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azmp_genReport2024.py
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'''
!!! This is the new script with new working tree.
For ResDoc and presentation on 2024 conditions.
This script can be run from anywhere, but a suggestion:
~/AZMP/state_reports/
The following subfolders will be created:
air_temperature/
bergs/
bottom_temp/
CIL/
climate_index/
LC_transport/
and figures further moved here:
./{year-in-review}/
Oct 2024
'''
import os
import csv
import matplotlib.pyplot as plt
import numpy as np
import datetime
import pandas as pd
#Provide the path to where custom packages are saved
import sys
sys.path.append(os.path.expanduser('~/github/AZMP-NL/python-toolbox/azmp_modules'))
import azmp_sections_tools as azst
import azmp_sections_climtools as azsct
import azmp_report_tools as azrt
import azmp_genreport as azgen
import azmp_utils as azu
import cc_tools as cc
import azmp_stn27_newtest as azS27
#Choose a year of interest
yoi = '2024'
#Choose a working directory name
work_name = 'reporting_2024'
## Preamble (create folders to dump figures and data)
if os.path.isdir('operation_files') != True: os.system('mkdir operation_files')
if os.path.isdir(yoi) != True: os.system('mkdir '+yoi)
if os.path.isdir('climate_indices') != True: os.system('mkdir climate_indices')
if os.path.isdir('air_temperature') != True: os.system('mkdir air_temperature')
if os.path.isdir('stn27') != True: os.system('mkdir stn27')
if os.path.isdir('bergs') != True: os.system('mkdir bergs')
if os.path.isdir('bottom_temp') != True: os.system('mkdir bottom_temp')
if os.path.isdir('bottom_saln') != True: os.system('mkdir bottom_saln')
if os.path.isdir('bottom_temp_stats') != True: os.system('mkdir bottom_temp_stats')
if os.path.isdir('AZMP_lines') != True: os.system('mkdir AZMP_lines')
if os.path.isdir('climate_indices') != True: os.system('mkdir climate_indices')
if os.path.isdir('NLCI') != True: os.system('mkdir NLCI')
if os.path.isdir('SAR_files') != True: os.system('mkdir SAR_files')
## ---- Yearly update ---- ##
# 0. Update CASTS and CABOTS.
# (separate scripts)
# Update CASTS from FRDR if wanted
print('Would you like to update CASTS? [yes/no]')
CASTS_up = input()
if CASTS_up == 'yes':
azgen.CASTS_update(years=np.arange(1912,int(yoi)).astype(str),version='CASTS_2023')
elif 'no':
print('No update to CASTS made.')
# 1. NAO, AO, AMO
azgen.nao(int(yoi),'~/data/AZMP/climate_indices/nao_data.csv')
azgen.ao(int(yoi),'~/data/AZMP/climate_indices/ao_data.csv')
azgen.amo(int(yoi),'~/data/AZMP/climate_indices/amo_data.csv')
os.system('cp NAO_winter_1950-'+yoi+'.png NAO_winter_1950-'+yoi+'_FR.png '+yoi)
os.system('mv *.pkl *.csv operation_files')
os.system('mv *.png climate_indices')
# 2. Air temperature (need to dowload AHCCD and download NUUK update) [Done 2023!]
%my_run azmp_dmi_nuukAirT.py
%my_run azmp_airTemp.py
os.system('cp air_temp_'+yoi+'.png air_temp_'+yoi+'_FR.png air_temp_anom.png air_temp_anom_FR.png air_temp_climate_index.png air_temp_climate_index_FR.png ./'+yoi+'/')
os.system('mv air_temp_'+yoi+'.png air_temp_'+yoi+'_FR.png air_temp_anom.png air_temp_anom_FR.png air_temp_climate_index.png air_temp_climate_index_FR.png air_temperature')
os.system('mv *.pkl operation_files')
# Air Scorecards
%my_run azmp_air_scorecards.py
os.system('cp scorecards_air.png scorecards_air_FR.png ./'+yoi+'/')
# delete tmp files
os.system('rm scorecards_*Air*.png scorecards_*nao*.png ')
os.system('mv *.png ./air_temperature')
os.system('mv *.csv ./operation_files')
# 3. SSTs [Received from PSG]
# -> [Received from PSG]
# 4. Station 27 [JON to UPDATE]
#%my_run viking2022.py # NOT IN 2022
#os.system('cp Viking2022.png Viking2022_FR.png ../2022')
#New version using only functions
#Isolate the stn27 data
file_location = './operation_files/stn27_all_casts.nc'
CASTS_path = '~/data/CASTS/*.nc'
s27_loc = [47.54667,-52.58667]
dc = .025
problem_casts = ['1988_10073164.nc','1990_02183115.nc','1991_11229001.nc']
azS27.stn27_dataisolate(file_location,CASTS_path,s27_loc,dc,problem_casts)
#Determine the temperature and salinity climatology
year_clim = [1991, 2020]
current_year = int(yoi)
df,df_monthly,weekly_clim,df_weekly,df_year,anom = azS27.stn27_climatology(file_location,year_clim,current_year)
#Save the current year monthly average
for variable in df_monthly:
csv_file = 'monthly_' + variable + '_' + str(current_year) + '.csv'
df_monthly[variable].T.to_csv(csv_file, float_format='%.4f')
#Create a temperature and salinity climatology figure
XLIM = [datetime.date(current_year, 1, 1), datetime.date(current_year, 12, 31)]
azS27.stn27_climatology_plot(weekly_clim,XLIM)
#Create a temperature and salinity current year plot
azS27.stn27_currentyear_plot(df_weekly,df_year,current_year,XLIM)
#Create a temperature and salinity anomaly year plot
azS27.stn27_anomaly_plot(anom,current_year,XLIM)
#Convert to subplots and remove individual plots
for variable in anom:
os.system('montage '+\
's27_'+variable+'_'+ yoi +'.png '+\
's27_'+variable+'_clim.png '+\
's27_'+variable+'_anom_'+ yoi + '.png '+\
'-tile 1x3 -geometry +10+10 -background white s27_'+variable+'_subplot_'+str(current_year)+'.png')
os.system('montage '+\
's27_'+variable+'_'+ yoi + '_FR.png '+\
's27_'+variable+'_clim_FR.png '+\
's27_'+variable+'_anom_'+ yoi +'_FR.png '+\
'-tile 1x3 -geometry +10+10 -background white s27_'+variable+'_subplot_'+str(current_year)+'_FR.png')
os.system('rm '+\
's27_'+variable+'_'+ yoi + '.png '+\
's27_'+variable+'_clim.png '+\
's27_'+variable+'_anom_'+ yoi + '.png')
os.system('rm '+\
's27_'+variable+'_'+ yoi + '_FR.png '+\
's27_'+variable+'_clim_FR.png '+\
's27_'+variable+'_anom_'+ yoi + '_FR.png')
os.system('mv s27*.png ./'+yoi+'/')
plt.close('all')
#Create a station occupation figure
azS27.station_occupations(file_location,current_year,year_clim)
#Isolate the density data
df_rho,df_sig,df_SA,df_CT,ds,Z = azS27.density_calculator(df,file_location)
#Save the MLD
azS27.MLD_calculator(df_SA,df_CT,df_rho,Z)
#Save the stratification
azS27.stratification_calculator(file_location)
#Create iroc output for salinity
years_flag = [1950,1980]
df = pd.read_pickle('S27_salinity_monthly.pkl')
df,anom_std,anom,annual_mean,ts_monthly_clim = azS27.anomaly_calculator(df,years_flag,current_year,year_clim)
iroc_stn27_S = pd.concat([annual_mean, anom, anom_std], axis=1, keys=['S', 'Sanom', 'Sanom_std'])
df = pd.read_pickle('S27_temperature_monthly.pkl')
df,anom_std,anom,annual_mean,ts_monthly_clim = azS27.anomaly_calculator(df,years_flag,current_year,year_clim)
iroc_stn27_T = pd.concat([annual_mean, anom, anom_std], axis=1, keys=['T', 'Tanom', 'Tanom_std'])
iroc_stn27 = pd.concat([iroc_stn27_T, iroc_stn27_S], axis=1)
iroc_stn27.index = iroc_stn27.index.year
#Set up the meta-data
with open('iroc_stn27.csv','w',newline='') as fd:
wr = csv.writer(fd, quoting=csv.QUOTE_ALL)
wr.writerow(['Station Description:','Station 27 - Newfoundland Shelf Temperature - Canada'])
wr.writerow(['Region:','2'])
wr.writerow(['Latitude:','47.55 N'])
wr.writerow(['Longitude:','52.59 W'])
wr.writerow(['Measurement Depth/Range:','0-176m'])
wr.writerow(['Long Term Averaging Period:','1991-2020','1991-2020'])
wr.writerow(['Long Term Mean:'])
wr.writerow(['Standard Deviation'])
wr.writerow(['Averaging Method:'])
wr.writerow(['Notes:'])
wr.writerow(['Data Source:','Northwest Atlantic Fisheries Centre - Canada'])
wr.writerow(['Contact Name (email):','Frederic Cyr ([email protected])'])
wr.writerow(['Location of source datasets:','Northwest Atlantic Fisheries Centre - Canada'])
wr.writerow(['Data Protection:','Include on ICES website for free distribution.'])
wr.writerow(['Year','Temperature °C','Temperature Anomaly °C','Temperature Anomaly Normalised °C','Salinity','Salinity Anomaly','Salinity Anomaly Normalised'])
#Append the data to the bottom
iroc_stn27.to_csv('iroc_stn27.csv', mode='a', sep=',', float_format='%0.3f', header=False)
#Create anomaly output for temperature
years_flag = [1950,1980]
df = pd.read_pickle('S27_temperature_monthly.pkl')
df,anom_std,anom,annual_mean,ts_monthly_clim = azS27.anomaly_calculator(df,years_flag,current_year,year_clim)
#Plot the temperature anomaly
XLIM = [anom_std.index[0],anom_std.index[-1]+datetime.timedelta(days=180)]
azS27.anomaly_plotter(anom_std,'temperature',XLIM=XLIM,YLIM=[-2,2])
#Plot the temperature climatology
azS27.climatology_plotter(ts_monthly_clim,annual_mean,'temperature')
#Determine the CIL metrics
cil_temp,cil_core,cil_coredepth,cil_thickness = azS27.CIL_calculator(df)
#Create the CIL figures
azS27.CIL_plotter(cil_temp,'CIL mean temperature','Température moyenne de la CIF','s27_CILtemp_anomaly',XLIM=XLIM,YLIM=[-3,3])
azS27.CIL_plotter(cil_core,'CIL core temperature','Température du coeur de la CIF','s27_CILcore_anomaly',XLIM=XLIM,YLIM=[-4,4])
azS27.CIL_plotter(cil_coredepth,'CIL core depth','Profondeur du coeur de la CIF','s27_CILcoredepth_anomaly',XLIM=XLIM,YLIM=[-4,4])
azS27.CIL_plotter(cil_thickness,'CIL thickness','Épaisseur de la CIF','s27_CILthickness_anomaly',XLIM=XLIM,YLIM=[-5,5])
#Create anomaly output for salinity
df = pd.read_pickle('S27_salinity_monthly.pkl')
df,anom_std,anom,annual_mean,ts_monthly_clim = azS27.anomaly_calculator(df,years_flag,current_year,year_clim)
#Plot the salinity anomaly
azS27.anomaly_plotter(anom_std,'salinity',XLIM=XLIM,YLIM=[-2.5,2.5])
#Get the stratification ready for plotting
strat_shallow_path = 'S27_stratif_0-50_monthly.pkl'
strat_deep_path = 'S27_stratif_10-150_monthly.pkl'
strat_monthly_shallow,strat_monthly_deep,anom,anom_std,strat_monthly_clim = azS27.stratification_plotter(
strat_shallow_path,
strat_deep_path,
years_flag,current_year,year_clim)
#Create a stratification barplot
azS27.stratification_barplot(anom_std)
#Create a stratification time series
azS27.stratification_timeseries(anom)
#Create the mean stratification time series
azS27.stratification_timeseries_mean(anom,strat_monthly_clim)
#Create a current year stratification bar plot
azS27.stratification_currentyear_barplot(strat_monthly_shallow,strat_monthly_deep,current_year,year_clim)
#Get the MLD ready for plotting
MLD_path = 'S27_MLD_monthly.pkl'
mld,anom,anom_std = azS27.MLD_processor(MLD_path,years_flag,year_clim,current_year)
#Bar plot the MLD
azS27.MLD_barplot(anom_std)
#Time series plot the MLD
azS27.MLD_timeseries(anom)
