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.yr1/'
start_date = dt.datetime(2013,1,1)
end_date = dt.datetime(2013,9,25)
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=False)
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	2013.0	4.0	11.0	20.848333	49.402167	-124.1555	2.6	None	None	None	11.82	mg/m^3	3.0	7.2	umol/L	27.743770	9.789540	2.57784	2013-04-11 20:50:54
1	2013.0	4.0	11.0	20.848333	49.402167	-124.1555	4.8	None	None	None	11.67	mg/m^3	2.9	7.3	umol/L	27.744457	9.787385	4.75906	2013-04-11 20:50:54
2	2013.0	4.0	11.0	20.848333	49.402167	-124.1555	10.2	None	None	None	NaN	mg/m^3	NaN	NaN	umol/L	27.749606	9.667316	10.1129	2013-04-11 20:50:54
3	2013.0	4.0	11.0	20.848333	49.402167	-124.1555	19.7	None	None	None	11.88	mg/m^3	10.6	18.5	umol/L	28.459715	9.127710	19.5313	2013-04-11 20:50:54
4	2013.0	4.0	11.0	20.848333	49.402167	-124.1555	29.9	None	None	None	NaN	mg/m^3	24.1	47.6	umol/L	29.363611	8.447678	29.6432	2013-04-11 20:50:54

In [3]:

plt.hist(df1.Month)

Out[3]:

(array([563.,   0.,   0.,   0.,   0., 217.,   0., 338.,   0.,  13.]),
 array([4. , 4.4, 4.8, 5.2, 5.6, 6. , 6.4, 6.8, 7.2, 7.6, 8. ]),
 <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	2013.0	4.0	3.0	14.939444	49.970833	-124.6825	4.7	None	None	None	...	190	0.548628	6.399379	0.440082	5.688290	0.268555	0.524411	27.609800	10.152428	4
1	2013.0	4.0	3.0	14.939444	49.970833	-124.6825	9.9	None	None	None	...	190	11.481730	26.726488	1.101039	5.689577	0.193569	0.341572	28.311152	8.812274	9
2	2013.0	4.0	3.0	14.939444	49.970833	-124.6825	21.3	None	None	None	...	190	26.325640	54.592690	1.316902	0.470348	0.081232	0.077165	29.395172	8.410794	19
3	2013.0	4.0	3.0	14.939444	49.970833	-124.6825	22.8	None	None	None	...	190	26.325640	54.592690	1.316902	0.470348	0.081232	0.077165	29.395172	8.410794	19
4	2013.0	4.0	3.0	14.939444	49.970833	-124.6825	25.2	None	None	None	...	190	26.856123	55.250294	1.246484	0.337995	0.069814	0.068710	29.566582	8.472906	20

5 rows × 30 columns

In [6]:

data.loc[data.Chlorophyll_Extracted>10]

Out[6]:

