fix: fix data due to the parsing error

data/clean/f_801_wenhao.py

@@ -2,14 +2,14 @@
 import re
 
 
-def f_801(text, seed=0):
+def f_801(text, seed=None):
     """
     Scramble the letters in each word of a given text, keeping the first and last letters of each word intact.
 
     Parameters:
     text (str): The text to be scrambled.
     seed (int, optional): A seed for the random number generator to ensure reproducible results.
-                          Defaults to 0.
+                          Defaults to None (not set).
 
     Returns:
     str: The scrambled text.
@@ -28,7 +28,8 @@ def f_801(text, seed=0):
     >>> f_801("Programming is fun, isn't it?", 42)
     "Prmiangmrog is fun, isn't it?"
     """
-    random.seed(seed)
+    if seed is not None:
+        random.seed(seed)
 
     def scramble_word(match):
         word = match.group(0)

data/processed/f_1708_hanhu_wo_doc.py

@@ -9,7 +9,7 @@ def f_335(request, session_expire_time):
     then sets this key in a cookie on an HttpResponse object with the specified expiration time.
 
     Parameters:
-    request (django.http.HttpRequest): The inco Django HttpRequest.
+    request (django.http.HttpRequest): The incoming Django HttpRequest.
     session_expire_time (int): The expiration time for the session cookie in seconds.
 
     Returns:

data/processed/f_1710_hanhu_wo_doc.py

@@ -10,7 +10,7 @@ def f_400(request, header, csv_data):
     CSV file in response to a user request on a Django web application.
 
     Parameters:
-    request (HttpRequest): The inco Django HttpRequest.
+    request (HttpRequest): The incoming Django HttpRequest.
     header (list of str): List of strings representing the header of the CSV file.
     csv_data (list of list of str): List of rows, with each row being a list of strings, to be written into the CSV file.
 

data/processed/f_1711_hanhu_wo_doc.py

@@ -10,7 +10,7 @@ def f_236(request, file_paths):
     is not utilized within the function but is required for compatibility with Django view structures.
 
     Parameters:
-    - request (HttpRequest): The inco Django HttpRequest, not used within the function.
+    - request (HttpRequest): The incoming Django HttpRequest, not used within the function.
     - file_paths (list of str): A list of file paths or file contents to be included in the zip.
 
     Returns:

data/processed/f_1712_hanhu_wo_doc.py

@@ -15,7 +15,7 @@ def f_475(template_folder):
 
     Returns:
     flask.app.Flask: A Flask application instance configured with a root route that handles POST requests.
-    The route logs inco request data as JSON and serves the 'index.html' template with the provided data.
+    The route logs incoming request data as JSON and serves the 'index.html' template with the provided data.
 
     Requirements:
     - flask.Flask

data/processed/f_1715_hanhu_wo_doc.py

@@ -146,7 +146,7 @@ def test_logout_route_redirects_to_login(self):
         with self.client as client:
             # Simulate an authenticated session
             with client.session_transaction() as sess:
-                sess['user_id'] = 'testuser'  # Assu the user loader can use this to load the user
+                sess['user_id'] = 'testuser'  # Assuming the user loader can use this to load the user
             # Manually set current_user for the duration of the test
             with patch('flask_login.utils._get_user') as mock_current_user:
                 mock_user = MagicMock()

data/processed/f_1728_hanhu_w_doc.py

@@ -93,7 +93,7 @@ def test_pdf_overlay_accuracy(self):
         mean, std_dev, num_samples = 0, 1, 1000
         _, fig = f_334(mean, std_dev, num_samples)
         ax = fig.axes[0]
-        line = ax.get_lines()[0]  # Assu the first line is the PDF
+        line = ax.get_lines()[0]  # Assuming the first line is the PDF
         x, y = line.get_data()
         expected_y = norm.pdf(x, mean, std_dev)
         np.testing.assert_array_almost_equal(y, expected_y, decimal=2)

