inter-run comparisons of N contained in phytoplankton¶
- S3
- Sentry Shoal
- Central Node
- JDF
In [1]:
import pandas as pd
import netCDF4 as nc
import datetime as dt
import subprocess
import requests
import matplotlib.pyplot as plt
import cmocean
import numpy as np
import os
import re
import dateutil as dutil
from salishsea_tools import viz_tools, places
import glob
import pickle
import matplotlib.dates as mdates
import matplotlib as mpl
mpl.rc('xtick', labelsize=14)
mpl.rc('ytick', labelsize=16)
mpl.rc('legend', fontsize=16)
mpl.rc('axes', titlesize=16)
mpl.rc('figure', titlesize=16)
mpl.rc('axes', labelsize=16)
mpl.rc('font', size=16)
mpl.rcParams['font.size'] = 16
mpl.rcParams['axes.titlesize'] = 16
mpl.rcParams['legend.numpoints'] = 1
%matplotlib inline
In [2]:
plist=['Sentry Shoal','S3','Central node','Central SJDF']
In [3]:
df1=pd.read_csv('/ocean/eolson/MEOPAR/obs/ONC/turbidity/nearSurface/search3928586/BritishColumbiaFerries_Tsawwassen-DukePoint_Turbidity-ChlorophyllandFluorescence_20140804T234330Z_20150604T070614Z-clean.csv',
skiprows=78,header=None,
names=('TimeUTC','CDOM','CDOMQC','Chlorophyll_ug','ChlQC','Turbidity_NTU','TurbQC','Lat','LatQC','Lon','LongQC'))
/home/eolson/anaconda3/envs/python36/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2698: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False. interactivity=interactivity, compiler=compiler, result=result)
In [4]:
df2=pd.read_csv('/ocean/eolson/MEOPAR/obs/ONC/turbidity/nearSurface/search3928586/BritishColumbiaFerries_Tsawwassen-DukePoint_Turbidity-ChlorophyllandFluorescence_20150604T070624Z_20160307T160206Z-clean.csv',
skiprows=78,header=None,
names=('TimeUTC','CDOM','CDOMQC','Chlorophyll_ug','ChlQC','Turbidity_NTU','TurbQC','Lat','LatQC','Lon','LongQC'))
/home/eolson/anaconda3/envs/python36/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2698: DtypeWarning: Columns (1,3,5) have mixed types. Specify dtype option on import or set low_memory=False. interactivity=interactivity, compiler=compiler, result=result)
In [5]:
df=pd.concat([df1.drop(df1[df1.TimeUTC<'2015'].index),df2.drop(df2[df2.TimeUTC>'2016'].index)],ignore_index=True)
In [6]:
dts=[dt.datetime(int(r[0:4]),int(r[5:7]),int(r[8:10]),int(r[11:13]),int(r[14:16]),int(r[17:19])) for r in df['TimeUTC']]
In [7]:
df=df.assign(dts=dts)
In [8]:
df['Lat']=pd.to_numeric(df['Lat'],errors='coerce')
df['Lon']=pd.to_numeric(df['Lon'],errors='coerce')
In [9]:
df.head()
Out[9]:
| TimeUTC | CDOM | CDOMQC | Chlorophyll_ug | ChlQC | Turbidity_NTU | TurbQC | Lat | LatQC | Lon | LongQC | dts | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2015-01-01T00:00:08.461Z | 16.5912 | 1 | 1.2463 | 1 | 4.5548 | 1 | 49.094471 | 8 | -123.426450 | 8 | 2015-01-01 00:00:08 |
| 1 | 2015-01-01T00:00:17.515Z | 16.948 | 1 | 1.2342 | 1 | 4.7864 | 1 | 49.094835 | 8 | -123.427538 | 8 | 2015-01-01 00:00:17 |
| 2 | 2015-01-01T00:00:27.689Z | 16.7696 | 1 | 1.2221 | 1 | 4.632 | 1 | 49.095233 | 8 | -123.428714 | 8 | 2015-01-01 00:00:27 |
| 3 | 2015-01-01T00:00:37.874Z | 16.7696 | 1 | 1.3673 | 1 | 4.632 | 1 | 49.095742 | 8 | -123.429833 | 8 | 2015-01-01 00:00:37 |
| 4 | 2015-01-01T00:00:48.048Z | 16.948 | 1 | 1.2826 | 1 | 4.632 | 1 | 49.096250 | 8 | -123.430950 | 8 | 2015-01-01 00:00:48 |
In [10]:
df['Lon'][0]
Out[10]:
-123.42645043
In [11]:
places.PLACES['S3']
Out[11]:
{'GEM2.5 grid ji': (138, 144),
'NEMO grid ji': (450, 258),
'lon lat': (-123.558, 49.125)}
In [12]:
111*.0226
Out[12]:
2.5086
In [13]:
llon=places.PLACES['S3']['lon lat'][0]-.01
ulon=places.PLACES['S3']['lon lat'][0]+.01
llat=places.PLACES['S3']['lon lat'][1]-.01
ulat=places.PLACES['S3']['lon lat'][1]+.01
iidfnd=(df.Lon>llon)&(df.Lon<ulon)&(df.Lat>llat)&(df.Lat<ulat)
In [14]:
fig,ax=plt.subplots(1,1,figsize=(20,4))
ax.plot(df.loc[iidfnd,['dts']],df.loc[iidfnd,['Chlorophyll_ug']],'k*')
ax.set_xlim(dt.datetime(2015,3,4),dt.datetime(2015,5,1))
dt.datetime(2014,12,31)+dt.timedelta(days=65),dt.datetime(2014,12,31)+dt.timedelta(days=75)
Out[14]:
(datetime.datetime(2015, 3, 6, 0, 0), datetime.datetime(2015, 3, 16, 0, 0))
In [15]:
with nc.Dataset('/ocean/eolson/MEOPAR/NEMO-forcing/grid/mesh_mask201702_noLPE.nc') as fm:
tmask=np.copy(fm.variables['tmask'])
umask=np.copy(fm.variables['umask'])
vmask=np.copy(fm.variables['vmask'])
navlon=np.copy(fm.variables['nav_lon'])
navlat=np.copy(fm.variables['nav_lat'])
e3t_0=np.copy(fm.variables['e3t_0'])
e3u_0=np.copy(fm.variables['e3u_0'])
e3v_0=np.copy(fm.variables['e3v_0'])
e1t=np.copy(fm.variables['e1t'])
e2t=np.copy(fm.variables['e2t'])
e1v=np.copy(fm.variables['e1v'])
e2u=np.copy(fm.variables['e2u'])
A=fm.variables['e1t'][0,:,:]*fm.variables['e2t'][0,:,:]*tmask[0,0,:,:]
t0=dt.datetime(2015,2,6)
fdur=10 # length of each results file in days
fnum=18 # number of results files per run
runlen=fdur*fnum # length of run in days
#stm=np.shape(tmask)
In [16]:
saveloc='/data/eolson/MEOPAR/SS36runs/calcFiles/comparePhytoN/'
baseloc='/data/eolson/MEOPAR/SS36runs/CedarRuns/'
dirname1='C07'
dirname2='B06'
dirnames=(dirname1,dirname2)
#dirnames=('spring2015_NewSink','spring2015_slowPP','spring2015_KhT','spring2015_diatHS')
varNameDict={'Sentry Shoal':'SentryShoal', 'S3':'S3', 'Central node':'CentralNode', 'Central SJDF':'CentralSJDF'}
with open('/ocean/eolson/MEOPAR/analysis-elise/notebooks/bioTuning/spathsMaster.txt') as f:
spaths = dict(x.strip().split() for x in f)
#spaths={'spring2015_NewSink':'/data/eolson/results/MEOPAR/SS36runs/CedarRuns/spring2015_NewSink/',
# 'spring2015_KhT':'/data/eolson/results/MEOPAR/SS36runs/CedarRuns/spring2015_KhT/',
# 'spring2015_uzoo':'/data/eolson/results/MEOPAR/SS36runs/CedarRuns/spring2015_uzoo/',
# 'spring2015_uzoo2':'/data/eolson/results/MEOPAR/SS36runs/CedarRuns/spring2015_uzoo2/',
# 'spring2015_slowPP':'/data/eolson/results/MEOPAR/SS36runs/OrcinusRuns/spring2015_slowPP/',
# 'spring2015_lowMuNano':'/data/eolson/results/MEOPAR/SS36runs/CedarRuns/spring2015_lowMuNano/',
# 'spring2015_SMELTBFastSink':'/data/eolson/results/MEOPAR/SS36runs/OrcinusRuns/spring2015_SMELTBFastSink/',
# 'spring2015_uzPref':'/data/eolson/results/MEOPAR/SS36runs/CedarRuns/spring2015_uzPref/',
# 'spring2015_hiNH':'/data/eolson/results/MEOPAR/SS36runs/CedarRuns/spring2015_hiNH/'}
ff=dict()
for idir in dirnames:
ff[idir]=dict()
for pl in plist:
print(spaths[idir]+'ts_'+idir+'_'+varNameDict[pl]+'.nc')
ff[idir][pl]=nc.Dataset(spaths[idir]+'ts_'+idir+'_'+varNameDict[pl]+'.nc')
#try:
# pl='Total'
# ff[idir][pl]=nc.Dataset(spaths[idir]+'ts_'+idir+'_'+varNameDict[pl]+'.nc')
#except:
# pass
/data/eolson/results/MEOPAR/SS36runs/OrcinusRuns/C07/ts_C07_SentryShoal.nc /data/eolson/results/MEOPAR/SS36runs/OrcinusRuns/C07/ts_C07_S3.nc /data/eolson/results/MEOPAR/SS36runs/OrcinusRuns/C07/ts_C07_CentralNode.nc /data/eolson/results/MEOPAR/SS36runs/OrcinusRuns/C07/ts_C07_CentralSJDF.nc /data/eolson/results/MEOPAR/SS36runs/OrcinusRuns/B06/ts_B06_SentryShoal.nc /data/eolson/results/MEOPAR/SS36runs/OrcinusRuns/B06/ts_B06_S3.nc /data/eolson/results/MEOPAR/SS36runs/OrcinusRuns/B06/ts_B06_CentralNode.nc /data/eolson/results/MEOPAR/SS36runs/OrcinusRuns/B06/ts_B06_CentralSJDF.nc
In [17]:
spaths[idir]+'ts_'+idir+'_'+varNameDict[pl]+'.nc'
Out[17]:
'/data/eolson/results/MEOPAR/SS36runs/OrcinusRuns/B06/ts_B06_CentralSJDF.nc'
In [18]:
times=dict()
for idir in dirnames:
f=ff[idir]['S3']
torig=dt.datetime.strptime(f.variables['time_centered'].time_origin,'%Y-%m-%d %H:%M:%S')
print(torig)
times[idir]=np.array([torig + dt.timedelta(seconds=ii) for ii in f.variables['time_centered'][:]])
1900-01-01 00:00:00 1900-01-01 00:00:00
In [19]:
lcol={dirname1:{'diatoms':'darkgreen','flagellates':'mediumblue','ciliates':'maroon'},
dirname2:{'diatoms':'c','flagellates':'blueviolet','ciliates':'orange'}}
lsty={dirname1:'-',
dirname2:'--'}
In [20]:
tmins=list()
tmaxs=list()
for idir in dirnames:
tmins.append(times[idir][0])
tmaxs.append(times[idir][-1])
#xl=(np.min(np.array(tmins)),np.max(np.array(tmaxs)))
xl=(np.min(np.array(tmins)),dt.datetime(2015,7,1))
In [21]:
sumVarTr={'diatoms':'diatSum','flagellates':'flagSum','ciliates':'myriSum'}
In [22]:
yearsFmt = mdates.DateFormatter('%b %d')
fig,ax=plt.subplots(len(plist),1,figsize=(16,3*(len(plist))))
pp=dict()
for ii in range(0,len(plist)):
pl=plist[ii]
if pl=='S3':
ax[ii].plot(df.loc[iidfnd,['dts']],[float(ik[0]) for ik in df.loc[iidfnd,['Chlorophyll_ug']].values],'.',color='lightgray',alpha=.5)
pp[ii]=dict()
for idir in dirnames:
f=ff[idir][pl]
for var in ('diatoms','flagellates','ciliates'):
try:
pp[ii][var]=ax[ii].plot(times[idir],2.0*np.sum(f.variables[var][:,:3,0,0]*f.variables['e3t'][:,:3,0,0],1)/np.sum(f.variables['e3t'][:,:3,0,0],1),
linestyle=lsty[idir],color=lcol[idir][var],alpha=.5,label=idir+' '+var)
except:
pp[ii][var]=ax[ii].plot(times[idir],2.0*np.sum(f.variables[var][:,:3,0,0]*np.tile(e3t_0[:,:3,0,0],(len(times[idir]),1)),1)/np.sum(np.tile(e3t_0[:,:3,0,0],(len(times[idir]),1)),1),
linestyle=lsty[idir],color=lcol[idir][var],alpha=.5,label=idir+' '+var)
try:
pp[ii]['tot']=ax[ii].plot(times[idir],2.0*np.sum((f.variables['diatoms'][:,:3,0,0]+f.variables['flagellates'][:,:3,0,0]+f.variables['ciliates'][:,:3,0,0])*f.variables['e3t'][:,:3,0,0],1)/np.sum(f.variables['e3t'][:,:3,0,0],1),
linestyle=lsty[idir],color='k',alpha=.6,label=idir+' '+'total')
except:
pp[ii]['tot']=ax[ii].plot(times[idir],2.0*np.sum((f.variables['diatoms'][:,:3,0,0]+f.variables['flagellates'][:,:3,0,0]+f.variables['ciliates'][:,:3,0,0])*np.tile(e3t_0[:,:3,0,0],(len(times[idir]),1)),1)/np.sum(np.tile(e3t_0[:,:3,0,0],(len(times[idir]),1)),1),
linestyle=lsty[idir],color='k',alpha=.6,label=idir+' '+'total')
ax[ii].set_title(pl)
ax[ii].set_ylabel('chl ($\mu$g/L)')
ax[ii].xaxis.set_major_formatter(yearsFmt)
ax[ii].set_xlim(xl)
ax[ii].set_ylim(0,40)
if ii==0:
ax[0].legend(bbox_to_anchor=(1,1),fontsize=12)
plt.tight_layout()
In [23]:
yearsFmt = mdates.DateFormatter('%b %d')
fig,ax=plt.subplots(len(plist),1,figsize=(16,3*(len(plist))))
pp=dict()
for ii in range(0,len(plist)):
pl=plist[ii]
if pl=='S3':
ax[ii].plot(df.loc[iidfnd,['dts']],[float(ik[0]) for ik in df.loc[iidfnd,['Chlorophyll_ug']].values],'.',color='lightgray',alpha=.5)
pp[ii]=dict()
for idir in dirnames:
f=ff[idir][pl]
for var in ('diatoms',):#,'flagellates','ciliates'):
try:
pp[ii][var]=ax[ii].plot(times[idir],2.0*np.sum(f.variables[var][:,:3,0,0]*f.variables['e3t'][:,:3,0,0],1)/np.sum(f.variables['e3t'][:,:3,0,0],1),
linestyle=lsty[idir],color=lcol[idir][var],alpha=.5,label=idir+' '+var)
except:
pp[ii][var]=ax[ii].plot(times[idir],2.0*np.sum(f.variables[var][:,:3,0,0]*np.tile(e3t_0[:,:3,0,0],(len(times[idir]),1)),1)/np.sum(np.tile(e3t_0[:,:3,0,0],(len(times[idir]),1)),1),
linestyle=lsty[idir],color=lcol[idir][var],alpha=.5,label=idir+' '+var)
#try:
# pp[ii]['tot']=ax[ii].plot(times[idir],2.0*np.sum((f.variables['diatoms'][:,:3,0,0]+f.variables['flagellates'][:,:3,0,0]+f.variables['ciliates'][:,:3,0,0])*f.variables['e3t'][:,:3,0,0],1)/np.sum(f.variables['e3t'][:,:3,0,0],1),
# linestyle=lsty[idir],color='k',alpha=.6,label=idir+' '+'total')
#except:
# pp[ii]['tot']=ax[ii].plot(times[idir],2.0*np.sum((f.variables['diatoms'][:,:3,0,0]+f.variables['flagellates'][:,:3,0,0]+f.variables['ciliates'][:,:3,0,0])*np.tile(e3t_0[:,:3,0,0],(len(times[idir]),1)),1)/np.sum(np.tile(e3t_0[:,:3,0,0],(len(times[idir]),1)),1),
# linestyle=lsty[idir],color='k',alpha=.6,label=idir+' '+'total')
ax[ii].set_title(pl)
ax[ii].set_ylabel('chl ($\mu$g/L)')
ax[ii].xaxis.set_major_formatter(yearsFmt)
ax[ii].set_xlim(xl)
ax[ii].set_ylim(0,40)
if ii==0:
ax[0].legend(bbox_to_anchor=(1,1),fontsize=12)
plt.tight_layout()
In [24]:
yearsFmt = mdates.DateFormatter('%b %d')
fig,ax=plt.subplots(len(plist),1,figsize=(16,3*(len(plist))))
pp=dict()
for ii in range(0,len(plist)):
pl=plist[ii]
if pl=='S3':
ax[ii].plot(df.loc[iidfnd,['dts']],[float(ik[0]) for ik in df.loc[iidfnd,['Chlorophyll_ug']].values],'.',color='lightgray',alpha=.5)
pp[ii]=dict()
for idir in dirnames:
f=ff[idir][pl]
for var in ('flagellates',):#,'flagellates','ciliates'):
try:
pp[ii][var]=ax[ii].plot(times[idir],2.0*np.sum(f.variables[var][:,:3,0,0]*f.variables['e3t'][:,:3,0,0],1)/np.sum(f.variables['e3t'][:,:3,0,0],1),
linestyle=lsty[idir],color=lcol[idir][var],alpha=.5,label=idir+' '+var)
except:
pp[ii][var]=ax[ii].plot(times[idir],2.0*np.sum(f.variables[var][:,:3,0,0]*np.tile(e3t_0[:,:3,0,0],(len(times[idir]),1)),1)/np.sum(np.tile(e3t_0[:,:3,0,0],(len(times[idir]),1)),1),
linestyle=lsty[idir],color=lcol[idir][var],alpha=.5,label=idir+' '+var)
#try:
# pp[ii]['tot']=ax[ii].plot(times[idir],2.0*np.sum((f.variables['diatoms'][:,:3,0,0]+f.variables['flagellates'][:,:3,0,0]+f.variables['ciliates'][:,:3,0,0])*f.variables['e3t'][:,:3,0,0],1)/np.sum(f.variables['e3t'][:,:3,0,0],1),
# linestyle=lsty[idir],color='k',alpha=.6,label=idir+' '+'total')
#except:
# pp[ii]['tot']=ax[ii].plot(times[idir],2.0*np.sum((f.variables['diatoms'][:,:3,0,0]+f.variables['flagellates'][:,:3,0,0]+f.variables['ciliates'][:,:3,0,0])*np.tile(e3t_0[:,:3,0,0],(len(times[idir]),1)),1)/np.sum(np.tile(e3t_0[:,:3,0,0],(len(times[idir]),1)),1),
# linestyle=lsty[idir],color='k',alpha=.6,label=idir+' '+'total')
ax[ii].set_title(pl)
ax[ii].set_ylabel('chl ($\mu$g/L)')
ax[ii].xaxis.set_major_formatter(yearsFmt)
ax[ii].set_xlim(xl)
ax[ii].set_ylim(0,40)
if ii==0:
ax[0].legend(bbox_to_anchor=(1,1),fontsize=12)
plt.tight_layout()
In [25]:
yearsFmt = mdates.DateFormatter('%b %d')
fig,ax=plt.subplots(len(plist),1,figsize=(16,3*(len(plist))))
pp=dict()
for ii in range(0,len(plist)):
pl=plist[ii]
if pl=='S3':
ax[ii].plot(df.loc[iidfnd,['dts']],[float(ik[0]) for ik in df.loc[iidfnd,['Chlorophyll_ug']].values],'.',color='lightgray',alpha=.5)
pp[ii]=dict()
for idir in dirnames:
f=ff[idir][pl]
for var in ('ciliates',):#,'flagellates','ciliates'):
try:
pp[ii][var]=ax[ii].plot(times[idir],2.0*np.sum(f.variables[var][:,:3,0,0]*f.variables['e3t'][:,:3,0,0],1)/np.sum(f.variables['e3t'][:,:3,0,0],1),
linestyle=lsty[idir],color=lcol[idir][var],alpha=.5,label=idir+' '+var)
except:
pp[ii][var]=ax[ii].plot(times[idir],2.0*np.sum(f.variables[var][:,:3,0,0]*np.tile(e3t_0[:,:3,0,0],(len(times[idir]),1)),1)/np.sum(np.tile(e3t_0[:,:3,0,0],(len(times[idir]),1)),1),
linestyle=lsty[idir],color=lcol[idir][var],alpha=.5,label=idir+' '+var)
#try:
# pp[ii]['tot']=ax[ii].plot(times[idir],2.0*np.sum((f.variables['diatoms'][:,:3,0,0]+f.variables['flagellates'][:,:3,0,0]+f.variables['ciliates'][:,:3,0,0])*f.variables['e3t'][:,:3,0,0],1)/np.sum(f.variables['e3t'][:,:3,0,0],1),
# linestyle=lsty[idir],color='k',alpha=.6,label=idir+' '+'total')
#except:
# pp[ii]['tot']=ax[ii].plot(times[idir],2.0*np.sum((f.variables['diatoms'][:,:3,0,0]+f.variables['flagellates'][:,:3,0,0]+f.variables['ciliates'][:,:3,0,0])*np.tile(e3t_0[:,:3,0,0],(len(times[idir]),1)),1)/np.sum(np.tile(e3t_0[:,:3,0,0],(len(times[idir]),1)),1),
# linestyle=lsty[idir],color='k',alpha=.6,label=idir+' '+'total')
ax[ii].set_title(pl)
ax[ii].set_ylabel('chl ($\mu$g/L)')
ax[ii].xaxis.set_major_formatter(yearsFmt)
ax[ii].set_xlim(xl)
ax[ii].set_ylim(0,40)
if ii==0:
ax[0].legend(bbox_to_anchor=(1,1),fontsize=12)
plt.tight_layout()
In [26]:
# one at a time
yearsFmt = mdates.DateFormatter('%b %d')
for idir in dirnames:
fig,ax=plt.subplots(len(plist),1,figsize=(16,3*(len(plist))))
pp=dict()
for ii in range(0,len(plist)):
pl=plist[ii]
if pl=='S3':
ax[ii].plot(df.loc[iidfnd,['dts']],[(1/2.0)*float(ik[0]) for ik in df.loc[iidfnd,['Chlorophyll_ug']].values],'.',color='lightgray',alpha=.5)
pp[ii]=dict()
f=ff[idir][pl]
for var in ('diatoms','flagellates','ciliates'):
try:
pp[ii][var]=ax[ii].plot(times[idir],np.sum(f.variables[var][:,:3,0,0]*f.variables['e3t'][:,:3,0,0],1)/np.sum(f.variables['e3t'][:,:3,0,0],1),
linestyle=lsty[idir],color=lcol[idir][var],alpha=.6,label=idir+' '+var)
except:
pp[ii][var]=ax[ii].plot(times[idir],np.sum(f.variables[var][:,:3,0,0]*np.tile(e3t_0[:,:3,0,0],(len(times[idir]),1)),1)/np.sum(np.tile(e3t_0[:,:3,0,0],(len(times[idir]),1)),1),
linestyle=lsty[idir],color=lcol[idir][var],alpha=.6,label=idir+' '+var)
ax[ii].set_title(pl)
ax[ii].set_ylabel('N (mmol m$^{-2}$)')
ax[ii].xaxis.set_major_formatter(yearsFmt)
ax[ii].set_xlim(xl)
if ii==0:
ax[0].legend(bbox_to_anchor=(1.25,1),fontsize=12)
fig.suptitle(idir)
plt.tight_layout()
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