/
voyages_congestion_breakdown_result.py
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/
voyages_congestion_breakdown_result.py
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import functools
import os
from multiprocessing.pool import Pool
from typing import List
import pandas as pd
from vortexasdk.api.entity_flattening import convert_to_flat_dict
from vortexasdk.api.search_result import Result
from vortexasdk.api.voyages import CongestionBreakdownItem
from vortexasdk.result_conversions import create_dataframe, create_list
from vortexasdk.logger import get_logger
logger = get_logger(__name__)
DEFAULT_COLUMNS = [
"avg_waiting_time",
"vessel_dwt",
"vessel_cubic_capacity",
"vessel_count",
"cargo_quantity",
"avg_waiting_time_laden",
"vessel_dwt_laden",
"vessel_cubic_capacity_laden",
"vessel_count_laden",
"avg_waiting_time_ballast",
"vessel_dwt_ballast",
"vessel_cubic_capacity_ballast",
"vessel_count_ballast",
"location_details.0.label",
]
class CongestionBreakdownResult(Result):
"""
Container class holdings search results returns from the voyages congestion breakdown endpoint.
Please note: you will require a subscription to our Freight module to access this endpoint.
This class has two methods, `to_list()`, and `to_df()`, allowing search results to be represented as a list of `CongestionBreakdownResult`(ies),
or as a `pd.DataFrame` , respectively.
"""
def to_list(self) -> List[CongestionBreakdownItem]:
"""Represent availability as a list."""
# noinspection PyTypeChecker
return create_list(super().to_list(), CongestionBreakdownItem)
def to_df(self, columns=DEFAULT_COLUMNS) -> pd.DataFrame:
"""
Represent availability as a `pd.DataFrame`.
# Arguments
columns: Output columns present in the `pd.DataFrame`.
Enter `columns='all'` to return all available columns.
Enter `columns=None` to use `voyages_congestion_breakdown_result.DEFAULT_COLUMNS`.
# Returns
`pd.DataFrame`, one row per `VesselAvailability`.
## Notes
By default, the columns returned are something along the lines of.
```python
DEFAULT_COLUMNS = [
'avg_waiting_time',
'vessel_dwt',
'vessel_cubic_capacity',
'vessel_count',
'cargo_quantity',
'avg_waiting_time_laden',
'vessel_dwt_laden',
'vessel_cubic_capacity_laden',
'vessel_count_laden',
'avg_waiting_time_ballast',
'vessel_dwt_ballast',
'vessel_cubic_capacity_ballast',
'vessel_count_ballast',
'location_details.0.label',
]
```
The exact default columns used can be found at `voyages_congestion_breakdown_result.DEFAULT_COLUMNS`
A near complete list of columns is given below
```
[
'avg_waiting_time',
'vessel_dwt',
'vessel_cubic_capacity',
'vessel_count',
'cargo_quantity',
'avg_waiting_time_laden',
'vessel_dwt_laden',
'vessel_cubic_capacity_laden',
'vessel_count_laden',
'avg_waiting_time_ballast',
'vessel_dwt_ballast',
'vessel_cubic_capacity_ballast',
'vessel_count_ballast',
'location_details.0.label',
'location_details.0.id',
'location_details.0.layer.0'
'location_details.0.layer.1'
'location_details.0.layer.2',
'location_details.1.label',
'location_details.1.id',
'location_details.1.layer.0'
'location_details.1.layer.1'
'location_details.1.layer.2'
]
```
"""
logger.debug(
"Converting each Voyage Congestion Breakdown to a flat dictionary"
)
flatten = functools.partial(convert_to_flat_dict, columns=columns)
with Pool(os.cpu_count()) as pool:
records = pool.map(flatten, super().to_list())
return create_dataframe(
columns=columns,
data=records,
logger_description="VoyagesCongestionBreakdown",
)