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 = 2013
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()
1402 data points
Year | Month | Day | Lat | Lon | Pressure | Depth | N | Si | Chlorophyll_Extracted | ConsT | AbsSal | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2013.0 | 4.0 | 11.0 | 49.402167 | -124.1555 | 2.6 | None | 3.0 | 7.2 | 11.82 | 9.789540 | 27.743770 |
1 | 2013.0 | 4.0 | 11.0 | 49.402167 | -124.1555 | 4.8 | None | 2.9 | 7.3 | 11.67 | 9.787385 | 27.744457 |
2 | 2013.0 | 4.0 | 11.0 | 49.402167 | -124.1555 | 10.2 | None | NaN | NaN | NaN | 9.667316 | 27.749606 |
3 | 2013.0 | 4.0 | 11.0 | 49.402167 | -124.1555 | 19.7 | None | 10.6 | 18.5 | 11.88 | 9.127710 | 28.459715 |
4 | 2013.0 | 4.0 | 11.0 | 49.402167 | -124.1555 | 29.9 | None | 24.1 | 47.6 | NaN | 8.447678 | 29.363611 |
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 01jan13 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 = 0.88 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 | -0.705091 | 277 | 5.76311 | 0.856422 |
1 | 15 m < z < 22 m | -1.43746 | 88 | 4.44962 | 0.747923 |
2 | z >= 22 m | -2.63274 | 666 | 3.39129 | 0.736723 |
3 | z > 50 m | -2.7322 | 448 | 3.11458 | 0.69476 |
4 | all | -2.01282 | 1031 | 4.24767 | 0.922814 |
5 | z < 15 m, JFM | nan | 0 | nan | nan |
6 | z < 15 m, Apr | 0.221857 | 100 | 6.49275 | 0.799564 |
7 | z < 15 m, MJJA | -1.33615 | 117 | 6.28087 | 0.838537 |
8 | z < 15 m, SOND | -1.01943 | 60 | 2.47975 | 0.93672 |
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 | -4.59431 | 277 | 12.3096 | 0.669077 |
1 | 15 m < z < 22 m | -3.39772 | 88 | 9.3143 | 0.708557 |
2 | z >= 22 m | -6.17965 | 666 | 8.16053 | 0.766 |
3 | z > 50 m | -6.36726 | 448 | 7.76973 | 0.781953 |
4 | all | -5.51626 | 1031 | 9.5464 | 0.828987 |
5 | z < 15 m, JFM | nan | 0 | nan | nan |
6 | z < 15 m, Apr | 1.72207 | 100 | 12.3433 | 0.719721 |
7 | z < 15 m, MJJA | -9.8764 | 117 | 14.404 | 0.547312 |
8 | z < 15 m, SOND | -4.82153 | 60 | 6.4065 | 0.827585 |
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 0x7fd38130c280>
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 0x7fd380ed0910>
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 | -2.19225 | 119 | 5.4082 | 0.422962 | -0.242606 | 119 | 0.54984 | 0.53862 |
1 | 15 m < z < 22 m | -1.75118 | 59 | 4.32839 | 0.357373 | -0.202992 | 59 | 0.488419 | 0.5408 |
2 | z >= 22 m | -2.88935 | 1 | 2.88935 | 0 | -0.782708 | 1 | 0.782708 | 0 |
3 | z > 50 m | nan | 0 | nan | nan | nan | 0 | nan | nan |
4 | all | -2.05077 | 179 | 5.06621 | 0.422177 | -0.232566 | 179 | 0.532013 | 0.595107 |
5 | z < 15 m, JFM | nan | 0 | nan | nan | nan | 0 | nan | nan |
6 | z < 15 m, Apr | -6.1843 | 39 | 9.13337 | 0.534324 | -0.388539 | 39 | 0.530882 | 0.663154 |
7 | z < 15 m, MJJA | 0.353238 | 40 | 2.16393 | 0.483901 | 0.0398585 | 40 | 0.600753 | 0.354704 |
8 | z < 15 m, SOND | -0.845496 | 40 | 0.999574 | 0.46913 | -0.382784 | 40 | 0.513537 | 0.32148 |
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 | -0.179863 | 283 | 1.42279 | 0.930141 |
1 | 15 m < z < 22 m | 0.0153107 | 89 | 0.799539 | 0.904124 |
2 | z >= 22 m | 0.0793752 | 663 | 0.387118 | 0.95285 |
3 | z > 50 m | 0.0955339 | 444 | 0.336071 | 0.952862 |
4 | all | 0.0029828 | 1035 | 0.839333 | 0.95574 |
5 | z < 15 m, JFM | nan | 0 | nan | nan |
6 | z < 15 m, Apr | 0.0119995 | 107 | 0.452013 | 0.849412 |
7 | z < 15 m, MJJA | -0.66093 | 118 | 2.11137 | 0.849246 |
8 | z < 15 m, SOND | 0.444908 | 58 | 0.656437 | 0.917777 |
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 | -0.0856546 | 283 | 2.38823 | 0.870242 |
1 | 15 m < z < 22 m | -0.014034 | 89 | 0.470325 | 0.950274 |
2 | z >= 22 m | 0.0334983 | 664 | 0.235318 | 0.990974 |
3 | z > 50 m | 0.0437047 | 445 | 0.192133 | 0.993465 |
4 | all | -0.00313359 | 1036 | 1.26986 | 0.931665 |
5 | z < 15 m, JFM | nan | 0 | nan | nan |
6 | z < 15 m, Apr | -0.411259 | 107 | 0.961515 | 0.876636 |
7 | z < 15 m, MJJA | 0.245837 | 118 | 3.55383 | 0.83318 |
8 | z < 15 m, SOND | -0.159385 | 58 | 0.655278 | 0.967828 |
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 | -0.0299516 | 283 | 1.94682 | 0.88734 |
1 | 15 m < z < 22 m | -0.0138766 | 89 | 0.45987 | 0.929599 |
2 | z >= 22 m | 0.0140328 | 663 | 0.212279 | 0.992412 |
3 | z > 50 m | 0.0196531 | 444 | 0.166968 | 0.994011 |
4 | all | -0.000393803 | 1035 | 1.04086 | 0.950278 |
5 | z < 15 m, JFM | nan | 0 | nan | nan |
6 | z < 15 m, Apr | -0.320471 | 107 | 0.776702 | 0.881392 |
7 | z < 15 m, MJJA | 0.318024 | 118 | 2.89463 | 0.849547 |
8 | z < 15 m, SOND | -0.201942 | 58 | 0.577531 | 0.964241 |
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 | -2.19225 | 119 | 5.4082 | 0.422962 |
1 | 15 m < z < 22 m | -1.75118 | 59 | 4.32839 | 0.357373 | |
2 | z >= 22 m | -2.88935 | 1 | 2.88935 | 0 | |
3 | z > 50 m | nan | 0 | nan | nan | |
4 | all | -2.05077 | 179 | 5.06621 | 0.422177 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | -6.1843 | 39 | 9.13337 | 0.534324 | |
7 | z < 15 m, MJJA | 0.353238 | 40 | 2.16393 | 0.483901 | |
8 | z < 15 m, SOND | -0.845496 | 40 | 0.999574 | 0.46913 | |
Chl log10 | 0 | z < 15 m | -0.242606 | 119 | 0.54984 | 0.53862 |
1 | 15 m < z < 22 m | -0.202992 | 59 | 0.488419 | 0.5408 | |
2 | z >= 22 m | -0.782708 | 1 | 0.782708 | 0 | |
3 | z > 50 m | nan | 0 | nan | nan | |
4 | all | -0.232566 | 179 | 0.532013 | 0.595107 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | -0.388539 | 39 | 0.530882 | 0.663154 | |
7 | z < 15 m, MJJA | 0.0398585 | 40 | 0.600753 | 0.354704 | |
8 | z < 15 m, SOND | -0.382784 | 40 | 0.513537 | 0.32148 | |
Density | 0 | z < 15 m | -0.0299516 | 283 | 1.94682 | 0.88734 |
1 | 15 m < z < 22 m | -0.0138766 | 89 | 0.45987 | 0.929599 | |
2 | z >= 22 m | 0.0140328 | 663 | 0.212279 | 0.992412 | |
3 | z > 50 m | 0.0196531 | 444 | 0.166968 | 0.994011 | |
4 | all | -0.000393803 | 1035 | 1.04086 | 0.950278 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | -0.320471 | 107 | 0.776702 | 0.881392 | |
7 | z < 15 m, MJJA | 0.318024 | 118 | 2.89463 | 0.849547 | |
8 | z < 15 m, SOND | -0.201942 | 58 | 0.577531 | 0.964241 | |
NO3 | 0 | z < 15 m | -0.705091 | 277 | 5.76311 | 0.856422 |
1 | 15 m < z < 22 m | -1.43746 | 88 | 4.44962 | 0.747923 | |
2 | z >= 22 m | -2.63274 | 666 | 3.39129 | 0.736723 | |
3 | z > 50 m | -2.7322 | 448 | 3.11458 | 0.69476 | |
4 | all | -2.01282 | 1031 | 4.24767 | 0.922814 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | 0.221857 | 100 | 6.49275 | 0.799564 | |
7 | z < 15 m, MJJA | -1.33615 | 117 | 6.28087 | 0.838537 | |
8 | z < 15 m, SOND | -1.01943 | 60 | 2.47975 | 0.93672 | |
Salinity | 0 | z < 15 m | -0.0856546 | 283 | 2.38823 | 0.870242 |
1 | 15 m < z < 22 m | -0.014034 | 89 | 0.470325 | 0.950274 | |
2 | z >= 22 m | 0.0334983 | 664 | 0.235318 | 0.990974 | |
3 | z > 50 m | 0.0437047 | 445 | 0.192133 | 0.993465 | |
4 | all | -0.00313359 | 1036 | 1.26986 | 0.931665 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | -0.411259 | 107 | 0.961515 | 0.876636 | |
7 | z < 15 m, MJJA | 0.245837 | 118 | 3.55383 | 0.83318 | |
8 | z < 15 m, SOND | -0.159385 | 58 | 0.655278 | 0.967828 | |
Temperature | 0 | z < 15 m | -0.179863 | 283 | 1.42279 | 0.930141 |
1 | 15 m < z < 22 m | 0.0153107 | 89 | 0.799539 | 0.904124 | |
2 | z >= 22 m | 0.0793752 | 663 | 0.387118 | 0.95285 | |
3 | z > 50 m | 0.0955339 | 444 | 0.336071 | 0.952862 | |
4 | all | 0.0029828 | 1035 | 0.839333 | 0.95574 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | 0.0119995 | 107 | 0.452013 | 0.849412 | |
7 | z < 15 m, MJJA | -0.66093 | 118 | 2.11137 | 0.849246 | |
8 | z < 15 m, SOND | 0.444908 | 58 | 0.656437 | 0.917777 | |
dSi | 0 | z < 15 m | -4.59431 | 277 | 12.3096 | 0.669077 |
1 | 15 m < z < 22 m | -3.39772 | 88 | 9.3143 | 0.708557 | |
2 | z >= 22 m | -6.17965 | 666 | 8.16053 | 0.766 | |
3 | z > 50 m | -6.36726 | 448 | 7.76973 | 0.781953 | |
4 | all | -5.51626 | 1031 | 9.5464 | 0.828987 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | 1.72207 | 100 | 12.3433 | 0.719721 | |
7 | z < 15 m, MJJA | -9.8764 | 117 | 14.404 | 0.547312 | |
8 | z < 15 m, SOND | -4.82153 | 60 | 6.4065 | 0.827585 |