To compute the logarithm base 2 with scimath, use the np.emath.log2() method in Python Numpy. The method returns the log base 2 of the x value(s). If x was a scalar, so is out, otherwise an array is returned. The 1st parameter, x is the value(s) whose log base 2 is (are) required.
Steps
At first, import the required libraries −
import numpy as np
Creating a numpy array using the array() method −
arr = np.array([np.inf, -np.inf, 16, np.exp(1), -np.exp(1), -32])
Display the array −
print("Our Array...\n",arr)Check the Dimensions −
print("\nDimensions of our Array...\n",arr.ndim)
Get the Datatype −
print("\nDatatype of our Array object...\n",arr.dtype)Get the Shape −
print("\nShape of our Array object...\n",arr.shape)
To compute the logarithm base 2 with scimath, use the np.emath.log2() method in Python Numpy. The method returns the log base 2 of the x value(s). If x was a scalar, so is out, otherwise an array is returned −
print("\nResult (log2)...\n",np.emath.log2(arr))Example
import numpy as np
# Creating a numpy array using the array() method
arr = np.array([np.inf, -np.inf, 16, np.exp(1), -np.exp(1), -32])
# Display the array
print("Our Array...\n",arr)
# Check the Dimensions
print("\nDimensions of our Array...\n",arr.ndim)
# Get the Datatype
print("\nDatatype of our Array object...\n",arr.dtype)
# Get the Shape
print("\nShape of our Array object...\n",arr.shape)
# To compute the logarithm base 2 with scimath, use the np.emath.log2() method in Python Numpy
# The method returns the log base 2 of the x value(s). If x was a scalar, so is out, otherwise an array is returned.
print("\nResult (log2)...\n",np.emath.log2(arr))Output
Our Array... [ inf -inf 16. 2.71828183 -2.71828183 -32. ] Dimensions of our Array... 1 Datatype of our Array object... float64 Shape of our Array object... (6,) Result (log2)... [ inf+0.j inf+4.53236014j 4. +0.j 1.44269504+0.j 1.44269504+4.53236014j 5. +4.53236014j]