# Results from Prodrisk

In this example, we will introduce different ways of extracting output from Prodrisk and demonstrate use of these methods.

## Extracting results from PyProdrisk
When using PyProdrisk, results are obtained through output attributes by calling the function *.get()*. A complete list of attributes, including their data types and input/output classification, can be found in [](../../../attribute-table.md).
An example is shown in [PyProdrisk: Basic example](../../interacting_prodrisk/pyprodrisk_basic/pyprodrisk_basic.md), where several results are extracted from a PyProdrisk run.

## Reading directly from result files
The Prodrisk results can be found in the the result files *detsimres.h5* and *enmres.h5*. *detsimres.hs* can provide results per module or per area, whereas *enmres.h5* can give results for the whole area only. 
Both can be viewed using an interfacing program like *HDFView*, or accessed directly  using e.g. python. 

![](./images/detsimres.png)
<figure>
    <figcaption>The folder structure of the detsimres.h5 file.</figcaption>
</figure>

![](./images/enmres.png)
<figure>
    <figcaption>The folder structure of the enmres.h5 file.</figcaption>
</figure>



Here is a basic example of how the income for the total system can be found using python.

```python
import numpy as np
import matplotlib.pyplot as plt
import h5py

folder_path = r"C:\PRODRISK\Aura"
nscenarios = 58
nweeks = 156

with h5py.File(folder_path + r'\detsimres.h5','r') as f:
        production = np.array(f['area_results']['hydro_prod']['val'])                   # resolution of price periods, GWh
        reservoir = np.array(f['area_results']['reservoir']['val'])                     # Weekly values, GWH

with h5py.File(folder_path + '/enmres.h5','r') as f:
        price = np.array(f['market_results']['price']['val'])                           # Øre/kWw

income = 0
endReservoir = 0
startReservoir = 0
for s in range(nscenarios):
    for w in range(nweeks):
        for d in range(7):
            income = income + production[s,0,w*7+d]*price[0,s,w]/100                    # GWh*NOK/kWH = MNOK
    endReservoir = endReservoir + reservoir[s,0,nweeks-1]*np.average(price[0,s,:])/100  # MNOK
    startReservoir = startReservoir + reservoir[s,0,0]*np.average(price[0,s,:])/100
adjustedIncome = (income+(endReservoir-startReservoir))/nscenarios                      # MNOK/år
income = income/nscenarios                                                              # MNOK/år


print("Average income:")
print(income)
print("Average adjusted income:")
print(adjustedIncome)
print("Average price: ",np.average(price[:,:,:]))
```

## Using Kurvetegn
Alternatively, the price, production and reservoir volumes can be extracted using the *kurvetegn* program. For more information about *kurvetegn*, we refer to **LTM** documentation.
Within *Kurvetegn*, you are presented with menus where you must choose the option you prefer. The option *VA* will give results from the whole system, whereas the option *SI* can give you results from the modules. 

Here is a basic example of how to extract production data for one module using *Kurvetegn.exe*.

![](./images/kurvetegn1.png)
<figure>
    <figcaption>Choose the option *SI*.</figcaption>
</figure>

![](./images/kurvetegn2.png)
<figure>
    <figcaption>Choose first week and last wek of extracted data, and all inflow alternatives/scenarios simulated.</figcaption>
</figure>

![](./images/kurvetegn3.png)
<figure>
    <figcaption>Two question marks shows all options. We want the module data *production* for the module *Aura*.</figcaption>
</figure>

![](./images/kurvetegn4.png)
<figure>
    <figcaption>Now we want to make this data readable by structuring it as a matrix. Choose the options .</figcaption>
</figure>

![](./images/kurvetegn5.png)
<figure>
    <figcaption>The data is now parallelized without including quantiles or mean value. The next step is to print the time series.</figcaption>
</figure>

![](./images/kurvetegn6.png)
<figure>
    <figcaption>Once the data is written to a file, we say that the file will not be used in a spreadsheet, that all weeks should be written and that all scenarios should be written, but without weekly numbers and with all scenarios on the same page. Then the program can be closed and the results can be found in the produced text file.</figcaption>
</figure>


## Using the ET program
Another useful program is the *ET* program. It gives you already processed energy, market- or hydropower results in a table form. For more information about *ET*, we refer to **LTM** documentation.

![](./images/et.png)
<figure>
    <figcaption>The program shows a list of available prints in the *ET* program.</figcaption>
</figure>