diff --git a/lastplot/data_cleanup.py b/lastplot/data_cleanup.py
index 693a96f..3fa7053 100644
--- a/lastplot/data_cleanup.py
+++ b/lastplot/data_cleanup.py
@@ -102,7 +102,7 @@ def replace_zero_values(row, data):
                 & (data["Regions"] == row["Regions"])
                 & (data["Genotype"] == row["Genotype"])
                 & (data["Values"] != 0)
-                ]
+            ]
             if not group_df.empty:
                 min_value = group_df["Values"].min()
                 if min_value != 0:
@@ -119,9 +119,9 @@ def replace_zero_values(row, data):
         os.makedirs(output_path + "/output")
 
     try:
-        with pd.ExcelWriter(
-                output_path + "/output/Output file.xlsx", engine="openpyxl", mode="a"
-        ) as writer:
+        with pd.ExcelWriter(output_path + "/output/Output file.xlsx") as writer:
+            df_eliminated.to_excel(writer, sheet_name="Eliminated Lipids")
+            print("Saving data to new Excel file")
     except PermissionError:
         print("Close the Excel file and try again.")
 
diff --git a/lastplot/graph_constructor.py b/lastplot/graph_constructor.py
index 3613598..d396a05 100644
--- a/lastplot/graph_constructor.py
+++ b/lastplot/graph_constructor.py
@@ -1,9 +1,11 @@
 import numpy as np
 from matplotlib.axes import Axes
 
+MIN_BAR_GAP = 0.005
+
 
 def mpl_calc_series(
-    n_groups, n_bars, group_width, bar_width, bar_gap, min_bar_gap=0.01
+    n_groups, n_bars, group_width, bar_width, bar_gap, min_bar_gap=MIN_BAR_GAP
 ):
     # IMPORTANT: This algorithm only produces correct bar widths when the
     # figure's width is determined exclusively by the n_groups given.
@@ -42,7 +44,7 @@ def mpl_calc_bar_width(n_bars, group_width, gap):
 
 
 def mpl_debug_series(
-    n_groups, n_bars, group_width, bar_width, bar_gap, ax: Axes, min_bar_gap=0.03
+    n_groups, n_bars, group_width, bar_width, bar_gap, ax: Axes, min_bar_gap=MIN_BAR_GAP
 ):
     debug = f"Input: w{bar_width:.2f}, g{bar_gap:.2f};"
     bar_gap *= n_groups
@@ -62,7 +64,6 @@ def mpl_debug_series(
         else:
             algorithm = "gapped"
     debug += f" Calc: w{bar_width:.2f}, g{bar_gap:.2f}, {algorithm};"
-    mpl_calc_series(n_groups, n_bars, group_width, bar_width, bar_gap)
     ax.axhline(0, linestyle="--", color="k")
     for x in range(n_groups):
         ax.axvline(x, color="gray", linewidth=0.5)
diff --git a/lastplot/log10_graphs.py b/lastplot/log10_graphs.py
index e8a2a56..8e0e26d 100644
--- a/lastplot/log10_graphs.py
+++ b/lastplot/log10_graphs.py
@@ -18,16 +18,16 @@
 
 # Graphs by log10 values
 def log_values_graph_lipid(
-        df_final,
-        control_name,
-        experimental_name,
-        output_path,
-        palette,
-        xlabel=None,
-        ylabel=None,
-        title=None,
-        show=True,
-        debug=False,
+    df_final,
+    control_name,
+    experimental_name,
+    output_path,
+    palette,
+    xlabel=None,
+    ylabel=None,
+    title=None,
+    show=True,
+    debug=False,
 ):
     """
     The `log_values_graph_lipid` function generates boxplots and statistical annotations to visualize the distribution of log 10 transformed values of single lipids across regions. It performs the following tasks:
@@ -198,16 +198,16 @@ def log_values_graph_lipid(
 
