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

# Comparisons between nowcast, spinup, and observed salinity in the Strait of Juan de Fuca

# 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')


# # Load observations

# In[4]:


ACTDR.load_dat('SOG_2000.dat')


# In[5]:


data = pd.DataFrame(ACTDR.CTD_DAT)

lon_min=-124.6; lat_min=48;
lon_max=-123.6; lat_max=48.6;

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)


# #Data model comparisons

# * Compare model salinity profiles with observed in this region. Apply to both spinup and nowcasts.
# * Focus on all depths

# In[9]:


paths = {'nowcast': '/data/dlatorne/MEOPAR/SalishSea/nowcast/',
        'spinup': '/ocean/dlatorne/MEOPAR/SalishSea/results/spin-up/'}
zmax= 250


# November

# In[10]:


month=11
fig=comparisons.compare_model_obs(month,2003,'Salinity',data_region,paths['spinup'],zmax=zmax,vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
fig=comparisons.compare_model_obs(month,2014,'Salinity',data_region,paths['nowcast'],zmax=zmax, vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
data_month=data_region[data_region['Month']==month]
data_month.hist('Year',bins = np.arange(data_month['Year'].min()-0.5,data_month['Year'].max()+1.5 ))
ax=plt.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)


# December - no data

# January - no data

# February

# In[11]:


month=2
fig=comparisons.compare_model_obs(month,2003,'Salinity',data_region,paths['spinup'],zmax=zmax,vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
fig=comparisons.compare_model_obs(month,2015,'Salinity',data_region,paths['nowcast'],zmax=zmax, vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
data_month=data_region[data_region['Month']==month]
data_month.hist('Year',bins = np.arange(data_month['Year'].min()-0.5,data_month['Year'].max()+1.5 ))
ax=plt.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)


# March 

# In[12]:


month=3
fig=comparisons.compare_model_obs(month,2003,'Salinity',data_region,paths['spinup'],zmax=zmax,vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
fig=comparisons.compare_model_obs(month,2015,'Salinity',data_region,paths['nowcast'],zmax=zmax, vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
data_month=data_region[data_region['Month']==month]
data_month.hist('Year',bins = np.arange(data_month['Year'].min()-0.5,data_month['Year'].max()+1.5 ))
ax=plt.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)


# April

# In[13]:


month=4
fig=comparisons.compare_model_obs(month,2003,'Salinity',data_region,paths['spinup'],zmax=zmax,vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
fig=comparisons.compare_model_obs(month,2015,'Salinity',data_region,paths['nowcast'],zmax=zmax, vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
data_month=data_region[data_region['Month']==month]
data_month.hist('Year',bins = np.arange(data_month['Year'].min()-0.5,data_month['Year'].max()+1.5 ))
ax=plt.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)


# May - no data

# June

# In[14]:


month=6
fig=comparisons.compare_model_obs(month,2003,'Salinity',data_region,paths['spinup'],zmax=zmax,vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
fig=comparisons.compare_model_obs(month,2015,'Salinity',data_region,paths['nowcast'],zmax=zmax, vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
data_month=data_region[data_region['Month']==month]
data_month.hist('Year',bins = np.arange(data_month['Year'].min()-0.5,data_month['Year'].max()+1.5 ))
ax=plt.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)


# July

# In[15]:


month=7
fig=comparisons.compare_model_obs(month,2003,'Salinity',data_region,paths['spinup'],zmax=zmax,vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
fig=comparisons.compare_model_obs(month,2015,'Salinity',data_region,paths['nowcast'],zmax=zmax, vmin=25,vmax=34,
                                  xmin=lon_min,xmax=lon_max,ymin=lat_min,ymax=lat_max)
data_month=data_region[data_region['Month']==month]
data_month.hist('Year',bins = np.arange(data_month['Year'].min()-0.5,data_month['Year'].max()+1.5 ))
ax=plt.gca()
ax.get_xaxis().get_major_formatter().set_useOffset(False)


# Consider comparing locations individually.

# # 2014

# In[16]:


data_2014 = data_region[data_region['Year']==2014]
data_2014.hist('Month')


# Unfortunately, no overlapping data with nowcasts 

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