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

# # Boundary Pass and ONC Central Node Results Time Series
# 
# Assemble time series results for Rich:
# 
# * NEMO-3.6 nowcast-green runs
# * full water column
# * hourly average results at Boundary Pass location of ONC mooring
# * hourly average results at ONC central node
# * 1-Jun-2016 through 30-Sep-2016
# 
# After the planned NEMO-3.6 hindcast runs it should be possible to increase the time resolution
# to 10 minute averages.

# In[1]:


import os
from pathlib import Path

import arrow
import xarray


# In[2]:


# ONC description of central node location:
# Depth: 297 m
# Latitude: 49.040066666
# Longitude: -123.425825
central_ji = (424, 266)
# ONC description of Boundary Pass mooring location:
# Depth: 230 m
# Latitude: 48.7665
# Longitude: -123.039556
boundary_ji = (343, 289)

start_date = arrow.get('2016-06-01')
end_date = arrow.get('2016-06-02')

results_archive = Path('/results/SalishSea/nowcast-green/')


# In[3]:


def write_csv(filehandle, time_counter, var):
    for i, t in enumerate(time_counter.values):
        mat_timestamp = arrow.get((str(t))).format('D-MMM-YYYY HH:mm:ss Z')
        line = ', '.join(str(v) for v in var.values[i])
        line = ', '.join((mat_timestamp, line))
        line = f'{line}\n'
        f.write(line)
        print(mat_timestamp)


# In[4]:


datasets = []
for a in arrow.Arrow.range('day', start_date, end_date):
    ddmmmyy = a.format('DDMMMYY').lower()
    yyyymmdd = a.format('YYYYMMDD')
    results_dir = f'{ddmmmyy}'
    grid_T_1h = f'SalishSea_1h_{yyyymmdd}_{yyyymmdd}_grid_T.nc'
    datasets.append(os.fspath(results_archive/results_dir/grid_T_1h))


# In[5]:


datasets


# In[6]:


get_ipython().run_cell_magic('time', '', "\nvar = 'vosaline'\nlocn = central_ji\nfilepath = Path('central_salinity.csv')\n\nwith filepath.open('wt') as f:\n    for dataset in datasets:\n        with xarray.open_dataset(dataset) as ds:\n            ds_var = ds.data_vars[var].isel(y=locn[0], x=locn[1])\n            write_csv(f, ds.time_counter, ds_var)\n")


# Best observed timings:
# 
# In notebook:
# 
# Using xarray.open_mf_dataset() for 1 day's results:
# 
#     CPU times: user 1min 41s, sys: 3min 53s, total: 5min 35s
#     Wall time: 5min 35s
# 
# Using xarray.open_dataset() for 1 day's results:
# 
#     CPU times: user 3.82 s, sys: 756 ms, total: 4.58 s
#     Wall time: 4.82 s
#     
# From command-line:
# 
# Using xarray.open_mf_dataset() for 1 day's results:
# 
# 
# Using xarray.open_dataset() for 1 day's results:
# 
#     real	0m11.714s
#     user	0m8.140s
#     sys	0m2.644s

# ## Depths

# In[12]:


with Path('depths.csv').open('wt') as f:
     with xarray.open_dataset(datasets[0]) as ds:
        for depth in ds.deptht.values:
            f.write(f'{depth}\n')


# In[ ]: