## distributions.Normal


Normal distribution implementation with optional truncation.


Usage

``` python
distributions.Normal(
    mean,
    sigma,
    minimum=None,
    random_seed=None,
)
```


A continuous probability distribution that follows the Gaussian bell curve. This implementation allows truncating the distribution at a minimum value.

This class conforms to the Distribution protocol and provides methods to sample from a normal distribution with specified mean and standard deviation.


## Methods

| Name | Description |
|----|----|
| [__init__()](#__init__) | Initialize a normal distribution. |
| [sample()](#sample) | Generate random samples from the normal distribution. |

------------------------------------------------------------------------


#### \_\_init\_\_()


Initialize a normal distribution.


Usage

``` python
__init__(mean, sigma, minimum=None, random_seed=None)
```


##### Parameters


`mean: float`  
The mean (μ) of the normal distribution.

`sigma: float`  
The standard deviation (σ) of the normal distribution.

`minimum: Optional[float] = None`  
If provided, truncates the distribution to this minimum value. Any sampled values below this minimum will be set to this value.

`random_seed: Optional[Union[int, SeedSequence]] = None`  
A random seed or SeedSequence to reproduce samples. If None, a unique sample sequence is generated.


------------------------------------------------------------------------


#### sample()


Generate random samples from the normal distribution.


Usage

``` python
sample(size=None)
```


##### Parameters


`size: Optional[Union[int, Tuple[int, …]]] = None`  
The number/shape of samples to generate:

- If None: returns a single sample as a float
- If int: returns a 1-D array with that many samples
- If tuple of ints: returns an array with that shape


##### Returns


`Union[float, NDArray[np.float64]]`  
Random samples from the normal distribution:

- A single float when size is None
- A numpy array of floats with shape determined by size parameter


##### Notes

If a minimum value was specified during initialization, any samples below this value will be truncated (set to the minimum value).