vortexasdk.endpoints.tonne_miles_breakdown

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TonneMilesBreakdown

TonneMilesBreakdown(self)

The Tonne-miles Breakdown Endpoint is used to retrieve the tonne-miles data as a time series. The aggregation is done on the Vessel Movements data hence very similar search parameters are accepted (minus: unit, size, offset). Additionally a parameter named breakdown_frequency can be used to specify the time series frequency.

A VesselMovement represents a single vessel moving between two locations.

The vessel may carry one cargo, many cargoes (co-loads), or zero cargos (ballast). The start and end locations for a VesselMovement may be on land (loadings and discharges), they may be STS Zones (STS events), or they may be Floating Storage.

search

TonneMilesBreakdown.search(self, breakdown_frequency: str = None, filter_time_min: datetime.datetime = datetime.datetime(2019, 10, 1, 0, 0), filter_time_max: datetime.datetime = datetime.datetime(2019, 10, 1, 1, 0), unit: str = 'b', filter_activity: str = None, filter_charterers: Union[str, List[str]] = None, filter_destinations: Union[str, List[str]] = None, filter_origins: Union[str, List[str]] = None, filter_owners: Union[str, List[str]] = None, filter_products: Union[str, List[str]] = None, filter_vessels: Union[str, List[str]] = None, filter_vessel_classes: Union[str, List[str]] = None, filter_vessel_status: str = None, filter_vessel_age_min: int = None, filter_vessel_age_max: int = None, filter_vessel_dwt_min: int = None, filter_vessel_dwt_max: int = None, filter_vessel_scrubbers: str = 'disabled', filter_vessel_flags: Union[str, List[str]] = None, filter_vessel_ice_class: Union[str, List[str]] = None, filter_vessel_propulsion: Union[str, List[str]] = None, exclude_origins: Union[str, List[str]] = None, exclude_destinations: Union[str, List[str]] = None, exclude_products: Union[str, List[str]] = None, exclude_vessels: Union[str, List[str]] = None, exclude_vessel_classes: Union[str, List[str]] = None, exclude_charterers: Union[str, List[str]] = None, exclude_owners: Union[str, List[str]] = None, exclude_vessel_flags: Union[str, List[str]] = None, exclude_vessel_ice_class: Union[str, List[str]] = None, exclude_vessel_propulsion: Union[str, List[str]] = None) -> vortexasdk.endpoints.timeseries_result.TimeSeriesResult

Find TonneMilesBreakdown matching the given search parameters.

Arguments

  • breakdown_frequency: Must be one of: 'day', 'week', 'doe_week', 'month', 'quarter' or 'year'.

  • filter_activity: Movement activity on which to base the time filter. Must be one of: 'loading_state', 'loading_start', 'loading_end', 'identified_for_loading_state', 'unloading_state', 'unloading_start', 'unloading_end', 'unloaded_state', 'storing_state', 'storing_start', 'storing_end', 'transiting_state', 'any_activity'.

  • filter_time_min: The UTC start date of the time filter.

  • filter_time_max: The UTC end date of the time filter.

  • unit: Unit of measurement. Enter 'b' for barrels or 't' for tonnes.

  • filter_charterers: A charterer ID, or list of charterer IDs to filter on.

  • filter_destinations: A geography ID, or list of geography IDs to filter on.

  • filter_origins: A geography ID, or list of geography IDs to filter on.

  • filter_owners: An corporation ID, or list of corporation IDs to filter on.

  • filter_products: A product ID, or list of product IDs to filter on.

  • filter_vessels: A vessel ID, or list of vessel IDs to filter on.

  • filter_vessel_classes: A vessel class, or list of vessel classes to filter on.

  • filter_vessel_status: The vessel status on which to base the filter. Enter 'vessel_status_ballast' for ballast vessels, 'vessel_status_laden_known' for laden vessels with known cargo (i.e. a type of cargo that Vortexa currently tracks) or 'vessel_status_laden_unknown' for laden vessels with unknown cargo (i.e. a type of cargo that Vortexa currently does not track).

  • filter_vessel_age_min: A number between 1 and 100 (representing years).

  • filter_vessel_age_max: A number between 1 and 100 (representing years).

  • filter_vessel_dwt_min: A number representing minimum deadweight tonnage of a vessel.

  • filter_vessel_dwt_max: A number representing maximum deadweight tonnage of a vessel.

  • filter_vessel_scrubbers: Either inactive 'disabled', or included 'inc' or excluded 'exc'.

  • filter_vessel_flags: A geography ID, or list of geography IDs to filter on.

  • filter_vessel_ice_class: An attribute ID, or list of attribute IDs to filter on.

  • filter_vessel_propulsion: An attribute ID, or list of attribute IDs to filter on.

  • exclude_origins: A geography ID, or list of geography IDs to exclude.

  • exclude_destinations: A geography ID, or list of geography IDs to exclude.

  • exclude_products: A product ID, or list of product IDs to exclude.

  • exclude_vessels: A vessel ID, or list of vessel IDs to exclude.

  • exclude_vessel_classes: A vessel class, or list of vessel classes to exclude.

  • exclude_charterers: A charterer ID, or list of charterer IDs to exclude.

  • exclude_owners: An owner ID, or list of owner IDs to exclude.

  • exclude_vessel_flags: A geography ID, or list of geography IDs to exclude.

  • exclude_vessel_ice_class: An attribute ID, or list of attribute IDs to exclude.

  • exclude_vessel_propulsion: An attribute ID, or list of attribute IDs to exclude.

Returns

TimeSeriesResult

Example

>>> from vortexasdk import TonneMilesBreakdown, Vessels
>>> from datetime import datetime
>>> new_wisdom = [g.id for g in Vessels().search("NEW WISDOM").to_list()]
>>> search_result = TonneMilesBreakdown().search(
...    unit='b',
...    breakdown_frequency='month',
...    filter_vessels=new_wisdom,
...    filter_time_min=datetime(2018, 1, 1),
...    filter_time_max=datetime(2018, 12, 31))
>>> df = search_result.to_df()

returns

key value count
0 2018-01-01 00:00:00+00:00 4.558499e+07 1
1 2018-02-01 00:00:00+00:00 4.393985e+07 1
2 2018-03-01 00:00:00+00:00 7.781776e+06 1
3 2018-04-01 00:00:00+00:00 8.041169e+07 1
4 2018-05-01 00:00:00+00:00 3.346161e+07 1
5 2018-06-01 00:00:00+00:00 5.731648e+07 1
6 2018-07-01 00:00:00+00:00 4.976054e+07 1
7 2018-08-01 00:00:00+00:00 3.022656e+06 1
8 2018-09-01 00:00:00+00:00 2.504909e+07 1
9 2018-10-01 00:00:00+00:00 6.269583e+07 1
10 2018-11-01 00:00:00+00:00 1.823642e+07 1
11 2018-12-01 00:00:00+00:00 3.137448e+07 1