import sys
!{sys.executable} -m pip install netCDF4
!{sys.executable} -m pip install xarray
import netCDF4
import opedia
import os
import numpy as np
import pandas as pd
import db
import common as com
import climatology as clim
import subset
from datetime import datetime, timedelta
import time
from math import pi
from bokeh.io import output_notebook
from bokeh.plotting import figure, show
from bokeh.layouts import column
from bokeh.models import DatetimeTickFormatter
from bokeh.palettes import all_palettes
from bokeh.models import HoverTool
from bokeh.embed import components
import jupyterInline as jup
import xarray as xr
if jup.jupytered():
    from tqdm import tqdm_notebook as tqdm
else:
    from tqdm import tqdm

def xarrayPlotDepthProfile(tables, variables, dt1, dt2, lat1, lat2, lon1, lon2, depth1, depth2, fname, exportDataFlag, marker='-', msize=25, clr='orangered'):
    p = []
    lw = 2
    w = 800
    h = 400
    TOOLS = 'pan,wheel_zoom,zoom_in,zoom_out,box_zoom, undo,redo,reset,tap,save,box_select,poly_select,lasso_select'
    for i in tqdm(range(len(tables)), desc='overall'):
        #if not db.hasField(tables[i], 'depth'):
        #    continue
        #if not iterative(tables[i]):    
        #    depths, y, yErr = depthProfile(tables[i], variables[i], dt1, dt2, lat1, lat2, lon1, lon2, depth1, depth2, fname, exportDataFlag)
        #else:    
        #    depths, y, yErr = depthProfile_iterative(tables[i], variables[i], dt1, dt2, lat1, lat2, lon1, lon2, depth1, depth2, fname, exportDataFlag)
        
        table = tables[i].sel(TIME = slice(dt1, dt2), LAT_C = slice(lat1, lat2), LON_C = slice(lon1, lon2), DEP_C = slice(depth1, depth2))
        depths = np.unique(table.variables['DEP_C'].values)
        
        varList = table.variables[variables[i]][:].values
        varList[varList < 0] = float('NaN')
        varList = varList.tolist()[0][:][0][0]
        
        varData = []
        for list in varList:
            varData.append(np.mean(list))
        
        output_notebook()
        p1 = figure(tools=TOOLS, toolbar_location="above", plot_width=w, plot_height=h)
        #p1.xaxis.axis_label = 'Depth'
        p1.yaxis.axis_label = variables[i] + ' [PLACEHOLDER]'
        leg = variables[i]
        cr = p1.circle(depths, varData, fill_color="grey", hover_fill_color="firebrick", fill_alpha=0.25, hover_alpha=0.3, line_color=None, hover_line_color="white", legend=leg, size=msize)
        p1.line(depths, varData, line_color=clr, line_width=lw, legend=leg)
        p1.add_tools(HoverTool(tooltips=None, renderers=[cr], mode='hline'))
        p.append(p1)
    dirPath = 'embed/'
    if not os.path.exists(dirPath):
        os.makedirs(dirPath)        
  #  if not inline:      ## if jupyter is not the caller
  #      output_file(dirPath + fname + ".html", title="Depth Profile")
    show(column(p))
    return

def iterative(table):
    table = table.lower()
    it = False
    if table.find('tblDarwin'.lower()) != -1:
        it = True
    if table.find('tblPisces'.lower()) != -1:
        it = True
    return it

def depthProfile(table, field, dt1, dt2, lat1, lat2, lon1, lon2, depth1, depth2, fname, exportDataFlag):
    df = subset.depthProfile(table, field, dt1, dt2, lat1, lat2, lon1, lon2, depth1, depth2)
    if exportDataFlag:
        exportData(df['depth'], df[field], df[field + '_std'], table, field, dt1, dt2, lat1, lat2, lon1, lon2, fname)    
    return df['depth'], df[field], df[field + '_std']

def depthProfile_iterative(table, field, dt1, dt2, lat1, lat2, lon1, lon2, depth1, depth2, fname, exportDataFlag):
    if db.isClimatology(table) and db.hasField(table, 'month'):
        m1 = clim.timeToMonth(dt1)
        m2 = clim.timeToMonth(dt2)
        if m2>m1:
            timesteps = range(m1, m2+1)
        else:
            timesteps = range(m2, m1+1)
        timesteps = ['2016-%2.2d-01' % m for m in timesteps]    
    elif table.lower().find('tblPisces'.lower()) != -1:        # currently (Nov 2018) only Pisces table has a calendar table. all datasets have to have a calendar  table. we can then remove thw else: clause below 
        calTable = table+'_Calendar'
        timesteps = com.timesBetween(calTable, dt1, dt2)   
    else:        
        delta = datetime.strptime(dt2, '%Y-%m-%d') - datetime.strptime(dt1, '%Y-%m-%d')
        timesteps = [(datetime.strptime(dt1, '%Y-%m-%d') + timedelta(days=x)).strftime('%Y-%m-%d') for x in range(delta.days+1)]

    zs, ys, y_stds = [], [], []
    for dt in timesteps:
        df = subset.depthProfile(table, field, dt, dt, lat1, lat2, lon1, lon2, depth1, depth2)
        if len(df[field]) < 1:
            continue
        zs.append(df['depth'])
        ys.append(df[field])
        y_stds.append(df[field + '_std'])

    depth = np.mean( np.stack(zs, axis=0), axis=0 )
    y = np.mean( np.stack(ys, axis=0), axis=0 )
    y_std = np.mean( np.stack(y_stds, axis=0), axis=0 )

    if exportDataFlag:
        exportData(depth, y, y_std, table, field, dt1, dt2, lat1, lat2, lon1, lon2, fname)    
    return depth, y, y_std

xFile = xr.open_dataset('http://3.88.71.225:80/thredds/dodsC/las/id-a1d60eba44/data_usr_local_tomcat_content_cbiomes_20190510_20_darwin_v0.2_cs510_darwin_v0.2_cs510_nutrients.nc.jnl')

tables = [xFile]     # see catalog.csv  for the complete list of tables and variable names
variables = ['O2']
startDate = '2010-04-30'   # PISCES is a weekly model, and here we are using monthly climatology of Darwin model
endDate = '2010-04-30'
lat1, lat2 = 20, 24
lon1, lon2 = -170, -150
depth1, depth2 = 0, 1500
fname = 'DEP'
exportDataFlag = False      # True if you you want to download data

xarrayPlotDepthProfile(tables, variables, startDate, endDate, lat1, lat2, lon1, lon2, depth1, depth2, fname, exportDataFlag)