diff --git a/harc_plot/calculate_histograms.py b/harc_plot/calculate_histograms.py
index 5e6d517..eda36c1 100755
--- a/harc_plot/calculate_histograms.py
+++ b/harc_plot/calculate_histograms.py
@@ -21,7 +21,7 @@ def calc_histogram(frame,attrs):
     ylim    = attrs['ylim']
     dy      = attrs['dy']
 
-    xbins   = gl.get_bins(xlim,dx)
+    xbins   = gl.get_bins(xlim,dx)[:-1]
     ybins   = gl.get_bins(ylim,dy)
 
     if len(frame) > 2:
@@ -93,7 +93,7 @@ def main(run_dct):
     if strip_time(sDate) != strip_time(eDate):
         dates   = dates[:-1]
 
-    for dt in tqdm.tqdm(dates):
+    for dt in tqdm.tqdm(dates,dynamic_ncols=True):
         nc_name = dt.strftime('%Y%m%d') + '.data.nc'
         nc_path = os.path.join(ncs_path,nc_name)
 
@@ -112,7 +112,7 @@ def main(run_dct):
                             filter_region=filter_region,filter_region_kind=filter_region_kind)
 
             if dft is None:
-                print('No data for {!s}'.format(ld_str))
+                tqdm.tqdm.write('No data for {!s}'.format(ld_str))
                 continue
             for xkey in xkeys:
                 dft[xkey]    = ld_inx*24 + dft[xkey]
@@ -181,8 +181,8 @@ def main(run_dct):
                 map_attrs['dy']             = 1
 
                 map_tmp = []
-                for tb_inx,tb_0 in enumerate(time_bins[:-1]):
-                    tb_1                = time_bins[tb_inx+1]
+                for tb_inx,tb_0 in enumerate(time_bins):
+                    tb_1                = time_bins[tb_inx] + xb_size_min/60.
                     tf                  = np.logical_and(frame[xkey] >= tb_0, frame[xkey] < tb_1)
                     tb_frame            = frame[tf].copy()
                     result              = calc_histogram(tb_frame,map_attrs)
diff --git a/harc_plot/geospace_env.py b/harc_plot/geospace_env.py
index da90a7d..7db9057 100644
--- a/harc_plot/geospace_env.py
+++ b/harc_plot/geospace_env.py
@@ -1,6 +1,8 @@
 from .omni import Omni
 
 class GeospaceEnv(object):
-    def __init__(self):
-        self.omni   = Omni()
+#    def __init__(self):
+#        self.omni   = Omni()
+    def __init__(self, years=[2016,2017]):     # Add year parameter. Kukkai, 20231002
+        self.omni   = Omni(years=years)        # Pass year parameter. Kukkai, 20231002
         self.symh   = self.omni.symh
diff --git a/harc_plot/omni.py b/harc_plot/omni.py
index 52f51f9..314f2c9 100644
--- a/harc_plot/omni.py
+++ b/harc_plot/omni.py
@@ -24,9 +24,12 @@ def date_parser(row):
     return dt
 
 class Omni():
-    def __init__(self):
-        self._load_omni()
-        self._load_symh()
+#    def __init__(self):       # Comment out. Kukkai, 20231002
+#        self._load_omni()     # Comment out. Kukkai, 20231002
+#        self._load_symh()     # Comment out. Kukkai, 20231002
+    def __init__(self,years=[2016,2017]):   # Add year parameter. Kukkai, 20231002
+        self._load_omni()             
+        self._load_symh(years=years)        # Pass years parameter. Kukkai, 20231002 
 
     def _load_omni(self,omni_csv='data/omni/omni_data_reduced.txt.bz2'):
         """
@@ -152,7 +155,7 @@ def _load_omni(self,omni_csv='data/omni/omni_data_reduced.txt.bz2'):
 
         self.df         = df 
 
-    def _load_symh(self,years=[2016,2017]):
+    def _load_symh(self,years=[2012,2013]):
         """
         Create and load SymH data/object.
         """
@@ -186,6 +189,8 @@ def plot_dst_kp(self,sTime,eTime,ax,xkey='index',xlabels=True,
             yy      = df['Dst_nT'].tolist()
             ylabel  = 'Dst [nT]'
         else:
+            print('symH df')
+            print(self.df_symasy)
             xx      = self.df_symasy.index
             yy      = self.df_symasy[dst_param].tolist()
             xlim    = (sTime,eTime)
diff --git a/harc_plot/visualize_histograms.py b/harc_plot/visualize_histograms.py
index a055dd1..cc9418c 100755
--- a/harc_plot/visualize_histograms.py
+++ b/harc_plot/visualize_histograms.py
@@ -1,7 +1,7 @@
 import os
 import glob
 import datetime
-from collections import OrderedDict
+from collections import OrderedDict, defaultdict
 import string
 import ast
 
@@ -153,6 +153,21 @@ def get_text(self,group,data_var):
 
         return lines
 
+    def get_text_sources_only(self,group,data_var):
+        
+        sc      = self.src_cnts[group][data_var]
+        srcs    = list(sc.index)
+        srcs.remove('sum')
+        srcs.sort()
+
+        lines   = []
+        lines.append('Data Sources:')
+        for src in srcs:
+            line    = '   {!s}'.format(src)
+            lines.append(line)
+
+        return lines
+
 def center_of_mass(da,c0,c1):
     """
     Calculate the center of mass of a 2D xarray.
@@ -168,13 +183,12 @@ def center_of_mass(da,c0,c1):
 class Sza(object):
     def __init__(self,xdct,sTime,eTime):
         """
-        Calculate percentages of data sources.
-        xdct: dictionary of datasets that have src_cnt attributes.
+        Determine the lat/lon for calculating the solar zenith angle
+        based on the center-of-mass of spot locations.
         """
         sza_dct = {}
-        # Add up the sources for each day.
         for group,ds in xdct.items():
-            for data_var in ds.data_vars:
+            for data_var in ds.keys():
                 da = ds[data_var].sum('freq_MHz')
 
                 for dim in da.dims:
@@ -352,43 +366,28 @@ def _load_ncs(self):
                     ds.coords[group]        = time_vec
                     ds.coords['ut_sTime']   = [self.sTime]
 
-                    if prefix == 'map':
-                        dt_vec      = np.array([dt_0 + pd.Timedelta(hours=x) for x in hrs])
-                        tf          = np.logical_and(dt_vec >= self.sTime, dt_vec < self.eTime)
-                        tmp_map_ds  = ds[{group:tf}].sum(group,keep_attrs=True)
-
-                        map_ds      = dss[prefix].get(group)
-                        if map_ds is None:
-                            map_ds      = tmp_map_ds
-                        else:
-                            map_attrs = map_ds['spot_density'].attrs
-                            map_ds += tmp_map_ds
-                            map_ds['spot_density'].attrs = map_attrs
-                        dss[prefix][group]  = map_ds
-                    else:
-                        if group not in dss[prefix]:
-                            dss[prefix][group]  = []
-                        dss[prefix][group].append(ds)
+                    if group not in dss[prefix]:
+                        dss[prefix][group]  = []
+                    dss[prefix][group].append(ds)
 
             mbz2.remove()
 
         # Process source counts - know what percentage of spots came from what sources.
-        self.sza        = Sza(dss['map'],self.sTime,self.eTime)
         self.src_cnts   = SrcCounts(dss['time_series'])
 
         # Concatenate Time Series Data
         xlim    = (0, (self.eTime-self.sTime).total_seconds()/3600.)
         print(' Concatenating data...')
-        prefix  = 'time_series'
-        xdct    = dss[prefix]
-        for group,ds_list in xdct.items():
-            ds          = xr.concat(ds_list,group)
-            for data_var in ds.data_vars:
-                print(prefix,group,data_var)
-                attrs   = ds[data_var].attrs
-                attrs.update({'xlim':str(xlim)})
-                ds[data_var].attrs = attrs
-            dss[prefix][group]  = ds
+        for prefix in prefixes:
+            xdct    = dss[prefix]
+            for group,ds_list in xdct.items():
+                ds          = xr.concat(ds_list,group)
+                for data_var in ds.data_vars:
+                    print(prefix,group,data_var)
+                    attrs   = ds[data_var].attrs
+                    attrs.update({'xlim':str(xlim)})
+                    ds[data_var].attrs = attrs
+                dss[prefix][group]  = ds
 
         self.datasets   = dss
 
@@ -442,21 +441,50 @@ def _format_timeticklabels(self,ax):
         if label is not None:
             ax.set_xlabel(label)
 
