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BigCodeBench / BigCodeBench v0.1.0_hf 5f2112d6-0f7e-5cdd-887b-0ab140f49178

Problem

Answer published by the source. Consult the official source to check your work against its answer.

complete prompt

import math
import numpy as np
from datetime import datetime
import pandas as pd


def task_func(
    start_time,
    end_time,
    step,
    columns=["Timestamp", "Sensor1", "Sensor2", "Sensor3", "SensorStatus"],
    sensor_statuses=["OK", "MAINTENANCE_REQUIRED", "ERROR"],
    random_seed=42,
):
    """
    Generate a DataFrame with detailed artificial sensor readings for specified timestamps
    and sensor statuses from a predefined list.

    The function generates sensor readings for Sensor1, Sensor2, and Sensor3 (or their
    corresponding named columns in the supplied column list) using sine, cosine, and tan
    functions, respectively, of the timestamp (converted to seconds), with a small random
    noise added to simulate real sensor data variability.
    SensorStatus is randomly chosen from the provided statuses for each timestamp.

    Parameters:
    - start_time (int): Start time in milliseconds since epoch.
    - end_time (int): End time in milliseconds since epoch. Must not be before start_time.
    - step (int): The interval in milliseconds between each generated data point. Must be positive.
                  This step defines the frequency at which data points are generated. If the step
                  does not neatly divide the interval between start_time and end_time into
                  equal-sized portions, the last timestamp may be excluded.
    - columns (list of str, optional): Names of the DataFrame columns to be included in the output.
                                       Defaults to: ['Timestamp', 'Sensor1', 'Sensor2', 'Sensor3', 'SensorStatus'].
                                       Regardless of naming, the function will populate the first column with
                                       timestamp, the middle columns with sensor data, and the final with status.
    - sensor_statuses (list of str, optional): Possible statuses for the sensors to randomly assign in the dataset.
                                               Defaults to: ['OK', 'MAINTENANCE_REQUIRED', 'ERROR'].
    - random_seed (int, optional): Seed for the random number generator to ensure reproducible results.
                                   Defaults to 42.

    Returns:
    - pd.DataFrame: Generated sensor readings for the given timestamps.

    Requirements:
    - math
    - datetime
    - numpy
    - pandas

    Example:
    >>> df = task_func(0, 5000, 1000)
    >>> type(df)
    <class 'pandas.core.frame.DataFrame'>
    >>> df.head(1)
                        Timestamp   Sensor1   Sensor2   Sensor3 SensorStatus
    0  1970-01-01 00:00:00.000000  0.049671  0.986174  0.064769        ERROR
    """

instruct prompt

Generate a DataFrame with detailed artificial sensor readings for specified timestamps and sensor statuses from a predefined list. The function generates sensor readings for Sensor1, Sensor2, and Sensor3 (or their corresponding named columns in the supplied column list) using sine, cosine, and tan functions, respectively, of the timestamp (converted to seconds), with a small random noise added to simulate real sensor data variability. SensorStatus is randomly chosen from the provided statuses for each timestamp.
The function should output with:
    pd.DataFrame: Generated sensor readings for the given timestamps.
You should write self-contained code starting with:

Code

import math
import numpy as np
from datetime import datetime
import pandas as pd
def task_func(
    start_time,
    end_time,
    step,
    columns=["Timestamp", "Sensor1", "Sensor2", "Sensor3", "SensorStatus"],
    sensor_statuses=["OK", "MAINTENANCE_REQUIRED", "ERROR"],
    random_seed=42,
):

code prompt

Code

import math
import numpy as np
from datetime import datetime
import pandas as pd
def task_func(
    start_time,
    end_time,
    step,
    columns=["Timestamp", "Sensor1", "Sensor2", "Sensor3", "SensorStatus"],
    sensor_statuses=["OK", "MAINTENANCE_REQUIRED", "ERROR"],
    random_seed=42,
):

entry point

task_func

libs

  • math
  • pandas
  • datetime
  • numpy

Discussion

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Source and history

task_id
5f2112d6-0f7e-5cdd-887b-0ab140f49178
task_key
default--v0~2e1~2e0~5fhf--5f2112d6-0f7e-5cdd-887b-0ab140f49178
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import