import numpy as np
import matplotlib.pyplot as plt
import os
import pandas as pd
import netCDF4 as nc
import datetime as dt
from salishsea_tools import evaltools as et, viz_tools
import gsw
import matplotlib.gridspec as gridspec
import matplotlib as mpl
import matplotlib.dates as mdates
import cmocean as cmo
import scipy.interpolate as sinterp
import pickle
import cmocean
import json
import f90nml
from collections import OrderedDict
fs=16
mpl.rc('xtick', labelsize=fs)
mpl.rc('ytick', labelsize=fs)
mpl.rc('legend', fontsize=fs)
mpl.rc('axes', titlesize=fs)
mpl.rc('axes', labelsize=fs)
mpl.rc('figure', titlesize=fs)
mpl.rc('font', size=fs)
mpl.rc('text', usetex=True)
mpl.rc('text.latex', preamble = r'''
\usepackage{txfonts}
\usepackage{lmodern}
''')
mpl.rc('font', family='sans-serif', weight='normal', style='normal')
import warnings
warnings.filterwarnings('ignore')
from IPython.display import Markdown, display
%matplotlib inline
from IPython.display import HTML
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
} else {
$('div.input').show();
}
code_show = !code_show
}
$( document ).ready(code_toggle);
</script>
<form action="javascript:code_toggle()"><input type="submit" value="Click here to toggle on/off the raw code."></form>''')
PATH= '/results2/SalishSea/nowcast-green.201905/'
year=2007
# Parameters
year = 2010
display(Markdown('''# Year: '''+ str(year)))
start_date = dt.datetime(year,1,1)
end_date = dt.datetime(year,12,31)
flen=1
namfmt='nowcast'
filemap={'nitrate':'ptrc_T','silicon':'ptrc_T','ammonium':'ptrc_T','diatoms':'ptrc_T',
'ciliates':'ptrc_T','flagellates':'ptrc_T','vosaline':'grid_T','votemper':'grid_T'}
fdict={'ptrc_T':1,'grid_T':1}
df1=et.loadDFO(datelims=(start_date,end_date))
print(len(df1),'data points')
df1[['Year','Month','Day','Lat','Lon','Pressure','Depth','N','Si','Chlorophyll_Extracted',
'ConsT','AbsSal']].head()
1992 data points
Year | Month | Day | Lat | Lon | Pressure | Depth | N | Si | Chlorophyll_Extracted | ConsT | AbsSal | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2010.0 | 6.0 | 2.0 | 51.6785 | -127.332 | 2.8 | None | NaN | NaN | NaN | 9.276901 | 28.399731 |
1 | 2010.0 | 6.0 | 2.0 | 51.6785 | -127.332 | 3.8 | None | NaN | NaN | NaN | 8.928219 | 30.240832 |
2 | 2010.0 | 6.0 | 2.0 | 51.6785 | -127.332 | 4.7 | None | NaN | NaN | NaN | 8.940301 | 30.222246 |
3 | 2010.0 | 6.0 | 2.0 | 51.6785 | -127.332 | 6.0 | None | NaN | NaN | NaN | 8.843436 | 30.294393 |
4 | 2010.0 | 6.0 | 2.0 | 51.6785 | -127.332 | 6.8 | None | NaN | NaN | NaN | 8.806168 | 30.317506 |
data=et.matchData(df1,filemap,fdict,start_date,end_date,'nowcast',PATH,1,quiet=True);
# density calculations:
data['rho']=gsw.rho(data['AbsSal'],data['ConsT'],data['Pressure'])
data['mod_rho']=gsw.rho(data['mod_vosaline'],data['mod_votemper'],
gsw.p_from_z(-1*data['Z'],data['Lat']))
# load chl to N ratio from namelist
nml=f90nml.read(os.path.join(PATH,'01jan'+str(year)[-2:],'namelist_smelt_cfg'))
mod_chl_N=nml['nampisopt']['zzn2chl']
print('Parameter values from 01jan'+str(year)[-2:]+' namelist_smelt_cfg:')
print(' Chl:N = ',mod_chl_N)
print(' zz_bfsi = ',nml['nampisrem']['zz_bfsi'])
print(' zz_remin_d_bsi = ',nml['nampisrem']['zz_remin_d_bsi'])
print(' zz_w_sink_d_bsi = ',nml['nampissink']['zz_w_sink_d_bsi'])
print(' zz_alpha_b_si = ',nml['nampissink']['zz_alpha_b_si'])
print(' zz_alpha_b_d = ',nml['nampissink']['zz_alpha_b_d'])
Parameter values from 01jan10 namelist_smelt_cfg: Chl:N = 2.0 zz_bfsi = 6e-05 zz_remin_d_bsi = 1.1e-06 zz_w_sink_d_bsi = 0.00028 zz_alpha_b_si = 0.92 zz_alpha_b_d = 0.0
# chlorophyll calculations
data['l10_obsChl']=np.log10(data['Chlorophyll_Extracted']+0.01)
data['l10_modChl']=np.log10(mod_chl_N*(data['mod_diatoms']+data['mod_ciliates']+data['mod_flagellates'])+0.01)
data['mod_Chl']=mod_chl_N*(data['mod_diatoms']+data['mod_ciliates']+data['mod_flagellates'])
data['Chl']=data['Chlorophyll_Extracted']
# prep and load dictionary to save stats in
if os.path.isfile('vET-HC1905-DFO-NutChlPhys-stats.json'):
with open('vET-HC1905-DFO-NutChlPhys-stats.json', 'r') as fstat:
statsDict = json.load(fstat);
statsDict[year]=dict();
else:
statsDict={year:dict()};
cm1=cmocean.cm.thermal
theta=-30
lon0=-123.9
lat0=49.3
with nc.Dataset('/data/eolson/results/MEOPAR/NEMO-forcing-new/grid/bathymetry_201702.nc') as bathy:
bathylon=np.copy(bathy.variables['nav_lon'][:,:])
bathylat=np.copy(bathy.variables['nav_lat'][:,:])
bathyZ=np.copy(bathy.variables['Bathymetry'][:,:])
def byDepth(ax,obsvar,modvar,lims):
ps=et.varvarPlot(ax,data,obsvar,modvar,'Z',(15,22),'z','m',('mediumseagreen','darkturquoise','navy'))
l=ax.legend(handles=ps)
ax.set_xlabel('Obs')
ax.set_ylabel('Model')
ax.plot(lims,lims,'k-',alpha=.5)
ax.set_xlim(lims)
ax.set_ylim(lims)
ax.set_aspect(1)
return ps,l
def byRegion(ax,obsvar,modvar,lims):
ps1=et.varvarPlot(ax,dJDF,obsvar,modvar,cols=('b',),lname='SJDF')
ps2=et.varvarPlot(ax,dSJGI,obsvar,modvar,cols=('c',),lname='SJGI')
ps3=et.varvarPlot(ax,dSOG,obsvar,modvar,cols=('y',),lname='SOG')
ps4=et.varvarPlot(ax,dNSOG,obsvar,modvar,cols=('m',),lname='NSOG')
l=ax.legend(handles=[ps1[0][0],ps2[0][0],ps3[0][0],ps4[0][0]])
ax.set_xlabel('Obs')
ax.set_ylabel('Model')
ax.plot(lims,lims,'k-',alpha=.5)
ax.set_xlim(lims)
ax.set_ylim(lims)
ax.set_aspect(1)
return (ps1,ps2,ps3,ps4),l
def bySeason(ax,obsvar,modvar,lims):
for axi in ax:
axi.plot(lims,lims,'k-')
axi.set_xlim(lims)
axi.set_ylim(lims)
axi.set_aspect(1)
axi.set_xlabel('Obs')
axi.set_ylabel('Model')
ps=et.varvarPlot(ax[0],JFM,obsvar,modvar,cols=('crimson','darkturquoise','navy'))
ax[0].set_title('Jan-Mar')
ps=et.varvarPlot(ax[1],Apr,obsvar,modvar,cols=('crimson','darkturquoise','navy'))
ax[1].set_title('Apr')
ps=et.varvarPlot(ax[2],MJJA,obsvar,modvar,cols=('crimson','darkturquoise','navy'))
ax[2].set_title('May-Aug')
ps=et.varvarPlot(ax[3],SOND,obsvar,modvar,cols=('crimson','darkturquoise','navy'))
ax[3].set_title('Sep-Dec')
return
def ErrErr(fig,ax,obsvar1,modvar1,obsvar2,modvar2,lims1,lims2):
m=ax.scatter(data[modvar1]-data[obsvar1],data[modvar2]-data[obsvar2],c=data['Z'],s=1,cmap='gnuplot')
cb=fig.colorbar(m,ax=ax,label='Depth (m)')
ax.set_xlim(lims1)
ax.set_ylim(lims2)
ax.set_aspect((lims1[1]-lims1[0])/(lims2[1]-lims2[0]))
return m,cb
fig, ax = plt.subplots(1,2,figsize = (13,6))
viz_tools.set_aspect(ax[0], coords = 'map')
ax[0].plot(data['Lon'], data['Lat'], 'ro',label='data')
ax[0].plot(data.loc[data.Si>75,['Lon']],data.loc[data.Si>75,['Lat']],'*',color='y',label='high Si')
grid = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc')
viz_tools.plot_coastline(ax[0], grid, coords = 'map',isobath=.1)
ax[0].set_ylim(48, 50.5)
ax[0].legend()
ax[0].set_xlim(-125.7, -122.5);
ax[0].set_title('Observation Locations');
viz_tools.set_aspect(ax[1], coords = 'map')
#ax[1].plot(data['Lon'], data['Lat'], 'ro',label='data')
dJDF=data.loc[(data.Lon<-123.6)&(data.Lat<48.6)]
ax[1].plot(dJDF['Lon'],dJDF['Lat'],'b.',label='JDF')
dSJGI=data.loc[(data.Lon>=-123.6)&(data.Lat<48.9)]
ax[1].plot(dSJGI['Lon'],dSJGI['Lat'],'c.',label='SJGI')
dSOG=data.loc[(data.Lat>=48.9)&(data.Lon>-124.0)]
ax[1].plot(dSOG['Lon'],dSOG['Lat'],'y.',label='SOG')
dNSOG=data.loc[(data.Lat>=48.9)&(data.Lon<=-124.0)]
ax[1].plot(dNSOG['Lon'],dNSOG['Lat'],'m.',label='NSOG')
grid = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc')
viz_tools.plot_coastline(ax[1], grid, coords = 'map')
ax[1].set_ylim(48, 50.5)
ax[1].legend()
ax[1].set_xlim(-125.7, -122.5);
# Also set up seasonal groupings:
iz=(data.Z<15)
JFM=data.loc[iz&(data.dtUTC<=dt.datetime(year,4,1)),:]
Apr=data.loc[iz&(data.dtUTC<=dt.datetime(year,5,1))&(data.dtUTC>dt.datetime(year,4,1)),:]
MJJA=data.loc[iz&(data.dtUTC<=dt.datetime(year,9,1))&(data.dtUTC>dt.datetime(year,5,1)),:]
SOND=data.loc[iz&(data.dtUTC>dt.datetime(year,9,1)),:]
statsubs=OrderedDict({'z < 15 m':data.loc[data.Z<15],
'15 m < z < 22 m':data.loc[(data.Z>=15)&(data.Z<22)],
'z >= 22 m':data.loc[data.Z>=22],
'z > 50 m':data.loc[data.Z>50],
'all':data,
'z < 15 m, JFM':JFM,
'z < 15 m, Apr':Apr,
'z < 15 m, MJJA':MJJA,
'z < 15 m, SOND': SOND})
obsvar='N'
modvar='mod_nitrate'
statsDict[year]['NO3']=OrderedDict()
for isub in statsubs:
statsDict[year]['NO3'][isub]=dict()
var=statsDict[year]['NO3'][isub]
var['N'],mmean,omean,var['Bias'],var['RMSE'],var['WSS']=et.stats(statsubs[isub].loc[:,[obsvar]],
statsubs[isub].loc[:,[modvar]])
tbl,tdf=et.displayStats(statsDict[year]['NO3'],level='Subset',suborder=list(statsubs.keys()))
tbl
Bias | N | RMSE | WSS | ||
---|---|---|---|---|---|
Subset | |||||
0 | z < 15 m | 0.436482 | 373 | 5.63537 | 0.875337 |
1 | 15 m < z < 22 m | -0.118248 | 87 | 3.9195 | 0.797161 |
2 | z >= 22 m | -0.722396 | 1001 | 2.39437 | 0.872329 |
3 | z > 50 m | -0.800966 | 819 | 2.30133 | 0.848664 |
4 | all | -0.390553 | 1461 | 3.59869 | 0.939591 |
5 | z < 15 m, JFM | nan | 0 | nan | nan |
6 | z < 15 m, Apr | 2.58616 | 157 | 6.17239 | 0.813508 |
7 | z < 15 m, MJJA | -3.1927 | 85 | 6.43877 | 0.821525 |
8 | z < 15 m, SOND | 0.214961 | 131 | 4.22654 | 0.873519 |
fig, ax = plt.subplots(1,2,figsize = (16,7))
ps,l=byDepth(ax[0],obsvar,modvar,(0,40))
ax[0].set_title('NO$_3$ ($\mu$M) By Depth')
ps,l=byRegion(ax[1],obsvar,modvar,(0,40))
ax[1].set_title('NO$_3$ ($\mu$M) By Region');
fig, ax = plt.subplots(1,4,figsize = (16,3.3))
bySeason(ax,obsvar,modvar,(0,30))
fig,ax=plt.subplots(1,1,figsize=(20,.3))
ax.plot(data.dtUTC,np.ones(np.shape(data.dtUTC)),'k.')
ax.set_xlim((dt.datetime(year,1,1),dt.datetime(year,12,31)))
ax.set_title('Data Timing')
ax.yaxis.set_visible(False)
fig,ax=plt.subplots(1,2,figsize=(12,4))
ax[0].set_xlabel('Density Error (kg m$^{-3}$)')
ax[0].set_ylabel('NO$_3$ ($\mu$M) Error')
m,cb=ErrErr(fig,ax[0],'rho','mod_rho',obsvar,modvar,(-3,3),(-15,15))
ax[1].set_xlabel('Salinity Error (g kg$^{-1}$)')
ax[1].set_ylabel('NO$_3$ ($\mu$M) Error')
m,cb=ErrErr(fig,ax[1],'AbsSal','mod_vosaline',obsvar,modvar,(-2.5,2.5),(-15,15))
obsvar='Si'
modvar='mod_silicon'
statsDict[year]['dSi']=OrderedDict()
for isub in statsubs:
statsDict[year]['dSi'][isub]=dict()
var=statsDict[year]['dSi'][isub]
var['N'],mmean,omean,var['Bias'],var['RMSE'],var['WSS']=et.stats(statsubs[isub].loc[:,[obsvar]],
statsubs[isub].loc[:,[modvar]])
tbl,tdf=et.displayStats(statsDict[year]['dSi'],level='Subset',suborder=list(statsubs.keys()))
tbl
Bias | N | RMSE | WSS | ||
---|---|---|---|---|---|
Subset | |||||
0 | z < 15 m | -3.09272 | 370 | 16.4275 | 0.598622 |
1 | 15 m < z < 22 m | -4.09204 | 85 | 9.56399 | 0.674404 |
2 | z >= 22 m | -3.72898 | 987 | 7.03738 | 0.843719 |
3 | z > 50 m | -3.93822 | 809 | 7.09057 | 0.835693 |
4 | all | -3.58713 | 1442 | 10.4179 | 0.825947 |
5 | z < 15 m, JFM | nan | 0 | nan | nan |
6 | z < 15 m, Apr | 10.7477 | 154 | 15.8423 | 0.674 |
7 | z < 15 m, MJJA | -16.198 | 85 | 20.3265 | 0.372485 |
8 | z < 15 m, SOND | -10.8597 | 131 | 14.1095 | 0.520334 |
mv=(0,80)
fig, ax = plt.subplots(1,2,figsize = (16,7))
ps,l=byDepth(ax[0],obsvar,modvar,mv)
ax[0].set_title('Dissolved Silica ($\mu$M) By Depth')
ps,l=byRegion(ax[1],obsvar,modvar,mv)
ax[1].set_title('Dissolved Silica ($\mu$M) By Region');
fig, ax = plt.subplots(1,4,figsize = (16,3.3))
bySeason(ax,obsvar,modvar,mv)
fig,ax=plt.subplots(1,1,figsize=(20,.3))
ax.plot(data.dtUTC,np.ones(np.shape(data.dtUTC)),'k.')
ax.set_xlim((dt.datetime(year,1,1),dt.datetime(year,12,31)))
ax.set_title('Data Timing')
ax.yaxis.set_visible(False)
fig,ax=plt.subplots(1,2,figsize=(12,4))
ax[0].set_xlabel('Density Error (kg m$^{-3}$)')
ax[0].set_ylabel('dSi Error ($\mu$M)')
m,cb=ErrErr(fig,ax[0],'rho','mod_rho',obsvar,modvar,(-3,3),(-25,25))
ax[1].set_xlabel('Salinity Error (g kg$^{-1}$)')
ax[1].set_ylabel('dSi Error ($\mu$M)')
m,cb=ErrErr(fig,ax[1],'AbsSal','mod_vosaline',obsvar,modvar,(-2.5,2.5),(-25,25))
fig, ax = plt.subplots(1,2,figsize = (15,8))
cols=('crimson','red','orangered','darkorange','gold','chartreuse','green','lightseagreen','cyan',
'darkturquoise','royalblue','lightskyblue','blue','darkblue','mediumslateblue','blueviolet',
'darkmagenta','fuchsia','deeppink','pink')
ii0=start_date
for ii in range(0,int((end_date-start_date).days/30)):
iii=(data.dtUTC>=(start_date+dt.timedelta(days=ii*30)))&(data.dtUTC<(start_date+dt.timedelta(days=(ii+1)*30)))
ax[0].plot(data.loc[iii,['mod_nitrate']].values-data.loc[iii,['N']].values, data.loc[iii,['Z']].values,
'.', color = cols[ii],label=str(ii))
ax[1].plot(data.loc[iii,['mod_silicon']].values-data.loc[iii,['Si']].values, data.loc[iii,['Z']].values,
'.', color = cols[ii],label=str(ii))
for axi in (ax[0],ax[1]):
axi.legend(loc=4)
axi.set_ylim(400,0)
axi.set_ylabel('Depth (m)')
ax[0].set_xlabel('Model - Obs')
ax[1].set_xlabel('Model - Obs')
ax[0].set_xlim(-15,15)
ax[1].set_xlim(-40,20)
ax[0].set_title('NO3')
ax[1].set_title('dSi')
Text(0.5, 1.0, 'dSi')
fig,ax=plt.subplots(1,2,figsize=(15,6))
p1=ax[0].plot(dJDF['N'],dJDF['Si'],'b.',label='SJDF')
p2=ax[0].plot(dSJGI['N'],dSJGI['Si'],'c.',label='SJGI')
p3=ax[0].plot(dSOG['N'],dSOG['Si'],'y.',label='SOG')
p4=ax[0].plot(dNSOG['N'],dNSOG['Si'],'m.',label='NSOG')
ax[0].plot(np.arange(0,41),1.35*np.arange(0,41)+6.46,'k-',label='OBC')
ax[0].set_title('Observed')
ax[0].set_xlabel('NO3')
ax[0].set_ylabel('dSi')
ax[0].set_xlim(0,40)
ax[0].set_ylim(0,85)
ax[0].legend()
p5=ax[1].plot(dJDF['mod_nitrate'],dJDF['mod_silicon'],'b.',label='SJDF')
p6=ax[1].plot(dSJGI['mod_nitrate'],dSJGI['mod_silicon'],'c.',label='SJGI')
p7=ax[1].plot(dSOG['mod_nitrate'],dSOG['mod_silicon'],'y.',label='SOG')
p8=ax[1].plot(dNSOG['mod_nitrate'],dNSOG['mod_silicon'],'m.',label='NSOG')
ax[1].plot(np.arange(0,41),1.35*np.arange(0,41)+6.46,'k-',label='OBC')
ax[1].set_title('Model')
ax[1].set_xlabel('NO3')
ax[1].set_ylabel('dSi')
ax[1].set_xlim(0,40)
ax[1].set_ylim(0,85)
ax[1].legend()
#ax[0].plot(np.arange(0,35),1.3*np.arange(0,35),'k-')
#ax[1].plot(np.arange(0,35),1.3*np.arange(0,35),'k-')
<matplotlib.legend.Legend at 0x7fb3c9797940>
fig,ax=plt.subplots(1,2,figsize=(15,6))
p1=ax[0].plot(dJDF['AbsSal'], dJDF['Si']-1.3*dJDF['N'],'b.',label='SJDF')
p2=ax[0].plot(dSJGI['AbsSal'],dSJGI['Si']-1.3*dSJGI['N'],'c.',label='SJGI')
p3=ax[0].plot(dSOG['AbsSal'],dSOG['Si']-1.3*dSOG['N'],'y.',label='SOG')
p4=ax[0].plot(dNSOG['AbsSal'],dNSOG['Si']-1.3*dNSOG['N'],'m.',label='NSOG')
ax[0].set_title('Observed')
ax[0].set_xlabel('S (g/kg)')
ax[0].set_ylabel('dSi-1.3NO3')
ax[0].set_xlim(10,35)
ax[0].set_ylim(0,45)
ax[0].legend()
p5=ax[1].plot(dJDF['mod_vosaline'],dJDF['mod_silicon']-1.3*dJDF['mod_nitrate'],'b.',label='SJDF')
p6=ax[1].plot(dSJGI['mod_vosaline'],dSJGI['mod_silicon']-1.3*dSJGI['mod_nitrate'],'c.',label='SJGI')
p7=ax[1].plot(dSOG['mod_vosaline'],dSOG['mod_silicon']-1.3*dSOG['mod_nitrate'],'y.',label='SOG')
p8=ax[1].plot(dNSOG['mod_vosaline'],dNSOG['mod_silicon']-1.3*dNSOG['mod_nitrate'],'m.',label='NSOG')
ax[1].set_title('Model')
ax[1].set_xlabel('S (g/kg)')
ax[1].set_ylabel('dSi-1.3NO3')
ax[1].set_xlim(10,35)
ax[1].set_ylim(0,45)
ax[1].legend()
<matplotlib.legend.Legend at 0x7fb3ec13dac0>
obsvar='l10_obsChl'
modvar='l10_modChl'
statsDict[year]['Chl log10']=OrderedDict()
for isub in statsubs:
statsDict[year]['Chl log10'][isub]=dict()
var=statsDict[year]['Chl log10'][isub]
var['N'],mmean,omean,var['Bias'],var['RMSE'],var['WSS']=et.stats(statsubs[isub].loc[:,[obsvar]],
statsubs[isub].loc[:,[modvar]])
obsvar='Chlorophyll_Extracted'
modvar='mod_Chl'
statsDict[year]['Chl']=OrderedDict()
for isub in statsubs:
statsDict[year]['Chl'][isub]=dict()
var=statsDict[year]['Chl'][isub]
var['N'],mmean,omean,var['Bias'],var['RMSE'],var['WSS']=et.stats(statsubs[isub].loc[:,[obsvar]],
statsubs[isub].loc[:,[modvar]])
tempD={'Chl log10':statsDict[year]['Chl log10'],'Chl':statsDict[year]['Chl']}
tbl,tdf=et.displayStatsFlex(tempD,('Variable','Subset','Metric',''),
['Order','Subset','Metric'],
['Variable','Metric'],
suborder=list(statsubs.keys()))
tbl
Variable | Chl | Chl log10 | |||||||
---|---|---|---|---|---|---|---|---|---|
Bias | N | RMSE | WSS | Bias | N | RMSE | WSS | ||
Subset | |||||||||
0 | z < 15 m | 0.2611 | 178 | 3.54728 | 0.620564 | 0.0133065 | 178 | 0.530094 | 0.67958 |
1 | 15 m < z < 22 m | -0.708091 | 80 | 2.61033 | 0.340501 | -0.119123 | 80 | 0.513023 | 0.542213 |
2 | z >= 22 m | -0.650431 | 10 | 1.36785 | 0.410637 | -0.175648 | 10 | 0.393922 | 0.658009 |
3 | z > 50 m | -0.871473 | 2 | 0.87154 | 0.13676 | -0.495606 | 2 | 0.496859 | 0.0896501 |
4 | all | -0.0622231 | 268 | 3.23439 | 0.609235 | -0.0332753 | 268 | 0.520566 | 0.682877 |
5 | z < 15 m, JFM | nan | 0 | nan | nan | nan | 0 | nan | nan |
6 | z < 15 m, Apr | -1.40571 | 53 | 4.05008 | 0.704272 | -0.205438 | 53 | 0.49759 | 0.658318 |
7 | z < 15 m, MJJA | 3.38347 | 49 | 5.02111 | 0.156187 | 0.501988 | 49 | 0.657165 | 0.352678 |
8 | z < 15 m, SOND | -0.589632 | 76 | 1.33315 | 0.490145 | -0.149219 | 76 | 0.455 | 0.592885 |
fig, ax = plt.subplots(1,2,figsize = (14,6))
ax[0].plot(np.arange(-.6,1.6,.1),np.arange(-.6,1.6,.1),'k-')
ps=et.varvarPlot(ax[0],data,'l10_obsChl','l10_modChl','Z',(5,10,15,20,25),'z','m',('crimson','darkorange','lime','mediumseagreen','darkturquoise','navy'))
ax[0].legend(handles=ps)
ax[0].set_xlabel('Obs')
ax[0].set_ylabel('Model')
ax[0].set_title('log10[Chl ($\mu$g/L)+0.01] By Depth')
ax[1].plot(np.arange(0,35),np.arange(0,35),'k-')
ps=et.varvarPlot(ax[1],data,'Chlorophyll_Extracted','mod_Chl','Z',(5,10,15,20,25),'z','m',('crimson','darkorange','lime','mediumseagreen','darkturquoise','navy'))
ax[1].legend(handles=ps)
ax[1].set_xlabel('Obs')
ax[1].set_ylabel('Model')
ax[1].set_title('Chl ($\mu$g/L) By Depth');
fig, ax = plt.subplots(1,2,figsize = (14,6))
obsvar='l10_obsChl'; modvar='l10_modChl'
ps,l=byRegion(ax[0],obsvar,modvar,(-.6,1.6))
ax[0].set_title('Log10 Chl ($\mu$g/L) By Region');
obsvar='Chlorophyll_Extracted'; modvar='mod_Chl'
ps,l=byRegion(ax[1],obsvar,modvar,(0,30))
ax[1].set_title('Chl ($\mu$g/L) By Region');
obsvar='ConsT'
modvar='mod_votemper'
statsDict[year]['Temperature']=OrderedDict()
for isub in statsubs:
statsDict[year]['Temperature'][isub]=dict()
var=statsDict[year]['Temperature'][isub]
var['N'],mmean,omean,var['Bias'],var['RMSE'],var['WSS']=et.stats(statsubs[isub].loc[:,[obsvar]],
statsubs[isub].loc[:,[modvar]])
tbl,tdf=et.displayStats(statsDict[year]['Temperature'],level='Subset',suborder=list(statsubs.keys()))
tbl
Bias | N | RMSE | WSS | ||
---|---|---|---|---|---|
Subset | |||||
0 | z < 15 m | -0.161648 | 414 | 0.933916 | 0.964602 |
1 | 15 m < z < 22 m | -0.333234 | 103 | 0.727572 | 0.849724 |
2 | z >= 22 m | -0.057767 | 1424 | 0.27014 | 0.963075 |
3 | z > 50 m | -0.0251931 | 1182 | 0.222897 | 0.973617 |
4 | all | -0.0945418 | 1941 | 0.517361 | 0.969683 |
5 | z < 15 m, JFM | nan | 0 | nan | nan |
6 | z < 15 m, Apr | -0.25794 | 182 | 0.536674 | 0.813524 |
7 | z < 15 m, MJJA | 0.161654 | 89 | 1.63197 | 0.934458 |
8 | z < 15 m, SOND | -0.240311 | 143 | 0.707776 | 0.967423 |
fig, ax = plt.subplots(1,2,figsize = (16,7))
ps,l=byDepth(ax[0],obsvar,modvar,(5,20))
ax[0].set_title('$\Theta$ ($^{\circ}$C) By Depth')
ps,l=byRegion(ax[1],obsvar,modvar,(5,20))
ax[1].set_title('$\Theta$ ($^{\circ}$C) By Region');
fig, ax = plt.subplots(1,4,figsize = (16,3.3))
bySeason(ax,obsvar,modvar,mv)
fig,ax=plt.subplots(1,1,figsize=(20,.3))
ax.plot(data.dtUTC,np.ones(np.shape(data.dtUTC)),'k.')
ax.set_xlim((dt.datetime(year,1,1),dt.datetime(year,12,31)))
ax.set_title('Data Timing')
ax.yaxis.set_visible(False)
obsvar='AbsSal'
modvar='mod_vosaline'
statsDict[year]['Salinity']=OrderedDict()
for isub in statsubs:
statsDict[year]['Salinity'][isub]=dict()
var=statsDict[year]['Salinity'][isub]
var['N'],mmean,omean,var['Bias'],var['RMSE'],var['WSS']=et.stats(statsubs[isub].loc[:,[obsvar]],
statsubs[isub].loc[:,[modvar]])
tbl,tdf=et.displayStats(statsDict[year]['Salinity'],level='Subset',suborder=list(statsubs.keys()))
tbl
Bias | N | RMSE | WSS | ||
---|---|---|---|---|---|
Subset | |||||
0 | z < 15 m | -0.27807 | 416 | 2.22411 | 0.877358 |
1 | 15 m < z < 22 m | -0.0382723 | 103 | 0.379658 | 0.975426 |
2 | z >= 22 m | 0.134756 | 1424 | 0.270924 | 0.986106 |
3 | z > 50 m | 0.154715 | 1182 | 0.255533 | 0.986066 |
4 | all | 0.0371967 | 1943 | 1.05855 | 0.942075 |
5 | z < 15 m, JFM | nan | 0 | nan | nan |
6 | z < 15 m, Apr | -0.794504 | 183 | 2.22976 | 0.734697 |
7 | z < 15 m, MJJA | 0.37373 | 89 | 2.76367 | 0.911056 |
8 | z < 15 m, SOND | -0.0246171 | 144 | 1.80315 | 0.91292 |
fig, ax = plt.subplots(1,2,figsize = (16,7))
ps,l=byDepth(ax[0],obsvar,modvar,(0,36))
ax[0].set_title('S$_A$ (g kg$^{-1}$) By Depth')
ps,l=byRegion(ax[1],obsvar,modvar,(0,36))
ax[1].set_title('S$_A$ (g kg$^{-1}$) By Region');
fig, ax = plt.subplots(1,4,figsize = (16,3.3))
bySeason(ax,obsvar,modvar,(0,36))
fig,ax=plt.subplots(1,1,figsize=(20,.3))
ax.plot(data.dtUTC,np.ones(np.shape(data.dtUTC)),'k.')
ax.set_xlim((dt.datetime(year,1,1),dt.datetime(year,12,31)))
ax.set_title('Data Timing')
ax.yaxis.set_visible(False)
obsvar='rho'
modvar='mod_rho'
statsDict[year]['Density']=OrderedDict()
for isub in statsubs:
statsDict[year]['Density'][isub]=dict()
var=statsDict[year]['Density'][isub]
var['N'],mmean,omean,var['Bias'],var['RMSE'],var['WSS']=et.stats(statsubs[isub].loc[:,[obsvar]],
statsubs[isub].loc[:,[modvar]])
tbl,tdf=et.displayStats(statsDict[year]['Density'],level='Subset',suborder=list(statsubs.keys()))
tbl
Bias | N | RMSE | WSS | ||
---|---|---|---|---|---|
Subset | |||||
0 | z < 15 m | -0.20296 | 414 | 1.7104 | 0.897306 |
1 | 15 m < z < 22 m | 0.0219064 | 103 | 0.351713 | 0.966073 |
2 | z >= 22 m | 0.112856 | 1424 | 0.222985 | 0.990307 |
3 | z > 50 m | 0.123388 | 1182 | 0.205437 | 0.9901 |
4 | all | 0.040669 | 1941 | 0.816713 | 0.961081 |
5 | z < 15 m, JFM | nan | 0 | nan | nan |
6 | z < 15 m, Apr | -0.564873 | 182 | 1.72494 | 0.739153 |
7 | z < 15 m, MJJA | 0.246827 | 89 | 2.1726 | 0.929733 |
8 | z < 15 m, SOND | -0.0222806 | 143 | 1.32094 | 0.920705 |
fig, ax = plt.subplots(1,2,figsize = (16,7))
ps,l=byDepth(ax[0],obsvar,modvar,(1010,1030))
ax[0].set_title('Density (kg m$^{-3}$) By Depth')
ps,l=byRegion(ax[1],obsvar,modvar,(1010,1030))
ax[1].set_title('Density (kg m$^{-3}$) By Region');
fig, ax = plt.subplots(1,4,figsize = (16,3.3))
bySeason(ax,obsvar,modvar,(1010,1030))
fig,ax=plt.subplots(1,1,figsize=(20,.3))
ax.plot(data.dtUTC,np.ones(np.shape(data.dtUTC)),'k.')
ax.set_xlim((dt.datetime(year,1,1),dt.datetime(year,12,31)))
ax.set_title('Data Timing')
ax.yaxis.set_visible(False)
def tsplot(ax,svar,tvar):
limsS=(0,36)
limsT=(5,20)
ss,tt=np.meshgrid(np.linspace(limsS[0],limsS[1],20),np.linspace(limsT[0],limsT[1],20))
rho=gsw.rho(ss,tt,np.zeros(np.shape(ss)))
r=ax.contour(ss,tt,rho,colors='k')
ps1=ax.plot(dJDF[svar],dJDF[tvar],'b.',label='SJDF')
ps2=ax.plot(dSJGI[svar],dSJGI[tvar],'c.',label='SJGI')
ps3=ax.plot(dSOG[svar],dSOG[tvar],'y.',label='SOG')
ps4=ax.plot(dNSOG[svar],dNSOG[tvar],'m.',label='NSOG')
l=ax.legend(handles=[ps1[0],ps2[0],ps3[0],ps4[0]],bbox_to_anchor=(1.55,1))
ax.set_ylim(limsT)
ax.set_xlim(limsS)
ax.set_ylabel('$\Theta$ ($^{\circ}$C)')
ax.set_xlabel('S$_A$ (g kg$^{-1}$)')
ax.set_aspect((limsS[1]-limsS[0])/(limsT[1]-limsT[0]))
return
fig,ax=plt.subplots(1,2,figsize=(16,4))
tsplot(ax[0],'AbsSal','ConsT')
ax[0].set_title('Observed')
tsplot(ax[1],'mod_vosaline','mod_votemper')
ax[1].set_title('Modelled')
Text(0.5, 1.0, 'Modelled')
# save stats dict to json file:
with open('vET-HC1905-DFO-NutChlPhys-stats.json', 'w') as fstat:
json.dump(statsDict, fstat, indent=4);
tbl,tdf=et.displayStats(statsDict[year],level='Variable',suborder=list(statsubs.keys()))
tbl
Bias | N | RMSE | WSS | |||
---|---|---|---|---|---|---|
Variable | Subset | |||||
Chl | 0 | z < 15 m | 0.2611 | 178 | 3.54728 | 0.620564 |
1 | 15 m < z < 22 m | -0.708091 | 80 | 2.61033 | 0.340501 | |
2 | z >= 22 m | -0.650431 | 10 | 1.36785 | 0.410637 | |
3 | z > 50 m | -0.871473 | 2 | 0.87154 | 0.13676 | |
4 | all | -0.0622231 | 268 | 3.23439 | 0.609235 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | -1.40571 | 53 | 4.05008 | 0.704272 | |