data/processed/f_1764_hanhu_wo_doc.py

@@ -26,7 +26,7 @@ def f_668(ROOT_DIR, DEST_DIR, SPECIFIC_HASH):
     - hashlib
 
     Examples:
-    >>> # Assu the correct paths are given for ROOT_DIR, DEST_DIR,
+    >>> # Assuming the correct paths are given for ROOT_DIR, DEST_DIR,
     >>> # and at least one file in ROOT_DIR matches SPECIFIC_HASH:
     >>> type(f_668('/path/to/root', '/path/to/dest', 'd41d8cd98f00b204e9800998ecf8427e')) is int
     True

data/processed/f_1893_hanhu_wo_doc.py

@@ -56,7 +56,7 @@ def test_return_type(self, mock_get):
     @patch('requests.get')
     def test_handle_exceptions(self, mock_get):
         """Test that the function handles exceptions properly by not including IPs with failed requests."""
-        mock_get.side_effect = [requests.exceptions.ConnectionError] * 4  # Assu a /30 subnet, resulting in 4 attempts.
+        mock_get.side_effect = [requests.exceptions.ConnectionError] * 4  # Assuming a /30 subnet, resulting in 4 attempts.
         result = f_341('192.168.0.0/30', 5)
         # The expected result is adjusted since the function no longer returns False for failed requests but instead skips them.
         expected_result = []  # Expecting an empty list due to ConnectionError.

data/processed/f_203_wending_chien_minor_w_doc.py

@@ -80,6 +80,6 @@ def test_empty_keys(self):
             f_605(self.data_dict, data_keys)
     def test_key_not_in_dict(self):
         # Test with a key that's not in the dictionary
-        data_keys = ['D']  # Assu 'D' is not in `data_dict`
+        data_keys = ['D']  # Assuming 'D' is not in `data_dict`
         with self.assertRaises(ValueError):
             f_605(self.data_dict, data_keys)

data/processed/f_204_wending_chien_edit_w_doc.py

@@ -54,7 +54,7 @@ class TestCases(unittest.TestCase):
     def setUp(self):
         random.seed(0)
         # Correctly set up the mock within the test execution context
-        self.patcher = patch('random.randint', side_effect=[i % 100 for i in range(800)])  # Assu 8 students and 100 course entries
+        self.patcher = patch('random.randint', side_effect=[i % 100 for i in range(800)])  # Assuming 8 students and 100 course entries
         self.mock_randint = self.patcher.start()
         self.grades_df = f_445()
         self.patcher.stop()

data/processed/f_219_ratna_edit_w_doc.py

@@ -63,7 +63,7 @@ def test_non_empty_data(self):
         updated_data = f_114(data, key, min_value, max_value)
         self.assertIsInstance(updated_data, pd.DataFrame)
         self.assertTrue(key in updated_data.columns)
-        self.assertEqual(len(updated_data), 3)  # Assu the length of the input data is 3
+        self.assertEqual(len(updated_data), 3)  # Assuming the length of the input data is 3
         self.assertTrue(all(min_value <= val <= max_value for val in updated_data[key]))
 
     def test_negative_values(self):

data/processed/f_219_wending_chien_edit_w_doc.py

@@ -56,7 +56,7 @@ def f_426(df):
 class TestCases(unittest.TestCase):
     def test_case_1(self):
         data_1 = pd.DataFrame({
-            'Title': ['How to code?', 'What is Python?', 'The art of program', 'How to cook?', 'What is life?'],
+            'Title': ['How to code?', 'What is Python?', 'The art of programming', 'How to cook?', 'What is life?'],
             'Views': [1000, 500, 200, 300, 800],
             'Likes': [500, 250, 100, 150, 600]
         })
@@ -86,7 +86,7 @@ def test_case_3(self):
         self.assertIsInstance(ax, matplotlib.axes.Axes, "The returned object should be of type Axes.")
     def test_case_4(self):
         data_4 = pd.DataFrame({
-            'Title': ['Learning to code', 'Python basics', 'Advanced program', 'Cooking basics',
+            'Title': ['Learning to code', 'Python basics', 'Advanced programming', 'Cooking basics',
                       'Life and philosophy'],
             'Views': [1100, 450, 220, 320, 850],
             'Likes': [550, 225, 110, 160, 425]

data/processed/f_221_wending_chien_edit_w_doc.py

@@ -60,9 +60,9 @@ class TestCases(unittest.TestCase):
     def setUp(self):
         # Sample data for testing
         self.DATA = {
-            'Title': ['How to code?', 'What is Python?', 'The art of program', 'How to cook?', 'What is life?'],
-            'Content': ['This is a tutorial about coding...', 'Python is a program language...',
-                        'Program is an art...', 'This is a cooking tutorial...', 'Life is complicated...']
+            'Title': ['How to code?', 'What is Python?', 'The art of programming', 'How to cook?', 'What is life?'],
+            'Content': ['This is a tutorial about coding...', 'Python is a programming language...',
+                        'Programming is an art...', 'This is a cooking tutorial...', 'Life is complicated...']
         }
         self.df_sample = pd.DataFrame(self.DATA)
     def test_case_1(self):
@@ -73,14 +73,14 @@ def test_case_1(self):
     def test_case_2(self):
         # Test with no interesting articles
         df_no_interesting = self.df_sample.copy()
-        df_no_interesting['Title'] = ['Coding 101', 'Python tutorial', 'Program basics', 'Cooking basics',
+        df_no_interesting['Title'] = ['Coding 101', 'Python tutorial', 'Programming basics', 'Cooking basics',
                                       'Life basics']
         ax = f_693(df_no_interesting)
         self.assertEqual(len(ax.patches), 0)  # No bars in the plot as no interesting articles
     def test_case_3(self):
         # Test with only one interesting article
         df_one_interesting = self.df_sample.copy()
-        df_one_interesting['Title'] = ['How to play guitar?', 'Python tutorial', 'Program basics', 'Cooking basics',
+        df_one_interesting['Title'] = ['How to play guitar?', 'Python tutorial', 'Programming basics', 'Cooking basics',
                                        'Life basics']
         ax = f_693(df_one_interesting)
         self.assertEqual(len(ax.patches), 5)  # 5 unique words in the interesting article

data/processed/f_222_wending_chien_edit_w_doc.py

@@ -24,9 +24,9 @@ def f_183(df):
     Example:
     >>> import pandas as pd
     >>> df_sample = pd.DataFrame({
-    ...    'Title': ['How to code?', 'What is Python?', 'The art of program', 'How to cook?', 'What is life?'],
-    ...    'Content': ['This is a tutorial about coding...', 'Python is a program language...',
-    ...                'Program is an art...', 'This is a cooking tutorial...', 'Life is complicated...']
+    ...    'Title': ['How to code?', 'What is Python?', 'The art of programming', 'How to cook?', 'What is life?'],
+    ...    'Content': ['This is a tutorial about coding...', 'Python is a programming language...',
+    ...                'Programming is an art...', 'This is a cooking tutorial...', 'Life is complicated...']
     ... })
     >>> f_183(df_sample)
     [0, 1, 0, 1]
@@ -48,9 +48,9 @@ class TestCases(unittest.TestCase):
     def setUp(self):
         """Prepare environment and variables for tests."""
         self.df_sample = pd.DataFrame({
-            'Title': ['How to code?', 'What is Python?', 'The art of program', 'How to cook?', 'What is life?'],
-            'Content': ['This is a tutorial about coding...', 'Python is a program language...',
-                        'Program is an art...', 'This is a cooking tutorial...', 'Life is complicated...']
+            'Title': ['How to code?', 'What is Python?', 'The art of programming', 'How to cook?', 'What is life?'],
+            'Content': ['This is a tutorial about coding...', 'Python is a programming language...',
+                        'Programming is an art...', 'This is a cooking tutorial...', 'Life is complicated...']
         })
         os.environ['OMP_NUM_THREADS'] = '1'  # Setup environment variable for deterministic parallel processing
     def tearDown(self):

data/processed/f_2246_hanhu_w_doc.py

@@ -42,7 +42,7 @@ def f_565(dic):
 
 import unittest
 from unittest.mock import patch
-import folium  # Assu the function f_565 and folium are imported or defined appropriately.
+import folium  # Assuming the function f_565 and folium are imported or defined appropriately.
 class TestCases(unittest.TestCase):
     def test_return_type(self):
         """Test that the function returns a tuple with a map and a dictionary."""

