diff --git a/IsoAutio/statistics.py b/IsoAutio/statistics.py
index 73dab5d..e71c4af 100644
--- a/IsoAutio/statistics.py
+++ b/IsoAutio/statistics.py
@@ -109,88 +109,6 @@ def bird_label_scores(automated_df,human_df):
 
     return pd.DataFrame(entry,index=[0])
 
-def plot_bird_Label_scores(automated_df,human_df,save_fig = False):
-    """
-    Function to visualize automated and human annotation scores across an audio clip.
-
-    Args:
-        automated_df (Dataframe) - Dataframe of automated labels for one clip
-        human_df (Dataframe) - Dataframe of human labels for one clip.
-        plot_fig (boolean) - Whether or not the efficiency statistics should be displayed.
-        save_fig (boolean) - Whether or not the plot should be saved within a file.
-
-    Returns:
-        Dataframe with statistics comparing the automated and human labeling.
-    """
-    duration = automated_df["CLIP LENGTH"].to_list()[0]
-    SAMPLE_RATE = automated_df["SAMPLE RATE"].to_list()[0]
-    # Initializing two arrays that will represent the human labels and automated labels with respect to
-    # the audio clip
-    #print(SIGNAL.shape)
-    human_arr = np.zeros((int(SAMPLE_RATE*duration),))
-    bot_arr = np.zeros((int(SAMPLE_RATE*duration),))
-
-    folder_name = automated_df["FOLDER"].to_list()[0]
-    clip_name = automated_df["IN FILE"].to_list()[0]
-    # Placing 1s wherever the au
-    for row in automated_df.index:
-        minval = int(round(automated_df["OFFSET"][row]*SAMPLE_RATE,0))
-        maxval = int(round((automated_df["OFFSET"][row] + automated_df["DURATION"][row]) *SAMPLE_RATE,0))
-        bot_arr[minval:maxval] = 1
-    for row in human_df.index:
-        minval = int(round(human_df["OFFSET"][row]*SAMPLE_RATE,0))
-        maxval = int(round((human_df["OFFSET"][row] + human_df["DURATION"][row])*SAMPLE_RATE,0))
-        human_arr[minval:maxval] = 1
-
-    human_arr_flipped = 1 - human_arr
-    bot_arr_flipped = 1 - bot_arr
-
-    true_positive_arr = human_arr*bot_arr
-    false_negative_arr = human_arr * bot_arr_flipped
-    false_positive_arr = human_arr_flipped * bot_arr
-    true_negative_arr = human_arr_flipped * bot_arr_flipped
-    IoU_arr = human_arr + bot_arr
-    IoU_arr[IoU_arr == 2] = 1
-
-    plt.figure(figsize=(22,10))
-    plt.subplot(7,1,1)
-    plt.plot(human_arr)
-    plt.title("Ground Truth for " + clip_name)
-    plt.subplot(7,1,2)
-    plt.plot(bot_arr)
-    plt.title("Automated Label for " + clip_name)
-
-    #Visualizing True Positives for the Automated Labeling
-    plt.subplot(7,1,3)
-    plt.plot(true_positive_arr)
-    plt.title("True Positive for " + clip_name)
-
-    #Visualizing False Negatives for the Automated Labeling
-    plt.subplot(7,1,4)
-    plt.plot(false_negative_arr)
-    plt.title("False Negative for " + clip_name)
-
-    plt.subplot(7,1,5)
-    plt.plot(false_positive_arr)
-    plt.title("False Positive for " + clip_name)
-
-    plt.subplot(7,1,6)
-    plt.plot(true_negative_arr)
-    plt.title("True Negative for " + clip_name)
-
-    plt.subplot(7,1,7)
-    plt.plot(IoU_arr)
-    plt.title("Union for " + clip_name)
-
-    plt.tight_layout()
-    if save_fig == True:
-        x = clip_name.split(".")
-        clip_name = x[0]
-        plt.save_fig(clip_name + "_label_plot.png")
-
-
-
-
 
 # Will have to adjust the isolate function so that it adds a sampling rate onto the dataframes.
 def automated_labeling_statistics(automated_df,manual_df):