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/linkHC201812/'
start_date = datetime.datetime(2017,1,1)
end_date = datetime.datetime(2017,12,31)
flen=1
namfmt='nowcast'
#varmap={'N':'nitrate','Si':'silicon','Ammonium':'ammonium'}
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'}
#gridmap={'nitrate':'tmask','silicon':'tmask','ammonium':'tmask'}
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]:
import pickle
data=pickle.load(open('/data/eolson/MEOPAR/SS36runs/calcFiles/evalMatches/dataDFO.pkl','rb'))
In [5]:
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')
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 [6]:
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: 1160 bias: -3.3667101992947224 RMSE: 5.888237330651951 WSS: 0.8672607422026446 15 m<=z<22 m: N: 314 bias: -2.4758662317361058 RMSE: 4.329926144577236 WSS: 0.7936470305798553 z>=22 m: N: 2418 bias: -1.0370727952873544 RMSE: 2.62811424334143 WSS: 0.9083896206441381 all: N: 3892 bias: -1.847494308055456 RMSE: 4.017139388667979 WSS: 0.9475337039573822
In [7]:
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[7]:
<matplotlib.text.Text at 0x7fb64bb18780>
In [8]:
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')
#ii4=(data.Z < 15)&(data.dtUTC<=dt.datetime(2016,4,1))&(data.dtUTC>dt.datetime(2016,2,1))
#ps=et.varvarPlot(ax[0],data.loc[ii4,:],obsvar,modvar,cols=('darkturquoise','navy'))
#ii5=(data.Z < 15)&(data.dtUTC<=dt.datetime(2016,5,1))&(data.dtUTC>dt.datetime(2016,4,1))
#ps=et.varvarPlot(ax[1],data.loc[ii5,:],obsvar,modvar,cols=('darkturquoise','navy'))
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: 685 bias: -3.2143418756245055 RMSE: 5.518963966423021 WSS: 0.8968999357308982 April: N: 188 bias: -4.329515312969524 RMSE: 6.4712158080217 WSS: 0.7622620462911704 May-Jun: N: 187 bias: -2.603768077613239 RMSE: 5.573742129364376 WSS: 0.8272290336519501 Sep-Oct: N: 100 bias: -4.0270613702714435 RMSE: 7.531335604647922 WSS: 0.7658565315011495
Out[8]:
[<matplotlib.lines.Line2D at 0x7fb64ab7f278>]
In [11]:
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 [12]:
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)')
Out[12]:
<matplotlib.text.Text at 0x7fa5547e60b8>
In [13]:
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[1].set_xlabel('model - obs Si')
ax[1].set_xlim(-40,20)
Out[13]:
(-40, 20)
Silicate¶
In [14]:
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: 517 bias: 2.5008700380011675 RMSE: 8.592049062387854 WSS: 0.8565845556526809 15 m<=z<22 m: N: 140 bias: 0.5921669578552198 RMSE: 6.589876419268737 WSS: 0.8464364739662879 z>=22 m: N: 958 bias: -1.0775190403565915 RMSE: 7.512859111751416 WSS: 0.7464839236785057 all: N: 1615 bias: 0.21274919076453358 RMSE: 7.80246843470353 WSS: 0.8814251294560127 obs Si < 50: N: 1060 bias: 3.572561176803873 RMSE: 7.357114248942437 WSS: 0.861408502966083
In [15]:
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)')
Out[15]:
<matplotlib.text.Text at 0x7fa554b8d0f0>
In [16]:
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(2015,4,1)),:],obsvar,modvar,cols=('crimson','darkturquoise','navy'))
ax[0].set_title('Feb-Mar')
ii1=(data.Z < 15)&(data.dtUTC<=dt.datetime(2015,5,1))&(data.dtUTC>dt.datetime(2015,4,1))
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(2015,9,1))&(data.dtUTC>dt.datetime(2015,5,1))
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(2015,12,1))&(data.dtUTC>dt.datetime(2015,9,1))
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(2016,4,1))&(data.dtUTC>dt.datetime(2016,2,1))
ps=et.varvarPlot(ax[0],data.loc[ii4,:],obsvar,modvar,cols=('darkturquoise','navy'))
ii5=(data.Z < 15)&(data.dtUTC<=dt.datetime(2016,5,1))&(data.dtUTC>dt.datetime(2016,4,1))
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(2015,4,1)),:],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: 0 bias: nan RMSE: nan WSS: nan 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/MEOPAR/tools/SalishSeaTools/salishsea_tools/evaltools.py:779: RuntimeWarning: invalid value encountered in double_scalars RMSE=np.sqrt(np.sum((mod-obs)**2)/N) /data/eolson/MEOPAR/tools/SalishSeaTools/salishsea_tools/evaltools.py:780: 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[16]:
[<matplotlib.lines.Line2D at 0x7fa554a22320>]
In [17]:
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)')
Out[17]:
<matplotlib.text.Text at 0x7fa555c8a390>
Ratios¶
In [18]:
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[18]:
[<matplotlib.lines.Line2D at 0x7fa552ef1c88>]
In [19]:
fig,ax=plt.subplots(1,2,figsize=(16,7))
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')
ax[0].set_ylabel('Si-1.3N')
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')
ax[1].set_ylabel('Si-1.3N')
ax[1].set_xlim(10,35)
ax[1].set_ylim(0,45)
ax[1].legend()
Out[19]:
<matplotlib.legend.Legend at 0x7fa552f64b70>
In [20]:
data.loc[data.Si>65,['Month','Lat','Lon','Z','Si']]
Out[20]:
| Month | Lat | Lon | Z | Si | |
|---|---|---|---|---|---|
| 80 | 2.0 | 49.027667 | -123.437833 | 297.5 | 66.85 |
| 81 | 2.0 | 49.027667 | -123.437833 | 311.8 | 67.82 |
| 97 | 2.0 | 49.163167 | -123.551000 | 347.1 | 66.29 |
| 98 | 2.0 | 49.163167 | -123.551000 | 367.2 | 68.34 |
| 109 | 2.0 | 49.250833 | -123.749000 | 390.9 | 72.03 |
| 140 | 2.0 | 49.402000 | -124.155167 | 272.6 | 65.08 |
| 153 | 2.0 | 49.466500 | -124.499500 | 316.1 | 69.51 |
| 169 | 2.0 | 49.730000 | -124.685000 | 347.9 | 68.70 |
| 184 | 2.0 | 49.885333 | -124.995333 | 307.2 | 66.13 |
| 185 | 2.0 | 49.483667 | -124.766667 | 0.6 | 66.38 |
| 231 | 4.0 | 48.651667 | -123.499667 | 98.7 | 83.80 |
| 370 | 4.0 | 49.030000 | -123.437000 | 314.0 | 67.87 |
| 389 | 4.0 | 49.164167 | -123.549833 | 298.0 | 65.52 |
| 390 | 4.0 | 49.164167 | -123.549833 | 347.1 | 69.04 |
| 391 | 4.0 | 49.164167 | -123.549833 | 368.5 | 71.44 |
| 411 | 4.0 | 49.318333 | -123.799000 | 297.5 | 66.13 |
| 412 | 4.0 | 49.318333 | -123.799000 | 336.6 | 67.33 |
| 430 | 4.0 | 49.401833 | -124.155167 | 247.9 | 67.95 |
| 431 | 4.0 | 49.401833 | -124.155167 | 272.0 | 68.41 |
| 450 | 4.0 | 49.443333 | -124.337667 | 297.6 | 68.10 |
| 451 | 4.0 | 49.443333 | -124.337667 | 315.7 | 69.80 |
| 487 | 4.0 | 49.592000 | -124.637667 | 163.9 | 66.94 |
| 503 | 4.0 | 49.726333 | -124.680000 | 297.7 | 68.27 |
| 504 | 4.0 | 49.726333 | -124.680000 | 346.2 | 73.59 |
| 520 | 4.0 | 49.883667 | -124.993833 | 308.2 | 66.64 |
| 807 | 6.0 | 49.706833 | -124.724333 | 197.9 | 73.17 |
| 808 | 6.0 | 49.706833 | -124.724333 | 287.2 | 75.08 |
| 825 | 6.0 | 49.670667 | -124.272333 | 198.7 | 69.14 |
| 826 | 6.0 | 49.670667 | -124.272333 | 343.8 | 78.96 |
| 843 | 6.0 | 49.250833 | -123.751500 | 198.2 | 70.85 |
| 844 | 6.0 | 49.250833 | -123.751500 | 388.9 | 71.47 |
| 848 | 6.0 | 49.054667 | -123.373167 | 1.4 | 66.25 |
| 1103 | 6.0 | 49.029667 | -123.436333 | 247.8 | 69.83 |
| 1104 | 6.0 | 49.029667 | -123.436333 | 298.1 | 67.66 |
| 1105 | 6.0 | 49.029667 | -123.436333 | 313.0 | 69.44 |
| 1121 | 6.0 | 49.163333 | -123.550333 | 297.9 | 69.22 |
| 1122 | 6.0 | 49.163333 | -123.550333 | 347.1 | 83.94 |
| 1123 | 6.0 | 49.163333 | -123.550333 | 370.5 | 83.76 |
| 1148 | 6.0 | 49.318167 | -123.799333 | 297.8 | 69.70 |
| 1149 | 6.0 | 49.318167 | -123.799333 | 334.5 | 80.44 |
| 1166 | 6.0 | 49.402000 | -124.154333 | 271.0 | 70.33 |
| 1184 | 6.0 | 49.443833 | -124.335667 | 247.8 | 70.53 |
| 1185 | 6.0 | 49.443833 | -124.335667 | 297.5 | 77.42 |
| 1186 | 6.0 | 49.443833 | -124.335667 | 313.8 | 79.93 |
| 1220 | 6.0 | 49.591500 | -124.639000 | 149.3 | 71.18 |
| 1221 | 6.0 | 49.591500 | -124.639000 | 164.3 | 73.79 |
| 1237 | 6.0 | 49.727000 | -124.680333 | 297.6 | 76.22 |
| 1238 | 6.0 | 49.727000 | -124.680333 | 348.7 | 82.07 |
| 1250 | 6.0 | 49.962000 | -125.147333 | 147.9 | 66.55 |
| 1264 | 6.0 | 49.883167 | -124.993500 | 173.7 | 65.36 |
| 1265 | 6.0 | 49.883167 | -124.993500 | 198.6 | 66.20 |
| 1266 | 6.0 | 49.883167 | -124.993500 | 248.1 | 69.74 |
| 1267 | 6.0 | 49.883167 | -124.993500 | 307.8 | 75.95 |
| 1544 | 9.0 | 49.705333 | -124.723667 | 284.6 | 67.10 |
| 1568 | 9.0 | 49.670500 | -124.271833 | 198.7 | 86.13 |
| 1612 | 10.0 | 48.651667 | -123.510000 | 99.8 | 68.23 |
| 1861 | 10.0 | 49.591833 | -124.637500 | 149.2 | 72.69 |
| 1862 | 10.0 | 49.591833 | -124.637500 | 164.4 | 73.88 |
| 1878 | 10.0 | 49.726667 | -124.680167 | 344.2 | 65.81 |
In [ ]:
Chlorophyll¶
In [21]:
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 [22]:
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: 317 bias: 0.16754256670499507 RMSE: 0.516460207187192 WSS: 0.5430666800992634 z>=15 m: N: 122 bias: 0.31157934452813224 RMSE: 0.5455013796664517 WSS: 0.5355417982343631 all: N: 439 bias: 0.2075710106558441 RMSE: 0.524692180709054 WSS: 0.6061606661057047 Chl z<15 m: N: 317 bias: 0.1488441276126382 RMSE: 5.19723353741412 WSS: 0.5162772178242793 z>=15 m: N: 122 bias: 0.6095566478038388 RMSE: 1.7420666867772108 WSS: 0.5266143677388557 all: N: 439 bias: 0.2768781309459549 RMSE: 4.510884610341626 WSS: 0.5723356851463115
In [23]:
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[23]:
<matplotlib.text.Text at 0x7fa554c345f8>
In [ ]:
In [ ]:
In [ ]: