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11_o2_annual_averaging.py
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11_o2_annual_averaging.py
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import pandas as pd
import numpy as np
import os
def annual_avg_on_density_surfaces(input_df: pd.DataFrame, output_file_name: str,
density_surfaces: list, station: str, input_file_names: str):
# Take annual average of oxygen data on potential density anomaly surfaces
df = input_df
# df columns are: Profile number, Time, Profile is interpolated,
# Depth [m] Oxygen [umol/kg], Potential density anomaly [kg/m]
# Potential density anomaly bin [kg/m]
# Convert time to pandas datetime
df['Datetime'] = pd.to_datetime(df.loc[:, 'Time'])
years_available = np.sort(np.unique(df.loc[:, 'Datetime'].dt.year))
obs_per_year = np.zeros((len(years_available), len(density_surfaces)),
dtype='int32')
# Initialize dataframe to hold annual averages
density_column = 'Potential density anomaly bin [kg/m]'
df_avg = pd.DataFrame(columns=['Year', density_column, 'Average oxygen [umol/kg]'])
# Take the average for each year and density level
for i in range(len(years_available)):
for j in range(len(density_surfaces)):
indexer = np.where(
(df.loc[:, 'Datetime'].dt.year == years_available[i]) &
(df.loc[:, density_column] == density_surfaces[j])
)[0]
# Append a new row to the end of the initialized df
if len(indexer) > 0:
df_avg.loc[len(df_avg)] = [
years_available[i],
density_surfaces[j],
np.nanmean(df.loc[indexer, 'Oxygen [umol/kg]'].to_numpy(float))
]
# Update summary statistics
obs_per_year[i, j] = len(indexer)
# Save summary statistics
summary_file = os.path.join(
os.path.dirname(output_file_name),
'{}_averaging_summary_statistics.txt'.format(station))
with open(summary_file, 'w') as txtfile:
txtfile.write('Input file: ' + input_file_names + '\n')
txtfile.write('Output file: ' + output_file_name + '\n')
txtfile.write('Number of observations:\n')
txtfile.write('Year')
for d in density_surfaces:
txtfile.write(', {} kg/m3'.format(d))
for i in range(len(years_available)):
txtfile.write('\n{}'.format(years_available[i]))
for j in range(len(obs_per_year[i])):
txtfile.write(', {}'.format(obs_per_year[i, j]))
df_avg.to_csv(output_file_name, index=False)
return
# for each station: P4 and P26, LB08
stn = 'P4'
# station_name = stn
# station_name = 'OSP'
# parent_dir = 'C:\\Users\\HourstonH\\Documents\\charles\\' \
# 'line_P_data_products\\csv\\has_osd_ctd_flags\\'
# parent_dir = 'C:\\Users\\HourstonH\\Documents\\charles\\' \
# 'bottom_oxygen\\'
# parent_dir = 'D:\\lineP\\processing\\'
parent_dir = ('C:\\Users\\hourstonh\\Documents\\charles\\line_P_data_products\\'
'update_jan2024_sopo\\csv_data\\')
o2_bin_dir = '10_bin_o2_to_select_densities'
o2_bin_file = os.path.join(parent_dir, o2_bin_dir, '{}_data.csv'.format(stn))
# o2_bin_file = os.path.join(parent_dir, o2_bin_dir,
# '{}_ctd_data_qc.csv'.format(stn))
avg_dir = '11_annual_avg_on_dens_surfaces'
avg_file = os.path.join(parent_dir, avg_dir, os.path.basename(o2_bin_file))
densities = [26.5, 26.7, 26.9]
# Pre-2022 data
pre_2022_file = f'D:\\lineP\\processing\\10_bin_o2_to_select_densities\\{stn}_data.csv'
df_merged = pd.read_csv(o2_bin_file)
df_merged = pd.concat((pd.read_csv(pre_2022_file), df_merged))
df_merged.reset_index(drop=True, inplace=True)
annual_avg_on_density_surfaces(df_merged, avg_file, densities, stn,
input_file_names=' & '.join([o2_bin_file, pre_2022_file]))
# ------------------------Bill's data------------------------
"""
import glob
bill_dir = 'C:\\Users\\HourstonH\\Documents\\charles\\' \
'line_P_data_products\\bill_crawford\\masked\\'
station_name = '26' # 26 4849
input_files = glob.glob(bill_dir + f'*{station_name}*masked.csv')
input_files.sort()
output_df_filename = bill_dir + f'CrawfordPena Line P 1950-2015 {station_name} oxy annual avg.csv'
dfout = pd.DataFrame(columns=['Year', 'Potential density anomaly bin [kg/m]',
'Average oxygen [umol/kg]'])
for f in input_files:
dfin = pd.read_csv(f)
# Remove almost-all nan lines
print(len(dfin))
dfin.dropna(axis='index', how='all', subset=['Date'], inplace=True)
print(len(dfin))
obs_years = [int(d) for d in dfin.loc[:, 'Date']]
years_available = np.sort(np.unique(obs_years))
sigma_theta = np.round(dfin.loc[0, 'Sigma_Theta (from CT and AS)'], 1)
print(sigma_theta)
# Find the name of the oxygen column
ox_umol_colname = None
for colname in ['Ox (umol/kg) ', 'Ox (mmol/kg) ', 'O2 (umol/kg) ']:
# I think the mmol is just a typo in Bill's file
if colname in dfin.columns:
ox_umol_colname = colname
if ox_umol_colname is None:
print('Error: oxygen (umol/kg) column not found in input dataset')
is_close_mask = dfin.loc[:, 'is_close_to_station']
for y in years_available:
year_mask = obs_years == y
# Take average
avg_ox = np.nanmean(dfin.loc[year_mask & is_close_mask, ox_umol_colname])
# Add to output df
dfout.loc[len(dfout), :] = [y, sigma_theta, avg_ox]
# Save dfout
dfout.to_csv(output_df_filename, index=False)
"""