Datasets:

Qdrant
/

NOAA-Buoy

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

from collections import defaultdict

FULL_DATA_SET_STRING = "../full_years_remove_flawed_rows.csv"
FULL_DATA_SET_PATH = Path(FULL_DATA_SET_STRING)
OUT_DATASET_STRING = "../trimmed_full_years_for_db.parquet"
OUT_DATASET_PATH = Path(OUT_DATASET_STRING)
OUT_FULL_DATASET_STRING = "../full_years_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("finding outlier rows")

        # calculate the rows to keep
        # for each column, is the z-score larger than 2 = loaded_np_data[NUMERIC_FIELDS].le(2)
        # are there less 2 columns meeting the condition above = keep the row
        output_np_data = loaded_np_data[loaded_np_data[NUMERIC_FIELDS].gt(2).sum(axis=1).lt(2)]

        print("exporting to parquet")
        output_np_data.set_index("TSTMP")
        output_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")

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

	from collections import defaultdict

	FULL_DATA_SET_STRING = "../full_years_remove_flawed_rows.csv"
	FULL_DATA_SET_PATH = Path(FULL_DATA_SET_STRING)
	OUT_DATASET_STRING = "../trimmed_full_years_for_db.parquet"
	OUT_DATASET_PATH = Path(OUT_DATASET_STRING)
	OUT_FULL_DATASET_STRING = "../full_years_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("finding outlier rows")

	# calculate the rows to keep
	# for each column, is the z-score larger than 2 = loaded_np_data[NUMERIC_FIELDS].le(2)
	# are there less 2 columns meeting the condition above = keep the row
	output_np_data = loaded_np_data[loaded_np_data[NUMERIC_FIELDS].gt(2).sum(axis=1).lt(2)]

	print("exporting to parquet")
	output_np_data.set_index("TSTMP")
	output_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")