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reader_bats_bottle.py
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import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import datetime
from auxiliary import *
def crdir(dirName):
# Create directory
try:
# Create target Directory
os.mkdir(dirName)
print("Directory " , dirName , " Created ")
except FileExistsError:
print("Directory " , dirName , " already exists")
def samp_date_hist(df,myvar='yyyymmdd',dir_output=''):
yyyy=[]
mm=[]
dd=[]
for yyyymmdd,var in zip(df['yyyymmdd'].values,df[myvar].values):
if(float(var) > -998.):
yyyy.append(int(str(yyyymmdd)[0:4]))
mm.append( int(str(yyyymmdd)[4:6]))
dd.append( int(str(yyyymmdd)[6:8]))
print(set(yyyy))
print(set(mm))
print(set(dd))
fig,axs=plt.subplots(2,1)
fig.set_size_inches(10,10)
# Annual year frequency histogram
ax=axs[0]
ax.hist(yyyy)
ax.set_xlabel('years')
ax.set_ylabel('N of samples')
ax=axs[1]
x=np.arange(1,13)
bins_edges=np.arange(0.5,13.5,1.0)
res, bins, patches=ax.hist(mm,bins=bins_edges,rwidth=0.5)
ax.set_xlabel('months')
ax.set_ylabel('N of samples')
ax.set_xticks(range(1,13))
ax.set_xticklabels(('J','F','M','A','M','J','J','A','S','O','N','D'))
fileout=dir_output + '/' + 'hyst_time_samples_' + myvar + '.png'
fig.savefig(fileout, format='png',dpi=150)
def samp_depth_hist(df,myvar='yyyymmdd',dir_output=''):
depth=[]
depth0_200=[]
depth0_500=[]
n_bins=100
for d,var in zip(df['Depth'].values,df[myvar].values):
if(float(var) > -998.):
depth.append(d)
if(d < 200.):
depth0_200.append(d)
if(d < 500.):
depth0_500.append(d)
fig,axs=plt.subplots(3,1)
fig.set_size_inches(10,14)
ax=axs[0]
ax.hist(depth,bins=n_bins)
dd = np.arange(0.,4000.,500.)
ax.set_xticks(dd)
ax.grid(linestyle="--", linewidth=0.5, color='.25', zorder=-10)
ax.set_xlabel('Depth [m]')
ax.set_ylabel('N of samples')
ax.set_title('Surface to bottom')
ax=axs[1]
ax.hist(depth0_500,bins=n_bins)
dd = np.arange(0.,500.,50.)
ax.set_xticks(dd)
ax.grid(linestyle="--", linewidth=0.5, color='.25', zorder=-10)
ax.set_xlabel('Depth [m]')
ax.set_ylabel('N of samples')
ax.set_title('Surface to 500 m depth')
ax=axs[2]
ax.hist(depth0_200,bins=n_bins)
dd = np.arange(0.,200.,10.)
ax.set_xticks(dd)
ax.grid(linestyle="--", linewidth=0.5, color='.25', zorder=-10)
ax.set_xlabel('Depth [m]')
ax.set_ylabel('N of samples')
ax.set_title('Surface to 200 m depth')
fig.suptitle(myvar)
fileout=dir_output + '/' + 'hyst_depth_samples_' + myvar + '.png'
fig.savefig(fileout, format='png',dpi=150)
def dump_gotm_file(dfin,var,dir_output=''):
file_gotm = dir_output + '/' + var + '.txt'
fid = open(file_gotm,'w')
yyyymmdd=df['yyyymmdd'].unique()
for mytime in yyyymmdd:
yyyy=int(str(mytime)[0:4])
mm =int(str(mytime)[4:6])
dd =int(str(mytime)[6:8])
data=df.loc[df['yyyymmdd'] == mytime]
time=data['time'].unique()
for hhmm in time:
HH=int(str(hhmm).zfill(4)[0:2])
MM=int(str(hhmm).zfill(4)[2:4])
profile=data.loc[(data['time'] == hhmm) & (data[var] > -998.0)]
nrows =profile.shape[0]
if nrows > 0:
# Write output file
current_date=datetime.datetime(yyyy, mm, dd, HH, MM, 0)
