{"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":"d5448d1c-9244-5a08-8687-06229ee478b5","task_key":"default--v0~2e1~2e0~5fhf--d5448d1c-9244-5a08-8687-06229ee478b5","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf d5448d1c-9244-5a08-8687-06229ee478b5","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\nfrom scipy.stats import norm\\ndef task_func(n_samples=1000, mu=0, sigma=1, random_seed=0):\\n\",\"complete_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\nfrom scipy.stats import norm\\n\\n\\ndef task_func(n_samples=1000, mu=0, sigma=1, random_seed=0):\\n    \\\"\\\"\\\"\\n    Generates a histogram and a probability density function (PDF) plot for a specified normal distribution.\\n\\n    This function draws n_samples from a normal distribution defined by mean (mu) and standard deviation (sigma),\\n    plots a histogram of the samples, and overlays the PDF of the normal distribution. The histogram's density\\n    is normalized, and the PDF is plotted with a red line with linewidth=2.\\n\\n    Parameters:\\n    - n_samples (int): Number of samples for the histogram. Must be greater than 0. Default is 1000.\\n    - mu (float): Mean for the normal distribution. Default is 0.\\n    - sigma (float): Standard deviation for the normal distribution. Must be greater than 0. Default is 1.\\n    - random_seed (int): Random seed for reproducibility. Defaults to 0.\\n\\n    Returns:\\n    - ax (matplotlib.axes._axes.Axes): Axes object with the histogram and PDF plotted.\\n    - samples (numpy.ndarray): Generated sample data.\\n\\n    Requirements:\\n    - numpy\\n    - matplotlib.pyplot\\n    - scipy.stats.norm\\n\\n    Example:\\n    >>> ax, samples = task_func()\\n    >>> type(ax)\\n    <class 'matplotlib.axes._axes.Axes'>\\n    >>> ax.get_xticklabels()\\n    [Text(-5.0, 0, '−5'), Text(-4.0, 0, '−4'), Text(-3.0, 0, '−3'), Text(-2.0, 0, '−2'), Text(-1.0, 0, '−1'), Text(0.0, 0, '0'), Text(1.0, 0, '1'), Text(2.0, 0, '2'), Text(3.0, 0, '3'), Text(4.0, 0, '4'), Text(5.0, 0, '5')]\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generates a histogram and a probability density function (PDF) plot for a specified normal distribution. This function draws n_samples from a normal distribution defined by mean (mu) and standard deviation (sigma), plots a histogram of the samples, and overlays the PDF of the normal distribution. The histogram's density is normalized, and the PDF is plotted with a red line with linewidth=2.\\nThe function should output with:\\n    ax (matplotlib.axes._axes.Axes): Axes object with the histogram and PDF plotted.\\n    samples (numpy.ndarray): Generated sample data.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\nfrom scipy.stats import norm\\ndef task_func(n_samples=1000, mu=0, sigma=1, random_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":[]}