#Bar plot the current year MLD
azS27.MLD_currentyear_barplot(mld,current_year,year_clim=[1991,2020])
#Get the scorecard plot ready
year_plot = [1981,int(yoi)]
T_anom,T_anom_std,T_clim_period_annual = azS27.scorecard_TS_processor('S27_temperature_monthly.pkl',year_clim,year_plot,'Temp')
S_anom,S_anom_std,S_clim_period_annual = azS27.scorecard_TS_processor('S27_salinity_monthly.pkl',year_clim,year_plot,'Sal')
CIL_anom,CIL_anom_std,CIL_clim_period = azS27.scorecard_CIL_processor('S27_CIL_summer_stats.pkl',year_clim,year_plot)
mld_clim,mld_anom_std = azS27.scorecard_MLD_processor('S27_MLD_monthly.pkl',year_clim,year_plot)
strat_clim_0to50,strat_anom_std_0to50 = azS27.scorecard_strat_processor('S27_stratif_0-50_monthly.pkl',year_clim,year_plot)
strat_clim_10to150,strat_anom_std_10to150 = azS27.scorecard_strat_processor('S27_stratif_10-150_monthly.pkl',year_clim,year_plot)
#Plot the scorecard
azS27.scorecard_plotter(
T_anom_std,
T_clim_period_annual,
'-- Vertically averaged temperature --',
u'-- Moyenne verticale de température --',
'T',years_present=True)
azS27.scorecard_plotter(
S_anom_std,
S_clim_period_annual,
'-- Vertically averaged salinity --',
u'-- Moyenne verticale de salinité --',
'S',years_present=False)
azS27.scorecard_plotter(
CIL_anom_std,
CIL_clim_period,
'-- Cold intermediate layer (CIL) properties --',
u'-- Propriétés de la couche intermédiaire froide (CIF) --',
'CIL',years_present=False)
azS27.scorecard_plotter(
mld_anom_std,
mld_clim,
'-- Mixed layer depth (MLD) --',
u'-- Profondeur de la couche de mélange (PCM) --',
'MLD',years_present=False)
azS27.scorecard_plotter(
strat_anom_std_0to50,
strat_clim_0to50,
'-- Stratification (0-50m) --',
u'-- Stratification (0-50m) --',
'strat_0-50m',years_present=False)
azS27.scorecard_plotter(
strat_anom_std_10to150,
strat_clim_10to150,
'-- Stratification (10-150m) --',
u'-- Stratification (10-150m) --',
'strat_10-150m',years_present=False)
#Montage all the figures together
os.system('convert scorecards_s27_T.png scorecards_s27_S.png scorecards_s27_CIL.png scorecards_s27_MLD.png scorecards_s27_strat_0-50m.png scorecards_s27_strat_10-150m.png -gravity East -append -geometry +1+1 scorecards_s27.png')
os.system('convert scorecards_s27_T_FR.png scorecards_s27_S_FR.png scorecards_s27_CIL_FR.png scorecards_s27_MLD_FR.png scorecards_s27_strat_0-50m_FR.png scorecards_s27_strat_10-150m_FR.png -gravity East -append -geometry +1+1 scorecards_s27_FR.png')
os.system('cp scorecards_s27.png scorecards_s27_FR.png ./'+yoi+'/')
os.system('rm scorecards_s27*.png')
# Clean the files:
os.system('montage s27_CILtemp_anomaly.png s27_CILcore_anomaly.png s27_CILcoredepth_anomaly.png -tile 1x3 -geometry +20+25 -background white s27_CIL_subplots.png')
os.system('montage s27_vert_temp_anomaly.png s27_vert_sal_anomaly.png -tile 1x2 -geometry +1+25 -background white s27_TS_subplots.png')
os.system('montage s27_vert_temp_anomalyFR.png s27_vert_sal_anomalyFR.png -tile 1x2 -geometry +1+25 -background white s27_TS_subplotsFR.png')
os.system('cp s27_mld_monthly.png s27_mld_plot.png s27_stratif_monthly_deep.png s27_stratif_monthly_shallow.png s27_stratif_plot_means_withlines.png ./'+yoi+'/')
os.system('cp s27_TS_subplots.png s27_TS_subplotsFR.png s27_CIL_subplots.png ./'+yoi+'/')
os.system('mv s27*.png ./stn27')
os.system('mv *.csv *.pkl ./operation_files')
# 5. Sea Ice (/home/cyrf0006/AZMP/state_reports/ice)
# -> [Received from PSG]
# 6. Icebergs (/home/cyrf0006/AZMP/state_reports/bergs) [FRED to UPDATE]
%my_run azmp_bergs.py
os.system('cp icebergs_climate*.png ./'+yoi+'')
#os.system('cp bergs_annual_FR.png bergs_annual.png bergs_monthly_FR.png bergs_monthly.png ../2023')
os.system('mv *.png ./bergs')
os.system('mv *.pkl ./operation_files')
# 7. bottom temperature maps (FINISHED/WORKING - 2023)
#Calculate the bottom temperature and salinity climatology
for season in ['spring','summer','fall']:
azu.get_bottomT_climato(
INFILES='~/data/CABOTS/CABOTS_'+season+'.nc',
lonLims=[-63, -45],
latLims=[42, 58],
bath_file='~/data/GEBCO/GEBCO_2023_sub_ice_topo.nc',
year_lims=[1991, 2020],
season=season,
time_adjust=False,
h5_outputfile='operation_files/Tbot_climato_'+season+'_0.10.h5'
)
azu.get_bottomS_climato(
INFILES='~/data/CABOTS/CABOTS_'+season+'.nc',
lonLims=[-63, -45],
latLims=[42, 58],
bath_file='~/data/GEBCO/GEBCO_2023_sub_ice_topo.nc',
year_lims=[1991, 2020],
season=season,
time_adjust=False,
h5_outputfile='operation_files/Sbot_climato_'+season+'_0.10.h5'
)
print(' -> '+season+' done!')
#Calculate the bottom temperature and salinity for specific years
for season in ['spring','fall']:
azrt.bottom_temperature(
season=season,
time_adjust=False,
year=yoi,
lonLims=[-63, -45],
latLims=[42, 58],
climato_file='operation_files/Tbot_climato_'+season+'_0.10.h5',
netcdf_path='~/data/CABOTS/CABOTS_'+season+'.nc',
CASTS_path='~/data/CASTS/'
)
azrt.bottom_salinity(
season=season,
time_adjust=False,
year=yoi,
lonLims=[-63, -45],
latLims=[42, 58],
climato_file='operation_files/Sbot_climato_'+season+'_0.10.h5',
netcdf_path='~/data/CABOTS/CABOTS_'+season+'.nc',
CASTS_path='~/data/CASTS/'
)
print(' -> '+season+' done!')
os.system('cp bottomT_spring'+yoi+'.png bottomT_spring'+yoi+'_FR.png bottomT_fall'+yoi+'.png bottomT_fall2023_FR.png '+yoi+'')
os.system('mv bottomT_*.png bottom_temp_*.png bottom_temp/')
os.system('mv bottomS_*.png bottom_sal_*.png bottom_saln/')
