diff --git a/analysis.py b/analysis.py
index bbf695e..6356167 100644
--- a/analysis.py
+++ b/analysis.py
@@ -17,26 +17,28 @@ def main():
     # parser.add_argument("dir", help="folder containing the cell folders with the fit results")
     # args = parser.parse_args()
 
-    dir_path = "results/invivo_results/"  # args.dir
+    # dir_path = "results/invivo_results/"  # args.dir
+    dir_path = "results/results_add__trial_more_iter_NM/invivo_results"  # args.dir
 
     # if not os.path.isdir(dir_path):
     #     print("Argument dir is not a directory.")
     #     parser.print_usage()
     #     exit(0)
-    # sensitivity_analysis(dir_path, max_models=2)
-    fits_info = get_fit_info(dir_path)
-
-    errors = calculate_percent_errors(fits_info)
-    create_boxplots(errors)
-    labels, corr_values, corrected_p_values = behaviour_correlations(fits_info, model_values=False)
-    create_correlation_plot(labels, corr_values, corrected_p_values)
-
-    labels, corr_values, corrected_p_values = parameter_correlations(fits_info)
-    create_correlation_plot(labels, corr_values, corrected_p_values)
-
-    create_parameter_distributions(get_parameter_values(fits_info))
-    cell_b, model_b = get_behaviour_values(fits_info)
-    create_behaviour_distributions(cell_b, model_b)
+    sensitivity_analysis(dir_path, max_models=3)
+
+    # fits_info = get_fit_info(dir_path)
+    #
+    # errors = calculate_percent_errors(fits_info)
+    # create_boxplots(errors)
+    # labels, corr_values, corrected_p_values = behaviour_correlations(fits_info, model_values=False)
+    # create_correlation_plot(labels, corr_values, corrected_p_values)
+    #
+    # labels, corr_values, corrected_p_values = parameter_correlations(fits_info)
+    # create_correlation_plot(labels, corr_values, corrected_p_values)
+    #
+    # create_parameter_distributions(get_parameter_values(fits_info))
+    # cell_b, model_b = get_behaviour_values(fits_info)
+    # create_behaviour_distributions(cell_b, model_b)
     pass
 
 
@@ -293,7 +295,7 @@ def sensitivity_analysis(dir_path, par_range=(0.5, 1.6, 0.1), contrast_range=(-0
                 if "vector_strength" in behaviours:
                     par_responses[par]["vector_strength"][i] = baseline.get_vector_strength()
 
-                fi_curve = FICurveModel(model_copy, contrasts, eod, trials=10)
+                fi_curve = FICurveModel(model_copy, contrasts, eod, trials=20)
 
                 if "f_inf_slope" in behaviours:
                     par_responses[par]["f_inf_slope"][i] = fi_curve.get_f_inf_slope()
@@ -304,7 +306,26 @@ def sensitivity_analysis(dir_path, par_range=(0.5, 1.6, 0.1), contrast_range=(-0
 
         model_behaviour_responses.append(par_responses)
 
-    pass
+    print("sensitivity analysis done!")
+    plot_sensitivity_analysis(model_behaviour_responses, behaviours, parameters, factors)
+
+
+def plot_sensitivity_analysis(responses, behaviours, parameters, factors):
+    fig, axes = plt.subplots(len(behaviours), len(parameters), sharex="all", sharey="row", figsize=(8, 8))
+    for i, behaviour in enumerate(behaviours):
+        for j, par in enumerate(parameters):
+
+            for model in responses:
+                axes[i, j].plot(factors, model[par][behaviour])
+
+            if j == 0:
+                axes[i, j].set_ylabel("{}".format(behaviour))
+            if i == 0:
+                axes[i, j].set_title("{}".format(par))
+
+    plt.tight_layout()
+    plt.show()
+    plt.close()
 
 
 if __name__ == '__main__':