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

# 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': '/results/SalishSea/nowcast/',
        'spinup': '/results/SalishSea/spin-up/'}


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ACTDR.load_dat('SOG_2000.dat')


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data = pd.DataFrame(ACTDR.CTD_DAT)


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data_fall = data[(data.Year>=2014) & (data.Month >=10)  ]


# In[7]:


fig,axm = plt.subplots(1,figsize=(8,5))
data_fall.plot(x='Longitude',y='Latitude',kind='scatter', marker='o',ax=axm)
viz_tools.plot_coastline(axm,grid_B,coords='map')


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


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data_fall.hist('Day')


# ## Southern Strait of Georgia

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lon_min=-124
lon_max=-123
lat_min=48.8
lat_max=49.35
data_SOG  = comparisons.isolate_region(data_fall,lon_min, lon_max, lat_min, lat_max)


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zmax= 50
grouped = data_SOG.groupby('Month')
model_year=2014
for month in grouped.groups.keys():
    fig = comparisons.compare_model_obs(month,model_year,'Salinity',data_SOG,paths['nowcast'],zmax=zmax,vmin=10,
                                        vmax=31.5, xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)


# In[22]:


month=10
fig=comparisons.compare_cast_hourly(month,2014,'Salinity',data_SOG,paths['nowcast'],zmax=zmax,vmin=10,vmax=31.5,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)


# In late October, most of the surface water in the southern Strait of Georgia is too salty.

# In[23]:


month=12
fig=comparisons.compare_cast_hourly(month,2014,'Salinity',data_SOG,paths['nowcast'],zmax=zmax,vmin=10,vmax=31.5,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)


# 
# ##Strait of Georgia North

# In[13]:


lon_min=-125.1
lon_max=-123
lat_min=49.35
lat_max=50
data_SOGN  = comparisons.isolate_region(data_fall,lon_min, lon_max, lat_min, lat_max)


# In[14]:


zmax= 50
grouped = data_SOGN.groupby('Month')
model_year=2014
for month in grouped.groups.keys():
    fig = comparisons.compare_model_obs(month,model_year,'Salinity',data_SOGN,paths['nowcast'],zmax=zmax,vmin=10,
                                        vmax=31.5, xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)


# In[15]:


month = 10
fig=comparisons.compare_cast_hourly(month,2014,'Salinity',data_SOGN,paths['nowcast'],zmax=zmax,vmin=20,vmax=31.5,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)


# * East of Texada Isalnd is too salty in the surface
# * Very north is too fresh in the surface. As we go furter south, we start to get too salty in the surface.

# ##Strait of Juan de Fuca

# In[24]:


lon_min=-125
lon_max=-123
lat_min=48
lat_max=48.48
data_JDF  = comparisons.isolate_region(data_fall,lon_min, lon_max, lat_min, lat_max)


# In[25]:


zmax= 200
grouped = data_JDF.groupby('Month')
model_year=2014
for month in grouped.groups.keys():
    fig = comparisons.compare_model_obs(month,model_year,'Salinity',data_JDF,paths['nowcast'],zmax=zmax,vmin=30,
                                        vmax=34, xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)


# In[26]:


month = 10
fig=comparisons.compare_cast_hourly(month,2014,'Salinity',data_JDF,paths['nowcast'],zmax=zmax,vmin=30,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)


# * Surface and intermediate JdF is too salty (above 150m)
# * Deep is ok (below 150m) but there aren't a lot of cats that go that deep. 
# * It looks like the salinity gradients are too small and too shallow in the model, especially west of -123.5.

# ## Haro

# In[30]:


lon_min=-123.49
lon_max=-122
lat_min=48.48
lat_max=48.8
data_haro  = comparisons.isolate_region(data_fall,lon_min, lon_max, lat_min, lat_max)


# In[31]:


zmax= 300
grouped = data_haro.groupby('Month')
model_year=2014
for month in grouped.groups.keys():
    fig = comparisons.compare_model_obs(month,model_year,'Salinity',data_haro,paths['nowcast'],zmax=zmax,
                                        vmin=28,vmax=32, xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)


# In[32]:


month = 10
fig=comparisons.compare_cast_hourly(month,2014,'Salinity',data_haro,paths['nowcast'],
                                    zmax=zmax,vmin=28,vmax=32,
                                    xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)



# * For these two casts, we look ok in Haro.

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