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

# In[1]:


import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import datetime as dt
import matplotlib.gridspec as gridspec
from matplotlib.colors import LogNorm
from salishsea_tools import evaltools as et

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


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df=et.loadPSFCTD()


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# path to model files:
PATH= '/results/SalishSea/nowcast-green.201812/'

# start and end dates for analysis:
start_date = dt.datetime(2015,1,1)
end_date = dt.datetime(2018,1,1)

# number of days per model file:
flen=1

# dictionary mapping desired model variables to the file types where they are found
filemap={'vosaline':'grid_T','votemper':'grid_T'}

# dictionary mapping model file types to their time resolution in hours (1 is hourly files, 24 is daily)
fdict={'ptrc_T':1,'grid_T':1}

# results format
# -- nowcast: files like 01jan15/SalishSea_1h_20150101_20150101_ptrc_T.nc
# -- long: files like SalishSea_1h_20150206_20150804_ptrc_T_20150427-20150506.nc, all in one directory
namfmt='nowcast'

data=et.matchData(df,filemap,fdict,start_date,end_date,namfmt,PATH,flen,preIndexed=False)


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fig = plt.figure(figsize = (7.5,3.25))
gs1=gridspec.GridSpec(1,4,left=.08,right=.91,bottom=.1,top=.97,
                      wspace=.2,width_ratios=[1,.1,1,.1])
axT=fig.add_subplot(gs1[0,0])
axS = fig.add_subplot(gs1[0,2])
gscb=gridspec.GridSpecFromSubplotSpec(7,1,subplot_spec=gs1[:,3])
axcb = fig.add_subplot(gscb[1:-1])

axS.plot((0,36),(0,36),'k-',alpha=.2)
axT.plot((0,25),(0,25),'k-',alpha=.2)

iiT=(~np.isnan(data['CT']))&(~np.isnan(data['mod_votemper']))
iiS=(~np.isnan(data['SA']))&(~np.isnan(data['mod_vosaline']))
counts, xedges, yedges, m1=axT.hist2d(data.loc[iiT,['CT']].values.flatten(),
                                      data.loc[iiT,['mod_votemper']].values.flatten(),bins=25*3,norm=LogNorm())
counts, xedges, yedges, m2=axS.hist2d(data.loc[iiS,['SA']].values.flatten(),
                                      data.loc[iiS,['mod_vosaline']].values.flatten(),bins=36*3,norm=LogNorm())

cb=fig.colorbar(m1,cax=axcb)
cb.set_label('Count')

ntick=np.arange(0,36,10)
ntickl=[str(i) for i in ntick]
axS.set_xlim((0,36))
axS.set_ylim((0,36))
axS.set_xticks(ntick)
axS.set_xticklabels(ntickl)
axS.set_yticks(ntick)
axS.set_yticklabels(ntickl)
    
stick=np.arange(0,25,10)
stickl=[str(i) for i in stick]
axT.set_xlim((0,25))
axT.set_ylim((0,25))
axT.set_xticks(stick)
axT.set_xticklabels(stickl)
axT.set_yticks(stick)
axT.set_yticklabels(stickl)
    
for ax in (axT,axS):
    ax.set_aspect(1, adjustable='box')
    
axT.set_ylabel('Modeled',fontsize=12)
axS.set_ylabel('Modeled',fontsize=12)
axT.set_xlabel('Observed',fontsize=12)
axS.set_xlabel('Observed',fontsize=12)
axS.set_xlim(5,35)
axS.set_ylim(5,35)

axS.set_title('S$_A$ (g/kg)',fontsize=12)
axT.set_title('$\Theta$ ($^{\circ}$C)',fontsize=12)



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data.keys()


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data[['station','ID', 'Patrol','Lat', 'Lon','Z','dtUTC','i', 'j', 'k',
       'pressure','chl', 'o2SAT', 'o2uM', 'conductivity', 'temperature', 'SA', 
      'CT']].to_csv('/data/eolson/results/MEOPAR/oldDBs/PSFCTD.csv',index=False)


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test=pd.read_csv('/data/eolson/results/MEOPAR/oldDBs/PSFCTD.csv')


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test


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data.head()


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