{"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":"951f8d87-9de5-5c57-af79-97b4a81f387b","task_key":"default--v0~2e1~2e0~5fhf--951f8d87-9de5-5c57-af79-97b4a81f387b","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 951f8d87-9de5-5c57-af79-97b4a81f387b","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"from datetime import datetime\\nimport pandas as pd\\nimport numpy as np\\ndef task_func(start_time, end_time, step, amplitude, period, seed=0):\\n\",\"complete_prompt\":\"from datetime import datetime\\nimport pandas as pd\\nimport numpy as np\\n\\n\\ndef task_func(start_time, end_time, step, amplitude, period, seed=0):\\n    \\\"\\\"\\\"\\n    Generate a time series with a given seasonality from the start UTC time to the end UTC time\\n    with a given step, and plot the time series with the seasonality.\\n\\n    Parameters:\\n    - start_time (int): The start epoch time in milliseconds.\\n    = end_time (int): The end epoch time in milliseconds.\\n    - step (int): The step in milliseconds between each data point. Must be at least 1.\\n    - amplitude (float): The amplitude of the seasonality.\\n    - period (int): The period of the seasonality in milliseconds. Must be at least 0.\\n    - seed (int): Random seed for reproducibility. Defaults to 0.\\n\\n    Returns:\\n    matplotlib.pyplot.Axes: A plot of the generated 'Time Series with Seasonality',\\n              with 'Timestamp' on x-axis and 'Value' on y-axis.\\n\\n    Requirements:\\n    - datetime.datetime\\n    - pandas\\n    - numpy\\n\\n    Example:\\n    >>> ax = task_func(0, 10000, 100, 1, 1000)\\n    >>> type(ax)\\n    <class 'matplotlib.axes._axes.Axes'>\\n    >>> ax.get_xticklabels()\\n    [Text(-20.0, 0, '1970-01-01 10:00:08.000000'), Text(0.0, 0, '1970-01-01 10:00:00.000000'), Text(20.0, 0, '1970-01-01 10:00:02.000000'), Text(40.0, 0, '1970-01-01 10:00:04.000000'), Text(60.0, 0, '1970-01-01 10:00:06.000000'), Text(80.0, 0, '1970-01-01 10:00:08.000000'), Text(100.0, 0, ''), Text(120.0, 0, '')]\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generate a time series with a given seasonality from the start UTC time to the end UTC time with a given step, and plot the time series with the seasonality.\\nThe function should output with:\\n    matplotlib.pyplot.Axes: A plot of the generated 'Time Series with Seasonality',\\n    with 'Timestamp' on x-axis and 'Value' on y-axis.\\nYou should write self-contained code starting with:\\n```\\nfrom datetime import datetime\\nimport pandas as pd\\nimport numpy as np\\ndef task_func(start_time, end_time, step, amplitude, period, seed=0):\\n```\",\"libs\":\"['pandas', 'datetime', 'numpy']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}