aircraft-database.com

aircraftdetective.processing.acftdb ¶

_read_aircraft_database ¶

_read_aircraft_database(
    path_json_aircraft_database=PATH_ZENODO_AIRCRAFT_DATABASE_AIRCRAFT_TYPES_FILE,
    dict_properties=None,
    dict_manufacturers=None,
)

Reads a JSON backup of the the entire aircraft-database.com (discontinued as of 01-2024) and returns a DataFrame with all aircraft models and their properties.

Since aircraft can have multiple engines and propertyValues, the function will explode these columns to have one row per engine and property. For example, the following JSON object:

{
"id":"1edbc3ab-ed02-6b78-b000-951ebcf1a8fa",
"aircraftFamily":"airplane",
"engineCount":1,
"engineFamily":"piston",
"engineModels":["1ed869d8-bac4-67a4-a133-df00b6f38f52","1ed869d8-e2ad-6d92-a133-799194a3de38"],
"iataCode":null,
"icaoCode":null,
"manufacturer":"1edbc3a6-8e32-6c24-b000-9ff27b37bf11",
"name":"1",
"propertyValues":[
    {"property":"1ec96f93-22b5-66f0-9933-45bd403e4df0","value":839},
    {"property":"1ec96f94-5471-66de-9933-a93eae676780","value":839},
    {"property":"1ec96f9c-4371-6098-9933-2d909f93f3cd","value":91}
],
"tags":[],
"url":"https:\/\/aircraft-database.com\/database\/aircraft-types\/1"
},

will be transformed into:

id	(...)	engineModels	propertyValues
1edbc3ab-ed02-6b78-b000-951ebcf1a8fa	(...)	1ed869d8-bac4-67a4-a133-df00b6f38f52	1ec96f93-22b5-66f0-9933-45bd403e4df0
1edbc3ab-ed02-6b78-b000-951ebcf1a8fa	(...)	1ed869d8-bac4-67a4-a133-df00b6f38f52	1ec96f94-5471-66de-9933-a93eae676780
1edbc3ab-ed02-6b78-b000-951ebcf1a8fa	(...)	1ed869d8-bac4-67a4-a133-df00b6f38f52	1ec96f9c-4371-6098-9933-2d909f93f3cd
1edbc3ab-ed02-6b78-b000-951ebcf1a8fa	(...)	1ed869d8-e2ad-6d92-a133-799194a3de38	1ec96f93-22b5-66f0-9933-45bd403e4df0
1edbc3ab-ed02-6b78-b000-951ebcf1a8fa	(...)	1ed869d8-e2ad-6d92-a133-799194a3de38	1ec96f94-5471-66de-9933-a93eae676780
1edbc3ab-ed02-6b78-b000-951ebcf1a8fa	(...)	1ed869d8-e2ad-6d92-a133-799194a3de38	1ec96f9c-4371-6098-9933-2d909f93f3cd

See Also

aircraft-database.com (archived)

Notes

With no parameters passed, the function will download the relevant JSON file from the aircraft-database.com Backup Zenodo repository.

Parameters:

Name	Type	Description	Default
`path_json_aircraft_database`	`str`	Path to the JSON file containing the aircraft models and their properties.	`PATH_ZENODO_AIRCRAFT_DATABASE_AIRCRAFT_TYPES_FILE`
`dict_properties`	`dict`	Dictionary mapping the property IDs to their names. If None, loaded from default path.	`None`
`dict_manufacturers`	`dict`	Dictionary mapping the manufacturer IDs to their names. If None, loaded from default path.	`None`

Returns:

Type	Description
`DataFrame`	`pint-pandas` DataFrame with the aircraft models and their properties.

Source code in aircraftdetective/processing/acftdb.py

def _read_aircraft_database(
    path_json_aircraft_database: str = PATH_ZENODO_AIRCRAFT_DATABASE_AIRCRAFT_TYPES_FILE,
    dict_properties: dict | None = None,
    dict_manufacturers: dict | None = None,
) -> pd.DataFrame:
    r"""
    Reads a JSON backup of the the entire [aircraft-database.com](https://web.archive.org/web/20231201220700/https://aircraft-database.com/)
    (discontinued as of 01-2024) and returns a DataFrame with all aircraft models and their properties.

