ampli/py/predict.py

from argparse import ArgumentParser, FileType
import numpy as np
import pandas as p
import statsmodels.formula.api as smf
import datetime as dt
import pickle
from pymodels import thirtyoffset, predweather, harmonic
from pprint import pprint

def main():
    parser = ArgumentParser(description=
                            'Predict from harmonic model of cluster')
    parser.add_argument("-m", "--model-file", dest="model_file",
                        help="filename for models",
                        default="../models/testmod.pkl",
                        type=FileType('rb'))
    # parser.add_argument("-w", "--weather",  dest="weather",
    #                     help="input weather pickle path",
    #                     metavar="PATH", required=True,
    #                     type=FileType('rb'))
    parser.add_argument("--weather-harmonics", dest="weather_harmonics",
                        help="number of harmonics for weather; default: 2",
                        type=int, default=2, metavar="NUM")
    parser.add_argument("--icp-harmonics", dest="icp_harmonics", nargs=3,
                        help="harmonics for icp fitting, default 2 3 3",
                        default=[2, 3, 3], type=int,
                        metavar="NUM")
    args = parser.parse_args()

    print(args)

    mods = pickle.load(args.model_file)

    pprint(mods)
    
    print(mods["max_temp"].predict())

    print(predweather(mods["max_temp"], "2019-01-01", "2019-02-01", harmonics = mods["weather_harmonics"]))

    args.model_file.close()


if __name__ == "__main__":
    main()