/
fixtures_result.py
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/
fixtures_result.py
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import functools
import os
from multiprocessing import Pool
from typing import List
import pandas as pd
from vortexasdk.api import Fixture
from vortexasdk.api.entity_flattening import convert_to_flat_dict
from vortexasdk.api.search_result import Result
from vortexasdk.logger import get_logger
from vortexasdk.result_conversions import create_dataframe, create_list
logger = get_logger(__name__)
DEFAULT_COLUMNS = [
"id",
"vessel.id",
"vessel.name",
"laycan_from",
"laycan_to",
"tones",
"fixing_timestamp",
"fulfilled",
"vtx_fulfilled",
"destination.label",
"origin.label",
"product.label",
"charterer.label",
]
class FixtureResult(Result):
"""Container class that holds the result obtained from calling the `Fixtures` endpoint."""
def to_list(self) -> List[Fixture]:
"""Represent Fixtures data as a list."""
# noinspection PyTypeChecker
return create_list(super().to_list(), Fixture)
def to_df(self, columns=DEFAULT_COLUMNS) -> pd.DataFrame:
"""
Represent Fixtures as a `pd.DataFrame`.
# Arguments
columns: The Fixtures columns we want in the dataframe.
Defaults to `columns = [
"id",
'vessels.corporate_entities.charterer.id',
'vessels.corporate_entities.charterer.label',
'vessels.corporate_entities.charterer.layer',
'vessels.corporate_entities.charterer.probability',
'vessels.corporate_entities.charterer.source',
'vessels.corporate_entities.effective_controller.id',
'vessels.corporate_entities.effective_controller.label',
'vessels.corporate_entities.effective_controller.layer',
'vessels.corporate_entities.effective_controller.probability',
'vessels.corporate_entities.effective_controller.source',
'vessels.corporate_entities.time_charterer.end_timestamp',
'vessels.corporate_entities.time_charterer.id',
'vessels.corporate_entities.time_charterer.label',
'vessels.corporate_entities.time_charterer.layer',
'vessels.corporate_entities.time_charterer.probability',
'vessels.corporate_entities.time_charterer.source',
'vessels.corporate_entities.time_charterer.start_timestamp',
'vessels.cubic_capacity',
'vessels.dwt',
'vessels.end_timestamp',
'vessels.fixture_fulfilled',
'vessels.fixture_id',
'vessels.id',
'vessels.imo',
'vessels.mmsi',
'vessels.name',
'vessels.start_timestamp',
'vessels.status',
'vessels.tags.end_timestamp',
'vessels.tags.start_timestamp',
'vessels.tags.tag',
'vessels.vessel_class',
'vessels.voyage_id',
"laycan_from",
"laycan_to",
"tones",
"fixing_timestamp",
"fulfilled",
"vtx_fulfilled",
"destination.label",
"destination.id",
"origin.label",
"origin.id",
"product.label",
"product.id",
"charterer.label",
"charterer.id",
]`.
A near complete list of columns is given below
```python
[
"id",
"vessel.id",
"vessel.name",
"laycan_from",
"laycan_to",
"tones",
"fixing_timestamp",
"fulfilled",
"vtx_fulfilled",
"destination.label",
"origin.label",
"product.label",
"charterer.label",
]
```
# Returns
`pd.DataFrame` of Fixtures.
"""
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="Fixtures",
)