numpy.log2() in Python

numpy.log2(arr, out = None, *, where = True, casting = ‘same_kind’, order = ‘K’, dtype = None, ufunc ‘log1p’) :
This mathematical function helps user to calculate Base-2 logarithm of x where x belongs to all the input array elements.

Parameters :

array    : [array_like]Input array or object.
out      : [ndarray, optional]Output array with same dimensions as Input array, 
           placed with result.
**kwargs : Allows you to pass keyword variable length of argument to a function. 
           It is used when we want to handle named argument in a function.
where    : [array_like, optional]True value means to calculate the universal 
           functions(ufunc) at that position, False value means to leave the 
           value in the output alone.

Return :



An array with Base-2 logarithmic value of x; 
where x belongs to all elements of input array. 

Code 1 : Working

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# Python program explaining
# log2() function
import numpy as np
  
in_array = [1, 3, 5, 2**8]
print ("Input array : ", in_array)
  
out_array = np.log2(in_array)
print ("Output array : ", out_array)
  
  
print("\nnp.log2(4**4) : ", np.log2(4**4))
print("np.log2(2**8) : ", np.log2(2**8))

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Output :

Input array :  [1, 3, 5, 256]
Output array :  [ 0.          1.5849625   2.32192809  8.        ]

np.log2(4**4) :  8.0
np.log2(2**8) :  8.0

 
Code 2 : Graphical representation

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# Python program showing
# Graphical representation of 
# log2() function
import numpy as np
import matplotlib.pyplot as plt
  
in_array = [1, 1.2, 1.4, 1.6, 1.8, 2]
out_array = np.log2(in_array)
  
print ("out_array : ", out_array)
  
plt.plot(in_array, in_array, color = 'blue', marker = "*")
  
# red for numpy.log2()
plt.plot(out_array, in_array, color = 'red', marker = "o")
plt.title("numpy.log2()")
plt.xlabel("out_array")
plt.ylabel("in_array")
plt.show()  

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Output :

out_array :  [ 0.          0.26303441  0.48542683  0.67807191  0.84799691  1.        ]


References :
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.exp.html
.



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