{"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":"ada7f6e5-b232-5541-a6ee-49de37e73392","task_key":"default--v0~2e1~2e0~5fhf--ada7f6e5-b232-5541-a6ee-49de37e73392","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf ada7f6e5-b232-5541-a6ee-49de37e73392","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(mean, std_dev, num_samples):\\n\",\"complete_prompt\":\"import numpy as np\\nfrom scipy.stats import norm\\nimport matplotlib.pyplot as plt\\n\\ndef task_func(mean, std_dev, num_samples):\\n    \\\"\\\"\\\"\\n    Generates a histogram of samples drawn from a normal distribution and overlays\\n    the probability density function (PDF) of the normal distribution. The plot is titled\\n    with the fit results, showing the mean and standard deviation used in the generation.\\n    The function returns both the plot and the samples generated.\\n\\n    Parameters:\\n        mean (float): The mean of the normal distribution.\\n        std_dev (float): The standard deviation of the normal distribution.\\n        num_samples (int): The number of samples to draw from the distribution.\\n\\n    Requirements:\\n    - numpy\\n    - scipy.stats.norm\\n    - matplotlib.pyplot\\n\\n    Notes:\\n    - The plot title is \\\"Fit results: mean = %.2f, std = %.2f\\\". This title format on the plot displays the mean and standard deviation\\n        of the normal distribution used to generate the histogram. The values are presented in a format where %.2f\\n        is replaced by the floating-point numbers corresponding to `mean` and `std_dev` respectively, rounded to two decimal places.\\n    - The number of bins is set to 30\\n\\n    Returns:\\n        tuple: A tuple containing:\\n            - matplotlib.figure.Figure: The figure object for the plot.\\n            - numpy.ndarray: An array of samples drawn from the normal distribution.\\n\\n    Examples:\\n    >>> import matplotlib\\n    >>> samples, fig = task_func(0, 1, 1000)\\n    >>> len(samples)\\n    1000\\n    >>> type(samples)\\n    <class 'numpy.ndarray'>\\n    >>> isinstance(fig, matplotlib.figure.Figure)\\n    True\\n\\n    Note: The actual values in the array depend on the random seed and will vary each time the function is called.\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generates a histogram of samples drawn from a normal distribution and overlays the probability density function (PDF) of the normal distribution. The plot is titled with the fit results, showing the mean and standard deviation used in the generation. The function returns both the plot and the samples generated.\\nNote that: Notes: The plot title is \\\"Fit results: mean = %.2f, std = %.2f\\\". This title format on the plot displays the mean and standard deviation of the normal distribution used to generate the histogram. The values are presented in a format where %.2f is replaced by the floating-point numbers corresponding to `mean` and `std_dev` respectively, rounded to two decimal places. The number of bins is set to 30 The actual values in the array depend on the random seed and will vary each time the function is called.\\nThe function should output with:\\n    tuple: A tuple containing:\\n    matplotlib.figure.Figure: The figure object for the plot.\\n    numpy.ndarray: An array of samples drawn from the normal distribution.\\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(mean, std_dev, num_samples):\\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":[]}