gotm_header= current_date.strftime("%Y-%m-%d %H:%M:%S\t" + str(nrows) + "\t2")
fid.write(gotm_header)
fid.write("\n")
for d,val in zip(profile['Depth'].values,profile[var].values):
fid.write(str(-d))
fid.write("\t")
fid.write(str(val))
fid.write("\n")
fid.close()
def create_monthly_clim(df,var,vlev,delta,ymin,ymax,conversion_var,mode):
Nrows=df.shape[0]
Nk=len(vlev)
clim=np.zeros((12,Nk))
conversion_factor=np.zeros((12,Nk)) + 1.0
for tt in range(0,12):
mm = tt + 1
time_filter_idx=[]
for index, row in df.iterrows():
yyyymmdd=row['yyyymmdd']
yyyy=int(str(int(yyyymmdd))[0:4])
month=int(str(int(yyyymmdd))[4:6])
# print(month)
if ( month == mm) and ( yyyy > ymin) and ( yyyy < ymax):
time_filter_idx.append(index)
dfclim=df.iloc[time_filter_idx,:].copy(deep=True)
for k,lev in enumerate(vlev):
if conversion_var != '':
rule1 = dfclim[conversion_var] > -998.0
rule2 = dfclim['Depth'] > lev-delta[k]
rule3 = dfclim['Depth'] <= lev+delta[k]
rule4 = dfclim['yyyymmdd'] > 0
#
df1 = dfclim.loc[rule1]
df2 = df1.loc[rule2]
df3 = df2.loc[rule3]
df4 = df3.loc[rule4]
sample_filtered=df4[conversion_var].values
conversion_factor[tt,k]=np.nanpercentile(sample_filtered,50.0)
rule1 = dfclim[var] > -998.0
rule2 = dfclim['Depth'] > lev-delta[k]
rule3 = dfclim['Depth'] <= lev+delta[k]
rule4 = dfclim['yyyymmdd'] > 0
#
df1 = dfclim.loc[rule1]
df2 = df1.loc[rule2]
df3 = df2.loc[rule3]
df4 = df3.loc[rule4]
sample_filtered=df4[var].values
clim[tt,k]=np.nanpercentile(sample_filtered,50.0)*conversion_factor[tt,k]
if mode == 0:
return df4
if mode == 1:
return conversion_factor
if mode == 2:
return clim
def dump_gotm_monthly_clim_file(indata,ymin,ymax,lev,var,dir_output=''):
nrows=len(lev)
file_gotm = dir_output + '/' + var + '_clim.txt'
fid = open(file_gotm,'w')
yyyy=int((ymax+ymin)/2)
# days per month
# [31,28,31,30,31,30,31,31,30,31,30,31]
dd =[16,15,16,16,16,16,16,16,16,16,16,16]
HH =[12,0 ,12, 0,12, 0,12,12, 0,12, 0,12]
for tt in range(12):
mm = tt +1
count = 1 # Start from 1 since we add -10000 m value
for zz,d in enumerate(lev):
if not np.isnan(indata[tt,zz]):
count += 1
# Write output file
current_date=datetime.datetime(yyyy, mm, dd[tt], HH[tt], 0, 0)
gotm_header= current_date.strftime("%Y-%m-%d %H:%M:%S\t" + str(count) + "\t2")
fid.write(gotm_header)
fid.write("\n")
for zz,d in enumerate(lev):
if not np.isnan(indata[tt,zz]):
fid.write(str(-d))
fid.write("\t")
fid.write(str(indata[tt,zz]))
fid.write("\n")
if not np.isnan(indata[tt,zz]):
fid.write(str(- 10984.0)) # The Mariana Trench depth
fid.write("\t")
fid.write(str(indata[tt,-1]))
fid.write("\n")
else:
fid.write(str(- 10984.0)) # The Mariana Trench depth
fid.write("\t")
fid.write(str(0.0))
fid.write("\n")
fid.close()
##################################
###MAIN CODE
##################################
# Selected levels to build climatology
vlev=[0., 10., 20., 40., 60., 80., 100., 120., 140., 160., 200., 250., 300., 400., 500., 600., 700., 800., 900., 1000., 1100., 1200., 1300., 1400., 1600., 1800., 2000., 2200., 2400., 2600., 2800., 3000., 3200., 3400., 3600., 3800., 4000., 4200.]
Nlev=len(vlev)
delta=np.zeros(Nlev)
for k in range(Nlev):
if vlev[k] <= 5.:
delta[k]=5.
if (vlev[k] >5.) and (vlev[k] <= 160.):
delta[k]=2.
if (vlev[k] > 160.) and (vlev[k] <= 300.):
delta[k]=5.
if (vlev[k] > 300.) and (vlev[k] <= 1400.):
delta[k]=10.
if (vlev[k] > 1400.):
delta[k]=20.
dirplots='PLOTS'
crdir(dirplots)
dir_gotm_txt='GOTM_INPUT'
crdir(dir_gotm_txt)
dir_gotm_clim_txt='GOTM_INPUT_CLIM'
crdir(dir_gotm_clim_txt)
df0 = pd.read_csv("DATA_BATS/bats_bottle.txt", sep="\t",skiprows = 59, engine='python')
#ordering data for ascending date and ascending depth
# yyyymmdd
# Depth positive downward
df = df0.sort_values(['yyyymmdd', 'time', 'Depth'], ascending=[True, True, True])
#-----------------------
#var_list=['Temp', 'Sal1','Sig-th','NO21','NO31', 'PO41','O2(1)','CO2', 'Alk','Si1','POC','PON','POP']
#for var in var_list:
# samp_date_hist(df,var, dirplots)
# samp_depth_hist(df,var, dirplots)
# dump_gotm_file(df,var, dir_gotm_txt)
#### Create climatology
ymin=2009
ymax=2020
# Variables with no unit conversion
# Temperature
print("processing var : Temperature")
var='Temp'
Temp=create_monthly_clim(df,var,vlev,delta,ymin,ymax,'',2)
dump_gotm_monthly_clim_file(Temp,ymin,ymax,vlev,var,dir_gotm_clim_txt)
# Salinity
print("processing var : Salinity")
var='Sal1'
Sal1=create_monthly_clim(df,var,vlev,delta,ymin,ymax,'',2)
dump_gotm_monthly_clim_file(Sal1,ymin,ymax,vlev,var,dir_gotm_clim_txt)
# Density scaling using in-situ computed density from T and S
print("processing var : RHO")
RHO=np.zeros((12,Nlev))
for mm in range(12):
for kk in range(Nlev):
s=Sal1[mm,kk]
t=Temp[mm,kk]
p=vlev[k]
RHO[mm,kk]=dens(s, t, p)
dump_gotm_monthly_clim_file(RHO,ymin,ymax,vlev,'RHO_insitu',dir_gotm_clim_txt)
# Variables with unit conversion
var_list=['PO41','O2(1)', 'Alk','Si1','POC']
for var in var_list:
print("processing var : ", var)
# indata_scaled = indata/1000. #1/Liter --> 1/m3
indata=create_monthly_clim(df,var,vlev,delta,ymin,ymax,'',2)
indata_scaled = indata * RHO/1000.
dump_gotm_monthly_clim_file(indata_scaled,ymin,ymax,vlev,var,dir_gotm_clim_txt)
var_list=['CO2']
for var in var_list:
print("processing var : ", var)
# indata_scaled = indata/1000. #1/Liter --> 1/m3
indata=create_monthly_clim(df,var,vlev,delta,ymin,ymax,'',2)
indata_scaled = indata * RHO/1000. *12.0
dump_gotm_monthly_clim_file(indata_scaled,ymin,ymax,vlev,var,dir_gotm_clim_txt)
var_list=['PON']
for var in var_list:
print("processing var : ", var)
# indata_scaled = indata/1000. #1/Liter --> 1/m3
indata=create_monthly_clim(df,var,vlev,delta,ymin,ymax,'',2)
indata_scaled = indata * RHO/ 1000. / 14.0
dump_gotm_monthly_clim_file(indata_scaled,ymin,ymax,vlev,var,dir_gotm_clim_txt)
var_list=['POP']
for var in var_list:
print("processing var : ", var)
# indata_scaled = indata/1000. #1/Liter --> 1/m3
indata=create_monthly_clim(df,var,vlev,delta,ymin,ymax,'',2)
indata_scaled = indata * RHO/ 1000. / 31.0
dump_gotm_monthly_clim_file(indata_scaled,ymin,ymax,vlev,var,dir_gotm_clim_txt)
############
# nitrites + nitrates requires unit conversion
############
print("processing var : NOX")
NO2=create_monthly_clim(df,'NO21',vlev,delta,ymin,ymax,'',2)
NO3=create_monthly_clim(df,'NO31',vlev,delta,ymin,ymax,'',2)
indata_scaled = (NO2+NO3)* RHO/1000.
dump_gotm_monthly_clim_file(indata_scaled,ymin,ymax,vlev,'NOX',dir_gotm_clim_txt)