 
 def log_values_graph_lipid_class(
-        df_final,
-        control_name,
-        experimental_name,
-        output_path,
-        palette,
-        xlabel=None,
-        ylabel=None,
-        title=None,
-        show=True,
-        debug=False,
+    df_final,
+    control_name,
+    experimental_name,
+    output_path,
+    palette,
+    xlabel=None,
+    ylabel=None,
+    title=None,
+    show=True,
+    debug=False,
 ):
     """
     The `log_values_graph_lipid_class` function generates boxplots to visualize the distribution of log 10 transformed values across different lipid classes within each region. It performs the following tasks:
@@ -277,12 +277,12 @@ def log_values_graph_lipid_class(
                 for g, genotype in enumerate(genotype_labels):
                     values = data[
                         (data["Lipids"] == lipid) & (data["Genotype"] == genotype)
-                        ]["Log10 Values"]
+                    ]["Log10 Values"]
 
                     bp = ax.boxplot(
                         values,
                         positions=[positions[j][g]],
-                        widths=bar_width,
+                        widths=width,
                         patch_artist=True,
                         boxprops=dict(facecolor=palette[g], color="k"),
                         medianprops=dict(color="k"),
@@ -334,16 +334,16 @@ def log_values_graph_lipid_class(
 
 
 def log_values_graph_class_average(
-        df_final,
-        control_name,
-        experimental_name,
-        output_path,
-        palette,
-        xlabel=None,
-        ylabel=None,
-        title=None,
-        show=True,
-        debug=False,
+    df_final,
+    control_name,
+    experimental_name,
+    output_path,
+    palette,
+    xlabel=None,
+    ylabel=None,
+    title=None,
+    show=True,
+    debug=False,
 ):
     """
     The `log_values_graph_class_average average` function generates boxplots and statistical annotations for visualizing average log 10 values of lipids classes
diff --git a/lastplot/zscores_graphs.py b/lastplot/zscores_graphs.py
index 3146e69..141bf8c 100644
--- a/lastplot/zscores_graphs.py
+++ b/lastplot/zscores_graphs.py
@@ -18,16 +18,16 @@
 
 # Graphs by Z scores
 def zscore_graph_lipid(
-        df_final,
-        control_name,
-        experimental_name,
-        output_path,
-        palette,
-        xlabel=None,
-        ylabel=None,
-        title=None,
-        show=True,
-        debug=False,
+    df_final,
+    control_name,
+    experimental_name,
+    output_path,
+    palette,
+    xlabel=None,
+    ylabel=None,
+    title=None,
+    show=True,
+    debug=False,
 ):
     """
     The `zscore_graph_lipid` function generates boxplots and statistical annotations for visualizing Z scores of lipids
@@ -200,16 +200,16 @@ def zscore_graph_lipid(
 
 
 def zscore_graph_lipid_class(
-        df_final,
-        control_name,
-        experimental_name,
-        output_path,
-        palette,
-        xlabel=None,
-        ylabel=None,
-        title=None,
-        show=True,
-        debug=False,
+    df_final,
+    control_name,
+    experimental_name,
+    output_path,
+    palette,
+    xlabel=None,
+    ylabel=None,
+    title=None,
+    show=True,
+    debug=False,
 ):
     """
     The `zscore_graph_lipid_class` function generates boxplots to visualize the distribution of Z scores across different lipid classes within each region. It performs the following tasks:
@@ -278,12 +278,12 @@ def zscore_graph_lipid_class(
                 for g, genotype in enumerate(genotype_data):
                     experimental_values = data[
                         (data["Lipids"] == lipid) & (data["Genotype"] == genotype)
-                        ]["Z Scores"]
+                    ]["Z Scores"]
 
                     bp = ax.boxplot(
                         experimental_values,
                         positions=[positions[j][g]],
-                        widths=bar_width,
+                        widths=width,
                         patch_artist=True,
                         boxprops=dict(facecolor=palette[g], color="k"),
                         medianprops=dict(color="k"),
@@ -333,16 +333,16 @@ def zscore_graph_lipid_class(
 
 
 def zscore_graph_class_average(
-        df_final,
-        control_name,
-        experimental_name,
-        output_path,
-        palette,
-        xlabel=None,
-        ylabel=None,
-        title=None,
-        show=True,
-        debug=False,
+    df_final,
+    control_name,
+    experimental_name,
+    output_path,
+    palette,
+    xlabel=None,
+    ylabel=None,
+    title=None,
+    show=True,
+    debug=False,
 ):
     """
     The `zscore_graph_class average` function generates boxplots and statistical annotations for visualizing average Z scores of lipids classes