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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") |