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

# In[1]:


import matplotlib.pyplot as plt
import numpy as np
import netCDF4 as nc
import datetime as dt
from salishsea_tools import evaltools as et, viz_tools, geo_tools
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()

get_ipython().run_line_magic('matplotlib', 'inline')


# In[2]:


df1=et.loadDFO()
df1.head()


# In[3]:


fig,ax=plt.subplots(1,1,figsize=(24,4))
ax.hist(df1['dtUTC'].values)


# In[4]:


df1.loc[df1.Si>=0.0]['dtUTC'].max()


# In[5]:


dfam=df1.loc[(df1.Ammonium>=0)]
dfam


# In[6]:


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'][0,:,:,:]


# In[7]:


m=plt.scatter(dfam.Lon,dfam.Lat,c=dfam.Ammonium)
plt.contour(navlon,navlat,tmask[0,:,:],levels=[.5,])
plt.colorbar(m)
plt.xlim((-124,-123))
plt.ylim(48,49.5)


# In[8]:


dfam2=dfam.loc[(dfam.Lat<49.2)]
dfam3=dfam.loc[(dfam.Lat<49.2)&(dfam.dtUTC>dt.datetime(2003,1,1))]
plt.scatter(dfam3.dtUTC,dfam3.Ammonium,c=dfam3.Lat)


# In[9]:


plt.scatter(dfam2.Ammonium,dfam2.Z,c=dfam2.Lat)


# In[10]:


np.unique(dfam2.loc[dfam2.Lat<49,['Lat','Lon']])


# In[11]:


j,i=geo_tools.find_closest_model_point(-123.43666667,   48.391,navlon,navlat)


# In[12]:


j,i


# In[13]:


sourceloc='/results/SalishSea/hindcast.201905/'
start=dt.datetime(2014,1,1)
end=dt.datetime(2016,1,1)
flist=et.index_model_files(start,end,sourceloc,'nowcast',1,'ptrc_T',1)


# In[14]:


data=list()
for ind, row in flist.iterrows():
    with nc.Dataset(row['paths']) as f:
        data.append(np.mean(f.variables['ammonium'][:,:,j,i],0,keepdims=True))


# In[15]:


data=np.concatenate(data,axis=0)


# In[16]:


times=[start+dt.timedelta(days=ii) for ii in \
       range(0,int((end-start).total_seconds()/(24*3600)+1))]


# In[19]:


fig,ax=plt.subplots(1,1,figsize=(16,3))
ax.plot(times,data);


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