'Lat': [np.NaN],
'Lon': [np.NaN],
'O2Inv': [np.NaN],
'O2EqInv': [np.NaN]
})
tic1 = time.time()
for i in np.arange(len(ArgoWMO)):
#for i in [16]:
tic2 = time.time()
dac = FileNames[i].split('/')[0]
WMO = ArgoWMO[i]
print('\n%%% ', WMO, ' %%%\n')
print(i, ' Floats completed; ', len(ArgoWMO) - i, ' Floats Left')
f = RF.ArgoDataLoader(DAC=dac, WMO=WMO)
# Make sure float measures oxygen
float_vars = list(f.keys())
float_vars = np.array(float_vars)
oxy_check = np.where(float_vars == 'DOXY')
flux_profcount = 0
if oxy_check[0].size != 0:
# Load float data and determine if use adjusted or not adjusted data
[pres, pres_QC,
pres_flag] = RF.DetermineAdjusted(raw_data=f.PRES.values,
raw_data_QC=f.PRES_QC.values,
a_data=f.PRES_ADJUSTED.values,
import numpy as np
import pandas as pd
import gsw
import RandomFxns as RF
import BCClassFxns as BCF
import time
import cmocean.cm as cmo
import glob
import cartopy as ct
from cartopy.mpl.ticker import (LongitudeFormatter, LatitudeFormatter)
import matplotlib.gridspec as gridspec
import scipy.integrate as integrate
OFiles = glob.glob(
'/Users/Ellen/Documents/GitHub/BGCArgo_BCGyre/CSVFiles/OverlapFiles_BC/*')
WMODacDict = RF.WMODacPair()
#iso = [27.70, 27.75, 27.80,27.85]
iso = [27.70, 27.725, 27.75, 27.775]
# Interpolate to same space and time steps
# Calculate MLD at each point
good_QC = [1, 2, 5, 8]
lat_N = 70.000
lat_S = 48.00
lon_E = -45.00
lon_W = -70.00
figx = 10
CheckFloats = pd.DataFrame({'FloatType': BC_List, 'FloatDir': BC_CheckList})
CheckFloats = CheckFloats.append(
pd.DataFrame({
'FloatType': Gyre_List,
'FloatDir': Gyre_CheckList
}))
df_count = 0
## Load Float data
for i in np.arange(CheckFloats.shape[0]):
fline = CheckFloats.iloc[i, 1]
dac = fline.split('/')[0]
wmo = fline.split('/')[1]
data = RF.ArgoDataLoader(DAC=dac, WMO=wmo)
lat = data.LATITUDE.values
lon = data.LONGITUDE.values
dates = data.JULD.values
date_reform = [[]] * dates.shape[0]
bad_index = []
for j in np.arange(len(date_reform)):
if np.isnat(dates[j]) == False:
date_reform[j] = datetime.fromisoformat(str(dates[j])[:-3])
else:
date_reform[j] = dates[j]
bad_index = bad_index + [j]
if len(bad_index) > 0:
MeanData=AllData.groupby(by='Date').mean()
StdData=AllData.groupby(by='Date').std()
date_list=MeanData.index.values.tolist()
FirstDate=date_list[0]
LastDate=date_list[-1]
# Get full range of dates in between first and last profile date
AllDates_df=pd.date_range(str(FirstDate),str(LastDate),freq='6H')
AllDates=[[]]*len(AllDates_df)
for i in np.arange(len(AllDates)):
AllDates[i]=str(AllDates_df[i])
AllDates=np.array(AllDates)
# Go through dates and match data to correct time period
MeanData = RF.MatchData2Dates(alldates=AllDates, dates=date_list, Data=MeanData)
# ## N16 ##
# Ft_N16=np.zeros(len(AllDates_df))
# Ft_N16[:]=np.NaN
# Fd_N16=np.zeros(len(AllDates_df))
# Fd_N16[:]=np.NaN
# Fp_N16=np.zeros(len(AllDates_df))
# Fp_N16[:]=np.NaN
# Fc_N16=np.zeros(len(AllDates_df))
# Fc_N16[:]=np.NaN
# ## L13 ##
# Ft_L13=np.zeros(len(AllDates_df))
# Ft_L13[:]=np.NaN
# Fd_L13=np.zeros(len(AllDates_df))
pres_range=np.arange(0,2001,step_size)
refdate=datetime(1990,1,1)
TSOsat=pd.DataFrame({'WMO': [np.NaN],'Date':[np.NaN],'Lat':[np.NaN],'Lon': [np.NaN],'Pres':[np.NaN],'Temp':[np.NaN],'Sal':[np.NaN],'Oxy':[np.NaN],'OxySat':[np.NaN],'Sigma0':[np.NaN]})
tic1=time.time()
for i in np.arange(len(ArgoWMO)):
#for i in [43]:
tic2=time.time()
dac=FileNames[i].split('/')[0]
WMO=ArgoWMO[i]
print('\n%%% ',WMO,' %%%\n')
print(i, ' Floats completed; ', len(ArgoWMO)-i,' Floats Left')
f = RF.ArgoDataLoader(DAC=dac, WMO=WMO)
# Make sure float measures oxygen
float_vars=list(f.keys())
float_vars=np.array(float_vars)
oxy_check = np.where(float_vars=='DOXY')
flux_profcount=0
if oxy_check[0].size != 0:
# Load float data and determine if use adjusted or not adjusted data
[pres, pres_QC, pres_flag]=RF.DetermineAdjusted(raw_data=f.PRES.values,raw_data_QC=f.PRES_QC.values,a_data=f.PRES_ADJUSTED.values, a_data_QC=f.PRES_ADJUSTED_QC.values)
[temp, temp_QC, temp_flag]=RF.DetermineAdjusted(raw_data=f.TEMP.values, raw_data_QC= f.TEMP_QC.values,a_data=f.TEMP_ADJUSTED.values, a_data_QC=f.TEMP_ADJUSTED_QC.values)
[sal, sal_QC, sal_flag]=RF.DetermineAdjusted(raw_data=f.PSAL.values, raw_data_QC=f.PSAL_QC.values,a_data=f.PSAL_ADJUSTED.values,a_data_QC=f.PSAL_ADJUSTED_QC.values)
[doxy, doxy_QC, doxy_flag]=RF.DetermineAdjusted(raw_data=f.DOXY.values, raw_data_QC=f.DOXY_QC.values,a_data=f.DOXY_ADJUSTED.values,a_data_QC=f.DOXY_ADJUSTED_QC.values)
'Fp_N16': [np.NaN],
'Fc_N16': [np.NaN],
'Fd_N16': [np.NaN],
'Ft_N16': [np.NaN]
})
for i in np.arange(len(ArgoWMO)):
#for i in [124]:
tic2 = time.time()
dac = ArgoDac[i]
WMO = ArgoWMO[i]
print('\n%%% ', WMO, ' %%%\n')
print(i, ' Floats completed; ', len(ArgoWMO) - i, ' Floats Left')
f = RF.ArgoDataLoader(DAC=dac, WMO=WMO)
# Make sure float measures oxygen
float_vars = list(f.keys())
float_vars = np.array(float_vars)
oxy_check = np.where(float_vars == 'DOXY')
flux_profcount = 0
if oxy_check[0].size != 0:
# Load float data and determine if use adjusted or not adjusted data
[pres, pres_QC,
pres_flag] = RF.DetermineAdjusted(raw_data=f.PRES.values,
raw_data_QC=f.PRES_QC.values,
a_data=f.PRES_ADJUSTED.values,
else:
MaxDate = AllDates_BC[-1]
dates_total_df = pd.date_range(str(MinDate), str(MaxDate), freq='6H')
dates_total = [[]] * len(dates_total_df)
for i in np.arange(len(dates_total_df)):
dates_total[i] = str(dates_total_df[i])
dates_total = np.array(dates_total)
######### BC #########
#L13_reform,L13_24hr_reform,L13_1wk_reform,N16_reform,N16_24hr_reform,N16_1wk_reform
# #[L13_reform=BC_L13, L13_24hr_reform=BC_L13_24hr, L13_1wk_reform=BC_L13_1wk, N16_reform=BC_N16, N16_24hr_reform=BC_N16_24hr, N16_1wk_reform=BC_N16_1wk]
# [BC_L13, BC_L13_24hr, BC_L13_1wk, BC_N16, BC_N16_24hr, BC_N16_1wk]=RF.MatchData2Dates(alldates=dates_total, dates=date_list_BC, L13=Ft_L13_BC, L13_24hr=Ft_L13_24hr_BC, L13_1wk=Ft_L13_1wk_BC,
# N16=Ft_N16_BC, N16_24hr=Ft_N16_24hr_BC , N16_1wk=Ft_N16_1wk_BC)
BC_Data_ref = RF.MatchData2Dates(alldates=dates_total,
dates=date_list_BC,
Data=Mean_BCData)
######### LSG #########
# [LSG_L13, LSG_L13_24hr, LSG_L13_1wk, LSG_N16, LSG_N16_24hr, LSG_N16_1wk]=RF.MatchData2Dates(alldates=dates_total, dates=date_list_G, L13=Ft_L13_G, L13_24hr=Ft_L13_24hr_G, L13_1wk=Ft_L13_1wk_G,
# N16=Ft_N16_G, N16_24hr=Ft_N16_24hr_G , N16_1wk=Ft_N16_1wk_G)
LSG_Data_ref = RF.MatchData2Dates(alldates=dates_total,
dates=date_list_G,
Data=Mean_GData)
BC_L13 = BC_Data_ref.loc[:, 'Ft_L13']
BC_N16 = BC_Data_ref.loc[:, 'Ft_N16']
LSG_L13 = LSG_Data_ref.loc[:, 'Ft_L13']
LSG_N16 = LSG_Data_ref.loc[:, 'Ft_N16']
## Moving Averages ##
34.91199875, 34.9109993, 34.90800095, 34.90800095, 34.9090004, 34.90999985,
34.9109993, 34.9109993, 34.91199875, 34.9129982, 34.9129982, 34.9109993,
34.90999985, 34.91199875, 34.9129982, 34.91500092, 34.91500092, np.NaN
])
T = np.array([
np.NaN, np.NaN, np.NaN, 9.77000046, 9.52799988, 9.47500038, 9.45800018,
9.44299984, 9.43599987, 9.42500019, 9.42000008, 9.40100002, 8.53299999,
6.71400023, 5.80999994, 5.20699978, 5.01599979, 4.81699991, 4.79500008,
4.66699982, 4.59499979, 4.6420002, 4.72300005, 4.70100021, 4.66200018,
4.66499996, 4.70100021, 4.72900009, 4.73199987, 4.76399994, 4.75500011,
4.72200012, 4.65199995, 4.55600023, 4.4920001, 4.45200014, 4.35599995,
4.30800009, 4.2750001, 4.24900007, 4.22100019, 4.20200014, 4.1880002,
4.18100023, 4.16499996, 4.16300011, 4.13999987, 4.12599993, 4.13600016,
4.12799978, 4.11899996, 4.11399984, 4.1079998, 4.10200024, 4.09200001,
4.079, 4.07499981, 4.05999994, 4.05499983, 4.05499983, 4.03100014,
4.01900005, 4.02699995, 4.04099989, 4.03900003, 4.0250001, 3.9849999,
3.9519999, 3.87899995, 3.86100006, 3.82399988, 3.80100012, 3.76399994,
3.73699999, 3.704, 3.69799995, 3.6730001, 3.65199995, 3.64400005, 3.648,
3.6400001, 3.62599993, 3.63000011, 3.63599992, 3.64100003, 3.63100004,
3.63400006, 3.6400001, 3.64400005, 3.65199995, 3.65899992, 3.67400002,
3.671, 3.65799999, 3.65100002, 3.6500001, 3.63700008, 3.625, 3.60899997,
3.58299994, 3.58800006, 3.55299997, 3.54099989, 3.50200009, 3.48699999,
3.45600009, 3.42799997, 3.41400003, 3.39499998, 3.38000011, 3.37299991,
3.36100006, 3.35299993, 3.33699989, 3.31200004, 3.29200006, 3.27900004,
3.26200008, 3.25699997, 3.24699998, np.NaN
])
LT = 58.886
LN = -40.20999999999998
mld = RF.MLD(Pres=P, Temp=T, Sal=S, Lat=LT, Lon=LN)
print('MLD: ', mld)
prof_count=0
for b in np.arange(len(floatlist)):
### For debugging and looking at specific floats ###
#for b in [10]:
WMO=floatlist[b]
dac=daclist[b]
bad_index=[]
#WMO=4900610
#dac='coriolis'
tic_float=time.time()
print('\n%%% '+str(WMO)+' %%%\n')
print(b, ' Floats completed; ', len(floatlist)-b,' Floats Left')
#BGCfile='/Users/Ellen/Desktop/ArgoGDAC/dac/'+dac+'/'+str(WMO)+'/'+str(WMO)+'_Sprof.nc'
f = RF.ArgoDataLoader(DAC=dac, WMO=WMO)
float_vars=list(f.keys())
float_vars=np.array(float_vars)
oxy_check = np.where(float_vars=='DOXY')
if oxy_check[0].size != 0:
reft=f.REFERENCE_DATE_TIME.values
dates=f.JULD.values
date_reform=[[]]*dates.shape[0]
for i in np.arange(len(date_reform)):
if np.isnat(dates[i]) == False:
date_reform[i]=datetime.fromisoformat(str(dates[i])[:-3])
else:
@author: Ellen """ import xarray as xr import matplotlib.pyplot as plt import RandomFxns as RF import numpy as np from datetime import datetime import time import gsw import RandomFxns2 as RF2 dac = 'meds' dictfloat = RF.WMODacPair() WMO = 4902384 #4900879 dac = dictfloat[WMO] # 4900494 # f = RF.ArgoDataLoader(DAC=dac, WMO=WMO) # plt.figure() # f.plot.scatter(x='JULD',y='PRES_ADJUSTED',hue='DOXY_ADJUSTED') # plt.xticks(rotation=45) # plt.gca().invert_yaxis() # plt.show() ## # Random parameters you can change