import calendar
import numpy as np
import pandas as pd
import re
import scipy.interpolate as interp
import urllib
import warnings
from datetime import datetime as dt,timedelta

from ..plot import Plot

from ..utils import *

try:
    import cartopy.feature as cfeature
    from cartopy import crs as ccrs
    from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
except:
    warnings.warn("Warning: Cartopy is not installed in your python environment. Plotting functions will not work.")

try:
    import matplotlib as mlib
    import matplotlib.lines as mlines
    import matplotlib.patheffects as path_effects
    import matplotlib.pyplot as plt
    import matplotlib.colors as col
    import matplotlib.ticker as mticker
    import matplotlib.patches as mpatches

except:
    warnings.warn("Warning: Matplotlib is not installed in your python environment. Plotting functions will not work.")

class RainPlot(Plot):
    
    def __init__(self):
        
        self.use_credit = True
    
    def plot_storm(self,storm,grid,levels,cmap,domain="dynamic",plot_all_dots=False,ax=None,track_labels=False,save_path=None,prop={},map_prop={}):
        
        r"""
        Creates a plot of a single storm track.
        
        Parameters
        ----------
        storm : str, tuple or dict
            Requested storm. Can be either string of storm ID (e.g., "AL052019"), tuple with storm name and year (e.g., ("Matthew",2016)), or a dict entry.
        domain : str
            Domain for the plot. Can be one of the following:
            "dynamic" - default. Dynamically focuses the domain using the storm track(s) plotted.
            "north_atlantic" - North Atlantic Ocean basin
            "pacific" - East/Central Pacific Ocean basin
            "lonW/lonE/latS/latN" - Custom plot domain
        plot_all_dots : bool
            Whether to plot dots for all observations along the track. If false, dots will be plotted every 6 hours. Default is false.
        ax : axes
            Instance of axes to plot on. If none, one will be generated. Default is none.
        prop : dict
            Property of storm track lines.
        map_prop : dict
            Property of cartopy map.
        """
        
        #Set default properties
        default_prop={'dots':True,'fillcolor':'category','cmap':None,'levels':None,'linecolor':'k','linewidth':1.0,'ms':7.5}
        default_map_prop={'res':'m','land_color':'#FBF5EA','ocean_color':'#EDFBFF','linewidth':0.5,'linecolor':'k','figsize':(14,9),'dpi':200}
        
        #Initialize plot
        prop = self.add_prop(prop,default_prop)
        map_prop = self.add_prop(map_prop,default_map_prop)
        self.plot_init(ax,map_prop,plot_geography=False)
        
        #--------------------------------------------------------------------------------------
        
        #get resolution corresponding to string in prop
        res = self.check_res(map_prop['res'])
        
        #fill oceans if specified
        self.ax.set_facecolor(map_prop['ocean_color'])
        ocean_mask = self.ax.add_feature(cfeature.OCEAN.with_scale(res),facecolor=map_prop['ocean_color'],edgecolor='face',zorder=3)
        lake_mask = self.ax.add_feature(cfeature.LAKES.with_scale(res),facecolor=map_prop['ocean_color'],edgecolor='face',zorder=1)
        continent_mask = self.ax.add_feature(cfeature.LAND.with_scale(res),facecolor=map_prop['land_color'],edgecolor='face',zorder=0)
        
        #draw geography
        states = self.ax.add_feature(cfeature.STATES.with_scale(res),linewidths=map_prop['linewidth'],
                                     linestyle='solid',edgecolor=map_prop['linecolor'],zorder=4)
        countries = self.ax.add_feature(cfeature.BORDERS.with_scale(res),linewidths=map_prop['linewidth'],
                                        linestyle='solid',edgecolor=map_prop['linecolor'],zorder=4)
        coastlines = self.ax.add_feature(cfeature.COASTLINE.with_scale(res),linewidths=map_prop['linewidth'],
                                         linestyle='solid',edgecolor=map_prop['linecolor'],zorder=4)
        
        #--------------------------------------------------------------------------------------
        
        #Retrieve grid coordinates and values
        plot_grid = True
        if isinstance(grid,dict):
            if 'lat' in grid.keys():
                grid_lat = grid['lat']
                grid_lon = grid['lon']
                grid_val = grid['grid']
            else:
                ms = grid['ms']
                mec = grid['mec']
                mew = grid['mew']
                minimum_threshold = grid['minimum_threshold']
                plot_grid = False
        else:
            grid_lat = grid.lat.values
            grid_lon = grid.lon.values
            grid_val = grid.values
        
        #Determine levels and colormap
        if levels is None:
            levels = [1,2,3,4,6,8,10,12,16,20]
        if cmap is None:
            cmap = plt.cm.YlGn
        norm = col.BoundaryNorm(levels,cmap.N)
        
        #Contour fill grid if requested
        if plot_grid:
            self.ax.contourf(grid_lon, grid_lat, grid, levels, cmap=cmap, norm=norm, zorder=2)
        
        #Plot dots if requested
        else:
            
            #Iterate over sorted data (so highest totals show up on top)
            iter_df = storm.rain.sort_values('Total')
            for _,row in iter_df.iterrows():
                
                #Retrieve rain total and determine color
                rain_value = row['Total']
                if rain_value < minimum_threshold: continue
                color = self.rgb(cmap(norm(rain_value),bytes=True)[:-1])
                
                #Specify additional kwargs
                ms_kwargs = {}
                if mec is not None: ms_kwargs = {'mec':mec,'mew':mew}
                self.ax.plot(row['Lon'],row['Lat'],'o',ms=ms,color=color,**ms_kwargs,transform=ccrs.PlateCarree())
        
        #Produce colorbar
        cs = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
        cs.set_array([])
        self.fig.colorbar(cs,ax=self.ax)
        
        #--------------------------------------------------------------------------------------
        
        #Keep record of lat/lon coordinate extrema
        max_lat = np.nanmax(storm.rain['Lat'].values)
        min_lat = np.nanmin(storm.rain['Lat'].values)
        max_lon = np.nanmax(storm.rain['Lon'].values)
        min_lon = np.nanmin(storm.rain['Lon'].values)

        #Get storm data
        storm_data = storm.dict

        #Retrieve storm data
        lats = storm_data['lat']
        lons = storm_data['lon']
        vmax = storm_data['vmax']
        styp = storm_data['type']
        sdate = storm_data['date']
                
        #Account for cases crossing dateline
        if self.proj.proj4_params['lon_0'] == 180.0:
            new_lons = np.array(lons)
            new_lons[new_lons<0] = new_lons[new_lons<0]+360.0
            lons = new_lons.tolist()

        #Force dynamic_tropical to tropical if an invest
        invest_bool = False
        if 'invest' in storm_data.keys() and storm_data['invest']:
            invest_bool = True
            if domain == 'dynamic_tropical': domain = 'dynamic'
        
        #Add to coordinate extrema
        if domain == 'dynamic_tropical':
            type_array = np.array(storm_data['type'])
            idx = np.where((type_array == 'SD') | (type_array == 'SS') | (type_array == 'TD') | (type_array == 'TS') | (type_array == 'HU'))
            use_lats = (np.array(storm_data['lat'])[idx]).tolist()
            use_lons = (np.array(lons)[idx]).tolist()
        else:
            use_lats = storm_data['lat']
            use_lons = np.copy(lons).tolist()

        #Iterate over storm data to plot
        for i,(i_lat,i_lon,i_vmax,i_mslp,i_date,i_type) in enumerate(zip(storm_data['lat'],lons,storm_data['vmax'],storm_data['mslp'],storm_data['date'],storm_data['type'])):

            #Determine line color, with SSHWS scale used as default
            if prop['linecolor'] == 'category':
                segmented_colors = True
                line_color = get_colors_sshws(np.nan_to_num(i_vmax))

            #Use user-defined colormap if another storm variable
            elif isinstance(prop['linecolor'],str) == True and prop['linecolor'] in ['vmax','mslp']:
                segmented_colors = True
                color_variable = storm_data[prop['linecolor']]
                if prop['levels'] is None: #Auto-determine color levels if needed
                    prop['levels'] = [np.nanmin(color_variable),np.nanmax(color_variable)]
                cmap,levels = get_cmap_levels(prop['linecolor'],prop['cmap'],prop['levels'])
                line_color = cmap((color_variable-min(levels))/(max(levels)-min(levels)))[i]

            #Otherwise go with user input as is
            else:
                segmented_colors = False
                line_color = prop['linecolor']

            #For tropical/subtropical types, color-code if requested
            if i > 0:
                if i_type in ['SD','TD','SS','TS','HU']:

                    #Plot underlying black and overlying colored line
                    self.ax.plot([lons[i-1],lons[i]],[storm_data['lat'][i-1],storm_data['lat'][i]],'-',
                                  linewidth=prop['linewidth']*1.33,color='k',zorder=5,
                                  transform=ccrs.PlateCarree())
                    self.ax.plot([lons[i-1],lons[i]],[storm_data['lat'][i-1],storm_data['lat'][i]],'-',
                                  linewidth=prop['linewidth'],color=line_color,zorder=6,
                                  transform=ccrs.PlateCarree())

                #For non-tropical types, plot dotted lines
                else:

                    #Restrict line width to 1.5 max
                    line_width = prop['linewidth'] + 0.0
                    if line_width > 1.5: line_width = 1.5

                    #Plot dotted line
                    self.ax.plot([lons[i-1],lons[i]],[storm_data['lat'][i-1],storm_data['lat'][i]],':',
                                  linewidth=line_width,color=line_color,zorder=6,
                                  transform=ccrs.PlateCarree(),
                                  path_effects=[path_effects.Stroke(linewidth=line_width*1.33, foreground='k'),
                                                path_effects.Normal()])

            #Plot dots if requested
            if prop['dots'] == True:
                
                #Skip if plot_all_dots == False and not in 0,6,12,18z
                if plot_all_dots == False:
                    if i_date.strftime('%H%M') not in ['0000','0600','1200','1800']: continue

                #Determine fill color, with SSHWS scale used as default
                if prop['fillcolor'] == 'category':
                    segmented_colors = True
                    fill_color = get_colors_sshws(np.nan_to_num(i_vmax))

                #Use user-defined colormap if another storm variable
                elif isinstance(prop['fillcolor'],str) == True and prop['fillcolor'] in ['vmax','mslp']:
                    segmented_colors = True
                    color_variable = storm_data[prop['fillcolor']]
                    if prop['levels'] is None: #Auto-determine color levels if needed
                        prop['levels'] = [np.nanmin(color_variable),np.nanmax(color_variable)]
                    cmap,levels = get_cmap_levels(prop['fillcolor'],prop['cmap'],prop['levels'])
                    fill_color = cmap((color_variable-min(levels))/(max(levels)-min(levels)))[i]

                #Otherwise go with user input as is
                else:
                    segmented_colors = False
                    fill_color = prop['fillcolor']

                #Determine dot type
                marker_type = '^'
                if i_type in ['SD','SS']:
                    marker_type = 's'
                elif i_type in ['TD','TS','HU']:
                    marker_type = 'o'

                #Plot marker
                self.ax.plot(i_lon,i_lat,marker_type,mfc=fill_color,mec='k',mew=0.5,
                             zorder=7,ms=prop['ms'],transform=ccrs.PlateCarree())

            #Label track dots
            if track_labels in ['valid_utc']:
                if track_labels == 'valid_utc':
                    strformat = '%H UTC \n%-m/%-d'
                    labels = {t.strftime(strformat):(x,y) for t,x,y in zip(sdate,lons,lats) if t.hour==0}
                    track = {t.strftime(strformat):(x,y) for t,x,y in zip(sdate,lons,lats)}
                self.plot_track_labels(self.ax, labels, track, k=.9)

        #--------------------------------------------------------------------------------------
        
        #Storm-centered plot domain
        if domain == "dynamic" or domain == "dynamic_tropical":
            
            bound_w,bound_e,bound_s,bound_n = self.dynamic_map_extent(min_lon,max_lon,min_lat,max_lat)
            self.ax.set_extent([bound_w,bound_e,bound_s,bound_n], crs=ccrs.PlateCarree())
            
        #Pre-generated or custom domain
        else:
            bound_w,bound_e,bound_s,bound_n = self.set_projection(domain)

        #Plot parallels and meridians
        #This is currently not supported for all cartopy projections.
        try:
            self.plot_lat_lon_lines([bound_w,bound_e,bound_s,bound_n],zorder=9)
        except:
            pass
        
        #--------------------------------------------------------------------------------------
        
        #Add left title
        type_array = np.array(storm_data['type'])
        if invest_bool == False:
            idx = np.where((type_array == 'SD') | (type_array == 'SS') | (type_array == 'TD') | (type_array == 'TS') | (type_array == 'HU'))
            tropical_vmax = np.array(storm_data['vmax'])[idx]
            
            #Coerce to include non-TC points if storm hasn't been designated yet
            add_ptc_flag = False
            if len(tropical_vmax) == 0:
                add_ptc_flag = True
                idx = np.where((type_array == 'LO') | (type_array == 'DB'))
            tropical_vmax = np.array(storm_data['vmax'])[idx]

            subtrop = classify_subtropical(np.array(storm_data['type']))
            peak_idx = storm_data['vmax'].index(np.nanmax(tropical_vmax))
            peak_basin = storm_data['wmo_basin'][peak_idx]
            storm_type = get_storm_classification(np.nanmax(tropical_vmax),subtrop,peak_basin)
            if add_ptc_flag == True: storm_type = "Potential Tropical Cyclone"
            left_title_string = f"{storm_type} {storm_data['name']}"
        else:
            #Use all indices for invests
            idx = np.array([True for i in type_array])
            add_ptc_flag = False
            tropical_vmax = np.array(storm_data['vmax'])
            
            #Determine letter in front of invest
            add_letter = 'L'
            if storm_data['id'][0] == 'C':
                add_letter = 'C'
            elif storm_data['id'][0] == 'E':
                add_letter = 'E'
            
            #Add title
            left_title_string = f"INVEST {storm_data['id'][2:4]}{add_letter}"

        #Add left title
        if plot_grid == True:
            left_title_string += "\nInterpolated WPC Storm Rainfall (in)"
        else:
            if minimum_threshold > 1:
                left_title_string += f"\nWPC Storm Rainfall (>{np.round(minimum_threshold,2)} inch)"
            else:
                left_title_string += f"\nWPC Storm Rainfall (inch)"
        self.ax.set_title(left_title_string,loc='left',fontsize=16,fontweight='bold')
        
        #Add right title
        ace = storm_data['ace']
        if add_ptc_flag == True: ace = 0.0
        type_array = np.array(storm_data['type'])
        
        #Get storm extrema for display
        mslp_key = 'mslp' if 'wmo_mslp' not in storm_data.keys() else 'wmo_mslp'
        if all_nan(np.array(storm_data[mslp_key])[idx]) == True:
            min_pres = "N/A"
        else:
            min_pres = int(np.nan_to_num(np.nanmin(np.array(storm_data[mslp_key])[idx])))
        if all_nan(np.array(storm_data['vmax'])[idx]) == True:
            max_wind = "N/A"
        else:
            max_wind = int(np.nan_to_num(np.nanmax(np.array(storm_data['vmax'])[idx])))
        start_date = dt.strftime(np.array(storm_data['date'])[idx][0],'%d %b %Y')
        end_date = dt.strftime(np.array(storm_data['date'])[idx][-1],'%d %b %Y')
        endash = u"\u2013"
        dot = u"\u2022"
        self.ax.set_title(f"{start_date} {endash} {end_date}\n{max_wind} kt {dot} {min_pres} hPa {dot} {ace:.1f} ACE",loc='right',fontsize=13)

        #--------------------------------------------------------------------------------------
        
        #Add plot credit
        warning_text=""
        if storm_data['source'] == 'ibtracs' and storm_data['source_info'] == 'World Meteorological Organization (official)':
            warning_text = f"This plot uses 10-minute averaged WMO official wind data converted\nto 1-minute average (factor of 0.88). Use this wind data with caution.\n\n"

            self.ax.text(0.99,0.01,warning_text,fontsize=9,color='k',alpha=0.7,
            transform=self.ax.transAxes,ha='right',va='bottom',zorder=10)
        
        credit_text = "Rainfall data (inch) from\nWeather Prediction Center (WPC)\n\n"
        self.ax.text(0.99,0.01,credit_text,fontsize=9,color='k',alpha=0.7,
                     transform=self.ax.transAxes,ha='right',va='bottom',zorder=10)
        
        credit_text = self.plot_credit()
        self.add_credit(credit_text)
        
        #--------------------------------------------------------------------------------------
                
        #Add legend
        if prop['fillcolor'] == 'category' and prop['dots'] == True:
            ex = mlines.Line2D([], [], linestyle='None', ms=prop['ms'], mec='k',mew=0.5, label='Non-Tropical', marker='^', color='w')
            sb = mlines.Line2D([], [], linestyle='None', ms=prop['ms'], mec='k',mew=0.5, label='Subtropical', marker='s', color='w')
            td = mlines.Line2D([], [], linestyle='None', ms=prop['ms'], mec='k',mew=0.5, label='Tropical Depression', marker='o', color=get_colors_sshws(33))
            ts = mlines.Line2D([], [], linestyle='None', ms=prop['ms'], mec='k',mew=0.5, label='Tropical Storm', marker='o', color=get_colors_sshws(34))
            c1 = mlines.Line2D([], [], linestyle='None', ms=prop['ms'], mec='k',mew=0.5, label='Category 1', marker='o', color=get_colors_sshws(64))
            c2 = mlines.Line2D([], [], linestyle='None', ms=prop['ms'], mec='k',mew=0.5, label='Category 2', marker='o', color=get_colors_sshws(83))
            c3 = mlines.Line2D([], [], linestyle='None', ms=prop['ms'], mec='k',mew=0.5, label='Category 3', marker='o', color=get_colors_sshws(96))
            c4 = mlines.Line2D([], [], linestyle='None', ms=prop['ms'], mec='k',mew=0.5, label='Category 4', marker='o', color=get_colors_sshws(113))
            c5 = mlines.Line2D([], [], linestyle='None', ms=prop['ms'], mec='k',mew=0.5, label='Category 5', marker='o', color=get_colors_sshws(137))
            self.ax.legend(handles=[ex,sb,td,ts,c1,c2,c3,c4,c5], prop={'size':11.5})

        elif prop['linecolor'] == 'category' and prop['dots'] == False:
            ex = mlines.Line2D([], [], linestyle='dotted', label='Non-Tropical', color='k')
            td = mlines.Line2D([], [], linestyle='solid', label='Sub/Tropical Depression', color=get_colors_sshws(33))
            ts = mlines.Line2D([], [], linestyle='solid', label='Sub/Tropical Storm', color=get_colors_sshws(34))
            c1 = mlines.Line2D([], [], linestyle='solid', label='Category 1', color=get_colors_sshws(64))
            c2 = mlines.Line2D([], [], linestyle='solid', label='Category 2', color=get_colors_sshws(83))
            c3 = mlines.Line2D([], [], linestyle='solid', label='Category 3', color=get_colors_sshws(96))
            c4 = mlines.Line2D([], [], linestyle='solid', label='Category 4', color=get_colors_sshws(113))
            c5 = mlines.Line2D([], [], linestyle='solid', label='Category 5', color=get_colors_sshws(137))
            self.ax.legend(handles=[ex,td,ts,c1,c2,c3,c4,c5], prop={'size':11.5}).set_zorder(10)

        elif prop['dots'] and segmented_colors == False:
            ex = mlines.Line2D([], [], linestyle='None', ms=prop['ms'], mec='k',mew=0.5, label='Non-Tropical', marker='^', color=prop['fillcolor'])
            sb = mlines.Line2D([], [], linestyle='None', ms=prop['ms'], mec='k',mew=0.5, label='Subtropical', marker='s', color=prop['fillcolor'])
            td = mlines.Line2D([], [], linestyle='None', ms=prop['ms'], mec='k',mew=0.5, label='Tropical', marker='o', color=prop['fillcolor'])
            handles=[ex,sb,td]
            self.ax.legend(handles=handles,fontsize=11.5).set_zorder(10)

        elif prop['dots'] == False and segmented_colors == False:
            ex = mlines.Line2D([], [], linestyle='dotted',label='Non-Tropical', color=prop['linecolor'])
            td = mlines.Line2D([], [], linestyle='solid',label='Tropical', color=prop['linecolor'])
            handles=[ex,td]
            self.ax.legend(handles=handles,fontsize=11.5).set_zorder(10)

        elif prop['dots'] == True:
            ex = mlines.Line2D([], [], linestyle='None', ms=prop['ms'], mec='k',mew=0.5, label='Non-Tropical', marker='^', color='w')
            sb = mlines.Line2D([], [], linestyle='None', ms=prop['ms'], mec='k',mew=0.5, label='Subtropical', marker='s', color='w')
            td = mlines.Line2D([], [], linestyle='None', ms=prop['ms'], mec='k',mew=0.5, label='Tropical', marker='o', color='w')
            handles=[ex,sb,td]
            for _ in range(7):
                handles.append(mlines.Line2D([], [], linestyle='-',label='',lw=0))
            l=self.ax.legend(handles=handles,fontsize=11.5).set_zorder(10)
            plt.draw()
            
            #Get the bbox
            bb = l.legendPatch.get_bbox().inverted().transform(self.fig.transFigure)
                
            #Define colorbar axis
            cax = self.fig.add_axes([bb.x0+0.47*bb.width, bb.y0+.057*bb.height, 0.015, .65*bb.height])
            norm = mlib.colors.Normalize(vmin=min(levels), vmax=max(levels))
            cbmap = mlib.cm.ScalarMappable(norm=norm, cmap=cmap)
            cbar = self.fig.colorbar(cbmap,cax=cax,orientation='vertical',\
                                     ticks=levels)
            
            cax.tick_params(labelsize=11.5)
            cax.yaxis.set_ticks_position('left')
            cbar.set_label(prop['fillcolor'],fontsize=11.5,rotation=90)
        
            rect_offset = 0.0
            if prop['cmap'] == 'category' and prop['fillcolor'] == 'vmax':
                cax.yaxis.set_ticks(np.linspace(min(levels),max(levels),len(levels)))
                cax.yaxis.set_ticklabels(levels)
                cax2 = cax.twinx()
                cax2.yaxis.set_ticks_position('right')
                cax2.yaxis.set_ticks((np.linspace(0,1,len(levels))[:-1]+np.linspace(0,1,len(levels))[1:])*.5)
                cax2.set_yticklabels(['TD','TS','Cat-1','Cat-2','Cat-3','Cat-4','Cat-5'],fontsize=11.5)
                cax2.tick_params('both', length=0, width=0, which='major')
                cax.yaxis.set_ticks_position('left')
                rect_offset = 0.7
            if prop['fillcolor'] == 'date':
                cax.set_yticklabels([f'{mdates.num2date(i):%b %-d}' for i in clevs],fontsize=11.5)
                
        else:
            ex = mlines.Line2D([], [], linestyle='dotted',label='Non-Tropical', color='k')
            td = mlines.Line2D([], [], linestyle='solid',label='Tropical', color='k')
            handles=[ex,td]
            for _ in range(7):
                handles.append(mlines.Line2D([], [], linestyle='-',label='',lw=0))
            l=self.ax.legend(handles=handles,fontsize=11.5).set_zorder(10)
            plt.draw()
            
            #Get the bbox
            bb = l.legendPatch.get_bbox().inverted().transform(self.fig.transFigure)
                
            #Define colorbar axis
            cax = self.fig.add_axes([bb.x0+0.47*bb.width, bb.y0+.057*bb.height, 0.015, .65*bb.height])
            norm = mlib.colors.Normalize(vmin=min(levels), vmax=max(levels))
            cbmap = mlib.cm.ScalarMappable(norm=norm, cmap=cmap)
            cbar = self.fig.colorbar(cbmap,cax=cax,orientation='vertical',\
                                     ticks=levels)
            
            cax.tick_params(labelsize=11.5)
            cax.yaxis.set_ticks_position('left')
            cbarlab = make_var_label(prop['linecolor'],storm_data)            
            cbar.set_label(cbarlab,fontsize=11.5,rotation=90)
        
            rect_offset = 0.0
            if prop['cmap'] == 'category' and prop['linecolor'] == 'vmax':
                cax.yaxis.set_ticks(np.linspace(min(levels),max(levels),len(levels)))
                cax.yaxis.set_ticklabels(levels)
                cax2 = cax.twinx()
                cax2.yaxis.set_ticks_position('right')
                cax2.yaxis.set_ticks((np.linspace(0,1,len(levels))[:-1]+np.linspace(0,1,len(levels))[1:])*.5)
                cax2.set_yticklabels(['TD','TS','Cat-1','Cat-2','Cat-3','Cat-4','Cat-5'],fontsize=11.5)
                cax2.tick_params('both', length=0, width=0, which='major')
                cax.yaxis.set_ticks_position('left')
                rect_offset = 0.7
            if prop['linecolor'] == 'date':
                cax.set_yticklabels([f'{mdates.num2date(i):%b %-d}' for i in clevs],fontsize=11.5)
                
        #-----------------------------------------------------------------------------------------
        
        #Save image if specified
        if save_path is not None and isinstance(save_path,str) == True:
            plt.savefig(save_path,bbox_inches='tight')
        
        #Return axis if specified, otherwise display figure
        return self.ax
