[data] new data

data/raw/ratna/copy_remove/f_306_haolan.py

@@ -0,0 +1,63 @@
+import matplotlib.pyplot as plt
+
+def f_306(number_list, bins):
+    fig, ax = plt.subplots()
+    ax.hist(number_list, bins=bins)
+    ax.set_title('Histogram')
+    ax.set_xlabel('Number')
+    ax.set_ylabel('Frequency')
+    plt.show()
+    return ax
+
+
+
+import unittest
+import matplotlib.pyplot as plt
+
+# Test data (this could be in a separate file or generated dynamically in real-world scenarios)
+test_data = {'small_dataset': [8, 8, 10, 2, 6, 8, 10, 2, 6, 7], 'large_dataset': [4, 9, 42, 79, 5, 60, 27, 58, 34, 61, 44, 68, 1, 78, 93, 11, 100, 69, 89, 45, 43, 7, 54, 31, 75, 64, 20, 93, 93, 95, 33, 19, 2, 6, 49, 18, 95, 62, 36, 52, 48, 61, 78, 61, 48, 17, 79, 4, 54, 63, 64, 37, 79, 22, 3, 24, 42, 1, 59, 25, 15, 53, 81, 86, 2, 34, 71, 80, 11, 36, 90, 37, 80, 48, 35, 66, 13, 57, 13, 16, 32, 42, 48, 96, 92, 60, 4, 14, 45, 45, 52, 88, 49, 71, 91, 77, 17, 27, 34, 18, 88, 41, 18, 65, 58, 18, 62, 77, 2, 5, 22, 2, 47, 39, 5, 17, 87, 85, 54, 7, 97, 32, 62, 92, 10, 45, 66, 58, 61, 25, 46, 10, 70, 60, 41, 5, 78, 79, 64, 36, 71, 45, 9, 11, 85, 51, 53, 71, 47, 88, 45, 37, 92, 72, 35, 70, 66, 28, 76, 97, 34, 13, 36, 88, 80, 86, 41, 91, 23, 2, 51, 61, 44, 50, 37, 90, 76, 45, 45, 51, 6, 12, 92, 16, 30, 74, 55, 58, 57, 77, 15, 51, 17, 48, 96, 89, 79, 16, 66, 30, 86, 53, 13, 61, 12, 66, 13, 94, 98, 82, 58, 19, 75, 22, 32, 24, 5, 49, 75, 16, 58, 36, 33, 79, 7, 58, 100, 54, 42, 74, 30, 52, 8, 68, 43, 97, 28, 47, 6, 51, 54, 62, 82, 4, 18, 82, 43, 72, 64, 97, 62, 90, 54, 1, 60, 27, 27, 42, 83, 100, 85, 73, 13, 5, 2, 96, 65, 28, 51, 28, 17, 35, 36, 71, 14, 53, 18, 23, 71, 85, 6, 1, 61, 68, 52, 9, 66, 37, 70, 91, 65, 59, 91, 55, 34, 86, 4, 48, 56, 55, 31, 21, 88, 41, 27, 81, 13, 34, 30, 42, 35, 94, 50, 82, 54, 4, 70, 52, 19, 38, 57, 89, 9, 35, 77, 79, 98, 29, 73, 92, 54, 38, 14, 71, 49, 15, 70, 16, 25, 79, 74, 76, 70, 7, 37, 36, 92, 51, 92, 37, 57, 10, 51, 3, 20, 66, 38, 1, 56, 15, 8, 46, 47, 75, 89, 24, 18, 84, 78, 66, 16, 76, 36, 58, 22, 96, 56, 22, 64, 9, 24, 74, 87, 50, 82, 1, 7, 73, 96, 91, 31, 61, 59, 95, 82, 92, 3, 37, 24, 22, 3, 54, 29, 52, 32, 82, 87, 42, 45, 4, 26, 96, 59, 42, 69, 51, 74, 25, 70, 90, 52, 30, 51, 69, 21, 8, 8, 65, 86, 26, 19, 61, 37, 58, 3, 21, 100, 7, 59, 5, 69, 38, 30, 11, 48, 9, 11, 7, 20, 46, 86, 63, 98, 51, 82, 51, 22, 18, 10, 34, 98, 54, 22, 51, 46, 54, 14, 79, 74, 84, 38, 25, 16, 28, 19, 100, 94, 87, 54, 81, 7, 56, 7, 7, 6, 1, 81, 40, 99, 88, 21, 28, 79, 74, 67, 16, 89, 17, 87, 86, 39, 75, 91, 87, 33, 25, 68, 25, 58, 96, 61, 92, 39, 50, 36, 30, 23, 28, 82, 52, 28, 23, 92, 17, 46, 62, 69, 80, 14, 96, 44, 98, 77, 39, 92, 69, 7, 22, 50, 12, 25, 76, 26, 34, 35, 99, 66, 97, 44, 79, 41, 41, 41, 41, 28, 17, 49, 79, 47, 56, 77, 27, 50, 6, 41, 59, 19, 15, 27, 58, 25, 62, 51, 12, 57, 38, 81, 88, 67, 82, 37, 8, 94, 77, 92, 88, 98, 59, 25, 9, 38, 48, 43, 23, 51, 11, 92, 32, 45, 46, 38, 54, 32, 45, 22, 65, 5, 66, 80, 84, 6, 80, 65, 14, 81, 19, 77, 7, 24, 46, 34, 53, 36, 48, 46, 81, 72, 55, 33, 66, 68, 34, 5, 14, 91, 35, 59, 61, 51, 92, 87, 10, 24, 33, 9, 89, 8, 28, 99, 4, 41, 56, 39, 25, 27, 80, 35, 28, 86, 21, 61, 73, 19, 68, 98, 70, 40, 89, 12, 31, 