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9a_generate_summary_tables.py
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9a_generate_summary_tables.py
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""" Sept 7, 2021
Generate summary tables for NEP climatology
Organize by instrument, season/year
"""
import pandas as pd
import numpy as np
import glob
from clim_helpers import date_string_to_datetime
from os.path import basename
def count_prof_by_szn(infiles):
# Now compute summary statistics for original files
# Initialize arrays to hold counts for each season
bot_count = np.zeros(4, dtype='int32')
ctd_count = np.zeros(4, dtype='int32')
pfl_count = np.zeros(4, dtype='int32')
gld_count = np.zeros(4, dtype='int32')
drb_count = np.zeros(4, dtype='int32')
# total_count1 = bot_count1 + ctd_count1
for f in infiles:
print(basename(f))
df = pd.read_csv(f)
# Get the indices of the start of each profile
prof_start_ind = np.unique(df.Profile_number, return_index=True)[1]
# Convert Date_string to pandas datetime??
# Create a new column for Date_string in pandas datetime format
df = date_string_to_datetime(df)
# Count the bottle and ctd data per season
for i in range(4):
szn_start = 3 * i + 1
szn_end = 3 * i + 3
bot_where1 = np.where((df.Instrument_type == 'BOT') &
(df.Time_pd.dt.month >= szn_start) &
(df.Time_pd.dt.month <= szn_end))[0]
bot_count[i] += len(np.intersect1d(prof_start_ind, bot_where1))
ctd_where1 = np.where((df.Instrument_type == 'CTD') &
(df.Time_pd.dt.month >= szn_start) &
(df.Time_pd.dt.month <= szn_end))[0]
ctd_count[i] += len(np.intersect1d(prof_start_ind, ctd_where1))
pfl_where1 = np.where((df.Instrument_type == 'PFL') &
(df.Time_pd.dt.month >= szn_start) &
(df.Time_pd.dt.month <= szn_end))[0]
pfl_count[i] += len(np.intersect1d(prof_start_ind, pfl_where1))
gld_where1 = np.where((df.Instrument_type == 'GLD') &
(df.Time_pd.dt.month >= szn_start) &
(df.Time_pd.dt.month <= szn_end))[0]
gld_count[i] += len(np.intersect1d(prof_start_ind, gld_where1))
drb_where1 = np.where((df.Instrument_type == 'DRB') &
(df.Time_pd.dt.month >= szn_start) &
(df.Time_pd.dt.month <= szn_end))[0]
drb_count[i] += len(np.intersect1d(prof_start_ind, drb_where1))
print(bot_count)
print(ctd_count)
print(pfl_count)
print(gld_count)
print(drb_count)
return bot_count, ctd_count, pfl_count, gld_count, drb_count
def make_summary_table(file_list1, file_list2):
# Make summary table of vvd table profiles
bot_count1, ctd_count1, pfl_count1, gld_count1, drb_count1 = count_prof_by_szn(file_list1)
bot_count2, ctd_count2, pfl_count2, gld_count2, drb_count2 = count_prof_by_szn(file_list2)
bot_in_out = np.zeros(len(bot_count1) * 2 + 2)
ctd_in_out = np.zeros(len(ctd_count1) * 2 + 2)
pfl_in_out = np.zeros(len(pfl_count1) * 2 + 2)
gld_in_out = np.zeros(len(gld_count1) * 2 + 2)
drb_in_out = np.zeros(len(drb_count1) * 2 + 2)
for i in range(len(bot_count1)):
bot_in_out[2 * i] = bot_count1[i]
for i in range(len(bot_count2)):