	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
312	2013.0	4.0	11.0	11.143889	49.029833	-123.436167	2.7	None	None	None	...	264	13.290957	27.664791	2.491055	0.618804	0.186262	0.059024	28.307165	8.962428	2
328	2013.0	4.0	11.0	20.848333	49.402167	-124.155500	2.6	None	None	None	...	204	5.680586	16.840712	2.244794	0.891690	0.224059	0.062258	24.934200	9.683742	2
329	2013.0	4.0	11.0	20.848333	49.402167	-124.155500	4.8	None	None	None	...	204	6.367824	17.839087	2.291835	0.886654	0.219737	0.062351	25.252041	9.556385	4
331	2013.0	4.0	11.0	20.848333	49.402167	-124.155500	19.7	None	None	None	...	204	16.340393	33.991100	2.620600	0.353107	0.138303	0.056986	28.383747	8.729556	18
343	2013.0	4.0	12.0	0.524722	49.443333	-124.338500	2.1	None	None	None	...	183	6.909050	18.532736	2.255384	1.271513	0.237962	0.063746	25.652113	9.804106	2
345	2013.0	4.0	12.0	0.524722	49.443333	-124.338500	9.8	None	None	None	...	183	10.057865	22.448006	2.731139	0.721713	0.208464	0.066235	27.310566	9.210092	9
346	2013.0	4.0	12.0	0.524722	49.443333	-124.338500	20.3	None	None	None	...	183	17.692408	37.115620	2.621187	0.252002	0.141715	0.064859	28.466013	8.765588	18
361	2013.0	4.0	12.0	3.034444	49.509667	-124.463833	2.5	None	None	None	...	173	10.293472	22.706141	2.644080	0.747765	0.236320	0.069472	27.512262	9.833039	2
363	2013.0	4.0	12.0	3.034444	49.509667	-124.463833	9.9	None	None	None	...	173	12.369872	26.672171	2.649645	0.642508	0.218012	0.071385	27.819229	9.370308	9
364	2013.0	4.0	12.0	3.034444	49.509667	-124.463833	19.7	None	None	None	...	173	21.331158	44.175171	2.291576	0.251590	0.114793	0.062665	29.115417	8.594073	18
374	2013.0	4.0	12.0	5.771389	49.592667	-124.638167	2.5	None	None	None	...	157	13.485579	28.211292	2.642432	0.921874	0.221238	0.070615	28.573500	9.244478	2
376	2013.0	4.0	12.0	5.771389	49.592667	-124.638167	9.8	None	None	None	...	157	13.928384	29.107447	2.742717	0.673595	0.205021	0.070713	28.660341	9.093882	9
377	2013.0	4.0	12.0	5.771389	49.592667	-124.638167	20.0	None	None	None	...	157	17.760056	36.804306	2.521186	0.458264	0.144252	0.064987	29.006037	8.897673	18
386	2013.0	4.0	12.0	8.661389	49.726833	-124.680167	2.4	None	None	None	...	168	5.878627	14.055737	2.861417	0.562675	0.242982	0.068882	26.807375	9.736365	2
388	2013.0	4.0	12.0	8.661389	49.726833	-124.680167	10.1	None	None	None	...	168	10.484881	21.830469	3.036012	0.395860	0.193837	0.064722	28.372532	9.302079	10
389	2013.0	4.0	12.0	8.661389	49.726833	-124.680167	19.9	None	None	None	...	168	20.420708	42.272526	2.266712	0.289438	0.115022	0.056619	29.119356	8.680081	18
403	2013.0	4.0	12.0	11.568333	49.883333	-124.993500	2.0	None	None	None	...	141	9.686026	20.600424	2.674127	1.007968	0.219518	0.061663	28.077101	9.495545	1
405	2013.0	4.0	12.0	11.568333	49.883333	-124.993500	10.0	None	None	None	...	141	22.331600	45.854149	2.028494	0.194754	0.104956	0.056663	29.604847	8.819620	9
406	2013.0	4.0	12.0	11.568333	49.883333	-124.993500	20.1	None	None	None	...	141	22.868708	46.786217	1.860957	0.187133	0.092785	0.053171	29.732216	8.851438	18
419	2013.0	4.0	12.0	15.183333	49.961333	-125.147000	2.6	None	None	None	...	129	19.138435	39.620335	2.080917	0.417651	0.131608	0.058667	29.357100	9.065556	2
422	2013.0	4.0	12.0	15.183333	49.961333	-125.147000	9.8	None	None	None	...	129	23.218901	47.357906	1.600570	0.264124	0.081919	0.046615	29.837925	8.960051	9
423	2013.0	4.0	12.0	15.183333	49.961333	-125.147000	20.3	None	None	None	...	129	24.789686	50.226654	1.406916	0.196404	0.060952	0.039555	30.035778	8.916419	18
431	2013.0	4.0	13.0	1.303611	49.318667	-123.800167	2.3	None	None	None	...	246	9.507198	23.660114	2.258392	1.403370	0.214779	0.067242	25.948977	9.184166	2
433	2013.0	4.0	13.0	1.303611	49.318667	-123.800167	9.7	None	None	None	...	246	10.198633	24.471125	2.326324	1.251530	0.207394	0.065604	26.300194	9.198015	9
447	2013.0	4.0	13.0	5.085000	49.163667	-123.554667	2.7	None	None	None	...	263	12.720423	26.064701	2.638867	0.634065	0.202250	0.068873	28.161551	9.131008	2

25 rows × 30 columns

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.Chlorophyll_Extracted>10,['Lon']],data.loc[data.Chlorophyll_Extracted>10,['Lat']],'m>',label='High Chl')

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: 274
  bias: -0.025224428377827124
  RMSE: 7.512139238211282
  WSS: 0.770628907071865
15 m<=z<22 m:
  N: 85
  bias: 1.5222854277666862
  RMSE: 7.229160593627256
  WSS: 0.5512098048993181
z>=22 m:
  N: 502
  bias: -1.264475923515409
  RMSE: 3.915863594613176
  WSS: 0.6653192694426678
all:
  N: 861
  bias: -0.5949873932869814
  RMSE: 5.66201140810254
  WSS: 0.89491834878871

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)')
ax.set_xlim(0,40)
ax.set_ylim(0,40)

Out[9]:

(0, 40)

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: 0
  bias: nan
  RMSE: nan
  WSS: nan
April:
  N: 127
  bias: -1.0808247719664053
  RMSE: 7.968982391966732
  WSS: 0.7237955278988251
May-Jun:
  N: 147
  bias: 0.8867568208449548
  RMSE: 7.093802142883446
  WSS: 0.7951388826088835
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 0x7f8763d643c8>]

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',ms=12)
dSJGI_N=data.loc[(data.Lon>=-123.6)&(data.Lat<48.9)&(data.Lat>48.5)]
ax.plot(dSJGI_N['Lon'],dSJGI_N['Lat'],'.',color='lawngreen',label='SJGI_N',ms=8)
dSJGI_S=data.loc[(data.Lon>=-123.6)&(data.Lat<48.5)]
ax.plot(dSJGI_S['Lon'],dSJGI_S['Lat'],'.',color='seagreen',label='SJGI_S',ms=8)
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,'N','mod_nitrate',cols=('lawngreen','darkturquoise','navy'),lname='SJGI_N')
ps3=et.varvarPlot(ax,dSJGI_S,'N','mod_nitrate',cols=('seagreen','darkturquoise','navy'),lname='SJGI_S')
ps4=et.varvarPlot(ax,dSOG,'N','mod_nitrate',cols=('y','darkturquoise','navy'),lname='SOG')
ps5=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],ps5[0][0]])
ax.set_xlabel('Obs')
ax.set_ylabel('Model')
ax.set_title('NO$_3$ ($\mu$M)')
ax.set_xlim(0,40)
ax.set_ylim(0,40)
ax.plot((0,40),(0,40),'-',color='grey',alpha=.5)

Out[12]:

[<matplotlib.lines.Line2D at 0x7f8763c034e0>]

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[0].set_xlim(-15,10)
ax[1].set_xlabel('model - obs Si')
ax[1].set_xlim(-40,20)
ax[0].plot(dSJGI['mod_nitrate']-dSJGI['N'],dSJGI['Z'],'ko',markeredgecolor='k',markerfacecolor='None')
ax[1].plot(dSJGI['mod_silicon']-dSJGI['Si'],dSJGI['Z'],'ko',markeredgecolor='k',markerfacecolor='None')

Out[13]:

[<matplotlib.lines.Line2D at 0x7f8763ac7898>]

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: 274
  bias: -4.462413137846617
  RMSE: 14.255203200939816
  WSS: 0.6095533382630076
15 m<=z<22 m:
  N: 85
  bias: 0.512954752304978
  RMSE: 10.717745368195413
  WSS: 0.6953964909079566
z>=22 m:
  N: 502
  bias: -1.7402726237992283
  RMSE: 6.141986145150249
  WSS: 0.8695515850185689
all:
  N: 861
  bias: -2.3841079012442066
  RMSE: 9.899680580994922
  WSS: 0.8595328626774601
obs Si < 50:
  N: 501
  bias: -2.0964356832637563
  RMSE: 11.451531382730845
  WSS: 0.70493515287917

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)')
ax.set_xlim(0,80)
ax.set_ylim(0,80)

Out[15]:

(0, 80)

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: 0
  bias: nan
  RMSE: nan
  WSS: nan
April:
  N: 127
  bias: -4.0894834402039315
  RMSE: 16.158707656296002
  WSS: 0.6230939151086754
May-Jun:
  N: 147
  bias: -4.784604101116162
  RMSE: 12.377172703591246
  WSS: 0.5583965552477621
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 0x7f8769c526d8>]

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_N,obsvar,modvar,cols=('lawngreen','darkturquoise','navy'),lname='SJGI_N')
ps3=et.varvarPlot(ax,dSJGI_S,obsvar,modvar,cols=('seagreen','darkturquoise','navy'),lname='SJGI_S')
ps4=et.varvarPlot(ax,dSOG,obsvar,modvar,cols=('y','darkturquoise','navy'),lname='SOG')
ps5=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],ps5[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)
ax.plot((0,80),(0,80),'-',color='grey',alpha=.5)

Out[17]:

[<matplotlib.lines.Line2D at 0x7f8768079a20>]

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['N'],dSJGI_N['Si'],'.',color='lawngreen',label='SJGI_N')
p2=ax[0].plot(dSJGI_S['N'],dSJGI_S['Si'],'.',color='seagreen',label='SJGI_S')
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_N['mod_nitrate'],dSJGI_N['mod_silicon'],'.',color='lawngreen',label='SJGI_N')
p6=ax[1].plot(dSJGI_S['mod_nitrate'],dSJGI_S['mod_silicon'],'.',color='seagreen',label='SJGI_S')
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 0x7f87637cd4a8>]

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_N['AbsSal'],dSJGI_N['Si']-1.3*dSJGI_N['N'],'.',color='lawngreen',label='SJGI_N')
p2=ax[0].plot(dSJGI_S['AbsSal'],dSJGI_S['Si']-1.3*dSJGI_S['N'],'.',color='seagreen',label='SJGI_S')
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_N['mod_vosaline'],dSJGI_N['mod_silicon']-1.3*dSJGI_N['mod_nitrate'],'.',color='lawngreen',label='SJGI_N')
p6=ax[1].plot(dSJGI_S['mod_vosaline'],dSJGI_S['mod_silicon']-1.3*dSJGI_S['mod_nitrate'],'.',color='seagreen',label='SJGI_S')
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 0x7f876367aba8>

In [20]:

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

Out[20]:

	Month	Lat	Lon	Z	Si
146	4.0	50.066000	-124.713333	29.6414	65.3
153	4.0	50.079833	-124.714167	19.9266	65.3
154	4.0	50.079833	-124.714167	29.7405	68.0
173	4.0	50.072333	-124.812333	125.079	65.5
174	4.0	50.072333	-124.812333	149.65	66.1
175	4.0	50.072333	-124.812333	200.07	71.4
176	4.0	50.072333	-124.812333	220.77	69.4
177	4.0	50.072333	-124.812333	223.84	69.4
178	4.0	50.072333	-124.812333	227.801	69.6
204	4.0	50.073167	-125.014167	59.9722	66.4
205	4.0	50.073167	-125.014167	73.1539	68.1
206	4.0	50.073167	-125.014167	74.5413	69.4
207	4.0	50.073167	-125.014167	77.6136	67.1
214	4.0	50.053833	-125.011667	87.9204	65.5
215	4.0	50.053833	-125.011667	89.9024	65.6
216	4.0	50.053833	-125.011667	91.8843	66.2
326	4.0	49.029833	-123.436167	296.546	69.1
327	4.0	49.029833	-123.436167	311.199	69.8
341	4.0	49.402167	-124.155500	248.216	67.8
342	4.0	49.402167	-124.155500	268.119	69.8
357	4.0	49.443333	-124.338500	247.918	65.6
358	4.0	49.443333	-124.338500	297.327	69.5
359	4.0	49.443333	-124.338500	314.751	73.7
385	4.0	49.592667	-124.638167	164.121	70.2
402	4.0	49.726833	-124.680167	347.012	71.1
418	4.0	49.883333	-124.993500	308.204	66.5
446	4.0	49.318667	-123.800167	339.601	68.0
462	4.0	49.163667	-123.554667	297.235	66.4
463	4.0	49.163667	-123.554667	351.484	74.8
464	4.0	49.163667	-123.554667	367.518	65.9
734	6.0	49.318500	-123.799500	297.528	65.6
749	6.0	49.401500	-124.154833	247.82	67.5
750	6.0	49.401500	-124.154833	270.1	69.4
764	6.0	49.442833	-124.336833	247.225	72.0
765	6.0	49.442833	-124.336833	296.139	72.1
766	6.0	49.442833	-124.336833	316.137	70.0
779	6.0	49.510333	-124.462333	188.788	65.5
790	7.0	49.591000	-124.638333	148.765	69.6
791	7.0	49.591000	-124.638333	162.932	74.2
822	7.0	49.883500	-124.993500	309.788	65.9
867	7.0	49.988667	-124.699167	46.7901	78.6
868	7.0	49.988667	-124.699167	48.6734	76.8
869	7.0	49.988667	-124.699167	50.2593	74.5
887	7.0	50.004667	-124.713333	66.514	82.3
888	7.0	50.004667	-124.713333	68.4962	76.8
889	7.0	50.004667	-124.713333	70.3793	77.4
897	7.0	50.017667	-124.721167	74.6408	84.8
898	7.0	50.017667	-124.721167	84.353	94.7
899	7.0	50.017667	-124.721167	86.236	96.2
900	7.0	50.017667	-124.721167	88.0198	99.1
908	7.0	50.032833	-124.729667	73.1541	85.7
909	7.0	50.032833	-124.729667	75.0371	87.3
910	7.0	50.032833	-124.729667	77.2175	88.1
1036	7.0	50.057000	-124.718167	60.4679	66.4
1037	7.0	50.057000	-124.718167	62.7475	65.8
1038	7.0	50.057000	-124.718167	64.5315	69.6
1066	7.0	50.073167	-125.014167	72.1628	70.8
1067	7.0	50.073167	-125.014167	74.4422	67.5
1068	7.0	50.073167	-125.014167	76.3252	69.9

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: 79
  bias: -0.23284336872740594
  RMSE: 0.6253349982353618
  WSS: 0.42095683676810636
z>=15 m:
  N: 40
  bias: -0.2838533573128714
  RMSE: 0.6217196229751139
  WSS: 0.4015041772473762
all:
  N: 119
  bias: -0.2499895833779826
  RMSE: 0.6241220827375371
  WSS: 0.47819830043915124


Chl
z<15 m:
  N: 79
  bias: -3.176542105966422
  RMSE: 7.062831816243247
  WSS: 0.40052935532029643
z>=15 m:
  N: 40
  bias: -2.61487728526257
  RMSE: 5.422735961643445
  WSS: 0.396611492547564
all:
  N: 119
  bias: -2.987747208250842
  RMSE: 6.557468306491702
  WSS: 0.4068955844426332

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 0x7f8763e25a20>

In [24]:

ii=(data.Lat>49.8)&(data.Lat<50.2)&(data.Lon>-124.8)&(data.Lon<-124.5)&(data.Z<10)
#,['Month','Z','mod_Chl','Chlorophyll_Extracted','mod_nitrate','N','mod_silicon','Si']]
plt.scatter(data.loc[ii,['N']],data.loc[ii,['mod_nitrate']],c=data.loc[ii,['Month']],vmin=1,vmax=11)
plt.colorbar()
plt.xlim(0,30)
plt.ylim(0,30)

Out[24]:

(0, 30)

In [25]:

plt.scatter(data.loc[~ii,['N']],data.loc[~ii,['mod_nitrate']],c=data.loc[~ii,['Month']],vmin=1,vmax=11)
plt.colorbar()
plt.xlim(0,30)
plt.ylim(0,30)

Out[25]:

(0, 30)

In [26]:

plt.scatter(data.loc[ii,['Si']],data.loc[ii,['mod_silicon']],c=data.loc[ii,['Month']])
plt.colorbar()
plt.xlim(0,60)
plt.ylim(0,60)

Out[26]:

(0, 60)

In [27]:

fspin=nc.Dataset('/results/SalishSea/spinup.201905/02mar13/SalishSea_1h_20130302_20130302_ptrc_T.nc')

In [28]:

ftest=nc.Dataset('/data/eolson/results/MEOPAR/SS36runs/CedarRuns/t15r15LinbfSi/SalishSea_1h_20150220_20150410_ptrc_T_20150302-20150311.nc')

In [29]:

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

Out[29]:

<matplotlib.colorbar.Colorbar at 0x7f876329c7b8>

In [30]:

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

Out[30]:

<matplotlib.colorbar.Colorbar at 0x7f87633b17f0>

In [31]:

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

Out[31]:

<matplotlib.colorbar.Colorbar at 0x7f8763388898>

In [32]:

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

Out[32]:

<matplotlib.colorbar.Colorbar at 0x7f87633d5550>

In [33]:

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(-15,10)
ax[1].set_xlabel('model - obs Si')
ax[1].set_xlim(-40,20)
ax[0].plot(dJDF['mod_nitrate']-dJDF['N'],dJDF['Z'],'ko',markeredgecolor='k',markerfacecolor='None')
ax[1].plot(dJDF['mod_silicon']-dJDF['Si'],dJDF['Z'],'ko',markeredgecolor='k',markerfacecolor='None')

Out[33]:

[<matplotlib.lines.Line2D at 0x7f8762e659b0>]

In [34]:

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,['N']].values, data.loc[iii,['Z']].values, 
        '.', color = cols[ii],label=str(ii))
    ax[1].plot(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('obs N')
#ax[0].set_xlim(-15,10)
ax[1].set_xlabel('obs Si')
#ax[1].set_xlim(-40,20)
ax[0].plot(dJDF['N'],dJDF['Z'],'ko',markeredgecolor='k',markerfacecolor='None')
ax[1].plot(dJDF['Si'],dJDF['Z'],'ko',markeredgecolor='k',markerfacecolor='None')

Out[34]:

[<matplotlib.lines.Line2D at 0x7f8762e7a0b8>]

In [35]:

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,['Z']].values, 
        '.', color = cols[ii],label=str(ii))
    ax[1].plot(data.loc[iii,['mod_silicon']].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 N')
ax[1].set_xlabel('model  Si')
ax[0].plot(dJDF['mod_nitrate'],dJDF['Z'],'ko',markeredgecolor='k',markerfacecolor='None')
ax[1].plot(dJDF['mod_silicon'],dJDF['Z'],'ko',markeredgecolor='k',markerfacecolor='None')

Out[35]:

[<matplotlib.lines.Line2D at 0x7f8768354588>]

In [36]:

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_votemper']].values-data.loc[iii,['ConsT']].values, data.loc[iii,['Z']].values, 
        '.', color = cols[ii],label=str(ii))
    ax[1].plot(data.loc[iii,['mod_vosaline']].values-data.loc[iii,['AbsSal']].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 T')
ax[0].set_xlim(-15,10)
ax[1].set_xlabel('model - obs S')
ax[1].set_xlim(-40,20)
ax[0].plot(dJDF['mod_votemper']-dJDF['ConsT'],dJDF['Z'],'ko',markeredgecolor='k',markerfacecolor='None')
ax[1].plot(dJDF['mod_vosaline']-dJDF['AbsSal'],dJDF['Z'],'ko',markeredgecolor='k',markerfacecolor='None')

Out[36]:

[<matplotlib.lines.Line2D at 0x7f8762b14b38>]

In [37]:

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,['ConsT']].values, data.loc[iii,['Z']].values, 
        '.', color = cols[ii],label=str(ii))
    ax[1].plot(data.loc[iii,['AbsSal']].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('obs T')
ax[0].set_xlim(0,23)
ax[1].set_xlabel('obs S')
ax[1].set_xlim(0,35)
ax[0].plot(dJDF['ConsT'],dJDF['Z'],'ko',markeredgecolor='k',markerfacecolor='None')
ax[1].plot(dJDF['AbsSal'],dJDF['Z'],'ko',markeredgecolor='k',markerfacecolor='None')

Out[37]:

[<matplotlib.lines.Line2D at 0x7f8763854c18>]

In [38]:

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_votemper']].values, data.loc[iii,['Z']].values, 
        '.', color = cols[ii],label=str(ii))
    ax[1].plot(data.loc[iii,['mod_vosaline']].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 T')
ax[0].set_xlim(0,23)
ax[1].set_xlabel('model S')
ax[1].set_xlim(0,35)
ax[0].plot(dJDF['mod_votemper'],dJDF['Z'],'ko',markeredgecolor='k',markerfacecolor='None')
ax[1].plot(dJDF['mod_vosaline'],dJDF['Z'],'ko',markeredgecolor='k',markerfacecolor='None')

Out[38]:

[<matplotlib.lines.Line2D at 0x7f8762b3aeb8>]

In [ ]: