{"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":"de8bc864-d82b-5172-b043-d94bebf9832c","task_key":"default--v0~2e1~2e0~5fhf--de8bc864-d82b-5172-b043-d94bebf9832c","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf de8bc864-d82b-5172-b043-d94bebf9832c","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\nfrom scipy.fft import fft\\nANGLES = np.arange(0, 2*np.pi, 0.01)\\ndef task_func(n_waves, seed=0):\\n\",\"complete_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\nfrom scipy.fft import fft\\n\\n\\nANGLES = np.arange(0, 2*np.pi, 0.01)\\n\\ndef task_func(n_waves, seed=0):\\n    \\\"\\\"\\\"\\n    Generate a series of n sine waves with increasing frequency with a fidelity of 0.01 radians as \\n    provided by the ANGLES array. The amplitude of each wave is 1. The function returns a list of\\n    numpy arrays with the y values of the sine waves. Additionally, calculate the Fast Fourier Transform\\n    (FFT) of the mixed signal and plot the histogram of the magnitude of the FFT data. If n_waves is less\\n    than 1, return an empty list for the sine waves, an empty array for the FFT data, and None for the axes\\n    object.\\n    \\n    Parameters:\\n    n_waves (int): The number of sine waves in the series.\\n    seed (int, Optional): The seed for the random number generator. Defaults to 0.\\n    \\n    Returns:\\n    list: A list of numpy arrays with the y values of the sine waves.\\n    np.array: FFT data.\\n    plt.Axes: The axes object of the plot.\\n    \\n    Requirements:\\n    - numpy\\n    - matplotlib.pyplot\\n    - scipy.fft\\n\\n    Example:\\n    >>> sine_waves, fft_data, ax = task_func(5)\\n    >>> len(sine_waves)\\n    5\\n    >>> fft_data.shape\\n    (629,)\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generate a series of n sine waves with increasing frequency with a fidelity of 0.01 radians as provided by the ANGLES array. The amplitude of each wave is 1. The function returns a list of numpy arrays with the y values of the sine waves. Additionally, calculate the Fast Fourier Transform (FFT) of the mixed signal and plot the histogram of the magnitude of the FFT data. If n_waves is less than 1, return an empty list for the sine waves, an empty array for the FFT data, and None for the axes object.\\nThe function should output with:\\n    list: A list of numpy arrays with the y values of the sine waves.\\n    np.array: FFT data.\\n    plt.Axes: The axes object of the plot.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\nfrom scipy.fft import fft\\nANGLES = np.arange(0, 2*np.pi, 0.01)\\ndef task_func(n_waves, 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":[]}