vortexasdk.endpoints.vessel_availability_timeseries

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VesselAvailabilityTimeseries

VesselAvailabilityTimeseries(self)

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

search

VesselAvailabilityTimeseries.search(self, 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_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_location: Union[str, List[str]] = None, filter_owners: Union[str, List[str]] = None, filter_effective_controllers: Union[str, List[str]] = None, filter_destination: Union[str, List[str]] = None, filter_region: str = None, filter_port: str = None, use_reference_port: bool = False, filter_days_to_arrival: List[Dict[str, int]] = None, filter_vessel_dwt_min: int = None, filter_vessel_dwt_max: int = None, filter_vessel_age_min: int = None, filter_vessel_age_max: int = None, filter_vessel_idle_min: int = None, filter_vessel_idle_max: int = None, filter_vessel_scrubbers: str = 'disabled', filter_recent_visits: str = None, filter_vessel_flags: Union[str, List[str]] = None, filter_vessel_ice_class: Union[str, List[str]] = None, filter_vessel_tags: Union[List[vortexasdk.api.shared_types.Tag], vortexasdk.api.shared_types.Tag] = None, filter_vessel_risk_level: 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_vessel_status: str = None, exclude_vessel_location: Union[str, List[str]] = None, exclude_owners: Union[str, List[str]] = None, exclude_effective_controllers: Union[str, List[str]] = None, exclude_destination: Union[str, List[str]] = None, exclude_filter_vessel_flags: Union[str, List[str]] = None, exclude_filter_vessel_ice_class: Union[str, List[str]] = None, exclude_filter_vessel_tags: Union[List[vortexasdk.api.shared_types.Tag], vortexasdk.api.shared_types.Tag] = None, exclude_filter_vessel_risk_level: Union[str, List[str]] = None) -> vortexasdk.endpoints.timeseries_result.TimeSeriesResult

Time series of the number of vessels that can be available to load a given cargo at a given port for every day in the specified range.

Arguments

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

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

  • filter_effective_controllers: An effective controller ID, or list of effective controller IDs to filter on.

  • filter_destination: A geography ID, or list of geography 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 'any_activity' for any other vessels

  • filter_vessel_location: A location ID, or list of location IDs to filter on.

  • filter_port: Filter by port ID.

  • filter_region: Filter by region ID - takes precedence over filter_port if provided. This should be used in conjunction with use_reference_port

  • filter_days_to_arrival: Filter availability by time to arrival in days`

  • use_reference_port: If this flag is enabled, we will return data for the reference port instead of the user selected one,

  • 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_idle_min: A number greater than 0 (representing idle days).

  • filter_vessel_idle_max: A number greater than 0 and filter_vessel_idle_min (representing idle days).

  • filter_vessel_dwt_min: A number between 0 and 550000.

  • filter_vessel_dwt_max: A number between 0 and 550000.

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

  • filter_recent_visits: Filter availability by each vessel's recent visits

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

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

  • filter_vessel_tags: A tag ID, or list of tag IDs to filter on.

  • filter_vessel_risk_level: A risk level ID, or list of risk level IDs to filter on.

  • 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_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

  • exclude_effective_controllers: An effective controller ID, or list of effective controller IDs to exclude.

  • exclude_vessel_location: A location ID, or list of location IDs to filter on.

  • exclude_destination: A location ID, or list of location IDs to filter on.

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

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

  • exclude_vessel_tags: A tag ID, or list of tag IDs to exclude.

  • exclude_vessel_risk_level: A risk level ID, or list of risk level IDs to exclude.

Returns

TimeSeriesResult

Example

Time series for the number of vessels available between 0 to 5 days, at port Rotterdam, over 4 days.

>>> from vortexasdk import VesselAvailabilityTimeseries, Geographies
>>> from datetime import datetime
>>> rotterdam = "68faf65af1345067f11dc6723b8da32f00e304a6f33c000118fccd81947deb4e"
>>> start = datetime(2021, 6, 17)
>>> end = datetime(2021, 6, 21)
>>> df = (VesselAvailabilityTimeseries().search(
...     filter_time_min=start,
...     filter_time_max=end,
...     filter_port=rotterdam,
...     filter_days_to_arrival={"min": 0, "max": 5},
... ).to_df())

Gives the following:

key value count
0 2021-06-23 00:00:00+00:00 19225923 224
1 2021-06-24 00:00:00+00:00 19634766 233
2 2021-06-25 00:00:00+00:00 19154857 228
3 2021-06-26 00:00:00+00:00 18410395 225