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

# In[1]:


import netCDF4 as nc
from matplotlib import pyplot as plt
import numpy as np
import glob
import pickle
from salishsea_tools import evaltools as et
import datetime as dt
import os
import re
import cmocean
get_ipython().run_line_magic('matplotlib', 'inline')


# In[2]:


with nc.Dataset('/ocean/eolson/MEOPAR/NEMO-forcing/grid/mesh_mask201702_noLPE.nc') as mesh:
    tmask=mesh.variables['tmask'][0,:,:,:]
    e1t=np.expand_dims(mesh.variables['e1t'][:,:,:],1)
    e2t=np.expand_dims(mesh.variables['e2t'][:,:,:],1)
SOGtmaskPath='/ocean/eolson/MEOPAR/northernNO3PaperCalcs/save/SOGtmask.pkl'
(tmaskSOG,_,_,_,_)=pickle.load(open(SOGtmaskPath,'rb'))


# In[3]:


idir='/data/eolson/results/MEOPAR/SS36runs/CedarRuns/shortTestTRD/'


# In[4]:


fP=nc.Dataset(glob.glob(idir+'/SalishSea_1d_*_dian_T*.nc')[0])


# In[5]:


fP.variables.keys()


# In[6]:


fP.variables['REM_DON']


# In[7]:


np.max(np.ma.masked_where(tmask==0,fP.variables['SMS_NO3'][0,:,:,:])),np.min(np.ma.masked_where(tmask==0,fP.variables['SMS_NO3'][0,:,:,:]))


# In[8]:


np.max(np.ma.masked_where(tmask==0,fP.variables['ATF_NO3'][0,:,:,:])),np.min(np.ma.masked_where(tmask==0,fP.variables['ATF_NO3'][0,:,:,:]))

np.max(np.ma.masked_where(tmask[0,:,:]==0,fP.variables['ATF_NO3'][0,0,:,:]/fP.variables['AFILTNO3'][0,0,:,:])),np.min(np.ma.masked_where(tmask[0,:,:]==0,fP.variables['ATF_NO3'][0,0,:,:]/fP.variables['AFILTNO3'][0,0,:,:]))
# In[9]:


np.max(np.ma.masked_where(tmask==0,fP.variables['RDB_NO3'][0,:,:,:])),np.min(np.ma.masked_where(tmask==0,fP.variables['RDB_NO3'][0,:,:,:]))


# In[10]:


np.max(np.ma.masked_where(tmask==0,fP.variables['RDN_NO3'][0,:,:,:])),np.min(np.ma.masked_where(tmask==0,fP.variables['RDN_NO3'][0,:,:,:]))


# In[11]:


np.max(np.ma.masked_where(tmask==0,fP.variables['NO3RAD'][0,:,:,:])),np.min(np.ma.masked_where(tmask==0,fP.variables['NO3RAD'][0,:,:,:]))


# In[12]:


np.max(np.ma.masked_where(tmask==0,fP.variables['REM_DON'][0,:,:,:])),np.min(np.ma.masked_where(tmask==0,fP.variables['REM_DON'][0,:,:,:]))


# In[13]:


np.max(np.ma.masked_where(tmask==0,fP.variables['REM_bSi'][0,:,:,:])),np.min(np.ma.masked_where(tmask==0,fP.variables['REM_bSi'][0,:,:,:]))


# In[14]:


np.max(np.ma.masked_where(tmask==0,fP.variables['REM_PON'][0,:,:,:])),np.min(np.ma.masked_where(tmask==0,fP.variables['REM_PON'][0,:,:,:]))


# In[15]:


np.max(np.ma.masked_where(tmask==0,fP.variables['SMS_NO3'][0,:,:,:])),np.min(np.ma.masked_where(tmask==0,fP.variables['SMS_NO3'][0,:,:,:]))


# In[16]:


np.max(fP.variables['NITR'][0,0,:,:]-fP.variables['REM_NO3'][0,0,:,:]),np.min(fP.variables['NITR'][0,0,:,:]-fP.variables['REM_NO3'][0,0,:,:])


# In[17]:


fig,ax=plt.subplots(1,3,figsize=(8,3))
m0=ax[0].pcolormesh(fP.variables['NITR'][0,0,:,:])
plt.colorbar(m0,ax=ax[0])
m1=ax[1].pcolormesh(fP.variables['REM_NO3'][0,0,:,:])
plt.colorbar(m1,ax=ax[1])
m2=ax[2].pcolormesh(fP.variables['NITR'][0,0,:,:]-fP.variables['REM_NO3'][0,0,:,:],cmap=cmocean.cm.balance)
plt.colorbar(m2,ax=ax[2])

fig,ax=plt.subplots(1,3,figsize=(8,3))
m0=ax[0].pcolormesh(fP.variables['BIOTRNO3'][0,0,:,:])
plt.colorbar(m0,ax=ax[0])
m1=ax[1].pcolormesh(fP.variables['SMS_NO3'][0,0,:,:])
plt.colorbar(m1,ax=ax[1])
m2=ax[2].pcolormesh(fP.variables['BIOTRNO3'][0,0,:,:]-fP.variables['SMS_NO3'][0,0,:,:],cmap=cmocean.cm.balance)
plt.colorbar(m2,ax=ax[2])
# In[ ]:





# In[ ]:





# In[6]:


fS=nc.Dataset(glob.glob(idir+'/SalishSea_1d_*_Sutil_V*.nc')[0])
fS.variables.keys()


# In[18]:


fS=nc.Dataset(glob.glob(idir+'/SalishSea_1d_*_Sutil_V*.nc')[0])
fS.variables.keys()


# In[8]:


np.max(fS.variables['LDF_NO3'][0,:,:,:]),np.min(fS.variables['LDF_NO3'][0,:,:,:])


# In[ ]:





# In[19]:


fS.variables['ATY_NO3']


# In[20]:


fig,ax=plt.subplots(1,3,figsize=(8,3))
m0=ax[0].pcolormesh(fS.variables['NO3TVDY'][0,:,0,:])
plt.colorbar(m0,ax=ax[0])
m1=ax[1].pcolormesh(fS.variables['ATY_NO3'][0,:,0,:])
plt.colorbar(m1,ax=ax[1])
m2=ax[2].pcolormesh(fS.variables['NO3TVDY'][0,:,0,:]-fS.variables['ATY_NO3'][0,:,0,:],cmap=cmocean.cm.balance)
plt.colorbar(m2,ax=ax[2])


# In[20]:


fig,ax=plt.subplots(1,3,figsize=(8,3))
m0=ax[0].pcolormesh(fS.variables['NO3TVDY'][0,:,0,:])
plt.colorbar(m0,ax=ax[0])
m1=ax[1].pcolormesh(fS.variables['ATY_NO3'][0,:,0,:])
plt.colorbar(m1,ax=ax[1])
m2=ax[2].pcolormesh(fS.variables['NO3TVDY'][0,:,0,:]-fS.variables['ATY_NO3'][0,:,0,:],cmap=cmocean.cm.balance)
plt.colorbar(m2,ax=ax[2])


# In[ ]: