{"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":"7e0e9c2d-f0cc-5d8e-8ebd-67af3b7e0cb1","task_key":"default--v0~2e1~2e0~5fhf--7e0e9c2d-f0cc-5d8e-8ebd-67af3b7e0cb1","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 7e0e9c2d-f0cc-5d8e-8ebd-67af3b7e0cb1","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nfrom scipy.optimize import curve_fit\\ndef task_func(x, y, labels):\\n\",\"complete_prompt\":\"import numpy as np\\nfrom scipy.optimize import curve_fit\\n\\n\\ndef task_func(x, y, labels):\\n    \\\"\\\"\\\"\\n    Fit an exponential curve to given data points and plot the curves with labels.\\n    It fits an exponential curve of the form: f(x) = a * exp(-b * x) + c\\n    to the provided x and y data points for each set of data and plots the fitted curves\\n    with the corresponding labels on a single matplotlib figure.\\n\\n    Parameters:\\n    - x (list of np.ndarray): List of numpy arrays, each representing the x-values of the data points for a dataset.\\n    - y (list of np.ndarray): List of numpy arrays, each representing the y-values of the data points for a dataset.\\n    - labels (list of str): List of strings, each representing the label for a dataset.\\n\\n    Returns:\\n    - matplotlib.figure.Figure: The figure object that contains the plotted curves.\\n\\n    Requirements:\\n    - numpy\\n    - scipy.optimize\\n\\n    Example:\\n    >>> x_data = [np.array([1,2,3]), np.array([4,5,6]), np.array([7,8,9])]\\n    >>> y_data = [np.array([4,5,6]), np.array([7,8,9]), np.array([10,11,12])]\\n    >>> labels = ['H2O', 'O2', 'CO2']\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Fit an exponential curve to given data points and plot the curves with labels. It fits an exponential curve of the form: f(x) = a * exp(-b * x) + c to the provided x and y data points for each set of data and plots the fitted curves with the corresponding labels on a single matplotlib figure.\\nThe function should output with:\\n    matplotlib.figure.Figure: The figure object that contains the plotted curves.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nfrom scipy.optimize import curve_fit\\ndef task_func(x, y, labels):\\n```\",\"libs\":\"['numpy', 'scipy']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}