Evaluate a 3D Laguerre series at points (x,y,z) with 4D array of coefficient using NumPy in Python

In this article, we will evaluate a 3D Laguerre Series at points (x,y) with 4 dimensional array of coefficient in Python.

laguerre.lagval3d()

The laguerre.lagval3d() is used to evaluate a 3D Laguerre series at points (x,y)  which returns the values of the multidimensional polynomial on points formed with triples of corresponding values from x, y, and z values, So we have to provide three lists such that each list has an x-point and y-point.

Syntax: laguerre.lagval3d(points,coefficient_array)

Parameters:

• Points(x,y): The first parameter can be a list of points - x and y.
• c: It is an numpy array of coefficients ordered such that it is of 3 Dimensions.

Return: multidimensional polynomial on points

Example 1:

In this example, we are creating numpy array with coefficients from 0 to 47 with (2*6) each and evaluate Laguerre Series at points [3,4],[1,2],[6,7].

# import numpy module
import numpy 
# import laguerre
from numpy.polynomial import laguerre

# Create 1d array of 6 elements
coefficient_array = numpy.arange(48).reshape(2,2,6,2)

# Display 
print(coefficient_array)

# display the Dimensions
print(coefficient_array.ndim)

# display Shape
print(coefficient_array.shape)

# Evaluate a 3D Laguerre series at 
# points (x,y) - [3,4],[1,2],[6,7]
print(laguerre.lagval3d([3,4],[1,2],[6,7],coefficient_array))

Output:

Example 2:

In this example we are creating NumPy array with coefficients from 0 to 47 with (2*6) each and evaluate Laguerre Series at points [0,0],[0,0] and [0,0].

# import numpy module
import numpy 
# import laguerre
from numpy.polynomial import laguerre

# Create 1d array of 6 elements
coefficient_array = numpy.arange(48).reshape(2,2,6,2)

# Display 
print(coefficient_array)

# display the Dimensions
print(coefficient_array.ndim)


# display Shape
print(coefficient_array.shape)

# Evaluate a 3D Laguerre series at points
# (x,y) - [0,0],[0,0],[0,0]
print(laguerre.lagval3d([0,0],[0,0],[0,0],coefficient_array))

Output: