{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"bigcodebench","formal_name":"BigCodeBench","introduction":"BigCodeBench poses 1,140 function-level tasks drawn across 139 libraries. It tests whether a model can compose several real APIs correctly rather than write one self-contained function.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://bigcode-bench.github.io/","indexing_mode":"noindex","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[]}},"task_id":"e72418c7-4429-5a58-8199-294bad97e33c","task_key":"default--v0~2e1~2e0~5fhf--e72418c7-4429-5a58-8199-294bad97e33c","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf e72418c7-4429-5a58-8199-294bad97e33c","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\nfrom scipy.ndimage import gaussian_filter\\ndef task_func(image, sigma=2):\\n\",\"complete_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\nfrom scipy.ndimage import gaussian_filter\\n\\ndef task_func(image, sigma=2):\\n    \\\"\\\"\\\"\\n    Apply a Gaussian filter to a given image and draw the original and filtered images side by side.\\n\\n    Parameters:\\n    - image (numpy.ndarray): The input image to apply the filter on.\\n    - sigma (float, optional): The sigma value for the Gaussian filter. Default is 2.\\n\\n    Returns:\\n    - ax (matplotlib.axes.Axes): Axes object containing the plot. Two plots with titles 'Original' and 'Filtered'. \\n    - filtered_image (numpy.ndarray): The numpy array of pixel values for the filtered image.\\n\\n    Raises:\\n    - ValueError: If sigma is non-positive.\\n    - TypeError: If the input is not a numpy array.\\n\\n    Requirements:\\n    - numpy\\n    - matplotlib.pyplot\\n    - scipy.ndimage\\n\\n    Example:\\n    >>> from skimage import data\\n    >>> ax, filtered_image = task_func(data.coins())\\n    >>> ax[0].get_title()  # Checking the title of the first subplot\\n    'Original'\\n    >>> ax[1].get_title()  # Checking the title of the second subplot\\n    'Filtered'\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Apply a Gaussian filter to a given image and draw the original and filtered images side by side.\\nThe function should raise the exception for: ValueError: If sigma is non-positive. TypeError: If the input is not a numpy array.\\nThe function should output with:\\n    ax (matplotlib.axes.Axes): Axes object containing the plot. Two plots with titles 'Original' and 'Filtered'.\\n    filtered_image (numpy.ndarray): The numpy array of pixel values for the filtered image.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\nfrom scipy.ndimage import gaussian_filter\\ndef task_func(image, sigma=2):\\n```\",\"libs\":\"['numpy', 'matplotlib', 'scipy']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"noindex","subproblems":[],"grids":[]}