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

# Compare a single cast from IOS observations to model.
# 
# IOS observations span many years. So, compare observations with model on day of, 7 days before and 7 days after. This can account for spring/neap variability but not interannual variabilty in discharge, winds, etc

# In[1]:


import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import ACTDR
import netCDF4 as nc
from salishsea_tools import viz_tools
import numpy as np
import datetime
from salishsea_tools import tidetools

import comparisons

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


# In[2]:


sns.set_style('darkgrid')


# In[3]:


grid_B = nc.Dataset('/data/nsoontie/MEOPAR/NEMO-forcing/grid/bathy_meter_SalishSea2.nc')
paths = {'nowcast': '/data/dlatorne/MEOPAR/SalishSea/nowcast/',
        'spinup': '/ocean/dlatorne/MEOPAR/SalishSea/results/spin-up/'}


# In[4]:


ACTDR.load_dat('SOG_2000.dat')


# In[5]:


data = pd.DataFrame(ACTDR.CTD_DAT)

lon_min=-123.1; lat_min=48.5;
lon_max=-123; lat_max=48.8;

data_region = comparisons.isolate_region(data, lon_min, lon_max, lat_min, lat_max)


# In[6]:


fig,axm = plt.subplots(1,figsize=(8,5))
data_region.plot(x='Longitude',y='Latitude',kind='scatter', marker='o',ax=axm)
viz_tools.plot_coastline(axm,grid_B,coords='map')
axm.set_xlim([lon_min,lon_max])
axm.set_ylim([lat_min, lat_max])


# In[7]:


data_region.hist('Month',bins=np.arange(0.5,13.5))


# In[8]:


data_region.hist('Year', bins= np.arange(data_region['Year'].min()-0.5, data_region['Year'].max() +1.5 ))
ax=plt.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)


# In[9]:


zmax= 200
grouped = data_region.groupby('Month')
for month in grouped.groups.keys():
    if month != 8 and month != 9 and month != 10: 
        if month ==11 or month ==12:
            model_year=2014
        else:
            model_year=2015
        fig = comparisons.compare_model_obs(month,model_year,'Salinity',data_region,paths['nowcast'],zmax=zmax,vmin=28,
                                            vmax=34, xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)


# These comparisons are still difficult. Let's separate each cast out. Focus on the eastern Juan de Fuca region.
# 
# #Cast by cast comparison, organized by month 
# 
# 
# Question - What does the stratification in Boundary Pass look like?
# 
# Plot 7 days before and after cast yeardate to attempt to capture spring/neap variability.
# 
# ###Nowcasts
# 

# In[10]:


vmin=28
month = 11
fig=comparisons.compare_cast_model(month,2014,'Salinity',data_region,paths['nowcast'],zmax=zmax,vmin=vmin,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)


# What is wrong with our depths?

# In[11]:


month = 12
fig=comparisons.compare_cast_model(month,2014,'Salinity',data_region,paths['nowcast'],zmax=zmax,vmin=vmin,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)


# In[12]:


month = 1
fig=comparisons.compare_cast_model(month,2015,'Salinity',data_region,paths['nowcast'],zmax=zmax,vmin=vmin,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)


# In[13]:


month = 4
fig=comparisons.compare_cast_model(month,2015,'Salinity',data_region,paths['nowcast'],zmax=zmax,vmin=vmin,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)


# In[14]:


month = 7
fig=comparisons.compare_cast_model(month,2015,'Salinity',data_region,paths['nowcast'],zmax=zmax,vmin=vmin,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)


# #Summary
# 
# 
# * Model is fresher in almost all comparisons at pretty much all depths. Does this mean that the water entering Haro Strait is too fresh?
# * Again, worried about this comparing mode/observations in different years when I know there is a lot of interannual variability in the surface salinity of SoG. 
# * Notice how the model is more or less stratified depending on the tidal cycle. 
# * There seems to be something strange about our model depths. 
# 

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