buoy-python/ZScore2023.py

import csv
from pathlib import Path
import numpy as np
import pandas as pd

from collections import defaultdict

FULL_DATA_SET_STRING = "../full_2023_remove_flawed_rows.csv"
FULL_DATA_SET_PATH = Path(FULL_DATA_SET_STRING)
OUT_DATASET_STRING = "../zscore_2023.parquet"
OUT_DATASET_PATH = Path(OUT_DATASET_STRING)
OUT_FULL_DATASET_STRING = "../full_2023_remove_flawed.parquet"
OUT_FULL_DATASET_PATH = Path(OUT_FULL_DATASET_STRING)

NUMERIC_FIELDS = ["WSPD","GST","WVHT","DPD","APD","PRES","ATMP","WTMP"]


def load_data(data_path):
    print("Loading data")

    with open(data_path, newline='') as csv_file:

        loaded_np_data = pd.read_csv(csv_file)

        print("Writing out the full Parquet file")
        loaded_np_data.to_parquet(OUT_FULL_DATASET_PATH)

        print("Applying Sin() to the two degrees columns")
        loaded_np_data["WDIR"] = np.sin(np.deg2rad(loaded_np_data["WDIR"]))
        loaded_np_data["MWD"] = np.sin(np.deg2rad(loaded_np_data["MWD"]))

        print("calculating z-scores")
        for var in NUMERIC_FIELDS:
            var_mean = np.mean(loaded_np_data[var])
            var_std = np.std(loaded_np_data[var])

            var_zscore = (loaded_np_data[var] - var_mean)/var_std
            loaded_np_data[var] = var_zscore


        print("exporting to parquet")
        loaded_np_data.set_index("TSTMP")
        loaded_np_data.to_parquet(OUT_DATASET_PATH)


if __name__ == '__main__':
    print("Start")

    # Load data
    all_data = load_data(FULL_DATA_SET_PATH)

    # Calculate mean and std dev for each non-date column
    # Going to need to sin(X) for any circular numbers (WDIR & MWD)


    # Write out data removing rows
    # Probably want to write out the sin(X) for any circular numbers
    print("finished")