adjust plot

scripts/clusterprosody.py

@@ -552,14 +552,14 @@ def plot_rmse_tts(speech_data,tts_data, tts_align,words,seg_aligns,cluster_id, v
             realign = np.mean([word_times[0][2],word_times[1][1]])
             rmse_xvals = [x - realign for x in rmse_xvals]
             word_times = [(w,s-realign,e-realign) for w,s,e in word_times]
-            plt.axvline(x= 0, color="gray", linestyle='-', linewidth=1, label=f"{word_times[0][0]} -> {word_times[1][0]} boundary")
+            plt.axvline(x= 0, color="gray", linestyle='--', linewidth=1, label=f"{word_times[0][0]} -> {word_times[1][0]} boundary")
 
         if len(word_times)>2:
             for i in range(1,len(word_times)-1):
                 bound_line = np.mean([word_times[i][2],word_times[i+1][1]])
-                plt.axvline(x=bound_line, color=colors[cc], linestyle='-', linewidth=1, label=f"Speaker {spk} -> {word_times[i+1][0]}")
+                plt.axvline(x=bound_line, color=colors[cc], linestyle='--', linewidth=1, label=f"Speaker {spk} -> {word_times[i+1][0]}")
                 
-        plt.scatter(rmse_xvals, rmse, color=colors[cc], label=f"Speaker {spk}")
+        plt.plot(rmse_xvals, rmse, color=colors[cc], label=f"Speaker {spk}")
         cc += 1
         if cc >= len(colors):
             cc=0
@@ -575,8 +575,8 @@ def plot_rmse_tts(speech_data,tts_data, tts_align,words,seg_aligns,cluster_id, v
     if len(tts_align)>2:
         for i in range(2,len(tts_align)):
             bound_line = tts_align[i][1]
-            plt.axvline(x=bound_line, color="black", linestyle='-', linewidth=1, label=f"TTS -> {tts_align[i][0]}")
-    plt.scatter(t_xvals, trmse, color="black", label=f"TTS {voice}")
+            plt.axvline(x=bound_line, color="black", linestyle='--', linewidth=1, label=f"TTS -> {tts_align[i][0]}")
+    plt.plot(t_xvals, trmse, color="black", label=f"TTS {voice}")
 
 
     #plt.legend()
@@ -589,7 +589,7 @@ def plot_rmse_tts(speech_data,tts_data, tts_align,words,seg_aligns,cluster_id, v
 def plot_rmse_cluster(speech_data,words,seg_aligns,cluster_id):
     colors = ["red", "green", "blue", "orange", "purple", "pink", "brown", "gray", "cyan"]
     cc = 0
-    fig = plt.figure(figsize=(10, 5))
+    fig, ax1 = plt.figure(figsize=(10, 5))
     plt.title(f"{words} - Energy - Cluster {cluster_id}")
     for k,v in speech_data.items():
 
@@ -607,14 +607,14 @@ def plot_rmse_cluster(speech_data,words,seg_aligns,cluster_id):
             realign = np.mean([word_times[0][2],word_times[1][1]])
             rmse_xvals = [x - realign for x in rmse_xvals]
             word_times = [(w,s-realign,e-realign) for w,s,e in word_times]
-            plt.axvline(x= 0, color="gray", linestyle='-', linewidth=1, label=f"{word_times[0][0]} -> {word_times[1][0]} boundary")
+            plt.axvline(x= 0, color="gray", linestyle='--', linewidth=1, label=f"{word_times[0][0]} -> {word_times[1][0]} boundary")
 
         if len(word_times)>2:
             for i in range(1,len(word_times)-1):
                 bound_line = np.mean([word_times[i][2],word_times[i+1][1]])
                 plt.axvline(x=bound_line, color=colors[cc], linestyle='--', linewidth=1, label=f"Speaker {spk} -> {word_times[i+1][0]}")
                 
-        plt.scatter(rmse_xvals, rmse, color=colors[cc], label=f"Speaker {spk}")
+        plt.plot(rmse_xvals, rmse, color=colors[cc], label=f"Speaker {spk}")
         cc += 1
         if cc >= len(colors):
             cc=0