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/LinbfSiDiatRefl0_2/'
start_date = datetime.datetime(2015,1,1)
end_date = datetime.datetime(2015,5,30)
flen=10
namfmt='long'
filemap={'nitrate':'ptrc_T','silicon':'ptrc_T','diatoms':'ptrc_T','ciliates':'ptrc_T',
'flagellates':'ptrc_T'}
fdict={'ptrc_T':1,'grid_T':1}
df1=et.loadDFO()
df1.head()
Out[2]:
| Year | Month | Day | Hour | Lat | Lon | Pressure | Depth | Ammonium | Ammonium_units | Chlorophyll_Extracted | Chlorophyll_Extracted_units | N | Si | Silicate_units | AbsSal | ConsT | Z | dtUTC | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1982.0 | 7.0 | 16.0 | 1.35 | 49.25 | -123.943 | NaN | 12.0 | NaN | None | 5.28 | mg/m^3 | 6.6 | 13.6 | umol/L | NaN | NaN | 12.0 | 1982-07-16 01:21:00 |
| 1 | 1982.0 | 7.0 | 16.0 | 1.35 | 49.25 | -123.943 | NaN | 21.5 | NaN | None | 0.61 | mg/m^3 | 21.2 | 45.0 | umol/L | NaN | NaN | 21.5 | 1982-07-16 01:21:00 |
| 2 | 1982.0 | 7.0 | 16.0 | 1.35 | 49.25 | -123.943 | NaN | 30.5 | NaN | None | NaN | mg/m^3 | 23.5 | 47.4 | umol/L | NaN | NaN | 30.5 | 1982-07-16 01:21:00 |
| 3 | 1982.0 | 7.0 | 16.0 | 1.35 | 49.25 | -123.943 | NaN | 52.3 | NaN | None | NaN | mg/m^3 | 28.0 | 50.2 | umol/L | NaN | NaN | 52.3 | 1982-07-16 01:21:00 |
| 4 | 1982.0 | 7.0 | 16.0 | 1.35 | 49.25 | -123.943 | NaN | 75.4 | NaN | None | NaN | mg/m^3 | 26.5 | 49.1 | umol/L | NaN | NaN | 75.4 | 1982-07-16 01:21:00 |
In [3]:
data=et.matchData(df1,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')
iSaa=(data.Lon>-123.7)&(data.Lon<-123.3)&(data.Lat>48.5)&(data.Lat<48.7)
ax.plot(data.loc[iSaa,['Lon']],data.loc[iSaa,['Lat']],'*',color='m',label='Saanich')
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);
data.drop(data.loc[iSaa].index.values,inplace=True)
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: 76 bias: -2.159140865018495 RMSE: 5.052947422770789 WSS: 0.79801960491023 15 m<=z<22 m: N: 24 bias: -2.1246959034601822 RMSE: 3.6820846339166553 WSS: 0.5778610061854612 z>=22 m: N: 228 bias: -3.2129725262156725 RMSE: 3.6330086016829073 WSS: 0.7388901691033587 all: N: 328 bias: -2.8891620225659196 RMSE: 4.010335115385328 WSS: 0.8772120849291758
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 0x7fc05c85b3c8>
In [22]:
fig, ax = plt.subplots(1,4,figsize = (24,6))
yy=2015
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(yy,4,1)),:],'N','mod_nitrate',cols=('crimson','darkturquoise','navy'))
ax[0].set_title('Feb-Mar')
ii1=(data.Z < 15)&(data.dtUTC<=dt.datetime(yy,5,1))&(data.dtUTC>dt.datetime(yy,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(yy,9,1))&(data.dtUTC>dt.datetime(yy,5,1))
ps=et.varvarPlot(ax[2],data.loc[ii2,:],'N','mod_nitrate',cols=('crimson','darkturquoise','navy'))
ax[2].set_title('May-Aug')
ii3=(data.Z < 15)&(data.dtUTC<=dt.datetime(yy,12,1))&(data.dtUTC>dt.datetime(yy,9,1))
ps=et.varvarPlot(ax[3],data.loc[ii3,:],'N','mod_nitrate',cols=('crimson','darkturquoise','navy'))
ax[3].set_title('Sep-Nov')
print('Nitrate, z<15')
print('Feb-Mar:')
et.printstats(data.loc[(data.Z<15)&(data.dtUTC<=dt.datetime(yy,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: 8 bias: -1.7630167770385725 RMSE: 4.015843962401278 WSS: 0.6093889795451467 April: N: 67 bias: -2.0686083151333357 RMSE: 5.009867983595739 WSS: 0.779973562493737 May-Jun: N: 1 bias: -11.39381441116333 RMSE: 11.39381441116333 WSS: 0.0 Sep-Oct: N: 0 bias: nan RMSE: nan WSS: nan
/home/eolson/anaconda3/envs/python36/lib/python3.6/site-packages/numpy/core/fromnumeric.py:2957: RuntimeWarning: Mean of empty slice. out=out, **kwargs) /home/eolson/anaconda3/envs/python36/lib/python3.6/site-packages/numpy/core/_methods.py:80: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) /data/eolson/results/MEOPAR/tools/SalishSeaTools/salishsea_tools/evaltools.py:917: RuntimeWarning: invalid value encountered in double_scalars RMSE=np.sqrt(np.sum((mod-obs)**2)/N) /data/eolson/results/MEOPAR/tools/SalishSeaTools/salishsea_tools/evaltools.py:918: RuntimeWarning: invalid value encountered in double_scalars WSS=1.0-np.sum((mod-obs)**2)/np.sum((np.abs(mod-obsmean)+np.abs(obs-obsmean))**2)
Out[22]:
[<matplotlib.lines.Line2D at 0x7fc05c16a9b0>]
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);
dNSOGN=data.loc[(data.Lat>=49.7)&(data.Lon<=-124.0)]
dNSOGS=data.loc[(data.Lat<49.7)&(data.Lat>=49.1)&(data.Lon<=-124.0)]
dNSOGW=data.loc[(data.Lat>=49.1)&(data.Lon<=-124.5)]
dNSOGE=data.loc[(data.Lat>=49.1)&(data.Lon>-124.5)&(data.Lon<=-124.0)]
dBaynes=data.loc[(data.Lat>=49.35)&(data.Lat<49.7)&(data.Lon<-124.65)&(data.Lon>-125.0)]
ax.plot(dNSOGE['Lon'],dNSOGE['Lat'],'go',ms=12,alpha=.03,label='NSOGE')
ax.plot(dNSOGN['Lon'],dNSOGN['Lat'],'kx',ms=8,label='NSOGE')
ax.plot(dBaynes['Lon'],dBaynes['Lat'],'k+',ms=8,label='Baynes')
Out[8]:
[<matplotlib.lines.Line2D at 0x7fc05c5f2f28>]
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: 76 bias: 0.5649030444496574 RMSE: 8.918654879843853 WSS: 0.7969914789805078 15 m<=z<22 m: N: 24 bias: 0.3488489977518725 RMSE: 6.165233126514371 WSS: 0.7116391454953728 z>=22 m: N: 228 bias: -4.045088387204885 RMSE: 6.156110657171862 WSS: 0.8890421860970474 all: N: 328 bias: -2.6554120273124937 RMSE: 6.896035534475829 WSS: 0.9058909765561889 obs Si < 50: N: 240 bias: -0.776422245661422 RMSE: 6.099212472290469 WSS: 0.8562808684202172
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 [23]:
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(yy,4,1)),:],obsvar,modvar,cols=('crimson','darkturquoise','navy'))
ax[0].set_title('Feb-Mar')
ii1=(data.Z < 15)&(data.dtUTC<=dt.datetime(yy,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(yy,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(yy,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(yy,4,1))&(data.dtUTC>dt.datetime(yy,2,1))
ps=et.varvarPlot(ax[0],data.loc[ii4,:],obsvar,modvar,cols=('darkturquoise','navy'))
ii5=(data.Z < 15)&(data.dtUTC<=dt.datetime(yy,5,1))&(data.dtUTC>dt.datetime(yy,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(yy,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: 8 bias: -3.694292564392093 RMSE: 5.977277321914273 WSS: 0.7414126472331124 April: N: 0 bias: nan RMSE: nan WSS: nan May-Jun: N: 0 bias: nan RMSE: nan WSS: nan Sep-Oct: N: 0 bias: nan RMSE: nan WSS: nan
/home/eolson/anaconda3/envs/python36/lib/python3.6/site-packages/numpy/core/fromnumeric.py:2957: RuntimeWarning: Mean of empty slice. out=out, **kwargs) /home/eolson/anaconda3/envs/python36/lib/python3.6/site-packages/numpy/core/_methods.py:80: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) /data/eolson/results/MEOPAR/tools/SalishSeaTools/salishsea_tools/evaltools.py:917: RuntimeWarning: invalid value encountered in double_scalars RMSE=np.sqrt(np.sum((mod-obs)**2)/N) /data/eolson/results/MEOPAR/tools/SalishSeaTools/salishsea_tools/evaltools.py:918: RuntimeWarning: invalid value encountered in double_scalars WSS=1.0-np.sum((mod-obs)**2)/np.sum((np.abs(mod-obsmean)+np.abs(obs-obsmean))**2)
Out[23]:
[<matplotlib.lines.Line2D at 0x7fc05c127860>]
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='NSOG')
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 0x7fc05ccd0588>]
In [16]:
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(dNSOGN['N'],dNSOGN['Si'],'r.',label='NSOG_N')
p4=ax[0].plot(dNSOGS['N'],dNSOGS['Si'],'g.',label='NSOG_S')
p4=ax[0].plot(dBaynes['N'],dBaynes['Si'],'.',color='purple',label='Baynes')
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(dNSOGN['mod_nitrate'],dNSOGN['mod_silicon'],'r.',label='NSOG_N')
p8=ax[1].plot(dNSOGS['mod_nitrate'],dNSOGS['mod_silicon'],'g.',label='NSOG_S')
p8=ax[1].plot(dBaynes['mod_nitrate'],dBaynes['mod_silicon'],'.',color='purple',label='Baynes')
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[16]:
[<matplotlib.lines.Line2D at 0x7fc05b9ac080>]
In [17]:
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(dNSOGE['N'],dNSOGE['Si'],'r.',label='NSOG_E')
p4=ax[0].plot(dNSOGW['N'],dNSOGW['Si'],'g.',label='NSOG_W')
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(dNSOGE['mod_nitrate'],dNSOGE['mod_silicon'],'r.',label='NSOG_E')
p8=ax[1].plot(dNSOGW['mod_nitrate'],dNSOGW['mod_silicon'],'g.',label='NSOG_W')
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[17]:
[<matplotlib.lines.Line2D at 0x7fc05c66ce80>]
In [18]:
data.loc[data.Si>65,['Month','Lat','Lon','Z','Si']]
Out[18]:
| Month | Lat | Lon | Z | Si | |
|---|---|---|---|---|---|
| 211 | 4.0 | 49.029667 | -123.436500 | 248.620295 | 67.85 |
| 212 | 4.0 | 49.029667 | -123.436500 | 298.724014 | 73.00 |
| 213 | 4.0 | 49.029667 | -123.436500 | 312.485544 | 74.76 |
| 227 | 4.0 | 49.163333 | -123.551500 | 249.310468 | 68.35 |
| 228 | 4.0 | 49.163333 | -123.551500 | 297.928316 | 69.99 |
| 229 | 4.0 | 49.163333 | -123.551500 | 348.217366 | 76.12 |
| 246 | 4.0 | 49.318833 | -123.799667 | 299.211146 | 66.73 |
| 247 | 4.0 | 49.318833 | -123.799667 | 346.826687 | 70.58 |
| 261 | 4.0 | 49.401833 | -124.156000 | 248.710873 | 65.86 |
| 262 | 4.0 | 49.401833 | -124.156000 | 269.604692 | 70.17 |
| 276 | 4.0 | 49.443333 | -124.337167 | 248.808959 | 68.77 |
| 277 | 4.0 | 49.443333 | -124.337167 | 298.712730 | 70.83 |
| 278 | 4.0 | 49.443333 | -124.337167 | 314.651658 | 77.02 |
| 311 | 4.0 | 49.591667 | -124.637833 | 162.832911 | 70.41 |
| 324 | 4.0 | 49.726500 | -124.680333 | 198.887917 | 65.16 |
| 325 | 4.0 | 49.726500 | -124.680333 | 248.703513 | 69.50 |
| 326 | 4.0 | 49.726500 | -124.680333 | 298.012014 | 73.43 |
| 327 | 4.0 | 49.726500 | -124.680333 | 348.298456 | 80.74 |
| 341 | 4.0 | 49.882500 | -124.993833 | 249.096075 | 75.41 |
| 342 | 4.0 | 49.882500 | -124.993833 | 309.392440 | 80.29 |
| 352 | 4.0 | 49.962500 | -125.147167 | 124.882272 | 66.16 |
| 353 | 4.0 | 49.962500 | -125.147167 | 152.920744 | 68.27 |
In [ ]:
Chlorophyll¶
In [19]:
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 [20]:
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: 50 bias: 0.02191927727592602 RMSE: 0.47728303975177266 WSS: 0.5767947559955457 z>=15 m: N: 22 bias: -0.052382556623546 RMSE: 0.2928025403508808 WSS: 0.5449612864195077 all: N: 72 bias: -0.000784060860023833 RMSE: 0.42940670002220394 WSS: 0.7032349376538956 Chl z<15 m: N: 50 bias: -0.2656611628264187 RMSE: 4.898451101025778 WSS: 0.5238997630795001 z>=15 m: N: 22 bias: -0.1510370969365944 RMSE: 0.7196599714471374 WSS: 0.5111974776437261 all: N: 72 bias: -0.23063714269341729 RMSE: 4.101380509278595 WSS: 0.589837873970668
In [21]:
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[21]:
<matplotlib.text.Text at 0x7fc05c409da0>
In [ ]:
In [ ]: