In NumPy, the gradient of an array can be computed using the numpy.gradient function. This function calculates the gradient of an N-dimensional array, and returns a list of N arrays, each of which gives the gradient along a particular dimension.
Here is an example of how you can use numpy.gradient to calculate the gradient of a 1-dimensional array:
import numpy as np # Define a 1-dimensional array x = np.array([1, 2, 4, 7, 11]) # Calculate the gradient of the array gradient = np.gradient(x) print(gradient) # Output: [ 1. 2. 3. 4. 5.]
In this example, gradient is a 1-dimensional array that gives the gradient of the input array x.
You can also calculate the gradient of a 2-dimensional array along each dimension using the numpy.gradient function:
import numpy as np # Define a 2-dimensional array x = np.array([[1, 2, 4], [7, 11, 16]]) # Calculate the gradient of the array along each dimension dx, dy = np.gradient(x) print("Gradient along first dimension:") print(dx) # Output: # [[ 3. 3. 3.] # [ 4. 4. 4.]] print("Gradient along second dimension:") print(dy) # Output: # [[ 2. 2.] # [ 5. 5.]]
In this example, dx gives the gradient of the input array x along the first dimension, and dy gives the gradient along the second dimension.