%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt

X =np.linspace(-6,6, 1024)
Y =np.sinc(X)
plt.title('A simple marker exercise')# a title notation
plt.xlabel('array variables') # adding xlabel
plt.ylabel(' random variables') # adding ylabel
plt.text(-5, 0.4, 'Matplotlib') # -5 is the x value and 0.4 is y value
plt.plot(X,Y, color ='r', marker ='o', markersize =9, markevery = 30, markerfacecolor='w', linewidth = 3.0, markeredgecolor = 'b')

def pq(I, mu, sigma):
    a = 1. / (sigma * np.sqrt(2. * np.pi))
    b = -1. / (2. * sigma ** 2)
    return a * np.exp(b * (I - mu) ** 2)

I =np.linspace(-6,6, 1024)

plt.plot(I, pq(I, 0., 1.), color = 'k', linestyle ='solid')
plt.plot(I, pq(I, 0., .5), color = 'k', linestyle ='dashed')
plt.plot(I, pq(I, 0., .25), color = 'k', linestyle ='dashdot')

# I have created a dictinary of styles
design = {
'facecolor' : 'y', # color used for the text box
'edgecolor' : 'g', 
'boxstyle' : 'round' 
}
plt.text(-4, 1.5, 'Matplot Lib', bbox = design)
plt.plot(X, Y, c='k')
plt.show()

#This sets the style of the box, which can either be 'round' or 'square'
#'pad': If 'boxstyle' is set to 'square', it defines the amount of padding between the text and the box's sides

cd C:\Users\tk\Desktop

from IPython.display import Image
Image(filename='text alignment.png')
#The horizontal alignment options are as follows:
#'center': This is relative to the center of the textbox
#'left': This is relative to the left side of the textbox
#'right': This is relative to the right-hand side of the textbox

X = np.linspace(-4, 4, 1024)
Y = .25 * (X + 4.) * (X + 1.) * (X - 2.)

plt.annotate('Big Data', 
ha ='center', va ='bottom',
xytext =(-1.5, 3.0), xy =(0.75, -2.7), 
arrowprops ={'facecolor': 'green', 'shrink':0.05, 'edgecolor': 'black'}) #arrow properties
plt.plot(X, Y)

#arrow styles are :

from IPython.display import Image
Image(filename='arrows.png')

x =np.linspace(0, 6,1024)
y1 =np.sin(x)
y2 =np.cos(x)
plt.xlabel('Sin Wave')
plt.ylabel('Cos Wave')
plt.plot(x, y1, c='b', lw =3.0, label ='Sin(x)') # labels are specified
plt.plot(x, y2, c ='r', lw =3.0, ls ='--', label ='Cos(x)')
plt.legend(loc ='best', shadow = True, fancybox = False, title ='Waves', ncol =1) # displays the labels
plt.grid(True, lw = 2, ls ='--', c='.75') # adds grid lines to the figure
plt.show()

#Paths for several kinds of shapes are available in the matplotlib.patches module
import matplotlib.patches as patches

dis = patches.Circle((0,0), radius = 1.0, color ='.75' )
plt.gca().add_patch(dis) # used to render the image.

dis = patches.Rectangle((2.5, -.5), 2.0, 1.0, color ='.75') #patches.rectangle((x & y coordinates), length, breadth)
plt.gca().add_patch(dis)

dis = patches.Ellipse((0, -2.0), 2.0, 1.0, angle =45, color ='.00')
plt.gca().add_patch(dis)

dis = patches.FancyBboxPatch((2.5, -2.5), 2.0, 1.0, boxstyle ='roundtooth', color ='g')
plt.gca().add_patch(dis)

plt.grid(True)
plt.axis('scaled') # displays the images within the prescribed axis
plt.show()

#FancyBox: This is like a rectangle but takes an additional boxstyle parameter 
#(either 'larrow', 'rarrow', 'round', 'round4', 'roundtooth', 'sawtooth', or 'square')


import matplotlib.patches as patches
theta = np.linspace(0, 2 * np.pi, 8) # generates an array
vertical = np.vstack((np.cos(theta), np.sin(theta))).transpose() # vertical stack clubs the two arrays. 
#print vertical, print and see how the array looks
plt.gca().add_patch(patches.Polygon(vertical, color ='y'))
plt.axis('scaled')
plt.grid(True)
plt.show()

#The matplotlib.patches.Polygon()constructor takes a list of coordinates as the inputs, that is, the vertices of the polygon

# a polygon can be imbided into a circle
theta = np.linspace(0, 2 * np.pi, 6) # generates an array
vertical = np.vstack((np.cos(theta), np.sin(theta))).transpose() # vertical stack clubs the two arrays. 
#print vertical, print and see how the array looks
plt.gca().add_patch(plt.Circle((0,0), radius =1.0, color ='b'))
plt.gca().add_patch(plt.Polygon(vertical, fill =None, lw =4.0, ls ='dashed', edgecolor ='w'))
plt.axis('scaled')
plt.grid(True)
plt.show()


#In matplotlib, ticks are small marks on both the axes of a figure
import matplotlib.ticker as ticker
X = np.linspace(-12, 12, 1024)
Y = .25 * (X + 4.) * (X + 1.) * (X - 2.)
pl =plt.axes() #the object that manages the axes of a figure
pl.xaxis.set_major_locator(ticker.MultipleLocator(5))
pl.xaxis.set_minor_locator(ticker.MultipleLocator(1))
plt.plot(X, Y, c = 'y')
plt.grid(True, which ='major') # which can take three values: minor, major and both
plt.show()

name_list = ('Omar', 'Serguey', 'Max', 'Zhou', 'Abidin')
value_list = np.random.randint(0, 99, size = len(name_list))
pos_list = np.arange(len(name_list))
ax = plt.axes()
ax.xaxis.set_major_locator(ticker.FixedLocator((pos_list)))
ax.xaxis.set_major_formatter(ticker.FixedFormatter((name_list)))
plt.bar(pos_list, value_list, color = '.75',align = 'center')
plt.show()