In [1]:
# imports
from sqlalchemy import create_engine, Column, String, Integer, Float, Date, MetaData, Table, type_coerce
from sqlalchemy.orm import mapper, create_session
import csv
from sqlalchemy import case
import numpy as np
from sqlalchemy.ext.automap import automap_base
import matplotlib.pyplot as plt
import sqlalchemy.types as types
import numbers
from sqlalchemy.sql import and_, or_, not_, func
from sqlalchemy.sql import select
import datetime as dt
import os
import re
from mpl_toolkits.basemap import Basemap
import netCDF4 as nc
import pandas as pd
%matplotlib inline
In [2]:
# First, calculate river concentrations from EC data at Hope and Gravesend
# NOTE: am assuming all forms of N are reported in grams of N, so that mole conversion is
# by division by 14.006720 (mol. weight N), rather than weights of full molecules. This
# is consistent with NO23 values in Susan's notebook.
# This is consistent with report in 'precdetectlimits.pdf', although it is rather old.
# However, assume Si in grams of SiO2 (mo. weight = 60.08). This is consistent with
# Si data in Susan's notebook (http://nbviewer.jupyter.org/url/bitbucket.org/salishsea/tools/
# raw/tip/I_ForcingFiles/Rivers/FraserRiverNutrients.ipynb?at=default&fileviewer=file-
# view-default)
# Also appears to result in rough consistency with Debby's river Si and NO3 values
mwN=14.006720
#mwSiO3=76.083820
mwSiO2=60.08
mwAmm=17.031 ## ammonia NH3 g/mol
#
# nh4 muM (N) = nh4_cst from mean of data at Hope/Gravesend
# don muM (N) = don_cst from mean of data at Hope/Gravesend
# - only 1 data point available. Should use it or set to zero? Sutton (2013) suggests DON may be unreactive on timescales
# of exchange of water in SoG, but Clair (2013) reports that organic nitrogen dominated total nitrogen in
# Canadian rivers.
# -> for now, using available data point
# no3 muM (N) -> daily from smoothed data
# sil muM (Si) -> daily from smoothed data
# dia muM (N) = 0.001
dia = 0.001
# phy muM (N) = 0.001
phy = 0.001
# mes muM (N) = 0.001
mes = 0.001
# zoo muM (N) = 0.001
zoo = 0.001
# pon muM (N) = 0.0
pon = 0.0
# bsi muM (Si) = 0.0
don=0.0
bsi = 0.0
# oxy ? empty
oxy = 160
# other rivers const vals:
const_si=59.57 ## updated June 2019
const_no=6.18 ## updated June 2019
const_nh=4.30 ## updated June 2019
const_tur=1.0 # default river turbidity (NTU)
In [3]:
# load EC Rivers database
dbpath='/ocean/eolson/MEOPAR/obs/ECRivers/ECRiversDB'
engine = create_engine('sqlite:///'+dbpath+'.sqlite')
Base = automap_base()
Base.prepare(engine, reflect=True)
Profs=Base.classes.profiles
session = create_session(bind = engine, autocommit = False, autoflush = True)
# relevant data types:
#('NITROGEN DISSOLVED NITRATE',)
#('NITROGEN DISSOLVED NO3 & NO2',)
#('NITROGEN DISSOLVED ORGANIC (CALCD.)',)
#('NITROGEN NITRITE',)
#('NITROGEN TOTAL',)
#('NITROGEN TOTAL DISSOLVED',)
#('NITROGEN TOTAL ORGANIC (CALCD.)',)
#('SALINITY',)
#('SILICA DISSOLVED',)
#('SILICA EXTRACTABLE',)
#('SILICA REACTIVE',)
#('SILICON DISSOLVED',)
#('SILICON EXTRACTABLE',)
#('SILICON TOTAL',)
#('TURBIDITY',)
In [4]:
#define function to plot results of query as repeating fxn of year-day if output rows contain Year, Month, Day, value:
def plotYMDV(qout,pcolor,ptitle):
Y=[]
M=[]
D=[]
Amm=[]
date=[]
for Y0, M0, D0, Amm0 in qout:
Y.append(Y0)
M.append(M0)
D.append(D0)
Amm.append(Amm0)
date.append(dt.date(Y0,M0,D0))
Y=np.array(Y)
M=np.array(M)
D=np.array(D)
Amm=np.array(Amm)
date=np.array(date)
YD=0.0*Y
for i in range(0,len(Y)):
YD[i]=date[i].timetuple().tm_yday
plt.plot(YD,Amm,'.',color=pcolor)
plt.plot(YD+365.0,Amm,'.',color=pcolor)
plt.title(ptitle)
plt.plot((366,366),(0,np.max(Amm)),'k--')
return(YD,Amm)
# definte function to smooth data with gaussian filter, plot result, and return dict of days, values
def gsmooth(YD,Amm,L):
allt=np.arange(1,367)
fil=np.empty(np.size(allt))
s=L/2.355
sdict={}
for t in allt:
diff=[min(abs(x-t),abs(x-t+365), abs(x-t-365)) for x in YD]
weight=[np.exp(-.5*x**2/s**2) if x <= 3*L else 0.0 for x in diff]
#weight=[np.exp(-.5*x**2/s**2) for x in diff]
fil[t-1]=np.sum(weight*Amm)/np.sum(weight)
sdict[t]=fil[t-1]
plt.plot(allt,fil,'k-')
plt.plot(allt+365,fil,'k-')
diff_ex=[min(abs(x-t),abs(x-t+365), abs(x-t-365)) for x in allt]
weight_ex=[np.exp(-.5*x**2/s**2) if x <= 3*L else 0.0 for x in diff_ex]
plt.plot(allt,weight_ex/np.sum(weight_ex)*np.max(2.0*Amm),'k:')
plt.plot(allt+365,weight_ex/np.sum(weight_ex)*np.max(2.0*Amm),'k:')
return(sdict)
#plt.plot(YD,weight,'*')
In [5]:
# calculate mean DON from data at Hope and Gravesend:
qdataG=session.query(Profs.Year, Profs.Month, Profs.Day, Profs.value).\
filter(and_(
Profs.variable_name=='NITROGEN DISSOLVED ORGANIC (CALCD.)',
Profs.station_name=='Fraser River (Main Arm) at Gravesend Reach - Buoy'))
if qdataG.count()>0:
YDG, valG = plotYMDV(qdataG.all(),'r','DON, red=Gravesend, blue=Hope')
qdataH=session.query(Profs.Year, Profs.Month, Profs.Day, Profs.value).\
filter(and_(
Profs.variable_name=='NITROGEN DISSOLVED ORGANIC (CALCD.)',
Profs.station_name.like('%Hope%')))
if qdataH.count()>0:
YDH, valH = plotYMDV(qdataH.all(),'b',)
else:
YDH=[]
valH=[]
units=session.query(Profs.unit_code).\
filter(and_(
Profs.variable_name=='NITROGEN DISSOLVED ORGANIC (CALCD.)',
or_(Profs.station_name=='Fraser River (Main Arm) at Gravesend Reach - Buoy',
Profs.station_name.like('%Hope%')))).group_by(Profs.unit_code).all()
for row in units:
print('units:',row)
val=np.concatenate((valG,valH))
don_cst=np.mean(val)
print('don_cst =', don_cst/mwN*1000, 'uM N ....DON"T USE')
units: ('MG/L',)
don_cst = 17.1346325192 uM N ....DON"T USE
In [6]:
def returnDF(fname):
obsdir='/ocean/eolson/MEOPAR/obs/ECRivers/SOGRivers'
df=pd.read_csv(os.path.join(obsdir,fname))
temp=[dt.datetime.strptime(s, "%Y-%m-%d %H:%M:%S") for s in df['Sample time']]
df['TS']=temp
df['YD']=[(t-dt.datetime(t.year-1,12,31)).days for t in temp]
return df
df0=dict()
df0['Fraser-Hope']=returnDF('BC08MF0001_FraserHope_19790717_to_20170125.csv')
df0['Fraser-Gravesend']=returnDF('BC08MH0453_FraserGravesend_20080902_to_20161104.csv')
In [7]:
# calculate mean NH4 from data at Hope and Gravesend
llNH=list()
for ff in df0.keys():
llNH=llNH+[(ff,i) for i in df0[ff].keys() if ((re.search('[Aa]mm',i) is not None) \
and (re.search('[Dd]is',i) is not None))]
print(llNH)
plt.plot(df0['Fraser-Hope']['YD'],
df0['Fraser-Hope']['Ammonia Dissolved [Lab: 1] [VMV: 7557]'],'r.')
plt.plot(df0['Fraser-Gravesend']['YD'],
df0['Fraser-Gravesend']['Ammonia Dissolved [Lab: -54] [VMV: 1109]'],'b.')
val=np.concatenate((df0['Fraser-Hope']['Ammonia Dissolved [Lab: 1] [VMV: 7557]'],
df0['Fraser-Gravesend']['Ammonia Dissolved [Lab: -54] [VMV: 1109]']))
nh4_cst=np.nanmean(val)
print('nh4_cst =', nh4_cst/mwAmm*1000, 'uM')
[('Fraser-Gravesend', 'Ammonia Dissolved [Lab: -54] [VMV: 1109]'), ('Fraser-Hope', 'Ammonia Dissolved [Lab: 1] [VMV: 7557]')]
nh4_cst = 4.23255330777 uM
In [8]:
# NO2+NO3 at Hope and Gravesend
llNO3=list()
for ff in df0.keys():
llNO3=llNO3+[(ff,i) for i in df0[ff].keys() if ((re.search('[Nn]itrate',i) is not None and re.search('[Nn]itrite',i) is None and re.search('[Dd]is',i) is not None))]
print('NO3')
for (ff,i) in llNO3:
print(ff,i,np.min(df0[ff]['TS'][df0[ff][i]>=0]),np.max(df0[ff]['TS'][df0[ff][i]>=0]))
plt.plot(df0['Fraser-Hope']['YD'],
df0['Fraser-Hope']['Nitrogen Dissolved Nitrate [Lab: 1] [VMV: 7315]'],'r.')
plt.plot(df0['Fraser-Hope']['YD'],
df0['Fraser-Hope']['Nitrogen Dissolved Nitrate [Lab: 36] [VMV: 7315]'],'m.')
plt.plot(df0['Fraser-Hope']['YD'],
df0['Fraser-Hope']['Nitrogen Dissolved Nitrate [Lab: -54] [VMV: 1115]'],'.',color='darkorange')
plt.plot(df0['Fraser-Gravesend']['YD'],
df0['Fraser-Gravesend']['Nitrogen Dissolved Nitrate [Lab: -54] [VMV: 1115]'],'b.')
plt.plot(df0['Fraser-Hope']['YD']+365,
df0['Fraser-Hope']['Nitrogen Dissolved Nitrate [Lab: 1] [VMV: 7315]'],'r.')
plt.plot(df0['Fraser-Hope']['YD']+365,
df0['Fraser-Hope']['Nitrogen Dissolved Nitrate [Lab: 36] [VMV: 7315]'],'m.')
plt.plot(df0['Fraser-Hope']['YD']+365,
df0['Fraser-Hope']['Nitrogen Dissolved Nitrate [Lab: -54] [VMV: 1115]'],'.',color='darkorange')
plt.plot(df0['Fraser-Gravesend']['YD']+365,
df0['Fraser-Gravesend']['Nitrogen Dissolved Nitrate [Lab: -54] [VMV: 1115]'],'b.')
val=np.concatenate((df0['Fraser-Hope']['Nitrogen Dissolved Nitrate [Lab: 1] [VMV: 7315]'],
df0['Fraser-Hope']['Nitrogen Dissolved Nitrate [Lab: 36] [VMV: 7315]'],
df0['Fraser-Hope']['Nitrogen Dissolved Nitrate [Lab: -54] [VMV: 1115]'],
df0['Fraser-Gravesend']['Nitrogen Dissolved Nitrate [Lab: -54] [VMV: 1115]']))
YD=np.concatenate((df0['Fraser-Hope']['YD'],
df0['Fraser-Hope']['YD'],
df0['Fraser-Hope']['YD'],
df0['Fraser-Gravesend']['YD']))
ii=~np.isnan(val)
no3dict=gsmooth(YD[ii],val[ii],60)
#print(i,np.max([v for v in no3dict.values()]))
NO3 Fraser-Gravesend Nitrogen Dissolved Nitrate [Lab: -54] [VMV: 1115] 2009-05-13 20:20:00 2015-04-30 20:45:00 Fraser-Hope Nitrogen Dissolved Nitrate [Lab: 1] [VMV: 7315] 1994-04-26 17:00:00 1994-09-27 21:15:00 Fraser-Hope Nitrogen Dissolved Nitrate [Lab: 36] [VMV: 7315] 1994-10-11 21:35:00 1998-12-08 22:10:00 Fraser-Hope Nitrogen Dissolved Nitrate [Lab: -54] [VMV: 1115] 2006-09-26 21:45:00 2015-05-25 18:11:00
In [9]:
# Si at Hope and Gravesend
ll=list()
for ff in df0.keys():
ll=ll+[(ff,i) for i in df0[ff].keys() if ((re.search('[Ss]ilica',i) is not None) and (re.search('[Dd]is',i) is not None)) ]
print('Si')
for (ff,i) in ll:
print(ff,i,np.min(df0[ff]['TS'][df0[ff][i]>=0]),np.max(df0[ff]['TS'][df0[ff][i]>=0]))
plt.plot(df0['Fraser-Hope']['YD'],
df0['Fraser-Hope']['Silica Dissolved [Lab: 36] [VMV: 103469]'],'r.')
plt.plot(df0['Fraser-Hope']['YD']+365,
df0['Fraser-Hope']['Silica Dissolved [Lab: 36] [VMV: 103469]'],'r.')
YD=df0['Fraser-Hope']['YD']
val=df0['Fraser-Hope']['Silica Dissolved [Lab: 36] [VMV: 103469]']
ii=~np.isnan(val)
sidict=gsmooth(YD[ii],val[ii],60)
Si Fraser-Hope Silica Dissolved [Lab: 36] [VMV: 103469] 1991-03-08 18:10:00 1999-12-21 22:10:00
In [10]:
# find fraser location
mesh=nc.Dataset('/ocean/eolson/MEOPAR/NEMO-forcing/grid/mesh_mask201702.nc')
tmask=mesh.variables['tmask'][0,0,:,:]
f=nc.Dataset('/results/forcing/rivers/R201702DFraCElse_y2016m01d23.nc') # example for dims
plt.pcolormesh(np.ma.masked_where(tmask==0,f.variables['rorunoff'][0,:,:]))
plt.colorbar()
plt.xlim(350,399)
plt.ylim(360,540)
plt.plot((380,398,398,380,380),(400,400,520,520,400),'k-')
Out[10]:
[<matplotlib.lines.Line2D at 0x7f2e49a88438>]
In [11]:
f.variables['time_counter'][:]
Out[11]:
masked_array(data = [--],
mask = [ True],
fill_value = 9.96921e+36)
In [12]:
# create daily files, use data to initialize fields
year=2016
fname='/data/eolson/MEOPAR/NEMO-forcing-new/rivers-climatology/bio/R201906_bioClim_'
for yearday in range(1,367):
t2=dt.date(year, 1, 1) + dt.timedelta(days = yearday - 1)
#datestr='y'+t2.strftime('%Y')+'m'+t2.strftime('%m')+'d'+t2.strftime('%d')
datestr='m'+t2.strftime('%m')+'d'+t2.strftime('%d')
new=nc.Dataset(fname+datestr+'.nc','w')
#Copy dimensions
for dname, the_dim in f.dimensions.items():
#print (dname, len(the_dim) if not the_dim.isunlimited() else None)
new.createDimension(dname, len(the_dim) if not the_dim.isunlimited() else None)
#new_tc=new.createVariable('time_counter',float,('time_counter'),zlib=True)
#new_tc[:]=f.variables['time_counter'][:]
new_run=new.createVariable('no3',float,('time_counter', 'y', 'x'),zlib=True)
new_run[:,:,:]=const_no # other river value
new_run[:,400:520,380:]=no3dict[yearday]/mwN*1000.0 # convert to muM; Fraser value
new_run=new.createVariable('sil',float,('time_counter', 'y', 'x'),zlib=True)
new_run[:,:,:]=const_si # other river value
new_run[:,400:520,380:]=sidict[yearday]/mwSiO2*1000.0 # convert to muM; Fraser value
new.close()
In [13]:
# create constants file
f=nc.Dataset('/results/forcing/rivers/R201702DFraCElse_y2016m01d23.nc') # example for dims
year=2016
fnameC='/data/eolson/MEOPAR/NEMO-forcing-new/rivers-climatology/bio/R201906_bioConst'
new=nc.Dataset(fnameC+'.nc','w')
#Copy dimensions
for dname, the_dim in f.dimensions.items():
#print (dname, len(the_dim) if not the_dim.isunlimited() else None)
new.createDimension(dname, len(the_dim) if not the_dim.isunlimited() else None)
#new_tc=new.createVariable('time_counter',float,('time_counter'),zlib=True)
#new_tc[:]=f.variables['time_counter'][:]
new_run=new.createVariable('no3',float,('time_counter', 'y', 'x'),zlib=True)
new_run[:,:,:]=const_no # other rivers
new_run[:,400:520,380:]=np.mean([no3dict[yearday]/mwN*1000.0 for yearday in range(1,367)])
new_run=new.createVariable('nh4',float,('time_counter', 'y', 'x'),zlib=True)
new_run[:,:,:]=const_nh # other river value
new_run[:,400:520,380:]=nh4_cst/mwAmm*1000.0 # convert to muM; Fraser value
new_run=new.createVariable('sil',float,('time_counter', 'y', 'x'),zlib=True)
new_run[:]=const_si # other river value
new_run[:,400:520,380:]=np.mean([sidict[yearday]/mwSiO2*1000.0 for yearday in range(1,367)])
new_run=new.createVariable('dia',float,('time_counter', 'y', 'x'),zlib=True)
new_run[:]=dia
new_run=new.createVariable('phy',float,('time_counter', 'y', 'x'),zlib=True)
new_run[:]=phy
new_run=new.createVariable('mes',float,('time_counter', 'y', 'x'),zlib=True)
new_run[:]=mes
new_run=new.createVariable('zoo',float,('time_counter', 'y', 'x'),zlib=True)
new_run[:]=zoo
new_run=new.createVariable('don',float,('time_counter', 'y', 'x'),zlib=True)
new_run[:]=don
new_run=new.createVariable('pon',float,('time_counter', 'y', 'x'),zlib=True)
new_run[:]=pon
new_run=new.createVariable('bsi',float,('time_counter', 'y', 'x'),zlib=True)
new_run[:]=bsi
new_run=new.createVariable('tur',float,('time_counter', 'y', 'x'),zlib=True)
new_run[:]=const_tur
new.close()
In [14]:
session.close()
engine.dispose()
In [15]:
f.close()
mesh.close()
In [16]:
year=2016
for yearday in range(1,367):
t2=dt.date(year, 1, 1) + dt.timedelta(days = yearday - 1)
#datestr='y'+t2.strftime('%Y')+'m'+t2.strftime('%m')+'d'+t2.strftime('%d')
datestr='m'+t2.strftime('%m')+'d'+t2.strftime('%d')
new=nc.Dataset(fname+datestr+'.nc')
print(yearday,[(ikey, np.min(new.variables[ikey]), np.max(new.variables[ikey])) for ikey in new.variables.keys()])
new.close()
1 [('no3', 6.1799999999999997, 8.2761044385521991), ('sil', 57.08212707723338, 59.57)]
2 [('no3', 6.1799999999999997, 8.2882785962522743), ('sil', 57.206163580199082, 59.57)]
3 [('no3', 6.1799999999999997, 8.2994811508050681), ('sil', 57.329740172292432, 59.57)]
4 [('no3', 6.1799999999999997, 8.3097422957544573), ('sil', 57.452843088808478, 59.57)]
5 [('no3', 6.1799999999999997, 8.3190924830764814), ('sil', 57.575459917952642, 59.57)]
6 [('no3', 6.1799999999999997, 8.3275622467559174), ('sil', 57.697579976153257, 59.57)]
7 [('no3', 6.1799999999999997, 8.335182031345342), ('sil', 57.819194697302521, 59.57)]
8 [('no3', 6.1799999999999997, 8.3419820273049403), ('sil', 57.940298032288815, 59.57)]
9 [('no3', 6.1799999999999997, 8.347992014782081), ('sil', 58.06088685553464, 59.57)]
10 [('no3', 6.1799999999999997, 8.3532412174565813), ('sil', 58.180961373846529, 59.57)]
11 [('no3', 6.1799999999999997, 8.3577581677750405), ('sil', 58.300525533589997, 59.57)]
12 [('no3', 6.1799999999999997, 8.3615705848992636), ('sil', 58.419587421660282, 59.57)]
13 [('no3', 6.1799999999999997, 8.3647052663268013), ('sil', 58.538159655556683, 59.57)]
14 [('no3', 6.1799999999999997, 8.3671879941058815), ('sil', 58.656259757986291, 59.57)]
15 [('no3', 6.1799999999999997, 8.3690434562051923), ('sil', 58.773910511271517, 59.57)]
16 [('no3', 6.1799999999999997, 8.3702951835098478), ('sil', 58.891140287107483, 59.57)]
17 [('no3', 6.1799999999999997, 8.3709655026844718), ('sil', 59.007983347211464, 59.57)]
18 [('no3', 6.1799999999999997, 8.3710755048617571), ('sil', 59.124480110786024, 59.57)]
19 [('no3', 6.1799999999999997, 8.3706450300838178), ('sil', 59.240677384888244, 59.57)]
20 [('no3', 6.1799999999999997, 8.3696926671118295), ('sil', 59.356628554282757, 59.57)]
21 [('no3', 6.1799999999999997, 8.3682357681479882), ('sil', 59.472393727748241, 59.57)]
22 [('no3', 6.1799999999999997, 8.3662904778163565), ('sil', 59.57, 59.588039838143551)]
23 [('no3', 6.1799999999999997, 8.3638717756908445), ('sil', 59.57, 59.703640694292837)]
24 [('no3', 6.1799999999999997, 8.3609935314903332), ('sil', 59.57, 59.819276983026114)]
25 [('no3', 6.1799999999999997, 8.3576685719585395), ('sil', 59.57, 59.935036220408811)]
26 [('no3', 6.1799999999999997, 8.3539087584889025), ('sil', 59.57, 60.051012651672657)]
27 [('no3', 6.1799999999999997, 8.349725074277508), ('sil', 59.57, 60.167307099898551)]
28 [('no3', 6.1799999999999997, 8.3451277199737248), ('sil', 59.57, 60.284026764046693)]
29 [('no3', 6.1799999999999997, 8.3401262165584242), ('sil', 59.57, 60.401284967266172)]
30 [('no3', 6.1799999999999997, 8.3347295142408147), ('sil', 59.57, 60.51920085704112)]
31 [('no3', 6.1799999999999997, 8.3289461061946852), ('sil', 59.57, 60.63789905892326)]
32 [('no3', 6.1799999999999997, 8.3227841457506564), ('sil', 59.57, 60.757509286051636)]
33 [('no3', 6.1799999999999997, 8.316251565877506), ('sil', 59.57, 60.878165906826055)]
34 [('no3', 6.1799999999999997, 8.3093561996377794), ('sil', 59.57, 61.000007473369472)]
35 [('no3', 6.1799999999999997, 8.3021059003083071), ('sil', 59.57, 61.123176213543715)]
36 [('no3', 6.1799999999999997, 8.2945086599383036), ('sil', 59.57, 61.2478174893145)]
37 [('no3', 6.1799999999999997, 8.2865727250959633), ('sil', 59.57, 61.374079224321768)]
38 [('no3', 6.1799999999999997, 8.2783067084946946), ('sil', 59.57, 61.502111303569059)]
39 [('no3', 6.1799999999999997, 8.2697196953832606), ('sil', 59.57, 61.632064947924647)]
40 [('no3', 6.1799999999999997, 8.2608213434550279), ('sil', 59.57, 61.764092066132378)]
41 [('no3', 6.1799999999999997, 8.2516219751300834), ('sil', 59.57, 61.898344587015082)]
42 [('no3', 6.1799999999999997, 8.2421326612054777), ('sil', 59.57, 62.034973774167938)]
43 [('no3', 6.1799999999999997, 8.2323652947107373), ('sil', 59.57, 62.174129525537943)]
44 [('no3', 6.1799999999999997, 8.2223326541131634), ('sil', 59.57, 62.315959660014812)]
45 [('no3', 6.1799999999999997, 8.2120484549737061), ('sil', 59.57, 62.460609193147583)]
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241 [('no3', 2.9026033647849321, 6.1799999999999997), ('sil', 49.444832999632013, 59.57)]
242 [('no3', 2.9112450276281239, 6.1799999999999997), ('sil', 49.281979607889518, 59.57)]
243 [('no3', 2.9210980758298195, 6.1799999999999997), ('sil', 49.122036542969958, 59.57)]
244 [('no3', 2.9321619148309077, 6.1799999999999997), ('sil', 48.965207754517579, 59.57)]
245 [('no3', 2.9444356240256431, 6.1799999999999997), ('sil', 48.811698027134952, 59.57)]
246 [('no3', 2.957917960381121, 6.1799999999999997), ('sil', 48.661712005517202, 59.57)]
247 [('no3', 2.9726073582281272, 6.1799999999999997), ('sil', 48.515453221179087, 59.57)]
248 [('no3', 2.9885019247254738, 6.1799999999999997), ('sil', 48.373123126836305, 59.57)]
249 [('no3', 3.0055994305377247, 6.1799999999999997), ('sil', 48.234920144195172, 59.57)]
250 [('no3', 3.0238972955202059, 6.1799999999999997), ('sil', 48.101038730952737, 59.57)]
251 [('no3', 3.0433925693265231, 6.1799999999999997), ('sil', 47.971668472369309, 59.57)]
252 [('no3', 3.0640819070452885, 6.1799999999999997), ('sil', 47.846993202583647, 59.57)]
253 [('no3', 3.0859615401153788, 6.1799999999999997), ('sil', 47.727190160385192, 59.57)]
254 [('no3', 3.1090272429591472, 6.1799999999999997), ('sil', 47.612429184083069, 59.57)]
255 [('no3', 3.1332742958715345, 6.1799999999999997), ('sil', 47.502871949359232, 59.57)]
256 [('no3', 3.1586974448535017, 6.1799999999999997), ('sil', 47.39867125394148, 59.57)]
257 [('no3', 3.1852908592223446, 6.1799999999999997), ('sil', 47.299970352477551, 59.57)]
258 [('no3', 3.2130480878313925, 6.1799999999999997), ('sil', 47.20690234436077, 59.57)]
259 [('no3', 3.2419620149054134, 6.1799999999999997), ('sil', 47.119589617357548, 59.57)]
260 [('no3', 3.2720248164807919, 6.1799999999999997), ('sil', 47.038143348957711, 59.57)]
261 [('no3', 3.3032279184586346, 6.1799999999999997), ('sil', 46.962663067442037, 59.57)]
262 [('no3', 3.335561957322267, 6.1799999999999997), ('sil', 46.893236274181554, 59.57)]
263 [('no3', 3.3690167444922117, 6.1799999999999997), ('sil', 46.829938128030427, 59.57)]
264 [('no3', 3.4035812352755657, 6.1799999999999997), ('sil', 46.772831193016415, 59.57)]
265 [('no3', 3.4392435032695929, 6.1799999999999997), ('sil', 46.721965249388894, 59.57)]
266 [('no3', 3.4759907209883516, 6.1799999999999997), ('sil', 46.677377168420051, 59.57)]
267 [('no3', 3.5138091473782427, 6.1799999999999997), ('sil', 46.639090850696917, 59.57)]
268 [('no3', 3.5526841227150907, 6.1799999999999997), ('sil', 46.607117227439623, 59.57)]
269 [('no3', 3.5926000712679538, 6.1799999999999997), ('sil', 46.581454324032116, 59.57)]
270 [('no3', 3.6335405119202675, 6.1799999999999997), ('sil', 46.562087384603288, 59.57)]
271 [('no3', 3.6754880767770319, 6.1799999999999997), ('sil', 46.548989056459277, 59.57)]
272 [('no3', 3.718424537605669, 6.1799999999999997), ('sil', 46.542119632193057, 59.57)]
273 [('no3', 3.7623308397910376, 6.1799999999999997), ('sil', 46.541427347920759, 59.57)]
274 [('no3', 3.8071871432968374, 6.1799999999999997), ('sil', 46.546848734901097, 59.57)]
275 [('no3', 3.8529728699549044, 6.1799999999999997), ('sil', 46.558309021952198, 59.57)]
276 [('no3', 3.8996667562776191, 6.1799999999999997), ('sil', 46.575722585857342, 59.57)]
277 [('no3', 3.9472469108034445, 6.1799999999999997), ('sil', 46.598993446045142, 59.57)]
278 [('no3', 3.9956908749073961, 6.1799999999999997), ('sil', 46.628015800421451, 59.57)]
279 [('no3', 4.0449756858830401, 6.1799999999999997), ('sil', 46.662674598182839, 59.57)]
280 [('no3', 4.0950779410373359, 6.1799999999999997), ('sil', 46.702846145604639, 59.57)]
281 [('no3', 4.1459738614977946, 6.1799999999999997), ('sil', 46.74839874043677, 59.57)]
