pywrdrb.pre.PredictedDiversionPreprocessor

class pywrdrb.pre.PredictedDiversionPreprocessor(start_date=None, end_date=None, modes=('regression_disagg',), use_log=True, remove_zeros=False, use_const=False)

Predicts catchment inflows at Montague and Trenton using specified modes (e.g., regression, perfect foresight, moving average).

load()

Load NJ diversions and catchment water consumption data.

process()

Run the full prediction workflow.

save()

Save predicted diversion time series to pywrdrb/data/diversions.

get_prediction_node_lag_combinations()

Return dictionary of predicted diversion column names and their defining (node, lag, mode) tuples.

input_dirs

Input files used for prediction.

Type:

dict

output_dirs

Output locations for predicted timeseries.

Type:

dict

timeseries_data

DataFrame containing the timeseries data. Is None until load() is called.

Type:

DataFrame

predicted_timeseries

DataFrame containing the predicted timeseries. Is None until process() is called.

Type:

DataFrame

catchment_wc

DataFrame containing the average water consumption data for node catchments.

Type:

DataFrame

Example usage: ```python from pywrdrb.pre import PredictedDiversionPreprocessor diversion_predictor = PredictedDiversionPreprocessor(

start_date=”1983-10-01”, end_date=”2016-12-31”, modes=(“regression_disagg”,),

) diversion_predictor.process() diversion_predictor.save() ```

__init__(start_date=None, end_date=None, modes=('regression_disagg',), use_log=True, remove_zeros=False, use_const=False)

Initialize the PredictedDiversionPreprocessor.

Parameters:

• start_date (str, None) – Start date for the time series. If None, match the input data.

• end_date (str, None) – End date for the time series. If None, match the input data.

• modes (tuple) – Modes to use for prediction. Default is (‘regression_disagg’,). Options include: “regression_disagg”, “perfect_foresight”, “moving_average”, “same_day”.

• use_log (bool) – Whether to use log transformation for model vars. Default is True.

• remove_zeros (bool) – Whether to remove zero values. Default is False.

• use_const (bool) – Whether to use a constant/intercept in regression. Default is False.

Return type:

None

Methods

__init__([start_date, end_date, modes, ...])	Initialize the PredictedDiversionPreprocessor.
get_prediction_node_lag_combinations()	Return dict of predicted diversion column names formatted as (node, lag, mode) tuples.
load()	Load NJ diversions and catchment WC data (used for structural compatibility).
make_predictions(regressions)	Generate lead-time predictions using the timeseries data and trained models.
process()	Run full prediction workflow.
save()	Save predicted diversion time series to CSV.
train_regressions()	Train the AR models for different node, lag combinations.