/
freight_pricing_timeseries.py
97 lines (72 loc) · 5.85 KB
/
freight_pricing_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%2Ffreight_pricing_timeseries.ipynb)
"""
from typing import Any, Dict, List, Union
from vortexasdk.endpoints.timeseries_result import TimeSeriesResult
from vortexasdk.api.shared_types import to_ISODate
from datetime import datetime
from vortexasdk.endpoints.endpoints import FREIGHT_PRICING_TIMESERIES
from vortexasdk.logger import get_logger
from vortexasdk.operations import Search
from vortexasdk.utils import convert_to_list
logger = get_logger(__name__)
class FreightPricingTimeseries(Search):
""" """
def __init__(self):
Search.__init__(self, FREIGHT_PRICING_TIMESERIES)
def search(
self,
time_min: datetime = datetime(2021, 9, 1),
time_max: datetime = datetime(2021, 11, 1),
routes: Union[List[str], str] = None,
breakdown_frequency: str = None,
breakdown_property: str = None,
) -> TimeSeriesResult:
"""
Time series of the selected pricing information for given routes in the specified time range.
# Arguments
time_min: The UTC start date of the time filter.
time_max: The UTC end date of the time filter.
breakdown_frequency: Must be one of: `'day'`, `'week'`, `'doe_week'`, `'month'`, `'quarter'` or `'year'`.
breakdown_property: Property used to build the value of the aggregation. Must be one of the following: `route`, `cost`, `tce`.
routes: Used to filter by specific routes. Must be one of the following:
- Clean routes - `TC1`, `TC2_37`, `TC5`, `TC6`, `TC7`, `TC8`, `TC9`, `TC10`, `TC11`, `TC12`, `TC14`, `TC15`, `TC16`, `TC17`, `TC18`, `TC19`.
- Dirty routes - `TD1`, `TD2`, `TD3C`, `TD6`, `TD7`, `TD8`, `TD9`, `TD12`, `TD14`, `TD15`, `TD17`, `TD18`, `TD19`, `TD20`, `TD21`, `TD22`, `TD23`, `TD24`, `TD25`, `TD26`.
- BLPG routes - `BLPG1`, `BLPG2`, `BLPG3`.
# Returns
`TimeSeriesResult`
# Example
Time series for the WS rate of the TD3C route between 1st and 15th November 2021.
```python
>>> from vortexasdk import FreightPricingTimeseries
>>> from datetime import datetime
>>> start = datetime(2021, 11, 1)
>>> end = datetime(2021, 11, 15)
>>> df = (FreightPricingTimeseries().search(
... time_min=start,
... time_max=end,
... routes=['TD3C'],
... breakdown_property='rate',
... breakdown_frequency='day')
... .to_df()).head(2)
```
Gives the following:
| | key | value | count |
|---:|:-------------------------|-------------------:|--------:|
| 0 | 2021-11-01 00:00:00+00:00| 46.04999923706055 | 1 |
| 1 | 2021-11-02 00:00:00+00:00| 45.13999938964844 | 1 |
"""
api_params: Dict[str, Any] = {
"time_min": to_ISODate(time_min),
"time_max": to_ISODate(time_max),
"routes": convert_to_list(routes),
"breakdown_frequency": breakdown_frequency,
"breakdown_property": breakdown_property,
}
response = super().search_with_client(
response_type="breakdown", **api_params
)
return TimeSeriesResult(
records=response["data"], reference=response["reference"]
)