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 as dt
import glob
import gsw

%matplotlib inline

In [2]:

PATH= '/results/SalishSea/spinup.201905/'
start_date = dt.datetime(2015,1,1)
end_date = dt.datetime(2015,5,25)
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),excludeSaanich=True)
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	2015.0	2.0	11.0	11.068611	48.300833	-124.000333	1.9	None	None	None	NaN	mg/m^3	15.31	32.14	umol/L	29.227507	9.859421	1.884	2015-02-11 11:04:07
1	2015.0	2.0	11.0	11.068611	48.300833	-124.000333	6.6	None	None	None	2.57	mg/m^3	17.13	33.90	umol/L	29.484341	9.777243	6.54434	2015-02-11 11:04:07
2	2015.0	2.0	11.0	11.068611	48.300833	-124.000333	6.7	None	None	None	NaN	mg/m^3	NaN	NaN	umol/L	29.484839	9.771987	6.6435	2015-02-11 11:04:07
3	2015.0	2.0	11.0	11.068611	48.300833	-124.000333	11.0	None	None	None	NaN	mg/m^3	NaN	NaN	umol/L	30.144549	9.439995	10.9071	2015-02-11 11:04:07
4	2015.0	2.0	11.0	11.068611	48.300833	-124.000333	11.0	None	None	None	NaN	mg/m^3	20.62	37.65	umol/L	30.157913	9.433733	10.9071	2015-02-11 11:04:07

In [3]:

plt.hist(df1.Month)

Out[3]:

(array([ 48.,   0.,   0.,   0.,   0.,  13.,   0.,   0.,   0., 261.]),
 array([2. , 2.2, 2.4, 2.6, 2.8, 3. , 3.2, 3.4, 3.6, 3.8, 4. ]),
 <a list of 10 Patch objects>)

In [4]:

data=et.matchData(df1,filemap, fdict, start_date, end_date, namfmt, PATH, flen)

In [5]:

data.head()

Out[5]:

	Year	Month	Day	Hour	Lat	Lon	Pressure	Depth	Ammonium	Ammonium_units	...	i	mod_nitrate	mod_silicon	mod_ammonium	mod_diatoms	mod_ciliates	mod_flagellates	mod_vosaline	mod_votemper	k
0	2015.0	2.0	11.0	5.296111	48.613333	-123.243833	1.6	None	None	None	...	240	20.908375	41.903946	0.638635	0.019805	0.098637	0.212311	29.256762	9.020932	1
1	2015.0	2.0	11.0	5.296111	48.613333	-123.243833	6.2	None	None	None	...	240	20.914202	41.455513	0.617860	0.018714	0.092254	0.199061	29.470306	9.060081	6
2	2015.0	2.0	11.0	5.296111	48.613333	-123.243833	6.2	None	None	None	...	240	20.914202	41.455513	0.617860	0.018714	0.092254	0.199061	29.470306	9.060081	6
3	2015.0	2.0	11.0	5.296111	48.613333	-123.243833	6.2	None	None	None	...	240	20.914202	41.455513	0.617860	0.018714	0.092254	0.199061	29.470306	9.060081	6
4	2015.0	2.0	11.0	5.296111	48.613333	-123.243833	11.4	None	None	None	...	240	20.968466	41.242725	0.605153	0.017784	0.087073	0.188642	29.609785	9.068625	11

5 rows × 30 columns

In [6]:

# because excludeSaanich was set to false when ran:
data=data.loc[~((data.Lat>48.47)&(data.Lat<48.67)&(data.Lon>-123.6)&(data.Lon<-123.43))].copy(deep=True)

In [7]:

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 [8]:

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: 68
  bias: -1.3744180341327947
  RMSE: 4.826313245388962
  WSS: 0.7788838705648012
15 m<=z<22 m:
  N: 22
  bias: -1.381714357896282
  RMSE: 3.3981884450841378
  WSS: 0.5444664128854448
z>=22 m:
  N: 204
  bias: -2.7336635114632415
  RMSE: 3.256797531730143
  WSS: 0.7590760345147262
all:
  N: 294
  bias: -2.3181139405892814
  RMSE: 3.689369904385841
  WSS: 0.8788838627698865

In [9]:

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[9]:

<matplotlib.text.Text at 0x7f6ae7715588>

In [10]:

fig, ax = plt.subplots(1,4,figsize = (24,6))
yy=data.dtUTC[0].year
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-Jun')
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-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(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: -2.2403355407714827
  RMSE: 3.7928710065499054
  WSS: 0.716568273455414
April:
  N: 60
  bias: -1.2589623665809633
  RMSE: 4.947823435813866
  WSS: 0.7424378139794274
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[10]:

[<matplotlib.lines.Line2D at 0x7f6ae5858ac8>]

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 0x7f6ae5a9a5c0>

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('Si')
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')

Si
z<15 m:
  N: 68
  bias: 3.3246145220363807
  RMSE: 9.221577889244738
  WSS: 0.7645394417157766
15 m<=z<22 m:
  N: 22
  bias: 1.8717096016623742
  RMSE: 6.8909094183993185
  WSS: 0.6510489804786874
z>=22 m:
  N: 204
  bias: -3.3028993786082523
  RMSE: 6.057750745396781
  WSS: 0.8742068374858244
all:
  N: 294
  bias: -1.382789369051146
  RMSE: 6.977433505876548
  WSS: 0.8823755917304841
obs Si < 50:
  N: 230
  bias: 0.4408218554621186
  RMSE: 6.188039264253743
  WSS: 0.8240010117745775

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 0x7f6ae5539cc0>

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(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(yy,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(yy,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(yy,9,1))
ps=et.varvarPlot(ax[3],data.loc[ii3,:],obsvar,modvar,cols=('crimson','darkturquoise','navy'))
ax[3].set_title('Sep-Oct')

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: -4.041343231201175
  RMSE: 5.585430165595218
  WSS: 0.8016681074022802
April:
  N: 60
  bias: 4.306742222468053
  RMSE: 9.602928090870495
  WSS: 0.7291162637374727
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[16]:

[<matplotlib.lines.Line2D at 0x7f6ae773cdd8>]

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 0x7f6ae57d2f60>

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 0x7f6ae52ea908>]

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 0x7f6ae5186e10>

In [20]:

data.loc[data.Si>65,['Month','Lat','Lon','Z','Si']]

Out[20]:

	Month	Lat	Lon	Z	Si
212	4.0	49.318833	-123.799667	299.211	66.73
213	4.0	49.318833	-123.799667	346.827	70.58
227	4.0	49.401833	-124.156000	248.711	65.86
228	4.0	49.401833	-124.156000	269.605	70.17
242	4.0	49.443333	-124.337167	248.809	68.77
243	4.0	49.443333	-124.337167	298.713	70.83
244	4.0	49.443333	-124.337167	314.652	77.02
277	4.0	49.591667	-124.637833	162.833	70.41
290	4.0	49.726500	-124.680333	198.888	65.16
291	4.0	49.726500	-124.680333	248.704	69.50
292	4.0	49.726500	-124.680333	298.012	73.43
293	4.0	49.726500	-124.680333	348.298	80.74
307	4.0	49.882500	-124.993833	249.096	75.41
308	4.0	49.882500	-124.993833	309.392	80.29
318	4.0	49.962500	-125.147167	124.882	66.16
319	4.0	49.962500	-125.147167	152.921	68.27

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: 44
  bias: -0.057691514295968804
  RMSE: 0.5014988507234466
  WSS: 0.48590346677850116
z>=15 m:
  N: 20
  bias: -0.027602349424534045
  RMSE: 0.23860869627155126
  WSS: 0.6528490753303224
all:
  N: 64
  bias: -0.048288650273645456
  RMSE: 0.43669086747151314
  WSS: 0.6476297274517048


Chl
z<15 m:
  N: 44
  bias: -1.3246390363878828
  RMSE: 4.923879484183097
  WSS: 0.44343561775342955
z>=15 m:
  N: 20
  bias: -0.14161111587286013
  RMSE: 0.7175855613279856
  WSS: 0.4986438872824067
all:
  N: 64
  bias: -0.954942811226938
  RMSE: 4.1023249971165985
  WSS: 0.5063561946376409

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 0x7f6ae5555c50>

In [24]:

fspin=nc.Dataset('/results/SalishSea/spinup.201905/01jan15/SalishSea_1h_20150101_20150101_ptrc_T.nc')

In [25]:

ftest=nc.Dataset('/data/eolson/results/MEOPAR/SS36runs/CedarRuns/t15r15LinbfSi/SalishSea_1h_20150101_20150219_ptrc_T_20150101-20150110.nc')

In [26]:

fig,ax=plt.subplots(1,2,figsize=(10,8))
ax[0].pcolormesh(fspin.variables['diatoms'][0,0,:,:])
ax[1].pcolormesh(ftest.variables['diatoms'][0,0,:,:])

Out[26]:

<matplotlib.collections.QuadMesh at 0x7f6ae4ffb3c8>

In [27]:

plt.pcolormesh(fspin.variables['diatoms'][0,0,:,:]-ftest.variables['diatoms'][0,0,:,:])
plt.colorbar()

Out[27]:

<matplotlib.colorbar.Colorbar at 0x7f6ae4f74320>

In [28]:

plt.pcolormesh(fspin.variables['microzooplankton'][0,0,:,:]-ftest.variables['microzooplankton'][0,0,:,:])
plt.colorbar()

Out[28]:

<matplotlib.colorbar.Colorbar at 0x7f6ae4e2d550>

In [ ]: