DFO Nutrient Comparison¶
In [1]:
import sqlalchemy
from sqlalchemy import (create_engine, Column, String, Integer, Float, MetaData,
Table, type_coerce, ForeignKey, case)
from sqlalchemy.orm import mapper, create_session, relationship, aliased, Session
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy import case
import numpy as np
from sqlalchemy.ext.automap import automap_base
import matplotlib.pyplot as plt
import sqlalchemy.types as types
from sqlalchemy.sql import and_, or_, not_, func
from sqlalchemy.sql import select
import os
from os.path import isfile
import pandas as pd
import netCDF4 as nc
import datetime as dt
from salishsea_tools import evaltools as et, viz_tools
import datetime
import glob
import gsw
%matplotlib inline
In [2]:
PATH= '/data/eolson/MEOPAR/SS36runs/CedarRuns/NewLONoSiT/'
start_date = datetime.datetime(2017,1,1)
end_date = datetime.datetime(2017,10,27)
flen=10
namfmt='long'
filemap={'nitrate':'ptrc_T','silicon':'ptrc_T','ammonium':'ptrc_T','diatoms':'ptrc_T','ciliates':'ptrc_T',
'flagellates':'ptrc_T'}
fdict={'ptrc_T':1,'grid_T':1}
df1=et.loadDFO()
df1.head()
df2=df1.loc[(df1.Lat>48.9)&(df1.Lat<49.5)&(df1.Lon>-124)]
In [3]:
data=et.matchData(df2,filemap, fdict, start_date, end_date, namfmt, PATH, flen)
In [4]:
fig, ax = plt.subplots(figsize = (6,6))
viz_tools.set_aspect(ax, coords = 'map')
ax.plot(data['Lon'], data['Lat'], 'ro',label='data')
ax.plot(data.loc[(data.Lon < -123.5) & (data.Lat < 48.6),['Lon']],
data.loc[(data.Lon < -123.5) & (data.Lat < 48.6),['Lat']],
'bo', label = 'Juan de Fuca')
ax.plot(data.loc[data.Si>75,['Lon']],data.loc[data.Si>75,['Lat']],'*',color='y',label='high Si')
ax.plot(data.loc[(data.Lat>48.8)&(data.Lat<49.5)&(data.Lon>-124),['Lon']],data.loc[(data.Lat>48.8)&(data.Lat<49.5)&(data.Lon>-124),['Lat']],'s',color='c',label='Central SOG')
grid = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc')
viz_tools.plot_coastline(ax, grid, coords = 'map')
ax.set_ylim(48, 50.5)
ax.legend()
ax.set_xlim(-125.7, -122.5);
Nitrate¶
In [5]:
N_s, modmean_s, obsmean_s, bias_s, RMSE_s, WSS_s = et.stats(data.loc[data.Z<15,['N']],data.loc[data.Z<15,['mod_nitrate']])
N_i, modmean_i, obsmean_i, bias_i, RMSE_i, WSS_i = et.stats(data.loc[(data.Z>=15)&(data.Z<22),['N']],data.loc[(data.Z>=15)&(data.Z<22),['mod_nitrate']])
N_d, modmean_d, obsmean_d, bias_d, RMSE_d, WSS_d = et.stats(data.loc[data.Z>=22,['N']],data.loc[data.Z>=22,['mod_nitrate']])
N, modmean, obsmean, bias, RMSE, WSS = et.stats(data.loc[:,['N']],data.loc[:,['mod_nitrate']])
print('Nitrate')
print('z<15 m:')
print(' N: {}\n bias: {}\n RMSE: {}\n WSS: {}'.format(N_s,bias_s,RMSE_s,WSS_s))
print('15 m<=z<22 m:')
print(' N: {}\n bias: {}\n RMSE: {}\n WSS: {}'.format(N_i,bias_i,RMSE_i,WSS_i))
print('z>=22 m:')
print(' N: {}\n bias: {}\n RMSE: {}\n WSS: {}'.format(N_d,bias_d,RMSE_d,WSS_d))
print('all:')
print(' N: {}\n bias: {}\n RMSE: {}\n WSS: {}'.format(N,bias,RMSE,WSS))
Nitrate z<15 m: N: 65 bias: -4.920397658503973 RMSE: 7.671056007934723 WSS: 0.8178866003508698 15 m<=z<22 m: N: 24 bias: -2.8696303788820856 RMSE: 5.309957888369662 WSS: 0.7932227412036283 z>=22 m: N: 185 bias: -0.5969267000765406 RMSE: 2.5191581267253147 WSS: 0.7460408360099606 all: N: 274 bias: -1.8216365562412022 RMSE: 4.551275730798269 WSS: 0.9244274809096328
In [6]:
fig, ax = plt.subplots(figsize = (8,8))
ps=et.varvarPlot(ax,data,'N','mod_nitrate','Z',(15,22),'z','m',('mediumseagreen','darkturquoise','navy'))
ax.legend(handles=ps)
ax.set_xlabel('Obs')
ax.set_ylabel('Model')
ax.set_title('NO$_3$ ($\mu$M)')
Out[6]:
<matplotlib.text.Text at 0x7f1ffdea4fd0>
In [7]:
fig, ax = plt.subplots(1,4,figsize = (24,6))
for axi in ax:
axi.plot(np.arange(0,30),np.arange(0,30),'k-')
ps=et.varvarPlot(ax[0],data.loc[(data.Z<15)&(data.dtUTC<=dt.datetime(2017,4,1)),:],'N','mod_nitrate',cols=('crimson','darkturquoise','navy'))
ax[0].set_title('Feb-Mar')
ii1=(data.Z < 15)&(data.dtUTC<=dt.datetime(2017,5,1))&(data.dtUTC>dt.datetime(2017,4,1))
ps=et.varvarPlot(ax[1],data.loc[ii1,:],'N','mod_nitrate',cols=('crimson','darkturquoise','navy'))
ax[1].set_title('April')
ii2=(data.Z < 15)&(data.dtUTC<=dt.datetime(2017,9,1))&(data.dtUTC>dt.datetime(2017,5,1))
ps=et.varvarPlot(ax[2],data.loc[ii2,:],'N','mod_nitrate',cols=('crimson','darkturquoise','navy'))
ax[2].set_title('May-Jun')
ii3=(data.Z < 15)&(data.dtUTC<=dt.datetime(2017,12,1))&(data.dtUTC>dt.datetime(2017,9,1))
ps=et.varvarPlot(ax[3],data.loc[ii3,:],'N','mod_nitrate',cols=('crimson','darkturquoise','navy'))
ax[3].set_title('Sep-Oct')
print('Nitrate, z<15')
print('Feb-Mar:')
et.printstats(data.loc[(data.Z<15)&(data.dtUTC<=dt.datetime(2017,4,1)),:],'N','mod_nitrate')
print('April:')
et.printstats(data.loc[ii1,:],'N','mod_nitrate')
print('May-Jun:')
et.printstats(data.loc[ii2,:],'N','mod_nitrate')
print('Sep-Oct:')
et.printstats(data.loc[ii3,:],'N','mod_nitrate')
fig,ax=plt.subplots(1,1,figsize=(24,1))
plt.plot(data.dtUTC,np.ones(np.shape(data.dtUTC)),'k.')
Nitrate, z<15 Feb-Mar: N: 12 bias: -4.297724253336586 RMSE: 4.414697752750363 WSS: 0.19608706577786994 April: N: 11 bias: -1.2297649296847268 RMSE: 4.738499375387916 WSS: 0.6873941838994079 May-Jun: N: 19 bias: -2.6440489030198044 RMSE: 4.313946702957443 WSS: 0.6842830767726542 Sep-Oct: N: 23 bias: -8.890817972991776 RMSE: 11.402663907708648 WSS: 0.5423480274427703
Out[7]:
[<matplotlib.lines.Line2D at 0x7f1ffd219a90>]
In [8]:
fig, ax = plt.subplots(figsize = (8,8))
viz_tools.set_aspect(ax, coords = 'map')
ax.plot(data['Lon'], data['Lat'], 'ro',label='data')
dJDF=data.loc[(data.Lon<-123.6)&(data.Lat<48.6)]
ax.plot(dJDF['Lon'],dJDF['Lat'],'b.',label='JDF')
dSJGI=data.loc[(data.Lon>=-123.6)&(data.Lat<48.9)]
ax.plot(dSJGI['Lon'],dSJGI['Lat'],'c.',label='SJGI')
dSOG=data.loc[(data.Lat>=48.9)&(data.Lon>-124.0)]
ax.plot(dSOG['Lon'],dSOG['Lat'],'y.',label='SOG')
dNSOG=data.loc[(data.Lat>=48.9)&(data.Lon<=-124.0)]
ax.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, grid, coords = 'map')
ax.set_ylim(48, 50.5)
ax.legend()
ax.set_xlim(-125.7, -122.5);
In [9]:
fig, ax = plt.subplots(figsize = (8,8))
ps1=et.varvarPlot(ax,dJDF,'N','mod_nitrate',cols=('b','darkturquoise','navy'),lname='SJDF')
ps2=et.varvarPlot(ax,dSJGI,'N','mod_nitrate',cols=('c','darkturquoise','navy'),lname='SJGI')
ps3=et.varvarPlot(ax,dSOG,'N','mod_nitrate',cols=('y','darkturquoise','navy'),lname='SOG')
ps4=et.varvarPlot(ax,dNSOG,'N','mod_nitrate',cols=('m','darkturquoise','navy'),lname='NSOGF')
ax.legend(handles=[ps1[0][0],ps2[0][0],ps3[0][0],ps4[0][0]])
ax.set_xlabel('Obs')
ax.set_ylabel('Model')
ax.set_title('NO$_3$ ($\mu$M)')
ax.set_xlim(0,35)
ax.set_ylim(0,35)
Out[9]:
(0, 35)
In [10]:
fig, ax = plt.subplots(1,2,figsize = (17,8))
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 N')
ax[0].set_xlim(-20,20)
ax[1].set_xlabel('model - obs Si')
ax[1].set_xlim(-40,20)
Out[10]:
(-40, 20)
Silicate¶
In [11]:
print('Nitrate')
print('z<15 m:')
et.printstats(data.loc[data.Z<15,:],'Si','mod_silicon')
print('15 m<=z<22 m:')
et.printstats(data.loc[(data.Z>=15)&(data.Z<22),:],'Si','mod_silicon')
print('z>=22 m:')
et.printstats(data.loc[data.Z>=22,:],'Si','mod_silicon')
print('all:')
et.printstats(data,'Si','mod_silicon')
print('obs Si < 50:')
et.printstats(data.loc[data.Si<50,:],'Si','mod_silicon')
Nitrate z<15 m: N: 65 bias: -12.707437172302836 RMSE: 19.820199305354276 WSS: 0.6402152223765163 15 m<=z<22 m: N: 24 bias: -6.8209352906545035 RMSE: 12.703821503133883 WSS: 0.7069811562531892 z>=22 m: N: 185 bias: -2.515602599272853 RMSE: 6.639347166486058 WSS: 0.8350030285221062 all: N: 274 bias: -5.3104830074484255 RMSE: 11.708578436815374 WSS: 0.82127744817519 obs Si < 50: N: 108 bias: -3.1306586592727257 RMSE: 14.542890923662105 WSS: 0.6654865470609095
In [12]:
fig, ax = plt.subplots(figsize = (8,8))
ps=et.varvarPlot(ax,data,'Si','mod_silicon','Z',(15,22),'z','m',('mediumseagreen','darkturquoise','navy'))
ax.legend(handles=ps)
ax.set_xlabel('Obs')
ax.set_ylabel('Model')
ax.set_title('dSi ($\mu$M)')
ax.set_xlim(0,80)
ax.set_ylim(0,80)
Out[12]:
(0, 80)
In [13]:
obsvar='Si'; modvar='mod_silicon'
fig, ax = plt.subplots(1,4,figsize = (24,6))
for axi in ax:
axi.plot(np.arange(0,70),np.arange(0,70),'k-')
ps=et.varvarPlot(ax[0],data.loc[(data.Z<15)&(data.dtUTC<=dt.datetime(2017,4,1)),:],obsvar,modvar,cols=('crimson','darkturquoise','navy'))
ax[0].set_title('Feb-Mar')
ii1=(data.Z < 15)&(data.dtUTC<=dt.datetime(2017,5,1))&(data.dtUTC>dt.datetime(2017,4,1))
ps=et.varvarPlot(ax[1],data.loc[ii1,:],obsvar,modvar,cols=('crimson','darkturquoise','navy'))
ax[1].set_title('April')
ii2=(data.Z < 15)&(data.dtUTC<=dt.datetime(2017,9,1))&(data.dtUTC>dt.datetime(2017,5,1))
ps=et.varvarPlot(ax[2],data.loc[ii2,:],obsvar,modvar,cols=('crimson','darkturquoise','navy'))
ax[2].set_title('May-Jun')
ii3=(data.Z < 15)&(data.dtUTC<=dt.datetime(2017,12,1))&(data.dtUTC>dt.datetime(2017,9,1))
ps=et.varvarPlot(ax[3],data.loc[ii3,:],obsvar,modvar,cols=('crimson','darkturquoise','navy'))
ax[3].set_title('Sep-Oct')
ii4=(data.Z < 15)&(data.dtUTC<=dt.datetime(2017,4,1))&(data.dtUTC>dt.datetime(2017,2,1))
ps=et.varvarPlot(ax[0],data.loc[ii4,:],obsvar,modvar,cols=('darkturquoise','navy'))
ii5=(data.Z < 15)&(data.dtUTC<=dt.datetime(2017,5,1))&(data.dtUTC>dt.datetime(2017,4,1))
ps=et.varvarPlot(ax[1],data.loc[ii5,:],obsvar,modvar,cols=('darkturquoise','navy'))
print('Silicate, z<15')
print('Feb-Mar:')
et.printstats(data.loc[(data.Z<15)&(data.dtUTC<=dt.datetime(2017,4,1)),:],obsvar,modvar)
print('April:')
et.printstats(data.loc[ii1,:],obsvar,modvar)
print('May-Jun:')
et.printstats(data.loc[ii2,:],obsvar,modvar)
print('Sep-Oct:')
et.printstats(data.loc[ii3,:],obsvar,modvar)
fig,ax=plt.subplots(1,1,figsize=(24,1))
plt.plot(data.dtUTC,np.ones(np.shape(data.dtUTC)),'k.')
Silicate, z<15 Feb-Mar: N: 12 bias: -7.424685745239259 RMSE: 7.4451981814857495 WSS: 0.10962531770795336 April: N: 11 bias: 8.029087267788977 RMSE: 10.935235604716244 WSS: 0.5459844975452466 May-Jun: N: 19 bias: -7.894512257826957 RMSE: 10.24369279514843 WSS: 0.8225258864508787 Sep-Oct: N: 23 bias: -29.35701801320781 RMSE: 30.61709033021411 WSS: 0.3337175264153165
Out[13]:
[<matplotlib.lines.Line2D at 0x7f1ffe6562b0>]
In [14]:
fig, ax = plt.subplots(figsize = (8,8))
ps1=et.varvarPlot(ax,dJDF,obsvar,modvar,cols=('b','darkturquoise','navy'),lname='SJDF')
ps2=et.varvarPlot(ax,dSJGI,obsvar,modvar,cols=('c','darkturquoise','navy'),lname='SJGI')
ps3=et.varvarPlot(ax,dSOG,obsvar,modvar,cols=('y','darkturquoise','navy'),lname='SOG')
ps4=et.varvarPlot(ax,dNSOG,obsvar,modvar,cols=('m','darkturquoise','navy'),lname='NSOGF')
ax.legend(handles=[ps1[0][0],ps2[0][0],ps3[0][0],ps4[0][0]])
ax.set_xlabel('Obs')
ax.set_ylabel('Model')
ax.set_title('Si ($\mu$M)')
ax.set_xlim(0,80)
ax.set_ylim(0,80)
Out[14]:
(0, 80)
Ratios¶
In [15]:
fig,ax=plt.subplots(1,2,figsize=(16,7))
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].set_title('Observed')
ax[0].set_xlabel('N')
ax[0].set_ylabel('Si')
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].set_title('Model')
ax[1].set_xlabel('N')
ax[1].set_ylabel('Si')
