vortexasdk.endpoints.fleet_utilisation_speed_breakdown

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FleetUtilisationSpeedBreakdown

FleetUtilisationSpeedBreakdown(self)

Please note: you will require a subscription to our Freight module to access this endpoint.

search

FleetUtilisationSpeedBreakdown.search(self, breakdown_frequency: str = None, breakdown_unit: str = None, breakdown_property: 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_ship_to_ship: bool = None, filter_charterer_exists: bool = 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.breakdown_result.BreakdownResult

Average speed of vessels. For frequencies other than ‘day’, the average daily speed within that period is returned.

Arguments

  • breakdown_unit: Must be one of: 'mps', 'kmh', 'kn'.

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

  • breakdown_property: Property on the vessel movement used to build the value of the aggregation. By default it is “quantity”. Must be one of the following: 'quantity’, ‘vessel_class’, ‘vessel_flag’, ‘origin_region’, ‘origin_trading_region’, ‘origin_trading_sub_region’, ‘origin_country’, ‘origin_port’, ‘origin_terminal’, ‘destination_region’, ‘destination_trading_region’, ‘destination_trading_sub_region’, ‘destination_country’, ‘destination_port’, ‘destination_terminal’, 'product_group', 'product_group_product', 'product_category', 'product_grade'.

  • 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

BreakdownResult

Example

Average daily speed by week and knots, over the last month, from Middle East to China; broken down by vessel class.

>>> from vortexasdk import FleetUtilisationSpeedBreakdown
>>> from datetime import datetime
>>> search_result = FleetUtilisationSpeedBreakdown().search(
...    filter_vessel_status="vessel_status_laden_known",
...    filter_origins="80aa9e4f3014c3d96559c8e642157edbb2b684ea0144ed76cd20b3af75110877",
...    filter_destinations="934c47f36c16a58d68ef5e007e62a23f5f036ee3f3d1f5f85a48c572b90ad8b2",
...    filter_time_min=datetime(2020, 12, 19),
...    filter_time_max=datetime(2021, 1, 18),
...    breakdown_frequency="week",
...    breakdown_property="vessel_class",
...    breakdown_unit="kn")
>>> df = search_result.to_df()

returns

key value count breakdown.0.value breakdown.0.count breakdown.0.label
0 2020-12-14 00:00:00+00:00 154.99280077816653 141 17.5539329197942 3 'qflex'
1 2020-12-21 00:00:00+00:00 157.30489158419476 143.71428571428572 17.94667624904423 5.142857142857143 'qmax'
2 2020-12-28 00:00:00+00:00 139.81578683072956 149.28571428571428 15.522854583046652 2.857142857142857 'qflex'
3 2021-01-04 00:00:00+00:00 164.77469345951883 147.85714285714286 17.22505391874332 3.5714285714285716 'qmax'
4 2021-01-11 00:00:00+00:00 169.1270520125691 142 16.70538969861004 2.5714285714285716 'conventional'
5 2021-01-18 00:00:00+00:00 162.0734332502441 135 17.190140952526683 3 'qmax'