diff --git a/punit_responses.py b/punit_responses.py
index cf1c3c8..efa2be2 100644
--- a/punit_responses.py
+++ b/punit_responses.py
@@ -8,6 +8,9 @@ from model import simulate, load_models
 from IPython import embed
 import matplotlib.pyplot as plt
 
+data_folder = "data"
+
+
 def append_settings(section, sec_name, sec_type, settings):
     section = section.create_section(sec_name, sec_type)
     for k in settings.keys():
@@ -140,7 +143,7 @@ def get_pre_stimulus(eodf, duration=2, df=20, contrast=20, dt=1./20000):
 def simulate_responses(stimulus_params, model_params, repeats=10, deltaf=20):
     cell_params = model_params.copy()
     cell = cell_params["cell"]
-    filename = "cell_%s.nix" % cell
+    filename = os.path.join(data_folder, "cell_%s.nix" % cell)
     del cell_params["cell"]
     del cell_params["EODf"]
     conditions = ["other", "self"]
@@ -193,8 +196,6 @@ def main():
     
     for cell_id in range(len(models)):
         model_params = models[cell_id]
-        
-
         baseline_spikes = get_baseline_response(model_params, duration=30)
         save_baseline_response( "cell_%s.nix" % model_params["cell"], "baseline response", baseline_spikes, model_params)
 
@@ -209,7 +210,7 @@ def main():
                                     1./stimulus_params["chirp_frequency"])
             stimulus_params["chirp_times"] = chirp_times     
             simulate_responses(stimulus_params, model_params, repeats=25, deltaf=deltaf)
-        exit()
+        exit() # the first cell only for now!
 
 
 if __name__ == "__main__":
diff --git a/response_discriminability.py b/response_discriminability.py
index 3a4fb66..96d8d05 100644
--- a/response_discriminability.py
+++ b/response_discriminability.py
@@ -130,7 +130,7 @@ def create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, curr
         despine(ax, ["top", "bottom", "left", "right"], True)
         ax.set_ylim([-1.25, 1.25])
         ax.legend(ncol=2, loc=(0.01, -0.5), fontsize=7, markerscale=0.5, frameon=False)
-        
+        """
         # for the largest contrast plot the raster with psth, only a section of the data (e.g. 1s)
         t, rates, spikes = get_firing_rate(block_map, current_df, all_contrasts[0], condition, kernel_width=0.001)
         avg_resp = np.mean(rates, axis=0)
@@ -153,9 +153,9 @@ def create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, curr
         if i > 0:
             ax.set_yticklabels([])
         despine(ax, ["top", "right"], False)
-        
+        """
         # for all other contrast plot the firing rate alone
-        for j in range(1, len(all_contrasts)):
+        for j in range(0, len(all_contrasts)):
             contrast = all_contrasts[j]
             t, rates, _ = get_firing_rate(block_map, current_df, contrast, condition)
             avg_resp = np.mean(rates, axis=0)
@@ -177,18 +177,32 @@ def create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, curr
             if i > 0:
                 ax.set_yticklabels([])
             despine(ax, ["top", "right"], False)
+            if i == 2:
+                ax.text(max_time + 0.025*max_time, 350, "c=%.3f" % all_contrasts[j], 
+                        color="#d62728", ha="left", fontsize=7)
+
         if i == 1:
             ax.set_xlabel("time [ms]")
         if i == 0:
             ax.set_ylabel("frequency [Hz]", va="center")
             ax.yaxis.set_label_coords(-0.45, 3.5)
-
+        
     name = figure_name if figure_name is not None else "chirp_responses.pdf"
     name = (name + ".pdf") if ".pdf" not in name else name
     plt.savefig(os.path.join(figure_folder, name))
     plt.close()
 
 
+def chrip_detection_soliloquy(spikes, chirp_times, kernel_width=0.0005):
+    # 
+    pass
+
+
+def chirp_detection(block_map, all_dfs, all_contrasts, all_conditions, current_df=None, current_condition=None):
+
+    pass
+
+
 def process_cell(filename, dfs=[], contrasts=[], conditions=[]):
     nf = nix.File.open(filename, nix.FileMode.ReadOnly)
     block_map, all_contrasts, all_dfs, all_conditions  = sort_blocks(nf)
@@ -196,12 +210,12 @@ def process_cell(filename, dfs=[], contrasts=[], conditions=[]):
         baseline_spikes = read_baseline(block_map["baseline"])
     else:
         print("ERROR: no baseline data for file %s!" % filename)
-    fig_name = filename.split(".nix")[0] + "_df_20Hz.pdf"
+    fig_name = filename.split(os.path.sep)[-1].split(".nix")[0] + "_df_20Hz.pdf"
     create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, 20, figure_name=fig_name)
-    fig_name = filename.split(".nix")[0] + "_df_-100Hz.pdf"
+    fig_name = filename.split(os.path.sep)[-1].split(".nix")[0] + "_df_-100Hz.pdf"
     create_response_plot(block_map, all_dfs, all_contrasts, all_conditions, -100, figure_name=fig_name)
-    
-    
+    chirp_detection(block_map, all_dfs, all_contrasts, all_conditions)
+
     
     nf.close()