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

# In[1]:


import netCDF4 as nc
import numpy as np
import matplotlib.pyplot as plt
from salishsea_tools import geo_tools, nc_tools, tidetools, viz_tools
import xarray as xr
import datetime
import pandas as pd
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:90% !important; }</style>"))

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


# In[2]:


from IPython.display import HTML

HTML('''<script>
code_show=true; 
function code_toggle() {
 if (code_show){
 $('div.input').hide();
 } else {
 $('div.input').show();
 }
 code_show = !code_show
} 
$( document ).ready(code_toggle);
</script>
<form action="javascript:code_toggle()"><input type="submit" value="Click here to toggle on/off the raw code."></form>''')


# In[5]:


grid = nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/bathymetry_201702.nc')
ferry_data = 'https://salishsea.eos.ubc.ca/erddap/tabledap/ubcONCTWDP1mV18-01'
nowcast_data = 'https://salishsea.eos.ubc.ca/erddap/griddap/ubcSSg3DBiologyFields1hV17-02'
bathy, X, Y = tidetools.get_bathy_data(grid)
ferry = nc.Dataset(ferry_data)
nowcast = xr.open_dataset(nowcast_data)


# In[117]:


plt.style.use('/ocean/vdo/MEOPAR/biomodelevalpaper/bioModelEvalPaper.mplstyle')


# In[26]:


ferry = pd.read_csv('https://salishsea.eos.ubc.ca/erddap/tabledap/ubcONCTWDP1mV18-01.csv?time%2Clongitude%2Clatitude%2Cchlorophyll&time%3E=2014-09-13T00%3A00%3A00Z&time%3C=2018-01-08T23%3A59%3A00Z')
ferry = ferry.drop(ferry.index[0])


# In[22]:


nc.num2date(ferry.variables['s.time'][1101000], ferry.variables['s.time'].units)


# In[23]:


nc.num2date(ferry.variables['s.time'][-1], ferry.variables['s.time'].units)


# In[10]:


import pickle


# In[11]:


HINDCAST_PATH= '/results/SalishSea/nowcast-green/'


# In[12]:


import os


# In[47]:


ferry = ferry.dropna()


# In[74]:


list_of_model_chl = np.array([])
list_of_ferry_chl = np.array([])
list_of_lons = np.array([])
#unit = ferry.variables['s.time'].units
for n in ferry.index: #2563687):
    #if ((ferry.variables['s.latitude'][n].mask == False) 
        #and (ferry.variables['s.chlorophyll'][n].mask == False)):
    Yind, Xind = geo_tools.find_closest_model_point(float(ferry.longitude[n]), 
                                                    float(ferry.latitude[n]), 
                                                    X, Y, land_mask = bathy.mask)
    date = datetime.datetime.strptime(ferry.time[n][:-1], '%Y-%m-%dT%H:%M:%S')
    sub_dir = date.strftime('%d%b%y').lower()
    datestr = date.strftime('%Y%m%d')
    fname = 'SalishSea_1h_{}_{}_ptrc_T.nc'.format(datestr, datestr)
    nuts = nc.Dataset(os.path.join(HINDCAST_PATH, sub_dir, fname))
    if date.minute < 30:
        before = datetime.datetime(year = date.year, month = date.month, day = date.day, 
                           hour = (date.hour), minute = 30) - datetime.timedelta(hours=1)
        after = before + datetime.timedelta(hours=1)
        sub_dir2 = after.strftime('%d%b%y').lower()
        datestr2 = after.strftime('%Y%m%d')
        fname2 = 'SalishSea_1h_{}_{}_ptrc_T.nc'.format(datestr2, datestr2)
        nuts2 = nc.Dataset(os.path.join(HINDCAST_PATH, sub_dir2, fname2))
        delta = (date - before).seconds / 3600
        chl_val = 1.6*((1-delta)*(nuts.variables['diatoms'][before.hour, 1, Yind, Xind] 
                       + nuts.variables['ciliates'][before.hour,1,Yind, Xind] 
                       + nuts.variables['flagellates'][before.hour,1,Yind,Xind]) + 
                   (delta)*(nuts2.variables['diatoms'][after.hour, 1, Yind, Xind] 
                       + nuts2.variables['ciliates'][after.hour,1,Yind, Xind] 
                       + nuts2.variables['flagellates'][after.hour,1,Yind,Xind]))
    if date.minute >= 30:
        before = datetime.datetime(year = date.year, month = date.month, day = date.day, 
                               hour = (date.hour), minute = 30)
        after = before + datetime.timedelta(hours=1)
        sub_dir2 = after.strftime('%d%b%y').lower()
        datestr2 = after.strftime('%Y%m%d')
        fname2 = 'SalishSea_1h_{}_{}_ptrc_T.nc'.format(datestr2, datestr2)
        nuts2 = nc.Dataset(os.path.join(HINDCAST_PATH, sub_dir2, fname2))
        delta = (date - before).seconds / 3600
        chl_val = 1.6*((1-delta)*(nuts.variables['diatoms'][before.hour, 1, Yind, Xind] 
                       + nuts.variables['ciliates'][before.hour,1,Yind, Xind] 
                       + nuts.variables['flagellates'][before.hour,1,Yind,Xind]) + 
                   (delta)*(nuts.variables['diatoms'][after.hour, 1, Yind, Xind] 
                       + nuts.variables['ciliates'][after.hour,1,Yind, Xind] 
                       + nuts.variables['flagellates'][after.hour,1,Yind,Xind]))
    list_of_ferry_chl = np.append(list_of_ferry_chl, float(ferry.chlorophyll[n]))
    list_of_model_chl = np.append(list_of_model_chl, chl_val)
    list_of_lons = np.append(list_of_lons, float(ferry.longitude[n]))


# In[75]:


list_of_lons.shape


# In[76]:


list_of_model_chl.shape


# In[77]:


bounds = pickle.load(open('bounds.pkl', 'rb'))


# In[78]:


def make_plot(n):
    fig, ax = plt.subplots(figsize = (10,10))
    c, xedge, yedge, im = ax.hist2d(list_of_ferry_chl[(bounds[n]<=list_of_lons) 
                                                       &  (list_of_lons< bounds[n+1])], 
                                    list_of_model_chl[(bounds[n]<=list_of_lons) 
                                                       &  (list_of_lons< bounds[n+1])], 
                                    bins = 100, norm=LogNorm())
    fig.colorbar(im, ax=ax)
    ax.set_xlabel('Ferry Data')
    ax.set_ylabel('Nowcast-green')
    ax.plot(np.arange(0,35), 'r-')
    ax.set_title(str(bounds[n]) + ' < lon < ' + str(bounds[n+1]))
    print('bias =  ' + str(-np.mean(list_of_ferry_chl[(bounds[n]<=list_of_lons) 
                                                       & (list_of_lons< bounds[n+1])]) + 
                           np.mean(list_of_model_chl[(bounds[n]<=list_of_lons) 
                                                      & (list_of_lons< bounds[n+1])])))
    print('RMSE = ' + str(np.sqrt(np.sum((list_of_model_chl[(bounds[n]<=list_of_lons) 
                                                             &  (list_of_lons< bounds[n+1])] - 
                                          list_of_ferry_chl[(bounds[n]<=list_of_lons) 
                                                             & (list_of_lons< bounds[n+1])])**2) 
                                  / len(list_of_model_chl[(bounds[n]<=list_of_lons) 
                                                           &  (list_of_lons< bounds[n+1])]))))
    xbar = np.mean(list_of_ferry_chl[(bounds[n]<=list_of_lons) &  (list_of_lons< bounds[n+1])])
    print('Willmott = ' + str(1-(np.sum((list_of_model_chl[(bounds[n]<=list_of_lons) 
                                                            & (list_of_lons< bounds[n+1])] - 
                                         list_of_ferry_chl[(bounds[n]<=list_of_lons) 
                                                            &  (list_of_lons< bounds[n+1])])**2)  / 
                                 np.sum((np.abs(list_of_model_chl[(bounds[n]<=list_of_lons) 
                                                                   &  (list_of_lons< bounds[n+1])] 
                                                - xbar) 
                                         + np.abs(list_of_ferry_chl[(bounds[n]<=list_of_lons) 
                                                                     &  (list_of_lons< bounds[n+1])] 
                                                  - xbar))**2))))


