%matplotlib inline 
import matplotlib.pyplot as plt #importing matplot lib library
import numpy as np 
x = range(100) 
#print x, print and check what is x
y =[val**2 for val in x] 
#print y
plt.plot(x,y) #plotting x and y

x = np.linspace(0, 2*np.pi, 100)
y =np.sin(x)
plt.plot(x,y)

x= np.linspace(-3,2, 200)
Y = x ** 2 - 2 * x + 1.
plt.plot(x,Y)

# plotting multiple plots
x =np.linspace(0, 2 * np.pi, 100)
y = np.sin(x)
z = np.cos(x)
plt.plot(x,y) 
plt.plot(x,z)
plt.show()

# Matplot lib picks different colors for different plot. 

cd C:\Users\tk\Desktop\Matplot

data = np.loadtxt('numpy.txt')
plt.plot(data[:,0], data[:,1]) # plotting column 1 vs column 2
# The text in the numpy.txt should look like this
# 0 0
# 1 1
# 2 4
# 4 16
# 5 25
# 6 36

data1 = np.loadtxt('scipy.txt') # load the file
print data1.T

for val in data1.T: #loop over each and every value in data1.T
    plt.plot(data1[:,0], val) #data1[:,0] is the first row in data1.T
    
# data in scipy.txt looks like this:
# 0 0  6
# 1 1  5
# 2 4  4 
# 4 16 3
# 5 25 2
# 6 36 1


sct = np.random.rand(20, 2)
print sct
plt.scatter(sct[:,0], sct[:,1]) # I am plotting a scatter plot.

ghj =[5, 10 ,15, 20, 25]
it =[ 1, 2, 3, 4, 5]
plt.bar(ghj, it) # simple bar graph

ghj =[5, 10 ,15, 20, 25]
it =[ 1, 2, 3, 4, 5]
plt.bar(ghj, it, width =5)# you can change the thickness of a bar, by default the bar will have a thickness of 0.8 units

ghj =[5, 10 ,15, 20, 25]
it =[ 1, 2, 3, 4, 5]
plt.barh(ghj, it) # barh is a horizontal bar graph

new_list = [[5., 25., 50., 20.], [4., 23., 51., 17.], [6., 22., 52., 19.]]
x = np.arange(4) 
plt.bar(x + 0.00, new_list[0], color ='b', width =0.25)
plt.bar(x + 0.25, new_list[1], color ='r', width =0.25)
plt.bar(x + 0.50, new_list[2], color ='g', width =0.25)

#plt.show()

#Stacked Bar charts
p = [5., 30., 45., 22.]
q = [5., 25., 50., 20.]
x =range(4)
plt.bar(x, p, color ='b')
plt.bar(x, q, color ='y', bottom =p) 

# plotting more than 2 values
A = np.array([5., 30., 45., 22.])
B = np.array([5., 25., 50., 20.])
C = np.array([1., 2., 1., 1.])
X = np.arange(4)
plt.bar(X, A, color = 'b')
plt.bar(X, B, color = 'g', bottom = A)
plt.bar(X, C, color = 'r', bottom = A + B) # for the third argument, I use A+B
plt.show()

black_money = np.array([5., 30., 45., 22.]) 
white_money = np.array([5., 25., 50., 20.])
z = np.arange(4)
plt.barh(z, black_money, color ='g')
plt.barh(z, -white_money, color ='r')# - notation is needed for generating, back to back charts


#Pie charts
y = [5, 25, 45, 65]
plt.pie(y)

#Histograms
d = np.random.randn(100)
plt.hist(d, bins = 20)

d = np.random.randn(100)
plt.boxplot(d)
#1) The red bar is the median of the distribution
#2) The blue box includes 50 percent of the data from the lower quartile to the upper quartile. 
#   Thus, the box is centered on the median of the data.


d = np.random.randn(100, 5) # generating multiple box plots
plt.boxplot(d)