### Install OMIEData

Source: https://context7.com/acruzgarcia/omiedata/llms.txt

Use pip to install the package.

```bash
pip install OMIEData
```

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

### Install OMIEData from GitHub

Source: https://github.com/acruzgarcia/omiedata/blob/dev/README.md

Install the OMIEData package directly from its GitHub repository.

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

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

### Install OMIEData from Wheel file

Source: https://github.com/acruzgarcia/omiedata/blob/dev/README.md

Install OMIEData using a downloaded wheel (.whl) file.

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

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

### Install OMIEData from Tarball

Source: https://github.com/acruzgarcia/omiedata/blob/dev/README.md

Install OMIEData using a downloaded source tarball (.tar.gz) file.

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

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

### Install OMIEData from PyPI

Source: https://github.com/acruzgarcia/omiedata/blob/dev/README.md

Install the latest version of the OMIEData package from the Python Package Index.

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

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

### Download and Plot Spanish Electricity Prices

Source: https://context7.com/acruzgarcia/omiedata/llms.txt

Imports OMIE marginal price data for a specified date range, filters for Spanish prices, and plots hourly price data for peak and off-peak hours using matplotlib. Ensure matplotlib is installed for visualization.

```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

# Download price data
date_ini = dt.datetime(2023, 1, 1)
date_end = dt.datetime(2023, 6, 30)

df = OMIEMarginalPriceFileImporter(
    date_ini=date_ini,
    date_end=date_end
).read_to_dataframe(verbose=True)

df.sort_values(by='DATE', axis=0, inplace=True)

# Filter Spanish prices
df_prices = df[df.CONCEPT == str(DataTypeInMarginalPriceFile.PRICE_SPAIN)]

# Plot hourly prices (comparing peak hour 12 with off-peak hour 23)
plt.figure(figsize=(12, 6))
plt.plot(df_prices.DATE, df_prices.H12, label='Hour 12 (Peak)')
plt.plot(df_prices.DATE, df_prices.H23, label='Hour 23 (Off-Peak)')
plt.xlabel('Date')
plt.ylabel('Price (€/MWh)')
plt.title('Spanish Electricity Prices')
plt.legend()
plt.grid(True)
plt.show()
```

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

### Two-Step Workflow: Download Energy by Technology then Read

Source: https://context7.com/acruzgarcia/omiedata/llms.txt

Downloads energy by technology data files to a local folder and then reads them into a DataFrame. Allows filtering by specific technologies.

```python
import datetime as dt
from OMIEData.DataImport.omie_data_importer_from_folder import OMIEDataImporterFromFolder
from OMIEData.Downloaders.energy_by_technology_downloader import EnergyByTechnologyDownloader
from OMIEData.FileReaders.energy_by_technology_files_reader import EnergyByTechnologyHourlyFileReader
from OMIEData.Enums.all_enums import SystemType, TechnologyType

# Step 1: Download energy by technology files
working_folder = '/tmp/omie_energy'
date_ini = dt.datetime(2023, 6, 1)
date_end = dt.datetime(2023, 10, 31)

# Download for Iberian system
downloader = EnergyByTechnologyDownloader(system=SystemType.IBERIAN)
errors = downloader.download_data(
    date_ini=date_ini,
    date_end=date_end,
    output_folder=working_folder
)

# Step 2: Read with technology filter (only nuclear and combined cycle)
data_types = [TechnologyType.NUCLEAR, TechnologyType.COMBINED_CYCLE]
file_reader = EnergyByTechnologyHourlyFileReader(types=data_types)

df = OMIEDataImporterFromFolder(
    absolute_path=working_folder,
    file_reader=file_reader
).read_to_dataframe(verbose=True)

df.sort_values(by=['DATE', 'HOUR'], axis=0, inplace=True)
print(df)
```

