## UtilisationCalculator


Compute time-weighted ambulance utilisation from an event log.


Usage

``` python
UtilisationCalculator(
    log,
    warm_up_period,
    data_collection_period,
    capacity,
)
```


## Attributes


`log: pd.DataFrame`  
Event log from a vidigi EventLogger.

`warm_up_period: float`  
Length of the warm-up period - observations before this time are excluded.

`data_collection_period: float`  
Length of the data collection period.

`run_length: float`  
Total run length (including warm-up and data collection period).

`capacity: int`  
Total number of ambulances.


## Methods

| Name | Description |
|----|----|
| [__init__()](#__init__) | Initialise UtilisationCalculator. |
| [from_model()](#from_model) | Construct a UtilisationCalculator from a completed Model instance. |
| [from_model_at_time()](#from_model_at_time) | Construct a UtilisationCalculator from a running Model, up to now. |
| [mean_utilisation()](#mean_utilisation) | Return mean time-weighted ambulance utilisation. |
| [state_changes_df()](#state_changes_df) | Return the time-weighted ambulance utilisation intervals. |

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


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


Initialise UtilisationCalculator.


Usage

``` python
__init__(log, warm_up_period, data_collection_period, capacity)
```


##### Parameters


`log: pd.DataFrame`  
Event log from a vidigi EventLogger.

`warm_up_period: float`  
Length of the warm-up period - observations before this time are excluded.

`data_collection_period: float`  
Length of the data collection period.

`capacity: int`  
Total number of ambulances.


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


#### from_model()


Construct a UtilisationCalculator from a completed Model instance.


Usage

``` python
from_model(model)
```


This "classmethod" makes it easier to set up the UtilisationCalculator as you can just write UtilisationCalculator.from_model(model) instead of manually passing all the arguments.


##### Parameters


`model: Model`  
A model instance that has already been executed (model.run()).


##### Returns


`UtilisationCalculator`  


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


#### from_model_at_time()


Construct a UtilisationCalculator from a running Model, up to now.


Usage

``` python
from_model_at_time(model, current_time)
```


Intended for use during warm-up audits, where the model has not yet finished. warm_up_period is forced to 0 and run_length is set to current_time so the full elapsed period is included.

This "classmethod" makes it easier to set up the UtilisationCalculator as you can just write UtilisationCalculator.from_model_at_time(model) instead of manually passing all the arguments.


##### Parameters


`model: Model`  
A model instance that has been run up to current_time.

`current_time: float`  
The simulation time to treat as the end of the observation window.


##### Returns


`UtilisationCalculator`  


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


#### mean_utilisation()


Return mean time-weighted ambulance utilisation.


Usage

``` python
mean_utilisation()
```


##### Returns


`float`  
Mean time-weighted ambulance utilisation.


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


#### state_changes_df()


Return the time-weighted ambulance utilisation intervals.


Usage

``` python
state_changes_df()
```


##### Returns


`state_changes: pd.DataFrame`  
Columns: time, busy, interval_duration, utilisation. One row per state-change interval during the data collection period. `busy` is the number of ambulances in use during that interval.