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

# # Test `tracer_thalweg_and_surface` Module
# 
# Render figure object produced by the `nowcast.figures.research.tracer_thalweg_and_surface` module.
# 
# Set-up and function call replicates as nearly as possible what is done in the `nowcast.workers.make_plots` worker
# to help ensure that the module will work in the nowcast production context.

# Notebooks like this should be developed in a
# [Nowcast Figures Development Environment](https://salishsea-nowcast.readthedocs.io/en/latest/figures/fig_dev_env.html)
# so that all of the necessary dependency packages are installed.
# The development has to be done on a workstation that has the Salish Sea Nowcast system `/results/` parition mounted.

# In[1]:


# reload lets us pull in changes from modules that we edit
from importlib import reload


# In[2]:


# imports fro the top of the make_plots worker
import datetime
from glob import glob
import logging
import os
from pathlib import Path

import matplotlib

import arrow
import cmocean
from nemo_nowcast import NowcastWorker
import netCDF4 as nc
import scipy.io as sio

from nowcast import lib
from nowcast.figures.research import tracer_thalweg_and_surface


# In[3]:


# some extra imports that we need to simulate how the nowcast system works
import io

import yaml


# In[4]:


# render figure objects in the notebook
get_ipython().run_line_magic('matplotlib', 'inline')


# ## Simulate Nowcast System Config
# 
# The `config` multiline string contains the pieces of `nowcast/config/nowcast.yaml`
# that are needed to set up the conditions as `nowcast.workers.make_plots` does
# before calling the figure module `make_figure()` function.
# 
# The `yaml.load()` call at the bottom of the cell converts the string into a `config` dict.

# In[5]:


config = '''
    run_types:
        nowcast-green:
            bathymetry: /SalishSeaCast/grid/bathymetry_201702.nc
            mesh_mask: /SalishSeaCast/grid/mesh_mask201702.nc

    run:
        results_archive:
            nowcast-green: /results2/SalishSea/nowcast-green.201806/
'''

config = yaml.load(io.StringIO(config))


# ## Simulate the `make_plots` Command-line Interface
# 
# `make_plots` gets `run_type`, `plot_type`, and `run_date` from the command-line.

# In[10]:


run_type = 'nowcast-green'
plot_type = 'research'
run_date = arrow.get('2019-02-22')


# ## Simulate the Set-up of Variables and Paths in the `make_plots.make_plots()` Function
# 
# The `make_plots()` function uses paths defined in the nowcast system configuration file
# to set up a collection of commonly used variables, paths, and datasets.
# These are just the ones that we need for our test.

# In[11]:


dmy = run_date.format('DDMMMYY').lower()
results_dir = Path(config['run']['results_archive'][run_type], dmy)
bathy = nc.Dataset(config['run_types'][run_type]['bathymetry'])
mesh_mask = nc.Dataset(config['run_types']['nowcast-green']['mesh_mask'])


# ## Simulate Dataset Loading in `_prep_nowcast_green_research_fig_functions()`
# 
# This has the same effect as:
# ```python
# ptrc_T_hr = _results_dataset('1h', 'ptrc_T', results_dir)
# ```

# In[12]:


yyyymmdd = run_date.format('YYYYMMDD')
ptrc_T_hr = nc.Dataset(results_dir/f'SalishSea_1h_{yyyymmdd}_{yyyymmdd}_ptrc_T.nc')


# ## Render the Figure
# 
# The `%%timeit` cell magic lets us keep an eye on how log the figure takes to process.
# Setting `-n1 -r1` prevents it from processing the figure more than once
# as it might try to do to generate better statistics.
# 
# The `reload()` calls reload imported modules so that any edits you have made will take effect here.
# 
# The `make_figure()` call should be the same as goes in the `make_plots` worker module.

# In[13]:


get_ipython().run_cell_magic('timeit', '-n1 -r1', "\nreload(tracer_thalweg_and_surface)\n\nfig = tracer_thalweg_and_surface.make_figure(\n    ptrc_T_hr.variables['nitrate'], bathy, mesh_mask,\n    cmap=cmocean.cm.matter, depth_integrated=False\n)\n")


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