diff --git a/metocean_stats/plots/extreme.py b/metocean_stats/plots/extreme.py
index fbdcf66..0cc5215 100644
--- a/metocean_stats/plots/extreme.py
+++ b/metocean_stats/plots/extreme.py
@@ -894,8 +894,9 @@ def plot_prob_non_exceedance_fitted_3p_weibull(data, var='hs', output_file='plot
     return fig
 
 
-def plot_tp_for_given_hs(data: pd.DataFrame, var_hs: str, var_tp: str,output_file='tp_for_given_hs.png'):
-    df = tables.table_tp_for_given_hs(data=data, var_hs=var_hs, var_tp=var_tp, bin_width=1,output_file=False)
+def plot_tp_for_given_hs(data: pd.DataFrame, var_hs: str, var_tp: str, output_file='tp_for_given_hs.png'):
+
+    df = tables.table_tp_for_given_hs(data=data, var_hs=var_hs, var_tp=var_tp, output_file=False)
     # Plot the 2D histogram
     fig, ax = plt.subplots()
     plt.hist2d(data[var_hs], data[var_tp], bins=50, cmap='hot', cmin=1)
diff --git a/metocean_stats/tables/extreme.py b/metocean_stats/tables/extreme.py
index 58dddf7..3e337ff 100644
--- a/metocean_stats/tables/extreme.py
+++ b/metocean_stats/tables/extreme.py
@@ -239,16 +239,20 @@ def table_Hs_Tpl_Tph_return_values(data,var_hs='hs',var_tp='tp',periods=[1,10,10
     return df
 
 
-def table_tp_for_given_hs(data: pd.DataFrame, var_hs: str,var_tp: str, bin_width=1, max_hs=20, output_file='table_perc_tp_for_hs.csv'):
+def table_tp_for_given_hs(data: pd.DataFrame, var_hs: str,var_tp: str, bin_width=None,max_hs=None, output_file='table_perc_tp_for_hs.csv'):
     df=data
     a1, a2, a3, b1, b2, b3, pdf_Hs, h, t3,h3,X,hs_tpl_tph = stats.joint_distribution_Hs_Tp(data=data,var_hs=var_hs,var_tp=var_tp,periods=[1000])
     # Create bins
-    min_hs = 0.5
-    max_hs = max_hs
+    if max_hs is None:
+        max_hs = int(np.ceil(df[var_hs].max()))
+
+    if bin_width is None:
+        bin_width = max_hs/20
+    
+    min_hs = bin_width/2
     bins = np.arange(min_hs, max_hs + bin_width, bin_width)
     bin_labels = [f"[{bins[i]}, {bins[i+1]})" for i in range(len(bins)-1)]
     bin_centers = [(bins[i] + bins[i+1]) / 2 for i in range(len(bins)-1)]
-
     # Bin the HS values
     df[var_hs+'_bin'] = pd.cut(df[var_hs], bins=bins, labels=bin_labels, right=False)
     
@@ -298,9 +302,15 @@ def table_tp_for_rv_hs(data: pd.DataFrame, var_hs: str,var_tp: str, bin_width=1,
     return result_df
 
 
-def table_hs_for_given_wind(data: pd.DataFrame, var_hs: str,var_wind: str, bin_width=2, max_wind=40, output_file='table_perc_tp_for_wind.csv'):
+def table_hs_for_given_wind(data: pd.DataFrame, var_hs: str,var_wind: str, bin_width=None, max_wind=None, output_file='table_perc_tp_for_wind.csv'):
     df=data
     # Create bins
+    if max_wind is None:
+        max_wind = int(np.ceil(df[var_wind].max()))
+
+    if bin_width is None:
+        bin_width = max_wind/10
+
     min_wind = 0
     bins = np.arange(min_wind, max_wind + bin_width, bin_width)
     bin_labels = [f"[{bins[i]}, {bins[i+1]})" for i in range(len(bins)-1)]
@@ -353,7 +363,7 @@ def table_hs_for_given_wind(data: pd.DataFrame, var_hs: str,var_wind: str, bin_w
     return result_df
 
 def table_wave_induced_current(ds, var_hs,var_tp,max_hs= 20, depth=200,ref_depth=50,spectrum='JONSWAP',output_file='wave_induced_current_depth200.csv'):
-    df = table_tp_for_given_hs(ds, var_hs,var_tp, bin_width=1, max_hs=max_hs, output_file=None)
+    df = table_tp_for_given_hs(ds, var_hs,var_tp, bin_width=1,max_hs=max_hs, output_file=None)
     df['Us(P5) [m/s]'], df['Tu(P5-model) [s]'] = aux_funcs.calculate_Us_Tu(df['Hs[m]'],df['Tp(P5-model) [s]'], depth=depth, ref_depth=ref_depth,spectrum=spectrum)
     df['Us(Mean) [m/s]'], df['Tu(Mean-model) [s]'] = aux_funcs.calculate_Us_Tu(df['Hs[m]'],df['Tp(Mean-model) [s]'], depth=depth, ref_depth=ref_depth,spectrum=spectrum)
     df['Us(P95) [m/s]'], df['Tu(P95-model) [s]'] = aux_funcs.calculate_Us_Tu(df['Hs[m]'],df['Tp(P95-model) [s]'], depth=depth, ref_depth=ref_depth,spectrum=spectrum)