Datasets:

Qdrant
/

NOAA-Buoy

import csv
import time
from time import strptime
from datetime import datetime
from pathlib import Path

# UGLY - the non 2023 functions should be more generic given a certain start location - that way we don't have to repeat
# logic

# Function for Years
YEARS_LOCATION = "../orig_downloads/csv"
LOCATION_2023 = "../orig_downloads/2023/csv"

YEARS_PATH = Path(YEARS_LOCATION)
YEARS_PATH_2023 = Path(LOCATION_2023)

FINAL_BIG_FILE = "../full_years_remove_flawed_rows.csv"
FINAL_BIG_FILE_2023 = "../full_2023_remove_flawed_rows.csv"

HEADER = "#YY,MM,DD,hh,mm,WDIR,WSPD,GST,WVHT,DPD,APD,MWD,PRES,ATMP,WTMP,DEWP,VIS,TIDE\n"
FINAL_HEADER = ["TSTMP", "#YY","MM","DD", "hh","mm","WDIR","WSPD","GST","WVHT","DPD","APD","MWD","PRES","ATMP","WTMP"]


# Deal with the difference between files and get them standardized
def standardize():
    for read_path in YEARS_PATH.rglob('*.csv'):
        out_file_name = "fixed_" + read_path.name
        write_path = str(read_path).replace(read_path.name, out_file_name)
        with open(read_path, newline='') as read_file, open(write_path, 'w', newline='\n') as write_file:
            year = read_path.name[6:10]
            year = int(year)
            if year <= 2006:
                # First write the new header line
                read_file.readline()

                write_file.write(HEADER)
                for line in read_file:
                    line = line.strip()
                    if line[len(line)-1] == ",":
                        line_array = line[:-1].split(',')
                    else:
                        line_array = line.split(',')

                    # pre 1999 we need to make the year 4 digits
                    if year <= 1998:
                        line_array[0] = "19" + (line_array[0])

                    # Add tide with a value of 99.00 for all years pre 2000
                    if year < 2000:
                        line_array.append('99.0')

                    # Add 0 in for mm pre 2005 (header and values)
                    if year < 2005:
                        line_array.insert(4, '0')

                    # Changes are done, write the line
                    write_file.write(','.join(line_array) + "\n")
            if year > 2006:

                # Remove second header line from 2007 onwards
                read_file.readline()
                read_file.readline()

                # Add the first line back and just write the rest of the lines
                write_file.write(HEADER)
                for line in read_file:
                    line = line.strip()
                    if line[len(line)-1] == ",":
                        line = line[0:-1]
                    write_file.write(line + "\n")

# Now remove the columns we don't want and erase rows with a lot of missing values in columns we care about
def winnow_down(big_file_name, read_location):

    # need to be become missing data
    nine9_0 = {"WVHT", "WSPD", "GST", "DPD", "APD"}
    nine99_0 = {"ATMP", "WTMP"}
    nine99 = {"WDIR", "MWD"}
    if_all_missing = {"DPD","APD"}
    remove_me = {"DEWP", "VIS", "TIDE"}


    # Set up the file to write to
    with open(big_file_name, 'w', newline='') as file:
        fieldnames = FINAL_HEADER
        output_csvfile = csv.DictWriter(file, fieldnames=fieldnames)

        output_csvfile.writeheader()
        for read_path in read_location.rglob('fixed_*.csv'):
            print(read_path)
            with open(read_path, newline='') as csv_file:
                csv_reader = csv.DictReader(csv_file)

                # row is not an ordered dict
                for row in csv_reader:

                    # Check to see if we are missing key data - if so delete the row and move along
                    delete_row = 0.0
                    if row["WSPD"] == "99.0":
                        delete_row = delete_row + 1.0
                    if row["WVHT"] == "99.0" or row["WVHT"] == "99.00":
                        delete_row = delete_row + 1.0
                    if row["WTMP"] == "999.0":
                        delete_row = delete_row + 1.0
                    # if DPD and APD are missing along with any of the above then we remove
                    for key in if_all_missing:
                        if row[key] == "99.0" or row[key] == "99.00":
                            delete_row = delete_row + 0.5


                    if delete_row >= 2.0:
                        # Two strikes you are out and we go on to the next row
                        continue

                    # Remove observations at least 2 of these columns with null values in wspd (99.0)  wvht (99.0) and wtmp (999.0)
                    # WD MWD = 999, GST DPD APD = 99.0, PRES = 9999.0, ATMP WTMP = 999.0
                    # For those left we need to convert these to missing(just a blank)
                    for key in nine99:
                        if row[key] == '999':
                            row[key] = ''
                    for key in nine9_0:
                        if row[key] == '99.0' or row[key] == '99.00':
                            row[key] = ''
                    for key in nine99_0:
                        if row[key] == '999.0':
                            row[key] = ''
                    if row["PRES"] == '9999.0':
                        row["PRES"] = ''

                    # remove columns DEMP, VIS, TIDE
                    for key in remove_me:
                        del row[key]

                    # Finally we need to convert Y, M, D, m into a timestamp and that will be the key
                    # Buoy 42002 is in Lousiana, UTC -5
                    timestamp_string = row["#YY"] + "-" + row["MM"] + "-" + row["DD"] + " " + row["hh"] + ":" + row["mm"] + "-" + "-0500"
                    row["TSTMP"] = datetime.strptime(timestamp_string, "%Y-%m-%d %H:%M-%z")

                    # Ok we are ready to write a new row to our database
                    output_csvfile.writerow(row)

# Function for 2023
def standardize2023():
    for read_path in YEARS_PATH_2023.rglob('*.csv'):
        out_file_name = "fixed_" + read_path.name
        write_path = str(read_path).replace(read_path.name, out_file_name)
        with open(read_path, newline='') as read_file, open(write_path, 'w', newline='\n') as write_file:
            # Remove second header line from 2007 onwards
            read_file.readline()
            read_file.readline()

            # Add the first line back and just write the rest of the lines
            write_file.write(HEADER)
            for line in read_file:
                line = line.strip()
                if line[len(line)-1] == ",":
                    line = line[0:-1]
                write_file.write(line + "\n")



if __name__ == '__main__':
    print("start")
    #standardize()
    winnow_down(FINAL_BIG_FILE, YEARS_PATH)
    #standardize2023()
    winnow_down(FINAL_BIG_FILE_2023, YEARS_PATH_2023)
    print("finished")

	import csv
	import time
	from time import strptime
	from datetime import datetime
	from pathlib import Path

	# UGLY - the non 2023 functions should be more generic given a certain start location - that way we don't have to repeat
	# logic

	# Function for Years
	YEARS_LOCATION = "../orig_downloads/csv"
	LOCATION_2023 = "../orig_downloads/2023/csv"

	YEARS_PATH = Path(YEARS_LOCATION)
	YEARS_PATH_2023 = Path(LOCATION_2023)

	FINAL_BIG_FILE = "../full_years_remove_flawed_rows.csv"
	FINAL_BIG_FILE_2023 = "../full_2023_remove_flawed_rows.csv"

	HEADER = "#YY,MM,DD,hh,mm,WDIR,WSPD,GST,WVHT,DPD,APD,MWD,PRES,ATMP,WTMP,DEWP,VIS,TIDE\n"
	FINAL_HEADER = ["TSTMP", "#YY","MM","DD", "hh","mm","WDIR","WSPD","GST","WVHT","DPD","APD","MWD","PRES","ATMP","WTMP"]


	# Deal with the difference between files and get them standardized
	def standardize():
	for read_path in YEARS_PATH.rglob('*.csv'):
	out_file_name = "fixed_" + read_path.name
	write_path = str(read_path).replace(read_path.name, out_file_name)
	with open(read_path, newline='') as read_file, open(write_path, 'w', newline='\n') as write_file:
	year = read_path.name[6:10]
	year = int(year)
	if year <= 2006:
	# First write the new header line
	read_file.readline()

	write_file.write(HEADER)
	for line in read_file:
	line = line.strip()
	if line[len(line)-1] == ",":
	line_array = line[:-1].split(',')
	else:
	line_array = line.split(',')

	# pre 1999 we need to make the year 4 digits
	if year <= 1998:
	line_array[0] = "19" + (line_array[0])

	# Add tide with a value of 99.00 for all years pre 2000
	if year < 2000:
	line_array.append('99.0')

	# Add 0 in for mm pre 2005 (header and values)
	if year < 2005:
	line_array.insert(4, '0')

	# Changes are done, write the line
	write_file.write(','.join(line_array) + "\n")
	if year > 2006:

	# Remove second header line from 2007 onwards
	read_file.readline()
	read_file.readline()

	# Add the first line back and just write the rest of the lines
	write_file.write(HEADER)
	for line in read_file:
	line = line.strip()
	if line[len(line)-1] == ",":
	line = line[0:-1]
	write_file.write(line + "\n")

	# Now remove the columns we don't want and erase rows with a lot of missing values in columns we care about
	def winnow_down(big_file_name, read_location):

	# need to be become missing data
	nine9_0 = {"WVHT", "WSPD", "GST", "DPD", "APD"}
	nine99_0 = {"ATMP", "WTMP"}
	nine99 = {"WDIR", "MWD"}
	if_all_missing = {"DPD","APD"}
	remove_me = {"DEWP", "VIS", "TIDE"}


	# Set up the file to write to
	with open(big_file_name, 'w', newline='') as file:
	fieldnames = FINAL_HEADER
	output_csvfile = csv.DictWriter(file, fieldnames=fieldnames)

	output_csvfile.writeheader()
	for read_path in read_location.rglob('fixed_*.csv'):
	print(read_path)
	with open(read_path, newline='') as csv_file:
	csv_reader = csv.DictReader(csv_file)

	# row is not an ordered dict
	for row in csv_reader:

	# Check to see if we are missing key data - if so delete the row and move along
	delete_row = 0.0
	if row["WSPD"] == "99.0":
	delete_row = delete_row + 1.0
	if row["WVHT"] == "99.0" or row["WVHT"] == "99.00":
	delete_row = delete_row + 1.0
	if row["WTMP"] == "999.0":
	delete_row = delete_row + 1.0
	# if DPD and APD are missing along with any of the above then we remove
	for key in if_all_missing:
	if row[key] == "99.0" or row[key] == "99.00":
	delete_row = delete_row + 0.5


	if delete_row >= 2.0:
	# Two strikes you are out and we go on to the next row
	continue

	# Remove observations at least 2 of these columns with null values in wspd (99.0) wvht (99.0) and wtmp (999.0)
	# WD MWD = 999, GST DPD APD = 99.0, PRES = 9999.0, ATMP WTMP = 999.0
	# For those left we need to convert these to missing(just a blank)
	for key in nine99:
	if row[key] == '999':
	row[key] = ''
	for key in nine9_0:
	if row[key] == '99.0' or row[key] == '99.00':
	row[key] = ''
	for key in nine99_0:
	if row[key] == '999.0':
	row[key] = ''
	if row["PRES"] == '9999.0':
	row["PRES"] = ''

	# remove columns DEMP, VIS, TIDE
	for key in remove_me:
	del row[key]

	# Finally we need to convert Y, M, D, m into a timestamp and that will be the key
	# Buoy 42002 is in Lousiana, UTC -5
	timestamp_string = row["#YY"] + "-" + row["MM"] + "-" + row["DD"] + " " + row["hh"] + ":" + row["mm"] + "-" + "-0500"
	row["TSTMP"] = datetime.strptime(timestamp_string, "%Y-%m-%d %H:%M-%z")

	# Ok we are ready to write a new row to our database
	output_csvfile.writerow(row)

	# Function for 2023
	def standardize2023():
	for read_path in YEARS_PATH_2023.rglob('*.csv'):
	out_file_name = "fixed_" + read_path.name
	write_path = str(read_path).replace(read_path.name, out_file_name)
	with open(read_path, newline='') as read_file, open(write_path, 'w', newline='\n') as write_file:
	# Remove second header line from 2007 onwards
	read_file.readline()
	read_file.readline()

	# Add the first line back and just write the rest of the lines
	write_file.write(HEADER)
	for line in read_file:
	line = line.strip()
	if line[len(line)-1] == ",":
	line = line[0:-1]
	write_file.write(line + "\n")



	if __name__ == '__main__':
	print("start")
	#standardize()
	winnow_down(FINAL_BIG_FILE, YEARS_PATH)
	#standardize2023()
	winnow_down(FINAL_BIG_FILE_2023, YEARS_PATH_2023)
	print("finished")