/
freight_pricing_result.py
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/
freight_pricing_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.freight_pricing import FreightPricing
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 = [
"short_code",
"record_date",
"rate",
"rate_unit",
"cost",
"cost_unit",
"tce",
"tce_unit",
"predictions.outlook_1d.prediction",
"predictions.outlook_1d.rating",
]
class FreightPricingResult(Result):
"""
Container class holdings search results returns from the freight pricing endpoint.
This class has two methods, `to_list()`, and `to_df()`, allowing search results to be represented as a list
or as a `pd.DataFrame` , respectively.
"""
def to_list(self) -> List[FreightPricing]:
"""Represent availability as a list."""
# noinspection PyTypeChecker
return create_list(super().to_list(), FreightPricing)
@staticmethod
def format_prediction_outlooks(records: List):
"""
This method formats the freight_pricing records to replace a list of predictions
with a dictionary where each key is the prediction outlook.
We do this so that when the freight pricing object is flattened to be consumed in a dataframe,
the column names are predictable, without having list indices in the column name.
`predictions.0.prediction` will become `predictions.outlook_1d.prediction`
This also means specific outlooks can be passed in the `columns` argument to the `to_df()` method.
E.G.
Input:
{
predictions: [
{
prediction_type: "outlook_1d",
prediction: "firm",
rating: "medium"
},
{
prediction_type: "outlook_4d",
prediction: "firm",
rating: "low"
}
]
}
Output:
{
predictions: {
outlook_1d: {
prediction: "firm",
rating: "medium",
prediction_type: "outlook_1d"
},
outlook_4d: {
prediction: "firm",
rating: "low",
prediction_type: "outlook_4d"
}
}
}
"""
formatted_records = []
for record in records:
formatted_predictions = None
new_record = {}
if record["predictions"]:
formatted_predictions = {}
for fp_prediction in record["predictions"]:
formatted_predictions[fp_prediction["prediction_type"]] = {
**fp_prediction
}
new_record = {
**record,
"predictions": formatted_predictions,
}
formatted_records.append(new_record)
return formatted_records
def to_df(self, columns="all") -> pd.DataFrame:
"""
Represent freight pricing 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 `freight_pricing_result.DEFAULT_COLUMNS`.
# Returns
`pd.DataFrame`, one row per `FreightPricing`.
## Notes
By default, the columns returned are something along the lines of.
```python
DEFAULT_COLUMNS = [
'short_code',
'record_date',
'rate',
'rate_unit',
'cost',
'cost_unit',
'tce',
'tce_unit',
'predictions.outlook_1d.prediction',
'predictions.outlook_1d.rating',
]
```
The exact default columns used can be found at `freight_pricing.DEFAULT_COLUMNS`
A near complete list of columns is given below
```
[
'id',
'short_code',
'rate',
'rate_precision',
'rate_unit',
'cost',
'cost_precision,
'cost_unit',
'tce',
'tce_precision',
'tce_unit',
'record_date',
'predictions.outlook_1d.prediction',
'predictions.outlook_1d.rating',
'predictions.outlook_2d.prediction',
'predictions.outlook_2d.rating',
'predictions.outlook_3d.prediction',
'predictions.outlook_3d.rating',
'predictions.outlook_4d.prediction',
'predictions.outlook_4d.rating',
]
```
"""
logger.debug(
"Converting each Freight Pricing object to a flat dictionary"
)
flatten = functools.partial(convert_to_flat_dict, columns=columns)
with Pool(os.cpu_count()) as pool:
records = pool.map(
flatten, self.format_prediction_outlooks(super().to_list())
)
return create_dataframe(
columns=columns,
data=records,
logger_description="FreightPricing",
)