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fleet_utilisation_avg_distance_timeseries.py
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fleet_utilisation_avg_distance_timeseries.py
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"""
Try me out in your browser:
[](https://mybinder.org/v2/gh/VorTECHsa/python-sdk/master?filepath=docs%2Fexamples%2Ftry_me_out%2Futilisation_avg_distance_timeseries.ipynb)
"""
from datetime import datetime
from typing import Any, Dict, List, Union
from vortexasdk.endpoints.breakdown_result import BreakdownResult
from vortexasdk.endpoints.endpoints import (
FLEET_UTILISATION_TIMESERIES_AVG_DISTANCE,
)
from vortexasdk.api.shared_types import Tag, to_ISODate
from vortexasdk.api import ID
from vortexasdk.operations import Search
from vortexasdk.utils import convert_to_list, sts_param_value
class FleetUtilisationAvgDistanceTimeseries(Search):
"""
Please note: you will require a subscription to our Freight module to access this endpoint.
"""
def __init__(self):
Search.__init__(self, FLEET_UTILISATION_TIMESERIES_AVG_DISTANCE)
# noinspection PyUnresolvedReferences
def search(
self,
timeseries_frequency: str = None,
timeseries_unit: str = None,
timeseries_property: str = None,
filter_products: Union[ID, List[ID]] = None,
filter_charterers: Union[ID, List[ID]] = None,
filter_owners: Union[ID, List[ID]] = None,
filter_origins: Union[ID, List[ID]] = None,
filter_destinations: Union[ID, List[ID]] = None,
filter_vessels: Union[ID, List[ID]] = None,
filter_vessel_classes: Union[ID, List[ID]] = None,
filter_vessel_flags: Union[ID, List[ID]] = None,
filter_vessel_ice_class: Union[ID, List[ID]] = None,
filter_vessel_propulsion: Union[ID, List[ID]] = None,
filter_vessel_tags: Union[List[Tag], Tag] = None,
filter_vessel_risk_levels: Union[ID, List[ID]] = 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(2019, 10, 1, 0),
filter_time_max: datetime = datetime(2019, 10, 1, 1),
filter_vessel_status: Union[ID, List[ID]] = None,
exclude_origins: Union[ID, List[ID]] = None,
exclude_destinations: Union[ID, List[ID]] = None,
exclude_products: Union[ID, List[ID]] = None,
exclude_vessels: Union[ID, List[ID]] = None,
exclude_vessel_classes: Union[ID, List[ID]] = None,
exclude_charterers: Union[ID, List[ID]] = None,
exclude_owners: Union[ID, List[ID]] = None,
exclude_vessel_flags: Union[ID, List[ID]] = None,
exclude_vessel_ice_class: Union[ID, List[ID]] = None,
exclude_vessel_propulsion: Union[ID, List[ID]] = None,
exclude_vessel_tags: Union[List[Tag], Tag] = None,
exclude_vessel_risk_levels: Union[ID, List[ID]] = None,
) -> 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._
```python
>>> 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 |
"""
sts_filter = sts_param_value(filter_ship_to_ship)
crossfilters = {
"filter_ship_to_ship": sts_filter["x_filter"],
# if charterer toggle is True, apply cross filter
# else make it false
"filter_charterer_exists": filter_charterer_exists == True,
}
exclude_params: Dict[str, Any] = {
"filter_destinations": convert_to_list(exclude_destinations),
"filter_products": convert_to_list(exclude_products),
"filter_vessels": convert_to_list(exclude_vessels),
"filter_vessel_classes": convert_to_list(exclude_vessel_classes),
"filter_owners": convert_to_list(exclude_owners),
"filter_origins": convert_to_list(exclude_origins),
"filter_charterers": convert_to_list(exclude_charterers),
"filter_vessel_flags": convert_to_list(exclude_vessel_flags),
"filter_vessel_ice_class": convert_to_list(
exclude_vessel_ice_class
),
"filter_vessel_propulsion": convert_to_list(
exclude_vessel_propulsion
),
"filter_vessel_risk_levels": convert_to_list(
exclude_vessel_risk_levels
),
"filter_ship_to_ship": sts_filter["exclude"],
}
api_params: Dict[str, Any] = {
"timeseries_frequency": timeseries_frequency,
"timeseries_property": timeseries_property,
"timeseries_unit": timeseries_unit,
"filter_activity": filter_activity,
"filter_time_min": to_ISODate(filter_time_min),
"filter_time_max": to_ISODate(filter_time_max),
"filter_vessel_age_min": filter_vessel_age_min,
"filter_vessel_age_max": filter_vessel_age_max,
"filter_vessel_dwt_min": filter_vessel_dwt_min,
"filter_vessel_dwt_max": filter_vessel_dwt_max,
"filter_vessel_status": convert_to_list(filter_vessel_status),
"filter_charterers": convert_to_list(filter_charterers),
"filter_owners": convert_to_list(filter_owners),
"filter_products": convert_to_list(filter_products),
"filter_vessels": convert_to_list(filter_vessels),
"filter_vessel_classes": convert_to_list(filter_vessel_classes),
"filter_vessel_flags": convert_to_list(filter_vessel_flags),
"filter_destinations": convert_to_list(filter_destinations),
"filter_origins": convert_to_list(filter_origins),
"filter_vessel_ice_class": convert_to_list(
filter_vessel_ice_class
),
"filter_vessel_propulsion": convert_to_list(
filter_vessel_propulsion
),
"vessel_tags": convert_to_list(filter_vessel_tags),
"vessel_tags_excluded": convert_to_list(exclude_vessel_tags),
"filter_vessel_risk_levels": filter_vessel_risk_levels,
"filter_vessel_scrubbers": filter_vessel_scrubbers,
"filter_ship_to_ship": filter_ship_to_ship,
"exclude": exclude_params,
"crossfilters": crossfilters,
}
response = super().search_with_client(**api_params)
return BreakdownResult(
records=response["data"], reference=response["reference"]
)