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
# 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)) |
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
# 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() |
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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