7 | z < 15 m, MJJA | 3.38347 | 49 | 5.02111 | 0.156187 | |
8 | z < 15 m, SOND | -0.589632 | 76 | 1.33315 | 0.490145 | |
Chl log10 | 0 | z < 15 m | 0.0133065 | 178 | 0.530094 | 0.67958 |
1 | 15 m < z < 22 m | -0.119123 | 80 | 0.513023 | 0.542213 | |
2 | z >= 22 m | -0.175648 | 10 | 0.393922 | 0.658009 | |
3 | z > 50 m | -0.495606 | 2 | 0.496859 | 0.0896501 | |
4 | all | -0.0332753 | 268 | 0.520566 | 0.682877 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | -0.205438 | 53 | 0.49759 | 0.658318 | |
7 | z < 15 m, MJJA | 0.501988 | 49 | 0.657165 | 0.352678 | |
8 | z < 15 m, SOND | -0.149219 | 76 | 0.455 | 0.592885 | |
Density | 0 | z < 15 m | -0.20296 | 414 | 1.7104 | 0.897306 |
1 | 15 m < z < 22 m | 0.0219064 | 103 | 0.351713 | 0.966073 | |
2 | z >= 22 m | 0.112856 | 1424 | 0.222985 | 0.990307 | |
3 | z > 50 m | 0.123388 | 1182 | 0.205437 | 0.9901 | |
4 | all | 0.040669 | 1941 | 0.816713 | 0.961081 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | -0.564873 | 182 | 1.72494 | 0.739153 | |
7 | z < 15 m, MJJA | 0.246827 | 89 | 2.1726 | 0.929733 | |
8 | z < 15 m, SOND | -0.0222806 | 143 | 1.32094 | 0.920705 | |
NO3 | 0 | z < 15 m | 0.436482 | 373 | 5.63537 | 0.875337 |
1 | 15 m < z < 22 m | -0.118248 | 87 | 3.9195 | 0.797161 | |
2 | z >= 22 m | -0.722396 | 1001 | 2.39437 | 0.872329 | |
3 | z > 50 m | -0.800966 | 819 | 2.30133 | 0.848664 | |
4 | all | -0.390553 | 1461 | 3.59869 | 0.939591 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | 2.58616 | 157 | 6.17239 | 0.813508 | |
7 | z < 15 m, MJJA | -3.1927 | 85 | 6.43877 | 0.821525 | |
8 | z < 15 m, SOND | 0.214961 | 131 | 4.22654 | 0.873519 | |
Salinity | 0 | z < 15 m | -0.27807 | 416 | 2.22411 | 0.877358 |
1 | 15 m < z < 22 m | -0.0382723 | 103 | 0.379658 | 0.975426 | |
2 | z >= 22 m | 0.134756 | 1424 | 0.270924 | 0.986106 | |
3 | z > 50 m | 0.154715 | 1182 | 0.255533 | 0.986066 | |
4 | all | 0.0371967 | 1943 | 1.05855 | 0.942075 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | -0.794504 | 183 | 2.22976 | 0.734697 | |
7 | z < 15 m, MJJA | 0.37373 | 89 | 2.76367 | 0.911056 | |
8 | z < 15 m, SOND | -0.0246171 | 144 | 1.80315 | 0.91292 | |
Temperature | 0 | z < 15 m | -0.161648 | 414 | 0.933916 | 0.964602 |
1 | 15 m < z < 22 m | -0.333234 | 103 | 0.727572 | 0.849724 | |
2 | z >= 22 m | -0.057767 | 1424 | 0.27014 | 0.963075 | |
3 | z > 50 m | -0.0251931 | 1182 | 0.222897 | 0.973617 | |
4 | all | -0.0945418 | 1941 | 0.517361 | 0.969683 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | -0.25794 | 182 | 0.536674 | 0.813524 | |
7 | z < 15 m, MJJA | 0.161654 | 89 | 1.63197 | 0.934458 | |
8 | z < 15 m, SOND | -0.240311 | 143 | 0.707776 | 0.967423 | |
dSi | 0 | z < 15 m | -3.09272 | 370 | 16.4275 | 0.598622 |
1 | 15 m < z < 22 m | -4.09204 | 85 | 9.56399 | 0.674404 | |
2 | z >= 22 m | -3.72898 | 987 | 7.03738 | 0.843719 | |
3 | z > 50 m | -3.93822 | 809 | 7.09057 | 0.835693 | |
4 | all | -3.58713 | 1442 | 10.4179 | 0.825947 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | 10.7477 | 154 | 15.8423 | 0.674 | |
7 | z < 15 m, MJJA | -16.198 | 85 | 20.3265 | 0.372485 | |
8 | z < 15 m, SOND | -10.8597 | 131 | 14.1095 | 0.520334 |