In [1]:

    

import matplotlib.pyplot as plt
import numpy as np

import collections

import tensorflow as tf
import tensorflow_probability as tfp
tfd = tfp.distributions
tfb = tfp.bijectors

tfe = tf.contrib.eager
tfe.enable_eager_execution()

from __future__ import print_function


    

In [2]:

    

from scipy.stats import geom


    

In [7]:

    

rvs = geom.rvs(0.1, size=1000)


    

In [8]:

    

plt.plot(rvs)


    

    
Out[8]:





[<matplotlib.lines.Line2D at 0x2b8feb41c4d0>]






    

In [14]:

    

import pandas as pd
a = pd.DataFrame([[1,2,3], [4,5,9]])
a.mean(axis=0)


    

    
Out[14]:





0    2.5
1    3.5
2    6.0
dtype: float64

In [15]:

    

a.divide(a.mean(axis=0))


    

    
Out[15]:







  

    

      

      
0
      
1
      
2
    
  
  

    

      
0
      
0.4
      
0.571429
      
0.5
    
    

      
1
      
1.6
      
1.428571
      
1.5
    
  

In [16]:

    

import cv2
import numpy as np

ann_img = np.zeros((30,30,3)).astype('uint8')
ann_img[ 3 , 4 ] = 1 # this would set the label of pixel 3,4 as 1

cv2.imwrite( "ann_1.png" ,ann_img )


    

    
Out[16]:





True

In [18]:

    

ann_img[ 3 , 4 ]


    

    
Out[18]:





array([1, 1, 1], dtype=uint8)

In [19]:

    

a = {'d': 1, 'e': 2}
b = {'e': 2, 'd': 1}
a == b


    

    
Out[19]:





True

In [20]:

    

a.keys()


    

    
Out[20]:





['e', 'd']

In [ ]: