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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Interact Exercise 4
Imports
Step2:  Line with Gaussian noise
Write a function named random_line that creates x and y data for a line with y direction random noise that has a normal distribution $N(0,\sigma^2)$
Step5:  Write a function named plot_random_line that takes the same arguments as random_line and creates a random line using random_line and then plots the x and y points using Matplotlib's scatter function
Step6:  Use interact to explore the plot_random_line function using
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Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
Explanation: Interact Exercise 4
Imports
End of explanation
def random_line(m, b, sigma, size=10):
    Create a line y = m*x + b + N(0,sigma**2) between x=[-1.0,1.0]
    
    Parameters
    ----------
    m : float
        The slope of the line.
    b : float
        The y-intercept of the line.
    sigma : float
        The standard deviation of the y direction normal distribution noise.
    size : int
        The number of points to create for the line.
    
    Returns
    -------
    x : array of floats
        The array of x values for the line with `size` points.
    y : array of floats
        The array of y values for the lines with `size` points.
    
    # YOUR CODE HERE
    x=np.linspace(-1,1,size)
    y=m*x+b + sigma*np.random.randn(size)
    return x,y
m = 0.0; b = 1.0; sigma=0.0; size=3
x, y = random_line(m, b, sigma, size)
assert len(x)==len(y)==size
assert list(x)==[-1.0,0.0,1.0]
assert list(y)==[1.0,1.0,1.0]
sigma = 1.0
m = 0.0; b = 0.0
size = 500
x, y = random_line(m, b, sigma, size)
assert np.allclose(np.mean(y-m*x-b), 0.0, rtol=0.1, atol=0.1)
assert np.allclose(np.std(y-m*x-b), sigma, rtol=0.1, atol=0.1)
Explanation: Line with Gaussian noise
Write a function named random_line that creates x and y data for a line with y direction random noise that has a normal distribution $N(0,\sigma^2)$:
$$
y = m x + b + N(0,\sigma^2)
$$
Be careful about the sigma=0.0 case.
End of explanation
def ticks_out(ax):
    Move the ticks to the outside of the box.
    ax.get_xaxis().set_tick_params(direction='out', width=1, which='both')
    ax.get_yaxis().set_tick_params(direction='out', width=1, which='both')
def plot_random_line(m, b, sigma, size=10, color='red'):
    Plot a random line with slope m, intercept b and size points.
    # YOUR CODE HERE
    f=plt.figure(figsize=(9,6))
    x=np.linspace(-1,1,size)
    y=m*x+b + sigma*np.random.randn(size)
    plt.scatter(x,y)
    plt.ylim(-10,10)
    plt.xlim(-1.1,1.1)
    plt.title("Plot of Line With Set Slope and Y-Intercept, with Random Noise Added Along Slope")
    plt.xlabel("X-Axis")
    plt.ylabel("Y-Axis")
    plt.tick_params(direction='out')
    plt.tight_layout
plot_random_line(5.0, -1.0, 2.0, 50)
assert True # use this cell to grade the plot_random_line function
Explanation: Write a function named plot_random_line that takes the same arguments as random_line and creates a random line using random_line and then plots the x and y points using Matplotlib's scatter function:
Make the marker color settable through a color keyword argument with a default of red.
Display the range $x=[-1.1,1.1]$ and $y=[-10.0,10.0]$.
Customize your plot to make it effective and beautiful.
End of explanation
# YOUR CODE HERE
interact (plot_random_line,m=(-10.0,10.0,0.1),b=(-5.0,5.0,0.1),sigma=(0,5.0,0.01),size=(10,100,10),color={'red':'r.','green':'g.','blue':'b.'})
#### assert True # use this cell to grade the plot_random_line interact
Explanation: Use interact to explore the plot_random_line function using:
m: a float valued slider from -10.0 to 10.0 with steps of 0.1.
b: a float valued slider from -5.0 to 5.0 with steps of 0.1.
sigma: a float valued slider from 0.0 to 5.0 with steps of 0.01.
size: an int valued slider from 10 to 100 with steps of 10.
color: a dropdown with options for red, green and blue.
End of explanation
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