data/processed/f_2248_hanhu_w_doc.py

@@ -90,5 +90,5 @@ def test_map_initialization(self, mock_map):
         """Test that the map is initialized with correct latitude and longitude."""
         locations = {'Loc1': {'Lat': 0, 'Lon': 0}, 'Loc2': {'Lat': 4, 'Lon': 4}}
         f_555(locations)
-        # Assu that the map is initialized at the location of the first entry in the dictionary
+        # Assuming that the map is initialized at the location of the first entry in the dictionary
         mock_map.assert_called_with(location=[0, 0], zoom_start=4)

data/processed/f_240_haolan_ratna_edit_w_doc.py

@@ -7,7 +7,7 @@
 def f_687(df, dct):
     """
     This function calculates and returns the mean, median, mode, and variance for specified features in a DataFrame. 
-    It replaces certain values in the DataFrame based on a provided dictionary mapping before perfor the calculations.
+    It replaces certain values in the DataFrame based on a provided dictionary mapping before performing the calculations.
     
     Parameters:
     df (DataFrame): The input DataFrame.

data/processed/f_2656_hanhu_wo_doc.py

@@ -5,7 +5,7 @@
 def f_471():
     """
     The f_471 method is a specialized handler for processing HTTP POST requests within a server setup,
-    primarily designed to handle JSON-formatted data. It meticulously checks inco requests to ensure they contain the
+    primarily designed to handle JSON-formatted data. It meticulously checks incoming requests to ensure they contain the
     expected 'data' key and have a Content-Type header set to application/json.
     If a request fails these checks, the method responds with an error status and a message indicating the specific validation failure.
     Conversely, when a request satisfies these criteria, it acknowledges with a success message,

data/processed/f_2657_hanhu_wo_doc.py

@@ -14,13 +14,13 @@
 
 def f_542():
     """
-    Creates an HTTP POST request handler for processing inco data. The data is expected
+    Creates an HTTP POST request handler for processing incoming data. The data is expected
     to be in JSON format with a key 'data'. The handler responds with a 200 success message
     if the data is valid, or an error message otherwise. 
     The type of the response can be retrieved as 'content-type' and the length of the response as 'content-length'.
 
     Returns:
-        function: A class that handles HTTP POST requests and validates inco data.
+        function: A class that handles HTTP POST requests and validates incoming data.
 
     Requirements:
     - cgi

data/processed/f_2659_hanhu_wo_doc.py

@@ -6,7 +6,7 @@
 
 def f_245(smtp_server, smtp_port, smtp_username, smtp_password):
     """
-    Creates an HTTP POST request handler that processes inco email data and sends
+    Creates an HTTP POST request handler that processes incoming email data and sends
     an email. The email data must be a JSON object with 'subject', 'message', and 'to' keys.
     The type of the response can be retrieved as 'content-type' and the length of the response as 'content-length'.
     

data/processed/f_280_haolan_ratna_edit_wo_doc.py

@@ -26,7 +26,7 @@ def f_540(directory):
 
     Example:
     >>> f_540("/path/to/directory")
-    (3, ['jquery-1.js', 'jquery-2.js', 'jquery-ui.js'])  # Assu 3 jQuery files were removed
+    (3, ['jquery-1.js', 'jquery-2.js', 'jquery-ui.js'])  # Assuming 3 jQuery files were removed
     """
     logging.basicConfig(filename='jquery_removal.log', level=logging.INFO,
                         format='%(asctime)s - %(levelname)s - %(message)s')

data/processed/f_287_haolan_ratna_edit_wo_doc.py

@@ -22,7 +22,7 @@ def f_408(filename):
     - shutil
 
     Example:
-    >>> f_408('vmware-cmd.bat') # Assu successful execution
+    >>> f_408('vmware-cmd.bat') # Assuming successful execution
     0
     >>> f_408('nonexistent.bat') # If backup fails or file doesn't exist
     -1

data/processed/f_3031_hanhu_w_doc.py

@@ -108,5 +108,5 @@ def test_plot_lines(self):
         """Test that the plot includes both real and imaginary parts of the complex wave."""
         _, _, ax = f_160(self.amplitude, self.frequency, self.time)
         lines = ax.get_lines()
-        # Assu the first line is the real part and the second line is the imaginary part
+        # Assuming the first line is the real part and the second line is the imaginary part
         self.assertEqual(len(lines), 2, "Plot does not contain two lines for real and imaginary parts")

data/processed/f_3047_hanhu_w_doc.py

@@ -58,7 +58,7 @@ def test_large_numbers(self):
     def test_negative_numbers(self):
         """Test function with a negative number."""
         with self.assertRaises(ValueError):
-            f_325([-1])  # Assu we want to enforce non-negative integers only
+            f_325([-1])  # Assuming we want to enforce non-negative integers only
     def test_very_large_number(self):
         """Test function with a very large number to check for performance or overflow issues."""
         number = 20  # A reasonable choice to avoid excessive computation time in tests

data/processed/f_3320_hanhu_w_doc.py

@@ -7,7 +7,7 @@
 def f_205(X, Y):
     """
     Trains a simple neural network on given input data and target labels. The function:
-    - Splits the data into a training set (75%) and a test set (25%), assu the input dimension is always 2.
+    - Splits the data into a training set (75%) and a test set (25%), assuming the input dimension is always 2.
     - Constructs a Sequential model with one dense hidden layer and a sigmoid activation function.
     - Compiles the model using binary cross-entropy loss and SGD optimizer with a specified learning rate.
     - Fits the model to the training data (without verbose output), also evaluating it on the test set as validation data.

data/processed/f_345_jenny_w_doc.py

@@ -135,7 +135,7 @@ def test_case_8(self):
         result = f_694(P, T)
         self.assertTrue(np.allclose(result, np.zeros((3, 15))))
     def test_case_9(self):
-        # Test DataFrame output for correct column names, ensuring they match expected feature na convention
+        # Test DataFrame output for correct column names, ensuring they match expected feature naming convention
         P = np.random.rand(3, 3)
         T = np.random.rand(3, 4, 4)
         result = f_694(P, T)

data/processed/f_3587_hanhu_wo_doc.py

@@ -35,7 +35,7 @@ def f_443(src_dir, dest_dir, ext):
     >>> moved_files = f_443(test_src_dir, test_dest_dir, test_ext)
     >>> len(moved_files) > 0  # Check if any files were moved
     True
-    >>> 'test_file.txt' in [os.path.basename(path) for path in moved_files]  # Assu test_file.txt exists in test_src_dir
+    >>> 'test_file.txt' in [os.path.basename(path) for path in moved_files]  # Assuming test_file.txt exists in test_src_dir
     True
     >>> os.listdir(test_dest_dir)  # Verify that files were moved, and no duplicates exist in the destination
     ['test_file.txt']

data/processed/f_3665_hanhu_wo_doc.py

@@ -42,7 +42,7 @@ def default(self, obj):
 import unittest
 from datetime import datetime
 from decimal import Decimal
-import pytz  # Assu pytz is used for timezone information in datetime objects
+import pytz  # Assuming pytz is used for timezone information in datetime objects
 class TestCases(unittest.TestCase):
     def test_datetime_serialization(self):
         """Ensure datetime objects are serialized to an ISO 8601 string."""

data/processed/f_382_jenny_wo_doc.py

@@ -31,7 +31,7 @@ def f_704(
                   equal-sized portions, the last timestamp may be excluded.
     - columns (list of str, optional): Names of the DataFrame columns to be included in the output.
                                        Defaults to: ['Timestamp', 'Sensor1', 'Sensor2', 'Sensor3', 'SensorStatus'].
-                                       Regardless of na, the function will populate the first column with
+                                       Regardless of naming, the function will populate the first column with
                                        timestamp, the middle columns with sensor data, and the final with status.
     - sensor_statuses (list of str, optional): Possible statuses for the sensors to randomly assign in the dataset.
                                                Defaults to: ['OK', 'MAINTENANCE_REQUIRED', 'ERROR'].

data/processed/f_385_jenny_wo_doc.py

@@ -107,7 +107,7 @@ def test_case_5(self):
             "2023-01-03 Not a valid entry\n"
             "WARNING - This log entry is missing its timestamp\n"
             "2023-01-04 15:00:00.000000 - INFO - System update completed\n"
-            "Some random text not confor to the log format\n"
+            "Some random text not conforming to the log format\n"
             "2023-01-04 16:00:00.000000 - ERROR - Error in processing\n"
         )
         log_file_path = self._create_temp_log_file("log5.txt", content)