{"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":"3eb69a2b-46f9-51fe-bdf6-0145750f910b","task_key":"default--v0~2e1~2e0~5fhf--3eb69a2b-46f9-51fe-bdf6-0145750f910b","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 3eb69a2b-46f9-51fe-bdf6-0145750f910b","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"from scipy.optimize import curve_fit\\nimport matplotlib.pyplot as plt\\ndef task_func(l, x_data, plot=False):\\n\",\"complete_prompt\":\"from scipy.optimize import curve_fit\\nimport matplotlib.pyplot as plt\\n\\ndef task_func(l, x_data, plot=False):\\n    \\\"\\\"\\\"\\n    Adjust a quadratic curve to the specified data and return the parameters and fitted values.\\n    \\n    Parameters:\\n    l (numpy array): The input y-values.\\n    x_data (numpy array): The x-values corresponding to l.\\n    plot (bool, optional): If True, a plot will be returned. Default is False.\\n    \\n    Returns:\\n    tuple: A tuple containing the following:\\n        - params (numpy array): Parameters of the fitted curve.\\n        - fitted_values (numpy array): Fitted y-values for the provided x_data.\\n        - ax (matplotlib.axes._axes.Axes, optional): Axes object of the plot if plot=True.\\n\\n    Requirements:\\n    - scipy.optimize.curve_fit\\n    - matplotlib.pyplot\\n\\n    Example:\\n    >>> import numpy as np\\n    >>> l = np.array([1, 4, 9, 16, 25])\\n    >>> x_data = np.array([1, 2, 3, 4, 5])\\n    >>> params, fitted_values = task_func(l, x_data)\\n    >>> print(fitted_values)\\n    [ 1.  4.  9. 16. 25.]\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Adjust a quadratic curve to the specified data and return the parameters and fitted values.\\nThe function should output with:\\n    tuple: A tuple containing the following:\\n    params (numpy array): Parameters of the fitted curve.\\n    fitted_values (numpy array): Fitted y-values for the provided x_data.\\n    ax (matplotlib.axes._axes.Axes, optional): Axes object of the plot if plot=True.\\nYou should write self-contained code starting with:\\n```\\nfrom scipy.optimize import curve_fit\\nimport matplotlib.pyplot as plt\\ndef task_func(l, x_data, plot=False):\\n```\",\"libs\":\"['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":[]}