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.
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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' |