55, 92, 4, 52, 14, 13, 5, 91, 41, 56, 36, 70, 39, 51, 51, 39, 42, 39, 32, 84, 77, 31, 42, 46, 36, 59, 20, 30, 87, 3, 71, 34, 3, 43, 31, 81, 75, 53, 65, 77, 43, 92, 77, 46, 62, 24, 71, 80, 33, 10, 72, 75, 24, 79, 9, 20, 9, 58, 9, 72, 17, 15, 49, 82, 20, 39, 39, 29, 81, 42, 72, 60, 91, 6, 81, 85, 15, 38, 79, 60, 24, 20, 58, 97, 100, 34, 74, 66, 56, 55, 8, 61, 79, 86, 94, 75, 23, 53, 60, 71, 95, 47, 82, 98, 45, 3, 16, 53, 15, 100, 42, 37, 76, 59, 19, 40, 88, 8, 9, 42, 53, 83, 37, 86, 84, 3, 37, 14, 3, 66, 43, 22, 22, 3, 21, 94, 29, 13, 49, 30, 4, 3, 4, 2, 83, 41, 92, 21, 64, 50, 66, 39, 88, 29, 81, 8, 19, 41, 46, 50, 53, 41, 50, 74, 32, 22, 50, 21, 37, 3, 78, 7, 37, 97, 5, 50, 64, 1, 17, 43, 52, 52, 82, 47, 20, 66, 16, 51, 63, 92, 83, 53, 61, 99, 61, 37, 41, 63, 7, 8, 93, 7, 45, 74, 2, 68, 16, 12, 93, 99, 32, 32, 68, 9, 39, 67, 81, 6, 23, 30, 67, 49, 40, 6, 29, 29, 95, 88, 64, 54, 24, 16, 80, 24, 26, 56, 44, 20, 35, 93, 49, 5, 33, 1, 40, 94, 18, 73, 44, 85, 98, 25, 24, 84, 75, 68, 48, 96, 5, 81, 13, 90, 37, 26, 9, 52, 31, 88, 46, 40, 8, 63, 65, 50, 74, 86, 100, 86, 66, 24, 35, 95, 80, 30, 49, 16, 57, 14, 80, 28, 13, 28, 71, 3, 2, 94, 24, 43, 8, 53, 86, 25, 75, 59, 59, 48, 71, 19, 34, 72, 4, 17, 2, 60, 51, 21, 9, 32, 29, 25, 81, 32, 37, 93, 93, 65, 52, 48, 96, 78], 'uniform_dataset': [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5], 'empty_dataset': [], 'mixed_dataset': [30, 40, 20, 1, 20, 50, 1, 50, 20, 20, 1, 50, 20, 50, 10, 10, 1, 20, 20, 20, 20, 20, 1, 1, 40, 30, 30, 30, 30, 50, 1, 10, 40, 1, 30, 20, 40, 30, 50, 20, 50, 30, 40, 20, 20, 10, 40, 10, 50, 20]}
+
+def run_tests():
+    suite = unittest.TestSuite()
+    suite.addTest(unittest.makeSuite(TestCases))
+    runner = unittest.TextTestRunner()
+    runner.run(suite)
+
+class TestCases(unittest.TestCase):
+    def test_case_1(self):
+        ax = f_306(test_data["small_dataset"], 5)
+        self.assertIsInstance(ax, plt.Axes)
+        self.assertEqual(ax.get_title(), "Histogram")
+        self.assertEqual(ax.get_xlabel(), "Number")
+        self.assertEqual(ax.get_ylabel(), "Frequency")
+
+    def test_case_2(self):
+        ax = f_306(test_data["large_dataset"], 10)
+        self.assertIsInstance(ax, plt.Axes)
+        self.assertEqual(ax.get_title(), "Histogram")
+        self.assertEqual(ax.get_xlabel(), "Number")
+        self.assertEqual(ax.get_ylabel(), "Frequency")
+
+    def test_case_3(self):
+        ax = f_306(test_data["uniform_dataset"], 3)
+        self.assertIsInstance(ax, plt.Axes)
+        self.assertEqual(ax.get_title(), "Histogram")
+        self.assertEqual(ax.get_xlabel(), "Number")
+        self.assertEqual(ax.get_ylabel(), "Frequency")
+
+    def test_case_4(self):
+        ax = f_306(test_data["empty_dataset"], 5)
+        self.assertIsInstance(ax, plt.Axes)
+        self.assertEqual(ax.get_title(), "Histogram")
+        self.assertEqual(ax.get_xlabel(), "Number")
+        self.assertEqual(ax.get_ylabel(), "Frequency")
+
+    def test_case_5(self):
+        ax = f_306(test_data["mixed_dataset"], 6)
+        self.assertIsInstance(ax, plt.Axes)
+        self.assertEqual(ax.get_title(), "Histogram")
+        self.assertEqual(ax.get_xlabel(), "Number")
+        self.assertEqual(ax.get_ylabel(), "Frequency")
+
+if __name__ == "__main__":
+    run_tests()

data/raw/ratna/copy_remove/f_313_haolan.py

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+import numpy as np
+from scipy.stats import norm
+import matplotlib.pyplot as plt
+
+def f_313(length):
+    """
+    Create a normal distribution with a given length, plot its histogram alongside the 
+    probability density function, and return the distribution and the plot.
+    
+    Parameters:
+    - length (int): The length of the distribution to be generated.
+    
+    Returns:
+    - tuple: A tuple containing:
+        1. numpy array with the normal distribution.
+        2. matplotlib Axes object representing the plot.
+    
+    Requirements:
+    - numpy
+    - scipy.stats
+    - matplotlib.pyplot
+    
+    Constants:
+    - MU (mean): 0
+    - SIGMA (standard deviation): 1
+    
+    Example:
+    >>> distribution, ax = f_312(1000)
+    >>> print(type(distribution))
+    <class 'numpy.ndarray'>
+    >>> print(type(ax))
+    <class 'matplotlib.axes._subplots.AxesSubplot'>
+    """
+
+    MU = 0
+    SIGMA = 1
+
+    distribution = np.random.normal(MU, SIGMA, length)
+    fig, ax = plt.subplots()
+    ax.hist(distribution, 30, density=True, label='Histogram')
+    ax.plot(np.sort(distribution), norm.pdf(np.sort(distribution), MU, SIGMA), 
+            linewidth=2, color='r', label='PDF')
+    ax.legend()
+
+    return distribution, ax
+
+import unittest
+import numpy as np
+import matplotlib.pyplot as plt
+
+def run_tests():
+    suite = unittest.TestSuite()
+    suite.addTest(unittest.makeSuite(TestCases))
+    runner = unittest.TextTestRunner()
+    runner.run(suite)
+
+class TestCases(unittest.TestCase):
+    def test_case_1(self):
+        distribution, ax = f_313(1000)
+        self.assertIsInstance(distribution, np.ndarray, "Expected distribution to be a numpy array")
+        self.assertIsInstance(ax, plt.Axes, "Expected ax to be a matplotlib Axes object")
+
+    def test_case_2(self):
+        length = 500
+        distribution, _ = f_313(length)
+        self.assertEqual(len(distribution), length, f"Expected distribution length to be {length}")
+
+    def test_case_3(self):
+        distribution, _ = f_313(1000)
+        mean = distribution.mean()
+        std_dev = distribution.std()
+        self.assertAlmostEqual(mean, 0, delta=0.1, msg=f"Expected mean to be close to 0, got {mean}")
+        self.assertAlmostEqual(std_dev, 1, delta=0.1, msg=f"Expected std_dev to be close to 1, got {std_dev}")
+
+    def test_case_4(self):
+        distribution, ax = f_313(1000)
+        lines = ax.get_lines()
+        self.assertEqual(len(lines), 1, "Expected one line representing PDF in the plot")
+        bars = [rect for rect in ax.get_children() if isinstance(rect, plt.Rectangle)]
+        self.assertGreater(len(bars), 1, "Expected multiple bars representing histogram in the plot")
+
+    def test_case_5(self):
+        distribution, _ = f_313(2000)
+        self.assertEqual(distribution.shape, (2000,), "Expected shape of distribution to match input length")
+if __name__ == "__main__":
+    run_tests()

data/raw/ratna/copy_remove/f_314_haolan.py

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+import os
+
+def f_314(directory):
+    """
+    Removes all jQuery files (JavaScript files containing 'jquery' in their name) from a specified directory.
+
+    Parameters:
+    directory (str): The directory path.
+
+    Returns:
+    tuple: A tuple containing two elements:
+        - int: The number of files removed.
+        - list: The names of the removed files.
+
+    Raises:
+    FileNotFoundError: If the specified directory does not exist.
+
+    Example:
+    >>> f_280("/path/to/directory")
+    (3, ['jquery-1.js', 'jquery-2.js', 'jquery-ui.js'])  # Assuming 3 jQuery files were removed
+    """
+
+    # Check if directory exists
+    if not os.path.exists(directory):
+        raise FileNotFoundError(f"Directory '{directory}' does not exist.")
+
+    # Get all files in the directory
+    files = os.listdir(directory)
+
+    # Remove jQuery files
+    removed_files = 0
+    removed_file_names = []
+    for file in files:
+        if 'jquery' in file and file.endswith('.js'):
+            os.remove(os.path.join(directory, file))
+            removed_files += 1
+            removed_file_names.append(file)
+
+    return removed_files, removed_file_names
+
+import unittest
+from unittest.mock import MagicMock, patch
+
+class TestF1590(unittest.TestCase):
+
+    @patch('os.path.exists')
+    @patch('os.listdir')
+    @patch('os.remove')
+    def test_remove_jquery_files(self, mock_remove, mock_listdir, mock_exists):
+        mock_exists.return_value = True
+        mock_listdir.return_value = ['jquery-1.js', 'jquery-2.js', 'jquery-ui.js', 'otherfile.txt', 'example.js']
+        removed_count, removed_files = f_314('/fake/directory')
+        self.assertEqual(removed_count, 3)
+        self.assertListEqual(removed_files, ['jquery-1.js', 'jquery-2.js', 'jquery-ui.js'])
+
+    @patch('os.path.exists')
+    @patch('os.listdir')
+    def test_empty_directory(self, mock_listdir, mock_exists):
+        mock_exists.return_value = True
+        mock_listdir.return_value = []
+        removed_count, removed_files = f_314('/fake/empty/directory')
+        self.assertEqual(removed_count, 0)
+        self.assertListEqual(removed_files, [])
+
+    @patch('os.path.exists')
+    def test_nonexistent_directory(self, mock_exists):
+        mock_exists.return_value = False
+        with self.assertRaises(FileNotFoundError):
+            f_314('/fake/nonexistent/directory')
+
+    # More tests can be added here
+
+def run_tests():
+    suite = unittest.TestSuite()
+    suite.addTest(unittest.makeSuite(TestF1590))
+    runner = unittest.TextTestRunner()
+    runner.run(suite)
+
+if __name__ == '__main__':
+    run_tests()

data/raw/ratna/copy_remove/f_319_haolan.py

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+import networkx as nx
+import matplotlib.pyplot as plt
+import random
+import numpy as np
+
+# Constants
+N_NODES = 10
+
+def f_319(data):
+    '''
+    Add a new key "a" with the value 1 to the "data" dictionary, create a random graph with "data" as node attributes, 
+    and visualize the graph, where the nodes are labeled with the value of key "a".
+
+    Parameters:
+    - data (dict): The input data as a dictionary.
+
+    Returns:
+    - Graph (networkx.Graph): The generated NetworkX graph.
+    - Axes (matplotlib.axes._subplots.AxesSubplot): The Axes object representing the plot.
+
+    Requirements:
+    - networkx: Used for creating and handling the graph.
+    - matplotlib: Used for visualizing the graph.
+    - random: Used internally by networkx for generating random graphs.
+    - numpy: Used internally by networkx and matplotlib.
+
+    Example:
+    >>> data = {'key1': 'value1', 'key2': 'value2', 'key3': 'value1'}
+    >>> graph, axes = f_319(data)
+    '''
+    # Add new key 'a' with value 1
+    data['a'] = 1
+
+    # Generate a random graph with `data` as node attributes
+    G = nx.gnp_random_graph(N_NODES, 0.5, seed=1)
+    for node in G.nodes():
+        G.nodes[node].update(data)
+
+    # Visualize the graph
+    fig, ax = plt.subplots()
+    pos = nx.spring_layout(G, seed=1)
+    labels = nx.get_node_attributes(G, 'a')
+    nx.draw(G, pos, with_labels=True, labels=labels, ax=ax)
+
+    return G, ax
+
+
+
+import unittest
+import networkx as nx
+import matplotlib.pyplot as plt
+import random
+import numpy as np
+
+# Blackbox test cases
+def run_tests():
+    suite = unittest.TestSuite()
+    suite.addTest(unittest.makeSuite(TestCases))
+    runner = unittest.TextTestRunner()
+    runner.run(suite)
+
+class TestCases(unittest.TestCase):
+    def test_case_1(self):
+        # Input 1: Test with an empty dictionary
+        data = {}
+        graph, ax = f_319(data)
+
+        # Assertions
+        self.assertIsInstance(graph, nx.Graph)
+        self.assertEqual(len(graph.nodes), N_NODES)
+        for node_data in graph.nodes.data():
+            self.assertIn('a', node_data[1])
+            self.assertEqual(node_data[1]['a'], 1)
+        self.assertIsInstance(ax, plt.Axes)
+
+    def test_case_2(self):
+        # Input 1: Test with an empty dictionary
+        data = {}
+        graph, ax = f_319(data)
+
+        # Assertions
+        self.assertIsInstance(graph, nx.Graph)
+        self.assertEqual(len(graph.nodes), N_NODES)
+        for node_data in graph.nodes.data():
+            self.assertIn('a', node_data[1])
+            self.assertEqual(node_data[1]['a'], 1)
+        self.assertIsInstance(ax, plt.Axes)
+
+    def test_case_3(self):
+        # Input 1: Test with an empty dictionary
+        data = {}
+        graph, ax = f_319(data)
+
+        # Assertions
+        self.assertIsInstance(graph, nx.Graph)
+        self.assertEqual(len(graph.nodes), N_NODES)
+        for node_data in graph.nodes.data():
+            self.assertIn('a', node_data[1])
+            self.assertEqual(node_data[1]['a'], 1)
+        self.assertIsInstance(ax, plt.Axes)
+
+    def test_case_4(self):
+        # Input 1: Test with an empty dictionary
+        data = {}
+        graph, ax = f_319(data)
+
+        # Assertions
+        self.assertIsInstance(graph, nx.Graph)
+        self.assertEqual(len(graph.nodes), N_NODES)
+        for node_data in graph.nodes.data():
+            self.assertIn('a', node_data[1])
+            self.assertEqual(node_data[1]['a'], 1)
+        self.assertIsInstance(ax, plt.Axes)
+
+    def test_case_5(self):
+        # Input 1: Test with an empty dictionary
+        data = {}
+        graph, ax = f_319(data)
+
+        # Assertions
+        self.assertIsInstance(graph, nx.Graph)
+        self.assertEqual(len(graph.nodes), N_NODES)
+        for node_data in graph.nodes.data():
+            self.assertIn('a', node_data[1])
+            self.assertEqual(node_data[1]['a'], 1)
+        self.assertIsInstance(ax, plt.Axes)
+if __name__ == "__main__":
+    run_tests()