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

# I've noticed in some places that the model bathymetry in Boundary Pass doesn't seem to match with reality. 
# 
# See these two notebooks:
# * http://nbviewer.ipython.org/urls/bitbucket.org/salishsea/analysis/raw/tip/Nancy/strat/comparisons/ONC%20Moorings.ipynb
# * http://nbviewer.ipython.org/urls/bitbucket.org/salishsea/analysis/raw/tip/Nancy/strat/comparisons/Fall%202014%20IOS%20data.ipynb
# 
# Is this a consistent error or is it just a discrepancy with the closest model grid point? I will investigate by looking at the bathymetry near the observations points as well.

# In[2]:


import sys
import pandas as pd
sys.path.insert(0, './strat/comparisons/')
import ONC
import ACTDR
import comparisons
from salishsea_tools import tidetools
import netCDF4 as nc
import matplotlib.pyplot as plt
import datetime
get_ipython().run_line_magic('matplotlib', 'inline')


# # Model files

# In[3]:


grid = nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/bathy_meter_SalishSea2.nc')

bathy = grid.variables['Bathymetry'][:]
lons = grid.variables['nav_lon'][:]
lats = grid.variables['nav_lat'][:]


# ## ONC Mooring Point

# In[4]:


csvfilename = ('/ocean/nsoontie/MEOPAR/ONC/BoundaryPass//'
               'BoundaryPass_BoundaryPassMooring_CTD_20140411T175025Z_20150408T235058Z-NaN_clean.csv')


# In[5]:


data, lat, lon, depth =  ONC.load_mooring_csv(csvfilename)
print(lat,lon, depth)


# In[6]:


j,i = tidetools.find_closest_model_point(lon,lat,lons, lats, bathy)
print(j,i, bathy[j,i])


# At the closest grid point, the model bathymetry is 30m shallower. What about neighbouring points?

# In[7]:


stp = 3
print(bathy[j-stp:j+stp,i-stp:i+stp])


# In[8]:


plt.pcolormesh(lons, lats,bathy,vmin=205,vmax=234)
cbar = plt.colorbar()
cbar.set_label('Bahtymetry [m]')
plt.plot([lons[j-stp,i-stp], lons[j-stp,i+stp]], [lats[j-stp,i-stp], lats[j-stp,i+stp] ] ,'k')
plt.plot([lons[j-stp,i-stp], lons[j+stp,i-stp]], [lats[j-stp,i-stp], lats[j+stp,i-stp] ] ,'k')
plt.plot([lons[j+stp,i+stp], lons[j-stp,i+stp]], [lats[j+stp,i+stp], lats[j-stp,i+stp] ],'k')
plt.plot([lons[j+stp,i+stp], lons[j+stp,i-stp]], [lats[j+stp,i+stp], lats[j+stp,i-stp] ],'k')
plt.axis([-123.2,-123, 48.7,48.8])
plt.plot(lon, lat,'r*')


# No where in this neighourhood do we get close to 234m depth. Is the depth record in the ONC file accurate?

# # IOS Cast

# In[9]:


ACTDR.load_dat('strat/comparisons/SOG_2000.dat')
data = pd.DataFrame(ACTDR.CTD_DAT)


# In[10]:


lon_min=-123.49
lon_max=-122
lat_min=48.75
lat_max=48.8
data_BP = comparisons.isolate_region(data,lon_min, lon_max, lat_min, lat_max)
data_fall_BP  = data_BP[(data_BP.Year>=2014) & (data_BP.Month >=10)  ]



# In[11]:


lon = data_fall_BP.Longitude
lat = data_fall_BP.Latitude
depths = data_fall_BP.Depth
print(lon.values[0], lat.values[0], max(depths.values[0]))


# In[12]:


j,i = tidetools.find_closest_model_point(lon.values[0],lat.values[0],lons, lats, bathy)
print(j,i, bathy[j,i])


# The model is 80m shallower! Neighbouring points

# In[13]:


stp = 3
print(bathy[j-stp:j+stp,i-stp:i+stp])


# In[14]:


plt.pcolormesh(lons, lats,bathy,vmin=131,vmax=211)
cbar = plt.colorbar()
cbar.set_label('Bahtymetry [m]')
plt.plot([lons[j-stp,i-stp], lons[j-stp,i+stp]], [lats[j-stp,i-stp], lats[j-stp,i+stp] ] ,'k')
plt.plot([lons[j-stp,i-stp], lons[j+stp,i-stp]], [lats[j-stp,i-stp], lats[j+stp,i-stp] ] ,'k')
plt.plot([lons[j+stp,i+stp], lons[j-stp,i+stp]], [lats[j+stp,i+stp], lats[j-stp,i+stp] ],'k')
plt.plot([lons[j+stp,i+stp], lons[j+stp,i-stp]], [lats[j+stp,i+stp], lats[j+stp,i-stp] ],'k')
plt.axis([-123.1,-123, 48.7,48.8])
plt.plot(lon.values[0], lat.values[0],'r*')


# The point at find_closest_model_point is too shallow but there are neighouring points at an appropriate depth. 

# In[ ]: