#
# Copyright (c) 2018 James Hume (www.jeh-tech.com). All rights reserved.
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#    This product includes software developed by James Hume (www.jeh-tech.com)
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#
import matplotlib.pyplot as pl
import numpy as np
import matplotlib.animation as animation
import math
import numpy as np

##
## Project a vector onto the line defined by the vector v
## x - vector (numpy array) to be projected onto the line L
## v - vector (numpy array) defining the line L: L = {a * v}
def proj_x_onto_L(x, v):
    return (x.dot(v) / np.power(np.linalg.norm(v), 2)) * v

##
## Plot a 2D vector starting at an origin
## vec - vector (array or list) to plot, starting at origin
## origin - origin point of vector
def plot_vec(vec, origin, *args, **kwargs):
    if origin is not None:
        return ax.plot([origin[0], origin[0] + vec[0]], [origin[1], origin[1] + vec[1]], *args, **kwargs)[0]
    else:
        return ax.plot([0, vec[0]], [0, vec[1]], *args, **kwargs)[0]

##
## Plot 2D line plane
##
## Get the limits of the x-axis and plot a line that spans the entire x-axis to make
## the line look "infinite"
##
## vec - Vector (array or list) defining the line vector m if L = {a * m + b}
## yintercept - This is b if L = {a * m + b}
def plot_line_plane(vec, yintercept, *args, **kwargs):
    xmin, xmax = ax.get_xlim()
    m = vec[1]/vec[0]
    ymin = xmin * m + yintercept
    ymax = xmax * m + yintercept

    return ax.plot([xmin, xmax], [ymin, ymax], *args, **kwargs)[0]

def annotate_line(line, annotate_at_x, annotate_txt, xytext):
    x1, x2 = line.get_xdata()
    y1, y2 = line.get_ydata()
    
    m = (y2 - y1) / (x2 - x1)
    yintercept = y1 - m*x1

    pl.annotate( annotate_txt
               , xy=(annotate_at_x, annotate_at_x * m + yintercept)
               , xytext=xytext
               , textcoords='offset points'
               , fontsize='medium'
               , arrowprops=dict(shrink=0.05, connectionstyle="arc3,rad=0.1", fc=line.get_color())
    )

##
## Setup figure and axis so that graph axis at (0,0) and so that x/y limits 
## leave enough room to make figure look "good"
fig, ax = pl.subplots()
ax.set_xlim([-1.5, 2.5])
ax.set_ylim([-0.5, 2.5])
ax.spines['left'].set_position('zero')
ax.spines['bottom'].set_position('zero')
ax.spines['top'].set_color('none')
ax.spines['right'].set_color('none')

##
## Define noisy data points
data_points = [
    np.array([-1, 0]),
    np.array([0, 1]),
    np.array([1, 2]),
    np.array([2, 1])
]

##
## A color for each noisey data point
colours = ['red', 'green', 'blue', 'purple']

##
## Define the least squares solution
lss_vec = np.array([1., 2./5.])
lss_yintercept = np.array([0, 4./5.])

##
## Build projection of each data point vector onto LSS
projections = []
for dp in data_points:
    projections.append(proj_x_onto_L(dp, lss_vec))    

##
## Plot the data points
for i, (dp, c) in enumerate(zip(data_points, colours)):
    ax.plot(dp[0], dp[1], 'x', color=c)
    ax.text(dp[0], dp[1] + 0.03, '$\\vec{{d_{}}}$'.format(i), color=c)

##
## Plot the least squares solution line
lss_line = plot_line_plane(lss_vec, lss_yintercept[1], color='grey')
lss_line_through_origin = plot_line_plane(lss_vec, 0, color='black')


##
## Plot the projections - and here we see that the projections are onto the line
## passing through the origin, because to project onto a vector, it must pass through
## the origin because if L = c \vec{v}, for all real c's, when c is zero, it passes
## through origin.
orthogs = []
for dp, p, c in zip(data_points, projections, colours):
    l = plot_vec(p - dp, dp, color=c, alpha=0.5)
    orthogs.append(l)

p2 = plot_vec(projections[2], [0, 0], color=colours[2], alpha=1.)

annotate_line(lss_line, -1.25, r'Least squares solution', (-40, 35))
annotate_line(lss_line_through_origin, 2, 'LSS through origin', (-30, -30))
annotate_line(p2, 1., 'Formula for $\\mathrm{{proj}}_L(\\vec{{d_2}})$ projects onto\nLLS through origin, not onto actual LSS!!', (-50, -95))

fig.show()
pl.show()