    Since aircraft can have multiple engines and `propertyValues`,
    the function will explode these columns to have one row per engine and property.
    For example, the following JSON object:

    ```
    {
    "id":"1edbc3ab-ed02-6b78-b000-951ebcf1a8fa",
    "aircraftFamily":"airplane",
    "engineCount":1,
    "engineFamily":"piston",
    "engineModels":["1ed869d8-bac4-67a4-a133-df00b6f38f52","1ed869d8-e2ad-6d92-a133-799194a3de38"],
    "iataCode":null,
    "icaoCode":null,
    "manufacturer":"1edbc3a6-8e32-6c24-b000-9ff27b37bf11",
    "name":"1",
    "propertyValues":[
        {"property":"1ec96f93-22b5-66f0-9933-45bd403e4df0","value":839},
        {"property":"1ec96f94-5471-66de-9933-a93eae676780","value":839},
        {"property":"1ec96f9c-4371-6098-9933-2d909f93f3cd","value":91}
    ],
    "tags":[],
    "url":"https:\/\/aircraft-database.com\/database\/aircraft-types\/1"
    },
    ```

    will be transformed into:

    | id                                   | (...) | engineModels                         | propertyValues                       |
    |--------------------------------------|-------|--------------------------------------|--------------------------------------|
    | 1edbc3ab-ed02-6b78-b000-951ebcf1a8fa | (...) | 1ed869d8-bac4-67a4-a133-df00b6f38f52 | 1ec96f93-22b5-66f0-9933-45bd403e4df0 |
    | 1edbc3ab-ed02-6b78-b000-951ebcf1a8fa | (...) | 1ed869d8-bac4-67a4-a133-df00b6f38f52 | 1ec96f94-5471-66de-9933-a93eae676780 |
    | 1edbc3ab-ed02-6b78-b000-951ebcf1a8fa | (...) | 1ed869d8-bac4-67a4-a133-df00b6f38f52 | 1ec96f9c-4371-6098-9933-2d909f93f3cd |
    | 1edbc3ab-ed02-6b78-b000-951ebcf1a8fa | (...) | 1ed869d8-e2ad-6d92-a133-799194a3de38 | 1ec96f93-22b5-66f0-9933-45bd403e4df0 |
    | 1edbc3ab-ed02-6b78-b000-951ebcf1a8fa | (...) | 1ed869d8-e2ad-6d92-a133-799194a3de38 | 1ec96f94-5471-66de-9933-a93eae676780 |
    | 1edbc3ab-ed02-6b78-b000-951ebcf1a8fa | (...) | 1ed869d8-e2ad-6d92-a133-799194a3de38 | 1ec96f9c-4371-6098-9933-2d909f93f3cd |

    See Also
    --------
    - [aircraft-database.com (archived)](https://web.archive.org/web/20231201220700/https://aircraft-database.com/)

    Notes
    -----
    With no parameters passed, the function will download the relevant JSON file 
    from the [aircraft-database.com Backup Zenodo repository](https://doi.org/10.5281/zenodo.14382244).

    Parameters
    ----------
    path_json_aircraft_database : str
        Path to the JSON file containing the aircraft models and their properties.
    dict_properties : dict, optional
        Dictionary mapping the property IDs to their names. If None, loaded from default path.
    dict_manufacturers : dict, optional
        Dictionary mapping the manufacturer IDs to their names. If None, loaded from default path.

    Returns
    -------
    pd.DataFrame
        [`pint-pandas`](https://pint-pandas.readthedocs.io/en/latest/) DataFrame with the aircraft models and their properties.
    """
    if dict_manufacturers is None:
        dict_manufacturers = _read_manufacturers_database()

    if dict_properties is None:
        dict_properties = _read_properties_database()

    df = pd.read_json(path_json_aircraft_database)
    df = df.loc[df['aircraftFamily'].isin(['airplane'])] # remove helicopters, amphibious, etc.
    df = df[df['tags'].str.len() == 0] # remove entries with tags (e.g. ['military'])

    df['manufacturer'] = df['manufacturer'].map(dict_manufacturers)

    df = df.explode('engineModels').reset_index(drop=True)
    df = df.explode('propertyValues').reset_index(drop=True)

    df_properties = pd.json_normalize(df['propertyValues'])
    df_properties['property'] = df_properties['property'].map(dict_properties)

    df_properties_pivot = df_properties.pivot(columns='property', values='value')

    df = pd.concat(
        objs=[
            df,
            df_properties_pivot
        ],
        axis=1
    )

    df = _rename_columns_and_set_units(
        df=df,
        column_names_and_units=[
            ('id', '_id_aircraft', 'str'),
            ('name', 'Aircraft Designation', 'str'),
            ('manufacturer', 'Aircraft Manufacturer', 'str'),
            ('engineModels', '_id_engine', 'str'),
            ('engineCount', 'Engine Count', 'Int64'),
            ('Fuel Capacity [L]', 'Fuel Capacity', 'pint[l]'),
            ('Mlw [kg]', 'MLW', 'pint[kg]'),
            ('Mtow [kg]', 'MTOW', 'pint[kg]'),
            ('Mtw [kg]', 'MTW', 'pint[kg]'),
            ('Mzfw [kg]', 'MZFW', 'pint[kg]'),
            ('Mmo', 'MMO', 'pint[dimensionless]'),
            ('Maximum Operating Altitude [ft]', 'Maximum Operating Altitude', 'pint[ft]'),
            ('Oew [kg]', 'OEW', 'pint[kg]'),
            ('Wing Area [m²]', 'Wing Area', 'pint[m**2]'),
            ('Wingspan (Canard) [m]', 'Wingspan (Canard)', 'pint[m]'),
            ('Wingspan (Winglets) [m]', 'Wingspan (winglets)', 'pint[m]'),
            ('Wingspan [m]', 'Wingspan', 'pint[m]'),
            ('Height [m]', 'Height', 'pint[m]'),
        ],
        return_only_renamed_columns=True,
    )

    df_grouped = df.groupby(
        by=[
            'Aircraft Designation',
            '_id_engine',
        ],
        as_index=False,
    ).first()

    return df_grouped

_read_engine_database ¶

_read_engine_database(
    path_json_engine_database=PATH_ZENODO_AIRCRAFT_DATABASE_ENGINE_MODELS_FILE,
    dict_properties=None,
    dict_manufacturers=None,
)

Given the path to a JSON file containing the backup of the aircraft-database.com (discontinued as of 01-2024) engine database, a dictionary of property IDs and their names, and a dictionary of manufacturer IDs and their names, returns a DataFrame with the engine models and their properties.

The propertyValues column contains a list of dictionaries with the properties and their values. For example, one entry in the propertyValues column might look like this:

name	propertyValues
foo	[{'property': 'alpha', 'value': 107.95}, {'property': 'beta', 'value': 'Air'}]
bar	[{'property': 'alpha', 'value': 12}

name	alpha	beta
foo	107.95	Air
bar	12	NaN

[ {'property': '1ecd84bb-a0fc-6e28-8b2d-99154ce73d02', 'value': 107.95}, {'property': '1ecd84bf-2ea5-6c86-8b2d-0b9aa407e174', 'value': 'Air'} ] Exploding the propertyValues column will create one row per property and value:

name	(...)	propertyValues
110 7DF	(...)	{'property': '1ecd84bb-a0fc-6e28-8b2d-99154ce73d02', 'value': 107.95}
110 7DF	(...)	{'property': '1ecd84bf-2ea5-6c86-8b2d-0b9aa407e174', 'value': 'Air'}

Using pd.json_normalize will create two columns property and value:

name	(...)	property	value
110 7DF	(...)	1ecd84bb-a0fc-6e28-8b2d-99154ce73d02	107.95
110 7DF	(...)	1ecd84bf-2ea5-6c86-8b2d-0b9aa407e174	Air

Using the dictionary of property names, we can map the property column to the actual property name:

name	(...)	property	value
110 7DF	(...)	Bore [mm]	107.95
110 7DF	(...)	Cooling System	Air

Finally, we can pivot the property column to have one column per property:

name	(...)	Bore [mm]	Cooling System
110 7DF	(...)	107.95	NaN
110 7DF	(...)	NaN	Air

Which we can group by engine designation (=name) and aggregate the values:

name	(...)	Bore [mm]	Cooling System
110 7DF	(...)	107.95	Air

Notes

With no parameters passed, the function will download the relevant JSON file from the aircraft-database.com Backup Zenodo repository.

Parameters:

Name	Type	Description	Default
`path_json_engine_database`	`str`	Path to the JSON file containing the engine models and their properties.	`PATH_ZENODO_AIRCRAFT_DATABASE_ENGINE_MODELS_FILE`
`dict_properties`	`dict`	Dictionary mapping the property IDs to their names. If None, loaded from default path.	`None`
`dict_manufacturers`	`dict`	Dictionary mapping the manufacturer IDs to their names. If None, loaded from default path.	`None`

Returns:

Type	Description
`DataFrame`	`pint-pandas` DataFrame with the engine models and their properties.

Source code in aircraftdetective/processing/acftdb.py

def _read_engine_database(
    path_json_engine_database: str = PATH_ZENODO_AIRCRAFT_DATABASE_ENGINE_MODELS_FILE,
    dict_properties: dict | None = None,
    dict_manufacturers: dict | None = None,
) -> pd.DataFrame:
    """
    Given the path to a JSON file containing the backup of the [aircraft-database.com](https://web.archive.org/web/20231201220700/https://aircraft-database.com/)
    (discontinued as of 01-2024) engine database, a dictionary of property IDs and their names, and a dictionary of manufacturer IDs and their names,
    returns a DataFrame with the engine models and their properties.

    The `propertyValues` column contains a list of dictionaries with the properties and their values.
    For example, one entry in the `propertyValues` column might look like this:

    | name | propertyValues |
    |------|----------------|
    | foo  | [{'property': 'alpha', 'value': 107.95}, {'property': 'beta', 'value': 'Air'}] |
    | bar  | [{'property': 'alpha', 'value': 12} |

    | name | alpha | beta |
    |------|-------|------|
    | foo  | 107.95| Air  |
    | bar  | 12    | NaN  |

    [
        {'property': '1ecd84bb-a0fc-6e28-8b2d-99154ce73d02', 'value': 107.95},
        {'property': '1ecd84bf-2ea5-6c86-8b2d-0b9aa407e174', 'value': 'Air'}
    ]
    Exploding the `propertyValues` column will create one row per property and value:

    | name    | (...) | propertyValues                                                        |
    |---------|-------|-----------------------------------------------------------------------|
    | 110 7DF | (...) | {'property': '1ecd84bb-a0fc-6e28-8b2d-99154ce73d02', 'value': 107.95} |
    | 110 7DF | (...) | {'property': '1ecd84bf-2ea5-6c86-8b2d-0b9aa407e174', 'value': 'Air'}  |

    Using `pd.json_normalize` will create two columns `property` and `value`:

    | name    | (...) | property                             | value  |
    |---------|-------|--------------------------------------|--------|
    | 110 7DF | (...) | 1ecd84bb-a0fc-6e28-8b2d-99154ce73d02 | 107.95 |
    | 110 7DF | (...) | 1ecd84bf-2ea5-6c86-8b2d-0b9aa407e174 | Air    |

    Using the dictionary of property names, we can map the `property` column to the actual property name:

    | name    | (...) | property       | value  |
    |---------|-------|----------------|--------|
    | 110 7DF | (...) | Bore [mm]      | 107.95 |
    | 110 7DF | (...) | Cooling System | Air    |

    Finally, we can pivot the `property` column to have one column per property:

    | name    | (...) | Bore [mm] | Cooling System |
    |---------|-------|-----------|----------------|
    | 110 7DF | (...) | 107.95    | NaN            |
    | 110 7DF | (...) | NaN       | Air            |

    Which we can group by engine designation (=name) and aggregate the values:

    | name    | (...) | Bore [mm] | Cooling System |
    |---------|-------|-----------|----------------|
    | 110 7DF | (...) | 107.95    | Air            |

    Notes
    -----
    With no parameters passed, the function will download the relevant JSON file 
    from the [aircraft-database.com Backup Zenodo repository](https://doi.org/10.5281/zenodo.14382244).

    Parameters
    ----------
    path_json_engine_database : str
        Path to the JSON file containing the engine models and their properties.
    dict_properties : dict, optional
        Dictionary mapping the property IDs to their names. If None, loaded from default path.
    dict_manufacturers : dict, optional
        Dictionary mapping the manufacturer IDs to their names. If None, loaded from default path.

    Returns
    -------
    pd.DataFrame
        [`pint-pandas`](https://pint-pandas.readthedocs.io/en/latest/) DataFrame with the engine models and their properties.  
    """
    if dict_properties is None:
        dict_properties = _read_properties_database()

    if dict_manufacturers is None:
        dict_manufacturers = _read_manufacturers_database()

    df = pd.read_json(path_json_engine_database)
    df = df.loc[df['engineFamily'].isin(['turbofan', 'turbojet', 'turboprop'])]

    df['manufacturer'] = df['manufacturer'].map(dict_manufacturers)

    df = df.explode('propertyValues').reset_index(drop=True)

    df_properties = pd.json_normalize(df['propertyValues'])
    df_properties['property'] = df_properties['property'].map(dict_properties)

    df_properties_pivot = df_properties.pivot(columns='property', values='value')

    df = pd.concat(
        objs=[
            df,
            df_properties_pivot
        ],
        axis=1
    )

    df = _rename_columns_and_set_units(
        df=df,
        column_names_and_units=[
            ('id', '_id_engine', 'str'), # aircraft-database.com internal id, not needed here
            ('name', 'Engine Designation', 'str'),
            ('engineFamily', 'Engine Family', 'str'),
            ('manufacturer', 'Engine Manufacturer', 'str'),
            # ('Bypass Ratio', 'Bypass Ratio', 'pint[dimensionless]'), # bypass ration of EASA/ICAO Engine Database seems more reliable
            ('Overall Pressure Ratio', 'Overall Pressure Ratio', 'pint[dimensionless]'),
            ('Dry Weight [kg]', 'Dry Weight', 'pint[kg]'),
            ('Fan Diameter [m]', 'Fan Diameter', 'pint[m]'),
            ('Max. Continuous Thrust [kN]', 'Max. Continuous Thrust', 'pint[kN]'),
        ],
        return_only_renamed_columns=True,
    )

    df_grouped = df.groupby(
        by='Engine Designation',
        as_index=False,
    ).first()

    return df_grouped

_read_manufacturers_database ¶

_read_manufacturers_database(
    path_json_manufacturers=PATH_ZENODO_AIRCRAFT_DATABASE_MANUFACTURERS_FILE,
)

Reads a JSON backup of the the aircraft-database.com (discontinued as of 01-2024) table of manufacturers and their values and returns it as a dictionary.

The JSON object and returned dictionary are of the form:

{
    '1edc26ff-b373-6418-bf55-35eb9f006d3f': 'AAMSA',
    '1ed62800-0b15-6f68-b5ec-05fb44d7d394': 'ABC Motors',
    '1ed62836-1733-6ee0-b5ec-8352ea69874f': 'ADC',
    (...)
}

Notes

With no parameters passed, the function will download the relevant JSON file from the aircraft-database.com Backup Zenodo repository.

Parameters:

Name	Type	Description	Default
`path_json_manufacturers`	`str`	Path or URL of the JSON file containing the manufacturers and their values.	`PATH_ZENODO_AIRCRAFT_DATABASE_MANUFACTURERS_FILE`

Returns:

Type	Description
`dict`	Dictionary with the `aircraft-database.com` manufacturer IDs as keys and the manufacturer names as values.

Source code in aircraftdetective/processing/acftdb.py

def _read_manufacturers_database(
    path_json_manufacturers: str = PATH_ZENODO_AIRCRAFT_DATABASE_MANUFACTURERS_FILE
) -> dict:
    """
    Reads a JSON backup of the the [aircraft-database.com](https://web.archive.org/web/20231201220700/https://aircraft-database.com/)
    (discontinued as of 01-2024) table of `manufacturers` and their values and returns it as a dictionary.

    The JSON object and returned dictionary are of the form:

    ```
    {
        '1edc26ff-b373-6418-bf55-35eb9f006d3f': 'AAMSA',
        '1ed62800-0b15-6f68-b5ec-05fb44d7d394': 'ABC Motors',
        '1ed62836-1733-6ee0-b5ec-8352ea69874f': 'ADC',
        (...)
    }
    ```

    Notes
    -----
    With no parameters passed, the function will download the relevant JSON file 
    from the [aircraft-database.com Backup Zenodo repository](https://doi.org/10.5281/zenodo.14382244).

    Parameters
    ----------
    path_json_manufacturers : str
        Path or URL of the JSON file containing the manufacturers and their values.

    Returns
    -------
    dict
        Dictionary with the `aircraft-database.com` manufacturer IDs as keys and the manufacturer names as values.
    """
    df_manufacturers = pd.read_json(path_json_manufacturers)
    df_manufacturers = df_manufacturers[['id', 'name']]
    dict_manufacturers = df_manufacturers.set_index('id')['name'].to_dict()

    return dict_manufacturers

_read_properties_database ¶

_read_properties_database(
    path_json_properties=PATH_ZENODO_AIRCRAFT_DATABASE_PROPERTIES_FILE,
)

Reads a JSON backup of the the aircraft-database.com (discontinued as of 01-2024) table of properties and their values and returns it as a dictionary.

The JSON object and returned dictionary are of the form:

{
    '1ec882d7-fc13-611c-b99a-5724c0f1cad1': 'Afterburner [None]',
    '1ed95aff-38d7-6552-9ae8-b35cc598320e': 'Battery Capacity [kilowatt-hour]',
    '1ed95b07-4149-69c6-9ae8-c3b0ecc17a8d': 'Battery Technology [None]',
    (...)
}

Notes

With no parameters passed, the function will download the relevant JSON file from the aircraft-database.com Backup Zenodo repository.

Parameters:

Name	Type	Description	Default
`path_json_properties`	`str`	Path to the JSON file containing the properties and their values.	`PATH_ZENODO_AIRCRAFT_DATABASE_PROPERTIES_FILE`

Returns:

Type	Description
`dict`	Dictionary with the `aircraft-database.com` property IDs as keys and the property names with units as values.

Source code in aircraftdetective/processing/acftdb.py

def _read_properties_database(
    path_json_properties: str = PATH_ZENODO_AIRCRAFT_DATABASE_PROPERTIES_FILE
) -> dict:
    """
    Reads a JSON backup of the the [aircraft-database.com](https://web.archive.org/web/20231201220700/https://aircraft-database.com/)
    (discontinued as of 01-2024) table of `properties` and their values and returns it as a dictionary.

    The JSON object and returned dictionary are of the form:

    ```
    {
        '1ec882d7-fc13-611c-b99a-5724c0f1cad1': 'Afterburner [None]',
        '1ed95aff-38d7-6552-9ae8-b35cc598320e': 'Battery Capacity [kilowatt-hour]',
        '1ed95b07-4149-69c6-9ae8-c3b0ecc17a8d': 'Battery Technology [None]',
        (...)
    }
    ```

    Notes
    -----
    With no parameters passed, the function will download the relevant JSON file 
    from the [aircraft-database.com Backup Zenodo repository](https://doi.org/10.5281/zenodo.14382244).

    Parameters
    ----------
    path_json_properties : str
        Path to the JSON file containing the properties and their values.

    Returns
    -------
    dict
        Dictionary with the `aircraft-database.com` property IDs as keys and the property names with units as values.
    """

    dict_units = {
        None: 'dimensionless',
        'kilowatt-hour': 'kWh',
        'volt': 'V',
        'millimetre': 'mm',
        'cubic-centimetre': 'cm³',
        'kilogram': 'kg',
        'metre': 'm',
        'litre': 'L',
        'horsepower': 'hp',
        'kilowatt': 'kW',
        'kilonewton': 'kN',
        'decanewton-metre': 'daN*m',
        'foot': 'ft',
        'knot': 'kn',
        'cubic-metre': 'm³',
        'square-metre': 'm²',
    }

    df_properties = pd.read_json(path_json_properties)

    # capitalize words in name column
    df_properties['name'] = df_properties['name'].apply(lambda x:x.title()) 
    # replace unit names with abbreviations
    df_properties['unit'] = df_properties['unit'].map(dict_units)

    df_properties['value'] = df_properties.apply(
        lambda row: f"{row['name']} [{row['unit']}]"
        if row['unit'] != 'dimensionless'
        else row['name'],
        axis=1
    )

    dict_properties = df_properties.set_index('id')['value'].to_dict()

    return dict_properties

enrich_aircraft_database ¶

enrich_aircraft_database(
    path_json_aircraft_database=PATH_ZENODO_AIRCRAFT_DATABASE_AIRCRAFT_TYPES_FILE,
    path_json_engine_database=PATH_ZENODO_AIRCRAFT_DATABASE_ENGINE_MODELS_FILE,
    path_json_properties=PATH_ZENODO_AIRCRAFT_DATABASE_PROPERTIES_FILE,
    path_json_manufacturers=PATH_ZENODO_AIRCRAFT_DATABASE_MANUFACTURERS_FILE,
)

Enriches the aircraft database with engine properties by merging the aircraft and engine databases from the aircraft-database.com (discontinued as of 01-2024).

This function serves as a high-level wrapper that first processes the aircraft and engine datasets separately using helper functions and then combines them.