diff --git a/bowtie.py b/bowtie.py
index 97d09374c999685bf495a984fd95009a2f8e3567..ed5860806e4b1f54e7431d560f255795a1034295 100644
--- a/bowtie.py
+++ b/bowtie.py
@@ -189,7 +189,9 @@ def calculate_bowtie_gf(response_data,
                         sigma=3,
                         plot=False,
                         gfactor_confidence_level=0.9,
-                        return_gf_stddev=False):
+                        return_gf_stddev=False,
+                        gmin=1E-5,
+                        gmax=10):
     """
     Calculates the bowtie geometric factor for a single channel
     :param return_gf_box: True if the margin of the channel geometric factor is requested.
@@ -237,7 +239,7 @@ def calculate_bowtie_gf(response_data,
     # Mathematically, this implies normalization of the random variable to its mean.
     non_zero_gf = np.mean(multi_geometric_factors, axis=0) > 0
     multi_geometric_factors_usable = multi_geometric_factors[:, non_zero_gf]
-    print(len(multi_geometric_factors), len(multi_geometric_factors_usable))
+    # print(len(multi_geometric_factors), len(multi_geometric_factors_usable))
     means = np.exp(np.mean(np.log(multi_geometric_factors_usable), axis=0))  # logarithmic mean of geometric factor curves by energy bin
 
     gf_stddev_abs = np.std(multi_geometric_factors_usable, axis=0)
@@ -269,11 +271,11 @@ def calculate_bowtie_gf(response_data,
     # gf_cross = gf[bowtie_cross_index]  # The mean geometric factor for the bowtie crossing point
 
     gf_cross = geometric_mean(multi_geometric_factors_usable[:, bowtie_cross_index])
-    energy_cross = energy_grid_local[bowtie_cross_index]
+    energy_cross = energy_grid_local[non_zero_gf][bowtie_cross_index]
 
     if plot:
         plot_multi_geometric(geometric_factors=multi_geometric_factors, response_data=response_data,
-                             emin=emin, emax=emax, gmin=1E-5, gmax=10)
+                             emin=emin, emax=emax, gmin=gmin, gmax=gmax)
 
     if return_gf_stddev:
         gf_upper = np.quantile(multi_geometric_factors_usable[:, bowtie_cross_index], gfactor_confidence_level)