OMIEData

https://pypi.org/project/OMIEData/
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OMIEData is a Python package to import electricity market data from OMIE (Iberian Peninsula's...
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### Install OMIEData from local files

Source: https://pypi.org/project/OMIEData

Commands to install specific distribution files.

```bash
python -m pip install OMIEData-VERSION-py3-none-any.whl
```

```bash
python -m pip install OMIEData-VERSION.tar.gz
```

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

### Install OMIEData from GitHub

Source: https://pypi.org/project/OMIEData/0.4.0.0

Install the OMIEData package directly from its GitHub repository.

```bash
python -m pip install git+https://github.com/acruzgarcia/OMIEData
```

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

### Install OMIEData from local tar.gz file

Source: https://pypi.org/project/OMIEData/0.4.0.0

Install OMIEData from a local .tar.gz file. Replace VERSION with the actual version number.

```bash
python -m pip install OMIEData-VERSION.tar.gz
```

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

### Install OMIEData using pip

Source: https://pypi.org/project/OMIEData/0.4.0.0

Install the latest version of the OMIEData package from PyPI.

```bash
python -m pip install OMIEData
```

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

### Install OMIEData from local wheel file

Source: https://pypi.org/project/OMIEData/0.4.0.0

Install OMIEData from a local .whl file. Replace VERSION with the actual version number.

```bash
python -m pip install OMIEData-VERSION-py3-none-any.whl
```

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

### Import Energy by Technology Data

Source: https://pypi.org/project/OMIEData/0.4.0.0

Use OMIEEnergyByTechnologyImporter to download energy data by technology. Ensure to specify the date range and system type. The data is then sorted by date and hour.

```python
df = OMIEEnergyByTechnologyImporter(date_ini=dateIni,
                                    date_end=dateEnd,
                                    system_type=system_type).read_to_dataframe(verbose=True)
df.sort_values(by=['DATE', 'HOUR'], axis=0, inplace=True)
print(df)
```

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

### Import Energy by Technology Data

Source: https://pypi.org/project/OMIEData

Use OMIEEnergyByTechnologyImporter to download energy data by technology. Ensure to sort the DataFrame by date and hour for proper analysis.

```python
from OMIEData.DataImport.omie_energy_by_technology_importer import OMIEEnergyByTechnologyImporter
import datetime as dt

dateIni = dt.datetime(2020, 6, 1)
dateEnd = dt.datetime(2020, 6, 1)
system_type = "Nuclear"

# This can take time, it is downloading the files from the website..
df = OMIEEnergyByTechnologyImporter(date_ini=dateIni,
                                    date_end=dateEnd,
                                    system_type=system_type).read_to_dataframe(verbose=True)
df.sort_values(by=['DATE', 'HOUR'], axis=0, inplace=True)
print(df)
```

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

### Download Hourly Energy by Technology

Source: https://pypi.org/project/OMIEData/0.4.0.0

Imports hourly energy demand data, broken down by technology, for a specified system type (e.g., Spain) within a given date range.

```python
import datetime as dt
from OMIEData.Enums.all_enums import SystemType
from OMIEData.DataImport.omie_energy_by_technology_importer import OMIEEnergyByTechnologyImporter

dateIni = dt.datetime(2020, 6, 1)
dateEnd = dt.datetime(2020, 7, 30)
system_type = SystemType.SPAIN
```

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

### Download Hourly Electricity Prices and Demand

Source: https://pypi.org/project/OMIEData/0.4.0.0

Imports hourly electricity prices and demand data for Spain and Portugal within a specified date range. This operation may take time as it downloads files from the website. The resulting DataFrame is sorted by date.

```python
import datetime as dt
import matplotlib.pyplot as plt

from OMIEData.DataImport.omie_marginalprice_importer import OMIEMarginalPriceFileImporter
from OMIEData.Enums.all_enums import DataTypeInMarginalPriceFile

dateIni = dt.datetime(2020, 1, 1)
dateEnd = dt.datetime(2022, 3, 22)

# This can take time, it is downloading the files from the website..
df = OMIEMarginalPriceFileImporter(date_ini=dateIni, date_end=dateEnd).read_to_dataframe(verbose=True)
df.sort_values(by='DATE', axis=0, inplace=True)
print(df)
```

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

### Sample DataFrame output

Source: https://pypi.org/project/OMIEData

Representation of the data structure returned by the importer.

```text
            DATE         CONCEPT        H1  ...       H22       H23       H24
0     2020-01-01        PRICE_SP     41.88  ...     45.60     42.90     37.55
1     2020-01-01        PRICE_PT     41.88  ...     45.60     42.90     37.55
2     2020-01-01         ENER_IB  18132.30  ...  22492.60  21800.90  19946.30
3     2020-01-01  ENER_IB_BILLAT  26488.50  ...  32611.70  31523.70  29088.30
4     2020-01-02        PRICE_SP     35.40  ...     42.00     38.60     33.39
          ...             ...       ...  ...       ...       ...       ...
3241  2022-03-21        PRICE_PT    218.69  ...    261.44    240.29    228.88
3245  2022-03-22        PRICE_PT    223.00  ...    256.00    242.18    212.99
3246  2022-03-22         ENER_IB  20652.20  ...  27113.50  24167.60  21841.50
3244  2022-03-22        PRICE_SP    223.00  ...    256.00    242.18    212.99
3247  2022-03-22  ENER_IB_BILLAT  29840.30  ...  38281.20  34781.90  31872.50
[3248 rows x 26 columns]
```

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

### Filter and Plot Electricity Prices

Source: https://pypi.org/project/OMIEData/0.4.0.0

Filters the previously downloaded DataFrame to include only prices in Spain and then plots the 'H12' and 'H23' hourly prices against the date.

```python
# Just prices in spain
str_price_spain = str(DataTypeInMarginalPriceFile.PRICE_SPAIN)
dfPrices = df[df.CONCEPT == str_price_spain]

# Plotting
plt.figure()
plt.plot(dfPrices.DATE, dfPrices.H12, label='H12')
plt.plot(dfPrices.DATE, dfPrices.H23, label='H23')
plt.legend()
plt.show()
```

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

### Import Supply/Demand Curves Data

Source: https://pypi.org/project/OMIEData/0.4.0.0

Utilize OMIESupplyDemandCurvesImporter to fetch supply and demand curve data. Requires specifying the date range and hour. The resulting DataFrame is sorted by date and hour.

```python
import datetime as dt
from OMIEData.DataImport.omie_supply_demand_curve_importer import OMIESupplyDemandCurvesImporter

dateIni = dt.datetime(2020, 6, 1)
dateEnd = dt.datetime(2020, 6, 1)
hour = 1

# This can take time, it is downloading the files from the website..
df = OMIESupplyDemandCurvesImporter(date_ini=dateIni, date_end=dateEnd, hour=hour).read_to_dataframe(verbose=True)
df.sort_values(by=['DATE', 'HOUR'], axis=0, inplace=True)
print(df)
```
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