{"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":"index","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[],"history":"","construction":"","limitations":"","saturation":"","scores":[]}},"task_id":"4dd51cbf-6d40-5cb4-8991-df108d6cbaca","task_key":"default--v0~2e1~2e0~5fhf--4dd51cbf-6d40-5cb4-8991-df108d6cbaca","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 4dd51cbf-6d40-5cb4-8991-df108d6cbaca","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nfrom scipy.stats import norm\\nimport matplotlib.pyplot as plt\\ndef task_func(length):\\n\",\"complete_prompt\":\"import numpy as np\\nfrom scipy.stats import norm\\nimport matplotlib.pyplot as plt\\n\\ndef task_func(length):\\n    \\\"\\\"\\\"\\n    Create a normal distribution with a given length, plot its histogram alongside the \\n    probability density function, and return the distribution and the plot.\\n    \\n    Parameters:\\n    - length (int): The length of the distribution to be generated.\\n    \\n    Returns:\\n    - tuple: A tuple containing:\\n        1. numpy array with the normal distribution.\\n        2. matplotlib Axes object representing the plot.\\n    \\n    Requirements:\\n    - numpy\\n    - scipy.stats.norm\\n    - matplotlib.pyplot\\n    \\n    Note:\\n    - This function use this constant MU (mean): 0, SIGMA (standard deviation): 1\\n    \\n    Example:\\n    >>> np.random.seed(0)\\n    >>> distribution, ax = task_func(1000)\\n    >>> print(type(distribution))\\n    <class 'numpy.ndarray'>\\n    >>> len(ax.get_lines())\\n    1\\n    >>> plt.close()\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Create a normal distribution with a given length, plot its histogram alongside the probability density function, and return the distribution and the plot.\\nNote that: This function use this constant MU (mean): 0, SIGMA (standard deviation): 1\\nThe function should output with:\\n    tuple: A tuple containing:\\n    1. numpy array with the normal distribution.\\n    2. matplotlib Axes object representing the plot.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nfrom scipy.stats import norm\\nimport matplotlib.pyplot as plt\\ndef task_func(length):\\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":"index","subproblems":[],"grids":[]}