{"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":"b66ca888-7aef-58c2-86e1-e4a31fa4fba9","task_key":"default--v0~2e1~2e0~5fhf--b66ca888-7aef-58c2-86e1-e4a31fa4fba9","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf b66ca888-7aef-58c2-86e1-e4a31fa4fba9","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import matplotlib.pyplot as plt\\nfrom scipy.optimize import curve_fit\\ndef task_func(X, Y):\\n\",\"complete_prompt\":\"import matplotlib.pyplot as plt\\nfrom scipy.optimize import curve_fit\\n\\n\\ndef task_func(X, Y):\\n    \\\"\\\"\\\"\\n    Adjust a quadratic function to the given data (X, Y) and plot the data along with the fit.\\n\\n    Parameters:\\n    - X (list or numpy.array): The X data points.\\n    - Y (list or numpy.array): The Y data points.\\n\\n    Returns:\\n    tuple:\\n    - list: The optimized parameters of the quadratic function (a, b, c).\\n    - matplotlib.axes.Axes: The plot showing the scatter data points and the quadratic fit.\\n\\n    Requirements:\\n    - matplotlib.pyplot\\n    - scipy.optimize.curve_fit\\n\\n    Example:\\n    >>> import numpy as np\\n    >>> np.random.seed(42)\\n    >>> X = np.linspace(-10, 10, 100)\\n    >>> Y = 3*X**2 + 2*X + 1 + np.random.normal(0, 20, len(X))\\n    >>> params, ax = task_func(X, Y)\\n    >>> params\\n    [3.0366511660907975, 2.1379326607136035, -2.3233168384548284]\\n    >>> type(ax)\\n    <class 'matplotlib.axes._axes.Axes'>\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Adjust a quadratic function to the given data (X, Y) and plot the data along with the fit.\\nThe function should output with:\\n    tuple:\\n    list: The optimized parameters of the quadratic function (a, b, c).\\n    matplotlib.axes.Axes: The plot showing the scatter data points and the quadratic fit.\\nYou should write self-contained code starting with:\\n```\\nimport matplotlib.pyplot as plt\\nfrom scipy.optimize import curve_fit\\ndef task_func(X, Y):\\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":[]}