inter-run comparisons of N contained in phytoplankton¶

- S3
- Sentry Shoal
- Central Node
- JDF

In [1]:

import pandas as pd
import xarray as xr
import datetime as dt
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, evaltools as et
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

definitions¶

In [2]:

ChlToN=1.8
rundict={'202007H':'/home/sallen/202007/202007H',
         '202007I':'/home/sallen/202007/202007I',
         '201812':'/results/SalishSea/nowcast-green.201812/',
         '201905':'/results2/SalishSea/nowcast-green.201905/'}
namfmt={'202007H':'long',
        '202007I':'long',
        '201812':'nowcast',
        '201905':'nowcast'}
tres={'202007H':24,
      '202007I':24,
      '201812':1,
      '201905':24}
cols={'202007H':'c',
      '202007I':'g',
      '201812':'b',
      '201905':'m'}
startdate=dt.datetime(2015,2,1)
enddate=dt.datetime(2015,4,30)
yearstr='2015'
len(rundict)

Out[2]:

load ferry data¶

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,usecols=['TimeUTC','Chlorophyll_ug','ChlQC','Lat','LatQC','Lon','LongQC'],header=None,
        names=('TimeUTC','CDOM','CDOMQC','Chlorophyll_ug','ChlQC','Turbidity_NTU','TurbQC','Lat','LatQC','Lon','LongQC'),
               dtype={'TimeUTC':str,'Chlorophyll_ug':str,'ChlQC':int,'Lat':str,'LatQC':int,
                      'Lon':str,'LongQC':int})
df2=pd.read_csv('/ocean/eolson/MEOPAR/obs/ONC/turbidity/nearSurface/search3928586/BritishColumbiaFerries_Tsawwassen-DukePoint_Turbidity-ChlorophyllandFluorescence_20150604T070624Z_20160307T160206Z-clean.csv',
        skiprows=78,usecols=['TimeUTC','Chlorophyll_ug','ChlQC','Lat','LatQC','Lon','LongQC'],header=None,
        names=('TimeUTC','CDOM','CDOMQC','Chlorophyll_ug','ChlQC','Turbidity_NTU','TurbQC','Lat','LatQC','Lon','LongQC'),
               dtype={'TimeUTC':str,'Chlorophyll_ug':str,'ChlQC':int,'Lat':str,'LatQC':int,
                      'Lon':str,'LongQC':int})
df=pd.concat([df1.drop(df1[df1.TimeUTC<yearstr].index),df2.drop(df2[df2.TimeUTC>'2016'].index)],ignore_index=True)
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']]
df=df.assign(dts=dts)
df['Lat']=pd.to_numeric(df['Lat'],errors='coerce')
df['Lon']=pd.to_numeric(df['Lon'],errors='coerce')
df['Chlorophyll_ug']=pd.to_numeric(df['Chlorophyll_ug'],errors='coerce')
df.head()

Out[3]:

	TimeUTC	Chlorophyll_ug	ChlQC	Lat	LatQC	Lon	LongQC	dts
0	2015-01-01T00:00:08.461Z	1.2463	1	49.094471	8	-123.426450	8	2015-01-01 00:00:08
1	2015-01-01T00:00:17.515Z	1.2342	1	49.094835	8	-123.427538	8	2015-01-01 00:00:17
2	2015-01-01T00:00:27.689Z	1.2221	1	49.095233	8	-123.428714	8	2015-01-01 00:00:27
3	2015-01-01T00:00:37.874Z	1.3673	1	49.095742	8	-123.429833	8	2015-01-01 00:00:37
4	2015-01-01T00:00:48.048Z	1.2826	1	49.096250	8	-123.430950	8	2015-01-01 00:00:48

In [4]:

ij,ii=places.PLACES['S3']['NEMO grid ji']
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)
fig,ax=plt.subplots(1,1,figsize=(15,3))
ax.plot(df.loc[iidfnd,['dts']],df.loc[iidfnd,['Chlorophyll_ug']],'k*',ms=2)
ax.set_xlim(dt.datetime(2015,1,1),dt.datetime(2015,9,1))

Out[4]:

(16436.0, 16679.0)

In [5]:

diat=dict()
myri=dict()
nano=dict()
tt=dict()
for key in rundict:
    print(key,dt.datetime.now())
    fin=et.index_model_files(startdate,enddate,rundict[key],namfmt[key],1,'ptrc_T',tres[key])
    fobj=xr.open_mfdataset(fin['paths'])
    tt[key]=fobj.time_centered if 'time_centered' in fobj.coords else fobj.time_counter
    diat[key]=fobj.diatoms.isel(deptht=slice(0,3),y=ij,x=ii).mean(dim='deptht').compute()
    myri[key]=fobj.ciliates.isel(deptht=slice(0,3),y=ij,x=ii).mean(dim='deptht').compute()
    nano[key]=fobj.flagellates.isel(deptht=slice(0,3),y=ij,x=ii).mean(dim='deptht').compute()
    fobj.close()

202007H 2021-09-09 15:25:06.453462
202007I 2021-09-09 15:26:51.044000
201812 2021-09-09 15:28:11.287778
201905 2021-09-09 15:55:36.597983

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-5-83190ed7fb31> in <module>
      7     fin=et.index_model_files(startdate,enddate,rundict[key],namfmt[key],1,'ptrc_T',tres[key])
      8     fobj=xr.open_mfdataset(fin['paths'])
----> 9     tt[key]=fobj.time_centered
     10     diat[key]=fobj.diatoms.isel(deptht=slice(0,3),y=ij,x=ii).mean(dim='deptht').compute()
     11     myri[key]=fobj.ciliates.isel(deptht=slice(0,3),y=ij,x=ii).mean(dim='deptht').compute()

~/anaconda3/envs/py39/lib/python3.9/site-packages/xarray/core/common.py in __getattr__(self, name)
    237                 with suppress(KeyError):
    238                     return source[name]
--> 239         raise AttributeError(
    240             "{!r} object has no attribute {!r}".format(type(self).__name__, name)
    241         )

AttributeError: 'Dataset' object has no attribute 'time_centered'

In [16]:

ChlToN=1.8
fig,ax=plt.subplots(3,1,figsize=(15,3*3))
fig.subplots_adjust(hspace=.3)
for iax in ax:
    iax.plot(df.loc[iidfnd,['dts']],df.loc[iidfnd,['Chlorophyll_ug']],'*',ms=2,color='gray',label='Ferry')
    iax.set_xlim(startdate,enddate)
for key in rundict:
    ax[0].plot(tt[key],ChlToN*(diat[key]+myri[key]+nano[key]),'-',color=cols[key],label=key)
    ax[1].plot(tt[key],ChlToN*(diat[key]),'-',color=cols[key],label=key)
    ax[2].plot(tt[key],ChlToN*(nano[key]),'-',color=cols[key],label=key)
    ax[0].set_title('S3 Total Phyto Chl (mg/m3)')
    ax[1].set_title('S3 Diatoms Chl (mg/m3)')
    ax[2].set_title('S3 Flagellates Chl (mg/m3)')
ax[0].legend(fontsize=12)

Out[16]:

<matplotlib.legend.Legend at 0x7fd612e88f70>

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