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BigCodeBench / BigCodeBench v0.1.0_hf 1268c156-c89d-59ef-a072-df2ddee09769

Problem

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

complete prompt

import numpy as np
import matplotlib.pyplot as plt
from scipy.fft import fft


def task_func(signal, precision=2, seed=777):
    """
    Calculate the one-dimensional discrete N-point Fourier Transform (DFT) for a real or complex sequence (signal) 
    using the Fast Fourier Transform (FFT) algorithm. Plot the original signal and the transformed signal, rounding 
    the transformed signal values to the specified accuracy. The title of the plots will be 'Original Signal' and 'Transformed Signal'.

    Parameters:
    - signal (array): An array representing the signal.
    - precision (int, optional): The number of decimal places to which to round the transformed signal values. 
                                 Defaults to 2.
    - seed (int, optional): The seed for the random number generator. Defaults to 777.

    Returns:
    - ndarray: A numpy array of transformed signal values (rounded to the specified precision).
    - tuple: A tuple containing the Axes objects for the original signal and transformed signal plots.

    Requirements:
    - numpy
    - matplotlib
    - scipy

    Example:
    >>> signal = np.array([0., 1., 0., -1.])
    >>> transformed_signal, (ax1, ax2) = task_func(signal)
    >>> print(transformed_signal)
    [0.-0.j 0.-2.j 0.-0.j 0.+2.j]
    """

instruct prompt

Calculate the one-dimensional discrete N-point Fourier Transform (DFT) for a real or complex sequence (signal) using the Fast Fourier Transform (FFT) algorithm. Plot the original signal and the transformed signal, rounding the transformed signal values to the specified accuracy. The title of the plots will be 'Original Signal' and 'Transformed Signal'.
The function should output with:
    ndarray: A numpy array of transformed signal values (rounded to the specified precision).
    tuple: A tuple containing the Axes objects for the original signal and transformed signal plots.
You should write self-contained code starting with:

Code

import numpy as np
import matplotlib.pyplot as plt
from scipy.fft import fft
def task_func(signal, precision=2, seed=777):

code prompt

Code

import numpy as np
import matplotlib.pyplot as plt
from scipy.fft import fft
def task_func(signal, precision=2, seed=777):

entry point

task_func

libs

  • numpy
  • matplotlib
  • scipy

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Source and history

task_id
1268c156-c89d-59ef-a072-df2ddee09769
task_key
default--v0~2e1~2e0~5fhf--1268c156-c89d-59ef-a072-df2ddee09769
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import