Regional Map Method

import sys
!{sys.executable} -m pip install netCDF4
!{sys.executable} -m pip install xarray
import opedia
import math
import common as com
from opedia import plotRegional as REG
import netCDF4
import xarray as xr
import numpy as np
from datetime import datetime
from dateutil.parser import parse
from bokeh.plotting import figure, show, output_file
from bokeh.layouts import column
from bokeh.palettes import all_palettes
from bokeh.models import HoverTool, LinearColorMapper, BasicTicker, ColorBar, DatetimeTickFormatter
from bokeh.models.annotations import Title
from bokeh.embed import components
from tqdm import tqdm_notebook as tqdm
from netCDF4 import num2date, date2num

def regionalMap(tables, variabels, dt1, dt2, lat1, lat2, lon1, lon2, depth1, depth2, fname, exportDataFlag):
    for i in tqdm(range(len(tables)), desc='overall'):
        
        unit = tables[i].variables[variables[i]].attrs['units']
        
        toDateTime = tables[i].indexes['TIME'].to_datetimeindex()
        tables[i]['TIME'] = toDateTime
        table = tables[i].sel(TIME = slice(startDate, endDate), LAT_C = slice(lat1, lat2), LON_C = slice(lon1, lon2), DEP_C = slice(depth1, depth2))
        
        varData = table.variables[variables[i]][0,0,:,:].values       
        
        lats = table.variables['LAT_C'].values.tolist()
        lons = table.variables['LON_C'].values.tolist()
        
        shape = (len(lats), len(lons))
        
        varData.reshape(shape)

        varData[varData < 0] = float('NaN')
        varData = [np.asarray(varData)]
        lats = [np.asarray(lats)]
        lons = [np.asarray(lons)]
        
        bokehMap(varData, unit, 'regional', lats, lons, unit, 'OTHER', variables[i])

def bokehMap(data, subject, fname, lat, lon, units, tables, variabels):
    TOOLS="crosshair,pan,zoom_in,wheel_zoom,zoom_out,box_zoom,reset,save,"
    p = []
    for ind in range(len(data)):

        w, h = com.canvasRect(dw=np.max(lon[ind])-np.min(lon[ind]), dh=np.max(lat[ind])-np.min(lat[ind]))
        p1 = figure(tools=TOOLS, toolbar_location="right", title=subject[ind], plot_width=w, plot_height=h, x_range=(np.min(lon[ind]), np.max(lon[ind])), y_range=(np.min(lat[ind]), np.max(lat[ind])))
        p1.xaxis.axis_label = 'Longitude'
        p1.yaxis.axis_label = 'Latitude'
    
        unit = units
        
        bounds = com.getBounds(variabels[ind])
        
        paletteName = com.getPalette(variabels[ind])
        low, high = bounds[0], bounds[1]
        
        if low == None:
            low, high = np.nanmin(data[ind].flatten()), np.nanmax(data[ind].flatten())
        color_mapper = LinearColorMapper(palette=paletteName, low=low, high=high)
        p1.image(image=[data[ind]], color_mapper=color_mapper, x=np.min(lon[ind]), y=np.min(lat[ind]), dw=np.max(lon[ind])-np.min(lon[ind]), dh=np.max(lat[ind])-np.min(lat[ind]))
        p1.add_tools(HoverTool(
            tooltips=[
                ('longitude', '$x'),
                ('latitude', '$y'),
                (variabels[ind]+unit, '@image'),
            ],
            mode='mouse'
        ))
        color_bar = ColorBar(color_mapper=color_mapper, ticker=BasicTicker(),
                        label_standoff=12, border_line_color=None, location=(0,0))
        p1.add_layout(color_bar, 'right')
        p.append(p1)
    if len(p) > 0:
       # if not inline:      ## if jupyter is not the caller
       #     dirPath = 'embed/'
       #     if not os.path.exists(dirPath):
       #         os.makedirs(dirPath)        
       #     output_file(dirPath + fname + ".html", title="Regional Map")
        show(column(p))
    return