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 = 2014
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()
1577 data points
Year | Month | Day | Lat | Lon | Pressure | Depth | N | Si | Chlorophyll_Extracted | ConsT | AbsSal | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2014.0 | 2.0 | 11.0 | 48.299833 | -123.9995 | 4.5 | None | 30.1 | 49.4 | NaN | 7.163426 | 32.134762 |
1 | 2014.0 | 2.0 | 11.0 | 48.299833 | -123.9995 | 5.2 | None | 30.2 | 49.3 | NaN | 7.160991 | 32.203660 |
2 | 2014.0 | 2.0 | 11.0 | 48.299833 | -123.9995 | 10.2 | None | 30.2 | 49.0 | NaN | 7.182615 | 32.260506 |
3 | 2014.0 | 2.0 | 11.0 | 48.299833 | -123.9995 | 20.1 | None | 30.2 | 48.7 | NaN | 7.185608 | 32.333712 |
4 | 2014.0 | 2.0 | 11.0 | 48.299833 | -123.9995 | 30.4 | None | 30.1 | 48.6 | NaN | 7.184490 | 32.340248 |
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 01jan14 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 | -2.71079 | 391 | 6.10419 | 0.863714 |
1 | 15 m < z < 22 m | -2.31533 | 89 | 4.24997 | 0.837409 |
2 | z >= 22 m | -2.92494 | 1013 | 3.64597 | 0.813922 |
3 | z > 50 m | -3.13506 | 707 | 3.59048 | 0.72917 |
4 | all | -2.83252 | 1493 | 4.45582 | 0.921497 |
5 | z < 15 m, JFM | -4.48265 | 6 | 4.50295 | 0.0638573 |
6 | z < 15 m, Apr | -5.86743 | 134 | 8.54581 | 0.692818 |
7 | z < 15 m, MJJA | -1.30758 | 123 | 5.1934 | 0.870879 |
8 | z < 15 m, SOND | -0.671543 | 128 | 3.24017 | 0.959008 |
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 | -5.26375 | 391 | 12.2093 | 0.714165 |
1 | 15 m < z < 22 m | -5.11649 | 89 | 8.90557 | 0.778079 |
2 | z >= 22 m | -7.53891 | 1013 | 10.1091 | 0.726525 |
3 | z > 50 m | -7.48189 | 707 | 9.42922 | 0.739717 |
4 | all | -6.79867 | 1493 | 10.6351 | 0.810776 |
5 | z < 15 m, JFM | -6.04632 | 6 | 6.10112 | 0.475428 |
6 | z < 15 m, Apr | -9.51914 | 134 | 15.4928 | 0.655779 |
7 | z < 15 m, MJJA | -7.64349 | 123 | 12.8569 | 0.563802 |
8 | z < 15 m, SOND | 1.51457 | 128 | 6.59431 | 0.895011 |
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 0x7fb4d97993a0>
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 0x7fb4fcc67be0>
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 | -1.85552 | 174 | 5.80477 | 0.611356 | -0.164615 | 174 | 0.472819 | 0.777024 |
1 | 15 m < z < 22 m | -0.955263 | 83 | 2.0212 | 0.573277 | -0.21734 | 83 | 0.450473 | 0.657297 |
2 | z >= 22 m | 1.16183 | 2 | 1.34692 | 0.500949 | 0.376406 | 2 | 0.377427 | 0.689551 |
3 | z > 50 m | nan | 0 | nan | nan | nan | 0 | nan | nan |
4 | all | -1.54372 | 259 | 4.89492 | 0.633327 | -0.177333 | 259 | 0.465102 | 0.772098 |
5 | z < 15 m, JFM | nan | 0 | nan | nan | nan | 0 | nan | nan |
6 | z < 15 m, Apr | -3.15057 | 45 | 7.46919 | 0.673556 | -0.142723 | 45 | 0.529455 | 0.745536 |
7 | z < 15 m, MJJA | -3.19195 | 42 | 7.8846 | 0.371716 | -0.145491 | 42 | 0.507133 | 0.292711 |
8 | z < 15 m, SOND | -0.540489 | 87 | 2.91939 | 0.604657 | -0.18517 | 87 | 0.421857 | 0.781781 |
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 | 0.0282261 | 398 | 1.00409 | 0.965847 |
1 | 15 m < z < 22 m | 0.00340123 | 91 | 0.53409 | 0.96935 |
2 | z >= 22 m | 0.0577557 | 1016 | 0.413138 | 0.966209 |
3 | z > 50 m | 0.082276 | 711 | 0.386462 | 0.961004 |
4 | all | 0.04666 | 1505 | 0.631736 | 0.976004 |
5 | z < 15 m, JFM | -0.383229 | 9 | 0.403843 | 0.177809 |
6 | z < 15 m, Apr | 0.155507 | 132 | 0.403387 | 0.834023 |
7 | z < 15 m, MJJA | -0.127715 | 128 | 1.40399 | 0.88675 |
8 | z < 15 m, SOND | 0.081423 | 129 | 0.988307 | 0.946601 |
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 | -0.295234 | 398 | 1.49418 | 0.894453 |
1 | 15 m < z < 22 m | -0.207555 | 91 | 0.550653 | 0.92467 |
2 | z >= 22 m | -0.114353 | 1017 | 0.313131 | 0.983448 |
3 | z > 50 m | -0.053339 | 712 | 0.204746 | 0.992006 |
4 | all | -0.167787 | 1506 | 0.821309 | 0.953873 |
5 | z < 15 m, JFM | -1.63249 | 9 | 1.69979 | 0.423635 |
6 | z < 15 m, Apr | -0.857404 | 132 | 1.35308 | 0.61986 |
7 | z < 15 m, MJJA | -0.121361 | 128 | 1.44319 | 0.936825 |
8 | z < 15 m, SOND | 0.200782 | 129 | 1.65723 | 0.869908 |
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 | -0.232919 | 398 | 1.24924 | 0.909702 |
1 | 15 m < z < 22 m | -0.161505 | 91 | 0.481891 | 0.913929 |
2 | z >= 22 m | -0.0966703 | 1016 | 0.269176 | 0.987758 |
3 | z > 50 m | -0.0531424 | 711 | 0.17254 | 0.993558 |
4 | all | -0.136622 | 1505 | 0.689678 | 0.968328 |
5 | z < 15 m, JFM | -1.22667 | 9 | 1.27686 | 0.446718 |
6 | z < 15 m, Apr | -0.687467 | 132 | 1.09475 | 0.631117 |
7 | z < 15 m, MJJA | -0.0670616 | 128 | 1.29812 | 0.933735 |
8 | z < 15 m, SOND | 0.136959 | 129 | 1.34265 | 0.884984 |
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 | -1.85552 | 174 | 5.80477 | 0.611356 |
1 | 15 m < z < 22 m | -0.955263 | 83 | 2.0212 | 0.573277 | |
2 | z >= 22 m | 1.16183 | 2 | 1.34692 | 0.500949 | |
3 | z > 50 m | nan | 0 | nan | nan | |
4 | all | -1.54372 | 259 | 4.89492 | 0.633327 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | -3.15057 | 45 | 7.46919 | 0.673556 | |
7 | z < 15 m, MJJA | -3.19195 | 42 | 7.8846 | 0.371716 | |
8 | z < 15 m, SOND | -0.540489 | 87 | 2.91939 | 0.604657 | |
Chl log10 | 0 | z < 15 m | -0.164615 | 174 | 0.472819 | 0.777024 |
1 | 15 m < z < 22 m | -0.21734 | 83 | 0.450473 | 0.657297 | |
2 | z >= 22 m | 0.376406 | 2 | 0.377427 | 0.689551 | |
3 | z > 50 m | nan | 0 | nan | nan | |
4 | all | -0.177333 | 259 | 0.465102 | 0.772098 | |
5 | z < 15 m, JFM | nan | 0 | nan | nan | |
6 | z < 15 m, Apr | -0.142723 | 45 | 0.529455 | 0.745536 | |
7 | z < 15 m, MJJA | -0.145491 | 42 | 0.507133 | 0.292711 | |
8 | z < 15 m, SOND | -0.18517 | 87 | 0.421857 | 0.781781 | |
Density | 0 | z < 15 m | -0.232919 | 398 | 1.24924 | 0.909702 |
1 | 15 m < z < 22 m | -0.161505 | 91 | 0.481891 | 0.913929 | |
2 | z >= 22 m | -0.0966703 | 1016 | 0.269176 | 0.987758 | |
3 | z > 50 m | -0.0531424 | 711 | 0.17254 | 0.993558 | |
4 | all | -0.136622 | 1505 | 0.689678 | 0.968328 | |
5 | z < 15 m, JFM | -1.22667 | 9 | 1.27686 | 0.446718 | |
6 | z < 15 m, Apr | -0.687467 | 132 | 1.09475 | 0.631117 | |
7 | z < 15 m, MJJA | -0.0670616 | 128 | 1.29812 | 0.933735 | |
8 | z < 15 m, SOND | 0.136959 | 129 | 1.34265 | 0.884984 | |
NO3 | 0 | z < 15 m | -2.71079 | 391 | 6.10419 | 0.863714 |
1 | 15 m < z < 22 m | -2.31533 | 89 | 4.24997 | 0.837409 | |
2 | z >= 22 m | -2.92494 | 1013 | 3.64597 | 0.813922 | |
3 | z > 50 m | -3.13506 | 707 | 3.59048 | 0.72917 | |
4 | all | -2.83252 | 1493 | 4.45582 | 0.921497 | |
5 | z < 15 m, JFM | -4.48265 | 6 | 4.50295 | 0.0638573 | |
6 | z < 15 m, Apr | -5.86743 | 134 | 8.54581 | 0.692818 | |
7 | z < 15 m, MJJA | -1.30758 | 123 | 5.1934 | 0.870879 | |
8 | z < 15 m, SOND | -0.671543 | 128 | 3.24017 | 0.959008 | |
Salinity | 0 | z < 15 m | -0.295234 | 398 | 1.49418 | 0.894453 |
1 | 15 m < z < 22 m | -0.207555 | 91 | 0.550653 | 0.92467 | |
2 | z >= 22 m | -0.114353 | 1017 | 0.313131 | 0.983448 | |
3 | z > 50 m | -0.053339 | 712 | 0.204746 | 0.992006 | |
4 | all | -0.167787 | 1506 | 0.821309 | 0.953873 | |
5 | z < 15 m, JFM | -1.63249 | 9 | 1.69979 | 0.423635 | |
6 | z < 15 m, Apr | -0.857404 | 132 | 1.35308 | 0.61986 | |
7 | z < 15 m, MJJA | -0.121361 | 128 | 1.44319 | 0.936825 | |
8 | z < 15 m, SOND | 0.200782 | 129 | 1.65723 | 0.869908 | |
Temperature | 0 | z < 15 m | 0.0282261 | 398 | 1.00409 | 0.965847 |
1 | 15 m < z < 22 m | 0.00340123 | 91 | 0.53409 | 0.96935 | |
2 | z >= 22 m | 0.0577557 | 1016 | 0.413138 | 0.966209 | |
3 | z > 50 m | 0.082276 | 711 | 0.386462 | 0.961004 | |
4 | all | 0.04666 | 1505 | 0.631736 | 0.976004 | |
5 | z < 15 m, JFM | -0.383229 | 9 | 0.403843 | 0.177809 | |
6 | z < 15 m, Apr | 0.155507 | 132 | 0.403387 | 0.834023 | |
7 | z < 15 m, MJJA | -0.127715 | 128 | 1.40399 | 0.88675 | |
8 | z < 15 m, SOND | 0.081423 | 129 | 0.988307 | 0.946601 | |
dSi | 0 | z < 15 m | -5.26375 | 391 | 12.2093 | 0.714165 |
1 | 15 m < z < 22 m | -5.11649 | 89 | 8.90557 | 0.778079 | |
2 | z >= 22 m | -7.53891 | 1013 | 10.1091 | 0.726525 | |
3 | z > 50 m | -7.48189 | 707 | 9.42922 | 0.739717 | |
4 | all | -6.79867 | 1493 | 10.6351 | 0.810776 | |
5 | z < 15 m, JFM | -6.04632 | 6 | 6.10112 | 0.475428 | |
6 | z < 15 m, Apr | -9.51914 | 134 | 15.4928 | 0.655779 | |
7 | z < 15 m, MJJA | -7.64349 | 123 | 12.8569 | 0.563802 | |
8 | z < 15 m, SOND | 1.51457 | 128 | 6.59431 | 0.895011 |