## distributions.Poisson


Poisson distribution implementation.


Usage

``` python
distributions.Poisson(
    rate,
    random_seed=None,
)
```


Used to simulate number of events that occur in an interval of time. E.g. number of items in a batch.

This class conforms to the Distribution protocol.


## Sources:

Law (2007 pg. 308) Simulation modelling and analysis.


## Methods

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

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


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


Initialize a Poisson distribution.


Usage

``` python
__init__(rate, random_seed=None)
```


##### Parameters


`rate: float`  
Mean number of events in time period.

`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 Poisson 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 an integer
- If int: returns a 1-D array with that many samples
- If tuple of ints: returns an array with that shape


##### Returns


`Union[int, NDArray[np.int_]]`  
Random samples from the Poisson distribution:

- A single integer when size is None
- A numpy array of integers with shape determined by size parameter