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/ISink/'
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: -2.7880771945809073
  RMSE: 5.6853853925544895
  WSS: 0.7633127129092379
15 m<=z<22 m:
  N: 24
  bias: -1.7404224936167374
  RMSE: 3.4188357884580998
  WSS: 0.5908285326714213
z>=22 m:
  N: 228
  bias: -3.056236578958078
  RMSE: 3.4854615536997806
  WSS: 0.7461024182826334
all:
  N: 328
  bias: -2.897823008040824
  RMSE: 4.0975012773676465
  WSS: 0.8762318844327222

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 0x7f194a8d57b8>

In [7]:

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: -1.689829902648924
  RMSE: 4.030407130591838
  WSS: 0.5983486896736766
April:
  N: 67
  bias: -2.8231060936379784
  RMSE: 5.784050320202453
  WSS: 0.7301890764029948
May-Jun:
  N: 1
  bias: -9.22711929321289
  RMSE: 9.22711929321289
  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[7]:

[<matplotlib.lines.Line2D at 0x7f194a7d1160>]

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 0x7f194b3f5908>]

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: -0.17663343479758709
  RMSE: 9.205546465679362
  WSS: 0.7889228481979149
15 m<=z<22 m:
  N: 24
  bias: 1.198184906641643
  RMSE: 6.3276894086130415
  WSS: 0.7120188792564173
z>=22 m:
  N: 228
  bias: -3.526008158232031
  RMSE: 5.763415481133185
  WSS: 0.9018266437156519
all:
  N: 328
  bias: -2.4042608639089096
  RMSE: 6.756843102962755
  WSS: 0.9125335804016701
obs Si < 50:
  N: 240
  bias: -0.669873097896577
  RMSE: 6.24813919933905
  WSS: 0.8561078326156601

In [12]:

fig, ax = plt.subplots(figsize = (8,8))
ps=et.varvarPlot(ax,data,'Si','mod_silicon','Z',(15,22),'z','m',('mediumseagreen','darkturquoise','navy'))
ax.legend(handles=ps)
ax.set_xlabel('Obs')
ax.set_ylabel('Model')
ax.set_title('dSi ($\mu$M)')
ax.set_xlim(0,80)
ax.set_ylim(0,80)

Out[12]:

(0, 80)

In [13]:

obsvar='Si'; modvar='mod_silicon'
fig, ax = plt.subplots(1,4,figsize = (24,6))
for axi in ax:
    axi.plot(np.arange(0,70),np.arange(0,70),'k-')
ps=et.varvarPlot(ax[0],data.loc[(data.Z<15)&(data.dtUTC<=dt.datetime(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: -3.499762077331546
  RMSE: 5.873158827025997
  WSS: 0.7429703895563348
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[13]:

[<matplotlib.lines.Line2D at 0x7f194a9e7eb8>]

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 0x7f194a93f7f0>]

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 0x7f194a7a2630>]

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 0x7f194b42add8>]

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: 0.06874266587202998
  RMSE: 0.5010812521292989
  WSS: 0.5398107217473685
z>=15 m:
  N: 22
  bias: -0.0335685587064455
  RMSE: 0.21336765567331373
  WSS: 0.739622435132758
all:
  N: 72
  bias: 0.03748090280638469
  RMSE: 0.4339048747597358
  WSS: 0.7106096863758413


Chl
z<15 m:
  N: 50
  bias: 0.05790611280202862
  RMSE: 4.785705585972614
  WSS: 0.5556877162076209
z>=15 m:
  N: 22
  bias: -0.1307793293080547
  RMSE: 0.6526755926285418
  WSS: 0.6185038403925129
all:
  N: 72
  bias: 0.00025222771283628376
  RMSE: 4.004373611064325
  WSS: 0.6227692495119259

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 0x7f194b7382b0>

In [22]:

PATH= '/data/eolson/MEOPAR/SS36runs/CedarRuns/ISink/'
ftest=nc.Dataset(PATH+'SalishSea_1h_20150501_20150530_ptrc_T_20150521-20150530.nc')
fspin=nc.Dataset('/results/SalishSea/spinup.201905/21may15/SalishSea_1h_20150521_20150521_ptrc_T.nc')

In [23]:

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[23]:

<matplotlib.collections.QuadMesh at 0x7f194b02a710>

In [24]:

test=data.loc[(data['Chlorophyll_Extracted']>=0)&(data.Lon<-123.6)&(data.Lat<48.6)]

In [28]:

test.loc[:,['Year','Month','Day','Lat','Lon','Z','Chlorophyll_Extracted','mod_Chl']]

Out[28]:

	Year	Month	Day	Lat	Lon	Z	Chlorophyll_Extracted	mod_Chl
25	2015.0	2.0	11.0	48.300833	-124.000333	6.544340	2.57	0.960437
31	2015.0	2.0	11.0	48.300833	-124.000333	30.439317	0.42	0.546162
75	2015.0	4.0	2.0	48.499833	-124.733167	1.586497	3.43	0.494309
77	2015.0	4.0	2.0	48.499833	-124.733167	11.997578	2.52	0.411824
78	2015.0	4.0	2.0	48.499833	-124.733167	21.020086	1.06	0.712107
89	2015.0	4.0	2.0	48.469000	-124.548500	1.883969	4.69	1.830897
91	2015.0	4.0	2.0	48.469000	-124.548500	11.501857	1.19	1.190068
92	2015.0	4.0	2.0	48.469000	-124.548500	20.920999	0.67	0.595168
103	2015.0	4.0	2.0	48.308167	-124.066500	1.685682	7.15	3.278023
105	2015.0	4.0	2.0	48.308167	-124.066500	11.204568	9.42	1.825634
116	2015.0	4.0	3.0	48.260833	-123.720167	1.685690	11.13	1.291974
118	2015.0	4.0	3.0	48.260833	-123.720167	11.601229	4.99	1.020297

In [38]:

import cmocean
fig, ax = plt.subplots(figsize = (6,6))
viz_tools.set_aspect(ax, coords = 'map')
m=ax.scatter(test['Lon']+0.2*np.random.rand(len(test['Lon'])), test['Lat']+0.2*np.random.rand(len(test['Lon'])), c=test['mod_Chl']-test['Chlorophyll_Extracted'],vmin=-10,vmax=10,cmap=cmocean.cm.balance)
viz_tools.plot_coastline(ax, grid, coords = 'map')
plt.colorbar(m)

Out[38]:

<matplotlib.colorbar.Colorbar at 0x7f1930be4dd8>

In [39]:

test=et.loadDFO()

In [48]:

test2=test.loc[(test['Chlorophyll_Extracted']>=0)&(test.Lon<-123.6)&(test.Lat<48.6)&(test.Month==6)]

In [ ]:

In [49]:

test2

Out[49]:

	Year	Month	Day	Hour	Lat	Lon	Pressure	Depth	Ammonium	Ammonium_units	Chlorophyll_Extracted	Chlorophyll_Extracted_units	N	Si	Silicate_units	AbsSal	ConsT	Z	dtUTC
11	2008.0	6.0	20.0	11.473056	48.470000	-124.547167	2.0	NaN	NaN	None	5.30	mg/m^3	21.30	37.70	umol/L	31.731979	9.648685	1.983125	2008-06-20 11:28:23
13	2008.0	6.0	20.0	11.473056	48.470000	-124.547167	9.9	NaN	NaN	None	3.51	mg/m^3	25.00	39.90	umol/L	31.827200	9.083090	9.816280	2008-06-20 11:28:23
14	2008.0	6.0	20.0	11.473056	48.470000	-124.547167	19.5	NaN	NaN	None	1.91	mg/m^3	26.80	42.10	umol/L	31.946372	8.948434	19.334645	2008-06-20 11:28:23
25	2008.0	6.0	20.0	13.969167	48.501167	-124.734667	2.0	NaN	NaN	None	4.37	mg/m^3	21.80	37.00	umol/L	31.779231	9.465428	1.983119	2008-06-20 13:58:09
27	2008.0	6.0	20.0	13.969167	48.501167	-124.734667	9.7	NaN	NaN	None	3.69	mg/m^3	23.30	37.30	umol/L	31.845634	9.169437	9.617948	2008-06-20 13:58:09
28	2008.0	6.0	20.0	13.969167	48.501167	-124.734667	20.2	NaN	NaN	None	0.76	mg/m^3	29.40	45.90	umol/L	32.482873	8.245013	20.028618	2008-06-20 13:58:09
925	2009.0	6.0	21.0	2.736389	48.260333	-123.721333	2.0	NaN	NaN	None	4.97	mg/m^3	20.20	34.60	umol/L	31.818120	9.778811	1.983163	2009-06-21 02:44:11
927	2009.0	6.0	21.0	2.736389	48.260333	-123.721333	10.3	NaN	NaN	None	5.27	mg/m^3	20.60	34.80	umol/L	31.831216	9.726881	10.213083	2009-06-21 02:44:11
928	2009.0	6.0	21.0	2.736389	48.260333	-123.721333	20.6	NaN	NaN	None	4.97	mg/m^3	22.10	36.50	umol/L	31.990000	9.281489	20.425656	2009-06-21 02:44:11
937	2009.0	6.0	21.0	5.835833	48.309833	-124.067667	1.9	NaN	NaN	None	17.64	mg/m^3	14.80	25.80	umol/L	31.855916	10.077544	1.883997	2009-06-21 05:50:09
939	2009.0	6.0	21.0	5.835833	48.309833	-124.067667	10.1	NaN	NaN	None	10.68	mg/m^3	17.20	29.40	umol/L	31.855544	10.077846	10.014730	2009-06-21 05:50:09
940	2009.0	6.0	21.0	5.835833	48.309833	-124.067667	20.5	NaN	NaN	None	13.34	mg/m^3	17.30	28.50	umol/L	31.930037	9.960018	20.326415	2009-06-21 05:50:09
950	2009.0	6.0	21.0	9.702500	48.470167	-124.548500	1.7	NaN	NaN	None	12.89	mg/m^3	12.80	22.70	umol/L	31.797204	10.447325	1.685657	2009-06-21 09:42:09
952	2009.0	6.0	21.0	9.702500	48.470167	-124.548500	9.7	NaN	NaN	None	20.82	mg/m^3	13.40	22.00	umol/L	32.227928	10.043882	9.617975	2009-06-21 09:42:09
953	2009.0	6.0	21.0	9.702500	48.470167	-124.548500	20.0	NaN	NaN	None	0.94	mg/m^3	26.30	42.30	umol/L	32.455806	8.610200	19.830381	2009-06-21 09:42:09
964	2009.0	6.0	21.0	12.248611	48.500667	-124.734333	1.8	NaN	NaN	None	14.93	mg/m^3	8.30	14.70	umol/L	32.114649	10.288480	1.784808	2009-06-21 12:14:55
966	2009.0	6.0	21.0	12.248611	48.500667	-124.734333	10.9	NaN	NaN	None	4.22	mg/m^3	23.40	37.00	umol/L	32.238194	9.025464	10.807766	2009-06-21 12:14:55
967	2009.0	6.0	21.0	12.248611	48.500667	-124.734333	20.1	NaN	NaN	None	7.72	mg/m^3	16.00	24.50	umol/L	32.655002	9.332093	19.929473	2009-06-21 12:14:55
1365	2010.0	6.0	23.0	17.985556	48.501167	-124.733833	2.1	NaN	NaN	None	1.21	mg/m^3	21.90	36.20	umol/L	31.192511	10.693282	2.082275	2010-06-23 17:59:08
1367	2010.0	6.0	23.0	17.985556	48.501167	-124.733833	10.7	NaN	NaN	None	1.97	mg/m^3	22.20	36.00	umol/L	31.734785	10.334393	10.609463	2010-06-23 17:59:08
1368	2010.0	6.0	23.0	17.985556	48.501167	-124.733833	20.7	NaN	NaN	None	1.67	mg/m^3	22.90	36.60	umol/L	31.796463	9.924836	20.524351	2010-06-23 17:59:08
1381	2010.0	6.0	23.0	21.547500	48.469167	-124.543833	1.8	NaN	NaN	None	2.70	mg/m^3	19.70	34.10	umol/L	31.625980	10.839667	1.784813	2010-06-23 21:32:51
1383	2010.0	6.0	23.0	21.547500	48.469167	-124.543833	10.8	NaN	NaN	None	2.35	mg/m^3	21.30	35.20	umol/L	31.735047	10.256759	10.708646	2010-06-23 21:32:51
1384	2010.0	6.0	23.0	21.547500	48.469167	-124.543833	21.1	NaN	NaN	None	0.79	mg/m^3	24.60	38.20	umol/L	31.941687	9.245623	20.920998	2010-06-23 21:32:51
1395	2010.0	6.0	24.0	3.106111	48.309333	-124.065667	1.8	NaN	NaN	None	1.05	mg/m^3	21.60	35.90	umol/L	30.990361	10.610546	1.784839	2010-06-24 03:06:22
1396	2010.0	6.0	24.0	3.106111	48.309333	-124.065667	5.9	NaN	NaN	None	1.13	mg/m^3	21.60	36.10	umol/L	30.974905	10.626953	5.850249	2010-06-24 03:06:22
1397	2010.0	6.0	24.0	3.106111	48.309333	-124.065667	10.9	NaN	NaN	None	1.09	mg/m^3	22.20	36.40	umol/L	31.032588	10.504902	10.807956	2010-06-24 03:06:22
1398	2010.0	6.0	24.0	3.106111	48.309333	-124.065667	20.4	NaN	NaN	None	0.72	mg/m^3	22.50	35.90	umol/L	31.393976	9.616825	20.227267	2010-06-24 03:06:22
1408	2010.0	6.0	24.0	7.188333	48.259500	-123.717000	1.7	NaN	NaN	None	0.80	mg/m^3	21.10	35.80	umol/L	30.778134	10.567536	1.685690	2010-06-24 07:11:18
1410	2010.0	6.0	24.0	7.188333	48.259500	-123.717000	10.9	NaN	NaN	None	0.83	mg/m^3	21.80	36.00	umol/L	30.830208	10.494354	10.808005	2010-06-24 07:11:18
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
14308	2008.0	6.0	20.0	5.140833	48.260333	-123.721500	20.1	NaN	NaN	None	1.61	mg/m^3	27.40	43.60	umol/L	31.988824	8.653933	19.929912	2008-06-20 05:08:27
14317	2008.0	6.0	20.0	7.683333	48.310500	-124.068167	2.0	NaN	NaN	None	3.47	mg/m^3	22.30	39.30	umol/L	31.309083	9.425148	1.983154	2008-06-20 07:41:00
14319	2008.0	6.0	20.0	7.683333	48.310500	-124.068167	9.9	NaN	NaN	None	4.06	mg/m^3	26.20	42.60	umol/L	31.743512	8.860797	9.816423	2008-06-20 07:41:00
14320	2008.0	6.0	20.0	7.683333	48.310500	-124.068167	19.7	NaN	NaN	None	3.50	mg/m^3	26.60	42.80	umol/L	31.809192	8.801711	19.533226	2008-06-20 07:41:00
44511	2016.0	6.0	6.0	9.816944	48.299333	-123.998833	3.9	NaN	NaN	None	1.27	mg/m^3	22.49	37.04	umol/L	31.670660	10.022609	3.867136	2016-06-06 09:49:01
44524	2016.0	6.0	16.0	14.563333	48.500500	-124.734500	3.6	NaN	NaN	None	2.65	mg/m^3	18.35	32.96	umol/L	30.709232	11.719243	3.569601	2016-06-16 14:33:48
44526	2016.0	6.0	16.0	14.563333	48.500500	-124.734500	10.1	NaN	NaN	None	1.90	mg/m^3	18.44	32.84	umol/L	30.708073	11.512623	10.014555	2016-06-16 14:33:48
44527	2016.0	6.0	16.0	14.563333	48.500500	-124.734500	19.6	NaN	NaN	None	0.81	mg/m^3	21.04	35.14	umol/L	31.396285	10.166112	19.433738	2016-06-16 14:33:48
44538	2016.0	6.0	16.0	17.624444	48.470500	-124.547667	1.9	NaN	NaN	None	2.11	mg/m^3	19.27	34.28	umol/L	31.017351	11.385460	1.883969	2016-06-16 17:37:28
44541	2016.0	6.0	16.0	17.624444	48.470500	-124.547667	10.0	NaN	NaN	None	0.79	mg/m^3	22.84	37.42	umol/L	31.641506	9.680428	9.915431	2016-06-16 17:37:28
44542	2016.0	6.0	16.0	17.624444	48.470500	-124.547667	20.1	NaN	NaN	None	0.60	mg/m^3	23.86	38.61	umol/L	31.809808	9.492293	19.929528	2016-06-16 17:37:28
44554	2016.0	6.0	16.0	22.022500	48.309667	-124.066833	1.6	NaN	NaN	None	1.90	mg/m^3	17.74	30.80	umol/L	30.605620	11.778187	1.586525	2016-06-16 22:01:21
44556	2016.0	6.0	16.0	22.022500	48.309667	-124.066833	9.9	NaN	NaN	None	1.44	mg/m^3	18.90	32.78	umol/L	30.697195	10.994266	9.816424	2016-06-16 22:01:21
44557	2016.0	6.0	16.0	22.022500	48.309667	-124.066833	20.2	NaN	NaN	None	0.96	mg/m^3	20.82	34.94	umol/L	30.989751	10.216089	20.028970	2016-06-16 22:01:21
44567	2016.0	6.0	17.0	1.200833	48.260000	-123.720333	2.7	NaN	NaN	None	1.17	mg/m^3	15.95	30.01	umol/L	30.376460	11.775605	2.677265	2016-06-17 01:12:03
44570	2016.0	6.0	17.0	1.200833	48.260000	-123.720333	10.0	NaN	NaN	None	1.15	mg/m^3	17.07	30.69	umol/L	30.488583	10.947426	9.915622	2016-06-17 01:12:03
44571	2016.0	6.0	17.0	1.200833	48.260000	-123.720333	19.8	NaN	NaN	None	1.14	mg/m^3	17.07	30.58	umol/L	31.216834	10.073979	19.632465	2016-06-17 01:12:03
46099	2017.0	6.0	5.0	11.983333	48.300333	-124.003167	5.1	5.0	NaN	None	1.40	mg/m^3	19.31	32.86	umol/L	30.776208	10.253985	5.000000	2017-06-05 11:59:00
46877	2017.0	6.0	22.0	17.129167	48.500000	-124.734500	1.3	1.3	NaN	None	2.20	mg/m^3	15.08	29.00	umol/L	30.576887	12.066188	1.300000	2017-06-22 17:07:45
46881	2017.0	6.0	22.0	17.129167	48.500000	-124.734500	9.3	9.2	NaN	None	2.25	mg/m^3	15.81	29.75	umol/L	30.765576	11.704150	9.200000	2017-06-22 17:07:45
46882	2017.0	6.0	22.0	17.129167	48.500000	-124.734500	20.0	19.8	NaN	None	1.23	mg/m^3	20.54	34.35	umol/L	31.277714	10.277012	19.800000	2017-06-22 17:07:45
46893	2017.0	6.0	22.0	20.935000	48.469500	-124.546000	0.6	0.6	NaN	None	2.80	mg/m^3	12.56	26.56	umol/L	30.585129	12.390519	0.600000	2017-06-22 20:56:06
46897	2017.0	6.0	22.0	20.935000	48.469500	-124.546000	10.2	10.1	NaN	None	1.77	mg/m^3	20.24	34.21	umol/L	30.611670	10.915805	10.100000	2017-06-22 20:56:06
46898	2017.0	6.0	22.0	20.935000	48.469500	-124.546000	19.7	19.5	NaN	None	1.18	mg/m^3	21.17	34.89	umol/L	30.809728	10.235778	19.500000	2017-06-22 20:56:06
46910	2017.0	6.0	23.0	1.790556	48.310667	-124.067000	1.1	1.1	NaN	None	1.41	mg/m^3	20.19	34.47	umol/L	30.600015	11.502959	1.100000	2017-06-23 01:47:26
46912	2017.0	6.0	23.0	1.790556	48.310667	-124.067000	10.2	10.1	NaN	None	1.25	mg/m^3	19.92	34.01	umol/L	30.703519	10.774795	10.100000	2017-06-23 01:47:26
46913	2017.0	6.0	23.0	1.790556	48.310667	-124.067000	20.6	20.4	NaN	None	1.01	mg/m^3	20.97	35.16	umol/L	30.779220	10.450065	20.400000	2017-06-23 01:47:26
46923	2017.0	6.0	23.0	4.822778	48.259667	-123.719333	1.2	1.2	NaN	None	1.25	mg/m^3	19.69	33.36	umol/L	30.498009	10.782388	1.200000	2017-06-23 04:49:22
46926	2017.0	6.0	23.0	4.822778	48.259667	-123.719333	10.1	10.0	NaN	None	1.19	mg/m^3	19.70	33.31	umol/L	30.527568	10.725600	10.000000	2017-06-23 04:49:22
46927	2017.0	6.0	23.0	4.822778	48.259667	-123.719333	20.6	20.4	NaN	None	1.17	mg/m^3	20.49	33.75	umol/L	30.961474	10.351940	20.400000	2017-06-23 04:49:22

187 rows × 19 columns

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