%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.
    """
    f=np.linspace(-1.0,1.0, size)
    N=np.empty(size)
    if sigma == 0.0:
        g=m*f+b
    else:
        for i in range(size):
            N[i]=np.random.normal(0,sigma**2)
        g=m*f+b+N
    return(f, g)

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."""
   
    X, Y = random_line(m, b, sigma, size=10)
    plt.scatter(X,Y, c=color)
    plt.title('A line with Gaussian Noise')
    plt.xlabel('x')
    plt.ylabel('y')
    plt.xlim(-1.1,1.1)
    plt.ylim(-10,10)
    plt.tick_params(axis='both', length=0)

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
"""


interact(plot_random_line, m=(-10.0,10.0,0.1), b=(-5.0,5.0,.1), sigma=(0.0,5.0,.01), size=(10,100,10), color=('red', 'green', 'blue'))

#### 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
"""


