diff --git a/requirements.txt b/requirements.txt
index 17db2d2..a6f6dec 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,8 @@
 sphinx
 sphinx-rtd-theme
-numpy
-pandas
-seaborn
+numpy~=1.26.4
+pandas~=2.2.1
+seaborn~=0.13.2
+
+matplotlib~=3.8.4
+scipy~=1.13.1
\ No newline at end of file
diff --git a/src/Utilities/_init_.py b/src/Utilities/_init_.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/Utilities/fix_no_dose_error_composite.py b/src/Utilities/fix_no_dose_error_composite.py
new file mode 100644
index 0000000..e22039d
--- /dev/null
+++ b/src/Utilities/fix_no_dose_error_composite.py
@@ -0,0 +1,22 @@
+import sys
+import os
+
+# Add the src directory to the sys.path
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from src import io_snc
+
+def main():
+    """
+    main function to fix measured files
+
+    Returns
+    -------
+
+    """
+    # ask user for file path of measured file
+    file_path = input("Please enter the file path: ")
+    return file_path
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/src/correction_coefficients/correction_coefficients.py b/src/correction_coefficients/correction_coefficients.py
index bce771e..635ba87 100644
--- a/src/correction_coefficients/correction_coefficients.py
+++ b/src/correction_coefficients/correction_coefficients.py
@@ -4,7 +4,7 @@
 The module includes the following functions:
 
 - `get_pr_data_from_acm_files`: Extracts the average counts/50ms from ACM files in a specified directory.
-- `exponential_fit`: Defines an exponential fitting function for curve fitting.
+- `saturation_fit`: Defines an exponential fitting function for curve fitting.
 - `get_correction_coefficients`: Calculates the correction coefficients based on the average counts/50ms and relative signal values.
 - `plot_correction_curve`: Plots the correction curve with the data points and the fitted exponential function.
 - `plot_counts_per_50ms`: Plots the counts per 50ms at different nominal dose rates.
@@ -17,10 +17,14 @@
 import os
 import sys
 
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
 import seaborn as sns
 from scipy.optimize import curve_fit
+import datetime
+
+
 
 # Add the src directory to the sys.path
 sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
@@ -42,36 +46,49 @@ def get_pr_data_from_acm_files(pr_measurement_folder):
         List of tuples containing the file name and the average counts/50ms for each nominal dose rate.
     """
     diode_numbers = np.array([758, 759, 760, 761, 692, 693, 694, 695, 626, 627, 628, 629]).astype(str)
+
+    # initialize a dictionary to store the average counts/50ms  for each file over all
+    # diodes in the middle 200 frames.
+    counts_per_50ms_dict = {}
+
+    # initialize a list to store the average counts per 50ms for each file
     counts_per_50ms_list = []
 
     for acm_file in os.listdir(pr_measurement_folder):
         acm_file_path = os.path.join(pr_measurement_folder, acm_file)
         frame_data_df, diode_data_df, bkrnd_and_calibration_df = io_snc.parse_acm_file(acm_file_path)
 
-        # Ensure the frame data is sorted by time or frame number if necessary
-        diode_data_df.sort_index(inplace=True)
-
-        # Calculate the count rate by differencing the accumulated counts
-        diode_data_diff = diode_data_df.diff().iloc[1:]
-
         # Grab the count rate from the middle 100 frames of the measurement
-        num_frames = len(diode_data_diff)
+        num_frames = len(diode_data_df)
         start_frame = num_frames // 2 - 50
         end_frame = start_frame + 100
-        middle_frames = diode_data_diff.iloc[start_frame:end_frame]
+        middle_frames = diode_data_df.iloc[start_frame:end_frame]
+
+        # Calculate the count rate by differencing the accumulated counts
+        diode_data_diff_df = middle_frames.diff().iloc[1:]
 
         # Extract the count rate for the specified diodes in the middle frames
-        count_rate_df = middle_frames.loc[:, diode_numbers]
+        count_rate_df = diode_data_diff_df.loc[:, diode_numbers]
+
+        # Calculate the average count rate for each frame across the selected diodes
+        avg_count_rate_per_frame = count_rate_df.mean(axis=1)
+        avg_count_rate_per_frame = avg_count_rate_per_frame.values
+        counts_per_50ms_dict[acm_file] = avg_count_rate_per_frame
 
         # Calculate the average count rate for the selected diodes
         avg_count_rate = count_rate_df.mean().mean()
         counts_per_50ms_list.append((acm_file, avg_count_rate))
 
+    # Convert the dictionary to a DataFrame
+    counts_per_50ms_df = pd.DataFrame.from_dict(counts_per_50ms_dict, orient='index')
+    # Save the DataFrame to a CSV file
+    counts_per_50ms_df.to_csv('counts_per_50ms.csv')
+
     counts_per_50ms_list = sorted(counts_per_50ms_list, key=lambda x: x[1])
     return counts_per_50ms_list
 
 
-def exponential_fit(x, a, b, c):
+def saturation_func(x, a, b, c):
     """
     Exponential fitting function.
 
@@ -91,7 +108,7 @@ def exponential_fit(x, a, b, c):
     array_like
         The calculated dependent variable values.
     """
-    return a * np.exp(b * x) + c
+    return c - a * np.exp(-b * x)
 
 
 def get_correction_coefficients(counts_per_50ms, relative_signal):
@@ -111,10 +128,49 @@ def get_correction_coefficients(counts_per_50ms, relative_signal):
         The fitted coefficients (a, b, c) for the exponential correction function.
     """
     counts_per_50ms = np.array([data[1] for data in counts_per_50ms])
+    initial_guess = [0.035, 5.21e-5, 1.0]
+    # noinspection PyTupleAssignmentBalance
+    params, covariance = curve_fit(saturation_func, counts_per_50ms,
+                                   relative_signal, p0=initial_guess)
+    a_fit, b_fit, c_fit = params
 
-    # Perform the curve fitting
-    popt, _ = curve_fit(exponential_fit, counts_per_50ms, relative_signal)
-    return tuple(popt)
+    return a_fit, b_fit, c_fit, covariance
+
+def get_coefficient_file_path(date=None):
+    """
+    Get the path to the file where the correction coefficients will be written.
+
+    Returns
+    -------
+    str
+        The file path for the correction coefficients.
+    """
+    # get user input on type of whether it DPP or PR
+    coefficient_file_directory = input("Enter the directory to the file where the correction coefficients will be written: ")
+
+    # Get the DRD type, ArcCheck SN from the user
+    DRD_type = input("Enter the DRD type: ")
+    ArcCheck_SN = input("Enter the ArcCheck SN: ")
+
+    # Get today's date
+    today = datetime.date.today()
+    date = today.strftime("%Y-%m-%d")
+
+    # construct coefficient file name from DRD type, ArcCheck SN and Date
+    coefficient_file_name = f"{DRD_type}_{ArcCheck_SN}_{date}.txt"
+
+    # construct the full path to the coefficient file
+    coefficient_file_path = os.path.join(coefficient_file_directory, coefficient_file_name)
+
+    return coefficient_file_path
+
+def write_correction_coefficients_to_file(coefficient_file_path, a_fit, b_fit, c_fit, covariance):
+    with open(coefficient_file_path, 'w') as f:
+        # Write the variable names and their values to the file
+        f.write(f"a_fit = {a_fit}\n")
+        f.write(f"b_fit = {b_fit}\n")
+        f.write(f"c_fit = {c_fit}\n")
+        f.write(f"covariance = {covariance}\n")
 
 
 def plot_correction_curve(counts_per_50ms, relative_signal, a_fit, b_fit, c_fit):
@@ -138,7 +194,7 @@ def plot_correction_curve(counts_per_50ms, relative_signal, a_fit, b_fit, c_fit)
 
     # Generate fitted curve data points
     x_fit = np.linspace(counts_per_50ms.min(), counts_per_50ms.max(), 500)
-    y_fit = exponential_fit(x_fit, a_fit, b_fit, c_fit)
+    y_fit = saturation_func(x_fit, a_fit, b_fit, c_fit)
 
     # Set Seaborn style
     sns.set(style="whitegrid")
@@ -148,7 +204,7 @@ def plot_correction_curve(counts_per_50ms, relative_signal, a_fit, b_fit, c_fit)
     sns.lineplot(x=x_fit, y=y_fit, label=f'Exponential fit, a={a_fit:.4f}, b={b_fit:.2e}, c={c_fit:.3f}')
     sns.scatterplot(x=counts_per_50ms, y=relative_signal, label='Data points')
 
-    plt.ioff()  # Turn off interactive mode
+    #plt.ioff()  # Turn off interactive mode
 
     # Plot the data points and the fitted curve
     plt.figure(figsize=(8, 6))
@@ -162,8 +218,8 @@ def plot_correction_curve(counts_per_50ms, relative_signal, a_fit, b_fit, c_fit)
     plt.title('Pulse rate (MU/min)')
     plt.legend()
     plt.grid(True)
-
     plt.show()
+    plt.savefig('counts_per_50ms.png')
 
 
 def plot_counts_per_50ms(counts_per_50ms):
@@ -179,7 +235,7 @@ def plot_counts_per_50ms(counts_per_50ms):
 
     # Set Seaborn style
     sns.set(style="whitegrid")
-
+    #plt.ioff()  # Turn off interactive mode
     # Create a plot with Seaborn
     plt.figure(figsize=(10, 6))
     sns.scatterplot(x=range(len(counts_per_50ms)), y=counts_per_50ms, label='Counts/50ms')
@@ -190,20 +246,23 @@ def plot_counts_per_50ms(counts_per_50ms):
     plt.title('Counts per 50ms at different nominal dose rates')
     plt.legend()
     plt.grid(True)
-
     plt.show()
+    plt.savefig('counts_per_50ms_nominal.png')
 
 
 def main():
     """
     Main function to execute the correction coefficient calculations and plotting.
     """
+
     pr_measurement_folder = r"P:\02_QA Equipment\02_ArcCheck\05_Commissoning\03_NROAC\Dose Rate Dependence Fix\NRO PR Coefficient"
     counts_per_50ms = get_pr_data_from_acm_files(pr_measurement_folder)
-    plot_counts_per_50ms(counts_per_50ms)
+    # plot_counts_per_50ms(counts_per_50ms)
     relative_signal = np.array([0.964, 0.971, 0.980, 0.988, 0.994, 0.995, 1.000, 1.000])
-    a_fit, b_fit, c_fit = get_correction_coefficients(counts_per_50ms, relative_signal)
-    plot_correction_curve(counts_per_50ms, relative_signal, a_fit, b_fit, c_fit)
+    a_fit, b_fit, c_fit, covariance = get_correction_coefficients(counts_per_50ms, relative_signal)
+    coefficient_file_path = get_coefficient_file_path()
+    write_correction_coefficients_to_file(coefficient_file_path, a_fit, b_fit, c_fit, covariance)
+    # plot_correction_curve(counts_per_50ms, relative_signal, a_fit, b_fit, c_fit)
 
 
 if __name__ == "__main__":
diff --git a/src/corrections.py b/src/corrections.py
index 1ff9adf..73a6703 100644
--- a/src/corrections.py
+++ b/src/corrections.py
@@ -73,7 +73,9 @@ def get_intrinsic_corrections(array_data):
 
 def apply_jager_corrections(counts_accumulated_df, bkrnd_and_calibration_df, intrinsic_corrections=None):
     """Apply Jager pulse rate and dose per pulse corrections."""
-    a_pr, b_pr, c_pr = 0.035, 5.21 * 10 ** -5, 1
+
+    # a_pr, b_pr, c_pr = 0.035, 5.21 * 10 ** -5, 1 # Jager coefficients from paper
+    a_pr, b_pr, c_pr = 0.03921672747199777, 6.499040838838414e-055, 0.999142799734291 # Coefficients from NRO Measurements
     jager_pr_coefficients = np.array([a_pr, b_pr, c_pr])
 
     a_dpp, b_dpp, c_dpp = 0.0978, 3.33 * 10 ** -5, 1.011
diff --git a/src/main.py b/src/main.py
index 727a98e..1dd16fa 100644
--- a/src/main.py
+++ b/src/main.py
@@ -199,3 +199,4 @@ def main():
 if __name__ == "__main__":
     main()
 
+
diff --git a/src/plots_for_presentation/difference_plot.py b/src/plots_for_presentation/difference_plot.py
new file mode 100644
index 0000000..d545344
--- /dev/null
+++ b/src/plots_for_presentation/difference_plot.py
@@ -0,0 +1,167 @@
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+
+# Load data
+file_path = r'P:\02_QA Equipment\02_ArcCheck\05_Commissoning\03_NROAC\Dose Rate Dependence Fix\Test on script\ACResultsPowerQuerySummary.txt'
+df = pd.read_csv(file_path, delimiter='\t')
+
+# Ensure numeric columns are converted from strings to numbers
+for col in ['3%3mm', '2%3mm', '1%3mm']:
+    df[col] = pd.to_numeric(df[col], errors='coerce')
+
+# Split data into Original and PR correction
+original_df = df[df['Correction'] == 'Original'].set_index('Plan Name')
+pr_df = df[df['Correction'] == 'PR'].set_index('Plan Name')
+
+
+# Check if data is numeric
+if (original_df[['3%3mm', '2%3mm', '1%3mm']].dtypes == 'float64').all() and (pr_df[['3%3mm', '2%3mm', '1%3mm']].dtypes == 'float64').all():
+    # Calculate differences
+    difference_df = pr_df[['3%3mm', '2%3mm', '1%3mm']].subtract(original_df[['3%3mm', '2%3mm', '1%3mm']])
+else:
+    print("Data is not numeric. Cannot perform subtraction.")
+
+
+# Reset index to use for plotting
+difference_df.reset_index(inplace=True)
+
+# Melt the dataframe for seaborn plotting
+melted_df = difference_df.melt(id_vars=['Plan Name'], value_vars=['3%3mm', '2%3mm', '1%3mm'], var_name='Metric', value_name='Difference')
+
+# Define a function to map the improvement/deterioration to colors
+def color_mapper(val):
+    if val >= 0:
+        return 'green'
+    else:
+        return 'red'
+
+# Melt the original and PR dataframes
+original_melted = original_df.reset_index().melt(id_vars=['Plan Name'], value_vars=['3%3mm', '2%3mm', '1%3mm'], var_name='Metric', value_name='Original')
+pr_melted = pr_df.reset_index().melt(id_vars=['Plan Name'], value_vars=['3%3mm', '2%3mm', '1%3mm'], var_name='Metric', value_name='PR')
+
+import matplotlib as mpl
+
+import matplotlib as mpl
+
+# Create the plot
+fig, axs = plt.subplots(3, 1, figsize=(10, 24))
+sns.set(style="whitegrid")
+
+metrics = ['3%3mm', '2%3mm', '1%3mm']
+
+for i, metric in enumerate(metrics):
+    # Create the plot for each metric
+    fig, ax1 = plt.subplots(figsize=(10, 8))
+    sns.set(style="whitegrid")
+
+    # Plot the differences
+    subset_df = melted_df[melted_df['Metric'] == metric]
+    colors = subset_df['Difference'].apply(color_mapper)
+    sns.scatterplot(x=subset_df['Difference'], y=subset_df['Plan Name'], hue=colors, palette=['red', 'green'], legend=False, s=100, marker='^', ax=ax1)
+
+    # Set the limits for the first x-axis and set label color to dark grey
+    ax1.set_xlim([-10, 10])
+    ax1.set_xlabel('Differences', color='darkgrey')
+
+    # Reduce the font size
+    mpl.rcParams.update({'font.size': 10})
+
+    # Create a second x-axis
+    ax2 = ax1.twiny()
+
+    # Plot the original values
+    subset_df = original_melted[original_melted['Metric'] == metric]
+    sns.scatterplot(x=subset_df['Original'], y=subset_df['Plan Name'], color='darkgrey', legend=False, s=100, marker='o', alpha=0.5, ax=ax2)
+
+    # Plot the PR values
+    subset_df = pr_melted[pr_melted['Metric'] == metric]
+    sns.scatterplot(x=subset_df['PR'], y=subset_df['Plan Name'], color='darkblue', legend=False, s=100, marker='o', alpha=0.5, ax=ax2)
+
+    # Set the limits for the second x-axis and set label color to dark grey
+    ax2.set_xlim([50, 110])
+    ax2.set_xlabel('Pass Rates', color='darkgrey')
+
+    # Add labels and title
+    ax1.set_ylabel('Plan Name')
+    ax1.set_title(f'PR {metric}', loc='left')  # Set title to just the metric and align left
+    ax1.axvline(0, color='gray', linestyle='--')
+
+    plt.tight_layout()
+    plt.ioff()
+    plt.savefig(f'PR_{metric}_correction_plot.png', dpi=300)
+
+
+
+
+# Load data for DPP correction
+dpp_df = df[df['Correction'] == 'DPP'].set_index('Plan Name')
+
+# Check if data is numeric
+if (original_df[['3%3mm', '2%3mm', '1%3mm']].dtypes == 'float64').all() and (dpp_df[['3%3mm', '2%3mm', '1%3mm']].dtypes == 'float64').all():
+    # Calculate differences
+    difference_dpp_df = dpp_df[['3%3mm', '2%3mm', '1%3mm']].subtract(original_df[['3%3mm', '2%3mm', '1%3mm']])
+else:
+    print("Data is not numeric. Cannot perform subtraction.")
+
+# Reset index to use for plotting
+difference_dpp_df.reset_index(inplace=True)
+
+# Melt the dataframe for seaborn plotting
+melted_dpp_df = difference_dpp_df.melt(id_vars=['Plan Name'], value_vars=['3%3mm', '2%3mm', '1%3mm'], var_name='Metric', value_name='Difference')