# For NAFO STACFEN and STACFIS input: [NEED TO DO]
# -> check if we have everything needed.
# bottom scorecards (arange year+1)
azrt.bottom_scorecards(path='~/data/CABOTS/csv_averages/', years=[1980, int(yoi)], clim_year=[1991, 2020])
azrt.bottomS_scorecards(path='~/data/CABOTS/csv_averages/', years=[1980, int(yoi)], clim_year=[1991, 2020])
os.system('cp scorecards_botT_spring.png scorecards_botT_spring_FR.png scorecards_botT_fall_FR.png scorecards_botT_fall.png scorecards_botS_spring.png scorecards_botS_spring_FR.png '+yoi+'')
os.system('mv *.png *.csv bottom_temp_stats/')
# bottom temperature bar plots [need to flag years if coverage insufficient] (FINISHED/WORKING - 2023)
%my_run azmp_bottomT_mean_anomaly.py
os.system('cp bottomT_anomalies_climateindex.png bottomT_anomalies_climateindex_FR.png '+yoi)
os.system('mv *.pkl operation_files/')
os.system('mv *.png bottom_temp_stats/')
## --------------- Sections plots ------------- ## (FINISHED/WORKING - 2023)
year = int(yoi)
sections = ['SI', 'BB', 'FC', 'WB']
seasons = ['summer']
#Ensure that climatologies have been set up
for section in sections:
for season in seasons:
azsct.section_clim(
SECTION=section,
SEASON=season,
YEARS=[1928,int(yoi)],
CLIM_YEAR=[1991,2020],
dlat=2,
dlon=2,
z1=2,
dz=5,
dc=0.2,
CASTS_path='~/data/CASTS/',
bath_path='~/data/GEBCO/GEBCO_2023_sub_ice_topo.nc'
)
#os.system('mv temp_section_*.png AZMP_lines/')
os.system('mv *.pkl *.csv operation_files/')
variables = ['temperature', 'salinity']
sections = ['SI', 'BB', 'FC']
for section in sections:
print('Processing Section - ' + section)
for season in seasons:
for var in variables:
#Ensure that STANDARD_SECTIONS.xlsx has been put in operation_files/
#Esnure that bathymetry .txt files are in operation_files/
azst.seasonal_section_plot(
VAR=var,
SECTION=section,
SEASON=season,
bath_path='~/data/GEBCO/GEBCO_2023_sub_ice_topo.nc',
CASTS_path='~/data/CASTS/',
YEAR=year,
ZMAX=500,
STATION_BASED=True
)
plt.close('all')
command = 'montage temperature_' + section + '_' + season + '_' + str(year) + '.png salinity_' + section + '_' + season + '_' + str(year) + '.png -tile 2x1 -geometry +10+10 -background white ' + section + '_stn_' + season + '_' + str(year) + '.png'
os.system(command)
command = 'montage temperature_' + section + '_' + season + '_' + str(year) + '_FR.png salinity_' + section + '_' + season + '_' + str(year) + '_FR.png -tile 2x1 -geometry +10+10 -background white ' + section + '_stn_' + season + '_' + str(year) + '_FR.png'
os.system(command)
os.system('cp ' + section + '_stn_' + season + '_' + str(year) + '.png '+yoi)
os.system('cp ' + section + '_stn_' + season + '_' + str(year) + '_FR.png '+yoi)
os.system('mv *.png AZMP_lines/')
os.system('mv *.csv *.pkl operation_files/')
# Section CIL [DONE 2023]
%my_run azmp_CIL_scorecards.py # update year in script!
os.system('cp scorecards_CIL.png scorecards_CIL_FR.png ./'+yoi)
os.system('mv *.png AZMP_lines/')
#(FINISHED/WORKING - 2023)
%my_run azmp_CIL_mean_anomaly.py
os.system('cp CIL_volume_climateindex.png CIL_volume_climateindex_FR.png ./'+yoi)
os.system('mv *.png AZMP_lines/')
os.system('mv *.pkl operation_files/')
## ----------- NLCI ---------------- ##
%my_run azmp_climate_index.py
os.system('cp NL_climate_index_ms*.png ./'+yoi)
os.system('mv *.png NLCI/')
os.system('mv *.csv operation_files/')
# FC: !!! CHECK BELOW !!!
## ----------- AZMP SAR / IROC ---------------- ##
%my_run azmp_sar_input.py
#os.system('cp NL_climate_index_ms_scorecards_FR.png NL_climate_index_ms_scorecards.png NL_climate_index_ms_FR.png NL_climate_index_ms.png ../'+yoi)
## ----------- CSAS DATA ---------------- ##
#%my_run csas_crab_stats.py
#%my_run azmp_bottomT_habitat.py
#%my_run NSRF_bottomT.py
#%my_run NSRF_bottomS.py
#%my_run NSRF_bottomT_habitat.py
#%my_run azmp_bottomT_shrimp_habitat.py
#azrt.bottom_stats(years=np.arange(1980, 2020), season='summer') # for crab 4R
#azrt.bottom_temperature(season='summer', year='2020') # for crab 4R
# SFAs (clim fill is new in 2022)
#Calculate the bottom temperature and salinity climatology
for season in ['summer']:
azu.get_bottomT_climato(
INFILES='~/data/CABOTS/CABOTS_'+season+'.nc',
lonLims=[-70, -56],
latLims=[57, 67],
bath_file='~/data/GEBCO/GEBCO_2023_sub_ice_topo.nc',
year_lims=[2006, 2021],
time_adjust=False,
season=season,
h5_outputfile='operation_files/Tbot_NSRFx_timeadjust_climato_'+season+'_2006-2021_0.10.h5'
)
azu.get_bottomS_climato(
INFILES='~/data/CABOTS/CABOTS_'+season+'.nc',
lonLims=[-70, -56],
latLims=[57, 67],
bath_file='~/data/GEBCO/GEBCO_2023_sub_ice_topo.nc',
year_lims=[2006, 2021],
time_adjust=False,
season=season,
h5_outputfile='operation_files/Sbot_NSRFx_timeadjust_climato_'+season+'_2006-2021_0.10.h5'
)
print(' -> '+season+' done!')
#Calculate the bottom temperature and salinity for specific years
for season in ['summer']:
azrt.bottom_temperature(
season=season,
year=yoi,
lonLims=[-70, -56],
latLims=[57, 67],
time_adjust=False,
climato_file='operation_files/Tbot_NSRFx_timeadjust_climato_'+season+'_2006-2021_0.10.h5',
netcdf_path='~/data/CABOTS/CABOTS_'+season+'.nc',
CASTS_path='~/data/CASTS/'
)
azrt.bottom_salinity(
season=season,
year=yoi,
lonLims=[-70, -56],
latLims=[57, 67],
time_adjust=False,
climato_file='operation_files/Sbot_NSRFx_timeadjust_climato_'+season+'_2006-2021_0.10.h5',
netcdf_path='~/data/CABOTS/CABOTS_'+season+'.nc',
CASTS_path='~/data/CASTS/'
)
print(' -> '+season+' done!')
os.system('cp sfa*.png '+yoi)
os.system('mv sfa_bottomT*.png bottom_temp/')
os.system('mv sfa_bottomS*.png bottom_saln/')
os.system('rm bottom_temp*.png ')
os.system('rm bottom_sal*.png ')
#Create the sfa bottom scorecards
azrt.sfa_bottom_scorecards(path='~/data/CABOTS/csv_averages/', season='summer', years=np.arange(2006,int(year)+1),clim_year=[2006,2020])
azrt.sfa_bottomS_scorecards(path='~/data/CABOTS/csv_averages/', years=np.arange(2006,int(year)+1),clim_year=[2006,2020])
os.system('cp scorecards_botT_SFA2-4_summer.png scorecards_botT_SFA2-4_summer_FR.png '+yoi)
os.system('cp scorecards_botS_SFA2-4_summer.png scorecards_botS_SFA2-4_summer_FR.png '+yoi)
os.system('mv scorecards_botT_SFA*.png bottomT*.csv bottom_temp/')
os.system('mv scorecards_botS_SFA*.png bottomS*.csv bottom_saln/')
# In ~/research/lobster:
%my_run lfa_sst_extract.py
%my_run lfa_sst_analysis.py
## ----------- OA data for BGC ResDoc ---------------- ##
os.system('cd /home/cyrf0006/AZMP/oa/NL_obs')
years = [2019, 2020]
seasons = ['summer', 'fall']
variables = ['Omega_Aragonite_(unitless)', 'pH_Total_(total_scale)', 'Oxygen_Saturation_(%)', 'Dissolved_Oxygen_(mL/L)']
depth = 'bottom'
for year in years:
print('-> ' + str(year))
for season in seasons:
print(' --> ' + season)
for variable in variables:
print(' ---> ' + variable)
cc.seasonal_map_NL(variable, year, season, depth)
plt.close('all')
# for 2020:
#montage AZMP_OA_2020_summer_OmegaA_surface.png AZMP_OA_2020_summer_pH_surface.png AZMP_OA_2020_summer_DO_perc_surface.png -tile 1x3 -geometry +10+10 -background white AZMP_OA_2020_summer_surface.png
# for 2021:
#montage AZMP_OA_2021_summer_OmegaA_surface.png AZMP_OA_2021_summer_pH_surface.png AZMP_OA_2021_summer_DO_perc_surface.png -tile 1x3 -geometry +10+10 -background white AZMP_OA_2021_summer_surface.png
# Montage 2 years in review:
os.system('montage NL_OA_2019_summer_pH_bottom.png NL_OA_2019_summer_OmegaA_bottom.png NL_OA_2020_summer_pH_bottom.png NL_OA_2020_summer_OmegaA_bottom.png -tile 2x2 -geometry +10+10 -background white NL_OA_summer_2019-2020.png')
os.system('montage NL_OA_2019_summer_pH_bottom.png NL_OA_2019_fall_pH_bottom.png NL_OA_2020_summer_pH_bottom.png NL_OA_2020_fall_pH_bottom.png -tile 2x2 -geometry +10+10 -background white NL_pH_summer-fall_2019-2020.png')
os.system('montage NL_OA_2019_summer_DO_perc_bottom.png NL_OA_2019_fall_DO_perc_bottom.png NL_OA_2020_summer_DO_perc_bottom.png NL_OA_2020_fall_DO_perc_bottom.png -tile 2x2 -geometry +10+10 -background white NL_DO_perc_2019-2020.png')
# Montage for same year summer and fall:
os.system('montage NL_OA_2019_summer_pH_bottom.png NL_OA_2019_fall_pH_bottom.png NL_OA_2019_summer_OmegaA_bottom.png NL_OA_2019_fall_OmegaA_bottom.png -tile 2x2 -geometry +10+10 -background white NL_OA_2019.png')
os.system('montage NL_OA_2020_summer_pH_bottom.png NL_OA_2020_fall_pH_bottom.png NL_OA_2020_summer_OmegaA_bottom.png NL_OA_2020_fall_OmegaA_bottom.png -tile 2x2 -geometry +10+10 -background white NL_OA_2020.png')
# Montage in French:
os.system('montage NL_OA_2019_summer_pH_bottom_FR.png NL_OA_2019_summer_OmegaA_bottom_FR.png NL_OA_2020_summer_pH_bottom_FR.png NL_OA_2020_summer_OmegaA_bottom_FR.png -tile 2x2 -geometry +10+10 -background white NL_OA_summer_2019-2020_FR.png')
os.system('montage NL_OA_2019_summer_pH_bottom_FR.png NL_OA_2019_fall_pH_bottom_FR.png NL_OA_2020_summer_pH_bottom_FR.png NL_OA_2020_fall_pH_bottom_FR.png -tile 2x2 -geometry +10+10 -background white NL_pH_summer-fall_2019-2020_FR.png')
os.system('montage NL_OA_2019_summer_DO_perc_bottom_FR.png NL_OA_2019_fall_DO_perc_bottom_FR.png NL_OA_2020_summer_DO_perc_bottom_FR.png NL_OA_2020_fall_DO_perc_bottom_FR.png -tile 2x2 -geometry +10+10 -background white NL_DO_perc_2019-2020_FR.png')
os.system('montage NL_OA_2019_summer_pH_bottom_FR.png NL_OA_2019_fall_pH_bottom_FR.png NL_OA_2019_summer_OmegaA_bottom_FR.png NL_OA_2019_fall_OmegaA_bottom_FR.png -tile 2x2 -geometry +10+10 -background white NL_OA_2019_FR.png')
os.system('montage NL_OA_2020_summer_pH_bottom_FR.png NL_OA_2020_fall_pH_bottom_FR.png NL_OA_2020_summer_OmegaA_bottom_FR.png NL_OA_2020_fall_OmegaA_bottom_FR.png -tile 2x2 -geometry +10+10 -background white NL_OA_2020_FR.png')