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")