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

### Two-Step Workflow: Download Marginal Prices then Read

Source: https://context7.com/acruzgarcia/omiedata/llms.txt

Downloads marginal price data files to a local folder and then reads them into a DataFrame. Useful for caching or repeated analysis. You can filter by specific data types.

```python
import datetime as dt
from OMIEData.DataImport.omie_data_importer_from_folder import OMIEDataImporterFromFolder
from OMIEData.Downloaders.marginal_price_downloader import MarginalPriceDownloader
from OMIEData.FileReaders.marginal_price_file_reader import MarginalPriceFileReader
from OMIEData.Enums.all_enums import DataTypeInMarginalPriceFile

# Step 1: Download files to local folder
working_folder = '/tmp/omie_data'
date_ini = dt.datetime(2023, 1, 1)
date_end = dt.datetime(2023, 12, 31)

# Download marginal price data files
downloader = MarginalPriceDownloader()
errors = downloader.download_data(
    date_ini=date_ini,
    date_end=date_end,
    output_folder=working_folder
)

# Step 2: Read from local folder (can filter specific data types)
data_types = [
    DataTypeInMarginalPriceFile.PRICE_SPAIN,
    DataTypeInMarginalPriceFile.ENERGY_IBERIAN
]
file_reader = MarginalPriceFileReader(types=data_types)

df = OMIEDataImporterFromFolder(
    absolute_path=working_folder,
    file_reader=file_reader
).read_to_dataframe(verbose=True)

df.sort_values(by='DATE', axis=0, inplace=True)
print(df)
```

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

### Download Supply/Demand Curves

Source: https://github.com/acruzgarcia/omiedata/blob/dev/README.md

Initializes an importer for OMIE supply and demand curves within a specified date range and hour. Further implementation is needed to retrieve the data.

```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


```

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

### Import Marginal Prices with OMIEMarginalPriceFileImporter

Source: https://context7.com/acruzgarcia/omiedata/llms.txt

Retrieves daily market marginal prices and energy volumes. Use the DataTypeInMarginalPriceFile enum to filter for specific metrics like Spanish or Portuguese prices.

```python
import datetime as dt
import pandas as pd
from OMIEData.DataImport.omie_marginalprice_importer import OMIEMarginalPriceFileImporter
from OMIEData.Enums.all_enums import DataTypeInMarginalPriceFile

# Define date range for data retrieval
date_ini = dt.datetime(2023, 1, 1)
date_end = dt.datetime(2023, 1, 31)

# Download and import marginal prices (downloads from OMIE website)
df = OMIEMarginalPriceFileImporter(
    date_ini=date_ini,
    date_end=date_end
).read_to_dataframe(verbose=True)

# Sort by date for chronological order
df.sort_values(by='DATE', axis=0, inplace=True)

# Filter for Spanish prices only
price_spain_str = str(DataTypeInMarginalPriceFile.PRICE_SPAIN)
df_prices = df[df.CONCEPT == price_spain_str]

# Access hourly columns (H1-H24)
print(df_prices[['DATE', 'CONCEPT', 'H1', 'H12', 'H24']])
```

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

### Download Hourly Electricity Prices and Demand

Source: https://github.com/acruzgarcia/omiedata/blob/dev/README.md

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 OMIE 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)
```

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

### Import Energy by Technology with OMIEEnergyByTechnologyImporter

Source: https://context7.com/acruzgarcia/omiedata/llms.txt

Retrieves hourly energy generation data broken down by technology. Specify the system type using the SystemType enum to target Spain, Portugal, or the Iberian system.

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

# Define date range
date_ini = dt.datetime(2023, 6, 1)
date_end = dt.datetime(2023, 6, 30)

# Choose system type: SPAIN, PORTUGAL, or IBERIAN
system_type = SystemType.SPAIN

# Download and import energy by technology data
df = OMIEEnergyByTechnologyImporter(
    date_ini=date_ini,
    date_end=date_end,
    system_type=system_type
).read_to_dataframe(verbose=True)

# Sort by date and hour
df.sort_values(by=['DATE', 'HOUR'], axis=0, inplace=True)
print(df)
```

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

### Import Supply and Demand Curves with OMIESupplyDemandCurvesImporter

Source: https://context7.com/acruzgarcia/omiedata/llms.txt

Retrieves price-volume pairs for aggregated supply and demand curves for a specific hour.

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

# Define date range and hour (1-24)
date_ini = dt.datetime(2023, 6, 15)
date_end = dt.datetime(2023, 6, 15)
hour = 12  # Hour 12 (noon)

# Download and import supply/demand curves
df = OMIESupplyDemandCurvesImporter(
    date_ini=date_ini,
    date_end=date_end,
    hour=hour
).read_to_dataframe(verbose=True)

# Sort by date and hour
df.sort_values(by=['DATE', 'HOUR'], axis=0, inplace=True)
print(df)
```

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

### Download Hourly Energy by Technology

Source: https://github.com/acruzgarcia/omiedata/blob/dev/README.md

Imports hourly energy demand data, broken down by technology, for a specified system type (e.g., Spain) and date range. This operation may take time as it downloads files from the OMIE website. The resulting DataFrame is sorted by date and hour.

```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

# 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 Intra-Day Market Prices

Source: https://context7.com/acruzgarcia/omiedata/llms.txt

Downloads intra-day market prices for a specified trading session and date range. Files are saved with a naming convention indicating the session and date.

```python
import datetime as dt
from OMIEData.Downloaders.intra_day_price_downloader import IntraDayPriceDownloader

# Define date range
date_ini = dt.datetime(2023, 5, 1)
date_end = dt.datetime(2023, 5, 31)
working_folder = '/tmp/omie_intraday'

# Download intra-day prices for session 2
session_number = 2  # Sessions typically range from 1-6
downloader = IntraDayPriceDownloader(session=session_number)
errors = downloader.download_data(
    date_ini=date_ini,
    date_end=date_end,
    output_folder=working_folder
)
# Files saved as: PrecioIntra_2_YYYYMMDD.txt
```

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

### Import OMIE Adjustment Prices to DataFrame

Source: https://context7.com/acruzgarcia/omiedata/llms.txt

Imports hourly adjustment prices directly into a pandas DataFrame. Specify the date range for the data you need.

```python
import datetime as dt
from OMIEData.DataImport.omie_adjustmentprice_importer import OMIEAdjustmentPriceFileImporter

# Define date range
date_ini = dt.datetime(2023, 3, 1)
date_end = dt.datetime(2023, 3, 31)

# Download and import adjustment prices
df = OMIEAdjustmentPriceFileImporter(
    date_ini=date_ini,
    date_end=date_end
).read_to_dataframe(verbose=True)

print(df)
# Output:
#         DATE  H1   H2   ...  H23   H24
# 0 2023-03-01  5.2  4.8  ...  6.1   5.5
# ...
```

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

### Import and Process OMIE Supply Demand Data

Source: https://github.com/acruzgarcia/omiedata/blob/dev/README.md

Use this snippet to download supply and demand curve data for a specified date range and hour. The data is then processed into a pandas DataFrame and sorted by date and hour. Set verbose=True for detailed output during download.

```python
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)
```

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

### DataTypeInMarginalPriceFile Enum Usage

Source: https://context7.com/acruzgarcia/omiedata/llms.txt

Demonstrates how to use the DataTypeInMarginalPriceFile enumeration for filtering marginal price data when reading files. Shows available types and how to filter a DataFrame.

```python
from OMIEData.Enums.all_enums import DataTypeInMarginalPriceFile

# Available data types and their string representations
print(str(DataTypeInMarginalPriceFile.PRICE_SPAIN))           # 'PRICE_SP'
print(str(DataTypeInMarginalPriceFile.PRICE_PORTUGAL))        # 'PRICE_PT'
print(str(DataTypeInMarginalPriceFile.ENERGY_IBERIAN))        # 'ENER_IB'
print(str(DataTypeInMarginalPriceFile.ENERGY_IBERIAN_WITH_BILLATERAL))  # 'ENER_IB_BILLAT'

# Use for filtering DataFrame results
df_spain_prices = df[df.CONCEPT == str(DataTypeInMarginalPriceFile.PRICE_SPAIN)]
df_portugal_prices = df[df.CONCEPT == str(DataTypeInMarginalPriceFile.PRICE_PORTUGAL)]
df_energy = df[df.CONCEPT == str(DataTypeInMarginalPriceFile.ENERGY_IBERIAN)]
```

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

### Filter and Plot Spain Prices

Source: https://github.com/acruzgarcia/omiedata/blob/dev/README.md

Filters the previously imported DataFrame to include only prices in Spain and then plots the prices for hours H12 and H23.

```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()
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.