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plot_Maps_Fast.py
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plot_Maps_Fast.py
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# coding: utf-8
# In[ ]:
'''
This code is part of the SIPN2 project focused on improving sub-seasonal to seasonal predictions of Arctic Sea Ice.
If you use this code for a publication or presentation, please cite the reference in the README.md on the
main page (https://github.com/NicWayand/ESIO).
Questions or comments should be addressed to [email protected]
Copyright (c) 2018 Nic Wayand
GNU General Public License v3.0
'''
'''
Plot forecast maps with all available models.
'''
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from collections import OrderedDict
import itertools
import numpy as np
import numpy.ma as ma
import pandas as pd
import struct
import os
import xarray as xr
import glob
import datetime
import cartopy.crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import seaborn as sns
np.seterr(divide='ignore', invalid='ignore')
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
import json
from esio import EsioData as ed
from esio import ice_plot
from esio import import_data
import subprocess
import dask
from dask.distributed import Client
import timeit
# General plotting settings
sns.set_style('whitegrid')
sns.set_context("talk", font_scale=.8, rc={"lines.linewidth": 2.5})
# In[ ]:
def remove_small_contours(p, thres=10):
for level in p.collections:
for kp,path in reversed(list(enumerate(level.get_paths()))):
# go in reversed order due to deletions!
# include test for "smallness" of your choice here:
# I'm using a simple estimation for the diameter based on the
# x and y diameter...
verts = path.vertices # (N,2)-shape array of contour line coordinates
diameter = np.max(verts.max(axis=0) - verts.min(axis=0))
if diameter<thres: # threshold to be refined for your actual dimensions!
del(level.get_paths()[kp]) # no remove() for Path objects:(
# In[ ]:
def get_figure_init_times(fig_dir):
# Get list of all figures
fig_files = glob.glob(os.path.join(fig_dir,'*.png'))
init_times = list(reversed(sorted(list(set([os.path.basename(x).split('_')[3] for x in fig_files])))))
return init_times
# In[ ]:
def update_status(ds_status=None, fig_dir=None, int_2_days_dict=None, NweeksUpdate=3):
# Get list of all figures
fig_files = glob.glob(os.path.join(fig_dir,'*.png'))
# For each figure
for fig_f in fig_files:
# Get the init_time from file name
cit = os.path.basename(fig_f).split('_')[3]
# Get the forecast int from file name
cft = int(os.path.basename(fig_f).split('_')[4].split('.')[0])
# Check if current it and ft were requested, otherwise skip
if (np.datetime64(cit) in ds_status.init_time.values) & (np.timedelta64(int_2_days_dict[cft]) in ds_status.fore_time.values):
# Always update the last 3 weeks (some models have lagg before we get them)
# Check if cit is one of the last NweeksUpdate init times in init_time
if (np.datetime64(cit) not in ds_status.init_time.values[-NweeksUpdate:]):
ds_status.status.loc[dict(init_time=cit, fore_time=int_2_days_dict[cft])] = 1
return ds_status
# In[ ]:
def Update_PanArctic_Maps():
# Plotting Info
runType = 'forecast'
variables = ['sic']
metrics_all = {'sic':['anomaly','mean','SIP'], 'hi':['mean']}
#metrics_all = {'sic':['SIP']}
updateAll = False
# Some models are terrible/have serious issues, so don't include in MME
MME_NO = ['hcmr']
# Define Init Periods here, spaced by 7 days (aprox a week)
# Now
cd = datetime.datetime.now()
cd = datetime.datetime(cd.year, cd.month, cd.day) # Set hour min sec to 0.
# Hardcoded start date (makes incremental weeks always the same)
start_t = datetime.datetime(1950, 1, 1) # datetime.datetime(1950, 1, 1)
# Params for this plot
Ndays = 7 # time period to aggregate maps to (default is 7)
Npers = 29 # number of periods to plot (from current date) (default is 14)
init_slice = np.arange(start_t, cd, datetime.timedelta(days=Ndays)).astype('datetime64[ns]')
init_slice = init_slice[-Npers:] # Select only the last Npers of periods (weeks) since current date
# Forecast times to plot
weeks = pd.to_timedelta(np.arange(0,5,1), unit='W')
months = pd.to_timedelta(np.arange(2,12,1), unit='M')
years = pd.to_timedelta(np.arange(1,2), unit='Y') - np.timedelta64(1, 'D') # need 364 not 365
slices = weeks.union(months).union(years).round('1d')
da_slices = xr.DataArray(slices, dims=('fore_time'))
da_slices.fore_time.values.astype('timedelta64[D]')
print(da_slices)
# Help conversion between "week/month" period used for figure naming and the actual forecast time delta value
int_2_days_dict = dict(zip(np.arange(0,da_slices.size), da_slices.values))
days_2_int_dict = {v: k for k, v in int_2_days_dict.items()}
#############################################################
# Load in Data
#############################################################
E = ed.EsioData.load()
mod_dir = E.model_dir
# Get median ice edge by DOY
median_ice_fill = xr.open_mfdataset(os.path.join(E.obs_dir, 'NSIDC_0051', 'agg_nc', 'ice_edge.nc')).sic
# Get mean sic by DOY
mean_1980_2010_sic = xr.open_dataset(os.path.join(E.obs_dir, 'NSIDC_0051', 'agg_nc', 'mean_1980_2010_sic.nc')).sic
# Get average sip by DOY
mean_1980_2010_SIP = xr.open_dataset(os.path.join(E.obs_dir, 'NSIDC_0051', 'agg_nc', 'hist_SIP_1980_2010.nc')).sip
# Climatology model
cmod = 'climatology'
all_files = os.path.join(mod_dir,cmod,runType,'sipn_nc', str(cd.year)+'*.nc')
files = glob.glob(all_files)
obs_clim_model = xr.open_mfdataset(sorted(files),
chunks={'time': 30, 'x': 304, 'y': 448},
concat_dim='time', autoclose=True, parallel=True)
obs_clim_model = obs_clim_model['sic']
# Get recent observations
ds_81 = xr.open_mfdataset(E.obs['NSIDC_0081']['sipn_nc']+'_yearly/*.nc', concat_dim='time', autoclose=True, parallel=True)#,
# Define models to plot
models_2_plot = list(E.model.keys())
models_2_plot = [x for x in models_2_plot if x not in ['piomas','MME','MME_NEW','uclsipn']] # remove some models
models_2_plot = [x for x in models_2_plot if E.icePredicted[x]] # Only predictive models
# Get # of models and setup subplot dims
Nmod = len(models_2_plot) + 4#(+3 for obs, MME, and clim)
Nc = int(np.floor(np.sqrt(Nmod)))
# Max number of columns == 5 (plots get too small otherwise)
Nc = np.min([Nc,5])
Nr = int(np.ceil(Nmod/Nc))
print(Nr, Nc, Nmod)
assert Nc*Nr>=Nmod, 'Need more subplots'
for cvar in variables:
# Define fig dir and make if doesn't exist
fig_dir = os.path.join(E.fig_dir, 'model', 'all_model', cvar, 'maps_weekly')
if not os.path.exists(fig_dir):
os.makedirs(fig_dir)
# Make requested dataArray as specified above
ds_status = xr.DataArray(np.ones((init_slice.size, da_slices.size))*np.NaN,
dims=('init_time','fore_time'),
coords={'init_time':init_slice,'fore_time':da_slices})
ds_status.name = 'status'
ds_status = ds_status.to_dataset()
# Check what plots we already have
if not updateAll:
print("Removing figures we have already made")
ds_status = update_status(ds_status=ds_status, fig_dir=fig_dir, int_2_days_dict=int_2_days_dict)
print(ds_status.status.values)
# Drop IC/FT we have already plotted (orthoginal only)
ds_status = ds_status.where(ds_status.status.sum(dim='fore_time')<ds_status.fore_time.size, drop=True)
print("Starting plots...")
# For each init_time we haven't plotted yet
start_time_cmod = timeit.default_timer()
for it in ds_status.init_time.values:
print(it)
it_start = it-np.timedelta64(Ndays,'D') + np.timedelta64(1,'D') # Start period for init period (it is end of period). Add 1 day because when
# we select using slice(start,stop) it is inclusive of end points. So here we are defining the start of the init AND the start of the valid time.
# So we need to add one day, so we don't double count.
# For each forecast time we haven't plotted yet
ft_to_plot = ds_status.sel(init_time=it)
ft_to_plot = ft_to_plot.where(ft_to_plot.isnull(), drop=True).fore_time
for ft in ft_to_plot.values:
print(ft.astype('timedelta64[D]'))
cs_str = format(days_2_int_dict[ft], '02') # Get index of current forcast week
week_str = format(int(ft.astype('timedelta64[D]').astype('int')/Ndays) , '02') # Get string of current week
cdoy_end = pd.to_datetime(it + ft).timetuple().tm_yday # Get current day of year end for valid time
cdoy_start = pd.to_datetime(it_start + ft).timetuple().tm_yday # Get current day of year end for valid time
it_yr = str(pd.to_datetime(it).year)
it_m = str(pd.to_datetime(it).month)
# Get datetime64 of valid time start and end
valid_start = it_start + ft
valid_end = it + ft
# Loop through variable of interest + any metrics (i.e. SIP) based on that
for metric in metrics_all[cvar]:
# Set up plotting info
if cvar=='sic':
if metric=='mean':
cmap_c = matplotlib.colors.ListedColormap(sns.color_palette("Blues_r", 10))
cmap_c.set_bad(color = 'lightgrey')
c_label = 'Sea Ice Concentration (-)'
c_vmin = 0
c_vmax = 1
elif metric=='SIP':
cmap_c = matplotlib.colors.LinearSegmentedColormap.from_list("", ["white","orange","red","#990000"])
cmap_c.set_bad(color = 'lightgrey')
c_label = 'Sea Ice Probability (-)'
c_vmin = 0
c_vmax = 1
elif metric=='anomaly':
# cmap_c = matplotlib.colors.ListedColormap(sns.color_palette("coolwarm", 9))
cmap_c = matplotlib.colors.LinearSegmentedColormap.from_list("", ["red","white","blue"])
cmap_c.set_bad(color = 'lightgrey')
c_label = 'SIC Anomaly to 1980-2010 Mean'
c_vmin = -1
c_vmax = 1
elif cvar=='hi':
if metric=='mean':
cmap_c = matplotlib.colors.ListedColormap(sns.color_palette("Reds_r", 10))
cmap_c.set_bad(color = 'lightgrey')
c_label = 'Sea Ice Thickness (m)'
c_vmin = 0
c_vmax = None
else:
raise ValueError("cvar not found.")
MME_list = []
# New Plot
start_time_plot = timeit.default_timer()
(f, axes) = ice_plot.multi_polar_axis(ncols=Nc, nrows=Nr, Nplots=Nmod)
############################################################################
# OBSERVATIONS #
############################################################################
# Plot Obs (if available)
ax_num = 0
axes[ax_num].set_title('Observed')
#ds_model = ds_model.sel(init_time=slice(it_start, it))
da_obs_c = ds_81.sic.sel(time=slice(valid_start,valid_end))
# Check we found any times in target valid time range
if da_obs_c.time.size>0:
#if ((it + ft) in ds_81.time.values):
if metric=='mean':
da_obs_c = da_obs_c.mean(dim='time') #ds_81.sic.sel(time=(it + ft))
elif metric=='SIP':
da_obs_c = (da_obs_c >= 0.15).mean(dim='time').astype('int').where(da_obs_c.isel(time=0).notnull())
elif metric=='anomaly':
da_obs_VT = da_obs_c.mean(dim='time')
da_obs_mean = mean_1980_2010_sic.isel(time=slice(cdoy_start,cdoy_end)).mean(dim='time')
da_obs_c = da_obs_VT - da_obs_mean
else:
raise ValueError('Not implemented')
da_obs_c.plot.pcolormesh(ax=axes[ax_num], x='lon', y='lat',
transform=ccrs.PlateCarree(),
add_colorbar=False,
cmap=cmap_c,
vmin=c_vmin, vmax=c_vmax)
axes[ax_num].set_title('Observed')
# Overlay median ice edge
#if metric=='mean':
#po = median_ice_fill.isel(time=cdoy).plot.contour(ax=axes[ax_num], x='xm', y='ym',
#=('#bc0f60'),
#linewidths=[0.5],
#levels=[0.5])
#remove_small_contours(po, thres=10)
else: # When were in the future (or obs are missing)
if metric=='anomaly': # Still get climatological mean for model difference
da_obs_mean = mean_1980_2010_sic.isel(time=slice(cdoy_start,cdoy_end)).mean(dim='time')
elif metric=='SIP': # Plot this historical mean SIP
da_obs_c = mean_1980_2010_SIP.isel(time=slice(cdoy_start,cdoy_end)).mean(dim='time')
da_obs_c.plot.pcolormesh(ax=axes[ax_num], x='lon', y='lat',
transform=ccrs.PlateCarree(),
add_colorbar=False,
cmap=cmap_c,
vmin=c_vmin, vmax=c_vmax)
axes[ax_num].set_title('Hist. Obs.')
