np.polynomial.hermite_e.hermeval2d method
The np.polynomial.hermite_e.hermeval2d from the NumPy library is used to Evaluate a 2-D Hermite_e series at points(x,y) in Python. If the parameters x and y are tuples or lists, they are converted to arrays otherwise they are treated as scalars and must have the same shape after conversion. In either case, x and y or their elements must support multiplication and addition with themselves as well as with the elements of c. If c is a one-dimensional array, a one is implicitly appended to its shape to make it two-dimensional. The final shape will be c.shape[2:] + x.shape.
Syntax: np.polynomial.hermite_e.hermeval2d(x, y, c)
Parameters :
- x , y: array like compatible objects.
- c: array like object.
Returns : The values of the two-dimensional polynomial at coordinates formed by corresponding pairs of x and y values.
Example 1:
The NumPy package is imported. An array is created which represents a 3D array of coefficients of the Hermite series. np.polynomial.hermite_e.hermeval2d(x, y, c) is used to evaluate a 2-D Hermite series, in the below example, arrays are given for x and y parameters which represent multiple points. The shape, datatype, and dimension of the array are found by using the .shape, .dtype, and .ndim attributes.
Python3
# import packages import numpy as np from numpy.polynomial import hermite_e as mit # array of coefficients array = np.array([[[ 5 , 6 ],[ 7 , 8 ],[ 9 , 10 ]]]) print (array) # shape of the array is print ( "Shape of the array is : " ,array.shape) # dimension of the array print ( "The dimension of the array is : " ,array.ndim) # evaluating a 2-d hermite series at point(x,y) # with 3D coeffiecients print (mit.hermeval2d([ 1 , 1 ],[ 2 , 2 ],array)) |
Output:
[[[ 5 6] [ 7 8] [ 9 10]]] Shape of the array is : (1, 3, 2) The dimension of the array is : 3 [[46. 46.] [52. 52.]]
Example 2:
The NumPy package is imported. An array is created using np.arange(12).reshape(2, 2, 3) which represents a 3D array of coefficients of the Hermite series. np.polynomial.hermite_e.hermeval2d(x, y, c) is used to evaluate a 2-D Hermite series, The shape, datatype, and dimension of the array are found by using the .shape, .dtype, and .ndim attributes.
Python3
# import packages import numpy as np from numpy.polynomial import hermite_e as mit # array of coefficients array = np.arange( 12 ).reshape( 2 , 2 , 3 ) print (array) # shape of the array is print ( "Shape of the array is : " ,array.shape) # dimension of the array print ( "The dimension of the array is : " ,array.ndim) # evaluating a 2-d hermite series at point(x,y) # with 3D coeffiecients print (mit.hermeval2d([ 1 , 1 ],[ 2 , 2 ],array)) |
Output:
[[[ 0 1 2] [ 3 4 5]] [[ 6 7 8] [ 9 10 11]]] Shape of the array is : (2, 2, 3) The dimension of the array is : 3 [[30. 30.] [36. 36.] [42. 42.]]
Evaluate a 2-D Hermite_e series at points (x,y) with 3D array of coefficient using NumPy in Python
In this article, we will cover how to evaluate a 2-D Hermite_e series at points (x,y) with a 3D array of coefficients using NumPy in Python.