bot_in_out[2 * i + 1] = bot_count2[i]
for i in range(len(ctd_count1)):
ctd_in_out[2 * i] = ctd_count1[i]
for i in range(len(bot_count2)):
ctd_in_out[2 * i + 1] = ctd_count2[i]
for i in range(len(pfl_count1)):
pfl_in_out[2 * i] = pfl_count1[i]
for i in range(len(pfl_count2)):
pfl_in_out[2 * i + 1] = pfl_count2[i]
for i in range(len(gld_count1)):
gld_in_out[2 * i] = gld_count1[i]
for i in range(len(gld_count2)):
gld_in_out[2 * i + 1] = gld_count2[i]
for i in range(len(drb_count1)):
drb_in_out[2 * i] = drb_count1[i]
for i in range(len(drb_count2)):
drb_in_out[2 * i + 1] = drb_count2[i]
bot_in_out[-2] = sum(bot_count1)
bot_in_out[-1] = sum(bot_count2)
ctd_in_out[-2] = sum(ctd_count1)
ctd_in_out[-1] = sum(ctd_count2)
pfl_in_out[-2] = sum(pfl_count1)
pfl_in_out[-1] = sum(pfl_count2)
gld_in_out[-2] = sum(gld_count1)
gld_in_out[-1] = sum(gld_count2)
drb_in_out[-2] = sum(drb_count1)
drb_in_out[-1] = sum(drb_count2)
total_in_out = bot_in_out + ctd_in_out + pfl_in_out + gld_in_out + drb_in_out
colnames = ['JFM_in', 'JFM_out', 'AMJ_in', 'AMJ_out', 'JAS_in', 'JAS_out',
'OND_in', 'OND_out', 'Total_in', 'Total_out']
df_sum = pd.DataFrame(data=np.array([bot_in_out, ctd_in_out, pfl_in_out, gld_in_out,
drb_in_out, total_in_out]),
columns=colnames, dtype='int32')
# rename rows in df
df_sum.rename(index={df_sum.index[0]: 'num_BOT'}, inplace=True)
df_sum.rename(index={df_sum.index[1]: 'num_CTD'}, inplace=True)
df_sum.rename(index={df_sum.index[2]: 'num_PFL'}, inplace=True)
df_sum.rename(index={df_sum.index[3]: 'num_GLD'}, inplace=True)
df_sum.rename(index={df_sum.index[4]: 'num_DRB'}, inplace=True)
df_sum.rename(index={df_sum.index[5]: 'num_total'}, inplace=True)
# Add columns for MTH_%
df_sum.insert(loc=2, column='JFM_%', value=[
df_sum.loc['num_BOT', 'JFM_out']/df_sum.loc['num_BOT', 'JFM_in'],
df_sum.loc['num_CTD', 'JFM_out']/df_sum.loc['num_CTD', 'JFM_in'],
df_sum.loc['num_PFL', 'JFM_out'] / df_sum.loc['num_PFL', 'JFM_in'],
df_sum.loc['num_GLD', 'JFM_out'] / df_sum.loc['num_GLD', 'JFM_in'],
df_sum.loc['num_DRB', 'JFM_out'] / df_sum.loc['num_DRB', 'JFM_in'],
df_sum.loc['num_total', 'JFM_out']/df_sum.loc['num_total', 'JFM_in']])
df_sum.insert(loc=5, column='AMJ_%', value=[
df_sum.loc['num_BOT', 'AMJ_out']/df_sum.loc['num_BOT', 'AMJ_in'],
df_sum.loc['num_CTD', 'AMJ_out']/df_sum.loc['num_CTD', 'AMJ_in'],
df_sum.loc['num_PFL', 'AMJ_out'] / df_sum.loc['num_PFL', 'AMJ_in'],
df_sum.loc['num_GLD', 'AMJ_out'] / df_sum.loc['num_GLD', 'AMJ_in'],
df_sum.loc['num_DRB', 'AMJ_out'] / df_sum.loc['num_DRB', 'AMJ_in'],
df_sum.loc['num_total', 'AMJ_out']/df_sum.loc['num_total', 'AMJ_in']])
df_sum.insert(loc=8, column='JAS_%', value=[
df_sum.loc['num_BOT', 'JAS_out']/df_sum.loc['num_BOT', 'JAS_in'],
df_sum.loc['num_CTD', 'JAS_out']/df_sum.loc['num_CTD', 'JAS_in'],
df_sum.loc['num_PFL', 'JAS_out'] / df_sum.loc['num_PFL', 'JAS_in'],
df_sum.loc['num_GLD', 'JAS_out'] / df_sum.loc['num_GLD', 'JAS_in'],
df_sum.loc['num_DRB', 'JAS_out'] / df_sum.loc['num_DRB', 'JAS_in'],
df_sum.loc['num_total', 'JAS_out']/df_sum.loc['num_total', 'JAS_in']])
df_sum.insert(loc=11, column='OND_%', value=[
df_sum.loc['num_BOT', 'OND_out']/df_sum.loc['num_BOT', 'OND_in'],
df_sum.loc['num_CTD', 'OND_out']/df_sum.loc['num_CTD', 'OND_in'],
df_sum.loc['num_PFL', 'OND_out'] / df_sum.loc['num_PFL', 'OND_in'],
df_sum.loc['num_GLD', 'OND_out'] / df_sum.loc['num_GLD', 'OND_in'],
df_sum.loc['num_DRB', 'OND_out'] / df_sum.loc['num_DRB', 'OND_in'],
df_sum.loc['num_total', 'OND_out']/df_sum.loc['num_total', 'OND_in']])
df_sum.insert(loc=14, column='Total_%', value=[
df_sum.loc['num_BOT', 'Total_out'] / df_sum.loc['num_BOT', 'Total_in'],
df_sum.loc['num_CTD', 'Total_out'] / df_sum.loc['num_CTD', 'Total_in'],
df_sum.loc['num_PFL', 'Total_out'] / df_sum.loc['num_PFL', 'Total_in'],
df_sum.loc['num_GLD', 'Total_out'] / df_sum.loc['num_GLD', 'Total_in'],
df_sum.loc['num_DRB', 'Total_out'] / df_sum.loc['num_DRB', 'Total_in'],
df_sum.loc['num_total', 'Total_out'] / df_sum.loc['num_total', 'Total_in']])
return df_sum
# Set variable
var_name = 'Sal' # Oxy Temp Sal
var_folder = 'salinity' # salinity temperature oxygen
# Do for first and last
dir1 = 'C:\\Users\\HourstonH\\Documents\\NEP_climatology\\data\\' \
'value_vs_depth\\1_original\\'
files1 = glob.glob(dir1 + '*{}*.csv'.format(var_name))
dir9 = 'C:\\Users\\HourstonH\\Documents\\NEP_climatology\\data\\' \
'value_vs_depth\\9_gradient_check\\'
files9 = glob.glob(dir9 + '*{}*_grad_check_done.csv'.format(var_name))
df_out = make_summary_table(files1, files9)
# Export
df_name = 'C:\\Users\\HourstonH\\Documents\\NEP_climatology\\data_explore\\' \
'{}\\profile_counts\\{}_summary_prof_count_table_19.csv'.format(
var_folder, var_name)
# Want to keep index
df_out.to_csv(df_name, index=True)
# Do at each processing step
indir = 'C:\\Users\\HourstonH\\Documents\\NEP_climatology\\data\\value_vs_depth\\'
subdirs = ['1_original', '3_filtered_for_duplicates', '4_latlon_check',
'5_filtered_for_quality_flag', '6_filtered_for_nans', '7_depth_check',
'8_range_check', '9_gradient_check']
outdir = 'C:\\Users\\HourstonH\\Documents\\NEP_climatology\\data_explore\\' \
'{}\\profile_counts\\'.format(var_folder)
for j in range(len(subdirs) - 1):
# Check for files with "done" in the file name
infiles1 = glob.glob(indir + subdirs[j] + '\\*{}*done.csv'.format(var_name))
infiles2 = glob.glob(indir + subdirs[j + 1] + '\\*{}*done.csv'.format(var_name))
if len(infiles1) == 0:
infiles1 = glob.glob(indir + subdirs[j] + '\\*{}*.csv'.format(var_name))
if len(infiles2) == 0:
infiles2 = glob.glob(indir + subdirs[j + 1] + '\\*{}*.csv'.format(var_name))
df_out = make_summary_table(infiles1, infiles2)
# Index the number in the subdir name, which is the order of cleaning
step1 = subdirs[j][0]
step2 = subdirs[j + 1][0]
outname = outdir + '{}_summary_prof_count_table{}{}.csv'.format(
var_name, step1, step2)
df_out.to_csv(outname, index=True)