-    def plot(self,baseout_dir='output',xlim=None,ylim=None,xunits='datetime',
-            plot_sza=True,subdir=None,geospace_env=None,plot_region=None,
+    def plot(self,baseout_dir='output',fname_suffix=None,xlim=None,ylim=None,xunits='datetime',title=None,
+            plot_sza=True,subdir=None,geospace_env=None,plot_region=None,no_percentages=False,
             plot_kpsymh=True,plot_goes=True,plot_f107=False,axvlines=None,axvlines_kw={},axvspans=None,time_format={},
             xkeys=None,log_z=None,**kwargs):
+
         if self.datasets is None:
             return
 
         if geospace_env is None:
             geospace_env    = GeospaceEnv()
 
+
         self.time_format    = time_format
-        xlim_in = xlim
         if axvlines_kw is None:
             axvlines_kw = {}
 
+        # Determine xlims ######################
+        xlim_in = xlim
+        xlims   = defaultdict(dict)
+        for group,ds in self.datasets['time_series'].items():
+            for data_var in ds.data_vars:
+                xlims[group][data_var] = \
+                    ast.literal_eval(ds[data_var].attrs.get('xlim',xlim_in))
+
+        # Concatenate Maps Across Time #########  
+        maps = defaultdict(dict)
+        szas = defaultdict(dict)
+        for group,ds in self.datasets['time_series'].items():
+            if xkeys is not None:
+                if group not in xkeys: continue
+            for data_var in ds.data_vars:
+                map_da  = self.datasets['map'][group]['spot_density']
+                xlim = xlims[group][data_var]
+                if xlim is not None:
+                    tf              = np.logical_and(map_da[group] >= xlim[0], map_da[group] < xlim[1])
+                    attrs           = map_da.attrs
+                    map_da          = map_da.loc[{group: map_da[group][tf]}].copy()
+                    map_da.attrs    = attrs
+                map_da  = map_da.sum(group,keep_attrs=True)
+                maps[group][data_var] = map_da
+
+        if plot_sza:
+            sza = Sza(maps,self.sTime,self.eTime)
+
         fpaths = [] # Keep track of paths of all plotted figures.
         for group,ds in self.datasets['time_series'].items():
 
@@ -464,7 +492,7 @@ def plot(self,baseout_dir='output',xlim=None,ylim=None,xunits='datetime',
             if xkeys is not None:
                 if group not in xkeys: continue
 
-            map_da  = self.datasets['map'][group]['spot_density']
+            map_da  = maps[group][data_var]
 
             outdir  = os.path.join(baseout_dir,group)
             if subdir is not None:
@@ -478,9 +506,7 @@ def plot(self,baseout_dir='output',xlim=None,ylim=None,xunits='datetime',
                 if ylim is None:
                     ylim = ast.literal_eval(data_da.attrs.get('ylim','None'))
 
-                xlim    = xlim_in
-                if xlim is None:
-                    xlim = ast.literal_eval(data_da.attrs.get('xlim','None'))
+                xlim = xlims[group][data_var]
 
                 if xunits == 'datetime':
                     hrs         = np.array(data_da.coords[group])
@@ -528,6 +554,7 @@ def plot(self,baseout_dir='output',xlim=None,ylim=None,xunits='datetime',
 #                col_1_span  = 65
 
                 axs_to_adjust   = []
+                axs_stackplot   = []
 
                 pinx = -1
                 if plot_kpsymh:
@@ -548,6 +575,7 @@ def plot(self,baseout_dir='output',xlim=None,ylim=None,xunits='datetime',
                         self._format_timeticklabels(ax)
                         ax.set_xlabel('')
                     axs_to_adjust   += omni_axs
+                    axs_stackplot   += omni_axs
 
                 ######################################## 
                 if plot_goes:
@@ -561,6 +589,7 @@ def plot(self,baseout_dir='output',xlim=None,ylim=None,xunits='datetime',
                     ax.tick_params(**tick_params)
                     plot_letter(pinx,ax)
                     axs_to_adjust.append(ax)
+                    axs_stackplot.append(ax)
                     self._format_timeticklabels(ax)
                     ax.set_xlabel('')
 
@@ -575,6 +604,7 @@ def plot(self,baseout_dir='output',xlim=None,ylim=None,xunits='datetime',
                     ax.tick_params(**tick_params)
                     plot_letter(pinx,ax)
                     axs_to_adjust.append(ax)
+                    axs_stackplot.append(ax)
                     self._format_timeticklabels(ax)
                     ax.set_xlabel('')
                 
@@ -604,8 +634,8 @@ def plot(self,baseout_dir='output',xlim=None,ylim=None,xunits='datetime',
                             ha='center',transform=ax.transAxes,fontdict=fdict)
 
                     if plot_sza:
-                        sza_lat = self.sza.sza[group]['lat'] 
-                        sza_lon = self.sza.sza[group]['lon'] 
+                        sza_lat = sza.sza[group]['lat'] 
+                        sza_lon = sza.sza[group]['lon'] 
                         ax.scatter([sza_lon],[sza_lat],marker='*',s=600,color='yellow',
                                         edgecolors='black',zorder=500,lw=3)
 
@@ -641,7 +671,7 @@ def plot(self,baseout_dir='output',xlim=None,ylim=None,xunits='datetime',
 #                    result      = data.plot.pcolormesh(x=data_da.attrs['xkey'],y=data_da.attrs['ykey'],ax=ax,robust=robust,cbar_kwargs=cbar_kwargs)
 
                     if plot_sza:
-                        self.sza.plot(group,ax)
+                        sza.plot(group,ax)
 
                     xlbl    = ax.get_xlabel()
                     if xlbl == 'ut_hrs':
@@ -673,6 +703,7 @@ def plot(self,baseout_dir='output',xlim=None,ylim=None,xunits='datetime',
                     self._format_timeticklabels(ax)
                     if inx != len(freqs)-1:
                         ax.set_xlabel('')
+                    axs_stackplot.append(ax)
 
                     hist_ax = ax
 
@@ -689,18 +720,21 @@ def plot(self,baseout_dir='output',xlim=None,ylim=None,xunits='datetime',
                     l('{!s}-\n{!s}'.format(date_str_0,date_str_1))
 
                 l('Ham Radio Networks')
-                l('N Spots = {!s}'.format(map_sum))
-                lines   += self.src_cnts.get_text(group,data_var)
+                l('N Spots = {:d}'.format(int(map_sum)))
+                if no_percentages:
+                    lines   += self.src_cnts.get_text_sources_only(group,data_var)
+                else:
+                    lines   += self.src_cnts.get_text(group,data_var)
                 txt     = '\n'.join(lines)
 
-                if not plot_kpsymh and not plot_goes:
+                if not plot_kpsymh and not plot_goes and not plot_f107:
                     xpos        = 0.025
                     ypos        = 1.005
                     fdict       = {'size':38,'weight':'bold'}
                     va          = 'bottom'
                 else:
                     xpos        = 0.025
-                    ypos        = 0.995
+                    ypos        = 1.005
                     fdict       = {'size':38,'weight':'bold'}
                     va          = 'top'
 
@@ -711,11 +745,25 @@ def plot(self,baseout_dir='output',xlim=None,ylim=None,xunits='datetime',
                 for ax_0 in axs_to_adjust:
                     gl.adjust_axes(ax_0,hist_ax)
 
+
+                if title is not None:
+                    xpos        = 0.500
+                    ypos        = 1.005
+                    fdict       = {'size':48,'weight':'bold'}
+                    va          = 'bottom'
+                    ha          = 'center'
+
+                    fig.text(xpos,ypos,title,fontdict=fdict,va=va,ha=ha)
+
                 sTime_str   = self.sTime.strftime('%Y%m%d.%H%MUT')
                 eTime_str   = self.eTime.strftime('%Y%m%d.%H%MUT')
                 date_str    = '-'.join([sTime_str,eTime_str])
 
-                fname   = '.'.join([date_str,self.basename,group,data_var,'png']).replace('.bz2','')
+                if fname_suffix is None:
+                    fname   = '.'.join([date_str,self.basename,group,data_var,'png']).replace('.bz2','')
+                else:
+                    fname   = '.'.join([date_str,self.basename,group,data_var,fname_suffix,'png']).replace('.bz2','')
+
                 fpath   = os.path.join(outdir,fname)
                 fig.savefig(fpath,bbox_inches='tight')
                 print('--> {!s}'.format(fpath))
diff --git a/scripts/agu2020_solarcycle/adjust_swap.sh b/scripts/agu2020_solarcycle/adjust_swap.sh
new file mode 100755
index 0000000..8f8b5a6
--- /dev/null
+++ b/scripts/agu2020_solarcycle/adjust_swap.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+
+# Turn off all swap processes
+sudo swapoff -a
+
+# Resize the swap
+#  if = input file
+#  of = output file
+#  bs = block size
+#  count = multiplier of blocks
+sudo dd if=/dev/zero of=/swapfile bs=1G count=200 status=progress
+
+# Change Permission
+sudo chmod 600 /swapfile
+
+# Make the file usable as swap
+sudo mkswap /swapfile
+
+# Activate the swapfile
+sudo swapon /swapfile
+
+# Edit /etc/fstab and add the new swapfile if it isn’t already there
+# /swapfile none swap sw 0 0
+
+# Check the amount of swap available
+grep SwapTotal /proc/meminfo
diff --git a/scripts/agu2020_solarcycle/agu2020_plot_RBN.py b/scripts/agu2020_solarcycle/agu2020_plot_RBN.py
new file mode 100755
index 0000000..2b37b19
--- /dev/null
+++ b/scripts/agu2020_solarcycle/agu2020_plot_RBN.py
@@ -0,0 +1,114 @@
+#!/usr/bin/env python3
+"""
+Script covering the entire histogram workflow process.
+"""
+import os
+import datetime
+import bz2
+import pickle
+
+import hashlib
+
+import harc_plot
+from harc_plot import visualize_histograms as vh
+
+
+def hash_rd(run_dict):
+    """
+    Quick hash of the run dictionary for checking that pickle files are up-to-date.
+    """
+    rd_ = repr(sorted(run_dict.items())).encode('utf-8')
+    m   = hashlib.sha1()
+    m.update(rd_)
+    return m.hexdigest()
+
+
+def load_nc_cache(rd,reset_cache=False):
+    rd_hash     = hash_rd(rd)
+    sTime_str   = rd['sTime'].strftime('%Y%m%d')
+    eTime_str   = rd['eTime'].strftime('%Y%m%d')
+    fname       = '{!s}-{!s}.{!s}.p.bz2'.format(sTime_str,eTime_str,rd_hash)
+
+    cache_dir   = os.path.join(rd['baseout_dir'],'cache')
+    fpath       = os.path.join(cache_dir,fname)
+
+    if not os.path.exists(cache_dir):
+        os.makedirs(cache_dir)
+
+
+    if os.path.exists(fpath) and (reset_cache is True):
+        os.remove(fpath)
+
+    if os.path.exists(fpath):
+        print('Loading cached file: {!s}'.format(fpath))
+        with bz2.BZ2File(fpath,'rb') as fl:
+            nc_obj = pickle.load(fl)
+    else:
+        nc_obj              = vh.ncLoader(**rd)
+        nc_obj.rd_original  = rd
+
+        print('Saving cached file: {!s}'.format(fpath))
+        with bz2.BZ2File(fpath,'wb') as fl:
+            pickle.dump(nc_obj,fl)
+
+    return nc_obj
+
+
+def main(data_source_name='WSPRNet_RBN'):
+    region          = 'World'
+    run_name        = '-'.join([region,data_source_name])
+#    run_name        = region
+    data_dir        = os.path.join('data/solarcycle_3hr_250km/histograms',run_name)
+    plot_dir        = os.path.join('output/galleries/solarcycle_3hr_250km',run_name)
+
+    xkeys       = ['slt_mid','ut_hrs']
+    sTime       = datetime.datetime(2009,1,1)
+    eTime       = datetime.datetime(2020,1,1)
+
+#    sTime       = datetime.datetime(2015,1,1)
+#    eTime       = datetime.datetime(2015,2,1)
+
+    rgc_lim     = (0, 10000)
+
+    #geo_env     = harc_plot.GeospaceEnv()
+    geo_env = None
+
+    # Visualization ################################################################
+    ### Visualize Observations
+    rd = {}
+    rd['srcs']                  = os.path.join(data_dir,'*.data.nc.bz2')
+    rd['baseout_dir']           = plot_dir
+    rd['sTime']                 = sTime
+    rd['eTime']                 = eTime
+    rd['plot_region']           = region
+    rd['geospace_env']          = geo_env
+    rd['plot_sza']              = False
+    rd['plot_trend']            = True 
+    rd['plot_kpsymh']           = False
+    rd['plot_goes']             = False
+    rd['plot_f107']             = True
+    rd['log_z']                 = False
+    rd['band_keys']             = [28, 21, 14, 7, 3, 1]
+    rd['xkeys']                 = xkeys
+
+    nc      = load_nc_cache(rd,reset_cache=True)
+    fpaths  = nc.plot(**rd)
+
+    print()
+    if fpaths is not None:
+        for fpath in fpaths:
+            print('http://arrow.lan/~w2naf/code/harc_plot/scripts/agu2020_solarcycle/'+fpath)
+    print()
+
+if __name__ == '__main__':
+#    dsns = data_src_names = []
+#    dsns.append('WSPRNet')
+#    dsns.append('RBN')
+#    dsns.append('WSPRNet_RBN')
+#    for dsn in data_src_names:
+#        main(dsn)
+
+    main('RBN')
+
+
+import ipdb; ipdb.set_trace()
diff --git a/scripts/agu2020_solarcycle/agu2020_plot_WSPRNet.py b/scripts/agu2020_solarcycle/agu2020_plot_WSPRNet.py
new file mode 100755
index 0000000..8e88bd5
--- /dev/null
+++ b/scripts/agu2020_solarcycle/agu2020_plot_WSPRNet.py
@@ -0,0 +1,114 @@
+#!/usr/bin/env python3
+"""
+Script covering the entire histogram workflow process.
+"""
+import os
+import datetime
+import bz2
+import pickle
+
+import hashlib
+
+import harc_plot
+from harc_plot import visualize_histograms as vh
+
+
+def hash_rd(run_dict):
+    """
+    Quick hash of the run dictionary for checking that pickle files are up-to-date.
+    """
+    rd_ = repr(sorted(run_dict.items())).encode('utf-8')
+    m   = hashlib.sha1()
+    m.update(rd_)
+    return m.hexdigest()
+
+
+def load_nc_cache(rd,reset_cache=False):
+    rd_hash     = hash_rd(rd)
+    sTime_str   = rd['sTime'].strftime('%Y%m%d')
+    eTime_str   = rd['eTime'].strftime('%Y%m%d')
+    fname       = '{!s}-{!s}.{!s}.p.bz2'.format(sTime_str,eTime_str,rd_hash)
+
+    cache_dir   = os.path.join(rd['baseout_dir'],'cache')
+    fpath       = os.path.join(cache_dir,fname)
+
+    if not os.path.exists(cache_dir):
+        os.makedirs(cache_dir)
+
+
+    if os.path.exists(fpath) and (reset_cache is True):
+        os.remove(fpath)
+
+    if os.path.exists(fpath):
+        print('Loading cached file: {!s}'.format(fpath))
+        with bz2.BZ2File(fpath,'rb') as fl:
+            nc_obj = pickle.load(fl)
+    else:
+        nc_obj              = vh.ncLoader(**rd)
+        nc_obj.rd_original  = rd
+
+        print('Saving cached file: {!s}'.format(fpath))
+        with bz2.BZ2File(fpath,'wb') as fl:
+            pickle.dump(nc_obj,fl)
+
+    return nc_obj
+
+
+def main(data_source_name='WSPRNet_RBN'):
+    region          = 'World'
+    run_name        = '-'.join([region,data_source_name])
+#    run_name        = region
+    data_dir        = os.path.join('data/solarcycle_3hr_250km/histograms',run_name)
+    plot_dir        = os.path.join('output/galleries/solarcycle_3hr_250km',run_name)
+
+    xkeys       = ['slt_mid','ut_hrs']
+    sTime       = datetime.datetime(2009,1,1)
+    eTime       = datetime.datetime(2020,1,1)
+
+#    sTime       = datetime.datetime(2015,1,1)
+#    eTime       = datetime.datetime(2015,2,1)
+
+    rgc_lim     = (0, 10000)
+
+    #geo_env     = harc_plot.GeospaceEnv()
+    geo_env = None
+
+    # Visualization ################################################################
+    ### Visualize Observations
+    rd = {}
+    rd['srcs']                  = os.path.join(data_dir,'*.data.nc.bz2')
+    rd['baseout_dir']           = plot_dir
+    rd['sTime']                 = sTime
+    rd['eTime']                 = eTime
+    rd['plot_region']           = region
+    rd['geospace_env']          = geo_env
+    rd['plot_sza']              = False
+    rd['plot_trend']            = True 
+    rd['plot_kpsymh']           = False
+    rd['plot_goes']             = False
+    rd['plot_f107']             = True
+    rd['log_z']                 = False
+    rd['band_keys']             = [28, 21, 14, 7, 3, 1]
+    rd['xkeys']                 = xkeys
+
+    nc      = load_nc_cache(rd,reset_cache=True)
+    fpaths  = nc.plot(**rd)
+
+    print()
+    if fpaths is not None:
+        for fpath in fpaths:
+            print('http://arrow.lan/~w2naf/code/harc_plot/scripts/agu2020_solarcycle/'+fpath)
+    print()
+
+if __name__ == '__main__':
+#    dsns = data_src_names = []
+#    dsns.append('WSPRNet')
+#    dsns.append('RBN')
+#    dsns.append('WSPRNet_RBN')
+#    for dsn in data_src_names:
+#        main(dsn)
+
+    main('WSPRNet')
+
+
+import ipdb; ipdb.set_trace()
diff --git a/scripts/agu2020_solarcycle/agu2020_plot_WSPRNet_RBN.py b/scripts/agu2020_solarcycle/agu2020_plot_WSPRNet_RBN.py
new file mode 100755
index 0000000..301c496
--- /dev/null
+++ b/scripts/agu2020_solarcycle/agu2020_plot_WSPRNet_RBN.py
@@ -0,0 +1,114 @@
+#!/usr/bin/env python3
+"""
+Script covering the entire histogram workflow process.
+"""
+import os
+import datetime
+import bz2
+import pickle
+
+import hashlib
+
+import harc_plot
+from harc_plot import visualize_histograms as vh
+
+
+def hash_rd(run_dict):
+    """
+    Quick hash of the run dictionary for checking that pickle files are up-to-date.
+    """
+    rd_ = repr(sorted(run_dict.items())).encode('utf-8')
+    m   = hashlib.sha1()
+    m.update(rd_)
+    return m.hexdigest()
+
+
+def load_nc_cache(rd,reset_cache=False):
+    rd_hash     = hash_rd(rd)
+    sTime_str   = rd['sTime'].strftime('%Y%m%d')
+    eTime_str   = rd['eTime'].strftime('%Y%m%d')
+    fname       = '{!s}-{!s}.{!s}.p.bz2'.format(sTime_str,eTime_str,rd_hash)
+
+    cache_dir   = os.path.join(rd['baseout_dir'],'cache')
+    fpath       = os.path.join(cache_dir,fname)
+
+    if not os.path.exists(cache_dir):
+        os.makedirs(cache_dir)
+
+
+    if os.path.exists(fpath) and (reset_cache is True):
+        os.remove(fpath)
+
+    if os.path.exists(fpath):
+        print('Loading cached file: {!s}'.format(fpath))
+        with bz2.BZ2File(fpath,'rb') as fl:
+            nc_obj = pickle.load(fl)
+    else:
+        nc_obj              = vh.ncLoader(**rd)
+        nc_obj.rd_original  = rd
+
+        print('Saving cached file: {!s}'.format(fpath))
+        with bz2.BZ2File(fpath,'wb') as fl:
+            pickle.dump(nc_obj,fl)
+
+    return nc_obj
+
+
+def main(data_source_name='WSPRNet_RBN'):
+    region          = 'World'
+    run_name        = '-'.join([region,data_source_name])
+#    run_name        = region
+    data_dir        = os.path.join('data/solarcycle_3hr_250km/histograms',run_name)
+    plot_dir        = os.path.join('output/galleries/solarcycle_3hr_250km',run_name)
+
+    xkeys       = ['slt_mid','ut_hrs']
+    sTime       = datetime.datetime(2009,1,1)
+    eTime       = datetime.datetime(2020,1,1)
+
+#    sTime       = datetime.datetime(2015,1,1)
+#    eTime       = datetime.datetime(2015,2,1)
+
+    rgc_lim     = (0, 10000)
+
+    #geo_env     = harc_plot.GeospaceEnv()
+    geo_env = None
+
+    # Visualization ################################################################
+    ### Visualize Observations
+    rd = {}
+    rd['srcs']                  = os.path.join(data_dir,'*.data.nc.bz2')
+    rd['baseout_dir']           = plot_dir
+    rd['sTime']                 = sTime
+    rd['eTime']                 = eTime
+    rd['plot_region']           = region
+    rd['geospace_env']          = geo_env
+    rd['plot_sza']              = False
+    rd['plot_trend']            = True 
+    rd['plot_kpsymh']           = False
+    rd['plot_goes']             = False
+    rd['plot_f107']             = True
+    rd['log_z']                 = False
+    rd['band_keys']             = [28, 21, 14, 7, 3, 1]
+    rd['xkeys']                 = xkeys
+
+    nc      = load_nc_cache(rd,reset_cache=True)
+    fpaths  = nc.plot(**rd)
+
+    print()
+    if fpaths is not None:
+        for fpath in fpaths:
+            print('http://arrow.lan/~w2naf/code/harc_plot/scripts/agu2020_solarcycle/'+fpath)
+    print()
+
+if __name__ == '__main__':
+#    dsns = data_src_names = []
+#    dsns.append('WSPRNet')
+#    dsns.append('RBN')
+#    dsns.append('WSPRNet_RBN')
+#    for dsn in data_src_names:
+#        main(dsn)
+
+    main('WSPRNet_RBN')
+
+
+import ipdb; ipdb.set_trace()
diff --git a/scripts/agu2020_solarcycle/calcHist.py b/scripts/agu2020_solarcycle/calcHist.py
new file mode 100755
index 0000000..ce9dea5
--- /dev/null
+++ b/scripts/agu2020_solarcycle/calcHist.py
@@ -0,0 +1,82 @@
+#!/usr/bin/env python3
+"""
+Script covering the entire histogram workflow process.
+"""
+import os
+import datetime
+import dateutil.parser
+import argparse
+import harc_plot
+
+
+#data_sources: list, i.e. [1,2]
+#    0: dxcluster
+#    1: WSPRNet
+#    2: RBN
+
+dsd = data_src_dict = {}
+dsd['WSPRNet']      = {'data_sources':[1]}
+dsd['RBN']          = {'data_sources':[2]}
+dsd['WSPRNet_RBN']  = {'data_sources':[1,2]}
+
+
+def main(start='2015-01-01',stop='2015-02-01',
+        data_source='WSPRNet_RBN',test_mode=False):
+
+    region          = 'World'
+    rgc_lim         = (0, 10000)
+    xkeys           = ['slt_mid']
+    params          = ['spot_density']
+
+    sTime           = dateutil.parser.isoparse(start)
+    eTime           = dateutil.parser.isoparse(stop)
+
+    if test_mode:
+#        sTime       = datetime.datetime(2015,1,1)
+#        eTime       = datetime.datetime(2015,2,1)
+        run_name    = '-'.join([region,data_source,'test'])
+    else:
+#        sTime       = datetime.datetime(2009,1,1)
+#        eTime       = datetime.datetime(2020,1,1)
+        run_name    = '-'.join([region,data_source])
+
+    data_dir        = os.path.join('data/solarcycle_3hr_250km/histograms',run_name)
+    plot_dir        = os.path.join('output/galleries/solarcycle_3hr_250km',run_name)
+
+    #geo_env        = harc_plot.GeospaceEnv()
+    geo_env         = None
+
+    # Create histogram NetCDF Files ################################################
+    rd  = {}
+    rd['sDate']                 = sTime
+    rd['eDate']                 = eTime
+    rd['params']                = params
+    rd['xkeys']                 = xkeys
+    rd['rgc_lim']               = rgc_lim
+    rd['filter_region']         = region
+    rd['filter_region_kind']    = 'mids'
+    rd['xb_size_min']           = 6*60.
+    rd['yb_size_km']            = 250.
+    rd['loc_sources']           = None
+    rd['data_sources']          = dsd[data_source]['data_sources']
+    rd['reprocess']             = False
+    rd['output_dir']            = data_dir
+    rd['band_obj']              = harc_plot.gl.BandData()
+    harc_plot.calculate_histograms.main(rd)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data_sources',default=['WSPRNet','RBN','WSPRNet_RBN'],help='[WSPRNet, RBN, WSPRNet_RBN]')
+    parser.add_argument('--start',default='2015-01-01')
+    parser.add_argument('--stop', default='2015-02-01')
+    args = parser.parse_args()
+
+    data_sources = ['WSPRNet_RBN']
+
+    for data_source in data_sources:
+        rd  = {}
+        rd['data_source']   = data_source
+        rd['start']         = args.start
+        rd['stop']          = args.stop
+        print(rd)
+        main(**rd)
diff --git a/scripts/agu2020_solarcycle/calcHist_test.py b/scripts/agu2020_solarcycle/calcHist_test.py
new file mode 100755
index 0000000..21546d2
--- /dev/null
+++ b/scripts/agu2020_solarcycle/calcHist_test.py
@@ -0,0 +1,81 @@
+#!/usr/bin/env python3
+"""
+Script covering the entire histogram workflow process.
+"""
+import os
+import datetime
+import dateutil.parser
+import argparse
+import harc_plot
+
+
+#data_sources: list, i.e. [1,2]
+#    0: dxcluster
+#    1: WSPRNet
+#    2: RBN
+
+dsd = data_src_dict = {}
+dsd['WSPRNet']      = {'data_sources':[1]}
+dsd['RBN']          = {'data_sources':[2]}
+dsd['WSPRNet_RBN']  = {'data_sources':[1,2]}
+
+
+def main(start='2015-01-01',stop='2015-02-01',
+        data_source='WSPRNet_RBN',test_mode=False):
+
+    region          = 'World'
+    rgc_lim         = (0, 10000)
+    xkeys           = ['slt_mid']
+    params          = ['spot_density']
+
+    sTime           = dateutil.parser.isoparse(start)
+    eTime           = dateutil.parser.isoparse(stop)
+
+    if test_mode:
+#        sTime       = datetime.datetime(2015,1,1)
+#        eTime       = datetime.datetime(2015,2,1)
+        run_name    = '-'.join([region,data_source,'test'])
+    else:
+#        sTime       = datetime.datetime(2009,1,1)
+#        eTime       = datetime.datetime(2020,1,1)
+        run_name    = '-'.join([region,data_source])
+
+    data_dir        = os.path.join('data/solarcycle_3hr_250km/histograms',run_name)
+    plot_dir        = os.path.join('output/galleries/solarcycle_3hr_250km',run_name)
+
+    #geo_env        = harc_plot.GeospaceEnv()
+    geo_env         = None
+
+    # Create histogram NetCDF Files ################################################
+    rd  = {}
+    rd['sDate']                 = sTime
+    rd['eDate']                 = eTime
+    rd['params']                = params
+    rd['xkeys']                 = xkeys
+    rd['rgc_lim']               = rgc_lim
+    rd['filter_region']         = region
+    rd['filter_region_kind']    = 'mids'
+    rd['xb_size_min']           = 6*60.
+    rd['yb_size_km']            = 250.
+    rd['loc_sources']           = None
+    rd['data_sources']          = dsd[data_source]['data_sources']
+    rd['reprocess']             = True
+    rd['output_dir']            = data_dir
+    rd['band_obj']              = harc_plot.gl.BandData()
+    harc_plot.calculate_histograms.main(rd)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data_sources',default=['WSPRNet','RBN','WSPRNet_RBN'],help='[WSPRNet, RBN, WSPRNet_RBN]')
+    parser.add_argument('--start',default='2015-01-01')
+    parser.add_argument('--stop', default='2015-02-01')
+    args = parser.parse_args()
+
+    for data_source in args.data_sources:
+        rd  = {}
+        rd['data_source']   = data_source
+        rd['start']         = args.start
+        rd['stop']          = args.stop
+        rd['test_mode']     = True
+        print(rd)
+        main(**rd)
diff --git a/scripts/agu2020_solarcycle/csv_check.py b/scripts/agu2020_solarcycle/csv_check.py
new file mode 100755
index 0000000..868aaeb
--- /dev/null
+++ b/scripts/agu2020_solarcycle/csv_check.py
@@ -0,0 +1,32 @@
+#!/usr/bin/env python3
+
+import os
+import datetime
+
+import numpy as np
+import pandas as pd
+
+
+#    """
+#    Load spots from CSV file and filter for network/location source quality.
+#    Also provide range and regional filtering, compute midpoints, ut_hrs,
+#    and slt_mid.
+#
+#    data_sources: list, i.e. [1,2]
+#    ¦   0: dxcluster
+#    ¦   1: WSPRNet
+#    ¦   2: RBN
+#
+#    loc_sources: list, i.e. ['P','Q']
+#    ¦   P: user Provided
+#    ¦   Q: QRZ.com or HAMCALL
+#    ¦   E: Estimated using prefix
+#    """
+
+csv_path = 'data/spot_csvs/2009-11-01.csv.bz2'
+df  	 = pd.read_csv(csv_path,parse_dates=['occurred'])
+
+tf = df['source'] == 2
+df_rbn = df[tf].copy()
+
+import ipdb; ipdb.set_trace()
diff --git a/scripts/agu2020_solarcycle/dayNight.py b/scripts/agu2020_solarcycle/dayNight.py
new file mode 100755
index 0000000..6451fb1
--- /dev/null
+++ b/scripts/agu2020_solarcycle/dayNight.py
@@ -0,0 +1,173 @@
+#!/usr/bin/env python3
+"""
+Script covering the entire histogram workflow process.
+"""
+import os
+import datetime
+import bz2
+import pickle
+
+import argparse
+
+import hashlib
+
+import numpy as np
+import pandas as pd
+
+import harc_plot
+from harc_plot import visualize_histograms as vh
+
+
+def hash_rd(run_dict):
+    """
+    Quick hash of the run dictionary for checking that pickle files are up-to-date.
+    """
+    rd_ = repr(sorted(run_dict.items())).encode('utf-8')
+    m   = hashlib.sha1()
+    m.update(rd_)
+    return m.hexdigest()
+
+
+def load_nc_cache(rd,reprocess=False):
+    rd_hash     = hash_rd(rd)
+    sTime_str   = rd['sTime'].strftime('%Y%m%d')
+    eTime_str   = rd['eTime'].strftime('%Y%m%d')
+    fname       = '{!s}-{!s}.{!s}.p.bz2'.format(sTime_str,eTime_str,rd_hash)
+
+    cache_dir   = os.path.join(rd['baseout_dir'],'cache')
+    fpath       = os.path.join(cache_dir,fname)
+
+    if not os.path.exists(cache_dir):
+        os.makedirs(cache_dir)
+
+
+    if os.path.exists(fpath) and (reprocess is True):
+        os.remove(fpath)
+
+    if os.path.exists(fpath):
+        print('Loading cached file: {!s}'.format(fpath))
+        with bz2.BZ2File(fpath,'rb') as fl:
+            nc_obj = pickle.load(fl)
+    else:
+        nc_obj              = vh.ncLoader(**rd)
+        nc_obj.rd_original  = rd
+
+        print('Saving cached file: {!s}'.format(fpath))
+        with bz2.BZ2File(fpath,'wb') as fl:
+            pickle.dump(nc_obj,fl)
+
+    return nc_obj
+
+
+def main(data_source='WSPRNet_RBN',diurnal='day',test_mode=False):
+
+    region          = 'World'
+    rgc_lim         = (0, 10000)
+    xkeys           = ['slt_mid']
+
+    if test_mode:
+        sTime       = datetime.datetime(2015,1,1)
+        eTime       = datetime.datetime(2015,2,1)
+        run_name    = '-'.join([region,data_source,'test'])
+    else:
+        sTime       = datetime.datetime(2009,1,1)
+        eTime       = datetime.datetime(2020,1,1)
+        run_name    = '-'.join([region,data_source])
+
+    data_dir        = os.path.join('data/solarcycle_3hr_250km/histograms',run_name)
+    plot_dir        = os.path.join('output/galleries/solarcycle_3hr_250km',run_name)
+
+    #geo_env        = harc_plot.GeospaceEnv()
+    geo_env         = None
+
+    # Visualization ################################################################
+    ### Visualize Observations
+    rd = {}
+    rd['srcs']                  = os.path.join(data_dir,'*.data.nc.bz2')
+    rd['baseout_dir']           = plot_dir
+    rd['sTime']                 = sTime
+    rd['eTime']                 = eTime
+    rd['plot_region']           = region
+    rd['geospace_env']          = geo_env
+    rd['plot_sza']              = False
+    rd['plot_trend']            = True 
+    rd['plot_kpsymh']           = False
+    rd['plot_goes']             = False
+    rd['plot_f107']             = True
+    rd['log_z']                 = True
+    rd['band_keys']             = [28, 21, 14, 7, 3, 1]
+    rd['xkeys']                 = xkeys
+
+#    nc      = load_nc_cache(rd,reprocess=True)
+    nc      = vh.ncLoader(**rd)
+
+    # Select Day / Night ###########################################################
+    rd['no_percentages'] = True
+    for prefix in ['time_series','map']:
+        ds          = nc.datasets[prefix]['slt_mid']                 # Pull out the dataset of interest.
+        attrs       = ds['spot_density'].attrs
+        slt_mids    = ds['slt_mid'] % 24                                # Modulo 24 so we see what hour of the day it actually is
+
+        dx          = float(attrs.get('dx'))
+
+        stride      = 12. / dx
+        assert (stride - np.floor(stride)) == 0, \
+                'Stride must be an integer value. Recalculate histograms so 12./dx is an integer.'
+        stride      = int(stride)
+
+        if diurnal == 'night':
+            tf      = np.logical_or(slt_mids >=18, slt_mids < 6)            # Find where it is night only.
+            ds      = ds.loc[{'slt_mid': ds['slt_mid'][tf]}].copy()         # Select the night times in the data array.
+
+            ds      = ds.rolling({'slt_mid':stride}).sum()                   # Rolling sum of every 4 points.
+
+            tf      = (ds['slt_mid']%24) == 18                          # Only keep those data points starting at LT == 18 hr.
+            ds      = ds.loc[{'slt_mid': ds['slt_mid'][tf]}].copy()
+            rd['fname_suffix']  = 'NIGHT'
+            rd['title']         = 'Night (18 - 06 SLT)'
+
+        elif diurnal == 'day':
+            tf      = np.logical_and(slt_mids >=6, slt_mids < 18)            # Find where it is day only.
+            ds      = ds.loc[{'slt_mid': ds['slt_mid'][tf]}].copy()  # Select the night times in the data array.
+
+            ds      = ds.rolling({'slt_mid':stride}).sum()                    # Rolling sum of every 4 points.
+
+            tf      = (ds['slt_mid']%24) == 6                            # Only keep those data points starting at LT == 6 hr.
+            ds      = ds.loc[{'slt_mid': ds['slt_mid'][tf]}].copy()
+            rd['fname_suffix']  = 'DAY'
+            rd['title']         = 'Day (06 - 18 SLT)'
+
+        ds['spot_density'].attrs = attrs 
+        nc.datasets[prefix]['slt_mid'] = ds                          # Put the data array back into the plotting object.
+
+    fpaths  = nc.plot(**rd)
+
+    print()
+    if fpaths is not None:
+        for fpath in fpaths:
+            print('http://arrow.lan/~w2naf/code/harc_plot/scripts/agu2020_solarcycle/'+fpath)
+    print()
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data_source',default='WSPRNet_RBN',help='[WSPRNet, RBN, WSPRNet_RBN]')
+    parser.add_argument('--diurnals', default=['day','night',None])
+    args = parser.parse_args()
+
+    diurnals    = args.diurnals
+    data_source = args.data_source
+    test_mode   = False
+
+#    diurnals    = ['night']
+#    data_source = 'WSPRNet'
+#    test_mode   = True
+
+#    diurnals    = [None]
+
+    for diurnal in diurnals:
+        rd = {}
+        rd['diurnal']           = diurnal
+        rd['data_source']       = data_source
+        rd['test_mode']         = test_mode
+        print(rd)
+        main(**rd)
diff --git a/scripts/agu2020_solarcycle/dayNight.py.bak b/scripts/agu2020_solarcycle/dayNight.py.bak
new file mode 100755
index 0000000..b6f9cdd
--- /dev/null
+++ b/scripts/agu2020_solarcycle/dayNight.py.bak
@@ -0,0 +1,174 @@
+#!/usr/bin/env python3
+"""
+Script covering the entire histogram workflow process.
+"""
+import os
+import datetime
+import bz2
+import pickle
+
+import hashlib
+
+import numpy as np
+import pandas as pd
+
+import harc_plot
+from harc_plot import visualize_histograms as vh
+
+
+def hash_rd(run_dict):
+    """
+    Quick hash of the run dictionary for checking that pickle files are up-to-date.
+    """
+    rd_ = repr(sorted(run_dict.items())).encode('utf-8')
+    m   = hashlib.sha1()
+    m.update(rd_)
+    return m.hexdigest()
+
+
+def load_nc_cache(rd,reset_cache=False):
+    rd_hash     = hash_rd(rd)
+    sTime_str   = rd['sTime'].strftime('%Y%m%d')
+    eTime_str   = rd['eTime'].strftime('%Y%m%d')
+    fname       = '{!s}-{!s}.{!s}.p.bz2'.format(sTime_str,eTime_str,rd_hash)
+
+    cache_dir   = os.path.join(rd['baseout_dir'],'cache')
+    fpath       = os.path.join(cache_dir,fname)
+
+    if not os.path.exists(cache_dir):
+        os.makedirs(cache_dir)
+
+
+    if os.path.exists(fpath) and (reset_cache is True):
+        os.remove(fpath)
+
+    if os.path.exists(fpath):
+        print('Loading cached file: {!s}'.format(fpath))
+        with bz2.BZ2File(fpath,'rb') as fl:
+            nc_obj = pickle.load(fl)
+    else:
+        nc_obj              = vh.ncLoader(**rd)
+        nc_obj.rd_original  = rd
+
+        print('Saving cached file: {!s}'.format(fpath))
+        with bz2.BZ2File(fpath,'wb') as fl:
+            pickle.dump(nc_obj,fl)
+
+    return nc_obj
+
+
+def main(data_source_name='WSPRNet_RBN'):
+    diurnal = 'night'
+
+    region          = 'World'
+    run_name        = '-'.join([region,data_source_name,'test'])
+#    run_name        = region
+    data_dir        = os.path.join('data/solarcycle_3hr_250km/histograms',run_name)
+    plot_dir        = os.path.join('output/galleries/solarcycle_3hr_250km',run_name)
+
+    xkeys       = ['slt_mid','ut_hrs']
+#    xkeys       = ['slt_mid']
+#    sTime       = datetime.datetime(2009,1,1)
+#    eTime       = datetime.datetime(2020,1,1)
+
+    sTime       = datetime.datetime(2015,1,1)
+    eTime       = datetime.datetime(2015,2,1)
+
+    rgc_lim     = (0, 10000)
+
+    #geo_env     = harc_plot.GeospaceEnv()
+    geo_env = None
+
+    # Visualization ################################################################
+    ### Visualize Observations
+    rd = {}
+    rd['srcs']                  = os.path.join(data_dir,'*.data.nc.bz2')
+    rd['baseout_dir']           = plot_dir
+    rd['sTime']                 = sTime
+    rd['eTime']                 = eTime
+    rd['plot_region']           = region
+    rd['geospace_env']          = geo_env
+    rd['plot_sza']              = False
+    rd['plot_trend']            = True 
+    rd['plot_kpsymh']           = False
+    rd['plot_goes']             = False
+    rd['plot_f107']             = True
+    rd['log_z']                 = False
+    rd['band_keys']             = [28, 21, 14, 7, 3, 1]
+    rd['xkeys']                 = xkeys
+
+    nc      = load_nc_cache(rd,reset_cache=False)
+
+    # Time Series ##################################################################
+    ts_slt      = nc.datasets['time_series']['slt_mid']                 # Pull out the dataset of interest.
+    attrs       = ts_slt['spot_density'].attrs
+    slt_mids    = ts_slt['slt_mid'] % 24                                # Modulo 24 so we see what hour of the day it actually is
+
+    if diurnal == 'night':
+        tf          = np.logical_or(slt_mids >=18, slt_mids < 6)            # Find where it is night only.
+        ts_slt      = ts_slt.loc[{'slt_mid': ts_slt['slt_mid'][tf]}].copy() # Select the night times in the data array.
+
+        ts_slt      = ts_slt.rolling({'slt_mid':4}).sum()                   # Rolling sum of every 4 points.
+
+        tf          = (ts_slt['slt_mid']%24) == 18                          # Only keep those data points starting at LT == 18 hr.
+        ts_slt      = ts_slt.loc[{'slt_mid': ts_slt['slt_mid'][tf]}].copy()
+
+    elif diurnal == 'day':
+        tf          = np.logical_and(slt_mids >=6, slt_mids < 18)            # Find where it is day only.
+        ts_slt      = ts_slt.loc[{'slt_mid': ts_slt['slt_mid'][tf]}].copy()  # Select the night times in the data array.
+
+        ts_slt      = ts_slt.rolling({'slt_mid':4}).sum()                    # Rolling sum of every 4 points.
+
+        tf          = (ts_slt['slt_mid']%24) == 6                            # Only keep those data points starting at LT == 6 hr.
+        ts_slt      = ts_slt.loc[{'slt_mid': ts_slt['slt_mid'][tf]}].copy()
+
+    ts_slt['spot_density'].attrs = attrs 
+    nc.datasets['time_series']['slt_mid'] = ts_slt                          # Put the data array back into the plotting object.
+
+    # Maps #########################################################################
+    map_slt      = nc.datasets['map']['slt_mid']                        # Pull out the dataset of interest.
+    attrs       = map_slt['spot_density'].attrs
+    slt_mids    = map_slt['slt_mid'] % 24                                # Modulo 24 so we see what hour of the day it actually is
+
+    if diurnal == 'night':
+        tf          = np.logical_or(slt_mids >=18, slt_mids < 6)            # Find where it is night only.
+        map_slt      = map_slt.loc[{'slt_mid': map_slt['slt_mid'][tf]}].copy() # Select the night times in the data array.
+
+        map_slt      = map_slt.rolling({'slt_mid':4}).sum()                   # Rolling sum of every 4 points.
+
+        tf          = (map_slt['slt_mid']%24) == 18                          # Only keep those data points starting at LT == 18 hr.
+        map_slt      = map_slt.loc[{'slt_mid': map_slt['slt_mid'][tf]}].copy()
+
+    elif diurnal == 'day':
+        tf          = np.logical_and(slt_mids >=6, slt_mids < 18)            # Find where it is day only.
+        map_slt      = map_slt.loc[{'slt_mid': map_slt['slt_mid'][tf]}].copy()  # Select the night times in the data array.
+
+        map_slt      = map_slt.rolling({'slt_mid':4}).sum()                    # Rolling sum of every 4 points.
+
+        tf          = (map_slt['slt_mid']%24) == 6                            # Only keep those data points starting at LT == 6 hr.
+        map_slt      = map_slt.loc[{'slt_mid': map_slt['slt_mid'][tf]}].copy()
+
+    map_slt['spot_density'].attrs = attrs 
+    nc.datasets['map']['slt_mid'] = map_slt                          # Put the data array back into the plotting object.
+
+
+    fpaths  = nc.plot(**rd)
+
+    print()
+    if fpaths is not None:
+        for fpath in fpaths:
+            print('http://arrow.lan/~w2naf/code/harc_plot/scripts/agu2020_solarcycle/'+fpath)
+    print()
+
+if __name__ == '__main__':
+#    dsns = data_src_names = []
+#    dsns.append('WSPRNet')
+#    dsns.append('RBN')
+#    dsns.append('WSPRNet_RBN')
+#    for dsn in data_src_names:
+#        main(dsn)
+
+    main('WSPRNet_RBN')
+
+
+import ipdb; ipdb.set_trace()
diff --git a/scripts/agu2020_solarcycle/harc_plot b/scripts/agu2020_solarcycle/harc_plot
new file mode 120000
index 0000000..559e6d2
--- /dev/null
+++ b/scripts/agu2020_solarcycle/harc_plot
@@ -0,0 +1 @@
+../../harc_plot
\ No newline at end of file
diff --git a/scripts/agu2020_solarcycle/ipython/OMNI2_H0_MRG1HR_227742.csv.bz2 b/scripts/agu2020_solarcycle/ipython/OMNI2_H0_MRG1HR_227742.csv.bz2
new file mode 100644
index 0000000..6798f1f
Binary files /dev/null and b/scripts/agu2020_solarcycle/ipython/OMNI2_H0_MRG1HR_227742.csv.bz2 differ
diff --git a/scripts/agu2020_solarcycle/ipython/SW Indices.ipynb b/scripts/agu2020_solarcycle/ipython/SW Indices.ipynb
new file mode 100644
index 0000000..2301899
--- /dev/null
+++ b/scripts/agu2020_solarcycle/ipython/SW Indices.ipynb	
@@ -0,0 +1,453 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Space Weather 2009 - 2020"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from scipy import signal\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Matplotlib settings to make the plots look a little nicer.\n",
+    "plt.rcParams['font.size']      = 18\n",
+    "plt.rcParams['font.weight']    = 'bold'\n",
+    "plt.rcParams['axes.grid']      = True\n",
+    "plt.rcParams['axes.xmargin']   = 0\n",
+    "#plt.rcParams['axes.ymargin']   = 0\n",
+    "plt.rcParams['grid.linestyle'] = ':'\n",
+    "plt.rcParams['figure.figsize'] = (10,6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fname = \"OMNI2_H0_MRG1HR_227742.csv\"\n",
+    "date_col = 'TIME_AT_CENTER_OF_HOUR_yyyy-mm-ddThh:mm:ss.sssZ'\n",
+    "df = pd.read_csv(fname,parse_dates=[date_col],comment=\"#\")\n",
+    "df.rename(columns={date_col:'Time_UT'},inplace=True)\n",
+    "\n",
+    "df['Time_UT'] = df['Time_UT'].map(lambda x:x.replace(tzinfo=None))\n",
+    "df.set_index('Time_UT',inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>DAILY_SUNSPOT_NO_</th>\n",
+       "      <th>DAILY_F10.7_</th>\n",
+       "      <th>3-H_KP*10_</th>\n",
+       "      <th>1-H_DST_nT</th>\n",
+       "      <th>1-H_AE_nT</th>\n",
+       "      <th>3-H_AP_nT</th>\n",
+       "      <th>1-H_PC(N)-INDEX_</th>\n",
+       "      <th>PROTON_QI_</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Time_UT</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2009-01-01 00:30:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>66.6</td>\n",
+       "      <td>17</td>\n",
+       "      <td>-7</td>\n",
+       "      <td>44</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.0206</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2009-01-01 01:30:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>66.6</td>\n",
+       "      <td>17</td>\n",
+       "      <td>-5</td>\n",
+       "      <td>51</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.0177</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2009-01-01 02:30:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>66.6</td>\n",
+       "      <td>17</td>\n",
+       "      <td>-4</td>\n",
+       "      <td>68</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.0111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2009-01-01 03:30:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>66.6</td>\n",
+       "      <td>23</td>\n",
+       "      <td>-5</td>\n",
+       "      <td>75</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.0197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2009-01-01 04:30:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>66.6</td>\n",
+       "      <td>23</td>\n",
+       "      <td>-7</td>\n",
+       "      <td>153</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.0087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-31 19:30:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>68.2</td>\n",
+       "      <td>13</td>\n",
+       "      <td>-2</td>\n",
+       "      <td>9999</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.0071</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-31 20:30:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>68.2</td>\n",
+       "      <td>13</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>9999</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.0086</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-31 21:30:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>68.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>-1</td>\n",
+       "      <td>9999</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.0052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-31 22:30:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>68.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>-3</td>\n",
+       "      <td>9999</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.0040</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019-12-31 23:30:00</th>\n",
+       "      <td>0</td>\n",
+       "      <td>68.2</td>\n",
+       "      <td>7</td>\n",
+       "      <td>-5</td>\n",
+       "      <td>9999</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.0209</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>96408 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     DAILY_SUNSPOT_NO_  DAILY_F10.7_  3-H_KP*10_  1-H_DST_nT  \\\n",
+       "Time_UT                                                                        \n",
+       "2009-01-01 00:30:00                  0          66.6          17          -7   \n",
+       "2009-01-01 01:30:00                  0          66.6          17          -5   \n",
+       "2009-01-01 02:30:00                  0          66.6          17          -4   \n",
+       "2009-01-01 03:30:00                  0          66.6          23          -5   \n",
+       "2009-01-01 04:30:00                  0          66.6          23          -7   \n",
+       "...                                ...           ...         ...         ...   \n",
+       "2019-12-31 19:30:00                  0          68.2          13          -2   \n",
+       "2019-12-31 20:30:00                  0          68.2          13          -1   \n",
+       "2019-12-31 21:30:00                  0          68.2           7          -1   \n",
+       "2019-12-31 22:30:00                  0          68.2           7          -3   \n",
+       "2019-12-31 23:30:00                  0          68.2           7          -5   \n",
+       "\n",
+       "                     1-H_AE_nT  3-H_AP_nT  1-H_PC(N)-INDEX_  PROTON_QI_  \n",
+       "Time_UT                                                                  \n",
+       "2009-01-01 00:30:00         44          6               0.9      0.0206  \n",
+       "2009-01-01 01:30:00         51          6               0.6      0.0177  \n",
+       "2009-01-01 02:30:00         68          6               0.9      0.0111  \n",
+       "2009-01-01 03:30:00         75          9               0.8      0.0197  \n",
+       "2009-01-01 04:30:00        153          9               1.9      0.0087  \n",
+       "...                        ...        ...               ...         ...  \n",
+       "2019-12-31 19:30:00       9999          5               0.4      0.0071  \n",
+       "2019-12-31 20:30:00       9999          5               0.5      0.0086  \n",
+       "2019-12-31 21:30:00       9999          3               0.5      0.0052  \n",
+       "2019-12-31 22:30:00       9999          3               0.4      0.0040  \n",
+       "2019-12-31 23:30:00       9999          3               0.2      0.0209  \n",
+       "\n",
+       "[96408 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['DAILY_SUNSPOT_NO_', 'DAILY_F10.7_', '3-H_KP*10_', '1-H_DST_nT',\n",
+       "       '1-H_AE_nT', '3-H_AP_nT', '1-H_PC(N)-INDEX_', 'PROTON_QI_'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prms  = {}\n",
+    "prms['DAILY_F10.7_'] = {'bad':900}\n",
+    "prms['3-H_KP*10_']   = {}\n",
+    "prms['1-H_DST_nT']   = {}\n",
+    "prms['1-H_AE_nT']    = {'bad':9999}\n",
+    "\n",
+    "#prms['DAILY_SUNSPOT_NO_'] = {}\n",
+    "#prms['DAILY_F10.7_'] = {}\n",
+    "#prms['3-H_KP*10_'] = {}\n",
+    "#prms['1-H_DST_nT'] = {}\n",
+    "#prms['1-H_AE_nT'] = {}\n",
+    "#prms['3-H_AP_nT'] = {}\n",
+    "#prms['1-H_PC(N)-INDEX_'] = {}\n",
+    "#prms['PROTON_QI_'] = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "DAILY_F10.7_ 900\n",
+      "1-H_AE_nT 9999\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set NaNs\n",
+    "for prm,prmd in prms.items():\n",
+    "    bad = prmd.get('bad')\n",
+    "    if bad:\n",
+    "        tf = df[prm] >= bad\n",
+    "        df.loc[tf,prm] = np.nan\n",
+    "        print(prm,bad)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x1008 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sTime = datetime.datetime(2009,1,1)\n",
+    "eTime = datetime.datetime(2020,1,1)\n",
+    "plot_spectrogram = False\n",
+    "\n",
+    "xlim  = (sTime,eTime)\n",
+    "\n",
+    "nrows = len(prms)\n",
+    "if plot_spectrogram:\n",
+    "    nrows += len(prms)\n",
+    "\n",
+    "fig = plt.figure(figsize=(20,nrows*3.5))\n",
+    "ax_inx = 0\n",
+    "axs      = []\n",
+    "axs_spec = []\n",
+    "for prm,prmd in prms.items():\n",
+    "    prm_lbl = prmd.get('label',prm)\n",
+    "    xx      = df.index\n",
+    "    yy      = df[prm]    \n",
+    "    yy_filt = yy.rolling(90*24,center=True).apply(lambda x: np.nanmean(x))\n",
+    "    \n",
+    "    ax_inx += 1\n",
+    "    ax  = fig.add_subplot(nrows,1,ax_inx)\n",
+    "    axs.append(ax)   \n",
+    "    ax.plot(xx,yy)\n",
+    "    ax.plot(xx,yy_filt,lw=3)\n",
+    "    ax.set_xlim(xlim)\n",
+    "    ax.set_ylabel(prm_lbl)    \n",
+    "    if ax_inx != nrows:\n",
+    "        ax.set_xticklabels([])        \n",
+    "              \n",
+    "    if plot_spectrogram:\n",
+    "        ax_inx += 1\n",
+    "        ax  = fig.add_subplot(nrows,1,ax_inx)\n",
+    "        axs_spec.append(ax)\n",
+    "        f_spec, t_spec, Sxx = signal.spectrogram(yy_filt, 24) # Samples Per Day\n",
+    "        xx_spec = [df.index[0] + datetime.timedelta(days=x) for x in t_spec]\n",
+    "        mpbl = ax.pcolormesh(xx_spec, f_spec, 10.*np.log10(Sxx)[:-1,:-1])\n",
+    "        cbar = fig.colorbar(mpbl,label='PSD [dB]')\n",
+    "        ax.set_ylabel(prm+'\\nFrequency [Day$^{-1}$]')\n",
+    "        ax.set_xlim(xlim)\n",
+    "        ax.set_ylim(0,2)\n",
+    "\n",
+    "        if ax_inx != nrows:\n",
+    "            ax.set_xticklabels([])\n",
+    "        \n",
+    "    if ax_inx == nrows:\n",
+    "        ax.set_xlabel('Time [UT]')  \n",
+    "\n",
+    "fig.tight_layout()\n",
+    "\n",
+    "if plot_spectrogram:\n",
+    "    widths = []\n",
+    "    for ax in axs_spec:\n",
+    "        widths.append(ax.get_position().bounds[2])\n",
+    "    spec_width = max(widths)\n",
+    "\n",
+    "    for ax in axs + axs_spec:\n",
+    "        axp = ax.get_position().bounds\n",
+    "        ax.set_position((axp[0],axp[1],spec_width,axp[3]))\n",
+    "            \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/scripts/agu2020_solarcycle/start_calcHist.sh b/scripts/agu2020_solarcycle/start_calcHist.sh
new file mode 100755
index 0000000..82752dc
--- /dev/null
+++ b/scripts/agu2020_solarcycle/start_calcHist.sh
@@ -0,0 +1,23 @@
+#!/bin/bash
+session=calcHist
+
+tmux new-session -d -s $session 'htop';         # start new detached tmux session, run htop
+
+tmux split-window;
+tmux send './calcHist.py --start=2009-01-01 --stop=2013-01-01' ENTER;
+
+tmux split-window;
+tmux send './calcHist.py --start=2013-01-01 --stop=2015-01-01' ENTER;
+
+tmux select-layout even-vertical
+
+tmux split-window -h -t 0;
+tmux send './calcHist.py --start=2015-01-01 --stop=2017-01-01' ENTER;
+
+tmux split-window -h -t 2;
+tmux send './calcHist.py --start=2017-01-01 --stop=2019-01-01' ENTER;
+
+tmux split-window -h -t 4;
+tmux send './calcHist.py --start=2019-01-01 --stop=2020-01-01' ENTER;
+
+tmux a;                                         # open (attach) tmux session.
diff --git a/scripts/agu2020_solarcycle/start_dayNight.sh b/scripts/agu2020_solarcycle/start_dayNight.sh
new file mode 100755
index 0000000..9f19a76
--- /dev/null
+++ b/scripts/agu2020_solarcycle/start_dayNight.sh
@@ -0,0 +1,16 @@
+#!/bin/bash
+session=plot
+
+tmux new-session -d -s $session 'htop';         # start new detached tmux session, run htop
+
+tmux split-window;
+tmux send './dayNight.py --data_source WSPRNet' ENTER;
+
+tmux split-window;
+tmux send './dayNight.py --data_source RBN' ENTER;
+
+tmux split-window;
+tmux send './dayNight.py --data_source WSPRNet_RBN' ENTER;
+
+tmux select-layout even-vertical
+tmux a;                                         # open (attach) tmux session.
diff --git a/scripts/basic_histogram/histograms.py b/scripts/basic_histogram/histograms.py
index 2bf8d9e..3db29f2 100755
--- a/scripts/basic_histogram/histograms.py
+++ b/scripts/basic_histogram/histograms.py
@@ -12,12 +12,14 @@
 plot_dir    = os.path.join('output/galleries/histograms',run_name)
 params      = ['spot_density']
 xkeys       = ['ut_hrs','slt_mid']
-sTime       = datetime.datetime(2017,7,1)
-eTime       = datetime.datetime(2017,7,2)
+sTime       = datetime.datetime(2016,8,26)
+eTime       = datetime.datetime(2016,8,27)
 region      = run_name
 rgc_lim     = (0, 10000)
 
-geo_env     = harc_plot.GeospaceEnv()
+# geo_env     = harc_plot.GeospaceEnv()  # Comment out and add 2 following lines. Kukkai, 20231002
+years = [sTime.year + dy for dy in range(eTime.year-sTime.year+1)]   # Added. Kukkai, 20231002
+geo_env     = harc_plot.GeospaceEnv(years=years)                     # Added. Kukkai, 20231002
 
 # Create histogram NetCDF Files ################################################
 rd  = {}