Missing volumes in query_contracted_reserve_prices_procured_capacity results

[christianfosli]

Steps to reproduce

start = pd.Timestamp("2024-12-12", tz="Europe/Oslo")
end = pd.Timestamp("2024-12-13", tz="Europe/Oslo")
df = client.query_contracted_reserve_prices_procured_capacity(
        country_code=Area.NO_2,
        process_type="A47",  # mFRR
        type_marketagreement_type="A01",  # Daily
        start=start,
        end=end,
    )
print(df)

Expected

Dataframe with procured capacity prices and volumes

Actual

Dataframe with procured capacity volumes prices but prices volumes are missing

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 24 entries, 2024-12-12 00:00:00+01:00 to 2024-12-12 23:00:00+01:00
Freq: h
Data columns (total 2 columns):
 #   Column                     Non-Null Count  Dtype
---  ------                     --------------  -----
 0   (Procured capacity, Down)  24 non-null     float64
 1   (Procured capacity, Up)    24 non-null     float64
dtypes: float64(2)
memory usage: 576.0 bytes
                          Procured capacity
                                       Down      Up
2024-12-12 00:00:00+01:00               3.0   22.00
2024-12-12 01:00:00+01:00               5.5   13.00
2024-12-12 02:00:00+01:00               5.5    6.00
2024-12-12 03:00:00+01:00               5.5   13.00
...

The raw xml response contains both prices (procurement_Price.amount) and volumes (quantity), e.g:

<Point>
    				
    <position>1</position>
    				
    <quantity>180</quantity>
    				
    <procurement_Price.amount>3.00</procurement_Price.amount>
    				
    <imbalance_Price.category>A07</imbalance_Price.category>
    			
</Point>

[christianfosli]

After some further troubleshooting I see it's actually the volumes that are missing, not the prices.

The column names (("Procured Capacity", {direction})) led me to believe they were volumes, but the datapoints are actually the price values.

Original issue description updated.

[christianfosli]

After investigating a bit further, this looks to be as designed from #305 implementation.

However, the way I understand the Entsoe UI, this endpoint has replaced legacy endpoints for both prices and volumes:

(and the legacy query_contracted_reserve_amount endpoint returns no data for the market/areas I'm interested in)

So I think it would be better to include both in the returned dataframe.