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rename model_date to model for requests #16

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Merged
nsteins merged 1 commit into from
Mar 25, 2024
Merged

rename model_date to model for requests #16

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52 changes: 26 additions & 26 deletions watttime/api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -177,7 +177,7 @@ def get_historical_jsons(
signal_type: Optional[
Literal["co2_moer", "co2_aoer", "health_damage"]
] = "co2_moer",
model_date: Optional[Union[str, date]] = None,
model: Optional[Union[str, date]] = None,
) -> List[dict]:
"""
Base function to scrape historical data, returning a list of .json responses.
Expand All @@ -187,7 +187,7 @@ def get_historical_jsons(
end (datetime): inclusive end, with a UTC timezone.
region (str): string, accessible through the /my-access endpoint, or use the free region (CAISO_NORTH)
signal_type (str, optional): one of ['co2_moer', 'co2_aoer', 'health_damage']. Defaults to "co2_moer".
model_date (Optional[Union[str, date]], optional): Optionally provide a model_date, used for versioning models.
model (Optional[Union[str, date]], optional): Optionally provide a model, used for versioning models.
Defaults to None.

Raises:
Expand All @@ -206,9 +206,9 @@ def get_historical_jsons(
start, end = self._parse_dates(start, end)
chunks = self._get_chunks(start, end)

# No model_date will default to the most recent model version available
if model_date is not None:
params["model_date"] = model_date
# No model will default to the most recent model version available
if model is not None:
params["model"] = model

for c in chunks:
params["start"], params["end"] = c
Expand All @@ -227,7 +227,7 @@ def get_historical_jsons(

# the API should not let this happen, but ensure for sanity
unique_models = set([r["meta"]["model"]["date"] for r in responses])
chosen_model = model_date or max(unique_models)
chosen_model = model or max(unique_models)
if len(unique_models) > 1:
responses = [
r for r in responses if r["meta"]["model"]["date"] == chosen_model
Expand All @@ -243,7 +243,7 @@ def get_historical_pandas(
signal_type: Optional[
Literal["co2_moer", "co2_aoer", "health_damage"]
] = "co2_moer",
model_date: Optional[Union[str, date]] = None,
model: Optional[Union[str, date]] = None,
include_meta: bool = False,
):
"""
Expand All @@ -259,7 +259,7 @@ def get_historical_pandas(
pd.DataFrame: _description_
"""
responses = self.get_historical_jsons(
start, end, region, signal_type, model_date
start, end, region, signal_type, model
)
df = pd.json_normalize(
responses, record_path="data", meta=["meta"] if include_meta else []
Expand All @@ -277,7 +277,7 @@ def get_historical_csv(
signal_type: Optional[
Literal["co2_moer", "co2_aoer", "health_damage"]
] = "co2_moer",
model_date: Optional[Union[str, date]] = None,
model: Optional[Union[str, date]] = None,
):
"""
Retrieves historical data from a specified start date to an end date and saves it as a CSV file.
Expand All @@ -288,12 +288,12 @@ def get_historical_csv(
end (Union[str, datetime]): The end date for retrieving historical data. It can be a string in the format "YYYY-MM-DD" or a datetime object.
region (str): The region for which historical data is requested.
signal_type (Optional[Literal["co2_moer", "co2_aoer", "health_damage"]]): The type of signal for which historical data is requested. Default is "co2_moer".
model_date (Optional[Union[str, date]]): The date of the model for which historical data is requested. It can be a string in the format "YYYY-MM-DD" or a date object. Default is None.
model (Optional[Union[str, date]]): The date of the model for which historical data is requested. It can be a string in the format "YYYY-MM-DD" or a date object. Default is None.

Returns:
None, results are saved to a csv file in the user's home directory.
"""
df = self.get_historical_pandas(start, end, region, signal_type, model_date)
df = self.get_historical_pandas(start, end, region, signal_type, model)

out_dir = Path.home() / "watttime_historical_csvs"
out_dir.mkdir(exist_ok=True)
Expand Down Expand Up @@ -370,7 +370,7 @@ def get_forecast_json(
signal_type: Optional[
Literal["co2_moer", "co2_aoer", "health_damage"]
] = "co2_moer",
model_date: Optional[Union[str, date]] = None,
model: Optional[Union[str, date]] = None,
horizon_hours: int = 24,
) -> Dict:
"""
Expand All @@ -383,7 +383,7 @@ def get_forecast_json(
region (str): The region for which forecast data is requested.
signal_type (str, optional): The type of signal to retrieve forecast data for. Defaults to "co2_moer".
Valid options are "co2_moer", "co2_aoer", and "health_damage".
model_date (str or date, optional): The date of the model version to use for the forecast data.
model (str or date, optional): The date of the model version to use for the forecast data.
If not provided, the most recent model version will be used.
horizon_hours (int, optional): The number of hours to forecast. Defaults to 24. Minimum of 0 provides a "nowcast" created with the forecast, maximum of 72.

Expand All @@ -398,9 +398,9 @@ def get_forecast_json(
"horizon_hours": horizon_hours,
}

# No model_date will default to the most recent model version available
if model_date is not None:
params["model_date"] = model_date
# No model will default to the most recent model version available
if model is not None:
params["model"] = model

url = "{}/v3/forecast".format(self.url_base)
headers = {"Authorization": "Bearer " + self.token}
Expand All @@ -414,7 +414,7 @@ def get_forecast_pandas(
signal_type: Optional[
Literal["co2_moer", "co2_aoer", "health_damage"]
] = "co2_moer",
model_date: Optional[Union[str, date]] = None,
model: Optional[Union[str, date]] = None,
include_meta: bool = False,
horizon_hours: int = 24,
) -> pd.DataFrame:
Expand All @@ -430,7 +430,7 @@ def get_forecast_pandas(
Returns:
pd.DataFrame: _description_
"""
j = self.get_forecast_json(region, signal_type, model_date, horizon_hours)
j = self.get_forecast_json(region, signal_type, model, horizon_hours)
return pd.json_normalize(
j, record_path="data", meta=["meta"] if include_meta else []
)
Expand All @@ -443,7 +443,7 @@ def get_historical_forecast_json(
signal_type: Optional[
Literal["co2_moer", "co2_aoer", "health_damage"]
] = "co2_moer",
model_date: Optional[Union[str, date]] = None,
model: Optional[Union[str, date]] = None,
horizon_hours: int = 24,
) -> List[Dict[str, Any]]:
"""
Expand All @@ -454,7 +454,7 @@ def get_historical_forecast_json(
end (Union[str, datetime]): The end date of the historical forecast. Can be a string or a datetime object.
region (str): The region for which to retrieve the forecast data.
signal_type (Optional[Literal["co2_moer", "co2_aoer", "health_damage"]]): The type of signal to retrieve. Defaults to "co2_moer".
model_date (Optional[Union[str, date]]): The date of the model version to use. Defaults to None.
model (Optional[Union[str, date]]): The date of the model version to use. Defaults to None.
horizon_hours (int, optional): The number of hours to forecast. Defaults to 24. Minimum of 0 provides a "nowcast" created with the forecast, maximum of 72.

Returns:
Expand All @@ -477,9 +477,9 @@ def get_historical_forecast_json(
start, end = self._parse_dates(start, end)
chunks = self._get_chunks(start, end, chunk_size=timedelta(days=1))

# No model_date will default to the most recent model version available
if model_date is not None:
params["model_date"] = model_date
# No model will default to the most recent model version available
if model is not None:
params["model"] = model

for c in chunks:
params["start"], params["end"] = c
Expand Down Expand Up @@ -507,7 +507,7 @@ def get_historical_forecast_pandas(
signal_type: Optional[
Literal["co2_moer", "co2_aoer", "health_damage"]
] = "co2_moer",
model_date: Optional[Union[str, date]] = None,
model: Optional[Union[str, date]] = None,
horizon_hours: int = 24,
) -> pd.DataFrame:
"""
Expand All @@ -519,14 +519,14 @@ def get_historical_forecast_pandas(
region (str): The region for which the historical forecast data is retrieved.
signal_type (Optional[Literal["co2_moer", "co2_aoer", "health_damage"]], optional):
The type of signal for the historical forecast data. Defaults to "co2_moer".
model_date (Optional[Union[str, date]], optional): The model date for the historical forecast data. Defaults to None.
model (Optional[Union[str, date]], optional): The model date for the historical forecast data. Defaults to None.
horizon_hours (int, optional): The number of hours to forecast. Defaults to 24. Minimum of 0 provides a "nowcast" created with the forecast, maximum of 72.

Returns:
pd.DataFrame: A pandas DataFrame containing the historical forecast data.
"""
json_list = self.get_historical_forecast_json(
start, end, region, signal_type, model_date, horizon_hours
start, end, region, signal_type, model, horizon_hours
)
out = pd.DataFrame()
for json in json_list:
Expand Down