## pricing.TowerModel


A frequency and a piecewise Pareto severity that reproduce a tower,


Usage


``` python
pricing.TowerModel()
```


a PML curve or a set of references.


## Attributes

| Name | Description |
|----|----|
| [frequency](#frequency) | Expected number of losses a year above the lowest threshold. |
| [severity](#severity) | The fitted severity. |

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#### frequency


Expected number of losses a year above the lowest threshold.


`frequency: float`


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


#### severity


The fitted severity.


`severity: PiecewisePareto`


## Methods

| Name | Description |
|----|----|
| [excess_frequency()](#excess_frequency) | Expected number of losses a year above [x](pricing.TabulatedCurve.md#prospicio.pricing.TabulatedCurve.x). |
| [layer_loss()](#layer_loss) | Expected loss a year to [limit](reinsurance.Layer.md#prospicio.reinsurance.Layer.limit) xs [attachment](reinsurance.Layer.md#prospicio.reinsurance.Layer.attachment). |

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


#### excess_frequency()


Expected number of losses a year above [x](pricing.TabulatedCurve.md#prospicio.pricing.TabulatedCurve.x).


Usage


``` python
excess_frequency(x)
```


##### Parameters


`x: float`  


##### Returns


`float`  


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


#### layer_loss()


Expected loss a year to [limit](reinsurance.Layer.md#prospicio.reinsurance.Layer.limit) xs [attachment](reinsurance.Layer.md#prospicio.reinsurance.Layer.attachment).


Usage


``` python
layer_loss(limit, attachment)
```


##### Parameters


`limit: float`  

`attachment: float`  


##### Returns


`float`