{"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":"28c9c96e-1187-5d6e-b871-2e832012c515","task_key":"default--v0~2e1~2e0~5fhf--28c9c96e-1187-5d6e-b871-2e832012c515","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 28c9c96e-1187-5d6e-b871-2e832012c515","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nfrom scipy import stats\\nimport matplotlib.pyplot as plt\\ndef task_func(mu=0, sigma=1, sample_size=1000, seed=0):\\n\",\"complete_prompt\":\"import numpy as np\\nfrom scipy import stats\\nimport matplotlib.pyplot as plt\\n\\n\\ndef task_func(mu=0, sigma=1, sample_size=1000, seed=0):\\n    \\\"\\\"\\\"\\n    Generate a sample from a normal distribution with a given mean and a standard deviation and plot the histogram \\n    together with the probability density function. Returns the Axes object representing the plot and the empirical\\n    mean and standard deviation of the sample.\\n\\n    Parameters:\\n    - mu (float): The mean of the normal distribution. Default is 0.\\n    - sigma (float): The standard deviation of the normal distribution. Default is 1.\\n    - sample_size (int): The size of the sample to generate. Default is 1000.\\n\\n    Returns:\\n    - ax (matplotlib.axes._axes.Axes): Axes object with the plotted histogram and normal PDF, with the title format of 'Normal Distribution with $\\\\\\\\mu = %0.2f, \\\\\\\\sigma = %0.2f$'.\\n    - float: The empirical mean of the sample.\\n    - float: The empirical standard deviation of the sample.\\n\\n    Requirements:\\n    - numpy for data generation.\\n    - scipy.stats for statistical functions.\\n    - matplotlib.pyplot for plotting.\\n\\n    Example:\\n    >>> ax, mean, std = task_func(0, 1, 1000)\\n    >>> type(ax)\\n    <class 'matplotlib.axes._axes.Axes'>\\n    >>> print(round(mean, 3))\\n    -0.045\\n    >>> print(round(std, 3))\\n    0.987\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generate a sample from a normal distribution with a given mean and a standard deviation and plot the histogram together with the probability density function. Returns the Axes object representing the plot and the empirical mean and standard deviation of the sample.\\nThe function should output with:\\n    ax (matplotlib.axes._axes.Axes): Axes object with the plotted histogram and normal PDF, with the title format of 'Normal Distribution with $\\\\\\\\mu = %0.2f, \\\\\\\\sigma = %0.2f$'.\\n    float: The empirical mean of the sample.\\n    float: The empirical standard deviation of the sample.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nfrom scipy import stats\\nimport matplotlib.pyplot as plt\\ndef task_func(mu=0, sigma=1, sample_size=1000, seed=0):\\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":[]}