DFO Si Plots¶
In [1]:
import numpy as np
import matplotlib.pyplot as plt
import os
import pandas as pd
import netCDF4 as nc
import datetime as dt
from salishsea_tools import evaltools as et, viz_tools
import gsw
import matplotlib.gridspec as gridspec
import matplotlib as mpl
import matplotlib.dates as mdates
import cmocean as cmo
import scipy.interpolate as sinterp
import pickle
import cmocean
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)
mpl.rc('legend', fontsize=12)
mpl.rc('axes', titlesize=12)
mpl.rc('axes', labelsize=12)
mpl.rc('figure', titlesize=12)
mpl.rc('font', size=12)
mpl.rc('text', usetex=True)
mpl.rc('text.latex', preamble = ','.join(r'''
\usepackage{txfonts}
\usepackage{lmodern}
'''.split()))
mpl.rc('font', family='sans-serif', weight='normal', style='normal')
%matplotlib inline
In [2]:
df=et.loadDFO()
df.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]:
with nc.Dataset('/ocean/eolson/MEOPAR/NEMO-forcing/grid/mesh_mask201702_noLPE.nc') as mesh:
navlon=mesh.variables['nav_lon'][:,:]
navlat=mesh.variables['nav_lat'][:,:]
tmask=mesh.variables['tmask'][:,:,:,:]
gdept=mesh.variables['gdept_1d'][0,:]
e3t0=mesh.variables['e3t_0'][0,:,:,:]
bathy=np.sum(e3t0,0)
with nc.Dataset('/results/SalishSea/hindcast.201812/01jan16/SalishSea_1h_20160101_20160101_ptrc_T.nc') as ftemp:
bounds=np.copy(ftemp.variables['deptht_bounds'][:,:])
In [4]:
#d1=et._gridHoriz(df.loc[df.Z>150].copy(deep=True),tmask,navlon,navlat,wrapSearch=False,resetIndex=True);
PATH= '/data/eolson/MEOPAR/SS36runs/linkHC201812/'
start_date = dt.datetime(2015,1,1)
end_date = dt.datetime(2018,12,31)
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}
d1=et.matchData(df,filemap, fdict, start_date, end_date, namfmt, PATH, flen)
#d1=et._gridHoriz(df.copy(deep=True),tmask,navlon,navlat,wrapSearch=False,resetIndex=True);
#d1['k']=np.nan
#for ind, row in d1.iterrows():
# ik=et._getZInd_bin(row['Z'],None,boundsFlag=True)
# if (not np.isnan(ik)) and (tmask[0,ik,row['j'],row['i']]==1):
# d1.loc[ind,['k']]=ik
(Lat,Lon)= 49.29933333333334 -122.983 not matched to domain (Lat,Lon)= 49.304833333333335 -122.94116666666666 not matched to domain (Lat,Lon)= 50.4882 -126.3484 not matched to domain (Lat,Lon)= 50.6318 -126.4979 not matched to domain (Lat,Lon)= 50.8046 -126.5291 not matched to domain (Lat,Lon)= 50.8762 -126.6183 not matched to domain (Lat,Lon)= 50.9086 -126.5451 not matched to domain progress: 0.0% progress: 97.48488984207448%
In [17]:
pickle.dump(d1,open('/data/eolson/MEOPAR/SS36runs/calcFiles/evalMatches/dataDFO_k.pkl','wb'))
In [5]:
d1['YD']=[(dt.datetime(int(yy),int(mm),int(dd))-dt.datetime(int(yy),1,1)).total_seconds()/(24*3600) for yy,mm,dd in zip(d1['Year'].values,d1['Month'].values,d1['Day'].values)]
In [6]:
np.unique(d1.loc[(d1.N==0)&(d1.Z>20),['Year']])
Out[6]:
array([], dtype=float64)
In [7]:
d1.loc[(d1.N==0)&(d1.Z>20)]
Out[7]:
| Year | Month | Day | Hour | Lat | Lon | Pressure | Depth | Ammonium | Ammonium_units | ... | mod_nitrate | mod_silicon | mod_ammonium | mod_diatoms | mod_ciliates | mod_flagellates | mod_vosaline | mod_votemper | k | YD |
|---|
0 rows × 31 columns
In [8]:
d1.loc[(d1.Si==0)&(d1.Z>20)].head()
Out[8]:
| Year | Month | Day | Hour | Lat | Lon | Pressure | Depth | Ammonium | Ammonium_units | ... | mod_nitrate | mod_silicon | mod_ammonium | mod_diatoms | mod_ciliates | mod_flagellates | mod_vosaline | mod_votemper | k | YD |
|---|
0 rows × 31 columns
In [10]:
def binmean(ddf,latEdges,lonEdges):
latbins=np.digitize(ddf.Lat,latEdges)
lonbins=np.digitize(ddf.Lon,lonEdges)
midLats=.5*(latEdges[:-1]+latEdges[1:])
midLons=.5*(lonEdges[:-1]+lonEdges[1:])
xxlon,xxlat=np.meshgrid(midLons,midLats)
sums=np.zeros(np.shape(xxlon))
counts=np.zeros(np.shape(xxlon))
means=np.nan*np.ones(np.shape(xxlon))
for jj in range(0,len(midLats)):
for ii in range(0,len(midLons)):
if len(ddf.loc[(latbins==jj+1)&(lonbins==ii+1),['Si']])>=1:
means[jj,ii]=np.sum(ddf.loc[(latbins==jj+1)&(lonbins==ii+1),['Si']])/len(ddf.loc[(latbins==jj+1)&(lonbins==ii+1),['mod_silicon']])
return xxlon, xxlat, means
In [11]:
fig,ax=plt.subplots(12,4,figsize=(16,50))
#kk=30 #36,34,32,30
simin=40
simax=80
for mon in range(1,13):
iax=ax[mon-1,0]
kk=26
iax.contourf(navlon,navlat,tmask[0,kk,:,:],levels=(0,.5,1.5),lw=.5,colors=('darkgray','k','k'))
viz_tools.set_aspect(iax,coords='map')
idf=d1.loc[(d1.k==kk)&(d1.Si>=0)&(d1.Month==mon)]
lo,la,mm=binmean(idf,latEdges=np.linspace(47,51,31),lonEdges=np.linspace(-126,-122,41))
#iax.pcolormesh(lo,la,np.ma.masked_where(np.isnan(mm),mm))
mesh=iax.scatter(lo,la,c=mm,s=6,vmin=simin,vmax=simax)
plt.colorbar(mesh,ax=iax)
iax.set_title('Depth='+str(gdept[kk]))
iax.set_ylabel('Month='+str(mon))
iax.set_xlim(-126,-122)
iax.set_ylim(47.1,50.5)
iax=ax[mon-1,1]
kk=27
iax.contourf(navlon,navlat,tmask[0,kk,:,:],levels=(0,.5,1.5),lw=.5,colors=('darkgray','k','k'))
viz_tools.set_aspect(iax,coords='map')
idf=d1.loc[(d1.k==kk)&(d1.Si>=0)&(d1.Month==mon)]
lo,la,mm=binmean(idf,latEdges=np.linspace(47,51,31),lonEdges=np.linspace(-126,-122,41))
#iax.pcolormesh(lo,la,np.ma.masked_where(np.isnan(mm),mm))
mesh=iax.scatter(lo,la,c=mm,s=6,vmin=simin,vmax=simax)
plt.colorbar(mesh,ax=iax)
iax.set_title('Depth='+str(gdept[kk]))
iax.set_xlim(-126,-122)
iax.set_ylim(47.1,50.5)
iax=ax[mon-1,2]
kk=28
iax.contourf(navlon,navlat,tmask[0,kk,:,:],levels=(0,.5,1.5),lw=.5,colors=('darkgray','k','k'))
viz_tools.set_aspect(iax,coords='map')
idf=d1.loc[(d1.k==kk)&(d1.Si>=0)&(d1.Month==mon)]
lo,la,mm=binmean(idf,latEdges=np.linspace(47,51,31),lonEdges=np.linspace(-126,-122,41))
#iax.pcolormesh(lo,la,np.ma.masked_where(np.isnan(mm),mm))
mesh=iax.scatter(lo,la,c=mm,s=6,vmin=simin,vmax=simax)
plt.colorbar(mesh,ax=iax)
iax.set_title('Depth='+str(gdept[kk]))
iax.set_xlim(-126,-122)
iax.set_ylim(47.1,50.5)
iax=ax[mon-1,3]
kk=36
iax.contourf(navlon,navlat,tmask[0,kk,:,:],levels=(0,.5,1.5),lw=.5,colors=('darkgray','k','k'))
viz_tools.set_aspect(iax,coords='map')
idf=d1.loc[(d1.k==kk)&(d1.Si>=0)&(d1.Month==mon)]
lo,la,mm=binmean(idf,latEdges=np.linspace(47,51,31),lonEdges=np.linspace(-126,-122,41))
#iax.pcolormesh(lo,la,np.ma.masked_where(np.isnan(mm),mm))
mesh=iax.scatter(lo,la,c=mm,s=6,vmin=simin,vmax=simax)
plt.colorbar(mesh,ax=iax)
iax.set_title('Depth='+str(gdept[kk]))
iax.set_xlim(-126,-122)
iax.set_ylim(47.1,50.5)
In [12]:
dfVic=d1.loc[(d1.Lat>48.15)&(d1.Lat<48.4)&(d1.Lon>-123.3)&(d1.Lon<-123.1)]
fig,ax=plt.subplots(12,4,figsize=(16,50))
#kk=30 #36,34,32,30
simin=40
simax=80
for mon in range(1,13):
iax=ax[mon-1,0]
kk=26
iax.contourf(navlon,navlat,tmask[0,kk,:,:],levels=(0,.5,1.5),lw=.5,colors=('darkgray','w','w'))
viz_tools.set_aspect(iax,coords='map')
idf=dfVic.loc[(dfVic.k==kk)&(dfVic.Si>=0)&(dfVic.Month==mon)]
lo,la,mm=binmean(idf,latEdges=np.linspace(47,51,31),lonEdges=np.linspace(-126,-122,41))
#iax.pcolormesh(lo,la,np.ma.masked_where(np.isnan(mm),mm))
mesh=iax.scatter(lo,la,c=mm,s=6,vmin=simin,vmax=simax)
plt.colorbar(mesh,ax=iax)
iax.set_title('Depth='+str(gdept[kk]))
iax.set_ylabel('Month='+str(mon))
iax.set_xlim(-126,-122)
iax.set_ylim(47.1,50.5)
iax=ax[mon-1,1]
kk=27
iax.contourf(navlon,navlat,tmask[0,kk,:,:],levels=(0,.5,1.5),lw=.5,colors=('darkgray','w','w'))
viz_tools.set_aspect(iax,coords='map')
idf=dfVic.loc[(dfVic.k==kk)&(dfVic.Si>=0)&(dfVic.Month==mon)]
lo,la,mm=binmean(idf,latEdges=np.linspace(47,51,31),lonEdges=np.linspace(-126,-122,41))
#iax.pcolormesh(lo,la,np.ma.masked_where(np.isnan(mm),mm))
mesh=iax.scatter(lo,la,c=mm,s=6,vmin=simin,vmax=simax)
plt.colorbar(mesh,ax=iax)
iax.set_title('Depth='+str(gdept[kk]))
iax.set_xlim(-126,-122)
iax.set_ylim(47.1,50.5)
iax=ax[mon-1,2]
kk=28
iax.contourf(navlon,navlat,tmask[0,kk,:,:],levels=(0,.5,1.5),lw=.5,colors=('darkgray','w','w'))
viz_tools.set_aspect(iax,coords='map')
idf=dfVic.loc[(dfVic.k==kk)&(dfVic.Si>=0)&(dfVic.Month==mon)]
lo,la,mm=binmean(idf,latEdges=np.linspace(47,51,31),lonEdges=np.linspace(-126,-122,41))
#iax.pcolormesh(lo,la,np.ma.masked_where(np.isnan(mm),mm))
mesh=iax.scatter(lo,la,c=mm,s=6,vmin=simin,vmax=simax)
plt.colorbar(mesh,ax=iax)
iax.set_title('Depth='+str(gdept[kk]))
iax.set_xlim(-126,-122)
iax.set_ylim(47.1,50.5)
iax=ax[mon-1,3]
kk=36
iax.contourf(navlon,navlat,tmask[0,kk,:,:],levels=(0,.5,1.5),lw=.5,colors=('darkgray','w','w'))
viz_tools.set_aspect(iax,coords='map')
idf=dfVic.loc[(dfVic.k==kk)&(dfVic.Si>=0)&(dfVic.Month==mon)]
lo,la,mm=binmean(idf,latEdges=np.linspace(47,51,31),lonEdges=np.linspace(-126,-122,41))
#iax.pcolormesh(lo,la,np.ma.masked_where(np.isnan(mm),mm))
mesh=iax.scatter(lo,la,c=mm,s=6,vmin=simin,vmax=simax)
plt.colorbar(mesh,ax=iax)
iax.set_title('Depth='+str(gdept[kk]))
iax.set_xlim(-126,-122)
iax.set_ylim(47.1,50.5)
In [13]:
fig,ax=plt.subplots(1,2,figsize=(10,4))
#kk=30 #36,34,32,30
simin=40
simax=80
for mon in range(1,12):
iax=ax[0];#ax[mon-1,1]
kk=34
iax.contour(navlon,navlat,tmask[0,0,:,:],levels=(.5,),linewidths=(.5,),colors=('w','w','w'))
iax.contourf(navlon,navlat,tmask[0,kk,:,:],levels=(0,.5,1.5),colors=('lightgray','darkgray','k'))
viz_tools.set_aspect(iax,coords='map')
d1bath=np.array([bathy[j,i] for i,j in zip(d1['i'],d1['j'])])
df0=d1.loc[(d1.Lon<-123.3)&(d1.Lon>-123.6)&(d1.Lat<49.2)&(d1.Lat>48.85)&(d1.Si>=0)&(d1bath>400)]
lo,la,mm=binmean(df0,latEdges=np.linspace(47,51,31),lonEdges=np.linspace(-126,-122,41))
#iax.pcolormesh(lo,la,np.ma.masked_where(np.isnan(mm),mm))
mesh=iax.scatter(lo,la,c=mm,s=6,vmin=simin,vmax=simax,zorder=2)
plt.colorbar(mesh,ax=iax)
#iax.set_title('Depth='+str(gdept[kk]))
Out[13]:
<matplotlib.colorbar.Colorbar at 0x7f3d1ce085f8>
In [14]:
times=np.array([dt.datetime(int(yy),int(mm),int(dd)) for yy,mm,dd in zip(df0.Year,df0.Month,df0.Day)])
In [23]:
Tcol='orangered'
Scol='teal'
Sicol='darkviolet'
Sicol2='fuchsia'
Chcol='limegreen'
fig=plt.figure(figsize=(12,7))
h=.15
w=.35
axPos=((.1,.7,w,h),(.1,.5,w,h),(.1,.3,w,h),(.1,.1,w,h))
axPos2=((.6,.7,w,h),(.6,.5,w,h),(.6,.3,w,h),(.6,.1,w,h))
axSi=dict(); axS=dict(); axT=dict(); axCh=dict()
for i in range(0,4):
axS[i]=fig.add_axes(axPos[i])
axT[i]=axS[i].twinx()
axT[i].yaxis.tick_left()
axT[i].spines['left'].set_position(('outward', 40))
axT[i].spines['left'].set_color(Tcol)
axCh[i]=axS[i].twinx()
axCh[i].spines['right'].set_position(('outward', 40))
axCh[i].spines['right'].set_color(Chcol)
axSi[i]=axS[i].twinx()
axSi[i].spines['right'].set_color(Sicol)
axS[i].spines['left'].set_color(Scol)
pSi2,=axSi[i].plot(times[df0.k==(ik-2)],df0.loc[df0.k==(ik-2),['mod_silicon']],'o',color=Sicol2,alpha=.5)
pSi,=axSi[i].plot(times[df0.k==ik],df0.loc[df0.k==ik,['mod_silicon']],'o',color=Sicol,alpha=.5)
pSi2,=axSi[i].plot(times[df0.k==(ik-2)],df0.loc[df0.k==(ik-2),['mod_silicon']],'o',color=Sicol2,alpha=.5)
#pS,=axS[i].plot(timesV,dfV['SA'],'-',color=Scol,alpha=.5)
#pT,=axT[i].plot(timesV,dfV['CT'],'-',color=Tcol,alpha=.5)
#pCh,=axCh[i].plot(timesCh,dfCh['Chl_ugl'],'.',color=Chcol,alpha=.5)
axSi[i].spines['left'].set_visible(False)
axCh[i].spines['left'].set_visible(False)
axS[i].spines['right'].set_visible(False)
axT[i].spines['right'].set_visible(False)
axS[0].set_xlim((dt.datetime(2009,1,1),dt.datetime(2011,1,1)))
axS[1].set_xlim((dt.datetime(2011,1,1),dt.datetime(2013,1,1)))
axS[2].set_xlim((dt.datetime(2013,1,1),dt.datetime(2015,1,1)))
axS[3].set_xlim((dt.datetime(2015,1,1),dt.datetime(2017,1,1)))
axT[0].yaxis.set_label_position("left")
axT[0].set_ylabel('CT ($^{\circ}$C)',color=Tcol)
axT[1].spines['left'].set_position(('outward', 55))
axS[1].set_ylabel('SA (g kg$^{-1}$)',color=Scol)
axSi[2].set_ylabel('dSi ($\muup$M)',color=Sicol)
axCh[2].spines['right'].set_position(('outward', 55))
axCh[3].set_ylabel('Chl ($\muup$g L$^{-1}$)',color=Chcol)
plt.legend((pT,pS,pSi2,pSi,pCh),('Conservative Temperature (Central Node)','Absolute Salinity (Central Node)',
str(int(bounds[ik-2,0]))+'--'+str(int(bounds[ik-2,1]))+' m Silicate (DFO)',
str(int(bounds[ik,0]))+'--'+str(int(bounds[ik,1]))+' m Silicate (DFO)','Chlorophyll (ferry)'),loc=8,ncol=3,
bbox_to_anchor=[0.5,-1,0,0])
#model side
for i in range(0,4):
axS[i]=fig.add_axes(axPos2[i])
axT[i]=axS[i].twinx()
axT[i].yaxis.tick_left()
axT[i].spines['left'].set_position(('outward', 40))
axT[i].spines['left'].set_color(Tcol)
axCh[i]=axS[i].twinx()
axCh[i].spines['right'].set_position(('outward', 40))
axCh[i].spines['right'].set_color(Chcol)
axSi[i]=axS[i].twinx()
axSi[i].spines['right'].set_color(Sicol)
axS[i].spines['left'].set_color(Scol)
pSi2,=axSi[i].plot(times[df0.k==(ik-2)],df0.loc[df0.k==(ik-2),['Si']],'o',color=Sicol2,alpha=.5)
pSi,=axSi[i].plot(times[df0.k==ik],df0.loc[df0.k==ik,['Si']],'o',color=Sicol,alpha=.5)
pS,=axS[i].plot(timesV,dfV['SA'],'-',color=Scol,alpha=.5)
pT,=axT[i].plot(timesV,dfV['CT'],'-',color=Tcol,alpha=.5)
pCh,=axCh[i].plot(timesCh,dfCh['Chl_ugl'],'.',color=Chcol,alpha=.5)
axSi[i].spines['left'].set_visible(False)
axCh[i].spines['left'].set_visible(False)
axS[i].spines['right'].set_visible(False)
axT[i].spines['right'].set_visible(False)
Out[23]:
<matplotlib.legend.Legend at 0x7fdd9b422a58>
In [16]:
Tcol='orangered'
Scol='teal'
Sicol='darkviolet'
Sicol2='fuchsia'
Chcol='limegreen'
fig=plt.figure(figsize=(12,7))
h=.15
w=.35
axPos=((.1,.7,w,h),(.1,.5,w,h),(.1,.3,w,h),(.1,.1,w,h))
axPos2=((.6,.7,w,h),(.6,.5,w,h),(.6,.3,w,h),(.6,.1,w,h))
axSi=dict(); axS=dict(); axT=dict(); axCh=dict()
for i in range(0,4):
axS[i]=fig.add_axes(axPos[i])
axT[i]=axS[i].twinx()
axT[i].yaxis.tick_left()
axT[i].spines['left'].set_position(('outward', 40))
axT[i].spines['left'].set_color(Tcol)
axCh[i]=axS[i].twinx()
axCh[i].spines['right'].set_position(('outward', 40))
axCh[i].spines['right'].set_color(Chcol)
axSi[i]=axS[i].twinx()
axSi[i].spines['right'].set_color(Sicol)
axS[i].spines['left'].set_color(Scol)
pSi2,=axSi[i].plot(times[df0.k==(ik-2)],df0.loc[df0.k==(ik-2),['mod_silicon']],'o',color=Sicol2,alpha=.5)
pSi,=axSi[i].plot(times[df0.k==ik],df0.loc[df0.k==ik,['mod_silicon']],'o',color=Sicol,alpha=.5)
pSi2,=axSi[i].plot(times[df0.k==(ik-2)],df0.loc[df0.k==(ik-2),['mod_silicon']],'o',color=Sicol2,alpha=.5)
#pS,=axS[i].plot(timesV,dfV['SA'],'-',color=Scol,alpha=.5)
#pT,=axT[i].plot(timesV,dfV['CT'],'-',color=Tcol,alpha=.5)
#pCh,=axCh[i].plot(timesCh,dfCh['Chl_ugl'],'.',color=Chcol,alpha=.5)
axSi[i].spines['left'].set_visible(False)
axCh[i].spines['left'].set_visible(False)
axS[i].spines['right'].set_visible(False)
axT[i].spines['right'].set_visible(False)
axS[0].set_xlim((dt.datetime(2009,1,1),dt.datetime(2011,1,1)))
axS[1].set_xlim((dt.datetime(2011,1,1),dt.datetime(2013,1,1)))
axS[2].set_xlim((dt.datetime(2013,1,1),dt.datetime(2015,1,1)))
axS[3].set_xlim((dt.datetime(2015,1,1),dt.datetime(2017,1,1)))
axT[0].yaxis.set_label_position("left")
axT[0].set_ylabel('CT ($^{\circ}$C)',color=Tcol)
axT[1].spines['left'].set_position(('outward', 55))
axS[1].set_ylabel('SA (g kg$^{-1}$)',color=Scol)
axSi[2].set_ylabel('dSi ($\muup$M)',color=Sicol)
axCh[2].spines['right'].set_position(('outward', 55))
axCh[3].set_ylabel('Chl ($\muup$g L$^{-1}$)',color=Chcol)
plt.legend((pT,pS,pSi2,pSi,pCh),('Conservative Temperature (Central Node)','Absolute Salinity (Central Node)',
str(int(bounds[ik-2,0]))+'--'+str(int(bounds[ik-2,1]))+' m Silicate (DFO)',
str(int(bounds[ik,0]))+'--'+str(int(bounds[ik,1]))+' m Silicate (DFO)','Chlorophyll (ferry)'),loc=8,ncol=3,
bbox_to_anchor=[0.5,-1,0,0])
--------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-16-4f2373cfb47d> in <module>() 22 axSi[i].spines['right'].set_color(Sicol) 23 axS[i].spines['left'].set_color(Scol) ---> 24 pSi2,=axSi[i].plot(times[df0.k==(ik-2)],df0.loc[df0.k==(ik-2),['mod_silicon']],'o',color=Sicol2,alpha=.5) 25 pSi,=axSi[i].plot(times[df0.k==ik],df0.loc[df0.k==ik,['mod_silicon']],'o',color=Sicol,alpha=.5) 26 pSi2,=axSi[i].plot(times[df0.k==(ik-2)],df0.loc[df0.k==(ik-2),['mod_silicon']],'o',color=Sicol2,alpha=.5) NameError: name 'ik' is not defined
In [51]:
timesVic=np.array([dt.datetime(int(yy),int(mm),int(dd)) for yy,mm,dd in zip(dfVic.Year,dfVic.Month,dfVic.Day)])
In [59]:
fig,ax=plt.subplots(1,1,figsize=(12,3))
simin=20; simax=60
m1=ax.scatter(timesVic,dfVic.k,c=dfVic.Si,s=6,vmin=simin,vmax=simax)
fig.colorbar(m1)
ax.set_xlim((dt.datetime(1999,1,1),dt.datetime(2019,1,1)))
Out[59]:
(729755.0, 737060.0)
In [63]:
fig,ax=plt.subplots(1,1,figsize=(12,3))
ii=dfVic.k==27
ax.plot(timesVic[ii],dfVic.Si[ii],'k.')
ax.set_xlim((dt.datetime(1999,1,1),dt.datetime(2019,1,1)))
Out[63]:
(729755.0, 737060.0)
In [24]:
ik=36
ax=axs[0]
ax.plot(timesV,20*(dfV['PracticalSalinity_psu']-np.nanmean(dfV['PracticalSalinity_psu'])),'-',color='teal')
ax.plot(timesV,10*(dfV['Temperature_C']-np.nanmean(dfV['Temperature_C'])),'-',color='coral')
ax.plot(times[df0.k==ik],df0.loc[df0.k==ik,['Si']]-np.nanmean(df0.loc[df0.k==ik,['Si']]),'o',color='deeppink')
ax.set_xlim((dt.datetime(2009,1,1),dt.datetime(2011,1,1)))
ax=axs[1]
ax.plot(timesV,20*(dfV['PracticalSalinity_psu']-np.nanmean(dfV['PracticalSalinity_psu'])),'-',color='teal')
ax.plot(timesV,10*(dfV['Temperature_C']-np.nanmean(dfV['Temperature_C'])),'-',color='coral')
ax.plot(times[df0.k==ik],df0.loc[df0.k==ik,['Si']]-np.nanmean(df0.loc[df0.k==ik,['Si']]),'o',color='deeppink')
ax.set_xlim((dt.datetime(2011,1,1),dt.datetime(2013,1,1)))
ax=axs[2]
ax.plot(timesV,20*(dfV['PracticalSalinity_psu']-np.nanmean(dfV['PracticalSalinity_psu'])),'-',color='teal')
ax.plot(timesV,10*(dfV['Temperature_C']-np.nanmean(dfV['Temperature_C'])),'-',color='coral')
ax.plot(times[df0.k==ik],df0.loc[df0.k==ik,['Si']]-np.nanmean(df0.loc[df0.k==ik,['Si']]),'o',color='deeppink')
ax.set_xlim((dt.datetime(2013,1,1),dt.datetime(2015,1,1)))
ax=axs[3]
ax.plot(timesV,20*(dfV['PracticalSalinity_psu']-np.nanmean(dfV['PracticalSalinity_psu'])),'-',color='teal')
ax.plot(timesV,10*(dfV['Temperature_C']-np.nanmean(dfV['Temperature_C'])),'-',color='coral')
ax.plot(times[df0.k==ik],df0.loc[df0.k==ik,['Si']]-np.nanmean(df0.loc[df0.k==ik,['Si']]),'o',color='deeppink')
ax.set_xlim((dt.datetime(2015,1,1),dt.datetime(2017,1,1)))
Out[24]:
(735599.0, 736330.0)
In [25]:
fig,ax=plt.subplots(1,3,figsize=(16,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
for valm, groupedm in df0.groupby(['Month']):
ik=37
ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
stds=groupedm.groupby(['k']).agg({'Si':np.std})
ns=groupedm.groupby(['k'])['Si'].count()
ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
label=str(int(valm)))
ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],
bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('Si vs CT, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')
Out[25]:
<matplotlib.text.Text at 0x7fdd9b175b38>
In [26]:
fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
for valm, groupedm in df0.groupby(['Month']):
ik=36
ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)
means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
stds=groupedm.groupby(['k']).agg({'Si':np.std})
ns=groupedm.groupby(['k'])['Si'].count()
ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
label=str(int(valm)))
ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],
bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')
Out[26]:
<matplotlib.text.Text at 0x7fdda7391390>
In [27]:
fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
for valm, groupedm in df0.groupby(['Month']):
ik=34
ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)
means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
stds=groupedm.groupby(['k']).agg({'Si':np.std})
ns=groupedm.groupby(['k'])['Si'].count()
ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
label=str(int(valm)))
ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],
bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')
Out[27]:
<matplotlib.text.Text at 0x7fddc8447ac8>
In [28]:
fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
for valm, groupedm in df0.groupby(['Month']):
ik=32
ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)
means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
stds=groupedm.groupby(['k']).agg({'Si':np.std})
ns=groupedm.groupby(['k'])['Si'].count()
ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
label=str(int(valm)))
ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],
bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')
Out[28]:
<matplotlib.text.Text at 0x7fdda54a9a90>
In [29]:
fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
for valm, groupedm in df0.groupby(['Month']):
ik=30
ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)
means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
stds=groupedm.groupby(['k']).agg({'Si':np.std})
ns=groupedm.groupby(['k'])['Si'].count()
ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
label=str(int(valm)))
ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],
bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')
Out[29]:
<matplotlib.text.Text at 0x7fddcc742b00>
In [30]:
fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
for valm, groupedm in df0.groupby(['Month']):
ik=29
ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)
means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
stds=groupedm.groupby(['k']).agg({'Si':np.std})
ns=groupedm.groupby(['k'])['Si'].count()
ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
label=str(int(valm)))
ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],
bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')
Out[30]:
<matplotlib.text.Text at 0x7fddccdafcf8>
In [31]:
fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
for valm, groupedm in df0.groupby(['Month']):
ik=28
ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)
means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
stds=groupedm.groupby(['k']).agg({'Si':np.std})
ns=groupedm.groupby(['k'])['Si'].count()
ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
label=str(int(valm)))
ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],
bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')
Out[31]:
<matplotlib.text.Text at 0x7fdda54b5240>
In [32]:
fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
for valm, groupedm in df0.groupby(['Month']):
ik=34
ax[1].plot(groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['AbsSal']],
groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['Si']].values/groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['N']].values,
'o',color=col[int(valm)],alpha=1)
ax[2].plot(groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['ConsT']],
groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['Si']].values/groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['N']].values,
'o',color=col[int(valm)],alpha=1)
ax[3].plot(groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['AbsSal']],
groupedm.loc[(groupedm.k==ik)&(groupedm.N>=0),['ConsT']].values,
'o',color=col[int(valm)],alpha=1)
means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
stds=groupedm.groupby(['k']).agg({'Si':np.std})
ns=groupedm.groupby(['k'])['Si'].count()
ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
label=str(int(valm)))
ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[1],ps[2],ps[3],ps[4],ps[5],ps[6],ps[7],ps[8],ps[9],ps[10],ps[11],ps[12]],
bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')
Out[32]:
<matplotlib.text.Text at 0x7fddccd666d8>
In [33]:
df0.loc[(df0.k==34)].groupby(['Year'])['Si'].count()
Out[33]:
Year 1978.0 1 1996.0 7 1997.0 4 1998.0 6 1999.0 2 2000.0 9 2001.0 11 2002.0 16 2003.0 14 2004.0 11 2005.0 13 2006.0 12 2007.0 11 2008.0 12 2009.0 12 2010.0 20 2011.0 15 2012.0 12 2013.0 9 2014.0 14 2015.0 9 2016.0 12 2017.0 12 2018.0 9 Name: Si, dtype: int64
In [34]:
df0.loc[(df0.k==35)].groupby(['Year'])['Si'].count()
Out[34]:
Year 1996.0 3 1997.0 3 1998.0 3 1999.0 1 2000.0 3 2001.0 3 2002.0 3 2003.0 3 2004.0 2 2005.0 2 2006.0 3 2008.0 3 2010.0 4 2011.0 3 2013.0 2 2014.0 3 2015.0 1 2016.0 2 2017.0 4 2018.0 2 Name: Si, dtype: int64
In [35]:
df0.loc[(df0.k==36)].groupby(['Year'])['Si'].count()
Out[35]:
Year 1996.0 3 1997.0 2 1998.0 3 1999.0 1 2000.0 3 2001.0 5 2002.0 7 2003.0 9 2004.0 6 2005.0 6 2006.0 5 2007.0 5 2008.0 5 2009.0 8 2010.0 11 2011.0 3 2012.0 5 2013.0 4 2014.0 5 2015.0 3 2016.0 6 2017.0 4 2018.0 2 Name: Si, dtype: int64
In [36]:
fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
df1=df0.loc[df0.Year==2010]
for valm, groupedm in df1.groupby(['Month']):
ik=34
ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)
means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
stds=groupedm.groupby(['k']).agg({'Si':np.std})
ns=groupedm.groupby(['k'])['Si'].count()
ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
label=str(int(valm)))
ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[ii] for ii in range(1,13) if ii in ps.keys()],
bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('2010 Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')
Out[36]:
<matplotlib.text.Text at 0x7fddc8742748>
In [37]:
fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
df1=df0.loc[df0.Year==2010]
for valm, groupedm in df1.groupby(['Month']):
ik=35
ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)
means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
stds=groupedm.groupby(['k']).agg({'Si':np.std})
ns=groupedm.groupby(['k'])['Si'].count()
ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
label=str(int(valm)))
ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[ii] for ii in range(1,13) if ii in ps.keys()],
bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title('2010 Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')
Out[37]:
<matplotlib.text.Text at 0x7fdda6ffca20>
In [38]:
fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
df1=df0.loc[df0.Year==2002]
for valm, groupedm in df1.groupby(['Month']):
ik=34
ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)
means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
stds=groupedm.groupby(['k']).agg({'Si':np.std})
ns=groupedm.groupby(['k'])['Si'].count()
ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
label=str(int(valm)))
ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[ii] for ii in range(1,13) if ii in ps.keys()],
bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title(' 2002 Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')
Out[38]:
<matplotlib.text.Text at 0x7fdda527d198>
In [39]:
fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
df1=df0.loc[df0.Year==2003]
for valm, groupedm in df1.groupby(['Month']):
ik=36
ax[1].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
ax[2].plot(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],'o',color=col[int(valm)],alpha=1)
ax[3].plot(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],'o',color=col[int(valm)],alpha=1)
means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
stds=groupedm.groupby(['k']).agg({'Si':np.std})
ns=groupedm.groupby(['k'])['Si'].count()
ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
label=str(int(valm)))
ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[ii] for ii in range(1,13) if ii in ps.keys()],
bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title(' 2003 Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')
Out[39]:
<matplotlib.text.Text at 0x7fdda7d3b2e8>
In [40]:
df1.keys()
Out[40]:
Index(['Year', 'Month', 'Day', 'Hour', 'Lat', 'Lon', 'Pressure', 'Depth',
'Ammonium', 'Ammonium_units', 'Chlorophyll_Extracted',
'Chlorophyll_Extracted_units', 'N', 'Si', 'Silicate_units', 'AbsSal',
'ConsT', 'Z', 'dtUTC', 'j', 'i', 'k', 'YD'],
dtype='object')
In [41]:
fig,ax=plt.subplots(1,4,figsize=(20,4))
fig.subplots_adjust(wspace=.5)
col=('midnightblue','steelblue','darkturquoise','lime',
'seagreen','darkkhaki','darkorange','saddlebrown','firebrick',
'lightcoral','fuchsia','mediumorchid','blueviolet')
ps=dict()
df1=df0.loc[df0.Year==2003]
for valm, groupedm in df1.groupby(['Month']):
ik=36
cmin=0
cmax=365
if np.sum(groupedm.k==ik)>0:
ax[1].scatter(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['Si']],c=groupedm.loc[groupedm.k==ik,['YD']],
vmin=cmin,vmax=cmax,cmap=plt.get_cmap('hsv'))
ax[2].scatter(groupedm.loc[groupedm.k==ik,['ConsT']],groupedm.loc[groupedm.k==ik,['Si']],c=groupedm.loc[groupedm.k==ik,['YD']],
vmin=cmin,vmax=cmax,cmap=plt.get_cmap('hsv'))
m=ax[3].scatter(groupedm.loc[groupedm.k==ik,['AbsSal']],groupedm.loc[groupedm.k==ik,['ConsT']],c=groupedm.loc[groupedm.k==ik,['YD']],
vmin=cmin,vmax=cmax,cmap=plt.get_cmap('hsv'))
means=groupedm.groupby(['k']).agg({'Si':np.mean,'Z':np.mean})
stds=groupedm.groupby(['k']).agg({'Si':np.std})
ns=groupedm.groupby(['k'])['Si'].count()
ps[int(valm)],=ax[0].plot(means['Si'][ns>2],means['Z'][ns>2],'-',color=col[int(valm)],
label=str(int(valm)))
ax[0].errorbar(means['Si'][ns>2],means['Z'][ns>2],xerr=stds['Si'][ns>2],color=col[int(valm)])
ax[0].legend(handles=[ps[ii] for ii in range(1,13) if ii in ps.keys()],
bbox_to_anchor=(1.3, 1.0))
ax[0].set_ylim(400,50)
ax[0].set_xlim(30,100)
ax[0].set_ylabel('Depth (m)')
ax[0].set_xlabel('dSi ($\muup$M Si)')
ax[1].set_ylabel('dSi ($\muup$M Si)')
ax[1].set_title(' 2003 Si vs SA, '+str(int(bounds[ik,0]))+'m$<$Z$<$'+str(int(bounds[ik,1]))+'m')
fig.colorbar(m,ax=ax[3])
Out[41]:
<matplotlib.colorbar.Colorbar at 0x7fdda77e1550>
In [42]:
df1.loc[df1.k==ik,['Year','Month','Day','Lat','Lon','Pressure','Si','AbsSal','ConsT']]
Out[42]:
| Year | Month | Day | Lat | Lon | Pressure | Si | AbsSal | ConsT | |
|---|---|---|---|---|---|---|---|---|---|
| 40566 | 2003.0 | 4.0 | 26.0 | 49.163000 | -123.550167 | 371.7 | 70.7 | 31.364321 | 9.209034 |
| 40599 | 2003.0 | 4.0 | 27.0 | 49.162833 | -123.549500 | 375.0 | 71.6 | 31.352728 | 9.208075 |
| 40699 | 2003.0 | 6.0 | 19.0 | 49.164000 | -123.548167 | 352.7 | 73.4 | 31.260225 | 9.148055 |
| 40700 | 2003.0 | 6.0 | 19.0 | 49.164000 | -123.548167 | 369.9 | 75.0 | 31.274537 | 9.164992 |
| 40732 | 2003.0 | 6.0 | 20.0 | 49.163333 | -123.549500 | 352.3 | 73.2 | 31.275391 | 9.166978 |
| 40733 | 2003.0 | 6.0 | 20.0 | 49.163333 | -123.549500 | 372.2 | 72.9 | 31.293075 | 9.181456 |
| 40807 | 2003.0 | 9.0 | 5.0 | 49.162333 | -123.547500 | 372.3 | 56.9 | 31.491569 | 9.452973 |
| 40914 | 2003.0 | 12.0 | 4.0 | 49.163667 | -123.547833 | 353.2 | 65.9 | 31.417017 | 9.665293 |
| 40915 | 2003.0 | 12.0 | 4.0 | 49.163667 | -123.547833 | 373.8 | 65.3 | 31.423961 | 9.656753 |
In [43]:
plt.scatter(df1.loc[df1.k==ik,['Lon']],df1.loc[df1.k==ik,['Lat']],c=df1.loc[df1.k==ik,['Si']])
plt.xlim(-123.58,-123.53)
plt.ylim(49.16,49.17)
Out[43]:
(49.16, 49.17)
In [ ]:
In [ ]: