#!/usr/bin/env python
# coding: utf-8

# # 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
get_ipython().run_line_magic('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)


# ### 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()


# 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))


# 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()


# 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)


# In[ ]: