Merge pull request #116 from UCSD-E4E/minor_bug_fixes

Corrected typos and deleted redundant file
UCSD-E4E · May 30, 2022 · f98ba0f · f98ba0f
2 parents 97faf00 + f2873f6
commit f98ba0f
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Showing 4 changed files with 5 additions and 109 deletions.
diff --git a/PyHa/microfaune_package/microfaune/data_augmentation.py b/PyHa/microfaune_package/microfaune/data_augmentation.py
@@ -49,14 +49,14 @@ def generate_augmentation(self, spec, y_val, my_range=5, to_display=False):
         """ data augmentation of one Spectrogram
         Parameters
         ----------
-        spec spectogram
+        spec spectrogram
         y_val classification value
 
         Returns
         -------
         list_s
-            list of Spectograms with Y list (duplicate from y input)
-            the first Spectograms is the given input S
+            list of Spectrograms with Y list (duplicate from y input)
+            the first Spectrograms is the given input S
         list_y
             All y have the value of the given y
         """

diff --git a/PyHa/microfaune_package/microfaune/dataaugmentation.py b/PyHa/microfaune_package/microfaune/dataaugmentation.py
diff --git a/PyHa/microfaune_package/microfaune/labeling.py b/PyHa/microfaune_package/microfaune/labeling.py
@@ -246,7 +246,7 @@ def extract_labels(json_path, start_time, duration):
         Returns
         -------
             labels: list
-                List of labelson the audio extract, each label is a dictionary with keys 'id', 'start', 'end' and 'annotation'
+                List of labels on the audio extract, each label is a dictionary with keys 'id', 'start', 'end' and 'annotation'
         """
     data_dict = read_json_file(json_path)
     labels = []

diff --git a/PyHa/statistics.py b/PyHa/statistics.py
@@ -367,7 +367,7 @@ def clip_IoU(automated_df, manual_df):
 
 def matrix_IoU_Scores(IoU_Matrix, manual_df, threshold):
     """
-    Function that takes in the IoU Matrix from the clip_IoU function and ouputs
+    Function that takes in the IoU Matrix from the clip_IoU function and outputs
     the number of true positives and false positives, as well as calculating
     the precision, recall, and f1 metrics.