+
+# Melt the DPP dataframe
+dpp_melted = dpp_df.reset_index().melt(id_vars=['Plan Name'], value_vars=['3%3mm', '2%3mm', '1%3mm'], var_name='Metric', value_name='DPP')
+
+# Create the plot for DPP
+fig, axs = plt.subplots(3, 1, figsize=(10, 24))
+sns.set(style="whitegrid")
+
+for i, metric in enumerate(metrics):
+    metrics = ['3%3mm', '2%3mm', '1%3mm']
+
+    for i, metric in enumerate(metrics):
+        # Create the plot for each metric
+        fig, ax1 = plt.subplots(figsize=(10, 8))
+        sns.set(style="whitegrid")
+
+        # Plot the differences
+        subset_df = melted_dpp_df[melted_df['Metric'] == metric]
+        colors = subset_df['Difference'].apply(color_mapper)
+        sns.scatterplot(x=subset_df['Difference'], y=subset_df['Plan Name'], hue=colors, palette=['green', 'red'],
+                        legend=False, s=100, marker='^', ax=ax1)
+
+        # Set the limits for the first x-axis and set label color to dark grey
+        ax1.set_xlim([-10, 10])
+        ax1.set_xlabel('Differences', color='darkgrey')
+
+        # Reduce the font size
+        mpl.rcParams.update({'font.size': 10})
+
+        # Create a second x-axis
+        ax2 = ax1.twiny()
+
+        # Plot the original values
+        subset_df = original_melted[original_melted['Metric'] == metric]
+        sns.scatterplot(x=subset_df['Original'], y=subset_df['Plan Name'], color='darkgrey', legend=False, s=100,
+                        marker='o', alpha=0.5, ax=ax2)
+
+        # Plot the PR values
+        subset_df = dpp_melted[dpp_melted['Metric'] == metric]
+        sns.scatterplot(x=subset_df['DPP'], y=subset_df['Plan Name'], color='darkblue', legend=False, s=100, marker='o',
+                        alpha=0.5, ax=ax2)
+
+        # Set the limits for the second x-axis and set label color to dark grey
+        ax2.set_xlim([50, 110])
+        ax2.set_xlabel('Pass Rates', color='darkgrey')
+
+        # Add labels and title
+        ax1.set_ylabel('Plan Name')
+        ax1.set_title(f'DPP {metric}', loc='left')  # Set title to just the metric and align left
+        ax1.axvline(0, color='gray', linestyle='--')
+
+        plt.tight_layout()
+        plt.ioff()
+        plt.savefig(f'DPP_{metric}_correction_plot.png', dpi=300)
+
diff --git a/src/plots_for_presentation/difference_plot_dpp.py b/src/plots_for_presentation/difference_plot_dpp.py
new file mode 100644
index 0000000..6b9e8b7
--- /dev/null
+++ b/src/plots_for_presentation/difference_plot_dpp.py
@@ -0,0 +1,54 @@
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+# Load data
+file_path = r'P:\02_QA Equipment\02_ArcCheck\05_Commissoning\03_NROAC\Dose Rate Dependence Fix\Test on script\ACResultsPowerQuerySummary.txt'
+df = pd.read_csv(file_path, delimiter='\t')
+
+# Ensure numeric columns are converted from strings to numbers
+for col in ['3%3mm', '2%3mm', '1%3mm']:
+    df[col] = pd.to_numeric(df[col], errors='coerce')
+
+# Split data into Original and DPP correction
+original_df = df[df['Correction'] == 'Original'].set_index('Plan Name')
+dpp_df = df[df['Correction'] == 'DPP'].set_index('Plan Name')
+
+# Melt the original and DPP dataframes
+original_melted = original_df.reset_index().melt(id_vars=['Plan Name'], value_vars=['3%3mm', '2%3mm', '1%3mm'], var_name='Metric', value_name='Original')
+dpp_melted = dpp_df.reset_index().melt(id_vars=['Plan Name'], value_vars=['3%3mm', '2%3mm', '1%3mm'], var_name='Metric', value_name='DPP')
+
+metrics = ['3%3mm', '2%3mm', '1%3mm']
+
+for i, metric in enumerate(metrics):
+    # Create the plot for each metric
+    fig, ax = plt.subplots(figsize=(10, 8))
+    sns.set(style="whitegrid")
+    # Ensure grid is turned on
+    ax.grid(True)
+    # Plot the original values
+    subset_df = original_melted[original_melted['Metric'] == metric]
+    sns.scatterplot(x=subset_df['Original'], y=subset_df['Plan Name'], color='darkgrey', legend=False, s=100, marker='o', alpha=0.5, ax=ax)
+
+    # Plot the DPP values
+    subset_df = dpp_melted[dpp_melted['Metric'] == metric]
+    sns.scatterplot(x=subset_df['DPP'], y=subset_df['Plan Name'], color='darkblue', legend=False, s=100, marker='o', alpha=0.5, ax=ax)
+
+    # Set the limits for the x-axis and set label color to dark grey
+    ax.set_xlim([50, 110])
+    ax.set_xlabel('Pass Rates', color='darkgrey')
+
+    # Add labels and title
+    ax.set_ylabel('Plan Name')
+    ax.set_title(f'DPP {metric}', loc='left')  # Set title to just the metric and align left
+
+    # Add legend