282 [('no3', 4.1976393544172419, 6.1799999999999997), ('sil', 46.799193330546601, 59.57)]
283 [('no3', 4.2500500722569639, 6.1799999999999997), ('sil', 46.855084191901597, 59.57)]
284 [('no3', 4.303181467895743, 6.1799999999999997), ('sil', 46.915919621276636, 59.57)]
285 [('no3', 4.3570088443654198, 6.1799999999999997), ('sil', 46.981542638976016, 59.57)]
286 [('no3', 4.4115073981244448, 6.1799999999999997), ('sil', 47.051791696405708, 59.57)]
287 [('no3', 4.4666522548989516, 6.1799999999999997), ('sil', 47.126501384095043, 59.57)]
288 [('no3', 4.5224184972613566, 6.1799999999999997), ('sil', 47.205503135172364, 59.57)]
289 [('no3', 4.5787811833187488, 6.1799999999999997), ('sil', 47.288625919975765, 59.57)]
290 [('no3', 4.6357153560055409, 6.1799999999999997), ('sil', 47.375696927393747, 59.57)]
291 [('no3', 4.6931960427223078, 6.1799999999999997), ('sil', 47.466542228702252, 59.57)]
292 [('no3', 4.7511982452494399, 6.1799999999999997), ('sil', 47.560987420426052, 59.57)]
293 [('no3', 4.8096969200464637, 6.1799999999999997), ('sil', 47.658858242489821, 59.57)]
294 [('no3', 4.8686669492773742, 6.1799999999999997), ('sil', 47.759981168665966, 59.57)]
295 [('no3', 4.9280831030870313, 6.1799999999999997), ('sil', 47.864183966617311, 59.57)]
296 [('no3', 4.987919993858422, 6.1799999999999997), ('sil', 47.971296225338733, 59.57)]
297 [('no3', 5.0481520233170851, 6.1799999999999997), ('sil', 48.081149847879935, 59.57)]
298 [('no3', 5.1087533235445326, 6.1799999999999997), ('sil', 48.193579508179809, 59.57)]
299 [('no3', 5.1696976930887892, 6.1799999999999997), ('sil', 48.308423071019519, 59.57)]
300 [('no3', 5.2309585294566974, 6.1799999999999997), ('sil', 48.425521973957125, 59.57)]
301 [('no3', 5.292508759380163, 6.1799999999999997), ('sil', 48.544721571947889, 59.57)]
302 [('no3', 5.3543207683234053, 6.1799999999999997), ('sil', 48.665871444030508, 59.57)]
303 [('no3', 5.4163663307007655, 6.1799999999999997), ('sil', 48.788825662798757, 59.57)]
304 [('no3', 5.4786165423106059, 6.1799999999999997), ('sil', 48.913443027917772, 59.57)]
305 [('no3', 5.5410417564812979, 6.1799999999999997), ('sil', 49.039587264065879, 59.57)]
306 [('no3', 5.6036115253388914, 6.1799999999999997), ('sil', 49.167127185110949, 59.57)]
307 [('no3', 5.6662945476002458, 6.1799999999999997), ('sil', 49.295936826058039, 59.57)]
308 [('no3', 5.729058624128526, 6.1799999999999997), ('sil', 49.425895544086352, 59.57)]
309 [('no3', 5.7918706224484486, 6.1799999999999997), ('sil', 49.556888090844673, 59.57)]
310 [('no3', 5.8546964512223347, 6.1799999999999997), ('sil', 49.68880465774776, 59.57)]
311 [('no3', 5.9175010455686881, 6.1799999999999997), ('sil', 49.821540895981713, 59.57)]
312 [('no3', 5.980248363952156, 6.1799999999999997), ('sil', 49.954997913506361, 59.57)]
313 [('no3', 6.0429013971455721, 6.1799999999999997), ('sil', 50.089082250621829, 59.57)]
314 [('no3', 6.1054221896618204, 6.1799999999999997), ('sil', 50.223705835916192, 59.57)]
315 [('no3', 6.1677718738058855, 6.1799999999999997), ('sil', 50.358785924645325, 59.57)]
316 [('no3', 6.1799999999999997, 6.2299107163160174), ('sil', 50.494245020799916, 59.57)]
317 [('no3', 6.1799999999999997, 6.2917981774517067), ('sil', 50.63001078459169, 59.57)]
318 [('no3', 6.1799999999999997, 6.3533929821159489), ('sil', 50.76601592690708, 59.57)]
319 [('no3', 6.1799999999999997, 6.4146532024972931), ('sil', 50.902198091475128, 59.57)]
320 [('no3', 6.1799999999999997, 6.4755363515708426), ('sil', 51.038499726572411, 59.57)]
321 [('no3', 6.1799999999999997, 6.535999486622516), ('sil', 51.174867946831732, 59.57)]
322 [('no3', 6.1799999999999997, 6.5959993218767785), ('sil', 51.311254385988917, 59.57)]
323 [('no3', 6.1799999999999997, 6.6554923492084397), ('sil', 51.447615041818487, 59.57)]
324 [('no3', 6.1799999999999997, 6.7144349658377021), ('sil', 51.583910113364389, 59.57)]
325 [('no3', 6.1799999999999997, 6.7727836078476562), ('sil', 51.720103831300385, 59.57)]
326 [('no3', 6.1799999999999997, 6.8304948883460224), ('sil', 51.856164281984384, 59.57)]
327 [('no3', 6.1799999999999997, 6.8875257390682281), ('sil', 51.992063225528923, 59.57)]
328 [('no3', 6.1799999999999997, 6.9438335542407446), ('sil', 52.127775908133842, 59.57)]
329 [('no3', 6.1799999999999997, 6.9993763355260654), ('sil', 52.263280869420264, 59.57)]
330 [('no3', 6.1799999999999997, 7.0541128369523234), ('sil', 52.39855974479908, 59.57)]
331 [('no3', 6.1799999999999997, 7.1080027087445155), ('sil', 52.533597063219879, 59.57)]
332 [('no3', 6.1799999999999997, 7.1610066390764757), ('sil', 52.66838004101718, 59.57)]
333 [('no3', 6.1799999999999997, 7.2130864928240657), ('sil', 52.802898371856813, 59.57)]
334 [('no3', 6.1799999999999997, 7.2642054464799539), ('sil', 52.937144013465286, 59.57)]
335 [('no3', 6.1799999999999997, 7.3143281184806836), ('sil', 53.071110971741184, 59.57)]
336 [('no3', 6.1799999999999997, 7.3634206942730271), ('sil', 53.204795082638206, 59.57)]
337 [('no3', 6.1799999999999997, 7.4114510455308915), ('sil', 53.33819379261783, 59.57)]
338 [('no3', 6.1799999999999997, 7.4583888429873708), ('sil', 53.471305938545918, 59.57)]
339 [('no3', 6.1799999999999997, 7.5042056624305058), ('sil', 53.604131527619131, 59.57)]
340 [('no3', 6.1799999999999997, 7.5488750834526019), ('sil', 53.736671518524894, 59.57)]
341 [('no3', 6.1799999999999997, 7.592372780558164), ('sil', 53.868927604836152, 59.57)]
342 [('no3', 6.1799999999999997, 7.6346766063094238), ('sil', 54.000902001519179, 59.57)]
343 [('no3', 6.1799999999999997, 7.6757666661535309), ('sil', 54.132597236164095, 59.57)]
344 [('no3', 6.1799999999999997, 7.7156253845973382), ('sil', 54.264015945991751, 59.57)]
345 [('no3', 6.1799999999999997, 7.754237562399954), ('sil', 54.395160681989339, 59.57)]
346 [('no3', 6.1799999999999997, 7.7915904244102148), ('sil', 54.526033721852585, 59.57)]
347 [('no3', 6.1799999999999997, 7.8276736576749286), ('sil', 54.656636892999479, 59.57)]
348 [('no3', 6.1799999999999997, 7.8624794394268411), ('sil', 54.786971407309224, 59.57)]
349 [('no3', 6.1799999999999997, 7.8960024545129697), ('sil', 54.917037709221923, 59.57)]
350 [('no3', 6.1799999999999997, 7.9282399018424172), ('sil', 55.046835338766492, 59.57)]
351 [('no3', 6.1799999999999997, 7.9591914894412179), ('sil', 55.176362811078555, 59.57)]
352 [('no3', 6.1799999999999997, 7.9888594176324315), ('sil', 55.305617514166428, 59.57)]
353 [('no3', 6.1799999999999997, 8.0172483500209957), ('sil', 55.434595626367347, 59.57)]
354 [('no3', 6.1799999999999997, 8.0443653718908052), ('sil', 55.56329205510351, 59.57)]
355 [('no3', 6.1799999999999997, 8.0702199357420792), ('sil', 55.691700398422753, 59.57)]
356 [('no3', 6.1799999999999997, 8.0948237938433092), ('sil', 55.819812930648453, 59.57)]
357 [('no3', 6.1799999999999997, 8.1181909176580547), ('sil', 55.947620613450894, 59.57)]
358 [('no3', 6.1799999999999997, 8.1403374043036578), ('sil', 56.075113133478176, 59.57)]
359 [('no3', 6.1799999999999997, 8.161281370233592), ('sil', 56.202278967449601, 59.57)]
360 [('no3', 6.1799999999999997, 8.1810428325623885), ('sil', 56.329105475543237, 59.57)]
361 [('no3', 6.1799999999999997, 8.1996435786654853), ('sil', 56.455579023648681, 59.57)]
362 [('no3', 6.1799999999999997, 8.2171070248225977), ('sil', 56.581685134675631, 59.57)]
363 [('no3', 6.1799999999999997, 8.2334580648882838), ('sil', 56.707408669124732, 59.57)]
364 [('no3', 6.1799999999999997, 8.2487229102184187), ('sil', 56.832734034515347, 59.57)]
365 [('no3', 6.1799999999999997, 8.2629289221420184), ('sil', 56.957645423143013, 59.57)]
366 [('no3', 6.1799999999999997, 8.2761044385521991), ('sil', 57.08212707723338, 59.57)]
In [17]:
new=nc.Dataset(fnameC+'.nc')
print(yearday,[(ikey, np.min(new.variables[ikey]), np.max(new.variables[ikey])) for ikey in new.variables.keys()])
new.close()
366 [('no3', 6.1235179805710107, 6.1799999999999997), ('nh4', 4.2325533077690913, 4.2999999999999998), ('sil', 59.554968806333505, 59.57), ('dia', 0.001, 0.001), ('phy', 0.001, 0.001), ('mes', 0.001, 0.001), ('zoo', 0.001, 0.001), ('don', 0.0, 0.0), ('pon', 0.0, 0.0), ('bsi', 0.0, 0.0), ('tur', 1.0, 1.0)]
In [ ]: