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 = 2009
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()
1122 data points
Year | Month | Day | Lat | Lon | Pressure | Depth | N | Si | Chlorophyll_Extracted | ConsT | AbsSal | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2009.0 | 9.0 | 12.0 | 51.678333 | -127.331667 | 5.3 | None | 19.3 | 30.7 | 1.01 | 9.232036 | 30.451543 |
1 | 2009.0 | 9.0 | 12.0 | 51.675000 | -127.283667 | 5.5 | None | 19.8 | 31.8 | 0.79 | 9.186134 | 30.750086 |
2 | 2009.0 | 9.0 | 17.0 | 48.499333 | -124.733500 | 1.5 | None | 0.7 | 24.3 | 15.17 | 14.772244 | 32.085822 |
3 | 2009.0 | 9.0 | 17.0 | 48.499333 | -124.733500 | 5.4 | None | 0.8 | 23.8 | NaN | 14.764574 | 32.088876 |
4 | 2009.0 | 9.0 | 17.0 | 48.499333 | -124.733500 | 10.9 | None | 2.6 | 24.3 | 3.59 | 14.159156 | 32.282807 |
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 01jan09 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 | 1.12796 | 241 | 6.32333 | 0.858144 |
1 | 15 m < z < 22 m | 0.748741 | 80 | 3.55367 | 0.747282 |
2 | z >= 22 m | 0.0975869 | 776 | 2.39782 | 0.841754 |
3 | z > 50 m | -0.108215 | 588 | 2.13845 | 0.847405 |
4 | all | 0.371437 | 1097 | 3.7111 | 0.915259 |
5 | z < 15 m, JFM | nan | 0 | nan | nan |
6 | z < 15 m, Apr | 3.95674 | 60 | 9.14013 | 0.516977 |
7 | z < 15 m, MJJA | -0.803055 | 62 | 3.72539 | 0.933943 |
8 | z < 15 m, SOND | 0.707765 | 119 | 5.62354 | 0.854868 |
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 | -4.44601 | 241 | 11.8623 | 0.816693 |
1 | 15 m < z < 22 m | -3.79019 | 80 | 7.39976 | 0.764308 |
2 | z >= 22 m | -3.6108 | 778 | 6.59373 | 0.799729 |
3 | z > 50 m | -3.60678 | 590 | 6.77256 | 0.784428 |
4 | all | -3.80701 | 1099 | 8.10069 | 0.865874 |
5 | z < 15 m, JFM | nan | 0 | nan | nan |
6 | z < 15 m, Apr | 4.03938 | 60 | 13.331 | 0.394536 |
7 | z < 15 m, MJJA | -6.35367 | 62 | 9.52994 | 0.830581 |
8 | z < 15 m, SOND | -7.73046 | 119 | 12.1676 | 0.684511 |
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 0x7f2b27ac9370>
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 0x7f2b2672c8e0>
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 | -2.61205 | 160 | 8.94811 | 0.350828 | -0.0015592 | 160 | 0.502321 | 0.745257 |
1 | 15 m < z < 22 m | -1.17608 | 79 | 3.36087 | 0.342782 | -0.0383487 | 79 | 0.490198 | 0.612162 |
2 | z >= 22 m | -0.234486 | 1 | 0.234486 | 0 | -0.0842336 | 1 | 0.0842336 | 0 |
3 | z > 50 m | nan | 0 | nan | nan | nan | 0 | nan | nan |
4 | all | -2.12947 | 240 | 7.55628 | 0.352338 | -0.0140136 | 240 | 0.497337 | 0.73716 |
5 | z < 15 m, JFM | nan | 0 | nan | nan | nan | 0 | nan | nan |
6 | z < 15 m, Apr | -10.2441 | 40 | 17.3065 | 0.474607 | -0.475099 | 40 | 0.726567 | 0.611964 |
7 | z < 15 m, MJJA | -0.704902 | 39 | 4.05881 | 0.661663 | 0.112151 | 39 | 0.407112 | 0.639543 |
8 | z < 15 m, SOND | 0.238585 | 81 | 1.52307 | 0.599935 | 0.177538 | 81 | 0.397405 | 0.75714 |
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 | 0.023639 | 244 | 1.4207 | 0.942963 |
1 | 15 m < z < 22 m | -0.206281 | 81 | 0.642737 | 0.920731 |
2 | z >= 22 m | -0.0626896 | 778 | 0.470799 | 0.93255 |
3 | z > 50 m | -0.0100127 | 590 | 0.442389 | 0.937771 |
4 | all | -0.0541372 | 1103 | 0.795723 | 0.95304 |
5 | z < 15 m, JFM | nan | 0 | nan | nan |
6 | z < 15 m, Apr | 0.110444 | 60 | 0.400089 | 0.711228 |
7 | z < 15 m, MJJA | 0.446745 | 62 | 2.37849 | 0.877163 |
8 | z < 15 m, SOND | -0.234073 | 122 | 1.0407 | 0.955068 |
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 | -0.336329 | 244 | 1.87368 | 0.89086 |
1 | 15 m < z < 22 m | -0.12303 | 81 | 0.426764 | 0.963202 |
2 | z >= 22 m | 0.145867 | 778 | 0.34555 | 0.98092 |
3 | z > 50 m | 0.186182 | 590 | 0.327591 | 0.981713 |
4 | all | 0.0194515 | 1103 | 0.934993 | 0.944222 |
5 | z < 15 m, JFM | nan | 0 | nan | nan |
6 | z < 15 m, Apr | -0.715961 | 60 | 1.10126 | 0.79516 |
7 | z < 15 m, MJJA | -1.01601 | 62 | 3.26089 | 0.869287 |
8 | z < 15 m, SOND | 0.195787 | 122 | 1.01047 | 0.930358 |
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 | -0.260878 | 244 | 1.59456 | 0.901594 |
1 | 15 m < z < 22 m | -0.0623826 | 81 | 0.381269 | 0.955658 |
2 | z >= 22 m | 0.12318 | 778 | 0.30079 | 0.984131 |
3 | z > 50 m | 0.146373 | 590 | 0.279697 | 0.983849 |
4 | all | 0.024594 | 1103 | 0.798098 | 0.957876 |
5 | z < 15 m, JFM | nan | 0 | nan | nan |
6 | z < 15 m, Apr | -0.572594 | 60 | 0.895731 | 0.79336 |
7 | z < 15 m, MJJA | -0.856234 | 62 | 2.80215 | 0.875496 |
8 | z < 15 m, SOND | 0.194983 | 122 | 0.836831 | 0.936495 |
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 | -2.61205 | 160 | 8.94811 | 0.350828 |
1 | 15 m < z < 22 m | -1.17608 | 79 | 3.36087 | 0.342782 | |
2 | z >= 22 m | -0.234486 | 1 | 0.234486 | 0 | |
3 | z > 50 m | nan | 0 | nan | nan | |
4 | all | -2.12947 | 240 | 7.55628 | 0.352338 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | -10.2441 | 40 | 17.3065 | 0.474607 | |
7 | z < 15 m, MJJA | -0.704902 | 39 | 4.05881 | 0.661663 | |
8 | z < 15 m, SOND | 0.238585 | 81 | 1.52307 | 0.599935 | |
Chl log10 | 0 | z < 15 m | -0.0015592 | 160 | 0.502321 | 0.745257 |
1 | 15 m < z < 22 m | -0.0383487 | 79 | 0.490198 | 0.612162 | |
2 | z >= 22 m | -0.0842336 | 1 | 0.0842336 | 0 | |
3 | z > 50 m | nan | 0 | nan | nan | |
4 | all | -0.0140136 | 240 | 0.497337 | 0.73716 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | -0.475099 | 40 | 0.726567 | 0.611964 | |
7 | z < 15 m, MJJA | 0.112151 | 39 | 0.407112 | 0.639543 | |
8 | z < 15 m, SOND | 0.177538 | 81 | 0.397405 | 0.75714 | |
Density | 0 | z < 15 m | -0.260878 | 244 | 1.59456 | 0.901594 |
1 | 15 m < z < 22 m | -0.0623826 | 81 | 0.381269 | 0.955658 | |
2 | z >= 22 m | 0.12318 | 778 | 0.30079 | 0.984131 | |
3 | z > 50 m | 0.146373 | 590 | 0.279697 | 0.983849 | |
4 | all | 0.024594 | 1103 | 0.798098 | 0.957876 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | -0.572594 | 60 | 0.895731 | 0.79336 | |
7 | z < 15 m, MJJA | -0.856234 | 62 | 2.80215 | 0.875496 | |
8 | z < 15 m, SOND | 0.194983 | 122 | 0.836831 | 0.936495 | |
NO3 | 0 | z < 15 m | 1.12796 | 241 | 6.32333 | 0.858144 |
1 | 15 m < z < 22 m | 0.748741 | 80 | 3.55367 | 0.747282 | |
2 | z >= 22 m | 0.0975869 | 776 | 2.39782 | 0.841754 | |
3 | z > 50 m | -0.108215 | 588 | 2.13845 | 0.847405 | |
4 | all | 0.371437 | 1097 | 3.7111 | 0.915259 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | 3.95674 | 60 | 9.14013 | 0.516977 | |
7 | z < 15 m, MJJA | -0.803055 | 62 | 3.72539 | 0.933943 | |
8 | z < 15 m, SOND | 0.707765 | 119 | 5.62354 | 0.854868 | |
Salinity | 0 | z < 15 m | -0.336329 | 244 | 1.87368 | 0.89086 |
1 | 15 m < z < 22 m | -0.12303 | 81 | 0.426764 | 0.963202 | |
2 | z >= 22 m | 0.145867 | 778 | 0.34555 | 0.98092 | |
3 | z > 50 m | 0.186182 | 590 | 0.327591 | 0.981713 | |
4 | all | 0.0194515 | 1103 | 0.934993 | 0.944222 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | -0.715961 | 60 | 1.10126 | 0.79516 | |
7 | z < 15 m, MJJA | -1.01601 | 62 | 3.26089 | 0.869287 | |
8 | z < 15 m, SOND | 0.195787 | 122 | 1.01047 | 0.930358 | |
Temperature | 0 | z < 15 m | 0.023639 | 244 | 1.4207 | 0.942963 |
1 | 15 m < z < 22 m | -0.206281 | 81 | 0.642737 | 0.920731 | |
2 | z >= 22 m | -0.0626896 | 778 | 0.470799 | 0.93255 | |
3 | z > 50 m | -0.0100127 | 590 | 0.442389 | 0.937771 | |
4 | all | -0.0541372 | 1103 | 0.795723 | 0.95304 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | 0.110444 | 60 | 0.400089 | 0.711228 | |
7 | z < 15 m, MJJA | 0.446745 | 62 | 2.37849 | 0.877163 | |
8 | z < 15 m, SOND | -0.234073 | 122 | 1.0407 | 0.955068 | |
dSi | 0 | z < 15 m | -4.44601 | 241 | 11.8623 | 0.816693 |
1 | 15 m < z < 22 m | -3.79019 | 80 | 7.39976 | 0.764308 | |
2 | z >= 22 m | -3.6108 | 778 | 6.59373 | 0.799729 | |
3 | z > 50 m | -3.60678 | 590 | 6.77256 | 0.784428 | |
4 | all | -3.80701 | 1099 | 8.10069 | 0.865874 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | 4.03938 | 60 | 13.331 | 0.394536 | |
7 | z < 15 m, MJJA | -6.35367 | 62 | 9.52994 | 0.830581 | |
8 | z < 15 m, SOND | -7.73046 | 119 | 12.1676 | 0.684511 |