Return the gradient of an N-dimensional array and specify edge order in Python

The gradient is computed using second order accurate central differences in the interior points and either first or second order accurate one-sides (forward or backwards) differences at the boundaries. The returned gradient hence has the same shape as the input array. The 1st parameter, f is an Ndimensional array containing samples of a scalar function. The 2nd parameter is the varargs i.e. the spacing between f values. Default unitary spacing for all dimensions.

The 3rd parameter is the edge_order{1, 2} i.e. the Gradient is calculated using N-th order accurate differences at the boundaries. Default: 1. The 4th parameter is the Gradient, which is calculated only along the given axis or axes. The default (axis = None) is to calculate the gradient for all the axes of the input array. axis may be negative, in which case it counts from the last to the first axis. The method returns a list of ndarrays corresponding to the derivatives of f with respect to each dimension. Each derivative has the same shape as f.

Steps

At first, import the required libraries −

import numpy as np

Creating a numpy array using the array() method. We have added elements of float type −

arr = np.array([20, 35, 57, 70, 85, 120], dtype = float)


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)

The gradient is computed using second order accurate central differences in the interior points and either first or second order accurate one-sides (forward or backwards) differences at the boundaries. The returned gradient hence has the same shape as the input array −

print("\nResult (gradient)...\n",np.gradient(arr, edge_order=2))


Example

import numpy as np

# Creating a numpy array using the array() method
# We have added elements of float type
arr = np.array([20, 35, 57, 70, 85, 120], dtype = float)

# 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)

# The gradient is computed using second order accurate central differences in the interior points and either first or second order accurate one-sides (forward or backwards) differences at the boundaries. The returned gradient hence has the same shape as the input array.
print("\nResult (gradient)...\n",np.gradient(arr, edge_order=2))

Output

Our Array...
[ 20. 35. 57. 70. 85. 120.]

Dimensions of our Array...
1

Datatype of our Array object...
float64

[11.5 18.5 17.5 14. 25. 45. ]

Updated on: 28-Feb-2022

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