+    legend_labels = ['Original', 'DPP Corrected']
+    legend_colors = ['darkgrey', 'darkblue']
+    patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=legend_colors[i],
+                label="{:s}".format(legend_labels[i]) )[0]  for i in range(len(legend_labels))]
+    ax.legend(handles=patches, bbox_to_anchor=(0, 0.5), loc='center left', ncol=1)
+
+    plt.tight_layout()
+    plt.savefig(f'DPP_{metric}_pass_rate_plot.png', dpi=300)
+    plt.show()
\ No newline at end of file
diff --git a/src/plots_for_presentation/difference_plot_pr.py b/src/plots_for_presentation/difference_plot_pr.py
new file mode 100644
index 0000000..fe5168a
--- /dev/null
+++ b/src/plots_for_presentation/difference_plot_pr.py
@@ -0,0 +1,54 @@
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+# Load data
+file_path = r'P:\02_QA Equipment\02_ArcCheck\05_Commissoning\03_NROAC\Dose Rate Dependence Fix\Test on script\ACResultsPowerQuerySummary.txt'
+df = pd.read_csv(file_path, delimiter='\t')
+
+# Ensure numeric columns are converted from strings to numbers
+for col in ['3%3mm', '2%3mm', '1%3mm']:
+    df[col] = pd.to_numeric(df[col], errors='coerce')
+
+# Split data into Original and PR correction
+original_df = df[df['Correction'] == 'Original'].set_index('Plan Name')
+pr_df = df[df['Correction'] == 'PR'].set_index('Plan Name')
+
+# Melt the original and PR dataframes
+original_melted = original_df.reset_index().melt(id_vars=['Plan Name'], value_vars=['3%3mm', '2%3mm', '1%3mm'], var_name='Metric', value_name='Original')
+pr_melted = pr_df.reset_index().melt(id_vars=['Plan Name'], value_vars=['3%3mm', '2%3mm', '1%3mm'], var_name='Metric', value_name='PR')
+
+metrics = ['3%3mm', '2%3mm', '1%3mm']
+
+for i, metric in enumerate(metrics):
+    # Create the plot for each metric
+    fig, ax = plt.subplots(figsize=(10, 8))
+    sns.set(style="whitegrid")
+
+    # Plot the original values
+    subset_df = original_melted[original_melted['Metric'] == metric]
+    sns.scatterplot(x=subset_df['Original'], y=subset_df['Plan Name'], color='darkgrey', legend=False, s=100, marker='o', alpha=0.5, ax=ax)
+
+    # Plot the PR values
+    subset_df = pr_melted[pr_melted['Metric'] == metric]
+    sns.scatterplot(x=subset_df['PR'], y=subset_df['Plan Name'], color='green', legend=False, s=100, marker='o', alpha=0.5, ax=ax)
+
+    # Set the limits for the x-axis and set label color to dark grey
+    ax.set_xlim([50, 110])
+    ax.set_xlabel('Pass Rates', color='darkgrey')
+
+    # Add labels and title
+    ax.set_ylabel('Plan Name')
+    ax.set_title(f'PR {metric}', loc='left')  # Set title to just the metric and align left
+
+    # Add legend
+    legend_labels = ['Original', 'PR Corrected']
+    legend_colors = ['darkgrey', 'green']
+    patches = [plt.plot([],[], marker="o", ms=10, ls="", mec=None, color=legend_colors[i],
+                label="{:s}".format(legend_labels[i]) )[0]  for i in range(len(legend_labels))]
+    ax.legend(handles=patches, bbox_to_anchor=(0, 0.5), loc='center left', ncol=1)
+
+
+    plt.tight_layout()
+    plt.savefig(f'PR_{metric}_pass_rate_plot.png', dpi=300)
+    plt.show()
\ No newline at end of file
diff --git a/src/plots_for_presentation/legend.py b/src/plots_for_presentation/legend.py
new file mode 100644
index 0000000..509ab7f
--- /dev/null
+++ b/src/plots_for_presentation/legend.py
@@ -0,0 +1,19 @@
+import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D
+
+# Create legend handles manually
+legend_elements = [Line2D([0], [0], marker='o', color='w', markerfacecolor='darkblue', markersize=10, label='Pass Rate Corrected'),
+                   Line2D([0], [0], marker='o', color='w', markerfacecolor='darkgrey', markersize=10, label='Pass Rate Uncorrected'),
+                   Line2D([0], [0], marker='^', color='w', markerfacecolor='green', markersize=10, label='Pass Rate Difference Better'),
+                   Line2D([0], [0], marker='^', color='w', markerfacecolor='red', markersize=10, label='Pass Rate Difference Worse')]
+
+# Create the figure and the axes
+fig, ax = plt.subplots()
+
+# Add the legend to the plot
+ax.legend(handles=legend_elements, loc='center')
+
+# Hide axes
+ax.axis('off')
+plt.savefig(f'legend.png', dpi=300)
+plt.show()
\ No newline at end of file
diff --git a/src/plots_for_presentation/summaryboxplots.py b/src/plots_for_presentation/summaryboxplots.py
index ece3faa..ecb73d0 100644
--- a/src/plots_for_presentation/summaryboxplots.py
+++ b/src/plots_for_presentation/summaryboxplots.py
@@ -7,23 +7,27 @@
 data = pd.read_csv(file_path, sep='\t')
 
 # Melt the data
-data_melted = data.melt(id_vars=['Plan Name', '(mm)', 'Custom'],
+data_melted = data.melt(id_vars=['Plan Name', '(mm)', 'Correction'],
                         value_vars=['3%3mm', '2%3mm', '1%3mm'],
                         var_name='Percentage', value_name='Value')
 
+# Filter the data to only include 'Original' and 'DPP'
+data_melted_filtered = data_melted[data_melted['Correction'].isin(['Original', 'DPP'])]
+
 # Set the theme
 sns.set_theme(style="white")
 
 # Set up the matplotlib figure with smaller size
 plt.figure(figsize=(20, 5))
 
+plt.figure(figsize=(20, 5))
 # Creating box plots with a custom color palette
-palette = {'Original': '#1f77b4', 'DPP': '#2ca02c', 'PR': '#17becf', 'PRI': '#ff7f0e'}
-y_limits = (75, data_melted['Value'].max() + 5)  # Set minimum value to 75 and adjust the max limit for clarity
+palette = {'Original': '#1f77b4', 'DPP': '#2ca02c'}
+y_limits = (75, data_melted_filtered['Value'].max() + 5)  # Set minimum value to 75 and adjust the max limit for clarity
 
 for i, percentage in enumerate(['3%3mm', '2%3mm', '1%3mm'], start=1):
     plt.subplot(1, 3, i)
-    sns.boxplot(x='Custom', y='Value', data=data_melted[data_melted['Percentage'] == percentage], palette=palette)
+    sns.boxplot(x='Correction', y='Value', data=data_melted_filtered[data_melted_filtered['Percentage'] == percentage], palette=palette)
     plt.title(f'{percentage}', fontsize=12)  # Increase title font size
     plt.ylim(y_limits)
     plt.xticks(rotation=45, fontsize=10)  # Increase x-tick labels font size