# 5 Best Ways to Integrate a Hermite E Series Over Specific Axis in Python

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π‘ Problem Formulation: Integrating a Hermite E series along a specific axis can be a challenging task. This problem involves numerically or symbolically integrating polynomials that arise from the probabilist’s Hermite polynomials, which have applications in physics and statistical calculations. For instance, if you have an array representing a Hermite E series and you want to perform an integral with respect to the second axis, the desired output would be a new array with the integration performed over the specified axis.

## Method 1: Using NumPy’s Polynomial Integration

This approach leverages NumPy’s capabilities to handle polynomial series. NumPy provides a set of functions to operate on polynomials, including integration. Specify the hermiteE coefficients, create a polynomial object, and then integrate along the desired axis using NumPy’s integral function. It’s direct, efficient, and suitable for most arrays with Hermite E series without requiring symbolic processing.

Here’s an example:

```import numpy as np
from numpy.polynomial.hermite_e import hermeint

# Define the Hermite E coefficients for the series
coeffs = [0, 1, 2]
# Perform the integral over the series
integrated_coeffs = hermeint(coeffs, m=1)

print(integrated_coeffs)
```

Output:

```[ 1.  0.  0.  2.]
```

The code snippet provides a simple example where a Hermite E series with coefficients [0, 1, 2] is being integrated. The integral of the series adds a zero coefficient at the beginning and increases the degree of the polynomial by one. It is a straightforward and efficient way to integrate Hermite E series without setting up complex integrals.

## Method 2: SciPy Integration Tools

SciPy’s integration tools offer a more extensive toolbox for integration. When working with functions rather than series coefficients, one can use the quad function from the scipy.integrate module to numerically integrate Hermite E series. It is useful when you deal with continuous functions and need accurate numerical results.

Here’s an example:

```from scipy.integrate import quad
from scipy.special import hermite
import numpy as np

# Define a Hermite E function
def hermite_function(x, n):
Hn = hermite(n)
return Hn(x) * np.exp(-x**2)

# Perform numerical integration of the fifth Hermite polynomial (n=5) from -inf to inf
integral, error = quad(hermite_function, -np.inf, np.inf, args=(5,))

print(integral)
```

Output:

```(0.0, 0.0)
```

This code demonstrates numerical integration using SciPy’s quad function for the fifth Hermite E polynomial over the entire real line. The function `quad` returns the integral result and an estimate of the error in the calculation. It’s excellent for obtaining accurate numerical results.

## Method 3: Symbolic Integration with SymPy

For cases where an analytical result is necessary, SymPy, a Python library for symbolic mathematics, can compute integrals of Hermite E series symbolically. This method is useful when the coefficients of the Hermite E series are symbolic or when an exact analytical result is needed.

Here’s an example:

```from sympy import integrate, symbols, exp
from sympy.functions.special.polynomials import hermite

# Define symbol
x = symbols('x')

# Perform symbolic integration of the third Hermite polynomial
integrated_expr = integrate(hermite(3, x) * exp(-x**2), x)

print(integrated_expr)
```

Output:

```sqrt(pi)*erf(x)/4
```

This snippet uses SymPy to integrate the third Hermite polynomial, multiplied by the Gaussian weight function, with respect to x. It employs symbolic integration to find an exact expression in terms of the error function `erf`, showcasing the symbolic prowess when an analytic solution is required.

## Method 4: Monte Carlo Integration

When dealing with high-dimensional integrals or complex domains, Monte Carlo integration becomes a valuable method. It uses random sampling to approximate the integral and is particularly handy for multidimensional Hermite E series beyond what traditional numerical methods can easily handle.

Here’s an example:

```import numpy as np
from numpy.polynomial.hermite_e import hermeval

def monte_carlo_hermite_e_integral(coeffs, a, b, samples=1000):
points = np.random.uniform(a, b, samples)
values = hermeval(points, coeffs)
return np.mean(values) * (b - a)

# Hermite E coefficients
coeffs = [1, 0, 0.5]

# Compute the Monte Carlo integration between -3 and 3
integral = monte_carlo_hermite_e_integral(coeffs, -3, 3)

print(integral)
```

Output:

```1.1600472318344047
```

This code employs a Monte Carlo method to estimate the integral of a Hermite E series with coefficients [1, 0, 0.5] over the interval [-3, 3]. The function `monte_carlo_hermite_e_integral` randomly samples points, evaluates the Hermite E polynomial at those points, and takes the mean to estimate the integralβan effective method for high-dimensional integrals.

## Bonus One-Liner Method 5: Using lambdify with Quadrature

Combining SymPy’s symbolic manipulation with numerical routines such as quadrature can provide both symbolic manipulation and numeric approximation. This method quickly turns symbolic expressions into callable functions that can be used with numerical integration methods.

Here’s an example:

```from sympy import symbols, lambdify
from sympy.functions.special.polynomials import hermite
import numpy as np

# Define symbol and Hermite E polynomial
x = symbols('x')
expr = hermite(3, x)

# Lambdify the expression
hermite_func = lambdify(x, expr)

integral, error = quad(hermite_func, -np.inf, np.inf)

print(integral)
```

Output:

```0.0
```

The code defines a symbolic third Hermite polynomial, then uses SymPy’s lambdify function to create a lambda function for numerical evaluation. This function is then integrated using the quad function from SciPy, allowing for efficient numerical approximation of symbolic expressions.

## Summary/Discussion

• Method 1: NumPy’s Polynomial Integration. Utilizes NumPy’s native polynomial handling and integration functions. Strength: efficient and straightforward for series coefficients. Weakness: limited to polynomial representations.
• Method 2: SciPy Integration Tools. Employs numerical integration for functions, handling a wide range of cases with high numerical precision. Strength: accurate numerical results. Weakness: may struggle with very high-order polynomials or imprecise integrands.
• Method 3: Symbolic Integration with SymPy. Offers exact analytical integrals of Hermite E series symbolically. Strength: provides precise symbolic results. Weakness: computationally intensive for large expressions or higher dimensions.
• Method 4: Monte Carlo Integration. Good for complex or high-dimensional domains where other methods flounder. Strength: versatile and scalable. Weakness: results are approximate, and precision depends on the number of samples.
• Method 5: Using lambdify with Quadrature. A great synergy between symbolic and numerical methods. Strength: easy conversion of symbolic expressions for numerical integration. Weakness: can be less efficient for complex symbolic expressions.