############################################################################
# Plot climatology trend #
############################################################################
i = 2
cmod = 'climatology'
axes[i].set_title('clim. trend')
# Check if we have any valid times in range of target dates
ds_model = obs_clim_model.where((obs_clim_model.time>=valid_start) & (obs_clim_model.time<=valid_end), drop=True)
if 'time' in ds_model.lat.dims:
ds_model.coords['lat'] = ds_model.lat.isel(time=0).drop('time') # Drop time from lat/lon dims (not sure why?)
# If we have any time
if ds_model.time.size > 0:
# Average over time
ds_model = ds_model.mean(dim='time')
if metric=='mean': # Calc ensemble mean
ds_model = ds_model
elif metric=='SIP': # Calc probability
# Issue of some ensemble members having missing data
ocnmask = ds_model.notnull()
ds_model = (ds_model>=0.15).where(ocnmask)
elif metric=='anomaly': # Calc anomaly in reference to mean observed 1980-2010
ds_model = ds_model - da_obs_mean
# Add back lat/long (get dropped because of round off differences)
ds_model['lat'] = da_obs_mean.lat
ds_model['lon'] = da_obs_mean.lon
else:
raise ValueError('metric not implemented')
if 'doy' in ds_model.coords:
ds_model = ds_model.drop(['doy'])
if 'xm' in ds_model.coords:
ds_model = ds_model.drop(['xm'])
if 'ym' in ds_model.coords:
ds_model = ds_model.drop(['ym'])
# Build MME
if cmod not in MME_NO: # Exclude some models (bad) from MME
ds_model.coords['model'] = cmod
MME_list.append(ds_model)
# Plot
p = ds_model.plot.pcolormesh(ax=axes[i], x='lon', y='lat',
transform=ccrs.PlateCarree(),
add_colorbar=False,
cmap=cmap_c,
vmin=c_vmin, vmax=c_vmax)
axes[i].set_title('clim. trend')
# Clean up for current model
ds_model = None
###########################################################
# Plot Models in SIPN format #
###########################################################
# Plot all Models
p = None # initlaize to know if we found any data
for (i, cmod) in enumerate(models_2_plot):
print(cmod)
i = i+3 # shift for obs, MME, and clim
axes[i].set_title(E.model[cmod]['model_label'])
# Load in Model
# Find only files that have current year and month in filename (speeds up loading)
all_files = os.path.join(E.model[cmod][runType]['sipn_nc'], '*'+it_yr+'*'+it_m+'*.nc')
# Check we have files
files = glob.glob(all_files)
if not files:
continue # Skip this model
# Load in model
ds_model = xr.open_mfdataset(sorted(files),
chunks={'fore_time': 1, 'init_time': 1, 'nj': 304, 'ni': 448},
concat_dim='init_time', autoclose=True, parallel=True)
ds_model.rename({'nj':'x', 'ni':'y'}, inplace=True)
# Select init period and fore_time of interest
ds_model = ds_model.sel(init_time=slice(it_start, it))
# Check we found any init_times in range
if ds_model.init_time.size==0:
print('init_time not found.')
continue
# Select var of interest (if available)
if cvar in ds_model.variables:
# print('found ',cvar)
ds_model = ds_model[cvar]
else:
print('cvar not found.')
continue
# Get Valid time
ds_model = import_data.get_valid_time(ds_model)
# Check if we have any valid times in range of target dates
ds_model = ds_model.where((ds_model.valid_time>=valid_start) & (ds_model.valid_time<=valid_end), drop=True)
if ds_model.fore_time.size == 0:
print("no fore_time found for target period.")
continue
# Average over for_time and init_times
ds_model = ds_model.mean(dim=['fore_time','init_time'])
if metric=='mean': # Calc ensemble mean
ds_model = ds_model.mean(dim='ensemble')
elif metric=='SIP': # Calc probability
# Issue of some ensemble members having missing data
# ds_model = ds_model.where(ds_model>=0.15, other=0).mean(dim='ensemble')
ok_ens = ((ds_model.notnull().sum(dim='x').sum(dim='y'))>0) # select ensemble members with any data
ds_model = ((ds_model.where(ok_ens, drop=True)>=0.15) ).mean(dim='ensemble').where(ds_model.isel(ensemble=0).notnull())
elif metric=='anomaly': # Calc anomaly in reference to mean observed 1980-2010
ds_model = ds_model.mean(dim='ensemble') - da_obs_mean
# Add back lat/long (get dropped because of round off differences)
ds_model['lat'] = da_obs_mean.lat
ds_model['lon'] = da_obs_mean.lon
else:
raise ValueError('metric not implemented')
#print("Calc metric took ", (timeit.default_timer() - start_time), " seconds.")
# drop ensemble if still present
if 'ensemble' in ds_model:
ds_model = ds_model.drop('ensemble')
# Build MME
if cmod not in MME_NO: # Exclude some models (bad) from MME
ds_model.coords['model'] = cmod
if 'xm' in ds_model:
ds_model = ds_model.drop(['xm','ym']) #Dump coords we don't use
MME_list.append(ds_model)
# Plot
#start_time = timeit.default_timer()
p = ds_model.plot.pcolormesh(ax=axes[i], x='lon', y='lat',
transform=ccrs.PlateCarree(),
add_colorbar=False,
cmap=cmap_c,
vmin=c_vmin, vmax=c_vmax)
#print("Plotting took ", (timeit.default_timer() - start_time), " seconds.")
# Overlay median ice edge
#if metric=='mean':
#po = median_ice_fill.isel(time=cdoy).plot.contour(ax=axes[i], x='xm', y='ym',
#colors=('#bc0f60'),
#linewidths=[0.5],
#levels=[0.5]) #, label='Median ice edge 1981-2010')
#remove_small_contours(po, thres=10)
axes[i].set_title(E.model[cmod]['model_label'])
# Clean up for current model
ds_model = None
# MME
ax_num = 1
if MME_list: # If we had any models for this time
# Concat over all models
ds_MME = xr.concat(MME_list, dim='model')
# Set lat/lon to "model" lat/lon (round off differences)
if 'model' in ds_MME.lat.dims:
ds_MME.coords['lat'] = ds_MME.lat.isel(model=0).drop('model')
# Plot average
# Don't include some models (i.e. climatology and dampedAnomaly)
mod_to_avg = [m for m in ds_MME.model.values if m not in ['climatology','dampedAnomaly']]
pmme = ds_MME.sel(model=mod_to_avg).mean(dim='model').plot.pcolormesh(ax=axes[ax_num], x='lon', y='lat',
transform=ccrs.PlateCarree(),
add_colorbar=False,
cmap=cmap_c,vmin=c_vmin, vmax=c_vmax)
# Overlay median ice edge
#if metric=='mean':
#po = median_ice_fill.isel(time=cdoy).plot.contour(ax=axes[ax_num], x='xm', y='ym',
# colors=('#bc0f60'),
# linewidths=[0.5],
# levels=[0.5])
#remove_small_contours(po, thres=10)
# Save all models for given target valid_time
out_metric_dir = os.path.join(E.model['MME'][runType]['sipn_nc'], metric)
if not os.path.exists(out_metric_dir):
os.makedirs(out_metric_dir)
out_init_dir = os.path.join(out_metric_dir, pd.to_datetime(it).strftime('%Y-%m-%d'))
if not os.path.exists(out_init_dir):
os.makedirs(out_init_dir)
out_nc_file = os.path.join(out_init_dir, pd.to_datetime(it+ft).strftime('%Y-%m-%d')+'.nc')
# Add observations
# Check if exits (if we have any observations for this valid period)
# TODO: require ALL observations to be present, otherwise we could only get one day != week avg
if ds_81.sic.sel(time=slice(valid_start,valid_end)).time.size > 0:
da_obs_c.coords['model'] = 'Observed'
# Drop coords we don't need
da_obs_c = da_obs_c.drop(['hole_mask','xm','ym'])
if 'time' in da_obs_c:
da_obs_c = da_obs_c.drop('time')
if 'xm' in ds_MME:
ds_MME = ds_MME.drop(['xm','ym'])
# Add obs
ds_MME_out = xr.concat([ds_MME,da_obs_c], dim='model')
else:
ds_MME_out = ds_MME
# Add init and valid times
ds_MME_out.coords['init_start'] = it_start
ds_MME_out.coords['init_end'] = it
ds_MME_out.coords['valid_start'] = it_start+ft
ds_MME_out.coords['valid_end'] = it+ft
ds_MME_out.coords['fore_time'] = ft
ds_MME_out.name = metric
# Save
ds_MME_out.to_netcdf(out_nc_file)
axes[ax_num].set_title('MME')
# Make pretty
f.subplots_adjust(right=0.8)
cbar_ax = f.add_axes([0.85, 0.15, 0.05, 0.7])
if p:
cbar = f.colorbar(p, cax=cbar_ax, label=c_label)
if metric=='anomaly':
cbar.set_ticks(np.arange(-1,1.1,0.2))
else:
cbar.set_ticks(np.arange(0,1.1,0.1))
#cbar.set_ticklabels(np.arange(0,1,0.05))
# Set title of all plots
init_time_2 = pd.to_datetime(it).strftime('%Y-%m-%d')
init_time_1 = pd.to_datetime(it_start).strftime('%Y-%m-%d')
valid_time_2 = pd.to_datetime(it+ft).strftime('%Y-%m-%d')
valid_time_1 = pd.to_datetime(it_start+ft).strftime('%Y-%m-%d')
plt.suptitle('Initialization Time: '+init_time_1+' to '+init_time_2+'\n Valid Time: '+valid_time_1+' to '+valid_time_2,
fontsize=15) # +'\n Week '+week_str
plt.subplots_adjust(top=0.85)
# Save to file
f_out = os.path.join(fig_dir,'panArctic_'+metric+'_'+runType+'_'+init_time_2+'_'+cs_str+'.png')
f.savefig(f_out,bbox_inches='tight', dpi=200)
print("saved ", f_out)
print("Figure took ", (timeit.default_timer() - start_time_plot)/60, " minutes.")
# Mem clean up
plt.close(f)
p = None
ds_MME= None
da_obs_c = None
da_obs_mean = None
# Done with current it
print("Took ", (timeit.default_timer() - start_time_cmod)/60, " minutes.")
# Update json file
json_format = get_figure_init_times(fig_dir)
json_dict = [{"date":cd,"label":cd} for cd in json_format]
json_f = os.path.join(fig_dir, 'plotdates_current.json')
with open(json_f, 'w') as outfile:
json.dump(json_dict, outfile)
# Make into Gifs
for cit in json_format:
subprocess.call(str("/home/disk/sipn/nicway/python/ESIO/scripts/makeGif.sh " + fig_dir + " " + cit), shell=True)
print("Finished plotting panArctic Maps.")
# In[ ]:
if __name__ == '__main__':
# Start up Client
client = Client()
#dask.config.set(scheduler='threads') # overwrite default with threaded scheduler
# Call function
Update_PanArctic_Maps()
# In[ ]:
# # Run below in case we need to just update the json file and gifs
# fig_dir = '/home/disk/sipn/nicway/public_html/sipn/figures/model/all_model/sic/maps_weekly'
# json_format = get_figure_init_times(fig_dir)
# json_dict = [{"date":cd,"label":cd} for cd in json_format]
# json_f = os.path.join(fig_dir, 'plotdates_current.json')
# with open(json_f, 'w') as outfile:
# json.dump(json_dict, outfile)
# # Make into Gifs
# # TODO fig_dir hardcoded to current variable
# for cit in json_format:
# subprocess.call(str("/home/disk/sipn/nicway/python/ESIO/scripts/makeGif.sh " + fig_dir + " " + cit), shell=True)
# print("Finished plotting panArctic Maps.")