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-')
Out[15]:
[<matplotlib.lines.Line2D at 0x7f1ffe232ac8>]
In [16]:
data.loc[data.Si>65,['Month','Lat','Lon','Z','Si']]
Out[16]:
| Month | Lat | Lon | Z | Si | |
|---|---|---|---|---|---|
| 14 | 2.0 | 49.027667 | -123.437833 | 297.5 | 66.85 |
| 15 | 2.0 | 49.027667 | -123.437833 | 311.8 | 67.82 |
| 31 | 2.0 | 49.163167 | -123.551000 | 347.1 | 66.29 |
| 32 | 2.0 | 49.163167 | -123.551000 | 367.2 | 68.34 |
| 43 | 2.0 | 49.250833 | -123.749000 | 390.9 | 72.03 |
| 88 | 4.0 | 49.030000 | -123.437000 | 314.0 | 67.87 |
| 107 | 4.0 | 49.164167 | -123.549833 | 298.0 | 65.52 |
| 108 | 4.0 | 49.164167 | -123.549833 | 347.1 | 69.04 |
| 109 | 4.0 | 49.164167 | -123.549833 | 368.5 | 71.44 |
| 129 | 4.0 | 49.318333 | -123.799000 | 297.5 | 66.13 |
| 130 | 4.0 | 49.318333 | -123.799000 | 336.6 | 67.33 |
| 157 | 6.0 | 49.250833 | -123.751500 | 198.2 | 70.85 |
| 158 | 6.0 | 49.250833 | -123.751500 | 388.9 | 71.47 |
| 162 | 6.0 | 49.054667 | -123.373167 | 1.4 | 66.25 |
| 197 | 6.0 | 49.029667 | -123.436333 | 247.8 | 69.83 |
| 198 | 6.0 | 49.029667 | -123.436333 | 298.1 | 67.66 |
| 199 | 6.0 | 49.029667 | -123.436333 | 313.0 | 69.44 |
| 215 | 6.0 | 49.163333 | -123.550333 | 297.9 | 69.22 |
| 216 | 6.0 | 49.163333 | -123.550333 | 347.1 | 83.94 |
| 217 | 6.0 | 49.163333 | -123.550333 | 370.5 | 83.76 |
| 242 | 6.0 | 49.318167 | -123.799333 | 297.8 | 69.70 |
| 243 | 6.0 | 49.318167 | -123.799333 | 334.5 | 80.44 |
In [ ]:
Chlorophyll¶
In [17]:
data['l10_obsChl']=np.log10(data['Chlorophyll_Extracted']+0.01)
data['l10_modChl']=np.log10(2*(data['mod_diatoms']+data['mod_ciliates']+data['mod_flagellates'])+0.01)
data['mod_Chl']=2*(data['mod_diatoms']+data['mod_ciliates']+data['mod_flagellates'])
In [18]:
print('log10[Chl+0.01]')
print('z<15 m:')
et.printstats(data.loc[data.Z<15,:],'l10_obsChl','l10_modChl')
print('z>=15 m:')
et.printstats(data.loc[data.Z>=15,:],'l10_obsChl','l10_modChl')
print('all:')
et.printstats(data,'l10_obsChl','l10_modChl')
print('\n')
print('Chl')
print('z<15 m:')
et.printstats(data.loc[data.Z<15,:],'Chlorophyll_Extracted','mod_Chl')
print('z>=15 m:')
et.printstats(data.loc[data.Z>=15,:],'Chlorophyll_Extracted','mod_Chl')
print('all:')
et.printstats(data,'Chlorophyll_Extracted','mod_Chl')
log10[Chl+0.01] z<15 m: N: 94 bias: -0.04497990725614304 RMSE: 0.45026047340536035 WSS: 0.6253052858931223 z>=15 m: N: 25 bias: 0.24624135631199395 RMSE: 0.5900435826932964 WSS: 0.3692663005960831 all: N: 119 bias: 0.01620103046825533 RMSE: 0.48299510379578237 WSS: 0.6772831626907425 Chl z<15 m: N: 94 bias: -2.088345563727173 RMSE: 5.905776871146265 WSS: 0.5497301187047098 z>=15 m: N: 25 bias: 0.24996662272363912 RMSE: 1.6987049534532326 WSS: 0.17686745650405478 all: N: 119 bias: -1.5971035077501115 RMSE: 5.3063231707009235 WSS: 0.5799711724865728
In [19]:
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]')
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)')
Out[19]:
<matplotlib.text.Text at 0x7f1ffddcef98>
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