vortexasdk.endpoints.fleet_utilisation_avg_distance_timeseries

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FleetUtilisationAvgDistanceTimeseries

FleetUtilisationAvgDistanceTimeseries(self)

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

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FleetUtilisationAvgDistanceTimeseries.search(self, timeseries_frequency: str = None, timeseries_unit: str = None, timeseries_property: str = None, filter_products: Union[str, List[str]] = None, filter_charterers: Union[str, List[str]] = None, filter_owners: Union[str, List[str]] = None, filter_origins: Union[str, List[str]] = None, filter_destinations: Union[str, List[str]] = None, filter_vessels: Union[str, List[str]] = None, filter_vessel_classes: Union[str, List[str]] = None, 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, filter_vessel_tags: Union[List[vortexasdk.api.shared_types.Tag], vortexasdk.api.shared_types.Tag] = None, filter_vessel_risk_levels: Union[str, List[str]] = None, filter_vessel_scrubbers: str = 'disabled', 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_activity: 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), filter_vessel_status: 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, exclude_vessel_tags: Union[List[vortexasdk.api.shared_types.Tag], vortexasdk.api.shared_types.Tag] = None, exclude_vessel_risk_levels: Union[str, List[str]] = None) -> vortexasdk.endpoints.breakdown_result.BreakdownResult

Sum of the average distance for each day. For frequencies other than ‘day’, the values returned are calculated by summing each daily average distance bucket and returning the total.

Arguments

  • timeseries_unit: A numeric metric to be calculated for each time bucket. Must be one of 'b', 'bpd', 't', 'tpd', 'c', 'cpd', corresponding to barrels, barrels per day, metric tonnes, metric tonnes per day, cargo movement count, cargo movement count per day, respectively.

  • timeseries_frequency: Frequency denoting the granularity of the time series. Must be one of the following: 'day', 'week', 'doe_week', 'month', 'quarter', 'year'.

  • timeseries_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_time_min: The UTC start date of the time filter.

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

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

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

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

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

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

  • filter_vessel_ice_class: A vessel ice class ID, or list of vessel ice class IDs to filter on.

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

  • filter_vessel_tag: A time bound vessel tag, or list of time bound vessel tags to filter on.

  • filter_vessel_risk_levels: A vessel risk level, or list of vessel risk levels to filter on.

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

  • 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_age_min: A number between 0 and 550000.

  • filter_vessel_age_max: A number between 0 and 550000.

  • filter_activity: Movement activity on which to base the time filter. Must be one of: 'loading_state', 'oil_on_water_state', 'unloading_state', 'ship_to_ship', 'storing_state', 'transiting_state'

  • 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 'any_activity' for any other vessels.

  • filter_charterer_exists: A boolean to include or exclude the records to those that have a charterer.

  • filter_ship_to_ship: A boolean to include or exclude the records to those that are involved in an STS.

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

  • exclude_filter_charterers: A charterer entity ID, or list of product IDs to exclude.

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

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

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

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

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

  • exclude_filter_vessel_ice_class: A vessel ice class ID, or list of vessel ice class IDs to exclude.

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

  • exclude_filter_vessel_tag: A time bound vessel tag, or list of time bound vessel tags to exclude.

  • exclude_filter_vessel_risk_levels: A vessel risk level, or list of vessel risk levels to exclude.

Returns

BreakdownResult

Example

Average distance traveled per day, by laden vessels between Middle East and China, in nautical miles.

>>> from vortexasdk import FleetUtilisationAvgDistanceTimeseries
>>> from datetime import datetime
>>> search_result = FleetUtilisationAvgDistanceTimeseries().search(
...    filter_vessel_status="vessel_status_laden_known",
...    filter_origins="80aa9e4f3014c3d96559c8e642157edbb2b684ea0144ed76cd20b3af75110877",
...    filter_destinations="934c47f36c16a58d68ef5e007e62a23f5f036ee3f3d1f5f85a48c572b90ad8b2",
...    filter_time_min=datetime(2021, 1, 11),
...    filter_time_max=datetime(2021, 1, 18),
...    timeseries_unit="nmi",
...    timeseries_frequency="day",
...    timeseries_property="quantity")
>>> df = search_result.to_df()

Gives the following:

key value count breakdown.0.label breakdown.0.value breakdown.0.count
0 2021-01-11 00:00:00+00:00 249.769869 141 "quantity" 249.769869 141
1 2021-01-12 00:00:00+00:00 253.188081 137 "quantity" 253.188081 137
2 2021-01-13 00:00:00+00:00 250.312211 139 "quantity" 250.312211 139
3 2021-01-14 00:00:00+00:00 255.139234 138 "quantity" 255.139234 138
4 2021-01-15 00:00:00+00:00 256.738301 140 "quantity" 256.738301 140
5 2021-01-16 00:00:00+00:00 258.256194 141 "quantity" 258.256194 141
6 2021-01-17 00:00:00+00:00 255.120250 137 "quantity" 255.120250 137
7 2021-01-18 00:00:00+00:00 253.602907 132 "quantity" 253.602907 132