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BigCodeBench / BigCodeBench v0.1.0_hf 3eb69a2b-46f9-51fe-bdf6-0145750f910b

Problem

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

complete prompt

from scipy.optimize import curve_fit
import matplotlib.pyplot as plt

def task_func(l, x_data, plot=False):
    """
    Adjust a quadratic curve to the specified data and return the parameters and fitted values.
    
    Parameters:
    l (numpy array): The input y-values.
    x_data (numpy array): The x-values corresponding to l.
    plot (bool, optional): If True, a plot will be returned. Default is False.
    
    Returns:
    tuple: A tuple containing the following:
        - params (numpy array): Parameters of the fitted curve.
        - fitted_values (numpy array): Fitted y-values for the provided x_data.
        - ax (matplotlib.axes._axes.Axes, optional): Axes object of the plot if plot=True.

    Requirements:
    - scipy.optimize.curve_fit
    - matplotlib.pyplot

    Example:
    >>> import numpy as np
    >>> l = np.array([1, 4, 9, 16, 25])
    >>> x_data = np.array([1, 2, 3, 4, 5])
    >>> params, fitted_values = task_func(l, x_data)
    >>> print(fitted_values)
    [ 1.  4.  9. 16. 25.]
    """

instruct prompt

Adjust a quadratic curve to the specified data and return the parameters and fitted values.
The function should output with:
    tuple: A tuple containing the following:
    params (numpy array): Parameters of the fitted curve.
    fitted_values (numpy array): Fitted y-values for the provided x_data.
    ax (matplotlib.axes._axes.Axes, optional): Axes object of the plot if plot=True.
You should write self-contained code starting with:

Code

from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
def task_func(l, x_data, plot=False):

code prompt

Code

from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
def task_func(l, x_data, plot=False):

entry point

task_func

libs

  • matplotlib
  • scipy

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Source and history

Official source

initial import