In [1]:

    

%pylab inline
import statsmodels as sm
import matplotlib.cm as cm


    

    




Populating the interactive namespace from numpy and matplotlib

In [7]:

    

width, height = 100, 100
np.random.seed(42)

# Mirch
r = np.random.normal(loc=1, scale=0.001, size=(width, height))
#r = np.ones((width, height))
g = np.zeros((width, height))
#g = np.random.normal(loc=0.001, scale=0.001, size=(width, height))
#b = np.random.normal(loc=1, scale=0.01, size=(width, height))
b = np.zeros((width, height))
#b = np.random.normal(loc=0.001, scale=0.001, size=(width, height))

mirch = np.dstack((r, g, b))  # stacks 3 h x w arrays -> h x w x 3

# Haldi
b = np.zeros((width, height))
#r = np.ones((width, height))
g = np.random.normal(loc=1, scale=0.001, size=(width, height))
#g = np.random.normal(loc=0.001, scale=0.001, size=(width, height))
#b = np.random.normal(loc=1, scale=0.01, size=(width, height))
r = np.random.normal(loc=1, scale=0.001, size=(width, height))

haldi = np.dstack((r,g,b))




# Namak
b = np.random.normal(loc=1, scale=0.001, size=(width, height))
#r = np.ones((width, height))
g = np.random.normal(loc=1, scale=0.001, size=(width, height))
#g = np.random.normal(loc=0.001, scale=0.001, size=(width, height))
#b = np.random.normal(loc=1, scale=0.01, size=(width, height))
r = np.random.normal(loc=1, scale=0.001, size=(width, height))
namak = np.dstack((r,g,b))

# Kali Mirch

b = np.random.normal(loc=1, scale=0.1, size=(width, height))
#r = np.ones((width, height))
g = np.random.normal(loc=1, scale=0.1, size=(width, height))
#g = np.random.normal(loc=0.001, scale=0.001, size=(width, height))
#b = np.random.normal(loc=1, scale=0.01, size=(width, height))
r = np.random.normal(loc=1, scale=0.1, size=(width, height))

kmirch = np.dstack((r,g,b))


    

In [8]:

    

plt.imshow(mirch, cmap=cm.RdYlGn,)


    

    
Out[8]:





<matplotlib.image.AxesImage at 0x2aec232a4690>






    

In [9]:

    

plt.imshow(haldi)


    

    
Out[9]:





<matplotlib.image.AxesImage at 0x2aec2336e410>






    

In [10]:

    

plt.imshow(0.5*mirch+0.5*haldi)


    

    
Out[10]:





<matplotlib.image.AxesImage at 0x2aec233de350>






    

In [12]:

    

plt.imshow(kmirch, cmap='gray')


    

    
Out[12]:





<matplotlib.image.AxesImage at 0x2aec234b53d0>






    

In [14]:

    

plt.imshow(np.random.random((width, height)), cmap='gray')


    

    
Out[14]:





<matplotlib.image.AxesImage at 0x2aec2359a090>






    

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