Reformated code to fit good coding style. Ran coverage again to see coverage results.

htmlcov/index.html

@@ -93,7 +93,7 @@
     <div class="content">
         <p>
             <a class="nav" href="https://coverage.readthedocs.io">coverage.py v5.1</a>,
-            created at 2021-03-16 18:01
+            created at 2021-03-16 20:38
         </p>
     </div>
 </div>

htmlcov/status.json

-{"format":2,"version":"5.1","globals":"6f970bb3fd818c0086bb8745517713d8","files":{"sentiment_py":{"hash":"b80045c1e80c9b6b65165d4976b72548","index":{"nums":[1,269,0,85,104,9,43],"html_filename":"sentiment_py.html","relative_filename":"sentiment.py"}},"tests_py":{"hash":"9efee8c6f89bda3badab9e1807a6b3dd","index":{"nums":[1,95,0,1,2,1,1],"html_filename":"tests_py.html","relative_filename":"tests.py"}}}}
+{"format":2,"version":"5.1","globals":"6f970bb3fd818c0086bb8745517713d8","files":{"sentiment_py":{"hash":"b80045c1e80c9b6b65165d4976b72548","index":{"nums":[1,269,0,85,104,9,43],"html_filename":"sentiment_py.html","relative_filename":"sentiment.py"}},"tests_py":{"hash":"4b0cb5acfe4ac9eee3f1aa789b8d76ae","index":{"nums":[1,95,0,1,2,1,1],"html_filename":"tests_py.html","relative_filename":"tests.py"}}}}
\ No newline at end of file

sentiment.py

@@ -290,8 +290,6 @@ prompt_result = ''
 
 
 def main():
-    # Comment for test commit
-    # Comment for second test commit
     global prompt_result
     options = tuple(MenuOption)
     try:

tests.py

@@ -19,6 +19,7 @@ class TestComputeCollectionOfTokens(unittest.TestCase):
         unique_tokens = set(sentiment.get_all_tokens(list_reviews))
         self.assertEqual(75, len(unique_tokens))
 
+
 class TestComputeDocumentFrequency(unittest.TestCase):
     def test_frequency_of_three(self):
         list_reviews = ['+ absolutely detestable', '- bad bad bad']
@@ -70,7 +71,8 @@ class TestCalculateTFIDFScore(unittest.TestCase):
         all_tokens = sentiment.get_all_tokens(list_reviews)
         unique_tokens = set(all_tokens)
         token_map = sentiment.get_token_map(all_tokens, list_reviews)
-        total_negative_review_tokens, total_positive_review_tokens = sentiment.calculate_total_positives_and_negatives(token_map)
+        total_negative_review_tokens, total_positive_review_tokens = sentiment.calculate_total_positives_and_negatives(
+            token_map)
         sentiment.set_tfidf_score(unique_tokens, token_map, total_negative_review_tokens, total_positive_review_tokens)
 
         self.assertEqual(1.4381194289844768, token_map['positively'].tfidf_score)
@@ -96,6 +98,7 @@ class TestSetTFIDFClassification(unittest.TestCase):
         self.assertEqual(sentiment.TokenClassification.POSITIVE, token_map['positively'].token_classification)
         self.assertEqual(sentiment.TokenClassification.POSITIVE, token_map['introspective'].token_classification)
 
+
 class TestSetTokenFrequencyOfSentiment(unittest.TestCase):
     def test_set_token_frequency_of_sentiment(self):
         list_reviews = ['+ absolutely detestable ; would not watch again',
@@ -125,7 +128,8 @@ class TestShowSentenceStatistics(unittest.TestCase):
             list_reviews)
         # correct_output = 'The sentence has 5 negative, 0 neutral, 1 positive, and 1 unknown token(s).\nThe sentence has an average tf-idf score of -0.18812093738509109'
         negative, neutral, positive, unknown, total_tfidf_score = sentiment.prompt_for_sentence_statistics(False,
-                                                                                                           token_map, 'this quiet , introspective and entertaining independent is worth seeking .')
+                                                                                                           token_map,
+                                                                                                           'this quiet , introspective and entertaining independent is worth seeking .')
         self.assertEqual(0, negative)
         self.assertEqual(2, neutral)
         self.assertEqual(9, positive)
@@ -150,8 +154,10 @@ class TestSaveStopWordList(unittest.TestCase):
         list_reviews = ['0 reeks of rot and hack work from start to finish .',
                         '- plays like a series of vignettes -- clips of a film that are still looking for a common through-line .',
                         '+ it shows us a slice of life that \'s very different from our own and yet instantly recognizable .']
-        stop_words = ['.\n', 'a\n', 'of\n', 'to\n', '--\n', 'it\n', 'us\n', '\'s\n', 'rot\n', 'and\n', 'are\n', 'for\n', 'our\n', 'own\n', 'yet\n', 'hack\n', 'work\n', 'from\n', 'like\n',
-                      'film\n', 'that\n', 'life\n', 'very\n', 'reeks\n', 'start\n', 'plays\n', 'clips\n', 'still\n', 'shows\n', 'slice\n', 'finish\n', 'series\n', 'common\n', 'looking\n',
+        stop_words = ['.\n', 'a\n', 'of\n', 'to\n', '--\n', 'it\n', 'us\n', '\'s\n', 'rot\n', 'and\n', 'are\n', 'for\n',
+                      'our\n', 'own\n', 'yet\n', 'hack\n', 'work\n', 'from\n', 'like\n',
+                      'film\n', 'that\n', 'life\n', 'very\n', 'reeks\n', 'start\n', 'plays\n', 'clips\n', 'still\n',
+                      'shows\n', 'slice\n', 'finish\n', 'series\n', 'common\n', 'looking\n',
                       'vignettes\n', 'different\n', 'instantly\n', 'through-line\n', 'recognizable\n']
         all_tokens, token_map, total_negative_review_tokens, total_positive_review_tokens, unique_tokens = sentiment.create_data_structures(
             list_reviews)
@@ -169,7 +175,8 @@ class TestShowAdjustedSentenceStatistics(unittest.TestCase):
         all_tokens, token_map, total_negative_review_tokens, total_positive_review_tokens, unique_tokens = sentiment.create_data_structures(
             list_reviews)
         # correct_output = 'The sentence has 5 negative, 0 neutral, 1 positive, and 1 unknown token(s).\nThe sentence has an average tf-idf score of -0.18812093738509109'
-        stop_words, negative, neutral, positive, unknown, total_tfidf_score = sentiment.prompt_for_sentence_statistics(True, token_map, 'this quiet , introspective and entertaining independent is worth seeking .')
+        stop_words, negative, neutral, positive, unknown, total_tfidf_score = sentiment.prompt_for_sentence_statistics(
+            True, token_map, 'this quiet , introspective and entertaining independent is worth seeking .')
         self.assertEqual(2, stop_words)
         self.assertEqual(0, negative)
         self.assertEqual(1, neutral)