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Maria Elijah

NumPy’s library provides a method called ** heaviside()**, which is used to calculate the heaviside step function of the input array. This is done element by element.

Note:A list of lists can be used to create a two-dimensional (2D) array in Python.

```
numpy.heaviside(x1,x2,out=None, where=True)
```

`x1`

: This represents the input array.`x2`

: This is array-like, and represents the value of the function when`x1`

is 0.`x2`

is commonly assumed to be 0.5. However, 0 and 1 are also used.`out`

: This parameter is optional. It specifies where the result is stored.`where`

: This parameter is optional. It represents the condition in which the input gets broadcasted.

Note:If the shape of`x1`

is not equal to the shape of`x2`

, they must be broadcasted to a common shape.

The `numpy.heaviside()`

method returns the sign of each number of the input array.

The following code demonstrates how to use the `numpy.heaviside()`

method for Python two-dimensional (2D) arrays.

# import numpy import numpy as np # create 2D array using np.array x1 = np.array([[-7, -3.4 , 1.2], [2, 3 , -9.5]]) x2 = 0.5 # Compute the heaviside step function of the array # using np.heaviside() result = np.heaviside(x1,x2) print(result)

- Line 2: We import the
`numpy`

library. - Lines 4: We create a 2D array called
`x1`

. - Line 5: We create a variable
`x2`

and assign a value to it. - Line 8: We use the
`np.heaviside()`

method to compute the heaviside step function of the input array. - Line 10: We display the result.

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