# In[105]:


def make_log_plot(n):
    fig, ax = plt.subplots(figsize = (10,10))
    c, xedge, yedge, im = ax.hist2d(list_of_ferry_chl2[(bounds[n]<=list_of_lons2) 
                                                       &  (list_of_lons2< bounds[n+1])], 
                                    list_of_model_chl2[(bounds[n]<=list_of_lons2) 
                                                       &  (list_of_lons2< bounds[n+1])], 
                                    bins = 100, norm=LogNorm())
    fig.colorbar(im, ax=ax)
    ax.set_xlabel('Ferry Data')
    ax.set_ylabel('Nowcast-green')
    ax.plot(np.arange(0,35), 'r-')
    ax.set_title(str(bounds[n]) + ' < lon < ' + str(bounds[n+1]))
    print('bias =  ' + str(-np.mean(list_of_ferry_chl2[(bounds[n]<=list_of_lons2) 
                                                       & (list_of_lons2< bounds[n+1])]) + 
                           np.mean(list_of_model_chl2[(bounds[n]<=list_of_lons2) 
                                                      & (list_of_lons2< bounds[n+1])])))
    print('RMSE = ' + str(np.sqrt(np.sum((list_of_model_chl2[(bounds[n]<=list_of_lons2) 
                                                             &  (list_of_lons2< bounds[n+1])] - 
                                          list_of_ferry_chl2[(bounds[n]<=list_of_lons2) 
                                                             & (list_of_lons2< bounds[n+1])])**2) 
                                  / len(list_of_model_chl2[(bounds[n]<=list_of_lons2) 
                                                           &  (list_of_lons2< bounds[n+1])]))))
    xbar = np.mean(list_of_ferry_chl2[(bounds[n]<=list_of_lons2) &  (list_of_lons2< bounds[n+1])])
    print('Willmott = ' + str(1-(np.sum((list_of_model_chl2[(bounds[n]<=list_of_lons2) 
                                                            & (list_of_lons2< bounds[n+1])] - 
                                         list_of_ferry_chl2[(bounds[n]<=list_of_lons2) 
                                                            &  (list_of_lons2< bounds[n+1])])**2)  / 
                                 np.sum((np.abs(list_of_model_chl2[(bounds[n]<=list_of_lons2) 
                                                                   &  (list_of_lons2< bounds[n+1])] 
                                                - xbar) 
                                         + np.abs(list_of_ferry_chl2[(bounds[n]<=list_of_lons2) 
                                                                     &  (list_of_lons2< bounds[n+1])] 
                                                  - xbar))**2))))


# In[67]:


output = open('ferry_chl.pkl', 'wb')
pickle.dump(list_of_ferry_chl, output)
output.close()
output = open('model_chl.pkl', 'wb')
pickle.dump(list_of_model_chl, output)
output.close()
output = open('chl_lons.pkl', 'wb')
pickle.dump(list_of_lons, output)
output.close()


# In[4]:


list_of_model_chl = pickle.load(open('model_chl.pkl', 'rb'))
list_of_ferry_chl = pickle.load(open('ferry_chl.pkl', 'rb'))


# In[118]:


fig, ax = plt.subplots(figsize = (12,12))
viz_tools.plot_coastline(ax, grid, coords = 'map')
viz_tools.set_aspect(ax, coords = 'map')
ax.set_xlim(-124.25, -122.75)
ax.set_ylim(48, 49.5)
for p in range(11):
    ax.plot((bounds[p], bounds[p]), (48, 49.5), '--', color = 'grey')


# In[55]:


from matplotlib.colors import LogNorm


# In[82]:


list_of_model_chl = list_of_model_chl[list_of_ferry_chl < 25]
list_of_lons = list_of_lons[list_of_ferry_chl < 25]
list_of_ferry_chl = list_of_ferry_chl[list_of_ferry_chl < 25]


# In[119]:


fig, ax = plt.subplots(figsize = (10,10))
c, xedge, yedge, im = ax.hist2d(list_of_ferry_chl, list_of_model_chl, 
                                bins = 100, norm=LogNorm())
fig.colorbar(im, ax=ax)
ax.set_xlabel('Ferry Data')
ax.set_ylabel('Nowcast-green')
ax.plot(np.arange(0,35), 'r-')
ax.set_title('Ferry vs Nowcast-green Chl')
print('bias =  ' + str(-np.mean(list_of_ferry_chl) + np.mean(list_of_model_chl)))
print('RMSE = ' + str(np.sqrt(np.sum((list_of_model_chl - list_of_ferry_chl)**2) 
                              / len(list_of_model_chl))))
xbar = np.mean(list_of_ferry_chl)
print('Willmott = ' + str(1-(np.sum((list_of_model_chl - list_of_ferry_chl)**2)  / 
                             np.sum((np.abs(list_of_model_chl - xbar) 
                                     + np.abs(list_of_ferry_chl - xbar))**2))))


# In[120]:


make_plot(0)


# In[121]:


make_plot(1)


# In[122]:


make_plot(2)


# In[123]:


make_plot(3)


# In[124]:


make_plot(4)


# In[125]:


make_plot(5)


# In[126]:


make_plot(6)


# In[127]:


make_plot(7)


# In[128]:


make_plot(8)


# In[129]:


make_plot(9)


# # Log(Chl)

# In[103]:


list_of_ferry_chl2 = np.log10(list_of_ferry_chl[list_of_ferry_chl > 0])
list_of_model_chl2 = np.log10(list_of_model_chl[list_of_ferry_chl > 0])
list_of_lons2 = list_of_lons[list_of_ferry_chl > 0]


# In[130]:


fig, ax = plt.subplots(figsize = (10,10))
c, xedge, yedge, im = ax.hist2d(list_of_ferry_chl2, list_of_model_chl2, 
                                bins = 100, norm=LogNorm())
fig.colorbar(im, ax=ax)
ax.set_xlabel('Ferry Data')
ax.set_ylabel('Nowcast-green')
ax.plot(np.arange(0,35), 'r-')
ax.set_title('Ferry vs Nowcast-green Log10(Chl)')
print('bias =  ' + str(-np.mean(list_of_ferry_chl2) + np.mean(list_of_model_chl2)))
print('RMSE = ' + str(np.sqrt(np.sum((list_of_model_chl2 - list_of_ferry_chl2)**2) 
                              / len(list_of_model_chl2))))
xbar = np.mean(list_of_ferry_chl2)
print('Willmott = ' + str(1-(np.sum((list_of_model_chl2 - list_of_ferry_chl2)**2)  / 
                             np.sum((np.abs(list_of_model_chl2 - xbar) 
                                     + np.abs(list_of_ferry_chl2 - xbar))**2))))


# In[131]:


make_log_plot(0)


# In[132]:


make_log_plot(1)


# In[133]:


make_log_plot(2)


# In[134]:


make_log_plot(3)


# In[135]:


make_log_plot(4)


# In[136]:


make_log_plot(5)


# In[137]:


make_log_plot(6)


# In[138]:


make_log_plot(7)


# In[139]:


make_log_plot(8)


# In[140]:


make_log_plot(9)


# In[141]:


fig, ax = plt.subplots(figsize = (10,6))
ax.plot(bounds[:-1], [0.671300651096, 0.787866750802, 0.813574305729,0.770953337192,0.722419353156,
                      0.673618297025,0.680863445623,0.801244440856,0.822053268011,0.755213496942], 
        'bo', label = 'salinity')
ax.plot(bounds[:-1], [0.606096675921, 0.661740900677, 0.677210364228, 0.669705472444, 
                      0.627474238704, 0.576622408505, 0.481705152104, 0.459957502523, 
                      0.427640182739, 0.410616257023], 'ro', label = 'chl')
ax.plot(bounds[:-1], [0.625474931503, 0.649335129276, 0.654179032163, 0.64124886339, 0.61568651551,
                      0.598083087025, 0.5546428283, 0.549722376184, 0.569900510582, 0.597208825939],
       'go', label = 'log10(chl)')
ax.legend()
ax.grid('on')
ax.set_title('Willmott Skill Score by Longitude');


# In[ ]: