From c3a6cf1aeb7e27493e48d0532a9f9cb1f45f01cb Mon Sep 17 00:00:00 2001 From: Jiwoo Lee Date: Tue, 7 Nov 2023 09:52:19 -0800 Subject: [PATCH 01/69] initial commit --- pcmdi_metrics/sea_ice/__init__.py | 1 + .../sea_ice/generate_sector_masks.py | 197 ++++ .../sea_ice/ice_area_cmip5_ssmi_reg_rms.py | 994 ++++++++++++++++++ pcmdi_metrics/sea_ice/sector_mask_defs.py | 61 ++ 4 files changed, 1253 insertions(+) create mode 100644 pcmdi_metrics/sea_ice/__init__.py create mode 100644 pcmdi_metrics/sea_ice/generate_sector_masks.py create mode 100755 pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py create mode 100644 pcmdi_metrics/sea_ice/sector_mask_defs.py diff --git a/pcmdi_metrics/sea_ice/__init__.py b/pcmdi_metrics/sea_ice/__init__.py new file mode 100644 index 000000000..430da4d9e --- /dev/null +++ b/pcmdi_metrics/sea_ice/__init__.py @@ -0,0 +1 @@ +import sector_mask_defs diff --git a/pcmdi_metrics/sea_ice/generate_sector_masks.py b/pcmdi_metrics/sea_ice/generate_sector_masks.py new file mode 100644 index 000000000..0be90b03c --- /dev/null +++ b/pcmdi_metrics/sea_ice/generate_sector_masks.py @@ -0,0 +1,197 @@ +import cdms2 +import MV2 as MV +import string +import os,sys +from pcmdi_metrics.pcmdi.pmp_parser import * +import pcmdi_metrics +import parser +import collections +from sector_mask_defs import * + +import argparse +from argparse import RawTextHelpFormatter + +P = PMPParser() + +P.add_argument("--mp", "--modpath", + type = str, + dest = 'modpath', + default = '', + help = "Explicit path to model monthly PR climatology") +P.add_argument("-o", "--obspath", + type = str, + dest = 'obspath', + default = '', + help = "Explicit path to obs monthly PR climatology") +P.add_argument("--outpd", "--outpathdata", + type = str, + dest = 'outpathdata', + default = '/export/gleckler1/processing/metrics_package/my_test/sea_ice/git_data/', + help = "Output path for sector scale masks") + +args = P.parse_args(sys.argv[1:]) +sec_mask_dir = args.outpathdata + +#Factors +factor1=1.e-6 #model units are m^2, converting to km^2 +factor2=1.e-2 #model units are %, converting to non-dimen +a=6371.009 #Earth radii in km +pi=22./7. +factor3=4.*pi*a*a # Earth's surface area in km2 +dc=0.15 #minimum ice concentration contour + +pin = '/work/gleckler1/processed_data/cmip5clims-historical/sic/cmip5.MOD.historical.r1i1p1.mo.seaIce.OImon.sic.ver-1.1980-2005.SC.nc' + +pins = string.replace(pin,'MOD','*') + +lst = os.popen('ls ' + pins).readlines() + +mods = [] +mods_failed = [] +for l in lst: + mod = string.split(l,'.')[1] + if mod not in mods: mods.append(mod) + +#w =sys.stdin.readline() + +var = 'sic' +factor2 = 1 + +mods = ['ACCESS1-3'] + +for mod in mods: + try: + fc = string.replace(pin,'MOD',mod) + f=cdms2.open(fc) + sic=f(var, squeeze=1) + sic_grid=sic.getGrid() + lat=sic.getLatitude() + lon=sic.getLongitude() + sic=MV.multiply(sic,factor2) + + print 'CMIP5-native= ',MV.max(sic) + if MV.rank(lat)==1: + tmp2d=f(var,time=slice(0,1),squeeze=1) + lats=MV.zeros(tmp2d.shape) + for ii in range (0,len(lon)): + lats[:,ii]=lat[:] + else: + lats=lat + + if MV.rank(lon)==1: + tmp2d=f(var,time=slice(0,1),squeeze=1) + lons=MV.zeros(tmp2d.shape) + for ii in range (0,len(lat)): + lons[ii,:]=lon[:] + else: + lons=lon + + f.close() + + +####################################################### +### areacello + area_dir = '/work/cmip5/fx/fx/areacello/' + alist = os.listdir(area_dir) # LIST OF ALL AREACELLO FILES + + for a in alist: + if string.find(a,mod) != -1: + areaf = a + print mod,' ', a +# w = sys.stdin.readline() + + g = cdms2.open(area_dir + areaf) + + try: + area = g('areacello') + except: + area = g('areacella') + area = MV.multiply(area,factor1) + area = MV.multiply(area,factor1) + + g.close() + + land_mask=MV.zeros(area.shape) + +# Reading the ocean/land grid cell masks (sftof/sftlf) + mask_dir = '/work/cmip5/fx/fx/sftof/' + mlist = os.listdir(mask_dir) # LIST OF ALL AREACELLO FILES + + for m in mlist: + if string.find(m,mod) != -1: + maskf = m + print mod,' ', m + + s = cdms2.open(mask_dir + maskf) + try: + frac = s('sftof') + if (mod != 'MIROC5' and mod!= 'GFDL-CM2p1' and mod!='GFDL-CM3' and mod!='GFDL-ESM2M'): + frac = MV.multiply(frac,factor2) + print mod,MV.max(frac) + area = MV.multiply(area,frac) + land_mask = MV.multiply(1,(1-frac)) + except: + frac = s('sftlf') + if (mod != 'MIROC5' or mod!= 'GFDL-CM2p1' or mod!='GFDL-CM3' or mod!='GFDL-ESM2M'): + frac = MV.multiply(frac,factor2) + area = MV.multiply(area,(1-frac)) + land_mask = MV.multiply(1,frac) + s.close() + +# Creating regional masks +# GFDL and bcc model grids have shift of 80 + if (mod[0:4] == 'GFDL' or mod[0:3] == 'bcc') : + lons_a=MV.where(MV.less(lons,-180.),lons+360.,lons) + lons_p=MV.where(MV.less(lons,0.),lons+360.,lons) + else: + lons_a=MV.where(MV.greater(lons,180.),lons-360.,lons) + lons_p=lons + + print 'CMIP5' + print 'lons_na= ',MV.min(lons_a),MV.max(lons_a) + print 'lons_np= ',MV.min(lons_p),MV.max(lons_p) +# mask_arctic=MV.zeros(area.shape) +# mask_antarctic=MV.zeros(area.shape) + mask_ca=MV.zeros(area.shape) + mask_na=MV.zeros(area.shape) + mask_np=MV.zeros(area.shape) + mask_sa=MV.zeros(area.shape) + mask_sp=MV.zeros(area.shape) + mask_io=MV.zeros(area.shape) + +############################################################### + + sectors = ['ca','na','np','sp','sa','io'] + + for sector in sectors: + mask = getmask(sector,lats,lons,lons_a,lons_p,land_mask) + + g = cdms2.open(sec_mask_dir + 'mask_' + mod + '_' + sector + '.nc','w+') + mask.id = 'mask' + g.write(mask) +# land_mask.id = 'sftof' +# g.write(land_mask) + g.close() + + print 'got it!',' ', mask.shape + w = sys.stdin.readline() + +# Calculate the Total Sea Ice Concentration/Extent/Area +# ice_area = MV.multiply(sic,1) #SIC + area = MV.multiply(1,area) #SIE + ice_area = MV.multiply(sic,area) #SIA + ice_area = MV.where(MV.greater_equal(sic,0.15),ice_area,0.) #Masking out the sic<0.15 + +# arctic=MV.logical_and(MV.greater_equal(lats,35.),MV.less(lats,87.2)) #SSM/I limited to 87.2N + mask_arctic=MV.logical_and(MV.greater_equal(lats,45.),MV.less(lats,90.)) #Adding currently in SSM/I 100% in the area >87.2N + mask_antarctic=MV.logical_and(MV.greater_equal(lats,-90.),MV.less(lats,-55.)) + + except: + 'Failed for ', mod + mods_failed.append(mod) + +print 'failed for ', mods_failed + +# Calculate the Sea Ice Covered Area + + diff --git a/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py b/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py new file mode 100755 index 000000000..e54196aad --- /dev/null +++ b/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py @@ -0,0 +1,994 @@ +import cdms2 as cdms +import MV2 as MV +import string, os, sys +import cdutil, genutil +import cdtime, datetime +import gc + +import numpy as np +import pylab +import matplotlib.pyplot as plt + +from cdms2.selectors import Selector + +def tgrid(t): + + import cdtime + + time=t[:]*0. + if t[0]==0. : dt=0. + else : dt=0.5 # centered in the midlle of the month + time[0]=dt/12. + nmonths=len(t) # monthy time series + + for it in range (1,nmonths) : + time[it]=time[it-1]+1./12. + + time=time+cdtime.reltime(t[0],t.units).tocomp().year + + return time + +value = 0 +cdms.setNetcdfShuffleFlag(value) +cdms.setNetcdfDeflateFlag(value) +cdms.setNetcdfDeflateLevelFlag(value) + +cdms.setAutoBounds('on') + +#Factors +factor1=1.e-6 #model units are m^2, converting to km^2 +factor2=1.e-2 #model units are %, converting to non-dimen +a=6371.009 #Earth radii in km +pi=22./7. +factor3=4.*pi*a*a # Earth's surface area in km2 +dc=0.15 #minimum ice concentration contour + + +#Observations +dlist_n=['ssmi_nt_n_names.asc','ssmi_bt_n_names.asc'] +dlist_s=['ssmi_nt_s_names.asc','ssmi_bt_s_names.asc'] + +annual_cycle_obs_arctic=[] +annual_cycle_obs_antarctic=[] +data_n=MV.zeros([324]) +obs_n=MV.zeros([324,2]) +ta_ca=MV.zeros([324]) +obs_ca=MV.zeros([324,2]) +ta_na=MV.zeros([324]) +obs_na=MV.zeros([324,2]) +ta_np=MV.zeros([324]) +obs_np=MV.zeros([324,2]) +data_s=MV.zeros([324]) +obs_s=MV.zeros([324,2]) +ta_sa=MV.zeros([324]) +obs_sa=MV.zeros([324,2]) +ta_sp=MV.zeros([324]) +obs_sp=MV.zeros([324,2]) +ta_io=MV.zeros([324]) +obs_io=MV.zeros([324,2]) + +# SSM/I Arctic + +for dl in range(0,len(dlist_n)): + + f=open(dlist_n[dl]) + lines_n=f.readlines() + f.close + +# Reading the ocean/ice grid cell area (area) +# Reading the sea ice concentration (ice_con) + for i in range(0,len(lines_n)): + filename=lines_n[i].strip('\t\n\r') + print filename + obs=cdms.open(filename) + lats_n=obs('lat') + lons_n=obs('lon') + area_n=obs('area') + sic_n=obs('ice_con') + sic_n=MV.multiply(sic_n,factor2) + area_n=MV.multiply(area_n,factor1) + + obs.close() + +# Creating regional masks + lons_p=MV.where(MV.less(lons_n,0.),lons_n+360.,lons_n) + lons_a=lons_n + + print 'Obs' + print 'lons_na= ',MV.min(lons_a),MV.max(lons_a) + print 'lons_np= ',MV.min(lons_p),MV.max(lons_p) + + mask_ca=MV.zeros(area_n.shape) + mask_na=MV.zeros(area_n.shape) + mask_np=MV.zeros(area_n.shape) + +# Arctic Regions +#Central Arctic + lat_bound1=MV.logical_and(MV.greater(lats_n,80.),MV.less_equal(lats_n,87.2)) + lat_bound2=MV.logical_and(MV.greater(lats_n,65.),MV.less_equal(lats_n,87.2)) + lat_bound3=MV.logical_and(MV.greater(lats_n,87.2),MV.less_equal(lats_n,90.)) + lon_bound1=MV.logical_and(MV.greater(lons_a,-120.),MV.less_equal(lons_a,90.)) + lon_bound2=MV.logical_and(MV.greater(lons_p,90.),MV.less_equal(lons_p,240.)) + reg1_ca=MV.logical_and(lat_bound1,lon_bound1) + reg2_ca=MV.logical_and(lat_bound2,lon_bound2) + mask_ca=MV.where(MV.logical_or(reg1_ca,reg2_ca),1,0) + mask_pole=MV.where(lat_bound3,1,0) + +#NA region + lat_bound=MV.logical_and(MV.greater(lats_n,35.),MV.less_equal(lats_n,80.)) + lon_bound=MV.logical_and(MV.greater(lons_a,-120.),MV.less_equal(lons_a,90.)) + mask_na=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) + +#NP region + lat_bound=MV.logical_and(MV.greater(lats_n,35.),MV.less_equal(lats_n,65.)) + lon_bound=MV.logical_and(MV.greater(lons_p,90.),MV.less_equal(lons_p,240.)) + mask_np=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) + + area_sic_pole=MV.where(MV.equal(mask_pole,True),MV.multiply(1,area_n),0.) + +#Masking out sic<0.15 + ice_area = MV.where(MV.greater_equal(sic_n,0.15),area_n,0.) #Masking out sic<0.15 + +# Ice Extent +# area_sic_arctic=MV.multiply(1,ice_area) +# area_sic_ca=MV.where(MV.equal(mask_ca,True),MV.multiply(1,ice_area),0.) +# area_sic_na=MV.where(MV.equal(mask_na,True),MV.multiply(1,ice_area),0.) +# area_sic_np=MV.where(MV.equal(mask_np,True),MV.multiply(1,ice_area),0.) +# Ice Area + area_sic_arctic=MV.multiply(sic_n,ice_area) + area_sic_ca=MV.where(MV.equal(mask_ca,True),MV.multiply(sic_n,ice_area),0.) + area_sic_na=MV.where(MV.equal(mask_na,True),MV.multiply(sic_n,ice_area),0.) + area_sic_np=MV.where(MV.equal(mask_np,True),MV.multiply(sic_n,ice_area),0.) + + data_n[i]=MV.add(MV.sum(area_sic_arctic),MV.sum(area_sic_pole)) + ta_ca[i]=MV.add(MV.sum(area_sic_ca),MV.sum(area_sic_pole)) + ta_na[i]=MV.sum(area_sic_na) + ta_np[i]=MV.sum(area_sic_np) + + print 'data_n= ',data_n[i] + print 'ta_na= ',ta_na[i] + print 'ta_np= ',ta_np[i] + + print MV.average(sic_n) + + obs_n[:,dl]=MV.array(data_n,id='sic') + obs_ca[:,dl]=MV.array(ta_ca,id='sic') + obs_na[:,dl]=MV.array(ta_na,id='sic') + obs_np[:,dl]=MV.array(ta_np,id='sic') + +# SSM/I Antarctic +for dl in range(0,len(dlist_s)): + g=open(dlist_s[dl]) + lines_s=g.readlines() + g.close + + + for i in range(0,len(lines_s)): + filename=lines_s[i].strip('\t\n\r') + print filename + obs=cdms.open(filename) + lats_s=obs('lat') + lons_s=obs('lon') + area_s=obs('area') + sic_s=obs('ice_con') + sic_s=MV.multiply(sic_s,factor2) + area_s=MV.multiply(area_s,factor1) + + + obs.close() + +# Creating regional masks + lons_sa=lons_s + lons_sp=MV.where(MV.less(lons_s,0.),lons_s+360,lons_s) + lons_io=lons_sp + + print 'Obs' + print 'lons_sa= ',MV.min(lons_sa),MV.max(lons_sa) + print 'lons_sp= ',MV.min(lons_sp),MV.max(lons_sp) + + mask_sa=MV.zeros(area_s.shape) + mask_sp=MV.zeros(area_s.shape) + mask_io=MV.zeros(area_s.shape) + +# Antarctic Regions + lat_bound=MV.logical_and(MV.greater(lats_s,-90.),MV.less_equal(lats_s,-40.)) + +# SA region +# lon_bound=MV.logical_and(MV.greater(lons_sa,-60.),MV.less_equal(lons_sa,30.)) + lon_bound=MV.logical_and(MV.greater(lons_sa,-60.),MV.less_equal(lons_sa,20.)) + mask_sa=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) + +# SP region +# lon_bound=MV.logical_and(MV.greater(lons_sp,130.),MV.less_equal(lons_sp,300.)) + lon_bound=MV.logical_and(MV.greater(lons_sp,90.),MV.less_equal(lons_sp,300.)) + mask_sp=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) + +# Indian Ocean (IO) region +# lon_bound=MV.logical_and(MV.greater(lons_sp,30.),MV.less_equal(lons_sp,130.)) + lon_bound=MV.logical_and(MV.greater(lons_sp,20.),MV.less_equal(lons_sp,90.)) + mask_io=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) + +#Masking out sic<0.15 + ice_area = MV.where(MV.greater_equal(sic_s,dc),area_s,0.) #Masking out sic<0.15 +# Ice Extent +# area_sic_antarctic=MV.multiply(1,ice_area) +# area_sic_sa=MV.where(MV.equal(mask_sa,True),MV.multiply(1,ice_area),0.) +# area_sic_sp=MV.where(MV.equal(mask_sp,True),MV.multiply(1,ice_area),0.) +# area_sic_io=MV.where(MV.equal(mask_io,True),MV.multiply(1,ice_area),0.) +# Ice Area + area_sic_antarctic=MV.multiply(sic_s,ice_area) + area_sic_sa=MV.where(MV.equal(mask_sa,True),MV.multiply(sic_s,area_s),0.) + area_sic_sp=MV.where(MV.equal(mask_sp,True),MV.multiply(sic_s,area_s),0.) + area_sic_io=MV.where(MV.equal(mask_io,True),MV.multiply(sic_s,area_s),0.) + + data_s[i]=MV.sum(area_sic_antarctic) + ta_sa[i]=MV.sum(area_sic_sa) + ta_sp[i]=MV.sum(area_sic_sp) + ta_io[i]=MV.sum(area_sic_io) + + print MV.average(sic_s) + + obs_s[:,dl]=MV.array(data_s,id='sic') + obs_s=MV.masked_equal(obs_s,-9999.) + obs_sa[:,dl]=MV.array(ta_sa,id='sic') + obs_sp[:,dl]=MV.array(ta_sp,id='sic') + obs_io[:,dl]=MV.array(ta_io,id='sic') + +# Create Time Axis +years=[] +months=[] +for iy in range(1979,2006): + for im in range(1,13): + years.append(int(iy)) + months.append(int(im)) + +timeax=[] +for date in zip(years,months): + yr,mo = date + print yr + c=cdtime.comptime(yr,mo) + print c + print c.torel("days since 1979-1-1").value + timeax=timeax+[int(c.torel("days since 1979-1-1").value)] + +time=cdms.createAxis(timeax) +time.id='time' +time.units='days since 1979-1-1' +bounds=cdutil.times.setAxisTimeBoundsMonthly(time) +obs_n.setAxis(0,time) +obs_ca.setAxis(0,time) +obs_na.setAxis(0,time) +obs_np.setAxis(0,time) +obs_s.setAxis(0,time) +obs_sa.setAxis(0,time) +obs_sp.setAxis(0,time) +obs_io.setAxis(0,time) + +# Calculate Annual Cycle (1979-2005) +cdutil.setTimeBoundsMonthly(obs_n) +cdutil.setTimeBoundsMonthly(obs_s) +annual_cycle_obs_arctic=np.array(cdutil.ANNUALCYCLE.climatology(obs_n[0:324])) +annual_cycle_obs_ca=np.array(cdutil.ANNUALCYCLE.climatology(obs_ca[0:324])) +annual_cycle_obs_na=np.array(cdutil.ANNUALCYCLE.climatology(obs_na[0:324])) +annual_cycle_obs_np=np.array(cdutil.ANNUALCYCLE.climatology(obs_np[0:324])) +annual_cycle_obs_antarctic=np.array(cdutil.ANNUALCYCLE.climatology(obs_s[0:324])) +annual_cycle_obs_sa=np.array(cdutil.ANNUALCYCLE.climatology(obs_sa[0:324])) +annual_cycle_obs_sp=np.array(cdutil.ANNUALCYCLE.climatology(obs_sp[0:324])) +annual_cycle_obs_io=np.array(cdutil.ANNUALCYCLE.climatology(obs_io[0:324])) + +annual_cycle_std_obs_arctic=np.zeros((12,2)) +annual_cycle_std_obs_ca=np.zeros((12,2)) +annual_cycle_std_obs_na=np.zeros((12,2)) +annual_cycle_std_obs_np=np.zeros((12,2)) +annual_cycle_std_obs_antarctic=np.zeros((12,2)) +annual_cycle_std_obs_sa=np.zeros((12,2)) +annual_cycle_std_obs_sp=np.zeros((12,2)) +annual_cycle_std_obs_io=np.zeros((12,2)) + +for im in range (0,12): + annual_cycle_std_obs_arctic[im,:]=np.array(genutil.statistics.std(obs_n[im:324:12,:])) + annual_cycle_std_obs_ca[im,:]=np.array(genutil.statistics.std(obs_ca[im:324:12,:])) + annual_cycle_std_obs_na[im,:]=np.array(genutil.statistics.std(obs_na[im:324:12,:])) + annual_cycle_std_obs_np[im,:]=np.array(genutil.statistics.std(obs_np[im:324:12,:])) + annual_cycle_std_obs_antarctic[im,:]=np.array(genutil.statistics.std(obs_s[im:324:12,:])) + annual_cycle_std_obs_sa[im,:]=np.array(genutil.statistics.std(obs_sa[im:324:12,:])) + annual_cycle_std_obs_sp[im,:]=np.array(genutil.statistics.std(obs_sp[im:324:12,:])) + annual_cycle_std_obs_io[im,:]=np.array(genutil.statistics.std(obs_io[im:324:12,:])) + + +# NSIDC-0192 +# SSM/I Arctic +# Area +dlist_n=['nasateam/gsfc.nasateam.month.area.1978-2010.n.asc','bootstrap/gsfc.bootstrap.month.area.1978-2010.n.asc'] +dlist_s=['nasateam/gsfc.nasateam.month.area.1978-2010.s.asc','bootstrap/gsfc.bootstrap.month.area.1978-2010.s.asc'] +# Extent +#dlist_n=['nasateam/gsfc.nasateam.month.extent.1978-2010.n.asc','bootstrap/gsfc.bootstrap.month.extent.1978-2010.n.asc'] +#dlist_s=['nasateam/gsfc.nasateam.month.extent.1978-2010.s.asc','bootstrap/gsfc.bootstrap.month.extent.1978-2010.s.asc'] +obs1_n=MV.zeros([324,2]) +obs1_ca=MV.zeros([324,2]) +obs1_na=MV.zeros([324,2]) +obs1_np=MV.zeros([324,2]) +obs1_s=MV.zeros([324,2]) +obs1_sa=MV.zeros([324,2]) +obs1_sp=MV.zeros([324,2]) +obs1_io=MV.zeros([324,2]) + +for dl in range(0,len(dlist_n)): + + years=[] + months=[] + data_n=[] + data_ca=[] + data_na=[] + data_np=[] + data_s=[] + data_sa=[] + data_sp=[] + data_io=[] + + f=open('/export/ivanova2/IceData/AreaExtent/NSIDC-0192/ice-extent/'+dlist_n[dl]) + lines_n=f.readlines() + f.close + + g=open('/export/ivanova2/IceData/AreaExtent/NSIDC-0192/ice-extent/'+dlist_s[dl]) + lines_s=g.readlines() + g.close + + for line in lines_n: +# val1,val2,val3i,val4,val5=map(int,line.split()) + sp=line.split() + try: + val0=int(sp[0]) + val1=int(sp[1]) + val3=float(sp[3]) + val4=float(sp[4]) + val5=float(sp[5]) + val6=float(sp[6]) + val7=float(sp[7]) + val8=float(sp[8]) + val9=float(sp[9]) + val10=float(sp[10]) + val11=float(sp[11]) + val12=float(sp[12]) + years.append(val0) + months.append(val1) + data_n.append(val3) + data_np.append(val4+val5) + data_na.append(val6+val7+val8+val9+val11+val12) + data_ca.append(val10) + except: + pass + obs1_n[:,dl]=MV.array(data_n[0:324],id='sic') + obs1_ca[:,dl]=MV.array(data_ca[0:324],id='sic') + obs1_na[:,dl]=MV.array(data_na[0:324],id='sic') + obs1_np[:,dl]=MV.array(data_np[0:324],id='sic') + + for line in lines_s: + sp=line.split() + try: + val3=float(sp[3]) + val4=float(sp[4]) + val5=float(sp[5]) + val6=float(sp[6]) + val7=float(sp[7]) + val8=float(sp[8]) + data_s.append(val3) + data_sa.append(val4) + data_io.append(val5) + data_sp.append(val6+val7+val8) + except: + pass + + obs1_s[:,dl]=MV.array(data_s[0:324],id='sic') + obs1_sa[:,dl]=MV.array(data_sa[0:324],id='sic') + obs1_sp[:,dl]=MV.array(data_sp[0:324],id='sic') + obs1_io[:,dl]=MV.array(data_io[0:324],id='sic') + +obs1_n=MV.masked_equal(obs1_n,-999.) +obs1_ca=MV.masked_equal(obs1_ca,-999.) +obs1_na=MV.masked_equal(obs1_na,-999.) +obs1_np=MV.masked_equal(obs1_np,-999.) +obs1_s=MV.masked_equal(obs1_s,-999.) +obs1_sa=MV.masked_equal(obs1_sa,-999.) +obs1_sp=MV.masked_equal(obs1_sp,-999.) +obs1_io=MV.masked_equal(obs1_io,-999.) +obs1_n=MV.multiply(obs1_n,factor1) +obs1_ca=MV.multiply(obs1_ca,factor1) +obs1_np=MV.multiply(obs1_np,factor1) +obs1_na=MV.multiply(obs1_na,factor1) +obs1_s=MV.multiply(obs1_s,factor1) +obs1_sa=MV.multiply(obs1_sa,factor1) +obs1_sp=MV.multiply(obs1_sp,factor1) +obs1_io=MV.multiply(obs1_io,factor1) + + +# Create Time Axis +timeax=[] +for date in zip(years,months): + yr,mo = date + print yr + c=cdtime.comptime(yr,mo) + print c + print c.torel("days since 1979-1-1").value + timeax=timeax+[int(c.torel("days since 1979-1-1").value)] + +time=cdms.createAxis(timeax[0:324]) +time.id='time' +time.units='days since 1979-1-1' +bounds=cdutil.times.setAxisTimeBoundsMonthly(time) +obs1_n.setAxis(0,time) +obs1_ca.setAxis(0,time) +obs1_na.setAxis(0,time) +obs1_np.setAxis(0,time) +obs1_s.setAxis(0,time) +obs1_sa.setAxis(0,time) +obs1_sp.setAxis(0,time) +obs1_io.setAxis(0,time) + + +# Calculate Annual Cycle (1979-2005) +cdutil.setTimeBoundsMonthly(obs1_n) +cdutil.setTimeBoundsMonthly(obs1_s) +annual_cycle_obs1_arctic=np.array(cdutil.ANNUALCYCLE.climatology(obs1_n[0:324])) +annual_cycle_obs1_ca=np.array(cdutil.ANNUALCYCLE.climatology(obs1_ca[0:324])) +annual_cycle_obs1_na=np.array(cdutil.ANNUALCYCLE.climatology(obs1_na[0:324])) +annual_cycle_obs1_np=np.array(cdutil.ANNUALCYCLE.climatology(obs1_np[0:324])) +annual_cycle_obs1_antarctic=np.array(cdutil.ANNUALCYCLE.climatology(obs1_s[0:324])) +annual_cycle_obs1_sa=np.array(cdutil.ANNUALCYCLE.climatology(obs1_sa[0:324])) +annual_cycle_obs1_sp=np.array(cdutil.ANNUALCYCLE.climatology(obs1_sp[0:324])) +annual_cycle_obs1_io=np.array(cdutil.ANNUALCYCLE.climatology(obs1_io[0:324])) + +annual_cycle_std_obs1_arctic=np.zeros((12,2)) +annual_cycle_std_obs1_ca=np.zeros((12,2)) +annual_cycle_std_obs1_na=np.zeros((12,2)) +annual_cycle_std_obs1_np=np.zeros((12,2)) +annual_cycle_std_obs1_antarctic=np.zeros((12,2)) +annual_cycle_std_obs1_sa=np.zeros((12,2)) +annual_cycle_std_obs1_sp=np.zeros((12,2)) +annual_cycle_std_obs1_io=np.zeros((12,2)) + +for im in range (0,12): + annual_cycle_std_obs1_arctic[im,:]=np.array(genutil.statistics.std(obs1_n[im:324:12,:])) + annual_cycle_std_obs1_ca[im,:]=np.array(genutil.statistics.std(obs1_ca[im:324:12,:])) + annual_cycle_std_obs1_na[im,:]=np.array(genutil.statistics.std(obs1_na[im:324:12,:])) + annual_cycle_std_obs1_np[im,:]=np.array(genutil.statistics.std(obs1_np[im:324:12,:])) + annual_cycle_std_obs1_antarctic[im,:]=np.array(genutil.statistics.std(obs1_s[im:324:12,:])) + annual_cycle_std_obs1_sa[im,:]=np.array(genutil.statistics.std(obs1_sa[im:324:12,:])) + annual_cycle_std_obs1_sp[im,:]=np.array(genutil.statistics.std(obs1_sp[im:324:12,:])) + annual_cycle_std_obs1_io[im,:]=np.array(genutil.statistics.std(obs1_io[im:324:12,:])) + +# Calculate the Obs RMS +rms_arctic_obs1=genutil.statistics.rms(annual_cycle_obs1_arctic[:,1],annual_cycle_obs1_arctic[:,0],axis=0) +rms_antarctic_obs1=genutil.statistics.rms(annual_cycle_obs1_antarctic[:,1],annual_cycle_obs1_antarctic[:,0],axis=0) +rms_ca_obs1=genutil.statistics.rms(annual_cycle_obs1_ca[:,1],annual_cycle_obs1_ca[:,0],axis=0) +rms_na_obs1=genutil.statistics.rms(annual_cycle_obs1_na[:,1],annual_cycle_obs1_na[:,0],axis=0) +rms_np_obs1=genutil.statistics.rms(annual_cycle_obs1_np[:,1],annual_cycle_obs1_np[:,0],axis=0) +rms_sa_obs1=genutil.statistics.rms(annual_cycle_obs1_sa[:,1],annual_cycle_obs1_sa[:,0],axis=0) +rms_sp_obs1=genutil.statistics.rms(annual_cycle_obs1_sp[:,1],annual_cycle_obs1_sp[:,0],axis=0) +rms_io_obs1=genutil.statistics.rms(annual_cycle_obs1_io[:,1],annual_cycle_obs1_io[:,0],axis=0) + + +# CMIP5 Native grid +var = 'sic' +#obs = ['NASATEAM','BOOTSTRAP'] +#mods = ['Obs-NASATEAM','Obs-BOOTSTRAP','CMIP5 Native grid','CMIP5 Interpolated'] +mods_obs = ['CMIP5 MME','Obs-NASATEAM','Obs-BOOTSTRAP'] +obs_mods = ['Obs-NASATEAM','Obs-BOOTSTRAP','CMIP5 MME Native grid'] +lines_m = ['ro-','rd-','ro--','co-','c*-','cd-','c-','bo-','b^-','b*-','co--','go--','g^--','gd--','g-','m*--','y*-'] #,'m^-','yo--'] +#lines_d = ['bo-','g*-'] +#cols_d=['b','g'] + +flist=open('./cmip5_sic_names_all_xml_012413_conserv.asc') +#flist=open('./cmip5_sic_names_all_xml_012413_conserv_ccsm4.asc') +#flist=open('./cmip5_sic_names_all_xml_121212_conserv.asc') +#flist=open('./cmip5_sic_names_all_xml_121212_ncar.asc') +#flist=open('./cmip5_sic_names_all_xml_121212.asc') +#flist=open('./cmip5_sic_names_all_xml_102412_hadgem.asc') +fnames=flist.readlines() + + +glist=open('./cmip5_areacell_names_nc_012413_conserv.asc') +#glist=open('./cmip5_areacell_names_nc_012413_conserv_ccsm4.asc') +#glist=open('./cmip5_areacell_names_nc.asc') +#glist=open('./cmip5_areacell_names_all_xml_121212.asc') +#glist=open('./cmip5_areacell_names_all_xml_121212.asc') +#glist=open('./cmip5_areacell_names_all_xml_010813_conserv.asc') +#glist=open('./cmip5_areacell_names_all_xml_121212_conserv.asc') +#glist=open('./cmip5_areacell_names_all_xml_121212_ncar.asc') +#glist=open('./cmip5_areacell_names_all_xml_102412_hadgem.asc') +gnames=glist.readlines() + + +# Dictionary with the model names and runs and versions +mod_runs={} +mods=[] +for i in range (0,len(fnames)) : + sp=fnames[i].split('.') + if (sp[3]=='r1i1p1'): + mods.append(sp[1]) +for mod in mods: + runs=[] + vers=[] + for i in range(0,len(fnames)): + rn=fnames[i].split('.') + if (rn[1]==mod): + runs.append(rn[3]) + vers.append(rn[7].strip('\t\n\r')) + mod_runs[mod]=[runs,vers] + del runs + del vers + +ann_arctic=np.zeros((12,len(mods))) +ann_antarctic=np.zeros((12,len(mods))) +ann_ca=np.zeros((12,len(mods))) +ann_na=np.zeros((12,len(mods))) +ann_np=np.zeros((12,len(mods))) +ann_sa=np.zeros((12,len(mods))) +ann_sp=np.zeros((12,len(mods))) +ann_io=np.zeros((12,len(mods))) +std_arctic=np.zeros((324,len(mods))) +std_antarctic=np.zeros((324,len(mods))) +std_ca=np.zeros((324,len(mods))) +std_na=np.zeros((324,len(mods))) +std_np=np.zeros((324,len(mods))) +std_sa=np.zeros((324,len(mods))) +std_sp=np.zeros((324,len(mods))) +std_io=np.zeros((324,len(mods))) +annual_cycle_std_mod_arctic=np.zeros((12)) +annual_cycle_std_mod_antarctic=np.zeros((12)) +annual_cycle_std_mod_ca=np.zeros((12)) +annual_cycle_std_mod_na=np.zeros((12)) +annual_cycle_std_mod_np=np.zeros((12)) +annual_cycle_std_mod_sa=np.zeros((12)) +annual_cycle_std_mod_sp=np.zeros((12)) +annual_cycle_std_mod_io=np.zeros((12)) +ann_arctic_mma = np.zeros((12)) +ann_antarctic_mma = np.zeros((12)) +ann_ca_mma = np.zeros((12)) +ann_na_mma = np.zeros((12)) +ann_np_mma = np.zeros((12)) +ann_sa_mma = np.zeros((12)) +ann_sp_mma = np.zeros((12)) +ann_io_mma = np.zeros((12)) +rms_ann_arctic=np.zeros((len(dlist_n),len(mods))) +rms_ann_antarctic=np.zeros((len(dlist_s),len(mods))) +rms_ann_ca=np.zeros((len(dlist_n),len(mods))) +rms_ann_na=np.zeros((len(dlist_n),len(mods))) +rms_ann_np=np.zeros((len(dlist_n),len(mods))) +rms_ann_sa=np.zeros((len(dlist_s),len(mods))) +rms_ann_sp=np.zeros((len(dlist_s),len(mods))) +rms_ann_io=np.zeros((len(dlist_s),len(mods))) + +nm=0 +i=-1 + +for mod in mods: + i = i + 1 + runs=mod_runs[mod][0] + vers=mod_runs[mod][1] + +# Reading the ocean/ice grid cell area (areacello) + gfile=gnames[i].strip('\t\n\r') + print gfile + g = cdms.open(gfile) + try: + area = g('areacello') + except: + area = g('areacella') + area = MV.multiply(area,factor1) + area = MV.multiply(area,factor1) + + g.close() + + total_area_arctic=MV.zeros([324]) + total_area_antarctic=MV.zeros([324]) + total_area_ca=MV.zeros([324]) + total_area_na=MV.zeros([324]) + total_area_np=MV.zeros([324]) + total_area_sa=MV.zeros([324]) + total_area_sp=MV.zeros([324]) + total_area_io=MV.zeros([324]) + + nr=0 # Number fo individual model runs + + for ir in range(0,len(runs)): + nr=nr+1 +# Reading the sea ice concentration (sic) + infile='/work/cmip5/historical/seaIce/mo/sic/' + 'cmip5.'+mod+'.historical.'+runs[ir]+'.mo.seaIce.sic.'+vers[ir]+'.xml' + print infile + + f=cdms.open(infile) + if (mod == 'HadGEM2-CC' and (runs[ir]=='r1i1p1' or runs[ir]=='r3i1p1')) or mod == 'HadGEM2-ES' and (runs[ir]=='r2i1p1'or runs[ir]=='r3i1p1'or runs[ir]=='r4i1p1') : + sic=f(var,time=('1978-12-1','2006-1-1')) + else: + sic=f(var,time=('1979-1-1','2006-1-1')) + print MV.average(sic) + t=sic.getTime() + lat=sic.getLatitude() + lon=sic.getLongitude() + sic=MV.multiply(sic,factor2) + + if MV.rank(lat)==1: + tmp2d=f(var,time=slice(0,1),squeeze=1) + lats=MV.zeros(tmp2d.shape) + for ii in range (0,len(lon)): + lats[:,ii]=lat[:] + else: + lats=lat + + if MV.rank(lon)==1: + tmp2d=f(var,time=slice(0,1),squeeze=1) + lons=MV.zeros(tmp2d.shape) + for ii in range (0,len(lat)): + lons[ii,:]=lon[:] + else: + lons=lon + + f.close() + +# Creating regional masks + lons_a=MV.where(MV.greater(lons,180.),lons-360,lons) + lons_p=lons + print 'CMIP5' + print 'lons_na= ',MV.min(lons_a),MV.max(lons_a) + print 'lons_np= ',MV.min(lons_p),MV.max(lons_p) + mask_ca=MV.zeros(area.shape) + mask_na=MV.zeros(area.shape) + mask_np=MV.zeros(area.shape) + mask_sa=MV.zeros(area.shape) + mask_sp=MV.zeros(area.shape) + mask_io=MV.zeros(area.shape) + +#Arctic Regions +#Central Arctic +# lat_bound1=MV.logical_and(MV.greater(lats,80.),MV.less_equal(lats,87.2)) +# lat_bound2=MV.logical_and(MV.greater(lats,65.),MV.less_equal(lats,87.2)) + lat_bound1=MV.logical_and(MV.greater(lats,80.),MV.less_equal(lats,90.)) + lat_bound2=MV.logical_and(MV.greater(lats,65.),MV.less_equal(lats,90.)) + lon_bound1=MV.logical_and(MV.greater(lons_a,-120.),MV.less_equal(lons_a,90.)) + lon_bound2=MV.logical_and(MV.greater(lons_p,90.),MV.less_equal(lons_p,240.)) + reg1_ca=MV.logical_and(lat_bound1,lon_bound1) + reg2_ca=MV.logical_and(lat_bound2,lon_bound2) + mask_ca=MV.where(MV.logical_or(reg1_ca,reg2_ca),1,0) + +#NA region + lat_bound=MV.logical_and(MV.greater(lats,35.),MV.less_equal(lats,80.)) + lon_bound=MV.logical_and(MV.greater(lons_a,-120.),MV.less_equal(lons_a,90.)) + mask_na=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) + +#NP region + lat_bound=MV.logical_and(MV.greater(lats,35.),MV.less_equal(lats,65.)) + lon_bound=MV.logical_and(MV.greater(lons_p,90.),MV.less_equal(lons_p,240.)) + mask_np=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) + +#Antarctic Regions + lat_bound=MV.logical_and(MV.greater(lats,-90.),MV.less_equal(lats,-40.)) + +#SA region + lon_bound=MV.logical_and(MV.greater(lons_a,-60.),MV.less_equal(lons_a,20.)) + mask_sa=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) + +#SP region +# lon_bound=MV.logical_and(MV.greater(lons_p,130.),MV.less_equal(lons_p,300.)) + lon_bound=MV.logical_and(MV.greater(lons_p,90.),MV.less_equal(lons_p,300.)) + + mask_sp=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) + +#IO region +# lon_bound=MV.logical_and(MV.greater(lons_p,30.),MV.less_equal(lons_p,130.)) + lon_bound=MV.logical_and(MV.greater(lons_p,20.),MV.less_equal(lons_p,90.)) + mask_io=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) + +# Calculate the Total Sea Ice Area + ice_area = MV.multiply(sic,area) + ice_area = MV.where(MV.greater_equal(sic,0.15),ice_area,0.) #Masking out the sic<0.15 + +# arctic=MV.logical_and(MV.greater_equal(lats,35.),MV.less(lats,87.2)) #SSM/I limited to 87.2N + arctic=MV.logical_and(MV.greater_equal(lats,35.),MV.less(lats,90.)) #Adding currently in SSM/I 100% in the area >87.2N + antarctic=MV.logical_and(MV.greater_equal(lats,-90.),MV.less(lats,-40.)) + + for nt in range (len(t)): + aice_arctic=MV.where(MV.equal(arctic,True),ice_area[nt],0.) + aice_antarctic=MV.where(MV.equal(antarctic,True),ice_area[nt],0.) + area_sic_ca=MV.where(MV.equal(mask_ca,True),ice_area[nt],0.) + area_sic_na=MV.where(MV.equal(mask_na,True),ice_area[nt],0.) + area_sic_np=MV.where(MV.equal(mask_np,True),ice_area[nt],0.) + area_sic_sa=MV.where(MV.equal(mask_sa,True),ice_area[nt],0.) + area_sic_sp=MV.where(MV.equal(mask_sp,True),ice_area[nt],0.) + area_sic_io=MV.where(MV.equal(mask_io,True),ice_area[nt],0.) + total_area_arctic[nt] = total_area_arctic[nt]+MV.sum(aice_arctic) + total_area_antarctic[nt] = total_area_antarctic[nt]+MV.sum(aice_antarctic) + total_area_ca[nt]=total_area_ca[nt] + MV.sum(area_sic_ca) + total_area_na[nt]=total_area_na[nt] + MV.sum(area_sic_na) + total_area_np[nt]=total_area_np[nt] + MV.sum(area_sic_np) + total_area_sa[nt]=total_area_sa[nt] + MV.sum(area_sic_sa) + total_area_sp[nt]=total_area_sp[nt] + MV.sum(area_sic_sp) + total_area_io[nt]=total_area_io[nt] + MV.sum(area_sic_io) + +# print 'total_area_arctic= ',total_area_arctic[nt] +# print 'total_area_na= ',total_area_na[nt] +# print 'total_area_np= ',total_area_np[nt] + +# Individual Model Ensemble Mean + total_area_arctic = MV.divide(total_area_arctic,nr) + total_area_antarctic = MV.divide(total_area_antarctic,nr) + total_area_ca=MV.divide(total_area_ca,nr) + total_area_na=MV.divide(total_area_na,nr) + total_area_np=MV.divide(total_area_np,nr) + total_area_sa=MV.divide(total_area_sa,nr) + total_area_sp=MV.divide(total_area_sp,nr) + total_area_io=MV.divide(total_area_io,nr) + +#Annual cycle + total_area_arctic.setAxis(0,t) + cdutil.setTimeBoundsMonthly(total_area_arctic) + annual_cycle_arctic=cdutil.ANNUALCYCLE.climatology(total_area_arctic) + + total_area_antarctic.setAxis(0,t) + cdutil.setTimeBoundsMonthly(total_area_antarctic) + annual_cycle_antarctic=cdutil.ANNUALCYCLE.climatology(total_area_antarctic) + + total_area_ca.setAxis(0,t) + cdutil.setTimeBoundsMonthly(total_area_ca) + annual_cycle_ca=cdutil.ANNUALCYCLE.climatology(total_area_ca) + + total_area_na.setAxis(0,t) + cdutil.setTimeBoundsMonthly(total_area_na) + annual_cycle_na=cdutil.ANNUALCYCLE.climatology(total_area_na) + + total_area_np.setAxis(0,t) + cdutil.setTimeBoundsMonthly(total_area_np) + annual_cycle_np=cdutil.ANNUALCYCLE.climatology(total_area_np) + + total_area_sa.setAxis(0,t) + cdutil.setTimeBoundsMonthly(total_area_sa) + annual_cycle_sa=cdutil.ANNUALCYCLE.climatology(total_area_sa) + + total_area_sp.setAxis(0,t) + cdutil.setTimeBoundsMonthly(total_area_sp) + annual_cycle_sp=cdutil.ANNUALCYCLE.climatology(total_area_sp) + + total_area_io.setAxis(0,t) + cdutil.setTimeBoundsMonthly(total_area_io) + annual_cycle_io=cdutil.ANNUALCYCLE.climatology(total_area_io) + + ann_arctic[:,i]=np.array(annual_cycle_arctic) + ann_antarctic[:,i]=np.array(annual_cycle_antarctic) + ann_ca[:,i]=np.array(annual_cycle_ca) + ann_na[:,i]=np.array(annual_cycle_na) + ann_np[:,i]=np.array(annual_cycle_np) + ann_sa[:,i]=np.array(annual_cycle_sa) + ann_sp[:,i]=np.array(annual_cycle_sp) + ann_io[:,i]=np.array(annual_cycle_io) + + +# Calculating the CMIP5 STD + + std_arctic[:,i]=np.array(total_area_arctic) + std_antarctic[:,i]=np.array(total_area_antarctic) + std_ca[:,i]=np.array(total_area_ca) + std_na[:,i]=np.array(total_area_na) + std_np[:,i]=np.array(total_area_np) + std_sa[:,i]=np.array(total_area_sa) + std_sp[:,i]=np.array(total_area_sp) + std_io[:,i]=np.array(total_area_io) + + + ann_arctic_mma = ann_arctic_mma + np.array(annual_cycle_arctic) + ann_antarctic_mma = ann_antarctic_mma + np.array(annual_cycle_antarctic) + ann_ca_mma = ann_ca_mma + np.array(annual_cycle_ca) + ann_na_mma = ann_na_mma + np.array(annual_cycle_na) + ann_np_mma = ann_np_mma + np.array(annual_cycle_np) + ann_sa_mma = ann_sa_mma + np.array(annual_cycle_sa) + ann_sp_mma = ann_sp_mma + np.array(annual_cycle_sp) + ann_io_mma = ann_io_mma + np.array(annual_cycle_io) + nm = nm + 1 + +# Calculating the CMIP5 RMS + for j in range (0,2) : + rms_ann_arctic[j,i]=genutil.statistics.rms(ann_arctic[:,i],annual_cycle_obs_arctic[:,j],axis=0) + rms_ann_antarctic[j,i]=genutil.statistics.rms(ann_antarctic[:,i],annual_cycle_obs_antarctic[:,j],axis=0) + rms_ann_ca[j,i]=genutil.statistics.rms(ann_ca[:,i],annual_cycle_obs_ca[:,j],axis=0) + rms_ann_na[j,i]=genutil.statistics.rms(ann_na[:,i],annual_cycle_obs_na[:,j],axis=0) + rms_ann_np[j,i]=genutil.statistics.rms(ann_np[:,i],annual_cycle_obs_np[:,j],axis=0) + rms_ann_sa[j,i]=genutil.statistics.rms(ann_sa[:,i],annual_cycle_obs_sa[:,j],axis=0) + rms_ann_sp[j,i]=genutil.statistics.rms(ann_sp[:,i],annual_cycle_obs_sp[:,j],axis=0) + rms_ann_io[j,i]=genutil.statistics.rms(ann_io[:,i],annual_cycle_obs_io[:,j],axis=0) + + +# CMIP5 MME +ann_arctic_mma = ann_arctic_mma/nm +ann_antarctic_mma = ann_antarctic_mma/nm +ann_ca_mma = ann_ca_mma/nm +ann_na_mma = ann_na_mma/nm +ann_np_mma = ann_np_mma/nm +ann_sa_mma = ann_sa_mma/nm +ann_sp_mma = ann_sp_mma/nm +ann_io_mma = ann_io_mma/nm + +[ni,nj]=std_arctic.shape +tta_std_arctic=MV.zeros([12,324/12,len(mods)]) +tta_std_antarctic=MV.zeros([12,324/12,len(mods)]) +tta_std_ca=MV.zeros([12,324/12,len(mods)]) +tta_std_na=MV.zeros([12,324/12,len(mods)]) +tta_std_np=MV.zeros([12,324/12,len(mods)]) +tta_std_sa=MV.zeros([12,324/12,len(mods)]) +tta_std_sp=MV.zeros([12,324/12,len(mods)]) +tta_std_io=MV.zeros([12,324/12,len(mods)]) + +for im in range (0,12): + tta_std_arctic[im,:,:]=std_arctic[im:ni:12,:] + tta_std_antarctic[im,:,:]=std_antarctic[im:ni:12,:] + tta_std_ca[im,:,:]=std_ca[im:ni:12,:] + tta_std_na[im,:,:]=std_na[im:ni:12,:] + tta_std_np[im,:,:]=std_np[im:ni:12,:] + tta_std_sa[im,:,:]=std_sa[im:ni:12,:] + tta_std_sp[im,:,:]=std_sp[im:ni:12,:] + tta_std_io[im,:,:]=std_io[im:ni:12,:] + +[nt,nx,ny]=tta_std_arctic.shape +ttta_std_arctic=MV.reshape(tta_std_arctic,(nt,nx*ny)) +ttta_std_ca=MV.reshape(tta_std_ca,(nt,nx*ny)) +ttta_std_na=MV.reshape(tta_std_na,(nt,nx*ny)) +ttta_std_np=MV.reshape(tta_std_np,(nt,nx*ny)) +[nt,nx,ny]=tta_std_antarctic.shape +ttta_std_antarctic=MV.reshape(tta_std_antarctic,(nt,nx*ny)) +ttta_std_sa=MV.reshape(tta_std_sa,(nt,nx*ny)) +ttta_std_sp=MV.reshape(tta_std_sp,(nt,nx*ny)) +ttta_std_io=MV.reshape(tta_std_io,(nt,nx*ny)) + +for im in range (0,12): + annual_cycle_std_mod_arctic[im]=np.array(genutil.statistics.std(ttta_std_arctic[im,:])) + annual_cycle_std_mod_antarctic[im]=np.array(genutil.statistics.std(ttta_std_antarctic[im,:])) + annual_cycle_std_mod_ca[im]=np.array(genutil.statistics.std(ttta_std_ca[im,:])) + annual_cycle_std_mod_na[im]=np.array(genutil.statistics.std(ttta_std_na[im,:])) + annual_cycle_std_mod_np[im]=np.array(genutil.statistics.std(ttta_std_np[im,:])) + annual_cycle_std_mod_sa[im]=np.array(genutil.statistics.std(ttta_std_sa[im,:])) + annual_cycle_std_mod_sp[im]=np.array(genutil.statistics.std(ttta_std_sp[im,:])) + annual_cycle_std_mod_io[im]=np.array(genutil.statistics.std(ttta_std_io[im,:])) + +#Plot + +# Bar Plots of the RMS +labels=["ACCESS1-3","BNU-ESM","CCSM4","CESM1-BGC","CESM1-CAM5-1-FV2","CESM1-CAM5","CESM1-FASTCHEM","CNRM-CM5","CSIRO-Mk3-6-0","CanCM4","CanESM2","GFDL-CM2p1","GFDL-CM3","GFDL-ES\ +M2G","GFDL-ESM2M","GISS-E2-H-CC","GISS-E2-H","GISS-E2-R-CC","GISS-E2-R","HadCM3","HadGEM2-AO","HadGEM2-CC","HadGEM2-ES","IPSL-CM5A-MR","IPSL-CM5B-LR","MIROC-ESM-CHEM","MI\ +ROC-ESM","MIROC4h","MIROC5","MPI-ESM-LR","MPI-ESM-MR","MPI-ESM-P","NorESM1-ME","bcc-csm1-1-m","bcc-csm1-1"] +#labels=["CCSM4","CESM1-BGC","CESM1-CAM5-1-FV2","CESM1-CAM5","CESM1-FASTCHEM","CNRM-CM5","CSIRO-Mk3-6-0","CanCM4","CanESM2","GFDL-CM3","GFDL-ES\ +#M2G","GFDL-ESM2M","GISS-E2-H-CC","GISS-E2-H","GISS-E2-R","HadCM3","HadGEM2-CC","HadGEM2-ES","IPSL-CM5A-MR","IPSL-CM5B-LR","MIROC-ESM-CHEM","MI\ +#ROC-ESM","MIROC4h","MIROC5","MPI-ESM-LR","MPI-ESM-MR","MPI-ESM-P","NorESM1-ME","bcc-csm1-1"] +#labels=["HadCM3","HadGEM2-CC"] +mlabels=np.append(labels,"RMS-Obs") +rms_arctic=np.append(rms_ann_arctic[0,:],rms_arctic_obs) +rms_antarctic=np.append(rms_ann_antarctic[0,:],rms_antarctic_obs) +rms_ca=np.append(rms_ann_ca[0,:],rms_ca_obs) +rms_na=np.append(rms_ann_na[0,:],rms_na_obs) +rms_np=np.append(rms_ann_np[0,:],rms_np_obs) +rms_sa=np.append(rms_ann_sa[0,:],rms_sa_obs) +rms_sp=np.append(rms_ann_sp[0,:],rms_sp_obs) +rms_io=np.append(rms_ann_io[0,:],rms_io_obs) + + +ind = np.arange(len(mods)) # the x locations for the groups +#ind = np.arange(len(mods)+1) # the x locations for the groups +width=0.3 +n=len(ind)-1 + +#fig1 = plt.figure(1) +plt.subplot(411) +plt.bar(ind,rms_ann_arctic[0,:],width,color='r') +plt.hold +plt.bar(ind[n]+2.5*width,rms_arctic_obs,width,color='b') +plt.xticks(ind+width/2.,mlabels,rotation=20,size=8) +#plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) +#plt.text +plt.hold +#plt.ylim(0.,ymax) +plt.ylabel('RMS of Total Sea Ice Area, 10${^6}$km${^2}$') +#plt.title('Arctic') +plt.annotate('Arctic', (0.5, 0.9), xycoords='axes fraction',size=15) +plt.grid(True) +#plt.legend(mods_obs,bbox_to_anchor=(0.0, 0., 1, 1), bbox_transform=plt.gcf().transFigure) + +#fig2 = plt.figure(2) +plt.subplot(412) +plt.bar(ind,rms_ann_ca[0,:],width,color='r') +plt.bar(ind[n]+2.5*width,rms_ca_obs,width,color='b') +#plt.bar(ind+width,rms_ann_na[1,:],width,color='b') +plt.xticks(ind+width/2.,mlabels,rotation=20,size=8) +#plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) +plt.ylabel('RMS of Total Sea Ice Area, 10${^6}$km${^2}$') +#plt.title('Central Arctic Sector') +plt.annotate('Central Arctic Sector', (0.4, 0.9), xycoords='axes fraction',size=15) +plt.grid(True) +plt.hold + +#fig3 = plt.figure(3) +plt.subplot(413) +plt.bar(ind,rms_ann_na[0,:],width,color='r') +plt.bar(ind[n]+2.5*width,rms_na_obs,width,color='b') +#plt.bar(ind+width,rms_ann_na[1,:],width,color='b') +plt.xticks(ind+width/2.,mlabels,rotation=20,size=8) +#plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) +plt.ylabel('RMS of Total Sea Ice Area, 10${^6}$km${^2}$') +#plt.title('North Atlantic Arctic Sector') +plt.annotate('North Atlantic Arctic Sector', (0.4, 0.9), xycoords='axes fraction',size=15) +plt.grid(True) +plt.hold + +#fig4 = plt.figure(4) +plt.subplot(414) +plt.bar(ind,rms_ann_np[0,:],width,color='r') +# Plot the annual cycle +plt.bar(ind[n]+2.5*width,rms_np_obs,width,color='b') +#plt.bar(ind+width,rms_ann_np[1,:],width,color='b') +plt.xticks(ind+width/2.,mlabels,rotation=20,size=8) +#plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) +plt.hold +#plt.ylim(0.,ymax) +plt.ylabel('RMS of Total Sea Ice Area, 10${^6}$km${^2}$') +#plt.title('North Pacific Arctic Sector') +plt.annotate('North Pacific Arctic Sector', (0.4, 0.9), xycoords='axes fraction',size=15) +plt.grid(True) +#plt.legend(mods_obs,loc=(.55,0.65)) + +plt.show() + +# Antarctic +fig5=plt.figure(5) +plt.subplot(411) +plt.bar(ind,rms_ann_antarctic[0,:],width,color='r') +plt.bar(ind[n]+2.5*width,rms_antarctic_obs,width,color='b') +#plt.bar(ind+width,rms_ann_antarctic[1,:],width,color='b') +plt.xticks(ind+width/2.,mlabels,rotation=20,size=8) +#plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) +plt.hold +#plt.ylim(0.,ymax) +plt.ylabel('RMS of Total Sea Ice Area, 10${^6}$km${^2}$') +#plt.title('Antarctic') +plt.annotate('Antarctic', (0.5, 0.9), xycoords='axes fraction',size=15) +#plt.legend(mods_obs,bbox_to_anchor=(0.0, 0., 1, 1), bbox_transform=plt.gcf().transFigure) +plt.grid(True) + +#fig6 = plt.figure(6) +plt.subplot(412) +plt.bar(ind,rms_ann_sa[0,:],width,color='r') +plt.bar(ind[n]+2.5*width,rms_sa_obs,width,color='b') +#plt.bar(ind+width,rms_ann_sa[1,:],width,color='b') +plt.xticks(ind+width/2.,mlabels,rotation=20,size=8) +#plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) +plt.hold +#plt.ylim(0.,ymax) +plt.ylabel('RMS of Total Sea Ice Area, 10${^6}$km${^2}$') +#plt.title('South Atlantic Antarctic Sector') +plt.annotate('South Atlantic Ocean Antarctic Sector', (0.4, 0.9), xycoords='axes fraction',size=15) +plt.grid(True) + +#fig7 = plt.figure(7) +plt.subplot(413) +plt.bar(ind,rms_ann_sp[0,:],width,color='r') +plt.bar(ind[n]+2.5*width,rms_sp_obs,width,color='b') +#plt.bar(ind+width,rms_ann_sp[1,:],width,color='b') +plt.xticks(ind+width/2.,mlabels,rotation=20,size=8) +plt.hold +#plt.ylim(0.,ymax) +plt.ylabel('RMS of Total Sea Ice Area, 10${^6}$km${^2}$') +#plt.title('South Pacific Antarctic Sector') +plt.annotate('South Pacific Ocean Antarctic Sector', (0.4, 0.9), xycoords='axes fraction',size=15) +#plt.legend(obs_mods,loc=(.05,0.65)) +plt.grid(True) + +#fig8 = plt.figure(8) +plt.subplot(414) +plt.bar(ind,rms_ann_io[0,:],width,color='r') +plt.bar(ind[n]+2.5*width,rms_io_obs,width,color='b') +#plt.bar(ind+width,rms_ann_io[1,:],width,color='b') +plt.xticks(ind+width/2.,mlabels,rotation=20,size=8) +#plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) +plt.hold + +plt.ylabel('RMS of Total Sea Ice Area, 10${^6}$km${^2}$') +plt.annotate('South Indian Ocean Antarctic Sector', (0.4, 0.9), xycoords='axes fraction',size=15) +#plt.title('South Indian Ocean Antarctic Sector') +plt.grid(True) + +plt.show() diff --git a/pcmdi_metrics/sea_ice/sector_mask_defs.py b/pcmdi_metrics/sea_ice/sector_mask_defs.py new file mode 100644 index 000000000..0fd2d78e8 --- /dev/null +++ b/pcmdi_metrics/sea_ice/sector_mask_defs.py @@ -0,0 +1,61 @@ +def getmask(sector,lats,lons,lons_a,lons_p,land_mask): + import MV2 as MV +#Arctic Regions +#Central Arctic + if sector == 'ca': + lat_bound1=MV.logical_and(MV.greater(lats,80.),MV.less_equal(lats,90.)) + lat_bound2=MV.logical_and(MV.greater(lats,65.),MV.less_equal(lats,90.)) + lon_bound1=MV.logical_and(MV.greater(lons_a,-120.),MV.less_equal(lons_a,90.)) + lon_bound2=MV.logical_and(MV.greater(lons_p,90.),MV.less_equal(lons_p,240.)) + reg1=MV.logical_and(lat_bound1,lon_bound1) + reg2=MV.logical_and(lat_bound2,lon_bound2) + mask=MV.where(MV.logical_or(reg1,reg2),1,0) + mask=MV.where(MV.equal(land_mask,0),0,mask) # 0 - Land + +#NA region + if sector == 'na': + lat_bound=MV.logical_and(MV.greater(lats,45.),MV.less_equal(lats,80.)) + lon_bound=MV.logical_and(MV.greater(lons_a,-120.),MV.less_equal(lons_a,90.)) + lat_bound3=MV.logical_and(MV.greater(lats,45.),MV.less_equal(lats,50.)) + lon_bound3=MV.logical_and(MV.greater(lons_a,30.),MV.less_equal(lons_a,60.)) + reg3=MV.logical_and(lat_bound3,lon_bound3) + + mask=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) + mask=MV.where(MV.equal(reg3,True),0,mask) # Masking out the Black and Caspian Seas + mask=MV.where(MV.equal(land_mask,True),0,mask) # 0 - Land + mask=MV.where(MV.equal(land_mask,0),0,mask) # 0 - Land + +#NP region + if sector == 'np': + lat_bound=MV.logical_and(MV.greater(lats,45.),MV.less_equal(lats,65.)) + lon_bound=MV.logical_and(MV.greater(lons_p,90.),MV.less_equal(lons_p,240.)) + mask=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) + mask=MV.where(MV.equal(land_mask,0),0,mask) # 0 - Land + +#Antarctic Regions + +#SA region + if sector == 'sa': + lat_bound=MV.logical_and(MV.greater(lats,-90.),MV.less_equal(lats,-55.)) + lon_bound=MV.logical_and(MV.greater(lons_a,-60.),MV.less_equal(lons_a,20.)) + mask=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) + mask=MV.where(MV.equal(land_mask,0),0,mask) # 0 - Land + +#SP region + if sector == 'sp': + lat_bound=MV.logical_and(MV.greater(lats,-90.),MV.less_equal(lats,-55.)) + lon_bound=MV.logical_and(MV.greater(lons_p,90.),MV.less_equal(lons_p,300.)) + mask=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) + mask=MV.where(MV.equal(land_mask,0),0,mask) # 0 - Land + +#IO region + if sector == 'io': + lat_bound=MV.logical_and(MV.greater(lats,-90.),MV.less_equal(lats,-55.)) + lon_bound=MV.logical_and(MV.greater(lons_p,20.),MV.less_equal(lons_p,90.)) + mask=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) + mask=MV.where(MV.equal(land_mask,0),0,mask) # 0 - Land + + return mask + + + From 5c7b40ca5913d7337f164934cf44de750470f1ed Mon Sep 17 00:00:00 2001 From: Jiwoo Lee Date: Tue, 7 Nov 2023 09:56:25 -0800 Subject: [PATCH 02/69] style fix (pre-commit) --- .../sea_ice/generate_sector_masks.py | 12 +- .../sea_ice/ice_area_cmip5_ssmi_reg_rms.py | 2 +- pcmdi_metrics/sea_ice/sector_mask_defs.py | 132 ++++++++++-------- 3 files changed, 80 insertions(+), 66 deletions(-) diff --git a/pcmdi_metrics/sea_ice/generate_sector_masks.py b/pcmdi_metrics/sea_ice/generate_sector_masks.py index 0be90b03c..b30b9ac56 100644 --- a/pcmdi_metrics/sea_ice/generate_sector_masks.py +++ b/pcmdi_metrics/sea_ice/generate_sector_masks.py @@ -24,7 +24,7 @@ default = '', help = "Explicit path to obs monthly PR climatology") P.add_argument("--outpd", "--outpathdata", - type = str, + type = str, dest = 'outpathdata', default = '/export/gleckler1/processing/metrics_package/my_test/sea_ice/git_data/', help = "Output path for sector scale masks") @@ -95,8 +95,8 @@ alist = os.listdir(area_dir) # LIST OF ALL AREACELLO FILES for a in alist: - if string.find(a,mod) != -1: - areaf = a + if string.find(a,mod) != -1: + areaf = a print mod,' ', a # w = sys.stdin.readline() @@ -161,9 +161,9 @@ ############################################################### - sectors = ['ca','na','np','sp','sa','io'] + sectors = ['ca','na','np','sp','sa','io'] - for sector in sectors: + for sector in sectors: mask = getmask(sector,lats,lons,lons_a,lons_p,land_mask) g = cdms2.open(sec_mask_dir + 'mask_' + mod + '_' + sector + '.nc','w+') @@ -193,5 +193,3 @@ print 'failed for ', mods_failed # Calculate the Sea Ice Covered Area - - diff --git a/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py b/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py index e54196aad..11acf0216 100755 --- a/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py +++ b/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py @@ -5,7 +5,7 @@ import cdtime, datetime import gc -import numpy as np +import numpy as np import pylab import matplotlib.pyplot as plt diff --git a/pcmdi_metrics/sea_ice/sector_mask_defs.py b/pcmdi_metrics/sea_ice/sector_mask_defs.py index 0fd2d78e8..94bb22b21 100644 --- a/pcmdi_metrics/sea_ice/sector_mask_defs.py +++ b/pcmdi_metrics/sea_ice/sector_mask_defs.py @@ -1,61 +1,77 @@ -def getmask(sector,lats,lons,lons_a,lons_p,land_mask): - import MV2 as MV -#Arctic Regions -#Central Arctic - if sector == 'ca': - lat_bound1=MV.logical_and(MV.greater(lats,80.),MV.less_equal(lats,90.)) - lat_bound2=MV.logical_and(MV.greater(lats,65.),MV.less_equal(lats,90.)) - lon_bound1=MV.logical_and(MV.greater(lons_a,-120.),MV.less_equal(lons_a,90.)) - lon_bound2=MV.logical_and(MV.greater(lons_p,90.),MV.less_equal(lons_p,240.)) - reg1=MV.logical_and(lat_bound1,lon_bound1) - reg2=MV.logical_and(lat_bound2,lon_bound2) - mask=MV.where(MV.logical_or(reg1,reg2),1,0) - mask=MV.where(MV.equal(land_mask,0),0,mask) # 0 - Land - -#NA region - if sector == 'na': - lat_bound=MV.logical_and(MV.greater(lats,45.),MV.less_equal(lats,80.)) - lon_bound=MV.logical_and(MV.greater(lons_a,-120.),MV.less_equal(lons_a,90.)) - lat_bound3=MV.logical_and(MV.greater(lats,45.),MV.less_equal(lats,50.)) - lon_bound3=MV.logical_and(MV.greater(lons_a,30.),MV.less_equal(lons_a,60.)) - reg3=MV.logical_and(lat_bound3,lon_bound3) - - mask=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) - mask=MV.where(MV.equal(reg3,True),0,mask) # Masking out the Black and Caspian Seas - mask=MV.where(MV.equal(land_mask,True),0,mask) # 0 - Land - mask=MV.where(MV.equal(land_mask,0),0,mask) # 0 - Land - -#NP region - if sector == 'np': - lat_bound=MV.logical_and(MV.greater(lats,45.),MV.less_equal(lats,65.)) - lon_bound=MV.logical_and(MV.greater(lons_p,90.),MV.less_equal(lons_p,240.)) - mask=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) - mask=MV.where(MV.equal(land_mask,0),0,mask) # 0 - Land - -#Antarctic Regions - -#SA region - if sector == 'sa': - lat_bound=MV.logical_and(MV.greater(lats,-90.),MV.less_equal(lats,-55.)) - lon_bound=MV.logical_and(MV.greater(lons_a,-60.),MV.less_equal(lons_a,20.)) - mask=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) - mask=MV.where(MV.equal(land_mask,0),0,mask) # 0 - Land - -#SP region - if sector == 'sp': - lat_bound=MV.logical_and(MV.greater(lats,-90.),MV.less_equal(lats,-55.)) - lon_bound=MV.logical_and(MV.greater(lons_p,90.),MV.less_equal(lons_p,300.)) - mask=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) - mask=MV.where(MV.equal(land_mask,0),0,mask) # 0 - Land - -#IO region - if sector == 'io': - lat_bound=MV.logical_and(MV.greater(lats,-90.),MV.less_equal(lats,-55.)) - lon_bound=MV.logical_and(MV.greater(lons_p,20.),MV.less_equal(lons_p,90.)) - mask=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) - mask=MV.where(MV.equal(land_mask,0),0,mask) # 0 - Land - - return mask +def getmask(sector, lats, lons, lons_a, lons_p, land_mask): + import MV2 as MV + # Arctic Regions + # Central Arctic + if sector == "ca": + lat_bound1 = MV.logical_and(MV.greater(lats, 80.0), MV.less_equal(lats, 90.0)) + lat_bound2 = MV.logical_and(MV.greater(lats, 65.0), MV.less_equal(lats, 90.0)) + lon_bound1 = MV.logical_and( + MV.greater(lons_a, -120.0), MV.less_equal(lons_a, 90.0) + ) + lon_bound2 = MV.logical_and( + MV.greater(lons_p, 90.0), MV.less_equal(lons_p, 240.0) + ) + reg1 = MV.logical_and(lat_bound1, lon_bound1) + reg2 = MV.logical_and(lat_bound2, lon_bound2) + mask = MV.where(MV.logical_or(reg1, reg2), 1, 0) + mask = MV.where(MV.equal(land_mask, 0), 0, mask) # 0 - Land + # NA region + if sector == "na": + lat_bound = MV.logical_and(MV.greater(lats, 45.0), MV.less_equal(lats, 80.0)) + lon_bound = MV.logical_and( + MV.greater(lons_a, -120.0), MV.less_equal(lons_a, 90.0) + ) + lat_bound3 = MV.logical_and(MV.greater(lats, 45.0), MV.less_equal(lats, 50.0)) + lon_bound3 = MV.logical_and( + MV.greater(lons_a, 30.0), MV.less_equal(lons_a, 60.0) + ) + reg3 = MV.logical_and(lat_bound3, lon_bound3) + mask = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) + mask = MV.where( + MV.equal(reg3, True), 0, mask + ) # Masking out the Black and Caspian Seas + mask = MV.where(MV.equal(land_mask, True), 0, mask) # 0 - Land + mask = MV.where(MV.equal(land_mask, 0), 0, mask) # 0 - Land + + # NP region + if sector == "np": + lat_bound = MV.logical_and(MV.greater(lats, 45.0), MV.less_equal(lats, 65.0)) + lon_bound = MV.logical_and( + MV.greater(lons_p, 90.0), MV.less_equal(lons_p, 240.0) + ) + mask = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) + mask = MV.where(MV.equal(land_mask, 0), 0, mask) # 0 - Land + + # Antarctic Regions + + # SA region + if sector == "sa": + lat_bound = MV.logical_and(MV.greater(lats, -90.0), MV.less_equal(lats, -55.0)) + lon_bound = MV.logical_and( + MV.greater(lons_a, -60.0), MV.less_equal(lons_a, 20.0) + ) + mask = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) + mask = MV.where(MV.equal(land_mask, 0), 0, mask) # 0 - Land + + # SP region + if sector == "sp": + lat_bound = MV.logical_and(MV.greater(lats, -90.0), MV.less_equal(lats, -55.0)) + lon_bound = MV.logical_and( + MV.greater(lons_p, 90.0), MV.less_equal(lons_p, 300.0) + ) + mask = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) + mask = MV.where(MV.equal(land_mask, 0), 0, mask) # 0 - Land + + # IO region + if sector == "io": + lat_bound = MV.logical_and(MV.greater(lats, -90.0), MV.less_equal(lats, -55.0)) + lon_bound = MV.logical_and( + MV.greater(lons_p, 20.0), MV.less_equal(lons_p, 90.0) + ) + mask = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) + mask = MV.where(MV.equal(land_mask, 0), 0, mask) # 0 - Land + + return mask From 7e7cc6fc1d3513c6a975dceac68c65d02b14a7a4 Mon Sep 17 00:00:00 2001 From: Jiwoo Lee Date: Tue, 7 Nov 2023 12:32:58 -0800 Subject: [PATCH 03/69] python 2 style to python 3 style, pre-commit fix --- .../sea_ice/generate_sector_masks.py | 363 +-- .../sea_ice/ice_area_cmip5_ssmi_reg_rms.py | 1973 +++++++++-------- 2 files changed, 1281 insertions(+), 1055 deletions(-) diff --git a/pcmdi_metrics/sea_ice/generate_sector_masks.py b/pcmdi_metrics/sea_ice/generate_sector_masks.py index b30b9ac56..6f8b0b082 100644 --- a/pcmdi_metrics/sea_ice/generate_sector_masks.py +++ b/pcmdi_metrics/sea_ice/generate_sector_masks.py @@ -1,195 +1,218 @@ +import os +import string +import sys + import cdms2 import MV2 as MV -import string -import os,sys -from pcmdi_metrics.pcmdi.pmp_parser import * -import pcmdi_metrics -import parser -import collections from sector_mask_defs import * -import argparse -from argparse import RawTextHelpFormatter +from pcmdi_metrics.pcmdi.pmp_parser import * P = PMPParser() -P.add_argument("--mp", "--modpath", - type = str, - dest = 'modpath', - default = '', - help = "Explicit path to model monthly PR climatology") -P.add_argument("-o", "--obspath", - type = str, - dest = 'obspath', - default = '', - help = "Explicit path to obs monthly PR climatology") -P.add_argument("--outpd", "--outpathdata", - type = str, - dest = 'outpathdata', - default = '/export/gleckler1/processing/metrics_package/my_test/sea_ice/git_data/', - help = "Output path for sector scale masks") +P.add_argument( + "--mp", + "--modpath", + type=str, + dest="modpath", + default="", + help="Explicit path to model monthly PR climatology", +) +P.add_argument( + "-o", + "--obspath", + type=str, + dest="obspath", + default="", + help="Explicit path to obs monthly PR climatology", +) +P.add_argument( + "--outpd", + "--outpathdata", + type=str, + dest="outpathdata", + default="/export/gleckler1/processing/metrics_package/my_test/sea_ice/git_data/", + help="Output path for sector scale masks", +) args = P.parse_args(sys.argv[1:]) sec_mask_dir = args.outpathdata -#Factors -factor1=1.e-6 #model units are m^2, converting to km^2 -factor2=1.e-2 #model units are %, converting to non-dimen -a=6371.009 #Earth radii in km -pi=22./7. -factor3=4.*pi*a*a # Earth's surface area in km2 -dc=0.15 #minimum ice concentration contour +# Factors +factor1 = 1.0e-6 # model units are m^2, converting to km^2 +factor2 = 1.0e-2 # model units are %, converting to non-dimen +a = 6371.009 # Earth radii in km +pi = 22.0 / 7.0 +factor3 = 4.0 * pi * a * a # Earth's surface area in km2 +dc = 0.15 # minimum ice concentration contour -pin = '/work/gleckler1/processed_data/cmip5clims-historical/sic/cmip5.MOD.historical.r1i1p1.mo.seaIce.OImon.sic.ver-1.1980-2005.SC.nc' +pin = "/work/gleckler1/processed_data/cmip5clims-historical/sic/cmip5.MOD.historical.r1i1p1.mo.seaIce.OImon.sic.ver-1.1980-2005.SC.nc" -pins = string.replace(pin,'MOD','*') +pins = string.replace(pin, "MOD", "*") -lst = os.popen('ls ' + pins).readlines() +lst = os.popen("ls " + pins).readlines() mods = [] mods_failed = [] -for l in lst: - mod = string.split(l,'.')[1] - if mod not in mods: mods.append(mod) +for li in lst: + mod = string.split(li, ".")[1] + if mod not in mods: + mods.append(mod) -#w =sys.stdin.readline() +# w =sys.stdin.readline() -var = 'sic' +var = "sic" factor2 = 1 -mods = ['ACCESS1-3'] +mods = ["ACCESS1-3"] for mod in mods: - try: - fc = string.replace(pin,'MOD',mod) - f=cdms2.open(fc) - sic=f(var, squeeze=1) - sic_grid=sic.getGrid() - lat=sic.getLatitude() - lon=sic.getLongitude() - sic=MV.multiply(sic,factor2) - - print 'CMIP5-native= ',MV.max(sic) - if MV.rank(lat)==1: - tmp2d=f(var,time=slice(0,1),squeeze=1) - lats=MV.zeros(tmp2d.shape) - for ii in range (0,len(lon)): - lats[:,ii]=lat[:] - else: - lats=lat - - if MV.rank(lon)==1: - tmp2d=f(var,time=slice(0,1),squeeze=1) - lons=MV.zeros(tmp2d.shape) - for ii in range (0,len(lat)): - lons[ii,:]=lon[:] - else: - lons=lon - - f.close() - - -####################################################### -### areacello - area_dir = '/work/cmip5/fx/fx/areacello/' - alist = os.listdir(area_dir) # LIST OF ALL AREACELLO FILES - - for a in alist: - if string.find(a,mod) != -1: - areaf = a - print mod,' ', a -# w = sys.stdin.readline() - - g = cdms2.open(area_dir + areaf) - - try: - area = g('areacello') - except: - area = g('areacella') - area = MV.multiply(area,factor1) - area = MV.multiply(area,factor1) - - g.close() - - land_mask=MV.zeros(area.shape) - -# Reading the ocean/land grid cell masks (sftof/sftlf) - mask_dir = '/work/cmip5/fx/fx/sftof/' - mlist = os.listdir(mask_dir) # LIST OF ALL AREACELLO FILES - - for m in mlist: - if string.find(m,mod) != -1: - maskf = m - print mod,' ', m - - s = cdms2.open(mask_dir + maskf) - try: - frac = s('sftof') - if (mod != 'MIROC5' and mod!= 'GFDL-CM2p1' and mod!='GFDL-CM3' and mod!='GFDL-ESM2M'): - frac = MV.multiply(frac,factor2) - print mod,MV.max(frac) - area = MV.multiply(area,frac) - land_mask = MV.multiply(1,(1-frac)) - except: - frac = s('sftlf') - if (mod != 'MIROC5' or mod!= 'GFDL-CM2p1' or mod!='GFDL-CM3' or mod!='GFDL-ESM2M'): - frac = MV.multiply(frac,factor2) - area = MV.multiply(area,(1-frac)) - land_mask = MV.multiply(1,frac) - s.close() - -# Creating regional masks -# GFDL and bcc model grids have shift of 80 - if (mod[0:4] == 'GFDL' or mod[0:3] == 'bcc') : - lons_a=MV.where(MV.less(lons,-180.),lons+360.,lons) - lons_p=MV.where(MV.less(lons,0.),lons+360.,lons) - else: - lons_a=MV.where(MV.greater(lons,180.),lons-360.,lons) - lons_p=lons - - print 'CMIP5' - print 'lons_na= ',MV.min(lons_a),MV.max(lons_a) - print 'lons_np= ',MV.min(lons_p),MV.max(lons_p) -# mask_arctic=MV.zeros(area.shape) -# mask_antarctic=MV.zeros(area.shape) - mask_ca=MV.zeros(area.shape) - mask_na=MV.zeros(area.shape) - mask_np=MV.zeros(area.shape) - mask_sa=MV.zeros(area.shape) - mask_sp=MV.zeros(area.shape) - mask_io=MV.zeros(area.shape) - -############################################################### - - sectors = ['ca','na','np','sp','sa','io'] - - for sector in sectors: - mask = getmask(sector,lats,lons,lons_a,lons_p,land_mask) - - g = cdms2.open(sec_mask_dir + 'mask_' + mod + '_' + sector + '.nc','w+') - mask.id = 'mask' - g.write(mask) -# land_mask.id = 'sftof' -# g.write(land_mask) - g.close() - - print 'got it!',' ', mask.shape - w = sys.stdin.readline() - -# Calculate the Total Sea Ice Concentration/Extent/Area -# ice_area = MV.multiply(sic,1) #SIC - area = MV.multiply(1,area) #SIE - ice_area = MV.multiply(sic,area) #SIA - ice_area = MV.where(MV.greater_equal(sic,0.15),ice_area,0.) #Masking out the sic<0.15 - -# arctic=MV.logical_and(MV.greater_equal(lats,35.),MV.less(lats,87.2)) #SSM/I limited to 87.2N - mask_arctic=MV.logical_and(MV.greater_equal(lats,45.),MV.less(lats,90.)) #Adding currently in SSM/I 100% in the area >87.2N - mask_antarctic=MV.logical_and(MV.greater_equal(lats,-90.),MV.less(lats,-55.)) - - except: - 'Failed for ', mod - mods_failed.append(mod) - -print 'failed for ', mods_failed + try: + fc = string.replace(pin, "MOD", mod) + f = cdms2.open(fc) + sic = f(var, squeeze=1) + sic_grid = sic.getGrid() + lat = sic.getLatitude() + lon = sic.getLongitude() + sic = MV.multiply(sic, factor2) + + print("CMIP5-native= ", MV.max(sic)) + if MV.rank(lat) == 1: + tmp2d = f(var, time=slice(0, 1), squeeze=1) + lats = MV.zeros(tmp2d.shape) + for ii in range(0, len(lon)): + lats[:, ii] = lat[:] + else: + lats = lat + + if MV.rank(lon) == 1: + tmp2d = f(var, time=slice(0, 1), squeeze=1) + lons = MV.zeros(tmp2d.shape) + for ii in range(0, len(lat)): + lons[ii, :] = lon[:] + else: + lons = lon + + f.close() + + ####################################################### + ### areacello + area_dir = "/work/cmip5/fx/fx/areacello/" + alist = os.listdir(area_dir) # LIST OF ALL AREACELLO FILES + + for a in alist: + if string.find(a, mod) != -1: + areaf = a + print(mod, " ", a) + + # w = sys.stdin.readline() + + g = cdms2.open(area_dir + areaf) + + try: + area = g("areacello") + except: + area = g("areacella") + area = MV.multiply(area, factor1) + area = MV.multiply(area, factor1) + + g.close() + + land_mask = MV.zeros(area.shape) + + # Reading the ocean/land grid cell masks (sftof/sftlf) + mask_dir = "/work/cmip5/fx/fx/sftof/" + mlist = os.listdir(mask_dir) # LIST OF ALL AREACELLO FILES + + for m in mlist: + if string.find(m, mod) != -1: + maskf = m + print(mod, " ", m) + + s = cdms2.open(mask_dir + maskf) + try: + frac = s("sftof") + if ( + mod != "MIROC5" + and mod != "GFDL-CM2p1" + and mod != "GFDL-CM3" + and mod != "GFDL-ESM2M" + ): + frac = MV.multiply(frac, factor2) + print(mod, MV.max(frac)) + area = MV.multiply(area, frac) + land_mask = MV.multiply(1, (1 - frac)) + except: + frac = s("sftlf") + if ( + mod != "MIROC5" + or mod != "GFDL-CM2p1" + or mod != "GFDL-CM3" + or mod != "GFDL-ESM2M" + ): + frac = MV.multiply(frac, factor2) + area = MV.multiply(area, (1 - frac)) + land_mask = MV.multiply(1, frac) + s.close() + + # Creating regional masks + # GFDL and bcc model grids have shift of 80 + if mod[0:4] == "GFDL" or mod[0:3] == "bcc": + lons_a = MV.where(MV.less(lons, -180.0), lons + 360.0, lons) + lons_p = MV.where(MV.less(lons, 0.0), lons + 360.0, lons) + else: + lons_a = MV.where(MV.greater(lons, 180.0), lons - 360.0, lons) + lons_p = lons + + print("CMIP5") + print("lons_na= ", MV.min(lons_a), MV.max(lons_a)) + print("lons_np= ", MV.min(lons_p), MV.max(lons_p)) + # mask_arctic=MV.zeros(area.shape) + # mask_antarctic=MV.zeros(area.shape) + mask_ca = MV.zeros(area.shape) + mask_na = MV.zeros(area.shape) + mask_np = MV.zeros(area.shape) + mask_sa = MV.zeros(area.shape) + mask_sp = MV.zeros(area.shape) + mask_io = MV.zeros(area.shape) + + ############################################################### + + sectors = ["ca", "na", "np", "sp", "sa", "io"] + + for sector in sectors: + mask = getmask(sector, lats, lons, lons_a, lons_p, land_mask) + + g = cdms2.open(sec_mask_dir + "mask_" + mod + "_" + sector + ".nc", "w+") + mask.id = "mask" + g.write(mask) + # land_mask.id = 'sftof' + # g.write(land_mask) + g.close() + + print("got it!", " ", mask.shape) + w = sys.stdin.readline() + + # Calculate the Total Sea Ice Concentration/Extent/Area + # ice_area = MV.multiply(sic,1) #SIC + area = MV.multiply(1, area) # SIE + ice_area = MV.multiply(sic, area) # SIA + ice_area = MV.where( + MV.greater_equal(sic, 0.15), ice_area, 0.0 + ) # Masking out the sic<0.15 + + # arctic=MV.logical_and(MV.greater_equal(lats,35.),MV.less(lats,87.2)) #SSM/I limited to 87.2N + mask_arctic = MV.logical_and( + MV.greater_equal(lats, 45.0), MV.less(lats, 90.0) + ) # Adding currently in SSM/I 100% in the area >87.2N + mask_antarctic = MV.logical_and( + MV.greater_equal(lats, -90.0), MV.less(lats, -55.0) + ) + + except: + "Failed for ", mod + mods_failed.append(mod) + +print("failed for ", mods_failed) # Calculate the Sea Ice Covered Area diff --git a/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py b/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py index 11acf0216..2752bea6a 100755 --- a/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py +++ b/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py @@ -1,547 +1,649 @@ import cdms2 as cdms +import cdtime +import cdutil +import genutil +import matplotlib.pyplot as plt import MV2 as MV -import string, os, sys -import cdutil, genutil -import cdtime, datetime -import gc - import numpy as np -import pylab -import matplotlib.pyplot as plt -from cdms2.selectors import Selector def tgrid(t): + import cdtime - import cdtime + time = t[:] * 0.0 + if t[0] == 0.0: + dt = 0.0 + else: + dt = 0.5 # centered in the midlle of the month + time[0] = dt / 12.0 + nmonths = len(t) # monthy time series - time=t[:]*0. - if t[0]==0. : dt=0. - else : dt=0.5 # centered in the midlle of the month - time[0]=dt/12. - nmonths=len(t) # monthy time series + for it in range(1, nmonths): + time[it] = time[it - 1] + 1.0 / 12.0 - for it in range (1,nmonths) : - time[it]=time[it-1]+1./12. + time = time + cdtime.reltime(t[0], t.units).tocomp().year - time=time+cdtime.reltime(t[0],t.units).tocomp().year + return time - return time value = 0 cdms.setNetcdfShuffleFlag(value) cdms.setNetcdfDeflateFlag(value) cdms.setNetcdfDeflateLevelFlag(value) -cdms.setAutoBounds('on') - -#Factors -factor1=1.e-6 #model units are m^2, converting to km^2 -factor2=1.e-2 #model units are %, converting to non-dimen -a=6371.009 #Earth radii in km -pi=22./7. -factor3=4.*pi*a*a # Earth's surface area in km2 -dc=0.15 #minimum ice concentration contour - - -#Observations -dlist_n=['ssmi_nt_n_names.asc','ssmi_bt_n_names.asc'] -dlist_s=['ssmi_nt_s_names.asc','ssmi_bt_s_names.asc'] - -annual_cycle_obs_arctic=[] -annual_cycle_obs_antarctic=[] -data_n=MV.zeros([324]) -obs_n=MV.zeros([324,2]) -ta_ca=MV.zeros([324]) -obs_ca=MV.zeros([324,2]) -ta_na=MV.zeros([324]) -obs_na=MV.zeros([324,2]) -ta_np=MV.zeros([324]) -obs_np=MV.zeros([324,2]) -data_s=MV.zeros([324]) -obs_s=MV.zeros([324,2]) -ta_sa=MV.zeros([324]) -obs_sa=MV.zeros([324,2]) -ta_sp=MV.zeros([324]) -obs_sp=MV.zeros([324,2]) -ta_io=MV.zeros([324]) -obs_io=MV.zeros([324,2]) +cdms.setAutoBounds("on") + +# Factors +factor1 = 1.0e-6 # model units are m^2, converting to km^2 +factor2 = 1.0e-2 # model units are %, converting to non-dimen +a = 6371.009 # Earth radii in km +pi = 22.0 / 7.0 +factor3 = 4.0 * pi * a * a # Earth's surface area in km2 +dc = 0.15 # minimum ice concentration contour + + +# Observations +dlist_n = ["ssmi_nt_n_names.asc", "ssmi_bt_n_names.asc"] +dlist_s = ["ssmi_nt_s_names.asc", "ssmi_bt_s_names.asc"] + +annual_cycle_obs_arctic = [] +annual_cycle_obs_antarctic = [] +data_n = MV.zeros([324]) +obs_n = MV.zeros([324, 2]) +ta_ca = MV.zeros([324]) +obs_ca = MV.zeros([324, 2]) +ta_na = MV.zeros([324]) +obs_na = MV.zeros([324, 2]) +ta_np = MV.zeros([324]) +obs_np = MV.zeros([324, 2]) +data_s = MV.zeros([324]) +obs_s = MV.zeros([324, 2]) +ta_sa = MV.zeros([324]) +obs_sa = MV.zeros([324, 2]) +ta_sp = MV.zeros([324]) +obs_sp = MV.zeros([324, 2]) +ta_io = MV.zeros([324]) +obs_io = MV.zeros([324, 2]) # SSM/I Arctic -for dl in range(0,len(dlist_n)): - - f=open(dlist_n[dl]) - lines_n=f.readlines() - f.close - -# Reading the ocean/ice grid cell area (area) -# Reading the sea ice concentration (ice_con) - for i in range(0,len(lines_n)): - filename=lines_n[i].strip('\t\n\r') - print filename - obs=cdms.open(filename) - lats_n=obs('lat') - lons_n=obs('lon') - area_n=obs('area') - sic_n=obs('ice_con') - sic_n=MV.multiply(sic_n,factor2) - area_n=MV.multiply(area_n,factor1) - - obs.close() - -# Creating regional masks - lons_p=MV.where(MV.less(lons_n,0.),lons_n+360.,lons_n) - lons_a=lons_n - - print 'Obs' - print 'lons_na= ',MV.min(lons_a),MV.max(lons_a) - print 'lons_np= ',MV.min(lons_p),MV.max(lons_p) - - mask_ca=MV.zeros(area_n.shape) - mask_na=MV.zeros(area_n.shape) - mask_np=MV.zeros(area_n.shape) - -# Arctic Regions -#Central Arctic - lat_bound1=MV.logical_and(MV.greater(lats_n,80.),MV.less_equal(lats_n,87.2)) - lat_bound2=MV.logical_and(MV.greater(lats_n,65.),MV.less_equal(lats_n,87.2)) - lat_bound3=MV.logical_and(MV.greater(lats_n,87.2),MV.less_equal(lats_n,90.)) - lon_bound1=MV.logical_and(MV.greater(lons_a,-120.),MV.less_equal(lons_a,90.)) - lon_bound2=MV.logical_and(MV.greater(lons_p,90.),MV.less_equal(lons_p,240.)) - reg1_ca=MV.logical_and(lat_bound1,lon_bound1) - reg2_ca=MV.logical_and(lat_bound2,lon_bound2) - mask_ca=MV.where(MV.logical_or(reg1_ca,reg2_ca),1,0) - mask_pole=MV.where(lat_bound3,1,0) - -#NA region - lat_bound=MV.logical_and(MV.greater(lats_n,35.),MV.less_equal(lats_n,80.)) - lon_bound=MV.logical_and(MV.greater(lons_a,-120.),MV.less_equal(lons_a,90.)) - mask_na=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) - -#NP region - lat_bound=MV.logical_and(MV.greater(lats_n,35.),MV.less_equal(lats_n,65.)) - lon_bound=MV.logical_and(MV.greater(lons_p,90.),MV.less_equal(lons_p,240.)) - mask_np=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) - - area_sic_pole=MV.where(MV.equal(mask_pole,True),MV.multiply(1,area_n),0.) - -#Masking out sic<0.15 - ice_area = MV.where(MV.greater_equal(sic_n,0.15),area_n,0.) #Masking out sic<0.15 - -# Ice Extent -# area_sic_arctic=MV.multiply(1,ice_area) -# area_sic_ca=MV.where(MV.equal(mask_ca,True),MV.multiply(1,ice_area),0.) -# area_sic_na=MV.where(MV.equal(mask_na,True),MV.multiply(1,ice_area),0.) -# area_sic_np=MV.where(MV.equal(mask_np,True),MV.multiply(1,ice_area),0.) -# Ice Area - area_sic_arctic=MV.multiply(sic_n,ice_area) - area_sic_ca=MV.where(MV.equal(mask_ca,True),MV.multiply(sic_n,ice_area),0.) - area_sic_na=MV.where(MV.equal(mask_na,True),MV.multiply(sic_n,ice_area),0.) - area_sic_np=MV.where(MV.equal(mask_np,True),MV.multiply(sic_n,ice_area),0.) - - data_n[i]=MV.add(MV.sum(area_sic_arctic),MV.sum(area_sic_pole)) - ta_ca[i]=MV.add(MV.sum(area_sic_ca),MV.sum(area_sic_pole)) - ta_na[i]=MV.sum(area_sic_na) - ta_np[i]=MV.sum(area_sic_np) - - print 'data_n= ',data_n[i] - print 'ta_na= ',ta_na[i] - print 'ta_np= ',ta_np[i] - - print MV.average(sic_n) - - obs_n[:,dl]=MV.array(data_n,id='sic') - obs_ca[:,dl]=MV.array(ta_ca,id='sic') - obs_na[:,dl]=MV.array(ta_na,id='sic') - obs_np[:,dl]=MV.array(ta_np,id='sic') +for dl in range(0, len(dlist_n)): + f = open(dlist_n[dl]) + lines_n = f.readlines() + f.close + + # Reading the ocean/ice grid cell area (area) + # Reading the sea ice concentration (ice_con) + for i in range(0, len(lines_n)): + filename = lines_n[i].strip("\t\n\r") + print(filename) + obs = cdms.open(filename) + lats_n = obs("lat") + lons_n = obs("lon") + area_n = obs("area") + sic_n = obs("ice_con") + sic_n = MV.multiply(sic_n, factor2) + area_n = MV.multiply(area_n, factor1) + + obs.close() + + # Creating regional masks + lons_p = MV.where(MV.less(lons_n, 0.0), lons_n + 360.0, lons_n) + lons_a = lons_n + + print("Obs") + print("lons_na= ", MV.min(lons_a), MV.max(lons_a)) + print("lons_np= ", MV.min(lons_p), MV.max(lons_p)) + + mask_ca = MV.zeros(area_n.shape) + mask_na = MV.zeros(area_n.shape) + mask_np = MV.zeros(area_n.shape) + + # Arctic Regions + # Central Arctic + lat_bound1 = MV.logical_and( + MV.greater(lats_n, 80.0), MV.less_equal(lats_n, 87.2) + ) + lat_bound2 = MV.logical_and( + MV.greater(lats_n, 65.0), MV.less_equal(lats_n, 87.2) + ) + lat_bound3 = MV.logical_and( + MV.greater(lats_n, 87.2), MV.less_equal(lats_n, 90.0) + ) + lon_bound1 = MV.logical_and( + MV.greater(lons_a, -120.0), MV.less_equal(lons_a, 90.0) + ) + lon_bound2 = MV.logical_and( + MV.greater(lons_p, 90.0), MV.less_equal(lons_p, 240.0) + ) + reg1_ca = MV.logical_and(lat_bound1, lon_bound1) + reg2_ca = MV.logical_and(lat_bound2, lon_bound2) + mask_ca = MV.where(MV.logical_or(reg1_ca, reg2_ca), 1, 0) + mask_pole = MV.where(lat_bound3, 1, 0) + + # NA region + lat_bound = MV.logical_and( + MV.greater(lats_n, 35.0), MV.less_equal(lats_n, 80.0) + ) + lon_bound = MV.logical_and( + MV.greater(lons_a, -120.0), MV.less_equal(lons_a, 90.0) + ) + mask_na = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) + + # NP region + lat_bound = MV.logical_and( + MV.greater(lats_n, 35.0), MV.less_equal(lats_n, 65.0) + ) + lon_bound = MV.logical_and( + MV.greater(lons_p, 90.0), MV.less_equal(lons_p, 240.0) + ) + mask_np = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) + + area_sic_pole = MV.where(MV.equal(mask_pole, True), MV.multiply(1, area_n), 0.0) + + # Masking out sic<0.15 + ice_area = MV.where( + MV.greater_equal(sic_n, 0.15), area_n, 0.0 + ) # Masking out sic<0.15 + + # Ice Extent + # area_sic_arctic=MV.multiply(1,ice_area) + # area_sic_ca=MV.where(MV.equal(mask_ca,True),MV.multiply(1,ice_area),0.) + # area_sic_na=MV.where(MV.equal(mask_na,True),MV.multiply(1,ice_area),0.) + # area_sic_np=MV.where(MV.equal(mask_np,True),MV.multiply(1,ice_area),0.) + # Ice Area + area_sic_arctic = MV.multiply(sic_n, ice_area) + area_sic_ca = MV.where( + MV.equal(mask_ca, True), MV.multiply(sic_n, ice_area), 0.0 + ) + area_sic_na = MV.where( + MV.equal(mask_na, True), MV.multiply(sic_n, ice_area), 0.0 + ) + area_sic_np = MV.where( + MV.equal(mask_np, True), MV.multiply(sic_n, ice_area), 0.0 + ) + + data_n[i] = MV.add(MV.sum(area_sic_arctic), MV.sum(area_sic_pole)) + ta_ca[i] = MV.add(MV.sum(area_sic_ca), MV.sum(area_sic_pole)) + ta_na[i] = MV.sum(area_sic_na) + ta_np[i] = MV.sum(area_sic_np) + + print("data_n= ", data_n[i]) + print("ta_na= ", ta_na[i]) + print("ta_np= ", ta_np[i]) + + print(MV.average(sic_n)) + + obs_n[:, dl] = MV.array(data_n, id="sic") + obs_ca[:, dl] = MV.array(ta_ca, id="sic") + obs_na[:, dl] = MV.array(ta_na, id="sic") + obs_np[:, dl] = MV.array(ta_np, id="sic") # SSM/I Antarctic -for dl in range(0,len(dlist_s)): - g=open(dlist_s[dl]) - lines_s=g.readlines() - g.close - - - for i in range(0,len(lines_s)): - filename=lines_s[i].strip('\t\n\r') - print filename - obs=cdms.open(filename) - lats_s=obs('lat') - lons_s=obs('lon') - area_s=obs('area') - sic_s=obs('ice_con') - sic_s=MV.multiply(sic_s,factor2) - area_s=MV.multiply(area_s,factor1) - - - obs.close() - -# Creating regional masks - lons_sa=lons_s - lons_sp=MV.where(MV.less(lons_s,0.),lons_s+360,lons_s) - lons_io=lons_sp - - print 'Obs' - print 'lons_sa= ',MV.min(lons_sa),MV.max(lons_sa) - print 'lons_sp= ',MV.min(lons_sp),MV.max(lons_sp) - - mask_sa=MV.zeros(area_s.shape) - mask_sp=MV.zeros(area_s.shape) - mask_io=MV.zeros(area_s.shape) - -# Antarctic Regions - lat_bound=MV.logical_and(MV.greater(lats_s,-90.),MV.less_equal(lats_s,-40.)) - -# SA region -# lon_bound=MV.logical_and(MV.greater(lons_sa,-60.),MV.less_equal(lons_sa,30.)) - lon_bound=MV.logical_and(MV.greater(lons_sa,-60.),MV.less_equal(lons_sa,20.)) - mask_sa=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) - -# SP region -# lon_bound=MV.logical_and(MV.greater(lons_sp,130.),MV.less_equal(lons_sp,300.)) - lon_bound=MV.logical_and(MV.greater(lons_sp,90.),MV.less_equal(lons_sp,300.)) - mask_sp=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) - -# Indian Ocean (IO) region -# lon_bound=MV.logical_and(MV.greater(lons_sp,30.),MV.less_equal(lons_sp,130.)) - lon_bound=MV.logical_and(MV.greater(lons_sp,20.),MV.less_equal(lons_sp,90.)) - mask_io=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) - -#Masking out sic<0.15 - ice_area = MV.where(MV.greater_equal(sic_s,dc),area_s,0.) #Masking out sic<0.15 -# Ice Extent -# area_sic_antarctic=MV.multiply(1,ice_area) -# area_sic_sa=MV.where(MV.equal(mask_sa,True),MV.multiply(1,ice_area),0.) -# area_sic_sp=MV.where(MV.equal(mask_sp,True),MV.multiply(1,ice_area),0.) -# area_sic_io=MV.where(MV.equal(mask_io,True),MV.multiply(1,ice_area),0.) -# Ice Area - area_sic_antarctic=MV.multiply(sic_s,ice_area) - area_sic_sa=MV.where(MV.equal(mask_sa,True),MV.multiply(sic_s,area_s),0.) - area_sic_sp=MV.where(MV.equal(mask_sp,True),MV.multiply(sic_s,area_s),0.) - area_sic_io=MV.where(MV.equal(mask_io,True),MV.multiply(sic_s,area_s),0.) - - data_s[i]=MV.sum(area_sic_antarctic) - ta_sa[i]=MV.sum(area_sic_sa) - ta_sp[i]=MV.sum(area_sic_sp) - ta_io[i]=MV.sum(area_sic_io) - - print MV.average(sic_s) - - obs_s[:,dl]=MV.array(data_s,id='sic') - obs_s=MV.masked_equal(obs_s,-9999.) - obs_sa[:,dl]=MV.array(ta_sa,id='sic') - obs_sp[:,dl]=MV.array(ta_sp,id='sic') - obs_io[:,dl]=MV.array(ta_io,id='sic') +for dl in range(0, len(dlist_s)): + g = open(dlist_s[dl]) + lines_s = g.readlines() + g.close + + for i in range(0, len(lines_s)): + filename = lines_s[i].strip("\t\n\r") + print(filename) + obs = cdms.open(filename) + lats_s = obs("lat") + lons_s = obs("lon") + area_s = obs("area") + sic_s = obs("ice_con") + sic_s = MV.multiply(sic_s, factor2) + area_s = MV.multiply(area_s, factor1) + + obs.close() + + # Creating regional masks + lons_sa = lons_s + lons_sp = MV.where(MV.less(lons_s, 0.0), lons_s + 360, lons_s) + lons_io = lons_sp + + print("Obs") + print("lons_sa= ", MV.min(lons_sa), MV.max(lons_sa)) + print("lons_sp= ", MV.min(lons_sp), MV.max(lons_sp)) + + mask_sa = MV.zeros(area_s.shape) + mask_sp = MV.zeros(area_s.shape) + mask_io = MV.zeros(area_s.shape) + + # Antarctic Regions + lat_bound = MV.logical_and( + MV.greater(lats_s, -90.0), MV.less_equal(lats_s, -40.0) + ) + + # SA region + # lon_bound=MV.logical_and(MV.greater(lons_sa,-60.),MV.less_equal(lons_sa,30.)) + lon_bound = MV.logical_and( + MV.greater(lons_sa, -60.0), MV.less_equal(lons_sa, 20.0) + ) + mask_sa = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) + + # SP region + # lon_bound=MV.logical_and(MV.greater(lons_sp,130.),MV.less_equal(lons_sp,300.)) + lon_bound = MV.logical_and( + MV.greater(lons_sp, 90.0), MV.less_equal(lons_sp, 300.0) + ) + mask_sp = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) + + # Indian Ocean (IO) region + # lon_bound=MV.logical_and(MV.greater(lons_sp,30.),MV.less_equal(lons_sp,130.)) + lon_bound = MV.logical_and( + MV.greater(lons_sp, 20.0), MV.less_equal(lons_sp, 90.0) + ) + mask_io = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) + + # Masking out sic<0.15 + ice_area = MV.where( + MV.greater_equal(sic_s, dc), area_s, 0.0 + ) # Masking out sic<0.15 + # Ice Extent + # area_sic_antarctic=MV.multiply(1,ice_area) + # area_sic_sa=MV.where(MV.equal(mask_sa,True),MV.multiply(1,ice_area),0.) + # area_sic_sp=MV.where(MV.equal(mask_sp,True),MV.multiply(1,ice_area),0.) + # area_sic_io=MV.where(MV.equal(mask_io,True),MV.multiply(1,ice_area),0.) + # Ice Area + area_sic_antarctic = MV.multiply(sic_s, ice_area) + area_sic_sa = MV.where(MV.equal(mask_sa, True), MV.multiply(sic_s, area_s), 0.0) + area_sic_sp = MV.where(MV.equal(mask_sp, True), MV.multiply(sic_s, area_s), 0.0) + area_sic_io = MV.where(MV.equal(mask_io, True), MV.multiply(sic_s, area_s), 0.0) + + data_s[i] = MV.sum(area_sic_antarctic) + ta_sa[i] = MV.sum(area_sic_sa) + ta_sp[i] = MV.sum(area_sic_sp) + ta_io[i] = MV.sum(area_sic_io) + + print(MV.average(sic_s)) + + obs_s[:, dl] = MV.array(data_s, id="sic") + obs_s = MV.masked_equal(obs_s, -9999.0) + obs_sa[:, dl] = MV.array(ta_sa, id="sic") + obs_sp[:, dl] = MV.array(ta_sp, id="sic") + obs_io[:, dl] = MV.array(ta_io, id="sic") # Create Time Axis -years=[] -months=[] -for iy in range(1979,2006): - for im in range(1,13): - years.append(int(iy)) - months.append(int(im)) - -timeax=[] -for date in zip(years,months): - yr,mo = date - print yr - c=cdtime.comptime(yr,mo) - print c - print c.torel("days since 1979-1-1").value - timeax=timeax+[int(c.torel("days since 1979-1-1").value)] - -time=cdms.createAxis(timeax) -time.id='time' -time.units='days since 1979-1-1' -bounds=cdutil.times.setAxisTimeBoundsMonthly(time) -obs_n.setAxis(0,time) -obs_ca.setAxis(0,time) -obs_na.setAxis(0,time) -obs_np.setAxis(0,time) -obs_s.setAxis(0,time) -obs_sa.setAxis(0,time) -obs_sp.setAxis(0,time) -obs_io.setAxis(0,time) +years = [] +months = [] +for iy in range(1979, 2006): + for im in range(1, 13): + years.append(int(iy)) + months.append(int(im)) + +timeax = [] +for date in zip(years, months): + yr, mo = date + print(yr) + c = cdtime.comptime(yr, mo) + print(c) + print(c.torel("days since 1979-1-1").value) + timeax = timeax + [int(c.torel("days since 1979-1-1").value)] + +time = cdms.createAxis(timeax) +time.id = "time" +time.units = "days since 1979-1-1" +bounds = cdutil.times.setAxisTimeBoundsMonthly(time) +obs_n.setAxis(0, time) +obs_ca.setAxis(0, time) +obs_na.setAxis(0, time) +obs_np.setAxis(0, time) +obs_s.setAxis(0, time) +obs_sa.setAxis(0, time) +obs_sp.setAxis(0, time) +obs_io.setAxis(0, time) # Calculate Annual Cycle (1979-2005) cdutil.setTimeBoundsMonthly(obs_n) cdutil.setTimeBoundsMonthly(obs_s) -annual_cycle_obs_arctic=np.array(cdutil.ANNUALCYCLE.climatology(obs_n[0:324])) -annual_cycle_obs_ca=np.array(cdutil.ANNUALCYCLE.climatology(obs_ca[0:324])) -annual_cycle_obs_na=np.array(cdutil.ANNUALCYCLE.climatology(obs_na[0:324])) -annual_cycle_obs_np=np.array(cdutil.ANNUALCYCLE.climatology(obs_np[0:324])) -annual_cycle_obs_antarctic=np.array(cdutil.ANNUALCYCLE.climatology(obs_s[0:324])) -annual_cycle_obs_sa=np.array(cdutil.ANNUALCYCLE.climatology(obs_sa[0:324])) -annual_cycle_obs_sp=np.array(cdutil.ANNUALCYCLE.climatology(obs_sp[0:324])) -annual_cycle_obs_io=np.array(cdutil.ANNUALCYCLE.climatology(obs_io[0:324])) - -annual_cycle_std_obs_arctic=np.zeros((12,2)) -annual_cycle_std_obs_ca=np.zeros((12,2)) -annual_cycle_std_obs_na=np.zeros((12,2)) -annual_cycle_std_obs_np=np.zeros((12,2)) -annual_cycle_std_obs_antarctic=np.zeros((12,2)) -annual_cycle_std_obs_sa=np.zeros((12,2)) -annual_cycle_std_obs_sp=np.zeros((12,2)) -annual_cycle_std_obs_io=np.zeros((12,2)) - -for im in range (0,12): - annual_cycle_std_obs_arctic[im,:]=np.array(genutil.statistics.std(obs_n[im:324:12,:])) - annual_cycle_std_obs_ca[im,:]=np.array(genutil.statistics.std(obs_ca[im:324:12,:])) - annual_cycle_std_obs_na[im,:]=np.array(genutil.statistics.std(obs_na[im:324:12,:])) - annual_cycle_std_obs_np[im,:]=np.array(genutil.statistics.std(obs_np[im:324:12,:])) - annual_cycle_std_obs_antarctic[im,:]=np.array(genutil.statistics.std(obs_s[im:324:12,:])) - annual_cycle_std_obs_sa[im,:]=np.array(genutil.statistics.std(obs_sa[im:324:12,:])) - annual_cycle_std_obs_sp[im,:]=np.array(genutil.statistics.std(obs_sp[im:324:12,:])) - annual_cycle_std_obs_io[im,:]=np.array(genutil.statistics.std(obs_io[im:324:12,:])) +annual_cycle_obs_arctic = np.array(cdutil.ANNUALCYCLE.climatology(obs_n[0:324])) +annual_cycle_obs_ca = np.array(cdutil.ANNUALCYCLE.climatology(obs_ca[0:324])) +annual_cycle_obs_na = np.array(cdutil.ANNUALCYCLE.climatology(obs_na[0:324])) +annual_cycle_obs_np = np.array(cdutil.ANNUALCYCLE.climatology(obs_np[0:324])) +annual_cycle_obs_antarctic = np.array(cdutil.ANNUALCYCLE.climatology(obs_s[0:324])) +annual_cycle_obs_sa = np.array(cdutil.ANNUALCYCLE.climatology(obs_sa[0:324])) +annual_cycle_obs_sp = np.array(cdutil.ANNUALCYCLE.climatology(obs_sp[0:324])) +annual_cycle_obs_io = np.array(cdutil.ANNUALCYCLE.climatology(obs_io[0:324])) + +annual_cycle_std_obs_arctic = np.zeros((12, 2)) +annual_cycle_std_obs_ca = np.zeros((12, 2)) +annual_cycle_std_obs_na = np.zeros((12, 2)) +annual_cycle_std_obs_np = np.zeros((12, 2)) +annual_cycle_std_obs_antarctic = np.zeros((12, 2)) +annual_cycle_std_obs_sa = np.zeros((12, 2)) +annual_cycle_std_obs_sp = np.zeros((12, 2)) +annual_cycle_std_obs_io = np.zeros((12, 2)) + +for im in range(0, 12): + annual_cycle_std_obs_arctic[im, :] = np.array( + genutil.statistics.std(obs_n[im:324:12, :]) + ) + annual_cycle_std_obs_ca[im, :] = np.array( + genutil.statistics.std(obs_ca[im:324:12, :]) + ) + annual_cycle_std_obs_na[im, :] = np.array( + genutil.statistics.std(obs_na[im:324:12, :]) + ) + annual_cycle_std_obs_np[im, :] = np.array( + genutil.statistics.std(obs_np[im:324:12, :]) + ) + annual_cycle_std_obs_antarctic[im, :] = np.array( + genutil.statistics.std(obs_s[im:324:12, :]) + ) + annual_cycle_std_obs_sa[im, :] = np.array( + genutil.statistics.std(obs_sa[im:324:12, :]) + ) + annual_cycle_std_obs_sp[im, :] = np.array( + genutil.statistics.std(obs_sp[im:324:12, :]) + ) + annual_cycle_std_obs_io[im, :] = np.array( + genutil.statistics.std(obs_io[im:324:12, :]) + ) # NSIDC-0192 # SSM/I Arctic # Area -dlist_n=['nasateam/gsfc.nasateam.month.area.1978-2010.n.asc','bootstrap/gsfc.bootstrap.month.area.1978-2010.n.asc'] -dlist_s=['nasateam/gsfc.nasateam.month.area.1978-2010.s.asc','bootstrap/gsfc.bootstrap.month.area.1978-2010.s.asc'] +dlist_n = [ + "nasateam/gsfc.nasateam.month.area.1978-2010.n.asc", + "bootstrap/gsfc.bootstrap.month.area.1978-2010.n.asc", +] +dlist_s = [ + "nasateam/gsfc.nasateam.month.area.1978-2010.s.asc", + "bootstrap/gsfc.bootstrap.month.area.1978-2010.s.asc", +] # Extent -#dlist_n=['nasateam/gsfc.nasateam.month.extent.1978-2010.n.asc','bootstrap/gsfc.bootstrap.month.extent.1978-2010.n.asc'] -#dlist_s=['nasateam/gsfc.nasateam.month.extent.1978-2010.s.asc','bootstrap/gsfc.bootstrap.month.extent.1978-2010.s.asc'] -obs1_n=MV.zeros([324,2]) -obs1_ca=MV.zeros([324,2]) -obs1_na=MV.zeros([324,2]) -obs1_np=MV.zeros([324,2]) -obs1_s=MV.zeros([324,2]) -obs1_sa=MV.zeros([324,2]) -obs1_sp=MV.zeros([324,2]) -obs1_io=MV.zeros([324,2]) - -for dl in range(0,len(dlist_n)): - - years=[] - months=[] - data_n=[] - data_ca=[] - data_na=[] - data_np=[] - data_s=[] - data_sa=[] - data_sp=[] - data_io=[] - - f=open('/export/ivanova2/IceData/AreaExtent/NSIDC-0192/ice-extent/'+dlist_n[dl]) - lines_n=f.readlines() - f.close - - g=open('/export/ivanova2/IceData/AreaExtent/NSIDC-0192/ice-extent/'+dlist_s[dl]) - lines_s=g.readlines() - g.close - - for line in lines_n: -# val1,val2,val3i,val4,val5=map(int,line.split()) - sp=line.split() - try: - val0=int(sp[0]) - val1=int(sp[1]) - val3=float(sp[3]) - val4=float(sp[4]) - val5=float(sp[5]) - val6=float(sp[6]) - val7=float(sp[7]) - val8=float(sp[8]) - val9=float(sp[9]) - val10=float(sp[10]) - val11=float(sp[11]) - val12=float(sp[12]) - years.append(val0) - months.append(val1) - data_n.append(val3) - data_np.append(val4+val5) - data_na.append(val6+val7+val8+val9+val11+val12) - data_ca.append(val10) - except: - pass - obs1_n[:,dl]=MV.array(data_n[0:324],id='sic') - obs1_ca[:,dl]=MV.array(data_ca[0:324],id='sic') - obs1_na[:,dl]=MV.array(data_na[0:324],id='sic') - obs1_np[:,dl]=MV.array(data_np[0:324],id='sic') - - for line in lines_s: - sp=line.split() - try: - val3=float(sp[3]) - val4=float(sp[4]) - val5=float(sp[5]) - val6=float(sp[6]) - val7=float(sp[7]) - val8=float(sp[8]) - data_s.append(val3) - data_sa.append(val4) - data_io.append(val5) - data_sp.append(val6+val7+val8) - except: - pass - - obs1_s[:,dl]=MV.array(data_s[0:324],id='sic') - obs1_sa[:,dl]=MV.array(data_sa[0:324],id='sic') - obs1_sp[:,dl]=MV.array(data_sp[0:324],id='sic') - obs1_io[:,dl]=MV.array(data_io[0:324],id='sic') - -obs1_n=MV.masked_equal(obs1_n,-999.) -obs1_ca=MV.masked_equal(obs1_ca,-999.) -obs1_na=MV.masked_equal(obs1_na,-999.) -obs1_np=MV.masked_equal(obs1_np,-999.) -obs1_s=MV.masked_equal(obs1_s,-999.) -obs1_sa=MV.masked_equal(obs1_sa,-999.) -obs1_sp=MV.masked_equal(obs1_sp,-999.) -obs1_io=MV.masked_equal(obs1_io,-999.) -obs1_n=MV.multiply(obs1_n,factor1) -obs1_ca=MV.multiply(obs1_ca,factor1) -obs1_np=MV.multiply(obs1_np,factor1) -obs1_na=MV.multiply(obs1_na,factor1) -obs1_s=MV.multiply(obs1_s,factor1) -obs1_sa=MV.multiply(obs1_sa,factor1) -obs1_sp=MV.multiply(obs1_sp,factor1) -obs1_io=MV.multiply(obs1_io,factor1) +# dlist_n=['nasateam/gsfc.nasateam.month.extent.1978-2010.n.asc','bootstrap/gsfc.bootstrap.month.extent.1978-2010.n.asc'] +# dlist_s=['nasateam/gsfc.nasateam.month.extent.1978-2010.s.asc','bootstrap/gsfc.bootstrap.month.extent.1978-2010.s.asc'] +obs1_n = MV.zeros([324, 2]) +obs1_ca = MV.zeros([324, 2]) +obs1_na = MV.zeros([324, 2]) +obs1_np = MV.zeros([324, 2]) +obs1_s = MV.zeros([324, 2]) +obs1_sa = MV.zeros([324, 2]) +obs1_sp = MV.zeros([324, 2]) +obs1_io = MV.zeros([324, 2]) + +for dl in range(0, len(dlist_n)): + years = [] + months = [] + data_n = [] + data_ca = [] + data_na = [] + data_np = [] + data_s = [] + data_sa = [] + data_sp = [] + data_io = [] + + f = open("/export/ivanova2/IceData/AreaExtent/NSIDC-0192/ice-extent/" + dlist_n[dl]) + lines_n = f.readlines() + f.close + + g = open("/export/ivanova2/IceData/AreaExtent/NSIDC-0192/ice-extent/" + dlist_s[dl]) + lines_s = g.readlines() + g.close + + for line in lines_n: + # val1,val2,val3i,val4,val5=map(int,line.split()) + sp = line.split() + try: + val0 = int(sp[0]) + val1 = int(sp[1]) + val3 = float(sp[3]) + val4 = float(sp[4]) + val5 = float(sp[5]) + val6 = float(sp[6]) + val7 = float(sp[7]) + val8 = float(sp[8]) + val9 = float(sp[9]) + val10 = float(sp[10]) + val11 = float(sp[11]) + val12 = float(sp[12]) + years.append(val0) + months.append(val1) + data_n.append(val3) + data_np.append(val4 + val5) + data_na.append(val6 + val7 + val8 + val9 + val11 + val12) + data_ca.append(val10) + except: + pass + obs1_n[:, dl] = MV.array(data_n[0:324], id="sic") + obs1_ca[:, dl] = MV.array(data_ca[0:324], id="sic") + obs1_na[:, dl] = MV.array(data_na[0:324], id="sic") + obs1_np[:, dl] = MV.array(data_np[0:324], id="sic") + + for line in lines_s: + sp = line.split() + try: + val3 = float(sp[3]) + val4 = float(sp[4]) + val5 = float(sp[5]) + val6 = float(sp[6]) + val7 = float(sp[7]) + val8 = float(sp[8]) + data_s.append(val3) + data_sa.append(val4) + data_io.append(val5) + data_sp.append(val6 + val7 + val8) + except: + pass + + obs1_s[:, dl] = MV.array(data_s[0:324], id="sic") + obs1_sa[:, dl] = MV.array(data_sa[0:324], id="sic") + obs1_sp[:, dl] = MV.array(data_sp[0:324], id="sic") + obs1_io[:, dl] = MV.array(data_io[0:324], id="sic") + +obs1_n = MV.masked_equal(obs1_n, -999.0) +obs1_ca = MV.masked_equal(obs1_ca, -999.0) +obs1_na = MV.masked_equal(obs1_na, -999.0) +obs1_np = MV.masked_equal(obs1_np, -999.0) +obs1_s = MV.masked_equal(obs1_s, -999.0) +obs1_sa = MV.masked_equal(obs1_sa, -999.0) +obs1_sp = MV.masked_equal(obs1_sp, -999.0) +obs1_io = MV.masked_equal(obs1_io, -999.0) +obs1_n = MV.multiply(obs1_n, factor1) +obs1_ca = MV.multiply(obs1_ca, factor1) +obs1_np = MV.multiply(obs1_np, factor1) +obs1_na = MV.multiply(obs1_na, factor1) +obs1_s = MV.multiply(obs1_s, factor1) +obs1_sa = MV.multiply(obs1_sa, factor1) +obs1_sp = MV.multiply(obs1_sp, factor1) +obs1_io = MV.multiply(obs1_io, factor1) # Create Time Axis -timeax=[] -for date in zip(years,months): - yr,mo = date - print yr - c=cdtime.comptime(yr,mo) - print c - print c.torel("days since 1979-1-1").value - timeax=timeax+[int(c.torel("days since 1979-1-1").value)] - -time=cdms.createAxis(timeax[0:324]) -time.id='time' -time.units='days since 1979-1-1' -bounds=cdutil.times.setAxisTimeBoundsMonthly(time) -obs1_n.setAxis(0,time) -obs1_ca.setAxis(0,time) -obs1_na.setAxis(0,time) -obs1_np.setAxis(0,time) -obs1_s.setAxis(0,time) -obs1_sa.setAxis(0,time) -obs1_sp.setAxis(0,time) -obs1_io.setAxis(0,time) +timeax = [] +for date in zip(years, months): + yr, mo = date + print(yr) + c = cdtime.comptime(yr, mo) + print(c) + print(c.torel("days since 1979-1-1").value) + timeax = timeax + [int(c.torel("days since 1979-1-1").value)] + +time = cdms.createAxis(timeax[0:324]) +time.id = "time" +time.units = "days since 1979-1-1" +bounds = cdutil.times.setAxisTimeBoundsMonthly(time) +obs1_n.setAxis(0, time) +obs1_ca.setAxis(0, time) +obs1_na.setAxis(0, time) +obs1_np.setAxis(0, time) +obs1_s.setAxis(0, time) +obs1_sa.setAxis(0, time) +obs1_sp.setAxis(0, time) +obs1_io.setAxis(0, time) # Calculate Annual Cycle (1979-2005) cdutil.setTimeBoundsMonthly(obs1_n) cdutil.setTimeBoundsMonthly(obs1_s) -annual_cycle_obs1_arctic=np.array(cdutil.ANNUALCYCLE.climatology(obs1_n[0:324])) -annual_cycle_obs1_ca=np.array(cdutil.ANNUALCYCLE.climatology(obs1_ca[0:324])) -annual_cycle_obs1_na=np.array(cdutil.ANNUALCYCLE.climatology(obs1_na[0:324])) -annual_cycle_obs1_np=np.array(cdutil.ANNUALCYCLE.climatology(obs1_np[0:324])) -annual_cycle_obs1_antarctic=np.array(cdutil.ANNUALCYCLE.climatology(obs1_s[0:324])) -annual_cycle_obs1_sa=np.array(cdutil.ANNUALCYCLE.climatology(obs1_sa[0:324])) -annual_cycle_obs1_sp=np.array(cdutil.ANNUALCYCLE.climatology(obs1_sp[0:324])) -annual_cycle_obs1_io=np.array(cdutil.ANNUALCYCLE.climatology(obs1_io[0:324])) - -annual_cycle_std_obs1_arctic=np.zeros((12,2)) -annual_cycle_std_obs1_ca=np.zeros((12,2)) -annual_cycle_std_obs1_na=np.zeros((12,2)) -annual_cycle_std_obs1_np=np.zeros((12,2)) -annual_cycle_std_obs1_antarctic=np.zeros((12,2)) -annual_cycle_std_obs1_sa=np.zeros((12,2)) -annual_cycle_std_obs1_sp=np.zeros((12,2)) -annual_cycle_std_obs1_io=np.zeros((12,2)) - -for im in range (0,12): - annual_cycle_std_obs1_arctic[im,:]=np.array(genutil.statistics.std(obs1_n[im:324:12,:])) - annual_cycle_std_obs1_ca[im,:]=np.array(genutil.statistics.std(obs1_ca[im:324:12,:])) - annual_cycle_std_obs1_na[im,:]=np.array(genutil.statistics.std(obs1_na[im:324:12,:])) - annual_cycle_std_obs1_np[im,:]=np.array(genutil.statistics.std(obs1_np[im:324:12,:])) - annual_cycle_std_obs1_antarctic[im,:]=np.array(genutil.statistics.std(obs1_s[im:324:12,:])) - annual_cycle_std_obs1_sa[im,:]=np.array(genutil.statistics.std(obs1_sa[im:324:12,:])) - annual_cycle_std_obs1_sp[im,:]=np.array(genutil.statistics.std(obs1_sp[im:324:12,:])) - annual_cycle_std_obs1_io[im,:]=np.array(genutil.statistics.std(obs1_io[im:324:12,:])) +annual_cycle_obs1_arctic = np.array(cdutil.ANNUALCYCLE.climatology(obs1_n[0:324])) +annual_cycle_obs1_ca = np.array(cdutil.ANNUALCYCLE.climatology(obs1_ca[0:324])) +annual_cycle_obs1_na = np.array(cdutil.ANNUALCYCLE.climatology(obs1_na[0:324])) +annual_cycle_obs1_np = np.array(cdutil.ANNUALCYCLE.climatology(obs1_np[0:324])) +annual_cycle_obs1_antarctic = np.array(cdutil.ANNUALCYCLE.climatology(obs1_s[0:324])) +annual_cycle_obs1_sa = np.array(cdutil.ANNUALCYCLE.climatology(obs1_sa[0:324])) +annual_cycle_obs1_sp = np.array(cdutil.ANNUALCYCLE.climatology(obs1_sp[0:324])) +annual_cycle_obs1_io = np.array(cdutil.ANNUALCYCLE.climatology(obs1_io[0:324])) + +annual_cycle_std_obs1_arctic = np.zeros((12, 2)) +annual_cycle_std_obs1_ca = np.zeros((12, 2)) +annual_cycle_std_obs1_na = np.zeros((12, 2)) +annual_cycle_std_obs1_np = np.zeros((12, 2)) +annual_cycle_std_obs1_antarctic = np.zeros((12, 2)) +annual_cycle_std_obs1_sa = np.zeros((12, 2)) +annual_cycle_std_obs1_sp = np.zeros((12, 2)) +annual_cycle_std_obs1_io = np.zeros((12, 2)) + +for im in range(0, 12): + annual_cycle_std_obs1_arctic[im, :] = np.array( + genutil.statistics.std(obs1_n[im:324:12, :]) + ) + annual_cycle_std_obs1_ca[im, :] = np.array( + genutil.statistics.std(obs1_ca[im:324:12, :]) + ) + annual_cycle_std_obs1_na[im, :] = np.array( + genutil.statistics.std(obs1_na[im:324:12, :]) + ) + annual_cycle_std_obs1_np[im, :] = np.array( + genutil.statistics.std(obs1_np[im:324:12, :]) + ) + annual_cycle_std_obs1_antarctic[im, :] = np.array( + genutil.statistics.std(obs1_s[im:324:12, :]) + ) + annual_cycle_std_obs1_sa[im, :] = np.array( + genutil.statistics.std(obs1_sa[im:324:12, :]) + ) + annual_cycle_std_obs1_sp[im, :] = np.array( + genutil.statistics.std(obs1_sp[im:324:12, :]) + ) + annual_cycle_std_obs1_io[im, :] = np.array( + genutil.statistics.std(obs1_io[im:324:12, :]) + ) # Calculate the Obs RMS -rms_arctic_obs1=genutil.statistics.rms(annual_cycle_obs1_arctic[:,1],annual_cycle_obs1_arctic[:,0],axis=0) -rms_antarctic_obs1=genutil.statistics.rms(annual_cycle_obs1_antarctic[:,1],annual_cycle_obs1_antarctic[:,0],axis=0) -rms_ca_obs1=genutil.statistics.rms(annual_cycle_obs1_ca[:,1],annual_cycle_obs1_ca[:,0],axis=0) -rms_na_obs1=genutil.statistics.rms(annual_cycle_obs1_na[:,1],annual_cycle_obs1_na[:,0],axis=0) -rms_np_obs1=genutil.statistics.rms(annual_cycle_obs1_np[:,1],annual_cycle_obs1_np[:,0],axis=0) -rms_sa_obs1=genutil.statistics.rms(annual_cycle_obs1_sa[:,1],annual_cycle_obs1_sa[:,0],axis=0) -rms_sp_obs1=genutil.statistics.rms(annual_cycle_obs1_sp[:,1],annual_cycle_obs1_sp[:,0],axis=0) -rms_io_obs1=genutil.statistics.rms(annual_cycle_obs1_io[:,1],annual_cycle_obs1_io[:,0],axis=0) +rms_arctic_obs1 = genutil.statistics.rms( + annual_cycle_obs1_arctic[:, 1], annual_cycle_obs1_arctic[:, 0], axis=0 +) +rms_antarctic_obs1 = genutil.statistics.rms( + annual_cycle_obs1_antarctic[:, 1], annual_cycle_obs1_antarctic[:, 0], axis=0 +) +rms_ca_obs1 = genutil.statistics.rms( + annual_cycle_obs1_ca[:, 1], annual_cycle_obs1_ca[:, 0], axis=0 +) +rms_na_obs1 = genutil.statistics.rms( + annual_cycle_obs1_na[:, 1], annual_cycle_obs1_na[:, 0], axis=0 +) +rms_np_obs1 = genutil.statistics.rms( + annual_cycle_obs1_np[:, 1], annual_cycle_obs1_np[:, 0], axis=0 +) +rms_sa_obs1 = genutil.statistics.rms( + annual_cycle_obs1_sa[:, 1], annual_cycle_obs1_sa[:, 0], axis=0 +) +rms_sp_obs1 = genutil.statistics.rms( + annual_cycle_obs1_sp[:, 1], annual_cycle_obs1_sp[:, 0], axis=0 +) +rms_io_obs1 = genutil.statistics.rms( + annual_cycle_obs1_io[:, 1], annual_cycle_obs1_io[:, 0], axis=0 +) # CMIP5 Native grid -var = 'sic' -#obs = ['NASATEAM','BOOTSTRAP'] -#mods = ['Obs-NASATEAM','Obs-BOOTSTRAP','CMIP5 Native grid','CMIP5 Interpolated'] -mods_obs = ['CMIP5 MME','Obs-NASATEAM','Obs-BOOTSTRAP'] -obs_mods = ['Obs-NASATEAM','Obs-BOOTSTRAP','CMIP5 MME Native grid'] -lines_m = ['ro-','rd-','ro--','co-','c*-','cd-','c-','bo-','b^-','b*-','co--','go--','g^--','gd--','g-','m*--','y*-'] #,'m^-','yo--'] -#lines_d = ['bo-','g*-'] -#cols_d=['b','g'] - -flist=open('./cmip5_sic_names_all_xml_012413_conserv.asc') -#flist=open('./cmip5_sic_names_all_xml_012413_conserv_ccsm4.asc') -#flist=open('./cmip5_sic_names_all_xml_121212_conserv.asc') -#flist=open('./cmip5_sic_names_all_xml_121212_ncar.asc') -#flist=open('./cmip5_sic_names_all_xml_121212.asc') -#flist=open('./cmip5_sic_names_all_xml_102412_hadgem.asc') -fnames=flist.readlines() - - -glist=open('./cmip5_areacell_names_nc_012413_conserv.asc') -#glist=open('./cmip5_areacell_names_nc_012413_conserv_ccsm4.asc') -#glist=open('./cmip5_areacell_names_nc.asc') -#glist=open('./cmip5_areacell_names_all_xml_121212.asc') -#glist=open('./cmip5_areacell_names_all_xml_121212.asc') -#glist=open('./cmip5_areacell_names_all_xml_010813_conserv.asc') -#glist=open('./cmip5_areacell_names_all_xml_121212_conserv.asc') -#glist=open('./cmip5_areacell_names_all_xml_121212_ncar.asc') -#glist=open('./cmip5_areacell_names_all_xml_102412_hadgem.asc') -gnames=glist.readlines() +var = "sic" +# obs = ['NASATEAM','BOOTSTRAP'] +# mods = ['Obs-NASATEAM','Obs-BOOTSTRAP','CMIP5 Native grid','CMIP5 Interpolated'] +mods_obs = ["CMIP5 MME", "Obs-NASATEAM", "Obs-BOOTSTRAP"] +obs_mods = ["Obs-NASATEAM", "Obs-BOOTSTRAP", "CMIP5 MME Native grid"] +lines_m = [ + "ro-", + "rd-", + "ro--", + "co-", + "c*-", + "cd-", + "c-", + "bo-", + "b^-", + "b*-", + "co--", + "go--", + "g^--", + "gd--", + "g-", + "m*--", + "y*-", +] # ,'m^-','yo--'] +# lines_d = ['bo-','g*-'] +# cols_d=['b','g'] + +flist = open("./cmip5_sic_names_all_xml_012413_conserv.asc") +# flist=open('./cmip5_sic_names_all_xml_012413_conserv_ccsm4.asc') +# flist=open('./cmip5_sic_names_all_xml_121212_conserv.asc') +# flist=open('./cmip5_sic_names_all_xml_121212_ncar.asc') +# flist=open('./cmip5_sic_names_all_xml_121212.asc') +# flist=open('./cmip5_sic_names_all_xml_102412_hadgem.asc') +fnames = flist.readlines() + + +glist = open("./cmip5_areacell_names_nc_012413_conserv.asc") +# glist=open('./cmip5_areacell_names_nc_012413_conserv_ccsm4.asc') +# glist=open('./cmip5_areacell_names_nc.asc') +# glist=open('./cmip5_areacell_names_all_xml_121212.asc') +# glist=open('./cmip5_areacell_names_all_xml_121212.asc') +# glist=open('./cmip5_areacell_names_all_xml_010813_conserv.asc') +# glist=open('./cmip5_areacell_names_all_xml_121212_conserv.asc') +# glist=open('./cmip5_areacell_names_all_xml_121212_ncar.asc') +# glist=open('./cmip5_areacell_names_all_xml_102412_hadgem.asc') +gnames = glist.readlines() # Dictionary with the model names and runs and versions -mod_runs={} -mods=[] -for i in range (0,len(fnames)) : - sp=fnames[i].split('.') - if (sp[3]=='r1i1p1'): - mods.append(sp[1]) +mod_runs = {} +mods = [] +for i in range(0, len(fnames)): + sp = fnames[i].split(".") + if sp[3] == "r1i1p1": + mods.append(sp[1]) for mod in mods: - runs=[] - vers=[] - for i in range(0,len(fnames)): - rn=fnames[i].split('.') - if (rn[1]==mod): - runs.append(rn[3]) - vers.append(rn[7].strip('\t\n\r')) - mod_runs[mod]=[runs,vers] - del runs - del vers - -ann_arctic=np.zeros((12,len(mods))) -ann_antarctic=np.zeros((12,len(mods))) -ann_ca=np.zeros((12,len(mods))) -ann_na=np.zeros((12,len(mods))) -ann_np=np.zeros((12,len(mods))) -ann_sa=np.zeros((12,len(mods))) -ann_sp=np.zeros((12,len(mods))) -ann_io=np.zeros((12,len(mods))) -std_arctic=np.zeros((324,len(mods))) -std_antarctic=np.zeros((324,len(mods))) -std_ca=np.zeros((324,len(mods))) -std_na=np.zeros((324,len(mods))) -std_np=np.zeros((324,len(mods))) -std_sa=np.zeros((324,len(mods))) -std_sp=np.zeros((324,len(mods))) -std_io=np.zeros((324,len(mods))) -annual_cycle_std_mod_arctic=np.zeros((12)) -annual_cycle_std_mod_antarctic=np.zeros((12)) -annual_cycle_std_mod_ca=np.zeros((12)) -annual_cycle_std_mod_na=np.zeros((12)) -annual_cycle_std_mod_np=np.zeros((12)) -annual_cycle_std_mod_sa=np.zeros((12)) -annual_cycle_std_mod_sp=np.zeros((12)) -annual_cycle_std_mod_io=np.zeros((12)) + runs = [] + vers = [] + for i in range(0, len(fnames)): + rn = fnames[i].split(".") + if rn[1] == mod: + runs.append(rn[3]) + vers.append(rn[7].strip("\t\n\r")) + mod_runs[mod] = [runs, vers] + del runs + del vers + +ann_arctic = np.zeros((12, len(mods))) +ann_antarctic = np.zeros((12, len(mods))) +ann_ca = np.zeros((12, len(mods))) +ann_na = np.zeros((12, len(mods))) +ann_np = np.zeros((12, len(mods))) +ann_sa = np.zeros((12, len(mods))) +ann_sp = np.zeros((12, len(mods))) +ann_io = np.zeros((12, len(mods))) +std_arctic = np.zeros((324, len(mods))) +std_antarctic = np.zeros((324, len(mods))) +std_ca = np.zeros((324, len(mods))) +std_na = np.zeros((324, len(mods))) +std_np = np.zeros((324, len(mods))) +std_sa = np.zeros((324, len(mods))) +std_sp = np.zeros((324, len(mods))) +std_io = np.zeros((324, len(mods))) +annual_cycle_std_mod_arctic = np.zeros((12)) +annual_cycle_std_mod_antarctic = np.zeros((12)) +annual_cycle_std_mod_ca = np.zeros((12)) +annual_cycle_std_mod_na = np.zeros((12)) +annual_cycle_std_mod_np = np.zeros((12)) +annual_cycle_std_mod_sa = np.zeros((12)) +annual_cycle_std_mod_sp = np.zeros((12)) +annual_cycle_std_mod_io = np.zeros((12)) ann_arctic_mma = np.zeros((12)) ann_antarctic_mma = np.zeros((12)) ann_ca_mma = np.zeros((12)) @@ -550,445 +652,546 @@ def tgrid(t): ann_sa_mma = np.zeros((12)) ann_sp_mma = np.zeros((12)) ann_io_mma = np.zeros((12)) -rms_ann_arctic=np.zeros((len(dlist_n),len(mods))) -rms_ann_antarctic=np.zeros((len(dlist_s),len(mods))) -rms_ann_ca=np.zeros((len(dlist_n),len(mods))) -rms_ann_na=np.zeros((len(dlist_n),len(mods))) -rms_ann_np=np.zeros((len(dlist_n),len(mods))) -rms_ann_sa=np.zeros((len(dlist_s),len(mods))) -rms_ann_sp=np.zeros((len(dlist_s),len(mods))) -rms_ann_io=np.zeros((len(dlist_s),len(mods))) - -nm=0 -i=-1 +rms_ann_arctic = np.zeros((len(dlist_n), len(mods))) +rms_ann_antarctic = np.zeros((len(dlist_s), len(mods))) +rms_ann_ca = np.zeros((len(dlist_n), len(mods))) +rms_ann_na = np.zeros((len(dlist_n), len(mods))) +rms_ann_np = np.zeros((len(dlist_n), len(mods))) +rms_ann_sa = np.zeros((len(dlist_s), len(mods))) +rms_ann_sp = np.zeros((len(dlist_s), len(mods))) +rms_ann_io = np.zeros((len(dlist_s), len(mods))) + +nm = 0 +i = -1 for mod in mods: - i = i + 1 - runs=mod_runs[mod][0] - vers=mod_runs[mod][1] - -# Reading the ocean/ice grid cell area (areacello) - gfile=gnames[i].strip('\t\n\r') - print gfile - g = cdms.open(gfile) - try: - area = g('areacello') - except: - area = g('areacella') - area = MV.multiply(area,factor1) - area = MV.multiply(area,factor1) - - g.close() - - total_area_arctic=MV.zeros([324]) - total_area_antarctic=MV.zeros([324]) - total_area_ca=MV.zeros([324]) - total_area_na=MV.zeros([324]) - total_area_np=MV.zeros([324]) - total_area_sa=MV.zeros([324]) - total_area_sp=MV.zeros([324]) - total_area_io=MV.zeros([324]) - - nr=0 # Number fo individual model runs - - for ir in range(0,len(runs)): - nr=nr+1 -# Reading the sea ice concentration (sic) - infile='/work/cmip5/historical/seaIce/mo/sic/' + 'cmip5.'+mod+'.historical.'+runs[ir]+'.mo.seaIce.sic.'+vers[ir]+'.xml' - print infile - - f=cdms.open(infile) - if (mod == 'HadGEM2-CC' and (runs[ir]=='r1i1p1' or runs[ir]=='r3i1p1')) or mod == 'HadGEM2-ES' and (runs[ir]=='r2i1p1'or runs[ir]=='r3i1p1'or runs[ir]=='r4i1p1') : - sic=f(var,time=('1978-12-1','2006-1-1')) - else: - sic=f(var,time=('1979-1-1','2006-1-1')) - print MV.average(sic) - t=sic.getTime() - lat=sic.getLatitude() - lon=sic.getLongitude() - sic=MV.multiply(sic,factor2) - - if MV.rank(lat)==1: - tmp2d=f(var,time=slice(0,1),squeeze=1) - lats=MV.zeros(tmp2d.shape) - for ii in range (0,len(lon)): - lats[:,ii]=lat[:] - else: - lats=lat - - if MV.rank(lon)==1: - tmp2d=f(var,time=slice(0,1),squeeze=1) - lons=MV.zeros(tmp2d.shape) - for ii in range (0,len(lat)): - lons[ii,:]=lon[:] - else: - lons=lon - - f.close() - -# Creating regional masks - lons_a=MV.where(MV.greater(lons,180.),lons-360,lons) - lons_p=lons - print 'CMIP5' - print 'lons_na= ',MV.min(lons_a),MV.max(lons_a) - print 'lons_np= ',MV.min(lons_p),MV.max(lons_p) - mask_ca=MV.zeros(area.shape) - mask_na=MV.zeros(area.shape) - mask_np=MV.zeros(area.shape) - mask_sa=MV.zeros(area.shape) - mask_sp=MV.zeros(area.shape) - mask_io=MV.zeros(area.shape) - -#Arctic Regions -#Central Arctic -# lat_bound1=MV.logical_and(MV.greater(lats,80.),MV.less_equal(lats,87.2)) -# lat_bound2=MV.logical_and(MV.greater(lats,65.),MV.less_equal(lats,87.2)) - lat_bound1=MV.logical_and(MV.greater(lats,80.),MV.less_equal(lats,90.)) - lat_bound2=MV.logical_and(MV.greater(lats,65.),MV.less_equal(lats,90.)) - lon_bound1=MV.logical_and(MV.greater(lons_a,-120.),MV.less_equal(lons_a,90.)) - lon_bound2=MV.logical_and(MV.greater(lons_p,90.),MV.less_equal(lons_p,240.)) - reg1_ca=MV.logical_and(lat_bound1,lon_bound1) - reg2_ca=MV.logical_and(lat_bound2,lon_bound2) - mask_ca=MV.where(MV.logical_or(reg1_ca,reg2_ca),1,0) - -#NA region - lat_bound=MV.logical_and(MV.greater(lats,35.),MV.less_equal(lats,80.)) - lon_bound=MV.logical_and(MV.greater(lons_a,-120.),MV.less_equal(lons_a,90.)) - mask_na=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) - -#NP region - lat_bound=MV.logical_and(MV.greater(lats,35.),MV.less_equal(lats,65.)) - lon_bound=MV.logical_and(MV.greater(lons_p,90.),MV.less_equal(lons_p,240.)) - mask_np=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) - -#Antarctic Regions - lat_bound=MV.logical_and(MV.greater(lats,-90.),MV.less_equal(lats,-40.)) - -#SA region - lon_bound=MV.logical_and(MV.greater(lons_a,-60.),MV.less_equal(lons_a,20.)) - mask_sa=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) - -#SP region -# lon_bound=MV.logical_and(MV.greater(lons_p,130.),MV.less_equal(lons_p,300.)) - lon_bound=MV.logical_and(MV.greater(lons_p,90.),MV.less_equal(lons_p,300.)) - - mask_sp=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) - -#IO region -# lon_bound=MV.logical_and(MV.greater(lons_p,30.),MV.less_equal(lons_p,130.)) - lon_bound=MV.logical_and(MV.greater(lons_p,20.),MV.less_equal(lons_p,90.)) - mask_io=MV.where(MV.logical_and(lat_bound,lon_bound),1,0) - -# Calculate the Total Sea Ice Area - ice_area = MV.multiply(sic,area) - ice_area = MV.where(MV.greater_equal(sic,0.15),ice_area,0.) #Masking out the sic<0.15 - -# arctic=MV.logical_and(MV.greater_equal(lats,35.),MV.less(lats,87.2)) #SSM/I limited to 87.2N - arctic=MV.logical_and(MV.greater_equal(lats,35.),MV.less(lats,90.)) #Adding currently in SSM/I 100% in the area >87.2N - antarctic=MV.logical_and(MV.greater_equal(lats,-90.),MV.less(lats,-40.)) - - for nt in range (len(t)): - aice_arctic=MV.where(MV.equal(arctic,True),ice_area[nt],0.) - aice_antarctic=MV.where(MV.equal(antarctic,True),ice_area[nt],0.) - area_sic_ca=MV.where(MV.equal(mask_ca,True),ice_area[nt],0.) - area_sic_na=MV.where(MV.equal(mask_na,True),ice_area[nt],0.) - area_sic_np=MV.where(MV.equal(mask_np,True),ice_area[nt],0.) - area_sic_sa=MV.where(MV.equal(mask_sa,True),ice_area[nt],0.) - area_sic_sp=MV.where(MV.equal(mask_sp,True),ice_area[nt],0.) - area_sic_io=MV.where(MV.equal(mask_io,True),ice_area[nt],0.) - total_area_arctic[nt] = total_area_arctic[nt]+MV.sum(aice_arctic) - total_area_antarctic[nt] = total_area_antarctic[nt]+MV.sum(aice_antarctic) - total_area_ca[nt]=total_area_ca[nt] + MV.sum(area_sic_ca) - total_area_na[nt]=total_area_na[nt] + MV.sum(area_sic_na) - total_area_np[nt]=total_area_np[nt] + MV.sum(area_sic_np) - total_area_sa[nt]=total_area_sa[nt] + MV.sum(area_sic_sa) - total_area_sp[nt]=total_area_sp[nt] + MV.sum(area_sic_sp) - total_area_io[nt]=total_area_io[nt] + MV.sum(area_sic_io) - -# print 'total_area_arctic= ',total_area_arctic[nt] -# print 'total_area_na= ',total_area_na[nt] -# print 'total_area_np= ',total_area_np[nt] - -# Individual Model Ensemble Mean - total_area_arctic = MV.divide(total_area_arctic,nr) - total_area_antarctic = MV.divide(total_area_antarctic,nr) - total_area_ca=MV.divide(total_area_ca,nr) - total_area_na=MV.divide(total_area_na,nr) - total_area_np=MV.divide(total_area_np,nr) - total_area_sa=MV.divide(total_area_sa,nr) - total_area_sp=MV.divide(total_area_sp,nr) - total_area_io=MV.divide(total_area_io,nr) - -#Annual cycle - total_area_arctic.setAxis(0,t) - cdutil.setTimeBoundsMonthly(total_area_arctic) - annual_cycle_arctic=cdutil.ANNUALCYCLE.climatology(total_area_arctic) - - total_area_antarctic.setAxis(0,t) - cdutil.setTimeBoundsMonthly(total_area_antarctic) - annual_cycle_antarctic=cdutil.ANNUALCYCLE.climatology(total_area_antarctic) - - total_area_ca.setAxis(0,t) - cdutil.setTimeBoundsMonthly(total_area_ca) - annual_cycle_ca=cdutil.ANNUALCYCLE.climatology(total_area_ca) - - total_area_na.setAxis(0,t) - cdutil.setTimeBoundsMonthly(total_area_na) - annual_cycle_na=cdutil.ANNUALCYCLE.climatology(total_area_na) - - total_area_np.setAxis(0,t) - cdutil.setTimeBoundsMonthly(total_area_np) - annual_cycle_np=cdutil.ANNUALCYCLE.climatology(total_area_np) - - total_area_sa.setAxis(0,t) - cdutil.setTimeBoundsMonthly(total_area_sa) - annual_cycle_sa=cdutil.ANNUALCYCLE.climatology(total_area_sa) - - total_area_sp.setAxis(0,t) - cdutil.setTimeBoundsMonthly(total_area_sp) - annual_cycle_sp=cdutil.ANNUALCYCLE.climatology(total_area_sp) - - total_area_io.setAxis(0,t) - cdutil.setTimeBoundsMonthly(total_area_io) - annual_cycle_io=cdutil.ANNUALCYCLE.climatology(total_area_io) - - ann_arctic[:,i]=np.array(annual_cycle_arctic) - ann_antarctic[:,i]=np.array(annual_cycle_antarctic) - ann_ca[:,i]=np.array(annual_cycle_ca) - ann_na[:,i]=np.array(annual_cycle_na) - ann_np[:,i]=np.array(annual_cycle_np) - ann_sa[:,i]=np.array(annual_cycle_sa) - ann_sp[:,i]=np.array(annual_cycle_sp) - ann_io[:,i]=np.array(annual_cycle_io) - - -# Calculating the CMIP5 STD - - std_arctic[:,i]=np.array(total_area_arctic) - std_antarctic[:,i]=np.array(total_area_antarctic) - std_ca[:,i]=np.array(total_area_ca) - std_na[:,i]=np.array(total_area_na) - std_np[:,i]=np.array(total_area_np) - std_sa[:,i]=np.array(total_area_sa) - std_sp[:,i]=np.array(total_area_sp) - std_io[:,i]=np.array(total_area_io) - - - ann_arctic_mma = ann_arctic_mma + np.array(annual_cycle_arctic) - ann_antarctic_mma = ann_antarctic_mma + np.array(annual_cycle_antarctic) - ann_ca_mma = ann_ca_mma + np.array(annual_cycle_ca) - ann_na_mma = ann_na_mma + np.array(annual_cycle_na) - ann_np_mma = ann_np_mma + np.array(annual_cycle_np) - ann_sa_mma = ann_sa_mma + np.array(annual_cycle_sa) - ann_sp_mma = ann_sp_mma + np.array(annual_cycle_sp) - ann_io_mma = ann_io_mma + np.array(annual_cycle_io) - nm = nm + 1 - -# Calculating the CMIP5 RMS - for j in range (0,2) : - rms_ann_arctic[j,i]=genutil.statistics.rms(ann_arctic[:,i],annual_cycle_obs_arctic[:,j],axis=0) - rms_ann_antarctic[j,i]=genutil.statistics.rms(ann_antarctic[:,i],annual_cycle_obs_antarctic[:,j],axis=0) - rms_ann_ca[j,i]=genutil.statistics.rms(ann_ca[:,i],annual_cycle_obs_ca[:,j],axis=0) - rms_ann_na[j,i]=genutil.statistics.rms(ann_na[:,i],annual_cycle_obs_na[:,j],axis=0) - rms_ann_np[j,i]=genutil.statistics.rms(ann_np[:,i],annual_cycle_obs_np[:,j],axis=0) - rms_ann_sa[j,i]=genutil.statistics.rms(ann_sa[:,i],annual_cycle_obs_sa[:,j],axis=0) - rms_ann_sp[j,i]=genutil.statistics.rms(ann_sp[:,i],annual_cycle_obs_sp[:,j],axis=0) - rms_ann_io[j,i]=genutil.statistics.rms(ann_io[:,i],annual_cycle_obs_io[:,j],axis=0) + i = i + 1 + runs = mod_runs[mod][0] + vers = mod_runs[mod][1] + + # Reading the ocean/ice grid cell area (areacello) + gfile = gnames[i].strip("\t\n\r") + print(gfile) + g = cdms.open(gfile) + try: + area = g("areacello") + except: + area = g("areacella") + area = MV.multiply(area, factor1) + area = MV.multiply(area, factor1) + + g.close() + + total_area_arctic = MV.zeros([324]) + total_area_antarctic = MV.zeros([324]) + total_area_ca = MV.zeros([324]) + total_area_na = MV.zeros([324]) + total_area_np = MV.zeros([324]) + total_area_sa = MV.zeros([324]) + total_area_sp = MV.zeros([324]) + total_area_io = MV.zeros([324]) + + nr = 0 # Number fo individual model runs + + for ir in range(0, len(runs)): + nr = nr + 1 + # Reading the sea ice concentration (sic) + infile = ( + "/work/cmip5/historical/seaIce/mo/sic/" + + "cmip5." + + mod + + ".historical." + + runs[ir] + + ".mo.seaIce.sic." + + vers[ir] + + ".xml" + ) + print(infile) + + f = cdms.open(infile) + if ( + (mod == "HadGEM2-CC" and (runs[ir] == "r1i1p1" or runs[ir] == "r3i1p1")) + or mod == "HadGEM2-ES" + and (runs[ir] == "r2i1p1" or runs[ir] == "r3i1p1" or runs[ir] == "r4i1p1") + ): + sic = f(var, time=("1978-12-1", "2006-1-1")) + else: + sic = f(var, time=("1979-1-1", "2006-1-1")) + print(MV.average(sic)) + t = sic.getTime() + lat = sic.getLatitude() + lon = sic.getLongitude() + sic = MV.multiply(sic, factor2) + + if MV.rank(lat) == 1: + tmp2d = f(var, time=slice(0, 1), squeeze=1) + lats = MV.zeros(tmp2d.shape) + for ii in range(0, len(lon)): + lats[:, ii] = lat[:] + else: + lats = lat + + if MV.rank(lon) == 1: + tmp2d = f(var, time=slice(0, 1), squeeze=1) + lons = MV.zeros(tmp2d.shape) + for ii in range(0, len(lat)): + lons[ii, :] = lon[:] + else: + lons = lon + + f.close() + + # Creating regional masks + lons_a = MV.where(MV.greater(lons, 180.0), lons - 360, lons) + lons_p = lons + print("CMIP5") + print("lons_na= ", MV.min(lons_a), MV.max(lons_a)) + print("lons_np= ", MV.min(lons_p), MV.max(lons_p)) + mask_ca = MV.zeros(area.shape) + mask_na = MV.zeros(area.shape) + mask_np = MV.zeros(area.shape) + mask_sa = MV.zeros(area.shape) + mask_sp = MV.zeros(area.shape) + mask_io = MV.zeros(area.shape) + + # Arctic Regions + # Central Arctic + # lat_bound1=MV.logical_and(MV.greater(lats,80.),MV.less_equal(lats,87.2)) + # lat_bound2=MV.logical_and(MV.greater(lats,65.),MV.less_equal(lats,87.2)) + lat_bound1 = MV.logical_and(MV.greater(lats, 80.0), MV.less_equal(lats, 90.0)) + lat_bound2 = MV.logical_and(MV.greater(lats, 65.0), MV.less_equal(lats, 90.0)) + lon_bound1 = MV.logical_and( + MV.greater(lons_a, -120.0), MV.less_equal(lons_a, 90.0) + ) + lon_bound2 = MV.logical_and( + MV.greater(lons_p, 90.0), MV.less_equal(lons_p, 240.0) + ) + reg1_ca = MV.logical_and(lat_bound1, lon_bound1) + reg2_ca = MV.logical_and(lat_bound2, lon_bound2) + mask_ca = MV.where(MV.logical_or(reg1_ca, reg2_ca), 1, 0) + + # NA region + lat_bound = MV.logical_and(MV.greater(lats, 35.0), MV.less_equal(lats, 80.0)) + lon_bound = MV.logical_and( + MV.greater(lons_a, -120.0), MV.less_equal(lons_a, 90.0) + ) + mask_na = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) + + # NP region + lat_bound = MV.logical_and(MV.greater(lats, 35.0), MV.less_equal(lats, 65.0)) + lon_bound = MV.logical_and( + MV.greater(lons_p, 90.0), MV.less_equal(lons_p, 240.0) + ) + mask_np = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) + + # Antarctic Regions + lat_bound = MV.logical_and(MV.greater(lats, -90.0), MV.less_equal(lats, -40.0)) + + # SA region + lon_bound = MV.logical_and( + MV.greater(lons_a, -60.0), MV.less_equal(lons_a, 20.0) + ) + mask_sa = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) + + # SP region + # lon_bound=MV.logical_and(MV.greater(lons_p,130.),MV.less_equal(lons_p,300.)) + lon_bound = MV.logical_and( + MV.greater(lons_p, 90.0), MV.less_equal(lons_p, 300.0) + ) + + mask_sp = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) + + # IO region + # lon_bound=MV.logical_and(MV.greater(lons_p,30.),MV.less_equal(lons_p,130.)) + lon_bound = MV.logical_and( + MV.greater(lons_p, 20.0), MV.less_equal(lons_p, 90.0) + ) + mask_io = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) + + # Calculate the Total Sea Ice Area + ice_area = MV.multiply(sic, area) + ice_area = MV.where( + MV.greater_equal(sic, 0.15), ice_area, 0.0 + ) # Masking out the sic<0.15 + + # arctic=MV.logical_and(MV.greater_equal(lats,35.),MV.less(lats,87.2)) #SSM/I limited to 87.2N + arctic = MV.logical_and( + MV.greater_equal(lats, 35.0), MV.less(lats, 90.0) + ) # Adding currently in SSM/I 100% in the area >87.2N + antarctic = MV.logical_and(MV.greater_equal(lats, -90.0), MV.less(lats, -40.0)) + + for nt in range(len(t)): + aice_arctic = MV.where(MV.equal(arctic, True), ice_area[nt], 0.0) + aice_antarctic = MV.where(MV.equal(antarctic, True), ice_area[nt], 0.0) + area_sic_ca = MV.where(MV.equal(mask_ca, True), ice_area[nt], 0.0) + area_sic_na = MV.where(MV.equal(mask_na, True), ice_area[nt], 0.0) + area_sic_np = MV.where(MV.equal(mask_np, True), ice_area[nt], 0.0) + area_sic_sa = MV.where(MV.equal(mask_sa, True), ice_area[nt], 0.0) + area_sic_sp = MV.where(MV.equal(mask_sp, True), ice_area[nt], 0.0) + area_sic_io = MV.where(MV.equal(mask_io, True), ice_area[nt], 0.0) + total_area_arctic[nt] = total_area_arctic[nt] + MV.sum(aice_arctic) + total_area_antarctic[nt] = total_area_antarctic[nt] + MV.sum(aice_antarctic) + total_area_ca[nt] = total_area_ca[nt] + MV.sum(area_sic_ca) + total_area_na[nt] = total_area_na[nt] + MV.sum(area_sic_na) + total_area_np[nt] = total_area_np[nt] + MV.sum(area_sic_np) + total_area_sa[nt] = total_area_sa[nt] + MV.sum(area_sic_sa) + total_area_sp[nt] = total_area_sp[nt] + MV.sum(area_sic_sp) + total_area_io[nt] = total_area_io[nt] + MV.sum(area_sic_io) + + # print 'total_area_arctic= ',total_area_arctic[nt] + # print 'total_area_na= ',total_area_na[nt] + # print 'total_area_np= ',total_area_np[nt] + + # Individual Model Ensemble Mean + total_area_arctic = MV.divide(total_area_arctic, nr) + total_area_antarctic = MV.divide(total_area_antarctic, nr) + total_area_ca = MV.divide(total_area_ca, nr) + total_area_na = MV.divide(total_area_na, nr) + total_area_np = MV.divide(total_area_np, nr) + total_area_sa = MV.divide(total_area_sa, nr) + total_area_sp = MV.divide(total_area_sp, nr) + total_area_io = MV.divide(total_area_io, nr) + + # Annual cycle + total_area_arctic.setAxis(0, t) + cdutil.setTimeBoundsMonthly(total_area_arctic) + annual_cycle_arctic = cdutil.ANNUALCYCLE.climatology(total_area_arctic) + + total_area_antarctic.setAxis(0, t) + cdutil.setTimeBoundsMonthly(total_area_antarctic) + annual_cycle_antarctic = cdutil.ANNUALCYCLE.climatology(total_area_antarctic) + + total_area_ca.setAxis(0, t) + cdutil.setTimeBoundsMonthly(total_area_ca) + annual_cycle_ca = cdutil.ANNUALCYCLE.climatology(total_area_ca) + + total_area_na.setAxis(0, t) + cdutil.setTimeBoundsMonthly(total_area_na) + annual_cycle_na = cdutil.ANNUALCYCLE.climatology(total_area_na) + + total_area_np.setAxis(0, t) + cdutil.setTimeBoundsMonthly(total_area_np) + annual_cycle_np = cdutil.ANNUALCYCLE.climatology(total_area_np) + + total_area_sa.setAxis(0, t) + cdutil.setTimeBoundsMonthly(total_area_sa) + annual_cycle_sa = cdutil.ANNUALCYCLE.climatology(total_area_sa) + + total_area_sp.setAxis(0, t) + cdutil.setTimeBoundsMonthly(total_area_sp) + annual_cycle_sp = cdutil.ANNUALCYCLE.climatology(total_area_sp) + + total_area_io.setAxis(0, t) + cdutil.setTimeBoundsMonthly(total_area_io) + annual_cycle_io = cdutil.ANNUALCYCLE.climatology(total_area_io) + + ann_arctic[:, i] = np.array(annual_cycle_arctic) + ann_antarctic[:, i] = np.array(annual_cycle_antarctic) + ann_ca[:, i] = np.array(annual_cycle_ca) + ann_na[:, i] = np.array(annual_cycle_na) + ann_np[:, i] = np.array(annual_cycle_np) + ann_sa[:, i] = np.array(annual_cycle_sa) + ann_sp[:, i] = np.array(annual_cycle_sp) + ann_io[:, i] = np.array(annual_cycle_io) + + # Calculating the CMIP5 STD + + std_arctic[:, i] = np.array(total_area_arctic) + std_antarctic[:, i] = np.array(total_area_antarctic) + std_ca[:, i] = np.array(total_area_ca) + std_na[:, i] = np.array(total_area_na) + std_np[:, i] = np.array(total_area_np) + std_sa[:, i] = np.array(total_area_sa) + std_sp[:, i] = np.array(total_area_sp) + std_io[:, i] = np.array(total_area_io) + + ann_arctic_mma = ann_arctic_mma + np.array(annual_cycle_arctic) + ann_antarctic_mma = ann_antarctic_mma + np.array(annual_cycle_antarctic) + ann_ca_mma = ann_ca_mma + np.array(annual_cycle_ca) + ann_na_mma = ann_na_mma + np.array(annual_cycle_na) + ann_np_mma = ann_np_mma + np.array(annual_cycle_np) + ann_sa_mma = ann_sa_mma + np.array(annual_cycle_sa) + ann_sp_mma = ann_sp_mma + np.array(annual_cycle_sp) + ann_io_mma = ann_io_mma + np.array(annual_cycle_io) + nm = nm + 1 + + # Calculating the CMIP5 RMS + for j in range(0, 2): + rms_ann_arctic[j, i] = genutil.statistics.rms( + ann_arctic[:, i], annual_cycle_obs_arctic[:, j], axis=0 + ) + rms_ann_antarctic[j, i] = genutil.statistics.rms( + ann_antarctic[:, i], annual_cycle_obs_antarctic[:, j], axis=0 + ) + rms_ann_ca[j, i] = genutil.statistics.rms( + ann_ca[:, i], annual_cycle_obs_ca[:, j], axis=0 + ) + rms_ann_na[j, i] = genutil.statistics.rms( + ann_na[:, i], annual_cycle_obs_na[:, j], axis=0 + ) + rms_ann_np[j, i] = genutil.statistics.rms( + ann_np[:, i], annual_cycle_obs_np[:, j], axis=0 + ) + rms_ann_sa[j, i] = genutil.statistics.rms( + ann_sa[:, i], annual_cycle_obs_sa[:, j], axis=0 + ) + rms_ann_sp[j, i] = genutil.statistics.rms( + ann_sp[:, i], annual_cycle_obs_sp[:, j], axis=0 + ) + rms_ann_io[j, i] = genutil.statistics.rms( + ann_io[:, i], annual_cycle_obs_io[:, j], axis=0 + ) # CMIP5 MME -ann_arctic_mma = ann_arctic_mma/nm -ann_antarctic_mma = ann_antarctic_mma/nm -ann_ca_mma = ann_ca_mma/nm -ann_na_mma = ann_na_mma/nm -ann_np_mma = ann_np_mma/nm -ann_sa_mma = ann_sa_mma/nm -ann_sp_mma = ann_sp_mma/nm -ann_io_mma = ann_io_mma/nm - -[ni,nj]=std_arctic.shape -tta_std_arctic=MV.zeros([12,324/12,len(mods)]) -tta_std_antarctic=MV.zeros([12,324/12,len(mods)]) -tta_std_ca=MV.zeros([12,324/12,len(mods)]) -tta_std_na=MV.zeros([12,324/12,len(mods)]) -tta_std_np=MV.zeros([12,324/12,len(mods)]) -tta_std_sa=MV.zeros([12,324/12,len(mods)]) -tta_std_sp=MV.zeros([12,324/12,len(mods)]) -tta_std_io=MV.zeros([12,324/12,len(mods)]) - -for im in range (0,12): - tta_std_arctic[im,:,:]=std_arctic[im:ni:12,:] - tta_std_antarctic[im,:,:]=std_antarctic[im:ni:12,:] - tta_std_ca[im,:,:]=std_ca[im:ni:12,:] - tta_std_na[im,:,:]=std_na[im:ni:12,:] - tta_std_np[im,:,:]=std_np[im:ni:12,:] - tta_std_sa[im,:,:]=std_sa[im:ni:12,:] - tta_std_sp[im,:,:]=std_sp[im:ni:12,:] - tta_std_io[im,:,:]=std_io[im:ni:12,:] - -[nt,nx,ny]=tta_std_arctic.shape -ttta_std_arctic=MV.reshape(tta_std_arctic,(nt,nx*ny)) -ttta_std_ca=MV.reshape(tta_std_ca,(nt,nx*ny)) -ttta_std_na=MV.reshape(tta_std_na,(nt,nx*ny)) -ttta_std_np=MV.reshape(tta_std_np,(nt,nx*ny)) -[nt,nx,ny]=tta_std_antarctic.shape -ttta_std_antarctic=MV.reshape(tta_std_antarctic,(nt,nx*ny)) -ttta_std_sa=MV.reshape(tta_std_sa,(nt,nx*ny)) -ttta_std_sp=MV.reshape(tta_std_sp,(nt,nx*ny)) -ttta_std_io=MV.reshape(tta_std_io,(nt,nx*ny)) - -for im in range (0,12): - annual_cycle_std_mod_arctic[im]=np.array(genutil.statistics.std(ttta_std_arctic[im,:])) - annual_cycle_std_mod_antarctic[im]=np.array(genutil.statistics.std(ttta_std_antarctic[im,:])) - annual_cycle_std_mod_ca[im]=np.array(genutil.statistics.std(ttta_std_ca[im,:])) - annual_cycle_std_mod_na[im]=np.array(genutil.statistics.std(ttta_std_na[im,:])) - annual_cycle_std_mod_np[im]=np.array(genutil.statistics.std(ttta_std_np[im,:])) - annual_cycle_std_mod_sa[im]=np.array(genutil.statistics.std(ttta_std_sa[im,:])) - annual_cycle_std_mod_sp[im]=np.array(genutil.statistics.std(ttta_std_sp[im,:])) - annual_cycle_std_mod_io[im]=np.array(genutil.statistics.std(ttta_std_io[im,:])) - -#Plot +ann_arctic_mma = ann_arctic_mma / nm +ann_antarctic_mma = ann_antarctic_mma / nm +ann_ca_mma = ann_ca_mma / nm +ann_na_mma = ann_na_mma / nm +ann_np_mma = ann_np_mma / nm +ann_sa_mma = ann_sa_mma / nm +ann_sp_mma = ann_sp_mma / nm +ann_io_mma = ann_io_mma / nm + +[ni, nj] = std_arctic.shape +tta_std_arctic = MV.zeros([12, 324 / 12, len(mods)]) +tta_std_antarctic = MV.zeros([12, 324 / 12, len(mods)]) +tta_std_ca = MV.zeros([12, 324 / 12, len(mods)]) +tta_std_na = MV.zeros([12, 324 / 12, len(mods)]) +tta_std_np = MV.zeros([12, 324 / 12, len(mods)]) +tta_std_sa = MV.zeros([12, 324 / 12, len(mods)]) +tta_std_sp = MV.zeros([12, 324 / 12, len(mods)]) +tta_std_io = MV.zeros([12, 324 / 12, len(mods)]) + +for im in range(0, 12): + tta_std_arctic[im, :, :] = std_arctic[im:ni:12, :] + tta_std_antarctic[im, :, :] = std_antarctic[im:ni:12, :] + tta_std_ca[im, :, :] = std_ca[im:ni:12, :] + tta_std_na[im, :, :] = std_na[im:ni:12, :] + tta_std_np[im, :, :] = std_np[im:ni:12, :] + tta_std_sa[im, :, :] = std_sa[im:ni:12, :] + tta_std_sp[im, :, :] = std_sp[im:ni:12, :] + tta_std_io[im, :, :] = std_io[im:ni:12, :] + +[nt, nx, ny] = tta_std_arctic.shape +ttta_std_arctic = MV.reshape(tta_std_arctic, (nt, nx * ny)) +ttta_std_ca = MV.reshape(tta_std_ca, (nt, nx * ny)) +ttta_std_na = MV.reshape(tta_std_na, (nt, nx * ny)) +ttta_std_np = MV.reshape(tta_std_np, (nt, nx * ny)) +[nt, nx, ny] = tta_std_antarctic.shape +ttta_std_antarctic = MV.reshape(tta_std_antarctic, (nt, nx * ny)) +ttta_std_sa = MV.reshape(tta_std_sa, (nt, nx * ny)) +ttta_std_sp = MV.reshape(tta_std_sp, (nt, nx * ny)) +ttta_std_io = MV.reshape(tta_std_io, (nt, nx * ny)) + +for im in range(0, 12): + annual_cycle_std_mod_arctic[im] = np.array( + genutil.statistics.std(ttta_std_arctic[im, :]) + ) + annual_cycle_std_mod_antarctic[im] = np.array( + genutil.statistics.std(ttta_std_antarctic[im, :]) + ) + annual_cycle_std_mod_ca[im] = np.array(genutil.statistics.std(ttta_std_ca[im, :])) + annual_cycle_std_mod_na[im] = np.array(genutil.statistics.std(ttta_std_na[im, :])) + annual_cycle_std_mod_np[im] = np.array(genutil.statistics.std(ttta_std_np[im, :])) + annual_cycle_std_mod_sa[im] = np.array(genutil.statistics.std(ttta_std_sa[im, :])) + annual_cycle_std_mod_sp[im] = np.array(genutil.statistics.std(ttta_std_sp[im, :])) + annual_cycle_std_mod_io[im] = np.array(genutil.statistics.std(ttta_std_io[im, :])) + +# Plot # Bar Plots of the RMS -labels=["ACCESS1-3","BNU-ESM","CCSM4","CESM1-BGC","CESM1-CAM5-1-FV2","CESM1-CAM5","CESM1-FASTCHEM","CNRM-CM5","CSIRO-Mk3-6-0","CanCM4","CanESM2","GFDL-CM2p1","GFDL-CM3","GFDL-ES\ -M2G","GFDL-ESM2M","GISS-E2-H-CC","GISS-E2-H","GISS-E2-R-CC","GISS-E2-R","HadCM3","HadGEM2-AO","HadGEM2-CC","HadGEM2-ES","IPSL-CM5A-MR","IPSL-CM5B-LR","MIROC-ESM-CHEM","MI\ -ROC-ESM","MIROC4h","MIROC5","MPI-ESM-LR","MPI-ESM-MR","MPI-ESM-P","NorESM1-ME","bcc-csm1-1-m","bcc-csm1-1"] -#labels=["CCSM4","CESM1-BGC","CESM1-CAM5-1-FV2","CESM1-CAM5","CESM1-FASTCHEM","CNRM-CM5","CSIRO-Mk3-6-0","CanCM4","CanESM2","GFDL-CM3","GFDL-ES\ -#M2G","GFDL-ESM2M","GISS-E2-H-CC","GISS-E2-H","GISS-E2-R","HadCM3","HadGEM2-CC","HadGEM2-ES","IPSL-CM5A-MR","IPSL-CM5B-LR","MIROC-ESM-CHEM","MI\ -#ROC-ESM","MIROC4h","MIROC5","MPI-ESM-LR","MPI-ESM-MR","MPI-ESM-P","NorESM1-ME","bcc-csm1-1"] -#labels=["HadCM3","HadGEM2-CC"] -mlabels=np.append(labels,"RMS-Obs") -rms_arctic=np.append(rms_ann_arctic[0,:],rms_arctic_obs) -rms_antarctic=np.append(rms_ann_antarctic[0,:],rms_antarctic_obs) -rms_ca=np.append(rms_ann_ca[0,:],rms_ca_obs) -rms_na=np.append(rms_ann_na[0,:],rms_na_obs) -rms_np=np.append(rms_ann_np[0,:],rms_np_obs) -rms_sa=np.append(rms_ann_sa[0,:],rms_sa_obs) -rms_sp=np.append(rms_ann_sp[0,:],rms_sp_obs) -rms_io=np.append(rms_ann_io[0,:],rms_io_obs) +labels = [ + "ACCESS1-3", + "BNU-ESM", + "CCSM4", + "CESM1-BGC", + "CESM1-CAM5-1-FV2", + "CESM1-CAM5", + "CESM1-FASTCHEM", + "CNRM-CM5", + "CSIRO-Mk3-6-0", + "CanCM4", + "CanESM2", + "GFDL-CM2p1", + "GFDL-CM3", + "GFDL-ES\ +M2G", + "GFDL-ESM2M", + "GISS-E2-H-CC", + "GISS-E2-H", + "GISS-E2-R-CC", + "GISS-E2-R", + "HadCM3", + "HadGEM2-AO", + "HadGEM2-CC", + "HadGEM2-ES", + "IPSL-CM5A-MR", + "IPSL-CM5B-LR", + "MIROC-ESM-CHEM", + "MI\ +ROC-ESM", + "MIROC4h", + "MIROC5", + "MPI-ESM-LR", + "MPI-ESM-MR", + "MPI-ESM-P", + "NorESM1-ME", + "bcc-csm1-1-m", + "bcc-csm1-1", +] +# labels=["CCSM4","CESM1-BGC","CESM1-CAM5-1-FV2","CESM1-CAM5","CESM1-FASTCHEM","CNRM-CM5","CSIRO-Mk3-6-0","CanCM4","CanESM2","GFDL-CM3","GFDL-ES\ +# M2G","GFDL-ESM2M","GISS-E2-H-CC","GISS-E2-H","GISS-E2-R","HadCM3","HadGEM2-CC","HadGEM2-ES","IPSL-CM5A-MR","IPSL-CM5B-LR","MIROC-ESM-CHEM","MI\ +# ROC-ESM","MIROC4h","MIROC5","MPI-ESM-LR","MPI-ESM-MR","MPI-ESM-P","NorESM1-ME","bcc-csm1-1"] +# labels=["HadCM3","HadGEM2-CC"] +mlabels = np.append(labels, "RMS-Obs") +rms_arctic = np.append(rms_ann_arctic[0, :], rms_arctic_obs) +rms_antarctic = np.append(rms_ann_antarctic[0, :], rms_antarctic_obs) +rms_ca = np.append(rms_ann_ca[0, :], rms_ca_obs) +rms_na = np.append(rms_ann_na[0, :], rms_na_obs) +rms_np = np.append(rms_ann_np[0, :], rms_np_obs) +rms_sa = np.append(rms_ann_sa[0, :], rms_sa_obs) +rms_sp = np.append(rms_ann_sp[0, :], rms_sp_obs) +rms_io = np.append(rms_ann_io[0, :], rms_io_obs) ind = np.arange(len(mods)) # the x locations for the groups -#ind = np.arange(len(mods)+1) # the x locations for the groups -width=0.3 -n=len(ind)-1 +# ind = np.arange(len(mods)+1) # the x locations for the groups +width = 0.3 +n = len(ind) - 1 -#fig1 = plt.figure(1) +# fig1 = plt.figure(1) plt.subplot(411) -plt.bar(ind,rms_ann_arctic[0,:],width,color='r') +plt.bar(ind, rms_ann_arctic[0, :], width, color="r") plt.hold -plt.bar(ind[n]+2.5*width,rms_arctic_obs,width,color='b') -plt.xticks(ind+width/2.,mlabels,rotation=20,size=8) -#plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) -#plt.text +plt.bar(ind[n] + 2.5 * width, rms_arctic_obs, width, color="b") +plt.xticks(ind + width / 2.0, mlabels, rotation=20, size=8) +# plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) +# plt.text plt.hold -#plt.ylim(0.,ymax) -plt.ylabel('RMS of Total Sea Ice Area, 10${^6}$km${^2}$') -#plt.title('Arctic') -plt.annotate('Arctic', (0.5, 0.9), xycoords='axes fraction',size=15) +# plt.ylim(0.,ymax) +plt.ylabel("RMS of Total Sea Ice Area, 10${^6}$km${^2}$") +# plt.title('Arctic') +plt.annotate("Arctic", (0.5, 0.9), xycoords="axes fraction", size=15) plt.grid(True) -#plt.legend(mods_obs,bbox_to_anchor=(0.0, 0., 1, 1), bbox_transform=plt.gcf().transFigure) +# plt.legend(mods_obs,bbox_to_anchor=(0.0, 0., 1, 1), bbox_transform=plt.gcf().transFigure) -#fig2 = plt.figure(2) +# fig2 = plt.figure(2) plt.subplot(412) -plt.bar(ind,rms_ann_ca[0,:],width,color='r') -plt.bar(ind[n]+2.5*width,rms_ca_obs,width,color='b') -#plt.bar(ind+width,rms_ann_na[1,:],width,color='b') -plt.xticks(ind+width/2.,mlabels,rotation=20,size=8) -#plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) -plt.ylabel('RMS of Total Sea Ice Area, 10${^6}$km${^2}$') -#plt.title('Central Arctic Sector') -plt.annotate('Central Arctic Sector', (0.4, 0.9), xycoords='axes fraction',size=15) +plt.bar(ind, rms_ann_ca[0, :], width, color="r") +plt.bar(ind[n] + 2.5 * width, rms_ca_obs, width, color="b") +# plt.bar(ind+width,rms_ann_na[1,:],width,color='b') +plt.xticks(ind + width / 2.0, mlabels, rotation=20, size=8) +# plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) +plt.ylabel("RMS of Total Sea Ice Area, 10${^6}$km${^2}$") +# plt.title('Central Arctic Sector') +plt.annotate("Central Arctic Sector", (0.4, 0.9), xycoords="axes fraction", size=15) plt.grid(True) plt.hold -#fig3 = plt.figure(3) +# fig3 = plt.figure(3) plt.subplot(413) -plt.bar(ind,rms_ann_na[0,:],width,color='r') -plt.bar(ind[n]+2.5*width,rms_na_obs,width,color='b') -#plt.bar(ind+width,rms_ann_na[1,:],width,color='b') -plt.xticks(ind+width/2.,mlabels,rotation=20,size=8) -#plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) -plt.ylabel('RMS of Total Sea Ice Area, 10${^6}$km${^2}$') -#plt.title('North Atlantic Arctic Sector') -plt.annotate('North Atlantic Arctic Sector', (0.4, 0.9), xycoords='axes fraction',size=15) +plt.bar(ind, rms_ann_na[0, :], width, color="r") +plt.bar(ind[n] + 2.5 * width, rms_na_obs, width, color="b") +# plt.bar(ind+width,rms_ann_na[1,:],width,color='b') +plt.xticks(ind + width / 2.0, mlabels, rotation=20, size=8) +# plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) +plt.ylabel("RMS of Total Sea Ice Area, 10${^6}$km${^2}$") +# plt.title('North Atlantic Arctic Sector') +plt.annotate( + "North Atlantic Arctic Sector", (0.4, 0.9), xycoords="axes fraction", size=15 +) plt.grid(True) plt.hold -#fig4 = plt.figure(4) +# fig4 = plt.figure(4) plt.subplot(414) -plt.bar(ind,rms_ann_np[0,:],width,color='r') +plt.bar(ind, rms_ann_np[0, :], width, color="r") # Plot the annual cycle -plt.bar(ind[n]+2.5*width,rms_np_obs,width,color='b') -#plt.bar(ind+width,rms_ann_np[1,:],width,color='b') -plt.xticks(ind+width/2.,mlabels,rotation=20,size=8) -#plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) +plt.bar(ind[n] + 2.5 * width, rms_np_obs, width, color="b") +# plt.bar(ind+width,rms_ann_np[1,:],width,color='b') +plt.xticks(ind + width / 2.0, mlabels, rotation=20, size=8) +# plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) plt.hold -#plt.ylim(0.,ymax) -plt.ylabel('RMS of Total Sea Ice Area, 10${^6}$km${^2}$') -#plt.title('North Pacific Arctic Sector') -plt.annotate('North Pacific Arctic Sector', (0.4, 0.9), xycoords='axes fraction',size=15) +# plt.ylim(0.,ymax) +plt.ylabel("RMS of Total Sea Ice Area, 10${^6}$km${^2}$") +# plt.title('North Pacific Arctic Sector') +plt.annotate( + "North Pacific Arctic Sector", (0.4, 0.9), xycoords="axes fraction", size=15 +) plt.grid(True) -#plt.legend(mods_obs,loc=(.55,0.65)) +# plt.legend(mods_obs,loc=(.55,0.65)) plt.show() # Antarctic -fig5=plt.figure(5) +fig5 = plt.figure(5) plt.subplot(411) -plt.bar(ind,rms_ann_antarctic[0,:],width,color='r') -plt.bar(ind[n]+2.5*width,rms_antarctic_obs,width,color='b') -#plt.bar(ind+width,rms_ann_antarctic[1,:],width,color='b') -plt.xticks(ind+width/2.,mlabels,rotation=20,size=8) -#plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) +plt.bar(ind, rms_ann_antarctic[0, :], width, color="r") +plt.bar(ind[n] + 2.5 * width, rms_antarctic_obs, width, color="b") +# plt.bar(ind+width,rms_ann_antarctic[1,:],width,color='b') +plt.xticks(ind + width / 2.0, mlabels, rotation=20, size=8) +# plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) plt.hold -#plt.ylim(0.,ymax) -plt.ylabel('RMS of Total Sea Ice Area, 10${^6}$km${^2}$') -#plt.title('Antarctic') -plt.annotate('Antarctic', (0.5, 0.9), xycoords='axes fraction',size=15) -#plt.legend(mods_obs,bbox_to_anchor=(0.0, 0., 1, 1), bbox_transform=plt.gcf().transFigure) +# plt.ylim(0.,ymax) +plt.ylabel("RMS of Total Sea Ice Area, 10${^6}$km${^2}$") +# plt.title('Antarctic') +plt.annotate("Antarctic", (0.5, 0.9), xycoords="axes fraction", size=15) +# plt.legend(mods_obs,bbox_to_anchor=(0.0, 0., 1, 1), bbox_transform=plt.gcf().transFigure) plt.grid(True) -#fig6 = plt.figure(6) +# fig6 = plt.figure(6) plt.subplot(412) -plt.bar(ind,rms_ann_sa[0,:],width,color='r') -plt.bar(ind[n]+2.5*width,rms_sa_obs,width,color='b') -#plt.bar(ind+width,rms_ann_sa[1,:],width,color='b') -plt.xticks(ind+width/2.,mlabels,rotation=20,size=8) -#plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) +plt.bar(ind, rms_ann_sa[0, :], width, color="r") +plt.bar(ind[n] + 2.5 * width, rms_sa_obs, width, color="b") +# plt.bar(ind+width,rms_ann_sa[1,:],width,color='b') +plt.xticks(ind + width / 2.0, mlabels, rotation=20, size=8) +# plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) plt.hold -#plt.ylim(0.,ymax) -plt.ylabel('RMS of Total Sea Ice Area, 10${^6}$km${^2}$') -#plt.title('South Atlantic Antarctic Sector') -plt.annotate('South Atlantic Ocean Antarctic Sector', (0.4, 0.9), xycoords='axes fraction',size=15) +# plt.ylim(0.,ymax) +plt.ylabel("RMS of Total Sea Ice Area, 10${^6}$km${^2}$") +# plt.title('South Atlantic Antarctic Sector') +plt.annotate( + "South Atlantic Ocean Antarctic Sector", + (0.4, 0.9), + xycoords="axes fraction", + size=15, +) plt.grid(True) -#fig7 = plt.figure(7) +# fig7 = plt.figure(7) plt.subplot(413) -plt.bar(ind,rms_ann_sp[0,:],width,color='r') -plt.bar(ind[n]+2.5*width,rms_sp_obs,width,color='b') -#plt.bar(ind+width,rms_ann_sp[1,:],width,color='b') -plt.xticks(ind+width/2.,mlabels,rotation=20,size=8) +plt.bar(ind, rms_ann_sp[0, :], width, color="r") +plt.bar(ind[n] + 2.5 * width, rms_sp_obs, width, color="b") +# plt.bar(ind+width,rms_ann_sp[1,:],width,color='b') +plt.xticks(ind + width / 2.0, mlabels, rotation=20, size=8) plt.hold -#plt.ylim(0.,ymax) -plt.ylabel('RMS of Total Sea Ice Area, 10${^6}$km${^2}$') -#plt.title('South Pacific Antarctic Sector') -plt.annotate('South Pacific Ocean Antarctic Sector', (0.4, 0.9), xycoords='axes fraction',size=15) -#plt.legend(obs_mods,loc=(.05,0.65)) +# plt.ylim(0.,ymax) +plt.ylabel("RMS of Total Sea Ice Area, 10${^6}$km${^2}$") +# plt.title('South Pacific Antarctic Sector') +plt.annotate( + "South Pacific Ocean Antarctic Sector", + (0.4, 0.9), + xycoords="axes fraction", + size=15, +) +# plt.legend(obs_mods,loc=(.05,0.65)) plt.grid(True) -#fig8 = plt.figure(8) +# fig8 = plt.figure(8) plt.subplot(414) -plt.bar(ind,rms_ann_io[0,:],width,color='r') -plt.bar(ind[n]+2.5*width,rms_io_obs,width,color='b') -#plt.bar(ind+width,rms_ann_io[1,:],width,color='b') -plt.xticks(ind+width/2.,mlabels,rotation=20,size=8) -#plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) +plt.bar(ind, rms_ann_io[0, :], width, color="r") +plt.bar(ind[n] + 2.5 * width, rms_io_obs, width, color="b") +# plt.bar(ind+width,rms_ann_io[1,:],width,color='b') +plt.xticks(ind + width / 2.0, mlabels, rotation=20, size=8) +# plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) plt.hold -plt.ylabel('RMS of Total Sea Ice Area, 10${^6}$km${^2}$') -plt.annotate('South Indian Ocean Antarctic Sector', (0.4, 0.9), xycoords='axes fraction',size=15) -#plt.title('South Indian Ocean Antarctic Sector') +plt.ylabel("RMS of Total Sea Ice Area, 10${^6}$km${^2}$") +plt.annotate( + "South Indian Ocean Antarctic Sector", (0.4, 0.9), xycoords="axes fraction", size=15 +) +# plt.title('South Indian Ocean Antarctic Sector') plt.grid(True) plt.show() From 7d3e823924c52c860e692c2a5757b7e09ad77ded Mon Sep 17 00:00:00 2001 From: Jiwoo Lee Date: Tue, 7 Nov 2023 13:13:45 -0800 Subject: [PATCH 04/69] explicit import for functions --- pcmdi_metrics/sea_ice/generate_sector_masks.py | 4 ++-- pcmdi_metrics/sea_ice/sector_mask_defs.py | 5 +++-- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/pcmdi_metrics/sea_ice/generate_sector_masks.py b/pcmdi_metrics/sea_ice/generate_sector_masks.py index 6f8b0b082..857b4b70a 100644 --- a/pcmdi_metrics/sea_ice/generate_sector_masks.py +++ b/pcmdi_metrics/sea_ice/generate_sector_masks.py @@ -4,9 +4,9 @@ import cdms2 import MV2 as MV -from sector_mask_defs import * +from sector_mask_defs import getmask -from pcmdi_metrics.pcmdi.pmp_parser import * +from pcmdi_metrics.mean_climate.lib.pmp_parser import PMPParser P = PMPParser() diff --git a/pcmdi_metrics/sea_ice/sector_mask_defs.py b/pcmdi_metrics/sea_ice/sector_mask_defs.py index 94bb22b21..d9af3cc06 100644 --- a/pcmdi_metrics/sea_ice/sector_mask_defs.py +++ b/pcmdi_metrics/sea_ice/sector_mask_defs.py @@ -1,6 +1,7 @@ -def getmask(sector, lats, lons, lons_a, lons_p, land_mask): - import MV2 as MV +import MV2 as MV + +def getmask(sector, lats, lons, lons_a, lons_p, land_mask): # Arctic Regions # Central Arctic if sector == "ca": From 62de671c91419723ead88175bed76907cea2d46a Mon Sep 17 00:00:00 2001 From: Jiwoo Lee Date: Tue, 7 Nov 2023 13:54:42 -0800 Subject: [PATCH 05/69] pre-commit fix: do not use bare except --- pcmdi_metrics/sea_ice/generate_sector_masks.py | 10 ++++++---- pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py | 6 +++--- 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/pcmdi_metrics/sea_ice/generate_sector_masks.py b/pcmdi_metrics/sea_ice/generate_sector_masks.py index 857b4b70a..77d8fcb30 100644 --- a/pcmdi_metrics/sea_ice/generate_sector_masks.py +++ b/pcmdi_metrics/sea_ice/generate_sector_masks.py @@ -111,10 +111,12 @@ try: area = g("areacello") - except: + except Exception: area = g("areacella") area = MV.multiply(area, factor1) - area = MV.multiply(area, factor1) + area = MV.multiply( + area, factor1 + ) # Question from Jiwoo (2023-11-7): Why this line repeats two times? g.close() @@ -142,7 +144,7 @@ print(mod, MV.max(frac)) area = MV.multiply(area, frac) land_mask = MV.multiply(1, (1 - frac)) - except: + except Exception: frac = s("sftlf") if ( mod != "MIROC5" @@ -209,7 +211,7 @@ MV.greater_equal(lats, -90.0), MV.less(lats, -55.0) ) - except: + except Exception: "Failed for ", mod mods_failed.append(mod) diff --git a/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py b/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py index 2752bea6a..fa7e7ee71 100755 --- a/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py +++ b/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py @@ -408,7 +408,7 @@ def tgrid(t): data_np.append(val4 + val5) data_na.append(val6 + val7 + val8 + val9 + val11 + val12) data_ca.append(val10) - except: + except Exception: pass obs1_n[:, dl] = MV.array(data_n[0:324], id="sic") obs1_ca[:, dl] = MV.array(data_ca[0:324], id="sic") @@ -428,7 +428,7 @@ def tgrid(t): data_sa.append(val4) data_io.append(val5) data_sp.append(val6 + val7 + val8) - except: + except Exception: pass obs1_s[:, dl] = MV.array(data_s[0:324], id="sic") @@ -675,7 +675,7 @@ def tgrid(t): g = cdms.open(gfile) try: area = g("areacello") - except: + except Exception: area = g("areacella") area = MV.multiply(area, factor1) area = MV.multiply(area, factor1) From ef09d8f36907f7b4bf9a3119ae91e156dc13301a Mon Sep 17 00:00:00 2001 From: Jiwoo Lee Date: Tue, 7 Nov 2023 14:17:55 -0800 Subject: [PATCH 06/69] clean up --- .../sea_ice/ice_area_cmip5_ssmi_reg_rms.py | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py b/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py index fa7e7ee71..eaf62fc4d 100755 --- a/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py +++ b/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py @@ -693,7 +693,7 @@ def tgrid(t): nr = 0 # Number fo individual model runs - for ir in range(0, len(runs)): + for run, ver in zip(runs, vers): nr = nr + 1 # Reading the sea ice concentration (sic) infile = ( @@ -701,19 +701,16 @@ def tgrid(t): + "cmip5." + mod + ".historical." - + runs[ir] + + run + ".mo.seaIce.sic." - + vers[ir] + + ver + ".xml" ) print(infile) f = cdms.open(infile) - if ( - (mod == "HadGEM2-CC" and (runs[ir] == "r1i1p1" or runs[ir] == "r3i1p1")) - or mod == "HadGEM2-ES" - and (runs[ir] == "r2i1p1" or runs[ir] == "r3i1p1" or runs[ir] == "r4i1p1") - ): + if ((mod == "HadGEM2-CC" and (run in ["r1i1p1", "r3i1p1"]) + or mod == "HadGEM2-ES" and (run in ["r2i1p1", "r3i1p1", "r4i1p1"]))): sic = f(var, time=("1978-12-1", "2006-1-1")) else: sic = f(var, time=("1979-1-1", "2006-1-1")) @@ -814,7 +811,7 @@ def tgrid(t): MV.greater_equal(sic, 0.15), ice_area, 0.0 ) # Masking out the sic<0.15 - # arctic=MV.logical_and(MV.greater_equal(lats,35.),MV.less(lats,87.2)) #SSM/I limited to 87.2N + # arctic=MV.logical_and(MV.greater_equal(lats,35.),MV.less(lats,87.2)) # SSM/I limited to 87.2N arctic = MV.logical_and( MV.greater_equal(lats, 35.0), MV.less(lats, 90.0) ) # Adding currently in SSM/I 100% in the area >87.2N From dae3443e05b211bd080a3c91076fecfd837eea49 Mon Sep 17 00:00:00 2001 From: Jiwoo Lee Date: Tue, 7 Nov 2023 14:18:52 -0800 Subject: [PATCH 07/69] clean up --- pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py b/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py index eaf62fc4d..b3a73c2ee 100755 --- a/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py +++ b/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py @@ -8,8 +8,6 @@ def tgrid(t): - import cdtime - time = t[:] * 0.0 if t[0] == 0.0: dt = 0.0 From 46b955cde186392f26ea7bdbd26c29ac56cc317d Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Tue, 28 Nov 2023 15:30:28 -0800 Subject: [PATCH 08/69] add file names --- .../sea_ice/ice_area_cmip5_ssmi_reg_rms.py | 340 +++++++----------- 1 file changed, 135 insertions(+), 205 deletions(-) diff --git a/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py b/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py index b3a73c2ee..a7a9c2f8a 100755 --- a/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py +++ b/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py @@ -41,8 +41,9 @@ def tgrid(t): # Observations -dlist_n = ["ssmi_nt_n_names.asc", "ssmi_bt_n_names.asc"] -dlist_s = ["ssmi_nt_s_names.asc", "ssmi_bt_s_names.asc"] + +dlist_n = ["/home/ordonez4/seaice/ssmi_nt_n_names.asc", "/home/ordonez4/seaice/ssmi_bt_n_names.asc"] +dlist_s = ["/home/ordonez4/seaice/ssmi_nt_s_names.asc", "/home/ordonez4/seaice/ssmi_bt_s_names.asc"] annual_cycle_obs_arctic = [] annual_cycle_obs_antarctic = [] @@ -89,9 +90,9 @@ def tgrid(t): lons_p = MV.where(MV.less(lons_n, 0.0), lons_n + 360.0, lons_n) lons_a = lons_n - print("Obs") - print("lons_na= ", MV.min(lons_a), MV.max(lons_a)) - print("lons_np= ", MV.min(lons_p), MV.max(lons_p)) + #print("Obs") + #print("lons_na= ", MV.min(lons_a), MV.max(lons_a)) + #print("lons_np= ", MV.min(lons_p), MV.max(lons_p)) mask_ca = MV.zeros(area_n.shape) mask_na = MV.zeros(area_n.shape) @@ -166,11 +167,11 @@ def tgrid(t): ta_na[i] = MV.sum(area_sic_na) ta_np[i] = MV.sum(area_sic_np) - print("data_n= ", data_n[i]) - print("ta_na= ", ta_na[i]) - print("ta_np= ", ta_np[i]) + #print("data_n= ", data_n[i]) + #print("ta_na= ", ta_na[i]) + #print("ta_np= ", ta_np[i]) - print(MV.average(sic_n)) + #print(MV.average(sic_n)) obs_n[:, dl] = MV.array(data_n, id="sic") obs_ca[:, dl] = MV.array(ta_ca, id="sic") @@ -201,9 +202,9 @@ def tgrid(t): lons_sp = MV.where(MV.less(lons_s, 0.0), lons_s + 360, lons_s) lons_io = lons_sp - print("Obs") - print("lons_sa= ", MV.min(lons_sa), MV.max(lons_sa)) - print("lons_sp= ", MV.min(lons_sp), MV.max(lons_sp)) + #print("Obs") + #print("lons_sa= ", MV.min(lons_sa), MV.max(lons_sa)) + #print("lons_sp= ", MV.min(lons_sp), MV.max(lons_sp)) mask_sa = MV.zeros(area_s.shape) mask_sp = MV.zeros(area_s.shape) @@ -255,7 +256,7 @@ def tgrid(t): ta_sp[i] = MV.sum(area_sic_sp) ta_io[i] = MV.sum(area_sic_io) - print(MV.average(sic_s)) + #print(MV.average(sic_s)) obs_s[:, dl] = MV.array(data_s, id="sic") obs_s = MV.masked_equal(obs_s, -9999.0) @@ -274,10 +275,10 @@ def tgrid(t): timeax = [] for date in zip(years, months): yr, mo = date - print(yr) + #print(yr) c = cdtime.comptime(yr, mo) - print(c) - print(c.torel("days since 1979-1-1").value) + #print(c) + #print(c.torel("days since 1979-1-1").value) timeax = timeax + [int(c.torel("days since 1979-1-1").value)] time = cdms.createAxis(timeax) @@ -344,13 +345,21 @@ def tgrid(t): # NSIDC-0192 # SSM/I Arctic # Area +#dlist_n = [ +# "nasateam/gsfc.nasateam.month.area.1978-2010.n.asc", +# "bootstrap/gsfc.bootstrap.month.area.1978-2010.n.asc", +#] +#dlist_s = [ +# "nasateam/gsfc.nasateam.month.area.1978-2010.s.asc", +# "bootstrap/gsfc.bootstrap.month.area.1978-2010.s.asc", +#] dlist_n = [ - "nasateam/gsfc.nasateam.month.area.1978-2010.n.asc", - "bootstrap/gsfc.bootstrap.month.area.1978-2010.n.asc", + "/p/user_pub/hoang1-backups/ARCHIVE/ivanova2/IceData/AreaExtent/NSIDC-0192/ice-extent/nasateam/gsfc.nasateam.month.area.1978-2010.n.asc", + "/p/user_pub/hoang1-backups/ARCHIVE/ivanova2/IceData/AreaExtent/NSIDC-0192/ice-extent/bootstrap/gsfc.bootstrap.month.area.1978-2010.n.asc" ] dlist_s = [ - "nasateam/gsfc.nasateam.month.area.1978-2010.s.asc", - "bootstrap/gsfc.bootstrap.month.area.1978-2010.s.asc", + "/p/user_pub/hoang1-backups/ARCHIVE/ivanova2/IceData/AreaExtent/NSIDC-0192/ice-extent/nasateam/gsfc.nasateam.month.area.1978-2010.s.asc", + "/p/user_pub/hoang1-backups/ARCHIVE/ivanova2/IceData/AreaExtent/NSIDC-0192/ice-extent/bootstrap/gsfc.bootstrap.month.area.1978-2010.s.asc" ] # Extent # dlist_n=['nasateam/gsfc.nasateam.month.extent.1978-2010.n.asc','bootstrap/gsfc.bootstrap.month.extent.1978-2010.n.asc'] @@ -376,11 +385,11 @@ def tgrid(t): data_sp = [] data_io = [] - f = open("/export/ivanova2/IceData/AreaExtent/NSIDC-0192/ice-extent/" + dlist_n[dl]) + f = open(dlist_n[dl]) lines_n = f.readlines() f.close - g = open("/export/ivanova2/IceData/AreaExtent/NSIDC-0192/ice-extent/" + dlist_s[dl]) + g = open(dlist_s[dl]) lines_s = g.readlines() g.close @@ -451,15 +460,14 @@ def tgrid(t): obs1_sp = MV.multiply(obs1_sp, factor1) obs1_io = MV.multiply(obs1_io, factor1) - # Create Time Axis timeax = [] for date in zip(years, months): yr, mo = date - print(yr) + #print(yr) c = cdtime.comptime(yr, mo) - print(c) - print(c.torel("days since 1979-1-1").value) + #print(c) + #print(c.torel("days since 1979-1-1").value) timeax = timeax + [int(c.torel("days since 1979-1-1").value)] time = cdms.createAxis(timeax[0:324]) @@ -522,6 +530,7 @@ def tgrid(t): annual_cycle_std_obs1_io[im, :] = np.array( genutil.statistics.std(obs1_io[im:324:12, :]) ) +print(annual_cycle_std_obs1_arctic) # Calculate the Obs RMS rms_arctic_obs1 = genutil.statistics.rms( @@ -548,7 +557,19 @@ def tgrid(t): rms_io_obs1 = genutil.statistics.rms( annual_cycle_obs1_io[:, 1], annual_cycle_obs1_io[:, 0], axis=0 ) - +import pickle +import sys + +data_dict = {"Arctic": rms_arctic_obs1.data.item(), + "Antarctic": rms_antarctic_obs1.data.item(), + "CA": rms_ca_obs1.data.item(), + "NA": rms_na_obs1.data.item(), + "NP": rms_np_obs1.data.item(), + "SA": rms_sa_obs1.data.item(), + "SP": rms_sp_obs1.data.item(), + "IO": rms_io_obs1.data.item()} +with open('obs_rms_data.pkl','wb') as handle: + pickle.dump(data_dict,handle) # CMIP5 Native grid var = "sic" @@ -578,7 +599,8 @@ def tgrid(t): # lines_d = ['bo-','g*-'] # cols_d=['b','g'] -flist = open("./cmip5_sic_names_all_xml_012413_conserv.asc") +flist = open("/home/ordonez4/seaice/cmip5_sic_names_all_xml_012413_conserv.asc") +#flist = open("./cmip5_sic_names_all_xml_012413_conserv.asc") # flist=open('./cmip5_sic_names_all_xml_012413_conserv_ccsm4.asc') # flist=open('./cmip5_sic_names_all_xml_121212_conserv.asc') # flist=open('./cmip5_sic_names_all_xml_121212_ncar.asc') @@ -586,8 +608,8 @@ def tgrid(t): # flist=open('./cmip5_sic_names_all_xml_102412_hadgem.asc') fnames = flist.readlines() - -glist = open("./cmip5_areacell_names_nc_012413_conserv.asc") +glist = open("/home/ordonez4/seaice/cmip5_areacell_names_nc_012413_conserv.asc") +#glist = open("./cmip5_areacell_names_nc_012413_conserv.asc") # glist=open('./cmip5_areacell_names_nc_012413_conserv_ccsm4.asc') # glist=open('./cmip5_areacell_names_nc.asc') # glist=open('./cmip5_areacell_names_all_xml_121212.asc') @@ -694,15 +716,23 @@ def tgrid(t): for run, ver in zip(runs, vers): nr = nr + 1 # Reading the sea ice concentration (sic) + #infile = ( + # "/work/cmip5/historical/seaIce/mo/sic/" + # + "cmip5." + # + mod + # + ".historical." + # + run + # + ".mo.seaIce.sic." + # + ver + # + ".xml" + #) infile = ( - "/work/cmip5/historical/seaIce/mo/sic/" + "/p/user_pub/pmp/pmp_results/pmp_v1.1.2/additional_xmls/latest/v20231104/cmip5/historical/seaIce/mon/sic/" + "cmip5." - + mod - + ".historical." - + run - + ".mo.seaIce.sic." - + ver - + ".xml" + + "historical." + + mod + "." + + run + "." + + "mon.sic.xml" ) print(infile) @@ -718,7 +748,7 @@ def tgrid(t): lon = sic.getLongitude() sic = MV.multiply(sic, factor2) - if MV.rank(lat) == 1: + if np.ndim(lat) == 1: tmp2d = f(var, time=slice(0, 1), squeeze=1) lats = MV.zeros(tmp2d.shape) for ii in range(0, len(lon)): @@ -726,7 +756,7 @@ def tgrid(t): else: lats = lat - if MV.rank(lon) == 1: + if np.ndim(lon) == 1: tmp2d = f(var, time=slice(0, 1), squeeze=1) lons = MV.zeros(tmp2d.shape) for ii in range(0, len(lat)): @@ -739,9 +769,9 @@ def tgrid(t): # Creating regional masks lons_a = MV.where(MV.greater(lons, 180.0), lons - 360, lons) lons_p = lons - print("CMIP5") - print("lons_na= ", MV.min(lons_a), MV.max(lons_a)) - print("lons_np= ", MV.min(lons_p), MV.max(lons_p)) + #print("CMIP5") + #print("lons_na= ", MV.min(lons_a), MV.max(lons_a)) + #print("lons_np= ", MV.min(lons_p), MV.max(lons_p)) mask_ca = MV.zeros(area.shape) mask_na = MV.zeros(area.shape) mask_np = MV.zeros(area.shape) @@ -949,14 +979,14 @@ def tgrid(t): ann_io_mma = ann_io_mma / nm [ni, nj] = std_arctic.shape -tta_std_arctic = MV.zeros([12, 324 / 12, len(mods)]) -tta_std_antarctic = MV.zeros([12, 324 / 12, len(mods)]) -tta_std_ca = MV.zeros([12, 324 / 12, len(mods)]) -tta_std_na = MV.zeros([12, 324 / 12, len(mods)]) -tta_std_np = MV.zeros([12, 324 / 12, len(mods)]) -tta_std_sa = MV.zeros([12, 324 / 12, len(mods)]) -tta_std_sp = MV.zeros([12, 324 / 12, len(mods)]) -tta_std_io = MV.zeros([12, 324 / 12, len(mods)]) +tta_std_arctic = MV.zeros([12, int(324 / 12), len(mods)]) +tta_std_antarctic = MV.zeros([12, int(324 / 12), len(mods)]) +tta_std_ca = MV.zeros([12, int(324 / 12), len(mods)]) +tta_std_na = MV.zeros([12, int(324 / 12), len(mods)]) +tta_std_np = MV.zeros([12, int(324 / 12), len(mods)]) +tta_std_sa = MV.zeros([12, int(324 / 12), len(mods)]) +tta_std_sp = MV.zeros([12, int(324 / 12), len(mods)]) +tta_std_io = MV.zeros([12, int(324 / 12), len(mods)]) for im in range(0, 12): tta_std_arctic[im, :, :] = std_arctic[im:ni:12, :] @@ -998,20 +1028,19 @@ def tgrid(t): # Bar Plots of the RMS labels = [ "ACCESS1-3", - "BNU-ESM", - "CCSM4", - "CESM1-BGC", - "CESM1-CAM5-1-FV2", - "CESM1-CAM5", - "CESM1-FASTCHEM", +# "BNU-ESM", +# "CCSM4", +# "CESM1-BGC", +# "CESM1-CAM5-1-FV2", +# "CESM1-CAM5", +# "CESM1-FASTCHEM", "CNRM-CM5", "CSIRO-Mk3-6-0", "CanCM4", "CanESM2", "GFDL-CM2p1", "GFDL-CM3", - "GFDL-ES\ -M2G", + "GFDL-ESM2G", "GFDL-ESM2M", "GISS-E2-H-CC", "GISS-E2-H", @@ -1024,30 +1053,29 @@ def tgrid(t): "IPSL-CM5A-MR", "IPSL-CM5B-LR", "MIROC-ESM-CHEM", - "MI\ -ROC-ESM", + "MIROC-ESM", "MIROC4h", "MIROC5", "MPI-ESM-LR", "MPI-ESM-MR", "MPI-ESM-P", "NorESM1-ME", - "bcc-csm1-1-m", - "bcc-csm1-1", +# "bcc-csm1-1-m", +# "bcc-csm1-1", ] # labels=["CCSM4","CESM1-BGC","CESM1-CAM5-1-FV2","CESM1-CAM5","CESM1-FASTCHEM","CNRM-CM5","CSIRO-Mk3-6-0","CanCM4","CanESM2","GFDL-CM3","GFDL-ES\ # M2G","GFDL-ESM2M","GISS-E2-H-CC","GISS-E2-H","GISS-E2-R","HadCM3","HadGEM2-CC","HadGEM2-ES","IPSL-CM5A-MR","IPSL-CM5B-LR","MIROC-ESM-CHEM","MI\ # ROC-ESM","MIROC4h","MIROC5","MPI-ESM-LR","MPI-ESM-MR","MPI-ESM-P","NorESM1-ME","bcc-csm1-1"] # labels=["HadCM3","HadGEM2-CC"] mlabels = np.append(labels, "RMS-Obs") -rms_arctic = np.append(rms_ann_arctic[0, :], rms_arctic_obs) -rms_antarctic = np.append(rms_ann_antarctic[0, :], rms_antarctic_obs) -rms_ca = np.append(rms_ann_ca[0, :], rms_ca_obs) -rms_na = np.append(rms_ann_na[0, :], rms_na_obs) -rms_np = np.append(rms_ann_np[0, :], rms_np_obs) -rms_sa = np.append(rms_ann_sa[0, :], rms_sa_obs) -rms_sp = np.append(rms_ann_sp[0, :], rms_sp_obs) -rms_io = np.append(rms_ann_io[0, :], rms_io_obs) +rms_arctic = np.append(rms_ann_arctic[0, :], rms_arctic_obs1) +rms_antarctic = np.append(rms_ann_antarctic[0, :], rms_antarctic_obs1) +rms_ca = np.append(rms_ann_ca[0, :], rms_ca_obs1) +rms_na = np.append(rms_ann_na[0, :], rms_na_obs1) +rms_np = np.append(rms_ann_np[0, :], rms_np_obs1) +rms_sa = np.append(rms_ann_sa[0, :], rms_sa_obs1) +rms_sp = np.append(rms_ann_sp[0, :], rms_sp_obs1) +rms_io = np.append(rms_ann_io[0, :], rms_io_obs1) ind = np.arange(len(mods)) # the x locations for the groups @@ -1055,138 +1083,40 @@ def tgrid(t): width = 0.3 n = len(ind) - 1 -# fig1 = plt.figure(1) -plt.subplot(411) -plt.bar(ind, rms_ann_arctic[0, :], width, color="r") -plt.hold -plt.bar(ind[n] + 2.5 * width, rms_arctic_obs, width, color="b") -plt.xticks(ind + width / 2.0, mlabels, rotation=20, size=8) -# plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) -# plt.text -plt.hold -# plt.ylim(0.,ymax) -plt.ylabel("RMS of Total Sea Ice Area, 10${^6}$km${^2}$") -# plt.title('Arctic') -plt.annotate("Arctic", (0.5, 0.9), xycoords="axes fraction", size=15) -plt.grid(True) -# plt.legend(mods_obs,bbox_to_anchor=(0.0, 0., 1, 1), bbox_transform=plt.gcf().transFigure) - -# fig2 = plt.figure(2) -plt.subplot(412) -plt.bar(ind, rms_ann_ca[0, :], width, color="r") -plt.bar(ind[n] + 2.5 * width, rms_ca_obs, width, color="b") -# plt.bar(ind+width,rms_ann_na[1,:],width,color='b') -plt.xticks(ind + width / 2.0, mlabels, rotation=20, size=8) -# plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) -plt.ylabel("RMS of Total Sea Ice Area, 10${^6}$km${^2}$") -# plt.title('Central Arctic Sector') -plt.annotate("Central Arctic Sector", (0.4, 0.9), xycoords="axes fraction", size=15) -plt.grid(True) -plt.hold - -# fig3 = plt.figure(3) -plt.subplot(413) -plt.bar(ind, rms_ann_na[0, :], width, color="r") -plt.bar(ind[n] + 2.5 * width, rms_na_obs, width, color="b") -# plt.bar(ind+width,rms_ann_na[1,:],width,color='b') -plt.xticks(ind + width / 2.0, mlabels, rotation=20, size=8) -# plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) -plt.ylabel("RMS of Total Sea Ice Area, 10${^6}$km${^2}$") -# plt.title('North Atlantic Arctic Sector') -plt.annotate( - "North Atlantic Arctic Sector", (0.4, 0.9), xycoords="axes fraction", size=15 -) -plt.grid(True) -plt.hold - -# fig4 = plt.figure(4) -plt.subplot(414) -plt.bar(ind, rms_ann_np[0, :], width, color="r") -# Plot the annual cycle -plt.bar(ind[n] + 2.5 * width, rms_np_obs, width, color="b") -# plt.bar(ind+width,rms_ann_np[1,:],width,color='b') -plt.xticks(ind + width / 2.0, mlabels, rotation=20, size=8) -# plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) -plt.hold -# plt.ylim(0.,ymax) -plt.ylabel("RMS of Total Sea Ice Area, 10${^6}$km${^2}$") -# plt.title('North Pacific Arctic Sector') -plt.annotate( - "North Pacific Arctic Sector", (0.4, 0.9), xycoords="axes fraction", size=15 -) -plt.grid(True) -# plt.legend(mods_obs,loc=(.55,0.65)) - -plt.show() - -# Antarctic -fig5 = plt.figure(5) -plt.subplot(411) -plt.bar(ind, rms_ann_antarctic[0, :], width, color="r") -plt.bar(ind[n] + 2.5 * width, rms_antarctic_obs, width, color="b") -# plt.bar(ind+width,rms_ann_antarctic[1,:],width,color='b') -plt.xticks(ind + width / 2.0, mlabels, rotation=20, size=8) -# plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) -plt.hold -# plt.ylim(0.,ymax) -plt.ylabel("RMS of Total Sea Ice Area, 10${^6}$km${^2}$") -# plt.title('Antarctic') -plt.annotate("Antarctic", (0.5, 0.9), xycoords="axes fraction", size=15) -# plt.legend(mods_obs,bbox_to_anchor=(0.0, 0., 1, 1), bbox_transform=plt.gcf().transFigure) -plt.grid(True) - -# fig6 = plt.figure(6) -plt.subplot(412) -plt.bar(ind, rms_ann_sa[0, :], width, color="r") -plt.bar(ind[n] + 2.5 * width, rms_sa_obs, width, color="b") -# plt.bar(ind+width,rms_ann_sa[1,:],width,color='b') -plt.xticks(ind + width / 2.0, mlabels, rotation=20, size=8) -# plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) -plt.hold -# plt.ylim(0.,ymax) -plt.ylabel("RMS of Total Sea Ice Area, 10${^6}$km${^2}$") -# plt.title('South Atlantic Antarctic Sector') -plt.annotate( - "South Atlantic Ocean Antarctic Sector", - (0.4, 0.9), - xycoords="axes fraction", - size=15, -) -plt.grid(True) - -# fig7 = plt.figure(7) -plt.subplot(413) -plt.bar(ind, rms_ann_sp[0, :], width, color="r") -plt.bar(ind[n] + 2.5 * width, rms_sp_obs, width, color="b") -# plt.bar(ind+width,rms_ann_sp[1,:],width,color='b') -plt.xticks(ind + width / 2.0, mlabels, rotation=20, size=8) -plt.hold -# plt.ylim(0.,ymax) -plt.ylabel("RMS of Total Sea Ice Area, 10${^6}$km${^2}$") -# plt.title('South Pacific Antarctic Sector') -plt.annotate( - "South Pacific Ocean Antarctic Sector", - (0.4, 0.9), - xycoords="axes fraction", - size=15, -) -# plt.legend(obs_mods,loc=(.05,0.65)) -plt.grid(True) - -# fig8 = plt.figure(8) -plt.subplot(414) -plt.bar(ind, rms_ann_io[0, :], width, color="r") -plt.bar(ind[n] + 2.5 * width, rms_io_obs, width, color="b") -# plt.bar(ind+width,rms_ann_io[1,:],width,color='b') -plt.xticks(ind + width / 2.0, mlabels, rotation=20, size=8) -# plt.xticks(ind[n]+3*width,mlabels[n+1],rotation=20) -plt.hold - -plt.ylabel("RMS of Total Sea Ice Area, 10${^6}$km${^2}$") -plt.annotate( - "South Indian Ocean Antarctic Sector", (0.4, 0.9), xycoords="axes fraction", size=15 -) -# plt.title('South Indian Ocean Antarctic Sector') -plt.grid(True) - -plt.show() +import pickle +import sys +with open('model_antarctic_plotting.pkl', 'wb') as handle: + pickle.dump(rms_ann_antarctic,handle) +with open('model_sp_plotting.pkl', 'wb') as handle: + pickle.dump(rms_ann_sp,handle) +with open('model_sa_plotting.pkl', 'wb') as handle: + pickle.dump(rms_ann_sa,handle) +with open('model_io_plotting.pkl', 'wb') as handle: + pickle.dump(rms_ann_io,handle) +with open('model_na_plotting.pkl', 'wb') as handle: + pickle.dump(rms_ann_na,handle) +with open('model_ca_plotting.pkl', 'wb') as handle: + pickle.dump(rms_ann_ca,handle) +with open('model_np_plotting.pkl', 'wb') as handle: + pickle.dump(rms_ann_np,handle) +sys.exit() +for sector in sector_list: + fig7 = plt.figure(7) + plt.subplot(413) + plt.bar(ind, rms_ann_sp[0, :], width, color="r") + plt.bar(ind[n] + 2.5 * width, rms_sp_obs1, width, color="b") + # plt.bar(ind+width,rms_ann_sp[1,:],width,color='b') + plt.xticks(ind + width / 2.0, mlabels, rotation=20, size=8) + #plt.hold + # plt.ylim(0.,ymax) + plt.ylabel("RMS of Total Sea Ice Area, 10${^6}$km${^2}$") + # plt.title('South Pacific Antarctic Sector') + plt.annotate( + "South Pacific Ocean Antarctic Sector", + (0.4, 0.9), + xycoords="axes fraction", + size=15, + ) + # plt.legend(obs_mods,loc=(.05,0.65)) + plt.grid(True) + fig7.savefig("fig7.png") From 9b3a27a38f126f19bd4f41f7781b58d212918170 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 30 Nov 2023 08:28:04 -0800 Subject: [PATCH 09/69] update regions --- .../sea_ice/ice_area_cmip5_ssmi_reg_rms.py | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py b/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py index a7a9c2f8a..bb938cb11 100755 --- a/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py +++ b/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py @@ -122,7 +122,7 @@ def tgrid(t): # NA region lat_bound = MV.logical_and( - MV.greater(lats_n, 35.0), MV.less_equal(lats_n, 80.0) + MV.greater(lats_n, 45.0), MV.less_equal(lats_n, 80.0) ) lon_bound = MV.logical_and( MV.greater(lons_a, -120.0), MV.less_equal(lons_a, 90.0) @@ -131,7 +131,7 @@ def tgrid(t): # NP region lat_bound = MV.logical_and( - MV.greater(lats_n, 35.0), MV.less_equal(lats_n, 65.0) + MV.greater(lats_n, 45.0), MV.less_equal(lats_n, 65.0) ) lon_bound = MV.logical_and( MV.greater(lons_p, 90.0), MV.less_equal(lons_p, 240.0) @@ -212,7 +212,7 @@ def tgrid(t): # Antarctic Regions lat_bound = MV.logical_and( - MV.greater(lats_s, -90.0), MV.less_equal(lats_s, -40.0) + MV.greater(lats_s, -90.0), MV.less_equal(lats_s, -55.0) ) # SA region @@ -343,6 +343,7 @@ def tgrid(t): # NSIDC-0192 + # SSM/I Arctic # Area #dlist_n = [ @@ -796,21 +797,21 @@ def tgrid(t): mask_ca = MV.where(MV.logical_or(reg1_ca, reg2_ca), 1, 0) # NA region - lat_bound = MV.logical_and(MV.greater(lats, 35.0), MV.less_equal(lats, 80.0)) + lat_bound = MV.logical_and(MV.greater(lats, 45.0), MV.less_equal(lats, 80.0)) lon_bound = MV.logical_and( MV.greater(lons_a, -120.0), MV.less_equal(lons_a, 90.0) ) mask_na = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) # NP region - lat_bound = MV.logical_and(MV.greater(lats, 35.0), MV.less_equal(lats, 65.0)) + lat_bound = MV.logical_and(MV.greater(lats, 45.0), MV.less_equal(lats, 65.0)) lon_bound = MV.logical_and( MV.greater(lons_p, 90.0), MV.less_equal(lons_p, 240.0) ) mask_np = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) # Antarctic Regions - lat_bound = MV.logical_and(MV.greater(lats, -90.0), MV.less_equal(lats, -40.0)) + lat_bound = MV.logical_and(MV.greater(lats, -90.0), MV.less_equal(lats, -55.0)) # SA region lon_bound = MV.logical_and( @@ -841,9 +842,9 @@ def tgrid(t): # arctic=MV.logical_and(MV.greater_equal(lats,35.),MV.less(lats,87.2)) # SSM/I limited to 87.2N arctic = MV.logical_and( - MV.greater_equal(lats, 35.0), MV.less(lats, 90.0) + MV.greater_equal(lats, 45.0), MV.less(lats, 90.0) ) # Adding currently in SSM/I 100% in the area >87.2N - antarctic = MV.logical_and(MV.greater_equal(lats, -90.0), MV.less(lats, -40.0)) + antarctic = MV.logical_and(MV.greater_equal(lats, -90.0), MV.less(lats, -55.0)) for nt in range(len(t)): aice_arctic = MV.where(MV.equal(arctic, True), ice_area[nt], 0.0) @@ -1087,6 +1088,8 @@ def tgrid(t): import sys with open('model_antarctic_plotting.pkl', 'wb') as handle: pickle.dump(rms_ann_antarctic,handle) +with open('model_arctic_plotting.pkl', 'wb') as handle: + pickle.dump(rms_ann_arctic,handle) with open('model_sp_plotting.pkl', 'wb') as handle: pickle.dump(rms_ann_sp,handle) with open('model_sa_plotting.pkl', 'wb') as handle: From 752d3436c130dd3bbdeee26a3c835b2d82982174 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Fri, 8 Dec 2023 10:28:45 -0800 Subject: [PATCH 10/69] add changes --- .../sea_ice/ice_area_cmip5_ssmi_reg_rms.py | 29 +++++++++++++------ 1 file changed, 20 insertions(+), 9 deletions(-) diff --git a/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py b/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py index bb938cb11..3b53bfb37 100755 --- a/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py +++ b/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py @@ -535,28 +535,28 @@ def tgrid(t): # Calculate the Obs RMS rms_arctic_obs1 = genutil.statistics.rms( - annual_cycle_obs1_arctic[:, 1], annual_cycle_obs1_arctic[:, 0], axis=0 + annual_cycle_obs_arctic[:, 1], annual_cycle_obs_arctic[:, 0], axis=0 ) rms_antarctic_obs1 = genutil.statistics.rms( - annual_cycle_obs1_antarctic[:, 1], annual_cycle_obs1_antarctic[:, 0], axis=0 + annual_cycle_obs_antarctic[:, 1], annual_cycle_obs_antarctic[:, 0], axis=0 ) rms_ca_obs1 = genutil.statistics.rms( - annual_cycle_obs1_ca[:, 1], annual_cycle_obs1_ca[:, 0], axis=0 + annual_cycle_obs_ca[:, 1], annual_cycle_obs_ca[:, 0], axis=0 ) rms_na_obs1 = genutil.statistics.rms( - annual_cycle_obs1_na[:, 1], annual_cycle_obs1_na[:, 0], axis=0 + annual_cycle_obs_na[:, 1], annual_cycle_obs_na[:, 0], axis=0 ) rms_np_obs1 = genutil.statistics.rms( - annual_cycle_obs1_np[:, 1], annual_cycle_obs1_np[:, 0], axis=0 + annual_cycle_obs_np[:, 1], annual_cycle_obs_np[:, 0], axis=0 ) rms_sa_obs1 = genutil.statistics.rms( - annual_cycle_obs1_sa[:, 1], annual_cycle_obs1_sa[:, 0], axis=0 + annual_cycle_obs_sa[:, 1], annual_cycle_obs_sa[:, 0], axis=0 ) rms_sp_obs1 = genutil.statistics.rms( - annual_cycle_obs1_sp[:, 1], annual_cycle_obs1_sp[:, 0], axis=0 + annual_cycle_obs_sp[:, 1], annual_cycle_obs_sp[:, 0], axis=0 ) rms_io_obs1 = genutil.statistics.rms( - annual_cycle_obs1_io[:, 1], annual_cycle_obs1_io[:, 0], axis=0 + annual_cycle_obs_io[:, 1], annual_cycle_obs_io[:, 0], axis=0 ) import pickle import sys @@ -846,6 +846,8 @@ def tgrid(t): ) # Adding currently in SSM/I 100% in the area >87.2N antarctic = MV.logical_and(MV.greater_equal(lats, -90.0), MV.less(lats, -55.0)) + + for nt in range(len(t)): aice_arctic = MV.where(MV.equal(arctic, True), ice_area[nt], 0.0) aice_antarctic = MV.where(MV.equal(antarctic, True), ice_area[nt], 0.0) @@ -863,6 +865,15 @@ def tgrid(t): total_area_sa[nt] = total_area_sa[nt] + MV.sum(area_sic_sa) total_area_sp[nt] = total_area_sp[nt] + MV.sum(area_sic_sp) total_area_io[nt] = total_area_io[nt] + MV.sum(area_sic_io) + #plt.pcolormesh(aice_arctic.data) + #plt.colorbar() + #plt.savefig("figs/"+mod+"_"+run+"_arctic.png") + #plt.close() + #plt.colorbar() + #plt.pcolormesh(area_sic_io.data) + #plt.savefig("figs/"+mod+"_"+run+"_io.png") + #plt.close() + #continue # print 'total_area_arctic= ',total_area_arctic[nt] # print 'total_area_na= ',total_area_na[nt] @@ -967,7 +978,7 @@ def tgrid(t): rms_ann_io[j, i] = genutil.statistics.rms( ann_io[:, i], annual_cycle_obs_io[:, j], axis=0 ) - +sys.exit() # CMIP5 MME ann_arctic_mma = ann_arctic_mma / nm From 6aac671ab2f092b8656cc4d435f6898241452066 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Fri, 8 Dec 2023 10:29:24 -0800 Subject: [PATCH 11/69] first draft --- pcmdi_metrics/sea_ice/ice_driver.py | 323 ++++++++++++++++++++++++ pcmdi_metrics/sea_ice/sea_ice_parser.py | 149 +++++++++++ 2 files changed, 472 insertions(+) create mode 100644 pcmdi_metrics/sea_ice/ice_driver.py create mode 100644 pcmdi_metrics/sea_ice/sea_ice_parser.py diff --git a/pcmdi_metrics/sea_ice/ice_driver.py b/pcmdi_metrics/sea_ice/ice_driver.py new file mode 100644 index 000000000..0562c748a --- /dev/null +++ b/pcmdi_metrics/sea_ice/ice_driver.py @@ -0,0 +1,323 @@ +import xarray as xr +import xcdat as xc +import numpy as np +import matplotlib.pyplot as plt +import glob +import json +import os + + +from pcmdi_metrics.mean_climate.lib import compute_statistics + +def rms_t(dm, do, var=None): + """Computes rms""" + if dm is None and do is None: # just want the doc + return { + "Name": "Temporal Mean Square Error", + "Abstract": "Compute Temporal Mean Square Error", + "Contact": "pcmdi-metrics@llnl.gov", + } + ds = dm.copy(deep=True) + ds["diff_square"] = ((dm[var] - do[var]) ** 2) / len(dm["time"]) + stat = ds["diff_square"].data + return stat + +def rms_model(dm, do, var=None): + """Computes rms""" + if dm is None and do is None: # just want the doc + return { + "Name": "Mean Square Error", + "Abstract": "Compute Mean Square Error", + "Contact": "pcmdi-metrics@llnl.gov", + } + ds = dm.copy(deep=True) + ds["diff_square"] = ((dm[var] - do[var]) ** 2) + stat = ds["diff_square"].data + return stat + +parser = create_sea_ice_parser.create_sea_ice_parser() +parameter = parser.get_parameter(argparse_vals_only=False) + +# Parameters +# I/O settings +case_id = parameter.case_id +model_list = parameter.test_data_set +realization = parameter.realization +variable_list = parameter.vars +filename_template = parameter.filename_template +test_data_path = parameter.test_data_path +reference_data_path = parameter.reference_data_path +reference_data_set = parameter.reference_data_set +reference_sftlf_template = parameter.reference_sftlf_template +metrics_output_path = parameter.metrics_output_path +area_template = parameter.grid_area +ModUnitsAdjust = parameter.ModUnitsAdjust +ObsUnitsAdjust = parameter.ObsUnitsAdjust +plots = parameter.plots +msyear = parameter.msyear +meyear = parameter.meyear +osyear = parameter.osyear +oeyear = parameter.oeyear + + +# Verifying output directory +metrics_output_path = utilities.verify_output_path(metrics_output_path, case_id) + +if isinstance(reference_data_set, list): + # Fix a command line issue + reference_data_set = reference_data_set[0] + +# Verify years +ok_mod = utilities.verify_years( + msyear, + meyear, + msg="Error: Model msyear and meyear must both be set or both be None (unset).", +) +ok_obs = utilities.verify_years( + osyear, + oeyear, + msg="Error: Obs osyear and oeyear must both be set or both be None (unset).", +) + +# Initialize output.json file +meta = metadata.MetadataFile(metrics_output_path) + +# Initialize other directories +nc_dir = os.path.join(metrics_output_path, "netcdf") +os.makedirs(nc_dir, exist_ok=True) +if plots: + plot_dir_maps = os.path.join(metrics_output_path, "plots", "maps") + os.makedirs(plot_dir_maps, exist_ok=True) + +# Setting up model realization list +find_all_realizations, realizations = utilities.set_up_realizations(realization) + +#### Do Obs part + +f_nt_n = "/home/ordonez4/seaice/data/icecon_ssmi_nt_n.nc" +f_nt_s = "/home/ordonez4/seaice/data/icecon_ssmi_nt_s.nc" +f_bt_n = "/home/ordonez4/seaice/data/icecon_ssmi_bt_n.nc" +f_bt_s = "/home/ordonez4/seaice/data/icecon_ssmi_bt_s.nc" + +obs = xc.open_dataset(f_nt_n) +# Get regions +data_arctic = obs[var].where(obs.lat > 0) +data_antarctic = obs[var].where(obs.lat < 0) +data_ca1 = obs[var].where(((obs.lat > 80) & (obs.lat <= 87.2) & (obs.lon > -120) & (obs.lon <= 90))) +data_ca2 = obs[var].where(((obs.lat > 65) & (obs.lat < 87.2)) & ((obs.lon > 90) | (obs.lon <= -120))) +data_ca = data_ca1 + data_ca2 +data_np = obs[var].where((obs.lat > 35) & (obs.lat <= 65) & ((obs.lon > 90) | (obs.lon <= -120))) +data_na = obs[var].data.where((obs.lat > 45) & (obs.lat <= 80) & (obs.lon > -120) & (obs.lon <= 90)) +data_na = data_na - data_na.where((obs.lat > 45) & (obs.lat <= 50) & (obs.lon > 30) & (obs.lon <= 60)) +data_sa = obs[var].where( + (obs.lat > -90) & (obs.lat <= -55) & + (obs.lon > -60) & (obs.lon <= 20)) +data_sp = obs[var].where( + (obs.lat > -90) & (obs.lat <= -55) & + ((obs.lon > 90) | (obs.lon <= -60))) +data_io = obs[var].where( + (obs.lat > -90) & (obs.lat <= -55) & + (obs.lon > 20) & (obs.lon <= 90)) +# Get ice extent TODO - convert area units? +total_extent_arctic_obs = (data_arctic.where(data_arctic > 15) * area).sum(skipna=True) +total_extent_antarctic_obs = (data_antarctic.where(data_antarctic > 15) * area).sum(skipna=True) +total_extent_ca_obs = (data_ca.where(data_ca > 15) * obs.area).sum(("lon","lat"),skipna=True) +total_extent_np_obs = (data_np.where(data_np > 15) * obs.area).sum(("lon","lat"),skipna=True) +total_extent_na_obs = (data_na.where(data_na > 15) * obs.area).sum(("lon","lat"),skipna=True) +total_extent_sa_obs = (data_sa.where(data_sa > 15) * obs.area).sum(("lon","lat"),skipna=True) +total_extent_sp_obs = (data_sp.where(data_sp > 15) * obs.area).sum(("lon","lat"),skipna=True) +total_extent_io_obs = (data_io.where(data_io > 15) * obs.area).sum(("lon","lat"),skipna=True) + +clim_arctic_obs = total_extent_arctic_obs.temporal.climatology(freq="month") +clim_antarctic_obs = total_area_antarctic_obs.temporal.climatology(freq="month") +clim_ca_obs = total_extent_ca_obs.temporal.climatology(freq="month") +clim_np_obs = total_extent_np_obs.temporal.climatology(freq="month") +clim_na_obs = total_extent_na_obs.temporal.climatology(freq="month") +clim_sa_obs = total_extent_sa_obs.temporal.climatology(freq="month") +clim_sp_obs = total_extent_sp_obs.temporal.climatology(freq="month") +clim_io_obs = total_extent_io_obs.temporal.climatology(freq="month") + +# Get climatology +# get errors for climo and mean + +#### Do model part +# Loop over models +for model in model_loop_list: + if find_all_realizations: + tags = {"%(model)": model, "%(model_version)": model, "%(realization)": "*"} + test_data_full_path = os.path.join(test_data_path, filename_template) + test_data_full_path = utilities.replace_multi(test_data_full_path, tags) + ncfiles = glob.glob(test_data_full_path) + realizations = [] + for ncfile in ncfiles: + realizations.append(ncfile.split("/")[-1].split(".")[3]) + print("=================================") + print("model, runs:", model, realizations) + list_of_runs = realizations + else: + list_of_runs = realizations + + metrics_dict["RESULTS"][model] = {} + + mean_extents = None + # Loop over realizations + for run in list_of_runs: + + # Get areacello + + if run == reference_data_set: + units_adjust = ObsUnitsAdjust + else: + units_adjust = ModUnitsAdjust + + metrics_dict["RESULTS"][model][run] = {} + + # Find model data, determine number of files, check if they exist + tags = { + "%(variable)": varname, + "%(model)": model, + "%(model_version)": model, + "%(realization)": run, + } + test_data_full_path = os.path.join(test_data_path, filename_template) + test_data_full_path = utilities.replace_multi(test_data_full_path, tags) + area_path = utilities.replace_multi(area_template,tags) + start_year = msyear + end_year = meyear + yrs = [str(start_year), str(end_year)] # for output file names + test_data_full_path = glob.glob(test_data_full_path) + test_data_full_path.sort() + if len(test_data_full_path) == 0: + print("") + print("-----------------------") + print("Not found: model, run, variable:", model, run, varname) + continue + else: + print("") + print("-----------------------") + print("model, run, variable:", model, run, varname) + print("test_data (model in this case) full_path:") + for t in test_data_full_path: + print(" ", t) + + # Load and prep data + ds = utilities.load_dataset(test_data_full_path) + area = utilities.load_dataset(area_path) #TODO: only once per model + + if any(ds.lon < -180) | any(ds.lon > 360): + print("Invalid longitude range") + continue + + # Get time slice if year parameters exist + if start_year is not None: + ds = utilities.slice_dataset(ds, start_year, end_year) + else: + # Get labels for start/end years from dataset + yrs = [str(int(ds.time.dt.year[0])), str(int(ds.time.dt.year[-1]))] + + # Compute climatologies at some point + + data_arctic = ds[var].where(ds.lat > 0) + data_antarctic = ds[var].where(ds.lat < 0) + data_ca1 = ds[var].where(((ds.lat > 80) & (ds.lat <= 87.2) & (ds.lon > -120) & (ds.lon <= 90))) + data_ca2 = ds[var].where(((ds.lat > 65) & (ds.lat < 87.2)) & ((ds.lon > 90) | (ds.lon <= -120))) + data_ca = data_ca1 + data_ca2 + data_np = ds[var].where((ds.lat > 35) & (ds.lat <= 65) & ((ds.lon > 90) | (ds.lon <= -120))) + data_na = ds[var].data.where((ds.lat > 45) & (ds.lat <= 80) & (ds.lon > -120) & (ds.lon <= 90)) + data_na = data_na - data_na.where((ds.lat > 45) & (ds.lat <= 50) & (ds.lon > 30) & (ds.lon <= 60)) + data_sa = ds[var].where( + (ds.lat > -90) & (ds.lat <= -55) & + (ds.lon > -60) & (ds.lon <= 20)) + data_sp = ds[var].where( + (ds.lat > -90) & (ds.lat <= -55) & + ((ds.lon > 90) | (ds.lon <= -60))) + data_io = ds[var].where( + (ds.lat > -90) & (ds.lat <= -55) & + (ds.lon > 20) & (ds.lon <= 90)) + + if mean_extents is None: + total_extent_arctic = np.zeros((len(list_of_runs),len(ds.time))) + total_extent_antarctic = np.zeros((len(list_of_runs),len(ds.time))) + total_extent_ca = np.zeros((len(list_of_runs),len(ds.time))) + total_extent_np = np.zeros((len(list_of_runs),len(ds.time))) + total_extent_na = np.zeros((len(list_of_runs),len(ds.time))) + total_extent_sa = np.zeros((len(list_of_runs),len(ds.time))) + total_extent_sp = np.zeros((len(list_of_runs),len(ds.time))) + total_extent_io = np.zeros((len(list_of_runs),len(ds.time))) + + total_extent_arctic[run_ind,:] = (data_arctic.where(data_arctic > 15) * area).sum(skipna=True) + total_extent_antarctic[run_ind,:] = (data_antarctic.where(data_antarctic > 15) * area).sum(skipna=True) + total_extent_ca[run_ind,:] = (data_ca.where(data_ca > 15) * area).sum(("lon","lat"),skipna=True) + total_extent_np[run_ind,:] = (data_np.where(data_np > 15) * area).sum(("lon","lat"),skipna=True) + total_extent_na[run_ind,:] = (data_na.where(data_na > 15) * area).sum(("lon","lat"),skipna=True) + total_extent_sa[run_ind,:] = (data_sa.where(data_sa > 15) * area).sum(("lon","lat"),skipna=True) + total_extent_sp[run_ind,:] = (data_sp.where(data_sp > 15) * area).sum(("lon","lat"),skipna=True) + total_extent_io[run_ind,:] = (data_io.where(data_io > 15) * area).sum(("lon","lat"),skipna=True) + + # Get average total over all realizations for this model + # Get annual cycle of model average tseries + # Get error compared to obs + total_extent_arctic = np.mean(total_extent_arctic,axis=0) + total_extent_antarctic = np.mean(total_extent_antarctic,axis=0) + total_extent_ca = np.mean(total_extent_ca,axis=0) + total_extent_np = np.mean(total_extent_np,axis=0) + total_extent_na = np.mean(total_extent_na,axis=0) + total_extent_sa = np.mean(total_extent_sa,axis=0) + total_extent_sp = np.mean(total_extent_sp,axis=0) + total_extent_io = np.mean(total_extent_io,axis=0) + + total_extent_arctic = ds.copy(data=total_extent_arctic).temporal.climatology(freq="month") + total_extent_antarctic = ds.copy(data=total_extent_antarctic).temporal.climatology(freq="month") + total_extent_ca = ds.copy(data=total_extent_ca).temporal.climatology(freq="month") + total_extent_np = ds.copy(data=total_extent_np).temporal.climatology(freq="month") + total_extent_na = ds.copy(data=total_extent_na).temporal.climatology(freq="month") + total_extent_sa = ds.copy(data=total_extent_sa).temporal.climatology(freq="month") + total_extent_sp = ds.copy(data=total_extent_sp).temporal.climatology(freq="month") + total_extent_io = ds.copy(data=total_extent_io).temporal.climatology(freq="month") + + # get RMS for model mean annual cycle + + metrics_tmp = metrics_dict.copy() + metrics_tmp["DIMENSIONS"]["model"] = model + metrics_tmp["DIMENSIONS"]["realization"] = list_of_runs + metrics_tmp["RESULTS"] = {model: metrics_dict["RESULTS"][model]} + metrics_path = "{0}_block_extremes_metrics.json".format(model) + utilities.write_to_json(metrics_output_path, metrics_path, metrics_tmp) + + meta.update_metrics( + model, + os.path.join(metrics_output_path, metrics_path), + model + " results", + "Seasonal metrics for block extrema for single dataset", + ) + + # reset for next model + mean_extents = None + +# Output single file with all models +model_write_list = model_loop_list.copy() +if "Reference" in model_write_list: + model_write_list.remove("Reference") +metrics_dict["DIMENSIONS"]["model"] = model_write_list +utilities.write_to_json( + metrics_output_path, "block_extremes_metrics.json", metrics_dict +) +fname = os.path.join(metrics_output_path, "block_extremes_metrics.json") +meta.update_metrics( + "All", fname, "All results", "Seasonal metrics for block extrema for all datasets" +) + + +# Update and write metadata file +try: + with open(fname, "r") as f: + tmp = json.load(f) + meta.update_provenance("environment", tmp["provenance"]) +except Exception: + # Skip provenance if there's an issue + print("Error: Could not get provenance from extremes json for output.json.") + +meta.update_provenance("modeldata", test_data_path) +if reference_data_path is not None: + meta.update_provenance("obsdata", reference_data_path) +meta.write() \ No newline at end of file diff --git a/pcmdi_metrics/sea_ice/sea_ice_parser.py b/pcmdi_metrics/sea_ice/sea_ice_parser.py new file mode 100644 index 000000000..b5617c8f5 --- /dev/null +++ b/pcmdi_metrics/sea_ice/sea_ice_parser.py @@ -0,0 +1,149 @@ +#!/usr/bin/env python +from pcmdi_metrics.mean_climate.lib import pmp_parser + + +def create_sea_ice_parser(): + parser = pmp_parser.PMPMetricsParser() + parser.add_argument( + "--case_id", + dest="case_id", + help="Defines a subdirectory to the metrics output, so multiple" + + "cases can be compared", + required=False, + ) + + parser.add_argument( + "-v", + "--vars", + type=str, + nargs="+", + dest="vars", + help="Variables to use", + required=False, + ) + + parser.add_argument( + "-r", + "--reference_data_set", + default=None, + type=str, + nargs="+", + dest="reference_data_set", + help="List of observations or models that are used as a " + + "reference against the test_data_set", + required=False, + ) + + parser.add_argument( + "--reference_data_path", + default=None, + dest="reference_data_path", + help="Path for the reference climitologies", + required=False, + ) + + parser.add_argument( + "-t", + "--test_data_set", + type=str, + nargs="+", + dest="test_data_set", + help="List of observations or models to test " + + "against the reference_data_set", + required=False, + ) + + parser.add_argument( + "--test_data_path", + dest="test_data_path", + help="Path for the test climitologies", + required=False, + ) + + parser.add_argument( + "--realization", + dest="realization", + help="A simulation parameter", + required=False, + ) + + parser.add_argument( + "--filename_template", + dest="filename_template", + help="Template for climatology files", + required=False, + ) + + parser.add_argument( + "--metrics_output_path", + dest="metrics_output_path", + default=None, + help="Directory of where to put the results", + required=False, + ) + + parser.add_argument( + "--filename_output_template", + dest="filename_output_template", + help="Filename for the interpolated test climatologies", + required=False, + ) + + parser.add_argument( + "--grid_area", + des="areacell", + help="Filename template for grid area", + required=True + ) + + parser.add_argument( + "--output_json_template", + help="Filename template for results json files", + required=False, + ) + + parser.add_argument( + "--debug", + dest="debug", + action="store_true", + help="Turn on debugging mode by printing more information to track progress", + required=False, + ) + parser.add_argument( + "--plots", + action="store_true", + help="Set to True to generate figures.", + required=False, + ) + parser.add_argument( + "--osyear", dest="osyear", type=int, help="Start year for reference data set" + ) + parser.add_argument( + "--msyear", dest="msyear", type=int, help="Start year for model data set" + ) + parser.add_argument( + "--oeyear", dest="oeyear", type=int, help="End year for reference data set" + ) + parser.add_argument( + "--meyear", dest="meyear", type=int, help="End year for model data set" + ) + parser.add_argument( + "--ObsUnitsAdjust", + type=tuple, + default=(False, 0, 0, None), + help="For unit adjust for OBS dataset. For example:\n" + "- (True, 'divide', 100.0, 'hPa') # Pa to hPa\n" + "- (True, 'subtract', 273.15, 'C') # degK to degC\n" + "- (False, 0, 0, None) # No adjustment (default)", + ) + parser.add_argument( + "--ModUnitsAdjust", + type=tuple, + default=(False, 0, 0, None), + help="For unit adjust for model dataset. For example:\n" + "- (True, 'divide', 100.0, 'hPa') # Pa to hPa\n" + "- (True, 'subtract', 273.15, 'C') # degK to degC\n" + "- (False, 0, 0, None) # No adjustment (default)", + ) + + return parser From 809882f8ef8e71cf8c150f8761152eb6dad8269f Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Mon, 18 Dec 2023 09:16:04 -0800 Subject: [PATCH 12/69] changes --- pcmdi_metrics/sea_ice/ice_driver.py | 265 ++++++++++++++++++---------- 1 file changed, 175 insertions(+), 90 deletions(-) diff --git a/pcmdi_metrics/sea_ice/ice_driver.py b/pcmdi_metrics/sea_ice/ice_driver.py index 0562c748a..cb83d80a7 100644 --- a/pcmdi_metrics/sea_ice/ice_driver.py +++ b/pcmdi_metrics/sea_ice/ice_driver.py @@ -9,7 +9,7 @@ from pcmdi_metrics.mean_climate.lib import compute_statistics -def rms_t(dm, do, var=None): +def mse_t(dm, do, var=None): """Computes rms""" if dm is None and do is None: # just want the doc return { @@ -18,11 +18,11 @@ def rms_t(dm, do, var=None): "Contact": "pcmdi-metrics@llnl.gov", } ds = dm.copy(deep=True) - ds["diff_square"] = ((dm[var] - do[var]) ** 2) / len(dm["time"]) + ds["diff_square"] = np.sum((dm[var] - do[var]) ** 2) / len(dm["time"]) stat = ds["diff_square"].data return stat -def rms_model(dm, do, var=None): +def mse_model(dm, do, var=None): """Computes rms""" if dm is None and do is None: # just want the doc return { @@ -31,10 +31,19 @@ def rms_model(dm, do, var=None): "Contact": "pcmdi-metrics@llnl.gov", } ds = dm.copy(deep=True) - ds["diff_square"] = ((dm[var] - do[var]) ** 2) + if var is not None: + ds["diff_square"] = ((dm[var] - do[var]) ** 2) + else: + ds["diff_square"] = ((dm - do) ** 2) stat = ds["diff_square"].data return stat +def to_ice_con_ds(da,obs): + ds = xr.Dataset(data_vars={"ice_con": da, + "time_bnds": obs.time_bnds}, + coords = {"time": obs.time}) + return ds + parser = create_sea_ice_parser.create_sea_ice_parser() parameter = parser.get_parameter(argparse_vals_only=False) @@ -43,7 +52,7 @@ def rms_model(dm, do, var=None): case_id = parameter.case_id model_list = parameter.test_data_set realization = parameter.realization -variable_list = parameter.vars +varname = parameter.vars filename_template = parameter.filename_template test_data_path = parameter.test_data_path reference_data_path = parameter.reference_data_path @@ -51,9 +60,10 @@ def rms_model(dm, do, var=None): reference_sftlf_template = parameter.reference_sftlf_template metrics_output_path = parameter.metrics_output_path area_template = parameter.grid_area +area_var = parameter.area_var ModUnitsAdjust = parameter.ModUnitsAdjust ObsUnitsAdjust = parameter.ObsUnitsAdjust -plots = parameter.plots +#plots = parameter.plots msyear = parameter.msyear meyear = parameter.meyear osyear = parameter.osyear @@ -99,54 +109,101 @@ def rms_model(dm, do, var=None): f_bt_n = "/home/ordonez4/seaice/data/icecon_ssmi_bt_n.nc" f_bt_s = "/home/ordonez4/seaice/data/icecon_ssmi_bt_s.nc" -obs = xc.open_dataset(f_nt_n) -# Get regions -data_arctic = obs[var].where(obs.lat > 0) -data_antarctic = obs[var].where(obs.lat < 0) -data_ca1 = obs[var].where(((obs.lat > 80) & (obs.lat <= 87.2) & (obs.lon > -120) & (obs.lon <= 90))) -data_ca2 = obs[var].where(((obs.lat > 65) & (obs.lat < 87.2)) & ((obs.lon > 90) | (obs.lon <= -120))) -data_ca = data_ca1 + data_ca2 -data_np = obs[var].where((obs.lat > 35) & (obs.lat <= 65) & ((obs.lon > 90) | (obs.lon <= -120))) -data_na = obs[var].data.where((obs.lat > 45) & (obs.lat <= 80) & (obs.lon > -120) & (obs.lon <= 90)) -data_na = data_na - data_na.where((obs.lat > 45) & (obs.lat <= 50) & (obs.lon > 30) & (obs.lon <= 60)) -data_sa = obs[var].where( - (obs.lat > -90) & (obs.lat <= -55) & - (obs.lon > -60) & (obs.lon <= 20)) -data_sp = obs[var].where( - (obs.lat > -90) & (obs.lat <= -55) & - ((obs.lon > 90) | (obs.lon <= -60))) -data_io = obs[var].where( - (obs.lat > -90) & (obs.lat <= -55) & - (obs.lon > 20) & (obs.lon <= 90)) -# Get ice extent TODO - convert area units? -total_extent_arctic_obs = (data_arctic.where(data_arctic > 15) * area).sum(skipna=True) -total_extent_antarctic_obs = (data_antarctic.where(data_antarctic > 15) * area).sum(skipna=True) -total_extent_ca_obs = (data_ca.where(data_ca > 15) * obs.area).sum(("lon","lat"),skipna=True) -total_extent_np_obs = (data_np.where(data_np > 15) * obs.area).sum(("lon","lat"),skipna=True) -total_extent_na_obs = (data_na.where(data_na > 15) * obs.area).sum(("lon","lat"),skipna=True) -total_extent_sa_obs = (data_sa.where(data_sa > 15) * obs.area).sum(("lon","lat"),skipna=True) -total_extent_sp_obs = (data_sp.where(data_sp > 15) * obs.area).sum(("lon","lat"),skipna=True) -total_extent_io_obs = (data_io.where(data_io > 15) * obs.area).sum(("lon","lat"),skipna=True) - -clim_arctic_obs = total_extent_arctic_obs.temporal.climatology(freq="month") -clim_antarctic_obs = total_area_antarctic_obs.temporal.climatology(freq="month") -clim_ca_obs = total_extent_ca_obs.temporal.climatology(freq="month") -clim_np_obs = total_extent_np_obs.temporal.climatology(freq="month") -clim_na_obs = total_extent_na_obs.temporal.climatology(freq="month") -clim_sa_obs = total_extent_sa_obs.temporal.climatology(freq="month") -clim_sp_obs = total_extent_sp_obs.temporal.climatology(freq="month") -clim_io_obs = total_extent_io_obs.temporal.climatology(freq="month") +arctic_clims = {} +arctic_means = {} +arctic_files = {"nt": f_nt_n, "bt": f_bt_n} +for source in arctic_files: + obs = xc.open_dataset(arctic_files[source]) + obs["ice_con"] = obs["ice_con"] * 0.01 + obs["area"] = obs["area"] * 1e-6 + # Get regions + data_arctic = obs[var].where(obs.lat > 0) + data_ca1 = obs[var].where(((obs.lat > 80) & (obs.lat <= 87.2) & (obs.lon > -120) & (obs.lon <= 90))) + data_ca2 = obs[var].where(((obs.lat > 65) & (obs.lat < 87.2)) & ((obs.lon > 90) | (obs.lon <= -120))) + data_ca = obs[var].where((data_ca1 > 0) | (data_ca2 > 0)) + data_np = obs[var].where((obs.lat > 35) & (obs.lat <= 65) & ((obs.lon > 90) | (obs.lon <= -120))) + data_na = obs[var].where((obs.lat > 45) & (obs.lat <= 80) & (obs.lon > -120) & (obs.lon <= 90)) + + # Get ice extent TODO - convert area units? + total_extent_arctic_obs = (data_arctic.where(data_arctic > 15) * obs.area).sum(("x","y"),skipna=True) + total_extent_ca_obs = (data_ca.where(data_ca > 0.15) * obs.area).sum(("x","y"),skipna=True) + total_extent_np_obs = (data_np.where(data_np > 0.15) * obs.area).sum(("x","y"),skipna=True) + total_extent_na_obs = (data_na.where(data_na > 0.15) * obs.area).sum(("x","y"),skipna=True) + + clim_arctic_obs = to_ice_con_ds(total_extent_arctic_obs,obs).temporal.climatology(var,freq="month") + clim_ca_obs = to_ice_con_ds(total_extent_ca_obs,obs).temporal.climatology(var,freq="month") + clim_np_obs = to_ice_con_ds(total_extent_np_obs,obs).temporal.climatology(var,freq="month") + clim_na_obs = to_ice_con_ds(total_extent_na_obs,obs).temporal.climatology(var,freq="month") + + arctic_clims[source] = { + "arctic": clim_arctic_obs, + "ca": clim_ca_obs, + "np": clim_np_obs, + "na": clim_na_obs + } + + arctic_means[source] = { + "arctic": total_extent_arctic_obs.mean("time"), + "ca": total_extent_ca_obs.mean("time"), + "np": total_extent_np_obs.mean("time"), + "na": total_extent_na_obs.mean("time") + } + obs.close() + +antarctic_clims = {} +antarctic_means = {} +antarctic_files = {"nt": f_nt_s, "bt": f_bt_s} +for source in antarctic_files: + obs = xc.open_dataset(antarctic_files[source]) + obs["ice_con"] = obs["ice_con"] * 0.01 + obs["area"] = obs["area"] * 1e-6 + data_antarctic = obs[var].where(obs.lat < 0) + data_sa = obs[var].where( + (obs.lat > -90) & (obs.lat <= -55) & + (obs.lon > -60) & (obs.lon <= 20)) + data_sp = obs[var].where( + (obs.lat > -90) & (obs.lat <= -55) & + ((obs.lon > 90) | (obs.lon <= -60))) + data_io = obs[var].where( + (obs.lat > -90) & (obs.lat <= -55) & + (obs.lon > 20) & (obs.lon <= 90)) + + total_extent_antarctic_obs = (data_antarctic.where(data_antarctic > 15) * obs.area).sum(("x","y"),skipna=True) + total_extent_sa_obs = (data_sa.where(data_sa > 0.15) * obs.area).sum(("x","y"),skipna=True) + total_extent_sp_obs = (data_sp.where(data_sp > 0.15) * obs.area).sum(("x","y"),skipna=True) + total_extent_io_obs = (data_io.where(data_io > 0.15) * obs.area).sum(("x","y"),skipna=True) + + clim_antarctic_obs = to_ice_con_ds(total_extent_antarctic_obs,obs).temporal.climatology(var,freq="month") + clim_sa_obs = to_ice_con_ds(total_extent_sa_obs,obs).temporal.climatology(var,freq="month") + clim_sp_obs = to_ice_con_ds(total_extent_sp_obs,obs).temporal.climatology(var,freq="month") + clim_io_obs = to_ice_con_ds(total_extent_io_obs,obs).temporal.climatology(var,freq="month") + + antarctic_clims[source] = { + "antarctic": clim_antarctic_obs, + "io": clim_io_obs, + "sp": clim_sp_obs, + "sa": clim_sa_obs + } + + antarctic_means[source] = { + "antarctic": total_extent_antarctic_obs.mean("time"), + "io": total_extent_io_obs.mean("time"), + "sp": total_extent_sp_obs.mean("time"), + "sa": total_extent_sa_obs.mean("time") + } + obs.close() # Get climatology # get errors for climo and mean #### Do model part # Loop over models + for model in model_loop_list: if find_all_realizations: tags = {"%(model)": model, "%(model_version)": model, "%(realization)": "*"} test_data_full_path = os.path.join(test_data_path, filename_template) - test_data_full_path = utilities.replace_multi(test_data_full_path, tags) + test_data_full_path = replace_multi(test_data_full_path, tags) ncfiles = glob.glob(test_data_full_path) realizations = [] for ncfile in ncfiles: @@ -161,7 +218,7 @@ def rms_model(dm, do, var=None): mean_extents = None # Loop over realizations - for run in list_of_runs: + for run_ind,run in enumerate(list_of_runs): # Get areacello @@ -180,8 +237,8 @@ def rms_model(dm, do, var=None): "%(realization)": run, } test_data_full_path = os.path.join(test_data_path, filename_template) - test_data_full_path = utilities.replace_multi(test_data_full_path, tags) - area_path = utilities.replace_multi(area_template,tags) + test_data_full_path = replace_multi(test_data_full_path, tags) + area_path = replace_multi(area_template,tags) start_year = msyear end_year = meyear yrs = [str(start_year), str(end_year)] # for output file names @@ -201,16 +258,22 @@ def rms_model(dm, do, var=None): print(" ", t) # Load and prep data - ds = utilities.load_dataset(test_data_full_path) - area = utilities.load_dataset(area_path) #TODO: only once per model + ds = load_dataset(test_data_full_path) + area = load_dataset(area_path) #TODO: only once per model + area[areaname] = area[areaname] * area_units_adjust + + xvar = get_longitude(ds).name + yvar = get_latitude(ds).name - if any(ds.lon < -180) | any(ds.lon > 360): + if (ds.lon < -180).any() | (ds.lon > 360).any(): print("Invalid longitude range") continue + if ds.lon.min() > -1: + ds["lon"] = ds.lon - 180 # Get time slice if year parameters exist if start_year is not None: - ds = utilities.slice_dataset(ds, start_year, end_year) + ds = slice_dataset(ds, start_year, end_year) else: # Get labels for start/end years from dataset yrs = [str(int(ds.time.dt.year[0])), str(int(ds.time.dt.year[-1]))] @@ -219,12 +282,12 @@ def rms_model(dm, do, var=None): data_arctic = ds[var].where(ds.lat > 0) data_antarctic = ds[var].where(ds.lat < 0) - data_ca1 = ds[var].where(((ds.lat > 80) & (ds.lat <= 87.2) & (ds.lon > -120) & (ds.lon <= 90))) - data_ca2 = ds[var].where(((ds.lat > 65) & (ds.lat < 87.2)) & ((ds.lon > 90) | (ds.lon <= -120))) + data_ca1 = ds[var].where(((ds.lat > 80) & (ds.lat <= 87.2) & (ds.lon > -120) & (ds.lon <= 90)),0) + data_ca2 = ds[var].where(((ds.lat > 65) & (ds.lat < 87.2)) & ((ds.lon > 90) | (ds.lon <= -120)),0) data_ca = data_ca1 + data_ca2 - data_np = ds[var].where((ds.lat > 35) & (ds.lat <= 65) & ((ds.lon > 90) | (ds.lon <= -120))) - data_na = ds[var].data.where((ds.lat > 45) & (ds.lat <= 80) & (ds.lon > -120) & (ds.lon <= 90)) - data_na = data_na - data_na.where((ds.lat > 45) & (ds.lat <= 50) & (ds.lon > 30) & (ds.lon <= 60)) + data_np = ds[var].where((ds.lat > 35) & (ds.lat <= 65) & ((ds.lon > 90) | (ds.lon <= -120)),0) + data_na = ds[var].where((ds.lat > 45) & (ds.lat <= 80) & (ds.lon > -120) & (ds.lon <= 90),0) + data_na = data_na - data_na.where((ds.lat > 45) & (ds.lat <= 50) & (ds.lon > 30) & (ds.lon <= 60),0) data_sa = ds[var].where( (ds.lat > -90) & (ds.lat <= -55) & (ds.lon > -60) & (ds.lon <= 20)) @@ -236,44 +299,66 @@ def rms_model(dm, do, var=None): (ds.lon > 20) & (ds.lon <= 90)) if mean_extents is None: - total_extent_arctic = np.zeros((len(list_of_runs),len(ds.time))) - total_extent_antarctic = np.zeros((len(list_of_runs),len(ds.time))) - total_extent_ca = np.zeros((len(list_of_runs),len(ds.time))) - total_extent_np = np.zeros((len(list_of_runs),len(ds.time))) - total_extent_na = np.zeros((len(list_of_runs),len(ds.time))) - total_extent_sa = np.zeros((len(list_of_runs),len(ds.time))) - total_extent_sp = np.zeros((len(list_of_runs),len(ds.time))) - total_extent_io = np.zeros((len(list_of_runs),len(ds.time))) - - total_extent_arctic[run_ind,:] = (data_arctic.where(data_arctic > 15) * area).sum(skipna=True) - total_extent_antarctic[run_ind,:] = (data_antarctic.where(data_antarctic > 15) * area).sum(skipna=True) - total_extent_ca[run_ind,:] = (data_ca.where(data_ca > 15) * area).sum(("lon","lat"),skipna=True) - total_extent_np[run_ind,:] = (data_np.where(data_np > 15) * area).sum(("lon","lat"),skipna=True) - total_extent_na[run_ind,:] = (data_na.where(data_na > 15) * area).sum(("lon","lat"),skipna=True) - total_extent_sa[run_ind,:] = (data_sa.where(data_sa > 15) * area).sum(("lon","lat"),skipna=True) - total_extent_sp[run_ind,:] = (data_sp.where(data_sp > 15) * area).sum(("lon","lat"),skipna=True) - total_extent_io[run_ind,:] = (data_io.where(data_io > 15) * area).sum(("lon","lat"),skipna=True) + total_extent_arctic = (data_arctic.where(data_arctic > 15)/100 * area[areaname]).sum((xvar,yvar),skipna=True) + total_extent_antarctic = (data_antarctic.where(data_antarctic > 15)/100 * area[areaname]).sum((xvar,yvar),skipna=True) + total_extent_ca = (data_ca.where(data_ca > 15)/100 * area[areaname]).sum((xvar,yvar),skipna=True) + total_extent_np = (data_np.where(data_np > 15)/100 * area[areaname]).sum((xvar,yvar),skipna=True) + total_extent_na = (data_na.where(data_na > 15)/100 * area[areaname]).sum((xvar,yvar),skipna=True) + total_extent_sa = (data_sa.where(data_sa > 15)/100 * area[areaname]).sum((xvar,yvar),skipna=True) + total_extent_sp = (data_sp.where(data_sp > 15)/100 * area[areaname]).sum((xvar,yvar),skipna=True) + total_extent_io = (data_io.where(data_io > 15)/100 * area[areaname]).sum((xvar,yvar),skipna=True) + else: + total_extent_arctic = total_extent_arctic + (data_arctic.where(data_arctic > 15) * area[areaname]).sum((xvar,yvar),skipna=True) + total_extent_antarctic = total_extent_antarctic + (data_antarctic.where(data_antarctic > 15) * area[areaname]).sum((xvar,yvar),skipna=True) + total_extent_ca = total_extent_ca + (data_ca.where(data_ca > 15) * area[areaname]).sum((xvar,yvar),skipna=True) + total_extent_np = total_extent_np + (data_np.where(data_np > 15) * area[areaname]).sum((xvar,yvar),skipna=True) + total_extent_na = total_extent_na + (data_na.where(data_na > 15) * area[areaname]).sum((xvar,yvar),skipna=True) + total_extent_sa = total_extent_sa + (data_sa.where(data_sa > 15) * area[areaname]).sum((xvar,yvar),skipna=True) + total_extent_sp = total_extent_sp + (data_sp.where(data_sp > 15) * area[areaname]).sum((xvar,yvar),skipna=True) + total_extent_io = total_extent_io + (data_io.where(data_io > 15) * area[areaname]).sum((xvar,yvar),skipna=True) # Get average total over all realizations for this model # Get annual cycle of model average tseries # Get error compared to obs - total_extent_arctic = np.mean(total_extent_arctic,axis=0) - total_extent_antarctic = np.mean(total_extent_antarctic,axis=0) - total_extent_ca = np.mean(total_extent_ca,axis=0) - total_extent_np = np.mean(total_extent_np,axis=0) - total_extent_na = np.mean(total_extent_na,axis=0) - total_extent_sa = np.mean(total_extent_sa,axis=0) - total_extent_sp = np.mean(total_extent_sp,axis=0) - total_extent_io = np.mean(total_extent_io,axis=0) - - total_extent_arctic = ds.copy(data=total_extent_arctic).temporal.climatology(freq="month") - total_extent_antarctic = ds.copy(data=total_extent_antarctic).temporal.climatology(freq="month") - total_extent_ca = ds.copy(data=total_extent_ca).temporal.climatology(freq="month") - total_extent_np = ds.copy(data=total_extent_np).temporal.climatology(freq="month") - total_extent_na = ds.copy(data=total_extent_na).temporal.climatology(freq="month") - total_extent_sa = ds.copy(data=total_extent_sa).temporal.climatology(freq="month") - total_extent_sp = ds.copy(data=total_extent_sp).temporal.climatology(freq="month") - total_extent_io = ds.copy(data=total_extent_io).temporal.climatology(freq="month") + + # TODO: compute only for dask arrays + total_extent_arctic = (total_extent_arctic / len(list_of_runs)).compute().to_dataset(name=varname) + total_extent_antarctic = (total_extent_antarctic / len(list_of_runs)).compute().to_dataset(name=varname) + total_extent_ca = (total_extent_ca / len(list_of_runs)).compute().to_dataset(name=varname) + total_extent_np = (total_extent_np / len(list_of_runs)).compute().to_dataset(name=varname) + total_extent_na = (total_extent_na / len(list_of_runs)).compute().to_dataset(name=varname) + total_extent_sa = (total_extent_sa / len(list_of_runs)).compute().to_dataset(name=varname) + total_extent_sp = (total_extent_sp / len(list_of_runs)).compute().to_dataset(name=varname) + total_extent_io = (total_extent_io / len(list_of_runs)).compute().to_dataset(name=varname) + + total_extent_arctic.time.attrs.pop("bounds") + total_extent_antarctic.time.attrs.pop("bounds") + total_extent_ca.time.attrs.pop("bounds") + total_extent_np.time.attrs.pop("bounds") + total_extent_na.time.attrs.pop("bounds") + total_extent_sa.time.attrs.pop("bounds") + total_extent_sp.time.attrs.pop("bounds") + total_extent_io.time.attrs.pop("bounds") + + total_extent_arctic = total_extent_arctic.bounds.add_missing_bounds() + total_extent_antarctic = total_extent_antarctic.bounds.add_missing_bounds() + total_extent_ca = total_extent_ca.bounds.add_missing_bounds() + total_extent_np = total_extent_np.bounds.add_missing_bounds() + total_extent_na = total_extent_na.bounds.add_missing_bounds() + total_extent_sa = total_extent_sa.bounds.add_missing_bounds() + total_extent_sp = total_extent_sp.bounds.add_missing_bounds() + total_extent_io = total_extent_io.bounds.add_missing_bounds() + + clim_extent_arctic = total_extent_arctic.temporal.climatology("ice_con",freq=varname) + clim_extent_antarctic = total_extent_antarctic.temporal.climatology("ice_con",freq=varname) + clim_extent_ca = total_extent_ca.temporal.climatology(varname,freq="month") + clim_extent_np = total_extent_np.temporal.climatology(varname,freq="month") + clim_extent_na = total_extent_na.temporal.climatology(varname,freq="month") + clim_extent_sa = total_extent_sa.temporal.climatology(varname,freq="month") + clim_extent_sp = total_extent_sp.temporal.climatology(varname,freq="month") + clim_extent_io = total_extent_io.temporal.climatology(varname,freq="month") + + # get RMS for model mean annual cycle From 1d4b6546671a7cbc3cc996d55c55b01db604461d Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Wed, 20 Dec 2023 16:42:04 -0800 Subject: [PATCH 13/69] more updates --- pcmdi_metrics/sea_ice/ice_driver.py | 962 +++++++++++++++--------- pcmdi_metrics/sea_ice/sea_ice_parser.py | 57 +- 2 files changed, 641 insertions(+), 378 deletions(-) diff --git a/pcmdi_metrics/sea_ice/ice_driver.py b/pcmdi_metrics/sea_ice/ice_driver.py index cb83d80a7..5544f43cc 100644 --- a/pcmdi_metrics/sea_ice/ice_driver.py +++ b/pcmdi_metrics/sea_ice/ice_driver.py @@ -5,404 +5,644 @@ import glob import json import os +import datetime +import cftime +import dask - +from sea_ice_parser import create_sea_ice_parser from pcmdi_metrics.mean_climate.lib import compute_statistics +from pcmdi_metrics.io import xcdat_openxml +from pcmdi_metrics.io.base import Base + +class MetadataFile: + # This class organizes the contents for the CMEC + # metadata file called output.json, which describes + # the other files in the output bundle. + + def __init__(self, metrics_output_path): + self.outfile = os.path.join(metrics_output_path, "output.json") + self.json = { + "provenance": { + "environment": "", + "modeldata": "", + "obsdata": "", + "log": "", + }, + "metrics": {}, + "data": {}, + "plots": {}, + } + + def update_metrics(self, kw, filename, longname, desc): + tmp = {"filename": filename, "longname": longname, "description": desc} + self.json["metrics"].update({kw: tmp}) + return + + def update_data(self, kw, filename, longname, desc): + tmp = {"filename": filename, "longname": longname, "description": desc} + self.json["data"].update({kw: tmp}) + return + + def update_plots(self, kw, filename, longname, desc): + tmp = {"filename": filename, "longname": longname, "description": desc} + self.json["plots"].update({kw: tmp}) + + def update_provenance(self, kw, data): + self.json["provenance"].update({kw: data}) + return -def mse_t(dm, do, var=None): - """Computes rms""" + def update_index(self, val): + self.json["index"] = val + return + + def write(self): + with open(self.outfile, "w") as f: + json.dump(self.json, f, indent=4) + +def mse_t(dm, do, weights=None): + """Computes mse""" if dm is None and do is None: # just want the doc return { "Name": "Temporal Mean Square Error", "Abstract": "Compute Temporal Mean Square Error", "Contact": "pcmdi-metrics@llnl.gov", } - ds = dm.copy(deep=True) - ds["diff_square"] = np.sum((dm[var] - do[var]) ** 2) / len(dm["time"]) - stat = ds["diff_square"].data + if weights is None: + stat = np.sum(((dm.data - do.data) ** 2)) / len(dm, axis=0) + else: + stat = np.sum(((dm.data - do.data) ** 2) * weights, axis=0) + if isinstance(stat,dask.array.core.Array): + stat = stat.compute() return stat def mse_model(dm, do, var=None): - """Computes rms""" + """Computes mse""" if dm is None and do is None: # just want the doc return { "Name": "Mean Square Error", "Abstract": "Compute Mean Square Error", "Contact": "pcmdi-metrics@llnl.gov", } - ds = dm.copy(deep=True) - if var is not None: - ds["diff_square"] = ((dm[var] - do[var]) ** 2) - else: - ds["diff_square"] = ((dm - do) ** 2) - stat = ds["diff_square"].data + if var is not None: # dataset + stat = ((dm[var].data - do[var].data) ** 2) + else: # dataarray + stat = ((dm - do) ** 2) + if isinstance(stat,dask.array.core.Array): + stat = stat.compute() return stat + +def get_longitude(ds: xr.Dataset) -> xr.DataArray: + key_lon = xc.axis.get_dim_keys(ds, axis="X") + lon = ds[key_lon] + return(lon) + + +def get_latitude(ds: xr.Dataset) -> xr.DataArray: + key_lat = xc.axis.get_dim_keys(ds, axis="Y") + lat = ds[key_lat] + return(lat) + +def slice_dataset(ds, start_year, end_year): + cal = ds.time.encoding["calendar"] + start_time = cftime.datetime(start_year, 1, 1, calendar=cal) - datetime.timedelta( + days=0 + ) + end_time = cftime.datetime(end_year + 1, 1, 1, calendar=cal) - datetime.timedelta( + days=1 + ) + ds = ds.sel(time=slice(start_time, end_time)) + return ds + def to_ice_con_ds(da,obs): ds = xr.Dataset(data_vars={"ice_con": da, "time_bnds": obs.time_bnds}, coords = {"time": obs.time}) return ds -parser = create_sea_ice_parser.create_sea_ice_parser() -parameter = parser.get_parameter(argparse_vals_only=False) - -# Parameters -# I/O settings -case_id = parameter.case_id -model_list = parameter.test_data_set -realization = parameter.realization -varname = parameter.vars -filename_template = parameter.filename_template -test_data_path = parameter.test_data_path -reference_data_path = parameter.reference_data_path -reference_data_set = parameter.reference_data_set -reference_sftlf_template = parameter.reference_sftlf_template -metrics_output_path = parameter.metrics_output_path -area_template = parameter.grid_area -area_var = parameter.area_var -ModUnitsAdjust = parameter.ModUnitsAdjust -ObsUnitsAdjust = parameter.ObsUnitsAdjust -#plots = parameter.plots -msyear = parameter.msyear -meyear = parameter.meyear -osyear = parameter.osyear -oeyear = parameter.oeyear - - -# Verifying output directory -metrics_output_path = utilities.verify_output_path(metrics_output_path, case_id) - -if isinstance(reference_data_set, list): - # Fix a command line issue - reference_data_set = reference_data_set[0] - -# Verify years -ok_mod = utilities.verify_years( - msyear, - meyear, - msg="Error: Model msyear and meyear must both be set or both be None (unset).", -) -ok_obs = utilities.verify_years( - osyear, - oeyear, - msg="Error: Obs osyear and oeyear must both be set or both be None (unset).", -) - -# Initialize output.json file -meta = metadata.MetadataFile(metrics_output_path) - -# Initialize other directories -nc_dir = os.path.join(metrics_output_path, "netcdf") -os.makedirs(nc_dir, exist_ok=True) -if plots: - plot_dir_maps = os.path.join(metrics_output_path, "plots", "maps") - os.makedirs(plot_dir_maps, exist_ok=True) - -# Setting up model realization list -find_all_realizations, realizations = utilities.set_up_realizations(realization) - -#### Do Obs part - -f_nt_n = "/home/ordonez4/seaice/data/icecon_ssmi_nt_n.nc" -f_nt_s = "/home/ordonez4/seaice/data/icecon_ssmi_nt_s.nc" -f_bt_n = "/home/ordonez4/seaice/data/icecon_ssmi_bt_n.nc" -f_bt_s = "/home/ordonez4/seaice/data/icecon_ssmi_bt_s.nc" - -arctic_clims = {} -arctic_means = {} -arctic_files = {"nt": f_nt_n, "bt": f_bt_n} -for source in arctic_files: - obs = xc.open_dataset(arctic_files[source]) - obs["ice_con"] = obs["ice_con"] * 0.01 - obs["area"] = obs["area"] * 1e-6 - # Get regions - data_arctic = obs[var].where(obs.lat > 0) - data_ca1 = obs[var].where(((obs.lat > 80) & (obs.lat <= 87.2) & (obs.lon > -120) & (obs.lon <= 90))) - data_ca2 = obs[var].where(((obs.lat > 65) & (obs.lat < 87.2)) & ((obs.lon > 90) | (obs.lon <= -120))) - data_ca = obs[var].where((data_ca1 > 0) | (data_ca2 > 0)) - data_np = obs[var].where((obs.lat > 35) & (obs.lat <= 65) & ((obs.lon > 90) | (obs.lon <= -120))) - data_na = obs[var].where((obs.lat > 45) & (obs.lat <= 80) & (obs.lon > -120) & (obs.lon <= 90)) - - # Get ice extent TODO - convert area units? - total_extent_arctic_obs = (data_arctic.where(data_arctic > 15) * obs.area).sum(("x","y"),skipna=True) - total_extent_ca_obs = (data_ca.where(data_ca > 0.15) * obs.area).sum(("x","y"),skipna=True) - total_extent_np_obs = (data_np.where(data_np > 0.15) * obs.area).sum(("x","y"),skipna=True) - total_extent_na_obs = (data_na.where(data_na > 0.15) * obs.area).sum(("x","y"),skipna=True) - - clim_arctic_obs = to_ice_con_ds(total_extent_arctic_obs,obs).temporal.climatology(var,freq="month") - clim_ca_obs = to_ice_con_ds(total_extent_ca_obs,obs).temporal.climatology(var,freq="month") - clim_np_obs = to_ice_con_ds(total_extent_np_obs,obs).temporal.climatology(var,freq="month") - clim_na_obs = to_ice_con_ds(total_extent_na_obs,obs).temporal.climatology(var,freq="month") - - arctic_clims[source] = { - "arctic": clim_arctic_obs, - "ca": clim_ca_obs, - "np": clim_np_obs, - "na": clim_na_obs - } - - arctic_means[source] = { - "arctic": total_extent_arctic_obs.mean("time"), - "ca": total_extent_ca_obs.mean("time"), - "np": total_extent_np_obs.mean("time"), - "na": total_extent_na_obs.mean("time") - } - obs.close() - -antarctic_clims = {} -antarctic_means = {} -antarctic_files = {"nt": f_nt_s, "bt": f_bt_s} -for source in antarctic_files: - obs = xc.open_dataset(antarctic_files[source]) - obs["ice_con"] = obs["ice_con"] * 0.01 - obs["area"] = obs["area"] * 1e-6 - data_antarctic = obs[var].where(obs.lat < 0) - data_sa = obs[var].where( - (obs.lat > -90) & (obs.lat <= -55) & - (obs.lon > -60) & (obs.lon <= 20)) - data_sp = obs[var].where( - (obs.lat > -90) & (obs.lat <= -55) & - ((obs.lon > 90) | (obs.lon <= -60))) - data_io = obs[var].where( - (obs.lat > -90) & (obs.lat <= -55) & - (obs.lon > 20) & (obs.lon <= 90)) - - total_extent_antarctic_obs = (data_antarctic.where(data_antarctic > 15) * obs.area).sum(("x","y"),skipna=True) - total_extent_sa_obs = (data_sa.where(data_sa > 0.15) * obs.area).sum(("x","y"),skipna=True) - total_extent_sp_obs = (data_sp.where(data_sp > 0.15) * obs.area).sum(("x","y"),skipna=True) - total_extent_io_obs = (data_io.where(data_io > 0.15) * obs.area).sum(("x","y"),skipna=True) - - clim_antarctic_obs = to_ice_con_ds(total_extent_antarctic_obs,obs).temporal.climatology(var,freq="month") - clim_sa_obs = to_ice_con_ds(total_extent_sa_obs,obs).temporal.climatology(var,freq="month") - clim_sp_obs = to_ice_con_ds(total_extent_sp_obs,obs).temporal.climatology(var,freq="month") - clim_io_obs = to_ice_con_ds(total_extent_io_obs,obs).temporal.climatology(var,freq="month") - - antarctic_clims[source] = { - "antarctic": clim_antarctic_obs, - "io": clim_io_obs, - "sp": clim_sp_obs, - "sa": clim_sa_obs - } +def adjust_units(ds,adjust_tuple): + action_dict = { + "multiply": "*", + "divide": "/", + "add": "+", + "subtract": "-"} + if adjust_tuple[0]: + print("Converting units by ",adjust_tuple[1],adjust_tuple[2]) + cmd = " ".join(["ds",str(action_dict[adjust_tuple[1]]),str(adjust_tuple[2])]) + ds = eval(cmd) + return ds - antarctic_means[source] = { - "antarctic": total_extent_antarctic_obs.mean("time"), - "io": total_extent_io_obs.mean("time"), - "sp": total_extent_sp_obs.mean("time"), - "sa": total_extent_sa_obs.mean("time") - } - obs.close() - -# Get climatology -# get errors for climo and mean - -#### Do model part -# Loop over models - -for model in model_loop_list: - if find_all_realizations: - tags = {"%(model)": model, "%(model_version)": model, "%(realization)": "*"} - test_data_full_path = os.path.join(test_data_path, filename_template) - test_data_full_path = replace_multi(test_data_full_path, tags) - ncfiles = glob.glob(test_data_full_path) - realizations = [] - for ncfile in ncfiles: - realizations.append(ncfile.split("/")[-1].split(".")[3]) - print("=================================") - print("model, runs:", model, realizations) - list_of_runs = realizations +def verify_output_path(metrics_output_path, case_id): + if metrics_output_path is None: + metrics_output_path = datetime.datetime.now().strftime("v%Y%m%d") + if case_id is not None: + metrics_output_path = metrics_output_path.replace("%(case_id)", case_id) + if not os.path.exists(metrics_output_path): + print("\nMetrics output path not found.") + print("Creating metrics output directory", metrics_output_path) + try: + os.makedirs(metrics_output_path) + except Exception as e: + print("\nError: Could not create metrics output path", metrics_output_path) + print(e) + print("Exiting.") + sys.exit() + return metrics_output_path + +def verify_years(start_year, end_year, msg="Error: Invalid start or end year"): + if start_year is None and end_year is None: + return + elif start_year is None or end_year is None: + # If only one of the two is set, exit. + print(msg) + print("Exiting") + sys.exit() + +def set_up_realizations(realization): + find_all_realizations = False + if realization is None: + realization = "" + realizations = [realization] + elif isinstance(realization, str): + if realization.lower() in ["all", "*"]: + find_all_realizations = True + else: + realizations = [realization] + elif isinstance(realization, list): + realizations = realization + + return find_all_realizations, realizations + +def load_dataset(filepath): + # Load an xarray dataset from the given filepath. + # If list of netcdf files, opens mfdataset. + # If list of xmls, open last file in list. + if filepath[-1].endswith(".xml"): + # Final item of sorted list would have most recent version date + ds = xcdat_openxml.xcdat_openxml(filepath[-1]) + elif len(filepath) > 1: + ds = xc.open_mfdataset(filepath, chunks=None) else: - list_of_runs = realizations + ds = xc.open_dataset(filepath[0]) + return ds - metrics_dict["RESULTS"][model] = {} +def replace_multi(string, rdict): + # Replace multiple keyworks in a string template + # based on key-value pairs in 'rdict'. + for k in rdict.keys(): + string = string.replace(k, rdict[k]) + return string + +if __name__ == "__main__": + + parser = create_sea_ice_parser() + parameter = parser.get_parameter(argparse_vals_only=False) + + # Parameters + # I/O settings + case_id = parameter.case_id + realization = parameter.realization + var = parameter.var + filename_template = parameter.filename_template + test_data_path = parameter.test_data_path + model_list = parameter.test_data_set + reference_data_path = parameter.reference_data_path + reference_data_set = parameter.reference_data_set + metrics_output_path = parameter.metrics_output_path + area_template = parameter.area_template + area_var = parameter.area_var + AreaUnitsAdjust = parameter.AreaUnitsAdjust + ModUnitsAdjust = parameter.ModUnitsAdjust + ObsUnitsAdjust = parameter.ObsUnitsAdjust + #plots = parameter.plots + msyear = parameter.msyear + meyear = parameter.meyear + osyear = parameter.osyear + oeyear = parameter.oeyear + + print(model_list) + model_list.sort() + # Verifying output directory + metrics_output_path = verify_output_path(metrics_output_path, case_id) + + if isinstance(reference_data_set, list): + # Fix a command line issue + reference_data_set = reference_data_set[0] + + # Verify years + ok_mod = verify_years( + msyear, + meyear, + msg="Error: Model msyear and meyear must both be set or both be None (unset).", + ) + ok_obs = verify_years( + osyear, + oeyear, + msg="Error: Obs osyear and oeyear must both be set or both be None (unset).", + ) - mean_extents = None - # Loop over realizations - for run_ind,run in enumerate(list_of_runs): + # Initialize output.json file + meta = MetadataFile(metrics_output_path) + + # Initialize other directories + nc_dir = os.path.join(metrics_output_path, "netcdf") + os.makedirs(nc_dir, exist_ok=True) + #if plots: + # plot_dir_maps = os.path.join(metrics_output_path, "plots", "maps") + # os.makedirs(plot_dir_maps, exist_ok=True) + + # Setting up model realization list + find_all_realizations, realizations = set_up_realizations(realization) + + #### Do Obs part + ObsUnitsAdjust=(True,"multiply",1e-2) + ObsAreaUnitsAdjust=(False,0,0) + + f_nt_n = "/home/ordonez4/seaice/data/icecon_ssmi_nt_n_edited.nc" + f_nt_s = "/home/ordonez4/seaice/data/icecon_ssmi_nt_s_edited.nc" + f_bt_n = "/home/ordonez4/seaice/data/icecon_ssmi_bt_n_edited.nc" + f_bt_s = "/home/ordonez4/seaice/data/icecon_ssmi_bt_s_edited.nc" + + arctic_clims = {} + arctic_means = {} + arctic_files = {"nt": f_nt_n, "bt": f_bt_n} + obs_var="ice_con" + + print("OBS: Arctic") + for source in arctic_files: + obs = xc.open_dataset(arctic_files[source]) + obs[obs_var] = adjust_units(obs[obs_var],ObsUnitsAdjust) + obs["area"] = adjust_units(obs["area"],ObsAreaUnitsAdjust) + # Get regions + data_arctic = obs[obs_var].where((obs.lat > 0),0) + data_ca1 = obs[obs_var].where(((obs.lat > 80) & (obs.lat <= 87.2) & (obs.lon > -120) & (obs.lon <= 90)),0) + data_ca2 = obs[obs_var].where(((obs.lat > 65) & (obs.lat < 87.2)) & ((obs.lon > 90) | (obs.lon <= -120)),0) + data_ca = obs[obs_var].where((data_ca1 > 0) | (data_ca2 > 0),0) + data_np = obs[obs_var].where((obs.lat > 35) & (obs.lat <= 65) & ((obs.lon > 90) | (obs.lon <= -120)),0) + data_na = obs[obs_var].where((obs.lat > 45) & (obs.lat <= 80) & (obs.lon > -120) & (obs.lon <= 90),0) + data_na = data_na - data_na.where((obs.lat > 45) & (obs.lat <= 50) & (obs.lon > 30) & (obs.lon <= 60),0) + + # Get ice extent + total_extent_arctic_obs = (data_arctic.where(data_arctic > 0.15) * obs.area).sum(("x","y"),skipna=True) + total_extent_ca_obs = (data_ca.where(data_ca > 0.15) * obs.area).sum(("x","y"),skipna=True) + total_extent_np_obs = (data_np.where(data_np > 0.15) * obs.area).sum(("x","y"),skipna=True) + total_extent_na_obs = (data_na.where(data_na > 0.15) * obs.area).sum(("x","y"),skipna=True) + + clim_arctic_obs = to_ice_con_ds(total_extent_arctic_obs,obs).temporal.climatology(obs_var,freq="month") + clim_ca_obs = to_ice_con_ds(total_extent_ca_obs,obs).temporal.climatology(obs_var,freq="month") + clim_np_obs = to_ice_con_ds(total_extent_np_obs,obs).temporal.climatology(obs_var,freq="month") + clim_na_obs = to_ice_con_ds(total_extent_na_obs,obs).temporal.climatology(obs_var,freq="month") + + arctic_clims[source] = { + "arctic": clim_arctic_obs, + "ca": clim_ca_obs, + "np": clim_np_obs, + "na": clim_na_obs + } - # Get areacello + arctic_means[source] = { + "arctic": total_extent_arctic_obs.mean("time",skipna=True).data.item(), + "ca": total_extent_ca_obs.mean("time",skipna=True).data.item(), + "np": total_extent_np_obs.mean("time",skipna=True).data.item(), + "na": total_extent_na_obs.mean("time",skipna=True).data.item() + } + obs.close() + + antarctic_clims = {} + antarctic_means = {} + antarctic_files = {"nt": f_nt_s, "bt": f_bt_s} + print("OBS: Antarctic") + for source in antarctic_files: + obs = xc.open_dataset(antarctic_files[source]) + obs[obs_var] = adjust_units(obs[obs_var],ObsUnitsAdjust) + obs["area"] = adjust_units(obs["area"],ObsAreaUnitsAdjust) + data_antarctic = obs[obs_var].where((obs.lat < 0),0) + data_sa = obs[obs_var].where( + (obs.lat > -90) & (obs.lat <= -55) & + (obs.lon > -60) & (obs.lon <= 20),0) + data_sp = obs[obs_var].where( + (obs.lat > -90) & (obs.lat <= -55) & + ((obs.lon > 90) | (obs.lon <= -60)),0) + data_io = obs[obs_var].where( + (obs.lat > -90) & (obs.lat <= -55) & + (obs.lon > 20) & (obs.lon <= 90),0) + + total_extent_antarctic_obs = (data_antarctic.where(data_antarctic > 0.15) * obs.area).sum(("x","y"),skipna=True) + total_extent_sa_obs = (data_sa.where(data_sa > 0.15) * obs.area).sum(("x","y"),skipna=True) + total_extent_sp_obs = (data_sp.where(data_sp > 0.15) * obs.area).sum(("x","y"),skipna=True) + total_extent_io_obs = (data_io.where(data_io > 0.15) * obs.area).sum(("x","y"),skipna=True) + + clim_antarctic_obs = to_ice_con_ds(total_extent_antarctic_obs,obs).temporal.climatology(obs_var,freq="month") + clim_sa_obs = to_ice_con_ds(total_extent_sa_obs,obs).temporal.climatology(obs_var,freq="month") + clim_sp_obs = to_ice_con_ds(total_extent_sp_obs,obs).temporal.climatology(obs_var,freq="month") + clim_io_obs = to_ice_con_ds(total_extent_io_obs,obs).temporal.climatology(obs_var,freq="month") + + antarctic_clims[source] = { + "antarctic": clim_antarctic_obs, + "io": clim_io_obs, + "sp": clim_sp_obs, + "sa": clim_sa_obs + } - if run == reference_data_set: - units_adjust = ObsUnitsAdjust - else: - units_adjust = ModUnitsAdjust + antarctic_means[source] = { + "antarctic": total_extent_antarctic_obs.mean("time",skipna=True).data.item(), + "io": total_extent_io_obs.mean("time",skipna=True).data.item(), + "sp": total_extent_sp_obs.mean("time",skipna=True).data.item(), + "sa": total_extent_sa_obs.mean("time",skipna=True).data.item() + } + obs.close() + + obs_clims = {"nt": {},"bt":{}} + obs_means = {"nt": {},"bt":{}} + for item in antarctic_clims["nt"]: + obs_clims["nt"][item] = antarctic_clims["nt"][item] + obs_means["nt"][item] = antarctic_means["nt"][item] + obs_clims["bt"][item] = antarctic_clims["bt"][item] + obs_means["bt"][item] = antarctic_means["bt"][item] + for item in arctic_clims["nt"]: + obs_clims["nt"][item] = arctic_clims["nt"][item] + obs_means["nt"][item] = arctic_means["nt"][item] + obs_clims["bt"][item] = arctic_clims["bt"][item] + obs_means["bt"][item] = arctic_means["bt"][item] + + # Get climatology + # get errors for climo and mean + + #### Do model part + # Loop over models + + clim_wts = [31., 28., 31., 30., 31., 30., 31., 31., 30., 31., 30., 31.] + clim_wts = [x/365 for x in clim_wts] + mse = {} + metrics = { + "DIMENSIONS": { + "json_structure": [ + "model", + "obs", + "region", + "index", + "statistic", + ], + "region": {}, + "index": { + "monthly_clim": "Monthly climatology of extent", + "total_extent": "Sum of ice coverage where concentration > 15%" + }, + "statistic": { + "mse": "Mean Square Error (10^12 km^4)" + }, + "model": model_list, + }, + "RESULTS": {}, + } - metrics_dict["RESULTS"][model][run] = {} + find_all_realizations, realizations = set_up_realizations(realization) - # Find model data, determine number of files, check if they exist + for model in model_list: tags = { - "%(variable)": varname, + "%(variable)": var, "%(model)": model, - "%(model_version)": model, - "%(realization)": run, - } - test_data_full_path = os.path.join(test_data_path, filename_template) - test_data_full_path = replace_multi(test_data_full_path, tags) - area_path = replace_multi(area_template,tags) - start_year = msyear - end_year = meyear - yrs = [str(start_year), str(end_year)] # for output file names - test_data_full_path = glob.glob(test_data_full_path) - test_data_full_path.sort() - if len(test_data_full_path) == 0: - print("") - print("-----------------------") - print("Not found: model, run, variable:", model, run, varname) - continue + "%(model_version)": model + } + if find_all_realizations: + test_data_full_path = os.path.join(test_data_path, filename_template) + test_data_full_path = replace_multi(test_data_full_path, tags) + ncfiles = glob.glob(test_data_full_path) + realizations = [] + for ncfile in ncfiles: + realizations.append(ncfile.split("/")[-1].split(".")[3]) + print("=================================") + print("model, runs:", model, realizations) + list_of_runs = realizations else: - print("") - print("-----------------------") - print("model, run, variable:", model, run, varname) - print("test_data (model in this case) full_path:") - for t in test_data_full_path: - print(" ", t) - - # Load and prep data - ds = load_dataset(test_data_full_path) - area = load_dataset(area_path) #TODO: only once per model - area[areaname] = area[areaname] * area_units_adjust + list_of_runs = realizations + + # Model grid area + area = load_dataset(replace_multi(area_template,tags)) + area[area_var] = adjust_units(area[area_var],AreaUnitsAdjust) + + totals_dict = {"arctic": 0, + "ca": 0, + "na": 0, + "np": 0, + "antarctic": 0, + "sp": 0, + "sa": 0, + "io": 0} + mse[model] = { + "nasateam": {}, + "bootstrap": {} + } - xvar = get_longitude(ds).name - yvar = get_latitude(ds).name - - if (ds.lon < -180).any() | (ds.lon > 360).any(): - print("Invalid longitude range") - continue - if ds.lon.min() > -1: - ds["lon"] = ds.lon - 180 - - # Get time slice if year parameters exist - if start_year is not None: - ds = slice_dataset(ds, start_year, end_year) - else: - # Get labels for start/end years from dataset - yrs = [str(int(ds.time.dt.year[0])), str(int(ds.time.dt.year[-1]))] + # Loop over realizations + for run_ind,run in enumerate(list_of_runs): + + # Find model data, determine number of files, check if they exist + tags = { + "%(variable)": var, + "%(model)": model, + "%(model_version)": model, + "%(realization)": run, + } + test_data_full_path = os.path.join(test_data_path, filename_template) + test_data_full_path = replace_multi(test_data_full_path, tags) + area_path = replace_multi(area_template,tags) + start_year = msyear + end_year = meyear + yrs = [str(start_year), str(end_year)] # for output file names + test_data_full_path = glob.glob(test_data_full_path) + test_data_full_path.sort() + if len(test_data_full_path) == 0: + print("") + print("-----------------------") + print("Not found: model, run, variable:", model, run, var) + continue + else: + print("") + print("-----------------------") + print("model, run, variable:", model, run, var) + print("test_data (model in this case) full_path:") + for t in test_data_full_path: + print(" ", t) + + # Load and prep data + ds = load_dataset(test_data_full_path) + ds[var] = adjust_units(ds[var],ModUnitsAdjust) + xvar = get_longitude(ds).name + yvar = get_latitude(ds).name + if (ds.lon < -180).any() | (ds.lon > 360).any(): + print("Invalid longitude range") + continue + if ds.lon.min() >= 0: + ds["lon"] = ds.lon - 180 + + # Get time slice if year parameters exist + if start_year is not None: + ds = slice_dataset(ds, start_year, end_year) + else: + # Get labels for start/end years from dataset + yrs = [str(int(ds.time.dt.year[0])), str(int(ds.time.dt.year[-1]))] + + # Get regions + data_arctic = ds[var].where(ds.lat > 0, 0) + data_antarctic = ds[var].where(ds.lat < 0, 0) + data_ca1 = ds[var].where(( + (ds.lat > 80) & + (ds.lat <= 87.2) & + (ds.lon > -120) & + (ds.lon <= 90)),0) + data_ca2 = ds[var].where( + ((ds.lat > 65) & (ds.lat < 87.2)) & + ((ds.lon > 90) | (ds.lon <= -120)),0) + data_ca = data_ca1 + data_ca2 + data_np = ds[var].where( + (ds.lat > 35) & + (ds.lat <= 65) & + ((ds.lon > 90) | (ds.lon <= -120)),0) + data_na = ds[var].where( + (ds.lat > 45) & + (ds.lat <= 80) & + (ds.lon > -120) & + (ds.lon <= 90),0) + data_na = data_na - data_na.where( + (ds.lat > 45) & + (ds.lat <= 50) & + (ds.lon > 30) & + (ds.lon <= 60),0) + data_sa = ds[var].where( + (ds.lat > -90) & (ds.lat <= -55) & + (ds.lon > -60) & (ds.lon <= 20)) + data_sp = ds[var].where( + (ds.lat > -90) & (ds.lat <= -55) & + ((ds.lon > 90) | (ds.lon <= -60))) + data_io = ds[var].where( + (ds.lat > -90) & (ds.lat <= -55) & + (ds.lon > 20) & (ds.lon <= 90)) + + regions_dict = { + "arctic": data_arctic, + "antarctic": data_antarctic, + "ca": data_ca, + "np": data_np, + "na": data_na, + "sa": data_sa, + "sp": data_sp, + "io": data_io + } + + # Running sum of all realizations + for rgn in regions_dict: + data = regions_dict[rgn] + totals_dict[rgn] = totals_dict[rgn] + (data.where(data > 0.15, 0) * area[area_var]).sum((xvar,yvar),skipna=True) + + ds.close() - # Compute climatologies at some point - - data_arctic = ds[var].where(ds.lat > 0) - data_antarctic = ds[var].where(ds.lat < 0) - data_ca1 = ds[var].where(((ds.lat > 80) & (ds.lat <= 87.2) & (ds.lon > -120) & (ds.lon <= 90)),0) - data_ca2 = ds[var].where(((ds.lat > 65) & (ds.lat < 87.2)) & ((ds.lon > 90) | (ds.lon <= -120)),0) - data_ca = data_ca1 + data_ca2 - data_np = ds[var].where((ds.lat > 35) & (ds.lat <= 65) & ((ds.lon > 90) | (ds.lon <= -120)),0) - data_na = ds[var].where((ds.lat > 45) & (ds.lat <= 80) & (ds.lon > -120) & (ds.lon <= 90),0) - data_na = data_na - data_na.where((ds.lat > 45) & (ds.lat <= 50) & (ds.lon > 30) & (ds.lon <= 60),0) - data_sa = ds[var].where( - (ds.lat > -90) & (ds.lat <= -55) & - (ds.lon > -60) & (ds.lon <= 20)) - data_sp = ds[var].where( - (ds.lat > -90) & (ds.lat <= -55) & - ((ds.lon > 90) | (ds.lon <= -60))) - data_io = ds[var].where( - (ds.lat > -90) & (ds.lat <= -55) & - (ds.lon > 20) & (ds.lon <= 90)) - - if mean_extents is None: - total_extent_arctic = (data_arctic.where(data_arctic > 15)/100 * area[areaname]).sum((xvar,yvar),skipna=True) - total_extent_antarctic = (data_antarctic.where(data_antarctic > 15)/100 * area[areaname]).sum((xvar,yvar),skipna=True) - total_extent_ca = (data_ca.where(data_ca > 15)/100 * area[areaname]).sum((xvar,yvar),skipna=True) - total_extent_np = (data_np.where(data_np > 15)/100 * area[areaname]).sum((xvar,yvar),skipna=True) - total_extent_na = (data_na.where(data_na > 15)/100 * area[areaname]).sum((xvar,yvar),skipna=True) - total_extent_sa = (data_sa.where(data_sa > 15)/100 * area[areaname]).sum((xvar,yvar),skipna=True) - total_extent_sp = (data_sp.where(data_sp > 15)/100 * area[areaname]).sum((xvar,yvar),skipna=True) - total_extent_io = (data_io.where(data_io > 15)/100 * area[areaname]).sum((xvar,yvar),skipna=True) - else: - total_extent_arctic = total_extent_arctic + (data_arctic.where(data_arctic > 15) * area[areaname]).sum((xvar,yvar),skipna=True) - total_extent_antarctic = total_extent_antarctic + (data_antarctic.where(data_antarctic > 15) * area[areaname]).sum((xvar,yvar),skipna=True) - total_extent_ca = total_extent_ca + (data_ca.where(data_ca > 15) * area[areaname]).sum((xvar,yvar),skipna=True) - total_extent_np = total_extent_np + (data_np.where(data_np > 15) * area[areaname]).sum((xvar,yvar),skipna=True) - total_extent_na = total_extent_na + (data_na.where(data_na > 15) * area[areaname]).sum((xvar,yvar),skipna=True) - total_extent_sa = total_extent_sa + (data_sa.where(data_sa > 15) * area[areaname]).sum((xvar,yvar),skipna=True) - total_extent_sp = total_extent_sp + (data_sp.where(data_sp > 15) * area[areaname]).sum((xvar,yvar),skipna=True) - total_extent_io = total_extent_io + (data_io.where(data_io > 15) * area[areaname]).sum((xvar,yvar),skipna=True) - - # Get average total over all realizations for this model - # Get annual cycle of model average tseries - # Get error compared to obs - - # TODO: compute only for dask arrays - total_extent_arctic = (total_extent_arctic / len(list_of_runs)).compute().to_dataset(name=varname) - total_extent_antarctic = (total_extent_antarctic / len(list_of_runs)).compute().to_dataset(name=varname) - total_extent_ca = (total_extent_ca / len(list_of_runs)).compute().to_dataset(name=varname) - total_extent_np = (total_extent_np / len(list_of_runs)).compute().to_dataset(name=varname) - total_extent_na = (total_extent_na / len(list_of_runs)).compute().to_dataset(name=varname) - total_extent_sa = (total_extent_sa / len(list_of_runs)).compute().to_dataset(name=varname) - total_extent_sp = (total_extent_sp / len(list_of_runs)).compute().to_dataset(name=varname) - total_extent_io = (total_extent_io / len(list_of_runs)).compute().to_dataset(name=varname) - - total_extent_arctic.time.attrs.pop("bounds") - total_extent_antarctic.time.attrs.pop("bounds") - total_extent_ca.time.attrs.pop("bounds") - total_extent_np.time.attrs.pop("bounds") - total_extent_na.time.attrs.pop("bounds") - total_extent_sa.time.attrs.pop("bounds") - total_extent_sp.time.attrs.pop("bounds") - total_extent_io.time.attrs.pop("bounds") - - total_extent_arctic = total_extent_arctic.bounds.add_missing_bounds() - total_extent_antarctic = total_extent_antarctic.bounds.add_missing_bounds() - total_extent_ca = total_extent_ca.bounds.add_missing_bounds() - total_extent_np = total_extent_np.bounds.add_missing_bounds() - total_extent_na = total_extent_na.bounds.add_missing_bounds() - total_extent_sa = total_extent_sa.bounds.add_missing_bounds() - total_extent_sp = total_extent_sp.bounds.add_missing_bounds() - total_extent_io = total_extent_io.bounds.add_missing_bounds() - - clim_extent_arctic = total_extent_arctic.temporal.climatology("ice_con",freq=varname) - clim_extent_antarctic = total_extent_antarctic.temporal.climatology("ice_con",freq=varname) - clim_extent_ca = total_extent_ca.temporal.climatology(varname,freq="month") - clim_extent_np = total_extent_np.temporal.climatology(varname,freq="month") - clim_extent_na = total_extent_na.temporal.climatology(varname,freq="month") - clim_extent_sa = total_extent_sa.temporal.climatology(varname,freq="month") - clim_extent_sp = total_extent_sp.temporal.climatology(varname,freq="month") - clim_extent_io = total_extent_io.temporal.climatology(varname,freq="month") - - - - # get RMS for model mean annual cycle - - metrics_tmp = metrics_dict.copy() - metrics_tmp["DIMENSIONS"]["model"] = model - metrics_tmp["DIMENSIONS"]["realization"] = list_of_runs - metrics_tmp["RESULTS"] = {model: metrics_dict["RESULTS"][model]} - metrics_path = "{0}_block_extremes_metrics.json".format(model) - utilities.write_to_json(metrics_output_path, metrics_path, metrics_tmp) - - meta.update_metrics( - model, - os.path.join(metrics_output_path, metrics_path), - model + " results", - "Seasonal metrics for block extrema for single dataset", + for rgn in regions_dict: + # Set up metrics dictionary + for key in ["nasateam","bootstrap"]: + mse[model][key][rgn] = { + "monthly_clim": {"mse": None}, + "total_extent": {"mse": None} + } + + # Average all realizations, fix bounds, get climatologies and totals + total_rgn = (totals_dict[rgn] / len(list_of_runs)).to_dataset(name=var) + #total_rgn.time.attrs.pop("bounds") + total_rgn = total_rgn.bounds.add_missing_bounds() + clim_extent = total_rgn.temporal.climatology(var,freq="month") + total = total_rgn.mean("time")[var].data + + # Get errors, convert to 1e-12 km^-4 + mse[model]["nasateam"][rgn]["monthly_clim"]["mse"] = str(mse_t(clim_extent[var],obs_clims["nt"][rgn]["ice_con"],weights=clim_wts)*1e-12) + mse[model]["bootstrap"][rgn]["monthly_clim"]["mse"] = str(mse_t(clim_extent[var],obs_clims["bt"][rgn]["ice_con"],weights=clim_wts)*1e-12) + mse[model]["nasateam"][rgn]["total_extent"]["mse"] = str(mse_model(total,obs_means["nt"][rgn])*1e-12) + mse[model]["bootstrap"][rgn]["total_extent"]["mse"] = str(mse_model(total,obs_means["bt"][rgn])*1e-12) + + metrics["RESULTS"]=mse + + metricsfile = os.path.join(metrics_output_path,"metrics_demo.json") + JSON = Base(metrics_output_path,"metrics_demo.json") + json_structure = metrics["DIMENSIONS"]["json_structure"] + JSON.write( + metrics, + json_structure=json_structure, + sort_keys=True, + indent=4, + separators=(",", ": "), ) - - # reset for next model - mean_extents = None - -# Output single file with all models -model_write_list = model_loop_list.copy() -if "Reference" in model_write_list: - model_write_list.remove("Reference") -metrics_dict["DIMENSIONS"]["model"] = model_write_list -utilities.write_to_json( - metrics_output_path, "block_extremes_metrics.json", metrics_dict -) -fname = os.path.join(metrics_output_path, "block_extremes_metrics.json") -meta.update_metrics( - "All", fname, "All results", "Seasonal metrics for block extrema for all datasets" -) - - -# Update and write metadata file -try: - with open(fname, "r") as f: - tmp = json.load(f) - meta.update_provenance("environment", tmp["provenance"]) -except Exception: - # Skip provenance if there's an issue - print("Error: Could not get provenance from extremes json for output.json.") - -meta.update_provenance("modeldata", test_data_path) -if reference_data_path is not None: - meta.update_provenance("obsdata", reference_data_path) -meta.write() \ No newline at end of file + meta.update_metrics("metrics", metricsfile, "metrics_JSON", "JSON file containig regional sea ice metrics") + + sector_list = ["Central Arctic Sector", + "North Atlantic Sector", + "North Pacific Sector", + "Indian Ocean Sector", + "South Atlantic Sector", + "South Pacific Sector"] + sector_short = ["ca","na","np","io","sa","sp"] + fig7,ax7 = plt.subplots(6,1,figsize=(5,9)) + mlabels = model_list+["bootstrap"] + ind = np.arange(len(mlabels)) # the x locations for the groups + # ind = np.arange(len(mods)+1) # the x locations for the groups + width = 0.3 + n = len(ind) - 1 + for inds,sector in enumerate(sector_list): + # Assemble data + mse_clim = [] + mse_ext = [] + rgn = sector_short[inds] + for model in model_list: + mse_clim.append(float(metrics["RESULTS"][model]["nasateam"][rgn]["monthly_clim"]["mse"])) + mse_ext.append(float(metrics["RESULTS"][model]["nasateam"][rgn]["total_extent"]["mse"])) + mse_clim.append(mse_t(obs_clims["bt"][rgn]["ice_con"],obs_clims["nt"][rgn]["ice_con"],weights=clim_wts)*1e-12) + mse_ext.append(mse_model(obs_means["bt"][rgn],obs_means["nt"][rgn])*1e-12) + + # Make figure + ax7[inds].bar(ind, mse_clim, width, color="b") + ax7[inds].bar(ind, mse_ext, width, color="r") + #ax7[inds].bar(ind[n], obs[sector_short[inds]]**2, width, color="b") + if inds==len(sector_list)-1: + ax7[inds].set_xticks(ind + width / 2.0, mlabels, rotation=90, size=5) + else: + ax7[inds].set_xticks(ind + width/2.0,labels="") + datamax=np.max(mse_clim) + ymax = (datamax)*1.3 + ax7[inds].set_ylim(0.,ymax) + if ymax < 1: + ticks = np.linspace(0,1,5) + labels = [str(round(x,1)) for x in ticks] + elif ymax < 4: + ticks = np.linspace(0,round(ymax),num=round(ymax/2)*4+1) + labels = [str(round(x,1)) for x in ticks] + elif ymax < 10: + ticks = range(0,round(ymax)) + labels = [str(round(x,0)) for x in ticks] + elif ymax > 10: + ticks = range(0,round(ymax),2) + labels = [str(round(x,0)) for x in ticks] + ax7[inds].set_yticks(ticks,labels,fontsize=6) + + ax7[inds].set_ylabel("10${^12}$km${^4}$",size=6) + ax7[inds].grid(True,linestyle=":") + ax7[inds].annotate( + sector, + (0.35, 0.8), + xycoords="axes fraction", + size=8, + ) + figfile=os.path.join(metrics_output_path,"demo_fig.png") + plt.savefig(figfile) + meta.update_plots("bar_chart", figfile, "regional_bar_chart", "Bar chart of regional MSE") + + # Update and write metadata file + try: + with open(os.path.join(metricsfile), "r") as f: + tmp = json.load(f) + meta.update_provenance("environment", tmp["provenance"]) + except Exception: + # Skip provenance if there's an issue + print("Error: Could not get provenance from metrics json for output.json.") + + meta.update_provenance("modeldata", test_data_path) + if reference_data_path is not None: + meta.update_provenance("obsdata", reference_data_path) + meta.write() \ No newline at end of file diff --git a/pcmdi_metrics/sea_ice/sea_ice_parser.py b/pcmdi_metrics/sea_ice/sea_ice_parser.py index b5617c8f5..31d3a25e6 100644 --- a/pcmdi_metrics/sea_ice/sea_ice_parser.py +++ b/pcmdi_metrics/sea_ice/sea_ice_parser.py @@ -14,11 +14,19 @@ def create_sea_ice_parser(): parser.add_argument( "-v", - "--vars", + "--var", type=str, nargs="+", - dest="vars", - help="Variables to use", + dest="var", + help="Name of model sea ice concentration variable", + required=False, + ) + + parser.add_argument( + "--area_var", + type=str, + dest="area_var", + help="Name of model area variable", required=False, ) @@ -90,10 +98,10 @@ def create_sea_ice_parser(): ) parser.add_argument( - "--grid_area", - des="areacell", - help="Filename template for grid area", - required=True + "--area_template", + dest="area_template", + help="Filename template for model grid area", + required=False ) parser.add_argument( @@ -130,20 +138,35 @@ def create_sea_ice_parser(): parser.add_argument( "--ObsUnitsAdjust", type=tuple, - default=(False, 0, 0, None), - help="For unit adjust for OBS dataset. For example:\n" - "- (True, 'divide', 100.0, 'hPa') # Pa to hPa\n" - "- (True, 'subtract', 273.15, 'C') # degK to degC\n" - "- (False, 0, 0, None) # No adjustment (default)", + default=(False, 0, 0), + help="Factor to convert obs sea ice concentration to decimal. For example:\n" + "- (True, 'divide', 100.0) # percentage to decimal\n" + "- (False, 0, 0) # No adjustment (default)", ) parser.add_argument( "--ModUnitsAdjust", type=tuple, - default=(False, 0, 0, None), - help="For unit adjust for model dataset. For example:\n" - "- (True, 'divide', 100.0, 'hPa') # Pa to hPa\n" - "- (True, 'subtract', 273.15, 'C') # degK to degC\n" - "- (False, 0, 0, None) # No adjustment (default)", + default=(False, 0, 0), + help="Factor to convert model sea ice concentration to decimal. For example:\n" + "- (True, 'divide', 100.0) # percentage to decimal\n" + "- (False, 0, 0) # No adjustment (default)", + ) + parser.add_argument( + "--AreaUnitsAdjust", + type=tuple, + default=(False, 0, 0), + help="Factor to convert area data to km^2. For example:\n" + "- (True, 'multiply', 1e-6) # m^2 to km^2\n" + "- (False, 0, 0) # No adjustment (default)", + ) + + parser.add_argument( + "--ObsAreaUnitsAdjust", + type=tuple, + default=(False, 0, 0), + help="Factor to convert area data to km^2. For example:\n" + "- (True, 'multiply', 1e-6) # m^2 to km^2\n" + "- (False, 0, 0) # No adjustment (default)", ) return parser From ecd5e5aa3db0eca70e56355f5766a61be3ab3586 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Fri, 22 Dec 2023 13:10:25 -0800 Subject: [PATCH 14/69] changes --- pcmdi_metrics/sea_ice/ice_driver.py | 64 ++++++++++++++--------------- 1 file changed, 30 insertions(+), 34 deletions(-) diff --git a/pcmdi_metrics/sea_ice/ice_driver.py b/pcmdi_metrics/sea_ice/ice_driver.py index 5544f43cc..530ff3494 100644 --- a/pcmdi_metrics/sea_ice/ice_driver.py +++ b/pcmdi_metrics/sea_ice/ice_driver.py @@ -103,17 +103,6 @@ def get_latitude(ds: xr.Dataset) -> xr.DataArray: lat = ds[key_lat] return(lat) -def slice_dataset(ds, start_year, end_year): - cal = ds.time.encoding["calendar"] - start_time = cftime.datetime(start_year, 1, 1, calendar=cal) - datetime.timedelta( - days=0 - ) - end_time = cftime.datetime(end_year + 1, 1, 1, calendar=cal) - datetime.timedelta( - days=1 - ) - ds = ds.sel(time=slice(start_time, end_time)) - return ds - def to_ice_con_ds(da,obs): ds = xr.Dataset(data_vars={"ice_con": da, "time_bnds": obs.time_bnds}, @@ -166,6 +155,7 @@ def set_up_realizations(realization): elif isinstance(realization, str): if realization.lower() in ["all", "*"]: find_all_realizations = True + realizations=[""] else: realizations = [realization] elif isinstance(realization, list): @@ -244,15 +234,14 @@ def replace_multi(string, rdict): # Initialize output.json file meta = MetadataFile(metrics_output_path) - # Initialize other directories - nc_dir = os.path.join(metrics_output_path, "netcdf") - os.makedirs(nc_dir, exist_ok=True) + #if plots: # plot_dir_maps = os.path.join(metrics_output_path, "plots", "maps") # os.makedirs(plot_dir_maps, exist_ok=True) # Setting up model realization list find_all_realizations, realizations = set_up_realizations(realization) + print("Find all realizations:",find_all_realizations) #### Do Obs part ObsUnitsAdjust=(True,"multiply",1e-2) @@ -273,6 +262,7 @@ def replace_multi(string, rdict): obs = xc.open_dataset(arctic_files[source]) obs[obs_var] = adjust_units(obs[obs_var],ObsUnitsAdjust) obs["area"] = adjust_units(obs["area"],ObsAreaUnitsAdjust) + #mask=create_land_sea_mask(obs,lon_key="lon",lat_key="lat") # Get regions data_arctic = obs[obs_var].where((obs.lat > 0),0) data_ca1 = obs[obs_var].where(((obs.lat > 80) & (obs.lat <= 87.2) & (obs.lon > -120) & (obs.lon <= 90)),0) @@ -394,20 +384,21 @@ def replace_multi(string, rdict): "model": model_list, }, "RESULTS": {}, + "model_year_range": {} } - find_all_realizations, realizations = set_up_realizations(realization) - for model in model_list: tags = { "%(variable)": var, "%(model)": model, - "%(model_version)": model + "%(model_version)": model, + "%(realization)": "*" } if find_all_realizations: - test_data_full_path = os.path.join(test_data_path, filename_template) - test_data_full_path = replace_multi(test_data_full_path, tags) - ncfiles = glob.glob(test_data_full_path) + test_data_full_path_tmp = os.path.join(test_data_path, filename_template) + test_data_full_path_tmp = replace_multi(test_data_full_path_tmp, tags) + ncfiles = glob.glob(test_data_full_path_tmp) + print(ncfiles) realizations = [] for ncfile in ncfiles: realizations.append(ncfile.split("/")[-1].split(".")[3]) @@ -416,10 +407,10 @@ def replace_multi(string, rdict): list_of_runs = realizations else: list_of_runs = realizations - - # Model grid area - area = load_dataset(replace_multi(area_template,tags)) - area[area_var] = adjust_units(area[area_var],AreaUnitsAdjust) + print(list_of_runs) + + start_year = msyear + end_year = meyear totals_dict = {"arctic": 0, "ca": 0, @@ -446,10 +437,6 @@ def replace_multi(string, rdict): } test_data_full_path = os.path.join(test_data_path, filename_template) test_data_full_path = replace_multi(test_data_full_path, tags) - area_path = replace_multi(area_template,tags) - start_year = msyear - end_year = meyear - yrs = [str(start_year), str(end_year)] # for output file names test_data_full_path = glob.glob(test_data_full_path) test_data_full_path.sort() if len(test_data_full_path) == 0: @@ -465,23 +452,29 @@ def replace_multi(string, rdict): for t in test_data_full_path: print(" ", t) + # Model grid area + print(area_template) + print(replace_multi(area_template,tags)) + area = load_dataset(replace_multi(area_template,tags)) + area[area_var] = adjust_units(area[area_var],AreaUnitsAdjust) + # Load and prep data ds = load_dataset(test_data_full_path) ds[var] = adjust_units(ds[var],ModUnitsAdjust) xvar = get_longitude(ds).name yvar = get_latitude(ds).name - if (ds.lon < -180).any() | (ds.lon > 360).any(): - print("Invalid longitude range") - continue + if (ds.lon < -180).any(): + ds["lon"] = ds.lon.where(ds.lon >= -180, ds.lon+360) if ds.lon.min() >= 0: - ds["lon"] = ds.lon - 180 + ds["lon"] = ds.lon.where(ds.lon >= 180, ds.lon-360) # Get time slice if year parameters exist if start_year is not None: - ds = slice_dataset(ds, start_year, end_year) + ds = ds.sel({"time":slice("{0}-01-01".format(start_year), "{0}-12-31".format(end_year))}) + yr_range = [str(start_year),str(end_year)] else: # Get labels for start/end years from dataset - yrs = [str(int(ds.time.dt.year[0])), str(int(ds.time.dt.year[-1]))] + yr_range = [str(int(ds.time.dt.year[0])),str(int(ds.time.dt.year[-1]))] # Get regions data_arctic = ds[var].where(ds.lat > 0, 0) @@ -557,6 +550,9 @@ def replace_multi(string, rdict): mse[model]["bootstrap"][rgn]["monthly_clim"]["mse"] = str(mse_t(clim_extent[var],obs_clims["bt"][rgn]["ice_con"],weights=clim_wts)*1e-12) mse[model]["nasateam"][rgn]["total_extent"]["mse"] = str(mse_model(total,obs_means["nt"][rgn])*1e-12) mse[model]["bootstrap"][rgn]["total_extent"]["mse"] = str(mse_model(total,obs_means["bt"][rgn])*1e-12) + + # Update year list + metrics["model_year_range"][model] = [str(start_year),str(end_year)] metrics["RESULTS"]=mse From c47558694d4755ec947429fb44b5e2f256fcc8fa Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Fri, 22 Dec 2023 13:10:36 -0800 Subject: [PATCH 15/69] add parameter file --- pcmdi_metrics/sea_ice/parameter_file.py | 41 +++++++++++++++++++++++++ 1 file changed, 41 insertions(+) create mode 100644 pcmdi_metrics/sea_ice/parameter_file.py diff --git a/pcmdi_metrics/sea_ice/parameter_file.py b/pcmdi_metrics/sea_ice/parameter_file.py new file mode 100644 index 000000000..364ab9e20 --- /dev/null +++ b/pcmdi_metrics/sea_ice/parameter_file.py @@ -0,0 +1,41 @@ +# CMIP5 +#========= +# Model settings +#test_data_set=["ACCESS1-3","GISS-E2-H","NorESM1-M"] +#test_data_set=["ACCESS1-3"] +#realization=["*"] +#test_data_path = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2/additional_xmls/latest/v20231104/cmip5/historical/seaIce/mon/sic/" +#filename_template= "cmip5.historical.%(model).%(realization).mon.sic.xml" +#var="sic" +#msyear=1981 +#meyear=2010 +#ModUnitsAdjust=(True,"multiply",1e-2) + +# Model area file +#area_template="/p/user_pub/hoang1-backups/ARCHIVE/ivanova2/IceMetrics/CMIP5/AREACELLO/areacello_fx_%(model)_historical_r0i0p0.nc" +#area_var = "areacello" +#AreaUnitsAdjust = (True, "multiply", 1e-6) + +# CMIP6 +#======= +test_data_set=["E3SM-1-0","MIROC6"] +realization="*" +test_data_path="/p/user_pub/cmip/CMIP6/CMIP/*/%(model)/historical/%(realization)/SImon/siconc/*/*/" +filename_template="siconc_SImon_%(model)_historical_%(realization)_*_*.nc" +var="siconc" +msyear=1981 +meyear=2010 +ModUnitsAdjust=(True,"multiply",1e-2) + +area_template="/p/css03/esgf_publish/CMIP6/CMIP/*/%(model)/historical/%(realization)/fx/areacella/*/*/areacella_fx_%(model)_historical_%(realization)_*.nc" +area_var="areacella" +AreaUnitsAdjust=(True,"multiply",1e-6) + + +# Reference is hard coded currently so this is a placeholder + +#ObsUnitsAdjust=(True,"multiply",1e-2) +#reference_data_set=None +#osyear=1981 +#oeyear=2010 +#obsvar="" \ No newline at end of file From 123313a93a024f21f11ac60505f4f6251225b567 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Fri, 22 Dec 2023 13:11:05 -0800 Subject: [PATCH 16/69] changes --- pcmdi_metrics/sea_ice/ice_driver.py | 60 ++++++++++++++--------------- 1 file changed, 30 insertions(+), 30 deletions(-) diff --git a/pcmdi_metrics/sea_ice/ice_driver.py b/pcmdi_metrics/sea_ice/ice_driver.py index 530ff3494..966cd0c84 100644 --- a/pcmdi_metrics/sea_ice/ice_driver.py +++ b/pcmdi_metrics/sea_ice/ice_driver.py @@ -294,7 +294,7 @@ def replace_multi(string, rdict): "arctic": total_extent_arctic_obs.mean("time",skipna=True).data.item(), "ca": total_extent_ca_obs.mean("time",skipna=True).data.item(), "np": total_extent_np_obs.mean("time",skipna=True).data.item(), - "na": total_extent_na_obs.mean("time",skipna=True).data.item() + "na": total_extent_na_obs.mean("time",skipna=True).data.item() } obs.close() @@ -308,13 +308,13 @@ def replace_multi(string, rdict): obs["area"] = adjust_units(obs["area"],ObsAreaUnitsAdjust) data_antarctic = obs[obs_var].where((obs.lat < 0),0) data_sa = obs[obs_var].where( - (obs.lat > -90) & (obs.lat <= -55) & + (obs.lat > -90) & (obs.lat <= -55) & (obs.lon > -60) & (obs.lon <= 20),0) data_sp = obs[obs_var].where( - (obs.lat > -90) & (obs.lat <= -55) & + (obs.lat > -90) & (obs.lat <= -55) & ((obs.lon > 90) | (obs.lon <= -60)),0) data_io = obs[obs_var].where( - (obs.lat > -90) & (obs.lat <= -55) & + (obs.lat > -90) & (obs.lat <= -55) & (obs.lon > 20) & (obs.lon <= 90),0) total_extent_antarctic_obs = (data_antarctic.where(data_antarctic > 0.15) * obs.area).sum(("x","y"),skipna=True) @@ -338,10 +338,10 @@ def replace_multi(string, rdict): "antarctic": total_extent_antarctic_obs.mean("time",skipna=True).data.item(), "io": total_extent_io_obs.mean("time",skipna=True).data.item(), "sp": total_extent_sp_obs.mean("time",skipna=True).data.item(), - "sa": total_extent_sa_obs.mean("time",skipna=True).data.item() + "sa": total_extent_sa_obs.mean("time",skipna=True).data.item() } obs.close() - + obs_clims = {"nt": {},"bt":{}} obs_means = {"nt": {},"bt":{}} for item in antarctic_clims["nt"]: @@ -424,7 +424,7 @@ def replace_multi(string, rdict): "nasateam": {}, "bootstrap": {} } - + # Loop over realizations for run_ind,run in enumerate(list_of_runs): @@ -475,41 +475,41 @@ def replace_multi(string, rdict): else: # Get labels for start/end years from dataset yr_range = [str(int(ds.time.dt.year[0])),str(int(ds.time.dt.year[-1]))] - + # Get regions data_arctic = ds[var].where(ds.lat > 0, 0) data_antarctic = ds[var].where(ds.lat < 0, 0) data_ca1 = ds[var].where(( - (ds.lat > 80) & - (ds.lat <= 87.2) & - (ds.lon > -120) & + (ds.lat > 80) & + (ds.lat <= 87.2) & + (ds.lon > -120) & (ds.lon <= 90)),0) data_ca2 = ds[var].where( - ((ds.lat > 65) & (ds.lat < 87.2)) & + ((ds.lat > 65) & (ds.lat < 87.2)) & ((ds.lon > 90) | (ds.lon <= -120)),0) data_ca = data_ca1 + data_ca2 data_np = ds[var].where( - (ds.lat > 35) & - (ds.lat <= 65) & + (ds.lat > 35) & + (ds.lat <= 65) & ((ds.lon > 90) | (ds.lon <= -120)),0) data_na = ds[var].where( - (ds.lat > 45) & - (ds.lat <= 80) & - (ds.lon > -120) & - (ds.lon <= 90),0) + (ds.lat > 45) & + (ds.lat <= 80) & + (ds.lon > -120) & + (ds.lon <= 90),0) data_na = data_na - data_na.where( - (ds.lat > 45) & - (ds.lat <= 50) & - (ds.lon > 30) & + (ds.lat > 45) & + (ds.lat <= 50) & + (ds.lon > 30) & (ds.lon <= 60),0) data_sa = ds[var].where( - (ds.lat > -90) & (ds.lat <= -55) & + (ds.lat > -90) & (ds.lat <= -55) & (ds.lon > -60) & (ds.lon <= 20)) data_sp = ds[var].where( - (ds.lat > -90) & (ds.lat <= -55) & + (ds.lat > -90) & (ds.lat <= -55) & ((ds.lon > 90) | (ds.lon <= -60))) data_io = ds[var].where( - (ds.lat > -90) & (ds.lat <= -55) & + (ds.lat > -90) & (ds.lat <= -55) & (ds.lon > 20) & (ds.lon <= 90)) regions_dict = { @@ -527,9 +527,9 @@ def replace_multi(string, rdict): for rgn in regions_dict: data = regions_dict[rgn] totals_dict[rgn] = totals_dict[rgn] + (data.where(data > 0.15, 0) * area[area_var]).sum((xvar,yvar),skipna=True) - + ds.close() - + for rgn in regions_dict: # Set up metrics dictionary for key in ["nasateam","bootstrap"]: @@ -537,20 +537,20 @@ def replace_multi(string, rdict): "monthly_clim": {"mse": None}, "total_extent": {"mse": None} } - + # Average all realizations, fix bounds, get climatologies and totals total_rgn = (totals_dict[rgn] / len(list_of_runs)).to_dataset(name=var) #total_rgn.time.attrs.pop("bounds") total_rgn = total_rgn.bounds.add_missing_bounds() clim_extent = total_rgn.temporal.climatology(var,freq="month") total = total_rgn.mean("time")[var].data - + # Get errors, convert to 1e-12 km^-4 mse[model]["nasateam"][rgn]["monthly_clim"]["mse"] = str(mse_t(clim_extent[var],obs_clims["nt"][rgn]["ice_con"],weights=clim_wts)*1e-12) mse[model]["bootstrap"][rgn]["monthly_clim"]["mse"] = str(mse_t(clim_extent[var],obs_clims["bt"][rgn]["ice_con"],weights=clim_wts)*1e-12) mse[model]["nasateam"][rgn]["total_extent"]["mse"] = str(mse_model(total,obs_means["nt"][rgn])*1e-12) mse[model]["bootstrap"][rgn]["total_extent"]["mse"] = str(mse_model(total,obs_means["bt"][rgn])*1e-12) - + # Update year list metrics["model_year_range"][model] = [str(start_year),str(end_year)] @@ -616,7 +616,7 @@ def replace_multi(string, rdict): ticks = range(0,round(ymax),2) labels = [str(round(x,0)) for x in ticks] ax7[inds].set_yticks(ticks,labels,fontsize=6) - + ax7[inds].set_ylabel("10${^12}$km${^4}$",size=6) ax7[inds].grid(True,linestyle=":") ax7[inds].annotate( From 8186e1521a09efa0ee0e98c595d93284c7138a41 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Wed, 3 Jan 2024 16:22:01 -0800 Subject: [PATCH 17/69] more fixes --- pcmdi_metrics/sea_ice/ice_driver.py | 104 ++++++++++++++++------------ 1 file changed, 61 insertions(+), 43 deletions(-) diff --git a/pcmdi_metrics/sea_ice/ice_driver.py b/pcmdi_metrics/sea_ice/ice_driver.py index 966cd0c84..7850ee9ef 100644 --- a/pcmdi_metrics/sea_ice/ice_driver.py +++ b/pcmdi_metrics/sea_ice/ice_driver.py @@ -14,6 +14,8 @@ from pcmdi_metrics.io import xcdat_openxml from pcmdi_metrics.io.base import Base +import sys + class MetadataFile: # This class organizes the contents for the CMEC # metadata file called output.json, which describes @@ -386,6 +388,7 @@ def replace_multi(string, rdict): "RESULTS": {}, "model_year_range": {} } + print(model_list) for model in model_list: tags = { @@ -401,13 +404,19 @@ def replace_multi(string, rdict): print(ncfiles) realizations = [] for ncfile in ncfiles: - realizations.append(ncfile.split("/")[-1].split(".")[3]) + basename = ncfile.split("/")[-1] + if len(basename.split(".")) <= 2: + if basename.split("_")[4] not in realizations: + realizations.append(basename.split("_")[4]) + else: + if basename.split(".")[3] not in realizations: + realizations.append(basename.split(".")[3]) + print("=================================") print("model, runs:", model, realizations) list_of_runs = realizations else: list_of_runs = realizations - print(list_of_runs) start_year = msyear end_year = meyear @@ -425,6 +434,11 @@ def replace_multi(string, rdict): "bootstrap": {} } + # Model grid area + print(replace_multi(area_template,tags)) + area = xc.open_dataset(glob.glob(replace_multi(area_template,tags))[0]) + area[area_var] = adjust_units(area[area_var],AreaUnitsAdjust) + # Loop over realizations for run_ind,run in enumerate(list_of_runs): @@ -452,21 +466,22 @@ def replace_multi(string, rdict): for t in test_data_full_path: print(" ", t) - # Model grid area - print(area_template) - print(replace_multi(area_template,tags)) - area = load_dataset(replace_multi(area_template,tags)) - area[area_var] = adjust_units(area[area_var],AreaUnitsAdjust) - # Load and prep data ds = load_dataset(test_data_full_path) ds[var] = adjust_units(ds[var],ModUnitsAdjust) - xvar = get_longitude(ds).name - yvar = get_latitude(ds).name - if (ds.lon < -180).any(): - ds["lon"] = ds.lon.where(ds.lon >= -180, ds.lon+360) - if ds.lon.min() >= 0: - ds["lon"] = ds.lon.where(ds.lon >= 180, ds.lon-360) + try: + xvar = get_longitude(ds).name + yvar = get_latitude(ds).name + except Exception as e: + print("Could not get latitude or longitude variables") + print(" Error:",e) + continue + print("xvar", xvar) + print("yvar", yvar) + if (ds[xvar] < -180).any(): + ds[xvar] = ds[xvar].where(ds[xvar] >= -180, ds[xvar]+360) + if ds[xvar].min() >= 0: + ds[xvar] = ds[xvar].where(ds[xvar] >= 180, ds[xvar]-360) # Get time slice if year parameters exist if start_year is not None: @@ -477,40 +492,40 @@ def replace_multi(string, rdict): yr_range = [str(int(ds.time.dt.year[0])),str(int(ds.time.dt.year[-1]))] # Get regions - data_arctic = ds[var].where(ds.lat > 0, 0) - data_antarctic = ds[var].where(ds.lat < 0, 0) + data_arctic = ds[var].where(ds[yvar] > 0, 0) + data_antarctic = ds[var].where(ds[yvar] < 0, 0) data_ca1 = ds[var].where(( - (ds.lat > 80) & - (ds.lat <= 87.2) & - (ds.lon > -120) & - (ds.lon <= 90)),0) + (ds[yvar] > 80) & + (ds[yvar] <= 87.2) & + (ds[xvar] > -120) & + (ds[xvar] <= 90)),0) data_ca2 = ds[var].where( - ((ds.lat > 65) & (ds.lat < 87.2)) & - ((ds.lon > 90) | (ds.lon <= -120)),0) + ((ds[yvar] > 65) & (ds[yvar] < 87.2)) & + ((ds[xvar] > 90) | (ds[xvar] <= -120)),0) data_ca = data_ca1 + data_ca2 data_np = ds[var].where( - (ds.lat > 35) & - (ds.lat <= 65) & - ((ds.lon > 90) | (ds.lon <= -120)),0) + (ds[yvar] > 35) & + (ds[yvar] <= 65) & + ((ds[xvar] > 90) | (ds[xvar] <= -120)),0) data_na = ds[var].where( - (ds.lat > 45) & - (ds.lat <= 80) & - (ds.lon > -120) & - (ds.lon <= 90),0) + (ds[yvar] > 45) & + (ds[yvar] <= 80) & + (ds[xvar] > -120) & + (ds[xvar] <= 90),0) data_na = data_na - data_na.where( - (ds.lat > 45) & - (ds.lat <= 50) & - (ds.lon > 30) & - (ds.lon <= 60),0) + (ds[yvar] > 45) & + (ds[yvar] <= 50) & + (ds[xvar] > 30) & + (ds[xvar] <= 60),0) data_sa = ds[var].where( - (ds.lat > -90) & (ds.lat <= -55) & - (ds.lon > -60) & (ds.lon <= 20)) + (ds[yvar] > -90) & (ds[yvar] <= -55) & + (ds[xvar] > -60) & (ds[xvar] <= 20)) data_sp = ds[var].where( - (ds.lat > -90) & (ds.lat <= -55) & - ((ds.lon > 90) | (ds.lon <= -60))) + (ds[yvar] > -90) & (ds[yvar] <= -55) & + ((ds[xvar] > 90) | (ds[xvar] <= -60))) data_io = ds[var].where( - (ds.lat > -90) & (ds.lat <= -55) & - (ds.lon > 20) & (ds.lon <= 90)) + (ds[yvar] > -90) & (ds[yvar] <= -55) & + (ds[xvar] > 20) & (ds[xvar] <= 90)) regions_dict = { "arctic": data_arctic, @@ -530,7 +545,9 @@ def replace_multi(string, rdict): ds.close() + print("Calculating model average metrics") for rgn in regions_dict: + print(rgn) # Set up metrics dictionary for key in ["nasateam","bootstrap"]: mse[model][key][rgn] = { @@ -609,12 +626,13 @@ def replace_multi(string, rdict): elif ymax < 4: ticks = np.linspace(0,round(ymax),num=round(ymax/2)*4+1) labels = [str(round(x,1)) for x in ticks] - elif ymax < 10: - ticks = range(0,round(ymax)) - labels = [str(round(x,0)) for x in ticks] elif ymax > 10: - ticks = range(0,round(ymax),2) + ticks = range(0,round(ymax),5) labels = [str(round(x,0)) for x in ticks] + else: + ticks = range(0,round(ymax)) + labels = [str(round(x,0)) for x in ticks] + ax7[inds].set_yticks(ticks,labels,fontsize=6) ax7[inds].set_ylabel("10${^12}$km${^4}$",size=6) From d0f9a0b8fb16f7bf1f41ae92c996114c4067b7b0 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Wed, 3 Jan 2024 16:22:16 -0800 Subject: [PATCH 18/69] new cases --- pcmdi_metrics/sea_ice/parameter_file.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/pcmdi_metrics/sea_ice/parameter_file.py b/pcmdi_metrics/sea_ice/parameter_file.py index 364ab9e20..fd02357ff 100644 --- a/pcmdi_metrics/sea_ice/parameter_file.py +++ b/pcmdi_metrics/sea_ice/parameter_file.py @@ -18,16 +18,16 @@ # CMIP6 #======= -test_data_set=["E3SM-1-0","MIROC6"] +#test_data_set=["E3SM-1-0"] realization="*" -test_data_path="/p/user_pub/cmip/CMIP6/CMIP/*/%(model)/historical/%(realization)/SImon/siconc/*/*/" -filename_template="siconc_SImon_%(model)_historical_%(realization)_*_*.nc" +#test_data_path="/p/user_pub/cmip/CMIP6/CMIP/*/%(model)/historical/%(realization)/SImon/siconc/*/v20190806/" +#filename_template="siconc_SImon_%(model)_historical_%(realization)_*_*.nc" var="siconc" -msyear=1981 +msyear=1991 meyear=2010 ModUnitsAdjust=(True,"multiply",1e-2) -area_template="/p/css03/esgf_publish/CMIP6/CMIP/*/%(model)/historical/%(realization)/fx/areacella/*/*/areacella_fx_%(model)_historical_%(realization)_*.nc" +area_template="/p/css03/esgf_publish/CMIP6/CMIP/*/%(model)/historical/*/fx/areacella/*/*/areacella_fx_%(model)_historical_%(realization)_*.nc" area_var="areacella" AreaUnitsAdjust=(True,"multiply",1e-6) From 724fe347f002423f3e308530dc4f7a9371d209b9 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Wed, 3 Jan 2024 16:22:30 -0800 Subject: [PATCH 19/69] add file --- pcmdi_metrics/sea_ice/sea_ice_parallel.py | 96 +++++++++++++++++++++++ 1 file changed, 96 insertions(+) create mode 100644 pcmdi_metrics/sea_ice/sea_ice_parallel.py diff --git a/pcmdi_metrics/sea_ice/sea_ice_parallel.py b/pcmdi_metrics/sea_ice/sea_ice_parallel.py new file mode 100644 index 000000000..5afacffcc --- /dev/null +++ b/pcmdi_metrics/sea_ice/sea_ice_parallel.py @@ -0,0 +1,96 @@ +import glob +import os + +import xsearch as xs + +from pcmdi_metrics.mean_climate.lib.pmp_parser import PMPParser +from pcmdi_metrics.misc.scripts import parallel_submitter +from pcmdi_metrics.precip_variability.lib import AddParserArgument + +num_cpus = 20 + +# Read parameters +P = PMPParser() +P = AddParserArgument(P) +param = P.get_parameter() +mip = "cmip6" +exp = "historical" +var = param.var +mod = None +frq = "mon" + +if mod is None: + pathDict = xs.findPaths(exp, var, frq, mip_era=mip.upper()) +else: + pathDict = xs.findPaths( + exp, var, frq, mip_era=mip.upper(), model=mod + ) +# Get which area variable needed +print("Reading external variable attribute") +#pathDB = xs.addAttribute(pathDict, 'external_variables') +areacello = xs.findPaths("historical","areacello","fx",cmipTable="Ofx") +areacella = xs.findPaths("historical","areacella","fx") +path_list = sorted(list(pathDict.keys())) +print("Number of datasets:", len(path_list)) + +cmd_list = [] +log_list = [] +model_list = xs.getGroupValues(pathDict,'model') +print(model_list) +for model in model_list[7:11]: + path = xs.getValuesForFacet(pathDict,'model',model)[0] + basename = os.path.basename(glob.glob(os.path.join(path,"*"))[0]) + + dir_template = "/".join(path.split("/")[0:9]) + "/%(realization)/" + "/".join(path.split("/")[10:13]) +"/*/" + file_template = "_".join(basename.split("_")[0:4]) + "_%(realization)_" + basename.split("_")[5] + "_*-*.nc" + + single=xs.getValuesForFacet(pathDict,'model',model) + empty = [{} for item in single] + d1=zip(single,empty) + db=dict(d1) + db = xs.addAttribute(db, 'external_variables') + + #area_var = pathDB[path]["external_variables"] + try: + area_var = db[single[0]]['external_variables'] + except: + print("No external variables") + print("Guessing areacello") + area_var = 'areacello' + if area_var == "areacello": # Same for all realizations + try: + apath = xs.getValuesForFacet(areacello,'model',model) + abase = os.path.basename(glob.glob(os.path.join(apath[0],'*'))[0]) + area_path = os.path.join(apath[0],abase) + except: + print("No areacello for model ",model) + print(apath) + continue + ## Make filename template + #area_path = "/".join(apath[0].split("/")[0:9]) + "/%(realization)/" + "/".join(apath[0].split("/")[10:]) + elif area_var == "areacella": # Different for each realization + apath = xs.getValuesForFacet(areacella,'model',model) + abase = os.path.basename(glob.glob(os.path.join(apath[0],'*'))[0]) + abase = "_".join(abase.split("_")[0:4]) + "_%(realization)_" + "_".join(abase.split("_")[5:]) + # Make filename template + area_dir = "/".join(apath[0].split("/")[0:9]) + "/%(realization)/" + "/".join(apath[0].split("/")[10:]) + area_path = os.path.join(area_dir,abase) + else: + "Area variable not found." + continue + + cmd_list.append( + "python -u ice_driver.py -p parameter_file.py --case_id " + model + \ + " --test_data_set '" + model + "' --test_data_path '" + \ + dir_template + "' --filename_template '" + file_template + \ + "' --area_template '" + area_path + "' --area_var " + area_var + ) + #log_list.append("log_" + mip + "_" + var + "_" + mymodel) + +print(cmd_list) +#parallel_submitter( +# cmd_list, +# log_dir="./log", +# logfilename_list=log_list, +# num_workers=num_cpus, +#) From 3177b3517225cde2cbdb4f8d556e53d3bc533f70 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 4 Jan 2024 10:59:15 -0800 Subject: [PATCH 20/69] add readme --- pcmdi_metrics/sea_ice/README.md | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 pcmdi_metrics/sea_ice/README.md diff --git a/pcmdi_metrics/sea_ice/README.md b/pcmdi_metrics/sea_ice/README.md new file mode 100644 index 000000000..d9b5e69d8 --- /dev/null +++ b/pcmdi_metrics/sea_ice/README.md @@ -0,0 +1,5 @@ +Sea ice metrics driver + +Example command: + +python -u ice_driver.py -p parameter_file.py --case_id E3SM-1-0 --test_data_set 'E3SM-1-0' --test_data_path '/p/css03/esgf_publish/CMIP6/CMIP/UCSB/E3SM-1-0/historical/%(realization)/SImon/siconc/gr/*/' --filename_template 'siconc_SImon_E3SM-1-0_historical_%(realization)_gr_*-*.nc' --area_template '/p/user_pub/work/CMIP6/CMIP/E3SM-Project/E3SM-1-0/historical/r1i1p1f1/Ofx/areacello/gr/v20210127/areacello_Ofx_E3SM-1-0_historical_r1i1p1f1_gr.nc' --area_var areacello \ No newline at end of file From c97bc293c079ebd8e60bd361a57d561bbe5d1fad Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 4 Jan 2024 10:59:57 -0800 Subject: [PATCH 21/69] fix formatting --- pcmdi_metrics/sea_ice/README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/pcmdi_metrics/sea_ice/README.md b/pcmdi_metrics/sea_ice/README.md index d9b5e69d8..91eb52452 100644 --- a/pcmdi_metrics/sea_ice/README.md +++ b/pcmdi_metrics/sea_ice/README.md @@ -2,4 +2,6 @@ Sea ice metrics driver Example command: -python -u ice_driver.py -p parameter_file.py --case_id E3SM-1-0 --test_data_set 'E3SM-1-0' --test_data_path '/p/css03/esgf_publish/CMIP6/CMIP/UCSB/E3SM-1-0/historical/%(realization)/SImon/siconc/gr/*/' --filename_template 'siconc_SImon_E3SM-1-0_historical_%(realization)_gr_*-*.nc' --area_template '/p/user_pub/work/CMIP6/CMIP/E3SM-Project/E3SM-1-0/historical/r1i1p1f1/Ofx/areacello/gr/v20210127/areacello_Ofx_E3SM-1-0_historical_r1i1p1f1_gr.nc' --area_var areacello \ No newline at end of file +``` +python -u ice_driver.py -p parameter_file.py --case_id E3SM-1-0 --test_data_set 'E3SM-1-0' --test_data_path '/p/css03/esgf_publish/CMIP6/CMIP/UCSB/E3SM-1-0/historical/%(realization)/SImon/siconc/gr/*/' --filename_template 'siconc_SImon_E3SM-1-0_historical_%(realization)_gr_*-*.nc' --area_template '/p/user_pub/work/CMIP6/CMIP/E3SM-Project/E3SM-1-0/historical/r1i1p1f1/Ofx/areacello/gr/v20210127/areacello_Ofx_E3SM-1-0_historical_r1i1p1f1_gr.nc' --area_var areacello +``` \ No newline at end of file From c9ef21e5fc9118a616a3949e56c662ceade6f9af Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Wed, 10 Jan 2024 11:49:15 -0800 Subject: [PATCH 22/69] fix coord issues --- pcmdi_metrics/sea_ice/ice_driver.py | 809 ++++++++++++++++++---------- 1 file changed, 513 insertions(+), 296 deletions(-) diff --git a/pcmdi_metrics/sea_ice/ice_driver.py b/pcmdi_metrics/sea_ice/ice_driver.py index 7850ee9ef..647d5fb4e 100644 --- a/pcmdi_metrics/sea_ice/ice_driver.py +++ b/pcmdi_metrics/sea_ice/ice_driver.py @@ -1,20 +1,37 @@ -import xarray as xr -import xcdat as xc -import numpy as np -import matplotlib.pyplot as plt +import datetime import glob import json import os -import datetime -import cftime -import dask +import sys +import dask +import matplotlib.pyplot as plt +import numpy as np +import xarray as xr +import xcdat as xc from sea_ice_parser import create_sea_ice_parser -from pcmdi_metrics.mean_climate.lib import compute_statistics + from pcmdi_metrics.io import xcdat_openxml from pcmdi_metrics.io.base import Base -import sys +from pcmdi_metrics.io import ( # noqa + get_axis_list, + get_data_list, + get_latitude_bounds_key, + get_latitude_key, + get_latitude, + get_latitude_bounds, + get_longitude_bounds_key, + get_longitude_key, + get_longitude, + get_longitude_bounds, + get_time, + get_time_bounds, + get_time_bounds_key, + get_time_key, + select_subset, +) + class MetadataFile: # This class organizes the contents for the CMEC @@ -61,6 +78,27 @@ def write(self): with open(self.outfile, "w") as f: json.dump(self.json, f, indent=4) + +def find_lon(ds): + for key in ds.coords: + if key in ["lon", "longitude"]: + return key + for key in ds.keys(): + if key in ["lon", "longitude"]: + return key + return None + + +def find_lat(ds): + for key in ds.coords: + if key in ["lat", "latitude"]: + return key + for key in ds.keys(): + if key in ["lat", "latitude"]: + return key + return None + + def mse_t(dm, do, weights=None): """Computes mse""" if dm is None and do is None: # just want the doc @@ -73,10 +111,11 @@ def mse_t(dm, do, weights=None): stat = np.sum(((dm.data - do.data) ** 2)) / len(dm, axis=0) else: stat = np.sum(((dm.data - do.data) ** 2) * weights, axis=0) - if isinstance(stat,dask.array.core.Array): + if isinstance(stat, dask.array.core.Array): stat = stat.compute() return stat + def mse_model(dm, do, var=None): """Computes mse""" if dm is None and do is None: # just want the doc @@ -85,44 +124,31 @@ def mse_model(dm, do, var=None): "Abstract": "Compute Mean Square Error", "Contact": "pcmdi-metrics@llnl.gov", } - if var is not None: # dataset - stat = ((dm[var].data - do[var].data) ** 2) - else: # dataarray - stat = ((dm - do) ** 2) - if isinstance(stat,dask.array.core.Array): + if var is not None: # dataset + stat = (dm[var].data - do[var].data) ** 2 + else: # dataarray + stat = (dm - do) ** 2 + if isinstance(stat, dask.array.core.Array): stat = stat.compute() return stat -def get_longitude(ds: xr.Dataset) -> xr.DataArray: - key_lon = xc.axis.get_dim_keys(ds, axis="X") - lon = ds[key_lon] - return(lon) - - -def get_latitude(ds: xr.Dataset) -> xr.DataArray: - key_lat = xc.axis.get_dim_keys(ds, axis="Y") - lat = ds[key_lat] - return(lat) - -def to_ice_con_ds(da,obs): - ds = xr.Dataset(data_vars={"ice_con": da, - "time_bnds": obs.time_bnds}, - coords = {"time": obs.time}) +def to_ice_con_ds(da, obs): + ds = xr.Dataset( + data_vars={"ice_con": da, "time_bnds": obs.time_bnds}, coords={"time": obs.time} + ) return ds -def adjust_units(ds,adjust_tuple): - action_dict = { - "multiply": "*", - "divide": "/", - "add": "+", - "subtract": "-"} + +def adjust_units(ds, adjust_tuple): + action_dict = {"multiply": "*", "divide": "/", "add": "+", "subtract": "-"} if adjust_tuple[0]: - print("Converting units by ",adjust_tuple[1],adjust_tuple[2]) - cmd = " ".join(["ds",str(action_dict[adjust_tuple[1]]),str(adjust_tuple[2])]) + print("Converting units by ", adjust_tuple[1], adjust_tuple[2]) + cmd = " ".join(["ds", str(action_dict[adjust_tuple[1]]), str(adjust_tuple[2])]) ds = eval(cmd) return ds + def verify_output_path(metrics_output_path, case_id): if metrics_output_path is None: metrics_output_path = datetime.datetime.now().strftime("v%Y%m%d") @@ -140,6 +166,7 @@ def verify_output_path(metrics_output_path, case_id): sys.exit() return metrics_output_path + def verify_years(start_year, end_year, msg="Error: Invalid start or end year"): if start_year is None and end_year is None: return @@ -149,6 +176,7 @@ def verify_years(start_year, end_year, msg="Error: Invalid start or end year"): print("Exiting") sys.exit() + def set_up_realizations(realization): find_all_realizations = False if realization is None: @@ -157,7 +185,7 @@ def set_up_realizations(realization): elif isinstance(realization, str): if realization.lower() in ["all", "*"]: find_all_realizations = True - realizations=[""] + realizations = [""] else: realizations = [realization] elif isinstance(realization, list): @@ -165,6 +193,7 @@ def set_up_realizations(realization): return find_all_realizations, realizations + def load_dataset(filepath): # Load an xarray dataset from the given filepath. # If list of netcdf files, opens mfdataset. @@ -178,6 +207,7 @@ def load_dataset(filepath): ds = xc.open_dataset(filepath[0]) return ds + def replace_multi(string, rdict): # Replace multiple keyworks in a string template # based on key-value pairs in 'rdict'. @@ -185,8 +215,8 @@ def replace_multi(string, rdict): string = string.replace(k, rdict[k]) return string -if __name__ == "__main__": +if __name__ == "__main__": parser = create_sea_ice_parser() parameter = parser.get_parameter(argparse_vals_only=False) @@ -206,7 +236,7 @@ def replace_multi(string, rdict): AreaUnitsAdjust = parameter.AreaUnitsAdjust ModUnitsAdjust = parameter.ModUnitsAdjust ObsUnitsAdjust = parameter.ObsUnitsAdjust - #plots = parameter.plots + # plots = parameter.plots msyear = parameter.msyear meyear = parameter.meyear osyear = parameter.osyear @@ -236,18 +266,17 @@ def replace_multi(string, rdict): # Initialize output.json file meta = MetadataFile(metrics_output_path) - - #if plots: + # if plots: # plot_dir_maps = os.path.join(metrics_output_path, "plots", "maps") # os.makedirs(plot_dir_maps, exist_ok=True) # Setting up model realization list find_all_realizations, realizations = set_up_realizations(realization) - print("Find all realizations:",find_all_realizations) + print("Find all realizations:", find_all_realizations) #### Do Obs part - ObsUnitsAdjust=(True,"multiply",1e-2) - ObsAreaUnitsAdjust=(False,0,0) + ObsUnitsAdjust = (True, "multiply", 1e-2) + ObsAreaUnitsAdjust = (False, 0, 0) f_nt_n = "/home/ordonez4/seaice/data/icecon_ssmi_nt_n_edited.nc" f_nt_s = "/home/ordonez4/seaice/data/icecon_ssmi_nt_s_edited.nc" @@ -257,46 +286,74 @@ def replace_multi(string, rdict): arctic_clims = {} arctic_means = {} arctic_files = {"nt": f_nt_n, "bt": f_bt_n} - obs_var="ice_con" + obs_var = "ice_con" print("OBS: Arctic") for source in arctic_files: obs = xc.open_dataset(arctic_files[source]) - obs[obs_var] = adjust_units(obs[obs_var],ObsUnitsAdjust) - obs["area"] = adjust_units(obs["area"],ObsAreaUnitsAdjust) - #mask=create_land_sea_mask(obs,lon_key="lon",lat_key="lat") + obs[obs_var] = adjust_units(obs[obs_var], ObsUnitsAdjust) + obs["area"] = adjust_units(obs["area"], ObsAreaUnitsAdjust) + # mask=create_land_sea_mask(obs,lon_key="lon",lat_key="lat") # Get regions - data_arctic = obs[obs_var].where((obs.lat > 0),0) - data_ca1 = obs[obs_var].where(((obs.lat > 80) & (obs.lat <= 87.2) & (obs.lon > -120) & (obs.lon <= 90)),0) - data_ca2 = obs[obs_var].where(((obs.lat > 65) & (obs.lat < 87.2)) & ((obs.lon > 90) | (obs.lon <= -120)),0) - data_ca = obs[obs_var].where((data_ca1 > 0) | (data_ca2 > 0),0) - data_np = obs[obs_var].where((obs.lat > 35) & (obs.lat <= 65) & ((obs.lon > 90) | (obs.lon <= -120)),0) - data_na = obs[obs_var].where((obs.lat > 45) & (obs.lat <= 80) & (obs.lon > -120) & (obs.lon <= 90),0) - data_na = data_na - data_na.where((obs.lat > 45) & (obs.lat <= 50) & (obs.lon > 30) & (obs.lon <= 60),0) + data_arctic = obs[obs_var].where((obs.lat > 0), 0) + data_ca1 = obs[obs_var].where( + ((obs.lat > 80) & (obs.lat <= 87.2) & (obs.lon > -120) & (obs.lon <= 90)), 0 + ) + data_ca2 = obs[obs_var].where( + ((obs.lat > 65) & (obs.lat < 87.2)) & ((obs.lon > 90) | (obs.lon <= -120)), + 0, + ) + data_ca = obs[obs_var].where((data_ca1 > 0) | (data_ca2 > 0), 0) + data_np = obs[obs_var].where( + (obs.lat > 35) & (obs.lat <= 65) & ((obs.lon > 90) | (obs.lon <= -120)), 0 + ) + data_na = obs[obs_var].where( + (obs.lat > 45) & (obs.lat <= 80) & (obs.lon > -120) & (obs.lon <= 90), 0 + ) + data_na = data_na - data_na.where( + (obs.lat > 45) & (obs.lat <= 50) & (obs.lon > 30) & (obs.lon <= 60), 0 + ) + # TODO: land/sea masking to remove lakes # Get ice extent - total_extent_arctic_obs = (data_arctic.where(data_arctic > 0.15) * obs.area).sum(("x","y"),skipna=True) - total_extent_ca_obs = (data_ca.where(data_ca > 0.15) * obs.area).sum(("x","y"),skipna=True) - total_extent_np_obs = (data_np.where(data_np > 0.15) * obs.area).sum(("x","y"),skipna=True) - total_extent_na_obs = (data_na.where(data_na > 0.15) * obs.area).sum(("x","y"),skipna=True) + total_extent_arctic_obs = ( + data_arctic.where(data_arctic > 0.15) * obs.area + ).sum(("x", "y"), skipna=True) + total_extent_ca_obs = (data_ca.where(data_ca > 0.15) * obs.area).sum( + ("x", "y"), skipna=True + ) + total_extent_np_obs = (data_np.where(data_np > 0.15) * obs.area).sum( + ("x", "y"), skipna=True + ) + total_extent_na_obs = (data_na.where(data_na > 0.15) * obs.area).sum( + ("x", "y"), skipna=True + ) - clim_arctic_obs = to_ice_con_ds(total_extent_arctic_obs,obs).temporal.climatology(obs_var,freq="month") - clim_ca_obs = to_ice_con_ds(total_extent_ca_obs,obs).temporal.climatology(obs_var,freq="month") - clim_np_obs = to_ice_con_ds(total_extent_np_obs,obs).temporal.climatology(obs_var,freq="month") - clim_na_obs = to_ice_con_ds(total_extent_na_obs,obs).temporal.climatology(obs_var,freq="month") + clim_arctic_obs = to_ice_con_ds( + total_extent_arctic_obs, obs + ).temporal.climatology(obs_var, freq="month") + clim_ca_obs = to_ice_con_ds(total_extent_ca_obs, obs).temporal.climatology( + obs_var, freq="month" + ) + clim_np_obs = to_ice_con_ds(total_extent_np_obs, obs).temporal.climatology( + obs_var, freq="month" + ) + clim_na_obs = to_ice_con_ds(total_extent_na_obs, obs).temporal.climatology( + obs_var, freq="month" + ) arctic_clims[source] = { "arctic": clim_arctic_obs, "ca": clim_ca_obs, "np": clim_np_obs, - "na": clim_na_obs + "na": clim_na_obs, } arctic_means[source] = { - "arctic": total_extent_arctic_obs.mean("time",skipna=True).data.item(), - "ca": total_extent_ca_obs.mean("time",skipna=True).data.item(), - "np": total_extent_np_obs.mean("time",skipna=True).data.item(), - "na": total_extent_na_obs.mean("time",skipna=True).data.item() + "arctic": total_extent_arctic_obs.mean("time", skipna=True).data.item(), + "ca": total_extent_ca_obs.mean("time", skipna=True).data.item(), + "np": total_extent_np_obs.mean("time", skipna=True).data.item(), + "na": total_extent_na_obs.mean("time", skipna=True).data.item(), } obs.close() @@ -306,46 +363,65 @@ def replace_multi(string, rdict): print("OBS: Antarctic") for source in antarctic_files: obs = xc.open_dataset(antarctic_files[source]) - obs[obs_var] = adjust_units(obs[obs_var],ObsUnitsAdjust) - obs["area"] = adjust_units(obs["area"],ObsAreaUnitsAdjust) - data_antarctic = obs[obs_var].where((obs.lat < 0),0) + obs[obs_var] = adjust_units(obs[obs_var], ObsUnitsAdjust) + obs["area"] = adjust_units(obs["area"], ObsAreaUnitsAdjust) + data_antarctic = obs[obs_var].where((obs.lat < 0), 0) data_sa = obs[obs_var].where( - (obs.lat > -90) & (obs.lat <= -55) & - (obs.lon > -60) & (obs.lon <= 20),0) + (obs.lat > -90) & (obs.lat <= -55) & (obs.lon > -60) & (obs.lon <= 20), 0 + ) data_sp = obs[obs_var].where( - (obs.lat > -90) & (obs.lat <= -55) & - ((obs.lon > 90) | (obs.lon <= -60)),0) + (obs.lat > -90) & (obs.lat <= -55) & ((obs.lon > 90) | (obs.lon <= -60)), 0 + ) data_io = obs[obs_var].where( - (obs.lat > -90) & (obs.lat <= -55) & - (obs.lon > 20) & (obs.lon <= 90),0) + (obs.lat > -90) & (obs.lat <= -55) & (obs.lon > 20) & (obs.lon <= 90), 0 + ) + # TODO: land/sea masking to remove lakes; do antarctica for consistency - total_extent_antarctic_obs = (data_antarctic.where(data_antarctic > 0.15) * obs.area).sum(("x","y"),skipna=True) - total_extent_sa_obs = (data_sa.where(data_sa > 0.15) * obs.area).sum(("x","y"),skipna=True) - total_extent_sp_obs = (data_sp.where(data_sp > 0.15) * obs.area).sum(("x","y"),skipna=True) - total_extent_io_obs = (data_io.where(data_io > 0.15) * obs.area).sum(("x","y"),skipna=True) + total_extent_antarctic_obs = ( + data_antarctic.where(data_antarctic > 0.15) * obs.area + ).sum(("x", "y"), skipna=True) + total_extent_sa_obs = (data_sa.where(data_sa > 0.15) * obs.area).sum( + ("x", "y"), skipna=True + ) + total_extent_sp_obs = (data_sp.where(data_sp > 0.15) * obs.area).sum( + ("x", "y"), skipna=True + ) + total_extent_io_obs = (data_io.where(data_io > 0.15) * obs.area).sum( + ("x", "y"), skipna=True + ) - clim_antarctic_obs = to_ice_con_ds(total_extent_antarctic_obs,obs).temporal.climatology(obs_var,freq="month") - clim_sa_obs = to_ice_con_ds(total_extent_sa_obs,obs).temporal.climatology(obs_var,freq="month") - clim_sp_obs = to_ice_con_ds(total_extent_sp_obs,obs).temporal.climatology(obs_var,freq="month") - clim_io_obs = to_ice_con_ds(total_extent_io_obs,obs).temporal.climatology(obs_var,freq="month") + clim_antarctic_obs = to_ice_con_ds( + total_extent_antarctic_obs, obs + ).temporal.climatology(obs_var, freq="month") + clim_sa_obs = to_ice_con_ds(total_extent_sa_obs, obs).temporal.climatology( + obs_var, freq="month" + ) + clim_sp_obs = to_ice_con_ds(total_extent_sp_obs, obs).temporal.climatology( + obs_var, freq="month" + ) + clim_io_obs = to_ice_con_ds(total_extent_io_obs, obs).temporal.climatology( + obs_var, freq="month" + ) antarctic_clims[source] = { "antarctic": clim_antarctic_obs, "io": clim_io_obs, "sp": clim_sp_obs, - "sa": clim_sa_obs + "sa": clim_sa_obs, } antarctic_means[source] = { - "antarctic": total_extent_antarctic_obs.mean("time",skipna=True).data.item(), - "io": total_extent_io_obs.mean("time",skipna=True).data.item(), - "sp": total_extent_sp_obs.mean("time",skipna=True).data.item(), - "sa": total_extent_sa_obs.mean("time",skipna=True).data.item() + "antarctic": total_extent_antarctic_obs.mean( + "time", skipna=True + ).data.item(), + "io": total_extent_io_obs.mean("time", skipna=True).data.item(), + "sp": total_extent_sp_obs.mean("time", skipna=True).data.item(), + "sa": total_extent_sa_obs.mean("time", skipna=True).data.item(), } obs.close() - obs_clims = {"nt": {},"bt":{}} - obs_means = {"nt": {},"bt":{}} + obs_clims = {"nt": {}, "bt": {}} + obs_means = {"nt": {}, "bt": {}} for item in antarctic_clims["nt"]: obs_clims["nt"][item] = antarctic_clims["nt"][item] obs_means["nt"][item] = antarctic_means["nt"][item] @@ -363,8 +439,8 @@ def replace_multi(string, rdict): #### Do model part # Loop over models - clim_wts = [31., 28., 31., 30., 31., 30., 31., 31., 30., 31., 30., 31.] - clim_wts = [x/365 for x in clim_wts] + clim_wts = [31.0, 28.0, 31.0, 30.0, 31.0, 30.0, 31.0, 31.0, 30.0, 31.0, 30.0, 31.0] + clim_wts = [x / 365 for x in clim_wts] mse = {} metrics = { "DIMENSIONS": { @@ -378,30 +454,42 @@ def replace_multi(string, rdict): "region": {}, "index": { "monthly_clim": "Monthly climatology of extent", - "total_extent": "Sum of ice coverage where concentration > 15%" - }, - "statistic": { - "mse": "Mean Square Error (10^12 km^4)" + "total_extent": "Sum of ice coverage where concentration > 15%", }, + "statistic": {"mse": "Mean Square Error (10^12 km^4)"}, "model": model_list, }, "RESULTS": {}, - "model_year_range": {} + "model_year_range": {}, } print(model_list) for model in model_list: + start_year = msyear + end_year = meyear + + totals_dict = { + "arctic": 0, + "ca": 0, + "na": 0, + "np": 0, + "antarctic": 0, + "sp": 0, + "sa": 0, + "io": 0, + } + mse[model] = {"nasateam": {}, "bootstrap": {}} + tags = { - "%(variable)": var, - "%(model)": model, - "%(model_version)": model, - "%(realization)": "*" - } + "%(variable)": var, + "%(model)": model, + "%(model_version)": model, + "%(realization)": "*", + } if find_all_realizations: test_data_full_path_tmp = os.path.join(test_data_path, filename_template) test_data_full_path_tmp = replace_multi(test_data_full_path_tmp, tags) ncfiles = glob.glob(test_data_full_path_tmp) - print(ncfiles) realizations = [] for ncfile in ncfiles: basename = ncfile.split("/")[-1] @@ -411,170 +499,279 @@ def replace_multi(string, rdict): else: if basename.split(".")[3] not in realizations: realizations.append(basename.split(".")[3]) - - print("=================================") + + print("\n=================================") print("model, runs:", model, realizations) list_of_runs = realizations else: list_of_runs = realizations - start_year = msyear - end_year = meyear - - totals_dict = {"arctic": 0, - "ca": 0, - "na": 0, - "np": 0, - "antarctic": 0, - "sp": 0, - "sa": 0, - "io": 0} - mse[model] = { - "nasateam": {}, - "bootstrap": {} - } - # Model grid area - print(replace_multi(area_template,tags)) - area = xc.open_dataset(glob.glob(replace_multi(area_template,tags))[0]) - area[area_var] = adjust_units(area[area_var],AreaUnitsAdjust) - - # Loop over realizations - for run_ind,run in enumerate(list_of_runs): - - # Find model data, determine number of files, check if they exist - tags = { + print(replace_multi(area_template, tags)) + area = xc.open_dataset(glob.glob(replace_multi(area_template, tags))[0]) + area[area_var] = adjust_units(area[area_var], AreaUnitsAdjust) + + if len(list_of_runs) > 1: + # Loop over realizations + for run_ind, run in enumerate(list_of_runs): + # Find model data, determine number of files, check if they exist + tags = { "%(variable)": var, "%(model)": model, "%(model_version)": model, "%(realization)": run, - } - test_data_full_path = os.path.join(test_data_path, filename_template) - test_data_full_path = replace_multi(test_data_full_path, tags) - test_data_full_path = glob.glob(test_data_full_path) - test_data_full_path.sort() - if len(test_data_full_path) == 0: - print("") - print("-----------------------") - print("Not found: model, run, variable:", model, run, var) - continue - else: - print("") - print("-----------------------") - print("model, run, variable:", model, run, var) - print("test_data (model in this case) full_path:") - for t in test_data_full_path: - print(" ", t) - - # Load and prep data - ds = load_dataset(test_data_full_path) - ds[var] = adjust_units(ds[var],ModUnitsAdjust) - try: - xvar = get_longitude(ds).name - yvar = get_latitude(ds).name - except Exception as e: - print("Could not get latitude or longitude variables") - print(" Error:",e) - continue - print("xvar", xvar) - print("yvar", yvar) - if (ds[xvar] < -180).any(): - ds[xvar] = ds[xvar].where(ds[xvar] >= -180, ds[xvar]+360) - if ds[xvar].min() >= 0: - ds[xvar] = ds[xvar].where(ds[xvar] >= 180, ds[xvar]-360) - - # Get time slice if year parameters exist - if start_year is not None: - ds = ds.sel({"time":slice("{0}-01-01".format(start_year), "{0}-12-31".format(end_year))}) - yr_range = [str(start_year),str(end_year)] - else: - # Get labels for start/end years from dataset - yr_range = [str(int(ds.time.dt.year[0])),str(int(ds.time.dt.year[-1]))] - - # Get regions - data_arctic = ds[var].where(ds[yvar] > 0, 0) - data_antarctic = ds[var].where(ds[yvar] < 0, 0) - data_ca1 = ds[var].where(( - (ds[yvar] > 80) & - (ds[yvar] <= 87.2) & - (ds[xvar] > -120) & - (ds[xvar] <= 90)),0) - data_ca2 = ds[var].where( - ((ds[yvar] > 65) & (ds[yvar] < 87.2)) & - ((ds[xvar] > 90) | (ds[xvar] <= -120)),0) - data_ca = data_ca1 + data_ca2 - data_np = ds[var].where( - (ds[yvar] > 35) & - (ds[yvar] <= 65) & - ((ds[xvar] > 90) | (ds[xvar] <= -120)),0) - data_na = ds[var].where( - (ds[yvar] > 45) & - (ds[yvar] <= 80) & - (ds[xvar] > -120) & - (ds[xvar] <= 90),0) - data_na = data_na - data_na.where( - (ds[yvar] > 45) & - (ds[yvar] <= 50) & - (ds[xvar] > 30) & - (ds[xvar] <= 60),0) - data_sa = ds[var].where( - (ds[yvar] > -90) & (ds[yvar] <= -55) & - (ds[xvar] > -60) & (ds[xvar] <= 20)) - data_sp = ds[var].where( - (ds[yvar] > -90) & (ds[yvar] <= -55) & - ((ds[xvar] > 90) | (ds[xvar] <= -60))) - data_io = ds[var].where( - (ds[yvar] > -90) & (ds[yvar] <= -55) & - (ds[xvar] > 20) & (ds[xvar] <= 90)) - - regions_dict = { - "arctic": data_arctic, - "antarctic": data_antarctic, - "ca": data_ca, - "np": data_np, - "na": data_na, - "sa": data_sa, - "sp": data_sp, - "io": data_io - } - - # Running sum of all realizations - for rgn in regions_dict: - data = regions_dict[rgn] - totals_dict[rgn] = totals_dict[rgn] + (data.where(data > 0.15, 0) * area[area_var]).sum((xvar,yvar),skipna=True) - - ds.close() - - print("Calculating model average metrics") - for rgn in regions_dict: - print(rgn) - # Set up metrics dictionary - for key in ["nasateam","bootstrap"]: - mse[model][key][rgn] = { - "monthly_clim": {"mse": None}, - "total_extent": {"mse": None} } - - # Average all realizations, fix bounds, get climatologies and totals - total_rgn = (totals_dict[rgn] / len(list_of_runs)).to_dataset(name=var) - #total_rgn.time.attrs.pop("bounds") - total_rgn = total_rgn.bounds.add_missing_bounds() - clim_extent = total_rgn.temporal.climatology(var,freq="month") - total = total_rgn.mean("time")[var].data - - # Get errors, convert to 1e-12 km^-4 - mse[model]["nasateam"][rgn]["monthly_clim"]["mse"] = str(mse_t(clim_extent[var],obs_clims["nt"][rgn]["ice_con"],weights=clim_wts)*1e-12) - mse[model]["bootstrap"][rgn]["monthly_clim"]["mse"] = str(mse_t(clim_extent[var],obs_clims["bt"][rgn]["ice_con"],weights=clim_wts)*1e-12) - mse[model]["nasateam"][rgn]["total_extent"]["mse"] = str(mse_model(total,obs_means["nt"][rgn])*1e-12) - mse[model]["bootstrap"][rgn]["total_extent"]["mse"] = str(mse_model(total,obs_means["bt"][rgn])*1e-12) - - # Update year list - metrics["model_year_range"][model] = [str(start_year),str(end_year)] - - metrics["RESULTS"]=mse - - metricsfile = os.path.join(metrics_output_path,"metrics_demo.json") - JSON = Base(metrics_output_path,"metrics_demo.json") + test_data_full_path = os.path.join(test_data_path, filename_template) + test_data_full_path = replace_multi(test_data_full_path, tags) + test_data_full_path = glob.glob(test_data_full_path) + test_data_full_path.sort() + if len(test_data_full_path) == 0: + print("") + print("-----------------------") + print("Not found: model, run, variable:", model, run, var) + continue + else: + print("") + print("-----------------------") + print("model, run, variable:", model, run, var) + print("test_data (model in this case) full_path:") + for t in test_data_full_path: + print(" ", t) + + # Load and prep data + ds = load_dataset(test_data_full_path) + ds[var] = adjust_units(ds[var], ModUnitsAdjust) + xvar = find_lon(ds) + yvar = find_lat(ds) + if xvar is None or yvar is None: + print("Could not get latitude or longitude variables") + continue + if (ds[xvar] < -180).any(): + ds[xvar] = ds[xvar].where(ds[xvar] >= -180, ds[xvar] + 360) + # if ds[xvar].min() >= 0: + # ds[xvar] = ds[xvar].where(ds[xvar] <= 180, ds[xvar]-360) + + # Get time slice if year parameters exist + if start_year is not None: + ds = ds.sel( + { + "time": slice( + "{0}-01-01".format(start_year), + "{0}-12-31".format(end_year), + ) + } + ) + yr_range = [str(start_year), str(end_year)] + else: + # Get labels for start/end years from dataset + yr_range = [ + str(int(ds.time.dt.year[0])), + str(int(ds.time.dt.year[-1])), + ] + + # Get regions + data_arctic = ds[var].where(ds[yvar] > 0, 0) + data_antarctic = ds[var].where(ds[yvar] < 0, 0) + + # Define regions depending on -180:180 or 0:360 map + if (ds[xvar] > 180).any(): + data_ca1 = ds[var].where( + ( + (ds[yvar] > 80) + & (ds[yvar] <= 87.2) + & ((ds[xvar] > 240) | (ds[xvar] <= 90)) + ), + 0, + ) + data_ca2 = ds[var].where( + ((ds[yvar] > 65) & (ds[yvar] < 87.2)) + & ((ds[xvar] > 90) & (ds[xvar] <= 240)), + 0, + ) + data_ca = data_ca1 + data_ca2 + data_np = ds[var].where( + (ds[yvar] > 35) + & (ds[yvar] <= 65) + & ((ds[xvar] > 90) & (ds[xvar] <= 240)), + 0, + ) + data_na = ds[var].where( + (ds[yvar] > 45) + & (ds[yvar] <= 80) + & ((ds[xvar] > 240) | (ds[xvar] <= 90)), + 0, + ) + data_na = data_na - data_na.where( + (ds[yvar] > 45) + & (ds[yvar] <= 50) + & (ds[xvar] > 30) + & (ds[xvar] <= 60), + 0, + ) + data_sa = ds[var].where( + (ds[yvar] > -90) + & (ds[yvar] <= -55) + & ((ds[xvar] > 300) | (ds[xvar] <= 20)), + 0, + ) + data_sp = ds[var].where( + (ds[yvar] > -90) + & (ds[yvar] <= -55) + & ((ds[xvar] > 90) & (ds[xvar] <= 300)), + 0, + ) + data_io = ds[var].where( + (ds[yvar] > -90) + & (ds[yvar] <= -55) + & (ds[xvar] > 20) + & (ds[xvar] <= 90), + 0, + ) + else: + data_ca1 = ds[var].where( + ( + (ds[yvar] > 80) + & (ds[yvar] <= 87.2) + & (ds[xvar] > -120) + & (ds[xvar] <= 90) + ), + 0, + ) + data_ca2 = ds[var].where( + ((ds[yvar] > 65) & (ds[yvar] < 87.2)) + & ((ds[xvar] > 90) | (ds[xvar] <= -120)), + 0, + ) + data_ca = data_ca1 + data_ca2 + data_np = ds[var].where( + (ds[yvar] > 35) + & (ds[yvar] <= 65) + & ((ds[xvar] > 90) | (ds[xvar] <= -120)), + 0, + ) + data_na = ds[var].where( + (ds[yvar] > 45) + & (ds[yvar] <= 80) + & (ds[xvar] > -120) + & (ds[xvar] <= 90), + 0, + ) + data_na = data_na - data_na.where( + (ds[yvar] > 45) + & (ds[yvar] <= 50) + & (ds[xvar] > 30) + & (ds[xvar] <= 60), + 0, + ) + data_sa = ds[var].where( + (ds[yvar] > -90) + & (ds[yvar] <= -55) + & (ds[xvar] > -60) + & (ds[xvar] <= 20), + 0, + ) + data_sp = ds[var].where( + (ds[yvar] > -90) + & (ds[yvar] <= -55) + & ((ds[xvar] > 90) | (ds[xvar] <= -60)), + 0, + ) + data_io = ( + ds[var].where( + (ds[yvar] > -90) + & (ds[yvar] <= -55) + & (ds[xvar] > 20) + & (ds[xvar] <= 90) + ), + 0, + ) + + regions_dict = { + "arctic": data_arctic.copy(deep=True), + "antarctic": data_antarctic.copy(deep=True), + "ca": data_ca.copy(deep=True), + "np": data_np.copy(deep=True), + "na": data_na.copy(deep=True), + "sa": data_sa.copy(deep=True), + "sp": data_sp.copy(deep=True), + "io": data_io.copy(deep=True), + } + ds.close() + # Running sum of all realizations + for rgn in regions_dict: + data = regions_dict[rgn] + # coordinates aren't always the same as lat/lon names, + # especially if lat/lon are 2D + if len(data[xvar].dims) == 2: + lon_j, lon_i = data[xvar].dims + elif len(data[xvar].dims) == 1: + lon_j = xvar + lon_i = yvar + # area data doesn't always use same coordinates as siconc data in CMIP6 + # so we multiply by area.data, dropping the coordinates + totals_dict[rgn] = totals_dict[rgn] + ( + data.where(data > 0.15, 0) * area[area_var].data + ).sum((lon_j, lon_i), skipna=True) + + print("\n-------------------------------------------") + print("Calculating model regional average metrics \nfor ", model) + print("--------------------------------------------") + for rgn in totals_dict: + print(rgn) + # Set up metrics dictionary + for key in ["nasateam", "bootstrap"]: + mse[model][key][rgn] = { + "monthly_clim": {"mse": None}, + "total_extent": {"mse": None}, + } + + # Average all realizations, fix bounds, get climatologies and totals + total_rgn = (totals_dict[rgn] / len(list_of_runs)).to_dataset(name=var) + # total_rgn.time.attrs.pop("bounds") + total_rgn = total_rgn.bounds.add_missing_bounds() + clim_extent = total_rgn.temporal.climatology(var, freq="month") + total = total_rgn.mean("time")[var].data + + # Get errors, convert to 1e-12 km^-4 + mse[model]["nasateam"][rgn]["monthly_clim"]["mse"] = str( + mse_t( + clim_extent[var], + obs_clims["nt"][rgn]["ice_con"], + weights=clim_wts, + ) + * 1e-12 + ) + mse[model]["bootstrap"][rgn]["monthly_clim"]["mse"] = str( + mse_t( + clim_extent[var], + obs_clims["bt"][rgn]["ice_con"], + weights=clim_wts, + ) + * 1e-12 + ) + mse[model]["nasateam"][rgn]["total_extent"]["mse"] = str( + mse_model(total, obs_means["nt"][rgn]) * 1e-12 + ) + mse[model]["bootstrap"][rgn]["total_extent"]["mse"] = str( + mse_model(total, obs_means["bt"][rgn]) * 1e-12 + ) + + # Update year list + metrics["model_year_range"][model] = [str(start_year), str(end_year)] + else: + for rgn in totals_dict: + # Set up metrics dictionary + for key in ["nasateam", "bootstrap"]: + mse[model][key][rgn] = { + "monthly_clim": {"mse": None}, + "total_extent": {"mse": None}, + } + metrics["model_year_range"][model] = ["", ""] + + metrics["RESULTS"] = mse + + metricsfile = os.path.join(metrics_output_path, "metrics_demo.json") + JSON = Base(metrics_output_path, "metrics_demo.json") json_structure = metrics["DIMENSIONS"]["json_structure"] JSON.write( metrics, @@ -583,69 +780,89 @@ def replace_multi(string, rdict): indent=4, separators=(",", ": "), ) - meta.update_metrics("metrics", metricsfile, "metrics_JSON", "JSON file containig regional sea ice metrics") - - sector_list = ["Central Arctic Sector", - "North Atlantic Sector", - "North Pacific Sector", - "Indian Ocean Sector", - "South Atlantic Sector", - "South Pacific Sector"] - sector_short = ["ca","na","np","io","sa","sp"] - fig7,ax7 = plt.subplots(6,1,figsize=(5,9)) - mlabels = model_list+["bootstrap"] + meta.update_metrics( + "metrics", + metricsfile, + "metrics_JSON", + "JSON file containig regional sea ice metrics", + ) + + sector_list = [ + "Central Arctic Sector", + "North Atlantic Sector", + "North Pacific Sector", + "Indian Ocean Sector", + "South Atlantic Sector", + "South Pacific Sector", + ] + sector_short = ["ca", "na", "np", "io", "sa", "sp"] + fig7, ax7 = plt.subplots(6, 1, figsize=(5, 9)) + mlabels = model_list + ["bootstrap"] ind = np.arange(len(mlabels)) # the x locations for the groups # ind = np.arange(len(mods)+1) # the x locations for the groups width = 0.3 n = len(ind) - 1 - for inds,sector in enumerate(sector_list): + for inds, sector in enumerate(sector_list): # Assemble data mse_clim = [] mse_ext = [] rgn = sector_short[inds] for model in model_list: - mse_clim.append(float(metrics["RESULTS"][model]["nasateam"][rgn]["monthly_clim"]["mse"])) - mse_ext.append(float(metrics["RESULTS"][model]["nasateam"][rgn]["total_extent"]["mse"])) - mse_clim.append(mse_t(obs_clims["bt"][rgn]["ice_con"],obs_clims["nt"][rgn]["ice_con"],weights=clim_wts)*1e-12) - mse_ext.append(mse_model(obs_means["bt"][rgn],obs_means["nt"][rgn])*1e-12) + mse_clim.append( + float(metrics["RESULTS"][model]["nasateam"][rgn]["monthly_clim"]["mse"]) + ) + mse_ext.append( + float(metrics["RESULTS"][model]["nasateam"][rgn]["total_extent"]["mse"]) + ) + mse_clim.append( + mse_t( + obs_clims["bt"][rgn]["ice_con"], + obs_clims["nt"][rgn]["ice_con"], + weights=clim_wts, + ) + * 1e-12 + ) + mse_ext.append(mse_model(obs_means["bt"][rgn], obs_means["nt"][rgn]) * 1e-12) # Make figure ax7[inds].bar(ind, mse_clim, width, color="b") ax7[inds].bar(ind, mse_ext, width, color="r") - #ax7[inds].bar(ind[n], obs[sector_short[inds]]**2, width, color="b") - if inds==len(sector_list)-1: + # ax7[inds].bar(ind[n], obs[sector_short[inds]]**2, width, color="b") + if inds == len(sector_list) - 1: ax7[inds].set_xticks(ind + width / 2.0, mlabels, rotation=90, size=5) else: - ax7[inds].set_xticks(ind + width/2.0,labels="") - datamax=np.max(mse_clim) - ymax = (datamax)*1.3 - ax7[inds].set_ylim(0.,ymax) + ax7[inds].set_xticks(ind + width / 2.0, labels="") + datamax = np.max(mse_clim) + ymax = (datamax) * 1.3 + ax7[inds].set_ylim(0.0, ymax) if ymax < 1: - ticks = np.linspace(0,1,5) - labels = [str(round(x,1)) for x in ticks] + ticks = np.linspace(0, 1, 5) + labels = [str(round(x, 1)) for x in ticks] elif ymax < 4: - ticks = np.linspace(0,round(ymax),num=round(ymax/2)*4+1) - labels = [str(round(x,1)) for x in ticks] + ticks = np.linspace(0, round(ymax), num=round(ymax / 2) * 4 + 1) + labels = [str(round(x, 1)) for x in ticks] elif ymax > 10: - ticks = range(0,round(ymax),5) - labels = [str(round(x,0)) for x in ticks] + ticks = range(0, round(ymax), 5) + labels = [str(round(x, 0)) for x in ticks] else: - ticks = range(0,round(ymax)) - labels = [str(round(x,0)) for x in ticks] + ticks = range(0, round(ymax)) + labels = [str(round(x, 0)) for x in ticks] - ax7[inds].set_yticks(ticks,labels,fontsize=6) + ax7[inds].set_yticks(ticks, labels, fontsize=6) - ax7[inds].set_ylabel("10${^12}$km${^4}$",size=6) - ax7[inds].grid(True,linestyle=":") + ax7[inds].set_ylabel("10${^12}$km${^4}$", size=6) + ax7[inds].grid(True, linestyle=":") ax7[inds].annotate( sector, (0.35, 0.8), xycoords="axes fraction", size=8, ) - figfile=os.path.join(metrics_output_path,"demo_fig.png") + figfile = os.path.join(metrics_output_path, "demo_fig.png") plt.savefig(figfile) - meta.update_plots("bar_chart", figfile, "regional_bar_chart", "Bar chart of regional MSE") + meta.update_plots( + "bar_chart", figfile, "regional_bar_chart", "Bar chart of regional MSE" + ) # Update and write metadata file try: @@ -659,4 +876,4 @@ def replace_multi(string, rdict): meta.update_provenance("modeldata", test_data_path) if reference_data_path is not None: meta.update_provenance("obsdata", reference_data_path) - meta.write() \ No newline at end of file + meta.write() From 50085d43bd5cd61fefe73c166e47174e104b0f58 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Wed, 10 Jan 2024 11:49:50 -0800 Subject: [PATCH 23/69] update for symlinks --- pcmdi_metrics/sea_ice/parameter_file.py | 26 ++++++++++++++++++------- 1 file changed, 19 insertions(+), 7 deletions(-) diff --git a/pcmdi_metrics/sea_ice/parameter_file.py b/pcmdi_metrics/sea_ice/parameter_file.py index fd02357ff..ea915e9a7 100644 --- a/pcmdi_metrics/sea_ice/parameter_file.py +++ b/pcmdi_metrics/sea_ice/parameter_file.py @@ -18,19 +18,31 @@ # CMIP6 #======= -#test_data_set=["E3SM-1-0"] +case_id="cmip6" +test_data_set=[ + "E3SM-1-0", + "CanESM5", + "CAS-ESM2-0", + "GFDL-ESM4", + "E3SM-2-0", + "MIROC6", + "ACCESS-CM2", + "ACCESS-ESM1-5" + ] realization="*" -#test_data_path="/p/user_pub/cmip/CMIP6/CMIP/*/%(model)/historical/%(realization)/SImon/siconc/*/v20190806/" -#filename_template="siconc_SImon_%(model)_historical_%(realization)_*_*.nc" +test_data_path="links_siconc/%(model)/historical/%(realization)/siconc/" +filename_template="siconc_SImon_%(model)_historical_%(realization)_*_*.nc" var="siconc" -msyear=1991 -meyear=2010 +msyear=1981 +meyear=2000 ModUnitsAdjust=(True,"multiply",1e-2) -area_template="/p/css03/esgf_publish/CMIP6/CMIP/*/%(model)/historical/*/fx/areacella/*/*/areacella_fx_%(model)_historical_%(realization)_*.nc" -area_var="areacella" +area_template="links_area/%(model)/*.nc" +area_var="areacello" AreaUnitsAdjust=(True,"multiply",1e-6) +metrics_output_path="demo/%(case_id)/" + # Reference is hard coded currently so this is a placeholder From 50da0c39d8c7c3db3ff4126c0026037de97adf8c Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Wed, 10 Jan 2024 11:51:44 -0800 Subject: [PATCH 24/69] fix formatting --- pcmdi_metrics/sea_ice/parameter_file.py | 81 ++++++++++++------------- 1 file changed, 40 insertions(+), 41 deletions(-) diff --git a/pcmdi_metrics/sea_ice/parameter_file.py b/pcmdi_metrics/sea_ice/parameter_file.py index ea915e9a7..7cd09858f 100644 --- a/pcmdi_metrics/sea_ice/parameter_file.py +++ b/pcmdi_metrics/sea_ice/parameter_file.py @@ -1,53 +1,52 @@ # CMIP5 -#========= +# ========= # Model settings -#test_data_set=["ACCESS1-3","GISS-E2-H","NorESM1-M"] -#test_data_set=["ACCESS1-3"] -#realization=["*"] -#test_data_path = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2/additional_xmls/latest/v20231104/cmip5/historical/seaIce/mon/sic/" -#filename_template= "cmip5.historical.%(model).%(realization).mon.sic.xml" -#var="sic" -#msyear=1981 -#meyear=2010 -#ModUnitsAdjust=(True,"multiply",1e-2) +# test_data_set=["ACCESS1-3","GISS-E2-H","NorESM1-M"] +# test_data_set=["ACCESS1-3"] +# realization=["*"] +# test_data_path = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2/additional_xmls/latest/v20231104/cmip5/historical/seaIce/mon/sic/" +# filename_template= "cmip5.historical.%(model).%(realization).mon.sic.xml" +# var="sic" +# msyear=1981 +# meyear=2010 +# ModUnitsAdjust=(True,"multiply",1e-2) # Model area file -#area_template="/p/user_pub/hoang1-backups/ARCHIVE/ivanova2/IceMetrics/CMIP5/AREACELLO/areacello_fx_%(model)_historical_r0i0p0.nc" -#area_var = "areacello" -#AreaUnitsAdjust = (True, "multiply", 1e-6) +# area_template="/p/user_pub/hoang1-backups/ARCHIVE/ivanova2/IceMetrics/CMIP5/AREACELLO/areacello_fx_%(model)_historical_r0i0p0.nc" +# area_var = "areacello" +# AreaUnitsAdjust = (True, "multiply", 1e-6) # CMIP6 -#======= -case_id="cmip6" -test_data_set=[ - "E3SM-1-0", +# ======= +case_id = "cmip6" +test_data_set = [ + "E3SM-1-0", "CanESM5", - "CAS-ESM2-0", - "GFDL-ESM4", - "E3SM-2-0", - "MIROC6", - "ACCESS-CM2", - "ACCESS-ESM1-5" - ] -realization="*" -test_data_path="links_siconc/%(model)/historical/%(realization)/siconc/" -filename_template="siconc_SImon_%(model)_historical_%(realization)_*_*.nc" -var="siconc" -msyear=1981 -meyear=2000 -ModUnitsAdjust=(True,"multiply",1e-2) + "CAS-ESM2-0", + "GFDL-ESM4", + "E3SM-2-0", + "MIROC6", + "ACCESS-CM2", + "ACCESS-ESM1-5", +] +realization = "*" +test_data_path = "links_siconc/%(model)/historical/%(realization)/siconc/" +filename_template = "siconc_SImon_%(model)_historical_%(realization)_*_*.nc" +var = "siconc" +msyear = 1981 +meyear = 2000 +ModUnitsAdjust = (True, "multiply", 1e-2) -area_template="links_area/%(model)/*.nc" -area_var="areacello" -AreaUnitsAdjust=(True,"multiply",1e-6) +area_template = "links_area/%(model)/*.nc" +area_var = "areacello" +AreaUnitsAdjust = (True, "multiply", 1e-6) -metrics_output_path="demo/%(case_id)/" +metrics_output_path = "demo/%(case_id)/" # Reference is hard coded currently so this is a placeholder - -#ObsUnitsAdjust=(True,"multiply",1e-2) -#reference_data_set=None -#osyear=1981 -#oeyear=2010 -#obsvar="" \ No newline at end of file +# ObsUnitsAdjust=(True,"multiply",1e-2) +# reference_data_set=None +# osyear=1981 +# oeyear=2010 +# obsvar="" From 6a1a2cc24159b7c3bd38f14393a06de3c44c6621 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 11 Jan 2024 16:16:46 -0800 Subject: [PATCH 25/69] rework regions --- pcmdi_metrics/sea_ice/ice_driver.py | 304 +++++++++++++--------------- 1 file changed, 144 insertions(+), 160 deletions(-) diff --git a/pcmdi_metrics/sea_ice/ice_driver.py b/pcmdi_metrics/sea_ice/ice_driver.py index 647d5fb4e..85cddd7ee 100644 --- a/pcmdi_metrics/sea_ice/ice_driver.py +++ b/pcmdi_metrics/sea_ice/ice_driver.py @@ -13,6 +13,7 @@ from pcmdi_metrics.io import xcdat_openxml from pcmdi_metrics.io.base import Base +from pcmdi_metrics.utils import create_land_sea_mask from pcmdi_metrics.io import ( # noqa get_axis_list, @@ -79,6 +80,110 @@ def write(self): json.dump(self.json, f, indent=4) +def sea_ice_regions(ds, var, xvar, yvar): + # Two sets of region definitions are provided, one for + # -180:180 and one for 0:360 longitude ranges + data_arctic = ds[var].where(ds[yvar] > 0, 0) + data_antarctic = ds[var].where(ds[yvar] < 0, 0) + if (ds[xvar] > 180).any(): # 0 to 360 + data_ca1 = ds[var].where( + ( + (ds[yvar] > 80) + & (ds[yvar] <= 87.2) + & ((ds[xvar] > 240) | (ds[xvar] <= 90)) + ), + 0, + ) + data_ca2 = ds[var].where( + ((ds[yvar] > 65) & (ds[yvar] < 87.2)) + & ((ds[xvar] > 90) & (ds[xvar] <= 240)), + 0, + ) + data_ca = data_ca1 + data_ca2 + data_np = ds[var].where( + (ds[yvar] > 35) & (ds[yvar] <= 65) & ((ds[xvar] > 90) & (ds[xvar] <= 240)), + 0, + ) + data_na = ds[var].where( + (ds[yvar] > 45) & (ds[yvar] <= 80) & ((ds[xvar] > 240) | (ds[xvar] <= 90)), + 0, + ) + data_na = data_na - data_na.where( + (ds[yvar] > 45) & (ds[yvar] <= 50) & (ds[xvar] > 30) & (ds[xvar] <= 60), + 0, + ) + data_sa = ds[var].where( + (ds[yvar] > -90) + & (ds[yvar] <= -40) + & ((ds[xvar] > 300) | (ds[xvar] <= 20)), + 0, + ) + data_sp = ds[var].where( + (ds[yvar] > -90) + & (ds[yvar] <= -40) + & ((ds[xvar] > 90) & (ds[xvar] <= 300)), + 0, + ) + data_io = ds[var].where( + (ds[yvar] > -90) & (ds[yvar] <= -40) & (ds[xvar] > 20) & (ds[xvar] <= 90), + 0, + ) + else: # -180 to 180 + data_ca1 = ds[var].where( + ( + (ds[yvar] > 80) + & (ds[yvar] <= 87.2) + & (ds[xvar] > -120) + & (ds[xvar] <= 90) + ), + 0, + ) + data_ca2 = ds[var].where( + ((ds[yvar] > 65) & (ds[yvar] < 87.2)) + & ((ds[xvar] > 90) | (ds[xvar] <= -120)), + 0, + ) + data_ca = data_ca1 + data_ca2 + data_np = ds[var].where( + (ds[yvar] > 35) & (ds[yvar] <= 65) & ((ds[xvar] > 90) | (ds[xvar] <= -120)), + 0, + ) + data_na = ds[var].where( + (ds[yvar] > 45) & (ds[yvar] <= 80) & (ds[xvar] > -120) & (ds[xvar] <= 90), + 0, + ) + data_na = data_na - data_na.where( + (ds[yvar] > 45) & (ds[yvar] <= 50) & (ds[xvar] > 30) & (ds[xvar] <= 60), + 0, + ) + data_sa = ds[var].where( + (ds[yvar] > -90) & (ds[yvar] <= -40) & (ds[xvar] > -60) & (ds[xvar] <= 20), + 0, + ) + data_sp = ds[var].where( + (ds[yvar] > -90) + & (ds[yvar] <= -40) + & ((ds[xvar] > 90) | (ds[xvar] <= -60)), + 0, + ) + data_io = ds[var].where( + (ds[yvar] > -90) & (ds[yvar] <= -40) & (ds[xvar] > 20) & (ds[xvar] <= 90), + 0, + ) + + regions_dict = { + "arctic": data_arctic.copy(deep=True), + "ca": data_ca.copy(deep=True), + "np": data_np.copy(deep=True), + "na": data_na.copy(deep=True), + "antarctic": data_antarctic.copy(deep=True), + "sa": data_sa.copy(deep=True), + "sp": data_sp.copy(deep=True), + "io": data_io.copy(deep=True), + } + return regions_dict + + def find_lon(ds): for key in ds.coords: if key in ["lon", "longitude"]: @@ -293,9 +398,12 @@ def replace_multi(string, rdict): obs = xc.open_dataset(arctic_files[source]) obs[obs_var] = adjust_units(obs[obs_var], ObsUnitsAdjust) obs["area"] = adjust_units(obs["area"], ObsAreaUnitsAdjust) - # mask=create_land_sea_mask(obs,lon_key="lon",lat_key="lat") + # Remove land areas (including lakes) + mask = create_land_sea_mask(obs, lon_key="lon", lat_key="lat") + obs[obs_var] = obs[obs_var].where(mask < 1) # Get regions - data_arctic = obs[obs_var].where((obs.lat > 0), 0) + rgn_dict = sea_ice_regions(obs, obs_var, "lon", "lat") + """data_arctic = obs[obs_var].where((obs.lat > 0), 0) data_ca1 = obs[obs_var].where( ((obs.lat > 80) & (obs.lat <= 87.2) & (obs.lon > -120) & (obs.lon <= 90)), 0 ) @@ -312,22 +420,21 @@ def replace_multi(string, rdict): ) data_na = data_na - data_na.where( (obs.lat > 45) & (obs.lat <= 50) & (obs.lon > 30) & (obs.lon <= 60), 0 - ) - # TODO: land/sea masking to remove lakes + )""" # Get ice extent total_extent_arctic_obs = ( - data_arctic.where(data_arctic > 0.15) * obs.area + rgn_dict["arctic"].where(rgn_dict["arctic"] > 0.15) * obs.area + ).sum(("x", "y"), skipna=True) + total_extent_ca_obs = ( + rgn_dict["ca"].where(rgn_dict["ca"] > 0.15) * obs.area + ).sum(("x", "y"), skipna=True) + total_extent_np_obs = ( + rgn_dict["np"].where(rgn_dict["np"] > 0.15) * obs.area + ).sum(("x", "y"), skipna=True) + total_extent_na_obs = ( + rgn_dict["na"].where(rgn_dict["na"] > 0.15) * obs.area ).sum(("x", "y"), skipna=True) - total_extent_ca_obs = (data_ca.where(data_ca > 0.15) * obs.area).sum( - ("x", "y"), skipna=True - ) - total_extent_np_obs = (data_np.where(data_np > 0.15) * obs.area).sum( - ("x", "y"), skipna=True - ) - total_extent_na_obs = (data_na.where(data_na > 0.15) * obs.area).sum( - ("x", "y"), skipna=True - ) clim_arctic_obs = to_ice_con_ds( total_extent_arctic_obs, obs @@ -365,7 +472,11 @@ def replace_multi(string, rdict): obs = xc.open_dataset(antarctic_files[source]) obs[obs_var] = adjust_units(obs[obs_var], ObsUnitsAdjust) obs["area"] = adjust_units(obs["area"], ObsAreaUnitsAdjust) - data_antarctic = obs[obs_var].where((obs.lat < 0), 0) + # Remove land areas (including lakes) + mask = create_land_sea_mask(obs, lon_key="lon", lat_key="lat") + obs[obs_var] = obs[obs_var].where(mask < 1) + rgn_dict = sea_ice_regions(obs, obs_var, "lon", "lat") + """data_antarctic = obs[obs_var].where((obs.lat < 0), 0) data_sa = obs[obs_var].where( (obs.lat > -90) & (obs.lat <= -55) & (obs.lon > -60) & (obs.lon <= 20), 0 ) @@ -374,21 +485,20 @@ def replace_multi(string, rdict): ) data_io = obs[obs_var].where( (obs.lat > -90) & (obs.lat <= -55) & (obs.lon > 20) & (obs.lon <= 90), 0 - ) - # TODO: land/sea masking to remove lakes; do antarctica for consistency + )""" total_extent_antarctic_obs = ( - data_antarctic.where(data_antarctic > 0.15) * obs.area + rgn_dict["antarctic"].where(rgn_dict["antarctic"] > 0.15) * obs.area + ).sum(("x", "y"), skipna=True) + total_extent_sa_obs = ( + rgn_dict["sa"].where(rgn_dict["sa"] > 0.15) * obs.area + ).sum(("x", "y"), skipna=True) + total_extent_sp_obs = ( + rgn_dict["sp"].where(rgn_dict["sp"] > 0.15) * obs.area + ).sum(("x", "y"), skipna=True) + total_extent_io_obs = ( + rgn_dict["io"].where(rgn_dict["io"] > 0.15) * obs.area ).sum(("x", "y"), skipna=True) - total_extent_sa_obs = (data_sa.where(data_sa > 0.15) * obs.area).sum( - ("x", "y"), skipna=True - ) - total_extent_sp_obs = (data_sp.where(data_sp > 0.15) * obs.area).sum( - ("x", "y"), skipna=True - ) - total_extent_io_obs = (data_io.where(data_io > 0.15) * obs.area).sum( - ("x", "y"), skipna=True - ) clim_antarctic_obs = to_ice_con_ds( total_extent_antarctic_obs, obs @@ -511,7 +621,7 @@ def replace_multi(string, rdict): area = xc.open_dataset(glob.glob(replace_multi(area_template, tags))[0]) area[area_var] = adjust_units(area[area_var], AreaUnitsAdjust) - if len(list_of_runs) > 1: + if len(list_of_runs) > 0: # Loop over realizations for run_ind, run in enumerate(list_of_runs): # Find model data, determine number of files, check if they exist @@ -529,7 +639,7 @@ def replace_multi(string, rdict): print("") print("-----------------------") print("Not found: model, run, variable:", model, run, var) - continue + break else: print("") print("-----------------------") @@ -545,11 +655,9 @@ def replace_multi(string, rdict): yvar = find_lat(ds) if xvar is None or yvar is None: print("Could not get latitude or longitude variables") - continue + break if (ds[xvar] < -180).any(): ds[xvar] = ds[xvar].where(ds[xvar] >= -180, ds[xvar] + 360) - # if ds[xvar].min() >= 0: - # ds[xvar] = ds[xvar].where(ds[xvar] <= 180, ds[xvar]-360) # Get time slice if year parameters exist if start_year is not None: @@ -570,132 +678,8 @@ def replace_multi(string, rdict): ] # Get regions - data_arctic = ds[var].where(ds[yvar] > 0, 0) - data_antarctic = ds[var].where(ds[yvar] < 0, 0) - - # Define regions depending on -180:180 or 0:360 map - if (ds[xvar] > 180).any(): - data_ca1 = ds[var].where( - ( - (ds[yvar] > 80) - & (ds[yvar] <= 87.2) - & ((ds[xvar] > 240) | (ds[xvar] <= 90)) - ), - 0, - ) - data_ca2 = ds[var].where( - ((ds[yvar] > 65) & (ds[yvar] < 87.2)) - & ((ds[xvar] > 90) & (ds[xvar] <= 240)), - 0, - ) - data_ca = data_ca1 + data_ca2 - data_np = ds[var].where( - (ds[yvar] > 35) - & (ds[yvar] <= 65) - & ((ds[xvar] > 90) & (ds[xvar] <= 240)), - 0, - ) - data_na = ds[var].where( - (ds[yvar] > 45) - & (ds[yvar] <= 80) - & ((ds[xvar] > 240) | (ds[xvar] <= 90)), - 0, - ) - data_na = data_na - data_na.where( - (ds[yvar] > 45) - & (ds[yvar] <= 50) - & (ds[xvar] > 30) - & (ds[xvar] <= 60), - 0, - ) - data_sa = ds[var].where( - (ds[yvar] > -90) - & (ds[yvar] <= -55) - & ((ds[xvar] > 300) | (ds[xvar] <= 20)), - 0, - ) - data_sp = ds[var].where( - (ds[yvar] > -90) - & (ds[yvar] <= -55) - & ((ds[xvar] > 90) & (ds[xvar] <= 300)), - 0, - ) - data_io = ds[var].where( - (ds[yvar] > -90) - & (ds[yvar] <= -55) - & (ds[xvar] > 20) - & (ds[xvar] <= 90), - 0, - ) - else: - data_ca1 = ds[var].where( - ( - (ds[yvar] > 80) - & (ds[yvar] <= 87.2) - & (ds[xvar] > -120) - & (ds[xvar] <= 90) - ), - 0, - ) - data_ca2 = ds[var].where( - ((ds[yvar] > 65) & (ds[yvar] < 87.2)) - & ((ds[xvar] > 90) | (ds[xvar] <= -120)), - 0, - ) - data_ca = data_ca1 + data_ca2 - data_np = ds[var].where( - (ds[yvar] > 35) - & (ds[yvar] <= 65) - & ((ds[xvar] > 90) | (ds[xvar] <= -120)), - 0, - ) - data_na = ds[var].where( - (ds[yvar] > 45) - & (ds[yvar] <= 80) - & (ds[xvar] > -120) - & (ds[xvar] <= 90), - 0, - ) - data_na = data_na - data_na.where( - (ds[yvar] > 45) - & (ds[yvar] <= 50) - & (ds[xvar] > 30) - & (ds[xvar] <= 60), - 0, - ) - data_sa = ds[var].where( - (ds[yvar] > -90) - & (ds[yvar] <= -55) - & (ds[xvar] > -60) - & (ds[xvar] <= 20), - 0, - ) - data_sp = ds[var].where( - (ds[yvar] > -90) - & (ds[yvar] <= -55) - & ((ds[xvar] > 90) | (ds[xvar] <= -60)), - 0, - ) - data_io = ( - ds[var].where( - (ds[yvar] > -90) - & (ds[yvar] <= -55) - & (ds[xvar] > 20) - & (ds[xvar] <= 90) - ), - 0, - ) + regions_dict = sea_ice_regions(ds, var, xvar, yvar) - regions_dict = { - "arctic": data_arctic.copy(deep=True), - "antarctic": data_antarctic.copy(deep=True), - "ca": data_ca.copy(deep=True), - "np": data_np.copy(deep=True), - "na": data_na.copy(deep=True), - "sa": data_sa.copy(deep=True), - "sp": data_sp.copy(deep=True), - "io": data_io.copy(deep=True), - } ds.close() # Running sum of all realizations for rgn in regions_dict: @@ -770,8 +754,8 @@ def replace_multi(string, rdict): metrics["RESULTS"] = mse - metricsfile = os.path.join(metrics_output_path, "metrics_demo.json") - JSON = Base(metrics_output_path, "metrics_demo.json") + metricsfile = os.path.join(metrics_output_path, "sea_ice_metrics.json") + JSON = Base(metrics_output_path, "sea_ice_metrics.json") json_structure = metrics["DIMENSIONS"]["json_structure"] JSON.write( metrics, @@ -858,7 +842,7 @@ def replace_multi(string, rdict): xycoords="axes fraction", size=8, ) - figfile = os.path.join(metrics_output_path, "demo_fig.png") + figfile = os.path.join(metrics_output_path, "MSE_bar_chart.png") plt.savefig(figfile) meta.update_plots( "bar_chart", figfile, "regional_bar_chart", "Bar chart of regional MSE" From 814cee5e50ceb926e86097ce6615bd08d7adfee2 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 11 Jan 2024 16:17:01 -0800 Subject: [PATCH 26/69] update links --- pcmdi_metrics/sea_ice/demo_param_file.py | 39 ++++++++++++++++++++++++ 1 file changed, 39 insertions(+) create mode 100644 pcmdi_metrics/sea_ice/demo_param_file.py diff --git a/pcmdi_metrics/sea_ice/demo_param_file.py b/pcmdi_metrics/sea_ice/demo_param_file.py new file mode 100644 index 000000000..1d7d452b2 --- /dev/null +++ b/pcmdi_metrics/sea_ice/demo_param_file.py @@ -0,0 +1,39 @@ +# Sea ice metrics parameter file + +# List of models to include in analysis +test_data_set = [ + "E3SM-1-0" +] + +# realization can be a single realization, a list of realizations, or "*" for all realizations +realization = "r1i2p2f1" + +# test_data_path is a template for the model data parent directory +test_data_path = "/p/user_pub/pmp/demo/sea-ice/links_siconc/%(model)/historical/%(realization)/siconc/" + +# filename_template is a template for the model data file name +# combine it with test_data_path to get complete data path +filename_template = "siconc_SImon_%(model)_historical_%(realization)_*_*.nc" + +# The name of the sea ice variable in the model data +var = "siconc" + +# Start and end years for model data +msyear = 1981 +meyear = 2010 + +# Factor for adjusting model data to decimal rather than percent units +ModUnitsAdjust = (True, "multiply", 1e-2) + +# Template for the grid area file +area_template = "/p/user_pub/pmp/demo/sea-ice/links_area/%(model)/*.nc" + +# Area variable name; likely 'areacello' or 'areacella' for CMIP6 +area_var = "areacello" + +# Factor to convert area units to km-2 +AreaUnitsAdjust = (True, "multiply", 1e-6) + +# Directory for writing outputs +case_id = "ex1" +metrics_output_path = "sea_ice_demo/%(case_id)/" From 145c942016cef8181dc4dee934f68fcda166fede Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 11 Jan 2024 16:17:28 -0800 Subject: [PATCH 27/69] add notebook draft --- pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb | 1705 ++++++++++++++++++++++ 1 file changed, 1705 insertions(+) create mode 100644 pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb diff --git a/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb b/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb new file mode 100644 index 000000000..fbe28da5d --- /dev/null +++ b/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb @@ -0,0 +1,1705 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "acb8d42e", + "metadata": {}, + "source": [ + "# Sea Ice Demo" + ] + }, + { + "cell_type": "markdown", + "id": "848c69e5", + "metadata": {}, + "source": [ + "The PCMDI Metrics sea ice driver produces metrics that compare modeled and observed sea ice extent. These metrics are the mean square errors of the total and climatological ice extent. Ice extent is defined as the area covered by sea ice concentration of >= 15%." + ] + }, + { + "cell_type": "markdown", + "id": "6bfd3b73", + "metadata": {}, + "source": [ + "This demo uses three CMIP6 models. The 'siconc' and 'areacello' variables are needed and can be found in the following directories. In addition, six other models are available that can be added to the analyses in this demo:\n", + "\n", + "/p/user_pub/pmp/demo/sea-ice/links_siconc \n", + "/p/user_pub/pmp/demo/sea-ice/links_area" + ] + }, + { + "cell_type": "markdown", + "id": "00d48042", + "metadata": {}, + "source": [ + "Add some info about observations and sectors" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a82ee330", + "metadata": {}, + "outputs": [], + "source": [ + "# To open and display one of the graphics\n", + "from IPython.display import display_png, JSON, Image" + ] + }, + { + "cell_type": "markdown", + "id": "5294910f", + "metadata": {}, + "source": [ + "## Basic example" + ] + }, + { + "cell_type": "markdown", + "id": "2540cd5d", + "metadata": {}, + "source": [ + "The PMP drivers can all read user arguments from parameter files. We provide a demo parameter file, which is shown below. Comments (beginning with a '#') explain each of the parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6e4fa38d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Sea ice metrics parameter file\n", + "\n", + "# List of models to include in analysis\n", + "test_data_set = [\n", + " \"E3SM-1-0\"\n", + "]\n", + "\n", + "# realization can be a single realization, a list of realizations, or \"*\" for all realizations\n", + "realization = \"r1i2p2f1\"\n", + "\n", + "# test_data_path is a template for the model data parent directory\n", + "test_data_path = \"/p/user_pub/pmp/demo/sea-ice/links_siconc/%(model)/historical/%(realization)/siconc/\"\n", + "\n", + "# filename_template is a template for the model data file name\n", + "# combine it with test_data_path to get complete data path\n", + "filename_template = \"siconc_SImon_%(model)_historical_%(realization)_*_*.nc\"\n", + "\n", + "# The name of the sea ice variable in the model data\n", + "var = \"siconc\"\n", + "\n", + "# Start and end years for model data\n", + "msyear = 1981\n", + "meyear = 2010\n", + "\n", + "# Factor for adjusting model data to decimal rather than percent units\n", + "ModUnitsAdjust = (True, \"multiply\", 1e-2)\n", + "\n", + "# Template for the grid area file\n", + "area_template = \"/p/user_pub/pmp/demo/sea-ice/links_area/%(model)/*.nc\"\n", + "\n", + "# Area variable name; likely 'areacello' or 'areacella' for CMIP6\n", + "area_var = \"areacello\"\n", + "\n", + "# Factor to convert area units to km-2\n", + "AreaUnitsAdjust = (True, \"multiply\", 1e-6)\n", + "\n", + "# Directory for writing outputs\n", + "case_id = \"ex1\"\n", + "metrics_output_path = \"sea_ice_demo/%(case_id)/\"\n", + "\n" + ] + } + ], + "source": [ + "with open(\"demo_param_file.py\") as f:\n", + " print(f.read())" + ] + }, + { + "cell_type": "markdown", + "id": "38dbe853", + "metadata": {}, + "source": [ + "To see all of the parameters available for the sea ice metrics, run the --help command as shown here:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9d6c1fbf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: ice_driver.py [-h] [--parameters PARAMETERS]\n", + " [--diags OTHER_PARAMETERS [OTHER_PARAMETERS ...]]\n", + " [--case_id CASE_ID] [-v VAR [VAR ...]]\n", + " [--area_var AREA_VAR]\n", + " [-r REFERENCE_DATA_SET [REFERENCE_DATA_SET ...]]\n", + " [--reference_data_path REFERENCE_DATA_PATH]\n", + " [-t TEST_DATA_SET [TEST_DATA_SET ...]]\n", + " [--test_data_path TEST_DATA_PATH]\n", + " [--realization REALIZATION]\n", + " [--filename_template FILENAME_TEMPLATE]\n", + " [--metrics_output_path METRICS_OUTPUT_PATH]\n", + " [--filename_output_template FILENAME_OUTPUT_TEMPLATE]\n", + " [--area_template AREA_TEMPLATE]\n", + " [--output_json_template OUTPUT_JSON_TEMPLATE] [--debug]\n", + " [--plots] [--osyear OSYEAR] [--msyear MSYEAR]\n", + " [--oeyear OEYEAR] [--meyear MEYEAR]\n", + " [--ObsUnitsAdjust OBSUNITSADJUST]\n", + " [--ModUnitsAdjust MODUNITSADJUST]\n", + " [--AreaUnitsAdjust AREAUNITSADJUST]\n", + " [--ObsAreaUnitsAdjust OBSAREAUNITSADJUST]\n", + "\n", + "options:\n", + " -h, --help show this help message and exit\n", + " --parameters PARAMETERS, -p PARAMETERS\n", + " --diags OTHER_PARAMETERS [OTHER_PARAMETERS ...], -d OTHER_PARAMETERS [OTHER_PARAMETERS ...]\n", + " Path to other user-defined parameter file. (default:\n", + " None)\n", + " --case_id CASE_ID Defines a subdirectory to the metrics output, so\n", + " multiplecases can be compared (default: None)\n", + " -v VAR [VAR ...], --var VAR [VAR ...]\n", + " Name of model sea ice concentration variable (default:\n", + " None)\n", + " --area_var AREA_VAR Name of model area variable (default: None)\n", + " -r REFERENCE_DATA_SET [REFERENCE_DATA_SET ...], --reference_data_set REFERENCE_DATA_SET [REFERENCE_DATA_SET ...]\n", + " List of observations or models that are used as a\n", + " reference against the test_data_set (default: None)\n", + " --reference_data_path REFERENCE_DATA_PATH\n", + " Path for the reference climitologies (default: None)\n", + " -t TEST_DATA_SET [TEST_DATA_SET ...], --test_data_set TEST_DATA_SET [TEST_DATA_SET ...]\n", + " List of observations or models to test against the\n", + " reference_data_set (default: None)\n", + " --test_data_path TEST_DATA_PATH\n", + " Path for the test climitologies (default: None)\n", + " --realization REALIZATION\n", + " A simulation parameter (default: None)\n", + " --filename_template FILENAME_TEMPLATE\n", + " Template for climatology files (default: None)\n", + " --metrics_output_path METRICS_OUTPUT_PATH\n", + " Directory of where to put the results (default: None)\n", + " --filename_output_template FILENAME_OUTPUT_TEMPLATE\n", + " Filename for the interpolated test climatologies\n", + " (default: None)\n", + " --area_template AREA_TEMPLATE\n", + " Filename template for model grid area (default: None)\n", + " --output_json_template OUTPUT_JSON_TEMPLATE\n", + " Filename template for results json files (default:\n", + " None)\n", + " --debug Turn on debugging mode by printing more information to\n", + " track progress (default: False)\n", + " --plots Set to True to generate figures. (default: False)\n", + " --osyear OSYEAR Start year for reference data set (default: None)\n", + " --msyear MSYEAR Start year for model data set (default: None)\n", + " --oeyear OEYEAR End year for reference data set (default: None)\n", + " --meyear MEYEAR End year for model data set (default: None)\n", + " --ObsUnitsAdjust OBSUNITSADJUST\n", + " Factor to convert obs sea ice concentration to\n", + " decimal. For example: - (True, 'divide', 100.0) #\n", + " percentage to decimal - (False, 0, 0) # No adjustment\n", + " (default) (default: (False, 0, 0))\n", + " --ModUnitsAdjust MODUNITSADJUST\n", + " Factor to convert model sea ice concentration to\n", + " decimal. For example: - (True, 'divide', 100.0) #\n", + " percentage to decimal - (False, 0, 0) # No adjustment\n", + " (default) (default: (False, 0, 0))\n", + " --AreaUnitsAdjust AREAUNITSADJUST\n", + " Factor to convert area data to km^2. For example: -\n", + " (True, 'multiply', 1e-6) # m^2 to km^2 - (False, 0, 0)\n", + " # No adjustment (default) (default: (False, 0, 0))\n", + " --ObsAreaUnitsAdjust OBSAREAUNITSADJUST\n", + " Factor to convert area data to km^2. For example: -\n", + " (True, 'multiply', 1e-6) # m^2 to km^2 - (False, 0, 0)\n", + " # No adjustment (default) (default: (False, 0, 0))\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING] yaksa: 10 leaked handle pool objects\n" + ] + } + ], + "source": [ + "%%bash\n", + "python ice_driver.py --help" + ] + }, + { + "cell_type": "markdown", + "id": "9bfa9c97", + "metadata": {}, + "source": [ + "The PMP drivers are run on the command line. In this Jupyter Notebook, we use the bash cell magic function %%bash to run command line functions from the notebook.\n", + "\n", + "The PMP sea ice metrics driver call follows the basic format:\n", + "ice_driver.py -p parameter_file.py --additional arguments\n", + "\n", + "The following cell runs the driver with the demo parameter file we saw above." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "d6ff0052", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-11 13:30:07,104 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", + "2024-01-11 13:30:17,291 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", + "2024-01-11 13:30:27,185 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", + "2024-01-11 13:30:34,909 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", + "2024-01-11 13:30:42,526 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "INFO::2024-01-11 13:31::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n", + "2024-01-11 13:31:42,825 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['E3SM-1-0']\n", + "Find all realizations: False\n", + "OBS: Arctic\n", + "Converting units by multiply 0.01\n", + "Converting units by multiply 0.01\n", + "OBS: Antarctic\n", + "Converting units by multiply 0.01\n", + "Converting units by multiply 0.01\n", + "['E3SM-1-0']\n", + "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n", + "Converting units by multiply 1e-06\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r1i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_201001-201112.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-------------------------------------------\n", + "Calculating model regional average metrics \n", + "for E3SM-1-0\n", + "--------------------------------------------\n", + "arctic\n", + "ca\n", + "na\n", + "np\n", + "antarctic\n", + "sp\n", + "sa\n", + "io\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING] yaksa: 10 leaked handle pool objects\n" + ] + } + ], + "source": [ + "%%bash\n", + "python ice_driver.py -p demo_param_file.py" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "dfd75e12", + "metadata": {}, + "outputs": [], + "source": [ + "# Explain MSE" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "9a46fb89", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"DIMENSIONS\": {\n", + " \"index\": {\n", + " \"monthly_clim\": \"Monthly climatology of extent\",\n", + " \"total_extent\": \"Sum of ice coverage where concentration > 15%\"\n", + " },\n", + " \"json_structure\": [\n", + " \"model\",\n", + " \"obs\",\n", + " \"region\",\n", + " \"index\",\n", + " \"statistic\"\n", + " ],\n", + " \"model\": [\n", + " \"E3SM-1-0\"\n", + " ],\n", + " \"region\": {},\n", + " \"statistic\": {\n", + " \"mse\": \"Mean Square Error (10^12 km^4)\"\n", + " }\n", + " },\n", + " \"RESULTS\": {\n", + " \"E3SM-1-0\": {\n", + " \"bootstrap\": {\n", + " \"antarctic\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.4063417787853511\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.155238367232\"\n", + " }\n", + " },\n", + " \"arctic\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"4.356016677100606\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.942134849536\"\n", + " }\n", + " },\n", + " \"ca\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.10201965034604724\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.011096832\"\n", + " }\n", + " },\n", + " \"io\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.04652064438963821\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.008945589248\"\n", + " }\n", + " },\n", + " \"na\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.2475039028131327\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.57598345216\"\n", + " }\n", + " },\n", + " \"np\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.41030185147377524\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.184947277824\"\n", + " }\n", + " },\n", + " \"sa\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.4186260179820069\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.301095288832\"\n", + " }\n", + " },\n", + " \"sp\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.7197147567464404\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.062149992448\"\n", + " }\n", + " }\n", + " },\n", + " \"nasateam\": {\n", + " \"antarctic\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.3784661114628296\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.499939508224\"\n", + " }\n", + " },\n", + " \"arctic\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"7.2725043134342915\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"5.161479569408\"\n", + " }\n", + " },\n", + " \"ca\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.10145201711023501\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.08221581312\"\n", + " }\n", + " },\n", + " \"io\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.028741228802380923\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.007070034944\"\n", + " }\n", + " },\n", + " \"na\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"2.0495937214356794\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.316665032704\"\n", + " }\n", + " },\n", + " \"np\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.6113302091333116\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.279103602688\"\n", + " }\n", + " },\n", + " \"sa\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.06886884722313671\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.031630178304\"\n", + " }\n", + " },\n", + " \"sp\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.7205462829629394\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.641328283648\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"json_structure\": [\n", + " \"model\",\n", + " \"obs\",\n", + " \"region\",\n", + " \"index\",\n", + " \"statistic\"\n", + " ],\n", + " \"json_version\": 3.0,\n", + " \"model_year_range\": {\n", + " \"E3SM-1-0\": [\n", + " \"1981\",\n", + " \"2010\"\n", + " ]\n", + " },\n", + " \"provenance\": {\n", + " \"commandLine\": \"ice_driver.py -p demo_param_file.py\",\n", + " \"conda\": {\n", + " \"Platform\": \"linux-64\",\n", + " \"PythonVersion\": \"3.8.15.final.0\",\n", + " \"Version\": \"23.1.0\",\n", + " \"buildVersion\": \"not installed\"\n", + " },\n", + " \"date\": \"2024-01-11 13:31:29\",\n", + " \"openGL\": {\n", + " \"GLX\": {\n", + " \"client\": {},\n", + " \"server\": {}\n", + " }\n", + " },\n", + " \"osAccess\": false,\n", + " \"packages\": {\n", + " \"PMP\": \"v3.0.2-11-g06b151f\",\n", + " \"PMPObs\": \"See 'References' key below, for detailed obs provenance information.\",\n", + " \"blas\": \"0.3.24\",\n", + " \"cdat_info\": \"8.2.1\",\n", + " \"cdms\": \"3.1.5\",\n", + " \"cdp\": \"1.7.0\",\n", + " \"cdtime\": \"3.1.4\",\n", + " \"cdutil\": \"8.2.1\",\n", + " \"clapack\": null,\n", + " \"esmf\": \"0.8.2\",\n", + " \"esmpy\": \"8.4.2\",\n", + " \"genutil\": \"8.2.1\",\n", + " \"lapack\": \"3.9.0\",\n", + " \"matplotlib\": null,\n", + " \"mesalib\": null,\n", + " \"numpy\": \"1.22.4\",\n", + " \"python\": \"3.10.13\",\n", + " \"scipy\": \"1.11.3\",\n", + " \"uvcdat\": null,\n", + " \"vcs\": null,\n", + " \"vtk\": null,\n", + " \"xarray\": \"2023.10.1\",\n", + " \"xcdat\": \"0.5.0\"\n", + " },\n", + " \"platform\": {\n", + " \"Name\": \"gates.llnl.gov\",\n", + " \"OS\": \"Linux\",\n", + " \"Version\": \"3.10.0-1160.71.1.el7.x86_64\"\n", + " },\n", + " \"userId\": \"ordonez4\"\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "with open(\"sea_ice_demo/ex1/sea_ice_metrics.json\") as f:\n", + " print(f.read())" + ] + }, + { + "cell_type": "markdown", + "id": "d74b6752", + "metadata": {}, + "source": [ + "Some text about the output figure" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "c6dfa7a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sea_ice_demo/ex1/MSE_bar_chart.png\r\n" + ] + } + ], + "source": [ + "!ls {\"sea_ice_demo/ex1/MSE_bar_chart.png\"}" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "d14e933a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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V586dg67rpsnJjK8Tc2JOoZaTx+Mxakd3czKLkG/osbGxRvN1OBw+33/72muvYfr06SgvL8eaNWva/UwwAGRnZyMmJsa4eS9L6b1cZ0VFBSoqKgAAZWVlqKysBNA85e+9JnZJSYnxMaWioiLU1NQAaD6j1/uRn/z8fONjTnl5eWhoaAAA5OTkwOl0+lwMwel0GmcRNzQ0GBcBcTgcxhdZ1NbWoqCgAABQU1NjzFBUV1cbl7SsqqpCaWkpAKCyshJlZWVhk9Pp06dRXFwMTdNMk5MZXyfmxJxCLacvvvgCf//736FpWrdyOnDgAMwi5M9yLy0txfr167F9+3Y899xzGDFiBBYtWgQAePnll6FpGh566CHk5+fjrbfewsaNG9tcj8vlgsvlMu7X19cjPj4edrsdsbGxxp6bLMs+saqqsFgsRixJEiRJajf2eDyQZdmIFUWBxWIxYqB5j7BlbLVaIYQwYu8epzfWdR2KorQba5oGIYQRt5UHc2JOzIk5MSf/nOx2O+Li4kxxlnvIN3Sg+Sz3AwcOYOjQodiyZQsefvhhbNq0CfX19Vi0aBG++eYbqKqK3//+97j++us7tE4zfVQhnOm6jtraWlx99dU+n6EmIrqUnqodZuoFYdHQA8FML2I4U1UVBQUFyMjI4CUciajDeqp2mKkXsKGb4EUkIqKuMVMv4BwnBZWu6zh16pRxxikRUUewdvhjQ6eg0nUdx48f5x8lEXUKa4c/TrmbYJqFiIi6xky9IOBH6N6vRSRqi67rOHnyJPeyiahTWDv8Bey04vnz5yMhIQGffPIJxo0bh+effz5Qm6Iw5n0fbPDgwfzYGhF1GGuHv4BNuefm5qK+vh6nT5/GypUrA7GJbjHTNAsREXWNmXpBwHZrZs6cicTERH61HV2Spmn4/PPPjSs6ERF1BGuHv4BeyWP06NEYPXo0amtrsWvXLly4cMFo8I8++mggN01hQgiBCxcuYPjw4cEeChGFEdYOf73yxsPs2bPR2NiIESNGICEhAQkJCb2xWQoDiqJg4sSJvEocEXUKa4e/XnkmBg4ciBUrVvTGpijMeL8paeTIkZBlOdjDIaIwwdrhr1ca+pIlSzB//nwkJSXBYrEAAJ555pne2DSFgaampmAPgYjCEGuHr15p6OvWrcPDDz+MwYMH98bmKIzIsozU1NRgD4OIwgxrh79eaejDhg3DsmXLemNTFGY0TUNFRQVGjx7NaTMi6jDWDn+90tCbmpowc+ZMnyl3XmiGiIio5/RKQ3/yySd7YzMUhmRZRmJiYrCHQURhhrXDX698bE0IgWnTphm3U6dO9cZmKQxomobS0lJeHIKIOoW1w1+vNPSNGzeiuLgYALBp0yYUFBR06vdXrVoFm82GxYsXw+12G4/v27cP8fHxmD59Om655ZYeHTP1nujo6GAPgYjCEGuHr15p6Nu2bcP69euxcuVK/POf/8TGjRs7/LulpaU4c+YMCgsLMWbMGOzYscNn+fe//33s27cPe/fu7elhUy+QZRmjRo3iSS1E1CmsHf4C2tBXr16NRx99FE8//TSuueYa7Ny5E5Ikdeqyr8XFxcjMzAQAzJo1C0VFRT7Ld+7cCZvNht/+9rc9OnbqHaqq4uDBg1BVNdhDIaIwwtrhL6Anxc2ZM8fn/sKFCzu9DofDgUGDBgEAYmJiYLfbjWUTJkzAsWPHAAB33HEHpk6divHjx7e5HpfLBZfLZdyvr68HAOP9F++/siz7xKqqwmKxGLEkSZAkqd3Y4/FAlmUjVhQFFovFiIHm/4gtY6vVCiGEEeu6Dk3TjFjXdSiK0m6saRqEEEbcVh6hmpMQAjExMbBYLKbJyYyvE3NiTqGWk67rRu3obk5mEdAj9KlTp6K2thYOhwNTp041Too7e/Zsh9cRGxtrNF+Hw4F+/foZy/r06YOIiAhERETg9ttvx6efftruerKzsxETE2Pc4uPjAQDl5eUAgIqKClRUVAAAysrKUFlZCaB5yr+qqgoAUFJSgurqagBAUVERampqAAAFBQWora0FAOTn58PhcAAA8vLy0NDQAADIycmB0+mEqqrIycmBqqpwOp3IyckBADQ0NCAvL8/IMz8/HwBQW1trnHNQU1NjzFBUV1ejpKQEAFBVVYXS0lIAQGVlJcrKysImp7Nnz+LMmTOQZdk0OZnxdWJOzCnUcvryyy9RV1cHWZa7ldOBAwdgFgH7PnQAuOuuu3DddddBURTs3bsXW7duRUJCAmbMmGG8cN+mtLQU69evx/bt2/Hcc89hxIgRWLRoEYDmo2zv99f+4Ac/wIMPPoiMjIw219PWEXp8fDzsdjtiY2PDek81nPe+3W43Dh48iLS0NEiSZIqczPg6MSfmFGo5uVwuHDp0CGlpacb1TbqSk91uR1xcnCm+Dz2gDf3mm2/GBx98AKB5b2rJkiV49tln8Ytf/KLDDR1oPsv9wIEDGDp0KLZs2YKHH34YmzZtwquvvopXXnkFiqJgypQpeOGFFzq8TjN9qX0403Ud1dXViI+PhyT1yjmaRGQCPVU7zNQLAtrQMzIykJeXh6ioKADN0yuLFy9GcXExzp07F6jNdoiZXkQiIuoaM/WCgB4S/eY3vzHe/waAK6+8Ert27cKLL74YyM1SGFFVFQUFBaY6MYWIAo+1w19Az3IfN24cAKCxsRFXXHEFAECSJNx1112B3CyFEUmSkJCQwOl2IuoU1g5/AWvoR48eBdB82dfXXnuNX8ZCbZIkiV+rS0SdxtrhL2C7Nk899RQOHTqEQ4cOGR8PIGpNVVXk5+dz2oyIOoW1w1/AjtCfeeYZ48vnp06dGqjNUJiTJAmJiYmcNiOiTmHt8Bewhp6amgpVVbFjxw4UFRXBbrejX79+mDJlCubPn298HpEub5IkYcCAAcEeBhGFGdYOfwHdtbn33nvxxRdfYOnSpVizZg3uueceHD9+HPfee28gN0thxOPxIDc3Fx6PJ9hDIaIwwtrhL6CHySdPnsTrr7/u89j48ePbvZobXX5kWcbEiRP5jUlE1CmsHf4C2tDT0tKwZMkSZGZmom/fvqivr0deXh7S0tICuVkKI5Ik+Vyfn4ioI1g7/AX0SnEAcPjwYRQXF8PhcCA2Nhbp6elISUkJ5CY7xExXBwpnHo8HeXl5yMzMhNVqDfZwiChM9FTtMFMvCHhDb8u2bduwZMmS3t6sDzO9iOFMCIGGhgZceeWVxhcsEBF9m56qHWbqBQGdcvdeXKYlIQQ2bdoU9IZOocFisYT9HxER9T7WDn8Bbejp6elYsGABWk8CnDx5MpCbpTDi8XiQk5ODrKwsTrkTUYexdvgLaEMfM2YMXnjhBcTFxfk8Pnv27EBulsKIoijIzMzkdQmIqFNYO/wF9Jl4//33jS9laemdd94J5GYpzPAPkoi6grXDV0AvLNOnTx9elo8uSVVV5OTk8HrMRNQprB3+gnKWeygw05mN4UwIAVVVoSgKz3Inog7rqdphpl4QFofPq1atgs1mw+LFi+F2u43Hc3JyMHnyZEydOhUPPfRQEEdI3cE9bCLqCtYOXyHf0EtLS3HmzBkUFhZizJgx2LFjh7EsMTERBQUF+PDDD2G323Hw4MEgjpS6QlVV5OXl8Q+TiDqFtcNfyDf04uJiZGZmAgBmzZqFoqIiY9nQoUONkyKsVuslT5BwuVyor6/3uQGApmnGv23Fqqr6xLquXzL2eDw+sfcdDW8shPCLAfjEuq77xN7/sO3Fmqb5xOGUkyzLmD17NqxWq2lyMuPrxJyYU6jlJEmSUTu6m5NZhHxDdzgcxvsaMTExsNvtfj/z8ccfo7a21vj+9bZkZ2cjJibGuMXHxwMAysvLAQAVFRWoqKgAAJSVlaGyshJA8wxBVVUVAKCkpATV1dUAgKKiItTU1AAACgoKUFtbCwDIz8+Hw+EAAOTl5aGhoQFA89sDTqfT50QOp9OJnJwcAEBDQwPy8vKMnPPz8wEAtbW1KCgoAADU1NQYOzTV1dUoKSkBAFRVVaG0tBQAUFlZibKysrDKqbCwEEIIU+VkxteJOTGnUMuppKQEQohu5XTgwAGYhghxL730knjttdeEEEIcPHhQ/PjHP/ZZXl1dLaZMmSLOnj17yfU4nU5RV1dn3KqrqwUAYbfbhRBCqKoqVFX1iz0ej0+sadolY7fb7RPruu4T67ruFwshfGJN03xij8dzyVhVVZ+4rTxCNSen0yn27NljbMMMOZnxdWJOPZ8TIER0tFtIkm7EFosuAF1ER7sFoAuLxRsLIUktY01ERbWMPQIQQpY1ERnZHCtKy1gVEREtY1UAQlitqrBam+OICFUoijf2GHFkpEcoimbEstwcR0V5hCR5Y7cR91ZOV17ZZNSO7rxO58+fFwBEXV2dCHchf5Z7aWkp1q9fj+3bt+O5557DiBEjsGjRIgDAxYsXkZWVhZdeegmJiYmdWq+ZzmwkovDDD3V0X090LzP1gpCfck9NTcXAgQNhs9lw9OhRzJ8/H8uXLwcAvPjiizh+/DgeeughTJ8+Hfv37w/yaKmzdF2H3W433s8iIuoISWLtaC3kj9ADxUx7ZeHM4/EgPz8fM2bM4PWY6bLCI/TuiYryYNeu7tcOM/UCNnQTvIhEFH7Y0LuPU+6+Qn7KncxN13WcPXuW02ZE1CmSxNrRGhs6BZWu6ygvL+cfJRF1SkQEa0drnHI3wTQLEYUfTrl3H6fcffEInYJK13WcOnWKe9lE1CmyzNrRGhs6BZWu6zh+/Dj/KOmyI2DhrRs3p3IFa0crnHI3wTQLEYUhzrl3Xw+0LzP1Ah6hU1Dpuo6TJ09yL5uIOkVXFNaOVtjQKaj4HjoRdYUuy6wdrXDK3QTTLEQUhjjl3n2ccvfBI/Ruslh4687NatXw+eefG99TTETUEZqisHa0woZOQSVJAhcuXMBlOlFERF0kJIm1oxUl2AMIdwKcNusWN4CJ/IMkos5R3G5MnDgx2MMIKTxCp6DSFAX/+Mc/OG1GRJ3C2uGPDZ2CS5LQ1NQU7FEQUbhh7fDDKXcKKtntRmpqarCHQURhhrXDX1gcoa9atQo2mw2LFy+G2+02HldVFUuXLoXNZsPKlSuDOELqKs1qRXl5OafNiKhTWDv8hXxDLy0txZkzZ1BYWIgxY8Zgx44dxrI9e/ZgyJAhKCwsRGNjI4qKioI4UiIiouAJ+YZeXFyMzMxMAMCsWbN8mvalllF4kD0eJCYmQpblYA+FiMIIa4e/kH8P3eFwYNCgQQCAmJgY2O12n2XeK/u0Xtaay+WCy+Uy7tfV1QEALly4AADGtI0syz6xqqqwWCxGLEkSJEn6dwxAjYyE5HZDEgKeyEjI3jgqCorLBYs3djoBAGqr2Op0QlgsUCMjYXU6oVss0CIiYHW5oFss0CMioLhc0CUJuqJAcbuhyzJ0WYbidkOTZQhJguLxQJNlQJIgezzQlOaXV1ZVaFYroOuQNQ2q1QqLN46IgKRpkLyxqkLS9V7LyRMVhfL9+5GUlASLxQJd16EoCjRNgxDCiNt6bTr1OrWKPR4PZFk2YkVRYLFYjBhofkunZWy1WiGEMGJd16FpmhF7x95ezJyYk09OAGtEN3JyR0fjs3/VDq+uvE7evmGGz7OHfEOPjY1FfX09gOYG3q9fvw4tay07Oxtr1qzxe3z48OHdH2SLHQWf+F//eTsUC+Ebe9fTMtZ1wHsOgaY13y4Vq+q/t+PxtB23OCfBJ+6tnJxOYPp0EF2WWCO6nlNTU4/WjoaGBsTExPTY+oIh5Bt6eno61q9fjyVLliA3NxdTpkzxWZaXl4eMjAzk5uZi2bJl7a7niSeewCOPPGLc13UddrsdcXFxsPCaykFTX1+P+Ph4VFdXh/11lImo9/RU7RBCoKGhwZgJDmch39BTU1MxcOBA2Gw2DB06FKtXr8by5cuxadMmzJ07F7t27YLNZkNqaiomTZrU7noiIyMRGRnp89hVV10V4NFTR/Xt25cNnYg6rSdqR7gfmXtdtt+2RqHBTN90RES9h7XDX8if5U5ERETfjg2dgioyMhLPPvus39shRESXwtrhj1PuREREJsAjdCIiIhNgQyciIjIBNnQiIiITYEMnIiIyATZ0IiIiE2BDJyIiMgE2dCIiIhNgQyciIjIBNnQiIiITCPmGXl5ejilTpmDatGmYPXs2Ll68aCxTVRVLly6FzWbDypUrgzhKIiKi4Ar5hn7DDTfgo48+wv79+3HTTTfhL3/5i7Fsz549GDJkCAoLC9HY2IiioqIgjpSIiCh4Qr6hW61WI25sbMSoUaOM+8XFxcjMzAQAzJo1iw2diIguW0qwB9ARf/vb3/Doo4/CarXiscceMx53OBzG9+DGxMTAbre3uw6XywWXy2Xc13UddrsdcXFxsFgsgRs8ERGFLCEEGhoaMGjQIEhSyB/jXlJYNPTbbrsNpaWleP755/HKK6/g8ccfBwDExsaivr4eQHNz79evX7vryM7Oxpo1a3plvEREFF6qq6sxZMiQYA+jW0K+obtcLuP7bmNiYuB2u41l6enpyMvLQ0ZGBnJzc7Fs2bJ21/PEE0/gkUceMe7X1dVh6NChOHHiBGJjY6FpGgBAlmWfWFVVWCwWI5YkCZIktRt7PB7IsmzEiqLAYrEYMdB8Ml/L2Gq1QghhxLquQ9M0I9Z1HYqitBtrmgYhhBG3lUeo5uR2u1FSUoL09HRIkmSKnMz4OjEn5hRqOblcLhw8eBDp6enGLGtXcrLb7bj22mtx5ZVXItyFfEP/29/+hhdeeAGSJKF///7YunUrli9fjk2bNmHu3LnYtWsXbDYbUlNTMWnSpHbXExkZaewYtBQbG2tM21Pv03UdycnJuOqqq8J+uouIek9P1w4zvPVqEUKIYA8iGOrr6xETE4O6ujo2dCKiy5SZegEPiSioVFVFfn4+VFUN9lCIKIywdvhjQ6egkiQJiYmJnG4nok5h7fAX8u+hk7lJkoQBAwYEexhEFGZYO/xx14aCyuPxIDc3Fx6PJ9hDIaIwwtrhjw2dgkqWZUycOBGyLAd7KEQURlg7/HHKnYJKkqRLXhCIiKgtrB3+eIROQeXxePDOO+9w2oyIOoW1wx8bOgWVoiiw2WzGFaSIiDqCtcMfGzoFlcViQd++fUPyKk3Dhw/HqFGjfD7nOmHCBOzbt6/T6zpx4gReeeUVv/WXl5d3eB35+fmwWCzYvn27z+O/+c1vcPbsWeP+z3/+c6xatarTY2xp3759yMvLM+6fPn0aN998c6fW4fF4sGLFCtx4441ITk7GmDFj8Otf/7rLY2rrOaTLVyjXjmBhQ6eg8ng82L17d8hOm7lcLmzevLlb61BVtUea0ebNmzF9+nS/8bRu6D2hdUMfNGgQPvjgg06t43e/+x3OnDmDTz/9FJ9++ik++eQTzJw5s8tj6s5zyIuPmE+o145gYEOnoFIUBZmZmSE7bbZmzRr84he/QGNjo9+yr7/+Gt/73vcwduxYJCYm+jSb4cOH47nnnsPNN9+Me+65Bw8++CCOHj2KlJQU3H777cbP7dy5E5MnT8a1116LdevWtTsOh8OBnJwcvPnmm/jss89w/PhxAMDatWtx+vRpLFiwACkpKTh8+LDP7x05cgQ2mw3jxo3DmDFjkJ2dbSxbunQp/uu//gu33norrr/+esybNw9utxuHDx/Gxo0bsW3bNqSkpGDt2rU4ceIErr76auN3i4uLYbPZkJycjKSkJOzevdtvzF9++SUGDhxovLZRUVG48cYbjeWvv/460tLSMG7cOEybNs1ntuK///u/MXbsWCQnJyM9PR2NjY1tPoeHDh3CpEmTkJSUhJtuugkfffQRABjjXbt2LWw2G1588cV2n1sKT6FeO4JCXKbq6uoEAFFXVxfsoVzWdF0Xbrdb6Loe7KH4GTZsmDhy5IhYtGiRWLdunRBCiPHjx4sPPvhACCHEwoULxeOPPy6EEOLrr78WQ4YMEQcOHDB+94EHHjDy+uCDD8T48eP91v+Tn/xECCHE2bNnRd++fcVXX33V5lg2bNggFi5cKIQQ4ic/+Yl48skn/cbp9eyzz4qf/vSnQggh6uvrhdPpFEII0djYKFJSUsTBgweFEELcc889YtKkSaKxsVGoqiomT54s3njjDb91CCFEVVWViIuLE0IIcf78eXHNNdeIjz76SAghhKZp4vz5835jLi8vF0OGDBFjxowR9913n3jzzTeFqqpCCCE+/PBDkZWVZYytoKBAJCUlCSGE2Lp1q0hPTzf+Nu12u1BV1e85dLlcIj4+Xrz33ntCCCEKCwvFwIEDxcWLF0VVVZUAIP7whz+0+XxS+Oup2mGmXsAjdAoqVVWRk5MT0lOi69atw29+8xucP3/e5/H3338fP/7xjwEAAwYMwLx587B3715j+b333vut7+8tXrwYANC/f3+MGDECVVVVbf7c5s2bja8H/tGPfoStW7caXw15KU1NTbjvvvswduxYpKen4+TJkz5H8fPmzUN0dDRkWcZNN91kHPlfSnFxMcaMGYPJkycDaP/jQzfeeCOOHz+OF198EcOGDcOzzz5rHFnv3r0bn376KdLS0pCSkoKHH34Y586dg9vtxttvv43//M//NL4oIzY2ts3PGh87dgwRERHGNP7UqVMxYMAAlJWVAWieEVi0aNG35kPhKRxqR2/jXAUFlaIoyMrKCulpsxEjRmDRokVtTom3btgt7/fp0+db1x0VFWXE3u9pbu3w4cM4cuQIHnjgAWP9tbW1eO+99zB79uxLrv/JJ5/ENddcg9LSUiiKgnnz5sHpdHZq+90RERGBGTNmYMaMGbjvvvvwH//xH7Db7RBCYNmyZVi7dm2X1y2EaHOHyfvYd77zHZ4wZWLhUDt6G4/QKejCYQ/7Zz/7GbZv347Tp08bj916663G++bnzp3DX/7yF8yYMaPN3+/bty/q6uq6tO1XX30VP/3pT3Hy5EmcOHECJ06cwPr1642T4y617gsXLmDIkCFQFAXHjh3D3/72tw5t81LrnDx5MioqKlBUVASg+Xup7Xa7388VFBSgpqbGuP/xxx+jX79+uOqqqzB37lxs27YN1dXVxjoOHToEALj99tvx8ssvo76+HkDz+QOapvmNadSoUXC5XMjPzwcAFBUV4ezZsxg7dmyHcqTwFw61ozexoVNQqaqKvLy8kP/D7N+/P1asWOHToH73u9+hrKwMSUlJuPnmm/HUU0/hpptuavP3k5KScMMNNyAxMdHnpLhv43Q68cYbbxhT81533XUXcnNz8fXXX2PFihW499572zwp7umnn8arr76KiRMn4umnn253h6O1733vezh06JBxUlxLsbGx+Mtf/oLVq1cjKSkJqamp+PDDD/3W8eWXX2L27NkYM2YMUlJSkJ2djd27d0OSJGRkZOCXv/wl7rjjDiQnJyMxMRF//OMfAQB333037rzzTkyaNAkpKSnIysqCy+Xyew4jIiKwc+dOPPXUU0hKSsJPfvIT/OlPf8J3vvOdDj+/FL7CpXb0JosQQgR7EMFgpi+1JyKirjFTLwj5I/SPP/4YNpsN06ZNw8KFC30+c5iTk4PJkydj6tSpeOihh4I4SuoqIQTq6+txme5XElEXsXb4C/mGPnjwYOTm5mL//v247rrrsGvXLmNZYmIiCgoK8OGHH8Jut+PgwYPBGyh1iaqqKCws5LQZEXUKa4e/kD89cODAgUZstVp9zmgcOnRou8soPFit1m89U5uIqDXWDn8hf4Tu9eWXX+L999/HnDlz/JZ9/PHHqK2tRWpqaru/73K5UF9f73MDYHyWV9O0NmNVVX1iXdcvGXs8Hp/YOx3kjYUQfjEAn1jXdZ/YuwfaXqxpmk8cTjmpqopz585B13XT5GTG14k5MadQy8nj8Ri1o7s5mUVYNPT6+nrcfffd2LJlC6xWq8+yr776CitXrsTWrVsvuY7s7GzExMQYt/j4eAAwLjdZUVGBiooKAEBZWRkqKysBAKWlpcbFPkpKSoyP2RQVFRlnPBcUFKC2thZA8xdoOBwOAEBeXh4aGhoANL/f73Q6fS6G4HQ6kZOTAwBoaGgwrp3tcDiMj+LU1taioKAAAFBTU2N8VKi6uholJSUAgKqqKpSWlgIAKisrjQtrhENOp0+fRnFxMTRNM01OZnydmBNzCrWcvvjiC/z973+HpmndyunAgQMwi5A/y13TNNx55534yU9+gltuucVn2cWLF5GVlYWXXnoJiYmJl1yPy+WCy+Uy7tfX1yM+Ph52ux2xsbHGnpssyz6xqqqwWCxGLEkSJElqN/Z4PJBl2YgVRYHFYjFioHmPsGVstVohhDBi7x6nN9Z1HYqitBtrmgYhhBG3lQdzYk7MiTkxJ/+c7HY74uLiTHGWe8g39DfffBMPPfSQcbGI//zP/0R+fj42bdqE7OxsbNiwASNHjgTQ/EUa06ZN69B6zfRRhXCm6zpqa2tx9dVXQ5LCYsKIiEJAT9UOM/WCkG/ogWKmFzGcqaqKgoICZGRk8KRGIuqwnqodZuoFbOgmeBGJiKhrzNQLOMdJQaXrOk6dOmWccUpE1BGsHf7Y0LvJYuGtO7crrtBx/Phx/lESUafoOmtHawFv6J999lmgN0FhzOVS+P45EXWaorB2tBawZ2L+/PlISEjAJ598gnHjxuH5558P1KYojCmKjpMnqxEfH8+z3Imow3RdR3U1a0dLAWvoDzzwAOrr6zF48GCsXLkyUJuhMCfLze+DDR48mH+URNRh3vfQWTv+LWDPwsyZM5GYmMhvwqFLcrkUTJ48mdNmRNQpisLa0VpAn4nRo0dj9OjRqK2txa5du3DhwgWjwT/66KOB3DSFCUXR8PnnVbj22mshy3Kwh0NEYULTNFRVsXa01CvzFLNnz0ZjYyNGjBiBhIQEJCQk9MZmKQxIkvDZ0SMi6gghWDta65W5ioEDB2LFihW9sSkKM263gokTJwZ7GEQUZhSFtaO1XmnoS5Yswfz585GUlASLxQIAeOaZZ3pj0xTiFEXDP/5RiZEjR3LajIg6zPsta6wd/9YrDX3dunV4+OGHMXjw4N7YHIURSQKampqCPQwiCkOsHb56paEPGzYMy5Yt641NUZhxu2WkpqYGexhEFGZkmbWjtV5p6E1NTZg5c6bPlDsvNEMAYLVqKC+vwOjRozltRkQdpmkaKipYO1rqlYb+5JNP9sZmiIiILlu90tCFEJg+fbpx/4033uiNzVIY8HhkJCYmBnsYRBRmZJm1o7Ve+Rz6xo0bUVxcDADYtGkTCgoKemOzFAYiIjSUlpZC07RgD4WIwoimsXa01itH6Nu2bcMPfvADDB48GIqiYOPGjZ36/YaGBtx666347LPP8Pe//91nr2zfvn24++67kZCQAFmWsXfv3p4ePgWQrgPR0dHBHgYRhSHWDl8BbeirV682ToK75pprsHPnTixatAiPPvpop06Ki46Oxttvv43Vq1e3ufz73/8+/ud//qdHxky9S1VljBo1KtjDIKIwI8usHa0FdMp9zpw5mD17NmbPno2FCxfiD3/4g/FYZyiKgv79+7e7fOfOnbDZbPjtb3/b7s+4XC7U19f73AAY0zWaprUZq6rqE+u67hdHRamQJG/sMeLoaA8kSRixxSIACERHewAIWCzeuPkSqP+OdURFtYxVAM3fTBYZqf7rOWkZa4iIaBk3j9dq1WC1NscRERoUxRurRhwZqUJRdCOW5d7NKTrajQMHDhjPp6qqxmvQMu6J16ll7PF4fGLv5SO9sRDCLwbgE+u67hN7x9tezJyYE3PquZxcLpdRO7qbk1kEtKFPnToVtbW1cDgcmDp1KqZNm4Zp06bh7NmzPbaNCRMm4NixY9i7dy/ee+89fPzxx23+XHZ2NmJiYoxbfHw8AKC8vBwAUFFRgYqKCgBAWVkZKisrAQClpaWoqqoCAJSUlKC6uhoAUFRUhJqaGgDA888XICmpFgCwYUM+Ro50AAA2b87D4MENAIA338xBv35OREerePPNHERHq+jXz4k338wBAAwe3IDNm/MAACNHOrBhQz4AICmpFs8/33zOQVpaDdasKQIATJ9ejccfLwEAZGVVYeXKUgDAggWVuP/+MgDAD39YgR/+sDmn++8vw4IFzTmtXFmKrKzmnB5/vATTpzfntGZNEdLSejenCRO+Rl1dHSwWC6qrq1FS0pxTVVUVSkubc6qsrERZWVm3X6eCggLU1jbnlJ+fD4ejOae8vDw0NDTnlJOTA6fTCVVVkZOTA1VV4XQ6kZPTnFNDQwPy8ppzcjgcyM9vzqm2ttY4N6SmpgZFRc2vE3NiTswpMDl9+eWXaGxshMVi6VZOBw4cgFlYRACvbH/XXXfhuuuug6Io2Lt3L7Zu3YqEhATMmDHDeOE6Y+nSpVi1alW7Zza+/PLLiIyMbPMiNi6XCy6Xy7hfX1+P+Ph42O12xMbGGntusiz7xKqqwmKxGLEkSZAkyYhlWUJUlAq3W4KuS4iK8sDtlqHrEqKjPXC5FOi6BdHRHjidCoQAoqNVNDUpsFiaj4SbmqyQJIHISG+sIyJCg9PpjXU4nQpkWYei6HC5FCiKDln2xhokScDt9sbNF2zxHp17PDIiIjToevMUd0SECl23QFVlREaq0DQJqiohMlKFqkrQtN7NyenUoSgKdF2HrjfHmqZBCGHEbb02nXmdWscejweyLBuxoiiwWCxGDDTvubeMrVYrhBBGrOs6NE0zYu/Y24uZE3NiTqGXk91uR1xcHOrq6tC3b1+/3hFWRABNnz7diE+ePCmmTZsm8vPzxc0339yl9d1zzz3iyJEjPo/V1dUZ8aJFi8T+/fs7tK66ujoBwOf3uwLgrTu3yEiP+Oijj4TH4+nW60BElxePp2dqR0/1glAQ0JPiNE2D0+lEVFQUhg4dij179mDx4sU4cuRIp9eVlZWFw4cP49ixY1i+fDmKi4uxadMmvPXWW3jllVegKAqmTJmCjIyMAGRCgaJpEgYPHgxJ6pVPUBKRSUgSa0drAZ1y/+STTzBkyBAMGDDAeEzXdbz11lu46667ArXZDqmvr0dMTEy3p1n+dRI/dQO/zpiIgqWnekEoCOiuzbhx4zBgwAA0Njb+e4OSFPRmTqEjMlJFQUGBqc40JaLAU1XWjtYCNuV+9OhRAM0fS3jttddM+2UsAjxE7w5dlVGTcJLTZkTUKZIkISEhgbWjhYA19Keeegrf+973IIQwPh5A1JqkaRg8eHCwh0FEYcb7Hjr9W8Aa+jPPPGN8V+3UqVMDtRkKc2pUFAry85GRkWF8pIWI6Nt4p9xZO/4tYM9CamoqVFXFjh07UFRUBLvdjn79+mHKlCmYP38+XwACAEhuNxITEzltRkSdIkkSa0crAT3L/e6778bo0aMxa9YsxMTEwOFwIDc3FxUVFXj99dcDtdkO6bEzG3mae/fxNHciChIzneUe0MPkkydP+jXu8ePH87PiZPBERSE/NxczZsyA1WoN9nCIKEx4PB7k5+ezdrQQ0IaelpaGJUuWIDMzE3379kV9fT3y8vKQlpYWyM1SGJHdbkycOBGyLAd7KEQURmRZZu1oJaBT7gBw+PBhFBcXw+FwIDY2Funp6UhJSQnkJjuEU+4hhFPuRBQknHLvhJSUFL8Gvm3bNixZsiTQm6Yw4ImORt477yAzM5PTZkTUYR6PB3l5eawdLQS0oXsvLtOSEAKbNm1iQycAgOJywWaz8VMPRNQpiqKwdrQS0GciPT0dCxYsQOtZ/ZMnTwZysxRGLLoe9tNcRNT7LBYLa0crAW3oY8aMwQsvvIC4uDifx2fPnh3IzVIY8URHI2f3bmRlZXHajIg6zOPxICcnh7WjhYCeFHfx4kVcccUVIfnBf54UFxqExQLnN98gKioKFj6XRNRBQgjj67m7Uzt4UlwH9enTJ5CrJzMQgu+BEVGXsHb4Cr1DZ7qsqNHRyMnJ4VcgElGnqKrK2tFKwD+HHqo45R4aBADV7YaiKJxyJ6IOE0JAVdVu1w4zTbmHxRH6qlWrYLPZsHjxYrjdbuPxpqYmzJkzB9OmTcNtt90Gu90exFFSl1gs3MMmoi5h7fAV8g29tLQUZ86cQWFhIcaMGYMdO3YYy959910kJiZi//79WLhwYdC/8IU6T42KQl5eHv8wiahTVFVl7Wgl5Bt6cXExMjMzAQCzZs1CUVGRsWzkyJFobGwEADgcDvTv37/d9bhcLtTX1/vcAEDTNOPftmJVVX1iXdf946go6P86k9/TMo6OhmgZWywQ3hjNZ3h7oqMBAEKSjFiXJHiiooxY9cayDDUysjlWFCPWFAVqRIQRa97YaoX2r49zaBER0P51AonaMo6MhN4y/td1kXsrJ9ntxuzZs2G1WqHruvHHqWmaT9wjr1OL2OPx+MTed568sRDCLwbgE+u67hN7x9tezJyYE3PquZwkSTJqR3dzMouQb+gOh8N4XyMmJsZnWj0hIQHl5eVITEzEtm3bcOedd7a7nuzsbMTExBi3+Ph4AEB5eTkAoKKiAhUVFQCAsrIyVFZWAmieIaiqqgIAlJSUoLq6GgBQVFSEmpoaAEDB88+jNikJAJC/YQMcI0cCAPI2b0bD4MEAgJw334SzX7/mk8DefBNqdDSc/foh5803AQANgwcjb/Pm5pxHjkT+hg0AgNqkJBQ8/zwAoCYtDUVr1gAAqqdPR8njjwMAqrKyULpyJQCgcsEClN1/f3NOP/whKn74w+ac7r8flQsWNOe0ciWqsrKac3r8cVRPn96c05o1qPnXF+f0Wk7p6SgsLIQQAtXV1SgpKWnOqaoKpaWlzTlVVqKsrKz7r1NBAWpra5tzys+Hw+FozikvDw0NDc055eTA6XT6nHDjdDqRk5PTnFNDA/Ly8ppzcjiQn5/fnFNtLQoKCppzqqkxdjyZE3NiToHLqaSkBEKIbuV04MABmIYIcS+99JJ47bXXhBBCHDx4UPz4xz/2WbZmzRohhBA7d+4Ujz32WLvrcTqdoq6uzrhVV1cLAMJutwshhFBVVaiq6hd7PB6fWNM03xgQnqgooUmSEIBwt4yjo4XeMrZYhO6NAaFbLMIdHS0EIHRJMmJNkoQ7KsqIPd5YloUnMrI5VhQjVhVFeCIijFj1xlarUK3W5jgiQqiK0jzelnFkpNBaxrLcqzk5+/QRe/bsEW63W2iaJjwej/EatIzbem069Tq1ir3b88a6rvvEuq77xUIIn1jTNJ/YO972YubEnJhTz+XU1NRk1I7u5HT+/HkBQNTV1YlwF/JnuZeWlmL9+vXYvn07nnvuOYwYMQKLFi0CALz88svQNA0PPfQQ8vPz8dZbb2Hjxo0dWi/Pcg8hof1fkIhMjGe596LU1FQMHDgQNpsNR48exfz587F8+XIAwOLFi/Huu+9i+vTpeOaZZ/DII48EebTUWbokwW63G+9nERF1hK7rrB2thPwReqDwCD00eKKikL9rF2bMmMHrMRNRh3k8HuTn53e7dpjpCJ0NnQ09+C7P/4JEFALM1NBDfsqdzE2XJJw9e5bTZkTUKbqus3a0woZOQaVHRKC8vJx/lETUKbqus3a0wil3TrkH3+X5X5CIQgCn3Il6iC7LOHXqFPeyiahTdF1n7WiFDZ2CSlcUHD9+nH+URNQpuq6zdrTCKXdOuQff5flfkIhCAKfciXqIrig4efIk97KJqFN0XWftaIUNnYKK76ETUVfwPXR/nHLnlHvwXZ7/BYkoBHDKnaiHaIqCzz//3PieYiKijtA0jbWjFTZ0CiohSbhw4QIu04kiIuoiIQRrRytKsAdAlzfF7cbEiRODPQwiCjOKorB2tMIjdAoqTVHwj3/8g9NmRNQpmqaxdrTChk7BJUloamoK9iiIKAyxdvjilDsFlex2IzU1NdjDIOp9/IRMt8gAUvn+uY+wOEJftWoVbDYbFi9eDLfbbTyuqiqWLl0Km82GlStXBnGE1FWa1Yry8nJOmxFRp7B2+Av5hl5aWoozZ86gsLAQY8aMwY4dO4xle/bswZAhQ1BYWIjGxkYUFRUFcaRERETBE/INvbi4GJmZmQCAWbNm+TTtSy2j8CB7PEhMTIQsy8EeChGFEdYOfyH/HrrD4cCgQYMAADExMbDb7T7LvFf2ab2sNZfLBZfLZdyvq6sDAFy4cAEAjGkbWZZ9YlVVYbFYjFiSJEiS9O8YgBoZCcnthiQEPJGRkL1xVBQUlwsWb+x0AgDUVrHV6YSwWKBGRsLqdEK3WKBFRMDqckG3WKBHREBxuaBLEnRFgeJ2Q5dl6LIMxe2GJssQkgTF44Emy4AkQfZ4oCnNL6+sqtCsVkDXIWsaVKsVFm8cEQFJ0yB5Y1WFpOu9lpMnKgrl+/cjKSkJFosFuq5DURRomgYhhBG39dp06nVqFXs8HsiybMSKosBisRgx0PyWTsvYarVCCGHEuq5D0zQj9o69vZg5MSefnADWiG7k5I6Oxmf/qh1eXXmdvH3DDJ9nD/mGHhsbi/r6egDNDbxfv34dWtZadnY21qxZ4/f48OHDuz/IFjsKPvG//vN2KBbCN/aup2Ws64D3HAJNa75dKlbVf2/H42k7bnFOgk/cWzk5ncD06SC6LLFGdD2npqYerR0NDQ2IiYnpsfUFQ8g39PT0dKxfvx5LlixBbm4upkyZ4rMsLy8PGRkZyM3NxbJly9pdzxNPPIFHHnnEuK/rOux2O+Li4mDh2aZBU19fj/j4eFRXV4f9dZSJqPf0VO0QQqChocGYCQ5nId/QU1NTMXDgQNhsNgwdOhSrV6/G8uXLsWnTJsydOxe7du2CzWZDamoqJk2a1O56IiMjERkZ6fPYVVddFeDRU0f17duXDZ2IOq0nake4H5l7XbbftkahwUzfdEREvYe1w1/In+VORERE344NnYIqMjISzz77rN/bIUREl8La4Y9T7kRERCbAI3QiIiITYEMnIiIyATZ0IiIiE2BDJyIiMgE2dCIiIhNgQyciIjIBNnQiIiITYEMnIiIyATZ0IiIiEwiLht7Q0IC0tDT06dMH5eXlPstUVcXSpUths9mwcuXKII2QiIgouMKioUdHR+Ptt9/GggUL/Jbt2bMHQ4YMQWFhIRobG1FUVBSEERIREQVXWDR0RVHQv3//NpcVFxcjMzMTADBr1iw2dCIiuiwpwR5AdzkcDuO7cGNiYmC329v8OZfLBZfLZdzXdR12ux1xcXGwWCy9MlYiIgotQgg0NDRg0KBBkKSwOMZtV9g39NjYWNTX1wNobu79+vVr8+eys7OxZs2a3hwaERGFierqagwZMiTYw+iWsG/o6enpyMvLQ0ZGBnJzc7Fs2bI2f+6JJ57AI488Ytyvq6vD0KFDceLECcTGxkLTNACALMs+saqqsFgsRixJEiRJajf2eDyQZdmIFUWBxWIxYqD5RL6WsdVqhRDCiHVdh6ZpRqzrOhRFaTfWNA1CCCNuK49QzcntdqOkpATp6emQJMkUOZnxdWJOzCnUcnK5XDh48CDS09ONWdau5GS323HttdfiyiuvRLgLm4aelZWFw4cP49ixY1i+fDmKi4uxadMmzJ07F7t27YLNZkNqaiomTZrU5u9HRkYiMjLS7/HY2Fhjyp56n67rSE5OxlVXXRX2011E1Ht6unaY4a1XixBCBHsQwVBfX4+YmBjU1dWxoRMRXabM1At4SERBpaoq8vPzoapqsIdCRGGEtcMfGzoFlSRJSExM5HQ7EXUKa4e/sHkPncxJkiQMGDAg2MMgojDD2uGPuzYUVB6PB7m5ufB4PMEeChGFEdYOf2zoFFSyLGPixImQZTnYQyGiMMLa4Y9T7hRUkiS1ezEgIqL2sHb44xE6BZXH48E777zDaTMi6hTWDn9s6BRUiqLAZrMZV5AKJcOHD8eoUaN8PhYzYcIE7Nu3r9PrOnHiBF555RW/9bf+OuD2fldRFKSkpCA5ORkTJkzABx980OkxAMDp06dx8803G/d3796N0aNHIyUlBUeOHEFKSgqampo6tc61a9ciMTERycnJGDVqFFavXt2lsQHNl29+/vnnu/z7dPkI5doRLGzoFFQWiwV9+/YN2as0uVwubN68uVvrUFW1zYbeGVdddRUOHz6MTz/9FD/72c+wcOFCdOWaUIMGDfLZGdi4cSPWrl2Lw4cPY+zYsTh8+DCio6M7vL6dO3ciNzcXBw8exKeffory8nL88Ic/7PS4vLrT0Pl55MtLqNeOYGBDp6DyeDzYvXt3yE6brVmzBr/4xS/Q2Njot+zrr7/G9773PYwdOxaJiYk+DXv48OF47rnncPPNN+Oee+7Bgw8+iKNHjyIlJQW333678XM7d+7E5MmTce2112LdunUdGtNtt92G2tpanD9/Hj/84Q8xYcIEJCUlYc6cOTh79qzxc1u2bPE5qj9x4gROnDiBq6++GgCwYsUKFBYW4rHHHsPkyZMBNBfJixcvAgAqKiowc+ZMJCUlISkpCRs3bvQby5dffomrr74aUVFRAJqPmpKTk43lubm5mDp1KsaPH4+0tDQUFBRccnwPPvggHA4HUlJSMGHCBADA559/jltvvRVJSUlISUnBrl27jHVYLBasX78e06dPxxNPPNGh54/MIdRrR1CIy1RdXZ0AIOrq6oI9lMuaruuisbFR6Loe7KH4GTZsmDhy5IhYtGiRWLdunRBCiPHjx4sPPvhACCHEwoULxeOPPy6EEOLrr78WQ4YMEQcOHDB+94EHHjDy+uCDD8T48eP91v+Tn/xECCHE2bNnRd++fcVXX33lN46qqioRFxdn3H/11VfF0KFDhRBCnDt3zng8Oztb/PjHPza2l5CQIE6fPi2EEOKbb74R33zzjd+6pk2bJvbs2WPcByAaGhqEx+MRI0eOFH/84x+NZS235VVTUyNGjx4thg8fLu6++26xefNm0djYKIQQ4vjx42LSpEnG31hlZaUYNGiQcLvdHR6fEELcdNNNYtOmTUIIIf75z3+Kfv36iS+//NIY73PPPec3LjK/nqodZuoFfPOBgi7U3wNbt24d0tLS8OCDD/o8/v777+PTTz8FAAwYMADz5s3D3r17cdNNNwEA7r333m+dDly8eDEAoH///hgxYgSqqqowePBgv5/zHrUCwODBg/HXv/4VAPCHP/wBr7/+OlwuF5qamjBw4EAAwDvvvIMlS5bgP/7jPwAAV1xxRadyPnbsGFRVxcKFC43HvEf2LQ0cOBBHjhzBgQMH8NFHH+Gll17Ciy++iAMHDuC9997D559/joyMDJ/fqa6u7vD4GhoacPjwYfzoRz8CAIwcORJTp07Fhx9+iEWLFgFAu9+wSOYX6rWjt/HZoKBSVRU5OTnIysqC1WoN9nDaNGLECCxatKjNKfHWDbvl/T59+nzrur1T1cC/v+KxLd730Fv68MMPsWHDBhQVFaF///7461//irVr137rNnuaLMuYPHkyJk+ejBUrVuCaa65BeXk5hBCYNWsWtm3b1uV1i3+dJ9Dd55nMJxxqR2/je+gUVIqiICsrK+T3tH/2s59h+/btOH36tPHYrbfearxvfu7cOfzlL3/BjBkz2vz9vn37oq6urkfHdOHCBfTt2xf9+vWD2+3Gpk2bjGVz587Ftm3bcObMGQBAY2Njm+cBtOeGG25AREQE/vSnPxmP1dbW+v3coUOHcPz4ceP+P/7xD3g8HsTHxyMzMxPvvfeez5n8JSUllxxf37590djYaOzY9O3bFykpKXjttdcAAMePH8dHH32EKVOmdDgXMqdwqR29iQ2dgi4czk7u378/VqxYgZqaGuOx3/3udygrK0NSUhJuvvlmPPXUU8Z0e2tJSUm44YYbkJiY6HNSXHd897vfxXXXXYdRo0Zh5syZxpQ8AGRkZODpp59GZmYmkpOTMW3aNJw7d67D61YUBbt378Yrr7yCsWPHIikpCTt37vT7ufPnz2Px4sUYNWoUUlNT8aMf/QhvvPEG+vfvj5EjR2L79u247777kJycjNGjR+O3v/3tJcfXr18/LF68GGPHjjVOivvDH/6A7du3Izk5GfPnz8err76K+Pj47j15ZArhUDt6E78P3QTfgRvOPB4Pp82IqNN6qnaYqRewoZvgRSQioq4xUy8Iiyn3VatWwWazYfHixXC73cbjTU1NmDNnDqZNm4bbbrsNdrs9iKOkrhBCoL6+vksXSSGiyxdrh7+Qb+ilpaU4c+YMCgsLMWbMGOzYscNY9u677yIxMRH79+/HwoUL8frrrwdxpNQVqqqisLCQ74URUaewdvgL+YZeXFyMzMxMAMCsWbNQVFRkLBs5cqRx5q7D4UD//v3bXY/L5UJ9fb3PDQA0TTP+bStWVdUn1nX9krHH4/GJvXuP3lgI4RcD8Il1XfeJvf9h24s1TfOJwyknWZYxc+ZMWK1W0+RkxteJOTGnUMtJkiSjdnQ3J7MI+YbucDiM9zViYmJ8ptUTEhJQXl6OxMREbNu2DXfeeWe768nOzkZMTIxx854l6/1ITUVFBSoqKgAAZWVlqKysBNA8Q1BVVQWg+SM31dXVAICioiLjjOeCggLjIz35+flwOBwAgLy8PDQ0NAAAcnJy4HQ6jc9OqqoKp9OJnJwcAM0X0MjLyzNyzs/PB9D8USHv5TJramqMHZrq6mrjI0BVVVUoLS0FAFRWVqKsrCxscjp9+jQKCgqg67ppcjLj68ScmFOo5fTFF1/gwIED0HW9WzkdOHAAZhHyJ8W9/PLL+M53voMlS5bg0KFD2Lp1KzZs2GAsO3fuHJ555hn8+c9/RklJCX71q1+1uR6XywWXy2Xcr6+vR3x8POx2O2JjY409N1mWfWJVVWGxWIxYkiRIktRu7PF4IMuyESuKAovFYsRA8x5hy9hqtUIIYcS6rkPTNCPWdR2KorQba5oGIYQRt5VHqObkcrnwwQcf4JZbboEsy6bIyYyvE3NiTqGWk9PpxL59+3DLLbdAkqQu52S32xEXF2eKk+JCvqGXlpZi/fr12L59O5577jnjql1Ac0PXNA0PPfQQ8vPz8dZbb7X5BRJtMdOZjURE1DVm6gUhP+WempqKgQMHwmaz4ejRo5g/fz6WL18OoPk62O+++y6mT5+OZ555Bo888kiQR0udpes6zp49a7yfRUTUEawd/kL+CD1QzLRXFs5UVUVBQQEyMjJ4CUci6rCeqh1m6gVs6CZ4EYmIqGvM1AtCfsqdzE3XdZw6dYrTZkTUKawd/tjQKah0Xcfx48f5R0lEncLa4S/gDf2zzz4L9CYojCmKwvfPiajTWDv8BeyZmD9/PhISEvDJJ59g3LhxeP755wO1KQpj3gvKxMfHG58lJSL6Nqwd/gLW0B944AHU19dj8ODBWLlyZaA2Q2HO+z7Y4MGD+UdJRB3G2uEvYM/CzJkzkZiYyG/CoUtSFAWTJ0/mtBkRdQprh7+APhOjR4/G6NGjUVtbi127duHChQtGg3/00UcDuWkKE5qmoaqqCtdeey1kWQ72cIgoTLB2+OuVeYrZs2ejsbERI0aMQEJCAhISEnpjsxQGhBA+O3pERB3B2uGvV+YqBg4ciBUrVvTGpnqdxRLsEYQ7BUJMDPYgiCjMKIqCiRNZO1rqlYa+ZMkSzJ8/H0lJSbD8qwM+88wzvbFpCnGKouEf/6jEyJEjOW1GRB2maRoqK1k7WuqVhr5u3To8/PDDGDx4cG9sjsKIJAFNTU3BHgYRhSHWDl+90tCHDRuGZcuW9camKMy43TJSU1ODPQwiCjOyzNrRWq809KamJsycOdNnyp0XmiEAsFo1lJdXYPTo0Zw2I6IO0zQNFRWsHS31SkN/8skne2MzREREl61eaehCCEyfPt24/8Ybb/TGZikMeDwyEhMTgz0MIgozssza0VqvfA5948aNKC4uBgBs2rQJBQUFvbFZCgMRERpKS0uhaVqwh0JEYUTTWDta65Uj9G3btuEHP/gBBg8eDEVRsHHjxk79/qpVq3DgwAEMHToUW7ZsQUREBABg3759uPvuu5GQkABZlrF3795ADJ8CSNeB6OjoYA+DiMIQa4evgB6hr169Go8++iiefvppXHPNNdi5cyckSerUZV9LS0tx5swZFBYWYsyYMdixY4fP8u9///vYt28fm3mYUlUZo0aN4kktRNQpssza0VpAG/qcOXMwe/ZszJ49GwsXLsQf/vAH47GOKi4uRmZmJgBg1qxZKCoq8lm+c+dO2Gw2/Pa3v73kelwuF+rr631uAIzpGk3T2oxVVfWJdV33i6OiVEiSN/YYcXS0B5IkjNhiEQAEoqM9AAQsFm8MSFLLWEdUVMtYBQDIso7IyOZYUVrGGiIiWsbN47VaNVitzXFEhAZF8caqEUdGqlAU3YhluXdzio5248CBA8bzqaqq8Rq0jHvidWoZezwen9h7+UhvLITwiwH4xLqu+8Te8bYXMyfmxJx6LieXy2XUju7mZBYBbehTp05FbW0tHA4Hpk6dimnTpmHatGk4e/Zsh9fhcDjQt29fAEBMTAzsdruxbMKECTh27Bj27t2L9957Dx9//HG768nOzkZMTIxxi4+PBwCUl5cDACoqKlBRUQEAKCsrQ2VlJYDmGYKqqioAQElJCaqrqwEARUVFqKmpAQA8/3wBkpJqAQAbNuRj5EgHAGDz5jwMHtwAAHjzzRz06+dEdLSKN9/MQXS0in79nHjzzRwAwODBDdi8OQ8AMHKkAxs25AMAkpJq8fzzzeccpKXVYM2a5h2a6dOr8fjjJQCArKwqrFxZCgBYsKAS999fBgD44Q8r8MMfNud0//1lWLCgOaeVK0uRldWc0+OPl2D69Oac1qwpQlpa7+Y0YcLXqKurg8ViQXV1NUpKmnOqqqpCaWlzTpWVlSgrK+v261RQUIDa2uac8vPz4XA055SXl4eGhuaccnJy4HQ6oaoqcnJyoKoqnE4ncnKac2poaEBeXnNODocD+fnNOdXW1hrnhtTU1Bg7nsyJOTGnwOT05ZdforGxERaLpVs5HThwAGZhEQG8sv1dd92F6667DoqiYO/evdi6dSsSEhIwY8YM44X7Ni+//DK+853vYMmSJTh06BC2bt2KDRs2tPlzkZGR7V7AxuVyweVyGffr6+sRHx8Pu92O2NhYY89NlmWfWFVVWCwWI5YkCZIkGbEsS4iKUuF2S9B1CVFRHrjdMnRdQnS0By6XAl23IDraA6dTgRBAdLSKpiYFFkvzkXBTkxWSJBAZ6Y11RERocDq9sQ6nU4Es61AUHS6XAkXRIcveWIMkCbjd3rj5gi3eo3OPR0ZEhAZdb57ijohQoesWqKqMyEgVmiZBVSVERqpQVQma1rs5OZ06FEWBruvQ9eZY0zQIIYy4rdemM69T69jj8UCWZSNWFAUWi8WIgeY995ax1WqFEMKIdV2HpmlG7B17ezFzYk7MKfRystvtiIuLQ11dnXHwGLZEAE2fPt2IT548KaZNmyby8/PFzTff3OF1fPLJJ2Lx4sVCCCHWrVsn3njjDWNZXV2dES9atEjs37+/w+utq6sTAHzW0RUAb925RUZ6xEcffSQ8Hk+3Xgciurx4PD1TO3qqF4SCgE65a5oGp9MJABg6dCj27NmD//3f/8WRI0c6vI7U1FQMHDgQNpsNR48exfz587F8+XIAwFtvvYWbbroJkydPxuDBg5GRkRGQPChwNE3C4MGDIUm98glKIjIJSWLtaC2gU+6ffPIJhgwZggEDBhiP6bqOt956C3fddVegNtsh9fX1iImJ6fY0C78+tfv4dcZEFCw91QtCQUB3bcaNG4cBAwagsbHx3xuUpKA3cwodkZEqCgoKTHWmKREFnqqydrQWsIZ+9OhRHD16FJ999hl+/vOfB2ozFOZUVUJCQgKnzYioUySJtaO1gF0p7qmnnsL3vvc9CCGMjwcQteZ9D52IqDO876HTvwWsoT/zzDPGd9VOnTo1UJuhMBcVpSI/vwAZGRnGR1qIiL6Nd8qdtePfAvYspKamQlVV7NixA0VFRbDb7ejXrx+mTJmC+fPn8wUgAIDbLSExMZHTZkTUKZLE2tFaQM9yv/vuuzF69GjMmjULMTExcDgcyM3NRUVFBV5//fVAbbZDeJZ76OBZ7kQULGY6yz2gh8knT570a9zjx4/n58XJEBXlQW5uPmbMmAGr1Rrs4RBRmPB4PMjPZ+1oKaANPS0tDUuWLEFmZib69u2L+vp65OXlIS0tLZCbpTDidsuYOHEivzGJiDpFllk7WgvolDsAHD58GMXFxXA4HIiNjUV6ejpSUlICuckO4ZR76OCUOxEFC6fcOyElJcWvgW/btg1LliwJ9KYpDERHe/DOO3nIzMzktBkRdZjH40FeHmtHSwFt6EePHvV7TAiBTZs2saETAMDlUmCz2fipByLqFEVh7WgtoM9Eeno6FixYgNaz+idPngzkZimM6Lol7Ke5iKj3WSysHa0FtKGPGTMGL7zwAuLi4nwenz17diA3S2EkOtqD3btzkJWVxWkzIuowj8eDnBzWjpYCelLcxYsXccUVV4TkB/95UlxosFgEvvnGiaioKFj4ZBJRBwkh4HR2v3bwpLgO6tOnTyBXTyYgBPgeGBF1CWuHr9A7dKbLSnS0ipycHH4FIhF1iqqydrQWFg191apVsNlsWLx4Mdxut/F4Tk4OJk+ejKlTp+Khhx4K4gipq5qaFGRlZXFPm4g6RVFYO1oL+YZeWlqKM2fOoLCwEGPGjMGOHTuMZYmJiSgoKMCHH34Iu92OgwcP9vr4BCy8deOmW2TuYRNRl7B2+Ar5hl5cXIzMzEwAwKxZs1BUVGQsGzp0qLF3ZrVauacWhtSoKOTl5fEPk4g6RVVV1o5WQr6hOxwO48zDmJgY2O12v5/5+OOPUVtba3z/eltcLhfq6+t9bgCgaZrxb1uxqqo+sa7r/nFUFPR/ncnvaRlHR0O0jC0WCG8MQFgs8ERHAwCEJBmxLknwREUZseqNZRlqZGRzrChGrCkK1IgII9a8sdUK7V8f59AiIqD9a4dHbRlHRkJvGf/rusi9lZPsdmP27NmwWq3Qdd3449Q0zSfukdepRezxeHxi74c9vLEQwi8G4BPruu4Te8fbXsycmBNz6rmcJEkyakd3czKLkG/osbGxRvN1OBzo16+fz/KvvvoKK1euxNatWy+5nuzsbMTExBi3+Ph4AEB5eTkAoKKiAhUVFQCAsrIyVFZWAmie8q+qqgIAlJSUoLq6GgBQVFSEmpoaAEDB88+jNikJAJC/YQMcI0cCAPI2b0bD4MEAgJw334SzXz+o0dHIefNNqNHRcPbrh5w33wQANAwejLzNm5vzHDkS+Rs2AABqk5JQ8PzzAICatDQUrVkDAKiePh0ljz8OAKjKykLpypUAgMoFC1B2//3NOf3wh6j44Q+bc7r/flQuWNCc08qVqMrKas7p8cdRPX16c05r1qDmX1+c02s5paejsLAQQghUV1ejpKSkOaeqKpSWljbnVFmJsrKy7r9OBQWora1tzik/Hw6HozmnvDw0NDQ055STA6fT6XPCjdPpRE5OTnNODQ3Iy8trzsnhQH5+fnNOtbUoKChozqmmxphJYk7MiTkFLqeSkhIIIbqV04EDB2AaIsR98sknYvHixUIIIdatWyfeeOMNY1lDQ4Ow2WziyJEj37oep9Mp6urqjFt1dbUAIOx2uxBCCFVVhaqqfrHH4/GJNU3zjQHhiYoSmiQJAQh3yzg6WugtY4tF6N4YELrFItzR0UIAQpckI9YkSbijoozY441lWXgiI5tjRTFiVVGEJyLCiFVvbLUK1WptjiMihKoozeNtGUdGCq1lLMu9mpOzTx+xZ88e4Xa7haZpwuPxGK9By7it16ZTr1Or2Ls9b6zruk+s67pfLITwiTVN84m9420vZk7MiTn1XE5NTU1G7ehOTufPnxcARF1dnQh3Af+2tZ6watUqHDhwAEOHDsWWLVvw8MMPY9OmTcjOzsaGDRsw8l9Hj2vWrMG0adM6tM4eu5gAL4bSfaH/X5CITMpMF5YJi4YeCGzooUGXJDjOncNVV10VklcUJKLQpOs6HA5Ht2uHmRo6KygFlRYRgYMHDxonqxARdYSmaawdrfAInUfowXd5/hckohDAI3SiHqJLEs6ePWt8hISIqCN0XWftaIUNnYJKj4hAeXk5/yiJqFN0XWftaIVT7pxyD77L878gEYUATrkT9RBdlnHq1CnuZRNRp+i6ztrRChs6BZWuKDh+/Dj/KImoU3RdZ+1ohVPunHIPvsvzvyARhQBOuRP1EF1RcPLkSe5lE1Gn6LrO2tEKGzoFFd9DJ6Ku4Hvo/jjlzin34Ls8/wsSUQjglDtRD9EUBZ9//jkv30hEnaJpGmtHK2zoFFRCknDhwgVcphNFRNRFQgjWjlaUYA+ALm+K242JEycGexhEFGYURWHtaIVH6BRUmqLgH//4B6fNiKhTNE1j7WiFDZ2CS5LQ1NQU7FEQURhi7fDFKXcKKtntRmpqarCHQURhRpZl1o5WwuIIfdWqVbDZbFi8eDHcbrfxuKqqWLp0KWw2G1auXBnEEVJXaVYrysvLOW1GRJ2iaRprRysh39BLS0tx5swZFBYWYsyYMdixY4exbM+ePRgyZAgKCwvR2NiIoqKiII6UiKjjLBbeunOLjg72Kxh6Qr6hFxcXIzMzEwAwa9Ysn6Z9qWUUHmSPB4mJiZBlOdhDIaIw4vHIrB2thPx76A6HA4MGDQIAxMTEwG63+yzzXtmn9bLWXC4XXC6Xcb+urg4AcOHCBQAwpm1kWfaJVVWFxWIxYkmSIEnSv2MAamQkJLcbkhDwREZC9sZRUVBcLli8sdMJAFBbxVanE8JigRoZCavTCd1igRYRAavLBd1igR4RAcXlgi5J0BUFitsNXZahyzIUtxuaLENIEhSPB5osA5IE2eOBpjS/vLKqQrNaAV2HrGlQrVZYvHFEBCRNg+SNVRWSrvdaTp6oKJTv34+kpCRYLBboug5FUaBpGoQQRtzWa9Op16lV7PF4IMuyESuKAovFYsRA81s6LWOr1QohhBHrug5N04zYO/b2YubEnFrmVIdY1ohu5OSOjsb+/e8iKSnJqOtdeZ28fcMMn2cP+YYeGxuL+vp6AM0NvF+/fh1a1lp2djbWrFnj9/jw4cO7P8gWOwo+8b/+83YoFsI39q6nZazrgPccAk1rvl0qVtV/b8fjaTtucU6CT9xbOTmdwPTpILossUZ0Paemph6tHQ0NDYiJiemx9QVDyDf09PR0rF+/HkuWLEFubi6mTJnisywvLw8ZGRnIzc3FsmXL2l3PE088gUceecS4r+s67HY74uLiYOH12IOmvr4e8fHxqK6uDvvrKBNR7+mp2iGEQENDgzETHM5CvqGnpqZi4MCBsNlsGDp0KFavXo3ly5dj06ZNmDt3Lnbt2gWbzYbU1FRMmjSp3fVERkYiMjLS57GrrroqwKOnjurbty8bOhF1Wk/UjnA/Mve6bL9tjUKDmb7piIh6D2uHv5A/y52IiIi+HRs6BVVkZCSeffZZv7dDiIguhbXDH6fciYiITIBH6ERERCbAhk5ERGQCbOhEREQmwIZORERkAmzoREREJsCGTkREZAJs6ERERCbAhk5ERGQCbOhEREQmEBYNvaGhAWlpaejTpw/Ky8t9lqmqiqVLl8Jms2HlypVBGiEREVFwhUVDj46Oxttvv40FCxb4LduzZw+GDBmCwsJCNDY2oqioKAgjJCIiCq6Q/z50AFAUBf37929zWXFxMebMmQMAmDVrFoqKijB58mS/n3O5XHC5XMZ9Xddht9sRFxcHi8USmIETEVFIE0KgoaEBgwYNgiSFxTFuu8KioV+Kw+Ewvgs3JiYGdru9zZ/Lzs7GmjVrenNoREQUJqqrqzFkyJBgD6Nbwr6hx8bGor6+HkBzc+/Xr1+bP/fEE0/gkUceMe7X1dVh6NChOHHiBGJjY6FpGgBAlmWfWFVVWCwWI5YkCZIktRt7PB7IsmzEiqLAYrEYMdD8vn/L2Gq1QghhxLquQ9M0I9Z1HYqitBtrmgYhhBG3lUeo5uR2u1FSUoL09HRIkmSKnMz4OjEn5hRqOblcLhw8eBDp6enGLGtXcrLb7bj22mtx5ZVXItyFfUNPT09HXl4eMjIykJubi2XLlrX5c5GRkW1+b25sbKxxhE+9T9d1JCcn46qrrgr76S4i6j09XTvM8NZr2FTQrKws5OXl4f7778fWrVuxfPlyAMDcuXNRXV0Nm82G6OhoTJo0Kcgjpc6QJAmDBw9mMyeiTmHt8GcRQohgDyIY6uvrERMTg7q6Oh6hB5GqqigoKEBGRoYxHUdE9G16qnaYqRdw14aCSpIkJCYmci+biDqFtcMfD4koqCRJwoABA4I9DCIKM6wd/rhrQ0Hl8XiQm5sLj8cT7KEQURhh7fDHhk5BJcsyJk6cCFmWgz0UIgojrB3+OOVOQSVJUrvXDiAiag9rhz8eoVNQeTwevPPOO5w2I6JOYe3wx4ZOQaUoCmw2Gz+yRkSdwtrhjw2dgspisaBv374heZWm4cOH+31db0csXboUGzZsAAA888wz+OMf/9ij46qrq8Py5csxYsQIjBo1ChMmTMDbb7/do9voqj//+c8YP348UlJSMHr0aNxyyy3Qdb3L6/vNb36Ds2fP9uAIySxCuXYEC3dtKKg8Hg9ycnKQlZUFq9Ua7OH0uLVr1/bo+oQQyMrKQmpqKv75z39CURQcPnwYs2fPxu9//3vMnDmzR7fXGWfOnMGDDz6IgwcPYtiwYQCATz75pFsF9ze/+Q1uvfXWTn88qeV1w8mczF47uoJH6BRUiqIgMzMz5Ivv9OnT8dhjj8FmsyEhIQEPPvigsezUqVO45ZZbkJSUhDvuuAO1tbXGspZH63v37sWkSZOQmpqKxMREbNmypUPrb2nv3r04efIkfv3rXxvPWUpKCp566imsW7fO+Ln//u//xtixY5GcnIz09HQ0NjYCAF5//XWkpaVh3LhxmDZtmjEDceTIEdhsNowbNw5jxoxBdna2Tw7/9V//hVtvvRXXX3895s2bB7fb7Te2mpoaKIqCuLg447Fx48YZDb2yshKzZ8/GxIkTkZycjJdeesn4ueLiYthsNiQnJyMpKQm7d+/G2rVrcfr0aSxYsAApKSk4fPgwLl68iGXLliExMRGJiYk+36A4ffp0PPXUU7jllluCumNDvSNcakevEpepuro6AUDU1dUFeyiXNV3XhdvtFrquB3sofoYNGyaOHDkihBBi2rRpYv78+UJVVdHY2CiGDx8uioqKhBBCzJs3T/z85z8XQghx/Phx0adPH/Hiiy8KIYS45557jNhutwtVVYUQQpw/f14MGzZMnD59+lvX39J///d/i9tvv93v8U8++URcccUVQgghtm7dKtLT043/297tfvjhhyIrK0s4nU4hhBAFBQUiKSlJCCFEfX298XhjY6NISUkRBw8eNHKYNGmSaGxsFKqqismTJ4s33njDbwyapol58+aJ2NhYceedd4rnn39efPXVV0IIIVRVFRMmTBAVFRVCCCG++eYbMXbsWPHxxx+L8+fPi2uuuUZ89NFHxnrOnz/v9xoIIcSjjz4qFi9eLDRNExcvXhQpKSnirbfeMp7DrKws4Xa723tJyUR6qnaYqRfwCJ2CSlVV5OTkQFXVYA/lW911112QZRnR0dFISUnB8ePHAQAffPAB7rvvPgDAiBEjcMstt7T5++fPn8f/+T//B4mJiZgxYwZqa2vx2Weffev6W2trClu0+EqGt99+G//5n/9pXJc6NjYWsixj9+7d+PTTT5GWloaUlBQ8/PDDOHfuHNxuN5qamnDfffdh7NixSE9Px8mTJ3H48GFjnfPmzUN0dDRkWcZNN93U5tgkScLOnTtRVFSEWbNm4aOPPsKNN96Izz//HMeOHcNnn32Gu+66CykpKZg8eTIaGhpw9OhRFBcXY8yYMZg8ebKxnvY+jvT+++/jwQcfhCRJ+M53voMlS5bg/fffN5bffffdnH69TIRT7egtnKugoFIUBVlZWWExbRYVFWXE3u9U7owHH3wQc+fOxc6dO2GxWDBu3Dg4nc5OrX/cuHH43e9+B7fbjYiICOPxv//97xg3btwlty+EwLJly9p8X//JJ5/ENddcg9LSUiiKgnnz5nV6bF6jRo3CqFGjsHz5csyaNQt//etfMXPmTFx99dU+Owle77zzziXH3TqH1js0Le/36dOnw+ui8BZOtaO38Aidgi7c97BnzJiB3//+9wCAEydOYO/evW3+3IULFzBs2DBYLBYUFBTg008/7fS2brnlFsTHx+OnP/2p8bwdPnwY69atw5NPPgkAuP322/Hyyy+jvr4eAOBwOKBpGubOnYtt27ahuroaQPP3SR86dMgY25AhQ6AoCo4dO4a//e1vnR7bqVOn8NFHH/nkW1VVhYSEBNxwww244oorsG3bNmP5559/DrvdjsmTJ6OiogJFRUXGuOx2OwCgb9++qKurM37ntttuw//9v/8XQgh888032L59O2699dZOj5XMIdxrR09jQ6egUlUVeXl5Yf2H+dvf/hb79u1DUlISVq1a1W6D+dWvfoXVq1cjPT0dW7duRVpaWqe3ZbFY8O6778LpdGLkyJG44YYb8KMf/Qgvv/wyvvvd7wJonna+8847MWnSJKSkpCArKwsulwsZGRn45S9/iTvuuAPJyclITEw0PlL39NNP49VXX8XEiRPx9NNPY8aMGZ0em6qqWLt2La6//nqkpKTAZrPhnnvuwR133AFFUbBnzx689dZbSEpKwo033oj77rsPTU1NiI2NxV/+8hesXr0aSUlJSE1NxYcffggAWLFiBe69917jpLif/exnsFgsGDt2LNLS0nD77bdjwYIFnR4rhT8z1I6exu9DN8F34BIRUdeYqRfwCJ2CSgiB+vp6XKb7lUTURawd/sKioa9atQo2mw2LFy/2+fxrU1MT5syZg2nTpuG2224z3nej8KGqKgoLCzltRkSdwtrhL+QbemlpKc6cOYPCwkKMGTMGO3bsMJa9++67SExMxP79+7Fw4UK8/vrrQRwpdYXVasXs2bP5USMi6hTWDn8h39CLi4uRmZkJAJg1a5ZxJiwAjBw50rgClsPhQP/+/dtdj8vlQn19vc8NADRNM/5tK1ZV1Sf2Xpe6vdjj8fjE3ukgbyyE8IsB+MS6rvvE3j3Q9mJN03zicMpJVVWcO3cOuq6bJiczvk7MiTmFWk4ej8eoHd3NySxCvqE7HA7jRIWYmBifafWEhASUl5cjMTER27Ztw5133tnuerKzsxETE2Pc4uPjAcC49GVFRQUqKioAAGVlZaisrATQPENQVVUFACgpKTE+8lNUVISamhoAQEFBgXG5z/z8fDgcDgBAXl4eGhoaAAA5OTlwOp0+F0NwOp3IyckBADQ0NCAvL8/IOT8/HwBQW1uLgoICAM2X1vTu0FRXV6OkpAQAUFVVhdLSUgDNl9csKysLm5xOnz6N4uJiaJpmmpzM+DoxJ+YUajl98cUX+Pvf/w5N07qV04EDB2AWIX+W+8svv2xcEerQoUPYunWrcW3sl19+GefOncMzzzyDP//5zygpKcGvfvWrNtfjcrngcrmM+/X19YiPj4fdbkdsbKyx5ybLsk+sqiosFosRS5IESZLajT0eD2RZNmJFUWCxWIwY8P3iCFVVYbVaIYQwYu8epzfWdR2KorQba5oGIYQRt5UHc2JOzIk5MSf/nOx2O+Li4kxxlnvIX2InPT0d69evx5IlS5Cbm4spU6b4LPdeIvKqq64y9hDbEhkZicjISL/HZVn2+bd13PIqRB2JW76f05nYYrEYsfc/Wkfj9sYeDjkBgN1ux9VXX22anMz4OjEn5hRqOVksFpw/fx5XX311j+UU7kJ+yj01NRUDBw6EzWbD0aNHMX/+fCxfvhwAsHjxYrz77ruYPn06nnnmGTzyyCNBHi11lq7rKC8v79Z3ZhPR5Ye1w1/IT7kHipkuJkBERF1jpl4Q8kfoZG66ruPUqVPcyyaiTmHt8MeGTkGl6zqOHz/OP0oi6hTWDn+ccjfBNAsREXWNmXpBwI/QP/vss0BvgsKYrus4efIk97KJqFNYO/wF7Hz9+fPnIyEhAZ988gnGjRuH559/PlCbojDmfR9s8ODBPh9lIyK6FNYOfwGbcs/NzUV9fT1Onz6NlStXBmIT3WKmaRYiIuoaM/WCgO3WzJw5E4mJifxqO7okTdPw+eefG1d0IiLqCNYOfwG9RM7o0aMxevRo1NbWYteuXbhw4YLR4B999NFAbprChBACFy5cwPDhw4M9FCIKI6wd/nrljYfZs2ejsbERI0aMQEJCAhISEnpjsxQGFEXBxIkTTXX5RSIKPNYOf73yTAwcOBArVqzojU1RmPF+U9LIkSN9rrtMRHQprB3+eqWhL1myBPPnz0dSUhIsFgsA4JlnnumNTVMYaGpqCvYQiCgMsXb46pWGvm7dOjz88MMYPHhwb2yOwogsy0hNTQ32MIgozLB2+OuVhj5s2DAsW7asNzZFYUbTNFRUVGD06NGcNiOiDmPt8NcrDb2pqQkzZ870mXLnhWaIiIh6Tq809CeffLI3NkNhSJZlJCYmBnsYRBRmWDv89crH1oQQmDZtmnE7depUb2yWwoCmaSgtLeXFIYioU1g7/PVKQ9+4cSOKi4sBAJs2bUJBQUGnfn/VqlWw2WxYvHgx3G638fi+ffsQHx+P6dOn45ZbbunRMVPviY6ODvYQiCgMsXb46pWGvm3bNqxfvx4rV67EP//5T2zcuLHDv1taWoozZ86gsLAQY8aMwY4dO3yWf//738e+ffuwd+/enh429QJZljFq1Cie1EJEncLa4S+gDX316tV49NFH8fTTT+Oaa67Bzp07IUlSpy77WlxcjMzMTADArFmzUFRU5LN8586dsNls+O1vf9ujY6feoaoqDh48CFVVgz0UIgojrB3+AnpS3Jw5c3zuL1y4sNPrcDgcGDRoEAAgJiYGdrvdWDZhwgQcO3YMAHDHHXdg6tSpGD9+fJvrcblccLlcxv36+noAMN5/8f4ry7JPrKoqLBaLEUuSBEmS2o09Hg9kWTZiRVFgsViMGGj+j9gytlqtEEIYsa7r0DTNiHVdh6Io7caapkEIYcRt5RGqOQkhEBMTA4vFYpqczPg6MSfmFGo56bpu1I7u5mQWAT1Cnzp1Kmpra+FwODB16lTjpLizZ892eB2xsbFG83U4HOjXr5+xrE+fPoiIiEBERARuv/12fPrpp+2uJzs7GzExMcYtPj4eAFBeXg4AqKioQEVFBQCgrKwMlZWVAJqn/KuqqgAAJSUlqK6uBgAUFRWhpqYGAFBQUIDa2loAQH5+PhwOBwAgLy8PDQ0NAICcnBw4nU6oqoqcnByoqgqn04mcnBwAQENDA/Ly8ow88/PzAQC1tbXGOQc1NTXGDEV1dTVKSkoAAFVVVSgtLQUAVFZWoqysLGxyOnv2LM6cOQNZlk2TkxlfJ+bEnEItpy+//BJ1dXWQZblbOR04cABmEbDvQweAu+66C9dddx0URcHevXuxdetWJCQkYMaMGcYL921KS0uxfv16bN++Hc899xxGjBiBRYsWAWg+yvZ+f+0PfvADPPjgg8jIyGhzPW0docfHx8NutyM2Njas91TDee/b7Xbj4MGDSEtLgyRJpsjJjK8Tc2JOoZaTy+XCoUOHkJaWZlzfpCs52e12xMXFmeL70APa0G+++WZ88MEHAJr3ppYsWYJnn30Wv/jFLzrc0IHms9wPHDiAoUOHYsuWLXj44YexadMmvPrqq3jllVegKAqmTJmCF154ocPrNNOX2oczXddRXV2N+Ph4SFKvnKNJRCbQU7XDTL0goA09IyMDeXl5iIqKAtA8vbJ48WIUFxfj3Llzgdpsh5jpRSQioq4xUy8I6CHRb37zG+P9bwC48sorsWvXLrz44ouB3CyFEVVVUVBQYKoTU4go8Fg7/AX0LPdx48YBABobG3HFFVcAACRJwl133RXIzVIYkSQJCQkJnG4nok5h7fAXsIZ+9OhRAM2XfX3ttdf4ZSzUJkmS+LW6RNRprB3+ArZr89RTT+HQoUM4dOiQ8fEAotZUVUV+fj6nzYioU1g7/AXsCP2ZZ54xvnx+6tSpgdoMhTlJkpCYmMhpMyLqFNYOfwFr6KmpqVBVFTt27EBRURHsdjv69euHKVOmYP78+cbnEenyJkkSBgwYEOxhEFGYYe3wF9Bdm3vvvRdffPEFli5dijVr1uCee+7B8ePHce+99wZysxRGPB4PcnNz4fF4gj0UIgojrB3+AnqYfPLkSbz++us+j40fP77dq7nR5UeWZUycOJHfmEREncLa4S+gDT0tLQ1LlixBZmYm+vbti/r6euTl5SEtLS2Qm6UwIkmSz/X5iYg6grXDX0CvFAcAhw8fRnFxMRwOB2JjY5Geno6UlJRAbrJDzHR1oHDm8XiQl5eHzMxMWK3WYA+HiMJET9UOM/WCgDf0tmzbtg1Llizp7c36MNOLGM6EEGhoaMCVV15pfMECEdG36anaYaZeENApd+/FZVoSQmDTpk1Bb+gUGiwWS9j/ERFR72Pt8BfQhp6eno4FCxag9STAyZMnA7lZCiMejwc5OTnIysrilDsRdRhrh7+ANvQxY8bghRdeQFxcnM/js2fPDuRmKYwoioLMzExel4CIOoW1w19An4n333/f+FKWlt55551AbpbCDP8giagrWDt8BfTCMn369OFl+eiSVFVFTk4Or8dMRJ3C2uEvKGe5hwIzndkYzoQQUFUViqLwLHci6rCeqh1m6gVhcfi8atUq2Gw2LF68GG6323g8JycHkydPxtSpU/HQQw8FcYTUHdzDJqKuYO3wFfINvbS0FGfOnEFhYSHGjBmDHTt2GMsSExNRUFCADz/8EHa7HQcPHgziSKkrVFVFXl4e/zCJqFNYO/yFfEMvLi5GZmYmAGDWrFkoKioylg0dOtQ4KcJqtfIEiTBktVpxxx138GMnRNQprB3+Qr6hOxwO432NmJgY2O12v5/5+OOPUVtba3z/eltcLhfq6+t9bgCgaZrxb1uxqqo+sa7rl4w9Ho9P7D1FwRsLIfxiAD6xrus+sXcPtL1Y0zSfOJxy0jQNFy5cgBDCNDmZ8XViTswp1HJSVdWoHd3NySxCvqHHxsYazdfhcPhdjP+rr77CypUrsXXr1kuuJzs7GzExMcYtPj4eAFBeXg4AqKioQEVFBQCgrKwMlZWVAJqn/KuqqgAAJSUlqK6uBgAUFRWhpqYGAFBQUIDa2loAQH5+PhwOBwAgLy8PDQ0NAJrf73c6nT5nZjqdTuTk5AAAGhoakJeXZ+SZn58PAKitrUVBQQEAoKamxpihqK6uRklJCQCgqqoKpaWlAIDKykqUlZWFTU6nTp1CQUEBVFU1TU5mfJ2YE3MKtZyOHz+OwsJCqKrarZwOHDgAswj5s9xLS0uxfv16bN++Hc899xxGjBiBRYsWAQAuXryIrKwsvPTSS0hMTLzkelwuF1wul3G/vr4e8fHxsNvtiI2NNfbcZFn2iVVVhcViMWJJkiBJUruxx+OBLMtG7D0D0xsDMM7M9MZWq9U4Y9NqtULXdWiaZsS6rkNRlHZjTdMghDDitvJgTsyJOTEn5uSfk91uR1xcnCnOcg/5hg40n+V+4MABDB06FFu2bMHDDz+MTZs2ITs7Gxs2bMDIkSMBAGvWrMG0adM6tE4zfVQhnOm6DofDgauuuorXLCCiDuup2mGmXhAWDT0QzPQihjOPx4P8/HzMmDGDJ7cQUYf1VO0wUy9gQzfBi0hERF1jpl7AOU4KKl3XcfbsWeOMUyKijmDt8MeGTkGl6zrKy8v5R0lEncLa4Y9T7iaYZiEioq4xUy/gEToFla7rOHXqFPeyiahTWDv8saFTUOm6juPHj/OPkog6hbXDH6fcTTDNQkREXWOmXsAjdAoqXddx8uRJ7mUTUaewdvhjQ6eg4vtgRNQVrB3+OOVugmkWIiLqGjP1Ah6hU1BpmobPP//c+BIFIqKOYO3wx4ZOQSWEML7TmIioo1g7/CnBHgBd3hRFwcSJE4M9DCIKM6wd/niETkGlaRr+8Y9/cNqMiDqFtcMfGzoFXVNTU7CHQERhiLXDF6fcKahkWUZqamqwh0FEYYa1wx+P0CmoNE1DeXk5p83osmOx8NadW0QEa0drYdHQV61aBZvNhsWLF8PtdhuPq6qKpUuXwmazYeXKlUEcIRFR5whYeOvGrQnRwX4JQ07IN/TS0lKcOXMGhYWFGDNmDHbs2GEs27NnD4YMGYLCwkI0NjaiqKio18cX7L3UcL8piozExETIstzrrx0RhS/Z42HtaCXk30MvLi5GZmYmAGDWrFnYsmULfvCDHxjL5syZYywrKirC5MmT21yPy+WCy+Uy7tfV1QEALly4AADGtI0syz6xqqqwWCxGLEkSJEkyYkBCZKQKt1uCEBIiIz1wu2UIISEqygOXS4EQFkRFeeB0Nj/dUVFqq9gKi0UgMtIb64iI0OByeWMdLpcCSdKhKDrcbgWyrEOWvbEGSRLweLwx4PHIUJTmPFRVhtWqQdcBTZNhtarQdQs0TUZEhApNk6BpEiIiVKiqBF3vvZyiojzYv78cSUlJsFgs0HUdiqJA0zQIIYy4rdemM69T69jj8UCWZSNWFAUWi8WIm5831Se2Wq0QQhixruvQNM2IvWNvL2ZOzMknJwCeqCgoLhcsQjTHTmdzHq1iq9MJYbFAjYyE1emEbrFAi4iA1eWCbrFAj4iA4nJBlyToigLF7YYuy9BlGYrbDU2WISQJiscDTZYBSYLs8UD71/Mmqyo0qxXQdciaBtVqhcUbR0RA0jRI3lhVIek61MhISG43JCHgiYyE7I17KSd3dDQ+278fSUlJRl3vyutkt9sBwBSfZw/5hu5wODBo0CAAQExMjPHke5d5L9XXellr2dnZWLNmjd/jw4cP7/YYW+wn+MT/+r/boVgI39i7npaxrgPedxw0rfl2qVhV/70dj6ftuMU7GD5xb+XkdALTp4Po8sQi0fWcmpp6tHg0NDQgJiamx9YXDCHf0GNjY1FfXw+guYH369evQ8tae+KJJ/DII48Y93Vdh91uR1xcHCwWS4BGT9+mvr4e8fHxqK6uDvvrKBNR7+mp2iGEQENDg3HgGM5CvqGnp6dj/fr1WLJkCXJzczFlyhSfZXl5ecjIyEBubi6WLVvW7noiIyMRGRnp89hVV10VqGFTJ/Xt25cNnYg6rSdqR7gfmXuF/ElxqampGDhwIGw2G44ePYr58+dj+fLlAIC5c+eiuroaNpsN0dHRmDRpUpBHS0REFByX7denUmgw01cXElHvYe3wF/JH6GRukZGRePbZZ/3eDiEiuhTWDn88QiciIjIBHqETERGZABs6ERGRCbChExERmQAbOhERkQmwoRMREZkAGzoREZEJsKETERGZABs6ERGRCbChExERmQAbOhERkQmERUNvaGhAWloa+vTpg/Lycp9lqqpi6dKlsNlsWLlyZZBGSEREFFxh0dCjo6Px9ttvY8GCBX7L9uzZgyFDhqCwsBCNjY0oKioKwgiJiIiCSwn2ADpCURT079+/zWXFxcWYM2cOAGDWrFkoKirC5MmT/X7O5XLB5XIZ93Vdh91uR1xcHCwWS2AGTkREIU0IgYaGBgwaNAiSFBbHuO0Ki4Z+KQ6Hw/gu3JiYGNjt9jZ/Ljs7G2vWrOnNoRERUZiorq7GkCFDgj2Mbgn7hh4bG4v6+noAzc29X79+bf7cE088gUceecS4X1dXh6FDh+LEiROIjY2FpmkAAFmWfWJVVWGxWIxYkiRIktRu7PF4IMuyESuKAovFYsRA8/v+LWOr1QohhBHrug5N04xY13UoitJurGkahBBG3FYeoZqT2+1GSUkJ0tPTIUmSKXIy4+vEnJhTqOXkcrlw8OBBpKenG7OsXcnJbrfj2muvxZVXXolwF/YNPT09HXl5ecjIyEBubi6WLVvW5s9FRkYiMjLS7/HY2FjjCJ96n67rSE5OxlVXXRX2011E1Ht6unaY4a3XsKmgWVlZyMvLw/3334+tW7di+fLlAIC5c+eiuroaNpsN0dHRmDRpUpBHSp0hSRIGDx7MZk5EncLa4c8ihBDBHkQw1NfXIyYmBnV1dTxCDyJVVVFQUICMjAxjOo6I6Nv0VO0wUy/grg0FlSRJSExM5F42EXUKa4c/HhJRUEmShAEDBgR7GEQUZlg7/HHXhoLK4/EgNzcXHo8n2EMhojDC2uGPDZ2CSpZlTJw4EbIsB3soRBRGWDv8ccqdgkqSpHavHUBE1B7WDn88Qqeg8ng8eOeddzhtRkSdwtrhjw2dgkpRFNhsNn5kjYg6hbXDHxs6BZXFYkHfvn1D8ipNf/7znzF+/HikpKRg9OjRuOWWW6DrerfW+fOf/xxut9u4v3TpUmzYsKHDv9/Q0IA+ffrgvvvu83l8165dKCkpMe7v27cPEyZM6NZYT5w4gVdeecXnsaysLBw/frxT69m4cSOSkpKQnJyMUaNGYfHixd0aV+vnkC5PoVw7goUNnYLK4/Fg9+7dITdtdubMGTz44IP485//jMOHD6OiogIvvPBCt4vHmjVrutWM/t//+38YN24cdu7ciYsXLxqPt27oPaGthp6Tk4OEhIQOr+PQoUP4n//5H+zbtw+ffvopKioq8NOf/rRb4+rqc6iqare2S6ElVGtHMLGhU1ApioLMzMyQmzarqamBoiiIi4szHhs3bpzR0A8dOoRJkyYhKSkJN910Ez766CMAzU3w6quvNn7n4sWLxu88+OCDAIDJkycjJSUFZ8+eBQAcPXoUt956K66//nrMmzfvks1q8+bNeOyxx2Cz2fDWW28BaG6yf/3rX/GrX/0KKSkpePXVV31+R1VVzJw5ExMmTMCNN96IxYsXo7GxEQCwdetWzJw5E4sWLcLYsWMxYcIEfPHFF8Z4jx49ipSUFNx+++0AgOHDh6O8vBwAcOrUKSxYsABJSUlISkrCz372M7/xVldXIyYmxrgCl8Viwbhx44zlBw8exIwZMzBhwgRjR8XrnXfewcSJE5GcnIyUlBQcOHCgzefw66+/xve+9z2MHTsWiYmJPjshw4cPx3PPPYebb74Z99xzT7vPK4WfUK0dQSUuU3V1dQKAqKurC/ZQLmu6rgu32y10XQ/2UHxomibmzZsnYmNjxZ133imef/558dVXXwkhhHC5XCI+Pl689957QgghCgsLxcCBA8XFixdFVVWViIuLM9bT0NAgWv6ZARANDQ3G/XvuuUdMmjRJNDY2ClVVxeTJk8Ubb7zR5pjKy8vFoEGDhKqqYteuXWLy5Mk+63nxxReN+x988IEYP368EKL5Oa6trTXiBx98ULzwwgtCCCG2bNkiYmJixIkTJ4QQQjz22GPigQce8FuH17Bhw8SRI0eEEEJMnz5dPP/888ays2fP+o35m2++EVOmTBEDBw4U3//+98WLL74o7Ha7EEKICxcuiNTUVHH69GkhhBDnzp0TQ4cOFTU1NeLYsWPimmuuEceOHRNCCOF2u4XD4WjzOVy4cKF4/PHHhRBCfP3112LIkCHiwIEDxngfeOCBkPv/Rd3XU7XDTL2AR+gUVKqqIicnJ+SmQyVJws6dO1FUVIRZs2bho48+wo033ojPP/8cx44dQ0REBGbOnAkAmDp1KgYMGICysrIubWvevHmIjo6GLMu46aab2n2PevPmzViyZAlkWcbs2bPxxRdfoKKi4lvXL4TA//7v/yI1NRVJSUl45513cPjwYWP51KlTMWzYMADApEmTOvQe+cWLF1FUVIT/7//7/4zH+vfv7/dzV1xxBQoLC5GTk4PJkyfjz3/+M5KSkmC321FUVIQvvvgC3/3ud5GSkoJbb70VQggcO3YMf/vb35CVlYXrr78eAGC1WhETE9PmWN5//338+Mc/BgAMGDAA8+bNw969e43l9957L99nNaFQrR3BxLkKCipFUZCVlRWy02ajRo3CqFGjsHz5csyaNQt//etfceutt7bZICwWi8/3TQOA0+n81m1ERUUZsfe7mlvzeDzYvn07rFYr3nzzTQBAY2Mjfv/73+OFF1645PrfeOMN7N+/HwUFBbjyyivxu9/9DgUFBZ3afndYLBakpqYiNTUVDz/8MMaMGYN9+/YhMjISSUlJPmPx8k7rd2Yb7d3v06dP1wZOIS3Ua0cw8Aidgi4U97BPnTplvC8OABcuXEBVVRUSEhIwatQouFwu5OfnAwCKiopw9uxZjB07FgMHDoSqqjh27BgAYNu2bT7rvfLKK1FXV9fp8ezevRsjRozAqVOncOLECZw4cQIfffQRtm3bBo/Hg759+7a73gsXLiAuLg5XXnklGhoasHXr1g5t81Lr7NOnD6ZOnYr//d//NR47d+6c38/94x//8Jm5qK6uxrlz5zBixAhMnjwZlZWVxvMIAIcPH4bb7cbMmTPx7rvv4p///CeA5h0a71haP4e33nqr8b75uXPn8Je//AUzZszoUI4U3kKxdgQTGzoFlaqqyMvLC7k/TFVVsXbtWlx//fVISUmBzWbDPffcgzvuuAMRERHYuXMnnnrqKSQlJeEnP/kJ/vSnP+E73/kOFEXB7373O3z3u99FRkYGXC6Xz3p/+tOfYsaMGT4nxXXE5s2b/T7ulZiYiEGDBmHPnj24++678cYbb7R5UtySJUtw8eJFjBkzBvPmzYPNZuvQNpOSknDDDTcgMTHROCmupddffx1///vfceONNyI5ObnNj981Njbi4Ycfxg033ICUlBTMnTvXOHkvNjYWe/bswS9+8QskJydjzJgxePzxx6HrOq677jps3rwZixYtMk489O4ktX4Of/e736GsrAxJSUm4+eab8dRTT+Gmm27q6FNLYSpUa0cw8fvQTfAduERE1DVm6gVhcYS+atUq2Gw2LF682OcjPU1NTZgzZw6mTZuG2267DXa7PYijpK4QQqC+vh6X6X4lEXURa4e/kG/opaWlOHPmDAoLCzFmzBjs2LHDWPbuu+8iMTER+/fvx8KFC/H6668HcaTUFaqqorCwkNNmRNQprB3+Qr6hFxcXIzMzEwAwa9YsFBUVGctGjhxpXCDD4XC0+bEZCm1WqxWzZ8+G1WoN9lCIKIywdvgL+YbucDiM9zViYmJ8ptUTEhJQXl6OxMREbNu2DXfeeWe763G5XKivr/e5ATA+YqRpWpuxqqo+sfda3u3FHo/HJ/ZOB3ljIYRfDMAn1nXdJ/bugbYXa5rmE4dTTqqq4ty5c9B13TQ5mfF1Yk7MKdRy8ng8Ru3obk5mEfINPTY21mi+DofD5/tvX3vtNUyfPh3l5eVYs2YN1q5d2+56srOzERMTY9zi4+MB/PvzrhUVFcZFOsrKylBZWQmgecq/qqoKAFBSUoLq6moAzR9VqqmpAQAUFBSgtrYWAJCfnw+Hw4H/v717D4+ivvcH/t6Z2U2CQEzwQoWIFWkFIRIVg0CAYk+gwUsteEE8AbSWnnNUegGPt6pYfTgPPtRaPadivVfFp4Lld9DU7IEIiSYmgosxGDFgxBWiNCzJrobdncv398e602w20IRkMzvD+/U8+/DJbpj5fjLJ57MzO/MdAPB6vQiFQgBi03OGw+GEyRDC4TDKysoAxG664fV6zTzjl/K0traa1+m2tLSYRyj8fr85d3dzczN8Ph8AoKmpybxMyA45HThwADU1NdB13TE5OXE7MSfmlG45ffrpp3j33Xeh63qfcqqtrYVTpP1Z7j6fD2vWrMGLL76Ihx56CGeffTYWLFgAAPjjH/8IXddxyy23oKKiAn/5y1/wxBNPdLucSCSScAlRMBhEXl4eAoEAcnJyzHdusiwnxJqmweVymbEkSZAk6aixqqqQZdmMFUWBy+UyYyD2jrBz7Ha7IYQw4/g7znhsGAYURTlqrOs6hBAJk5p0zYM5MSfmxJyYU3JOgUAAw4YNc8RZ7mnf0IHYWe61tbU488wz8eyzz+LWW2/F2rVrEQwGsWDBAnzzzTfQNA3PPPOMOVXkP+OkSxXszDAMtLa24pRTToEkpf0BIyJKE/1VO5zUC2zR0FPBSRvRzjRNQ2VlJaZPn84pHImox/qrdjipF7ChO2AjEhHR8XFSL+AxTrKUYRjYv3+/ecYpEVFPsHYkY0MnSxmGgb179/KPkoh6hbUjGQ+5O+AwCxERHR8n9YKU76Hv2rUr1asgGzMMA/v27eO7bCLqFdaOZCk7rXjevHkYPXo03n//fVxwwQVYvXp1qlZFNhb/HGzEiBG8bI2Ieoy1I1nKDrmXl5cjGAziwIEDWLZsWSpW0SdOOsxCRETHx0m9IGVva2bPno3x48fz1nZ0TLquY8+ePeaMTkREPcHakSylM3mMHTsWY8eORWtrKzZu3IjDhw+bDf72229P5arJJoQQOHz4MM466yyrh0JENsLakWxAPniYO3cuOjo6cPbZZ2P06NEYPXr0QKyWbEBRFEyaNImzxBFRr7B2JBuQn8Tw4cNx2223DcSqyGbid0oaM2YMZFm2ejhEZBOsHckGpKGXlpZi3rx5yM/Ph8vlAgDce++9A7FqsoEjR45YPQQisiHWjkQD0tAffPBB3HrrrRgxYsRArI5sRJZlFBQUWD0MIrIZ1o5kA9LQR40ahRtvvHEgVkU2o+s6GhsbMXbsWB42I6IeY+1INiAN/ciRI5g9e3bCIXdONENERNR/BqSh33XXXQOxGrIhWZYxfvx4q4dBRDbD2pFsQC5bE0JgxowZ5mP//v0DsVqyAV3X4fP5ODkEEfUKa0eyAWnoTzzxBGpqagAAa9euRWVlZa/+//Lly1FUVISFCxciGo2az2/duhV5eXmYOXMmLr300n4dMw2crKwsq4dARDbE2pFoQBr6Cy+8gDVr1mDZsmX45JNP8MQTT/T4//p8Pnz55ZeoqqrCuHHjsH79+oTXr732WmzduhVbtmzp72HTAJBlGeeeey5PaiGiXmHtSJbShr5ixQrcfvvtuOeee3D66adjw4YNkCSpV9O+1tTUoLi4GAAwZ84cVFdXJ7y+YcMGFBUV4dFHH+3XsdPA0DQN7733HjRNs3ooRGQjrB3JUnpS3GWXXZbw9TXXXNPrZbS1teGMM84AAGRnZyMQCJivXXTRRdi9ezcA4Morr8S0adNw4YUXdrucSCSCSCRifh0MBgHA/Pwl/q8sywmxpmlwuVxmLEkSJEk6aqyqKmRZNmNFUeByucwYiP0ido7dbjeEEGZsGAZ0XTdjwzCgKMpRY13XIYQw4+7ySNechBDIzs6Gy+VyTE5O3E7MiTmlW06GYZi1o685OUVK99CnTZuG1tZWtLW1Ydq0aeZJcQcPHuzxMnJycszm29bWhtzcXPO1wYMHw+PxwOPx4IorrsAHH3xw1OWsWrUK2dnZ5iMvLw8A0NDQAABobGxEY2MjAKC+vh5NTU0AYof8m5ubAQB1dXXw+/0AgOrqarS0tAAAKisr0draCgCoqKhAW1sbAMDr9SIUCgEAysrKEA6HoWkaysrKoGkawuEwysrKAAChUAher9fMs6KiAgDQ2tpqnnPQ0tJiHqHw+/2oq6sDADQ3N8Pn8wEAmpqaUF9fb5ucDh48iC+//BKyLDsmJyduJ+bEnNItp88//xzt7e2QZblPOdXW1sIpUnY/dAC47rrrcM4550BRFGzZsgXPPfccRo8ejVmzZpkb7p/x+XxYs2YNXnzxRTz00EM4++yzsWDBAgCxvez4/Wuvv/56/PznP8f06dO7XU53e+h5eXkIBALIycmx9TtVO7/7jkajeO+991BYWAhJkhyRkxO3E3NiTumWUyQSwfbt21FYWGjOb3I8OQUCAQwbNswR90NPaUP/wQ9+gLfeegtA7N1UaWkp7rvvPvz2t7/tcUMHYme519bW4swzz8Szzz6LW2+9FWvXrsVTTz2FJ598EoqiYOrUqXj44Yd7vEwn3dTezgzDgN/vR15eHiRpQM7RJCIH6K/a4aRekNKGPn36dHi9XmRmZgKIHV5ZuHAhampq8Pe//z1Vq+0RJ21EIiI6Pk7qBSndJfr9739vfv4NAEOGDMHGjRvx2GOPpXK1ZCOapqGystJRJ6YQUeqxdiRL6VnuF1xwAQCgo6MDgwYNAgBIkoTrrrsulaslG5EkCaNHj+bhdiLqFdaOZClr6B999BGA2LSvzz//PG/GQt2SJIm31SWiXmPtSJaytzZ33303tm/fju3bt5uXBxB1pWkaKioqeNiMiHqFtSNZyvbQ7733XvPm89OmTUvVasjmJEnC+PHjediMiHqFtSNZyhp6QUEBNE3D+vXrUV1djUAggNzcXEydOhXz5s0zr0ekE5skSTjttNOsHgYR2QxrR7KUvrVZsmQJPv30UyxevBgrV67EokWLsHfvXixZsiSVqyUbUVUV5eXlUFXV6qEQkY2wdiRL6W7yvn378Oc//znhuQsvvPCos7nRiUeWZUyaNIl3TCKiXmHtSJbShl5YWIjS0lIUFxdj6NChCAaD8Hq9KCwsTOVqyUYkSUqYn5+IqCdYO5KldKY4ANi5cydqamrQ1taGnJwcTJ48GRMnTkzlKnvESbMD2ZmqqvB6vSguLobb7bZ6OERkE/1VO5zUC1Le0LvzwgsvoLS0dKBXm8BJG9HOhBAIhUIYMmSIeYMFIqJ/pr9qh5N6QUoPuccnl+lMCIG1a9da3tApPbhcLtv/ERHRwGPtSJbShj558mTMnz8fXQ8C7Nu3L5WrJRtRVRVlZWUoKSnhIXci6jHWjmQpbejjxo3Dww8/jGHDhiU8P3fu3FSulmxEURQUFxdzXgIi6hXWjmQp/Uls3rzZvClLZ2+88UYqV0s2wz9IIjoerB2JUjqxzODBgzktHx2TpmkoKyvjfMxE1CusHcksOcs9HTjpzEY7E0JA0zQoisKz3Imox/qrdjipF9hi93n58uUoKirCwoULEY1GzefLysowZcoUTJs2DbfccouFI6S+4DtsIjoerB2J0r6h+3w+fPnll6iqqsK4ceOwfv1687Xx48ejsrISb7/9NgKBAN577z0LR0rHQ9M0eL1e/mESUa+wdiRL+4ZeU1OD4uJiAMCcOXNQXV1tvnbmmWeaJ0W43e5jniARiUQQDAYTHgCg67r5b3expmkJsWEYx4xVVU2I459oxGMhRFIMICE2DCMhjv/CHi3WdT0htlNOsixj7ty5cLvdjsnJiduJOTGndMtJkiSzdvQ1J6dI+4be1tZmfq6RnZ2NQCCQ9D07duxAa2uref/17qxatQrZ2dnmIy8vDwDQ0NAAAGhsbERjYyMAoL6+Hk1NTQBiRwiam5sBAHV1dfD7/QCA6upqtLS0AAAqKyvR2toKAKioqEBbWxsAwOv1IhQKAYh9PBAOhxNO5AiHwygrKwMAhEIheL1eM+eKigoAQGtrKyorKwEALS0t5hsav9+Puro6AEBzczN8Ph8AoKmpCfX19bbKqaqqCkIIR+XkxO3EnJhTuuVUV1cHIUSfcqqtrYVTpP1JcX/84x9x0kknobS0FNu3b8dzzz2Hxx9/3Hz9iy++wHXXXYe//vWvOPXUU4+6nEgkgkgkYn4dDAaRl5eHQCCAnJwc852bLMsJsaZpcLlcZixJEiRJOmqsqipkWTbj+Akb8RiAeSJHPHa73eYJHvE9VV3XzdgwDCiKctRY13UIIcy4uzzSNadIJIL/+7//w+zZsyHLsiNycuJ2Yk7MKd1yCofD2Lx5M2bPnm1eTXU8OQUCAQwbNswRJ8WlfUP3+XxYs2YNXnzxRTz00EM4++yzsWDBAgDA119/jZKSEvzP//wPxo8f36vlOunMRiIiOj5O6gVpf8i9oKAAw4cPR1FRET766CPMmzcPS5cuBQA89thj2Lt3L2655RbMnDkT27Zts3i01FuGYSAQCJifZxER9QRrR7K030NPFSe9K7MzVVVRUVGBWbNmcT5mIuqx/qodTuoFbOgO2IhERHR8nNQL0v6QOzmbYRg4ePAgD5sRUa+wdiRjQydLGYaBhoYG/lESUa+wdiTjrWrIUoqiYNasWVYPg2jA8dYFfaVACNaOzriHTpYyDAP79+/nu2wi6hVZZu3oig2dLGUYBvbu3cs/SiLqFUVh7eiKZ7k74MxGIrIfHnLvu/7oXk7qBdxDJ0sZhoF9+/bxXTadcARcfPThoStu1o4u2NDJUvwMnYiOhyHLrB1d8JC7Aw6zEJEN8Zh73/VD+3JSL+AeOllK13Xs2bPHvCsSEVFP6IrC2tEFGzpZSgiBw4cP4wQ9UEREx0lIEmtHF5xYhiylKAomTZpk9TCIyGaUaJS1owvuofeRy8VHXx5ut46PP/6Yh82IqFd0RWHt6IINnSwlScCRI0esHgYR2Y0ksXZ0wUPuZKloVEZBQYHVwyAim5GjUdaOLriHTpZyu3U0NDTwsBkR9YrudrN2dGGLhr58+XIUFRVh4cKFiEaj5vOapmHx4sUoKirCsmXLLBmb1bMl2f1xBFmWbDciIqdJ+4bu8/nw5ZdfoqqqCuPGjcP69evN1zZt2oSRI0eiqqoKHR0dqK6utnCkdDxkVcX48eMhy7LVQyEiG2HtSJb2n6HX1NSguLgYADBnzhw8++yzuP76683XLrvsMvO16upqTJkypdvlRCIRRCIR8+v29nYAwOHDhwHAPGwjy3JCrGkaXC6XGUuSBEmS/hED0DIyIEWjkISAmpEBOR5nZkKJROCKx+EwAEDrErvDYQiXC1pGBtzhMAyXC7rHA3ckAsPlguHxQIlEYEgSDEWBEo3CkGUYsgwlGoUuyxCSBEVVocsyIEmQVRW6Etu8sqZBd7sBw4Cs69DcbrjisccDSdchxWNNg2QYA5aTmpmJhm3bkJ+fD5fLBcMwoCgKdF2HEMKMu9s2vdpOXWJVVSHLshkrigKXy2XGQOwIUOfY7XZDCGHGhmFA13Uzjo/9aDFzYk4JOQGsEX3IKZqVhV3f1o6449lOgUAAABxxPXvaN/S2tjacccYZAIDs7Gzzhx9/LT5VX9fXulq1ahVWrlyZ9PxZZ53V90F2eqOQEH/7y9ujWIjEOL6czrFhAPGPHHQ99jhWrGn/WI+qdh93+ggjIR6onMJhYOZMEJ2QWCOOP6cjR/q1doRCIWRnZ/fb8qyQ9g09JycHwWAQQKyB5+bm9ui1ru6880786le/Mr82DAOBQADDhg2Di3MqWyYYDCIvLw9+v9/28ygT0cDpr9ohhEAoFDJ3HO0s7Rv65MmTsWbNGpSWlqK8vBxTp05NeM3r9WL69OkoLy/HjTfeeNTlZGRkICMjI+G5k08+OVXDpl4aOnQoGzoR9Vp/1A6775nHpf1JcQUFBRg+fDiKiorw0UcfYd68eVi6dCkA4PLLL4ff70dRURGysrJwySWXWDxaIiIia5ywt0+l9OCkWxcS0cBh7UiW9nvo5GwZGRm47777kj4OISI6FtaOZNxDJyIicgDuoRMRETkAGzoREZEDsKETERE5ABs6ERGRA7ChExEROQAbOhERkQOwoRMRETkAGzoREZEDsKETERE5ABs6ERGRA7ChExEROQAbOhERkQMoVg/AKoZh4MCBAxgyZAhcLpfVwyEiIgsIIRAKhXDGGWdAkuy9j3vCNvQDBw4gLy/P6mEQEVEa8Pv9GDlypNXD6JMTtqEPGTIEQGwjDh061OLRnLg0TUNtbS0KCwuhKCfsryMR9VJ/1Y5gMIi8vDyzJ9jZCVtB44fZhw4dyoZuIcMwkJ+fj5NPPtn2h7uIaOD0d+1wwkevJ2xDp/QgSRJGjBhh9TCIyGZYO5Jxl4gspWkaKioqoGma1UMhIhth7UjGhk6WkiQJ48eP5+F2IuoV1o5kPOROlpIkCaeddprVwyAim2HtSJb2b2127NiBoqIizJgxA9dccw1UVTVf0zQNixcvRlFREZYtW2bhKOl4qaqK8vLyhO1KRPTPsHYkS/uGPmLECJSXl2Pbtm0455xzsHHjRvO1TZs2YeTIkaiqqkJHRweqq6utGygdF1mWMWnSJMiybPVQiMhGWDuSpX1DHz58OAYNGgQAcLvdCdcb1tTUoLi4GAAwZ86cYzb0SCSCYDCY8AAAXdfNf7uLNU1LiA3DOGasqmpCLIRIiIUQSTGAhNgwjIQ4ftLH0WJd1xNiO+UExC4dlCTJMTk5cTsxJ+aUbjkJIcza0decnCLtG3rc559/js2bN+Oyyy4zn2trazOvIc/OzkYgEDjq/1+1ahWys7PNR3yWuIaGBgBAY2MjGhsbAQD19fVoamoCAPh8PjQ3NwMA6urq4Pf7AQDV1dVoaWkBAFRWVqK1tRUAUFFRgba2NgCA1+tFKBQCAJSVlSEcDkPTNJSVlUHTNITDYZSVlQEAQqEQvF6vmVdFRQUAoLW1FZWVlQCAlpYW802L3+9HXV0dAKC5uRk+nw8A0NTUhPr6etvk9MUXX+CNN96AqqqOycmJ24k5Mad0y2nPnj0oKyuDqqp9yqm2thZO4RLxt1JpLBgM4vLLL8ef/vQnfO973zOf/8///E/MnTsX06dPx/r16/HZZ59h+fLl3S4jEokgEokkLDMvLw+BQAA5OTnmOzdZlhNiTdPgcrnMWJIkSJJ01FhVVciybMaKosDlcpkxEHtH2Dl2u90QQpixYRjQdd2MDcOAoihHjXVdhxDCjLvLI11z0nUdwWAQJ598MoQQjsjJiduJOTGndMtJ0zSEQiGcfPLJ5t728eQUCAQwbNgwtLe323+SMZHmNE0Tl112mdi8eXPSa6+99pq4++67hRBC/PSnPxXV1dU9Xm57e7sAINrb2/ttrOQsGzZsEBdccIE4//zzxbnnnitmzZoldF3v0zLvu+8+EYlEzK8XLVokHnvssR7931GjRonvf//74vzzzxdjx44Vjz/++HGP40c/+pHYs2ePEEKIPXv2iIKCAjFx4kTxzDPPiJtuuklUVlb2ann9/bN65JFHxFdffXXc/5+op5zUC9K+ob/88ssiNzdXzJgxQ8yYMUO88sor4mc/+5kQQghVVUVpaamYNm2auPXWW3u1XCdtRDuLRqNi48aNIhqNWj2UBC0tLeLUU08Vn332mfncjh07hGEYfVouABEKhcyve9vQP/zwQyGEEJ9//rnIzs4WH3zwQZ/GI4QQ//Vf/yX+/d///bj/fyp+Vp1z7Q1VVY97nWQv/VU7nNQL0r6hp4qTNqKdGYYhOjo6+two+9v7778vvvOd7yQ0387ee+89MXnyZDFhwgQxadIk8fbbbwshhGhubhbDhg0zvy8UCon4gbClS5cKAGLChAni/PPPF1999ZVYtGiR+Ld/+zdx6aWXijFjxoirrroqYQ++s65NbtKkSeLVV18Va9asERdddJGYOHGimDRpknj33XfN76murhbTpk0T+fn5YsKECWLjxo0Jy3r++efF6aefLk499VRx/vnni127dokZM2aITZs2CSGEaGtrEzfddJMYP368yM/PF0uWLOn1z+qTTz4RJSUl4qKLLhL5+fniv//7v485vpUrVwq3220ejfD5fCIUCoklS5aI8847T5x33nni/vvvN5cxY8YMcdddd4lZs2aJWbNmdTsGcp7+qh1O6gVs6A7YiHZmGIaIRqNp19B1XRc/+clPRE5Ojvjxj38sVq9eLb744gshhBCRSETk5eWJN998UwghRFVVlRg+fLj4+uuvj9nQheh+D/2SSy4RHR0dQtM0MWXKFPHyyy93O6bODb2+vl4MGTJEfPLJJ+LgwYPm99TU1IjzzjtPCCHEoUOHxOmnny7eeecdM6dDhw4lLeu+++4Tv/71r81ldG7oixcvFrfccot5+Lzzunrys9I0TVx00UWisbFRCCHEN998IyZMmCB27NjR4/EJIcTtt98uFi5cKHRdF19//bWYOHGi+Mtf/mKOt6SkJO2O8lBq9VftcFIv4ExxZKn42a8lJSVwu91WD8ckSRI2bNiAjz/+GNu2bcPf/vY3PPTQQ9i+fTuOHDkCj8eD2bNnAwCmTZuG0047DfX19fjOd77T63X95Cc/QVZWFgDg4osvxt69e4/6vfPnz0dmZiYGDRqEZ555BmPGjIHX68VDDz2EQ4cOQVEUfPTRR4hGo6ipqcG4ceMwZcoUM6fc3Nxeje3111/Hjh07zOk1Tz311KTvOdbPKhqNYteuXbjuuuvM7w+FQvjoo4/Q0tLS4/Ft3rwZjz76KCRJwkknnYTS0lJs3rwZV199NQDgX//1X9Pq94dSL11rh5XY0MlSiqKgpKQkbe+Ffu655+Lcc8/F0qVLMWfOHPzv//4vfvjDH3Z7q0WXy5VwFjEAhMPhf7qOzMxMM46fgXs069evx/jx482vo9Eo5s2bh61bt+LCCy9EMBhEdnY2otFoT1PsN939rGbPno1TTjkFO3fuTPr+N954o8fLFkIk/cw7fz148ODjHjfZU7rXDivY5jp0cq50nNhh//79eOedd8yvDx8+jObmZowePRrnnnsuIpGIec1sdXU1Dh48iAkTJmD48OHQNA27d+8GALzwwgsJyx0yZAja29v7bZzhcBiqqprzKjz22GPma1OmTEFjY6N5Da9hGMecq6E7V1xxBR5++GHzsqC///3vSd9zrJ/V97//fQwaNCjh57Bnzx4EAoFjjm/o0KEJP6d/+Zd/wZ/+9CcIIfDNN9/gxRdfxA9/+MNe5ULOk461w0ps6GQpTdPg9XrT7g9T0zQ88MAD+N73voeJEyeiqKgIixYtwpVXXgmPx4MNGzbg7rvvRn5+Pn7xi1/g1VdfxUknnQRFUfCHP/wBP/rRjzB9+vSEuQ8A4Ne//jVmzZqFiRMn4uDBg30e59ChQ/HAAw/g4osvxvTp05GRkWG+lpOTg7/+9a9YsWIF8vPzUVBQgLfffrtXy3/kkUfQ0dGB8ePHY+LEibjrrruSvudYPytFUbBp0yb85S9/QX5+Ps477zz89Kc/xZEjR445vttuuw1LlizBxIkTsXPnTvzmN7+By+XChAkTUFhYiCuuuALz58/v2w+PbC1da4eVbDGxTCrED006YjIBIiI6Lk7qBdxDJ0sJIRAMBnGCvq8kouPE2pGMDZ0spWkaqqqqeNiMiHqFtSMZTw/so25OdqZecUOIuVYPgohsxu12Y+5c1o7OuIdOlpKk2JnN8bOoiYh6In5VBGvHP7Chk6U8Hh3vvfdewrXbRET/jK6zdnTFs9z7eGYjD7n33Yn5G0hE6YBnuRP1E0kycPDgQR42I6JeMQzWjq7Y0MlSHo+BhoYG/lESUa8YBmtHV7Zo6KFQCIWFhRg8eDAaGhoSXtu6dSvy8vIwc+ZMXHrppRaNkI5XOKxg1qxZnI+ZiHpFUVg7urJFQ8/KysLrr79+1Kker732WmzduhVbtmwZ4JFRX8mygf379/NdNhH1imGwdnRli4auKEq3t22M27BhA4qKivDoo48O4KioPyiKgb179/KPkoh6xTBYO7qyRUM/losuugi7d+/Gli1b8Oabb2LHjh3dfl8kEkEwGEx4ADAvedB1vdtY07SEOP7L0znOzNQgSfFYNeOsLBWSJMzY5RIABLKyVAACLlc8BiSpc2wgM7NzHJsJSZYNZGTEYkXpHOvweDrHsfG63Trc7ljs8ehQlHismXFGhgZFMcxYlgc2J02TMGXKFCiKAsMwzFmfdF1PiPtjO3WOVVVNiOMXe8RjIURSDCAhNgwjIY6P92gxc2JOzKn/cnK5XGbt6GtOTpHyhr5r166ULn/w4MHweDzweDy44oor8MEHH3T7fatWrUJ2drb5iN9uMv6ZfGNjIxobGwEA9fX1aGpqAgD4fD40NzcDAOrq6uD3+wHEbpnZ0tICAFi9uhL5+a0AgMcfr8CYMW0AgKef9mLEiBAAYN26MuTmhpGVpWHdujJkZWnIzQ1j3boyAMCIESE8/bQXADBmTBsefzx2a878/FasXl0JACgsbMHKlbFbTc6c6ccdd9QBAEpKmrFsmQ8AMH9+E26+uR4AcMMNjbjhhlhON99cj/nzYzktW+ZDSUkspzvuqMPMmbGcVq6sRmHhwOY0ZcoBvPXWWzAMA36/H3V1sZyam5vh88VyampqQn19fZ+3U2VlJVpbYzlVVFSgrS2Wk9frRSgUy6msrAzhcBiapqGsrAyapiEcDqOsLJZTKBSC1xvLqa2tzbyFamtrKyorYzm1tLSYtwRlTsyJOaUmp08//RRvv/02DMPoU061tbVwDJEiP/nJT8SKFSvEpZdeKlasWNEvy1y0aJH48MMPE55rb2834wULFoht27Z1+3/D4bBob283H36/XwAQgUBACCGEpmlC07SkWFXVhFjX9YQYECIzUxWSFI+jZpyVFRWSZJixy2UIwBBZWVEBGMLlisdCSFLnWBeZmZ1jVQBCyLIuMjJisaJ0jjXh8XSONQEI4XZrwu2OxR6PJhQlHqtmnJGhCkXRzViWBzanQYMi4u233zZ/nqqqmtugc9zdtunNduoaR6PRhNgwjITYMIykWAiREOu6nhDHx3u0mDkxJ+bUfzmFw2GzdvQlp0OHDgkACb3ErlI2sUx5eTmCwSAOHDiAZcuW9Xl5JSUl2LlzJ0aNGoWlS5eipqYGa9euxVNPPYUnn3wSiqJg6tSpePjhh3u0PE4skz44sQwRWcVJE8ukdKa4xsZGlJeX4xe/+EWqVnHc2NDTg6LoaGxsxne/+13Ismz1cIjIJnRdR3Nz32uHkxp6Si/gGzt2LMaOHYvW1lZs3LgRhw8fNk+WuP3221O5arIJSRI4fPgwzjrrLKuHQkQ2IgRrR1cDcpb73Llz0dHRgbPPPhujR4/G6NGjB2K1ZAPRqIJJkyZxcggi6hVFYe3oakB+EsOHD8dtt902EKsim1EUHR9/3IQxY8bwkDsR9Ziu62hqYu3obEAaemlpKebNm4f8/Hy4vv3Q+d577x2IVVOakyTgyJEjVg+DiGyItSPRgDT0Bx98ELfeeitGjBgxEKsjG4lGZRQUFFg9DCKyGVlm7ehqQBr6qFGjcOONNw7Eqshm3G4dDQ2NGDt2LA+bEVGP6bqOxkbWjs4GpKEfOXIEs2fPTjjkvnr16oFYNRER0QlhQBr6XXfdNRCrIRtSVRnjx4+3ehhEZDOyzNrR1YBctiaEwIwZM8zH/v37B2K1ZAMejw6fz2feNIGIqCd0nbWjqwFp6E888QRqamoAAGvXrjUn3ScyjNj97omIeou1I1FKp36Ni0ajuP766zFixAgoioI1a9akepX/FKd+TR+cy52IrOKkqV9Tuoe+YsUK3H777bjnnntw+umnY8OGDZAkidO+ksnj0fDee+856p7ERJR6msba0VVKT4q77LLLEr6+5pprUrk6siHDcCEnJ8e8+oGIqCdcLtaOrlLa0KdNm4aNGzdCURRcdtll5rWCr776aipXSzaiaTLOOeccq4dBRDYjy6wdXaX0kPvChQvh8/ng8/kwc+ZM7N27FwDwxz/+sVfLCYVCKCwsxODBg9HQ0JDwmqZpWLx4MYqKivrlvus0sDIyNFRXV/OwGRH1iqaxdnSV0ob+1Vdf4cEHH8T999+Pl156CTfddBPeeuutXi8nKysLr7/+OubPn5/02qZNmzBy5EhUVVWho6MD1dXV/TF0GiC6LmHEiBGQpAG54IKIHEKSWDu6SulPQtd1hMNhAMCZZ56JTZs24ZFHHsGHH37Yq+UoioJTTz2129dqampQXFwMAJgzZw4bus1omoRRo0bxj5KIekWSWDu6SulP4ve//z2CwaD59ZAhQ7Bx40Y89thj/baOtrY281KD7OxsBAKBbr8vEokgGAwmPACYkxLout5trGlaQmwYRlKcmalBkuKxasZZWSokSZixyyUACGRlqQAEXK54DEhS59hAZmbnOHZISZYNZGTEYkXpHOvweDrHsfG63Trc7ljs8ehQlHismXFGhgZFMcxYlgc2p0GDoti2bZv584wfPtN1PSHuj+3UOVZVNSGOX70Zj4UQSTGAhNgwjIQ4Pt6jxcyJOTGn/sspEomYtaOvOTlFShv6BRdcgNNOOw0dHR3/WKEk4brrruu3deTk5JjNua2tDbm5ud1+36pVq5CdnW0+8vLyAMD8TL6xsRGNjY0AgPr6ejQ1NQEAfD4fmpubAQB1dXXw+/0AgOrqarS0tAAAVq+uRH5+KwDg8ccrMGZMGwDg6ae9GDEiBABYt64MublhZGVpWLeuDFlZGnJzw1i3rgwAMGJECE8/7QUAjBnThscfrwAA5Oe3YvXq2EQ8hYUtWLkydgRi5kw/7rijDgBQUtKMZct8AID585tw8831AIAbbmjEDTfEcrr55nrMnx/LadkyH0pKYjndcUcdZs6M5bRyZTUKCwc2pwsuOAhVVSFJEvx+P+rqYjk1NzfD54vl1NTUhPr6+j5vp8rKSrS2xnKqqKhAW1ssJ6/Xi1AollNZWRnC4TA0TUNZWRk0TUM4HEZZWSynUCgErzeWU1tbGyoqYjm1traaEya1tLSYR4qYE3NiTqnJ6fPPP4fL5YIkSX3Kqba2Fo4hUmTXrl1i165doqGhQaxYsaJflrlo0SLx4YcfJjz32muvibvvvlsIIcRPf/pTUV1d3e3/DYfDor293Xz4/X4BQAQCASGEEJqmCU3TkmJVVRNiXdcTYkCIzExVSFI8jppxVlZUSJJhxi6XIQBDZGVFBWAIlyseCyFJnWNdZGZ2jlUBCCHLusjIiMWK0jnWhMfTOdYEIITbrQm3OxZ7PJpQlHismnFGhioURTdjWR74nFRVFUIIoev/iDVNS4i72za92U5d42g0mhAbhpEQG4aRFAshEmJd1xPi7vJgTsyJOaV3TocOHRIARHt7u7C7lM0Ud9VVV+Gqq66CEAJvvvkm1q1b16fllZSUYOfOnRg1ahSWLl2KmpoarF27Fpqm4aabbsKnn36KgoIC/OEPf+jR8jhTXHrIzNTwxhuVmD59OhRlQO4VREQOoGkaKiv7XjucNFNcyhq6z+czbz6/d+9ejB49OhWrOW5s6OlBkgy0tLTilFNO4cktRNRjhmGgtbXvtcNJDT1lu0QFBQXQNA3r169HdXU1AoEAcnNzMXXqVMybN497YwQAMAwJp512mtXDICKbkSTWjq5Suku0ZMkSfPrpp1i8eDFWrlyJRYsWYe/evViyZEkqV0s2kpmpory83Dy7lYioJ1SVtaOrlO4m79u3D3/+858Tnrvwwgsxffr0VK6WbCQalTFp0iRzWmAiop6QZdaOrlLa0AsLC1FaWori4mIMHToUwWAQXq8XhYWFqVwt2YhhSEe91JCI6GgkibWjq5TfD33nzp2oqalBW1sbcnJyMHnyZEycODGVq+wRnhSXHrKyVLz6qhfFxcVwu91WD4eIbEJVVXi9fa8dTjopLuUNvTsvvPACSktLB3q1CdjQ04MkCRw+HMKQIUN4G0Qi6jEhBEKhvtcOJzX0lB5y/+ijj5KeE0Jg7dq1ljd0Sg+G4bL9HxERDTyXi7Wjq5Q29MmTJ2P+/PnoehBg3759qVwt2UhWlor/9//KUFJSwkPuRNRjqqqirIy1o7OUNvRx48bh4YcfxrBhwxKenzt3bipXSzYSDisoLi7mvARE1CuKwtrRVUp/Eps3b8agQYOSnn/jjTdSuVqyESHAP0giOi6sHYlSOrHM4MGDOZ0nHVNW1j/uwkRE1FOd7+BGMZac5Z4OeJZ7uhCIRjUoisKz3Imox4QQ0LS+1w6e5U4mATahvhAuF8LaNzx0RkS9Fm/oFMPj4WQpLTMTXq+Xh82IqFc0TWPt6MIWDX358uUoKirCwoULEY1Gzee3bt2KvLw8zJw5E5deeqmFI6Tj5T5yBFdeeSUvOyGiXnG73awdXaR9Q/f5fPjyyy9RVVWFcePGYf369QmvX3vttdi6dSu2bNli0QipL4QkIRgMJs1VQER0LEII1o4u0r6h19TUoLi4GAAwZ84cVFdXJ7y+YcMGFBUV4dFHH7VieNRHWkYGqqqqeNiMiHpF0zTWji7SvqG3tbWZZx5mZ2cjEAiYr1100UXYvXs3tmzZgjfffBM7duw46nIikQiCwWDCAwB0XTf/7S7WNC0hNgwjOc7MhPHt5Xlq5zgrC6Jz7HJBxGPETghTs7IAxPZU47EhSVAzM81Yi8eyDC0jIxYrihnrigLN4zFjPR673dC/PRylezzQvz15ROscZ2TA6Bx/eyvCgcpJjkYxe/ZsuN1uGIZh/nHqup4Q98t26hSrqpoQx9/lx2MhRFIMICE2DCMhjo/3aDFzYk7Mqf9ykiTJrB19zckp0r6h5+TkmM23ra0t4XZ5gwcPhsfjgcfjwRVXXIEPPvjgqMtZtWoVsrOzzUdeXh4AoKGhAQDQ2NiIxsZGAEB9fT2ampoAxA75Nzc3AwDq6urg9/sBANXV1WhpaQEAVK5ejdb8fABAxeOPo23MGACA9+mnERoxAgBQtm4dwrm50LKyULZuHbSsLIRzc1G2bh0AIDRiBLxPPx3Lc8wYVDz+OACgNT8flatXAwBaCgtRvXIlAMA/cybq7rgDANBcUgLfsmUAgKb581F/882xnG64AY033BDL6eab0TR/fiynZcvQXFISy+mOO+CfOTOW08qVaPn21rYDldOByZNRWVkJwzDg9/tRV1cXy6m5GT6fL5ZTUxPq6+v7vp0qK9Ha2hrLqaICbW1tsZy8XoRCoVhOZWUIh8MJ17iGw2GUlZXFcgqF4PV6Yzm1taGioiKWU2srKisrY9uppcU8ksScmBNzSk1On376KWpra2EYRp9yqq2thWOINPf++++LhQsXCiGEePDBB8XLL79svtbe3m7GCxYsENu2bTvqcsLhsGhvbzcffr9fABCBQEAIIYSmaULTtKRYVdWEWNf1xBgQamam0CVJCEBEO8dZWcLoHLtcwojHgDBcLhHNyhICEIYkmbEuSSKamWnGajyWZaFmZMRiRTFjTVGE6vGYsRaP3W6hud2x2OMRmqLExts5zsgQeudYlgc0p/BJJ4m//e1vIhqNCl3Xhaqq5jboHHe3bXq1nbrE8fXFY8MwEmLDMJJiIURCrOt6Qhwf79Fi5sScmFP/5XTkyBGzdvQlp0OHDgkACf3Ermwxsczy5ctRW1uLM888E88++yxuvfVWrF27Fk899RSefPJJKIqCqVOn4uGHH+7xMvttMgFOhtJ36f8rSEQO5aSJZWzR0FOBDT09GJKE1pYWnHLKKZwmmIh6zDAMtLa29rl2OKmhs4KSpQyPBw0NDeYJKkREPWEYBmtHF5wzjyylhMOYNWuW1cMgGnA8uNdXCoRg7eiMe+hkKUOWsX//fr7LJqJekWWDtaMLNnSylKEo2Lt3L/8oiahXFMVg7eiCJ8XxpDjrnZi/gnSCY+nou/4oHTwpjqifGIqCffv28V02nXAEXHz04aErbtaOLtjQyVL8DJ2IjgdrRzIecuchd+udmL+CdKJj7ei7fqgdPORO1E90RcGePXvMmyYQEfUEa0cyNnSylJAkHD58mPc0JqJeYe1IxollyFJKNIpJkyZZPQwishnWjmTcQydL6YqCjz/+mIfNiKhXWDuSsaGTtSQJR44csXoURGQ3rB1JeMidLCVHoygoKLB6GERkM6wdyWyxh758+XIUFRVh4cKFiEaj5vOapmHx4sUoKirCsmXLLBwhHS/d7UZDQwMPmxFRr7B2JEv7hu7z+fDll1+iqqoK48aNw/r1683XNm3ahJEjR6KqqgodHR2orq62cKRERETWSfuGXlNTg+LiYgDAnDlzEpr2sV4je5BVFePHj4csy1YPhYhshLUjWdp/ht7W1oYzzjgDAJCdnY1AIJDwWnxmn66vdRWJRBCJRMyv29vbAQCHDx8GAPOwjSzLCbGmaXC5XGYsSRIkSfpHDEDLyIAUjUISAmpGBuR4nJkJJRKBKx6HwwAArUvsDochXC5oGRlwh8MwXC7oHg/ckQgMlwuGxwMlEoEhSTAUBUo0CkOWYcgylGgUuixDSBIUVYUuy4AkQVZV6Eps88qaBt3tBgwDsq5Dc7vhisceDyRdhxSPNQ2SYQxYTmpmJhq2bUN+fj5cLhcMw4CiKNB1HUIIM+5u2/RqO3WJVVWFLMtmrCgKXC6XGQOxj3Q6x263G0IIMzYMA7qum3F87EeLmRNzSsgJYI3oQ07RrCzs+rZ2xB3Pdor3DSdcz572DT0nJwfBYBBArIHn5ub26LWuVq1ahZUrVyY9f9ZZZ/V9kJ3eKCTE3/7y9igWIjGOL6dzbBhA/BwCXY89jhVr2j/Wo6rdx53OSUiIByqncBiYORNEJyTWiOPP6ciRfq0doVAI2dnZ/bY8K6R9Q588eTLWrFmD0tJSlJeXY+rUqQmveb1eTJ8+HeXl5bjxxhuPupw777wTv/rVr8yvDcNAIBDAsGHD4OKcypYJBoPIy8uD3++3/TzKRDRw+qt2CCEQCoXMI8F2lvYNvaCgAMOHD0dRURHOPPNMrFixAkuXLsXatWtx+eWXY+PGjSgqKkJBQQEuueSSoy4nIyMDGRkZCc+dfPLJKR499dTQoUPZ0Imo1/qjdth9zzzuhL3bGqUHJ93piIgGDmtHsrQ/y52IiIj+OTZ0slRGRgbuu+++pI9DiIiOhbUjGQ+5ExEROQD30ImIiByADZ2IiMgB2NCJiIgcgA2diIjIAdjQyTL79+9HbW0t9u/fb/VQiMhmDh06hEOHDlk9jLTCs9zJEvfccw9UVTWnblQUBQ899JDVwyIiG7j33nsRDAahqiqGDh2KVatWWT2ktJD2U7+SM4VCITz66KPm17/+9a8tHA0R2Ul2djYeeOABAMBdd91l8WjSBxs6WWLIkCG48847zT30rKwsq4dERDaxbds2aJoGVVXx4Ycf4sknn8TPfvYzq4dlOR5yJ8t88cUX+OKLLzBy5EiMHDnS6uEQkU1s27Yt6bkZM2ZYMJL0wpPiyDIjR47E5MmT8eSTT1o9FCKykbPOOgvbt2/Htm3bsG3bNjbzb7Ghk+V4P3oi6o0HHngAzc3NmDFjBg4fPmz1cNIGGzpZbuXKlfj73/9u9TCIyCZOO+00DBo0CEVFRRg0aJDVw0kbbOhkiWg0mvC45557rB4SEdnE5ZdfjmuuuQbXX389vvOd71g9nLTBs9zJEvn5+ZgyZQoAQAiBd9991+IREZFd+Hw+/Md//AdeeeUVq4eSVtjQyRLz58/Hgw8+aH79yCOPWDgaIrKT7du349VXX0V2djYAoLi42OIRpQdetkaW0zQNisL3lkTUM88//7wZu1wulJaWWjia9MHP0Mlyt99+u9VDICIbiUQiWLRoERYtWoRgMGj1cNIGd4vIcqNGjbJ6CERkE3feeSfeeecdfPbZZxBC4JNPPsEtt9xi9bDSAg+5kyUqKyvNWAgBl8uF6dOnWzgiIrKDffv2obq6GlOmTIEsyzj99NPhdrutHlZa4CF3ssSCBQvwxBNPoLGxEbt378bHH39s9ZCIyAZGjRqFSCSC4cOH41e/+lXCybUnOjZ0skRzczN+/OMfo76+HocPH8bNN99s9ZCIyCbq6+uxZcsWLFq0CB0dHVYPJ22woZMlPB4Pvvvd7+Kkk07CV199ZfVwiMhGvv76azz77LOYNWsWvv76a6uHkzb4GTpZYtKkSTjnnHNwzTXXYNCgQXC5XLyWlIh6pLa2FoWFhQCAqqoqFBUVWTyi9MCz3MkS8bNSg8EgQqGQxaMhIrvYvXs3XnrpJeTk5EAIgeeee44N/Vts6GSJc845B1OnTsXHH3+M3/3ud7j66qutHhIR2cArr7yC3bt345VXXoEkSawdnfAzdLLEyy+/DAD43e9+hwceeAAvvviixSMiIju47777sG7dOgwbNgy5ubm4+OKLrR5S2mBDJ0t0dHRA13UoioLhw4fj5JNPtnpIRGQTv/zlL5Gfn48JEybgl7/8pdXDSRts6GSJgoICzJs3D0uWLAEAZGZmWjwiIrKLIUOGoKioCEVFRdwZ6IRnuRMRka08+eST2Lx5M1wuF37wgx/g5z//udVDSgts6GSJBx54AH6/H2PHjsXbb7+N/Px83H///VYPi4hsYteuXQCA8847z+KRpA8ecidLfPLJJ/jTn/6E8vJyvPbaazh48KDVQyIim1i2bBlee+01vPbaa7jtttusHk7a4GVrZInBgwcDgLlXLsuyhaMhIjtRFAW/+c1vAPD2y52xoZMl4rdM/fDDD3HJJZdw+lci6jFN0/Db3/4WkiQhHA5bPZy0wYZOlvD7/QCAnTt3AgCGDRtm4WiIyE7OOussvPPOOwCAqVOnWjya9MHP0MkSn3/+Obxer/nvF198YfWQiMgmPv/8c6xfvx7r16/HgQMHrB5O2uAeOlni6quvRktLi/nv/PnzrR4SEdlAeXk5Wltb8dJLL8HlcvHjuk542RoREdnG888/n/TcokWLLBhJ+mFDJyIicgB+hk5EROQAbOhEREQOwIZORETkAGzoREREDsCGTkRE5ABs6ERERA7Ahk5EROQAbOhEREQOwIZORETkAGzoREREDsCGTkRE5ABs6ERERA7Ahk5EROQAbOhEREQOwIZORETkAGzoREREDsCGTkRE5ABs6ERERA7Ahk5EROQAbOhEREQOwIZORETkAGzoREREDsCGTkRE5ABs6ERERA7Ahk5EROQAbOhEREQOwIZORETkAGzoREREDsCGTkRE5ABs6ERERA7Ahk5EROQAbOhEREQOwIZORETkAGzoREREDsCGTkRE5ABs6ERERA7Ahk5EROQAbOhEREQOwIZORETkAGzoREREDsCGTkRE5ABs6ERERA7Ahk5EROQAbOhEREQOwIZORETkAGzoREREDsCGTkRE5ABs6ERERA7Ahk5EROQAbOhEREQOwIZORETkAGzoREREDsCGTkRE5ABs6ERERA7w/wHYipVjt7H7dQAAAABJRU5ErkJggg==" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = Image(\"sea_ice_demo/ex1/MSE_bar_chart.png\")\n", + "display_png(a)" + ] + }, + { + "cell_type": "markdown", + "id": "a9b323ec", + "metadata": {}, + "source": [ + "## Working with multiple realizations" + ] + }, + { + "cell_type": "markdown", + "id": "0c427a07", + "metadata": {}, + "source": [ + "The sea ice driver can generate metrics based on an average of all available realizations. To do so, provide an asterisk \\* as the value to the --realization argument on the command line. Options passed on the command line will supercede arguments in the parameter file. \n", + "\n", + "In addition, we set the --case_id value to 'ex2' to save results in a new directory." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "5f8174e1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-11 13:33:49,583 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", + "2024-01-11 13:34:00,293 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", + "2024-01-11 13:34:10,830 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", + "2024-01-11 13:34:18,848 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", + "2024-01-11 13:34:26,735 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['E3SM-1-0']\n", + "Find all realizations: True\n", + "OBS: Arctic\n", + "Converting units by multiply 0.01\n", + "Converting units by multiply 0.01\n", + "OBS: Antarctic\n", + "Converting units by multiply 0.01\n", + "Converting units by multiply 0.01\n", + "['E3SM-1-0']\n", + "\n", + "=================================\n", + "model, runs: E3SM-1-0 ['r1i2p2f1', 'r2i2p2f1', 'r3i2p2f1', 'r4i2p2f1']\n", + "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n", + "Converting units by multiply 1e-06\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r1i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_201001-201112.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r2i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_201001-201312.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r3i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_201001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO::2024-01-11 13:37::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n", + "2024-01-11 13:37:10,054 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model, run, variable: E3SM-1-0 r4i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_201001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-------------------------------------------\n", + "Calculating model regional average metrics \n", + "for E3SM-1-0\n", + "--------------------------------------------\n", + "arctic\n", + "ca\n", + "na\n", + "np\n", + "antarctic\n", + "sp\n", + "sa\n", + "io\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING] yaksa: 10 leaked handle pool objects\n" + ] + } + ], + "source": [ + "%%bash\n", + "python ice_driver.py -p demo_param_file.py --realization '*' --case_id \"ex2\"" + ] + }, + { + "cell_type": "markdown", + "id": "cadb1306", + "metadata": {}, + "source": [ + "Since we have averaged four different realizations, the resulting statistics are different than seen in example 1. " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "d6cb5f07", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = Image(\"sea_ice_demo/ex2/MSE_bar_chart.png\")\n", + "display_png(a)" + ] + }, + { + "cell_type": "markdown", + "id": "499d3935", + "metadata": {}, + "source": [ + "# Working with multiple models" + ] + }, + { + "cell_type": "markdown", + "id": "51b008db", + "metadata": {}, + "source": [ + "Along with using multiple realizations, we can include multiple models in a single analysis. The model data must all follow a single filename template. All model inputs must use the same name and units for the sea ice variable.\n", + "\n", + "The example below shows how to use three models in the analysis, with all available realizations. The models are listed as inputs to the --test_data_set flag.\n", + "\n", + "Want to add more models? Six other model sea ice datasets are available in the directories linked in the notebook introduction." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "679d7289", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-11 13:37:14,718 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", + "2024-01-11 13:37:22,367 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", + "2024-01-11 13:37:29,926 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", + "2024-01-11 13:37:35,806 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", + "2024-01-11 13:37:41,433 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "2024-01-11 13:38:16,606 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "2024-01-11 13:38:59,446 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['E3SM-1-0', 'CanESM5', 'CAS-ESM2-0']\n", + "Find all realizations: True\n", + "OBS: Arctic\n", + "Converting units by multiply 0.01\n", + "Converting units by multiply 0.01\n", + "OBS: Antarctic\n", + "Converting units by multiply 0.01\n", + "Converting units by multiply 0.01\n", + "['CAS-ESM2-0', 'CanESM5', 'E3SM-1-0']\n", + "\n", + "=================================\n", + "model, runs: CAS-ESM2-0 ['r2i1p1f1', 'r1i1p1f1', 'r4i1p1f1', 'r3i1p1f1']\n", + "/p/user_pub/pmp/demo/sea-ice/links_area/CAS-ESM2-0/*.nc\n", + "Converting units by multiply 1e-06\n", + "\n", + "-----------------------\n", + "model, run, variable: CAS-ESM2-0 r2i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/CAS-ESM2-0/historical/r2i1p1f1/siconc/siconc_SImon_CAS-ESM2-0_historical_r2i1p1f1_gn_185001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: CAS-ESM2-0 r1i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/CAS-ESM2-0/historical/r1i1p1f1/siconc/siconc_SImon_CAS-ESM2-0_historical_r1i1p1f1_gn_185001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: CAS-ESM2-0 r4i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/CAS-ESM2-0/historical/r4i1p1f1/siconc/siconc_SImon_CAS-ESM2-0_historical_r4i1p1f1_gn_185001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: CAS-ESM2-0 r3i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/CAS-ESM2-0/historical/r3i1p1f1/siconc/siconc_SImon_CAS-ESM2-0_historical_r3i1p1f1_gn_185001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-------------------------------------------\n", + "Calculating model regional average metrics \n", + "for CAS-ESM2-0\n", + "--------------------------------------------\n", + "arctic\n", + "ca\n", + "na\n", + "np\n", + "antarctic\n", + "sp\n", + "sa\n", + "io\n", + "\n", + "=================================\n", + "model, runs: CanESM5 ['r2i1p1f1', 'r1i1p1f1', 'r3i1p1f1']\n", + "/p/user_pub/pmp/demo/sea-ice/links_area/CanESM5/*.nc\n", + "Converting units by multiply 1e-06\n", + "\n", + "-----------------------\n", + "model, run, variable: CanESM5 r2i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/CanESM5/historical/r2i1p1f1/siconc/siconc_SImon_CanESM5_historical_r2i1p1f1_gn_185001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: CanESM5 r1i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/CanESM5/historical/r1i1p1f1/siconc/siconc_SImon_CanESM5_historical_r1i1p1f1_gn_185001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: CanESM5 r3i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/CanESM5/historical/r3i1p1f1/siconc/siconc_SImon_CanESM5_historical_r3i1p1f1_gn_185001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-------------------------------------------\n", + "Calculating model regional average metrics \n", + "for CanESM5\n", + "--------------------------------------------\n", + "arctic\n", + "ca\n", + "na\n", + "np\n", + "antarctic\n", + "sp\n", + "sa\n", + "io\n", + "\n", + "=================================\n", + "model, runs: E3SM-1-0 ['r1i2p2f1', 'r2i2p2f1', 'r3i2p2f1', 'r4i2p2f1']\n", + "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n", + "Converting units by multiply 1e-06\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r1i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_201001-201112.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r2i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_199001-199912.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO::2024-01-11 13:41::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n", + "2024-01-11 13:41:40,126 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_201001-201312.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r3i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_201001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r4i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_201001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-------------------------------------------\n", + "Calculating model regional average metrics \n", + "for E3SM-1-0\n", + "--------------------------------------------\n", + "arctic\n", + "ca\n", + "na\n", + "np\n", + "antarctic\n", + "sp\n", + "sa\n", + "io\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING] yaksa: 10 leaked handle pool objects\n" + ] + } + ], + "source": [ + "%%bash\n", + "python ice_driver.py -p demo_param_file.py \\\n", + "--test_data_set \"E3SM-1-0\" \"CanESM5\" \"CAS-ESM2-0\" \\\n", + "--realization '*' \\\n", + "--case_id \"ex3\"" + ] + }, + { + "cell_type": "markdown", + "id": "9a17ffee", + "metadata": {}, + "source": [ + "The output JSON now includes metrics for all three models." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "b07dbb8b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"DIMENSIONS\": {\n", + " \"index\": {\n", + " \"monthly_clim\": \"Monthly climatology of extent\",\n", + " \"total_extent\": \"Sum of ice coverage where concentration > 15%\"\n", + " },\n", + " \"json_structure\": [\n", + " \"model\",\n", + " \"obs\",\n", + " \"region\",\n", + " \"index\",\n", + " \"statistic\"\n", + " ],\n", + " \"model\": [\n", + " \"CAS-ESM2-0\",\n", + " \"CanESM5\",\n", + " \"E3SM-1-0\"\n", + " ],\n", + " \"region\": {},\n", + " \"statistic\": {\n", + " \"mse\": \"Mean Square Error (10^12 km^4)\"\n", + " }\n", + " },\n", + " \"RESULTS\": {\n", + " \"CAS-ESM2-0\": {\n", + " \"bootstrap\": {\n", + " \"antarctic\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"7.019108133567357\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.871674069649\"\n", + " }\n", + " },\n", + " \"arctic\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"4.294828188493619\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.574135052089\"\n", + " }\n", + " },\n", + " \"ca\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"2.312010146130351\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.508629991489\"\n", + " }\n", + " },\n", + " \"io\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.5482737233471119\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.4375293316\"\n", + " }\n", + " },\n", + " \"na\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.3222710090779452\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.2349881371580625\"\n", + " }\n", + " },\n", + " \"np\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.4959409821003755\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.20888410478754005\"\n", + " }\n", + " },\n", + " \"sa\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.8350528909264078\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.31861565606025\"\n", + " }\n", + " },\n", + " \"sp\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.8884341936515745\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.55017451890625\"\n", + " }\n", + " }\n", + " },\n", + " \"nasateam\": {\n", + " \"antarctic\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"5.361855796372524\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.7509782281\"\n", + " }\n", + " },\n", + " \"arctic\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"3.102515619474768\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.014536401489\"\n", + " }\n", + " },\n", + " \"ca\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.2011225819926112\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.10311066877225\"\n", + " }\n", + " },\n", + " \"io\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.2941932037036493\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.2330913741213906\"\n", + " }\n", + " },\n", + " \"na\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.10314171465111885\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.0092604534765625\"\n", + " }\n", + " },\n", + " \"np\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.7242297392262352\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.308342541796\"\n", + " }\n", + " },\n", + " \"sa\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.7939506012779867\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.0374762169500625\"\n", + " }\n", + " },\n", + " \"sp\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.8582638182690185\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.0361786077455625\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"CanESM5\": {\n", + " \"bootstrap\": {\n", + " \"antarctic\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"5.003557981289977\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"4.727954476996\"\n", + " }\n", + " },\n", + " \"arctic\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.4970427723932969\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.182362643225\"\n", + " }\n", + " },\n", + " \"ca\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.07462988368836171\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.006555978961\"\n", + " }\n", + " },\n", + " \"io\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.3934786848364646\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.34699639609599997\"\n", + " }\n", + " },\n", + " \"na\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.7952224983965241\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.627756720721\"\n", + " }\n", + " },\n", + " \"np\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.0203118874520511\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.005536982968946289\"\n", + " }\n", + " },\n", + " \"sa\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.536937657840359\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.2153022640455625\"\n", + " }\n", + " },\n", + " \"sp\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.2839539580227173\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.257345086596\"\n", + " }\n", + " }\n", + " },\n", + " \"nasateam\": {\n", + " \"antarctic\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"10.954517499493136\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"10.728592355208999\"\n", + " }\n", + " },\n", + " \"arctic\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"4.042630538014595\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.863775991201\"\n", + " }\n", + " },\n", + " \"ca\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.23422690133312402\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.22377015289225\"\n", + " }\n", + " },\n", + " \"io\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.6677608897121026\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.5894072416443906\"\n", + " }\n", + " },\n", + " \"na\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.5789066828733234\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.39437247806025\"\n", + " }\n", + " },\n", + " \"np\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.0034760984469305203\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.0005681876709726563\"\n", + " }\n", + " },\n", + " \"sa\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.8465014279664933\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.69702377952025\"\n", + " }\n", + " },\n", + " \"sp\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"2.954188845737339\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"2.7984095576025623\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"E3SM-1-0\": {\n", + " \"bootstrap\": {\n", + " \"antarctic\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.7320920509215254\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.542355652608\"\n", + " }\n", + " },\n", + " \"arctic\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"4.121180025982386\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.9232087080959999\"\n", + " }\n", + " },\n", + " \"ca\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.0806544487713556\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.00532082688\"\n", + " }\n", + " },\n", + " \"io\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.084337580469155\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.031675101184\"\n", + " }\n", + " },\n", + " \"na\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.1610708077444354\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.538802618368\"\n", + " }\n", + " },\n", + " \"np\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.3851183687251\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.169368174592\"\n", + " }\n", + " },\n", + " \"sa\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.5307715522442514\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.411421212672\"\n", + " }\n", + " },\n", + " \"sp\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.574440321460334\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.006880287744\"\n", + " }\n", + " }\n", + " },\n", + " \"nasateam\": {\n", + " \"antarctic\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.8229523470238773\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.132947017728\"\n", + " }\n", + " },\n", + " \"arctic\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"7.046055592965159\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"5.130596384768\"\n", + " }\n", + " },\n", + " \"ca\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.12243728740127617\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.101844279296\"\n", + " }\n", + " },\n", + " \"io\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.026688722221047654\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"4.7558262499999997e-07\"\n", + " }\n", + " },\n", + " \"na\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.9424239970669361\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.260132499456\"\n", + " }\n", + " },\n", + " \"np\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.5810669209888607\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.25988712038399997\"\n", + " }\n", + " },\n", + " \"sa\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.11104463714637731\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.073196494848\"\n", + " }\n", + " },\n", + " \"sp\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.3599127340389943\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.402562646016\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"json_structure\": [\n", + " \"model\",\n", + " \"obs\",\n", + " \"region\",\n", + " \"index\",\n", + " \"statistic\"\n", + " ],\n", + " \"json_version\": 3.0,\n", + " \"model_year_range\": {\n", + " \"CAS-ESM2-0\": [\n", + " \"1981\",\n", + " \"2010\"\n", + " ],\n", + " \"CanESM5\": [\n", + " \"1981\",\n", + " \"2010\"\n", + " ],\n", + " \"E3SM-1-0\": [\n", + " \"1981\",\n", + " \"2010\"\n", + " ]\n", + " },\n", + " \"provenance\": {\n", + " \"commandLine\": \"ice_driver.py -p demo_param_file.py --test_data_set E3SM-1-0 CanESM5 CAS-ESM2-0 --realization * --case_id ex3\",\n", + " \"conda\": {\n", + " \"Platform\": \"linux-64\",\n", + " \"PythonVersion\": \"3.8.15.final.0\",\n", + " \"Version\": \"23.1.0\",\n", + " \"buildVersion\": \"not installed\"\n", + " },\n", + " \"date\": \"2024-01-11 13:41:26\",\n", + " \"openGL\": {\n", + " \"GLX\": {\n", + " \"client\": {},\n", + " \"server\": {}\n", + " }\n", + " },\n", + " \"osAccess\": false,\n", + " \"packages\": {\n", + " \"PMP\": \"v3.0.2-11-g06b151f\",\n", + " \"PMPObs\": \"See 'References' key below, for detailed obs provenance information.\",\n", + " \"blas\": \"0.3.24\",\n", + " \"cdat_info\": \"8.2.1\",\n", + " \"cdms\": \"3.1.5\",\n", + " \"cdp\": \"1.7.0\",\n", + " \"cdtime\": \"3.1.4\",\n", + " \"cdutil\": \"8.2.1\",\n", + " \"clapack\": null,\n", + " \"esmf\": \"0.8.2\",\n", + " \"esmpy\": \"8.4.2\",\n", + " \"genutil\": \"8.2.1\",\n", + " \"lapack\": \"3.9.0\",\n", + " \"matplotlib\": null,\n", + " \"mesalib\": null,\n", + " \"numpy\": \"1.22.4\",\n", + " \"python\": \"3.10.13\",\n", + " \"scipy\": \"1.11.3\",\n", + " \"uvcdat\": null,\n", + " \"vcs\": null,\n", + " \"vtk\": null,\n", + " \"xarray\": \"2023.10.1\",\n", + " \"xcdat\": \"0.5.0\"\n", + " },\n", + " \"platform\": {\n", + " \"Name\": \"gates.llnl.gov\",\n", + " \"OS\": \"Linux\",\n", + " \"Version\": \"3.10.0-1160.71.1.el7.x86_64\"\n", + " },\n", + " \"userId\": \"ordonez4\"\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "with open(\"sea_ice_demo/ex3/sea_ice_metrics.json\") as f:\n", + " print(f.read())" + ] + }, + { + "cell_type": "markdown", + "id": "f48b3856", + "metadata": {}, + "source": [ + "Now the resulting bar chart shows three different models along with the bootstrap vs. nasateam comparison." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "41aa14a3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = Image(\"sea_ice_demo/ex3/MSE_bar_chart.png\")\n", + "display_png(a)" + ] + }, + { + "cell_type": "markdown", + "id": "bc43281a", + "metadata": {}, + "source": [ + "# Other examples?" + ] + }, + { + "cell_type": "markdown", + "id": "7b1d5694", + "metadata": {}, + "source": [ + "Use --msyear and --meyear flags to change model year range" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4b45449", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:pmp_si] *", + "language": "python", + "name": "conda-env-pmp_si-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From b04d9b7b7cc03c4ad0eb36d6d8e4a79e80f04fab Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Wed, 17 Jan 2024 12:46:22 -0800 Subject: [PATCH 28/69] add realizations --- pcmdi_metrics/sea_ice/ice_driver.py | 175 +++++++++++++++++----------- 1 file changed, 104 insertions(+), 71 deletions(-) diff --git a/pcmdi_metrics/sea_ice/ice_driver.py b/pcmdi_metrics/sea_ice/ice_driver.py index 85cddd7ee..246857123 100644 --- a/pcmdi_metrics/sea_ice/ice_driver.py +++ b/pcmdi_metrics/sea_ice/ice_driver.py @@ -15,24 +15,6 @@ from pcmdi_metrics.io.base import Base from pcmdi_metrics.utils import create_land_sea_mask -from pcmdi_metrics.io import ( # noqa - get_axis_list, - get_data_list, - get_latitude_bounds_key, - get_latitude_key, - get_latitude, - get_latitude_bounds, - get_longitude_bounds_key, - get_longitude_key, - get_longitude, - get_longitude_bounds, - get_time, - get_time_bounds, - get_time_bounds_key, - get_time_key, - select_subset, -) - class MetadataFile: # This class organizes the contents for the CMEC @@ -238,9 +220,11 @@ def mse_model(dm, do, var=None): return stat -def to_ice_con_ds(da, obs): +def to_ice_con_ds(da, ds): + # Convert sea ice data array to dataset using + # coordinates from another dataset ds = xr.Dataset( - data_vars={"ice_con": da, "time_bnds": obs.time_bnds}, coords={"time": obs.time} + data_vars={"ice_con": da, "time_bnds": ds.time_bnds}, coords={"time": ds.time} ) return ds @@ -556,6 +540,7 @@ def replace_multi(string, rdict): "DIMENSIONS": { "json_structure": [ "model", + "realization", "obs", "region", "index", @@ -578,17 +563,36 @@ def replace_multi(string, rdict): start_year = msyear end_year = meyear - totals_dict = { - "arctic": 0, - "ca": 0, - "na": 0, - "np": 0, - "antarctic": 0, - "sp": 0, - "sa": 0, - "io": 0, + # totals_dict = { + # "arctic": 0, + # "ca": 0, + # "na": 0, + # "np": 0, + # "antarctic": 0, + # "sp": 0, + # "sa": 0, + # "io": 0, + # } + real_dict = { + "arctic": {"model_mean": 0}, + "ca": {"model_mean": 0}, + "na": {"model_mean": 0}, + "np": {"model_mean": 0}, + "antarctic": {"model_mean": 0}, + "sp": {"model_mean": 0}, + "sa": {"model_mean": 0}, + "io": {"model_mean": 0}, + } + mse[model] = { + "arctic": {"model_mean": {"nasateam": {}, "bootstrap": {}}}, + "ca": {"model_mean": {"nasateam": {}, "bootstrap": {}}}, + "na": {"model_mean": {"nasateam": {}, "bootstrap": {}}}, + "np": {"model_mean": {"nasateam": {}, "bootstrap": {}}}, + "antarctic": {"model_mean": {"nasateam": {}, "bootstrap": {}}}, + "sp": {"model_mean": {"nasateam": {}, "bootstrap": {}}}, + "sa": {"model_mean": {"nasateam": {}, "bootstrap": {}}}, + "io": {"model_mean": {"nasateam": {}, "bootstrap": {}}}, } - mse[model] = {"nasateam": {}, "bootstrap": {}} tags = { "%(variable)": var, @@ -693,60 +697,79 @@ def replace_multi(string, rdict): lon_i = yvar # area data doesn't always use same coordinates as siconc data in CMIP6 # so we multiply by area.data, dropping the coordinates - totals_dict[rgn] = totals_dict[rgn] + ( - data.where(data > 0.15, 0) * area[area_var].data - ).sum((lon_j, lon_i), skipna=True) + rgn_total = (data.where(data > 0.15, 0) * area[area_var].data).sum( + (lon_j, lon_i), skipna=True + ) + real_dict[rgn][run] = rgn_total + # totals_dict[rgn] = totals_dict[rgn] + rgn_total + real_dict[rgn]["model_mean"] = ( + real_dict[rgn]["model_mean"] + rgn_total + ) print("\n-------------------------------------------") print("Calculating model regional average metrics \nfor ", model) print("--------------------------------------------") - for rgn in totals_dict: + for rgn in real_dict: print(rgn) - # Set up metrics dictionary - for key in ["nasateam", "bootstrap"]: - mse[model][key][rgn] = { - "monthly_clim": {"mse": None}, - "total_extent": {"mse": None}, - } # Average all realizations, fix bounds, get climatologies and totals - total_rgn = (totals_dict[rgn] / len(list_of_runs)).to_dataset(name=var) - # total_rgn.time.attrs.pop("bounds") - total_rgn = total_rgn.bounds.add_missing_bounds() - clim_extent = total_rgn.temporal.climatology(var, freq="month") - total = total_rgn.mean("time")[var].data - - # Get errors, convert to 1e-12 km^-4 - mse[model]["nasateam"][rgn]["monthly_clim"]["mse"] = str( - mse_t( - clim_extent[var], - obs_clims["nt"][rgn]["ice_con"], - weights=clim_wts, - ) - * 1e-12 + # total_rgn = (totals_dict[rgn] / len(list_of_runs)).to_dataset(name=var) + real_dict[rgn]["model_mean"] = real_dict[rgn]["model_mean"] / len( + list_of_runs ) - mse[model]["bootstrap"][rgn]["monthly_clim"]["mse"] = str( - mse_t( - clim_extent[var], - obs_clims["bt"][rgn]["ice_con"], - weights=clim_wts, + + for run in real_dict[rgn]: + # Set up metrics dictionary + if run not in mse[model][rgn]: + mse[model][rgn][run] = {} + for key in ["nasateam", "bootstrap"]: + mse[model][rgn][run].update( + { + key: { + "monthly_clim": {"mse": None}, + "total_extent": {"mse": None}, + } + } + ) + + run_data = real_dict[rgn][run].to_dataset(name=var) + # total_rgn.time.attrs.pop("bounds") + # total_rgn = total_rgn.bounds.add_missing_bounds() + run_data = run_data.bounds.add_missing_bounds() + clim_extent = run_data.temporal.climatology(var, freq="month") + total = run_data.mean("time")[var].data + + # Get errors, convert to 1e12 km^-4 + mse[model][rgn][run]["nasateam"]["monthly_clim"]["mse"] = str( + mse_t( + clim_extent[var], + obs_clims["nt"][rgn]["ice_con"], + weights=clim_wts, + ) + * 1e-12 + ) + mse[model][rgn][run]["bootstrap"]["monthly_clim"]["mse"] = str( + mse_t( + clim_extent[var], + obs_clims["bt"][rgn]["ice_con"], + weights=clim_wts, + ) + * 1e-12 + ) + mse[model][rgn][run]["nasateam"]["total_extent"]["mse"] = str( + mse_model(total, obs_means["nt"][rgn]) * 1e-12 + ) + mse[model][rgn][run]["bootstrap"]["total_extent"]["mse"] = str( + mse_model(total, obs_means["bt"][rgn]) * 1e-12 ) - * 1e-12 - ) - mse[model]["nasateam"][rgn]["total_extent"]["mse"] = str( - mse_model(total, obs_means["nt"][rgn]) * 1e-12 - ) - mse[model]["bootstrap"][rgn]["total_extent"]["mse"] = str( - mse_model(total, obs_means["bt"][rgn]) * 1e-12 - ) # Update year list metrics["model_year_range"][model] = [str(start_year), str(end_year)] else: - for rgn in totals_dict: + for rgn in mse[model]: # Set up metrics dictionary for key in ["nasateam", "bootstrap"]: - mse[model][key][rgn] = { + mse[model][rgn]["model_mean"][key] = { "monthly_clim": {"mse": None}, "total_extent": {"mse": None}, } @@ -793,10 +816,18 @@ def replace_multi(string, rdict): rgn = sector_short[inds] for model in model_list: mse_clim.append( - float(metrics["RESULTS"][model]["nasateam"][rgn]["monthly_clim"]["mse"]) + float( + metrics["RESULTS"][model][rgn]["model_mean"]["nasateam"][ + "monthly_clim" + ]["mse"] + ) ) mse_ext.append( - float(metrics["RESULTS"][model]["nasateam"][rgn]["total_extent"]["mse"]) + float( + metrics["RESULTS"][model][rgn]["model_mean"]["nasateam"][ + "total_extent" + ]["mse"] + ) ) mse_clim.append( mse_t( @@ -811,6 +842,8 @@ def replace_multi(string, rdict): # Make figure ax7[inds].bar(ind, mse_clim, width, color="b") ax7[inds].bar(ind, mse_ext, width, color="r") + # ax7[inds].errorbar(ind, mse_clim, yerr=clim_err, fmt="o", color="r") + # ax7[inds].errorbar(ind, mse_ext, yerr=ext_err, fmt="o", color="r") # ax7[inds].bar(ind[n], obs[sector_short[inds]]**2, width, color="b") if inds == len(sector_list) - 1: ax7[inds].set_xticks(ind + width / 2.0, mlabels, rotation=90, size=5) From bf8977e4418e43e4d288401cb287bbffc6415fa9 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Fri, 19 Jan 2024 11:26:57 -0800 Subject: [PATCH 29/69] add figure script --- pcmdi_metrics/sea_ice/create_sector_plots.py | 146 +++++++++++++++++++ 1 file changed, 146 insertions(+) create mode 100644 pcmdi_metrics/sea_ice/create_sector_plots.py diff --git a/pcmdi_metrics/sea_ice/create_sector_plots.py b/pcmdi_metrics/sea_ice/create_sector_plots.py new file mode 100644 index 000000000..910c14a52 --- /dev/null +++ b/pcmdi_metrics/sea_ice/create_sector_plots.py @@ -0,0 +1,146 @@ +import cartopy.crs as ccrs +import matplotlib.colors as colors +import matplotlib.pyplot as plt +import numpy as np +import regionmask +import xcdat as xc + +from pcmdi_metrics.utils import create_land_sea_mask + +# ---------- +# Arctic +# ---------- +print("Creating Arctic map") +# Load and process data +f_os_n = "/p/user_pub/PCMDIobs/obs4MIPs_input/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/*nh*" +obs = xc.open_mfdataset(f_os_n) +obs = obs.sel({"time": slice("1988-01-01", "2020-12-31")}).mean("time").compute() +mask = create_land_sea_mask(obs, lon_key="lon", lat_key="lat") +obs["ice_conc"] = obs["ice_conc"].where(mask < 1) +ds = obs.assign_coords( + xc=obs["lon"], yc=obs["lat"] +) # Assign these variables to Coordinates, which were originally data variables + +# Set up regions +region_NA = np.array([[-120, 45], [-120, 80], [90, 80], [90, 45]]) +region_NP = np.array([[90, 45], [90, 65], [240, 65], [240, 45]]) +names = ["North_Atlantic", "North_Pacific"] +abbrevs = ["NA", "NP"] +arctic_regions = regionmask.Regions( + [region_NA, region_NP], names=names, abbrevs=abbrevs, name="arctic" +) + +# Do plotting +cmap = colors.LinearSegmentedColormap.from_list("", [[0, 85 / 255, 182 / 255], "white"]) +proj = ccrs.NorthPolarStereo() +ax = plt.subplot(111, projection=proj) +ax.set_global() +ds.ice_conc.plot.pcolormesh( + ax=ax, x="xc", y="yc", transform=ccrs.PlateCarree(), cmap=cmap +) +arctic_regions.plot_regions( + ax=ax, + add_label=False, + label="abbrev", + line_kws={"color": [0.2, 0.2, 0.25], "linewidth": 3}, +) +ax.set_extent([-180, 180, 43, 90], ccrs.PlateCarree()) +ax.coastlines(color=[0.3, 0.3, 0.3]) +plt.annotate( + "North Atlantic", + (0.5, 0.2), + xycoords="axes fraction", + horizontalalignment="right", + verticalalignment="bottom", + color="white", +) +plt.annotate( + "North Pacific", + (0.65, 0.88), + xycoords="axes fraction", + horizontalalignment="right", + verticalalignment="bottom", + color="white", +) +plt.annotate( + "Central\nArctic ", + (0.56, 0.56), + xycoords="axes fraction", + horizontalalignment="right", + verticalalignment="bottom", +) +ax.set_facecolor([0.55, 0.55, 0.6]) +plt.title("Arctic regions with mean\nOSI-SAF ice concentration\n1988-2020") +plt.savefig("Arctic_regions.png") +plt.close() +obs.close() + +# ---------- +# Antarctic +# ---------- +print("Creating Antarctic map") +# Load and process data +f_os_s = "/p/user_pub/PCMDIobs/obs4MIPs_input/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/*sh*" +obs = xc.open_mfdataset(f_os_s) +obs = obs.sel({"time": slice("1988-01-01", "2020-12-31")}).mean("time").compute() +mask = create_land_sea_mask(obs, lon_key="lon", lat_key="lat") +obs["ice_conc"] = obs["ice_conc"].where(mask < 1) +ds = obs.assign_coords( + xc=obs["lon"], yc=obs["lat"] +) # Assign these variables to Coordinates, which were originally data variables + +# Set up regions +region_IO = np.array([[20, -90], [90, -90], [90, -55], [20, -55]]) +region_SA = np.array([[20, -90], [-60, -90], [-60, -55], [20, -55]]) +region_SP = np.array([[90, -90], [300, -90], [300, -55], [90, -55]]) +names = ["Indian Ocean", "South Atlantic", "South Pacific"] +abbrevs = ["IO", "SA", "SP"] +arctic_regions = regionmask.Regions( + [region_IO, region_SA, region_SP], names=names, abbrevs=abbrevs, name="antarctic" +) + +# Do plotting +cmap = colors.LinearSegmentedColormap.from_list("", [[0, 85 / 255, 182 / 255], "white"]) +proj = ccrs.SouthPolarStereo() +ax = plt.subplot(111, projection=proj) +ax.set_global() +ds.ice_conc.plot.pcolormesh( + ax=ax, x="xc", y="yc", transform=ccrs.PlateCarree(), cmap=cmap +) +arctic_regions.plot_regions( + ax=ax, + add_label=False, + label="abbrev", + line_kws={"color": [0.2, 0.2, 0.25], "linewidth": 3}, +) +ax.set_extent([-180, 180, -55, -90], ccrs.PlateCarree()) +ax.coastlines(color=[0.3, 0.3, 0.3]) +plt.annotate( + "South Pacific", + (0.50, 0.2), + xycoords="axes fraction", + horizontalalignment="right", + verticalalignment="bottom", + color="black", +) +plt.annotate( + "Indian\nOcean", + (0.93, 0.66), + xycoords="axes fraction", + horizontalalignment="right", + verticalalignment="bottom", + color="black", +) +plt.annotate( + "South Atlantic", + (0.54, 0.82), + xycoords="axes fraction", + horizontalalignment="right", + verticalalignment="bottom", + color="black", +) +ax.set_facecolor([0.55, 0.55, 0.6]) +plt.title("Antarctic regions with mean\nOSI-SAF ice concentration\n1988-2020") +plt.savefig("Antarctic_regions.png") +plt.close() +obs.close() From 47f7f7e6bdc74d2807935e194cea7fee80cf65c1 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Fri, 19 Jan 2024 15:06:01 -0800 Subject: [PATCH 30/69] add script for demo plots --- .../sea_ice/make_demo_sea_ice_plots.py | 43 +++++++++++++++++++ 1 file changed, 43 insertions(+) create mode 100644 pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py diff --git a/pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py b/pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py new file mode 100644 index 000000000..acbad7a3b --- /dev/null +++ b/pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py @@ -0,0 +1,43 @@ +import cftime +import matplotlib.pyplot as plt +import xarray as xr +import xcdat as xc + +ds = xc.open_mfdataset( + "/p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_*_*.nc" +) +area = xc.open_dataset( + "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/areacello_Ofx_E3SM-1-0_historical_r1i1p1f1_gr.nc" +) + +arctic = (ds.where(ds.lat > 0) * 1e-2 * area.areacello * 1e-6).sum(("lat", "lon")) + +# Time series plot +arctic.siconc.sel({"time": slice("1981-01-01", "2010-12-31")}).plot() +plt.title("E3SM-1-0 Arctic montly sea ice extent") +plt.ylabel("Extent (km2)") +plt.xlim( + [ + cftime.DatetimeNoLeap(1981, 1, 16, 12, 0, 0, 0, has_year_zero=True), + cftime.DatetimeNoLeap(2010, 12, 16, 12, 0, 0, 0, has_year_zero=True), + ] +) +plt.savefig("E3SM_arctic_tseries.png") +plt.close() + +# Climatology plot +arctic_ds = xr.Dataset( + data_vars={"siconc": arctic.siconc, "time_bnds": ds.time_bnds}, + coords={"time": ds.time}, +) +arctic_clim = arctic_ds.sel( + {"time": slice("1980-01-01", "2010-12-31")} +).temporal.climatology("siconc", freq="month") +arctic_clim["time"] = [x for x in range(1, 13)] +arctic_clim.siconc.plot() +plt.title("E3SM-1-0 Arctic climatological sea ice extent") +plt.xlabel("Month") +plt.ylabel("Extent (km2)") +plt.xlim([1, 12]) +plt.savefig("E3SM_arctic_clim.png") +plt.close() From 07ed06c7112610656a409cb928c879349dfda37e Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Fri, 19 Jan 2024 15:24:36 -0800 Subject: [PATCH 31/69] rerun --- pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb | 2052 ++++++++++++++++------ 1 file changed, 1481 insertions(+), 571 deletions(-) diff --git a/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb b/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb index fbe28da5d..501765363 100644 --- a/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb +++ b/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb @@ -22,9 +22,10 @@ "metadata": {}, "source": [ "This demo uses three CMIP6 models. The 'siconc' and 'areacello' variables are needed and can be found in the following directories. In addition, six other models are available that can be added to the analyses in this demo:\n", - "\n", + "```\n", "/p/user_pub/pmp/demo/sea-ice/links_siconc \n", - "/p/user_pub/pmp/demo/sea-ice/links_area" + "/p/user_pub/pmp/demo/sea-ice/links_area\n", + "```" ] }, { @@ -32,7 +33,15 @@ "id": "00d48042", "metadata": {}, "source": [ - "Add some info about observations and sectors" + "The observation dataset provided is a satellite derived sea ice concentration dataset from EUMETSAT OSI SAF. More information about this data can be found at the [osi-450-a product page](https://osi-saf.eumetsat.int/products/osi-450-a)." + ] + }, + { + "cell_type": "markdown", + "id": "0b854017", + "metadata": {}, + "source": [ + "These maps show the different regions used in the analysis along with the mean observed sea ice concentration. The code to generate these figures can be found in the script `create_sector_plots.py`." ] }, { @@ -46,6 +55,33 @@ "from IPython.display import display_png, JSON, Image" ] }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6a7eb6da", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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GRgZf+MIXWLduXb/zN2zYwOWXX05eXh5Wq5X09HTmzJnDrbfeGjNv4cKFTJgw4ZD2EAwGeeyxx5g5cyZJSUk4HA7y8/O57LLLePXVVwc8JyMjA0VR+u1cAnD33XejKEq/rz/+8Y8H3dPChQv7LUsjOXY88sgjx7TAd288Hg933313TA/gCE899RSKolBaWnpcri2RSE5NZBKI5IzhD3/4A7fccguzZs3igQceID8/n/Lycv70pz8xf/58fve73/Gd73wnOv+NN97g0ksvZeHChTzwwANkZmZSU1PDpk2beOGFF3jwwQePaB9f//rXeeWVV7jlllu45557sFqtFBcX8/bbb/POO+9w+eWX9znn9ddfj9YkfPzxx6M9i/vj7bffxu12x4wVFBQcdE+PPPLIEdyJ5HB45JFHSElJ4dprrz3ma3s8Hu655x6APkL+4osvZt26df32aZZIJGcwQiI5A1izZo1QVVVccsklIhgMxhwLBoPikksuEaqqijVr1kTHzznnHFFYWNhnvhBC6Loe837BggVi/Pjxg+6juLhYAOKnP/1pv8cPXDfCxRdfLCwWi1iyZIlQVVVUVFT0mXPXXXcJQDQ0NAy6D8mJZ/z48WLBggWHNDcQCPT7czcQDQ0NAhB33XXXkW1OIpGccUgXsOSM4L777kNRFP785z9jMsUavk0mE4888giKovCrX/0qOt7U1ERKSkqf+XDwtmgHo6mpCWBAa0x/61ZXV/P222+zbNkyfvjDH2IYxjF3JfbnAvb7/fzsZz9j7Nix2Gw2kpOTWbRoEWvXro3OEULwyCOPMGXKFOx2O4mJiXzhC1+guLj4kK67e/durrrqKtLT07FareTl5XH11VfH9Fbevn07l112GYmJidhsNqZMmcLTTz8ds86HH36Ioig8//zz/N///R9ZWVnEx8ezePFi9uzZ0+e6b7/9Nueddx5utxuHw8HYsWO57777YuZs2rSJSy+9lKSkJGw2G1OnTuWf//xnzJyIe3XFihV8+9vfJiUlheTkZK644gqqq6uj84YNG8aOHTtYuXJl1C0f6cAS2fvf//53br31VrKzs7FarRQVFdHQ0MBNN93EuHHjcLlcpKWlce6557J69ero2qWlpdHOKPfcc090/YilcSAX8BNPPMHkyZOx2WwkJSVx+eWXs2vXrpg51157LS6Xi6KiIpYuXYrL5SI3N5dbb731oP2vJRLJyY8UgJLTHl3XWbFiBTNmzCAnJ6ffObm5uUyfPp3ly5ej6zoQbhu2YcMGbr75ZjZs2EAwGDzqvYwdO5aEhATuuece/vKXvxxSXNZTTz2Frutcf/31LF68mPz8fJ544okBW5Lpuk4oFIq+IvdzOIRCIS666CJ+/vOfc8kll/Dqq6/y1FNPMXfuXMrLy6Pz/ud//odbbrmFxYsX89prr/HII4+wY8cO5s6dO2gbva1btzJz5kzWr1/Pz372M9566y3uu+8+/H4/gUAAgD179jB37lx27NjB73//e1555RXGjRvHtddeywMPPNBnzTvuuIOysjL+9re/8Ze//IV9+/axbNmymGfw+OOPs3TpUgzD4NFHH+W///0vN998M5WVldE5K1asYN68ebS2tvLoo4/y73//mylTpvDlL3+5X/F94403Yjabee6553jggQf48MMP+drXvhY9/uqrrzJ8+HCmTp3KunXrWLduXZ94z9tvv53y8vLontLS0mhubgbgrrvu4o033uDJJ59k+PDhLFy4MBrvl5mZydtvvw3ADTfcEF3/zjvvHPDZ33fffdxwww2MHz+eV155hd/97nd89tlnzJkzh3379sXMDQaDXHrppZx33nn8+9//5vrrr+e3v/0t999//4DrSySSU4ChNkFKJMeb2tpaAYgrr7zyoPO+/OUvC0DU1dUJIYRobGwU8+fPF4AAhNlsFnPnzhX33Xef6OjoiDn3UF3AQgjxxhtviJSUlOi6ycnJ4otf/KL4z3/+02euYRhixIgRIjs7W4RCISFEj6v3gw8+iJkbGT/wlZ2dPeieFixYEOOefOaZZwQg/vrXvw54zrp16wQgHnzwwZjxiooKYbfbxW233XbQa5577rkiISFB1NfXDzjnyiuvFFarVZSXl8eMX3TRRcLhcIjW1lYhhBArVqwQgFi6dGnMvH/+858CEOvWrRNCCNHR0SHi4+PF/PnzhWEYA153zJgxYurUqX3csJdcconIzMyMuuqffPJJAYibbropZt4DDzwgAFFTUxMdG8gFHNn7OeecM+B+IoRCIREMBsV5550nLr/88uj4wVzAkT2WlJQIIYRoaWkRdru9z7MqLy8XVqtVfOUrX4mOXXPNNQIQ//znP2PmLl26VIwePXrQ/UokkpMXaQGUSLoR3RY1RVEASE5OZvXq1WzcuJFf/epXXHbZZezdu5fbb7+diRMn0tjYOOBahmEMaIVbunQp5eXlvPrqq/zgBz9g/PjxvPbaa1x66aUxSSgAK1eupKioiGuuuQZN0wC47rrrUBSFJ554ot9rv//++2zcuDH6evPNNw/7Wbz11lvYbDauv/76Aee8/vrrKIrC1772tZh7zcjIYPLkyf1mpEbweDysXLmSL33pS1H3ZX8sX76c8847j9zc3Jjxa6+9Fo/H0yd7+9JLL415P2nSJADKysoAWLt2Le3t7dx0003Rz/lAioqK2L17N1/96lcBYu5t6dKl1NTU9HErD3bdQ+Hzn/98v+OPPvoo06ZNw2azYTKZMJvNfPDBB33ctYfKunXr8Hq9fZJRcnNzOffcc/nggw9ixhVFYdmyZTFjkyZNOqx7k0gkJx9SAEpOe1JSUnA4HJSUlBx0XmlpKQ6Hg6SkpJjxGTNm8KMf/YiXXnqJ6upqvv/971NaWtqvCzLC9ddfj9lsjr7OO++8mON2u53Pfe5z/PrXv46KvHHjxvGnP/2JHTt2ROc9/vjjAFx++eW0trbS2tqK2+1m/vz5vPzyy7S2tva59uTJk5kxY0b0FREjh0NDQwNZWVkHjXWsq6tDCEF6enrMvZrNZtavX39QgdzS0oKu6wO65CM0NTX1Gy+ZlZUVPd6b5OTkmPdWqxUAr9cbvS/goNeNuK5/8IMf9Lmvm266CaDPvQ123UOhv/t86KGH+Pa3v83s2bN5+eWXWb9+PRs3buTCCy88rLV7c7A41KysrD7P1OFwYLPZYsasVis+n++Iri+RSE4OZBkYyWmPpmksWrSIt99+m8rKyn6//CsrK/nkk0+46KKLopa2/jCbzdx111389re/Zfv27QPOu/vuu2OseXFxcQfdY15eHt/85je55ZZb2LFjB+PHj6etrY2XX34ZgJkzZ/Z73nPPPRcVJceS1NRU1qxZg2EYA4rAlJQUFEVh9erVUcHTm/7GIiQlJaFpWkzcXX8kJydTU1PTZzySYJGSknLQ8w8kYm082HUja95+++1cccUV/c4ZPXr0YV33UOjPIvnss8+ycOFC/vznP8eMd3R0HPF1ImJ1oOd6uM9UIpGcmkgLoOSM4Pbbb0cIwU033dQnKULXdb797W8jhOD222+Pjvf3BQlEXW8RK1R/DBs2LMYKFxEMHR0ddHZ2HtK6zz33HF6vl5///OesWLGizyslJWVAN/DRctFFF+Hz+Q6abXzJJZcghKCqqirmXiOviRMnDniu3W5nwYIFvPTSSwe1FJ533nksX748JqMW4JlnnsHhcHDWWWcd1n3NnTsXt9vNo48+OmASzejRoxk5ciRbt27t975mzJgxqKDvD6vVethWO0VR+gjpzz77rI/r+3AsjnPmzMFut/Pss8/GjFdWVkZd7hKJ5PRHWgAlZwTz5s3j4Ycf5pZbbmH+/Pl85zvfIS8vL1oIesOGDTz88MPMnTs3es4FF1xATk4Oy5YtY8yYMRiGwaeffsqDDz6Iy+Xie9/73mHvY8+ePVxwwQVceeWVLFiwgMzMTFpaWnjjjTf4y1/+wsKFC6N7ePzxx0lMTOQHP/hBHxccwNVXX81DDz3E1q1bmTx58pE/nH646qqrePLJJ/nWt77Fnj17WLRoEYZhsGHDBsaOHcuVV17JvHnz+OY3v8l1113Hpk2bOOecc3A6ndTU1LBmzRomTpzIt7/97QGv8dBDDzF//nxmz57Nj3/8Y0aMGEFdXR3/+c9/eOyxx4iLi+Ouu+7i9ddfZ9GiRfz0pz8lKSmJf/zjH7zxxhs88MADfQpeD4bL5eLBBx/kxhtvZPHixXzjG98gPT2doqIitm7dGu2Y8thjj3HRRRdxwQUXcO2115KdnU1zczO7du1i8+bNvPTSS4f9TCdOnMgLL7zAiy++yPDhw7HZbAcVyRAW2T//+c+56667WLBgAXv27OFnP/sZBQUFhEKh6Ly4uDjy8/P597//zXnnnUdSUhIpKSnRUjO9SUhI4M477+SOO+7g6quv5qqrrqKpqYl77rkHm83GXXfdddj3JpFITkGGMgNFIjnRrFu3TnzhC18Q6enpwmQyibS0NHHFFVeItWvX9pn74osviq985Sti5MiRwuVyCbPZLPLy8sTXv/51sXPnzpi5h5oF3NLSIu69915x7rnniuzsbGGxWITT6RRTpkwR9957r/B4PEIIIbZu3SoAccsttwy41u7duwUgvvvd7wohjq4Q9IFZwEII4fV6xU9/+lMxcuRIYbFYRHJysjj33HP7PKsnnnhCzJ49WzidTmG320VhYaG4+uqrxaZNmwa97s6dO8UXv/hFkZycLCwWi8jLyxPXXnut8Pl80Tnbtm0Ty5YtE263W1gsFjF58mTx5JNPxqwTyaR96aWXYsZLSkoE0Gf+m2++KRYsWCCcTqdwOBxi3Lhx4v7774+Zs3XrVvGlL31JpKWlCbPZLDIyMsS5554rHn300eicSIbtxo0b+93PihUromOlpaXi/PPPF3FxcQIQ+fn5B927EEL4/X7xgx/8QGRnZwubzSamTZsmXnvtNXHNNddEz4/w/vvvi6lTpwqr1SoAcc0118TsMZIFHOFvf/ubmDRpkrBYLMLtdovLLrtM7NixI2bONddcI5xOZ599RX7WJBLJqYsixAB+EIlEIpFIJBLJaYmMAZRIJBKJRCI5w5ACUCKRSCQSieQMQwpAiUQikUgkkjMMKQAlEolEIpFIzjCkAJRITnM6Ojq47bbbOP/880lNTUVRFO6+++4+84QQ/P73v2fMmDFYrVYyMzP59re/TUtLS5+5tbW1fOc732H48OHY7Xby8/O54YYbKC8v7zN3xYoVLFmyhLS0NFwuF5MmTeL3v/99n3qM/aHrOg899BAXXnghOTk5OBwOxo4dy49//ON+u6AA/OEPf4jeQ0FBAffccw/BYDBmziuvvMJVV13FiBEjsNvtDBs2jK9+9avs27ev3zXff/995syZg8PhICUlhWuvvZb6+vpB9y+RSCQnLUOchSyRSI4zJSUlwu12i3POOUfceOONAhB33XVXn3n/+7//K1RVFbfddpt49913xcMPPyzi4+PF9OnTRSAQiM7z+Xxi5MiRIiUlRfzpT38SK1asEI8++qhIT08X2dnZor29PTr3vffeE6qqioULF4rXXntNvPfee+K73/2uAMTNN9886N47OjpEXFyc+OY3vyleeuklsWLFCvHggw+KxMREMW7cuGjZnAj33nuvUBRF3H777WLFihXigQceEBaLRXzjG9+ImTdr1ixx6aWXiieeeEJ8+OGH4u9//7sYO3ascLlcYvv27TFzP/zwQ2EymcRll10m3n33XfHss8+K7OxsMWHChJhyNRKJRHIqIQWgRHKaYxiGMAxDCCFEQ0NDvwKwsrJSaJoWrSkY4bnnnhOA+Mtf/hIde++99wQg/va3v/U795VXXomOffWrXxVWq1V0dnbGzD3//PNFfHz8oHsPhUKisbGxz/hLL70kAPH3v/89OtbY2ChsNpv45je/GTP3F7/4hVAUJabGXV1dXZ81q6qqhNlsFjfccEPM+MyZM8W4ceNEMBiMjn300UcCEI888sig9yCRSCQnI9IFLJGc5iiK0m+f2d6sX78eXddZunRpzPgll1wCEO1JDOF+yECfLhwJCQkAMV1LzGYzFosFu93eZ25/3U0ORNO0aO/a3syaNQuAioqK6Njbb7+Nz+fjuuuui5l73XXXIYTgtddei46lpaX1WTMrK4ucnJyYNauqqti4cSNf//rXMZl6GifNnTuXUaNG8eqrrw56DxKJRHIyIgWgRCIhEAgA9Ok7azabURSFzz77LDo2b948pk+fzt13383GjRvp7Oxk8+bN3HHHHUybNo3FixdH537rW98iEAhw8803U11dTWtrK3//+9959dVXue222454v8uXLwdg/Pjx0bHt27cD9GmvlpmZSUpKSvT4QBQXF1NWVtbvmpMmTeozf9KkSYOuKZFIJCcrUgBKJBLGjRsHwEcffRQzvnbtWoQQNDU1RcdMJhMrVqxg+PDhzJo1i7i4OKZPn05CQgLvvfde1EIIMHv2bJYvX86rr75KdnY2iYmJXHfddfziF7/g1ltvPaK9VlVV8eMf/5gZM2ZELZQATU1NWK1WnE5nn3OSkpJi7uFAQqEQN9xwAy6Xi+9///sxa0bOP9w1JRKJ5GTGNPgUiURyujN58mTOOeccfv3rXzN69GiWLFnCzp07+da3voWmaahqz++KwWCQL3/5y2zfvp2//vWvjB49mpKSEu69916WLFnC8uXLo+7hTz75hMsvv5zZs2fz2GOP4XQ6Wb58OT/5yU/w+XzceeedABiGgWEY0WsoioKmaX322dzczNKlSxFC8OKLL8bsK3LeQAx0TAjBDTfcwOrVq3n55ZfJzc095HMHc61LJBLJyYoUgBKJBICXXnqJa6+9li996UsAWCwWvv/97/P+++/HlFx5/PHHeeutt9i4cSMzZswA4Oyzz2b+/PkUFhby8MMPc9dddwHw//7f/yM9PZ1XX301KugWLVqEqqrcfffdfPWrX2X48OFcf/31PP3009FrLFiwgA8//DBmfy0tLSxZsoSqqiqWL1/O8OHDY44nJyfj8/nweDw4HI6YY83NzUyfPr3PPQshuPHGG3n22Wd5+umnueyyy/qsCfRr6Wtubu7XMiiRSCSnAtIFLJFIgHBixJtvvkldXR1bt26lvr6en/3sZ+zdu5dzzjknOu/TTz9F0zSmTZsWc/7w4cNJTk6OiYv79NNPmT59eh9r3syZMzEMg127dgFE4wkjr8ceeyxmfktLC4sXL6akpIT33nuv35i8SOzftm3bYsZra2tpbGxkwoQJMeMR8ffkk0/yt7/9ja997Wt91oycc+CakbED15RIJJJTBSkAJRJJDGlpaUyaNAm3282jjz5KV1cX3/nOd6LHs7Ky0HWdjRs3xpy3d+9empqayMnJiZm7adOmPkWf161bBxCdO2zYMGbMmBF9jR49Ojo3Iv6Ki4t59913mTp1ar/7vvDCC7HZbDz11FMx40899RSKovC5z30uOiaE4Bvf+AZPPvkkjz32WJ/M4QjZ2dnMmjWLZ599NuYe1q9fz549e7jiiiv6PU8ikUhOdqQLWCI5A3jrrbfo6uqio6MDgJ07d/Kvf/0LgKVLl+JwOPjrX/8KQGFhIa2trbz11ls8/vjj/PKXv4yx9l133XX89re/5fOf/zw/+clPGD16NMXFxfzyl7/E6XTyrW99Kzr3+9//PjfffDPLli3jf/7nf3A4HHzwwQc8+OCDLF68mMmTJx90316vlwsuuIAtW7bw8MMPEwqFWL9+ffR4amoqhYWFQDgp4yc/+Ql33nknSUlJnH/++WzcuJG7776bG2+8MZroAnDzzTfz+OOPc/311zNx4sSYNa1Wa4zIvP/++1myZAlf/OIXuemmm6ivr+fHP/4xEyZMGFA4SiQSyUnPENYglEgkJ4j8/HwB9PsqKSkRQgjx2GOPibFjxwqHwyFcLpc4++yzxWuvvdbvevv27RNf//rXxbBhw4TVahV5eXniy1/+ckyx5Qgvv/yymD9/vkhJSRFOp1OMHz9e/PznP+9THLo/SkpKBtw3IK655po+5/zud78To0aNEhaLReTl5Ym77rorppPJYM8jPz+/z5rvvvuuOOuss4TNZhNJSUni6quv7reYtEQikZwqKEIIcYI1p0QikUgkEolkCJExgBKJRCKRSCRnGFIASiQSiUQikZxhSAEokUgkEolEcoYhBaBEIpFIJBLJGYYUgBKJRCKRSCRnGFIASiQSiUQikZxhSAEokUgkEolEcoYhO4Gcgvh8PgKBwFBvQyKRSCSHicViwWazHddrHKvviBOxV8nQIQXgKYbP58OemAW+lqHeikQikUgOk4yMDEpKSo6bsPL5fBQUFFBbW3vUax3vvUqGFikATzECgUBY/F38DJgdQ70diUQikRwqQQ+1b1xNIBA4bqIqEAhQW1tLeXkF8fHxR7xOe3s7eXm5x3WvkqFFCsBTFbNDCkCJRCKR9EtcfBxx8XFHfL5Adok93ZFJIBKJRCKRSCRnGNICKJFIJBLJaYYQ4dfRnC85vZECUCKRSCSS0wwpACWDIQWgRCI5tggDk+7FZHjCf+peNMMffakiiGYEUEUIRYRQhY4iDECgIKA79kigAApCURGKCUPRMBQThmLGUC3oqgVdtaKrVkKqnZDW/VIdCFX+0yaRSCQHQ/4rKZFIDh0hMOldWEOtWINtWELt3a8OzHonZr0Lk+7pFnJDR0i1EtScBDUXAVM8QVMcAVM8fpMbvzmBgCkOFG1I9yiRHE96fpU68vMlpzdSAEokkj4oQscabMYeaMQWaMQWbMYWbMEabEETwaHe3qCYDD8mw4892NzvcYGC35yAz5yI35yE15yCz5KC15KMoVpP8G4lkmOPEAJxFH7cozlXcmogBaBEcoajGgEc/joc/locgXrsgXrsgSYUjKHe2nFDQWALtmALtgDFMcf8JjceSxpeaxoeSzpd1gxCJtfQbFQikUiOE1IASiRnEkJgCzbh9FXh8lfj9FVjCzahHKfLhVQbIdWGrtmi8Xq6YsFQzRiKGaFoGIqGULRwzJ/SqzKVECgYKMJAETqq0FFFMPwywnGEmuFDM3xhi98xcj1bQ21YQ20kevZFxwKaiy5rFl22LDptWXgsGTLOUHJSI5NAJIMh/wWTSE5nhMAeqCfOW0acrxKXrxKT4TvqZQ00AmY3fpObQHd8XdAUF427C2pOQpo9VtAdb4QIi0HdE41HtIQ6ul/hWEVrsBVNHH6PVIveicWzl0TPXgAMRaPLmkmnLZcOex6d1iyEaj7WdySRHDEyBlAyGFIASiSnGZZgG/HeEuI9pcT5yo9K8AW0OLyWZHzmZHyWJPzmJHzmRIJaHCjHy254hCgKumZH1+z4Se5/TrdItHa7f23BZmyBJuzBRqzBlkO2IKpCJ85XSZyvkszWdRhodNmyaLcPo91RgMeSfvI9H4lEIumFFIASySmOInRcvkrcnv24PcXYBkh8OBgCFa8lBY81HY8lHa8lFa8lFV07zXqAdotEj2bHY8uKPSRCYTEYaAjHRAbqcPjrDinpRUUnzldBnK+C7JbVBFUH7Y4C2hyFtNuHnX7PUXLSI5NAJIMhBaBEcgqiGgHiPcUkdu3D7dl/2G7NgBZHpy2bLlsWXdYsPJbUM96FKRQTXms6Xms6zXETugcNbMEWnN3xkk5/NfZAw6Axk2bDQ3LnDpI7dyBQ6bDn0uoYSatzJEHTkfdnlcSiCJ1Uo5JMYz+gUK/m0aDmElIsQ721IUfGAEoGQwpAieQUQTUCuD37SezcjdtbjCr0Qz7XZ0oIx6rZcumw5xI0xR/HnZ5GKCo+SzI+SzJNcRMB0HQfTn8Vcd5KXL5ynP7ag7qOFQzivWXEe8vIa3qfTmsWLa4xtDhHSzF4hJiFj9Ghj8nS92PFh93uRFEU8jy7MVBpUjOpV/Op0YbjVc7MZyxjACWDIQWgRHISowideE8xSZ27SPAUoYrQIZ0XVO102IfR7hhGuz1fCr5jiK7ZaHcU0u4oBEA1/MR5y7tFXkl3aZmBcfmrcfmryW1aTocth2bXOFqco9E1+4nY/mnBqNAmRrCbzJx80tKycbniURQFr9dDc3MdHxV3MEFfz+jQx3xqPpcarXCotyyRnHRIASiRnGwIgSNQS3LHDpI6dx5yEkeXNYM2+3DaHIV4rBkyCeEEYahW2pwjaXOOBMASbMXtKcbtKSbOV3ZQS20kkSS38X3aHIU0xU2gzTFcdik5CCbhJ0/fTXbecAoKRsccs9sdZGcX8KVsCIXGsXfvZ5ga3mWPmMFebcYZ9XdCuoAlgyEFoERykqDpXpI7d5DS/hn2YOOg88OxZXm0OEfS5hgh3YknCQFzAg3uaTS4p3XHapaQ4AnHapoMf7/nqBgkevaR6NlHUHXQFDeexrjJ+C1JJ3j3JzFCkGaUMT60Fouqk5WVf9DpJpOJsWOn4nTGQekm4oxmPjWfi66cGbGuMglEMhhSAEokQ4kQOP3VpLZvIbFrz6BxfQKVdns+La4xtDpGSLfhSY6hWmh1jabVNRpF6MR5S0ns3EOCZ9+AYtBseMho20hG20Y6bLk0xE+l1TkScQZbBZ1GCxNCH5FmVJCQkMyIEfOxWgfPrFYUhfz8kTidcai7tuAMvMZGy4VnbFygRNIbKQAlkiFAMYIkde4krX0LjkD9oPPDsWJju2PFHCdgh5JjjVC0aOxguQgR7ykhqXNnd2xn/8I/UlomqDlpiJtEQ/zUM64tnV10MC/wGm6bSmHhDJKT01EO05WbkpLB1Knz2L59Iwv9LxJQbN2dZsJWLgVBnFkHBDV6Cusslx2HOzmxyCQQyWBIASiRnEDMoQ5S27eQ2r4Vk+E96Fy/yU2TazxNcRMImBNOzAYlJwShmKJxg5ruI7FrN8kdO3D5q/qdb9a7yGpdR0brBlpcY6h3zwjHeZ7mqCLEjMA7xFsVpk2bj9l85OVdXK54pk2bT3V1GUIYgNIdEhgWk+Ekki6CdZVYRRd+xXlM7mGokDGAksGQAlAiOQHYAo2kt35MUudOVIwB5xlotDpH0hg/mQ5b3hkVtH6moms2GuOn0Bg/BWugiZSOz0ju2I65n18QVAySO3eS3LmTDlsutQmzaLcPPz1/ToRgYmg1iTQxfvzcoxJ/ESwWK8OGjRrwuM/noa6uErfRSL12agtAiWQwpACUSI4jDl81ma3rSPDsP+g8v8lNQ/wUmuImEpIu3jMWvyWZquRFVCedTULXPlLbtxDnq+x3bpyvgrjaCjyWVGoTZtPiHHNiey8fL4TAJVrI1feQp+9m1OjJxMUlnJBLW612VFXDLRqpF3mYCWAXHZiFn2Y145SKw5RJIJLBkAJQIjkOuLwVZLZ8RLyv/KDz2uzDqHdPP32tOJIjQigmWlxjaXGNxRZoIK3tE5I7d/ZbB9IRaGB4/ev4TGuoTZxDk2vcKVdGxiT8pBhVpBnljDKV4A/4UBSV3LwRZGTknpA9CCHYv38nuqHjooWLQn/DRM/z/th8IXVawQnZy7FAxgBKBkMKQInkGHIows9QNJpc46l3z8BnSTmBu5OcivgsqZSnXkhV0jmktm8lrX0zZr2rzzxbqJVhDW+R2bKOmoSzaIqbcEpYBJP1Ks7V3sMX9GK3O0lKyiQpKRW3OxlNO3FCtqJiP1VVJezVZjBa30RGRi6JialYLBa2bl2PiZ6e0BbhJV0vo0YrIKRYT9geJZJjiRSAEskxwOGrIbtlNfHe0gHnhFQb9fFTaXBPIyTjiySHia45qE2cQ13CTJI6dpLethF7sKnPPGuolWGNb5PRtoHqxHm0OMeetNZlh9HGOeItVNXKrFnnYrcPTfiD3++jpGQ3LUoavu7kj/z8Udhs9mjCyKjQJlxGKw7RTqaxHw0DKx6KTNP6XVMROmlGeTiuV00jqAxetuZYIpNAJIMhBaBEchRYA01kN68i0bNvwDlBzUmteyaN8VMwVNmkXnJ0CMVEU/wkmuImkuDZR0bLOpyBuj7zbMEWhte/jseygaqkBbTbC04qIWgSfpaZ/oMQFqZMOXiSh9froaxsL+3tzdExRVFRFAVVVVEUleJOOyPdQVRVJTMzj+Tk9D7rCCHo7GynoaGa1NQs4uLcQDg5ZPjwsRQV7yY3pBDCRHV1GcOHj0FRVCZNmkVdXSXWum3E2UxkZY2mubme1PYKiogVgBbhIV/fxSR1K4FgT61Hm83BptAoSo72wR0qRykApQ/49EcKQInkCDCFushq+YiUjq3RWmIHEtBc1CacRWPcJIQq/6pJjjGKQqtzFK2OkcR7S8hsWYvLX91nmiPQwMjaf9Fuy6MqeeFJUz5mdGgT/pDvoOVdAgEfZWX7qKyuIICNKm0EBhoK4Rp+KjoqBgo6Kjo72gySjBpUtTJGAAYCfurrq9hcXIdbNCGApqY6pk8/G1XVUBSF3NxC4uMT2blzM56AoKqqGI+ng2AwQEJCMmPGTGX06O66gYqCoig0t+5GE0F0xYzbaKAgtI0sYx+gkpyZRVbWMDRNo6OjlYqKYjL04hMmAEX3f0dzvuT0Rn4rSSSHgWKESG/bREbrOjQR7HdOQHNSmzCHxvhJCEX+FZMcZxSFdsdw2u0FxHtLyWpZjdNf22davK+cuKpnaHJNoDrp7CFtHaiKEDn6HrJy83E4+ha2DoWCVFTsp7KyBFVV2WOaSYk2MaaN229mV/CDDX0TRKYG3ic90IQQBk1NDdTVVdDUFLaQdikF7DbNxqc4Wej9FxUVxeTnj4ye63YnMX362ezc+Qltbc2UNfnwKHGktReRkpJBXFwCQgja21vYvr8MgZN0vYQCfQdJohar1U529mgyMnJjRK3d7qSpqZ5Qa//dXySSoUB+O0kkh4IQJHj2kdO0Amuord8pIdVGbcJZ1MdPRahnRr/RY8mTN00kwWnm8l9vHuqtRLlmQTYPXzuWxOvej45947xc7vx8IdlJNv73md0kOE18bmY6U2/7aAh3SrcQLKDdPgy3p4js5tV9ekorQErndhK79lCbMJs696whsU5nGsVY8Peb4RsKBfnkk1UEAn72KJPZr00l2E+iRX/iD+CsLJ2amnbWr/+AQMBPm5JChTaXKm0kAaWndeJedQqUbSU1NTNGhFosViZPnkN5eRGlpXtIs+s0exx89tl6FEUlGAwAEFBSCGBheugDEhKSyc6e3t2lJDbxRghBa2sT7e3N6ErikTyuI0LGAEoGQwpAiWQQbIEmcpveJ95b1u9xQzFR555BbcJsDPXUzwh88qaJXLswhx//Yw/3/7s4On7ZzDRe++F0lC+9dVTr56faKf3TQqb8cA1byzqOaq1rFmTz1P+bFH1f0+Jj9e4WfvTsHkobDt5p5VB4cW0Nb25piL6Ps5v44w3j+N+nd/HyhjraPEFUReEPb/X/szEkKEq4y4ijkOTOHWQ1r8Gixz5nTQTJbllDSsd2KpLPpc054mDLHXMxkB/aSUJCcr/Wv+LiXQSDAd4zXYlHdR/22v+uK2ScXkcNw6mwjKFd7T/Tfp9pOlmBInbs2MTYsdNwueKjxyI9hBMSkqmsLMbjqSUQUmhXEmjQcmhUc2hSszkn8BKZmXmMGjWp32u0tTVTUrKHtrYmWpQ09g2QMHI8kAJQMhhSAEokA6AYQTJb15LRuhGln+4dAk4Kd9rxwBvQ+dFlw3ns/XJau/rWnjtSzNqxT0Jo8wQZ/b1VKIrCmGwnj31jAv/50XSm/HANxlF+ifmCBr5uiw9AXooNi0nljc0N1PZy53X5++/lO6QoKk1xE2l2jiGt/RMyW9ajiUDMFGuolRF1r9DqKKQieTEBc6zg6k/8FYS2IlCp0kb2m9lqER5y9H3YRQc7TPP6JJ64jBaSRQ2ZmX3FUEtLIzU15XxmOueIxB9AtTaCam1gQRtBV8x8bL6Y6d538W5eQ2HhOLKy8mP6DLvdSbjdSfj9PmprK6itrSDB9ylmAjSqOQjUAfsSl5XtpbR0L21KMnvMF1Gn5kPo6H8pkUiOFVIASiT9EO/ZT17je1hD7f0e77DlUJF8Lt6TJKD+WPP+tiZGpDu4/XOF/Ogfewacd8XsdH72pZGMyHBS0+LjD2+X8dDrpdHjJX9cwN+WVzIiw8Hls9J5bWMd1yzIAeDTX88H4MMdTSy65+PoObcuK+DWS4ZhMam8sLaGW57aRUgfWMkJAXVtYWFT2+rnnn8V8Y+bJzMiw0m8w8QvrxrF1GHxmE0Kn5Z28P2nd7GlpOdzdTtMPPC10Vw2Ix23w0RRrYcfP7eHNzY3xLiAe1sbS/60EIBh/+9Drl2Y3ccFfN2iHG69ZBgjMpw0dwZ4eUMd331i5yE+/WOLUM3UJZxFk2siWS1ruhOXYknw7CfeW0Z14jzq3DNA0aK6rbcIdBsNTAitBWCSvpaUlAz+0zqNZjWTdKOMHH0PaUY5oKBiUK/m0aDlxVwrT99FABspKbFZuroeYv22HXjULMq0ccfhSfSlU01kteXzjAutQxRtp6WlgdGjJ/dJSrFabeTnjyQvbwQ1NeWwbxtBrOFUlAFMZaFQCIvFyirli0OSfS2TQCSDIQWgRNILk95FbuMHJHXt7vd4QIujMnlhd9utk6ekxrFGNwR3PL+X5743md+/VUZVs6/PnGkF8fzz+1O5+6V9vLi2hrmjEnnkxvE0dQR5emVVdN4PLy3g5y/v596Xw+3w/vh2ORvvm8t5P/uYHRUdBEI9XzSLxidR0+Jn0T0fMyLDwYu3TOHT0nb+9kH/7dD6wxsIW+PMJoU4m8bTK6u4+cmw+Lr1kgLevH0GI29eSadPR1HgrTtmEGcz8bU/bGV/nYdxOS70fkyHL66toaLJxwc/ncXM29dS0eiloT3QZ963luTx0DVj+PE/9vDWpw24HWbmjT5xsV8DETI5KU+9gK/NzeDlD7fj8lfFHFdFiJzmlSR17qIs9UJ8tsyoCFAUUBWFUYFPsNsdTJ48l/r6KmprK5gTfB0DBRVBXFwC6enjSUvLYuvWdVxs28RTXbECMNWowGHSqa2tJCMjB1XVaG9vobR0LxbhZZ152Qn9u2UoJrabz6ZBzWFK0wo6Nq1i7NipJCQk95mrKApZWfnoug7FW9BRESK733Xj4txUVhZjsfoIYO93zvFEuoAlgyEFoEQCIARJnTvJbfoAk9FX7AhU6twzqUmcc8bU8nttYx2flrZzz5dGcOOj2/sc/99LCvhgW1NU2O2rCYunH15aECMAl29v4sH/9hS/yDfCX4ZNHYGo5S5CS2eQ7zy+A0PAnuou3tjSwHkTkg9ZAGYn2fjhpQVUNHrZW93FjorOmOP/85fttDy5hAXjknhjcwOLJ6Ywa0QCY7+/in01HgBK6vt30/mCBk0d4f02tPfde4SffL6QB/9bwu97xQVu2t9/4tBQ8Mt9k1GyJ5HYsZMx7e/j98dmpjoC9Yyp+jv1CbOoSZyPoYa7cSTSSIZRQl7eZKxWG7m5heTkDKe9vZX29maSktJwOntCITIz89lbtAObtROfEo71swoPnYqbNJOPffu2UV6+j4ZQPPF6PV1KPDvN5x2x6/doqdMKWKmmMjXwAYGt68jIyMVkMnf31DVQFAW3O4nExFRyc4cTDAaoqCgacL1IfGOc0UyT1r9IlEiGEikAJWc8plAn+Y3vkuDp/x/zDlsu5SlLzsi2bT/6xx6W/3QWD/63tM+xsdlO/r2pPmbsoz0t3HLxMFSFaPzdpv39u9H7Y0dlZ0zcXk2Ln4l5B4+vTHCa6XhmCQrgtJn4pLiNKx7cQlAXpMZb+NmXR3Lu+GTSEyxoqoLDopGXEhahU4bFUdnki4q/oyU13kJ2ko0Ptvft0HEyEHXrotLmnsAncSPIaFpFctuWGLewgiC9dQPurn0sWzidR2tnMaJ9U3drNoXOzjaczvhuUZSI293Xwpmenk1x8S5y9d0UadMYpm9ndGgjdpOgy2fgNyVTbyRgiBC7zBdRr+YNees6n+JinWUZI/Ut2Nt20OAzY3RXHNQI4aoqRVEUEhKSSU5Ox+WKx+fzUlKym1AoSHp6DvHx4WfR0FBDCDNtAyShHG+kBVAyGFIASs5oEjt3k9f4br9Wv5BqozJ5EU2uCae1u/dgrN7VwjtbG/nlV0bx1IexVjhFUfp8SfT3mA4nQSJ4QKyfEKAO8ujbPSGm/egjDCGoawvg6XW9p26aSGq8hVue3kVZgxd/0GDdL+ZgMYWFhjfQN7nnaIi4n082esfxKb2knjDZqc24gHb3BLLq3sbmb4g5zxZs5t333icppYskcwm6Itiz51MACgvHkZMzfMBrmkxm0tOz8dTuIEvfT7xoJisrn2HDRuPzedmxYxNuo4PlpktoVft27RgyFJV9punsE9PhgKR+u9FOulFGRlspaa07EUJgoFLT6kMA1dVlpKZmkp8/iurqMsq1MUPWK1hwdM08pP47/ZECUHJGouk+8hrfI6lrV7/Hm52jqUheTMgke/b++B97+PTX89lb3RUzvrOyk/ljYi0/c0clsre666DZt4FQWHRpgym7Q8QQgv11/Vvwzh6bxE1/28Fb3aVccpJtpMb3uPA/K+sgJ9nGyEzHMbECdvp0Suo9nDchmQ93NA9+wglCiL7ivPd7rz2b4mHXkdK8ntSGNTFZ7wqCxMa1VFpTaMu7gKAzl9zONejFO0lNzcRqHTi+LTMzn5qaclLjLIwcOZ+4uAQAzGYLY8ZMYevWdSSbqk8uAXgQvGo8pepESpmIJoJoBMPxfYoCwiBX38voho9paFgJQIll4hDvWCIZGCkAJWccLm8FBfWv96mNBhDUHJQnL6HVNXoIdnZysr2ik3+srua7F+XHjD/4egkb75vLTz5fyItra5gzKpHvXJjPTX/bAYQ7NfRHfbeV7sIpqVQ2+/AFDNq9x67UTG+Kaj18/ZxsNhW3EW838euvjYmxEK7a1cyqnc28fOs0/vfpXRTVehiT7UQIeGdr40FWHpi7Xyri0W+Mp749wFtbGoizm5g3OpE/vj20tQJ7i8DIn+EMViVcIRqVhqS5dLhGkVXzOnZfbDcRs7+R5KIXaMteTFXKWST69lJRsZ8RIyYMeM24ODezZp2LzWaPKZdiGAb79++gQ0mkRDs1RZKumNHpVfBdUakwjaFaK6RA3wYwZPGMQHfs4lFkAUsf8GnP0AZcSCQnEmGQ1byaUTXP9yv+mp2j2ZFzvRR//XDni3v7lA7ZUtLOl367hSvnZrL9wbP52ZdG8tN/7osmgAzUqUE3BDc/uZP/WZJL9WPn8u/bjl9x3Ov/vI1Ep4kt98/j798JZzTXt8UmPXz+wS1s3N/G89+bws7fns0DXxtzVNbJZ1ZWcctTu7jp/Dx2PHQ2r/9oOiMzHUd7K0fNYFEMka97nyWF0mHXUJ+6AHFATJ4idBIq3yG++DUazCOpq6sKZ8T2g67r+Hwe7HZHn1p55eX76OzsYIv5PIzTrF2irpgpMk2j6AQWfe4PQU8c4BG9hnT3khOBIqTMP6Vob2/H7XbD5/4F5qH/UjlVMIc6KKj/L3G+vtmkIdVKecr5tLjGDsHOJJLjT0/yhxJ9r3Qb/hRF6XU8/F7tPm7z15NR9V+svvo+a+omJ2lug8mTZ5CenhNzLBgMsG3bBto62hk+bBR5eYXRNmpVVaWUle1jjzadveaZx/GuT0KCHnjtC7S1tREfHz/4/CMg8h2xo6SOuLgjv0ZHRzvjC9KP614lQ8vp9auXRNIP8Z5iCupf7zfRo92WR2naxaddJ48znYj7eSAr5JlKbxdwROxFj9EjBiN1/0KODKpHXEdq/Ye46jfErKWFumhqgn37ikhLy46u5fd7+eyzDbR4dKq0CSil22luricuzk1NTQVBA8q0CSe0LZpEIumLFICS0xdhkNnyEZmt6/q4LwUqVUlnU+eeOeSlJyTHHin8eugv+SNG7EXHw5Y/Ve3+U1FQVVAVM76CCzESR+IqfhU1GJsMVFFRw8qVK5kzZw66HuSzz8JC8SPL5+hSE6jWRjC1430a22so0aZQap1IQDnxhZHPNGQMoGQwpACUnJZoupeC+v/i9pb2OeY3uSlOW4bHlnXiNyaRDMJvZlccFwGrqUqM1S/s5u0RgX2Fn4KqKDisKqqiYCSNoNX5LZxFr2JtL45Zu6amhrfeehO320qXFs96yzJ8argQcouawXLLV1EwEIp2zO9rqNBEEJdowSSCNKlZJ12pKFkHUDIYUgBKTjvs/joK617DGurbfaHFMZKy1IvQtb5N7CWSE4kidDKMUkJYaNByj4vbOqJJVEU5IL5P6Xbz9oxpqhJ9RVzAmqpgMakoCoR0gWF20TTiK9irPyKh9kOUXqkCXq8Pr9dPRcosfFZXn40ITg/xl6JXcp55BR5PT5eZanU4W80Lh6zmn0RyJEgBKDmtSOjcTUHDm6gitqyIQKUyaQH17hkn3W/qJzvHyyJ1pmIWPvL1nUxWtxII+mhR0mjQco/5M44Rf90Cr3eCR9jS1yP+TJqCqVsAqt3WQrX7vR70k97wHnXOaXiUFJpT59JpyyKv/AWE0TsLWJDb+B6WYAuVSQtPu/CKYaHtTAytweJMIidnOE5nHD6fF23vZ7gD/+IT8xLa1LSh3iYgC0FLBkcKQMnpgRBktnxEVuvaPoeCmpP96ZfRZcvp50TJYEjxd2ywii5GhzZSIPYgBCSlZNPZ2U6d59hbow8Uf6ZuUaf1cvMqvax+Zk3BbFK753Wv0R0dqAIpDe/i9uymTUmlyZaMbgg67XlY0vMxmsoIHtABJb1tE7ZAEyVpy04La7sidCaEPmKYvoPs7AIKC8eidIvb+PhE4uLc7Ny5mXM6X6ZJyaBSG021VjikFkHpApYMhhSAklMexQgyrOFNkrr29DnWac2iOP0ymeV7GEiL33FACL5g/S9eo4vs7JFkZuZhsVjZsuUj/McoIaK3Ybt3uZeIKzdi5esd4xcZs5jUsAjUwueJ6DrgatmM27MbRVEwCR+q7iHHtwt3sIJd1vkEMheR1LKZtPYtMftxe0sYXf0sRRlfIGBOOCb3OBSYhJ8ZwXdINmoYNWoimZn5febY7U6mTp1HQ0MNdXWVJLesZIq+mk+1syk3jRuCXUskgyMFoOSUxhTqorDuFVz+mj7HGuImUZGy5LQKPD9RSBF4bMnTd9He3sLkyWeRkJAChLMsPZ5OvErhUa/fu85fj/UvNr7PpIVfvWP9ItY/i0nFblG7+zuLaCFgIcDd+gnp6WFrZZp3J9meT1DRMZstbFSS0FULXSlL8FgzyGt4F5Uea6A92MyYqr+zP+MKumzZR32fQ8HE4BoSjAamTp4V/ez6Q1VV0tOzSU/Pxu/3UVq6h8m1K9EVE1XaqBO44zAyC1gyGFIASk5ZrIEmRtb+q0+yh0ChIvlcGuKnyXi/I0AKv6NDEToWfASwIxQVKx6ms5bk9JwYAeHxdBIKBWkyZx7d9Q4Qf0ovF68CWEwqJk3BrKl9RKBJVbCYFBxWDZOmoKAgCAvASD9nJeTB6cwAFAyjmbi4dOrrq/lEzEZXevoqN8VNxGdOorDuVcx6T19ls+FlVM0LlKRecsp12UnXS8gx9jJ69JSDir8DsVptjBo1CSEE0+qWE8JMnVZwHHfaFxkDKBkMKQAlpyROXxUjal/uU9xZVyzsT7+MDseJ/cdWIokwNbicbKMIAB924i0GBjB8eGynmdbWJhRFoU3LOOJrHSj+elv9IokcvcWfWYu1BJpUBafNFI39U7rzeoUARQh0XUc1/JhMZsaMmYIQgq1b1+FwuCjX+7o2u2zZ7Mq+mhG1L+MINETHVaEzvP7fVOrnhhOxTgE0EWS+sgJXUhrp6YdvvVQUhYKC0VTX1VKgbzvhAlAiGQwpACWnHO6uIobX/6dPpq/f5KYo4/P4LIf+m/qZTrpeypjQekq1iZSZxg/1dk5JehuZk/Rqso0i8vNHYrXaCAT8BAJ+UlOzsFhiEwLa2pppVtIwFHPY7naYJpewha93rF9YdEQTPbr/NGsqZlNE/KnRTN+IIIzE/RkGELb/Rd/rAS8AZrMFIQT79m2jra2FdeZLEFr/Gb5BUzx7sr7C8Lr/4vb21AtUgNym5ZhDnVQlLTjprfNZ+n4CIT8jRkzo08v4UPB4OtmxYxMBrHxmWnjsNzgIMglEMhhSAEpOKZI7tpHf8HZM/TGALmsGRemfJ2RyDtHOTi1sopMJwTVkGiUAFOjbpAA8ChQUEAYTgmuIi0sgP3/UQUWDEIK2tiaa1MNPEBiwr68SSfLoKfFiM6u9hJ7aY/nr/tNh1VAVhaBuENTDMWNKd81AQwgIhgWgyWSmtHQvNTXlbDEtolE7eEa9oVopyriCvMb3Se34NOZYRtvHmHQPZakXntRlYvL0XSQmpmC3H17P9Y6OVsrL99PQWIMPJ+ssy/CoQ9BL9yhjAKUCPP2RAlByypDWupHc5hV9xlsdwylJuxRDtfRzluRAMvX9zDaWo5k0FMWG1+/nM/OCod7WScPhJsBEhFiesYt40cSIEfMGtRj5fB4CAT/Nlqyoxe1w6B3vF37fYwUMWwCVmNp+pn7i/5xWDYtJRSt+HUV10JU8H90QqN2LCt1Hcv0qAJqa6qisLGan6SwqTWMOcZMq5SlLCJjiyW5ZFXMopXM7JsNHcfqlCCX2a0gTQeyiE5voolNNwKccUFT6BOAyWkgStWRkHF6/4uLiXVRU7KdLiafItIBKbRSGMjRfszIGUDIYUgBKTn6EILN1LVktH/U51OiaSFnqBSe1JeFkIt5oZJb+PolJaSQkJFNUtIOdprk0q0eXiHCmYRVdZOrFXJheyeYagVO0EUcbaek5xMcnDnp+S0sDoNCiHl78X8TtG7X60dPVw9Qt/Ho6eCh9Czx3Hwtn/IJq+LE0fYpZCNpMWfjsw1AUgS3YQGr1v7EbHWTnjaCiYj8l2kT2m6Ye3oNSFGoTzyJgcjGs4a0Yy32Cp4jJNU+hpI3AKrxYRQdJajuhUDBmiU7FTYOaQ6OaQ5OaTfAE1NbL1XdjMplJSUk/5HOamxuoqNjPbtMs9mlT5b9JkpMeKQAlJzdCkN28koy2j/scqnXPpirpnJM+luhkwSK8zAi+g8PpIjk5nd17PqNCG0OJNmmot3ZSMZD1T1G6kyMUKAhtZ2RoMy0tLsYmOLDbE7Hbc8jIOLRi483NDbRomd2Fgg8t/u9g4i9qAexO5jCbYku9hOv+hV8Wk4pCuPSL2lKGEAKXK560urcoy70GV1cRqY0fEDQnMnXqPHbt2kwbiew0zTnkZ4gQJIka4owWnKINp7UNa1Iy3ubGmGmarxmtdisjCrNwOl1YLCnYbHasVjsWi5WurnZaWhpJbS3B692Bgcqn5kXHtayKInRy9T2kZ2ejqgOXkGptbULTNOLiEggGA+zZs5UGNYd92slRfUDGAEoGQwpAycmLEOQ0LSe9/ZM+hyqTFlCXMHsINnVqoooQMwNvkWDykZMzjp27t1KljmSr6eQPxj8ZiJZWUcNiy29KgBBMnToPk8l8WGsZhk5LSyN12szDun7v/z9Q/EWsewpErYCRJJCI1U+NCsJwZjCA0rYfm83BhAkz2bRpFfmVz2AKdZCRkcuIERMoLy+io6uLT61XIFQNtbtMDAwsEBKMWiYEPyJR1AMKNpsdu92JIzOTQG4a27fvQdd7agXqAT/19e0sXDgNiyU2jMPhcJGamgWE3ealpXtR6z5Ax0StNvyQn9/hkGpUYsVLRkbegHMMw2DHjk2EQiGGDx9LZ2crnQGDT62LTpq/T1IASgZDCkDJyYkQ5DZ9QFr75thhoDxlCY3xh+mKOoP59awyXlyzixSlkQkTzmLFp3tASeFT86IB3VT9fYedyV8IPWVWwn/agh0A6Lp+2AKwra0Zw9CpN+dFa+4d9NoH1vmjp9RLRASqvQSeWVNiYgHVbtGqKuF2b9EagAporUUkJaditdoZNWoS+/Zto3DMFNLTc2hpaaS0vIhiyww8pjQsqoIQENLDucLRnxEhcIgOko0q0vQyMo0SnM54CgvPwu1OQlVjf8bS03P48MMPCQQC0bGmpiZWrFjBokWL+ojACDabg9GjJ9PV1U6Wp+i4CECnVSPe20YIE07nwN2DmprqCIWCVKijoHgnANvN5w1JvKJEcqRIASg5+ei2/PUVfwqlqRfRHDdhiDZ2CiIEz35UTJ5RyrgJM+jq6iBBNLLGcnm/4q93aZG+BwdWKqezOOxdVFlVIJEmhgc+IS9vBFbr4fe5bW5uwGKx0qEkAz2u5cj/HwpKL/Gn9bLsmU0H9PxVezKCewtDk6ag+ZvB30pS0kgAUlMzSUnJQFEUamsr2LVnG+2WXOpcs7AJFcMI2/40VUHxt5FsVEVfDtGJAOJcbrKyJpKRkTdgIkxycjLnnXcey5cvx+/393ouzXz44YcsXLhwQBEYXlNhRnqQzS2H9qwAEKK7OLftoA+5y6/jEJ341LiDJvLU1lbQoqTxqeU8avThuEQrVerIw9jQ8Uf0KulzpOdLTm+kAJScXAhBdvOHfdy+AoWStItpccm+moeCSfiZGFxFoqjHKdoZMWIC7e0tVFYWU6mNolXLADFw/9j+v/uUaAxc+F34CzmkixhxeDqJwUhGrMtoZol4jWo9FafoIM7pJD//yOLQmpvrqTaNiHlOh+s1PDDrV1PDsX0HtnlTI5bC7vkxx9r2IxSVhITkXvtQKCvbR2npHlqcE6hwn4sdDasQePw68YEKRnk+xGG0AuB0xpOQkEpCwhgSEpIP2RqakJAQFYE+X08x96amJlauXMnChQsxm/tfKxQKsLPBINtcSbOSitfoSQpRhN6n9aMiDCaFVpKn76ZNSaZWLUBXTPix06am0qEmx8w3Gx6y4wb+QPx+H83N9ZSbwpnzdVoBdYd01ycW6QKWDIYUgJKTisyWNWS0bYwZC4u/ZbS4DrH8xJmOEEwKriJPKSUjMxeTKYPS0j34Q4IS0xT2mcKlLQ4sJNzTQ1YZUJBEBGBE/EFYVAghMES4vp1ucEiuzZOd3s/gquQN1NXpjIz34/UGGTNmeh/X5qHg83nweDops+TAAfkFA1peB9gTgElTw5Y/TY3G+kXi/SKxgEq36zf6vvtco7mIBHcSmtbzNVBXV0lp6R4aE+fS5J6Drbs3cEgXJHVsZXjnhyQkJJGVNZ2EhGTM5iMvveR2u6Mi0Ov1RscbGxtZtWoVCxYswGTq+xWVlJROqL6KRO9/AehS4mlV0rDgI9moZoP54midQk0EmR58jwxRTv6w0XR2tpHS9ln4nkJBBAr7tcnsMc1E0cxouocsfR8dHQMnfzQ3h+VetXb0PZwlkqFECkDJSUN66wayWtfFjPVY/qT4O1Ry9T1kG0WMGjuVtLRs9u79jA7Dzjr75XgJF8qOWISgR9BFWof1Ti6A/uuB9T4fQAgFpdvioKrhLhKCyJ+nthhUMaivryY9PYcRI46uWHZzc7j8S6Mamy3cW4hHOPCZHSj+FHongPT8GXlFRH3E9auqoKndn3PIC20lJA3rcVt2dLSyZ89ndMZPoDNlHtaIdVcIMppXkNi5hezsYRQWjkM5RiVO4uPjOffcc/nggw9iLIH19fWsWbOGs88+G02LFWMjR05gxIjx+HweOjpaaW9vpaMj3FYvFHIyq+tNAkEbAgWn0oWiqIyfMIukpNSYdQxDp7KyBLX0M3L8e+iy5pBhlBNSVFJTBy7PYzaHLY4ZRilV6kjESVruRVoAJYMhBaDkpCClfSs5zStjxgRQmrpUun0Pk/Ghj0hPzyYtLRshDFpaGqhTC6PiD2Jbh/VOJogVgj1r9vdlEIlDCx8PWwAjwk8BDAGKKtCN8Nq9Y4pOhS8XhbBrNU1UEQz6SU8/tBIvB6OpqZYWLRNdtcaEVA7sdo+dE/4z/D8mTYmJ8Yt2AIla/mK7hKi9LiDqt6IKPXpPgYCPHTs24bel05F9IRZFwxACJeRjSsvztLQ3MnLkRLKy8o/6GRxIbxHYOyawpqaG9evXM2fOnD7WVkVRsNud2O1O0tJ6+vT6/T7q66u7awkKrFY7CQnJOBx9kzNUVSMvbwTJyenU1VXS1taI1ZpEYeH4g8Z2ulxuINzzOV/ZyTrLpRjKwBbDoeJExwCGQiHuvvtu/vGPf1BbW0tmZibXXnstP/nJT6KfnxCCe+65h7/85S+0tLQwe/Zs/vSnPzF+vOxCNBRIASgZchK69pLX+G6f8fKU82mOk/8wHC4aIeLiEgGori7D6/NSZR0dk2wQtRipSl8hSN+yI/1Zo3q7LCPWPkPpFn6EwwINET5qCED0XvTgpUSGmqg7XFXI8u3F4XDhch1dO6+GhhqamxuodSzuc53eFtfexw58PH3m9MpOjmQCq2rP59PjGibaBUQRAqX2E1JSMrBYrBiGzo4dn+DRTbQXfD4ax6f4W0mqfomOUDuTJs0mMfH49dh2u90sWrSIDz74gGCwpxB0eXk5VquV6dOnH1I/XqvVRm7u4WUHO51xDB8+9pDn22x2Zs1axMcfr8AuOg7rWqcz999/P48++ihPP/0048ePZ9OmTVx33XW43W6+973vAfDAAw/w0EMP8dRTTzFq1CjuvfdelixZwp49e4iLGzjrWnJ8ODlt15IzBpe3nIK6//bp7VuRtIjG+ClDs6lTGLvoQCHczzUQ8FFSsodK8zjaTeGOBkokM/SAZAFTTNuwA16qilnreVlMarSvbOQV877XGrFJCUTFSTgWTTkky9eJJiKeFAVUI0BqcD9padmHJEAGwu/3snfvZzSYC6lQR8W4aGNKu6g9r4jVLhK319s9b9KUPkWgo3N6Pddo1q/a85lpneXgbYxa82prK8Nu1PwrEOa48HmhThJLX8Cp+Jg6dd5xFX8REhMTWbhwYZ+4v3379rFjx47jfv3DwW53kpiYSpuaelJa/6DHBXw0r8Nh3bp1XHbZZVx88cUMGzaML3zhC5x//vls2rSpez+Chx9+mP/7v//jiiuuYMKECTz99NN4PB6ee+654/AEJIMhBaBkyLAFGimsexUVPWa8JmEO9QmHXiRXEkYRBtMC72OzWklNzWT//l14dRN7zWeFjyuxlqaICIwReqYeERcVeyYl9qX1fZmi/989X1N7jvebndotBjl5hGDP81Gi4smKF5Oi09xcj9/vG3yRfhBCsHv3VjyGhSLXuaiqGnaTH5BN3VNqpqdlm6rEWmsjmbyHIkbDLuLuz8bUvZbQoex9HA4Xbnc4+7WxsYaAK5+AIzv8a1jIj7vkn9jxMWnSWf26T48XKSkpnH322X1cvtu2baO4uPiE7aM/hBDs3PkJO3Z8wp49W+nsbBvS/QzGsRKA7e3tMa/ebvrezJ8/nw8++IC9e/cCsHXrVtasWcPSpUsBKCkpoba2lvPPPz96jtVqZcGCBaxdu/b4PgxJv0gXsGRIMIc6GFnzEiYj9h+ThrjJVCfOH6JdndqMCm0iSdQxduwcVFWjvr6KUts8dM2OekC8WaQ8iKZC75IhB8YBRuaH/+xOVOge720gEEKgK5FsYAVDFRiGgqqIcCwZYXew0fsLpnsV0e0e7l1c+ES6hg8shRMRf6qqEFQTmDx5Djt3fsInn6xixowFWCyH14u2srKE1tZGityfQ1ftGKGeX3gOFH8HJuBENhd5HDHu+gOuc+Aji4hIkxb+bE2agtj/LnTWMnrKHBRFIRgM0NzSjC8znBmOESK+7GWswSYmTZ2LzWY/rHs9FmRkZDBnzhw++ii29/fHH3+MzWYjKyvrhO6nqamOurpKWloaCYWC+LHhUeLpUoZRrp28yWmC/hO4Dud8gNzc2NaId911F3fffXef+T/60Y9oa2tjzJgxaJqGruv84he/4KqrrgKgtrYWgPT02P7K6enplJWVHcVOJUeKFICSE45qBBhR+zIWPTZ+psUxkvKUJUNvCjoFiTcaGaV/wrBho3G7k6itrQCgzToMm6ZGBVVQFwfUj4t9RdyJPUkEsaKvt6Wut0gTIiz2BAqG0SMCNQN0QwEMDNG7XEwkPjDy/wIhlG5heOzU32DlbKLzUHoJ3fB9JzrNZCRY2ZN4Ic5xmbR8+vJhXTsUClJeXkRlZTHV9qm0mPMIhozwtaP766+7R08sZgRV7Um2gUjMX9/PIvylLzCpKqoCpu7P1KQpiPptULORkSMnEh+fAIRrEioYqCljcOlNmCrex+qpYMKk2TidRxfzeDTk5eXh8/n45JOeeqBCCD766COWLFlCQkLCCdmHx9PJ9u0b6VASqVEnUWspoE1NHfzE04iKigri43t+FqzW/n8BevHFF3n22Wd57rnnGD9+PJ9++im33HILWVlZXHPNNdF5B1qvhRBHFV4hOXKkAJScWIRBQf1/cQTqY4Y7rVmUpF3CQK3JJD24jQYMVDqUpKgCsIkuINxmSwhBWdk+mq2FhCwpWHr922o1h8VDWKopsRbA3pmkal9RBLFjQFSwRcq/CAGG2mMJ1A2BZggURUU3eou/sFCMWAYVQVg4QjhZRDn60jG93bkHK6nSW9RG3ePdcXZpbguar56OvcuJi0s4ZOtfXV0lRUU78OuCKttMKuwzCYQMgrqIiriI+Ou9j4jltbcFVouIP8IiOTo3GkfZIw7DS4fHetz7KsLfgVL0X9LTs8nMDPe41XWdkroGNFc6eVUv0Nxcj9VqY+T46THFoYeKUaNG4fF42LVrV3QsFAqxcuVKzj//fOz242+djDzbIm0KlabDt/apIoRA6VOc+kQQ/nk4iizg7nPj4+NjBOBA/PCHP+THP/4xV155JQATJ06krKyM++67j2uuuYaMjHBpnUiGcIT6+vo+VkHJiUEKwFOU3NAu/GoKXsWFT3ERxHJKWM6ym1eS4NkfM+YzJVCUcQVCPbyeqmcidtHBvMCraOi0Kimss1xGSLFgdFcVDgT8WCxWAgEfHmcmZlNEHPTE/4Vj/1SsngrcwUqaXVNQTc4Y8dfbHRwhag3sNS6g23Inui18YaFiGAqGEGhqWASCgaaGxwwjbPEzVFD0sADUje41DYFQwDAUjrS7SO8M5Rhx189fj96Wt577gXunlfFY+RisHUV07XkZh83KuHH9x6UKIWhvb0FRFGw2ByDYuWc7rdZhlCYspEs4wpa/XvfR2+IRdf0S6+LVVCXmXkCJsY4qhD+rA4lY/6LnK6DUfoymGIwYMQGl21W/a9dm/C3hosY+h4vRoyeTlpZ9RAWujxeTJ0/G4/HEuAg9Hg+rV6/m3HPP7bdQ9LHEbneSkJBMTvteKjk8AWgTncwN/Jt0J7wcvIKAcmLd6Se6DqDH4+nzs6NpGoZhAFBQUEBGRgbvvfceU6dOBSAQCLBy5Uruv//+I9+o5IiRAvAUZXxoLWal5+NTVQ2r1Ua5P5EybRwBxU6SUYNVeNAIYRJBNMLlFSq00dSohSdcMCZ1bO/T5SOk2ijK/AK65jihezlVGR38GJvZxKhRU9i9+1MmBNewzXw255lXoFjicLniUFUVtzuZJF8FreZZ+AM6yb69tNvySfduozVuEvGde8hqWQ6AWRW0O88JJ4CoCkFd9Fid+vkWiGSgRo6HRZ8S7RVL9//rRlj8hePVIhbASHxg2AoIBoYRFj5694+jIYDuGEIgJjYwwoB1CXtZKFUEU/1v41Xi2Gs/G1O3GI2cGnGNRkSqqoT/vGdKKQCTTNsI7HyHpOR0xo6dGtMxI4LP52Xfvm00N/e2aCsIxUxJ3GI8hiX6DA2jx/IX2W/M3rsFeCRm78CknejaMe/6/jWOJIpEsrM1I4BSu4nMzPxoiZfi4l00NdWhqipjx04jOTn9pHTDKYrC7Nmz6erqorGxMTre1NTExx9/zJw5c477vu12FwWhFtaHDv0ci/ByqfoKhkXH7ze4wvIyr4SuIHD8tjnkLFu2jF/84hfk5eUxfvx4tmzZwkMPPcT1118PhD/LW265hV/+8peMHDmSkSNH8stf/hKHw8FXvvKVId79mYkUgKco8+adj6oq+P0+/H4vPl/4T0tnG6lt4Zp6mmbCZrOjaSY0TUNVNUKhIOlt7+FwrGN18CxalTRMBNEI0a4koStH3trpYDh91eQ3vBMzZqCyP/1z+M1Jx+WapxtOo5UcYy/DCieQkpJJWlojoq6IYMiCL+hh2rSzUdWwJTApKZXW4t2YlRD2QCkFrW8RUqyYhJ+Mto8wFDOZmXkoikKo7jO60uahqWY0NZy1G/lKDekG1oaPsXRV4HcOI5A0AUWxRQViRJAYhkD0Eo2GqqAaAtUgJhkkIgJ1Q3QLIhVDDbePC7tEu+cYoHfbuyJuz4F658bE7nWPqapCdmAX6XoJAF1GFo2mkVjNKoaAUDBEol5FWqCYduswOp2FuE0+bHYnr7ROIsG3H23fi+TmFlJQMKbfuKXq6jJKSnYRVOy0516OYU0k1NWM8LfSThJ+wn+XjIH2TY8bWFXAZtZ6CjsrPcf6kze9rYk9e+rO/FUiIr37mdRtRtH95OQUAFBTU05lZTijdvToyaSkDNz14mRA0zTOPvts3nvvPTo7O6PjZWVlJCQkMG7c8S0UL4RBXRdoliC6cmheinS9FK+/i0mTzsJisbJm00bmKv9hrbL4hInAY5UEcqj84Q9/4M477+Smm26ivr6erKws/ud//oef/vSn0Tm33XYbXq+Xm266KVoI+t1335U1AIcIRRxNkIDkhNPe3o7b7ebeex/uN0NPCEFLSwMWixWnM77f347b2looK9tLS0tDzHiHksBay2UElGNrjTOFOhlb9QwWvTNmvDTlQpriJx3Ta53OjAhtZryxiblzz0fXQ3z88QpKRCE5xj4KC0aRlzciOre5uZ5t2z7GZ83E5q/BZDLjcMTR3t4MhAvmzpixAI+nky1bPqJ5+NcxXDmYVANLezFB1zCEZsZStwFH9QfExbnp7GzHZLLQnHcFwt1TbLd3HGDE1SwE6IYg1C30IvF/ERGoG2HRGNQj73sJxG4XcVAX3QklkeuIfqxfPSJJdI9ZlCDDfevJCWwjIz0bXdepbWpmb/rXUQOtJPr2kuQrwmx4UBQFr5ZAwJJKvGcvVquNzszziKtdgd3uZOLEWX3+Dnk8nezZs5X29ha6EqfSmb4AXbWhGwJ/0MAfMvAFdAKh8P33dEnpm94SseBFyudEsrEHEoCRZxt5lr1d+5qq4LBq2MwqLpsJu0XFYYHQ6p8DkJCQgmHotLe3AoLMzHxGjZp4pD+OJ5y2tjbeffddQqEeU5yiKCxYsCAmpuxY09raxCdbN9KuJLPRctEhuXI1EWSR/3nyUt2MGzcNj6eTVRs34g8ZrH7rn7S1tR1SXN2REPmO+PDTclxxR36Nzo52Fk7JO657lQwt0gJ4mqEoCklJaQed43YnMmnSbDo72wkGA2iaCcPQ2bVrM3MC/2Wd5dJjFq+iCJ3h9f/pI/7q4qdL8XeYZOglJCaloKoqe/eGC+Na8OPHTnZ2QczciLvR5q8lMzOfhIQkUlOz6OhoRVU1VFXFZDJjtYY/59TAftr9GmmVr9DV1Y7VasdfeDmKw44AkpLS8Pl8BIN+bKFmhLlHbEasUUKEBU8EzQhbAfsXgBEXr4GmhjOFw/GBItpVRFWM6NzIWG/3be9i1qbu+MI4735Ge1diMnwUDh9DTk4BoVCI9k2rGF/3OEIIrFYbqVlZpKZm4fd72LlzMwkmHzkjJtDW1oy/9A38itJH/Om6Tnl5EWUVxehmNy35XyHgyA3vSYhu0WoQDBno4bCncGyhoqCKcJeU7qlRLKZwzFTEcqf2KsfTuzMLHJDxa0Rc793vFaKuY7NJDYs/qxbO/O0m5MpC0cykZFho3LuOzMzY8h4nO263m7lz57Jq1aromBCCtWvXcsEFF+ByHZ96hQkJycyadhbbtn3MvMCrbDBfjEd1H/QcXTGzzzQNe8NqQqGJOBwuFsyaxSefrDkue5RIjgQpAM9gDmxtNWnSWXy0aQNnBV7nY8tF+JSj/wc1u+lD4nyVMWPt9nwqkxcd9dpnGiaCNDS18u6qFdjw8Kl2DpON1YwoHIemxWYZRsTCyJETYvq3xscnxsyzWKzk5hZSUbEOJ2vBGc/48TOpqirBv/M5cGWCJY6yqkpEXB5G5lko7gK0PpnAYVetEm3xJkAFM0pP0kd3Aog/1CPs6I4N1NRIEomIWg81VSXUPS+kh13MRnfJiASHKerOtZlVQsEgma2riO/aQlJSGiNHzupOyACz2cL48dNpaKghJSWD+PjEXjGMbiZPnkN8fCKqqpKdPYzOzhGEQoFoAWQhBE1NdRQV7cAXCNCZPJuOlLkxHSCMqOWSaJFnTVXQYtrlhe/N1r3v3s4XNcbyF1uGJ2Il7K/kixHNKu6p9ee0alhM4a4fgZpwzK3ZnUXyjHCcVbClAvauQzkFM+6zs7OZNGkSn332WXQsEAiwZs0alixZ0ufvwbEiLi6BqVPnsW3bx8z3vsJ283zq1GEHdQl7lLBIDIVCmExm7HYn06fP57XXXjkuezyQE50EIjn1kAJQEsXpjGPejFl89tkGFvlfYJdpNqXa+CMuzZLQuYf09k9ixvymeIrTlslyL0fAasvnydN3cWFGDc/XTyGEGUUX/faoTUxMZerUeX0E34EoisLw4WNJT8+hq6udlJRMVFUlOTmNpqY6KiuLaQ12oc+8DdVkibYbM2lqVMBE6/gBdIu0iONSARRVoAgFVSgYikBV1agoDGg9LtLeLt7Il1fEjRzsFo0Rt6il28plMamo3iYSa/+NNdDIiJETo7GNvYmPT+z3WSiK0qfkSe/n6fF0UlS0g5aWBgJxhXTkLyZgTgTR00ZJCIGqKgRCRrT2Xu+aipGsW7tFiz6rgVzCB/ZjVntnXvcnAA2B3v3eYlKxmcPCz2pWsZk1TDlTCFqsOLImRM/T9bAL9WTK9j0cxo0bR3NzM5WVPb9YtrS08MknnzBr1qzjdl273cnUqfPYtWsL1pb30dGoV/Mo0SbQpOX0ma93J+lFnjdwQkX3iY4BlJx6SAEoicHpjGfmzIWUlOzGVL2GbH0f+0zTaFRzMJRD/3GxBpsZ1vBWzJihaOxP/5zM+D1CdMVMiWkSf26cBGrYJQzgcDj7zFVVdVDx1xunMw6nsycQW1EUUlIycDhcbNz4IUp7GSJxJBDJnI2UfwmX7cMI/ykMgXpAuRKhKKiExYqKEi4Ro/TE9vVY/noyXCNCyIgKQBVfUEdRwhYu/G0kNW7G6qtG89SGy7RMm9+vGD4SOjvbqagoor6+BsMSj6fgCwTiRhIyBFqfuNrwPi0mBU3VCKoGaqiLhM5tBCxpGK5sFLMjKtgMocSUxAmXzem1WjT794Di1JHnSU8soOjOyHbZtGhGc8R6COAcdhbmwjlRIQkghN695qkpABVF4ayzzuLdd9+lvb09Or5//35SU1MpKCg4yNlHh9lsYdKk2Xi9Hhoba3DXV5HR+To7xFxKtIkxWT6h7q/Xzs42HA7XSZllLTmzkQJQ0geTyczIkRNJS8tm377tJHW9hapqVJFHuTaWei3/oOcrIsTwuv+gidh8t/LkxXitJ3fG4amEXYQ7qdTVVZOVlddvmZKjvobdid3uxFv8OnrhMpTEEeGiz92KJGLRQg2LIFUBg55SJzFdL9SweFGV7iQGQ0FBRC2GESugIcJWNFUNz9G6XcRWs0qwq4nU+o3YW7aiaSaSklJxpY8kKyv/mNx/a2sT5eVFtLQ0oNgSMI28EG/iFFRDxdJVD6YEFC3s9uttkTO6rX4AuqHi6Kwlvrkn3itkTcXvyMHnKsRwFyIUNSbBJZLQcSAR4afECDjRLegMHFYzRvf7cO/fnj31zAehiB5BeYpbAAHMZjPz58/vkxSyadMmkpOTj3vSgt3uIDe3kMzMPLZs+YgJno9wilZ2mOYjuoW1R4mnQ0lg9+5PKSvbS3p6LgkJKcd1X72RLmDJYEgBKBkQtzuJ6dPPxuPppKmpDkdDDZmdb7KOZTT24/KIkNP0YZ9OH02u8TTFyaSPY0mVNoJ40YRRvJsdxWUsmX/OMY+BUhSFyZPPYs+ez2jZ9RzGxBtQ3DlRi5JQItkN3SJQF32yVnu/iSQtRJIiwpYsJRofFxaCAnvlu5hb9xCMKyAQV0jI7MbRtAlLyw4sZgu5BWOOmeiL0NRUx/btGzHFZWCf8AVM6RPQdZ1Q1Va0qg2onjp8uRcQSutbEFogcFhMmE0qZk1Bc2XhKYMxY6YgBLS3N9PauhdvyxaMGif+xAl4UmahWMJW14gADD+f2G/eA9vCCcJiUwg16iaPxBxGrH8RYR6pw6j2WkHprgXY3Fwf7QpyKuJ2u5k5cybr1q2LjoVCoWi7uGNdJLqzs52GhhqcThdOZzwOh5Pduz/F4wknuBXo4cSs7eZzwntRrHxouZIkUUOefxf+8n3s27f9mO7p4BxdJxDpBD79kQJQclAURYm6B3NzC9m27WPO7nibt40v4VX7/pbt7ioirX1zzJjXnCx7/B5DkvUqvIoLj+pmq3kROhqjtaLjZtGxWu2MHj2Z9evfRwl5AaKWJsMIJ34ohBt3hC1VA39xRI4KRemeH2upMASoIT+2xk9wu5MJ+IrxNH/WvQ8buSPGkZGRd8yFrq7rFBXtwJ42EvvUq9G9bfiLl+Ov3ARBDylJaTR7uu9B6UnyiGQjh2vvhcWfzaxiUpx4AMMwyMzMIyMj/AtTZ2cbVVWl1NZuQCgageyF4dZ8vURwpO5h9Jn1iv8zqWqfeZEs6Uhbv/Bz7C6hYwj07lhCrfszs6cMJ77gLPbuXY/P5yUUCtDV1UF+/igSE0+chepYMGzYMOrr69m/v6e7UGtrK1u3bmX69OnH9FolJbtoam5CCTcsRFFUhDDYbD4Pr+Ii3miiQzkg7EJRaFayaLZksV3MJ0/fCLx9TPc1EDIGUDIYUgBKDhlFURg7diqbN69hpv9tPrJcHpMFZwp1kt8n7s9EcfqlGOrxKTB9ppGmlzE7+CZdSjwrLV9Ex0SmXkJqRuZxjTEKhcJdZMxWO0QTHBRQBcIARP9fGP3tKSz6erqGRFzJEEms0BBCkJGRQ3p6Dj6fB4+nk4SE5Gih62NNRUURfr+X3MmXUr/z3/gqN6OpKlkZuWRnD8Nms7Nq1ZsYihkhYsWfQriOH4BihAiUfUx78Uo0zYTdHhuf6XK5w0kwqgV/ynQQYdd4+DEpPQkekefR3ZO5d0s/0etZG0aPOAyLvPB+IrUCdUNgNqlhN7sRqTmokD79c6jolJduwuFOwzCguKqCaQnJp1ys2rRp02hsbKStrS06tnfvXjIzM8nKyjom1/D7vTQ3N1CTtISiYB4WfwNxRiNBrFRpowBoVg9+rZBipVYdftA5EsmJRApAyWERLqkxg8CWj5gVfJNN5gsIKjYQgmENb2M2vDHzK5LPxWdJHaLdnl64jBamB98jISGZUGs7E0IfUamNxoaH1NRj80U3EG1t4QLS2BN7ypMcYOwTQkTrD/Ya7ePChO7MWAWUiOuzl+hQVCs2m53a2krS0rKx2RzRki7HA6+3i/Ly/SSPWYSndie+ik0UFoYtjRE3YkQAo1n6GLLVSOZzw3YoexOvz0dWVh75+aOwWKwxcz2eTqqry9Az56KbXFg1JSbZI/r0ut9bTErPs4aomzzaXk7p6TQSiQOM9ACOCEW9u7+y6LZa6t31FDNmfpGCOV/EYbfQVrOf7W/+gaamzJO+M8iBmEwm5s2bxzvvvIOu69Hx9evXs3TpUmw221Ffo6GhBoAuczpmzUU7NhqC2Ye9TlCxDj7pGCFjACWDcepGAUuGDJcrnokTZ5GhNjA/8Aouo4WUjq24vcUx81ocI2mMmzxEuzz9GBn6hAA2JkyYyejCkeTqe8jW92CxWA8r4/dIqK+vQksqxGSL6441I1oSBkCv3oxe9iG6t7VXZ4++bdBiXsS2QtNUBYtJxWpWUcd9idbWRpqa6o7rfQEUFe1AtTiwpxXSuO1tcnNHkJMzPCaGLCIswvvuuSnDEBitZbi2/g6x5+XuLPoFjBw5sY/4A7BYbMTFJWCtW4+1bU94UOjga0GEwklTardF0NxL/ClEnlH4FRF6kSLYJrVH/EVqCkK3JbBXEe5AyOguVi3wBgy8IQiEDOzJOSiKitfbdZye8vHF7XYzderUmDG/38/GjRuPMg4uTHx8IlarjVH1z5HTtQ67pvdqh3iIezTqmR1446j3cqiEM+uP7iU5vZEWQMkRkZCQzLRp89m+fRNz2l+isakj5nhAc1KWeoGM+ztGWEUXWcZ+Rgwfg6aZSE3NZP/+naTrZZhtluPu/m1ra8Y69pyo6NBUJRpjFCr+EKNkOYpiwly5Gj1xLKq3HiNxNCLvXER38eYDiVgQI8fUbiGjqQopuWOp3x+uRXg8LVKhUJDm5gZAULHyr8TFJTBs2Kj+9wrYi19BlJoQFjdY4gEFtb0Y4Ypn0qSzBo2hM5lMTJ48hz17PsUoewVREw+BjnA2tNmFd/gXCDmzu+MC6S6nE1sUumdTEQtqREQr3d1RYuMG9e6We5G4xUjWcFCHYS3hTOUGUypCGKec9a83I0aMoKamhqqqquhYZWUlJSUlDB9+dK7X+PhEZs5cSHl5EVRsIsP7GVXaKErVMXSoyYMvAIwKfUKS2Tv4RInkBCEFoOSIsdudTJkyhzfffANV6DHHylKXynp/xwhF6EwPvEsAa7R9l9VqD3eq8HQixPFtpB5xfyo2d9TqF7VKaQpG7ZbufYbLcZiaw9mQqrcBf85ChAiXJzGpSszvA70TRpTuJAWTFrZk+UMGKXEu6uurBhSQxwKTyczs2Yvo6uogEPCRnJzRbzKNxWJjxoxz8Hg68ft9+Hxe/H4vwWCQjNFTSE/PPuQ9aprG2LHTcLvL8Pt92Gz5WK02ysuL0PY+Q2fuUkLJYcu5qoYtf5G8kEjCSXSt3q5zJfaYIQShoIjpLNJdsQeTFo4tBPAOO4/6Nc9jszn6xCyeSiiKwqxZs3jzzTfx+/3R8c2bN5ORkYHDcXT/HmmaiYKCMWRk5FJdXYajbjfDAp/RqqRSax1HnZGKV4kniKXfX3zn5NvYv1/vZ+Xjg0wCkQyGFICSo6K4uASPxxcz1hA3iXbH8SvGekYhBJNCK0mhnslT5mAy9STdJCamRktQHA/q6irxej34/WGrhWa2hwWIqsRYoxJmXkeoo5pQRy3esg2IkB+EgbDEResC6t0ZqRGhpygKavdXjNbtvjRrKhZTuIuF3aIRKFxMoPIvtLY2kph4/OJIDzXG0OmMx+k8NvXlFEUhO3tYzFhiYgr79m1HlL+O39uAN3sRGlp3oE44azqcJt3TKaQ3QggMuotuI9D1HrFudNcBNITA1K0SzSaF/e55uP0hnOmFNO7/mJqa8lO6NIzNZmPmzJmsWdNTgzEYDLJx40bOOeecY/KLhN3upLBwHAUFY2hurqe2toKEplWMifw8aybc7kT+3vW5mOL5z1SN5Sx920DLHnNkDKBkMKQAlBwxHR0dbN26NWZM01QKXM349DLq1bwhdQG7jXrGhD5GoBLETLuaQp2aj1dxoQAh5eTPTC7UPyVP38PoMVNwu3vi/IQQdHS0ApCYeGguqMMhFAqyd+9nCEVFUc3Ej70Qc1IOukGMG1JRwByXgjkuBcOYiG3Y2fh8foKKjaAhIARqxUoUXyPC7MLIXQQmK6oiUNQe12VvEagqCnaLRlxqJi2J6ezdu43p08855nXdTjZUVWPUqEm4XPEUFX2M6qvHW3A5htkBqggLQMJZH6raO2O455taUSDYXRcwInZ6spVFtA4jgKnbFawbgpTCmfiaStm3dwMuVzxxcQkn8M6PLbm5uQwbNozS0tLoWHV1NaWlpce0S4iqqqSkZJCSkkEwGOj+ZcmD1+uhtHQP3879kD/VL452dbGoxuCLSiQnkNP7X1TJcUMIwcaNG2Oy7gBmzZpFa2st8W1v0qhmsdM0hzY17aivZxFesvQiOpVEWtT0gzZhj+4l8CY2esXcGEWMY330ba06jJ2mOXSpCUe9v+NFh5KEgD6WvsbGGtrbWwBISzv8bMSD4fF0UltbgWEY5F5wG5o9npChENQFqhKJSushJlhcs4LFDLqBCmiNn0DlSuz2eDo62ghWbSMUPwqBggh6EUEfqhEAPQgiBMIIC5Wgj5C3FQgLmebmd3C7E7FYLFitVmw2GzabDYfDgcPhwGaznXLlS/ojbBkswOGIY+fOTzDteIRA2iyMjJkIsyPcNg4F0Z1dE3n0Id3oThgJizqXNdwarsMbCtulQiEIdWGEPKghD0L3EtK9BAwPbboHJehB97UjhMGePVuZMWPBkD2DY8G0adOora3F5+vxTmzevJnMzMxjkhV8IGazBbPZAiQAYBg65eVFuMzT6NKS0BSDcf41xMW5j/m1B0JaACWDIQWg5IgoKSmhri42Q3PEiBEMG1aAEMNobq6nuHg3KZ6XqVJHsMc087CFliIMBAqJoo7pgXexE85QNFDZa5rBPtPBC71uNi8m1ajEIToY7WigszNcJ2zMmCnouo61vIis4D9ZYbqCdvXkLIBbr+WzU8xBKV9HXJyblJRMoKcciM1mP6bWGp/PwyefrMIwDKxpozEs7mgwkNZdky7SrcIQPa3ehBAEPe10NVbQ1VSNv7WWYEcDwdZqjFAQiAjYLqiv7//iByEQaKe1tX3A45qm4XK5iI+PJz4+HrfbTWJiIi6X65RseZaYmMKMGQuoqNhPTc0a9PoN6OkzCaXPRrE4wnUXI234uhHBLjRvI5ZQM2ajma6WOlRvMyLQBUY4w1gAevdLVTVEt3Axmy04LBbcOcNPaetfBKvVysyZM1m9enV0LBAIsHnzZubOnXvcr5+XN4L6+mou0F9jf6AARVVxGU0MHz7juF87guj+72jOl5zeSAEoOWz8fj9btmyJGXM4HEyZMgUIWzGSk9NJSkqjtrYCS+kesgNFNCkZVGqjqdYKCfVTD8smOglhxiq85Os7yNV3AwomgiTEuxk79iwCAR9btnzEmNDHlGljCSgDx241aTk0dbes2xyCFHMlc4Kv09nZTmHhODTNxO7dWxgV2sQmy4XH7Pkca3QivWd7hExaWhZOZ1z3+LGzfJWXF6GYHWQs+B7C5CBkCFTR3ftXCEKtFaBZECYnHY3VeOpL8DaU4m0sJ+TtGPwCxwld12lra4spBgzhrNvExESSk5NJSUkhNTX1uFiAjgdWq40RI8aTl1dIRUUx1dXr0Go/RmTMwIgvwPA0Mjy0m4oOgc1XSzAYEXkKut1BksOFIzUJszkTi6VH6IVf1mPeTeVkIycnh9zcXCoqKqJjZWVlFBQUkJmZeVyvraoaEybMpKqqBHNbNZ1dHWRn5Z9QC6BEMhhSAEoOmy1bthAIBGLGZsyYgdkc65ZVFIXMzDzS0rJpbKylrq6S5JaVTAitpk1Jo1VNZb9pCgYa44MfkWPsi55rMpnJyMlF0zRUVSMnZzhtbc3s3fsZiqKyVZt/UPHXH41aDtvFXKj8CCEMqqpKaVSz2GY++8gfxjFAEQYpRiVu0Uic0UycaMYlWgCVECbMBMjKyic5OT3mvIgAPKZ7UcJFhgOGgmIIfOXrIeRFtThp2fkB3rYm/H6dQODEZTMeDaFQiIaGBhoaGqJjbreb9PR0MjIySE9PP+ljCy0WG4WF48jNLaSyspjq6g3o1WtRFJU2h5MMhwtH0jAcDhdOpwu73XncOqacakyfPp3a2lqCwWB0bNOmTVx00UXH/XN3OFyMHDkRCP+Coqoqfr9vkLOOHdIFLBmMk/tfPslJR0NDAyUlJTFjubm5ZGcPHIemaRrp6dmkp2fj9/ui8Wv19dtIMaqwCQ8Ok05B4YTuLNewBTFiodB1nb17P6OurhKTyUxQaKQaFYiQQr2Wh09xHfL+S7SJxIsmqNpNo5LFOvOlQ5qoogida+P+Q2NjLUEsJMc7cTrjcDjSAQVdD6GqGtnZ+SdkP3l5I6mtraRj5QOYC5fQtOU1fL4Qfr/eT5ePw8NqtUbj9axWK15vBz6fByEEdrst+uXodMaRlzcCwzAwDANd1wmFQgQCAQKBAH6/H5/Ph9frxev1HnbB2oilcO/evaiqSnp6OtnZ2eTk5GC324/qHo8nFouV4cPHkpc3gkDAj93uiLEKS/pit9uZOnUqH3/8cXSss7OTXbt2MXHixBO2j6GwtkoBKBkMKQAlh4xhGGzatClmzGQyMW3atAHPObCGm9VqIyMjN9paLF40k5aWRWHh+H47J4RCIbZt20BHRys6GhZVZXheLkmtTWS2l0AIqtRCtpjPQygaCMEdozbz9O44qtQRoKj8ZnYFP9gQrp+HorDNdA4eJY4KbUwf8Rczt9cY0Gf8aLGLDiYGV9HUVM/48TNITk4f8kQGk8lMamo+u3btwF/+wmF/CahmK9aETNRgK5rhxeVykZaWwYgRY7qD5GMJdxwwUBSVVave6B7zkZ2d1e/8AzEMA6/XS2dnJx0dHbS3t9PW1kZra2tMAsDBzq+pqaGmpoZNmzaRmppKXl4eeXl5J62r2GQyx5QDkhyc4cOHU1xcTGNjY3Rs586dFBQU4HId+i+PpxrhOoBHEwMoOd2RAlByyOzfv5/W1taYsYkTJw5YYDUUCvLRR+8AkJ6eQ1xcAjabneLiXXi9XSiKwvjxM0lOTsPv91FSsof6+koMI5zRqKoaoVCw230jyErPpLBwXFQYBIMBGhpqUIq2owShWU1npvlTtm3zMA04x7WJNwOLgR5hpwodAewz9QRjRwRe7/e9xd4PNuTym9kVRy8EhUGSqCVdLyPNKCdeNKOqGuMnzCQpaej6JQshaG5upri4mLKyshh32cFQFIX4+HggiKoKkmZ9DVfOeLr2r8az912mTFmI25006BqKokXj1wBGjpxwyAJHVVWcTidOp5P09FgXudfrpbm5maamJhobG2lqaiIUCh10vYi7ePPmzWRlZVFQUEB2dvYpmUgiCaMoCjNmzOCdd97p6aFsGGzevJlzzjlniHcnkQwdUgBKDomurq4+Nf8cDgejRvVtmxVB03p+vOrqKqNdHdqVJIYnu2hqqqOsbC9VVSW0tjaiqiqlYjgW/LiMVmx0oaFjNlsYPXpynxg4s9lCVlY+FosFfcdmskUxcUmZjB49BUVR2Lt3G+cEXuaTT9yEQkE+Fwyg6yH82NlkOZ9mNQuAH67P5idjtyGEICEhXFOvtwiM/H9EAB4oGH+wPockUUOmXky6UYpJBAkpFkKYCWEhpJjRMZFk1GDDi9lsISkljeTkYSQmpg6ZNScYDFJWVsa+ffv6CPuBiI+PJzMzk4yMDFJTU1FVla1b19EVCJGQMQzVZKK9bBV2u7PfGMX9+3eiaRr5+aNirJ1ms4WpU+fhcsUfs/g1u91OdnZ2NDzBMAyam5upq6ujpqaGxsbGAd3HQoj/z955h7dxXln/N4PO3nuvEkWJ6lazLFnuvaQ4zY6zcXqcuun54iRO32STbLKpGyfObrpL4irZki2rd1ESSVHsvXcSJMrMfH8MBsAAoLrEYhw9eAi8094ZQIODc+89l46ODjo6OrBarRQWFlJYWEhk5NztlPFmRnx8PMXFxZw5c8Y71tHRQVdX1xUvCJkphEPAYZwLYQIYxnnh6NGjQcqQ3W7nueeeY8OGDSQmBpsRC4JARcVa6upOYrePM0QSPYZcGo1LODI6QbqhkesiupictNMp5GKV7WQrdQiCQFx8EjExqURHxxEbm3jWhO2kpHRWrbgWk8mMxeIL261YsYHOzhbGx0d1FZDd3e2sHXmOQTENBYFYuZ/jx9XWUd1iHidN1zIlRAURvVCQJIn3Rj3DwEAPZrOFpNQ0zGYrkuT2PtxuB5I0QWRkGsnJGcTExM1oqHdiYoIzZ87Q0NBwTrVPEARSUlLIysoiMzNTR4BkWaKq6gjj9gkyr/sQpqg4FCDhmofo2fckx47tYfHi1bouG+3tjdqeg3ruxsTEcyWhGvcmkZSUxKJFi3A6nd7esZ2dndNei6mpKaqqqqiuriYrK4sFCxaQlDQ7bYPCmB6LFy+mpaUlqE3crbfeOi8V3jABDONcCBPAMM6J0dFR2tvbQy6z2+1s27aNlJQUNmzYgMWiz+OLi0tk5crrGBjopa2tnrjRwyxUjtFBDk7ByuleJ/HKIBnCMAkJySQnLyUxMfWCVbGoqOAWXYIgkpkZ7PyfkpJJW1s9qRPjKIpMRESOJww9ibm+igzXn8jIyKWtzYIoioiiSIY0hdtt8M7rswey+e6KRk6dOsTY2DCLFq0gMTFtxnP4zoaRkRGqq6tpaWk5Z+FESkoKubm5ZGdnY7FYkCS3p5K7jfz8UiRJoqrqMCNjw2Sue4iIxGzckvqNE51aiHXLR2nf9TuOHt1DWdlyIiKivCbHHR1NTExM7+l3tWA2m8nNzSU3NxdJkujq6qK1tZX29vYgg3NQVcG2tjba2tpITk6mrKyM9PT0Wf2eh+GD2WymoqJCVxAyOjpKQ0MDxcXFMzizMMKYGYQJYBjnxPHjx8+5Tm9vL8888wwlJSUsXbpU94taEASSklJJSkplYmKMgYEeIvu7GRsbJi4uifT05SQkpFw1Ow5RFMnNDRW6jic+Ppnm5lp6ezuQJAlFUStRV3CS1/ZYKMnLIzMzn28uqeWlvcdJMIyxZMlaXZu22YaRkRFOnTpFa2vrWdeLjIykoKCA/Px8r9LndDqoqTlGf383sqySovj4JJqaTjMxOUnepkewJRXg9rQf03qEWGNTyb/h47TtfoLKyn1Bx7qadhjnA4PBQFZWFllZWbhcLlpbW4MKB/zR19fHzp07SUhIoLy8nIyMjDARnAPIz8/nzJkzupSHkydPkpeXF2RjNdehcGmFHGEBcP4jTADDOCt6e3vp6OjQjWlecYFQFIXa2loaGxtZuXIleXl5QetERkZ7bT7cbhcGg3FWfXEajSaKisopKir3jimKgsMxRVtbPQ3NdZxpbiHCKJFiMVFevu6K+PFdDoyPj3Py5EldT9RQyMjIoLi4OKSaZbeP09vbQVRcMmvv+gCvPPktTpw4gMlkomDLR7AmZCHLCqIAiqB2m9X2YImIpujGD+MYaMaAhNmgYBQUWk/spK+jgdbWerKzC2fV+w9gMpm8OX/Dw8PU1dXR3NwcsoBkcHCQN954g8TERCoqKoIKUcKYXRBFkeXLl7Njxw7vmMPhoKamhiVLlszgzC4/1Ar7S6gCDseA5z3CBDCMaaEoSpD6Z7FYuPPOO7Hb7ezevZvR0eBQnsvlYt++fZw6dYprr72W2NjQ7vdzxcpCENSWa8XFi8nOLqKtrR6320VRUfl5WZVcbTidTqqqqjhz5gyyHLoBvcFgoKCggNLSUqKjpyewcXGJFBcvpq7uJFX7t5JStAJzdCpxecsRbXFIft6AGgkURQHRw+mMRjO2jAWYjAIRZrU/bUrhMqr+9VOamk4zNTVJcfGiWetnFxcXx6pVq6ioqKChoYHa2lomJyeD1hsYGGDHjh1kZGSwdOnSaT/zYcw8NN9H/x+2p0+fpri4eFb7QIYRxuVGmACGMS06OjoYGBjQjZWXl2MymYiNjeX222+npaWFQ4cOhUygHxsb48UXXyQjI4O1a9diNs8+snSh0IjgbIQsyzQ2NnLixAldors/TCYTJSUllJaWBuVrToeMjFwMBgP19ZW43S6iUguJzVuJQRRQ+Z+CiIAieHwfwWPjo7dZVABJVhAFgfK7P4F16685c+YkDsckZWXLdVXjsw1ms5mFCxdSUlJCS0sL1dXVjI0Ft77r7Oykq6uL4uJiFi9ePC8+8/MRFRUVdHZ2elUuSZI4deoUq1atmuGZXT6Ei0DCOBdm58/uMGYcsixz4sQJ3VhUVBRFRUW6sdzcXO677z4WLFgwbSivs7OTp59+mhMnTkyrSIVxaRgcHOSVV17h0KFDIcmf0Whk0aJF3HXXXSxZsuS8yZ+G1NQs1q69gYULl+Ma7qDx5R8yNTHiCTOp6xhEAaNBxGgQMIhq/2ABNSwsyQpuSUZWQPZssPC2D7Hh/o8xOjbCgQOvMTIydMnX4UpDU05vu+021qxZE9JIWFEUzpw5w/PPP09TU1M4lDYLERsbS0FBgW6soaGB8fHxGZrR5YdyGR5hzG+ECWAYIdHa2srIyIhubMmSJSHtEkRRZNmyZdx7773T5kApikJVVRXPPvtsUE5hGBcPt9vN0aNH2bZtG4ODg0HLRVGkpKSEO++8kyVLllySIiWKBlJSMqioWIskSTS88D1GO2u9YWCf+gcCgvrX81xdrnWG8S1PzlnIuns+hNvt4vjxPXR0NF/0/K4mRFEkPz+f22+/nZUrV4Yk1A6Hg/379/P666/PK2IxX1BeXq5r0aYoCidPnpzBGYURxtVFmACGEQRZljl16pRuLC4ujpycnLNuZ7FYuP7667npppumNcx1OBy88cYbvPTSS+EvxUtET08PL730ErW1tSFVpqysLG677TZWrFhxWdua2WyRLF++noiIaNre+C2tr/+K/jN7cDvsXtUglBisqn8gy6oKKMtqm8CM/IXc8t6vYDJbqa8/xenTxy7bXK80RFGkuLiYO++8k7KyspA/kLq7u3nxxRc5c+ZMWA2cRYiIiAiKaLS0tITMa56L0IpALuURxvxGmACGEYSWlpag/KbFixefd7VmYmIid911F6tWrZrW2mV4eJjnnnuOvXv3nrM9Vxh6SJLE0aNH2bFjR0gSHRMTw+bNm7n22mvPWuBxKbBYbCxZcg0LFizF6h6l99g/6T7+gvdLw1/50+D9YsGjBnrHID4lk7s/+j1iE9MZGBqgvr7qgr6AHI4pGhqqaWiovoxnef4wmUxUVFRw++23ezuP+EOSJI4cOcKOHTuYmJiYgRmGEQplZWW6e5QWqZgXUHx5gBfzCMeA5z/CBDAMHWRZDroBxsfHh/xSOxeKioq4//77KSwsnHadlpYWnnrqKU6fPn3B+38zYmRkhK1bt1JbWxu0TBRFlixZwi233EJaWtoVn4sgCKSmZrFkyTVYLBYMFjUfTlECQsD+hSCeMLCCgiyr3zGCoP61Wq3c86Gvs3TjHXR0NHH69HEk6dw/DoaG+jlwYAddXa20tzcyPj5zCk5UVBQbN27k2muvDdkju7e3l5deeumcnoxhXB1YrdYgE+hQP4DnIsI5gGGcC2ECGIYO7e3tl6T+BUIURVavXs0999wzbfssWZY5duwYzz77LL29vRd1nPkORVFoaGhg69atQbmZAMnJydx6660sWrRIl9d0JSHLEu3tjRw/vheHYwpTtPr++hM/UfAQQd25aAqg4n0uoJJGRYHFa27muns/QF9fFwcPvkZnZ8tZi4dkWTXsfs/HvozFYqWjo+nKnvh5QAu/B4YYQbVJ2rNnD4cOHQrZcSSMq4uFCxcGqYDV1TOjJIcRxtVEmACG4UWo8Ed8fDwZGRmXvG+bzcaNN97Ili1bpvXampycZPv27bzyyishvdberHC73Rw4cICDBw8GEQatAGfLli3ExAS3w7uSaGqqpbGxBjkyhYzVbyUhbxkGUcAgCjryJ3q8AUP9iPCGhD2vBUElhEXlq3j7x79FTuky6upOcvjwTjo7W+jt7aSvr1On8sXGJiCIIh2tLazdchc9PR04naFtcK4mTCYTq1atYvPmzSHVwPr6el555ZVwLuwMw2KxBKmAzc3Ncz5Ufynh30u1kAljbmD2Gm+FcdXR1dWla5EEao7M5ezUkJKSwj333MPp06eprKwMqez09/fz7LPPUlhYyMqVK+dlo/bzxcTEBLt27WJoKNgiJTY2lvXr18+I6fDUlJ2OjmZSym8ibcmNgGoDIyB47GBUM2hB8JlCC57X4PtyEQQ1U1D0UwxFAURRIDo+mc33vZ+KdTdzYPtT1NXpKzSjo+PIyMglKSkNsyWCmkNvkJNThCAI7N+/nciYOAxAbm4J8fGh1eergbS0NG699VYOHz5MS0uLbtnQ0BBbt25l3bp1pKenz9AMwygtLeXMmTPeH1iyLHP69GlWrFgxwzO7eIQ7gYRxLoQJYBhe1NTU6F5HR0eTlZV1RY61YMECioqKOHDgwLT5UA0NDbS0tLB8+fKz5hHOV/T19bFr166Qvn7FxcUsW7bsqoV7A9Hd3Y4oiiQu2Iii4CF8noeIlwCKHsKnKXvac38rGLVziOALF3tIoUYWk9JzuO1dn8TlcuB2y8iyRHdrHaePvE5tbSVnzqjEcPlt72Wk9iDLlq3DmpKNc3KU9sbTnDixn5SUTDIycomOjpuRHxRms5m1a9eSlpbG4cOHdUqu0+lk586dLF26lNLS0lnXGu/NAJvNRkFBAXV1dd6xxsbGsJl3GPMaYQIYBqAaCQfm3y1cuPCKflkajUbWr19PRUUFu3btClIfQQ1/Hjx4kKqqKtavX09iYuIVm89sQlNTEwcPHgxSSI1GI9dcc805LXmuNIxGkzo30eglc6LgMYAWBYyiviUcgF/XOH2OIP6kUIXaJ9qAwWDw7EPAIFpxm1T7mLzSpeQvWMbIQA+nj+0iIjqexJR0klLv9oahDaLA6s0yNcf3sHfbP+g9vhdRNGAymXG5HKSmZlFcfPH5rRcKQRAoKCggMTExqI2ioigcO3aMsbExVqxY8aZWvWcKCxYsoL6+3qt8ud1u6urqWLRo0QzP7OJwqYUcYf1v/iNMAMMACKrCtVqt5OXlXZVjR0VFceutt9LR0cH+/ftxOp1B60xMTLBt2zZSU1PZsGHDvP1VriWgB3ZhAVWR3bhx41XP9QsFURRRFBlBdiMKRg/hUsmfwUP+DKJP5VMUEARf1xBvqFdQQ8eKolBz+HVaTh/DaDLRVneSgvJV3PiWDyKIHkMZD0mURcFbTRyflMo1N7zFYzDtCx+DJ4QlCCxYtoHixWvp6Wymo6kWx9QkkuSmav82oqPjSE+/umQ6NjaWm266if3799Pe3q5bVl9fj91uZ926dZhMc6NX9nxBVFQU2dnZuohEXV0dCxYsmDGl/VIQbgUXxrkQ/pkZBna7PSgMW1JSctVvepmZmdx7770sWrRoWlWmp6eHp59+mmPHjs27tnKyLHPkyJGQ5C89PZ2bbrppVpA/gP7+bqJSCjGZrd7Wb5rqphE7LRys++v/8KwPsPu5J9j/8p9wjA5gcDmIioqmr7PZezxtXaNBxGQQMBkEz3F944HH1+UbigZSMgtYuuFWVm+5j/U3v434lAwGBnpm4OqpBSIbNmxg8eLgvtKdnZ3s2LFj2n7OYVw5LFy4UPd6cnKStra2GZpNGGFcWYQJYBjU1dXpEn4NBkNI+4qrAc3L7r777pu2+lhRFE6fPs3TTz89b/zUZFlm//79uhwkDSUlJWzcuHHWqJ7t7U0MDfWRkL8Mo0EN9/oTMK0K2L8S2H+ZqhCqyzxN5GiuOghAdnYhoigwPj5KUfk1PjKHtj+8RSYmT99hk99Ddwwtn9CPEGoFJ4M9bQz1dpKScuH+lpcLgiBQXl7O+vXrg35sDQ4O8uqrr2K322dodm9OJCQkkJycrBubrtPObEe4E0gY50KYAL7JIUkSDQ0NurH8/PyQvU2vJsxmM9dddx233XbbtN0sND+1F154YU63b5Ikid27dwdViAIsX758VuWE9fV10tBQRfqi68hcuJYYmxGz0aPKGUUP8fJ/6Mmfv1ooCmAU1e1ufc9niYyMobJyH729XVSsv5XV19/tKxDxUxC1Y2j7C3z4SKivEMXopxoaDQI1R3ZiNltJSrryhtnnQk5ODps3bw4i+KOjo7z66qthm5irjNLSUt3rwcFBBgYGZmg2F4+wEXQY50I4B/BNjtbW1qBQU0lJyQzNJhixsbHccccdNDc3c/jwYVwuV9A6o6OjvPDCC2RlZbF27dpp28/NRmjkr7OzUzcuiiJr166d8WIPfzgcU5xpOE1S/lKK196L0SBiMxvUXD/Bl3cnq6l3fmSQoIIQUImZyagqiDmFpbzrsz+kp60eg8FIalY+JqNBF0r27y4CWsGIoPui0kQLRfH0G/b3NRNAkdU5iqKoFpjMEmKdnJzMjTfeyGuvvaZT/SYmJti+fTtbtmwhKipqBmf45kFmZiaRkZE6H8C6urppjezDCGOuYnbc/cKYMdTX1+tep6amzoiv3LmQl5fHfffdd1abjPb2dp566ilOnTp1lWd3cZAkiT179gSRP4PBwMaNG2cV+WtoqObo0V2IBiNlmx7AZjEQYTFgMgpYzQYsJtGjsInesLBWGGIQtTCx4FELRW/41qviiQJmo4Hs/FIycwuxmg2esK7op+qhU/9E0ZdHqFcHCc45FPGGk0VBICO3hMnJCbq728998lcJMTEx3HjjjUGKt91uD/cQvooQRTEoBaa1tZWpqakZmtHFIWwEHca5ECaAb2IMDw/T39+vGwt0xJ9NEEWR5cuXc++995KSkhJyHVmWOXnyJE8//XQQsZpN0HL+Ojo6dONGo5HNmzfPqCmwLEtMTfk6sYyPj9Le3khyfgWr7/oIsTHRRFqMRFgM2LxETQ3l6smff9hXwCiqJNFsVJU/k9+6RoM6ZjaK3ofJELyeKOhJYPBzv2KUgAIU/1zEokUrWLB0HbW1x6mpOcbY2PCsyHmKiIjghhtuCPoRNjExwY4dO8Idcq4SCgoKdOqwLMs0Nc18i8ELgcIlEsCZPoEwrjjCBPBNjMDcP5vNRmbmzCXFny8sFgtbtmzhpptuCtliC8DhcLBz505efvnlWZdDpSgKhw8fDipg0chfYBL6lYYkSYyODtPT047b7aK5+QwHDmyntbUeh2OKrq4WTCYzS298N8npOUSYDdjMIlaTiMWj6GkKnab06dQ93UP0VPKKPsLoTxCNfiqhUdTtz7cuquWMtyjE/7mAv7G0P/nzGU2DyWTk5rc+ws1vfYSRsWGOHt3N/v3bOXPmBAMDPcjyzPXotVqtbNmyhbi4ON34+Pg4r7/+ekibpDAuL6xWK9nZ2bqxhoaGWfEj4XyhXIZ/YcxvzJ1kqTAuKyRJorm5WTcW+Kt3tiMxMZG7776buro6jh07FtQnF9RWW8899xy5ubmsXr16VuQHnjx5Moh8GwwGNm3adNXzjEZHhzhx4gCS5A5a1tR0mqYm1R8yf+n12CwmLCZfSFbr8iErCoIiIChqXp72CfKFZX3EzKB5+uFrDeefKxi4nn/7OAjdnkpRAMGnWHj7C3vMA9VXglcREQS8X24Ll62jePFqulvraKg5Ts2R3XR1tWI2W1i58jpMppmpvLZYLGzevJkdO3YwMjLiHR8eHuaNN95g8+bNc9Kbbi6hqKhIV5g1NjZGX1/ftNGHMMKYa5j5b8MwZgTt7e1BSkJBQcEMzebSUFxcTGFhIYcOHaKxsTHkOi0tLbS1tXnbbc0UGhoaqKqq0o2JosjGjRuvivI3Pj5KZ2cz4+MjTE1N4nI5ScspZv0tb6ez+Qy9nc2ULFlLZmEZg70djI0MgWAgOasQo8ngtVrR7FUAJFklVqICigi+8gyff59/eBb0reBCFYvoCaBv/oqiz//UijvU55qpjIAiqJ1HRBQUQX2hjSmKemw8xtRGo5HM/IVk5i9k420P0NfZwp//+xv093eRnp575d6Mc8BqtbJ58+agSuC+vj7279/PunXrwm3jriCSk5OJjo5mbGzMO9bY2DhnCGDYCDqMcyFMAN+kCCRKqampc7rKUBRFrrnmGhYvXszu3btD2jbIsszRo0epqalh3bp1V/1G3t3dzaFDh3RjgiCwbt060tKunB2J2+2is7OFkZEBBgf7iIxJILtoMdFxScQlppC/cBlGk5nUrHxAq6CFpLQcEtPUQhTNSsXfTw+0PCPfN4U/IfF59/lCsGo1LzoCqZE9jfz5ijy0/UxPchQUND9wXc6T5/iy51y05wZRVSwVTSIU9N9yiiKQnJFHVkEZPT2dM0oAQU3L2Lx5M6+88oquCKG1tZWoqCgqKipmcHbzG4IgUFhYyPHjx71jbW1trFixYk50aQkTwDDOhTABfBPCbrfT06PvgDBX1b9AREREcNNNN9Hd3c2+fftCVu5NTk6yfft2kpOT2bBhA1ar9YrPa2xsjN27dweFMFesWBGUa3S5cebMCQYH+0jKyGfTxjspWnwNZpMxiMhpEAQQtLgqeP30/NU7PTTFT7/cX+XT+vxq6qFveWiSOL3ti36uiiKo0qPnuYzibT2HKICsbqEIAoJnmYi6nrqRgKwjsOq2pRXX8OrTv8PhmMRisV3Q9b7ciIqKYtOmTbz66qu43b5QfXV1NbGxsVetZeObEXl5eVRWVur6A7e3t5Ofnz/DMwsjjEvH3En4CuOyoaWlRUdEjEYjWVlZMzijy4+0tDTuvfdeKioqps1r7Ovr45lnnuHQoUNXtK2cy+XijTfeCPIwXLhw4WWvulYUhfHxUZqbazl58iCHDr1OX18XG+9+H3e97/OULl2P0WjU/brXOmX4F1LoijUCDJb9q26ne/iqgfVG0L4QL96Qr3/hhpc0BhBHrXVcoO2Lphhq62vPwUcuBX9y6X8MIRSZVVFYtgJBEOnr67qs78/FIj4+ng0bNgSFfA8ePMjg4OAMzWr+w2azBVXkB+ZOz1aEi0DCOBfCCuCbEIEdJ7Kzs2dFccSVQFlZGSUlJezfv3/anp719fU0NzezYsWKy66EKorCgQMHgjqVZGVlXXL4TpIkxsdHURQZRVEYGxump6cDu30Mg8FIen4ZybFJpGTlU7BolSc0qujCr16ChP9rH0Hyjvm9ng7+Sp0o+pMw9bko6I8X/DxAMRR8+/LNT32mfTn55/cpilroIaP41kdAUBSfGIiPGGohYLUlneIlxYIAFlsEoijOqqrP9PR0VqxYweHDh71jmpH4zTffPOPde+Yr8vLydJZSPT09TE5OYrPNrDJ8LoRDwGGcC/PzWz+MaTEyMsLQ0JBubL6HkIxGIxs2bGBsbIxdu3bpqio1uN1uDhw4QFVVFRs2bCA+Pv6yHLu2tjaIeMbFxbFmzZpLSuAfGOihsaUe+5jvvRSNJjIKK1i6cDWZhWVYzCavWuZVwbzFFX7qWSjyFSL8eq5qXP/Qruj33Le9T8nzH5v22PgTxMDwsocIKoCIl9zKqCFeL9nFb58BoV51DmoIWZZ9+YCKAo6pSSTJjcVy5dMDLgTFxcUMDw/rDNwnJibYt28f1113Xbgo5AogMzMTo9HoDb8rikJra+uMFpOFEcblQJgAvskQ6D1ns9nmTFXbpSI6OprbbruN9vZ2Dhw4ENJPbXx8nJdffpm0tDTWr18f1J/1QjAwMKBLIAe1x/G11157yUnk7e1NmMwWNr/z8whGCwgGzBHRmM0WT1cOMag7hskoeos4NDVOLcDQQqT6cKkkK0H5d174k0H/YXz70KuKgk7N8y/w8H8eSDj9VUhA105Os3TBY/USWO3rrwJqcobge+p9rs7BQwI940YDmMwWens7SU7OmFXEavny5YyMjNDX1+cd6+rqoqamhrKyshmc2fyEliLjH/qdCwQwrACGcS6EcwDfZAgkgNnZ2XPK++9yICsri3vvvZeysrJpv9i7u7t5+umnOX78+EXlB7pcLvbu3RuklK1du/ayVFtbLFZEk5W41Fwi41OJiE3CYrFiNKhdNPTGyfocPm/3DVH05un5vP3w/jX7deYweR7+z/3H/NczBnT4MBpEv2P65mLyaxtn9Hbu8OUGagTVWyXsJajqw5cTyLS5iF4l0e/aCVouIPocQFH0vTZbbNz+jg8zONRHff2pWRUKNhgMrF+/Pqh46cSJEyGr38O4dOTm6qvB+/v7Z31rPuUyPMKY33hzffO/yTEyMhKUizab+s1eTYiiSEVFBffdd9+0bdcURaGmpoZnnnlm2vzB6XDkyJGgDiRlZWVkZGRc9Jz9IYoiktvlvUlrZMts9LRV8yNWgb10fYTQV/Dhb8As+hEv/+eB60z30K2vhZpFH8HyL8bwKpL+xNTbWSQgP1AjbYQidYEhY18xSSD8SZ//uvp9CRQsWMqWux+ks7OFtraGoP3MJGw2G+vWrdONKYrC3r17g4qNwrh0pKamBqn2F3pPCCOM2YYwAXwTIfCGZbPZrnrnidkGs9nMpk2buOWWW4iOjg65jtPpZPfu3bz44otBBDoU2tragvqGJiUlsXjx4ssyZ0VRmJgYIzoh1Vshq6vA9Vbx6lU3o+inDHrWOZuC5h8+1lXuCv7mzb6HP2nUCF+o9XxKn68q2JuX6Kfa+ZRAn2IHoQicnrz5xn0P/fXzN43235dflbBnm8Wrr+OazXfR1HSanp72s74nVxupqaksWrRINzY+Ph6UdhDGpcNgMAQ5JbS3T/95mA1QFOWSH2HMb4QJ4JsIgTes7OzsWZXbNJOIj4/njjvuYM2aNdNWRI+MjPDCCy+we/dunR+bPxwOR5DZs8lkYu3atZcl1O5wTFJZuZ/R0SEyCsoRBJ+a5+ubKwb89SN/0yiCQeTPQ+D8iZtGkHRE7mxEz28dXwHIhRE/X+hXT+ZCETsNIQmh569G/rxhLr98wOkKUtbeeA+LVmzgzJmTDA31EYixsWEOHNjB6OjwJb+/F4ry8nISExN1Y/X19XR3d1/1ucx3BPp19vX1MTk5OUOzOTe8puiX8AhjfiNMAN8kmJiYCKr+nW/ef5cD+fn53H///ZSUlExLjtva2njqqaeCWroBHD16FIfDoRtbsWLFZeuy0t7exPj4CBvf+gmyFq7x5smZjSIWk4jF76/VJGIzG7CaRKxmUZcHGCpMawhB+vxJnv/DP5Qbcr3pyKAWEvbL4RPPSfx8KqCvotlX2RxYMILf6xvWLOJ//+fn3nFFUbzt4GR5GgLoFxJWz1PkhnvfS05RGVVVRxgfH9G9J01NtTgck9TWHqetrZGRkavnyyeKImvXrg3qC3zw4MFpf6SEcXFIS0sL+nHY0dExQ7M5P4Tz/8I4G8IE8E2CwBuV2Wy+Kr1n5yJEUWTFihXcc889014jWZY5ceIEzzzzjFdt6erqCjKJzczMvKw2O5LkJjopg6TsBYCPOPkTOpNBwGQUdIUgRlHwkkOzUV1u8hRn+J6LvsIMz8Nf9Rvo6+VbX/ksN61bzNLCRLasXsBHH34bB/a87s230+X3eYmiX8hXQEfcNNKnEb+33XUTX//yZ73ETwwig/ocwECSPp2erR1TU/1kRfE+AtWOUKHj7vZGyldtJCU9i5MnDzI1ZQdgZGSQoaE+Vm66C7cs09hYQ21t5VUNn0VHR7Ns2TLd2MTEBCdPnrxqc3gzwGAwBOUL+/sDhhHGXEPYBuZNgsAbVUZGxpuu+vdCYbVaueGGG+jr62Pv3r3Y7fagdaampnjttdeIj48PWm4ymVi1atVlDbNLkoRotOKWZG+Va1AHDr9Qry7c6hfeBH9rFb39igZBEBA9wlJ7WwsP3LWF6JhYPveVb1K6sBy328Wu17fzzS9/mq27jgWTMV0+XoDVjP/ygJCtgBCy7ZymysnISJLk62giCsiy5gnj5/2n+PYvAJKnmFtWFCTZZ/xsFGW625tIy8zBZFJtfxTteIKC2+Xgb7/6DoqisGTlOsbHxzhx4gBxcYkMDfWTmJrFNZvvYsXG22lvruWFJ3/E2NgwMTHxF/DOXhqKiopobW2lt7fXO1ZbW0teXt5l87QMQ/1B559L3d3djSRJQQrsbMCl5vGFcwDnP8IM4E0At9sd1Ps3MzNzhmYz95CcnMzdd9/N8uXLp73RDw0NBYV+ly5detm7BYyNDRMVn6r63AmB4VyC8vy0/EBvQUdAeNZX5OELr/oUOZ8S9tgXPgmCwNMvvcFtd91HYXEJpQsX8f4PP8pTL76OKAqMj43wlX//GNeU57KsOI13338rtdUn0XwHf/KDx7l18xqe/tufWLuslIV5qXzk/Q8yMTaGAHzqo+9n355d/PZXPyM93kp6vJW2lhb27nqDtDgrr23fxk2b1pKTEsOBvXtobmrgve98CxWlOZTmJnHHDevZvXNHUIUw6BU9TfFzywpnqo7y869/jP/7+Tf5y6++h2PK7lMoPdfFOWVHURQSElI4eXQ/OQULiIpLwC1AZuFCbrr/3zAYDZjNJrILFhIZHU919REaG2uYnLw6ViGCILBq1SrdjzpFUTh8+HD4i/wyIiND7wkpSVLQvXW2IJwDGMa5ECaAbwL09vbqvOwEQSAtLW0GZzQ3UVpayn333XdeId3Y2FgKCwsvy3FlWaKycj8nTx5kcnKC1PxyQB/61Yo+dN57BiE08RP9K3p9CqH20IVYgZGhQd547RXe8/AHiIyM1C0TBYG4uHgE4P3vvp/+vh6e+PMz/OvVPZQvWca733I7o8ODXsPmluZGtr34HE/++Rme/MvT7N+7i5/95AeIIjz+3R+ycvUa3v3Q+zhZ28LJMy1kZWd7ids3vvYlvvy1b7L7YCWLFi/GPm5ny00389dnXmTr6wfYdP2NvO/db6GzQ1VogtvM+aqDZUXBLSm0N9UyNWUnO7uQno4WXnv+r+q2fttFx8ZjtlixWm2kp2VTdXQPq667nXd//Ovc+a6PkZqV5z2eyWjgzoc+ReHia+jp66S2ttLzHsr09nYyMTF2WT4ToRATExNkBN3f3x/U+jGMi4fFYgkquunqmh39osMI40IRJoBvAgTeoJKSki6pw8WbGUajkbVr13LXXXeRkJAw7XojIyPs3bv3spjFSpLE8HA/bgSSMgvILCjDaFD73noJn5b7ZxA9RNBXdevLzQsgfX7qnxDw8BZjCAKtzY0oikJhcalOIfRXCQ/s2cmZmir++7f/x9JlKygsLOKr3/gOMTGxvPT8s94wsyzL/OS/f0NZ+SLWrt/AW97+Tna/oeYQxsbGYTabiYiIIC09zZN0b/ASwC98+f+xecsNFBQWkpCQSPmSJTz08AcoW1ROYWERn//K18nNzefVrS8EFIX45yWqg1o18NKNdxGfnEl/fzcpKRmcOXUERZZ0qpkgCCxeeS2dnS10dqpkKrtwoWeZdgQfktOy2Hz3Q5QuuQaXy0lHRxMHD+6gpuboFfcTLCsrCyo4qqysDBeEXEYE5gHO1orrsBF0GOdCmAC+CRB4gwqrf5eOyMhIbr75ZjZt2hTUkUFDa2srL7zwAidPnrykL2Cj0YTVakMQRNbf+1EEg9lbGGEyqMUdVrNa8Wszq+RP12XDkxcYSPpEwUfixICHt0BDUHvqgl8RB8HrnzpxjImJcZYtyGJhXrL30dbaTGtzo1eBy87JJTom2ls0kpaWRn9fr7doRIPg90+jV8uWrdRV505MTPCN//dFNq5ZyoK8VIqzE6mvq6Wzvc17PPD99e/+oRFck9nC5rd8CKfTwcBAL/aJMWoqD/Gr732OZ//4M5yOKQTgxnvezfs+8y3e8m+f5dFv/pqIqBjfXP0VRg8jFAVQBAN2+zgNjTXklC4lOS0bWZYu+nNwPjAYDEEFIXa7ndra2it63DcTAgng6OjorOwKMhMh4I6ODt797neTmJhIREQES5cu5ciRI35zUnjsscfIyMjAZrOxadOmkG4KYVwdhItA5jnsdnuQefF0nS/CuHCkp6dz7733Ul1dTU1NTVB/YUmSOHXqFI2NjSxdupScnJzzKgppaKimq6sVo9GEyWRCkmRGBrqoOfw6Jatv8RSABLZ303fwCLRJEf2KP842B22Jdv8vKipGEAQa689gEIWg3r8AiiKTkprG3/+1zbfMc4zY2FivAmcymgIKUQQURUHUFYIEWrqofyMjIz3HVAe+8dUvsGP7Kzz2ze+Rm1+A1Wbj/Q+9A5fLqc8B9MtlFD22Nzazes2cbgEhKY3Nb/kQr/7158QmpPDi3/8Hk9FEfdVR/vizb/LWhz9FbEISyWlZJKRkEtgZUMHXM1n70lQUKFlyDUajibKVm4iOT+b5P/wHU6NX3iImMzOT1NRUXW5adXU1RUVFWCyWK378+Y74+HjMZrPu/3pPTw8FBQUzOKuZx9DQEOvXr2fz5s289NJLpKSk0NDQQFxcnHed73//+/zoRz/i97//PSUlJTz++OPceOON1NbWTmvEH8aVQ5gAznMEJiibTKZwVeAVQFlZGQsWLKCuro6TJ08GteOy2+3s3buXuro6li9fPm34WFEUurpacbmcSJKbkhXXMeVw4Zi0ExGbRH7FdYgC5/D08yh7BJJAX14cTG+krEFbnJCQwHXX38gf/ueXvP+DHyXCS8RUjIwMU7F0GX29PVjMRnJy8zzLfWodnrngyVvUxjXip6l/FrMZWZK8ZBV8YQrRoxJqJGv/vj28410Pcvtdd6MoMD4+RntrC8L6jd5jaPPUeg8LgoIogEtSkEUwGQVckkhe6VLueO/neOEPPyA2NoHy8pVMTU1y6tQhfv+Tr1G+Yj2ZeSUUli1DEPSBE3/S54/UrEKSMwtRFIXRoT46m09TWKjv3HElIAgCy5Yt4+WXX/aOud1uqqurg9TBMC4coiiSmpqqqwaejQTwalcBf+973yM7O5snnnjCO+afL60oCj/+8Y/58pe/zH333QfAH/7wB1JTU/nTn/7EBz/4wYueaxgXh3AIeJ4jkACmpqaG7V+uEERRpLS0lDvvvJOioqKQKltfXx9bt27lwIEDIbsIjIwMUld30tt2zBqfxeJNb2fFrQ+zcN2dWKw2XdWv6Cnw8BolT1fc4Wf47AsF682VQ5o3ex7f/Y+fIMsyt9+4gRefe4bmxnrqzpzmd7/+OXfdfB2bNt/AytVrePhdb+P17a/Q3trC4UP7+O7jX6Py2FFf+BVNifMQQ78cOgHIycnlyOGDtLa2MDgwoH4JCXoyp73MLyjkheee5eSJSqpOneDD738IWZG9ip/oiVf7rgfe3EjveyZodjmQkl1EcmYBoGAwGImMjGb58g3ExsRz4uAbPPvH/2KwtyvINBr05E9RVPVUklW7GZekULnvVQwGI2lpV8d8PT4+ntzcXN1YXV3drO5cMZeQmpqqe93T0zPrqq2vdg7gv/71L1auXMlb3/pWUlJSWLZsGb/5zW+8y5uamuju7uamm27yjlksFq677jr27t17kWcZxqUgzATmOfr69K2rUlJSZmgmbx5YLBZWrVrFLbfcMu31bmxs5Pnnn6empgZJ8uWFjY0NIxqMFK+4AUEQaD9T6fWv05Qsf+uWQDVPDa+ehfjplunNmgW/scBHXn4+r+7cz/prr+MbX/kCm9ct54H7bmf3G6/x/R/9F6Io8Jd//JO16zfwyY99kLUryvnAw++hra2F1NQUH+HDP8fQf/7qvD/+yU9jMBhYs2IJhbnptLe3+vif54lGAr/13f8gNi6eO266jvc8cB+bt9zIkiXLfMqnd13/6yXolEfwa6EnCixceR3DwwPY7eMAmExmSksrKC2tAMBsMfvlJoaGRv6cboXWxlpee+Y31B7dSUZGLgbD1Qu6LF68OMiy5PTp01ft+PMZgQRwcnKS8fHxGZrNlcXo6KjuEWh3paGxsZFf/OIXFBcXs3XrVj70oQ/x6KOP8uSTTwK+XPTAa5eamjprC2nmOwRltv1sCeOsGB0dJTY2lscf/zFW69k95ux2O//85z91Y7fccks4BHwVoSgK7e3tHDt2bNpEca2TQ0ZGBkND/Zw8eYCVt76PqKRsZEUmMi7VS1r81T+zUSTGZiTCYvC2fLOY1OIPk8FX+RpohwJMS2BChYX9x/xbp3mXB4R6/Z8H7k4jJOcbfvYe1+/42i3Lv6uHb5makyfL6nJZ1jp+gNMtI8mq/YvTLeOSFO9rl2dsasrBn3/yORLjEikuXuw9fl9fF9XVR/j4Yz/HaotCQdEdT52HOjdN9WtpamD7n/4Di9lCYmIaOTlF0/aZvlI4cOAAjY2N3tdGo5G77rornAt4iVAUhWeeeUZHhq655ppzhoGnpib5ylc+ycjICDExMWdd92KhfUf817+qsUVefF7d5MQYH7+rLGj8a1/7Go899ljQuNlsZuXKlTo179FHH+XQoUPs27ePvXv3sn79ejo7O3V56I888ghtbW26lIUwrg7COYDzGIHqn8lkIjY2doZm8+aEIAhkZ2eTkZFBbW0tVVVVQRXBY2NjvPHGG6SkpJCYGI0giFgiY4iKT1G7VkiKN5w5PtxL55kjDHY2sOaWdxFhScUlKZgkGdkoeMiQz//PV6Eq6Aigd34I3ipf7XXwOfieK9OMhyJ8oULg0xHD4PUEvRWL59jq9uoyz1MERdAVYqAICIIah/Weu2d99TooajGLou5XlkH0jJnMZhavvZnD25/Cao0gO7sQh2OS9vZGTGYrZktEEPkLJJuSDGOjY+z8638SYYugomLdjHWKWLRoEU1NTd5r6Xa7qauro7y8fEbmM18gCAIpKSm6PMDe3t5ZlQd4uXIA29radGR1uh8P6enpQT6UCxcu5KmnngJ87hPd3d06Atjb2xukCoZxdRAmgPMY/f39utfJycnh/L8ZgsFgoKysjLy8PE6cOEFTU1PQOr29vfT29pJTWk5kfAaiAPaRAXb+6VvYohIwGE2M9LVhMBgQRQO7//kb7v23LyLJIpKshh1lWSUiGvzDnRoZ8l8GegXvXARuOgRuq192DlLpl+an78kbsJ1XftQIojqmFoZ4Xnv6v2lbih7qqHhC4AoKBjzkTxBQ3DJGg7qmosjIosCy9bcgOR0c2/U8XV0tuFwuLBFR3PXgJ5FBlfrQdxRRX6tKoyQrtNQew+GYYunS9TPaJiwqKoqcnBydGfSZM2dYsGDBVVcj5xuSk5N1BDDwfjvTuFQvP23bmJiY81Ir169fH2Q3dObMGW8uan5+PmlpabzyyiveYiSn08nOnTv53ve+dwkzDeNiEb4DzGME3pCSkpJmaCZhaIiIiGDNmjUUFxdz5MgRBgYGgtZprT3FX37w76y++X6SM7KQXE5sRhGQKCtbTkJCKhMTYxw/vpc9L/0fm+56ELcs4JYEJIOCrOgJnz4/0HccfRhXGwsmiP7rhPpCCbVtqO2nw3Rz8oeXsHmUvEA10LutIoCgIHj++kLW6laaCigKeOxn9PmQBlFAVkSWb7qbtPyFnD6yE4PRzKot9xMTE4PLrXjD6lq41+n2Oz5qSHqwp5WIiKhzpmlcDSxcuFBHAB0OBy0tLZetU82bFYH307GxMRwOx5s2vP6pT32KdevW8e1vf5u3ve1tHDx4kF//+tf8+te/BtT/M5/85Cf59re/TXFxMcXFxXz7298mIiKCd77znTM8+zcnwgRwnsLtdjM0NKQbC2xhFMbMIT4+nry8NCRpggm7gsupT6yeso/zxjN/ICYhCZPgprCwDIvFZzgdExNHcXE5NUfeICUjl4o11+N0K5iMCkbZF9QNRf7OFq49Z+4ewfYQ0+X1nb9+qOJsaoUghCaB3uMI/gKhGub1Kos+4dBTbOJRDQFR9lROKx7yJ2pt4iA9t4SU7GLvft2SjCRrVcOq2udyy0y5ZN35yzIM93Viscw8+QP1sxboC3jmzBkKCgouSOUNQ4+4uDgMBoOuiGtgYICMjIwZnJUPl9rP90K3XbVqFc888wxf/OIX+cY3vkF+fj4//vGPede73uVd53Of+xyTk5N85CMfYWhoiGuuuYZt27aFPQBnCOF44DzF8PBwUDurMAGcGbhcToaGfPmYsixTU3OUnp4Obnrr+3j4az9j+fV3YTCagrYdHexnYGCSAwcOBFUZpqfnkJmZx64X/kRPe5NafOCWcUuKJ/9HXc8//08L1QZW3wYuC24L59ve1wZOCEn+9Ps/+8MfQsAjFD74yPt4x9vu963vUeN8z/33p80twBNR0FRAn0eiViWsttYTMRlFtTrYz2PRrRWMuGWcbrVoxOn2FZA4XDJTThmXJJNRuIShoT4GBnqnOZOri9LSUt3r4eHhkOpzGOcPg8EQ5Oc5OHjljb7PFzPRCeSOO+7g5MmTTE1NUVNTwyOPPKJbLggCjz32GF1dXUxNTbFz585wPuoMIkwA5ykCb0QxMTGYTMEEI4wrC7fbRWXlfk6cOMDUlB2AlpYzDA72UbHmBtwytFXvJzE1kfs+/hgFi1eF3E9HRycvvPAClZWVOpPpgoIyIiKieOUfv0FQXLgkhUmnhBxw8w5U9s5N/LT8wWBi5lvX99i/by8xEWbuufM27/HOR1yajgy2NDcTZTVyovK4jkz+4If/yS9/8zudIuk9n3Mc10f2/Amtr0Wef1cVk6d9nskoYvKYbmsFM5IMbkklg5Lfhdby/2QFKtbdRHpuCW1t9bjdbhoaqmloqD73BblCSE9P93ZS0VBfXz9Ds5k/mM0EMIwwzoVwCHieIvBGNF3niTCuHGRZpqrqCC7JhdFooqenncjIaHp62klIyeDY3q269Vfd/ghRkZCYaGN01InLJQXtr7q6msbGRioqKsjPz0cURRYsWMrRo7v45Xc+S1pmHgWli7j19rswimplqyD6yJ0GL+mbRsHzH/PfLqgy1/Pyj3/4PR/6yMf4wxP/Q3tbKzkBJsT+UBQFSZKCiiPORhi1SK5Wxa4LBwtnVys0Yqj251VpnOiJG4uCWiCCp3OKKICsqGFhb1GNp7jDaFA8lb7qOQiKp9raz2pGkhXPnARKKtax81+/59Ch13G5HCiKQlpaNpGXYM1xsRBFkaKiIiorK71jra2trFixIvzD8BIQeF+dTaqq4vl3KduHMb8RVgDnKcIEcOYxMNDD8HA/N73to2QVL6G5+QxVVUewxcTjdNiJi0tk48bbWb16MwCjHTX0t56moKCYpCQbCYnRqv1LAKampjhw4ADbtm2jv7+fyMhoKirWkhifTFvjaV574e84XDKyongtSzToFL9QCqD/On4hXvCpZ7ruIaLA5KSdp5/6Ox/84Ie49bbb+d8/PundlyjArjdex2Y28Oq2raxfs5rYKBt7du9CURR++B8/oLyslLjoCEqK8vn+d7+DIAiULSgCYN01K4m0GrnlxusRgA++/3088Nb7vHNUFIUf//AHLFu8gNSEKJYsKORHP/iOn4KpdUfRrHHUXD99WNunHmrdU0K12TOKPkVQ+2v0exj8urAIAhQsXIbZYiMjv4R3feLbRETH0tERXP19tZCfn697PyVJorW1dcbmMx8QeF+dmpqaNd1WZiIEHMbcQpgAzkNIksTo6KhuLGz+fPUxNNRHbGIaaXkLyF+4AltkDLGJqYwPD+CamqKoaBGCoBIogNa6SuLjk8nIyEUQBKwW2LTpWvLzQ6tpg4ODvPLKK+zduxej0UJ2dhGSJLH++lswGlSrE9WjTtEVTARSysBwLviRooBtQuX6/ePvf6WkpJTS0lLe+c538eSTv/cqc/740he/wDce/xbHT1SxePESvvrlL/Gj//g+X/zilzlWeYrfP/m/pKSqnVN27d0PwAsvbaWxpZ0//+0fIa/B1776JX78ox/wuS98iQNHT/CbJ54kJSVVn/eI4O1EEnxeIbqkiD4fRX/yp3UL8eYHeomf4EcwBS9pt0ZE8ciXfsqd7/kk8UmpLLnmBrq723E6Q3dSuNKw2WxBBQrNzc0zMpf5gqioqCA7neHh4ZmZTBhhXCDCIeB5iJGRkaBKzbi4uJmZzJsUiqIwZh8nf+FyAAZ7WnE5JkmISyAuKpbs7AJvW7D+/i5Add7Pyyny7iMmJp7U1ExSUzNJT0/lyJEjOBxS0LFaWlpob28nIyMVt9vNtZtvRhB8OWkGWSU2+JE79a+P/PmPh4KO+AWs9ocnnuAd71Qr/W6+5RYmHhnntde2s2XLDR7CpW7w/x57jBtvvBGA0dExfv6zn/KfP/kp737wIQAKCgtZv34DAMlJyQAkJiZ5DWQDFYmxsTF+8fP/4j/+86e8+z0PoSgKBQWFrF23Qc3N81jBqBt7CKHis3FRFT8FZE+AWRRQjf48oXMFNVyMoJpF4+s8IgoCsiJ4Qr4B10pRvETQIPpusYtXX8fBHc/Q2dlCXl7JtNf6SiIvL4+Ojg7v697eXux2OxERETMyn7kOURSJjY3VhX6HhoZ0RsczhatdBRzG3ENYAZyHGBkZ0b2OjIwM5/lcZdjt44yPDJJdVI5BFGg8sZ+EhGSKisrJyyvxkj+7fZyuLjUMJ4oGEhNTsdkiyMkporzcVxCSm1tIWVkJiYk2rFZz0PEkSaKtrZPBQQdnampA0bpSKDqSEhT29Qv1nq3IYzrU1tZy6NBB3vb2tyMIaquxt7z1bfzh908EEcqVK33nU3u6BofDwebNW877mgaqj9o+Nm2+PuS6Wgg48HwDl2shYi08rLXa0ymABr0iqKmFWphbe/gvB3yWNYrCodefQ5blS+rOcKnIzMwMuhf4mxmHceEIjK7MFgVQuQz/wpjfCCuA8xCBBDCs/l19OJ1TAMQlpqBITsbGRkhLywpaz2739QfOzy/1EsP8/AVB66amZtLT005UVCQJCdmcOnVKVxEM4HA4+fF3v8vC8nLe+4EPkFtQiCSrqqEgqH2C/YkfhAgJhyB806mDv3/id7jdbvJzs71jiqJgMpkYGhrSfTlGRkZ6CzdsNp9H3ln4pffYoUiTtg/Bbz2tv5ugqAvUl+qAFg7WlECVxCle8qto+5IBjzeg7NmHZgOjFoEIyKKCLAsIgoxBFrxEW/YohIqnJZwiqA+3y0Hl/u2kp+fMmPoHqnVJZmamLvTb1tYWZBMTxvkjsL1m4P03jDBmK8IK4DxEYP7flWo6HkYwhocHOH36GKdPHwfUsO5gbwegEBUVF7S+oqgmwpmZ+WRlnb2PaFSU+kXjcjnIy8vhjjvuICcnO+S6NadO8YVPfILHv/wF/v0j72XbS8/5qYDB5M9bAOHn7+f/8IemEEpuN//3v3/k+z/4Dw4fOeZ9HDl6nNzcXP78p//T5w96jyVQXFKCzWbj9de2h5y/2ayqnD7y6gtbaygqKvbsY0dw7qLgp9L5Ez/8lTowGkRPXp+AySBiMgqYjSIWk/qwmgxYzaI3/8/gKRLRlMNAlVBTCgOvmdliJTO3BIdj6qyh9quBnJwc3ev+/n6mpqZmaDZzH4EEcGxsDFmWZ2g2PoSLQMI4F8IK4DxEIAEMvEGFceXQ3t7IwEAPmZl5LLr2TmITkmk5fRxBEIiKCrb/SEhIYfHi1cTHJ0+7z8nJCY4f30tRUTlmsxWnc4qammO4XA7cbjsLyvIRxAhqTlXptlMUhVOVJxEE+MNvf0tebg4rV67wLvcnf9OpgWfDCy88z9DQEO97378Ffcbuu+9+nnjid3z0ox8Lua3VauWz//45vvTFL2A2m1mzdj39/X1UV1fx8MP/RkpKCjabjVe2biUzMwur1ar7ISMI6j4+/Zl/56tf1vaxjr6+Pmqqq3jPe9+nI7yaEigICpreJ3htYNSCGQUFrWWcoghqAExQlUFZBoP/z2VZ1QsVAQTPuKB4Hqi5l/65hgAFC5ey66W/IUlur9I7E0hNTcVoNOJ2uwH1c9LV1UV+fv6MzWkuI/AHtiRJ2O12oqKiZmhGKhQuMQfwss0kjNmKsAI4zyBJUlDHiLACePWQl1eKIAgYI6LY9dwf+L8ff5HO5tNEREQhioag9dVuAilnVYWcTidOp4Pq6iM4nVNYrTZEUWTNhut4/0c/zVce/yGPffd7fOyznyEqREslRYHRUQdf/vxX2bN737THCRUKPptY9cTvfseWLTfoyJ+2zX3330/l8eMcPXp02u2//JWv8slPfZpvfP0xli5ZxLvf9Q76etWOKUajkR/+54/57W9/TUFuFm+9/95gJVKAL3zpKzz6iU/x+DceY8XSct77nnfS19cX4mj68xR0Fb9MU/2LX36gJ1dQ9BlHa9sJwvS5g6LgUyULFy5FUWRaW2fWgNloNHoLazR0dnbO0GzmPqxWa1Be5djY2AzNRg/lEh6zEU888QR///vfg8b//ve/84c//GEGZjS3ISgzmZEcxgVjdHSU2NhYHn/8xyEbzY+OjvLCCy/oxu6//35vSC2MK4/m5lra2hpIS8tmeHgAg8mE2+n0+v1dKEZHhzl2bDdpadnceOe9rLpmnTdUqyhqR4rh4UF+9oNv0NfThaxY6O8b1vUo9ceatav5+KMfJi8vJyh0qj6/qGmeE9qdJvCG49+2btptQ2wTar/ac9mzT0VRvONuyeeLqCp+IMvacsW7LzlgO8Vj8qx1/vDP99OMn325f/7H9s1Xu6aHdr7Inq3qF1hpaQVpaaFD+FcaDQ0NHDx40PvabDZz7733IophTeBisG3bNl0l8IoVKygpCc71nJqa5Ctf+SQjIyNX7Ie59h3xvX+cxBpx8abjU/YxPv+WxVd0rheK0tJSfvnLX7J5s/5eunPnTj7wgQ9QW1s7QzObmwj/b59nCPzlabFYwuTvKiM3t4T162+huHgxUVExGI1GpqbsF5wXNDk5QU3NUU6cUD3xPvDop1mzbgOiqIYsB/r7qDl1AsntZv+u1xga6KOiYg0RNpHs7EQ2bLw25H737zvIg+9+Pz/58X8zNjY+Lfk7lwJ4oTjbvs51HP9cQnV9we958H5C7U6XC+hXFewzb/YZRAf7AoauitZVFaNXENVWclpOoEos80sXe7c1mWbu/2WgAuh0OmdN9epcRGC4dzYogIqiXPJjtqGlpSVkqkJubm7Y1PwiEM4BnGeYmJjQvZ7pPJQ3I/wLJ+z2cdJzCxga6MPhmMRmizzH1j4MD/fT29tJxfLV3HbPW0lLzwR8atif//Abak4dJyUtg4mxMRISUomPT2bJkjVUVu7D7RohMzOBoSE7drs+yV+SJP7216fYtvVVPvDB93HPPbfpWrNN5/un9cM9F87Vms3/PAKPc65tvabWAdXB/st8+/NVBouCgCLo8/sETakT8CqBgih4nyuKgKyoeX6yphwKamhYlhRvSzoUBVH07Evx9AyW3BhEEQUBSQanW+b43lcAyMkpIjExdfoTvcKIjIwkOjpaR1R6enrCHYMuEtEBqReBaTgzgfnoA5iSksKJEyfIy8vTjVdWVpKYmDgzk5rDCCuA8wyBN54wAZw5jIwMMjU1iSypyp/ReHYvRpfLSW9vB8PD/YyPjxIfn4LNFsHpqhMkJKkdMrR7st1u53RVJcnJGfR2dzIxMUZysmo+GxERRXn5KlpbmoiOjiIhwcrixYuwWCxBxxweHuH73/tPHnzPBzly5Pi0qp+Az9BZCKmvBazvr4xNSya1seDcvrN5EJ7r6MEqpqfPr19LOF9nEL8HvqIN/+fePD4P8dOWA0FKiaJAa+Npnn3yp/zssY/wq+98hl0vP8XU5AQmg8DN976H6Jg4bwHGTCI1VU9Ae3t7Z2gmcx+RkfofdrOBAM5HPPDAAzz66KO89tprSJKEJEns2LGDT3ziEzzwwAMzPb05h7ACOM8QVgBnFpIk0dnZTE9POxMTY0RFxTIxPorVGnHOkF9vbwf19VVB45nZebhdbvBL+TSZzYgGAzExcUxNjTMxMUFCgkoSZVmmu1s19731jjs4dGA/jQ11xMebGB9XcDgVXE69f2B9fSMf+fCnuf76jXz80Q+SmenrZBCK8GljZ1MDA4lYYFWuup9zJ5yflzIoBC/X9q2RQFlW/JibKv8Jnr+qkue3AYruuRaC1iqJ/aG9kmSFno5WnvrdD7GYrWRnFeBwTHJs90sc37OVLXe9gxVrr2N8bIS01JnJ/fNHcnIy9fW+gpT+/n5PC78rlAQ6jxF4n52YmJjxa3mpxRyzUADk8ccfp6WlhS1btnhb8MmyzIMPPsi3v/3tGZ7d3EOYAM4zBBLAcIunq4v+/i4aG2soXbKK0oo1tNSe4MShXSxduvac22Zk5DExMebtDKJBEAREjweJxlGMRiOZ2XmMjY2QkJBKTIwbu32MoaF++vq6mJgY5eH3f5DGug4ee/w77N+3h//+6X+Snp7MN7/1HX7x379h5+u7guawY8cb7N69j48/+kHe/vb7zqn2nQ8RDLldAGGbzuz5QuHjcIK3C4f/MbRwMOBpD6foWsZpqygeixeVNAqeFnI+8qeFiGWtAEVRrWK2//OPxCWmsqCo3BtSz8kpprm5lq1PP8n+115AURTM5mA19mojOVlvPeR0OhkbG5s1Cf9zCYH3WUmScDqdIVX3q4VLzeObjTmAZrOZv/71r3zzm9+ksrISm83G4sWLyc3NnempzUmECeA8g91u170OE8Cri6mpSQxGE7e94yO0NtRSeXAnJSVLiImJP+e2giBQUrKEvLxSpqbsOBxTjI4O0d7aSGtTPYvKK3TrZ+cWcOzgXhYuXEZNzVGOHt2NKBqIjU3gK489TnHJAjZtUYsYNmzYyKKyciy2CCIjrHzrO49x7Ogxfvyf/01DfaNuv06nix/+x89YvryCkuIirhRCqXaXd/8++qeqMeCfE+h55fkr+Obi7R+sXjvZ6+mn37+3Shi1Kniwr5vOljoWLlymy6e0WKyUllaQkZFHV1cLcrREZOTMk6yIiAhsNhuTk5PesYGBgTABvAiEus/a7fYZJYDzGSUlJSGrrMO4MIQJ4DyCJEk4HA7dWJgAXj309nbS0nKG4iVrcLkV9m9/lqiomGmtPsbGRujoaMJoNGGx2LBYLBiNZkwmE0ajieHhfjo6mhEEkX/940/kFxYTGRGhkhVZwelwYDAYGRkZpLe3k8TEJBaULcJgMNLX10vpgoUoCpw8eZJf/PTHjI4O8eDD7+fmW25DFGD58mX8/g+/4l//eoEffO/HQfPr7xsISQBDh1v9yJbHZDlQFTxXKDeQsJ3PcUPtH084V1vVv1CDwMIQBN8sBTw2MZ4iEE9oGFEAWasW9pybIiPJCk63jIIa/j11ZC8Gg5HERH2FrYbo6Fiio5dMfwJXGYIgkJCQQEdHh3dscHAwbAh9ERBFMYhM2+32oD7BVxPzsQhEkiR+//vfs337dnp7e4OcFXbs2DFDM5ubCBPAeYRQ7Zz8e66GceXQ19dJTc1RSpasYcu972NooIeOptMkJKRQX38Kh2OKRYtWekKTMq2tDbS21ROfkIgiyXQ1tyLLoX37bLYI2lqa6OvuIrKg0Dve39eNzab2142OjsPhcHLieCWyLPHG69uxWiysWLWG3//mN0xMjJGUlM6TT/yWiIgIyssXk5qSjMEgcu+9d/L3vz1Dc1OLd99JSYksWz49WTkbGfMvFjmf0HCoPMBLCQn7cz2F4LkGKoG65X7hYO256Hnqv71//2DJ0yMYwGAyz6kcukACGLaCuXgEEsCZbq83H3MAP/GJT/D73/+e22+/nfLy8jn1f202IkwA5xECbziiKIY9AK8Senu7SM7I5Yb7348oisQmpJCfv4CWljpAQZZl1bTZ7aKq6ggjIwPcfPu93HzXWzAYjEiyjGNqErt9gsmJcQb7+9mx7TmaG85gtUYwOWnHYDR6jYkB+nt7iI1JICYmnvLylezb9yqCIGKzqarvwf37WLl6DbfeeQe//+0vPQRT4Jc//ylms5lf/eZ3REdHcfz4CR35A3jkkYewWq1B5xmqovfsti3nlyPofxufzuZFO6Y/udMv0+8lFAnU1Xngx/x0mwaTQF3+n9+BZQ/5k2SFwrIVHNn5HIODfSQlzZzFy4UgLi5O93p4eHjGixfmKgL/v/iTwTAuD/7yl7/wt7/9jdtuu22mpzIvECaA8wiBNxyr1Rq+kV8lmEwmJEFAEEXUqKHAXe//PAISx/e+yusv/JWBgW6ammpxuZx8/HNfo3RBmddbThQErLYIrLYIhMRkcvIKWLJ8Ja+8+Czbnn+G/PwFZGTl6LpT5OYXcqryCJLk9raZ23LjTQiCgNvt5h3vfA8CsHr1Gl55+QU62ttYsHARp2uqcDqdvPrKNu67/37+8ud/6M4lPT2VO++65bzP/UJz+c4ZykVPAjWEDgtP//kOJn2+whAdCVR37js46EigWuDhK/6QFZX0yYr2Wv2bmJYFQFXVIYqKysnMzJv+JGcJAgmg0+lkamoqHDm4CAResxlXAOdhCNhsNlNUdOXykt9sCBPAeYTA/L9QCk4YVwaiKDLld/0VwC0rxEdayM7NJzo2jurqoySmpPO+j/47GRkZavswD7GQFXA6HLz47F/Zu/MVUtMzeOiRj3Hrnfdxyx33+fartSgDHvrQp9m94wVe+udTOJ0OUtPSeNeD78NkMuo89uLjY/nO939IQ309CAJf/+oXWbFyFddt2kxvbx+7d+n7A7/jnW/1WiycL85N6kKHgy+mEORsKuC5tw2oDg58ElAlrOUzakfSWsW5ZbUFn0oEFURBoP7kAe9xIiLmhv1SZGQkBoNB1zZwdHQ0TAAvAoEFH4H346uN+VgF/JnPfIaf/OQn/OxnPwuLG5cBYQI4jxB4wwmHf68exsdHiU/LQZFlBE8/VZdjimee+Q09HS189AvfYXJinOi4eCJtNrUvraKpSdDSVMdf//ALBnp7iI1NoKOthb6+XhwOJ/mFRV7iovWflWQFGYGNN9zJTbfdg318FIvZgMFg8PWjBQQUtS2Z0cTChQsRBPj5L35NUnIyoiDwf//7F10itc1m5fbbbw55jpd6v/UngdP5Al4p6NL+/E5EwFch7FMJQdbmiVbpq153t6TgcMk43TIuSUYQwGwUObj9WQ69/i8ANm68fc58Oan5o9G63L+xsbEgk+gwzo1ZRwCZfzmAu3fv5rXXXuOll15i0aJFmEx6c/2nn356hmY2NxEmgPMIgTecsAXB1cPUlJ3m2uP84rFHyCtZwobb38VTv/wGDsckIPDsn37N2x/+OGaz2Ut4XJJMa1M9B/e8xpF9O4mMjGbFims5deoQAL/5rx8AsOmGW3nLO98L6MM6AgLVJ44yMjRAQ00tUVExiKKBf/vQw8gKPPGrJwD4tw89jCiq3QmioqJI8vN/e+WV13TnccMNm4mKivQZI/sh1Jg/rgaR0+bhPaY2do71vOuHPCe9KqjgFzb2PNPIn9uj+E1NTTEyNEhCYhL2iTEO73zO7xhzg/xpiIqK0hHAcBeLi8NsI4DzEXFxcdx7770zPY15gzABnEdwufTdHcIK4NXDkiVrmJgYY3R0iKbaStz2Cez2cVas2MjU1CQ1VUf49Y++zsMf/TxRMTEoCtTVVvO7nz6OxWIlN7eErKwCRFFk8eLVnD59jMzMfJxOJ6+/+hIxsXHcdPs93rBObfUpBFHkmb/+gcF+XwuvL371697nD3/wYUSPsjU6Ns5H3v8ghUXFWK1WDAYj9973FurO1OvO45ZbbzjreZ6NBF5sKPdK4Xz27zsXT7GKn1+g4CkkAVX9c0kybknm6P6dbP3H71AUhYVLryG/eBGKopCTU0RqatYVOpsrh1BdLMK4cATebwPvx1cb8zEH8IknnpjpKcwrhAngPILT6dS9ns8EUFFkOjqaPZXONiwWKxaLFdMM2XBEREQRERHFyMigpwhDfS8sFiuRkdHExyfT1d5MX18PkdExyIrC8UN7MJstXHPNFt2cIyKiWL78WkC1lwF0xsJ7d77CU3/6nfd1VlYBNlsEdXWnvKqDIKidKSRFrcB943VV6Wuor8Nmi2RycgJZ1rcCj4uLZdmyc/vUnUsJnE0IbDsXCroqYb/KY58iqOByq4+erg5effaPJCenExERTc3xA6xYcx2rNtzIod2vMD4+SnJyhlcNiotLmvWKYKBXaKCZfBjnh8BwZOD9+GpjPuYAaujr66O2ttZjnl8S1NUmjPNDmADOIwT+4gy8Ic0nDA720dBQDYIIii+HzWCJIDk+keTkdOLikhBF8Sx7uTRMTdk5cEA1HjUaTcTGJjAw0EN6eg5dXa3k5ZV6bF8OMzIyyHW33EtsfCKSp/CjaOESju57jaGhPm8fX4Dh4QE6OprIzS1hcLCPiIhoNt90B5KskpWDe3eSmJhKfn4p4+OjxMUlYTSaaG9v4p/P/IMlS5cjCAKSrPDkb55ANLl4ffs2UlMzyc4uorb2OEajidFR/Rf9NWtWeauJNfiKIPQIRQLPxxZmOpxLrbuUL6PzVRpDeUWDh0jLCm5J5tV//RmzyUxJSQWiKDI42Mu2Z//EI5/9Jhm5RRzY+RK1tce9+6yoWEtcXOJFz/1qIJAAhu1LLg6B91uXyxW21LnMmJiY4OMf/zhPPvmkN3fZYDDw4IMP8l//9V/hxgcXiDABnEdwu9261xdayTmXMDjYizU6kWX3fxnH5CiOiRGmxkcY72thsKWS7u6DmEwWcnOLSU/PuSJE0GKxERubwMjIIG63i4GBHlZsuJnohFS6/vUkfX2dNLecISY2AbPZws6Xn2Hny88giiJWaySFCxaRkZ1PTc0x4uISiYyMQRCgpaUOk9nMsWO7sVojkSQ3ky71ZqcoYLVF0N/TxejoMImJKZjNarV3amoWDXW1TE46MVvMuCSZgYEeqqoOY7HYMBpNnDp1CLPFxDe/9V0+9ckv6M5nxcplutcXq/RdSGh3unUvpML3fNY7u3G1fh+a1Y5bVnC6FaacElMuiQn7JPU1lRgMBpqaajCZLERHx9LR0cyRPdtZvfFmypevZXxsjKnJCX71/c8zOjo06wlgoFvATNuXzFWE+sEtSdKM3YfnYxHIpz/9aXbu3Mlzzz3H+vXrAbUw5NFHH+Uzn/kMv/jFL2Z4hnML85chvAnxZiCADscU4+Mj9Pf3kFi0CoNBxBoZhyUilqgkiMsuJ7XiFsb6OxmofYP6+qN0dDSRn19KUlL6Zf01LggCS5euQ5ZlGhur6ehoJiu/hPS8hVisVmqP7yPDbKGh+hhWq42FC5fjdruYmrIzOTlBXfUJ3G4XWbkFSJJEd3cbjqlJsvMKKSgpo725gZHhQUoXLcMtKZ7zdxAdm0Bc4ih1dSc5c0bBbLZgNlsYHx8FYNxuJ9bzZbRo6RKqqg6DoOB02ilduJAHH3oYo8nC4MCg7nzKyxepN/0A4hdKBTzXZQxFuM5pBu23jY6Q+e0oeJ/nj1Bz9t+f5q+oVVm7JQWXW235dqbmJJWH9xKfnM7okNqiz2KNwOlQ1bLqyoOs2XQLCmCNjMIWGcXiFes5eXg3LpeTgoIFCMKVU6MvBYEEUJIk3G73vLx/XEmEul4zeR3nYw7gU089xT/+8Q82bdrkHbvtttuw2Wy87W1vCxPAC0T4f/g8wnwkgFNTdgYH+xgc7GVsbBinU81xM1mjSSm+xmO6DIoggKxgNAiAgcjETKxrHiCxdCO9J16kuvoo0dFxlJZWEBkZfVnnKIoiiYmp9PV18c8//heCIBAZGYNbduOYnCQ9PYf8/AVB74ckSTQ319LcUKsb7+nsoKezHafTgSiKjI+PYZ+cAkHgT7/6D5rqqgFYUL6UpSvX0d/bzdDQICND/XS1t/DnJ/+H2++6n9SMTJo8+3ZMTdE3NUVfXy+Gf/sg9fWNumNGRESQlZ2pki2PJ8p0JG++RbS07znNY1HyKH8ut0xvXx///NsfOXH0ABER0URGRpORnkN/fzdWawRf/f6vsI+PYbZaMIj6auI7HniExJQMXn/xbyiKQlHRohk6w7MjlFuAw+GYF/ePq4npCGAYlw92uz2kRVFKSko4d/UiEP4fPo8Q2Bjbv3BgLuLkyQMMDvYBAjGp+cSXLCY6KYvo5Gxs0QmIHiYieKQjURRQZAVRBKOoLrPFZ5C98d+w9zbQf/D/OHp0N6WlFaSkZFzWucbHJ7NmzQ1MTk4wMjLIyMgARqOJrMWFWK2hTXUNBgMpKZm0t6tkLC+vlOzsQm+4WpLcdHe3cfLIPgxGEzmFC2mqq2bJkjU4HJOcPnWcsiWr2HTr/bz8r79yZN/rpKZmcezwfoxGI2dOVzE8NEhUVCxZWfmcPn0cgOiYGFpb23RzycvLQRAEZAVEfCRQvb7T5wJeTZyPInGxuYKa158sq8UeU06JV19+nq3P/R0BWLBgKSkpmV4F2eVyMjw8gMVsxBwfj+KxilEUAYMoePI8BWLjkwBISJi9SeqhiMtMV7DORYRKMwm8J19NzEcFcO3atXzta1/jySef9CrXk5OTfP3rX2ft2rUzPLu5hzABnEfwd/OH0DekuQSHw0F0WjHZ6x/EYovAIAqYDAKiKCAKQoAS5SGBgoAigMFz6hJqR6/I1CIib/siPYf/QU3NUUZHByksLLusYTlBELzVwOnpOWdd1+Vy0tnZQktLHQnJaUTHJdBcV01fXxdFRYuIi0vEYDCSmZnP6OgwJw/vUSmYINLT0+59r5vqT7No5QYOvbGN9PRcUlIy6OlpxzE1xfCQGuIdHx/h9OnjxMbG8el//wIOxxRvvKb3/xufGOPMmTOUlJR4jnPZLst5Yya+cLR8P0XRevrC8Ng4T/7m51RVHiYzM4+8vFKMRl9+l9vtpq+vk803360rfBEQEAUQRZVIy4pCVGwcgDdPczZCFEWMRqNOrQorVxcOURSD+lcH3pOvJhTPv0vZfrbhJz/5CbfccgtZWVlUVFQgCALHjx/HarWydevWmZ7enEOYAM4jzDcFMDY2nsHxEWJiojB4SJ9B9Bnt+ifvq5YHAoKihoQFAQyygOzJ6ZIVBUm0kLr6HZgS8ug4+gyxsQkkJ19eJTAQfX2d1NWdoqBgIcnJGQwO9tDT08Hw8ACyInPNdbew4aZ7MZjMtDTU8voLf6Gq6jBr1mzBYFD/e1osNmyR0azeeAttdVUMDamqaGJiCjFx8fzsW5/F4ZjCbLZQWbmPgqJSYuPjAcjMyuYTn/0iB/a8QXtbCy88/08O7t/H0JA+0X9qcoKvfunz5Obl8+GPfIzCwkJEUfB2wrgchHC6dnDn3E4zar5ClcJahxW1xZvMzu3bqKo8THFxORkZebp13W43VVWHUBSFles2I8l6ZVQQ1M+lqGYkkJ6VjygaaGysprh4MTZb5EXN8UrDYDCECeBlgCiKOtI3kwrgfER5eTl1dXX87//+L6dPn0ZRFB544AHe9a53hdsXXgTCBHAeIfALcK7bD8TExNPZ2QKuSYy2SC8h0Z2W4mvhBQoiIHuUGEQFZAFEVQVU1xRJKF7HYO1OBgf7iI6Ow2g06RSey4nOzlZEg4Ha2koaGqpxu11kZOdzw7U3sHDpNUTFxKtt2xTILSzl7nd9jF9/7zNUVu4nP38BcXGJDA/3k5SRTWRsAmNjozidDgRBZHR0mH2vbyU2NoEVK65lYKCXyMgoPvOlryNJbhISErlu8/W8vv0V/vn034iIiPZ6QwqCAfCF+VSiM0lLcxP//OezfPwTn8YICKJPDdTCwOdLCK9WZ5DzQaAyA74QmdZez+mWcbhkFq24lgN7d1JfX43RaCIhIZXGxloGBnqZnLQjiiI33fNuurp76ezu1YXKQfNf9PzwkBXKr9nCyYM7qaqqJC+v5Cqf+fkh8F7R3NwcNoS+CAQqfjPppTcfQ8AANpuNRx55ZKanMS8QJoDzCPONAI6NjWC0RGAymzF4iIj/OXm7NoQggYpm5qaRQAARtAZfEcn5dDcfobtbzYXLySkiL6/0kq6ZLMs0NZ1Gktze/QwP93PPO/6N6NgEujtaKVu6ioSkNG+xgfaWuV1OBNFAXGIib33/v/PGS3/nxIn9REbGMDExyg1r38fJQ7twOCZZvnwDUVGxCIKAwzFFc3MtXV2t9Pf3kJKWhiCImExmbrvrPlAUXnr+n6Sn51BcvBhBEJiYGGP37j3Y7T4V0OVyoBHCvt4efvWLn1G6YAEbr91AhE311rrSljAXiukqhYPnMP3ENbNcTSl2ywpmWxRv/8AX2PqPJ6ipOcbEhJuREb033h9+8fOLmHE/dXUtF7Hd1UdTUxNNTU0zPY05j3AO4OXFd77zHVJTU3nf+96nG//d735HX18fn//852doZnMTYQIYxqyEVgCRuXiTp3+uL+zrRUDXBsU3jCAIajHDNCQwa9X9JBStw6A4cNW/RktLHQ7HJAsW6L3wLgSyrPruTU6qyklCchpZecUsXnYNtshIShctxS1JjAwN0NnezP6dWxkbHWZ8dBjHpB2LLYLs/FJyChdw61sfob+/h9ef/T1puQuIzyph+z+/QnJyOtHRcQA4HJOcOnWYqSk7BoMRk8lMadkS+vv72P78S7zjfQ8xZbfjdDqIjU3wXkO1ClpPinp62khJTSA+PoH6ujN0dnSx87XtVJ08wac/81ldV4wLLQa5UBJ4Nb93NK80WfYpgbICVW9s4973foLm2pP87NvfuIozCmM+Yab7Ac/GPL5Lwa9+9Sv+9Kc/BY0vWrSIBx54IEwALxBhAhjGrERPTweS5Ca7XG2JdjbKoe/goM8XEwQ1/CuK6EigYLYQnZKLokBkWgly0uu0HXne2+HhYmA0GlmxYiOtrfW0tTWgKDIPfvjfsUVGoigwPjbKX373U5obThMZFYNzapKUlExi0nIwmy04HJP0d7bSUFOJwl9Ytv4m7v7IdxANRrpaGxgd6mfp0nXe41VW7kOSJB569P/x6vP/oOVMJTu2Pk99bRUf+NhnOHHsMOPjY1gsNhoaanC5nKSn53hzC/2hKAojwyMkJ6exZMk1xMcn09bWwIH9+2hpbiYvL897rdX157YdjFb163vua5tlEAWuuf2tiAKkpmeH87jCuGgEeiyGcWno7u4mPT09aDw5OZmurq4ZmNHcRpgAziPM9ZCvBkVR6OhoIjm/Alt0gpf8+ZOPwN+1gSRQ0Fow+f3VSKAg+JL0NWXKGp8JqEbTNtuFtRNyOCYZGRny9iNWCaSC2+HAZDJ65/zMn35Nb3cHmRm5dHS0EBubEOQNl5sLsizR0dFM5b7tTIyPs/b2hxjs6wDUvEhQFdLJSTtvefDDZGbnMdDZTHp6DikpmVRW7uPLn/mId58REZE4HJM0NFTT1FRLYeFCHA59EYjb7aaiYgOxsQnesYyMPKYco3zm059kyw038pGPfPSyk75L7QKi7uPsa4fK/fN/7u0VLKiFRhaT6C02GldCF0NYLOoX+/nPU13T7XKiKEpIEj6TUKug5Vnd/zWMC8N8DAFnZ2ezZ88e8vPzdeN79uwhI+PKFvTNR8yuu1AYlxVz9Wbe3t6I3T5O2dIbvEUfgWFEjSToSa+vjYQoqiRQ9lQGa8n+gqiOKfgIpYBARKxqLlpXd4LS0goslnNXlMmyTHt7Ix2dTTgDQj033nYvN91xP4LBgFtScEpu6mtOkJNTTE5OEWlp09vEiKKBrKwCdT6V+1i66V4c9nEstkjv+drtapg5MSkVl3OKiYkxUlOziItLZNGilYBAY2M1k5MT2O0TZGbmMTw8oHYgqTuF262frywrQX5wBoOBzIxiOjs6ePWVbdxw400UFxdjEC+sGORSP4ba5/hcXUIuZB6a/Yvsyf/TfjwYDQIRZhFZEVEUkJz63D9RFPnN355Rw8YKON0ybslXaS7Lags5/x6wimf/sgL1Jw+w/alfk5VVQE5OEYoiMzDQS0JC8nl95sKY3fj73/+uq6CeyR/l85EAvv/97+eTn/wkLpeL66+/HoDt27fzuc99js985jMzPLu5hzABnEcIDF3OxdCV3T5Oc3Mt2Ys3EZua6yV/oRA07F3RWxWCiILsWdtD+zx/tbwvBVEAW0w8C2/6II17/sLhw2+Qn7+AxMSUab+UFUWhsnIfo6NDrLhmA7fe+w6mJu2MDg8QH59AVnau11jYLSl0tDYhSZJXYYuKigm53/HxUdrbG+nt7cRstqAoMrVHd2GLjMExaUeWZURR9L63o8MDGMxWFEX2WowkJaXpchFzc0vIzS3C5XJy6tRhJibGsFgsOBw+53xZVpictBMZGRMwnxHv8y987rM8+N73ce89d4NwYSRwtiNQVdbOaXR4WLdeVEyMt6JYI3ahvig1b0FfBbX6WctfuJwV191J5b5tdHa2IMtq1Wh2dhEFBQsu+3mFcXUReM+d616ssw2f+9znGBwc5CMf+QhOpxNQw+yf//zn+eIXvzjDs5t7CBPAeYTAm81MmpBeDCYmxjhxYj+W6CQKV98e9OvZP68P/EN3fuFfz4C/MqQVBAuK76+2dGKkn6Ydvyar4gayy9aSk/9lqnf+jbrTh6mrUwsm0tNzSE3N0lnFCIJAYmIqExOjHDu0l+72dhIT03j3Bz+MzWr1+sHhqV6uOXEIk8lMTEzctOff399DVdUhACKjYjxm1wJOxyTpBYsAhZGRQeLjk4iJiSMpKY3n/v4kj371R1gsVgYHe0lMTAFU78D09Bzy8koxm9VWX2azleXLNwBw/PhxRkdrvMeWJCWkR11KSibJyRnIskRz8xme/P3vWL1qFVlZGedVEHJ2376zbupZJyB8e5Zl5wtN/fMncFoVsFYEoqmCfX29um3jExK96p8k41P+POqfrBUlCQHhZe1hMLJ8090sWLmJmkOvExMXz5nKPUzZw5Yrcx1aGN0fM0kA56MRtCAIfO973+OrX/0qNTU12Gw2iouLg9oZtre3k5GRESbg50CYAM4jzGUF0G4f5/jxvVhiU6i49UMYzVZf5akSupLUnx/qOzLgtYfRVQr75QQKKB510YhzfIDGPX9luOU4sUnpWCJiKFq6EbdzitGBburrq2hsrCEpKY2oqDji4hKIjo4jJ6eI9PQcOjqa6OxsoaOjmS88eojsnHyiY2OZtE8wabdjt9sZHR4iNTXzrJ1HYmPjycsrZWRkgJGRIWRF5vq3PUpqXhmyLGOLimNgoIf4+CQEQSAnp4ijR3fT1tzAsjXXcWz/ToqLywFVYSwpWTLtsaKionSvRdFERERUyHUFQcBgMJKXV0p7eyO7dr3BAw88cFULQs6X613oXALJn+RRbF2SjKJAZ0enbv2klBTdOm5JI4CarY+i++Eie4im6GlNqL22RsSw7Lq7MIgCXS1n6BrS94MOY+4h1A+SGSWA8zAErCEqKopVq1ZNu7ysrIzjx49TUFBwFWc19xAmgPMIgZ0/5pIC2N3dhmA0s/zOj2O2+oowpiN/02E6oui/H0EARfasJzkwGAzYbFG4RrrpHer0hvfcbhculxpmMEfE4MRAY2MNRqOR9etvBsBkMpOXV0pubgl2+xgjI0OMjAwyPjqG0WjEYDDhmJxEliUSE4ObmPvDZDKTm1sMFCPLMqdOHWLv809w68NfxRYZQ1reQnqbTnnXj4qKxWg00VB3GqfTgSief+eXmBh9qHdqyoEsy+foHqMgigae+9e/eMtb3orJZLhsPYJDh1EvTP07L0XRb3sFrfrXZwHjllQvQKdb/Qy0NTfptk9Oz8LllnHL/oqhSgDdklZFrHiPpXjIpUd89o7LsnpsWYHhgd4LLjwKY/Yh1P12rndjmquYq/nvVxthAjiPEJjEP1faOSmKQn9/F0l5SzBbI1AUtUrXt9z3PKgYZLr8wBBhYCVgvG7H/zDSXuXpuZtHWlq2bh+TkxNUVx9lfHyE+PRClm68i5d/95i3QCPweJGRMURGxpCRkatbVlm5j8nJiQtK8hdFkQULlnLo0Osc3Pq/XHvvh5m0j2My6UMdgiAwPO6g4eh+kpJUewRJcjM+Pkp0dNy0CkRcXJzutSzLDA8Pk5iYOO2cDAYjCxcuparqCPX19SxYUHpOte1ijKA14uR9HbD92ZZpY0KIz48u9Aue3r++jh2SrOCSFKackhoGlmVaGhp0+05Mz2F8SvLOUVUA/UK/gKwI3uMqnuO63IqvkwracdX3qq+zZdZ2CAnj/BHqfht4T76amM8KYBiXB2ECOI8wVwng+PgIk5N2SguWquTPj/1p9yD/cLA//PMAQy0PXNdb8SmAJTIOgOLiclJTs4LWdzimGB8fwRoZS9mG+xgd6UVRFOLiki7o/Kam1ErSC/kycLmcNDRU4Xa7MFtsuCUFW3QCvc3VjI2NYLXaaGo6jcvlJDIhA5fLxdBQP+Pjo1RXH2FycoKoqFgyM/Mwm63esLGGlpYzREZGMDHhKwTp6+s7KwEEiIlRi1iGhga9YwoKKMIFh14vBuf7y/6snwP8yJn/jwm/bsWKotDZ3oZ9Yly3bWpOAU637N2PRh79FT7Q/9Dw/+xqiqM/ERUEYU6la4QRGrOOAKJXzS9m+zDmN8IEcB7BZNL3s3W5XNOsOXugKAoNDTVExKaQmLVAtW8JQeZChf90RSLnuFtpVZjalzVAwZr7kFxT1NYeRRAEUlIydduMjAwAsOSG9+Bu3EfGytuwWKx0dbUQGxt/Xuc3NTWJ06larpzL+02SJJxOB+Pjwx6rFvX962qqYWx0hAXr76a3/ihHj+7yEAwo2fgOIjLKueHBL7Hn7z/m6NHdKIqvUri2thKAhIQUb5i6vr4KSXKjKPrPR3d3NwsWnL0S1WQyY7FY6O3pOa/zv1RcLhUiuPBDX8ErigIGUfWKFAUBGYXTJyt1+4iJT8QWk4BDI4CKj9SBh0B6ioyEgA+kIKiKo3Y82btYIDG7lO7uRiTJjdUagc0WiSiK2O3j2O3jSJKboqLycEL7LEfg/VYQhPB7FsasRpgAziPMRQLY19fFyMgAS2//MKLBwAVwOh0RDFQK9et51D9ZofvMASb6WrBExpG95HpKr3snZwSoqTlCX183xcXlmM0WJibGaG6uIyeniJTchZgLF2EyiizacBdHt/8No9FEYWGZjoQ6HJNIkuQtppBlierqw5itNqbs42dVA3p62jl9+rj3tSgaMBiMpKfn0NbWwFB3M2kFi1n/7q/T3XAMye0kIWcxgiUaAFtcOte995tUPvsT+vo6WbrhVsrX305ipEj1yUq2/e2XHDu2G4Blq9ZSf7qayUkXdrvLbw49uFyuoM+RPwRBwGg0093THfI6X6ov4HSLzhUWPuvxvNv41D3/sK0GQRAwiAIGUQ3jVh8/oluet2AxkuybR2CVpLf4CDWFwf+zoT2VPZ9D3zlB3tLraTjsZnC0H3t7Y5DKqbb+C+sxsx2B91v/FpYzAS2X+VK2n6uYL00RrjTCBHAeYS4SwM7OZhKySknOKdONe/O2zuMmFIoIBnd/UHA77DTt+Yt33b76gyy965MUb3w3MSn5NO77Bw7HJMuXb6CxsQar1cayex7FZBIxGlRysGj1DRhEkUOv/IWpKTsLFy5DFA10dbXQ0FCNLMtEREQRFRXD2NgwTpeLxWtv4fiu53C5XF5LlkBERcUiiiI5BSXExSdw/NBeVq7ciNFo8raVEwQBo9lMeulqZI8FCXhyzCQZl2yg+PZPUiIIRFoMdAxOMTRuwBFTzIb3fo+x9hPERRi58YbNmAwir297jl/+9JfeOciyzPHjh8jLyycxMZWpKTu1tZXExMSTm1uM0WjyWF1IiJfxBjvdW3yh4+c8DvrQr2bboqUFgKriGUSBkeFhGmpO6bYvKl/u2U+g5YvgIX+K+lfR9qQE/SDRSKj2XFYgIbOU+MwSbGYDFoOC0T3Knhf+l+a6KrKzC8nLKw0rSXMAmi+dhrP9kLoaeDPnAM5l8no1ESaA8whms1n3eqYbkZ8fBEzWKG8OlT5hf/r/xKEKQc71n14wGEEQSU5KIysrn2PH9jDS1UBsdhkdJ7djNJrIzS1mfHyUwcFeVt32MEaTCZNRxCiqxEAQYNHqLUTHJ7Pr2d+wb98rHjIhUbL8OlJyy2ipOcJIZx2JBctJL1lBYkomwu7n6e3tCFlAAqrfYEFBGfX1p8jOLwJgasruDRuLoogggFFU25XJooIoa3mNMOmU1SIGt4wkw8SUxITDzZRLxi3JdO/6FY6BZvU6TQ5w0x33c+0Nd/D69l2crqryzqOruwe7fRBBEFEUj9n06BB9fZ0kJKQgigYmJ+0sX7HKFz71I4Nny8kM5Svm/377xpSQ44H7Oxd8RMtXjavm7KnkzS0pjE9JuGXZex3dknrOh/fu0uXlmcwWFiyuAEFQq4W919pjKyTgUQ8FTAZVARRFX1hYu04GEZB9RSCyonjzAl1uhaGjLwEwMjxIRkYuBQULz/+Ew5hRBBLAwPvx1cZ8zgGsr6+noaGBjRs3YrPZCLRfqq6uDreGOw+ECeA8QqAZZuANaTbCYDAguX1KZaCCd17WHtMpRegVF9FoJm/d22nZ/3fs9jEAHBNDTPS34Rgforx8lbcTicViI7N4hc/A1/OFLnrChIULK0hI+hJNNUcxmy2kZOaRnluCJCsUL1qOwyVjd0qMT0lIQFrhMlqaT+J2u8jNLQkZosjIyMVkMlFXpypPQ0P99Pd3E5eSTVpeGQKeAhnZQ5dFj52IJ7dRy2/097QTBQFnfwOOgWZKSpbgdE6x/cWn6elo5t3/9lE233STjgBOjE8RG2MhPjEFa0QkRoNA+eIKDh/cy+BgH263izvvupslFRXnfE9CiYQ6QjgN+Qu9vxDk0e95iIaA3mNoSps/AdQI3ITDrVvXJalhszde2ao7VtmyVVisNhwu2Wv54vJ4AYLvc2E0CMiy2k8YWUAUFfCrCg51PSxGEYOobpOw7i5AYdeul4jPKw15LcKYnQj8wR14Pw7j0jEwMMDb3/52duzYgSAI1NXVUVBQwPvf/37i4uL44Q9/CKg9g8M4N8JxhXmEwBvOXFAARdGA5HZ6vqQVL5kJFb5QAh7eDgxablfAskASKQoCqcWrWXT7p5CMqiWLrCiM97cjCCKjo8M0NtZgiU1h5e2PIBoMHmsQj+GvrNmaqKG9+OQMlm+8g8VrbiQjtwTRo9CZDOqXucUoInqUoUXX3osiCHR1tU57LQRBIDk5A5PJjNFopK2tAafLyerb3ofRZPKGGkVR8O5Xe6jFMz5HPkmWcQ21EWGCqbZDREZGk5aWTW5uCeXlqzlddZLv/r/PEh8XgS0i0IPOBrJMT0cLbc0NvPivpxgaGMThmKR0YRl333tfyJBkIOE7l/oXiLP5/gXvL2CZ3yNwfe2HgNd6xUP+7E6JSaeMwyV7Cd3e5/7GiWNH6e5o1+1/1cbrPT8mFO9nwumWmXLKTDplJp0Sk06JKaeMy2MN46349evI4KsA9uVomYyiGv41id6H2WrTteoLY/ZjthFA9fN3CY9ZqAF+6lOfwmg00traSoTffevtb387L7/88gzObG4irADOIwTecKampmZoJucPURRwS+4gNShQ0vcPC+rX04iDLx9Lg68dly/5HyAiPoPyO/8d99QokTEJNO5/BoPB4M3ZWbTxfhLTcr25YW5JQRAURFlGkESvR6E2B6OodRn2VHt6FljNIo6DW4muuJXBvhYkl5PFS9efNUF5cLCXyckJFqzYxEBPBwuvuZnYpDT13P1a3omipw+vqF4nSdZUJjXMONKwl9aDz5CYXYbothMREe09bmJiCitWbKSu7iS//9VPSUqKpa3VRzZ6e/tZs2YdZrOZjo4mRFEkPT2XoaE+Tp8+zmNf/TLf/8EPsdmsIf32AnGu0G9wvmbgumcnhmfbVqv8Vomfz+x5yiUzPiUx4ZCwmkQU8Nq7bH/uGd0+UtIzySstx+n2dQrRCOOUS/bzABSwmNTnNrMInl7Jmhoa+ENF9uQjGj3q33BvO8N97SiyRHxyOr2dLSQnZ2A0mjCZzNPmj4YxOxB4v51xAjgPcwC3bdvG1q1bycrS23YVFxfT0tIyQ7OauwgTwHkEm01vNOxwqN0dZnMCuSwrYDLoChpAywdUgoyEA4mfgq+qEvC0d/PlEypoeXKejg+edQ1GI8boBARBIG3hegZaTtDQUE18RhFRiZne7g2SrKgKm6zglkBRZG8uoDpPAcVzeUVPEYrJAEaD+uWfs+4unG6Jka56AKKj9R04/KEoMs3NZ4iOjmPlje/A5eksIXuuA4qP4KovFRQP4zQa1HFJFLD3NdJ26J8kJqYy0tOA2+kgOjpWdyyr1UZ5+Sr6+rpobNS3IXO73VRXV7Ns2TJdzmJCQgpLl67jyJFdPPWPv/Hggw/5vS9+hRTT8Fsv6ZuOKJ5F4ZsO0+9L8XvvNeKn5kc63apiN2p343DJXvPmoy8/RXd3N021Vbp9bbzlDkwGkUmn20P+VJXP5faoh54WcKIgICsKBhEUxXdrDfL+86rcPk9KlyTzyt9+zvhwv+7Yx4/v9T5fu/bGMAmcxQgkgFardYZmMn8xMTGhU/409Pf3zzjhnosIE8B5hMAbjqIoOByOIGI4U7Dbx+nv72Z4uB9JklAUGbt9gpjMRJ+CxvRhPO21P/FT//pUJlHQW3D4E4HBtiqa9/0Dg8lCUsEKspfeCEBkXCoVd36SjhM7KF51EwgiLsmn9ljNoqfPK4iCSrZEQUAUUZP6EZAkN5OjI5gsFsxmGyaTibbG05w89AYTfZ10dbVQULDwrO3a2toaGR8f4Y6Hv4SCpvJ5ukdoKpMfIdZCvoqgzkOSFZDd1O38I9EpuZSVLGRiYpSjR3czNjYSdDzV+zCDlJQMFGUfzc3N3mVnzpyhuLg4qGewyWRGliWeeuoptm59hQ9+8AOsX7+eb3/7x2zevBqD0ciq1Wv85uunyPq/rwFzCZ0fp5x1eaj1tH3Lsi/vTyNrTrdK3CadEsMTbsam3DTv/hcLNt/LyVefpuS6u3npCx/S7Ss2PpHF11yHw7O9w6WGfO0OtchmxO7G6ZaZ6KzC3XMKs9lEwYpbSInVKxTajxFZCXxouYQKhcs20VF7lOHeNq8HpIbExFRMppktKgjj7JicnNS9nun7bmBKxMVsP9uwceNGnnzySb75zW8CPhP1H/zgB2zevHmGZzf3ECaA8wgWiyWoBZrdbp/RG5HL5aSzs5m+vi4mJsYQjWai04oxmSMQDAYijEaS8yt02/j3TNWgD+VqoT29jYcoCCiCgmDwXQPtUrjdLuq2/1adEwLtx14kpXgVEdHxCIJARHQ8JRvux2QQ1WrM7iaaj72KJTqJuKQ0EtLziE7MQBQEjLKa52dERJElqva9TPWhHdjHhgEwW23c+/DnqDm6l/oT+wAoKlpEZmZ+yGs0Pj7CmTMnGRsbZsm6m0nOzPeQOQUZAUGTiVA8KmDADjwxaYMo0F27H8fEMEvKKhBFkejoOKKj4xjzzG06lJeX09LS4r1usixz9OhRrr322qCQdXx8MooiMzIyyL/+9U9+8YtfYrePc/jwTgCuueYaqqqqKCwq5r63vI2FCxd6w+OB+zpXcYe6TuhlvvB/iHHUz4am/Lk9lbta+Fb7HKWuvoPTr6kh30M7tzE8rL9O1952H4pgwO0hjy5J8RLBKQ8RdLoVxhp2Y7R340KgqrOa/A99B8GTZ6qpkG7Jv4BEzTtUVWkJ19Q4EkaKr7mN5Ixczuz9J3Un9lFRsRaHw0F8fBJ2+xhWa2S4v+wsRSABnGkFcD6GgH/wgx+wadMmDh8+jNPp5HOf+xxVVVUMDg6yZ8+emZ7enEOYAM4jiKKIzWbDbvflc9nt9nO297oSkGWZzs5mWlrqkBGJy15Eeu4S4rIWIhrMalWtoBU0CN4cN38Ekj51zGfi608ABdTuDQaPTYdWDqEpg5JbwhQZj2tiCFCIjo7DaI3BLSuIgqaiCeoXs9tN1Wv/h+ScQhjooOPkawiiyA3v/y5Gs82bK2Y2irhH2jm842kSE1MpKF/tCePW8sIf/xNbTDwpKRnk5BQTGRk97bWanJxgbGyYhcuvZc0Nb2HS5UvA9poLK/5P9PCRK635vOrVp2HZsvVBilIgoqOjKS4u5syZM96xjo4OWltbyc3N9Y6ZTGaWLLkGWZbYtesl6urqiIqKYcmSNURFxdDe3sShQ4dIS8umob6Br/+/L/PuBx/m1ttux2AQEVFCkEC/594xZdpl02/j21ar+NUIny9vTyVyKhlTw7A56+6i5pU/8+q2Z3XzSkrPYvGaTZ71fcTP4ZaZcvkKSJxuBXlqjOTkDFJSMjl2bDfDfe1E5BbrJqilIPj7DsqKghGFoy/9hqFOte+wIAisuv4eIqLjqKk5hsFgpLr6MIqiYLFYKSxcRFJSmvc6ut0uJEnCYgmHHGcKbrc7yHUhMjJyhmYzf1FWVsaJEyf4xS9+gcFgYGJigvvuu4+PfvSjpKenz/T05hzCBHCeISIiIogAXk0oisLgYC8NDdVMTtlJT8shcf3DWCNiPKTPL38ugAgEdkjwhXr99u8x4fX/q5JHz1/ZlwPoD4PJwuL7voJ7ahxpchhjRCxOSUGQVSUmofN1xnI2I8vQ13ScyeEeli3bQExMHHb7OIcOvc5kXxOpBYtxesKBigKxKTnYouKQZQmz2YLL5cDlchETE0d/VxsFBQvPSv4AkpLSSU3NpPb4XpZsuA1rdCKyLGiOwogecusjgXoInvwzgyiQu2gt7Sdfp76+moqKNR6POuG8woeLFy+mtbVVl8t0+PBhkpOTg/Ju/EPZy5dvQBDURMj8/FJyc4sRRdVHsKGhhid//z80NzfxyAc+hNViZrrg0vmEhf3H9abKvjFv6NftC6/6F244XDJTLhn7pIPhgV4SEpM5XlOHc0qv4Gx5y3uREXG7ZS8B9Kp/LvW5qizKEJHM6GgPeXklAEyODXrD9RpvF0XBky+o7l8QwF2zjTONNQx1NlJRsRaz2UJnZzMHtz9DVGwCk5MTIAgUlS0jf8Ey6k4dprr6CPHxyeTnLyAyMppTpw4zNj5MRnouubnFKIqC2+3Cao0Id0S4Sgh1nw2Vq3Y1MV87gaSlpfH1r399pqcxLxAmgPMMgTediYmJq3r8hoZqOjqaiEkvJnf53aSOn2bEEu39MtSIjIAAooKgoNmkeb+8fXl/PpXP/16kVV3KfiFALR9O1uw2vLYbSCQWmwAAoyNJREFUvpw951g/w60nGGk/yXhfK1FpJRRc9xCKqCYPTzplXG4ZQ2QygMeGIw6bLRKLxcZQ5xmKFy3zqkCyrGAQRVbe8HYObfsTR4/uAsBqjaBi7Wa2P/dn4uOTznnNZFlicnLS29831H1X8LCI4M4SHo9CVAVTNJtYct397Hnm5wwM9JKUlHrO42swm82sWLFCF0pxOp3s2bOHLVu2TFtMpJE/Ddp6giBSVLSIqKhYdu18ncKiYm699VZdGDswx1MdU4Kugb8KHIhApdif/Kl5f3Iw+Zty8twvPo/LMYnBlkJbQ4Nun2WrryOzcCEuT9jW5af+Of0emq8giQsYq6tFlmUMBiP2saFp8hr1r+WsFbTtfJ78/AXExalKfVFROTExCZw5U+ndqL7qKPVVR4mMjichJYPB3k6Ghvq8+ylasobGU4dob2/0jmVk5FJcvDj0RQvjsiLwPms2m8/a+vFqYD7mAD7xxBNERUXx1re+VTf+97//HbvdzkMPPTRDM5ubCBPAeYbApP2rSQD7+rro6GgiueIOUhZeR1KXmhOmqnQquVHzrxRvhwSDx9NOI3tuWa3UNIjqurISqjBE8fZUVWQ3JoOI2Wzy5gZqEAUBx1gf/a3HGWk7ydRwF6JoICEhmZSCMpqba+k+8QoZy++kL+06Urp3Uh+zHmNkCqa4HLq720hOzkAQBOLjk2g7U8mGW96OQTRgNKihZqNBoKh8JQULFjPU20FkRATtlfs4cWgXCQkp51T/AKqrjzIxMcKSJWuIjkvyVv+CjygF0j/F75mnFMRrR5OWv4i03AU0NdWQmJhyQSpQdnY2OTk5tLb6/Ar7+/s5cuQIK1eu1O0rN7eEmJi4c+4zLS2L3t4O9uzexU0336IW6YTs2hyMYFVQCVrmnw6gFX1MTEle8qf173W4fcUbDVVHcDkmmZx0eUOvGqLiEll3xzt1ps8AU568vylP2FcrFFIAXBOIogGj0YTFYmNiZNCn/uErcPJ/KwQEzBGRGIxmRkYGcTod3irflJQMYmLicTimiIqKweGYxG4fZ2ioj97eTu8+kjLysFijEAQjgmgAWWLDbe/C5ZziwKtPYbHYyM4uDCuBVxjj4+O614H34TAuD7773e/yy1/+Mmg8JSWFD3zgA2ECeIEIE8B5hsAbT+CN6UpB6xsblbmYxNKNuCSF1oQNmI0iVvBW0WpqDMj0HnsOgzWatMU3oDW4GLW7yR/eDUB70rUYRX3I2J/8SS4HPa9+H4djiuiscmJzFhOZugCj2cLkYAd91dsZbj2BwWAkMTGVwkUriI9P8SbRd3W14KGnyIpCc/wGDB4FyZq9nMGT/8ThmMRisZGensOxY3toqz9JRt4CZE++j8kcqaqPBivpOUWIyOxuPkNfdzsVFWvOed1kWWJwsJfNd72HomXXee1wwEeSRBFdjqSmbsoyIKhFIoIgIIi+sPqSjXey7Y8/YHh44LxUSO8xBYFVq1YxMDCg+/FQX19PZGQkZWW+ns1auDMQqs2JrCtWSEnJpPb0cfp6+0hOSUYM4dt4NvgXfEBgVbie/GkVvw637CVv4OsE4nS6OfTi73A6JYaH9dYdgiiy5R0fxmixeQmephz2jToZm3Rjd0g43DIut6owygqYRlqIiYlHFEXMZguTE2N6+xdt//jyNRUFBKONsuvfQ83OP3P48E6Ki8tJTlZbWFmtNqxWtZAkIiKKiIgokpLSKCwso7+/h56eNga61MKdiIgoUpLT6Ovr4sCrT7H6xreRnV1IU9Npens7WLBgGVFR01sQhXFpCLzPzob8v/lYBNLS0kJ+fnAxXW5uru5HaxjnhzABnGcIvPGMj48TaKp8uSHLMtXVRxEs0aSveouq6ilgMBvUwgq3giTLukrIwZPPM9aghhqtMUkk5C0FwGISaY7fQN7Qbm/1pBZg9C8MkBWF3iP/wOGYJDMzn8GBRlpajiMYjETEpTMx0IbVGkFJyRJSUzND2q84XG7ibeqXoiAIXlVPcoMtfQnj1S9QWbmftLRscnKKiI6OY8+Lf2Z8bAjJrbYQs0ZEkZiWQ1J6DnFJ6Zw+spOejiZPSO/cxMvhUAlIbGIaBlHw5kEGvl2614pwVpd+UYCkjEKi45Pp6+u8IAIIavhqw4YNvPrqq0iSr5iksrISs9lMUVHRWbfv7m7jzJkTxMUlkZVVQEJCMklJqdTViezds4u77rlPr2AGGH77lmhjwdW+/kUUGrHzkT/FG6rVureAL5d0YnwEl0tiYMAe9CW3+pYHSMkpwe1XROJ0K9gdEiN2N+NTbm/fZa3YRAGUsQ5iMzO9129qYtR7bC1VQUcCEVRzcQGS8yuISMqjfu/fqa4+Sl7eBLlaAUkIiKLBa9/jdE4hSTI2m5r6kZ+/gMbGGva88CTX3PIult7wVl75809pba2nrGz5Wd+3MC4es1EBnI85gCkpKZw4cYK8vDzdeGVl5YwUO851hAngPEN0tD7k6Ha7mZqauqJWMC0tZxgfHyX3+o8gmFTlxOixJtG8+gQBFFnBOdrNWMNuxpoPU1S0iJGRIboO/JnIhHSMUSkYRDAZBFoTNmAQ9MTH/3Y02rCXiY4TlJUtJzk5g/z8BUxOTtDf383IyCDZC5aSkpIRlJ+mQZLcyG4HZlu0qsSghqMBZBEUi5WIvDVMNOxiZGQAKCI7u4Dq6qNkZOQSH5+ELCvY7eOMjwxwuqMRh2OKyMhoKpas8eZznQsOh1p4EBEd5/Wu88d07dXOdm9WlwkYjRfvG5eQkMCaNWuCrBUOHTqELMuUlIRW/0D9QQAwPNzP8HA/ZrOF6Og4ZFnGPjmpKoSeFAD1TZ3+ZKarBNe2kmU1RKv19nW6fdW6mvrnJYCe/YwNDTIyFnwNi5Zfy8J1N+KWVeKokcopl4TdYxw9PuX2huh1nWYMFi9ZNpksjNpH9cVMgfmqguLXY1rAHBFDyeaHaTv6Is0nthMfn0RMTPxZ3iEVZrO+8tdkMlNSsgSDwcCBl/+Pa257LwUV11J39HUkyY3BEL7lXwmMjY3pXgfeh2cKs4/CXRoeeOABHn30UaKjo9m4cSMAO3fu5BOf+AQPPPDADM9u7iF8N5hniIiIwGAw6JSb0dHRK0oA7fYJTzsyT2hWVhCMolf9kCUXEx0nGa/ZyujoEGazxeuLl5aWzdGju2l+4w/kb/4gojUGo8FHxvyLBdSODQrD9fvoP/E8mZn53nAZgM0WSXZ2IdnZhecxa23/vlxErzWNx+tPyCxjomEX2dmq4pWcnMGaNfFYLKGvpdvtwmAwXpDa2t/fgyiKWKLi1YKCc/zqVsOJfipZwPqaguh2Ohjq66CkZMl5zyUQOTk52O12jh07phs/cuQIk5OTLFmyJOS5ZmbmkZiY4gk/duJ0OhgY6OE9D76XG265w8P5PDHt0M42ISrB9eRYU4FlRfEaPGvETyvQ0PJHtf25JYXayiM89duf4HLq+7ZmFC9hzV0PAQJuSa/+TfkZP086ZW9nFv+KdsWawOSkqgJFRETR2dmCfXwUS0S0Z776whZBAEHx9HYWFK9HYubSmxnqOM3p08dZsWLjRXn+CYJAYeEiZFnm4Et/YNHaW5BlicrKfSxduu6sZuRhXDhkWZ61BHC+4fHHH6elpYUtW7Z4i2wkSeKhhx7iW9/61gzPbu4hTADnGQRBIDo6WmdoOzo6Smrq+VeDXihKSys4efIAnW/8EmtKEcaIeMyR8UTGJDDe385w/R7cbhdxcUmUla0gMTHVWylqMBhZtGglx4/vo/b575K8aAuJpRsRRFNA6FrB7bDTffgfjHecIiMjl4KChRc9Z1EUEUQD430tRGUvVUspBE9BisdMeqj1IDZbJLGxCd7tpiN/AEaj6YLmMDw8QEdHEys234eEEUnSM6GzqX/+eWX+y2VPjmRny2mAoBZwF4oFCxbgcrk4deqUbry6uprR0VHWrFnj7aHsD6s1gvz8hfT1daEoCrm5edx9zz043Yo3DUAN//qIVFCFM9oPCl+oVztvyU/xG5106ypzXZJmDK6qa3j289qLz7LzX38Jkv7S8hey8W0fAcHgVf00z0CtbdykU8Lhkr1VwaIARgMYtY4zlngmx3sASE5Op76hipbTRyhaep2vAl7RiK2+yEclgepDFg0UbHgXp577IbW1x8nLK8Vmi5z2R4XT6cBoNAVVaAuCQHHxYmRZomrfVuKS0hgf7p+VYb25Drvd7lW9NcwGAjgfcwDNZjN//etfefzxxzl+/Dg2m43FixfrvErDOH+ECeA8RExMjI4AjoyMXNHjGY1GFi9eTUtLHXb7II7RDsanJumT3BiNJlJTs8jIyCUiInReTEREFKtXb6KlpY72k9sYajhIQtEaLHGZWOIycE+OMNp+ipGmQ8huJ4sWrSAp6dJMPwVBIDeniOba3SiCgfRld/pytAQQ3JMMt1SSl1dyWfInGxtrkGWZvLxSjEYj/f3d1NQcJS4ukYWrb/Aqe1p1dKhDagTPv9pZsz3xkSWFxuOvc2rnP4iKiiEi4tK/iBYvXowoipw4cUI33t7eztatW1m3bh0JCQlB21mtNhYvXk1V1REGBwd48YXnaWpqYnhkhHvufycZ2dkAGEURUdQKJARdaFX2XBeNAGpVuf5+fEMTLtWTT1K8KqpBFLzV0RNjo7z4f7+i7uSRoDlmFpdz7ds+hmA0e1XDSafkVRSnnCoBnHBIfj2FNcVOxGoyqHOzJDLZZ0dRFEwmM/FxSbTVHCR/yUZkRVWYvaFggr9cBS8JVoiMT6Vg3VtpPvA0fYdeRxQNREZGExubQHZ2IbW1x1EU1UB8aspORESU10MwcJ+lpRXIskxfXyfl5avCIeArgMD7q9lsnvEuIDB/COCnP/1pvvnNbxIZGcmnP/3poOU7duzwPv/Rj350Nac25xG+G8xDxMbqVZ8rTQBBVb8KC8t0Y263C1EUzyvkpG2fnp5DY2MNfVWvIrt9zvpaJW9+/gJvZeSlIje3mN7eDpzjA4B/rpnMSEsliqKQmpp19p2cByRJor29CUWR6e/vIjk5g/b2RpKT07nhvV9GEQwoiuJTwqYhf1phghYO1cigv2G2IsucOfgyKSkZlJZWTOvdd6FYtGgRFouFw4cP61SksbExtm3bRllZGYsWLfr/7P13YGTpXecLf845lZNKKuXcSp3zdPdMT3TOARuDMXnZe/deAwvLLrAscPHeZe1dLsvyvi+b4IIxYBsbbGxjG3scxuOZ6Qmds1qtnGPlfMLz/nGqSipJ3epWt1pq9fnYGqlOnXPqqdBV3/qF729FyvLatXPoukYikeDTf/HneL1+dF3nP175LX7x136Hzu4ehA0UIRVEmyj6X5fGuRnGYq1fsbs3nSumZXXiGQ1NN0riSgJshXGAN86/zgtf/gyZZHzFfeo58iQn3v/zGJKymPY1RCHSJ8p8/4rd2ZIklbp7dWOxsxhnCCEMMpmU2ZFb20hv7wXikTk8FdWIwvi5UhRTwNIu4SLmbFNBVcdjVLYdJDc/RDo6hZ6YYaz3DJOTwxiGgd9fQffBJ2hobueFr/01ly+/zuHDT60SCZTZvfsw7e07b/kFzOLeWP7+WlFR8cjb7nzqU5/i3/27f8ev/Mqv8Md//MeA+T717//9v+dP//RPiUQinDhxgv/23/4be/fuve25zp8/j6qqpb9vxaP+mK8HSwBuQ1YTgBvdCbwad5sSBTMauG/fMYQQZDIpksk4druDioqq+yZmiiQSUdLpJG2PHS+IDcjG5xl+/o8Qhk5VVc19Ga8Vi4URwuDNP/ZLXHvj+4wPX2Pn0Tfx2Nt+DEmSS35xq4m/pWKhKDaW/xRnykoSLIxcIZ9J0Lhz/31/vLq6uvB6vZw6daps7JUQgqtXrzIyMsLhw4dpamoqWfbY7Q4kSWLPnqMlmxRd13j11e9y4eIFGtu6ygywlSW2P0XxVxRm2ULEL5nViKc14hmNZNZMzQKFKKIp0OKzE5z6+ucYv3ll5R2RJE6848McfO59ZFWBKIyF0w1QNVONZVW91FBStINZ+tyUZgzrsinqnGandSqVKNm1yLLC8MUX2fPMh9ELGcKiX2HpuaVomyPKujYlJGSbg4qmXQSbdyNJUL//bfS/8nfs2HuMvUef4dI/fYZzL34DVc2jKDYMQ1/1OZck2RJ/G8jy+dHL3383C8HiSMn1Hr8eTp8+zZ/+6Z9y4EB5/fEf/MEf8Ed/9Ef85V/+JT09Pfz+7/8+b3vb27hx48ZtU+YvvPDCqn9b3DuWANyGBIPBssu5XG7DO4HvN5IklbzPNorR0X7s3ip8DTvNqJohiPSfQpJttDS1lTWY3AvR6Dwur5/Gjv3UtO0ltjBDsLoeIRYbXZYbIy+NDBXr+opiz6xTW5xuURxLZ6TnufL9v6GysuaOOkjXQ0NDA+94xzs4deoUCwsLZdclk0leeuklqqur2bdvH/X19Rw//qZl90swPz9jihWbi7xm4LAtnySy1NuPgtAyrViSWZ1YWiWcVImkVNI5HUmSyIbHGb/yAoYhMzsxwcxw36r1bm5fgLd/7P+koXMvqi5KNYZ6YaSbtsT3rxgJXFpXaN4HM92uAum8jk2W0CUPmqOSGzcukk4naG3tpq2tm4HzLxBq3091c3dZjWZ5Olgs1gcWH6dCdDCvgSILbIqMK1DNUz/6LwlPDPDtz/0xU0NXcbs97Nx5kNrapvsu+C3ujOUCcPn772axGSngZDLJT/7kT/Jnf/Zn/P7v//6Scwn++I//mN/+7d/mQx/6EACf+cxnqKur43Of+xz/4l/8i/Uv1GLdWAJwG+Lz+bDZbGgFrzqASCTyUAnAjSaRiDI/P03z4z+OJCvmvFc1R3z4NE31TezYseuOz6XrOtlsCq93daPdaHSBxvbdKIqEZkj4Ks2GnFLUb+mEj1XSg0XRUGxMKAq/4uVs3gA9y41v/xl2WWLPniMbGu31+Xy89a1v5erVq1y9enWF0Jqfn+cHP/gBFRUVdHd309bWhsPhIJ/P0td3mYWFGTr3Hafn0JNk8gZOu4EQEkKRMISEZEgl0VeMwmVVg5lYjoWEymwshyJLRFIqqUyOmdf+mshEP4logmxWu8WqoWPfEd78o7+AwxsoeFMu1gwWxbWmm40exfrCoregVoq0ipJg0zWBEAZGwT9yru1j+OZOoQ+fJxSqp6Wlk3B4lkvf/WtO/vi/xe50l8SjIcrrARetYso/uAUCSUioukFsboLLZ7/KwvhNPB4/u3cfpqam4ZZWRxYbj67rxOPl5QVbRQDeL5bfP6fTidPpXHXfX/zFX+Q973kPb33rW8sE4NDQENPT07z97W8vO8+zzz7LqVOnLAG4SVgCcBsiSRLBYJD5+fnStkgkQmPj/YloPexomkp//zWc/hqCbUdKXoXJ6ZsYapaGhtY7Ok82m6a//yqRyDyGobNz50Hq61tW7KfrGm5fAFky69wMsVj0v5RbCb+iUFlqcaKVBIvAMHRGXvob8skwR448ta7U+90iyzL79++nubmZM2fOlL3WisRiMc6cOcO5c2cJhUIIkcUf8PGun/hFuvYexWGTC+lPELKZgjf/I8rGrmVVnUzeYCaaYzaeN39H00TGbpC+8lWy0fmS/c1qeAKVPPX+n2THvmMIJLL5xfnAxcey+LgWI3/pvHmb2fzyx3sxKlisC/S7C3YUeMhWvxlP/Dpzc1P4fAF27jzE2bMv0vvyP7DvzR8reRGWRhmy0utwURyavzVNZfLc15ntfQmPx8/evUcJheqtmqctQDQaXfEFaKsIwPsVAWxpKX9P+73f+z0+8YlPrNj/b//2bzl37hynT59ecd309DTACjeKuro6RkZG1r9Ii3vCEoDblMrKyrIP5XA4vImr2ToYhsGF65fIZNK0P/PzSLKCKKR/XVOvYbPZcbk8a55HCEFf32WS2Txtbd1oTh83+85TUVGF210+jcVud5BNmx5xRa/BssEeS2ptil6Ky8WfbgjCSZWctihMNMNAkSX04R8Qm7jO/v3HH3itV2VlJW9961sZGRnh0qVLq86eNgzB3Jz5WpyfT/LNL36e9q5zNLfvoKmlhR1tTYRCVUh2G0KSMIQgkdFIZHTiGZWFcJSZ6Rn6+keYHh8hNjVEbmEUjFtH+wAUm42Dz76XI8+9B8XhLDXQaMbSGkqW/KbsslqYJawuE33qkjnAYI4vXGo3E3N1MD8/SltbN263h46O3dzsfZWGXY/jr20vPa/FZh5JKjTwFOZhF38bQmDoOqOv/DWJiWt0dOymqWmHlerdQix/X/X7/avaIm0G96sGcGxsjEBgMbuxWvRvbGyMX/mVX+H555+/bQf08i8tm1GbbrGIJQC3KcttOSwBuEg2vkD9vjcTbOwqTYvQDcFMzo7PFyi9IQkhuH79HLW1TVRX15edY2Fhhkhkjp63/AKh1n2ok+cY6z1DLpctE4C6rpHNppFlpfSNutjtu7SmrGjjohaiTLpBSXiomhmRymuikJo0/xZCUCcmOH/x27S19VBVVbvhj91qSJJEe3s7LS0tDA8Pc/369RXGuEuZGhthaqz8W78kSXi8XpwuF4qioGoGuVyOXCaFscTU/E6QZQmPx8HTP/7LtHQfwBCmaCsKq1K6t1j3p9+qwQbz8dfN44vTRYrRwEXTbhnZMD/IDCGIuLqpCl/jpZe+yYEDj9PQ0MbU1Ch9r3yJIx/8VwhRLuBMMUjJY7AkDg2dqdc/T2riKnv3PkYotHFenhbrIxKJlF1ezQ5ps7hfEcBAIFAmAFfj7NmzzM7OcvTo0dI2Xdf54Q9/yJ/8yZ9w48YNwIwENjQsWnjNzs5uqEetxe2xBOA2ZflcxHQ6TSaTeeTrAGVZJuCvIDU3XJrHWkzhqPEpQpWLHXyzs5PMzU0BrBCAw8M3qGjcSXXbPlDTXPrhP1BT07hiBNzgYC+6YXDgyfeULEBgaY3XYhOAUWjuKEaZSmlJzSCV1QuXTQFiGAKvXXD9+U8TDFbfdnbsg0JRFDo7O+no6GBqaoqBgQEmJibuyHxYCEEqmSS1bKbq3WC3y3g8dqqqKjj8/l+mqr51ifgzo67aEvG3WPcnymxgtNJMYaP0O6+ZaemlXoPFWb8SotCBbIrAuLOVwcr30pH6AdPTo1RWVtPVtY8LF04xce01GnafLBlDl/wNDcoafYQQzJz5e1ITl0vm6RZbj+WlD1tJAD5I3vKWt3D58uWybT//8z/Prl27+M3f/E06Ojqor6/nO9/5DocPHwYgn8/z4osv8p//83/ejCVbYAnAbYvf71/RCLKwsEBz87372j3s+P1BxicH0XUVQyiFgv48ejqMr1Dvomkqvb2m51Rzc0fZ8blcllQqQfdjxxECBl/7Koaao6ur3M8qFgszOTnC8bf9GN5gdSnaWOr0FIumzks//Iu1acXmh6xqkMhqpjDJxNDDg2gLA8TCg+SzGfbvP76l0iiSJNHY2EgwGEDTYnT17CGfh4vnL5DL5dY+wV0QCoWora1BVZOk0zFCzd3sftPP4Ct0QavaYqRu8TFeFHJLm2lU3ShNF8ktEX4lOxjNjBjqRvloN0kykMXiNBOBTNTdzbwURZ5/FV3XqKiooq6umf5XvkgqMknjwXcgOzzl0cZCt3HLwksIIRifuYbPF0DTVMbHh/D5Anc8Y9pi41FVdYUH4PIv3pvJg+wC9vv97Nu3r2yb1+slFAqVtv/qr/4qn/zkJ+nu7qa7u5tPfvKTeDwePvaxj61/kRb3hCUAtymyLBMKhZiZmSltm5+ff2QFoKapTE+PMTs7QSIRQ3F40NQ8QnEVivFNA+NitGp4uK90rM9X7usVjZrf+r11XUSmB5m5+QY9PftXTGIYGLhqGvYeeVMpZbg08geLM24XZ88upn1zxWkXhYYELZci8eJ/AWHg8wWorQpRs3PflvR4M2skL9HQWM/v/N7vIisO0tkcFy5e5/q16wz19zMxNsrs1BSqml/7fEjkbQG8SpZQ0EdPzx5qamrK6pHGxgYZHLxGYqqPQOA4EjKKDFIh2pZT9dJjWhR+mfxiZLWYXk/n9DITaFUrpn6NwvO4OHNZkkDTC3WdkrlSWZKQbBB176I6/DJzc9PU1zfT03MAr9fPaP9p5gbO4q1uR8ul0fIZ1EyM6iMfQa7eXTivRGfnPq5dP8eNGxeRZAVZkjhx4s3Y7Y6NeMos7pLlNkiyLFNZuTH2S+thqa3Qeo+/n/zGb/wGmUyGj3/84yUj6Oeff35LjM17VLEE4Damurq6TADOzc1t4mo2B13XmZwcZnS0H13X8TbupmHv+wg270aXHejFdCASkt2DquZJpRJMTA4DrDCgFkIwMTGMt7oVxeln/uJ3cTrd1Nev7ByWZQVXsAZdgG4YK75RL00BFq1dimnGohVJTlsyjSIxC0Jn165D92VCyUYSDs8Si4X5xX/577HZHGiGwGazs2vvXjp37V4ipAzisRiRcIRYPEE4lmIhnuP8cJzpuMF8RiaiOknjAUmhSz1LvX6a+vq6sm5nIQQLC9N4PD5Sw+f4zkt/z7EP/BKBmrYl6fXFEXK5gqjLFGori53GxZm/sbS2RCgudgEXo3XGkidTN0CSRKmxR5IgrxlIHj8JVzs3b17CMHQaG9toaemkrq6ZkZGb5LILuGx2MuTJqVmSyQSOCoOztuO4HAr+HoU9e36EgNdBjcfgG//rtxgd7V8xccdic1ie/g0Gg9hs1kdqkR/84AdllyVJ4hOf+MSqHcQWm4P1at3GVFdXl10Oh8NomvZIvEkJYTA9Pc7ISB+5fJ7KjuPU7n87stNvRvkKkyqMQv2VECA7veTzOYaGenH7qtDyGQKBxZoeXdfp7T1PIhFl79M/jUCQnOkvpelsNnspFWsYOj5fBRMjNwjPz+IOmM9FMU1YFH+lGjTDjPoVLUpUXTAXz5PN64XrBF5fDUawkd7ei/j9wS0Z+SsyOztBc0sLPbv3Luu6NR/vIpIk4fVXYHP7cVZqiFiexHyG1MIC0XyWmJono+sYhjkibty2iy71DcbHh2hv7ymdR9c1YrEwu469jd7T30GWFc587X/QuPdJ2o68uzTiLZs3G2pySzz+Ujkz6lcUgMVoYGn0nqD0Gin37Cs0fmB28RQFoFyY+5vK6QxWvY/W+A8wbl4mEKjE5wvgcDjp7jbTYplMivPnX8HwNZH270EUppqIggm2LtkYOPdd+hNTyLLCxMQwra1dVhRwCzA7O1t2uaamZpNWsjpLJ8us93iL7c32VwKPMDU1NaWRXGBGW8LhMLW1m9Mt+iC5fPk0kcgc/uYDNB94J+6KWjJ5nebpF+nzn8TjNFNqZprE/ECXHT5mZydQ1Tw9T/84fS99Aa/XFFn5fI4rV06TSiU4+K7/HV9DJ9m8Tj4VIatmOXXqeSRJwm534HA4yWTS6LqGx+Mjr4OiGciShCxTsHBZUnummb9TOb0UoUrnTCGi6gYiOow8ex65sor22mr6opMr0s1bCV3XmJ+f4UMf+fGSgXVR5Jo+eOXNMHnNIJnVmU/kGVvIMDiTZiqSI5ZWyarGYvOMJMjJXobsR5BHzhIMhko1cYpiw253oOZzKDaH2eiTijN89ttUdj6J4vKRyeuk8zqprCn28gWbl2RWJ5Nf3FaMxpaaPcSinUYxrbbo27c4u7jo7WgYArkw0i6ry4R9B6lMXiaTSeH1+pmdnSASmae6up7BwWvYbHainT+OU7KhGQZuh1Iaiafpgrmr38PlcBAMVuNyuVfMW7Z48BiGsSICuPUE4IOfBGLxcGEJwG2M3W4nGAyWWRXMzMw8EgIwl8sQ7HyCxsc+hCxLJWuNPv9JJMmsuUNe9iYnK6hqHndtF+6G/cAXSKeTjI72MzU1iq5rnPjwr+GuakHVBQ6bzIEf+bfI2QiKnkbNJsilE+TScezuCqrbD+AI1IEkkVNNzz4BBTuRRQPiYsdvKmvWqBWjUFomhtz3ZYiPYHe6ic5miNsc+HyBB2L2vF4WFsxRb8cff6okbov+eouTThb9DjN5g2hKZSqSY3g2w9Bshtl4nkxeNzt4l4hFCRh0Hickprl+/RxHjz6Dw+E0I4leP6lknKrmncQm+1AUG8Ggj7zshrxOtiCsk1mdVK4YBTQFYNFap9iBW4r8icUI8a0+D0vbRaHXuGDxY87/FSxIFdTZ6rh27Swul4dsNo1uDzAzM46ueFjo+Gnskovl7mmaLrApEjaXn+qqSjo6dt/358pifYTDYfRl9kRbTQBaWKyFJQC3ObW1tWUCcHnaYjtipnYNhDAjMYpsfhjbFBl3wavN3M/cXyr0bro7nsVb10NVz1PEMmb39OhoP7KsEAxWseudH8dbUY0hBHZFwmFT8LtD2JRqbLI55aMoNkuWLbqBMITp+2dQEh2qtpj2Le6bzGqlrl89Nobj5hdxSBo79x+nsrKGCxdeJR4P4/NtbUuQaHSBpuZWXIEQmbxeVjdXnL9b7HzOawZT0RxDs2mujyfpnUwxvpAlntbK6uyKGMJsCLnqeTuexBf44Wsvs6+nh7q6JlKpJG53GNF4AmIx9NQMnoMfQS10X+dUg0zeIJnVCqLPfOzThchrUfTB6hM5ZAm0JQJ26fIkqSAECyLQ9BwU5uQXbFyr+ggN6fN4tTmSTccwPI24M6MYjgA4gijLBGaxlEDXDaTCFxOLrcPS2mqAioqK2xogbwZbrQnEYuthCcBtTl1dXcmEE8zC5e1eBzg+Pkg2m6am9QASpsBTCn0cZvRvsYuziCSBI9SBt7EbSZYwZJnW5/4FAb+XqrpmbIrNrO2i/INfFOrDzFjAopecWhB/RlEwGGYasdh1WmY/UogAppc0IbgmXsFrlzh48JlSureyspp4PLyla//A7IaMRhbIlerZWOKdtyj+tILBcjytEU6qLCRVYmlTBN9qgoEhBDZJIid5ORv4cfYlv8nwcB+1tU1oWp587WE8zUewNxw2b0MIoikVISCe0YhnNCJJlXhGI53TUXWz3q94axKUTLqXrqDk1XiLvJhYegIhEIXyAt0AIQkkRWHKewxZBq9dwSEg7W4zI3zF28V8rcqShGEIHA6Z7MQF8ok5atpP3OvTYnEfWS4At6KZsVUDaLEW21cFWABmBHB5HeDc3FyZG/t2Ip1OMjTUS6jnSSoaesymi8L4NVH4QS73zJNlkA2JvelTDDmeAsDjlLE1dON2yAgUM31JsYHDjAhJxXMWtmmG+QGu6uXTJYoiUDeEaTGilUcAi4LRbEQwRYlTV/F4fGW1fqFQLSMjffj9wQf1cN4xqppncnKEublJstkMNpsdvRD5LI01K3U9U/DgM7txE1mNWFojntYKad+VHdNFiqlV3RCkZS+6pKAoCoah4/H4yMWmcBfFZSGdW7TZyeQXmz2yhU7gYoq6+HxKUjEevPQ2F30EyyKDJUufgiUMkhkplM0vGMVRcUICRZZMPxokNF2gyAJJKZ7DfD1Khf0MQ0dEB7A1djF36RvU1DRQVWWlF7cKmqatcFTYigLQwmItLAG4zbHb7YRCobKC5ampqW0rAA3DQFFsRAbP4PKFqN75FKCUPtzlglcbhVTtokgTXHE/jssQKAYYhU7OYjpXkSXk4gf6ktsrTn9YqimXWoYU/f+KfyeyWpkP3eJoMbP5w0xFgmYPMjc/xJUrpwkGQ3i9AQKBICdPvn1LdoBOTY0yPHyDI8dPMjk6hsPhKhNLRSuWxVm7pujN5vVCSlYrjLszSmnYW1EU205ZYsx1hFDmW5w9+xKKYiMdnoSUiiEKo9WMxaYNs8vX9PozH+tFU+gi5kQPVl3D0ojKcvFX/LsoAktRRGFuVXVRikIXBaksSSCb30dkGfKawGk30Pq/TXzsVRwH3o5Q03g82/Pf6sPK7OwsxpJWdkmStmRdtdUEYrEWlgB8BGhoaFghALcrPl+A48ffxPDwDSbO/yPzN0/h9IUKdVwCWzaM7q5G8dWCvwmpeg92m1Lo4JTMTlW1aP1hfphrilnLZXYNL0sdI5WijBLSorXLkokTpfFjBQGY1wyiKc08t7F0vBgl8ZBteTuyr4756BUWBq4BUFfXxK5dhzfjYV2TYNC0uTnx1Jvo3LnPvG+6WJL+XYyKLY3+ZfIGiYzZmZtTF6dsrEVxisckbeQrfpJdiW/hzc6QrtmNnjVtYzRjMVIrS1Kh03ex9k8t1P2VR3cpdfIWWTobemkJQKkzuNQFvEQEFo4VohAlFgIhFsXh4v7F5hZzo6YLbNNnAVgYeB2HJ0AsVm44bLG5TE9Pl10OhUI4HFvvS5lVA2ixFpYAfASor68vm9MYj8dJpVJ4vd5NXNXGYbc76O7eT0NDG+Pjg+hqvPChKyE5nOj5KJGBfgxDx33ilyBYjyKbH77FD35NF9htEnnN7MQs2nLAyuL/Yt0YLKYnNV0wE8uVvObUJebOxciYVKj1EoDIJ/Em+3Ckx9G8zcjVu/HWduJijnRinGCwmvb2XQ/wUbw7/P4KXC43l86dpqNnX0kMGWIx4lUUflqh7i6d04lnNKIplUShAaZYN7kWohDhc9hksnIFFys+QoVT4PN5Caa1JY0bxedOLKZ+CwKwOFNZLwRzpEL93mL0bnHb8tteLv6KfxePlaRivaBkPgCyhFzYV9UMbLKyciqMEEhCR1XzNDW1MzExjNPponPf8fU+LRYbwOTkZNnlrZpNsSKAFmthCcBHgKqqKhwOB/n8Yifh5OQk3d3dm7iqjcfnC7Br16EV2+fnp1lYmEFpfRLZV1uq1dINQb4QNZJlUDQJm2yUxF+xlnK5AIykVMCsdSuKiqKJcLHGrDiFYtEGRSAZeXyJXvypXlypEUDC5/OTmrqOmHweA8jb7AUx27ql5v2uhqZpuD3eUkq1WOe4mOo2LVeKwm82lmcykuXSaILZWL7k+3erRovlCEGp0cTtUEjrMlpKI5HRcDsUXHYZl8P0zNMMYTaBFOoMl6aaV4i5ZaIOFiN0q+5/i7UZhkCSMRtCCqng4jGyLJXqIYvIkoRNi5MHqqrqCAZr8PsrcDq3Vnfpo0wikSCRSJRt26oC0MJiLSwB+AggyzKNjY0MDw+Xtk1MTGx7AbgaiUSM69fPY6vdg6PrbaWP9WJEqZiOBVPwKfJimjecVMvm+S7VY0VhV6wnLKY9l4qfvLbYlCBL0Dr3DQKZfoLBEDXd+6mpaTDNjNU84fAssqwQCtUiy1vf+DeRiKJpKrv2HVqseVwm/kyrG51oSmU+kWcynGM8nGUunieeMWsji557d4OqCUBH0w3siozdJmFTZBw2U4RpmHYzTptsPsdGeUfvndzerbqSV+wnAGkxDVxKGUvF1HZxUoxAkiV+vvE6AF+LHzTnCedNceF0uvB6rRmpW42JiYmyyy6Xi6qqqlvsvblYXcAWa2EJwEeE5QJwZmYGVVWx27euofD9RtM0rl07i+Srx7Pvw6AoJQFYnABRHAGWzOplBf/F2r7iW+LyWFzxg75odlycIKEboBWMkLPqonGsTw/jywzR2bmH5uaOsnPZ7Y4tP+t3KZOTI4yN9WOz2ens6ianUdbkUoyAZvI6c/E8c/Ec09E8k+EsU9Ec8cLcXUPcudBaiiEEqlZ8ngwMIZlpd5uZ/qUQfc2qeinNf6/psbVYWuu3GoosYVMWd3h/4CLfyx/BpiXIAU6ne+MWZ7FulgvAxsbGLR2ZtyScxe2wBOAjQkNDwwo7mKmpKVpbWzd5ZQ+O/v4rqGqOwPEfR3Y4yyZSpHLFEWGLpsBFUVcUdsXI4GosFv0vNjwsrX0rdhN7tAXas69TnR/A6XRTU9P4IO76hhKJzOF0uWira+Urn/0cb/nwR8vEX0415+8mMjrjCxmmo3mmozlmYzkWkmohHSvuSZAJBJpejFrIZud2XirNHS42nBSjf+sRmnfDapqgOCoOFu1wPj25m59vvM7X4gdx2gWyGkdRbNvap/NhJZfLrbB/aWpq2qTVWFjcO9a7zCOCw+Ggrq6urINtbGxsVQGo6xq6rm/pebN3y9zcJDMz4wQOfBhXoLrQcWt2kmoFj7hUTivNgy1ev/T3ap2hUJ4qKYrG4gixYuRQ1Q2EYbAv8VWCTkFrzwHq6pqRZflBPgwbgiwr1NY18BMf/3Ve+PLflgyWNV2QK4y4i6VV5uJ5hucyzMbyzMXzRFMqqZy+OOu3QLGR4m4opl51Q4KC2BNQ8gLMa2IxAniPYvNO17NoKL2Y+i3WkoJZ8ych8Tcze/E6zccrHg3jtaJ/W5KJiYmyf+uKolBfX7+JK7o9SzvW13u8xfbGEoCPEM3NzWUCcHJyctWpICMj/YyN9VNVVUN9fQuhUN1DUYd2K3K5DNdvXsfVsA9f69HSx7FeiNAtJNTSfNh0Ti+leo1lUcClSNIS0be0aYDFFPDSyGFeE/zr9tNcuZJiz56n8fsrHsA931iGh/tIJKLkchmcHjextMr+t36ITN6M6OVUg1ROJ5ZebPgYm88STppdv5m8mRpe+tBKrLTauVNKYsuQSs0kdsWcwJLXzG7sByH+lq5naa3A4qSPwvWFV2KxY1g3BEYubjV9bFHGxsbKLjc0NGzpSK1VA2ixFlv31Wtx32lububMmTOly5qmMT09TXNzeb1ZQ0Mrk5PDhMNzhMNz2Gx26uqaqKlpxO8PPlRRKyEEvb0XkRQ7wQMfNIvxi5MoNKMkUorzYUszYQupw9W83m59W6wQj4siUjA1NYrPF9gW4g8gnU4QDs9iszsQNheprI4im16K4aRaavaYiuSYiuSYieUYW8iWjLCXp9VL/nilBoqV9it3giHMaJ+qS8iSsRjBfUDirxiNRBRKLooRPxkU2YysFKOAkqEhCQUhZNNrMh/H6bcE4FYjn8+v8P9b/r5pYfGwYQnARwi3201tbS2zs7OlbSMjIyveyNxuD11de7lx4yKyLKNpKhMTw0xMDKMoNoLBEJWVNVRV1eByebZ0EfT4+CDR6DxVJ/4Zkt2zOIZMM42I00vEXyavl8yLl9b03YqlJYFFe5jF4xaNgw1D4BRpwuFZOjv3bNRdfSAsLMzi9fpxudwEAlXMzU3xk//q/8HmdJdSrqpuEEmaKd+ZWI7paI65eJ5YWkPVCpGuZY0Yyzuqi8bai155d6fcStHXO7Bs2QhKIrCU+i0aiYNdkZD0LFXRs1RGzyBkB7nqQyhNJ8DQyGRyCGEgSQ/PF63tzvj4eNn0D1mWt7wALHah38vxFtsbSwA+pGQyaVyuu68Vam1tLROAExMTq3YD19U1Y7M5mJkZJxyewTAMc1KG7GI2qTG/cA0JA5fLTWVlDW1t3VuuczGRiDE01Itnx1PYQp0li5eiJ186pzMRzpLIFL3hFo2glwvA23q+lfZdFH1LLztEmiPZb5i1crUPb9G4pqlcufIGgDl3N5eloqIKh8uDKMy4VXWDdM5gLp5nNm4Kv2LKN6saixNPMB8bqTgil8XHcdXvE8Lc8W6E3GZnsIQw/Qdl2XQRVGQJu6QTip2lJnEaB3kam9rRdZ2pqVcQIo2j513ELv41AwPX6erau7l3wKLEyMhI2eXGxsYt76BgGUFbrIUlAB9SJiaGqawM3fVxLS0tnD17tiRsdF1nYmKC9vb2sv0kSaK6uo7q6jo0TWNhYZrZ2UkikTkkkUTzNqMGd5LNxTAWLrOwMMPevccIBIL34d7dO7qucf36OWR/A86ut6IW7D/UkiWIQTKrkSjU/pVsSJbU7hVZGuG8VYPCijFhhT+8RoSj2W8QtOfYv/+JLTnH905Ip5M4nW5kRWHnngMEquowkNl34q2lbmdVN9Pp8bTGXDzPfFwlklJJZDSyeXP0WlH4LaX0+K72wC6LDD5sFA2htYI/ZDB5hYb4Swgkunr20dDQxvT0GFNTI7hbH8NV1YansZXJyRE6O/ds6ej6o0Imk2FmZqZs26PknmCxfbEE4EPK5PQ4nZ27cLvvbpyby+Wivr6+bB7w8PDwCgG4FJvNRl1dM3V1zahqnvn5acbGBkhNvUSq+V3M1J7EN/wlLl48xc6dh6it3TxrEyEEuVyW4eEbZPJ5Akc/gpBsaJrpxWfagZiWL2bqVyuMBhNl0bvl51xqGF123ZLty7uBHSLN8cxXqPQo7N//1LoitluB0dF+hoZ6qa1txKbYqapr4cm3f7gUNc2qOqomSOdN8RdOqmYNYNpsrsmWav7KHyOpNDdt8XJZQ03hcS9FBu+hQWQzKZYB5DXBlGMvjmoHlYmL9PVdRlHsaJo5ScaemsBf38J0PE11dZ0l/rYIo6OjZa9bm832UNi/WBFAi7WwBOBDSlbycf36OQ4derLUlCGEQTQaZn5+ilwui9cboKKiikCgsqxbra2trUwATk9Pk8lkcLvXFih2u4OGhlZqahq5efMys6Nfpb6+havtH8Ez8R2uXz/H2Fg/FRUhgsEQFRVVDyzqNTh4nYmJYQzDNFx27/kgkru6zIg5nTMjfpORHOGkuliPxhpveMs+i1cIwWXiUQjYnXsZCcHBg0881JY68XgEgNnZSSRJwmZ3lNLpxbnHZkRVJ5o2o37RtFryVTQtYcpHvElL/1NMs7OY/jXrAKUV0cKHEbMr3DQEdzgVZmx7yFUdpHnmywwP3+DYsWfJ53OMXf8mievfBKCx84lNXrVFkaGhobLLzc3NW7r7t4go/O9ejrfY3mz9V7HFqlywvwl/4luMjNxkx46d5HJZLl9+nVQqgdPpZlwN0RgfYXT0JiBRWVnNnj1HsdlstLS0cObMGTRNA0zxMjw8zO7du+/49m02G7t2HaKyspqbNy/TEv9zRht/jJhzB+70EOHpMRwTQ9hsdo4efeaBRL/m5qZwVHfiaDmB7K0FV2VJ/OU0g/mESjytEc9opHJamSfcWkLjTmsAi1TrozTq/ezadeihFn8AXV17iUbn0XVTWHfse5x0Xi9ETg3mE2aDRzipspDIE0mZf6dzZvQvp5rPQfGxNiNbhULAgggsi7IWfy9Jqy/66T0YKoxZqowpcpKXrOQhi5ec5EGX7r7uq1hSoOqQzRvYZLNeMln3LLa+P+fs2ZcIherp7t6Poigoio2Kiq05XuxRIxKJEIlEyrbdLluylbAigBZrYQnAh5S4XMMwe8mNDtLQ0MqlS68RzsBZxweJUA8OM7Lic0QJGZPsjrwG186yb9+xkghc+s12cHCQXbt23VXaSZIk6utb8PuDXLt2ltrBTzNW837Gqt5BXhM49Sg9c19gaOg6u3cf2YiHoYRhGGSzGSp69mKv3YVuiJJAKdb7pQop33Rh6oexRPit972uPGW5+Herdg2fr+KhbfpQ1TxXrpxm9+4juFweKitrwKbwzLt/ApevkpxmPraZvEEkqRFJmZG/WHppWt0oiey1LFhWi/YtF38P6gMppI9zQv0mtoKFzFLO2t/KpLL6DG2nSFGvDyOjI5AQyOZvSQZJRug2klILqu41xay3jmzPT0DkKunJPhR0Tpx4Mzbb1m4ueJRYHv3zeDzU1dVt0mosLO4vlgB8iJmWd9ChX+bcuZdQVRVVCiKQlhiqSSSlSpJyJUmpgscj36Cv7xI7dx6ko6Oj7M0tHo8zPz9PTU3NXa/D6/Vz5MjTZtPFwjdINvzvqJKDnBJkMvAk9tnv0NjYvqFRjWw2DQgMZ1VJeJTGkBUEXyqnm5GrJZM+1kz93obVLEokJBShUqOPUFvb89DWcc3OThKPR5ibm6SlpROfr4LJqVHq23aVib94RmM+YXb6xtIqiYxeig5qhdSvbiyJtLLYaLOcslGFQjxw8ecSSVr0Xrq081RXVrFv32MYhkE+n2NiYojJyRGSUpBd6mt4RIIFuYGw3IBbJGjTr1NvjCBJoAoFCYGEgbzsq4WRk4hnWom43wFUowc6Maq7aQtojD//n5mcHKG1tWvj76zFmui6vkIAtre3PzQ+qFYE0GItLAH4ELMgNzIhd9KkDgAQEBGcIrv6vkozF3gT8sz3UBSFzs69+Hw+kslkaZ+BgYF1CUAwxyJ1d+/n9OkXqAyfIh18DiEEc6491NjOMzJykwMHTqzr3HdCKhU3/3CH0AqdvkWRksmboiRd6PbNqeUTIe70je5OUpACQa0+jIJOdXXDOu/N5hONzgOQz+cAcDpd5HMZkukMhuwgkzdIZDTCyTyzsRyRlBn5y+QNsqopsrVC44dR/Fk2mWD5o7ki5fsAxV+NPsoJ9RsoskJdQxOdnXuRZQVZVrDZ7OTzOSRJolkM0mVcxOFw0pwfQGjm4ny+AA0Ne6mtbSqL4C3aCBmoap6FhVlGR/vxTP4dad/PIFx+tOgIozfewGZzMD4+SEtL50P7xWE7MTY2Rj6fL9vW0dGxSau5e6waQIu1sATgw4wkcdH+HJX5WTwiQSBQyUyu7Za7Tyg9KEKDyRfRdZ3Ozg4uXrxUun50dJQjR47gcKyvacPpdNHa2o0xfIEF737SShVIEtO+x/BEvkUyGcfnC6zr3GsxPz+N4q9H2H1ompn+zaoF8ZczvemK3ahLo0twb290y8WJJEGD3o/fX4Hb7bmHe7Q5xONRhoauE40uAJBKJQBIJKJUhOpK4i+ZNdO+CwmVhaRZW1mM/Km6abStGaI0VWW54C5vmFlMw5e8FB9w2rfaGMfldPHYY8+tWuC/c+dBRkf7kSYuYAiDffuO4XZ7iccj2Gz2W053KU38kBScTjeNjW1UVlZz4cIpQqOfYbb2rTgG/t401/YHrDFwW4iBgYGyy7W1tfj9/k1ajYXF/ccSgA85uuTge46PIWNg5G0rulWXM2rbgybZOTrzPQKBqrK0WzHlsXPnznWvp7l5BzdGx6lJnmO44q3Y82EcehKHw8XYWP+G1AIahs5ceAH3jqfQDVB1U4QUBWAmr5NVF8e83aqp4PY1amvvAyAbKrXGKNXVq9eJbWWEMLh8+XU0TeX4M2/H6XDy2ovfZmpqlEhknqaeQ2QLkb94ZtHuJZbWSGX1UmpY00VJ/BWtX4wyYVe8vcLvJZeNTRB/AF4Rx+Px3bK702az09Gxm6amduLxCF6vH0kym6vuFrfbS2trFwMD16iunUVzujl69Bkr6reFiMViZYb5AJ2dnZu0mvVhpYAt1sISgNsBScbgzutSJpVudOw8kfoOHo+DVCpXuu7mzZv09Nxd7ZphGKW6GEmS8Ug54nYfAEdjn0fTVCLeTvKzg3R0ZO77xJBEIobQcthCXWgSJWNiTTf9/UxhYoqRxVFvxdTcnd3Gne7Xrl9FQaemZvO8ENeLJMns3fsYg4PXeeOHz5e+HPT1mVHi6tbdhRSvQTSlln7CSZVsye7FjLAWZyIvFX3LTbKL25bX+8ED/vARBl3ueVyuyjV3dTrd1NTc++tXkmSEANnIYbfbLfG3xbh582bZZafTSUtLyyatZn1YAtBiLSwB+Igyo7TzbfmjvDP0ZVKpRZf7RCLB9PQ0DQ1r168ZhsHQUC/j44P4fAGqq+txu31omkrU3oJNMkrWIXZFQpIk5uamaG6+v3U0Ho8PkNASM0jeJtM8uOi9ZghUbbEZYSOjS26RpEc7TVNT+0OZ/gUIBkMcPvwkCwszDAxcJZvN0H7ozXQefSea7GQ2bvonzsXzzMXzjMxlSkbamlGwezGKnb/lBtkrIn+iOCu5cPkBR/0kYbBHO0WTfpNULktDw63LJ+43uVwWWZbQ8lkcivU2vJVQVXVF80dnZyeKomzSiiwsNgbrnecRJiP5+Qf5pzhk/58oaqq0/dq1q2sKwEwmxbVr50gkE8SrHieWjZAZ60fXdQzJRtJRjyM/hxAGfn+QSHaBrG/HhghAu92B319Bbv4mSu0Rc76stHQMl7EY/dtAkbFXfRmPQ6G9ff0p9PuJpqmEw7PE41FA0NLStWaNWT6fY25uitnZCbLZDIrNYfay2lwk0qa3XyKjkcyandVFnz9VN8p8FfVbmGuvrPd78MKvyB7tFB36FVpaOgiF6ggE1o4A3g+KXcVK03HcqUEky/ZlSzE4OFjySAWzjvNhS/9C4d/YPR5vsb2xBOCjjqwwXnGStvnvlDbNzs4Ri8WoqFi9sH12doK+vss4HA4G6n+CrKMO4YURQ8OXHUUAulCQhfkmWlFRRXx8kHT1CeKT3yKXu/9p4KqqGkYmBnALvZROE6LYDWw2hWyk0KjWx2kwhujqOnJffdwMQ2dhYZZsNkMoVFuIdq51jMHk5AgjI31omoqnohotl2FqaoyKiir8/iB+fwV+fwWaphKPR4nHI8TjEdLpJJIs07BjL11PfIBAyz4MyU6qYKUTL9T/JTJaaYZyUfyZdi/m414UgLAoAouivMw7cZPEX6t2jQ79Ml1d+2hqat/w2xNCkE4nicXCzM5OAhAPnUSNTVKpZEwjbCsNvOkYhkFfX1/ZtqamJny+tf/dbTXEsq779Rxvsb2xBOAjjiQMnG43hmxHNtTS9osXz/PMM8+V7SuEYGTkJiMjfcS8u5mofAuGvNgxLCQbCXdHIdUqyMmLHXMSkLQ34EfZkCigzxcALYvIp9Alb8EKZnFc2UYLjSpjErvded+sX3RdY2DgGrOzk+i6hiTJDA5ew+PxFUbshaiqqllhObKwMMvg4DUymTQte56g6/i7UNxBjHyasSsvEZsZYmamOCFmEa83QGPPEWqaOgi27EFxeBe7qLMaqi6IpFTiGY1oqjhNxWysUYvib4kANJYIvZIUXBENvC8P1V3jEkkO6T+kvqFtw8SfYegkEjFisTDxeIRYLIymqQgkNFcdycb3ktcc6I5GmH+Ns2dfoqNjN1VV67Nhsrg/TExMlFljAffUFGdhsZWxBOAjiix0mvUbdOnn8Yo4cY/E0ve9ycmpsvnAQghu3rzC1NQIWXuIjL0Wlog/STJFnmEsFvHnJLMOLpmMmb/xk/S0Mzs7ed8FYDabAdlGXvagl6JRphDJ5o37elur4SOG2+29L1EcIQTXr58nEovg7XwGX/MhFE8lkYle1NleIuHrTE6OYLPZ8Xr9aJqKpqmoqoph6FQ19bDviR+hoqYZVTPI5XSEcNB44G2022RsCuiZGPG5MewON8G6VhSHq/TcJbM6mUSevCbIqjqZvGmmPRfPk8joJLJmBDCdK2/8WNrQUZz7uxWDCE36TSRJoqNj14acP5NJce7cy2iaCooDyd9MoqqbnLsZzd2IUBymQNYFsZpnqWnfByPf5fLl1zly5Cn8/uCGrMtibXp7e8suV1ZWrtsbdbOxmkAs1sISgI8oT+W/RIVYoLq6gdbWg6RSSV544Yel64Uwo4CPP34SgMnJYaamRhCSgktdoD76InFPF6o9uGKygxkBEhhCwel0EY2GMVCIZSWw78Yf+zrT02PU19+frrpcLsP4+CCGtwmjUOu3dPrEg8BlmDWU95rKMwyd/v6rLCzM4jr4k9jqd5EDYjEN1d1JtnEHNbs+wA5nhtTQyxj5NG6HG8XhRnF4cAcbqG7ZaU6ByWoYBqRyGpIk4bKbj4vDJmN3VRBqq0QgyBsCI6sXGmYMElmNdK7ooWjW+aVzOvOJvDlVJaeXDJ9Lo96MJU0fm5TWvSOEoEW/QXVN/YaNXBsZuYmmqbirmvHseJKorZl4wl5qepGKX5IMFZeRAC2HzV8Hc8O4XA9n89B2YG5ujvn5+bJtdzsecyth1QBarIUlAB9RBDLV1fXs3XsUAL+/gvb2doaHh0v7jIyMcuTIYzgcDjKZNLrixmZkcThdxAwfqlz+YVWy+2BRCPRLe2nhLHnZi2bAgqOTGdce9BtX8Pkq7tkYWtNULl9+g6xwYHT/SFn0TwiIZ7TS2jaSIdt+QvHnGRi4Smfn3rv60BBCoKp5YrEFBgd7yeSy0PVesr5OYtEcsiQVhJiOEOBxKKScPny734tNKRgNF84lyxKxjI5Nlsx5yJpBPK1hUyTcDoW8Q8Zpl3HYZGyyORJP003T5mK9ZDipkspqpPOLPoqxtDnfdyGhohY6qs3GGuPhEH4FKsQ8fhGhrm7jfBqbmnagKArR6AILZ7+AALwN7yQWOIAkSchqnNqRz2LTzOk1GSArSdTWNmG3r8+E3eLeuXbtWtllj8dDa2vrJq3m3rFqAC3WwhKAjygzShuhyAUSiVhpisGePXvKBKBhCHp7r3PgwEGzDk22IXRBLpdluubdCNkOt4iyFdMPY55jgMGCe3epFmzY9yZ82hxXr57h6NGn7ykS09t7gVRWxdj380jOABRr/gprkoujkTf4vWxK6eSSeBomXsJud9LWdnuBMT8/w8jIDfL5HHlVBWGmqW2hboarn8bpqMWZVE0RV6jFy6oGXqfCQlJFMwRuh4osSUgSBUEnYbfJ2BUJWZLQjMK83rSGwybjdRnkVAWXw9xHkaXCzGTB8FyabN4gklLJ5I2CtYtRSPEapXrKxekeZsTvYRJ/AM36DRwO57oMnO8Us8FmPwD5fJahoRvI098mZatG9TTi0JPYtDjt7TupqKjC5fLgdDqRpIdjxux2JBKJMDk5WbZt586dD83cXwuL9WAJwEeUMXknLfkbnDv3EoFAJU1N7VRXN9Dc3Mz4+Hhpv4GBQfbs2Yuu60iKDVlXEMLAqcdILZkiAuVCq/i3kB2MeJ8GQAYkJHRJoTfwbg5GPm+OxArVEwxWEQhUotylJ1oul0UEO5C8NaYgKUb/gERGQ5aLo7g2NgooSTDu2E+VIwfDb1BX13TLdJ6mafT1XUTx1+Nu6sGmeFBlD3HDh+JvYGE0QX4ujd9lK/jkiVI9XjJrWrAsJMwoniyZUT+PQ8Fpl3HZZVwOGUWWUDXBXDyPADJ5HZ9LweNQzONkyYz86WaUMFsweI6lVfKaKEX5DMN8SstFX9G4eXOmdqyXPzg2wmuv3aC2tvmBiS2Hw0V3935SqQS10//IZNvPIitmlC8SmcPnC+By3d+OeIu75+rVq2WXHQ4HXV1dm7Sa+4NVA2ixFpYAfETJyAG+7/wYdcYwO5KXiV8/j8NxjdraxjIBmM1mGRgYKHSiKkiShM8XJJDqZcF7ALFs9lzx0lIvvuJlIQDJ3JBXgtyoeD8NmfPExsewjd4snLuCtrZuQqG6O7ofoVAtifH+kjgxjYghmTXF34OKABYjcVN6Hc3cPn0yNtaPqhu49v4YuiNAIm121UZSKsnJFLmCt15OzaMUBKwhIKeaUcJ0zihF8GQZFNlM77rsMvGMhtNmCkDNEKRzOvZC+lfVBBmHeayZ+l2M7mVV09IlldNLdi5FSmJvtb8fEvH3hyfGmJubRlXz1NU1P9DblmWZPXuO8OrpU9TNfhut+0dx7/tR0uNvcOXKaQ4dOklFRdUDXZPFItFolLGxsbJtO3fuvOVYwIcFqwbQYi0e7le4xT0hJJlppYNppQO/sUCXdoH8dB9er5tUKlPa79q1a+zb140UGUEXApfLgzc+gT99k7inp8zvrczgVyxuW/puUiyPSzqbuOlsQpHApYVpss1QkT3LlSunaW/fSWtr15q1dIlEDMMRRNVN0aIW6tkWhae01njk+4IQ5v1yyaaVzmppbSEEk5MjjI4Po7Q8QUTzMLuQRjcEyaxGJm8wn8ijaubiZXlRWAqxaGSt6pBTpVKnrXk/zX3tipkGlgsj8QxDYFNMMZjTDOw5Uzgujssr1v4Zpb/h1m/+Qqz+91bn37zewpO50+yoCN1z3el6cLk87Nu1l2vXzhKc/TaSbMPu9qJGYXDwOocPP/nA12Rhsjz6Z7fb6enp2aTVWFg8OCwBaAFAQg5x3vEWJvQu9riehyUCMJvNoqoyTqeLjCqYnZ0AoC52ipinpywqZBTHrYmlgrDQ/ShJ5SniIpKE6qxmylbLrGs/DfbXGR5+mWQyxq5dh1ZNCwthMDx8k3B4llzre1E1M/Wr6mbUqyhwDOPBqJTi/bELc67ycgGYTie5ceMi8XiEZPAgKe9xotOm+IulNdJ5MwKnL1mvMEBHICGtiLSJZRJNCNALBsw5zUBCKkVhNcOs9cvKBopsbi8+PsUfwygfy7YWmzKz9x743V2XuHBhhubmY5u2hpqaBlpaupiauojT6cLpdOEO1eH1+tc+2GJDiEajjI6Olm3buXMnDsc2aMa5xxSwFQLc/lgC0KKMWaWNSOCnOZD8C8gtGgP29vby3HNPc/ny68QrjxGR6wrSBIr/KQo9KKQGYZkAK4TJBBgIJGE2KxRPoRsCxSYTr34Se6CBuaGvkjr7EvX1LdTUNJbm6+ZyGa5fP08sFiFV9yypwD5EoUO2aF6c15b40234o2auXZLg3Q3TDA3JqGoeh8OJYeiMjg4wOtqP7ggy3fRRRtU6HFEDWYJo2vTT01cRqiuMlO+QYmq2eJhuCFTMdHFRdy+N1BrLPiXutIH5YRF/AF+/NEO320soVLup6+jo2LVh/oMWd8+lS5fKLtvt9m1j/Gx1AVushSUALVagSi56q97PrqnPlbblcjkmJ2dobe1mdPQMmYqTaLKbynQvmmRDkh3EdTc5pQpDmBYhxYkgAHYjjYMcOVslFArwDSGQEChqGq8RxmuE8ehhXHoMu01G8tWTycUYGr7J0FAvXq+fYDDEzMwEeclFsvOnyHmaC/N+zShWOlfwsys0OGh6oZFhg9/LiqLpr6YPcYJhzp17mR07djI62k8mkyJV8wQz/uNEsxKZvMpCQsVhk8hpxoavrSgIDX3ldTaRZ7d2GgOJpFTJrNJKDu/GLuhBIwQ1xgQ1NU0Praebxf1nYWGBiYmJsm27du3aHtE/C4s7wBKAFquScjcT9XQSTA+UtvX29vKe97yHZDKGEnsdXddWHHe68p+hyT6zKUPXCGljRG0N7Et9i6A+iYadlK2GnBLApcfw6GFshbSpJMl4PD7cHq+ZpjQMMppOWugIRwUJRy3q3BhJ9w7iTe9CsntK9X7xtFaW0lSXdLI+iBigEGYzRlqp4kLFR9md+Dq53guk7PWEmz+M8NQRS6pk80apkzarbu43bIfIcCL/DbwiSl5y4xUXSeiV/MDx43ceBnwI8IgETjIEApXrOn52dpKRkT58vgDBYDWhUB0Oh/M+r9LiQSKE4MKFC2XbnE7nton+gdUEYrE2lgC0uCWTlU9TkR4opQ1VVeXq1Ss89thxwHwTnZub5MaNi2SFi5veZ8lIXnTdwDAEe9LfpUG7iY6CgkFjYxsul4dEIspgJEJnlQ2vdwcejw+v14/L5SmL0AhhkMvlmJkZZ3T0Jvmsm+GuX0UIgSJL6AXj4kzejPoVJ4CY3nbFGbUPLlWpFM2XHX5Oez9EtZgmam/BZ9hJzGWIZ7StkzYVgg/ZvkxWS3Po0BP4fBVEIvNcuvQaVWKKsNQIQqCgoUsbMzHjQVEppgHWJQBnZye4fv08SlUHaj7PbN9lHI4bHDv23IZNErHYeKamppidnS3btnv3buz27fOcWjYwFmthCUCLW5Jx1hL27SGUXHTIv3mzn8pKP+3tXSiKjfM3h8nIjVzzvhNdcZcsRBQjS4N2k7q6Zny+AJHIPC0tnSVvvD3Lbsus6zsHgKrmyWTS5HKZsn2MJTUtOc2M7mXzRpn4K5oVq4XLD6YC0ETVDRw2mVhaw2GzMSe3oKuCZC5XZk69VbiRqaPZuMns7BReb4BgMITb7WFH7jIBeYFW/TpBIpyyvYd5pRmvEaVF78VAYUzZSUZ+8N2066HSmMHt9t71lI1odMEUf/WH0Dveh8Npx56LkHn9vzE6OmDV8j2kGIaxIvrn8Xiszl+LRw5LAFrclsnKp6lM3kBmsYDs0qXLTE+PUF/fgkuLMuw+iCq7MXRT4eiGQBVORm17UOb76OjYTXNzx21vJxKZZ25uqnR5vuI4qq8Cw+4DR4C8rQLZ7kIu2KEYwpyQUZpMUZg/XPS2e1C1f0sRS7z6sqrYcPPpe0KSOG9/C3E9hBh7nWQyxv79x6ivbyUz1EujMUR1dR2aVslTiW8yo9dRbUxis5kzbXfmz3DFdpIh28HNvidrUmlMryv6F4nMY7c7iTW/G0UHNaNh1wFhWBMiHmIGBweJxWJl2/bv34+iKJu0oo3BagKxWAtLAFrclry9grmKw9TFzpS2ZbMaN7PNZMYnsCOISVWlLtaiDYwh4IbtBB3aTYaHb9DTc+C2t1Nf30IwGGJ8fJCJiWEW/EcxbB7T285WMEM2QMfAMDB965aOJSsYQJtCsBAt3OQqlq38/tmlnaNDu0hKqiAt+VFiYYQQNDXtKIxKq8HpdKHrGleunMFrZGlsPERNTQNCCF5++Vvs1M5seQGoCJWAWCAQ2HfXx2YyKVRPPaouoeqmsHfNnMONoLl5x/1eqsUDQFXVFZ2/wWCQ9vb2zVnQBmLVAFqshSUALdZkKniSUOIqNmMxJatHp/le009RKc0TlepBF4XGjcUpETnJxXlOsH/qZRoaWvH7g7e9nbm5aRIJ85u5XYuRt5vdqIu1LAUjZG2p8FvsOC6mn40tmG7daszLjeyRTuOW5vB6A1RW7kCWzQhIfX1LaT9FsXHw4ONlx6pqHkmSuakceaBrXg9BMYeMWFcEMJNJUe8LMJ3XkWXT8sgbvkZ1dZ1V//eQcuXKFXK5XNm2w4cPb8uIrlUDaLEW2+9Vb3Hf0RUXk5Xlkwo8+VmCieuElabSN8XSrFhjcUTYiLKXuFRFf//VNVMKc3MTLKTyRDy7ySkVJV/BpfV9RcPnxY5fylLAxTXc65vfdicq13NOeRbDMKira2LHjjuvZ0ulEghhEJYbNnCF94dKYxpFUdZltux2e5iKJBCGjqoZSOlZbNk5amoaN2ClFhtNPB6nr6+vbFtTUxP19fWbtCILi83FEoAWd8Rc4CAZe6hsW2P4RWQtYxo+iyXCa8nUCiHJXLE/TSweYWTk5m1vw+PxgTPIZM27FxtKltb3GaLkMWgISqnexbQvWyb9+zAwoXSzIDXQ33+NZDJ+x8cFApXY7Q4a9MENXN39oVYfpaIitC7/v7a2HqRcFGf4ArohcMSuoyg2qqpqNmClFhuJEIKzZ89iLBl1I8syhw4d2rxFbTArRnKu48die2MJQIs7Q1IYq35r2SabkaVh4UWz5s9YKf6KLMiN3LAdY2Skj4WFcuuFpXg8fmzpSVpm/gF7PlwSc0vHlenGYjdw+ZvVoli03rzWxiEyvCX3OUJiisrK6rvytZNlmdraJpr0PiSxirv0FsEh0oTEFNXV64vwuN1ebDY7h5xDGIbAHe+lurq+lCq3eHgYHx9nenq6bNvOnTsJBB6OTvb1YNYA3sv/LLY7lgC0uGMS7jYi3nKrhOr4RVyZicU3jVu8a9xUjjIjt3LjxgWEWH3gbGNjGx07dhLSJwjFTqNqxrJoH+Vp3iVvVMXri+lgi9uj4iApVyAAny9wVzVtqponlYrjIIeD7MYt8h6p00cACIXq1nV8ODyDpqm8Kj2DV0Sw58NW+vchRFVVzp49W7bN7Xazd+/eTVqRhcXWwBKAFnfFWOjNK4yBW+e+DUK/fdRNkrhpO4qq5kuNHsux2ey0tnYRCtXiVucAljV7LGn0EIs2L0W7g2L0z2JthKTwmv19XLc9zsjYEBcuvEI6nVzzuHQ6yfnzr5BMxnnN/l5y0tYdG1dvDFFRUbXuqR3T0+P4fBXkHCE8+RkAKirWN03EYvO4fPkymUy5p+jhw4e3lenzaiyd972un82+AxYbjtUFbHFXqLYAU5VP0hz+QWmbJz9HbfQMM8ETqx5TrY/Tpl8lKZkfnrFY+JZdmYZhoCgKbm0BQ9eRFKUk6kr1fWW/zTcr3Vjs/rVE4B0iSQzYDjMvN3Ek+V1SZ1+is3M3DQ1tSJKEEIJsNk0iESUej5JIREkkYrjdHr4tf4S0XLHZ9+CWKCJPjTFOKLS+0V75fI5weJbpyjeZG1JTuFweq/v3IWNhYWFF40d9fT2tra2btKIHh9UFbLEWlgC0uGtmKo5SlbyKJz9X2tYYeYWot5ucvWrF/goqjcZiw0Amk1qxj2EYDA/fYGB8CodIk7TVYwiBZFAyVBaIMq8/UTB+Xmz8wKpcWQcxuZYfOj7CL1Q9z82bV5ieHkOWFdLpJKqaByAlBYhItUSVPYzpu9Dku5uq8aCpN4ZR0KmuXl/6d3x8EFmWmbD1IKc16jIL6+okttg8DMPgjTfeKHMfkGWZxx57bF1NQRYW2w1LAFrcPZLCSPU72DX5N6U5wbLQaJv7Fn0NP2EqtiXMyO3MS41Ui0nAnK+aTMZxuTzU1zdTVVVLLpdhfHwQhxDc8LyZec8+JEAWAglKUT/TYsaM9mnGYjRQNx785I/thC7Z+dPIe6i2j/Nu22vY7XbeSO4gaq8jKteSl9ybvcQ7RhYaO7U3qKyswe2++xR1Pp9jYmKYYcdhUroDOwa6LizR8JBx7do1otFo2bZ9+/bh9z8aQv5eWzmsL9PbH6sG0GJdpF2NzAaOlm3zZ8epiZ9fubMk8brjPbzi+ADn7W/mqnSEwVwNc3OTTEwMA2bH5f79Zgq5Jt9fSu2WxN0q4m9pXWAxQvgw8+mP7+cffv3BmSu/8HvH+a8/u7ts27zSzF9lfpQ/j3+APvtxZpW2+yL+xBffxQeO1d7zee6ETv0CbpGiq2t9Rf7j44PkDZkRx0F0Q5DK6eR00DTtPq/UYqOIRqNcvXq1bFtFRQW7d+++xRHbD8sGxmItLAFosW4mq54mZyuvA2sKv4hDja7Y15BshOVGpuV2QKLBGEaSJIJB01tQ1zUGBq6Skzz0uZ8pNHUs2ssUU7+abpTE32o1gfeDT398P+KL7+I3P1A+v/gDx2oRX3zXPZ+/rcaN+OK7ONh2/yIRLrtM+C/eysJfvAWXvfyf9bN7qhBffBcVno0P+P/eR7o4/wdPrthe/799j386P7/ht+8WCbq187Q2t5u+kndJMfo35jhADjeabr6u5pU2otH529oYWWwNDMPg9ddfL/P8kySJEydObMuJHxYW68X612CxbgzZwUjNO8u2KUKlfe6bcAurlxP5b7JbP0NtbSPHj7+JlpZOAAYGrhFLZTjrfj8pObg40o3F5g99SZOH2eG2cU0fmbzOb36gg6D3/oomu7IxacQPP17PlbEE18aTfOjE1ptsMBPLk9dWf03cTz7q/x5uh0JbW8/aO69CLLaAYeiM2feVust1QzBm30vc1c6NGxfJ53Nrn2gJ0egC166dY3j4BpHIPLq+db0TtwNXr14lHA6Xbdu1axehUOgWR2xPrAigxVpYAtDinki425jzHyrb5s+OUxc7ver+OclNIBCku3s/LpcHIQSzs5NMTY1yw3GSpBIqRPzEktq+pWneJcKPW5tP3yvfvbzAdDTHb32w87b7fehEHVf+y1NkP/sOhv7kWX7tve1l1w/9ybP89oc6+fTH9xP9y7fyZ//HPob/23MAXPh/nkJ88V288HvHy4751+/bweT/ehPzf/4W/uQX9mC7A9H4C29q5m9emuRvXprkF97cXNreVuPmB58wU+vRv3wb4ovv4tMf37/qOX7y6UZOf+ok8c+8jak/fTOf/ZcHqQksNnsUI4lv3hfi9KdOkvrrt/PKf3icngazzu5nn23iEx/p5lB7APHFdyG++C5+9tkmYGUKuKnKxed/5SALf/EWkn/1Nk5/6iTHu+6hq1gIPl7zPPPzU3R07MFmW59wVxTzOMMwSt6SYP4+Kz8HCPr7r9zRuVQ1z40bF7l48VWmEzmGx8e5dOk1Ll58dV1rs1ibcDi8IvUbCATYv3/11/x2RtyHH4vtjdUEYnHPjIeeI5AZxqlFS9sawy8Td7eTcZZ3YS7IjcTjI8zNTZFIRJmdnSSXyzCrtDFm24tU7PZFQgKkJcquWP9nRgWLNYEb4/2nG4J/9/k+PvcrB/n//tMIE+GVhsdHdgT44r86zCf+7iZfODXFyZ5K/vs/38tCQuUzL06U9vv19+/gP3xpgN//0gAAf/KtUU5/6iRv+b/f4OpYgry2eAfetLeKqUiON/37N+iq9/CFXz3EheE4/+/3xm+51o46D0/0BPnQfzmHhMQf/+xudtS6GZrNMDaf4UN/eI4v/5sj9PzKi8TTGpn86pE4h03md7/Qx43JFLUVTv7rz+7iLz++n/f8p3IT3f/40R7+9V/1MhfP8z//t738xf+5n6f+r9f4wqkp9rX6eOfBGt76H94AIJZeWTfndSq8+IkTTISzvP8/n2M6muNIRwB5nU0WkjA4oP2AsbEBOjv3UFfXtK7zACWbFxs5MsteWFnJw1XlBDvnX0HXtZJYXI1EIsbly2YacqH2baQrD+OwSdQM/gUul/W2ux7y+Sxzc9PU1jZit6/sQtc0jVOnTpV1/UqSxOOPP46iPHrTW4oR7Hs53mJ7Y70TWdwzhuxgqPbd7Jz83GJXMDo7Zr/O9aafQciL3mmjyi6a9Jtcu3aWPC4mlQ4mHd1ElQazy1IIRNH3Bcm0glkiDEoisPDetJHvUV85PcOF4Tj//se6+Of/c2XU59feu4PvXV4oCbubU2n2NPv49ffvKBOA37+ywH/5x6HS5TbDbKpYSOSZieXLzhlJqvzSn1/FEHBjMsU3zs/xln2h2wrAf/amJv7pwhzRlCm2vnVxnn/2pmZ+9ws3MQSEkyoAs7H8qoKsyKdfWLyNodkM//LT1zn9qZN4nQqp3GLa8rf/to8fXjdTbP/pq4N887cew2mXyaoGyayOZogV92spH3uqkZqAg2O/dYpIylzbwEz6lvvfDkWoHFG/Q60xxq5dh6ira177oNtQFIB2YaZ5l76+JAlmpGZ6hEE0GiYUWr2pJZNJcfny6yTlKuZaf4Sk4UXKG+jZBHJmjur2w/e0xkcRIQTXr18gGp1naKiXxsY2Wlu7ynwZz507RyKRKDtu7969j1zq18LiTrFSwBb3hZSrmZmKciNot7pAy8ILZdt0ycFrjvdyyv5+nnf+DJftzxJWGhHIy9K6S7z9hFjys7jPgxj59pufvcHPPtvE7qaVDQW7m7y8ciNStu2VGxG6G7zIS4JZZwbid3x7V8eTpbQjwFQkR23FrSdZyBL87LNm+rfI3/xwkp99tqlsDXfCofYAX/n1Iwz/t+eIf+Zt/KCQmm6tLu8CvjSy+CE7FTGFUm3gzn0BD7X7OT8cL4m/9WITOR7Pf51qY4KD+x+7Z/EHSyKARm7FlwshIKNUkpV9RCJzqxxtNpFcvvw6NpuNsboPkTS8aLqBTY1SN/dtJEmmqurBdENvJ6anR4lG58m2f4B09TFGJ8a4ePHVkk/l2NgYAwMDZcdUVVU90uPerBpAi7WwIoAW943JqqfwZ4bxFsZmAdQkLhB3txL17Spt0yQnC0p5mk4gkJBMYYdAxpxEIUkSmP8viUHd2Ji072q8dD3Cty/O88mP9fCXPyiPwpnTMli2beU5lkbP1kLVy08oBLcVcu84VENzyMUXfvVQ2XabIvP2g9V868Kddd56nArP/84xnr84z0/9/y4yF8/TWu3m+d85hsNWvgBVX0whF9NE8l2ozVuloO+WndppauR5Dhw4ccvJMndLUQC6WD0iqQuYV1rxTPWSTicIBqtpbt6BLCtkMimuXj1DMg8zbT+NV43imX0BpzqPM7+Ay+mge+9Ra5rIXaDrGmNjg4yN9ZMPHSRftQ9DCHKVe3EM/BUXLrxKV9d+Xn/99bLjbDYbJ0+efKS7fu+1js/Sf9ufR/dfh8V9R0gKQ7XvXTEruH3uW6taw6w4fklaVzeWRv/MNyPT8+++L3tN/u1nb/C+o7Wc7CkXGdfGkzy1q3zbyZ5K+iZTZVG85RS7YZW7DdGtwi+8uZnPvzLJod94peznb16a4Bfe3HLHt7er0UtNwMG//dwNXu6NFOoA737aR14z1rxfl0YTHGr3U+ldvxDyGHHa9au0tHTeN/EH5qSIysoaevKvETBWRvkcNpkRz5MMuU4wkPQxPNzHhQuvMjMzzrlzLxPJKcy2fhRnvI+Gsc/TIMbYEXLT072HY8feRCi0vskkjyKpVII33niB0dF+UqFjJBveRl4z0HRB3lHD7I6fI5EzeOGF76Oq5dHkI0eOPDKGz1uFT33qUxw7dgy/309tbS0f/OAHuXHjRtk+Qgg+8YlP0NjYiNvt5rnnnlvRtGPx4LAEoMV9JecIMVb91rJtisjTMfNVJGP1+rOS8KNg+CzK7V7ArPtbvv+D4spYks++NMkvv6utbPt/+foQb9kf4nc+3El3g4efebaJX3pnG3+4pN5vNWZjedI5nXceqqG2wkHAvb5AfLXfwfuO1vKZH0xwdSxZ9vOZH0zw/sdqqfY7GJnLYBiC9x41L3udKwviR+ez5FSDX35nGztq3bzvaC2/++Hbd0CvxvBshh21bg62+Qn57fzXkxMr9vn8y5NMR3N85dePcHJnkB21bj50oo7Hu4N3fDvd+llcDjvNzTvueo1rsWfPUar8Ho5n/5EKY64sqmsYgrzkZMT1GJd97+Os70eZT6r09l5gRmpmqvVn8CycITT/As3NO3jssWfo6TlAY2P7I9mIcC/MzU2S1SXCO/8PUg1vRpPs6IY5/lHTBXl7kFhGIZcrF3+tra10dHTc4qyPEGWlM3f/c7dvtC+++CK/+Iu/yGuvvcZ3vvMdNE3j7W9/O6nU4ujPP/iDP+CP/uiP+JM/+RNOnz5NfX09b3vb21bUblo8GCwBaHHfWfDtY8G3p2ybNz9Dy8L3b3nM8rqTpVM9imnGzaxJ+d0v9LE8rnV+KM6P/dfzfPRkA1f+y9P83z/Wzf/1xZtlDSCroRuCf/npa/yLt7Uw+b/ezFd/Y33TP37m2UZSWZ3vXVlYcd0LV8MkMho//Uwjk5Ecv/d3N/lPH+th5s/ezJ/8wp4V+88n8vzcf7/ER56o59ofPc2//WAH/+avb6zYby2+9PoM37owzwu/d4L5P38rF+zHV+yj6oK3//5pZuN5vvlbj3H5D5/i336gA/12YdNluEQKn6/itp2468Vms7F//3GqfA6eyH6JnblXsGHWmv3+kRGyqkFONQ3JI3Itp30/xmX3O7jkeTfy5CtUxC+xc+dBOjv3IEnWW+x6SSbjVPl9CGewlBXQ9EVrKNeVvyA7X/5vzefzcfz4cWtsHw++BvBb3/oWP/dzP8fevXs5ePAgn/70pxkdHeXs2bOF9Qj++I//mN/+7d/mQx/6EPv27eMzn/kM6XSaz33ucxvwCFishSSsXu+Hing8TkVFBXzw78Hu2ezl3BLZyLF74q9xqeWGrEM17ybs33fbY4vv3bIklUVfim9KD6L5w2Jrs1d9hb2OAY4ff9OG3YZh6IyPDzEy0kdcVPCK5ycolmjK0mLdo26Y86ob9AEOZr9NZ+cemputCNS98vrr3yfi20eq8S2l8g+z/lfgnDuHcfUfykSKLMu87W1vo6qqavMWvQbZbIbf+Z1fJRaLEQgENuQ2ip8RR/7V8yjOu5+FXUTPpTj3X9++7rX29/fT3d3N5cuX2bdvH4ODg3R2dnLu3DkOH17shP/ABz5AMBjkM5/5zLrXarE+rK+nFhuCITsZqPvginrAtvnn8eSmb3vs0lpAS+tZrEZCqiSTSWEYGzdVQ5YVWlu76OjYjU9ESl88zGgUpVSkOaYQMrJZc7aaR53F3aGqebLZNFlHTWEWOIszwdUs4sY3V7w3HD16dEuLvwfN3Rg+3+oHTEG59CeXW3sSjhCCX/u1X+Opp55i3z7zC//0tPm+X1dXXgdbV1dXus7iwWIJQIsNI+uoZrT67WXbZKHRMfMVbPravm9Fk+flQtDK7lgk5SAAmcz6/APvBk3TsNns/KdjY4uTZ5bWqxb+/s1jWdK+HgYGrjM+PsSFC6dIp5Mbvr7tyMzMOJIkkXC1m+l2vfBYGwa261/CUMtFSHt7O52dd1+zup25XynglpYWKioqSj+f+tSn1rztX/qlX+LSpUt8/vOfX3Hd8vR8ye3B4oFjCUCLDSXs38tsoLzGzanF6Zj5CpK4dfRmqeCzooAWy0lKZufvgxBYmqaWonpLJ88sf13+zrk23rS3hYi9mYGBq8RiYSYnhzd8fdsNIQSTkyPU1DSQk9xohVGQuiGQhl5AXugt27+iooJjx45ZImKDGBsbIxaLlX5+67d+67b7//Iv/zJf+9rXeOGFF2huXvTmrK83Z5Qvj/bNzs6uiApaPBgsAWix4YyF3kTCVW7S68+O0zL/nTXVXbEZxEoHWywlj4s8zgciAFU1j93u4N+83rJC/C19XeqGwOVyc83/Pq7V/wIJVxvz8zNYZdZ3RzabJpNJEQrVYxTq/rKqgZi5gn30B2X7ShK0ttbd1eznyckRzpz5IQMD1wiHZwmHZ5mcHGFoqJfR0f77fG82j3vpAF46Ri4QCJT9OJ2rG9MLIfilX/olvvzlL/P973+fHTvKO/R37NhBfX093/nOd0rb8vk8L774IidPnty4B8LillhG0BYbj6QwWPsBdk/8FQ59sd2/JnGJrD3EbPDYqocJYb7BF02ioTwCY/EII0kkpcoNF4C5XJZ4PIzXG4BbT7dDCPjUY6P8xysdSJJBBC84j+CP/QOpVAKfb2MK/rcjLpcHl8tDODyL6jSQDQni47j6vrxi39raADt29NzyXJlMinB4jkAgiM9XgSRJzMyMM5cSzKdmcI8PAiCQSv6VLS2d2yKa+KCNoH/xF3+Rz33uc3z1q1/F7/eXIn0VFRW43W4kSeJXf/VX+eQnP0l3dzfd3d188pOfxOPx8LGPfeweVmqxXiwBaPFA0Gxe+us/xK7JzyKLRT/A5vAL5OxBYt7u2x4v7umtzOJh4A9PjPFvXm+54/0TcpB0euqO9k2lEng8vrv6YM9k0ly8eIpoXuGf9GfWzJf8xuvNPGF8g7zkZMj3LFFbI7rkYGFhxhKAd4EkSdTXNzM6OoBRl0NoWYI3Pr/CRzQQcNLe3sHg4HVsNhs1NY1UVdUgywq6rjEy0s/4+CBCmEbodXXNdHfvJ5GI0m97ihFlL14Rw0AmK3k5oL3IXs/0thB/cO9Zk7s99n/8j/8BwHPPPVe2/dOf/jQ/93M/B8Bv/MZvkMlk+PjHP04kEuHEiRM8//zzlmn3JmEJQIsHRsZZx1DNu+mc/VppmwTsmP06fY0fJe1sWPMcVvRv+3I34g8oRABvrllEPjc3xbVrZ2lq2kFX153Phg2HZ8nlsrzs/Gmy0spZ0MvZoV2gUhvAwIZfm2aw8j1kHbUsLMzQ1nb7LzgW5SiKzezwzicI3PwiipYquz7ta6c5EGV0bJAFuQGHyDI7ewZFsREK1TI6G8VOjn7lMIO2g7TqvTBzikCgEiEEMgZIEikpWDpnwFgwI70W6+JOSh0kSeITn/gEn/jEJzZ+QRZrYglAiwdK1LeLCTVCU+Sl0jZFqHRNf4nexp8ibw+W7W8JPotbEZNqMAydVCqOz1ex6j6aptLffwWXy8PExBAej4/GxrZV912Ox2N6qClCY4UL+DKcIsVO7TQtzR3U17dw7dpZvPN/DYC7oun2B1uUoWkqo6P9TDt34xv6BvZs+Ui+uLuNmzUfph8NCYO85AbAZ4Rp1PupnxsmKTdy3f44GcmMLA0q+znhukp/vzl2bJ/2CjI6A7bD2ESOferLBMU8FRUHH+yd3UCW1vGt93iL7Y0lAC0eONPBx3GqEaqTV0rb7Hqa7um/40bjT6IpW9fg2mLrEJHrkWWFSGT+lgJwaOgGmqbxvP0jdCoXkPqvEAyG8HjWjuh5vaZ48IswKYKl7aulqt0iiYJOXV0zXq+fI0eeYmpqFL+/koqK+zer+FFgcnIETVNJJqJ4ksNl12XsIQZrPwCSgkr5aL2kXEWffJw+Vk6fQZL5O/VH2aFcplnvwyei7NFewynSNOoD+JQcXTsPUVe3fcT6g64BtHj4sLqALR48ksRozTuIu8sjMS41Qtf03yMbaxuNWlgYksIUTYTDc6teH49HmJwcZseOnWQkP9dsT5AWHkZG+u7o/Ha7E5vNjl+E19xXwbQ0kmXzLVVRbDQ3d1jibx0IIQhHc3ji5c+TqngZaPgwuuJa13k1ycFN21FecP4ELzg+Sr9yiAoxj5sUe/Ycob6+GSEMVPU23T4WFtsISwBabApCUhio+yBpR03Zdm9ums6ZryAJ7RZHWjyMeI0o7doV/MYCTpHCZ4Rxi3sfAD8vNxOLhVd8aBuGQV/fZXy+Cv4/40+b2yQbN21HmZ2dJJWKr3luSZLwev34jbUFYLFWodhwYLF+5uaiZFLlXwJ1ycFAw4+SswXvy20k5Uqu25/gtP2dxKUq+vouk0olOH/+Fc6ceZF8/uH/EvqgZwFbPHxYAtBi0zBkJ/31P0rOVl54HciMsGPmH8H6MH3o+cMTY0hC5332r7Nfe4nn8l/k7bm/4k35L/BO/Qv3fP4peQd5ofDGGy8wNjaAYeiEw3NcvPgqqVScb+bfhpAW3+ZGlV2kJD/Dw3cWBfR6/QTuIAIYlWvRsDE/b420uhf6+vq4ebPci08gM9H8I+Td9fd9CpAmOXnd8R6iOZkzZ35IOJkjnpe5fv0chqEzMzPO2bMv0dt7AcN4uN6PLAFosRZWDaDFpqLa/Nys/zF2Tn4Wu5Epba9M36R97psM17zHmv32kPKHJ8YA6NQvksolOHTopDneKxGjv/8KN8See76NjBzg+86PsVM7Q37wGiMjfei6TkSq5Yb9vcTkxQiz24gTFHP02R7DO/8CiUQUvz942/N7vX68YhRJ6AjJrDlbrVtZl+xMyR34ZyZobe3eNlYiD5LBwUHOnj1btk0AM83vI+ntwNCMDRElWcnH6473sE99mev2x1GExsno1zh16jvousac3ERVcop8PsvevY+hKNbHpsX2wHolW2w6OUcV/Q0foWfy8yhCLW0PJa9hSDZGq99hicCHlP/r1QBvM75JY3MHFRVVAIyNDZDByw3bY/flNvKSh8v2ZxhU9tOq9zJvb2JObil7zfiMMO/gK+TVHP/k/GckpfMMDvZy4MCJ24o1RbEhI5DuwIlyXOmhJdNHJDJHVVXtfblvjwrDw8O88cYbK7YbdQew+YI4jSQ5w71ht5+QQ7zq/EDp8iXxDAExz5BjP0m5ipA+wbHIP6FdfJX9+0+URgNuZYozq+/leIvtjSUALbYEaWc9/fUfpnv678uMomsSlxCSzFjobZYIfMj4N6+3sFd/GUWx095uTmtYWJhlfn6asNxFi96HJtlJS37CcuM9315KruS6/MSK7bLQeAdfKV32ijhXbE/ii36D6elRGhpubQuj62Zzh7Gs43Q15uVmFqR6BgevU1lZY0UB75DR0VFee+21FbYjNTVB7MoQLfEhAGRZIRCo5G8z70eVVh9Hdr8YsZX7RS4oTZySPsDjiW/w/Kuvc2JPN6FQ3ZZ+jh+0EbTFw4clAC22DEl3KwN1H6Rz+sumUWuB2vgFQGIs9FZLBD5MCMEe+wCVlXWltNno6E0Amox+msVA6UP/m85/ji7Z7+vNS8KgRe+l0egnZ+TZs/sQ16+fR0ZjTmllxNiN1tdLMFiD27269ZBh6MiycmevO0niqv1JQqkvMT09RkND6329P9uR0dFRTp06tUL87d69m4MHDyKEQTabIZ1OksmkGB3t55j+T7zmeB+GtLYov5/E5RpedvwIh9Xvc/XqGZJSBUe6W6mvb93SQtDC4lZYAtBiSxH3dDBY9wE6Z76KVCYCzyMJg9Hqt1si8CHBJyJkc2lCobrStq6ufei6itcbwG53MDBwjZvjM/dd/AG061fYp71CRUUVDQ0HyefzCCAuVQNw1XaSGmOcb57u5UNPHy59iBuGzsTEMOl0klgsTMa483RfTvKQw8Xs7IQlANdgZGSEV199dYX46+np4eDBg0iShCQpeDy+km9jIFCJcek1ftL9ZV5JdpOWAqUfTdr4tGxaruAV549QaUzToV2kr+8ymqbR0tK54bd9t1gRQIu1sASgxZYj5u1msPZ9dMx+DWlJHUpN4iKSMBipeQdIVgP7VqfOGEHHRjBYXdrm95cbNicSMSJy3fJD7wuN+gChUB379h0DIJmMIQEn81/lhu0Ys0obF2xv4qT6NSYnq2hqagcgEllgcPA6MamalFTHnO3ORtR5jSiPq18n4JTo7t6/IfdpuzA0NMTrr7++Qvx1d3dz5MiRW0bUKiqq2LPnKENDvezUTmNjsVwkj5O0FCAvucjjJie5icnVRKVaUlIFPdoZWvXrSBict7+VeaV53euPyPWcddTjzn2JcHh2awpAqwbQYg0sAWixJYn6djLI++mY/ceySGB18jKyyDNc+95SV6bF1sQronhdjpI58nLy+RwLsRhR2wbMyRWCCjFPRcXiuX2+Crq69tLff5V6Y4hZuRWviBGTqhgd7aexsRVJkslmU+jI/NDx4Tv+oqEIlZP5r1Llkdm//yQu18Y1LDzs3Lx5kzNnzqzY3tXVxdGjR9dMp4ZCdYRCdQghUNU82WyabDZNJmP+VtU8mpYil8uSzV4EQMOODRWbzU5e08lK3nu+H35jgUoxS339oXs+10ZgRQAt1sISgBZblqhvJ4OSRMfM18pEYFXqBvKMymDtBxDy/U8dWtwfJuVu2rK9xGJhgsHQiusHB69joDCm7Lz/Ny5JaNhKTRxFpqZGAcji4XH169QY4wDk8zA/P01NTSOZTNqcIXsb8WcTOewih4JOSgpgQ8VFmh07HrPE3y0QQnD9+nUuXry44rqenp7bRv5WQ5IkHA4nDoeTQGD1iSuqmieRiBKPR5FlmeHhG9y0HSUp3/uEli7tAk6nm5qae29gsrDYDCwBaLGliXp7GKj7IB2zX0UWix/mwfQg3dN/x0Ddh9Y9GspiY5mXm0hLfubmplYIwGh0npmZca7bniUvbczs56RUSTqdLF1OpRKkUgnG5B469Iv4nDZ6eo4jhODKldMMDd0gGl0wJ4VIDbc8b5t2lQPaSxSnpUakWnKKaWb+sJkFr5dodAGn04XbfWeRNCEEFy5coLe3d8V1u3bt4tChQxvSSGG3O6iqqqWqqpb5+WmEEOS5d4HuNuI0Gjdpbt5zywj3ZmNFAC3WYmu+ci0slhDzdtFf/6MrGgX82XF6pj6PXbv3kWIWG4AkoaMgSVJZrZcQBjdvXsFud5CQqzZs4ktCriSdXnxtzM5OouLgqu1JJCTq6pqpqqotdShnMin6p8LcELu5ajsJLJpZFxbOTvUNDmg/pLGxlf37T9DTc4CgmKXTOUNbW88j4f8Xj0e4ePFV3njjhTuam2sYBq+99tqq4m///v0bJv6WU11dT2NjOwf0lwnpE/d0rk79Ig6bjYaGO6sP3QzEffix2N5YEUCLh4KEu42bDT9G1/TfYzMW53R68nPsmvgbbjZ8hKyj+jZnsNgMHCLHxMQQyWSMgwefQJIkDMNAkiRUNc9T/ANDyj6u2J++77edlIKk073ouo6iKESjC9RWVvCpA3N8+uVd2EbNDk6Hw4mOzAuOnyikfhfFSHHqhyQM9ms/pE2/zo4du2hp6SyJltraxkdmOoSmqVy/fg4wTbKlNWokVVXl5ZdfZnp65Yi8w4cPs2vXrg1Z563o6tpDJpPksci3eVH+MbKS767PEdInaNWvc9V2mCcfkefdYntiRQAtHhpSriZuNH6MvFKednLoCXZOfhZfZnSTVmYBgBDmJJcl0T4n5ni/WCxMODwLmMLhscee5amn3klLSyctei82sXYk6W6ZkdvRBIyNmbNla2oaiETmSKeT/NyTLVy1nWR0cpzh4RskpCoycmBViyFZaBxVn6dN72XnzoO0tnaVRaweFfGn6zpXrpwmmzWf0x07dmGz3fq+p9Npvve9760Qf5Ik8fjjjz9w8Wfetszu3Ufw2gU92um7Pr5OH+aE+g2qKyv5hce3+hdOgRDr/7FigNsfSwBaPFRkHTXcaPwpsvbyIm6bkaN76otUJa5s0soebTxGnGfyf8e7c/8v78v9Tw7lvwfL6uFcrvJaP0Wx0dTUjg2NJv3mfV9TWq5gQDnE6OgA2WyaxsZWsnj423MRfv2NNgZtB3nyxLPU1jYxJXfQrN/AIdJl57CJHCfy36CJEfbte4z6+q2b8ttIhBBcvXqGSDyBkGQ0Z4jGxlv7HEYiEZ5//nkikUjZdkVRePrpp9mxY8dGL/mW2O0Ompp20KL33VWhW5Pex2Pqt6irrmHfvmNbXvgXawDv5cdie7O1X8EWFquQt1fQ2/iTdE1/GV9usrRdxmDH3DdxqWEmK5+2DKMfENX6GE+Lb2N32WltPUQul4HhGzzdKPHfp/45z+T/jh3VXrxe/4pjnU43oVAdbeFrjCh77vtz1m87QlfuPOHwHI2NbTRXVyDmB9AkO1Gplt+90IVEB08aXyGozwGwIDUwrvQwoXRzIv8NapUw+/Y9Xppl/CgSj0eIROaYsO+jRb1CjUdmZmaC2tqmFU0QExMTnDp1Ck3TyrY7nU6effZZQqGVHeEPGpvNjoRAxrjtmD+byOEVMWqNUXZpp6mvb6GnZ/+aqW8Li4cBSwBaPJToioe+hh9nx+zXqUyXR48aoq/hyi8wXPseDHnrD21/WJGEzi7tDTr1C/gra9i9+zB2u/l4C2EwPHyDkL2Tzlo/8/PT9PVdorq6gcrKUNkHaENDKwsLp6kQc8Sk+9tEoUt2ElIVyWQMgKamdnS9H3/iIpqqMqjsp9KYwS/C7N9/glwuw/z8NKHwi+zSXsct59m//ySBQPC+ruthY35+miwehuXduOQozvQCN25cxOl0U1lppkJvZ/Pi8/l47rnn8PtXfgnYDPz+CmRJ4mT+K5xxvKO8FlAYdOoX6dAu4WIxItzc3EFHx+6HZuzbvSZxrQDg9scSgBYPLUK2M1j3AZrDP6AuVm4sW5m+iXPyswzU/Qh5e3BzFriN8RoRjqjfJSDCdHbsprm5o+yDsa2th7m5ad4iv8xfRT9EJxewR/qYmhqlsrKG/fuPl/avqqrF6XSxP/8SN2zHmVPub5o1LDcwP99HR8cegsEQwWAIIQSnT/+AjszlgkH0iVKEr6GhlWh0gYGBa7S07LHE3/w0I+OjTCq7iUrVvOZ4H3aR5Z18Gl1XAdA0jdOnTzM8PLzi+JqaGp5++mmcTucDXvmtCQQqOXz4JFevnuHNuc8yJXcwYttLDg+H1O9TKWZoamyjoqIKt9uH2+29bb3jVmSxlm/9x1tsbx6uV7SFxXIkmfHQm8naK2md/27Z6DhPfo7dE3/FYO37SHg2r+Zou1FhzPGc9g84XS52735yxXg3gEQiSjqd4JLtGfI2N9ftT3BdPE6jvZ+jke+ysDBLdbU5Ak6SJHbuPIh98DqVya/zBu9kRrl/z9egcoAd+SvMzU3Q0NBWus3Ozr3oukZNTcOKqE4wGOLo0fvfmfywMTExTH//FWbkDq7bHi9t1zAtmTRNI5lM8vLLL6+o9wNob2/n+PHjKMrWm9rj9wc5evQZpqfH8E2N0Jwxm4Xcbg87d558pFP+Fo8GlgC02BbMBw6Ts1fRMfNVbEa2tN1mZOme/jsmK59mOvi4VRd4HwgY8xiGzuHDT5ZSvssZHLyOzxdgRN29uFGSmJS7aJWv8/q1m7zrqZpS/VhlZQ1HjlRz9uxLNGb671kAykKnUsxQZUwRNGYRSPj95Y1DodD29+y7F/L5LAMDVxlW9nDZ9kzZvx0hKegozM3N88orb5DPr+ziPnjwILt3b+2Uqd3uoKWlk+bmDqLRBVKpBA0NLVu+weNOsIygLdbCqmS12DYk3G1cb/ppMvbyInMJaIq8ROfMl1D0zOYsbhvxM3tMgX0rE2BNU0kkoryR3bNynJokMaQcwCeiZLPpZVdJVFfX0yYNI4nyEW53iiw0nCLFcfUbnMx/lV3aG9QbwzQ1tuHzBdZ1zkeVqakxNKHQa1v5xclmZEnEs1y50rtC/NlsNp555hn27NmzpcXfUiRJorKymubmHdtC/IFlBG2xNtvjlW5hUSBvr6S36adon/0nKtN9ZdcF04PsnvgMQ7XvJ+Wy5neuF7fbtHPJZtN4PCuNdCVJxjAMsgW/RofI0KOdISP5SEhVBI0ZZFleYQsD5rSGkZE+quUJ5pRb24wsRxYaPdoZeowLpdqlnTsPUlVVy/j4EK2tneu5q48sQgimpkaYULpQpcXaPadI0Za/gH/+IulcdsVxbreLhoYqpqcHmZsbxWazYbPZsdvt+HwVVFSEyGbTTEwMmd3imFG4HTt2WwL9PmNFAC3WwhKAFtsOQ3YyWPcB6qOv0xh5qawu0KnF2Tn5OSaqnmam4riVEl4Hv3Ohm/fwAzKZ9KrXF9O6xdnNjXo/O/QrKIqCrpnbfIHKVWeoer1+XC4PP1J5mT+N3JkArNbHOaC9iI8kLS1dBAJB7HYHfn8QSZLo6HjwhsMPO5IkYbc7qcpPYRM5NMmJ31jgmezfMzsbxzBWqgOXy0ZFhY1MNoFALvy7E6z2LywhVRKWGwGJSmOK+fDLtLd20tbWhSxvvXpBC4vtiCUALbYnksR05eOkXA3smP1H7PqiWJEwaA6/iD8zwnDNu9Fsdz8O6lGmQswjI26Z3puZGQcgLZkRnRpjnIqKKg4efIJcLksqFcft9q56rCRJ1NQ0Mjk5jGJTV8x/Xk6dPsxx9Z+oqKiip+eZVSOSFutjz54jZM+9zOP5rxOTqqiIXmY6uTLqJwBnoJK0v42btm5mpTZYIu5lQ8NJGrvI4yKFgcK83FT68iUJnW79HIyeI5tNsXv3kQd1F7c1VhewxVpYAtBiW2PWBf4sO2b/EX92vOy6iswwe8Y/zUjNO4l5uzdphQ8XilA5on4Xvz+w6lSMXC5Df/9V6uqaWIg1ARA0ZvD7zU5bl8uNy+W+7W00NrYxNjbAR71foqqqls9O7iIi1SNWMd9t1Pvxev2lOcMW60dV8wghcDjMlK/b7WXPnqPcuHEJY3KUTE5dcUxe8TFU+z6S7ltb9xiyjQwBMkCclePThKTQZztGFi/y7Iu0tHRZ6eD7gOUDaLEWVhOIxbZHtfnpa/gok8GTK97U7EaGrpl/oG3un5CN3Kas72Fij/YqASnBrl2HV6RwhRD09V1GURQ+E317afuC3Mjc3CSGcWeNHS6Xm927D2Gz2RgbG+DJ/Ffp1M+v3FEIaowxqqrqLPF3F0Qic5w/f4p0OommqUxPj3Pp0uucOvUdXn31O1y9ehYwn8+5uTCjo/PkVhF/MXcH15p/7rbi726YULoxkIhE5u7L+SwsLG6PFQG0eDSQZKaqniLpbqF99hs49GTZ1dWJy4WU8LtIuts2aZFbm3/XfZbLl6/S3rG7LNWaz+eYnZ1genqMVCrBG/Z3osqu0vUTSjdNuQGmp8dpbLyzx7a2tona2iaEEJw69TysUklWIeZwkqWqquae79ujwuzsBL29F9CFxMunX8ct5zAMg4qKKq7aHmeX9gaSBKlUitdff52ZmZkV5zCQmah6htmKY/e1hjZkTCIjqKy0ns/7gdUEYrEWlgC0eKRIuNu41vzztM19a8UIOacWZ+fUF5gNHGKi6lkMeetMLtgKZLNm1+bQUC+xWJjKymoikXnmFsyIzbTczqj92dIkjwpjjn3qS1SJGWw2Ow7H3Y/lE0KgaSo528q0ca0xhqLYCAQqVznSYjnJZJzr188zJvfQbz/CHu1VdrW5+bPJx8jk/OwSr2GTDCTJxTe/+c0Vs3wBMvYQQ7XvJeOsu+/ra9QHcLtXnxltcfdYNYAWa2EJQItHDl1xM1j3QUKJy7QsfB9FlPuY1cYvUJEeZLT67cQ9HZu0yq3HH409hdN5mEZ9gMZwP1ULV4lK1YzZTjKhdKNKrrL9K41pqsQMPT0HqKtruuvuTiEMbty4gIFETF5ZO1arj1IZql61m9hiJbpuCrp+22GSciVvON7NG1OAZNZ27sidJZLUmJi4tOrxad8Oeqs/iJBv35izHmShU28MUVPTZqXzLSweEJYAtHg0kSQWAgdIuNton/sm/uxY2dVOLU739N8T9u5mrPrNaMrqXauPGjnJy5DtAEO2Ayji9l2608oO9msvMzMzjqrmCQZD+HwVdyzYIpEFZmcnuWp7irhcnhb0GDGqxDRVVfvv6f48ShSbO1wiTZIlY86ETl30dWYjqVWPs9ttuAI+zgc+hJDuv0WLIlT2aK9iJ09tbcN9P/+jitUEYrEWlgC0eKTJ2yvoa/goNfHzNIVfRBHlxe5VqesEMoNMVD3DvP+Q5Ru4hLUsWrKSj/P2N9OUuEltrLe0/eDBJwgGQ7c50iQYrMLpdFGpTjNMQegJQciY5DnpeRS3h+pqSzDcKcUJFzYWX+O+zBit88/jVhdWPcbnc+Hz2eh1HMPYAPFXo49xQP0BPjnNjs49eL1W9+/9wqoBtFgLSwBaWEgScxVHiHk6aJ/7pxXRQJuRo23+O1QnrjAaeitplyU67pRxuZtKY7p02e323tIDcDmSJFNREaJptp8rxpPUiTF2aJcJijmc/gr27j12y1nEFivJ500Pv6zkwaYlaQ6/SCh5ddV9c0oAUdVG2lXBkBRkTNl539cT0sd5QvsGFcEQPT3HSxNmLCwsHgyWALSwKJC3B+lr+CihxGWawy9gW2YL481NsWvyr1nw72ei6lk0xfrAuh2S0DmkvkCzcZPu7v00NLTecX2XEIKhoV5mZyeYknfw5vzf4iBHZWUNzc0nqKystmrF7pJcLosQAk+8n/bI6RW1r2DO7ZgNHGGy6mkMeePEtd9Y4Jj6bYKV1ezbd8yq49wArAigxVpYAtDCYimF2sCYp4OWhReoSl0vvxrTMiaY6mM6+ASzFUcQkvXPaDmy0DiqfocGMcLuPUeoqTFnL+u6xs2bV/D7g9TXNxOPR4nHI8TjEZLJGEIsThjJ53NctZ2kTb9KbdBPd/cT1qSPdSKEYHJyktm5NA3aK6vuk3bUMlL9jo2NcAtBQMxzPP8tgl4Xe/YcscTfBiEK/7uX4y22N9Ynl4XFKmg2H0N172M+vZ/Whe/gUiNl19uMHM3hH1ATv8B41TNEvTut+sACilA5pn6LOibp7NpHKpUEpvD7g/T2nicci6HMjNPffwUAFQcRuY6YtBddsiEJA0kIovYaFuRG9mqnqKs7ZIm/dRIOh7lw4cKqnn4AuuRgoupp5gKHYZVpK/eMEATFLA36IA3GIF4Rx+Vys3//CWy2+99RbGFiRQAt1sISgBYWtyHhaeea++epjZ2lIXJqRZOIU4vSOfs1Us4GxqueJelu3aSVbh069IvUGONUhuq4dvMGMjoK5hQQFTuvO96HInQ8Ik5EriMhVd1SPFfr5vg+v7/iga1/u5BMJrl06RIjIyO33Gfet4+JqmfuyzxsSeh4RAKPiOMVcTwihlfEqTDmcJMih4u2+hpqanYRDFr2PRYWm40lAC0s1kBINmaCJwj79tAUfpFQ8tqKfby5KXZO/S0x9w4mq54i7Xx0G0VGlD2EjClYGGdW7uKy/Rls5AkZk0SlGpJy1ZrnkIROq36dHu0sdrvTiv7dBZlMhqtXrzIwMIBhGKvuk3LWMxZ6CylX0z3fnt9Y4KD6A4JiDqmQNjTnPnsYyVXR1VRLKFRHMFiFtBERRotVsSKAFmthCUALiztEtfkZrn0vs4GjtCx8H19uYsU+FZkhKiaGiHi6mao8uSETE7Y6ecnDa/b34hMRklIlSBIqTsaXdZJ6jSh7tFP02k6QkAu2MMKg2ehjp3YGj0hQW9tEe3uP1fBxB2SzWa5fv87NmzfR9dXnLquKh/GqNxH27bn3kgUh6NAvsUt7jYDXS1PTPlwuL263B6fTjSRJHL+3W7C4B6waQIu1sASghcVdknY1cKPxYwRTfTRFfriiPhCgMn2TyvTNghB8goyzfhNWuolIEknp9pE+J2nqjREatTEuyE+Rl1zs1E7jFxGqq+tpbz9ijQW7AzKZDL29vbcVfjabDbdb5lrw3YTt9z7dxilSHFa/T40xTlPTDjo6dt31pBcLC4vNxRKAFhbrQZKI+nYS9XZRnbhEY+QUdn3lJIWiEIy5dzAdfJyku2UTFrs1SUpBwPQGPJD6IQCVlTXs2PEUfn9w8xb2kJBMJunt7b1tqleWZbq7u9mzZw+XLr1KNDfELPcmAIPGNCfy38RAYf/+E1RV1ax9kMWD5x5TwFYAcPtjCUALi3tBUpgPHGbBt4/a+Dnqo69jM7IrdqvIDFGRGSLpbGQmeJyop2tjOi63KkLgFgncJIlJ1eiSgzxuVBzU1jYRCFQiSRIVFWvXBz7qhMNhent7GR0dRdziE16SJDo6Oti7dy9er2m8XVvbRH58kEviWYx1Whe5RJJj+W9RHfCwb59lxL2VEfc4C86qAdz+WALQwuI+IGQ7M8ETzAUOURs7R13s9KpC0JebxDfzFbK2ILMVj7Hg34shOzdhxQ+GDu0CDfoQNfI8uq4BpjiZo4E5pYW85CSTSdHa2rXJK93aGIbB5OQkfX19t7RzAfOxbW9vZ+/evfj95enz2tpGRkb6qJHHmFF23PUaZKFxLP8tBAp79z5miT8Li4ccSwBaWNxHDNnJdKVpEF0bO09t7Ax2I71iP5cWpXXhuzSFf8h84ABzgUPk7Nsv+nWQM7h9Hqqru/D5AjgcLmKxMFWROWojZzCETja7fQXwvZLP5xkcHOTmzZskk8lb7ifLMjt27GDPnj34fLfvmDZYX63eAfWH+EWEY0eewOGwnrOtzr0G8KwA4PbHEoAWFhuAKQQfZ6biKNWJS9TFTuPU4iv2U0SeutgZ6mJniLnbmQ8cIurpBGl7FNSnNBvNofqyCJ/PF6CpqR3DMIjHw9jtlphYihCCcDhMf38/IyMjt2zsALO5o7Ozk127duHx3H40YTS6gIFEWL57iyKfEaHFuEF3937Lk/EhQdyjD8ytygsstg+WALSw2ECEbGeu4ihzgUNUpm5QH30DT3521X0rMsNUZIZRFS/z/n0s+A+Qs1c+4BXfXwxk8vlc2Yi3IrIsEwxWb9LKth75fJ7h4WEGBgaIRqO33dflctHT00NXVxdO550J6EwmRVbyoUt3P32jTb+K3e6gvr75ro+1WMQwbi3mLSweNJYAtLB4EEgKEd8eIt7d+LJj1MXOUJHuZzUnNrueoiH6Og3R10k6m5j37yPi2/VQ1grG5BomJ4eJxRZobu6gtrbJmgCxBMMwmJ6eZmhoiPHx8Vt28xYJBoPs3LmTtrY2FOXuosQulxuXSJlRobvwAFSESot+g4amVsvq5R7QdY2vvbbSRH6jsJpALNbCEoAWFg8SSSLpbiXpbsWhRqiJX6A6cXnVhhEAX24CX26C1oXvEnN3EvbvIebuQMgPxz/d0/Z3EjIm6MxcJHXjIhdv3ORATxcNDY/uyDwhBPPz84yMjDA6Okoul7vt/pIk0dLSQnd3NzU1Nes2xXa5PMgYuEiR5c4nqzTrfdjJ09DQtq7btTCJROapNlaax28UVg2gxVo8HJ8iFhbbkLy9konQm5isfIqqVC/V8UurThcBkIVOZbqPynQfumQn6u0i4t1F3N2OkO8+pffAkCQWlGYWlGZ8Rphu7Rx9fZcAHikRKIRgYWGB0dFRxsbGSKdXNgYtx+v10tHRQWdnJ263+57X4HabdjAd2kWu2U6uGQVURJ42/Tpd2jlCoTpcrntfw6NMKFTHvn3H+Na3vvVAbs+qAbRYC0sAWlhsMkK2s+Dfz4J/P678PNXxi1Qlr2H//7d3Z7Fx3Icdx78zs7MXyeXyEEWRS1KkLNm6YsuUHdupHQMOHLhB06YHXCAI+pA8BH0K8hSgDwlSIAH6EOTJRvLS5qFHHCBA09pF6jRF7fi2LMsUrYOieIla3tz7mNmZ6QMdRjIpkZJIUdL+PsBwgdk5/ktid3/8n3553eOtwKWtcIa2whk8wyYX6yfTsJ9sfADPunO/pAtmKyftZ6jVbDg/hG1HaG+/d5fK8zyP2dlZpqenmZ6eplxe/+95JdM06e7uZt++fXR2dm7pEnjxeCMDA4cILg7REGT5wP4CnrF2KpdwUKK/NsRe7zQhauzZ3UV///3rXFFuhGEYJJNtO10MkVUKgCJ3kEq4nUvtzzDd9jSJ0kXa8qdpLl3EZP3O41bgrtYMBhgUI11k4/vIxvsphztufb3XrWYYDIWeJBKUsc6e5JFHniYSie50qTYtCAKCwMcwzHXDWbFYJJ1Ok06nmZmZoVarbeq6bW1t9Pf309vbu+lBHTejp2eAeLyR0JkPMN3/5qT9DCYeocAFoN8bYiA4Axjs6e4llRpQzd9dSn0AZSMKgCJ3oMCwyDbsJ9uwH8urkCyeo7VwhqbKFMY1PtUNgtU+g93Lr+FacXKxveRjfeSjvTj2HTJ9h2Fyyn6aXv+fuXDhNIcPH9/pEm2K59UYHj7B8vL86r5jx54kl8szOzvLzMwMudzaqX6uJZFI0NfXR19f35pJm7dTW1sHhw8fxx96hy9W/+mq5ywrRE/vfrq6+jTR811OfQBlIwqAInc4z4qymHiQxcSDhGoFWoojtBTP0li5dM0wCGB7JdoKH9NWWBl5WA0lyUdTFKIpCrEU1VDLjtUQukaU++47wpkzH7CwMEN7e+eOlGOzPK/G0NC7zC9lGfP201kewnE8Xn75lRu6TiKRoLe3l56eHpqbm7e0ifdGtLS08/DDT1IuF3Fdh/Hx87hulVgsTl/f/h0pk4jcXgqAIneRWqiR+eZjzDcfw/JKJEujJIsjJMrjmMH1mxsjtQyRQob2wmkAXDNGMdpFMbKHUmQPxchuPOv6kwlvpX+4+Ch/03qJkZHTJJNthEJ31mCWWq1GJpNhaWmJsbFRMtkcvufTxAcUb+A6bW1tpFIpUqkUiURi28p7o+LxRpaX5zl/cQSXCAWzHbs6v/GJclcIVn/cwvlyT1MAFLlLeVZ8dfCI4bs0VaZoLo2SKI0RrWU2PN/2yysBsjS6uq8aSlAOd1AKd1COdFC221Ymoza2Ye4+w+Cl4rN80ftXJiZG2Lfv0NbfYxOCIKBcLpPNZslkMmQyGZaXl8nlcjc1EjIcDtPZ2UlXVxd79uwhGr3z+jj6vs/Jk2+QL+SYtI5yNvQIfd4ZdrnT+L63I/P9+b6PYRg7Vit6r9EoYNmIAqDIPSAwbXLxAXLxAQDCboZEeYxEeZKm8iSha4wo/rRILUekliNZurC6z8eiardQCbdSsVup2i1UQ0mqdhLXarylZuSy0cTlIEVz6drr3G6FIAhwHIdCobC65XI58vk8uVwO13Vv+tq+YVGIdBOJWHTbs+zZ08WRI49sYem33tLSPIVClrfsL1Mwkxyp/Y5e7xyNjQlYd3ry7VWplDh16i06O3vVBC1ymygAityDHDvJgn2MhcQxCAJizjyNlUs0VqZoqlzC9jbfiGniEXMXiLkLa57zMXFDTTifbK7ViGs1ULMacK04NSuGZ8aoWVF8I7xuWAwHZcLhGx/5GgQBnufhOA6O41CtVqlWq1QqFcrl8upWKpUolUqbHpG7Ec+wKUa7KERT5KMpipHu1Ym5+ysvsrg4Sz6foakpuSX32w5zc9PEYg0k3Tkerf4XPhb79x9lz54ejO2o7b0G3/fJZpc4f/4jKpWylkrbQhoEIhtRABS51xnGSnNupIP55ochCAjXcjRUp2mopGmozhB3ZjfsQ7geE59ILUuklt3w2ADwzAi+YeOb9sqjEcIJFriQj5BOL682Aa5Mt7Ky+b6/utVqtdXNdd1tb6YyDINEIkEymSSXm6e1tZWXvOfhGk2k/xv+a465/8PJk2/Q17ef3t77bmugWo/rOszMTOE4VQYGDuK6VRYW0gRBwGHjHbq699LXt/+2jvrN5zNMTo6yvDyP59UoGEliOGSzS2QyizQ3t6op+BZpGhjZiAKgSL0xDBy7GcduZrnxk353gU/MWSDmzBF35ohV54i5izdUU7jhbYGQXwWqXDmtoQfkHIdcLr9l97oZoVCI5uZmkskkyWSSlpYWkskktr0yOGVxcZbTp9/jqPU7RoxBKsba5dQKZgu/C3+FA7UT+OMfkE5PEo83EYvFiceb6Ojoui1Bq1ZzmZy8QDa7TD6fIQhW1hiORuO4rrManO+//0F2705te3k+bWpqlEsLWcZDx5gL95E12tnrDTOQO0X21Fvs2tXFoUMP3/ZyidQTBUARAcNcrSVcumK35ZWJOQtE3CWin2wRN0uklrmpGsOdZhgGDQ0NNDQ00NTUhGVBrVals7Ob7u4eLOvaH4ltbbvZt+8Q7ugIvd5ZLpsDXAw9SNbsuOq4wLA4Zz/KrNVHV+0C8WyeeCZHUzDFxYsf09HRTTLZRkNDgni8EdPc2hrCSqXE6dPvUamUmQz2smwdZNq6jwdq78GF05/UspoEgU9ra8fGF9wGxWKetDXASOgPc0COh44wbh3mbzt+w/T0GEEQqBbwFmgQiGxEAVBErsmzYhRiPRRiPVc/EQSEvCLhWo5wLU+4lsP2CtheEbtWwPZKhPwyIa903bkKt1I4HCYSiRCJRIjFYkSjUWKxGPF4nIaGBuLxOPF4fDVwnT8/RDo9QYlGymPDnBu7QKqjjViske7uvevW1KVSA3R29jAzc4nyxSmedH7J2/aXWLB61hybMXeTMf+w1F04KNHnnSE/e5aGmSlgpQ9l3mjlUFcjPT37iERubdWNbHaJ4eH3ydQivGv/BQWzdfW5j+zPs2B28VT4ndWm9p2Y7Nn3PcrlInmrde2ThsEvZu7nMf8C+XyGRKLltpfvXqE+gLIRBUARuXGGQS3USC3USOl6xwUBZuBg+VVCXgXLr2IGLqbvYgYujf4i99U+JJXah22HV/v9XTkdiGVZmKaJaZqEQiEsyyIUCmHbNrZtEw6HsW37hmrSFhZmSKcnGAo9ybh1mMYgQ8o7R34+TTK4iONUOHDgM+ueGwrZpFL9dHfvZWjoXQaXf8Nr5l9SNq6/modjxBkJDTISGiQUVGkKlkj4iySDOebmxpibu0xf3wF27dpzU4Ni8vksp069TTmI8kbkK1SNhjXHXLb2MzgY4a23XqWzs/eG77EVFhZmCYKAvLl+uFs0u8gY7QwPv8+xY3+kpehukvoAykYUAEVkY0FAm3+ZXf4UC2aKRbOLYDODGwwD34jgmxHc0NpJkCPeKA3uxxw8ePC21EYFQcDi4hznzw8xY/Yxbh0Gw6BgtHDWfAyA/bUT2LPv09//wHXLZBgGBw8eo3TidQad/+bN8J/hG5ubP69mRFg29rBs7mECOBOUecj9Le6F00xMnOeRR56+4d9HOBwmkWghyC7yVPUXjIeOMGINrhl5/fdvx/m869DcfHtr14IgYHLyAmPj55g1+1g2Oon7WZ52/o3Xwn+1WlvpGyHeDX+JLxsvMTT0Dg899ISWpRPZBjs7PE1E7mxBQLs3xVcjL/GE+yseYIjH3f/gK/4/ctR9jd3eGAl/nnBQuqkqg6Q/90nN3vavAhIEAWfOfMDw8HvM1Zr5yP78utPSTFiHCAJIpyc3vKZthzl0aJBWFnjc+XeS/uxNlc0xYrwb/hKvh/8c13Uolzc/+GZqapRLl8aw7QgPPfQ4g4NP0WC57K+dxMZZW+agCsDw8Pt89NE7zM1dvqky34hazWV4+H3Gx89xPnSc9+znCDB4xvkXLPw13QSqRpxX/D+lWq0wMXF+28t3L/p9F8Bb2W7GCy+8QH9/P9FolMHBQV5//fWtfWGyZVQDKCLr2uVNcqD2Pq3BLEG0mSNHHqG1tYNCIcvc3GXM+VH2VoevOMOgaDQyb/YwY/azYHYTXKdGLOEvsN8/RU/vfdve2T8IAkZGhpibn+GE/Sxpa996BwErYWzC3E/48jip1MCGTcuJRJLPfOazXLhwmtbiL5kyD3DW/uzKKOEgoDFYpmQk8I2NP24rnzTbOk51k6/L5+LFMwBMT4+xe3eK+fk0vu/xnv3HuMbapuRFq5vHBp9hYWGGE6OzZDIf0tHRtan7bcR1HSYnL1As5ohG40QiMSKRKFNTo2RLDift55i19gKQ8s6tnpc329Zcq2i2MF1L0Vy+bicDuYbgip83f/6N+fnPf863vvUtXnjhBT73uc/xk5/8hOeee46PP/6Y3t6d6XIg16YAKCJrNPmLPOa+TNZo5+jRR2lp2bUa0pqakjQ1JRkYOIjjVHCcKtXqymOplKdtcZy9lY+xLIu8FyMZdsk5Fo4RwSWKY6xsbX6aeLyR3t5bW/khCHyWluaZmZmiUMhh22HC4cgVjxHK5QLp9CSnQk+vG/6iQYHHnV9xyTrASOg4F60H6XPO89FHb3Po0MOEw9dfzi2ZbGNw8CnS6Uns8XP0uaNM00uvNY3rOsRiDbzsfZmimbzudSo0UKKRbHaJ9vbOTbx6g3A4SsEJmHPaKE+cZ9bcy4j9BTLmtc//uw8PAAfos4Zprr22iftcn+97TE+PMzExAsB0kCKaKRAPZglTJW+08F74T1Zff4c3wVF35b5j1uFrXrdqxHGc5Vsun9weP/rRj/j617/ON77xDQB+/OMf8+tf/5oXX3yRH/7whztcOvk0BcC7lav/imX75IMwOTdK3y6Ix5uoVivXPNa2V0IWQGtrB93dAxSLBZaX59nlexiGQRsrzYCu6+K6S3ieQxDA3r1HN13b9WmlUpG5uUuMXpolQomc0cqC2YtNlUhQIRzkiQRzhClj4nM+dJypoG/Ne8cKqgw6/0kkWKbLqDISPkjeiPE6z/LQwm/Jvf1bHnjgoU2NSG1t7SCRaGFqapRwbpbm5t0YhsHU1Ci7QmcpWkc3XDpvzmujZT5Nd3f/pn4P+/YdZmjoHQqmzbuhrxIYoZXJFb2NPyO8WhW35lKpbG6pwPXUajU+/PBNKpUyE9YDXAgdwzXiqyvKWYGDRwg8E6OW40H3/+j0x0g0r9T6pbIfcsnvuWrE9O8VXYOiX7il8t1JKpVrv4+2nFO6tYEcn7xPcrncVbt/P9J+ze0chxMnTvCd73znqv3PPvssb7755i0URLaLEWiyn7tKpVKhv7+fmZmZnS6KiIjcoM7OTsbGxohGr1+rfLO28juisbGRQuHqdbq/+93v8r3vfW/NsZcvX6a7u5s33niDJ554YnX/D37wA372s59x7ty5NefIzlIN4F0mGo0yNjaG46zt3C0iIne2cDi8beEPtvY7Yr3JuNer/bvSp4/XhN53LgXAu1A0Gt3WDxAREbl77cR3RHt7O5Zlral5nJubY/futc37svM0DYyIiIjcknA4zODgIK+++upV+1999dWrmoTlzqEaQBEREbll3/72t/na177G8ePHefzxx/npT3/K5OQk3/zmN3e6aLIOBUARERG5Zc8//zyLi4t8//vfJ51Oc+TIEV555RX6+vp2umiyDo0CFhEREakz6gMoIiIiUmcUAEVERETqjAKgiIiISJ1RABQRERGpMwqAIiIiInVGAVBERESkzigAioiIiNQZBUARERGROqMAKCIiIlJnFABFRERE6owCoIiIiEidUQAUERERqTMKgCIiIiJ1RgFQREREpM4oAIqIiIjUGQVAERERkTqjACgiIiJSZxQARUREROqMAqCIiIhInVEAFBEREakzCoAiIiIidUYBUERERKTOKACKiIiI1BkFQBEREZE6owAoIiIiUmcUAEVERETqjAKgiIiISJ1RABQRERGpMwqAIiIiInVGAVBERESkzigAioiIiNQZBUARERGROqMAKCIiIlJnFABFRERE6owCoIiIiEidUQAUERERqTMKgCIiIiJ1RgFQREREpM4oAIqIiIjUGQVAERERkTqjACgiIiJSZxQARUREROqMAqCIiIhInVEAFBEREakzCoAiIiIidUYBUERERKTO/D/SNwmyoVsDUgAAAABJRU5ErkJggg==" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = Image(\"Arctic_regions.png\")\n", + "b = Image(\"Antarctic_regions.png\")\n", + "display_png(a,b)" + ] + }, { "cell_type": "markdown", "id": "5294910f", @@ -54,6 +90,43 @@ "## Basic example" ] }, + { + "cell_type": "markdown", + "id": "2161a4b3", + "metadata": {}, + "source": [ + "This first case will work with sea ice concentration ouput from a single model, E3SM-1-0. Two overview plots are shown below to visualize the Arctic sea ice in this model.\n", + "\n", + "The code to generate these figures can be found in `make_demo_sea_ice_plots.py`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "068142a6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = Image(\"E3SM_arctic_tseries.png\")\n", + "b = Image(\"E3SM_arctic_clim.png\")\n", + "display_png(a,b)" + ] + }, { "cell_type": "markdown", "id": "2540cd5d", @@ -64,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "id": "6e4fa38d", "metadata": {}, "outputs": [ @@ -76,7 +149,7 @@ "\n", "# List of models to include in analysis\n", "test_data_set = [\n", - " \"E3SM-1-0\"\n", + " \"E3SM-1-0\",\n", "]\n", "\n", "# realization can be a single realization, a list of realizations, or \"*\" for all realizations\n", @@ -111,7 +184,19 @@ "# Directory for writing outputs\n", "case_id = \"ex1\"\n", "metrics_output_path = \"sea_ice_demo/%(case_id)/\"\n", - "\n" + "\n", + "# Settings for the observational data\n", + "reference_data_path_nh = \"/work/ordonez4/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc\"\n", + "reference_data_path_sh = \"/work/ordonez4/ice_conc_sh_ease2-250_cdr-v3p0_198801-202012.nc\"\n", + "ObsUnitsAdjust=(True,\"multiply\",1e-2)\n", + "reference_data_set=\"OSI-SAF\"\n", + "osyear=1988\n", + "oeyear=2020\n", + "obs_var=\"ice_conc\"\n", + "ObsAreaUnitsAdjust = (False, 0, 0)\n", + "obs_area_template = None\n", + "obs_area_var = None\n", + "obs_cell_area = 625 #km 2\n" ] } ], @@ -130,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "id": "9d6c1fbf", "metadata": {}, "outputs": [ @@ -140,8 +225,8 @@ "text": [ "usage: ice_driver.py [-h] [--parameters PARAMETERS]\n", " [--diags OTHER_PARAMETERS [OTHER_PARAMETERS ...]]\n", - " [--case_id CASE_ID] [-v VAR [VAR ...]]\n", - " [--area_var AREA_VAR]\n", + " [--case_id CASE_ID] [-v VAR] [--obs_var OBS_VAR]\n", + " [--area_var AREA_VAR] [--obs_area_var OBS_AREA_VAR]\n", " [-r REFERENCE_DATA_SET [REFERENCE_DATA_SET ...]]\n", " [--reference_data_path REFERENCE_DATA_PATH]\n", " [-t TEST_DATA_SET [TEST_DATA_SET ...]]\n", @@ -151,6 +236,9 @@ " [--metrics_output_path METRICS_OUTPUT_PATH]\n", " [--filename_output_template FILENAME_OUTPUT_TEMPLATE]\n", " [--area_template AREA_TEMPLATE]\n", + " [--obs_area_template_nh OBS_AREA_TEMPLATE_NH]\n", + " [--obs_area_template_sh OBS_AREA_TEMPLATE_SH]\n", + " [--obs_cell_area OBS_CELL_AREA]\n", " [--output_json_template OUTPUT_JSON_TEMPLATE] [--debug]\n", " [--plots] [--osyear OSYEAR] [--msyear MSYEAR]\n", " [--oeyear OEYEAR] [--meyear MEYEAR]\n", @@ -167,10 +255,13 @@ " None)\n", " --case_id CASE_ID Defines a subdirectory to the metrics output, so\n", " multiplecases can be compared (default: None)\n", - " -v VAR [VAR ...], --var VAR [VAR ...]\n", - " Name of model sea ice concentration variable (default:\n", + " -v VAR, --var VAR Name of model sea ice concentration variable (default:\n", + " None)\n", + " --obs_var OBS_VAR Name of obs sea ice concentration variable (default:\n", " None)\n", " --area_var AREA_VAR Name of model area variable (default: None)\n", + " --obs_area_var OBS_AREA_VAR\n", + " Name of reference data area variable (default: None)\n", " -r REFERENCE_DATA_SET [REFERENCE_DATA_SET ...], --reference_data_set REFERENCE_DATA_SET [REFERENCE_DATA_SET ...]\n", " List of observations or models that are used as a\n", " reference against the test_data_set (default: None)\n", @@ -192,6 +283,15 @@ " (default: None)\n", " --area_template AREA_TEMPLATE\n", " Filename template for model grid area (default: None)\n", + " --obs_area_template_nh OBS_AREA_TEMPLATE_NH\n", + " Filename template for obs grid area in Northern\n", + " Hemisphere (default: None)\n", + " --obs_area_template_sh OBS_AREA_TEMPLATE_SH\n", + " Filename template for obs grid area in Southern\n", + " Hemisphere (default: None)\n", + " --obs_cell_area OBS_CELL_AREA\n", + " For equal area grids, the cell area in km (default:\n", + " None)\n", " --output_json_template OUTPUT_JSON_TEMPLATE\n", " Filename template for results json files (default:\n", " None)\n", @@ -250,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 6, "id": "d6ff0052", "metadata": {}, "outputs": [ @@ -258,13 +358,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-11 13:30:07,104 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", - "2024-01-11 13:30:17,291 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", - "2024-01-11 13:30:27,185 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", - "2024-01-11 13:30:34,909 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", - "2024-01-11 13:30:42,526 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "INFO::2024-01-11 13:31::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n", - "2024-01-11 13:31:42,825 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n" + "2024-01-19 15:01:38,777 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "INFO::2024-01-19 15:02::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n", + "2024-01-19 15:02:44,427 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n" ] }, { @@ -275,11 +371,9 @@ "Find all realizations: False\n", "OBS: Arctic\n", "Converting units by multiply 0.01\n", - "Converting units by multiply 0.01\n", "OBS: Antarctic\n", "Converting units by multiply 0.01\n", - "Converting units by multiply 0.01\n", - "['E3SM-1-0']\n", + "Model list: ['E3SM-1-0']\n", "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n", "Converting units by multiply 1e-06\n", "\n", @@ -334,7 +428,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 7, "id": "dfd75e12", "metadata": {}, "outputs": [], @@ -344,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 8, "id": "9a46fb89", "metadata": { "scrolled": false @@ -362,6 +456,7 @@ " },\n", " \"json_structure\": [\n", " \"model\",\n", + " \"realization\",\n", " \"obs\",\n", " \"region\",\n", " \"index\",\n", @@ -377,135 +472,179 @@ " },\n", " \"RESULTS\": {\n", " \"E3SM-1-0\": {\n", - " \"bootstrap\": {\n", - " \"antarctic\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.4063417787853511\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.155238367232\"\n", - " }\n", - " },\n", - " \"arctic\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"4.356016677100606\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.942134849536\"\n", - " }\n", - " },\n", - " \"ca\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.10201965034604724\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.011096832\"\n", - " }\n", - " },\n", - " \"io\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.04652064438963821\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.008945589248\"\n", - " }\n", - " },\n", - " \"na\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.2475039028131327\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.57598345216\"\n", - " }\n", - " },\n", - " \"np\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.41030185147377524\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.184947277824\"\n", - " }\n", - " },\n", - " \"sa\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.4186260179820069\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.301095288832\"\n", - " }\n", - " },\n", - " \"sp\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.7197147567464404\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.062149992448\"\n", + " \"antarctic\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.4635192339671928\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.139646926848\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.4635192339671928\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.139646926848\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"arctic\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"5.476181000101471\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.628078727168\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"5.476181000101471\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.628078727168\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"ca\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.05045644169895609\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.007755424768\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.05045644169895609\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.007755424768\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"io\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.04955696515353039\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.00991997952\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.04955696515353039\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.00991997952\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"na\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"2.3482121752568643\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.576847409152\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"2.3482121752568643\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.576847409152\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"np\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.6264518797177615\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.287947685888\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.6264518797177615\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.287947685888\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"sa\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.3797729615722766\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.297013608448\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.3797729615722766\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.297013608448\"\n", + " }\n", " }\n", " }\n", " },\n", - " \"nasateam\": {\n", - " \"antarctic\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.3784661114628296\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.499939508224\"\n", - " }\n", - " },\n", - " \"arctic\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"7.2725043134342915\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"5.161479569408\"\n", - " }\n", - " },\n", - " \"ca\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.10145201711023501\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.08221581312\"\n", - " }\n", - " },\n", - " \"io\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.028741228802380923\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.007070034944\"\n", - " }\n", - " },\n", - " \"na\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"2.0495937214356794\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.316665032704\"\n", - " }\n", - " },\n", - " \"np\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.6113302091333116\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.279103602688\"\n", - " }\n", - " },\n", - " \"sa\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.06886884722313671\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.031630178304\"\n", - " }\n", - " },\n", - " \"sp\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.7205462829629394\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.641328283648\"\n", + " \"sp\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.6767107661262813\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.078223351808\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.6767107661262813\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.078223351808\"\n", + " }\n", " }\n", " }\n", " }\n", @@ -513,6 +652,7 @@ " },\n", " \"json_structure\": [\n", " \"model\",\n", + " \"realization\",\n", " \"obs\",\n", " \"region\",\n", " \"index\",\n", @@ -533,7 +673,7 @@ " \"Version\": \"23.1.0\",\n", " \"buildVersion\": \"not installed\"\n", " },\n", - " \"date\": \"2024-01-11 13:31:29\",\n", + " \"date\": \"2024-01-19 15:02:30\",\n", " \"openGL\": {\n", " \"GLX\": {\n", " \"client\": {},\n", @@ -587,12 +727,12 @@ "id": "d74b6752", "metadata": {}, "source": [ - "Some text about the output figure" + "This driver also outputs a bar chart that visualizes the mean square error metrics. Since there is only one model and one realization in this instance, the bar chart looks very simple." ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 9, "id": "c6dfa7a6", "metadata": {}, "outputs": [ @@ -610,13 +750,13 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 10, "id": "d14e933a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" + "image/png": 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7du0yLlPKy8vDqVOnANSe0eu55Cc7O9u4zCkrKwsOhwMAkJmZiZqaGp/JEGpqaoyziB0OhzEJiN1uN25kUV5ejpycHADAqVOnjBGKsrIyY0rL0tJSFBYWAgAOHz6MoqIi5sScmBNzMnVOR48exX/+8x9omtaknAoKCmAWAX+We2FhIVasWIENGzbgueeeQ58+fTB9+nQAwKuvvgpN0/DQQw8hOzsb77zzDlatWlVvO06nE06n03heWVmJmJgY2Gw2REVFGVtusiz7xKqqwmKxGLEkSZAkqcHY7XZDlmUjVhQFFovFiIHaLULv2Gq1QghhxJ4tTk+s6zoURWkw1jQNQggjri8P5sScmBNzYk51c7LZbIiOjjbFWe4BX9CB2rPcCwoK0LNnT6xduxbz58/H6tWrUVlZienTp+OHH36Aqqr4+9//jp/97GeNatNMlyoQEbU1uq6jvLwc11xzjc/8C1fKTLUgKAp6SzDTl0hE1NaoqoqcnBykpKQ0afpXM9UCToJLRERBR1GUJt0PwIwC/qQ4IiKiS+m6jhMnThhnqxMLOhERBSFd13HkyBEWdC8ccicioqCjKApSUlL83Y2A0uJ76J7bIhIRETUXXddx/Phx7qF7abE99ClTpqBv37748ssvMWTIEDz//PMttSoiImpjPMfQu3fv3qTL1sykxQr63LlzUVlZie7du2PhwoUttRoiImqDFEVBUlKSv7sRUFpssyY9PR2xsbG8tR0RETU7TdPwzTffGLPBUQufFDdgwAAMGDAA5eXl2LJlC86fP28U+EcffbQlV01ERCYmhMD58+fRu3dvf3clYLTKgYcJEyagqqoKffr0Qd++fdG3b9/WWC0REZmUoigYPnx4k2aJM5tW+SS6du2KBQsWtMaqiIioDfDcZa1fv36QZdnf3QkIrVLQZ86ciSlTpiAuLg4WiwUA8PTTT7fGqomIyKSqq6v93YWA0ioFfdmyZZg/fz66d+/eGqsjIiKTk2UZCQkJ/u5GQGmVgt6rVy/MmTOnNVZFRERtgKZpKCkpwYABAzjkflGrFPTq6mqkp6f7DLlzohkiIqLm0yoF/cknn2yN1RARURshyzJiY2P93Y2A0ioFXQiB1NRU4/nbb7/dGqslIiKT0jQNRUVFiIuL45D7Ra1yHfqqVauQn58PAFi9ejVycnKu6O8XL16M5ORkzJgxAy6Xy3h9586diImJQWpqKm6++eZm7TMREQW28PBwf3choLRKQV+/fj1WrFiBhQsX4uuvv8aqVasa/beFhYU4ffo0cnNzMXDgQGzatMln+a9+9Svs3LkTO3bsaO5uExFRgJJlGf379+feuZcWLeiPPPIIHn30Ufz+97/Htddei82bN0OSpCua9jU/Px9paWkAgPHjxyMvL89n+ebNm5GcnIyXXnqpWftORESBS1VV7N69G6qq+rsrAaNFj6FPnDjR5/m0adOuuA273Y5u3boBACIjI2Gz2Yxlw4YNw6FDhwAAt99+O0aPHo2hQ4fW247T6YTT6TSeV1ZWAoAxsb/nv7Is+8SqqsJisRixJEmQJKnB2O12Q5ZlI1YUBRaLxYiB2n+I3rHVaoUQwoh1XYemaUas6zoURWkw1jQNQggjri8P5sScmBNzMlNOuq4jMjISFoulyTmZRYvuoY8ePRrl5eWw2+0YPXo0xowZgzFjxuDMmTONbiMqKsoovna7HZ06dTKWtW/fHiEhIQgJCcGkSZOwb9++BttZvnw5IiMjjUdMTAwAoLi4GABQUlKCkpISAEBRUREOHz4MoHbIv7S0FACwa9culJWVAQDy8vJw6tQpAEBOTg7Ky8sBANnZ2bDb7QCArKwsOBwOAEBmZiZqamqgqioyMzOhqipqamqQmZkJAHA4HMjKyjLyzM7OBgCUl5cb5xycOnXKGKEoKyvDrl27AAClpaUoLCwEABw+fBhFRUXMiTkxJ+Zk6py+/fZbVFRUQJblJuVUUFAAs7CIFry/6Z133omf/vSnUBQFO3bswLp169C3b1+MGzfO+OJ+TGFhIVasWIENGzbgueeeQ58+fTB9+nQAtXvZERERAIC77roL8+bNQ0pKSr3t1LeHHhMTA5vNhqioqKDeUjXj1jdzYk7MiTldLien04k9e/YgMTHRmN/kanKy2WyIjo5GRUWFUU+CVYsW9LFjx+KTTz4BULs1NXPmTDzzzDN49tlnG13Qgdqz3AsKCtCzZ0+sXbsW8+fPx+rVq/H666/jtddeg6IoGDVqFF544YVGt1lZWYnIyEhTfIlERG2NrusoKytDTEwMJOnqB5vNVAtatKCnpKQgKysLYWFhAGqHV2bMmIH8/HycPXu2pVbbKGb6EomI6OqYqRa06DH0F1980Tj+DQAdOnTAli1b8Le//a0lV0tERCanqipycnJMdVJbU7XoHrpHVVUV2rVr19KruSJm2iojImprdF3HqVOncN1113HI/aIWu2zt4MGDAGqnfX3jjTd4MxYiImo2kiTxltyXaLEh96eeegp79uzBnj17jMsDiIiImoOqqsjOzuaQu5cW20N/+umnjZvPjx49uqVWQ0REbZAkSYiNjW3ScLvZtFhBT0hIgKqq2LRpE/Ly8mCz2dCpUyeMGjUKU6ZMMa5HJCIiulKSJKFLly7+7kZAadFNm9mzZ+Po0aOYNWsWlixZgnvvvRdHjhzB7NmzW3K1RERkcm63G9u3b4fb7fZ3VwJGi+4mHz9+HG+++abPa0OHDm1wNjciIqLGkGUZw4cP593WvLRoQU9MTMTMmTORlpaGiIgIVFZWIisrC4mJiS25WiIiMjlJknzu7UGtcB363r17kZ+fD7vdjqioKIwYMQLx8fEtucpGMdO1h0REbY3b7UZWVhbS0tJgtVqvuh0z1YJWmVjmUuvXr8fMmTNbe7U+zPQlEhG1NUIIOBwOdOjQwbg5y9UwUy1o0SF3z+Qy3oQQWL16td8LOhERBS+LxRL0Bbi5tWhBHzFiBKZOnYpLBwGOHz/ekqslIiKTc7vdyMzMREZGRpOG3M2kRQv6wIED8cILLyA6Otrn9QkTJrTkaomIyOQURUFaWhrnNPHSosfQL1y4gHbt2gXkTD5mOm5CRNTWCCGgqioUReEx9ItatNK2b98+IIs5EREFN1VVkZmZybncvbDaEhFR0FEUBRkZGRxy9xIUBX3x4sVITk7GjBkz4HK5jNczMzORlJSE0aNH46GHHvJjD4mIqLVx79xXwBf0wsJCnD59Grm5uRg4cCA2bdpkLIuNjUVOTg4+++wz2Gw27N692489JSKi1qKqKrKysljUvQR8Qc/Pz0daWhoAYPz48cjLyzOW9ezZ0xhusVqtlx16cTqdqKys9HkAgKZpxn/ri1VV9Yl1Xb9s7Ha7fWLPOYeeWAhRJwbgE+u67hN7/sE2FGua5hMzJ+bEnJiT2XOSJAkTJkyA1Wptck5mEfAF3W63G2ceRkZGwmaz1XnPF198gfLycuP+6/VZvnw5IiMjjUdMTAwAoLi4GABQUlKCkpISAEBRUREOHz4MoHaEoLS0FACwa9culJWVAQDy8vJw6tQpAEBOTg7Ky8sBANnZ2bDb7QCArKwsOBwOALWHB2pqanxO5KipqUFmZiYAwOFwICsry8g5OzsbAFBeXo6cnBwAwKlTp4wNmrKyMuzatQsAUFpaisLCQgDA4cOHUVRUxJyYE3NiTqbPadeuXRBCNCmngoICmIVfpn69Eq+++ip+8pOfYObMmdizZw/WrVuHlStXGsu/++473HnnnXj33XfRuXPnBttxOp1wOp3G88rKSsTExMBmsyEqKsrYcpNl2SdWVRUWi8WIJUmCJEkNxm63G7IsG7HnkgpPDMC41MITW61W4xIMq9UKXdehaZoR67oORVEajDVNgxDCiOvLgzkxJ+bEnMyUU01NDT7++GOkp6cbV1NdTU42mw3R0dGmuGwt4At6YWEhVqxYgQ0bNuC5555Dnz59MH36dAC117lnZGTglVdeQWxs7BW1a6ZrD4mI6OqYqRYE/JB7QkICunbtiuTkZBw8eBBTpkzBgw8+CAD429/+hiNHjuChhx5CamoqPv30Uz/3loiIWoOu67DZbMaxcAqCPfSWYqatMiKitsbtdiM7Oxvjxo3j7VMv4hX5REQUdKxWK9LT0/3djYAS8EPuREREl9J1HWfOnOGQuxcWdCIiCjq6rqO4uJgF3QuH3ImIKOgoioJx48b5uxsBhXvoREQUdHRdx4kTJ7iH7oUFnYiIgo6u6zhy5AgLuhcOuRMRUdBRFAUpKSn+7kZA4R46EREFHV3Xcfz4ce6he2FBJyKioMNj6HVxyJ2IiIKOoihISkrydzcCCvfQiYgo6Giahm+++ca4oxqxoBMRURASQuD8+fNoo7cjqReH3ImIKOgoioLhw4f7uxsBhXvoREQUdDRNw1dffcUhdy/cQycCsHffaRwoOdvg8hsHdEb84K6t2CMi+jHV1dX+7kJAYUFvIovF3z2gZhG6HZC/bXi51hNw3tt6/aFmx0Ot5iLLMhISEvzdjYASFAV98eLFKCgoQM+ePbF27VqEhIQAAFRVxf33348jR45gyJAheOmll/zcUwpaznRAangPHXrn1usLEf0oTdNQUlKCAQMGQJZlf3cnIAT8MfTCwkKcPn0aubm5GDhwIDZt2mQs27ZtG3r06IHc3FxUVVUhLy/Pjz2l4NYV0Ac1/ACH24kosAX8Hnp+fj7S0tIAAOPHj8fatWtx1113GcsmTpxoLMvLy+NEA0QEALgWe9EFB4zn+9/yXd75xhvRNT6+dTtFzUaWZcTGxvq7GwEl4Au63W5Ht27dAACRkZGw2Ww+yyIiIupddimn0wmn02k8r6ioAACcP38eAIwzJWVZ9olVVYXFYjFiSZIgSZIRAxJCQ1W4XBKEkBAa6obLJUMICWFhbjidCoSwICzMjZqa2o87LEy9JLbCYhEIDfXEOkJCNDidnliH06lAknQoig6XS4Es65BlT6xBkgTcbk8MuN0yFKU2D1WVYbVq0HVA02RYrSp03QJNkxESokLTJGiahJAQFaoqQdeZkx0doYaFQampqV1fWBisNTUQFgvU0FBYa2qgWyzQQkJgdTqhWyzQQ0KgOJ3QJQm6okBxuaDLMnRZhuJyQZNlCEmC4nZDk2VAkiC73dCU2s9NVlVoViug65A1DarVCosnDgmBpGmQPLGqQtJ1qKGhkFwuSELAHRoK2ROHhUFxOmHxxF55tJWc3hYC33n9Bvzv3b6/CTEApoWEBFVOZvyerjYnV3g4Dnz4IeLi4ozv9NLf78b8lnvqhhmuZw/4gh4VFYXKykoAtQW8U6dOjVp2qeXLl2PJkiV1Xu/du3eT++i1neATX/y326hYCN/Y0453rOuAy1Uba1rt43Kxqv7fetzu+mNPe5fGbT2njmZMqq3l1Aj/FWw5mfF7utqcqquB1FQ0F4fDgcjIyGZrzx8CvqCPGDECK1aswMyZM7F9+3aMGjXKZ1lWVhZSUlKwfft2zJkzp8F2nnjiCSxatMh4rus6bDYboqOjYeGp6kREQaWyshIxMTEoKyszRmqvhhACDofDGAkOZhYRBOMMl57lPn/+fKxevRqqquK+++7D0aNHkZCQgJdfftnfXSUiolZQWVmJyMhIVFRUNKmgm0lQFHQiIiJvLOh1Bfxla0RERPTjWNCJiCjohIaG4plnnkFoaKi/uxIwOORORERkAtxDJyIiMgEWdCIiIhNgQSciIjIBFnQiIiITYEEnIiIyARZ0IiIiE2BBJyIiMgEWdCIiIhNgQSciIjIBFnQiIiITYEEnIiIygYAv6MXFxRg1ahTGjBmDCRMm4MKFC8YyVVUxa9YsJCcnY+HChX7sJRERkX8F/M1Z3G43rFYrAGDJkiXo06cP7rnnHgDAu+++iy+++ALLli3DAw88gNmzZyMpKalR7eq6jpMnT6JDhw6wWCwt1n8iIgpcQgg4HA5069YNkhTw+7iXpfi7Az/GU8wBoKqqCv379zee5+fnY+LEiQCA8ePHIy8vr9EF/eTJk4iJiWnezhIRUVAqKytDjx49/N2NJgn4gg4A//73v/Hoo4/CarXiscceM1632+2IiIgAAERGRsJmszXYhtPphNPpNJ57BiaOHTuGqKgoaJoGAJBl2SdWVRUWi8WIJUmCJEkNxm63G7IsG7GiKLBYLEYM1B4q8I6tViuEEEas6zo0TTNiXdehKEqDsaZpEEIYcX15MCfmxJyYk5lycjqd2L17N0aMGGGMsl5NTjabDddffz06dOiAYBcU4wu33norCgsLMXXqVLz22mvG61FRUaisrARQW9w7derUYBvLly9HZGSk8ejZsycA4Ntvv0VERAROnDiBEydOICIiAseOHcP333+PiIgIHDlyBOfOnUNERAS++uorVFRUICIiAsXFxfjhhx8QERGBvXv3wuVyISIiAnv27IGu64iIiMB//vMfWCwWRERE4LPPPkNISAjatWuHzz77DO3atUNISAg+++wzREREwGKx4D//+Q8iIiKg6zr27NmDiIgIuFwu7N27FxEREfjhhx9QXFyMiIgIVFRU4KuvvkJERATOnTuHI0eOICIiAt9//z2OHTvGnJgTc2JOps7Jbrejffv26NixY5NyKikpAQBTHHoN+GPoTqcToaGhAIDVq1fD5XJh/vz5AOoeQ58zZw5GjhzZYDvee+iVlZWIiYmBzWbjHjpzYk7MiTm10ZxsNhuio6ONAh/MAr6gv//++3jhhRcgSRI6d+6MdevW4Xe/+x1Wr14NVVVx33334ejRo0hISMDLL7/c6HYrKysRGRlpii+RiKitUVUVOTk5SElJMTYUroaZakHAF/SWYqYvkYiordF1HeXl5bjmmmuadHa6mWpBUJwUR0RE5E2SJHTp0sXf3QgoQXFSHBERkTe3243t27fD7Xb7uysBgwWdiIiCjizLGD58OGRZ9ndXAgaH3ImIKOhIknTZS5XbooDfQ//iiy+QnJyMMWPGYNq0aXWGV/73f/8X48aNQ0pKCnbt2uWnXhIRUWtyu9344IMPOOTuJeALevfu3bF9+3Z8+umn+OlPf4otW7YYy06ePImtW7dix44dyMnJwU033eS/jhIRUatRFAXJyclNumTNbAK+oHft2hXt2rUDUDuvu/eX99FHHyE0NBS33nor7rnnHp87sRE1Ve/evdG/f3+oqmq8NmzYMOzcufOK2zp27JjPLIee9ouLixvdRnZ2NiwWCzZs2ODz+osvvogzZ84Yz//4xz9i8eLFV9xHbzt37kRWVpbx/OTJkxg7duwVteF2u7FgwQLceOONGDx4MAYOHIi//OUvV92n+j5Dars8s9GZYYa35hLwBd3j22+/xccff2zcjAUAvv/+e9jtdvz73/9GUlISVq5c2eDfO51OVFZW+jwAGDMJaZpWb6yqqk+s6/plY7fb7RN7LvP3xEKIOjEAn1jXdZ/YU1AaijVN84mZU/PkBNT+u3n99dd9hvW8+9uYnGpqaoxi5J2TR2Nzev3115GamorXX3/dJydPQffOSdf1Jn1PO3fuxPbt243+du3aFf/+97+v6Ht68cUXcfr0aXz55ZfG4+abb77q7+mbb74xCvqV5lRdXf2j31Mg/dsz4/9PzZ1TTU0Ntm7dCrfb3eSczCIoCnplZSXuuecerF271ufuax07dsTYsWNhsVgwbtw4HDx4sME2Lp3L3XOnNc8eUklJiTGnb1FREQ4fPgwAKCwsRGlpKQBg165dKCsrAwDk5eXh1KlTAICcnByUl5cDqN2LstvtAICsrCw4HA4AQGZmJmpqaqCqKjIzM6GqKmpqapCZmQkAcDgcxh6R3W5HdnY2AKC8vBw5OTkAgFOnTiEvLw9A7Z2BPOcMlJaWorCwEABw+PBhFBUVMadmyKmqqgp/+MMf8Oyzz+Ldd98FUPsDsXv3bgDA119/jdTUVAwaNAgDBw7Eo48+auQUExOD5557DqNGjcKkSZMwb948HDhwAAMHDsSkSZNw+PBhuFwubN68GUOHDkWvXr2wbNmyBnPasWMHPvjgA2zcuBH79u1Dfn4+AOD+++/HyZMnMXXqVPTv3x+5ubkAavdmHQ4H9u/fj8GDByMhIQEDBw7EnDlzjO8pPT0dv/71rzF27Fj07t0bkydPxpkzZ/D//t//w6pVq/DGG2+gf//+WLp0KXbt2oVrrrnG+J7+/ve/Izk5GbGxsRgwYAC2bt1a53vat28funbtioMHD+Lw4cMICwuDy+Uyclq6dCmGDBmCIUOGYNiwYfjkk0+M7+mZZ57BoEGDcMMNN+Cmm25CVVUVZs+ejYMHDyI+Ph6jRo1CTU0NCgoKMGjQIMTFxWH48OFYsWIFgNr/r6+55hosXboUo0aNwm9/+9ug+rdnxv+fmjun7777Dtdeey0URWlSTgUFBTANEeBUVRUTJ04UH3/8cZ1l+/btE7NmzRJCCLFx40bxzDPPNNhOTU2NqKioMB5lZWUCgLDZbMZ6VFWtE7vdbp9Y07TLxi6XyyfWdd0n1nW9TiyE8Ik1TfOJ3W73ZWNVVX3i+vJgTleeU69evURRUZGYPn26WLJkiRBCiKFDh4p///vfQgghfvnLX4pHHnlECCHEqVOnRI8ePURBQYHQNE306tVLzJ0718jjk08+EUOHDvXJqVevXuK3v/2tUFVVnDp1SkRERIjjx4/Xm9NLL70kfvnLXwohhFiwYIF44oknjPf06tVL7N+/38jpmWeeEb/73e+EruuisrJSOBwOoeu6+OGHH8TgwYPFrl27hK7r4p577hEjR44UP/zwg6iurhZJSUliw4YNwuVyiWeeeUYsWrTI6O+RI0dEdHS0EEKIs2fPimuvvVZ8/vnnQlVV4XQ6xblz5+p8N/v27RM9evQQAwcOFPfdd5/YuHGjqKmpEaqqis8++0z8/Oc/F1VVVUIIIT755BMRFxcnhBBizZo1YsSIEaKiokK4XC5RXl4uVFUV//73v8XQoUON76mmpkbExMSI999/X+i6LnJyckTXrl3FhQsXxNGjRwUA8dZbbwXlvz0z/v/U3Dm53W5RXV0tdF1vUk7nzp0TAERFRYUIdgF/NsE777yDvLw8OBwOPPvss/iv//ovZGdnY/Xq1YiLi8N1112H1NRUhIeH46233mqwndDQUOMmL9481zB6X8voHXsfs29M7D2CcCWxxWIxYs9NAxobN9R35tS0nDw5LFu2DImJifjNb37j086OHTuwb98+ALVD0pMnT8aOHTuMkzNnz55d5xpZz996Xp8xYwZkWUbXrl3Rp08ffPvtt8adAL37u27dOixfvhwA8MADDyA9PR3PPvtsgzlJkgSLxYLq6mo8/PDD2Lt3LyRJQllZGfbt24fhw4dDkiRMnjzZOEflpptuQmlpqc/352nfe2rNgoICDBw4EElJSUYul14+JMsy4uLicOTIEXz22WfIy8vDM888gzfffBMffPABtm7diqKiIp+bKZ09exYulwsffvgh/uu//suYhjM6OrrO52G1WrF//36EhIRgwoQJAIDk5GR06dIFRUVFuO666xAWFobp06fDYrEE3b89M/7/1Nw5CSGwfft2ZGRk+LTdlJyCXcBnMn36dEyfPt3ntV/96ldG/Kc//am1u0RtTJ8+fTB9+nQsW7aszrJLT8jxft6+ffsfbTssLMyIPXeButTevXuxf/9+zJ0712i/vLwcH330kVHMGvLkk0/i2muvRWFhIRRFweTJk1FTU3NF62+KkJAQjBs3DuPGjcP999+P6667DjabDUIIzJkzB0uXLr3qtoUQ9Z4Q5XntJz/5CU+YMjFFUZCRkWGqgtxUQXEMncjf/vCHP2DDhg04efKk8dott9xinKR19uxZvPvuuxg3bly9f++5l/PVeP311/Hwww/j+PHjOHbsGI4dO4YVK1ZgzZo1P9r2+fPn0aNHDyiKgkOHDhkntv2Yy7WZlJSEkpIS47imruuw2Wx13peTk2McbwVq55To1KkTOnbsiNtuuw3r1683jmPqF++FDQCTJk3Cq6++apy4arfboWlanT71798fTqfTOO6al5eHM2fOYNCgQY3KkYKfmU5oaw7ctCFqhM6dO2PBggV4+umnjddefvllzJs3D3FxcdB1HU899VSDcyHExcXhhhtuQGxsLPr06YP33nuvUeutqanB22+/jU8//dTn9TvvvBOPPfYYvv/+eyxYsACzZ89Gu3btsG7dOp/3/f73v8c999yDt956C717925wg+NSv/jFL/Dmm28iPj4ekydPxsyZM41lUVFRePfdd/Hwww/D4XDAYrHg2WefxaRJk3za+Pbbb/Hb3/4WNTU1CAkJQfv27bF161ZIkoSUlBT86U9/wu233w5N0+B2uzFhwgQMGzYM99xzD06ePImRI0fCarWiXbt2+Pjjj+v9DDdv3owFCxbghx9+QFhYGP75z3/iJz/5Cc6ePduoPCl4qaqKrKysOkPubRlvn2qCW+YREdHVMVMt4JA7EREFHSEEKisr0Ub3SesV8EPuX3zxBX77299CkiRce+21eOutt4zhlczMTCxbtgySJCE+Pv6yE8sQXc7efadxoKThYdobB3RG/OCurdgjIrocVVWRm5uLtLQ0DrlfFPBD7qdPn0ZERATatWuHJ598EgkJCfjlL38JoPYYXbdu3aAoCu666y787ne/w/DhwxvVbnMNs/AkWpMIfQOQv214udYTcN7bev2hZhfYv3TkL2Yacg/4PfSuXf9vr+jSudw91+vWt4zoijjTAekyJ1LpnVuvL0T0o3Rdh91uR8eOHX3mSWjLguZTqG8ud48vvvgC5eXlSEhIaPDvW3Iu97AwFZLkid1GHB7uhiQJI7ZYBACB8HA3AAGLxRMDkuQd6wgL845rL82QZR2hobWxonjHGkJCvOPa/lqtGqzW2jgkRIOieGLViENDVSiKbsSy3FZzuhbQByE8tD8sIhbQYxEe2h/QY2ERsQgPjw7CnMz4PTUtp7Y677kZc3K5XNi1a5fRb87lHiQFvaG53IHa+XwXLlxY53KdS7XkXO7PP5+DuLjaOY1XrsxGv352AMCaNVno3r12TuONGzPRqVMNwsNVbNyYifBwFZ061WDjxto5jbt3d2DNmto5jfv1s2Plytpra+PiyvH887VzGicmnsKSJbXX/qamluHxx2vnNM7IKMXChbXzNE+dehgPPFA7p/Hdd5fg7rtrc3rggSJMnVqb08KFhcjIqM3p8cd3ITW1NqclS/KQmMicmJN5c2qr856bMafvvvsO0dHRsFqtnMvdo1Unmr0Kl5vL3eFwiOTkZLF///4fbael5nIHhAgLcwtJ8sQuIw4PdwlJ0o3YYtEFoIvwcJcAdGGxeGIhJMk71kRYmHfsFoAQsqyJ0NDaWFG8Y1WEhHjHqgCEsFpVYbXWxiEhqlAUT+w24tBQt1AUzYhlmTkxJ3PmJETbnffcjDm5XC5x8uRJoWka53K/KOBPitu4cSMeeughY/Yn77ncly9fjpUrV6Jfv34AgCVLlmDMmDGNapcnxRG1LYH9S0dXSlVV5OTkICUlpUnnT5nppLiAL+gthQWdqG1pm7909GPMVNCD4hg6ERGRN13XceLECePkNmJBJyKiIKTrOo4cOcKC7qXFC/qBAwdaehVERNTGKIrS5OPnZtNin8SUKVPQt29ffPnllxgyZAief/75lloVERG1Mbquo6ysDDExMZxY5qIW+xTmzp2L4cOH47bbbmtyMXc4HEhMTET79u2N68Y9KisrMWnSJIwdOxYPP/xwk9ZDRETBgcfQ62qxgp6eno7Y2NhmuRNOeHg43n//fUydOrXOstWrV+P222/HJ598gurqanNNEkBERPVSFAVJSUkccvfSop/EgAEDMGDAAJSXl2PLli04f/68UeAfffTRRrejKAo6d65/Lu2jR49i3LhxAIAhQ4YgNzcXiYmJTe88EREFLE3TUFpaiuuvvx6yLPu7OwGhVQ48TJgwAVVVVejTpw/69u2Lvn37NlvbAwYMMKYL/Pjjj43pBy/FudwDZz5t5sSc/JVTW5333Iw5qaqKc+fOQQjBudwvapWC3rVrVyxYsABTpkwxHs3l/vvvx4EDB3DLLbegffv2Pndn88a53ANnPm3mxJz8lVNbnffcjDmVlZVBkiQoisK53D1aY37ZTZs2icmTJ4s//vGPYsmSJWLJkiVX1c6999572Xnb77vvPlFaWlrvMs7lHhjzaTMn5sS53DmXe3Pk5HQ6xYEDB4x+cy73VprLPSEhAfPnz0f37t2N19LT06+ojYyMDOzduxe9evXCgw8+iPz8fKxevRp79+7Fb3/7W8iyjJkzZ+Lee+9tVHuc+pWobeHUr+aiaRqKiooQFxfXpGPoZpr6tVUK+h133IEtW7a09GquCAs6UdvCgk71MVNBb5Xz/aurq5Geno64uDhYLlZATjRDRERXS9M0lJSUYMCAATzL/aJWKehPPvlka6yGiIiozWqVgi6EQGpqqvH87bffbo3VEhGRScmyjNjYWH93I6C0ymVrq1atQn5+PoDamd08lyMQERFdDU3TUFhYaFxbTq20h75+/Xrcdddd6N69OxRFwapVq1pjtUREZGLh4eH+7kJAadGz3B955BHjJLgffvgBW7duxfTp02GxWK7opDiHw4FbbrkFBw4cwH/+8x+fYZadO3finnvuQd++fSHLMnbs2NGoNnmWO5G5XYu96IL/u33zWxt8l3e+8UZ0jY9v3U5RwOFZ7o00ceJEn+fTpk27qnY8N2d55JFH6l3+q1/9Cv/zP/9zVW0T1UeAW2rB7g0A33o933K37/KeABo3awUFIjUkBIWffYaEhATeoOWiFj2GPnr0aJSXl8Nut2P06NEYM2YMxowZgzNnzlxRO5e7OQsAbN68GcnJyXjppZcafA/ncg+c+bSDIScBwB0eDgFAWCxwXxzaE5JkxLokwR0WZsSqJ5ZlqKGhtbGiGLGmKFBDQoxY88RWKzSrtTYOCYF28cdJ9Y5DQ6F7xxcv01HDwqBfvBe02zsOD4fwji2WNpfTzaGhuAPAJAC3KUqd+FaLJehyMuP3dLU56ZKEyMhIWCwWzuV+UYsW9BkzZqCwsBCFhYVITU3FkSNHAACvvvpqs61j2LBhOHToEHbs2IGPPvoIX3zxRb3v41zugTOfdjDkpIaHI3PjRqjh4ajp1AmZGzcCABzduyNrzRoAgL1fP2SvXAkAKI+LQ87Fw0inEhORt2QJAKAsNRW7Hn8cAFCakYHChQsBAIenTkXRAw8AAEruvhsld9fuPhY98AAOX7xNcOHChSjNyAAA7Hr8cZRdvFIkb8kSnLp4R8Gc559HeVwcACB75UrY+/UDAGStWQPHxZkZMzduRE2nTm0up31r12IQgJgbbsD3q1ZhEIDr4uNh+8tfMAiAGDky6HIy4/d0tTl9m5aGiooKyLLMudw9WnJe2dTUVCM+fvy4GDNmjMjOzhZjx469qvZ+bC73V155RaxZs6beZZzLPTDm0w6WnHRAuMLDhQ4I3WIRrvBwIQChS5IRa5IkXGFhRuz2xLIs3KGhtbGiGLGqKMIdEmLEqie2WoVqtdbGISFCVRQhAOH2jkNDheYdy3JtHBYmNEkSAhAu7zg8XOjescXCnJiTqXKq+clPxGeffWb8RnMu99pb2bWY5ORkUV1dbTyvrKwUt912m7jmmmuuqr36Crr3lzB9+nTx6aefNqqtioqKZvkSAT7M+PB7B/jgg4/LPjRFEceOHTMK89VqrloQCFr0TIIXX3wRlZWVCLt4LKRDhw7YsmUL3nnnnStuy3NzlkOHDvncnOWdd97Ba6+9BkVRMGrUKKSkpDR3GkREFGAkVUWvXr383Y2A0io3Z6mqqkK7du1aejVXhJet0eXwLHeiwKaGhiIvKwtJSUlNOsvdTJettdhJcQcPHsTBgwdx4MAB/PGPf2yp1RARURskqSr69u0LSWqVCU+DQosNuT/11FP4xS9+ASGEcTYhERFRc5A0Dd0vnk1PtVqsoD/99NNISEgAUHs9OhERUXNRw8KQk52NlJQUTixzUYt9CgkJCVBVFZs2bUJeXh5sNhs6deqEUaNGYcqUKfwCiIjoqkkuF2JjYznk7qVFP4nZs2fj6NGjmDVrFpYsWYJ7770XR44cwezZs6+oHYfDgcTERLRv396YCMbjyJEjSEhIQFhYGC5cuNCc3SciogAl6Tq6dOnCgu6lRXeTjx8/jjfffNPntaFDh17xpWWXm8v9uuuuw86dO3H77bc3qa9ERBQ83GFhyN6+HePGjYP14vS1bV2LbtokJiZi5syZ2LBhA9577z1s2LABM2fOROLFqQMb63Jzubdr1w6RkZE/2gbncudc7pzLnTkxJ/PkBF3HkCFDIMsy53K/qEUL+gsvvIBFixbB4XDgwIEDuHDhAhYtWoQXXnihJVdbL87lzrncOZc7c2JO5snp+PjxOHbsGCRJ4lzuHv6Ynu6NN964qr+73FzuY8aMEQ6Ho8G/5VzunMudc7kzJ+ZknpyqO3QQ27ZtEy6Xi3O5X9SiM8UdPHiwvg0IzJ07F59//vkVtzdr1iwsXrwYsbGxdZalpqbi/fffR/v27RvVFmeKo8vhTHFEgU1IEhznz6NDhw6wNOGH2EwzxbXoSXEjRozA1KlTcek2w/Hjx6+4rYbmcj9//jx++ctfYt++fbjtttvw6KOP4uc//3lzpUBERAHIoutBX4CbW4vuoY8YMQIffPABoqOjfV6fMGECPvjgg5ZabaNwD50uh3voRIHNffG4fEZGRpPOcjfTHnqLFvQLFy6gXbt2AXmdIAs6XQ4LOlFgExYLan74AWFhYRxyv6hFh9wbezybiIjoigjBGUcvEXi7zkRERD9CDQ9HZmamqa4jbyoWdCIiCjpKdTUyMjK4l+4lKAr64sWLkZycjBkzZsDlchmvV1dXY+LEiRgzZgxuvfVW2Gw2P/aSiIhajcXCvfNLBHxBLywsxOnTp5Gbm4uBAwdi06ZNxrIPP/wQsbGx+PTTTzFt2rQ688YTEZE5qWFhyMrKYlH3EvAFPT8/H2lpaQCA8ePHIy8vz1jWr18/VFVVAQDsdnuD870DnMudc7lzLnfmxJzMlJPkdmPChAmwWq2cy/2igC/odrvduJQgMjLSZ1i9b9++KC4uRmxsLNavX4877rijwXY4lzvncudc7syJOZkopwkTsGvXLgghOJe7h98mnW2kV155xZj7fffu3eI3v/mNz7IlS5YIIYTYvHmzeOyxxxpsh3O5cy53zuXOnJiTeXLiXO51tejEMs2hsLAQK1aswIYNG/Dcc8+hT58+mD59OgDg1VdfhaZpeOihh5CdnY133nkHq1atalS7nFiGLocTyxAFgWYoX2aaWCbgh9wTEhLQtWtXJCcn4+DBg5gyZQoefPBBAMCMGTPw4YcfIjU1FU8//TQWLVrk594SEVFr0CUJNpvNOBZOLTz1ayDjHjpdDvfQiQKbOywM2Vu2YNy4cZzL/SJekU9EREHHWlOD9PR0f3cjoAT8kDsREdGldEnCmTNnOOTuhQWdiIiCjh4SguLiYhZ0LxxyJyKioKPU1GDcuHH+7kZACYo99IbmcvdYvnw5hg0b5oeeERGRP+iyjBMnTnAP3UvAF/TLzeUOAA6Hw5jtjYiI2gZdUXDkyBEWdC8BX9AvN5c7ALz00kv4zW9+44+uERGRnyhOJ1JSUnj7VC8BX9AvN5d7RUUF9u/fj6SkpB9thzdn4c1ZeHMW5sSczJOTOywMpaWl0HWdN2e5KOALelRUlFF87XY7OnXqZCx78cUX8dBDDzWqHd6chTdn4c1ZmBNzMldOX331FXRd581ZPPw7lfyP+/LLL8WMGTOEEEIsW7ZMvP3228ayu+++W6Snp4v09HQREREhli9f3mA7vDkLb87Cm7MwJ+Zkspzc7sv+fvPmLAFo8eLFKCgoQM+ePbF27VrMnz8fq1ev9nnPsGHDsGfPnka3yalf6XI49StRYNMUBaUlJbj++ushXzwMcDXMNPVrUBT0lsCCTpfDgk4U2NSQEBR+9hkSEhKadGKcmQo6Tw8kIqKgo7hcGD58uL+7EVAC/qQ4IiKiS2mKgq+++so4c51Y0ImIKBhJEqqrq/3di4DCIXciIgo6ssuFhIQEf3cjoATFHnpDc7mrqopZs2YhOTkZCy9eY0lEROanWa0oLi7mkLuXgC/ol5vLfdu2bejRowdyc3NRVVVVZ1pYIiKitiLgC/rl5nL/sXneiYjInGS3G7GxsU26Bt1sAv4Yut1uR7du3QDUncv9cvO8X8rpdMLpdBrPKyoqAADnz58H8H9zusuy7BOrqgqLxWLEkiRBkiQjBiSEhqpwuSQIISE01A2XS4YQEsLC3HA6FQhhQViYGzU1tR93WJh6SWyFxSIQGuqJdYSEaHA6PbEOp1OBJOlQFB0ulwJZ1iHLnliDJAm43Z4YcLtlY25zVZVhtWrQdUDTZFitKnTdAk2TERKiQtMkaJqEkBAVqipB15lTBWrnqlZqamrXFxYGa00NhMUCNTQU1poa6BYLtJAQWJ1O6BYL9JAQKE4ndEmCrihQXC7osgxdlqG4XNBkGUKSoLjd0GQZkCTIbrcxZ7asqrXzaus6ZE2DarXC4olDQiBpGiRPrKqQdB1qaCgklwuSEHCHhkL2xGFhUJxOWDyxVx7MiTmZISdXeDgOfPop4i5OP1vf73djfss9dcMMU7IEfEG/3Fzul1t2qeXLl2PJxfmAvfXu3bvJffTaTvCJL/7bbVQshG/sacc71nXAcwqBptU+Lhd733PA7a4/9r69vHfc1nPqaMakmBNzMlNO1dXAxTnmm4PD4UBkZGSztecPAV/QR4wYgRUrVmDmzJnYvn07Ro0a5bMsKysLKSkp2L59O+bMmdNgO0888QQWLVpkPNd1HTabDdHR0bBwujcioqBSWVmJmJgYlJWVNWmGNyEEHA6HMRIczIJi6teG5nJXVRX33Xcfjh49ioSEBLz88sv+7ioREbUCM03Z2lyCoqATERF5Y0GvK+DPciciIqIfx4JORERBJzQ0FM888wxCQ0P93ZWAwSF3IiIiE+AeOhERkQmwoBMREZkACzoREZEJsKATERGZAAs6ERGRCbCgExERmQALOhERkQmwoBMREZkACzoREZEJBEVBdzgcSExMRPv27VFcXOyzTFVVzJo1C8nJyVi4cKGfekhERORfQVHQw8PD8f7772Pq1Kl1lm3btg09evRAbm4uqqqqkJeX54ceEhER+VdQFHRFUdC5c+d6l+Xn5yMtLQ0AMH78eBZ0IiJqkxR/d6Cp7Ha7cS/cyMhI2Gy2et/ndDrhdDqN57quw2azITo6GhaLpVX6SkREgUUIAYfDgW7dukGSgmIft0FBX9CjoqJQWVkJoLa4d+rUqd73LV++HEuWLGnNrhERUZAoKytDjx49/N2NJgn6gj5ixAhkZWUhJSUF27dvx5w5c+p93xNPPIFFixYZzysqKtCzZ08cO3YMUVFR0DQNACDLsk+sqiosFosRS5IESZIajN1uN2RZNmJFUWCxWIwYqD2Rzzu2Wq0QQhixruvQNM2IdV2HoigNxpqmQQhhxPXlwZyYE3NiTmbKyel0Yvfu3RgxYoQxyno1OdlsNlx//fXo0KEDgl3QFPSMjAzs3bsXhw4dwoMPPoj8/HysXr0at912G7Zs2YLk5GQkJCRg5MiR9f59aGgoQkND67weFRVlDNkTEVFw0HUdgwcPRseOHZtlqNwMh14tQgjh7074Q2VlJSIjI1FRUcGCTkTURpmpFgT3GQBERNQmqaqK7OxsqKrq764EDBZ0IiIKOpIkITY2NujPTG9OQXMMnYiIyEOSJHTp0sXf3Qgo3LQhIqKg43a7sX37drjdbn93JWCwoBMRUdCRZRnDhw+HLMv+7krA4JA7EREFHUmSGpxIrK3iHjoREQUdt9uNDz74gEPuXljQiRrQu3dv9O/f3+eymGHDhmHnzp1X3NaxY8fw2muv1Wn/0tsBN/S3iqIgPj4egwcPxrBhw/DJJ59ccR8A4OTJkxg7dqzxfOvWrRgwYADi4+Oxf/9+xMfHo7q6+oraXLp0KWJjYzF48GD0798fjzzyyFX1Daidvvn555+/6r+ntkNRFCQnJxuzzxELOtFlOZ1OrFmzpkltqKpab0G/Eh07dsTevXuxb98+/OEPf8C0adNwNXNCdevWzWdjYNWqVVi6dCn27t2LQYMGYe/evQgPD290e5s3b8b27duxe/du7Nu3D8XFxbj77ruvuF8eTSnovB65bbFYLIiIiDDFDG/NhQWd6DKWLFmCZ599FlVVVXWWff/99/jFL36BQYMGITY21qdg9+7dG8899xzGjh2Le++9F/PmzcPBgwcRHx+PSZMmGe/bvHkzkpKScP3112PZsmWN6tOtt96K8vJynDt3DnfffTeGDRuGuLg4TJw4EWfOnDHet3btWp+9+mPHjuHYsWO45pprAAALFixAbm4uHnvsMSQlJQGo/ZG8cOECAKCkpATp6emIi4tDXFwcVq1aVacv3377La655hqEhYUBqN1rGjx4sLF8+/btGD16NIYOHYrExETk5ORctn/z5s2D3W5HfHw8hg0bBgD45ptvcMsttyAuLg7x8fHYsmWL0YbFYsGKFSuQmpqKJ554olGfH5mD2+3G1q1bOeTuTbRRFRUVAoCoqKjwd1coQPXq1Uvs379fTJ8+XSxbtkwIIcTQoUPFJ598IoQQYtq0aeLxxx8XQgjx/fffix49eoiCggLjb+fOnSt0XRdCCPHJJ5+IoUOH1mn/t7/9rRBCiDNnzoiIiAjx3Xff1elHaWmpiI6ONp6//vrromfPnkIIIc6ePWu8vnz5cvGb3/zGWF/fvn3FyZMnhRBC/PDDD+KHH36o09aYMWPEtm3bjOcAhMPhEG63W/Tr10/84x//MJZ5r8vj1KlTYsCAAaJ3797innvuEWvWrBFVVVVCCCGOHDkiRo4cafw/dvjwYdGtWzfhcrka3T8hhLjpppvE6tWrhRBCfP3116JTp07i22+/Nfr73HPP1ekXmZ+u66Kqqsr4f+xqmakW8OAD0Y9YtmwZEhMTMW/ePJ/XP/74Y+zbtw8A0KVLF0yePBk7duzATTfdBACYPXv2jw4HzpgxAwDQuXNn9OnTB6WlpejevXud93n2WgGge/fueO+99wAAb731Ft588004nU5UV1eja9euAIAPPvgAM2fOxHXXXQcAaNeu3RXlfOjQIaiqimnTphmvefbsvXXt2hX79+9HQUEBPv/8c7zyyiv429/+hoKCAnz00Uf45ptvkJKS4vM3ZWVlje6fw+HA3r17cd999wEA+vXrh9GjR+Ozzz7D9OnTAaDBOyyS+fH4uS9+GkQ/ok+fPpg+fXq9Q+KXFmzv5+3bt//Rtj1D1cD/3eKxPp5j6N4+++wzrFy5Enl5eejcuTPee+89LF269EfX2dxkWUZSUhKSkpKwYMECXHvttSguLoYQAuPHj8f69euvum1x8TyBpn7OZD6qqiIzMxMZGRmwWq3+7k5A4DF0okb4wx/+gA0bNuDkyZPGa7fccotx3Pzs2bN49913MW7cuHr/PiIiAhUVFc3ap/PnzyMiIgKdOnWCy+XC6tWrjWW33XYb1q9fj9OnTwMAqqqq6j0PoCE33HADQkJC8M9//tN4rby8vM779uzZgyNHjhjPv/rqK7jdbsTExCAtLQ0fffSRz5n8u3btumz/IiIiUFVVZWzYREREID4+Hm+88QYA4MiRI/j8888xatSoRudC5qQoCjIyMriX7oUFnagROnfujAULFuDUqVPGay+//DKKiooQFxeHsWPH4qmnnjKG2y8VFxeHG264AbGxsT4nxTXFz3/+c/z0pz9F//79kZ6ebgzJA0BKSgp+//vfIy0tDYMHD8aYMWNw9uzZRretKAq2bt2K1157DYMGDUJcXBw2b95c533nzp3DjBkz0L9/fyQkJOC+++7D22+/jc6dO6Nfv37YsGED7r//fgwePBgDBgzASy+9dNn+derUCTNmzMCgQYOMk+LeeustbNiwAYMHD8aUKVPw+uuvIyYmpmkfHpkCr2zwxfuhm+AeuEREbY3b7W6WIXcz1QKOVRARUdCxWq24/fbb/d2NgBIUQ+6LFy9GcnIyZsyYAZfLZbxeXV2NiRMnYsyYMbj11lths9n82EsiImotQghUVlZe1QRLZhXwBb2wsBCnT59Gbm4uBg4ciE2bNhnLPvzwQ8TGxuLTTz/FtGnT8Oabb/qxp0RE1FpUVUVubi6Po3sJ+IKen5+PtLQ0AMD48eORl5dnLOvXr59x5q7dbkfnzp0bbMfpdKKystLnAQCaphn/rS9WVdUn1nX9srHb7faJPVuPnlgIUScG4BPruu4Te/7BNhRrmuYTMyfmxJyYk9lzkiQJ6enpsFqtTc7JLAK+oNvtduNEhcjISJ9h9b59+6K4uBixsbFYv3497rjjjgbbWb58OSIjI42H5yxZzyU1JSUlKCkpAQAUFRXh8OHDAGpHCEpLSwHUXnJTVlYGAMjLyzPOeM7JyTEu6cnOzobdbgcAZGVlweFwAAAyMzNRU1NjXDupqipqamqQmZkJoHYCjaysLCPn7OxsALWXCnmmyzx16pSxQVNWVmZcAlRaWorCwkIAwOHDh1FUVMScmBNzYk6mzuno0aMoKCiArutNyqmgoABmEfBnub/66qv4yU9+gpkzZ2LPnj1Yt24dVq5caSw7e/Ysnn76afzrX//Crl278Oc//7nedpxOJ5xOp/G8srISMTExsNlsiIqKMrbcZFn2iVVVhcViMWJJkiBJUoOx2+2GLMtGrCgKLBaLEQO1W4TesdVqhRDCiHVdh6ZpRqzrOhRFaTDWNA1CCCOuLw/mxJyYE3MyU041NTXYuXMnbr75ZkiSdNU52Ww2REdH8yz31jBixAisWLECM2fOxPbt2+tMKOG5wX3Hjh2NLcT6hIaGIjQ0tM7rsiz7/PfS2HvSgsbE3pdPXElssViM2PMPrbFxQ31nTsyJOTEns+YUFhaG8ePH41JNySnYBfyQe0JCArp27Yrk5GQcPHgQU6ZMwYMPPgigdh7sDz/8EKmpqXj66aexaNEiP/eWiIhag67rOHPmjHEsnIJgyL2lmGkyASKitkZVVeTk5CAlJaVJe9lmqgXmGWsgIqI2Q1GUBu+d0FYF/JA7ERHRpXRdx4kTJzjk7oUFnYiIgo6u6zhy5AgLupcWL+gHDhxo6VUQEVEboyhKk4+fm02LfRJTpkxB37598eWXX2LIkCF4/vnnW2pVRETUxui6jrKyMsTExBiXsrV1LVbQ586di8rKSnTv3h0LFy5sqdUQEVEb5DmG3r17dxb0i1rsU0hPT0dsbCzvhENERM1OURQkJSVxyN1Li34SAwYMwIABA1BeXo4tW7bg/PnzRoF/9NFHW3LVRFdk777TOFBytsHlNw7ojPjBXVuxR0R0OZqmobS0FNdff73PjHBtWats2kyYMAEzZsxAnz59WmN1rcpi8XcPqFmEbgfkbxtervUEnPe2Xn+o2XGw0FyEEDh//jx69+7t764EjFYp6F27dsWCBQtaY1VEV8eZDkgN76FDb/jWvETU+hRFwfDhw/3djYDSKgV95syZmDJlCuLi4mC5uEv79NNPt8aqiRqpK6BzSJ0oWGiahsOHD6Nfv34ccr+oVQr6smXLMH/+fHTv3r01VkdERG1AdXW1v7sQUFqloPfq1Qtz5sxpjVUREVEbIMsyEhIS/N2NgNIqBb26uhrp6ek+Q+6caIaIiK6WpmkoKSnBgAEDOOR+UasU9CeffLI1VkNERNRmtUpBF0IgNTXVeP7222+3xmqJiMikZFlGbGysv7sRUFplvrxVq1YhPz8fALB69Wrk5OS0xmqJiMikNE1DYWEhNE3zd1cCRqvsoa9fvx533XUXunfvDkVRsGrVqiv6+8WLF6OgoAA9e/bE2rVrERISAgDYuXMn7rnnHvTt2xeyLGPHjh0t0X0iIgpA4eHh/u5CQGnRPfRHHnkEjz76KH7/+9/j2muvxebNmyFJ0hVN+1pYWIjTp08jNzcXAwcOxKZNm3yW/+pXv8LOnTtZzImI2hBZltG/f3+eEOelRQv6xIkTMWHCBEyYMAHTpk3DW2+9ZbzWWPn5+UhLSwMAjB8/Hnl5eT7LN2/ejOTkZLz00kuXbcfpdKKystLnAcAYrtE0rd5YVVWfWNf1OnFYmApJ8sRuIw4Pd0OShBFbLAKAQHi4G4CAxeKJAUnyjnWEhXnHKgBAlnWEhtbGiuIdawgJ8Y5r+2u1arBaa+OQEA2K4olVIw4NVaEouhHLMnNiTubNSdd1qKp62VjTNJ+4OX4jvGO32+0Te+5v4YmFEHViAD6xrus+cVvMyel0oqCgwOhrU3IyixYt6KNHj0Z5eTnsdjtGjx6NMWPGYMyYMThz5kyj27Db7YiIiAAAREZGwmazGcuGDRuGQ4cOYceOHfjoo4/wxRdfNNjO8uXLERkZaTxiYmIAAMXFxQCAkpISlJSUAACKiopw+PBhALUjBKWlpQCAXbt2oaysDACQl5eHU6dOAQCefz4HcXHlAICVK7PRr58dALBmTRa6d3cAADZuzESnTjUID1excWMmwsNVdOpUg40bMwEA3bs7sGZNFgCgXz87Vq7MBgDExZXj+edrzzlITDyFJUtqN2hSU8vw+OO7AAAZGaVYuLAQADB16mE88EARAODuu0tw9921OT3wQBGmTq3NaeHCQmRk1Ob0+OO7kJpam9OSJXlITGROzMm8OZWXlxvn8Jw6dcrYQSgrK8OuXbU5lZaWorCwNqfDhw+jqKg2p6b8RuTk5KC8vDan7Oxs2O21OWVlZcHhqM0pMzMTNTU1UFUVmZmZUFUVNTU1yMyszcnhcCArqzYnu92O7Oy2ndO3336LqqoqWCyWJuVUUFAA0xAt6Fe/+pV46qmnxDPPPCNGjx4tvvnmGyGEEGPHjm10G6+88op44403hBBC7N69W/zmN79p8H1r1qxpsJ2amhpRUVFhPMrKygQAYbPZhBBCqKoqVFWtE7vdbp9Y0zSfGBAiLMwtJMkTu4w4PNwlJEk3YotFF4AuwsNdAtCFxeKJhZAk71gTYWHesVsAQsiyJkJDa2NF8Y5VERLiHasCEMJqVYXVWhuHhKhCUTyx24hDQ91CUTQjlmXmxJzMmZMQQmiaJtxu92VjVVV94vp+F67kN+LS2OVy+cS6rvvEuq7XiYUQPrGmaT4xc7r6nM6dOycAiIqKChHsWrSgp6amGvHx48fFmDFjRHZ29hUV9C+//FLMmDFDCCHEsmXLxNtvv20s8/4Cpk+fLj799NNGt1tRUdEsX2LtPZz44IOPQH+QubjdbvH5558bRf1qNVctCAQtOuSuaRpqamoAAD179sS2bdvw17/+Ffv37290GwkJCejatSuSk5Nx8OBBTJkyBQ8++CAA4J133sFNN92EpKQkdO/eHSkpKS2SBxERBRZJktC9e3dIUqtcfR0ULEII0VKNf/nll+jRowe6dOlivKbrOt555x3ceeedLbXaRqmsrERkZCQqKiqMY/RXg/dDJwoOLfdLR8GsuWpBIGjRTZshQ4agS5cuqKqq+r8VSpLfizkREQU3VVWRk5NjqrPUm6rFCvrBgwdx8OBBHDhwAH/84x9bajVERNQGSZKEvn37csjdS4vNFPfUU0/hF7/4BYQQxuUBREREzcFzDJ3+T4sV9Kefftq4V+3o0aNbajVERNQGeYbcU1JSoCitMot5wGuxTyEhIQGqqmLTpk3Iy8uDzWZDp06dMGrUKEyZMoVfABERXTVJkhAbG8shdy8tWlVnz56NAQMGYNasWYiMjITdbsf27dsxe/ZsvPnmmy25aiIiMjFJknyuoKIWLujHjx+vU7iHDh3K68WJiKhJ3G43srOzMW7cOFitVn93JyC0aEFPTEzEzJkzkZaWhoiICFRWViIrKwuJiYktuVoiIjI5WZYxfPhw3m3NS4tOLAMAe/fuRX5+Pux2O6KiojBixAjEx8e35CobhRPLELUtnFiG6mOmiWVa/My0+Pj4OgV8/fr1mDlzZkuvmoiITMrtdiMrKwtpaWkccr+oRQv6wYMH67wmhMDq1atZ0ImI6KopioLk5GReMeWlRT+JESNGYOrUqbh0VP/48eMtuVoiIjI5i8US9EPkza1FC/rAgQPxwgsvIDo62uf1CRMmtORqiYjI5NxuNzIzM5GRkcEh94ta9KS4CxcuoF27dgF54T9PiiNqW3hSnLkIIVBTU4OwsDBYmvBDzJPiGql9+/Yt2TwREbVhPH7ui58GEZnStdiLLjhgPN//lu/yzjfeiK4BcAktXR1VVTnkfomgKOiLFy9GQUEBevbsibVr1yIkJAQAkJmZiWXLlkGSJMTHx2PlypV+7imZhQCPpQS7NwB86/V8y92+y3sCuLcV+0PNSwGQ4XJxL91L4B3cvkRhYSFOnz6N3NxcDBw4EJs2bTKWxcbGIicnB5999hlsNht2797tx54SUSBJB3DHZR7pfugTNSOLBaqq+rsXASXgC3p+fj7S0tIAAOPHj0deXp6xrGfPnsbWmdVq5ZYaERm6Ahh0mUdX/3WNmoEaFoasrCwWdS8BX9Dtdrtx5mFkZCRsNlud93zxxRcoLy837r9eH6fTicrKSp8HAGiaZvy3vlhVVZ9Y1/U6cViYCknyxG4jDg93Q5KEEVssAoBAeLgbgIDF4okBSfKOdYSFece1/2BlWUdoaG2sKN6xhpAQ77i2v1arBqu1Ng4J0aAonlg14tBQFYqiG7EsMyeLRUAAcIeHQwAQFgvc4eEAACFJRqxLEtxhYUasemJZhhoaWhsrihFrigL14uEiTVGgeWKrFdrFY4BaSAi0ixumqnccGgrdO744f7UaFgb94lUkbu84PBzCO7ZYmBNzMlVOktuNCRMmwGq1Nvj73djfcrMI+IIeFRVlFF+73Y5OnTr5LP/uu++wcOFCrFu37rLtLF++HJGRkcYjJiYGAFBcXAwAKCkpQUlJCQCgqKgIhw8fBlA75F9aWgoA2LVrF8rKygAAeXl5OHXqFADg+edzEBdXDgBYuTIb/frZAQBr1mShe3cHAGDjxkx06lSD8HAVGzdmIjxcRadONdi4MRMA0L27A2vWZAEA+vWzY+XKbABAXFw5nn8+BwCQmHgKS5bUjlCkppbh8cd3AQAyMkqxcGEhAGDq1MN44IEiAMDdd5fg7rtrc3rggSJMnVqb08KFhcjIqM3p8cd3ITW1NqclS/KQmMicOnWqgRoejsyNG6GGh6OmUydkbtwIAHB0746sNWsAAPZ+/ZB98byN8rg45Dz/PADgVGIi8pYsAQCUpaZi1+OPAwBKMzJQuHAhAODw1KkoeuABAEDJ3Xej5O7aA7xFDzyAw1OnAgAKFy5EaUYGAGDX44+jLDUVAJC3ZAlOXbzBUc7zz6M8Lg4AkL1yJez9+gEAstasgaN7dwBA5saNqOnUiTkxJ3PlNGECdu3aBSEEDh8+jKKi2t+IK/0tLygogGmIAPfll1+KGTNmCCGEWLZsmXj77beNZQ6HQyQnJ4v9+/f/aDs1NTWioqLCeJSVlQkAwmazCSGEUFVVqKpaJ3a73T6xpmk+MSBEWJhbSJIndhlxeLhLSJJuxBaLLgBdhIe7BKALi8UTCyFJ3rEmwsK8Y7cAhJBlTYSG1saK4h2rIiTEO1YFIITVqgqrtTYOCVGFonhitxGHhrqFomhGLMvMyWLRhQ4IV3i40AGhWyzCFR4uBCB0STJiTZKEKyzMiN2eWJaFOzS0NlYUI1YVRbhDQoxY9cRWq1Ct1to4JESoiiIEINzecWio0LxjWa6Nw8KEJklCAMLlHYeHC907tliYE3MyVU7VHTqIbdu2CZfL1eDvd2N+y8+dOycAiIqKiiZUqsDQ4ndbaw6XnuU+f/58rF69GsuXL8fKlSvR7+KW4ZIlSzBmzJhGtcmJZehyeJY7URBohvJlpollgqKgtwQWdLocFnSiwKZLEuxnz6Jjx45Nmo3UTAU94I+hExERXUoLCcHu3buNE90oSCaWISIi8matqUF6OmcT8MY9dCIiCjq6JOHMmTPG5WfEgk5EREFIDwlBcXExC7oXDrkTEVHQUWpqMG7cOH93I6BwD52IiIKOLss4ceIE99C9sKATEVHQ0RUFR44cYUH3wiF3IiIKOorTiZSUFH93I6BwD52IiIKOrig4fvw499C9sKATEVHQ4TH0ujjkTkREQUdxOpGUlOTvbgQU7qETEVHQ0RQF33zzDad+9cKCTkREQUdIEs6fP482en+xenHInYiIgo7icmH48OH+7kZA4R46EREFHU1R8NVXX3HI3QsLOhERBR9JQnV1tb97EVA45E5EREFHdrmQkJDg724ElKDYQ1+8eDGSk5MxY8YMuFwu43VVVTFr1iwkJydj4cKFfuwhERG1Js1qRXFxMYfcvQR8QS8sLMTp06eRm5uLgQMHYtOmTcaybdu2oUePHsjNzUVVVRXy8vL82FMiIiL/CfiCnp+fj7S0NADA+PHjfYr25ZYREZF5yW43YmNjIcuyv7sSMAL+GLrdbke3bt0AAJGRkbDZbD7LIiIi6l12KafTCafTaTyvqKgAAJw/fx4AjGEbWZZ9YlVVYbFYjFiSJEiSZMSAhNBQFS6XBCEkhIa64XLJEEJCWJgbTqcCISwIC3Ojpqb24w4LUy+JrbBYBEJDPbGOkBANTqcn1uF0KpAkHYqiw+VSIMs6ZNkTa5AkAbfbEwNutwxFqc1DVWVYrRp0HdA0GVarCl23QNNkhISo0DQJmiYhJESFqkrQdeZUAUANC4NSU1O7vrAwWGtqICwWqKGhsNbUQLdYoIWEwOp0QrdYoIeEQHE6oUsSdEWB4nJBl2XosgzF5YImyxCSBMXthibLgCRBdruhKbWfm6yq0KxWQNchaxpUqxUWTxwSAknTIHliVYWk61BDQyG5XJCEgDs0FLInDguD4nTC4om98mBOzMkMObnCw3Hg008RFxdn/K5f+vvdmN9yT90ww/XsAV/Qo6KiUFlZCaC2gHfq1KlRyy61fPlyLFmypM7rvXv3bnIfvbYTfOKL/3YbFQvhG3va8Y51HfCcQqBptY/Lxar6f+txu+uPvU5J8Inbek4dzZgUc2JOZsqpuhpITUVzcTgciIyMbLb2/CHgC/qIESOwYsUKzJw5E9u3b8eoUaN8lmVlZSElJQXbt2/HnDlzGmzniSeewKJFi4znuq7DZrMhOjoaFoulRXMgIqLmVVlZiZiYGJSVlRkjtVdDCAGHw2GMBAcziwiCcYbFixejoKAAPXv2xNq1azF//nysXr0aqqrivvvuw9GjR5GQkICXX37Z310lIqJWUFlZicjISFRUVDSpoJtJUBR0IiIibyzodQX8We5ERET041jQiYgo6ISGhuKZZ55BaGiov7sSMDjkTkREZALcQyciIjIBFnQiIiITYEEnIiIyARZ0IiIiE2BBJyIiMgEWdCIiIhNgQSciIjIBFnQiIiITYEEnIiIygaAo6A6HA4mJiWjfvj2Ki4t9lqmqilmzZiE5ORkLFy70Uw+JiIj8KygKenh4ON5//31MnTq1zrJt27ahR48eyM3NRVVVFfLy8vzQQyIiIv9S/N2BxlAUBZ07d653WX5+PiZOnAgAGD9+PPLy8pCUlFTnfU6nE06n03iu6zpsNhuio6NhsVhapuNERBTQhBBwOBzo1q0bJCko9nEbFBQF/XLsdrtxL9zIyEjYbLZ637d8+XIsWbKkNbtGRERBoqysDD169PB3N5ok6At6VFQUKisrAdQW906dOtX7vieeeAKLFi0ynldUVKBnz544duwYoqKioGkaAECWZZ9YVVVYLBYjliQJkiQ1GLvdbsiybMSKosBisRgxUHvc3zu2Wq0QQhixruvQNM2IdV2HoigNxpqmQQhhxPXlwZyYE3NiTmbKyel0Yvfu3RgxYoQxyno1OdlsNlx//fXo0KEDgl3QF/QRI0YgKysLKSkp2L59O+bMmVPv+0JDQ+u9b25UVJSxh09ERMFB13UMHjwYHTt2bJahcjMceg2aAwYZGRnIysrCAw88gHXr1uHBBx8EANx2220oKytDcnIywsPDMXLkSD/3lIiIWpokSejevXvQH/duThYhhPB3J/yhsrISkZGRqKio4B46EVGQUVUVOTk5SElJMYbyr4aZagE3bYiIKOhIkoTY2FjuoXsJ+mPoRETU9kiShC5duvi7GwGFmzZERBR03G43tm/fDrfb7e+uBAwWdCIiCjqyLGP48OGQZdnfXQkYHHInIqKgI0lSg/OOtFXcQycioqDjdrvxwQcfcMjdCws6EREFHUVRkJyc3KRL1syGBZ2oAb17965zu97GmDVrFlauXAkAePrpp/GPf/yjWftVUVGBBx98EH369EH//v0xbNgwvP/++826jqv1r3/9C0OHDkV8fDwGDBiAm2++GbquX3V7L774Is6cOdOMPSSzsFgsiIiIMMUMb82FmzZELWjp0qXN2p4QAhkZGUhISMDXX38NRVGwd+9eTJgwAX//+9+Rnp7erOu7EqdPn8a8efOwe/du9OrVCwDw5ZdfNukH98UXX8Qtt9xyxZcnec8bTubkdruRmZmJjIwMWK1Wf3cnIHAPnagRUlNT8dhjjyE5ORl9+/bFvHnzjGUnTpzAzTffjLi4ONx+++0oLy83lnnvre/YsQMjR45EQkICYmNjsXbt2ka1723Hjh04fvw4/vKXvxgFKz4+Hk899RSWLVtmvO+///u/MWjQIAwePBgjRoxAVVUVAODNN99EYmIihgwZgjFjxhgjEPv370dycjKGDBmCgQMHYvny5T45/PrXv8Ytt9yCn/3sZ5g8eTJcLledvp06dQqKoiA6Otp4bciQIUZBP3z4MCZMmIDhw4dj8ODBeOWVV4z35efnIzk5GYMHD0ZcXBy2bt2KpUuX4uTJk5g6dSri4+Oxd+9eXLhwAXPmzEFsbCxiY2N97qCYmpqKp556CjfffLNfN2yodSiKgrS0NG64eRNtVEVFhQAgKioq/N0VClC9evUS+/fvF0IIMWbMGDFlyhShqqqoqqoSvXv3Fnl5eUIIISZPniz++Mc/CiGEOHLkiGjfvr3429/+JoQQ4t577zVim80mVFUVQghx7tw50atXL3Hy5Mkfbd/bf//3f4tJkybVef3LL78U7dq1E0IIsW7dOjFixAjj37ZnvZ999pnIyMgQNTU1QgghcnJyRFxcnBBCiMrKSuP1qqoqER8fL3bv3m3kMHLkSFFVVSVUVRVJSUni7bffrtMHTdPE5MmTRVRUlLjjjjvE888/L7777jshhBCqqophw4aJkpISIYQQP/zwgxg0aJD44osvxLlz58S1114rPv/8c6Odc+fO1fkOhBDi0UcfFTNmzBCapokLFy6I+Ph48c477xifYUZGhnC5XA19pWQiuq4Ll8sldF1vUjtmqgXcQydqpDvvvBOyLCM8PBzx8fE4cuQIAOCTTz7B/fffDwDo06cPbr755nr//ty5c/jlL3+J2NhYjBs3DuXl5Thw4MCPtn+p+oawhdctGd5//33813/9lzEvdVRUFGRZxtatW7Fv3z4kJiYiPj4e8+fPx9mzZ+FyuVBdXY37778fgwYNwogRI3D8+HHs3bvXaHPy5MkIDw+HLMu46aab6u2bJEnYvHkz8vLyMH78eHz++ee48cYb8c033+DQoUM4cOAA7rzzTsTHxyMpKQkOhwMHDx5Efn4+Bg4ciKSkJKOdhi5H+vjjjzFv3jxIkoSf/OQnmDlzJj7++GNj+T333MPh1zZCVVVkZmZCVVV/dyVgcKyCqJHCwsKM2HNP5Ssxb9483Hbbbdi8eTMsFguGDBmCmpqaK2p/yJAhePnll+FyuRASEmK8/p///AdDhgy57PqFEJgzZ069x/WffPJJXHvttSgsLISiKJg8efIV982jf//+6N+/Px588EGMHz8e7733HtLT03HNNdf4bCR4fPDBB5ft96U5XLpB4/28ffv2jW6LgpuiKMjIyOCQuxfuoRM10bhx4/D3v/8dAHDs2DHs2LGj3vedP38evXr1gsViQU5ODvbt23fF67r55psRExODhx9+2Ciqe/fuxbJly/Dkk08CACZNmoRXX30VlZWVAAC73Q5N03Dbbbdh/fr1KCsrA1B7P+k9e/YYfevRowcURcGhQ4fw73//+4r7duLECXz++ec++ZaWlqJv37644YYb0K5dO6xfv95Y/s0338BmsyEpKQklJSXIy8sz+mWz2QAAERERqKioMP7m1ltvxf/7f/8PQgj88MMP2LBhA2655ZYr7iuZA/fOfXHThqiJXnrpJcycORP//Oc/8bOf/azBAvPnP/8Zv/71r/HnP/8ZAwcORGJi4hWvy2Kx4MMPP8QjjzyCfv36ISQkBO3bt8err76Kn//85wBqh51PnjyJkSNHwmq1ol27dvj444+RkpKCP/3pT7j99tuhaRrcbjcmTJiAYcOG4fe//z3uuecevPXWW+jduzfGjRt3xX1TVRVLly5FaWkp2rVrB1VVce+99+L2228HAGzbtg2/+93v8D//8z/QNA2dO3fGW2+9he7du+Pdd9/Fww8/DIfDAYvFgmeffRaTJk3CggULMHv2bLRr1w7r1q3DH/7wB8yfPx+DBg0CAPzyl7/E1KlTr7ivFPxUVUVWVhbPcvfC+6Gb4B64RER0dcxUCzjkTkREQUcIgcrKSrTRfdJ6BUVBX7x4MZKTkzFjxgyf61+rq6sxceJEjBkzBrfeeqtx3I2IiMxNVVXk5ubyOLqXgC/ohYWFOH36NHJzczFw4EBs2rTJWPbhhx8iNjYWn376KaZNm4Y333zTjz0lIqLWYrVaMWHCBB4/9xLwBT0/Px9paWkAgPHjxxtnwgJAv379jBmw7HY7Onfu3GA7TqcTlZWVPg8A0DTN+G99saqqPrFnXuqGYrfb7RN7hoM8sRCiTgzAJ9Z13Sf2bIE2FGua5hMzJ+bEnJiT2XNyu904e/YsdF1vck5mEfAF3W63GycqREZG+gyr9+3bF8XFxYiNjcX69etxxx13NNjO8uXLERkZaTxiYmIAwJj6sqSkBCUlJQCAoqIiHD58GEDtCEFpaSkAYNeuXcYlP3l5eTh16hQAICcnx5juMzs7G3a7HQCQlZUFh8MBAMjMzERNTY3PZAg1NTXIzMwEADgcDmRlZRk5Z2dnAwDKy8uRk5MDoHZqTc8GTVlZGXbt2gUAKC0tRWFhIYDa6TWLioqYE3NiTszJ1DkdPXoU//nPf6BpWpNyKigogFkE/Fnur776qjEj1J49e7Bu3TpjbuxXX30VZ8+exdNPP41//etf2LVrF/785z/X247T6YTT6TSeV1ZWIiYmBjabDVFRUcaWmyzLPrGqqrBYLEYsSRIkSWowdrvdkGXZiBVFgcViMWLA98YRqqrCarVCCGHEni1OT6zrOhRFaTDWNA1CCCOuLw/mxJyYE3NiTnVzstlsiI6ONsVZ7gF/HfqIESOwYsUKzJw5E9u3b8eoUaN8lnumiOzYsaOxhVif0NBQhIaG1nldlmWf/14ae89C1JjY+3jOlcQWi8WIPf/QGhs31HfmxJyYE3Mya04WiwXnzp3DNddc02w5BbuAH3JPSEhA165dkZycjIMHD2LKlCl48MEHAQAzZszAhx9+iNTUVDz99NNYtGiRn3tLREStQdd1FBcXG8fCKQiG3FuKmSYTICKiq2OmWhDwe+hERESX0nUdJ06c4B66FxZ0IiIKOrqu48iRIyzoXsxzNgAREbUZiqIgJSXF390IKC2+h37gwIGWXgUREbUxuq7j+PHj3EP30mJ76FOmTEHfvn3x5ZdfYsiQIXj++edbalVERNTGeI6hd+/e3biUra1rsYI+d+5cVFZWonv37li4cGFLrYaIiNogRVGQlJTk724ElBbbrElPT0dsbCxvbUdERM1O0zR88803xmxw1MInxQ0YMAADBgxAeXk5tmzZgvPnzxsF/tFHH23JVRMRkYkJIXD+/Hn07t3b310JGK1y4GHChAmoqqpCnz590LdvX/Tt27c1VktERCalKAqGDx9uqqlbm6pVPomuXbtiwYIFrbEqIiJqAzx3WevXr5/PnO1tWasU9JkzZ2LKlCmIi4uDxWIBADz99NOtsWoiIjKp6upqf3choLRKQV+2bBnmz5+P7t27t8bqiIjI5GRZRkJCgr+7EVBapaD36tULc+bMaY1VERFRG6BpGkpKSjBgwAAOuV/UKgW9uroa6enpPkPunGiGiIio+bRKQX/yySdbYzVERNRGyLKM2NhYf3cjoLRKQRdCIDU11Xj+9ttvt8ZqiYjIpDRNQ1FREeLi4jjkflGrXIe+atUq5OfnAwBWr16NnJycK/r7xYsXIzk5GTNmzIDL5TJe37lzJ2JiYpCamoqbb765WftMRESBLTw83N9dCCitUtDXr1+PFStWYOHChfj666+xatWqRv9tYWEhTp8+jdzcXAwcOBCbNm3yWf6rX/0KO3fuxI4dO5q720REFKBkWUb//v25d+6lRQv6I488gkcffRS///3vce2112Lz5s2QJOmKpn3Nz89HWloaAGD8+PHIy8vzWb5582YkJyfjpZdeata+ExFR4FJVFbt374aqqv7uSsBo0WPoEydO9Hk+bdq0K27DbrejW7duAIDIyEjYbDZj2bBhw3Do0CEAwO23347Ro0dj6NCh9bbjdDrhdDqN55WVlQBgTOzv+a8syz6xqqqwWCxGLEkSJElqMHa73ZBl2YgVRYHFYjFioPYfondstVohhDBiXdehaZoR67oORVEajDVNgxDCiOvLgzkxJ+bEnMyUk67riIyMhMViaXJOZtGie+ijR49GeXk57HY7Ro8ejTFjxmDMmDE4c+ZMo9uIiooyiq/dbkenTp2MZe3bt0dISAhCQkIwadIk7Nu3r8F2li9fjsjISOMRExMDACguLgYAlJSUoKSkBABQVFSEw4cPA6gd8i8tLQUA7Nq1C2VlZQCAvLw8nDp1CgCQk5OD8vJyAEB2djbsdjsAICsrCw6HAwCQmZmJmpoaqKqKzMxMqKqKmpoaZGZmAgAcDgeysrKMPLOzswEA5eXlxjkHp06dMkYoysrKsGvXLgBAaWkpCgsLAQCHDx9GUVERc2JOzIk5mTqnb7/9FhUVFZBluUk5FRQUwCwsogXvb3rnnXfipz/9KRRFwY4dO7Bu3Tr07dsX48aNM764H1NYWIgVK1Zgw4YNeO6559CnTx9Mnz4dQO1edkREBADgrrvuwrx585CSklJvO/XtocfExMBmsyEqKiqot1TNuPXNnJgTc2JOl8vJ6XRiz549SExMNOY3uZqcbDYboqOjUVFRYdSTYNWiBX3s2LH45JNPANRuTc2cORPPPPMMnn322UYXdKD2LPeCggL07NkTa9euxfz587F69Wq8/vrreO2116AoCkaNGoUXXnih0W1WVlYiMjLSFF8iEVFbo+s6ysrKEBMTA0m6+sFmM9WCFi3oKSkpyMrKQlhYGIDa4ZUZM2YgPz8fZ8+ebanVNoqZvkQiIro6ZqoFLXoM/cUXXzSOfwNAhw4dsGXLFvztb39rydUSEZHJqaqKnJwcU53U1lQtuofuUVVVhXbt2rX0aq6ImbbKiIjaGl3XcerUKVx33XUccr+oxS5bO3jwIIDaaV/feOMN3oyFiIiajSRJvCX3JVpsyP2pp57Cnj17sGfPHuPyACIiouagqiqys7M55O6lxfbQn376aePm86NHj26p1RARURskSRJiY2ObNNxuNi1W0BMSEqCqKjZt2oS8vDzYbDZ06tQJo0aNwpQpU4zrEYmIiK6UJEno0qWLv7sRUFp002b27Nk4evQoZs2ahSVLluDee+/FkSNHMHv27JZcLRERmZzb7cb27dvhdrv93ZWA0aK7ycePH8ebb77p89rQoUMbnM2NiIioMWRZxvDhw3m3NS8tWtATExMxc+ZMpKWlISIiApWVlcjKykJiYmJLrpaIiExOkiSfe3tQK1yHvnfvXuTn58NutyMqKgojRoxAfHx8S66yUcx07SERUVvjdruRlZWFtLQ0WK3Wq27HTLWgVSaWudT69esxc+bM1l6tDzN9iUREbY0QAg6HAx06dDBuznI1zFQLWnTI3TO5jDchBFavXu33gk5ERMHLYrEEfQFubi1a0EeMGIGpU6fi0kGA48ePt+RqiYjI5NxuNzIzM5GRkdGkIXczadGCPnDgQLzwwguIjo72eX3ChAktuVoiIjI5RVGQlpbGOU28tOgx9AsXLqBdu3YBOZOPmY6bEBG1NUIIqKoKRVF4DP2iFq207du3D8hiTkREwU1VVWRmZnIudy+stkREFHQURUFGRgaH3L0ERUFfvHgxkpOTMWPGDLhcLuP1zMxMJCUlYfTo0XjooYf82EMiImpt3Dv3FfAFvbCwEKdPn0Zubi4GDhyITZs2GctiY2ORk5ODzz77DDabDbt37/ZjT4mIqLWoqoqsrCwWdS8BX9Dz8/ORlpYGABg/fjzy8vKMZT179jSGW6xWK4deiIjaCKvVittvv52XrHkJ+IJut9uNMw8jIyNhs9nqvOeLL75AeXm5cf/1+jidTlRWVvo8AEDTNOO/9cWqqvrEuq5fNna73T6x5yICTyyEqBMD8Il1XfeJPVugDcWapvnEzIk5MSfmZPacVFXF+fPnIYRock5mEfAFPSoqyii+dru9zmT83333HRYuXIh169Zdtp3ly5cjMjLSeMTExAAAiouLAQAlJSUoKSkBABQVFeHw4cMAaof8S0tLAQC7du1CWVkZACAvLw+nTp0CAOTk5KC8vBwAkJ2dDbvdDgDIysqCw+EAUHu8v6amxufMzJqaGmRmZgIAHA4HsrKyjDyzs7MBAOXl5cjJyQEAnDp1yhihKCsrw65duwAApaWlKCwsBAAcPnwYRUVFzIk5MSfmZOqcjhw5gtzcXKiq2qScCgoKYBZ+mcv9ShQWFmLFihXYsGEDnnvuOfTp0wfTp08HUHude0ZGBl555RXExsZeth2n0wmn02k8r6ysRExMDGw2G6KioowtN1mWfWJVVWGxWIxYkiRIktRg7Ha7IcuyEXuukfTEAIxrJz2x1Wo1rqm0Wq3QdR2aphmxrutQFKXBWNM0CCGMuL48mBNzYk7MiTnVzclmsyE6OtoU16EHfEEHas9yLygoQM+ePbF27VrMnz8fq1evxvLly7Fy5Ur069cPALBkyRKMGTOmUW2aaTIBIqK2Rtd12O12dOzYsUnznZipFgRFQW8JZvoSiYjaGrfbjezsbIwbN463T72Ip4UTEVHQsVqtSE9P93c3AkrAnxRHRER0KV3XcebMGeNsdWJBJyKiIKTrOoqLi1nQvXDInYiIgo6iKBg3bpy/uxFQuIdORERBR9d1nDhxgnvoXljQiYgo6Oi6jiNHjrCge+GQOxERBR1FUZCSkuLvbgQU7qETEVHQ0XUdx48f5x66FxZ0IiIKOjyGXheH3ImIKOgoioKkpCR/dyOgcA+diIiCjqZp+Oabb4wbsBALOhERBSEhhHE/dKrFIXciIgo6iqJg+PDh/u5GQOEeOhERBR1N0/DVV19xyN0L99CJAOzddxoHSs42uPzGAZ0RP7hrK/aIiH5MdXW1v7sQUFjQm8hi8XcPqFmEbgfkbxtervUEnPe2Xn+o2fFQq7nIsoyEhAR/dyOgsKATAYAzHZAa3kOH3rn1+kJEP0rTNJSUlGDAgAGQZdnf3QkIQVHQFy9ejIKCAvTs2RNr165FSEgIAEBVVdx///04cuQIhgwZgpdeesnPPaXg1RXQOaRORMEr4E+KKywsxOnTp5Gbm4uBAwdi06ZNxrJt27ahR48eyM3NRVVVFfLy8vzYUyIKJNdiLwbhLeOx/y3fx+m9e/3dRWoCWZYRGxvLvXMvAb+Hnp+fj7S0NADA+PHjsXbtWtx1113GsokTJxrL8vLyGpw5yOl0wul0Gs8rKioAAOfPnwcA40xJWZZ9YlVVYbFYjFiSJEiSZMSAhNBQFS6XBCEkhIa64XLJEEJCWJgbTqcCISwIC3Ojpqb24w4LUy+JrbBYBEJDPbGOkBANTqcn1uF0KpAkHYqiw+VSIMs6ZNkTa5AkAbfbEwNutwxFqc1DVWVYrRp0HdA0GVarCl23QNNkhISo0DQJmiYhJESFqkrQdeZkR0eoYWFQampq1xcWBmtNDYTFAjU0FNaaGugWC7SQEFidTugWC/SQEChOJ3RJgq4oUFwu6LIMXZahuFzQZBlCkqC43dBkGZAkyG43NKX2c5NVFZrVCug6ZE2DarXC4olDQiBpGiRPrKqQdB1qaCgklwuSEHCHhkL2xGFhUJxOWDyxVx5tJae3hcB3Xr8B/3u3729CDIBpISFBlZMZv6erzckVHo4DH36IuLg44zu99Pe7Mb/lNpsNAExxPXvAF3S73Y5u3boBACIjI40P37MsIiKi3mWXWr58OZYsWVLn9d69eze5j17bCT7xxX+7jYqF8I097XjHug64XLWxptU+Lher6v+tx+2uP/a0d2nc1nPqaMak2lpOjfBfwZaTGb+nq82puhpITUVzcTgciIyMbLb2/CHgC3pUVBQqKysB1BbwTp06NWrZpZ544gksWrTIeK7rOmw2G6Kjo2HhqepEREGlsrISMTExKCsrM3bsroYQAg6Hw9hxDGYBX9BHjBiBFStWYObMmdi+fTtGjRrlsywrKwspKSnYvn075syZ02A7oaGhCA0N9XmtY8eOLdVtIiJqBREREU0q6ACCfs/cI+BPiktISEDXrl2RnJyMgwcPYsqUKXjwwQcBALfddhvKysqQnJyM8PBwjBw50s+9JSIi8g+LMMOZAERE1KZUVlYiMjISFRUVTd5DN4uA30MnIiK6VGhoKJ555pk6h1LbMu6hExERmQD30ImIiEyABZ2IiMgEWNCJiIhMgAWdiIjIBFjQiYiITIAFnYiIyARY0ImIiEyABZ2IiMgEWNCJiIhMgAWdiIjIBIKioDscDiQmJqJ9+/YoLi72WaaqKmbNmoXk5GQsXLjQTz0kIiLyr6Ao6OHh4Xj//fcxderUOsu2bduGHj16IDc3F1VVVcjLy/NDD4mIiPxL8XcHGkNRFHTu3LneZfn5+Zg4cSIAYPz48cjLy0NSUlKd9zmdTjidTuO5ruuw2WyIjo6GxWJpmY4TEVFAE0LA4XCgW7dukKSg2MdtUFAU9Mux2+3GvXAjIyNhs9nqfd/y5cuxZMmS1uwaEREFibKyMvTo0cPf3WiSoC/oUVFRqKysBFBb3Dt16lTv+5544gksWrTIeF5RUYGePXvi2LFjiIqKgqZpAABZln1iVVVhsViMWJIkSJLUYOx2uyHLshErigKLxWLEQO1xf+/YarVCCGHEuq5D0zQj1nUdiqI0GGuaBiGEEdeXB3NiTsyJOZkpJ6fTid27d2PEiBHGKOvV5GSz2XD99dejQ4cOCHZBX9BHjBiBrKwspKSkYPv27ZgzZ0697wsNDUVoaGid16Oioow9fCIiCg66rmPw4MHo2LFjswyVm+HQa9AcMMjIyEBWVhYeeOABrFu3Dg8++CAA4LbbbkNZWRmSk5MRHh6OkSNH+rmnRETU0iRJQvfu3YP+uHdzsgghhL874Q+VlZWIjIxERUUF99CJiIKMqqrIyclBSkqKMZR/NcxUC7hpQ0REQUeSJMTGxnIP3UvQH0MnIqK2R5IkdOnSxd/dCCjctCEioqDjdruxfft2uN1uf3clYLCgExFR0JFlGcOHD4csy/7uSsDgkDsREQUdSZIanHekreIeOhERBR23240PPviAQ+5eWNCJiCjoKIqC5OTkJl2yZjYs6EQN+Ne//oWhQ4ciPj4eAwYMwM033wxd15vU5h//+Ee4XC7j+axZs7By5cpG/73D4UD79u1x//33+7y+ZcsW7Nq1y3i+c+dODBs2rEl9PXbsGF577TWf1zIyMnDkyJEramfVqlWIi4vD4MGD0b9/f8yYMaNJ/br0M6S2yWKxICIiwhQzvDUXFnSiepw+fRrz5s3Dv/71L+zduxclJSV44YUXmvzjsWTJkiYVo//93//FkCFDsHnzZly4cMF4/dKC3hzqK+iZmZno27dvo9vYs2cP/ud//gc7d+7Evn37UFJSgocffrhJ/braz1BV1SatlwKL2+3G1q1bOeTuhQWdqB6nTp2CoiiIjo42XhsyZIhR0Pfs2YORI0ciLi4ON910Ez7//HMAtUXwmmuuMf7mwoULxt/MmzcPAJCUlIT4+HicOXMGAHDw4EHccsst+NnPfobJkydftlitWbMGjz32GJKTk/HOO+8AqC2y7733Hv785z8jPj4er7/+us/fqKqK9PR0DBs2DDfeeCNmzJiBqqoqAMC6deuQnp6O6dOnY9CgQRg2bBiOHj1q9PfgwYOIj4/HpEmTAAC9e/dGcXExAODEiROYOnUq4uLiEBcXhz/84Q91+ltWVobIyEhjBi6LxYIhQ4YYy3fv3o1x48Zh2LBhxoaKxwcffIDhw4dj8ODBiI+PR0FBQb2f4ffff49f/OIXGDRoEGJjY302Qnr37o3nnnsOY8eOxb333tvg50rBR1EUpKWlccjdm2ijKioqBABRUVHh765QANI0TUyePFlERUWJO+64Qzz//PPiu+++E0II4XQ6RUxMjPjoo4+EEELk5uaKrl27igsXLojS0lIRHR1ttONwOIT3/2YAhMPhMJ7fe++9YuTIkaKqqkqoqiqSkpLE22+/XW+fiouLRbdu3YSqqmLLli0iKSnJp52//e1vxvNPPvlEDB06VAghhK7rory83IjnzZsnXnjhBSGEEGvXrhWRkZHi2LFjQgghHnvsMTF37tw6bXj06tVL7N+/XwghRGpqqnj++eeNZWfOnKnT5x9++EGMGjVKdO3aVfzqV78Sf/vb34TNZhNCCHH+/HmRkJAgTp48KYQQ4uzZs6Jnz57i1KlT4tChQ+Laa68Vhw4dEkII4XK5hN1ur/cznDZtmnj88ceFEEJ8//33okePHqKgoMDo79y5c4Wu6/V+phS8dF0XLperyd+tmWoB99CJ6iFJEjZv3oy8vDyMHz8en3/+OW688UZ88803OHToEEJCQpCeng4AGD16NLp06YKioqKrWtfkyZMRHh4OWZZx0003NXiMes2aNZg5cyZkWcaECRNw9OhRlJSU/Gj7Qgj89a9/RUJCAuLi4vDBBx9g7969xvLRo0ejV69eAICRI0c26hj5hQsXkJeXh9/97nfGa507d67zvnbt2iE3NxeZmZlISkrCv/71L8TFxcFmsyEvLw9Hjx7Fz3/+c8THx+OWW26BEAKHDh3Cv//9b2RkZOBnP/sZAMBqtSIyMrLevnz88cf4zW9+AwDo0qULJk+ejB07dhjLZ8+ezeOsJqSqKjIzM3koxQvHKoguo3///ujfvz8efPBBjB8/Hu+99x5uueWWeguExWLxud80ANTU1PzoOsLCwozYc6/mS7ndbmzYsAFWqxUbN24EAFRVVeHvf/87Xnjhhcu2//bbb+PTTz9FTk4OOnTogJdffhk5OTlXtP6msFgsSEhIQEJCAubPn4+BAwdi586dCA0NRVxcnE9fPDzD+leyjoaet2/f/uo6TgFNURRkZGRwyN0L99CJ6nHixAnjuDgAnD9/HqWlpejbty/69+8Pp9OJ7OxsAEBeXh7OnDmDQYMGoWvXrlBVFYcOHQIArF+/3qfdDh06oKKi4or7s3XrVvTp0wcnTpzAsWPHcOzYMXz++edYv3493G43IiIiGmz3/PnziI6ORocOHeBwOLBu3bpGrfNybbZv3x6jR4/GX//6V+O1s2fP1nnfV1995TNyUVZWhrNnz6JPnz5ISkrC4cOHjc8RAPbu3QuXy4X09HR8+OGH+PrrrwHUbtB4+nLpZ3jLLbcYx83Pnj2Ld999F+PGjWtUjhTcuHfuiwWdqB6qqmLp0qX42c9+hvj4eCQnJ+Pee+/F7bffjpCQEGzevBlPPfUU4uLi8Nvf/hb//Oc/8ZOf/ASKouDll1/Gz3/+c6SkpMDpdPq0+/DDD2PcuHE+J8U1xpo1a+pc7hUbG4tu3bph27ZtuOeee/D222/Xe1LczJkzceHCBQwcOBCTJ09GcnJyo9YZFxeHG264AbGxscZJcd7efPNN/Oc//8GNN96IwYMH13v5XVVVFebPn48bbrgB8fHxuO2224yT96KiorBt2zY8++yzGDx4MAYOHIjHH38cuq7jpz/9KdasWYPp06cbJx56NpIu/QxffvllFBUVIS4uDmPHjsVTTz2Fm266qbEfLQUpVVWRlZXFou6F90M3wT1wiYjo6pipFgTFHvrixYuRnJyMGTNm+FzSU11djYkTJ2LMmDG49dZbYbPZ/NhLIiJqLUIIVFZWoo3uk9Yr4At6YWEhTp8+jdzcXAwcOBCbNm0yln344YeIjY3Fp59+imnTpuHNN9/0Y0+JiKi1qKqK3NxcDrl7CfiCnp+fj7S0NADA+PHjkZeXZyzr16+fMUGG3W6v97IZIiIyH6vVigkTJsBqtfq7KwEj4Au63W43jmtERkb6DKv37dsXxcXFiI2Nxfr163HHHXc02I7T6URlZaXPA4BxiZGmafXGqqr6xJ65vBuK3W63T+wZDvLEQog6MQCfWNf/f3t3Hx1VfeYB/Htf5g2BmCCKhqAFcQFjTFQMAgkp2sAGqx7JahEX0BXt2aLZo+jR6mrDymEPHqpW1kpdpSAr51josotNlzk1K4lNJKDBNJJqxIgjGxbiMJkgmZn78ts/hrk7A4ECyWRmbr6fc+b4zNzk3ueZ4Dzz+903MyGOfQM9XWwYRkLMmlgTa2JNdq9J0zQcOXIEpmn2uya7SPuGnp2dbTXfQCCQcP/bDRs2oKysDK2traiursaKFStOu55Vq1YhKyvLeuTl5QH4//Nd29rarIt0tLS0oL29HUB0yr+jowMA0NTUBJ/PByB6qlJnZycAoK6uDl1dXQCA2tpaBAIBAIDX60VPTw+A6OU5Q6FQwsUQQqEQampqAERvuuH1eq06Y6fydHV1WefpdnZ2WjMUPp/PunZ3R0cHmpubAQDt7e3WaUKsiTWxJtZk15q+/PJLfPjhhzAMo1817dq1C3aR9ke5Nzc3Y82aNdi0aRNWrlyJ8ePHY8GCBQCAX/7ylzAMA8uWLUNtbS3eeecdvPbaa32uJxwOJ5xCFAwGkZeXB7/fj+zsbOubm6IoCbGu65AkyYplWYYsy6eNNU2DoihWrKoqJEmyYiD6jTA+djgcEEJYcewbZyw2TROqqp42NgwDQoiEi5qcXAdrYk2siTWxplNr8vv9GDVqlC2Ock/7hg5Ej3LftWsXxo0bh/Xr1+Phhx/GunXrEAwGsWDBAnz33XfQdR1vvvmmdanIv8ROpyoQEQ01pmmiq6sLF110EWT5/Ceb7dQLMqKhJ4Od/ohEREONruuoq6tDaWlpvy7/aqdewIvgEhFRxlFVlZf4PUnaHxRHRER0MtM0cfDgQetodWJDJyKiDGSaJvbv38+GHodT7kRElHFUVUVpaWmq00grSR+hf/rpp8neBBERDTGmaeLAgQMcocdJ2gh9/vz5mDBhAj7++GNcd911WL16dbI2RUREQ0xsH3pubm6/Tluzk6Q19AcffBDBYBC5ubmoqqpK1maIiGgIUlUV06dPT3UaaSVpX2vmzJmD/Px83tqOiIgGnGEY+OKLL6yrwVGSD4qbPHkyJk+ejK6uLmzbtg1Hjx61GvwTTzyRzE0TEZGNCSFw9OhRXHHFFalOJW0Myo6HefPm4fjx4xg/fjwmTJiACRMmDMZmiYjIplRVxdSpU/t1lTi7GZR3YsyYMXjkkUcGY1NERDQExO6yNnHiRCiKkup00sKgNPRFixZh/vz5KCgogCRJAIBnn312MDZNREQ21dvbm+oU0sqgNPTnn38eDz/8MHJzcwdjc0REZHOKoqCoqCjVaaSVQWnol19+Oe6///7B2BQREQ0BhmGgra0NkydP5pT7CYPS0Ht7ezFnzpyEKXdeaIaIiGjgDEpD/+lPfzoYmyEioiFCURTk5+enOo20MigNXQiBsrIy6/nbb789GJslIiKbMgwDLS0tKCgo4JT7CYNyHvprr72GxsZGAMC6detQV1d3Tr+/fPlylJSUYOHChYhEItbr77//PvLy8lBWVoabb755QHMmIqL05vF4Up1CWhmUhr5x40asWbMGVVVV+Pzzz/Haa6+d9e82Nzfj0KFDqK+vx5QpU7Bly5aE5XfffTfef/99vPfeewOdNhERpSlFUTBp0iSOzuMktaE//vjjeOKJJ/DMM8/gkksuwdatWyHL8jld9rWxsRHl5eUAgLlz56KhoSFh+datW1FSUoKXX355QHMnIqL0pes6du/eDV3XU51K2kjqPvRbb7014fldd911zusIBAK47LLLAABZWVnw+/3WshtuuAGfffYZAOD222/HzJkzcf311/e5nnA4jHA4bD0PBoMAYF3YP/ZfRVESYl3XIUmSFcuyDFmWTxtrmgZFUaxYVVVIkmTFQPQfYnzscDgghLBi0zRhGIYVm6YJVVVPGxuGASGEFfdVB2tiTayJNdmpJtM0kZWVBUmS+l2TXSR1hD5z5kx0dXUhEAhg5syZmDVrFmbNmoXDhw+f9Tqys7Ot5hsIBJCTk2MtGz58OJxOJ5xOJ2677TZ88sknp13PqlWrkJWVZT3y8vIAAK2trQCAtrY2tLW1AQBaWlrQ3t4OIDrl39HRAQBoamqCz+cDADQ0NKCzsxMAUFdXh66uLgBAbW0tAoEAAMDr9aKnpwcAUFNTg1AoBF3XUVNTA13XEQqFUFNTAwDo6emB1+u16qytrQUAdHV1WcccdHZ2WjMUPp8PTU1NAICOjg40NzcDANrb29HS0sKaWBNrYk22runrr79Gd3c3FEXpV027du2CXUgiifc3/dGPfoQrr7wSqqrivffew69//WtMmDABs2fPtv5wf0lzczPWrFmDTZs2YeXKlRg/fjwWLFgAIDrKHjlyJADgnnvuwY9//GOUlpb2uZ6+Ruh5eXnw+/3Izs7O6G+qdvz2zZpYE2tiTWeqKRwOY8+ePSguLraub3I+Nfn9fowaNQrd3d1WP8lUSW3o3//+9/Hf//3fAKLfphYtWoTnnnsO//RP/3TWDR2IHuW+a9cujBs3DuvXr8fDDz+MdevW4V//9V/xq1/9CqqqYsaMGXjhhRfOep3BYBBZWVm2+CMSEQ01pmnC5/MhLy8Psnz+k8126gVJbeilpaXwer1wu90AotMrCxcuRGNjI44cOZKszZ4VO/0RiYjo/NipFyR1H/pLL71k7f8GgBEjRmDbtm145ZVXkrlZIiKyOV3XUVdXZ6uD2vorqSP0mOPHj2PYsGHJ3sw5sdO3MiKiocY0TXR2duLSSy/llPsJSTttbd++fQCil33dsGEDb8ZCREQDRpZl3pL7JEmbcn/66aexZ88e7Nmzxzo9gIiIaCDouo7a2lpOucdJ2gj92WeftW4+P3PmzGRthoiIhiBZlpGfn9+v6Xa7SVpDLyoqgq7r2LJlCxoaGuD3+5GTk4MZM2Zg/vz51vmIRERE50qWZVx88cWpTiOtJPWrzX333Ycvv/wSS5YsQXV1NRYvXoz9+/fjvvvuS+ZmiYjI5jRNw44dO6BpWqpTSRtJHSYfOHAAb731VsJr119//Wmv5kaUKns/OYRP205/bYSrJ49G4bVjBjEjIjoTRVEwdepU3m0tTlIbenFxMRYtWoTy8nKMHDkSwWAQXq8XxcXFydzsoDpxxUHKdK4dgPL16Zcb44Dw4sHLhwZc8k/QpcEky3LCvT1oEM5D37t3LxobGxEIBJCdnY1p06ahsLAwmZs8KwN17iEbul0cAuQzXL3QHA2AI/RMxoZuL5qmwev1ory8HA6H47zXY6fz0AflwjIn27hxIxYtWjTYm03Ahk40tLCh24sQAj09PRgxYoR1c5bzYaeGntQp99jFZeIJIbBu3bqUN3QiIspckiRlfAMeaElt6NOmTUNlZSVOngQ4cOBAMjdLREQ2p2kaampqUFFR0a8pdztJakOfMmUKXnjhBYwaNSrh9Xnz5iVzs0REZHOqqqK8vJzXNImT1H3ox44dw7Bhw9LySj7ch040tHAfur0IIaDrOlRV5T70E5L61Wb48OHJXD0R0Wldgr24GJ9az//0b4nLR199NcakwRk3dH50XeeU+0k4V0HUBwFOvWS6DQDiryyw7d7E5eMA8MoCmUsFUBGJcMo9Tka8E8uXL8euXbswbtw4rF+/Hk6nEwBQU1OD559/HrIso7CwEGvXrk1xpkSULuYAOMOVBTB6sBKh5JAka8qdotJv5/ZJmpubcejQIdTX12PKlCnYsmWLtSw/Px91dXX44IMP4Pf7sXv37hRmSkTpZAyAa87w4GWCMpvudsPr9fL2qXHSvqE3NjaivLwcADB37lw0NDRYy8aNG2d9O3M4HGf8phYOhxEMBhMeAGAYhvXfvmJd1xNi0zRPid1uHbIcizUr9ng0yLKwYkkSAAQ8Hg2AgCTFYkCW42MTbnd8HP0HqygmXK5orKrxsQGnMz42TrwnBhyOaOx0GlDVWKxbsculQ1VNK1YU1iRJAgKA5vFAABCSBM3jAQAIWbZiU5ahud1WrMdiRYHuckVjVbViQ1Whn5hdMlQVRix2OGCc2AdoOJ0wTvw71uNjlwtmfHzi+tW62w3zxEGnWnzs8UDEx5LEmliTrWqSNQ3z5s2Dw+E47ef32X6W20XaN/RAIGAdeZiVlQW/33/Kz3z00Ufo6uqy7r/el1WrViErK8t65OXlAQBaW1sBAG1tbWhrawMAtLS0oL29HUB0hqCjowMA0NTUBJ/PBwBoaGhAZ2cnAGD16joUFHQBANaurcXEiQEAwBtveJGb2wMA2Ly5Bjk5IXg8OjZvroHHoyMnJ4TNm2sAALm5PXjjDS8AYOLEANaurQUAFBR0YfXqOgBAcXEnqqujX2jKynx48skmAEBFRQeqqpoBAJWV7Vi6tAUAcO+9bbj33mhNS5e2oLIyWlNVVTMqKqI1PflkE8rKojVVVzeguJg15eSEoHs8qNm8GbrHg1BODmo2bwYA9OTmwvvGGwCAwMSJqD2xm6eroAB1q1cDADqLi9FQXQ0A8JWVoenJJwEAHRUVaK6qAgC0V1aiZelSAEDbvfei7d7oDt6WpUvRXlkJAGiuqkJHRQUAoOnJJ+ErKwMANFRXo/PE/RDqVq9GV0EBAKB27VoEJk4EAHjfeAM9ubkAgJrNmxHKyWFNrMleNc2bh6amJggh0N7ejpaW6GfEuX6W79q1C7Yh0tyrr74qNmzYIIQQYvfu3eInP/lJwnKfzydmzJghDh8+fMb1hEIh0d3dbT18Pp8AIPx+vxBCCF3Xha7rp8SapiXEhmEkxIAQbrcmZDkWR6zY44kIWTatWJJMAZjC44kIwBSSFIuFkOX42BBud3ysCUAIRTGEyxWNVTU+1oXTGR/rAhDC4dCFwxGNnU5dqGos1qzY5dKEqhpWrCisSZJMYQIi4vEIExCmJImIxyMEIExZtmJDlkXE7bZiLRYritBcrmisqlasq6rQnE4r1mOxwyF0hyMaO51CV1UhAKHFxy6XMOJjRYnGbrcwZFkIQETiY49HmPGxJLEm1mSrmnpHjBDbt28XkUjktJ/fZ/NZ/u233woAoru7ux+dKj2k5Fru56K5uRlr1qzBpk2bsHLlSowfPx4LFiwAED3PvaKiAq+++iry8/PPab08D53OhEe5E2WAAWhfdjoPPe2n3IuKijBmzBiUlJRg3759mD9/Ph566CEAwCuvvIL9+/dj2bJlKCsrw86dO1OcLRERDQZTluH3+6194ZSiu62lA47Q6Uw4QidKb5rbjdpt2zB79mzePvUEnsBHREQZxxEKYc6cOalOI62k/ZQ7ERHRyUxZxuHDhznlHocNnYiIMo7pdKK1tZUNPQ6n3ImIKOOooRBmz56d6jTSCkfoRESUcUxFwcGDBzlCj8OGTkREGcdUVezfv58NPQ6n3ImIKOOo4TBKS0tTnUZa4QidiIgyjqmqOHDgAEfocdjQiYgo43Af+qk45U5ERBlHDYcxffr0VKeRVjhCJyKijGOoKr744gvrHufEhk5ERBlIyDKOHj2KIXo7kj5xyp2IiDKOGolg6tSpqU4jrXCETkREGcdQVfz5z3/mlHscNnQiIso8soze3t5UZ5FWOOVOREQZR4lEUFRUlOo00gpH6ERElHEMhwOtra2cco+TEQ19+fLlKCkpwcKFCxGJRKzXdV3HkiVLUFJSgqqqqhRmSERElFpp39Cbm5tx6NAh1NfXY8qUKdiyZYu1bPv27Rg7dizq6+tx/PhxNDQ0pDBTIiIaLIqmIT8/H4qipDqVtJH2+9AbGxtRXl4OAJg7dy7Wr1+Pe+65x1p26623WssaGhpOe+WgcDiMcDhsPe/u7gYAHD16FACsaRtFURJiXdchSZIVy7IMWZatGJDhcumIRGQIIcPl0hCJKBBChtutIRxWIYQEt1tDKBR9u91u/aTYAUkScLlisQmn00A4HItNhMMqZNmEqpqIRFQoiglFicUGZFlA02IxoGkKVDVah64rcDgMmCZgGAocDh2mKcEwFDidOgxDhmHIcDp16LoM02RN3QB0txtqKBTdntsNRygEIUnQXS44QiGYkgTD6YQjHIYpSTCdTqjhMExZhqmqUCMRmIoCU1GgRiIwFAVClqFqGgxFAWQZiqbBUKPvm6LrMBwOwDShGAZ0hwNSLHY6IRsG5Fis65BNE7rLBTkSgSwENJcLSix2u6GGw5BicVwdrIk12aGmiMeDT3fuREFBgfW5fvLn99l8lvv9fgCwxfnsad/QA4EALrvsMgBAVlaW9ebHlo0cObLPZSdbtWoVqqurT3n9iiuu6HeOcd8TEuIT/3bPKhYiMY6tJz42TSC2x8Ewoo8zxbr+/9vRtL7juD0YCfFQr+lCOxbFmliTnWrq7QXKyjBQenp6kJWVNWDrS4W0b+jZ2dkIBoMAog08JyfnrJad7KmnnsKjjz5qPTdNE36/H6NGjYIkSUnKnoiIkiEYDCIvLw8+n88a2J0PIQR6enqsgWMmS/uGPm3aNKxZswaLFi3Cjh07MGPGjIRlXq8XpaWl2LFjB+6///7TrsflcsHlciW8duGFFyYrbSIiGgQjR47sV0MHkPEj85i0PyiuqKgIY8aMQUlJCfbt24f58+fjoYceAgD88Ic/hM/nQ0lJCTweD2666aYUZ0tERJQakrDDkQBERDSkBINBZGVlobu7u98jdLtI+xE6ERHRyVwuF5577rlTdqUOZRyhExER2QBH6ERERDbAhk5ERGQDbOhEREQ2wIZORERkA2zoRERENsCGTkREZANs6ERERDbAhk5ERGQDbOhEREQ2wIZORERkAxnR0Ht6elBcXIzhw4ejtbU1YZmu61iyZAlKSkpQVVWVogyJiIhSKyMausfjwbvvvovKyspTlm3fvh1jx45FfX09jh8/joaGhhRkSERElFpqqhM4G6qqYvTo0X0ua2xsxK233goAmDt3LhoaGjB9+vRTfi4cDiMcDlvPTdOE3+/HqFGjIElSchInIqK0JoRAT08PLrvsMshyRoxxTysjGvqZBAIB6164WVlZ8Pv9ff7cqlWrUF1dPZipERFRhvD5fBg7dmyq0+iXjG/o2dnZCAaDAKLNPScnp8+fe+qpp/Doo49az7u7uzFu3Dh89dVXyM7OhmEYAABFURJiXdchSZIVy7IMWZZPG2uaBkVRrFhVVUiSZMVAdL9/fOxwOCCEsGLTNGEYhhWbpglVVU8bG4YBIYQV91UHa2JNrIk12ammcDiM3bt3Y9q0adYs6/nU5Pf78b3vfQ8jRoxApsv4hj5t2jR4vV6UlpZix44duP/++/v8OZfLBZfLdcrr2dnZ1gifiIgyg2mauPbaa3HhhRcOyFS5HXa9ZswOg4qKCni9XixduhS//vWv8dBDDwEAfvjDH8Ln86GkpAQejwc33XRTijMlIqJkk2UZubm5Gb/feyBJQgiR6iRSIRgMIisrC93d3RyhExFlGF3XUVdXh9LSUmsq/3zYqRfwqw0REWUcWZaRn5/PEXqcjN+HTkREQ48sy7j44otTnUZa4VcbIiLKOJqmYceOHdA0LdWppA02dCIiyjiKomDq1KlQFCXVqaQNTrkTEVHGkWX5tNcdGao4QiciooyjaRp+97vfcco9Dhs6ERFlHFVVUVJS0q9T1uyGDZ3oNH7729/i+uuvR2FhISZPnoybb74Zpmn2a50/+9nPEIlErOdLlizB2rVrz+p3r7jiCkyaNAmFhYWYMmUK/uVf/uW886ioqMD+/fsBAPv378d1112HoqIirF+/Hg888ADq6+vPaX0D/V699NJLOHz48Hn/PtmfJEkYOXKkLa7wNmDEENXd3S0AiO7u7lSnQmmos7NTjB49Wnz11VfWax999JEwTbNf6wUgenp6rOeLFy8Wr7zyyln97uWXXy7+9Kc/CSGE+Prrr0VWVpb45JNP+pWPEEL88z//s/j7v//78/79ZLxX8bWeC03TznublFkikYjYtm2biEQi/VqPnXoBR+hEfejs7ISqqhg1apT12nXXXWeNBvbs2YObbroJBQUFuPHGG/HHP/4RAPDVV1/hoosusn7n2LFj1u/8+Mc/BgBMnz4dhYWF1gh03759uOWWW3DVVVfhzjvvTBjBn05eXh6uuuoqfP755/j5z3+OqVOnoqioCDfeeCN27dpl/VxjYyNKSkpw7bXXoqCgAP/xH/8BIDrab21txcaNG/Hiiy/iN7/5DQoLC7Fv3z6UlZXh3XffBRC9idEDDzyAa665Btdee22f90r4S+9Ve3s75s2bh6lTp+Laa6/Fq6++esb8VqxYgf/5n/9BZWUlCgsLsXfvXhw7dgz3338/8vPzkZ+fn3DnxLKyMjz99NO4+eabMWfOnL/43pE9qKqK8vJyTrnHS/U3ilSx07cyGniGYYg777xTZGdnizvuuEOsXr1afPPNN0IIIcLhsMjLyxP/9V//JYQQor6+XowZM0YcO3ZMdHR0iFGjRlnr6enpEfH/m6GPEfpNN90kjh8/LnRdF9OnTxdvv/12nznFj1pbWlrEiBEjxOeffy4OHz5s/UxjY6O4+uqrhRBCfPvtt+KSSy4Rf/zjH62avv3221PW9dxzz4nHHnvMWsesWbPE9u3bhRBCLFmyRCxbtkwYhiGEEAnbOpv3Std1ccMNN4i2tjYhhBDfffeduOaaa8RHH3101vkJIcQTTzwhFi5cKAzDEMeOHROFhYXinXfesfKtqKjo90iNMotpmiISifR71sxOvYBfbYj6IMsytm7dij//+c/YuXMnfv/732PlypXYs2cPent74XQ6rdHgzJkzcfHFF6OlpQWXXnrpOW/rzjvvhMfjAQDceOON1r7tvlRWVsLtdmPYsGF48803MXHiRHi9XqxcuRLffvstVFXFvn37EIlE0NjYiClTpmD69OlWTed6ms+7776Ljz76yLq85ujRo0/5mTO9V5FIBJ9++il+9KMfWT/f09ODffv2obOz86zz+8Mf/oCXX34ZsizjggsuwKJFi/CHP/wBf/M3fwMA+Nu//Vs4HI5zqo0ym67rqKmpQUVFBf/2J7ChE53BpEmTMGnSJDz00EOYO3cu/vM//xO33HJLnwfiSJKUcL9pAAiFQn9xG26324pj92o+nS1btiA/P996HolEMH/+fLz//vu4/vrrrRtNnM20/UDr672aM2cOLrroIuzdu/eUn//d73531usWQpzynsc/Hz58+HnnTZlJVVVUVFRwyj0O96ET9eHgwYPWfnEAOHr0KDo6OjBhwgRMmjQJ4XAYtbW1AICGhgYcPnwY11xzDcaMGQNd1/HZZ58BADZu3Jiw3hEjRqC7u3vA8gyFQtA0DXl5eQCAV155xVo2ffp0tLW1oaGhAUD0/tF+v/+c1n/bbbfhhRdesI5YP3LkyCk/c6b36q/+6q8wbNiwhPfhiy++gN/vP2N+I0eOTHiffvCDH+D111+HEALfffcdNm3ahFtuueWcaiH7OdOX36GIDZ2oD7quY8WKFbjqqqtQWFiIkpISLF68GLfffjucTie2bt2Kp59+GgUFBfiHf/gH/OY3v8EFF1wAVVXxi1/8An/913+N0tJShMPhhPU+9thjmD17dsJBcf0xcuRIrFixAjfeeCNKS0vhcrmsZdnZ2fj3f/93PP744ygoKEBRURE++OCDc1r/iy++iOPHjyM/Px+FhYX46U9/esrPnOm9UlUV27dvxzvvvIOCggJcffXVeOCBB9Db23vG/B555BHcd9991kFx//iP/whJknDNNdeguLgYt912GyorK/v35lFG03UdXq+XTT0O74dug3vgEhHR+bFTL8iIEfry5ctRUlKChQsXJuwb7O3txa233opZs2bhBz/4wTlPJxIRUWYSQiAYDGKIjkn7lPYNvbm5GYcOHUJ9fT2mTJmCLVu2WMt+//vfIz8/Hzt37sRdd92Ft956K4WZEhHRYNF1HfX19Zxyj5P2Db2xsRHl5eUAgLlz51oH0ADAxIkTcfz4cQBAIBDo85QaIiKyH4fDgXnz5vGUtThp39ADgYC1XyMrKythWn3ChAlobW1Ffn4+Nm7ciDvuuOO06wmHwwgGgwkPANYpRoZh9Bnrup4Qx472PV2saVpCHJsOisVCiFNiAAmxaZoJcewb6OliwzASYtbEmlgTa7J7TZqm4ciRIzBNs9812UXaN/Ts7Gyr+QYCgYQLT2zYsAFlZWVobW1FdXU1VqxYcdr1rFq1CllZWdYjdppPa2srAKCtrQ1tbW0AgJaWFrS3twOITvl3dHQAAJqamuDz+QBET1Xq7OwEANTV1aGrqwsAUFtbi0AgAADwer3o6ekBANTU1CAUClkXQ9B1HaFQCDU1NQCiF9vwer1WnbFTorq6ulBXVwcgeonN2AyFz+dDU1MTAKCjowPNzc0AopfZbGlpYU2siTWxJlvX9OWXX+LDDz+EYRj9qin+UsmZLu2Pcm9ubsaaNWuwadMmrFy5EuPHj8eCBQsAAL/85S9hGAaWLVuG2tpavPPOO3jttdf6XE84HE44hSgYDCIvLw9+vx/Z2dnWNzdFURJiXdchSZIVy7IMWZZPG2uaBkVRrFhVVUiSZMVA9BthfOxwOCCEsOLYN85YbJomVFU9bWwYBoQQCRc1ObkO1sSaWBNrYk2n1uT3+zFq1ChbHOWe9g0diB7lvmvXLowbNw7r16/Hww8/jHXr1iEYDGLBggX47rvvoOs63nzzTVx11VVntU47napARDTUmKaJrq4uXHTRRdalic+HnXpBRjT0ZLDTH5GIaKjRdR11dXUoLS3t1+Vf7dQLeBFcIiLKOKqqYvbs2alOI62k/UFxREREJzNNEwcPHrSOVieO0IkAAHs/OYRP20698UjM1ZNHo/DaMYOYERGdiWma2L9/Py655JJ+7UO3Ezb0furjLpqUiVw7AOXr0y83xgHhxYOXDw24oXm0kH2pqorS0tJUp5FWkt7QP/30U1x99dXJ3gxR/4TnAPLpR+gweRVConRimiZ8Ph/y8vI4Qj8haQ19/vz5mDBhAj7++GNcd911WL16dbI2RTQAxgAmp9SJMkVsH3pubi4b+glJa+gPPvgggsEgcnNzUVVVlazNEBHREKSqKqZPn57qNNJK0r7WzJkzB/n5+by1HRERDTjDMPDFF19YV4OjJO9Dnzx5MiZPnoyuri5s27YNR48etRr8E088kcxNExGRjQkhcPToUVxxxRWpTiVtDMqOh3nz5uH48eMYP348JkyYgAkTJgzGZomIyKZUVcXUqVP7dZU4uxmUd2LMmDF45JFHBmNTREQ0BMTusjZx4kQoipLqdNLCoDT0RYsWYf78+SgoKIB04sTtZ599djA2TURENtXb25vqFNLKoDT0559/Hg8//DByc3MHY3NERGRziqKgqKgo1WmklUFp6Jdffjnuv//+wdgUERENAYZhoK2tDZMnT+aU+wmD0tB7e3sxZ86chCl3XmiGiIho4AxKQ//pT386GJshIqIhQlEU5OfnpzqNtDIoDV0IgbKyMuv522+/PRibJSIimzIMAy0tLSgoKOCU+wmDch76a6+9hsbGRgDAunXrUFdXd06/v3z5cpSUlGDhwoWIRCLW6++//z7y8vJQVlaGm2++eUBzJiKi9ObxeFKdQloZlIa+ceNGrFmzBlVVVfj888/x2muvnfXvNjc349ChQ6ivr8eUKVOwZcuWhOV333033n//fbz33nsDnTYREaUpRVEwadIkjs7jJLWhP/7443jiiSfwzDPP4JJLLsHWrVshy/I5Xfa1sbER5eXlAIC5c+eioaEhYfnWrVtRUlKCl19+eUBzJyKi9KXrOnbv3g1d11OdStpI6j70W2+9NeH5XXfddc7rCAQCuOyyywAAWVlZ8Pv91rIbbrgBn332GQDg9ttvx8yZM3H99df3uZ5wOIxwOGw9DwaDAGBd2D/2X0VREmJd1yFJkhXLsgxZlq0YkOF264hEZJimDLdbQySiwDRleDwawmEVpinB49EQCqkQAvB4dPT2qpAkwO3W0dvrgCwLuFyx2ITTaSAUisUmQiEVimJCVU2EwypU1YSixGIDsiwQicRiIBJR4HBE69A0BU6nAdMEdF2B06nDNCXougKXS4dhyNB1GS6XDl2XYRisiTXZrybAAdM0YZomVFU9bWwYBoQQVtzX58K5fEacHGuaBkVRrFhVVUiSZMVAtFnFxw6HA0IIKzZNE4ZhWPFQrMk0TWRlZUGSpH7XZBdJHaHPnDkTXV1dCAQCmDlzJmbNmoVZs2bh8OHDZ72O7Oxsq/kGAgHk5ORYy4YPHw6n0wmn04nbbrsNn3zyyWnXs2rVKmRlZVmPvLw8AEBraysAoK2tDW1tbQCAlpYWtLe3A4hO+Xd0dAAAmpqa4PP5AAANDQ3o7OwEAKxeXYeCgi4AwNq1tZg4MQAAeOMNL3JzewAAmzfXICcnBI9Hx+bNNfB4dOTkhLB5cw0AIDe3B2+84QUATJwYwNq1tQCAgoIurF4dPeaguLgT1dXRGYqyMh+efLIJAFBR0YGqqmYAQGVlO5YubQEA3HtvG+69N1rT0qUtqKyM1lRV1YyKimhNTz7ZhLKyaE3V1Q0oLmZNrMm+NXV1dVnH8HR2dlozfj6fD01N0Zo6OjrQ3Bytqb29HS0t0Zr68xlRV1eHrq5oTbW1tQgEojV5vV709ERrqqmpQSgUgq7rqKmpga7rCIVCqKmJ1tTT0wOvN1pTIBBAbe3Qrunrr79Gd3c3FEXpV027du2CbYgkuvvuu8XTTz8tnnvuOTFz5kzxxRdfCCGE+P73v3/W6/j444/FwoULhRBCPP/88+Ltt9+2lnV3d1vxggULxM6dO0+7nlAoJLq7u62Hz+cTAITf7xdCCKHrutB1/ZRY07SE2DCMhBgQwu3WhCzH4ogVezwRIcumFUuSKQBTeDwRAZhCkmKxELIcHxvC7Y6PNQEIoSiGcLmisarGx7pwOuNjXQBCOBy6cDiisdOpC1WNxZoVu1yaUFXDihWFNbEme9YkhBCGYQhN084Y67qeEPf1uXAunxEnx5FIJCE2TTMhNk3zlFgIkRAbhpEQD8WaQqGQ+OCDD6xcz7emb7/9VgBI6CeZKqkNvayszIoPHDggZs2aJWpra8+poQshxGOPPSZmzpwp7rnnHhEOh8WDDz4ohBDi9ddfF1OnThU33XSTWL58+Tmts7u7e0D+iAAffPCRCQ+yF8MwxFdffWU15vM1UL0gHUhCCJGs0X9paSm8Xi/cbjeA6PTKwoUL0djYiCNHjiRrs2clGAwiKysL3d3dGDly5Hmv58SF74gozSXvk44y2UD1gnSQ1H3oL730krX/GwBGjBiBbdu24ZVXXknmZomIyOZ0XUddXZ2tDmrrr6SO0GOOHz+OYcOGJXsz54QjdKKhhSN0ezFNE52dnbj00ktPnHF0fuw0Qk/aaWv79u0DAAghsGHDBt6MhYiIBowsy7wl90mSNuX+9NNPY8+ePdizZ491egAREdFA0HUdtbW1nHKPk7QR+rPPPmvdfH7mzJnJ2gwREQ1BsiwjPz+/X9PtdpO0hl5UVARd17FlyxY0NDTA7/cjJycHM2bMwPz5860rBhEREZ0rWZZx8cUXpzqNtJLUrzb33XcfvvzySyxZsgTV1dVYvHgx9u/fj/vuuy+ZmyUiIpvTNA07duyApmmpTiVtJHWYfODAAbz11lsJr11//fUoLS1N5maJiMjmFEXB1KlTebe1OElt6MXFxVi0aBHKy8sxcuRIBINBeL1eFBcXJ3OzRERkc7IsJ9zbgwbhPPS9e/eisbERgUAA2dnZmDZtGgoLC5O5ybPC89CJhhaeh24vmqbB6/WivLwcDofjvNdjp/PQB+XCMifbuHEjFi1aNNibTcCGTjS0sKHbixACPT09GDFiBKR+fBDbqaEndco9dnGZeEIIrFu3LuUNnYiIMpckSRnfgAdaUhv6tGnTUFlZiZMnAQ4cOJDMzRIRkc1pmoaamhpUVFT0a8rdTpLa0KdMmYIXXngBo0aNSnh93rx5ydwsERHZnKqqKC8v5zVN4iR1H/qxY8cwbNiwtLySD/ehEw0t3IduL0II6LoOVVW5D/2EpHba4cOHp2UzJyKizKbrOmpqangt9zjstkRElHFUVUVFRQWn3ONkRENfvnw5SkpKsHDhQkQiEev1mpoaTJ8+HTNnzsSyZctSmCEREQ02js4TpX1Db25uxqFDh1BfX48pU6Zgy5Yt1rL8/HzU1dXhgw8+gN/vx+7du1OYKRERDRZd1+H1etnU46R9Q29sbER5eTkAYO7cuWhoaLCWjRs3zppucTgcZ5x6CYfDCAaDCQ8AMAzD+m9fsa7rCbFpmqfEbrcOWY7FmhV7PBpkWVixJAkAAh6PBkBAkmIxIMvxsQm3Oz6O/oNVFBMuVzRW1fjYgNMZHxsn3hMDDkc0djoNqGos1q3Y5dKhqqYVKwprYk32rck0TasBnC42DCMhHojPiPhY07SEOHZcciwWQpwSA0iITdNMiIdiTbIsY968eXA4HP2uyS7SvqEHAgHryMOsrCz4/f5Tfuajjz5CV1eXdf/1vqxatQpZWVnWIy8vDwDQ2toKAGhra0NbWxsAoKWlBe3t7QCiMwQdHR0AgKamJvh8PgBAQ0MDOjs7AQCrV9ehoKALALB2bS0mTgwAAN54w4vc3B4AwObNNcjJCcHj0bF5cw08Hh05OSFs3lwDAMjN7cEbb3gBABMnBrB2bS0AoKCgC6tX1wEAios7UV0d/UJTVubDk082AQAqKjpQVdUMAKisbMfSpS0AgHvvbcO990ZrWrq0BZWV0ZqqqppRURGt6cknm1BWFq2puroBxcWsiTXZt6auri7U1UVr6uzstAYIPp8PTU3Rmjo6OtDcHK2pvb0dLS3RmvrzGVFXV4eurmhNtbW1CASiNXm9XvT0RGuqqalBKBRKONgrFAqhpiZaU09PD7zeaE2BQAC1taypqakJQoh+1bRr1y7Yhkhzr776qtiwYYMQQojdu3eLn/zkJwnLfT6fmDFjhjh8+PAZ1xMKhUR3d7f18Pl8AoDw+/1CCCF0XRe6rp8Sa5qWEBuGkRADQrjdmpDlWByxYo8nImTZtGJJMgVgCo8nIgBTSFIsFkKW42NDuN3xsSYAIRTFEC5XNFbV+FgXTmd8rAtACIdDFw5HNHY6daGqsVizYpdLE6pqWLGisCbWZM+ahBDCMAyhadoZY13XE+K+PhfO5TPi5DgSiSTEpmkmxKZpnhILIRJiwzAS4qFYU29vr9i+fbuIRCL9qunbb78VAER3d7fIdCm5lvu5aG5uxpo1a7Bp0yasXLkS48ePx4IFCwBEz3OvqKjAq6++ivz8/HNaL89DJxpa0vuTjlKF56EPoqKiIowZMwYlJSXYt28f5s+fj4ceeggA8Morr2D//v1YtmwZysrKsHPnzhRnS0REg8E0Tfj9fmtfOKXobmvpgCN0oqFlaH7S2ZemaaitrcXs2bN5+9QTeEY+ERFlHIfDgTlz5qQ6jbSS9lPuREREJzNNE4cPH+aUexw2dCIiyjimaaK1tZUNPQ6n3ImIKOOoqorZs2enOo20whE6ERFlHNM0cfDgQY7Q47ChExFRxjFNE/v372dDj8MpdyIiyjiqqqK0tDTVaaQVjtCJiCjjmKaJAwcOcIQehw2diIgyDvehn4pT7kRElHFUVcX06dNTnUZa4QidiIgyjmEY+OKLL6x7nBNH6ERkU5dgLy7Gp9bzP/1b4vLRV1+NMYWFg5sUDRghBI4ePYorrrgi1amkDTZ0oj4I8K47mW4DgK/jnm+7N3H5OACLBzEfGlgqgKm8404CNnQisqU5AI6cYfnowUqEksJQVbT/+c+YOHEiFEVJdTppgQ2diGxpzIkH2ZQso7e3N9VZpBU2dCIiyjhKJIKioqJUp5FWMuIo9+XLl6OkpAQLFy5EJBKxXtd1HUuWLEFJSQmqqqpSmCEREQ0mw+FAa2srj3KPk/YNvbm5GYcOHUJ9fT2mTJmCLVu2WMu2b9+OsWPHor6+HsePH0dDQ0MKMyUiIkqdtG/ojY2NKC8vBwDMnTs3oWmfaRkREdmXomnIz8/nAXFx0n4feiAQwGWXXQYAyMrKgt/vT1g2cuTIPpedLBwOIxwOW8+7u7sBAEePHgUAa9pGUZSEWNd1SJJkxbIsQ5ZlKwZkuFw6IhEZQshwuTREIgqEkOF2awiHVQghwe3WEApF3263Wz8pdkCSBFyuWGzC6TQQDsdiE+GwClk2oaomIhEVimJCUWKxAVkW0LRYDGiaAlWN1qHrChwOA6YJGIYCh0OHaUowDAVOpw7DkGEYMpxOHbouwzRZUzcA3e2GGgpFt+d2wxEKQUgSdJcLjlAIpiTBcDrhCIdhShJMpxNqOAxTlmGqKtRIBKaiwFQUqJEIDEWBkGWomgZDUQBZhqJpMNTo+6boOgyHAzBNKIYB3eGAFIudTsiGATkW6zpk04TuckGORCALAc3lghKL3W6o4TCkWBxXB2tiTXaoKeLx4NOdO1FQUGB9rp/8+X02n+WxviFscApc2jf07OxsBINBANEGnpOTc1bLTrZq1SpUV1ef8vpAXJQg7ntCQnzi3+5ZxUIkxrH1xMemCcQOITCM6ONMsa7//3Y0re847pCEhHio13ShHYtiTazJTjX19gJlZRgoPT09yMrKGrD1pULaN/Rp06ZhzZo1WLRoEXbs2IEZM2YkLPN6vSgtLcWOHTtw//33n3Y9Tz31FB599FHruWma8Pv9GDVqFCSJFxEhIsokwWAQeXl58Pl81kzt+RBCoKenx5oJzmSSyIB5huXLl2PXrl0YN24c1q9fj4cffhjr1q2Druv4u7/7O3z55ZcoKirCL37xi1SnSkREgyAYDCIrKwvd3d39auh2khENnYiIKB4b+qnS/ih3IiIi+svY0ImIKOO4XC4899xzcLlcqU4lbXDKnYiIyAY4QiciIrIBNnQiIiIbYEMnIiKyATZ0IiIiG2BDJyKijHLw4EHs2rULBw8eTHUqaYVHuRMRUcZ45plnoGmaddlXVVWxcuXKVKeVFtL+Wu5EREQxPT09ePnll63njz32WAqzSS9s6ERElDFGjBiBp556yhqhezyeVKeUNjjlTkREGeWbb77BN998g7Fjx2Ls2LGpTidt8KA4IiLKKGPHjsW0adPwq1/9KtWppBU2dCIiykiSJKU6hbTCKXciIspYR44cwejRo1OdRlrgCJ2IiDJGJBJJeDzzzDOpTilt8Ch3IiLKGAUFBZg+fToAQAiBDz/8MMUZpQ82dCIiyhiVlZV4/vnnrecvvvhiCrNJL9yHTkREGUnXdagqx6Ux3IdOREQZ6Yknnkh1CmmFDZ2IiDLS5ZdfnuoU0gqn3ImIKGPU1dVZsRACkiShtLQ0hRmlDzZ0IiLKGLm5uZg1axZmzZplXVjmwQcfTHFW6YFT7kRElDE6Ojpwxx13oKWlBUePHsXSpUtTnVLaYEMnIqKM4XQ68b3vfQ8XXHAB/vd//zfV6aQVTrkTEVHGmDp1Kq688krcddddGDZsGCRJQnl5earTSgs8gY+IiDLGsmXLAADBYBA9PT0pzia9cMqdiIgyxpVXXonFixejuLgYH3zwAS699NJUp5Q22NCJiChjvP322wCAn//851ixYgU2bdqU4ozSBxs6ERFljOPHj8MwDKiqijFjxuDCCy9MdUppgw2diIgyRlFREebPn4/77rsPAOB2u1OcUfrgUe5EREQ2wKPciYgoY6xYsQI+nw+TJ0/GBx98gIKCAvzsZz9LdVppgVPuRESUMT7//HO8/vrr2LFjB37729/i8OHDqU4pbbChExFRxhg+fDgAWKNyRVFSmE16YUMnIqKMEbtl6p/+9CcA4OVf47ChExFRxvD5fACAvXv3AgBGjRqVwmzSCxs6ERFljK+//hper9f67zfffJPqlNIGT1sjIqKMsWHDhlNeW7x4cQoyST9s6ERERDbAKXciIiIbYEMnIiKyATZ0IiIiG2BDJyIisgE2dCIiIhtgQyciIrIBNnQiIiIbYEMnIiKyATZ0IiIiG2BDJyIisgE2dCIiIhtgQyciIrIBNnQiIiIbYEMnIiKyATZ0IiIiG2BDJyIisgE2dCIiIhtgQyciIrIBNnQiIiIbYEMnIiKyATZ0IiIiG2BDJyIisgE2dCIiIhtgQyciIrIBNnQiIiIbYEMnIiKyATZ0IiIiG2BDJyIisgE2dCIiIhtgQyciIrIBNnQiIiIbYEMnIiKyATZ0IiIiG2BDJyIisgE2dCIiIhtgQyciIrIBNnQiIiIbYEMnIiKyATZ0IiIiG2BDJyIisgE2dCIiIhtgQyciIrIBNnQiIiIbYEMnIiKyATZ0IiIiG2BDJyIisgE2dCIiIhtgQyciIrIBNnQiIiIbYEMnIiKyATZ0IiIiG2BDJyIisgE2dCIiIhtgQyciIrIBNnQiIiIb+D+4XfTOkE9L4QAAAABJRU5ErkJggg==" }, "metadata": {}, "output_type": "display_data" @@ -647,7 +787,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 11, "id": "5f8174e1", "metadata": {}, "outputs": [ @@ -655,11 +795,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-11 13:33:49,583 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", - "2024-01-11 13:34:00,293 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", - "2024-01-11 13:34:10,830 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", - "2024-01-11 13:34:18,848 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", - "2024-01-11 13:34:26,735 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + "2024-01-19 15:03:39,967 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" ] }, { @@ -670,11 +806,9 @@ "Find all realizations: True\n", "OBS: Arctic\n", "Converting units by multiply 0.01\n", - "Converting units by multiply 0.01\n", "OBS: Antarctic\n", "Converting units by multiply 0.01\n", - "Converting units by multiply 0.01\n", - "['E3SM-1-0']\n", + "Model list: ['E3SM-1-0']\n", "\n", "=================================\n", "model, runs: E3SM-1-0 ['r1i2p2f1', 'r2i2p2f1', 'r3i2p2f1', 'r4i2p2f1']\n", @@ -747,22 +881,22 @@ " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_201001-201412.nc\n", "Converting units by multiply 0.01\n", "\n", - "-----------------------\n" + "-----------------------\n", + "model, run, variable: E3SM-1-0 r4i2p2f1 siconc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO::2024-01-11 13:37::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n", - "2024-01-11 13:37:10,054 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n" + "INFO::2024-01-19 15:06::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n", + "2024-01-19 15:06:48,149 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "model, run, variable: E3SM-1-0 r4i2p2f1 siconc\n", "test_data (model in this case) full_path:\n", " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_185001-185912.nc\n", " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_186001-186912.nc\n", @@ -801,13 +935,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "[WARNING] yaksa: 10 leaked handle pool objects\n" + "[WARNING] yaksa: 10 leaked handle pool objects\n", + "\n", + "real\t4m4.264s\n", + "user\t4m23.113s\n", + "sys\t1m18.596s\n" ] } ], "source": [ "%%bash\n", - "python ice_driver.py -p demo_param_file.py --realization '*' --case_id \"ex2\"" + "time python ice_driver.py -p demo_param_file.py --realization '*' --case_id \"ex2\"" ] }, { @@ -815,18 +953,18 @@ "id": "cadb1306", "metadata": {}, "source": [ - "Since we have averaged four different realizations, the resulting statistics are different than seen in example 1. " + "Since we have averaged four different realizations, the resulting statistics are different than seen in example 1. The bar chart now contains error bars showing the overall spread among the realizations." ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 12, "id": "d6cb5f07", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -859,7 +997,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 13, "id": "679d7289", "metadata": {}, "outputs": [ @@ -867,13 +1005,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-11 13:37:14,718 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", - "2024-01-11 13:37:22,367 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", - "2024-01-11 13:37:29,926 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", - "2024-01-11 13:37:35,806 [WARNING]: bounds.py(_create_bounds:398) >> The 'y' coordinate variable is missing a 'units' attribute. Assuming 'units' is 'degrees_north'.\n", - "2024-01-11 13:37:41,433 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "2024-01-11 13:38:16,606 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "2024-01-11 13:38:59,446 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + "2024-01-19 15:07:49,272 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "2024-01-19 15:08:26,554 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "2024-01-19 15:09:10,957 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" ] }, { @@ -884,11 +1018,9 @@ "Find all realizations: True\n", "OBS: Arctic\n", "Converting units by multiply 0.01\n", - "Converting units by multiply 0.01\n", "OBS: Antarctic\n", "Converting units by multiply 0.01\n", - "Converting units by multiply 0.01\n", - "['CAS-ESM2-0', 'CanESM5', 'E3SM-1-0']\n", + "Model list: ['CAS-ESM2-0', 'CanESM5', 'E3SM-1-0']\n", "\n", "=================================\n", "model, runs: CAS-ESM2-0 ['r2i1p1f1', 'r1i1p1f1', 'r4i1p1f1', 'r3i1p1f1']\n", @@ -1019,8 +1151,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO::2024-01-11 13:41::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n", - "2024-01-11 13:41:40,126 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n" + "INFO::2024-01-19 15:12::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n", + "2024-01-19 15:12:17,350 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n" ] }, { @@ -1093,13 +1225,17 @@ "name": "stderr", "output_type": "stream", "text": [ - "[WARNING] yaksa: 10 leaked handle pool objects\n" + "[WARNING] yaksa: 10 leaked handle pool objects\n", + "\n", + "real\t5m29.588s\n", + "user\t5m6.419s\n", + "sys\t1m59.480s\n" ] } ], "source": [ "%%bash\n", - "python ice_driver.py -p demo_param_file.py \\\n", + "time python ice_driver.py -p demo_param_file.py \\\n", "--test_data_set \"E3SM-1-0\" \"CanESM5\" \"CAS-ESM2-0\" \\\n", "--realization '*' \\\n", "--case_id \"ex3\"" @@ -1115,7 +1251,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 14, "id": "b07dbb8b", "metadata": {}, "outputs": [ @@ -1131,6 +1267,7 @@ " },\n", " \"json_structure\": [\n", " \"model\",\n", + " \"realization\",\n", " \"obs\",\n", " \"region\",\n", " \"index\",\n", @@ -1148,403 +1285,1175 @@ " },\n", " \"RESULTS\": {\n", " \"CAS-ESM2-0\": {\n", - " \"bootstrap\": {\n", - " \"antarctic\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"7.019108133567357\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"3.871674069649\"\n", - " }\n", - " },\n", - " \"arctic\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"4.294828188493619\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.574135052089\"\n", - " }\n", - " },\n", - " \"ca\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"2.312010146130351\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.508629991489\"\n", - " }\n", - " },\n", - " \"io\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.5482737233471119\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.4375293316\"\n", - " }\n", - " },\n", - " \"na\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.3222710090779452\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.2349881371580625\"\n", - " }\n", - " },\n", - " \"np\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.4959409821003755\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.20888410478754005\"\n", - " }\n", - " },\n", - " \"sa\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.8350528909264078\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.31861565606025\"\n", - " }\n", - " },\n", - " \"sp\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.8884341936515745\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.55017451890625\"\n", + " \"antarctic\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"7.083854826293509\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.792162821153895\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i1p1f1\": {\n", + " \"OSI-SAF\": 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\"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.5653155943768037\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.255321079808\"\n", + " }\n", + " }\n", + " },\n", + " \"r4i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.605146184785239\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.272687104\"\n", + " }\n", " }\n", " }\n", " },\n", - " \"nasateam\": {\n", - " \"antarctic\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.8229523470238773\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.132947017728\"\n", - " }\n", - " },\n", - " \"arctic\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"7.046055592965159\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"5.130596384768\"\n", - " }\n", - " },\n", - " \"ca\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.12243728740127617\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.101844279296\"\n", - " }\n", - " },\n", - " \"io\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.026688722221047654\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"4.7558262499999997e-07\"\n", - " }\n", - " },\n", - " \"na\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.9424239970669361\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.260132499456\"\n", - " }\n", - " },\n", - " \"np\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.5810669209888607\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.25988712038399997\"\n", - " }\n", - " },\n", - " \"sa\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.11104463714637731\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.073196494848\"\n", - " }\n", - " },\n", - " \"sp\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.3599127340389943\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.402562646016\"\n", + " \"sa\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.4924799868799379\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.406647668736\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.3797729615722766\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.297013608448\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.43324236598783966\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.3584606208\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.54670122730152\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.455321387008\"\n", + " }\n", + " }\n", + " },\n", + " \"r4i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.6355585206799742\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.53622751232\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"sp\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.5282094877035928\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.01284434432\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.6767107661262813\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.078223351808\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.4522165451285096\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.000495858944\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.5318409136802855\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.009201968127999999\"\n", + " }\n", + " }\n", + " },\n", + " \"r4i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.501167141217253\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.003074912256\"\n", + " }\n", " }\n", " }\n", " }\n", @@ -1552,6 +2461,7 @@ " },\n", " \"json_structure\": [\n", " \"model\",\n", + " \"realization\",\n", " \"obs\",\n", " \"region\",\n", " \"index\",\n", @@ -1580,7 +2490,7 @@ " \"Version\": \"23.1.0\",\n", " \"buildVersion\": \"not installed\"\n", " },\n", - " \"date\": \"2024-01-11 13:41:26\",\n", + " \"date\": \"2024-01-19 15:12:03\",\n", " \"openGL\": {\n", " \"GLX\": {\n", " \"client\": {},\n", @@ -1634,18 +2544,18 @@ "id": "f48b3856", "metadata": {}, "source": [ - "Now the resulting bar chart shows three different models along with the bootstrap vs. nasateam comparison." + "Now the resulting bar chart shows three different models with their spread." ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 15, "id": "41aa14a3", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" 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bRAgh+hdqtcAbYfHmg8Viwfjx43Hs2DF88803SExM1Jbt3r0bjzzyCHr16gVBELBz584gttRz2e8dxrI/5je5fOnz6Xj5hdEBbFHzMJrpi3iB8od4inLHXVgMuZtMJnz++ecuz8LW98ADD2D37t1hW8wBYP6cFOz4PMvt9R2fZ+HQnrmYPyclCK26OVmWUVBQQMNexCOUP8RTlDvuwuIKXRTFJh9fAYAtW7Zg3759mD59OhYuXBjAlvlOXFxbiKL7+VVSYmd07HhLEFrUPAaDAZMnTw52M0iYovwhnqLccRcWV+g3MnjwYHz//ffYuXMntm3bpt1c05DNZoPZbHb5An6ZFlJRlEZjWZZdYufdt03FkiS5xM5bFJwxY8wtBhzDR1IjZ5qqqmpnoE3FiqK4xIHsk6IouHz5Mmw2W+N9uh6rquoSh3KfbrqfqE8+65Msy6iqqoLdbtdNn/S4n0KxT5Ik4dKlS1BV1es+6UXYF/SoqCgYjUYYjUbce++9+Pbbbxv9uZUrVyImJkb7ck5L6Zyus7S0FKWlpQCAo0eP4uTJkwCA4uJibU7s/fv3a48pFRYWoqKiAoDjjl7nIz95eXnaY065ubmwWCwAgJycHFitVpfJEKxWq3YXscViwe7du9zafbmqCvn5jvfWKyoqUFhYCMDx4RfOKS3LyspQXFwMADh58iSOHj0asD5du3YNBw4cwLZt2xrtk3Nik+rqau3DOSorK0O6TzfbT9Qn3/Xpp59+0ubL10uf9LifQrFPP/74I7755hsoiuJVn/bt2we9CKu73GfPno1Fixa53BRnNpu1OxMfeughPPnkk0hPT3f7vzabDTabzeX/JSQkoKqqCrGxsdqZmyAILrEsy+A4Tot5ngfP803GkiRBEAQtFkURHMdpMQCXiTRkWYbBYABjDBXnzYi/428u7T7/49Po0MEEURShqipUVXWLFUUBY0yLG+tHsPrkjJ1n0c64sX5Qn6hP1CfqU6D7VFVVhQ4dOujiLvewKeiZmZk4cuQIunfvjvnz56OoqAjZ2dl499138c4770AURYwcObLZnwoVio8qXLp0DZ16/MXltYunnwnp99BVVUVlZSVuvfVWl2egCWkOyh/iKV/lTijWAk+FTUH3tVDcieFY0GVZRn5+PtLT02kKRtJilD/EU77KnVCsBZ6ivyDiFVEUMXbs2GA3g4Qpyh/iKcoddzTGRbyiqirOnj2r3TFKSEtQ/hBPUe64o4JOvKKqKk6dOkV/VMQjlD/EU5Q77mjInXhFFMVGnyogpDkof4inKHfc+f0K3fmxiESfVFXFmTNn6CyZeITyh3iKcsed367Qp02bhl69euHw4cNISUnBqlWr/LUpEkTO97Hi4+PpsSPSYpQ/xFOUO+78VtCfeOIJmM1mxMfHh+386uTmRFHEiBEjgt0MEqYof4inKHfc+e20ZuLEiUhMTEQrfcy91VAUBT/88IM2IxMhLUH5QzxFuePOrzfF9e3bF3379kVlZSW2bt2KK1euaAX+2Wef9eemSYAwxnDlyhX06NEj2E0hYYjyh3iKcsddQO5ynzx5MrKystCzZ89AbI4EkCiKGDJkSLCbQcIU5Q/xFOWOu4AU9C5dumDBggWB2BQJMOcnHfXu3RuCIAS7OSTMUP4QT1HuuAtIQZ81axamTZuGpKQkcBwHAHjppZcCsWkSAHV1dcFuAgljlD/EU5Q7rgJS0FesWIGnnnoK8fHxgdgcCSBBEDBw4MBgN4OEKcof4inKHXcBKejdu3fHnDlzArEpEmCKoqC0tBR9+/alYS/SYpQ/xFOUO+4CUtDr6uowceJElyF3mmiGEEII8Z2AFPTnn38+EJsJiuvnJ77TxvXbTp18t2p/TAkgCAISExN9v2LSKlD+EE9R7rgLyHx5jDGMHj1a+zp79mwgNksCQFEUFBcX0+QOxCOUP8RTlDvuAlLQ16xZg6KiIgBAdnY28vPzW/T/LRYLUlNTERUVhZKSEpdlsixj9uzZSEtLoylmg8RkMgW7CSSMUf4QT1HuuApIQd+wYQNef/11LFy4EP/617+wZs2aFv1/k8mEzz//HNOnT3db9tlnn6Fr164oKChAbW0tCgsLfdVs0gyCIKBPnz50UwrxCOUP8RTljju/FvTFixfj2WefxYsvvojOnTtjy5Yt4Hm+xdO+iqKIjh07NrqsqKgIEyZMAABMmjSJCnqAybKMAwcOQJblYDeFhCHKH+Ipyh13fr0pbsqUKS7fz5gxw+fbqK6uRnR0NAAgJiYGVVVVjf6czWaDzWbTvjebzQCgvf/i/FcQBJdYlmVwHKfFPM+D53ktBnhERsqw23moKo/ISAl2uwBV5WEySbDZRKgqB5NJgtUqgjHAZJJRVyeC44DISBl1dQbwPIMxQoa1Qbs5XkWEUYbVKkIQVIiiCptNhCiqEARnrIDnGex2ZwzY7QIMBkc/JEmA0ahAUZrXp4axJEkQBEGLRVEEx3GQJAkcx6Fdu3aQZVk7U5ZlGQaDAYwxLVZVFYqiaLGqqhBFsclYURQwxrS4sX3Tkv3Ukj6Joqj1o35MffJ9nxhjiI2NhaIouumTHvdTKPZJVVXExMSA4ziv+6QXfr1CHzVqFCorK1FdXY1Ro0ZpN8VdvHjRZ9uIjY3VinN1dTXat2/f6M+tXLkSMTEx2ldCQgIAaO/Jl5aWorS0FABw9OhRnDx5EgBQXFyMsrIyAMD+/ftRXl4OACgsLERFRQUAYNWqfCQlVQIAVq/OQ+/e1QCAtWtzER9vAQBs2pSD9u2tMJlkbNqUA5NJRvv2VmzalAMAiI+34K9/3eXW7rv7VWHVKsc9B6mpFVi2zDECkZFRjiVL9gMAMjPLsHBhMQBg+vSTmDfvKADg4YdL8fDDjj7Nm9eyPuXn56Oy0tGnvLw8VFc7+pSbmwuLxdGnnJwcSJKEHj16YPv27ZBlGVarFTk5jj5ZLBbk5uZq+yYvLw8AUFlZqd1HUVFRoY2qlJeXY/9+R5/KyspQXOzo08mTJ3H06FGv91Nz+2S1WiHLMnJycqhPfu7TuXPncMcdd2Dfvn266ZMe91Mo9umnn35CTU0NBEHwqk/79u2DXnDMj59v+uCDD+KOO+6AKIrYuXMn1q9fj169emHs2LHajmuJ2bNnY9GiRS6PKnzyySc4dOgQVqxYgXnz5mHOnDkYPny42/9t7Ao9ISEBVVVV2hUC0PIzVUHw5RW6GVbuby7t5qxPI8Jo8skVem2t78++AccfR0pKCiIjIwHQFQX1qfl9UlUVBw8eREpKCoxGoy76pMf9FIp9stlsOHjwIFJTU7X5TTzpU1VVFTp06ICamhpttDdc+bWgjxkzBrt2Oa46f/rpJ8yaNQtLly7FK6+80uKCnpmZiSNHjqB79+6YP38+ioqKkJ2dDVmW8fjjj+PHH3/EwIED8be//e3mK4OjoMfExHi9E337HPo1oM1fXF+qfQbALT5Zuz/2tKqqKC8vR0JCwvW3IAhpPsof4ilf5Y6vakEo8GtBT09PR25urnblZrFYkJWVhaKiIly6dMlfm20WKuiEEEL0VND9ekr817/+VXt/GwDatm2LrVu34s033/TnZkkAybKM/Px8Xd1YQgKH8od4inLHnV/vck9JSQEA1NbWok0bx5ymPM/jwQcf9OdmSQDxPI9evXrRcCnxCOUP8RTljju/FfTjx48DcEz7+ve//50+jEWneJ6nj8UlHqP8IZ6i3HHnt1ObF154AQcPHsTBgwe1xwOI/siyjLy8PBr2Ih6h/CGeotxx57cr9Jdeekn78PlRo0b5azMkyHieR2JiIg17EY9Q/hBPUe6481tBHzhwIGRZxubNm1FYWIiqqiq0b98eI0eOxLRp07TnEUl443kenXz5Ga+kVaH8IZ6i3HHn11Obxx57DD/++CNmz56NZcuW4dFHH8WpU6fw2GOP+XOzJIAkScL27du1SWYIaQnKH+Ipyh13fr1MPnPmDN5//32X1wYNGoT09HR/bpYEkCAIGDJkCH3iEfEI5Q/xFOWOO78W9NTUVMyaNQsTJkxAdHQ0zGYzcnNzkZqa6s/NkgDieb7J+fMJuRnKH+Ipyh13fh1yf+211/DMM8/AYrHg2LFjuHr1Kp555hm89tpr/twsCSBJkvDFF1/QsBfxCOUP8RTljju/Tv3alA0bNmDWrFmB3qwLmvrVNxhjsFgsaNu2rfYBCYQ0F+UP8ZSvckdPU7/6dcjdOblMfYwxZGdnB72ghx4LwDUyvz13AYAJYFEA2ga6UTfFcVzY/xGQ4KH8IZ6i3HHn14I+bNgwTJ8+HQ0HAc6cOePPzYYnw2HAkO/+uukDx79SOiCNDmybmkGSJOTk5CAzMxMGgyHYzSFhhvKHeIpyx51fh9yHDRuGL774Ah06dHB5ffLkyfjiiy/8tdlmCb0hdwvAXW16sQ+u0P015G61WhEZGUlDpqTFKH+Ip3yVO3oacvdrQb969SratGkTkjP5hF5B9z9/FXRZliGKIh2QSYtR/hBP+Sp39FTQ/Vppo6KiQrKYE9+RZRk5OTk0nzLxCOUP8RTljrug3OUeCugK3VfrpCss4jnKH+IpukJ3FxaXz4sWLUJaWhqysrJgt9u113fv3o2EhARkZGRg3LhxQWxh60ZnyMQblD/EU5Q7rkK+oBcXF+P8+fMoKChAv379sHnzZpflDzzwAHbv3o2dO3cGqYWtmyzLyM3NpT8s4hHKH+Ipyh13IV/Qi4qKMGHCBADApEmTUFhY6LJ8y5YtSEtLwxtvvHHD9dhsNpjNZpcvAFAURfu3sViWZZdYVVW3ODJSBs87Y0mLTSYJPM+0mOMYAAaTSQLAwHHOGOD5+rGKyMj6sSNhBUFFRIQjFsX6sQKjsX7saK/BoMBgcMRGY8v6VD+WJMkldr5LI0kSRFHEvffeC8AxBMYY02Zuqh+rquoSO/8Im4oVRXGJfbGfmtun+v2gPvm3T4Ig4L777gPHcbrpkx73Uyj2ied5TJ48GQaDwes+6UXIF/Tq6mrtfY2YmBhUVVVpywYPHozvv/8eO3fuxLZt23Do0KEm17Ny5UrExMRoXwkJCQCAkpISAEBpaSlKS0sBAEePHsXJkycBOEYIysrKAAD79+9HeXk5AKCwsBAVFRUAgFWr8pGUVAkAWL06D717VwMA1q7NRXy8BQCwaVMO2re3wmSSsWlTDkwmGe3bW7FpUw4AID7egrVrcwEAvXtXY/XqPABAUlIlVq1yPJ+emlqBZcscJzQZGeVYsmQ/ACAzswwLFxYDAKZPP4l5844CAB5+uBQPP+zo07x5LetTfn4+KisdfcrLy0N1taNPubm5sFgcfcrJyUFdXR2uXLmi3ZxitVqRk+Pok8ViQW5urrYf8/IcfaqsrER+vqNPFRUV2klaeXk59u939KmsrAzFxY4+nTx5EkePHvV6PzW3T1ar1eWGG+qT//r0008/wWw266pPetxPodqn/fv3gzHmVZ/27dsHvQj5m+Lefvtt3HLLLZg1axYOHjyI9evXY/Xq1Y3+XEREBObMmdPoemw2G2w2m/a92WxGQkICqqqqEBsbq525CYLgEsuyDI7jtJjnefA8r8WCwCMyUobdzkNVeURGSrDbBagqD5NJgs0mQlU5mEwSrFYRjAEmk4y6OhEc57i6r6szgOcZIiKcsQqjUYHV6oxVWK0iBEGFKKqw2USIogpBcMYKeJ7BbnfGgN0uIFb8GVGogCwLMIgKCgsBXhCgXO9TdNeuMHXs6NanhrEkSRAEQYudN6E4z7hzc3MxduxYmEwmAI4zXoPBoN20YjAYoKoqFEXRYlVVIYpik7GiKGCMaXFj+6Yl+6klfRJFUetH/Zj65Ps+KYqCHTt2YOzYsYiIiNBFn/S4n0KxT1arFTt27MDEiRO1p6k86VNVVRU6dOigi5viQr6gFxcX4/XXX8fGjRvx6quvomfPnpg5cyYAR1F27oCHHnoITz75ZLM/mrU13OU+Bi9jDJY1uTx96VKMfvnlwDWIEEJCDN3lHkADBw5Ely5dkJaWhuPHj2PatGmYP38+AODjjz/G0KFDMWLECMTHx9PnrDdwAPOxDjvcXs/asQNzDx1CyvXfozdUVUVVVZX2fhQhLUH5QzxFueMu5K/Q/aU1XKEDQBtcwhJ0cnntmYsXcUvHjj5ZvyRJyMvLw9ixY2k+ZdJilD/EU77KHT1dofv1w1mI/hkMBkycODHYzSBhivKHeIpyx13ID7mT0KaqKi5evEjDXsQjlD/EU5Q77qigE6+oqoqSkhL6oyIeofwhnqLccUdD7sQroihi7NixwW4GCVOUP8RTlDvu6AqdeEVVVZw9e5bOkolHKH+Ipyh33FFBJ15RVRWnTp2iPyriEcof4inKHXc05E68IooiPf9PPEb5QzxFueOOCnqIYvDNA+7XAPyl4YudOjXyk55RRRHlP/yAhIQEbfpFElgVFRZUnL/a5PK4LlGIi2sbwBY1n6qqKC8vp/whLUa5444KOvGKKgg4e/Ys4uPj6Y8qSLLfO4xlf8xvcvnS59Px8gujA9ii5nO+D0r5Q1qKcscdzRQXojPF+fMK/RkAt/hk7de1zhQKGRUVFhw/cQnjp3zg8vqOz7MQ284U0lfohASbnmaKo9Ma4hVFFPHDDz9on2pEAi8uri2SEju7vZ6U2BkpA+NCupgrikL5QzxCueOOCjrxCuN5XLlyBa10oId4iTFG+UM8Qrnjjt5DJ14R7XYMGTIk2M0gfmCpqMDViooml0fFxaFtXJxX2xBFkfKHeIRyxx0VdOIVRRRx8sQJ9O7dG4IgBLs5xIcOZ2cjf9myJpenL12K0S+/7NU2FEXByZMnKX+CKFyfkqDccUcFnXiH51FXVxfsVoQtn95U2cb1W2+fTozCfHREGh7DeJfXs3bsgCk2FlFeXp07Uf4EVzg/JUG544oKuo5ZAFxq5PULAEwAogB4e94t2O0YOHCgl2shoegq4qA2cojonJSEWzp29Mk2BEGg/Amy+XNSkDYi4YZPSYQiyh13YXFT3KJFi5CWloasrCzY7XbtdVmWMXv2bKSlpWHhwoVBbGFoOgzgg0Ze/wDAu9eXe0sxGFBSUkJ3moagi+gEBs6rr4to5DK/UyfH0IIPvhSjkfInyML1KQlFUSh3Ggj5K/Ti4mKcP38eBQUFePXVV7F582Y89NBDAIDPPvsMXbt2xfr16zFv3jwUFhZixIgRQW5x6EgBcOcNlofmeTchpLlC+y2bCrTFLzdVHjrUYLkPbqokrkK+oBcVFWHChAkAgEmTJmHdunVaQS8qKsKUKVO0ZVTQXbWF90PqNyNIEhITE/28FaJXlD+hwAJwjbw5x11/c4559ubcEGRjDH65qfLdQa7Lvb2pUhAEyp0GQr6gV1dX47bbbgMAxMTEoKqqymWZc2afhssastlssNlsABzPL547dw4AcOXKFQDQhm0EQXCJZVkGx3FazPM8eJ7XYoBHRIQMu50HYzwiIiTY7QIY4xEZKcFmE8EYh8hICVar49cdGSk3iA3gOIaICGes4rIxAgabDSrHQTUaIdpsUHkeqihCtNuhCgJUQYBot0MRBDCehyhJUAQB4HkIkgRFdGxDkGUoBgOgqhAUBbLBAM4ZG43gFQW8M5Zl8KoKOSICvN0OnjFIEREQnHFkJESbDdz1mFNVlOTno0+fPoiIiADgeCvEYDCAMabFqqpCURQtVlUVoig2GSuKAsaYFje2b1qynxrGkiRBEAQtFkURHMdpsbMf9WN/9AkQIIrK9W0IMBgUqCqgKAIMBhmqykFRBBiNMhSFh6LwMBplyDIPVXXkns1+DWBVALO65Hwh3xmxai06REroanX8bciRkRCtVi02WK1gHAc5IgIGqxXq9WFwg82GGgCVogirLLus9yTHIUIQEC3LaONl7jGex/GCAvTp0wcGgyFk91Oo5h7gOF4YjSpsNhE8r0IUVdjtIgRBhSA4YwU8zyBJzhiQJEfuKfxeMKEIaPg4d+RaAMCzaiGekwtafIy4yBjKBQE7GwyJjwdgEEV0XrYMNcuXN5p7zTnu2U0mHPvySyQlJWnr9mQ/OeuGHp5nD/mCHhsbC7PZDMBRwNu3b9+sZQ2tXLkSyxp5BKdHjx5et/H6eYJbbLU2P2bMNb7VuR7GflmpqgLOewgUxfF1o7j+gViSGo/r3ZPgErekU6ND8w7YcOGr3YQGN/z+2hnUr/PNSb76K21QzAHgj4z98rovco8+McsrvjpENGXV9S8ALT9GNPL+9h/rb7ip3GtOp+rqgIyMm3egmSwWC2JiYny2vmAI+YI+bNgwvP7665g1axa2b9+OkSNHuizLzc1Feno6tm/fjjlz5jS5nueeew7PPPMMAMeZmNlshiRJ6NChAzh/TcjeCpjNZiQkJKC8vDzs50EmgUf5Qzzlq9xhjMFisWgjweEs5Av6wIED0aVLF6SlpaFbt25YvHgx5s+fj+zsbEydOhVbt25FWloaBg4ciOHDhze5noiICG1IGEDYn4mFmujoaDogE49R/hBP+SJ39FIPWu2nrRHf0NMnFZHAo/whnqLccRcWz6ETQggh5MaooBOvREREYOnSpS5vZxDSXJQ/xFOUO+5oyJ0QQgjRAbpCJ4QQQnSACjohhBCiA1TQCSGEEB2ggk4IIYToABV0QgghRAeooBNCCCE6QAWdEEII0QEq6IQQQogOUEEnhBBCdIAKOiGEEKIDVNAJIYQQHaCCTgghhOiAGOwGBANjDGazGRaLBW3btgXHccFuEiGEkCBgjMFiseC2224Dz4f3NW6rLOgWiwXt2rULdjMIIYSEiPLycnTt2jXYzfBKqyzobdu2RXl5ORISElBeXo7o6OhgNylsybKMffv2ITU1FaLYKtOJeIHyh3jKV7ljNpuRkJCAtm3b+rB1wdEq/4I4jtOKeHR0NBV0L6iqiqSkJLRr1y7sh6tI4FH+EE/5Onf08NZrqyzoxHd4nkd8fHywm0HCFOUP8RTljruQPyUuKSnByJEjMXr0aEyePBlXr17VlsmyjNmzZyMtLQ0LFy4MYitbL1mWkZeXB1mWg90UEoYof4inKHfchXxBv+uuu7B37158/fXXGDp0KD755BNt2WeffYauXbuioKAAtbW1KCwsDGJLWyee55GYmEjDpcQjlD/EU5Q77kL+N2EwGLS4trYWffr00b4vKirChAkTAACTJk26YUG32Wwwm80uXwCgKIr2b2OxLMsusaqqN4wlSXKJGWMuMWPMLQbgEquq6hI7z0CbihVFcYkD2SeO49CxY0coiqKbPulxP4VqnwCgU6dOUFVVN33S434KxT4xxtC+fXvwPO91n/Qi5As6AHz11VcYOHAgdu3ahV69emmvV1dXaze0xcTEoKqqqsl1rFy5EjExMdpXQkICAMeQPgCUlpaitLQUAHD06FGcPHkSAFBcXIyysjIAwP79+1FeXg4AKCwsREVFBQAgPz8flZWVAIC8vDxUV1cDAHJzc2GxWAAAOTk5sFqtkGUZOTk5kGUZVqsVOTk5AByP0uXm5mr9ysvLAwBUVlYiPz8fAFBRUaGdtJSXl2P//v0AgLKyMhQXFwMATp48iaNHjwasTxaLBdu3b9dVn/S4n0K1T6dPn8b27duxd+9e3fRJj/spFPv0ww8/4Msvv4QkSV71ad++fdALjjlPpcLAqlWroKoqlixZAgD4wx/+gMmTJyM9PR2bN2/G6dOnsWjRokb/r81mg81m0753PqpQVVWF2NhY7cxNEASXWJZlcBynxTzPg+f5JmNJkiAIghaLogiO47QYcJwR1o8NBgMYY1qsqioURdFiVVUhimKTsfPq2Bk31g9/9YnneVRXVyMqKgpGo1EXfdLjfgrVPjn/FqOioiCKoi76pMf9FIp9kiQJ1dXV6NChgzYi4Emfqqqq0KFDB9TU1IT9E08hX9BtNhsiIiIAANnZ2bDb7XjqqacAAJ988gkOHTqEFStWYN68eZgzZw6GDx/erPWazWbExMToYicSQgjxjJ5qQcgPuX/11VcYPXo0xowZg507d+Lxxx/H/PnzAQBTp05FeXk50tLSYDKZml3Mie9IkoQvvvhCe++LkJag/CGeotxxF/JX6P6ip7OyYHLOg0xz4hNPUP4QT/kqd/RUC0L+Cp2ENuese3o8GPfo0QN9+vRxuQt28ODB2L17d4vXdfr0abzzzjtu63felNkceXl54DgOGzdudHn9r3/9Ky5evKh9//LLLzd5L0lz7d69W7tZCQDOnTuHMWPGtGgdkiRhwYIFuPvuuzFgwAD069cPf/nLX1x+piX509jvkLReej72eIoKOvGKJEn49NNPdTvsZbPZsHbtWq/WIcuyT4rR2rVrkZGR4daehgXdFxoW9Ntuuw27du1q0Tr+9re/4fz58/j222/x7bff4vDhw5g4caLLz7Qkf7z5Herp0STioPdjjyeooBOviKKICRMm6PaDNZYtW4ZXXnkFtbW1bssuXLiA3/zmN+jfvz8SExNdik2PHj3w6quvYsyYMXj00Ufx5JNP4vjx40hOTsa9996r/dyWLVswYsQI3H777VixYkWT7aiurkZOTg42bdqEY8eO4dSpUwCA5cuX49y5c5g+fTqSk5Nx5MgRl//33XffIS0tDSkpKejXrx9WrlypLZs9ezb+/d//HePHj8edd96J+++/H3a7HUeOHMGaNWuwYcMGJCcnY/ny5Th9+jRuvfVW7f8WFRUhLS0NAwYMQFJSEj799FO3Nv/000/o0qWLlhuRkZG4++67teXvv/8+Ro0ahaVLl2L8+PEuoxX//d//jf79+2PAgAEYNmwYamtrG/0dHjx4EMOHD0dSUhKGDh2KvXv3AoDW3uXLlyMtLQ1vvvlmk7/b1q6iwoLDxRVNflVUWILdxEbp/djjERbiDh48yEaNGsXS09PZb3/7W2a3212Wb9q0iY0ZM4alpaWxffv2NXu9NTU1DACrqanxdZNbFVVVmd1uZ6qqBrspPte9e3f23XffsZkzZ7IVK1YwxhgbNGgQ27VrF2OMsRkzZrAlS5Ywxhi7cOEC69q1q5aD3bt3Z0888YT2e9m1axcbNGiQ2/qffvppxhhjFy9eZNHR0eznn39utC2rV69mM2bMYIwx9vTTT7Pnn3/erZ1OS5cuZf/5n//JGGPMbDYzq9XKGGOstraWJScnswMHDjDGGHv00UfZ8OHDWW1tLZNlmY0YMYJ9+OGHbutgjLGysjLWoUMHxhhjly9fZp07d2Z79+5ljDGmKAq7fPmyW5tLSkpY165dWb9+/djcuXPZpk2bmCzLjDHG9uzZwzIzM1ldXR2z2+3s66+/ZklJSYwxxtavX8+GDRum/W1WVVUxWZbdfoc2m40lJCSwbdu2McYYKygoYF26dGFXr15lZWVlDAD74IMPGv19kl8sXbGboc3yJr+Wrtgd7CY2ylfHHj3VgpC/Qo+Pj8f27dvx9ddf44477sDWrVu1ZefOncOnn36KnTt3Ij8/H0OHDg1eQ1up+hNG6NWKFSvw17/+FZcvX3Z5fceOHfjd734HwDHb2f3334+dO3dqyx977LGbvr+XlZUFAOjYsSN69uypTXzR0Nq1azFnzhwAwOOPP47169fXmw2L4XjpJZcrqgsXr+JwcQX27T+NrKxH0b9/fwwbNgxnzpxxuYq///77YTKZIAgChg4dql3530hRURH69euHESNGAHBMwdm+fXu3n7v77rtx6tQpvPnmm+jevTuWLl2qXVl/+umn+Pbbb5Gamoo777wTTz31FC5dugS73Y7PP/8c//Zv/6bdoBQbGwtBENzW//3338NoNGrD+KNGjUKnTp20CUYiIyMxc+bMm/antZs/JwU7Ps9ye33H51k4tGcu5s9JCUKrbq41HHtaKuTHKrp06aLFBoPBZXhl27ZtiIiIwD333IO4uDi8/fbbiIqKCkYzWy1RFJGZmanrYa+ePXti5syZjQ6JNyzY9b9vTi5GRkZqcf3JVuo7cuQIvvvuOzzxxBPa+isrK7Ft2zZMnjwZlqs2PDBrC8B3dvwH+2EAdmzc8i5g24rhw+5AcXExRFHE/fffD6vV2qLte8NoNGLs2LEYO3Ys5s6di7i4OFRVVYExhjlz5mDZsmXahCMtvbmJMdbo/3G+dsstt9ANU80QF9cWouh+bZeU2BkdO94ShBY1T2s49rRUyF+hO/3000/YsWMHpkyZor124cIFVFdX46uvvsKIESOwevXqJv8/zeXuvz5JkqS7Pjm/d8ZLlizBxo0bce7cOa1d48aNw9tvvw3AkYuffPIJxo4dq623fp+io6NRU1Pj0ienm/Xp3XffxX/8x3+grKwMp0+fxg8//IA///nPWLt2LWRZRlxcB/x1VToa+uqzhzB2dBfcM24ABEHAiRMn8NVXX2l9crazsX0WHR2N6upql33jNGzYMJSWlqKwsBCKosBut6OqqsqtH7t27UJFRYX2+qFDh9C+fXu0bdsWU6dOxYYNG3DmzBnIsgy73a5N6Tl58mS8/fbbMJvNkCRJW3ebNm1QU1Oj7ae77roLNpsNubm5YIxh7969uHjxIvr376/tv3DNvWD8PTWkqKHfJ+fsnzSXu0NYFHSz2YxHHnkE69atc/mwlnbt2mHMmDHgOA5jx47F8ePHm1wHzeXunz5dvXoVX331Fb788kvd9Mm5n2pra7U+HThwAAsWLEBFRQUOHDgAwHFD2q5du5CUlITRo0dj2rRpGDp0KCoqKrQDjbNPSUlJ6Nq1K+666y7ce++9OHnyJOx2u1uffvzxR5c+nThxAh9++CH69+/v0qeMjAxs374dW7duxby5j+Nvf30BqHsLUB19BYCePW7Bn/+8AuvXZ2Pw4MF44YUX0K9fP6iqCqvVip9//tltP1mtVuTl5eE3v/kN9u3bhz59+mD58uW4ePGiduC7evUqli1bhsWLF+Puu+/G3XffjT179rjtp2+++QaTJ09G7969kZiYiJUrV+JPf/oTzpw5g/T0dMyePRtTpkxBv379cNddd+G9994DAHTr1g3jx4/H8OHD0adPH0yaNAk2mw3nz59Hr169kJiYiJEjR0JVVXz00UdYsGABkpKSsHDhQixYsAC33HILrl69qhWHcMy9YBwjGjpz5qeQ7tOpU6ewc+dOyLJMc7lfF/ITyyiKgl//+td4+umnMW7cOJdlR48exf/8z/9g3bp1+H//7//hxIkTePnllxtdD83lTn3Sc58uX65Dpx6uz3hfKPsPdOoUFbZ90uN+CtU+VVVZ3fKn4seF6NI5Omz7RHO5h6BNmzbh97//Pfr37w8A+Ld/+zfk5eUhOzsbAPD888+jsLAQJpMJH3zwQaM35zRGT7MDBROjmb5CwqVL19wOyBdPP+PVe6CWigpcrahocnlUXBzaxsV5vH6A8idU+CN//M1XuaOnWhDydxPMnDnT7U7VBx54QIv/+Mc/BrpJpB5ZllFQUIAJEya4vB1Cwt/h7GzkL1vW5PL0pUsxuokRseai/CGeotxxF/IFnYQ2g8GAyZMnB7sZxA9S5s9HQloaPhg/3uX1rB07YIqNRZSXV+cA5Q/xHOWOOyroxCuqqqK6uhrt2rUDz4fFPZakmdrGxYFv5JGgzklJuKVjR59sg/KHeIpyxx39FohXFEXBgQMHmnz0hZAbofwhnqLccUdX6MQrBoPB7QM3CGkuyh/iKcoddyF/hX7o0CGkpaVh9OjRmDFjhssn6+Tk5GDEiBEYNWoUfv/73wexla2Xqqq4ePGiy8QjhDQX5Q/xFOWOu5Av6Deayz0xMRH5+fnYs2cPqqqqtAk/SOCoqoqSkhL6oyIeofwhnqLccRfyBb1Lly5o06YNAPe53Lt166Z933BZQzT1q3/6JAgCxowZo/VHD30K1/3UkC/6pDQyLaYv+8TzPMaOHauttzXsp1DtU0OhPvUrx3FIT0/XJpahqV/DoKA7NTaXu9OhQ4dQWVmJgQMHNvn/aepX//SptrYW5eXluupTuO6nhq5evep1n4qKitzWe+7sWZ/16cyZMzh79myr2k+h2qeGQn3q1x9//BGFhYVQVZWmfr0u5GeKAxwz+UydOhX/+7//izvvvNNl2c8//4wHH3wQn3zyCTre4FEamvrVP30CHH/kQ4cO1T65K9z7FI77yV9Tv169cAFv3Haby3qfPn8epg4dfNInVVXxzTffYOjQoTAajbrfT77uk9FoAM+rMBpVWK0iBEGFKKqw2USIogpBcMYKeJ7BbnfGgN0uwGBw9EOSrEAb1/wR7AuhyNGIiJChKDxkmUdEhAxZ5qEoPCIjZdjtPFSVR2SkBLtdgKryMJkk2GwiVJWDySTBahXBGGC3+3Y/2Ww2fPPNNxg5cqQ2UxxN/erngn7s2DHcfffdHv9/RWl6LverV68iMzMTb731FhITE1u0Xj1N90eIv6buvHbpEv7SqZPLa89cvOiz59CJd3w3W+41t4KO2mcA+G7q11C9dNRTLfDbkPu0adPw7LPPYuHChXj22Wc9Xs/HH3+MwsJCvPLKK8jIyMBHH32E+fPnAwDefPNNnDp1Cr///e+RkZGBr7/+2lfNJ82kqirOnDlDN6YQj1D+EE9R7rjz23PoTzzxBMxmM+Lj47Fw4UKP13Ojudyfe+45PPfcc161k3hHVVWcPXsW8fHxNFsTaTHKH+Ipyh13fvstTJw4EYmJiQiDt+iJF0RRxIgRI274hAEhTaH8IZ6i3HHn199E37590bdvX1RWVmLr1q24cuWKVuC9GYYnoUNRFJSVleH222+HIAjBbg4JM5Q/+hWFCrTFLx+/W3G4wXIvP36XcsddQE5tJk+ejKysLPTs2TMQmyMBxBjDlStX0KNHj2A3hYQhyh/9GoJsjMEvH7/77iDX5d5+/C7ljruAFPQuXbpgwYIFHv9/i8WC8ePH49ixY/jmm29c7mg3m814+OGHYbFYkJKSgtdff90XTSbNJIoihgwZEuxmkDBF+aNfBzAfp5GGx+Cfj9+l3HEXkDsJZs2ahWnTpmHZsmVYvnw5li9f3qL/bzKZ8Pnnn2P69Oluy7Kzs3Hfffdh165dqKur09UkAeFAURScOHGCPvGIeITyR7+uIg4XkOT2euekJMSlpHg13A5Q7jQmIFfoK1aswFNPPYX4+HiP/r8oik1OGvPjjz9qU0empKSgoKAAqampHreVtFxdXV2wm0DCGOUP8RTljquAXKF3794dc+bMwcSJE7UvX+nbt682XeCOHTu06Qcbornc/dMnnueRnJwMVVV106dw3U8NhcNc7hzHYeDAgWCMtZr95Os+8byKyEhHewVBRUSEIxbF+rECo7F+7GivwaBos8U1JIiO1yMiZIiiqsWC4IgjI2XwvDOWtNhkksDzTHu9IV/tJwDo37+/NiMczeUeoIJeV1eHiRMnYvHixXj22Wd9eof73LlzcezYMYwfPx5RUVHo0qVLoz9Hc7n7p0/Xrl3Dd999p6s+BXI/cRwwb14p5s0rBccBCxcexcMPnwTHAc8/X4z77y8DxwErVuzHxInl4Djg9dcLkZZWAY4D3n47H4MGVaLBZG4AgIEpV8Fxjj517GjFLbc4+nTLLTI6dnT0ieOA7t0t2Lw5FxwH9OtXjQ0b8sBxwKBBlVi3zv9zuZeUlGDv3r0hvZ9a0qdA515SUiVWrXL0KTW1AsuWOfqUkVGOJUscfcrMLMPChY4+TZ9+EvPmOfr08MOlePjhxudyHz/eMZf7kiX7kZHh6NOyZYVITXX0adWqfCQlOfq0enUeevd29Gnt2lzExzv69E72Drf12nw4l/vu3buhKArN5X5dQOZyb2wGt9GjR7d4PbNnz8aiRYuanOZ17ty5ePHFFxu965HmcvdPnziOQ2lpKXr37o2IiAhd9CmQ+0kU68+nLcBoVKCqgCwLMBplqCoHWRaaMZ92ndvUnZz1P8DUKJf5tE0mGXV1IjjOcYVVV2cAzzNERDhjFUajAqvVEbczXMDTNv/N5c4Yw/fff4/evXvDYDCE7H4K1dwL9bnc20eew9NW17da/+PCBUTExnq9n+x2O06cOOEytXhrn8s9IO+hM8aQkZGhff/hhx+2eB2ZmZk4cuQIvv/+e8yfPx9FRUXIzs7GkSNH8PTTT0MQBMyaNavJRxgiIiK0glOf8/nF+s8x1o/rT1rQnNhgMHgUcxynxc5Ea27cVNsD1af+/fujPj30qanYH32SpF9iu71+/Et7bbbGY6u16T9hpjom+q6r+6UfzpixX2JV5erFPKxW/pfY5r5+nue136Uv9lPDE/RQ3U+hmnv195miOIotAMiyowg74l/aWz+un3sNKdd/rjm5Z7W651jD153q98Ob/WQ0GpGU5H7TnTf7KdwFpCdr1qxBREQEhg8fjuzsbBQXF+Ohhx5q0TqcQzROs2fPBgAkJydj9+7dPmopaSlFUXD06FEkJSXR5A6kxSh/iKcod9wFpKBv2LABDz30EOLj4yGKItasWROIzZIAMZlMwW4CCWOUP8FmAbhL7i9zFwCYABYFoG2gG9UslDuu/FrQFy9erH1ObefOnbFlyxbMnDkTzz77LFatWuXPTZMAEQQBffr0CXYzSJii/AkBhsOAId/9ddMHjn+ldEBq+T1P/ka5486vBX3KlCku38+YMcOfmyNBIMsyiouLMXDgQF29F6UHF9EJHVHr1TquAfhLwxcbu6XeQ7LRiOI9eyh/gklKAeQ7m17OogLXlhagY487v/4WRo0aha1bt0IURUyZMkV7n+Mf//iHPzdLAojjOMTGxmojMYS0BKeqlD9B1xZgoTmkfiN07HHn1+fQs7KyUFxcjOLiYmRkZODUqVMAgLfffrtF67FYLEhNTUVUVJT23LjT7t27kZCQgIyMDIwbN85nbSfNIwgC7rjjDrophXhEkGXKH+IROva482tBv3DhAlasWIGXX34ZH3zwAR5//HHs2rWrxeu50VzuAPDAAw9g9+7d2Llzp7dNJi0kyzIKCwt1NdsSCRw5IoLyh3iEjj3u/FrQFUWB1WoFAHTr1g2fffYZ/ud//gffffddi9Zzo7ncAWDLli1IS0vDG2+80eTP0NSv/ukTx3G47bbbtMk49NCnQO+n+tNvGo0KRNEZy1rcnOk3G2LXn9eVTCYwjgNzxgAYx0G6focw43ktVnkeUmSkFiuNzN2gCgLk668rogjZaNRixRkbDFCuP1+sGI1QnJOk1I8jIgCOQ3x8vDZhSCjvp1DNPV9N/epN7gVj6lfGGOLi4sDzPE39ep1fC/pf//pXrXACQNu2bbF161a8+eabPtvG4MGD8f3332Pnzp3Ytm0bDh061OjP0dSv/umT3W5HfHw8tm3bpps+BXo/1Z9+c968o5g+3dGnhQuLkZnp6FNzpt9s6Or1T7PK2bQJ1vbtIZtMyNm0CbLJBGv79sjZtMnRp/h45K5d6+hT797IW73a0aekJBS99JLbes+NHIn9S5Y4+pSZieKFCx19mj4dR+fNc+ynhx9G6cMPO/bTvHk4eX10rXjhQpRlZjr205IlODtqFLp3745vvvkm5PdTqOaer6Z+deYeA4c9C59Haeb9YOCQt2QFfsiYCAYOuctex5nUNDBw+GLV26hIGgQGDltXb8Cl3v3AwOEfazfjSnx3MHCNT/3ap4+jTxwHS/fuyN28GeA4VPfrh7wNGwCOQ+WgQch/+22A41CRlobC118HOA7lEydi/4oVAMfhzPTpqKysBM/zNPXrdQGZ+rW2thZt2rTxej03m/r17bffRkREBObMmeO2jKZ+9U+fAMeBa+jQoYi8fmUX7n3Sy9SvF6yr0Em9Cslkgmi1AoxBNpkg1tUBHAc5MhKGujownoccEQFDXZ3jqtxohMFqhcrzuGow4I16fzcA8LQgwCSKEG02KKIIxvMQ7XbHlTfPQ7DbtatzQZIcV+2qCkGWIRuN4JxxRARUQcA3X36JoUOHwmg0hux+CtXc893Ur7/kniSLbvuJVxTwzliWwSsK5MhI8HY7eFWFFBkJwRmbTBBtNnCqiprISPzt+iit038AiDCZbpp7qtEI0WqFKghQr+ebKopQBQGizQbbLbfgmy++wMiRI7Ub42jqVz85fvw4AMewyN///ne/PXduNpu1nVBQUIAnn3yy0Z+jqV/90ydVVXHHHXcgIiJC+6MK9z7dKA6nqV+560OKhnofManFjGkxp6pazKsq+OsHYF5VITQo5gDAKwpE54Gy3nClSyz9MtQq2O1aLNaPbTaogoBevXppxRwI3f0Uqrnnq6lf6+dew/3UaFyvUBvqx/XyTWxQzAGAq/czN8o9LVYU8NfzjZdl8NfzzGC14o477nD5XQA09atfvPDCC/jNb34Dxpg2tOGNpuZy//jjj/HOO+9AFEWMHDkS6enpPmg9aS6e5z3+nHtCeEWh/CEeodxx57chd+cD/wBw6tQp9OrVyx+b8ZjZbEZMTIwuhlmCSZZl5OfnIz09XVdnuoHiu0dor7kNuV+s/ZNfJpZ5BsAtXq31F3JkJPK/+ILyx0P+eASbwXcr9Wf++Cp39FQL/PYXNHDgQMiyjM2bN6OwsBBVVVVo3749Ro4ciWnTptEfr07wPI/ExESXIS9Cmou32yl/iEcod9z5tao+9thj6Nu3L2bPno2YmBhUV1dj+/bteOyxx/D+++/7c9MkQHieRycfTgVKWhdeVSl/iEcod9z5taCfOXPGrXAPGjSI3ufWEUmSkJeXh7Fjx7rcyENIc0iRkcjbvp3yh7QY5Y47v45VpKamYtasWdi4cSP++c9/YuPGjZg1axZSU1NbtJ4bTf166tQpDBw4EJGRkbh69aovm0+aQRAEDBkyhKZfJB4R7HbKH+IRyh13fr1Cf+2113DkyBEUFRWhvLwcsbGxeOaZZ5CcnNyi9Tinfl28eLHbsri4OOzevRv33Xefj1pNWoLnebRv3z7YzSBhildVyh/iEcodd36/My05OdmtgG/YsAGzZs1q9jpuNPWrLyasIZ6TJAm5ubmYMGECDXuRFpNMJuR+8QXlD2kxyh13fh1yP378uNvXsWPHkJ2d7c/NNormcvdPnwRBwKhRo7T+6KFPNJd74OZy5xUFaWlp2u8qlPdTqOaeP+Zyb7if1Prx9SFuOTISqjPH6scmk5Z78vVcqs+Zh8CNc8/5f+vnmyqKWswpCkaMGKHN6Edzufu5oA8bNgx//vOf8dprr2lff/7zn3HmzBl/brZRNJe7f/pks9nQpk0bfPnll7rpE83lHri53H9OT0d0dDSKiopCfj+Fau75ei73xvZTeUaGo0/LlqHi+j1Q+atWoTIpydGn1atR3bu3o09r18JyfcKXHY1cvNliY5uVe/nXZxetSE1F4bJljv2UkaHl3ulJk/DDDz+A4ziay/06v87lPmzYMHzxxRfo0KGDy+uTJ0/GF1980eL13Wgu94yMDHz++eeIiopq9P/SXO7+6RNjDF9++SXuuecemK6faYd7n8JtLneb/RoYuwyYPnDJ+a9sf0d7tRa3RkpIsF4OybncFYMB2zZuxIQJExARERGy+ylUc681z+VubdsW299/H5mZmS7T9rbmudz9WtCvXr2KNm3a+OTBf+fUr927d3eZ+vXKlSv47W9/i0OHDiE5ORnPPvssfvWrX910fXqaHSiYGGOwWq2IjIzU5nInzeeTX5nha8CQ3+TipdIuvCzt8mjV/p4pjnEcrNeuUf54KJRnirMAuATggwavZwEwAYgC0NaL9fsqd/RUCwLyaWuhSE87MZicVw3OqwzSMr75lVkA7pdHNg9hkMvSOGZBHDx7pNPvBR2AbLdT/ngolAv61wCaPs0E0gGM9mL9vsodPdUCmn+VeMX5fl9mZibdaRo0bQH2y7VOCiqC2JaWkU0myh+dSgFw5w2WN/7maPNR7rijK3QdnJUFE12heyeUr7AAukIPdaGeP/5EV+juaFZ74jU9PfZBAozjKH+IZyh33IRFQV+0aBHS0tKQlZUFu92uvV5XV4cpU6Zg9OjRuOeee1BVVRXEVrZOsiwjNzeX/rCIR+TISMof4hHKHXchX9CLi4tx/vx5FBQUoF+/fti8ebO27Msvv0RiYiK+/vprzJgxgz7BLQgMBgPuu+8+eg9LhywALjTy+gUAFdeXe8tQV0f5QzxCueMu5At6UVERJkyYAACYNGmSNhEEAPTu3Ru1tbUAHBMtNDU9LPEfxhjMZjNa6a0YunYY7o8c4fpr715f7i3G85Q/xCOUO+5CvqBXV1drNyrExMS4DKv36tULJSUlSExMxIYNG/DrX/+6yfXQ1K/+6ZMkScjPz0ddXZ1u+hSuU79GRkpaXH/6TU+nfk3mODxmNGIugDk8j8cMBkcsCHjMYEAKvJ/61d6mDQoKCmC1WkN+P4Vq7oXy1K/eTDt8s6lfbbfcgvz8fO33T1O/hkFBj42N1YpvdXW1y6fr/P3vf0dGRgZKSkqwbNkyLF++vMn10NSv/umToiiYOHEivvrqK930KVynfl29Og+9e1/vU73pNz2d+tU2YAD+9ec/Iw4Ahg3Dj6+8gjgA8pgx+On559EW3k/9en7YMEyePBkHDhwI+f0UqrkXylO/ejPt8M2mfv15zBh07twZBoOBpn51YiHu8OHDLCsrizHG2IoVK9iHH36oLXvrrbfYm2++yRhjbOfOnWz+/PlNrsdqtbKamhrtq7y8nAFgVVVVjDHGZFlmsiy7xZIkucSKotwwttvtLrGqqi6xqqpuMWPMJVYUxSWWJOmGsSzLLnFj/fBXn2RZZpWVlcxqteqmT4HcTwBjBoPMDAZHbDTKTBSdsaTFERESE0VFiwXBEUdGSoznnbGd8bzCGMDsJhNTef6XmOOY6owBpnIcs5tMjAFM5XktVnie2SMjtVhyxoLApIgIRyyKWiyLIpOMRi2WnbHBwGSDwREbjUwWRcYAJtWPIyKYZDSyy5cvM5vNFtL7KVRzD2CM5xUWGSkxgDFBUFhEhCMWxfqxzIzG+nHTudfYflLqx4LgiCMjmeLMsfpxgHLPbjSyixcvMkVRvNpPly9fZgBYTU0NC3dh8Rz6okWLsG/fPnTr1g3r1q3DU089hezsbJjNZsycORPXrl2DLMt47733cOedN5rK4Bd6evYwmCRJQl5eHsaOHUs3p3igNT9HDDiGavO2bqX88VBrzh9f5Y6eakFYFHR/0NNOJOGrNR+QNa3zEOQTrT5/fJA7eqoFIf8eOgltqqri4sWL2g0mhLSEyvOUP8QjlDvuqKATr6iqipKSEvqjIh5RjUbKH+IRyh13NOSug2EWEr5a/ZApQEPuXmj1+UND7i7oCp14RVVVnD17ls6SiUdUQaD8IR6h3HEXFgW9qbncnVauXInBgwcHoWVEVVWcOnWK/qiIR1RRpPwhHqHccRfyBf1Gc7kDjskWnJPDkMATRRHp6ekQr88kRUhLiDYb5Q/xCOWOu5Av6Deayx0A3njjDfzud7+76Xpo6lf/9ElRFJw+fRo2m003fQr0fgrVqV+bM/2mt1O/ypGROHPmDOx2e8jvp1DNvdY69asUGYmysjKoqkpTv14X8gX9RnO519TU4LvvvsOIESNuuh6a+tU/faqtrcXPP/+Mbdu26aZPNPVr86ff9Hbq15/GjMHZs2dRVFQU8vspVHOvtU79WpaZiRMnTkBVVZr61cnfU9F566233mJ///vfGWOMHThwgP3ud7/Tlr388sssPz+fMcbYoEGDbrgemvqV+hSKfWrtU78qohgW+ylUc681T/0qi6JP9hNN/RpAxcXFeP3117Fx40a8+uqr6NmzJ2bOnAkAeOSRR3Dp0iUAjqH55557Dkuun73djJ4eVQgmRVFQVlaG22+/HcL1oTjSfK39sSNFFFFWWkr546HWnD++yh091YKQH3IfOHAgunTpgrS0NBw/fhzTpk3D/PnzAQDvv/8+tm3bhm3btqF3797NLubEdxhjuHLlCn0mMfEI43nKH+IRyh13IX+F7i96Oisj4as1X2FpWuchyCdaff7QxDIuQv4KnYQ2RVFw4sQJ7e5RQlpCEUXKH+IRyh13VNCJ1+rq6oLdBBKueJ7yh3iGcscNDbmHyDBLRYUFFeevNrk8rksU4uLaBrBFJBBa/ZApQEPuXmj1+UND7i7CYoqdRYsWYd++fejWrRvWrVsH4/XJK2RZxty5c3Hq1CmkpKTgjTfeCHjbfPYHZSgCDDd4HlJKBaQJXm3CH8dNRVFQWlqKvn370l3KpMUUgwGlJSWUP6TFKHfchfyQ+42mfv3ss8/QtWtXFBQUoLa21m0WOUIIIaS1CPkr9IZTv65btw4PPfSQtmzKlCnassLCwmbNGheSpOGA3L/p5SwqcG1pAUEQkJiYGOxmkDAlSBLlD/EI5Y67kC/o1dXVuO222wC4T/16o2lhG7LZbLDZbAAcz06fO3cOAHDlyhUAv8zpLgiCSyzLMjiO02Ke58HzvBYDPCIiZNjtPBjjEREhwW4XwBiPyEgJNpsIxjhERkqwWh2/7shIuUFsAMcxREQYr8cqjEYFNpszVmGzMfB8NURRhd0uQhBUCIIzVsDzDJLkjAFJEnAbX4wO+B6KykHkGfa+A/AcB0VVwXEcOvXrhw79+rn1qWEsSRIEQdBiURTBcRwkSQLHcSgpKUGfPn0QcX2OZVmWYTAYwBjTYud8y85YVVWIothkrCgKGGNa3Ni+acl+akmfnB/2IMuyS+yPPgHCL/NnywIMBgWqCiiKAINBhqpyUBQBRqMMReGhKDyMRhmyzENVG889M3PMcy3abOAYc8RWq2MbDWKD1QrGcZAjImCwWqFyHBSjEQabDSrHQTUaIdpsUHkeqihCtNuhCgJUQYBot0MRBDCehyhJUAQB4HkIkqTNAy7IsmNOd1WFoCiQDQZwzthoBON5HC8oQJ8+fWAwGEJ2P4Vq7gH1jxEieF5t0TGisdwzK3DbT7yigHfGsgxeVSFHRIC328EzBikiAoIzDlDu2U0mHPv6ayRdn37W0/3krBt6uJ0s5At6bGys9kEq1dXVaN++fbOWNbRy5Uosuz4fcH09evTwuo3XzxPc4uu526yYMdfYuZ76saoCzk+PVRTH143icypwzrkhFRg135PeEX+r/9kQ1z9vwy2u/6nB9ePGci8GCH7ytaRT6ekgnvP1boqp/w3QsuQDApd7dXXA9TnmfcFisSAmJsZn6wuGkC/ow4YNw+uvv45Zs2Zh+/btGDlypMuy3NxcpKenY/v27ZgzZ06T63nuuefwzDPPAHCciZnNZkiShA4dOoDzx62irYTZbEZCQgLKy8vD/g5REniUP8RTvsodxhgsFos2EhzOQr6g15/6tVu3bli8eDHmz5+P7OxsTJ06FVu3bkVaWhoGDhyI4cOHN7meiIgIbUgYQNifiYWa6OhoOiATj1H+EE/5Inf0Ug9a7XPoxDf09AwnCTzKH+Ipyh13If/YGiGEEEJujgo68UpERASWLl3q8nYGIc1F+UM8RbnjjobcCSGEEB2gK3RCCCFEB6igE0IIITpABZ0QQgjRASrohBBCiA5QQSeEEEJ0gAo6IYQQogNU0AkhhBAdoIJOCCGE6AAVdEIIIUQHqKATQgghOkAFnRBCCNEBKuiEEEKIDojBbkBzWCwWjB8/HseOHcM333yDxMREbZksy5g7dy5OnTqFlJQUvPHGGzddH2MMZrMZFosFbdu2Bcdx/mw+IYSQEMUYg8ViwW233QaeD+9r3LAo6CaTCZ9//jkWL17stuyzzz5D165dsX79esybNw+FhYUYMWLEDddnsVjQrl07P7WWEEJIuCkvL0fXrl2D3QyvhEVBF0URHTt2bHRZUVERpkyZAgCYNGlSkwXdZrPBZrMBcJyRHT9+HP369cPp06cRGxsLRVEAAIIguMSyLIPjOC3meR48zzcZS5IEQRC0WBRFcBynxYBjVKF+bDAYwBjTYlVVoSiKFquqClEUm4wVRQFjTIsb64e/+gQA+/btw6BBgxAZGamLPulxP4Vqn1RVxYEDBzBo0CAYjUZd9EmP+ykU+2Sz2XDgwAEMGzZMG2X1pE9VVVW4/fbb0bZt24ZlI+yE9/gCgOrqakRHRwMAYmJiUFVV1ejPrVy5EjExMYiJiUG7du3Qr18/AMBPP/2E6OhonD17FmfPnkV0dDROnz6NCxcuIDo6GqdOncLly5cRHR2NEydOoKamBtHR0SgpKcG1a9cQHR2NI0eOwG63Izo6GgcPHoSqqoiOjsY333wDjuMQHR2NPXv2wGg0ok2bNtizZw/atGkDo9GIPXv2IDo6GhzH4ZtvvkF0dDRUVcXBgwcRHR0Nu92OI0eOIDo6GteuXUNJSQmio6NRU1ODEydOIDo6GpcvX8apU6cQHR2NCxcu4PTp0wHrU2RkJBITE/HNN9/opk963E+h2ieLxYKkpCScOHFCN33S434KxT5VV1cjKioK7dq186pPpaWlAKCLt145xhgLdiOaa/bs2Vi0aJHLe+h/+MMfMHnyZKSnp2Pz5s04ffo0Fi1a5PZ/61+hA4DZbEZCQgKqqqroCp36RH2iPlGfWmmfqqqq0KFDB63Ah7OwL+iffPIJDh06hBUrVmDevHmYM2cOhg8fftN1mc1mxMTE6GInBpMsy8jPz0d6err2x0pIc1H+EE/5Knf0VAvCZsg9MzMTubm5mDdvHtavX4/58+cDAKZOnYry8nKkpaXBZDI1q5gT3+F5HomJiWF/dygJDsof4inKHXdhdYXuS3o6KyOEEOIZPdUCOrUhXpEkCdu3b9fueCekJSh/iKcod9xRQSdeEQQBQ4YMgSAIwW4KCUOUP8RTlDvu6C4U4hWe59G+fftgN4OEKcof4inKHXd0hU68IkkSvvjiCxr2Ih6h/CGeotxxRwWdeEUURaSlpenykaMePXqgT58+kGVZe23w4MHYvXt3i9d1+vRpvPPOO27rLykpadb/FUURycnJGDBgAAYPHoxdu3a1uA0AcO7cOYwZM0b7/tNPP0Xfvn2RnJyM7777DsnJyairq2vROpcvX47ExEQMGDAAffr0aXSK5qY0zJ/q6mqsWrWqRdsnrZOejz2eooJOvOKcEUoPsyw1xmazYe3atV6tQ5blRgt6S7Rr1w5HjhzBt99+i//6r//CjBkz4MkDKrfddpvLycCaNWuwfPlyHDlyBP3798eRI0dgMpmavb4tW7Zg+/btOHDgAL799luUlJTg4Ycfbvb/b5g/3hT0+ideRP/0fuzxBBV04hVJkvDpp5/qdthr2bJleOWVV1BbW+u27MKFC/jNb36D/v37IzEx0aVg9+jRA6+++irGjBmDRx99FE8++SSOHz+O5ORk3HvvvdrPbdmyBSNGjMDtt9+OFStWNKtN99xzDyorK3H58mU8/PDDGDx4MJKSkjBlyhRcvHhR+7l169a5XNWfPn0ap0+fxq233goAWLBgAQoKCvCHP/xB+/wDjuNw9epVAEBpaSkmTpyIpKQkJCUlYc2aNW5t+emnn3Drrbdq8/iLoogBAwZoy7dv345Ro0Zh0KBBSE1NRX5+vkv7BgwYgNtvvx2DBg3C6dOn8eSTT6K6uhrJyckYPHgwAOCHH37A+PHjkZSUhOTkZGzdulVbB8dxeP3115GRkYHnnnuuWb8/og96P/Z4hLVSNTU1DACrqakJdlPCmqqqrLa2lqmqGuym+Fz37t3Zd999x2bOnMlWrFjBGGNs0KBBbNeuXYwxxmbMmMGWLFnCGGPswoULrGvXrmzfvn3a/33iiSe038uuXbvYoEGD3Nb/9NNPM8YYu3jxIouOjmY///yzWzvKyspYhw4dtO/fffdd1q1bN8YYY5cuXdJeX7lyJfvd736nba9Xr17s3LlzjDHGrl27xq5du+a2rtGjR7PPPvtM+x4As1gsTJIk1rt3b/bRRx9py+pvy6miooL17duX9ejRgz3yyCNs7dq1rLa2ljHG2KlTp9jw4cO1v7GTJ0+y2267jdntdq19Z8+eZbW1tezq1auNto8xxoYOHcqys7MZY4z961//Yu3bt2c//fST1t5XX33VrV1E/3x17NFTLaA3H4jX9P4e1ooVK5Camoonn3zS5fUdO3bg22+/BQB06tQJ999/P3bu3ImhQ4cCAB577LGbDgdmZWUBADp27IiePXuirKwM8fHxbj/nvGoFgPj4ePzzn/8EAHzwwQd4//33YbPZUFdXhy5dugAAvvjiC8yaNQtxcXEAgDZt2rSoz99//z1kWcaMGTO015xX9vV16dIF3333Hfbt24e9e/firbfewptvvol9+/Zh27Zt+OGHH5Cenu7yf8rLy13a55zju7HflcViwZEjR/D4448DAHr37o1Ro0Zhz549mDlzJgBgzpw5Leob0Q+9H3tain4bIaKiwoKK81ebXB7XJQpxcaH38X6yLCMnJweZmZkwGAzBbo5f9OzZEzNnzmx0SLxhEar/fVRU1E3X7RyqBn75AInGON9Dr2/Pnj1YvXo1CgsL0bFjR/zzn//E8uXLb7pNXxMEASNGjMCIESOwYMECdO7cGSUlJWCMYdKkSdiwYUOT//dm+cOu3yfg7e+Z6E9rOPa0FL2HHiKy3zuMQaPebfIr+73DwW5io0RRRGZmpu7PlP/rv/4LGzduxLlz57TXxo8fr71vfunSJXzyyScYO3Zso//f+dGPvnTlyhVER0ejffv2sNvtyM7O1pZNnToVGzZswPnz5wEAtbW1jd4H0JS77roLRqMR//jHP7TXKisr3X7u4MGDOHXqlPb9iRMnIEkSEhISMGHCBGzbts3lTv79+/e7tK+yshKZmZmw2+2ora1FdHQ0amtrtROb6OhoJCcn4+9//zsA4NSpU9i7dy9GjhzZ7L4QfWotx56WoIIeIubPScGOz7PcXt/xeRYO7ZmL+XNSgtCq5mkNdxd37NgRCxYsQEVFhfba3/72Nxw9ehRJSUkYM2YMXnjhBW24vaGkpCTcddddSExMdLkpzhu/+tWvcMcdd6BPnz6YOHGiNiQPAOnp6XjxxRcxYcIEDBgwAKNHj8alS5eavW5RFPHpp5/inXfeQf/+/ZGUlIQtW7a4/dzly5eRlZWFPn36YODAgXj88cfx4YcfomPHjujduzc2btyIuXPnYsCAAejbty/eeOMNl/ZNnDgRgwYNQkZGBi5duoT27dsjKysL/fv3126K++CDD7Bx40YMGDAA06ZNw7vvvouEhATvfnlEF1rDsacl6MNZQmhC/kuXrqFTj7+4vHbx9DPo2PGWILXo5iRJomEv4jHKH+IpX+VOKNYCT9FYBfGKwWDAfffdF+xmkDBF+UM8RbnjjobciVcYYzCbzR5NckII5Q/xFOWOOyroxCuyLKOgoIDeyyIeofwhnqLccRcWQ+6LFi3Cvn370K1bN6xbtw5GoxEAUFdXh9/+9rewWCwwGo346KOP6NN3AsxgMGDy5MnBbgYJU5Q/xFOUO+5C/gq9uLgY58+fR0FBAfr164fNmzdry7788kskJibi66+/xowZM/D+++8HsaWtk6qqqKqqgqqqwW4KCUOUP8RTlDvuQr6gFxUVYcKECQCASZMmobCwUFvWu3dv7dna6upqdOzYMShtbM0URcGBAwegKEqwm0LCEOUP8RTljruQL+jV1dXaowQxMTGoqqrSlvXq1QslJSVITEzEhg0b8Otf/7rJ9dhsNpjNZpcvAFoyKIrSaCzLskvsPBtsKpYkySV23rDhjBljbjHguMFDauS9IFVVtfeImooVRXGJA9knURS1E65G+3Q9VlXVJQ7lPt10P1GffNYnQRAwceJEcBynmz7pcT+FYp94nse4ceNgMBi87pNehHxBj42N1YpvdXW1y3vkf//735GRkYGSkhIsW7bshtNerly5EjExMdqXc2IK5yxWpaWlKC0tBQAcPXoUJ0+eBOAY8i8rKwPgmOWqvLwcAFBYWKhNMpKfn6/NopWXl4fq6moAQG5uLiwWCwAgJycHVqtVm65QlmVYrVbk5OQAcMxZvXu3+2dcX66q0j6hqqKiQhuhKC8v12bdKisrQ3FxMQDg5MmTOHr0aMD6VFtbi4qKiib7lJubq+27vLw8AI4Zx0K5TzfbT9Qn3/XpzJkzuHjxoq76pMf9FIp9+vHHH1FUVARVVb3q0759+6AXIT+xTHFxMV5//XVs3LgRr776qjavNgC8/fbbUBQFv//975GXl4ePP/640Y94BBxX6DabTfvebDYjISEBVVVViI2N1c7cBEFwiWVZBsdxWszzPHie12JB4BEZKcNu56GqPCIjJdjtAlSVh8kkwWYToaocTCYJVqsIxgCTSUZdnQiOAyIjZdTVGcDzDMYIM6zc31zazVmfRoTRBKtVhCCoEEUVNpsIUVQhCM5YAc8z2O3OGLDbBRgMjn5IkgCjUUFtbfP61DCWJAmCIGix84M0nGfT+fn5GDFihDYvuSzLMBgMYIxpsaqqUBRFi1VVhSiKTcaKooAxpsWN7ZuW7KeW9Mk5laTzQ0OoT/7rk6qq2LNnD0aMGAGj0aiLPulxP4Vin2w2G/bs2YPRo0drc/t70qeqqip06NBBFxPLhHxBB9zvcn/qqaeQnZ0Ns9mMmTNn4tq1a5BlGe+99x7uvPPOZq3TV7MD3eTDtFroGtDGdaY41D4DwDczxYX+niaEkMDS00xxYVHQ/YEKum+oqoqKigrExcWB50P+HRwSYih/iKd8lTt6Kuj0F0S8oqoqTp06RY+OEI9Q/hBPUe6483tBP3bsmL83QYJIFEWkp6fTRxgSj1D+EE9R7rjz229i2rRp6NWrFw4fPoyUlBSsWrXKX5siQaSqKsrLy5GQkEBDpqTFKH+Ipyh33PmtoD/xxBMwm82Ij4/HwoUL/bUZEmSqquLs2bOIj4+nPyrSYpQ/xFOUO+78VtAnTpyI0tJSnD171l+bICFAFEWMGDEi2M0gYYryJ/gqKiyoOH+1yeVxXaIQF9c2gC1qHsodd35986Fv377o27cvKisrsXXrVly5ckWbQejZZ5/156ZJgCiKgrKyMtx+++0QBCHYzSFhhvIn+LLfO4xlf8xvcvnS59Px8gujA9ii5qHccReQcYrJkyejtrYWPXv2RK9evdCrV69AbJYEAGPM5USNkJag/Am++XNSsOPzLLfXd3yehUN75mL+nJQgtOrmKHfcBeT2wC5dumDBggWB2FQYswDcJfeXuQsATACLAhCaw15DhgwJdjNImKL8Cb64uLYQRfdru6TEzujY0TdzYPgD5Y67gBT0WbNmYdq0aUhKStKm6HvppZcCsenwYTgMGBoZ9jJ94PhXSgek0Bz2OnnyJHr37k3DXqTFKH+Ipyh33AWkoK9YsQJPPfUU4uPjA7G58CSlAPINpq1lUYFrSwvV1dUFuwkkjFH+EE9R7rgKSEHv3r075syZE4hNhbG2AAu9IfWbEQQBAwcODHYzSJii/PGOT6eebuP6badOPlw3fD/1NOWOu4AU9Lq6OkycONFlyJ0mmtEHRVFQWlqKvn370rAXaTHKH+Ipyh13ASnozz//fCA2QwghhLRaASnojDFkZGRo33/44YeB2CwJAEEQkJiYGOxmkDBF+UM8RbnjLiDPoa9ZswZFRUUAgOzsbOTnNz2JAQkviqKguLgYiqIEuykkDFH+EE9R7rgLyBX6hg0b8NBDDyE+Ph6iKGLNmjWB2CwJEJPJFOwmkDBG+RNs4TkHBkC50xDH/DjNzuLFi7Wb4K5du4ZPP/0UM2fOBMdxLbopbtGiRdi3bx+6deuGdevWwWg0AgB2796NRx55BL169YIgCNi5c2ez1+mrD7X36V2mfkYTKhGiLz45/hi+bnwODCcfzYERqscfX9WCUODXK/QpU6a4fD9jxowWr6O4uBjnz59HQUEBXn31VWzevBkPPfSQtvyBBx7An//8Z6/bSjwjyzKKi4sxcOBA+lxi0mKUPyEgTOfAoNxx59f30EeNGoXKykpUV1dj1KhRGD16NEaPHo2LFy82ex1FRUWYMGECAGDSpEkoLCx0Wb5lyxakpaXhjTfe8GnbSfNwHIfY2FhtJIaQlqD8CQVtARbX9FeIDrdT7rjza0HPyspCcXExiouLkZGRgVOnTgEA3n777Wavo7q6WhsGiYmJQVVVlbZs8ODB+P7777Fz505s27YNhw4danI9NpsNZrPZ5QuAdkOFoiiNxrIsu8SqqrrFkZEyeN4ZS1psMkngeabFHMcAMJhMEgAGjnPGAM/Xj1VERtaPZQCAIKiIiHDEolg/VmA01o8d7TUYFBgMjthobFmf6seSJLnEzndpJEkCz/Po1asXVFUFYwyMMUiSo+31Y1VVXWJZlm8YK4riEvtiPzW3T/X7QX3yb584jsMdd9wBxphu+hTo/eTLY4QoOmNZiyMiZIiiqsWC4Nlxz9f7CQB69OgBQRC83k964deCfuHCBaxYsQIvv/wyPvjgAzz++OPYtWtXi9YRGxurFd/q6mq0b99eWxYVFQWj0Qij0Yh7770X3377bZPrWblyJWJiYrSvhIQEAEBJSQkAoLS0FKWlpQCAo0eP4uTJkwAcQ/5lZWUAgP3796O8vBwAUFhYiIqKCgDAqlX5SEqqBACsXp2H3r2rAQBr1+YiPt4CANi0KQft21thMsnYtCkHJpOM9u2t2LQpBwAQH2/B2rW5AIDevauxenUeACApqRKrVjne30pNrcCyZY4RioyMcixZsh8AkJlZhoULiwEA06efxLx5RwEADz9ciocfdvRp3ryW9Sk/Px+VlY4+5eXlobra0afc3FxYLI4+5eTk4OrVq9i7dy9ycnIgyzKsVitychx9slgsyM3N1fZdXp6jT5WVldqTDhUVFdqoS3l5Ofbvd/SprKwMxcWOPp08eRJHjx71ej81t09WqxWyLFOfAtCn06dPo7CwEHv37tVNnwK9n3x5jJg+3dGnhQuLkZnp6NOSJfuRkeHo07JlhUhN9ey45+v9dOrUKXz11VeQZdmr/bRv3z7oBvOjtLQ0VldXp31vNpvZ1KlT2a233trsdRw+fJhlZWUxxhhbsWIF+/DDD7VlNTU1Wjxz5kz29ddfN7keq9XKampqtK/y8nIGgFVVVTHGGJNlmcmy7BZLkuQSK4riEgOMRUZKjOedsV2LTSY743lVizlOZYDKTCY7A1TGcc6YMZ6vHyssMrJ+LDGAMUFQWESEIxbF+rHMjMb6scwAxgwGmRkMjthobH6fGsZ2u90lVlVVi2VZZmVlZcxqtTJVVZmqqsxutzPGmEusKIpLLEnSDWNZll3ixvZNS/ZTS/pUvx/UJ//2SZIkdvr0aWaz2XTTp0DuJ18fI0TRGUtaHBEhMVFUtFgQPDvu+Xo/2e129uOPPzJFUbzaT5cvX2YAXOpJuPLrXe6HDx9G165d0anepMCqquLjjz/Ggw8+2Oz1NLzL/amnnkJ2djbeffddvPPOOxBFESNHjsRrr73W7HXSXe6EkHBHxx/v6ekud78WdKfa2lq0adPm5j8YQFTQfUOWZRQWFmLEiBF0pylpMcof77Tm44+vckdPBd1v76EfP34cx48fx7Fjx/Dyyy/7azMkyJw3xfF8QCYdJDpD+UM8Rbnjzm+nxC+88AJ+85vfgDGm3XxA9Ifnefqce+Ixyh/iKcodd34r6C+99JL2WbWjRo3y12ZIkMmyjPz8fKSnp9OQKWkxyh/iKcodd377LQwcOBCyLGPz5s0oLCxEVVUV2rdvj5EjR2LatGm0A3SC53kkJibSsBfxCOUP8RTljju/3hT3yCOPoG/fvpg0aRJiYmJQXV2N7du3o7S0FO+//76/NtssdFMcISTc0fHHe3q6Kc6vl8lnzpxxK9yDBg1Cenq6PzdLAkiSJOTl5WHs2LEwGAzBbg4JM5Q/xFOUO+78WtBTU1Mxa9YsTJgwAdHR0TCbzcjNzUVqaqo/N0sCSBAEDBkyBIIgBLspJAxR/hBPUe648/tz6EeOHEFRURGqq6sRGxuLYcOGITk52Z+bbBYacieEhDs6/niPhtxbIDk52a2Ab9iwAbNmzfL3plu9KFSgLSq07ysON1geF4e2cXFebUOSJOTm5mLChAk07EVajPKHeIpyx51fr9CPHz/u9hpjDE888QT27t3rr802S2u4Qh+DlzEGy5pcnr50KUZ7OekPYwwWiwVt27aljzEkLUb5451w+pX5utL4KnfoCr2Zhg0bhunTp6PhOcOZM2f8uVly3QHMx2mk4TGMd3k9a8cOmGJjEeXl1Tng+EzicP8jIMFD+UM8Rbnjzq8FvV+/fnjttdfQoUMHl9cnT57sz82S664iDmoju7hzUhJu6djRJ9uQJAk5OTnIzMykYS/SYpQ/xFOUO+78OuR+9epVtGnTJiQf/G8NQ+4A0AaXsASdXF575uJFnxV0xhisVisiIyNpyJS0GOWPd8LpV+aPIXdf5I6ehtz9WmmjoqJCspiHAwbOJ18XGxRzAECnTo4jgS++eJ5m/SNeofwhnqLccUXVlnhFNpmQk5MDWZaD3RQShmRZpvzRqShUIA6Hta+Kw65floqKm6/kBih33AXk89BDUagPuTP4ZsXXAPylwWvPALjFJ2sHGADZbocoijRkSlqMMQZZlil/PBTKvzJ/P2Xjq9yhIfcAW7RoEdLS0pCVlQW73a69npOTgxEjRmDUqFH4/e9/H8QWtmIcR2fIxCuUP/p0APOxDjvcXs/asQNzDx1Cyvz5Xm+DcsdVyBf04uJinD9/HgUFBejXrx82b96sLUtMTER+fj727NmDqqoqHDhwIIgtDT0WABcaef0CgIrry70lR0YiNzeX/rCIR2RZpvzRqauIwwUkub3eOSkJcSkpXk9qRbnjLuQLelFRESZMmAAAmDRpEgoLC7Vl3bp1026KMBgMdINEA4cBfNDI6x8AePf6cm8Z6upw33330WMjxCMGg4HyJ8T46oZcf9+UazAaKXcaCPmCXl1drb2vERMTg6qqKrefOXToECorKzFw4MAm12Oz2WA2m12+AEBRFO3fxmJZll1iVVXd4shIGTzvjCUtNpkk8DzTYo5jABhMJgkAA8c5Y4Dn68cqpMhIAIDK85CdsSBAjohwxKKoxYooQjYatVi5HieLIuaIIuYCmCOKmCMImAvgsetxCgA5IgLq9RMhOSIC6vUPOpAjI6Fef0JBqh+bTGD1YlUQUFNTA7vdDsYYGGOQJEc/6seqqrrEzrPqpmJFUVxiX+yn+rEkSS6x81YSZ+xsO/XJv31SFAVms9ml7eHep0DvJ55XERnpaK8gqIiIcMSiWD9WYDTWjx3tNRgUGAyO2GhUIIrX+2E0Qql/XPDwGOE8dtXHrv8MADCe12KV51t03JONRly5cgWMMa/3k16EfEGPjY3Vim91dTXat2/vsvznn3/GwoULsX79+huuZ+XKlYiJidG+EhISAAAlJSUAgNLSUpSWlgIAjh49ipMnTwJwDPmXlZUBAPbv34/y8nIAQGFhISqu36W5alU+kpIqAQCrV+ehd+9qAMDatbmIj3cMbG/alIP27a0wmWRs2pQDk0lG+/ZWbNqUAwCIj7dg7dpcAEDv3tXIW70aAFCZlIT8VasAABWpqShc5rjJpDwjA/uXLAEAlGVmonjhQgDAyenTcXTePMfPPPIIrjzyCOIAXJw/H5bf/hZxACr+4z9QN3ky2gLYv2QJyjMyHH1atgwV1z8JL3/VKlQmOYbL8lavRnXv3gCA3LVrYYmPBwDkbNqEq126oKCgAF9++SVkWYbVakVOjqNPFosFubm52r7Ly8tz9KmyEvn5+Y4+VVRooy7l5eXYv3+/o09lZSguLnb06eRJHD161Ov9lJ+fj8pKx37Ky8tDdbVjP+Xm5sJiceynnJwcWK1WlztoqU/+69OZM2dQUFCgqz4Fej8lJVVi1SpHn1JTK7BsmaNPGRnlWLLE0afMzDIsXOjo0/TpJzFvnqNPDz9ciocfdvRp3ryjmD79ep8WLkRZZqajTx4eIywAPnvuOTT0c9u22LxiBSwALPHxyF271tGn3r1bdNw7NXUqCgoKIMuyV/tp3759bm0MVyF/l3txcTFef/11bNy4Ea+++ip69uyJmTNnAnBMXJOZmYm33noLiYmJN1yPzWaDzWbTvjebzUhISEBVVRViY2O1MzdBEFxiWZbBcZwW8zwPnue1WBB4REbKsNt5qCqPyEgJdrsAVeVhMkmw2USoKgeTSYLVKoIxwGSSUVcnguMcV/d1dQbwPENEhDNWYTXeAoPVCpXnoRqNEK1WqIIAVRQh2mxQRRGqIEC02aCIIhjPQ7TbHWfVPA/BbodyfShKkCTHVbuqQpBlyEYjOGccEQFeUcA7Y1kGryiQIyPB2+3gVcdogeCMTSaINhs4Z2y1AtevaJxveciyDIPBoN2FajAYoKoqFEXRYlVVIYpik7GiKGCMaXFj+6Yl+6lhLEkSBEHQYuedss7Y2Q/qE/UplPtkNDqOF0ajCqtVhCCoEEUVNpsIUVQhCM5YAc8z2O3OGLDbBe3qXJIEGI0KVBWQZNEnx4h8VUX+DY7J6QDSeR5yRAQMdXVQeR6K0diy415dndf7qaqqCh06dNDFXe4hX9ABx13u+/btQ7du3bBu3To89dRTyM7OxsqVK7F69Wr0vn5muGzZMowePbpZ62wtj635m8rzqL50Ce3ataNJhEiLqaqK6upqyh8P+eP446tjjwXA1RssjwLQ1ov1++rYo6fH1sKioPsDFXTfkCIjkbd1K8aOHUs3p5AWkyQJeXl5lD8eCuWC7m++OvZQQdcBKug+1DpTiJCga80FHYBPjj16Kug0xkW8ovI8Ll68qN0xSkhLqKpK+UM8Qsced1TQiVdUoxElJSX0R0U8oqoq5Q/xCB173NGQOw25e691phAhQUdD7jTkXh9doROvqIKAs2fP0lky8YiqqpQ/xCN07HFHBZ14RRVFnDp1iv6oiEdUVaX8IR6hY487GnKnIXfvtc4UIiToaMidhtzroyt04hVVFHHmzBk6SyYeUVWV8od4hI497qigE6/Q+1jEG/QeOvEUHXvc0ZA7Dbl7r3WmECFBR0PuNOReH12hE68ooogffvhB+xAEQlpCURTKH+IROva4o4JOvMJ4XvtMYkJaijFG+UM8QscedzTkTkPu3mudKURI0NGQOw2510dX6MQriijixIkTNOxFPKIoCuUP8Qgde9xRQSfe4XnU1dUFuxUkjFH+EI/QsccNDbnTkLv3WmcKERJ0NOROQ+71hcUV+qJFi5CWloasrCzY7XbtdVmWMXv2bKSlpWHhwoVBbGHrpRgMKCkpoWEv4hFFUSh/iEfo2OMu5At6cXExzp8/j4KCAvTr1w+bN2/Wln322Wfo2rUrCgoKUFtbi8LCwiC2lJDgqKiw4HBxRZNfFRWWYDeREBIAYrAbcDNFRUWYMGECAGDSpElYt24dHnroIW3ZlClTtGWFhYUYMWJE0NraGgmShMTExGA3o1XLfu8wlv0xv8nlS59Px8svjG7xei0VFbhaUdHk8qi4OLSNi2vxeusTBIHyh3iEjj3uQr6gV1dX47bbbgMAxMTEoKqqymWZ8z2PhssastlssNlsABzPvp47dw4AcOXKFQDQhm0EQXCJZVkGx3FazPM8eJ7XYoBHRIQMu50HYzwiIiTY7QIY4xEZKcFmE8EYh8hICVar49cdGSk3iA3gOIaICGes4rIxAgabDSrHQTUaIdpsUHkeqihCtNuhCgJUQYBot0MRBDCehyhJUAQB4HkIkgRFdGxDkGUoBgOgqhAUBbLBAM4ZG43gFQW8M5Zl8KoKOSICvN0OnjFIEREQnHFkJESbDdz1mFNVlOTno0+fPoiIiADgeCvEYDCAMabFqqpCURQtVlUVoig2GSuKAsaYFje2b1qynxrGkiRBEAQtFkURHMdpsbMf9WN/9Kl9ewGiqFzfhgCDQYGqAooiwGCQoaocFEWA0ShDUXgoCg+jUYYs81BVR+7Z7F0ApAGRX7kmvX08oEZi5Yp2WPZiTbNzz2hUYLMZMB4rMBRvNfk3Nfh3v0PGK694tZ8YYzh+/Dj69OkDg8EQsvspVHMPcO4zFTabCJ5XIYoq7HYRgqBCEJyxAp5nkCRnDEhS47lnVuDTY4RotTq20SA2WK1gHAc5IgIGqxUqx0ExGpt93LObTDj29ddISkrSctKT/eSsG3q4nSzkC3psbCzMZjMARwFv3759s5Y1tHLlSixbtszt9R49enjdxuvnCW7x9dxtVsyYa3yrcz2M/bJSVQWc9xAoiuPrRrEs/7IhSWo8rndPgkvckk6NbvnVH/mFr3YT3G74/aPj51uYe8517rj+1aT/7/9zfJGg8vUhIqb+N4BvjhFNxU0lX3M6VVcHZGTAVywWC2JiYny2vmAI+YI+bNgwvP7665g1axa2b9+OkSNHuizLzc1Feno6tm/fjjlz5jS5nueeew7PPPMMAMeZmNlshiRJ6NChAzh/3areCpjNZiQkJKC8vDzs7xAlgUf5Qzzlq9xhjMFisWgjweEs5Av6wIED0aVLF6SlpaFbt25YvHgx5s+fj+zsbEydOhVbt25FWloaBg4ciOHDhze5noiICG1IGEDYn4mFmujoaDogE49R/hBP+SJ39FIPWu1z6MQ39PQMJwk8yh/iKcoddyH/2BohhBBCbo4KOvFKREQEli5d6vJ2BiHNRflDPEW5446G3AkhhBAdoCt0QgghRAeooBNCCCE6QAWdEEII0QEq6IQQQogOUEEnhBBCdIAKOiGEEKIDVNAJIYQQHaCCTgghhOgAFXRCCCFEB8KioFssFqSmpiIqKgolJSUuy2RZxuzZs5GWloaFCxcGqYWEEEJIcIVFQTeZTPj8888xffp0t2WfffYZunbtioKCAtTW1qKwsDAILSSEEEKCK+Q/Dx0ARFFEx44dG11WVFSEKVOmAAAmTZqEwsJCjBgxwu3nbDYbbDYbAMcH2pvNZkiShA4dOoDjOP81nhBCSMhijMFiseC2224Dz4fFNW6TwqKg30h1dbX2WbgxMTGoqqpq9OdWrlyJZcuWBbJphBBCwkR5eTm6du0a7GZ4JewLemxsLMxmMwBHcW/fvn2jP/fcc8/hmWeeAeA4Izt37hz69euH06dPIzY2FoqiAAAEQXCJZVkGx3FazPM8eJ5vMpYkCYIgaLEoiuA4TosBx/v+9WODwQDGmBarqgpFUbRYVVWIothkrCgKGGNa3Fg//NUnANi3bx8GDRqEyMhIXfRJj/spVPukqioOHDiAQYMGwWg06qJPetxPodgnm82GAwcOYNiwYdooqyd9qqqqwu233462bds2LBthJ+wL+rBhw5Cbm4v09HRs374dc+bMafTnIiIiXD4315kAsbGx2hU+aTlVVTFgwAB07Ngx7IerSOCpqoqkpCTceuutlD+kRZzHnnbt2vkkd/Tw1mvY/AVlZmYiNzcX8+bNw/r16zF//nwAwNSpU1FeXo60tDSYTCYMHz48yC1tXXieR3x8PB2MiUcof4inKHfccYwxFuxGBIPZbEZMTAxqamroCt0LsiwjPz8f6enp2nAaIc1F+UM85avc0VMtoFMb4hWe55GYmEhnycQjlD/EU5Q77uiUmHiF53l06tQp2M0gYYryh3iKcscdndoQr0iShO3bt2t3vBPSEpQ/xFOUO+6ooBOvCIKAIUOGQBCEYDeFhCHKH+Ipyh13NOROvMLzfJPP/hNyM5Q/xFOUO+7oCp14RZIkfPHFFzTsRTxC+UM8Rbnjjh5b08GjCsHknAe5bdu2upiYgQQW5Q/xlK9yR0+1gK7QiVc4jkN0dLQuD8Y9evRw+7je5pg9ezZWr14NAHjppZfw0Ucf+bRdNTU1mD9/Pnr27Ik+ffpg8ODB+Pzzz326DU/93//9HwYNGoTk5GT07dsX48aNg6qqTf78zfLnr3/9Ky5evOiv5pIwpudjj6eooBOvSJKETz/9lIa9mrB8+XI88MADPlsfYwyZmZkwGAz417/+hRMnTuDdd9/F/PnzsX37dp9txxPnz5/Hk08+if/7v//DkSNHUFpaitdee+2GB9yb5Y+nBV2W5Rb/HxJe6Njjjgo68YooipgwYYLuZ/nKyMjAH/7wB6SlpaFXr1548skntWVnz57FuHHjkJSUhPvuuw+VlZXasvpX6zt37sTw4cMxcOBAJCYmYt26dc1af307d+7EmTNn8Je//EX7nScnJ+OFF17AihUrtJ/77//+b/Tv3x8DBgzAsGHDUFtbCwB4//33kZqaipSUFIwePVobgfjuu++QlpaGlJQU9OvXDytXrnTpw7//+79j/PjxuPPOO3H//ffDbre7ta2iogKiKKJDhw7aaykpKVpBP3nyJCZPnowhQ4ZgwIABeOutt7T8OXDgANLS0jBgwAAkJSXh008/xfLly3Hu3DlMnz4dycnJOHLkCK5evYo5c+YgMTERiYmJLp+gmJGRgRdeeAHjxo3DxIkTb7ZLSZhrLceeFmGtVE1NDQPAampqgt2UsKaqKrPb7UxV1WA3xee6d+/OvvvuO8YYY6NHj2bTpk1jsiyz2tpa1qNHD1ZYWMgYY+z+++9nL7/8MmOMsVOnTrGoqCj25ptvMsYYe/TRR7W4qqqKybLMGGPs8uXLrHv37uzcuXM3XX99//3f/83uvfdet9cPHz7M2rRpwxhjbP369WzYsGFabju3u2fPHpaZmcmsVitjjLH8/HyWlJTEGGPMbDZrr9fW1rLk5GR24MABrQ/Dhw9ntbW1TJZlNmLECPbhhx+6tUFRFHb//fez2NhY9utf/5qtWrWK/fzzz4wxxmRZZoMHD2alpaWMMcauXbvG+vfvzw4ePMjOnz/POnfuzPbu3aut5/Lly277gDHGnn32WZaVlcUURWFXr15lycnJ7OOPP9Z+h5mZmcxutze1S4mO+OrYo6daQFfoxCuyLCMnJ6dVDHE++OCDEAQBJpMJycnJOHXqFABg165dmDt3LgCgZ8+eGDduXKP///Lly/jtb3+LxMREjB07FpWVlTh27NhN199QY0PYrN69rZ9//jn+7d/+TbvBJzY2FoIg4NNPP8W3336L1NRUJCcn46mnnsKlS5dgt9tRV1eHuXPnon///hg2bBjOnDmDI0eOaOu8//77YTKZIAgChg4d2mjbeJ7Hli1bUFhYiEmTJmHv3r24++678cMPP+D777/HsWPH8OCDDyI5ORkjRoyAxWLBd999h9WrV6Nv374YMWKEtp6mHkfasWMHnnzySfA8j1tuuQWzZs3Cjh07tOWPPPIIDAZDo/+X6EtrOvY0F41VEK+IoojMzMxWMezl/Lx34JfPV26JJ598ElOnTsWWLVvAcRxSUlJgtVpbtP6UlBT87W9/g91uh9Fo1F7/5ptvkJKScsPtM8YwZ84cLF++3G3Z888/j86dO6O4uBiiKOL+++9vcduc+vTpgz59+mD+/PmYNGkS/vnPf2LixIm49dZbXU4SnG365z//ib17996w7fV/vuEJTf3vo6KimrUeEv5a07GnuegKnXittZ8hjx07Fu+99x4A4PTp09i5c2ejP3flyhV0794dHMchPz8f3377bYu3NW7cOCQkJOA///M/td/7kSNHsGLFCjz//PMAgHvvvRdvv/02zGYzAKC6uhqKomDq1KnYsGEDysvLATg+T/rgwYNa27p27QpRFPH999/jq6++anHbzp4961KYr1y5grKyMvTq1Qt33XUX2rRpgw0bNmjLf/jhB1RVVWHIkCEoLS1FYWGh1q6qqioAQHR0NGpqarT/c8899+B///d/wRjDtWvXsHHjRowfP77FbSX60NqPPQ1RQSdekWUZubm5rfoP64033sDu3buRlJSERYsWNVlg/vSnP2Hx4sUYNmwY1q9fj9TU1BZvi+M4fPnll7BarejduzfuuusuPP7443j77bfxq1/9CoBj2PnXv/41hg8fjuTkZGRmZsJmsyE9PR1//OMfcd9992HAgAFITEzUHql78cUX8e6772LIkCF48cUXMXbs2Ba3TZZlLF++HHfeeSeSk5ORlpaGRx99FPfddx9EUcRnn32Gjz/+GElJSbj77rsxd+5cWCwWHDhwAP/4xz+wePFiJCUlYeDAgdizZw8AYMGCBXjssce0m+L+67/+CxzHoX///khNTcW9996L6dOnt7itJPzRsccdTSyjg8kECCGEeEZPtYCu0IlXGGMwm81opeeFxEuUP8RTlDvuwqKgL1q0CGlpacjKynJ5/rWurg5TpkzB6NGjcc8992jvu5HAkWUZBQUFNOxFPEL5QzxFueMu5At6cXExzp8/j4KCAvTr1w+bN2/Wln355ZdITEzE119/jRkzZuD9998PYktbJ4PBgMmTJ9OjQsQjlD/EU5Q77kK+oBcVFWHChAkAgEmTJml3wgJA7969tRmwqqur0bFjxybXY7PZYDabXb4AQFEU7d/GYlmWXWLnvNRNxZIkucTO4SBnzBhziwG4xKqqusTOM9CmYkVRXOJA9klRFFy+fBk2m003fdLjfgrVPsmyjKqqKtjtdt30SY/7KRT7JEkSLl26BFVVve6TXoR8Qa+urtZuVIiJiXEZVu/VqxdKSkqQmJiIDRs24Ne//nWT61m5ciViYmK0r4SEBADQpr4sLS1FaWkpAODo0aM4efIkAMcIQVlZGQBg//792iM/hYWFqKioAADk5+dr033m5eWhuroaAJCbmwuLxQIAyMnJgdVqdZkMwWq1IicnBwBgsViQm5ur9TkvLw8AUFlZifz8fACOqTWdJzTl5eXYv38/AKCsrAzFxcUAHNNrHj16NGB9unbtGg4cOIBt27bppk963E+h2qeffvoJBw4cQFFRkW76pMf9FIp9+vHHH/HNN99AURSv+rRv3z7oRcjf5f72229rM0IdPHgQ69ev1+bGfvvtt3Hp0iW89NJL+L//+z/s378ff/rTnxpdj81mg81m0743m81ISEhAVVUVYmNjtTM3QRBcYlmWwXGcFvM8D57nm4wlSYIgCFosiiI4jtNiwHFGWD82GAxgjGmx84zTGauqClEUm4wVRQFjTIsb6wf1ifpEfaI+UZ/c+1RVVYUOHTro4i73kJ9iZ9iwYXj99dcxa9YsbN++HSNHjnRZ7pwisl27dtoZYmMiIiIQERHh9rogCC7/Nozrz0LUnLj++zktic+fv4qK81ebbH9clyjExbUFzzsGVZzJeKO2B6JPqqqisrISt956qzZjl/NnOI7T4vrtbU4czD7dKKY++bZPqqri4sWLuPXWW7V1hnufbhZTn3zTJ47jcPnyZdx6660+61O4C/meDBw4EF26dEFaWhq6deuGxYsXY/78+cjOzkZWVhZmzpyJzZs3Q5ZlbbaucJT93mEs+2N+k8uXPp+Ol18YHcAWNY+qqigpKUF6err2h0ZIc1H+EE9R7rgL+SF3fwm1yQQqKiw4fuISxk/5wOX1HZ9nIbadSbtCJ4QQ4juhVgu8EfJX6K1FXFxbiKL7WWZSYmd07HhLEFrUPKqqoqKiAnFxcXSWTFqM8od4inLHHf0WiFdUVcWpU6e0R0AIaQnKH+Ipyh13dIVOvCKKItLT04PdDBKmKH+Ipyh33Pn9Cv3YsWP+3gQJIlVVcebMGTpLJh6h/CGeotxx57cr9GnTpqFXr144fPgwUlJSsGrVKn9tigSRqqo4e/Ys4uPj6X0s0mKUP8RTlDvu/FbQn3jiCZjNZsTHx2PhwoX+2gwJMlEUMWLEiGA3o1WrqLA0aw6DUET5QzxFuePObwV94sSJKC0txdmzZ/21CRICFEVBWVkZbr/9dpdJHEjghOscBgDlD/Ec5Y47v94U17dvX/Tt2xeVlZXYunUrrly5ok3a/+yzz/pz0yRAGGO4cuUKevToEeymtFrz56QgbUTCDecwCFWUP8RTlDvuAnKX++TJk5GVlYWePXsGYnMkgERRxJAhQ4LdjFYtXOcwACh/iOcod9wFpKB36dIFCxYsCMSmSIA5P+mod+/eNOxFWozyh3iKcsddQAr6rFmzMG3aNCQlJWkf4PHSSy8FYtMkAOrq6oLdBBLGKH+Ipyh3XAWkoK9YsQJPPfUU4uPjA7E5EkCCIGDgwIHBbgbxA0tFBa5e/+zrxkTFxaFtXJxX26D8IZ6i3HEXkILevXt3zJkzJxCbIvUE4oCsKApKS0vRt29fGvbSmcPZ2chftqzJ5elLl2L0yy97tQ3KH+Ipyh13ASnodXV1mDhxosuQO00043+BOCAT/UqZPx8JaWn4YPx4l9ezduyAKTYWUV6eDBJCfCsgBf35558PxGaC4vr5ie+0cf22UyfPVxWF+eiINDwG/x2QBUFAYmKi1+shoadtXBx40f0Q0TkpCbd07OiTbVD+EE9R7rgLyHx5jDGMHj1a+6LJZgLjKuJwAUlur3dOSkJcSorXw+2AY9iruLgYiqJ4vS7S+lD+EE9R7rgLSEFfs2YNioqKAADZ2dnIz296VqvGLFq0CGlpacjKyoLdbtde3717NxISEpCRkYFx48b5tM2k+UwmU7CbQMIY5Q/xFOWOq4AU9A0bNuD111/HwoUL8a9//Qtr1qxp9v8tLi7G+fPnUVBQgH79+mHz5s0uyx944AHs3r0bO3fu9HWzSTMIgoA+ffrQTSnEI5Q/xFOUO+78WtAXL16MZ599Fi+++CI6d+6MLVu2gOf5Fk37WlRUhAkTJgAAJk2ahMLCQpflW7ZsQVpaGt544w2ftp00jyzLOHDgAGRZDnZTSBii/CGeotxx59eb4qZMmeLy/YwZM1q8jurqatx2220AgJiYGFRVVWnLBg8ejO+//x4AcN9992HUqFEYNGhQo+ux2Wyw2Wza92azGQC091+c/wqC4BLLsgyO47SY53nwPK/FAI/ISBl2Ow9V5REZKcFuF6CqPEwmCTabCFXlYDJJsFpFMAaYTDLq6kRwHBAZKaOuzgCeZzBGyLA2aDfHq4gwyrBaRQiCClFUYbOJEEUVguCMFfA8g93ujAG7XYDBoMDAFKBBviuyDEVRmuxTw1iSJAiCoMWiKILjOEiSBI7j0K5dO8iyrJ0py7IMg8EAxpgWq6oKRVG0WFVViKLYZKwoChhjWtzYvmnJfmpJn8TrN4LJsuwSh3KfGuP83ARv+qQ0crBUVVVbj7d9YowhNjYWiqK0iv1EffJdn1RVRUzM/9/e/cdHUd+JH3/tzOxuVggRqEiMQU/FQ8QIKEWQAFUPvKhtT7CtQlE5KX7vrFSrFutVxdazh9XWyrWlV6pnrfQULT01lYg5TTSUX8YiEjVipBEDGEPIItnd+fH5/rHsmM0mmOxuspvl/Xw89sF7d8PM553PO/OZ+ezsTAEejyflnHJFnx6hT5s2jebmZlpbW5k2bZp7Uty+fft6vIyhQ4e6g29rayvDhg1z3xs8eDA+nw+fz8eXv/xl/vrXv3a7nPvuu4+CggL3UVxcDMD27dsBqKuro66uDoBt27ZRX18PRKf8GxoaANi0aRONjY0A1NTU0HT4O97Ll1dRUtIMwIoVlYwe3QrAqlUVFBUFAVi9upxhw0IEAharV5cTCFgMGxZi9epyAIqKgvzsZ/+X0O4zx7awfHn0nIPJk5tYtiw6QzFzZiNLl24CoKysgSVLagGYO7eeRYu2ATB/fh1zr3g3YZlvvvnm5+ZUVVVFc3M0p8rKSlpbozlVVFQQDEZzKi8vxzRNTj75ZNatW4dlWYRCIcrLozkFg0EqKiqAaN9VVlYC0Nzc7J5H0dTU5M66NDY2smlTNKeGhgZqa6M51dfXs23btpT7qac5hUIhLMuivLx8QOXU2cGDB1POKXbuS0cf7d6dtpw++ugjTjvtNDZu3HjU9JPklJ6c/va3v3HgwAF0XU8pp40bN5IrPCq2G98HvvGNb3DaaadhGAYvvfQSjz76KKeeeioXXHCB23Gfp7a2lgceeIDHH3+ce++9l1NOOYUrr7wSiB5lDxkyBICrrrqK66+/nunTp3e5nK6O0IuLi2lpaXGPEKD3e6q6ns4j9DZCnp/HtdsT+g5+XyDpI/Rj1Md814o/m33JRx8xaMSItOx9Q/SPY+LEieTl5QFyRJGJnD75pJ0RJz8Y1897G25ixIjBKeV0cO9eHjo8QxbznT17CAwfnpacHMdhy5YtTJw4EZ/Pl/P9JDmlL6dwOMyWLVuYPHmye32TZHJqaWlh+PDhHDhwwB1PBqo+nXLfu3cvf/jDHwBYuHAhCxYs4K677urVMiZMmMDIkSMpLS1l1KhR3HrrrSxevJiVK1fy5JNP8utf/xrDMDj//PO7HcwB/H4/fr8/4fXYNHHHEys6xrECPFIcCnWMvW7c3n7kWKnPYsfxEGo3Er6HrhzNXb5ta9h2dFLFsjQsKxZ/1t6OsWnqmCSeMKIbhptjT/Lzer3dxo7jcOKJJ+L3+90/qtjPeDweN4798fQ07q4/UumnnuZ0pDibc+qsc38kk5PexfI1TXPXm2pOjuNQVFTkDuadfyYX+0lySk9OXq+XE088Me61VHMa6Po0E9u2CYVC5OXlMWrUKJ599lnmzZvHm2++2avl/OQnP4l7vnLlSgCuu+46rrvuurS1V/SepmmcdNJJmW6GGKCkfkSypHYS9eln6D/72c/cz78B8vPzWbt2LQ8//HBfrlb0I8uyqKqqyqkTS0T/kfoRyZLaSdSnR+gTJ04E4NChQxxzTHQuWdM0vvGNb/TlanPKPkZwHIeS/v+fAg92fjGV68l2ouk6p+7a1e2Z1kIciaZpnHrqqVI/otekdhL12YC+Y8cOIPrVmf/+7/+Wm7HkKM225ba4Immapkn9iKRI7STqs12bO+64gy1btrBlyxb36wEi91h5eVRWVsq0l0iKZVlSPyIpUjuJ+uwI/c4773RvPj9t2rS+Wo3IMC0SYdy4cTLtJZKiaZrUj0iK1E6iPhvQJ0yYgGVZrFmzhpqaGlpaWhg2bBjnn38+c+bMyamvCqRHEDwfJ7y6zXM8QwlRqIIUcjAD7ToyzXEYkcbP5MXRRdM0qR+RFKmdRH26a3Pttdfy/vvvc80117Bs2TKuvvpqdu7cybXXXtuXqx2YvK9D4PcJL18UuJZzAv+Pld5JGWjU5zPz8li3bp17kRkhesM0TakfkRSpnUR9epi8a9cufve738W9ds455xzxAjBHLXMiWKe7T7cSf036QhXs7xb1iB6JMGnSJLnjUZIOX/slPTpdlCgdBy/HAEtTX0y3dF2X+hFJkdpJ1KcD+uTJk1mwYAGzZs1iyJAhtLW1UVFRweTJk/tytQNUPqh899lEmjLYlp7THCfu+vpC9IamaVI/IilSO4n6dMr9/vvv5+abbyYYDPLWW29x8OBBbr75Zu6///6+XK3oR2YgwPPPPy/TXiIppmlK/YikSO0k6vMz08aPH8/48ePjXnvsscdYsGBBX69a9AMjHKa0tFROchRJMQxD6kckRWonUZ/+JmIXl+lIKcXKlStlQM8RHscZ8HcoEpnj8XikfkRSpHYS9emAft555zF37lw636F1165dfbla0Y/MQIDyP/2JsrKyuLspicxL9bLB0PeXDjYDAcpXr5b6Eb1mmibl5eVSOx306YA+duxY7r//foYPHx73+iWXXNKXqxWHBYHEb7bDXiAADAbyu3i/N4xQiFmzZsm0l0iK1I9IlmEYUjud9OlvYv369e5NWTp6/vnn+3K14rDXgaouXo992306MCPVlSglf1AieVI/IgVSO/H69LcxePDgvly8+BwTgdOP8H46escKBGTaSyRN6kcky7IsqZ1OZPcmh+WT+pT65zHa2ykrK5M9ZZEUqR+RLMMwpHY6GRBXtb/lllsoLS1l3rx5RCIR9/Xy8nKmTp3KtGnTuOGGGzLYwqOYxyN3OxLJk/oRKZDaiZf1A3ptbS179uyhurqasWPHsmbNGve9cePGUVVVxauvvkpLSwubN2/OYEuPTlZeHhUVFfKHJZIi9SOSZVmW1E4nWT+gb9iwgVmzZgFw8cUXU1NT4743atQod7rF6/XK1EsGeNvb+cpXviKfYYmkSP2IZHm9XqmdTrJ+QG9tbXUvHlBQUEBLS0vCz2zdupXm5mb3/utdCYfDtLW1xT0AbNt2/+0qtiwrLnYcJyHOy7PQtFhsunEgYKJpyo09HgUoAgETUHg8sRg0rWPsYOblAeBoGlYs1nUsvz8aG4Yb24aB5fO5sR2LvV7sw8Vu+3zYh3d4rI6x34/TMT58owMrLw/n8H2GzY5xIIDqEDu6zoEDB4hEIiilUEq5l2LsGDuOExfH9qq7i23bjovT0U8dY9M04+LYtRJicazt/ZGT12vj9UZjn8/GMGKx5cZ+v4VhOG6s64m111nHflIeDyoWA8rjwQwE3J+LxY6mxdWefbjGOupYh6nWnu3z0dbWFtcf2dpPuVh7Azkny7LYv38/SqmUc8oVWT+gDx061B18W1tbEy7G/+GHH7JkyRIeffTRIy7nvvvuo6CgwH0UFxcDsH37dgDq6uqoq6sDYNu2bdTX1wPRKf+GhgYANm3aRGNjIwA1NTU0NUVvoLJ8eRUlJc0ArFhRyejRrQCsWlVBUVH0LmmrV5czbFiIQMBi9epyAgGLYcNCrF5dDkBRUZBVqyoAGD26lcoVKwBoLimhavlyAJomT6Zm2TIAGmfOZNPS6H2wGsrKqF2yBID6uXPZtmhRNKf586mbPz+a06JF1M+dG81pyRIaysqiOS1dSuPMmdGcli2j6fCNc6qWL6e5pASAyhUraB09GoCKVasIFhUBUL56NQdHjqS6upo///nPWJZFKBSivDyaUzAYpKKiwu27ysrKaE7NzVRVRb9Q19TU5M66NDY2smnTpmhODQ3U1tZGc6qvZ9u2bSn3U1VVFc3N0X6qrKyktTXaTxUVFQSD0X4qLy8nFAq5Z9D2R07z59cxf340p0WLtjF3bjSnJUtqKSuL5rR06SZmzozmtGxZDZMnJ9ZeZwcLC91+Cg0bFj2jfPVqrECA0LBhlK9eHc2pqIiKVauiOY0eHVd7G+68M2G5H51/ftpqb9dFF1FdXT0g+ikXa28g57Rz506qq6uxLCulnDZu3Eiu8KjOl3HLMrW1tTzwwAM8/vjj3HvvvZxyyilceeWVABw8eJCysjJ+8YtfMG7cuCMuJxwOEw6H3edtbW0UFxfT0tLC0KFD3T03XdfjYsuy8Hg8bqxpGpqmubGua+TlWUQiGo6jkZdnEonoOI5GIGASDhs4jodAwCQUMlAKAgGL9nYDjyd6hNXe7kXTFH5/LHYI+QbhDYVwNA3H58MIhXB0HccwMMJhHMPA0XWMcBjbMFCahhGJRI9+NA09EnGPkHTTjB45OQ66ZWH5fHhisd+PZttosdiy0GwbKy8PLRJBc6KzBXosDgQwwmE8sTgUgsN7/7GPPCzLwuv1opRyY8dxsG3bjR3HwTCMbmPbtlGHv6PcXd/0pp86x6Zpouu6GxuGgcfjceNYHn2dk2Ho7tG5aer4fDaOA5al4/NZOI4Hy9Lx+y1sW8OyNPx+C8vSsO2OtdcOx8Rf021vaDkjnIOf9ZNSWIEARnt79GS0vDy87e0oTcPy+/G2t0ePyn0+t/YOer081OHvBuA7uk7gcB2mpfZMM+v7KRdrT3KKtr2lpYXhw4dz4MCBAX8p2awf0CF6lvvGjRsZNWoUjzzyCN/+9rdZuXIl9913HytWrGD04aPHZcuWMWNGzy6V0tbWRkFBQcqdmNb7WXeg6KMFp5mjabR+/DHHHnssmpb1Ez5ZJ33182nCgL7v0I/75NKvNwODUlrqZ6R+RLIcx6G1tTXl2knXWJANBsSA3hdkQE8PMy+PyrVrueCCC+TklCQc7QO61I9IlmmaVFZWplw7uTSgy2nhIiXeUIjZs2dnuhligJL6Ecnyer1SO53IHJdIiaNp7Nu3zz1jVIjekPoRyXIcR2qnEzlCFylxfD62b9/O9OnT5TNQ0WtSP5nX1BSkac/Bbt8vHDmYwsK+voh07zmOI7XTiQzoIiVGKMQFF1yQ6WaIAUrqJ/NW/vZ1lv17V/dljLrr+9O5+46U78uYdoZhSO10Irs1IiWOrrN7926Z9hJJkfrJvMULJ7L+uXkJr69/bh5bX72OxQsnZqBVn89xHKmdTuQIXaTEMQx27tzJ8ccfL9NeotekfjKvsDAfw0j83ZeMO57jjkvX9xnSz3EcqZ1OZEAXKTHCYaZPn57pZhzlguD5OOHVbZ7jGUqIQhWkkO4/I80kqR+RLMMwpHY6kd0akRLHMNi1a5dMe2WS93UI/D7h5YsC13JO4P+x0jspA43qGakfkSzHcaR2OpEjdJGS2GegRUVFMu2VKeZEsE53n27lnLi3C1Wwv1vUY1I/Ilmxz9Cldj4jA7pIiREOM3Xq1Ew34yiXD+qzrxVNpCktSw0CiRP5sBcIAIOja06J1I9IlmEYUjudyG6NSIltGLz33nvuTRBE7ngdSJzIj772m8Pvp0rqRyTLtm2pnU7kCF2kRGka+/fv5+STT850U0SaTQROP8L7g9OwDqmf3BVsauJgU/ezRYMLC8k/fIvfZCilpHY6kQFdpMSIRJg0KXtPuhLJyyf1KfXPI/WTmrTeHOqY+KcjRqS2uC+xki+xrNv3p991FzPuvjvp5RuGIbXTiUy5i5TYhsHbb78t014iKVI/uWszi3mE9Qmvz1u/nuu2bmXi4sUpLd+2bamdTuQIXaRG02hvb890K8RAJfWTsw5SiNPFEHN8SQmDjjsuLeuQ2oknA7pIiR6JMGHChEw3QwxQUj8iWbquS+10IlPuIiW218v27dtl2kskRepHJMu2bamdTgbEgH7LLbdQWlrKvHnziEQi7uuWZXHNNddQWlrKkiVLMthCIYTILfsYgcKT0mMfXZxZN2JE9Gy+VB+BQP//UrJc1g/otbW17Nmzh+rqasaOHcuaNWvc95599llOPPFEqqurOXToEDU1NRls6dFJN03GjRuHruuZbooYgKR+RLKkdhJl/WfoGzZsYNasWQBcfPHFPPLII1x11VXue5deeqn7Xk1NTbdXDgqHw4TDYSD6/cWPPvoIgP379wO40za6rsfFlmXh8XjcWNM0NE1zY9Dw+y0iEQ2lNPx+k0hERymNvDyTcNhAKQ95eSahUPTXnZdndYq9eDwKvz8WO3zi8+MNh3E8HhyfDyMcxtE0HMPAiERwdB1H1zEiEWxdR2kahmli6zpoGrppYhvRdeiWhe31guOg2zaW14snFvt8aLaNFostC81xsPx+tEgETSlMvx89FuflYYTDeA7HHsdhe1UVY8aMwe/3A9GZE6/Xi1LKjR3HwbZtN3YcB8Mwuo1t20Yp5cZd9U1v+qlzbJomuq67sWEYeDweN47l0THui5xAxzDsw+vQ8XptHAdsW8frtXAcD7at4/NZ2LaGbWv4fBaWpeE4XddemyKhn4xQKLqOTrE3FEJ5PFh+P95QCMfjwfb5+q32lKaxo7qaMWPG4PV6s7afsrX2ILq98PkcwmEDTXMwDIdIxEDXHXQ9FttomsI0YzGYZrT2FAexrRZQobht5qvGSIZbn/IFX4QT7LakthEH/H5Ch7e7MQeAUBpqLxII8NYrr1BSUuIuO5l+amlpcceFgS7rB/TW1lZOOOEEAAoKCtxffuy9IUOGdPleZ/fddx/LliV+JzIdFyXoWK8d41Co57FS8fEXYstR6rOFOg7EPnKw7ejjSLFlfbYi0+w67vARRlzcm6RmzEAkL93dVACpFV9sQf1Ve3LHrJSkq5vodML45bGgQ731ehvRaTAH+PeOP5NK7bW3w8yZCctPVjAYpKCgIG3Ly4SsH9CHDh1KW1sbEB3Ahw0b1qP3Orv99tu5+eabgeieWFtbG6ZpMnz4cDxpvTrD0aWtrY3i4mIaGxvdnSshekrqRyQrXbWjlCIYDLoHjgNZ1g/o5513Hg888AALFixg3bp1nH/++XHvVVRUMH36dNatW8fChQu7XY7f73enhIEBvyeWbYYMGSIbZJE0qR+RrHTUTq6MB1l/UtyECRMYOXIkpaWl7Nixgzlz5rD48BWGLrvsMhobGyktLSUQCDBlypQMt1YIIYTIDI/KhTMBRMa0tbVRUFDAgQMH5AhL9JrUj0iW1E6irD9CF9nN7/dz1113xX2cIURPSf2IZEntJJIjdCGEECIHyBG6EEIIkQNkQBdCCCFygAzoQgghRA6QAV0IIYTIATKgCyGEEDlABnQhhBAiB8iALoQQQuQAGdCFEEKIHCADuhBCCJEDZEAXQgghcoAM6EIIIUQOkAFdCCGEyAFGphuQCUop2traCAaD5Ofn4/F4Mt0kIYQQGaCUIhgMcsIJJ6BpA/sY96gc0IPBIMcee2ymmyGEECJLNDY2cuKJJ2a6GSk5Kgf0/Px8GhsbKS4uprGxkSFDhmS6SQOWZVls3LiRyZMnYxhHZTmJFEj9iGSlq3ba2tooLi4mPz8/ja3LjKPyL8jj8biD+JAhQ2RAT4HjOJSUlHDssccO+Okq0f+kfkSy0l07ufDR61E5oIv00TSNoqKiTDdDDFBSPyJZUjuJZJdYpMSyLCorK7EsK9NNEQOQ1I9IltROIhnQRUo0TWPcuHEyXSqSIvUjkiW1k0im3EVKNE1jxIgRmW6GGKCkfkSypHYSya6NSIlpmqxbtw7TNDPdFDEASf2IZEntJJIBXaRE13UmTZqEruuZbooYgKR+RLKkdhLJlLtIiaZpDBs2LNPNEAOU1I9IltROIjlCFykxTZPnn39epr1EUqR+RLKkdhJ5lFIq043IhLa2NgoKCjhw4IBcWCYFsesgyzXxRTKkfkSy0lU7uTQWyBG6SEnsqnu5uDF+5plnOOeccxg/fjxnnHEGF154IY7jpLTMu+++m0gk4j6/5pprWLFiRY//fzAYZPDgwVx33XVxr69du5ZNmza5z19++WXOPffclNr6wQcf8Otf/zrutbKyMnbu3Nmr5fzqV7+ipKSEs88+mzFjxjBv3jz3vWTqp/PvUBydcnnbkywZ0EVKTNPkT3/6U85Ne+3Zs4frr7+eZ555hjfeeIO6ujruv//+lDcey5YtS2kw+sMf/sDEiRN5+umnOXjwoPt65wE9Hboa0MvLyzn11FN7vIwtW7bwk5/8hJdffpm//vWv1NXV8d3vftd9P5n6SfZ3KBcgyS25uu1JhQzoIiWGYTBr1qycu7FGU1MThmEwfPhw97WJEye6A/qWLVuYMmUKJSUlfPGLX+S1114DooPgF77wBff/HDx40P0/119/PQBTp05l/Pjx7Nu3D4AdO3Zw0UUXcfrpp3P55ZcfcbBatWoV3/ve9ygtLeXJJ58EooPs//7v//LjH/+Y8ePH85vf/Cbu/1iWxezZszn33HM588wzmTdvHocOHQLg0UcfZfbs2Vx55ZWcddZZnHvuubz//vtue3fs2MH48eP58pe/DMDJJ5/M9u3bAdi9ezdz586lpKSEkpISfvCDHyS0t7GxkYKCAncq0+PxMHHiRPf92tpafvrTnzJlyhR3RyXm+eefZ9KkSZx99tmMHz+ejRs3dvk73Lt3L//0T//EWWedxbhx4+J2Qk4++WTuvfdevvSlL3H11Vd3+3sVA0+ubntSoo5SBw4cUIA6cOBAppsyoDmOoyKRiHIcJ9NNSSvbttXll1+uhg4dqr761a+q5cuXqw8//FAppVQ4HFbFxcXqhRdeUEopVV1drUaOHKkOHjyoGhoa1PDhw93lBINB1fHPDFDBYNB9fvXVV6spU6aoQ4cOKcuy1NSpU9UTTzzRZZu2b9+uTjjhBGVZllq7dq2aOnVq3HIefvhh9/n//d//qXPOOUcpFe2j5uZmN77++uvV/fffr5RS6pFHHlEFBQXqgw8+UEop9b3vfU9961vfSlhGzEknnaTefPNNpZRSM2fOVMuXL3ff27dvX0KbP/30U3X++eerkSNHqq9//evq4YcfVi0tLUoppfbv368mTJigdu3apRzHUR9//LEaNWqUampqUu+88446/vjj1TvvvKOUUioSiajW1tYuf4df+9rX1NKlS5VSSu3du1edeOKJauPGjW57v/Wtb+VcfYr0bXtyaSyQI3SREsuyKC8vz7npTE3TePrpp6mpqeHiiy/mtdde48wzz+S9997jnXfewefzMXv2bACmTZvGiBEj2LZtW1LruvzyywkEAui6zhe/+MVuP6NetWoVCxYsQNd1LrnkEt5//33q6uo+d/lKKX76058yYcIESkpKeP7553njjTfc96dNm8ZJJ50EwJQpU3r0GfnBgwepqanhpptucl877rjjEn7umGOOobq6mvLycqZOncozzzxDSUkJLS0t1NTU8P777zNjxgzGjx/PRRddhFKKd955hxdffJGysjJOP/10ALxeLwUFBV22Zf369fzrv/4rACNGjODyyy/npZdect+/9tpr5XPWHJSr255UyFxFlmhqCtK052C37xeOHExhYfbdr9cwDMrKynJ22mvMmDGMGTOGxYsXc/HFF/O///u/XHTRRV0OEB6PB8MwsG3bfS0UCn3uOvLy8txY1/UuN1CmafL444/j9XpZvXo1AIcOHeK3v/0t999//xGX/8QTT/DKK69QVVVFfn4+P//5z6mqqurV+lPh8XiYMGECEyZM4Nvf/jZjx47l5Zdfxu/3U1JSwksvvYRhGHG/09i0fm/W0d3zwYMHp5aAyEq5vu1JhhyhZ4mVv32dc6b9ptvHyt++nukmdisX95B3797tfi4OsH//fhoaGjj11FMZM2YM4XCYyspKAGpqati3bx9nnXUWI0eOxLIs3nnnHQAee+yxuOXm5+dz4MCBXrfnT3/6E6eccgq7d+/mgw8+4IMPPuC1117jsccewzRNhgwZ0u1y9+/fz/Dhw8nPzycYDPLoo4/2aJ1HWubgwYOZNm0aP/3pT93XPv7444Sfe/vtt+NmLhobG/n444855ZRTmDp1KvX19axfv959/4033iASiTB79mz+/Oc/8+677wLRHZpYWzr/Di+66CL3c/OPP/6YP/7xj1xwwQU9ylEMbLm47UmFDOhZYvHCiax/bl7C6+ufm8fWV69j8cKJXfyvzLMsi4qKipz7w7Isi3vuuYfTTz+d8ePHU1paytVXX81XvvIVfD4fTz/9NHfccQclJSV85zvf4amnnmLQoEEYhsHPf/5z/vEf/5Hp06cTDofjlvvd736XCy64IO6kuJ5YtWpV3Ne9AMaNG8cJJ5zAs88+yze/+U2eeOKJLk+KW7BgAQcPHmTs2LFcfvnllJaW9midJSUl/P3f/z3jxo1zT4rr6He/+x1/+ctfOPPMMzn77LO7/PrdoUOH+Pa3v83f//3fM378eC677DL35L2hQ4fyxz/+kdtuu42zzz6bsWPHsnTpUhzH4bTTTmPVqlVceeWV7omHsZ2kzr/Dn//852zbto2SkhK+9KUvcccdd/DFL36xp79aMUDl6rYnFQPiwjLBYJCLLrqIt956i7/85S+MGzfOfc+yLK677jp27tzJxIkTeeihh3q0zGy8mMDHH3/KiJMfjHtt3wc3c9xxgzLUIiGEyG3ZOBYka0AcoQcCAZ577jnmzp2b8N6zzz7LiSeeSHV1NYcOHaKmpiYDLTx6KaVoa2tjAOwXiiwk9SOSJbWTaEAM6IZhdHkGLcCGDRuYNWsWABdffHG3A3o4HKatrS3uAbgnMNm23WVsWVZcHLtSWHexaZpxcazYYrFSKiGGaHGaXUwdOY7jTil1F9u2HRf3Z06maVJVVUV7e3vXOR2OHceJi7M5p8/tJ8kpbTlFIhGqq6sJhUI5k1Mu9lM25hQOh6mqqnLbmkpOuWJADOhH0tra6k6TFBQU0NLS0uXP3XfffRQUFLiP4uJi4LOzaevq6tyvAG3bto36+nogeuGLhoYGADZt2kRjYyMQPRGqqakJgKqqKpqbmwGorKyktbUVgIqKCoLBIBC9+EcoFIr7qkUoFKK8vByIfqzw8sv/l9DuT1pa3DOSm5qa3B2WxsZG98pgDQ0N1NbWAlBfX++ehNQfOdm2zezZs3nxxRe7zKmiosLtp9hJZM3NzVmd0+f1k+SUvpz27NnDJZdcwubNm3Mmp1zsp2zM6cMPP+T444/H6/WmlNPGjRvJFQPiM/SYa665hltuuSXuM/Tvfe97XHLJJUyfPp01a9bwwQcfcMsttyT833A4HHeCUltbG8XFxbS0tDB06FB3z03X9bjYsiw8Ho8ba5qGpmndxqZpouu6G8e+jhOLIbpH2DH2er0opWja00bRaT+Pa/ee97/D8OEBDMPAcRwcx0mIbdtGKRX3lanOefRVTpqm0drayuDBg/H5fAk5xWLHcbBt2427yiNbcvq8fpKc0pdT7G9x8ODBGIaREznlYj9lY06madLa2srw4cPdGYFkcmppaWH48OE58Rn6gP8C33nnnUdFRQXTp09n3bp1LFy4sMuf8/v9+P3+hNd1XY/7t3Pc8TuOPYm9Xm9SscfjwdvF9yk1TXOXHyvAznF3be+PnEzTZMuWLVxwwQXud3/jcjocd9f2bMzpSLHklN6cTNNk8+bNXHDBBe4yB3pOnxdLTunJCeD111/nggsuiFt2KjkNdANmyr2srIyKigoWLVrEo48+yuLFiwG47LLLaGxspLS0lEAgwJQpUzLc0qOL1+tl9uzZcX9QQvSU1I9IltROogE15Z5O2fhVhYH4tTXHcWhubuYLX/hC3J6zED0h9SOSla7aycaxIFnyF5Qijyd9jxEjEpc/YkT6lt8XHMdh+/btKd8nXBydpH5EsqR2EskReop7ZekdKD+FY+KP0Dl0M5CeI/Sjs6eFEKJ7coQuxGGO47B7927ZSxZJkfoRyZLaSSQDukiJ4zjs3LlT/qhEUqR+RLKkdhLlzvn6IiMMw2D69OmZboYYoKR+RLKkdhL1+RH6W2+91derEBnkOA67du2SvWSRFKkfkSypnUR9doQ+Z84cTj31VF5//XUmTpzI8uXL+2pVIoNin2MVFRXJ145Er0n9iGRJ7STqs7Pc161bR1tbGx999BFLlizpi1WkRM5yF0IIIWe598Ds2bMZN26c3Noux9m2zXvvvedeM1mI3pD6EcmS2knUpyfFnXHGGZxxxhk0Nzezdu1a9u/f7w7wt912W1+uegAKgufjxJc9e4EAqMFAfn836nMppdi/fz8nn3xyppsiBiCpH5EsqZ1E/XJhmcmTJzNv3jyKiorc1+bMmdPXqz2irJty974C3qru3zengzkjpVXIZIkQQsTLpSn3fvna2siRI7nxxhuT/v+33HILGzduZNSoUTzyyCPubTrb29u54oorCAaD+Hw+/ud//odhw4alq9n9y5wI1undv68G919besG2berr6xk9enTcnY2E6AmpH5EsqZ1E/XJq4IIFC5gzZw7Lli3jnnvu4Z577unx/62trWXPnj1UV1czduxY1qxZ47735z//mXHjxvHKK6/wta99jd/97nd90fx+kg+qsPtHFk63x7S3t2e6CWIAk/oRyZLaidcvA/qPfvQjLrnkEs477zwmT57M5MmTe/x/N2zYwKxZswC4+OKLqampcd8bPXo0hw4dAqC1tZXjjjuu2+WEw2Ha2triHoB7QoVt213GlmXFxbHvPHaM8/IsNC0Wm24cCJhomnJjj0cBikDABBQeTywGTesYO+TldYwtAHTdwe+PxobRMbbx+TrG0fZ6vTZebzT2+XqXU8fYNM24OPYpjWmaaJrG+PHjcRwHpRRKKUwz2vaOseM4cbFlWUeMbduOi9PRTz3NqWMeklPf5uTxeJgwYQJKqZzJKRf7KRtzAjjrrLPQdT3lnHJFvwzoJ510EgsXLmT27Nnuo6daW1vdzzUKCgpoaWlx3zv11FPZvn0748aN47HHHuOrX/1qt8u57777KCgocB/FxcUAbN++HYC6ujrq6uoA2LZtG/X19UB0hqChoQGATZs20djYCEBNTQ1NTU0ALF9eRUlJMwArVlQyenQrAKtWVVBUFARg9epyhg0LEQhYrF5dTiBgMWxYiNWrywEoKgqyalUFAKNHt7JiRSUAJSXNLF8e/Wx98uQmli2L7tDMnNnI0qWbACgra2DJkloA5s6tZ9GibQDMn1/H/PnRnBYt6l1OVVVVNDdHc6qsrKS1NZpTRUUFwWA0p/Lycj799FPefPNNysvLsSyLUChEeXk0p2AwSEVFhduPlZXRnJqbm6mqiubU1NTk7qQ1NjayaVM0p4aGBmproznV19ezbdu2lPuppzmFQiEsy5Kc+iGnXbt2sX37dl577bWcyWmg9VNVdS2v1zbxP09Vs/Z/N/F6bROr/+dlnivfyuu1TZT/uTorc3r//fd5+eWX3an3ZPtp48aN5Ip+OSkuNoCXlJTgOXwWWU8vNPPLX/6SQYMGsWDBArZs2cKjjz7KihUr3Pc+/vhj7rzzTp555hk2bdrEj3/84y6XEw6HCYfD7vO2tjaKi4tpaWlh6NCh7p5bbG8vFseOImKxpmlomubGuq6Rl2cRiWg4jkZenkkkouM4GoGASThs4DgeAgGTUMhAKQgELNrbDTye6NF9e7sXTVP4/bHYweezCYVisUMoZKDrDobhEA4bGIaDrsdiG01TRCKxGCIR3T06N00dn8/m0KGe5dQ5Nk0TXdfd2DAMPB4Ppmni8Xioq6tj9OjR+P1+ILrH6/V6UUq5seM42Lbtxo7jYBhGt7Ft2yil3LirvulNP/UmJ8Mw3Dw6xpJT+nNSSvHOO+8wevRovF5vTuQ00Ppp2b9X8cMfv9rtNvgHS6dx9x0zsi6nSCTC22+/zZlnnum2NZl+amlpYfjw4TlxUly/DOivvPJKwmszZvTsjO3a2loeeOABHn/8ce69915OOeUUrrzySiA6oNu2zQ033EBlZSVPPvkkv/rVr3q03Kw7y70fyFnuQojOmpqC7Hj7Yy669Pdxr69/bh5Djw1QOHIwhYXZew5PqnLpLPd+mXJXSjFjxgz3sXv37h7/3wkTJjBy5EhKS0vZsWMHc+bMYfHixQDMmzePP//5z8ycOZM777yTm2++ua9SEN2wbZva2lq5uINIitRP5hUW5lMy7viE10vGHc/ECYVZO5hL7STql6+t/epXv8Lv9zNlyhRWrlxJbW0tV111VY///09+8pO45ytXrgRgyJAhPP/882ltq+i9QCCQ6SaIAUzqRyRLaidevwzojz32GFdddRVFRUUYhtHjaXGR/XRdZ8yYMZluhhigpH5EsqR2EvXpgH7rrbe6J8Edf/zxPP3001x55ZXcdtttcve1HGFZFrW1tUyYMME94UX0r6amIE17Dnb7fjZ/Bir1I5IltZOoT38Ll156adzzr33ta325OpEBHo+HoUOHujtuov+t/O3rLPv37i8bfNf3p3P3HaldNrivSP3krmBTEwcPf8WtK4MLC8kvLEx6+VI7ifp0QJ82bRpr167FMAwuvfRS9/J8Tz31VF+uVvQjXdc57bTTMt2Mo9rihRMpnVp8xLOUs5XUT+56feVKqpYt6/b96XfdxYy77056+VI7ifr0LPd58+ZRW1tLbW0tM2fOZOfOnUD062YiN1iWRU1NTU5dbWmgGahnKYPUTy6buHgx89avT3h93vr1XLd1KxMPf1spWVI7ifr0CH3v3r384Q9/AGDhwoUsWLCAu+66qy9XKfqZpmkUFRWhaf3yDUiRY6R+cld+YSFaF59tH19SwqAjXKa7p6R2EvXpgG7bNqFQiLy8PEaNGsWzzz7LvHnzePPNN/tytaIfaZrGSSedlOlmiAFK6kckS2onUZ/u2vzsZz9zb4ICkJ+fz9q1a3n44Yf7crWiH1mWRVVVlUx7iaRI/YhkSe0k6tMj9IkTJwJw6NAhjjnmGCC6V/WNb3yjL1cr+pGmaZx66qky7SWSIvUjkiW1k6jPBvQdO3YA0cu+/vd//7d87zxHxT7HEiIZUj8iWVI7ifps1+aOO+5gy5YtbNmyxb1Nncg9lmVRWVkp014iKVI/IllSO4n67Aj9zjvvZMKECUD0++ipuOWWW9i4cSOjRo3ikUcewefzAfDyyy/zzW9+k1NPPRVd13nppZdSbrfoHU3TGDdunEx7iaRI/YhkSe0k6rMBfcKECViWxZo1a6ipqaGlpYVhw4Zx/vnnM2fOnB5fqq+2tpY9e/ZQXV3Nvffey5o1a+Ju7PL1r3894eYtov9omsaIESMy3QwxQEn9iGRJ7STq012ba6+9lvfff59rrrmGZcuWcfXVV7Nz506uvfbaHi9jw4YNzJo1C4CLL76YmpqauPeffvppSktLeeihh464nHA4TFtbW9wDcG+9Z9t2l7FlWXGx4zgJcV6ehabFYtONAwETTVNu7PEoQBEImIDC44nFoGkdY4e8vI5xdEpJ1x38/mhsGB1jG5+vYxxtr9dr4/VGY5+vdzl1jE3TjIvV4Rurm6ZJJBLhhRde4NChQyilUEphmtG2d4wdx4mLY9Nk3cW2bcfF6einnubUMY+BlFNnAyGncDjMunXraG9vP2r6KVtz6sx2Us/JOtzGjtKVUygU4oUXXsA0zZT7KVf06YC+a9cuvv/97zNx4kROPfVUzjnnHL7//e+za9euHi+jtbXVvel8QUEBLS0t7nvnnnsu77zzDi+99BIvvPACW7du7XY59913HwUFBe6juLgYgO3btwNQV1dHXV0dANu2baO+vh6IzhA0NDQAsGnTJvd8gJqaGpoOX6d4+fIqSkqaAVixopLRo1sBWLWqgqKiIACrV5czbFiIQMBi9epyAgGLYcNCrF5dDkBRUZBVqyoAGD26lRUrKgEoKWlm+fLodbonT25i2bLoDs3MmY0sXboJgLKyBpYsqQVg7tx6Fi3aBsD8+XXMnx/NadGi3uVUVVVFc3M0p8rKSlpbozlVVFQQDEZzKi8vxzRNJk6cyIsvvohlWYRCIcrLozkFg0EqKircfqysjObU3NxMVVU0p6amJncnrbGxkU2bojk1NDRQWxvNqb6+nm3btqXcTz3NKRQKYVkW5eXlAyqnzg4ePJj1OX300UdMmjSJzZs3HzX9lK05dbZr199Szml9F1eKC6cpp8bGRvLz89F1PaV+2rhxY5f5D0QeFduV6gO33nore/fuZdasWQwZMoS2tjYqKio4/vjjuf/++3u0jF/+8pcMGjSIBQsWsGXLFh599FFWrFjR5c/5/X4WLlzY5XLC4TDhcNh93tbWRnFxMS0tLQwdOtTdc9N1PS62LAuPx+PGmqahaZob67pGXp5FJKLhOBp5eSaRiI7jaAQCJuGwgeN4CARMQiEDpSAQsGhvN/B4okf37e1eNE3h98diB5/PJhSKxQ6hkIGuOxiGQzhsYBgOuh6LbTRNEYnEYohEdPfo3DR1fD6bQ4d6llPn2DRNdF13Y8Mw8Hg8bgzRvdyOsdfrRSnlxo7jYNu2GzuOg2EY3ca2baOUcuOu+qY3/ZTrOX3ySTsjTn4wrub3NtzEiBGDB2xOudhP6c7J50vXNiIEx8TXjx5Zgm0Nwe+3sG0Ny9Lw+y0sS8O2e7bdG5b3Ed8JxZ+JftPevfiHDs2afmppaWH48OEcOHDAPXgcqPp0QAd444032LBhA62trQwdOpTzzjuP8ePH9/j/19bW8sADD/D4449z7733csopp3DllVcC0UE51gFXXXUV119/PdOnT+/Rctva2igoKEi5EwfSjX76oqdN06SiooJZs2bh9XrTvwLRIx9//GnCgL7vg5s57rhBGWpRz0j9pCZ9259PEwZ0Dt0MpFY/x/AxS4n/nPvmffvScunXdNVOusaCbNDnN5EdP358wgD+2GOPsWDBgh79/wkTJjBy5EhKS0sZNWoUt956K4sXL2blypU8+eST/PrXv8YwDM4///weD+YifQzDoLS0VO5HnKS07hAeE/803ecL9cUOodSPSJbUTqI+PUKPXVymI6UU3/rWt3jttdf6arU9IkfoIhtk+xFWR1I/2Sfb66cvj9DTRY7Qe+i8885j7ty5dN5n6M1JcSK7maZJeXk5ZWVlMmUqek3qRyRLaidRnw7oY8eO5f7772f48OFxr19yySV9uVpx2GCayKfJfd70eqf3CwvJLyxMaR2GYTBr1iyZ9hJJkfoRyZLaSdSnv4n169e7N2Xp6Pnnn+/L1YrDJrGSL7HMff6bc+Lfn37XXcy4++6U1yN/UCIVUj/ZaR8jOI5DKS3jU+DBzi+m8eQOIxJJ27JyQZ/+JQ0ePLgvFy8+x2YW8wGlXMtFca/PW7+ewNChDE7x6BxwvzMr0165pz9meKR+RLKsQEBqpxPZNc5hBynE6aKLjy8pSdtJKYZhUFZWJkdZOag/ZnikfkSyjPZ2qZ1O5DchUtbxIhgid/THDA9I/YgkeTxSO53IbWpESizLoqKiIqeuhyyiDlLIXkoSXj++pITCiRNTnm4HqR+RPCsvT2qnE9m1ESnxer185StfyXQzxAAl9SOS5W1vl9rpRI7QRUqUUrS1tSVca0CInpD6EclSmia104kM6FlK4UnLYx9dfEVkxIjoJabS8LAGDaK6ulqmvURSLMuS+hFJsfx+qZ1OZMpdpMTb3i4XChJJ83q9Uj8iKbLtSTQgjtBvueUWSktLmTdvHpEOFxIoLy9n6tSpTJs2jRtuuCGDLTx6OYdvP+g4TqabIgYgx3GkfkRSZNuTKOsH9NraWvbs2UN1dTVjx45lzZo17nvjxo2jqqqKV199lZaWFjZv3pzBlh6dbJ+PzZs3u/cdFqI3bNuW+hFJkW1Poqwf0Dds2MCsWbMAuPjii6mpqXHfGzVqlPsdRK/Xe8TvI4bDYdra2uIegFsMtm13GVuWFRfH9gY7xnl5FpoWi003DgRMNE25scejAEUgYAIKjycWg6Z1jB3MvDwguhdqxWJdx/L7o7FhuLFtGFg+nxvbsdjrxe7id9Lxdcvvx+kY63o0zsvD0aLlYXaMAwFUh9gIh93+UUqhlMI0Tfd5LHYcJy6Ofe7VXWzbdlycjn7qGJumGRfHTqyJxbG290dOXq+N1xuNfT4bw4jFlhv7/RaG4bixrn9Wex7tAHj2JvQz+h7wNJEX2N+r2svL6xD7Ez+fTGc/6brO7Nmz8Xg8Wd9P2Vp70T6LtlfXHfyH+8wwOsY2Pl/HOLH2OkvHNiK27epIHf4ZiJ7YFosdTevVdk+zLC688EK8Xm/K/ZQrsn5Ab21tdW9pV1BQQEtLS8LPbN26lebmZiZMmNDtcu677z4KCgrcR3FxMQDbt28HoK6ujrq6OgC2bdtGfX09EJ0haGhoAGDTpk00NjYCUFNTQ1NT9LKYy5dXUVLSDMCKFZWMHt0KwKpVFRQVBQFYvbqcYcNCBAIWq1eXEwhYDBsWYvXqcgCKioKsWlUBwOjRrVSuWAFAc0kJVcuXA9A0eTI1y6JX7mqcOZNNS5cC0FBWRu2SJQDUz53LtkWLojnNn0/9FVck/C7e/Na3aCgri+a0dCmNM2dGc1q2jKbJkwGoWr6c5pLod5ArV6ygdfRoACpWrSJYVARA+erVHPrCF2hqaqK8vBzLsgiFQpSXR3MKBoNUVERzam1tpbKyMppTczNVVVXRnJqa3J20xsZGNm3aFM2poYHa2tpoTvX1bNu2LeV+qqqqork52k+VlZW0tkb7qaKigmAw2k/l5eWEQiH3kqT9kdP8+XXMnx/NadGibcydG81pyZJaysqiOS1duomZM6M5LVtWw+TJn9XeiBNeg8DvE/oZ/xMQ+A1fmfNkr2pvxYpoTiUlzdx554aExX60e3fa+mnXrl3s27dvQPRTttZeSUkzy5dHc5o8uYlly6I5zZzZyNKl0ZzKyhpYsiSa09y59Sxa9FntXT6ntssdwnWTL+N1TyHlNyS/jVi/cmXCcsNDh1K+enU0p6IiKlatiuY0enSvtnvvX3IJGzZswHGclPpp48aNCW0cqPr0fujp8Mtf/pJBgwaxYMECtmzZwqOPPsqKw50O8OGHH/KNb3yDP/7xjxx3hMuZhsNhwuGw+7ytrY3i4mJaWloYOnSou+em63pcbFkWHo/HjTVNQ9M0N9Z1jbw8i0hEw3E08vJMIhEdx9EIBEzCYQPH8RAImIRCBkpBIGDR3m7g8USPsNrbvWiawu+PxQ4h3yC8oRCOpuH4fBihEI6u4xgGRjiMYxg4uo4RDmMbBkrTMCKR6F61pqFHItheL58qxUOd9kCXeL0MUgrdsrD8fjTbRovFloVm21h5eWiRCJoTnS3QY/Hho3LP4Rig6tlnmTp1KnmH96gty8Lr9aKUcmPHcbBt240dx8EwjG5j27ZRSrlxV33Tm37qHJumia7rbmwYBh6Px41jeXSM+yInw9DdIyTT1PH5bBwHLEvH57NwHA+WpeP3W9i2hmVp+P0WlqVh29HaC0c+RalD+PwWpqmhHA1/nkUkoqMcD3l5eYRDx/a49nw+m1AoGh/r3ct3wifE1c939uwhMHx4WvrJcRxeffVVpk6dis/ny9p+ytba8/lifeYQChnouoNhOITDBobhoOux2EbTFJFILIZIJFp7tlaFo7/a7bbzB04Vd1svJ7WNOJCXx89Dobjl3QT4AwG87e0oTcPy+/G2t+NoGrbP1+PtXnjwYF5du5YZM2bgOXxj+GT6qaWlheHDh+fE/dCzfkCvra3lgQce4PHHH+fee+/llFNO4corrwTg4MGDlJWV8Ytf/IJx48b1arnpuqn94TpKO0XqCw4CHwOdj93mAQFgMJCf8lqA7C6hrNZX9ZMux/AxSzt99fHmffvSdi8AkZr01E8QPAfdZ1uJv2h/oQpSyMHO/6lHurrb2s3AoKSW1oU0bHvSNRZkg6yfcp8wYQIjR46ktLSUHTt2MGfOHBYvXgzAww8/zM6dO7nhhhuYOXMmr7zySoZbm11eJ3Ew5/Brvzn8fqocXWf37t1ypmkWSdc1DPrjOgaOYUj9ZFw+qEL3MVE1xT2SHcz7mmx7Eg2I76H/5Cc/iXu+8vDnMrfffju33357Jpo0IEwETj/C++m4ua1jGOzcuZPjjz8eTcv6/UORZaR+RLKkdhINiAFdJCefNE2pH4ERDjN9+vQ+XovIVVI/IllSO4lkt0akxDEMdu3aJdNeIilSP7krCHTxZUr2Ak2H30+F1E4iGdBFSuRzLJEKqZ/c1dfn8EjtJMr6s9z7ytFwlnu/OTpLKC36on7SWTt9fpYySP2kIJvrJwhHPJ0uLd+ykbPc48gRukiJbRi89957cvlFkRSpn9yVDxQe4ZHqYC61k0gGdJESpWns379f7kkskiL1I5IltZNIznIXKTEiESZNmpTpZogBSupHJEtqJ5EcoYuU2IbB22+/LdNeIilSPyJZUjuJZEAXqdE02tvbM90KMVBJ/YhkSe0kkCl3kRI9EjniXe7EwBW7F0Bne0nfvQCkfkSypHYSyRG6SInt9bJ9+3aZ9spB/XEvAKkfkSypnUQD4gj9lltuYePGjYwaNYpHHnkEn88HRG8reN1117Fz504mTpzIQw89lOGWCpE7+uNeAEKI9Mn6I/Ta2lr27NlDdXU1Y8eOZc2aNe57zz77LCeeeCLV1dUcOnSImpqaDLb06KSbJuPGjUPX9Uw3RaRZX3+PGKR+RPKkdhJl/RH6hg0bmDVrFgAXX3wxjzzyCFdddZX73qWXXuq+V1NTw9SpU7tcTjgcJhwOA6CU4qOPPgJg//79AO60ja7rcbFlWXg8HjfWNA1N09wYNPx+i0hEQykNv98kEtFRSiMvzyQcNlDKQ16eSSgU/XXn5VmdYi8ej8Lvj8UOn/j8eMNhHI8Hx+fDCIdxNA3HMDAiERxdx9F1jEgEW9dRmoZhmti6DpqGbprYRnQdumVhe73gOOi2jeX14onFPh+abaPFYstCcxwsvx8tEkFTCtPvR4/FeXkY4TCew7HHcdheVcWYMWPw+/1AdObE6/WilHJjx3GwbduNHcfBMIxuY9u2UUq5cVd905t+6hybpomu625sGAYej8eNY3l0jPsiJ9AxDPvwOnS8XhvHAdvW8XotHMeDbev4fBa2rWHbGj6fhWVpOE7XtdemSOgnIxSKrqNT7A2FUB4Plt+PNxTC8Xiwfb5+qz2laeyormbMmDF4vd6s7adsrT2Ibi98Podw2EDTHAzDIRIx0HUHXY/FNpqmMM1YDKbZde212aR1G9FXtRcJBHjrlVcoKSlxt/PJ9FNLS4s7Lgx0WT+gt7a2csIJJwBQUFDg/vJj78Uu1df5vc7uu+8+li1blvD6ySefnHIbD+8nJMSHa7dHsVLx8Rdiy1Hqs4U6DkQi0di2o48jxZb12YpMs+s4trzOcW+SmjEDkbx0d1MBpFZ8sQX1V+3JHbNSku5uKuj4BNKzjeguTqX22tth5kzSJRgMUlBQkLblZULWD+hDhw6lra0NiA7gw4YN69F7nd1+++3cfPPNQHRPrK2tDdM0GT58OJ6+uiD7UaCtrY3i4mIaGxsH/HWQRf+T+hHJSlftKKUIBoPugeNAlvUD+nnnnccDDzzAggULWLduHeeff37cexUVFUyfPp1169axcOHCbpfj9/vdKWFgwO+JZZshQ4bIBlkkTepHJCsdtZMr40HWnxQ3YcIERo4cSWlpKTt27GDOnDksXrwYgMsuu4zGxkZKS0sJBAJMmTIlw60VQgghMuOovX2qSI9cuvWg6H9SPyJZUjuJsv4IXWQ3v9/PXXfdFfdxhhA9JfUjkiW1k0iO0IUQQogcIEfoQgghRA6QAV0IIYTIATKgCyGEEDlABnQhhBAiB8iALoQQQuQAGdCFEEKIHCADuhBCCJEDZEAXQgghcoAM6EIIIUQOkAFdCCGEyAEyoAshhBA5QAZ0IYQQIgcYmW5AJiilaGtrIxgMkp+fj8fjyXSThBBCZIBSimAwyAknnICmDexj3KNyQA8Ggxx77LGZboYQQogs0djYyIknnpjpZqTkqBzQ8/PzaWxspLi4mMbGRoYMGZLpJg1YlmWxceNGJk+ejGEcleUkUiD1I5KVrtppa2ujuLiY/Pz8NLYuM47KvyCPx+MO4kOGDJEBPQWO41BSUsKxxx474KerRP+T+hHJSnft5MJHr0flgC7SR9M0ioqKMt0MMUBJ/YhkSe0kkl1ikRLLsqisrMSyrEw3RQxAUj8iWVI7iWRAFynRNI1x48bJdKlIitSPSJbUTiKZchcp0TSNESNGZLoZYoCS+hHJktpJJLs2IiWmabJu3TpM08x0U8QAJPUjkiW1k0gGdJESXdeZNGkSuq5nuiliAJL6EcmS2kkkU+4iJZqmMWzYsEw3QwxQUj8iWVI7ieQIXaTENE2ef/55mfYSSZH6EcmS2knkUUqpTDciE9ra2igoKODAgQNyYZkUxK6DLNfEF8mQ+hHJSlft5NJYIEfoIiWxq+7l4sb4mWee4ZxzzmH8+PGcccYZXHjhhTiOk9Iy7777biKRiPv8mmuuYcWKFT36vyeffDJjxoxh/PjxjB07lv/8z/9Muh1lZWXs3LkTgJ07dzJx4kQmTJjAI488wnXXXUd1dXWvlpfs76q7+vnZz37Gvn37etUGcXTJ5W1P0lSW27Jli5o2bZqaPn26uuKKK1QkEnHfM01TXX311WratGnqxhtv7NVyDxw4oAB14MCBdDf5qBKJRNTatWvj+iUXNDU1qeOOO0598MEH7mtbt25VjuOktFxABYNB9/nVV1+tHn744R7935NOOkm9+eabSiml/va3v6mCggL117/+NaX2KKXUj3/8Y/Uv//IvSf//VH5X3dVPx1x7wzTNXv8fMTCla9uTS2NB1h+hFxUVsW7dOl555RVOO+001q5d67737LPPcuKJJ1JdXc2hQ4eoqanJXEOPUoZhMGvWrJy7sUZTUxOGYTB8+HD3tYkTJ7pHA1u2bGHKlCmUlJTwxS9+kddeew2ADz74gC984Qvu/zl48KD7f66//noApk6dyvjx490j0B07dnDRRRdx+umnc/nll8cdwXenuLiY008/nXfffZcHH3yQSZMmMWHCBL74xS+yceNG9+c2bNhAaWkpZ599NiUlJfzpT38Cokf727dv57HHHuOnP/0pTz31FOPHj2fHjh3MnDmT5557DoADBw5w3XXXcdZZZ3H22WezcOHCXv+u6uvrueSSS5g0aRJnn302v/jFL9yf27x5M8uXL+fcc89123fPPffw0UcfMXfuXMaPH88bb7zBwYMHWbhwIePGjWPcuHEsW7bMXcbMmTO54447uPDCC5k9e/bn/u5EbsjVbU9KMr1H0Rt33nmneuaZZ9znt956q3rllVeUUkqtWbNG3X///T1eVi7tlWWS4zgqEomkfOSabWzbVpdffrkaOnSo+upXv6qWL1+uPvzwQ6WUUuFwWBUXF6sXXnhBKaVUdXW1GjlypDp48KBqaGhQw4cPd5cTDAZVxz8zujhCnzJlijp06JCyLEtNnTpVPfHEE122qeNR67Zt21R+fr5699131b59+9yf2bBhgzrzzDOVUkp98skn6vjjj1evvfaam9Mnn3ySsKy77rpLffe733WXMWPGDPXss88qpZS65ppr1A033KBs21ZKqbh19eR3ZVmWOvfcc1VdXZ1SSqlPP/1UnXXWWWrr1q1u+1555RXlOE637VNKqdtuu03NmzdP2batDh48qMaPH6+efPJJt71lZWU5N0skjixd255cGguy/gg95m9/+xvr16/n0ksvdV9rbW11T2IoKCigpaWl2/8fDodpa2uLewDYtu3+21VsWVZcHPtcsLvYNM24WB0+5zAWK6USYiAudhwnLo5dq7i72LbtuLg/czJNk/Lyctrb23MmJ6UUHo+HP/zhD7z22mvMnj2b6upqzjzzTOrr69m+fTs+n49/+Id/wDRNpk2bxogRI3j99deJ6ZhTTKx9ndv+1a9+FZ/Ph67rnHvuudTX13eZE8DcuXM5++yzWbx4Mb/97W/5u7/7O7Zu3cqMGTM488wzuf7669mxYweffvopNTU1jB07lkmTJrk55efnx+Ua66dYOx3Hcd93HIfnnnuOW2+91W3Dcccdl9BPjuPw9NNPU11dzaxZs3jttdc488wzeeedd3jnnXd46623+MY3vsH48eOZMmUKwWCQHTt28OqrrzJmzBj2799PKBQCYNiwYXHX5o710/r167nuuuvweDwMGjSIefPm8eKLL7o1N3/+fAzDyInay7VtRF/lFA6HKS8vd9uaSk65YkAM6G1tbXzzm9/kkUcewev1uq8PHTrUHZhbW1uP+J3E++67j4KCAvdRXFwMwPbt2wGoq6ujrq4OgG3btrkb1draWhoaGgDYtGkTjY2NANTU1NDU1ARAVVUVzc3NAFRWVtLa2gpARUUFwWAQgPLyckKhEJZluUUYCoUoLy8HIBgMUlFR4eZSWVkJQHNzM1VVVUB0ajP2sUJjYyObNm0CoKGhgdraWiA6vblt27Z+y8myLP7hH/6BF198MWdy6thPp512GldffTX//M//zHnnncdTTz1FTU0NHo8nLifLsvjrX/+KYRhYluXmFDvxLJZTTMecWltb3Zz279/vTsV3zsmyLNasWcPDDz/MU089xdy5c3nppZeYO3cuDz74IA8++CBr165FKcWf//xn2tvbu8zJsiyUUu6Jb5FIhA8++MBty/79+91+im1ge9JPuq5z3nnnsXbtWsaNG8dvfvMblFIUFBTwhz/8gTfeeIPf/OY3vPjii8yfP593332XSCRCWVkZmzdvjuun2MY21k9KKTZv3uz2U11dnbtx/+STTwgEAjlXe7n495TOnD788ENGjhyJYRgp5dTxI6oBr49nAFJmWZa69NJL1fr16xPee+aZZ9Qdd9yhlFLquuuuUzU1Nd0uJxQKqQMHDriPxsZGBaiWlhZ3PZZlJcSmacbFsanH7uJIJBIXx6aDYnHHaaJYrJSKi23bjotjJ/p0F1uWFRd3lUdf5WTbtvr0009VOBzOmZwcx1GNjY3q5ZdfdnPau3evOv3009Uf//hHdfDgQVVcXKxefPFFFYlE1GuvvaZGjhypWltblWmaavDgwWr79u1KKaV+8pOfuFPulmWp/Px89eGHH7ptv/rqq9VDDz3ktv2mm25SP/jBD7rMKTYN3TGPTz75RPn9frV3714ViUTUD3/4Q7euP/nkEzVy5Eh3StuyLLVnzx7lOI466aST1Ouvv66Uin6UddNNN7l9MH36dPXss88q27bdKXfTNJVpmmrfvn0J/fTBBx+oV1991c2ppaVFnX766eqZZ55Rpmmqv//7v1ePPPKIm8fbb7+tPvnkE7Vv3z41cuRIVVlZqSKRiDJNU33yySfKNE111llnqVdffdXtp9tuu01985vfdKfcJ0yYoJ588knlOI6aPn26+t///d+cqb1c20b0VU6maapgMOjWdrI5ffLJJzkz5Z71ZxM8+eST1NTUEAwG+eEPf8j/+3//j8rKSlauXMlll13G2rVrKS0tZcKECUyZMqXb5fj9fvx+f8LrscsGdrx8YMe44wkXPYk7ziD0Jt6z5yBNew522/7CkYMpLMx37yykaZobd9f2/sjJNE1efPFFysrK3JOgYj/j8XjcuGN7exJnMieITtX9+7//O4sWLeKYY47BsiyuvvpqvvrVrwLw9NNPc+ONN/Lpp5+Sl5fHU089RUFBAQA///nPueyyyzjxxBP5x3/8x7j2fve73+WCCy4gEAi4Ryaaprm5dPwd9CSPYcOGcc899/DFL36RUaNG8eUvf9nNY/Dgwfzxj3/ku9/9LsFgEI/Hww9/+MO4n4FoP3Xsg1g/aprGQw89xE033URJSQk+n49JkybxX//1Xwn9dM8999DQ0BD3u/qnf/onIHry6k033cSDDz6Ibdscd9xx/P73v6eoqIinnnqKRYsWYRgGmqa57bvxxhu59tprOeaYY3j00Uf5wQ9+wLe//W1KSkoAuOKKK7jiiivc9sceuVB73cWSU3yslOKll16irKwsbtmp5DTQyYVlsuRiAnff+wrL/r2q2/fv+v507r5jRj+2SAwUTU3BHu0MCiESZdtYkIrc2TUZ4BYvnEjp1GIuuvT3ca+vf24eQ48NUDhycIZadmRKrvSVcSt/+3qf7AwGm5o4ePjz0q4MLiwkv7Cw18vtSOpHJEtqJ5EM6FmisDAfw0g8R7Fk3PEcd9ygDLSoZyzLcs9u7jjtJfpPX+0Mvr5yJVUdvu/d2fS77mLG3XcntewYqR+RLKmdRDKgi5R4vV4uueSSTDfjqNZXO4MTFy+muLSU3190Udzr89avJzB0KINTPDoHqR+RPKmdRDKgi5Q4jkNrayvHHnuse7KKyA35hYVoXZwwdHxJCYOOOy4t65D6EcmS2kkkv4UUeTzpe4wYkbj8ESPSt/y+YNs2mzdvjrtoihA9JfUjkiW1k0iO0EVKvF6vXD9bJE3qRyRLaieRHKGLlDiOw759+1K+rag4Okn9iGRJ7SSSAV2kxHEctm/fLn9UIilSPyJZUjuJZMo9awTB83Hiy569QADUYCD7Lg5iGAYXXHBBppshBiipH5EsqZ1EcoSeLbyvQ+D3ia8Hfg+B30Tfz0KO47B7927ZSxZJkfoRyZLaSSRH6NnCnAjW6d2/r7LzSnGO47Bz506OP/54+eqI6DWpH5EsqZ1EMqBnjXxQ2Tel/nkMw2D69OmZboYYoKR+RLKkdhL1+W7NW2+91derEBnkOA67du2SaS+RFKkfkSypnUR9NqDPmTOH2267jSVLlnDbbbeltKxgMMjkyZMZPHgw27dvj3vv5Zdfpri4mJkzZ3LhhRemtB7Re/I5lkiF1I9IltROoj6bcv/Wt75FW1sbRUVFLFmyJKVlBQIBnnvuOW699dYu3//617/OT37yk5TWIZJjGAZTp07NdDPEACX1I5IltZOoz47QZ8+ezbhx40jH7dYNw+C4I1w7+umnn6a0tJSHHnqo258Jh8O0tbXFPQD3soG2bXcZW5YVF8f2BjvGeXkWmhaLTTcOBEw0Tbmxx6MARSBgAgqPJxaDpnWMHfLyOsYWALru4Pdbh38nHWMbn69jHG2v12vj9UZjn693OXWMTdOMi2N9apomlmVRX19PKBRCKYVSCtOMtr1j7DhOXGxZ1hFj27bj4nT0U09z6pjHQMqps3TkZB9ub0fpzMk0Td577z3C4fBR00+SU3pyikQivPvuu267U8kpV/TpZ+hnnHEG3/nOd2hubuY3v/kN999/P8uXL2f58uVpW8e5557LO++8w0svvcQLL7zA1q1bu/y5++67j4KCAvdRXFwM4E7h19XVUVdXB8C2bduor68HoLa2loaGBgA2bdpEY2MjADU1NTQdvlf08uVVlJQ0A7BiRSWjR7cCsGpVBUVFQQBWry5n2LAQgYDF6tXlBAIWw4aFWL26HICioiCrVlUAMHp0KytWVAJQUtLM8uXRe11PntzEsmU1AMyc2cjSpZsAKCtrYMmSWgDmzq1n0aJtAMyfX8f8+dGcFi3qXU5VVVU0N0dzqqyspLU1mlNFRQXBYDSn8vJy2tvbaWlpYd26dViWRSgUorw8mlMwGKSiIppTa2srlZXRnJqbm6mqiubU1NRETU00p8bGRjZtiubU0NBAbW00p/r6erZt25ZyP/U0p1AohGVZlJeXD6icOjt48GDKOW3YsCFhuR/t3p3WnPbv389f/vKXo6afJKf05PTBBx/Q0NCAUiqlnDZu3Eiu8Kh0HEJ/jsmTJzNv3jyKiorc1+bMmdPr5VxzzTXccsstjBs3rsv3f/nLX+L3+1m4cGHCe+FwmHA47D5va2ujuLiYlpYWhg4d6u656boeF1uWhcfjcWNN09A0zY11XSMvzyIS0XAcjbw8k0hEx3E0AgGTcNjAcTwEAiahkIFSEAhYtLcbeDzRo/v2di+apvD7Y7GDz2cTCsVih1DIQNcdDMMhHDYwDAddj8U2mqaIRGIxRCK6e3Rumjo+n82hQz3LqXNsmia6rruxYRh4PB43huhebsfY6/WilHJjx3GwbduNHcfBMIxuY9u2UUq5cVd905t+yvWcPvmknREnPxhX83sbbmLEiMEp5XRw714eOuGEuOV+Z88eAsOHSz9JTjmRU0tLC8OHD+fAgQMMGTKEgaxfvrY2cuRIbrzxxj5Zdltbm9sJ1dXVXH/99V3+nN/vx+/3J7yu63rcv53jWAEeKQ6FOsZeN25vP3Ks1Gex43g6xBqhkJYQ27aGbUdjy9KwrFj8WXs7xqb5WRyJ6MTS6klOHWOv19ttbNs29fX1jB49Gs/hW7rFfsbj8bhx7I+np3F3/ZFKP/U0pyPF2ZxTZ537I5mc9C6Wr2mau95Uc7Jtm7fffpvRo0e76831fpKc0pMTwHvvvcfo0aPTltNA1y+ZLFiwgDlz5lBSUuJuZO68885eLaOsrIw33niDd955h8WLF7NhwwZWrlzJk08+ya9//WsMw+D888+X7yVmQHt7e6abIAYwqR+RLKmdeP0y5T5hwgS+/e1vx025Z/q2d21tbRQUFKQ8zdJX9xnvC33f0yJTPv7404Qp930f3Mxxxw1KabmffvwxD44YEffazfv2MegIJ6kKMZCkayzIBv1yhH7SSSd1+bm2GPhs26auro4zzjgjbnpLiJ6Q+hHJktpJ1C8Dent7O7Nnz46bck/nme5CCCHE0a5fBvTvf//7/bEakQG6rnf7rQPx+dL6kc0x8U87zZQnvcilqS+mW1I/IllSO4n65RY1SilmzJjhPnbv3t0fqxX9wLZtamtr3a+HiOyxjxEoPCk99tHFXsGIEdE9kTQ8bL9f6kckRbY9ifplQP/Vr37lXqBi5cqV7gUDRG4IBAKZboIYqBxH6kckTWonXr9MuT/22GNcddVVFBUVYRgGv/rVr/pjtaIf6LrOmDFjMt0MMUDpliX1I5Ii255EfXqEfuutt3Lbbbfxb//2bxx//PE8/fTTaJqW8t3XRPawLIvNmzfn1PWQRf+xfD6pH5EU2fYk6tMj9EsvvTTu+de+9rW+XJ3IAI/Hw9ChQ91vLwjRGx7HkfoRSZFtT6I+HdCnTZvG2rVrMQyDSy+91P2u4FNPPdWXqxX9SNd1TjvttEw3QwxQumVJ/YikyLYnUZ9Ouc+bN4/a2lpqa2uZOXMmO3fuBKI3URG5wbIsampqZNpLJMXy+6V+RFJk25OoT4/Q9+7dyx/+8AcAFi5cyIIFC7jrrrv6cpWin2maRlFRUdwNE4ToKc22pX5EUmTbk6hPB3TbtgmFQuTl5TFq1CieffZZ5s2bx5tvvtmXqxX9SNM0TjrppEw3QwxQmmVJ/YikyLYnUZ/u2vzsZz+jra3NfZ6fn8/atWt5+OGHe7WcYDDI5MmTGTx4MNu3b497z7IsrrnmGkpLS1myZEla2i16zrIsqqqqZNpLJMXy+6V+RFJk25OoTwf0iRMnMmLECA4dOvTZCjWNb3zjG71aTiAQ4LnnnmPu3LkJ7z377LOceOKJVFdXc+jQIWpqalJut+g5TdM49dRTZdpLJEWzLKkfkRTZ9iTqs9/Ejh072LFjB2+99RZ33313SssyDIPjurld44YNG5g1axYAF198sQzo/Uw+xxKpkM/QRbJk25Ooz34Td9xxB1u2bGHLli00Njb21WpobW1172FbUFBAS0tLlz8XDodpa2uLewDudYBt2+4ytiwrLnYcJyHOy7PQtFhsunEgYKJpyo09HgUoAgETUHg8sRg0rWPskJfXMY5OKem6g98fjQ2jY2zj83WMo+31em283mjs8/Uup46xaZpxsTp8Y3XTNDFNk5deeon29naUUiilMM1o2zvGjuPExbFpsu5i27bj4nT0U09z6phHf+TUuZ8MIxZbbuz3WxiG48a6nlh7nanDGzozEEB5PKhYDCiPB/PwZTOVprmxo2mYeXlubPv9Cct1dB3r8Ou2YWD5fG5sx2KvF9vrjcY+H7YRPV3H6hj7/UQGDaKyspJQKJT1/ZSLtTeQcwqHw7z00ktuW1PJKVf02YB+5513smDBAq6++mp+9KMf9dVqGDp0qDs4t7a2MmzYsC5/7r777qOgoMB9FBcXA7ifydfV1VFXVwfAtm3bqK+vB6C2tpaGhgYANm3a5O6c1NTU0NTUBMDy5VWUlDQDsGJFJaNHtwKwalUFRUVBAFavLmfYsBCBgMXq1eUEAhbDhoVYvbocgKKiIKtWVQAwenQrK1ZUAlBS0szy5dFr30+e3MSyZdEZiJkzG1m6dBMAZWUNLFlSC8DcufUsWrQNgPnz65g/P5rTokW9y6mqqorm5mhOlZWVtLZGc6qoqCAYjOZUXl5OJBLhjDPOoKKiAsuyCIVClJdHcwoGg1RUVLh9U1kZzam5udm9nn9TU5M7q9LY2MimTdGcGhoaqK2N5lRfX8+2bdtS7qee5hQKhbAsi/Ly8n7JqXM/zZ0bzWnJklrKyqI5LV26iZkzozktW1bD5MmJtdfZwcLCaE6rVxMaNgwrEKB89WqsQIDQsGGUr14dzamoiIpVq6I5jR5N5YoV0ZxKSthw550Jy/3o/PPZtDR6D7aGsjJqD5+7Uj93LtsWLYr20/z51M2fH+2nRYuoP/xxWe2SJTSUlUX7aelSdp9/PuPGjWPTpk1Z30+5WHsDOae//e1v+Hw+NE1LKaeNGzeSKzwqtivVByzLYs2aNdTU1NDS0sKwYcM4//zzmTNnDobR+xPsr7nmGm655Za4W+b98Y9/ZOvWrfzoRz9i0aJFLFy4kClTpiT833A4TDgcdp+3tbVRXFxMS0sLQ4cOdffcdF2Piy3LwuPxuLGmaWia5sa6rpGXZxGJaDiORl6eSSSi4zgagYBJOGzgOB4CAZNQyEApCAQs2tsNPJ7oEVZ7uxdNU/j9sdjB57MJhWKxQyhkoOsOhuEQDhsYhoOux2IbTVNEIrEYIhHdPeozTR2fz+bQoZ7l1Dk2TRNd193YMAw8Ho8bx/q6Y+z1elFKubHjONi27caO42AYRrexbdsopdy4q77pTT9la06GkdhPjgOWpePzWTiOB8vS8fstbFvDsjT8fgvL0rDtjrXXDsc8GFfze0PLGeEcxAwEMEIhUAorEMBobwePBysvD297O0rTsPx+vO3t0aNynw9vKISjaRz0enmow98NwHd0nYBhYITD2IaB0jSMSCR65K1p6JGIe3Sum2b0qN1x0C0Ly+fDE4v9fjTbRjPNrO+nXKw9ySna9paWFoYPH86BAwfc2d6Bqk8H9G9+85ucccYZXHzxxRQUFNDa2sq6deuoq6vjd7/7Xa+WVVZWxhtvvMFJJ53E4sWL2bBhAytXrsSyLP75n/+Z999/nwkTJvDzn/+8R8tra2ujoKAg5U4cSFcd7IueNk2TyspKLrjgAryHN+Ki59JXP58mDOj7Dv2Y4zjUzc/3eKk82Om1m4FBKS31M2ZeHpVr10r9iF5L17YnXWNBNujTAX369Old3iq1u9f7kwzo6eE4Dq2trRx77LFyckoSjvYB3dE0Wj/+WOpH9Fq6tj25NKD36YVlJk+ezIIFC5g1axZDhgyhra2NiooKJk+e3JerFf1I07Ruz1sQ4vNojiP1I5Ii255EfbpLfP/993PzzTcTDAZ56623OHjwIDfffDP3339/X65W9CPTNHn++efds1OF6A0zEJD6EUmRbU+iPj1CBxg/fjzjx4+Pe+2xxx5jwYIFfb3qo95gmsinyX3e9Hqn9wsLyT98JnSyDMOgtLQ0qZMchTDCYakfkRTZ9iTq09/Ejh07El5TSrFy5UoZ0PvBJFbyJZa5z39zTvz70++6ixkpXvTH4/EM+M+dBr4geD5OeHWb53iGEqJQBSnkYAba9fk8jiP1I5Ii255EfTqgn3feecydO5fO593t2rWrL1crDtvMYj6glGu5KO71eevXExg6lMEpHp1DdNqrvLycsrIyOUs5U7yvgzfxJNOLAtcCcJf5f9xt/l+vFxsEEncTYC8QAAYD+b1eajwzEKD8T3+S+hG9JtueRH06oI8dO5b777+f4cOHx71+ySWX9OVqxWEHKcTpoouPLylhUDeX0u0twzCYNWuWTHtlkjkRrNPdp1uJn4opVMGkFvs60NV3UX5/+N/pwIyklvwZIxSS+smwpqYgTXu6n8EpHDmYwsJUd93ST7Y9ifr0N7F+/XqOOeaYhNeff/75vlyt6GfyB5Vp+aA+2+BO7HDeRComAqcf4f3B6VjJ4QuDiMxZ+dvXWfbv3X+N+K7vT+fuO1LddesbUjvx+vQs98GDB8t3S3Ncx0s6itySDxQe4ZGOYzYrEJD6ybDFCyey/rl5Ca+vf24eW1+9jsULJ2agVZ9Ptj2JZPdGpMQwDMrKymRPWSTFaG+X+smwwsJ8DCPxwKtk3PEcd1y6LiGUfrLtSSSHzyJlsocskubxSP2IpEntxJMBXaTEsiz3TmtC9JaVlyf1I5Ii255EMlchUuL1evnKV76S6WaIAcrb3i71I5Ii255EcoQuUqKUoq2tLeFaA0L0hNI0qR+RFNn2JBoQA/ott9xCaWkp8+bNIxKJuK+//PLLFBcXM3PmTC688MIMtvDoZVkW1dXVMu0lkmL5/VI/Iimy7UmU9QN6bW0te/bsobq6mrFjx7JmzZq497/+9a/z8ssv89JLL2WohUc3r9fLJZdcIldqEknxtrdL/YikyLYnUdYP6Bs2bGDWrFkAXHzxxdTU1MS9//TTT1NaWspDDz10xOWEw2Ha2triHgC2bbv/dhVblhUXO46TEOflWWhaLDbdOBAw0TTlxh6PAhSBgAkoPJ5YDJrWMXbIy+sYR/dAdd3B74/GhtExtvH5OsbR9nq9Nl7DTvhd2D3IqWNsmmZcHJviMk0T27b55JNPCIfDKKVQSrl3P+oYO44TF8f2qruLbduOi9PRTz3NqWMe/ZGT12vj9UZjn8/GMGKx5cZ+v4VhOG6s60euPTMQQB2+BoQZCKA8HlQsBpTHgxkIRHPSNDd2NA0zL8+NrVis61h+fzQ2DDe2DQPL53NjOxZ7vdiHN7S2z4d9+KtFVsfY78fy+WhpaSESiWR9P2Vj7Xk80e1CIGDh8US3C3l50djr7Rjb+P0dYxuPJ1pvPp/NiBEkKDwh+jN5eRZer+PGhhGNA4FoHUZj042POcZE15Uba1p0W5fufjJNk48//hjHcVLup1yR9QN6a2urewH+goICWlpa3PfOPfdc3nnnHV566SVeeOEFtm7d2u1y7rvvPgoKCtxHcXExANu3bwegrq6Ouro6ALZt20Z9fT0QnSFoaGgAYNOmTTQ2NgJQU1NDU1P0ilzLl1dRUtIMwIoVlYwe3QrAqlUVFBVFL7u5enU5w4aFCAQsVq8uJxCwGDYsxOrV5QAUFQVZtaoCgNGjW1m74jEUHppKzuH55b9E4WHX5FIqlj2AwsN7M2dTufRHKDzUlV3Oq0u+j8LDm3Pn85dFS1B4eH3+Il664uaE38Wb995Lw+WXg8fDph/9iMbZs8HjoeaBB2gqLQWPh6pf/pLmc84Bj4fKxx6jdexY8HioWLOG4EkngcdDeXk5n44axebNm3nhhRewLItQKER5eTSnYDBIRUWF24+VlZUANDc3U1UVvTJVU1OTu5PW2NjIpk2bAGhoaKC2thaA+vp6tm3blnI/VVVV0dwc7afKykpaW6P9VFFRQTAY7afy8nJCoVDcRSv6Oqf58+uYPz+a06JF25g7N5rTkiW1lJVFc1q6dBMzZ0ZzWrashsmTj1x7FatWESwqiua0ejWhYcOiF3FZvRorECA0bBjlq1dHcyoqomLVqmhOo0dTuWJFNKeSEqqWL4/mNHkyNcuiN/ppnDmTTUuXRnMqK6N2yZJoTnPnsm3Romg/zZ9P3fz50X5atIj6uXOj/bRkCQ1lZdF+WrqUv114IZs3b2bDhg1Z30/ZWnslJc0sXx7NafLkJpYti+Y0c2YjS5dGcyora2DJkmhOc+fWs2hRYu11dtFFfwN6X3vdbffS3U/vv/8+f/nLX7BtO6V+2rhxY5f5D0QeleVnFPzyl79k0KBBLFiwgC1btvDoo4+y4vAGp/PP+f1+Fi5c2OVywuEw4XDYfd7W1kZxcTEtLS0MHTrU3XPTdT0utiwLj8fjxpqmoWmaG+u6Rl6eRSSi4TgaeXkmkYiO42gEAibhsIHjeAgETEIhA6Wie7bt7Ya7x9ve7kXTFH5/LHYI+QbhDYVwNA3H58MIhXB0HccwMMJhHMPA0XWMcBjbMFCahhGJRI9+NA09EsH2evlUKR7qtAe6xOtlkFLoloXl96PZNlostiw028bKy0OLRNAcBzMvDz0WBwIY4TCeWBwKweEjmtgFHizLwuv1opRy49hedCx2HAfDMLqNbdtGHb4saHd905t+6hybpomu625sGAYej8eNY3n0dU6GobtH56ap4/PZOA5Ylo7PZ+E4HixLx++3sG0Ny9Lw+y0sS8O2u64929G77ielsAIBjPb26Pe/8/LwtrejNA3L78fb3o6jadg+X1pqD0A3zehRu+NE683nwxOLY7VnmlnfT9laez5fdHvh8zmEQga67mAYDuGwgWE46HosttE0RSQSiyES6Vh7ITjmwbjthB5Zgm0N6VXtHWm7F4lkZz+1tLQwfPhwDhw4MODv3pb1A3ptbS0PPPAAjz/+OPfeey+nnHIKV155JRAdlGMdcNVVV3H99dczffr0Hi23ra2NgoKClDvR40n6vx6RIj0L/hR4sNNrNwPpuv6To2k0NzXxhS98QS7zm4S+qJ901U5/kPpJTfrq59OEAZ1D6dxSQLpHGsdxaG5uTrl20jUWZIOs/wuaMGECI0eOpLS0lB07djBnzhwWL14MwJNPPskXv/hFpk6dSlFRUY8Hc5E+js/H9u3b3c+jhOgNqR+RLMdxpHY6yfoj9L4iR+hpdHSWUFoc7UfogNRPCo7mI/R0kSN0IQ5zdJ3du3fLXrJIitSPSJbjOFI7nciALlLiGAY7d+6UPyqRFKkfkSzHcaR2OpFruYuUGOGwnLsgkib1I5JlGIbUTidyhC5S4hgGu3btkr1kkRSpn2wQBM/exJc9e8HTFH0/CzmOI7XTiQzoIiXyGahIhdRPFvC+DoHfJ74e+D0EfhN9PwvJZ+iJ5Cx3Ocs9dUdnCaWFnOWO1E8K0lM/QfAc7P5tNRjIT3kt2drNcpa7EIfZhsF7773nXpFJiN6Q+skG+aAKu3+kYTDvC7ZtS+10IgN6DgsCXXwyxl4gXZ+MKU1j//79ck9ikRSpH5EspZTUTicy5Z7DU+6vAFVHeH86MCPltZC9c2kDgEy5I/WTgr7a/vSFbO3mXJpyl6+t5bCJwOlHeH9wGtZhGwb1b7/N6NGj0XU9DUsURxOpH5Gs2F3WpHY+IwN6DsunHz790jTa29v7ei0iV0n9iBRI7cSTKfccnnLvN0dnCaWFTLkj9ZOCbJ5yH0wT+TS5z7du7fR+YSH5hYX93KpEMuXez2655RY2btzIqFGjeOSRR/D5fED0nrrXXXcdO3fuZOLEiTz00EMZbunRx/Z6qdu+nTPOOEOmvUSvSf3krkms5Essc5//5pz496ffdRcz7r476eXbtk1dXZ3UTgdZf5Z7bW0te/bsobq6mrFjx7JmzRr3vWeffZYTTzyR6upqDh06RE1NTQZbKoQQImYzi3mE9Qmvz1u/nuu2bmXi4dtgi/TJ+gF9w4YNzJo1C4CLL744btA+0nuif+imybhx42QPWSRF6id3HaSQvZQkvH58SQmFEyemPN2u67rUTidZP+Xe2trKCSecAEBBQQEtLS1x78U+8+j8XmfhcJhwOAxEv7/40UcfAbB//34A9+IEuq7HxZZl4fF43FjTNDRNc2PQ8PstIhENpTT8fpNIREcpjbw8k3DYQCkPeXkmoVD0152XZ3WKvXg8Cr8/Fjt84vPjDYdxPB4cnw8jHMbRNBzDwIhEcHQdR9cxIhFsXUdpGoZpYus6aBq6aWIb0XXoloXt9YLjoNs2lteLJxb7fGi2jRaLLQvNcbD8frRIBE0pTL8fPRbn5WGEw3gOxx7HYXtVFWPGjMHv9wPRj0K8Xi9KKTd2HAfbtt3YcRwMw+g2tm0bpZQbd9U3vemnzrFpmui67saGYeDxeNw4lkfHuC9yAh3DsA+vQ8frtXEcsG0dr9fCcTzYto7PZ2HbGrat4fNZWJaG43Rde22KhH4yQqHoOjrF3lAI5fFg+f14QyEcjwfb5+u32lOaxo7qasaMGYPX683afsrW2oPo9sLncwiHDTTNwTAcIhEDXXfQ9Vhso2kK04zFYJpd116LPSxt24gDfj+/Csdviw+MGEEoDbUXCQR4689/pqTks52GZPopNm7kwulkWT+gDx06lLa2NiA6gA8bNqxH73V23333sWzZsoTXTz755JTbGA53HR/ebvYoVio+/kJsOUp9tlDHgUgkGtt29HGk2LI+W5Fpdh3Hltc57k1SM9LybfajVrq7qQBSK77Ygvqr9uSOWSlJdzcVdHwCqW0jwp1Gc+DfO/5MKrXX3g4zZyYsP1nBYJCCgoK0LS8Tsn5AP++883jggQdYsGAB69at4/zzz497r6KigunTp7Nu3ToWLlzY7XJuv/12br75ZiC6J9bW1oZpmgwfPhxPNp8qmuXa2tooLi6msbFxwJ8hKvqf1I9IVrpqRylFMBh0Z4IHsqwf0CdMmMDIkSMpLS1l1KhR3HrrrSxevJiVK1dy2WWXsXbtWkpLS5kwYQJTpkzpdjl+v9+dEgYG/J5YthkyZIhskEXSpH5EstJRO7kyHhy130MX6ZFL3+EU/U/qRyRLaidR1p/lLoQQQojPJwO6SInf7+euu+6K+zhDiJ6S+hHJktpJJFPuQgghRA6QI3QhhBAiB8iALoQQQuQAGdCFEEKIHCADuhBCCJEDZEAXQggxoOzevZuNGzeye/fuTDclq8hZ7qLX3n77bX72s59x8OBB8vPz+fa3v83YsWMz3SwxQLz11lsUFhbyH//xHxw8eJAbbriBM844I9PNEgPEv/3bv2GapnvZV8MwuPfeezPdrKyQ9Zd+Fdnn3nvv5T//8z8ZMmQIbW1t3HjjjTz66KOZbpYYIFasWAHAv/zLvzBixAhuvPFG/ud//ifDrRIDRTAY5KGHHnKff/e7381ga7KLDOii12K3IAQO30JWiJ5rbGzkmGOO4ayzzgI44l0ShegsPz+f22+/3T1CDwQCmW5S1pABXfTa7bffzne/+12CwSBDhgzh1ltvzXSTxAByxRVXxO0ITpgwIYOtEQPNj370Iz788EM+/PBDvvzlL3PiiSdmuklZQz5DFyl57bXX4m5pK8TnqaqqSnhtutwTXSThzjvv5J577sl0M7KGzJeKlDz99NOZboIYYK688kp+9atfUVdXR11dHW+//XammyQGKI/Hk+kmZBU5Qhcp2bBhwxHvQy9EZ5FIhLVr1/LKK68watQobrvtNtkwi6R9/PHHHHfccZluRlaQI3TRa2+99Rb/8R//we7du5kyZYocpYte8fl8/N3f/R2DBg1i7969mW6OGGAikUjc49/+7d8y3aSsIUfoote++tWvcvfdd/Pwww9z880385//+Z/84he/yHSzxAAxadIkTjvtNL72ta9xzDHH4PF4mDVrVqabJQaIMWPGMHXqVACUUvzlL3+hrq4uw63KDnKWu+i1ESNGMH78eH7961/z7W9/mx07dmS6SWIAueGGGwBoa2sjGAxmuDVioJk7dy4/+tGP3Oc//elPM9ia7CJT7qLXYp+Z67rOz3/+cyZPnpzhFomB5LTTTuPqq69m8uTJvPrqqxQWFma6SWIA6TiYW5bFTTfdlMHWZBcZ0EWvnXrqqVRVVVFVVUVNTQ2XXHJJppskBpAnnngCgAcffJB77rmHxx9/PMMtEgPVbbfdlukmZBWZche9duWVVzJjxgxmzJgBRL86It8jFj116NAhbNvGMAxGjhzJsccem+kmiQHqpJNOynQTsoocoYtea2ho4Ktf/Srbtm2jtbWVRYsWZbpJYgCZMGECc+bM4dprrwUgLy8vwy0SA0lsdrCqqorx48d3eaGio5Wc5S6SsnnzZp566iksy+KBBx6Q7xGLHtuzZw8jR44kHA7j9/tpaGjg7/7u7zLdLDFAFBUVuTOEse3Ot771rQy3KjvIEbrotUmTJvHggw8yZcoUZs+ezYsvvpjpJokBJHapztjJTPfff38mmyMGmI4zhPv375cZwg7kM3TRa/K1IyFEpsQuTLRlyxa5MFEnMuUuei12Q5a3336bBx98kCuuuIJ/+Id/yHSzxAAxevRoJk2axObNmzn33HPZsmUL9fX1mW6WGCDkwkTdkyN00WtPPPEE559/vvu1o+9973syoIsek8FbpEJmCLsnA7roNfnakUjVL3/5S1577TUcxwE++266EJ/ntNNOS5ghFFFyUpzoNfnakUjVnj17ePzxx3niiSdkMBe9Ihcm6p4coYte+9rXvsaNN95IOBwG4Prrr89wi8RA89FHH/HHP/6RQYMGAchnoKLHZIawe3KELnpNvnYkUjVt2jTa2tpoampiz549mW6OGEBkhrB7MqALIfrdP/3TPzF48OBMN0MMQDfeeCNr165l0qRJAPzHf/xHhluUPeRra6LXOn/taOvWrbz77ruZbpYYQK677jrC4TAXXXQRL730Eo899limmyQGiHvuuYfGxkbOOOMMXn31VUpKSrj77rsz3aysIJ+hi17r+LUj0zTxer0ZbI0YiIYPH47H4+Hqq69m9+7dmW6OGEDeffddHn/8cWbPns26dev4l3/5l0w3KWvIlLtIyfe+971MN0EMMH/5y184//zz+dKXvsTll19OZWVlppskBpDYRzWxo3Jd1zPYmuwiA7pIidy+UPTWo48+ype//GVmz57NM888w+mnn57pJokBJLbNefPNNwHk8q8dyIAueu2dd94BoKmpiU8//ZS//vWvGW6RGEh8Pl/cc8OQT/5EzzU2NgLwxhtvANGPb0SUDOii1372s58B8MMf/pALL7xQzjIVvaKU4qWXXqKtrY3Kykr3anFC9MTf/vY3Kioq3H8//PDDTDcpa8iALnrNNE0AHMdh8uTJHHfccRlukRhIHnjgAd5++21uv/126urqeOCBBzLdJDGAXHHFFTQ1Nbn/zp07N9NNyhoy1yV6rbCwkEsvvZQbb7wRgE8//TTDLRIDic/n41//9V8z3QwxQF199dWZbkLWku+hi5RVV1dTWlqa6WYIIcRRTabcRVL27NnDj3/8Y8477zw2btyY6eYIIcRRT6bcRa9dddVVnHjiiVx55ZXs2rWLW265JdNNEkKIo54coYtemzhxIrt37+b//u//5PNzIYTIEjKgi16bNm0ajz76KFOmTMHr9TJ79uxMN0kIIY56MqCLXnv00Ufxer1MmTKFVatWUVRUlOkmCSHEUU8GdNFrna/0JbfBFEKIzJMBXfSaXOlLCCGyj3wPXfRaJBLhv/7rv9ixYwdjx47luuuuw+/3Z7pZQghxVJMBXQghhMgBMuUuhBBC5AAZ0IUQQogcIAO6EEIIkQNkQBdCCCFygAzoQgghRA6QAV0IIYTIATKgCyGEEDlABnQhhBAiB8iALoQQQuQAGdCFEEKIHCADuhBCCJEDZEAXQgghcoAM6EIIIUQOkAFdCCGEyAEyoAshhBA5QAZ0IYQQIgfIgC6EEELkABnQhRBCiBwgA7oQQgiRA2RAF0IIIXKAzXYZfAAAAMRJREFUDOhCCCFEDpABXQghhMgBMqALIYQQOUAGdCGEECIHyIAuhBBC5AAZ0IUQQogcIAO6EEIIkQNkQBdCCCFygAzoQgghRA6QAV0IIYTIATKgCyGEEDlABnQhhBAiB8iALoQQQuQAGdCFEEKIHCADuhBCCJEDZEAXQgghcoAM6EIIIUQOkAFdCCGEyAEyoAshhBA5QAZ0IYQQIgfIgC6EEELkABnQhRBCiBwgA7oQQgiRA2RAF0IIIXKADOhCCCFEDvj/7KoY1qDZSNUAAAAASUVORK5CYII=" }, "metadata": {}, "output_type": "display_data" From 6cddae10e80051a83d4b0811e9b851f576927f54 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Fri, 19 Jan 2024 15:25:11 -0800 Subject: [PATCH 32/69] generalize obs --- pcmdi_metrics/sea_ice/ice_driver.py | 487 +++++++++++++++------------- 1 file changed, 259 insertions(+), 228 deletions(-) diff --git a/pcmdi_metrics/sea_ice/ice_driver.py b/pcmdi_metrics/sea_ice/ice_driver.py index 246857123..2dda533d2 100644 --- a/pcmdi_metrics/sea_ice/ice_driver.py +++ b/pcmdi_metrics/sea_ice/ice_driver.py @@ -139,17 +139,17 @@ def sea_ice_regions(ds, var, xvar, yvar): 0, ) data_sa = ds[var].where( - (ds[yvar] > -90) & (ds[yvar] <= -40) & (ds[xvar] > -60) & (ds[xvar] <= 20), + (ds[yvar] > -90) & (ds[yvar] <= -55) & (ds[xvar] > -60) & (ds[xvar] <= 20), 0, ) data_sp = ds[var].where( (ds[yvar] > -90) - & (ds[yvar] <= -40) + & (ds[yvar] <= -55) & ((ds[xvar] > 90) | (ds[xvar] <= -60)), 0, ) data_io = ds[var].where( - (ds[yvar] > -90) & (ds[yvar] <= -40) & (ds[xvar] > 20) & (ds[xvar] <= 90), + (ds[yvar] > -90) & (ds[yvar] <= -55) & (ds[xvar] > 20) & (ds[xvar] <= 90), 0, ) @@ -220,11 +220,11 @@ def mse_model(dm, do, var=None): return stat -def to_ice_con_ds(da, ds): +def to_ice_con_ds(da, ds, obs_var): # Convert sea ice data array to dataset using # coordinates from another dataset ds = xr.Dataset( - data_vars={"ice_con": da, "time_bnds": ds.time_bnds}, coords={"time": ds.time} + data_vars={obs_var: da, "time_bnds": ds.time_bnds}, coords={"time": ds.time} ) return ds @@ -305,6 +305,15 @@ def replace_multi(string, rdict): return string +def get_xy_coords(ds, xvar): + if len(ds[xvar].dims) == 2: + lon_j, lon_i = ds[xvar].dims + elif len(ds[xvar].dims) == 1: + lon_j = find_lon(ds) + lon_i = find_lat(ds) + return lon_i, lon_j + + if __name__ == "__main__": parser = create_sea_ice_parser() parameter = parser.get_parameter(argparse_vals_only=False) @@ -317,12 +326,19 @@ def replace_multi(string, rdict): filename_template = parameter.filename_template test_data_path = parameter.test_data_path model_list = parameter.test_data_set - reference_data_path = parameter.reference_data_path + reference_data_path_nh = parameter.reference_data_path_nh + reference_data_path_sh = parameter.reference_data_path_sh reference_data_set = parameter.reference_data_set metrics_output_path = parameter.metrics_output_path area_template = parameter.area_template area_var = parameter.area_var AreaUnitsAdjust = parameter.AreaUnitsAdjust + obs_area_var = parameter.obs_area_var + obs_var = parameter.obs_var + obs_area_template_nh = parameter.obs_area_template_nh + obs_area_template_sh = parameter.obs_area_template_sh + obs_cell_area = parameter.obs_cell_area + ObsAreaUnitsAdjust = parameter.ObsAreaUnitsAdjust ModUnitsAdjust = parameter.ModUnitsAdjust ObsUnitsAdjust = parameter.ObsUnitsAdjust # plots = parameter.plots @@ -364,168 +380,153 @@ def replace_multi(string, rdict): print("Find all realizations:", find_all_realizations) #### Do Obs part - ObsUnitsAdjust = (True, "multiply", 1e-2) - ObsAreaUnitsAdjust = (False, 0, 0) - - f_nt_n = "/home/ordonez4/seaice/data/icecon_ssmi_nt_n_edited.nc" - f_nt_s = "/home/ordonez4/seaice/data/icecon_ssmi_nt_s_edited.nc" - f_bt_n = "/home/ordonez4/seaice/data/icecon_ssmi_bt_n_edited.nc" - f_bt_s = "/home/ordonez4/seaice/data/icecon_ssmi_bt_s_edited.nc" - arctic_clims = {} arctic_means = {} - arctic_files = {"nt": f_nt_n, "bt": f_bt_n} - obs_var = "ice_con" print("OBS: Arctic") - for source in arctic_files: - obs = xc.open_dataset(arctic_files[source]) - obs[obs_var] = adjust_units(obs[obs_var], ObsUnitsAdjust) - obs["area"] = adjust_units(obs["area"], ObsAreaUnitsAdjust) - # Remove land areas (including lakes) - mask = create_land_sea_mask(obs, lon_key="lon", lat_key="lat") - obs[obs_var] = obs[obs_var].where(mask < 1) - # Get regions - rgn_dict = sea_ice_regions(obs, obs_var, "lon", "lat") - """data_arctic = obs[obs_var].where((obs.lat > 0), 0) - data_ca1 = obs[obs_var].where( - ((obs.lat > 80) & (obs.lat <= 87.2) & (obs.lon > -120) & (obs.lon <= 90)), 0 - ) - data_ca2 = obs[obs_var].where( - ((obs.lat > 65) & (obs.lat < 87.2)) & ((obs.lon > 90) | (obs.lon <= -120)), - 0, - ) - data_ca = obs[obs_var].where((data_ca1 > 0) | (data_ca2 > 0), 0) - data_np = obs[obs_var].where( - (obs.lat > 35) & (obs.lat <= 65) & ((obs.lon > 90) | (obs.lon <= -120)), 0 - ) - data_na = obs[obs_var].where( - (obs.lat > 45) & (obs.lat <= 80) & (obs.lon > -120) & (obs.lon <= 90), 0 - ) - data_na = data_na - data_na.where( - (obs.lat > 45) & (obs.lat <= 50) & (obs.lon > 30) & (obs.lon <= 60), 0 - )""" - - # Get ice extent - total_extent_arctic_obs = ( - rgn_dict["arctic"].where(rgn_dict["arctic"] > 0.15) * obs.area - ).sum(("x", "y"), skipna=True) - total_extent_ca_obs = ( - rgn_dict["ca"].where(rgn_dict["ca"] > 0.15) * obs.area - ).sum(("x", "y"), skipna=True) - total_extent_np_obs = ( - rgn_dict["np"].where(rgn_dict["np"] > 0.15) * obs.area - ).sum(("x", "y"), skipna=True) - total_extent_na_obs = ( - rgn_dict["na"].where(rgn_dict["na"] > 0.15) * obs.area - ).sum(("x", "y"), skipna=True) - - clim_arctic_obs = to_ice_con_ds( - total_extent_arctic_obs, obs - ).temporal.climatology(obs_var, freq="month") - clim_ca_obs = to_ice_con_ds(total_extent_ca_obs, obs).temporal.climatology( - obs_var, freq="month" - ) - clim_np_obs = to_ice_con_ds(total_extent_np_obs, obs).temporal.climatology( - obs_var, freq="month" - ) - clim_na_obs = to_ice_con_ds(total_extent_na_obs, obs).temporal.climatology( - obs_var, freq="month" - ) + obs = load_dataset(reference_data_path_nh) + xvar = find_lon(obs) + yvar = find_lat(obs) + coord_i, coord_j = get_xy_coords(obs, xvar) + if osyear is not None: + obs = obs.sel( + { + "time": slice( + "{0}-01-01".format(osyear), + "{0}-12-31".format(oeyear), + ) + } + ).compute() # TODO: won't always need to compute + obs[obs_var] = adjust_units(obs[obs_var], ObsUnitsAdjust) + if obs_area_var is not None: + obs[obs_area_var] = adjust_units(obs[obs_area_var], ObsAreaUnitsAdjust) + area_val = obs[obs_area_var] + else: + area_val = obs_cell_area + # Remove land areas (including lakes) + mask = create_land_sea_mask(obs, lon_key=xvar, lat_key=yvar) + obs[obs_var] = obs[obs_var].where(mask < 1) + # Get regions + rgn_dict = sea_ice_regions(obs, obs_var, xvar, yvar) + + # Get ice extent + total_extent_arctic_obs = ( + rgn_dict["arctic"].where(rgn_dict["arctic"] > 0.15) * area_val + ).sum((coord_i, coord_j), skipna=True) + total_extent_ca_obs = (rgn_dict["ca"].where(rgn_dict["ca"] > 0.15) * area_val).sum( + (coord_i, coord_j), skipna=True + ) + total_extent_np_obs = (rgn_dict["np"].where(rgn_dict["np"] > 0.15) * area_val).sum( + (coord_i, coord_j), skipna=True + ) + total_extent_na_obs = (rgn_dict["na"].where(rgn_dict["na"] > 0.15) * area_val).sum( + (coord_i, coord_j), skipna=True + ) - arctic_clims[source] = { - "arctic": clim_arctic_obs, - "ca": clim_ca_obs, - "np": clim_np_obs, - "na": clim_na_obs, - } + clim_arctic_obs = to_ice_con_ds( + total_extent_arctic_obs, obs, obs_var + ).temporal.climatology(obs_var, freq="month") + clim_ca_obs = to_ice_con_ds(total_extent_ca_obs, obs, obs_var).temporal.climatology( + obs_var, freq="month" + ) + clim_np_obs = to_ice_con_ds(total_extent_np_obs, obs, obs_var).temporal.climatology( + obs_var, freq="month" + ) + clim_na_obs = to_ice_con_ds(total_extent_na_obs, obs, obs_var).temporal.climatology( + obs_var, freq="month" + ) - arctic_means[source] = { - "arctic": total_extent_arctic_obs.mean("time", skipna=True).data.item(), - "ca": total_extent_ca_obs.mean("time", skipna=True).data.item(), - "np": total_extent_np_obs.mean("time", skipna=True).data.item(), - "na": total_extent_na_obs.mean("time", skipna=True).data.item(), - } - obs.close() + arctic_clims = { + "arctic": clim_arctic_obs, + "ca": clim_ca_obs, + "np": clim_np_obs, + "na": clim_na_obs, + } + + arctic_means = { + "arctic": total_extent_arctic_obs.mean("time", skipna=True).data.item(), + "ca": total_extent_ca_obs.mean("time", skipna=True).data.item(), + "np": total_extent_np_obs.mean("time", skipna=True).data.item(), + "na": total_extent_na_obs.mean("time", skipna=True).data.item(), + } + obs.close() antarctic_clims = {} antarctic_means = {} - antarctic_files = {"nt": f_nt_s, "bt": f_bt_s} print("OBS: Antarctic") - for source in antarctic_files: - obs = xc.open_dataset(antarctic_files[source]) - obs[obs_var] = adjust_units(obs[obs_var], ObsUnitsAdjust) - obs["area"] = adjust_units(obs["area"], ObsAreaUnitsAdjust) - # Remove land areas (including lakes) - mask = create_land_sea_mask(obs, lon_key="lon", lat_key="lat") - obs[obs_var] = obs[obs_var].where(mask < 1) - rgn_dict = sea_ice_regions(obs, obs_var, "lon", "lat") - """data_antarctic = obs[obs_var].where((obs.lat < 0), 0) - data_sa = obs[obs_var].where( - (obs.lat > -90) & (obs.lat <= -55) & (obs.lon > -60) & (obs.lon <= 20), 0 - ) - data_sp = obs[obs_var].where( - (obs.lat > -90) & (obs.lat <= -55) & ((obs.lon > 90) | (obs.lon <= -60)), 0 - ) - data_io = obs[obs_var].where( - (obs.lat > -90) & (obs.lat <= -55) & (obs.lon > 20) & (obs.lon <= 90), 0 - )""" - - total_extent_antarctic_obs = ( - rgn_dict["antarctic"].where(rgn_dict["antarctic"] > 0.15) * obs.area - ).sum(("x", "y"), skipna=True) - total_extent_sa_obs = ( - rgn_dict["sa"].where(rgn_dict["sa"] > 0.15) * obs.area - ).sum(("x", "y"), skipna=True) - total_extent_sp_obs = ( - rgn_dict["sp"].where(rgn_dict["sp"] > 0.15) * obs.area - ).sum(("x", "y"), skipna=True) - total_extent_io_obs = ( - rgn_dict["io"].where(rgn_dict["io"] > 0.15) * obs.area - ).sum(("x", "y"), skipna=True) - - clim_antarctic_obs = to_ice_con_ds( - total_extent_antarctic_obs, obs - ).temporal.climatology(obs_var, freq="month") - clim_sa_obs = to_ice_con_ds(total_extent_sa_obs, obs).temporal.climatology( - obs_var, freq="month" - ) - clim_sp_obs = to_ice_con_ds(total_extent_sp_obs, obs).temporal.climatology( - obs_var, freq="month" - ) - clim_io_obs = to_ice_con_ds(total_extent_io_obs, obs).temporal.climatology( - obs_var, freq="month" - ) + obs = load_dataset(reference_data_path_sh) + xvar = find_lon(obs) + yvar = find_lat(obs) + coord_i, coord_j = get_xy_coords(obs, xvar) + if osyear is not None: + obs = obs.sel( + { + "time": slice( + "{0}-01-01".format(osyear), + "{0}-12-31".format(oeyear), + ) + } + ).compute() + obs[obs_var] = adjust_units(obs[obs_var], ObsUnitsAdjust) + if obs_area_var is not None: + obs[obs_area_var] = adjust_units(obs[obs_area_var], ObsAreaUnitsAdjust) + area_val = obs[obs_area_var] + else: + area_val = obs_cell_area + # Remove land areas (including lakes) + mask = create_land_sea_mask(obs, lon_key="lon", lat_key="lat") + obs[obs_var] = obs[obs_var].where(mask < 1) + rgn_dict = sea_ice_regions(obs, obs_var, "lon", "lat") + + total_extent_antarctic_obs = ( + rgn_dict["antarctic"].where(rgn_dict["antarctic"] > 0.15) * area_val + ).sum((coord_i, coord_j), skipna=True) + total_extent_sa_obs = (rgn_dict["sa"].where(rgn_dict["sa"] > 0.15) * area_val).sum( + (coord_i, coord_j), skipna=True + ) + total_extent_sp_obs = (rgn_dict["sp"].where(rgn_dict["sp"] > 0.15) * area_val).sum( + (coord_i, coord_j), skipna=True + ) + total_extent_io_obs = (rgn_dict["io"].where(rgn_dict["io"] > 0.15) * area_val).sum( + (coord_i, coord_j), skipna=True + ) - antarctic_clims[source] = { - "antarctic": clim_antarctic_obs, - "io": clim_io_obs, - "sp": clim_sp_obs, - "sa": clim_sa_obs, - } + clim_antarctic_obs = to_ice_con_ds( + total_extent_antarctic_obs, obs, obs_var + ).temporal.climatology(obs_var, freq="month") + clim_sa_obs = to_ice_con_ds(total_extent_sa_obs, obs, obs_var).temporal.climatology( + obs_var, freq="month" + ) + clim_sp_obs = to_ice_con_ds(total_extent_sp_obs, obs, obs_var).temporal.climatology( + obs_var, freq="month" + ) + clim_io_obs = to_ice_con_ds(total_extent_io_obs, obs, obs_var).temporal.climatology( + obs_var, freq="month" + ) - antarctic_means[source] = { - "antarctic": total_extent_antarctic_obs.mean( - "time", skipna=True - ).data.item(), - "io": total_extent_io_obs.mean("time", skipna=True).data.item(), - "sp": total_extent_sp_obs.mean("time", skipna=True).data.item(), - "sa": total_extent_sa_obs.mean("time", skipna=True).data.item(), - } - obs.close() - - obs_clims = {"nt": {}, "bt": {}} - obs_means = {"nt": {}, "bt": {}} - for item in antarctic_clims["nt"]: - obs_clims["nt"][item] = antarctic_clims["nt"][item] - obs_means["nt"][item] = antarctic_means["nt"][item] - obs_clims["bt"][item] = antarctic_clims["bt"][item] - obs_means["bt"][item] = antarctic_means["bt"][item] - for item in arctic_clims["nt"]: - obs_clims["nt"][item] = arctic_clims["nt"][item] - obs_means["nt"][item] = arctic_means["nt"][item] - obs_clims["bt"][item] = arctic_clims["bt"][item] - obs_means["bt"][item] = arctic_means["bt"][item] + antarctic_clims = { + "antarctic": clim_antarctic_obs, + "io": clim_io_obs, + "sp": clim_sp_obs, + "sa": clim_sa_obs, + } + + antarctic_means = { + "antarctic": total_extent_antarctic_obs.mean("time", skipna=True).data.item(), + "io": total_extent_io_obs.mean("time", skipna=True).compute().data.item(), + "sp": total_extent_sp_obs.mean("time", skipna=True).compute().data.item(), + "sa": total_extent_sa_obs.mean("time", skipna=True).compute().data.item(), + } + obs.close() + + obs_clims = {reference_data_set: {}} + obs_means = {reference_data_set: {}} + for item in antarctic_clims: + obs_clims[reference_data_set][item] = antarctic_clims[item] + obs_means[reference_data_set][item] = antarctic_means[item] + for item in arctic_clims: + obs_clims[reference_data_set][item] = arctic_clims[item] + obs_means[reference_data_set][item] = arctic_means[item] # Get climatology # get errors for climo and mean @@ -533,8 +534,10 @@ def replace_multi(string, rdict): #### Do model part # Loop over models + # Needs to weigh months by length for metrics later clim_wts = [31.0, 28.0, 31.0, 30.0, 31.0, 30.0, 31.0, 31.0, 30.0, 31.0, 30.0, 31.0] clim_wts = [x / 365 for x in clim_wts] + # Initialize JSON data mse = {} metrics = { "DIMENSIONS": { @@ -557,22 +560,12 @@ def replace_multi(string, rdict): "RESULTS": {}, "model_year_range": {}, } - print(model_list) + print("Model list:", model_list) for model in model_list: start_year = msyear end_year = meyear - # totals_dict = { - # "arctic": 0, - # "ca": 0, - # "na": 0, - # "np": 0, - # "antarctic": 0, - # "sp": 0, - # "sa": 0, - # "io": 0, - # } real_dict = { "arctic": {"model_mean": 0}, "ca": {"model_mean": 0}, @@ -584,14 +577,14 @@ def replace_multi(string, rdict): "io": {"model_mean": 0}, } mse[model] = { - "arctic": {"model_mean": {"nasateam": {}, "bootstrap": {}}}, - "ca": {"model_mean": {"nasateam": {}, "bootstrap": {}}}, - "na": {"model_mean": {"nasateam": {}, "bootstrap": {}}}, - "np": {"model_mean": {"nasateam": {}, "bootstrap": {}}}, - "antarctic": {"model_mean": {"nasateam": {}, "bootstrap": {}}}, - "sp": {"model_mean": {"nasateam": {}, "bootstrap": {}}}, - "sa": {"model_mean": {"nasateam": {}, "bootstrap": {}}}, - "io": {"model_mean": {"nasateam": {}, "bootstrap": {}}}, + "arctic": {"model_mean": {reference_data_set: {}}}, + "ca": {"model_mean": {reference_data_set: {}}}, + "na": {"model_mean": {reference_data_set: {}}}, + "np": {"model_mean": {reference_data_set: {}}}, + "antarctic": {"model_mean": {reference_data_set: {}}}, + "sp": {"model_mean": {reference_data_set: {}}}, + "sa": {"model_mean": {reference_data_set: {}}}, + "io": {"model_mean": {reference_data_set: {}}}, } tags = { @@ -690,11 +683,7 @@ def replace_multi(string, rdict): data = regions_dict[rgn] # coordinates aren't always the same as lat/lon names, # especially if lat/lon are 2D - if len(data[xvar].dims) == 2: - lon_j, lon_i = data[xvar].dims - elif len(data[xvar].dims) == 1: - lon_j = xvar - lon_i = yvar + lon_i, lon_j = get_xy_coords(data, xvar) # area data doesn't always use same coordinates as siconc data in CMIP6 # so we multiply by area.data, dropping the coordinates rgn_total = (data.where(data > 0.15, 0) * area[area_var].data).sum( @@ -722,15 +711,14 @@ def replace_multi(string, rdict): # Set up metrics dictionary if run not in mse[model][rgn]: mse[model][rgn][run] = {} - for key in ["nasateam", "bootstrap"]: - mse[model][rgn][run].update( - { - key: { - "monthly_clim": {"mse": None}, - "total_extent": {"mse": None}, - } + mse[model][rgn][run].update( + { + reference_data_set: { + "monthly_clim": {"mse": None}, + "total_extent": {"mse": None}, } - ) + } + ) run_data = real_dict[rgn][run].to_dataset(name=var) # total_rgn.time.attrs.pop("bounds") @@ -740,27 +728,20 @@ def replace_multi(string, rdict): total = run_data.mean("time")[var].data # Get errors, convert to 1e12 km^-4 - mse[model][rgn][run]["nasateam"]["monthly_clim"]["mse"] = str( + mse[model][rgn][run][reference_data_set]["monthly_clim"][ + "mse" + ] = str( mse_t( clim_extent[var], - obs_clims["nt"][rgn]["ice_con"], + obs_clims[reference_data_set][rgn][obs_var], weights=clim_wts, ) * 1e-12 ) - mse[model][rgn][run]["bootstrap"]["monthly_clim"]["mse"] = str( - mse_t( - clim_extent[var], - obs_clims["bt"][rgn]["ice_con"], - weights=clim_wts, - ) - * 1e-12 - ) - mse[model][rgn][run]["nasateam"]["total_extent"]["mse"] = str( - mse_model(total, obs_means["nt"][rgn]) * 1e-12 - ) - mse[model][rgn][run]["bootstrap"]["total_extent"]["mse"] = str( - mse_model(total, obs_means["bt"][rgn]) * 1e-12 + mse[model][rgn][run][reference_data_set]["total_extent"][ + "mse" + ] = str( + mse_model(total, obs_means[reference_data_set][rgn]) * 1e-12 ) # Update year list @@ -768,13 +749,15 @@ def replace_multi(string, rdict): else: for rgn in mse[model]: # Set up metrics dictionary - for key in ["nasateam", "bootstrap"]: - mse[model][rgn]["model_mean"][key] = { - "monthly_clim": {"mse": None}, - "total_extent": {"mse": None}, - } + mse[model][rgn]["model_mean"][reference_data_set] = { + "monthly_clim": {"mse": None}, + "total_extent": {"mse": None}, + } metrics["model_year_range"][model] = ["", ""] + # ----------------- + # Update metrics + # ----------------- metrics["RESULTS"] = mse metricsfile = os.path.join(metrics_output_path, "sea_ice_metrics.json") @@ -794,6 +777,9 @@ def replace_multi(string, rdict): "JSON file containig regional sea ice metrics", ) + # ---------------- + # Make figure + # ---------------- sector_list = [ "Central Arctic Sector", "North Atlantic Sector", @@ -804,52 +790,96 @@ def replace_multi(string, rdict): ] sector_short = ["ca", "na", "np", "io", "sa", "sp"] fig7, ax7 = plt.subplots(6, 1, figsize=(5, 9)) - mlabels = model_list + ["bootstrap"] + # mlabels = model_list + ["bootstrap"] + mlabels = model_list ind = np.arange(len(mlabels)) # the x locations for the groups # ind = np.arange(len(mods)+1) # the x locations for the groups width = 0.3 - n = len(ind) - 1 + # n = len(ind) - 1 + n = len(ind) for inds, sector in enumerate(sector_list): # Assemble data mse_clim = [] mse_ext = [] + clim_range = [] + ext_range = [] rgn = sector_short[inds] for model in model_list: mse_clim.append( float( - metrics["RESULTS"][model][rgn]["model_mean"]["nasateam"][ + metrics["RESULTS"][model][rgn]["model_mean"][reference_data_set][ "monthly_clim" ]["mse"] ) ) mse_ext.append( float( - metrics["RESULTS"][model][rgn]["model_mean"]["nasateam"][ + metrics["RESULTS"][model][rgn]["model_mean"][reference_data_set][ "total_extent" ]["mse"] ) ) - mse_clim.append( - mse_t( - obs_clims["bt"][rgn]["ice_con"], - obs_clims["nt"][rgn]["ice_con"], - weights=clim_wts, - ) - * 1e-12 - ) - mse_ext.append(mse_model(obs_means["bt"][rgn], obs_means["nt"][rgn]) * 1e-12) + # Get spread + clim_err = [] + ext_err = [] + for r in metrics["RESULTS"][model][rgn]: + if r != "model_mean": + clim_err.append( + float( + metrics["RESULTS"][model][rgn][r][reference_data_set][ + "monthly_clim" + ]["mse"] + ) + ) + ext_err.append( + float( + metrics["RESULTS"][model][rgn][r][reference_data_set][ + "total_extent" + ]["mse"] + ) + ) + clim_range.append(np.max(clim_err) - np.min(clim_err)) + ext_range.append(np.max(ext_err) - np.min(ext_err)) + + # mse_clim.append( + # mse_t( + # obs_clims["bt"][rgn]["ice_con"], + # obs_clims["nt"][rgn]["ice_con"], + # weights=clim_wts, + # ) + # * 1e-12 + # ) + # mse_ext.append(mse_model(obs_means["bt"][rgn], obs_means["nt"][rgn]) * 1e-12) + # clim_range.append(0) + # ext_range.append(0) # Make figure - ax7[inds].bar(ind, mse_clim, width, color="b") + ax7[inds].bar(ind - width / 2.0, mse_clim, width, color="b") + ax7[inds].errorbar( + ind - width / 2.0, + mse_clim, + yerr=clim_range, + fmt="none", + color=[0, 10 / 255, 130 / 255], + elinewidth=3, + capsize=3, + ) ax7[inds].bar(ind, mse_ext, width, color="r") - # ax7[inds].errorbar(ind, mse_clim, yerr=clim_err, fmt="o", color="r") - # ax7[inds].errorbar(ind, mse_ext, yerr=ext_err, fmt="o", color="r") - # ax7[inds].bar(ind[n], obs[sector_short[inds]]**2, width, color="b") + # https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.errorbar.html + ax7[inds].errorbar( + ind, + mse_ext, + yerr=ext_range, + fmt="none", + color=[130 / 255, 0, 0], + elinewidth=3, + capsize=3, + ) if inds == len(sector_list) - 1: ax7[inds].set_xticks(ind + width / 2.0, mlabels, rotation=90, size=5) else: ax7[inds].set_xticks(ind + width / 2.0, labels="") - datamax = np.max(mse_clim) + datamax = np.max(np.array(mse_clim) + (np.array(clim_range) / 2)) ymax = (datamax) * 1.3 ax7[inds].set_ylim(0.0, ymax) if ymax < 1: @@ -891,6 +921,7 @@ def replace_multi(string, rdict): print("Error: Could not get provenance from metrics json for output.json.") meta.update_provenance("modeldata", test_data_path) - if reference_data_path is not None: - meta.update_provenance("obsdata", reference_data_path) + if reference_data_path_nh is not None: + meta.update_provenance("obsdata_nh", reference_data_path_nh) + meta.update_provenance("obsdata_sh", reference_data_path_sh) meta.write() From c215ad9dd714ae06503fbbe0a73b70ad3ecebd4d Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Fri, 19 Jan 2024 15:27:35 -0800 Subject: [PATCH 33/69] add obs --- pcmdi_metrics/sea_ice/demo_param_file.py | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/pcmdi_metrics/sea_ice/demo_param_file.py b/pcmdi_metrics/sea_ice/demo_param_file.py index 1d7d452b2..ea914f09f 100644 --- a/pcmdi_metrics/sea_ice/demo_param_file.py +++ b/pcmdi_metrics/sea_ice/demo_param_file.py @@ -2,7 +2,7 @@ # List of models to include in analysis test_data_set = [ - "E3SM-1-0" + "E3SM-1-0", ] # realization can be a single realization, a list of realizations, or "*" for all realizations @@ -37,3 +37,16 @@ # Directory for writing outputs case_id = "ex1" metrics_output_path = "sea_ice_demo/%(case_id)/" + +# Settings for the observational data +reference_data_path_nh = "/work/ordonez4/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc" +reference_data_path_sh = "/work/ordonez4/ice_conc_sh_ease2-250_cdr-v3p0_198801-202012.nc" +ObsUnitsAdjust=(True,"multiply",1e-2) +reference_data_set="OSI-SAF" +osyear=1988 +oeyear=2020 +obs_var="ice_conc" +ObsAreaUnitsAdjust = (False, 0, 0) +obs_area_template = None +obs_area_var = None +obs_cell_area = 625 #km 2 \ No newline at end of file From 78ac0041b88133ce66cc555d69e648e4d7b118eb Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Fri, 19 Jan 2024 16:29:46 -0800 Subject: [PATCH 34/69] update figures --- pcmdi_metrics/sea_ice/create_sector_plots.py | 16 ++++++++-------- pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py | 4 ++-- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/pcmdi_metrics/sea_ice/create_sector_plots.py b/pcmdi_metrics/sea_ice/create_sector_plots.py index 910c14a52..1756d2841 100644 --- a/pcmdi_metrics/sea_ice/create_sector_plots.py +++ b/pcmdi_metrics/sea_ice/create_sector_plots.py @@ -12,9 +12,9 @@ # ---------- print("Creating Arctic map") # Load and process data -f_os_n = "/p/user_pub/PCMDIobs/obs4MIPs_input/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/*nh*" -obs = xc.open_mfdataset(f_os_n) -obs = obs.sel({"time": slice("1988-01-01", "2020-12-31")}).mean("time").compute() +f_os_n = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc" +obs = xc.open_dataset(f_os_n) +obs = obs.sel({"time": slice("1988-01-01", "2020-12-31")}).mean("time") mask = create_land_sea_mask(obs, lon_key="lon", lat_key="lat") obs["ice_conc"] = obs["ice_conc"].where(mask < 1) ds = obs.assign_coords( @@ -80,9 +80,9 @@ # ---------- print("Creating Antarctic map") # Load and process data -f_os_s = "/p/user_pub/PCMDIobs/obs4MIPs_input/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/*sh*" -obs = xc.open_mfdataset(f_os_s) -obs = obs.sel({"time": slice("1988-01-01", "2020-12-31")}).mean("time").compute() +f_os_s = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_sh_ease2-250_cdr-v3p0_198801-202012.nc" +obs = xc.open_dataset(f_os_s) +obs = obs.sel({"time": slice("1988-01-01", "2020-12-31")}).mean("time") mask = create_land_sea_mask(obs, lon_key="lon", lat_key="lat") obs["ice_conc"] = obs["ice_conc"].where(mask < 1) ds = obs.assign_coords( @@ -113,7 +113,7 @@ label="abbrev", line_kws={"color": [0.2, 0.2, 0.25], "linewidth": 3}, ) -ax.set_extent([-180, 180, -55, -90], ccrs.PlateCarree()) +ax.set_extent([-180, 180, -53, -90], ccrs.PlateCarree()) ax.coastlines(color=[0.3, 0.3, 0.3]) plt.annotate( "South Pacific", @@ -125,7 +125,7 @@ ) plt.annotate( "Indian\nOcean", - (0.93, 0.66), + (0.89, 0.66), xycoords="axes fraction", horizontalalignment="right", verticalalignment="bottom", diff --git a/pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py b/pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py index acbad7a3b..502fc9bbd 100644 --- a/pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py +++ b/pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py @@ -31,11 +31,11 @@ coords={"time": ds.time}, ) arctic_clim = arctic_ds.sel( - {"time": slice("1980-01-01", "2010-12-31")} + {"time": slice("1981-01-01", "2010-12-31")} ).temporal.climatology("siconc", freq="month") arctic_clim["time"] = [x for x in range(1, 13)] arctic_clim.siconc.plot() -plt.title("E3SM-1-0 Arctic climatological sea ice extent") +plt.title("E3SM-1-0 Arctic climatological sea ice extent\n1981-2010") plt.xlabel("Month") plt.ylabel("Extent (km2)") plt.xlim([1, 12]) From 25e2fda77cceee689e6a594708f9114ae44c56ad Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Fri, 19 Jan 2024 16:30:07 -0800 Subject: [PATCH 35/69] update obs --- pcmdi_metrics/sea_ice/demo_param_file.py | 4 ++-- pcmdi_metrics/sea_ice/parameter_file.py | 18 ++++++++++++------ 2 files changed, 14 insertions(+), 8 deletions(-) diff --git a/pcmdi_metrics/sea_ice/demo_param_file.py b/pcmdi_metrics/sea_ice/demo_param_file.py index ea914f09f..43a971f84 100644 --- a/pcmdi_metrics/sea_ice/demo_param_file.py +++ b/pcmdi_metrics/sea_ice/demo_param_file.py @@ -39,8 +39,8 @@ metrics_output_path = "sea_ice_demo/%(case_id)/" # Settings for the observational data -reference_data_path_nh = "/work/ordonez4/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc" -reference_data_path_sh = "/work/ordonez4/ice_conc_sh_ease2-250_cdr-v3p0_198801-202012.nc" +reference_data_path_nh = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc" +reference_data_path_sh = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_sh_ease2-250_cdr-v3p0_198801-202012.nc" ObsUnitsAdjust=(True,"multiply",1e-2) reference_data_set="OSI-SAF" osyear=1988 diff --git a/pcmdi_metrics/sea_ice/parameter_file.py b/pcmdi_metrics/sea_ice/parameter_file.py index 7cd09858f..c49c04665 100644 --- a/pcmdi_metrics/sea_ice/parameter_file.py +++ b/pcmdi_metrics/sea_ice/parameter_file.py @@ -18,7 +18,7 @@ # CMIP6 # ======= -case_id = "cmip6" +case_id = "cmip6_osi-saf" test_data_set = [ "E3SM-1-0", "CanESM5", @@ -45,8 +45,14 @@ # Reference is hard coded currently so this is a placeholder -# ObsUnitsAdjust=(True,"multiply",1e-2) -# reference_data_set=None -# osyear=1981 -# oeyear=2010 -# obsvar="" +reference_data_path_nh = "/p/user_pub/PCMDIobs/obs4MIPs_input/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/*nh*" +reference_data_path_sh = "/p/user_pub/PCMDIobs/obs4MIPs_input/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/*sh*" +ObsUnitsAdjust=(True,"multiply",1e-2) +reference_data_set="OSI-SAF" +osyear=1981 +oeyear=2010 +obs_var="ice_conc" +ObsAreaUnitsAdjust = (False, 0, 0) +obs_area_template = None #km2 +obs_area_var = None +obs_cell_area = 625 From b8396b67fba65f79a523851ad03e6b1110330f98 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Fri, 19 Jan 2024 16:30:22 -0800 Subject: [PATCH 36/69] add obs --- pcmdi_metrics/sea_ice/sea_ice_parser.py | 44 +++++++++++++++++++++++-- 1 file changed, 42 insertions(+), 2 deletions(-) diff --git a/pcmdi_metrics/sea_ice/sea_ice_parser.py b/pcmdi_metrics/sea_ice/sea_ice_parser.py index 31d3a25e6..785227abc 100644 --- a/pcmdi_metrics/sea_ice/sea_ice_parser.py +++ b/pcmdi_metrics/sea_ice/sea_ice_parser.py @@ -16,12 +16,19 @@ def create_sea_ice_parser(): "-v", "--var", type=str, - nargs="+", dest="var", help="Name of model sea ice concentration variable", required=False, ) + parser.add_argument( + "--obs_var", + type=str, + dest="obs_var", + help="Name of obs sea ice concentration variable", + required=False, + ) + parser.add_argument( "--area_var", type=str, @@ -30,6 +37,15 @@ def create_sea_ice_parser(): required=False, ) + parser.add_argument( + "--obs_area_var", + type=str, + dest="obs_area_var", + help="Name of reference data area variable", + required=False, + default=None, + ) + parser.add_argument( "-r", "--reference_data_set", @@ -101,7 +117,31 @@ def create_sea_ice_parser(): "--area_template", dest="area_template", help="Filename template for model grid area", - required=False + required=False, + ) + + parser.add_argument( + "--obs_area_template_nh", + dest="obs_area_template_nh", + help="Filename template for obs grid area in Northern Hemisphere", + required=False, + default=None, + ) + + parser.add_argument( + "--obs_area_template_sh", + dest="obs_area_template_sh", + help="Filename template for obs grid area in Southern Hemisphere", + required=False, + default=None, + ) + + parser.add_argument( + "--obs_cell_area", + dest="obs_cell_area", + help="For equal area grids, the cell area in km", + required=False, + default=None, ) parser.add_argument( From bf7b83ff7ee623506b49bbcbb2a63ef3f49a99e6 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Fri, 19 Jan 2024 17:23:13 -0800 Subject: [PATCH 37/69] rerun with changes --- pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb | 990 ++++++++++++----------- 1 file changed, 529 insertions(+), 461 deletions(-) diff --git a/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb b/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb index 501765363..a18d07c4f 100644 --- a/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb +++ b/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb @@ -13,7 +13,7 @@ "id": "848c69e5", "metadata": {}, "source": [ - "The PCMDI Metrics sea ice driver produces metrics that compare modeled and observed sea ice extent. These metrics are the mean square errors of the total and climatological ice extent. Ice extent is defined as the area covered by sea ice concentration of >= 15%." + "The PCMDI Metrics sea ice driver produces metrics that compare modeled and observed sea ice extent. This notebook demonstrates how to run the PCMDI Metrics sea ice code." ] }, { @@ -47,6 +47,33 @@ { "cell_type": "code", "execution_count": 1, + "id": "b6d75e4e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating Arctic map\n", + "Creating Antarctic map\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING] yaksa: 10 leaked handle pool objects\n" + ] + } + ], + "source": [ + "%%bash\n", + "python create_sector_plots.py" + ] + }, + { + "cell_type": "code", + "execution_count": 2, "id": "a82ee330", "metadata": {}, "outputs": [], @@ -57,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "6a7eb6da", "metadata": {}, "outputs": [ @@ -70,7 +97,7 @@ }, { "data": { - "image/png": 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+ "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -92,7 +119,7 @@ }, { "cell_type": "markdown", - "id": "2161a4b3", + "id": "f316897b", "metadata": {}, "source": [ "This first case will work with sea ice concentration ouput from a single model, E3SM-1-0. Two overview plots are shown below to visualize the Arctic sea ice in this model.\n", @@ -102,8 +129,27 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "068142a6", + "execution_count": 4, + "id": "a6cb929f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-19 16:32:53,136 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + ] + } + ], + "source": [ + "%%bash\n", + "python make_demo_sea_ice_plots.py" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3120f819", "metadata": {}, "outputs": [ { @@ -115,7 +161,7 @@ }, { "data": { - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -137,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "id": "6e4fa38d", "metadata": {}, "outputs": [ @@ -186,8 +232,8 @@ "metrics_output_path = \"sea_ice_demo/%(case_id)/\"\n", "\n", "# Settings for the observational data\n", - "reference_data_path_nh = \"/work/ordonez4/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc\"\n", - "reference_data_path_sh = \"/work/ordonez4/ice_conc_sh_ease2-250_cdr-v3p0_198801-202012.nc\"\n", + "reference_data_path_nh = \"/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc\"\n", + "reference_data_path_sh = \"/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_sh_ease2-250_cdr-v3p0_198801-202012.nc\"\n", "ObsUnitsAdjust=(True,\"multiply\",1e-2)\n", "reference_data_set=\"OSI-SAF\"\n", "osyear=1988\n", @@ -215,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "9d6c1fbf", "metadata": {}, "outputs": [ @@ -350,7 +396,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "d6ff0052", "metadata": {}, "outputs": [ @@ -358,9 +404,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-19 15:01:38,777 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "INFO::2024-01-19 15:02::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n", - "2024-01-19 15:02:44,427 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n" + "2024-01-19 16:34:01,834 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "INFO::2024-01-19 16:35::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n", + "2024-01-19 16:35:05,003 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n" ] }, { @@ -427,18 +473,18 @@ ] }, { - "cell_type": "code", - "execution_count": 7, - "id": "dfd75e12", + "cell_type": "markdown", + "id": "084440aa", "metadata": {}, - "outputs": [], "source": [ - "# Explain MSE" + "One of the primary outputs of the PMP is a JSON file containing the metrics values. In this case, the metrics are the mean square errors of the time mean and monthly mean ice extent. Ice extent is defined as the total area covered by sea ice concentration of >= 15%. The metrics are organized by model, realization, and reference dataset.\n", + "\n", + "The metrics JSON from this run is displayed below." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "9a46fb89", "metadata": { "scrolled": false @@ -673,7 +719,7 @@ " \"Version\": \"23.1.0\",\n", " \"buildVersion\": \"not installed\"\n", " },\n", - " \"date\": \"2024-01-19 15:02:30\",\n", + " \"date\": \"2024-01-19 16:34:51\",\n", " \"openGL\": {\n", " \"GLX\": {\n", " \"client\": {},\n", @@ -727,12 +773,12 @@ "id": "d74b6752", "metadata": {}, "source": [ - "This driver also outputs a bar chart that visualizes the mean square error metrics. Since there is only one model and one realization in this instance, the bar chart looks very simple." + "This driver also outputs a bar chart that visualizes the mean square error between the model and observations. Since there is only one model and one realization in this instance, the bar chart looks very simple. The red bar indicates the mean square error for the time mean ice extent, and the blue bar indicates the mean square error for the climatological ice extent." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "c6dfa7a6", "metadata": {}, "outputs": [ @@ -750,7 +796,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "d14e933a", "metadata": {}, "outputs": [ @@ -787,7 +833,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "5f8174e1", "metadata": {}, "outputs": [ @@ -795,7 +841,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-19 15:03:39,967 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + "2024-01-19 16:36:00,469 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" ] }, { @@ -889,8 +935,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO::2024-01-19 15:06::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n", - "2024-01-19 15:06:48,149 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n" + "INFO::2024-01-19 16:38::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n", + "2024-01-19 16:38:53,126 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n" ] }, { @@ -937,9 +983,9 @@ "text": [ "[WARNING] yaksa: 10 leaked handle pool objects\n", "\n", - "real\t4m4.264s\n", - "user\t4m23.113s\n", - "sys\t1m18.596s\n" + "real\t3m48.529s\n", + "user\t4m7.945s\n", + "sys\t1m15.284s\n" ] } ], @@ -958,7 +1004,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "d6cb5f07", "metadata": {}, "outputs": [ @@ -997,7 +1043,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "679d7289", "metadata": {}, "outputs": [ @@ -1005,64 +1051,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-19 15:07:49,272 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "2024-01-19 15:08:26,554 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "2024-01-19 15:09:10,957 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + "2024-01-19 16:39:48,532 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "2024-01-19 16:40:31,455 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "['E3SM-1-0', 'CanESM5', 'CAS-ESM2-0']\n", + "['E3SM-1-0', 'CanESM5', 'MIROC6']\n", "Find all realizations: True\n", "OBS: Arctic\n", "Converting units by multiply 0.01\n", "OBS: Antarctic\n", "Converting units by multiply 0.01\n", - "Model list: ['CAS-ESM2-0', 'CanESM5', 'E3SM-1-0']\n", - "\n", - "=================================\n", - "model, runs: CAS-ESM2-0 ['r2i1p1f1', 'r1i1p1f1', 'r4i1p1f1', 'r3i1p1f1']\n", - "/p/user_pub/pmp/demo/sea-ice/links_area/CAS-ESM2-0/*.nc\n", - "Converting units by multiply 1e-06\n", - "\n", - "-----------------------\n", - "model, run, variable: CAS-ESM2-0 r2i1p1f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/CAS-ESM2-0/historical/r2i1p1f1/siconc/siconc_SImon_CAS-ESM2-0_historical_r2i1p1f1_gn_185001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: CAS-ESM2-0 r1i1p1f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/CAS-ESM2-0/historical/r1i1p1f1/siconc/siconc_SImon_CAS-ESM2-0_historical_r1i1p1f1_gn_185001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: CAS-ESM2-0 r4i1p1f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/CAS-ESM2-0/historical/r4i1p1f1/siconc/siconc_SImon_CAS-ESM2-0_historical_r4i1p1f1_gn_185001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: CAS-ESM2-0 r3i1p1f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/CAS-ESM2-0/historical/r3i1p1f1/siconc/siconc_SImon_CAS-ESM2-0_historical_r3i1p1f1_gn_185001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-------------------------------------------\n", - "Calculating model regional average metrics \n", - "for CAS-ESM2-0\n", - "--------------------------------------------\n", - "arctic\n", - "ca\n", - "na\n", - "np\n", - "antarctic\n", - "sp\n", - "sa\n", - "io\n", + "Model list: ['CanESM5', 'E3SM-1-0', 'MIROC6']\n", "\n", "=================================\n", "model, runs: CanESM5 ['r2i1p1f1', 'r1i1p1f1', 'r3i1p1f1']\n", @@ -1144,21 +1147,7 @@ " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_196001-196912.nc\n", " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_197001-197912.nc\n", " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_198001-198912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_199001-199912.nc\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO::2024-01-19 15:12::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n", - "2024-01-19 15:12:17,350 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_199001-199912.nc\n", " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_200001-200912.nc\n", " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_201001-201312.nc\n", "Converting units by multiply 0.01\n", @@ -1173,7 +1162,22 @@ " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_189001-189912.nc\n", " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_190001-190912.nc\n", " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_191001-191912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_192001-192912.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-19 16:43:05,900 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "INFO::2024-01-19 16:47::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n", + "2024-01-19 16:47:24,512 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_193001-193912.nc\n", " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_194001-194912.nc\n", " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_195001-195912.nc\n", @@ -1218,6 +1222,52 @@ "antarctic\n", "sp\n", "sa\n", + "io\n", + "\n", + "=================================\n", + "model, runs: MIROC6 ['r2i1p1f1', 'r1i1p1f1', 'r4i1p1f1', 'r3i1p1f1']\n", + "/p/user_pub/pmp/demo/sea-ice/links_area/MIROC6/*.nc\n", + "Converting units by multiply 1e-06\n", + "\n", + "-----------------------\n", + "model, run, variable: MIROC6 r2i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r2i1p1f1/siconc/siconc_SImon_MIROC6_historical_r2i1p1f1_gn_185001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r2i1p1f1/siconc/siconc_SImon_MIROC6_historical_r2i1p1f1_gn_195001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: MIROC6 r1i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r1i1p1f1/siconc/siconc_SImon_MIROC6_historical_r1i1p1f1_gn_185001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r1i1p1f1/siconc/siconc_SImon_MIROC6_historical_r1i1p1f1_gn_195001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: MIROC6 r4i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r4i1p1f1/siconc/siconc_SImon_MIROC6_historical_r4i1p1f1_gn_185001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r4i1p1f1/siconc/siconc_SImon_MIROC6_historical_r4i1p1f1_gn_195001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: MIROC6 r3i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r3i1p1f1/siconc/siconc_SImon_MIROC6_historical_r3i1p1f1_gn_185001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r3i1p1f1/siconc/siconc_SImon_MIROC6_historical_r3i1p1f1_gn_195001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-------------------------------------------\n", + "Calculating model regional average metrics \n", + "for MIROC6\n", + "--------------------------------------------\n", + "arctic\n", + "ca\n", + "na\n", + "np\n", + "antarctic\n", + "sp\n", + "sa\n", "io\n" ] }, @@ -1227,16 +1277,16 @@ "text": [ "[WARNING] yaksa: 10 leaked handle pool objects\n", "\n", - "real\t5m29.588s\n", - "user\t5m6.419s\n", - "sys\t1m59.480s\n" + "real\t8m31.064s\n", + "user\t10m28.047s\n", + "sys\t2m23.983s\n" ] } ], "source": [ "%%bash\n", "time python ice_driver.py -p demo_param_file.py \\\n", - "--test_data_set \"E3SM-1-0\" \"CanESM5\" \"CAS-ESM2-0\" \\\n", + "--test_data_set \"E3SM-1-0\" \"CanESM5\" \"MIROC6\" \\\n", "--realization '*' \\\n", "--case_id \"ex3\"" ] @@ -1251,7 +1301,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "b07dbb8b", "metadata": {}, "outputs": [ @@ -1274,9 +1324,9 @@ " \"statistic\"\n", " ],\n", " \"model\": [\n", - " \"CAS-ESM2-0\",\n", " \"CanESM5\",\n", - " \"E3SM-1-0\"\n", + " \"E3SM-1-0\",\n", + " \"MIROC6\"\n", " ],\n", " \"region\": {},\n", " \"statistic\": {\n", @@ -1284,55 +1334,45 @@ " }\n", " },\n", " \"RESULTS\": {\n", - " \"CAS-ESM2-0\": {\n", + " \"CanESM5\": {\n", " \"antarctic\": {\n", " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"7.083854826293509\"\n", + " \"mse\": \"5.1043444982100254\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"3.792162821153895\"\n", + " \"mse\": \"4.816687734317558\"\n", " }\n", " }\n", " },\n", " \"r1i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"6.752607765078877\"\n", + " \"mse\": \"3.8203905542158574\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"3.174008188870785\"\n", + " \"mse\": \"3.551903219690635\"\n", " }\n", " }\n", " },\n", " \"r2i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"7.522817843681294\"\n", + " \"mse\": \"5.408472768567934\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"4.299438069656205\"\n", + " \"mse\": \"5.10073354794071\"\n", " }\n", " }\n", " },\n", " \"r3i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"7.411432112275569\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"4.185670546371886\"\n", - " }\n", - " }\n", - " },\n", - " \"r4i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"6.73436642084015\"\n", + " \"mse\": \"6.255511442006537\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"3.566113619835315\"\n", + " \"mse\": \"5.95826796378333\"\n", " }\n", " }\n", " }\n", @@ -1341,50 +1381,40 @@ " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"2.758010437166196\"\n", + " \"mse\": \"2.6739701578200408\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.06079693675193923\"\n", + " \"mse\": \"2.5552395000296997\"\n", " }\n", " }\n", " },\n", " \"r1i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"2.5432430859680046\"\n", + " \"mse\": \"1.9601839559074323\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.04967966166799988\"\n", + " \"mse\": \"1.8071711277770932\"\n", " }\n", " }\n", " },\n", " \"r2i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"2.6533567886155067\"\n", + " \"mse\": \"2.8686219657630323\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.041593297278908994\"\n", + " \"mse\": \"2.7646000598233362\"\n", " }\n", " }\n", " },\n", " \"r3i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"3.2748196947205086\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.13583746759696935\"\n", - " }\n", - " }\n", - " },\n", - " \"r4i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"2.6148538235748644\"\n", + " \"mse\": \"3.306431955856059\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.03643683449648478\"\n", + " \"mse\": \"3.1987918127469728\"\n", " }\n", " }\n", " }\n", @@ -1393,50 +1423,40 @@ " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"1.115678313720505\"\n", + " \"mse\": \"0.12445176930055403\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.2701677272629309\"\n", + " \"mse\": \"0.0752818530752368\"\n", " }\n", " }\n", " },\n", " \"r1i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"1.0137471293629183\"\n", + " \"mse\": \"0.06261975386075735\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.22276239505159934\"\n", + " \"mse\": \"0.04065017565672462\"\n", " }\n", " }\n", " },\n", " \"r2i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"1.0131920675189705\"\n", + " \"mse\": \"0.18876746901985617\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.22998029964085454\"\n", + " \"mse\": \"0.11135594838591391\"\n", " }\n", " }\n", " },\n", " \"r3i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"1.465998755519945\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.4115486967078753\"\n", - " }\n", - " }\n", - " },\n", - " \"r4i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.0093024589958346\"\n", + " \"mse\": \"0.14126431682827864\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.23624179590712016\"\n", + " \"mse\": \"0.08283366771548334\"\n", " }\n", " }\n", " }\n", @@ -1445,50 +1465,40 @@ " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.5587052132584508\"\n", + " \"mse\": \"0.3902350350581252\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.4441928847225293\"\n", + " \"mse\": \"0.3411097649596542\"\n", " }\n", " }\n", " },\n", " \"r1i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.44632580540645383\"\n", + " \"mse\": \"0.27378096542718267\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.3578341565945753\"\n", + " \"mse\": \"0.23052580580517984\"\n", " }\n", " }\n", " },\n", " \"r2i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.5914869387051985\"\n", + " \"mse\": \"0.39222062635127936\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.4697689210442217\"\n", + " \"mse\": \"0.34482149125771394\"\n", " }\n", " }\n", " },\n", " \"r3i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.6000166805923479\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.4744316230074157\"\n", - " }\n", - " }\n", - " },\n", - " \"r4i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.6073432623785198\"\n", + " \"mse\": \"0.5287500850978069\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.48098746056386565\"\n", + " \"mse\": \"0.4689404665141464\"\n", " }\n", " }\n", " }\n", @@ -1497,50 +1507,40 @@ " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.11035555723719193\"\n", + " \"mse\": \"1.8575586124643404\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.00014482621582919338\"\n", + " \"mse\": \"1.6617817141384847\"\n", " }\n", " }\n", " },\n", " \"r1i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.11224121667589122\"\n", + " \"mse\": \"1.5264155067552119\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"7.122307436612489e-05\"\n", + " \"mse\": \"1.3050483466111835\"\n", " }\n", " }\n", " },\n", " \"r2i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.10635922072937791\"\n", + " \"mse\": \"1.7640984838802416\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"4.950351702734517e-05\"\n", + " \"mse\": \"1.5843839089856835\"\n", " }\n", " }\n", " },\n", " \"r3i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.11799285312083212\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.000195800590518114\"\n", - " }\n", - " }\n", - " },\n", - " \"r4i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.10869083883245541\"\n", + " \"mse\": \"2.3388720869665747\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.0010720141006566098\"\n", + " \"mse\": \"2.1497244832528395\"\n", " }\n", " }\n", " }\n", @@ -1549,50 +1549,40 @@ " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.7365077844610202\"\n", + " \"mse\": \"0.0063419603431157535\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.31763482132964094\"\n", + " \"mse\": \"0.001033088420302666\"\n", " }\n", " }\n", " },\n", " \"r1i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.6739315691049321\"\n", + " \"mse\": \"0.005949894526659108\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.294929307420199\"\n", + " \"mse\": \"1.7412310294637067e-06\"\n", " }\n", " }\n", " },\n", " \"r2i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.7856044857813185\"\n", + " \"mse\": \"0.01271835367014484\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.3423126156354784\"\n", + " \"mse\": \"0.004687148872326894\"\n", " }\n", " }\n", " },\n", " \"r3i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.7381070087851462\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.3151345286459791\"\n", - " }\n", - " }\n", - " },\n", - " \"r4i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.752006940066863\"\n", + " \"mse\": \"0.005275638631907463\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.31905206886524895\"\n", + " \"mse\": \"0.0008574285177782025\"\n", " }\n", " }\n", " }\n", @@ -1601,50 +1591,40 @@ " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.9389315024588251\"\n", + " \"mse\": \"0.4618851114415225\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.31441652292728833\"\n", + " \"mse\": \"0.21877947801248515\"\n", " }\n", " }\n", " },\n", " \"r1i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.9000594613216378\"\n", + " \"mse\": \"0.3604933562263525\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.35776166739018606\"\n", + " \"mse\": \"0.16135560774807228\"\n", " }\n", " }\n", " },\n", " \"r2i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"1.0077412958752512\"\n", + " \"mse\": \"0.48465097876034335\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.3286640777431595\"\n", + " \"mse\": \"0.18590427961007985\"\n", " }\n", " }\n", " },\n", " \"r3i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.9846307446638192\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.31344833046401555\"\n", - " }\n", - " }\n", - " },\n", - " \"r4i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.8749517355191212\"\n", + " \"mse\": \"0.565345935295451\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.261759950703633\"\n", + " \"mse\": \"0.32530874506699275\"\n", " }\n", " }\n", " }\n", @@ -1653,94 +1633,94 @@ " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"1.7953944004662226\"\n", + " \"mse\": \"1.3466206749703824\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.5060218237368052\"\n", + " \"mse\": \"1.3264114860024545\"\n", " }\n", " }\n", " },\n", " \"r1i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"1.8616328897040875\"\n", + " \"mse\": \"1.0412143157666585\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.33230741080381965\"\n", + " \"mse\": \"1.0233677214618289\"\n", " }\n", " }\n", " },\n", " \"r2i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"1.8219508007941667\"\n", + " \"mse\": \"1.5844213803502167\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.6496841344585111\"\n", + " \"mse\": \"1.5597222118436824\"\n", " }\n", " }\n", " },\n", " \"r3i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"1.8111874101237784\"\n", + " \"mse\": \"1.4483893228104396\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.621650421425891\"\n", + " \"mse\": \"1.4270545631414926\"\n", " }\n", " }\n", - " },\n", - " \"r4i1p1f1\": {\n", + " }\n", + " }\n", + " },\n", + " \"E3SM-1-0\": {\n", + " \"antarctic\": {\n", + " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"1.7442886033736613\"\n", + " \"mse\": \"0.7772427941035078\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.45490790524514696\"\n", + " \"mse\": \"0.512854523904\"\n", " }\n", " }\n", - " }\n", - " }\n", - " },\n", - " \"CanESM5\": {\n", - " \"antarctic\": {\n", - " \"model_mean\": {\n", + " },\n", + " \"r1i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"5.1043444982100254\"\n", + " \"mse\": \"0.4635192339671928\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"4.816687734317558\"\n", + " \"mse\": \"0.139646926848\"\n", " }\n", " }\n", " },\n", - " \"r1i1p1f1\": {\n", + " \"r2i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"3.8203905542158574\"\n", + " \"mse\": \"0.7917153708317476\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"3.551903219690635\"\n", + " \"mse\": \"0.5296078848\"\n", " }\n", " }\n", " },\n", - " \"r2i1p1f1\": {\n", + " \"r3i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"5.408472768567934\"\n", + " \"mse\": \"0.9431708933066041\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"5.10073354794071\"\n", + " \"mse\": \"0.709116624896\"\n", " }\n", " }\n", " },\n", - " \"r3i1p1f1\": {\n", + " \"r4i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"6.255511442006537\"\n", + " \"mse\": \"1.1123064886611145\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"5.95826796378333\"\n", + " \"mse\": \"0.8482918891519999\"\n", " }\n", " }\n", " }\n", @@ -1749,40 +1729,50 @@ " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"2.6739701578200408\"\n", + " \"mse\": \"5.271005131039172\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"2.5552395000296997\"\n", + " \"mse\": \"3.602193842176\"\n", " }\n", " }\n", " },\n", - " \"r1i1p1f1\": {\n", + " \"r1i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"1.9601839559074323\"\n", + " \"mse\": \"5.476181000101471\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"1.8071711277770932\"\n", + " \"mse\": \"3.628078727168\"\n", " }\n", " }\n", " },\n", - " \"r2i1p1f1\": {\n", + " \"r2i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"2.8686219657630323\"\n", + " \"mse\": \"4.798813326297904\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"2.7646000598233362\"\n", + " \"mse\": \"3.0712725504\"\n", " }\n", " }\n", " },\n", - " \"r3i1p1f1\": {\n", + " \"r3i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"3.306431955856059\"\n", + " \"mse\": \"5.695229471419496\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"3.1987918127469728\"\n", + " \"mse\": \"4.135149109248\"\n", + " }\n", + " }\n", + " },\n", + " \"r4i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"5.16787172788022\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.6138642309119997\"\n", " }\n", " }\n", " }\n", @@ -1791,40 +1781,50 @@ " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.12445176930055403\"\n", + " \"mse\": \"0.06682122096680175\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.0752818530752368\"\n", + " \"mse\": \"0.014511187968\"\n", " }\n", " }\n", " },\n", - " \"r1i1p1f1\": {\n", + " \"r1i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.06261975386075735\"\n", + " \"mse\": \"0.05045644169895609\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.04065017565672462\"\n", + " \"mse\": \"0.007755424768\"\n", " }\n", " }\n", " },\n", - " \"r2i1p1f1\": {\n", + " \"r2i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.18876746901985617\"\n", + " \"mse\": \"0.04953964308899206\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.11135594838591391\"\n", + " \"mse\": \"0.007533873664\"\n", " }\n", " }\n", " },\n", - " \"r3i1p1f1\": {\n", + " \"r3i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.14126431682827864\"\n", + " \"mse\": \"0.09545969211386617\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.08283366771548334\"\n", + " \"mse\": \"0.026321457152\"\n", + " }\n", + " }\n", + " },\n", + " \"r4i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.08158619730649973\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.020952242176\"\n", " }\n", " }\n", " }\n", @@ -1833,40 +1833,50 @@ " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.3902350350581252\"\n", + " \"mse\": \"0.08859447654792228\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.3411097649596542\"\n", + " \"mse\": \"0.033486426112\"\n", " }\n", " }\n", " },\n", - " \"r1i1p1f1\": {\n", + " \"r1i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.27378096542718267\"\n", + " \"mse\": \"0.04955696515353039\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.23052580580517984\"\n", + " \"mse\": \"0.00991997952\"\n", " }\n", " }\n", " },\n", - " \"r2i1p1f1\": {\n", + " \"r2i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.39222062635127936\"\n", + " \"mse\": \"0.0709290381850532\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.34482149125771394\"\n", + " \"mse\": \"0.020307523584\"\n", " }\n", " }\n", " },\n", - " \"r3i1p1f1\": {\n", + " \"r3i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.5287500850978069\"\n", + " \"mse\": \"0.13171857892467173\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.4689404665141464\"\n", + " \"mse\": \"0.064746631168\"\n", + " }\n", + " }\n", + " },\n", + " \"r4i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.12394583688994158\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.055420383232\"\n", " }\n", " }\n", " }\n", @@ -1875,40 +1885,50 @@ " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"1.8575586124643404\"\n", + " \"mse\": \"2.2353377826268255\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"1.6617817141384847\"\n", + " \"mse\": \"1.514922442752\"\n", " }\n", " }\n", " },\n", - " \"r1i1p1f1\": {\n", + " \"r1i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"1.5264155067552119\"\n", + " \"mse\": \"2.3482121752568643\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"1.3050483466111835\"\n", + " \"mse\": \"1.576847409152\"\n", " }\n", " }\n", " },\n", - " \"r2i1p1f1\": {\n", + " \"r2i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"1.7640984838802416\"\n", + " \"mse\": \"1.986686713962093\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"1.5843839089856835\"\n", + " \"mse\": \"1.273763069952\"\n", " }\n", " }\n", " },\n", - " \"r3i1p1f1\": {\n", + " \"r3i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"2.3388720869665747\"\n", + " \"mse\": \"2.5126581069696856\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"2.1497244832528395\"\n", + " \"mse\": \"1.781503885312\"\n", + " }\n", + " }\n", + " },\n", + " \"r4i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"2.120027257004436\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.450146136064\"\n", " }\n", " }\n", " }\n", @@ -1917,40 +1937,50 @@ " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.0063419603431157535\"\n", + " \"mse\": \"0.5951950421264879\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.001033088420302666\"\n", + " \"mse\": \"0.268423725056\"\n", " }\n", " }\n", " },\n", - " \"r1i1p1f1\": {\n", + " \"r1i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.005949894526659108\"\n", + " \"mse\": \"0.6264518797177615\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"1.7412310294637067e-06\"\n", + " \"mse\": \"0.287947685888\"\n", " }\n", " }\n", " },\n", - " \"r2i1p1f1\": {\n", + " \"r2i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.01271835367014484\"\n", + " \"mse\": \"0.5857836656186229\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.004687148872326894\"\n", + " \"mse\": \"0.258358591488\"\n", " }\n", " }\n", " },\n", - " \"r3i1p1f1\": {\n", + " \"r3i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.005275638631907463\"\n", + " \"mse\": \"0.5653155943768037\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.0008574285177782025\"\n", + " \"mse\": \"0.255321079808\"\n", + " }\n", + " }\n", + " },\n", + " \"r4i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.605146184785239\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.272687104\"\n", " }\n", " }\n", " }\n", @@ -1959,40 +1989,50 @@ " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.4618851114415225\"\n", + " \"mse\": \"0.4924799868799379\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.21877947801248515\"\n", + " \"mse\": \"0.406647668736\"\n", " }\n", " }\n", " },\n", - " \"r1i1p1f1\": {\n", + " \"r1i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.3604933562263525\"\n", + " \"mse\": \"0.3797729615722766\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.16135560774807228\"\n", + " \"mse\": \"0.297013608448\"\n", " }\n", " }\n", " },\n", - " \"r2i1p1f1\": {\n", + " \"r2i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.48465097876034335\"\n", + " \"mse\": \"0.43324236598783966\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.18590427961007985\"\n", + " \"mse\": \"0.3584606208\"\n", " }\n", " }\n", " },\n", - " \"r3i1p1f1\": {\n", + " \"r3i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.565345935295451\"\n", + " \"mse\": \"0.54670122730152\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.32530874506699275\"\n", + " \"mse\": \"0.455321387008\"\n", + " }\n", + " }\n", + " },\n", + " \"r4i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.6355585206799742\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.53622751232\"\n", " }\n", " }\n", " }\n", @@ -2001,94 +2041,104 @@ " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"1.3466206749703824\"\n", + " \"mse\": \"0.5282094877035928\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"1.3264114860024545\"\n", + " \"mse\": \"0.01284434432\"\n", " }\n", " }\n", " },\n", - " \"r1i1p1f1\": {\n", + " \"r1i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"1.0412143157666585\"\n", + " \"mse\": \"0.6767107661262813\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"1.0233677214618289\"\n", + " \"mse\": \"0.078223351808\"\n", " }\n", " }\n", " },\n", - " \"r2i1p1f1\": {\n", + " \"r2i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"1.5844213803502167\"\n", + " \"mse\": \"0.4522165451285096\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"1.5597222118436824\"\n", + " \"mse\": \"0.000495858944\"\n", " }\n", " }\n", " },\n", - " \"r3i1p1f1\": {\n", + " \"r3i2p2f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"1.4483893228104396\"\n", + " \"mse\": \"0.5318409136802855\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"1.4270545631414926\"\n", + " \"mse\": \"0.009201968127999999\"\n", + " }\n", + " }\n", + " },\n", + " \"r4i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.501167141217253\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.003074912256\"\n", " }\n", " }\n", " }\n", " }\n", " },\n", - " \"E3SM-1-0\": {\n", + " \"MIROC6\": {\n", " \"antarctic\": {\n", " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.7772427941035078\"\n", + " \"mse\": \"83.57711925460697\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.512854523904\"\n", + " \"mse\": \"68.05560229888\"\n", " }\n", " }\n", " },\n", - " \"r1i2p2f1\": {\n", + " \"r1i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.4635192339671928\"\n", + " \"mse\": \"83.38600579613097\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.139646926848\"\n", + " \"mse\": \"67.918356283392\"\n", " }\n", " }\n", " },\n", - " \"r2i2p2f1\": {\n", + " \"r2i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.7917153708317476\"\n", + " \"mse\": \"83.83210652837262\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.5296078848\"\n", + " \"mse\": \"68.251656650752\"\n", " }\n", " }\n", " },\n", - " \"r3i2p2f1\": {\n", + " \"r3i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.9431708933066041\"\n", + " \"mse\": \"83.79334319896631\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.709116624896\"\n", + " \"mse\": \"68.22144507904\"\n", " }\n", " }\n", " },\n", - " \"r4i2p2f1\": {\n", + " \"r4i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"1.1123064886611145\"\n", + " \"mse\": \"83.29824611560043\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.8482918891519999\"\n", + " \"mse\": \"67.831450304512\"\n", " }\n", " }\n", " }\n", @@ -2097,50 +2147,50 @@ " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"5.271005131039172\"\n", + " \"mse\": \"0.7964690037128367\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"3.602193842176\"\n", + " \"mse\": \"0.35166486528\"\n", " }\n", " }\n", " },\n", - " \"r1i2p2f1\": {\n", + " \"r1i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"5.476181000101471\"\n", + " \"mse\": \"0.897558208169598\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"3.628078727168\"\n", + " \"mse\": \"0.338398609408\"\n", " }\n", " }\n", " },\n", - " \"r2i2p2f1\": {\n", + " \"r2i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"4.798813326297904\"\n", + " \"mse\": \"0.6413023948192471\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"3.0712725504\"\n", + " \"mse\": \"0.211103809536\"\n", " }\n", " }\n", " },\n", - " \"r3i2p2f1\": {\n", + " \"r3i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"5.695229471419496\"\n", + " \"mse\": \"0.77767869088113\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"4.135149109248\"\n", + " \"mse\": \"0.374499180544\"\n", " }\n", " }\n", " },\n", - " \"r4i2p2f1\": {\n", + " \"r4i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"5.16787172788022\"\n", + " \"mse\": \"0.9160660976325624\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"3.6138642309119997\"\n", + " \"mse\": \"0.516833574912\"\n", " }\n", " }\n", " }\n", @@ -2149,50 +2199,50 @@ " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.06682122096680175\"\n", + " \"mse\": \"0.08012887394477156\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.014511187968\"\n", + " \"mse\": \"0.021300850688\"\n", " }\n", " }\n", " },\n", - " \"r1i2p2f1\": {\n", + " \"r1i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.05045644169895609\"\n", + " \"mse\": \"0.09644034149447794\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.007755424768\"\n", + " \"mse\": \"0.009676776448\"\n", " }\n", " }\n", " },\n", - " \"r2i2p2f1\": {\n", + " \"r2i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.04953964308899206\"\n", + " \"mse\": \"0.08312628758340265\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.007533873664\"\n", + " \"mse\": \"0.019284629504\"\n", " }\n", " }\n", " },\n", - " \"r3i2p2f1\": {\n", + " \"r3i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.09545969211386617\"\n", + " \"mse\": \"0.07526427644741965\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.026321457152\"\n", + " \"mse\": \"0.0323789824\"\n", " }\n", " }\n", " },\n", - " \"r4i2p2f1\": {\n", + " \"r4i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.08158619730649973\"\n", + " \"mse\": \"0.07474956943685433\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.020952242176\"\n", + " \"mse\": \"0.027760095232\"\n", " }\n", " }\n", " }\n", @@ -2201,50 +2251,50 @@ " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.08859447654792228\"\n", + " \"mse\": \"2.481295899016718\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.033486426112\"\n", + " \"mse\": \"1.642160586752\"\n", " }\n", " }\n", " },\n", - " \"r1i2p2f1\": {\n", + " \"r1i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.04955696515353039\"\n", + " \"mse\": \"2.4925219153664493\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.00991997952\"\n", + " \"mse\": \"1.646709702656\"\n", " }\n", " }\n", " },\n", - " \"r2i2p2f1\": {\n", + " \"r2i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.0709290381850532\"\n", + " \"mse\": \"2.4959466246800366\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.020307523584\"\n", + " \"mse\": \"1.6535963566079999\"\n", " }\n", " }\n", " },\n", - " \"r3i2p2f1\": {\n", + " \"r3i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.13171857892467173\"\n", + " \"mse\": \"2.4778961139985274\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.064746631168\"\n", + " \"mse\": \"1.6392122531839999\"\n", " }\n", " }\n", " },\n", - " \"r4i2p2f1\": {\n", + " \"r4i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.12394583688994158\"\n", + " \"mse\": \"2.4589450695378665\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.055420383232\"\n", + " \"mse\": \"1.629173710848\"\n", " }\n", " }\n", " }\n", @@ -2253,50 +2303,50 @@ " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"2.2353377826268255\"\n", + " \"mse\": \"0.09909765129397402\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"1.514922442752\"\n", + " \"mse\": \"0.051428544512\"\n", " }\n", " }\n", " },\n", - " \"r1i2p2f1\": {\n", + " \"r1i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"2.3482121752568643\"\n", + " \"mse\": \"0.1197780144247023\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"1.576847409152\"\n", + " \"mse\": \"0.064398516224\"\n", " }\n", " }\n", " },\n", - " \"r2i2p2f1\": {\n", + " \"r2i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"1.986686713962093\"\n", + " \"mse\": \"0.06350924181643114\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"1.273763069952\"\n", + " \"mse\": \"0.014681411584\"\n", " }\n", " }\n", " },\n", - " \"r3i2p2f1\": {\n", + " \"r3i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"2.5126581069696856\"\n", + " \"mse\": \"0.07872592575887577\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"1.781503885312\"\n", + " \"mse\": \"0.037655158784\"\n", " }\n", " }\n", " },\n", - " \"r4i2p2f1\": {\n", + " \"r4i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"2.120027257004436\"\n", + " \"mse\": \"0.16268848865282248\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"1.450146136064\"\n", + " \"mse\": \"0.114331983872\"\n", " }\n", " }\n", " }\n", @@ -2305,50 +2355,50 @@ " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.5951950421264879\"\n", + " \"mse\": \"0.1074685296351375\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.268423725056\"\n", + " \"mse\": \"0.043079524352\"\n", " }\n", " }\n", " },\n", - " \"r1i2p2f1\": {\n", + " \"r1i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.6264518797177615\"\n", + " \"mse\": \"0.11746168062866781\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.287947685888\"\n", + " \"mse\": \"0.046970937344\"\n", " }\n", " }\n", " },\n", - " \"r2i2p2f1\": {\n", + " \"r2i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.5857836656186229\"\n", + " \"mse\": \"0.08798398576956203\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.258358591488\"\n", + " \"mse\": \"0.03501553664\"\n", " }\n", " }\n", " },\n", - " \"r3i2p2f1\": {\n", + " \"r3i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.5653155943768037\"\n", + " \"mse\": \"0.1243555932581142\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.255321079808\"\n", + " \"mse\": \"0.050896572416\"\n", " }\n", " }\n", " },\n", - " \"r4i2p2f1\": {\n", + " \"r4i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.605146184785239\"\n", + " \"mse\": \"0.10222095624994212\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.272687104\"\n", + " \"mse\": \"0.040308391936\"\n", " }\n", " }\n", " }\n", @@ -2357,50 +2407,50 @@ " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.4924799868799379\"\n", + " \"mse\": \"12.832756905129132\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.406647668736\"\n", + " \"mse\": \"10.706316427264\"\n", " }\n", " }\n", " },\n", - " \"r1i2p2f1\": {\n", + " \"r1i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.3797729615722766\"\n", + " \"mse\": \"12.968499777649717\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.297013608448\"\n", + " \"mse\": \"10.810716848128\"\n", " }\n", " }\n", " },\n", - " \"r2i2p2f1\": {\n", + " \"r2i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.43324236598783966\"\n", + " \"mse\": \"12.732441134907969\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.3584606208\"\n", + " \"mse\": \"10.638712635392\"\n", " }\n", " }\n", " },\n", - " \"r3i2p2f1\": {\n", + " \"r3i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.54670122730152\"\n", + " \"mse\": \"12.91876551982416\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.455321387008\"\n", + " \"mse\": \"10.769082089472\"\n", " }\n", " }\n", " },\n", - " \"r4i2p2f1\": {\n", + " \"r4i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.6355585206799742\"\n", + " \"mse\": \"12.712614806291402\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.53622751232\"\n", + " \"mse\": \"10.607433613312\"\n", " }\n", " }\n", " }\n", @@ -2409,50 +2459,50 @@ " \"model_mean\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.5282094877035928\"\n", + " \"mse\": \"16.158254589965146\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.01284434432\"\n", + " \"mse\": \"13.595991605247999\"\n", " }\n", " }\n", " },\n", - " \"r1i2p2f1\": {\n", + " \"r1i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.6767107661262813\"\n", + " \"mse\": \"15.90437524234963\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.078223351808\"\n", + " \"mse\": \"13.40484878336\"\n", " }\n", " }\n", " },\n", - " \"r2i2p2f1\": {\n", + " \"r2i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.4522165451285096\"\n", + " \"mse\": \"16.338122498955823\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.000495858944\"\n", + " \"mse\": \"13.727326797824\"\n", " }\n", " }\n", " },\n", - " \"r3i2p2f1\": {\n", + " \"r3i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.5318409136802855\"\n", + " \"mse\": \"16.167353763780575\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.009201968127999999\"\n", + " \"mse\": \"13.60793174016\"\n", " }\n", " }\n", " },\n", - " \"r4i2p2f1\": {\n", + " \"r4i1p1f1\": {\n", " \"OSI-SAF\": {\n", " \"monthly_clim\": {\n", - " \"mse\": \"0.501167141217253\"\n", + " \"mse\": \"16.22520331188068\"\n", " },\n", " \"total_extent\": {\n", - " \"mse\": \"0.003074912256\"\n", + " \"mse\": \"13.644895092736\"\n", " }\n", " }\n", " }\n", @@ -2469,28 +2519,28 @@ " ],\n", " \"json_version\": 3.0,\n", " \"model_year_range\": {\n", - " \"CAS-ESM2-0\": [\n", + " \"CanESM5\": [\n", " \"1981\",\n", " \"2010\"\n", " ],\n", - " \"CanESM5\": [\n", + " \"E3SM-1-0\": [\n", " \"1981\",\n", " \"2010\"\n", " ],\n", - " \"E3SM-1-0\": [\n", + " \"MIROC6\": [\n", " \"1981\",\n", " \"2010\"\n", " ]\n", " },\n", " \"provenance\": {\n", - " \"commandLine\": \"ice_driver.py -p demo_param_file.py --test_data_set E3SM-1-0 CanESM5 CAS-ESM2-0 --realization * --case_id ex3\",\n", + " \"commandLine\": \"ice_driver.py -p demo_param_file.py --test_data_set E3SM-1-0 CanESM5 MIROC6 --realization * --case_id ex3\",\n", " \"conda\": {\n", " \"Platform\": \"linux-64\",\n", " \"PythonVersion\": \"3.8.15.final.0\",\n", " \"Version\": \"23.1.0\",\n", " \"buildVersion\": \"not installed\"\n", " },\n", - " \"date\": \"2024-01-19 15:12:03\",\n", + " \"date\": \"2024-01-19 16:47:10\",\n", " \"openGL\": {\n", " \"GLX\": {\n", " \"client\": {},\n", @@ -2549,13 +2599,13 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "41aa14a3", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -2571,21 +2621,39 @@ "id": "bc43281a", "metadata": {}, "source": [ - "# Other examples?" + "# Further exploration" ] }, { "cell_type": "markdown", - "id": "7b1d5694", + "id": "19abc98d", "metadata": {}, "source": [ - "Use --msyear and --meyear flags to change model year range" + "Maybe you want to compare more models, or take a closer look at the model data? Here are links to the data for further exploration.\n", + "\n", + "Data for nine models is available here:\n", + "```\n", + "/p/user_pub/pmp/demo/sea-ice/links_siconc \n", + "/p/user_pub/pmp/demo/sea-ice/links_area\n", + "```\n", + "\n", + "The observational time series can be found at:\n", + "```\n", + "/p/user_pub/pmp/demo/sea-ice/EUMETSAT\n", + "```\n", + "\n", + "For some example plotting code using xcdat and matplotlib, see the scripts that were used to generate the introductory figures:\n", + "\n", + "```\n", + "create_sector_plots.py\n", + "make_demo_sea_ice_plots.py\n", + "```\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "a4b45449", + "id": "f1161f29", "metadata": {}, "outputs": [], "source": [] From c0a278a7c60bca758df56f3c3f6c9d523913d9ee Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Tue, 23 Jan 2024 15:23:25 -0800 Subject: [PATCH 38/69] rerun with markers --- pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb | 48 ++++++++++++------------ 1 file changed, 24 insertions(+), 24 deletions(-) diff --git a/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb b/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb index a18d07c4f..d4cd12a9c 100644 --- a/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb +++ b/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb @@ -137,7 +137,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-19 16:32:53,136 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + "2024-01-23 15:05:03,897 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" ] } ], @@ -404,9 +404,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-19 16:34:01,834 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "INFO::2024-01-19 16:35::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n", - "2024-01-19 16:35:05,003 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n" + "2024-01-23 15:06:06,266 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "INFO::2024-01-23 15:07::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n", + "2024-01-23 15:07:10,554 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n" ] }, { @@ -719,7 +719,7 @@ " \"Version\": \"23.1.0\",\n", " \"buildVersion\": \"not installed\"\n", " },\n", - " \"date\": \"2024-01-19 16:34:51\",\n", + " \"date\": \"2024-01-23 15:06:56\",\n", " \"openGL\": {\n", " \"GLX\": {\n", " \"client\": {},\n", @@ -802,7 +802,7 @@ "outputs": [ { "data": { - "image/png": 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" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -841,7 +841,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-19 16:36:00,469 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + "2024-01-23 15:08:06,985 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" ] }, { @@ -935,8 +935,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO::2024-01-19 16:38::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n", - "2024-01-19 16:38:53,126 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n" + "INFO::2024-01-23 15:11::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n", + "2024-01-23 15:11:20,578 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n" ] }, { @@ -983,9 +983,9 @@ "text": [ "[WARNING] yaksa: 10 leaked handle pool objects\n", "\n", - "real\t3m48.529s\n", - "user\t4m7.945s\n", - "sys\t1m15.284s\n" + "real\t4m10.243s\n", + "user\t4m28.848s\n", + "sys\t1m24.374s\n" ] } ], @@ -999,7 +999,7 @@ "id": "cadb1306", "metadata": {}, "source": [ - "Since we have averaged four different realizations, the resulting statistics are different than seen in example 1. The bar chart now contains error bars showing the overall spread among the realizations." + "Since we have averaged four different realizations, the resulting statistics are different than seen in example 1. The bar chart now contains markers showing the overall spread among the realizations." ] }, { @@ -1010,7 +1010,7 @@ "outputs": [ { "data": { - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -1051,8 +1051,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-19 16:39:48,532 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "2024-01-19 16:40:31,455 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + "2024-01-23 15:12:21,567 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "2024-01-23 15:13:05,799 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" ] }, { @@ -1169,9 +1169,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-19 16:43:05,900 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "INFO::2024-01-19 16:47::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n", - "2024-01-19 16:47:24,512 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n" + "2024-01-23 15:15:50,273 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "INFO::2024-01-23 15:20::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n", + "2024-01-23 15:20:09,013 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n" ] }, { @@ -1277,9 +1277,9 @@ "text": [ "[WARNING] yaksa: 10 leaked handle pool objects\n", "\n", - "real\t8m31.064s\n", - "user\t10m28.047s\n", - "sys\t2m23.983s\n" + "real\t8m48.404s\n", + "user\t10m38.508s\n", + "sys\t2m36.253s\n" ] } ], @@ -2540,7 +2540,7 @@ " \"Version\": \"23.1.0\",\n", " \"buildVersion\": \"not installed\"\n", " },\n", - " \"date\": \"2024-01-19 16:47:10\",\n", + " \"date\": \"2024-01-23 15:19:54\",\n", " \"openGL\": {\n", " \"GLX\": {\n", " \"client\": {},\n", @@ -2605,7 +2605,7 @@ "outputs": [ { "data": { - "image/png": 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" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" From c751f4ab2109ec79881e28d3bdc08e70abfbc5d4 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Tue, 23 Jan 2024 15:23:57 -0800 Subject: [PATCH 39/69] add scatter --- pcmdi_metrics/sea_ice/ice_driver.py | 121 +++++++++++++--------------- 1 file changed, 58 insertions(+), 63 deletions(-) diff --git a/pcmdi_metrics/sea_ice/ice_driver.py b/pcmdi_metrics/sea_ice/ice_driver.py index 2dda533d2..3bef04dc8 100644 --- a/pcmdi_metrics/sea_ice/ice_driver.py +++ b/pcmdi_metrics/sea_ice/ice_driver.py @@ -803,8 +803,11 @@ def get_xy_coords(ds, xvar): mse_ext = [] clim_range = [] ext_range = [] + clim_err_x = [] + clim_err_y = [] + ext_err_y = [] rgn = sector_short[inds] - for model in model_list: + for nmod, model in enumerate(model_list): mse_clim.append( float( metrics["RESULTS"][model][rgn]["model_mean"][reference_data_set][ @@ -819,74 +822,64 @@ def get_xy_coords(ds, xvar): ]["mse"] ) ) - # Get spread - clim_err = [] - ext_err = [] - for r in metrics["RESULTS"][model][rgn]: - if r != "model_mean": - clim_err.append( - float( - metrics["RESULTS"][model][rgn][r][reference_data_set][ - "monthly_clim" - ]["mse"] + # Get spread, only if there are multiple realizations + if len(metrics["RESULTS"][model][rgn].keys()) > 2: + for r in metrics["RESULTS"][model][rgn]: + if r != "model_mean": + clim_err_x.append(ind[nmod]) + clim_err_y.append( + float( + metrics["RESULTS"][model][rgn][r][reference_data_set][ + "monthly_clim" + ]["mse"] + ) ) - ) - ext_err.append( - float( - metrics["RESULTS"][model][rgn][r][reference_data_set][ - "total_extent" - ]["mse"] + ext_err_y.append( + float( + metrics["RESULTS"][model][rgn][r][reference_data_set][ + "total_extent" + ]["mse"] + ) ) - ) - clim_range.append(np.max(clim_err) - np.min(clim_err)) - ext_range.append(np.max(ext_err) - np.min(ext_err)) - - # mse_clim.append( - # mse_t( - # obs_clims["bt"][rgn]["ice_con"], - # obs_clims["nt"][rgn]["ice_con"], - # weights=clim_wts, - # ) - # * 1e-12 - # ) - # mse_ext.append(mse_model(obs_means["bt"][rgn], obs_means["nt"][rgn]) * 1e-12) - # clim_range.append(0) - # ext_range.append(0) - - # Make figure - ax7[inds].bar(ind - width / 2.0, mse_clim, width, color="b") - ax7[inds].errorbar( - ind - width / 2.0, - mse_clim, - yerr=clim_range, - fmt="none", - color=[0, 10 / 255, 130 / 255], - elinewidth=3, - capsize=3, - ) - ax7[inds].bar(ind, mse_ext, width, color="r") - # https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.errorbar.html - ax7[inds].errorbar( - ind, - mse_ext, - yerr=ext_range, - fmt="none", - color=[130 / 255, 0, 0], - elinewidth=3, - capsize=3, - ) + + # plot data + if len(model_list) < 4: + mark_size = 9 + elif len(model_list) < 12: + mark_size = 3 + else: + mark_size = 1 + ax7[inds].bar(ind - width / 2.0, mse_clim, width, color="b", label="Ann. Cycle") + ax7[inds].bar(ind, mse_ext, width, color="r", label="Ann. Mean") + if len(clim_err_x) > 0: + ax7[inds].scatter( + [x - width / 2.0 for x in clim_err_x], + clim_err_y, + marker="D", + s=mark_size, + color="k", + ) + ax7[inds].scatter(clim_err_x, ext_err_y, marker="D", s=mark_size, color="k") + # xticks if inds == len(sector_list) - 1: - ax7[inds].set_xticks(ind + width / 2.0, mlabels, rotation=90, size=5) + ax7[inds].set_xticks(ind + width / 2.0, mlabels, rotation=90, size=7) else: ax7[inds].set_xticks(ind + width / 2.0, labels="") - datamax = np.max(np.array(mse_clim) + (np.array(clim_range) / 2)) + # yticks + if len(clim_err_y) > 0: + datamax = np.max(np.array(clim_err_y)) + else: + datamax = np.max(np.array(mse_clim)) ymax = (datamax) * 1.3 ax7[inds].set_ylim(0.0, ymax) - if ymax < 1: + if ymax < 0.1: + ticks = np.linspace(0, 0.1, 6) + labels = [str(round(x, 3)) for x in ticks] + elif ymax < 1: ticks = np.linspace(0, 1, 5) labels = [str(round(x, 1)) for x in ticks] elif ymax < 4: - ticks = np.linspace(0, round(ymax), num=round(ymax / 2) * 4 + 1) + ticks = np.linspace(0, round(ymax), num=round(ymax / 2) * 2 + 1) labels = [str(round(x, 1)) for x in ticks] elif ymax > 10: ticks = range(0, round(ymax), 5) @@ -895,16 +888,18 @@ def get_xy_coords(ds, xvar): ticks = range(0, round(ymax)) labels = [str(round(x, 0)) for x in ticks] - ax7[inds].set_yticks(ticks, labels, fontsize=6) - - ax7[inds].set_ylabel("10${^12}$km${^4}$", size=6) + ax7[inds].set_yticks(ticks, labels, fontsize=8) + # labels etc + ax7[inds].set_ylabel("10${^12}$km${^4}$", size=8) ax7[inds].grid(True, linestyle=":") ax7[inds].annotate( sector, (0.35, 0.8), xycoords="axes fraction", - size=8, + size=9, ) + # Add legend, save figure + ax7[0].legend(loc="upper right", fontsize=6) figfile = os.path.join(metrics_output_path, "MSE_bar_chart.png") plt.savefig(figfile) meta.update_plots( From cf49fbdd79c4ff6fb00574735ed5b7286d2c3b49 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Wed, 24 Jan 2024 11:22:17 -0800 Subject: [PATCH 40/69] clean up comments --- pcmdi_metrics/sea_ice/ice_driver.py | 13 +------------ 1 file changed, 1 insertion(+), 12 deletions(-) diff --git a/pcmdi_metrics/sea_ice/ice_driver.py b/pcmdi_metrics/sea_ice/ice_driver.py index 3bef04dc8..4a1fd9e36 100644 --- a/pcmdi_metrics/sea_ice/ice_driver.py +++ b/pcmdi_metrics/sea_ice/ice_driver.py @@ -341,7 +341,6 @@ def get_xy_coords(ds, xvar): ObsAreaUnitsAdjust = parameter.ObsAreaUnitsAdjust ModUnitsAdjust = parameter.ModUnitsAdjust ObsUnitsAdjust = parameter.ObsUnitsAdjust - # plots = parameter.plots msyear = parameter.msyear meyear = parameter.meyear osyear = parameter.osyear @@ -371,10 +370,6 @@ def get_xy_coords(ds, xvar): # Initialize output.json file meta = MetadataFile(metrics_output_path) - # if plots: - # plot_dir_maps = os.path.join(metrics_output_path, "plots", "maps") - # os.makedirs(plot_dir_maps, exist_ok=True) - # Setting up model realization list find_all_realizations, realizations = set_up_realizations(realization) print("Find all realizations:", find_all_realizations) @@ -528,11 +523,7 @@ def get_xy_coords(ds, xvar): obs_clims[reference_data_set][item] = arctic_clims[item] obs_means[reference_data_set][item] = arctic_means[item] - # Get climatology - # get errors for climo and mean - #### Do model part - # Loop over models # Needs to weigh months by length for metrics later clim_wts = [31.0, 28.0, 31.0, 30.0, 31.0, 30.0, 31.0, 31.0, 30.0, 31.0, 30.0, 31.0] @@ -562,6 +553,7 @@ def get_xy_coords(ds, xvar): } print("Model list:", model_list) + # Loop over models and realizations to generate metrics for model in model_list: start_year = msyear end_year = meyear @@ -690,7 +682,6 @@ def get_xy_coords(ds, xvar): (lon_j, lon_i), skipna=True ) real_dict[rgn][run] = rgn_total - # totals_dict[rgn] = totals_dict[rgn] + rgn_total real_dict[rgn]["model_mean"] = ( real_dict[rgn]["model_mean"] + rgn_total ) @@ -721,8 +712,6 @@ def get_xy_coords(ds, xvar): ) run_data = real_dict[rgn][run].to_dataset(name=var) - # total_rgn.time.attrs.pop("bounds") - # total_rgn = total_rgn.bounds.add_missing_bounds() run_data = run_data.bounds.add_missing_bounds() clim_extent = run_data.temporal.climatology(var, freq="month") total = run_data.mean("time")[var].data From e9c6bc7dc60d0ee7fbecbd47e8d845da3e71a135 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Wed, 24 Jan 2024 13:31:25 -0800 Subject: [PATCH 41/69] rerun, add text --- pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb | 66 ++++++++++++++---------- 1 file changed, 39 insertions(+), 27 deletions(-) diff --git a/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb b/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb index d4cd12a9c..2a4123b13 100644 --- a/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb +++ b/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb @@ -13,7 +13,14 @@ "id": "848c69e5", "metadata": {}, "source": [ - "The PCMDI Metrics sea ice driver produces metrics that compare modeled and observed sea ice extent. This notebook demonstrates how to run the PCMDI Metrics sea ice code." + "**Summary** \n", + "The PCMDI Metrics sea ice driver produces metrics that compare modeled and observed sea ice extent. This notebook demonstrates how to run the PCMDI Metrics sea ice code.\n", + "\n", + "**Demo author list** \n", + "Ana Ordonez, Jiwoo Lee, Paul Durack, Peter Gleckler\n", + "\n", + "**Reference** \n", + "Ivanova, D. P., P. J. Gleckler, K. E. Taylor, P. J. Durack, and K. D. Marvel, 2016: Moving beyond the Total Sea Ice Extent in Gauging Model Biases. J. Climate, 29, 8965–8987, https://doi.org/10.1175/JCLI-D-16-0026.1. " ] }, { @@ -21,6 +28,7 @@ "id": "6bfd3b73", "metadata": {}, "source": [ + "## Demo data\n", "This demo uses three CMIP6 models. The 'siconc' and 'areacello' variables are needed and can be found in the following directories. In addition, six other models are available that can be added to the analyses in this demo:\n", "```\n", "/p/user_pub/pmp/demo/sea-ice/links_siconc \n", @@ -33,7 +41,10 @@ "id": "00d48042", "metadata": {}, "source": [ - "The observation dataset provided is a satellite derived sea ice concentration dataset from EUMETSAT OSI SAF. More information about this data can be found at the [osi-450-a product page](https://osi-saf.eumetsat.int/products/osi-450-a)." + "The observation dataset provided is a satellite derived sea ice concentration dataset from EUMETSAT OSI SAF. More information about this data can be found at the [osi-450-a product page](https://osi-saf.eumetsat.int/products/osi-450-a). The path to this data is:\n", + "```\n", + "/p/user_pub/pmp/demo/sea-ice/EUMETSAT\n", + "```" ] }, { @@ -41,7 +52,8 @@ "id": "0b854017", "metadata": {}, "source": [ - "These maps show the different regions used in the analysis along with the mean observed sea ice concentration. The code to generate these figures can be found in the script `create_sector_plots.py`." + "## Sectors\n", + "This code block produces maps that show the different regions used in the analysis along with the mean observed sea ice concentration. The code to generate these figures can be found in the script `create_sector_plots.py`." ] }, { @@ -137,7 +149,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-23 15:05:03,897 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + "2024-01-24 13:04:18,737 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" ] } ], @@ -404,9 +416,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-23 15:06:06,266 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "INFO::2024-01-23 15:07::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n", - "2024-01-23 15:07:10,554 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n" + "2024-01-24 13:05:18,942 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "INFO::2024-01-24 13:06::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n", + "2024-01-24 13:06:21,672 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n" ] }, { @@ -719,7 +731,7 @@ " \"Version\": \"23.1.0\",\n", " \"buildVersion\": \"not installed\"\n", " },\n", - " \"date\": \"2024-01-23 15:06:56\",\n", + " \"date\": \"2024-01-24 13:06:07\",\n", " \"openGL\": {\n", " \"GLX\": {\n", " \"client\": {},\n", @@ -802,7 +814,7 @@ "outputs": [ { "data": { - "image/png": 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" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -841,7 +853,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-23 15:08:06,985 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + "2024-01-24 13:07:18,993 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" ] }, { @@ -935,8 +947,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO::2024-01-23 15:11::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n", - "2024-01-23 15:11:20,578 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n" + "INFO::2024-01-24 13:10::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n", + "2024-01-24 13:10:29,016 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n" ] }, { @@ -983,9 +995,9 @@ "text": [ "[WARNING] yaksa: 10 leaked handle pool objects\n", "\n", - "real\t4m10.243s\n", - "user\t4m28.848s\n", - "sys\t1m24.374s\n" + "real\t4m7.504s\n", + "user\t4m22.717s\n", + "sys\t1m23.538s\n" ] } ], @@ -1010,7 +1022,7 @@ "outputs": [ { "data": { - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -1051,8 +1063,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-23 15:12:21,567 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "2024-01-23 15:13:05,799 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + "2024-01-24 13:11:32,256 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "2024-01-24 13:12:15,349 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" ] }, { @@ -1169,9 +1181,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-23 15:15:50,273 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "INFO::2024-01-23 15:20::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n", - "2024-01-23 15:20:09,013 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n" + "2024-01-24 13:14:59,902 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "INFO::2024-01-24 13:19::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n", + "2024-01-24 13:19:01,364 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n" ] }, { @@ -1277,9 +1289,9 @@ "text": [ "[WARNING] yaksa: 10 leaked handle pool objects\n", "\n", - "real\t8m48.404s\n", - "user\t10m38.508s\n", - "sys\t2m36.253s\n" + "real\t8m32.955s\n", + "user\t10m20.702s\n", + "sys\t2m33.213s\n" ] } ], @@ -2540,7 +2552,7 @@ " \"Version\": \"23.1.0\",\n", " \"buildVersion\": \"not installed\"\n", " },\n", - " \"date\": \"2024-01-23 15:19:54\",\n", + " \"date\": \"2024-01-24 13:18:47\",\n", " \"openGL\": {\n", " \"GLX\": {\n", " \"client\": {},\n", @@ -2605,7 +2617,7 @@ "outputs": [ { "data": { - "image/png": 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6/J+hQ4fSBx98QIqiUJs2bejbb7+td951v5fS0lICQGVlZeprH330ESUlJRGRq6ADoAsXLjRq2QNJ3SKlNytWrKA777zTY4OPC7o37nJnfpEkCRkZGbo73ujuYq9vgI5bUVERDh48iIiICPWxdOlSnD17Vp2mc+fOHv+nbdu2Vx3wVfdWwMePH8eUKVMQExODsLAwzJw5E+Xl5Y1qhyRJ+OCDDzBr1iwAQGhoKKZNm+bV7V77M0+dOgWz2XzVWxI35NSpU0hKSmrUtIqioHPnztixYwcqKyvx17/+FRs2bFCXr6ioCMuXL/f4jg8cOIDTp0/j559/RnV1daM/q7S0FMHBwR7rpFu3bigtLVWfh4aGIiIiovGNZdfESy+9hC+//NJj8C0RwWKxXDfXrm8MLujML0ajEZMmTdLdKPfk5GQkJibib3/7W4PTxMXFYeDAgaisrFQfFosF//nPfxr1GQ2de1339cceewyxsbE4dOgQLBYLPvzww0b/iG3fvh0//fQTfve736Fz587o3Lkztm3bhq+++grFxcX1fmZCQgLsdnuDI4Ovds54QkKCOkDqamrnjzseO3asegpcXFwc3nzzTY/v+NKlS3juuecQFRWFNm3aNPhZdZeza9eusNlsHqcwFRYWqsfvG9M2Fjj0PCDXV5y9zG962zsHXD8Wf/zjH7Fq1Sr88Y9/VAehHTt2DHPmzMGpU6dw991346effsI777wDm80GWZZx9OhR7Nq1q1Gf0alTJ5w6dUod0dsQi8WC0NBQhIWFoaSkBG+88Uaj25GWlobJkyfjP//5Dw4cOIADBw7g2LFj6NGjR4MD1Tp16oQpU6bgscceQ1lZGRRFQV5envoddOrUCSdOnGjwMxcsWIDU1FR8++23UBQF586dQ15eXr3TrlmzBpmZmbh48SKICP/85z+xa9cuDB06FADw1FNP4Y033sC//vUvEBGqq6vx9ddfo7S0FIIgYN68eVi6dCmOHz8OIsLRo0dx6tSpepczNjYWo0ePxq9//WtcunQJxcXFWLlypdp7wVhrxwWd+UWSJGRmZuqyqN9999344osv8Pnnn6N79+6IiIjAjBkzcOONN6JLly5o164dvv76a+zcuROJiYmIjIzEQw895NHlfiX33XcfwsLC0LFjxyt287711lvYvn07wsLCMGXKFPU83Ks5c+YMvvjiCyxZskTdO3c/fvWrX+G9995rcE9/06ZNiIuLw//7f/8PEREReOyxx1BTUwMAWL58OdatW4f27dvjiSee8Pq/U6dOxVtvvYUnn3wS4eHhGDRoUIMXnQkJCcHixYvRtWtXREREYN68eXjppZfw4IMPAnCtg1WrVmHevHlo3749brjhBqSmpqpnErz++usYO3Ysxo0bh7CwMNx3332oqKgAACxatAhff/01IiIicPfddwMA/vrXv6KmpgYJCQm4/fbbcdddd2HZsmWN+j5ZYOEud298P3Qd3AOXMXZ9s9lsKCwsxA033IDg4OCWXpyAVve70lMtCMg99IKCAgwdOhTJyckYPHgwDh065DUNEeGZZ55B79690bdvX4wePbrRx+2YdngrmfmD86d10/pua++//z7atm2LS5cuAQByc3MhCEK9F0MiIsiyzLlTS0AW9AULFmD+/Pk4duwYli1bhjlz5nhNs23bNmRnZ+PAgQPIz8/H2LFjNbvjD2s8SZKwe/duXXa5s+bH+dNMrsHdWWrfbU1LvXr1whdffAHAdUW6QYMG1TsdEcFqtXJBryXgCvq5c+ewf/9+zJw5EwAwffp0FBYWoqioyGtau90Om82mbuXXHq3Krg2TyYS77roLJpOppReFtUKcP61Xc9xtDQCmTJmCbdu2AQAOHTqEXr16AXAV8F/96lcYPXo07rjjDpw5cwYRERH45S9/iVGjRmHYsGHqmRsDBgzAY489hiFDhiAlJaUZv4XAEnAFvaSkBDExMeppUIIgID4+3uMUGwC45557MHr0aHTu3BldunTBzp078dvf/rbB+drtdlgsFo8HAHWEsSzL9caSJHnE7sE4DcVOp9Mjdm89umMi8ooBeMSKonjE7r2XhmJZlj3ia9kmWZZx/vx52O123bRJj+spUNskSRIqKirgcDh006aWXE/u/6MF93xqz7N2vHPnTtxxxx148MEH8fHHH6uvJycn44svvsDw4cORmZkJIsL58+eRnp6OzZs345133lHbT0RecUREBGpqapCTk6Pe6IWI8PnnnyMiIgJZWVlYtWoVUlJS4HQ68ac//QnffPMNnnnmGWzcuBFEhMrKSjz//PPIyclRTz2t247a60YvAq6gA/A6r7C+BN2/fz+OHDmC06dP48yZMxg7diyeeuqpBueZkpKC8PBw9REXFwcA6rGZw4cP4/DhwwCA/Px8FBQUAADy8vLU6w3n5uaq5+bm5OSoN+jIzs5WL/SRlZWFyspKAEBmZqZ6EZGMjAzYbDaPC7HYbDZkZGQAAKxWKzIzMwEAlZWVyMrKAgCUl5cjOzsbgOvWgu7rWJeUlCA3NxeA61xa92lBBQUFyM/Pv2ZtunTpEvbt24cdO3bopk16XE+B2qbi4mLs27cP3333nW7a1BLr6YcffgDg2ii4ePEitGC32wG47tTnvrvgxYsX4XQ6UVpaih9//FG929q2bdvUjZrk5GQoioK4uDiUlZWBiNC7d29UV1eja9euuHDhgrpDpSiKx86V+5rrY8eOxWOPPYbp06dDURRUV1fj0KFD2Lp1K0aMGIElS5agoqICFosFzz77LIYNG4bf/e53OHPmDGw2G8LDw9XrKZjNZgBAdXW12iaHw6Gum71792ryfQUEXy8x11x++uknCgsLUy/xpygKderUiQoLCz2me/LJJ+n1119Xnx88eJDi4+MbnK/NZqOqqir1UVJSQgCooqKCiFyXoHRfbrJ27HQ6PWJZlq8YOxwOj9h9WUJ3XPtSqe7Y3U53LMuyR+z+LhqKJUnyiOtrB7eJ28Rt0m+brFYrHTp06L/3VdDgYu7u5XIvR+14zZo1tGXLFvX1Rx55hI4dO0Yvv/wyffrpp6QoCq1fv57S0tLo5MmTNH36dJJlmaqrq2nkyJFq+92Xb3XHaWlp9Mc//pHOnTun3q9g1qxZlJ+fT59++imtWLFC/Uy73U779u2j++67jxRFoU8++YRmzZpFiqLQwIED1XkOGTLEY9lramroP//5j3qt/vPnz/OlX+tT+564voqOjkb//v3x4YcfAnDd1ScxMRGJiYke03Xr1g07d+5Uu50+++wz9OnTp8H5ms1mhIWFeTwAwGAwqP/WFxuNRo/YfSWphmKTyeQRu3sb3LEgCF4xAI9YFEWP2H34oaHYYDB4xNeyTUSEn3/+GQaDQTdt0uN6CtQ2Aa5xM6Io6qZNLbme3P9HC+751J6nO96yZQtGjRqlvj5u3Dhs3rxZXR7361eKV61ahaKiIvW7qH1f86ioKPzv//6vx7Lcc889qKiowJgxYzB69Gh88MEH6NGjB8rKynDnnXeqg/Nqt79uXLsdtdeNXvh9Hvodd9wBQRBARDh27Bh69uypdgv56ujRo5g9ezbOnz+PsLAwbNq0Cb1798bcuXMxefJkTJ48GXa7HU899RR2796NoKAgdOnSBRs3bvQq/A3R07mHLUmSJGRnZ2PEiBG6+sNg1wbnjzaux/PQ6fIo99DQ0CZtxOj5PHS/C/qLL76IgQMHYurUqVi8eDHWrFmj1bI1Kz2tRMbY9e16LOi+0nNB97vL/Xe/+x0kScLy5cvVgRPs+qEoCk6fPq2OGGWsKTh/mK+ICA6Hg89Dr0WTY+gzZszAo48+ip49e2oxO9aKKIqCEydO8A8y8wnnj7aut+LmHrXeFHr+jvha7jroZmGMXd9kWUZBQQHatGmDqKgovqVoA9yDeKurq5GUlASDwaCrWqDpKJTDhw/jtddew8mTJz1O1nef38n0R1EUlJSUIC4uju8lzZqM80cbBoMBXbt2RWlpab1X1dQjunwtd/cZNo0lCAK6du2qnpmgJ5oW9Pvvvx8PP/wwHn30UV1+Wcyb+xhobGws/yCzJuP80U67du2QlJSknsqrd5Ik4d///jduvvnmJp0hYTKZdFufNO1yHzBgAPbv36/V7JqVnrpZGGOM+UZPtUDTTeIJEyZgx44dWs6SBThZlnH8+HH1so+MNQXnD/MV5443TQv62LFjMWPGDISHhyM6OhpRUVGIjo7W8iNYgCEiXLhwQdcjR1nz4fxhvuLc8aZpl3uPHj2watUqDBgwwOMYRUJCglYfoRk9dbMwxhjzjZ5qgaaD4iIjIzFjxgwtZ8kCnPt0GfcpIIw1BecP8xXnjjdNu9ynTZuGDRs2oKKiAtXV1eqD6VtNTU1LLwJrxTh/mK84dzxp2uVe+7QT9w1bBEEIyEELeupmYYwx5hs91QJN99AvXboERVGgKApkWYaiKOpN5Jk+ybKMgwcPBuRGGwt8nD/MV5w73jQt6A899JDH86qqKkycOFHLj2CMMcZYPTQt6MnJyVi4cCEA4OLFi5gwYQIef/xxLT+CBRiDwYA+ffrwoBTmE84f5ivOHW+aFvTXX38dP/30E15//XVMmTIF//M//4O5c+dq+REswMiyjLy8PO72Yj7h/GG+4tzxpklBrz2i/e2338aWLVswePBgzJ8/36dR7gUFBRg6dCiSk5MxePBgHDp0yGuaXbt2oU2bNujXr5/64BGPLSMkJKSlF4G1Ypw/zFecO540GeUuiqLHqPbas/RllPuYMWPw8MMPY/bs2di8eTPefPNNfPfddx7T7Nq1C7/+9a/xww8/+LTMehrZyBhjzDd6qgWa7KHXHtXu/rf2aPemOHfuHPbv34+ZM2cCAKZPn47CwsLr5paArY0kSdi3b5/H7XIZayzOH+Yrzh1vAXe/wpKSEsTExKi3wxMEAfHx8SguLvaa9ujRoxgwYAAGDRqEd95554rztdvtsFgsHg8A6gaHLMv1xpIkecSKolwxdjqdHrG7t8IdE5FXDMAjVhTFI3YnbEOxLMse8bVsEwBERERAkiTdtEmP6ylQ20REaN++vbojoIc26XE9BWKbFEVBeHi42gvsT5v0QpOCfurUKYwfPx7JyclYunQpbDab+t5tt93W5PnVvVl9fUcFBgwYgNLSUuzfvx9bt27Fhg0b8PHHHzc4z5SUFISHh6uPuLg4AMDBgwcBAIcPH8bhw4cBAPn5+SgoKAAA5OXlobCwEACQm5uLkpISAEBOTo56jn12djbKy8sBAFlZWaisrAQAZGZmwmq1AgAyMjJgs9kgSRIyMjIgSRJsNhsyMjIAAFarFZmZmQCAyspKZGVlAQDKy8uRnZ0NACgrK0NOTg4A14ZPbm4uAKCwsBB5eXkAXOMP8vPzr1mbnE4nEhMT8eWXX+qmTXpcT4HapjNnzqBHjx7Yu3evbtqkx/UUiG0qLi5GVVUVDAaDX23au3cv9EKTY+iTJk3CXXfdhVtvvRV/+MMfcPz4cezYsQOhoaHo37+/mkiNce7cOSQlJeH8+fMwGo0gInTp0gXff/89EhMTG/x/KSkpOHPmDP74xz/W+77dbofdblefWywWxMXFoaKiQt1DAFynQtSOJUmCIAhqLIoiRFFsMHY6nTAYDGpsNBohCIIaA64twtqxyWQCEamx+1CFO1YUBUajscFYlmUQkRrX147mahPg+uMYMGAAgoODddEmPa6nQG2Toij44YcfMGDAAAQFBemiTXpcT4HYJrvdjh9++AFDhgxRdwJ9aVNFRQUiIyN1cQwdpIH+/ft7PH/ttddo0KBBVFlZ6fVeY4wcOZLee+89IiL6+9//TkOGDPGa5syZMyTLMhERWSwWGjp0KKWlpTX6M6qqqggAVVVVNXn52H/JskxFRUXqumCsKTh/mK+0yh091QJN7rZW99S05cuXIygoCGPHjlW7Xppi48aNmD17NlauXImwsDBs2rQJADB37lxMnjwZkydPxpYtW7B+/XoYjUZIkoT77rsPjzzyiBbNYU0gimJA3h6XtQ6cP8xXnDveNOlynzZtGhYsWIAJEyZ4vP7WW2/h17/+tTr4IJDo6VSFliRJEnJycjB06FC1O42xxuL8Yb7SKnf0VAs0KejuY9Nms9nrvdOnTyM2Ntbfj9CcnlZiS3LfgKdLly4ed9tjrDE4f5ivtModPdUCTf6CzGazWszdowjdArGYM+2IoojY2Fj+MWY+4fxhvuLc8ab5N/HGG29oPUsWwCRJQlZWlq7O5WTXDucP8xXnjje/D1olJCSgZ8+eAFznix89evSqF3lh+iGKIvr06cNbycwnnD/MV5w73vwu6HfccQfeffdd9TnfLvX6IooioqOjW3oxWCvF+cN8xbnjze9Nm9///vcez9evX+/vLFkr4nQ68eWXX6oXmWGsKTh/mK84d7z5vYceERGhxsXFxeo11+Pj4xEfH+/v7FmAMxgMGDRoEAwGQ0svCmuFOH+Yrzh3vGly4ueRI0fw6KOPorCwEPHx8SAilJSU4IYbbkBaWhpuuukmLT6GBSBRFNGhQ4eWXgzWSnH+MF9x7njTZDTB7NmzsXTpUpSVlWHv3r3Izc1FWVkZlixZglmzZmnxESxAOZ1OfP7559ztxXzC+cN8xbnjTZMLy/Ts2RNHjx5t8nstSU8XE2hJRASr1YrQ0FCvu+QxdjWcP8xXWuWOnmqBJnvoHTt2xF/+8hePS7wqioJNmzYhMjJSi49gAUoQBISFhfGPMfMJ5w/zFeeON00K+qZNm/D++++jY8eO6NOnD26++WZERkaqrzP9cjqd+PTTT7nbi/mE84f5inPHmyZd7m4///yzetP4uLg4REVFaTVrzempm6UlERFsNhuCg4N5S5k1GecP85VWuaOnWqDp7Y2ioqICuoiz5sF3yWL+4PxhvuLc8dTs18xLTk5u7o9gLUiSJGRkZPD1lJlPOH+Yrzh3vGnS5X7o0KEG3xs3bhzOnDnTpPkVFBRg1qxZKC8vR0REBN5//3306tXLY5qsrCw8//zzsFqtEEURU6ZMwauvvtrorhc9dbO0JCKCJEkwGo3cZcqajPOH+Uqr3NFTLdCkv6JPnz5ITExEfdsG5eXlTZ7fggULMH/+fMyePRubN2/GnDlz8N1333lM0759e6Snp6Nbt26w2WwYN24c0tPT8dBDD/ncDuYb9x8VY77g/GG+4tzxpEmXe0JCAvbs2YPCwkKvR6dOnZo0r3PnzmH//v2YOXMmAGD69OkoLCxEUVGRx3T9+/dHt27dAADBwcHo168fTp48qUVzWBNIkoTMzEzu9mI+4fxhvuLc8aZJQZ88eXKDxXTKlClNmldJSQliYmLUrS5BEBAfH69eI74+Z8+exebNmzFp0qQGp7Hb7bBYLB4PAJBlWf23vliSJI/Yfa59Q7HT6fSI3b0W7piIvGIAHrGiKB6xO2EbimVZ9oivZZuMRiMmT56stkEPbdLjegrUNhkMBkyZMgWCIOimTXpcT4HYJlEUcdddd8FkMvndJr3QpKCnpqZi2LBh9b63bt26Js+v7vGQKx3mt1gsuOeee7Bs2TIMGDCgwelSUlIQHh6uPuLi4gAABw8eBAAcPnwYhw8fBgDk5+ejoKAAAJCXl4fCwkIAQG5urnpaXk5ODsrKygAA2dnZ6qGFrKwsVFZWAgAyMzNhtVoBABkZGbDZbB4DOWw2GzIyMgAAVqsVmZmZAIDKykpkZWUBcB2yyM7OBgCUlZUhJycHgGvDJzc3FwBQWFiIvLw8AK7xB/n5+desTTU1Nbhw4YKu2qTH9RSobSouLobFYtFVm/S4ngK1Tbm5uSAiv9q0d+9e6IWm56Fr4dy5c0hKSsL58+dhNBpBROjSpQu+//57JCYmekxrtVoxfvx4TJw4ES+++OIV52u322G329XnFosFcXFxqKioQPv27dUtN4PB4BFLkgRBENRYFEWIothg7HQ6YTAY1Ng9YMMdA57HfSRJgslkUgd4mEwmKIoCWZbVWFEUGI3GBmNZlkFEalxfO5qrTUSEzMxMjBkzBiEhIbpokx7XU6C2SZZlfP311xgzZgzMZrMu2qTH9RSIbbLZbPj6668xfvx4iKLoc5sqKioQGRmpi0FxAVfQAWDUqFGYPXu2Oiju97//Pb7//nuPaS5evIjx48fjzjvvxMsvv9zkzwj0kY3uaxQzxhhrPoFeC5qi2c9D98XGjRuxceNGJCcnY9WqVUhLSwMAzJ07F9u2bQPg6ubPzc3F1q1b0a9fP/Tr1w+vvfZaSy62ZlJTUxEeHo7U1NSWXpSrUhQFFRUVHtfxZ6yxOH+Yrzh3vAXkHvq1EKhbZampqVi0aJH6fO3atVi4cGHLLdBVOJ1OZGVlYcyYMTCZTC29OKyV4fxhvtIqdwK1FviCC3oArcS6xdwt0Is6Y4y1VoFYC3zFBT1AVqLVakV4eHi9I/oFQUBVVVVAHlNXFAXl5eXo2LGjOjCFscbi/GG+0ip3Aq0W+IP/ggJEaGgo1qxZU+97a9asCchiDrj+qA4ePMjHsZhPOH+Yrzh3vPEeeoBtlbW2Y+iMMdaaBWot8AXvoTO/KIqC06dP81Yy8wnnD/MV5443LugBpL5BcYsWLdLs9DX31Zu0pCgKTpw4wX9UzCecP8xXnDveuMs9QLpZmntQXGpqKhYvXow1a9ZwFz5jrEHX20WtAq0W+IP30ANEcw6Kc+/5E5Gme/yAayv51KlTvJUcQJqjJ6a5cP4EltZ2USvOHU9c0P0kCNo9Fi1aCGBtnU9Yi0WLFvox3+btxufjWIElNTUVYWFhreIHGeD8CSTNueHfHDh3vHGXu5/dLHVuDKeRVACLAawB4E/3uBVAOIDWdW478w2fIcF8dT1f1Iq73FkzWwigFP4VcwAIhWujwJtW57bLsozjx4+rdzViLaO5B1Q2F86flme1WrF48eJ631u8eHHAHsLh3PHGBT0gpQLoevnfwEZEuHDhwhXvWc+al9VqrXfvCnAV9UD9QQY4fwJBaGgopk6dWu97U6dODdhePM4db9zlHnBd7qkAFtV6vha+76lzl/v1wGq1XjGHLRYLr2ed0ub3xwrgSr+BFrh6+/wTqJWGu9xZM6lbzAFgEdZCAPn0CMPaeoo5AKwhQmhYmN8j+WSTCUeOHOFuLx9pMZgyLCwU3oMp3dYiLCxUk89pjj19WZY5f1rc1darNuv9zJkzmszHjXPHGxf0gGGFayCct8Xw/U9qIYBpdV6bBv+PzqtEETU1NVrNjfns2ya+3lTNN3qe86elxcD7V8Jt2uX3/XUvYmNjce+992owr//i3PHEBT1gXGEAG3zv8EoFsLXOa1uh3dF5g8OB/v37w2AwaDRH1nRn4L2W3bZeft8f/+050nqgncFg4PwJCP9A/Zv+/9Bg3vfCnZ9bt27VrKhz7ngLyIJeUFCAoUOHIjk5GYMHD8ahQ4fqnS4tLQ1JSUno3r075s+fD0mSrvGSas37PPS18O8Iev37/P7t9dcmm0w4ePAgd3u1qObcw/I+DKRlUZdlmfMnYNQu6toXczetijrnjreALOgLFizA/PnzcezYMSxbtgxz5szxmqawsBAvvvgi9uzZg+PHj+Ps2bNIS0trgaXVmquoC/CvmANX2uf3b6+fBaLm2MOywntMh0ugj55nvvoHgNPQppg33HO0detWzY+pswAc5X7u3DkkJyejvLwcRqMRRIQuXbrg+++/R2JiojrdG2+8gaKiIrz99tsAgIyMDKxevRq7du1q1OcE7ih3FwsEzQquluPm6xVYKdSqaJ8/7j0iLfawrjz6mUfPt7zm+P0haDPTazJ2XoPfHj2Ncje29ALUVVJSgpiYGBiNrkUTBAHx8fEoLi72KOjFxcVISEhQnycmJqK4uLjB+drtdtjtdgCu8xfdW4cXLlwAALXbxmAweMSSJEEQBDUWRRGiKKoxIMJsluBwiCASYTY74XAYQCQiONgJu90IIgHBwU7YbK42BQdLdWITBIFgNrtjBY4gMyx2OxRBgBIUBKPdDkUUoRiNMDocUAwGKAYDjA4HZIMBJIowOp2QDQZAFGFwOiFf/g4fkSTYTSa8oCh4VZbxS5MJFxQFBlmGFBQEUZYhumNJgqgokMxmiA4HRCI4zWYY3HFwMIx2O4TLsaAoOJidjRtvvBFmsxkAIEkSTCYTiEiNFUWBLMtqrCgKjEZjg7EsyyAiNa5v3TRlPdWNnU4nDAaDGhuNRgiCoMbudtSOm6NNgAFGo3z5MwwwmWQoCiDLBphMEhRFgCwbEBQkQZZFyLKIoCAJkiRCUerLvT/jMG1Fx+AvYLSL6noy2myuz6gTm2w2kCBAMpthstmgCALkoCCY7HacEQT0CzLDbrdDFEUYjUY4HA4YDAYYDAacCQtD9FVyzyBJkE0mwJ1vJhOEWrlHoohDmZm48cYbYTKZAnY9BWruAa7fi6AgBXa7EaKowGhU4HAYYTAoMBjcsQxRJDid7hhwOuvPPYsMr/Xky2/ERSIEBwfDdjnf6saWy/lWX+415nfPERKC/3z7Lfr27av+zvuynioqKtS60NoFXEEHXEW8toa+6NrTXW1lpKSkYMWKFV6v195I8NXl7QSv+HLuNiom8ow7uudD9N+ZKgrgcLhiWXY9rhTXHlPgdAIAngPw3OUYwH/nVzduSqNGjgTzXT2ryStuymq6EfAv+dwzqhUrigLH5Q+WZRmyLLs+pwm512CjRowA853GPxEIr/0E0OY3AlCLuTvu6l74q+Reg42qqQFGjYJW3He8bM0CrqDHxcWhtLRU3UIlIpSUlCA+Pt5juvj4eBQVFanPT5065TVNbc8//zyWLFkCwFX8LRYLnE4nIiMjvTYgWONZLBbExcWhpKSk1XdXsWuP84f5SqvcISJYrVbExGhxel7LCriCHh0djf79++PDDz/E7NmzsWXLFiQmJnrtSU+fPh3Dhg3DSy+9hOjoaGzYsAEPPPBAg/M1m81qlzCAVr8lFmjCwsL4B5n5jPOH+UqL3NFLPQjIUe4bN27Exo0bkZycjFWrVqmj1+fOnYtt27YBALp164YVK1bg9ttvR/fu3REdHV3vaHjGGGPsehBwo9xZ66KnEaLs2uP8Yb7i3PEWkHvorPUwm814+eWXPQ5nMNZYnD/MV5w73ngPnTHGGNMB3kNnjDHGdIALOmOMMaYDXNAZY4wxHeCCzhhjjOkAF3TGGGNMB7igM8YYYzrABZ0xxhjTAS7ojDHGmA5wQWeMMcZ0gAs6Y4wxpgNc0BljjDEd4ILOGGOM6YCxpRegJRARLBYLrFYrQkNDIQhCSy8SY4yxFkBEsFqtiImJgSi27n3c67KgW61WREREtPRiMMYYCxAlJSXo2rVrSy+GX67Lgh4aGoqSkhLExcWhpKQEYWFhLb1IrZYkSdi7dy+GDBkCo/G6TCfmB84f5iutcsdisSAuLg6hoaEaLl3LuC7/ggRBUIt4WFgYF3Q/KIqCvn37IiIiotV3V7Frj/OH+Urr3NHDodfrsqAz7YiiiNjY2JZeDNZKcf4wX3HueAvITeI777wTffv2Rb9+/TB8+HAcOHCg3unS0tKQlJSE7t27Y/78+ZAk6douKIMkScjKyuLvnvmE84f5inPHW0AW9I8//hj5+fk4cOAAli5dikcffdRrmsLCQrz44ovYs2cPjh8/jrNnzyItLa0Flvb6Jooi+vTpw92lzCecP8xXnDveAvKbqD0Cvaqqqt4VtnnzZkybNg2dOnWCIAh47LHHkJ6efg2XkgGuP6ro6Gj+o2I+4fxhvuLc8Raw38TDDz+MuLg4/OY3v8GmTZu83i8uLkZCQoL6PDExEcXFxQ3Oz263w2KxeDwAQJZl9d/6YkmSPGJFUa4YO51Oj5iIPGIi8ooBeMSKonjE7i6lhmJZlj3ia9kmh8OBHTt2oLq6Wjdt0uN6CtQ22e12fPnll6ipqdFNm/S4ngKxTTabDTt27IDT6fS7TXoRsAX9gw8+QElJCV599VU888wz9U5Te1SiO4EakpKSgvDwcPURFxcHADh48CAA4PDhwzh8+DAAID8/HwUFBQCAvLw8FBYWAgByc3NRUlICAMjJyUFZWRkAIDs7G+Xl5QCArKwsVFZWAgAyMzNhtVoBABkZGbDZbJAkCRkZGZAkCTabDRkZGQBc58ZnZmYCACorK5GVlQUAKC8vR3Z2NgCgrKwMOTk5AFznTObm5gJwHX7Iy8sDABQUFCA/P/+atcnpdGLAgAH46quvdNMmPa6nQG3TmTNnMGjQIOzbt083bdLjegrENpWUlCA0NBQGg8GvNu3duxd6IdDVKmEACAkJQWlpKSIjI9XX3njjDRQVFeHtt98G4Eqc1atXY9euXfXOw263w263q8/d5x5WVFSgffv26pabwWDwiCVJgiAIaiyKIkRRbDB2Op0wGAxqbDQaIQiCGgOuLcLasclkAhGpsaIokGVZjRVFgdFobDCWZRlEpMb1tYPbxG3iNnGbuE3ebaqoqEBkZCSqqqpa/ynMFGCqqqro9OnT6vN//OMfFBsbS4qieEx34sQJ6tKlC509e5YURaF77rmH1q9f36TPAUBVVVWaLfv1yOFw0Pbt28nhcLT0orRqCQkJtHXr1lb9Gb169aLPPvusSf+H84f5Sqvc0VMtCLgu96qqKkydOhU333wzbrnlFrz99tvYvn07BEHA3LlzsW3bNgBAt27dsGLFCtx+++3o3r07oqOjMWfOnBZe+uuP0WjE8OHDdXmVr1GjRsFgMKhdeYCrW1AQBBQVFfk137Vr1/q/gADGjBmDkJAQXLhwodk+oz71zf8///kP7r777ibNx50/qampSE5ORmhoKKKiojBu3Di/vmO32bNnY9GiRX7PhwUePf/2+Crgvom4uDj1GEld7777rsfzefPmYd68eddisVgDal91T4/at2+P559/Hp9//rnf8yIidSCOFk6ePIldu3ahffv2+Oijj/DUU09pNu9rRRAEbNu2DevWrcP27dvRp08fVFZWIjMzMyCu3FW7m5gFFr3/9vgi4PbQWevidDrx6aefqqNT9eaJJ55ATk6OOkCnLiLCm2++ie7du6NDhw6YMGECTp48qb6fmJiIlJQU3HrrrWjTpg3uv/9+7N69G88++yzatWuHiRMnqtMeO3YMt956K0JDQzFy5Eh10E5D/vznP6Nfv3741a9+5XENhqVLlzb4GW7FxcW44447EBUVhfbt2+Ouu+7y2COePXs25s2bhwceeAChoaHo2bOnOj6lofknJibik08+Uefx1VdfYciQIYiIiECXLl2QkpLitRxOpxPp6ekYPXo0+vTpA8B12ur999/vcRbL119/jcGDByMiIgK9e/dWe+oA18jnP/zhD7jxxhsRGhqKpKQk7NixA3/4wx/w0Ucf4Z133kG7du3Qu3dvAK6BWPPnz0eXLl3QpUsXPPbYY7h06RIAoKioCIIg4L333kOPHj34SmQBTO+/PT5p6T7/lqKn4yYtSVEUqq6u9hrjoAcjR46kNWvW0MqVK+m2224jIqILFy4QACosLCQiok2bNlFMTAzl5+dTTU0NLVmyhG666SZyOp1E5DpunZycTEeOHCFJkshut6vzrS0hIYF69+5NJ06coJqaGpo4cSLNmjWrwWWTJIliY2MpNTWVTpw4QYIg0L/+9S+vZa/7Ge5j6IWFhZSRkUE1NTVUVVVFM2bMoHHjxqnTzpo1i9q1a0c7d+4kSZLod7/7HSUkJDR6/vv376eQkBDavHkzORwOqqyspO+++86rHYqi0KZNm6hdu3b06quv0p49e6impsZjmh9//JEiIiJo586dJMsy7d69m8LCwujIkSNERJSamko33HAD/fDDD6QoCp06dYoOHTqktmPhwoUe83vkkUdo9OjRVF5eTj///DONHDmS5s2bp34vAGjq1Kl04cIFunTpUoPrgLUsrX579FQLeA+d+U3vXZKLFi3CqVOnPPY+3f7yl7/g6aefxs0334zg4GCsXLkSpaWlHoeNHn/8cfTs2RMGgwFBQUENfs5TTz2Fbt26ITg4GL/4xS/wr3/9q8Fpv/zyS5w7dw4PPvggunXrhttvv71JV0pMTEzExIkTERwcjLCwMLzwwgvIzs72OCRw1113YcyYMTAYDHjkkUdw6tQpnD9/vlHz/9Of/oQHHngA06dPh8lkQnh4OG699dZ6p33wwQfx5z//GTk5ObjrrrsQGRmJefPmqXvNGzduxOzZszFmzBiIoohhw4bh7rvvxscffwwAWL9+PV555RUMHDgQgiAgPj4eN910U72fpSgK/vrXvyIlJQWRkZHo2LEjVq5ciQ8++MCj7S+//DIiIiLQpk2bRrWXtQy9//Y0FRd05pfa55fqVUhICF5++WUsX75cPQ3GrbS0FImJiepzs9mMmJgYlJaWqq/Fx8c36nM6d+6sxm3btlXP5a1PWloaJk2ahKioKADArFmz8Ne//hU1NTWN+qyff/4ZDz30EOLi4hAWFoYRI0bA4XB4fGbd5QFwxWWq7dSpU0hKSrrqdO78mTp1Kj7//HNcuHABX375JTIzM/Haa68BcHWDb9iwAREREerj008/xZkzZ5r0We522+12j3XWrVs32O129ZxqoPHrjLWc6+G3p6m4oDO/GI1GTJo0SfdbynPmzIGiKF5XLezatavHsWeHw4EzZ86ga9eu6mt1L03p76Uqf/75Z3z22WfYuXMnOnfujM6dO+O5555DZWUl/vGPfzTqM55//nlUV1dj//79sFgs6hgBauRlKa42/4SEBBw/fvyq86mbP4IgYNiwYZgxYwb+/e9/A3ANlF24cCEqKyvVx8WLF7F+/fqrflbd5YyKikJQUJDHOissLITZbEbHjh0b3T7W8q6X356m4KxlfrsetpANBgNee+01rFy50uP1mTNnYt26dTh06BDsdjt+85vfIDY2FoMHD25wXp06dcKJEyd8XpYPPvgAHTp0wJEjR3DgwAEcOHAABw8exOzZs9Vu96t9hsViQZs2bRAREYHz589jxYoVTVqGq81/3rx5SE9Px9atWyFJEqqqqvD999/XO+2f//xnfPrpp+qVxg4ePIhPP/0UQ4cOBQAsWLAA7733Hr755hvIsgy73Y7vvvtOvRrYggULsGLFChw4cABEhOLiYvW9Tp06eQxSFEURDz30EF544QVUVFTg/PnzeOGFF/DLX/6Si3grdD389jQFZzDziyRJyMzMvC7+sKZPn44ePXp4vPbwww/jV7/6Fe6++2507twZP/74Iz777LMr7jUsWrQIX3/9NSIiIpp83jbg6m5//PHHERsbq+6hd+7cGUuXLsWuXbvw448/XvUzVqxYgePHj6N9+/a4/fbb6x0JfyVXm/+AAQOwZcsWvPbaa+jQoQNuuukmfPvtt17TSZKEoqIivPnmm+jWrRtCQ0MxdepUPPjgg1i2bBkAoH///khPT8dvfvMbREVFITY2Fi+++KJ65cenn34ajz/+OO6//36EhoZi3Lhx6n0d5s6di9OnT6N9+/bo27cvACA1NRWJiYno1asXevfujR49euCtt95qUvtZy7uefnsaq1Vc+rU5WCwWhIeH6+Nyf4xdlpqaisWLF2PNmjVYuHBhSy8OYwFPT7WA99CZX4gIFoul0cdeWfNJTU3FokWLQERYtGgRUlNTW3qRrorzh/mKc8cbF3TmF0mSsHv3bu72amHuYl5bayjqnD/MV5w73rjLXQfdLOz6ZrVaER4eXu+eiiAIqKqqQmhoaAssGWOBT0+1gPfQA1Rjz/dtaYqioKKiQtNrlLOmCQ0NxZo1a+p9b82aNQFdzDl/mK84d7xxQQ9AqampCA8PD/juUgCQZRn79u3zuuAKu7YWLlzodfeztWvXBvzAOM4f5ivOHW98Rn6AqX0s1P1vIP8om0wmjB8/vqUXg7VSnD/MV5w73jTdQ9++fbvf87DZbJg6dSqSk5PRr18/TJgwod77ImdlZWHIkCHo1asX+vTpgxdeeKHVj3ZsjQObFEXBuXPnuNurhbXG3AE4f5jvOHe8+T0o7o477oAgCCAiHDt2DD179kRmZqbP87PZbMjKysLEiRMhCALWrVuHbdu2ec0zLy8P4eHh6NatG2w2G8aNG4cnnngCDz30UKM+J9AGQrTWgU2SJCE7OxsjRozgSzC2kNaaOwDnD/OdVrkTaLXAH37vod9666144okn8NVXX+Hee+/1q5gDQHBwMCZNmgRBENT51750o1v//v3RrVs39f/069ev3ulai9Y6sMloNGLMmDH8Y9yCWmvuAJw/zHecO978Lui/+93vIEkSli9fDofDocUyefjDH/6Ae+6554rTnD17Fps3b8akSZManMZut8NisXg8AKgDKmRZrjeWJMkjdnfvNBQ7nU6P2L3X5I6JyCsGXBdJeOKJJ7B27VqIoojg4GAAroFNTz75JABXF5P7nMvasSzLHnFDbaqqqtK8TbIso7S0FHa7vd42uWNFUTzi+trhS5taYj0FYpsWLlyI1NRU9XrkISEh6qC4QG6TJEk4ffo0HA7HdbGeuE3atcnpdKK4uBiKovjdJr3Q5Bj6jBkz8Oijj6Jnz55azE61cuVKFBQUqLdRrI/FYsE999yDZcuWYcCAAQ1Ol5KSgvDwcPURFxcHwHUjCAA4fPiwekOH/Px8FBQUAHB17RcWFgIAcnNzUVJSAgDIyclBWVkZACA7O1u99WJWVpZ6k4nMzEz19LOMjAzYbDaPW/7ZbDZkZGQAcHWbZmZmYuHChXj77bexbt06rF27Fg8++KB6J6yysjLk5OQAAEpKStR7bhcWFiIvLw8AUFBQgPz8fK82paen47HHHkNqaqqmbaqursaJEyewY8eOBtsEAJWVlcjKygIAlJeXa9KmllxPWrVJEIB58w5j3rzDEARg4cJ8zJxZAEEAli/Pw733FkIQgFdfzcX48SUQBODNN3MwfHgZBAFYvz4bAweWQxCAtm17IinpbQBAWtpHePPNRyAIrjZFRdnQtq2rTW3bSoiKcrVJEICEBCs2b86EIAC9elXigw+yIAjAwIHlWL8+G4IADB/ePOupuLgYJ06cwHfffRfQ60mPuaeHNh08eBCKovjVpr1790I3KEC98cYbNHDgQLpw4UKD01gsFrrtttvot7/97VXnZ7PZqKqqSn2UlJQQAKqoqCAiIkmSSJIkr9jpdHrEsixfMXY4HB6xoigesaIoXjERecSyLKvLJcsyOZ3OK8aSJHnEdduxdu1aCgoKIqPRSAAoNTW1RdpUO/a3TYGynvxtE0BkMklkMrnioCCJjEZ37FRjs9lJRqOsxgaDKw4OdpIoumMHieJaAgQKCUklUVQIIAoJcZAgKAQoFBLiIEAhQXDHRKJYO5YpOLh27CSAyGC4vtcTt0m/bTp//jwBoKqqKmrtNL1S3OHDh/Haa6/h5MmTHt0Y7i2qxnrrrbfw0Ucf4euvv0b79u3rnebixYsYP3487rzzTrz88stNXlY9DYS4kvpGPwPanaOsKApKSkoQFxfHt5/0weWhIhpJBbCo1vO1ALQ75bE5TiLh/GG+0ip39FQLNC3oN998Mx5++GEMHDgQBoNBfX3kyJGNnkdpaSni4uLUWykCgNlsxt69ezF37lxMnjwZkydPxmuvvYZXXnkFvXv3Vv/vfffdhxdeeKFRn6OnldiQazH6WZIk5ObmYvDgwTw4xQfaFfS6xdxtLbQq6s1R0Dl/mK+0yh091QJNC/qAAQOwf/9+rWbXrPS0Eq+kuffQmX+0KehWAOEA6vtTFgBUAfB/pHsrv8wDY/XSUy3QtI9rwoQJ2LFjh5azZH5q7kuCyrKM48eP8+UXW1QogPpPW3O9HrinrXH+MF9x7njTtKCPHTsWM2bMQHh4OKKjoxEVFYXo6GgtP4L5wF3UBUHQfM+ciHDhwoVWf5W+1m8hgGl1XpsGLY+hNwfOH+Yrzh1vmna59+jRA6tWrcKAAQM8jqEnJCRo9RGa0VM3S2NZrdaAvsjI9eh6P4bOWEvTUy3QdA89MjISM2bMQLdu3ZCQkKA+WGBojmIuyzKOHDnC3V4tygpgcQPvLb78fmDi/GG+4tzxpmlBnzZtGjZs2ICKigpUV1erD6ZvNTU1Lb0I17nWewwd4PxhvuPc8aRpl3vtcwHdN2wRBCEgt6ACvZuFu8evD63pPHSLhXOS6U+g14Km0HQP/dKlS1AURb22rqIo6mUCWeOlpqYiPDw84G99Cbi6vQ4ePBiQG23Xn4VwFXEBWhdzoHlykvOH+Ypzx5umBb3urUurqqowceJELT8i4AiC1g/XeeNEhEWLFkEQUjWbN7seLITrvHNtiznw35xsDRuajF2PNC3oycnJ6ilRFy9exIQJE/D4449r+RE6V99I5UWXXw9MBoMBffr08TirgbU0LbvFvXNSy6LO+cN8xbnjTdNj6ADwwAMPoH///sjMzMQ999xT71XKAoFWx0202/Nt/qt9NcdpR7IsIz8/H3379uU/LB8Eds9Jwzmp1aWDOX+Yr7TKHT6GXkftEe1vv/02tmzZgsGDB2P+/Pk8yr3RWu9I5ZCQkJZeBNYsGs7JNWvWaDZAjvOH+Ypzx5Mme+iiKHqMaq89S72Pctd+D6v5RirzKOXAE9h76G6eOcn3AWB6wnvoddQe1e7+t/Zod9YUrpHK7nHKhEUgCH4/1kJAeFgYUjUexSeZzdi3b5/H7XKZ3vz3srLTpk3TtJhLksT5w3zCueMt4G5A/PTTTyMxMRGCIODgwYP1TrNr1y60adMG/fr1Ux/6usDAQpRCu3HK7v0rgvZD7ARFQfv27SG0jl1N5pNUAFsBAFu3btV0lLsgCJw/zCecO940KeinTp3C+PHjkZycjKVLl8Jms6nv3XbbbU2a14wZM7Bnz56rXjK2V69eOHDggPrQ17GUVHSFNoW3ucfNGyQJPXr04AFNutX8o9w5f5gvOHe8aVLQH3/8cUyePBnp6ekoLy/H2LFjYbW6rh9du7g3xogRI9C1a1ctFquVunzOL/wvvNfiCt+S2YycnBzu9tKlhjNo8eLF6t+4PyRJ4vxhPuHc8aZJQT979iyefPJJDBw4EJs2bcJdd92FsWPHoqqqqtm6Q44ePYoBAwZg0KBBeOedd646vd1uh8Vi8XgAUI/xy7JcbyxJkkesKIpXHBwsQRTdsVONQ0KcEEVSY0EgAISQECcAgiC4Y0AUCSEhfwCwCKIoIjg4GACwRBTxh8uxYjBAMptdsdGoxrLRCCkoSI3ly3EbkwlrTSYAQFBQEIxGoxqvMRoRCldBVi6/LpnNUC5v7UrBwVAuX8rXWTsOCQHVigVFQUxMDGRZBhGBiOB0utpUO1YUxSN2/xE2FMuy7BFrsZ5qx06n0yN2D+R0x+5lvxZtMplkmEzy5XUjw2h0x5Iam80SjEZFjQ0GrXPPHSsIDnbHbREcvBaAa2/IfDnfjEYj1q5di9DQUL/XEwDExsaqY24CeT3pMfdac5uICF26dIEoin63SS80O22ttuXLl+P+++/32FPX0oABA1BaWor9+/dj69at2LBhAz7++OMr/p+UlBSEh4erj7i4OABQj9MfPnwYhw8fBgDk5+ejoKAAAJCXl4fCwkIAQG5uLkpKSgAAOTk56mVtV6/ORt++5QCAdeuykJRUCQBIS8tEbKyr/enpGejQwYaQEAnp6RkICZHQoYMN6ekZAIDY2LNIS4sCACQlJWHdunUAgL59+0JYvRpWAGVDhiBnxQoAQMmoUch97jkAQOGkSci7PFCpYMYM5M+b52rTzJkYM3Mm1gKYN28eZsyYAQB4b+FC3DVpkqtNzz2HklGjXG1asQJlQ4YAALJXr0Z5374AgKx161CZlAQAyExLgzU2FgCQkZ4OR1gYYmNjsWPHDkiSBJvNhowMV5usVisyMzMBAJWVlcjKygIAlJeXIzs7GwBQVlaGnJwcV5tKSpCbm+tqU2Eh8vLyXG0qKEB+fr7f6yk7Oxvl5a71lJWVhcpK13rKzMxU8zQjIwM2mw2SJCEjI+OatGnmzMOYOdPVpnnz8jFjhqtNCxfmYdIkV5ueey4Xo0a52rRiRQ6GDGk49wgC/p62GRdiE0AQkJ6egUsdouAIaYv09Aw4QtriUocopKdngCDgQmwC/p62GQQBPyf1wifrPgBBQFnfgfh8tYBpAIYMGYIVl3NvyahR+H8VFYAgoPDee5G3fDkgCCiYORP5CxcCgoDD8+bh8Lx5gCAgf+FCFMycCQgC8pYvR+G99wKCgNxXX8XpiRORkJCA77//PuDXkx5zrzW36dSpUygvL4coin61ae/evdAN0sDUqVPpiy++8Hr9zTffJEEQfJpnQkIC/fvf/27UtCtXrqSnnnrqitPYbDaqqqpSHyUlJQSAKioqiIhIkiSSJMkrdjqdHrEsyx4xQBQc7CRRdMcONQ4JcZAoKmosCAoBCoWEOAhQSBDcMZEoKhQSkkoASBRFCg4OVuPU4GAigGSDgZxmsys2GtVYMhrJGRSkxpI7NplIMpmIAEoNCiKT0UhrAXIGBZFkNBIB5DSbSa4dGwyuODiYZFEkAshROw4JIaVW7AgOpl27dlF1dTUpikKKopDD4SAi8ohlWfaInU7nFWNJkjzi+tZNU9ZT3djhcHjEiqJ4xO5lb+42AUQmk0QmkysOCpLIaHTHTjU2m51kNMpqbDA0nHv1rSdFEEhxxwApgkCOkBAigBRRVGNZFMnhzrfLuQeADAYDmc1mAkBGo5FSm5B7Uu18q5N79jZt6Ntvv6WampqAXk96zL3W3iabzUa7du1Sl9XXNp0/f54AUFVVFbV2mpyHbrfbAUDtkqvt9OnTiL28R9cUiYmJ2L59O/r06eP1XllZGTp16gRRFGG1WjFhwgTMmTMHjz76aKPn31rOQ18L7Ua7nwEQo9G83BSDAWWnTqldX6xpmuOIFEGbmTb/tQs5f5jv3Df/8jd3+Dz0Osxms1rM3V0abk0t5k8++SS6du2K0tJSjBs3Dj169AAAzJ07F9u2bQMAbNmyBTfffDNuueUW3HrrrbjjjjvwyCOPaNCSQOB5HrqWp65pNXK+NlGWERsbyz/GOnQtrl3I+cN8JYoi504dml/L/YknnmjUILWWFrh76C4WCJpd7LU575ItBQcj+/PPMWLECHXQHWu8QN5Dd+P8YYFIkiRkZ2f7nTt62kP3+y8oISEBPXv2BOAadXj06NFWUdADXXMVc9R6rsWPsuhwoE+fPryVzHzC+cN8JYoi504dfhf0O+64A++++676nG+XGjiudh76o/B/w0FUFERHR/s5Fxaomn2DkPOH+UgURc6dOvzetPn973/v8Xz9+vX+zpJp5FocA3UGB+PLL79Uzx9l+nEtLkzE+cN85XQ6OXfq8HsPPSIiQo2Li4tRXFwMAIiPj0d8fLy/s2d+cu9FLar12lpodwzU4HBg0KBBfPlFHXJvEC6q5z2tNgg5f5ivDAYD504dmoxCOXLkCB599FEUFhYiPj4eRISSkhLccMMNSEtLw0033aTFxzAfuYv3Yrh+iLW88aWoKOjQoYOGc2SBpLk3CDl/mK9EUeTcqUOT0QSzZ8/G0qVLUVZWhr179yI3NxdlZWVYsmQJZs2apcVHMD8thOu8Ya3vYu0MCcHnn3/O3V465jqREpqfSglw/jDfOZ1Ozp06NDltrWfPnjh69GiT32tJgX7amtanHjUXEkVYL1xAaGgo38bQB63htDU3K7Q7+8KN84f5iohgtVr9zh09nbamyR56x44d8Ze//EW92D3guorPpk2bEBkZqcVHsAAlKArCwsL4x/g6oHUxBzh/mO8EQeDcqUOTgr5p0ya8//776NixI/r06YObb74ZkZGR6utMv5whIfj000+524v5hPOH+crpdHLu1KHpleJ+/vln9Q42cXFxiIqK0mrWmuMud22QIMB26RKCg4N5S9kHranLvTlw/jBfERFsNpvfuaOnLndNr7UYFRUV0EWcNQMivmQn8x3nD/MD546nZr9mXnJycnN/BGtBUkiIep9jxpqK84f5qvY91pmLJl3uhw4davC9cePG4cyZM/5+hOa4y10bBEByOGA0GrnL1AfXfZc7OH+Yb4gIkiT5nTvc5V5Hnz59kJiYiPq2DcrLy7X4CBaoBEH9o2KsyTh/mB84dzxp0uWekJCAPXv2oLCw0OvRqVOnJs+voKAAQ4cORXJyMgYPHlxvDwAR4ZlnnkHv3r3Rt29fjB49GsePH9eiOawJpOBgZGZmcrcX8wnnD/OVJEmcO3VoUtAnT56MkydP1vvelClTmjy/BQsWYP78+Th27BiWLVuGOXPmeE2zbds2ZGdn48CBA8jPz8fYsWOxfPnyJn8W84+ppgZTpkyByWRq6UVhrRDnD/OVyWTi3KlDk4KempqKYcOG1fveunXrmjSvc+fOYf/+/Zg5cyYAYPr06SgsLERRUZHXtHa7HTabDUQEi8WCrl27NnnZmX9IFGGxWOo93MLY1XD+MF+5f/c5d/4r4O4MX1JSgpiYGPW4iCAIiI+PV+/i5nbPPfdg9OjR6Ny5M7p06YKdO3fit7/9bYPztdvtsFgsHg8AkGVZ/be+WJIkj9h9NbzacXCwBFF0x041DglxQhRJjQWBABBCQpwACILgjgFRrB0rcAYHAwAUUYTkjg0GSGazKzYa1Vg2GiEFBamx7I5NJsiXt17loCDIl79TqXZsNkOpHV++c5EUHAxFdKWHs3YcEgKqFTuDg5GdnY2amhoQEYhIvdBD7VhRFI/Y3U3WUCzLskesxXqqHTudTo/Y/aPgjt3Lfi3aZDLJMJlccVCQDKPRHUtqbDZLMBoVNTYYrpx7ddcTCQLIHcN1/rczJMTVJlFUY0UUr2nuOdq0we7du2Gz2QJ+Pekx91pzm+x2O7Kzs9Vl9adNehFwBR2A14jF+rbA9u/fjyNHjuD06dM4c+YMxo4di6eeeqrBeaakpCA8PFx9xMXFAQAOHjwIADh8+DAOHz4MAMjPz0dBQQEAIC8vD4WFhQCA3Nxc9cI5OTk5KCsrAwCsXp2Nvn1dg//WrctCUlIlACAtLROxsa67RqenZ6BDBxtCQiSkp2cgJERChw42pKdnAABiY61IS8sEACQlVSLrcs9Ged++yF69GgBQNmQIclasAACUjBqF3OeeAwAUTpqEvIWuW2YUzJiB/HnzXG2aOROHL/d05M+bh4IZM1xtWrgQhZMmudr03HMoGTXK1aYVK1A2ZAgAIHv1apT37QsAyFq3DpVJSQCAzLQ0WGNjAQAZ6emQQ0Iwfvx4fPXVV5AkCTabDRkZrjZZrVZkZrraVFlZiaysLFebysuRnZ3talNZGXJyclxtKilBbm6uq02FhcjLy3O1qaAA+fn5fq+n7OxsdZBmVlYWKitd6ykzMxNWq2s9ZWRkwGazeZwS09xtmjnzMGbOdLVp3rx8zJjhatPChXmYNMnVpueey8WoUa42rViRgyFDrpx7ddeTrUMH1yli6emQQkJg69ABGenprjbFxiIzLc3VpqSka5p7Z2+9FXfddRf27dsX8OtJj7nXmttUWlqKTp06wWQy+dWmvXv3QjcowPz0008UFhZGTqeTiIgURaFOnTpRYWGhx3RPPvkkvf766+rzgwcPUnx8fIPztdlsVFVVpT5KSkoIAFVUVBARkSRJJEmSV+x0Oj1iWZY9YoAoONhJouiOHWocEuIgUVTUWBAUAhQKCXEQoJAguGMiUawdy+QIDiYCSBZFcrpjg4GcZrMrNhrVWDIayRkUpMaSOzaZSDKZXHFQEElGIxFAztqx2Uxy7dhgcMXBwSSLIhFAjtpxSAgptWLJYKDy8nKy2WykKAopikIOh0Ndd+5YlmWP2L1+G4olSfKI61s3TVlPdWOHw+ERK4riEbuXvbnbBBCZTBKZTK44KEgio9EdO9XYbHaS0SirscHQcO7Vt54UQSDFHQOkCAI5QkKIAFJEUY1lUbymuecMCqLz58+T3W4P6PWkx9xr7W1yOBx07tw5kmXZrzadP3+eAFBVVRW1dgFX0ImIRo4cSe+99x4REf3973+nIUOGeE3z5ptv0p133qkmQEpKCk2aNKnRn1FVVaXJSgSa59FsM9b44QgOph07dqjrgTXN9Zw7nD/MHw6HQ5Pc0aoWBAJNr+WulaNHj2L27Nk4f/48wsLCsGnTJvTu3Rtz587F5MmTMXnyZNjtdjz11FPYvXs3goKC0KVLF2zcuBGJiYmN+gy+sIyGAi+FWo3r/cIyADh/WIvS04VlArKgXwtc0LWhiCLKy8rQsWNHiGJADskIaNd7Qef8Yb5SFAXl5eV+546eCjr/BTG/KEFBOHjwoDpilLGm4PxhvlIUhXOnDt5D5z10/12fKaSJ630PHQDnD2tRvIfO2GWKwYDTp0/zVjLzCecP85WiKJw7dXBBZ35RjEacOHGC/6iYTzh/mK8UReHcqYO73LnL3X/XZwppgrvcwfnDWhR3uTN2mWI04tSpU7yVzHzC+cN8pSgK504dXNCZX/gYKPMH5w/zFR9D98Zd7tzl7r/rM4U0wV3u4PxhLYq73Bm7TDYacfz4cfUuRow1BecP85Usy5w7dXBBZ34hUcSFCxdwnXb0MD9x/gQe953SAh0Rce7UwQWd+cXocGDQoEHq/esZawrOn8CSmpqK8PBwpKamaj5vrTcUjEYj504dXNCZX2SjEUeOHOFuL+YTzp/AkZqaikWLFoGIsGjRIk2LenNsKMiyzLlTB2/aMP+IImpqalp6KVhrxfkTENzFvDb384ULF2o2b63m6ca544lHufMod/9dnymkCR7lDs6fFma1Wq/4G2ixWBAaGurTvOvbUACAtWvXalbU/cWj3JtZQUEBhg4diuTkZAwePBiHDh2qd7q0tDQkJSWhe/fumD9/PiRJusZLymSTCQcPHuRuL+YTzh/9slqtWLx4cb3vLV682O9j6rIsc+7UEZAFfcGCBZg/fz6OHTuGZcuWYc6cOV7TFBYW4sUXX8SePXtw/PhxnD17FmlpaS2wtIwx1jIEwf/H1XZKw8J8nW8oiKbWO8+pU6f6vNfPGhZwXe7nzp1DcnIyysvLYTQaQUTo0qULvv/+eyQmJqrTvfHGGygqKsLbb78NAMjIyMDq1auxa9euRn0Od7lrKLBSqFXhLndw/vhBm/yxArhClzsAX0rvlefq+3w9aJA7eupyD7hBcSUlJYiJiVFPRRAEAfHx8SguLvYo6MXFxUhISFCfJyYmori4uMH52u122O12AK7zF8+cOQMAuHDhAgCo3TYGg8EjliQJgiCosSiKEEVRjQERZrMEh0MEkQiz2QmHwwAiEcHBTtjtRhAJCA52wmZztSk4WKoTmyAIBLPZHSs4H2SGyW6HIghQgoJgtNuhiCIUoxFGhwOKwQDFYIDR4YBsMIBEEUanE7LBAIgiDE4n5MvfoUGSIJtMgKLAIMuQTCYI7jgoCKIsQ3THkgRRUSCZzRAdDohEcJrNMLjj4GAY7XYIl2NBUXAwOxs33ngjzGYzAECSJJhMJhCRGiuKAlmW1VhRFBiNxgZjWZZBRGpc37ppynqqGzudThgMBjU2Go0QBEGN3e2oHTdHmwADjEb58mcYYDLJUBRAlg0wmSQoigBZNiAoSIIsi5BlEUFBEiRJhKLUn3sWgtd6Mtpsrs+oE5tsNpAgQDKbYbLZoAgC5KCga5Z7JIo4tHs3brzxRphMpoBdT4Gae4Dr9yIoSIHdboQoKjAaFTgcRhgMCgwGdyxDFAlOpzsGnM7aubcKJtOLXt/Va7IMZ1AQKn34jThLhODgYNgu51vd+IzNhs5+5J4jJAT/+fZb9O3bV/2d92U9VVRUqHWhtQu4gg64inhtDX3Rtae72spISUnBihUrvF6vvZHgq8vbCV7x5dxtVEzkGXd0z4fovzNVFMDhcMWy7HpcKa49psDprD92z69u3JRGjRwJ5jutV1M44F/yuWd0rXJvxAgw3zXPanI9eQ7Ac1r8RgBqMXfHN7oX3tfcq6kBRo2CVqxWK8LDwzWbX0sIuIIeFxeH0tJSdQuViFBSUoL4+HiP6eLj41FUVKQ+P3XqlNc0tT3//PNYsmQJAFfxt1gscDqdiIyM9NqAYI1nsVgQFxeHkpKSVt9dxa49zh/mK61yh4hgtVoRExOj4dK1jIAr6NHR0ejfvz8+/PBDzJ49G1u2bEFiYqLXnvT06dMxbNgwvPTSS4iOjsaGDRvwwAMPNDhfs9msdgkDaPVbYoEmLCyMf5CZzzh/mK+0yB291IOAHOW+ceNGbNy4EcnJyVi1apU6en3u3LnYtm0bAKBbt25YsWIFbr/9dnTv3h3R0dH1joZnjDHGrgcBN8qdtS56GiHKrj3OH+Yrzh1vAbmHzloPs9mMl19+2eNwBmONxfnDfMW544330BljjDEd4D10xhhjTAe4oDPGGGM6wAWdMcYY0wEu6IwxxpgOcEFnjDHGdIALOmOMMaYDXNAZY4wxHeCCzhhjjOkAF3TGGGNMBwKuoD/99NNITEyEIAg4ePBgg9OlpaUhKSkJ3bt3x/z58yHVvrEvY4wxdp0JuII+Y8YM7NmzBwkJCQ1OU1hYiBdffBF79uzB8ePHcfbsWfWObIwxxtj1KOAK+ogRI9C1a9crTrN582ZMmzYNnTp1giAIeOyxx5Cenn6NlpAxxhgLPMaWXgBfFBcXe+zBJyYmori4+Ir/x263w263AwCICBaLBU6nE5GRkRAEoVmXlzHGWGAiIlitVsTExEAUA24ft0laZUEH4FGEG3PDuJSUFKxYsaI5F4kxxlgrVVJSctXe4UDXKgt6fHw8ioqK1OenTp1CfHz8Ff/P888/jyVLlgBwbQCcOXMGvXr1QlFREdq3bw9ZlgEABoPBI5YkCYIgqLEoihBFscHY6XTCYDCosdFohCAIagwAkiR5xCaTCUSkxoqiQJZlNVYUBUajscFYlmUQkRrX147mahMA7N27FwMHDkRwcLAu2qTH9RSobVIUBfv27cPAgQMRFBSkizbpcT0FYpvsdjv27duHW2+9Vd3B86VNFRUVuOGGGxAaGlq3bLQ6rbKgT58+HcOGDcNLL72E6OhobNiwAQ888MAV/4/ZbIbZbFafuxOgffv2CAsLa9bl1TNFUXDLLbcgKiqq1XdXsWtPURT07dsXHTt25PxhTeL+7YmIiNAkd/Rw6DXg/oKefPJJdO3aFaWlpRg3bhx69OgBAJg7dy62bdsGAOjWrRtWrFiB22+/Hd27d0d0dDTmzJnTkot93RJFEbGxsfxjzHzC+cN8xbnjTaDGHIDWIYvFgvDwcFRVVfEeuh8kSUJ2djZGjBihdqcx1licP8xXWuWOnmoBb9owv4iiiD59+vBWMvMJ5w/zFeeON94kZn4RRRHR0dEtvRisleL8Yb7i3PHGmzbML06nE19++aU64p2xpuD8Yb7i3PHGBZ35xWAwYNCgQTAYDC29KKwV4vxhvuLc8cYFnflFFEV06NCBj2P5KTExEZ988kmLLsPu3bs9Lqxhs9kwbdo0REREYPDgwV7va4Hzh/mKc8cbfxPML06nE59//rkuu71GjRoFg8GA/Px89bXKykoIguBxYSNf5rt27Vq/li0xMREhISFo164dOnbsiEmTJqGgoMCveQ4fPhylpaXq8y1btuDo0aP46aefkJub6/V+U5SVleGhhx5C586dERoaim7dumHx4sWa5I8gCDhw4IDP/5+1Tnr+7fEVF3TmF6PRiOHDh+v2lKP27dvj+eef12ReRKReuUoL6enpuHjxIk6ePInQ0FDMmjVLs3kDrrsaJicne1yQyVe//OUvERwcjCNHjqCqqgpfffUV+vXrFxD5w7debp0CIXcCDRd05hdBEBAWFqaLqyzV54knnkBOTg6ys7PrfZ+I8Oabb6J79+7o0KEDJkyYgJMnT6rvJyYmIiUlBbfeeivatGmD+++/H7t378azzz6Ldu3aYeLEieq0x44dw6233orQ0FCMHDkSJSUljVrGsLAw/PKXv8S///1vAMCyZcuQkJCA0NBQ9OrVC3//+989pv/Xv/6FMWPGoEOHDoiKisKvfvUrAMCuXbsQEREBAFi6dCl++9vfYvv27WjXrh1efvllj/cBwOFw4KWXXkL37t0RGhqKm2++Gfv37693Gb///ns88sgj6lW9unfvjlmzZqn5I0mSOq/IyEhMnjwZZ86cUf//2bNnMXPmTMTExCAiIgIjRoxATU0NBg8eDAAYOnQo2rVrh5UrVwIAfvjhB9x+++2IiIhAr169PO7G+Morr+Duu+/G448/jg4dOuDZZ59t1PfMAovef3t8QtepqqoqAkBVVVUtvSitmsPhoE8++YQcDkdLL4rmRo4cSWvWrKGVK1fSbbfdRkREFy5cIABUWFhIRESbNm2imJgYys/Pp5qaGlqyZAnddNNN5HQ6iYgoISGBkpOT6ciRIyRJEtntdnW+tSUkJFDv3r3pxIkTVFNTQxMnTqRZs2Y1uGwJCQm0detWdZnuu+8+GjFiBBERffjhh/TTTz+RJEmUnp5OZrOZTp48SUREpaWlFBYWRm+//TbV1NTQpUuXKDs7m4iIvvnmGwoPD1c/4+WXX6YpU6aoz+u+v3jxYho4cCAdO3aMFEWhI0eOUFFRUb3LO378eBowYABt2rSJjh49qr7uzp8lS5bQmDFj6MyZM2S322np0qU0fPhwIiKSZZkGDRpEs2bNooqKCnI6nbR7926y2WxERASA8vLy1HleuHCBIiMj6Q9/+AM5HA7atWsXtW3blvbs2aO2y2Aw0HvvvUdOp5MuXbrU4PfMApdWvz16qgVc0HWwEluSoihUXV1NiqK09KJozl14q6urKSYmhrZu3epV0MeNG0erVq1S/4/NZqPQ0FD65z//SUSuwlu3eDdU0NevX68+//DDD6lPnz4NLltCQgK1adOGIiIiKCYmhqZPn95gMb3lllvoww8/JCKiVatW0ejRo+udrikFXVEUatOmDX377bcNLmNtVVVV9PLLL1P//v3JaDRSfHw8ffTRR6QoCl26dInatm1LBw4cUKevqakhURSpuLiYvv/+e2rbti1VV1fXO++6Bf3DDz+kG2+80WOaefPm0bx589R23XLLLY1abha4tPrt0VMt4C535je9H8MKCQnByy+/jOXLl3sdAy8tLUViYqL63Gw2IyYmxmPw2NXuBOjWuXNnNW7bti2sVusVp//oo49w4cIFnD59Gps3b0ZCQgIAYM2aNejduzfCw8MRERGBgwcPory8HIDrzoRJSUmNWp4r+fnnn1FdXd3oeYWFheGVV17B/v37ceHCBTz99NN4+OGHcfjwYVRWVuLSpUsYMWIEIiIiEBERgc6dOyMoKAglJSU4deoUYmNjERIS0qjPqrtOANf9H3xZJyyw6f23p6m4oDO/SJKEjIwM3Q8smjNnDhRFwaZNmzxe79q1q8eId4fDgTNnznic3lX3tJrmPM1mz549eOWVV/DBBx/gwoULqKysRJ8+fUCXb9mQkJCA48eP+/05UVFRaNOmjU/zateuHZYuXYrw8HD8+9//xt69e9GmTRvs3bsXlZWV6qOmpgZDhw5FQkICTp8+jZqamnrnV/cYat11ArgG+F1pnbDW53r57WkKzmrmF6PRiEmTJul+S9lgMOC1115TB125zZw5E+vWrcOhQ4dgt9vxm9/8BrGxsepgrfp06tQJJ06caJbltFgsMBqNiIqKgqIo+POf/4yDBw+q7//iF79Abm4uNmzYALvdjurqauzevbvJnyMIAubNm4elS5fi+PHjICIcPXoUp06dqnf6Z555BgcOHIDD4YDD4cC7776LS5cuYfDgwbj77ruxYMECLF26VB0IeP78efzf//0fAGDQoEHo2bMnnnzySVRWVkKSJOzZswd2ux2A9/c5adIknDt3Du+88w4kScLu3bvx17/+FQ8//HCT28kC1/Xy29MUXNCZ366XLeTp06ert/N1e/jhh/GrX/0Kd999Nzp37owff/wRn3322RV/ZBYtWoSvv/4aERERuPvuuzVdxgkTJmD69Om4+eabERMTg//85z+4/fbb1fe7du2Kr7/+Gn/961/RqVMnJCYmYvPmzT591uuvv46xY8di3LhxCAsLw3333YeKiop6p7Xb7XjggQcQGRmJzp0747333sOnn36KxMRESJKElJQU3HbbbRgzZgxCQ0MxcOBAZGZmAnDtTX/22Weorq5Gz5490bFjR/zmN7+BoigAgN/97nd4+umn0b59e6xatQrt27fHF198gQ8//BCRkZGYP38+1q9fj2HDhvnUTha4rpffnsbi26fq4JZ5LcnpdCIjIwOTJk2CyWRq6cVhrQznD/OVVrmjp1oQkAW9oKAAs2bNQnl5OSIiIvD++++jV69eHtMQEZYtW4aMjAwYDAZERkbif//3f732oBqip5XIGGPMN3qqBQHZ5b5gwQLMnz8fx44dw7JlyzBnzhyvabZt24bs7GwcOHAA+fn5GDt2LJYvX94CS3t9IyJYLBYE4HYhawU4f5ivOHe8BVxBP3fuHPbv34+ZM2cCcB23LCwsrPfa2Xa7HTabTV2xWt84gl2de9ARH8tivuD8Yb7i3PEWcAW9pKQEMTEx6qAiQRAQHx+P4uJij+nuuecejB49Gp07d0aXLl2wc+dO/Pa3v21wvna7HRaLxeMBQD2vWJblemNJkjxi90CchmKn0+kRu7ce3TERecUAPGJFUTxid8I2FMuy7BFfyza5R5q626CHNulxPQVqmwwGA+666y4IgqCbNulxPQVim0RRxPjx42Eymfxuk14EXEEHvM8rra9LZf/+/Thy5AhOnz6NM2fOYOzYsXjqqacanGdKSgrCw8PVR1xcHACop/QcPnwYhw8fBgDk5+erd67Ky8tDYWEhACA3N1c9rSYnJwdlZWUAgOzsbPXCHVlZWaisrAQAZGZmqhcHycjIgM1m8zh30mazISMjAwBgtVrVUb2VlZXIysoCAJSXl6vXES8rK0NOTg4A14ZPbm4uANc5tnl5eQBc4w/cdwe7Fm2qrq7Gzz//rKs26XE9BWqbTp06hYqKCl21SY/rKRDbdPLkSezduxeKovjVpr1790IvAm5Q3Llz55CUlITz58/DaDSCiNClSxd8//33Hld/euqppxAfH49ly5YBAP7zn/9g0qRJDZ4Ha7fb1fNWAddAiLi4OFRUVKB9+/bqlpvBYPCIJUmCIAhqLIoiRFFsMHY6nTAYDGpsNBohCIIaA64twtqxyWQCEamxoiiQZVmNFUWB0WhsMJZlGUSkxvW1o7naRETIysrCiBEj1Ct5tfY26XE9BWqbZFnGrl27MGLECJjNZl20SY/rKRDbZLPZsGvXLowdO1a9UJAvbaqoqEBkZKQuBsVpWtC3b9+uyXm1o0aNwuzZszF79mxs3rwZv//97/H99997TPPWW2/hyy+/xPbt22EymbBq1Srs3r0bn3/+eaM+Q08jGxljjPlGT7XA74J+xx13QBAEEBGOHTuGnj17ql0ovjp69Chmz56N8+fPIywsDJs2bULv3r0xd+5cTJ48GZMnT4bdbsdTTz2F3bt3IygoCF26dMHGjRu9ruHcED2txJakKArKy8vRsWNHvpwmazLOH+YrrXJHT7XA74L+4osvYuDAgZg6dSoWL16MNWvWaLVszUpPK7ElSZKE7OxsjBgxgi/ByJqM84f5Sqvc0VMt0KTLffPmzdi/fz+qqqrw9ttva7FczU5PK5Exxphv9FQLNOnjmjFjBh599FH07NlTi9mxVkRRFJw+fVo9BYSxpuD8Yb7i3PGm2UGrHj164Omnn9ZqdqyVUBQFJ06c4D8q5hPOH+Yrzh1vmo5yP3z4MF577TWcPHnS42R993mDgURP3SyMMcZ8o6daoOkolPvvvx8PP/wwHn30URgMBi1nzQKUoigoKSlBXFwcj1IOEFarFaGhoS29GI3C+cN8xbnjTdNvwWQy4ZlnnsGYMWMwcuRI9cH0i49jBZbU1FSEh4cjNTW1pRelUTh/mK84d7xpWtAnTJiAHTt2aDlLFuCMRiOGDh3KpxwFgNTUVCxatAhEhEWLFrWKos75w3zFueNN04I+duxYzJgxA+Hh4YiOjkZUVBSio6O1/AgWYGRZxvHjx9VLLLKW4S7mtbWGos75w3zFueNN04K+YMECvP/++8jLy8O+ffvwww8/YN++fVp+BAswRIQLFy7wPYlbkNVqxeLFi+t9b/HixeqNMgIR5w/zFeeON037KiIjIzFjxgwtZ8kCnNFoxKBBg1p6Ma5roaGhmDp1KrZu3er13tSpUwN6gBznD/MV5443TffQp02bhg0bNqCiogLV1dXqg+mXLMs4cuQId3u1IKvVik8++aTe9z755JOA3kPn/GG+4tzxpuke+vLlywEATzzxhHrDFkEQ+AvXuZqampZehFZLELSYSyiAqQC899CJpiIsTJs99Obq2eT8Yb7i3PGk6YVlampq1Htiu507dy4gB8bp6WICrPXSpqBbAYQDqO9PWQBQBVfR9w8fqmR6pKdaoGmX+0MPPeTxvKqqChMnTtTyI1iAkWUZBw8e5F6YFuXeQ6/PVGhRzJsL5w/zFeeON00LenJyMhYuXAgAuHjxIiZMmIDHH39cy49gjHmxAvikgfc+ufw+Y0zvNO1yB4AHHngA/fv3R2ZmJu655x6vc2MDhZ66WVjrpU2XOwCkAlhUz+trASzU5BO4y53pkZ5qgSZ76LVHtL/99tvYsmULBg8ejPnz5/s0yr2goABDhw5FcnIyBg8ejEOHDnlNs2vXLrRp0wb9+vVTHzxA4tqTZRl5eXnc7dXiFsJVvGtbC62KeXPh/GG+4tzxpsko93bt2nmMaici/PDDD3j99dd9GuW+YMECzJ8/H7Nnz8bmzZsxZ84cfPfdd17T9erVCz/88IMWTWB+qDsQkrUUd/FeDGANAr2Yu3H+MF9x7njSZA9dURTIsuzxr/vR1GJ+7tw57N+/HzNnzgQATJ8+HYWFhSgqKtJiUZnGDAYDbrzxRr67XsBYCNeo9tZRzDl/mK84d7wF3D3nSkpKEBMTo15wXxAExMfHo7i42Gvao0ePYsCAARg0aBDeeeedK87XbrfDYrF4PACoGxyyLNcbS5LkEbvv7NNQ7HQ6PWL3EAV3TEReMQCPWFEUj9h9b/mGYlmWPeJr2San04nc3FzU1NTopk3Xej2ZTDJMJlccFCTDaHTHkhqbzRKMRkWNDQZXHBwsQRTdsfNyHIqQECdE0dWmkBAnBIEAEEJCnAAIguCOAVGsHSsIDq4du5bXYGie9eRwOLBv3z7YbLaAX096zL3W3Ca73Y69e/eqy+pPm/RCk4J+6tQpjB8/HsnJyVi6dClsNpv63m233dbk+Ql1RgrVN25vwIABKC0txf79+7F161Zs2LABH3/8cYPzTElJQXh4uPqIi4sDABw8eBAAcPjwYRw+fBgAkJ+fj4KCAgBAXl4eCgsLAQC5ubkoKSkBAOTk5KCsrAwAkJ2djfLycgBAVlYWKisrAQCZmZnqVboyMjJgs9kgSRIyMjIgSRJsNhsyMjIAuK72lZmZCQCorKxEVlYWAKC8vBzZ2dkAgLKyMuTk5ABwbfjk5uYCAAoLC5GXlwfANf4gPz//mrXJbrcjPDwcmZmZumnTtV5PM2cexsyZrjbNm5ePGTNcbVq4MA+TJrna9NxzuRg1ytWmFStyMGSIq02rV2ejb19Xm9aty0JSkqtNaWmZiI11tSk9PQMdOtgQEiIhPT0DISESOnSwIT3d1abYWCvS0lxtSkqqxLp1rjb17VuO1atdbRoypHnW0+nTp9G+fXvs3bs34NeTHnOvNbepuLgY1dXVEATBrzbt3bsXeqHJKPdJkybhrrvuwq233oo//OEPOH78OHbs2IHQ0FD0799fTaTGOHfuHJKSknD+/HkYjUYQEbp06YLvv/8eiYmJDf6/lJQUnDlzBn/84x/rfd9ut8Nut6vPLRYL4uLiUFFRgfbt26tbbgaDwSOWJAmCIKixKIoQRbHB2Ol0wmAwqLHRaIQgCGoMuLYIa8cmkwlEpMbuQxXuWFEUGI3GBmNZlkFEalxfO7hNgdkmo9Gg7p07nQYEBclQFECSDAgKkqAoAiTJALNZgiyLkCQRZrMESRIhyyKCgyU4HCIURURwsBMOhwGKIiIkxAm73QhFERAS4oTNZgQREBIioabGCEFw7d3X1JggigSz2R0rCAqSYbO5YwU2mxEGgwKb7fpdT9wm/bapoqICkZGRuhjlDtJA//79PZ6/9tprNGjQIKqsrPR6rzFGjhxJ7733HhER/f3vf6chQ4Z4TXPmzBmSZZmIiCwWCw0dOpTS0tIa/RlVVVUEgKqqqpq8fOy/nE4n/fOf/ySn09nSi9IquU4Gax2P5sD5w3ylVe7oqRZoMsq97qlpy5cvR1BQEMaOHevTjSE2btyI2bNnY+XKlQgLC8OmTZsAAHPnzsXkyZMxefJkbNmyBevXr4fRaIQkSbjvvvvwyCOPaNEc1gSiKCI2NhaiGHDDMVgrwPnDfMW5402TLvdp06ZhwYIFmDBhgsfrb731Fn7961+rgw8CiZ4uJsBaL+0uLNP8+MIyTI/0VAs02bT529/+htGjR3u9vmTJEnXgAdMnSZKQnZ2tq5Gi7Nrh/GG+4tzxpklBN5vNMJvNAKCOInSLjY3V4iNYgBJFEd27d+duL+YTzh/mK84db5p/E2+88YbWs2QBjI9jMX9w/jBfce548/ubSEhIwJ133ok777wTd9xxB7Zv367FcrFWQpIkZGVlcbcX8wnnD/MV5443v0e533HHHXj33XfV53y71OuLKIro06cPbyUzn3D+MF9x7njze5R7ZWUlIiIiNFqca0dPIxtZ68Wj3BlrWXqqBX5v2tQu5sXFxdizZw/27NlT77XXmf44nU58+eWX6jWYGWsKzh/mK84db5pcWObIkSN49NFHUVhYiPj4eBARSkpKcMMNNyAtLQ033XSTFh/DApDBYMCgQYP4jkfMJ5w/zFecO940KeizZ8/GM888g+nTp3u8vnnzZsyaNUu9mD7TH1EU0aFDh5ZeDNZKcf4wX3HueNNkNMGFCxe8ijkAzJgxA1VVVVp8BAtQTqcTn3/+OXd7MZ9w/jBfce5406Sgd+zYEX/5y188LvGqKAo2bdqEyMhILT6CBSij0Yjhw4erd1FirCk4f5ivOHe8aXIt9+PHj2PBggXIy8tDTEwMBEFAaWkp+vfvjw0bNiA5OVmLZdWUnkY2staLR7kz1rL0VAs02bTp0aMHdu7ciZ9//lm9dntcXByioqK0mD0LYE6nExkZGZg0aRJMJlNLLw5rRlarFaGhoZrOk/OH+Ypzx5sme+itkZ62yloSEcFmsyE4OBhCa9rdDBCt5ytLhSAsxpo1a7Bw4ULN5sr5w3ylVe7oqRY0+yV2ArG7nWmLj2HpXSqARSAiLFq0CKmpqZrOnfOH+Ypzx5Mm38ahQ4cafO/ixYtafAQLUJIkcbeXrrmKeW2LFrmea7GnzvnDfMW5402TLndRFJGYmIj6ZnX69Gk4HI4mza+goACzZs1CeXk5IiIi8P7776NXr14e02RlZeH555+H1WqFKIqYMmUKXn311UZ3veipm6UlEREkSYLRaOQuUx8E9ldmBRAOwPvvWhAEVFVV+X1MnfOH+Uqr3NFTLdCkyz0hIQF79uxBYWGh16NTp05Nnt+CBQswf/58HDt2DMuWLcOcOXO8pmnfvj3S09Nx6NAh/PDDD/j222+Rnp6uRXNYE/HdjvQqFMDUet+ZOnWqZgPkOH+Yrzh3PGlS0CdPnoyTJ0/W+96UKVOaNK9z585h//79mDlzJgBg+vTpKCwsRFFRkcd0/fv3R7du3QAAwcHB6NevX4PLwJqPJEnIzMzkP6wAQhA0eVggANha72ds3boVVkFwdTH48ZDatuX8YT7h3x5vmhT01NRUDBs2rN731q1b16R5lZSUICYmRh3sIAgC4uPjr3izl7Nnz2Lz5s2YNGlSg9PY7XZYLBaPBwDIsqz+W18sSZJH7L54TkOx0+n0iN2HIdwxEXnFADxiRVE8YnfCNhTLsuwRX8s2GY1GTJ48WW2DHtp0rdeTySTDZHLFQUEyjEZ3LKmx2SzBaFTU2GBwxcHBEkTRHTvV2BkSArp8W0lnSAhIEEDuGAAJApwhIa42iaIaK6IIZ3AwAMAiigi+HBsMBpjNZgCugUhmsxlWALLRCCkoyNUmoxGyOzaZIF8+rikHBUG+/Pcs1Y7NZhicTkyZMgWCIAT8etJj7rXmNomiiLvuugsmk8nvNulFQN5Itu7xkCsd5rdYLLjnnnuwbNkyDBgwoMHpUlJSEB4erj7i4uIAAAcPHgQAHD58GIcPHwYA5Ofno6CgAACQl5eHwsJCAEBubq56nn1OTg7KysoAANnZ2SgvLwfgOrZfWVkJAMjMzITVagUAZGRkwGazqQM5JEmCzWZDRkYGANc5vpmZmQBct6TNysoCAJSXlyM7OxsAUFZWhpycHACuDR/3NfILCwuRl5cHwDX+ID8//5q1qaamBhcuXNBVm671epo58zBmznS1ad68fMyY4WrTwoV5mDTJ1abnnsvFqFGuNq1YkYMhQ1xtWr06G337utq0bl0WkpIutyktDdbYWFeb0tNh69ABUkgIMtLTIYWEwNahAzIuH6KyxsYiMy3N1aakJGRd3gg39e2LDatXAwCGDBmCFStWAABGjRqFt557DjEACidNQt7lwXEFM2Ygf94813qaOROHL/ey5c+bh4IZM1zraeFCFF7e8M597jkUjxkDi8XSKtaTHnOvtbcpNzcXRORXm/bu3QvdoADz008/UVhYGDmdTiIiUhSFOnXqRIWFhV7TWiwWuu222+i3v/3tVedrs9moqqpKfZSUlBAAqqioICIiSZJIkiSv2Ol0esSyLF8xdjgcHrGiKB6xoihesbud7liWZY/Y/V00FEuS5BHX147mapPdbqfPPvuMLl26pJs2Xcv1BBCZTBKZTK44KEgio9EdO9XYbHaS0SirscHgioODnSSK7thBoigTAeQICSFFFP8bCwIp7hggRRDIERJCBJAiimosiyI5goPV2BkcTNMAMhgMZDabCQBNNxrJaTYTASQZjeQMClJjyR2bTCSZTK44KIgko5EIIGft2GwmW7t2tH37dqqurg7o9aTH3GvtbaqpqaHPPvuMHA6HX206f/48AaCqqipq7QLywjKjRo3C7NmzMXv2bGzevBm///3v8f3333tMc/HiRYwfPx533nknXn755SZ/hp5GNrLWqzkGdhO0n+m9cB1NnwbgH1rPPPB+gth1RE+1ICC73Ddu3IiNGzciOTkZq1atQtrl7sC5c+di27ZtAFzH7XNzc7F161b069cP/fr1w2uvvdaSi31dUhQFFRUVHjfmYfrzDwCnoX0xV0SR84f5hH97vAXkHvq1oKetspbkdDqRlZWFMWPG8MUdfNBa9tCbizM4GFmffML5w5pMq98ePdUCLug6WIms9breCzoA7nJnLUpPtSAgu9xZ66EoCs6dO8fdXswniihy/jCf8G+PNy7ozC+KouDgwYP8R8V8ogQFcf4wn/BvjzfuctdBNwtrvbjLHdzlHmCa4773gUxPtYD30JlfFEXB6dOneSuZ+UQxGDh/AkhqairCw8M1v0Vuc+DfHm9c0JlfFEXBiRMn+I+K+UQxGjl/AkRqaioWLWq++95rjX97vHGXuw66WVjrxV3u4C73AOAu5nWtXbtWk/veBzI91QLeQ2d+URQFp06d4q1k5hPFaOT8aWFWq7XeYg4AixYtUq/LHmj4t8cbF3TmFz6OxfzBx9CZr/i3xxt3ueugm4W1XtzlDu5y94M2+WMFcKXfQAsA/0e9B+pq1lMt4D105hdZlnH8+HH1PsOMNYVsNHL+tLhQuG67U59p0KKYNwf+7fHGBZ35hYhw4cKFK96znrGGkChy/rQ4K4BPGnjvk8vv++/MmTOazMeNf3u8cUFnfjEajRg0aBCMRmNLLwprhYwOB+dPiwsFsKaB99ZAmz30exEbG4t7771Xg3m58G+PNy7ozC+yLOPIkSPc7cV8IhuNnD8BYSGAtXVeW3v5dX/dC2ArAGDr1q2aFXX+7fHGBT1ABeqpIvWpqalp6UVgrZUocv4EDHdRF9AcxdxNy6LOueOJR7kH4MhG90UetL6ow/V2jebWgEe5I3CHP7cCzZE/FgiadLKfARB7hfdPA4jx90M0yJ1ArgVNFZB76AUFBRg6dCiSk5MxePBgHDp0qN7p0tLSkJSUhO7du2P+/PmQJOkaL6n2al+xScvLL6ampiIsLEzzyznKsoyDBw9ytxfziWwycf4EmNayyc+54y0gC/qCBQswf/58HDt2DMuWLcOcOXO8piksLMSLL76IPXv24Pjx4zh79izS0tJaYGm1U9/lF7Uo6s21kcAYYw2JwZVPhvN775x5owDz008/UXh4ODmdTiIiUhSFOnXqRIWFhR7TrV69mp544gn1+eeff04jR45s9OdUVVURAKqqqtJisf1msVgIQIMPi8Xi03zXrl1b7/zWrl2rcQuYL1x9hto+mmWmzflgPmsN+TOtzm/PtADLnUCrBf4IuPH+JSUliImJUU9FEAQB8fHxKC4uRmJiojpdcXExEhIS1OeJiYkoLi5ucL52ux12ux2AK2Xd50ReuHABANRuG4PB4BFLkgRBENRYFEWIoqjG7duLMJslOBwiiESYzU44HAYQiQgOdsJuN4JIQHCwEzabq03BwVKd2ARBOIPg4GDYbDYIgoCgoCDY7XY1PhMWhk6iCMVohNHhgGIwQDEYYHQ4IBsMIFGE0emEbDAAogiD04lKoxG/vtx+k8kERVEgyzJMJhOWLl2KGYsWISQoCKIsQ5RlSEFBECUJoqJAMpshOhwQieA0m2Fwx8HBMNrtEC7HgqLg4Fdf4cYbb4TZbAYASJIEk8kEIlLj2p+tKAoURYHRaGwwlmUZRKTG9a2bpqynurHT6YTBYFBjo9EIQRDU2N2O2nFztAkwwGiUL3+GASaTDEUBZNkAk0mCogiQZQOCgiTIsghZFhEUJEGSRChK/blnIXitJ6PN5vqMOrHJZgMJAiSzGSabDYogQA4KgsluhyIIUIKCYLTboTQx9+TL35tBkiCbTICiwCDLkEwmCO44KAgkiji0ezduvPFGmEymgF1PgZp7gAmCoCAoSIHdboQoKjAaFTgcRhgMCgwGdyxDFAlOpzsGnM76c88iw2s9+fMb8Z7NBieAr4ODMc5mw3sAzmuQe46QEPzn22/Rt29f9Xfel/VUUVGh1oXWLuAKOuAq4rU19EXXnu5qKyMlJQUrVqzwer32RoKvLm8neMWXfzcbFRPVjslj48Nut+NGAFAUwOFwTSTLrseV4lpjCpxOp1fcFfjv/OrGTWnUyJFgvqs99KPWavKIm7KawgF/ku+/M6od+5F7V23UiBFgvtN6NYXXfgJo8xtxOd4OIKL26/7kXk0NMGoUtGK1WhEeHq7Z/FpCwBX0uLg4lJaWqluoRISSkhLEx8d7TBcfH4+ioiL1+alTp7ymqe3555/HkiVLALiKpMVigdPpRGRkpNcGBGs8i8WCuLg4lJSUtPoRouza4/xhvtIqd4gIVqsVMTGt/6h+wBX06Oho9O/fHx9++CFmz56NLVu2IDEx0WtPevr06Rg2bBheeuklREdHY8OGDXjggQcanK/ZbFa7hAG0+i2xQBMWFsY/yMxnnD/MV1rkjl7qQUCOct+4cSM2btyI5ORkrFq1Sh29PnfuXGzbtg0A0K1bN6xYsQK33347unfvjujo6HpHwzPGGGPXg+v2wjJMG3q6KAO79jh/mK84d7wF5B46az3MZjNefvllj8MZjDUW5w/zFeeON95DZ4wxxnSA99AZY4wxHeCCzhhjjOkAF3TGGGNMB7igM8YYYzrABZ0xxhjTAS7ojDHGmA5wQWeMMcZ0gAs6Y4wxpgNc0BljjDEd4ILOGGOM6QAXdMYYY0wHAu5+6NcCEcFiscBqtSI0NBSCILT0IjHGGGsBRASr1YqYmBiIYuvex70uC7rVakVERERLLwZjjLEAUVJSgq5du7b0YvjluizooaGhKCkpQVxcHEpKSvheun6QJAl79+7FkCFDYDRel+nE/MD5w3ylVe5YLBbExcUhNDRUw6VrGdflX5AgCGoRDwsL44LuB0VR0LdvX0RERLT67ip27XH+MF9pnTt6OPR6XRZ0ph1RFBEbG9vSi8FaKc4f5ivOHW8BuUl85513om/fvujXrx+GDx+OAwcO1DtdWloakpKS0L17d8yfPx+SJF3bBWWQJAlZWVn83TOfcP4wX3HueAvIgv7xxx8jPz8fBw4cwNKlS/Hoo496TVNYWIgXX3wRe/bswfHjx3H27FmkpaW1wNJe30RRRJ8+fbi7lPmE84f5inPHW0B+E7VHoFdVVdW7wjZv3oxp06ahU6dOEAQBjz32GNLT06/hUjLA9UcVHR3Nf1TMJ5w/zFecO94C9pt4+OGHERcXh9/85jfYtGmT1/vFxcVISEhQnycmJqK4uLjB+dntdlgsFo8HAMiyrP5bXyxJkkesKMoVY6fT6RETkUdMRF4xAI9YURSP2N2l1FAsy7JHfC3b5HA4sGPHDlRXV+umTXpcT4HaJrvdji+//BI1NTW6aZMe11Mgtslms2HHjh1wOp1+t0kvAragf/DBBygpKcGrr76KZ555pt5pao9KdCdQQ1JSUhAeHq4+4uLiAAAHDx4EABw+fBiHDx8GAOTn56OgoAAAkJeXh8LCQgBAbm4uSkpKAAA5OTkoKysDAGRnZ6O8vBwAkJWVhcrKSgBAZmYmrFYrACAjIwM2mw2SJCEjIwOSJMFmsyEjIwOA69z4zMxMAEBlZSWysrIAAOXl5cjOzgYAlJWVIScnB4DrnMnc3FwArsMPeXl5AICCggLk5+dfszY5nU4MGDAAX331lW7apMf1FKhtOnPmDAYNGoR9+/bppk16XE+B2KaSkhKEhobCYDD41aa9e/dCLwS6WiUMACEhISgtLUVkZKT62htvvIGioiK8/fbbAFyJs3r1auzataveedjtdtjtdvW5+9zDiooKtG/fXt1yMxgMHrEkSRAEQY1FUYQoig3GTqcTBoNBjY1GIwRBUGPAtUVYOzaZTCAiNVYUBbIsq7GiKDAajQ3GsiyDiNS4vnZwm7hN3CZuE7fJu00VFRWIjIxEVVVV6z+FmQJMVVUVnT59Wn3+j3/8g2JjY0lRFI/pTpw4QV26dKGzZ8+Soih0zz330Pr165v0OQCoqqpKs2W/HjkcDtq+fTs5HI6WXpSAc8stt9B7771HREQffvgh3XbbbS27QAGI84f5Sqvc0VMtCLgu96qqKkydOhU333wzbrnlFrz99tvYvn07BEHA3LlzsW3bNgBAt27dsGLFCtx+++3o3r07oqOjMWfOnBZe+uuP0WjE8OHDdXmVr1GjRmHt2rWazOsXv/iF2m3YHJxOJ1asWIHu3bsjJCQEcXFxWLx4MS5evNhsn+mPvXv3YvTo0YiOjsYvfvELDBw4EO+//77f8921axdf1vk6oeffHl8F3DcRFxenHiOp69133/V4Pm/ePMybN+9aLBZrQO2r7rGW89BDD6GgoAAff/wx+vXrhxMnTuCxxx7DnXfeiW+//RYmk6mlF1FltVoxYcIEpKSkqMdPDxw4gJ9//rmFl8yldtcwC1z82+Mt4PbQWevidDrx6aefqqNT9cq95/fuu+8iLi4OkZGRWLZsmcc069atU9974YUXPN57//330a9fP/X5W2+9haSkJISGhqJ79+5Yt26d+l5RUREEQcBf/vIX9OjRAxEREZg9e3aD3/GuXbuwbds2bN26FQMHDoTBYEBycjK2bt2KY8eO4aOPPlKn/eqrrzBkyBBERESgS5cuSElJUd/7+uuvMXjwYERERKB3795qbxjgGuj0//7f/0N4eDi6dOmCJ554AjU1Ner7iYmJWL16NW699VaEhoZi5MiR6qCjuo4ePYpLly5h/vz5AFzjX/r164dJkyap05w7dw6/+MUvEBMTg5iYGCxatMhjDMy//vUvjBkzBh06dEBUVBR+9atf4fz585g4cSKqqqrQrl07tGvXDrt37wYAfPjhh7jpppsQERGBYcOGqQPEAFdPzLJly3DnnXeibdu2+OKLL+pdbhZYrpffniZp6T7/lqKn4yYtSVEUqq6u9hrjoAcjR46kNWvWEBHRN998Q6Io0tNPP001NTV06NAhatOmDX3zzTdERLRz504KCwujnJwcstvttHz5cjIYDOox9Pfee49uueUWdd6bN2+m4uJiUhSFsrKyKDg4mPbs2UNERIWFhQSA/ud//kcdUxIbG6vOq67nnnuOhg8fXu97M2fOpAcffJCIiPbv308hISG0efNmcjgcVFlZSd999x0REf34448UERFBO3fuJFmWaffu3RQWFkZHjhwhIqLs7Gzav38/SZJEJ06coBtvvJFeffVV9XMSEhKod+/edOLECaqpqaGJEyfSrFmz6l0mi8VCUVFRdN9999HWrVvp5MmTHvmjKAoNGTKElixZQpcuXaLy8nIaNWoU/eY3vyEiotLSUgoLC6O3336bampq6NKlS5Sdna2up/DwcI/Py87Opnbt2tG3335LDoeD1qxZQ1FRUVRZWamu56ioKNq7d6+azyzwafXbo6dawHvozG/XS/ckESElJQXBwcG46aabMHToUPzrX/8CAHz00Uf4xS9+gdtuuw1BQUF45ZVX0LZt2wbnNX36dMTFxUEQBIwePRrjx4/3OkPjlVdeQVhYGGJiYjBx4kT1s+oqLy9HTExMve/FxMSoXdl/+tOf8MADD2D69OkwmUwIDw/HrbfeCgDYuHEjZs+ejTFjxkAURQwbNgx33303Pv74YwDA8OHD0b9/f/z/9u48Por6/h/4a449wpGEhHCFhIgkHIaAIGJbD07FYEEwta1QpXJ4YA1frX6p1SJtFcELFBV/LUWqmOJXxQMjRo01YIRgicZI0AgJhLAcISRZSHZ3Zj6f3x+bnWZzQLJHdjN5Px+PffDO7jLz+WTe2ffMZz8zI0kShg4dijvuuKNFe++55x4MHToUVqsV8+bNa7O9vXv3Rn5+PmJiYnD//ffj4osvxhVXXIF9+/YBAL766iuUlpbiySefRI8ePRAbG4uHHnoIr7/+OgD30fb48eNx9913w2q1okePHrjqqqva/F3/85//xPz583H11VfDZDJh2bJl6NOnDz744AP9Pbfccgsuv/xyCIKAiIiINpdFwkt3+expLyroxC9Nzy81usjISPTo0UP/uWfPnvr5tseOHfO60JHJZMLAgQPbXNaWLVswbtw49OnTB9HR0cjOztbP6fUYMGBAq+tqrm/fvjh27Firrx07dgxxcXEAgMOHDyM5ObnV95WXl2PDhg2Ijo7WH++++66+3L1792LatGno378/IiMj8dBDD/ncXgAYNmwYNmzYgAMHDmDjxo0YOnQoZs2aBc45ysvLUVNTg5iYGL0tGRkZOHHixAX70ZqjR48iKSnJ67mLLroIR48e1X9OTExs9/JIeOhOnz3tRQWd+EWWZaSnp3f7PeVBgwbh8OHD+s+KougX4GjuyJEjuO2227BmzRqcOnUKNTU1SE9Pv+DFkdoyffp07NmzR79ohkddXR0+/PBDTJ8+HQAwZMgQ/Pjjj60uIyEhAZmZmaipqdEfZ8+exUsvvQQA+PWvf43Jkyfj0KFDqKurw+OPP+5ze5uSZRnz58/HH/7wB1RWVqK6uhoJCQno16+fV1tqa2v1Gfvn60drlwEdPHgwysvLvZ4rLy/H4MGDz/v/SHijz56WKIuJ32gP2V3wtmzZgj179sDlcuHPf/4zzp071+p7z549C865fh3q7Oxsfba3L6ZMmYL09HTMmTMH+/btg6Zp+OGHHzBnzhxcfPHFmDdvHgD3WSFZWVnYtm0bVFVFbW0tdu/eDQC44447sGnTJnz22WfQNA1OpxNffvmlfsWturo6REdHo2fPnigpKdELvS8OHDiA1atXo7y8HIwxVFVVYf369UhJSUFsbCwmTJiAxMREPPzww7Db7eCc4/Dhw/pktXnz5qGgoAAbNmyA0+lEfX29Pvmtf//+sNvtXjPm58+fjy1btuCLL76Aqqp4/vnncfr0aa9JeKRros8eb1TQiV9UVUVOTk63/8OaNm0a/vKXv+Cmm27CwIEDwRhDampqq+8dNWoU/vjHP2LKlCmIjY3F1q1bMWvWLL/Wv3XrVsyePRsZGRno2bMnJk+ejNTUVHz88ccwm80AgHHjxuGtt97CY489hpiYGIwcORKff/45AODSSy9FVlYWHn74YcTFxSE+Ph6PPPKIPrP85ZdfxlNPPYVevXrhzjvvxK9+9Suf29q7d28UFhbiqquuQlRUFEaOHImTJ0/i/fffB+C+qtf777+PyspKjBw5ElFRUZg5c6Z+VD548GB88skneP3119G/f38kJSXhzTffBAAMHz4cCxcu1Ge079q1C9dccw2ef/55LFy4ELGxsfjXv/6FDz/8kM5Xb0NbX9+EG/rsaalLXPo1GOrq6hAVFWWMy/0RQkgAzJ07F9u2bcOcOXPw9ttvh7o5ncJItYCO0IlfOOeoq6sLyPeppPuh/AkfnmIOANu2bcPcuXND3KLzo9xpiQo68Yuqqti5cycNexGfUP6Eh6bF3CPcizrlTks05G6AYRZCCPHVsWPHEB8f3+brlZWVbV7nwAiMVAvoCJ34hTGG6upqMMZC3RTSBVH++EcQ/H+cp5YDcL8eiPUEGuVOS1TQiV80TcPevXv1+w4T0hGUP+FgEIA5bbw2p/H18EO50xINuRtgmIUQ0j0F9sh3LoCm36PPARC4me7hWmmMVAsCeoS+fft2v5fhcDhw4403IiUlBWPHjsWMGTNaXOUJAHJzczFx4kSMGjUKqamp+OMf/0izHUOAMYaTJ0/SsBfxCeVPOHkb/z1SD2wxDwbKnZb8LujTp0/Htddei+nTp2Pp0qW49tpr/W7UkiVL8P333+Prr7/GDTfcoN9msak+ffogKysL+/fvx1dffYXPP/8cWVlZfq+bdAxjDMXFxfRHRXxC+RNu3gZQiXAv5gDlTmv8LuhXXHEF7r77bnz88ceYO3euX5ewBACr1Yr09HQIjWNJV1xxBQ4dOtTifZdeeimGDh2q/5+xY8e2+j4SXLIsY8qUKXQ9ZeITyp9wFJ7fmTdHudOS3wX9L3/5C1RVxUMPPQSXyxWINnl57rnn8POf//y87zl+/DjefPPN816b2el0oq6uzusBQJ9QoWlaq7Gqql6xZ2+wrVhRFK/Y8zWAJ+act4gBeMWMMa/Yc55lW7GmaV5xZ/ZJ0zQcPXoUTqfTMH0y4nYK1z6pqorKykq4XC7D9Kmzt5MoMlit7vZKEoPF4o5luWmswWxuGrvbazJpMJncsdmsQZY9sarHFosKWWZ6LEnu2GpVIYqeWNHjiAgFosj1WBA4gMBvJ0VRcOTIETDG/N5ORhGQ79AzMjJw++23Y/jw4YFYnO7xxx9HaWkpHnvssTbfU1dXh5///Od48MEHMW7cuDbft2rVKkRFRemPhIQEAEBxcTEAoKSkRL8RRVFREUpLSwEAhYWF+l2sCgoKUFFRAQDIz8/X76aVl5en30oyNzcXNTU1AICcnBz9FpLZ2dlwOBxet/xzOBzIzs4GANjtdn10o6amBrm5uQDc97rOy8sDANhsNuTn5wMAKioqUFBQAAAoKytDYWEhAKC0tBRFRUWd1qf6+nocPHgQO3bsMEyfjLidwrVPR44cwcGDB/Hll18apk+dvZ3S0qqwZo27TxMn2rBypbtPkyZVYPlyd5/S08uQmenuU0ZGKRYvdvdp/vwSzJ/v7tPixUXIyHD3KTOzEOnp7j4tX16ASZPcfVq5Mh8TJ7r7tGZNHtLS3H1avz4XycnuPm3cmIP4eHefsrKyERPjQEREcLaTZ8jdn+20Z88eGEXYznJ/6qmn8K9//QuffPJJmzdRsNvtuO6663D99dfjkUceOe/ynE6nfqMJwL0jkJCQgOrqavTp00ffc5MkyStWVRWCIOixKIoQRbHNWFEUSJKkx7IsQxAEPQbce4RNY5PJBM65Hnv2OD0xYwyyLLcZa5oGzrket9YP6hP1ifpkvD6ZzSaIIoPZzOBwyJAkBllmcDplyDKDJHliDaLI4XJ5YsDlkvSjc0WRYDZrYAxQVQlmswrGBKiqBItFhaaJUFURFosKVRWhaSKsVhUulwjGRFitClwuCYyJiIhQ4HTKYExARIQCh0MG54DLFZ7bqbq6GrGxsYaY5R7Qgl5SUoLHHnsMhw4d8hrG8OxRtdczzzyDLVu24JNPPkGfPn1afc/Zs2dx3XXX4dprr8WKFSs63FYjnaoQSowxVFRUICEhge4pTTqM8sc/wbhgS7AE+tAxULljpFoQ0NkEN998M2699VbcfvvtkCTJp2UcPXoU999/P4YOHYrJkycDACwWC/bs2YNFixZh1qxZmDVrFtatW4eCggKcO3dOvwbxL37xC/zxj38MWH/IhTHGUFlZifj4ePpAJh1G+UN8RbnTUkCP0MeNG4d9+/YFanFBZaS9MkJI99Sdj9ADxUi1IKC7NTNmzMCOHTsCuUgS5jRNw48//kiXXyQ+ofwhvqLcaSmgBX3q1KnIyMhAVFQU+vXrh7i4OPTr1y+QqyBhhnOOM2fO0FX6iE8of4ivKHdaCuiQ+7Bhw/DEE09g3LhxXt+hDxkyJFCrCBgjDbMQQronGnL3n5FqQUAnxcXGxiIjIyOQiyRhTtM0lJaWIjk52eeJkKT7ovwhvqLcaSmgQ+5z5szBhg0bUF1djfr6ev1BjK2hoSHUTSBdGOUP8RXljreADrk3PXVAEARwziEIQlhOWjDSMAshpHuiIXf/GakWBPQI/dy5c/qVfDRNA2NMv/QhMSZN01BcXByWO20k/FH+EF9R7rQU0IJ+yy23eP1cW1uL66+/PpCrIIQQQkgrAlrQU1JSkJmZCcB9adYZM2bgrrvuCuQqSJiRJAmpqak0KYX4hPKH+Ipyp6WAFvTVq1fjxIkTWL16NWbPno1f/vKXWLRoUSBXQcKMpmkoLCykYS/iE8qf7uOHH34I6PIod1oKSEFvOqP9hRdewFtvvYXLL78cS5YsoVnu3UBERESom0C6MMqf7iANw4cPR1paWkCXSrnjLSCz3EVR9JrV3nSRNMudEEKCo2vMck8D8K3+0+jRo/V7l4cDI9WCgByhN53V7vm36Wx3YlyqqmLv3r1et8slpL0of4zOu5gDwLfffhuQI3XKnZbonnPEL4IgoE+fPhC6xqECCTOUP+GHQwjI43sIaF7MPb799lv8IAjuIQYfH0JEBOVOMwEp6IcPH8Z1112HlJQU3H///XA4HPprP/nJTzq0rHvvvRdJSUkQBAHFxcWtvuff//43evTogbFjx+oPumJQaEiShGHDhtFMU+ITyh/jGujn6xciqSrlTjMBKeh33XUXZs2ahaysLFRVVWHq1Kmw2+0A4FXc2yMjIwO7du264A1dRo0aha+//lp/0OSI0FBVFfn5+TTsRXxC+WNcvQGsbeO1tY2v+0O1WCh3mglIQT9+/DiWLl2K8ePHY/PmzZg5cyamTp2K2traDg+HXH311Rg8eHAgmkU6gSiKiI+P97rsLyHtRfljbJloWdTXNj7vL1HTKHeaCdhpa0099NBDuPnmm72O1APt+++/x7hx4zBhwgS8+OKLF3y/0+lEXV2d1wOAPmlP07RWY1VVvWLG2HljRVG8Ys+Mf0/MOW8RA/CKGWNesWcPtK1Y0zSvuDP7JAgCEhMToWmaYfpkxO0Urn0C3LdX9kyiNUKfOns7iSKD1epuryQxWCzuWJabxhrM5qaxu70mkwaTyR2bzRpkubEfZjM02X0zTtViAWsaNw5xq1YrWGMxVZrGERHgTeJ7BQFr4T7FbC2AewUBSuOIKhdFPWaiCMVq1WPVE0sSVIvFHcuyHnNAL+j+biejCEhBHzlyJHbs2OH13O9//3vccsstOHjwYCBW4WXcuHE4evQo9u3bh23btmHDhg144403zvt/Vq1ahaioKP2RkJAAAPr39CUlJSgpKQEAFBUVobS0FABQWFiIsrIyAEBBQQEqKioAAPn5+fp16vPy8lBVVQUAyM3NRU1NDQAgJydH36HJzs6Gw+GAqqrIzs6GqqpwOBzIzs4GANjtduTk5AAAampqkJubCwCoqqpCXl4eAMBmsyE/Px8AUFFRgYKCAgBAWVkZCgsLAQClpaX6KSGd0aezZ88iLy/PUH0y4nYK1z6Vl5cjLy8PX3zxhWH61NnbKS2tCmvWuPs0caINK1e6+zRpUgWWL3f3KT29DJmZ7j5lZJRi8WJ3n+bPL8H8+e4+LV5chIyMxj5lZqIsPd3dp+XLUTFpkrtPK1fCNnGiu09r1qCqcbZ67vr1qElOdvdp40bY4+PdfcrKgiMmBndHRCArKwt3R0TAEROD7Kwsd5/i45GzcaO7T8nJyF2/3r2d0tKQt2aNeztNnIj8lSvd22nSJBQsXw4AODhrFnJycqCqql/bac+ePTCKgJyH7nQ6AQCWxj2npiorKxHfuHE7IikpCdu3b0dqauoF37tq1SocO3YMzz///Hnb6Gkn4D73MCEhAdXV1ejTp4++5yZJklesqioEQdBjURQhimKbsaIokCRJj2VZhiAIegy49wibxiaTCZxzPfac7ueJGWOQZbnN2HN07Ilb60ew+iSKImw2G+Li4mA2mw3RJyNup3DtEwCcOHECcXFxkGXZEH3qzO1kNpsgigxmM4PDIUOSGGSZwemUIcsMkuSJNYgih8vliQGXS9KPzhVFgtmsgTFAUWWoZjMExiCpKlSLBaKmQfTEqgpR06BarRBdLoiMQbFaIXniiAjITicET+xwAJxDjYiA3NAACAJUqxWmhgZwUYRqscDU0AAmitDMZpgcDjBRBDObITscYJIEJsuQnU4wWQaTJMhOJxSLBbYffsDgwYP1UQ5ftlN1dTViY2MNcR56QG+fCrj3bi+66CK/l3O+gm6z2dC/f3+Iogi73Y4ZM2Zg4cKFuP3229u9fCNdTIAQ0j0F44wtji50GlgAypeRakHAZxM8+eSTfv3/pUuXYvDgwTh69CimTZuGYcOGAQAWLVqE9957DwDw1ltvYfTo0RgzZgyuuOIKTJ8+Hb/97W/9bjvpOFVVkZuba6jvoUjnofwhvlKtVsqdZvw+Qh8yZAiGDx8OwD1p4/vvv8eRI0cC0rhgMtJeWSgxxlBVVYW+ffvSbFPSYZQ//unOR+hMFFFls/mdO0aqBbK/C5g+fTr+/ve/6z/T7VK7F1EU0a9fv1A3g3RRlD/EVyJjlDvN+L1L/NRTT3n9/NJLL/m7SNKFKIqCjz76SD+FhpCOoPwhvlKsVsqdZvw+Qo+OjtbjI0eO6MPtiYmJSExM9HfxJMxJkoQJEybQ5ReJTyh/iK8kl4typxm/CzoAHDhwALfffjvKysqQmJgIzjkqKipw0UUXYePGjRg5cmQgVkPCkCiKiImJCXUzSBdF+UN8JTJGudNMQGahLFiwAPfffz9sNhv27NmDgoIC2Gw23HfffbjtttsCsQoSphRFwQcffEDDXsQnlD/EV0pEBOVOMwE5D3348OH4/vvvO/xaKBlpZmMocc5ht9vRu3dvuo0h6TDKH/9051nuXBRhP3PG79wxUi0IyBF637598eqrr+rXxgXcp6Ns3rwZsbGxgVgFCVOCICAyMpI+jIlPKH+IrwTGKHeaCUhB37x5M1555RX07dsXqampGD16NGJjY/XniXEpioJ3332Xhr2ITyh/iK+UiAjKnWYCeunXU6dO6Re8T0hIQFxcXKAWHXBGGmYJJc45HA4HrFYr7SmTDqP88U+3HnIXBDjOnfM7d4xUCwIyy90jLi4urIs4CQ7PTSQI8QXlD/FJ4w10yH8F/VqLKSkpwV4FCaGmt3okpKMof4iv1IgIyp1mAjLkvn///jZfmzZtGo4dO+bvKgLOSMMsoeS5paPnFpCEdATlj3+69ZA7ANXl8jt3jFQLAjJekZqaiqSkJLS2b1BVVRWIVZAw1vTezYR0FOUP8YkgUO40E5Ah9yFDhmDXrl0oKytr8ejfv38gVkHClKqqyMnJoWEv4hPKH+Ir1Wql3GkmIAV91qxZOHToUKuvzZ49u8PLKy0txU9/+lOkpKTg8ssvb3VIn3OOBx54AJdccgnS0tIwefJk/Pjjjx1eF/GPyWTC7NmzYTKZQt0U0gVR/hBfmRoaKHeaCUhBX7duHa688spWX1u/fn2Hl3fHHXdgyZIl+OGHH/Dggw9i4cKFLd7z3nvvIS8vD19//TWKioowdepUPPTQQx1eF/EP5xx1dXWtft1CyIVQ/hBfcVGk3Gkm6LPcO+rkyZPYt28f5s+fDwC46aabUFZWhvLy8hbvdTqdcDgc+ofC4MGDO7m1RFVV7Ny5k4a9iE8of4ivVIuFcqeZsCvoFRUVGDRokD7RQRAEJCYm6rdl9fj5z3+OyZMnY8CAARg4cCA+/fRT/PnPf25zuU6nE3V1dV4PANA0Tf+3tVhVVa/Yc3nbtmJFUbxiz96jJ+act4gBeMWMMa/Yk7BtxZqmecWd2SdZlpGenq73wQh9MuJ2Ctc+SZKEmTNnQhAEw/Sps7eTKDJYre72ShKDxeKOZblprMFsbhq722syaTCZ3LHZrEGWG/thNkNr/AxWLRawpnHj7UpVqxVMdJcQpWkcEQHeNBYEcE8M9wVhlIgId59EUY+ZKEKxWvVY9cSSBNVicceyrMeiouC6666DyWTyezsZRdgVdAAtTkFobUhl3759OHDgACorK3Hs2DFMnToV99xzT5vLXLVqFaKiovRHQkICAKC4uBgAUFJSgpKSEgBAUVERSktLAQCFhYUoKysDABQUFOhXwsvPz4fNZgMA5OXl6bP5c3NzUVNTAwDIycmB3W4HAGRnZ8PhcHidd+twOJCdnQ0AsNvtyMnJAQDU1NQgNzcXgPssgby8PACAzWZDfn4+APeOT0FBAQCgrKwMhYWFANzzD4qKijqtT/X19Th16pSh+mTE7RSufTp8+DCqq6sN1afO3k5paVVYs8bdp4kTbVi50t2nSZMqsHy5u0/p6WXIzHT3KSOjFIsXu/s0f34J5s9392nx4iJkZDT2KTMTZY076gXLl6Ni0iR3n1auhG3iRHef1qxBVVqau0/r16MmOdndp40bYY+Pd/cpKwuOmBj3OeNZWVAjIuCIiUF2Vpa7T/HxyNm40d2n5GTkNn5FW5WWhrw1a9zbaeJE5K9c6d5OkyahYPlyAMChmTOxZ88eMMb82k579uyBYfAwc+LECR4ZGckVReGcc84Y4/379+dlZWVe71u6dClfvXq1/nNxcTFPTExsc7kOh4PX1tbqj4qKCg6AV1dXc845V1WVq6raIlYUxSvWNO28scvl8ooZY14xY6xF7OmnJ9Y0zSv2/C7ailVV9Ypb60ew+uR0OvmHH37Iz507Z5g+GXE7hWufHA4H37FjB6+vrzdMnzpzOwGci6LGrVaFA5xLksYtFncsy01jlZvNTWOVA5ybTCo3mdyx2axyWVY5B7hiNnNVlt2xxcK1prEkuWOrlWuiyDnAXU3jiAjOmsaCwJknBjgTBO6KiOAc4EwU9VgTRe6yWvVY8cSSxBWLxR3Lsh439OrFP/zwQ+5yufzaTqdPn+YAeG1tLe/qAnot90CZNGkSFixYgAULFuDNN9/EU089hd27d3u955lnnsFHH32E7du3w2Qy4YknnsDOnTvxwQcftGsd4X4xAc8tJQkhpC3d+cIyAIAAlK9wrwUdEZZD7i+//DJefvllpKSk4IknnsDGxiGZRYsW4b333gMALF26FImJiRg9ejTS0tLw2Wef4YUXXghlswNm3bp1iIqKwrp160LdlAtijOHkyZNet84lpL0of4ivmChS7jQTlkfonSFc98rWrVuHZcuW6T+vXbsWmZmZAVl2MI76VVVFXl4err76arpiE+kwyh//dOcjdNVqRd4HH/idO+FaC3wRlkfo3VXzYg4Ay5YtC8iRerCO+mVZxpQpU+jDmPiE8of4SnY4KHeaoSP0MNkrs9vtiIqKanVGvyAIqK2t9fnoOphH/Ywx2Gw2DBw4EKJI+4ekYyh//NOdj9CZJMF2+LDfuRNutcAf9BcUJnr37o1nn3221deeffbZgBVzIHBH/YD7A/ngwYP0PRbxCeUP8RWTZcqdZugIPcz2yubOnYtt27bpP8+ZMwdvv/22T8sK5lE/IST0uvMROgCa5d4MHaGHkXXr1nkVcwDYtm2bz0fTwTrqb4oxhsOHD9NeMvEJ5Q/xFZNlyp1mqKD7SRAC9bBj2bL/aXUdy5b9DwTB7tNyly3LBDDHa3lz5swJ6HfolZWV9EdFfEL5Q3zFJIlypxkq6GGjN4DWj6bdz/t6NL0OQOCO+puTZRk//elPaaYp8QnlD/GV7HRS7jRDBT2stHI0DYBjGTiEDj/qIEDAslbX9D/LlsEegKEFzWTCjz/+qN/0gJCO0DSN8of4RJNlyp1mqKCHlVaOphuf9UWwjvmb4qKIM2fO0D2JiU8455Q/xCf02dMSFfSwYQfQ+nfo/9P4ajiSXS5MmDCBhr3CiOfuXV2BLMuUP8Qn9NnTEhX0sNH28bSvR9Nt7yIEbidBk2UcOHCAhr3CxLp16xAZGdkl7gMAuIfcKX+IL+izpyUq6GElE8Bar2fWNj7ri84YcocooqGhIRBLIn5qehGhQF48yCNYR/6UP8Qn9NnTAhX0sOMu6gL8K+beS/MWiOV6SC4XLr30UkiSFKAlEl8E+4qAwTrylySJ8of4hD57WgrLgl5aWoqf/vSnSElJweWXX479+/e3+r6NGzciOTkZF198MZYsWQJVVTu5pcGSiVoEruh6inqgdhKa0kwmFBcX07BXCNnt9hbF3GPZsmV+H1kH88hf0zTKH+IT+uxpKSwL+h133IElS5bghx9+wIMPPoiFCxe2eE9ZWRkeeeQR7Nq1Cz/++COOHz+u3zfdCAJ9QdZMIKA7CaR7CPaRPyEkcMLuWu4nT55ESkoKqqqqIMsyOOcYOHAgdu/ejaSkJP19Tz75JMrLy/HCCy8AALKzs7FmzRr8+9//btd6AnX93mBcSxnoftdT7q4Ckz92AOfL4Tr4Pq2y7eXW1dXRvQBCjK7lTtdybyrs5vtXVFRg0KBB+qkIgiAgMTERR44c8SroR44cwZAhQ/Sfk5KScOTIkTaX63Q64XQ6AbjPfT127BgA4MyZMwCgD9tIkuQVq6oKQRD0WBRFiKKox4AIi0WFyyWCcxEWiwKXSwLnIqxWBU6nDM4FWK0KHA53n6xWtVlsgiBwWCyemOG02QKT0wkmCGBmM2SnE0wUwWQZsssFJklgkgTZ5YImSeCiCFlRoEkSIIqQFAVa4+9QUlVoJhPAGCRNg2oyQfDEZjNETYPoiVUVImNQLRaILhdEzqFYLJA8sdUK2emE0BgLjKE4Lw8jRoyAxWIBAKiqCpPJBM65HjPGoGmaHjPGIMtym7GmaeCc63Fr26Yj26l5rCgKJEnSY1mWIQiCHnv60TQORp8ACbKsNa5DgsmkgTFA0ySYTCoYE6BpEsxmFZomQtNEmM0qVFUEY57cOwfOAYvFApfLBc45rFYrnE4nOOcot/ZDtMPhXofVCrlJbHI4wAUBqsUCk8MBJgjQzGaYnE7UCgKSzRY4nU6IoghZluFyuSBJEiRJQl1kJFQ/c4+LIvbn5GDEiBEwmUxhu53CNfcA9+eF2czgdMoQRQZZZnC5ZEgSgyR5Yg2iyKEonhhQlNZzr05DQD8jZB9yrz2fe66ICHz3+edIS0vTP+d92U7V1dV6Xejqwq6gA+4i3lRbv+im77vQxli1ahVWrlzZ4vmmOwm+atxPaBE35m67Ys69476e5XD+34UyBrhc7ljT3I/zxU3nFChK67Fnec3jjnTqmmtAfBe4zfTfHxxNtlNSR5PPs5wmMWMMrsYVa5oGTdMw2P2D/7l39dUgvgv0R0RU0x+AwHxGtBW3I/fa7FRDAzBpEgLFc3fKrizsCnpCQgKOHj2q76FyzlFRUYHExESv9yUmJqK8vFz/+fDhwy3e09Qf/vAH3HfffQDcxb+urg6KoiA2NrbFDgRpv7q6OiQkJKCioqLLD1eRzkf5Q3wVqNzhnMNut2PQoEEBbF1ohF1B79evHy699FK89tprWLBgAd566y0kJSW1OJK+6aabcOWVV+JPf/oT+vXrhw0bNuBXv/pVm8u1WCz6kDCALr8nFm4iIyPpA5n4jPKH+CoQuWOUehCWs9xffvllvPzyy0hJScETTzyhz15ftGgR3nvvPQDA0KFDsXLlSvzsZz/DxRdfjH79+rU6G54QQgjpDsJuljvpWow0Q5R0Psof4ivKnZbC8giddB0WiwUrVqzw+jqDkPai/CG+otxpiY7QCSGEEAOgI3RCCCHEAKigE0IIIQZABZ0QQggxACrohBBCiAFQQSeEEEIMgAo6IYQQYgBU0AkhhBADoIJOCCGEGAAVdEIIIcQAqKATQgghBkAFnRBCCDEAKuiEEEKIAcihbkAocM5RV1cHu92O3r17QxCEUDeJEEJICHDOYbfbMWjQIIhi1z7G7ZYF3W63Izo6OtTNIIQQEiYqKiowePDgUDfDL92yoPfu3RsVFRVISEhARUUFIiMjQ92kLktVVezZswcTJ06ELHfLdCJ+oPwhvgpU7tTV1SEhIQG9e/cOYOtCo1v+BQmCoBfxyMhIKuh+YIwhLS0N0dHRXX64inQ+yh/iq0DnjhG+eu2WBZ0EjiiKiI+PD3UzSBdF+UN8RbnTEu0SE7+oqorc3FyoqhrqppAuiPKH+IpypyUq6MQvoigiNTWVhkuJTyh/iK8od1qiIXfiF1EU0a9fv1A3g3RRlD/EV5Q7LYX9rs29996LpKQkCIKA4uJi/flJkyZh6NChGDt2LMaOHYtnn302hK3svhRFwUcffQRFUULdFNIFUf4QX1HutBT2R+gZGRl48MEHceWVV7Z47bnnnsMNN9wQglYRD0mSMGHCBEiSFOqmkC6I8of4inKnpbAv6FdffXWom0DOQxRFxMTEhLoZpIui/CG+otxpKeyH3M/ngQcewOjRo/HLX/4Shw4dOu97nU4n6urqvB4AoGma/m9rsaqqXjFj7LyxoiheMefcK+act4gBeMWMMa/YM4uzrVjTNK+4M/vkcrmwfft21NfXG6ZPodpOjz76KGbPnh3UPj3yyCO48cYbg9anv/71r/j1r3/d7u3kdDrxwQcfoKGhoctspwv1qSvmXlfsk8PhwPbt26Eoit99MoouW9BfffVVlJSUoKioCFddddUFh95XrVqFqKgo/ZGQkAAA+vfyJSUlKCkpAQAUFRWhtLQUAFBYWIiysjIAQEFBASoqKgAA+fn5sNlsAIC8vDxUVVUBAHJzc1FTUwMAyMnJgd1uBwBkZ2fD4XBAVVVkZ2dDVVU4HA5kZ2cDcF+ONicnBwBQU1OD3NxcAEBVVRXy8vIAADabDfn5+QDclyksKCgAAJSVlaGwsBAAUFpaiqKiok7rk6qq+OlPf4qPP/7YMH3ybKe//e1vuOGGGxAXF4eoqCiMGDECf/7znwPSpzVr1mDUqFEt+lRXV9fhPr344osQBAH33XefV582bNiAsWPHevWptLQUjLGAbKdXXnkFqampXttp5syZyMrKavd2stlsuOqqq7B371588MEHmDx5Mvr06YM+ffogLS0Ny5cv9/vvafv27ejTp0+Xyj0jfUYEq09Hjx5FTEwMZFn2q0979uyBYfAuYsiQIfzbb79t83WLxcKrqqrafN3hcPDa2lr9UVFRwQHw6upqzjnnqqpyVVVbxIqieMWapp03drlcXjFjzCtmjLWIOedesaZpXrGiKOeNVVX1ilvrB/Wp4326+OKL+UMPPcTPnj3LGxoaeHFxMd+6dWtA+rRx40Y+ZswYr36sWLGCz5o1q8N9uvrqq3lMTAwfN26cV5/+8Y9/8DFjxnj16eGHH+azZ88OyHbatGkTHzNmTEC2U3V1NY+OjuYvvfQSr6+v5w6HgxcUFPB3333X79z79NNPeVRUlF+552k3/T0Zr0+nT5/mAHhtbS3v6rpkQVcUhR8/flx/7c033+SJiYkdWl5tba1hNmIouVwu/s477+h/iEZx6tQpDoAfOXKkzfccP36c/+IXv+B9+/blCQkJ/KGHHtI/YDzFrqkxY8bwTZs28X379nGLxcJFUeQ9e/bkPXv25IcPH+YrVqzgN9xwA1+6dCmPioriCQkJ/F//+td521laWsoB8HfeeYcLgsC//vprzjk/7zpmz56t//8HHniAJyYm8l69evGRI0fyN954Q3/ts88+41FRUfxvf/sbHzx4MI+JieEPPPBAh5Zvs9n4vHnz+MCBA3lUVBS/6qqreH19vf66J3++/PJLbjKZ9A/Z1pw4cYLfcsstfODAgXzgwIE8MzOTOxwO/fWvvvqKT548mffp04f37duX33PPPbyqqopbrVYOQG9nXl4e55zzV199lY8YMYJHRUXxn/3sZ3zfvn36sq655hr+wAMP8OnTp/MePXrw995777zbgXS+QH32GKkWhH1Bv/vuu3l8fDyXJIn379+fX3zxxfzs2bN8/PjxPDU1laelpfEpU6boH2TtZaSNGEqMMV5fX6/vkRsFY4yPGDGCT506lW/dupWXl5e3eM+UKVP4Lbfcwu12Oy8vL+ejRo3ijz32GOf8/AW9rddXrFjBTSYTf/3117mqqnzz5s28V69evK6urs12Ll++nF966aWcc86vvvpq/rvf/U5/ra11NC24r732Gj9x4gRXVZVnZWVxi8XCDx06xDl3F3RRFPm9997LGxoa+P79+3mPHj34Z5991q7la5rGJ0yYwG+77TZeXV3NFUXhO3fu9CrCnvypra3lcXFx/Be/+AV/5513uM1m81ouY4xPnDiR33ffffzcuXO8qqqKT5o0iT/88MOcc86PHj3KIyMj+QsvvMAbGhr4uXPn9MLt2TFpKi8vj/fq1Yt//vnn3OVy8WeffZbHxcXxmpoazrm7oMfFxfE9e/bobSThJVCfPUaqBWFf0IPFSBsxlJoOpRmNzWbj9913Hx81ahQXRZGPHDmS5+TkcM7dBQSAV+HZsmULT05O5pz7XtAnTpyo/8wY42azmX/11Vettk9VVT5w4EC+du1azjnnf//733lMTIxeMNtT0JsbM2YMf+211zjn7kIoCAI/d+6c/vq0adP4U0891a7l7969m/fs2fO8xbBp/pSWlvI77riDDx06lAuCwC+//HL+n//8h3POeUFBAY+JifE6gs/JyeFDhw7lnHP+xBNP8MmTJ7e6jtYK+qJFi/idd97p9VxKSgrfsmUL59xd0DMzM9tsNwm9QH32GKkWdNlJcSQ8NJ3sYjQDBgzA008/je+++w6nTp3C9ddfjzlz5qC6uhpHjx6F1WrFgAED9PcPHToUR48e9XudHoIgICIiQp9g1Fx2djaqqqpwyy23AAB+8YtfoKGhAdu2bWv3+p599llccskliIqKQnR0NIqLi/WJToD7boQ9evTQf+7Zs2eb7Wnu8OHDiI+PR0RERJvvaZo/w4YNw4YNG3Dw4EEcPXoUw4YNw6xZs8A5R3l5OWpqahATE4Po6GhER0cjIyMDJ06c0NeVnJzc7n4fPXoUSUlJXs9ddNFFXtsvMTGx3csjnc/Inz2+ooJO/CLLMtLT0w1/L+uYmBg8+uijOHfuHMrKyjB48GA4HA69oADQnweAXr16ob6+3msZx48f1+NAXH9648aNYIxh9OjRGDBgAFJSUqAoCjZu3NiudezatQuPPvoo/vnPf+LMmTOoqalBamqqfsrRhVxo+UOGDEFlZSUaGhrafE9b+TNo0CAsX74clZWVqK6uRkJCAvr164eamhr9UVtbi7Nnz+rr+vHHH9vdzsGDB6O8vNzrufLycn37tad/JLS6y2dPR1DGEr8ZcQ/5zJkzePjhh3HgwAFomob6+no888wziImJwYgRIxAfH4/Jkyfj97//Pc6dO4cjR47g8ccfx2233QYAGDt2LA4dOoSdO3dCVVWsWbMGp0+f1pffv39/2Gy28xa78zlx4gQ++OAD/POf/8TXX3+tP95//318+umnKC8vv+A66urqIMsy4uLiwBjDP/7xD6/LK1/IhZY/YcIEDB8+HEuXLkVNTQ1UVcWuXbvgdDq93qeqKg4cOIDVq1ejvLwcjDHU1NRg/fr1SElJQWxsLCZMmIDExEQ8/PDDsNvt4Jzj8OHD+PDDDwEA8+bNQ0FBATZs2ACn04n6+nrs3LlTb6fdbsepU6f0dc6fPx9btmzBF198AVVV8fzzz+P06dNIT09vd/9J6Bnxs8cfVNCJX1RVRU5OjuH+sMxmMyorK5Geno6oqCgkJibiiy++wI4dO9CzZ08AwOuvv46GhgYMGTIEP/vZzzBz5kw8+OCDAIBhw4ZhzZo1yMjIwMCBA+F0OnHJJZfoy58yZQquuOIKxMfHIzo6GkeOHOlQ+zZv3ozExET86le/woABA/THjBkzMH78ePzjH/+44DpmzJiBm266CaNHj8agQYPw3Xff4Wc/+1m723Ch5YuiiPfffx/19fUYPnw4+vbti4cffli/oAfw3/yJiIhAYWEhrrrqKkRGRmL48OE4deoU3n//fQDuy3y+//77qKysxMiRIxEVFYWZM2fqR+WDBw/GJ598gtdffx39+/dHUlIS3nzzTQDA8OHDsXDhQowcORLR0dHYtWsXrrnmGjz//PNYuHAhYmNj8a9//QsffvghoqOjO7QdjOjYsWOhbkK7GPWzxx8Cb+/4msHU1dUhKioKtbW1iIyMDHVzCCEk5ObOnYtt27Zhzpw5ePvtt0PdnE5hpFpAR+jEL5xz1NXVtft7V0KaovwJH55iDgDbtm3D3LlzQ9yi86PcaYkKOvGLqqr698SEdBTlT3hoWsw9wr2oU+60REPuBhhmIYQQXx07dgzx8fFtvl5ZWYlBgwZ1Yos6l5FqAR2hE78wxlBdXe010YmQ9qL88Y8g+P+Ij+993nXEx/cOyHoCjXKnJSroxC+apmHv3r36bQkJ6QjKH+Iryp2WaMjdAMMshJDuKXBHvusALGvl+bUAMgOyhnCtNEaqBUE9Qt++fXswF0/CAGMMJ0+epGEv4hPKn3CRCXfxbmotAlXMg4Fyp6WAF/Tp06fj2muvxfTp07F06VJce+21gV4FCSOMMRQXF9MfFfEJ5U848RR1AeFezAHKndYEfMj9kUcewfjx43HjjTfif/7nf/Dss88GcvEBY6RhFkJI9xSMyWaAHcD5J8r5gobcgy/gR+h/+ctfoKoqHnroIbhcrkAvnoQZxhgqKytpL5n4hPInHAW+mAcD5U5LQfkOPSMjA7fffjuGDx8ejMWTMMIYw8GDB+mPiviE8of4inKnJZrlboBhFkJI9xScIffgqKw8FpYXqDFSLQjqLPeSkhLMnz8fP/3pT3H55ZfrD2IcjDEcPnyY9pKJTyh/uou5iI+PD+ilZCl3WgrqneFvvvlm3Hrrrbj99tshSVIwV0VCxPM9Vnx8PESRrlNEOobypzuYC8D7pi+BuJMb5U5LQR1yHzduHPbt2xesxfvFSMMshJDuKfyH3P9bzJsKp9uzGqkWBHW3ZsaMGdixY4dfy7j33nuRlJQEQRBQXFysP3/y5EnMmDEDycnJSE1Nxa5du/xtLvGBpmn48ccf6fKLxCeUP+GHQwjIoxICWivmgPtI/ZifF4fXTCbKnWaCWtCnTp2KjIwMREVFoV+/foiLi0O/fv06tIyMjAzs2rULQ4YM8Xp++fLluOKKK1BaWopNmzZh3rx5dBu9EOCc48yZM3RPYuITyh/iKy6KlDvNBPU79DvuuAOvvPIKxo0b5/N36FdffXWrz7/xxhsoKysDAEyYMAH9+/fHrl27MGnSJF+bS3wgyzImTJgQ6maQLoryx7gGAZiD1o/R5zS+7g/Z5aLcaSaoR+ixsbHIyMjA0KFDMWTIEP3hr9OnT4Mxhri4OP25pKQkHDlypM3/43Q6UVdX5/UAoA/XaJrWaqyqqlfsmVHZVqwoilfs2Xv0xJzzFjEAr5gx5hV7Rh7aijVN84o7s0+qqqKkpAQOh8MwfTLidgrXPimKggMHDsDpdBqmT529nUSRwWp1t1eSGCwWdyzLTWMNZnPT2N1ek0mDyeSOzWYNstzYD7MZmuw+3lMtFrCmcePBmWq1gjVORlOaxhER4I3x1ogIzGn8oj8iIgIAMEcQsLUx5qIIpTFmogjFatVj1RNLElSLxR3Lsh67rFbs379f3xb+bCejCGpBnzNnDjZs2IDq6mrU19frj0AQms0GudCwy6pVqxAVFaU/EhISAED/Xr6kpAQlJSUAgKKiIpSWlgIACgsL9ZGAgoICVFRUAADy8/Nhs9kAAHl5eaiqqgIA5ObmoqamBgCQk5MDu90OAMjOzobD4YCqqsjOzoaqqnA4HMjOzgYA2O125OTkAABqamqQm5sLAKiqqkJeXh4AwGazIT8/HwBQUVGBgoICAEBZWRkKCwsBAKWlpSgqKurUPtXX1+Ojjz4yVJ+MuJ3CsU9Hjx5FQ0MDdu/ebZg+dfZ2Skurwpo17j5NnGjDypXuPk2aVIHly919Sk8vQ2amu08ZGaVYvNjdp/nzSzB/vrtPixcXISOjsU+ZmShLT3f3aflyVDSOfOavXAnbxInuPq1Zg6q0NHef1q9HTXKyu08bN8IeH+/uU1YWtsTE4OaICGRlZeHmiAhsiYlBdlaWu0/x8cjZuNHdp+Rk5K5f795OaWnIW7PGvZ0mTkT+ypXu7TRpEgqWLwcAHL7+ehw9etTv7bRnzx4YRVBnuTc9lUAQBHDOIQiCT5MYkpKSsH37dqSmpgIAevbsifLycv0o/fLLL8eaNWvaHHJ3Op1wOp36z3V1dUhISEB1dTX69Omjt0mSJK9YVVUIgqDHoihCFMU2Y0VRIEmSHsuyDEEQ9Bhw7xE2jU0mEzjneswYg6ZpeswYgyzLbcaapoFzrset9YP6RH2iPhmvT2azCaLIYDYzOBwyJIlBlhmcThmyzCBJnliDKHK4XJ4YcLkk/ehcUSSYzRoYAxRVhmo2Q2AMkqpCtVggahpET6yqEDUNqtUK0eWCyBgUqxWSJ46IgOx0QvDEDgfAOY5GRGBwQwMgCFCtVpgaGsBFEarFAlNDA5goQjObYXI4wEQRzGyG7HCASRKYLEN2OsFkGUySIDud0GQZvKHB7+1UXV2N2NhYQ8xyD2pBb2ho0IdZPE6ePNnhiXFAy4K+YMECJCUl4dFHH8XevXtx00034dChQ/ofwYUY6VSFUNI0DSUlJRg5ciRda4B0GOWPf4Jx2hpH2J8LBwDQTCaU7Nvnd+4YqRYEdcj9lltu8fq5trYW119/fYeWsXTpUgwePBhHjx7FtGnTMGzYMADA6tWrkZ+fj+TkZCxYsACvvvpqu4s5IYQQYjRBPUL/3//9XzgcDqxbtw5nz57F9OnTsXDhQixatChYq2w3I+2VEUK6p+58hA4gIPdkNVItCOoR+urVq3HixAmsXr0as2fPxi9/+cuwKOYkcDRNQ2FhIV3cgfiE8of4SjObKXeaCcoYddOZ7C+88AKuv/56TJ06FUuWLEF9fT169OgRjNWSEGk+T4KQjqD8IT5hjHKnmaAMuYui6DWrvekqfJ3lHmhGGmYhhHRPNOROQ+5NBWXI3XNKRdN/PY9wKOYkcFRVxd69ew11cQbSeSh/iK9Us5lypxm65xzxiyAI6NOnT4sL/RDSHpQ/xFcCY5Q7zQSloB8+fBjXXXcdUlJScP/998PhcOiv/eQnPwnGKkmISJKEYcOG0TnExCeUP8RXkqpS7jQTlIJ+1113YdasWcjKykJVVRWmTp2qX96waXEnXZ+qqsjPz6dhL+ITyh/iK9ViodxpJigF/fjx41i6dCnGjx+PzZs3Y+bMmZg6dSpqa2tpeMRgRFFEfHy812V+CWkvyh/iK1HTKHeaCfppawDw0EMPwWw2ex2pE2MQRTEgd9Aj3RPlD/GVqKqUO80EZddm5MiR2LFjh9dzv//973HLLbfg4MGDwVglCRFVVZGXl0fDXsQnlD/EV6rFQrnTTFDOQ/fc1czSeN/apiorKxHfeGu9UDLSuYehxBiDzWbDwIEDaeiLdBjlj3+683noTJJgO3zY79wxUi0Iyl+QxWLRi7nn/rMe4VDMSeDQd6DEH5Q/xFf0HXpLQf9NPPnkk8FeBQkhVVWRm5tLw17EJ5Q/xFeq1Uq500zAJ8UNGTIEw4cPBwBwzvH999/jxRdfDPRqSJgQRRGpqam0l0x8QvlDfCW6XJQ7zQS8oE+fPh1///vf9Z/vuuuuQK+ChBFRFNGvX79QN4N0UZQ/xFciY5Q7zQR81+app57y+vmll14K9CpIGFEUBR999BEURQl1U0gXRPlDfKVYrZQ7zQT8CD06OlqPjxw5giNHjgAAEhMTkZiYGOjVkRCTJAkTJkygyy8Sn1D+EF9JLhflTjNBubDMgQMHcPvtt6OsrAyJiYngnKOiogIXXXQRNm7ciJEjRwZjtSQERFFETExMqJtBuijKH+IrkTHKnWaCMptgwYIFuP/++2Gz2bBnzx4UFBTAZrPhvvvuw2233RaMVZIQURQFH3zwAQ17EZ9Q/hBfKRERlDvNBOXCMsOHD8f333/f4dc6k5EuJhBKnHPY7Xb07t2brtNPOozyxz/d+cIyXBRhP3PG79wxUi0IyhF637598eqrr4Ixpj/HGMPmzZsRGxsbjFWSEBEEAZGRkfRhTHxC+UN8JTBGudNMUAr65s2b8corr6Bv375ITU3F6NGjERsbqz8fKElJSRgxYgTGjh2LsWPHYuvWrQFbNmkfRVHw7rvv0rAX8QnlD/GVEhFBudNMUIbcPU6dOoWKigoAQEJCAuLi4gK6/KSkJGzfvh2pqakd/r9GGmYJJc45HA4HrFYr7SmTDqP88U+3HnIXBDjOnfM7d4xUC4Iyy90jLi4u4EWchB9ZDmoaEYOj/CE+4Zxyp5lOv2ZeSkpKQJc3b948jB49GosWLcKpU6fafJ/T6URdXZ3XAwA0TdP/bS1WVdUr9swLaCtWFMUr9gyAeGLOeYsYgFfMGPOKPdcqbivWNM0r7sw+KYqC7OxsNDQ0GKZPRtxO4donl8uF7OxsOBwOw/Sps7eTKDJYre72ShKDxeKOZblprMFsbhq722syaTCZ3LHZrEGWG/thNkNrLJaqxQLWNG4871u1WsEaL7uqNI0jIsCbxoIA7onhPrJWIiLcfRJFPWaiCMVq1WPVE0sS1MabfTFZ1mNn797Izs7Wf//+bCejCEpB379/f5uPs2fPBmw9eXl5+Oabb7Bv3z7Exsae95S4VatWISoqSn8kJCQAAIqLiwEAJSUlKCkpAQAUFRWhtLQUAFBYWKjfMa6goED/CiE/Px82m01vR1VVFQAgNzcXNTU1AICcnBzY7XYA0D+0VFXVk9DhcCA7OxsAYLfbkZOTAwCoqalBbm4uAKCqqgp5eXkAAJvNhvz8fABARUUFCgoKALjvaFdYWAgAKC0tRVFRUaf1SVVVTJ8+HR9//LFh+mTE7RSufbLZbEhPT8fevXsN06fO3k5paVVYs8bdp4kTbVi50t2nSZMqsHy5u0/p6WXIzHT3KSOjFIsXu/s0f34J5s9392nx4iJkZDT2KTMTZenp7j4tX46KSZPcfVq5EraJE919WrMGVWlp7j6tX4+a5GR3nzZuhL3xrprZWVlwxMRAjYhAdlYW1IgIOGJikJ2V5e5TfDxyNm509yk5Gbnr17u3U1oa8tascW+niRORv3KleztNmoSC5csBAEcnT8aAAQMgy7Jf22nPnj0wiqB8hy6KIpKSktDaoisrK+FyuQK9SthsNqSkpOh/HM05nU79Pu2A+3uThIQEVFdXo0+fPvqemyRJXrGqqhAEQY9FUYQoim3GiqJAkiQ9lmUZgiDoMeDeI2wam0wmcM71mDEGTdP0mDEGWZbbjDVNA28cfmqrH8HqkyRJcDgckGUZJpPJEH0y4nYK1z4JggCXywVZlvW2d/U+deZ2MptNEEUGs5nB4ZAhSQyyzOB0ypBlBknyxBpEkcPl8sSAyyXpR+eKIsFs1sAYoKgyVLMZAmOQVBWqxQJR0yB6YlWFqGlQrVaILhdExqBYrZA8cUQEZKcTgid2OADOoUZEQG5oAAQBqtUKU0MDuChCtVhgamgAE0VoZjNMDgeYKIKZzZAdDjBJApNlyE4nmCyDSRJkpxOqyQRHdTV69uypH237sp2qq6sRGxtriO/Qg1LQL7roInzxxRcYNGhQi9cSEhL0PSN/nDt3Doqi6JeafeaZZ/DOO+/oe98XYqSJEKHkGXJPT0/XCzoh7UX545/uPClOaTzq9zd3jFQLgjKjYNasWTh06FCrBX327NkBWceJEydw00036XvTQ4cOxT//+c+ALJu0n8lkCtg2Jd0P5Q/xlamhgXKnmaCethbOjLRXFkp0pS/iD8of/3TnI3S6UlxLdGd44hdVVbFz505DzRQlnYfyh/hKtVgod5qhI3QD7JURQrqn7nyEDgAIQPkyUi2gI3TiF8YYqqurva7bT0h7Uf4QX7HGGeqUO/9FBZ34RdM07N27Vz89hJCOoPwhvtLMZsqdZmjI3QDDLISQ7omG3GnIvSk6Qid+YYzh5MmTNOxFfEL5Q3zFRJFypxkq6MQvjDEUFxfTHxXxCeUP8RUzmyl3mqEhdwMMsxBCuicacqch96boCJ34hTGGyspK2ksmPqH8Ib5ikkS50wwVdOIXxhgOHjxIf1TEJ5Q/xFdMlil3mqEhdwMMsxBCuicacqch96boCJ34hTGGw4cP014y8QnlD/EVk2XKnWaooBO/0HegxB+UP8RX9B16SzTkboBhFkJI90RD7jTk3hQdoRO/aJqGH3/8kS6/SHxC+UN8pcky5U4zVNCJXzjnOHPmDLrpQA/xE+UP8RUXRcqdZmjI3QDDLIQ0Zbfb0bt371A3g3QCGnKnIfem6Ag9TB07dizUTWgXTdNw4MABGvYKE+vWrUNUVBTWrVsX8GXb7faAL5Pyh/hKk2XKnWaooIehuXPnIj4+HnPnzg3ocoPxgQwADQ0NQVku6Zh169Zh2bJl4Jxj2bJlAS3qwdxRoPwhPhFFyp3meDdVW1vLAfDa2tpQN8XLnDlzOAD9MWfOnIAsd+3atVwQBL527dqALI+El7Vr13rljecRiO3dfNmUQ+HDPeYc2EdQFhqsRwCEay3wRZcu6D/88AP/yU9+wpOTk/mECRP4d9991+7/G44bsXkxD1RRD+YHsqqq/Ntvv+WqqgZsmaRj6urqWs0bz6Ours7nZQdzR4Fzyh9/deeCrppMAcmdcKwFvpKDPAAQVHfccQeWLFmCBQsW4M0338TChQvx5ZdfdmobAjcp5RiAba2+sm3bNgjCMQCDfFjuOgDLvJ5Ztsz9c2Zmpg/LI4EUjElNzfk+z8eO5rnjsWzZMtx+++00+Y6QMNJlZ7mfPHkSKSkpqKqqgizL4Jxj4MCB2L17N5KSki74/wM1szFwH8h2AOdrRx2Ajn54nn+ZdXV19IEcYoHJn2MA4tt8tRK+7Qr+AGD4eV7/HkCKD8ttoWt+BIUFmuXuf+4YaZZ7lz1Cr6iowKBBgyDL7i4IgoDExEQcOXKk1YLudDrhdDoBuAeVPLPIz5w5AwD6TElJkrxiVVUhCIIei6IIURT1GBBhsahwuURwLsJiUeBySeBchNWqwOmUwbkAq1WBw+Fuq9WqNotNEAQOi2U1HI7/hSAIMJvNcDqdEAQBT5jNuNMZCSaKYLIM2eUCkyQwSYLsckGTJHBRhKwo0CQJEEVIioJKWUYaZKiqCpPJBMYYNE3T42ORkYgzmyFqGkRNg2o2Q1RViIxBtVggulwQOYdisUDyxFYrZKcTQmMsMIbijz/GiBEjYLFYAEBfH+e8zXUzxiDLcpuxpmngnOtxa9umI9upeawoCiRJ0mNZliEIgh57+tE0DkafAAmyrDWuQ4LJpIExQNMkmEwqGBOgaRLMZhWaJkLTRJjNKlRVBGOe3OsBzm+AxfIxXC4XOOewWq1wOp2YyTksVitqHQ73OqxWyE1ik8MBLghQLRaYHA4wQYBmNsPkdKKfIGCs2YyvnU6IoghZluFyuSBJElIlCQNcLpxpI/e0xt+bpKrQTCaAMUiaBtVkguCJzWZwUcT+nTsxYsQImEymsN1O4Zp7gAmCwGA2MzidMkSRQZYZXC4ZksQgSZ5YgyhyKIonBhSl9dyr09BiO/nzGSH7kHtMEMDMZshOZ5ufe66ICHz3+edIS0vTP+d92U7V1dV6XejyQjLQHwBfffUVHzVqlNdzl112Gf/8889bff+KFSvO+z0jPehBD3rQo/s+KioqOqN0BVWXHnJPTk7G6dOn2zXk3vwIva6uDoqiIDY2FkJnfJFpUHV1dUhISEBFRUWXH64inY/yh/gqULnDOYfdbsegQYMaR127ri475N6vXz9ceumleO2117BgwQK89dZbSEpKavP7c4vFog8JA0BUVFQntbR7iIyMpA9k4jPKH+KrQOSOUepBly3oAPDyyy9jwYIFePzxxxEZGYnNmzeHukmEEEJISHTpgj58+PBOP02NEEIICUdd+wsDEnIWiwUrVqzw+jqDkPai/CG+otxpqctOiiOEEELIf9EROiGEEGIAVNAJIYQQA6CCTgghhBgAFXRCCCHEAKigE0IIIQZABZ0QQggxACrohBBCiAFQQSeEEEIMgAo6IYQQYgBU0AkhhBADoIJOCCGEGAAVdEIIIcQAuvTtU33FOUddXR3sdjt69+4NQRBC3SRCCCEhwDmH3W7HoEGDIIpd+xi3WxZ0u92O6OjoUDeDEEJImKioqMDgwYND3Qy/dMuC3rt3b1RUVCAhIQEVFRWIjIwMdZO6LFVVsWfPHkycOBGy3C3TifiB8of4KlC5U1dXh4SEBPTu3TuArQuNsP4Lcjgc+NWvfoX9+/ejR48eGDBgADZs2ICkpCScPHkSt956Kw4ePAiLxYINGzbgyiuvbNdyBUHQi3hkZCQVdD8wxpCWlobo6OguP1xFOh/lD/FVoHPHCF+9hv1f0JIlS/D999/j66+/xg033IAlS5YAAJYvX44rrrgCpaWl2LRpE+bNmwdVVUPc2u5HFEXEx8fThzHxCeUP8RXlTkth/ZuwWq1IT0/X95yuuOIKHDp0CADwxhtvYOnSpQCACRMmoH///ti1a1fI2tpdqaqK3Nxc2pkiPqH8Ib6i3GkprAt6c8899xx+/vOf4/Tp02CMIS4uTn8tKSkJR44cafP/Op1O1NXVeT0AQNM0/d/WYlVVvWLG2HljRVG8Ys65V8w5bxED8IoZY16xJ2HbijVN84o7s0+CIOCSSy6BpmmG6ZMRt1O49gkAUlNTwRgzTJ+MuJ3CsU+cc4wcORKiKPrdJ6PoMgX98ccfR2lpKR577DEALb/v8CRQW1atWoWoqCj9kZCQAAAoLi4GAJSUlKCkpAQAUFRUhNLSUgBAYWEhysrKAAAFBQWoqKgAAOTn58NmswEA8vLyUFVVBQDIzc1FTU0NACAnJwd2ux0AkJ2dDYfDAVVVkZ2dDVVV4XA4kJ2dDcA98z4nJwcAUFNTg9zcXABAVVUV8vLyAAA2mw35+fkA3DMyCwoKAABlZWUoLCwEAJSWlqKoqKjT+uRyuRATE4MdO3YYpk9G3E7h2qfKykr069cPu3fvNkyfjLidwrFPhw8fxtGjRyGKol992rNnDwyDdwFPPvkkHz9+PD9z5oz+XI8ePfjJkyf1nydMmMA/++yzNpfhcDh4bW2t/qioqOAAeHV1Neecc1VVuaqqLWJFUbxiTdPOG7tcLq+YMeYVM8ZaxJxzr1jTNK9YUZTzxqqqesWt9SNYfXI6nfzDDz/k586dM0yfjLidwrVPDoeD79ixg9fX1xumT0bcTuHYp4aGBv7hhx9yl8vlV59Onz7NAfDa2lre1QmcX+DQNsSeeeYZbNmyBZ988gn69OmjP79gwQIkJSXh0Ucfxd69e3HTTTfh0KFD7T59oa6uDlFRUaitraVZ7n5gjKGmpoZmKROfUP4QXwUqd4xUC8K6oB89ehQJCQkYOnSofo6gxWLBnj17cOLECfzmN79BWVkZzGYzXnzxRVxzzTXtXraRNiIhhBDfGKkWhPUu8eDBg8E5x8GDB/H111/j66+/1r/v6N+/P3JyclBaWorvvvuuQ8WcBI6iKPjggw/0ySzEd48++ihuvPHGUDcDl1xyCbZv367//Le//Q0DBw5Er169UFhY2OJ1f1D+EF9R7rQU1gWdhD9ZlnHVVVcZ8ipf33//PX7+85+jb9++iIyMxIgRI7B69eqALPuVV17B2LFj/VrGo48+ClmW0atXL0RGRiI1NRWvvfaa32377rvvcMMNNwBwf2hmZmZi69atOHv2LC699FKv1zvq6aefRkpKCnr37o24uDhcf/31SExM9Dt/FixYgGXLlvm1DNK1GPmzx1dU0IlfPFfdM8JVlpqbOXMmxowZgyNHjuDMmTN46623MHTo0FA3y8sNN9yAs2fPoqamBn/605+wYMECfYZvIJw4cQINDQ1IS0vze1mvvfYann/+ebz99tuw2+0oLS3FkiVLwiJ/jHTqUndh5M8eX1FBJ35RFAXvvvuu4Ya9qqqqcPDgQdxxxx3o0aMHJEnCJZdcgl/84hf6e06cOIGbb74ZcXFxSExMxB//+Ee9MLR2BD527Fi88sorKCwsxJ133olvv/0WvXr1Qq9evfRrKGiahnvuuQfR0dFITEzE1q1b29VeURRx8803Izo6Gvv370dOTg4uu+wyREVFYeDAgbj77rvR0NCgv7+urg733HMPEhMTERkZiQkTJuin8SQlJeGdd95BYWEhhg8fDsD99dfFF1/s9brHxx9/jIkTJyI6OhoDBw7EqlWrWm3j7t27MXXqVKSmpgIAoqOjMWfOHHz99dd6/nzyySe4/PLLER0djUsuuQTvvfee/v8ZY3juuecwYsQI9O7dG8nJydixYweee+45bNmyBS+++CJ69eqFSy65BID7lKglS5Zg4MCBGDhwIO68806cO3cOAFBeXg5BELBp0yYMGzYM8fHx7fo9k/Bh1M8ev4R0jn0I1dbWGuZUhVBijPH6+nr9NBWjYIzxESNG8KlTp/KtW7fy8vLyFu+ZMmUKv+WWW7jdbufl5eV81KhR/LHHHuOcc75p0yY+ZswYr/ePGTOGb9q0qc3XV6xYwU0mE3/99de5qqp88+bNvFevXryurq7VNq5YsYLPnj2bc+4+PScrK4vLssx/+OEHnpeXx/ft28dVVeUHDx7kI0aM4H/961/1/ztnzhx+3XXX8crKSq5pGt+3bx8/deoU55zzIUOG8G3btnHOOS8rK+MAvE4Zbfr6vn37eEREBH/zzTe5y+XiNTU1/Msvv2y1vVlZWbxXr178r3/9K9+1axdvaGjwyp9vvvmGR0dH808//ZRrmsZ37tzJIyMj+YEDBzjnnK9bt45fdNFF/KuvvuKMMX748GG+f/9+zjnnt912G8/MzPRa329/+1s+efJkXlVVxU+dOsWvueYavnjxYq9+3XjjjfzMmTP83LlzrbaZhK9AffYYqRZQQTfARgylpueXGo3NZuP33XcfHzVqFBdFkY8cOZLn5ORwzjk/evQoB8BtNpv+/i1btvDk5GTOue8FfeLEifrPjDFuNpv5V1991Wr7VqxYwWVZ5lFRUTw2NpZfdtll/M0332z1vc8++yyfNm0a55zz48ePcwD88OHDrb63IwX9zjvv5L/97W9bXU5r/u///o+np6fzqKgo3qNHD75w4UJ+5swZzhjjd999N1+2bJnX+2+55Rb+5z//mXPO+YgRI/jmzZtbXW7zgq5pGrdYLHz37t36c1988QW3WCxc0zS9X4WFhe1uOwkvgfrsMVItoCF34pemV4AymgEDBuDpp5/Gd999h1OnTuH666/HnDlzUF1djaNHj8JqtWLAgAH6+4cOHYqjR4/6vU4PQRAQERGhX3WrNTNnzkRNTQ2qqqr06zEAwN69ezFt2jT0798fkZGReOihh/Sreh0+fBgWiwWJiYl+tdWzrOTk5Ha/PyMjAx988AHOnDmDjz76CB9//DEWLVoEVVVRXl6ODRs2IDo6Wn+8++67OHbsWIfXderUKTidTiQlJenPDR06FE6nU/89AAjI74CEhpE/e3xFBZ34RZZlpKenG36maUxMDB599FGcO3cOZWVlGDx4MBwOB06cOKG/x/M8APTq1Qv19fVeyzh+/LgeB/siKr/+9a8xefJkHDp0CHV1dXj88cf1yyMPGTIETqdT/87cH0OGDMGPP/7Y4f8nCAKuvPJK3HTTTWhoaIAsy0hISEBmZiZqamr0x9mzZ/HSSy9dcF3Nf59xcXEwm80oLy/XnysrK4PFYkHfvn3b/H+k6+gunz0dQdlM/GbEPeQzZ87g4YcfxoEDB6BpGurr6/HMM88gJiYGI0aMQHx8PCZPnozf//73OHfuHI4cOYLHH38ct912GwD3BLhDhw5h586dUFUVa9aswenTp/Xl9+/fHzabzWuiWiDV1dUhOjoaPXv2RElJiV4UPeuePXs27rzzTthsNjDGUFhY6NW+9lq8eDGysrKwbds2qKqK2tpa7N69u9X3btq0Ce+++65+ze/i4mK89957mDhxIgDgjjvuwKZNm/DZZ59B0zQ4nU58+eWX+qz9O+64AytXrsTXX38NzjmOHDmiv9a/f3/9ToyAu1Dfcsst+OMf/4jq6mqcPn0af/zjH/Gb3/yGiriBGPGzxx+U2cQvqqoiJyfHcH9YZrMZlZWVSE9PR1RUFBITE/HFF19gx44d6NmzJwDg9ddfR0NDA4YMGYKf/exnmDlzJh588EEAwLBhw7BmzRpkZGRg4MCBcDqd+uxrAJgyZQquuOIKxMfHIzo6+rx3CvTFyy+/jKeeegq9evXCnXfeiV/96lder2/evBkJCQm47LLLEB0djTvvvNOnnYtx48bhrbfewmOPPYaYmBiMHDkSn3/+eavvjY6OxtNPP61f+fHGG2/EzTffjNTUVKiqiksvvRRZWVl4+OGHERcXh/j4eDzyyCNwOp0AgHvvvRd33XUXbr75ZvTu3RvTpk3Tf2+LFi1CZWUl+vTpo59it27dOiQlJWHUqFG45JJLMGzYMDzzzDMd7mN34/mKI9wZ9bPHH2F96ddgMtLl/gghJBDmzp2Lbdu2Yc6cOXj77bdD3ZxOYaRaQEfoxC+cc9TV1V3w9rWEtIbyJ3x4ijkAbNu2DXPnzg1xi86PcqclKujEL6qq6t8TE9JRlD/hoWkx9wj3ok650xINuRtgmIUQQnx17Nix814pr7KyEoMGDerEFnUuI9UCOkInfmGMobq6GoyxUDeFdEGUP/4RBP8f8fG9z7uO+PjeAVlPoFHutBT2Bf3ee+9FUlISBEFAcXGx/vykSZMwdOhQjB07FmPHjsWzzz4bwlZ2X5qmYe/evdA0LdRNIV0Q5Q/xFeVOS2F/Rn5GRgYefPBBXHnllS1ee+6553y+jSMJDJPJhOuuuy7UzSBdFOVPOOgNYC2AZa28trbx9fBDudNSUI/Qt2/f7vcyrr76av3qWyT8MMZw8uRJGvYiPqH8CReZcBfvptY2Ph+eKHdaCnhBnz59Oq699lpMnz4dS5cuxbXXXhvoVegeeOABjB49Gr/85S+9rhLVGqfTibq6Oq8HAH24RtO0VmNVVb1iT/K0FSuK4hV75hx6Ys55ixiAV8wY84o9szjbijVN84o7s0+apuHbb7+F0+k0TJ+MuJ3CtU+qqqK4uBgul8swfers7SSKDFaru72SxGCxuGNZbhprMJubxu72mkwaTCZ3bDbfA1le2xivgyzfAwCwWFTIMtNjSXLHVqsKUfTEih5HRCgQRa7HgsABBH47KYqCb7/9Fowxv7eTUQS8oF9xxRW4++678fHHH2Pu3LnIyckJ9CoAAK+++ipKSkpQVFSEq6666oJD76tWrUJUVJT+SEhIAAD9e/mSkhL9MpJFRUUoLS0FABQWFqKsrAwAUFBQoF//Oj8/HzabDQCQl5en3/AhNzdXv7RlTk6OfmON7OxsOBwOrxsKOBwOZGdnA3Dfu9nzu6qpqUFubi4A93258/LyAAA2mw35+fkAgIqKChQUFABwX6O6sLAQAFBaWoqioqJO65Oqqrj66qv1KzYZoU9G3E7h2iebzYYpU6bosRH61NnbKS2tCmvWuPs0caINK1e6+zRpUgWWL3f3KT29DJmZ7j5lZJRi8WJ3n+bPL8H8+e4+LV5chIyM6wDUITPzJ0hPd/dp+fICTJrk7tPKlfmYONHdpzVr8pCW5u7T+vW5SE5292njxhzEx7v7lJWVjZgYByIiAr+dKioqEBkZCVmW/dpOe/bsgVEE5bS1N998E/v27UNtbS1eeOGFgCwzKSkJ27dvR2pqaquvW61WVFZWIjY2ttXXnU6nfglJwH2qQkJCAqqrq9GnTx99z02SJK9YVVUIgqDHoihCFMU2Y0VRIEmSHsuyDEEQ9Bhw7xE2jU0mEzjneuzZ4/TEjDHIstxmrGkaOOd63Fo/gtUnURRhs9n0m2EYoU9G3E7h2icAOHHiBOLi4iDLsiH61JnbyWw2QRQZzGYGh0OGJDHIMoPTKUOWGSTJE2sQRQ6XyxMDLpekH50rigSzWQNjgKpKMJtVMCZAVSVYLCo0TYSqirBYVKiqCE0TYbWqcLlEMCbCalXgcklgTEREhAKnUwZjAiIiFDgcMjgHXK7AbidFUWCz2TB48GB9lMOX7VRdXY3Y2FhDnLYWlElxGRkZGDt2rL4XFmiqquL06dPo378/AOCtt95C//792yzmAGCxWGCxWFo8L0mS17/N46Z38mlPbDKZfIoFQdBjT6K1N26r7Z3RJ1VVcejQIQwYMABC47kpXb1P54upT4Htk6qqOHjwIPr3768vs6v36UJxoPvEmAiHwx1rmrvYAoCquouwO/5ve5vGivLf2OVqGv+3vU5n67HD0TT+b3sbGlqPT506pZ/PHojtJAgCysvLMWjQIK/frz/bqasL+wvLLF26FO+++y6OHz+Ovn37olevXvjmm29wzTXXwOl0QhRF9O3bF8888wzGjBnT7uUa6WIChJDuKRjndwfHXADheY14I9WCoBb0kpISPPbYYzh06JDXxAPPdyChZKSNGEqMMVRUVCAhIYFuS0k6jPLHP12joLuLuUeginqgcsdItSCoYw0333wzbr31Vtx+++1eQx/EOBhjqKysRHx8PH0gkw6j/DE672IO/Pca8f4WdcqdloJ6hD5u3Djs27cvWIv3i5H2yggh3VN4H6EfAxD+14g3Ui0I6m7NjBkzsGPHjmCugoSYpmn48ccf6fKLxCeUP+GHQwjIo+48xRwAesfH+3VxeM1kotxpJqgFferUqcjIyEBUVBT69euHuLg49OvXL5irJJ2Mc44zZ87QPYmJTyh/iK+4KFLuNBPUIfdhw4bhiSeewLhx47y+Qx8yZEiwVtluRhpmIYR0T8EYcucI3ELXoe0rxAfkorIBKF9GqgVBnRQXGxuLjIyMYK6ChJimaSgtLUVycjJNfCQdRvljbJ6ivazJc2sRmGKuyTJKDxyg3GkiqEPuc+bMwYYNG1BdXY36+nr9QYyloaEh1E0gXRjlj7E1ve3LWgTwdi+iSLnTTFCH3JueSiAIAjjnEAQhLCYxGGmYhRDSPYX7kHtTdgThRqw05O4lqEfo586d06+9q2kaGGP6zQqIMWiahuLi4rDYSSNdD+VP9xHoYq6ZTJQ7zQS1oN9yyy1eP9fW1uL6668P5ioJIYSQbimoBT0lJQWZme5vTM6ePYsZM2bgrrvuCuYqSSeTJAmpqak0KYX4hPKH+EpSFMqdZoJa0FevXo0TJ05g9erVmD17Nn75y19i0aJFwVwl6WSapqGwsJCGvYhPKH+IrzSzmXKnmaCcttZ0JvsLL7yA66+/HlOnTsWSJUtQX1+PHj16BGO1JEQiIiJC3QTShVH+EJ8wRrnTTFBmuYui6DWrvekqaJY7IYQERlea5R4UNMvdS1CG3JvOavf823S2e0fce++9SEpKgiAIKC4u1p8/efIkZsyYgeTkZKSmpmLXrl2B7gZpB1VVsXfvXq/b4xLSXpQ/xFeq2Uy500zY33MuIyMDu3btanG52OXLl+OKK65AaWkpNm3ahHnz5tGGDQFBENCnTx8I4X3bJxKmKH+IrwTGKHeaCUpBP3z4MK677jqkpKTg/vvvh8Ph0F/7yU9+0qFlXX311Rg8eHCL59944w0sXboUADBhwgT079+fjtJDQJIkDBs2jGaaEp9Q/hBfSapKudNMUAr6XXfdhVmzZiErKwtVVVWYOnUq7HY7AHgVd1+dPn0ajDHExcXpzyUlJeHIkSNt/h+n04m6ujqvBwD9KwBN01qNVVX1ihlj540VRfGKPfMHPDHnvEUMwCtmjHnFnpGHtmJN07zizuyToij44osv0NDQYJg+GXE7hWufXC4X8vPz4XA4DNOnzt5OoshgtbrbK0kMFos7luWmsQazuWnsbq/JpMFkcsdmswZZbuyH2QxNds+ZVi0WsKZxYwFVrVawxquBKk3jiAjwprEggHtiAFwQoDROZuOiqMdMFKFYrXqsemJJgmqxuGNZ1mNnz5744osv9N+/P9vJKIJS0I8fP46lS5di/Pjx2Lx5M2bOnImpU6eitrY2YMMjzZdzobl9q1atQlRUlP5ISEgAAP17+ZKSEpSUlAAAioqKUFpaCgAoLCxEWVkZAKCgoAAVFRUAgPz8fP2qd3l5eaiqqgIA5ObmoqamBgCQk5Oj78hkZ2fD4XBAVVVkZ2dDVVU4HA5kZ2cDAOx2O3JycgAANTU1yM3NBQBUVVUhLy8PAGCz2ZCfnw8AqKioQEFBAQCgrKwMhYWFAIDS0lIUFRV1Wp9cLhcGDhyInJwcw/TJiNspXPtUWVmJ+Ph4FBQUGKZPnb2d0tKqsGaNu08TJ9qwcqW7T5MmVWD5cnef0tPLkJnp7lNGRikWL3b3af78Esyf7+7T4sVFyMho7FNmJsrS0919Wr4cFZMmufu0ciVsEye6+7RmDarS0tx9Wr8eNcnJ7j5t3Ah7vPte6NlZWXDExECNiEB2VhbUiAg4YmKQnZXl7lN8PHI2bnT3KTkZuevXu7dTWhry1qxxb6eJE5G/cqV7O02ahILlywEAR6ZPh6IoEEXRr+20Z88eGAYPguHDh7d47sknn+Tjx4/nw4YN82mZQ4YM4d9++63+c48ePfjJkyf1nydMmMA/++yzNv+/w+HgtbW1+qOiooID4NXV1ZxzzlVV5aqqtogVRfGKNU07b+xyubxixphXzBhrEXPOvWJN07xiRVHOG6uq6hW31g/qE/WJ+mS8PgGci6LGrVaFA5xLksYtFncsy01jlZvNTWOVA5ybTCo3mdyx2axyWVY5B7hiNnNVlt2xxcK1prEkuWOrlWuiyDnAXU3jiAjOmsaCwJknBjgTBO6KiOAc4EwU9VgTRe6yWvVY8cSSxBWLxR3Lsh6rshyQ7XT69GkOgNfW1vKuLigF/cYbb+Qffvhhi+effvppLgiCT8tsXtBvu+02vmLFCs455wUFBTwhIUHfuO1RW1trmI0YSoqi8M8//7xDv3tCPCh//OM+byuwj6AsNAgPxWIJSO4YqRYE5Tx0p9MJALA0ftfRlGeIrb2WLl2Kd999F8ePH0ffvn3Rq1cv/Pjjjzhx4gR+85vfoKysDGazGS+++CKuueaadi/XSOcehpLnhjsDBw70urseIe1B+eOf7nweOpMk2A4f9jt3jFQLgnr7VMD9fdRFF10UzFX4xEgbkRDSPXXngg6ALizTTNB3iZ988slgr4KEkKqqyM3NNdRMUdJ5KH+Ir1SrlXKnmYBfy33IkCEYPnw4APe3Md9//z1efPHFQK+GhAlRFJGamkrDpcQnlD/EV6LLRbnTTMAL+vTp0/H3v/9d/5lul2psoiiiX79+oW4G6aIof4ivRMYod5oJ+K7NU0895fXzSy+9FOhVkDCiKAo++ugj/SIXhHQE5Q/xlWK1Uu40E/Aj9OjoaD0+cuSIfvW2xMREJCYmBnp1JMQkScKECRPo8ovEJ5Q/xFeSy0W500xQ7od+4MAB3H777SgrK0NiYiI456ioqMBFF12EjRs3YuTIkcFYLQkBURQRExMT6maQLoryh/hKZIxyp5mgzCZYsGAB7r//fthsNuzZs0e/rON9992H2267LRirJCGiKAo++OADGvYiPqH8Ib5SIiIod5oJynnow4cPx/fff9/h1zqTkc49DCXOOex2O3r37k23MSQdRvnjn+58HjoXRdjPnPE7d4xUC4JyhN63b1+8+uqr+t1sAPcVoTZv3ozY2NhgrJKEiCAIiIyMpA9j4hPKH+IrgTHKnWaCUtA3b96MV155BX379kVqaipGjx6N2NhY/XliHIqi4N1336VhL+ITyh/iKyUignKnmaBe+vXUqVP6LeoSEhK87l8eakYaZgklzjkcDgesVivtKZMOo/zxT7cechcEOM6d8zt3jFQLgjLL3SMuLi6sijgJDlkOahoRg6P8IT7hnHKnmU6/Zl5KSkpnr5IEkaqqyM7OpuspE59Q/hBfqRERlDvNBGXIff/+/W2+Nm3aNBw7dizQq+wwIw2zhBLnHKqqQpZlGjIlHUb5459uPeQOQHW5/M4dI9WCoIxXpKamIikpCa3tK1RVVQVjlSSEPB/IhPiC8of4RBAod5oJypD7kCFDsGvXLpSVlbV49O/fP2DrSUpKwogRIzB27FiMHTsWW7duDdiySfuoqoqcnBwa9iI+ofwhvlKtVsqdZoKyazNr1iwcOnQIgwYNavHa7NmzA7quN998E6mpqQFdJmk/k8kU8G1Kug/KH+IrU0MD5U4zQSno69ata/O19evXB2OVJEToSl/EH5Q/xFdcFGGvq6PcaaLL3xl+3rx5GD16NBYtWoRTp061+T6n04m6ujqvBwBomqb/21qsqqpX7Ln6XVuxoihesWcegSfmnLeIAXjFjDGv2DOk1FasaZpX3Jl9UhQFeXl5aGhoMEyfjLidwrVPLpcLO3fuhMPhMEyfOns7iSKD1epuryQxWCzuWJabxhrM5qaxu70mkwaTyR2bzRpkubEfZjO0xu+mVYsFrGnceHcz1WoFE90lRGkaR0SAN40FAdwTw33+uBIR4e6TKOoxE0UoVqseq55YkqBaLO5YlvXY2bMn8vLy9N+/P9vJKLp0Qc/Ly8M333yDffv2ITY29rw3flm1ahWioqL0R0JCAgCguLgYAFBSUoKSkhIAQFFREUpLSwEAhYWFKCsrAwAUFBToF8rJz8+HzWbT2+GZ7Jebm4uamhoAQE5ODux2OwAgOzsbDofD6zQdh8OB7OxsAIDdbkdOTg4AoKamBrm5uQDckwjz8vIAADabDfn5+QCAiooKFBQUAADKyspQWFgIACgtLUVRUVGn9UnTNFx33XX4+OOPDdMnI26ncO3T8ePHMXPmTOzdu9cwfers7ZSWVoU1a9x9mjjRhpUr3X2aNKkCy5e7+5SeXobMTHefMjJKsXixu0/z55dg/nx3nxYvLkJGRmOfMjNRlp7u7tPy5aiYNMndp5UrYZs40d2nNWtQlZbm7tP69ahJTnb3aeNG2OPj3X3KyoIjJsZ9illWFtSICDhiYpCdleXuU3w8cjZudPcpORm5jSO4VWlpyFuzxr2dJk5E/sqV7u00aRIKli8HABydPBn9+/eHyWTyazvt2bMHRhHUK8V1JpvNhpSUFP2Pozmn0wmn06n/XFdXh4SEBFRXV6NPnz76npskSV6xqqoQBEGPRVGEKIptxoqiQJIkPfacUuGJAe9ZvaqqwmQy6afvmEwmMMagaZoeM8Ygy3KbsaZp4I0XWWirH8HqkyiKqKmpQa9evWA2mw3RJyNup3Dtk+dvsVevXpBl2RB96sztZDabIIoMZjODwyFDkhhkmcHplCHLDJLkiTWIIofL5YkBl0vSj84VRYLZrIExQFFlqGYzBMYgqSpUiwWipkH0xKoKUdOgWq0QXS6IjEGxWiF54ogIyE4nBE/scACcQ42IgNzQ4J6dbrXC1NAALopQLRaYGhrARBGa2QyTwwEmimBmM2SHA0ySwGQZstMJJstgkgTZ6YRiNqPm6FHExsbqoxy+bKfq6mrExsYa4rS1LlvQz507B0VREB0dDQB45pln8M477+h73xdipHMPQ0lRFOTm5mLKlCkwmUyhbg7pYih//NOdz0NXrFbkvvOO37ljpFrQZQv6oUOHcNNNN+l700OHDsW6deuQlJTUrv9vpI1ICOmeunNBBwAEoHwZqRZ02TPyhw4dqn/PRUKHMYaqqir07dsXotilp2SQEKD8Ib5iooiqkycpd5qg3wLxC2MMxcXF+oxRQjqC8of4ipnNlDvNdNkhd38ZaZiFENI90ZA7Dbk3RUfoxC+MMVRWVtJeMvEJ5Q/xFZMkyp1mqKATvzDGcPDgQfqjIj6h/CG+YrJMudMMDbkbYJiFENI90ZA7Dbk3RUfoxC+MMRw+fJj2kolPKH+Ir5gsU+40QwWd+IW+AyX+oPwhvqLv0FuiIfcwHWY5duxYq7efDbdlEkJCh4bcaci9KTpCD0Nz585FfHw85s6dG9bLBNx3M/rxxx/1ayYT0hGUP8RXmixT7jRDBT3MzJ07F9u2bQMAbNu2LSAFOBjL9OCc48yZM+imAz3ET5Q/xFdcFCl3mqEh9zAaZmlaeJuaM2cO3n777bBZJiEkPNCQOw25N0VH6GHi2LFjrRZewH1UfezYsbBYZnOapuHAgQM07BVG2rqFcDii/CG+0mSZcqcZKuh+EoTAPOLjBwEY3eo6RgMYFB/f4YUOio9vY4m+L7PFo0cPNDQ0BOm3Szpq3bp1iIyMxLp160LdlHaj/CE+EUXKnWZoyN3PYZbADXnZAUQBaLk5BAC1AHoHbIm+L7NV3TOFws66deuwbNky/ee1a9ciMzMzdA0iQUdD7jTk3lSXPkIvLS3FT3/6U6SkpODyyy/H/v37Q90kP/QG8GyrrzwL3wpv20v0fZnNaSYTiouLadgrxJoXcwBYtmxZQI/U9+3bF7BleWiaRvlDfEKfPS116YJ+xx13YMmSJfjhhx/w4IMPYuHChaFukp8yAaz1emZt47OBW6L/yyThxW63tyjmHsuWLQvId+qDBg3C+PHj6ToGhISxLjvkfvLkSaSkpKCqqgqyLINzjoEDB2L37t1ISkq64P8PvyH3ptZBwDI8i8AV3nUA/gcI6DJ1XTOFwkJg8ucHAMPP8/r3AFL8WP4gADb9p4EDBwZkQiXxHw2505B7U3KoG+CriooKDBo0CLLs7oIgCEhMTMSRI0daLehOpxNOpxOA+9xXzwfSmTNnAEAftpEkyStWVRWCIOixKIoQRVGPAREWiwqXSwTnIiwWBS6XBM5FWK0KnE4ZnAuwWhU4HO62Wq1qs9gEQeCwWDzxbSgz/y/6OJ2oEQQwsxmy0wkmimCyDNnlApMkMEmC7HJBkyRwUYSsKNAkCRBFSIoCrfF3I6kqbjWZMJcxRGkaqk0mCIxB0jSoZjNETYPoiVUVImNQLRaILhdEzqFYLJA8sdUK2emE0BgLjKE4Lw8jRoyAxWIBAKiqCpPJBM65HjPGoGmaHjPGIMtym7GmaeCc63Fr26Yj26l5rCgKJEnSY1mWIQiCHnv60TQORp8ACbKsNa5DgsmkgTFA0ySYTCoYE6BpEsxmFZomQtNEmM0qVFUEY57c6wfOR8FiOQiXywXOOaxWK5xOJzgfCas1Fg5HbTtzj8Fs1uB0euIxcDptEEURsizD5XLh5MmTSExM1Ic7/dlOnHPs378fI0aMgMlkCtvtFK65B3i2E4PTKUMUGWSZweWSIUkMkuSJNYgih6J4YkBRWs+9Og1QA/gZITsc7nU0i00OB7ggQLVYYHI4wAQBmtkMk9MJ1o7PPVdEBL77/HOkpaXpn/O+bKfq6mq9LnR5vIv66quv+KhRo7yeu+yyy/jnn3/e6vtXrFjB4Z4fRg960IMe9KCH16OioqIzSldQdekh9+TkZJw+fbpdQ+7Nj9Dr6uqgKApiY2MhBGfcvFuoq6tDQkICKioquvxwFel8lD/EV4HKHc457HY7Bg0a1Djq2nV12SH3fv364dJLL8Vrr72GBQsW4K233kJSUlKb359bLBZ9SBgAoqKiOqml3UNkZCR9IBOfUf4QXwUid4xSD7psQQeAl19+GQsWLMDjjz+OyMhIbN68OdRNIoQQQkKiSxf04cOH48svvwx1MwghhJCQ69pfGJCQs1gsWLFihdfXGYS0F+UP8RXlTktddlIcIYQQQv6LjtAJIYQQA6CCTgghhBgAFXRCCCHEAKigE0IIIQZABZ0QQggxACrohJBOVVNTg61bt+KZZ57Bs88+i3/961/6TZIIacu6detQVVUFACgtLcWVV16J/v374/LLL8c333wT4taFByropN3+3//7f3p85MgRTJkyBf369cNPfvITHDhwIIQtI13Fxo0bcfnll2P37t36HcN2796NK664Ahs3bgx180gY+9vf/oa+ffsCAH73u9/hT3/6E06cOIEXXngBd955Z4hbFx7oPHTSbuPGjcO+ffsAADfddBPmzp2LefPm4aOPPsITTzyBzz77LMQtJOFu+PDh+M9//oNevXp5PW+32zF+/Hj88MMPIWoZCXcjRozA/v37IYoiJk6ciD179uivjR49Gt9++20IWxce6Aid+OTQoUOYN28eAOC6665DbW1tiFtEugJBEHD27NkWz589e5buekjOa9GiRZg9ezby8/ORnp6O5cuXo6CgAE888QQuueSSUDcvLHTpa7mTzlVWVoabb74ZnHNUVlaivr4ePXr0AAC4XK4Qt450BU899RSuueYapKamIj4+HgBw9OhRfPfdd3j66adD3DoSzn7/+99j7NixWLt2LUpLS6EoCr755hv8/Oc/xz//+c9QNy8s0JA7abfPP//c6+fx48ejV69eOHnyJN544w3cc889IWoZ6Uo0TUNBQQGOHTsGzjni4+Nx+eWXQ5KkUDeNkC6NCjohJKTWr19PO4Pkgh599FEkJCRg4cKFXs+vX78eZ86cwSOPPBKiloUPKuik3aZMmXLe13NzczupJcRImk62JKQtl1xyCb799luIovfUL03TMHbsWJoUB/oOnXSAzWaD2WzGL3/5S8yYMYNuW0gCgo4pSHtwzlsUcwCQJAmapoWgReGHZrmTdispKcGWLVvgcDhw1113YfXq1SgrK0NKSgrNMiU+KygoCHUTSBcQGRmJoqKiFs9/88036N27dwhaFH5oyJ347LXXXkNmZiaWL1+OBx54INTNIV3c8uXL8cQTT4S6GSRM7dy5E7/5zW+wcOFCjB07FoIgYN++fdi0aRM2b96Mq6++OtRNDDkq6KRD9u/fj61bt+Kjjz5CcnIyMjIycP3118NsNoe6aaQLqK+vb/V5zjlGjBiBioqKTm4R6UqOHTuGl156Cfv37wfnHKNGjcLdd9+NQYMGhbppYYEKOmm31NRUSJKEm2++Gddffz2sVqvX66NGjQpRy0hXIUkShgwZ4vW9uSAI+rUN6HoG5EIaGhpw8OBBAMDFF1+MiIiIELcofFBBJ+02adIk/Wpeng9hD0EQaJY7uaCUlBR8/PHHGDJkSIvXEhIS6AidtMnlcuGBBx7A66+/jsTERHDOUVFRgXnz5mH16tU0SRc0y510wL///e9QN4F0cf/7v//b6qVfAWDlypWd3BrSldx7770wmUwoLy9Hz549AbgvGfyHP/wBv/vd77xuHtVd0RE6abeCggIkJiZiwIABAIBNmzZh27ZtGDJkCB599FHExsaGuIWEEKNKTk5GaWlpi+c550hJSWn1te6GTlsj7XbHHXfow1qffvopHnroIdx6662IjY1tcfUmQtrr2muvDXUTSBfQ1rFn86//ujMq6KTdGGPo06cPAOCNN97AnXfeiYyMDDz66KMoLy8PbeNIl3Xq1KlQN4F0AdOmTcO9996LhoYG/bn6+nr87ne/u+BVLLsLKuikQ5xOJzjnyMnJ8TqyUhQlhK0iXdmMGTNC3QTSBaxbtw6Ae/LkZZddhssuuwxDhgyBIAh47rnnQty68EDfoZN2+9vf/oZ169ahV69e6NWrFz755BMA7is1LVu2DJ999lmIW0gIMbpz5855nbbmmSBHqKCTDqqsrMTJkycxZswY/brKNpsNiqIgMTExxK0j4W7o0KFeP3PO9e9ABUHAoUOHQtQy0lV9+umneOaZZ/DBBx+EuikhR0PupN2ysrIQHx+PSy+9FF9++aX+/MCBA/Hee++FsGWkqxg+fLg+iXLHjh0oLi7Gt99+q/9LSFtyc3ORkpKCnj17ipUjZQAABpRJREFU4te//jW+/fZbjB8/Hvfddx9uv/32UDcvLNAROmm3pre5bH7LS7oFJmmvM2fOYNu2bXjzzTfhdDoxZ84c/OpXv0Lfvn1D3TQSxsaOHYunn34aV155JbZv345bb70Vq1atwr333hvqpoUNOkIn7dZ036/5fiDtF5L26tOnD26//Xa88847+M1vfoMVK1bg9ddfD3WzSBcwdepUWCwW3HTTTRg8eDAV82boSnGk3TyXfW0et/YzIa1RVRU5OTl44403UFJSgmuvvRa5ubkYM2ZMqJtGwpzdbkd2drb+s6ZpXj+np6eHollhhYbcSbvJsoyYmBhwzlFTU6Ofk845R21tLd1Yg1xQTEwMEhIScPPNN+u3wGyKPpRJW37729+2+ZogCPjHP/7Ria0JT1TQCSGdZsGCBW2O5tCHMiH+oYJOfKKqKk6cOAFN0/Tn6LQ1QkiwvPjii+d9/e677+6kloQv+g6ddNgzzzyDNWvWID4+Xj8XXRAEFBQUhLhlJNy9//77SEtL02+fumLFCv0GP2vXrsXFF18c4haScEWXCL4wOkInHZaSkoK9e/ciKioq1E0hXUxaWhp2796NHj16YNu2bXjwwQeRlZWFoqIivP766/rVBwkhHUdH6KTDkpKS9LuuEdIRoiiiR48eAIBt27ZhyZIl+nW5169fH+LWkXD25z//+byv/+lPf+qkloQvKuikw/r06YPLLrtMPyfUY82aNSFsFekKRFFEdXU1evbsiY8//hj333+//prD4Qhhy0i4+/Of/4zU1FTMnTsXffv2pWtftIIKOumw9PR0Or2I+GTFihW49NJLwRjDddddp59/vnPnTiQlJYW2cSSsHT16FG+++Sa2bdsGs9mMX/ziF5gzZ45++iyh79AJIZ1MVVXY7XavD+Jz586Bc45evXqFsGWkq7DZbPjXv/6F1atXY/Xq1bjttttC3aSwQEfopMO+//57/OEPf0BJSQmcTqf+PN0pi7THd999B0EQ0KdPH+zfvx8ffvghRowYgZkzZ4a6aSTMcc7x+eefY+vWrSgoKMCvf/1r/OxnPwt1s8IGFXTSYb/97W/x9NNP484778S///1v/POf/8S5c+dC3SzSBfz1r39FdnY2FEXBtGnTUFhYiClTpuDZZ5/Ff/7zH5rYRNp0zz33YPfu3bjqqqtw66234qWXXgp1k8IODbmTDrvsssvw1VdfYfTo0fotL6+88krs2rUrxC0j4W706NEoKiqCw+HAgAEDcOzYMfTs2RNOpxMTJkxAUVFRqJtIwpQoioiJidGvNOj5l3MOQRBw8uTJUDYvLNAROumwHj16QFEUpKWl4ZFHHsGgQYNQW1sb6maRLkCSJAiCgIiICKSmpqJnz54AAIvFol+kiJDWMMZC3YSwR39BpN1qa2tRXl6OV155BZqm4YUXXoAkSfjkk0/oGtykXWJiYnD27FkAwBdffKE/f+rUKZhMplA1ixBDoCF30m6/+c1vcNttt2HatGlez2/fvh3/93//h82bN4eoZaSrs9vtqK2txeDBg0PdFEK6LDpCJ+1WWFjYopgDwA033IB9+/aFoEWkq8nKytLjpkfovXv3xjvvvBOCFhFiHFTQSbud70pedJUv0h5PPvmkHv/ud7/zeo2+tiHEP1TQSbuNHj0aW7ZsafH866+/jlGjRoWgRaSrafoNX/Nv++jbP0L8Q7PcSbs9//zzmDNnDv7xj39g7NixEAQB+/btQ11dHQ2XknbxnGrUPG7tZ0JIx9CkONJhn376Kfbv3w/OOUaNGtXq9+qEtEaWZcTExIBzjpqaGv3yr5xz1NbWwuVyhbiFhHRdVNAJIYQQA6Dv0AkhhBADoIJOCCGEGAAVdEIIIcQAqKATQgghBkAFnRBCCDEAKuiEEEKIAVBBJ4QQQgyACjohhBBiAFTQCSGEEAOggk4IIYQYABV0QgghxACooBNCCCEGQAWdEEIIMQAq6IQQQogBUEEnhBBCDIAKOiGEEGIAVNAJIYQQA6CCTgghhBgAFXRCCCHEAKigE0IIIQZABZ0QQggxACrohBBCiAFQQSeEEEIMgAo6IYQQYgBU0AkhhBADoIJOCCGEGAAVdEIIIcQAqKATQgghBkAFnRBCCDEAKuiEEEKIAVBBJ4QQQgyACjohhBBiAFTQCSGEEAOggk4IIYQYABV0QgghxACooBNCCCEGQAWdEEIIMQAq6IQQQogBUEEnhBBCDIAKOiGEEGIAVNAJIYQQA/j/bA1z00WyMPAAAAAASUVORK5CYII=" 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" }, "metadata": {}, "output_type": "display_data" @@ -2631,7 +2643,7 @@ "source": [ "Maybe you want to compare more models, or take a closer look at the model data? Here are links to the data for further exploration.\n", "\n", - "Data for nine models is available here:\n", + "As a reminder, data for nine models is available here:\n", "```\n", "/p/user_pub/pmp/demo/sea-ice/links_siconc \n", "/p/user_pub/pmp/demo/sea-ice/links_area\n", From 29300c31365cf2c582c17203d33762eae5e27e28 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Wed, 24 Jan 2024 15:05:12 -0800 Subject: [PATCH 42/69] update labels --- pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py b/pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py index 502fc9bbd..659cd324b 100644 --- a/pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py +++ b/pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py @@ -14,8 +14,8 @@ # Time series plot arctic.siconc.sel({"time": slice("1981-01-01", "2010-12-31")}).plot() -plt.title("E3SM-1-0 Arctic montly sea ice extent") -plt.ylabel("Extent (km2)") +plt.title("E3SM-1-0 Arctic monthly sea ice extent") +plt.ylabel("Extent (km${^2}$)") plt.xlim( [ cftime.DatetimeNoLeap(1981, 1, 16, 12, 0, 0, 0, has_year_zero=True), @@ -36,8 +36,8 @@ arctic_clim["time"] = [x for x in range(1, 13)] arctic_clim.siconc.plot() plt.title("E3SM-1-0 Arctic climatological sea ice extent\n1981-2010") -plt.xlabel("Month") -plt.ylabel("Extent (km2)") +plt.xlabel("month") +plt.ylabel("Extent (km${^2}$)") plt.xlim([1, 12]) plt.savefig("E3SM_arctic_clim.png") plt.close() From 920855243bc8f4bebe43614d8eae98e94c10cf40 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Wed, 24 Jan 2024 15:10:33 -0800 Subject: [PATCH 43/69] label updates --- pcmdi_metrics/sea_ice/create_sector_plots.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/pcmdi_metrics/sea_ice/create_sector_plots.py b/pcmdi_metrics/sea_ice/create_sector_plots.py index 1756d2841..e91ee6937 100644 --- a/pcmdi_metrics/sea_ice/create_sector_plots.py +++ b/pcmdi_metrics/sea_ice/create_sector_plots.py @@ -36,7 +36,12 @@ ax = plt.subplot(111, projection=proj) ax.set_global() ds.ice_conc.plot.pcolormesh( - ax=ax, x="xc", y="yc", transform=ccrs.PlateCarree(), cmap=cmap + ax=ax, + x="xc", + y="yc", + transform=ccrs.PlateCarree(), + cmap=cmap, + cbar_kwargs={"label": "ice concentration (%)"}, ) arctic_regions.plot_regions( ax=ax, @@ -105,7 +110,12 @@ ax = plt.subplot(111, projection=proj) ax.set_global() ds.ice_conc.plot.pcolormesh( - ax=ax, x="xc", y="yc", transform=ccrs.PlateCarree(), cmap=cmap + ax=ax, + x="xc", + y="yc", + transform=ccrs.PlateCarree(), + cmap=cmap, + cbar_kwargs={"label": "ice concentration (%)"}, ) arctic_regions.plot_regions( ax=ax, From 0e9c7e982bd9e8af988cac2d542f59e78975b027 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Wed, 24 Jan 2024 15:28:38 -0800 Subject: [PATCH 44/69] rerun --- pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb | 48 ++++++++++++------------ 1 file changed, 24 insertions(+), 24 deletions(-) diff --git a/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb b/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb index 2a4123b13..b59483cb3 100644 --- a/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb +++ b/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb @@ -102,14 +102,14 @@ "outputs": [ { "data": { - "image/png": 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" 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" 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -149,7 +149,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-24 13:04:18,737 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + "2024-01-24 15:12:15,365 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" ] } ], @@ -166,14 +166,14 @@ "outputs": [ { "data": { - "image/png": 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" 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" 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -416,9 +416,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-24 13:05:18,942 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "INFO::2024-01-24 13:06::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n", - "2024-01-24 13:06:21,672 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n" + "2024-01-24 15:13:27,907 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "INFO::2024-01-24 15:14::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n", + "2024-01-24 15:14:27,887 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n" ] }, { @@ -731,7 +731,7 @@ " \"Version\": \"23.1.0\",\n", " \"buildVersion\": \"not installed\"\n", " },\n", - " \"date\": \"2024-01-24 13:06:07\",\n", + " \"date\": \"2024-01-24 15:14:13\",\n", " \"openGL\": {\n", " \"GLX\": {\n", " \"client\": {},\n", @@ -853,7 +853,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-24 13:07:18,993 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + "2024-01-24 15:15:33,747 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" ] }, { @@ -947,8 +947,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO::2024-01-24 13:10::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n", - "2024-01-24 13:10:29,016 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n" + "INFO::2024-01-24 15:18::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n", + "2024-01-24 15:18:12,068 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n" ] }, { @@ -995,9 +995,9 @@ "text": [ "[WARNING] yaksa: 10 leaked handle pool objects\n", "\n", - "real\t4m7.504s\n", - "user\t4m22.717s\n", - "sys\t1m23.538s\n" + "real\t3m44.357s\n", + "user\t3m58.921s\n", + "sys\t1m19.472s\n" ] } ], @@ -1063,8 +1063,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-24 13:11:32,256 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "2024-01-24 13:12:15,349 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + "2024-01-24 15:19:18,845 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "2024-01-24 15:20:03,407 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" ] }, { @@ -1181,9 +1181,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-24 13:14:59,902 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "INFO::2024-01-24 13:19::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n", - "2024-01-24 13:19:01,364 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n" + "2024-01-24 15:22:44,793 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "INFO::2024-01-24 15:27::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n", + "2024-01-24 15:27:10,888 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n" ] }, { @@ -1289,9 +1289,9 @@ "text": [ "[WARNING] yaksa: 10 leaked handle pool objects\n", "\n", - "real\t8m32.955s\n", - "user\t10m20.702s\n", - "sys\t2m33.213s\n" + "real\t8m59.147s\n", + "user\t10m47.294s\n", + "sys\t2m36.007s\n" ] } ], @@ -2552,7 +2552,7 @@ " \"Version\": \"23.1.0\",\n", " \"buildVersion\": \"not installed\"\n", " },\n", - " \"date\": \"2024-01-24 13:18:47\",\n", + " \"date\": \"2024-01-24 15:26:56\",\n", " \"openGL\": {\n", " \"GLX\": {\n", " \"client\": {},\n", From b9ec28c81723539dd0915053bf10cffb1a94b42e Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 11:34:47 -0800 Subject: [PATCH 45/69] add obs --- .../sea_ice/make_demo_sea_ice_plots.py | 26 ++++++++++++++++--- 1 file changed, 22 insertions(+), 4 deletions(-) diff --git a/pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py b/pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py index 659cd324b..3bf820142 100644 --- a/pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py +++ b/pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py @@ -12,16 +12,24 @@ arctic = (ds.where(ds.lat > 0) * 1e-2 * area.areacello * 1e-6).sum(("lat", "lon")) +f_os_n = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc" +obs = xc.open_dataset(f_os_n) +obs_area = 625 +obs_arctic = (obs.ice_conc.where(obs.lat > 0) * 1e-2 * obs_area).sum(("xc", "yc")) + # Time series plot -arctic.siconc.sel({"time": slice("1981-01-01", "2010-12-31")}).plot() -plt.title("E3SM-1-0 Arctic monthly sea ice extent") +arctic.siconc.sel({"time": slice("1981-01-01", "2010-12-31")}).plot(label="E3SM-1-0") +obs_arctic.plot(label="OSI-SAF") +plt.title("Arctic monthly sea ice extent") plt.ylabel("Extent (km${^2}$)") +plt.xlabel("time") plt.xlim( [ cftime.DatetimeNoLeap(1981, 1, 16, 12, 0, 0, 0, has_year_zero=True), cftime.DatetimeNoLeap(2010, 12, 16, 12, 0, 0, 0, has_year_zero=True), ] ) +plt.legend(loc="upper right", fontsize=9) plt.savefig("E3SM_arctic_tseries.png") plt.close() @@ -34,10 +42,20 @@ {"time": slice("1981-01-01", "2010-12-31")} ).temporal.climatology("siconc", freq="month") arctic_clim["time"] = [x for x in range(1, 13)] -arctic_clim.siconc.plot() -plt.title("E3SM-1-0 Arctic climatological sea ice extent\n1981-2010") + +obs_arc_ds = xr.Dataset( + data_vars={"ice_conc": obs_arctic, "time_bnds": obs.time_bnds}, + coords={"time": obs.time}, +) +obs_clim = obs_arc_ds.temporal.climatology("ice_conc", freq="month") +obs_clim["time"] = [x for x in range(1, 13)] + +arctic_clim.siconc.plot(label="E3SM-1-0") +obs_clim.ice_conc.plot(label="OSI-SAF") +plt.title("Arctic climatological sea ice extent\n1981-2010") plt.xlabel("month") plt.ylabel("Extent (km${^2}$)") plt.xlim([1, 12]) +plt.legend(loc="upper right", fontsize=9) plt.savefig("E3SM_arctic_clim.png") plt.close() From f7b8c40ee6c479e2bc35c631849f3b61a4934e89 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 12:02:34 -0800 Subject: [PATCH 46/69] rerun --- pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb | 46 ++++++++++++------------ 1 file changed, 24 insertions(+), 22 deletions(-) diff --git a/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb b/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb index b59483cb3..33a348fa0 100644 --- a/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb +++ b/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb @@ -136,6 +136,8 @@ "source": [ "This first case will work with sea ice concentration ouput from a single model, E3SM-1-0. Two overview plots are shown below to visualize the Arctic sea ice in this model.\n", "\n", + "For this demo, we start the OSI-SAF satellite data in 1988 as that avoids missing data in earlier parts of the record.\n", + "\n", "The code to generate these figures can be found in `make_demo_sea_ice_plots.py`." ] }, @@ -149,7 +151,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-24 15:12:15,365 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + "2024-01-25 11:37:13,752 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" ] } ], @@ -166,14 +168,14 @@ "outputs": [ { "data": { - "image/png": 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hvOtd78L06dNRq9UwMDCAo446Ct1uF3/+859J50AF5X564okn8Mc//hEf/vCHAWSfsYMOOgiLFy+2Prvqu2200UY49NBDM8futttu2HzzzTPfbfr06bjyyivx4IMPYu+998bWW2+NCy64gPRdJPOU3AshQvQ7AgDsU6xevRq77rorzj33XNHxJ510EhYvXpz5b8cdd7QCjg09dthhB+yxxx6Z/3bffffC5+r1OvbYYw/ss88+OO6443DrrbfiySefxDe/+U3jmJ///Ofx05/+FAsXLsTZZ5+NpUuX4l//9V+Nc9hmm23w9re/HZdccgkuueQSfOADH8gACz0GBgYy//34xz9mfd877rijMIaql5o+fTqWLl1aOGbZsmUAgE022cQ59q233oqnnnoK73//+zE0NITly5dj+fLlOPzww7FmzRpcccUVhWPyak1VM7XllluyvpcrXnzxxVLj5b93o9Fw/nx4eBgAknNpUqTOnj0bvV4PL730UubnSlGuotlsAgDWrl1Lnq90jM9+9rNYuXIl5s+fDwA499xzseWWW+Ld736387h9990X1113HTqdDo466ihsueWW2GmnnTLX+/nnn8fvf//7wr03ZcoURFGEJUuWAAAWLlyIt7zlLXj22Wdxzjnn4De/+Q3uu+++pOaTcx4A2v30/PPPA4jfjfn5HX/88QCQzM92/PLly9FoNArHP/fcc4Vj99prL7z+9a/H8PAwPvWpT5E335J59uN+ChGibAQVcJ9i3rx5mDdvnvX3IyMj+NrXvob58+dj+fLl2GmnnfCtb30rKRSfPHlyRqTw8MMP4/HHHyfvQkPEseWWW2L27NleRqJSqeALX/gCvv71r+PRRx+1fu7YY4/FkUceiV6vh/PPP9/6ufvuuy/z7zlz5rDmvfvuuxfGmD17NgBg5513xhVXXIFOp5MRtzzyyCMA/Mriiy++GABw9tln4+yzzzb+/hOf+ETmZ3lWRolrnnnmGcrXIcWmm27a1/GooRZfnSVWsWjRIlSrVWy88cbre1rWePWrX4158+bhhz/8IebNm4frr78ep59+Okn5/e53vxvvfve70Wq18Nvf/hZnnXUWPvShD2HbbbfFm970JsyYMQMTJkzAJZdcYjx+xowZAIDrrrsOq1evxjXXXINtttkm+b3UE49yP6m/ffLJJ+Owww4zfua1r32t8/jp06fjpptuMv5edwQAgFNPPRWPPPIIdt99d5xyyik45JBDsN122zm/Rz/mGSLEWEUAgOspjjnmGPztb3/Dz372M8yePRvXXnst3vnOd+KRRx7Ba17zmsLnL7roImy//fZ4y1veMgazfeXGE088gWeeeSaTYlm8eLGR7Vm0aBGGhoaMzKKK97znPXjPe96DadOm4Y1vfKP1cz7LGl9MmTLFOsZ73vMeXHjhhbj66qtxxBFHJD//8Y9/jNmzZ2OvvfayjvvSSy/h2muvxT777IMzzjij8PuLLroI8+fPx6OPPuoEknvvvTemTZuGCy64AB/4wAesaTudyZgwYYJ1PCDeNJ1yyim49dZb8ba3vc352X7Ga1/7WmyxxRa4/PLLcdJJJyXfZfXq1bj66qsTZTA3ms3mOmNwPve5z+HAAw/ERz/6UdRqNXz84x9nz22//fbDRhtthJtvvhkPPfQQ3vSmN+GQQw7BmWeeienTpzs3LeocqesLxKUFHDskPSj302tf+1q85jWvwcMPP4wzzzyT/TcOOeQQ/OxnP0O323U+IwBwyy234KyzzsLXvvY1fP7zn8duu+2GI444AnfffXfCINtYurLztMW6vJ9ChAACAFwv8de//hVXXHEFnnnmmYTVOemkk3DTTTfh0ksvLbw0Wq0W5s+fj6985StjMd2XRTz66KOFOjgAeNWrXoVNN90Uv//97/GFL3wB73vf+7DddtuhWq3ikUcewfe+9z1Mnz4dJ510UnLMP/3TP2H58uV473vfi5122gm1Wg1//OMf8b3vfQ/VahX//M//bJ3H4OAgfvGLX5T6Lvfff3+Szh0aGkIURcmYb3jDGzKMiinmzZuHAw44AJ/61KcwNDSEV7/61bjiiitw00034ac//amTCZo/fz6Gh4dxwgknGDtMTJ8+HfPnz8fFF1+M733ve9ZxJk+ejH//93/Hcccdh3/4h3/Axz/+cWy22WZ44okn8PDDDyelDzvvvDMA4Fvf+hbmzZuHWq2GXXbZJVlE9fj85z+PK6+8Eu9+97vxla98BXvuuSfWrl2LO+64A4cccgje+ta3Os+LNKrVKr797W/jwx/+MA455BB84hOfQKvVwne+8x0sX77cWD5AiZ133hm33347fvWrX2HWrFmYMmVK35ifAw44ADvuuCNuu+02HHnkkZg5c6b3mFNOOQXPPPMM3v72t2PLLbfE8uXLcc4552BgYAD77bcfgPgaXH311dh3333xhS98Abvssgt6vR4WLlyI//mf/8EXv/hF7LXXXjjggAPQaDTwwQ9+EF/+8pcxPDyM888/v5Aqpwb1fvrRj36EefPm4R3veAeOPvpobLHFFli2bBn+8Ic/4MEHH8RVV11l/Rsf+MAHMH/+fBx00EH43Oc+hz333BMDAwN45plncNttt+Hd73433vOe92Dx4sU48sgjsd9+++HUU09FtVrFlVdeiX333Rdf/vKX8f3vfx9A/O6ZMGEC5s+fjx122AGTJ0/G7NmzMXv27FLztMXOO++Mn/3sZ7jyyiuTDkbq+QoRoi8xlgqUDTUARNdee23y75///OcRgGjSpEmZ/+r1enT44YcXjr/88sujer2eUYSNl3CpgAFEF154YRRFUfTcc89FRx55ZPSqV70qmjhxYtRoNKLtttsu+uQnPxktXLgwM+bNN98cHXvssdGOO+4YTZs2LarX69GsWbOiww47LLr33nszn9VVwLbgqoA/+tGPWr+Pz8pCxcqVK6MTTjgh2nzzzaNGoxHtsssu0RVXXOE9brfddotmzpwZtVot62fe+MY3RjNmzIharVaiAr7qqquMn73xxhuj/fbbL5o0aVI0ceLEaMcdd4y+9a1vJb9vtVrRcccdF2266aZRpVLJKD/zKuAoiqKXXnop+tznPhdtvfXW0cDAQDRz5szo4IMPjv74xz86v9c222wTHXzwwYWfA4g+/elPZ3721FNPRQCi73znO5mfX3fdddFee+0VDQ4ORpMmTYre/va3R3fffXfmM0oF/OKLL2Z+brI1WbBgQbTPPvtEEydOjAAkCk712bzSVp3r2267LflZXgWsx2mnnZZYJFHihhtuiObNmxdtscUWUaPRiGbOnBkddNBBGZubKIqiVatWRV/72tei1772tVGj0YimTZsW7bzzztEXvvCF6Lnnnks+96tf/Sraddddo8HBwWiLLbaIvvSlL0X//d//XfgOpjCdryjy309RFEUPP/xwdPjhh0czZ86MBgYGos033zx629veFl1wwQXec9But6Pvfve7ybwnT54cve51r4s+8YlPRH/5y1+iTqcT7bffftFmm21WeN9+5zvfKbzLr7jiiuh1r3tdNDAwUHgHUObJuRf+9re/RQceeGA0ZcqUCEC0zTbbeL9viBCcqEQRU64YwhuVSgXXXntt0hLqyiuvxIc//GE89thjBbZm8uTJ2HzzzTM/e/vb346pU6fi2muvXV9TDhEixMs89thjD1QqlUKtaIgQIUJIIqSA10PMnTsX3W4XL7zwgrem76mnnsJtt92G66+/fj3NLkSIEC/XGBoawqOPPoobbrgBDzzwQNgUhggRom8RAGCfYtWqVUm7LCAGcgsWLMAmm2yC7bffHh/+8Idx1FFH4d///d8xd+5cLFmyBLfeeit23nlnHHTQQclxl1xyCWbNmuVUFIcIEWJ8xIMPPoi3vvWtmD59Ok499dQkqxAiRIgQZSOkgPsUt99+u7Fo/aMf/Sguu+wytNttnHHGGfjJT36CZ599FtOnT8eb3vQmnH766Ulhb6/XwzbbbIOjjjoK3/jGN9b3VwgRIkSIECFCjJMIADBEiBAhQoQIEWKcRegEEiJEiBAhQoQIMc4iAMAQIUKECBEiRIhxFgEAhggRIkSIECFCjLMIKuAS0ev1sGjRIkyZMsXaGitEiBAhQoQI8fKKKIqwcuVKzJ49G9Xq+OTCAgAsEYsWLcJWW2011tMIESJEiBAhQgji6aefxpZbbjnW0xiTCACwREyZMgVAfANNnTp1jGcTIkSIECFChKDE0NAQttpqq2QdH48RAGCJUGnfqVOnBgAYIkSIECFCvMJiPJdvjc/Ed4gQIUKECBEixDiOAABDhAgRIkSIECHGWQQAGCJEiBAhQoQIMc4iAMAQIUKECBEiRIhxFgEAhggRIkSIECFCjLMIADBEiBAhQoQIEWKcRQCAIUKECBEiRIgQ4ywCAAwRIkSIECFChBhnEQBgiBAhQoQIESLEOIsAAEOECBEiRIgQIcZZBAAYIkSIECFChAgxziIAwBAhQoQIESJEiHEWAQD2IdaMdHDWjX/Aw08vH+uphAgRIkSIECFCeCMAwD7Ef/zvE/jRnU/i3T+8e6ynEiJEiBAhQoQI4Y0AAPsQ9/51yVhP4WUTp13/GE67/rGxnkaIECFChAgRwhEBAPYhXlo9MtZTeFnEklUtXHbP33DZPX/DijXtsZ5OiBAhQoQIEcISAQD2IZYFsAMAWLR8bfL/l60JoDhEiBAhQoR4uUYAgCH6FouWDyf//yUhAHx62RocP/8BPLjwpX5NK8QYx0urRxBF0VhPI0SIECFCaBEAYJ9juN0d6ymMWegMoDQtfvz8B3HjI8/hwxf+rl/TCjGG8dsnl2Luv92C7/7Pn8Z6KiFChAgRQosAAPscQ8PjNx2cAYDCtPgjz64AAKwdx0B6QwpljfTIs0NjO5EQIUKECJGJAAD7HCuHO2M9hTGLRSvKM4AhNqxYOnofrG6N3+ciRIgQIV6OEQBgn2NorYz5uuH3i/Cpnz6AVa/ghbJsDWCvl9aJbTRxoC9zCjG2sWRVCwCwahxvjEKECBHi5RgBAPY5hoQL3Y/ueBL//ehzuOsvr1xPwWwKmA8AnxtKAeQmkxp9mVOIsY2lq+L74JW8sQkRIkSIDTECAOxzrBTWAC4bTZUtf4Xap7Q6XbywspX8+6XV/PPw1JLV6XjtXl/mFWJsY+nqUQZwnAPAKIpw6i8fxXdu/uOYzuNvS1bj/r8tG9M5hAgR4uURAQD2OYbWyha6FaOp4+XCFPJYx/MrWpl/S3wAn3xxVfL/14zIAcNzK4axIPRlflnEkpVpDeB4toJ5YWULP7737/jhbX9FqzN2Aqdjf3wfDv/RvXhuxbD/wyFChNigY4MBgHfeeScOPfRQzJ49G5VKBdddd533mPnz52PXXXfFxIkTMWvWLBxzzDFYunSp6O+rmjUJA9ju9hKGZPkr1FT6WS39C8hEIH99MWUAV7fki+THf3I/3nPe3Xh62RrxGCHKRxRFCQPY6UVodcYvq/uixo6vGKNNXhRFWLh0DXpRVrAVIkSI8RkbDABcvXo1dt11V5x77rmkz99111046qij8LGPfQyPPfYYrrrqKtx333047rjjRH9/xuQmAJkNjA76Vqx9ZaaAnxuKFxRVuyexgdFTwCPdHkYEgCGKIvzlhZWIIuCvGqM4HmO43cU9f10iOo/9iKHhDtrdlPUbqzTwyuE23n3uXTjn138Zk78PpGpoAGPWJnH1SBedUaHVeHYrCBEiRBwbDACcN28ezjjjDBx22GGkz//2t7/FtttuixNOOAFz5szBm9/8ZnziE5/A/fffL/r7myoAKEgB66DvlcoArhpl7LbeZCKAuJaRm/J7fiibllo7wmcBV490MTxaP7hk1SsTTPcrTv3lY/jQhb/D2bf8eUz+/tJV2bKAsVIC/+7JZXj4mRX42X0Lx+TvA9lzMVZlHnp9sbRWOUSIEBtObDAAkBt77703nnnmGdx4442IogjPP/88fvGLX+Dggw8WjbfplBgASl6sOugrAwB/++RSLFw6NmnP1qhx86xpgwDilN9KJuOTTxGuEtQB6qm2JTkAMt7iyvufBgBccMdfxWN0uj1xd5uluTIAKQP42yeX4nu3/BndnqyG8O+jpQBLV41dS7plq8d+k6f/XWmtcogQITacGNcAcP78+TjiiCPQaDSw+eabY6ONNsIPfvAD6zGtVgtDQ0OZ/1SkKWD+i1VPl0rZgfv/tgwf+H+/xT+ed7fo+LKhwNvUwQFMbNQA8OsA84zfGgFg0EGfDgZfSbFmpIOrH3imb/Of3KyLjz3s/Hvw1u/eLgKBeQZQagZ92vWP4Zz//Qv+7ymZenXh0ri0YKTbGzPgo7PRY6X01z1KAwMYIkSIcQsAH3/8cZxwwgk45ZRT8MADD+Cmm27CU089hU9+8pPWY8466yxMmzYt+W+rrbZKfleOAdTrg2SLwxX/F7M9y8aoA4cCCIMDVWw8UVYHOJxTR64WpIA3BAbwqvufwRevehhv+Mav8efnV5Yeb9oEman26lYHv39mBRavGMZigWo0n4KXMIBRFGGhYvBWy67nQk0M9OKqsVG/6mB4rEQgyzMAMDCAIUKM9xi3APCss87CPvvsgy996UvYZZdd8I53vAPnnXceLrnkEixevNh4zMknn4wVK1Yk/z399NPJ72ZMjkGPrAawPAP40NMvJf9/LNJcKQCsYeNJMeDgMoBqDMUgShgjHfS9UgGgboj9mcsfFI2hg60pgzIGUPd1lLBWS/sAAIeGO1gzuhGQsnd/1wDgC0JWdbjdxaPPrhA/WxkRyJjVAAYGMESIEGmMWwC4Zs0aVKvZr1+rxcDD9pJvNpuYOnVq5j8VigGUqID1rhlrRrpsn7C1I108qVmojIXdhvqbzXrKAHLYyCiKEvGGUhJLAKDOAI5lCvjah57Be8+/B4sFdhtt7fr95YVV6HT51/OZl1LQI62de0EDohLQkmfsJABQP3+SZ6vbi/DMsnQMqTDocz97CIf84C786vfmzaEvlvahBnDNSAfXPfSsGEAuX6uLQAIDGCLEeI8NBgCuWrUKCxYswIIFCwAATz31FBYsWICFC2Pl38knn4yjjjoq+fyhhx6Ka665Bueffz6efPJJ3H333TjhhBOw5557Yvbs2ey/n6aA+S/W/ILAfcHfl3P2Hwu7DcXeNQdqScqRs2DroHX6KABcI0gBZxnAsVMBf+HKh/HA31/Cj+54kn2sfi6iSAa+dNAjAU5Ali0TAcDc+ZcAej31LJnDc0PDGNEAtHRTcPNjzwMAfnrv30XH90MFfNr1j+HzVy7ASVc9LDpet5+R3hMvl7jyvoX47ZMyz9YQIULEscEAwPvvvx9z587F3LlzAQAnnngi5s6di1NOOQUAsHjx4gQMAsDRRx+Ns88+G+eeey522mknvP/978drX/taXHPNNaK/r2xgVrU6bMYlvyBwfcLyAFBabF8mFHs3OFBDox7fVm0Gc6W3fpuunUtu6Av8S2tGROxZ2dC/90Ctwj4+L7iQ1HXqDKA0dVoWAKp5Tx1NQUtsYBYvTwHgkGAOf1+6OvNvCQDU09/bbTqJfTyQBcNSEcjP738GAHDL48+LjtevobRn+apWB1+5+velepZf9Jsn8bZ/vz3TO5wTT7ywEv989SP44s9lQDhEiBBxyOWBL7PYf//9nfU5l112WeFnn/3sZ/HZz3629N+uVSvYaDTtCcQL3bSJ9ML7PODjMgR5BnEs0jsqbd2sVzFQVQCQDoSVAKRaSUULknZwL2oLbRTFIGTm1EH2OGVCN6BWYJYTw7kUvgQAPv1SuriubXcx0uklwJwaL6zU2DdB2nLtKJCdOXUQQ8OrEq9ITmRTwPz7Id8NRlIXqrcVrFb5gH7NSCc5F8DLpQZQ9o645fHn8LP7nsZfX1yFN79mhmiMM/7rDwCAy3+3ECe947Xs459aEl/T54aGEUURKhX+NRkabuPuvyzBW183E4MDNfbxIUJsCLHBMIBjGY16BY16FY1afDpXM4HLSzlGgFsjtDbHGI01AzhQj1/IHAZQWcBMGKhhUlOJQAQp4BzDIy36LxN/WJzaA+nMJjVaueuZvz8ooTOAgCzl98JQOQZQMZlKILWqxR+jbAr476O+mJNGhUUSBlAHgBLglE+Fj50KuLwRtCotkKjCgexmZkJDBrwUc9gVeI2q+PT8B/Gp+Q/i+2PYHSZEiLGOAAD7EM1R4FcfTfd1GMwXkAI+lSrjpojy/nnSGsAH/r4Mn/zPB0Q9dHUGsD7KAHLOg2IABwdqmNSIzwMXyEZRhBdHGR6lfB0LJfDji1IAmAfnlCgygIIawJey6TVJ+jTDAJYCgDELKgH0GQZQMAcFnrfffAqA8gygBDjlDbElIhC9nd8kIXDKGkHLAKDqIfzCypZIEf37Z5aL/m5mDlrqeLng2QCA34ymsH/xwDOl5xMixCs1AgDsQ6jU2sAoEBxh1p2pxXXOjEmZf1MjnyqVAsD3nn8vbnrsOfzbDY+zjy1bA6gfP2nUuJjrA7iy1UkWytclCz4PTN/zxBIcedHvCrVjnHhcYwAlBsrqGLXQSxjA54eyQEeSPtUZQIlwYW0OAIpUwHoNoAB8qftKtSgsywBKgJMSgMwe7ZIzNNxm1wnrQLhek7229ffKqlZHBOCeHb0eI52eaFPw+2dWJP9fmoZ+VgOAkmdDDyU4CxFiPEYAgH2IRj1eqFXBf6dHBz4jnV6yMG47CgC5DEFeLVtWBZxnLCihG0HXR+ukOEA4VRFXxT6AanGf3Kxjq43jBZ/L+Hz2iodw1xNL8I8/lHVUiaIIf1icmjdL+hkrFfCsjSYAKKYQKaE2BQpEyhjAsing+HsohTz3voyiKGGcANl3UOdf3Q9LV4+gxwBf7W6vdO2cun7bbToZQFybymUSn86pujnfQYV+DXuRzGj9Wa20IL/JoITOAEqVyItKAkDdZmv65AAAQ4zfCACwD9HMMYDtDv3lrF7KlUq6SOm1OpRQ4GmjUeGJRG2p1+YotoQTI4kPYC05D6wUsAKQ9VrSuowrAlGp840nDWDGKOjgMj4KfL20ps1iMFUMDXcy51KSAs73VeYuclEUJX93s9ExuABuuN3NqkZLMYAyX8cVa9sJiIznwGetVGnBFhvHYLrbi1jnM++pKQGAqk5tk0mNBJBzN3lPa8AriiDos90tbBS5IDSKIizSGFm9RIB6/IKnyzOA+hwk6XS9PKLJFEaFCLEhRbj7+xAq5alqANsMBlAtihMHaokBspQBnDlF1VrxX6yPLVrh/5AjdAawXAq4iokqBcysGVNjTBioYYoQRO6y5bTk/9/1BN/qIi/gENUA5gAgVwU83O5B4aTNp6ZpR07kgTP3nuz1omRToFLA3AVfMZDqfhrp9tgm54oBnNysJ8/XiwxWOJ/Cl6Wh02dDuQVwU+p5UQ9Xla3AfLWSbhS59kDL17Qz9zOXAVy2eiTDyEvqKdvdHp7XgKeEAfzbkrS8Yyw8U0OEeLlEAIB9iGaSAlYMICMFPAqSmpr6lZs2VAAwMaMWAcC0bk2yM29pDKBKAXNsYFRaZkKjlrAkXDV1SxOSqDopzhziz6fX7oaH+V0f8gBFUgOYpICnxawVd5HTQe9mCgAyF3vF7qhryWYQtTRbIgJhXk+1kdl0chPKfYU/j7S2dKMJfOCTv55rRrpsb8lWO70vp47Ogfs99BQwwM8SKMA4bcJAYrPEBWDP5nz7nh/iMYB5BlLCKj+3Yhg6CcztNw4Af1uagunQESXEeI4AAPsQSQpYqV8Z9TnKJkRXz7aZ9T1qV56YUQteao8+mzKAErsOneVIgLCgBnCwrolAuGku7Vwm9ZjMxVpPOep2LvTju85/c8aYvVEM3rg1gGqhbdarKdvDZgDjv7n19LgcYG2b16JQP48qHb9qmJfCVQzw5GY9AU5c0KDA14SBmqbS59+XEzXlLZc1MoFQrtL/6RwDyGVkFeM4bcJAopDngp88AHyBCQDz948EfOXNoyWm2rrAKwDAEOM5AgDsQzRGfe+SFDBjgRnpdkfHqGKgzmcQgZQxVAwgl2kBsgygxK5DX+QGBOdBfQfdBobbCs7IQgrBNACsFADhPGMkSwGXYwDXaqBlmhA4rW3H99DsaRNQEbBvag6NWjUBHJ1exErhqvt4UlPWXlCfRyxO4qv01aZiUrOOwYH4eC6bmm5uqpisuqIwQaQCfDUhI6uer4mNOqY0ZecyD764HpvDOU9MEQDM9dYuywCGFHCI8RwBAPYhCilgTupTZ62qfBVxr5cW/M+cEjNGkher7v3HfSl2ur3E1iJm3ySdQFQqvIqJo6lw7jwyXoSJEIXLAKaATdJCLc9ycNP5URQlYygGcM1Il8UkrtEW+6mDspTj2pHe6Bi1ZAwOiNQZYQXo9blRIlEyN+XfY1hLvw4IfDpTf8pqeh6YwEnvk90QCKSAlMncbHSTx60hVPWYA/WqmAFUAHCLUXU6NwWs7mu1oZDUUyoBiDqPZRlAqR3Oyyle6fMPMXYRAGAfIvUB5KeYMqxV4iPIX6AAjQFkAqeRTi+TtuYuDLpxcbzQlkgBD6Qq4NXMl3NLA5GSxT6eRzrnlQK7jXznjzzr4Yt2N4L6kzMmNxMmk8MCKuA0oZHWnHF9APUxpgnq1nRGt1atJDV8nGdDMdETGzVMnRDfE3z2TWemVYkGnwFs1mti4KTPQZIlAFLWctPRms4VTOCj/l6zVtXuCSYDONr9Y7etNwLAF4Goc6lqQiX1lGoTsuUmMnYcyHYL6mobaG6MdHqiEo9+xim/fBRv/tZtolaNIUIEANiHyNvAsFJMo6ClIaxb0xkVqeFusZOIjOEAFPvGZzKTRbJew+Aoo9qLpPWUNVG6Lx4j/S6xVxoXDKeMEcBPAeuAfnCgho1HlaucOsA03VdLustwU8DDWu3ctKRujT6GLuoBZM+G2shMatT7kwKu8cVJ+vWcMigTT5jrY2Ubi4QBZC746rwP1CtiIKuux2tmxn6GLzK7gahzOUPrj81n+ePvodTtLwk6gfTD2qfXi3Dwf/wGB3zvDpFdVL/ilsefx7PL1+KxxeVcHEKMzwgAsA+hUsB1QXpHT1tK/PNSpiVN7XBr+PIgZ7jdE7GYjXoVlUolSc9w/BAzi2Q9be7OeblmU8BCBjCXwuUyZ2qh3mhCDNy4KWCdQWzWq0mnAh4DWARvcuBUS4QkPAYwBfQARKlPZVScSQEzgI9uRdMPBlDKpib1sXUZOw6kz9jMqTF44qbC1Xlo1KqYMBBfEy57pe5l5RM6kjPJ9oU6l5ObtWQOXEY3DwC556HT7RU2lRIAuGJtG395YRWeXraWbdPUz1AbO0nddogQAQD2IVQKuCFI72QZQP7ikBb815MCcy5DsUZjjFRwXih6kTuQAmGOH2LCGGkLNcC1ktFVwPzFvtuLkr+niu3ZqtPROSjQtLbd5bEk7RTEVioVrf6OvkjpDOAUwfHxGKOeig2ZdUlyTzTU5oj/bKxRHpnNmihtqTM9EwY0YRBjY9LqFDdYcgawJmL5oyhKGDxV58utAVT39UBN9p4B0u8xdXAgKdPgbEz0ft/qXHI3JiMJEI7Pwyqt/SMl9HtCGZSX7e8sOb4f0e1FySZJ4v0aIkQAgH0IlQKW2LiMaKBFYiStsz3qpcwtbFaAYdqEgeS7cBSwCUsykG2JJ+0FrBZq/hhpsb3Ei1BnRFSqjQ8As11Z4p/JWEwACRvKAbKqfm9io47mgGyxTzYWAzUZCM1tCkQpYMUA6ilgwRyAXG2qlAEUpk5bhhQwp85Xv382UwwgNwXcSd0GEqN2BhAGNKP1Ri25PyWK6mY9rUNkn8skjdxIxCQcT0T9XE6fJO9RvSwDAMcGfOl2X0HNHEISAQD2IdIUcDkRiCR1mi72KQDsRTzxgV7wr4NIauTr3iTfQ09lVyoVEYhsJam21FKHV4dYFNSwU8Cjc1CABeClgXUgDKSbCg7LsUbV7zW0e0rI9uiLvQSM52sAWSngVnpvK3Z6FaMmU82hUauiVq3IWhSaVMDsespiGppnFZV+NmUAuSKQ+Ds3ammtMQcIA9l6ygQACtg3XVAjZQB1cRInDa3fE0pYJOvvnApJxgp86ecuAMAQkggAsA+hWBrJYjuipYBl4ol0oZ3YqCW7Yg6Dp6eAE58yxktRN3EGZClgtdCmLCIfROpMZGLKLbCiadSrmDbasksqnpjUqCf3A0cIonczATTgxGCV9RSwVHWqNgVSX8f8PSEZQ81hcrOesFaczdXahBHOtWoUsVYlVMAdnQEUbBK1zZzamLBrALumUhOZR+bgQC25Hhx2e1i7HqmgRrbBatSq2Hj0GX2JUYOnl4moOUiM85e+DBhAHQCGFHAISQQA2IdIUsAClWFGuCBhe7QUcKVSweQGXwiSAMCBesIActrJ6fYrgDQFbAY+kjrCTDpdBFqqqXqWyVCkILSaMKKcYvthLU0GyKyFknuiUcss9qyyAK2vsiR9q6cLAakKePS+bNbT4wWAI8+mSsoCMipgoUq+Wa+JUsA6eJOwXoDmA1hLPTK5hvO6MlxlPaQM4FRhPWVSMjMgS8m3tI2metdJ/Ah1Vb4EQPYj9HKIwACGkEQAgH2IRsEIulwKmMP25AUcqo0a56WUpJGbWQ8+arQKbI+AfUtsYOQgMisCUWyqZLHXRAdClWKzXkvATxkGUNLTWAf0UkHNsBFE8tm3ApPJ+h7KBkZPZfPvKaU4VR17pCUa/fQi5LGQ6cZGbbK49kaJDUytKhKrAdnnI2UABWIxDUzzn680hdsQlHnodYhThF1ZAGDZ6jQFLOm93o9YGRjAECUjAMA+RLOWLXTnLHLGFLDABkaBjUQJzGAp9NZhU0Qp4DwDWM4IOjMGS7GZLtZ1SQpYB4DCrg8pw1BNvouEAVTsoaQ7zFqtLrSRAYB8ADdBW+yltj6ADNCv1jqayFjI7BwkIq1M2rLZDx/AMiC0mpQ2RBGS7juUaGvvGcm57HR7CfiOGcByNYBiBrCbvmskbKp+PScLU/oAsORloALW5x1sYEJIou7/SAhfFFKfwrSl/mKOogiVSsV1KAA9BRxfSrXYsXquttIxGrX4OM6uOGGtcmIYyeJQJmWogy9ZCjgFXynbIyv4b9ZTrzNlqUI7Pk0XArKygmwKOL2HWBuLkkISHUACsk1BYgTdrGHlsDylP2GgPyItxbJzBFZxaz8TAyhxCqgl4qZ4jB5q1ZrtsOwYKo1ck4ph0u88oSGrAdTfdep4cYmFZmcjec8M1muYIsiWqFj2ckgBBxFIiJIRGMA+RCMvfmAwJSOZnXm6WFN39zp7B0D0UtQZI5VC5uyK88pVSSrbJhqQFMtn+xHLWMjSDGA9ZQB5KeAsA1gvAb4mNuI2bGofwQHTa7XaUnUtWkJbH0DWJzsFgKkIRALoC8IiIYs4IJiDDpAGhfWUOgMotUhSnxX7jWpK9mY9VQHzRCDpPSEV1JgYQFGZiJaGfqXawGQZwAAAQ/AjMIB9iKRgX5CuM5kXA/FCWSds7nXwBsiUyEnNWDP9gxIGMN8ST7rQZseQpoD56XS9HV3ZGsDBAY0BZKWAs2poSQcNvS60UqlgoFrFSLcnY/AaKevEEQ3Y2DfqHKIoSr6HrqhmWQsVRCASkZbGvpVIQ8djVEUs5IghfQtwWUTdCLqMSGu024+IAUzfdZOUWE3YKadRqyU1nSKv0Hq1nAhk9cvABmZtYABDlIvAAPYh8jWAIpsJrVE8QE8jrxnJLnINQW2O8o2bOFBP0iKcHWWeadHTllTlqU00IGoFp7MDAkud5kAfVMD1apLOHmYscvoiCWigRWIOPrrIShb8LANYHtBzNyYjWsuuic0UhErA14TcpoK3QSvW7/GUyPFnlQ+hRMyib7Bq1QoUCcgCkQYGUFI7pwC9RAWsP1+STQVgqQGUpIA1FpILnnq9KMsAjpkI5OWhAv7Vw4vw7h/ejaeXrRmzOYSQRQCAfYhGIV3Ht3ho1tICb4D+YlybUwGLGEDNcFdiA2Nj7wBaGjiKIoOQZHSxlaT86jWRoEZvVSVpPaaP0axXSzGAeRUwTxiUZYW5qcsoirKeb4L72g7oaWPoRe0T9dRpCRuYAck9od1Tkmcr3yZRwiLqDCCg+2zyRSDSVnD5mk6JClivv5MqkZUiulGT+RnqYFoqAlm+tg391I+VCOTl4gN45X1P4+Gnl+PWP74wZnMIIYsAAPsQec82KWtVrVaSHrTU+rl8DaCIAdREA5MF5qh6mgxIzwNAOxf5Oql4DD7wMXkqdnp0FlK3DUkYQG4KWGNDJTWAehoaKOkDOJCvC6Wdh0wPXa3gX6bAlaWA1YI2OBD71pVJW07IgWmJsGhwQAY4hgu2PuWEKIDeaUfGAErscNbmMg0iFbDOAAo71JStAdQBvUpDr2GCJ90CBhg7EcjLRQW8ZLQrCqcvdIiXRwQA2Ido5Hb3LNCSM/5N65R4KWCV7pPU5iiAMqkpbAXnYAApi6Xe6aCMalQXUDSYLCSQ/R5TB1PLjx6DadFB6IRGPAdOKzh9QwBoreAE1kB5Vpia+tTnK00Br8178DHH0Ov/JMcDprZ65RhAWRraLJCSsFYpA8ivNU5EIELmbLiT+x4CADhsrKekz6HXizIt7SQsohHQM55vAFiyKgt0pOnXF1e28C/XPoJHn10hOl5nHke6Pda16Geo88HpyBLi5REBAPYhmiWMoPWdOcBfINZa2B6RbYjWCaQMA8hVKiqWRO/XKkkBtwwpYIC+4Lc01kqlgHsRsJrRf9YkAhH5AKp7isnWRFGU1nQKawDVhqBRVz10BbY+ZVPAmjm5mgvneKDYCk5kHKxtKvTzSGeVs3MQvSNydaFcRhfQWqjpHYckNaGNbA0gSwSibbAkqm59vk2polpP6QvmAKQK4BmT41Z0UhXwLxc8i8t/txA/uvNJ0fH5PuVjkQaO6yFjBnAZsztNiLGPAAD7EMVWcLIXkj4GdcFf086pgAU789VaDWDCWonq1uJjK5UKq94qXycF8BfK2G9NSwELBDU6y9Gspyxi/kXrCl3EIQGArTybqqWyKTHS7SUWQmqxrjMBQz6FXMoHsCF7NhILmATElhGByFvBpV04amjW4nE4JszFPtnlOtwAMrcB9fcGatVS6tnBHJjmvGdGDAyg5F0JjDKZEoNy3YtQ2BJP3ZubTxsEEDOAnCyBikXLhwEAL64cZh8LFGsPx0II8tKakaQeMjCAr7wIALAPMZCrAeT43+UZQO5Cl9+ZN5Lj6aBDryNsjC5yZVJtAA/AtXPnAEjPKZW96/Si5EXUrNcyghrqGDoQrVQqSZE4ryuK5gMoagWXKwngsnda+jbvDUkdIw+cBgTsW97QmjsHVdOkfCl1EQi3prM/9XfVhI0F6Oei0NmlhL1RQQQiYBEHNCPoMrY+qQ+gwOJICL5ao++0SiX3PUSuC+n15FwL/fObTGomP1vFyBKoeGEU+L20Wsac5euTOZmKfsVSDfQtKwEA//uRxbj3r0v7MaUQjAgAsA9RTM0ICqOFdYTFfqclUsB6wb/Q4V+FSgNTgKzuUaZigFkLqaeilKCGa5ehwFMzpxqV9nZWrM/aNn+hlaqA1bXUF0hurZTuARiPxb8nChsTJqBfY/G3BAQ1nQWRlqw9oH5/Ujdo+d7O5VrB5e9LhgrYZATN2KjmVd1lWsENDtQS8MWpAUw9AEe9CEs8n4M5X0fqpkL/e1Oa9eRaSIQgL6yMU6dLBcBpuN1N7sFNJjXEcygbSgACyEUgL6wcxvGXP4hPzX+gX9MKQYwNBgDeeeedOPTQQzF79mxUKhVcd9113mNarRa++tWvYptttkGz2cSrXvUqXHLJJey/rRanOjNdB7he7jzgk68hlCzWkxrpC41zvInB4yz4enpKBZcJbWksW3I9mAtdvtBdxjCkqTIFfjgikLxqlAuE8+lbgP898opPkQI3dy45GwIgNQhOUsAZ9o3JZAqZUKB8Fw5bj2sO8MnbwJTqOV7LtkmU+nTKOoGkG8Uy9dKFDbfg+dRVxPE8ZNdDIppT8eLKVD3LTSGrusNKBZg5pSmeQ9nQBTHLVo+wgLSKRcuHEUXA8jXtMROyjNfYYDqBrF69GrvuuiuOOeYYvPe97yUdc/jhh+P555/HxRdfjFe/+tV44YUX0OnwHyLVs1dSW2P1+CK+kEZyCkHuSzGKorTgvlFLjuO8VNVc9RdqWm9VLgVMre/RgXB1dKEeqFYwAnqayaZmFgH6TCcQRg1hXhXOBbE5AQjATzvmrYWadd5i3e1FyX0tVXUn3pTN7PHA6D3R8I9R7ATCv546gItZpypLcWlPAcsZdskYiXpWS7+qWka9XtYWtkyDlAFUc+dlS9QzLhe86RvuRgYA9jLvH1eMJBvWCqYMDuClNW2RF+DzQ3EKuNuLMDTcxkYTCTf1aCgPwMnNeuJYMBZWMEs1BrDV6WFtu5t591Biycp0jNWtDhp1+nkIUS42GAA4b948zJs3j/z5m266CXfccQeefPJJbLLJJgCAbbfdttQcREXmls4P1AJv3RYB4L+YW50e1KZtQqMGjL7HVFpEgVv3HFJ2QcUAAwzrL1QVXOuS/HkEFHjqksew9SOmAshYiJLOI2FJOCngkinDNbnUK8A3gi6rLNfrwvKiASprtTpnZVMf7WkcRfTNid0ImnZ8R+tGont9jnQ5DL35npKkgPM2MBImUzeCjsegtZzMs6lcFbDpXMZ/vwQDKOgmYurswp2HnrGYLOidDsRs3RotM7Bs9QgLAKp075RmHZNGN0ljoQJemrPEWbpqBBM3YQLAVdm2ehtPCgBwfcUGkwLmxvXXX4899tgD3/72t7HFFltg++23x0knnYS1a9daj2m1WhgaGsr8p0eamqEDhoINDHOxth1PBYBrMqKBeoYdICtPtcVFBQc0mBhErg1MXnSgj0EGLgXfOB77lqlDrFcT648yXR+46b52twiEuWlkWxcPLusFaOpXZgpYV4wCSlnOFbPkricz/Wo0KGc+n/k2iRLWKn8uJGOYagABukI+XxYg2WiqaGrgq9OLyOlPPX0L8GtbgayvY210UwHIn1FpO7kXhrLKX66AQt/oTSqRhi4bS3Om2JI6QB0ASi11Qshig2EAufHkk0/irrvuwuDgIK699losWbIExx9/PJYtW2atAzzrrLNw+umnW8eU1u8BJiNo/0ux24sSOwppwb8qtld9Rhu53bm+WNjCxOANcFLABgCZpD6ZKeCsEIUpqOlkrUu4DEP2eqaKal49ZZqqA/i9gEe6xXPJrTvLCzjyxfI+Vlhd83q1kqbjhSyifj0btSpGOpz0q1kEQt2gmZ5PrgefrayAw7AXbGBKG0FrzJewPIKrAs4/G/rmst3roVn105Aj2ncAhDWA2n2lNhUjnZ4MTNdSAMgFLi+szAInrhBEL/WYLOjf3q94cWV23hIlsF5HWEbJ/NLqkcAeMmPcMoC9Xg+VSgXz58/HnnvuiYMOOghnn302LrvsMisLePLJJ2PFihXJf08//XTm99z6vfxLEZDZpwDlGUCT2pI6RpISydTw0YGoMYXMZHvy7EBmDtIUMDMdrxaX6qhNRRmvtIHcIkdmAEeP1+u6uEA27fuqNhXpOaWwwiOOOVDBV54JBfgbLJsIhKsibtTSulKuJ2KehdTBF9VL0FrnK7ivGqPAh7PRBFy9gHnXYqAWt7vM1t/xGPoimyqrQwTiHuwAL42sZywkxvlAWv+nQswADmgM4JjYwJRnAF/UU8BCBvAXDzyDuf92C668b6Ho+PEa4xYAzpo1C1tssQWmTZuW/GyHHXZAFEV45plnjMc0m01MnTo1858e3AVKf4GrYzkLvr7zVcdz+7amADB+iWTSIlQA2MnWIQK8esiEtdKUng0my5FXUwOaiTLTUqeZpAxlKeRmfVQwIFBKtnNsKrfey5VO59ZCpj6AvFopo6pbaOuTBYC8e9umRObOoVkGhBZqAAXAp1AnzAOyvV6UfDa/sZB6Q6rnjJsCzm90ATr4SmoAC99B4k+ZY/mFNZmSPtlAqgBWwQeAMVCa0KiJQWg/QtUAbrHRBADAMoGnoX4upGnsh59eHv/vM7K2euM1xi0A3GeffbBo0SKsWrUq+dmf//xnVKtVbLnllqIxuQxDPh0Rj8Hxz9MYwHxahMwApi8SAInSkToHwGIDw6iHNIMWXqrNLALhpfxs6TpuwX8zx5zxFNXmNBcdABbZVHF7wVwfXoCWklf3v4nRpaah83Vv+hjU75EvLZCCnqZmqcNd8F19srn1kA1hKlv/O/kx6N8jC6a5mxtdfAHEG03lqiN+vgSuC6lKX3Ye9L+nC2q47eTyKWAuABzWlPrq3T3MEJupWNXq4NjL7hMzZ6p+79UzJwOQdQPJi0AksWJtDDyXC70Ix2tsMABw1apVWLBgARYsWAAAeOqpp7BgwQIsXBjf2CeffDKOOuqo5PMf+tCHMH36dBxzzDF4/PHHceedd+JLX/oSjj32WEyYMEE0B67Dfr7HJ8Az/tXZosSKhgk61uZSwAA/jWysO6vTX6wmxojb7cBsRj06Btc4OJdiYjOIwnQ8UATD3O4ypnpMBYTJm4Ic28MtlnelgLnPhr6p4NrR5M8F93qaGUBmmYcjBcytRcyzZ1QwrZ+vfKaAei7K+gAO51pe6nNgA2HhZhcott4sI6jRzdY5noxAKgKZMTn28CuTAk6JBz4A/MH//gW3/vEF/PPVj7CPHW53k3koALhMIgJZWT4FnALA0I+YExsMALz//vsxd+5czJ07FwBw4oknYu7cuTjllFMAAIsXL07AIABMnjwZt9xyC5YvX4499tgDH/7wh3HooYfiP/7jP8Rz4Bbsp6kE7aXIAD75FyKQLrR8n7LiHLgv5gzoYKSAzawVM12XS9/q8yEzgAUjaB47UKgvktQA5uopuUIWl6k2mflKGMD4OF2BKwX00nrKpul7MGtTU2NwWQ3h4IB8DqY+2dz6uzzzxWW2M6UmVRkbauvsMkIVgbSLGzS+MCjP3knSt7mexoIx9I27xCQdAJ4fikHPDrOmAOCLQDLdm4RzAIAHF77EPkaF3uNcpYC5DGCr0830Wi/PAAYAyIkNRgW8//77O13IL7vsssLPXve61+GWW27p2xy4u2pX2pJVa6WzJH1gzhIrGmL61SXioCxSJgApXRwGDWwqZYwoigqMbJ3xHeI5ZM9lmiajLZJRFJWuAVT3nn5PsIULOS9CNcZIp0cCPn1JAXeziz3AY5W7Wm/ohE1l1s6Z6kq5myPTuRyoVdHpddm1wurZ5l7PlFVOVdl1bgo4qWWM/zaXAdQN0lVwv4eNAeTVAFrYVFGdbpUt/FOxshUDlTkzJuE3f1mCZTkxhS/0FDC3XEaPp5asYR+TzmGU6a9WsOkUGZOZ9xGUAsChkAIWxQbDAL4cIjVxjkgtcYwpYMYi1TIwgFJ/LnNqxg9cTAtt/P85QNZeA8ivD0q/B8dUWz/fA4WFli8C0Y/vRTQQ2e1FiSl3P2sAuQulkVnmXM9+poCNdaH0zZE+j74wgMy6s3xnF8k88swXV5zk9OmkMpk2BrBLayeXF1/oc5C+q7gAMjZqz4tA+DWAI9r7SloDqMBTypzxmCtTCpg7h063l6m/45iTA9mSmYlJHSKvG4n+9wG5lU3CAK4NDCAnAgDsY9S1Fyzl5dzK2TvE/5/efcINnGQ+ZfoYlN19pr5ImN4xMZniRdI4B973SHs7c1P6ZgYQoKZOiyCUW2Nk9GTkMi0uUQ7le/RMx8tU3SYVMOee0o/TgRNng2ZUInMtkkqIclIwLLM/yZvFS+aQt4FRICyKqO86w/PJBdN5MQzz+I62WS1YybAsddL3NtetQIVKqW+xcQwA83YqvlijCbUkSmYAeOLFVZl/c70MhzNdVXgMv4q8GnqlAABGUZQAwDUjXXLGJUQAgH2NfG9JX5jq1jjCBRODyFfn2dNcHAsX/ThABr7MylUeW2NmWujnEtBEA9KUfq79WX586xxMoIUJQlXa3iTIoafaihsTFvhKGMDyLKT5vpSBaf3+onjwuespqeUR8efM50LGAHLdBpwMINkaaLTEIlffCtA2BXmBlT4HOgNo7onMNQY3jcETgaTPWNJdhpl+VXOZNW1w9N89Fnump4ClNYC/fzprmcIFgHrGQ8pC5hlAiQhk9Ug38yysCHWA5AgAsI9Rzyj86ODJWOguLLbnF2cXa604aeS2ATjp/1/aC5i7ozQBWY6AQs2hUolVrwD43nVqkdTanymrC27aUqpcNQswhIyTUIGbAnrD9SyhAuaoRlU6q1atJNdTB2EU8NQxMKH8+rvivc1lt/PpcK6fobksgJ5piKKooALWx2oRgIuJARQzoYphF9YJ6/OQiEBS2yt+e0IVCsBNm5B2ruD4hSb2XVoKmMu+PbYoCwCHhnnAKbVJqiaZK24a+aVRsKZSyJIU8Ipc2jekgekRAGAfQ72YAdoilVf3ATzlqnGRZL8Us0a1+hgc1kq3oon/zWGMTKyVAj488GVKp1PSM/quXn0PdmeXHPjimkHrLdTSOfDqSk2Ag6tmzrfcisegp8r6mQKWmjCPaOdShf7/5RsTftoxPq78MypVv5p8Ojn3tn7fqDlUqxXWfWUSgYiZUEHXpOzx6TMuMXLWU/KStnw6oJ46mOowOUIUXQUsSWMDReA0xAROiWtCvcZyfciMMXoelIhEIgLJM35BCUyPAAD7GLFdBv2FYErhcoCTaYHiG9XaGUBawX9xgQOEauYMa8VkSboOH0BG2rJMO7q2A5CTaudMQLjKY63Mnoy8BSLp7GKo12qRvkf5FLCzBpBRH2u6ntR5qM2H/j2k3nVSgVOvFyVzTYGP2iTKRT3SdLo+hppPi2BA7BIWlTbE7kXoEZ4NsxCFnz7V37sSBe5It5fUIg42aiIQalIBS4UoKoa4NYBtUw2gbA7TR3v4igBgDrhK2tGN1wgAsM/BSdmZFjkJcDItkt1eRKpzctnA8BjA7K2UdAJh1FplAQcTMBgYQI5fmos5o7KQ5pQ8vWWWqscypQv18blz6IdogAe+7ClgugrYIJBiLDLqmunCIr37BKdDjZkJpd2XaiOYYSIFTCagAx8mCE3OhYwVzpZ5yMCw610lFXrp15ZSy2j2CuWzZ7pojeM0oEIHXoP1WlICJGIAB2qijihAKuJQIU0BD5aZw+gY0yeXYABzADDUANIjAMA+h3ohkNIiBnd8DoA0sQv6C5YkRDG23GKwPRYAyOk+4awBZHYjyezuGZY6RuaMWVuj1wap4JhBuxbJ+Pf0dLqxLV8/RAOUOThSwFyT9H6y44CW+qRsTAzgjZ9+NbCpjO+hs2tFf8oytj6M94yhnlKfD48BLGYrpOKkBvPZyJtA62NwUpf6+eR+ByAFPbXRNLqkY5AxBcxMv6rrpip3uClg/RmVpqHVNVEdUVYNd0ilLnrk5718bWAAqREAYJ9D8mKV1hi1DaAnU5xNeBjT7hVF1krK9gBcMUv5mjFzWz2+F6GUQQRSoFiv6ueyZAqY2TrMxCJKwbQ4ZVgyBRxFkZmFrNM3FW3DtQDS0gLKuVTPsCkFzK2/y4JI+n3ZGi1tqFTSMbiqT+PzxfgeJqN2gOcX6rTDEfb7zmyOCPeEqR2dJHWpvysk4Gutxt7pdcIcAJimgOvJPc5Ov46Cr01HwRdfBZwygGVB6IzJcQq404tYYhjAlAIODCA1AgDsc3CAj0nEIbGBsQEGyhzSuhhZtwMTa6XPidTTuCTjBLg92zh9lctY0bgWOWk6vVJJWRdaOr242ItVp0IFrjmdnpYm+Oq1Yp+++P83a/p9WcuMT5lD/r7k9Jg2p7JH2fESPoCcd4RuJC0VJymAlnk2GDW2NpafwwC6ajqlNYB6Sp+TTs+2zqRvKlTo7yuuIhvI+ucBPCCtoh8pYHXdlACDnwJW5EEtcy05DJ46F5tMStXQ3DRwQQUcACA5AgDsc4hq+LRFjlOwP2JgrSqVCgt0GO0ZGC/FEYsIRMJkSlWngLlujeOhZwKh6fHEFLAaQ0sBc9rBmYBTZh4M9q0fxsPSYvlE+VotHg/4r4dJdQqA5WdoO5c8M+nyHn4mKxlJKrthuC+p7HhSFmAsb2Cw4/k6X3VvS2tTmWyquVyFw7DbQSgHPLW0dw3XKxRIGcC8pQ51U6GriMuogBX4mqkA4Fq5CITrsZmOEc95YqOGSUIrGAUAN5o4MPrvkAKmRgCAfQ5OvdWIATAMMF7uJgYQ4Ik4WpqXU3I8h8W0sAOp0SwjFS4sUgf0tniaD6AkHa/PgWlFY+qBy/JU9J1LIZMptQ0x1kOSNgWm66ml6zzfQ/8bZkZWxoTG/6YraFMVsKwGMIoizQZG6JGZgJZiq0YycHKALw47XmQA6Spgc32rjJmWegn2g+XP9+tOPTY56dv4s0lXlQFeClgXkUzsQw2gYgBXMhlAHZDrzzqvN3MKhic166PzkAHAbaZPAsBvqzeeIwDAPkfCALLq50w7c/mLmfNi1b2cVEiMoAdsqTYh8OEKF8wpJn4NYCkDZcf34PgAFtPpDG/IkmC610tBi9lfksIA2gEk4L8nFFtar1ZQNQgwSOfSUIcY/5vBCjsBAx2E5ueRlibI0tBczzWXPRErdZq7LzmlIm4rGqYfotEtQPps8OaQ79fN3agCxa4onIwNkJpAqzGk6Vd1PuUp4JQ80GttOedCB5GTRz0RpQzgNptMBBCMoDkRAGCfg8d82eu1OEbQzfyLmWE+bGIAeemh4gKl/1sKvtSCSbWzcdZTCsE0t3bOmH7lWOpYPRX530N/IXPYHpPtiD4nKfuWqdfypYAt9zVPWGS+LxNQzzC0rhtS2ZxnI56HdJNnSkNzPTLt5Q2c1Gk+na7eGZROIC41dJnuMMn1YAiDpFY2+c816lW2Ihso9lXmzmGt5mdYq1Yy9xZ1wwykTOLMKXE7OnYKWGsiwK09T+eQrj9TRhlAbg2gAnzbTo8B4IrgA0iOAAD7HBLmq6xwwa7O44BIGQPYjxSwq1WV/nvnPAzsgARM9yd1arCBKVO3xigLMC5yHNFBxvNNT4dz6kLN34MKZE0LvT4eCQAarGj0f9NawRVTwByFfCfDAMp8AF2CGnJ7QBNwSmqN5el0DgPYMmyOuCKQsjWZTkEO0yRdHStpJTec23RzvEKBtIZQtU/Lpl857FuWAVzZkotA9CYIknMxWE9TwFwAqGxgttw4BoDcFPJ4jgAA+xxla/g4L0VT2lMfg/JSM9vAMFLAhp09wAPCrvMQ/w1ZmopVj2lcaHkdF5wMQ4kaQAkDqHsRSgAHIAfDphSwPp4/BVzclMT/Ziz2lvpYkTWQkH3Tn2HdBia5Lynn0iki4bFWRmNuDpuae8aVoTKnBtC0qSCDr4TdLp5LeX2sjE1V80jvpxIMIFMEoiuAgXx5BT2Vrea8aR9EIPo8qHOIx0jXH4kdDpCmxJUIhOo1GiIAwL6HpIavYWAHaGyPe6GlgEiXDUwZi4iG6HuYRQMcU+yMWrJsOzrhQlsWAObVlhIms6yAo1GrZno796M9INXb0bexobTdsiqqBQx7lgGkAw71PfPnkvWO6BXnwLb1MW6O6M+nib2Lx+BnGqR1iECqLi0tThJeTyB7LmPWi88ArtVMnAGeWTyAjAIY4PegB7KuBKkPYJtZQ5jdqEk8FVMCooSYZXQMVUPIPX48RwCAfQ5J+zHjS5GwyNlSZSwVsNEGRvBiz4OWKmORUyyHpW0XRy2ZTQEzFnuDmIXrt+ZMATNsfQrAiVHL2DEskhyWxGr6K2BabODLB+DsKeDyGxOJ/50p5cgx9q4XzkM5Wx/uQmk2GC8PptMaQNnmhgOE9THqJhZRKvRism/5nuEcNldFAnrquRpAdgo4Bjy69RcViOpKYsUA9iJg9Qjdi9DGAHLa4rW0ekau+X9+HlMHYwaQWjceIgDAvkeDATpMO2uWd52FMeLVfNltYHhpy1zdGsPP0PY9OIutsUBcUDtXJj3kAvSsYvs88Knz7wkpk2lXItMXSpN4AqDflya1J5CeF2nBv/5vSm1qJ7knDOwd6Ty4mTNSCtjhqUjvUFM0gpYYYtsZQLrHZRkPPvO5YDxf3eLzVbbOV5QCzjGAnHcEUEwB6/OhlquoZ2ygVhm1komP57SDa2k1gIAm/OOkgBNT7Brr2VIRRWnnkMmjNYQAj5EdzxEAYJ8j6YErTTsKbA1sDKBvsdbrQAaN5qqc2jkz09KPVDatz2fxXHAYPFc/Yqqyrn81gHkjaD6TaQKynV7kTfF460rLAFniDt/KbJdM6QNMht6hAl5/ZtSGFDCjWxBgZgAl59JmBC3d7HI3WGlrvpK1jCXmkH8+JBYsBRsYhmsDUEwBA9rmiMkADtZjAYdizzhWMPmOJnUmkM2sP8IUcHze4/+vUsDxGAEAUiIAwD4Hz+PLYI3AWOztXThoD5JeB9I0ikAIO3s1h9xizWlfphb8fKqMasSse9dluwSUA6HcNk9GP0PGy91kJB2Px/EBVNejaGgdz9GTfrUygPRzkda+CVPAHka4H+BLWhbA8kO0sJC8TaJpUyGsARSywrbSBJaVjOG+4oh6AHM9pMy0Xna8/jl1LvSxqGnHtbnUKT8FHIseJjaKm3Z6Clhlfmqjc6mN/pwOnPL143wwna4vgwNV1nsuHSP9bJYBDClgSgQA2Ofg7GKM1iOMl7ttoaS+UPTanbLdK4rCBcZCa2k1RV1gbN51Eh/AMikqE+hQnUkoANDW2YXD+BhZZY3B8n0Xm3qWIwJRgCGfAqaez6T/7UBWBZzWpso8NuM50dPpiZq5WgTTHFa5sLHhpIAN9xSHEdbnIfZ17AMrbMoUcOoQdQNmUzqcZGfjqoVkKv0bCQCkb65UWH0AhSpgfT5cJlOBcK4QRR+joAIW1CE26zX28fEY8bmoVOLvIOnNPJ4jAMA+h6SGL8s68V/MefaNyuCpBzi2MzCxHDLgBPBYK2vBPpnJNANZniDHVAPIW2hNPVclYNoqGuCkkW19eIlg2lZXShIN2O5Lagq4Dwxgx3dPMeoIjSIQhg9gmdIGYxqaa15sSr9yVN22Eg1OO7mOYuhlGyz9MyZVtrTkJtnYCM2o9blQ7UeKtXPqO9AEGMYUMJN9s1q4CMBXMydm4c5hoFZBraopqhkCjmSzmFNlc61kxmsEANjn4BismnfmnHSfe7H2LTD5WpTkeIGK2Fa35nuY9fStzdDaC1ps5sWMAm2zfQo9xaXPs24CgGVqrRjqurTQPT0PtWoFyoXEy6b2oQtH2RSwrgyUzsGeAqaXJpjtiQQ1gDkmVKQCNombmPelSSDFEwbln/H1V++sX6+yAidTna84Bayz69waPiEDmD9enw9lgwYULVwkHnzDOSDLnYNuAg3wNroqdBuZeA6BAeREAIB9DvVS7Jb0v5Oa/gJ0VZnJAiYer9yuOv63Og/u76Hvmm2iATJrVc/6rdUZjdpd6lluqs3kM0aqp/TUjPnuibhRfXGMrF8ZzYKlcD0FdjbFFDDvehbuS44C13ou6c+XicFjmVEbwLg+HgdMDxh6IvciWt2ZydqHJ/QqV2tsmwNHiKKD3ZrRCFqWhpbWU6rvUa1Wknc+VSymVMCDQh9Ac0s8GfuWMoC8za5rDKqNS7L+5JhQGQuZZzJDDSAlAgDsc3DqtUyLrd6s3qcqS2sIzQyer+7MarfBWmjNgKFGZBj0B9We8iMyRpbjWWyPsVG8jB0AuD6A5nOZ1lPSz6XUesSqLC/JWulzkqqAJfWxto0JTQU8yugawBenJ7LdDocxhsEIOv49/RnNpF8ZqU/fpoCVrTCAFo5XKFD+epTxVDT2HGf0Iwb0HrrVzFjclL6xtR+7BlDGAOr2K3kjaLYQJQ/eOCngknMY7xEAYJ+DV/tWVAGrxSEi7O5NjFP8b9pDkKfP83PgqBRt4M3HnukLkJWt8byUfMpVaS/gupYupFg8dAzXk9ULOAFOuetJZBj0621TElPT6WUK/k2sVWYMXwrYk4am1d+Zz6WIFTaCFvocbJ1dyjLTAE9lbypvKGPTRE0jR1HkVCKTrqcGxjNdVRg9qo3pdMb1BDyZAiYDWPABZLbEk9r6APYaQOocdHIhUTMTn+90Dmr9iY8TpYDz36POZzLHcwQA2Ofg2J+YPNvqjJd72U4g6iVQppOIDYRSW9qpF1a1kk3tAPSXmq1ujddD184OUMcwppgEbKpNUe2bQyfDAJazYLGl+6SdXfQ5UesQiyyk4HralMgMI2iT8XC7S2DoLaIenpjFc18yUvLmNmyMUhPL9/DdE7qCV5q2NHkAcscwfQ8u8DF7CcqYr3wNINUH0PS+46qZWzkBB9eSR3eQUAQCx94ISDNQeTEMh72zMoBBBEKKAAD7HBz7k5aRYUhfTr4HyarOIwInq90G56WaLNbZMXQ1s2uhtAEO/We+xdoGGOoMM2qzTUV6LaQ9jXkqYF+tFY0JrRjBNG0Mm+UHqxewB0T6GFmbGTXPisbGjvMZepPvXBT5N2g2FTArbWlIQ+uiHorydCTp+lDcaJYp86CmgPX3mLT+ztQFBOhDDSATdJjr75gp4LwKmJl+daaymQxewYuQCmJH7yldvSu1gcmLSHgp4JynYqgBZEUAgH0OarouLtgvLlL6C84HIm3MF9kHsOTxgIMB1BYs16mwpZDjMYkpYE/akuZFWPwe9Yx/nszPkLO7L1u3pi/UeppMH4MswBCCUP0zdVsKmGjr08xtKvRUW8+XDrfUIXJad5mVq/T6O68SmbTBsrGpfDbUBFpIwKlTPB6gp4D1lKCRAWSk9O0MYLnrSReBFMFX4svINoKWtYJz+zpyU8B5BS/zeKFJOlCsQZekgIfb2XdFqAHkxQYDAO+8804ceuihmD17NiqVCq677jrysXfffTfq9Tp222230vOoEV/uelqkqYk4qtUK1LpJLfq3+XP5AaDbBoa00Frq76iF6qY6KxXURarlmQPP6sLMAPrG0BuQmzwVeYpqmaWOLYUcj0lbKL3pVwaTaavJlKaAOex4ai2UZ4U5aUfDBi2TfvUp3N0MICVdZ62nZFjBmO6LRo0OWnyMru976NfKJODglBXUrXMol073ZSrSeRhYRKH/XVIDKFQBm7IVfBFIljnjtpJrGqxoqOybDYTyUsB5IUmoAeTEBgMAV69ejV133RXnnnsu67gVK1bgqKOOwtvf/va+zGOAuBvMpEUsNhHUDhg25qvlOX64nX0JpMcz0tCednSA+1yY7CHyY1BT2QXhA2NnbvoelYpm8eATs2TSXNIUsBv4UBnAPEsSj0FNI1uuJ6O42p8Cpj0bNmERQBfE2LwlOR1qdNChAxj/s6GuR5mNiQX4cNLhRrcBQQq4oMrmpYAbOWaaA5ysQLhOZ41Mtln6s8bq3lQv3hOU79HrperZggqYLQKRgWnAAL4Y/rWARh5klMgyEJpP31KZVMCRRg4AkBR1/0deGTFv3jzMmzePfdwnPvEJfOhDH0KtVmOxhrbQlaOu0B8000LZ6vSci1QURVZ7hgbxxWyzgdFfbiPdXoEh1MNqXaK9qF3zsIEF/Wfi/rV9sQ2potPrktN9+TE4KWAbmKamLW29oQE6YPCl01kdTawpYB+gV31K7QCw3ekBTfsY9rZ6qc2SL9Rn9IW2UqmgUatipNvzfo+O4XiAbskD2J8PTscgl+G8VDwBcFLAHla5BBDm1QAWnw99k9Hu9gpzLM6jeC44zNew5gc6Ie8DyKxDlHZVAVwMIK0bSR54SeZgM4LmtaPLrmFcNna8xwbDAEri0ksvxV//+leceuqppM+3Wi0MDQ1l/stHnZia0Qv2iwul/8WqA8xibQ7thWSrtdJfir6H0ca06CIEFxgm1QASQUv+e1CFKPrfsKmZOX6G+gLTFx9AYtrSmQImLtb9VODamExqHWL+e9S08gg/+LKBaRoDaOs9C9AXGa8SmWRGbb4vU1aYYwNTBC0UtqVlAdPUvshl7aqAdVgDmAOAvjCKxRh+iHr/WwV8lI9rKRsYBhMaz0NtsmRp6PzxAF+AkU8ji1LAyRgqBcxjMsd7jFsA+Je//AVf+cpXMH/+fNTrNCL0rLPOwrRp05L/ttpqq8JnqJYd+oskX7BPGUN/UAupMiLzlfeCUhF3jqACH/NiTU2fuhnA/oAWgNCRxMIiUl9q6uVfq1ZynQroL3crY0RMW9pAbPwzWorIl771XYueVgtprxkjpvSNdaGj8xDWQ3Lva8AEOnhjFLwIGXVKaS9gWZmIPg9T2rLLqPMVq9M9NZ2UDVrHch5Y59LwPfRNBWUMowqYkQJWApBGrYrq6HFc8GVSyUsVuHkGkFu/p7OQbEPs3BiiFHAna2cTagB5MS4BYLfbxYc+9CGcfvrp2H777cnHnXzyyVixYkXy39NPP134DLUVnMkDUAUlraH/TpquszGA+hx8Y9he7gCNPRuxLJIAo8i8Y/YzzApRfIucmYmkMoA2loPVVq9jmQMxbem01CF2fvApy33CIH2ONraGWgPoui+p30PKGNlS+vq/fZ6KaSrcvKmgqYDV82G+L31jdLq9RIWf8a7Ta9+8Xp02QQ0TCFsYRP1v+MYotBes8s+ltOOQPg8Ti0gSsxjel/IaQNkcAIMHH9OLcDhXv6fPgV8DWCIFbGMAAwAkxQZTA8iJlStX4v7778dDDz2Ez3zmMwCA3mjrtXq9jv/5n//B2972tsJxzWYTzaaj8AiMGkAL45QdwwGcRh8Sk4EyVzyRr7VS81o90iWkgB11Z9UqhtFzp4At6SX9Z2WtS9RnJsBRy2hLcxF3pbbzoICUUgnnr1V2DNtiTwNONt85IK1Z8rZh8zBnQAwYmlXzudTnWLYbSR6EApyUvC2dTjsPWVNtGfNlT4UzUsBe/zvafZmfR95uqulYCUziCX28sspy9Tdc9XfpvW1mAEkdURKD8mJNZqvTI3YTKV5TKhgHzO/9BHxRawCNNZ0yBlAqnmi1iw4ScisaOXhL2+rJzaTHc4xLADh16lQ88sgjmZ+dd955uPXWW/GLX/wCc+bMEY9NrdeyMUbxGP6if5vwAaAvDq3cw6MHV4lsAh3KEsfFhjoBZJ1YT0mwDeF46GXHoC72ZuCUEdR0eknhtym8PoDEripmSx0qm2q+LzNgutMzsnP6HAB52tImTuKMYasBpLKQ6nsYO9QQWV2bGEaStswzmXrPcFfooCarAtafDeo9URIIOzZoVEFNUVFNX/CdTGSLVwNostShHN8yPF96tiWKokJJUD5MmQJuKzibeKKMGbW0BjAPQjkpYDsDGGoAKbHBAMBVq1bhiSeeSP791FNPYcGCBdhkk02w9dZb4+STT8azzz6Ln/zkJ6hWq9hpp50yx8+cORODg4OFn3NDr61xhe2lqv/MmTrtA3M27GEA9b9jCxs7ANCUim7hAhXImgGgqkPs9CJyTWb+e1DTXLaUIwcAWoUoxPNgU2vGYxJTp8l5MJsw++bh6kZCTgE7ygrIAM6S7iMziAZPx3QOtIJ7G+DohwqYez3zYjOWnY3lGSU/G11ziYaqv+tFlPeMuQYwBT709K2NmZYa35c1F89b0ZjepaY5mBlAGfjiAkgFyLMt8XhjDFs8/FhG0PlWcIze0CE2oBrA+++/H3PnzsXcuXMBACeeeCLmzp2LU045BQCwePFiLFy4cJ3PgyoCoahf3eIJ1/E0hsHFtJABoBOI+kUgLiaUzEI6UoZklsLLcsgK5eta266Wx2LBzpRQF1o7m0quZbSAL13c4lSna3MQdyNxAkDeuZD2jk3Ut4aUPZmFtKQt1fG9yL9RtI7B9BvNXw9d6OVnllXq1Mxu+wG9674cvR6e72ETFtU5NYAlWX7AXJPJOt5QFqC/t2i1wsXvwfGFBAw2MEIzav35Ys/BwgBS6xjjMcxpZErrzhAbEAO4//77O5Vkl112mfP40047DaeddlrpeVDq9wB3DSAFwDkXSeKDSBGBUIGsE3Q4U8AOAEm0NrAxgMkc2vQ0lTX9KkxzKd84So2RzTiYrAJ2gHFqmsoHvrq9yPk92gTgRLaBKZECti/2vBpdVzqdo/TXI98lp2appwQ0JjIvfiAq/dU9ka+PVWO2u11/RxMPA6iEQVVLfatrk9eoVTHS6ZHZVGsq3HMt4tabZjBNrWUE3ClgjiWP6fjk9+4y877UAOZr+Diq8vjvFN+XXBYy38eXuiHJjpFdw0IvYF5sMAzgyyWonSNcKVxK0b+r9o76QrL1Agb0Oic7axVFkXMeatFysRw2lSNAtwVIX8oyIOv6HmzGyHQ9+uSXRgc95W1gTGlkjjq9FHAilDf4BTE20MLb2OSBlz6m/54wp5E5tW9WFTCzLMDZapFaW2qqnVOfcW3ykk1F8fmkbpg7NiBMZHwyPp1W31Q6i2gSgUitaKrV1DLLt0nMvqv0OkJuDWA5BtDcJpGZAs4ZQVPLGkxjpGlk3vcY7xEAYJ+D2hjctysGaDWATq80L9tjrs3R5+Bme8wKQxWUGiFXKjt5KZZirfznImOqba0ZI15PQ/0O1VLHXqNEu55uT0VaqswpLiIAWRIjLGRT43F5Y9g8+OiiIMP1JBtBm8E0tZ4ScLRAI34Pk+ggPw/qRtHW0cQ3BqU21WepYzPElrRqtD3j0paR1PMI2J+v1IbFXSaiG5Q3DHPwnUcV+VZw3F7ATisapp9hGQFH3kom2MDwIgDAPod6QUmNhwHaQule7Gm7SacCl8AYuV6qAI3JNLXbSubA9K4rey7jvymrGaOcS9cY3V6U+LXZ0pZlagCpKcM0RWVXhpPuS0M6kMyc9Vz3tn8MnSWxMUblrifv+cozgKyOJh4WkQxCy9THOupb858xhTOlT7TlsZ3LtIaQ9h0A+/eg1BG2EjArS7/aVPZUBk4HaKVSwDkGkFu/ZypN4M4h/67RS4Z8xuAqCgwgsx/xeI8AAPsclEJ5gJjmcrZQ8++qqak2qQgk+1I1zcP/crf1v9V/5q9l9DOZbi9CLT1kWWjpXTgc55IMQnPsQJIaKTEHdt2a/b5ymcXaBAP68b50fLrYy+5tF6NLfTZsrcc4Y1AYWaldFLnjkLPUhMnIGoRBSldCqVc2b0x4m4KiCpj2bLjU6dRzGf+dYkpd0lfZpgz3GTHb3lV8EUi237aqESXbwCTnoWhnQ2Xw8nWd6viIII5SMZJjAEMNIC8CAOxzcAGDNM1FARxlUoYUAOh6qQLpz6St4Mg1gBTjYML3ABx9mcl1UnYgTJ2DfbH3sUWKaSnOQe3Uy6XT6fdl/jwCVDbWDsYB2kKXMXG22PqQla+GGkAuaHGXeVBVwDJWmOQ24Hi+lIG5aQ6xktj/vrOZiwM0z1P99zZPRfL1NMxBvacooMP0nLMAZNf8rqJ6SyongbytD6cGMCuIyTOANOCk7hkpE6r/rWQOekkBEQAWmExmKnu8RwCAfQ5qPcmIZVcNUP3z/CyJr22Xa7GnFfynD7DJvJRiVUFJhVPTIlLjYB2MW/syE9PQ7nS6iwnVAGAhbUmshaSkgEuk01kpYCcL6QBv2kJuBNMEhj0L6IWMrgJvZWo6nfYntAU7NUCW2sDY2XHKxiRT5uFI4TrvCXUeSqSh7alwIoC0+ELqY1DUp6Z3jSwFbAGAjLId/V3FsVAxpZGp93R+jGwKmJd+zQu19HucCuCSFHC+jjCIQEgRAGCfg8owlDVyptQAAm7mymWfQkoBO16qALMXsDPVRgQtLpaDUqTurFGS2+FQGFmd4cjbafRDBEK2gXGwNZTv0XFsbBQAcF4LRzpe/xm1NtXWOozq4WdSAVMBeduStozHoF5TCwNIrul0iaxG50DYoMVzcNTpOp/xrnUOZFW3hU2l+gC6GPpalTYHAEarJWqbRcC+SeP2Xre1vaQAn4x4L8e+UYFXIsrRNkhcFjHfmk/f+FIBXCICSZTEoQaQEwEA9jnqxJcJBcC5Xmquvq9UlaFT6Uh4IbiKuwGiCKQfdWt9SluWMaN2pfTTl7vwPBBfrKS+yj4gS2CFXd8jZQZMaWg/cHKl4wEdRNKuZ57RpVokUe4JMiAvYYmTimqyY/BbTsreM5mUvBEM07MVUs9TfYw8IE+FA9TaVtcmTwbg6oTnQoVts9kkM4Dme0rCQurHiVvBadeD24c3LReJj9PtcKgp4DwDyPF0DBEAYN+jRq0xIqTaXCwHhTnT/45rDGPqlNBSx6ZoS8Yg1Nb0RwRCSFs6U+GuVN36AQyk48k2MvIxKCbMcmaangI2gTeAlnJ0bY64Ru1l7suOYZFMxyCmgK0dTYj2J31K6ZuYaYCYkne8J6jPl80GJmUQZUxqPAc66DBZZ6UlN31IAQuzHQ2G+lV9Ru/uw00Bm2sheeybadNMLfsB4muurlmzLjO0Hu8RAGCfgy8CsRfLuxnA0RezpaiZokZ2d46IHyhK7ZzpeDUPwJ2GdjFn7PZlDgBHAcLm88ArtncJFyieimVYkrT9mQxw9LSeycZ0XckUMKWjiWtDoP+c0o/YzEKmx7usJmw1ZwCdZShb3hBFkWaTZGZ8/PeE/d6meFy6SlUAagpYPZ8uo3YPEO7ZgHB8vK+tnrPWmJMCNjwfEhVw/r2vAAy13tluV0UBscX3JVU4qKJtWH8GmCAy2SDpaWQOk6l9RnUTofanDhFHAIB9Dn036VpgWk7QQWFKFLtgYd8o7eRcbA+BAXSBhXgO/hcrbZF0v9SctYyMFLBZiMJM1ZWcQxk1NI21ciySugDD8T0oynBpCtgGeNJ5lbue+n0mt1misRQuEEm5r3TTX5synLrRdAmkXMyV6/nUx3DeVwbLEBVsMYwlBewbo+1kIekMoOkZS4EPXYBhM4KW9l6nqvz1z2Q6idTTd4xLOJjMw/Du51iw2Lw6qe86ABhup981YQCZQHa8RwCAfQ594SOlPp3mxRTgJGNKfGxPkwBCfQwgy9BaqEQG9FS2w7yY0o6uD/VeUvFE8jJ0zEG35HDNQVrLqC8+roL9dZkCdn0HgFb7RikrAHwG5XYRiGKySt0TBAWuvggW+kMz2XF3aYGMOQNojItLWESxogH0tL5ZDe0bQ11Plw2ML43c60Wp/YneCo5TQ2hLAY/+u0V815VJAac10+n7Uj+vnJZ2+vuKkwq3eXVyUsDKy3CgVkxl+3whQ8QRAGCfQ39R0xgGR0rC8SC52AV9XNsLwcv2MHwATTt7gNYWz506pVk8uDuBlEwBMx36XYpPp3LVpWTWe66WZL4oSmTrPAjfw1V/148UMKkdnYNFzDBGhP61RkBOXGxpVk3+6wmY/O+4aWjTe4ZQokG+Huvuvox/b37f6efWKWZxCaSY7TvjcYrsGScFXFR1+99T+vH5c8lTARfZbX08aU9j3ZLH18kjoy4XpoAVA9jMANnAAHIiAMA+B7k9EsGChbQz96RmbMCFyvbQRCBudoBUm1Oi2N5lBM0BPqbvQVbnEbzOKOl0r62P857y1yFSFNm2gn/OYm9MtWkKP1uayZsCplxPF3jTGSNnNxEXe0cDXy42NLWj8W+OTGOQmWllA+MSgQg3R9l5+EVW7mfcwwBaLHWy71vZe6ZGFdRkAKDOANJTn7brQVUi2+qdRTWAupCFacFiYmSzG1XfuTTf25wUcNLNxNCVJYhAaBEAYJ+DnAImLJRu8YSbAfSxFD62h9O+rJQPIGFnTrbbEO7uXd9DvVx8Tdr7lfo0exHSXqyuHrqUdPqIAzjp47qNg/0gFLDf2y7wlpkDAdCbrmdV68NLsT9xsXd+L0FK+tXF8se/q1aKnXao5sXKg89pAyMUTwC8FLCZhaQ946kvY/Y8VCoVUgrWla3ggi9ALgKxpXCpAM7mI6jfkz72zXQ9qtUKCzyZ3nf6d6K+s4HsNaV0blLRShhA/VoEBpATAQD2OWrEHSmlLobm4SfbmSeLvYftofQCti0OFBGIs26NkGqz1eXkx6B8D9PikAJAzwKVMEb2MaSLpO6PJWXfKKDFd09xxCxG9Syh/o4qLHKy414WkX4uzIpqIivsSodTUsA9+yaPyjqp35vbJJZ/z9DS+ur5Mm00afV3rutRJzB47lZwNNbJZtZOYddV2EQ1dGNvWwqYRjy4x6Dd1/E8i+eTmv3Sf5+3e+LYuCS13wNpCjj4APIiAMA+R9wf05/6HHG8FGlG0PbFHvAzPq4UdDwuYZH01AdxmEyfWa5tV5upZRTWWrlYSHINoFN4QFC/eq4nKY3s6vrASIX7wJerUN2lOqUsEEn3DOt5oKeAfefSmQJ22eEQ5qBbuJi+CykFTGhfRheByMbwlZpwzL1d9c6+FmaJ60Fpn03Z+9Y1BjVTAdjBMJUJtc2BWnsej2F+V0lqGfV7u1atQGE5r7G35X1JeTbTMYrPOacWMkQAgOskSB58ZVOGDpUi4G955avt4bFW5sVB1dZ0hbU5A4SXmqtOSv8ZpVOBuQYw3l36GEDK9SSxkCVq39x+hgTmzDcHQm2q656gLBCuYn395+72Ze7yCIoZtKuNG2Wh1C1c3C3QHJs8B4CkLpQUY2/XHFwbG/3ntPIG1/cgMoDibiQOEEq0gbHZTUlSp0UVL/F6WuqdOeybbfOfKJEpNYC94gYpJj+IzLTV35KfTjcJUUINIC0CAFwHQas7K5macXhrAf6dkI8BpDjDewED4TxQWlXpf6swB0NbI9MYpFR2CQbQZWdDWiQd7J0+D8oi5wIMpE2J754S1nTqC4QvBWxllUl1iPbrCdDSp+p6uFLALiDssnDR50ZhZF2gh9o5wll/R6pVdm8KnF6fCZNpsmmiLfi2jigAsQ7RwQpTewF7FbycGkALgKO2nLR1RAE4TQjM7BtHSFK0J+IBetv3oIHp4ruC245uvEd9rP5wu93Gc889hzVr1mDTTTfFJptsMlZT6Xuol1RXmHakFHinRtC+nbltoXUvkhwVcBkRCKVuDRhdbBv2402F8voYFLsNExhmi0BcjBGJOesD0yJO93ksP0oaWgPx+RlxjKG3gis/B/kYrppOyiJlswxRQfH6dKVfKd2CMmO4FPIUpb+tVIQlAnFtTGgqYBfDLrabIvaftV0PjvecL/XpZe8s11Ox61HkLrkB7Jt/quMBYH9OqSbpfUkBG9536njVGca0JoRIY70ygKtWrcKPfvQj7L///pg2bRq23XZb7Ljjjth0002xzTbb4OMf/zjuu+++9TmldRI1AsPgZr7KpdoAQg2gY2HQf+62qXCPQRKBuPrw6mkNm2rUYfCq/1x6LhsJAPS9mP0F/zT2zQPIKQDOU0/pm4OPhaTUhVrvCQ9b4m09xkgBe1lEQlmAuxWcA0DqpQnOtCWBAXTeU8SCf2d9rAyE6mOQ6ludpvc+IGtWAetjuFXd9jnUiGDamjqVMICW1Cc1BWwGsjTwZBWiMNgz2zNG7pzUhxSwKWNB9UwNEcd6A4Df+973sO222+LCCy/E2972NlxzzTVYsGAB/vSnP+Hee+/Fqaeeik6ngwMOOADvfOc78Ze//GV9Ta3vQXmYXSwFRTzh6gWsj2utAfSwd2nq0858uUAPoDnsC20mKpWK39Da8z2SF4rQ7Jbao9OldFRzoIgn7MIFBlsjrCvth+cblVm2M9P9SwHbx6ADBnM6nc5C1iwqe8r16DjmQAdO9ueLx9DLU8CJZ5wDCPtAi4sBTFPAwlpjphG0jbWiMGc2RpWa+nRdD4oaWp9DI9c5iVruAsBaq0u11LEJtTgg1PS+43Y0Ge+x3lLA99xzD2677TbsvPPOxt/vueeeOPbYY3HBBRfg4osvxh133IHXvOY162t6fY0a4SFwpmZIhrt2VVz8cxoD6Kv3KmMbwvLncizWI107g+djzij1Xi6vNKoNjIspobj8k+1PpJY6WqF7rxcZQQnZg49kHGwZw5MyXB8pYMqC33EABgr7Rgb0BBayTAu1VulNgYdhJwneFDMtA9OAuwaQpPR3XA/KRjWeoy1tyReB2ICPVAQC0IgD9xwY38MikqLWQ9rPJY1BtI1BNXoPEcd6A4BXXXUV6XPNZhPHH3/8Op7Nug3Ky9nFwFGofLpiU8YAJipgl2qUKESRMqHpGF2vmtmUGornxgBfjq4sXgDoSEVzTJit9XeUtKNBmacib8LcrBYL8v01gP5Ngbdm7GWRAvYvdK6UI03VTa3RdYzhVAHzGEBnrXEfyjxInoqGc0ERogDpve0SxEhLTag2MP1IAavP5AEctyWesxZSCCJZDKDlnUlhY+Pfm+9LKrOtf0Z/3ynP1E4vCilgQqy3FPB4Cpppr/+F5Hqp0nsBmx9EKuBY151AqMyXbXdPFS7QfACLCy1VBGJTxQE8Ba8P+Ljr7+xjZF36hcIgglkt9XtIU8AUFpJaT0lqi+dYaN3PJ5FNpTzjDuDkV3zaNzckFbAPkJNSwA5mmtgDl5IOl2Yr6EbQ5udD/VsJD1xhe+9SmS+bFQ3ALwvIz6FBLHfp9aLkexYYQCIItW38Oa3gvEri4AXojfUCANeuXYtnn3228PPHHntsffz59R4qpeA0gnbW5tAXKHtxtjsdQN7ZO9N9HqbFcx662ovEqzz1pIDtrarUYi0DX3Qj6GJfShU0xsjNprJabjnmANiZDhobS2O+7BYsnhSwZw6sujUbK0wxWnewqQ0CEHYt1PocaP2+ZSAU8GQaCHWl3vdM2c0utWbMxciyMiblFdX5Z1T/Xt73hFc8IWd060Qg27ICWSKA1M33LbWMZVPApHpKa00mnUUc77HOAeAvfvELbL/99jjooIOwyy674He/+13yu4985CPr+s+PSVB2Qc7uE8nxrvSQnR3Qx7UtMK5aEoDGAPpYRB/DoD+g1ho+z0LnBS11wrl0vFR1I2hXj02K4S4l5ej3AaR8D3Odk1o77eeSlgKmgFCfutwq6vEwiJw5SO9LfXynpyKhFtK3wXJ3yfGngMvZwLjZdYBQA1g6le3foOlzdIofhLXGlJ7IgAbqLaBF/zu2sF0PbmcXl62P955Q6XBbCphYjwkUz0VZFpKVAvZ8j9AOzh/rHACeccYZePDBB/Hwww/jkksuwbHHHovLL78cALxNq1+pQXGWd+3uOW2ifGmusgvtiAP4eNk3zwspAwC9xfIy0MJhKFxG0PrnTOFiWijgjazAtQD6bi+Cut1sdWc+iwZv6rSPqWxraQIZcBBUp577UqokpszBV2PL+R5OM+oStiEUyw56Taf9vnR1RKGyb04jaML3IPUClhpBEwzrVdjuC27q1OwNSbwnRkVvtjlQTe8BQwqYragunwK2G1oHBtAX61wE0m63semmmwIA9thjD9x555047LDD8MQTT2SaQG9IkahfKfVzrp2c0wjanebygQ5fikp/qDq9yAjQ/Glo90st08bNw2RaxyCKBlxg2qUQ1BnSVqdrPV8uRpXXv9adMvQxoYB9sW7Uqhjp9KzAx1cDyBOzyAC9r3auH9Yl6SLjeL4cKnud0Y2iyPgeS5+vothGH1eafqUyJW5muvy59KX0M2DBke2gMkamjEeDACKdNk2Ekh3XGMquaqTbo3cMsoGvEowuxXUBSDeRtlZw/jnEx1cqRfN9Sp0woD9fZgaQJkQxv2uo5zLEemAAZ86cid///vfJv6dPn45bbrkFf/jDHzI/35DCl2Lq9SJtRypLMfn6nXp9AH2LvfZysD2MvsXBZ4eTvtTNXmn6PPxMZokUsGNx0M8PqSuKYwxaWz2ZaMDXeUL/uW+McjWAVOZr3YFQXw0gpwuHEXBo19jGVPhaLVLSr64yDypT4gLkHDGMbZNHfT4Bm4kzjbVKOh8ZfVPd71t9HqbvUSPap6jno+mqp3Tcl71elNaWWlLAPvBGsfUh94fOg1CC8wOgP1/VwuaHYruVGUNYQwgQagCDCMQb6xwA/ud//idmzpyZ+Vmj0cAVV1yBO+64o29/584778Shhx6K2bNno1Kp4LrrrnN+/pprrsEBBxyATTfdFFOnTsWb3vQm3HzzzX2Zi68VnKuINj6e/kKTAgZb/YQKCgD0p+vcqXBfDSHg75HpTZ2SUsD2MarV1IzaZQXTShhAU79TeorKx8jar6d2T0nbAzr638bHU9SW7vuKmgL21ba662t9KWDCpsDxPbKKast92bGDBYDGTLuVr/Hx3VFfR1vYbEcAWv/ZsubgGZZfCJwAdzqckkam1Oh6GUDH+45T5hGPYWatyODLWTpEA7K29KtKEdvCzY4zBTVCFXFmDGsKeMMsMetnrPMU8JZbbmn8+fDwMAYGBnDDDTeglwNK73rXu9h/Z/Xq1dh1111xzDHH4L3vfa/383feeScOOOAAnHnmmdhoo41w6aWX4tBDD8Xvfvc7zJ07l/339fB5Iek/l/ZttVHo6RjuF4qtDkSFEg30Ij/okJow+0Bs/Dsfw+A5DwSbCooAw5Xece3s9Z+tj4XWxaaWZd8ou3N/7RtRBWxTQ1MskhyCAQCk3q+uEotMzZelR3XyfPWhDtFVOwfA6usIuDdZJBbSszGhqrq9vbp9jJHzejBYYWMNoJ8RBvR6SEdZAEHVbZoHRRSkz8EIZKmt4CzlCdRewIn3qrM2lQpCLewdgQG0prIZvozjPdabEbQeN910Ez7ykY9g6dKlhd9VKhV0PTsQU8ybNw/z5s0jf/773/9+5t9nnnkmfvnLX+JXv/pVeQDoeanpD5jZGiH+WRTZG1qXVUv6Xuzqd8PtnpX5SlMJ7sXaLgKhzSH+rAcweGqUKODLNo9mvYpVLTsDqI/tYhjK1Fr5amt8x+tzk6bTE3PwEiIQXyu3vqaAfalsAgNoWuRq1Qoqlfj59LZa9DC6FPBlYgDzvo5Ny5vc9YyRUsAlDcrpGxv7edCFJEYGkADg3GIYGmuVvu/sLD/lnorHyM6D2xPZKagRdwJhpm+Nc6CNYRNqcer3Qg1g+VjnKWBTfOYzn8Hhhx+OxYsXo9frZf6TgL9+RK/Xw8qVK7HJJpuUHiv1v3MvcrVqxbwrzihP3WPYjWY9qVMPU6P/zic88Nc5+RhA+xz8zJeag4z1oszDtzPWgaGrddi6rAGksKk+MNwPE+YRz/XwGZSndW/u8+Ay3R1xdH0AeD6bpvNZqVS8i4zv+UrtjWQqYP382IBLFEVOAMYxUJaqmX2ZigT4EOxw9M9nxyjH8qtMBV0EYgDklBSwMos3sPTJc+FJAbs2FmQVsKejia/rkTMFTPZ1LJ8CDj6A5WNMGMAXXngBJ554IjbbbLOx+PPG+Pd//3esXr0ahx9+uPUzrVYLrVYr+ffQ0JDxc74H0aeU1F/u7W4PgwOOtl22WqmSKmD9d746J6kIxMcOAP4Uk68OUX0Hmieju9Dd1g0ky+iaAD1FBexJQxNTwK7rSQXTvnS6tBtJ/HP3gu+1N8ptjmrGlna0OUi9IQG/oppuBC0DXxQWklp/53w2fCn50r2d/cBHB2amjQGl2w+ldo7aQ9fZH1poLcRn70yZI179ndQ+xe2pSASh/UgBe2sAAwD0xZgwgO973/tw++23j8WfNsYVV1yB0047DVdeeWVBsKLHWWedhWnTpiX/bbXVVsbP+XZB1F01YH85Jy9WL/MlZ998zJV3ofXUpLQ9wAvQgKxwDpQUsO966GbQplDAsFEvquL0cdvdyOqpSE2VSa8FQD+XXtBCSHP5U8Cy+zK/OTKFD3RQVKMu3zl9bF/qs1QKWBkoG0BPpVLxPl/6Pe/qUFMGCPtqbEc8wiIK8NHHNrbFI4AOZ+cllbERqmeB9D1Mq/N11RC6QQupFRxRGW5jAH1zoLTl86ayLUItXgrYvGmmCmpCjBEDeO655+L9738/fvOb32DnnXfGwMBA5vcnnHDCepvLlVdeiY997GO46qqr8A//8A/Oz5588sk48cQTk38PDQ0ZQWDKfLlfzLbFwSfAiKLURsamlvTVSvlqlPTf2UEHMb3jYa0oINSuXC2XOs3Mw9cVxVYD6OmqkmWtImN61GaLkIzhu54kQE9lZN1sTS+KF4H8oq6nHKVqZpfdR/xz/+ao7UkBJ4yskK3Rfy6tAaSAFl+/74FaBSNd/wZLn29mDoQ2iT6Wf6Dqu57mVF9+Xs66Ut01QdjKzZXSp2wIALe6nGWRZHAKoNr6OL8HAdAD9qwJuRWcI51OTeHahFqUdH46DzMJQnErCBHHmADAyy+/HDfffDMmTJiA22+/PcOaVCqV9QYAr7jiChx77LG44oorcPDBB3s/32w20Ww2vZ/z3cS+BUqNMdLpGV9K+kvCxyL6GCObgCMzhq8G0JdqE/Yj1sfw9TT2LdTStnxACuysKeCuGwBmvAS7PeP39dYA+kQgqr6IdD1lgFz/ebsbIb+O6felNAVMKSuoVSvo9iK58IDQrcBbYkEs8/A9GzRPRZvIqgqg630+rbXGpNSpuzSBalBu3dh4ACSQPrtx2ttUd+a/nm41tKrZlosfKEA2vSdKMGeOd02aAvYBWbNCncoAti3gLf4Z73sUagCJNYT6PEMKWB5jAgC/9rWv4etf/zq+8pWvoGp5wXJj1apVeOKJJ5J/P/XUU1iwYAE22WQTbL311jj55JPx7LPP4ic/+QmAGPwdddRROOecc/DGN74Rzz33HABgwoQJmDZtWqm5+GqMfCwJ4K4xyqREpCmqPjCALlsEIG2xZEutkGoAPXQ+x4vQ1rXBB758xdHUtl/AKFAz7CG8VjTUGkBXCthTp5SCSP/3GOn2MAFZBKjfJz7xgzQFDMQpu24v8oOOEt1E1AJke75SRbSQFaakgLu+6+FeLFtEdpwmhpGlgHV7IvPxftCSnAePsTep17bhetQ8dYwqXO9M3/2g/87FIKoyEVuXLFJrP28dofmdSU2/ujabdCGKmU2lHh9/xnwuqN1lQoxRDeDIyAiOOOKIvoE/ALj//vsxd+7cxMLlxBNPxNy5c3HKKacAABYvXoyFCxcmn//Rj36ETqeDT3/605g1a1by3+c+97nSc/GmgCkGyI6Xe9ZQ1P1ithvu9gEAEuu1/OydnbXypgyJC1Q8hkdJ7GUAZWBaZ2CkghoqG+tWVHtYRA/j5Ot5mvU5k6XkfSngeH7ue7vjZa3coEUvsZAqon3PF4UxUvfb4IBvDrL7mgKcvN1hfClgX5lINb2vbfWx6T3hu54O8OXYpKnjqQyg6fmgpIBHHEIvnWmmMNPOFqJEFXB+c0KuAXRcD7YKuJACpjOA3hrAYATtjTFhAD/60Y/iyiuvxL/8y7/0bcz999/f+gIBgMsuuyzz73UpQumn/YnJWLRDWGipNYB9sWARLrTrBbRoL9a2Nf1KUxL7bGB8qWwaa1UOcJQx1fYBer3nqWkMdbypR6gKbx0i5b6sV4GWy1/SzZz5lat6KtvNXEltYCgq4JZnDJ96lQpCpT3LAUJpguee0r9btxcZWSVff2iemtnOAFJbqLlSn1Kzdz2L0u72jJ/x2vpU/ddTjW8ag94L2P6uoaZf1fOff29Tzayz88gBQPVshlZw3hgTANjtdvHtb38bN998M3bZZZeCCOTss88ei2n1LeqeFwqp9s2xk1KAxVYTo89B2gsY0PzvLN6M3i4BnvSrz/oEINRaEVPA8Rge4GM1gnargNMdtbkbg5rfcNveTcTvZ+gBLQRBja8GkDZGLDwwsU76jtx6Xzo2NvEYKt3nTgED/nvby0JaNmj6c+sF5J4awKaPvXMwHa22GsN8X/lT+jQmVHkqGg3nfYIa4ibRBt4yHU0MdaX62LZ7wifiyAInR/1diS4cStjhtnqyv2fqmY2qO50OeOoQhSpgn3VY/nhnGpqYAs5v0qi1kID9OQ81gPQYEwD4yCOPJKnaRx99dCymsE6j7qHBXcXAKlwpXErq1Pcw+4qz4/nZma9uL0pSJhTLDtPunlUDKGR79EXNdC7jdJ/7fPaDAaTW8PlYSL8hth8A+pgzL/s20jVuClxptvwcfOybDQhnx5ABWV+Nrg4E7KyT+3u0iOy4OwUcn2NrHaFH/KCukb0ncnZz5PJUtDKAnhQwS1jUK9aVAn5bH9/11DuJuIygoyhu62hrpegWgfiBSyLgcBzvGsNv6+NXQ+vj5M9n0/G+10PdbyZAzu4FXEjfusunjGPkU9kBAJJjTADgbbfdNhZ/dr2F1wiaWOgej2FiAEePd7AkPoNVX7N6fX4mxohS76WDsk6vuLv3sQOA35rAB958aUvf4gDQVcBlgA/ZB1B4PJCCKm86nQC+TAyej0kF/Clgn5F0PD8akJVasGTsUzw2Sz6BlMnyA0jv124vsoIOH7PsS7+6as4AWt2Z9770zIEK3gAHk+nxZPReTw9zRu2r7BJaUbr9uEy14+4yFbS7kdzWx2O7Bbitmnx148k8HO8Jav2d7b6oc1LAlvs71ADSw/6WXYfx05/+1Pq7L33pS+txJusm6snL3f1Co7E1dhsY10LrBRwlVcAUIYrPtNdXQ6iPLW1HF49hT8n7FgeAngImnUvD98iykD7GSAaE9TFsKWBfOh1w7659HVUAfwpYzaEfKWCrapRYA1gztOxSQW61SPGG9Kh4rUC26l7o/JsKCvgiptMtJue+ms5KpaJlTGyMrrsswN8tKP25ywgacAtBXNfU92zpx/sEMb7vYbP1ScCTI5Xt2vBSQGw8P/szyjWClvZE1j8jVTOHGMNewDfccEPh51/4whec4PCVEr4aQJ4Aww6cbAtcfLx7gSKZMDsAoG83mv+5C3xRzoPPZoKSRjadC9/ioP/cngI2+2rp4fJ9o7CQXjEMRVnuTdfR2TcnM10qBUxnIaVdbhIG0VOi4X6+ym2wBgg1X62EAXSzkFYm1NNWL1Me4S1XkbGIrnShirrj2QBSUObrD+3zG7WNQam/08cx3VeUXsBUT0W/cK+8GEafc3o8DTg5U+EET0b9b+RLd3wCStMYhRpAgtF7iDjGBAD+7Gc/w5FHHok777wz+dlnP/tZ/PznP98g0sO+omQK8+XaCXGAk7VGiaRcdS32KUtiU3zqPzY90D4fwfh35UGL63voi45tgfGmgD0LtW8OGRbSci58KX3SPeF5MdJYRPtiTWO2y6eABzwp4I5nHj7mzGc7os/BawNTgn0b8dQA+mxcEoNyB/vmAg3dXgT12PpSwID53lbXiALopWloX+pTB7EmcRKZAXQ8HzQbGHdNpl/w5r6nKAIM/f1RYACZreCkfc/13xdTwOUZQGpXlRBjBADf+c534oILLsA//uM/4v7778fxxx+Pa665Brfddhte97rXjcWU+hr+FDA9XWd6mF0PoAqqaMC1yDVdDCABxGYWGKOambDYe14INC9BVwrYvTgAdENsSgrYZZ8CuBY5WnqojKciCXw5WAJOSt8HZG2innh8+z3R60VeAOdrHUZ6vnyLtWdTECv44/8vTgF72XHCBouQaYjH8LP8pnNBSen7yxvcgJ4ubrJsVEdbb7rGADwqYJIIhKbK9tfoulPhrhRwa1QYZLJqcr3v9XCViviyDCpsXp2+jI8etvsipIDpMSYiEAD4wAc+gJdeeglvfvObsemmm+KOO+7Aq1/96rGaTl+DWpsjZUps9Hn2+Ph3Sq2bf9gprJWrbo0COID4XLS7XS/4soWvtobaVk+fs2kOru9BNYJ2nktH7ZsvRaXPr5wRtJuZJtUAuupCSwLIKIq0NlOOOkJHD1uagtddc0Z5vnyeab5NQaVSwUBViZN8KWCzKMEHOnzpW0BnXOyiHsB+PXx1vj4Pv+wchGloz/Wk1Amr1ptuE2b7PCgpYD+D5yvzoAFId1eVdIz8hpfs4edKARPNqG0KXqoRtMuFwmePFCKN9QYATzzxROPPZ86ciblz5+K8885LfvaK9wH0MAxlTZipfm0qTBYPHBDqZAAdxwOj56JdIpXtSfdR1K8uawKKHY5vZ8xRAfuYMxsLSReBUNg3C/Pl6PGpwgV8aGbU9hc8pRYScKdf9edF2ic7eb5cNYA+BS7x+Yo9FT0bC4uXoK/UhKYMV+kyw7kktPbT1atlhV7ebIWFyfRuuCmbxGoFI6CBJ5cHn03cFP/O/T28rDLR1scJQh1gOr0X7Mp0ffwyRtA2oZZ+LV0t8VwuFNR+xCHWIwB86KGHjD9/1atehaGhoeT3tgv+Sgqfqo2yWLuodE4KGYgftkHNTJbi4QcAjVotOb4wB8JLFUjPham2hrRAlbSB0cc3shyE7+FTAZPqKV2gJZlDmZQ+vf7OlwJ2LdZOYRALhLoV2ZQUsAm0ZLvkuIGTVXWqgHA/VPY+IDvSddTpdp1j+OoQ24T7MnnPOK7HQM1uOB//3sXyq5Q+YVPgA9PClD7HdsvFPNFUwK4UsHsevlQ21daHxGI6fASB0TXDY4djtqJxv6fy87D5AAL2zjD68aYxfO/KEGmsNwC4IYg7qOGvzYl/7hYNuFgrtyouP3Z+sdYfHrkNzOhu1AcAHS+EtmdHDLjTfYC/76s+RylDQa0BdHUCcQFZin+ej70rC4T1n7uuB6kulPA9jJuKnv5SJ9wTDnuiqqHGKT+2b6F1PV8+toYnsiqOEUVRmgK2dRPxGc4T7m1XFwwKoAfctjwUVtkHnqh9sqW1c4D2ziZ1EzGAJw8YB/wpfaqoxy+G8YNYl5JZ/a1BSwcal38rtRewTajl845N56AJ5ywp4FAD6A/3kx1CFN4XM6tWyl4z5jpeGSDrfy/5+4TaHsBX7+VnrQC38ICUHvKxHCTw5FJU+4GTTwVM6gRS2j6lfzWAJibTt8CpcNWF0lr7Oa4FwYAZ0O4JJ+PkX+ztKmDG8+lR8NK6w5g3eSodbjWCJgJ6yhxMm1XK/aCPL7V6oopZfOIHn7G3e1PhBk86MDTd300PCAWA4XZ8Twx62gP6jKDtKWD38YC7NCFjTeQAsk4bGCL7Zru3fDWl+d+ZXCioaegQ6xEALly4kPX5Z599dh3NZN1HzZMCptWt2W9iygIF2AGc/u+y6T5bp4PCGNIaQE9RMK+dnDAN3QcVsAt8kVhIH8tBSGW7QKh+r5JqAA3fo8U4lz5rIVv9UWYODhApBaEAt6+ynH1zzcPX9gvwMz4twkbTNQZlowrQylXcbGpZEYhi7zzXwpkKt5eq6HOwjaNYcxfrNKx6O3u6w/jqSm22PhT2zeUlWK2mrg2u75E+H3ZLHaoNTP6+0O81J5B1fI9QA0iP9QYA3/CGN+DjH/84/u///s/6mRUrVuDCCy/ETjvthGuuuWZ9Ta3vQd3JuRkjfwrYy75ZwJfPF0tFMwEthr6vyQJHYwBd6ToK4PA2myel62Qg1NsJpGShOyd924vMi1TZVLarqNo0hhHIehgKfX7S8xDPoZL5e3r4Wo/Fv3On+1IVMEEEUkIZ7gKRrXb6zElVn6TUJwG8+Z5xF/gh1TsTn3F/7ZyntKEPc4jHsYsfXBYq6j3qYwB9/rFWMQyh/s63cWe1tDMxgAQWUp9j/t6k2CPFx9vn4PNMDZHGeqsB/MMf/oAzzzwT73znOzEwMIA99tgDs2fPxuDgIF566SU8/vjjeOyxx7DHHnvgO9/5DubNm7e+ptb3SKXsPgbQv0i5UsCuNBdgf5ipC62qPZK+2AGqaMCRmvG8kEhpLkJHE6cKeMAOeoB0sZaaalOEKJnWYV27qtvdV9mfvvXNo+z1dNUyJsc70r+AG9BTFZ+24wEaA+iaQ6fbSwyUpTWALW2T5mtH52POaCp7ByD3KP1di76LLUqP9zFf7mfcx4RSzoOXARwd2+Sfp4/tTgHHv7PV1tUdzwaQvSfMxzOELDaLJI8wCUivqWn9oZyHnkeEqOyRqHY2xu/gmUOIONYbA7jJJpvgu9/9LhYtWoTzzz8f22+/PZYsWYK//OUvAIAPf/jDeOCBB3D33Xe/osEfoKWASxQlu3aUFOGDPn4euFBSlvr4ZvaOpgJ21c/RzgNNuUpZrFsOIOtiOXy74iQ1Q7CSKVsDWGYMxZz5eju72VSHMpxwHgYsrHR8fJT5jHUMwvWkpG+9BspSZbk2L6kynMIgUlkr173tMlqngOl4DAcg51wPYc20T/1KYcep1l22rAlFedrydHahOx54zgOJAbTNwb3ZzYxhSgFTDLE9Xp3ptfB/D6clTwCA3ljvRtCDg4M47LDDcNhhh63vP73ewptiIhXL+wGDi+0BdACYBV/c4m6n6a8PRDpThnSmxfpyJ6T8EgavXQShlHORMoDuVnBlPRWdQhZPv1Jfl4H4d7XM39Mj6cHrKQtwK8PpNZ1SQQ6gA6dy6XTfQuvqXuE0SafW2DrORdlWjfo8xKpsAnAC3ClgF1uUn4O9O4z7e/gAA60G0Aci3c8XJXXa8jGAvhai3s4wfvDlqwv1CYv0+dnYO8BnReMW1CQMvYvJdKTDQw0gPdYbAziewucpVdb/LrWBkTF4VAbQVftGrgF0AgY6O2BXAVOAj3+xpigly3QCoS209nNZrVZSuw0niJSBN4oXoXcMRk2nqZaRArz0MZQVUXYMSr1XWqIRRcWFitQL2JmGjn9WrdCAj7EG0MMWAf6i/7ICp76kgHv+e9u34PvSlrp9iul60kpN3OfSdy4oqU/fNe1Xf2kK+LK97yjt4NT8jClgQi/gjNpfWEfoyvz46sZDpBEA4DoI746WUixPSBm6GqwDWg2fNAVccrEHPHVnjBez6XiqoXXKADrSjs7jiSIQaQ0gMZ1OAR2Uxd70PShehEC6CJdtBWcaQy1c1PvSxQBSgBcgvy9dLAOFvdPnYTYoH91UWNgiAF7LDU6PaunGBPCkgDsEBpBo9eRLWwIWgRSjBtDKInrq71znUYWvBtDneOADb3VCCrisp6I+P2MKONfJwxQK6Nu8OmsEJbGzBtBDGoRIIwDAdRC+lwGpNidh70yF1VymJDsPCmsGeAADlUVU4MkIvvwpJoqPIOBOhysmU5rm0plU00tNfTeVYjWO4QItRLsN1/WgsFY0ME7cFAgBgwt8Ufza4vHLzcFlkh6P4QctLqUhRRUOEEFkCTELh5E11iFSNyYO1keBBVdd6UBJ26y8ebDtePe5dKcuXSnHeG72jZGKYR8D6Glp50vf0oyg3YI1n+UVkN4XpvUn38nDFL7ny1dCFY9hPxc+Q+0QaQQAuA7CVw/CEoGYFjnCYq+PXzSC9qtWAd9CS5wDZQzCeXClDH3zSICTA4RSaggBSxpZiR8s9g5Af8AXpS60tK8j9Xo6vSFd19MuZkn7g9KAk/Rc6t/R3GubwgDar2e6OXJ7ZLquZ5IudNxTVPsTMQNI3JhQLI6k7zrAz1r5zIM5ZQFSsZnPpxPw1wD60qdkOxxKCrgEk0lpiaf/rcIcqGpm1xwczyjlWoSIY0wA4MKFC41MShRFbMPol2O4lI4AzaDV3TuWxpTY6jmo6j6XeKIfNjAcmwr9b6b/plmXuJTIHC9Cfc6ZMRjmw07WypfSL3kuKeDNLyyyp8Mp91WlkprN5q8nxTIE8DwbhMWeXE9J6Ebiuhau+j19jtIxqMCJxrDbmTPfM+72EvTXK5ftBKLPz9jRhJCtqBFtYHyejK66Mx8D6DqPgP97UEyYqXWETgawY28Dqr8/bDWdvk23r7OLPj9XDaHNMzVEGmMCAOfMmYMXX3yx8PNly5Zhzpw5YzCj/oYvZUhjKVxpFUWhEwv2LUwLme1x1pzRQIvLBobSokn/fP7fpnZAepDEDwQLF8AMfEiKTZflB5VNJaRfKSbOZSw/aHOg3Vf59CmlllI/3jUH8rPhTAHLagCp34PkA+i0gfFYl1DYt9FNh1Po5dmYNFwpYMp9ybBgMYX+/MttlsqBUApz5mMAfVYyvvIGingidW8wj6E2eE4RiINZ1jdNUmNuny1QPIaDhXSQBiGyMSYAMIoio9XEqlWrMDg4OAYz6m80Mjegw7JDmJoh27jUzKlPugrYDmQp3nf5MfJBSTu6UgpU2xCamtnNWrmZzLJ9X6mpttFFzsV8Ee6pTi9Cz5JOpzO6BjaVaA1kY8i5c5CmwvUxTEpiSqtFtxk1dQ6OGkBP27DsHNzCBff3sC/41FZw7hQwJf2qroU8le1i2H2gByC07/RskHQ215bdGvak9X0MnpcBpBhBJ8+HrRMIpY7Q/u6vesC4/nNfLaPcBzC9zqEO0B3r1QfwxBNPBBAvqP/6r/+KiRMnJr/rdrv43e9+h9122219TmmdRKbIvNsrPLC0lKF9gelwF+uSC20vil+MmQerL0pi/+KgdvfdXmRNAZdJQ5NBZK2KkU7PzQBS6u9K1FoltYwuEEnwrlPzGNS6iVDsOuLfUwC9J4Vrubf7YVDOrU01bwr8qWiKgpfKAJqYL5INjCcFTKq/c4BQ6rkk9QKmtObzMJk+Bm+43XPXIRJKNLz9hD01gPEYUeHeiVWx8f+39wL2pIB9wCmnwDWRLG0vA+hnMn3Mct3yvk7m4E0BpxtV+xzsz2jGMzUogZ2xXgHgQw89BCDeDT3yyCNoNBrJ7xqNBnbddVecdNJJ63NK6yQKVhfN7O85KQn3Iidjvqg1SvqLaqTTy8w3BT3uQndXuo5S6A7E39MMAGlMS9kaQDXHla3iGFEUkUQgLmsgNnCS+gDmrp+eiiLXhTrSVFxmWgroU+DjKK8oUX9HsZJxLZRcGxjT92gRnlFfvRalTKNJOA/+TZ6Lmaa862jMlzuN7GBkKSykx0LFZ92Vzfr0Cn9rWHtv2HoBJwy90NYnr8A1ge7kXUVgMm1BsZJpdeyt3PwpYL8IxLVpVjW+nV7krCMMsZ4B4G233QYAOOaYY3DOOedg6tSp6/PPr7dwsVYArTbHld4hG+ZamC8q65VhjDo9TNKAbIuQ9gTsHnxd7eF0pbmAeEc3jOLuXn0vX71X08W+MUGLybsu2dk7wLBTNdoHRTXNg6/I4Kroh6iHChhsCz4V0LsEGCkTSrsnXM8XZYPmUvD6bWBcYxDeEY5UuP5zCjNdJp2efA8DY5PaExHYVF8rNwobKrwvfeb9/vo7rVa5EwGN7O+HR4V0lYpD/aqESeLUaZaFNL1WfWO43jEqfOuXt30nlckk2MC4RDmdnruncYgxqgG89NJLN1jwp8LZR5ewo3WngGkMoG2xpjIUenF1QUhCZBFtfXj1OXnVkpZFigpaEgbQZAOj2vJR/QwtbKpvDMpCS22r50rXeWsZrewbbw5lrIFsHpfkjYmznrJ8WUDyfDl7ItvZWHoq2+8D6KwB9Hi2sVrBGcVJtOvhSgF3KOybo7ZVn5szhetMZfvfE3p3GOMcPKCnVq1AZVxbhrrStKbT3mqxrK2Pzw4H8L/7XW0WgdGMh9fGxb650udmTQFTGEDPNXXVhIZIY733Albxv//7v/jf//1fvPDCC+jldl2XXHLJGM2qf9GoV7G23RULDygiELpaMtcLmLhAqTHW9rpiIYlNVaanUv12GeZUF9XQmmIETTYfFgJAUi9gIQsJ0IQLao4j3Z6VASxTA0gVP9jYUIo9EuAWT1BS4fEY5mcjHsNvBJ0CDofxMFkFbD+XrmfDxWzHPyeYg7vqSollAQOOFDDF29Hb75vBADprMh33ds2TAvYx7LG9UVwnbFZ1q5pOV5aAKALxpG8BuQLX1WZRPx5wgEgim+pVAbvUzB4w7GPHQ8QxJgDw9NNPx9e//nXssccemDVrlrP5/Cs1bLvzbi+Ces9RAINTZSgUYFABBxCncNe2u0UQSa4PcrOQtWrFudDq88wvEFwxjIkBbBHHSFLZuRpA/Xs4rWicrBURfFnOZRRFoNaFNupVoGVgdKn9pZN6SjmQtVnikFlIh3K1H2CasinQQWy+4J4sAkmupx0wuMtE3JYdFHU6qa6UeC5NAI5iA+NPfRKALOH5opRH2FgnUstIBQAN5zJtA0cBsTZAT2MhowiOvsrue8LXC1i/RlYg6i1NcJ9LX29ofWwfiLQxmSOdHg79wW+s44+XGBMAeMEFF+Cyyy7DRz7ykbH48+slbC8k/cGSikDIwgXLw8xlAIEyQhIzcKIeD9hfjL72TL45AOnCJ1Weck1/zQxFOfFDxhBb+D0o/akBHYS6BDU+FrFsDWB55aq7rR4FMKS/a3ejjFdeck/0oQawTAqYwpA7Mw3EZ0NtGqRlAeqes4Evks8mocaWYgRtt4EhzMGyuQJoDKAvdUqq863GDL83jewDTh4xDOBPRXtTwNYaQj8D6FMzp3Wltnuqi6eWrLGOP17Cv/qugxgZGcHee+/d1zHvvPNOHHrooZg9ezYqlQquu+467zF33HEHdt99dwwODmK77bbDBRdc0Lf5+EyYAVpqxp3SkC1y1IVWH8MKIoW1VhSbCxU2/7v+2sBQGcD89SS21euHEMXD6ALl74l+9OElK3ALAJBWj+mycKEalNNU9n7GKf68bIPlqgFMQKSTMXKrgCmp01QMI2NCgXQRNgE4kg2MpwcuS0lsup4kgZSdxdTnQOr2I2YAqeIJB5vq8dDz3ZuujRGQPrOujIe/lnH0e1iOp/Q09otAaHWl4z3GBAAed9xxuPzyy/s65urVq7Hrrrvi3HPPJX3+qaeewkEHHYS3vOUteOihh/Av//IvOOGEE3D11Vf3ZT5WpiUDAAkikFJF5hbwJgCA+RdCi7BA6cfnF/thgtGtCjtgUC8SHwvpMoKm1RHaQAfFAxDwAQ4ecLIxZ/pn7GOYFykuo+ta5CZYOh2osG1uuODNKDpg1EICbiBL6QRimkc/agDLekv2epGmwF23mQYXgKMAJ9+Cz/NNldkTJQygULgAuK+H2vDauoAAWipdCN4Af/rUdy68aWjWtbDNwZcCdoNxyhheJjPYwwAYoxTw8PAw/t//+3/49a9/jV122QUDAwOZ35999tnsMefNm4d58+aRP3/BBRdg6623xve//30AwA477ID7778f3/3ud/He976X/ffz4WNrGjW7GixzvKtmjGjBYiu2p6SAmxYRR/oicC/2Kg2Wr7/jzMG2o2QX/JdKp5tBJNd4uEwNoDWlP3p8pQJnHSKQinKsoIUInHpRzPjo9ZvDI/Ei5wOAPiBbitFltrQzbbAoKnu93sqmkKeLYQw1gG2/R6b+jijUIeqssLOOsDw7blNc9rR6Z6egxgMYKO87kp2N4z3hEy5w0tCuGkDXBivtiOI5DxQmsyQDaGPIOC0KbdczqQu1poDLq4B9IpDAAMYxJgDw97//fdLx49FHH838bn0JQu69914ceOCBmZ+94x3vwMUXX4x2u10ApQDQarXQarWSfw8NDVnHVzexrXbOz3I40kPUxbpWDrQAjhRwaRAqSAFbFlqyDYyw4B/Q/QyztW9k7ztH3VrZXsB9uZ5MYZGah76wr237WQ7AXx9LBdOq2XvNYH9B6eyivkM+KMDHpfokG0E7QCilBlD5TkamTj3ETEN6T5nYO1pK3lbDp7NQ7myHnX3LMpk0UU4+KPeVuo99KWCnCMSRaaAwgGQVcAnw5Xs+fK3gqHWIAOVc+lLAfh9A2xiu+yH+uVnlPN5iTACgMoQey3juueew2WabZX622WabodPpYMmSJZg1a1bhmLPOOgunn346aXzbDoSudLQvcmVVwBR1oArbQklPQ9tsYEYXOE8KGbA/zJQUlz5HE1PCvR4F8EUE9Lqxd2EOROBjtcMhMk6A/XpSQYv+N0Y6PUwcNbxtd3vJ9fAzgDZbHyJzps2x3e2hprW0I3cjIdSF+hTRNtUn3aapnNI/b9SeEaboxfqkFLDJDod4PSwp4Iw4ScgAUplMF3iifI80BewDTgRGVsgAurIEAG2j6EuflmUA1dycHWocm93458pn0zxG+h38DKDUjNqkvB+P4V8x1lH85je/wZFHHom9994bzz77LADgP//zP3HXXXettznk2UbVxNvGQp588slYsWJF8t/TTz9tHdvmf6duPGp6yDxGOQBIrXvTx7DZn3iNoG0iEEENYHGBoS1Q+hwLbCjxetg6mrSJIDQLWmwGyEQwbU2d+tlzL6PrSenXa1WovYg+xlqNGR1s0O5tG2DggND89aQYD+t/w+ypSLPUsdldkG1gnDWAfoY8DwAzc9DuCUqpiUtsRvYBzG/QtH/XHaUJLvUrtb7VbWczej0JANIHnJy+jA7WKU3pOxhAx7WIf+6/r/x2Nu53pqv0SP+5uw7R/T2S+8qq4HXXY8ZjUGsA3d9jvMeYAMCrr74a73jHOzBhwgQ8+OCDSVp15cqVOPPMM9fLHDbffHM899xzmZ+98MILqNfrmD59uvGYZrOJqVOnZv6zhY0xIhdW6wBQOIZ1DqwaQAuAo7aCs4pIOClgc4qJmk7P9DSW1p3ZwBczbemag6tGCYDVcJd6P8SfcYtASNZAhmuq6v+qFTqDZ7X1ITKhpjGomwJbh5p4DKotj7n2rS81gATA4erUQ66FJNUAykQ51NpUiuepPldTuFpnUq6HD7SQlOGWzTIADBOup+ta6D93C2po4MvOnJWfgw+E+oRavu8Q/849Rtqpx30exnuMCQA844wzcMEFF+DCCy/M1NrtvffeePDBB9fLHN70pjfhlltuyfzsf/7nf7DHHnsY6/+4YQNO5Joxl8qQuMDYXkiSxb7fNjAcH0Bb5wdKxwYgey4LHU3YNYDCtKUlPQcwOmDYUuGSGsASmwLTPBQDOGGg5q3j9Snkfdcirr+zAFmVviWyd84UMBn4mNXMdG9IO/Dx9sm2nofy9kSpz6Z7DBt46mipPtc94bJgSZ7xagVVJ4i0fw+SjYyljlEFZXPiVshTagDd7Btl7fD1NPalkX29gH3snT621AjaJ8jRx5bWALosZsZTjAkA/NOf/oR999238POpU6di+fLlojFXrVqFBQsWYMGCBQBim5cFCxZg4cKFAOL07VFHHZV8/pOf/CT+/ve/48QTT8Qf/vAHXHLJJbj44otx0kknif5+PnxMC2WRM9WERFGU3NRSyw4OY2RiezhzyNffqaAUuatIjWZlqdNKpWI1g6YzgGoO5o4oPrCgszVl1a9W1osEpj2qbuGmIAGADUpK32MEzQCh+TGo95Wr3orcYcYCZOldcuzMF7cmswjoy6uh6Zs8WwqYmEonsZA+VpkgmnOyb+UFGK6yAgqj61Kucm197Cpg98bA18WDIkCkG1rbRCB0BlDajSQwgHGMCQCcNWsWnnjiicLP77rrLmy33XaiMe+//37MnTsXc+fOBQCceOKJmDt3Lk455RQAwOLFixMwCABz5szBjTfeiNtvvx277bYb/u3f/g3/8R//0RcLGMC+s6aowVSkZpbpg9DtRVA4SlrnJEkB6wCwo82BoxrVx5CIQOzpPnntG7XuzM4A0tk3W8qQayXjqvfyha80wde9wjaPtSOc+9o8B2raErAzeGXrYwFOCth8X9LZOzv4opZIWAE9mZkedSsQClHiMSxMaLI58jGIdsaHrKh2AHoKs+xKx1PHcJkoUxhAZ5/tTC2k3wjax77Z7oumYw76z511iCV7Adc9KWTAz2T6RCA2o+vxFmOiAv7EJz6Bz33uc7jkkktQqVSwaNEi3HvvvTjppJMSwMaN/fffP8Mw5eOyyy4r/Gy//fZbZyln2wKTOMJTxA/1KjCS7cNLVcUBFAZQBpz0/+9b5PQFbKTbS16AnBrAhqXTAAd8Nes1rETHYcsjY1qoDIX6zHC7J1a/2rt4rD8bmMwYlhSwL2yLdYt4HvTPSFPZrnRdqgKm1WRK5+ACHWoMV+cIoHypic46WXsaewGc+XqmtV40n06XCITusymrAXSBcX0erjFcdlPqZ673vosBJKuhfSpgz33hE6Kk97Y/lW0bw1ey4vsOALytK133A2BPDY+3GBMA+OUvfxkrVqzAW9/6VgwPD2PfffdFs9nESSedhM985jNjMaW+h000kAAfjv2JxgBmiqKF9T0UKX8yhuF7tDhz0MUPOgPYpgMO20JJtXAByoNhpd7LM4Cd5IVKZ9+sKmCfCMTGhHZoTEs8h/6JQPQxhhkp4DTtmDsPnpe6HrZFhs6cmZ8NgG4vZAMNVCDrVgGrMWhdVaRCFPV7l5egv9uPJaVPZHTrltIIQP8esjRyVzOjllrRZOYhTAEPE4y9U9YqQq8XZWoe9XvM1fnIxQB2exG6nnvbK0QhZF3KehFyWsF5awBtgD4wgADGCAACwDe+8Q189atfxeOPP45er4cdd9wRkydPHqvp9D0SdiC/OBD8oFSY6pR0ZZ2PodABh/5CGSEuLoDGOmnARx3v6gepQtUyjnSzzJekBlBaOxf/neLuPIoiMlNi9UMUsG9iA2Qf28NgdEuxb6YawBHFWBHuKU9Kvy92NtTaORMDSKzT9YlAqHPoSw2gbWND7JKjxtDvQW6bw0Knnp7amMg2NvrPpCngjI2ME7x5agBJ/aHNm34gfX8Outg77XftXg9No7+lWwxDtdTx+gCWEKJ46xC9KWD38Zl5WFPAHkAfGEAAY1QDuHDhQkRRhIkTJ2KPPfbAnnvumYA/vU7vlRxNyw04TPCDUmFarHWw4FVb5tKvKqiME6AxZ3oamrgw5OeRrQFk2MBY7E+S7+EBofoc9IVSr6ck+wAKRST6Z2w1gFS2xlZrJRX1AEwGsFYcg5cCttVCMkCop/6OClqM3WGI/YSbVkBPU+DahAs6U0MdQ2oOrv9e6meYirTMc6AyqaYFn2wNZDmXmdQpgQH01wDKGMA08+OvATSNQbf1sdfPUeoI+2kD42cALeCNpAIuVwMYRCBxjAkAnDNnDl588cXCz5cuXYo5c+aMwYz6H1YDZKJFBGBeKJMCc8Zinx+DurPXx8jUABItJlSY0q8cBjBpkt7LMy1+g9f8HHQAR+1UkDk+LwIhskX6Z+z2J1IGkMFCWlgKTneY0jWAVjUzRwRiYdiJz5fTCJopArEJajjiCb1+mcLUJL/3zMH3HVRPYyB7LnSlv1TNzBWixOKysmyquaxA/zvmOXgYI8I8bE4DgFb77ertnAHj+U0ecVPh+B4UT0VXbSxA7QVsZyGB9PmyzYGiAvbd3746xGADE8eYAMB8sbGKVatWYXBwcAxm1P/wikA4akltDKpqFcg+YKYUrtQImgNi9b9jqgEs0wqOlwIuqiWzO2IiYCgpAtGPAei9Tvs1h7Kp0/gzxXPZYtnA9IHJ9IhAvDYwFhAaRen18KcuLcCHyULGYxQtkihjeMsCPNdTt5vSz6Wu9G96SkWsKWDiHOoO4MNVZNuBsLsjit+7zn9NSQyg476sVivWekiurY+5t7P/XFCBsLOe0utFGP/c9nylGwIXA6juLZkPYFABx7FeawBPPPFEAPFL51//9V8xceLE5Hfdbhe/+93vsNtuu63PKa2zsHUakHTA0F+K1HoxIFd/Z0gB81rBGRhELgA0qJkp5yEpCu5D7VurUzwPAEEEUjeLQDg1gKaUYVt7yfmYTFt6hiVE8amAOeArUwNIt4Hxpm85wiBhKtsOpjVW2NML2CYCoRpB59OvJlDq3xR4fB2Jz3irk63R5YjN6h4wzk1D5+sSKXOwXQtuRxSfCthVNuM2gvYzgGqenV5X7BRAYQApILbTKwpRMvMg1ABKU8C+riyAv7zAVjakIqSA41ivAPChhx4CEO+yH3nkETQajeR3jUYDu+66a9+MmMc6bD5lHAbQLALhpV8b9awAo9PtJao4KQPIWaj1MXTwlPTGZKSAy6QMzSlgGjsA2AED1TRY/4yu6tZfctSFspQQxaMCpgByE5CV2MAU7Wz4GxN9Dl2NTSWLevJgOgPI6SIrPeigxdztR1+oyV1VSoBp0xgcAGhr/UXdKOpMkJVFJKaRrWlo4hxsNYCUshm3DQztfTdQq2Btu3xZgdNU2wnesirwwWp2vpT7ipoCtr2vlLDQ5QPoe/e7BFaun4+3WK8A8LbbbgMAHHPMMTjnnHOcvXRf6WHvVMDxvzMAwA49Baz+zqpW+uBy6t70v2NaHNgiEEMdIosJ7ZkXB2nqk2M8nIJYswiEwkKa0hLZGiUfa1Wuk0g8Rnn2zagCTlLAlOvp+R4sSx0zaJHWAOrWNHUiAyhdrFX9XRRZni/hBg3gsfQmWx51PEXpr5jSXhSD8LTjDc8GBsgy4kA2hUv6DsJuQWVZK30OZRjAmNHtlBD1ELrLEL4DgIxvq/4zwF2D7usF7E/fpjWhptDrU6U1gIEBjIO2gvc5PvzhD1vB349+9KP1PJt1E/2pASyyNdTCats8ODv7+O8o4+biAkWuATTsSlsM0YE1veOpJdHDtDuXqGfLsG+J+MGQhq5W4F1oEwGHZXEo9z3KMUZJpwOqwTmyi1RsycPpqmJnpgFGCjh/LnUG0NtPuPh89nrp9/A9o3FP4+JCxbHDsQmLJBssqdI/Y18iALJ6b2fpxsQKxplpaG8HDcc8XBYqw8SMhw3AtYibI1ftG+X50sseXO0BaZ6KNhsY1d/ZPIaXQez5sya+GsBgAxPHmADAgw8+GF/84hcxMjKS/OzFF1/EoYceipNPPnksptT3sO/M+TWAJnaAonwFivV3rdH/pfgI6sebgBMlfWsbg7NA2YxBy9YySkQkxVZwCoTSgZPJ15GjfLWfBwpzVgT0HNACmM+lqgEkGUEngN6cCucBWY21YtzbPmFRveovCzCxRpxOPYC59kzC3pVKASfvKq3WmFFqYk9l8zd5thSwlMGjgmlXL+GMLY+LPTM8Wyq4vdNtZu9UEUjeZB2gbdyr1RSMm0ASZaNIN4K2gDdPCjjr7ehmEa1G0AEAAhgjAHjnnXfiV7/6Fd7whjfgsccew3/9139hp512wqpVq/Dwww+PxZT6HrYXM4sBNDBGHBsYIH1hqZeSvgv0LXCATQXMtYExqUZHvwerFjKvGuW1ggPMAJAlIhEuMPEYxRcjC3hp56HX0xdrpVqVpW/ZoMWRAibVtprEMPocSjKApNo5ywatk5xLYRqaeS5NzBdPke1OAXNqbE1WUaSNqs4adYugnnI9FWCXt9Vz1wDSAWTRioZqJu2qAWyTv4d5DL4aumhFQ7abctQRUq6Hr5WbT7SWGEFbjtfBrbUG0AKkVQQVcBxjAgD32msvPPTQQ9hll12w++674z3veQ+++MUv4tZbb8VWW201FlPqe9iKcVk1gI70EFuBm0sBkwFkcrwhPSScAyBVQ1t2xcKFksOm6kDY5NnGYTmMLCTjOwC5OkKWEMVeVpD/G755ZAFg/P85IhCp6ED/TFsIWmziCYlHpkmlTx3DmALmpPStvo6M+9Kx0aQcX9XqBI1AlnEupSIQE6scH0801NbGt9kTxZ+zbwxcNYBUIOqrZSQziA4G0HsuHOCJBAA9XVV8YNbHIOrXw8b0+/wMQw1gHGMCAAHgT3/6E+677z5sueWWqNfr+OMf/4g1a9aM1XT6HtYFRqACNqaACQwFUEzXcQCHfnw/RCA6iJQYYudfCNRm8/rf0VXAkhrAeIwig8fzATTZ+tBFJPEY5ZhMm3iCZQOjsQzDjBSw6XpyaiHjORiAbLI4+efgK9Fg+XRang0Kw+5KI0uN2gHeBsvU5pC7yTMymYwxbO0eqSy/1Vycmjq1PFsA/fkwveeAbH2rVMWbeOd5Sxvs4IksqLGwkNkx7N/Ddi3zY9hqABWD2LUxgF3/M+aqxwQCAFQxJgDwm9/8Jt70pjfhgAMOwKOPPor77rsvYQTvvffesZhS36MfL2bTTirp2SoUYLDBm3oZ6BYuCrwRTJyBdIExC0nkxsGSujWTqTZHBQzI2TdX6pNjfgzkvwd9jKYH0FNAi1MFLEzpc8C4/jkT4OAo7HtRttaIc2+bxElSdrzs95D6IQJmMQtXbDZg8G7rR31raREI8XvY6hj1f/vsomwpYJbhvCXbQe1OYyMeAP65NIkwKPdmqgL2ADhrCjirIrcd72RjLYK5/BjjPcYEAJ5zzjm47rrr8IMf/ACDg4N4/etfj//7v//DYYcdhv33338sptT36I8K2ABaGCwHkNbYqTG4Js7KtiADGJiLg1pMsylgwUJrWRzENYCcQnntb+hgmFUDaPgeHBCb7RRQrLUipZENL0apr6O0BtAEnDjnQZ+rscVhiXQ6Va0JlK/f08cQbypsvo4cFtHBAFKV/iYGjgVCLea/1HNh66BBtRbKtMQrsIjMLhyW+j2AkMq2jqG+B40JdfoAeubgSp9SUvI+Sx0fmPV1AuH4GVpTwAEAAljPPoAqHnnkEcyYMSPzs4GBAXznO9/BIYccMhZT6nvYa4wkNYDFBYacAs7NgwNY4uPtAg42C5lZrDk1gO6Xu9QGhpM6rVQqaNSrGOnkuqowvARNLyXOYg/E57wz0hWP4RKB0DcF8T2xVvNE5KiABwzgjVua4BKBcJ4tddzEUU96Vh2howaQzJwZ2JZ+qIBZIitHfWyZFLDEIzOv/KTOw1Y7R70eypJnpNMz1ADSRG8mdl2fA2UMmwI3eT6I6nZX/Z7v3iZ1E3GM4bNx8THDKjVsYxApbfFMTgXZMQIABNYzA3jQQQdhxYoVCfj7xje+geXLlye/X7p0KT71qU+tzymts0iLccszgKYFhrozzxsYc+qk4s8VHyQuYMgvMFEUsRa5AcvLXWIDMyKsAQTMZtCsNJeJaSEyFCpMC76kBtCmnqXEhAQAFpkzmggkXeSUoEZa25rdHNHV6RnGx7C54YiTyrRJLJtGtneHGf0erNpU+fcwte9i1QB6gI/v3rZ7hXK65JjHIPfhtfl0MupbbepVsojEAb7IKmAHA9jq+Mdw9QKO6yHdG3eqjQypHtNah2hvMzeeYr0CwJtvvhmtViv597e+9S0sW7Ys+Xen08Gf/vSn9TmldRbWnbmgE4i0jy+QAs3hnAiEAhb0eXZ7UbI756cMs+nX2Goh+ztXDFiKimWt4PRzST9eH0O/HhwhirEGkJHG1j+XBXB8FtLUlYV6PRXLp4QfgKwGMIrSQm+2ut3BAFKeDdUnGzAz07wNmpw5c5UF0BhdM+hI3jOEEot+bApctYw0AGhmfegt0Gw2MvF5oDDLvkyD71yoc12oAeRsdi0ikA6xzMMFfKjPuWsMyvWwbdiB+HlX736fibPVBoaQxTK1q9QjMIBxrFcAmPdXyv97QwqbZxvHn6sfReaqhm+4wADyFnsgBZEcAUf8OfVi7Gb+V/+dK3wGyFIbGG761WQGrVLALMsPgwEyOQVsAC1SIUqefSPfE7kUcBRFaQ0gqRVc+hn1/Tn1nPHnirWQXCBrtifiMIDlrE8ATYCRAU50JtMrAuHUAOrnQVjLKDU5t3XAoN4Xvn7hLG9Jm5DEB5xq6Wa5m/Hp5JwH2/cYHYNqcG6s36N6Cdrr5ygbNVs9JpAFdfYUcHx8DBYNQhTC+fSJQPJK7fEa6xUAjqcwFZlHUZS2zCKJH4qLA4WC1yO/WHPTt/pCqOq8uGnohIUcBU76DpnVEcVWGE2qAXQYQfch/UoTYJRndF1KYlIt5OiLMYrSlzEXtORrAFudXrKr5/gA6n+bLwIpXk8OeNM/l3m+GCIQVz9idgrYoJ6lpG/7oQJ2K5FpmzyVApbWESYp5Bzrw1Wu9iJYwBfHkidXakIsT8i88w3vmVKqbiKIdNrAkBlAO3iibCxsXV2A7LNmTwEXN4h6UICsK41tG3c8xnoFgJVKUUZPsZ14JYZpkev0Iqh3E2mBcSz20nQdl2mpVCrJoj7czjJ4ZMAwOgcFIHWmhmQ7YkgJdLVzSZmHSdnGPRdpDWCR5aC01TMBerGoxyhmoS8w+hhc0DIxdz2HtZpITo9rIJ07l40t6zsHWJgvBXwY6vQRYd2bPoY4BWwVm9EBXGL1VAbI1ouLPmdjYTKj5oxh60fMeV+Wrb/TAV7LYJzPqxPOAhRyCtgh4KCbapcDkS4RiX59B2w+gNr7wVRHSGkAkBiL97IZOBUhBRzHelUBR1GEo48+Gs1mEwAwPDyMT37yk5g0aRIAZOoDX+mRMRYdvdn0hZK1wHTkC0yereGyPUAMIte2u2IWcXD0c2vbMgbRVBTczuwk6QBQfzGz068JY6SPwUm/xteibQBvZOBjShmyasb0RaqHSU1+az+1qVDXUzG7A7UKaQ6q/m6k20u+PxsIO2vOeAInEwAc5GzQMgIpeo0vYF6weSpgiw2MRGRV4j1jMpPmgC+bcIDOAKb3zUi3l7z3OM9XsqkQnst6rYpqJWYhjRu0EgwgNQVsU0MDjFS2qwaQwAC6RBxqXvVqBVXLd6lbWgumP1Nz8HdlAUbvh2r2eQ42MHGsVwD40Y9+NPPvI488svCZo446an1NZ52Gaqrd7kbJzcZNfbpsYNgikNFFmtM+TUWSRh4pV0eYZxCp6aV8n85KpUJuz6TCpGbm1wAWGUCqw78+T6MIpARrxRmjNuol2OlFBQaQClry9wPHA1DFQK2CkW4KOvohAuGmgE2lBUkKWOhPybeBcXU0obwjskBHhawtnq5u57H8ZYUkNuaLbF2ig4bSc8iDL94Ga7jdM5Ym8LoFWQCgNwVsT31SlfaUOkInA+joBUz5HhkG0CFEcdcAZje6+XdTYADjWK8A8NJLL12ff27Mo1Grot1NPdtSk1lim6h+pIAtDCC1FzCQ1ivqNV8AowawngOADKsNoCgaaNQrpFSCHk1DXUu6OMja6sXz4TCAJpZEaIBsnANd2d0Z6SZAnAtadFY5iqLUA5ADAOtVYKRr8KeUp07ZKWB1PbUxhjnAydSppw81gGV9HbnzcIE3+jNaDsgq1qjgA0h831W1jY3RJJ0FvkrYTdViAGh6xqXdgjJjEGshnQremvs5tZUVRFFEuh6uXsCUeuVKJe4t3e1FFhDpP58ZRtgEhgMDCCCIQNZp5F3ZucDJ2e+UKgIZVWXmVcDUhTYeIwcixQyg9Dykn1MpIvVicKUS9DBZNEhVwFLwZVIBc1OfJuCjzgW9M0tWQMFN6evK8FanJ2QAs99DajuS9aeUsVb6ZoIjAnH5dHJr56RA1mZ4KzOcl38P0+aGVZpgYb7S+0Joy8NJv/rmQBgjebYyTgGcOZhT+glw8qaAHeAryRR4GEBDSQCAXNbFkQLWPCHzKl7qhtelJKa8t202T8AokA0MIIAAANdp5G/AYeZC2Q+DVsW+5QUYFBWyikQEMiKrIyyqRnk1Z/puUb2UuEDW6BvHUPAC7jpCacstaRq6n2IWqQ0MEN9XHBNoFfnFlgMWgPSeyqTamMyy2QeQ/nyY2JYWF8g6mExe+lYOWowMIPMZNylHOXY2tv6vPA89Q1s9TvrV1o+YsUkzttUj1Kwlc7CmoWnnISkJcLBefhGImkPWKkW/tpRewEBWkQ3Q33em93V+DJ97g72eMiiAVQQAuA4jfwNyFIaAW2VIXigV+9bJKjYpLaKSMfIAjtkKLu9FyGlTBWR3vdKWdikDKBNwAEW2Rff7oghRXCn9MqlPcS1jJ7spoC72tWolORdr2t1kczFIaAOnIjGkHv3b3FpIV1cWMgB0iEA4DKA07RmP4WoPKOvsQk3VpXMwANk+tHuUzMFWyyi2sxEw9Ha7KQID6CjRKCcCIXr4GURi3HnYegFTexqbfD7zc/CxkBQhirSjic0cejxGAIDrMPI3YMIAslWKRdDCrgHMM4ASEUi+jpD4PfLHc1PAOp2f2obIDJR1Y27FJrLb6inQwhSimBd7uohE/1wZP8O8JyIXtADZ+yrtAkI/fjCXKuMC+nyHG4D/PUzdYVgdNEqmHPXPZcAXs+YMKN5TnE47xvPA/h6Ge1tgkm7tBMKp4csYrUtAqJyFTJ/P9J3dTxGILwWcF8zpQS2z8J0HX9lNJmPTy3+P0fe2p27b5eNHfff7UvohAgBcp5FfaLkMoOnFzK7XyqmAJQzghJzvm9SKZridZSG5qlEgXSDYrJf2t6QsYv6llAWAQsBAVObl/46p0L1OEMMARTaUe08ByHhDctrAJXPILZQcs9zM8ToDyAaAxbTlMCON7FS+Mucw3DaNQbeiMTFv8fgE1irH0GfnwN1gSRlAdycQKZPJaZPo68Ih9RuV+HRahShEEYhpDGqZha2ulMq86eAuD+jJKWCLGEYyRuF7jB7v68s8HiIAwHUYihFZo9g3JgOYt3AByik29bGoCy2AghG01Dak3Y37CXMZQEAT1HSzoMWXSlCRtwXIjMEUgajj9ZcbT+EnU3zGcygyDOJaRmENIJAVBiU1gIwUcDN3b0trAHUGkJvKNp1LTi9gdXzH0CebqrI3Xk/GpkBdM70cQQfFlHMxmLQ4LM6ByvKXtYGpe1gnaWs+DviyglCBitdYo8up6RSmgPXzVATTtJpMGwuZiKwIiuyaRcRBTgHXzOBNH8NXU2ljERMmk7jp3pBjgwKA5513HubMmYPBwUHsvvvu+M1vfuP8/Pz587Hrrrti4sSJmDVrFo455hgsXbq0b/OZ2Ihddta2OwD4DGCeOQP4KUNbFw8O+1YAkckYvO8RH9sTMYD5F0KHCRgGahUo550868Q2gs4xgDXtheeegz19KwWhURQJbGDMKWCeNZCWAh7hX8888JEKWboa+EqfL2KPaoNikyMkGTSwyv1IQ0tqxvTj9Lo3ikLeudEsUZrA2ZjYQYckhWt4voQMIiAU1BjmQHm+fCIQagoYsAMfshF0QVlOPw82FS85fetIAVM37laBFEOUs6HHBgMAr7zySnz+85/HV7/6VTz00EN4y1vegnnz5mHhwoXGz99111046qij8LGPfQyPPfYYrrrqKtx333047rjj+janNHWaTb9SGcA8eAMEDOCoDYzybFMvec5inbdx4S74+mI63O6KGMCCoTVTpajXEaZ1Z0z7lDxoEaeQZSpioCioiRumgzWGDXxxGMCJGgMoSwFnQSgXCOc3FQBfuWquAaQzX43MfZ2bA7M8omUAXxwVMKCVmrSZ7wiDQIp7LvPARbfaILFvhk4gOqvJS5+aWEiG72pHlrYEtPvaYBZfxteRCmRr1QoURpSag9tawXHeVbaazrSjiRwAquvjbYvnYQAp1kIbemwwAPDss8/Gxz72MRx33HHYYYcd8P3vfx9bbbUVzj//fOPnf/vb32LbbbfFCSecgDlz5uDNb34zPvGJT+D+++/v25zUIrlmRMoAFlNMnIJiIF2Uoyg+VjejpoYu4uj1omQO1AW/Wq0kf0+3DeFY0SSLVDsPvuhjNHO7c2o6QkWRAaQVNOf/vpElEbJ3bWYaGih6IpapAcyKQDischbIUtM6KjJ9V5PSBJ69kEkZzvHPq412+9GP427QTHPg3Nsmw9tEpEW8Hi4GkKuobhvuS1YvYIPjgT6+c4wkfSo01fbWANJtYHRz8eSdTXjP2NLQHQH4kjKZXuBUoqaTmwJ2qZnLqoCp79wNOTYIADgyMoIHHngABx54YObnBx54IO655x7jMXvvvTeeeeYZ3HjjjYiiCM8//zx+8Ytf4OCDD7b+nVarhaGhocx/rsgrcFPwxXsxAynLwVUBZ8bQ2DcWA6j5AA5rCxWn5kt9ttVJx6CeB/2zedsQTh1HPuXHTztaWCuu6KDbSxR6bPuTBAiPzkFjTNjfI18DKEkBt7uJPySrBrAkA1itpoyueja4Hpn5OQBajSx1k5Y7l+kcqOy4nQGkfA+T4S2/Tlidx/IikPzmijqGKWXIB4CGMUq2atTnIbVxSQEkQ9RjES74UsD6GEURCJEBNKSx9TmVqensRwqYCsjTDb/Zz5Cz4d1QY4M4A0uWLEG328Vmm22W+flmm22G5557znjM3nvvjfnz5+OII45Ao9HA5ptvjo022gg/+MEPrH/nrLPOwrRp05L/ttpqK+e81IK4Jud/R2W+9Ac1r8DltFFTL43hdk/GvunpvpH0YaKmsvXPrh3psc+D/tmUMeLVAAL2RYrvA5ivW+MxiFGUzp+d+qxnF+tMSzwme1bwAWRcT10ZLukEkr+eirXhvJTzSmDus6E+p5dYcPtUN3PgicumukQ97FR2vtc2+T1jKDURfo+EhWSCN/UZPWXYGl24KxUa8CnrkWllzlgp4CJwSUUgdAbRzpzJmcy0vtV9b9v8EKkiEMCc0tfnRBWzlKkBtH6PAACT2KDOQL6/bhRF1p67jz/+OE444QSccsopeOCBB3DTTTfhqaeewic/+Unr+CeffDJWrFiR/Pf000875zMxZ5/CZb4qlUpxoSyTrsvU3/EZQL3eq1mvkgrMkzEa+hx4tZCArvrMpwwZgCGXRpbWAKoXiGrBxhUuALoQRdjGLVfHGItcuACwTAp4NKXf7mJtm1cSEM+hHAMIGJTEfRBgcDvlFNlUXhra1NGEoxoFNLP3PAvJTEMPt1NmWj0j7BrA3PWsVmh2G8rCaMTAADZqtN7pbpslOgCUCheA4iZRH48iArGxd6IUsIVF9HcTsTGADDbVUI+p/9u3WXUZQXN9AAtiFsWmMt41G2rUx3oC/YgZM2agVqsV2L4XXnihwAqqOOuss7DPPvvgS1/6EgBgl112waRJk/CWt7wFZ5xxBmbNmlU4ptlsotlskueVqIBHsgwgdWcOxAvEcLuHVqebKYrmLpQrWx2sHemmVjSCVnCZtl+MdF/891KWQXIeUrYmC1o4dRy2VBnfgiWXjqcyiAaFHreWMV+wXwY4pSpgHmgB0nt7WGOFOfdEkQHkX0+bKIfMADpVwDyBU8FUm3lPZZX+5VK4UrcBdezgQK20oTU3HZ/6fBbBOH0MNQeDETQHfOV74DKeD1cKmANC8/YniQqYJGYxdwNpaZt39/Fm8MZ556YpfbOtT6lWcMQNks0HsM18Rjfk2CDOQKPRwO67745bbrkl8/NbbrkFe++9t/GYNWvWoJor3q/VlGCiP70C1Ys1SQEL2Dc9dcqtq1ExQVMCDycMh2yxVopmDtujjxHPQWAbkizW8bFcG5jsGHnWicecJSwHE0DqdWtF5ospAikA4fKpU0kN4JqRlNEtwwCmaUvJfTk6hhi8xX87iiKNpeeByDxDT09D2xlA6hiDuRQuuwawngWA+v+yAaBQkW0yOGeXu+SEKPF49PSrXbjAVyKb2guWaYnH2awmTKiQAWxawBfHoNymAqZmTWzgDRD4AFptYDYI+FMqNpgzcOKJJ+Kiiy7CJZdcgj/84Q/4whe+gIULFyYp3ZNPPhlHHXVU8vlDDz0U11xzDc4//3w8+eSTuPvuu3HCCSdgzz33xOzZs/sypzQFHKuAJfV3E7RevvrDwLl5J2jgqawKWKL41D8/3O6KzKiTRU4owAC0F1veL024WI8wduXpGBYASE4ZZtkeiaItr4bmghYgd09IfADz/aGZi3382VRYBJSwYNGUq0kLNabFUcFTkXk9jSlgpiWO1Nh7oJZah0jrKW31tVz2zmzhwmMRbWlk6hxsHnwUEGkC9JwUsq32rcOwwzGB6V4vIgNZkyJbnxNvDmb2bb34AFoZXf7asaHGBpECBoAjjjgCS5cuxde//nUsXrwYO+20E2688UZss802AIDFixdnPAGPPvporFy5Eueeey6++MUvYqONNsLb3vY2fOtb3+rbnFIbGFUDyGcA9RSRWugqFd6Cb6oBZDGAhq4PnOP1z+vfo4xoIPWT4qiAzelTcgrXZgPDEaLUq0BLZxG5dYg5FpPR6so2hqwTyOj10G1gOCngHHCS3Fd5BlCafs2zkPrv6GPIPBXTayE3e9fZ9XguvPdMXGtcw5qRblIHyAXT+bQh91rUjQpe2bk09douUwOY1lT6z6dZBMIQT3hEIJTNZsp8FUU9gH9zY+9GQt8UmK6n/m/f+mUD49kxiGnknAqYazm1IccGAwAB4Pjjj8fxxx9v/N1ll11W+NlnP/tZfPazn11n89GBF5AygRNZtVJp/V2inq3XyAX/QPrArxzuJDWEHAFGWgPYEy32+hixElkChHMF/4LUp9UIWrjYi4QoljG4dYh51SkvFV6OtQLMrPAg4/h8Or4fDGCLWctoU0Nz5pH30GMDwITRjY+TgK98xyAJoE8AYKeLjmYu3iSa5ebThlIbGcV0ScZwKqpZ6lmbETRdiJLtBMJPIXd6EXq9CNVqZbTbDx/I6n6InOyRz0eQch6UN6p+PeMx+2EDw2uLF1TA9ghnYB1GXgSiFkoOAEyAU6fHtnfIj7F8zUjyM84Y2fStLAXc1FgKyfcoiB+IbvCmOSRpKmaqzdYKjmokHc/BnEamMrr5dF+ZFLA6l5LuMGV9APPXswwD2Gr3Mikudv1dwcOPpjrV/5Y0DZ1vaacvulxfx1QFzC/zSOyF2l22hYv+Oam4STFGxl7CzHOpjuOK5tRzXMaCJXnHCG1gMsbeo39XB1FSQ2sdFJMVuMIaQsB+LjvE+8JWh6iP2fCZSVu+B7eZwoYc4Qysw1BpMpUCXiOoldJTn8MaA8iahwKAa9vJz1hsj5YClnwHfQ7S71G2FRzgElCU68Ih8iIU2p/kGUCRGMYmwBCogNcIO4HYwJeUAdSBE599y4HQPjCZdAVvVoChs0/Uc5G2apQzgLqljgQA5q1HuHOoGxgjtp2Npb6WOg+fbxzJxiUReZl8HeniCSCdfzuzKWC0tDPUQjYJmxsbc8brBVyOTXXWAHKVxEEEYo0NKgX8cosJA+kiCaRMoCQF3BIyZ/EY8eeXr4kBIOUlYJpDtxdh5XA8htQGRq8jFDGAqgawx2e+rIXqTAawnHddPgXMrfcyM4AyA+X4WFl/6HRjUsoIuhQLqebQkwHA3HlI7Yn4tYzDuRpA6himXr6m33HmIAPTGgM4ei5r1QrJww/Q687i+5mdAjYwRmIrGgMAJAEnnw8gJY1sUK9KUshAsU5Yn6N7jHJsqq8OkQZki7Y+gCAFXKYG0KIkltiQbagRzsA6jLwKuEwKeK3un8esXVBg7aXRFDD7eG0xW7Z6FAAK09DDI5oQpVQrOIkNTAoiu70Iimzgsm9F9k6Qfk1EHDIGsNXpZurFpCBUYn0CpEBtdauTgDdRK7gSaUudAdS9/NiCmhJMqN6aL4oiNvjKtLTT0q/1aoVstG6rQ2TZTWmlCepcchiSFHzJOvWY2DeO7QhgUOlrY1H6ddtqADmqbJMQhQO+qtVKwUNPb/co7YjCEQbl6xBV8FLZbhApZe/iMcrVEabvTB6JsSFGAIDrMCZqqdMoikTpUz01I/HP0z+vGEDu8QO1lAl4afWIaIwJWrcCUTs6mwpYIgLpZj0VqXWE+q6014vYdh35MfT/JaehR897b7SdnAyEpgulxPoESAH9Cq2sgJcCzrFWJRjAllYfy6nfy9chlktDZ1lICfsWXw/+PdWPbkH6GJyWXyoKJutdpp+iCzhRAb2FXaeCaasHn8BM2sQAyt0G0mec1BElx8bqY1Guh6kOMTMG4XvYegF3iO+rhuF+UEE9n7aUfrLBCSKQAADXZSjQ04viF8JwkgKmZ971F7OYAczVAHLBW6VSScZYNsoiskUgo3POtKMTiA5K+d9pbE222J5X8A/EL0ZJDWDBxoW5OOTbyYnS0NoiNazVKknqQpetToVFEhGJEnBIvAh1E+aEhRSaUUdRVNKKJuvTyVK4J+xbOgZXwavmANA7PtjGkMyhIAJhgjcTWzPCZGTL1teafAT1cXyig/gzLiaTyYYmYjUZm2piADndTPQ5AMw0srUXcD9UwLR52I2gR++rUAMYAOC6DH0xWjvSTTqCSFPA5RlAWQpYH6MsA7hmJE1zsYrt+2IDU0uO1V3yKekhfQ5A/EKlKtqyY6QsRafbE6ehgfhclPMBTDcVlQpTBDJa36rm36hXyfVi8RxSBlAHTizwpQNZAausNgTRKJtalgHUz6W0LEBq4QIYGEAOi6gZraf1mHwGsBfFtcLUWq/keANzxlZU5+2NSnoZquCUabhsYLjnIhGB9HjvGVc6ncQAau9DU1cVXh1h3gaGV7/nFoHIxCycVPaGHgEArsOo16rJC2HF2nbqwSdhvtq98gygMAUMpEX/CQPIFYGMLjBDuhJZ0Dkin96R1QCmDCCn1iqTGtHSdRxDUX2hy5qz0r5HpVLJgEhJT2R9odQ7w7CEQY3sfPmtAVMGUGLADOQZQIkSOcumSlo16tdCryHk+XSmLGIZb8lCSzyhyEqdSwXyKZFhjTp8IKt3p1GtONksYi2v0ucBp7SFmhm0UL6LyQaG3VbPymQSBTlGU216Wr9arRjZ0BbjfJY1gu5LDaDH2FtChGxoEc7AOg4FlJZqqTKeCrj4YuYAJyDt5LEiSQHzL/uEHAPIbgXXUGloLWXIYTlyDGDCvgnqlFqdrog5K4Kv0RojYfpVIlzQxxjOMEYS0NITgR6geP2lJQGtTpq+rVcr4nMpURHnFbhJ6pQFnHQgywdvgLmOkNeVJZcCLmGRNKxbPTHeU2UBoCntKDeCzqehqRu8ImDgmjCbbGCkqeiEAWS+q0ypTy55YOoGIlESF3oBE10PbOwdIEgB22x9AgAMAHBdx8RcrdRArcICHaY2bmVUvPHxAgZQAcA17cy/qaFApzqeu9jbRCAc9k3fnUv6+ALZl4qs/q642NfY5yKtGUs9+CRz6IpSpwAwqVHP7OK5jLAuZlnVSu2JJGMMCzdHOqAfFj5fGQZQYCMDZMUsXNYLKPbJTovcZZY6iVsB43vo6tRWl1+barLDaTHBsK7yB/h9tusG1ktPYUp7+ZYWgQhTwDqT2Y90OK8XsJkBTDMvMvDW60WJV6TUBkYi3ttQI5yBdRwKKC1d1QLAT7+aCt35NYBV579pY2T/Jocd0I9fsUa42BdqAMsZIKsdOp+50hW0/PSr/mKTpvSb2mLdEggXmsnLPUoWe+6moFqtYPZGE5J/s+9r7TsrZpoNnLQXvKQdnT6PbB2hpDShB4mNTHYOWkqfUZ+UlImMZGsAJSKQVqeb2FZxQH2lUtFq1yI2y5IRWOUZPC5rlTCIvD7bppQhV9ld1gYGMIlAeJvV9PiUheRa+6RMpux72FTAbCPofApZE5VI08jSrMeGGAEAruPIp4A56V8gVwMoXGAKDKCoBrBcyk99D/UwSkFs0nJr9H85zJkOvpKaMS5zZQBwEvAVL/Z8uw0gZXyyDCAftABpTaZkU7DlxikA5PpCmgAgG7yZagCFTKSewpWIQKQMYnYMIQOYM9VWz4a0E8haQWs/AJn2Xdza1EqlUkg78m1gzAp7vno2BS0ZsZjABibLWDFNtZMaQCaQVUBc7wXc5YEe47lgMJk2FXCHaKptSkHn5yP2ARQIrTbUCGdgHYcCfEtXKQDIa76iFsW1JRjA/ItcUvuQB67sGsBCGpoJenIv96SrihB8rR3pGedFH6MnMlDWGcThkgxgq62NIax9GxpWjCx/U7DlRhOT/88FC3r6dWhtzDiVYQBV+pPbJlHfWEiYgawXoYxZyGwK+mEDIxojTYWvEaSA9b8nqQHMH6//r0RIoh9PTQErgKb3EFagp1oBSeWeN1GWdKjJgy9uCtjIZErPpVCV7e8mIkvfcgC5vRewbOO9IUY4A+s4JowCvqWrZSlgvcBbygDOnDKY+bdEBbzZ1OwYExq8ORRSyCUYxG4vNdXmMKp6WmGtUFDT0BZrScows0gKCv6B8gxgvVZNarZWrJEzgFttojOA/HtK3cdJCljMAKYem9zvkbVxKQHo2zIRCZDd3Ejqk/T6Pf1/WWIz3QZGyAAOGBhAStswFQURBzOVnTybwjphHSiqY9kK3pyYJduOTgbg+Clgg4KXuXaYAFw/UsC6+wLl749oYhp9PpQ2hfkNgQrJBmlDjXAG1nGoXfSykilgqdIRAGZvlAOAArZn9rQJmX+zgWzJNHTesiMBgE06o6r7rUnEE/oYI0IGz6QCZjNGBhsXPvAZZd+GO6I5AMCWG6cMoGRTkdSFlq4BLJECNtQRcp5Rk5+hvAZQOocsA5jU8InsptJni3su9Q2WxGvNxgBy6wjzAFLisZnvFy4ZQxf1ACVEINy2eib2TghkpZ1ZbL2AqcIcG3jjlBbYOoEkmxOi/+uGHOEMrOOYUEgBcxe58gzgxEYdG00cSP4taYI9KwciuYzPxpMGMv+WpoCBGMCtGV3kJIv1SFdjOUqIQNT1kDCArXZXY0lkDKDU/w5IwVYZayC9BlACANV9WLYGsNXW0/FCANjuJmUFHIGTyQZGCuiHtTlwSkXyNjBrJWIWTQ29RnhPGWtsJex4V9UyCkHLqJdgm2g5khyvK5HbWfBFfUbr1QqUBWTcqYfXjg4oMniqjo46h7KdQPTPZTqBMFTVNiPoDvF82tK3LEseWwpYCWIE77wNLcIZWMeRikCkKeD4Euk1gBK2RmfwRAzgRlkGkM+01DBjciOdA/Phq2nmpMMaAyhq/aUzgFKWQ8jINg0sCReQZxlAGSuc1t+VqAHUGECJpYK6D8UMoFa/p2o6+edBY9gFdYQ2I2hODGpzkIC3tE1itj6Wc2/rQHZYUF4BZFWXZYy51QLN7gQyeh5VZxcue5cRogi9CPNiFk73DBX5Prjqf6kpYF3lr2KEuTlJQKiQAfQbQXsAoNZZRmcROUIU9RlVj6lCuvHeECOcgXUcZVPATQMDKGFrspYdguNzKWBJzdfm01IWUQI41EK5ZiRNt0lqAKX1e0B2wZe1H9MZo9FiZKFx8HC7KwIM8Ri5+jvBPTFzSjP5/3qHF/IcyjKAmnp2WPhs6GBaApz0OUh9APUuN5KazkFtU9LtRaK+yDqYlmyugGzqUbLBSp7PfP0dsY4wn8KVKKp1RliNA8jaPY5owiDO8QUjaG4K2GB/wmdTsyBUH4/TTq7Ty9UAEsGsrx8xhwHMj6GuLaeJwIYa4Qys41AART3EpWxgyjCAG+ngi3/ZN9UWe31enNh8ajkQqhZK1dMYACYJagAzC1SJNJck5aczDFI7gsGMCric+GFFCQZQT2m9OOpzWWoOJRhAcSpcS31KxjCriGXXQgehvG5B6WfXjHSSxU5WA6iDN55jQVNLPUqyFVYbGFEKVzejFtQhFmoAOWOkgjWZWXw2hctNAScefm3NB5DJTufrKQFmJ5C6jQHkpYCBrJ1NWkNIV2QD5lR2MzCAAQCu68i/RMtYuCjLjvIMIH+xr1UrmQdKAiJnaQygqGasrtjU+DxUKnILFikAzIo4JAygCTAIWWEhY6TPQ4lAJPeUHjoop4b6m0MlGcB2N8KalowJzYqsBNdzdA69CEldqlQEotcAStK3QNrvmz/G6Bw6XREIBbKbozJsaqGVG/F8Vqtp+lW35WH5IebT0MkYslS2pO1YXrzATQGnNaFF8MYHgLKWdqrTRx4AdoiWNplayq4+B35bPiC9nlHENynfkCOcgXUc+ZcoXwSSXqLla+RsTVkACACbTExr+DjN7lVkU8CCmjHVTk6l0wdqrHnoHl2rW/xuB/oYmZoxYZ2T9EWU8QEcKZcCLlMDCAAnHbg9KhXglENezz62yADKmFAg7TEtVUPr6VdJJxBAzqbqIFQyB70+9iUNiEsNrfvBjpcSgeRUvJwUbmmG3sIAclpO6mNIGMQ0hau8CHkp4LwxuJqLPjdf5Dua9Ho8UU2qAk7Zu2xfZff5MBmDA7waQL07TSqoiaCy0hxQv6EGj+MPwY7NpmZTp1wjaOXZ1ulF4kUOALbYSGffZLh/o4kDeG5oWHQsUJ4BVMcsG13k2Cmqur5YK+arhGggaT8mE4HIW4elDKACoRJRDlCuEwgAfOZtr8Ex+8xhpeJVDBZqAGXfIR6j5PVsy1hh/doNCYFsnnUCZG4B7W4n06ubsznSU8BrBK3ggHRRb3VlbGpZI2ggPperWrm+ygIgnFqwlKsBVFegjCF2hzmHvC0QAHDbTuaNmLmG1iYlsi5KoXRwatSrBSsdbk3mYL2a2ZBkbHkCAxgYwHUdulISkAGfCdrLGZCxNbM0EYe0CfYmkxr+DzmiLAOojnlJKKhpZACgAj5SBlAoAtEARz9qABPrEqH9ycqWDDjpIQF/8RxSUQ/AB04687VijdocyVP6EgNknWVIPRWFjG6JWkZlXbM82RzJ6ikzaWj2szGakheWWOTTjhKGvKwqu5GbgySNrI8hEZHkwROXRVTXXl1HQMIA5q6FDgBJKmD1HYr1e9QxTF6AyhSbei7y7QEDAMxGOAPrOLbKAUAuaAGKxfEStkZXbK4cXai4sfHEcgBQB6GlGEAhAKxXK1C6BQUApTWAq1udJJXAAeTZBaqcaGC4o6lfmZ1Z8mBrLOph8vexxJ5InYvlQkCfAV+ClH48hyyTKa3p1FlIaa9s9WyU8bdMWsGVsIGRfA9rCljInnGtT0xz4KZfgayYRcpi6nMYYaeA0/eDCq6AUC8zAbIt2KS9gDuZPr4EEYcxBVwuHa7euXVCJ5HxEAEAruOYOqGOKRpDIgGA+YVSwgDqlHte0UuNN75quug4FZtr7eTyDu+UUC82VefEPZc6W7MiYUp4j0DC9qxNQTSHuSrbLxXQ2LvhNqLRd6p0wVdRhgGURn4OEisa9WysSZhQGZjO2MAIhSRDwpZ2WT9DmT+lAs8vCVv7Zeop10jZ8dEUcLukR2Y+BcxKv6bXs5QIpEwK2NgfmsMAZhW03BSwbguk/O9azHrKJBXezYJxqqG1sljJeBFq73xOX2VjDSCzlrEo6gnQBwg1gOs8KpUKttxkIv6weAiAbKHNAx1pvdaV//RGPLZoCHsLgdyH9twaa1odvHE72fH6oqbSuJzIsxzcekogXtyH273STIlSZJdRIktVwOoe0hWfUvVrOq/1/0LMA74yynAV0lZw/fBUVPcEvwaweE9I7aLkKeDi56UM4MphmRK5rApY/+yIEHwVat8kY2jMlarDlFpFAfIUMBAzXxMbdfa5tKuhacerXr/ZGsAUhFLqU43t6LhguMAAdjNjj/cIZ2E9hN4yS8IAzpicZeykis29tpuOY988R6TgBeJd2yf2exV23Woj0fF6vLRGYBycYzm4ixwATJ0Qt6RL7U/KqmdpL7P88Z1elLA9UgZQpRzr1Qq7rlPvygK8TBhAwUt52oRsi0H59UwZXamSWF0PvrF3CjrkLORofewaWWnDQK1aYGWkCvkhDQByGNl8KzdJClgvsRjp8GtL8yxkGS9BvV0kiwEsmwLWnivFxMp9AGVt+dRcO4YaQOq5zBuD6/Mg1wDmGEBp2c2GGuEsrIfQ6wAlAHBmLmUrSZW9XEJ9/z233YR9bLrIxSzHJMG5zAMGqdWFtN5Lf4GubElThlm2RwLeipuKMWAAc39T8j2m54GskE1dsXak8DPuGMPSTiB96OzSj3ti44k5MC28txWYHqhVSGrP5Hi9do6pOlVhSr/ybGRyLKQgBax30ZB0h8mLQLgpYN0PMa9+5YtAZOfBpQKmduBw2cBwGcC8mCUwgHGEFPB6CJ0BlLyYZ2q1c8Are/fyP1/YF3c/sQTvmbsl+9ik4D9hAPm3bwEAClOGUgNl/dopMY6UAZQKH4D+dHYpG3lVueS+zgNZqRhG3VMDNT6bmp+3vAZQ1gkk/pvZ+lhpq8Ylq9LjKbVeeqjz1g+Fvd6BQpJ+bQnBl7UOUSLi6PaSGl2JkrlMN5LBgWpGjMMtN7Gm44nPhjKtHjGkgJVJtC/yhtj6GNxaxoQBLOGksSHGKxdJvIIimwLmgxadAeSmHF9useXGE3HEG7YW7cDyzKeETS3LACaMkbBQvq6l2oaEAFD9TbW4SGpCXw4M4LbTJ2XnIAAteRAp9QFcLvQi1MdI/y2rAVzVkrVxA9KNzEur5eUReqtGyfF5drxMlx3FXFUqchFIGSNpxRhJRCDJGG1hClixZx2ZETSgs9JCBnDADITJPoJGBlBdD14KuIwKOKnxzTOAoQ0cgA0MAJ533nmYM2cOBgcHsfvuu+M3v/mN8/OtVgtf/epXsc0226DZbOJVr3oVLrnkkr7Pa6tNSqaAp5bzz9tQYupgFrxJzuVG+TQXc5FSNYTqRSIBDPlieb5tSPYekLA9L4cawDkzcgBQcG+XTQEn9XsK0AvuqTxY4l5PtSnRFzq+CjhbHiG5J3Sjdsnx6r5OW1bKGMBM6pS54c2KQPg1gPm0I6f7RWGMrqzdY8JiFhhACQAsB+CkqVP1OZMPIDUFbPIBTGoACb2AAa3LTsIABhGIHhtMCvjKK6/E5z//eZx33nnYZ5998KMf/Qjz5s3D448/jq233tp4zOGHH47nn38eF198MV796lfjhRdeQKcj88hzxRZlU8A6AzgGC/XLJTbPpcIlLEXZFPDUwXxvZxmTubbdFaeAywofAGBGvq50DF6Im01tYsJATVz3BgAzJqXfQzeGpsaUwSyglwCfQiqbeU9MGayjVq2gO2rZwVWWA+m5WyNsDQhkjdolz1ZeICWtr9XFE3Jj7y67+wWQrSEEhFY0GnumsAvnnkgsVHJCFGovYEBvHpBVv5JFIJbzQAWh6nx1exG6vQi1aoXN3rlsYLgMYB7IjmciRY8NBgCeffbZ+NjHPobjjjsOAPD9738fN998M84//3ycddZZhc/fdNNNuOOOO/Dkk09ik01iQcK22267TuY2dXAAZx22M0Y6vcLiTYnNppZv47YhhL5AAcAkQTp9ownlmK+puesnqSXRffz0f1MjX78nAS1TmnU069XkBT8WDGClUsG2MyYlFkmSl7IOvgYF5RF54YOMTS0HpqvVCjaeOJCpv+N+j4nNcnY4QJYBFBnWjz4LSXcZYR1j3LpLtlDrBsaSxb6ZYwAlIhB9jO5onUYZH8CEhWQxgKkIpNPtaf1vZbVzXP89neVrd3uoVWtaDSAxBdyHGkAbExoYwDg2iLMwMjKCBx54AAceeGDm5wceeCDuuece4zHXX3899thjD3z729/GFltsge233x4nnXQS1q5du07m+ME9t8ZH995WdKzOAHY1Sn28xWbrgAHkGgfn09ASRbZ6+aiXEvdlNKlZzyzQkjlUKpUMcBmrHfGcGWl5RFkVsOT4jSbmNwSSesq8mKXcPCQgdHqOhZSKQFRIzuW0UTCd1KYy7ylT+padRq6NgshuT1TwrzIsSQqYmXIEckymZA651KdIiKIBn6yghioCyc6Bq6jWP5c31eamkbNCElkNYL61XxCBxLFBMIBLlixBt9vFZpttlvn5Zptthueee854zJNPPom77roLg4ODuPbaa7FkyRIcf/zxWLZsmbUOsNVqodVqJf8eGhrq35dwhN5rVRVYj8fIM4AiEYjG+DRqVZZNBRB3dtGjH+bFEvA1c0oTf1u6BoBssQdiMPzs8njDM1alBXodoKgGUEsBywBg+XR6vg5RxGTqALAPXqGyGkBNBCKykcmBUKGRdNwTeWwYQGvtm0AEMtLpodOTMIDZFDC3iweQXr+17a6o/20qZJH6AKaAOc9kclPAOoDlMrJNCwMYUsBxbFBnIZ82iaLImkrp9WKX9vnz52PPPffEQQf9/+3de3RU5dU/8O+Ze66Te0IghhRBwChI6IKoWLSaFyuKUpW38IJY7ZIi/Jal3qiuQlltsf4qilq81CWIv1WgWrDWZuGKb7ipiIqJRaygqA2XQLgmIcBMMvP8/pick3NmguQ8Z5IZZr6ftbKWTM5Mzpkck529n72fH2HJkiVYsWLFWbOAixcvhtfr1T5KSkqifg3n0qbb4DvZpLutb6unzwBKrd9z2A3PkwkYwn+Qy/ww0peBZcu3Gbr1jLH6gViq6wS2GnwFhfnsuNNuM9xT0Qi+ZDKy+kA0GmVomWYW/RpbdT2iGVbL6drauYCVDKCuAUNiwb/+HADzWStAVz4NSDaihI+B0bKQciVg9XXsJva/jRgDYzLwUhSlm4YauSHOhhKwyYys1YHWiS4h3oW8vDzY7faIbF9TU1NEVlDVr18/9O/fH16vV3ts2LBhEEJg37593T5n/vz5aG5u1j727t0bvYugHik0dCpamwMo88seMJaBzZa5gOjs7VyQYa1jE+hqgAidQ2x+FPTP6so6yZyD/o+ANp9cA1dWmv77KVN+tb5Tj34to8x9GZ6FlLkn9F9XptIQWU43GQDqAgZtL2GzXd26DKClDlwtY2R+BItxDIz5cwiffyeThdSPgVGvxdw4HWP2rV0icxa5ltFkI8l3NIHIrgGUGcuTyBLiXXC5XKioqEBNTY3h8ZqaGlx++eXdPueKK67AgQMHcPLkSe2x3bt3w2azYcCA7ocUu91uZGZmGj6ob+mzFGlu87/krGZaAGMQKZftMf6ilPlr1JgBlPvfWN/RHKvZkoPy07X/lskA6s9bNjuebbX8mmG9BJydZm0NYH4USsB6cgGgtXJ6t1uoSe6rfLo9IFV+Dc++SZWAdXv5ynQid83QU+cAqlkruS5gdQaeqX2Zw+cASmRCwwO4dpPBdHj2DrC+BpAZQKOEeRfmzZuHl156CS+//DL+/e9/4xe/+AUaGhowa9YsAKHs3YwZM7Tjp06ditzcXNx55534/PPPsXnzZjzwwAP46U9/ipSUlLN9mZjhDRtSZLFT0VgClswApljLGEVjBwxDACiZyUz3xH4JcJHXgyenjMAzP7nM9A4c4WTKloD1eyInNQoBYKq1zHROmgv6GD7F5I4o4dQt5cyIKKdLDuU2dgHLBZHqiCX9Y2bOITzzZab82hU8BaQyThFBqNaA0fP3Qp/5UkfBmNqXWRfEBoNCahyO0268jvag9RKw2VJ0ZAaQg6D1Yv8bIEqmTJmCo0ePYtGiRWhsbER5eTmqq6tRWloKAGhsbERDQ4N2fHp6OmpqajB37lyMHj0aubm5uP322/Hb3/42VpfwnW4Z2R9rPt6LIYXp5z44gekzgDJbwaU47XDZQ9skyZeAu76u1Lq1tChnACU72jLiIAAEILUtYDRlW+zADW8kksmmRuMcslNdONYmvxcwELqvDrf6cNkF2VLPz0pzamNgzAah+oyRGrSYzwCGjleHUesfM3MOEZ2rJmbw6de+aSVgib2A1Rl6cl3AXWsA1e0FzfyhqD9ffyBoegAz0F0GUK4E7OumBNzzIPIsXcBJPE5NLz5+A0TJ7NmzMXv27G4/t2LFiojHhg4dGlE2jlcLbhqOi/tnomp4UaxPJab0IzdSJUuGmSlOHDnpky6TZVpsJMmOyABKlPx0AaBsIDusH5cwAMbsm2w53WlXDLsemD8HawEgEPrDQg0AZV/j9VmV+H8f/Ad3j/ue1POzU13YeyzURGf2DxNjCVhyDWBYBtCmRAboPXl+NEqf+uuQCUKBUMDjNxn0AMYu4DMS76X+fEMd1eosQhPrKcN2A7Favg29hrnvh7YVXHvY99PEdSSyhAoAE1mqy4EZlQNjfRoxpw++woff9lRWaigAlM2SGNYARqEELJMBLDBkAOWClhsu6YdvrmvDiJIsqecnCqsz+IBQQ40afMnIttgEAoQ6gb9sOmnpNUpz0/DIDcOlnguEvZemB0FHLwMou81i+F7AZnfA0L+GYTs6ieYJwJh9M/NzQl/61DKAJt5Lh02BTQGCIvReyJxDeBew2UC22yYQk+sIta3g1JE67cwA6vFdoPOKvgNXdh2HGsBJZwA91jJGESVgievQZwBdkiVgRVEw94eDcdWQfKnnx4vn/6cCAPDbm8ulnm91/R0QGlFkRXYUmpP02/tZbQKRZcymygVf/oCFLmB1N5LODKBsABk5usT8OkLZYdT6nwf+jqDUKBr1j8IzunWIZu5tRVEMmcwzHeaDyK4MYOj8O6QzgBbmAJ5lKziuAQxhBpDOK2MH5SLd7cCg/DTpztWszgBQvglENz/P4t6ximKutKPSjx455Y/+/tXnkwnlRfj3ognSwZs++yY7ENvqekqrY2AA4x8WsQsA5Xdm0Qc4agbP7B9Y6muoeyKb/UUfuQZQRJzbubgNGUDzwZuiKNqSgtP+QNc2bmYGQXfeQ2f8ujWAEsG0upOITCCrBmnh6yl7HrwZs3fG1+jZz0wtA6iN9TE/GzKRMQCk80q624GPHrnW1Mbo4bQMoGSnpNWuUX3w5rKb378WgGGga8uZ5A4AAfmgCYhOCdhqBjDT49RKbtLNSfr70sL7YYWVMUv6Mql6T5st4UbM2LSYAVQzRzI7gfgkS8BAKEhqDwTQ5pfrZtZKwB2Bri5gyZK8ryMglwEM7wKWbOCwMgdQmwsZsRUcA0CAJWA6D6W47JZGhqgZnzTJX9pWB0HnWNy9Ilx5MZs5rIhG+TUjbI9os2w2RQtE5YPQrufFQwbQdBew7v/pltNyGcDw74PZADJ89IiaPTM1Q6+bDKDZQFQN9vTDzWXK0Gfag1JNIIA+eApqu6qY+YNXHZ3THrEG0EoTiDC89rl4dO8DwDmA4ZgBpKTz398vQVOrD7dVyI0fyTQMgjb/izZN98vESufo//7yB9ixrxnXDe9+txvqmawU+aBFNaOyFO/8+xAqSuXGpwChQPRYm186eNP/QWN1pqIsfQbQbMChbh/mDwS1MS5mMzX67DxgpQQcCjq0tYgmvifqa5zUBW9mA1H1+3fSFzoPRQk1ZvSUGrCe1pWAZQNyv2w3c1gG0PQaQF0AqjLblMMM4HdjAEhJZ3BhBp75yWXSzzeUgCV+kERr141B+emGnTRIjtWt4ADgqiH5qPnFVSjJSZU+j9w0N/YcbpPOTFvNQkaDYQ2gRBna7egMALUMoLnXCA8AZUvAWvdrZ/BiJijvLug0G3Cor6FmAM0uFdE3gWjlW8lsqE/XlW0uED7bVnA97AK2Rw6CNr0GsPMa2gPGmYoy0xsSEQNAIpOMXcD8QXK+y3A74LAp6AgKS2vnBhdmWDqPn48fhH71Humu7KrhhRhSmI5L+mdZOg8rrM4zdDlsgE+3BtBkAOdx2uFy2HS/6OUygADQcrorg2emFN1dAC97Hid1AaAZ+uaHM37zwRug31c5oMuERmMMjHwGUHYNYOh15HZmSWQMAIlM0ncBywaA6oJ/ij1FUVCY6cH+E6cNAUxfu3poAa4eWiD9fI/Tjrfvuypm+zoDYSVg2QAQujWAEpkab4oTh1t9na9nshNZF1jo90M2cx4epx0ep00LmlwO841eERlAkwGLVgLWBW+ycxmNzSzmu4C7mkDMlYDDA0iZ19Cf75n2oNYdbqVpLJEwACQySV9qk/1LMs3tMOxXSrH1f2+7FN8caUNZXlqsT8WSWAZ/QNhAa4kAMHz2m0wQqQ8Azf7/6bDbYLcpCASFFgC6HDbYTKy/A0LrSg+2n5E6B6BryzU1ADS7prOr+SGA0+1yWS+XriFGKgMY1sXboWYAe7qLR9j6PUCXRezha9htXSN1fLqOaAaAIcyDEplktykYWZKF/Aw3iryecz+hGxkWx4ZQdF0+KA/TxpTG+jTOe2kuOwYXpKMo0xOx401PhGe6ZIIn/V7dMt2eauZJzULKBLJWdwsKbwIxex0e3V7AskGPPhjXdmaRyABGrAHsYTDt1q3fCwYFhBCm1wDqX0e/K0qsuuTjDX8LEUl4fVYl2gNCugSc7nEAzVE+KaIYUxQF/5h7JYJCSAVfKS7jrySZLntj8GX+HNxOG063B7QMoMxuP95Ua+dgtQSsrmUNCmgd1bL7KofWzpnPAEbuq2yyBKy7Zn8gCIdNgZAYiu1x2nDSFwqGT+mDYRE4xzMTHzOARBIcdpulMsLlg/KieDZE8cPjtCPVJZdbCN8mUSZ4spp9U4OLZgsZwCzDqCiJADBsDqBsCRgATpzqvA7TGcDILmC5DGAoalPnAfa4BKw7ztceNIzMkpmJ2HK6XQsgmQEMYQaQKAYe+K+LkOlx4EeX9ov1qRDFjfAmHNk1gCrZDCAAXQZQIgBMjU4QelIyA+i0K1qjmRoAms1kunVlZJk1meHb6nUE1Q7enpVvHbaua/B1BADd00wFgJ3XcULf1OO047S/xy+RsBgAEsVAmtuBeVUXxfo0iOJKbnq0M4Dy5VdrAaBuf2mZJhC1BOxXx8CYa0JRFAUpTjva/AEcPxWKdMyWgLUgVNesZiabGb4GUC0BO2w9ew1FUeBy2LT9iPWNODJrAJs7A2GnXYHTbsPpHr9C4mIJmIiI4kJ444hM8JVpNQOoKxmGzqHvg1C1TCrbBAJ0lXy17J3ZEnBYJhQwF0SGdwGbLQEDxjK0vgHE1FDszutQA2GWf7swACQiorgQGQBaC76kuoAdUVgDmGptu8judgIxKzNsVxTZnUDU98GmmMu8RYyBCZrv4NW/RnuHuSYSlRqAH5dcC5nIGAASEVFciGwC6fsRLOEBoFQJOMVaCdhlcQ4gAOSEraeUHQPTtS+z3VTmTS1bt4cNgjYTzOo7kc3uJKJSv38nmAGMwACQiIjigj4DaDbjpPJa7MB1RzsDaGEdomwTCGAcyg1INIFou7J0SD1fy96pawA7M4EOqQBQXwKWzQB2BoCSHeqJiAEgERHFBX0AaDbjpNLP4JMpnXYFPp2ZrxgOgpadAwh0kwE0uxdwWAbQbCZU2wquI2wQtKkScNduJF37AJu7J9T3XysBS/xRkKj4ThARUVzQB4ACcptlW10DGL72zWoG0Mo6xKDE4GNVZAbQ2hpA01vJhe0F3BG0UgLWZQAld0Vp5hrACAwAiYgoLqTrtkhU9581Sx8AqkGHGamdAUJb57ZhMo0oWYbsm/lzCC9zSmUA04xNIGYDOLV8ru5ZbjoD6AjbCq7DfAnXsAZQsglEDeCPtvk6/80SsIoBIBERxQWZkm84fcZO3cHCDH32Lvz1eipNl2VS9581IzUsSyXVBJLm1v7b47SZfm/DS9dmS+HusBKwmgl0yHYBS64BVP8gOHJSXQPIDKCKASARESUMfaAjFwBan0WoP4fTEucQPsLFagbQ6m4mgPkMYlcGUEAIoQWAZq6luzmAZtcAhr+XXAPYhe8EERElpMGFGaafEx74mB2gHO6URAYww2MsU0qtAdQFsjJZTKvb8rns+uyd0PbhNfM6Wgm4PdAVAJoMRMMDetl9qhMR3wkiIoobDpsitXZPr/r/jMPnjS0YPyTf9HP1M/wAwCORfdOTyUJmeqKRAey6jmhkAM2+D05dE8iZjq73wEwm0a0bJaNuJWe2BJwVPhCbcwA1DACJiChupLrsaNHtPytjeHEmhhdnSj03O3wNoMUMYD9viunnRJSALXYBy6ys9Iadg9k1gPr1e77Ohh5FMdkF7FQzgEGpJhLAOBYI4CBoPZaAiYgobqS5Y5uXCA8YzG6hplr1s7G44ZJ+eHTiMNPPDS8BywzEztC9jzLrED1OuyFYMpsBVAO99kAQvs4MoNthrhlFP0rGLzFHEIjMAIY32CQzBoBERBQ3HpowFADw398vicnXD1/7JpsBrByUiz9NG4WCDI/p50aWgK01osisQwSM2VDTawB1GUB1pI/Zodhq1tHXEdS6qc2eR3gm0+qazkTCEjAREcWNmy/rj1EXZKN/tvnSaTRErH2LQddoRBOIxXWIMhlAAPCmunCg+QwAiS7gzkxdR1Bo6yBNzyLUNYGo15BmsokjsguYAaAqoTKAy5YtQ1lZGTweDyoqKrBly5YePe+9996Dw+HAyJEje/cEiYjonC7ITYXdZn0moIwUp92wTi0WTQMep90QLMmUgPXUWXxmRSMDCMgPk9aXgE/5Q69hNiPrcdoNQTxLwF0SJgBcs2YN7rvvPjzyyCOoq6vDuHHjcP3116OhoeE7n9fc3IwZM2bghz/8YR+dKRERxStFUQxZwFh1jWZ49PsJx+ZXtb4cbjYTqm/WUPcTlt2NxNceRJsvlAGUCeD0nd3MAHZJmABwyZIluOuuu3D33Xdj2LBheOqpp1BSUoLnnnvuO593zz33YOrUqaisrOyjMyUionimDwBjFTBkpnSVOmV2AokGfUOM2fV7+izqSYsZQP0aQJkAUL8OkGNguiREAOj3+7F9+3ZUVVUZHq+qqsL7779/1uctX74ce/bswYIFC3r7FImI6DyRlWpthl406BtBZNcATh9bCgC4ffQAqecbS8DmzsFmU7TSdat0BrCrCeRU5xrAFIlBzvpAliXgLgnRBHLkyBEEAgEUFhYaHi8sLMTBgwe7fc6XX36Jhx9+GFu2bIHD0bO3wefzwefzaf9uaWmRP2kiIopL+tEhscoA6htBZOYAAsCjE4fhvy4uwuiB2VLP15dOzc4BBEKZy/ZAQFsD6DYZRGpNIB0BAKFB0FYzgNwLuEtCZABV4fOFhBDdzhwKBAKYOnUqfvOb32DIkCE9fv3FixfD6/VqHyUlsRlTQEREvUe/9i1W6+/03atOyXNwO+y4cnCedBYzK9XaOkQ1c9nq6ywBmy0jO7pKwKcslIDjIaCPRwkRAObl5cFut0dk+5qamiKyggDQ2tqKjz/+GHPmzIHD4YDD4cCiRYvw6aefwuFwoLa2ttuvM3/+fDQ3N2sfe/fu7ZXrISKi2FEDH7fDBluMupENJeAYrQG0WgpX1y5qJWDTGcDQ1/R3BNGmBYASJWBmALuVECVgl8uFiooK1NTU4JZbbtEer6mpwaRJkyKOz8zMxI4dOwyPLVu2DLW1tXj99ddRVlbW7ddxu91wu93RPXkiIoor6pqxWDYM6JtArM4BlGVlDAzQFbiqW/uZHgStywAGgqFRNlIZwDho6olHCREAAsC8efMwffp0jB49GpWVlXjxxRfR0NCAWbNmAQhl7/bv34+VK1fCZrOhvLzc8PyCggJ4PJ6Ix4mIKLmoJeBYBgvxkQGMTgm4qwvY5HZy2m4iAfg6ZxnKZPDYBdy9hAkAp0yZgqNHj2LRokVobGxEeXk5qqurUVoa6oJqbGw850xAIiIidc1YLHYBUWV6Yp8B1JeAZUbRuMJLEFVFtAAAEu5JREFUwBYygJbGwHReh9thi9mA8XiUMAEgAMyePRuzZ8/u9nMrVqz4zucuXLgQCxcujP5JERHReeV7+ekAgJKc1Jidg34QdMzmAOoyZ6FOXHOcDnUMjFwXcLdNIE75NYBc/2eUUAEgERGRVRcVZeDNOVegJDt2AaA+WIlVBlAfeBZ7ze/N3JUBlFsDqO7723qmHWfaO9cAus0HcQUZobX7WWH7Aic7BoBERERhLh2QFdOvr1+rFqtRNADw5pwrcLjVh4F5aaafqwauslvBZaeFSrdq8AfIlYCHFmXgkR8Nw7B+maafm8gYABIREcUZjy5YilUJGLAWCKe7Qxk3tXxrtgEj0+OA3aYgEBTaY2ZnCQKhGcE/u+p7pp+X6BJiDiAREVEiKc7qKrmer40L+kYWwHwGUFGUiCHOsZrLmIiYASQiIoozJTmpeOK2EYYdQc434ecuU8rOTnPhaJsfAPfxjTYGgERERHHoxxUDYn0KlmSEZQBlZvDph1Gzize6WAImIiKiqNMPswbkMoD6WYTMAEYXA0AiIiKKumhnAGX2AaazYwBIREREURetNYAqZgCjiwEgERERRV14BtAtlQFkANhbGAASERFR1IWvAZTZW9nYBMIScDQxACQiIqKoiywBW8wASmQQ6ewYABIREVHURZSALa4B5BiY6GIASERERFEX7S7gNDcDwGhiAEhERERR53bYDVk/qQygoQmEawCjiQEgERER9Qr9OkCZANAbthcwRQ8DQCIiIuoVahnYYVPgsJsPORx2GzI7X4NjYKKLASARERH1CnUUjEz2T5XT2QjCJpDoYgBIREREvULNAMo0gKgGZKcCAAoyPFE5JwrhikoiIiLqFeoaQCsZwN/dUo66hhMYU5YTrdMiMAAkIiKiXqKVgC1kAEtz01CamxatU6JOLAETERFRr1AbOKxkAKl38DtCREREvUIrAXOES9xhAEhERES9IoMZwLjF7wgRERH1iv5ZKQCAggx3jM+EwrEJhIiIiHrF+IsK8KepozB6YHasT4XCMAAkIiKiXmG3Kbjh0n6xPg3qBkvAREREREmGASARERFRkmEASERERJRkGAASERERJZmECgCXLVuGsrIyeDweVFRUYMuWLWc9du3atbjuuuuQn5+PzMxMVFZW4u233+7DsyUiIiKKjYQJANesWYP77rsPjzzyCOrq6jBu3Dhcf/31aGho6Pb4zZs347rrrkN1dTW2b9+Oq6++GjfeeCPq6ur6+MyJiIiI+pYihBCxPoloGDNmDEaNGoXnnntOe2zYsGG4+eabsXjx4h69xsUXX4wpU6bg17/+dY+Ob2lpgdfrRXNzMzIzM6XOm4iIiPoWf38nSAbQ7/dj+/btqKqqMjxeVVWF999/v0evEQwG0draipycnLMe4/P50NLSYvggIiIiOt8kRAB45MgRBAIBFBYWGh4vLCzEwYMHe/QaTzzxBNra2nD77bef9ZjFixfD6/VqHyUlJZbOm4iIiCgWEiIAVCmKYvi3ECLise6sWrUKCxcuxJo1a1BQUHDW4+bPn4/m5mbtY+/evZbPmYiIiKivJcRWcHl5ebDb7RHZvqampoisYLg1a9bgrrvuwmuvvYZrr732O491u91wu7mhNREREZ3fEiID6HK5UFFRgZqaGsPjNTU1uPzyy8/6vFWrVmHmzJn4y1/+ghtuuKG3T5OIiIgoLiREBhAA5s2bh+nTp2P06NGorKzEiy++iIaGBsyaNQtAqHy7f/9+rFy5EkAo+JsxYwaWLl2KsWPHatnDlJQUeL3emF0HERERUW9LmABwypQpOHr0KBYtWoTGxkaUl5ejuroapaWlAIDGxkbDTMAXXngBHR0duPfee3Hvvfdqj99xxx1YsWJFj76mOkGH3cBERETnD/X3doJMwpOSMHMAY2Hfvn3sBCYiIjpP7d27FwMGDIj1acQEA0ALgsEgDhw4gIyMjB51G8ejlpYWlJSUYO/evUk7DJOMeE+QHu8HCpcI94QQAq2trSguLobNlhDtEKYlTAk4Fmw2W8L85ZCZmXne/o9MvYP3BOnxfqBw5/s9kezr/ZMz7CUiIiJKYgwAiYiIiJIMA8Ak53a7sWDBAg64Jg3vCdLj/UDheE8kBjaBEBERESUZZgCJiIiIkgwDQCIiIqIkwwCQiIiIKMkwACQiIiJKMgwAE8DmzZtx4403ori4GIqi4I033jB8/tChQ5g5cyaKi4uRmpqKCRMm4MsvvzQcc/DgQUyfPh1FRUVIS0vDqFGj8Prrr3f79Xw+H0aOHAlFUVBfX99LV0WyonE/7NmzB7fccgvy8/ORmZmJ22+/HYcOHdI+/+233+Kuu+5CWVkZUlJSMGjQICxYsAB+v78vLpFMWrx4Mb7//e8jIyMDBQUFuPnmm7Fr1y7DMUIILFy4EMXFxUhJScH48eOxc+dOwzE+nw9z585FXl4e0tLScNNNN2Hfvn2GY44fP47p06fD6/XC6/Vi+vTpOHHiRG9fIpnQl/fD7t27MWnSJOTl5SEzMxNXXHEFNmzY0OvXSOfGADABtLW1YcSIEXj22WcjPieEwM0334yvv/4af//731FXV4fS0lJce+21aGtr046bPn06du3ahTfffBM7duzA5MmTMWXKFNTV1UW85oMPPoji4uJevSaSZ/V+aGtrQ1VVFRRFQW1tLd577z34/X7ceOONCAaDAIAvvvgCwWAQL7zwAnbu3Iknn3wSzz//PH71q1/16bVSz2zatAn33nsvPvjgA9TU1KCjowNVVVWGnwGPP/44lixZgmeffRYfffQRioqKcN1116G1tVU75r777sO6deuwevVqvPvuuzh58iQmTpyIQCCgHTN16lTU19dj/fr1WL9+Perr6zF9+vQ+vV76bn15P9xwww3o6OhAbW0ttm/fjpEjR2LixIk4ePBgn14zdUNQQgEg1q1bp/17165dAoD47LPPtMc6OjpETk6O+POf/6w9lpaWJlauXGl4rZycHPHSSy8ZHquurhZDhw4VO3fuFABEXV1dr1wHRYfM/fD2228Lm80mmpubtWOOHTsmAIiampqzfq3HH39clJWVRf8iKOqampoEALFp0yYhhBDBYFAUFRWJxx57TDvmzJkzwuv1iueff14IIcSJEyeE0+kUq1ev1o7Zv3+/sNlsYv369UIIIT7//HMBQHzwwQfaMVu3bhUAxBdffNEXl0YSeut+OHz4sAAgNm/erB3T0tIiAIh33nmnLy6NvgMzgAnO5/MBADwej/aY3W6Hy+XCu+++qz125ZVXYs2aNTh27BiCwSBWr14Nn8+H8ePHa8ccOnQIP/vZz/Dqq68iNTW1z66Boqcn94PP54OiKIYhrx6PBzabzXDPhGtubkZOTk4vnTlFU3NzMwBo369vvvkGBw8eRFVVlXaM2+3GD37wA7z//vsAgO3bt6O9vd1wTHFxMcrLy7Vjtm7dCq/XizFjxmjHjB07Fl6vVzuG4k9v3Q+5ubkYNmwYVq5ciba2NnR0dOCFF15AYWEhKioq+ury6CwYACa4oUOHorS0FPPnz8fx48fh9/vx2GOP4eDBg2hsbNSOW7NmDTo6OpCbmwu324177rkH69atw6BBgwCESoczZ87ErFmzMHr06FhdDlnUk/th7NixSEtLw0MPPYRTp06hra0NDzzwAILBoOGe0duzZw+eeeYZzJo1qy8vhyQIITBv3jxceeWVKC8vBwCtHFdYWGg4trCwUPvcwYMH4XK5kJ2d/Z3HFBQURHzNgoIClvziVG/eD4qioKamBnV1dcjIyIDH48GTTz6J9evXIysrq5evjM6FAWCCczqd+Nvf/obdu3cjJycHqamp2LhxI66//nrY7XbtuEcffRTHjx/HO++8g48//hjz5s3Dbbfdhh07dgAAnnnmGbS0tGD+/PmxuhSKgp7cD/n5+Xjttdfwj3/8A+np6fB6vWhubsaoUaMM94zqwIEDmDBhAm677TbcfffdfX1JZNKcOXPwr3/9C6tWrYr4nKIohn8LISIeCxd+THfH9+R1KDZ6834QQmD27NkoKCjAli1b8OGHH2LSpEmYOHHiWf+YpL7jiPUJUO+rqKhAfX09mpub4ff7kZ+fjzFjxmiZvD179uDZZ5/FZ599hosvvhgAMGLECGzZsgV/+tOf8Pzzz6O2thYffPBBxN6Po0ePxrRp0/DKK6/0+XWRnHPdDwBQVVWFPXv24MiRI3A4HMjKykJRURHKysoMr3XgwAFcffXVqKysxIsvvtjXl0ImzZ07F2+++SY2b96MAQMGaI8XFRUBCGV1+vXrpz3e1NSkZYGKiorg9/tx/PhxQ9anqakJl19+uXaMvltcdfjw4YhsEsVeb98PtbW1eOutt3D8+HFkZmYCAJYtW4aamhq88sorePjhh3v9GunsmAFMIl6vF/n5+fjyyy/x8ccfY9KkSQCAU6dOAQBsNuPtYLfbta7Pp59+Gp9++inq6+tRX1+P6upqAKHS8e9+97s+vAqKlrPdD3p5eXnIyspCbW0tmpqacNNNN2mf279/P8aPH49Ro0Zh+fLlEfcPxQ8hBObMmYO1a9eitrY2IpAvKytDUVERampqtMf8fj82bdqk/TKvqKiA0+k0HNPY2IjPPvtMO6ayshLNzc348MMPtWO2bduG5uZm7RiKvb66H872u8Vms2m/WyiGYtR8QlHU2toq6urqRF1dnQAglixZIurq6sR//vMfIYQQf/3rX8WGDRvEnj17xBtvvCFKS0vF5MmTtef7/X5x4YUXinHjxolt27aJr776Svzxj38UiqKIf/7zn91+zW+++YZdwHHK6v0ghBAvv/yy2Lp1q/jqq6/Eq6++KnJycsS8efO0z+/fv19ceOGF4pprrhH79u0TjY2N2gfFn5///OfC6/WKjRs3Gr5Xp06d0o557LHHhNfrFWvXrhU7duwQP/nJT0S/fv1ES0uLdsysWbPEgAEDxDvvvCM++eQTcc0114gRI0aIjo4O7ZgJEyaISy+9VGzdulVs3bpVXHLJJWLixIl9er303frqfjh8+LDIzc0VkydPFvX19WLXrl3i/vvvF06nU9TX1/f5dZMRA8AEsGHDBgEg4uOOO+4QQgixdOlSMWDAAOF0OsUFF1wgHn30UeHz+QyvsXv3bjF58mRRUFAgUlNTxaWXXhoxFkaPAWD8isb98NBDD4nCwkLhdDrF4MGDxRNPPCGCwaD2+eXLl3f7Nfg3ZXw62/dq+fLl2jHBYFAsWLBAFBUVCbfbLa666iqxY8cOw+ucPn1azJkzR+Tk5IiUlBQxceJE0dDQYDjm6NGjYtq0aSIjI0NkZGSIadOmiePHj/fBVVJP9eX98NFHH4mqqiqRk5MjMjIyxNixY0V1dXVfXCadgyKEEL2fZyQiIiKieMFFO0RERERJhgEgERERUZJhAEhERESUZBgAEhERESUZBoBERERESYYBIBEREVGSYQBIRERElGQYABJR0tm4cSMURcGJEydifSpERDHBQdBElPDGjx+PkSNH4qmnngIQ2tf02LFjKCwshKIosT05IqIYcMT6BIiI+prL5UJRUVGsT4OIKGZYAiaihDZz5kxs2rQJS5cuhaIoUBQFK1asMJSAV6xYgaysLLz11lu46KKLkJqailtvvRVtbW145ZVXMHDgQGRnZ2Pu3LkIBALaa/v9fjz44IPo378/0tLSMGbMGGzcuDE2F0pEZAIzgESU0JYuXYrdu3ejvLwcixYtAgDs3Lkz4rhTp07h6aefxurVq9Ha2orJkydj8uTJyMrKQnV1Nb7++mv8+Mc/xpVXXokpU6YAAO688058++23WL16NYqLi7Fu3TpMmDABO3bswODBg/v0OomIzGAASEQJzev1wuVyITU1VSv7fvHFFxHHtbe347nnnsOgQYMAALfeeiteffVVHDp0COnp6Rg+fDiuvvpqbNiwAVOmTMGePXuwatUq7Nu3D8XFxQCA+++/H+vXr8fy5cvx+9//vu8ukojIJAaAREQAUlNTteAPAAoLCzFw4ECkp6cbHmtqagIAfPLJJxBCYMiQIYbX8fl8yM3N7ZuTJiKSxACQiAiA0+k0/FtRlG4fCwaDAIBgMAi73Y7t27fDbrcbjtMHjURE8YgBIBElPJfLZWjeiIbLLrsMgUAATU1NGDduXFRfm4iot7ELmIgS3sCBA7Ft2zZ8++23OHLkiJbFs2LIkCGYNm0aZsyYgbVr1+Kbb77BRx99hD/84Q+orq6OwlkTEfUeBoBElPDuv/9+2O12DB8+HPn5+WhoaIjK6y5fvhwzZszAL3/5S1x00UW46aabsG3bNpSUlETl9YmIegt3AiEiIiJKMswAEhERESUZBoBERERESYYBIBEREVGSYQBIRERElGQYABIRERElGQaAREREREmGASARERFRkmEASERERJRkGAASERERJRkGgERERERJhgEgERERUZJhAEhERESUZP4/EFrCgdJXVTAAAAAASUVORK5CYII=" 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" 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -416,9 +418,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-24 15:13:27,907 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "INFO::2024-01-24 15:14::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n", - "2024-01-24 15:14:27,887 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n" + "2024-01-25 11:38:28,347 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "INFO::2024-01-25 11:39::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n", + "2024-01-25 11:39:27,529 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n" ] }, { @@ -731,7 +733,7 @@ " \"Version\": \"23.1.0\",\n", " \"buildVersion\": \"not installed\"\n", " },\n", - " \"date\": \"2024-01-24 15:14:13\",\n", + " \"date\": \"2024-01-25 11:39:13\",\n", " \"openGL\": {\n", " \"GLX\": {\n", " \"client\": {},\n", @@ -853,7 +855,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-24 15:15:33,747 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + "2024-01-25 11:40:29,821 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" ] }, { @@ -947,8 +949,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO::2024-01-24 15:18::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n", - "2024-01-24 15:18:12,068 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n" + "INFO::2024-01-25 11:43::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n", + "2024-01-25 11:43:28,092 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n" ] }, { @@ -995,9 +997,9 @@ "text": [ "[WARNING] yaksa: 10 leaked handle pool objects\n", "\n", - "real\t3m44.357s\n", - "user\t3m58.921s\n", - "sys\t1m19.472s\n" + "real\t4m0.717s\n", + "user\t4m12.255s\n", + "sys\t1m20.539s\n" ] } ], @@ -1063,8 +1065,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-24 15:19:18,845 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "2024-01-24 15:20:03,407 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + "2024-01-25 11:44:23,351 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "2024-01-25 11:45:05,516 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" ] }, { @@ -1181,9 +1183,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-24 15:22:44,793 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "INFO::2024-01-24 15:27::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n", - "2024-01-24 15:27:10,888 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n" + "2024-01-25 11:48:07,932 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "INFO::2024-01-25 11:52::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n", + "2024-01-25 11:52:30,866 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n" ] }, { @@ -1289,9 +1291,9 @@ "text": [ "[WARNING] yaksa: 10 leaked handle pool objects\n", "\n", - "real\t8m59.147s\n", - "user\t10m47.294s\n", - "sys\t2m36.007s\n" + "real\t9m2.167s\n", + "user\t10m50.119s\n", + "sys\t2m40.216s\n" ] } ], @@ -2552,7 +2554,7 @@ " \"Version\": \"23.1.0\",\n", " \"buildVersion\": \"not installed\"\n", " },\n", - " \"date\": \"2024-01-24 15:26:56\",\n", + " \"date\": \"2024-01-25 11:52:17\",\n", " \"openGL\": {\n", " \"GLX\": {\n", " \"client\": {},\n", From a50452d83dca426b12d3f33e652c54d4f68d7d0a Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 12:04:29 -0800 Subject: [PATCH 47/69] move file --- pcmdi_metrics/sea_ice/sea_ice_driver.py | 914 ++++++++++++++++++++++++ 1 file changed, 914 insertions(+) create mode 100644 pcmdi_metrics/sea_ice/sea_ice_driver.py diff --git a/pcmdi_metrics/sea_ice/sea_ice_driver.py b/pcmdi_metrics/sea_ice/sea_ice_driver.py new file mode 100644 index 000000000..4de860e95 --- /dev/null +++ b/pcmdi_metrics/sea_ice/sea_ice_driver.py @@ -0,0 +1,914 @@ +import datetime +import glob +import json +import os +import sys + +import dask +import matplotlib.pyplot as plt +import numpy as np +import xarray as xr +import xcdat as xc +from sea_ice_parser import create_sea_ice_parser + +from pcmdi_metrics.io import xcdat_openxml +from pcmdi_metrics.io.base import Base +from pcmdi_metrics.utils import create_land_sea_mask + + +class MetadataFile: + # This class organizes the contents for the CMEC + # metadata file called output.json, which describes + # the other files in the output bundle. + + def __init__(self, metrics_output_path): + self.outfile = os.path.join(metrics_output_path, "output.json") + self.json = { + "provenance": { + "environment": "", + "modeldata": "", + "obsdata": "", + "log": "", + }, + "metrics": {}, + "data": {}, + "plots": {}, + } + + def update_metrics(self, kw, filename, longname, desc): + tmp = {"filename": filename, "longname": longname, "description": desc} + self.json["metrics"].update({kw: tmp}) + return + + def update_data(self, kw, filename, longname, desc): + tmp = {"filename": filename, "longname": longname, "description": desc} + self.json["data"].update({kw: tmp}) + return + + def update_plots(self, kw, filename, longname, desc): + tmp = {"filename": filename, "longname": longname, "description": desc} + self.json["plots"].update({kw: tmp}) + + def update_provenance(self, kw, data): + self.json["provenance"].update({kw: data}) + return + + def update_index(self, val): + self.json["index"] = val + return + + def write(self): + with open(self.outfile, "w") as f: + json.dump(self.json, f, indent=4) + + +def sea_ice_regions(ds, var, xvar, yvar): + # Two sets of region definitions are provided, one for + # -180:180 and one for 0:360 longitude ranges + data_arctic = ds[var].where(ds[yvar] > 0, 0) + data_antarctic = ds[var].where(ds[yvar] < 0, 0) + if (ds[xvar] > 180).any(): # 0 to 360 + data_ca1 = ds[var].where( + ( + (ds[yvar] > 80) + & (ds[yvar] <= 87.2) + & ((ds[xvar] > 240) | (ds[xvar] <= 90)) + ), + 0, + ) + data_ca2 = ds[var].where( + ((ds[yvar] > 65) & (ds[yvar] < 87.2)) + & ((ds[xvar] > 90) & (ds[xvar] <= 240)), + 0, + ) + data_ca = data_ca1 + data_ca2 + data_np = ds[var].where( + (ds[yvar] > 35) & (ds[yvar] <= 65) & ((ds[xvar] > 90) & (ds[xvar] <= 240)), + 0, + ) + data_na = ds[var].where( + (ds[yvar] > 45) & (ds[yvar] <= 80) & ((ds[xvar] > 240) | (ds[xvar] <= 90)), + 0, + ) + data_na = data_na - data_na.where( + (ds[yvar] > 45) & (ds[yvar] <= 50) & (ds[xvar] > 30) & (ds[xvar] <= 60), + 0, + ) + data_sa = ds[var].where( + (ds[yvar] > -90) + & (ds[yvar] <= -40) + & ((ds[xvar] > 300) | (ds[xvar] <= 20)), + 0, + ) + data_sp = ds[var].where( + (ds[yvar] > -90) + & (ds[yvar] <= -40) + & ((ds[xvar] > 90) & (ds[xvar] <= 300)), + 0, + ) + data_io = ds[var].where( + (ds[yvar] > -90) & (ds[yvar] <= -40) & (ds[xvar] > 20) & (ds[xvar] <= 90), + 0, + ) + else: # -180 to 180 + data_ca1 = ds[var].where( + ( + (ds[yvar] > 80) + & (ds[yvar] <= 87.2) + & (ds[xvar] > -120) + & (ds[xvar] <= 90) + ), + 0, + ) + data_ca2 = ds[var].where( + ((ds[yvar] > 65) & (ds[yvar] < 87.2)) + & ((ds[xvar] > 90) | (ds[xvar] <= -120)), + 0, + ) + data_ca = data_ca1 + data_ca2 + data_np = ds[var].where( + (ds[yvar] > 35) & (ds[yvar] <= 65) & ((ds[xvar] > 90) | (ds[xvar] <= -120)), + 0, + ) + data_na = ds[var].where( + (ds[yvar] > 45) & (ds[yvar] <= 80) & (ds[xvar] > -120) & (ds[xvar] <= 90), + 0, + ) + data_na = data_na - data_na.where( + (ds[yvar] > 45) & (ds[yvar] <= 50) & (ds[xvar] > 30) & (ds[xvar] <= 60), + 0, + ) + data_sa = ds[var].where( + (ds[yvar] > -90) & (ds[yvar] <= -55) & (ds[xvar] > -60) & (ds[xvar] <= 20), + 0, + ) + data_sp = ds[var].where( + (ds[yvar] > -90) + & (ds[yvar] <= -55) + & ((ds[xvar] > 90) | (ds[xvar] <= -60)), + 0, + ) + data_io = ds[var].where( + (ds[yvar] > -90) & (ds[yvar] <= -55) & (ds[xvar] > 20) & (ds[xvar] <= 90), + 0, + ) + + regions_dict = { + "arctic": data_arctic.copy(deep=True), + "ca": data_ca.copy(deep=True), + "np": data_np.copy(deep=True), + "na": data_na.copy(deep=True), + "antarctic": data_antarctic.copy(deep=True), + "sa": data_sa.copy(deep=True), + "sp": data_sp.copy(deep=True), + "io": data_io.copy(deep=True), + } + return regions_dict + + +def find_lon(ds): + for key in ds.coords: + if key in ["lon", "longitude"]: + return key + for key in ds.keys(): + if key in ["lon", "longitude"]: + return key + return None + + +def find_lat(ds): + for key in ds.coords: + if key in ["lat", "latitude"]: + return key + for key in ds.keys(): + if key in ["lat", "latitude"]: + return key + return None + + +def mse_t(dm, do, weights=None): + """Computes mse""" + if dm is None and do is None: # just want the doc + return { + "Name": "Temporal Mean Square Error", + "Abstract": "Compute Temporal Mean Square Error", + "Contact": "pcmdi-metrics@llnl.gov", + } + if weights is None: + stat = np.sum(((dm.data - do.data) ** 2)) / len(dm, axis=0) + else: + stat = np.sum(((dm.data - do.data) ** 2) * weights, axis=0) + if isinstance(stat, dask.array.core.Array): + stat = stat.compute() + return stat + + +def mse_model(dm, do, var=None): + """Computes mse""" + if dm is None and do is None: # just want the doc + return { + "Name": "Mean Square Error", + "Abstract": "Compute Mean Square Error", + "Contact": "pcmdi-metrics@llnl.gov", + } + if var is not None: # dataset + stat = (dm[var].data - do[var].data) ** 2 + else: # dataarray + stat = (dm - do) ** 2 + if isinstance(stat, dask.array.core.Array): + stat = stat.compute() + return stat + + +def to_ice_con_ds(da, ds, obs_var): + # Convert sea ice data array to dataset using + # coordinates from another dataset + ds = xr.Dataset( + data_vars={obs_var: da, "time_bnds": ds.time_bnds}, coords={"time": ds.time} + ) + return ds + + +def adjust_units(ds, adjust_tuple): + action_dict = {"multiply": "*", "divide": "/", "add": "+", "subtract": "-"} + if adjust_tuple[0]: + print("Converting units by ", adjust_tuple[1], adjust_tuple[2]) + cmd = " ".join(["ds", str(action_dict[adjust_tuple[1]]), str(adjust_tuple[2])]) + ds = eval(cmd) + return ds + + +def verify_output_path(metrics_output_path, case_id): + if metrics_output_path is None: + metrics_output_path = datetime.datetime.now().strftime("v%Y%m%d") + if case_id is not None: + metrics_output_path = metrics_output_path.replace("%(case_id)", case_id) + if not os.path.exists(metrics_output_path): + print("\nMetrics output path not found.") + print("Creating metrics output directory", metrics_output_path) + try: + os.makedirs(metrics_output_path) + except Exception as e: + print("\nError: Could not create metrics output path", metrics_output_path) + print(e) + print("Exiting.") + sys.exit() + return metrics_output_path + + +def verify_years(start_year, end_year, msg="Error: Invalid start or end year"): + if start_year is None and end_year is None: + return + elif start_year is None or end_year is None: + # If only one of the two is set, exit. + print(msg) + print("Exiting") + sys.exit() + + +def set_up_realizations(realization): + find_all_realizations = False + if realization is None: + realization = "" + realizations = [realization] + elif isinstance(realization, str): + if realization.lower() in ["all", "*"]: + find_all_realizations = True + realizations = [""] + else: + realizations = [realization] + elif isinstance(realization, list): + realizations = realization + + return find_all_realizations, realizations + + +def load_dataset(filepath): + # Load an xarray dataset from the given filepath. + # If list of netcdf files, opens mfdataset. + # If list of xmls, open last file in list. + if filepath[-1].endswith(".xml"): + # Final item of sorted list would have most recent version date + ds = xcdat_openxml.xcdat_openxml(filepath[-1]) + elif len(filepath) > 1: + ds = xc.open_mfdataset(filepath, chunks=None) + else: + ds = xc.open_dataset(filepath[0]) + return ds + + +def replace_multi(string, rdict): + # Replace multiple keyworks in a string template + # based on key-value pairs in 'rdict'. + for k in rdict.keys(): + string = string.replace(k, rdict[k]) + return string + + +def get_xy_coords(ds, xvar): + if len(ds[xvar].dims) == 2: + lon_j, lon_i = ds[xvar].dims + elif len(ds[xvar].dims) == 1: + lon_j = find_lon(ds) + lon_i = find_lat(ds) + return lon_i, lon_j + + +if __name__ == "__main__": + parser = create_sea_ice_parser() + parameter = parser.get_parameter(argparse_vals_only=False) + + # Parameters + # I/O settings + case_id = parameter.case_id + realization = parameter.realization + var = parameter.var + filename_template = parameter.filename_template + test_data_path = parameter.test_data_path + model_list = parameter.test_data_set + reference_data_path_nh = parameter.reference_data_path_nh + reference_data_path_sh = parameter.reference_data_path_sh + reference_data_set = parameter.reference_data_set + metrics_output_path = parameter.metrics_output_path + area_template = parameter.area_template + area_var = parameter.area_var + AreaUnitsAdjust = parameter.AreaUnitsAdjust + obs_area_var = parameter.obs_area_var + obs_var = parameter.obs_var + obs_area_template_nh = parameter.obs_area_template_nh + obs_area_template_sh = parameter.obs_area_template_sh + obs_cell_area = parameter.obs_cell_area + ObsAreaUnitsAdjust = parameter.ObsAreaUnitsAdjust + ModUnitsAdjust = parameter.ModUnitsAdjust + ObsUnitsAdjust = parameter.ObsUnitsAdjust + msyear = parameter.msyear + meyear = parameter.meyear + osyear = parameter.osyear + oeyear = parameter.oeyear + + print(model_list) + model_list.sort() + # Verifying output directory + metrics_output_path = verify_output_path(metrics_output_path, case_id) + + if isinstance(reference_data_set, list): + # Fix a command line issue + reference_data_set = reference_data_set[0] + + # Verify years + ok_mod = verify_years( + msyear, + meyear, + msg="Error: Model msyear and meyear must both be set or both be None (unset).", + ) + ok_obs = verify_years( + osyear, + oeyear, + msg="Error: Obs osyear and oeyear must both be set or both be None (unset).", + ) + + # Initialize output.json file + meta = MetadataFile(metrics_output_path) + + # Setting up model realization list + find_all_realizations, realizations = set_up_realizations(realization) + print("Find all realizations:", find_all_realizations) + + #### Do Obs part + arctic_clims = {} + arctic_means = {} + + print("OBS: Arctic") + nh_files = glob.glob(reference_data_path_nh) + obs = load_dataset(nh_files) + xvar = find_lon(obs) + yvar = find_lat(obs) + coord_i, coord_j = get_xy_coords(obs, xvar) + if osyear is not None: + obs = obs.sel( + { + "time": slice( + "{0}-01-01".format(osyear), + "{0}-12-31".format(oeyear), + ) + } + ).compute() # TODO: won't always need to compute + obs[obs_var] = adjust_units(obs[obs_var], ObsUnitsAdjust) + if obs_area_var is not None: + obs[obs_area_var] = adjust_units(obs[obs_area_var], ObsAreaUnitsAdjust) + area_val = obs[obs_area_var] + else: + area_val = obs_cell_area + # Remove land areas (including lakes) + mask = create_land_sea_mask(obs, lon_key=xvar, lat_key=yvar) + obs[obs_var] = obs[obs_var].where(mask < 1) + # Get regions + rgn_dict = sea_ice_regions(obs, obs_var, xvar, yvar) + + # Get ice extent + total_extent_arctic_obs = ( + rgn_dict["arctic"].where(rgn_dict["arctic"] > 0.15) * area_val + ).sum((coord_i, coord_j), skipna=True) + total_extent_ca_obs = (rgn_dict["ca"].where(rgn_dict["ca"] > 0.15) * area_val).sum( + (coord_i, coord_j), skipna=True + ) + total_extent_np_obs = (rgn_dict["np"].where(rgn_dict["np"] > 0.15) * area_val).sum( + (coord_i, coord_j), skipna=True + ) + total_extent_na_obs = (rgn_dict["na"].where(rgn_dict["na"] > 0.15) * area_val).sum( + (coord_i, coord_j), skipna=True + ) + + clim_arctic_obs = to_ice_con_ds( + total_extent_arctic_obs, obs, obs_var + ).temporal.climatology(obs_var, freq="month") + clim_ca_obs = to_ice_con_ds(total_extent_ca_obs, obs, obs_var).temporal.climatology( + obs_var, freq="month" + ) + clim_np_obs = to_ice_con_ds(total_extent_np_obs, obs, obs_var).temporal.climatology( + obs_var, freq="month" + ) + clim_na_obs = to_ice_con_ds(total_extent_na_obs, obs, obs_var).temporal.climatology( + obs_var, freq="month" + ) + + arctic_clims = { + "arctic": clim_arctic_obs, + "ca": clim_ca_obs, + "np": clim_np_obs, + "na": clim_na_obs, + } + + arctic_means = { + "arctic": total_extent_arctic_obs.mean("time", skipna=True).data.item(), + "ca": total_extent_ca_obs.mean("time", skipna=True).data.item(), + "np": total_extent_np_obs.mean("time", skipna=True).data.item(), + "na": total_extent_na_obs.mean("time", skipna=True).data.item(), + } + obs.close() + + antarctic_clims = {} + antarctic_means = {} + print("OBS: Antarctic") + sh_files = glob.glob(reference_data_path_sh) + obs = load_dataset(sh_files) + xvar = find_lon(obs) + yvar = find_lat(obs) + coord_i, coord_j = get_xy_coords(obs, xvar) + if osyear is not None: + obs = obs.sel( + { + "time": slice( + "{0}-01-01".format(osyear), + "{0}-12-31".format(oeyear), + ) + } + ).compute() + obs[obs_var] = adjust_units(obs[obs_var], ObsUnitsAdjust) + if obs_area_var is not None: + obs[obs_area_var] = adjust_units(obs[obs_area_var], ObsAreaUnitsAdjust) + area_val = obs[obs_area_var] + else: + area_val = obs_cell_area + # Remove land areas (including lakes) + mask = create_land_sea_mask(obs, lon_key="lon", lat_key="lat") + obs[obs_var] = obs[obs_var].where(mask < 1) + rgn_dict = sea_ice_regions(obs, obs_var, "lon", "lat") + + total_extent_antarctic_obs = ( + rgn_dict["antarctic"].where(rgn_dict["antarctic"] > 0.15) * area_val + ).sum((coord_i, coord_j), skipna=True) + total_extent_sa_obs = (rgn_dict["sa"].where(rgn_dict["sa"] > 0.15) * area_val).sum( + (coord_i, coord_j), skipna=True + ) + total_extent_sp_obs = (rgn_dict["sp"].where(rgn_dict["sp"] > 0.15) * area_val).sum( + (coord_i, coord_j), skipna=True + ) + total_extent_io_obs = (rgn_dict["io"].where(rgn_dict["io"] > 0.15) * area_val).sum( + (coord_i, coord_j), skipna=True + ) + + clim_antarctic_obs = to_ice_con_ds( + total_extent_antarctic_obs, obs, obs_var + ).temporal.climatology(obs_var, freq="month") + clim_sa_obs = to_ice_con_ds(total_extent_sa_obs, obs, obs_var).temporal.climatology( + obs_var, freq="month" + ) + clim_sp_obs = to_ice_con_ds(total_extent_sp_obs, obs, obs_var).temporal.climatology( + obs_var, freq="month" + ) + clim_io_obs = to_ice_con_ds(total_extent_io_obs, obs, obs_var).temporal.climatology( + obs_var, freq="month" + ) + + antarctic_clims = { + "antarctic": clim_antarctic_obs, + "io": clim_io_obs, + "sp": clim_sp_obs, + "sa": clim_sa_obs, + } + + antarctic_means = { + "antarctic": total_extent_antarctic_obs.mean("time", skipna=True).data.item(), + "io": total_extent_io_obs.mean("time", skipna=True).compute().data.item(), + "sp": total_extent_sp_obs.mean("time", skipna=True).compute().data.item(), + "sa": total_extent_sa_obs.mean("time", skipna=True).compute().data.item(), + } + obs.close() + + obs_clims = {reference_data_set: {}} + obs_means = {reference_data_set: {}} + for item in antarctic_clims: + obs_clims[reference_data_set][item] = antarctic_clims[item] + obs_means[reference_data_set][item] = antarctic_means[item] + for item in arctic_clims: + obs_clims[reference_data_set][item] = arctic_clims[item] + obs_means[reference_data_set][item] = arctic_means[item] + + #### Do model part + + # Needs to weigh months by length for metrics later + clim_wts = [31.0, 28.0, 31.0, 30.0, 31.0, 30.0, 31.0, 31.0, 30.0, 31.0, 30.0, 31.0] + clim_wts = [x / 365 for x in clim_wts] + # Initialize JSON data + mse = {} + metrics = { + "DIMENSIONS": { + "json_structure": [ + "model", + "realization", + "obs", + "region", + "index", + "statistic", + ], + "region": {}, + "index": { + "monthly_clim": "Monthly climatology of extent", + "total_extent": "Sum of ice coverage where concentration > 15%", + }, + "statistic": {"mse": "Mean Square Error (10^12 km^4)"}, + "model": model_list, + }, + "RESULTS": {}, + "model_year_range": {}, + } + print("Model list:", model_list) + + # Loop over models and realizations to generate metrics + for model in model_list: + start_year = msyear + end_year = meyear + + real_dict = { + "arctic": {"model_mean": 0}, + "ca": {"model_mean": 0}, + "na": {"model_mean": 0}, + "np": {"model_mean": 0}, + "antarctic": {"model_mean": 0}, + "sp": {"model_mean": 0}, + "sa": {"model_mean": 0}, + "io": {"model_mean": 0}, + } + mse[model] = { + "arctic": {"model_mean": {reference_data_set: {}}}, + "ca": {"model_mean": {reference_data_set: {}}}, + "na": {"model_mean": {reference_data_set: {}}}, + "np": {"model_mean": {reference_data_set: {}}}, + "antarctic": {"model_mean": {reference_data_set: {}}}, + "sp": {"model_mean": {reference_data_set: {}}}, + "sa": {"model_mean": {reference_data_set: {}}}, + "io": {"model_mean": {reference_data_set: {}}}, + } + + tags = { + "%(variable)": var, + "%(model)": model, + "%(model_version)": model, + "%(realization)": "*", + } + if find_all_realizations: + test_data_full_path_tmp = os.path.join(test_data_path, filename_template) + test_data_full_path_tmp = replace_multi(test_data_full_path_tmp, tags) + ncfiles = glob.glob(test_data_full_path_tmp) + realizations = [] + for ncfile in ncfiles: + basename = ncfile.split("/")[-1] + if len(basename.split(".")) <= 2: + if basename.split("_")[4] not in realizations: + realizations.append(basename.split("_")[4]) + else: + if basename.split(".")[3] not in realizations: + realizations.append(basename.split(".")[3]) + + print("\n=================================") + print("model, runs:", model, realizations) + list_of_runs = realizations + else: + list_of_runs = realizations + + # Model grid area + print(replace_multi(area_template, tags)) + area = xc.open_dataset(glob.glob(replace_multi(area_template, tags))[0]) + area[area_var] = adjust_units(area[area_var], AreaUnitsAdjust) + + if len(list_of_runs) > 0: + # Loop over realizations + for run_ind, run in enumerate(list_of_runs): + # Find model data, determine number of files, check if they exist + tags = { + "%(variable)": var, + "%(model)": model, + "%(model_version)": model, + "%(realization)": run, + } + test_data_full_path = os.path.join(test_data_path, filename_template) + test_data_full_path = replace_multi(test_data_full_path, tags) + test_data_full_path = glob.glob(test_data_full_path) + test_data_full_path.sort() + if len(test_data_full_path) == 0: + print("") + print("-----------------------") + print("Not found: model, run, variable:", model, run, var) + break + else: + print("") + print("-----------------------") + print("model, run, variable:", model, run, var) + print("test_data (model in this case) full_path:") + for t in test_data_full_path: + print(" ", t) + + # Load and prep data + ds = load_dataset(test_data_full_path) + ds[var] = adjust_units(ds[var], ModUnitsAdjust) + xvar = find_lon(ds) + yvar = find_lat(ds) + if xvar is None or yvar is None: + print("Could not get latitude or longitude variables") + break + if (ds[xvar] < -180).any(): + ds[xvar] = ds[xvar].where(ds[xvar] >= -180, ds[xvar] + 360) + + # Get time slice if year parameters exist + if start_year is not None: + ds = ds.sel( + { + "time": slice( + "{0}-01-01".format(start_year), + "{0}-12-31".format(end_year), + ) + } + ) + yr_range = [str(start_year), str(end_year)] + else: + # Get labels for start/end years from dataset + yr_range = [ + str(int(ds.time.dt.year[0])), + str(int(ds.time.dt.year[-1])), + ] + + # Get regions + regions_dict = sea_ice_regions(ds, var, xvar, yvar) + + ds.close() + # Running sum of all realizations + for rgn in regions_dict: + data = regions_dict[rgn] + # coordinates aren't always the same as lat/lon names, + # especially if lat/lon are 2D + lon_i, lon_j = get_xy_coords(data, xvar) + # area data doesn't always use same coordinates as siconc data in CMIP6 + # so we multiply by area.data, dropping the coordinates + rgn_total = (data.where(data > 0.15, 0) * area[area_var].data).sum( + (lon_j, lon_i), skipna=True + ) + real_dict[rgn][run] = rgn_total + real_dict[rgn]["model_mean"] = ( + real_dict[rgn]["model_mean"] + rgn_total + ) + + print("\n-------------------------------------------") + print("Calculating model regional average metrics \nfor ", model) + print("--------------------------------------------") + for rgn in real_dict: + print(rgn) + + # Average all realizations, fix bounds, get climatologies and totals + # total_rgn = (totals_dict[rgn] / len(list_of_runs)).to_dataset(name=var) + real_dict[rgn]["model_mean"] = real_dict[rgn]["model_mean"] / len( + list_of_runs + ) + + for run in real_dict[rgn]: + # Set up metrics dictionary + if run not in mse[model][rgn]: + mse[model][rgn][run] = {} + mse[model][rgn][run].update( + { + reference_data_set: { + "monthly_clim": {"mse": None}, + "total_extent": {"mse": None}, + } + } + ) + + run_data = real_dict[rgn][run].to_dataset(name=var) + run_data = run_data.bounds.add_missing_bounds() + clim_extent = run_data.temporal.climatology(var, freq="month") + total = run_data.mean("time")[var].data + + # Get errors, convert to 1e12 km^-4 + mse[model][rgn][run][reference_data_set]["monthly_clim"][ + "mse" + ] = str( + mse_t( + clim_extent[var], + obs_clims[reference_data_set][rgn][obs_var], + weights=clim_wts, + ) + * 1e-12 + ) + mse[model][rgn][run][reference_data_set]["total_extent"][ + "mse" + ] = str( + mse_model(total, obs_means[reference_data_set][rgn]) * 1e-12 + ) + + # Update year list + metrics["model_year_range"][model] = [str(start_year), str(end_year)] + else: + for rgn in mse[model]: + # Set up metrics dictionary + mse[model][rgn]["model_mean"][reference_data_set] = { + "monthly_clim": {"mse": None}, + "total_extent": {"mse": None}, + } + metrics["model_year_range"][model] = ["", ""] + + # ----------------- + # Update metrics + # ----------------- + metrics["RESULTS"] = mse + + metricsfile = os.path.join(metrics_output_path, "sea_ice_metrics.json") + JSON = Base(metrics_output_path, "sea_ice_metrics.json") + json_structure = metrics["DIMENSIONS"]["json_structure"] + JSON.write( + metrics, + json_structure=json_structure, + sort_keys=True, + indent=4, + separators=(",", ": "), + ) + meta.update_metrics( + "metrics", + metricsfile, + "metrics_JSON", + "JSON file containig regional sea ice metrics", + ) + + # ---------------- + # Make figure + # ---------------- + sector_list = [ + "Central Arctic Sector", + "North Atlantic Sector", + "North Pacific Sector", + "Indian Ocean Sector", + "South Atlantic Sector", + "South Pacific Sector", + ] + sector_short = ["ca", "na", "np", "io", "sa", "sp"] + fig7, ax7 = plt.subplots(6, 1, figsize=(5, 9)) + # mlabels = model_list + ["bootstrap"] + mlabels = model_list + ind = np.arange(len(mlabels)) # the x locations for the groups + # ind = np.arange(len(mods)+1) # the x locations for the groups + width = 0.3 + # n = len(ind) - 1 + n = len(ind) + for inds, sector in enumerate(sector_list): + # Assemble data + mse_clim = [] + mse_ext = [] + clim_range = [] + ext_range = [] + clim_err_x = [] + clim_err_y = [] + ext_err_y = [] + rgn = sector_short[inds] + for nmod, model in enumerate(model_list): + mse_clim.append( + float( + metrics["RESULTS"][model][rgn]["model_mean"][reference_data_set][ + "monthly_clim" + ]["mse"] + ) + ) + mse_ext.append( + float( + metrics["RESULTS"][model][rgn]["model_mean"][reference_data_set][ + "total_extent" + ]["mse"] + ) + ) + # Get spread, only if there are multiple realizations + if len(metrics["RESULTS"][model][rgn].keys()) > 2: + for r in metrics["RESULTS"][model][rgn]: + if r != "model_mean": + clim_err_x.append(ind[nmod]) + clim_err_y.append( + float( + metrics["RESULTS"][model][rgn][r][reference_data_set][ + "monthly_clim" + ]["mse"] + ) + ) + ext_err_y.append( + float( + metrics["RESULTS"][model][rgn][r][reference_data_set][ + "total_extent" + ]["mse"] + ) + ) + + # plot data + if len(model_list) < 4: + mark_size = 9 + elif len(model_list) < 12: + mark_size = 3 + else: + mark_size = 1 + ax7[inds].bar(ind - width / 2.0, mse_clim, width, color="b", label="Ann. Cycle") + ax7[inds].bar(ind, mse_ext, width, color="r", label="Ann. Mean") + if len(clim_err_x) > 0: + ax7[inds].scatter( + [x - width / 2.0 for x in clim_err_x], + clim_err_y, + marker="D", + s=mark_size, + color="k", + ) + ax7[inds].scatter(clim_err_x, ext_err_y, marker="D", s=mark_size, color="k") + # xticks + if inds == len(sector_list) - 1: + ax7[inds].set_xticks(ind + width / 2.0, mlabels, rotation=90, size=7) + else: + ax7[inds].set_xticks(ind + width / 2.0, labels="") + # yticks + if len(clim_err_y) > 0: + datamax = np.max(np.array(clim_err_y)) + else: + datamax = np.max(np.array(mse_clim)) + ymax = (datamax) * 1.3 + ax7[inds].set_ylim(0.0, ymax) + if ymax < 0.1: + ticks = np.linspace(0, 0.1, 6) + labels = [str(round(x, 3)) for x in ticks] + elif ymax < 1: + ticks = np.linspace(0, 1, 5) + labels = [str(round(x, 1)) for x in ticks] + elif ymax < 4: + ticks = np.linspace(0, round(ymax), num=round(ymax / 2) * 2 + 1) + labels = [str(round(x, 1)) for x in ticks] + elif ymax > 10: + ticks = range(0, round(ymax), 5) + labels = [str(round(x, 0)) for x in ticks] + else: + ticks = range(0, round(ymax)) + labels = [str(round(x, 0)) for x in ticks] + + ax7[inds].set_yticks(ticks, labels, fontsize=8) + # labels etc + ax7[inds].set_ylabel("10${^1}{^2}$km${^4}$", size=8) + ax7[inds].grid(True, linestyle=":") + ax7[inds].annotate( + sector, + (0.35, 0.8), + xycoords="axes fraction", + size=9, + ) + # Add legend, save figure + ax7[0].legend(loc="upper right", fontsize=6) + plt.suptitle("Mean Square Error relative to " + reference_data_set, y=0.91) + figfile = os.path.join(metrics_output_path, "MSE_bar_chart.png") + plt.savefig(figfile) + meta.update_plots( + "bar_chart", figfile, "regional_bar_chart", "Bar chart of regional MSE" + ) + + # Update and write metadata file + try: + with open(os.path.join(metricsfile), "r") as f: + tmp = json.load(f) + meta.update_provenance("environment", tmp["provenance"]) + except Exception: + # Skip provenance if there's an issue + print("Error: Could not get provenance from metrics json for output.json.") + + meta.update_provenance("modeldata", test_data_path) + if reference_data_path_nh is not None: + meta.update_provenance("obsdata_nh", reference_data_path_nh) + meta.update_provenance("obsdata_sh", reference_data_path_sh) + meta.write() From 8b089c2341488d73145891b5de371a2026e71aa6 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 12:06:39 -0800 Subject: [PATCH 48/69] move nb --- doc/jupyter/Demo/Tutorial_sea_ice.ipynb | 2697 +++++++++++++++++++++++ 1 file changed, 2697 insertions(+) create mode 100644 doc/jupyter/Demo/Tutorial_sea_ice.ipynb diff --git a/doc/jupyter/Demo/Tutorial_sea_ice.ipynb b/doc/jupyter/Demo/Tutorial_sea_ice.ipynb new file mode 100644 index 000000000..33a348fa0 --- /dev/null +++ b/doc/jupyter/Demo/Tutorial_sea_ice.ipynb @@ -0,0 +1,2697 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "acb8d42e", + "metadata": {}, + "source": [ + "# Sea Ice Demo" + ] + }, + { + "cell_type": "markdown", + "id": "848c69e5", + "metadata": {}, + "source": [ + "**Summary** \n", + "The PCMDI Metrics sea ice driver produces metrics that compare modeled and observed sea ice extent. This notebook demonstrates how to run the PCMDI Metrics sea ice code.\n", + "\n", + "**Demo author list** \n", + "Ana Ordonez, Jiwoo Lee, Paul Durack, Peter Gleckler\n", + "\n", + "**Reference** \n", + "Ivanova, D. P., P. J. Gleckler, K. E. Taylor, P. J. Durack, and K. D. Marvel, 2016: Moving beyond the Total Sea Ice Extent in Gauging Model Biases. J. Climate, 29, 8965–8987, https://doi.org/10.1175/JCLI-D-16-0026.1. " + ] + }, + { + "cell_type": "markdown", + "id": "6bfd3b73", + "metadata": {}, + "source": [ + "## Demo data\n", + "This demo uses three CMIP6 models. The 'siconc' and 'areacello' variables are needed and can be found in the following directories. In addition, six other models are available that can be added to the analyses in this demo:\n", + "```\n", + "/p/user_pub/pmp/demo/sea-ice/links_siconc \n", + "/p/user_pub/pmp/demo/sea-ice/links_area\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "00d48042", + "metadata": {}, + "source": [ + "The observation dataset provided is a satellite derived sea ice concentration dataset from EUMETSAT OSI SAF. More information about this data can be found at the [osi-450-a product page](https://osi-saf.eumetsat.int/products/osi-450-a). The path to this data is:\n", + "```\n", + "/p/user_pub/pmp/demo/sea-ice/EUMETSAT\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "0b854017", + "metadata": {}, + "source": [ + "## Sectors\n", + "This code block produces maps that show the different regions used in the analysis along with the mean observed sea ice concentration. The code to generate these figures can be found in the script `create_sector_plots.py`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b6d75e4e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating Arctic map\n", + "Creating Antarctic map\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING] yaksa: 10 leaked handle pool objects\n" + ] + } + ], + "source": [ + "%%bash\n", + "python create_sector_plots.py" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a82ee330", + "metadata": {}, + "outputs": [], + "source": [ + "# To open and display one of the graphics\n", + "from IPython.display import display_png, JSON, Image" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6a7eb6da", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = Image(\"Arctic_regions.png\")\n", + "b = Image(\"Antarctic_regions.png\")\n", + "display_png(a,b)" + ] + }, + { + "cell_type": "markdown", + "id": "5294910f", + "metadata": {}, + "source": [ + "## Basic example" + ] + }, + { + "cell_type": "markdown", + "id": "f316897b", + "metadata": {}, + "source": [ + "This first case will work with sea ice concentration ouput from a single model, E3SM-1-0. Two overview plots are shown below to visualize the Arctic sea ice in this model.\n", + "\n", + "For this demo, we start the OSI-SAF satellite data in 1988 as that avoids missing data in earlier parts of the record.\n", + "\n", + "The code to generate these figures can be found in `make_demo_sea_ice_plots.py`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a6cb929f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-25 11:37:13,752 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + ] + } + ], + "source": [ + "%%bash\n", + "python make_demo_sea_ice_plots.py" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3120f819", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = Image(\"E3SM_arctic_tseries.png\")\n", + "b = Image(\"E3SM_arctic_clim.png\")\n", + "display_png(a,b)" + ] + }, + { + "cell_type": "markdown", + "id": "2540cd5d", + "metadata": {}, + "source": [ + "The PMP drivers can all read user arguments from parameter files. We provide a demo parameter file, which is shown below. Comments (beginning with a '#') explain each of the parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6e4fa38d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Sea ice metrics parameter file\n", + "\n", + "# List of models to include in analysis\n", + "test_data_set = [\n", + " \"E3SM-1-0\",\n", + "]\n", + "\n", + "# realization can be a single realization, a list of realizations, or \"*\" for all realizations\n", + "realization = \"r1i2p2f1\"\n", + "\n", + "# test_data_path is a template for the model data parent directory\n", + "test_data_path = \"/p/user_pub/pmp/demo/sea-ice/links_siconc/%(model)/historical/%(realization)/siconc/\"\n", + "\n", + "# filename_template is a template for the model data file name\n", + "# combine it with test_data_path to get complete data path\n", + "filename_template = \"siconc_SImon_%(model)_historical_%(realization)_*_*.nc\"\n", + "\n", + "# The name of the sea ice variable in the model data\n", + "var = \"siconc\"\n", + "\n", + "# Start and end years for model data\n", + "msyear = 1981\n", + "meyear = 2010\n", + "\n", + "# Factor for adjusting model data to decimal rather than percent units\n", + "ModUnitsAdjust = (True, \"multiply\", 1e-2)\n", + "\n", + "# Template for the grid area file\n", + "area_template = \"/p/user_pub/pmp/demo/sea-ice/links_area/%(model)/*.nc\"\n", + "\n", + "# Area variable name; likely 'areacello' or 'areacella' for CMIP6\n", + "area_var = \"areacello\"\n", + "\n", + "# Factor to convert area units to km-2\n", + "AreaUnitsAdjust = (True, \"multiply\", 1e-6)\n", + "\n", + "# Directory for writing outputs\n", + "case_id = \"ex1\"\n", + "metrics_output_path = \"sea_ice_demo/%(case_id)/\"\n", + "\n", + "# Settings for the observational data\n", + "reference_data_path_nh = \"/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc\"\n", + "reference_data_path_sh = \"/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_sh_ease2-250_cdr-v3p0_198801-202012.nc\"\n", + "ObsUnitsAdjust=(True,\"multiply\",1e-2)\n", + "reference_data_set=\"OSI-SAF\"\n", + "osyear=1988\n", + "oeyear=2020\n", + "obs_var=\"ice_conc\"\n", + "ObsAreaUnitsAdjust = (False, 0, 0)\n", + "obs_area_template = None\n", + "obs_area_var = None\n", + "obs_cell_area = 625 #km 2\n" + ] + } + ], + "source": [ + "with open(\"demo_param_file.py\") as f:\n", + " print(f.read())" + ] + }, + { + "cell_type": "markdown", + "id": "38dbe853", + "metadata": {}, + "source": [ + "To see all of the parameters available for the sea ice metrics, run the --help command as shown here:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9d6c1fbf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: ice_driver.py [-h] [--parameters PARAMETERS]\n", + " [--diags OTHER_PARAMETERS [OTHER_PARAMETERS ...]]\n", + " [--case_id CASE_ID] [-v VAR] [--obs_var OBS_VAR]\n", + " [--area_var AREA_VAR] [--obs_area_var OBS_AREA_VAR]\n", + " [-r REFERENCE_DATA_SET [REFERENCE_DATA_SET ...]]\n", + " [--reference_data_path REFERENCE_DATA_PATH]\n", + " [-t TEST_DATA_SET [TEST_DATA_SET ...]]\n", + " [--test_data_path TEST_DATA_PATH]\n", + " [--realization REALIZATION]\n", + " [--filename_template FILENAME_TEMPLATE]\n", + " [--metrics_output_path METRICS_OUTPUT_PATH]\n", + " [--filename_output_template FILENAME_OUTPUT_TEMPLATE]\n", + " [--area_template AREA_TEMPLATE]\n", + " [--obs_area_template_nh OBS_AREA_TEMPLATE_NH]\n", + " [--obs_area_template_sh OBS_AREA_TEMPLATE_SH]\n", + " [--obs_cell_area OBS_CELL_AREA]\n", + " [--output_json_template OUTPUT_JSON_TEMPLATE] [--debug]\n", + " [--plots] [--osyear OSYEAR] [--msyear MSYEAR]\n", + " [--oeyear OEYEAR] [--meyear MEYEAR]\n", + " [--ObsUnitsAdjust OBSUNITSADJUST]\n", + " [--ModUnitsAdjust MODUNITSADJUST]\n", + " [--AreaUnitsAdjust AREAUNITSADJUST]\n", + " [--ObsAreaUnitsAdjust OBSAREAUNITSADJUST]\n", + "\n", + "options:\n", + " -h, --help show this help message and exit\n", + " --parameters PARAMETERS, -p PARAMETERS\n", + " --diags OTHER_PARAMETERS [OTHER_PARAMETERS ...], -d OTHER_PARAMETERS [OTHER_PARAMETERS ...]\n", + " Path to other user-defined parameter file. (default:\n", + " None)\n", + " --case_id CASE_ID Defines a subdirectory to the metrics output, so\n", + " multiplecases can be compared (default: None)\n", + " -v VAR, --var VAR Name of model sea ice concentration variable (default:\n", + " None)\n", + " --obs_var OBS_VAR Name of obs sea ice concentration variable (default:\n", + " None)\n", + " --area_var AREA_VAR Name of model area variable (default: None)\n", + " --obs_area_var OBS_AREA_VAR\n", + " Name of reference data area variable (default: None)\n", + " -r REFERENCE_DATA_SET [REFERENCE_DATA_SET ...], --reference_data_set REFERENCE_DATA_SET [REFERENCE_DATA_SET ...]\n", + " List of observations or models that are used as a\n", + " reference against the test_data_set (default: None)\n", + " --reference_data_path REFERENCE_DATA_PATH\n", + " Path for the reference climitologies (default: None)\n", + " -t TEST_DATA_SET [TEST_DATA_SET ...], --test_data_set TEST_DATA_SET [TEST_DATA_SET ...]\n", + " List of observations or models to test against the\n", + " reference_data_set (default: None)\n", + " --test_data_path TEST_DATA_PATH\n", + " Path for the test climitologies (default: None)\n", + " --realization REALIZATION\n", + " A simulation parameter (default: None)\n", + " --filename_template FILENAME_TEMPLATE\n", + " Template for climatology files (default: None)\n", + " --metrics_output_path METRICS_OUTPUT_PATH\n", + " Directory of where to put the results (default: None)\n", + " --filename_output_template FILENAME_OUTPUT_TEMPLATE\n", + " Filename for the interpolated test climatologies\n", + " (default: None)\n", + " --area_template AREA_TEMPLATE\n", + " Filename template for model grid area (default: None)\n", + " --obs_area_template_nh OBS_AREA_TEMPLATE_NH\n", + " Filename template for obs grid area in Northern\n", + " Hemisphere (default: None)\n", + " --obs_area_template_sh OBS_AREA_TEMPLATE_SH\n", + " Filename template for obs grid area in Southern\n", + " Hemisphere (default: None)\n", + " --obs_cell_area OBS_CELL_AREA\n", + " For equal area grids, the cell area in km (default:\n", + " None)\n", + " --output_json_template OUTPUT_JSON_TEMPLATE\n", + " Filename template for results json files (default:\n", + " None)\n", + " --debug Turn on debugging mode by printing more information to\n", + " track progress (default: False)\n", + " --plots Set to True to generate figures. (default: False)\n", + " --osyear OSYEAR Start year for reference data set (default: None)\n", + " --msyear MSYEAR Start year for model data set (default: None)\n", + " --oeyear OEYEAR End year for reference data set (default: None)\n", + " --meyear MEYEAR End year for model data set (default: None)\n", + " --ObsUnitsAdjust OBSUNITSADJUST\n", + " Factor to convert obs sea ice concentration to\n", + " decimal. For example: - (True, 'divide', 100.0) #\n", + " percentage to decimal - (False, 0, 0) # No adjustment\n", + " (default) (default: (False, 0, 0))\n", + " --ModUnitsAdjust MODUNITSADJUST\n", + " Factor to convert model sea ice concentration to\n", + " decimal. For example: - (True, 'divide', 100.0) #\n", + " percentage to decimal - (False, 0, 0) # No adjustment\n", + " (default) (default: (False, 0, 0))\n", + " --AreaUnitsAdjust AREAUNITSADJUST\n", + " Factor to convert area data to km^2. For example: -\n", + " (True, 'multiply', 1e-6) # m^2 to km^2 - (False, 0, 0)\n", + " # No adjustment (default) (default: (False, 0, 0))\n", + " --ObsAreaUnitsAdjust OBSAREAUNITSADJUST\n", + " Factor to convert area data to km^2. For example: -\n", + " (True, 'multiply', 1e-6) # m^2 to km^2 - (False, 0, 0)\n", + " # No adjustment (default) (default: (False, 0, 0))\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING] yaksa: 10 leaked handle pool objects\n" + ] + } + ], + "source": [ + "%%bash\n", + "python ice_driver.py --help" + ] + }, + { + "cell_type": "markdown", + "id": "9bfa9c97", + "metadata": {}, + "source": [ + "The PMP drivers are run on the command line. In this Jupyter Notebook, we use the bash cell magic function %%bash to run command line functions from the notebook.\n", + "\n", + "The PMP sea ice metrics driver call follows the basic format:\n", + "ice_driver.py -p parameter_file.py --additional arguments\n", + "\n", + "The following cell runs the driver with the demo parameter file we saw above." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d6ff0052", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-25 11:38:28,347 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "INFO::2024-01-25 11:39::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n", + "2024-01-25 11:39:27,529 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['E3SM-1-0']\n", + "Find all realizations: False\n", + "OBS: Arctic\n", + "Converting units by multiply 0.01\n", + "OBS: Antarctic\n", + "Converting units by multiply 0.01\n", + "Model list: ['E3SM-1-0']\n", + "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n", + "Converting units by multiply 1e-06\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r1i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_201001-201112.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-------------------------------------------\n", + "Calculating model regional average metrics \n", + "for E3SM-1-0\n", + "--------------------------------------------\n", + "arctic\n", + "ca\n", + "na\n", + "np\n", + "antarctic\n", + "sp\n", + "sa\n", + "io\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING] yaksa: 10 leaked handle pool objects\n" + ] + } + ], + "source": [ + "%%bash\n", + "python ice_driver.py -p demo_param_file.py" + ] + }, + { + "cell_type": "markdown", + "id": "084440aa", + "metadata": {}, + "source": [ + "One of the primary outputs of the PMP is a JSON file containing the metrics values. In this case, the metrics are the mean square errors of the time mean and monthly mean ice extent. Ice extent is defined as the total area covered by sea ice concentration of >= 15%. The metrics are organized by model, realization, and reference dataset.\n", + "\n", + "The metrics JSON from this run is displayed below." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9a46fb89", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"DIMENSIONS\": {\n", + " \"index\": {\n", + " \"monthly_clim\": \"Monthly climatology of extent\",\n", + " \"total_extent\": \"Sum of ice coverage where concentration > 15%\"\n", + " },\n", + " \"json_structure\": [\n", + " \"model\",\n", + " \"realization\",\n", + " \"obs\",\n", + " \"region\",\n", + " \"index\",\n", + " \"statistic\"\n", + " ],\n", + " \"model\": [\n", + " \"E3SM-1-0\"\n", + " ],\n", + " \"region\": {},\n", + " \"statistic\": {\n", + " \"mse\": \"Mean Square Error (10^12 km^4)\"\n", + " }\n", + " },\n", + " \"RESULTS\": {\n", + " \"E3SM-1-0\": {\n", + " \"antarctic\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.4635192339671928\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.139646926848\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.4635192339671928\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.139646926848\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"arctic\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"5.476181000101471\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.628078727168\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"5.476181000101471\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.628078727168\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"ca\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.05045644169895609\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.007755424768\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.05045644169895609\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.007755424768\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"io\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.04955696515353039\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.00991997952\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.04955696515353039\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.00991997952\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"na\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"2.3482121752568643\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.576847409152\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"2.3482121752568643\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.576847409152\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"np\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.6264518797177615\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.287947685888\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.6264518797177615\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.287947685888\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"sa\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.3797729615722766\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.297013608448\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.3797729615722766\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.297013608448\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"sp\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.6767107661262813\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.078223351808\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.6767107661262813\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.078223351808\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"json_structure\": [\n", + " \"model\",\n", + " \"realization\",\n", + " \"obs\",\n", + " \"region\",\n", + " \"index\",\n", + " \"statistic\"\n", + " ],\n", + " \"json_version\": 3.0,\n", + " \"model_year_range\": {\n", + " \"E3SM-1-0\": [\n", + " \"1981\",\n", + " \"2010\"\n", + " ]\n", + " },\n", + " \"provenance\": {\n", + " \"commandLine\": \"ice_driver.py -p demo_param_file.py\",\n", + " \"conda\": {\n", + " \"Platform\": \"linux-64\",\n", + " \"PythonVersion\": \"3.8.15.final.0\",\n", + " \"Version\": \"23.1.0\",\n", + " \"buildVersion\": \"not installed\"\n", + " },\n", + " \"date\": \"2024-01-25 11:39:13\",\n", + " \"openGL\": {\n", + " \"GLX\": {\n", + " \"client\": {},\n", + " \"server\": {}\n", + " }\n", + " },\n", + " \"osAccess\": false,\n", + " \"packages\": {\n", + " \"PMP\": \"v3.0.2-11-g06b151f\",\n", + " \"PMPObs\": \"See 'References' key below, for detailed obs provenance information.\",\n", + " \"blas\": \"0.3.24\",\n", + " \"cdat_info\": \"8.2.1\",\n", + " \"cdms\": \"3.1.5\",\n", + " \"cdp\": \"1.7.0\",\n", + " \"cdtime\": \"3.1.4\",\n", + " \"cdutil\": \"8.2.1\",\n", + " \"clapack\": null,\n", + " \"esmf\": \"0.8.2\",\n", + " \"esmpy\": \"8.4.2\",\n", + " \"genutil\": \"8.2.1\",\n", + " \"lapack\": \"3.9.0\",\n", + " \"matplotlib\": null,\n", + " \"mesalib\": null,\n", + " \"numpy\": \"1.22.4\",\n", + " \"python\": \"3.10.13\",\n", + " \"scipy\": \"1.11.3\",\n", + " \"uvcdat\": null,\n", + " \"vcs\": null,\n", + " \"vtk\": null,\n", + " \"xarray\": \"2023.10.1\",\n", + " \"xcdat\": \"0.5.0\"\n", + " },\n", + " \"platform\": {\n", + " \"Name\": \"gates.llnl.gov\",\n", + " \"OS\": \"Linux\",\n", + " \"Version\": \"3.10.0-1160.71.1.el7.x86_64\"\n", + " },\n", + " \"userId\": \"ordonez4\"\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "with open(\"sea_ice_demo/ex1/sea_ice_metrics.json\") as f:\n", + " print(f.read())" + ] + }, + { + "cell_type": "markdown", + "id": "d74b6752", + "metadata": {}, + "source": [ + "This driver also outputs a bar chart that visualizes the mean square error between the model and observations. Since there is only one model and one realization in this instance, the bar chart looks very simple. The red bar indicates the mean square error for the time mean ice extent, and the blue bar indicates the mean square error for the climatological ice extent." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c6dfa7a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sea_ice_demo/ex1/MSE_bar_chart.png\r\n" + ] + } + ], + "source": [ + "!ls {\"sea_ice_demo/ex1/MSE_bar_chart.png\"}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d14e933a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = Image(\"sea_ice_demo/ex1/MSE_bar_chart.png\")\n", + "display_png(a)" + ] + }, + { + "cell_type": "markdown", + "id": "a9b323ec", + "metadata": {}, + "source": [ + "## Working with multiple realizations" + ] + }, + { + "cell_type": "markdown", + "id": "0c427a07", + "metadata": {}, + "source": [ + "The sea ice driver can generate metrics based on an average of all available realizations. To do so, provide an asterisk \\* as the value to the --realization argument on the command line. Options passed on the command line will supercede arguments in the parameter file. \n", + "\n", + "In addition, we set the --case_id value to 'ex2' to save results in a new directory." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5f8174e1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-25 11:40:29,821 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['E3SM-1-0']\n", + "Find all realizations: True\n", + "OBS: Arctic\n", + "Converting units by multiply 0.01\n", + "OBS: Antarctic\n", + "Converting units by multiply 0.01\n", + "Model list: ['E3SM-1-0']\n", + "\n", + "=================================\n", + "model, runs: E3SM-1-0 ['r1i2p2f1', 'r2i2p2f1', 'r3i2p2f1', 'r4i2p2f1']\n", + "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n", + "Converting units by multiply 1e-06\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r1i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_201001-201112.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r2i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_201001-201312.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r3i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_201001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r4i2p2f1 siconc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO::2024-01-25 11:43::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n", + "2024-01-25 11:43:28,092 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_201001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-------------------------------------------\n", + "Calculating model regional average metrics \n", + "for E3SM-1-0\n", + "--------------------------------------------\n", + "arctic\n", + "ca\n", + "na\n", + "np\n", + "antarctic\n", + "sp\n", + "sa\n", + "io\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING] yaksa: 10 leaked handle pool objects\n", + "\n", + "real\t4m0.717s\n", + "user\t4m12.255s\n", + "sys\t1m20.539s\n" + ] + } + ], + "source": [ + "%%bash\n", + "time python ice_driver.py -p demo_param_file.py --realization '*' --case_id \"ex2\"" + ] + }, + { + "cell_type": "markdown", + "id": "cadb1306", + "metadata": {}, + "source": [ + "Since we have averaged four different realizations, the resulting statistics are different than seen in example 1. The bar chart now contains markers showing the overall spread among the realizations." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "d6cb5f07", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = Image(\"sea_ice_demo/ex2/MSE_bar_chart.png\")\n", + "display_png(a)" + ] + }, + { + "cell_type": "markdown", + "id": "499d3935", + "metadata": {}, + "source": [ + "# Working with multiple models" + ] + }, + { + "cell_type": "markdown", + "id": "51b008db", + "metadata": {}, + "source": [ + "Along with using multiple realizations, we can include multiple models in a single analysis. The model data must all follow a single filename template. All model inputs must use the same name and units for the sea ice variable.\n", + "\n", + "The example below shows how to use three models in the analysis, with all available realizations. The models are listed as inputs to the --test_data_set flag.\n", + "\n", + "Want to add more models? Six other model sea ice datasets are available in the directories linked in the notebook introduction." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "679d7289", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-25 11:44:23,351 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "2024-01-25 11:45:05,516 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['E3SM-1-0', 'CanESM5', 'MIROC6']\n", + "Find all realizations: True\n", + "OBS: Arctic\n", + "Converting units by multiply 0.01\n", + "OBS: Antarctic\n", + "Converting units by multiply 0.01\n", + "Model list: ['CanESM5', 'E3SM-1-0', 'MIROC6']\n", + "\n", + "=================================\n", + "model, runs: CanESM5 ['r2i1p1f1', 'r1i1p1f1', 'r3i1p1f1']\n", + "/p/user_pub/pmp/demo/sea-ice/links_area/CanESM5/*.nc\n", + "Converting units by multiply 1e-06\n", + "\n", + "-----------------------\n", + "model, run, variable: CanESM5 r2i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/CanESM5/historical/r2i1p1f1/siconc/siconc_SImon_CanESM5_historical_r2i1p1f1_gn_185001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: CanESM5 r1i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/CanESM5/historical/r1i1p1f1/siconc/siconc_SImon_CanESM5_historical_r1i1p1f1_gn_185001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: CanESM5 r3i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/CanESM5/historical/r3i1p1f1/siconc/siconc_SImon_CanESM5_historical_r3i1p1f1_gn_185001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-------------------------------------------\n", + "Calculating model regional average metrics \n", + "for CanESM5\n", + "--------------------------------------------\n", + "arctic\n", + "ca\n", + "na\n", + "np\n", + "antarctic\n", + "sp\n", + "sa\n", + "io\n", + "\n", + "=================================\n", + "model, runs: E3SM-1-0 ['r1i2p2f1', 'r2i2p2f1', 'r3i2p2f1', 'r4i2p2f1']\n", + "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n", + "Converting units by multiply 1e-06\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r1i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_201001-201112.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r2i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_201001-201312.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r3i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_192001-192912.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-25 11:48:07,932 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "INFO::2024-01-25 11:52::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n", + "2024-01-25 11:52:30,866 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_201001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r4i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_201001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-------------------------------------------\n", + "Calculating model regional average metrics \n", + "for E3SM-1-0\n", + "--------------------------------------------\n", + "arctic\n", + "ca\n", + "na\n", + "np\n", + "antarctic\n", + "sp\n", + "sa\n", + "io\n", + "\n", + "=================================\n", + "model, runs: MIROC6 ['r2i1p1f1', 'r1i1p1f1', 'r4i1p1f1', 'r3i1p1f1']\n", + "/p/user_pub/pmp/demo/sea-ice/links_area/MIROC6/*.nc\n", + "Converting units by multiply 1e-06\n", + "\n", + "-----------------------\n", + "model, run, variable: MIROC6 r2i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r2i1p1f1/siconc/siconc_SImon_MIROC6_historical_r2i1p1f1_gn_185001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r2i1p1f1/siconc/siconc_SImon_MIROC6_historical_r2i1p1f1_gn_195001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: MIROC6 r1i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r1i1p1f1/siconc/siconc_SImon_MIROC6_historical_r1i1p1f1_gn_185001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r1i1p1f1/siconc/siconc_SImon_MIROC6_historical_r1i1p1f1_gn_195001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: MIROC6 r4i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r4i1p1f1/siconc/siconc_SImon_MIROC6_historical_r4i1p1f1_gn_185001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r4i1p1f1/siconc/siconc_SImon_MIROC6_historical_r4i1p1f1_gn_195001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: MIROC6 r3i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r3i1p1f1/siconc/siconc_SImon_MIROC6_historical_r3i1p1f1_gn_185001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r3i1p1f1/siconc/siconc_SImon_MIROC6_historical_r3i1p1f1_gn_195001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-------------------------------------------\n", + "Calculating model regional average metrics \n", + "for MIROC6\n", + "--------------------------------------------\n", + "arctic\n", + "ca\n", + "na\n", + "np\n", + "antarctic\n", + "sp\n", + "sa\n", + "io\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING] yaksa: 10 leaked handle pool objects\n", + "\n", + "real\t9m2.167s\n", + "user\t10m50.119s\n", + "sys\t2m40.216s\n" + ] + } + ], + "source": [ + "%%bash\n", + "time python ice_driver.py -p demo_param_file.py \\\n", + "--test_data_set \"E3SM-1-0\" \"CanESM5\" \"MIROC6\" \\\n", + "--realization '*' \\\n", + "--case_id \"ex3\"" + ] + }, + { + "cell_type": "markdown", + "id": "9a17ffee", + "metadata": {}, + "source": [ + "The output JSON now includes metrics for all three models." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b07dbb8b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"DIMENSIONS\": {\n", + " \"index\": {\n", + " \"monthly_clim\": \"Monthly climatology of extent\",\n", + " \"total_extent\": \"Sum of ice coverage where concentration > 15%\"\n", + " },\n", + " \"json_structure\": [\n", + " \"model\",\n", + " \"realization\",\n", + " \"obs\",\n", + " \"region\",\n", + " \"index\",\n", + " \"statistic\"\n", + " ],\n", + " \"model\": [\n", + " \"CanESM5\",\n", + " \"E3SM-1-0\",\n", + " \"MIROC6\"\n", + " ],\n", + " \"region\": {},\n", + " \"statistic\": {\n", + " \"mse\": \"Mean Square Error (10^12 km^4)\"\n", + " }\n", + " },\n", + " \"RESULTS\": {\n", + " \"CanESM5\": {\n", + " \"antarctic\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"5.1043444982100254\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"4.816687734317558\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"3.8203905542158574\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.551903219690635\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"5.408472768567934\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"5.10073354794071\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"6.255511442006537\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"5.95826796378333\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"arctic\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"2.6739701578200408\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"2.5552395000296997\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.9601839559074323\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.8071711277770932\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"2.8686219657630323\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"2.7646000598233362\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"3.306431955856059\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.1987918127469728\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"ca\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.12445176930055403\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.0752818530752368\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.06261975386075735\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.04065017565672462\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.18876746901985617\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.11135594838591391\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.14126431682827864\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.08283366771548334\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"io\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.3902350350581252\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.3411097649596542\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.27378096542718267\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.23052580580517984\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.39222062635127936\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.34482149125771394\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.5287500850978069\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.4689404665141464\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"na\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.8575586124643404\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.6617817141384847\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.5264155067552119\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.3050483466111835\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.7640984838802416\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.5843839089856835\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"2.3388720869665747\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"2.1497244832528395\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"np\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.0063419603431157535\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.001033088420302666\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.005949894526659108\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.7412310294637067e-06\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.01271835367014484\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.004687148872326894\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.005275638631907463\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.0008574285177782025\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"sa\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.4618851114415225\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.21877947801248515\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.3604933562263525\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.16135560774807228\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.48465097876034335\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.18590427961007985\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.565345935295451\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.32530874506699275\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"sp\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.3466206749703824\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.3264114860024545\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.0412143157666585\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.0233677214618289\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.5844213803502167\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.5597222118436824\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.4483893228104396\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.4270545631414926\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"E3SM-1-0\": {\n", + " \"antarctic\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.7772427941035078\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.512854523904\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.4635192339671928\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.139646926848\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.7917153708317476\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.5296078848\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.9431708933066041\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.709116624896\"\n", + " }\n", + " }\n", + " },\n", + " \"r4i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.1123064886611145\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.8482918891519999\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"arctic\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"5.271005131039172\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.602193842176\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"5.476181000101471\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.628078727168\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"4.798813326297904\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.0712725504\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"5.695229471419496\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"4.135149109248\"\n", + " }\n", + " }\n", + " },\n", + " \"r4i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"5.16787172788022\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.6138642309119997\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"ca\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.06682122096680175\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.014511187968\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.05045644169895609\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.007755424768\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.04953964308899206\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.007533873664\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.09545969211386617\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.026321457152\"\n", + " }\n", + " }\n", + " },\n", + " \"r4i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.08158619730649973\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.020952242176\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"io\": {\n", + " \"model_mean\": {\n", + " 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\"mse\": \"15.90437524234963\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"13.40484878336\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"16.338122498955823\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"13.727326797824\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"16.167353763780575\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"13.60793174016\"\n", + " }\n", + " }\n", + " },\n", + " \"r4i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"16.22520331188068\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"13.644895092736\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"json_structure\": [\n", + " \"model\",\n", + " \"realization\",\n", + " \"obs\",\n", + " \"region\",\n", + " \"index\",\n", + " \"statistic\"\n", + " ],\n", + " \"json_version\": 3.0,\n", + " \"model_year_range\": {\n", + " \"CanESM5\": [\n", + " \"1981\",\n", + " \"2010\"\n", + " ],\n", + " \"E3SM-1-0\": [\n", + " \"1981\",\n", + " \"2010\"\n", + " ],\n", + " \"MIROC6\": [\n", + " \"1981\",\n", + " \"2010\"\n", + " ]\n", + " },\n", + " \"provenance\": {\n", + " \"commandLine\": \"ice_driver.py -p demo_param_file.py --test_data_set E3SM-1-0 CanESM5 MIROC6 --realization * --case_id ex3\",\n", + " \"conda\": {\n", + " \"Platform\": \"linux-64\",\n", + " \"PythonVersion\": \"3.8.15.final.0\",\n", + " \"Version\": \"23.1.0\",\n", + " \"buildVersion\": \"not installed\"\n", + " },\n", + " \"date\": \"2024-01-25 11:52:17\",\n", + " \"openGL\": {\n", + " \"GLX\": {\n", + " \"client\": {},\n", + " \"server\": {}\n", + " }\n", + " },\n", + " \"osAccess\": false,\n", + " \"packages\": {\n", + " \"PMP\": \"v3.0.2-11-g06b151f\",\n", + " \"PMPObs\": \"See 'References' key below, for detailed obs provenance information.\",\n", + " \"blas\": \"0.3.24\",\n", + " \"cdat_info\": \"8.2.1\",\n", + " \"cdms\": \"3.1.5\",\n", + " \"cdp\": \"1.7.0\",\n", + " \"cdtime\": \"3.1.4\",\n", + " \"cdutil\": \"8.2.1\",\n", + " \"clapack\": null,\n", + " \"esmf\": \"0.8.2\",\n", + " \"esmpy\": \"8.4.2\",\n", + " \"genutil\": \"8.2.1\",\n", + " \"lapack\": \"3.9.0\",\n", + " \"matplotlib\": null,\n", + " \"mesalib\": null,\n", + " \"numpy\": \"1.22.4\",\n", + " \"python\": \"3.10.13\",\n", + " \"scipy\": \"1.11.3\",\n", + " \"uvcdat\": null,\n", + " \"vcs\": null,\n", + " \"vtk\": null,\n", + " \"xarray\": \"2023.10.1\",\n", + " \"xcdat\": \"0.5.0\"\n", + " },\n", + " \"platform\": {\n", + " \"Name\": \"gates.llnl.gov\",\n", + " \"OS\": \"Linux\",\n", + " \"Version\": \"3.10.0-1160.71.1.el7.x86_64\"\n", + " },\n", + " \"userId\": \"ordonez4\"\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "with open(\"sea_ice_demo/ex3/sea_ice_metrics.json\") as f:\n", + " print(f.read())" + ] + }, + { + "cell_type": "markdown", + "id": "f48b3856", + "metadata": {}, + "source": [ + "Now the resulting bar chart shows three different models with their spread." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "41aa14a3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = Image(\"sea_ice_demo/ex3/MSE_bar_chart.png\")\n", + "display_png(a)" + ] + }, + { + "cell_type": "markdown", + "id": "bc43281a", + "metadata": {}, + "source": [ + "# Further exploration" + ] + }, + { + "cell_type": "markdown", + "id": "19abc98d", + "metadata": {}, + "source": [ + "Maybe you want to compare more models, or take a closer look at the model data? Here are links to the data for further exploration.\n", + "\n", + "As a reminder, data for nine models is available here:\n", + "```\n", + "/p/user_pub/pmp/demo/sea-ice/links_siconc \n", + "/p/user_pub/pmp/demo/sea-ice/links_area\n", + "```\n", + "\n", + "The observational time series can be found at:\n", + "```\n", + "/p/user_pub/pmp/demo/sea-ice/EUMETSAT\n", + "```\n", + "\n", + "For some example plotting code using xcdat and matplotlib, see the scripts that were used to generate the introductory figures:\n", + "\n", + "```\n", + "create_sector_plots.py\n", + "make_demo_sea_ice_plots.py\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1161f29", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:pmp_si] *", + "language": "python", + "name": "conda-env-pmp_si-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 42401ee412668753ec1937cb78274cd53aaf77bd Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 12:06:57 -0800 Subject: [PATCH 49/69] add sea ice --- setup.py | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.py b/setup.py index 5755f7372..63e0d45f0 100644 --- a/setup.py +++ b/setup.py @@ -40,6 +40,7 @@ "pcmdi_metrics/precip_distribution/precip_distribution_driver.py", "pcmdi_metrics/cloud_feedback/cloud_feedback_driver.py", "pcmdi_metrics/extremes/extremes_driver.py", + "pcmdi_metrics/sea_ice/sea_ice_driver.py", ] entry_points = { From 68be632796876956b8e88172350d1a3ce73cb79c Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 12:39:51 -0800 Subject: [PATCH 50/69] make lib folder --- pcmdi_metrics/sea_ice/lib/__init.py__ | 1 + pcmdi_metrics/sea_ice/lib/sea_ice_parser.py | 212 ++++++++++++++++++++ 2 files changed, 213 insertions(+) create mode 100644 pcmdi_metrics/sea_ice/lib/__init.py__ create mode 100644 pcmdi_metrics/sea_ice/lib/sea_ice_parser.py diff --git a/pcmdi_metrics/sea_ice/lib/__init.py__ b/pcmdi_metrics/sea_ice/lib/__init.py__ new file mode 100644 index 000000000..06f0911d4 --- /dev/null +++ b/pcmdi_metrics/sea_ice/lib/__init.py__ @@ -0,0 +1 @@ +from .sea_ice_parser import create_sea_ice_parser diff --git a/pcmdi_metrics/sea_ice/lib/sea_ice_parser.py b/pcmdi_metrics/sea_ice/lib/sea_ice_parser.py new file mode 100644 index 000000000..785227abc --- /dev/null +++ b/pcmdi_metrics/sea_ice/lib/sea_ice_parser.py @@ -0,0 +1,212 @@ +#!/usr/bin/env python +from pcmdi_metrics.mean_climate.lib import pmp_parser + + +def create_sea_ice_parser(): + parser = pmp_parser.PMPMetricsParser() + parser.add_argument( + "--case_id", + dest="case_id", + help="Defines a subdirectory to the metrics output, so multiple" + + "cases can be compared", + required=False, + ) + + parser.add_argument( + "-v", + "--var", + type=str, + dest="var", + help="Name of model sea ice concentration variable", + required=False, + ) + + parser.add_argument( + "--obs_var", + type=str, + dest="obs_var", + help="Name of obs sea ice concentration variable", + required=False, + ) + + parser.add_argument( + "--area_var", + type=str, + dest="area_var", + help="Name of model area variable", + required=False, + ) + + parser.add_argument( + "--obs_area_var", + type=str, + dest="obs_area_var", + help="Name of reference data area variable", + required=False, + default=None, + ) + + parser.add_argument( + "-r", + "--reference_data_set", + default=None, + type=str, + nargs="+", + dest="reference_data_set", + help="List of observations or models that are used as a " + + "reference against the test_data_set", + required=False, + ) + + parser.add_argument( + "--reference_data_path", + default=None, + dest="reference_data_path", + help="Path for the reference climitologies", + required=False, + ) + + parser.add_argument( + "-t", + "--test_data_set", + type=str, + nargs="+", + dest="test_data_set", + help="List of observations or models to test " + + "against the reference_data_set", + required=False, + ) + + parser.add_argument( + "--test_data_path", + dest="test_data_path", + help="Path for the test climitologies", + required=False, + ) + + parser.add_argument( + "--realization", + dest="realization", + help="A simulation parameter", + required=False, + ) + + parser.add_argument( + "--filename_template", + dest="filename_template", + help="Template for climatology files", + required=False, + ) + + parser.add_argument( + "--metrics_output_path", + dest="metrics_output_path", + default=None, + help="Directory of where to put the results", + required=False, + ) + + parser.add_argument( + "--filename_output_template", + dest="filename_output_template", + help="Filename for the interpolated test climatologies", + required=False, + ) + + parser.add_argument( + "--area_template", + dest="area_template", + help="Filename template for model grid area", + required=False, + ) + + parser.add_argument( + "--obs_area_template_nh", + dest="obs_area_template_nh", + help="Filename template for obs grid area in Northern Hemisphere", + required=False, + default=None, + ) + + parser.add_argument( + "--obs_area_template_sh", + dest="obs_area_template_sh", + help="Filename template for obs grid area in Southern Hemisphere", + required=False, + default=None, + ) + + parser.add_argument( + "--obs_cell_area", + dest="obs_cell_area", + help="For equal area grids, the cell area in km", + required=False, + default=None, + ) + + parser.add_argument( + "--output_json_template", + help="Filename template for results json files", + required=False, + ) + + parser.add_argument( + "--debug", + dest="debug", + action="store_true", + help="Turn on debugging mode by printing more information to track progress", + required=False, + ) + parser.add_argument( + "--plots", + action="store_true", + help="Set to True to generate figures.", + required=False, + ) + parser.add_argument( + "--osyear", dest="osyear", type=int, help="Start year for reference data set" + ) + parser.add_argument( + "--msyear", dest="msyear", type=int, help="Start year for model data set" + ) + parser.add_argument( + "--oeyear", dest="oeyear", type=int, help="End year for reference data set" + ) + parser.add_argument( + "--meyear", dest="meyear", type=int, help="End year for model data set" + ) + parser.add_argument( + "--ObsUnitsAdjust", + type=tuple, + default=(False, 0, 0), + help="Factor to convert obs sea ice concentration to decimal. For example:\n" + "- (True, 'divide', 100.0) # percentage to decimal\n" + "- (False, 0, 0) # No adjustment (default)", + ) + parser.add_argument( + "--ModUnitsAdjust", + type=tuple, + default=(False, 0, 0), + help="Factor to convert model sea ice concentration to decimal. For example:\n" + "- (True, 'divide', 100.0) # percentage to decimal\n" + "- (False, 0, 0) # No adjustment (default)", + ) + parser.add_argument( + "--AreaUnitsAdjust", + type=tuple, + default=(False, 0, 0), + help="Factor to convert area data to km^2. For example:\n" + "- (True, 'multiply', 1e-6) # m^2 to km^2\n" + "- (False, 0, 0) # No adjustment (default)", + ) + + parser.add_argument( + "--ObsAreaUnitsAdjust", + type=tuple, + default=(False, 0, 0), + help="Factor to convert area data to km^2. For example:\n" + "- (True, 'multiply', 1e-6) # m^2 to km^2\n" + "- (False, 0, 0) # No adjustment (default)", + ) + + return parser From d7193d43c2982ef2c3a069be51bdbc2411108734 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 12:43:37 -0800 Subject: [PATCH 51/69] remove import --- pcmdi_metrics/sea_ice/__init__.py | 1 - 1 file changed, 1 deletion(-) diff --git a/pcmdi_metrics/sea_ice/__init__.py b/pcmdi_metrics/sea_ice/__init__.py index 430da4d9e..e69de29bb 100644 --- a/pcmdi_metrics/sea_ice/__init__.py +++ b/pcmdi_metrics/sea_ice/__init__.py @@ -1 +0,0 @@ -import sector_mask_defs From 15875adef4c8e517a8a2dfb5eac6355d6f37aea0 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 12:44:34 -0800 Subject: [PATCH 52/69] add shebang --- pcmdi_metrics/sea_ice/sea_ice_driver.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/pcmdi_metrics/sea_ice/sea_ice_driver.py b/pcmdi_metrics/sea_ice/sea_ice_driver.py index 4de860e95..85306087c 100644 --- a/pcmdi_metrics/sea_ice/sea_ice_driver.py +++ b/pcmdi_metrics/sea_ice/sea_ice_driver.py @@ -1,3 +1,5 @@ +#!/usr/bin/env python + import datetime import glob import json @@ -9,10 +11,10 @@ import numpy as np import xarray as xr import xcdat as xc -from sea_ice_parser import create_sea_ice_parser from pcmdi_metrics.io import xcdat_openxml from pcmdi_metrics.io.base import Base +from pcmdi_metrics.sea_ice.lib import create_sea_ice_parser from pcmdi_metrics.utils import create_land_sea_mask From ebafc2c78d2a6c0095e58c6a795e4cf14cbf7012 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 13:00:58 -0800 Subject: [PATCH 53/69] clean up --- pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb | 2697 ----------------- pcmdi_metrics/sea_ice/create_sector_plots.py | 156 - .../sea_ice/generate_sector_masks.py | 220 -- .../sea_ice/ice_area_cmip5_ssmi_reg_rms.py | 1136 ------- pcmdi_metrics/sea_ice/ice_driver.py | 911 ------ pcmdi_metrics/sea_ice/sea_ice_parser.py | 212 -- pcmdi_metrics/sea_ice/sector_mask_defs.py | 78 - 7 files changed, 5410 deletions(-) delete mode 100644 pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb delete mode 100644 pcmdi_metrics/sea_ice/create_sector_plots.py delete mode 100644 pcmdi_metrics/sea_ice/generate_sector_masks.py delete mode 100755 pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py delete mode 100644 pcmdi_metrics/sea_ice/ice_driver.py delete mode 100644 pcmdi_metrics/sea_ice/sea_ice_parser.py delete mode 100644 pcmdi_metrics/sea_ice/sector_mask_defs.py diff --git a/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb b/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb deleted file mode 100644 index 33a348fa0..000000000 --- a/pcmdi_metrics/sea_ice/Sea_ice_demo.ipynb +++ /dev/null @@ -1,2697 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "acb8d42e", - "metadata": {}, - "source": [ - "# Sea Ice Demo" - ] - }, - { - "cell_type": "markdown", - "id": "848c69e5", - "metadata": {}, - "source": [ - "**Summary** \n", - "The PCMDI Metrics sea ice driver produces metrics that compare modeled and observed sea ice extent. This notebook demonstrates how to run the PCMDI Metrics sea ice code.\n", - "\n", - "**Demo author list** \n", - "Ana Ordonez, Jiwoo Lee, Paul Durack, Peter Gleckler\n", - "\n", - "**Reference** \n", - "Ivanova, D. P., P. J. Gleckler, K. E. Taylor, P. J. Durack, and K. D. Marvel, 2016: Moving beyond the Total Sea Ice Extent in Gauging Model Biases. J. Climate, 29, 8965–8987, https://doi.org/10.1175/JCLI-D-16-0026.1. " - ] - }, - { - "cell_type": "markdown", - "id": "6bfd3b73", - "metadata": {}, - "source": [ - "## Demo data\n", - "This demo uses three CMIP6 models. The 'siconc' and 'areacello' variables are needed and can be found in the following directories. In addition, six other models are available that can be added to the analyses in this demo:\n", - "```\n", - "/p/user_pub/pmp/demo/sea-ice/links_siconc \n", - "/p/user_pub/pmp/demo/sea-ice/links_area\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "00d48042", - "metadata": {}, - "source": [ - "The observation dataset provided is a satellite derived sea ice concentration dataset from EUMETSAT OSI SAF. More information about this data can be found at the [osi-450-a product page](https://osi-saf.eumetsat.int/products/osi-450-a). The path to this data is:\n", - "```\n", - "/p/user_pub/pmp/demo/sea-ice/EUMETSAT\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "0b854017", - "metadata": {}, - "source": [ - "## Sectors\n", - "This code block produces maps that show the different regions used in the analysis along with the mean observed sea ice concentration. The code to generate these figures can be found in the script `create_sector_plots.py`." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "b6d75e4e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating Arctic map\n", - "Creating Antarctic map\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[WARNING] yaksa: 10 leaked handle pool objects\n" - ] - } - ], - "source": [ - "%%bash\n", - "python create_sector_plots.py" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a82ee330", - "metadata": {}, - "outputs": [], - "source": [ - "# To open and display one of the graphics\n", - "from IPython.display import display_png, JSON, Image" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "6a7eb6da", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "a = Image(\"Arctic_regions.png\")\n", - "b = Image(\"Antarctic_regions.png\")\n", - "display_png(a,b)" - ] - }, - { - "cell_type": "markdown", - "id": "5294910f", - "metadata": {}, - "source": [ - "## Basic example" - ] - }, - { - "cell_type": "markdown", - "id": "f316897b", - "metadata": {}, - "source": [ - "This first case will work with sea ice concentration ouput from a single model, E3SM-1-0. Two overview plots are shown below to visualize the Arctic sea ice in this model.\n", - "\n", - "For this demo, we start the OSI-SAF satellite data in 1988 as that avoids missing data in earlier parts of the record.\n", - "\n", - "The code to generate these figures can be found in `make_demo_sea_ice_plots.py`." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a6cb929f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-01-25 11:37:13,752 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" - ] - } - ], - "source": [ - "%%bash\n", - "python make_demo_sea_ice_plots.py" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "3120f819", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "a = Image(\"E3SM_arctic_tseries.png\")\n", - "b = Image(\"E3SM_arctic_clim.png\")\n", - "display_png(a,b)" - ] - }, - { - "cell_type": "markdown", - "id": "2540cd5d", - "metadata": {}, - "source": [ - "The PMP drivers can all read user arguments from parameter files. We provide a demo parameter file, which is shown below. Comments (beginning with a '#') explain each of the parameters." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "6e4fa38d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# Sea ice metrics parameter file\n", - "\n", - "# List of models to include in analysis\n", - "test_data_set = [\n", - " \"E3SM-1-0\",\n", - "]\n", - "\n", - "# realization can be a single realization, a list of realizations, or \"*\" for all realizations\n", - "realization = \"r1i2p2f1\"\n", - "\n", - "# test_data_path is a template for the model data parent directory\n", - "test_data_path = \"/p/user_pub/pmp/demo/sea-ice/links_siconc/%(model)/historical/%(realization)/siconc/\"\n", - "\n", - "# filename_template is a template for the model data file name\n", - "# combine it with test_data_path to get complete data path\n", - "filename_template = \"siconc_SImon_%(model)_historical_%(realization)_*_*.nc\"\n", - "\n", - "# The name of the sea ice variable in the model data\n", - "var = \"siconc\"\n", - "\n", - "# Start and end years for model data\n", - "msyear = 1981\n", - "meyear = 2010\n", - "\n", - "# Factor for adjusting model data to decimal rather than percent units\n", - "ModUnitsAdjust = (True, \"multiply\", 1e-2)\n", - "\n", - "# Template for the grid area file\n", - "area_template = \"/p/user_pub/pmp/demo/sea-ice/links_area/%(model)/*.nc\"\n", - "\n", - "# Area variable name; likely 'areacello' or 'areacella' for CMIP6\n", - "area_var = \"areacello\"\n", - "\n", - "# Factor to convert area units to km-2\n", - "AreaUnitsAdjust = (True, \"multiply\", 1e-6)\n", - "\n", - "# Directory for writing outputs\n", - "case_id = \"ex1\"\n", - "metrics_output_path = \"sea_ice_demo/%(case_id)/\"\n", - "\n", - "# Settings for the observational data\n", - "reference_data_path_nh = \"/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc\"\n", - "reference_data_path_sh = \"/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_sh_ease2-250_cdr-v3p0_198801-202012.nc\"\n", - "ObsUnitsAdjust=(True,\"multiply\",1e-2)\n", - "reference_data_set=\"OSI-SAF\"\n", - "osyear=1988\n", - "oeyear=2020\n", - "obs_var=\"ice_conc\"\n", - "ObsAreaUnitsAdjust = (False, 0, 0)\n", - "obs_area_template = None\n", - "obs_area_var = None\n", - "obs_cell_area = 625 #km 2\n" - ] - } - ], - "source": [ - "with open(\"demo_param_file.py\") as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "markdown", - "id": "38dbe853", - "metadata": {}, - "source": [ - "To see all of the parameters available for the sea ice metrics, run the --help command as shown here:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9d6c1fbf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "usage: ice_driver.py [-h] [--parameters PARAMETERS]\n", - " [--diags OTHER_PARAMETERS [OTHER_PARAMETERS ...]]\n", - " [--case_id CASE_ID] [-v VAR] [--obs_var OBS_VAR]\n", - " [--area_var AREA_VAR] [--obs_area_var OBS_AREA_VAR]\n", - " [-r REFERENCE_DATA_SET [REFERENCE_DATA_SET ...]]\n", - " [--reference_data_path REFERENCE_DATA_PATH]\n", - " [-t TEST_DATA_SET [TEST_DATA_SET ...]]\n", - " [--test_data_path TEST_DATA_PATH]\n", - " [--realization REALIZATION]\n", - " [--filename_template FILENAME_TEMPLATE]\n", - " [--metrics_output_path METRICS_OUTPUT_PATH]\n", - " [--filename_output_template FILENAME_OUTPUT_TEMPLATE]\n", - " [--area_template AREA_TEMPLATE]\n", - " [--obs_area_template_nh OBS_AREA_TEMPLATE_NH]\n", - " [--obs_area_template_sh OBS_AREA_TEMPLATE_SH]\n", - " [--obs_cell_area OBS_CELL_AREA]\n", - " [--output_json_template OUTPUT_JSON_TEMPLATE] [--debug]\n", - " [--plots] [--osyear OSYEAR] [--msyear MSYEAR]\n", - " [--oeyear OEYEAR] [--meyear MEYEAR]\n", - " [--ObsUnitsAdjust OBSUNITSADJUST]\n", - " [--ModUnitsAdjust MODUNITSADJUST]\n", - " [--AreaUnitsAdjust AREAUNITSADJUST]\n", - " [--ObsAreaUnitsAdjust OBSAREAUNITSADJUST]\n", - "\n", - "options:\n", - " -h, --help show this help message and exit\n", - " --parameters PARAMETERS, -p PARAMETERS\n", - " --diags OTHER_PARAMETERS [OTHER_PARAMETERS ...], -d OTHER_PARAMETERS [OTHER_PARAMETERS ...]\n", - " Path to other user-defined parameter file. (default:\n", - " None)\n", - " --case_id CASE_ID Defines a subdirectory to the metrics output, so\n", - " multiplecases can be compared (default: None)\n", - " -v VAR, --var VAR Name of model sea ice concentration variable (default:\n", - " None)\n", - " --obs_var OBS_VAR Name of obs sea ice concentration variable (default:\n", - " None)\n", - " --area_var AREA_VAR Name of model area variable (default: None)\n", - " --obs_area_var OBS_AREA_VAR\n", - " Name of reference data area variable (default: None)\n", - " -r REFERENCE_DATA_SET [REFERENCE_DATA_SET ...], --reference_data_set REFERENCE_DATA_SET [REFERENCE_DATA_SET ...]\n", - " List of observations or models that are used as a\n", - " reference against the test_data_set (default: None)\n", - " --reference_data_path REFERENCE_DATA_PATH\n", - " Path for the reference climitologies (default: None)\n", - " -t TEST_DATA_SET [TEST_DATA_SET ...], --test_data_set TEST_DATA_SET [TEST_DATA_SET ...]\n", - " List of observations or models to test against the\n", - " reference_data_set (default: None)\n", - " --test_data_path TEST_DATA_PATH\n", - " Path for the test climitologies (default: None)\n", - " --realization REALIZATION\n", - " A simulation parameter (default: None)\n", - " --filename_template FILENAME_TEMPLATE\n", - " Template for climatology files (default: None)\n", - " --metrics_output_path METRICS_OUTPUT_PATH\n", - " Directory of where to put the results (default: None)\n", - " --filename_output_template FILENAME_OUTPUT_TEMPLATE\n", - " Filename for the interpolated test climatologies\n", - " (default: None)\n", - " --area_template AREA_TEMPLATE\n", - " Filename template for model grid area (default: None)\n", - " --obs_area_template_nh OBS_AREA_TEMPLATE_NH\n", - " Filename template for obs grid area in Northern\n", - " Hemisphere (default: None)\n", - " --obs_area_template_sh OBS_AREA_TEMPLATE_SH\n", - " Filename template for obs grid area in Southern\n", - " Hemisphere (default: None)\n", - " --obs_cell_area OBS_CELL_AREA\n", - " For equal area grids, the cell area in km (default:\n", - " None)\n", - " --output_json_template OUTPUT_JSON_TEMPLATE\n", - " Filename template for results json files (default:\n", - " None)\n", - " --debug Turn on debugging mode by printing more information to\n", - " track progress (default: False)\n", - " --plots Set to True to generate figures. (default: False)\n", - " --osyear OSYEAR Start year for reference data set (default: None)\n", - " --msyear MSYEAR Start year for model data set (default: None)\n", - " --oeyear OEYEAR End year for reference data set (default: None)\n", - " --meyear MEYEAR End year for model data set (default: None)\n", - " --ObsUnitsAdjust OBSUNITSADJUST\n", - " Factor to convert obs sea ice concentration to\n", - " decimal. For example: - (True, 'divide', 100.0) #\n", - " percentage to decimal - (False, 0, 0) # No adjustment\n", - " (default) (default: (False, 0, 0))\n", - " --ModUnitsAdjust MODUNITSADJUST\n", - " Factor to convert model sea ice concentration to\n", - " decimal. For example: - (True, 'divide', 100.0) #\n", - " percentage to decimal - (False, 0, 0) # No adjustment\n", - " (default) (default: (False, 0, 0))\n", - " --AreaUnitsAdjust AREAUNITSADJUST\n", - " Factor to convert area data to km^2. For example: -\n", - " (True, 'multiply', 1e-6) # m^2 to km^2 - (False, 0, 0)\n", - " # No adjustment (default) (default: (False, 0, 0))\n", - " --ObsAreaUnitsAdjust OBSAREAUNITSADJUST\n", - " Factor to convert area data to km^2. For example: -\n", - " (True, 'multiply', 1e-6) # m^2 to km^2 - (False, 0, 0)\n", - " # No adjustment (default) (default: (False, 0, 0))\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[WARNING] yaksa: 10 leaked handle pool objects\n" - ] - } - ], - "source": [ - "%%bash\n", - "python ice_driver.py --help" - ] - }, - { - "cell_type": "markdown", - "id": "9bfa9c97", - "metadata": {}, - "source": [ - "The PMP drivers are run on the command line. In this Jupyter Notebook, we use the bash cell magic function %%bash to run command line functions from the notebook.\n", - "\n", - "The PMP sea ice metrics driver call follows the basic format:\n", - "ice_driver.py -p parameter_file.py --additional arguments\n", - "\n", - "The following cell runs the driver with the demo parameter file we saw above." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "d6ff0052", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-01-25 11:38:28,347 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "INFO::2024-01-25 11:39::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n", - "2024-01-25 11:39:27,529 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['E3SM-1-0']\n", - "Find all realizations: False\n", - "OBS: Arctic\n", - "Converting units by multiply 0.01\n", - "OBS: Antarctic\n", - "Converting units by multiply 0.01\n", - "Model list: ['E3SM-1-0']\n", - "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n", - "Converting units by multiply 1e-06\n", - "\n", - "-----------------------\n", - "model, run, variable: E3SM-1-0 r1i2p2f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_185001-185912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_186001-186912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_187001-187912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_188001-188912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_189001-189912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_190001-190912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_191001-191912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_192001-192912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_193001-193912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_194001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_195001-195912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_196001-196912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_197001-197912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_198001-198912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_199001-199912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_200001-200912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_201001-201112.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-------------------------------------------\n", - "Calculating model regional average metrics \n", - "for E3SM-1-0\n", - "--------------------------------------------\n", - "arctic\n", - "ca\n", - "na\n", - "np\n", - "antarctic\n", - "sp\n", - "sa\n", - "io\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[WARNING] yaksa: 10 leaked handle pool objects\n" - ] - } - ], - "source": [ - "%%bash\n", - "python ice_driver.py -p demo_param_file.py" - ] - }, - { - "cell_type": "markdown", - "id": "084440aa", - "metadata": {}, - "source": [ - "One of the primary outputs of the PMP is a JSON file containing the metrics values. In this case, the metrics are the mean square errors of the time mean and monthly mean ice extent. Ice extent is defined as the total area covered by sea ice concentration of >= 15%. The metrics are organized by model, realization, and reference dataset.\n", - "\n", - "The metrics JSON from this run is displayed below." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "9a46fb89", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"DIMENSIONS\": {\n", - " \"index\": {\n", - " \"monthly_clim\": \"Monthly climatology of extent\",\n", - " \"total_extent\": \"Sum of ice coverage where concentration > 15%\"\n", - " },\n", - " \"json_structure\": [\n", - " \"model\",\n", - " \"realization\",\n", - " \"obs\",\n", - " \"region\",\n", - " \"index\",\n", - " \"statistic\"\n", - " ],\n", - " \"model\": [\n", - " \"E3SM-1-0\"\n", - " ],\n", - " \"region\": {},\n", - " \"statistic\": {\n", - " \"mse\": \"Mean Square Error (10^12 km^4)\"\n", - " }\n", - " },\n", - " \"RESULTS\": {\n", - " \"E3SM-1-0\": {\n", - " \"antarctic\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.4635192339671928\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.139646926848\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.4635192339671928\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.139646926848\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"arctic\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"5.476181000101471\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"3.628078727168\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"5.476181000101471\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"3.628078727168\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"ca\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.05045644169895609\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.007755424768\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.05045644169895609\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.007755424768\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"io\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.04955696515353039\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.00991997952\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.04955696515353039\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.00991997952\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"na\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"2.3482121752568643\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.576847409152\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"2.3482121752568643\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.576847409152\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"np\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.6264518797177615\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.287947685888\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.6264518797177615\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.287947685888\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"sa\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.3797729615722766\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.297013608448\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.3797729615722766\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.297013608448\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"sp\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.6767107661262813\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.078223351808\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.6767107661262813\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.078223351808\"\n", - " }\n", - " }\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"json_structure\": [\n", - " \"model\",\n", - " \"realization\",\n", - " \"obs\",\n", - " \"region\",\n", - " \"index\",\n", - " \"statistic\"\n", - " ],\n", - " \"json_version\": 3.0,\n", - " \"model_year_range\": {\n", - " \"E3SM-1-0\": [\n", - " \"1981\",\n", - " \"2010\"\n", - " ]\n", - " },\n", - " \"provenance\": {\n", - " \"commandLine\": \"ice_driver.py -p demo_param_file.py\",\n", - " \"conda\": {\n", - " \"Platform\": \"linux-64\",\n", - " \"PythonVersion\": \"3.8.15.final.0\",\n", - " \"Version\": \"23.1.0\",\n", - " \"buildVersion\": \"not installed\"\n", - " },\n", - " \"date\": \"2024-01-25 11:39:13\",\n", - " \"openGL\": {\n", - " \"GLX\": {\n", - " \"client\": {},\n", - " \"server\": {}\n", - " }\n", - " },\n", - " \"osAccess\": false,\n", - " \"packages\": {\n", - " \"PMP\": \"v3.0.2-11-g06b151f\",\n", - " \"PMPObs\": \"See 'References' key below, for detailed obs provenance information.\",\n", - " \"blas\": \"0.3.24\",\n", - " \"cdat_info\": \"8.2.1\",\n", - " \"cdms\": \"3.1.5\",\n", - " \"cdp\": \"1.7.0\",\n", - " \"cdtime\": \"3.1.4\",\n", - " \"cdutil\": \"8.2.1\",\n", - " \"clapack\": null,\n", - " \"esmf\": \"0.8.2\",\n", - " \"esmpy\": \"8.4.2\",\n", - " \"genutil\": \"8.2.1\",\n", - " \"lapack\": \"3.9.0\",\n", - " \"matplotlib\": null,\n", - " \"mesalib\": null,\n", - " \"numpy\": \"1.22.4\",\n", - " \"python\": \"3.10.13\",\n", - " \"scipy\": \"1.11.3\",\n", - " \"uvcdat\": null,\n", - " \"vcs\": null,\n", - " \"vtk\": null,\n", - " \"xarray\": \"2023.10.1\",\n", - " \"xcdat\": \"0.5.0\"\n", - " },\n", - " \"platform\": {\n", - " \"Name\": \"gates.llnl.gov\",\n", - " \"OS\": \"Linux\",\n", - " \"Version\": \"3.10.0-1160.71.1.el7.x86_64\"\n", - " },\n", - " \"userId\": \"ordonez4\"\n", - " }\n", - "}\n" - ] - } - ], - "source": [ - "with open(\"sea_ice_demo/ex1/sea_ice_metrics.json\") as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "markdown", - "id": "d74b6752", - "metadata": {}, - "source": [ - "This driver also outputs a bar chart that visualizes the mean square error between the model and observations. Since there is only one model and one realization in this instance, the bar chart looks very simple. The red bar indicates the mean square error for the time mean ice extent, and the blue bar indicates the mean square error for the climatological ice extent." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c6dfa7a6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sea_ice_demo/ex1/MSE_bar_chart.png\r\n" - ] - } - ], - "source": [ - "!ls {\"sea_ice_demo/ex1/MSE_bar_chart.png\"}" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d14e933a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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zzTdd2vnyyy9RWVnpsi6aMmUKiAgrV66stly9Xl/t7z81NbUhHwu7TjzKvREJgoDHHnsMf//732E2m9GpU6cajxkCwJkzZ1BeXg4vL68aXy8pKQFgX7H2798f3t7eeO2119CpUyf4+PjAZDLhgQcewOXLl13e5+PjA29vb5fndDrdVVcczvR6Pe6//37cf//9AIDCwkKMHDkSH3zwAWbPno0uXbqgtLQU7du3r/betm3b1mkZNfn6668xYcIEPPjgg3j22WfRtm1baDQafPTRR/j444+rzX/l8cPS0lKIooj3338f77//fo3LcHymV+Pn51fr6N9r9eFqz9d2LDU0NFR5vS5t17UvZ86cAQA888wzeOaZZ2p8z9U+j4cffhhbtmzBwoUL0aNHDxgMBgiCgISEhGp/c/UxZcoUrFu3Dj/88AOGDx+OtLQ0WCwWlyJb1/8b9VVaWgqNRoOgoCCX5wVBQNu2bat9B84iIiIAAEaj8arLOHHiRLUfbSqVCgMGDMCAAQMA2E85mzp1Kr744gt8/PHHePzxx2tsa8iQIS6noE2ePFkZnwDY/1YfeughZfzOb7/9hnvvvRcvvvgipk+fDn9/fwD2Ee3e3t4YMWKEMlYgNjYWUVFRWLVqFZKTk6FWq136W9f/A+zG4oLeyJKSkvDSSy9h+fLleP3112udr3Xr1ggMDMTmzZtrfN1xmltmZiZOnTqFbdu2uZyKUpdBPO4QERGBGTNm4KmnnsJvv/2GLl26IDAwEKdPn642b03PeXt71zggr6SkBK1bt1Yer1mzBu3bt8cXX3zhsoVT22A+53kAoFWrVlCr1XjkkUfwxBNP1Piemn6ENMSVfbja84GBgSguLq72/KlTpwDA5bO4Wtt17Yujveeff95lsJmzzp071/h8RUUFNm7ciEWLFrmcVmWxWFBWVlavfl1p+PDhCA0NxcqVKzF8+HCsXLkSvXr1wh133OHS97r836ivwMBAiKKIc+fOuRR1IsLp06fRo0ePWt8bEhKCLl26ICMjA5cuXYKPj0+1eX7++WecOXNGGShYmxYtWuD555/HF198gby8vFrnW7FiBaqqqpTHV/6NXKlLly6YOHEi3n33XRw+fBg9e/bE4cOHsWPHDgD//6PkSt9//z0SEhKu2jZrHFzQG1lYWBieffZZHDx4EJMnT651vvvuuw///ve/IUkSevXqVet8jhW1TqdzeX7FihXu6fDvqqqqIAgCWrZsWe01xy5Wx9bkoEGDkJKSgv3797vsdv/888+rvTcqKgq5ubkuzx0+fBiHDh1yWUEJggAvLy+XwnT69OkaR7nXxMfHB4MGDUJOTg5iY2Nr3bprLEOGDMH69etx6tQp5XMEgE8++QQ+Pj7o3bu3W5fXuXNnxMTEYP/+/Vi8eHG93isIAoio2t/cv/71L0iS5PKcY566brU7fnS9++672L59O/bs2VPtb7mu/zdq49wnvV6vPD9kyBCkpKRgzZo1+Mtf/qI8v27dOly8eBFDhgy5arsvvvgiHn74YTzzzDP48MMPXV67ePEinnzySfj4+Li0XVxcXOPeliv/T9Wkth9cpaWl8PX1rfFv/ODBgy7tOnaV//Of/0THjh1d5r18+TLGjh2Ljz/+mAt6E8UFvQm48gpQNZk4cSI+++wzJCQkYO7cuejZsye0Wi2KioqwdetWjB07FuPGjUOfPn3QqlUrzJo1C4sWLYJWq8Vnn32G/fv3u7XPhw4dwvDhwzFx4kQMHDgQISEhOH/+PDZt2oR//OMfiI+PR58+fQAATz31FD7++GOMGjUKr732GoKDg/HZZ58pKxNnjzzyCCZNmoTHH38c48ePx4kTJ5CSklJtt6fjVK/HH38ciYmJMJlMePXVVxESEoKCgoI65fDee++hX79+6N+/P2bPno2oqChUVVXhyJEj+O6772q8WMaVysvLazzWrtPpEBcXV6d+1GTRokXYuHEjBg0ahJdeegkBAQH47LPPsGnTJqSkpMDPz++6267NihUrMHLkSAwfPhxJSUkICwtDWVkZ8vPzsXfvXnz11Vc1vs9gMGDAgAF488030bp1a0RFReGnn35CamqqshvXoWvXrgCAf/zjH/D19YW3tzfat2+PwMDAWvs1ZcoUvPHGG3j44Yeh1+vxxz/+0eX1uv7fqM2dd94JAHjjjTcwcuRIqNVqxMbGYujQoRg+fDiee+45VFZWom/fvsjNzcWiRYsQFxeHRx555Kqf50MPPYS9e/firbfewvHjxzFlyhQEBwfj0KFDeOedd3D06FF8/vnn6NChg/KeLl26YMiQIRg5ciSio6NhNpuxe/duvP322wgODq5xbMa1bN26FXPnzsWf/vQn9OnTB4GBgTh79izS0tKwefNmPProo2jXrh1EUcQnn3yC22+/HdOmTauxrdGjR2PDhg3V9lqwJqJxx+TdepxHuV9NTReWsdls9NZbb1G3bt3I29ubWrZsSbfddhvNnDmTCgoKlPl27txJ99xzD/n4+FBQUBBNmzaN9u7dW+30o9ouCuEYlX0158+fp9dee40GDx5MYWFh5OXlRS1atKDu3bvTa6+9Vu1UoQMHDtDQoUPJ29ubAgICaOrUqfTtt99WG/EsyzKlpKRQhw4dyNvbm/7whz9QZmZmjaPcly5dSlFRUaTT6ej222+nf/7znzX2HQA98cQTNeZhNBppypQpFBYWRlqtloKCgqhPnz702muvXTV/oquPcg8LC6v2eTpO37qyjdpGKv/66680evRo8vPzIy8vL+rWrZvL90f0/yPOv/rqq2v215EvAHrzzTdrfH3//v00YcIEatOmDWm1Wmrbti0NHjyYli9fXm2Zzt9bUVERjR8/nlq1akW+vr40YsQIysvLq/GshXfffZfat29ParXa5W/yylHuzvr06UMA6E9/+lONr9f1/0ZNLBYLTZs2jYKCgkgQBAKgnIZ5+fJleu655ygyMpK0Wi2FhITQ7Nmz6fz581dt01l6ejolJCRQYGAgabVaCgsLo0ceeYR+++23avOuWLGCHnjgAerQoQP5+PiQl5cXRUdH06xZs8hkMrnMW9dR7iaTif76179S3759qW3btqTRaMjX15d69epF77//vnKK2TfffEMA6N133621LcfI/7fffpuI+MIyTY1ARHRTf0Ew9rtt27Zh0KBB2Lp1q0df+5wxxm4GPm2NMcYY8wBc0BljjDEPwLvcGWOMMQ/AW+iMMcaYB+CCzhhjjHkALuiMMcaYB+CCzhhjjHkALuiMMcaYB+CCzhhjjHkALuiMMcaYB2iSBb2goAB9+vRBp06d0LNnTxw4cKDaPMePH0d8fHy97kfNGGOMeaomWdBnzpyJGTNm4PDhw5g/f36NdxgyGAx47bXXarwFJ2OMMXaraXIF/ezZs9i7dy8mTZoEABg/fjyMRiOOHz/uMl9AQAD69euHFi1aNEIvGWOMsaalyd0P3WQyITQ0FBqNvWuCICAiIgKFhYWIioq67nYtFgssFovyWJZllJWVITAwEIIgNLTbjDHGmiEiQlVVFUJDQ6FSNblt3HppcgUdQLUC647LzS9ZsgTJyckNbocxxpjnMZlMaNeuXWN3o0GaXEEPDw9HUVERRFGERqMBEcFkMiEiIqJB7T7//PN4+umnlccVFRWIiIjA8ePH0apVK0iSBABQq9UusSiKEARBiVUqFVQqVa2xzWaDWq1WYo1GA0EQlBiAkpsj1mq1ICIllmUZkiQpsSzL0Gg0tcaSJIGIlLimPDgnzolz4pw8KSeLxYJffvkFvXv3VjYCryensrIytG/fHr6+vmjumlxBb9OmDeLi4rBmzRokJSVh3bp1iIqKatDudgDQ6XTQ6XTVnm/VqhUMBkOD2maMMXZzybKMbt26wd/f3y27yj3h0GuTvH3qoUOHkJSUhNLSUhgMBqxevRpdunTBtGnTMGbMGIwZMwYWiwXR0dGwWCyoqKhAmzZt8Mgjj2DJkiV1WkZlZSX8/PxQUVHBBZ0x5hEkSYLNZmvsbjRpWq0WarVaeexJtaBJFvSbwZO+RMYYu3DhAoqKitwy5qg5ICJYLBbodLp6bV0LgoB27dqhZcuWADyrFjS5Xe6MMcbqR5IkFBUVwcfHB0FBQR6x+/haHMffHcfr6/qec+fOoaioCDExMS5b6p6ACzpjjDVzNpsNRISgoCDo9frG7k6TFhQUhOPHjysD+TxJ8z7pjjHGmOJmb5n36dMHixcvdmubH330EQYMGIB+/frhwQcfxIULF2qcT5ZlVFRUQJZlAPbLgScmJl6zfU/ee8Fb6IwxxurNZDIhMjISW7ZswQsvvOCWNn/44Qf897//xdatW6FWq5GTkwOr1VrjvIIgoEWLFh5doOuLt9AZY8zDCELDp2tZu3YtJk2ahOjoaBw5cgQA8PLLL+NPf/oTRowYgQEDBuDSpUs4fvw4+vTpg/HjxyM2NhY//vhjrW2mpaXhueeeU3aFx8XFwdfXF/369VPm+eMf/4hjx47hl19+waBBgxAfH4+3337bpZ09e/Zg0KBB6N+/P956663r+ASbJy7ojDVz8fHxePfddxu1Dy1btsSvv/7aqH1gN9eWLVswbNgwPPTQQ/jqq6+U5zt37ozNmzejf//+SvEuLS3FF198gXXr1uHDDz+stc3i4mKEhoa6PKfVahEXF4c9e/agsrISZWVl6NChA/7yl79gxYoV2Lp1K/7yl7+4vOe5557D119/je3bt+O///0vzpw548bMmy4u6IxdxY4dOzBy5Ei0atUK/v7+6NatG1JSUmrdDVgfL7/8Mu6///6Gd7IOLl68CIPBgF69ejW4raioKHzzzTcuz124cAF33nlnvdsSRREvvPACoqKi0LJlS4SEhOC+++5DVVVVg/vZFH7oeKqioiLk5uZi9OjRWLJkCTZu3Ki8FhcXB8B+1c/z588DALp27QqNRuPyXE1CQ0Nx8uTJas8/+uijWLNmDdatW4fx48cDAKxWKzp37gxBEKpdWObXX3/FuHHjEB8fj2PHjsFkMjU45+aACzpjtdi4cSNGjhyJ4cOHo6CgAOXl5fjiiy9w4MABFBcX35Q+iKLolna+/PJLqNVq/PLLL8jLy7spy6yLpUuXIiMjA1u3bsWFCxewf/9+PPDAAzdt+VdzMz+H5mbt2rV47733sHnzZmRkZOC2225Tdrs7H9N2nBNf03M1eeihh5CSkqJcsnX//v0oKytDjx49kJubi3//+9+YMGECAPvVP8+ePQtBEJSBcQ7dunXDt99+i23btmHv3r24++673ZN4E8cFnbEaEBGefPJJPPfcc3jqqafQunVrAMBtt92GVatWITIyEgBw9OhRjB49GkFBQYiMjMRrr72mrFxWrVqF7t2749VXX0WbNm0QHBysbDF+8803WLx4MTZu3IiWLVsqF7lISkrC1KlTMWHCBBgMBnz00UfIyclBv379EBAQgKCgIDz00EMoLS2tVz6pqal47LHHMGDAAKSmprq8Fh8fj/nz52PYsGFo0aIF/vOf/6CyshJz5sxBREQEDAYDevToAZPJhAcffBCFhYV46KGH0LJlS8yaNQuAfYW9b98+pc20tDR069YNBoMBkZGRWLVqVY392rVrF8aOHYv27dsDsF/6ecqUKS7X1f73v/+N2NhY+Pv7o0ePHti5c6fymtVqxUsvvYTo6Gj4+vrizjvvxN69ezFv3jxs374dzz33HFq2bImRI0cCAM6cOYMJEyYgKCgIERERePHFF5XCvW3bNvj7++Ojjz5CREQE7rnnnnp9xreSdevWYeDAgcrjIUOGuOx2r4ulS5fCaDS6PHfvvfeib9++iI+PR//+/bF48WJ4eXkBAIYOHQofHx8EBAQAAN566y3cf//9GDRoULU9MUuXLsUDDzyAQYMGISEhAWaz+TqybIboFlVRUUEAqKKiorG7wpqgQ4cOEQA6cuRIrfNcunSJIiMj6W9/+xtZLBY6ceIEdenShf71r38REdHKlStJo9FQSkoKWa1W2rp1K6nVaqXNRYsW0dixY13anDx5Mun1etq8eTNJkkQXL16kffv20fbt28lqtdLp06epf//+NG3aNOU9AwcOpHfeeafWfh48eJAA0P79++njjz+mwMBAslgsLu8PCgqi3bt3kyzLdOnSJRo3bhwNHz6cTp48SZIk0d69e+ncuXNERBQZGUnr1693WQYAysnJISKiDRs2UEBAAG3ZsoUkSaIzZ87Q3r17a+zbkiVLKDg4mN555x365ZdfyGazuby+adMmCgsLo//9738kSRKtW7eOAgICqKSkhIiI/vKXv9Ddd99Nhw8fJlmW6eDBg3T8+PFaP5fBgwfTww8/TFVVVXT8+HG644476PXXXycioq1bt5JKpaKZM2fSxYsX6eLFi7V+pk3N5cuX6cCBA3T58mUiIgIaPjU1S5cupa+//lp5LMsySZJEsizXq50rPytPqgVN8Gu7OTzpS2Tut2PHDgKg/KevyZdffkndu3d3ee4f//gHDR48mIjsBT04ONjl9Y4dO9LatWuJqPaCfuVzV1q/fj117NhReXytgv7ss88q/aysrCQfHx/68ssvXd4/d+5c5fHp06cJAJ04caLG9q5V0EeMGEHJyclXzcFBkiT65z//SYMHD6YWLVqQn58fPffccySKIhERJSQk0Lvvvuvynj59+tAnn3xCsiyTj48P/fTTTzW2feXnUlRURACouLhYee6zzz6jmJgYIrIXdAB0/vz5OvW9KbmySHma5ORkGjZsmMsPPi7o1TXaLveCggL06dMHnTp1Qs+ePXHgwIEa50tNTUVMTAyio6MxY8YMl+Nab731Frp27Yru3bujd+/e+OWXX25W95mHc+xir2mAjsPx48eRl5cHf39/ZZo3bx5Onz6tzNO2bVuX97Ro0eKaA76uvFXwkSNHMHbsWISGhsJgMGDSpEkoKSmpUx6iKOKTTz7B5MmTAQC+vr4YN25ctd3uzss8ceIEdDrddd+y+MSJE4iJianTvCqVCtOmTcOWLVtQXl6Ozz//HMuXL1f6d/z4cbzwwgsun/G+fftw8uRJnDt3DpcuXarzsoqKiuDt7e3ynXTo0AFFRUXKY19fX/j7+9c9WXZTvPTSS/j++++V268C9sNilZWVt8y16+ui0Qr6zJkzMWPGDBw+fBjz58/H1KlTq81jNBqxcOFC7NixA0eOHMHp06eV/+j79+/H+++/j127dmHfvn2YM2cOnnjiiZudBvNQnTp1QlRUFP7973/XOk94eDjuvvtulJeXK1NlZSV+++23Oi2jtls+Xvn8rFmzEBYWhgMHDqCyshJr1qyp80ps48aNOHPmDF599VW0bdsWbdu2xYYNG/DDDz+gsLCwxmVGRkbCYrHUOjL4WreqjIyMVAZI1YdGo0FCQgKGDBminAIXHh6Ot99+2+UzvnjxIhYsWICgoCD4+PjUuqwr+9muXTuYzWaXU5iMRiPatWtX59xY0yEIAgwGA19Yxkmj/PWePXsWe/fuxaRJkwAA48ePh9FoxPHjx13mW7t2LcaNG4fg4GAIgoBZs2YhLS1Ned1ms+HixYsAgPLycpf/mIw1hCAIeP/997F06VK8//77yiC0w4cPY+rUqThx4gTuu+8+nDlzBh9++CHMZjMkScKhQ4ewbdu2Oi0jODgYJ06cUEb01qayshK+vr4wGAwwmUx4880365xHamoqxowZg99++w379u3Dvn37cPjwYXTs2LHWgWrBwcEYO3YsZs2aheLiYsiyjJycHOUzCA4OxtGjR2td5syZM/Hee+/hp59+gizLOHv2LHJycmqc95133sGPP/6ICxcugIjw3//+F9u2bUOfPn0AAHPmzMGbb76J//3vfyAiXLp0CT/++COKioogCAKmT5+OefPm4ciRIyAiHDp0CCdOnKixn2FhYRg0aBCeeeYZXLx4EYWFhVi8eLGy94Kx5q5RCrrJZEJoaKiy+0QQBERERLhsMQBAYWGhMpoYsJ//6pinW7duePrpp9G+fXu0a9cO77zzDt5///1al2mxWFBZWekyAVBWppIk1RiLougSO0Yw1xbbbDaX2LEl5YiJqFoMwCWWZdkldhxmqC2WJMkl5pzck9Pw4cORnp6OTZs2ITo6Gv7+/khMTERMTAxCQkLg4+OD//znP9iyZQuioqIQGBiIhx9+GKdOnXI5NOSch2M5kiThgQcegMFgQOvWrZXdvI5lO+f0t7/9DRs3boTBYMDYsWNdzl13Xs6VOZ08eRL/+c9/8OSTTyI4OBjBwcEIDAxEcHAw5syZg5UrV7r0x/l7Wr16Ndq1a4c//OEP8Pf3x6xZs5RDBQsWLMCyZcvQqlUrzJ49u9r3dP/99+Ott97CE088AT8/P/To0QP79++v8XvS6/V44YUXEBYWBn9/f0yfPh0vvvgi/vjHPwIAhg8fjsWLF2P69Olo1aoV2rdvj/feew8WiwVEhKVLlyI+Ph733nsvDAYDEhMTUVZWBiLCnDlz8OOPP8Lf3x+jRo0CAKxZswaXLl1CZGQk+vbti5EjR2L+/PmQZdnl76qx//au9/+T89/PzYxlWb5qTETVYkcbtcV1Wb5jl/v19N35u/EY13PgvaH27NlDd9xxh8tzf/jDH6oNbpkzZw6lpKQoj/Py8qh9+/ZERHT8+HHq378/nTp1ioiI3n//fRo4cGCty1y0aBEBqDZlZWUREdGvv/5Kv/76KxER7d27l/Lz84mIKDs7mwoKCoiI6L///a8ygvann36ioqIiIiLasmULnTlzhoiINm/eTKWlpUREtHHjRmWgxTfffEOXLl0iq9VK33zzDVmtVrp06RJ98803RGQfmLFx40YiIiotLaXNmzcTEdGZM2doy5YtRGQf1OP4jI4fP07//e9/iYiooKCAsrOziYgoPz9fGVHMOXFOnNOtkdPWrVvpwIEDVFFRQZWVlUREZDabqaqqiojsA8EuXLhARPazMxwj+C9dukSXLl0iIqKLFy8q8YULF5RBY1VVVWQ2m4nIPqjScYZERUUFWa1WIiIqLy9XBqydP39eGdR4/vx5kiSJJEmqFhMRiaKoxDabjcrLy4mIyGq1Kp+LxWJxa06XL1+mnJwc5WyT9PR0jxkU1ygF/cyZM2QwGJQ/AFmWKTg4mIxGo8t8KSkp9PjjjyuPN23apBTtN998k2bPnq28duHCBRIEQflDupLZbKaKigplMplMBIDKysqIyP6H5Xivc2yz2VxiSZKuGlutVpfYMQLTEcuyXC12fAaOWJIkl9jxOdUWi6LoEteUB+fEOXFOnptTVVUVHThwgC5duqS053j/jYzvueceev31111Gm9cUO49Id8SONpzj1NRU8vHxoaqqKpJlmXbv3k0AKDc3t8bl22w25bm69v3y5cv022+/KT8ASktLPaagN8ou9zZt2iAuLg5r1qwBYL9IQVRUFKKiolzmGz9+PNavX48zZ86AiLB8+XJMnDgRgH106o4dO5Rb63333Xe4/fbba72/rU6ng8FgcJkAKPOr1eoaY41G4xI7Bs3UFmu1WpfYMWDDEQuCUC0G4BKrVCqX2HFoorZYrVa7xJwT58Q53Zo5Od4DQYCgUkFQqa4//r1fSptXxEVFRcrd1lRO89cUOy7P6hw72rsyvuOOO7B582YIgoC1a9eiR48eNS6fiJSxF7X18Wqx83fjKRptSOeKFSuwYsUKdOrUCUuXLlVGr0+bNg0bNmwAYC/aycnJ6Nu3L6Kjo9GmTRtlNPy4ceMwatQo/OEPf0C3bt2wbNky5QcCY4yxG+tG3G0NAMaOHavUgAMHDuCOO+4AYC/gf/7znzFo0CAMHToUp06dgr+/Px555BHEx8ejX79+yhiru+66C7NmzUKvXr2wZMmSG/gpNC0C0a15El9lZSX8/PxQUVGhbK0zxlhzZDabYTQa0b59e3h7e9ft/qfXco3ScN9992H9+vXYsWMHdu3aheeffx4vv/wyVCoVXnrpJbz44ovo1asXYmNjMXz4cPz2228wGo3KndBqsmrVKly4cAE//fQTnn76aaSnp8NkMuGZZ57B8ePHkZ2djVdeeQX/+9//8PHHHysDJFu0aIENGzZg9+7deP3119GhQwds3boV4eHhiIuLUwZl1vRZeVIt8Jx9DYwxxm4K57utybKMixcv4vnnnwfQsLutOQwbNgyPP/44Vq5cqVyn/cCBA1i/fj2ysrJARGjXrh0qKyvx0ksvYf/+/bBYLOjSpQsAoFWrVsoZUnq93t3pN1lc0BljjNWL425r48aNAwBMnTrVLXdbcxg3bhx++eUXdO/eXXnutttuw4QJE7Bw4UIA9tP8cnNzcfbsWWzfvh0bNmxQtvxv1YvN1PsYuvN9bxljjN16btTd1hxat26Nf/zjHy7PjR49GqWlpRg0aBAGDRqE1atXIzo6GsXFxRg6dOg1j83fCup0DH3o0KEQBAFEhMOHD6Nz587IyMi4Gf27YTzpuAlj7NZW7Rj6LYCIUFVVBV9f33ptkXvyMfQ6baH37t0bjz/+OH744Qc88MADzb6YM8YYa974Wu7V1amgv/rqqxBFES+88AKsVuuN7hNjjDF2VUQEq9XKd1tzUudBcYmJiejevTvS09NvZH8YY4xdp1utuFksFuUCO3XlyZ9RvUa5d+zYEU8++eSN6gtjjLHr4Liy3Llz5xAUFHTL7IbWarWwWCx1np+IcO7cOZer7nmSep+2lp+fj9dffx3Hjh1zuUtNdna2WzvGGGOsbtRqNdq1a4eioqJqt6H2VEQESZKgVqvr9QNGEAS0a9eu1suEN2f1LugTJkzAo48+iilTpnjkB8IYY81Ry5YtERMTo9xW1dOJoohff/0Vd955Z72ux67Vaj22dtW7oGu1Wjz77LM3oi+MMcYawPmmMbeCe+65p7G70KTU+8IyI0aMwObNmxu84IKCAvTp0wedOnVCz549ceDAgRrnS01NRUxMDKKjozFjxgyX3fyFhYUYPXo0OnfujNtuuw3vv/9+g/vFGGOs6ZMkCUeOHIEkSY3dlSaj3gV9yJAhSExMhJ+fH9q0aYOgoCC0adOm3gueOXMmZsyYgcOHD2P+/PnKXdScGY1GLFy4EDt27MCRI0dw+vRp5a5sRIRx48bh0UcfxaFDh5Cfn48HH3yw3v1gjDHW/BARzp8/79Gj1uur3ndb69ixI5YuXYq77rrLZdeO40L4dXH27Fl06tQJJSUl0Gg0ICKEhIRg165dLvdEf/PNN3H8+HF88MEHAID09HSkpKRg27Zt+PHHH/Hyyy9jx44d9em+wpOuDsQYY+z6eFItqPcWemBgIBITE9GhQwdERkYqU32YTCaEhoYqAxkEQUBERIRyL1uHwsJCl7ajoqKUeQ4cOICgoCBMnDgRcXFxGDduHI4dO1brMi0WCyorK10mAMruGkmSaoxFUXSJZVm+amyz2Vxix+8lR0xE1WIALrEsyy6x4zBDbbEkSS4x58Q5cU6ck6fnZLVaceDAAaXfDcnJU9S7oI8bNw7Lly9HWVkZLl26pEz1deVpBrXtKKjtLj02mw0//vgjFi5ciJycHIwcORITJ06sdXlLliyBn5+fMoWHhwMA8vLyANhPx8vPzwcA5ObmoqCgAACQk5Oj3EAgOzsbJpMJALBz504UFxcDALKyslBSUgIAyMzMRHl5OQAgIyMDVVVVAOx7F8xmM0RRRHp6OkRRhNlsVi7UU1VVpVxSt7y8HJmZmQCAkpISZGVlAQCKi4uxc+dOAPYfRY5TBY1GI3JycgDYxybk5uZyTpwT58Q5eXROJ06cQFFRUYNz2r17NzwG1ZMgCMqkUqmUf+vjzJkzZDAYyGazERGRLMsUHBxMRqPRZb6UlBR6/PHHlcebNm2igQMHEhHRV199Rf3791deu3jxIqlUKhJFscZlms1mqqioUCaTyUQAqKysjIiIRFFU3usc22w2l1iSpKvGVqvVJZZl2SWWZbla7PgMHLEkSS6x43OqLRZF0SWuKQ/OiXPinDgnzql6TqWlpQSAKioqqLmrd0F3l4EDB9LKlSuJyF6ce/XqVW2eo0ePUkhICJ0+fZpkWabRo0fTRx99REREFy5coA4dOlBRUREREa1bt45iY2PrvPyKigqP+RIZY+xWI4oi/frrr7VuxNWVJ9WCep+Hbjabq92ez3G5wfpYsWIFkpKSsHjxYhgMBqxevRoAMG3aNIwZMwZjxoxBhw4dkJycjL59+0KWZQwePFgZDd+iRQt8+OGHGDVqFIgI/v7++Pzzz+ubDmOMMeYR6j3Kfdy4cVi/fr3yuLy8HEOGDMH//vc/t3fuRvKkkY2MMcaujyfVgnoPiuvcuTPmzp0LALhw4QISEhIwe/Zst3eMMcYYq40kScjJyeELyzipd0FfunQpzpw5gzfeeANjx47FhAkTMG3atBvRN8YYY6xWer2+sbvQpNR5l7vzqWmXL1/GyJEjMWTIECxcuBAA4OPjc2N6eIN40m4Wxhhj18eTakGdC7pKpYIgCCAi5V+lEUFodrs9POlLZIyxW40oisjJyUFcXFy97rZ2JU+qBXX+FBxX1WGu6nEbXsZYI+JLfnsWQRDQqlWret0L3dPV+Rj6xYsXlbi0tPSGdIYxxhirC7VajY4dO95St4u9ljoV9D//+c94+OGH8fzzzwOActycMcYYawyiKGLnzp0edS32hqpTQS8vL8e3336LAQMG4JVXXrnRfWKMMcauSqVSISwsDCpVvU/W8lh1+iR0Oh0AYOTIkQgJCcGmTZtuaKcYY4yxq1GpVIiMjOSC7qROg+IeffRRJZ4+fToCAwNvWIcYY4yxa3Hscu/Tp0+DRrl7kjr9tBkwYIDL47i4uAYvuKCgAH369EGnTp3Qs2dPHDhwoMb5UlNTERMTg+joaMyYMaPa8RIiwpAhQ9C6desG94kxxljzoFKpEB0dzVvoTq7rk3jzzTcbvOCZM2dixowZOHz4MObPn6/cdMWZ0WjEwoULsWPHDhw5cgSnT59GamqqyzzLli1DVFRUg/vDGGOs+eBj6NXV6ZOIjIzEsGHDMGzYMAwdOhQbN25s0ELPnj2LvXv3YtKkSQCA8ePHw2g04vjx4y7zrV27FuPGjUNwcDAEQcCsWbOQlpamvF5QUIB///vfWLBgQYP6wxhjrHkRRRGZmZk8yt1JnQr60KFDkZGRgYyMDPzwww8YNWpUgxZqMpkQGhqqHPcQBAEREREoLCx0ma+wsBCRkZHK46ioKGUeWZYxffp0fPDBB9BqtddcpsViQWVlpcsEQLnCnSRJNcaiKLrEjgvsOMfe3iJUKkdsU2K93gaVipRYEAgAQa+3ASAIgiMGVCrnWIa3t3Ns/4NVq2XodPZYo3GOJXh5Ocf2/mq1ErRae+zlJUGjccSiEut0IjQaWYnVas6Jc/LcnGRZVgpAbbEkSS6xO9YRzrHNZnOJHVfddMREVC0G4BLLsuwS34o5ERFuv/12qFSqBufkKepU0N966y2Xxx999FGDF3zl1X1quwKt83zO87z11lsYMGAAunfvXqflLVmyBH5+fsoUHh4OAMjLywMA5OfnIz8/HwCQm5uLgoICAEBOTg6MRiMAIDs7GyaTCQCwc+dOFBcXAwBSUrIQG1sCAFi2LBMxMeUAgNTUDISFVQEA0tLSERBghl4vIi0tHXq9iIAAM9LS0gEAYWFVSE3NAADExJRj2bJMAEBsbAlSUrIAAL16FSM5eScAID7ehAULsgEACQlGzJ2bAwBITCzA9Om5AIBJk/IxaZI9p+nTc5GYaM9p7twcJCTYc1qwIBvx8fackpN3olcvzolz8tycSkpKkJVlz6m4uBg7d9pzMplMyM6252Q0GpGTY8+poKAAubn2nBqyjsjKykJJiT2nzMxMlJfbc8rIyEBVlT2n9PR0mM1miKKI9PR0iKIIs9mM9HR7TlVVVcjIsOdUXl6OzMxbO6cTJ06gqKgIKpWqQTnt3r0bHoPq6cSJE7R9+3bavn07nThxor5vJyKiM2fOkMFgIJvNRkREsixTcHAwGY1Gl/lSUlLo8ccfVx5v2rSJBg4cSEREo0aNovDwcIqMjKSwsDBSqVQUGRlJZWVlNS7TbDZTRUWFMplMJgKgzC+KIomiWC222WwusSRJLjFA5O1tI5XKEVuVWK+3kkolK7EgyATIpNdbCZBJEBwxkUrlHEvk7e0c2wggUqsl0unssUbjHIvk5eUciwQQabUiabX22MtLJI3GEduUWKezkUYjKbFazTlxTp6ZExGRJEnKeqe2WBRFl7im9UJ91hFXxlar1SWWZdkllmW5WuxYTzpiSZJc4lsxp8uXL9N//vMfslqtDcqptLSUAFBFRQU1d3Uu6Pn5+XTPPfdQ27ZtqWfPntSjRw9q27Yt3XPPPXTgwIF6L3jgwIG0cuVKIiL66quvqFevXtXmOXr0KIWEhNDp06dJlmUaPXo0ffTRR9XmMxqNFBgYWK/lV1RUuOVLtF8hmieeeGrqE/MskiRRaWmpUpivl7tqQVNQ5z/zXr160dq1a6s9/9VXX1GPHj3qveCDBw9S7969KSYmhu6++27Ky8sjIqKpU6fSt99+q8z3j3/8g6Kjo6l9+/Y0depU5RecMy7oPPHE07UmxmriSQW9zrdP7dy5Mw4dOlTv15oqd90yj2/0w1jzULc1HWsubDYbMjIyMGzYsDoNjK6NJ90+tc4n8LVu3Rqffvqpy21UZVnG6tWr+cpxjDHGbiqNRoP+/fvzVeKc1PmTWL16NWbOnIm5c+ciNDQUgiCgqKgIcXFxWLVq1Q3sImOMMeZKEIRmv0XtbnUu6B07dsSWLVtw7tw5Zbh/eHg4goKCbljnGGOMsZrYbDakp6cjISGhQbvcPUm991UEBQVxEWeMMdaoNBoNhg0bxrvcnbjlIridOnVyRzOMMcZYnXExd1XnT6O2u6EBwIULF9zSGcYYY6wuHFef413u/6/OBb1r166IiopCTWe5OS7/xxhjjN0MGo0GCQkJvJXupM6fRGRkJHbs2IHQ0NBqrzmui84YY4zdLKIockF3Uudj6GPGjMGxY8dqfG3s2LFu6xBjjDF2LaIoIiMjw6PultZQdb5SnKfhK8Uxdmu5Ndd07FpuySvFMcYYY00FEaGysrLGcV23qkYr6AUFBejTpw86deqEnj171jqKPjU1FTExMYiOjsaMGTOU3Su//vorBgwYgNtuuw133nknZsyYAYvFcjNTYIwx1khEUcT27dt5l7uTRivoM2fOxIwZM3D48GHMnz8fU6dOrTaP0WjEwoULsWPHDhw5cgSnT59GamoqAMDb2xvLli3DwYMHsW/fPlRUVODtt9++2WkwxhhrBFqtFqNGjeJT1pw0SkE/e/Ys9u7di0mTJgEAxo8fD6PRiOPHj7vMt3btWowbNw7BwcEQBAGzZs1CWloaACAmJgaxsbEAALVajR49etQ6aI8xxphnkWUZZWVlLjcMu9U1SkE3mUwIDQ1VTjcQBAEREREoLCx0ma+wsBCRkZHK46ioqGrzAMDFixfxr3/9C6NHj651mRaLBZWVlS4TAEiSpPxbUyyKokvs+ONxjr29RahUjtimxHq9DSoVKbEgEACCXm8DQBAERwyoVM6xDG9v59i+S0mtlqHT2WONxjmW4OXlHNv7q9VK0GrtsZeXBI3GEYtKrNOJ0GhkJVarOSfOyXNzkmVZ2UVbWyxJkkvsjnWEc2yz2VxixzFgR0xE1WIALrEsyy7xrZiT1WpFdna20u+G5OQpGm2Xu3DF8PDaBjY4z1fTPDabDX/84x8xbNiwq54+t2TJEvj5+SmT49z5vLw8AEB+fj7y8/MBALm5uSgoKAAA5OTkwGg0AgCys7OVG9Ps3LkTxcXFAICUlCzExtovrrNsWSZiYsoBAKmpGQgLqwIApKWlIyDADL1eRFpaOvR6EQEBZqSlpQMAwsKqkJqaAQCIiSnHsmWZAIDY2BKkpGQBAHr1KkZy8k4AQHy8CQsWZAMAEhKMmDs3BwCQmFiA6dNzAQCTJuVj0iR7TtOn5yIx0Z7T3Lk5SEiw57RgQTbi4+05JSfvRK9enBPn5Lk5lZSUICvLnlNxcTF27rTnZDKZkJ1tz8loNCInx55TQUEBcnPtOTVkHZGVlaVcgCszMxPl5facMjIyUFVlzyk9PR1ms1m5ApooijCbzUhPt+dUVVWFjAx7TuXl5cjMvLVzKioqQmBgILRabYNy2r17NzwGNYIzZ86QwWAgm81GRESyLFNwcDAZjUaX+VJSUujxxx9XHm/atIkGDhyoPLZarXT//ffTtGnTSJblqy7TbDZTRUWFMplMJgJAZWVlREQkiiKJolgtttlsLrEkSS4xQOTtbSOVyhFblVivt5JKJSuxIMgEyKTXWwmQSRAcMZFK5RxL5O3tHNsIIFKrJdLp7LFG4xyL5OXlHIsEEGm1Imm19tjLSySNxhHblFins5FGIymxWs05cU6emRMRkSRJynqntlgURZe4pvVCfdYRV8ZWq9Uldqy7HLEsy9Vix3rSEUuS5BLfijlZrVY6deoUSZLUoJxKS0sJAFVUVFBz1ygFnYho4MCBtHLlSiIi+uqrr6hXr17V5jl69CiFhITQ6dOnSZZlGj16NH300UdEZP9CHnjgAZoyZco1i3lNKioq3PIl2s9u5Yknnpr6xDyLzWajLVu2KAX+ermrFjQFjXZhmUOHDiEpKQmlpaUwGAxYvXo1unTpgmnTpmHMmDEYM2YMAOCf//wn3njjDciyjMGDB+Ojjz6CVqvFZ599hkmTJiE2NlbZLd+3b1988MEHdVo+X1iGsVtL46zpWFPnSReW4SvFcUFn7JZwa67pPJcsyyguLkZISAhUqusfDuZJBZ2vFMcYY6zZkWUZR48e5dPWnPBtahhjjDU7Go0GAwYMaOxuNCm8hc4YY6zZkWUZJ06c4C10J1zQGWOMNTuyLOPkyZNc0J3wLnfGGGPNjkajQZ8+fRq7G00Kb6EzxhhrdiRJwpEjR5TLuTIu6IwxxpohIsL58+dxi555XSPe5c5YDQh8gQGPw1+pR9EA6MHF3AVvoTPGGGt2JI0GBw8e5F3uTrigM8YYa35UKly+fLmxe9Gk8C53xhhjzY7aakVcXFxjd6NJaZJb6AUFBejTpw86deqEnj174sCBAzXOl5qaipiYGERHR2PGjBkedaN6xhhjtZO0WuTl5fEudydNsqDPnDkTM2bMwOHDhzF//nxMnTq12jxGoxELFy7Ejh07cOTIEZw+fRqpqamN0FvGGGOs8TW5gn727Fns3bsXkyZNAgCMHz8eRqMRx48fd5lv7dq1GDduHIKDgyEIAmbNmoW0tLRG6DFjjLGbTW2zoWvXrlCr1Y3dlSajyR1DN5lMCA0NhUZj75ogCIiIiEBhYSGioqKU+QoLCxEZGak8joqKQmFhYa3tWiwWWCwW5XFFRQUA4Pz58wCg7LZRq9UusSiKEARBiVUqFVQqlRIDKuh0IqxWFYhU0OlssFrVIFLB29sGi0UDIgHe3jaYzfacvL3FK2ItBIGg0zliGV5eEiwWRyzDYtFApZKh0ciwWjVQq2Wo1Y5YgkpFsNkcMWCzqaHR2PMQRTW0WgmyDEiSGlqtCFkWIElqeHmJkCQVJEkFLy8RoqiCLHNOFQBEb29ozGb78ry9oTWbQYIAUaeD1myGLAiQvLygtVggCwJkLy9oLBbIKhVkjQYaqxWyWg1ZrYbGaoWkVoNUKmhsNkhqNaBSQW2zQfr9b10tipC0WkCWoZYkiFotBEfs5QWVJEHliEURKlmGqNNBZbVCRQSbTge1I/b2hsZigeCInfLgnDgnT8jJqtfjt59+QmxsrLJev3L9XZd1eVlZGQB4xPnsTa6gA/Yi7qy2D9p5vmt9GUuWLEFycnK1551/JFwvp98JLvHvf7t1iolcY0c7zrEsA1arPZYk+3S12HlIgc1Wc+xo78r4Vs/J3xOT4pw4J0/K6fJlID4e7lJVVQU/Pz+3tdcYmlxBDw8PR1FREURRhEajARHBZDIhIiLCZb6IiAiX3fAnTpyoNo+z559/Hk8//bTyWJZllJWVITAwsNoPCMYYY01bZWUlwsPDYTKZYDAYrrsdIkJVVRVCQ0Pd2LvG0eQKeps2bRAXF4c1a9YgKSkJ69atQ1RUVLUt6fHjx6Nfv3546aWX0KZNGyxfvhwTJ06stV2dTgedTufynL+//w3IgDHG2M1iMBgaVNABNPstc4cmNygOAFasWIEVK1agU6dOWLp0qTJ6fdq0adiwYQMAoEOHDkhOTkbfvn0RHR2NNm3a1DganjHGGLsVCOQJIwEYY4zdUiorK+Hn54eKiooGb6F7iia5hc4YY4xdjU6nw6JFi6odSr2V8RY6Y4wx5gF4C50xxhjzAFzQGWOMMQ/ABZ0xxhjzAFzQGWOMMQ/ABZ0xxhjzAFzQGWOMMQ/ABZ0xxhjzAFzQGWOMMQ/ABZ0xxhjzAFzQGWOMMQ/ABZ0xxhjzAE2yoA8bNgyxsbHo3r07+vfvj3379tU4X2pqKmJiYhAdHY0ZM2ZAFMWb21HGGGOsiWiSN2cpLy+Hv78/AOCbb77BK6+8gr1797rMYzQa0bdvX+Tk5KBNmzYYO3YsRo0ahZkzZ9ZpGbIs49SpU/D19YUgCO5OgTHGWDNARKiqqkJoaChUqia5jVtnmsbuQE0cxRwAKioqavyQ165di3HjxiE4OBgAMGvWLKSkpNS5oJ86dQrh4eFu6S9jjLHmzWQyoV27do3djQZpkgUdAB599FFs3boVALB58+ZqrxcWFiIyMlJ5HBUVhcLCwlrbs1gssFgsymPHjonjx4+jVatWkCQJAKBWq11iURQhCIISq1QqqFSqWmObzQa1Wq3EGo0GgiAoMQCIougSa7VaEJESy7IMSZKUWJZlaDSaWmNJkkBESlxTHpwT58Q5cU6elJPFYsEvv/yC3r17K3tZryensrIytG/fHr6+vmjumuz+hU8++QQmkwmvvfYann322Rrncd5Vfq0jB0uWLIGfn58yRUREALD/MDAYDDh58iROnjwJg8GA48eP48yZMzAYDDh69ChKS0thMBhw8OBBVFRUwGAwIC8vDxcvXoTBYMC+fftgtVphMBiwZ88eyLIMg8GAXbt2QRAEGAwG7NixA15eXvDx8cGOHTvg4+MDLy8v7NixAwaDAYIgYNeuXTAYDJBlGXv27IHBYIDVasW+fftgMBhw8eJF5OXlwWAwoKKiAgcPHoTBYEBpaSmOHj0Kg8GAM2fO4Pjx45wT58Q5cU4enVN5eTlatmwJf3//BuWUn59frZ40V03yGPqV9Ho9ioqKEBgYqDz35ptv4vjx4/jggw8AAOnp6UhJScG2bdtqbOPKLfTKykqEh4ejrKyMt9A5J86Jc+KcbtGcysrKEBgYqBT45qzJFfTKykpcuHABoaGhAID169fjz3/+M0wmk8svqGPHjqFfv34ug+ISEhIwa9asOi/Hz8/PI75Exhi71YiiiKysLAwYMED5oXA9PKkWNLlj6BUVFRg/fjwuX74MlUqFoKAgbNy4EYIgYNq0aRgzZgzGjBmDDh06IDk5GX379oUsyxg8eDCmTp3a2N1njDF2E6hUKnTt2rXZj0x3pya3hX6zeNKvMsYYY9fHk2oB/7RhjDHW7NhsNnz//few2WyN3ZUmgws6Y4yxZketVqNHjx5Qq9WN3ZUmo8kdQ2eMMcauRaVSISAgoLG70aTwFjpjTUBUVBS++eabZr2MLl26YOPGjTesfcac2Ww2bNq0iXe5O+GCzlgt4uPjoVarkZubqzxXXl4OQRBw/PjxBrX77rvvNryDAAYPHgy9Xo/z58/fsGXUpKb2f/vtN9x3333X1d7bb7+NTp06wdfXF0FBQbj33nsb9Bk7JCUl4amnnmpwO6zp0Wg06N+/f4NOWfM0XNAZu4pWrVrh+eefd0tbRKRc6MIdjh07hm3btsHHxwefffaZ29q92dasWYP3338fX3/9NaqqqlBQUIAZM2Y0iSt38R0cmy7H1eiawt9JU8EFnbGrePzxx7Fz505kZWXV+DoR4e2330Z0dDQCAgIwYsQIHDt2THk9KioKS5YsQe/eveHj44MJEyZg+/bteO6559CyZUuMHDlSmffw4cPo3bs3fH19MXDgQJhMpqv27eOPP0b37t3x5z//Gampqcrz8+bNq3UZDoWFhRg6dCiCgoLQqlUrjBo1ymWLOCkpCdOnT8fEiRPh6+uLzp07K1dhrK39K3fp//DDD+jVqxf8/f0REhKCJUuW1JjHrl27MGTIEHTt2hWA/eZMEyZMcLlXw48//oiePXvC398fXbp0wYYNG5TXZFnG3//+d9x2223w9fVFTEwMNm/ejL///e/47LPP8OGHH6Jly5bo0qULAKCqqgozZsxASEgIQkJCMGvWLFy8eBGA/d4OgiBg5cqV6NixI8LCwq76HbDGY7PZ8O233/Iud2d0i6qoqCAAVFFR0dhdYU3UwIED6Z133qHFixfTPffcQ0RE58+fJwBkNBqJiGj16tUUGhpKubm5dPnyZXr66afp9ttvJ5vNRkREkZGR1KlTJzp48CCJokgWi0Vp11lkZCR16dKFjh49SpcvX6aRI0fS5MmTa+2bKIoUFhZG7733Hh09epQEQaD//e9/1fp+5TLWr19PRERGo5HS09Pp8uXLVFFRQYmJiXTvvfcq806ePJlatmxJW7ZsIVEU6dVXX6XIyMg6t793717S6/W0du1aslqtVF5eTj///HONuaSlpVHLli3ptddeox07dtDly5ddXt+/fz/5+/vTli1bSJIk2r59OxkMBjp48CAREb333nvUvn172rNnD8myTCdOnKADBw4oecydO9elvccee4wGDRpEJSUldO7cORo4cCBNnz5d+VwA0P3330/nz5+nixcv1vodsMYlyzJdunSJZFluUDueVAt4C52xa3jqqadw4sSJGgeUffrpp3jyySdx5513wtvbG4sXL0ZRURGys7OVeWbPno3OnTtDrVbDy8ur1uXMmTMHHTp0gLe3N/70pz/hf//7X63zfv/99zh79iweeughdOjQAX379nXZSr+WqKgojBw5Et7e3jAYDHjxxReRlZUFWZaVeUaNGoXBgwdDrVbjsccew4kTJ1BaWlqn9v/xj39g4sSJGD9+PLRaLfz8/NC7d+8a5504cSJWrlyJnTt3YtSoUQgMDMT06dOVreYVK1YgKSkJgwcPhkqlQr9+/XDffffhyy+/BAB89NFHePnll3H33XdDEARERETg9ttvr3FZsizj888/x5IlSxAYGIjWrVtj8eLF+OSTT1xyX7RoEfz9/eHj41OnfFnj4OPnrrigM3YNer0eixYtwgsvvFDtGHhRURGioqKUxzqdDqGhoSgqKlKec9zZ71ratm2rxC1atEBVVVWt86ampiIhIQFBQUEAgMmTJ+Pzzz/H5cuX67Ssc+fO4eGHH0Z4eDgMBgMGDBgAq9Xqsswr+wPgqn1yduLECcTExNRpXgBITEzEpk2bcP78eXz//ffIyMjA66+/DsC+G3z58uXw9/dXpm+//RanTp2q97LOnTsHi8Xi8p116NABFosFJSUlynN1/c5Y4xFFEenp6TzOwQkXdMbqYOrUqZBlGatXr3Z5vl27di7Hnq1WK06dOoV27dopz115remGXnv63Llz+O6777Blyxa0bdsWbdu2xYIFC1BeXo6vv/66Tst4/vnncenSJezduxeVlZXKGAGq45Wgr9V+ZGQkjhw5Uqe2nAmCgH79+iExMRG//vorACA8PBxz585FeXm5Ml24cAEfffTRNZd1ZT+DgoLg5eXl8p0ZjUbodDq0bt26zvmxxqfRaJCQkMBb6U74r5axOlCr1Xj99dexePFil+cnTZqEZcuW4cCBA7BYLPjrX/+KsLAw9OzZs9a2goODcfTo0evuyyeffIKAgAAcPHgQ+/btw759+5CXl4ekpCRlt/u1llFZWQkfHx/4+/ujtLQUycnJ9erDtdqfPn060tLSsH79eoiiiIqKCuzatavGeVeuXIlvv/0W5eXlAIC8vDx8++236NOnDwBg5syZWLlyJbZu3QpJkmCxWPDzzz8r97GeOXMmkpOTsW/fPhARCgsLldeCg4NdBimqVCo8/PDDePHFF1FWVobS0lK8+OKLeOSRR7iIN0O8de6K/4IZq6Px48ejY8eOLs89+uij+POf/4z77rsPbdu2xf79+/Hdd99ddavhqaeewo8//gh/f//rOm87NTUVs2fPRlhYmLKF3rZtW8ybNw/btm3D0aNHr7mM5ORkHDlyBK1atULfvn1rHAl/Nddq/6677sK6devw+uuvIyAgALfffjt++umnGtvy9/fH22+/jQ4dOsDX1xf3338/HnroIcyfPx8AEBcXh7S0NPz1r39FUFAQwsLCsHDhQlgsFgDAk08+idmzZ2PChAnw9fXFvffei8LCQgDAtGnTcPLkSbRq1QqxsbEAgPfeew9RUVG444470KVLF3Ts2BF/+9vf6pU/a3yiKCIjI4OLuhO+25oH3GGHMcbY9fGkWsBb6IwxxpodIkJlZWWdx33cCppcQTebzbj//vvRqVMndO/eHSNGjKjxEpCZmZno1asX7rjjDnTt2hUvvvgif7GMMXaLEEUR27dv513uTprcLnez2YzMzEyMHDkSgiBg2bJl2LBhAzIyMlzmy8nJgZ+fHzp06ACz2Yx7770Xjz/+OB5++OE6Lcddu1n4qoOMNQ9Na03Hmgre5X4DeXt7IyEhQbk+b+/evV1GqTrExcWhQ4cOynu6d+9e43yMMcY8jyzLKCsrc7kg0K2uyRX0K/3973/H6NGjrzrP6dOnsXbtWiQkJNQ6j8ViQWVlpcsEQLlQiCRJNcaiKLrEjj8e59jbW4RK5YhtSqzX26BSkRILAgEg6PU2AARBcMSASuUcy/D2do7tu5TUahk6nT3WaJxjCV5ezrG9v1qtBK3WHnt5SdBoHLGoxDqdCI1GVmK1mnPinDw3J1mWlV20tcWSJLnE7lhHOMc2m80lduwkdcREVC0G4BLLsuwS34o5Wa1WZGdnK/1uSE6eokkX9MWLF6OgoEC5YlRNKisrMXr0aMyfPx933XVXrfMtWbIEfn5+yhQeHg7Afs4rAOTn5yvnrubm5qKgoACAfde+0WgEAGRnZys3zNi5cyeKi4sBACkpWYiNtV9latmyTMTElAMAUlMzEBZmv7JWWlo6AgLM0OtFpKWlQ68XERBgRlpaOgAgLKwKqan2wwoxMeVYtiwTABAbW4KUFPtFP3r1KkZy8k4AQHy8CQsW2C8vmpBgxNy5OQCAxMQCTJ9uv93npEn5mDTJntP06blITLTnNHduDhIS7DktWJCN+Hh7TsnJO9GrF+fEOXluTiUlJcpFdIqLi7Fzpz0nk8mkXK7XaDQiJ8eeU0FBgXL73IasI7KyspQr0WVmZirn3GdkZChX30tPT4fZbHa5AprZbEZ6uj2nqqoq5dBjeXk5MjNv7ZyKiooQGBgIrVbboJx2794Nj+HOC8N/9913bmvrzTffpLvvvpvOnz9f6zyVlZV0zz330CuvvHLN9sxmM1VUVCiTyWQiAFRWVkZE9ptdiKJYLbbZbC6xJEkuMUDk7W0jlcoRW5VYr7eSSiUrsSDIBMik11sJkEkQHDGRSuUcS+Tt7RzbCCBSqyXS6eyxRuMci+Tl5RyLBBBptSJptfbYy0skjcYR25RYp7ORRiMpsVrNOXFOnpkTEZEkScqNc2qLRVF0iWtaL9RnHXFlbLVaXWLHzUUcsSzL1WIicoklSXKJb8WcrFYrnTp1iiRJalBOpaWlHnNzlgYPihs6dCgEQQAR4fDhw+jcuXO1AWz19be//Q2fffYZfvzxR7Rq1arGeS5cuIDhw4dj2LBhWLRoUb2XwYPiGLu18KA4zyKKIrKysjBgwIAGXf6VB8U56d27Nx5//HH88MMPeOCBBxpczIuKijBv3jyUl5dj0KBB6N69O3r16gXAftUnx32Q33vvPWRnZ2P9+vXo3r07unfvftVd84wxxjyHRqPB4MGD+VruTtxy2tratWuxd+9eVFRU4IMPPnBHv2443kJn7NbCW+ieRZZlFBcXIyQkpEHX4ect9CskJiZiypQp6Ny5szuaY4wxxq5KlmUcPXqUT1tz0uQuLHOz8BY6Y7eWW3NNx67Fk7bQ3XrwIT8/H6+//jqOHTvmcm6f4zQDxhhjzB1kWYbJZEJ4eDjf+vZ3bi3oEyZMwKOPPoopU6ZArVa7s2nGGGNMIcsyTp48ibCwMC7ov3NrQddqtXj22Wfd2SRjjDFWjUajQZ8+fRq7G02KW3/WjBgxAps3b3Znk4wxxlg1kiThyJEjyuVcmZu30IcMGYKxY8dCrVZDp9OBiCAIAs6ePevOxTDGGLvFERHOnz+PqKioxu5Kk+HWgj5z5kysWrUKd911Fx9DZ4wxdsNoNBr06NGjsbvRpLi1oAcGBiIxMdGdTTLGGGPVSJKEgoICxMTE8Abk79x6DH3cuHFYvnw5ysrKcOnSJWVijDHG3O3y5cuN3YUmxa0XlnE+dcBxwxZBEJrkoAW+sAxjtxa+sAyriSddWMatW+iyLCuTJEnKv4wxxpg7SZKEvLw8rjFO3FrQzWZztefOnTvnzkUwxhhjrAZuLegPPfSQy+Py8nKMGDHCnYtgjDHGoFar0bVrVx4Q58StBb1z586YO3cuAODChQtISEjA7Nmz3bkIxhhjDJIkIScnh3e5O3FrQV+6dCnOnDmDN954A2PHjsWECRMwbdq0erXx5JNPIioqCoIgIC8vr8Z5tm3bBh8fH3Tv3l2ZeLQjY4zdWvR6fWN3oUlxy3nozqemffDBBxg5ciSGDBmCGTNm4NKlS/Dx8alzW4mJiZg/fz769et31fnuuOMO7Nmz57r7zBhjrPlSq9W47bbbGrsbTYpbCnrLli1dTlMjIuzZswdvvPFGvU9bGzBggDu6xFiDEPh8RI/DX6lHEb28kLNjB+Li4qDRuPUaac2WW3a5X3ma2pWnr90Ihw4dwl133YUePXrgww8/vOb8FosFlZWVLhMApX+SJNUYi6LoEsuyXC329hahUjlimxLr9TaoVKTEgkAACHq9DQBBEBwxoFI5xzK8vZ1j+73l1WoZOp091micYwleXs6xvb9arQSt1h57eUnQaByxqMQ6nQiNRlZitZpzEgQCAbDp9SAAJAiw/b5rj1QqJZZVKti8vZVYdMRqNUSdzh5rNEosaTQQvbyUWHLEWi0krdYee3lB+n3lJDrHOh1k5/j3gUCitzfk36//YHOO9XqQcywInBPn5FE5ySoV/Pz8lI3GhqzLPYVbCvrFixeVuLS01B1NXtVdd92FoqIi7N27F+vXr8fy5cvx5ZdfXvU9S5YsgZ+fnzKFh4cDgHKcPj8/H/n5+QCA3NxcFBQUAABycnJgNBoBANnZ2TCZTACAnTt3ori4GACQkpKF2NgSAMCyZZmIiSkHAKSmZiAsrAoAkJaWjoAAM/R6EWlp6dDrRQQEmJGWlg4ACAurQmpqBgAgJqYcy5ZlAgBiY0uQkpIFAOjVqxjJyTsBAPHxJixYkA0ASEgwYu7cHABAYmIBpk/PBQBMmpSPSZPsOU2fnovERHtOc+fmICHBntOCBdmIj7fnlJy8E716cU4BAWaIej3S09Ig6vUwBwQgPS0NAFAVFoaM1FQAQHlMDDKXLQMAlMTGIislBQBQ3KsXdiYnAwBM8fHIXrAAAGBMSEDO74NGCxITkTt9OgAgf9Ik5E+aBADInT4dBb9fPjln7lwYExIAANkLFsAUHw8A2JmcjOJevQAAWSkpKImNBQBkLluG8pgYAEBGaiqqwsIAAOlpaTAHBHBOnJNH5VQ4bBgqKiqgVqtRUFCA3Fz7OqK+6/Ldu3fDY1ADzZkzh8aMGUMLFiwgIqLZs2c3tEkiIoqMjKRff/21TvMuXryY5syZc9V5zGYzVVRUKJPJZCIAVFZWRkREoiiSKIrVYpvN5hJLkuQSA0Te3jZSqRyxVYn1eiupVLISC4JMgEx6vZUAmQTBEROpVM6xRN7ezrGNACK1WiKdzh5rNM6xSF5ezrFIAJFWK5JWa4+9vETSaByxTYl1OhtpNJISq9WckyDIJANk1etJBkgWBLLq9UQAySqVEksqFVm9vZXY5ojVarLpdPZYo1FiUaMhm5eXEouOWKslUau1x15eJGo0RADZnGOdjiTnWK22x97eJKlURABZnWO9nmTnWBA4J87Jo3Iyt2hBO3bsUNbRNa2/67IuLy0tJQBUUVFRt+LUhDX40q+PPPIIPv30U/znP//BL7/8gtOnT9dpF/i1REVFYePGjejatWu114qLixEcHAyVSoWqqiqMGDECU6dOxZQpU+rcPl/6lV0NH0NnrGmTNRqYjhxBeHi4y2XH64sv/epE9/vxjJEjRyIkJASbNm1qUHtPPPEE2rVrh6KiItx7773o2LEjAGDatGnYsGEDAGDdunW488470a1bN/Tu3RtDhw7FY4891rBEGGOMNRsqUURkZGSDirmnafAWelZWlsvI9K+//hoPPPBAgzt2o/EWOrsa3kJnrGkTdTrszMhAnz59GjTKnbfQnVx5mllcXFxDm2SMMcauSiWKiI6O5i10J27/JN588013N8kYY4y5UEkSwsLCuKA7afDZ+JGRkejcuTMAgIhw6NAhtwyKY4wxxmojensjKzMTAwYM4AvL/K7Bn8LQoUPxr3/9S3nMN2NhjDF2o6msVnTt2pW30J00eFBceXk5/P393dSdm4cHxbGr4UFxjDUDDStfAHhQnAvnYl5YWIgdO3Zgx44dKCwsbGjTjDHGWI1s3t74/vvvYbPZGrsrTYZbDjwcPHgQU6ZMgdFoREREBIgIJpMJ7du3R2pqKm6//XZ3LIYxxhgDAKitVvTo0QPq368tz9xU0JOSkvDss89i/PjxLs+vXbsWkydPRnZ2tjsWwxhjjAEAVLKMgICAxu5Gk+KW0QTnz5+vVswB+73NKyoq3LEIxhhjTGHT67Fp0ybe5e7ELQW9devW+PTTT5Xb0QH2W6quXr0agYGB7lgEY4wxptBYLOjfvz+fsubELZ/E6tWrMXPmTMydOxehoaEQBAFFRUWIi4vDqlWr3LEIxhhjTCHIcrMfle5ubinoHTt2xJYtW3Du3DnlHrPh4eEICgpyR/OMMcaYC5tej/Rvv0VCQgK0Wm1jd6dJcOu+iqCgIC7ijDHGbjiN2Yxhw4bxLncnN/wSO506dbrRi2CMMXarIeJifgW3fBoHDhyo9bULFy64YxGMMcaYQtTrkZ6ezrvcnbiloHft2hVRUVGo6SqyJSUl9W6voKAAkydPRklJCfz9/bFq1SrccccdLvMQEebPn4/09HSo1WoEBgbin//8Jzp27HjdeTDGGGseNJcvIyEhgbfSnbhll3tkZCR27NgBo9FYbQoODq53ezNnzsSMGTNw+PBhzJ8/H1OnTq02z4YNG5CVlYV9+/YhNzcXQ4YMwQsvvOCOdBhjjDV1ggBRFBu7F02KWwr6mDFjcOzYsRpfGzt2bL3aOnv2LPbu3YtJkyYBAMaPHw+j0Yjjx49Xm9discBsNoOIUFlZiXbt2tW774wxxpof0dsbGRkZXNSduKWgv/fee+jXr1+Nry1btqxebZlMJoSGhiq7UQRBQERERLWbvYwePRqDBg1C27ZtERISgi1btuCVV16ptV2LxYLKykqXCQAkSVL+rSkWRdEldlw8xzn29hahUjlimxLr9TaoVKTEgkAACHq9DQBBEBwxoFI5xzK8vZ1j+x+sWi1Dp7PHGo1zLMHLyzm291erlaDV2mMvLwkajSMWlVinE6HRyEqsVnNOgkAg2E+LIQAkCLDp9QAAUqmUWFapYPP2VmLREavVEHU6e6zRKLGk0UD08lJiyRFrtZB+PwYoeXlB+v1vX3SOdTrIzvHv168Wvb0h/377SJtzrNeDnGNB4Jw4J4/KSWWzYdSoUdBqtbWuv+u6LvcUTfJGssIV9ySt6dj83r17cfDgQZw8eRKnTp3CkCFDMGfOnFrbXLJkCfz8/JQpPDwcAJCXlwcAyM/PR35+PgAgNzcXBQUFAICcnBwYjUYAQHZ2tnKe/c6dO1FcXAwASEnJQmysfazAsmWZiIkpBwCkpmYgLKwKAJCWlo6AADP0ehFpaenQ60UEBJiRlpYOAAgLq0JqagYAICamHMuWZQIAYmNLkJKSBQDo1asYyck7AQDx8SYsWGC/Rn5CghFz5+YAABITCzB9ei4AYNKkfEyaZM9p+vRcJCbac5o7NwcJCfacFizIRny8Pafk5J3o1YtzCggw2wfcpKVB1OthDghAeloaAKAqLAwZqakAgPKYGGT+/oO1JDYWWSkpAIDiXr2wMzkZAGCKj0f2ggUAAGNCAnLmzgUAFCQmInf6dABA/qRJyP99j1Tu9OkoSEwEAOTMnQtjQgIAIHvBApji4wEAO5OTUdyrFwAgKyUFJbGxAIDMZctQHhMDAMhITUVVWBgAID0tDeaAAM6Jc/KsnEaNQnZ2NogIBQUFyM21ryPquy7fvXs3PAY1MWfOnCGDwUA2m42IiGRZpuDgYDIajS7zPfHEE/TGG28oj/Py8igiIqLWds1mM1VUVCiTyWQiAFRWVkZERKIokiiK1WKbzeYSS5LkEgNE3t42UqkcsVWJ9XorqVSyEguCTIBMer2VAJkEwRETqVTOsUTe3s6xjQAitVoinc4eazTOsUheXs6xSACRViuSVmuPvbxE0mgcsU2JdTobaTSSEqvVnJMgyCQDZNXrSQZIFgSy6vVEAMkqlRJLKhVZvb2V2OaI1Wqy6XT2WKNRYlGjIZuXlxKLjlirJVGrtcdeXiRqNEQA2ZxjnY4k51ittsfe3iSpVEQAWZ1jvZ5k51gQOCfOyaNyuuzrS9999x1ZrdZa1991WZeXlpYSAKqoqLjOqtV0CERuuEO8m8XHxyMpKQlJSUlYu3Yt3nrrLezatctlnr/97W/4/vvvsXHjRmi1WixduhTbt2/Hpk2b6rQMd93U/oqdCcxDEPiLZazJc0P5clctaAqa5Hj/FStWICkpCYsXL4bBYMDq1asBANOmTcOYMWMwZswYPPHEE8jPz8edd94JLy8vhISEYMWKFY3cc8YYYzeDrFKhvKwM/v7+UKma5NHjm65JbqHfDLyFzq6Gt9AZa9ps3t7I/OYbDB48uEEXluEtdMYYY6wRac1mDB8+vLG70aTwfgrGGGPNjqxS4ezZs8rpZ4wLOmOMsWZI9vJCXl4eF3QnvMudMcZYs6MxmzF48ODG7kaTwlvojDHGmh1ZrcbJkyd5C90JF3TGGGPNjqzR4OjRo1zQnfAud8YYY82OxmLBgAEDGrsbTQpvoTPGGGt2ZI0GJ06c4C10J1zQGWOMNTt8DL063uXOGGOs2dFYLOjTp09jd6NJ4S10xhhjzY6k0eDIkSPKPc4ZF3TGGGPNEKlUOH/+PG7R25HUiHe5M8YYa3Y0Vit69OjR2N1oUngLnTHGWLMjaTQ4ePAg73J3wgWdMcZY86NS4fLly43diyaFd7kzxhhrdtRWK+Li4hq7G01Kk9xCLygoQJ8+fdCpUyf07NkTBw4cqHG+1NRUxMTEIDo6GjNmzIAoije5p4wxxhqDpNUiLy+Pd7k7aZIFfebMmZgxYwYOHz6M+fPnY+rUqdXmMRqNWLhwIXbs2IEjR47g9OnTSE1NbYTeMsYYY42vyRX0s2fPYu/evZg0aRIAYPz48TAajTh+/LjLfGvXrsW4ceMQHBwMQRAwa9YspKWlNUKPGWOM3Wxqmw1du3aFWq1u7K40GU3uGLrJZEJoaCg0GnvXBEFAREQECgsLERUVpcxXWFiIyMhI5XFUVBQKCwtrbddiscBisSiPKyoqAADnz58HAGW3jVqtdolFUYQgCEqsUqmgUqmUGFBBpxNhtapApIJOZ4PVqgaRCt7eNlgsGhAJ8Pa2wWy25+TtLV4RayEIBJ3OEcvw8pJgsThiGRaLBiqVDI1GhtWqgVotQ612xBJUKoLN5ogBm00NjcaehyiqodVKkGVAktTQakXIsgBJUsPLS4QkqSBJKnh5iRBFFWSZc6oAIHp7Q2M225fn7Q2t2QwSBIg6HbRmM2RBgOTlBa3FAlkQIHt5QWOxQFapIGs00FitkNVqyGo1NFYrJLUapFJBY7NBUqsBlQpqmw3S73/ralGEpNUCsgy1JEHUaiE4Yi8vqCQJKkcsilDJMkSdDiqrFSoi2HQ6qB2xtzc0FgsER+yUB+fEOXlCTla9Hr/99BNiY2OV9fqV6++6rMvLysoAwCPOZ29yBR2wF3FntX3QzvNd68tYsmQJkpOTqz3v/CPhejn9TnCJf//brVNM5Bo72nGOZRmwWu2xJNmnq8XOQwpstppjR3tXxrd6Tv6emBTnxDl5Uk6XLwPx8XCXqqoq+Pn5ua29xtDkCnp4eDiKioogiiI0Gg2ICCaTCRERES7zRUREuOyGP3HiRLV5nD3//PN4+umnlceyLKOsrAyBgYHVfkAwxhhr2iorKxEeHg6TyQSDwXDd7RARqqqqEBoa6sbeNY4mV9DbtGmDuLg4rFmzBklJSVi3bh2ioqKqbUmPHz8e/fr1w0svvYQ2bdpg+fLlmDhxYq3t6nQ66HQ6l+f8/f1vQAaMMcZuFoPB0KCCDqDZb5k7NLlBcQCwYsUKrFixAp06dcLSpUuV0evTpk3Dhg0bAAAdOnRAcnIy+vbti+joaLRp06bG0fCMMcbYrUAgTxgJwBhj7JZSWVkJPz8/VFRUNHgL3VM0yS10xhhj7Gp0Oh0WLVpU7VDqrYy30BljjDEPwFvojDHGmAfggs4YY4x5AC7ojDHGmAfggs4YY4x5AC7ojDHGmAfggs4YY4x5AC7ojDHGmAfggs4YY4x5AC7ojDHGmAdocgX9ySefRFRUFARBQF5eXq3zpaamIiYmBtHR0ZgxYwZE5/v6MsYYY7eYJlfQExMTsWPHDkRGRtY6j9FoxMKFC7Fjxw4cOXIEp0+fVu7IxhhjjN2KmlxBHzBgANq1a3fVedauXYtx48YhODgYgiBg1qxZSEtLu0k9ZIwxxpoeTWN34HoUFha6bMFHRUWhsLDwqu+xWCywWCzKY1mWUVZWhsDAQAiCcMP6yhhjrOkiIlRVVSE0NBQqVZPbxq2XZlnQAbgU4brcMG7JkiVITk6+kV1ijDHWTJlMpmvuHW7qmmVBj4iIwPHjx5XHJ06cQERExFXf8/zzz+Ppp59WHldUVCjttGrVCpIkAQDUarVLLIoiBEFQYpVKBZVKVWtss9mgVquVWKPRQBAEJQYAURRdYq1WCyJSYlmWIUmSEsuyDI1GU2ssSRKISIlryoNz4pw4J87Jk3KyWCz45Zdf0Lt3b2UD73pyKisrQ/v27eHr64vmrlkW9PHjx6Nfv3546aWX0KZNGyxfvhwTJ0686nt0Oh10Ol2151u1agWDwXCjusoYY+wGkGUZ3bp1g7+/v1t2lXvCodcmd8DgiSeeQLt27VBUVIR7770XHTt2BABMmzYNGzZsAAB06NABycnJ6Nu3L6Kjo9GmTRtMnTq1MbvNGGPsJlKpVAgLC2v2x73dSaC6HID2QJWVlfDz80NFRQVvoTPGWDMjiiKysrIwYMAAZVf+9fCkWsA/bRhjjDU7KpUKXbt25S10J83yGDpjjLFbm0qlQps2bRq7G00K/7RhjDHW7NhsNnz//few2WyN3ZUmgws6Y4yxZketVqNHjx5Qq9WN3ZUmgws6Y01AVFQUvvnmm0btw/bt210urGE2mzFu3Dj4+/ujZ8+e1V5nrDGpVCoEBATwMXQn/EkwVov4+Hio1Wrk5uYqz5WXl0MQBJcLG11Pu++++26D+hYVFQW9Xo+WLVuidevWSEhIQEFBQYPa7N+/P4qKipTH69atw6FDh3DmzBlkZ2dXe70+iouL8fDDD6Nt27bw9fVFhw4d8Je//KVB/XUQBAH79u1zS1us+bDZbNi0aRPvcnfCBZ2xq2jVqhWef/55t7RFRMqVq9whLS0NFy5cwLFjx+Dr64vJkye7rW3AflfDTp061XhBpvp65JFH4O3tjYMHD6KiogI//PADunfv3vBOugHferl50mg06N+/f4NOWfM0XNAZu4rHH38cO3fuRFZWVo2vExHefvttREdHIyAgACNGjMCxY8eU16OiorBkyRL07t0bPj4+mDBhArZv347nnnsOLVu2xMiRI5V5Dx8+jN69e8PX1xcDBw6EyWSqUx8NBgMeeeQR/PrrrwCA+fPnIzIyEr6+vrjjjjvw1Vdfucz/v//9D4MHD0ZAQACCgoLw5z//GQCwbds2+Pv7AwDmzZuHV155BRs3bkTLli2xaNEil9cBwGq14qWXXkJ0dDR8fX1x5513Yu/evTX2cdeuXXjssceUq3pFR0e7/ACx2WxKW4GBgRgzZgxOnTqlvH769GlMmjQJoaGh8Pf3x4ABA3D58mX07NkTANCnTx+0bNkSixcvBgDs2bMHffv2hb+/P+644w6XuzG+/PLLuO+++zB79mwEBATgueeeq9PnzJoWQRBgMBg84gpvbkO3qIqKCgJAFRUVjd0V1kQNHDiQ3nnnHVq8eDHdc889RER0/vx5AkBGo5GIiFavXk2hoaGUm5tLly9fpqeffppuv/12stlsREQUGRlJnTp1ooMHD5IoimSxWJR2nUVGRlKXLl3o6NGjdPnyZRo5ciRNnjy51r5FRkbS+vXrlT49+OCDNGDAACIiWrNmDZ05c4ZEUaS0tDTS6XR07NgxIiIqKioig8FAH3zwAV2+fJkuXrxIWVlZRES0detW8vPzU5axaNEiGjt2rPL4ytf/8pe/0N13302HDx8mWZbp4MGDdPz48Rr7O3z4cLrrrrto9erVdOjQoWqvP/vsszR48GA6deoUWSwWmjdvHvXv35+IiCRJoh49etDkyZOprKyMbDYbbd++ncxmMxERAaCcnBylrfPnz1NgYCD9/e9/J6vVStu2baMWLVrQjh07lLzUajWtXLmSbDYbXbx4sdbPmTVdVquVvvnmG7JarQ1qx5NqARd0D/gS2Y3hKLyXLl2i0NBQWr9+fbWCfu+999LSpUuV95jNZvL19aX//ve/RGQvvFcW79oK+kcffaQ8XrNmDXXt2rXWvkVGRpKPjw/5+/tTaGgojR8/vtZi2q1bN1qzZg0RES1dupQGDRpU43z1KeiyLJOPjw/99NNPtfbRWUVFBS1atIji4uJIo9FQREQEffbZZ0pbLVq0oH379inzX758mVQqFRUWFtKuXbuoRYsWdOnSpRrbvrKgr1mzhm677TaXeaZPn07Tp09X8urWrVud+s2aLlmW6dKlSyTLcoPa8aRawLvcGbsGvV6PRYsW4YUXXqh2DLyoqAhRUVHKY51Oh9DQUJfBY9e6E6BD27ZtlbhFixaoqqq66vyfffYZzp8/j5MnT2Lt2rWIjIwEALzzzjvo0qUL/Pz84O/vj7y8PJSUlACw35kwJiamTv25mnPnzuHSpUt1bstgMODll1/G3r17cf78eTz55JN49NFHkZ+fj5KSEly8eBEDBgyAv78//P390bZtW3h5ecFkMuHEiRMICwuDXq+v07Ku/E4A+/0fruc7YU0bHz93xQWdsTqYOnUqZFnG6tWrXZ5v166dy4h3q9WKU6dOuZzedeVpNTfyNJsdO3bg5ZdfxieffILz58+jvLwcXbt2Bf1+y4bIyEgcOXKkwcsJCgqCj4/PdbXVsmVLzJs3D35+fjhw4AACAwPh4+OD3bt3o7y8XJkuX76MPn36IDIyEidPnsTly5drbO/KY6hXfieAfYDf1b4T1vyIooj09HQe1OiE/6oZqwO1Wo3XX39dGXTlMGnSJCxbtgwHDhyAxWLBX//6V4SFhSmDtWoSHByMo0eP3pB+VlZWQqPRICgoCLIs4+OPP0ZeXp7y+p/+9CdkZ2dj+fLlsFgsuHTpErZv317v5QiCgOnTp2PevHk4cuQIiAiHDh3CiRMnapz/2Wefxb59+2C1WmG1WvGvf/0LFy9exN133w2VSoVZs2Zh3rx5ykDA0tJSfPHFFwCAHj16oHPnznjiiSdQXl4OURSxY8cOWCwWANU/z4SEBJw9exYffvghRFHE9u3b8fnnn+PRRx+td56s6dJoNEhISOCtdCdc0Bmro/Hjxyu383V49NFH8ec//xn33Xcf2rZti/379+O777676krmqaeewo8//gh/f3/cd999bu3jiBEjMH78eNx5550IDQ3Fb7/9hr59+yqvt2vXDj/++CM+//xzBAcHIyoqCmvXrr2uZb3xxhsYMmQI7r33XhgMBjz44IMoKyurcV6LxYKJEyciMDAQbdu2xcqVK/Htt98qu8aXLFmCe+65B4MHD4avry/uvvtuZGRkALBvTX/33Xe4dOkSOnfujNatW+Ovf/0rZFkGALz66qt48skn0apVKyxduhStWrXCf/7zH6xZswaBgYGYMWMGPvroI/Tr1++68mRNF2+du+Lbp3rALfMYY+xWY7PZkJ6ejoSEBGi12utux5NqQZPcQi8oKECfPn3QqVMn9OzZEwcOHKg2DxHh2WefRZcuXRAbG4tBgwa55dggY4yxpk+r1WLs2LENKuaepkkW9JkzZ2LGjBk4fPgw5s+fj6lTp1abZ8OGDcjKysK+ffuQm5uLIUOG4IUXXmiE3jLGGLvZiAiVlZW4RXcy16jJFfSzZ89i7969mDRpEgD7cUuj0VjjtbMtFgvMZrPyxfKNIxhj7NbgGPDIx9H/X5Mr6CaTCaGhocqgIkEQEBERgcLCQpf5Ro8ejUGDBqFt27YICQnBli1b8Morr9TarsViQWVlpcsEQDmvWJKkGmNRFF1ix0Cc2mKbzeYSO349OmIiqhYDcIllWXaJHX+wtcWSJLnEnBPnxDlxTp6ek0qlwvDhw6HVahuck6docgUdqH5eaU27VPbu3YuDBw/i5MmTOHXqFIYMGYI5c+bU2uaSJUvg5+enTOHh4QCgnNKTn5+P/Px8AEBubq5y56qcnBwYjUYAQHZ2tnJazc6dO1FcXAwAyMrKUi7ckZmZifLycgBARkaGcnGQ9PR0mM1ml3MnzWYz0tPTAQBVVVXKqN7y8nJkZmYCAEpKSpTriBcXF2Pnzp0A7D98srOzAdjPsc3JyQFgH3/guDsY58Q5cU6ck6fmdOzYMezevRuyLDcop927d8NTNLlR7mfPnkVMTAxKS0uh0WhARAgJCcGuXbtcrv40Z84cREREYP78+QCA3377DQkJCbWeB2uxWJTzVgH7yMbw8HCUlZWhVatWyi83tVrtEouiCEEQlFilUkGlUtUa22w2qNVqJdZoNBAEQYkB+y9C51ir1YKIlFiWZUiSpMSyLEOj0dQaS5IEIlLimvLgnDgnzolz8qSczGYztm3bhiFDhigXCrqenMrKyhAYGOgRo9zdWtA3btzolvNq4+PjkZSUhKSkJKxduxZvvfUWdu3a5TLP3/72N3z//ffYuHEjtFotli5diu3bt2PTpk11WoYnnarAGGPs+nhSLWhwQR86dCgEQQAR4fDhw+jcubOyC+V6HTp0CElJSSgtLYXBYMDq1avRpUsXTJs2DWPGjMGYMWNgsVgwZ84cbN++HV5eXggJCcGKFSuqXcO5Np70JTLG2K1GlmWUlJSgdevWDbqUryfVggYX9IULF+Luu+/G/fffj7/85S9455133NW3G8qTvkTGGLvViKKIrKwsDBgwoEGXf/WkWtDgQXGvvvoqRFHECy+8AKvV6o4+McYYY1el0WgwePBgvpa7E7eMck9MTMSUKVPQuXNndzTHGGOMXZUsyzh58qRy+hlz42lrHTt2xJNPPumu5hhjjLFaybKMo0ePckF34tZ9Ffn5+Xj99ddx7Ngxl5P1HecNMsYYY+6g0WgwYMCAxu5Gk+LWgj5hwgQ8+uijmDJlCtRqtTubZowxxhSyLMNkMiE8PLxBo9w9iVsLularxbPPPuvOJhljjLFqHMfQw8LCuKD/zq2fwogRI7B582Z3NskYY4xVo9Fo0KdPHx7l7sStn8SQIUMwduxYqNVq6HQ6EBEEQcDZs2fduRjGGGO3OEmSYDQa0b59ez7E+zu3FvSZM2di1apVuOuuu26ZD/iK+8gwxpqopnXXCtZQRITz58/X+eqgtwK3FvTAwEAkJia6s0nGGGOsGo1Ggx49ejR2N5oUtx5DHzduHJYvX46ysjJcunRJmRhjjDF3kiQJBw8eVO6oxtx8tzXnkYaOG7YIgtAkP3B3Xb+Xd7kz1jzwLnfPIkkScnNzERsb26BDvJ50LXe37nLnK/Ywxhi7GdRqNeLi4hq7G02KW3e5m83mas+dO3fOnYtgjDHGIEkS8vLymuQe4Mbi1oL+0EMPuTwuLy/HiBEj3LkIxhhjjNXArQW9c+fOmDt3LgDgwoULSEhIwOzZs925CMYYYwxqtRpdu3a9ZU6Rrgu3FvSlS5fizJkzeOONNzB27FhMmDAB06ZNq3c7BQUF6NOnDzp16oSePXviwIED1ebZtm0bfHx80L17d2W6fPmyO9JgjDHWxEmShJycHN7l7sQtg+KcT0374IMPMHLkSAwZMgQzZszApUuX4OPjU6/2Zs6ciRkzZiApKQlr167F1KlT8fPPP1eb74477sCePXsa3H/GGGPNj16vb+wuNCluOW1NpVK5nKbm3GR9T1s7e/YsOnXqhJKSEmg0GhARQkJCsGvXLpcrAm3btg3PPPPMdRd0Pm2NsVsLn7bGauJJp625ZZe7LMuQJMnlX8dU390hJpMJoaGhygX3BUFAREQECgsLq8176NAh3HXXXejRowc+/PDDq7ZrsVhQWVnpMgFQ+idJUo2xKIousePUPOfY21uESuWIbUqs19ugUpESCwIBIOj1NgAEQXDEgErlHMvw9naO7feWV6tl6HT2WKNxjiV4eTnH9v5qtRK0Wnvs5SVBo3HEohLrdCI0GlmJ1WrOiXPy3JxkWYYoileNJUlyid2xjnCObTabS+zYAHLERFQtBuASy7LsEt+KOVksFuzevVvpa0Ny8hRuKegXL15U4tLS0ga3J1yx2VvTToS77roLRUVF2Lt3L9avX4/ly5fjyy+/rLXNJUuWwM/PT5nCw8MBAHl5eQCA/Px85OfnAwByc3NRUFAAAMjJyYHRaAQAZGdnw2QyAQB27tyJ4uJiAEBKShZiY0sAAMuWZSImphwAkJqagbCwKgBAWlo6AgLM0OtFpKWlQ68XERBgRlpaOgAgLKwKqakZAICYmHIsW5YJAIiNLUFKShYAoFevYiQn7wQAxMebsGBBNgAgIcGIuXNzAACJiQWYPj0XADBpUj4mTbLnNH16LhIT7TnNnZuDhAR7TgsWZCM+3p5TcvJO9OrFOXFOnptTSUkJsrLsORUXF2PnTntOJpMJ2dn2nIxGI3Jy7DkVFBQgN9eeU0PWEVlZWSgpseeUmZmJ8nJ7ThkZGaiqsueUnp4Os9kMURSRnp4OURRhNpuRnm7PqaqqChkZ9pzKy8uRmXlr51RYWIhLly5BEIQG5bR79254DGqgOXPm0JgxY2jBggVERDR79uwGtXfmzBkyGAxks9mIiEiWZQoODiaj0XjV9y1evJjmzJlT6+tms5kqKiqUyWQyEQAqKysjIiJRFEkUxWqxzWZziSVJcokBIm9vG6lUjtiqxHq9lVQqWYkFQSZAJr3eSoBMguCIiVQq51gib2/n2EYAkVotkU5njzUa51gkLy/nWCSASKsVSau1x15eImk0jtimxDqdjTQaSYnVas6Jc/LMnIiIJElS1i21xaIousQ1rRfqs464MrZarS6xLMsusSzL1WLHutARS5LkEnNO159TaWkpAaCKigpq7hpc0CdNmkREROnp6ZScnNzggk5ENHDgQFq5ciUREX311VfUq1evavOcOnVK+UIqKyupT58+lJqaWudlVFRUuOVLtB+Z44knnpr6xDyLzWaj//73v0pRv17uqgVNQYN3uet0OgDAyJEjERISgk2bNjW0SaxYsQIrVqxAp06dsHTpUqSmpgIApk2bhg0bNgAA1q1bhzvvvBPdunVD7969MXToUDz22GMNXjZjjLGmT6VSISwszOUeIre6Bo9yz8rKwoABA5THX3/9NR544IEGd+xG41HujN1aGramY56KR7k7cS7mAPhi+Ywxxm44URSRlZXlUaPUG8rt+yrefPNNdzfJGGOMuVCpVIiOjuZd7k4afKW4yMhIdO7cGQBARDh06NA1zwlnjDHGGsJxDJ39vwYX9KFDh+Jf//qX8phvxsIYY+xGc+xyHzBggHIhsltdgwfFlZeXw9/f303duXl4UBxjtxYeFOdZZFlGSUkJWrdu3aDd7p40KK7BP2uci3lhYaFyidaIiAhEREQ0tHnGGGOsGpVKhTZt2jR2N5oUt+ynOHjwIKZMmQKj0YiIiAgQEUwmE9q3b4/U1FTcfvvt7lgMY4wxBsB+nfjMzEwMHjwYWq22sbvTJLiloCclJeHZZ5/F+PHjXZ5fu3YtJk+erFx7lzHGGHMHtVqNHj16QK1WN3ZXmgy3jPc/f/58tWIOAImJiaioqHDHIhhjjDGFSqVCQEAAn7bmxC2fROvWrfHpp58qt6MD7AMWVq9ejcDAQHcsgjHGGFPYbDZs2rRJueUqc9Mu99WrV2PmzJmYO3cuQkNDIQgCioqKEBcXh1WrVrljEYwxxphCo9Ggf//+fMqaE7d8Eh07dsSWLVtw7tw55R6z4eHhCAoKckfzjDHGmAtBEJr9aWbu5tafNkFBQVzEGWOM3XA2mw3p6elISEjgUe6/u+GjCTp16nSjF8EYY+wWo9FoMGzYMN7l7sQtn8SBAwdqfe3ChQvuWARjjDHmgou5K7d8Gl27dkVUVBRquopsSUmJOxbBGGOMKURR5F3uV3BLQY+MjMSOHTsQGhpa7bXw8PB6t1dQUIDJkyejpKQE/v7+WLVqFe644w6XeTIzM/H888+jqqoKKpUKY8eOxWuvvQaBL67O3IDAf0ceh79Sj6IBkGC18la6E7ccQx8zZgyOHTtW42tjx46td3szZ87EjBkzcPjwYcyfPx9Tp06tNk+rVq2QlpaGAwcOYM+ePfjpp5+QlpZW72UxxhhrhgQBoig2di+alAbfbc3dzp49i06dOqGkpAQajQZEhJCQEOzatQtRUVG1vm/OnDlo27Yt/vrXv9ZpOXy3NXY1vIXOWNNm0+uRnpbW4F3unnS3tSZ3zTyTyYTQ0FBlN4ogCIiIiFDu4laT06dPY+3atUhISKh1HovFgsrKSpcJACRJUv6tKRZF0SV2XA3POfb2FqFSOWKbEuv1NqhUpMSCQAAIer0NAEEQHDGgUjnHMry9nWP7r1C1WoZOZ481GudYgpeXc2zvr1YrQau1x15eEjQaRywqsU4nQqORlVit5pwEgUCwrzAIAAkCbHo9AIBUKiWWVSrYvL2VWHTEajVEnc4eazRKLGk0EL28lFhyxFotpN9XSJKXF6Tf//ZF51ing+wc/379atHbG/Lvl760Ocd6Pcg5FgTOiXPyqJxUNhtGjRoFrVZb6/q7rutyT9HkCjqAasfBr7YTobKyEqNHj8b8+fNx11131TrfkiVL4Ofnp0yOY/t5eXkAgPz8fOTn5wMAcnNzUVBQAADIycmB0WgEAGRnZysXztm5cyeKi4sBACkpWYiNtQ/+W7YsEzEx5QCA1NQMhIVVAQDS0tIREGCGXi8iLS0der2IgAAz0tLSAQBhYVVITc0AAMTElGPZskwAQGxsCVJSsgAAvXoVIzl5JwAgPt6EBQvsN71JSDBi7twcAEBiYgGmT88FAEyalI9Jk+w5TZ+ei8REe05z5+YgIcGe04IF2YiPt+eUnLwTvXpxTgEBZoi///oX9XqYAwKQ/vvhnKqwMGSkpgIAymNikLlsGQCgJDYWWSkpAIDiXr2wMzkZAGCKj0f2ggUAAGNCAnLmzgUAFCQmInf6dABA/qRJyJ80CQCQO306ChITAQA5c+fC+PuP1OwFC2CKjwcA7ExORnGvXgCArJQUlMTGAgAyly1DeUwMACAjNRVVYWEAgPS0NJgDAjgnzsmzcho1CtnZ2SAiFBQUIDfXvo6o77p89+7d8BjUxJw5c4YMBgPZbDYiIpJlmYKDg8loNFabt7Kyku655x565ZVXrtmu2WymiooKZTKZTASAysrKiIhIFEUSRbFabLPZXGJJklxigMjb20YqlSO2KrFebyWVSlZiQZAJkEmvtxIgkyA4YiKVyjmWyNvbObYRQKRWS6TT2WONxjkWycvLORYJINJqRdJq7bGXl0gajSO2KbFOZyONRlJitZpzEgSZZICsej3JAMmCQFa9ngggWaVSYkmlIqu3txLbHLFaTTadzh5rNEosajRk8/JSYtERa7UkarX22MuLRI2GCCCbc6zTkeQcq9X22NubJJWKCCCrc6zXk+wcCwLnxDl5VE6XfX3pu+/+r71/j4+ivPvH/9fMzmYTDklIJJiEHAQSUBCIFbDhIAdFDRXkJlpUrKlgoJa7tH5u+aIVMVYFEevdW6zkvpsK9ZDWYlGUqLGihhg5WGIRCRJgQzYYCCHkBOxhZq7fH+vOb9ckGMjCbjav5+OxD9+7O1xzvZO4771mrrnmHeF0Ojv8/O7MZ/nJkycFANHU1HT+BSvIBN05dACYPHkycnJykJOTg40bN2LNmjXYvn27zzatra246aabMH36dKxYseK898Fz6HQuPIdO1A34oXzxHPpFlp+fj/z8fKSnp2PVqlUo+O6QzIIFC7B582YAwB/+8Afs3LkTmzZtwujRozF69Gg89dRTgew2ERFdIroso6Ghwecunz1dUI7QLwWO0OlcOEInCm6u8HBsfestTJ06lbPcv8Mr8omIqNsx2+246aabAt2NoBKUh9yJiIjORZdl1NXV8ZC7FxZ0IiLqdvSwMOzdu5cF3QsPuRMRUbej2O2YOnVqoLsRVDhCJyKibkc3mXD06FGO0L2woBMRUbejKwoOHTrEgu6Fh9yJiKjbURwOTJo0KdDdCCocoRMRUbejKwqOHDnCEboXFnQiIup2eA69LR5yJyKibkdxOJCZmRnobgQVjtCJiKjb0RQFBw8eNO5xTizoRETUDQlZxqlTp9BDb0fSLh5yJyKibkdxOjFmzJhAdyOocIRORETdjqYo2L9/Pw+5e2FBJyKi7keWcfbs2UD3IqjwkDsREXU7JqcTGRkZge5GUAnKEXplZSUyMzORnp6OsWPHYt++fe1uV1BQgLS0NAwePBi5ublQVfUS95SIiAJBM5uxd+9eHnL3EpQFfeHChcjNzcWBAwewdOlSzJ8/v802VqsVy5cvR2lpKQ4ePIhjx46hoKAgAL0lIiIKvKAr6HV1ddi9ezfmzZsHAJgzZw6sViuqqqp8ttu4cSNmz56NAQMGQJIkLFq0CIWFhQHoMRERXWomlwsjRoyAyWQKdFeCRtCdQ7fZbEhISICiuLsmSRKSk5NRXV2N1NRUY7vq6mqkpKQYz1NTU1FdXd1huw6HAw6Hw3je1NQEADh16hQAGIdtTCaTT6yqKiRJMmJZliHLshEDMiwWFU6nDCFkWCwuOJ0mCCEjPNwFh0OBEBLCw12w2905hYer34vNkCQBi8UT6wgL0+BweGIdDocCWdahKDqcTgUmkw6TyRNrkGUBl8sTAy6XCYrizkNVTTCbNeg6oGkmmM0qdF2CppkQFqZC02RomoywMBWqKkPXmVMTADU8HIrd7t5feDjMdjuEJEG1WGC226FLErSwMJgdDuiSBD0sDIrDAV2WoSsKFKcTuskE3WSC4nRCM5kgZBmKywXNZAJkGSaXC9p3f+smVYVmNgO6DpOmQTWbIXnisDDImgbZE6sqZF2HarFAdjohCwGXxQKTJw4Ph+JwQPLEXnkwJ+YUCjk5IyLw9aefYuTIkcbn+vc/vzvzWd7Q0AAAIXE9e9AVdMBdxL119IP23u6HfhkrV65EXl5em9e9vyRcKK/vCT7xd3+7nYqF8I097XjHug44ne5Y09yPc8XeUwpcrvZjT3vfj3t6TtGhmBRzYk6hlNPZs8DkyfCXlpYWREVF+a29QAi6gp6UlISamhqoqgpFUSCEgM1mQ3Jyss92ycnJPofhjxw50mYbbw8//DAefPBB47mu62hoaEBsbGybLxBERBTcmpubkZSUBJvNhsjIyAtuRwiBlpYWJCQk+LF3gRF0BT0uLg4ZGRl49dVXkZOTgzfffBOpqaltRtJz5szBhAkT8NhjjyEuLg7r1q3D3LlzO2zXYrHAYrH4vBYdHX0RMiAiokslMjKySwUdQLcfmXsE3aQ4AMjPz0d+fj7S09OxatUqY/b6ggULsHnzZgDAoEGDkJeXh/Hjx2Pw4MGIi4trdzY8ERFRTyCJUJgJQEREPUpzczOioqLQ1NTU5RF6qAjKEToREdG5WCwWrFixos2p1J6MI3QiIqIQwBE6ERFRCGBBJyIiCgEs6ERERCGABZ2IiCgEsKATERGFABZ0IiKiEMCCTkREFAJY0ImIiEJAUBb0yspKZGZmIj09HWPHjsW+ffvabFNVVYXJkycjKioK1157bQB6SUREFDyCsqAvXLgQubm5OHDgAJYuXdruTVciIyPx5JNP4vXXXw9AD4mIiIJL0BX0uro67N69G/PmzQPgvk2q1Wr1ufc5AMTExGDChAno3bt3AHpJREQUXILufug2mw0JCQlQFHfXJElCcnIyqqur29wT/Xw4HA44HA7jua7raGhoQGxsLCRJ6mq3iYioGxJCoKWlBQkJCZDloBvjnpegK+gA2hRYf9w/ZuXKlcjLy+tyO0REFHpsNhsGDhwY6G50SdAV9KSkJNTU1EBVVSiKAiEEbDYbkpOTu9Tuww8/jAcffNB43tTUhOTkZFRVVaFfv37QNA0AYDKZfGJVVSFJkhHLsgxZljuMXS4XTCaTESuKAkmSjBiAkZsnNpvNEEIYsa7r0DTNiHVdh6IoHcaapkEIYcTt5cGcmBNzYk6hlJPD4cCuXbtw3XXXGYPAC8mpoaEBV1xxBfr27YvuLugKelxcHDIyMvDqq68iJycHb775JlJTU7t0uB1w3zu3vfvm9uvXD5GRkV1qm4iILi1d1zFq1ChER0f75VB5KJx6Dcr7oX/zzTfIycnByZMnERkZiQ0bNmD48OFYsGABZs6ciZkzZ8LhcGDw4MFwOBxoampCXFwc7rnnHqxcubJT+2hubkZUVBSamppY0ImIeqhQqgVBWdAvhVD6JRIR9TSqqqKkpASTJk0yDuVfiFCqBd17Sh8REfVIsixjxIgR3X5muj8F3Tl0IiKiHyLLMuLi4gLdjaDCrzZERNTtuFwufPDBB3C5XIHuStBgQSciom7HZDJhzJgxMJlMge5K0OAhdyIi6nZkWUZMTEyguxFUOEInukhGjx6N9evXAwBee+01ZGZmBrZDRCHE5XJhy5YtPOTuhQWdqAOTJ0/Gf//3f/ulrbvvvhtlZWV+aas9LpcLeXl5GDx4MCIiIpCUlITf/OY3aG1tvWj77IodO3ZgypQp6NevH6KjozFy5Ejjy09XfPLJJ4iOju5yOxT8FEXBxIkTu3TJWqhhQScKAXfddRc2bdqEN954A62trfjoo4/w73//G9OnTw+6EUxLSwtuvvlm/PSnP0VdXR1OnDiBgoKCoJmxrKpqoLtAnSBJEiIjI0NihTd/YUEn6gTPyO9Pf/oTkpKSEBsbi6VLl/pss3btWuO93/72tz7vrV+/HqNHjzae//73v0daWhr69u2LwYMHY+3atcZ7VVVVkCQJr7zyCoYMGYLo6Gjk5OR0WJg/+eQTbN68GZs2bcKPfvQjmEwmpKenY9OmTThw4ABee+01Y9sPP/wQ48aNQ3R0NOLj431WVvznP/+JsWPHIjo6GsOHD8fmzZuN94qLi3HttdciKioK8fHxeOCBB3D27Fnj/dTUVKxevRrXXXcd+vbti+uvvx42m63d/n7zzTc4ffo0cnNzYTabYTabMWbMGGRlZRnb1NXV4e6770ZCQgISEhLw61//2uduif/6178wdepUxMTEoH///vjP//xPnDx5ErfccguamprQp08f9OnTB9u2bQMAvPrqq7jyyisRHR2NCRMmoLy83Ghr8uTJWLp0KaZPn47evXvjvffea7ffFFxcLhfefvvtoPvCGlCih2pqahIARFNTU6C7QkHq+uuvF88//7wQQoiPP/5YyLIsfvWrX4mzZ8+Kffv2iV69eomPP/5YCCHERx99JCIjI0VZWZlwOBzikUceESaTSbz88stCCCFefvllMWrUKKPtjRs3iurqaqHruti6dasIDw8XpaWlQgghrFarACB++tOfiqamJnH06FGRmJhotPV9y5YtExMnTmz3vXnz5ok777xTCCHE7t27RUREhNi4caNwOp2isbFRfP7550IIIf7973+L6Oho8dFHHwlN08S2bdtEZGSk2L9/vxBCiJKSErF7926hqqo4dOiQGDZsmHjyySeN/aSkpIjhw4eLQ4cOibNnz4pbbrlF3Hvvve32qbm5WfTv31/cfvvt4q233hK1tbU+7+u6LsaNGycefPBBcfr0aVFfXy8mT54sHn30USGEEDU1NSIyMlK8+OKL4uzZs+L06dOipKTE+D1FRUX5tFdSUiL69OkjPv30U+F0OsXzzz8v+vfvLxobG4UQ7t9z//79xY4dO4Su6+LMmTPt9puCi+d3pet6l9oJpVrAETpRJwkhsHLlSoSHh+PKK69EZmYm/vWvfwFwT3q7++678eMf/xhhYWF4/PHH0bt37w7bmjNnDpKSkiBJEqZMmYKbbroJn3zyic82jz/+OCIjI5GQkIBbbrnF2Nf31dfXIyEhod33EhIScOLECQDA//7v/2Lu3LmYM2cOzGYzoqKicN111wEA8vPzkZOTg6lTp0KWZUyYMAE/+clP8MYbbwAAJk6ciIyMDJhMJgwaNAgLFy5s09/Fixdj0KBBCA8Px913391hf/v27YuysjLExMTgwQcfREJCAsaNG4fdu3cDAL744gtUVlbi2WefRa9evRAbG4tHHnkEr7/+OgD3aPtHP/oRHnjgAYSHh6NXr16YOHFihz/rv/zlL5g3bx4mTZoEs9mMX//61+jXrx+2bNlibHPXXXdh7NixkCQJERERHbZFwYXnz30FrKBXVlYiMzMT6enpGDt2LPbt29fudgUFBUhLS8PgwYORm5vrc35rzZo1GDFiBEaPHo3rrrsOu3btulTdpx4oMjISvXr1Mp737t0bLS0tAIBvv/0WKSkpxntmsxnx8fEdtvXaa6/hmmuuMSaFFRUVob6+3mebyy+/vN19fd9ll12Gb7/9tt33vv32W/Tv3x8AcOTIEaSlpbW7XVVVFdatW4fo6Gjj8fbbbxvt7tq1CzfccAMGDBiAyMhIPPLIIxfcXwAYMmQI1q1bh0OHDqGmpgZDhgzBzJkzIYRAVVUVGhsbERMTY/QlOzsbx48f/8E82lNTU9Pmbo1XXHEFampqjOddvT0zXXqqqqKoqIhzHrwErKAvXLgQubm5OHDgAJYuXYr58+e32cZqtWL58uUoLS3FwYMHcezYMRQUFAAA/v3vf+OFF17A9u3b8eWXX2Lx4sX45S9/eanTIALgHgkfOXLEeO5yuVBbW9vuttXV1bj33nuxevVqnDhxAo2NjcjKyoK4wPsk3XjjjdixYwesVqvP683NzXjvvfdw4403AgBSUlJw8ODBdttISkrCkiVL0NjYaDxaW1vx0ksvAQDuvPNOTJkyBYcPH0ZzczOefvrpC+7v9yUkJGDZsmU4evQoGhoakJSUhLi4OJ++NDU1GTP2z5VHe+t6Dxw4EFVVVT6vVVVVYeDAgef8dxTcFEVBVlYWR+leAvJXXFdXh927d2PevHkA3IcfrVZrm//pNm7ciNmzZ2PAgAGQJAmLFi1CYWGh8b7L5cLp06cBAI2NjT7/gxJdSnfeeSdee+017NixA06nE0888YTxt/l9ra2tEEIgLi4OsiyjqKgIxcXFF7zvqVOnIisrC7Nnz8bu3buhaRoOHDiA2bNnY/Dgwbj77rsBAPfffz8KCwuxadMmqKqKpqYmbN++HYD7C/bLL7+Mjz/+GJqmweFw4PPPP0dFRQUA95eD6Oho9O7dGxUVFUahvxD79+/HM888g6qqKui6jsbGRqxduxbp6emIjY3FmDFjkJycjEcffRQtLS0QQuDIkSPGZLW7774bO3fuxLp16+BwOHDmzBlj8tuAAQPQ0tJinGYAgHnz5uG1117DZ599BlVV8cILL+DkyZM+k/Coe+Lo3FdACrrNZkNCQoLxzUqSJCQnJ6O6utpnu+rqap/DmKmpqcY2o0aNwoMPPogrrrgCAwcOxPPPP48XXnihw306HA40Nzf7PABA0zTjv+3Fqqr6xLqunzN2uVw+sWcU44mFEG1iAD6xrus+seePtqNY0zSfmDn5Jyfvh6cv38/Ps8+pU6fi8ccfx5w5cxAfHw9VVTFixAifnDx9uOqqq/Dwww9j6tSpiI2NxV//+lfceuut58xJ13Wjj+3l9Le//Q0/+clPkJ2djd69e2PKlCkYPnw4PvzwQ0iSBCEEMjIy8Le//Q1PPfUUYmJicOWVV+LTTz+FEAIjRoxAYWEhHn30UfTv3x+JiYlYvny58aXkpZdewpo1a9CnTx8sWrQId9xxR5vfk67rPv31+H5OvXr1Qnl5OSZOnIjIyEgMHToUdXV1ePvtt42f69tvv42jR4/iyiuvRFRUFGbMmIFvvvkGQggMHDgQ77//Pl5//XUMGDAAqamp+Pvf/w4hBAYNGoT58+cbM9pLS0sxceJEPP/885g/fz5iY2NRWFiI9957D5GRkT6/12D62wvF/5/8nZPD4UBxcbHR167kFDIuylS7H/DFF1+Iq666yue1a6+9Vnz66ac+ry1evFisXr3aeL53715xxRVXCCGEqKqqEhMnThTffvutEEKIF154QVx//fUd7nPFihUCQJuHZ3bsV199Jb766ishhHs2cEVFhRBCiJ07d4rKykohhBCfffaZqKqqEkII8emnn4qamhohhHuG8/Hjx4UQQrz//vvi5MmTQggh3n33XWPm5FtvvSXOnDkjnE6neOutt4TT6RRnzpwRb731lhDCPdPy3XffFUIIcfLkSfH+++8LIYQ4fvy4+Oijj4QQ7tm9np9RVVWV+Oyzz4QQQlRWVoqdO3cKIYSoqKgQu3fvZk7MiTkxJ+bUiZyKiopCZpa7JISfToSdh7q6OqSlpeHkyZNQFAVCCMTHx2P79u0+k1eeffZZVFVV4cUXXwQAFBUVYfXq1fjkk0+wZs0aHD58GH/84x8BAKdPn0bfvn3hcrnaXazf4XD4XMfa3NyMpKQkNDQ0oF+/fsY3N5PJ5BOrqgpJkoxYlmXIstxh7Nm/J1YUBZIkGTHg/kboHZvNZmOEYDabjZGOJ9Z1HYqidBhrmgYhhBG3lwdzYk7MiTmFUk6qqqKlpQXR0dHGaPtCcmpoaEBsbCyampoQGRmJbu3Sf4dwu/76643rav/+97+LcePGtdnm0KFDIj4+Xhw7dkzoui5uvfVW8dJLLwkhhHjzzTfF1VdfLVpaWoQQQhQWFrYZ9Z9LKF17SETU0zidTvHuu+8Kp9PZpXZCqRYEbHqg57rXp59+GpGRkdiwYQMAYMGCBZg5cyZmzpyJQYMGIS8vD+PHj4eu65g6daoxG3727NnYtWsXrr32WlgsFvTt2xevvvpqoNIhIqJLyGw2Y8aMGYHuRlAJyCH3YNDc3IyoqKjQOMxCRNTDeK6QiI6O7tJlh6FUC3jxJRERdTuapmHXrl3GeXICeEU+ERF1O2azGTfddFOguxFUznuE/u67716MfhAREXWaruuoq6vzWfOgp+vUCP3GG280Fqc4cOAA/ud//qdLK1sRERF1ha7r2Lt3LyZNmsSle7/TqZ/CddddhwceeAAffvgh/uM//oPFnIiIAkpRFEydOpVruXvpVEH/3e9+B1VV8cgjj8DpdF7sPhEREZ2Trus4evQoD7l76fRxiuzsbNx3330YOnToxewPERHRD9J1HYcOHWJB98Lr0EPg2kMiIrowoVQLzvvkQ0VFBZ566ikcPnzY5y41O3fu9GvHiIiIOqLrOmw2G5KSkjgp7jvnXdDvuOMO/OxnP8N9993X7k1QiIiILjbPOfTExEQW9O+cd0E3m8146KGHLkZfiIiIOkVRFGRmZga6G0HlvL/W3HzzzXj//fe7vOPKykpkZmYiPT0dY8eOxb59+9rdrqCgAGlpaRg8eDByc3N9DvNXV1fj1ltvxdChQzFs2DC88MILXe4XEREFP03TcPDgQS796uW8C/q0adOQnZ2NqKgoxMXFoX///oiLizvvHS9cuBC5ubk4cOAAli5datxFzZvVasXy5ctRWlqKgwcP4tixYygoKAAACCEwe/Zs/OxnP8M333yDiooK3H777efdDyIi6n6EEDh16hR66Lzudp33LPchQ4Zg1apVuOaaa3zOoaekpHS6jbq6OqSnp6O+vh6KokAIgfj4eGzfvh2pqanGds8++yyqqqrw4osvAgCKioqwevVqfPLJJ/jnP/+Jxx9/HKWlpefTfUMozWwkIqILE0q14LxH6LGxscjOzsagQYOQkpJiPM6HzWZDQkKCscKPJElITk5GdXW1z3bV1dU+baemphrb7Nu3D/3798fcuXORkZGB2bNn4/Dhwx3u0+FwoLm52ecBwDhco2lau7Gqqj6x55rHjmKXy+UTe74veWIhRJsYgE+s67pP7DnN0FGsaZpPzJyYE3NiTqGek9PpxL59+4x+dyWnUHHeBX327NlYt24dGhoacObMGeNxviRJ8nne0YEC7+28t3G5XPjnP/+J5cuXo7y8HLfccgvmzp3b4f5WrlyJqKgo45GUlAQA2Lt3LwD35XgVFRUAgD179qCyshIAUF5eDqvVCsB9aZ7NZgMAlJWVoba2FgBQUlKC+vp6AMDWrVvR2NgIACguLkZLSwsA99EFu90OVVVRVFQEVVVht9tRVFQEAGhpaTGW1G1sbMTWrVsBAPX19SgpKQEA1NbWoqysDID7S5HnUkGr1Yry8nIA7rkJe/bsYU7MiTkxp5DO6ciRI6ipqelyTjt27EDIEOdJkiTjIcuy8d/zcfz4cREZGSlcLpcQQghd18WAAQOE1Wr12W716tXigQceMJ5v2bJFXH/99UIIIf7+97+LiRMnGu+dPn1ayLIsVFVtd592u100NTUZD5vNJgCIhoYGIYQQqqoa/9Y7drlcPrGmaeeMnU6nT6zruk+s63qb2PMz8MSapvnEnp9TR7Gqqj5xe3kwJ+bEnJgTc2qb08mTJwUA0dTUJLq78y7o/nL99deLl19+WQjhLs7jxo1rs82hQ4dEfHy8OHbsmNB1Xdx6663ipZdeEkII0draKgYNGiRqamqEEEK8+eabYuTIkZ3ef1NTU8j8EomIehpVVcVXX33V4SCus0KpFpz3deh2ux3h4eE+r504cQL9+/c/r3by8/ORk5ODp59+GpGRkdiwYQMAYMGCBZg5cyZmzpyJQYMGIS8vD+PHj4eu65g6daoxG75379744x//iBkzZkAIgejoaLz++uvnmw4REVFIOO9Z7rNnz8amTZuM542NjZg2bRr+9a9/+b1zF1MozWwkIqILE0q14LwnxQ0dOhRLliwBALS2tiIrKwu/+MUv/N4xIiKijmiahvLyci4s4+W8C/qqVatw/PhxPPPMM5g1axbuuOMOLFiw4GL0jYiIqEMRERGB7kJQ6fQhd+9L086ePYtbbrkF06ZNw/LlywEAvXr1ujg9vEhC6TALERFdmFCqBZ0u6LIsQ5IkCCGM/xqNSFK3O+wRSr9EIqKeRlVVlJeXIyMjw1ik7EKEUi3o9E/Bs6oOERFRoEmShH79+rVZpKwn63RBP336NHr37g0AOHnyJGJjYy9ap7oT/i0RdQ+8h0doMZlMGDJkSKC7EVQ6NSnuP//zP3HXXXfh4YcfBgDjvDkREVEgqKqKsrKykFqLvas6VdAbGxvx9ttvY9KkSXjiiScudp+IiIjOSZZlJCYmQpbP+2KtkNWpn4TFYgEA3HLLLYiPj8eWLVsuaqeIiIjORZZlpKSksKB76dQ59J/97GdGfP/99/P8ORERBZTnkHtmZmaXZrmHkk59tZk0aZLP84yMjIvSGSIios6QZRmDBw/mCN3LBf0knn322S7vuLKyEpmZmUhPT8fYsWOxb9++drcrKChAWloaBg8ejNzc3DYTIIQQmDZtGi677LIu94mIiLoHnkNvq1M/iZSUFEyfPh3Tp0/HjTfeiHfffbfLO164cCFyc3Nx4MABLF261LiLmjer1Yrly5ejtLQUBw8exLFjx1BQUOCzzdq1a5Gamtrl/hARUfehqiq2bt3KWe5eOlXQb7zxRhQXF6O4uBgffvghZsyY0aWd1tXVYffu3Zg3bx4AYM6cObBaraiqqvLZbuPGjZg9ezYGDBgASZKwaNEiFBYWGu9XVlbir3/9K5YtW9al/hARUfciyzJGjBjBEbqXTv0k1qxZ4/P8pZde6tJObTYbEhISjIkMkiQhOTkZ1dXVPttVV1cjJSXFeJ6ammpso+s67r//frz44oswm80/uE+Hw4Hm5mafBwBjyVpN09qNVVX1iT0r5nnH4eEqZNkTu4w4IsIFWRZGLEkCgEBEhAuAgCR5YkCWvWMd4eHesfsbqMmkw2Jxx4riHWsIC/OO3f01mzWYze44LEyDonhi1YgtFhWKohuxycScmFPo5qTrujGi6yjWNM0n9sdnhHfscrl8Ys8y2p5YCNEmBuAT67ruE/fEnIQQiImJgSzLXc4pVHSqoEdHRxtxdXU1SktLUVpa2qYAn4/vL9fX0ZLy3tt5b7NmzRpMmjQJo0eP7tT+Vq5ciaioKOORlJQEANi7dy8AoKKiAhUVFQCAPXv2oLKyEgBQXl4Oq9UKANi5cydsNhsAoKysDLW1tQCA1atLMHJkPQBg7dqtSEtrBAAUFBQjMbEFAFBYWISYGDsiIlQUFhYhIkJFTIwdhYVFAIDExBYUFBQDANLSGrF27VYAwMiR9Vi9ugQAMG5cLfLyygAAkyfbsGzZTgBAVpYVS5aUAwCysytx//17AADz5lVg3jx3TvffvwfZ2e6cliwpR1aWO6dly3Zi8mR3Tnl5ZRg3jjkxp9DNqb6+HiUl7pxqa2tRVubOyWazYedOd05WqxXl5e6cKisrsWePO6eufEaUlJSgvt6d09atW9HY6M6puLgYLS3unIqKimC326GqKoqKiqCqKux2O4qK3Dm1tLSguNidU2NjI7Zu7dk5HTx4EO+99x5cLleXctqxYwdChuikiooK8eMf/1hcfvnlYuzYsWLMmDHi8ssvFz/+8Y/Fvn37OtuMEEKI48ePi8jISOFyuYQQQui6LgYMGCCsVqvPdqtXrxYPPPCA8XzLli3i+uuvF0IIMWPGDJGUlCRSUlJEYmKikGVZpKSkiIaGhnb3abfbRVNTk/Gw2WwCgLG9qqpCVdU2scvl8ok1TfOJASHCw11Clj2x04gjIpxClnUjliRdALqIiHAKQBeS5ImFkGXvWBPh4d6xSwBCmEyasFjcsaJ4x6oIC/OOVQEIYTarwmx2x2FhqlAUT+wyYovFJRRFM2KTiTkxp9DMSQghNE0zPnc6ilVV9Ynb+1w4n8+I78dOp9Mn1nXdJ9Z1vU3s+Zz0xJqm+cQ9MSen0ynq6uqEpmldyunkyZMCgGhqahLdXacL+rhx48TGjRvbvP73v/9djBkz5rx3fP3114uXX37ZaGPcuHFttjl06JCIj48Xx44dE7qui1tvvVW89NJLbbazWq0iNjb2vPbf1NTkl1+ie4VoPvjgI9gfRO3xVy0IBp2eTXDq1CnMmTOnzevZ2dloamo67yMD+fn5yM/PR3p6OlatWmXMXl+wYAE2b94MABg0aBDy8vIwfvx4DB48GHFxce3Ohiciop7F5XJhy5Ytxnl3Oo/7oY8fPx6LFi3C3Xffbcwq1HUdr7zyCvLz841zHN2Fv+6By7utEXUPnfuko+5CCIGWlhb07du3S7dQ7ZH3Q9+wYQMWLlyIJUuWICEhAZIkoaamBhkZGVi/fv1F7CIREZEvSZK6fQH2t04X9CFDhuCjjz7CiRMnjNmBSUlJ6N+//0XrHBERUXtcLheKioqQlZXVqUuXe4LzXtG+f//+LOJERBRQiqJg+vTpvDGLF78ssZOenu6PZoiIiDqNxdxXp38aHd08BQBaW1v90hkiIqLO8CxWw0Pu/3+dLugjRoxAamoq2psU71ktiIiI6FJQFAVZWVkcpXvp9E8iJSUFpaWlSEhIaPOeZxlVIiKiS0VVVRZ0L50+hz5z5kwcPny43fdmzZrltw4RERH9EFVVUVxcHFI3V+mqTi8sE2q4sAxRz9IzP+noh4TSwjK8kSwREXU7Qgg0Nze3O6+rp2JBJyKibkdVVWzbto2H3L0ErKBXVlYiMzMT6enpGDt2bIeXxRUUFCAtLQ2DBw9Gbm6u8cv76quvMGnSJAwbNgxXX301cnNz4XA4LmUKREQUIGazGTNmzOAla14CVtAXLlyI3NxcHDhwAEuXLm33LmpWqxXLly9HaWkpDh48iGPHjhl3ZQsPD8fatWuxf/9+fPnll2hqasJzzz13qdMgIqIA0HUdDQ0N0HU90F0JGgEp6HV1ddi9ezfmzZsHAJgzZw6sViuqqqp8ttu4cSNmz56NAQMGQJIkLFq0CIWFhQCAtLQ0jBw5EgBgMpkwZsyYDmfhExFRaNE0Dbt27YKmaYHuStAISEG32WxISEgwrh+UJAnJycmorq722a66uhopKSnG89TU1DbbAMDp06fxpz/9CbfeemuH+3Q4HGhubvZ5ADD+GDRNazdWVdUn9nwb9I7Dw1XIsid2GXFEhAuyLIxYkgQAgYgIFwABSfLEgCx7xzrCw71j92kGk0mHxeKOFcU71hAW5h27+2s2azCb3XFYmAZF8cSqEVssKhRFN2KTiTkxp9DNSdd147RdR7GmaT6xPz4jvGOXy+UTeyZ1eWIhRJsYgE+s67pP3BNzkmUZ06ZNg9ls7nJOoSJgh9y/f//ajmYqem/X3jYulws//elPMX369HNeD79y5UpERUUZD89iOHv37gUAVFRUoKKiAgCwZ88eVFZWAgDKy8thtVoBADt37jTuNFdWVoba2loAwOrVJRg50r1a3tq1W5GW1ggAKCgoRmJiCwCgsLAIMTF2RESoKCwsQkSEipgYOwoLiwAAiYktKCgoBgCkpTVi7dqtAICRI+uxenUJAGDcuFrk5bnvOz95sg3Llu0EAGRlWbFkSTkAIDu7EvffvwcAMG9eBebNc+d0//17kJ3tzmnJknJkZblzWrZsJyZPdueUl1eGceOYE3MK3Zzq6+tRUuLOqba2FmVl7pxsNht27nTnZLVaUV7uzqmyshJ79rhz6spnRElJibGi5tatW9HY6M6puLgYLS3unIqKimC3240lTVVVhd1uR1GRO6eWlhYUF7tzamxsxNatPTunw4cP4/PPP4eu613KaceOHQgZIgCOHz8uIiMjhcvlEkIIoeu6GDBggLBarT7brV69WjzwwAPG8y1btojrr7/eeO50OsVtt90mFixYIHRdP+c+7Xa7aGpqMh42m00AEA0NDUIIIVRVFaqqtoldLpdPrGmaTwwIER7uErLsiZ1GHBHhFLKsG7Ek6QLQRUSEUwC6kCRPLIQse8eaCA/3jl0CEMJk0oTF4o4VxTtWRViYd6wKQAizWRVmszsOC1OFonhilxFbLC6hKJoRm0zMiTmFZk5CCKFpmvG501GsqqpP3N7nwvl8Rnw/djqdPrHns8sT67reJvZ8TnpiTdN84p6Yk91uF//85z+Nvl5oTidPnhQARFNTk+juArawzOTJk5GTk4OcnBxs3LgRa9aswfbt2322OXz4MCZMmIDy8nLExcVh1qxZyMrKwqJFi6CqKn76058iOjoaf/rTn9qM+H8IF5Yh6ll4uTK1hwvL+EF+fj7y8/ORnp6OVatWGbPXFyxYgM2bNwMABg0ahLy8PIwfPx6DBw9GXFycMRv+b3/7G/7xj3/giy++QEZGBkaPHo1f/vKXgUqHiIguIV3XcfToUc5y98KlXzlCJ+oReuYnXehSVRVlZWXIzMzs0g1aQmmEztvUEBFRt6MoCiZNmhTobgQVLv1KRETdjq7rOHLkCA+5e2FBJyKibofn0NviIXciIup2FEVBZmZmoLsRVDhCJyKibkfTNBw8eJBLv3rhCJ2oHQK8fCHk8FcaUkRYGE6VliI1NTXQXQkaLOhERNTtKE4nxowZE+huBBUeciciom5HUxTs37+fh9y9sKATEVH3I8s4e/ZsoHsRVHjInYiIuh2T04mMjIxAdyOoBOUIvbKyEpmZmUhPT8fYsWOxb9++drcrKChAWloaBg8ejNzc3JC6ry0REXVMM5uxd+9eHnL3EpQFfeHChcjNzcWBAwewdOlS44Ys3qxWK5YvX47S0lIcPHgQx44dM27wQkRE1NMEXUGvq6vD7t27MW/ePADAnDlzYLVaUVVV5bPdxo0bMXv2bAwYMACSJGHRokUoLCwMQI+JiOhSM7lcGDFiBEwmU6C7EjSC7hy6zWZDQkKCcfccSZKQnJyM6upqn+sNq6urkZKSYjxPTU1FdXV1h+06HA44HA7jeVNTEwDg1KlTAGActjGZTD6xqqqQJMmIZVmGLMtGDMiwWFQ4nTKEkGGxuOB0miCEjPBwFxwOBUJICA93wW535xQern4vNkOSBCwWT6wjLEyDw+GJdTgcCmRZh6LocDoVmEw6TCZPrEGWBVwuTwy4XCYoijsPVTXBbNag64CmmWA2q9B1CZpmQliYCk2ToWkywsJUqKoMXWdOTQDU8HAodrt7f+HhMNvtEJIE1WKB2W6HLknQwsJgdjigSxL0sDAoDgd0WYauKFCcTugmE3STCYrTCc1kgpBlKC4XNJMJkGWYXC5o3/2tm1QVmtkM6DpMmgbVbIbkicPCIGsaZE+sqpB1HarFAtnphCwEXBYLTJ44PByKwwHJE3vlwZyYUyjk5IyIwNeffoqRI0can+vf//zuzGd5Q0MDACAUbjwadAUdcBdxbx39oL23+6FfxsqVK5GXl9fmdX8sSuD1PcEn/u5vt1OxEL6xpx3vWNcBp9Mda5r7ca7Ye0qBy9V+7Gnv+3FPzyk6FJNiTswplHI6exaYPBn+0tLSgqioKL+1FwhBV9CTkpJQU1MDVVWhKAqEELDZbEhOTvbZLjk52ecw/JEjR9ps4+3hhx/Ggw8+aDzXdR0NDQ2IjY1t8wWCiIiCW3NzM5KSkmCz2bp0H3MhBFpaWpCQkODH3gVG0BX0uLg4ZGRk4NVXX0VOTg7efPNNpKamthlJz5kzBxMmTMBjjz2GuLg4rFu3DnPnzu2wXYvFAovF4vNadHT0RciAiIgulcjIyC4VdADdfmTuEXST4gAgPz8f+fn5SE9Px6pVq4zZ6wsWLMDmzZsBAIMGDUJeXh7Gjx+PwYMHIy4urt3Z8ERERD2BJEJhJgAREfUozc3NiIqKQlNTU5dH6KEiKEfoRERE52KxWLBixYo2p1J7Mo7QiYiIQgBH6ERERCGABZ2IiCgEsKATERGFABZ0IiKiEMCCTkREFAJY0ImIiEIACzoREVEIYEEnIiIKASzoREREISDoCvqvfvUrpKamQpIk7N27t8PtCgoKkJaWhsGDByM3Nxeq9319iYiIepigK+jZ2dkoLS1FSkpKh9tYrVYsX74cpaWlOHjwII4dO2bckY2IiKgnCrqCPmnSJAwcOPCc22zcuBGzZ8/GgAEDIEkSFi1ahMLCwkvUQyIiouCjBLoDF6K6utpnBJ+amorq6upz/huHwwGHw2E813UdDQ0NiI2NhSRJF62vREQUvIQQaGlpQUJCAmQ56Ma456VbFnQAPkW4MzeMW7lyJfLy8i5ml4iIqJuy2Ww/eHQ42HXLgp6cnIyqqirj+ZEjR5CcnHzOf/Pwww/jwQcfNJ43NTUZ7fTr1w+apgEATCaTT6yqKiRJMmJZliHLcoexy+WCyWQyYkVRIEmSEQOAqqo+sdlshhDCiHVdh6ZpRqzrOhRF6TDWNA1CCCNuLw/mxJyYE3MKpZwcDgd27dqF6667zhjgXUhODQ0NuOKKK9C3b190d92yoM+ZMwcTJkzAY489hri4OKxbtw5z584957+xWCywWCxtXu/Xrx8iIyMvVleJiOgi0HUdo0aNQnR0tF8OlYfCqdegO2Hwy1/+EgMHDkRNTQ1uuOEGDBkyBACwYMECbN68GQAwaNAg5OXlYfz48Rg8eDDi4uIwf/78QHabiIguIVmWkZiY2O3Pe/uTJDpzAjoENTc3IyoqCk1NTRyhExF1M6qqoqSkBJMmTTIO5V+IUKoF/GpDRETdjizLGDFiBEfoXrrlOXQiIurZZFlGXFxcoLsRVPjVhoiIuh2Xy4UPPvgALpcr0F0JGizoRETU7ZhMJowZMwYmkynQXQkaPORORETdjizLiImJCXQ3ggpH6ERB4vHHH8dtt93Wrffx9NNP484777xo7RN5uFwubNmyhYfcvbCgE3Xgm2++wa233orLLrsMkZGRGDZsGJ555hm/tL1+/XqMHj3aL2395S9/gSRJeOmlly7aPtrTXvuPPPLIBd8oaceOHZgyZQr69euH6OhojBw5EuvXr+9yPz/55BNER0d3uR0KLoqiYOLEiV26ZC3UsKATdWDGjBkYNWoUqqurcerUKbz55psYNGhQoLvVRkFBAWJiYrr1LYRbWlpw880346c//Snq6upw4sQJFBQUBM0sZlVVA90F+h5JkhAZGRkSK7z5jeihmpqaBADR1NQU6K5QEDpx4oQAIKqrqzvc5tixY+L2228Xl112mUhKShKPPPKIcLlcQgghXn75ZTFq1Cif7UeNGiVefvllsXv3bmGxWIQsy6J3796id+/e4siRI2LFihXiJz/5ifjlL38poqKiRFJSkvjrX/96zn5WVlYKAOKtt94SkiSJL7/8UgghzrmPWbNmGf/+oYceEsnJyaJPnz7iyiuvFG+88Ybx3scffyyioqLE//3f/4mBAweKmJgY8dBDD51X+7W1teLuu+8W8fHxIioqSkycOFGcOXOmTR67du0SZrNZaJrWYa7Hjx8Xd911l4iPjxfx8fFiyZIlwm63G+9/8cUXYsqUKaJfv37isssuE4sXLxb19fUiPDxcADD6WVJSIoQQ4pVXXhHDhg0TUVFRYvz48WL37t1GW9dff7146KGHxI033ih69eolNm/efM7fA116TqdTvPXWW8LpdHapnVCqBRyhE7UjNjYWw4YNw89//nO88cYbOHLkSJtt7rrrLpjNZlitVmzbtg1vvfUWVq9e/YNtZ2RkYN26dbj66qvR2tqK1tZW4+ZCH3zwAcaPH4+TJ0/iySefxIIFC9DS0tJhWwUFBcjIyMCsWbMwceJEY5R+rn14GzVqFHbt2oXGxkY89thjuOeee2C1Wo33W1pa8NVXX6GyshKlpaV48cUX8cknn3SqfV3XMXPmTCiKgq+//hr19fV4+umn210IZOjQoYiOjsbcuXPx9ttv49ixYz7vCyEwc+ZMXH755Th48CC++uor/Pvf/8aTTz4JADh69CimTp2K7OxsfPvttzhy5AjuuOMOxMbG4r333kNUVJTRz4kTJ2Lbtm34xS9+gfz8fJw4cQLZ2dm46aab0NTUZOxz/fr1ePLJJ9Ha2oobbrjhh36tdIkpioLp06fzkLsXFnSidkiShI8//hijRo1CXl4eBg0ahKuuugoffvghAHcB2bp1K5577jn06dMHKSkp+O1vf9vlc77XXHMN7rzzTphMJtxzzz1wOp04cOBAu9tqmoYNGzbg3nvvBQD87Gc/w2uvvQaHw9Hp/d19992Ii4uDyWTC3LlzMWzYMJSVlRnvCyGwcuVKhIeH48orr0RmZib+9a9/dartXbt2Yd++fXjppZfQr18/KIqCCRMmtHuTpL59+6KsrAwxMTF48MEHkZCQgHHjxmH37t0AgC+++AKVlZV49tln0atXL8TGxuKRRx7B66+/DgB49dVX8aMf/QgPPPAAwsPD0atXL0ycOLHDvv3lL3/BvHnzMGnSJJjNZvz6179Gv379sGXLFmObu+66C2PHjoUkSYiIiOhUznRpsZj7YkEn6sDll1+O5557Dl9//TVOnDiBW265BbNnz0ZDQwNqamoQHh6Oyy+/3Nh+0KBBqKmp6fI+PTyFpKMRelFREerr63HXXXcBAG6//XacPXsWmzZt6vT+nn/+eQwfPhxRUVGIjo7G3r17UV9fb7wfGRmJXr16Gc979+59ziMG3o4cOYLExMROF8MhQ4Zg3bp1OHToEGpqajBkyBDMnDkTQghUVVWhsbERMTExiI6ORnR0NLKzs3H8+HFjX2lpaZ3Ou6amBqmpqT6vXXHFFT6/vx+6JTMFlqqqKCoq4vwGLyzoRJ0QExODxx9/HKdPn4bVasXAgQNht9uNggLAeB0A+vTpgzNnzvi04X0Y2R/rTxcUFEDXdVx99dW4/PLLkZ6eDpfLZRx2/6F9lJaW4vHHH8df/vIXnDp1Co2NjRgxYgREJ+/X9EPtp6Sk4OjRozh79mznEvKSkJCAZcuW4ejRo2hoaEBSUhLi4uLQ2NhoPJqamtDa2mrs6+DBg53u58CBA1FVVeXzWlVVlfH76+jfUfBQFAVZWVkcpXvhXyxRO06dOoVHH30U+/fvh6ZpOHPmDH7/+98jJiYGw4YNQ2JiIqZMmYL/+q//wunTp1FdXY2nn37aOPw9evRoHD58GNu2bYOqqli9ejVOnjxptD9gwADU1tZeULEDgOPHj2PLli34y1/+gi+//NJ4vPPOO/joo49QVVX1g/tobm6Goijo378/dF3Hn//8Z+zdu7fTffih9seMGYOhQ4fil7/8JRobG6GqKkpLS9s9JbB//34888wzqKqqgq7raGxsxNq1a5Geno7Y2FiMGTMGycnJePTRR9HS0gIhBI4cOYL33nsPgPvUwc6dO7Fu3To4HA6cOXMG27ZtM/rZ0tKCEydOGPubN28eXnvtNXz22WdQVRUvvPACTp48iaysrE7nT4HH0bkvFnSidoSFheHo0aPIyspCVFQUkpOT8dlnn+H9999H7969AQCvv/46zp49i5SUFIwfPx4zZszA0qVLAbgPH69evRrZ2dmIj4+Hw+HA8OHDjfanTp2K6667DomJiYiOjkZ1dfV59W/Dhg1ITk7G3LlzcfnllxuPm2++GT/60Y/w5z//+Qf3cfPNN2POnDm4+uqrkZCQgK+//hrjx4/vdB9+qH1ZlvHOO+/gzJkzGDp0KC677DI8+uij0HW9TVt9+/ZFeXk5Jk6ciMjISAwdOhQnTpzAO++8A8C9zOc777yDo0eP4sorr0RUVBRmzJhhjMoHDhyIf/7zn3j99dcxYMAApKamYuPGjQDcE+7mz5+PK6+8EtHR0SgtLcX111+PF154AfPnz0dsbCz++te/4r333uP16t2IqqooLi5mUffC+6GHwD1wiYjowoRSLQjKEXplZSUyMzORnp6OsWPHYt++fW22EULgoYcewvDhwzFy5EhMmTKlw3NoREQUWoQQaG5u7vScj54gKAv6woULkZubiwMHDmDp0qWYP39+m202b96MkpISfPnll9izZw+mTZuGRx55JAC9JSKiS01VVWOOCrkFXUGvq6vD7t27MW/ePADAnDlzYLVa28xIBQCHwwG73W58U/OeoUpERKHLbDZjxowZMJvNge5K0Ai6gm6z2ZCQkGBciiBJEpKTk9tMuLn11lsxZcoUXH755YiPj8dHH32EJ554osN2HQ4HmpubfR6Ae3EOz3/bi1VV9Yk9E3o6il0ul0/sORzkiYUQbWIAPrGu6z6x5xtoR7GmaT4xc2JOzIk5hXpOLpcLJ06cgK7rXc4pVARdQQfQZrH99s6R7N69G/v378fRo0fx7bffYtq0aVi8eHGHba5cuRJRUVHGIykpCQCMy3QqKipQUVEBANizZw8qKysBAOXl5cZSmDt37oTNZgMAlJWVoba2FgBQUlJiLMaxdetWNDY2AgCKi4uNRTiKiopgt9t9FkOw2+0oKioC4F5is7i4GADQ2NiIrVu3AgDq6+tRUlICAKitrTVW8bLZbNi5cycA9/XP5eXlANzzD/bs2cOcmBNzYk4hndPhw4exfft2aJrWpZx27NiBUBF0s9zr6uqQlpaGkydPQlEUCCEQHx+P7du3+6zstHjxYiQnJxuXCX399dfIyspqd81twD1C977+tbm5GUlJSWhoaEC/fv2Mb24mk8knVlUVkiQZsSzLkGW5w9jlcsFkMhmxoiiQJMmIAfc3Qu/YbDZDCGHEnm+cnljXdSiK0mGsaRqEEEbcXh7MiTkxJ+bEnNrm1NDQgNjY2JCY5e7Xgv7uu+/iJz/5SZfbmTx5MnJycpCTk4ONGzdizZo12L59u882v//97/HBBx/g3XffhdlsxqpVq7Bt2zaftZjPJZQuVSAi6ml0XUd9fT0uu+yyLq3qF0q1oMsF/cYbb4QkSRBC4MCBAxg6dKhxCOVCffPNN8jJycHJkycRGRmJDRs2YPjw4ViwYAFmzpyJmTNnwuFwYPHixdi2bRvCwsIQHx+P/Pz8NuszdySUfolERD2NqqooKSnBpEmTurT8ayjVgi4X9OXLl+NHP/oRbrvtNvzmN7/B888/76++XVSh9EskIqILE0q1oMuT4n73u99BVVU88sgjcDqd/ugTERHROem6jqNHj7a7lHBP5ZdZ7tnZ2bjvvvswdOhQfzRHRER0Trqu49ChQyzoXoJulvulEkqHWYiI6MKEUi3w641kKyoq8NRTT+Hw4cM+F+t7rhskIiLyB13XYbPZkJSUxHvXf8evBf2OO+7Az372M9x3330wmUz+bJqIiMjgOYeemJjIgv4dvxZ0s9mMhx56yJ9NEhERtaEoCjIzMwPdjaDi1681N998M95//31/NklERNSGpmk4ePCgsRoc+XmEPm3aNMyaNQsmkwkWiwVCCEiShLq6On/uhoiIejghBE6dOtXpxcR6Ar8W9IULF2L9+vW45ppreA6diIguGkVRMGbMmEB3I6j4taDHxsYiOzvbn00SERG14bnLWlpaGgeQ3/HrOfTZs2dj3bp1aGhowJkzZ4wHERGRv509ezbQXQgqfl1YxvvSAc8NWyRJCspJC6G0mAAREV2YUKoFfh2he+5V67mvree/RERE/qRpGvbu3csa48WvBd1ut7d57cSJE/7cBREREbXDrwX9zjvv9Hne2NiIm2++2Z+7ICIigslkwogRIzghzotfC/rQoUOxZMkSAEBrayuysrLwi1/8wp+7ICIigqZpKC8v5yF3L34t6KtWrcLx48fxzDPPYNasWbjjjjuwYMGC826nsrISmZmZSE9Px9ixY7Fv374223zyySfo1asXRo8ebTw445GIqOeIiIgIdBeCil+uQ/e+NO3FF1/ELbfcgmnTpiE3NxdnzpxBr169zqu9hQsXIjc3Fzk5Odi4cSPmz5+Pzz//vM12V111Fb744osu95+IiLoXk8mEYcOGBbobQcUvI/Q+ffqgb9++6NOnD+Li4vDFF1/gmWeeMV4/H3V1ddi9ezfmzZsHAJgzZw6sViuqqqr80VUiIgoBqqpi165dPrfq7un8UtC/f5na9y9fOx82mw0JCQlQFPfBA0mSkJycjOrq6jbbfvPNN7jmmmswZswY/PGPfzxnuw6HA83NzT4PAEb/NE1rN1ZV1SfWdf2cscvl8ok9l/l7YiFEmxiAT6zruk/s+YPtKNY0zSdmTsyJOTGnUM9J13VERUUZa510JadQ4ZeCfvr0aSM+efJkl9uTJMnneXtr31xzzTWoqanB7t27sWnTJqxbtw5vvPFGh22uXLkSUVFRxiMpKQkAsHfvXgBARUUFKioqAAB79uxBZWUlAKC8vBxWqxUAsHPnTthsNgBAWVkZamtrAQAlJSWor68HAGzduhWNjY0AgOLiYrS0tAAAioqKYLfboaoqioqKoKoq7HY7ioqKAAAtLS0oLi4G4L46YOvWrQCA+vp6lJSUAABqa2tRVlYGwP3FZ+fOnQAAq9WK8vJyAO75B3v27GFOzIk5MaeQzqm6uhpNTU0wmUxdymnHjh0IFV1eKe4///M/UV1djauuugorV67EAw888IOj5XOpq6tDWloaTp48CUVRIIRAfHw8tm/ffs676qxcuRLffvstXnjhhXbfdzgccDgcxvPm5mYkJSWhoaEB/fr1M765mUwmn1hVVUiSZMSyLEOW5Q5jl8sFk8lkxIqiQJIkIwbc3wi9Y7PZDCGEEXuObHhiXdehKEqHsaZpEEIYcXt5MCfmxJyYUyjl5HA48MUXX2DcuHHGIPBCcmpoaEBsbGxIrBTX5YJ+zz334JVXXsF7772HXbt24dixY10q6AAwefJk5OTkGJPi1qxZg+3bt/tsU1tbiwEDBkCWZbS0tODmm2/G/Pnzcd9993VqH6G03B8RUU+j6zpsNhuSkpJ8lh0/X6FUC7p8yN1isQAAbrnlFsTHx2PLli1d7lR+fj7y8/ORnp6OVatWoaCgAACwYMECbN68GQDw5ptv4uqrr8aoUaNw3XXX4cYbb8TPf/7zLu+biIiCnyzLSElJ6VIxDzVdHqGXlJRg0qRJxvN//OMf+I//+I8ud+xiC6VvZUREPY2qqigrK0NmZqZxKP9ChFIt6PJXG+9iDgAZGRldbZKIiOicZFnG4MGDOUL34vefxLPPPuvvJomIiHzIsozExEQWdC9dXikuJSUFQ4cOBeC+vOybb77p8qQ4IiKic1FV1Tjl25VD7qGkyz+FG2+8EX/605+M57wZCxERXWyyLGPEiBEcoXvp8qS4xsZGREdH+6k7l04oTYQgIqILE0q1oMtfbbyLeXV1NUpLS1FaWtruUq1ERET+4HK58MEHHxjLxZKf7ra2f/9+3HfffbBarUhOToYQAjabDVdccQUKCgpw5ZVX+mM3REREANyrv40ZMwYmkynQXQkafinoOTk5eOihhzBnzhyf1zdu3Ih7773XWHs3FH1v2XkiClJdO7lIwUaWZcTExAS6G0HFL7MJTp061aaYA0B2djaampr8sQsiIiKDy+XCli1beMjdi18K+mWXXYZXXnnFuB0d4F5nd8OGDYiNjfXHLoiIiAyKomDixIm8ZM2LX34SGzZswMKFC7FkyRIkJCRAkiTU1NQgIyMD69ev98cuiIiIDJIkdftZ6f7ml4I+ZMgQfPTRRzhx4oRxj9mkpCT079/fH80TERH5cLlcKCoqQlZWFsxmc6C7ExT8eqyif//+LOJERHTRKYqC6dOn85C7l4u+xE56evrF3gUREfVALOa+/PLT2LdvX4fvtba2+mMXREREBlVVecj9e/xS0EeMGIHU1FS0t4psfX39ebdXWVmJe++9F/X19YiOjsb69etx1VVX+WyzdetWPPzww2hpaYEsy5g1axaefPJJSLwwnPxAgH9HIYe/0pCiAMhyOjlK9+KXn0RKSgpKS0uRkJDQ5r2kpKTzbm/hwoXIzc1FTk4ONm7ciPnz5+Pzzz/32aZfv34oLCzEoEGDYLfbccMNN6CwsBB33XXXBedBRETdhCRBVVUWdC9+OYc+c+ZMHD58uN33Zs2adV5t1dXVYffu3Zg3bx4AYM6cObBaraiqqvLZLiMjA4MGDQIAhIeHY/To0R32gYiIQosaHo7i4mKoqhrorgQNvxT0P/zhD5gwYUK7761du/a82rLZbEhISDC+dUmShOTk5HPe7OXYsWPYuHEjsrKyOtzG4XCgubnZ5wEAmqYZ/20vVlXVJ/YsnuMdh4erkGVP7DLiiAgXZFkYsSQJAAIRES4AApLkiQFZ9o51hId7x+4/WJNJh8XijhXFO9YQFuYdu/trNmswm91xWJgGRfHEqhFbLCoURTdik4k5SZKAAOCKiIAAICQJrogIAICQZSPWZRmu8HAjVj2xyQTVYnHHimLEmqJADQszYs0Tm83QvjsHqIWFQfvub1/1ji0W6N7xd+tXq+Hh0L+7faTLO46IgPCOJYk5MaeQykl2uTBjxgyYzeYOP787+1keKoLyRrLfPw9+rju8Njc349Zbb8XSpUtxzTXXdLjdypUrERUVZTw8pwL27t0LAKioqEBFRQUAYM+ePaisrAQAlJeXw2q1AgB27txpXGdfVlaG2tpaAMDq1SUYOdI9V2Dt2q1IS2sEABQUFCMxsQUAUFhYhJgYOyIiVBQWFiEiQkVMjB2FhUUAgMTEFhQUFAMA0tIasXbtVgDAyJH1WL26BAAwblwt8vLKAACTJ9uwbJl7jfysLCuWLCkHAGRnV+L++/cAAObNq8C8ee6c7r9/D7Kz3TktWVKOrCx3TsuW7cTkye6c8vLKMG4cc4qJsUONiEBRYSHUiAjYY2JQVFgIAGhJTERxQQEAoDEtDVu/+8JaP3IkSlavBgDUjhuHsrw8AIBt8mTsXLYMAGDNykL5kiUAgMrsbOy5/34AQMW8eaj47ojUnvvvR2V2NgCgfMkSWL/7krpz2TLYJk8GAJTl5aF23DgAQMnq1agfORIAsHXtWjSmpQEAigsK0JKYCAAoKiyEPSaGOTGn0Mppxgzs3LkTQghUVlZizx73Z8T5fpbv2LEDIUMEmePHj4vIyEjhcrmEEELoui4GDBggrFZrm22bm5vFj3/8Y/HEE0/8YLt2u100NTUZD5vNJgCIhoYGIYQQqqoKVVXbxC6XyyfWNM0nBoQID3cJWfbETiOOiHAKWdaNWJJ0AegiIsIpAF1IkicWQpa9Y02Eh3vHLgEIYTJpwmJxx4riHasiLMw7VgUghNmsCrPZHYeFqUJRPLHLiC0Wl1AUzYhNJuYkSbrQAeGMiBA6IHRJEs6ICCEAocuyEWuyLJzh4Ubs8sQmk3BZLO5YUYxYVRThCgszYtUTm81CNZvdcViYUBVFCEC4vGOLRWjescnkjsPDhSbLQgDC6R1HRAjdO5Yk5sScQiqns337infeeUc4nc4OP78781l+8uRJAUA0NTWdf8EKMpIQwXcPosmTJyMnJ8eYFLdmzRps377dZ5vW1lbcdNNNmD59OlasWHHe+/DXTe05qT40cZY7UTfgh/Llr1oQDILykHt+fj7y8/ORnp6OVatWoeC7QzILFizA5s2bAbjP2+/cuRObNm3C6NGjMXr0aDz11FOB7DYREV0iuiyjoaHB56ZgPV1QjtAvBY7Q6Vw4QicKbq7wcGx96y1MnTq1SwvLhNIInRfwERFRt2O223HTTTcFuhtBJSgPuRMREZ2LLsuoq6vjIXcvLOhERNTt6GFh2Lt3Lwu6Fx5yJyKibkex2zF16tRAdyOocIRORETdjm4y4ejRoxyhe2FBJyKibkdXFBw6dIgF3QsPuRMRUbejOByYNGlSoLsRVDhCJyKibkdXFBw5coQjdC8s6ERE1O3wHHpbPORORETdjuJwIDMzM9DdCCocoRMRUbejKQoOHjxo3OOcWNCJiKgbErKMU6dOoYfejqRdPORORETdjuJ0YsyYMYHuRlDhCJ2IiLodTVGwf/9+HnL3woJORETdjyzj7Nmzge5FUOEhdyIi6nZMTicyMjIC3Y2gEpQj9MrKSmRmZiI9PR1jx47Fvn372t2uoKAAaWlpGDx4MHJzc6Gq6iXuKRERBYJmNmPv3r085O4lKAv6woULkZubiwMHDmDp0qWYP39+m22sViuWL1+O0tJSHDx4EMeOHUNBQUEAektERBR4QVfQ6+rqsHv3bsybNw8AMGfOHFitVlRVVflst3HjRsyePRsDBgyAJElYtGgRCgsLA9BjIiK61EwuF0aMGAGTyRTorgSNoDuHbrPZkJCQAEVxd02SJCQnJ6O6uhqpqanGdtXV1UhJSTGep6amorq6usN2HQ4HHA6H8bypqQkAcOrUKQAwDtuYTCafWFVVSJJkxLIsQ5ZlIwZkWCwqnE4ZQsiwWFxwOk0QQkZ4uAsOhwIhJISHu2C3u3MKD1e/F5shSQIWiyfWERamweHwxDocDgWyrENRdDidCkwmHSaTJ9YgywIulycGXC4TFMWdh6qaYDZr0HVA00wwm1XougRNMyEsTIWmydA0GWFhKlRVhq4zpyYAang4FLvdvb/wcJjtdghJgmqxwGy3Q5ckaGFhMDsc0CUJelgYFIcDuixDVxQoTid0kwm6yQTF6YRmMkHIMhSXC5rJBMgyTC4XtO/+1k2qCs1sBnQdJk2DajZD8sRhYZA1DbInVlXIug7VYoHsdEIWAi6LBSZPHB4OxeGA5Im98mBOzCkUcnJGRODrTz/FyJEjjc/1739+d+azvKGhAQBC4nr2oCvogLuIe+voB+293Q/9MlauXIm8vLw2r3t/SbhQXt8TfOLv/nY7FQvhG3va8Y51HXA63bGmuR/nir2nFLhc7cee9r4f9/ScokMxKebEnEIpp7NngcmT4S8tLS2IioryW3uBEHQFPSkpCTU1NVBVFYqiQAgBm82G5ORkn+2Sk5N9DsMfOXKkzTbeHn74YTz44IPGc13X0dDQgNjY2DZfIIiIKLg1NzcjKSkJNpsNkZGRF9yOEAItLS1ISEjwY+8CI+gKelxcHDIyMvDqq68iJycHb775JlJTU9uMpOfMmYMJEybgscceQ1xcHNatW4e5c+d22K7FYoHFYvF5LTo6+iJkQEREl0pkZGSXCjqAbj8y9wi6SXEAkJ+fj/z8fKSnp2PVqlXG7PUFCxZg8+bNAIBBgwYhLy8P48ePx+DBgxEXF9fubHgiIqKeQBKhMBOAiIh6lObmZkRFRaGpqanLI/RQEZQjdCIionOxWCxYsWJFm1OpPRlH6ERERCGAI3QiIqIQwIJOREQUAljQiYiIQgALOhERUQhgQSciIgoBLOhEREQhgAWdiIgoBLCgExERhQAWdCIiohAQdAX9V7/6FVJTUyFJEvbu3dvhdgUFBUhLS8PgwYORm5sL1fu+vkRERD1M0BX07OxslJaWIiUlpcNtrFYrli9fjtLSUhw8eBDHjh0z7shGRETUEwVdQZ80aRIGDhx4zm02btyI2bNnY8CAAZAkCYsWLUJhYeEl6iEREVHwUQLdgQtRXV3tM4JPTU1FdXX1Of+Nw+GAw+Ewnuu6joaGBsTGxkKSpIvWVyIiCl5CCLS0tCAhIQGyHHRj3PPSLQs6AJ8i3Jkbxq1cuRJ5eXkXs0tERNRN2Wy2Hzw6HOy6ZUFPTk5GVVWV8fzIkSNITk4+5795+OGH8eCDDxrPm5qajHb69esHTdMAACaTySdWVRWSJBmxLMuQZbnD2OVywWQyGbGiKJAkyYgBQFVVn9hsNkMIYcS6rkPTNCPWdR2KonQYa5oGIYQRt5cHc2JOzIk5hVJODocDu3btwnXXXWcM8C4kp4aGBlxxxRXo27cvurtuWdDnzJmDCRMm4LHHHkNcXBzWrVuHuXPnnvPfWCwWWCyWNq/369cPkZGRF6urRER0Eei6jlGjRiE6Otovh8pD4dRr0J0w+OUvf4mBAweipqYGN9xwA4YMGQIAWLBgATZv3gwAGDRoEPLy8jB+/HgMHjwYcXFxmD9/fiC7TUREl5Asy0hMTOz25739SRKdOQEdgpqbmxEVFYWmpiaO0ImIuhlVVVFSUoJJkyYZh/IvRCjVAn61ISKibkeWZYwYMYIjdC/d8hw6ERH1bLIsIy4uLtDdCCr8akNERN2Oy+XCBx98AJfLFeiuBA0WdCIi6nZMJhPGjBkDk8kU6K4EDR5yJyKibkeWZcTExAS6G0GFI3SiIPH444/jtttuC3Q3MHz4cLz77rvG8//7v/9DfHw8+vTpg/Ly8jbvEwWCy+XCli1beMjdCws6UQe++eYb3HrrrbjssssQGRmJYcOG4ZlnnvFL2+vXr8fo0aO71Mbjjz8ORVHQp08fREZGYsSIEXj11Ve73Levv/4aP/nJTwC4PzSXLFmCv/3tb2htbUVGRobP++frueeeQ3p6Ovr27Yv+/fvjhhtu8Fn18ULl5OTg17/+dZfboe5DURRMnDixS5eshRoWdKIOzJgxA6NGjUJ1dTVOnTqFN998E4MGDQp0t3z85Cc/QWtrKxobG/HYY48hJycHFRUVfmv/+PHjOHv2LEaOHNnltl599VW88MIL+Mc//oGWlhZUVlYiNzc3KFboUlU10F2g8yRJEiIjI4Pi7ydYsKATtaO+vh6HDh3CwoUL0atXL5hMJgwfPhy33367sc3x48dxxx13oH///khOTsZvf/tbozC0NwIfPXo01q9fj/LycixatAhfffUV+vTpgz59+hh3C9Q0DYsXL0Z0dDSSk5Pxt7/9rVP9lWUZd9xxB6Kjo7Fv3z4UFxfj2muvRVRUFOLj4/HAAw/g7NmzxvbNzc1YvHgxkpOTERkZiTFjxsBmswFw373wrbfeQnl5OYYOHQoAGDhwIAYPHuzzvseHH36IcePGITo6GvHx8Vi5cmW7fdy+fTumTZuGESNGAACio6Nxxx13+Nw58Z///CfGjh2L6OhoDB8+3FgdEnAv9fk///M/GDZsGPr27Yu0tDS8//77+J//+R+89tpr+OMf/4g+ffpg+PDhAICWlhbk5uYiPj4e8fHxWLRoEU6fPg0AqKqqgiRJePnllzFkyBAkJiZ26udMwcPlcuHtt9/mIXcvLOhE7YiNjcWwYcPw85//HG+88QaOHDnSZpu77roLZrMZVqsV27Ztw1tvvYXVq1f/YNsZGRlYt24drr76arS2tqK1tdW4udAHH3yA8ePH4+TJk3jyySexYMECtLS0/GCbmqbhr3/9K5qamjBy5EhERETg//7v/9DQ0IDPPvsMH3/8MX7/+98b2+fk5ODgwYPYvn07Ghsb8b//+7+IiIho08+vv/4aAFBTU4NDhw612W95eTlmzZqFpUuX4sSJE9i/fz+mTJnSbh8nTJiAN954A0899RQ+++wz2O12n/f37NmD22+/HatWrUJDQwPy8/Nxzz334JtvvgEArF27Fv/93/+N1157Dc3Nzfjoo4+QkpKCX/3qV7j77rvxwAMPoLW11ejzkiVLcPDgQezduxdfffUV9u/fj9/85jc++9y8eTO++OILWK3WH/wZU3BRFAXTp0/nIXdvoodqamoSAERTU1Ogu0JBqra2Vjz44IPiqquuErIsiyuvvFIUFxcLIYSoqakRAERtba2x/WuvvSbS0tKEEEK8/PLLYtSoUT7tjRo1Srz88ssdvr9ixQoxbtw447mu6yIsLEx88cUX7fZvxYoVQlEUERUVJWJjY8W1114rNm7c2O62zz//vLjhhhuEEEIcO3ZMABBHjhxpd9uUlBSxadMmIYQQVqtVABCnTp1q9/1FixaJn//85+22056///3vIisrS0RFRYlevXqJBQsWiNbWViGEEA888ID49a9/7bP9XXfdJZ544gkhhBDDhg0TGzZsaLfde++9VyxZssR4rmmasFgsYvv27cZrn332mbBYLELTNCOv8vLyTvedgouu68LpdApd17vUTijVAo7QiTpw+eWX47nnnsPXX3+NEydO4JZbbsHs2bPR0NCAmpoahIeH4/LLLze2HzRoEGpqarq8Tw9JkhAREXHOEfqMGTPQ2NiI+vp67Nq1C3PmzAEA7Nq1CzfccAMGDBiAyMhIPPLII6ivrwfgvt2wxWL5wVsOd8aRI0eQlpbW6e2zs7OxZcsWnDp1Ch988AGKi4vx1FNPAXAfBl+3bh2io6ONx9tvv41vv/32vPd14sQJOBwOpKamGq8NGjQIDofD+DkA8MvPgAJDVVUUFRVx/oMXFnSiToiJicHjjz+O06dPw2q1YuDAgbDb7Th+/Lixjed1AOjTpw/OnDnj08axY8eM+GKvP33nnXdiypQpOHz4MJqbm/H0009DfHcfppSUFDgcDuOceVekpKTg4MGD5/3vJEnChAkTkJ2dja+++goAkJSUhCVLlqCxsdF4tLa24qWXXvrBfX3/59m/f3+EhYX5zKC3Wq2wWCy47LLLOvx31H0oioKsrCwecvfCv2aidpw6dQqPPvoo9u/fD03TcObMGfz+979HTEwMhg0bhsTEREyZMgX/9V//hdOnT6O6uhpPP/007r33XgDuCXCHDx/Gtm3boKoqVq9ejZMnTxrtDxgwALW1tT4T1fypubkZ0dHR6N27NyoqKoyi6Nn3rFmzsGjRItTW1kLXdZSXl/v0r7Puv/9+FBYWYtOmTVBVFU1NTdi+fXu727788st4++230djYCADYu3cv3n77bWRmZgIAFi5ciJdffhkff/wxNE2Dw+HA559/bszaX7hwIfLy8vDll19CCIHq6mrjvQEDBuDw4cPGvmRZxl133YXf/va3aGhowMmTJ/Hb3/4W99xzD4t4COHo3Bf/sonaERYWhqNHjyIrKwtRUVFITk7GZ599hvfffx+9e/cGALz++us4e/YsUlJSMH78eMyYMQNLly4FAAwZMgSrV69GdnY24uPj4XA4jNnXADB16lRcd911SExMRHR0tDHL3V/y8/OxZs0a9OnTB4sWLcLcuXN93t+wYQOSkpJw7bXXIjo6GosWLbqgLxfXXHMN3nzzTTz11FOIiYnBlVdeiU8//bTdbaOjo/Hcc89h0KBB6Nu3L2677Tbceeedxs8sIyMDhYWFePTRR9G/f38kJiZi+fLlcDgcAIBf/epX+MUvfoE77rgDffv2xQ033GD83BYsWICjR4+iX79+xiV2f/jDH5CamoqrrroKw4cPx5AhQ3wmBlL3pqoqiouLWdS98H7oIXAPXCIiujChVAuCcoReWVmJzMxMpKenY+zYsdi3b1+bbYQQeOihhzB8+HCMHDkSU6ZMuaBzeURE1P0IIdDc3IweOiZtV1AW9IULFyI3NxcHDhzA0qVLMX/+/DbbbN68GSUlJfjyyy+xZ88eTJs2DY888kgAektERJeaqqrGHBVyC7qCXldXh927d2PevHkAgDlz5sBqtba73rPD4YDdbje+qXlmGBMRUWgzm82YMWMGzGZzoLsSNIKuoNtsNiQkJBiXIkiShOTk5DaThm699VZMmTIFl19+OeLj4/HRRx/hiSee6LBdh8OB5uZmnwfgXmHL89/2YlVVfWJd188Zu1wun9hzOMgTCyHaxAB8Yl3XfWLPN9COYk3TfGLmxJyYE3MK9ZxcLhdOnDgBXde7nFOoCLqCDqDNYvvtnSPZvXs39u/fj6NHj+Lbb7/FtGnTsHjx4g7bXLlyJaKiooxHUlISAPelMwBQUVFhXAKzZ88eVFZWAnAvbelZFnLnzp3GtbtlZWWora0FAJSUlBiLVWzdutW4LKe4uNhYFKSoqAh2u91nMQS73Y6ioiIA7nWni4uLAQCNjY3YunUrAPea4iUlJQCA2tpalJWVAXB/8dm5cycA9/W15eXlANzzD/bs2cOcmBNzYk4hndPhw4exfft2aJrWpZx27NiBUBF0s9zr6uqQlpaGkydPQlEUCCEQHx+P7du3+6z65LmxhOeSl6+//hpZWVntrrkNuEfonstfAPfMxqSkJDQ0NKBfv37GNzeTyeQTq6oKSZKMWJZlyLLcYexyuWAymYxYURRIkmTEgPsboXdsNpshhDBizzdOT6zrOhRF6TDWNA1CCCNuLw/mxJyYE3NiTm1zamhoQGxsbEjMcvdrQX/33Xcv+D7J3iZPnoycnBzk5ORg48aNWLNmTZvFKn7/+9/jgw8+wLvvvguz2YxVq1Zh27Zt2LJlS6f2EUqXKhAR9TS6rqO+vh6XXXZZlxYLCqVa0OWCfuONN0KSJAghcODAAQwdOtQ4hHKhvvnmG+Tk5ODkyZOIjIzEhg0bMHz4cCxYsAAzZ87EzJkz4XA4sHjxYmzbtg1hYWGIj49Hfn6+zyj+XELpl0hE1NOoqoqSkhJMmjSpS8u/hlIt6HJBX758OX70ox/htttuw29+8xs8//zz/urbRRVKv0QiIrowoVQLujwp7ne/+x1UVcUjjzwCp9Ppjz4RERGdk67rOHr0qDFbnfw0yz07Oxv33Xcfhg4d6o/miIiIzknXdRw6dIgF3UvQzXK/VPx1mOV7V9gRUZDqmZ909ENC6ZC7X28kW1FRgaeeegqHDx/2uVjfc90gERGRP+i6DpvNhqSkJN4S9zt+Leh33HEHfvazn+G+++6DyWTyZ9NEREQGzzn0xMREFvTv+LWgm81mPPTQQ/5skoiIqA1FUZCZmRnobgQVv36tufnmm/H+++/7s0kiIqI2NE3DwYMHjdXgyM8j9GnTpmHWrFkwmUywWCwQQkCSJNTV1flzN0RE1MMJIXDq1KlOLybWE/i1oC9cuBDr16/HNddcw3PoRER00SiKgjFjxgS6G0HFrwU9NjYW2dnZ/mySiIioDc9d1tLS0jiA/I5fz6HPnj0b69atQ0NDA86cOWM8iIiI/O3s2bOB7kJQ8evCMt6XDnhu2CJJUlBOWuDCMkQ9CxeWofaE0sIyfh2he+5V67mvree/RERE/qRpGvbu3csa48WvBd1ut7d57cSJE/7cBREREbXDrwX9zjvv9Hne2NiIm2++2Z+7ICIigslkwogRIzghzotfC/rQoUOxZMkSAEBrayuysrLwi1/8wp+7ICIigqZpKC8v5yF3L34t6KtWrcLx48fxzDPPYNasWbjjjjuwYMGC826nsrISmZmZSE9Px9ixY7Fv374223zyySfo1asXRo8ebTw445GIqOeIiIgIdBeCil+uQ/e+NO3FF1/ELbfcgmnTpiE3NxdnzpxBr169zqu9hQsXIjc3Fzk5Odi4cSPmz5+Pzz//vM12V111Fb744osu95+IiLoXk8mEYcOGBbobQcUvI/Q+ffqgb9++6NOnD+Li4vDFF1/gmWeeMV4/H3V1ddi9ezfmzZsHAJgzZw6sViuqqqr80VUiIgoBqqpi165dPrfq7un8UtC/f5na9y9fOx82mw0JCQlQFPfBA0mSkJycjOrq6jbbfvPNN7jmmmswZswY/PGPfzxnuw6HA83NzT4PAEb/NE1rN1ZV1SfWdb1NHB6uQpY9scuIIyJckGVhxJIkAAhERLgACEiSJwZk2TvWER7uHbv/YE0mHRaLO1YU71hDWJh37O6v2azBbHbHYWEaFMUTq0ZssahQFN2ITSbmxJxCNydd140C0FGsaZpP7I/PCO/Y5XL5xJ6lQDyxEKJNDMAn1nXdJ+6JOem6jqioKGOtk67kFCr8UtBPnz5txCdPnuxye9L3Vmtpb+2ba665BjU1Ndi9ezc2bdqEdevW4Y033uiwzZUrVyIqKsp4JCUlAQD27t0LAKioqEBFRQUAYM+ePaisrAQAlJeXw2q1AgB27twJm80GACgrK0NtbS0AYPXqEowcWQ8AWLt2K9LSGgEABQXFSExsAQAUFhYhJsaOiAgVhYVFiIhQERNjR2FhEQAgMbEFBQXFAIC0tEasXbsVADByZD1Wry4BAIwbV4u8vDIAwOTJNixbthMAkJVlxZIl5QCA7OxK3H//HgDAvHkVmDfPndP99+9BdrY7pyVLypGV5c5p2bKdmDzZnVNeXhnGjWNOzCl0c6qvr0dJiTun2tpalJW5c7LZbNi5052T1WpFebk7p8rKSuzZ486pK58RJSUlqK9357R161Y0NrpzKi4uRkuLO6eioiLY7XaoqoqioiKoqgq73Y6iIndOLS0tKC5259TY2IitW3t2TtXV1WhqaoLJZOpSTjt27EDIEF20ePFiMXPmTLFs2TIhhBC/+MUvutTe8ePHRWRkpHC5XEIIIXRdFwMGDBBWq/Wc/+7pp58Wixcv7vB9u90umpqajIfNZhMARENDgxBCCFVVhaqqbWKXy+UTa5rmEwNChIe7hCx7YqcRR0Q4hSzrRixJugB0ERHhFIAuJMkTCyHL3rEmwsO9Y5cAhDCZNGGxuGNF8Y5VERbmHasCEMJsVoXZ7I7DwlShKJ7YZcQWi0soimbEJhNzYk6hmZMQQmiaZny2dBSrquoTt/e5cD6fEd+PnU6nT6zruk+s63qb2PNZ6Ik1TfOJe2JOdrtdlJaWGn290JxOnjwpAIimpibR3XV56dd77rkHr7zyCt577z3s2rULx44d+8HD3z9k8uTJyMnJMSbFrVmzBtu3b/fZpra2FgMGDIAsy2hpacHNN9+M+fPn47777uvUPrj0K1HPwqVfQ4uu67DZbEhKSvJZdvx8celXLxaLBQBwyy23ID4+Hlu2bOlyp/Lz85Gfn4/09HSsWrUKBQUFAIAFCxZg8+bNAIA333wTV199NUaNGoXrrrsON954I37+8593ed9ERBT8ZFlGSkpKl4p5qOnyCL2kpASTJk0ynv/jH//Af/zHf3S5YxcbR+hEPQtH6KFFVVWUlZUhMzPTmER9IThC9+JdzAEgIyOjq00SERGdkyzLGDx4MEfoXvz+k3j22Wf93SQREZEPWZaRmJjIgu6lyyvFpaSkYOjQoQDcl5d98803XZ4UR0REdC6qqhqnfLtyyD2UdPmncOONN+JPf/qT8Zw3YyEiootNlmWMGDGCI3QvXZ4U19jYiOjoaD9159LhpDiinoWT4qg9nBTnxbuYV1dXo7S0FKWlpe0u1UpEROQPLpcLH3zwgbFcLPnpbmv79+/HfffdB6vViuTkZAghYLPZcMUVV6CgoABXXnmlP3ZDREQEwH23tTFjxsBkMgW6K0HDLwU9JycHDz30EObMmePz+saNG3Hvvfcaa+8SERH5gyzLiImJCXQ3gopfZhOcOnWqTTEHgOzsbDQ1NfljF0RERAaXy4UtW7bwkLsXvxT0yy67DK+88opxOzrAvc7uhg0bEBsb649dEBERGRRFwcSJE3nJmhe//CQ2bNiAhQsXYsmSJUhISIAkSaipqUFGRgbWr1/vj10QEREZJEnq9rPS/c0vBX3IkCH46KOPcOLECeMes0lJSejfv78/miciIvLhcrlQVFSErKwsmM3mQHcnKPj1WEX//v1ZxImI6KJTFAXTp0/nIXcvF32JnfT09Iu9CyIi6oFYzH355aexb9++Dt9rbW31xy6IiIgMqqrykPv3+KWgjxgxAqmpqWhvFdn6+vrzbq+yshL33nsv6uvrER0djfXr1+Oqq67y2Wbr1q14+OGH0dLSAlmWMWvWLDz55JOQuBYrEVHIUxQFWVlZHKV78ctPIiUlBaWlpUhISGjzXlJS0nm3t3DhQuTm5iInJwcbN27E/Pnz8fnnn/ts069fPxQWFmLQoEGw2+244YYbUFhYiLvuuuuC8yAiou5DVVUWdC9+OYc+c+ZMHD58uN33Zs2adV5t1dXVYffu3Zg3bx4AYM6cObBaraiqqvLZLiMjA4MGDQIAhIeHY/To0R32gYiIQouqqiguLoaqqoHuStDwS0H/wx/+gAkTJrT73tq1a8+rLZvNhoSEBONblyRJSE5OPufNXo4dO4aNGzciKyurw20cDgeam5t9HgCgaZrx3/ZiVVV9Ys/iOd5xeLgKWfbELiOOiHBBloURS5IAIBAR4QIgIEmeGJBl71hHeLh37P6DNZl0WCzuWFG8Yw1hYd6xu79mswaz2R2HhWlQFE+sGrHFokJRdCM2mZgTcwrdnHRdNwpAR7GmaT6xPz4jvGOXy+UTe05VemIhRJsYgE+s67pP3BNzkmUZM2bMgNls7nJOoSIobyT7/fPg57rDa3NzM2699VYsXboU11xzTYfbrVy5ElFRUcbDcypg7969AICKigpUVFQAAPbs2YPKykoAQHl5OaxWKwBg586dxnX2ZWVlqK2tBQCsXl2CkSPdcwXWrt2KtLRGAEBBQTESE1sAAIWFRYiJsSMiQkVhYREiIlTExNhRWFgEAEhMbEFBQTEAIC2tEWvXbgUAjBxZj9WrSwAA48bVIi+vDAAwebINy5a518jPyrJiyZJyAEB2diXuv38PAGDevArMm+fO6f779yA7253TkiXlyMpy57Rs2U5MnuzOKS+vDOPGMSfmFLo51dfXo6TEnVNtbS3Kytw52Ww2454TVqsV5eXunCorK7FnjzunrnxGlJSUGPOJtm7disZGd07FxcVoaXHnVFRUBLvdbkz2UlUVdrsdRUXunFpaWlBc7M6psbERW7cyp507d0II0aWcduzYgZAhgszx48dFZGSkcLlcQgghdF0XAwYMEFartc22zc3N4sc//rF44oknfrBdu90umpqajIfNZhMARENDgxBCCFVVhaqqbWKXy+UTa5rmEwNChIe7hCx7YqcRR0Q4hSzrRixJugB0ERHhFIAuJMkTCyHL3rEmwsO9Y5cAhDCZNGGxuGNF8Y5VERbmHasCEMJsVoXZ7I7DwlShKJ7YZcQWi0soimbEJhNzYk6hmZMQQmiaZny2dBSrquoTt/e5cD6fEd+PnU6nT6zruk+s63qb2PNZ6Ik1TfOJe2JOZ8+eFe+8845wOp1dyunkyZMCgGhqahLdnSTEOYa/ATJ58mTk5OQYk+LWrFmD7du3+2zT2tqKm266CdOnT8eKFSvOex/+uqk9J9UTdQ/B90lHwcBftSAYBOUh9/z8fOTn5yM9PR2rVq1CQUEBAGDBggXYvHkzAPd5+507d2LTpk0YPXo0Ro8ejaeeeiqQ3SYioktE13U0NDT43BSspwvKEfqlwBE6Uc/SMz/pQpfL5cLWrVsxderULi0sE0ojdF7AR0RE3Y7ZbMZNN90U6G4ElaA85E5ERHQuuq6jrq6Oh9y9sKATEVG3o+s69u7dy4LuhYfciYio21EUBVOnTg10N4IKR+hERNTt6LqOo0ePcoTuhQWdiIi6HV3XcejQIRZ0LzzkTkRE3Y6iKJg0aVKguxFUOEInIqJuR9d1HDlyhCN0LyzoRETU7fAcels85E5ERN2OoijIzMwMdDeCCkfoRETU7WiahoMHDxr3OCcWdCIi6oaEEDh16hR66O1I2sVD7kTtEOBdd0IOf6UhRQEwhsXcB0foRETU7WiKgv379/OQuxcWdCIi6n5kGWfPng10L4IKD7kTEVG3Y3I6kZGREehuBJWgHKFXVlYiMzMT6enpGDt2LPbt29fudgUFBUhLS8PgwYORm5sLVVUvcU+JiCgQNLMZe/fu5SF3L0FZ0BcuXIjc3FwcOHAAS5cuxfz589tsY7VasXz5cpSWluLgwYM4duwYCgoKAtBbIiKiwAu6gl5XV4fdu3dj3rx5AIA5c+bAarWiqqrKZ7uNGzdi9uzZGDBgACRJwqJFi1BYWBiAHhMR0aVmcrkwYsQImEymQHclaATdOXSbzYaEhAQoirtrkiQhOTkZ1dXVSE1NNbarrq5GSkqK8Tw1NRXV1dUdtutwOOBwOIznTU1NAIBTp04BgHHYxmQy+cSqqkKSJCOWZRmyLBsxIMNiUeF0yhBChsXigtNpghAywsNdcDgUCCEhPNwFu92dU3i4+r3YDEkSsFg8sY6wMA0OhyfW4XAokGUdiqLD6VRgMukwmTyxBlkWcLk8MeBymaAo7jxU1QSzWYOuA5pmgtmsQtclaJoJYWEqNE2GpskIC1OhqjJ0nTk1AVDDw6HY7e79hYfDbLdDSBJUiwVmux26JEELC4PZ4YAuSdDDwqA4HNBlGbqiQHE6oZtM0E0mKE4nNJMJQpahuFzQTCZAlmFyuaB997duUlVoZjOg6zBpGlSzGZInDguDrGmQPbGqQtZ1qBYLZKcTshBwWSwweeLwcCgOByRP7JUHc2JOoZCTMyICX3/6KUaOHGl8rn//87szn+UNDQ0AEBLXswddQQfcRdxbRz9o7+1+6JexcuVK5OXltXnd+0vChfL6nuATf/e326lYCN/Y0453rOuA0+mONc39OFfsPaXA5Wo/9rT3/bin5xQdikkxJ+YUSjmdPQtMngx/aWlpQVRUlN/aC4SgK+hJSUmoqamBqqpQFAVCCNhsNiQnJ/tsl5yc7HMY/siRI2228fbwww/jwQcfNJ7ruo6GhgbExsa2+QJBRETBrbm5GUlJSbDZbIiMjLzgdoQQaGlpQUJCgh97FxhBV9Dj4uKQkZGBV199FTk5OXjzzTeRmpraZiQ9Z84cTJgwAY899hji4uKwbt06zJ07t8N2LRYLLBaLz2vR0dEXIQMiIrpUIiMju1TQAXT7kblH0E2KA4D8/Hzk5+cjPT0dq1atMmavL1iwAJs3bwYADBo0CHl5eRg/fjwGDx6MuLi4dmfDExER9QSSCIWZAERE1KM0NzcjKioKTU1NXR6hh4qgHKETERGdi8ViwYoVK9qcSu3JOEInIiIKARyhExERhQAWdCIiohDAgk5ERBQCWNCJiIhCAAs6ERFRCAi6leKIiIja09jYiA8++ABHjx6FJEmIj4/HTTfdhH79+gW6a0GBI3QiIgp6BQUFGDt2LLZv3w5d16FpGrZv347rrrvOWE20p+N16EREFPSGDh2Kf/3rX+jTp4/P6y0tLfjRj36EAwcOBKhnwYMjdCIiCnqSJKG1tbXN662trbxj5nd4Dp2IiILemjVrcP3112PEiBFITEwEANTU1ODrr7/Gc889F+DeBQceciciom5B0zTs3LkT3377LYQQSExMxNixY2EymQLdtaDAgk5ERN3S2rVrsXjx4kB3I2jwHDoREXVLf/7znwPdhaDCgk5ERN0SDzD74iF3IiLqllwuF8xmc6C7ETQ4Qiciom7JU8yXLVsW4J4EB47QiYgo6J05c6bd14UQGDZsGGw22yXuUfDhdehERBT0+vbti5SUFJ/z5pIkQQiB48ePB7BnwYMFnYiIgt7gwYPx4YcfIiUlpc17SUlJAehR8OE5dCIiCnr/3//3/7W79CsA5OXlXeLeBCeeQyciIgoBHKETEVG3NH369EB3IaiwoBMRUbd04sSJQHchqLCgExFRt3TzzTcHugtBhefQiYiIQgAvWyMioqA3aNAgn+dCCOM6dEmScPjw4QD1LHiwoBMRUdAbOnQo6uvrcdttt+H2229HYmJioLsUdHjInYiIuoVTp05h06ZN2LhxIxwOB2bPno25c+fisssuC3TXggILOhERdStOpxOvv/46/t//+39YsWIFfvWrXwW6S0GBh9yJiCjoqaqK4uJivPHGG6ioqMD06dOxdetWjBo1KtBdCxocoRMRUdCLiYlBUlIS7rjjDowePRqSJPm8n5WVFaCeBQ8WdCIiCno5OTltiriHJEn485//fIl7FHxY0ImIiEIAV4ojIqKg98477+DIkSPG8xUrVmDkyJG49dZbcejQoQD2LHiwoBMRUdD77W9/i/79+wMANm3ahNdffx1//vOfMXv2bCxcuDDAvQsOLOhERBT0ZFlGr169ALgLem5uLq699lrcd999aGhoCHDvggMLOhERBT1ZltHQ0ACHw4EPP/zQ59apdrs9gD0LHrwOnYiIgt6KFSuQkZEBXddx0003Gdefb9u2DampqYHtXJDgLHciIuoWVFVFS0sL+vXrZ7x2+vRpCCHQp0+fAPYsOHCETkRE3cLXX38NSZLQr18/7Nu3D++99x6GDRuGGTNmBLprQYEjdCIiCnpPPvkkioqK4HK5cMMNN6C8vBxTp05FcXExJk2ahMceeyzQXQw4FnQiIgp6V199Nfbs2QO73Y7LL78c3377LXr37g2Hw4ExY8Zgz549ge5iwHGWOxERBT2TyQRJkhAREYERI0agd+/eAACLxQJZZikDWNCJiKgbiImJQWtrKwDgs88+M14/ceIEzGZzoLoVVHjInYiIuq2WlhY0NTVh4MCBge5KwHGETkREQa+wsNCIvUfoffv2xVtvvRWAHgUfjtCJiCjoXXPNNdi9e3ebuL3nPRVH6EREFPS8x57fH4dyXOrGgk5EREFPkqR24/ae91Q85E5EREFPURTExMRACIHGxkZj+VchBJqamuB0OgPcw8BjQSciIgoBPOROREQUAljQiYiIQgALOhERUQhgQSciIgoBLOhEREQhgAWdiIgoBLCgExERhQAWdCIiohDAgk5ERBQCWNCJiIhCAAs6ERFRCGBBJyIiCgEs6ERERCGABZ2IiCgEsKATERGFABZ0IiKiEMCCTkREFAJY0ImIiEIACzoREVEIYEEnIiIKASzoREREIYAFnYiIKASwoBMREYUAFnQiIqIQwIJOREQUAljQiYiIQgALOhERUQhgQSciIgoBLOhEREQhgAWdiIgoBLCgExERhQAWdCIiohDAgk5ERBQCWNCJiIhCAAs6ERFRCGBBJyIiCgEs6ERERCGABZ2IiCgEsKATERGFABZ0IiKiEMCCTkREFAL+f4v6xF4xtjWiAAAAAElFTkSuQmCC" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "a = Image(\"sea_ice_demo/ex1/MSE_bar_chart.png\")\n", - "display_png(a)" - ] - }, - { - "cell_type": "markdown", - "id": "a9b323ec", - "metadata": {}, - "source": [ - "## Working with multiple realizations" - ] - }, - { - "cell_type": "markdown", - "id": "0c427a07", - "metadata": {}, - "source": [ - "The sea ice driver can generate metrics based on an average of all available realizations. To do so, provide an asterisk \\* as the value to the --realization argument on the command line. Options passed on the command line will supercede arguments in the parameter file. \n", - "\n", - "In addition, we set the --case_id value to 'ex2' to save results in a new directory." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "5f8174e1", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-01-25 11:40:29,821 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['E3SM-1-0']\n", - "Find all realizations: True\n", - "OBS: Arctic\n", - "Converting units by multiply 0.01\n", - "OBS: Antarctic\n", - "Converting units by multiply 0.01\n", - "Model list: ['E3SM-1-0']\n", - "\n", - "=================================\n", - "model, runs: E3SM-1-0 ['r1i2p2f1', 'r2i2p2f1', 'r3i2p2f1', 'r4i2p2f1']\n", - "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n", - "Converting units by multiply 1e-06\n", - "\n", - "-----------------------\n", - "model, run, variable: E3SM-1-0 r1i2p2f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_185001-185912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_186001-186912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_187001-187912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_188001-188912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_189001-189912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_190001-190912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_191001-191912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_192001-192912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_193001-193912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_194001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_195001-195912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_196001-196912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_197001-197912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_198001-198912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_199001-199912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_200001-200912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_201001-201112.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: E3SM-1-0 r2i2p2f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_185001-185912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_186001-186912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_187001-187912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_188001-188912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_189001-189912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_190001-190912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_191001-191912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_192001-192912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_193001-193912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_194001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_195001-195912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_196001-196912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_197001-197912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_198001-198912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_199001-199912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_200001-200912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_201001-201312.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: E3SM-1-0 r3i2p2f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_185001-185912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_186001-186912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_187001-187912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_188001-188912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_189001-189912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_190001-190912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_191001-191912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_192001-192912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_193001-193912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_194001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_195001-195912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_196001-196912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_197001-197912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_198001-198912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_199001-199912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_200001-200912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_201001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: E3SM-1-0 r4i2p2f1 siconc\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO::2024-01-25 11:43::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n", - "2024-01-25 11:43:28,092 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_185001-185912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_186001-186912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_187001-187912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_188001-188912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_189001-189912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_190001-190912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_191001-191912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_192001-192912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_193001-193912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_194001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_195001-195912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_196001-196912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_197001-197912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_198001-198912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_199001-199912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_200001-200912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_201001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-------------------------------------------\n", - "Calculating model regional average metrics \n", - "for E3SM-1-0\n", - "--------------------------------------------\n", - "arctic\n", - "ca\n", - "na\n", - "np\n", - "antarctic\n", - "sp\n", - "sa\n", - "io\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[WARNING] yaksa: 10 leaked handle pool objects\n", - "\n", - "real\t4m0.717s\n", - "user\t4m12.255s\n", - "sys\t1m20.539s\n" - ] - } - ], - "source": [ - "%%bash\n", - "time python ice_driver.py -p demo_param_file.py --realization '*' --case_id \"ex2\"" - ] - }, - { - "cell_type": "markdown", - "id": "cadb1306", - "metadata": {}, - "source": [ - "Since we have averaged four different realizations, the resulting statistics are different than seen in example 1. The bar chart now contains markers showing the overall spread among the realizations." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d6cb5f07", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "a = Image(\"sea_ice_demo/ex2/MSE_bar_chart.png\")\n", - "display_png(a)" - ] - }, - { - "cell_type": "markdown", - "id": "499d3935", - "metadata": {}, - "source": [ - "# Working with multiple models" - ] - }, - { - "cell_type": "markdown", - "id": "51b008db", - "metadata": {}, - "source": [ - "Along with using multiple realizations, we can include multiple models in a single analysis. The model data must all follow a single filename template. All model inputs must use the same name and units for the sea ice variable.\n", - "\n", - "The example below shows how to use three models in the analysis, with all available realizations. The models are listed as inputs to the --test_data_set flag.\n", - "\n", - "Want to add more models? Six other model sea ice datasets are available in the directories linked in the notebook introduction." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "679d7289", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-01-25 11:44:23,351 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "2024-01-25 11:45:05,516 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['E3SM-1-0', 'CanESM5', 'MIROC6']\n", - "Find all realizations: True\n", - "OBS: Arctic\n", - "Converting units by multiply 0.01\n", - "OBS: Antarctic\n", - "Converting units by multiply 0.01\n", - "Model list: ['CanESM5', 'E3SM-1-0', 'MIROC6']\n", - "\n", - "=================================\n", - "model, runs: CanESM5 ['r2i1p1f1', 'r1i1p1f1', 'r3i1p1f1']\n", - "/p/user_pub/pmp/demo/sea-ice/links_area/CanESM5/*.nc\n", - "Converting units by multiply 1e-06\n", - "\n", - "-----------------------\n", - "model, run, variable: CanESM5 r2i1p1f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/CanESM5/historical/r2i1p1f1/siconc/siconc_SImon_CanESM5_historical_r2i1p1f1_gn_185001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: CanESM5 r1i1p1f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/CanESM5/historical/r1i1p1f1/siconc/siconc_SImon_CanESM5_historical_r1i1p1f1_gn_185001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: CanESM5 r3i1p1f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/CanESM5/historical/r3i1p1f1/siconc/siconc_SImon_CanESM5_historical_r3i1p1f1_gn_185001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-------------------------------------------\n", - "Calculating model regional average metrics \n", - "for CanESM5\n", - "--------------------------------------------\n", - "arctic\n", - "ca\n", - "na\n", - "np\n", - "antarctic\n", - "sp\n", - "sa\n", - "io\n", - "\n", - "=================================\n", - "model, runs: E3SM-1-0 ['r1i2p2f1', 'r2i2p2f1', 'r3i2p2f1', 'r4i2p2f1']\n", - "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n", - "Converting units by multiply 1e-06\n", - "\n", - "-----------------------\n", - "model, run, variable: E3SM-1-0 r1i2p2f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_185001-185912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_186001-186912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_187001-187912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_188001-188912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_189001-189912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_190001-190912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_191001-191912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_192001-192912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_193001-193912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_194001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_195001-195912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_196001-196912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_197001-197912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_198001-198912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_199001-199912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_200001-200912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_201001-201112.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: E3SM-1-0 r2i2p2f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_185001-185912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_186001-186912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_187001-187912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_188001-188912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_189001-189912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_190001-190912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_191001-191912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_192001-192912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_193001-193912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_194001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_195001-195912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_196001-196912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_197001-197912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_198001-198912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_199001-199912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_200001-200912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_201001-201312.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: E3SM-1-0 r3i2p2f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_185001-185912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_186001-186912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_187001-187912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_188001-188912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_189001-189912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_190001-190912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_191001-191912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_192001-192912.nc\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-01-25 11:48:07,932 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "INFO::2024-01-25 11:52::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n", - "2024-01-25 11:52:30,866 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_193001-193912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_194001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_195001-195912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_196001-196912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_197001-197912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_198001-198912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_199001-199912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_200001-200912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_201001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: E3SM-1-0 r4i2p2f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_185001-185912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_186001-186912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_187001-187912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_188001-188912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_189001-189912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_190001-190912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_191001-191912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_192001-192912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_193001-193912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_194001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_195001-195912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_196001-196912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_197001-197912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_198001-198912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_199001-199912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_200001-200912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_201001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-------------------------------------------\n", - "Calculating model regional average metrics \n", - "for E3SM-1-0\n", - "--------------------------------------------\n", - "arctic\n", - "ca\n", - "na\n", - "np\n", - "antarctic\n", - "sp\n", - "sa\n", - "io\n", - "\n", - "=================================\n", - "model, runs: MIROC6 ['r2i1p1f1', 'r1i1p1f1', 'r4i1p1f1', 'r3i1p1f1']\n", - "/p/user_pub/pmp/demo/sea-ice/links_area/MIROC6/*.nc\n", - "Converting units by multiply 1e-06\n", - "\n", - "-----------------------\n", - "model, run, variable: MIROC6 r2i1p1f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r2i1p1f1/siconc/siconc_SImon_MIROC6_historical_r2i1p1f1_gn_185001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r2i1p1f1/siconc/siconc_SImon_MIROC6_historical_r2i1p1f1_gn_195001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: MIROC6 r1i1p1f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r1i1p1f1/siconc/siconc_SImon_MIROC6_historical_r1i1p1f1_gn_185001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r1i1p1f1/siconc/siconc_SImon_MIROC6_historical_r1i1p1f1_gn_195001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: MIROC6 r4i1p1f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r4i1p1f1/siconc/siconc_SImon_MIROC6_historical_r4i1p1f1_gn_185001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r4i1p1f1/siconc/siconc_SImon_MIROC6_historical_r4i1p1f1_gn_195001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: MIROC6 r3i1p1f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r3i1p1f1/siconc/siconc_SImon_MIROC6_historical_r3i1p1f1_gn_185001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r3i1p1f1/siconc/siconc_SImon_MIROC6_historical_r3i1p1f1_gn_195001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-------------------------------------------\n", - "Calculating model regional average metrics \n", - "for MIROC6\n", - "--------------------------------------------\n", - "arctic\n", - "ca\n", - "na\n", - "np\n", - "antarctic\n", - "sp\n", - "sa\n", - "io\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[WARNING] yaksa: 10 leaked handle pool objects\n", - "\n", - "real\t9m2.167s\n", - "user\t10m50.119s\n", - "sys\t2m40.216s\n" - ] - } - ], - "source": [ - "%%bash\n", - "time python ice_driver.py -p demo_param_file.py \\\n", - "--test_data_set \"E3SM-1-0\" \"CanESM5\" \"MIROC6\" \\\n", - "--realization '*' \\\n", - "--case_id \"ex3\"" - ] - }, - { - "cell_type": "markdown", - "id": "9a17ffee", - "metadata": {}, - "source": [ - "The output JSON now includes metrics for all three models." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "b07dbb8b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"DIMENSIONS\": {\n", - " \"index\": {\n", - " \"monthly_clim\": \"Monthly climatology of extent\",\n", - " \"total_extent\": \"Sum of ice coverage where concentration > 15%\"\n", - " },\n", - " \"json_structure\": [\n", - " \"model\",\n", - " \"realization\",\n", - " \"obs\",\n", - " \"region\",\n", - " \"index\",\n", - " \"statistic\"\n", - " ],\n", - " \"model\": [\n", - " \"CanESM5\",\n", - " \"E3SM-1-0\",\n", - " \"MIROC6\"\n", - " ],\n", - " \"region\": {},\n", - " \"statistic\": {\n", - " \"mse\": \"Mean Square Error (10^12 km^4)\"\n", - " }\n", - " },\n", - " \"RESULTS\": {\n", - " \"CanESM5\": {\n", - " \"antarctic\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"5.1043444982100254\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"4.816687734317558\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"3.8203905542158574\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"3.551903219690635\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"5.408472768567934\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"5.10073354794071\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"6.255511442006537\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"5.95826796378333\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"arctic\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"2.6739701578200408\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"2.5552395000296997\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.9601839559074323\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.8071711277770932\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"2.8686219657630323\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"2.7646000598233362\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"3.306431955856059\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"3.1987918127469728\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"ca\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.12445176930055403\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.0752818530752368\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.06261975386075735\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.04065017565672462\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.18876746901985617\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.11135594838591391\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.14126431682827864\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.08283366771548334\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"io\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.3902350350581252\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.3411097649596542\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.27378096542718267\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.23052580580517984\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.39222062635127936\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.34482149125771394\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.5287500850978069\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.4689404665141464\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"na\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.8575586124643404\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.6617817141384847\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.5264155067552119\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.3050483466111835\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.7640984838802416\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.5843839089856835\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"2.3388720869665747\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"2.1497244832528395\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"np\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.0063419603431157535\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.001033088420302666\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.005949894526659108\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.7412310294637067e-06\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.01271835367014484\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.004687148872326894\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.005275638631907463\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.0008574285177782025\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"sa\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.4618851114415225\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.21877947801248515\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.3604933562263525\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.16135560774807228\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.48465097876034335\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.18590427961007985\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.565345935295451\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.32530874506699275\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"sp\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.3466206749703824\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.3264114860024545\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.0412143157666585\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.0233677214618289\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.5844213803502167\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.5597222118436824\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.4483893228104396\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.4270545631414926\"\n", - " }\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"E3SM-1-0\": {\n", - " \"antarctic\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.7772427941035078\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.512854523904\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.4635192339671928\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.139646926848\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.7917153708317476\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.5296078848\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.9431708933066041\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.709116624896\"\n", - " }\n", - " }\n", - " },\n", - " \"r4i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.1123064886611145\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.8482918891519999\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"arctic\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"5.271005131039172\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"3.602193842176\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"5.476181000101471\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"3.628078727168\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"4.798813326297904\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"3.0712725504\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"5.695229471419496\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"4.135149109248\"\n", - " }\n", - " }\n", - " },\n", - " \"r4i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"5.16787172788022\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"3.6138642309119997\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"ca\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.06682122096680175\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.014511187968\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.05045644169895609\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.007755424768\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.04953964308899206\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.007533873664\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.09545969211386617\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.026321457152\"\n", - " }\n", - " }\n", - " },\n", - " \"r4i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.08158619730649973\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.020952242176\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"io\": {\n", - " \"model_mean\": {\n", - " 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\"mse\": \"15.90437524234963\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"13.40484878336\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"16.338122498955823\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"13.727326797824\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"16.167353763780575\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"13.60793174016\"\n", - " }\n", - " }\n", - " },\n", - " \"r4i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"16.22520331188068\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"13.644895092736\"\n", - " }\n", - " }\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"json_structure\": [\n", - " \"model\",\n", - " \"realization\",\n", - " \"obs\",\n", - " \"region\",\n", - " \"index\",\n", - " \"statistic\"\n", - " ],\n", - " \"json_version\": 3.0,\n", - " \"model_year_range\": {\n", - " \"CanESM5\": [\n", - " \"1981\",\n", - " \"2010\"\n", - " ],\n", - " \"E3SM-1-0\": [\n", - " \"1981\",\n", - " \"2010\"\n", - " ],\n", - " \"MIROC6\": [\n", - " \"1981\",\n", - " \"2010\"\n", - " ]\n", - " },\n", - " \"provenance\": {\n", - " \"commandLine\": \"ice_driver.py -p demo_param_file.py --test_data_set E3SM-1-0 CanESM5 MIROC6 --realization * --case_id ex3\",\n", - " \"conda\": {\n", - " \"Platform\": \"linux-64\",\n", - " \"PythonVersion\": \"3.8.15.final.0\",\n", - " \"Version\": \"23.1.0\",\n", - " \"buildVersion\": \"not installed\"\n", - " },\n", - " \"date\": \"2024-01-25 11:52:17\",\n", - " \"openGL\": {\n", - " \"GLX\": {\n", - " \"client\": {},\n", - " \"server\": {}\n", - " }\n", - " },\n", - " \"osAccess\": false,\n", - " \"packages\": {\n", - " \"PMP\": \"v3.0.2-11-g06b151f\",\n", - " \"PMPObs\": \"See 'References' key below, for detailed obs provenance information.\",\n", - " \"blas\": \"0.3.24\",\n", - " \"cdat_info\": \"8.2.1\",\n", - " \"cdms\": \"3.1.5\",\n", - " \"cdp\": \"1.7.0\",\n", - " \"cdtime\": \"3.1.4\",\n", - " \"cdutil\": \"8.2.1\",\n", - " \"clapack\": null,\n", - " \"esmf\": \"0.8.2\",\n", - " \"esmpy\": \"8.4.2\",\n", - " \"genutil\": \"8.2.1\",\n", - " \"lapack\": \"3.9.0\",\n", - " \"matplotlib\": null,\n", - " \"mesalib\": null,\n", - " \"numpy\": \"1.22.4\",\n", - " \"python\": \"3.10.13\",\n", - " \"scipy\": \"1.11.3\",\n", - " \"uvcdat\": null,\n", - " \"vcs\": null,\n", - " \"vtk\": null,\n", - " \"xarray\": \"2023.10.1\",\n", - " \"xcdat\": \"0.5.0\"\n", - " },\n", - " \"platform\": {\n", - " \"Name\": \"gates.llnl.gov\",\n", - " \"OS\": \"Linux\",\n", - " \"Version\": \"3.10.0-1160.71.1.el7.x86_64\"\n", - " },\n", - " \"userId\": \"ordonez4\"\n", - " }\n", - "}\n" - ] - } - ], - "source": [ - "with open(\"sea_ice_demo/ex3/sea_ice_metrics.json\") as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "markdown", - "id": "f48b3856", - "metadata": {}, - "source": [ - "Now the resulting bar chart shows three different models with their spread." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "41aa14a3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "a = Image(\"sea_ice_demo/ex3/MSE_bar_chart.png\")\n", - "display_png(a)" - ] - }, - { - "cell_type": "markdown", - "id": "bc43281a", - "metadata": {}, - "source": [ - "# Further exploration" - ] - }, - { - "cell_type": "markdown", - "id": "19abc98d", - "metadata": {}, - "source": [ - "Maybe you want to compare more models, or take a closer look at the model data? Here are links to the data for further exploration.\n", - "\n", - "As a reminder, data for nine models is available here:\n", - "```\n", - "/p/user_pub/pmp/demo/sea-ice/links_siconc \n", - "/p/user_pub/pmp/demo/sea-ice/links_area\n", - "```\n", - "\n", - "The observational time series can be found at:\n", - "```\n", - "/p/user_pub/pmp/demo/sea-ice/EUMETSAT\n", - "```\n", - "\n", - "For some example plotting code using xcdat and matplotlib, see the scripts that were used to generate the introductory figures:\n", - "\n", - "```\n", - "create_sector_plots.py\n", - "make_demo_sea_ice_plots.py\n", - "```\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f1161f29", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:pmp_si] *", - "language": "python", - "name": "conda-env-pmp_si-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/pcmdi_metrics/sea_ice/create_sector_plots.py b/pcmdi_metrics/sea_ice/create_sector_plots.py deleted file mode 100644 index e91ee6937..000000000 --- a/pcmdi_metrics/sea_ice/create_sector_plots.py +++ /dev/null @@ -1,156 +0,0 @@ -import cartopy.crs as ccrs -import matplotlib.colors as colors -import matplotlib.pyplot as plt -import numpy as np -import regionmask -import xcdat as xc - -from pcmdi_metrics.utils import create_land_sea_mask - -# ---------- -# Arctic -# ---------- -print("Creating Arctic map") -# Load and process data -f_os_n = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc" -obs = xc.open_dataset(f_os_n) -obs = obs.sel({"time": slice("1988-01-01", "2020-12-31")}).mean("time") -mask = create_land_sea_mask(obs, lon_key="lon", lat_key="lat") -obs["ice_conc"] = obs["ice_conc"].where(mask < 1) -ds = obs.assign_coords( - xc=obs["lon"], yc=obs["lat"] -) # Assign these variables to Coordinates, which were originally data variables - -# Set up regions -region_NA = np.array([[-120, 45], [-120, 80], [90, 80], [90, 45]]) -region_NP = np.array([[90, 45], [90, 65], [240, 65], [240, 45]]) -names = ["North_Atlantic", "North_Pacific"] -abbrevs = ["NA", "NP"] -arctic_regions = regionmask.Regions( - [region_NA, region_NP], names=names, abbrevs=abbrevs, name="arctic" -) - -# Do plotting -cmap = colors.LinearSegmentedColormap.from_list("", [[0, 85 / 255, 182 / 255], "white"]) -proj = ccrs.NorthPolarStereo() -ax = plt.subplot(111, projection=proj) -ax.set_global() -ds.ice_conc.plot.pcolormesh( - ax=ax, - x="xc", - y="yc", - transform=ccrs.PlateCarree(), - cmap=cmap, - cbar_kwargs={"label": "ice concentration (%)"}, -) -arctic_regions.plot_regions( - ax=ax, - add_label=False, - label="abbrev", - line_kws={"color": [0.2, 0.2, 0.25], "linewidth": 3}, -) -ax.set_extent([-180, 180, 43, 90], ccrs.PlateCarree()) -ax.coastlines(color=[0.3, 0.3, 0.3]) -plt.annotate( - "North Atlantic", - (0.5, 0.2), - xycoords="axes fraction", - horizontalalignment="right", - verticalalignment="bottom", - color="white", -) -plt.annotate( - "North Pacific", - (0.65, 0.88), - xycoords="axes fraction", - horizontalalignment="right", - verticalalignment="bottom", - color="white", -) -plt.annotate( - "Central\nArctic ", - (0.56, 0.56), - xycoords="axes fraction", - horizontalalignment="right", - verticalalignment="bottom", -) -ax.set_facecolor([0.55, 0.55, 0.6]) -plt.title("Arctic regions with mean\nOSI-SAF ice concentration\n1988-2020") -plt.savefig("Arctic_regions.png") -plt.close() -obs.close() - -# ---------- -# Antarctic -# ---------- -print("Creating Antarctic map") -# Load and process data -f_os_s = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_sh_ease2-250_cdr-v3p0_198801-202012.nc" -obs = xc.open_dataset(f_os_s) -obs = obs.sel({"time": slice("1988-01-01", "2020-12-31")}).mean("time") -mask = create_land_sea_mask(obs, lon_key="lon", lat_key="lat") -obs["ice_conc"] = obs["ice_conc"].where(mask < 1) -ds = obs.assign_coords( - xc=obs["lon"], yc=obs["lat"] -) # Assign these variables to Coordinates, which were originally data variables - -# Set up regions -region_IO = np.array([[20, -90], [90, -90], [90, -55], [20, -55]]) -region_SA = np.array([[20, -90], [-60, -90], [-60, -55], [20, -55]]) -region_SP = np.array([[90, -90], [300, -90], [300, -55], [90, -55]]) -names = ["Indian Ocean", "South Atlantic", "South Pacific"] -abbrevs = ["IO", "SA", "SP"] -arctic_regions = regionmask.Regions( - [region_IO, region_SA, region_SP], names=names, abbrevs=abbrevs, name="antarctic" -) - -# Do plotting -cmap = colors.LinearSegmentedColormap.from_list("", [[0, 85 / 255, 182 / 255], "white"]) -proj = ccrs.SouthPolarStereo() -ax = plt.subplot(111, projection=proj) -ax.set_global() -ds.ice_conc.plot.pcolormesh( - ax=ax, - x="xc", - y="yc", - transform=ccrs.PlateCarree(), - cmap=cmap, - cbar_kwargs={"label": "ice concentration (%)"}, -) -arctic_regions.plot_regions( - ax=ax, - add_label=False, - label="abbrev", - line_kws={"color": [0.2, 0.2, 0.25], "linewidth": 3}, -) -ax.set_extent([-180, 180, -53, -90], ccrs.PlateCarree()) -ax.coastlines(color=[0.3, 0.3, 0.3]) -plt.annotate( - "South Pacific", - (0.50, 0.2), - xycoords="axes fraction", - horizontalalignment="right", - verticalalignment="bottom", - color="black", -) -plt.annotate( - "Indian\nOcean", - (0.89, 0.66), - xycoords="axes fraction", - horizontalalignment="right", - verticalalignment="bottom", - color="black", -) -plt.annotate( - "South Atlantic", - (0.54, 0.82), - xycoords="axes fraction", - horizontalalignment="right", - verticalalignment="bottom", - color="black", -) -ax.set_facecolor([0.55, 0.55, 0.6]) -plt.title("Antarctic regions with mean\nOSI-SAF ice concentration\n1988-2020") -plt.savefig("Antarctic_regions.png") -plt.close() -obs.close() diff --git a/pcmdi_metrics/sea_ice/generate_sector_masks.py b/pcmdi_metrics/sea_ice/generate_sector_masks.py deleted file mode 100644 index 77d8fcb30..000000000 --- a/pcmdi_metrics/sea_ice/generate_sector_masks.py +++ /dev/null @@ -1,220 +0,0 @@ -import os -import string -import sys - -import cdms2 -import MV2 as MV -from sector_mask_defs import getmask - -from pcmdi_metrics.mean_climate.lib.pmp_parser import PMPParser - -P = PMPParser() - -P.add_argument( - "--mp", - "--modpath", - type=str, - dest="modpath", - default="", - help="Explicit path to model monthly PR climatology", -) -P.add_argument( - "-o", - "--obspath", - type=str, - dest="obspath", - default="", - help="Explicit path to obs monthly PR climatology", -) -P.add_argument( - "--outpd", - "--outpathdata", - type=str, - dest="outpathdata", - default="/export/gleckler1/processing/metrics_package/my_test/sea_ice/git_data/", - help="Output path for sector scale masks", -) - -args = P.parse_args(sys.argv[1:]) -sec_mask_dir = args.outpathdata - -# Factors -factor1 = 1.0e-6 # model units are m^2, converting to km^2 -factor2 = 1.0e-2 # model units are %, converting to non-dimen -a = 6371.009 # Earth radii in km -pi = 22.0 / 7.0 -factor3 = 4.0 * pi * a * a # Earth's surface area in km2 -dc = 0.15 # minimum ice concentration contour - -pin = "/work/gleckler1/processed_data/cmip5clims-historical/sic/cmip5.MOD.historical.r1i1p1.mo.seaIce.OImon.sic.ver-1.1980-2005.SC.nc" - -pins = string.replace(pin, "MOD", "*") - -lst = os.popen("ls " + pins).readlines() - -mods = [] -mods_failed = [] -for li in lst: - mod = string.split(li, ".")[1] - if mod not in mods: - mods.append(mod) - -# w =sys.stdin.readline() - -var = "sic" -factor2 = 1 - -mods = ["ACCESS1-3"] - -for mod in mods: - try: - fc = string.replace(pin, "MOD", mod) - f = cdms2.open(fc) - sic = f(var, squeeze=1) - sic_grid = sic.getGrid() - lat = sic.getLatitude() - lon = sic.getLongitude() - sic = MV.multiply(sic, factor2) - - print("CMIP5-native= ", MV.max(sic)) - if MV.rank(lat) == 1: - tmp2d = f(var, time=slice(0, 1), squeeze=1) - lats = MV.zeros(tmp2d.shape) - for ii in range(0, len(lon)): - lats[:, ii] = lat[:] - else: - lats = lat - - if MV.rank(lon) == 1: - tmp2d = f(var, time=slice(0, 1), squeeze=1) - lons = MV.zeros(tmp2d.shape) - for ii in range(0, len(lat)): - lons[ii, :] = lon[:] - else: - lons = lon - - f.close() - - ####################################################### - ### areacello - area_dir = "/work/cmip5/fx/fx/areacello/" - alist = os.listdir(area_dir) # LIST OF ALL AREACELLO FILES - - for a in alist: - if string.find(a, mod) != -1: - areaf = a - print(mod, " ", a) - - # w = sys.stdin.readline() - - g = cdms2.open(area_dir + areaf) - - try: - area = g("areacello") - except Exception: - area = g("areacella") - area = MV.multiply(area, factor1) - area = MV.multiply( - area, factor1 - ) # Question from Jiwoo (2023-11-7): Why this line repeats two times? - - g.close() - - land_mask = MV.zeros(area.shape) - - # Reading the ocean/land grid cell masks (sftof/sftlf) - mask_dir = "/work/cmip5/fx/fx/sftof/" - mlist = os.listdir(mask_dir) # LIST OF ALL AREACELLO FILES - - for m in mlist: - if string.find(m, mod) != -1: - maskf = m - print(mod, " ", m) - - s = cdms2.open(mask_dir + maskf) - try: - frac = s("sftof") - if ( - mod != "MIROC5" - and mod != "GFDL-CM2p1" - and mod != "GFDL-CM3" - and mod != "GFDL-ESM2M" - ): - frac = MV.multiply(frac, factor2) - print(mod, MV.max(frac)) - area = MV.multiply(area, frac) - land_mask = MV.multiply(1, (1 - frac)) - except Exception: - frac = s("sftlf") - if ( - mod != "MIROC5" - or mod != "GFDL-CM2p1" - or mod != "GFDL-CM3" - or mod != "GFDL-ESM2M" - ): - frac = MV.multiply(frac, factor2) - area = MV.multiply(area, (1 - frac)) - land_mask = MV.multiply(1, frac) - s.close() - - # Creating regional masks - # GFDL and bcc model grids have shift of 80 - if mod[0:4] == "GFDL" or mod[0:3] == "bcc": - lons_a = MV.where(MV.less(lons, -180.0), lons + 360.0, lons) - lons_p = MV.where(MV.less(lons, 0.0), lons + 360.0, lons) - else: - lons_a = MV.where(MV.greater(lons, 180.0), lons - 360.0, lons) - lons_p = lons - - print("CMIP5") - print("lons_na= ", MV.min(lons_a), MV.max(lons_a)) - print("lons_np= ", MV.min(lons_p), MV.max(lons_p)) - # mask_arctic=MV.zeros(area.shape) - # mask_antarctic=MV.zeros(area.shape) - mask_ca = MV.zeros(area.shape) - mask_na = MV.zeros(area.shape) - mask_np = MV.zeros(area.shape) - mask_sa = MV.zeros(area.shape) - mask_sp = MV.zeros(area.shape) - mask_io = MV.zeros(area.shape) - - ############################################################### - - sectors = ["ca", "na", "np", "sp", "sa", "io"] - - for sector in sectors: - mask = getmask(sector, lats, lons, lons_a, lons_p, land_mask) - - g = cdms2.open(sec_mask_dir + "mask_" + mod + "_" + sector + ".nc", "w+") - mask.id = "mask" - g.write(mask) - # land_mask.id = 'sftof' - # g.write(land_mask) - g.close() - - print("got it!", " ", mask.shape) - w = sys.stdin.readline() - - # Calculate the Total Sea Ice Concentration/Extent/Area - # ice_area = MV.multiply(sic,1) #SIC - area = MV.multiply(1, area) # SIE - ice_area = MV.multiply(sic, area) # SIA - ice_area = MV.where( - MV.greater_equal(sic, 0.15), ice_area, 0.0 - ) # Masking out the sic<0.15 - - # arctic=MV.logical_and(MV.greater_equal(lats,35.),MV.less(lats,87.2)) #SSM/I limited to 87.2N - mask_arctic = MV.logical_and( - MV.greater_equal(lats, 45.0), MV.less(lats, 90.0) - ) # Adding currently in SSM/I 100% in the area >87.2N - mask_antarctic = MV.logical_and( - MV.greater_equal(lats, -90.0), MV.less(lats, -55.0) - ) - - except Exception: - "Failed for ", mod - mods_failed.append(mod) - -print("failed for ", mods_failed) - -# Calculate the Sea Ice Covered Area diff --git a/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py b/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py deleted file mode 100755 index 3b53bfb37..000000000 --- a/pcmdi_metrics/sea_ice/ice_area_cmip5_ssmi_reg_rms.py +++ /dev/null @@ -1,1136 +0,0 @@ -import cdms2 as cdms -import cdtime -import cdutil -import genutil -import matplotlib.pyplot as plt -import MV2 as MV -import numpy as np - - -def tgrid(t): - time = t[:] * 0.0 - if t[0] == 0.0: - dt = 0.0 - else: - dt = 0.5 # centered in the midlle of the month - time[0] = dt / 12.0 - nmonths = len(t) # monthy time series - - for it in range(1, nmonths): - time[it] = time[it - 1] + 1.0 / 12.0 - - time = time + cdtime.reltime(t[0], t.units).tocomp().year - - return time - - -value = 0 -cdms.setNetcdfShuffleFlag(value) -cdms.setNetcdfDeflateFlag(value) -cdms.setNetcdfDeflateLevelFlag(value) - -cdms.setAutoBounds("on") - -# Factors -factor1 = 1.0e-6 # model units are m^2, converting to km^2 -factor2 = 1.0e-2 # model units are %, converting to non-dimen -a = 6371.009 # Earth radii in km -pi = 22.0 / 7.0 -factor3 = 4.0 * pi * a * a # Earth's surface area in km2 -dc = 0.15 # minimum ice concentration contour - - -# Observations - -dlist_n = ["/home/ordonez4/seaice/ssmi_nt_n_names.asc", "/home/ordonez4/seaice/ssmi_bt_n_names.asc"] -dlist_s = ["/home/ordonez4/seaice/ssmi_nt_s_names.asc", "/home/ordonez4/seaice/ssmi_bt_s_names.asc"] - -annual_cycle_obs_arctic = [] -annual_cycle_obs_antarctic = [] -data_n = MV.zeros([324]) -obs_n = MV.zeros([324, 2]) -ta_ca = MV.zeros([324]) -obs_ca = MV.zeros([324, 2]) -ta_na = MV.zeros([324]) -obs_na = MV.zeros([324, 2]) -ta_np = MV.zeros([324]) -obs_np = MV.zeros([324, 2]) -data_s = MV.zeros([324]) -obs_s = MV.zeros([324, 2]) -ta_sa = MV.zeros([324]) -obs_sa = MV.zeros([324, 2]) -ta_sp = MV.zeros([324]) -obs_sp = MV.zeros([324, 2]) -ta_io = MV.zeros([324]) -obs_io = MV.zeros([324, 2]) - -# SSM/I Arctic - -for dl in range(0, len(dlist_n)): - f = open(dlist_n[dl]) - lines_n = f.readlines() - f.close - - # Reading the ocean/ice grid cell area (area) - # Reading the sea ice concentration (ice_con) - for i in range(0, len(lines_n)): - filename = lines_n[i].strip("\t\n\r") - print(filename) - obs = cdms.open(filename) - lats_n = obs("lat") - lons_n = obs("lon") - area_n = obs("area") - sic_n = obs("ice_con") - sic_n = MV.multiply(sic_n, factor2) - area_n = MV.multiply(area_n, factor1) - - obs.close() - - # Creating regional masks - lons_p = MV.where(MV.less(lons_n, 0.0), lons_n + 360.0, lons_n) - lons_a = lons_n - - #print("Obs") - #print("lons_na= ", MV.min(lons_a), MV.max(lons_a)) - #print("lons_np= ", MV.min(lons_p), MV.max(lons_p)) - - mask_ca = MV.zeros(area_n.shape) - mask_na = MV.zeros(area_n.shape) - mask_np = MV.zeros(area_n.shape) - - # Arctic Regions - # Central Arctic - lat_bound1 = MV.logical_and( - MV.greater(lats_n, 80.0), MV.less_equal(lats_n, 87.2) - ) - lat_bound2 = MV.logical_and( - MV.greater(lats_n, 65.0), MV.less_equal(lats_n, 87.2) - ) - lat_bound3 = MV.logical_and( - MV.greater(lats_n, 87.2), MV.less_equal(lats_n, 90.0) - ) - lon_bound1 = MV.logical_and( - MV.greater(lons_a, -120.0), MV.less_equal(lons_a, 90.0) - ) - lon_bound2 = MV.logical_and( - MV.greater(lons_p, 90.0), MV.less_equal(lons_p, 240.0) - ) - reg1_ca = MV.logical_and(lat_bound1, lon_bound1) - reg2_ca = MV.logical_and(lat_bound2, lon_bound2) - mask_ca = MV.where(MV.logical_or(reg1_ca, reg2_ca), 1, 0) - mask_pole = MV.where(lat_bound3, 1, 0) - - # NA region - lat_bound = MV.logical_and( - MV.greater(lats_n, 45.0), MV.less_equal(lats_n, 80.0) - ) - lon_bound = MV.logical_and( - MV.greater(lons_a, -120.0), MV.less_equal(lons_a, 90.0) - ) - mask_na = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) - - # NP region - lat_bound = MV.logical_and( - MV.greater(lats_n, 45.0), MV.less_equal(lats_n, 65.0) - ) - lon_bound = MV.logical_and( - MV.greater(lons_p, 90.0), MV.less_equal(lons_p, 240.0) - ) - mask_np = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) - - area_sic_pole = MV.where(MV.equal(mask_pole, True), MV.multiply(1, area_n), 0.0) - - # Masking out sic<0.15 - ice_area = MV.where( - MV.greater_equal(sic_n, 0.15), area_n, 0.0 - ) # Masking out sic<0.15 - - # Ice Extent - # area_sic_arctic=MV.multiply(1,ice_area) - # area_sic_ca=MV.where(MV.equal(mask_ca,True),MV.multiply(1,ice_area),0.) - # area_sic_na=MV.where(MV.equal(mask_na,True),MV.multiply(1,ice_area),0.) - # area_sic_np=MV.where(MV.equal(mask_np,True),MV.multiply(1,ice_area),0.) - # Ice Area - area_sic_arctic = MV.multiply(sic_n, ice_area) - area_sic_ca = MV.where( - MV.equal(mask_ca, True), MV.multiply(sic_n, ice_area), 0.0 - ) - area_sic_na = MV.where( - MV.equal(mask_na, True), MV.multiply(sic_n, ice_area), 0.0 - ) - area_sic_np = MV.where( - MV.equal(mask_np, True), MV.multiply(sic_n, ice_area), 0.0 - ) - - data_n[i] = MV.add(MV.sum(area_sic_arctic), MV.sum(area_sic_pole)) - ta_ca[i] = MV.add(MV.sum(area_sic_ca), MV.sum(area_sic_pole)) - ta_na[i] = MV.sum(area_sic_na) - ta_np[i] = MV.sum(area_sic_np) - - #print("data_n= ", data_n[i]) - #print("ta_na= ", ta_na[i]) - #print("ta_np= ", ta_np[i]) - - #print(MV.average(sic_n)) - - obs_n[:, dl] = MV.array(data_n, id="sic") - obs_ca[:, dl] = MV.array(ta_ca, id="sic") - obs_na[:, dl] = MV.array(ta_na, id="sic") - obs_np[:, dl] = MV.array(ta_np, id="sic") - -# SSM/I Antarctic -for dl in range(0, len(dlist_s)): - g = open(dlist_s[dl]) - lines_s = g.readlines() - g.close - - for i in range(0, len(lines_s)): - filename = lines_s[i].strip("\t\n\r") - print(filename) - obs = cdms.open(filename) - lats_s = obs("lat") - lons_s = obs("lon") - area_s = obs("area") - sic_s = obs("ice_con") - sic_s = MV.multiply(sic_s, factor2) - area_s = MV.multiply(area_s, factor1) - - obs.close() - - # Creating regional masks - lons_sa = lons_s - lons_sp = MV.where(MV.less(lons_s, 0.0), lons_s + 360, lons_s) - lons_io = lons_sp - - #print("Obs") - #print("lons_sa= ", MV.min(lons_sa), MV.max(lons_sa)) - #print("lons_sp= ", MV.min(lons_sp), MV.max(lons_sp)) - - mask_sa = MV.zeros(area_s.shape) - mask_sp = MV.zeros(area_s.shape) - mask_io = MV.zeros(area_s.shape) - - # Antarctic Regions - lat_bound = MV.logical_and( - MV.greater(lats_s, -90.0), MV.less_equal(lats_s, -55.0) - ) - - # SA region - # lon_bound=MV.logical_and(MV.greater(lons_sa,-60.),MV.less_equal(lons_sa,30.)) - lon_bound = MV.logical_and( - MV.greater(lons_sa, -60.0), MV.less_equal(lons_sa, 20.0) - ) - mask_sa = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) - - # SP region - # lon_bound=MV.logical_and(MV.greater(lons_sp,130.),MV.less_equal(lons_sp,300.)) - lon_bound = MV.logical_and( - MV.greater(lons_sp, 90.0), MV.less_equal(lons_sp, 300.0) - ) - mask_sp = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) - - # Indian Ocean (IO) region - # lon_bound=MV.logical_and(MV.greater(lons_sp,30.),MV.less_equal(lons_sp,130.)) - lon_bound = MV.logical_and( - MV.greater(lons_sp, 20.0), MV.less_equal(lons_sp, 90.0) - ) - mask_io = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) - - # Masking out sic<0.15 - ice_area = MV.where( - MV.greater_equal(sic_s, dc), area_s, 0.0 - ) # Masking out sic<0.15 - # Ice Extent - # area_sic_antarctic=MV.multiply(1,ice_area) - # area_sic_sa=MV.where(MV.equal(mask_sa,True),MV.multiply(1,ice_area),0.) - # area_sic_sp=MV.where(MV.equal(mask_sp,True),MV.multiply(1,ice_area),0.) - # area_sic_io=MV.where(MV.equal(mask_io,True),MV.multiply(1,ice_area),0.) - # Ice Area - area_sic_antarctic = MV.multiply(sic_s, ice_area) - area_sic_sa = MV.where(MV.equal(mask_sa, True), MV.multiply(sic_s, area_s), 0.0) - area_sic_sp = MV.where(MV.equal(mask_sp, True), MV.multiply(sic_s, area_s), 0.0) - area_sic_io = MV.where(MV.equal(mask_io, True), MV.multiply(sic_s, area_s), 0.0) - - data_s[i] = MV.sum(area_sic_antarctic) - ta_sa[i] = MV.sum(area_sic_sa) - ta_sp[i] = MV.sum(area_sic_sp) - ta_io[i] = MV.sum(area_sic_io) - - #print(MV.average(sic_s)) - - obs_s[:, dl] = MV.array(data_s, id="sic") - obs_s = MV.masked_equal(obs_s, -9999.0) - obs_sa[:, dl] = MV.array(ta_sa, id="sic") - obs_sp[:, dl] = MV.array(ta_sp, id="sic") - obs_io[:, dl] = MV.array(ta_io, id="sic") - -# Create Time Axis -years = [] -months = [] -for iy in range(1979, 2006): - for im in range(1, 13): - years.append(int(iy)) - months.append(int(im)) - -timeax = [] -for date in zip(years, months): - yr, mo = date - #print(yr) - c = cdtime.comptime(yr, mo) - #print(c) - #print(c.torel("days since 1979-1-1").value) - timeax = timeax + [int(c.torel("days since 1979-1-1").value)] - -time = cdms.createAxis(timeax) -time.id = "time" -time.units = "days since 1979-1-1" -bounds = cdutil.times.setAxisTimeBoundsMonthly(time) -obs_n.setAxis(0, time) -obs_ca.setAxis(0, time) -obs_na.setAxis(0, time) -obs_np.setAxis(0, time) -obs_s.setAxis(0, time) -obs_sa.setAxis(0, time) -obs_sp.setAxis(0, time) -obs_io.setAxis(0, time) - -# Calculate Annual Cycle (1979-2005) -cdutil.setTimeBoundsMonthly(obs_n) -cdutil.setTimeBoundsMonthly(obs_s) -annual_cycle_obs_arctic = np.array(cdutil.ANNUALCYCLE.climatology(obs_n[0:324])) -annual_cycle_obs_ca = np.array(cdutil.ANNUALCYCLE.climatology(obs_ca[0:324])) -annual_cycle_obs_na = np.array(cdutil.ANNUALCYCLE.climatology(obs_na[0:324])) -annual_cycle_obs_np = np.array(cdutil.ANNUALCYCLE.climatology(obs_np[0:324])) -annual_cycle_obs_antarctic = np.array(cdutil.ANNUALCYCLE.climatology(obs_s[0:324])) -annual_cycle_obs_sa = np.array(cdutil.ANNUALCYCLE.climatology(obs_sa[0:324])) -annual_cycle_obs_sp = np.array(cdutil.ANNUALCYCLE.climatology(obs_sp[0:324])) -annual_cycle_obs_io = np.array(cdutil.ANNUALCYCLE.climatology(obs_io[0:324])) - -annual_cycle_std_obs_arctic = np.zeros((12, 2)) -annual_cycle_std_obs_ca = np.zeros((12, 2)) -annual_cycle_std_obs_na = np.zeros((12, 2)) -annual_cycle_std_obs_np = np.zeros((12, 2)) -annual_cycle_std_obs_antarctic = np.zeros((12, 2)) -annual_cycle_std_obs_sa = np.zeros((12, 2)) -annual_cycle_std_obs_sp = np.zeros((12, 2)) -annual_cycle_std_obs_io = np.zeros((12, 2)) - -for im in range(0, 12): - annual_cycle_std_obs_arctic[im, :] = np.array( - genutil.statistics.std(obs_n[im:324:12, :]) - ) - annual_cycle_std_obs_ca[im, :] = np.array( - genutil.statistics.std(obs_ca[im:324:12, :]) - ) - annual_cycle_std_obs_na[im, :] = np.array( - genutil.statistics.std(obs_na[im:324:12, :]) - ) - annual_cycle_std_obs_np[im, :] = np.array( - genutil.statistics.std(obs_np[im:324:12, :]) - ) - annual_cycle_std_obs_antarctic[im, :] = np.array( - genutil.statistics.std(obs_s[im:324:12, :]) - ) - annual_cycle_std_obs_sa[im, :] = np.array( - genutil.statistics.std(obs_sa[im:324:12, :]) - ) - annual_cycle_std_obs_sp[im, :] = np.array( - genutil.statistics.std(obs_sp[im:324:12, :]) - ) - annual_cycle_std_obs_io[im, :] = np.array( - genutil.statistics.std(obs_io[im:324:12, :]) - ) - - -# NSIDC-0192 - -# SSM/I Arctic -# Area -#dlist_n = [ -# "nasateam/gsfc.nasateam.month.area.1978-2010.n.asc", -# "bootstrap/gsfc.bootstrap.month.area.1978-2010.n.asc", -#] -#dlist_s = [ -# "nasateam/gsfc.nasateam.month.area.1978-2010.s.asc", -# "bootstrap/gsfc.bootstrap.month.area.1978-2010.s.asc", -#] -dlist_n = [ - "/p/user_pub/hoang1-backups/ARCHIVE/ivanova2/IceData/AreaExtent/NSIDC-0192/ice-extent/nasateam/gsfc.nasateam.month.area.1978-2010.n.asc", - "/p/user_pub/hoang1-backups/ARCHIVE/ivanova2/IceData/AreaExtent/NSIDC-0192/ice-extent/bootstrap/gsfc.bootstrap.month.area.1978-2010.n.asc" -] -dlist_s = [ - "/p/user_pub/hoang1-backups/ARCHIVE/ivanova2/IceData/AreaExtent/NSIDC-0192/ice-extent/nasateam/gsfc.nasateam.month.area.1978-2010.s.asc", - "/p/user_pub/hoang1-backups/ARCHIVE/ivanova2/IceData/AreaExtent/NSIDC-0192/ice-extent/bootstrap/gsfc.bootstrap.month.area.1978-2010.s.asc" -] -# Extent -# dlist_n=['nasateam/gsfc.nasateam.month.extent.1978-2010.n.asc','bootstrap/gsfc.bootstrap.month.extent.1978-2010.n.asc'] -# dlist_s=['nasateam/gsfc.nasateam.month.extent.1978-2010.s.asc','bootstrap/gsfc.bootstrap.month.extent.1978-2010.s.asc'] -obs1_n = MV.zeros([324, 2]) -obs1_ca = MV.zeros([324, 2]) -obs1_na = MV.zeros([324, 2]) -obs1_np = MV.zeros([324, 2]) -obs1_s = MV.zeros([324, 2]) -obs1_sa = MV.zeros([324, 2]) -obs1_sp = MV.zeros([324, 2]) -obs1_io = MV.zeros([324, 2]) - -for dl in range(0, len(dlist_n)): - years = [] - months = [] - data_n = [] - data_ca = [] - data_na = [] - data_np = [] - data_s = [] - data_sa = [] - data_sp = [] - data_io = [] - - f = open(dlist_n[dl]) - lines_n = f.readlines() - f.close - - g = open(dlist_s[dl]) - lines_s = g.readlines() - g.close - - for line in lines_n: - # val1,val2,val3i,val4,val5=map(int,line.split()) - sp = line.split() - try: - val0 = int(sp[0]) - val1 = int(sp[1]) - val3 = float(sp[3]) - val4 = float(sp[4]) - val5 = float(sp[5]) - val6 = float(sp[6]) - val7 = float(sp[7]) - val8 = float(sp[8]) - val9 = float(sp[9]) - val10 = float(sp[10]) - val11 = float(sp[11]) - val12 = float(sp[12]) - years.append(val0) - months.append(val1) - data_n.append(val3) - data_np.append(val4 + val5) - data_na.append(val6 + val7 + val8 + val9 + val11 + val12) - data_ca.append(val10) - except Exception: - pass - obs1_n[:, dl] = MV.array(data_n[0:324], id="sic") - obs1_ca[:, dl] = MV.array(data_ca[0:324], id="sic") - obs1_na[:, dl] = MV.array(data_na[0:324], id="sic") - obs1_np[:, dl] = MV.array(data_np[0:324], id="sic") - - for line in lines_s: - sp = line.split() - try: - val3 = float(sp[3]) - val4 = float(sp[4]) - val5 = float(sp[5]) - val6 = float(sp[6]) - val7 = float(sp[7]) - val8 = float(sp[8]) - data_s.append(val3) - data_sa.append(val4) - data_io.append(val5) - data_sp.append(val6 + val7 + val8) - except Exception: - pass - - obs1_s[:, dl] = MV.array(data_s[0:324], id="sic") - obs1_sa[:, dl] = MV.array(data_sa[0:324], id="sic") - obs1_sp[:, dl] = MV.array(data_sp[0:324], id="sic") - obs1_io[:, dl] = MV.array(data_io[0:324], id="sic") - -obs1_n = MV.masked_equal(obs1_n, -999.0) -obs1_ca = MV.masked_equal(obs1_ca, -999.0) -obs1_na = MV.masked_equal(obs1_na, -999.0) -obs1_np = MV.masked_equal(obs1_np, -999.0) -obs1_s = MV.masked_equal(obs1_s, -999.0) -obs1_sa = MV.masked_equal(obs1_sa, -999.0) -obs1_sp = MV.masked_equal(obs1_sp, -999.0) -obs1_io = MV.masked_equal(obs1_io, -999.0) -obs1_n = MV.multiply(obs1_n, factor1) -obs1_ca = MV.multiply(obs1_ca, factor1) -obs1_np = MV.multiply(obs1_np, factor1) -obs1_na = MV.multiply(obs1_na, factor1) -obs1_s = MV.multiply(obs1_s, factor1) -obs1_sa = MV.multiply(obs1_sa, factor1) -obs1_sp = MV.multiply(obs1_sp, factor1) -obs1_io = MV.multiply(obs1_io, factor1) - -# Create Time Axis -timeax = [] -for date in zip(years, months): - yr, mo = date - #print(yr) - c = cdtime.comptime(yr, mo) - #print(c) - #print(c.torel("days since 1979-1-1").value) - timeax = timeax + [int(c.torel("days since 1979-1-1").value)] - -time = cdms.createAxis(timeax[0:324]) -time.id = "time" -time.units = "days since 1979-1-1" -bounds = cdutil.times.setAxisTimeBoundsMonthly(time) -obs1_n.setAxis(0, time) -obs1_ca.setAxis(0, time) -obs1_na.setAxis(0, time) -obs1_np.setAxis(0, time) -obs1_s.setAxis(0, time) -obs1_sa.setAxis(0, time) -obs1_sp.setAxis(0, time) -obs1_io.setAxis(0, time) - - -# Calculate Annual Cycle (1979-2005) -cdutil.setTimeBoundsMonthly(obs1_n) -cdutil.setTimeBoundsMonthly(obs1_s) -annual_cycle_obs1_arctic = np.array(cdutil.ANNUALCYCLE.climatology(obs1_n[0:324])) -annual_cycle_obs1_ca = np.array(cdutil.ANNUALCYCLE.climatology(obs1_ca[0:324])) -annual_cycle_obs1_na = np.array(cdutil.ANNUALCYCLE.climatology(obs1_na[0:324])) -annual_cycle_obs1_np = np.array(cdutil.ANNUALCYCLE.climatology(obs1_np[0:324])) -annual_cycle_obs1_antarctic = np.array(cdutil.ANNUALCYCLE.climatology(obs1_s[0:324])) -annual_cycle_obs1_sa = np.array(cdutil.ANNUALCYCLE.climatology(obs1_sa[0:324])) -annual_cycle_obs1_sp = np.array(cdutil.ANNUALCYCLE.climatology(obs1_sp[0:324])) -annual_cycle_obs1_io = np.array(cdutil.ANNUALCYCLE.climatology(obs1_io[0:324])) - -annual_cycle_std_obs1_arctic = np.zeros((12, 2)) -annual_cycle_std_obs1_ca = np.zeros((12, 2)) -annual_cycle_std_obs1_na = np.zeros((12, 2)) -annual_cycle_std_obs1_np = np.zeros((12, 2)) -annual_cycle_std_obs1_antarctic = np.zeros((12, 2)) -annual_cycle_std_obs1_sa = np.zeros((12, 2)) -annual_cycle_std_obs1_sp = np.zeros((12, 2)) -annual_cycle_std_obs1_io = np.zeros((12, 2)) - -for im in range(0, 12): - annual_cycle_std_obs1_arctic[im, :] = np.array( - genutil.statistics.std(obs1_n[im:324:12, :]) - ) - annual_cycle_std_obs1_ca[im, :] = np.array( - genutil.statistics.std(obs1_ca[im:324:12, :]) - ) - annual_cycle_std_obs1_na[im, :] = np.array( - genutil.statistics.std(obs1_na[im:324:12, :]) - ) - annual_cycle_std_obs1_np[im, :] = np.array( - genutil.statistics.std(obs1_np[im:324:12, :]) - ) - annual_cycle_std_obs1_antarctic[im, :] = np.array( - genutil.statistics.std(obs1_s[im:324:12, :]) - ) - annual_cycle_std_obs1_sa[im, :] = np.array( - genutil.statistics.std(obs1_sa[im:324:12, :]) - ) - annual_cycle_std_obs1_sp[im, :] = np.array( - genutil.statistics.std(obs1_sp[im:324:12, :]) - ) - annual_cycle_std_obs1_io[im, :] = np.array( - genutil.statistics.std(obs1_io[im:324:12, :]) - ) -print(annual_cycle_std_obs1_arctic) - -# Calculate the Obs RMS -rms_arctic_obs1 = genutil.statistics.rms( - annual_cycle_obs_arctic[:, 1], annual_cycle_obs_arctic[:, 0], axis=0 -) -rms_antarctic_obs1 = genutil.statistics.rms( - annual_cycle_obs_antarctic[:, 1], annual_cycle_obs_antarctic[:, 0], axis=0 -) -rms_ca_obs1 = genutil.statistics.rms( - annual_cycle_obs_ca[:, 1], annual_cycle_obs_ca[:, 0], axis=0 -) -rms_na_obs1 = genutil.statistics.rms( - annual_cycle_obs_na[:, 1], annual_cycle_obs_na[:, 0], axis=0 -) -rms_np_obs1 = genutil.statistics.rms( - annual_cycle_obs_np[:, 1], annual_cycle_obs_np[:, 0], axis=0 -) -rms_sa_obs1 = genutil.statistics.rms( - annual_cycle_obs_sa[:, 1], annual_cycle_obs_sa[:, 0], axis=0 -) -rms_sp_obs1 = genutil.statistics.rms( - annual_cycle_obs_sp[:, 1], annual_cycle_obs_sp[:, 0], axis=0 -) -rms_io_obs1 = genutil.statistics.rms( - annual_cycle_obs_io[:, 1], annual_cycle_obs_io[:, 0], axis=0 -) -import pickle -import sys - -data_dict = {"Arctic": rms_arctic_obs1.data.item(), - "Antarctic": rms_antarctic_obs1.data.item(), - "CA": rms_ca_obs1.data.item(), - "NA": rms_na_obs1.data.item(), - "NP": rms_np_obs1.data.item(), - "SA": rms_sa_obs1.data.item(), - "SP": rms_sp_obs1.data.item(), - "IO": rms_io_obs1.data.item()} -with open('obs_rms_data.pkl','wb') as handle: - pickle.dump(data_dict,handle) - -# CMIP5 Native grid -var = "sic" -# obs = ['NASATEAM','BOOTSTRAP'] -# mods = ['Obs-NASATEAM','Obs-BOOTSTRAP','CMIP5 Native grid','CMIP5 Interpolated'] -mods_obs = ["CMIP5 MME", "Obs-NASATEAM", "Obs-BOOTSTRAP"] -obs_mods = ["Obs-NASATEAM", "Obs-BOOTSTRAP", "CMIP5 MME Native grid"] -lines_m = [ - "ro-", - "rd-", - "ro--", - "co-", - "c*-", - "cd-", - "c-", - "bo-", - "b^-", - "b*-", - "co--", - "go--", - "g^--", - "gd--", - "g-", - "m*--", - "y*-", -] # ,'m^-','yo--'] -# lines_d = ['bo-','g*-'] -# cols_d=['b','g'] - -flist = open("/home/ordonez4/seaice/cmip5_sic_names_all_xml_012413_conserv.asc") -#flist = open("./cmip5_sic_names_all_xml_012413_conserv.asc") -# flist=open('./cmip5_sic_names_all_xml_012413_conserv_ccsm4.asc') -# flist=open('./cmip5_sic_names_all_xml_121212_conserv.asc') -# flist=open('./cmip5_sic_names_all_xml_121212_ncar.asc') -# flist=open('./cmip5_sic_names_all_xml_121212.asc') -# flist=open('./cmip5_sic_names_all_xml_102412_hadgem.asc') -fnames = flist.readlines() - -glist = open("/home/ordonez4/seaice/cmip5_areacell_names_nc_012413_conserv.asc") -#glist = open("./cmip5_areacell_names_nc_012413_conserv.asc") -# glist=open('./cmip5_areacell_names_nc_012413_conserv_ccsm4.asc') -# glist=open('./cmip5_areacell_names_nc.asc') -# glist=open('./cmip5_areacell_names_all_xml_121212.asc') -# glist=open('./cmip5_areacell_names_all_xml_121212.asc') -# glist=open('./cmip5_areacell_names_all_xml_010813_conserv.asc') -# glist=open('./cmip5_areacell_names_all_xml_121212_conserv.asc') -# glist=open('./cmip5_areacell_names_all_xml_121212_ncar.asc') -# glist=open('./cmip5_areacell_names_all_xml_102412_hadgem.asc') -gnames = glist.readlines() - - -# Dictionary with the model names and runs and versions -mod_runs = {} -mods = [] -for i in range(0, len(fnames)): - sp = fnames[i].split(".") - if sp[3] == "r1i1p1": - mods.append(sp[1]) -for mod in mods: - runs = [] - vers = [] - for i in range(0, len(fnames)): - rn = fnames[i].split(".") - if rn[1] == mod: - runs.append(rn[3]) - vers.append(rn[7].strip("\t\n\r")) - mod_runs[mod] = [runs, vers] - del runs - del vers - -ann_arctic = np.zeros((12, len(mods))) -ann_antarctic = np.zeros((12, len(mods))) -ann_ca = np.zeros((12, len(mods))) -ann_na = np.zeros((12, len(mods))) -ann_np = np.zeros((12, len(mods))) -ann_sa = np.zeros((12, len(mods))) -ann_sp = np.zeros((12, len(mods))) -ann_io = np.zeros((12, len(mods))) -std_arctic = np.zeros((324, len(mods))) -std_antarctic = np.zeros((324, len(mods))) -std_ca = np.zeros((324, len(mods))) -std_na = np.zeros((324, len(mods))) -std_np = np.zeros((324, len(mods))) -std_sa = np.zeros((324, len(mods))) -std_sp = np.zeros((324, len(mods))) -std_io = np.zeros((324, len(mods))) -annual_cycle_std_mod_arctic = np.zeros((12)) -annual_cycle_std_mod_antarctic = np.zeros((12)) -annual_cycle_std_mod_ca = np.zeros((12)) -annual_cycle_std_mod_na = np.zeros((12)) -annual_cycle_std_mod_np = np.zeros((12)) -annual_cycle_std_mod_sa = np.zeros((12)) -annual_cycle_std_mod_sp = np.zeros((12)) -annual_cycle_std_mod_io = np.zeros((12)) -ann_arctic_mma = np.zeros((12)) -ann_antarctic_mma = np.zeros((12)) -ann_ca_mma = np.zeros((12)) -ann_na_mma = np.zeros((12)) -ann_np_mma = np.zeros((12)) -ann_sa_mma = np.zeros((12)) -ann_sp_mma = np.zeros((12)) -ann_io_mma = np.zeros((12)) -rms_ann_arctic = np.zeros((len(dlist_n), len(mods))) -rms_ann_antarctic = np.zeros((len(dlist_s), len(mods))) -rms_ann_ca = np.zeros((len(dlist_n), len(mods))) -rms_ann_na = np.zeros((len(dlist_n), len(mods))) -rms_ann_np = np.zeros((len(dlist_n), len(mods))) -rms_ann_sa = np.zeros((len(dlist_s), len(mods))) -rms_ann_sp = np.zeros((len(dlist_s), len(mods))) -rms_ann_io = np.zeros((len(dlist_s), len(mods))) - -nm = 0 -i = -1 - -for mod in mods: - i = i + 1 - runs = mod_runs[mod][0] - vers = mod_runs[mod][1] - - # Reading the ocean/ice grid cell area (areacello) - gfile = gnames[i].strip("\t\n\r") - print(gfile) - g = cdms.open(gfile) - try: - area = g("areacello") - except Exception: - area = g("areacella") - area = MV.multiply(area, factor1) - area = MV.multiply(area, factor1) - - g.close() - - total_area_arctic = MV.zeros([324]) - total_area_antarctic = MV.zeros([324]) - total_area_ca = MV.zeros([324]) - total_area_na = MV.zeros([324]) - total_area_np = MV.zeros([324]) - total_area_sa = MV.zeros([324]) - total_area_sp = MV.zeros([324]) - total_area_io = MV.zeros([324]) - - nr = 0 # Number fo individual model runs - - for run, ver in zip(runs, vers): - nr = nr + 1 - # Reading the sea ice concentration (sic) - #infile = ( - # "/work/cmip5/historical/seaIce/mo/sic/" - # + "cmip5." - # + mod - # + ".historical." - # + run - # + ".mo.seaIce.sic." - # + ver - # + ".xml" - #) - infile = ( - "/p/user_pub/pmp/pmp_results/pmp_v1.1.2/additional_xmls/latest/v20231104/cmip5/historical/seaIce/mon/sic/" - + "cmip5." - + "historical." - + mod + "." - + run + "." - + "mon.sic.xml" - ) - print(infile) - - f = cdms.open(infile) - if ((mod == "HadGEM2-CC" and (run in ["r1i1p1", "r3i1p1"]) - or mod == "HadGEM2-ES" and (run in ["r2i1p1", "r3i1p1", "r4i1p1"]))): - sic = f(var, time=("1978-12-1", "2006-1-1")) - else: - sic = f(var, time=("1979-1-1", "2006-1-1")) - print(MV.average(sic)) - t = sic.getTime() - lat = sic.getLatitude() - lon = sic.getLongitude() - sic = MV.multiply(sic, factor2) - - if np.ndim(lat) == 1: - tmp2d = f(var, time=slice(0, 1), squeeze=1) - lats = MV.zeros(tmp2d.shape) - for ii in range(0, len(lon)): - lats[:, ii] = lat[:] - else: - lats = lat - - if np.ndim(lon) == 1: - tmp2d = f(var, time=slice(0, 1), squeeze=1) - lons = MV.zeros(tmp2d.shape) - for ii in range(0, len(lat)): - lons[ii, :] = lon[:] - else: - lons = lon - - f.close() - - # Creating regional masks - lons_a = MV.where(MV.greater(lons, 180.0), lons - 360, lons) - lons_p = lons - #print("CMIP5") - #print("lons_na= ", MV.min(lons_a), MV.max(lons_a)) - #print("lons_np= ", MV.min(lons_p), MV.max(lons_p)) - mask_ca = MV.zeros(area.shape) - mask_na = MV.zeros(area.shape) - mask_np = MV.zeros(area.shape) - mask_sa = MV.zeros(area.shape) - mask_sp = MV.zeros(area.shape) - mask_io = MV.zeros(area.shape) - - # Arctic Regions - # Central Arctic - # lat_bound1=MV.logical_and(MV.greater(lats,80.),MV.less_equal(lats,87.2)) - # lat_bound2=MV.logical_and(MV.greater(lats,65.),MV.less_equal(lats,87.2)) - lat_bound1 = MV.logical_and(MV.greater(lats, 80.0), MV.less_equal(lats, 90.0)) - lat_bound2 = MV.logical_and(MV.greater(lats, 65.0), MV.less_equal(lats, 90.0)) - lon_bound1 = MV.logical_and( - MV.greater(lons_a, -120.0), MV.less_equal(lons_a, 90.0) - ) - lon_bound2 = MV.logical_and( - MV.greater(lons_p, 90.0), MV.less_equal(lons_p, 240.0) - ) - reg1_ca = MV.logical_and(lat_bound1, lon_bound1) - reg2_ca = MV.logical_and(lat_bound2, lon_bound2) - mask_ca = MV.where(MV.logical_or(reg1_ca, reg2_ca), 1, 0) - - # NA region - lat_bound = MV.logical_and(MV.greater(lats, 45.0), MV.less_equal(lats, 80.0)) - lon_bound = MV.logical_and( - MV.greater(lons_a, -120.0), MV.less_equal(lons_a, 90.0) - ) - mask_na = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) - - # NP region - lat_bound = MV.logical_and(MV.greater(lats, 45.0), MV.less_equal(lats, 65.0)) - lon_bound = MV.logical_and( - MV.greater(lons_p, 90.0), MV.less_equal(lons_p, 240.0) - ) - mask_np = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) - - # Antarctic Regions - lat_bound = MV.logical_and(MV.greater(lats, -90.0), MV.less_equal(lats, -55.0)) - - # SA region - lon_bound = MV.logical_and( - MV.greater(lons_a, -60.0), MV.less_equal(lons_a, 20.0) - ) - mask_sa = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) - - # SP region - # lon_bound=MV.logical_and(MV.greater(lons_p,130.),MV.less_equal(lons_p,300.)) - lon_bound = MV.logical_and( - MV.greater(lons_p, 90.0), MV.less_equal(lons_p, 300.0) - ) - - mask_sp = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) - - # IO region - # lon_bound=MV.logical_and(MV.greater(lons_p,30.),MV.less_equal(lons_p,130.)) - lon_bound = MV.logical_and( - MV.greater(lons_p, 20.0), MV.less_equal(lons_p, 90.0) - ) - mask_io = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) - - # Calculate the Total Sea Ice Area - ice_area = MV.multiply(sic, area) - ice_area = MV.where( - MV.greater_equal(sic, 0.15), ice_area, 0.0 - ) # Masking out the sic<0.15 - - # arctic=MV.logical_and(MV.greater_equal(lats,35.),MV.less(lats,87.2)) # SSM/I limited to 87.2N - arctic = MV.logical_and( - MV.greater_equal(lats, 45.0), MV.less(lats, 90.0) - ) # Adding currently in SSM/I 100% in the area >87.2N - antarctic = MV.logical_and(MV.greater_equal(lats, -90.0), MV.less(lats, -55.0)) - - - - for nt in range(len(t)): - aice_arctic = MV.where(MV.equal(arctic, True), ice_area[nt], 0.0) - aice_antarctic = MV.where(MV.equal(antarctic, True), ice_area[nt], 0.0) - area_sic_ca = MV.where(MV.equal(mask_ca, True), ice_area[nt], 0.0) - area_sic_na = MV.where(MV.equal(mask_na, True), ice_area[nt], 0.0) - area_sic_np = MV.where(MV.equal(mask_np, True), ice_area[nt], 0.0) - area_sic_sa = MV.where(MV.equal(mask_sa, True), ice_area[nt], 0.0) - area_sic_sp = MV.where(MV.equal(mask_sp, True), ice_area[nt], 0.0) - area_sic_io = MV.where(MV.equal(mask_io, True), ice_area[nt], 0.0) - total_area_arctic[nt] = total_area_arctic[nt] + MV.sum(aice_arctic) - total_area_antarctic[nt] = total_area_antarctic[nt] + MV.sum(aice_antarctic) - total_area_ca[nt] = total_area_ca[nt] + MV.sum(area_sic_ca) - total_area_na[nt] = total_area_na[nt] + MV.sum(area_sic_na) - total_area_np[nt] = total_area_np[nt] + MV.sum(area_sic_np) - total_area_sa[nt] = total_area_sa[nt] + MV.sum(area_sic_sa) - total_area_sp[nt] = total_area_sp[nt] + MV.sum(area_sic_sp) - total_area_io[nt] = total_area_io[nt] + MV.sum(area_sic_io) - #plt.pcolormesh(aice_arctic.data) - #plt.colorbar() - #plt.savefig("figs/"+mod+"_"+run+"_arctic.png") - #plt.close() - #plt.colorbar() - #plt.pcolormesh(area_sic_io.data) - #plt.savefig("figs/"+mod+"_"+run+"_io.png") - #plt.close() - #continue - - # print 'total_area_arctic= ',total_area_arctic[nt] - # print 'total_area_na= ',total_area_na[nt] - # print 'total_area_np= ',total_area_np[nt] - - # Individual Model Ensemble Mean - total_area_arctic = MV.divide(total_area_arctic, nr) - total_area_antarctic = MV.divide(total_area_antarctic, nr) - total_area_ca = MV.divide(total_area_ca, nr) - total_area_na = MV.divide(total_area_na, nr) - total_area_np = MV.divide(total_area_np, nr) - total_area_sa = MV.divide(total_area_sa, nr) - total_area_sp = MV.divide(total_area_sp, nr) - total_area_io = MV.divide(total_area_io, nr) - - # Annual cycle - total_area_arctic.setAxis(0, t) - cdutil.setTimeBoundsMonthly(total_area_arctic) - annual_cycle_arctic = cdutil.ANNUALCYCLE.climatology(total_area_arctic) - - total_area_antarctic.setAxis(0, t) - cdutil.setTimeBoundsMonthly(total_area_antarctic) - annual_cycle_antarctic = cdutil.ANNUALCYCLE.climatology(total_area_antarctic) - - total_area_ca.setAxis(0, t) - cdutil.setTimeBoundsMonthly(total_area_ca) - annual_cycle_ca = cdutil.ANNUALCYCLE.climatology(total_area_ca) - - total_area_na.setAxis(0, t) - cdutil.setTimeBoundsMonthly(total_area_na) - annual_cycle_na = cdutil.ANNUALCYCLE.climatology(total_area_na) - - total_area_np.setAxis(0, t) - cdutil.setTimeBoundsMonthly(total_area_np) - annual_cycle_np = cdutil.ANNUALCYCLE.climatology(total_area_np) - - total_area_sa.setAxis(0, t) - cdutil.setTimeBoundsMonthly(total_area_sa) - annual_cycle_sa = cdutil.ANNUALCYCLE.climatology(total_area_sa) - - total_area_sp.setAxis(0, t) - cdutil.setTimeBoundsMonthly(total_area_sp) - annual_cycle_sp = cdutil.ANNUALCYCLE.climatology(total_area_sp) - - total_area_io.setAxis(0, t) - cdutil.setTimeBoundsMonthly(total_area_io) - annual_cycle_io = cdutil.ANNUALCYCLE.climatology(total_area_io) - - ann_arctic[:, i] = np.array(annual_cycle_arctic) - ann_antarctic[:, i] = np.array(annual_cycle_antarctic) - ann_ca[:, i] = np.array(annual_cycle_ca) - ann_na[:, i] = np.array(annual_cycle_na) - ann_np[:, i] = np.array(annual_cycle_np) - ann_sa[:, i] = np.array(annual_cycle_sa) - ann_sp[:, i] = np.array(annual_cycle_sp) - ann_io[:, i] = np.array(annual_cycle_io) - - # Calculating the CMIP5 STD - - std_arctic[:, i] = np.array(total_area_arctic) - std_antarctic[:, i] = np.array(total_area_antarctic) - std_ca[:, i] = np.array(total_area_ca) - std_na[:, i] = np.array(total_area_na) - std_np[:, i] = np.array(total_area_np) - std_sa[:, i] = np.array(total_area_sa) - std_sp[:, i] = np.array(total_area_sp) - std_io[:, i] = np.array(total_area_io) - - ann_arctic_mma = ann_arctic_mma + np.array(annual_cycle_arctic) - ann_antarctic_mma = ann_antarctic_mma + np.array(annual_cycle_antarctic) - ann_ca_mma = ann_ca_mma + np.array(annual_cycle_ca) - ann_na_mma = ann_na_mma + np.array(annual_cycle_na) - ann_np_mma = ann_np_mma + np.array(annual_cycle_np) - ann_sa_mma = ann_sa_mma + np.array(annual_cycle_sa) - ann_sp_mma = ann_sp_mma + np.array(annual_cycle_sp) - ann_io_mma = ann_io_mma + np.array(annual_cycle_io) - nm = nm + 1 - - # Calculating the CMIP5 RMS - for j in range(0, 2): - rms_ann_arctic[j, i] = genutil.statistics.rms( - ann_arctic[:, i], annual_cycle_obs_arctic[:, j], axis=0 - ) - rms_ann_antarctic[j, i] = genutil.statistics.rms( - ann_antarctic[:, i], annual_cycle_obs_antarctic[:, j], axis=0 - ) - rms_ann_ca[j, i] = genutil.statistics.rms( - ann_ca[:, i], annual_cycle_obs_ca[:, j], axis=0 - ) - rms_ann_na[j, i] = genutil.statistics.rms( - ann_na[:, i], annual_cycle_obs_na[:, j], axis=0 - ) - rms_ann_np[j, i] = genutil.statistics.rms( - ann_np[:, i], annual_cycle_obs_np[:, j], axis=0 - ) - rms_ann_sa[j, i] = genutil.statistics.rms( - ann_sa[:, i], annual_cycle_obs_sa[:, j], axis=0 - ) - rms_ann_sp[j, i] = genutil.statistics.rms( - ann_sp[:, i], annual_cycle_obs_sp[:, j], axis=0 - ) - rms_ann_io[j, i] = genutil.statistics.rms( - ann_io[:, i], annual_cycle_obs_io[:, j], axis=0 - ) -sys.exit() - -# CMIP5 MME -ann_arctic_mma = ann_arctic_mma / nm -ann_antarctic_mma = ann_antarctic_mma / nm -ann_ca_mma = ann_ca_mma / nm -ann_na_mma = ann_na_mma / nm -ann_np_mma = ann_np_mma / nm -ann_sa_mma = ann_sa_mma / nm -ann_sp_mma = ann_sp_mma / nm -ann_io_mma = ann_io_mma / nm - -[ni, nj] = std_arctic.shape -tta_std_arctic = MV.zeros([12, int(324 / 12), len(mods)]) -tta_std_antarctic = MV.zeros([12, int(324 / 12), len(mods)]) -tta_std_ca = MV.zeros([12, int(324 / 12), len(mods)]) -tta_std_na = MV.zeros([12, int(324 / 12), len(mods)]) -tta_std_np = MV.zeros([12, int(324 / 12), len(mods)]) -tta_std_sa = MV.zeros([12, int(324 / 12), len(mods)]) -tta_std_sp = MV.zeros([12, int(324 / 12), len(mods)]) -tta_std_io = MV.zeros([12, int(324 / 12), len(mods)]) - -for im in range(0, 12): - tta_std_arctic[im, :, :] = std_arctic[im:ni:12, :] - tta_std_antarctic[im, :, :] = std_antarctic[im:ni:12, :] - tta_std_ca[im, :, :] = std_ca[im:ni:12, :] - tta_std_na[im, :, :] = std_na[im:ni:12, :] - tta_std_np[im, :, :] = std_np[im:ni:12, :] - tta_std_sa[im, :, :] = std_sa[im:ni:12, :] - tta_std_sp[im, :, :] = std_sp[im:ni:12, :] - tta_std_io[im, :, :] = std_io[im:ni:12, :] - -[nt, nx, ny] = tta_std_arctic.shape -ttta_std_arctic = MV.reshape(tta_std_arctic, (nt, nx * ny)) -ttta_std_ca = MV.reshape(tta_std_ca, (nt, nx * ny)) -ttta_std_na = MV.reshape(tta_std_na, (nt, nx * ny)) -ttta_std_np = MV.reshape(tta_std_np, (nt, nx * ny)) -[nt, nx, ny] = tta_std_antarctic.shape -ttta_std_antarctic = MV.reshape(tta_std_antarctic, (nt, nx * ny)) -ttta_std_sa = MV.reshape(tta_std_sa, (nt, nx * ny)) -ttta_std_sp = MV.reshape(tta_std_sp, (nt, nx * ny)) -ttta_std_io = MV.reshape(tta_std_io, (nt, nx * ny)) - -for im in range(0, 12): - annual_cycle_std_mod_arctic[im] = np.array( - genutil.statistics.std(ttta_std_arctic[im, :]) - ) - annual_cycle_std_mod_antarctic[im] = np.array( - genutil.statistics.std(ttta_std_antarctic[im, :]) - ) - annual_cycle_std_mod_ca[im] = np.array(genutil.statistics.std(ttta_std_ca[im, :])) - annual_cycle_std_mod_na[im] = np.array(genutil.statistics.std(ttta_std_na[im, :])) - annual_cycle_std_mod_np[im] = np.array(genutil.statistics.std(ttta_std_np[im, :])) - annual_cycle_std_mod_sa[im] = np.array(genutil.statistics.std(ttta_std_sa[im, :])) - annual_cycle_std_mod_sp[im] = np.array(genutil.statistics.std(ttta_std_sp[im, :])) - annual_cycle_std_mod_io[im] = np.array(genutil.statistics.std(ttta_std_io[im, :])) - -# Plot - -# Bar Plots of the RMS -labels = [ - "ACCESS1-3", -# "BNU-ESM", -# "CCSM4", -# "CESM1-BGC", -# "CESM1-CAM5-1-FV2", -# "CESM1-CAM5", -# "CESM1-FASTCHEM", - "CNRM-CM5", - "CSIRO-Mk3-6-0", - "CanCM4", - "CanESM2", - "GFDL-CM2p1", - "GFDL-CM3", - "GFDL-ESM2G", - "GFDL-ESM2M", - "GISS-E2-H-CC", - "GISS-E2-H", - "GISS-E2-R-CC", - "GISS-E2-R", - "HadCM3", - "HadGEM2-AO", - "HadGEM2-CC", - "HadGEM2-ES", - "IPSL-CM5A-MR", - "IPSL-CM5B-LR", - "MIROC-ESM-CHEM", - "MIROC-ESM", - "MIROC4h", - "MIROC5", - "MPI-ESM-LR", - "MPI-ESM-MR", - "MPI-ESM-P", - "NorESM1-ME", -# "bcc-csm1-1-m", -# "bcc-csm1-1", -] -# labels=["CCSM4","CESM1-BGC","CESM1-CAM5-1-FV2","CESM1-CAM5","CESM1-FASTCHEM","CNRM-CM5","CSIRO-Mk3-6-0","CanCM4","CanESM2","GFDL-CM3","GFDL-ES\ -# M2G","GFDL-ESM2M","GISS-E2-H-CC","GISS-E2-H","GISS-E2-R","HadCM3","HadGEM2-CC","HadGEM2-ES","IPSL-CM5A-MR","IPSL-CM5B-LR","MIROC-ESM-CHEM","MI\ -# ROC-ESM","MIROC4h","MIROC5","MPI-ESM-LR","MPI-ESM-MR","MPI-ESM-P","NorESM1-ME","bcc-csm1-1"] -# labels=["HadCM3","HadGEM2-CC"] -mlabels = np.append(labels, "RMS-Obs") -rms_arctic = np.append(rms_ann_arctic[0, :], rms_arctic_obs1) -rms_antarctic = np.append(rms_ann_antarctic[0, :], rms_antarctic_obs1) -rms_ca = np.append(rms_ann_ca[0, :], rms_ca_obs1) -rms_na = np.append(rms_ann_na[0, :], rms_na_obs1) -rms_np = np.append(rms_ann_np[0, :], rms_np_obs1) -rms_sa = np.append(rms_ann_sa[0, :], rms_sa_obs1) -rms_sp = np.append(rms_ann_sp[0, :], rms_sp_obs1) -rms_io = np.append(rms_ann_io[0, :], rms_io_obs1) - - -ind = np.arange(len(mods)) # the x locations for the groups -# ind = np.arange(len(mods)+1) # the x locations for the groups -width = 0.3 -n = len(ind) - 1 - -import pickle -import sys -with open('model_antarctic_plotting.pkl', 'wb') as handle: - pickle.dump(rms_ann_antarctic,handle) -with open('model_arctic_plotting.pkl', 'wb') as handle: - pickle.dump(rms_ann_arctic,handle) -with open('model_sp_plotting.pkl', 'wb') as handle: - pickle.dump(rms_ann_sp,handle) -with open('model_sa_plotting.pkl', 'wb') as handle: - pickle.dump(rms_ann_sa,handle) -with open('model_io_plotting.pkl', 'wb') as handle: - pickle.dump(rms_ann_io,handle) -with open('model_na_plotting.pkl', 'wb') as handle: - pickle.dump(rms_ann_na,handle) -with open('model_ca_plotting.pkl', 'wb') as handle: - pickle.dump(rms_ann_ca,handle) -with open('model_np_plotting.pkl', 'wb') as handle: - pickle.dump(rms_ann_np,handle) -sys.exit() -for sector in sector_list: - fig7 = plt.figure(7) - plt.subplot(413) - plt.bar(ind, rms_ann_sp[0, :], width, color="r") - plt.bar(ind[n] + 2.5 * width, rms_sp_obs1, width, color="b") - # plt.bar(ind+width,rms_ann_sp[1,:],width,color='b') - plt.xticks(ind + width / 2.0, mlabels, rotation=20, size=8) - #plt.hold - # plt.ylim(0.,ymax) - plt.ylabel("RMS of Total Sea Ice Area, 10${^6}$km${^2}$") - # plt.title('South Pacific Antarctic Sector') - plt.annotate( - "South Pacific Ocean Antarctic Sector", - (0.4, 0.9), - xycoords="axes fraction", - size=15, - ) - # plt.legend(obs_mods,loc=(.05,0.65)) - plt.grid(True) - fig7.savefig("fig7.png") diff --git a/pcmdi_metrics/sea_ice/ice_driver.py b/pcmdi_metrics/sea_ice/ice_driver.py deleted file mode 100644 index 4a1fd9e36..000000000 --- a/pcmdi_metrics/sea_ice/ice_driver.py +++ /dev/null @@ -1,911 +0,0 @@ -import datetime -import glob -import json -import os -import sys - -import dask -import matplotlib.pyplot as plt -import numpy as np -import xarray as xr -import xcdat as xc -from sea_ice_parser import create_sea_ice_parser - -from pcmdi_metrics.io import xcdat_openxml -from pcmdi_metrics.io.base import Base -from pcmdi_metrics.utils import create_land_sea_mask - - -class MetadataFile: - # This class organizes the contents for the CMEC - # metadata file called output.json, which describes - # the other files in the output bundle. - - def __init__(self, metrics_output_path): - self.outfile = os.path.join(metrics_output_path, "output.json") - self.json = { - "provenance": { - "environment": "", - "modeldata": "", - "obsdata": "", - "log": "", - }, - "metrics": {}, - "data": {}, - "plots": {}, - } - - def update_metrics(self, kw, filename, longname, desc): - tmp = {"filename": filename, "longname": longname, "description": desc} - self.json["metrics"].update({kw: tmp}) - return - - def update_data(self, kw, filename, longname, desc): - tmp = {"filename": filename, "longname": longname, "description": desc} - self.json["data"].update({kw: tmp}) - return - - def update_plots(self, kw, filename, longname, desc): - tmp = {"filename": filename, "longname": longname, "description": desc} - self.json["plots"].update({kw: tmp}) - - def update_provenance(self, kw, data): - self.json["provenance"].update({kw: data}) - return - - def update_index(self, val): - self.json["index"] = val - return - - def write(self): - with open(self.outfile, "w") as f: - json.dump(self.json, f, indent=4) - - -def sea_ice_regions(ds, var, xvar, yvar): - # Two sets of region definitions are provided, one for - # -180:180 and one for 0:360 longitude ranges - data_arctic = ds[var].where(ds[yvar] > 0, 0) - data_antarctic = ds[var].where(ds[yvar] < 0, 0) - if (ds[xvar] > 180).any(): # 0 to 360 - data_ca1 = ds[var].where( - ( - (ds[yvar] > 80) - & (ds[yvar] <= 87.2) - & ((ds[xvar] > 240) | (ds[xvar] <= 90)) - ), - 0, - ) - data_ca2 = ds[var].where( - ((ds[yvar] > 65) & (ds[yvar] < 87.2)) - & ((ds[xvar] > 90) & (ds[xvar] <= 240)), - 0, - ) - data_ca = data_ca1 + data_ca2 - data_np = ds[var].where( - (ds[yvar] > 35) & (ds[yvar] <= 65) & ((ds[xvar] > 90) & (ds[xvar] <= 240)), - 0, - ) - data_na = ds[var].where( - (ds[yvar] > 45) & (ds[yvar] <= 80) & ((ds[xvar] > 240) | (ds[xvar] <= 90)), - 0, - ) - data_na = data_na - data_na.where( - (ds[yvar] > 45) & (ds[yvar] <= 50) & (ds[xvar] > 30) & (ds[xvar] <= 60), - 0, - ) - data_sa = ds[var].where( - (ds[yvar] > -90) - & (ds[yvar] <= -40) - & ((ds[xvar] > 300) | (ds[xvar] <= 20)), - 0, - ) - data_sp = ds[var].where( - (ds[yvar] > -90) - & (ds[yvar] <= -40) - & ((ds[xvar] > 90) & (ds[xvar] <= 300)), - 0, - ) - data_io = ds[var].where( - (ds[yvar] > -90) & (ds[yvar] <= -40) & (ds[xvar] > 20) & (ds[xvar] <= 90), - 0, - ) - else: # -180 to 180 - data_ca1 = ds[var].where( - ( - (ds[yvar] > 80) - & (ds[yvar] <= 87.2) - & (ds[xvar] > -120) - & (ds[xvar] <= 90) - ), - 0, - ) - data_ca2 = ds[var].where( - ((ds[yvar] > 65) & (ds[yvar] < 87.2)) - & ((ds[xvar] > 90) | (ds[xvar] <= -120)), - 0, - ) - data_ca = data_ca1 + data_ca2 - data_np = ds[var].where( - (ds[yvar] > 35) & (ds[yvar] <= 65) & ((ds[xvar] > 90) | (ds[xvar] <= -120)), - 0, - ) - data_na = ds[var].where( - (ds[yvar] > 45) & (ds[yvar] <= 80) & (ds[xvar] > -120) & (ds[xvar] <= 90), - 0, - ) - data_na = data_na - data_na.where( - (ds[yvar] > 45) & (ds[yvar] <= 50) & (ds[xvar] > 30) & (ds[xvar] <= 60), - 0, - ) - data_sa = ds[var].where( - (ds[yvar] > -90) & (ds[yvar] <= -55) & (ds[xvar] > -60) & (ds[xvar] <= 20), - 0, - ) - data_sp = ds[var].where( - (ds[yvar] > -90) - & (ds[yvar] <= -55) - & ((ds[xvar] > 90) | (ds[xvar] <= -60)), - 0, - ) - data_io = ds[var].where( - (ds[yvar] > -90) & (ds[yvar] <= -55) & (ds[xvar] > 20) & (ds[xvar] <= 90), - 0, - ) - - regions_dict = { - "arctic": data_arctic.copy(deep=True), - "ca": data_ca.copy(deep=True), - "np": data_np.copy(deep=True), - "na": data_na.copy(deep=True), - "antarctic": data_antarctic.copy(deep=True), - "sa": data_sa.copy(deep=True), - "sp": data_sp.copy(deep=True), - "io": data_io.copy(deep=True), - } - return regions_dict - - -def find_lon(ds): - for key in ds.coords: - if key in ["lon", "longitude"]: - return key - for key in ds.keys(): - if key in ["lon", "longitude"]: - return key - return None - - -def find_lat(ds): - for key in ds.coords: - if key in ["lat", "latitude"]: - return key - for key in ds.keys(): - if key in ["lat", "latitude"]: - return key - return None - - -def mse_t(dm, do, weights=None): - """Computes mse""" - if dm is None and do is None: # just want the doc - return { - "Name": "Temporal Mean Square Error", - "Abstract": "Compute Temporal Mean Square Error", - "Contact": "pcmdi-metrics@llnl.gov", - } - if weights is None: - stat = np.sum(((dm.data - do.data) ** 2)) / len(dm, axis=0) - else: - stat = np.sum(((dm.data - do.data) ** 2) * weights, axis=0) - if isinstance(stat, dask.array.core.Array): - stat = stat.compute() - return stat - - -def mse_model(dm, do, var=None): - """Computes mse""" - if dm is None and do is None: # just want the doc - return { - "Name": "Mean Square Error", - "Abstract": "Compute Mean Square Error", - "Contact": "pcmdi-metrics@llnl.gov", - } - if var is not None: # dataset - stat = (dm[var].data - do[var].data) ** 2 - else: # dataarray - stat = (dm - do) ** 2 - if isinstance(stat, dask.array.core.Array): - stat = stat.compute() - return stat - - -def to_ice_con_ds(da, ds, obs_var): - # Convert sea ice data array to dataset using - # coordinates from another dataset - ds = xr.Dataset( - data_vars={obs_var: da, "time_bnds": ds.time_bnds}, coords={"time": ds.time} - ) - return ds - - -def adjust_units(ds, adjust_tuple): - action_dict = {"multiply": "*", "divide": "/", "add": "+", "subtract": "-"} - if adjust_tuple[0]: - print("Converting units by ", adjust_tuple[1], adjust_tuple[2]) - cmd = " ".join(["ds", str(action_dict[adjust_tuple[1]]), str(adjust_tuple[2])]) - ds = eval(cmd) - return ds - - -def verify_output_path(metrics_output_path, case_id): - if metrics_output_path is None: - metrics_output_path = datetime.datetime.now().strftime("v%Y%m%d") - if case_id is not None: - metrics_output_path = metrics_output_path.replace("%(case_id)", case_id) - if not os.path.exists(metrics_output_path): - print("\nMetrics output path not found.") - print("Creating metrics output directory", metrics_output_path) - try: - os.makedirs(metrics_output_path) - except Exception as e: - print("\nError: Could not create metrics output path", metrics_output_path) - print(e) - print("Exiting.") - sys.exit() - return metrics_output_path - - -def verify_years(start_year, end_year, msg="Error: Invalid start or end year"): - if start_year is None and end_year is None: - return - elif start_year is None or end_year is None: - # If only one of the two is set, exit. - print(msg) - print("Exiting") - sys.exit() - - -def set_up_realizations(realization): - find_all_realizations = False - if realization is None: - realization = "" - realizations = [realization] - elif isinstance(realization, str): - if realization.lower() in ["all", "*"]: - find_all_realizations = True - realizations = [""] - else: - realizations = [realization] - elif isinstance(realization, list): - realizations = realization - - return find_all_realizations, realizations - - -def load_dataset(filepath): - # Load an xarray dataset from the given filepath. - # If list of netcdf files, opens mfdataset. - # If list of xmls, open last file in list. - if filepath[-1].endswith(".xml"): - # Final item of sorted list would have most recent version date - ds = xcdat_openxml.xcdat_openxml(filepath[-1]) - elif len(filepath) > 1: - ds = xc.open_mfdataset(filepath, chunks=None) - else: - ds = xc.open_dataset(filepath[0]) - return ds - - -def replace_multi(string, rdict): - # Replace multiple keyworks in a string template - # based on key-value pairs in 'rdict'. - for k in rdict.keys(): - string = string.replace(k, rdict[k]) - return string - - -def get_xy_coords(ds, xvar): - if len(ds[xvar].dims) == 2: - lon_j, lon_i = ds[xvar].dims - elif len(ds[xvar].dims) == 1: - lon_j = find_lon(ds) - lon_i = find_lat(ds) - return lon_i, lon_j - - -if __name__ == "__main__": - parser = create_sea_ice_parser() - parameter = parser.get_parameter(argparse_vals_only=False) - - # Parameters - # I/O settings - case_id = parameter.case_id - realization = parameter.realization - var = parameter.var - filename_template = parameter.filename_template - test_data_path = parameter.test_data_path - model_list = parameter.test_data_set - reference_data_path_nh = parameter.reference_data_path_nh - reference_data_path_sh = parameter.reference_data_path_sh - reference_data_set = parameter.reference_data_set - metrics_output_path = parameter.metrics_output_path - area_template = parameter.area_template - area_var = parameter.area_var - AreaUnitsAdjust = parameter.AreaUnitsAdjust - obs_area_var = parameter.obs_area_var - obs_var = parameter.obs_var - obs_area_template_nh = parameter.obs_area_template_nh - obs_area_template_sh = parameter.obs_area_template_sh - obs_cell_area = parameter.obs_cell_area - ObsAreaUnitsAdjust = parameter.ObsAreaUnitsAdjust - ModUnitsAdjust = parameter.ModUnitsAdjust - ObsUnitsAdjust = parameter.ObsUnitsAdjust - msyear = parameter.msyear - meyear = parameter.meyear - osyear = parameter.osyear - oeyear = parameter.oeyear - - print(model_list) - model_list.sort() - # Verifying output directory - metrics_output_path = verify_output_path(metrics_output_path, case_id) - - if isinstance(reference_data_set, list): - # Fix a command line issue - reference_data_set = reference_data_set[0] - - # Verify years - ok_mod = verify_years( - msyear, - meyear, - msg="Error: Model msyear and meyear must both be set or both be None (unset).", - ) - ok_obs = verify_years( - osyear, - oeyear, - msg="Error: Obs osyear and oeyear must both be set or both be None (unset).", - ) - - # Initialize output.json file - meta = MetadataFile(metrics_output_path) - - # Setting up model realization list - find_all_realizations, realizations = set_up_realizations(realization) - print("Find all realizations:", find_all_realizations) - - #### Do Obs part - arctic_clims = {} - arctic_means = {} - - print("OBS: Arctic") - obs = load_dataset(reference_data_path_nh) - xvar = find_lon(obs) - yvar = find_lat(obs) - coord_i, coord_j = get_xy_coords(obs, xvar) - if osyear is not None: - obs = obs.sel( - { - "time": slice( - "{0}-01-01".format(osyear), - "{0}-12-31".format(oeyear), - ) - } - ).compute() # TODO: won't always need to compute - obs[obs_var] = adjust_units(obs[obs_var], ObsUnitsAdjust) - if obs_area_var is not None: - obs[obs_area_var] = adjust_units(obs[obs_area_var], ObsAreaUnitsAdjust) - area_val = obs[obs_area_var] - else: - area_val = obs_cell_area - # Remove land areas (including lakes) - mask = create_land_sea_mask(obs, lon_key=xvar, lat_key=yvar) - obs[obs_var] = obs[obs_var].where(mask < 1) - # Get regions - rgn_dict = sea_ice_regions(obs, obs_var, xvar, yvar) - - # Get ice extent - total_extent_arctic_obs = ( - rgn_dict["arctic"].where(rgn_dict["arctic"] > 0.15) * area_val - ).sum((coord_i, coord_j), skipna=True) - total_extent_ca_obs = (rgn_dict["ca"].where(rgn_dict["ca"] > 0.15) * area_val).sum( - (coord_i, coord_j), skipna=True - ) - total_extent_np_obs = (rgn_dict["np"].where(rgn_dict["np"] > 0.15) * area_val).sum( - (coord_i, coord_j), skipna=True - ) - total_extent_na_obs = (rgn_dict["na"].where(rgn_dict["na"] > 0.15) * area_val).sum( - (coord_i, coord_j), skipna=True - ) - - clim_arctic_obs = to_ice_con_ds( - total_extent_arctic_obs, obs, obs_var - ).temporal.climatology(obs_var, freq="month") - clim_ca_obs = to_ice_con_ds(total_extent_ca_obs, obs, obs_var).temporal.climatology( - obs_var, freq="month" - ) - clim_np_obs = to_ice_con_ds(total_extent_np_obs, obs, obs_var).temporal.climatology( - obs_var, freq="month" - ) - clim_na_obs = to_ice_con_ds(total_extent_na_obs, obs, obs_var).temporal.climatology( - obs_var, freq="month" - ) - - arctic_clims = { - "arctic": clim_arctic_obs, - "ca": clim_ca_obs, - "np": clim_np_obs, - "na": clim_na_obs, - } - - arctic_means = { - "arctic": total_extent_arctic_obs.mean("time", skipna=True).data.item(), - "ca": total_extent_ca_obs.mean("time", skipna=True).data.item(), - "np": total_extent_np_obs.mean("time", skipna=True).data.item(), - "na": total_extent_na_obs.mean("time", skipna=True).data.item(), - } - obs.close() - - antarctic_clims = {} - antarctic_means = {} - print("OBS: Antarctic") - obs = load_dataset(reference_data_path_sh) - xvar = find_lon(obs) - yvar = find_lat(obs) - coord_i, coord_j = get_xy_coords(obs, xvar) - if osyear is not None: - obs = obs.sel( - { - "time": slice( - "{0}-01-01".format(osyear), - "{0}-12-31".format(oeyear), - ) - } - ).compute() - obs[obs_var] = adjust_units(obs[obs_var], ObsUnitsAdjust) - if obs_area_var is not None: - obs[obs_area_var] = adjust_units(obs[obs_area_var], ObsAreaUnitsAdjust) - area_val = obs[obs_area_var] - else: - area_val = obs_cell_area - # Remove land areas (including lakes) - mask = create_land_sea_mask(obs, lon_key="lon", lat_key="lat") - obs[obs_var] = obs[obs_var].where(mask < 1) - rgn_dict = sea_ice_regions(obs, obs_var, "lon", "lat") - - total_extent_antarctic_obs = ( - rgn_dict["antarctic"].where(rgn_dict["antarctic"] > 0.15) * area_val - ).sum((coord_i, coord_j), skipna=True) - total_extent_sa_obs = (rgn_dict["sa"].where(rgn_dict["sa"] > 0.15) * area_val).sum( - (coord_i, coord_j), skipna=True - ) - total_extent_sp_obs = (rgn_dict["sp"].where(rgn_dict["sp"] > 0.15) * area_val).sum( - (coord_i, coord_j), skipna=True - ) - total_extent_io_obs = (rgn_dict["io"].where(rgn_dict["io"] > 0.15) * area_val).sum( - (coord_i, coord_j), skipna=True - ) - - clim_antarctic_obs = to_ice_con_ds( - total_extent_antarctic_obs, obs, obs_var - ).temporal.climatology(obs_var, freq="month") - clim_sa_obs = to_ice_con_ds(total_extent_sa_obs, obs, obs_var).temporal.climatology( - obs_var, freq="month" - ) - clim_sp_obs = to_ice_con_ds(total_extent_sp_obs, obs, obs_var).temporal.climatology( - obs_var, freq="month" - ) - clim_io_obs = to_ice_con_ds(total_extent_io_obs, obs, obs_var).temporal.climatology( - obs_var, freq="month" - ) - - antarctic_clims = { - "antarctic": clim_antarctic_obs, - "io": clim_io_obs, - "sp": clim_sp_obs, - "sa": clim_sa_obs, - } - - antarctic_means = { - "antarctic": total_extent_antarctic_obs.mean("time", skipna=True).data.item(), - "io": total_extent_io_obs.mean("time", skipna=True).compute().data.item(), - "sp": total_extent_sp_obs.mean("time", skipna=True).compute().data.item(), - "sa": total_extent_sa_obs.mean("time", skipna=True).compute().data.item(), - } - obs.close() - - obs_clims = {reference_data_set: {}} - obs_means = {reference_data_set: {}} - for item in antarctic_clims: - obs_clims[reference_data_set][item] = antarctic_clims[item] - obs_means[reference_data_set][item] = antarctic_means[item] - for item in arctic_clims: - obs_clims[reference_data_set][item] = arctic_clims[item] - obs_means[reference_data_set][item] = arctic_means[item] - - #### Do model part - - # Needs to weigh months by length for metrics later - clim_wts = [31.0, 28.0, 31.0, 30.0, 31.0, 30.0, 31.0, 31.0, 30.0, 31.0, 30.0, 31.0] - clim_wts = [x / 365 for x in clim_wts] - # Initialize JSON data - mse = {} - metrics = { - "DIMENSIONS": { - "json_structure": [ - "model", - "realization", - "obs", - "region", - "index", - "statistic", - ], - "region": {}, - "index": { - "monthly_clim": "Monthly climatology of extent", - "total_extent": "Sum of ice coverage where concentration > 15%", - }, - "statistic": {"mse": "Mean Square Error (10^12 km^4)"}, - "model": model_list, - }, - "RESULTS": {}, - "model_year_range": {}, - } - print("Model list:", model_list) - - # Loop over models and realizations to generate metrics - for model in model_list: - start_year = msyear - end_year = meyear - - real_dict = { - "arctic": {"model_mean": 0}, - "ca": {"model_mean": 0}, - "na": {"model_mean": 0}, - "np": {"model_mean": 0}, - "antarctic": {"model_mean": 0}, - "sp": {"model_mean": 0}, - "sa": {"model_mean": 0}, - "io": {"model_mean": 0}, - } - mse[model] = { - "arctic": {"model_mean": {reference_data_set: {}}}, - "ca": {"model_mean": {reference_data_set: {}}}, - "na": {"model_mean": {reference_data_set: {}}}, - "np": {"model_mean": {reference_data_set: {}}}, - "antarctic": {"model_mean": {reference_data_set: {}}}, - "sp": {"model_mean": {reference_data_set: {}}}, - "sa": {"model_mean": {reference_data_set: {}}}, - "io": {"model_mean": {reference_data_set: {}}}, - } - - tags = { - "%(variable)": var, - "%(model)": model, - "%(model_version)": model, - "%(realization)": "*", - } - if find_all_realizations: - test_data_full_path_tmp = os.path.join(test_data_path, filename_template) - test_data_full_path_tmp = replace_multi(test_data_full_path_tmp, tags) - ncfiles = glob.glob(test_data_full_path_tmp) - realizations = [] - for ncfile in ncfiles: - basename = ncfile.split("/")[-1] - if len(basename.split(".")) <= 2: - if basename.split("_")[4] not in realizations: - realizations.append(basename.split("_")[4]) - else: - if basename.split(".")[3] not in realizations: - realizations.append(basename.split(".")[3]) - - print("\n=================================") - print("model, runs:", model, realizations) - list_of_runs = realizations - else: - list_of_runs = realizations - - # Model grid area - print(replace_multi(area_template, tags)) - area = xc.open_dataset(glob.glob(replace_multi(area_template, tags))[0]) - area[area_var] = adjust_units(area[area_var], AreaUnitsAdjust) - - if len(list_of_runs) > 0: - # Loop over realizations - for run_ind, run in enumerate(list_of_runs): - # Find model data, determine number of files, check if they exist - tags = { - "%(variable)": var, - "%(model)": model, - "%(model_version)": model, - "%(realization)": run, - } - test_data_full_path = os.path.join(test_data_path, filename_template) - test_data_full_path = replace_multi(test_data_full_path, tags) - test_data_full_path = glob.glob(test_data_full_path) - test_data_full_path.sort() - if len(test_data_full_path) == 0: - print("") - print("-----------------------") - print("Not found: model, run, variable:", model, run, var) - break - else: - print("") - print("-----------------------") - print("model, run, variable:", model, run, var) - print("test_data (model in this case) full_path:") - for t in test_data_full_path: - print(" ", t) - - # Load and prep data - ds = load_dataset(test_data_full_path) - ds[var] = adjust_units(ds[var], ModUnitsAdjust) - xvar = find_lon(ds) - yvar = find_lat(ds) - if xvar is None or yvar is None: - print("Could not get latitude or longitude variables") - break - if (ds[xvar] < -180).any(): - ds[xvar] = ds[xvar].where(ds[xvar] >= -180, ds[xvar] + 360) - - # Get time slice if year parameters exist - if start_year is not None: - ds = ds.sel( - { - "time": slice( - "{0}-01-01".format(start_year), - "{0}-12-31".format(end_year), - ) - } - ) - yr_range = [str(start_year), str(end_year)] - else: - # Get labels for start/end years from dataset - yr_range = [ - str(int(ds.time.dt.year[0])), - str(int(ds.time.dt.year[-1])), - ] - - # Get regions - regions_dict = sea_ice_regions(ds, var, xvar, yvar) - - ds.close() - # Running sum of all realizations - for rgn in regions_dict: - data = regions_dict[rgn] - # coordinates aren't always the same as lat/lon names, - # especially if lat/lon are 2D - lon_i, lon_j = get_xy_coords(data, xvar) - # area data doesn't always use same coordinates as siconc data in CMIP6 - # so we multiply by area.data, dropping the coordinates - rgn_total = (data.where(data > 0.15, 0) * area[area_var].data).sum( - (lon_j, lon_i), skipna=True - ) - real_dict[rgn][run] = rgn_total - real_dict[rgn]["model_mean"] = ( - real_dict[rgn]["model_mean"] + rgn_total - ) - - print("\n-------------------------------------------") - print("Calculating model regional average metrics \nfor ", model) - print("--------------------------------------------") - for rgn in real_dict: - print(rgn) - - # Average all realizations, fix bounds, get climatologies and totals - # total_rgn = (totals_dict[rgn] / len(list_of_runs)).to_dataset(name=var) - real_dict[rgn]["model_mean"] = real_dict[rgn]["model_mean"] / len( - list_of_runs - ) - - for run in real_dict[rgn]: - # Set up metrics dictionary - if run not in mse[model][rgn]: - mse[model][rgn][run] = {} - mse[model][rgn][run].update( - { - reference_data_set: { - "monthly_clim": {"mse": None}, - "total_extent": {"mse": None}, - } - } - ) - - run_data = real_dict[rgn][run].to_dataset(name=var) - run_data = run_data.bounds.add_missing_bounds() - clim_extent = run_data.temporal.climatology(var, freq="month") - total = run_data.mean("time")[var].data - - # Get errors, convert to 1e12 km^-4 - mse[model][rgn][run][reference_data_set]["monthly_clim"][ - "mse" - ] = str( - mse_t( - clim_extent[var], - obs_clims[reference_data_set][rgn][obs_var], - weights=clim_wts, - ) - * 1e-12 - ) - mse[model][rgn][run][reference_data_set]["total_extent"][ - "mse" - ] = str( - mse_model(total, obs_means[reference_data_set][rgn]) * 1e-12 - ) - - # Update year list - metrics["model_year_range"][model] = [str(start_year), str(end_year)] - else: - for rgn in mse[model]: - # Set up metrics dictionary - mse[model][rgn]["model_mean"][reference_data_set] = { - "monthly_clim": {"mse": None}, - "total_extent": {"mse": None}, - } - metrics["model_year_range"][model] = ["", ""] - - # ----------------- - # Update metrics - # ----------------- - metrics["RESULTS"] = mse - - metricsfile = os.path.join(metrics_output_path, "sea_ice_metrics.json") - JSON = Base(metrics_output_path, "sea_ice_metrics.json") - json_structure = metrics["DIMENSIONS"]["json_structure"] - JSON.write( - metrics, - json_structure=json_structure, - sort_keys=True, - indent=4, - separators=(",", ": "), - ) - meta.update_metrics( - "metrics", - metricsfile, - "metrics_JSON", - "JSON file containig regional sea ice metrics", - ) - - # ---------------- - # Make figure - # ---------------- - sector_list = [ - "Central Arctic Sector", - "North Atlantic Sector", - "North Pacific Sector", - "Indian Ocean Sector", - "South Atlantic Sector", - "South Pacific Sector", - ] - sector_short = ["ca", "na", "np", "io", "sa", "sp"] - fig7, ax7 = plt.subplots(6, 1, figsize=(5, 9)) - # mlabels = model_list + ["bootstrap"] - mlabels = model_list - ind = np.arange(len(mlabels)) # the x locations for the groups - # ind = np.arange(len(mods)+1) # the x locations for the groups - width = 0.3 - # n = len(ind) - 1 - n = len(ind) - for inds, sector in enumerate(sector_list): - # Assemble data - mse_clim = [] - mse_ext = [] - clim_range = [] - ext_range = [] - clim_err_x = [] - clim_err_y = [] - ext_err_y = [] - rgn = sector_short[inds] - for nmod, model in enumerate(model_list): - mse_clim.append( - float( - metrics["RESULTS"][model][rgn]["model_mean"][reference_data_set][ - "monthly_clim" - ]["mse"] - ) - ) - mse_ext.append( - float( - metrics["RESULTS"][model][rgn]["model_mean"][reference_data_set][ - "total_extent" - ]["mse"] - ) - ) - # Get spread, only if there are multiple realizations - if len(metrics["RESULTS"][model][rgn].keys()) > 2: - for r in metrics["RESULTS"][model][rgn]: - if r != "model_mean": - clim_err_x.append(ind[nmod]) - clim_err_y.append( - float( - metrics["RESULTS"][model][rgn][r][reference_data_set][ - "monthly_clim" - ]["mse"] - ) - ) - ext_err_y.append( - float( - metrics["RESULTS"][model][rgn][r][reference_data_set][ - "total_extent" - ]["mse"] - ) - ) - - # plot data - if len(model_list) < 4: - mark_size = 9 - elif len(model_list) < 12: - mark_size = 3 - else: - mark_size = 1 - ax7[inds].bar(ind - width / 2.0, mse_clim, width, color="b", label="Ann. Cycle") - ax7[inds].bar(ind, mse_ext, width, color="r", label="Ann. Mean") - if len(clim_err_x) > 0: - ax7[inds].scatter( - [x - width / 2.0 for x in clim_err_x], - clim_err_y, - marker="D", - s=mark_size, - color="k", - ) - ax7[inds].scatter(clim_err_x, ext_err_y, marker="D", s=mark_size, color="k") - # xticks - if inds == len(sector_list) - 1: - ax7[inds].set_xticks(ind + width / 2.0, mlabels, rotation=90, size=7) - else: - ax7[inds].set_xticks(ind + width / 2.0, labels="") - # yticks - if len(clim_err_y) > 0: - datamax = np.max(np.array(clim_err_y)) - else: - datamax = np.max(np.array(mse_clim)) - ymax = (datamax) * 1.3 - ax7[inds].set_ylim(0.0, ymax) - if ymax < 0.1: - ticks = np.linspace(0, 0.1, 6) - labels = [str(round(x, 3)) for x in ticks] - elif ymax < 1: - ticks = np.linspace(0, 1, 5) - labels = [str(round(x, 1)) for x in ticks] - elif ymax < 4: - ticks = np.linspace(0, round(ymax), num=round(ymax / 2) * 2 + 1) - labels = [str(round(x, 1)) for x in ticks] - elif ymax > 10: - ticks = range(0, round(ymax), 5) - labels = [str(round(x, 0)) for x in ticks] - else: - ticks = range(0, round(ymax)) - labels = [str(round(x, 0)) for x in ticks] - - ax7[inds].set_yticks(ticks, labels, fontsize=8) - # labels etc - ax7[inds].set_ylabel("10${^12}$km${^4}$", size=8) - ax7[inds].grid(True, linestyle=":") - ax7[inds].annotate( - sector, - (0.35, 0.8), - xycoords="axes fraction", - size=9, - ) - # Add legend, save figure - ax7[0].legend(loc="upper right", fontsize=6) - figfile = os.path.join(metrics_output_path, "MSE_bar_chart.png") - plt.savefig(figfile) - meta.update_plots( - "bar_chart", figfile, "regional_bar_chart", "Bar chart of regional MSE" - ) - - # Update and write metadata file - try: - with open(os.path.join(metricsfile), "r") as f: - tmp = json.load(f) - meta.update_provenance("environment", tmp["provenance"]) - except Exception: - # Skip provenance if there's an issue - print("Error: Could not get provenance from metrics json for output.json.") - - meta.update_provenance("modeldata", test_data_path) - if reference_data_path_nh is not None: - meta.update_provenance("obsdata_nh", reference_data_path_nh) - meta.update_provenance("obsdata_sh", reference_data_path_sh) - meta.write() diff --git a/pcmdi_metrics/sea_ice/sea_ice_parser.py b/pcmdi_metrics/sea_ice/sea_ice_parser.py deleted file mode 100644 index 785227abc..000000000 --- a/pcmdi_metrics/sea_ice/sea_ice_parser.py +++ /dev/null @@ -1,212 +0,0 @@ -#!/usr/bin/env python -from pcmdi_metrics.mean_climate.lib import pmp_parser - - -def create_sea_ice_parser(): - parser = pmp_parser.PMPMetricsParser() - parser.add_argument( - "--case_id", - dest="case_id", - help="Defines a subdirectory to the metrics output, so multiple" - + "cases can be compared", - required=False, - ) - - parser.add_argument( - "-v", - "--var", - type=str, - dest="var", - help="Name of model sea ice concentration variable", - required=False, - ) - - parser.add_argument( - "--obs_var", - type=str, - dest="obs_var", - help="Name of obs sea ice concentration variable", - required=False, - ) - - parser.add_argument( - "--area_var", - type=str, - dest="area_var", - help="Name of model area variable", - required=False, - ) - - parser.add_argument( - "--obs_area_var", - type=str, - dest="obs_area_var", - help="Name of reference data area variable", - required=False, - default=None, - ) - - parser.add_argument( - "-r", - "--reference_data_set", - default=None, - type=str, - nargs="+", - dest="reference_data_set", - help="List of observations or models that are used as a " - + "reference against the test_data_set", - required=False, - ) - - parser.add_argument( - "--reference_data_path", - default=None, - dest="reference_data_path", - help="Path for the reference climitologies", - required=False, - ) - - parser.add_argument( - "-t", - "--test_data_set", - type=str, - nargs="+", - dest="test_data_set", - help="List of observations or models to test " - + "against the reference_data_set", - required=False, - ) - - parser.add_argument( - "--test_data_path", - dest="test_data_path", - help="Path for the test climitologies", - required=False, - ) - - parser.add_argument( - "--realization", - dest="realization", - help="A simulation parameter", - required=False, - ) - - parser.add_argument( - "--filename_template", - dest="filename_template", - help="Template for climatology files", - required=False, - ) - - parser.add_argument( - "--metrics_output_path", - dest="metrics_output_path", - default=None, - help="Directory of where to put the results", - required=False, - ) - - parser.add_argument( - "--filename_output_template", - dest="filename_output_template", - help="Filename for the interpolated test climatologies", - required=False, - ) - - parser.add_argument( - "--area_template", - dest="area_template", - help="Filename template for model grid area", - required=False, - ) - - parser.add_argument( - "--obs_area_template_nh", - dest="obs_area_template_nh", - help="Filename template for obs grid area in Northern Hemisphere", - required=False, - default=None, - ) - - parser.add_argument( - "--obs_area_template_sh", - dest="obs_area_template_sh", - help="Filename template for obs grid area in Southern Hemisphere", - required=False, - default=None, - ) - - parser.add_argument( - "--obs_cell_area", - dest="obs_cell_area", - help="For equal area grids, the cell area in km", - required=False, - default=None, - ) - - parser.add_argument( - "--output_json_template", - help="Filename template for results json files", - required=False, - ) - - parser.add_argument( - "--debug", - dest="debug", - action="store_true", - help="Turn on debugging mode by printing more information to track progress", - required=False, - ) - parser.add_argument( - "--plots", - action="store_true", - help="Set to True to generate figures.", - required=False, - ) - parser.add_argument( - "--osyear", dest="osyear", type=int, help="Start year for reference data set" - ) - parser.add_argument( - "--msyear", dest="msyear", type=int, help="Start year for model data set" - ) - parser.add_argument( - "--oeyear", dest="oeyear", type=int, help="End year for reference data set" - ) - parser.add_argument( - "--meyear", dest="meyear", type=int, help="End year for model data set" - ) - parser.add_argument( - "--ObsUnitsAdjust", - type=tuple, - default=(False, 0, 0), - help="Factor to convert obs sea ice concentration to decimal. For example:\n" - "- (True, 'divide', 100.0) # percentage to decimal\n" - "- (False, 0, 0) # No adjustment (default)", - ) - parser.add_argument( - "--ModUnitsAdjust", - type=tuple, - default=(False, 0, 0), - help="Factor to convert model sea ice concentration to decimal. For example:\n" - "- (True, 'divide', 100.0) # percentage to decimal\n" - "- (False, 0, 0) # No adjustment (default)", - ) - parser.add_argument( - "--AreaUnitsAdjust", - type=tuple, - default=(False, 0, 0), - help="Factor to convert area data to km^2. For example:\n" - "- (True, 'multiply', 1e-6) # m^2 to km^2\n" - "- (False, 0, 0) # No adjustment (default)", - ) - - parser.add_argument( - "--ObsAreaUnitsAdjust", - type=tuple, - default=(False, 0, 0), - help="Factor to convert area data to km^2. For example:\n" - "- (True, 'multiply', 1e-6) # m^2 to km^2\n" - "- (False, 0, 0) # No adjustment (default)", - ) - - return parser diff --git a/pcmdi_metrics/sea_ice/sector_mask_defs.py b/pcmdi_metrics/sea_ice/sector_mask_defs.py deleted file mode 100644 index d9af3cc06..000000000 --- a/pcmdi_metrics/sea_ice/sector_mask_defs.py +++ /dev/null @@ -1,78 +0,0 @@ -import MV2 as MV - - -def getmask(sector, lats, lons, lons_a, lons_p, land_mask): - # Arctic Regions - # Central Arctic - if sector == "ca": - lat_bound1 = MV.logical_and(MV.greater(lats, 80.0), MV.less_equal(lats, 90.0)) - lat_bound2 = MV.logical_and(MV.greater(lats, 65.0), MV.less_equal(lats, 90.0)) - lon_bound1 = MV.logical_and( - MV.greater(lons_a, -120.0), MV.less_equal(lons_a, 90.0) - ) - lon_bound2 = MV.logical_and( - MV.greater(lons_p, 90.0), MV.less_equal(lons_p, 240.0) - ) - reg1 = MV.logical_and(lat_bound1, lon_bound1) - reg2 = MV.logical_and(lat_bound2, lon_bound2) - mask = MV.where(MV.logical_or(reg1, reg2), 1, 0) - mask = MV.where(MV.equal(land_mask, 0), 0, mask) # 0 - Land - - # NA region - if sector == "na": - lat_bound = MV.logical_and(MV.greater(lats, 45.0), MV.less_equal(lats, 80.0)) - lon_bound = MV.logical_and( - MV.greater(lons_a, -120.0), MV.less_equal(lons_a, 90.0) - ) - lat_bound3 = MV.logical_and(MV.greater(lats, 45.0), MV.less_equal(lats, 50.0)) - lon_bound3 = MV.logical_and( - MV.greater(lons_a, 30.0), MV.less_equal(lons_a, 60.0) - ) - reg3 = MV.logical_and(lat_bound3, lon_bound3) - - mask = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) - mask = MV.where( - MV.equal(reg3, True), 0, mask - ) # Masking out the Black and Caspian Seas - mask = MV.where(MV.equal(land_mask, True), 0, mask) # 0 - Land - mask = MV.where(MV.equal(land_mask, 0), 0, mask) # 0 - Land - - # NP region - if sector == "np": - lat_bound = MV.logical_and(MV.greater(lats, 45.0), MV.less_equal(lats, 65.0)) - lon_bound = MV.logical_and( - MV.greater(lons_p, 90.0), MV.less_equal(lons_p, 240.0) - ) - mask = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) - mask = MV.where(MV.equal(land_mask, 0), 0, mask) # 0 - Land - - # Antarctic Regions - - # SA region - if sector == "sa": - lat_bound = MV.logical_and(MV.greater(lats, -90.0), MV.less_equal(lats, -55.0)) - lon_bound = MV.logical_and( - MV.greater(lons_a, -60.0), MV.less_equal(lons_a, 20.0) - ) - mask = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) - mask = MV.where(MV.equal(land_mask, 0), 0, mask) # 0 - Land - - # SP region - if sector == "sp": - lat_bound = MV.logical_and(MV.greater(lats, -90.0), MV.less_equal(lats, -55.0)) - lon_bound = MV.logical_and( - MV.greater(lons_p, 90.0), MV.less_equal(lons_p, 300.0) - ) - mask = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) - mask = MV.where(MV.equal(land_mask, 0), 0, mask) # 0 - Land - - # IO region - if sector == "io": - lat_bound = MV.logical_and(MV.greater(lats, -90.0), MV.less_equal(lats, -55.0)) - lon_bound = MV.logical_and( - MV.greater(lons_p, 20.0), MV.less_equal(lons_p, 90.0) - ) - mask = MV.where(MV.logical_and(lat_bound, lon_bound), 1, 0) - mask = MV.where(MV.equal(land_mask, 0), 0, mask) # 0 - Land - - return mask From 60c0e41348a4e622f0ed59d1394d8cb6c889c401 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 13:03:15 -0800 Subject: [PATCH 54/69] clean up --- .../sea_ice/make_demo_sea_ice_plots.py | 61 ------------------- .../sea_ice/{ => param}/parameter_file.py | 20 +++--- 2 files changed, 12 insertions(+), 69 deletions(-) delete mode 100644 pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py rename pcmdi_metrics/sea_ice/{ => param}/parameter_file.py (78%) diff --git a/pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py b/pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py deleted file mode 100644 index 3bf820142..000000000 --- a/pcmdi_metrics/sea_ice/make_demo_sea_ice_plots.py +++ /dev/null @@ -1,61 +0,0 @@ -import cftime -import matplotlib.pyplot as plt -import xarray as xr -import xcdat as xc - -ds = xc.open_mfdataset( - "/p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_*_*.nc" -) -area = xc.open_dataset( - "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/areacello_Ofx_E3SM-1-0_historical_r1i1p1f1_gr.nc" -) - -arctic = (ds.where(ds.lat > 0) * 1e-2 * area.areacello * 1e-6).sum(("lat", "lon")) - -f_os_n = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc" -obs = xc.open_dataset(f_os_n) -obs_area = 625 -obs_arctic = (obs.ice_conc.where(obs.lat > 0) * 1e-2 * obs_area).sum(("xc", "yc")) - -# Time series plot -arctic.siconc.sel({"time": slice("1981-01-01", "2010-12-31")}).plot(label="E3SM-1-0") -obs_arctic.plot(label="OSI-SAF") -plt.title("Arctic monthly sea ice extent") -plt.ylabel("Extent (km${^2}$)") -plt.xlabel("time") -plt.xlim( - [ - cftime.DatetimeNoLeap(1981, 1, 16, 12, 0, 0, 0, has_year_zero=True), - cftime.DatetimeNoLeap(2010, 12, 16, 12, 0, 0, 0, has_year_zero=True), - ] -) -plt.legend(loc="upper right", fontsize=9) -plt.savefig("E3SM_arctic_tseries.png") -plt.close() - -# Climatology plot -arctic_ds = xr.Dataset( - data_vars={"siconc": arctic.siconc, "time_bnds": ds.time_bnds}, - coords={"time": ds.time}, -) -arctic_clim = arctic_ds.sel( - {"time": slice("1981-01-01", "2010-12-31")} -).temporal.climatology("siconc", freq="month") -arctic_clim["time"] = [x for x in range(1, 13)] - -obs_arc_ds = xr.Dataset( - data_vars={"ice_conc": obs_arctic, "time_bnds": obs.time_bnds}, - coords={"time": obs.time}, -) -obs_clim = obs_arc_ds.temporal.climatology("ice_conc", freq="month") -obs_clim["time"] = [x for x in range(1, 13)] - -arctic_clim.siconc.plot(label="E3SM-1-0") -obs_clim.ice_conc.plot(label="OSI-SAF") -plt.title("Arctic climatological sea ice extent\n1981-2010") -plt.xlabel("month") -plt.ylabel("Extent (km${^2}$)") -plt.xlim([1, 12]) -plt.legend(loc="upper right", fontsize=9) -plt.savefig("E3SM_arctic_clim.png") -plt.close() diff --git a/pcmdi_metrics/sea_ice/parameter_file.py b/pcmdi_metrics/sea_ice/param/parameter_file.py similarity index 78% rename from pcmdi_metrics/sea_ice/parameter_file.py rename to pcmdi_metrics/sea_ice/param/parameter_file.py index c49c04665..5ea1037da 100644 --- a/pcmdi_metrics/sea_ice/parameter_file.py +++ b/pcmdi_metrics/sea_ice/param/parameter_file.py @@ -45,14 +45,18 @@ # Reference is hard coded currently so this is a placeholder -reference_data_path_nh = "/p/user_pub/PCMDIobs/obs4MIPs_input/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/*nh*" -reference_data_path_sh = "/p/user_pub/PCMDIobs/obs4MIPs_input/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/*sh*" -ObsUnitsAdjust=(True,"multiply",1e-2) -reference_data_set="OSI-SAF" -osyear=1981 -oeyear=2010 -obs_var="ice_conc" +reference_data_path_nh = ( + "/p/user_pub/PCMDIobs/obs4MIPs_input/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/*nh*" +) +reference_data_path_sh = ( + "/p/user_pub/PCMDIobs/obs4MIPs_input/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/*sh*" +) +ObsUnitsAdjust = (True, "multiply", 1e-2) +reference_data_set = "OSI-SAF" +osyear = 1981 +oeyear = 2010 +obs_var = "ice_conc" ObsAreaUnitsAdjust = (False, 0, 0) -obs_area_template = None #km2 +obs_area_template = None # km2 obs_area_var = None obs_cell_area = 625 From b6fd08cf7925c761b8904b8cae3de0e339df5b6f Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 13:03:58 -0800 Subject: [PATCH 55/69] edit command --- pcmdi_metrics/sea_ice/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pcmdi_metrics/sea_ice/README.md b/pcmdi_metrics/sea_ice/README.md index 91eb52452..40d555362 100644 --- a/pcmdi_metrics/sea_ice/README.md +++ b/pcmdi_metrics/sea_ice/README.md @@ -3,5 +3,5 @@ Sea ice metrics driver Example command: ``` -python -u ice_driver.py -p parameter_file.py --case_id E3SM-1-0 --test_data_set 'E3SM-1-0' --test_data_path '/p/css03/esgf_publish/CMIP6/CMIP/UCSB/E3SM-1-0/historical/%(realization)/SImon/siconc/gr/*/' --filename_template 'siconc_SImon_E3SM-1-0_historical_%(realization)_gr_*-*.nc' --area_template '/p/user_pub/work/CMIP6/CMIP/E3SM-Project/E3SM-1-0/historical/r1i1p1f1/Ofx/areacello/gr/v20210127/areacello_Ofx_E3SM-1-0_historical_r1i1p1f1_gr.nc' --area_var areacello +sea_ice_driver.py -p parameter_file.py ``` \ No newline at end of file From 65c60207015272b0b1a6c35a94edcea8780d7cd1 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 13:13:36 -0800 Subject: [PATCH 56/69] move files --- doc/jupyter/Demo/sea_ice_line_plots.py | 61 +++++++ .../jupyter/Demo/sea_ice_param.py | 12 +- doc/jupyter/Demo/sea_ice_sector_plots.py | 156 ++++++++++++++++++ 3 files changed, 223 insertions(+), 6 deletions(-) create mode 100644 doc/jupyter/Demo/sea_ice_line_plots.py rename pcmdi_metrics/sea_ice/demo_param_file.py => doc/jupyter/Demo/sea_ice_param.py (91%) create mode 100644 doc/jupyter/Demo/sea_ice_sector_plots.py diff --git a/doc/jupyter/Demo/sea_ice_line_plots.py b/doc/jupyter/Demo/sea_ice_line_plots.py new file mode 100644 index 000000000..3bf820142 --- /dev/null +++ b/doc/jupyter/Demo/sea_ice_line_plots.py @@ -0,0 +1,61 @@ +import cftime +import matplotlib.pyplot as plt +import xarray as xr +import xcdat as xc + +ds = xc.open_mfdataset( + "/p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_*_*.nc" +) +area = xc.open_dataset( + "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/areacello_Ofx_E3SM-1-0_historical_r1i1p1f1_gr.nc" +) + +arctic = (ds.where(ds.lat > 0) * 1e-2 * area.areacello * 1e-6).sum(("lat", "lon")) + +f_os_n = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc" +obs = xc.open_dataset(f_os_n) +obs_area = 625 +obs_arctic = (obs.ice_conc.where(obs.lat > 0) * 1e-2 * obs_area).sum(("xc", "yc")) + +# Time series plot +arctic.siconc.sel({"time": slice("1981-01-01", "2010-12-31")}).plot(label="E3SM-1-0") +obs_arctic.plot(label="OSI-SAF") +plt.title("Arctic monthly sea ice extent") +plt.ylabel("Extent (km${^2}$)") +plt.xlabel("time") +plt.xlim( + [ + cftime.DatetimeNoLeap(1981, 1, 16, 12, 0, 0, 0, has_year_zero=True), + cftime.DatetimeNoLeap(2010, 12, 16, 12, 0, 0, 0, has_year_zero=True), + ] +) +plt.legend(loc="upper right", fontsize=9) +plt.savefig("E3SM_arctic_tseries.png") +plt.close() + +# Climatology plot +arctic_ds = xr.Dataset( + data_vars={"siconc": arctic.siconc, "time_bnds": ds.time_bnds}, + coords={"time": ds.time}, +) +arctic_clim = arctic_ds.sel( + {"time": slice("1981-01-01", "2010-12-31")} +).temporal.climatology("siconc", freq="month") +arctic_clim["time"] = [x for x in range(1, 13)] + +obs_arc_ds = xr.Dataset( + data_vars={"ice_conc": obs_arctic, "time_bnds": obs.time_bnds}, + coords={"time": obs.time}, +) +obs_clim = obs_arc_ds.temporal.climatology("ice_conc", freq="month") +obs_clim["time"] = [x for x in range(1, 13)] + +arctic_clim.siconc.plot(label="E3SM-1-0") +obs_clim.ice_conc.plot(label="OSI-SAF") +plt.title("Arctic climatological sea ice extent\n1981-2010") +plt.xlabel("month") +plt.ylabel("Extent (km${^2}$)") +plt.xlim([1, 12]) +plt.legend(loc="upper right", fontsize=9) +plt.savefig("E3SM_arctic_clim.png") +plt.close() diff --git a/pcmdi_metrics/sea_ice/demo_param_file.py b/doc/jupyter/Demo/sea_ice_param.py similarity index 91% rename from pcmdi_metrics/sea_ice/demo_param_file.py rename to doc/jupyter/Demo/sea_ice_param.py index 43a971f84..fcff3da45 100644 --- a/pcmdi_metrics/sea_ice/demo_param_file.py +++ b/doc/jupyter/Demo/sea_ice_param.py @@ -41,12 +41,12 @@ # Settings for the observational data reference_data_path_nh = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc" reference_data_path_sh = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_sh_ease2-250_cdr-v3p0_198801-202012.nc" -ObsUnitsAdjust=(True,"multiply",1e-2) -reference_data_set="OSI-SAF" -osyear=1988 -oeyear=2020 -obs_var="ice_conc" +ObsUnitsAdjust = (True, "multiply", 1e-2) +reference_data_set = "OSI-SAF" +osyear = 1988 +oeyear = 2020 +obs_var = "ice_conc" ObsAreaUnitsAdjust = (False, 0, 0) obs_area_template = None obs_area_var = None -obs_cell_area = 625 #km 2 \ No newline at end of file +obs_cell_area = 625 # km 2 diff --git a/doc/jupyter/Demo/sea_ice_sector_plots.py b/doc/jupyter/Demo/sea_ice_sector_plots.py new file mode 100644 index 000000000..d9f65b790 --- /dev/null +++ b/doc/jupyter/Demo/sea_ice_sector_plots.py @@ -0,0 +1,156 @@ +import cartopy.crs as ccrs +import matplotlib.colors as colors +import matplotlib.pyplot as plt +import numpy as np +import regionmask +import xcdat as xc + +from pcmdi_metrics.utils import create_land_sea_mask + +# ---------- +# Arctic +# ---------- +print("Creating Arctic map") +# Load and process data +f_os_n = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc" +obs = xc.open_dataset(f_os_n) +obs = obs.mean("time") +mask = create_land_sea_mask(obs, lon_key="lon", lat_key="lat") +obs["ice_conc"] = obs["ice_conc"].where(mask < 1) +ds = obs.assign_coords( + xc=obs["lon"], yc=obs["lat"] +) # Assign these variables to Coordinates, which were originally data variables + +# Set up regions +region_NA = np.array([[-120, 45], [-120, 80], [90, 80], [90, 45]]) +region_NP = np.array([[90, 45], [90, 65], [240, 65], [240, 45]]) +names = ["North_Atlantic", "North_Pacific"] +abbrevs = ["NA", "NP"] +arctic_regions = regionmask.Regions( + [region_NA, region_NP], names=names, abbrevs=abbrevs, name="arctic" +) + +# Do plotting +cmap = colors.LinearSegmentedColormap.from_list("", [[0, 85 / 255, 182 / 255], "white"]) +proj = ccrs.NorthPolarStereo() +ax = plt.subplot(111, projection=proj) +ax.set_global() +ds.ice_conc.plot.pcolormesh( + ax=ax, + x="xc", + y="yc", + transform=ccrs.PlateCarree(), + cmap=cmap, + cbar_kwargs={"label": "ice concentration (%)"}, +) +arctic_regions.plot_regions( + ax=ax, + add_label=False, + label="abbrev", + line_kws={"color": [0.2, 0.2, 0.25], "linewidth": 3}, +) +ax.set_extent([-180, 180, 43, 90], ccrs.PlateCarree()) +ax.coastlines(color=[0.3, 0.3, 0.3]) +plt.annotate( + "North Atlantic", + (0.5, 0.2), + xycoords="axes fraction", + horizontalalignment="right", + verticalalignment="bottom", + color="white", +) +plt.annotate( + "North Pacific", + (0.65, 0.88), + xycoords="axes fraction", + horizontalalignment="right", + verticalalignment="bottom", + color="white", +) +plt.annotate( + "Central\nArctic ", + (0.56, 0.56), + xycoords="axes fraction", + horizontalalignment="right", + verticalalignment="bottom", +) +ax.set_facecolor([0.55, 0.55, 0.6]) +plt.title("Arctic regions with mean\nOSI-SAF ice concentration\n1988-2020") +plt.savefig("Arctic_regions.png") +plt.close() +obs.close() + +# ---------- +# Antarctic +# ---------- +print("Creating Antarctic map") +# Load and process data +f_os_s = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_sh_ease2-250_cdr-v3p0_198801-202012.nc" +obs = xc.open_dataset(f_os_s) +obs = obs.mean("time") +mask = create_land_sea_mask(obs, lon_key="lon", lat_key="lat") +obs["ice_conc"] = obs["ice_conc"].where(mask < 1) +ds = obs.assign_coords( + xc=obs["lon"], yc=obs["lat"] +) # Assign these variables to Coordinates, which were originally data variables + +# Set up regions +region_IO = np.array([[20, -90], [90, -90], [90, -55], [20, -55]]) +region_SA = np.array([[20, -90], [-60, -90], [-60, -55], [20, -55]]) +region_SP = np.array([[90, -90], [300, -90], [300, -55], [90, -55]]) +names = ["Indian Ocean", "South Atlantic", "South Pacific"] +abbrevs = ["IO", "SA", "SP"] +arctic_regions = regionmask.Regions( + [region_IO, region_SA, region_SP], names=names, abbrevs=abbrevs, name="antarctic" +) + +# Do plotting +cmap = colors.LinearSegmentedColormap.from_list("", [[0, 85 / 255, 182 / 255], "white"]) +proj = ccrs.SouthPolarStereo() +ax = plt.subplot(111, projection=proj) +ax.set_global() +ds.ice_conc.plot.pcolormesh( + ax=ax, + x="xc", + y="yc", + transform=ccrs.PlateCarree(), + cmap=cmap, + cbar_kwargs={"label": "ice concentration (%)"}, +) +arctic_regions.plot_regions( + ax=ax, + add_label=False, + label="abbrev", + line_kws={"color": [0.2, 0.2, 0.25], "linewidth": 3}, +) +ax.set_extent([-180, 180, -53, -90], ccrs.PlateCarree()) +ax.coastlines(color=[0.3, 0.3, 0.3]) +plt.annotate( + "South Pacific", + (0.50, 0.2), + xycoords="axes fraction", + horizontalalignment="right", + verticalalignment="bottom", + color="black", +) +plt.annotate( + "Indian\nOcean", + (0.89, 0.66), + xycoords="axes fraction", + horizontalalignment="right", + verticalalignment="bottom", + color="black", +) +plt.annotate( + "South Atlantic", + (0.54, 0.82), + xycoords="axes fraction", + horizontalalignment="right", + verticalalignment="bottom", + color="black", +) +ax.set_facecolor([0.55, 0.55, 0.6]) +plt.title("Antarctic regions with mean\nOSI-SAF ice concentration\n1988-2020") +plt.savefig("Antarctic_regions.png") +plt.close() +obs.close() From 2576403bbd6946531b4fd41fda2cc4bdcb5c1fb9 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 13:33:53 -0800 Subject: [PATCH 57/69] add param again --- doc/jupyter/Demo/sea_ice_param.py | 52 ------------------------------- 1 file changed, 52 deletions(-) diff --git a/doc/jupyter/Demo/sea_ice_param.py b/doc/jupyter/Demo/sea_ice_param.py index fcff3da45..e69de29bb 100644 --- a/doc/jupyter/Demo/sea_ice_param.py +++ b/doc/jupyter/Demo/sea_ice_param.py @@ -1,52 +0,0 @@ -# Sea ice metrics parameter file - -# List of models to include in analysis -test_data_set = [ - "E3SM-1-0", -] - -# realization can be a single realization, a list of realizations, or "*" for all realizations -realization = "r1i2p2f1" - -# test_data_path is a template for the model data parent directory -test_data_path = "/p/user_pub/pmp/demo/sea-ice/links_siconc/%(model)/historical/%(realization)/siconc/" - -# filename_template is a template for the model data file name -# combine it with test_data_path to get complete data path -filename_template = "siconc_SImon_%(model)_historical_%(realization)_*_*.nc" - -# The name of the sea ice variable in the model data -var = "siconc" - -# Start and end years for model data -msyear = 1981 -meyear = 2010 - -# Factor for adjusting model data to decimal rather than percent units -ModUnitsAdjust = (True, "multiply", 1e-2) - -# Template for the grid area file -area_template = "/p/user_pub/pmp/demo/sea-ice/links_area/%(model)/*.nc" - -# Area variable name; likely 'areacello' or 'areacella' for CMIP6 -area_var = "areacello" - -# Factor to convert area units to km-2 -AreaUnitsAdjust = (True, "multiply", 1e-6) - -# Directory for writing outputs -case_id = "ex1" -metrics_output_path = "sea_ice_demo/%(case_id)/" - -# Settings for the observational data -reference_data_path_nh = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc" -reference_data_path_sh = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_sh_ease2-250_cdr-v3p0_198801-202012.nc" -ObsUnitsAdjust = (True, "multiply", 1e-2) -reference_data_set = "OSI-SAF" -osyear = 1988 -oeyear = 2020 -obs_var = "ice_conc" -ObsAreaUnitsAdjust = (False, 0, 0) -obs_area_template = None -obs_area_var = None -obs_cell_area = 625 # km 2 From b37e1654ae5f3c8d7c3e845f3a02efabb40bd94a Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 13:57:36 -0800 Subject: [PATCH 58/69] update nb --- .../Demo/Demo_9_seaIceExtent_ivanova.ipynb | 2704 +++++++++++++++++ 1 file changed, 2704 insertions(+) create mode 100644 doc/jupyter/Demo/Demo_9_seaIceExtent_ivanova.ipynb diff --git a/doc/jupyter/Demo/Demo_9_seaIceExtent_ivanova.ipynb b/doc/jupyter/Demo/Demo_9_seaIceExtent_ivanova.ipynb new file mode 100644 index 000000000..b1e1ba9d0 --- /dev/null +++ b/doc/jupyter/Demo/Demo_9_seaIceExtent_ivanova.ipynb @@ -0,0 +1,2704 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "acb8d42e", + "metadata": {}, + "source": [ + "# Sea Ice Demo" + ] + }, + { + "cell_type": "markdown", + "id": "848c69e5", + "metadata": {}, + "source": [ + "**Summary** \n", + "The PCMDI Metrics sea ice driver produces metrics that compare modeled and observed sea ice extent. This notebook demonstrates how to run the PCMDI Metrics sea ice code.\n", + "\n", + "**Demo author list** \n", + "Ana Ordonez, Jiwoo Lee, Paul Durack, Peter Gleckler\n", + "\n", + "**Reference** \n", + "Ivanova, D. P., P. J. Gleckler, K. E. Taylor, P. J. Durack, and K. D. Marvel, 2016: Moving beyond the Total Sea Ice Extent in Gauging Model Biases. J. Climate, 29, 8965–8987, https://doi.org/10.1175/JCLI-D-16-0026.1. " + ] + }, + { + "cell_type": "markdown", + "id": "6bfd3b73", + "metadata": {}, + "source": [ + "## Demo data\n", + "This demo uses three CMIP6 models. The 'siconc' and 'areacello' variables are needed and can be found in the following directories. In addition, six other models are available that can be added to the analyses in this demo:\n", + "```\n", + "/p/user_pub/pmp/demo/sea-ice/links_siconc \n", + "/p/user_pub/pmp/demo/sea-ice/links_area\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "00d48042", + "metadata": {}, + "source": [ + "The observation dataset provided is a satellite derived sea ice concentration dataset from EUMETSAT OSI SAF. More information about this data can be found at the [osi-450-a product page](https://osi-saf.eumetsat.int/products/osi-450-a). The path to this data is:\n", + "```\n", + "/p/user_pub/pmp/demo/sea-ice/EUMETSAT\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "0b854017", + "metadata": {}, + "source": [ + "## Sectors\n", + "This code block produces maps that show the different regions used in the analysis along with the mean observed sea ice concentration. The code to generate these figures can be found in the script `sea_ice_sector_plots.py`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b6d75e4e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating Arctic map\n", + "Creating Antarctic map\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING] yaksa: 10 leaked handle pool objects\n" + ] + } + ], + "source": [ + "%%bash\n", + "python sea_ice_sector_plots.py" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a82ee330", + "metadata": {}, + "outputs": [], + "source": [ + "# To open and display one of the graphics\n", + "from IPython.display import display_png, JSON, Image" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6a7eb6da", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = Image(\"Arctic_regions.png\")\n", + "b = Image(\"Antarctic_regions.png\")\n", + "display_png(a,b)" + ] + }, + { + "cell_type": "markdown", + "id": "5294910f", + "metadata": {}, + "source": [ + "## Basic example" + ] + }, + { + "cell_type": "markdown", + "id": "f316897b", + "metadata": {}, + "source": [ + "This first case will work with sea ice concentration ouput from a single model, E3SM-1-0. Two overview plots are shown below to visualize the Arctic sea ice in this model.\n", + "\n", + "For this demo, we start the OSI-SAF satellite data in 1988 as that avoids missing data in earlier parts of the record.\n", + "\n", + "The code to generate these figures can be found in `sea_ice_line_plots.py`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a6cb929f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-25 13:41:42,705 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + ] + } + ], + "source": [ + "%%bash\n", + "python sea_ice_line_plots.py" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3120f819", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = Image(\"Arctic_tseries.png\")\n", + "b = Image(\"Arctic_clim.png\")\n", + "display_png(a,b)" + ] + }, + { + "cell_type": "markdown", + "id": "2540cd5d", + "metadata": {}, + "source": [ + "The PMP drivers can all read user arguments from parameter files. We provide a demo parameter file, which is shown below. Comments (beginning with a '#') explain each of the parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6e4fa38d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Sea ice metrics parameter file\n", + "\n", + "# List of models to include in analysis\n", + "test_data_set = [\n", + " \"E3SM-1-0\",\n", + "]\n", + "\n", + "# realization can be a single realization, a list of realizations, or \"*\" for all realizations\n", + "realization = \"r1i2p2f1\"\n", + "\n", + "# test_data_path is a template for the model data parent directory\n", + "test_data_path = \"/p/user_pub/pmp/demo/sea-ice/links_siconc/%(model)/historical/%(realization)/siconc/\"\n", + "\n", + "# filename_template is a template for the model data file name\n", + "# combine it with test_data_path to get complete data path\n", + "filename_template = \"siconc_SImon_%(model)_historical_%(realization)_*_*.nc\"\n", + "\n", + "# The name of the sea ice variable in the model data\n", + "var = \"siconc\"\n", + "\n", + "# Start and end years for model data\n", + "msyear = 1981\n", + "meyear = 2010\n", + "\n", + "# Factor for adjusting model data to decimal rather than percent units\n", + "ModUnitsAdjust = (True, \"multiply\", 1e-2)\n", + "\n", + "# Template for the grid area file\n", + "area_template = \"/p/user_pub/pmp/demo/sea-ice/links_area/%(model)/*.nc\"\n", + "\n", + "# Area variable name; likely 'areacello' or 'areacella' for CMIP6\n", + "area_var = \"areacello\"\n", + "\n", + "# Factor to convert area units to km-2\n", + "AreaUnitsAdjust = (True, \"multiply\", 1e-6)\n", + "\n", + "# Directory for writing outputs\n", + "case_id = \"ex1\"\n", + "metrics_output_path = \"sea_ice_demo/%(case_id)/\"\n", + "\n", + "# Settings for the observational data\n", + "reference_data_path_nh = \"/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc\"\n", + "reference_data_path_sh = \"/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_sh_ease2-250_cdr-v3p0_198801-202012.nc\"\n", + "ObsUnitsAdjust = (True, \"multiply\", 1e-2)\n", + "reference_data_set = \"OSI-SAF\"\n", + "osyear = 1988\n", + "oeyear = 2020\n", + "obs_var = \"ice_conc\"\n", + "ObsAreaUnitsAdjust = (False, 0, 0)\n", + "obs_area_template = None\n", + "obs_area_var = None\n", + "obs_cell_area = 625 # km 2\n", + "\n" + ] + } + ], + "source": [ + "with open(\"sea_ice_param.py\") as f:\n", + " print(f.read())" + ] + }, + { + "cell_type": "markdown", + "id": "38dbe853", + "metadata": {}, + "source": [ + "To see all of the parameters available for the sea ice metrics, run the --help command as shown here:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9d6c1fbf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: sea_ice_driver.py [-h] [--parameters PARAMETERS]\n", + " [--diags OTHER_PARAMETERS [OTHER_PARAMETERS ...]]\n", + " [--case_id CASE_ID] [-v VAR] [--obs_var OBS_VAR]\n", + " [--area_var AREA_VAR] [--obs_area_var OBS_AREA_VAR]\n", + " [-r REFERENCE_DATA_SET [REFERENCE_DATA_SET ...]]\n", + " [--reference_data_path_nh REFERENCE_DATA_PATH_NH]\n", + " [--reference_data_path_sh REFERENCE_DATA_PATH_SH]\n", + " [-t TEST_DATA_SET [TEST_DATA_SET ...]]\n", + " [--test_data_path TEST_DATA_PATH]\n", + " [--realization REALIZATION]\n", + " [--filename_template FILENAME_TEMPLATE]\n", + " [--metrics_output_path METRICS_OUTPUT_PATH]\n", + " [--filename_output_template FILENAME_OUTPUT_TEMPLATE]\n", + " [--area_template AREA_TEMPLATE]\n", + " [--obs_area_template_nh OBS_AREA_TEMPLATE_NH]\n", + " [--obs_area_template_sh OBS_AREA_TEMPLATE_SH]\n", + " [--obs_cell_area OBS_CELL_AREA]\n", + " [--output_json_template OUTPUT_JSON_TEMPLATE]\n", + " [--debug] [--plots] [--osyear OSYEAR]\n", + " [--msyear MSYEAR] [--oeyear OEYEAR] [--meyear MEYEAR]\n", + " [--ObsUnitsAdjust OBSUNITSADJUST]\n", + " [--ModUnitsAdjust MODUNITSADJUST]\n", + " [--AreaUnitsAdjust AREAUNITSADJUST]\n", + " [--ObsAreaUnitsAdjust OBSAREAUNITSADJUST]\n", + "\n", + "options:\n", + " -h, --help show this help message and exit\n", + " --parameters PARAMETERS, -p PARAMETERS\n", + " --diags OTHER_PARAMETERS [OTHER_PARAMETERS ...], -d OTHER_PARAMETERS [OTHER_PARAMETERS ...]\n", + " Path to other user-defined parameter file. (default:\n", + " None)\n", + " --case_id CASE_ID Defines a subdirectory to the metrics output, so\n", + " multiplecases can be compared (default: None)\n", + " -v VAR, --var VAR Name of model sea ice concentration variable (default:\n", + " None)\n", + " --obs_var OBS_VAR Name of obs sea ice concentration variable (default:\n", + " None)\n", + " --area_var AREA_VAR Name of model area variable (default: None)\n", + " --obs_area_var OBS_AREA_VAR\n", + " Name of reference data area variable (default: None)\n", + " -r REFERENCE_DATA_SET [REFERENCE_DATA_SET ...], --reference_data_set REFERENCE_DATA_SET [REFERENCE_DATA_SET ...]\n", + " List of observations or models that are used as a\n", + " reference against the test_data_set (default: None)\n", + " --reference_data_path_nh REFERENCE_DATA_PATH_NH\n", + " Path for the reference climatologies for southern\n", + " hemisphere (default: None)\n", + " --reference_data_path_sh REFERENCE_DATA_PATH_SH\n", + " Path for the reference climatologies for northern\n", + " hemisphere (default: None)\n", + " -t TEST_DATA_SET [TEST_DATA_SET ...], --test_data_set TEST_DATA_SET [TEST_DATA_SET ...]\n", + " List of observations or models to test against the\n", + " reference_data_set (default: None)\n", + " --test_data_path TEST_DATA_PATH\n", + " Path for the test climitologies (default: None)\n", + " --realization REALIZATION\n", + " A simulation parameter (default: None)\n", + " --filename_template FILENAME_TEMPLATE\n", + " Template for climatology files (default: None)\n", + " --metrics_output_path METRICS_OUTPUT_PATH\n", + " Directory of where to put the results (default: None)\n", + " --filename_output_template FILENAME_OUTPUT_TEMPLATE\n", + " Filename for the interpolated test climatologies\n", + " (default: None)\n", + " --area_template AREA_TEMPLATE\n", + " Filename template for model grid area (default: None)\n", + " --obs_area_template_nh OBS_AREA_TEMPLATE_NH\n", + " Filename template for obs grid area in Northern\n", + " Hemisphere (default: None)\n", + " --obs_area_template_sh OBS_AREA_TEMPLATE_SH\n", + " Filename template for obs grid area in Southern\n", + " Hemisphere (default: None)\n", + " --obs_cell_area OBS_CELL_AREA\n", + " For equal area grids, the cell area in km (default:\n", + " None)\n", + " --output_json_template OUTPUT_JSON_TEMPLATE\n", + " Filename template for results json files (default:\n", + " None)\n", + " --debug Turn on debugging mode by printing more information to\n", + " track progress (default: False)\n", + " --plots Set to True to generate figures. (default: False)\n", + " --osyear OSYEAR Start year for reference data set (default: None)\n", + " --msyear MSYEAR Start year for model data set (default: None)\n", + " --oeyear OEYEAR End year for reference data set (default: None)\n", + " --meyear MEYEAR End year for model data set (default: None)\n", + " --ObsUnitsAdjust OBSUNITSADJUST\n", + " Factor to convert obs sea ice concentration to\n", + " decimal. For example: - (True, 'divide', 100.0) #\n", + " percentage to decimal - (False, 0, 0) # No adjustment\n", + " (default) (default: (False, 0, 0))\n", + " --ModUnitsAdjust MODUNITSADJUST\n", + " Factor to convert model sea ice concentration to\n", + " decimal. For example: - (True, 'divide', 100.0) #\n", + " percentage to decimal - (False, 0, 0) # No adjustment\n", + " (default) (default: (False, 0, 0))\n", + " --AreaUnitsAdjust AREAUNITSADJUST\n", + " Factor to convert area data to km^2. For example: -\n", + " (True, 'multiply', 1e-6) # m^2 to km^2 - (False, 0, 0)\n", + " # No adjustment (default) (default: (False, 0, 0))\n", + " --ObsAreaUnitsAdjust OBSAREAUNITSADJUST\n", + " Factor to convert area data to km^2. For example: -\n", + " (True, 'multiply', 1e-6) # m^2 to km^2 - (False, 0, 0)\n", + " # No adjustment (default) (default: (False, 0, 0))\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING] yaksa: 10 leaked handle pool objects\n" + ] + } + ], + "source": [ + "%%bash\n", + "sea_ice_driver.py --help" + ] + }, + { + "cell_type": "markdown", + "id": "9bfa9c97", + "metadata": {}, + "source": [ + "The PMP drivers are run on the command line. In this Jupyter Notebook, we use the bash cell magic function %%bash to run command line functions from the notebook.\n", + "\n", + "The PMP sea ice metrics driver call follows the basic format:\n", + "ice_driver.py -p parameter_file.py --additional arguments\n", + "\n", + "The following cell runs the driver with the demo parameter file we saw above." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d6ff0052", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-25 13:42:49,930 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "INFO::2024-01-25 13:43::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex1/sea_ice_metrics.json\n", + "2024-01-25 13:43:52,348 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex1/sea_ice_metrics.json\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['E3SM-1-0']\n", + "\n", + "Metrics output path not found.\n", + "Creating metrics output directory sea_ice_demo/ex1/\n", + "Find all realizations: False\n", + "OBS: Arctic\n", + "Converting units by multiply 0.01\n", + "OBS: Antarctic\n", + "Converting units by multiply 0.01\n", + "Model list: ['E3SM-1-0']\n", + "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n", + "Converting units by multiply 1e-06\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r1i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_201001-201112.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-------------------------------------------\n", + "Calculating model regional average metrics \n", + "for E3SM-1-0\n", + "--------------------------------------------\n", + "arctic\n", + "ca\n", + "na\n", + "np\n", + "antarctic\n", + "sp\n", + "sa\n", + "io\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING] yaksa: 10 leaked handle pool objects\n" + ] + } + ], + "source": [ + "%%bash\n", + "sea_ice_driver.py -p sea_ice_param.py" + ] + }, + { + "cell_type": "markdown", + "id": "084440aa", + "metadata": {}, + "source": [ + "One of the primary outputs of the PMP is a JSON file containing the metrics values. In this case, the metrics are the mean square errors of the time mean and monthly mean ice extent. Ice extent is defined as the total area covered by sea ice concentration of >= 15%. The metrics are organized by model, realization, and reference dataset.\n", + "\n", + "The metrics JSON from this run is displayed below." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9a46fb89", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"DIMENSIONS\": {\n", + " \"index\": {\n", + " \"monthly_clim\": \"Monthly climatology of extent\",\n", + " \"total_extent\": \"Sum of ice coverage where concentration > 15%\"\n", + " },\n", + " \"json_structure\": [\n", + " \"model\",\n", + " \"realization\",\n", + " \"obs\",\n", + " \"region\",\n", + " \"index\",\n", + " \"statistic\"\n", + " ],\n", + " \"model\": [\n", + " \"E3SM-1-0\"\n", + " ],\n", + " \"region\": {},\n", + " \"statistic\": {\n", + " \"mse\": \"Mean Square Error (10^12 km^4)\"\n", + " }\n", + " },\n", + " \"RESULTS\": {\n", + " \"E3SM-1-0\": {\n", + " \"antarctic\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.4635192339671928\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.139646926848\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.4635192339671928\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.139646926848\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"arctic\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"5.476181000101471\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.628078727168\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"5.476181000101471\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.628078727168\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"ca\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.05045644169895609\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.007755424768\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.05045644169895609\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.007755424768\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"io\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.04955696515353039\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.00991997952\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.04955696515353039\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.00991997952\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"na\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"2.3482121752568643\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.576847409152\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"2.3482121752568643\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.576847409152\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"np\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.6264518797177615\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.287947685888\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.6264518797177615\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.287947685888\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"sa\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.3797729615722766\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.297013608448\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.3797729615722766\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.297013608448\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"sp\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.6767107661262813\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.078223351808\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.6767107661262813\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.078223351808\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"json_structure\": [\n", + " \"model\",\n", + " \"realization\",\n", + " \"obs\",\n", + " \"region\",\n", + " \"index\",\n", + " \"statistic\"\n", + " ],\n", + " \"json_version\": 3.0,\n", + " \"model_year_range\": {\n", + " \"E3SM-1-0\": [\n", + " \"1981\",\n", + " \"2010\"\n", + " ]\n", + " },\n", + " \"provenance\": {\n", + " \"commandLine\": \"/home/ordonez4/miniconda3/envs/pmp_si/bin/sea_ice_driver.py -p sea_ice_param.py\",\n", + " \"conda\": {\n", + " \"Platform\": \"linux-64\",\n", + " \"PythonVersion\": \"3.8.15.final.0\",\n", + " \"Version\": \"23.1.0\",\n", + " \"buildVersion\": \"not installed\"\n", + " },\n", + " \"date\": \"2024-01-25 13:43:38\",\n", + " \"openGL\": {\n", + " \"GLX\": {\n", + " \"client\": {},\n", + " \"server\": {}\n", + " }\n", + " },\n", + " \"osAccess\": false,\n", + " \"packages\": {\n", + " \"PMP\": \"v3.0.2-11-g06b151f\",\n", + " \"PMPObs\": \"See 'References' key below, for detailed obs provenance information.\",\n", + " \"blas\": \"0.3.24\",\n", + " \"cdat_info\": \"8.2.1\",\n", + " \"cdms\": \"3.1.5\",\n", + " \"cdp\": \"1.7.0\",\n", + " \"cdtime\": \"3.1.4\",\n", + " \"cdutil\": \"8.2.1\",\n", + " \"clapack\": null,\n", + " \"esmf\": \"0.8.2\",\n", + " \"esmpy\": \"8.4.2\",\n", + " \"genutil\": \"8.2.1\",\n", + " \"lapack\": \"3.9.0\",\n", + " \"matplotlib\": null,\n", + " \"mesalib\": null,\n", + " \"numpy\": \"1.22.4\",\n", + " \"python\": \"3.10.13\",\n", + " \"scipy\": \"1.11.3\",\n", + " \"uvcdat\": null,\n", + " \"vcs\": null,\n", + " \"vtk\": null,\n", + " \"xarray\": \"2023.10.1\",\n", + " \"xcdat\": \"0.5.0\"\n", + " },\n", + " \"platform\": {\n", + " \"Name\": \"gates.llnl.gov\",\n", + " \"OS\": \"Linux\",\n", + " \"Version\": \"3.10.0-1160.71.1.el7.x86_64\"\n", + " },\n", + " \"userId\": \"ordonez4\"\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "with open(\"sea_ice_demo/ex1/sea_ice_metrics.json\") as f:\n", + " print(f.read())" + ] + }, + { + "cell_type": "markdown", + "id": "d74b6752", + "metadata": {}, + "source": [ + "This driver also outputs a bar chart that visualizes the mean square error between the model and observations. Since there is only one model and one realization in this instance, the bar chart looks very simple. The red bar indicates the mean square error for the time mean ice extent, and the blue bar indicates the mean square error for the climatological ice extent." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c6dfa7a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "sea_ice_demo/ex1/MSE_bar_chart.png\r\n" + ] + } + ], + "source": [ + "!ls {\"sea_ice_demo/ex1/MSE_bar_chart.png\"}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "d14e933a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = Image(\"sea_ice_demo/ex1/MSE_bar_chart.png\")\n", + "display_png(a)" + ] + }, + { + "cell_type": "markdown", + "id": "a9b323ec", + "metadata": {}, + "source": [ + "## Working with multiple realizations" + ] + }, + { + "cell_type": "markdown", + "id": "0c427a07", + "metadata": {}, + "source": [ + "The sea ice driver can generate metrics based on an average of all available realizations. To do so, provide an asterisk \\* as the value to the --realization argument on the command line. Options passed on the command line will supercede arguments in the parameter file. \n", + "\n", + "In addition, we set the --case_id value to 'ex2' to save results in a new directory." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5f8174e1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-25 13:44:52,112 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['E3SM-1-0']\n", + "\n", + "Metrics output path not found.\n", + "Creating metrics output directory sea_ice_demo/ex2/\n", + "Find all realizations: True\n", + "OBS: Arctic\n", + "Converting units by multiply 0.01\n", + "OBS: Antarctic\n", + "Converting units by multiply 0.01\n", + "Model list: ['E3SM-1-0']\n", + "\n", + "=================================\n", + "model, runs: E3SM-1-0 ['r1i2p2f1', 'r2i2p2f1', 'r3i2p2f1', 'r4i2p2f1']\n", + "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n", + "Converting units by multiply 1e-06\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r1i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_201001-201112.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r2i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_201001-201312.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r3i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_201001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO::2024-01-25 13:48::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex2/sea_ice_metrics.json\n", + "2024-01-25 13:48:09,795 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex2/sea_ice_metrics.json\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----------------------\n", + "model, run, variable: E3SM-1-0 r4i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_201001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-------------------------------------------\n", + "Calculating model regional average metrics \n", + "for E3SM-1-0\n", + "--------------------------------------------\n", + "arctic\n", + "ca\n", + "na\n", + "np\n", + "antarctic\n", + "sp\n", + "sa\n", + "io\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING] yaksa: 10 leaked handle pool objects\n" + ] + } + ], + "source": [ + "%%bash\n", + "sea_ice_driver.py -p sea_ice_param.py --realization '*' --case_id \"ex2\"" + ] + }, + { + "cell_type": "markdown", + "id": "cadb1306", + "metadata": {}, + "source": [ + "Since we have averaged four different realizations, the resulting statistics are different than seen in example 1. The bar chart now contains markers showing the overall spread among the realizations." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "d6cb5f07", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = Image(\"sea_ice_demo/ex2/MSE_bar_chart.png\")\n", + "display_png(a)" + ] + }, + { + "cell_type": "markdown", + "id": "499d3935", + "metadata": {}, + "source": [ + "# Working with multiple models" + ] + }, + { + "cell_type": "markdown", + "id": "51b008db", + "metadata": {}, + "source": [ + "Along with using multiple realizations, we can include multiple models in a single analysis. The model data must all follow a single filename template. All model inputs must use the same name and units for the sea ice variable.\n", + "\n", + "The example below shows how to use three models in the analysis, with all available realizations. The models are listed as inputs to the --test_data_set flag.\n", + "\n", + "Want to add more models? Six other model sea ice datasets are available in the directories linked in the notebook introduction." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "679d7289", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-25 13:49:18,281 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "2024-01-25 13:50:02,067 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['E3SM-1-0', 'CanESM5', 'MIROC6']\n", + "\n", + "Metrics output path not found.\n", + "Creating metrics output directory sea_ice_demo/ex3/\n", + "Find all realizations: True\n", + "OBS: Arctic\n", + "Converting units by multiply 0.01\n", + "OBS: Antarctic\n", + "Converting units by multiply 0.01\n", + "Model list: ['CanESM5', 'E3SM-1-0', 'MIROC6']\n", + "\n", + "=================================\n", + "model, runs: CanESM5 ['r2i1p1f1', 'r1i1p1f1', 'r3i1p1f1']\n", + "/p/user_pub/pmp/demo/sea-ice/links_area/CanESM5/*.nc\n", + "Converting units by multiply 1e-06\n", + "\n", + "-----------------------\n", + "model, run, variable: CanESM5 r2i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/CanESM5/historical/r2i1p1f1/siconc/siconc_SImon_CanESM5_historical_r2i1p1f1_gn_185001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: CanESM5 r1i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/CanESM5/historical/r1i1p1f1/siconc/siconc_SImon_CanESM5_historical_r1i1p1f1_gn_185001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: CanESM5 r3i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/CanESM5/historical/r3i1p1f1/siconc/siconc_SImon_CanESM5_historical_r3i1p1f1_gn_185001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-------------------------------------------\n", + "Calculating model regional average metrics \n", + "for CanESM5\n", + "--------------------------------------------\n", + "arctic\n", + "ca\n", + "na\n", + "np\n", + "antarctic\n", + "sp\n", + "sa\n", + "io\n", + "\n", + "=================================\n", + "model, runs: E3SM-1-0 ['r1i2p2f1', 'r2i2p2f1', 'r3i2p2f1', 'r4i2p2f1']\n", + "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n", + "Converting units by multiply 1e-06\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r1i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_201001-201112.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r2i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_201001-201312.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r3i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_192001-192912.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-25 13:52:38,818 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", + "INFO::2024-01-25 13:57::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex3/sea_ice_metrics.json\n", + "2024-01-25 13:57:06,766 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex3/sea_ice_metrics.json\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_201001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: E3SM-1-0 r4i2p2f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_185001-185912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_186001-186912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_187001-187912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_188001-188912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_189001-189912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_190001-190912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_191001-191912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_192001-192912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_193001-193912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_194001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_195001-195912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_196001-196912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_197001-197912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_198001-198912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_199001-199912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_200001-200912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_201001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-------------------------------------------\n", + "Calculating model regional average metrics \n", + "for E3SM-1-0\n", + "--------------------------------------------\n", + "arctic\n", + "ca\n", + "na\n", + "np\n", + "antarctic\n", + "sp\n", + "sa\n", + "io\n", + "\n", + "=================================\n", + "model, runs: MIROC6 ['r2i1p1f1', 'r1i1p1f1', 'r4i1p1f1', 'r3i1p1f1']\n", + "/p/user_pub/pmp/demo/sea-ice/links_area/MIROC6/*.nc\n", + "Converting units by multiply 1e-06\n", + "\n", + "-----------------------\n", + "model, run, variable: MIROC6 r2i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r2i1p1f1/siconc/siconc_SImon_MIROC6_historical_r2i1p1f1_gn_185001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r2i1p1f1/siconc/siconc_SImon_MIROC6_historical_r2i1p1f1_gn_195001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: MIROC6 r1i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r1i1p1f1/siconc/siconc_SImon_MIROC6_historical_r1i1p1f1_gn_185001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r1i1p1f1/siconc/siconc_SImon_MIROC6_historical_r1i1p1f1_gn_195001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: MIROC6 r4i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r4i1p1f1/siconc/siconc_SImon_MIROC6_historical_r4i1p1f1_gn_185001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r4i1p1f1/siconc/siconc_SImon_MIROC6_historical_r4i1p1f1_gn_195001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-----------------------\n", + "model, run, variable: MIROC6 r3i1p1f1 siconc\n", + "test_data (model in this case) full_path:\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r3i1p1f1/siconc/siconc_SImon_MIROC6_historical_r3i1p1f1_gn_185001-194912.nc\n", + " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r3i1p1f1/siconc/siconc_SImon_MIROC6_historical_r3i1p1f1_gn_195001-201412.nc\n", + "Converting units by multiply 0.01\n", + "\n", + "-------------------------------------------\n", + "Calculating model regional average metrics \n", + "for MIROC6\n", + "--------------------------------------------\n", + "arctic\n", + "ca\n", + "na\n", + "np\n", + "antarctic\n", + "sp\n", + "sa\n", + "io\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[WARNING] yaksa: 10 leaked handle pool objects\n" + ] + } + ], + "source": [ + "%%bash\n", + "sea_ice_driver.py -p sea_ice_param.py \\\n", + "--test_data_set \"E3SM-1-0\" \"CanESM5\" \"MIROC6\" \\\n", + "--realization '*' \\\n", + "--case_id \"ex3\"" + ] + }, + { + "cell_type": "markdown", + "id": "9a17ffee", + "metadata": {}, + "source": [ + "The output JSON now includes metrics for all three models." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "b07dbb8b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"DIMENSIONS\": {\n", + " \"index\": {\n", + " \"monthly_clim\": \"Monthly climatology of extent\",\n", + " \"total_extent\": \"Sum of ice coverage where concentration > 15%\"\n", + " },\n", + " \"json_structure\": [\n", + " \"model\",\n", + " \"realization\",\n", + " \"obs\",\n", + " \"region\",\n", + " \"index\",\n", + " \"statistic\"\n", + " ],\n", + " \"model\": [\n", + " \"CanESM5\",\n", + " \"E3SM-1-0\",\n", + " \"MIROC6\"\n", + " ],\n", + " \"region\": {},\n", + " \"statistic\": {\n", + " \"mse\": \"Mean Square Error (10^12 km^4)\"\n", + " }\n", + " },\n", + " \"RESULTS\": {\n", + " \"CanESM5\": {\n", + " \"antarctic\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"5.1043444982100254\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"4.816687734317558\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"3.8203905542158574\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.551903219690635\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"5.408472768567934\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"5.10073354794071\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"6.255511442006537\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"5.95826796378333\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"arctic\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"2.6739701578200408\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"2.5552395000296997\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.9601839559074323\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.8071711277770932\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"2.8686219657630323\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"2.7646000598233362\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"3.306431955856059\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.1987918127469728\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"ca\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.12445176930055403\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.0752818530752368\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.06261975386075735\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.04065017565672462\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.18876746901985617\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.11135594838591391\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.14126431682827864\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.08283366771548334\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"io\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.3902350350581252\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.3411097649596542\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.27378096542718267\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.23052580580517984\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.39222062635127936\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.34482149125771394\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.5287500850978069\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.4689404665141464\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"na\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.8575586124643404\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.6617817141384847\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.5264155067552119\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.3050483466111835\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.7640984838802416\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.5843839089856835\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"2.3388720869665747\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"2.1497244832528395\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"np\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.0063419603431157535\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.001033088420302666\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.005949894526659108\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.7412310294637067e-06\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.01271835367014484\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.004687148872326894\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.005275638631907463\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.0008574285177782025\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"sa\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.4618851114415225\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.21877947801248515\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.3604933562263525\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.16135560774807228\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.48465097876034335\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.18590427961007985\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.565345935295451\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.32530874506699275\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"sp\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.3466206749703824\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.3264114860024545\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.0412143157666585\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.0233677214618289\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.5844213803502167\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.5597222118436824\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.4483893228104396\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"1.4270545631414926\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"E3SM-1-0\": {\n", + " \"antarctic\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.7772427941035078\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.512854523904\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.4635192339671928\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.139646926848\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.7917153708317476\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.5296078848\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.9431708933066041\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.709116624896\"\n", + " }\n", + " }\n", + " },\n", + " \"r4i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"1.1123064886611145\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.8482918891519999\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"arctic\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"5.271005131039172\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.602193842176\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"5.476181000101471\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.628078727168\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"4.798813326297904\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.0712725504\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"5.695229471419496\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"4.135149109248\"\n", + " }\n", + " }\n", + " },\n", + " \"r4i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"5.16787172788022\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"3.6138642309119997\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"ca\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.06682122096680175\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.014511187968\"\n", + " }\n", + " }\n", + " },\n", + " \"r1i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.05045644169895609\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.007755424768\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.04953964308899206\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.007533873664\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.09545969211386617\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.026321457152\"\n", + " }\n", + " }\n", + " },\n", + " \"r4i2p2f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"0.08158619730649973\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"0.020952242176\"\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"io\": {\n", + " \"model_mean\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": 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\"mse\": \"13.40484878336\"\n", + " }\n", + " }\n", + " },\n", + " \"r2i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"16.338122498955823\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"13.727326797824\"\n", + " }\n", + " }\n", + " },\n", + " \"r3i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"16.167353763780575\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"13.60793174016\"\n", + " }\n", + " }\n", + " },\n", + " \"r4i1p1f1\": {\n", + " \"OSI-SAF\": {\n", + " \"monthly_clim\": {\n", + " \"mse\": \"16.22520331188068\"\n", + " },\n", + " \"total_extent\": {\n", + " \"mse\": \"13.644895092736\"\n", + " }\n", + " }\n", + " }\n", + " }\n", + " }\n", + " },\n", + " \"json_structure\": [\n", + " \"model\",\n", + " \"realization\",\n", + " \"obs\",\n", + " \"region\",\n", + " \"index\",\n", + " \"statistic\"\n", + " ],\n", + " \"json_version\": 3.0,\n", + " \"model_year_range\": {\n", + " \"CanESM5\": [\n", + " \"1981\",\n", + " \"2010\"\n", + " ],\n", + " \"E3SM-1-0\": [\n", + " \"1981\",\n", + " \"2010\"\n", + " ],\n", + " \"MIROC6\": [\n", + " \"1981\",\n", + " \"2010\"\n", + " ]\n", + " },\n", + " \"provenance\": {\n", + " \"commandLine\": \"/home/ordonez4/miniconda3/envs/pmp_si/bin/sea_ice_driver.py -p sea_ice_param.py --test_data_set E3SM-1-0 CanESM5 MIROC6 --realization * --case_id ex3\",\n", + " \"conda\": {\n", + " \"Platform\": \"linux-64\",\n", + " \"PythonVersion\": \"3.8.15.final.0\",\n", + " \"Version\": \"23.1.0\",\n", + " \"buildVersion\": \"not installed\"\n", + " },\n", + " \"date\": \"2024-01-25 13:56:52\",\n", + " \"openGL\": {\n", + " \"GLX\": {\n", + " \"client\": {},\n", + " \"server\": {}\n", + " }\n", + " },\n", + " \"osAccess\": false,\n", + " \"packages\": {\n", + " \"PMP\": \"v3.0.2-11-g06b151f\",\n", + " \"PMPObs\": \"See 'References' key below, for detailed obs provenance information.\",\n", + " \"blas\": \"0.3.24\",\n", + " \"cdat_info\": \"8.2.1\",\n", + " \"cdms\": \"3.1.5\",\n", + " \"cdp\": \"1.7.0\",\n", + " \"cdtime\": \"3.1.4\",\n", + " \"cdutil\": \"8.2.1\",\n", + " \"clapack\": null,\n", + " \"esmf\": \"0.8.2\",\n", + " \"esmpy\": \"8.4.2\",\n", + " \"genutil\": \"8.2.1\",\n", + " \"lapack\": \"3.9.0\",\n", + " \"matplotlib\": null,\n", + " \"mesalib\": null,\n", + " \"numpy\": \"1.22.4\",\n", + " \"python\": \"3.10.13\",\n", + " \"scipy\": \"1.11.3\",\n", + " \"uvcdat\": null,\n", + " \"vcs\": null,\n", + " \"vtk\": null,\n", + " \"xarray\": \"2023.10.1\",\n", + " \"xcdat\": \"0.5.0\"\n", + " },\n", + " \"platform\": {\n", + " \"Name\": \"gates.llnl.gov\",\n", + " \"OS\": \"Linux\",\n", + " \"Version\": \"3.10.0-1160.71.1.el7.x86_64\"\n", + " },\n", + " \"userId\": \"ordonez4\"\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "with open(\"sea_ice_demo/ex3/sea_ice_metrics.json\") as f:\n", + " print(f.read())" + ] + }, + { + "cell_type": "markdown", + "id": "f48b3856", + "metadata": {}, + "source": [ + "Now the resulting bar chart shows three different models with their spread." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "41aa14a3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = Image(\"sea_ice_demo/ex3/MSE_bar_chart.png\")\n", + "display_png(a)" + ] + }, + { + "cell_type": "markdown", + "id": "bc43281a", + "metadata": {}, + "source": [ + "# Further exploration" + ] + }, + { + "cell_type": "markdown", + "id": "19abc98d", + "metadata": {}, + "source": [ + "Maybe you want to compare more models, or take a closer look at the model data? Here are links to the data for further exploration.\n", + "\n", + "As a reminder, data for nine models is available here:\n", + "```\n", + "/p/user_pub/pmp/demo/sea-ice/links_siconc \n", + "/p/user_pub/pmp/demo/sea-ice/links_area\n", + "```\n", + "\n", + "The observational time series can be found at:\n", + "```\n", + "/p/user_pub/pmp/demo/sea-ice/EUMETSAT\n", + "```\n", + "\n", + "For some example plotting code using xcdat and matplotlib, see the scripts that were used to generate the introductory figures:\n", + "\n", + "```\n", + "sea_ice_sector_plots.py\n", + "sea_ice_line_plots.py\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1161f29", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:pmp_si] *", + "language": "python", + "name": "conda-env-pmp_si-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 61a788fe85c2bd38470456df321377630150764e Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 13:58:06 -0800 Subject: [PATCH 59/69] update names --- doc/jupyter/Demo/Tutorial_sea_ice.ipynb | 2697 ----------------------- doc/jupyter/Demo/sea_ice_line_plots.py | 4 +- 2 files changed, 2 insertions(+), 2699 deletions(-) delete mode 100644 doc/jupyter/Demo/Tutorial_sea_ice.ipynb diff --git a/doc/jupyter/Demo/Tutorial_sea_ice.ipynb b/doc/jupyter/Demo/Tutorial_sea_ice.ipynb deleted file mode 100644 index 33a348fa0..000000000 --- a/doc/jupyter/Demo/Tutorial_sea_ice.ipynb +++ /dev/null @@ -1,2697 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "acb8d42e", - "metadata": {}, - "source": [ - "# Sea Ice Demo" - ] - }, - { - "cell_type": "markdown", - "id": "848c69e5", - "metadata": {}, - "source": [ - "**Summary** \n", - "The PCMDI Metrics sea ice driver produces metrics that compare modeled and observed sea ice extent. This notebook demonstrates how to run the PCMDI Metrics sea ice code.\n", - "\n", - "**Demo author list** \n", - "Ana Ordonez, Jiwoo Lee, Paul Durack, Peter Gleckler\n", - "\n", - "**Reference** \n", - "Ivanova, D. P., P. J. Gleckler, K. E. Taylor, P. J. Durack, and K. D. Marvel, 2016: Moving beyond the Total Sea Ice Extent in Gauging Model Biases. J. Climate, 29, 8965–8987, https://doi.org/10.1175/JCLI-D-16-0026.1. " - ] - }, - { - "cell_type": "markdown", - "id": "6bfd3b73", - "metadata": {}, - "source": [ - "## Demo data\n", - "This demo uses three CMIP6 models. The 'siconc' and 'areacello' variables are needed and can be found in the following directories. In addition, six other models are available that can be added to the analyses in this demo:\n", - "```\n", - "/p/user_pub/pmp/demo/sea-ice/links_siconc \n", - "/p/user_pub/pmp/demo/sea-ice/links_area\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "00d48042", - "metadata": {}, - "source": [ - "The observation dataset provided is a satellite derived sea ice concentration dataset from EUMETSAT OSI SAF. More information about this data can be found at the [osi-450-a product page](https://osi-saf.eumetsat.int/products/osi-450-a). The path to this data is:\n", - "```\n", - "/p/user_pub/pmp/demo/sea-ice/EUMETSAT\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "0b854017", - "metadata": {}, - "source": [ - "## Sectors\n", - "This code block produces maps that show the different regions used in the analysis along with the mean observed sea ice concentration. The code to generate these figures can be found in the script `create_sector_plots.py`." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "b6d75e4e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Creating Arctic map\n", - "Creating Antarctic map\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[WARNING] yaksa: 10 leaked handle pool objects\n" - ] - } - ], - "source": [ - "%%bash\n", - "python create_sector_plots.py" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a82ee330", - "metadata": {}, - "outputs": [], - "source": [ - "# To open and display one of the graphics\n", - "from IPython.display import display_png, JSON, Image" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "6a7eb6da", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "a = Image(\"Arctic_regions.png\")\n", - "b = Image(\"Antarctic_regions.png\")\n", - "display_png(a,b)" - ] - }, - { - "cell_type": "markdown", - "id": "5294910f", - "metadata": {}, - "source": [ - "## Basic example" - ] - }, - { - "cell_type": "markdown", - "id": "f316897b", - "metadata": {}, - "source": [ - "This first case will work with sea ice concentration ouput from a single model, E3SM-1-0. Two overview plots are shown below to visualize the Arctic sea ice in this model.\n", - "\n", - "For this demo, we start the OSI-SAF satellite data in 1988 as that avoids missing data in earlier parts of the record.\n", - "\n", - "The code to generate these figures can be found in `make_demo_sea_ice_plots.py`." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a6cb929f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-01-25 11:37:13,752 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" - ] - } - ], - "source": [ - "%%bash\n", - "python make_demo_sea_ice_plots.py" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "3120f819", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "a = Image(\"E3SM_arctic_tseries.png\")\n", - "b = Image(\"E3SM_arctic_clim.png\")\n", - "display_png(a,b)" - ] - }, - { - "cell_type": "markdown", - "id": "2540cd5d", - "metadata": {}, - "source": [ - "The PMP drivers can all read user arguments from parameter files. We provide a demo parameter file, which is shown below. Comments (beginning with a '#') explain each of the parameters." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "6e4fa38d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "# Sea ice metrics parameter file\n", - "\n", - "# List of models to include in analysis\n", - "test_data_set = [\n", - " \"E3SM-1-0\",\n", - "]\n", - "\n", - "# realization can be a single realization, a list of realizations, or \"*\" for all realizations\n", - "realization = \"r1i2p2f1\"\n", - "\n", - "# test_data_path is a template for the model data parent directory\n", - "test_data_path = \"/p/user_pub/pmp/demo/sea-ice/links_siconc/%(model)/historical/%(realization)/siconc/\"\n", - "\n", - "# filename_template is a template for the model data file name\n", - "# combine it with test_data_path to get complete data path\n", - "filename_template = \"siconc_SImon_%(model)_historical_%(realization)_*_*.nc\"\n", - "\n", - "# The name of the sea ice variable in the model data\n", - "var = \"siconc\"\n", - "\n", - "# Start and end years for model data\n", - "msyear = 1981\n", - "meyear = 2010\n", - "\n", - "# Factor for adjusting model data to decimal rather than percent units\n", - "ModUnitsAdjust = (True, \"multiply\", 1e-2)\n", - "\n", - "# Template for the grid area file\n", - "area_template = \"/p/user_pub/pmp/demo/sea-ice/links_area/%(model)/*.nc\"\n", - "\n", - "# Area variable name; likely 'areacello' or 'areacella' for CMIP6\n", - "area_var = \"areacello\"\n", - "\n", - "# Factor to convert area units to km-2\n", - "AreaUnitsAdjust = (True, \"multiply\", 1e-6)\n", - "\n", - "# Directory for writing outputs\n", - "case_id = \"ex1\"\n", - "metrics_output_path = \"sea_ice_demo/%(case_id)/\"\n", - "\n", - "# Settings for the observational data\n", - "reference_data_path_nh = \"/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc\"\n", - "reference_data_path_sh = \"/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_sh_ease2-250_cdr-v3p0_198801-202012.nc\"\n", - "ObsUnitsAdjust=(True,\"multiply\",1e-2)\n", - "reference_data_set=\"OSI-SAF\"\n", - "osyear=1988\n", - "oeyear=2020\n", - "obs_var=\"ice_conc\"\n", - "ObsAreaUnitsAdjust = (False, 0, 0)\n", - "obs_area_template = None\n", - "obs_area_var = None\n", - "obs_cell_area = 625 #km 2\n" - ] - } - ], - "source": [ - "with open(\"demo_param_file.py\") as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "markdown", - "id": "38dbe853", - "metadata": {}, - "source": [ - "To see all of the parameters available for the sea ice metrics, run the --help command as shown here:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "9d6c1fbf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "usage: ice_driver.py [-h] [--parameters PARAMETERS]\n", - " [--diags OTHER_PARAMETERS [OTHER_PARAMETERS ...]]\n", - " [--case_id CASE_ID] [-v VAR] [--obs_var OBS_VAR]\n", - " [--area_var AREA_VAR] [--obs_area_var OBS_AREA_VAR]\n", - " [-r REFERENCE_DATA_SET [REFERENCE_DATA_SET ...]]\n", - " [--reference_data_path REFERENCE_DATA_PATH]\n", - " [-t TEST_DATA_SET [TEST_DATA_SET ...]]\n", - " [--test_data_path TEST_DATA_PATH]\n", - " [--realization REALIZATION]\n", - " [--filename_template FILENAME_TEMPLATE]\n", - " [--metrics_output_path METRICS_OUTPUT_PATH]\n", - " [--filename_output_template FILENAME_OUTPUT_TEMPLATE]\n", - " [--area_template AREA_TEMPLATE]\n", - " [--obs_area_template_nh OBS_AREA_TEMPLATE_NH]\n", - " [--obs_area_template_sh OBS_AREA_TEMPLATE_SH]\n", - " [--obs_cell_area OBS_CELL_AREA]\n", - " [--output_json_template OUTPUT_JSON_TEMPLATE] [--debug]\n", - " [--plots] [--osyear OSYEAR] [--msyear MSYEAR]\n", - " [--oeyear OEYEAR] [--meyear MEYEAR]\n", - " [--ObsUnitsAdjust OBSUNITSADJUST]\n", - " [--ModUnitsAdjust MODUNITSADJUST]\n", - " [--AreaUnitsAdjust AREAUNITSADJUST]\n", - " [--ObsAreaUnitsAdjust OBSAREAUNITSADJUST]\n", - "\n", - "options:\n", - " -h, --help show this help message and exit\n", - " --parameters PARAMETERS, -p PARAMETERS\n", - " --diags OTHER_PARAMETERS [OTHER_PARAMETERS ...], -d OTHER_PARAMETERS [OTHER_PARAMETERS ...]\n", - " Path to other user-defined parameter file. (default:\n", - " None)\n", - " --case_id CASE_ID Defines a subdirectory to the metrics output, so\n", - " multiplecases can be compared (default: None)\n", - " -v VAR, --var VAR Name of model sea ice concentration variable (default:\n", - " None)\n", - " --obs_var OBS_VAR Name of obs sea ice concentration variable (default:\n", - " None)\n", - " --area_var AREA_VAR Name of model area variable (default: None)\n", - " --obs_area_var OBS_AREA_VAR\n", - " Name of reference data area variable (default: None)\n", - " -r REFERENCE_DATA_SET [REFERENCE_DATA_SET ...], --reference_data_set REFERENCE_DATA_SET [REFERENCE_DATA_SET ...]\n", - " List of observations or models that are used as a\n", - " reference against the test_data_set (default: None)\n", - " --reference_data_path REFERENCE_DATA_PATH\n", - " Path for the reference climitologies (default: None)\n", - " -t TEST_DATA_SET [TEST_DATA_SET ...], --test_data_set TEST_DATA_SET [TEST_DATA_SET ...]\n", - " List of observations or models to test against the\n", - " reference_data_set (default: None)\n", - " --test_data_path TEST_DATA_PATH\n", - " Path for the test climitologies (default: None)\n", - " --realization REALIZATION\n", - " A simulation parameter (default: None)\n", - " --filename_template FILENAME_TEMPLATE\n", - " Template for climatology files (default: None)\n", - " --metrics_output_path METRICS_OUTPUT_PATH\n", - " Directory of where to put the results (default: None)\n", - " --filename_output_template FILENAME_OUTPUT_TEMPLATE\n", - " Filename for the interpolated test climatologies\n", - " (default: None)\n", - " --area_template AREA_TEMPLATE\n", - " Filename template for model grid area (default: None)\n", - " --obs_area_template_nh OBS_AREA_TEMPLATE_NH\n", - " Filename template for obs grid area in Northern\n", - " Hemisphere (default: None)\n", - " --obs_area_template_sh OBS_AREA_TEMPLATE_SH\n", - " Filename template for obs grid area in Southern\n", - " Hemisphere (default: None)\n", - " --obs_cell_area OBS_CELL_AREA\n", - " For equal area grids, the cell area in km (default:\n", - " None)\n", - " --output_json_template OUTPUT_JSON_TEMPLATE\n", - " Filename template for results json files (default:\n", - " None)\n", - " --debug Turn on debugging mode by printing more information to\n", - " track progress (default: False)\n", - " --plots Set to True to generate figures. (default: False)\n", - " --osyear OSYEAR Start year for reference data set (default: None)\n", - " --msyear MSYEAR Start year for model data set (default: None)\n", - " --oeyear OEYEAR End year for reference data set (default: None)\n", - " --meyear MEYEAR End year for model data set (default: None)\n", - " --ObsUnitsAdjust OBSUNITSADJUST\n", - " Factor to convert obs sea ice concentration to\n", - " decimal. For example: - (True, 'divide', 100.0) #\n", - " percentage to decimal - (False, 0, 0) # No adjustment\n", - " (default) (default: (False, 0, 0))\n", - " --ModUnitsAdjust MODUNITSADJUST\n", - " Factor to convert model sea ice concentration to\n", - " decimal. For example: - (True, 'divide', 100.0) #\n", - " percentage to decimal - (False, 0, 0) # No adjustment\n", - " (default) (default: (False, 0, 0))\n", - " --AreaUnitsAdjust AREAUNITSADJUST\n", - " Factor to convert area data to km^2. For example: -\n", - " (True, 'multiply', 1e-6) # m^2 to km^2 - (False, 0, 0)\n", - " # No adjustment (default) (default: (False, 0, 0))\n", - " --ObsAreaUnitsAdjust OBSAREAUNITSADJUST\n", - " Factor to convert area data to km^2. For example: -\n", - " (True, 'multiply', 1e-6) # m^2 to km^2 - (False, 0, 0)\n", - " # No adjustment (default) (default: (False, 0, 0))\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[WARNING] yaksa: 10 leaked handle pool objects\n" - ] - } - ], - "source": [ - "%%bash\n", - "python ice_driver.py --help" - ] - }, - { - "cell_type": "markdown", - "id": "9bfa9c97", - "metadata": {}, - "source": [ - "The PMP drivers are run on the command line. In this Jupyter Notebook, we use the bash cell magic function %%bash to run command line functions from the notebook.\n", - "\n", - "The PMP sea ice metrics driver call follows the basic format:\n", - "ice_driver.py -p parameter_file.py --additional arguments\n", - "\n", - "The following cell runs the driver with the demo parameter file we saw above." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "d6ff0052", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-01-25 11:38:28,347 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "INFO::2024-01-25 11:39::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n", - "2024-01-25 11:39:27,529 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex1/sea_ice_metrics.json\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['E3SM-1-0']\n", - "Find all realizations: False\n", - "OBS: Arctic\n", - "Converting units by multiply 0.01\n", - "OBS: Antarctic\n", - "Converting units by multiply 0.01\n", - "Model list: ['E3SM-1-0']\n", - "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n", - "Converting units by multiply 1e-06\n", - "\n", - "-----------------------\n", - "model, run, variable: E3SM-1-0 r1i2p2f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_185001-185912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_186001-186912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_187001-187912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_188001-188912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_189001-189912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_190001-190912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_191001-191912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_192001-192912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_193001-193912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_194001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_195001-195912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_196001-196912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_197001-197912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_198001-198912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_199001-199912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_200001-200912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_201001-201112.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-------------------------------------------\n", - "Calculating model regional average metrics \n", - "for E3SM-1-0\n", - "--------------------------------------------\n", - "arctic\n", - "ca\n", - "na\n", - "np\n", - "antarctic\n", - "sp\n", - "sa\n", - "io\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[WARNING] yaksa: 10 leaked handle pool objects\n" - ] - } - ], - "source": [ - "%%bash\n", - "python ice_driver.py -p demo_param_file.py" - ] - }, - { - "cell_type": "markdown", - "id": "084440aa", - "metadata": {}, - "source": [ - "One of the primary outputs of the PMP is a JSON file containing the metrics values. In this case, the metrics are the mean square errors of the time mean and monthly mean ice extent. Ice extent is defined as the total area covered by sea ice concentration of >= 15%. The metrics are organized by model, realization, and reference dataset.\n", - "\n", - "The metrics JSON from this run is displayed below." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "9a46fb89", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"DIMENSIONS\": {\n", - " \"index\": {\n", - " \"monthly_clim\": \"Monthly climatology of extent\",\n", - " \"total_extent\": \"Sum of ice coverage where concentration > 15%\"\n", - " },\n", - " \"json_structure\": [\n", - " \"model\",\n", - " \"realization\",\n", - " \"obs\",\n", - " \"region\",\n", - " \"index\",\n", - " \"statistic\"\n", - " ],\n", - " \"model\": [\n", - " \"E3SM-1-0\"\n", - " ],\n", - " \"region\": {},\n", - " \"statistic\": {\n", - " \"mse\": \"Mean Square Error (10^12 km^4)\"\n", - " }\n", - " },\n", - " \"RESULTS\": {\n", - " \"E3SM-1-0\": {\n", - " \"antarctic\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.4635192339671928\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.139646926848\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.4635192339671928\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.139646926848\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"arctic\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"5.476181000101471\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"3.628078727168\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"5.476181000101471\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"3.628078727168\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"ca\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.05045644169895609\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.007755424768\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.05045644169895609\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.007755424768\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"io\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.04955696515353039\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.00991997952\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.04955696515353039\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.00991997952\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"na\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"2.3482121752568643\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.576847409152\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"2.3482121752568643\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.576847409152\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"np\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.6264518797177615\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.287947685888\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.6264518797177615\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.287947685888\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"sa\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.3797729615722766\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.297013608448\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.3797729615722766\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.297013608448\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"sp\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.6767107661262813\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.078223351808\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.6767107661262813\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.078223351808\"\n", - " }\n", - " }\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"json_structure\": [\n", - " \"model\",\n", - " \"realization\",\n", - " \"obs\",\n", - " \"region\",\n", - " \"index\",\n", - " \"statistic\"\n", - " ],\n", - " \"json_version\": 3.0,\n", - " \"model_year_range\": {\n", - " \"E3SM-1-0\": [\n", - " \"1981\",\n", - " \"2010\"\n", - " ]\n", - " },\n", - " \"provenance\": {\n", - " \"commandLine\": \"ice_driver.py -p demo_param_file.py\",\n", - " \"conda\": {\n", - " \"Platform\": \"linux-64\",\n", - " \"PythonVersion\": \"3.8.15.final.0\",\n", - " \"Version\": \"23.1.0\",\n", - " \"buildVersion\": \"not installed\"\n", - " },\n", - " \"date\": \"2024-01-25 11:39:13\",\n", - " \"openGL\": {\n", - " \"GLX\": {\n", - " \"client\": {},\n", - " \"server\": {}\n", - " }\n", - " },\n", - " \"osAccess\": false,\n", - " \"packages\": {\n", - " \"PMP\": \"v3.0.2-11-g06b151f\",\n", - " \"PMPObs\": \"See 'References' key below, for detailed obs provenance information.\",\n", - " \"blas\": \"0.3.24\",\n", - " \"cdat_info\": \"8.2.1\",\n", - " \"cdms\": \"3.1.5\",\n", - " \"cdp\": \"1.7.0\",\n", - " \"cdtime\": \"3.1.4\",\n", - " \"cdutil\": \"8.2.1\",\n", - " \"clapack\": null,\n", - " \"esmf\": \"0.8.2\",\n", - " \"esmpy\": \"8.4.2\",\n", - " \"genutil\": \"8.2.1\",\n", - " \"lapack\": \"3.9.0\",\n", - " \"matplotlib\": null,\n", - " \"mesalib\": null,\n", - " \"numpy\": \"1.22.4\",\n", - " \"python\": \"3.10.13\",\n", - " \"scipy\": \"1.11.3\",\n", - " \"uvcdat\": null,\n", - " \"vcs\": null,\n", - " \"vtk\": null,\n", - " \"xarray\": \"2023.10.1\",\n", - " \"xcdat\": \"0.5.0\"\n", - " },\n", - " \"platform\": {\n", - " \"Name\": \"gates.llnl.gov\",\n", - " \"OS\": \"Linux\",\n", - " \"Version\": \"3.10.0-1160.71.1.el7.x86_64\"\n", - " },\n", - " \"userId\": \"ordonez4\"\n", - " }\n", - "}\n" - ] - } - ], - "source": [ - "with open(\"sea_ice_demo/ex1/sea_ice_metrics.json\") as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "markdown", - "id": "d74b6752", - "metadata": {}, - "source": [ - "This driver also outputs a bar chart that visualizes the mean square error between the model and observations. Since there is only one model and one realization in this instance, the bar chart looks very simple. The red bar indicates the mean square error for the time mean ice extent, and the blue bar indicates the mean square error for the climatological ice extent." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "c6dfa7a6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "sea_ice_demo/ex1/MSE_bar_chart.png\r\n" - ] - } - ], - "source": [ - "!ls {\"sea_ice_demo/ex1/MSE_bar_chart.png\"}" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "d14e933a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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zzTdd2vnyyy9RWVnpsi6aMmUKiAgrV66stly9Xl/t7z81NbUhHwu7TjzKvREJgoDHHnsMf//732E2m9GpU6cajxkCwJkzZ1BeXg4vL68aXy8pKQFgX7H2798f3t7eeO2119CpUyf4+PjAZDLhgQcewOXLl13e5+PjA29vb5fndDrdVVcczvR6Pe6//37cf//9AIDCwkKMHDkSH3zwAWbPno0uXbqgtLQU7du3r/betm3b1mkZNfn6668xYcIEPPjgg3j22WfRtm1baDQafPTRR/j444+rzX/l8cPS0lKIooj3338f77//fo3LcHymV+Pn51fr6N9r9eFqz9d2LDU0NFR5vS5t17UvZ86cAQA888wzeOaZZ2p8z9U+j4cffhhbtmzBwoUL0aNHDxgMBgiCgISEhGp/c/UxZcoUrFu3Dj/88AOGDx+OtLQ0WCwWlyJb1/8b9VVaWgqNRoOgoCCX5wVBQNu2bat9B84iIiIAAEaj8arLOHHiRLUfbSqVCgMGDMCAAQMA2E85mzp1Kr744gt8/PHHePzxx2tsa8iQIS6noE2ePFkZnwDY/1YfeughZfzOb7/9hnvvvRcvvvgipk+fDn9/fwD2Ee3e3t4YMWKEMlYgNjYWUVFRWLVqFZKTk6FWq136W9f/A+zG4oLeyJKSkvDSSy9h+fLleP3112udr3Xr1ggMDMTmzZtrfN1xmltmZiZOnTqFbdu2uZyKUpdBPO4QERGBGTNm4KmnnsJvv/2GLl26IDAwEKdPn642b03PeXt71zggr6SkBK1bt1Yer1mzBu3bt8cXX3zhsoVT22A+53kAoFWrVlCr1XjkkUfwxBNP1Piemn6ENMSVfbja84GBgSguLq72/KlTpwDA5bO4Wtt17Yujveeff95lsJmzzp071/h8RUUFNm7ciEWLFrmcVmWxWFBWVlavfl1p+PDhCA0NxcqVKzF8+HCsXLkSvXr1wh133OHS97r836ivwMBAiKKIc+fOuRR1IsLp06fRo0ePWt8bEhKCLl26ICMjA5cuXYKPj0+1eX7++WecOXNGGShYmxYtWuD555/HF198gby8vFrnW7FiBaqqqpTHV/6NXKlLly6YOHEi3n33XRw+fBg9e/bE4cOHsWPHDgD//6PkSt9//z0SEhKu2jZrHFzQG1lYWBieffZZHDx4EJMnT651vvvuuw///ve/IUkSevXqVet8jhW1TqdzeX7FihXu6fDvqqqqIAgCWrZsWe01xy5Wx9bkoEGDkJKSgv3797vsdv/888+rvTcqKgq5ubkuzx0+fBiHDh1yWUEJggAvLy+XwnT69OkaR7nXxMfHB4MGDUJOTg5iY2Nr3bprLEOGDMH69etx6tQp5XMEgE8++QQ+Pj7o3bu3W5fXuXNnxMTEYP/+/Vi8eHG93isIAoio2t/cv/71L0iS5PKcY566brU7fnS9++672L59O/bs2VPtb7mu/zdq49wnvV6vPD9kyBCkpKRgzZo1+Mtf/qI8v27dOly8eBFDhgy5arsvvvgiHn74YTzzzDP48MMPXV67ePEinnzySfj4+Li0XVxcXOPeliv/T9Wkth9cpaWl8PX1rfFv/ODBgy7tOnaV//Of/0THjh1d5r18+TLGjh2Ljz/+mAt6E8UFvQm48gpQNZk4cSI+++wzJCQkYO7cuejZsye0Wi2KioqwdetWjB07FuPGjUOfPn3QqlUrzJo1C4sWLYJWq8Vnn32G/fv3u7XPhw4dwvDhwzFx4kQMHDgQISEhOH/+PDZt2oR//OMfiI+PR58+fQAATz31FD7++GOMGjUKr732GoKDg/HZZ58pKxNnjzzyCCZNmoTHH38c48ePx4kTJ5CSklJtt6fjVK/HH38ciYmJMJlMePXVVxESEoKCgoI65fDee++hX79+6N+/P2bPno2oqChUVVXhyJEj+O6772q8WMaVysvLazzWrtPpEBcXV6d+1GTRokXYuHEjBg0ahJdeegkBAQH47LPPsGnTJqSkpMDPz++6267NihUrMHLkSAwfPhxJSUkICwtDWVkZ8vPzsXfvXnz11Vc1vs9gMGDAgAF488030bp1a0RFReGnn35CamqqshvXoWvXrgCAf/zjH/D19YW3tzfat2+PwMDAWvs1ZcoUvPHGG3j44Yeh1+vxxz/+0eX1uv7fqM2dd94JAHjjjTcwcuRIqNVqxMbGYujQoRg+fDiee+45VFZWom/fvsjNzcWiRYsQFxeHRx555Kqf50MPPYS9e/firbfewvHjxzFlyhQEBwfj0KFDeOedd3D06FF8/vnn6NChg/KeLl26YMiQIRg5ciSio6NhNpuxe/duvP322wgODq5xbMa1bN26FXPnzsWf/vQn9OnTB4GBgTh79izS0tKwefNmPProo2jXrh1EUcQnn3yC22+/HdOmTauxrdGjR2PDhg3V9lqwJqJxx+TdepxHuV9NTReWsdls9NZbb1G3bt3I29ubWrZsSbfddhvNnDmTCgoKlPl27txJ99xzD/n4+FBQUBBNmzaN9u7dW+30o9ouCuEYlX0158+fp9dee40GDx5MYWFh5OXlRS1atKDu3bvTa6+9Vu1UoQMHDtDQoUPJ29ubAgICaOrUqfTtt99WG/EsyzKlpKRQhw4dyNvbm/7whz9QZmZmjaPcly5dSlFRUaTT6ej222+nf/7znzX2HQA98cQTNeZhNBppypQpFBYWRlqtloKCgqhPnz702muvXTV/oquPcg8LC6v2eTpO37qyjdpGKv/66680evRo8vPzIy8vL+rWrZvL90f0/yPOv/rqq2v215EvAHrzzTdrfH3//v00YcIEatOmDWm1Wmrbti0NHjyYli9fXm2Zzt9bUVERjR8/nlq1akW+vr40YsQIysvLq/GshXfffZfat29ParXa5W/yylHuzvr06UMA6E9/+lONr9f1/0ZNLBYLTZs2jYKCgkgQBAKgnIZ5+fJleu655ygyMpK0Wi2FhITQ7Nmz6fz581dt01l6ejolJCRQYGAgabVaCgsLo0ceeYR+++23avOuWLGCHnjgAerQoQP5+PiQl5cXRUdH06xZs8hkMrnMW9dR7iaTif76179S3759qW3btqTRaMjX15d69epF77//vnKK2TfffEMA6N133621LcfI/7fffpuI+MIyTY1ARHRTf0Ew9rtt27Zh0KBB2Lp1q0df+5wxxm4GPm2NMcYY8wBc0BljjDEPwLvcGWOMMQ/AW+iMMcaYB+CCzhhjjHkALuiMMcaYB+CCzhhjjHkALuiMMcaYB+CCzhhjjHkALuiMMcaYB2iSBb2goAB9+vRBp06d0LNnTxw4cKDaPMePH0d8fHy97kfNGGOMeaomWdBnzpyJGTNm4PDhw5g/f36NdxgyGAx47bXXarwFJ2OMMXaraXIF/ezZs9i7dy8mTZoEABg/fjyMRiOOHz/uMl9AQAD69euHFi1aNEIvGWOMsaalyd0P3WQyITQ0FBqNvWuCICAiIgKFhYWIioq67nYtFgssFovyWJZllJWVITAwEIIgNLTbjDHGmiEiQlVVFUJDQ6FSNblt3HppcgUdQLUC647LzS9ZsgTJyckNbocxxpjnMZlMaNeuXWN3o0GaXEEPDw9HUVERRFGERqMBEcFkMiEiIqJB7T7//PN4+umnlccVFRWIiIjA8ePH0apVK0iSBABQq9UusSiKEARBiVUqFVQqVa2xzWaDWq1WYo1GA0EQlBiAkpsj1mq1ICIllmUZkiQpsSzL0Gg0tcaSJIGIlLimPDgnzolz4pw8KSeLxYJffvkFvXv3VjYCryensrIytG/fHr6+vmjumlxBb9OmDeLi4rBmzRokJSVh3bp1iIqKatDudgDQ6XTQ6XTVnm/VqhUMBkOD2maMMXZzybKMbt26wd/f3y27yj3h0GuTvH3qoUOHkJSUhNLSUhgMBqxevRpdunTBtGnTMGbMGIwZMwYWiwXR0dGwWCyoqKhAmzZt8Mgjj2DJkiV1WkZlZSX8/PxQUVHBBZ0x5hEkSYLNZmvsbjRpWq0WarVaeexJtaBJFvSbwZO+RMYYu3DhAoqKitwy5qg5ICJYLBbodLp6bV0LgoB27dqhZcuWADyrFjS5Xe6MMcbqR5IkFBUVwcfHB0FBQR6x+/haHMffHcfr6/qec+fOoaioCDExMS5b6p6ACzpjjDVzNpsNRISgoCDo9frG7k6TFhQUhOPHjysD+TxJ8z7pjjHGmOJmb5n36dMHixcvdmubH330EQYMGIB+/frhwQcfxIULF2qcT5ZlVFRUQJZlAPbLgScmJl6zfU/ee8Fb6IwxxurNZDIhMjISW7ZswQsvvOCWNn/44Qf897//xdatW6FWq5GTkwOr1VrjvIIgoEWLFh5doOuLt9AZY8zDCELDp2tZu3YtJk2ahOjoaBw5cgQA8PLLL+NPf/oTRowYgQEDBuDSpUs4fvw4+vTpg/HjxyM2NhY//vhjrW2mpaXhueeeU3aFx8XFwdfXF/369VPm+eMf/4hjx47hl19+waBBgxAfH4+3337bpZ09e/Zg0KBB6N+/P956663r+ASbJy7ojDVz8fHxePfddxu1Dy1btsSvv/7aqH1gN9eWLVswbNgwPPTQQ/jqq6+U5zt37ozNmzejf//+SvEuLS3FF198gXXr1uHDDz+stc3i4mKEhoa6PKfVahEXF4c9e/agsrISZWVl6NChA/7yl79gxYoV2Lp1K/7yl7+4vOe5557D119/je3bt+O///0vzpw548bMmy4u6IxdxY4dOzBy5Ei0atUK/v7+6NatG1JSUmrdDVgfL7/8Mu6///6Gd7IOLl68CIPBgF69ejW4raioKHzzzTcuz124cAF33nlnvdsSRREvvPACoqKi0LJlS4SEhOC+++5DVVVVg/vZFH7oeKqioiLk5uZi9OjRWLJkCTZu3Ki8FhcXB8B+1c/z588DALp27QqNRuPyXE1CQ0Nx8uTJas8/+uijWLNmDdatW4fx48cDAKxWKzp37gxBEKpdWObXX3/FuHHjEB8fj2PHjsFkMjU45+aACzpjtdi4cSNGjhyJ4cOHo6CgAOXl5fjiiy9w4MABFBcX35Q+iKLolna+/PJLqNVq/PLLL8jLy7spy6yLpUuXIiMjA1u3bsWFCxewf/9+PPDAAzdt+VdzMz+H5mbt2rV47733sHnzZmRkZOC2225Tdrs7H9N2nBNf03M1eeihh5CSkqJcsnX//v0oKytDjx49kJubi3//+9+YMGECAPvVP8+ePQtBEJSBcQ7dunXDt99+i23btmHv3r24++673ZN4E8cFnbEaEBGefPJJPPfcc3jqqafQunVrAMBtt92GVatWITIyEgBw9OhRjB49GkFBQYiMjMRrr72mrFxWrVqF7t2749VXX0WbNm0QHBysbDF+8803WLx4MTZu3IiWLVsqF7lISkrC1KlTMWHCBBgMBnz00UfIyclBv379EBAQgKCgIDz00EMoLS2tVz6pqal47LHHMGDAAKSmprq8Fh8fj/nz52PYsGFo0aIF/vOf/6CyshJz5sxBREQEDAYDevToAZPJhAcffBCFhYV46KGH0LJlS8yaNQuAfYW9b98+pc20tDR069YNBoMBkZGRWLVqVY392rVrF8aOHYv27dsDsF/6ecqUKS7X1f73v/+N2NhY+Pv7o0ePHti5c6fymtVqxUsvvYTo6Gj4+vrizjvvxN69ezFv3jxs374dzz33HFq2bImRI0cCAM6cOYMJEyYgKCgIERERePHFF5XCvW3bNvj7++Ojjz5CREQE7rnnnnp9xreSdevWYeDAgcrjIUOGuOx2r4ulS5fCaDS6PHfvvfeib9++iI+PR//+/bF48WJ4eXkBAIYOHQofHx8EBAQAAN566y3cf//9GDRoULU9MUuXLsUDDzyAQYMGISEhAWaz+TqybIboFlVRUUEAqKKiorG7wpqgQ4cOEQA6cuRIrfNcunSJIiMj6W9/+xtZLBY6ceIEdenShf71r38REdHKlStJo9FQSkoKWa1W2rp1K6nVaqXNRYsW0dixY13anDx5Mun1etq8eTNJkkQXL16kffv20fbt28lqtdLp06epf//+NG3aNOU9AwcOpHfeeafWfh48eJAA0P79++njjz+mwMBAslgsLu8PCgqi3bt3kyzLdOnSJRo3bhwNHz6cTp48SZIk0d69e+ncuXNERBQZGUnr1693WQYAysnJISKiDRs2UEBAAG3ZsoUkSaIzZ87Q3r17a+zbkiVLKDg4mN555x365ZdfyGazuby+adMmCgsLo//9738kSRKtW7eOAgICqKSkhIiI/vKXv9Ddd99Nhw8fJlmW6eDBg3T8+PFaP5fBgwfTww8/TFVVVXT8+HG644476PXXXycioq1bt5JKpaKZM2fSxYsX6eLFi7V+pk3N5cuX6cCBA3T58mUiIgIaPjU1S5cupa+//lp5LMsySZJEsizXq50rPytPqgVN8Gu7OTzpS2Tut2PHDgKg/KevyZdffkndu3d3ee4f//gHDR48mIjsBT04ONjl9Y4dO9LatWuJqPaCfuVzV1q/fj117NhReXytgv7ss88q/aysrCQfHx/68ssvXd4/d+5c5fHp06cJAJ04caLG9q5V0EeMGEHJyclXzcFBkiT65z//SYMHD6YWLVqQn58fPffccySKIhERJSQk0Lvvvuvynj59+tAnn3xCsiyTj48P/fTTTzW2feXnUlRURACouLhYee6zzz6jmJgYIrIXdAB0/vz5OvW9KbmySHma5ORkGjZsmMsPPi7o1TXaLveCggL06dMHnTp1Qs+ePXHgwIEa50tNTUVMTAyio6MxY8YMl+Nab731Frp27Yru3bujd+/e+OWXX25W95mHc+xir2mAjsPx48eRl5cHf39/ZZo3bx5Onz6tzNO2bVuX97Ro0eKaA76uvFXwkSNHMHbsWISGhsJgMGDSpEkoKSmpUx6iKOKTTz7B5MmTAQC+vr4YN25ctd3uzss8ceIEdDrddd+y+MSJE4iJianTvCqVCtOmTcOWLVtQXl6Ozz//HMuXL1f6d/z4cbzwwgsun/G+fftw8uRJnDt3DpcuXarzsoqKiuDt7e3ynXTo0AFFRUXKY19fX/j7+9c9WXZTvPTSS/j++++V268C9sNilZWVt8y16+ui0Qr6zJkzMWPGDBw+fBjz58/H1KlTq81jNBqxcOFC7NixA0eOHMHp06eV/+j79+/H+++/j127dmHfvn2YM2cOnnjiiZudBvNQnTp1QlRUFP7973/XOk94eDjuvvtulJeXK1NlZSV+++23Oi2jtls+Xvn8rFmzEBYWhgMHDqCyshJr1qyp80ps48aNOHPmDF599VW0bdsWbdu2xYYNG/DDDz+gsLCwxmVGRkbCYrHUOjL4WreqjIyMVAZI1YdGo0FCQgKGDBminAIXHh6Ot99+2+UzvnjxIhYsWICgoCD4+PjUuqwr+9muXTuYzWaXU5iMRiPatWtX59xY0yEIAgwGA19Yxkmj/PWePXsWe/fuxaRJkwAA48ePh9FoxPHjx13mW7t2LcaNG4fg4GAIgoBZs2YhLS1Ned1ms+HixYsAgPLycpf/mIw1hCAIeP/997F06VK8//77yiC0w4cPY+rUqThx4gTuu+8+nDlzBh9++CHMZjMkScKhQ4ewbdu2Oi0jODgYJ06cUEb01qayshK+vr4wGAwwmUx4880365xHamoqxowZg99++w379u3Dvn37cPjwYXTs2LHWgWrBwcEYO3YsZs2aheLiYsiyjJycHOUzCA4OxtGjR2td5syZM/Hee+/hp59+gizLOHv2LHJycmqc95133sGPP/6ICxcugIjw3//+F9u2bUOfPn0AAHPmzMGbb76J//3vfyAiXLp0CT/++COKioogCAKmT5+OefPm4ciRIyAiHDp0CCdOnKixn2FhYRg0aBCeeeYZXLx4EYWFhVi8eLGy94Kx5q5RCrrJZEJoaKiy+0QQBERERLhsMQBAYWGhMpoYsJ//6pinW7duePrpp9G+fXu0a9cO77zzDt5///1al2mxWFBZWekyAVBWppIk1RiLougSO0Yw1xbbbDaX2LEl5YiJqFoMwCWWZdkldhxmqC2WJMkl5pzck9Pw4cORnp6OTZs2ITo6Gv7+/khMTERMTAxCQkLg4+OD//znP9iyZQuioqIQGBiIhx9+GKdOnXI5NOSch2M5kiThgQcegMFgQOvWrZXdvI5lO+f0t7/9DRs3boTBYMDYsWNdzl13Xs6VOZ08eRL/+c9/8OSTTyI4OBjBwcEIDAxEcHAw5syZg5UrV7r0x/l7Wr16Ndq1a4c//OEP8Pf3x6xZs5RDBQsWLMCyZcvQqlUrzJ49u9r3dP/99+Ott97CE088AT8/P/To0QP79++v8XvS6/V44YUXEBYWBn9/f0yfPh0vvvgi/vjHPwIAhg8fjsWLF2P69Olo1aoV2rdvj/feew8WiwVEhKVLlyI+Ph733nsvDAYDEhMTUVZWBiLCnDlz8OOPP8Lf3x+jRo0CAKxZswaXLl1CZGQk+vbti5EjR2L+/PmQZdnl76qx//au9/+T89/PzYxlWb5qTETVYkcbtcV1Wb5jl/v19N35u/EY13PgvaH27NlDd9xxh8tzf/jDH6oNbpkzZw6lpKQoj/Py8qh9+/ZERHT8+HHq378/nTp1ioiI3n//fRo4cGCty1y0aBEBqDZlZWUREdGvv/5Kv/76KxER7d27l/Lz84mIKDs7mwoKCoiI6L///a8ygvann36ioqIiIiLasmULnTlzhoiINm/eTKWlpUREtHHjRmWgxTfffEOXLl0iq9VK33zzDVmtVrp06RJ98803RGQfmLFx40YiIiotLaXNmzcTEdGZM2doy5YtRGQf1OP4jI4fP07//e9/iYiooKCAsrOziYgoPz9fGVHMOXFOnNOtkdPWrVvpwIEDVFFRQZWVlUREZDabqaqqiojsA8EuXLhARPazMxwj+C9dukSXLl0iIqKLFy8q8YULF5RBY1VVVWQ2m4nIPqjScYZERUUFWa1WIiIqLy9XBqydP39eGdR4/vx5kiSJJEmqFhMRiaKoxDabjcrLy4mIyGq1Kp+LxWJxa06XL1+mnJwc5WyT9PR0jxkU1ygF/cyZM2QwGJQ/AFmWKTg4mIxGo8t8KSkp9PjjjyuPN23apBTtN998k2bPnq28duHCBRIEQflDupLZbKaKigplMplMBIDKysqIyP6H5Xivc2yz2VxiSZKuGlutVpfYMQLTEcuyXC12fAaOWJIkl9jxOdUWi6LoEteUB+fEOXFOnptTVVUVHThwgC5duqS053j/jYzvueceev31111Gm9cUO49Id8SONpzj1NRU8vHxoaqqKpJlmXbv3k0AKDc3t8bl22w25bm69v3y5cv022+/KT8ASktLPaagN8ou9zZt2iAuLg5r1qwBYL9IQVRUFKKiolzmGz9+PNavX48zZ86AiLB8+XJMnDgRgH106o4dO5Rb63333Xe4/fbba72/rU6ng8FgcJkAKPOr1eoaY41G4xI7Bs3UFmu1WpfYMWDDEQuCUC0G4BKrVCqX2HFoorZYrVa7xJwT58Q53Zo5Od4DQYCgUkFQqa4//r1fSptXxEVFRcrd1lRO89cUOy7P6hw72rsyvuOOO7B582YIgoC1a9eiR48eNS6fiJSxF7X18Wqx83fjKRptSOeKFSuwYsUKdOrUCUuXLlVGr0+bNg0bNmwAYC/aycnJ6Nu3L6Kjo9GmTRtlNPy4ceMwatQo/OEPf0C3bt2wbNky5QcCY4yxG+tG3G0NAMaOHavUgAMHDuCOO+4AYC/gf/7znzFo0CAMHToUp06dgr+/Px555BHEx8ejX79+yhiru+66C7NmzUKvXr2wZMmSG/gpNC0C0a15El9lZSX8/PxQUVGhbK0zxlhzZDabYTQa0b59e3h7e9ft/qfXco3ScN9992H9+vXYsWMHdu3aheeffx4vv/wyVCoVXnrpJbz44ovo1asXYmNjMXz4cPz2228wGo3KndBqsmrVKly4cAE//fQTnn76aaSnp8NkMuGZZ57B8ePHkZ2djVdeeQX/+9//8PHHHysDJFu0aIENGzZg9+7deP3119GhQwds3boV4eHhiIuLUwZl1vRZeVIt8Jx9DYwxxm4K57utybKMixcv4vnnnwfQsLutOQwbNgyPP/44Vq5cqVyn/cCBA1i/fj2ysrJARGjXrh0qKyvx0ksvYf/+/bBYLOjSpQsAoFWrVsoZUnq93t3pN1lc0BljjNWL425r48aNAwBMnTrVLXdbcxg3bhx++eUXdO/eXXnutttuw4QJE7Bw4UIA9tP8cnNzcfbsWWzfvh0bNmxQtvxv1YvN1PsYuvN9bxljjN16btTd1hxat26Nf/zjHy7PjR49GqWlpRg0aBAGDRqE1atXIzo6GsXFxRg6dOg1j83fCup0DH3o0KEQBAFEhMOHD6Nz587IyMi4Gf27YTzpuAlj7NZW7Rj6LYCIUFVVBV9f33ptkXvyMfQ6baH37t0bjz/+OH744Qc88MADzb6YM8YYa974Wu7V1amgv/rqqxBFES+88AKsVuuN7hNjjDF2VUQEq9XKd1tzUudBcYmJiejevTvS09NvZH8YY4xdp1utuFksFuUCO3XlyZ9RvUa5d+zYEU8++eSN6gtjjLHr4Liy3Llz5xAUFHTL7IbWarWwWCx1np+IcO7cOZer7nmSep+2lp+fj9dffx3Hjh1zuUtNdna2WzvGGGOsbtRqNdq1a4eioqJqt6H2VEQESZKgVqvr9QNGEAS0a9eu1suEN2f1LugTJkzAo48+iilTpnjkB8IYY81Ry5YtERMTo9xW1dOJoohff/0Vd955Z72ux67Vaj22dtW7oGu1Wjz77LM3oi+MMcYawPmmMbeCe+65p7G70KTU+8IyI0aMwObNmxu84IKCAvTp0wedOnVCz549ceDAgRrnS01NRUxMDKKjozFjxgyX3fyFhYUYPXo0OnfujNtuuw3vv/9+g/vFGGOs6ZMkCUeOHIEkSY3dlSaj3gV9yJAhSExMhJ+fH9q0aYOgoCC0adOm3gueOXMmZsyYgcOHD2P+/PnKXdScGY1GLFy4EDt27MCRI0dw+vRp5a5sRIRx48bh0UcfxaFDh5Cfn48HH3yw3v1gjDHW/BARzp8/79Gj1uur3ndb69ixI5YuXYq77rrLZdeO40L4dXH27Fl06tQJJSUl0Gg0ICKEhIRg165dLvdEf/PNN3H8+HF88MEHAID09HSkpKRg27Zt+PHHH/Hyyy9jx44d9em+wpOuDsQYY+z6eFItqPcWemBgIBITE9GhQwdERkYqU32YTCaEhoYqAxkEQUBERIRyL1uHwsJCl7ajoqKUeQ4cOICgoCBMnDgRcXFxGDduHI4dO1brMi0WCyorK10mAMruGkmSaoxFUXSJZVm+amyz2Vxix+8lR0xE1WIALrEsyy6x4zBDbbEkSS4x58Q5cU6ck6fnZLVaceDAAaXfDcnJU9S7oI8bNw7Lly9HWVkZLl26pEz1deVpBrXtKKjtLj02mw0//vgjFi5ciJycHIwcORITJ06sdXlLliyBn5+fMoWHhwMA8vLyANhPx8vPzwcA5ObmoqCgAACQk5Oj3EAgOzsbJpMJALBz504UFxcDALKyslBSUgIAyMzMRHl5OQAgIyMDVVVVAOx7F8xmM0RRRHp6OkRRhNlsVi7UU1VVpVxSt7y8HJmZmQCAkpISZGVlAQCKi4uxc+dOAPYfRY5TBY1GI3JycgDYxybk5uZyTpwT58Q5eXROJ06cQFFRUYNz2r17NzwG1ZMgCMqkUqmUf+vjzJkzZDAYyGazERGRLMsUHBxMRqPRZb6UlBR6/PHHlcebNm2igQMHEhHRV199Rf3791deu3jxIqlUKhJFscZlms1mqqioUCaTyUQAqKysjIiIRFFU3usc22w2l1iSpKvGVqvVJZZl2SWWZbla7PgMHLEkSS6x43OqLRZF0SWuKQ/OiXPinDgnzql6TqWlpQSAKioqqLmrd0F3l4EDB9LKlSuJyF6ce/XqVW2eo0ePUkhICJ0+fZpkWabRo0fTRx99REREFy5coA4dOlBRUREREa1bt45iY2PrvPyKigqP+RIZY+xWI4oi/frrr7VuxNWVJ9WCep+Hbjabq92ez3G5wfpYsWIFkpKSsHjxYhgMBqxevRoAMG3aNIwZMwZjxoxBhw4dkJycjL59+0KWZQwePFgZDd+iRQt8+OGHGDVqFIgI/v7++Pzzz+ubDmOMMeYR6j3Kfdy4cVi/fr3yuLy8HEOGDMH//vc/t3fuRvKkkY2MMcaujyfVgnoPiuvcuTPmzp0LALhw4QISEhIwe/Zst3eMMcYYq40kScjJyeELyzipd0FfunQpzpw5gzfeeANjx47FhAkTMG3atBvRN8YYY6xWer2+sbvQpNR5l7vzqWmXL1/GyJEjMWTIECxcuBAA4OPjc2N6eIN40m4Wxhhj18eTakGdC7pKpYIgCCAi5V+lEUFodrs9POlLZIyxW40oisjJyUFcXFy97rZ2JU+qBXX+FBxX1WGu6nEbXsZYI+JLfnsWQRDQqlWret0L3dPV+Rj6xYsXlbi0tPSGdIYxxhirC7VajY4dO95St4u9ljoV9D//+c94+OGH8fzzzwOActycMcYYawyiKGLnzp0edS32hqpTQS8vL8e3336LAQMG4JVXXrnRfWKMMcauSqVSISwsDCpVvU/W8lh1+iR0Oh0AYOTIkQgJCcGmTZtuaKcYY4yxq1GpVIiMjOSC7qROg+IeffRRJZ4+fToCAwNvWIcYY4yxa3Hscu/Tp0+DRrl7kjr9tBkwYIDL47i4uAYvuKCgAH369EGnTp3Qs2dPHDhwoMb5UlNTERMTg+joaMyYMaPa8RIiwpAhQ9C6desG94kxxljzoFKpEB0dzVvoTq7rk3jzzTcbvOCZM2dixowZOHz4MObPn6/cdMWZ0WjEwoULsWPHDhw5cgSnT59GamqqyzzLli1DVFRUg/vDGGOs+eBj6NXV6ZOIjIzEsGHDMGzYMAwdOhQbN25s0ELPnj2LvXv3YtKkSQCA8ePHw2g04vjx4y7zrV27FuPGjUNwcDAEQcCsWbOQlpamvF5QUIB///vfWLBgQYP6wxhjrHkRRRGZmZk8yt1JnQr60KFDkZGRgYyMDPzwww8YNWpUgxZqMpkQGhqqHPcQBAEREREoLCx0ma+wsBCRkZHK46ioKGUeWZYxffp0fPDBB9BqtddcpsViQWVlpcsEQLnCnSRJNcaiKLrEjgvsOMfe3iJUKkdsU2K93gaVipRYEAgAQa+3ASAIgiMGVCrnWIa3t3Ns/4NVq2XodPZYo3GOJXh5Ocf2/mq1ErRae+zlJUGjccSiEut0IjQaWYnVas6Jc/LcnGRZVgpAbbEkSS6xO9YRzrHNZnOJHVfddMREVC0G4BLLsuwS34o5ERFuv/12qFSqBufkKepU0N966y2Xxx999FGDF3zl1X1quwKt83zO87z11lsYMGAAunfvXqflLVmyBH5+fsoUHh4OAMjLywMA5OfnIz8/HwCQm5uLgoICAEBOTg6MRiMAIDs7GyaTCQCwc+dOFBcXAwBSUrIQG1sCAFi2LBMxMeUAgNTUDISFVQEA0tLSERBghl4vIi0tHXq9iIAAM9LS0gEAYWFVSE3NAADExJRj2bJMAEBsbAlSUrIAAL16FSM5eScAID7ehAULsgEACQlGzJ2bAwBITCzA9Om5AIBJk/IxaZI9p+nTc5GYaM9p7twcJCTYc1qwIBvx8fackpN3olcvzolz8tycSkpKkJVlz6m4uBg7d9pzMplMyM6252Q0GpGTY8+poKAAubn2nBqyjsjKykJJiT2nzMxMlJfbc8rIyEBVlT2n9PR0mM1miKKI9PR0iKIIs9mM9HR7TlVVVcjIsOdUXl6OzMxbO6cTJ06gqKgIKpWqQTnt3r0bHoPq6cSJE7R9+3bavn07nThxor5vJyKiM2fOkMFgIJvNRkREsixTcHAwGY1Gl/lSUlLo8ccfVx5v2rSJBg4cSEREo0aNovDwcIqMjKSwsDBSqVQUGRlJZWVlNS7TbDZTRUWFMplMJgKgzC+KIomiWC222WwusSRJLjFA5O1tI5XKEVuVWK+3kkolK7EgyATIpNdbCZBJEBwxkUrlHEvk7e0c2wggUqsl0unssUbjHIvk5eUciwQQabUiabX22MtLJI3GEduUWKezkUYjKbFazTlxTp6ZExGRJEnKeqe2WBRFl7im9UJ91hFXxlar1SWWZdkllmW5WuxYTzpiSZJc4lsxp8uXL9N//vMfslqtDcqptLSUAFBFRQU1d3Uu6Pn5+XTPPfdQ27ZtqWfPntSjRw9q27Yt3XPPPXTgwIF6L3jgwIG0cuVKIiL66quvqFevXtXmOXr0KIWEhNDp06dJlmUaPXo0ffTRR9XmMxqNFBgYWK/lV1RUuOVLtF8hmieeeGrqE/MskiRRaWmpUpivl7tqQVNQ5z/zXr160dq1a6s9/9VXX1GPHj3qveCDBw9S7969KSYmhu6++27Ky8sjIqKpU6fSt99+q8z3j3/8g6Kjo6l9+/Y0depU5RecMy7oPPHE07UmxmriSQW9zrdP7dy5Mw4dOlTv15oqd90yj2/0w1jzULc1HWsubDYbMjIyMGzYsDoNjK6NJ90+tc4n8LVu3Rqffvqpy21UZVnG6tWr+cpxjDHGbiqNRoP+/fvzVeKc1PmTWL16NWbOnIm5c+ciNDQUgiCgqKgIcXFxWLVq1Q3sImOMMeZKEIRmv0XtbnUu6B07dsSWLVtw7tw5Zbh/eHg4goKCbljnGGOMsZrYbDakp6cjISGhQbvcPUm991UEBQVxEWeMMdaoNBoNhg0bxrvcnbjlIridOnVyRzOMMcZYnXExd1XnT6O2u6EBwIULF9zSGcYYY6wuHFef413u/6/OBb1r166IiopCTWe5OS7/xxhjjN0MGo0GCQkJvJXupM6fRGRkJHbs2IHQ0NBqrzmui84YY4zdLKIockF3Uudj6GPGjMGxY8dqfG3s2LFu6xBjjDF2LaIoIiMjw6PultZQdb5SnKfhK8Uxdmu5Ndd07FpuySvFMcYYY00FEaGysrLGcV23qkYr6AUFBejTpw86deqEnj171jqKPjU1FTExMYiOjsaMGTOU3Su//vorBgwYgNtuuw133nknZsyYAYvFcjNTYIwx1khEUcT27dt5l7uTRivoM2fOxIwZM3D48GHMnz8fU6dOrTaP0WjEwoULsWPHDhw5cgSnT59GamoqAMDb2xvLli3DwYMHsW/fPlRUVODtt9++2WkwxhhrBFqtFqNGjeJT1pw0SkE/e/Ys9u7di0mTJgEAxo8fD6PRiOPHj7vMt3btWowbNw7BwcEQBAGzZs1CWloaACAmJgaxsbEAALVajR49etQ6aI8xxphnkWUZZWVlLjcMu9U1SkE3mUwIDQ1VTjcQBAEREREoLCx0ma+wsBCRkZHK46ioqGrzAMDFixfxr3/9C6NHj651mRaLBZWVlS4TAEiSpPxbUyyKokvs+ONxjr29RahUjtimxHq9DSoVKbEgEACCXm8DQBAERwyoVM6xDG9v59i+S0mtlqHT2WONxjmW4OXlHNv7q9VK0GrtsZeXBI3GEYtKrNOJ0GhkJVarOSfOyXNzkmVZ2UVbWyxJkkvsjnWEc2yz2VxixzFgR0xE1WIALrEsyy7xrZiT1WpFdna20u+G5OQpGm2Xu3DF8PDaBjY4z1fTPDabDX/84x8xbNiwq54+t2TJEvj5+SmT49z5vLw8AEB+fj7y8/MBALm5uSgoKAAA5OTkwGg0AgCys7OVG9Ps3LkTxcXFAICUlCzExtovrrNsWSZiYsoBAKmpGQgLqwIApKWlIyDADL1eRFpaOvR6EQEBZqSlpQMAwsKqkJqaAQCIiSnHsmWZAIDY2BKkpGQBAHr1KkZy8k4AQHy8CQsWZAMAEhKMmDs3BwCQmFiA6dNzAQCTJuVj0iR7TtOn5yIx0Z7T3Lk5SEiw57RgQTbi4+05JSfvRK9enBPn5Lk5lZSUICvLnlNxcTF27rTnZDKZkJ1tz8loNCInx55TQUEBcnPtOTVkHZGVlaVcgCszMxPl5facMjIyUFVlzyk9PR1ms1m5ApooijCbzUhPt+dUVVWFjAx7TuXl5cjMvLVzKioqQmBgILRabYNy2r17NzwGNYIzZ86QwWAgm81GRESyLFNwcDAZjUaX+VJSUujxxx9XHm/atIkGDhyoPLZarXT//ffTtGnTSJblqy7TbDZTRUWFMplMJgJAZWVlREQkiiKJolgtttlsLrEkSS4xQOTtbSOVyhFblVivt5JKJSuxIMgEyKTXWwmQSRAcMZFK5RxL5O3tHNsIIFKrJdLp7LFG4xyL5OXlHIsEEGm1Imm19tjLSySNxhHblFins5FGIymxWs05cU6emRMRkSRJynqntlgURZe4pvVCfdYRV8ZWq9Uldqy7HLEsy9Vix3rSEUuS5BLfijlZrVY6deoUSZLUoJxKS0sJAFVUVFBz1ygFnYho4MCBtHLlSiIi+uqrr6hXr17V5jl69CiFhITQ6dOnSZZlGj16NH300UdEZP9CHnjgAZoyZco1i3lNKioq3PIl2s9u5Yknnpr6xDyLzWajLVu2KAX+ermrFjQFjXZhmUOHDiEpKQmlpaUwGAxYvXo1unTpgmnTpmHMmDEYM2YMAOCf//wn3njjDciyjMGDB+Ojjz6CVqvFZ599hkmTJiE2NlbZLd+3b1988MEHdVo+X1iGsVtL46zpWFPnSReW4SvFcUFn7JZwa67pPJcsyyguLkZISAhUqusfDuZJBZ2vFMcYY6zZkWUZR48e5dPWnPBtahhjjDU7Go0GAwYMaOxuNCm8hc4YY6zZkWUZJ06c4C10J1zQGWOMNTuyLOPkyZNc0J3wLnfGGGPNjkajQZ8+fRq7G00Kb6EzxhhrdiRJwpEjR5TLuTIu6IwxxpohIsL58+dxi555XSPe5c5YDQh8gQGPw1+pR9EA6MHF3AVvoTPGGGt2JI0GBw8e5F3uTrigM8YYa35UKly+fLmxe9Gk8C53xhhjzY7aakVcXFxjd6NJaZJb6AUFBejTpw86deqEnj174sCBAzXOl5qaipiYGERHR2PGjBkedaN6xhhjtZO0WuTl5fEudydNsqDPnDkTM2bMwOHDhzF//nxMnTq12jxGoxELFy7Ejh07cOTIEZw+fRqpqamN0FvGGGOs8TW5gn727Fns3bsXkyZNAgCMHz8eRqMRx48fd5lv7dq1GDduHIKDgyEIAmbNmoW0tLRG6DFjjLGbTW2zoWvXrlCr1Y3dlSajyR1DN5lMCA0NhUZj75ogCIiIiEBhYSGioqKU+QoLCxEZGak8joqKQmFhYa3tWiwWWCwW5XFFRQUA4Pz58wCg7LZRq9UusSiKEARBiVUqFVQqlRIDKuh0IqxWFYhU0OlssFrVIFLB29sGi0UDIgHe3jaYzfacvL3FK2ItBIGg0zliGV5eEiwWRyzDYtFApZKh0ciwWjVQq2Wo1Y5YgkpFsNkcMWCzqaHR2PMQRTW0WgmyDEiSGlqtCFkWIElqeHmJkCQVJEkFLy8RoqiCLHNOFQBEb29ozGb78ry9oTWbQYIAUaeD1myGLAiQvLygtVggCwJkLy9oLBbIKhVkjQYaqxWyWg1ZrYbGaoWkVoNUKmhsNkhqNaBSQW2zQfr9b10tipC0WkCWoZYkiFotBEfs5QWVJEHliEURKlmGqNNBZbVCRQSbTge1I/b2hsZigeCInfLgnDgnT8jJqtfjt59+QmxsrLJev3L9XZd1eVlZGQB4xPnsTa6gA/Yi7qy2D9p5vmt9GUuWLEFycnK1551/JFwvp98JLvHvf7t1iolcY0c7zrEsA1arPZYk+3S12HlIgc1Wc+xo78r4Vs/J3xOT4pw4J0/K6fJlID4e7lJVVQU/Pz+3tdcYmlxBDw8PR1FREURRhEajARHBZDIhIiLCZb6IiAiX3fAnTpyoNo+z559/Hk8//bTyWJZllJWVITAwsNoPCMYYY01bZWUlwsPDYTKZYDAYrrsdIkJVVRVCQ0Pd2LvG0eQKeps2bRAXF4c1a9YgKSkJ69atQ1RUVLUt6fHjx6Nfv3546aWX0KZNGyxfvhwTJ06stV2dTgedTufynL+//w3IgDHG2M1iMBgaVNABNPstc4cmNygOAFasWIEVK1agU6dOWLp0qTJ6fdq0adiwYQMAoEOHDkhOTkbfvn0RHR2NNm3a1DganjHGGLsVCOQJIwEYY4zdUiorK+Hn54eKiooGb6F7iia5hc4YY4xdjU6nw6JFi6odSr2V8RY6Y4wx5gF4C50xxhjzAFzQGWOMMQ/ABZ0xxhjzAFzQGWOMMQ/ABZ0xxhjzAFzQGWOMMQ/ABZ0xxhjzAFzQGWOMMQ/ABZ0xxhjzAFzQGWOMMQ/ABZ0xxhjzAE2yoA8bNgyxsbHo3r07+vfvj3379tU4X2pqKmJiYhAdHY0ZM2ZAFMWb21HGGGOsiWiSN2cpLy+Hv78/AOCbb77BK6+8gr1797rMYzQa0bdvX+Tk5KBNmzYYO3YsRo0ahZkzZ9ZpGbIs49SpU/D19YUgCO5OgTHGWDNARKiqqkJoaChUqia5jVtnmsbuQE0cxRwAKioqavyQ165di3HjxiE4OBgAMGvWLKSkpNS5oJ86dQrh4eFu6S9jjLHmzWQyoV27do3djQZpkgUdAB599FFs3boVALB58+ZqrxcWFiIyMlJ5HBUVhcLCwlrbs1gssFgsymPHjonjx4+jVatWkCQJAKBWq11iURQhCIISq1QqqFSqWmObzQa1Wq3EGo0GgiAoMQCIougSa7VaEJESy7IMSZKUWJZlaDSaWmNJkkBESlxTHpwT58Q5cU6elJPFYsEvv/yC3r17K3tZryensrIytG/fHr6+vmjumuz+hU8++QQmkwmvvfYann322Rrncd5Vfq0jB0uWLIGfn58yRUREALD/MDAYDDh58iROnjwJg8GA48eP48yZMzAYDDh69ChKS0thMBhw8OBBVFRUwGAwIC8vDxcvXoTBYMC+fftgtVphMBiwZ88eyLIMg8GAXbt2QRAEGAwG7NixA15eXvDx8cGOHTvg4+MDLy8v7NixAwaDAYIgYNeuXTAYDJBlGXv27IHBYIDVasW+fftgMBhw8eJF5OXlwWAwoKKiAgcPHoTBYEBpaSmOHj0Kg8GAM2fO4Pjx45wT58Q5cU4enVN5eTlatmwJf3//BuWUn59frZ40V03yGPqV9Ho9ioqKEBgYqDz35ptv4vjx4/jggw8AAOnp6UhJScG2bdtqbOPKLfTKykqEh4ejrKyMt9A5J86Jc+KcbtGcysrKEBgYqBT45qzJFfTKykpcuHABoaGhAID169fjz3/+M0wmk8svqGPHjqFfv34ug+ISEhIwa9asOi/Hz8/PI75Exhi71YiiiKysLAwYMED5oXA9PKkWNLlj6BUVFRg/fjwuX74MlUqFoKAgbNy4EYIgYNq0aRgzZgzGjBmDDh06IDk5GX379oUsyxg8eDCmTp3a2N1njDF2E6hUKnTt2rXZj0x3pya3hX6zeNKvMsYYY9fHk2oB/7RhjDHW7NhsNnz//few2WyN3ZUmgws6Y4yxZketVqNHjx5Qq9WN3ZUmo8kdQ2eMMcauRaVSISAgoLG70aTwFjpjTUBUVBS++eabZr2MLl26YOPGjTesfcac2Ww2bNq0iXe5O+GCzlgt4uPjoVarkZubqzxXXl4OQRBw/PjxBrX77rvvNryDAAYPHgy9Xo/z58/fsGXUpKb2f/vtN9x3333X1d7bb7+NTp06wdfXF0FBQbj33nsb9Bk7JCUl4amnnmpwO6zp0Wg06N+/f4NOWfM0XNAZu4pWrVrh+eefd0tbRKRc6MIdjh07hm3btsHHxwefffaZ29q92dasWYP3338fX3/9NaqqqlBQUIAZM2Y0iSt38R0cmy7H1eiawt9JU8EFnbGrePzxx7Fz505kZWXV+DoR4e2330Z0dDQCAgIwYsQIHDt2THk9KioKS5YsQe/eveHj44MJEyZg+/bteO6559CyZUuMHDlSmffw4cPo3bs3fH19MXDgQJhMpqv27eOPP0b37t3x5z//Gampqcrz8+bNq3UZDoWFhRg6dCiCgoLQqlUrjBo1ymWLOCkpCdOnT8fEiRPh6+uLzp07K1dhrK39K3fp//DDD+jVqxf8/f0REhKCJUuW1JjHrl27MGTIEHTt2hWA/eZMEyZMcLlXw48//oiePXvC398fXbp0wYYNG5TXZFnG3//+d9x2223w9fVFTEwMNm/ejL///e/47LPP8OGHH6Jly5bo0qULAKCqqgozZsxASEgIQkJCMGvWLFy8eBGA/d4OgiBg5cqV6NixI8LCwq76HbDGY7PZ8O233/Iud2d0i6qoqCAAVFFR0dhdYU3UwIED6Z133qHFixfTPffcQ0RE58+fJwBkNBqJiGj16tUUGhpKubm5dPnyZXr66afp9ttvJ5vNRkREkZGR1KlTJzp48CCJokgWi0Vp11lkZCR16dKFjh49SpcvX6aRI0fS5MmTa+2bKIoUFhZG7733Hh09epQEQaD//e9/1fp+5TLWr19PRERGo5HS09Pp8uXLVFFRQYmJiXTvvfcq806ePJlatmxJW7ZsIVEU6dVXX6XIyMg6t793717S6/W0du1aslqtVF5eTj///HONuaSlpVHLli3ptddeox07dtDly5ddXt+/fz/5+/vTli1bSJIk2r59OxkMBjp48CAREb333nvUvn172rNnD8myTCdOnKADBw4oecydO9elvccee4wGDRpEJSUldO7cORo4cCBNnz5d+VwA0P3330/nz5+nixcv1vodsMYlyzJdunSJZFluUDueVAt4C52xa3jqqadw4sSJGgeUffrpp3jyySdx5513wtvbG4sXL0ZRURGys7OVeWbPno3OnTtDrVbDy8ur1uXMmTMHHTp0gLe3N/70pz/hf//7X63zfv/99zh79iweeughdOjQAX379nXZSr+WqKgojBw5Et7e3jAYDHjxxReRlZUFWZaVeUaNGoXBgwdDrVbjsccew4kTJ1BaWlqn9v/xj39g4sSJGD9+PLRaLfz8/NC7d+8a5504cSJWrlyJnTt3YtSoUQgMDMT06dOVreYVK1YgKSkJgwcPhkqlQr9+/XDffffhyy+/BAB89NFHePnll3H33XdDEARERETg9ttvr3FZsizj888/x5IlSxAYGIjWrVtj8eLF+OSTT1xyX7RoEfz9/eHj41OnfFnj4OPnrrigM3YNer0eixYtwgsvvFDtGHhRURGioqKUxzqdDqGhoSgqKlKec9zZ71ratm2rxC1atEBVVVWt86ampiIhIQFBQUEAgMmTJ+Pzzz/H5cuX67Ssc+fO4eGHH0Z4eDgMBgMGDBgAq9Xqsswr+wPgqn1yduLECcTExNRpXgBITEzEpk2bcP78eXz//ffIyMjA66+/DsC+G3z58uXw9/dXpm+//RanTp2q97LOnTsHi8Xi8p116NABFosFJSUlynN1/c5Y4xFFEenp6TzOwQkXdMbqYOrUqZBlGatXr3Z5vl27di7Hnq1WK06dOoV27dopz115remGXnv63Llz+O6777Blyxa0bdsWbdu2xYIFC1BeXo6vv/66Tst4/vnncenSJezduxeVlZXKGAGq45Wgr9V+ZGQkjhw5Uqe2nAmCgH79+iExMRG//vorACA8PBxz585FeXm5Ml24cAEfffTRNZd1ZT+DgoLg5eXl8p0ZjUbodDq0bt26zvmxxqfRaJCQkMBb6U74r5axOlCr1Xj99dexePFil+cnTZqEZcuW4cCBA7BYLPjrX/+KsLAw9OzZs9a2goODcfTo0evuyyeffIKAgAAcPHgQ+/btw759+5CXl4ekpCRlt/u1llFZWQkfHx/4+/ujtLQUycnJ9erDtdqfPn060tLSsH79eoiiiIqKCuzatavGeVeuXIlvv/0W5eXlAIC8vDx8++236NOnDwBg5syZWLlyJbZu3QpJkmCxWPDzzz8r97GeOXMmkpOTsW/fPhARCgsLldeCg4NdBimqVCo8/PDDePHFF1FWVobS0lK8+OKLeOSRR7iIN0O8de6K/4IZq6Px48ejY8eOLs89+uij+POf/4z77rsPbdu2xf79+/Hdd99ddavhqaeewo8//gh/f//rOm87NTUVs2fPRlhYmLKF3rZtW8ybNw/btm3D0aNHr7mM5ORkHDlyBK1atULfvn1rHAl/Nddq/6677sK6devw+uuvIyAgALfffjt++umnGtvy9/fH22+/jQ4dOsDX1xf3338/HnroIcyfPx8AEBcXh7S0NPz1r39FUFAQwsLCsHDhQlgsFgDAk08+idmzZ2PChAnw9fXFvffei8LCQgDAtGnTcPLkSbRq1QqxsbEAgPfeew9RUVG444470KVLF3Ts2BF/+9vf6pU/a3yiKCIjI4OLuhO+25oH3GGHMcbY9fGkWsBb6IwxxpodIkJlZWWdx33cCppcQTebzbj//vvRqVMndO/eHSNGjKjxEpCZmZno1asX7rjjDnTt2hUvvvgif7GMMXaLEEUR27dv513uTprcLnez2YzMzEyMHDkSgiBg2bJl2LBhAzIyMlzmy8nJgZ+fHzp06ACz2Yx7770Xjz/+OB5++OE6Lcddu1n4qoOMNQ9Na03Hmgre5X4DeXt7IyEhQbk+b+/evV1GqTrExcWhQ4cOynu6d+9e43yMMcY8jyzLKCsrc7kg0K2uyRX0K/3973/H6NGjrzrP6dOnsXbtWiQkJNQ6j8ViQWVlpcsEQLlQiCRJNcaiKLrEjj8e59jbW4RK5YhtSqzX26BSkRILAgEg6PU2AARBcMSASuUcy/D2do7tu5TUahk6nT3WaJxjCV5ezrG9v1qtBK3WHnt5SdBoHLGoxDqdCI1GVmK1mnPinDw3J1mWlV20tcWSJLnE7lhHOMc2m80lduwkdcREVC0G4BLLsuwS34o5Wa1WZGdnK/1uSE6eokkX9MWLF6OgoEC5YlRNKisrMXr0aMyfPx933XVXrfMtWbIEfn5+yhQeHg7Afs4rAOTn5yvnrubm5qKgoACAfde+0WgEAGRnZys3zNi5cyeKi4sBACkpWYiNtV9latmyTMTElAMAUlMzEBZmv7JWWlo6AgLM0OtFpKWlQ68XERBgRlpaOgAgLKwKqan2wwoxMeVYtiwTABAbW4KUFPtFP3r1KkZy8k4AQHy8CQsW2C8vmpBgxNy5OQCAxMQCTJ9uv93npEn5mDTJntP06blITLTnNHduDhIS7DktWJCN+Hh7TsnJO9GrF+fEOXluTiUlJcpFdIqLi7Fzpz0nk8mkXK7XaDQiJ8eeU0FBgXL73IasI7KyspQr0WVmZirn3GdkZChX30tPT4fZbHa5AprZbEZ6uj2nqqoq5dBjeXk5MjNv7ZyKiooQGBgIrVbboJx2794Nj+HOC8N/9913bmvrzTffpLvvvpvOnz9f6zyVlZV0zz330CuvvHLN9sxmM1VUVCiTyWQiAFRWVkZE9ptdiKJYLbbZbC6xJEkuMUDk7W0jlcoRW5VYr7eSSiUrsSDIBMik11sJkEkQHDGRSuUcS+Tt7RzbCCBSqyXS6eyxRuMci+Tl5RyLBBBptSJptfbYy0skjcYR25RYp7ORRiMpsVrNOXFOnpkTEZEkScqNc2qLRVF0iWtaL9RnHXFlbLVaXWLHzUUcsSzL1WIicoklSXKJb8WcrFYrnTp1iiRJalBOpaWlHnNzlgYPihs6dCgEQQAR4fDhw+jcuXO1AWz19be//Q2fffYZfvzxR7Rq1arGeS5cuIDhw4dj2LBhWLRoUb2XwYPiGLu18KA4zyKKIrKysjBgwIAGXf6VB8U56d27Nx5//HH88MMPeOCBBxpczIuKijBv3jyUl5dj0KBB6N69O3r16gXAftUnx32Q33vvPWRnZ2P9+vXo3r07unfvftVd84wxxjyHRqPB4MGD+VruTtxy2tratWuxd+9eVFRU4IMPPnBHv2443kJn7NbCW+ieRZZlFBcXIyQkpEHX4ect9CskJiZiypQp6Ny5szuaY4wxxq5KlmUcPXqUT1tz0uQuLHOz8BY6Y7eWW3NNx67Fk7bQ3XrwIT8/H6+//jqOHTvmcm6f4zQDxhhjzB1kWYbJZEJ4eDjf+vZ3bi3oEyZMwKOPPoopU6ZArVa7s2nGGGNMIcsyTp48ibCwMC7ov3NrQddqtXj22Wfd2SRjjDFWjUajQZ8+fRq7G02KW3/WjBgxAps3b3Znk4wxxlg1kiThyJEjyuVcmZu30IcMGYKxY8dCrVZDp9OBiCAIAs6ePevOxTDGGLvFERHOnz+PqKioxu5Kk+HWgj5z5kysWrUKd911Fx9DZ4wxdsNoNBr06NGjsbvRpLi1oAcGBiIxMdGdTTLGGGPVSJKEgoICxMTE8Abk79x6DH3cuHFYvnw5ysrKcOnSJWVijDHG3O3y5cuN3YUmxa0XlnE+dcBxwxZBEJrkoAW+sAxjtxa+sAyriSddWMatW+iyLCuTJEnKv4wxxpg7SZKEvLw8rjFO3FrQzWZztefOnTvnzkUwxhhjrAZuLegPPfSQy+Py8nKMGDHCnYtgjDHGoFar0bVrVx4Q58StBb1z586YO3cuAODChQtISEjA7Nmz3bkIxhhjDJIkIScnh3e5O3FrQV+6dCnOnDmDN954A2PHjsWECRMwbdq0erXx5JNPIioqCoIgIC8vr8Z5tm3bBh8fH3Tv3l2ZeLQjY4zdWvR6fWN3oUlxy3nozqemffDBBxg5ciSGDBmCGTNm4NKlS/Dx8alzW4mJiZg/fz769et31fnuuOMO7Nmz57r7zBhjrPlSq9W47bbbGrsbTYpbCnrLli1dTlMjIuzZswdvvPFGvU9bGzBggDu6xFiDEPh8RI/DX6lHEb28kLNjB+Li4qDRuPUaac2WW3a5X3ma2pWnr90Ihw4dwl133YUePXrgww8/vOb8FosFlZWVLhMApX+SJNUYi6LoEsuyXC329hahUjlimxLr9TaoVKTEgkAACHq9DQBBEBwxoFI5xzK8vZ1j+73l1WoZOp091micYwleXs6xvb9arQSt1h57eUnQaByxqMQ6nQiNRlZitZpzEgQCAbDp9SAAJAiw/b5rj1QqJZZVKti8vZVYdMRqNUSdzh5rNEosaTQQvbyUWHLEWi0krdYee3lB+n3lJDrHOh1k5/j3gUCitzfk36//YHOO9XqQcywInBPn5FE5ySoV/Pz8lI3GhqzLPYVbCvrFixeVuLS01B1NXtVdd92FoqIi7N27F+vXr8fy5cvx5ZdfXvU9S5YsgZ+fnzKFh4cDgHKcPj8/H/n5+QCA3NxcFBQUAABycnJgNBoBANnZ2TCZTACAnTt3ori4GACQkpKF2NgSAMCyZZmIiSkHAKSmZiAsrAoAkJaWjoAAM/R6EWlp6dDrRQQEmJGWlg4ACAurQmpqBgAgJqYcy5ZlAgBiY0uQkpIFAOjVqxjJyTsBAPHxJixYkA0ASEgwYu7cHABAYmIBpk/PBQBMmpSPSZPsOU2fnovERHtOc+fmICHBntOCBdmIj7fnlJy8E716cU4BAWaIej3S09Ig6vUwBwQgPS0NAFAVFoaM1FQAQHlMDDKXLQMAlMTGIislBQBQ3KsXdiYnAwBM8fHIXrAAAGBMSEDO74NGCxITkTt9OgAgf9Ik5E+aBADInT4dBb9fPjln7lwYExIAANkLFsAUHw8A2JmcjOJevQAAWSkpKImNBQBkLluG8pgYAEBGaiqqwsIAAOlpaTAHBHBOnJNH5VQ4bBgqKiqgVqtRUFCA3Fz7OqK+6/Ldu3fDY1ADzZkzh8aMGUMLFiwgIqLZs2c3tEkiIoqMjKRff/21TvMuXryY5syZc9V5zGYzVVRUKJPJZCIAVFZWRkREoiiSKIrVYpvN5hJLkuQSA0Te3jZSqRyxVYn1eiupVLISC4JMgEx6vZUAmQTBEROpVM6xRN7ezrGNACK1WiKdzh5rNM6xSF5ezrFIAJFWK5JWa4+9vETSaByxTYl1OhtpNJISq9WckyDIJANk1etJBkgWBLLq9UQAySqVEksqFVm9vZXY5ojVarLpdPZYo1FiUaMhm5eXEouOWKslUau1x15eJGo0RADZnGOdjiTnWK22x97eJKlURABZnWO9nmTnWBA4J87Jo3Iyt2hBO3bsUNbRNa2/67IuLy0tJQBUUVFRt+LUhDX40q+PPPIIPv30U/znP//BL7/8gtOnT9dpF/i1REVFYePGjejatWu114qLixEcHAyVSoWqqiqMGDECU6dOxZQpU+rcPl/6lV0NH0NnrGmTNRqYjhxBeHi4y2XH64sv/epE9/vxjJEjRyIkJASbNm1qUHtPPPEE2rVrh6KiItx7773o2LEjAGDatGnYsGEDAGDdunW488470a1bN/Tu3RtDhw7FY4891rBEGGOMNRsqUURkZGSDirmnafAWelZWlsvI9K+//hoPPPBAgzt2o/EWOrsa3kJnrGkTdTrszMhAnz59GjTKnbfQnVx5mllcXFxDm2SMMcauSiWKiI6O5i10J27/JN588013N8kYY4y5UEkSwsLCuKA7afDZ+JGRkejcuTMAgIhw6NAhtwyKY4wxxmojensjKzMTAwYM4AvL/K7Bn8LQoUPxr3/9S3nMN2NhjDF2o6msVnTt2pW30J00eFBceXk5/P393dSdm4cHxbGr4UFxjDUDDStfAHhQnAvnYl5YWIgdO3Zgx44dKCwsbGjTjDHGWI1s3t74/vvvYbPZGrsrTYZbDjwcPHgQU6ZMgdFoREREBIgIJpMJ7du3R2pqKm6//XZ3LIYxxhgDAKitVvTo0QPq368tz9xU0JOSkvDss89i/PjxLs+vXbsWkydPRnZ2tjsWwxhjjAEAVLKMgICAxu5Gk+KW0QTnz5+vVswB+73NKyoq3LEIxhhjTGHT67Fp0ybe5e7ELQW9devW+PTTT5Xb0QH2W6quXr0agYGB7lgEY4wxptBYLOjfvz+fsubELZ/E6tWrMXPmTMydOxehoaEQBAFFRUWIi4vDqlWr3LEIxhhjTCHIcrMfle5ubinoHTt2xJYtW3Du3DnlHrPh4eEICgpyR/OMMcaYC5tej/Rvv0VCQgK0Wm1jd6dJcOu+iqCgIC7ijDHGbjiN2Yxhw4bxLncnN/wSO506dbrRi2CMMXarIeJifgW3fBoHDhyo9bULFy64YxGMMcaYQtTrkZ6ezrvcnbiloHft2hVRUVGo6SqyJSUl9W6voKAAkydPRklJCfz9/bFq1SrccccdLvMQEebPn4/09HSo1WoEBgbin//8Jzp27HjdeTDGGGseNJcvIyEhgbfSnbhll3tkZCR27NgBo9FYbQoODq53ezNnzsSMGTNw+PBhzJ8/H1OnTq02z4YNG5CVlYV9+/YhNzcXQ4YMwQsvvOCOdBhjjDV1ggBRFBu7F02KWwr6mDFjcOzYsRpfGzt2bL3aOnv2LPbu3YtJkyYBAMaPHw+j0Yjjx49Xm9discBsNoOIUFlZiXbt2tW774wxxpof0dsbGRkZXNSduKWgv/fee+jXr1+Nry1btqxebZlMJoSGhiq7UQRBQERERLWbvYwePRqDBg1C27ZtERISgi1btuCVV16ptV2LxYLKykqXCQAkSVL+rSkWRdEldlw8xzn29hahUjlimxLr9TaoVKTEgkAACHq9DQBBEBwxoFI5xzK8vZ1j+x+sWi1Dp7PHGo1zLMHLyzm291erlaDV2mMvLwkajSMWlVinE6HRyEqsVnNOgkAg2E+LIQAkCLDp9QAAUqmUWFapYPP2VmLREavVEHU6e6zRKLGk0UD08lJiyRFrtZB+PwYoeXlB+v1vX3SOdTrIzvHv168Wvb0h/377SJtzrNeDnGNB4Jw4J4/KSWWzYdSoUdBqtbWuv+u6LvcUTfJGssIV9ySt6dj83r17cfDgQZw8eRKnTp3CkCFDMGfOnFrbXLJkCfz8/JQpPDwcAJCXlwcAyM/PR35+PgAgNzcXBQUFAICcnBwYjUYAQHZ2tnKe/c6dO1FcXAwASEnJQmysfazAsmWZiIkpBwCkpmYgLKwKAJCWlo6AADP0ehFpaenQ60UEBJiRlpYOAAgLq0JqagYAICamHMuWZQIAYmNLkJKSBQDo1asYyck7AQDx8SYsWGC/Rn5CghFz5+YAABITCzB9ei4AYNKkfEyaZM9p+vRcJCbac5o7NwcJCfacFizIRny8Pafk5J3o1YtzCggw2wfcpKVB1OthDghAeloaAKAqLAwZqakAgPKYGGT+/oO1JDYWWSkpAIDiXr2wMzkZAGCKj0f2ggUAAGNCAnLmzgUAFCQmInf6dABA/qRJyP99j1Tu9OkoSEwEAOTMnQtjQgIAIHvBApji4wEAO5OTUdyrFwAgKyUFJbGxAIDMZctQHhMDAMhITUVVWBgAID0tDeaAAM6Jc/KsnEaNQnZ2NogIBQUFyM21ryPquy7fvXs3PAY1MWfOnCGDwUA2m42IiGRZpuDgYDIajS7zPfHEE/TGG28oj/Py8igiIqLWds1mM1VUVCiTyWQiAFRWVkZERKIokiiK1WKbzeYSS5LkEgNE3t42UqkcsVWJ9XorqVSyEguCTIBMer2VAJkEwRETqVTOsUTe3s6xjQAitVoinc4eazTOsUheXs6xSACRViuSVmuPvbxE0mgcsU2JdTobaTSSEqvVnJMgyCQDZNXrSQZIFgSy6vVEAMkqlRJLKhVZvb2V2OaI1Wqy6XT2WKNRYlGjIZuXlxKLjlirJVGrtcdeXiRqNEQA2ZxjnY4k51ittsfe3iSpVEQAWZ1jvZ5k51gQOCfOyaNyuuzrS9999x1ZrdZa1991WZeXlpYSAKqoqLjOqtV0CERuuEO8m8XHxyMpKQlJSUlYu3Yt3nrrLezatctlnr/97W/4/vvvsXHjRmi1WixduhTbt2/Hpk2b6rQMd93U/oqdCcxDEPiLZazJc0P5clctaAqa5Hj/FStWICkpCYsXL4bBYMDq1asBANOmTcOYMWMwZswYPPHEE8jPz8edd94JLy8vhISEYMWKFY3cc8YYYzeDrFKhvKwM/v7+UKma5NHjm65JbqHfDLyFzq6Gt9AZa9ps3t7I/OYbDB48uEEXluEtdMYYY6wRac1mDB8+vLG70aTwfgrGGGPNjqxS4ezZs8rpZ4wLOmOMsWZI9vJCXl4eF3QnvMudMcZYs6MxmzF48ODG7kaTwlvojDHGmh1ZrcbJkyd5C90JF3TGGGPNjqzR4OjRo1zQnfAud8YYY82OxmLBgAEDGrsbTQpvoTPGGGt2ZI0GJ06c4C10J1zQGWOMNTt8DL063uXOGGOs2dFYLOjTp09jd6NJ4S10xhhjzY6k0eDIkSPKPc4ZF3TGGGPNEKlUOH/+PG7R25HUiHe5M8YYa3Y0Vit69OjR2N1oUngLnTHGWLMjaTQ4ePAg73J3wgWdMcZY86NS4fLly43diyaFd7kzxhhrdtRWK+Li4hq7G01Kk9xCLygoQJ8+fdCpUyf07NkTBw4cqHG+1NRUxMTEIDo6GjNmzIAoije5p4wxxhqDpNUiLy+Pd7k7aZIFfebMmZgxYwYOHz6M+fPnY+rUqdXmMRqNWLhwIXbs2IEjR47g9OnTSE1NbYTeMsYYY42vyRX0s2fPYu/evZg0aRIAYPz48TAajTh+/LjLfGvXrsW4ceMQHBwMQRAwa9YspKWlNUKPGWOM3Wxqmw1du3aFWq1u7K40GU3uGLrJZEJoaCg0GnvXBEFAREQECgsLERUVpcxXWFiIyMhI5XFUVBQKCwtrbddiscBisSiPKyoqAADnz58HAGW3jVqtdolFUYQgCEqsUqmgUqmUGFBBpxNhtapApIJOZ4PVqgaRCt7eNlgsGhAJ8Pa2wWy25+TtLV4RayEIBJ3OEcvw8pJgsThiGRaLBiqVDI1GhtWqgVotQ612xBJUKoLN5ogBm00NjcaehyiqodVKkGVAktTQakXIsgBJUsPLS4QkqSBJKnh5iRBFFWSZc6oAIHp7Q2M225fn7Q2t2QwSBIg6HbRmM2RBgOTlBa3FAlkQIHt5QWOxQFapIGs00FitkNVqyGo1NFYrJLUapFJBY7NBUqsBlQpqmw3S73/ralGEpNUCsgy1JEHUaiE4Yi8vqCQJKkcsilDJMkSdDiqrFSoi2HQ6qB2xtzc0FgsER+yUB+fEOXlCTla9Hr/99BNiY2OV9fqV6++6rMvLysoAwCPOZ29yBR2wF3FntX3QzvNd68tYsmQJkpOTqz3v/CPhejn9TnCJf//brVNM5Bo72nGOZRmwWu2xJNmnq8XOQwpstppjR3tXxrd6Tv6emBTnxDl5Uk6XLwPx8XCXqqoq+Pn5ua29xtDkCnp4eDiKioogiiI0Gg2ICCaTCRERES7zRUREuOyGP3HiRLV5nD3//PN4+umnlceyLKOsrAyBgYHVfkAwxhhr2iorKxEeHg6TyQSDwXDd7RARqqqqEBoa6sbeNY4mV9DbtGmDuLg4rFmzBklJSVi3bh2ioqKqbUmPHz8e/fr1w0svvYQ2bdpg+fLlmDhxYq3t6nQ66HQ6l+f8/f1vQAaMMcZuFoPB0KCCDqDZb5k7NLlBcQCwYsUKrFixAp06dcLSpUuV0evTpk3Dhg0bAAAdOnRAcnIy+vbti+joaLRp06bG0fCMMcbYrUAgTxgJwBhj7JZSWVkJPz8/VFRUNHgL3VM0yS10xhhj7Gp0Oh0WLVpU7VDqrYy30BljjDEPwFvojDHGmAfggs4YY4x5AC7ojDHGmAfggs4YY4x5AC7ojDHGmAfggs4YY4x5AC7ojDHGmAfggs4YY4x5AC7ojDHGmAdocgX9ySefRFRUFARBQF5eXq3zpaamIiYmBtHR0ZgxYwZE5/v6MsYYY7eYJlfQExMTsWPHDkRGRtY6j9FoxMKFC7Fjxw4cOXIEp0+fVu7IxhhjjN2KmlxBHzBgANq1a3fVedauXYtx48YhODgYgiBg1qxZSEtLu0k9ZIwxxpoeTWN34HoUFha6bMFHRUWhsLDwqu+xWCywWCzKY1mWUVZWhsDAQAiCcMP6yhhjrOkiIlRVVSE0NBQqVZPbxq2XZlnQAbgU4brcMG7JkiVITk6+kV1ijDHWTJlMpmvuHW7qmmVBj4iIwPHjx5XHJ06cQERExFXf8/zzz+Ppp59WHldUVCjttGrVCpIkAQDUarVLLIoiBEFQYpVKBZVKVWtss9mgVquVWKPRQBAEJQYAURRdYq1WCyJSYlmWIUmSEsuyDI1GU2ssSRKISIlryoNz4pw4J87Jk3KyWCz45Zdf0Lt3b2UD73pyKisrQ/v27eHr64vmrlkW9PHjx6Nfv3546aWX0KZNGyxfvhwTJ0686nt0Oh10Ol2151u1agWDwXCjusoYY+wGkGUZ3bp1g7+/v1t2lXvCodcmd8DgiSeeQLt27VBUVIR7770XHTt2BABMmzYNGzZsAAB06NABycnJ6Nu3L6Kjo9GmTRtMnTq1MbvNGGPsJlKpVAgLC2v2x73dSaC6HID2QJWVlfDz80NFRQVvoTPGWDMjiiKysrIwYMAAZVf+9fCkWsA/bRhjjDU7KpUKXbt25S10J83yGDpjjLFbm0qlQps2bRq7G00K/7RhjDHW7NhsNnz//few2WyN3ZUmgws6Y4yxZketVqNHjx5Qq9WN3ZUmgws6Y01AVFQUvvnmm0btw/bt210urGE2mzFu3Dj4+/ujZ8+e1V5nrDGpVCoEBATwMXQn/EkwVov4+Hio1Wrk5uYqz5WXl0MQBJcLG11Pu++++26D+hYVFQW9Xo+WLVuidevWSEhIQEFBQYPa7N+/P4qKipTH69atw6FDh3DmzBlkZ2dXe70+iouL8fDDD6Nt27bw9fVFhw4d8Je//KVB/XUQBAH79u1zS1us+bDZbNi0aRPvcnfCBZ2xq2jVqhWef/55t7RFRMqVq9whLS0NFy5cwLFjx+Dr64vJkye7rW3AflfDTp061XhBpvp65JFH4O3tjYMHD6KiogI//PADunfv3vBOugHferl50mg06N+/f4NOWfM0XNAZu4rHH38cO3fuRFZWVo2vExHefvttREdHIyAgACNGjMCxY8eU16OiorBkyRL07t0bPj4+mDBhArZv347nnnsOLVu2xMiRI5V5Dx8+jN69e8PX1xcDBw6EyWSqUx8NBgMeeeQR/PrrrwCA+fPnIzIyEr6+vrjjjjvw1Vdfucz/v//9D4MHD0ZAQACCgoLw5z//GQCwbds2+Pv7AwDmzZuHV155BRs3bkTLli2xaNEil9cBwGq14qWXXkJ0dDR8fX1x5513Yu/evTX2cdeuXXjssceUq3pFR0e7/ACx2WxKW4GBgRgzZgxOnTqlvH769GlMmjQJoaGh8Pf3x4ABA3D58mX07NkTANCnTx+0bNkSixcvBgDs2bMHffv2hb+/P+644w6XuzG+/PLLuO+++zB79mwEBATgueeeq9PnzJoWQRBgMBg84gpvbkO3qIqKCgJAFRUVjd0V1kQNHDiQ3nnnHVq8eDHdc889RER0/vx5AkBGo5GIiFavXk2hoaGUm5tLly9fpqeffppuv/12stlsREQUGRlJnTp1ooMHD5IoimSxWJR2nUVGRlKXLl3o6NGjdPnyZRo5ciRNnjy51r5FRkbS+vXrlT49+OCDNGDAACIiWrNmDZ05c4ZEUaS0tDTS6XR07NgxIiIqKioig8FAH3zwAV2+fJkuXrxIWVlZRES0detW8vPzU5axaNEiGjt2rPL4ytf/8pe/0N13302HDx8mWZbp4MGDdPz48Rr7O3z4cLrrrrto9erVdOjQoWqvP/vsszR48GA6deoUWSwWmjdvHvXv35+IiCRJoh49etDkyZOprKyMbDYbbd++ncxmMxERAaCcnBylrfPnz1NgYCD9/e9/J6vVStu2baMWLVrQjh07lLzUajWtXLmSbDYbXbx4sdbPmTVdVquVvvnmG7JarQ1qx5NqARd0D/gS2Y3hKLyXLl2i0NBQWr9+fbWCfu+999LSpUuV95jNZvL19aX//ve/RGQvvFcW79oK+kcffaQ8XrNmDXXt2rXWvkVGRpKPjw/5+/tTaGgojR8/vtZi2q1bN1qzZg0RES1dupQGDRpU43z1KeiyLJOPjw/99NNPtfbRWUVFBS1atIji4uJIo9FQREQEffbZZ0pbLVq0oH379inzX758mVQqFRUWFtKuXbuoRYsWdOnSpRrbvrKgr1mzhm677TaXeaZPn07Tp09X8urWrVud+s2aLlmW6dKlSyTLcoPa8aRawLvcGbsGvV6PRYsW4YUXXqh2DLyoqAhRUVHKY51Oh9DQUJfBY9e6E6BD27ZtlbhFixaoqqq66vyfffYZzp8/j5MnT2Lt2rWIjIwEALzzzjvo0qUL/Pz84O/vj7y8PJSUlACw35kwJiamTv25mnPnzuHSpUt1bstgMODll1/G3r17cf78eTz55JN49NFHkZ+fj5KSEly8eBEDBgyAv78//P390bZtW3h5ecFkMuHEiRMICwuDXq+v07Ku/E4A+/0fruc7YU0bHz93xQWdsTqYOnUqZFnG6tWrXZ5v166dy4h3q9WKU6dOuZzedeVpNTfyNJsdO3bg5ZdfxieffILz58+jvLwcXbt2Bf1+y4bIyEgcOXKkwcsJCgqCj4/PdbXVsmVLzJs3D35+fjhw4AACAwPh4+OD3bt3o7y8XJkuX76MPn36IDIyEidPnsTly5drbO/KY6hXfieAfYDf1b4T1vyIooj09HQe1OiE/6oZqwO1Wo3XX39dGXTlMGnSJCxbtgwHDhyAxWLBX//6V4SFhSmDtWoSHByMo0eP3pB+VlZWQqPRICgoCLIs4+OPP0ZeXp7y+p/+9CdkZ2dj+fLlsFgsuHTpErZv317v5QiCgOnTp2PevHk4cuQIiAiHDh3CiRMnapz/2Wefxb59+2C1WmG1WvGvf/0LFy9exN133w2VSoVZs2Zh3rx5ykDA0tJSfPHFFwCAHj16oHPnznjiiSdQXl4OURSxY8cOWCwWANU/z4SEBJw9exYffvghRFHE9u3b8fnnn+PRRx+td56s6dJoNEhISOCtdCdc0Bmro/Hjxyu383V49NFH8ec//xn33Xcf2rZti/379+O777676krmqaeewo8//gh/f3/cd999bu3jiBEjMH78eNx5550IDQ3Fb7/9hr59+yqvt2vXDj/++CM+//xzBAcHIyoqCmvXrr2uZb3xxhsYMmQI7r33XhgMBjz44IMoKyurcV6LxYKJEyciMDAQbdu2xcqVK/Htt98qu8aXLFmCe+65B4MHD4avry/uvvtuZGRkALBvTX/33Xe4dOkSOnfujNatW+Ovf/0rZFkGALz66qt48skn0apVKyxduhStWrXCf/7zH6xZswaBgYGYMWMGPvroI/Tr1++68mRNF2+du+Lbp3rALfMYY+xWY7PZkJ6ejoSEBGi12utux5NqQZPcQi8oKECfPn3QqVMn9OzZEwcOHKg2DxHh2WefRZcuXRAbG4tBgwa55dggY4yxpk+r1WLs2LENKuaepkkW9JkzZ2LGjBk4fPgw5s+fj6lTp1abZ8OGDcjKysK+ffuQm5uLIUOG4IUXXmiE3jLGGLvZiAiVlZW4RXcy16jJFfSzZ89i7969mDRpEgD7cUuj0VjjtbMtFgvMZrPyxfKNIxhj7NbgGPDIx9H/X5Mr6CaTCaGhocqgIkEQEBERgcLCQpf5Ro8ejUGDBqFt27YICQnBli1b8Morr9TarsViQWVlpcsEQDmvWJKkGmNRFF1ix0Cc2mKbzeYSO349OmIiqhYDcIllWXaJHX+wtcWSJLnEnBPnxDlxTp6ek0qlwvDhw6HVahuck6docgUdqH5eaU27VPbu3YuDBw/i5MmTOHXqFIYMGYI5c+bU2uaSJUvg5+enTOHh4QCgnNKTn5+P/Px8AEBubq5y56qcnBwYjUYAQHZ2tnJazc6dO1FcXAwAyMrKUi7ckZmZifLycgBARkaGcnGQ9PR0mM1ml3MnzWYz0tPTAQBVVVXKqN7y8nJkZmYCAEpKSpTriBcXF2Pnzp0A7D98srOzAdjPsc3JyQFgH3/guDsY58Q5cU6ck6fmdOzYMezevRuyLDcop927d8NTNLlR7mfPnkVMTAxKS0uh0WhARAgJCcGuXbtcrv40Z84cREREYP78+QCA3377DQkJCbWeB2uxWJTzVgH7yMbw8HCUlZWhVatWyi83tVrtEouiCEEQlFilUkGlUtUa22w2qNVqJdZoNBAEQYkB+y9C51ir1YKIlFiWZUiSpMSyLEOj0dQaS5IEIlLimvLgnDgnzolz8qSczGYztm3bhiFDhigXCrqenMrKyhAYGOgRo9zdWtA3btzolvNq4+PjkZSUhKSkJKxduxZvvfUWdu3a5TLP3/72N3z//ffYuHEjtFotli5diu3bt2PTpk11WoYnnarAGGPs+nhSLWhwQR86dCgEQQAR4fDhw+jcubOyC+V6HTp0CElJSSgtLYXBYMDq1avRpUsXTJs2DWPGjMGYMWNgsVgwZ84cbN++HV5eXggJCcGKFSuqXcO5Np70JTLG2K1GlmWUlJSgdevWDbqUryfVggYX9IULF+Luu+/G/fffj7/85S9455133NW3G8qTvkTGGLvViKKIrKwsDBgwoEGXf/WkWtDgQXGvvvoqRFHECy+8AKvV6o4+McYYY1el0WgwePBgvpa7E7eMck9MTMSUKVPQuXNndzTHGGOMXZUsyzh58qRy+hlz42lrHTt2xJNPPumu5hhjjLFaybKMo0ePckF34tZ9Ffn5+Xj99ddx7Ngxl5P1HecNMsYYY+6g0WgwYMCAxu5Gk+LWgj5hwgQ8+uijmDJlCtRqtTubZowxxhSyLMNkMiE8PLxBo9w9iVsLularxbPPPuvOJhljjLFqHMfQw8LCuKD/zq2fwogRI7B582Z3NskYY4xVo9Fo0KdPHx7l7sStn8SQIUMwduxYqNVq6HQ6EBEEQcDZs2fduRjGGGO3OEmSYDQa0b59ez7E+zu3FvSZM2di1apVuOuuu26ZD/iK+8gwxpqopnXXCtZQRITz58/X+eqgtwK3FvTAwEAkJia6s0nGGGOsGo1Ggx49ejR2N5oUtx5DHzduHJYvX46ysjJcunRJmRhjjDF3kiQJBw8eVO6oxtx8tzXnkYaOG7YIgtAkP3B3Xb+Xd7kz1jzwLnfPIkkScnNzERsb26BDvJ50LXe37nLnK/Ywxhi7GdRqNeLi4hq7G02KW3e5m83mas+dO3fOnYtgjDHGIEkS8vLymuQe4Mbi1oL+0EMPuTwuLy/HiBEj3LkIxhhjjNXArQW9c+fOmDt3LgDgwoULSEhIwOzZs925CMYYYwxqtRpdu3a9ZU6Rrgu3FvSlS5fizJkzeOONNzB27FhMmDAB06ZNq3c7BQUF6NOnDzp16oSePXviwIED1ebZtm0bfHx80L17d2W6fPmyO9JgjDHWxEmShJycHN7l7sQtg+KcT0374IMPMHLkSAwZMgQzZszApUuX4OPjU6/2Zs6ciRkzZiApKQlr167F1KlT8fPPP1eb74477sCePXsa3H/GGGPNj16vb+wuNCluOW1NpVK5nKbm3GR9T1s7e/YsOnXqhJKSEmg0GhARQkJCsGvXLpcrAm3btg3PPPPMdRd0Pm2NsVsLn7bGauJJp625ZZe7LMuQJMnlX8dU390hJpMJoaGhygX3BUFAREQECgsLq8176NAh3HXXXejRowc+/PDDq7ZrsVhQWVnpMgFQ+idJUo2xKIousePUPOfY21uESuWIbUqs19ugUpESCwIBIOj1NgAEQXDEgErlHMvw9naO7feWV6tl6HT2WKNxjiV4eTnH9v5qtRK0Wnvs5SVBo3HEohLrdCI0GlmJ1WrOiXPy3JxkWYYoileNJUlyid2xjnCObTabS+zYAHLERFQtBuASy7LsEt+KOVksFuzevVvpa0Ny8hRuKegXL15U4tLS0ga3J1yx2VvTToS77roLRUVF2Lt3L9avX4/ly5fjyy+/rLXNJUuWwM/PT5nCw8MBAHl5eQCA/Px85OfnAwByc3NRUFAAAMjJyYHRaAQAZGdnw2QyAQB27tyJ4uJiAEBKShZiY0sAAMuWZSImphwAkJqagbCwKgBAWlo6AgLM0OtFpKWlQ68XERBgRlpaOgAgLKwKqakZAICYmHIsW5YJAIiNLUFKShYAoFevYiQn7wQAxMebsGBBNgAgIcGIuXNzAACJiQWYPj0XADBpUj4mTbLnNH16LhIT7TnNnZuDhAR7TgsWZCM+3p5TcvJO9OrFOXFOnptTSUkJsrLsORUXF2PnTntOJpMJ2dn2nIxGI3Jy7DkVFBQgN9eeU0PWEVlZWSgpseeUmZmJ8nJ7ThkZGaiqsueUnp4Os9kMURSRnp4OURRhNpuRnm7PqaqqChkZ9pzKy8uRmXlr51RYWIhLly5BEIQG5bR79254DGqgOXPm0JgxY2jBggVERDR79uwGtXfmzBkyGAxks9mIiEiWZQoODiaj0XjV9y1evJjmzJlT6+tms5kqKiqUyWQyEQAqKysjIiJRFEkUxWqxzWZziSVJcokBIm9vG6lUjtiqxHq9lVQqWYkFQSZAJr3eSoBMguCIiVQq51gib2/n2EYAkVotkU5njzUa51gkLy/nWCSASKsVSau1x15eImk0jtimxDqdjTQaSYnVas6Jc/LMnIiIJElS1i21xaIousQ1rRfqs464MrZarS6xLMsusSzL1WLHutARS5LkEnNO159TaWkpAaCKigpq7hpc0CdNmkREROnp6ZScnNzggk5ENHDgQFq5ciUREX311VfUq1evavOcOnVK+UIqKyupT58+lJqaWudlVFRUuOVLtB+Z44knnpr6xDyLzWaj//73v0pRv17uqgVNQYN3uet0OgDAyJEjERISgk2bNjW0SaxYsQIrVqxAp06dsHTpUqSmpgIApk2bhg0bNgAA1q1bhzvvvBPdunVD7969MXToUDz22GMNXjZjjLGmT6VSISwszOUeIre6Bo9yz8rKwoABA5THX3/9NR544IEGd+xG41HujN1aGramY56KR7k7cS7mAPhi+Ywxxm44URSRlZXlUaPUG8rt+yrefPNNdzfJGGOMuVCpVIiOjuZd7k4afKW4yMhIdO7cGQBARDh06NA1zwlnjDHGGsJxDJ39vwYX9KFDh+Jf//qX8phvxsIYY+xGc+xyHzBggHIhsltdgwfFlZeXw9/f303duXl4UBxjtxYeFOdZZFlGSUkJWrdu3aDd7p40KK7BP2uci3lhYaFyidaIiAhEREQ0tHnGGGOsGpVKhTZt2jR2N5oUt+ynOHjwIKZMmQKj0YiIiAgQEUwmE9q3b4/U1FTcfvvt7lgMY4wxBsB+nfjMzEwMHjwYWq22sbvTJLiloCclJeHZZ5/F+PHjXZ5fu3YtJk+erFx7lzHGGHMHtVqNHj16QK1WN3ZXmgy3jPc/f/58tWIOAImJiaioqHDHIhhjjDGFSqVCQEAAn7bmxC2fROvWrfHpp58qt6MD7AMWVq9ejcDAQHcsgjHGGFPYbDZs2rRJueUqc9Mu99WrV2PmzJmYO3cuQkNDIQgCioqKEBcXh1WrVrljEYwxxphCo9Ggf//+fMqaE7d8Eh07dsSWLVtw7tw55R6z4eHhCAoKckfzjDHGmAtBEJr9aWbu5tafNkFBQVzEGWOM3XA2mw3p6elISEjgUe6/u+GjCTp16nSjF8EYY+wWo9FoMGzYMN7l7sQtn8SBAwdqfe3ChQvuWARjjDHmgou5K7d8Gl27dkVUVBRquopsSUmJOxbBGGOMKURR5F3uV3BLQY+MjMSOHTsQGhpa7bXw8PB6t1dQUIDJkyejpKQE/v7+WLVqFe644w6XeTIzM/H888+jqqoKKpUKY8eOxWuvvQaBL67O3IDAf0ceh79Sj6IBkGC18la6E7ccQx8zZgyOHTtW42tjx46td3szZ87EjBkzcPjwYcyfPx9Tp06tNk+rVq2QlpaGAwcOYM+ePfjpp5+QlpZW72UxxhhrhgQBoig2di+alAbfbc3dzp49i06dOqGkpAQajQZEhJCQEOzatQtRUVG1vm/OnDlo27Yt/vrXv9ZpOXy3NXY1vIXOWNNm0+uRnpbW4F3unnS3tSZ3zTyTyYTQ0FBlN4ogCIiIiFDu4laT06dPY+3atUhISKh1HovFgsrKSpcJACRJUv6tKRZF0SV2XA3POfb2FqFSOWKbEuv1NqhUpMSCQAAIer0NAEEQHDGgUjnHMry9nWP7r1C1WoZOZ481GudYgpeXc2zvr1YrQau1x15eEjQaRywqsU4nQqORlVit5pwEgUCwrzAIAAkCbHo9AIBUKiWWVSrYvL2VWHTEajVEnc4eazRKLGk0EL28lFhyxFotpN9XSJKXF6Tf//ZF51ing+wc/379atHbG/Lvl760Ocd6Pcg5FgTOiXPyqJxUNhtGjRoFrVZb6/q7rutyT9HkCjqAasfBr7YTobKyEqNHj8b8+fNx11131TrfkiVL4Ofnp0yOY/t5eXkAgPz8fOTn5wMAcnNzUVBQAADIycmB0WgEAGRnZysXztm5cyeKi4sBACkpWYiNtQ/+W7YsEzEx5QCA1NQMhIVVAQDS0tIREGCGXi8iLS0der2IgAAz0tLSAQBhYVVITc0AAMTElGPZskwAQGxsCVJSsgAAvXoVIzl5JwAgPt6EBQvsN71JSDBi7twcAEBiYgGmT88FAEyalI9Jk+w5TZ+ei8REe05z5+YgIcGe04IF2YiPt+eUnLwTvXpxTgEBZoi///oX9XqYAwKQ/vvhnKqwMGSkpgIAymNikLlsGQCgJDYWWSkpAIDiXr2wMzkZAGCKj0f2ggUAAGNCAnLmzgUAFCQmInf6dABA/qRJyJ80CQCQO306ChITAQA5c+fC+PuP1OwFC2CKjwcA7ExORnGvXgCArJQUlMTGAgAyly1DeUwMACAjNRVVYWEAgPS0NJgDAjgnzsmzcho1CtnZ2SAiFBQUIDfXvo6o77p89+7d8BjUxJw5c4YMBgPZbDYiIpJlmYKDg8loNFabt7Kyku655x565ZVXrtmu2WymiooKZTKZTASAysrKiIhIFEUSRbFabLPZXGJJklxigMjb20YqlSO2KrFebyWVSlZiQZAJkEmvtxIgkyA4YiKVyjmWyNvbObYRQKRWS6TT2WONxjkWycvLORYJINJqRdJq7bGXl0gajSO2KbFOZyONRlJitZpzEgSZZICsej3JAMmCQFa9ngggWaVSYkmlIqu3txLbHLFaTTadzh5rNEosajRk8/JSYtERa7UkarX22MuLRI2GCCCbc6zTkeQcq9X22NubJJWKCCCrc6zXk+wcCwLnxDl5VE6XfX3pu+/+r71/j4+ivPvH/9fMzmYTDklIJJiEHAQSUBCIFbDhIAdFDRXkJlpUrKlgoJa7tH5u+aIVMVYFEevdW6zkvpsK9ZDWYlGUqLGihhg5WGIRCRJgQzYYCCHkBOxhZq7fH+vOb9ckGMjCbjav5+OxD9+7O1xzvZO4771mrrnmHeF0Ojv8/O7MZ/nJkycFANHU1HT+BSvIBN05dACYPHkycnJykJOTg40bN2LNmjXYvn27zzatra246aabMH36dKxYseK898Fz6HQuPIdO1A34oXzxHPpFlp+fj/z8fKSnp2PVqlUo+O6QzIIFC7B582YAwB/+8Afs3LkTmzZtwujRozF69Gg89dRTgew2ERFdIroso6Ghwecunz1dUI7QLwWO0OlcOEInCm6u8HBsfestTJ06lbPcv8Mr8omIqNsx2+246aabAt2NoBKUh9yJiIjORZdl1NXV8ZC7FxZ0IiLqdvSwMOzdu5cF3QsPuRMRUbej2O2YOnVqoLsRVDhCJyKibkc3mXD06FGO0L2woBMRUbejKwoOHTrEgu6Fh9yJiKjbURwOTJo0KdDdCCocoRMRUbejKwqOHDnCEboXFnQiIup2eA69LR5yJyKibkdxOJCZmRnobgQVjtCJiKjb0RQFBw8eNO5xTizoRETUDQlZxqlTp9BDb0fSLh5yJyKibkdxOjFmzJhAdyOocIRORETdjqYo2L9/Pw+5e2FBJyKi7keWcfbs2UD3IqjwkDsREXU7JqcTGRkZge5GUAnKEXplZSUyMzORnp6OsWPHYt++fe1uV1BQgLS0NAwePBi5ublQVfUS95SIiAJBM5uxd+9eHnL3EpQFfeHChcjNzcWBAwewdOlSzJ8/v802VqsVy5cvR2lpKQ4ePIhjx46hoKAgAL0lIiIKvKAr6HV1ddi9ezfmzZsHAJgzZw6sViuqqqp8ttu4cSNmz56NAQMGQJIkLFq0CIWFhQHoMRERXWomlwsjRoyAyWQKdFeCRtCdQ7fZbEhISICiuLsmSRKSk5NRXV2N1NRUY7vq6mqkpKQYz1NTU1FdXd1huw6HAw6Hw3je1NQEADh16hQAGIdtTCaTT6yqKiRJMmJZliHLshEDMiwWFU6nDCFkWCwuOJ0mCCEjPNwFh0OBEBLCw12w2905hYer34vNkCQBi8UT6wgL0+BweGIdDocCWdahKDqcTgUmkw6TyRNrkGUBl8sTAy6XCYrizkNVTTCbNeg6oGkmmM0qdF2CppkQFqZC02RomoywMBWqKkPXmVMTADU8HIrd7t5feDjMdjuEJEG1WGC226FLErSwMJgdDuiSBD0sDIrDAV2WoSsKFKcTuskE3WSC4nRCM5kgZBmKywXNZAJkGSaXC9p3f+smVYVmNgO6DpOmQTWbIXnisDDImgbZE6sqZF2HarFAdjohCwGXxQKTJw4Ph+JwQPLEXnkwJ+YUCjk5IyLw9aefYuTIkcbn+vc/vzvzWd7Q0AAAIXE9e9AVdMBdxL119IP23u6HfhkrV65EXl5em9e9vyRcKK/vCT7xd3+7nYqF8I097XjHug44ne5Y09yPc8XeUwpcrvZjT3vfj3t6TtGhmBRzYk6hlNPZs8DkyfCXlpYWREVF+a29QAi6gp6UlISamhqoqgpFUSCEgM1mQ3Jyss92ycnJPofhjxw50mYbbw8//DAefPBB47mu62hoaEBsbGybLxBERBTcmpubkZSUBJvNhsjIyAtuRwiBlpYWJCQk+LF3gRF0BT0uLg4ZGRl49dVXkZOTgzfffBOpqaltRtJz5szBhAkT8NhjjyEuLg7r1q3D3LlzO2zXYrHAYrH4vBYdHX0RMiAiokslMjKySwUdQLcfmXsE3aQ4AMjPz0d+fj7S09OxatUqY/b6ggULsHnzZgDAoEGDkJeXh/Hjx2Pw4MGIi4trdzY8ERFRTyCJUJgJQEREPUpzczOioqLQ1NTU5RF6qAjKEToREdG5WCwWrFixos2p1J6MI3QiIqIQwBE6ERFRCGBBJyIiCgEs6ERERCGABZ2IiCgEsKATERGFABZ0IiKiEMCCTkREFAJY0ImIiEJAUBb0yspKZGZmIj09HWPHjsW+ffvabFNVVYXJkycjKioK1157bQB6SUREFDyCsqAvXLgQubm5OHDgAJYuXdruTVciIyPx5JNP4vXXXw9AD4mIiIJL0BX0uro67N69G/PmzQPgvk2q1Wr1ufc5AMTExGDChAno3bt3AHpJREQUXILufug2mw0JCQlQFHfXJElCcnIyqqur29wT/Xw4HA44HA7jua7raGhoQGxsLCRJ6mq3iYioGxJCoKWlBQkJCZDloBvjnpegK+gA2hRYf9w/ZuXKlcjLy+tyO0REFHpsNhsGDhwY6G50SdAV9KSkJNTU1EBVVSiKAiEEbDYbkpOTu9Tuww8/jAcffNB43tTUhOTkZFRVVaFfv37QNA0AYDKZfGJVVSFJkhHLsgxZljuMXS4XTCaTESuKAkmSjBiAkZsnNpvNEEIYsa7r0DTNiHVdh6IoHcaapkEIYcTt5cGcmBNzYk6hlJPD4cCuXbtw3XXXGYPAC8mpoaEBV1xxBfr27YvuLugKelxcHDIyMvDqq68iJycHb775JlJTU7t0uB1w3zu3vfvm9uvXD5GRkV1qm4iILi1d1zFq1ChER0f75VB5KJx6Dcr7oX/zzTfIycnByZMnERkZiQ0bNmD48OFYsGABZs6ciZkzZ8LhcGDw4MFwOBxoampCXFwc7rnnHqxcubJT+2hubkZUVBSamppY0ImIeqhQqgVBWdAvhVD6JRIR9TSqqqKkpASTJk0yDuVfiFCqBd17Sh8REfVIsixjxIgR3X5muj8F3Tl0IiKiHyLLMuLi4gLdjaDCrzZERNTtuFwufPDBB3C5XIHuStBgQSciom7HZDJhzJgxMJlMge5K0OAhdyIi6nZkWUZMTEyguxFUOEInukhGjx6N9evXAwBee+01ZGZmBrZDRCHE5XJhy5YtPOTuhQWdqAOTJ0/Gf//3f/ulrbvvvhtlZWV+aas9LpcLeXl5GDx4MCIiIpCUlITf/OY3aG1tvWj77IodO3ZgypQp6NevH6KjozFy5Ejjy09XfPLJJ4iOju5yOxT8FEXBxIkTu3TJWqhhQScKAXfddRc2bdqEN954A62trfjoo4/w73//G9OnTw+6EUxLSwtuvvlm/PSnP0VdXR1OnDiBgoKCoJmxrKpqoLtAnSBJEiIjI0NihTd/YUEn6gTPyO9Pf/oTkpKSEBsbi6VLl/pss3btWuO93/72tz7vrV+/HqNHjzae//73v0daWhr69u2LwYMHY+3atcZ7VVVVkCQJr7zyCoYMGYLo6Gjk5OR0WJg/+eQTbN68GZs2bcKPfvQjmEwmpKenY9OmTThw4ABee+01Y9sPP/wQ48aNQ3R0NOLj431WVvznP/+JsWPHIjo6GsOHD8fmzZuN94qLi3HttdciKioK8fHxeOCBB3D27Fnj/dTUVKxevRrXXXcd+vbti+uvvx42m63d/n7zzTc4ffo0cnNzYTabYTabMWbMGGRlZRnb1NXV4e6770ZCQgISEhLw61//2uduif/6178wdepUxMTEoH///vjP//xPnDx5ErfccguamprQp08f9OnTB9u2bQMAvPrqq7jyyisRHR2NCRMmoLy83Ghr8uTJWLp0KaZPn47evXvjvffea7ffFFxcLhfefvvtoPvCGlCih2pqahIARFNTU6C7QkHq+uuvF88//7wQQoiPP/5YyLIsfvWrX4mzZ8+Kffv2iV69eomPP/5YCCHERx99JCIjI0VZWZlwOBzikUceESaTSbz88stCCCFefvllMWrUKKPtjRs3iurqaqHruti6dasIDw8XpaWlQgghrFarACB++tOfiqamJnH06FGRmJhotPV9y5YtExMnTmz3vXnz5ok777xTCCHE7t27RUREhNi4caNwOp2isbFRfP7550IIIf7973+L6Oho8dFHHwlN08S2bdtEZGSk2L9/vxBCiJKSErF7926hqqo4dOiQGDZsmHjyySeN/aSkpIjhw4eLQ4cOibNnz4pbbrlF3Hvvve32qbm5WfTv31/cfvvt4q233hK1tbU+7+u6LsaNGycefPBBcfr0aVFfXy8mT54sHn30USGEEDU1NSIyMlK8+OKL4uzZs+L06dOipKTE+D1FRUX5tFdSUiL69OkjPv30U+F0OsXzzz8v+vfvLxobG4UQ7t9z//79xY4dO4Su6+LMmTPt9puCi+d3pet6l9oJpVrAETpRJwkhsHLlSoSHh+PKK69EZmYm/vWvfwFwT3q7++678eMf/xhhYWF4/PHH0bt37w7bmjNnDpKSkiBJEqZMmYKbbroJn3zyic82jz/+OCIjI5GQkIBbbrnF2Nf31dfXIyEhod33EhIScOLECQDA//7v/2Lu3LmYM2cOzGYzoqKicN111wEA8vPzkZOTg6lTp0KWZUyYMAE/+clP8MYbbwAAJk6ciIyMDJhMJgwaNAgLFy5s09/Fixdj0KBBCA8Px913391hf/v27YuysjLExMTgwQcfREJCAsaNG4fdu3cDAL744gtUVlbi2WefRa9evRAbG4tHHnkEr7/+OgD3aPtHP/oRHnjgAYSHh6NXr16YOHFihz/rv/zlL5g3bx4mTZoEs9mMX//61+jXrx+2bNlibHPXXXdh7NixkCQJERERHbZFwYXnz30FrKBXVlYiMzMT6enpGDt2LPbt29fudgUFBUhLS8PgwYORm5vrc35rzZo1GDFiBEaPHo3rrrsOu3btulTdpx4oMjISvXr1Mp737t0bLS0tAIBvv/0WKSkpxntmsxnx8fEdtvXaa6/hmmuuMSaFFRUVob6+3mebyy+/vN19fd9ll12Gb7/9tt33vv32W/Tv3x8AcOTIEaSlpbW7XVVVFdatW4fo6Gjj8fbbbxvt7tq1CzfccAMGDBiAyMhIPPLIIxfcXwAYMmQI1q1bh0OHDqGmpgZDhgzBzJkzIYRAVVUVGhsbERMTY/QlOzsbx48f/8E82lNTU9Pmbo1XXHEFampqjOddvT0zXXqqqqKoqIhzHrwErKAvXLgQubm5OHDgAJYuXYr58+e32cZqtWL58uUoLS3FwYMHcezYMRQUFAAA/v3vf+OFF17A9u3b8eWXX2Lx4sX45S9/eanTIALgHgkfOXLEeO5yuVBbW9vuttXV1bj33nuxevVqnDhxAo2NjcjKyoK4wPsk3XjjjdixYwesVqvP683NzXjvvfdw4403AgBSUlJw8ODBdttISkrCkiVL0NjYaDxaW1vx0ksvAQDuvPNOTJkyBYcPH0ZzczOefvrpC+7v9yUkJGDZsmU4evQoGhoakJSUhLi4OJ++NDU1GTP2z5VHe+t6Dxw4EFVVVT6vVVVVYeDAgef8dxTcFEVBVlYWR+leAvJXXFdXh927d2PevHkA3IcfrVZrm//pNm7ciNmzZ2PAgAGQJAmLFi1CYWGh8b7L5cLp06cBAI2NjT7/gxJdSnfeeSdee+017NixA06nE0888YTxt/l9ra2tEEIgLi4OsiyjqKgIxcXFF7zvqVOnIisrC7Nnz8bu3buhaRoOHDiA2bNnY/Dgwbj77rsBAPfffz8KCwuxadMmqKqKpqYmbN++HYD7C/bLL7+Mjz/+GJqmweFw4PPPP0dFRQUA95eD6Oho9O7dGxUVFUahvxD79+/HM888g6qqKui6jsbGRqxduxbp6emIjY3FmDFjkJycjEcffRQtLS0QQuDIkSPGZLW7774bO3fuxLp16+BwOHDmzBlj8tuAAQPQ0tJinGYAgHnz5uG1117DZ599BlVV8cILL+DkyZM+k/Coe+Lo3FdACrrNZkNCQoLxzUqSJCQnJ6O6utpnu+rqap/DmKmpqcY2o0aNwoMPPogrrrgCAwcOxPPPP48XXnihw306HA40Nzf7PABA0zTjv+3Fqqr6xLqunzN2uVw+sWcU44mFEG1iAD6xrus+seePtqNY0zSfmDn5Jyfvh6cv38/Ps8+pU6fi8ccfx5w5cxAfHw9VVTFixAifnDx9uOqqq/Dwww9j6tSpiI2NxV//+lfceuut58xJ13Wjj+3l9Le//Q0/+clPkJ2djd69e2PKlCkYPnw4PvzwQ0iSBCEEMjIy8Le//Q1PPfUUYmJicOWVV+LTTz+FEAIjRoxAYWEhHn30UfTv3x+JiYlYvny58aXkpZdewpo1a9CnTx8sWrQId9xxR5vfk67rPv31+H5OvXr1Qnl5OSZOnIjIyEgMHToUdXV1ePvtt42f69tvv42jR4/iyiuvRFRUFGbMmIFvvvkGQggMHDgQ77//Pl5//XUMGDAAqamp+Pvf/w4hBAYNGoT58+cbM9pLS0sxceJEPP/885g/fz5iY2NRWFiI9957D5GRkT6/12D62wvF/5/8nZPD4UBxcbHR167kFDIuylS7H/DFF1+Iq666yue1a6+9Vnz66ac+ry1evFisXr3aeL53715xxRVXCCGEqKqqEhMnThTffvutEEKIF154QVx//fUd7nPFihUCQJuHZ3bsV199Jb766ishhHs2cEVFhRBCiJ07d4rKykohhBCfffaZqKqqEkII8emnn4qamhohhHuG8/Hjx4UQQrz//vvi5MmTQggh3n33XWPm5FtvvSXOnDkjnE6neOutt4TT6RRnzpwRb731lhDCPdPy3XffFUIIcfLkSfH+++8LIYQ4fvy4+Oijj4QQ7tm9np9RVVWV+Oyzz4QQQlRWVoqdO3cKIYSoqKgQu3fvZk7MiTkxJ+bUiZyKiopCZpa7JISfToSdh7q6OqSlpeHkyZNQFAVCCMTHx2P79u0+k1eeffZZVFVV4cUXXwQAFBUVYfXq1fjkk0+wZs0aHD58GH/84x8BAKdPn0bfvn3hcrnaXazf4XD4XMfa3NyMpKQkNDQ0oF+/fsY3N5PJ5BOrqgpJkoxYlmXIstxh7Nm/J1YUBZIkGTHg/kboHZvNZmOEYDabjZGOJ9Z1HYqidBhrmgYhhBG3lwdzYk7MiTmFUk6qqqKlpQXR0dHGaPtCcmpoaEBsbCyampoQGRmJbu3Sf4dwu/76643rav/+97+LcePGtdnm0KFDIj4+Xhw7dkzoui5uvfVW8dJLLwkhhHjzzTfF1VdfLVpaWoQQQhQWFrYZ9Z9LKF17SETU0zidTvHuu+8Kp9PZpXZCqRYEbHqg57rXp59+GpGRkdiwYQMAYMGCBZg5cyZmzpyJQYMGIS8vD+PHj4eu65g6daoxG3727NnYtWsXrr32WlgsFvTt2xevvvpqoNIhIqJLyGw2Y8aMGYHuRlAJyCH3YNDc3IyoqKjQOMxCRNTDeK6QiI6O7tJlh6FUC3jxJRERdTuapmHXrl3GeXICeEU+ERF1O2azGTfddFOguxFUznuE/u67716MfhAREXWaruuoq6vzWfOgp+vUCP3GG280Fqc4cOAA/ud//qdLK1sRERF1ha7r2Lt3LyZNmsSle7/TqZ/CddddhwceeAAffvgh/uM//oPFnIiIAkpRFEydOpVruXvpVEH/3e9+B1VV8cgjj8DpdF7sPhEREZ2Trus4evQoD7l76fRxiuzsbNx3330YOnToxewPERHRD9J1HYcOHWJB98Lr0EPg2kMiIrowoVQLzvvkQ0VFBZ566ikcPnzY5y41O3fu9GvHiIiIOqLrOmw2G5KSkjgp7jvnXdDvuOMO/OxnP8N9993X7k1QiIiILjbPOfTExEQW9O+cd0E3m8146KGHLkZfiIiIOkVRFGRmZga6G0HlvL/W3HzzzXj//fe7vOPKykpkZmYiPT0dY8eOxb59+9rdrqCgAGlpaRg8eDByc3N9DvNXV1fj1ltvxdChQzFs2DC88MILXe4XEREFP03TcPDgQS796uW8C/q0adOQnZ2NqKgoxMXFoX///oiLizvvHS9cuBC5ubk4cOAAli5datxFzZvVasXy5ctRWlqKgwcP4tixYygoKAAACCEwe/Zs/OxnP8M333yDiooK3H777efdDyIi6n6EEDh16hR66Lzudp33LPchQ4Zg1apVuOaaa3zOoaekpHS6jbq6OqSnp6O+vh6KokAIgfj4eGzfvh2pqanGds8++yyqqqrw4osvAgCKioqwevVqfPLJJ/jnP/+Jxx9/HKWlpefTfUMozWwkIqILE0q14LxH6LGxscjOzsagQYOQkpJiPM6HzWZDQkKCscKPJElITk5GdXW1z3bV1dU+baemphrb7Nu3D/3798fcuXORkZGB2bNn4/Dhwx3u0+FwoLm52ecBwDhco2lau7Gqqj6x55rHjmKXy+UTe74veWIhRJsYgE+s67pP7DnN0FGsaZpPzJyYE3NiTqGek9PpxL59+4x+dyWnUHHeBX327NlYt24dGhoacObMGeNxviRJ8nne0YEC7+28t3G5XPjnP/+J5cuXo7y8HLfccgvmzp3b4f5WrlyJqKgo45GUlAQA2Lt3LwD35XgVFRUAgD179qCyshIAUF5eDqvVCsB9aZ7NZgMAlJWVoba2FgBQUlKC+vp6AMDWrVvR2NgIACguLkZLSwsA99EFu90OVVVRVFQEVVVht9tRVFQEAGhpaTGW1G1sbMTWrVsBAPX19SgpKQEA1NbWoqysDID7S5HnUkGr1Yry8nIA7rkJe/bsYU7MiTkxp5DO6ciRI6ipqelyTjt27EDIEOdJkiTjIcuy8d/zcfz4cREZGSlcLpcQQghd18WAAQOE1Wr12W716tXigQceMJ5v2bJFXH/99UIIIf7+97+LiRMnGu+dPn1ayLIsVFVtd592u100NTUZD5vNJgCIhoYGIYQQqqoa/9Y7drlcPrGmaeeMnU6nT6zruk+s63qb2PMz8MSapvnEnp9TR7Gqqj5xe3kwJ+bEnJgTc2qb08mTJwUA0dTUJLq78y7o/nL99deLl19+WQjhLs7jxo1rs82hQ4dEfHy8OHbsmNB1Xdx6663ipZdeEkII0draKgYNGiRqamqEEEK8+eabYuTIkZ3ef1NTU8j8EomIehpVVcVXX33V4SCus0KpFpz3deh2ux3h4eE+r504cQL9+/c/r3by8/ORk5ODp59+GpGRkdiwYQMAYMGCBZg5cyZmzpyJQYMGIS8vD+PHj4eu65g6daoxG75379744x//iBkzZkAIgejoaLz++uvnmw4REVFIOO9Z7rNnz8amTZuM542NjZg2bRr+9a9/+b1zF1MozWwkIqILE0q14LwnxQ0dOhRLliwBALS2tiIrKwu/+MUv/N4xIiKijmiahvLyci4s4+W8C/qqVatw/PhxPPPMM5g1axbuuOMOLFiw4GL0jYiIqEMRERGB7kJQ6fQhd+9L086ePYtbbrkF06ZNw/LlywEAvXr1ujg9vEhC6TALERFdmFCqBZ0u6LIsQ5IkCCGM/xqNSFK3O+wRSr9EIqKeRlVVlJeXIyMjw1ik7EKEUi3o9E/Bs6oOERFRoEmShH79+rVZpKwn63RBP336NHr37g0AOHnyJGJjYy9ap7oT/i0RdQ+8h0doMZlMGDJkSKC7EVQ6NSnuP//zP3HXXXfh4YcfBgDjvDkREVEgqKqKsrKykFqLvas6VdAbGxvx9ttvY9KkSXjiiScudp+IiIjOSZZlJCYmQpbP+2KtkNWpn4TFYgEA3HLLLYiPj8eWLVsuaqeIiIjORZZlpKSksKB76dQ59J/97GdGfP/99/P8ORERBZTnkHtmZmaXZrmHkk59tZk0aZLP84yMjIvSGSIios6QZRmDBw/mCN3LBf0knn322S7vuLKyEpmZmUhPT8fYsWOxb9++drcrKChAWloaBg8ejNzc3DYTIIQQmDZtGi677LIu94mIiLoHnkNvq1M/iZSUFEyfPh3Tp0/HjTfeiHfffbfLO164cCFyc3Nx4MABLF261LiLmjer1Yrly5ejtLQUBw8exLFjx1BQUOCzzdq1a5Gamtrl/hARUfehqiq2bt3KWe5eOlXQb7zxRhQXF6O4uBgffvghZsyY0aWd1tXVYffu3Zg3bx4AYM6cObBaraiqqvLZbuPGjZg9ezYGDBgASZKwaNEiFBYWGu9XVlbir3/9K5YtW9al/hARUfciyzJGjBjBEbqXTv0k1qxZ4/P8pZde6tJObTYbEhISjIkMkiQhOTkZ1dXVPttVV1cjJSXFeJ6ammpso+s67r//frz44oswm80/uE+Hw4Hm5mafBwBjyVpN09qNVVX1iT0r5nnH4eEqZNkTu4w4IsIFWRZGLEkCgEBEhAuAgCR5YkCWvWMd4eHesfsbqMmkw2Jxx4riHWsIC/OO3f01mzWYze44LEyDonhi1YgtFhWKohuxycScmFPo5qTrujGi6yjWNM0n9sdnhHfscrl8Ys8y2p5YCNEmBuAT67ruE/fEnIQQiImJgSzLXc4pVHSqoEdHRxtxdXU1SktLUVpa2qYAn4/vL9fX0ZLy3tt5b7NmzRpMmjQJo0eP7tT+Vq5ciaioKOORlJQEANi7dy8AoKKiAhUVFQCAPXv2oLKyEgBQXl4Oq9UKANi5cydsNhsAoKysDLW1tQCA1atLMHJkPQBg7dqtSEtrBAAUFBQjMbEFAFBYWISYGDsiIlQUFhYhIkJFTIwdhYVFAIDExBYUFBQDANLSGrF27VYAwMiR9Vi9ugQAMG5cLfLyygAAkyfbsGzZTgBAVpYVS5aUAwCysytx//17AADz5lVg3jx3TvffvwfZ2e6cliwpR1aWO6dly3Zi8mR3Tnl5ZRg3jjkxp9DNqb6+HiUl7pxqa2tRVubOyWazYedOd05WqxXl5e6cKisrsWePO6eufEaUlJSgvt6d09atW9HY6M6puLgYLS3unIqKimC326GqKoqKiqCqKux2O4qK3Dm1tLSguNidU2NjI7Zu7dk5HTx4EO+99x5cLleXctqxYwdChuikiooK8eMf/1hcfvnlYuzYsWLMmDHi8ssvFz/+8Y/Fvn37OtuMEEKI48ePi8jISOFyuYQQQui6LgYMGCCsVqvPdqtXrxYPPPCA8XzLli3i+uuvF0IIMWPGDJGUlCRSUlJEYmKikGVZpKSkiIaGhnb3abfbRVNTk/Gw2WwCgLG9qqpCVdU2scvl8ok1TfOJASHCw11Clj2x04gjIpxClnUjliRdALqIiHAKQBeS5ImFkGXvWBPh4d6xSwBCmEyasFjcsaJ4x6oIC/OOVQEIYTarwmx2x2FhqlAUT+wyYovFJRRFM2KTiTkxp9DMSQghNE0zPnc6ilVV9Ynb+1w4n8+I78dOp9Mn1nXdJ9Z1vU3s+Zz0xJqm+cQ9MSen0ynq6uqEpmldyunkyZMCgGhqahLdXacL+rhx48TGjRvbvP73v/9djBkz5rx3fP3114uXX37ZaGPcuHFttjl06JCIj48Xx44dE7qui1tvvVW89NJLbbazWq0iNjb2vPbf1NTkl1+ie4VoPvjgI9gfRO3xVy0IBp2eTXDq1CnMmTOnzevZ2dloamo67yMD+fn5yM/PR3p6OlatWmXMXl+wYAE2b94MABg0aBDy8vIwfvx4DB48GHFxce3Ohiciop7F5XJhy5Ytxnl3Oo/7oY8fPx6LFi3C3Xffbcwq1HUdr7zyCvLz841zHN2Fv+6By7utEXUPnfuko+5CCIGWlhb07du3S7dQ7ZH3Q9+wYQMWLlyIJUuWICEhAZIkoaamBhkZGVi/fv1F7CIREZEvSZK6fQH2t04X9CFDhuCjjz7CiRMnjNmBSUlJ6N+//0XrHBERUXtcLheKioqQlZXVqUuXe4LzXtG+f//+LOJERBRQiqJg+vTpvDGLF78ssZOenu6PZoiIiDqNxdxXp38aHd08BQBaW1v90hkiIqLO8CxWw0Pu/3+dLugjRoxAamoq2psU71ktiIiI6FJQFAVZWVkcpXvp9E8iJSUFpaWlSEhIaPOeZxlVIiKiS0VVVRZ0L50+hz5z5kwcPny43fdmzZrltw4RERH9EFVVUVxcHFI3V+mqTi8sE2q4sAxRz9IzP+noh4TSwjK8kSwREXU7Qgg0Nze3O6+rp2JBJyKibkdVVWzbto2H3L0ErKBXVlYiMzMT6enpGDt2bIeXxRUUFCAtLQ2DBw9Gbm6u8cv76quvMGnSJAwbNgxXX301cnNz4XA4LmUKREQUIGazGTNmzOAla14CVtAXLlyI3NxcHDhwAEuXLm33LmpWqxXLly9HaWkpDh48iGPHjhl3ZQsPD8fatWuxf/9+fPnll2hqasJzzz13qdMgIqIA0HUdDQ0N0HU90F0JGgEp6HV1ddi9ezfmzZsHAJgzZw6sViuqqqp8ttu4cSNmz56NAQMGQJIkLFq0CIWFhQCAtLQ0jBw5EgBgMpkwZsyYDmfhExFRaNE0Dbt27YKmaYHuStAISEG32WxISEgwrh+UJAnJycmorq722a66uhopKSnG89TU1DbbAMDp06fxpz/9CbfeemuH+3Q4HGhubvZ5ADD+GDRNazdWVdUn9nwb9I7Dw1XIsid2GXFEhAuyLIxYkgQAgYgIFwABSfLEgCx7xzrCw71j92kGk0mHxeKOFcU71hAW5h27+2s2azCb3XFYmAZF8cSqEVssKhRFN2KTiTkxp9DNSdd147RdR7GmaT6xPz4jvGOXy+UTeyZ1eWIhRJsYgE+s67pP3BNzkmUZ06ZNg9ls7nJOoSJgh9y/f//ajmYqem/X3jYulws//elPMX369HNeD79y5UpERUUZD89iOHv37gUAVFRUoKKiAgCwZ88eVFZWAgDKy8thtVoBADt37jTuNFdWVoba2loAwOrVJRg50r1a3tq1W5GW1ggAKCgoRmJiCwCgsLAIMTF2RESoKCwsQkSEipgYOwoLiwAAiYktKCgoBgCkpTVi7dqtAICRI+uxenUJAGDcuFrk5bnvOz95sg3Llu0EAGRlWbFkSTkAIDu7EvffvwcAMG9eBebNc+d0//17kJ3tzmnJknJkZblzWrZsJyZPdueUl1eGceOYE3MK3Zzq6+tRUuLOqba2FmVl7pxsNht27nTnZLVaUV7uzqmyshJ79rhz6spnRElJibGi5tatW9HY6M6puLgYLS3unIqKimC3240lTVVVhd1uR1GRO6eWlhYUF7tzamxsxNatPTunw4cP4/PPP4eu613KaceOHQgZIgCOHz8uIiMjhcvlEkIIoeu6GDBggLBarT7brV69WjzwwAPG8y1btojrr7/eeO50OsVtt90mFixYIHRdP+c+7Xa7aGpqMh42m00AEA0NDUIIIVRVFaqqtoldLpdPrGmaTwwIER7uErLsiZ1GHBHhFLKsG7Ek6QLQRUSEUwC6kCRPLIQse8eaCA/3jl0CEMJk0oTF4o4VxTtWRViYd6wKQAizWRVmszsOC1OFonhilxFbLC6hKJoRm0zMiTmFZk5CCKFpmvG501GsqqpP3N7nwvl8Rnw/djqdPrHns8sT67reJvZ8TnpiTdN84p6Yk91uF//85z+Nvl5oTidPnhQARFNTk+juArawzOTJk5GTk4OcnBxs3LgRa9aswfbt2322OXz4MCZMmIDy8nLExcVh1qxZyMrKwqJFi6CqKn76058iOjoaf/rTn9qM+H8IF5Yh6ll4uTK1hwvL+EF+fj7y8/ORnp6OVatWGbPXFyxYgM2bNwMABg0ahLy8PIwfPx6DBw9GXFycMRv+b3/7G/7xj3/giy++QEZGBkaPHo1f/vKXgUqHiIguIV3XcfToUc5y98KlXzlCJ+oReuYnXehSVRVlZWXIzMzs0g1aQmmEztvUEBFRt6MoCiZNmhTobgQVLv1KRETdjq7rOHLkCA+5e2FBJyKibofn0NviIXciIup2FEVBZmZmoLsRVDhCJyKibkfTNBw8eJBLv3rhCJ2oHQK8fCHk8FcaUkRYGE6VliI1NTXQXQkaLOhERNTtKE4nxowZE+huBBUeciciom5HUxTs37+fh9y9sKATEVH3I8s4e/ZsoHsRVHjInYiIuh2T04mMjIxAdyOoBOUIvbKyEpmZmUhPT8fYsWOxb9++drcrKChAWloaBg8ejNzc3JC6ry0REXVMM5uxd+9eHnL3EpQFfeHChcjNzcWBAwewdOlS44Ys3qxWK5YvX47S0lIcPHgQx44dM27wQkRE1NMEXUGvq6vD7t27MW/ePADAnDlzYLVaUVVV5bPdxo0bMXv2bAwYMACSJGHRokUoLCwMQI+JiOhSM7lcGDFiBEwmU6C7EjSC7hy6zWZDQkKCcfccSZKQnJyM6upqn+sNq6urkZKSYjxPTU1FdXV1h+06HA44HA7jeVNTEwDg1KlTAGActjGZTD6xqqqQJMmIZVmGLMtGDMiwWFQ4nTKEkGGxuOB0miCEjPBwFxwOBUJICA93wW535xQern4vNkOSBCwWT6wjLEyDw+GJdTgcCmRZh6LocDoVmEw6TCZPrEGWBVwuTwy4XCYoijsPVTXBbNag64CmmWA2q9B1CZpmQliYCk2ToWkywsJUqKoMXWdOTQDU8HAodrt7f+HhMNvtEJIE1WKB2W6HLknQwsJgdjigSxL0sDAoDgd0WYauKFCcTugmE3STCYrTCc1kgpBlKC4XNJMJkGWYXC5o3/2tm1QVmtkM6DpMmgbVbIbkicPCIGsaZE+sqpB1HarFAtnphCwEXBYLTJ44PByKwwHJE3vlwZyYUyjk5IyIwNeffoqRI0can+vf//zuzGd5Q0MDACAUbjwadAUdcBdxbx39oL23+6FfxsqVK5GXl9fmdX8sSuD1PcEn/u5vt1OxEL6xpx3vWNcBp9Mda5r7ca7Ye0qBy9V+7Gnv+3FPzyk6FJNiTswplHI6exaYPBn+0tLSgqioKL+1FwhBV9CTkpJQU1MDVVWhKAqEELDZbEhOTvbZLjk52ecw/JEjR9ps4+3hhx/Ggw8+aDzXdR0NDQ2IjY1t8wWCiIiCW3NzM5KSkmCz2bp0H3MhBFpaWpCQkODH3gVG0BX0uLg4ZGRk4NVXX0VOTg7efPNNpKamthlJz5kzBxMmTMBjjz2GuLg4rFu3DnPnzu2wXYvFAovF4vNadHT0RciAiIgulcjIyC4VdADdfmTuEXST4gAgPz8f+fn5SE9Px6pVq4zZ6wsWLMDmzZsBAIMGDUJeXh7Gjx+PwYMHIy4urt3Z8ERERD2BJEJhJgAREfUozc3NiIqKQlNTU5dH6KEiKEfoRERE52KxWLBixYo2p1J7Mo7QiYiIQgBH6ERERCGABZ2IiCgEsKATERGFABZ0IiKiEMCCTkREFAJY0ImIiEIACzoREVEIYEEnIiIKASzoREREISDoCvqvfvUrpKamQpIk7N27t8PtCgoKkJaWhsGDByM3Nxeq9319iYiIepigK+jZ2dkoLS1FSkpKh9tYrVYsX74cpaWlOHjwII4dO2bckY2IiKgnCrqCPmnSJAwcOPCc22zcuBGzZ8/GgAEDIEkSFi1ahMLCwkvUQyIiouCjBLoDF6K6utpnBJ+amorq6upz/huHwwGHw2E813UdDQ0NiI2NhSRJF62vREQUvIQQaGlpQUJCAmQ56Ma456VbFnQAPkW4MzeMW7lyJfLy8i5ml4iIqJuy2Ww/eHQ42HXLgp6cnIyqqirj+ZEjR5CcnHzOf/Pwww/jwQcfNJ43NTUZ7fTr1w+apgEATCaTT6yqKiRJMmJZliHLcoexy+WCyWQyYkVRIEmSEQOAqqo+sdlshhDCiHVdh6ZpRqzrOhRF6TDWNA1CCCNuLw/mxJyYE3MKpZwcDgd27dqF6667zhjgXUhODQ0NuOKKK9C3b190d92yoM+ZMwcTJkzAY489hri4OKxbtw5z584957+xWCywWCxtXu/Xrx8iIyMvVleJiOgi0HUdo0aNQnR0tF8OlYfCqdegO2Hwy1/+EgMHDkRNTQ1uuOEGDBkyBACwYMECbN68GQAwaNAg5OXlYfz48Rg8eDDi4uIwf/78QHabiIguIVmWkZiY2O3Pe/uTJDpzAjoENTc3IyoqCk1NTRyhExF1M6qqoqSkBJMmTTIO5V+IUKoF/GpDRETdjizLGDFiBEfoXrrlOXQiIurZZFlGXFxcoLsRVPjVhoiIuh2Xy4UPPvgALpcr0F0JGizoRETU7ZhMJowZMwYmkynQXQkaPORORETdjizLiImJCXQ3ggpH6ERB4vHHH8dtt93Wrffx9NNP484777xo7RN5uFwubNmyhYfcvbCgE3Xgm2++wa233orLLrsMkZGRGDZsGJ555hm/tL1+/XqMHj3aL2395S9/gSRJeOmlly7aPtrTXvuPPPLIBd8oaceOHZgyZQr69euH6OhojBw5EuvXr+9yPz/55BNER0d3uR0KLoqiYOLEiV26ZC3UsKATdWDGjBkYNWoUqqurcerUKbz55psYNGhQoLvVRkFBAWJiYrr1LYRbWlpw880346c//Snq6upw4sQJFBQUBM0sZlVVA90F+h5JkhAZGRkSK7z5jeihmpqaBADR1NQU6K5QEDpx4oQAIKqrqzvc5tixY+L2228Xl112mUhKShKPPPKIcLlcQgghXn75ZTFq1Cif7UeNGiVefvllsXv3bmGxWIQsy6J3796id+/e4siRI2LFihXiJz/5ifjlL38poqKiRFJSkvjrX/96zn5WVlYKAOKtt94SkiSJL7/8UgghzrmPWbNmGf/+oYceEsnJyaJPnz7iyiuvFG+88Ybx3scffyyioqLE//3f/4mBAweKmJgY8dBDD51X+7W1teLuu+8W8fHxIioqSkycOFGcOXOmTR67du0SZrNZaJrWYa7Hjx8Xd911l4iPjxfx8fFiyZIlwm63G+9/8cUXYsqUKaJfv37isssuE4sXLxb19fUiPDxcADD6WVJSIoQQ4pVXXhHDhg0TUVFRYvz48WL37t1GW9dff7146KGHxI033ih69eolNm/efM7fA116TqdTvPXWW8LpdHapnVCqBRyhE7UjNjYWw4YNw89//nO88cYbOHLkSJtt7rrrLpjNZlitVmzbtg1vvfUWVq9e/YNtZ2RkYN26dbj66qvR2tqK1tZW4+ZCH3zwAcaPH4+TJ0/iySefxIIFC9DS0tJhWwUFBcjIyMCsWbMwceJEY5R+rn14GzVqFHbt2oXGxkY89thjuOeee2C1Wo33W1pa8NVXX6GyshKlpaV48cUX8cknn3SqfV3XMXPmTCiKgq+//hr19fV4+umn210IZOjQoYiOjsbcuXPx9ttv49ixYz7vCyEwc+ZMXH755Th48CC++uor/Pvf/8aTTz4JADh69CimTp2K7OxsfPvttzhy5AjuuOMOxMbG4r333kNUVJTRz4kTJ2Lbtm34xS9+gfz8fJw4cQLZ2dm46aab0NTUZOxz/fr1ePLJJ9Ha2oobbrjhh36tdIkpioLp06fzkLsXFnSidkiShI8//hijRo1CXl4eBg0ahKuuugoffvghAHcB2bp1K5577jn06dMHKSkp+O1vf9vlc77XXHMN7rzzTphMJtxzzz1wOp04cOBAu9tqmoYNGzbg3nvvBQD87Gc/w2uvvQaHw9Hp/d19992Ii4uDyWTC3LlzMWzYMJSVlRnvCyGwcuVKhIeH48orr0RmZib+9a9/dartXbt2Yd++fXjppZfQr18/KIqCCRMmtHuTpL59+6KsrAwxMTF48MEHkZCQgHHjxmH37t0AgC+++AKVlZV49tln0atXL8TGxuKRRx7B66+/DgB49dVX8aMf/QgPPPAAwsPD0atXL0ycOLHDvv3lL3/BvHnzMGnSJJjNZvz6179Gv379sGXLFmObu+66C2PHjoUkSYiIiOhUznRpsZj7YkEn6sDll1+O5557Dl9//TVOnDiBW265BbNnz0ZDQwNqamoQHh6Oyy+/3Nh+0KBBqKmp6fI+PTyFpKMRelFREerr63HXXXcBAG6//XacPXsWmzZt6vT+nn/+eQwfPhxRUVGIjo7G3r17UV9fb7wfGRmJXr16Gc979+59ziMG3o4cOYLExMROF8MhQ4Zg3bp1OHToEGpqajBkyBDMnDkTQghUVVWhsbERMTExiI6ORnR0NLKzs3H8+HFjX2lpaZ3Ou6amBqmpqT6vXXHFFT6/vx+6JTMFlqqqKCoq4vwGLyzoRJ0QExODxx9/HKdPn4bVasXAgQNht9uNggLAeB0A+vTpgzNnzvi04X0Y2R/rTxcUFEDXdVx99dW4/PLLkZ6eDpfLZRx2/6F9lJaW4vHHH8df/vIXnDp1Co2NjRgxYgREJ+/X9EPtp6Sk4OjRozh79mznEvKSkJCAZcuW4ejRo2hoaEBSUhLi4uLQ2NhoPJqamtDa2mrs6+DBg53u58CBA1FVVeXzWlVVlfH76+jfUfBQFAVZWVkcpXvhXyxRO06dOoVHH30U+/fvh6ZpOHPmDH7/+98jJiYGw4YNQ2JiIqZMmYL/+q//wunTp1FdXY2nn37aOPw9evRoHD58GNu2bYOqqli9ejVOnjxptD9gwADU1tZeULEDgOPHj2PLli34y1/+gi+//NJ4vPPOO/joo49QVVX1g/tobm6Goijo378/dF3Hn//8Z+zdu7fTffih9seMGYOhQ4fil7/8JRobG6GqKkpLS9s9JbB//34888wzqKqqgq7raGxsxNq1a5Geno7Y2FiMGTMGycnJePTRR9HS0gIhBI4cOYL33nsPgPvUwc6dO7Fu3To4HA6cOXMG27ZtM/rZ0tKCEydOGPubN28eXnvtNXz22WdQVRUvvPACTp48iaysrE7nT4HH0bkvFnSidoSFheHo0aPIyspCVFQUkpOT8dlnn+H9999H7969AQCvv/46zp49i5SUFIwfPx4zZszA0qVLAbgPH69evRrZ2dmIj4+Hw+HA8OHDjfanTp2K6667DomJiYiOjkZ1dfV59W/Dhg1ITk7G3LlzcfnllxuPm2++GT/60Y/w5z//+Qf3cfPNN2POnDm4+uqrkZCQgK+//hrjx4/vdB9+qH1ZlvHOO+/gzJkzGDp0KC677DI8+uij0HW9TVt9+/ZFeXk5Jk6ciMjISAwdOhQnTpzAO++8A8C9zOc777yDo0eP4sorr0RUVBRmzJhhjMoHDhyIf/7zn3j99dcxYMAApKamYuPGjQDcE+7mz5+PK6+8EtHR0SgtLcX111+PF154AfPnz0dsbCz++te/4r333uP16t2IqqooLi5mUffC+6GHwD1wiYjowoRSLQjKEXplZSUyMzORnp6OsWPHYt++fW22EULgoYcewvDhwzFy5EhMmTKlw3NoREQUWoQQaG5u7vScj54gKAv6woULkZubiwMHDmDp0qWYP39+m202b96MkpISfPnll9izZw+mTZuGRx55JAC9JSKiS01VVWOOCrkFXUGvq6vD7t27MW/ePADAnDlzYLVa28xIBQCHwwG73W58U/OeoUpERKHLbDZjxowZMJvNge5K0Ai6gm6z2ZCQkGBciiBJEpKTk9tMuLn11lsxZcoUXH755YiPj8dHH32EJ554osN2HQ4HmpubfR6Ae3EOz3/bi1VV9Yk9E3o6il0ul0/sORzkiYUQbWIAPrGu6z6x5xtoR7GmaT4xc2JOzIk5hXpOLpcLJ06cgK7rXc4pVARdQQfQZrH99s6R7N69G/v378fRo0fx7bffYtq0aVi8eHGHba5cuRJRUVHGIykpCQCMy3QqKipQUVEBANizZw8qKysBAOXl5cZSmDt37oTNZgMAlJWVoba2FgBQUlJiLMaxdetWNDY2AgCKi4uNRTiKiopgt9t9FkOw2+0oKioC4F5is7i4GADQ2NiIrVu3AgDq6+tRUlICAKitrTVW8bLZbNi5cycA9/XP5eXlANzzD/bs2cOcmBNzYk4hndPhw4exfft2aJrWpZx27NiBUBF0s9zr6uqQlpaGkydPQlEUCCEQHx+P7du3+6zstHjxYiQnJxuXCX399dfIyspqd81twD1C977+tbm5GUlJSWhoaEC/fv2Mb24mk8knVlUVkiQZsSzLkGW5w9jlcsFkMhmxoiiQJMmIAfc3Qu/YbDZDCGHEnm+cnljXdSiK0mGsaRqEEEbcXh7MiTkxJ+bEnNrm1NDQgNjY2JCY5e7Xgv7uu+/iJz/5SZfbmTx5MnJycpCTk4ONGzdizZo12L59u882v//97/HBBx/g3XffhdlsxqpVq7Bt2zaftZjPJZQuVSAi6ml0XUd9fT0uu+yyLq3qF0q1oMsF/cYbb4QkSRBC4MCBAxg6dKhxCOVCffPNN8jJycHJkycRGRmJDRs2YPjw4ViwYAFmzpyJmTNnwuFwYPHixdi2bRvCwsIQHx+P/Pz8NuszdySUfolERD2NqqooKSnBpEmTurT8ayjVgi4X9OXLl+NHP/oRbrvtNvzmN7/B888/76++XVSh9EskIqILE0q1oMuT4n73u99BVVU88sgjcDqd/ugTERHROem6jqNHj7a7lHBP5ZdZ7tnZ2bjvvvswdOhQfzRHRER0Trqu49ChQyzoXoJulvulEkqHWYiI6MKEUi3w641kKyoq8NRTT+Hw4cM+F+t7rhskIiLyB13XYbPZkJSUxHvXf8evBf2OO+7Az372M9x3330wmUz+bJqIiMjgOYeemJjIgv4dvxZ0s9mMhx56yJ9NEhERtaEoCjIzMwPdjaDi1681N998M95//31/NklERNSGpmk4ePCgsRoc+XmEPm3aNMyaNQsmkwkWiwVCCEiShLq6On/uhoiIejghBE6dOtXpxcR6Ar8W9IULF2L9+vW45ppreA6diIguGkVRMGbMmEB3I6j4taDHxsYiOzvbn00SERG14bnLWlpaGgeQ3/HrOfTZs2dj3bp1aGhowJkzZ4wHERGRv509ezbQXQgqfl1YxvvSAc8NWyRJCspJC6G0mAAREV2YUKoFfh2he+5V67mvree/RERE/qRpGvbu3csa48WvBd1ut7d57cSJE/7cBREREbXDrwX9zjvv9Hne2NiIm2++2Z+7ICIigslkwogRIzghzotfC/rQoUOxZMkSAEBrayuysrLwi1/8wp+7ICIigqZpKC8v5yF3L34t6KtWrcLx48fxzDPPYNasWbjjjjuwYMGC826nsrISmZmZSE9Px9ixY7Fv374223zyySfo1asXRo8ebTw445GIqOeIiIgIdBeCil+uQ/e+NO3FF1/ELbfcgmnTpiE3NxdnzpxBr169zqu9hQsXIjc3Fzk5Odi4cSPmz5+Pzz//vM12V111Fb744osu95+IiLoXk8mEYcOGBbobQcUvI/Q+ffqgb9++6NOnD+Li4vDFF1/gmWeeMV4/H3V1ddi9ezfmzZsHAJgzZw6sViuqqqr80VUiIgoBqqpi165dPrfq7un8UtC/f5na9y9fOx82mw0JCQlQFPfBA0mSkJycjOrq6jbbfvPNN7jmmmswZswY/PGPfzxnuw6HA83NzT4PAEb/NE1rN1ZV1SfWdf2cscvl8ok9l/l7YiFEmxiAT6zruk/s+YPtKNY0zSdmTsyJOTGnUM9J13VERUUZa510JadQ4ZeCfvr0aSM+efJkl9uTJMnneXtr31xzzTWoqanB7t27sWnTJqxbtw5vvPFGh22uXLkSUVFRxiMpKQkAsHfvXgBARUUFKioqAAB79uxBZWUlAKC8vBxWqxUAsHPnTthsNgBAWVkZamtrAQAlJSWor68HAGzduhWNjY0AgOLiYrS0tAAAioqKYLfboaoqioqKoKoq7HY7ioqKAAAtLS0oLi4G4L46YOvWrQCA+vp6lJSUAABqa2tRVlYGwP3FZ+fOnQAAq9WK8vJyAO75B3v27GFOzIk5MaeQzqm6uhpNTU0wmUxdymnHjh0IFV1eKe4///M/UV1djauuugorV67EAw888IOj5XOpq6tDWloaTp48CUVRIIRAfHw8tm/ffs676qxcuRLffvstXnjhhXbfdzgccDgcxvPm5mYkJSWhoaEB/fr1M765mUwmn1hVVUiSZMSyLEOW5Q5jl8sFk8lkxIqiQJIkIwbc3wi9Y7PZDCGEEXuObHhiXdehKEqHsaZpEEIYcXt5MCfmxJyYUyjl5HA48MUXX2DcuHHGIPBCcmpoaEBsbGxIrBTX5YJ+zz334JVXXsF7772HXbt24dixY10q6AAwefJk5OTkGJPi1qxZg+3bt/tsU1tbiwEDBkCWZbS0tODmm2/G/Pnzcd9993VqH6G03B8RUU+j6zpsNhuSkpJ8lh0/X6FUC7p8yN1isQAAbrnlFsTHx2PLli1d7lR+fj7y8/ORnp6OVatWoaCgAACwYMECbN68GQDw5ptv4uqrr8aoUaNw3XXX4cYbb8TPf/7zLu+biIiCnyzLSElJ6VIxDzVdHqGXlJRg0qRJxvN//OMf+I//+I8ud+xiC6VvZUREPY2qqigrK0NmZqZxKP9ChFIt6PJXG+9iDgAZGRldbZKIiOicZFnG4MGDOUL34vefxLPPPuvvJomIiHzIsozExEQWdC9dXikuJSUFQ4cOBeC+vOybb77p8qQ4IiKic1FV1Tjl25VD7qGkyz+FG2+8EX/605+M57wZCxERXWyyLGPEiBEcoXvp8qS4xsZGREdH+6k7l04oTYQgIqILE0q1oMtfbbyLeXV1NUpLS1FaWtruUq1ERET+4HK58MEHHxjLxZKf7ra2f/9+3HfffbBarUhOToYQAjabDVdccQUKCgpw5ZVX+mM3REREANyrv40ZMwYmkynQXQkafinoOTk5eOihhzBnzhyf1zdu3Ih7773XWHs3FH1v2XkiClJdO7lIwUaWZcTExAS6G0HFL7MJTp061aaYA0B2djaampr8sQsiIiKDy+XCli1beMjdi18K+mWXXYZXXnnFuB0d4F5nd8OGDYiNjfXHLoiIiAyKomDixIm8ZM2LX34SGzZswMKFC7FkyRIkJCRAkiTU1NQgIyMD69ev98cuiIiIDJIkdftZ6f7ml4I+ZMgQfPTRRzhx4oRxj9mkpCT079/fH80TERH5cLlcKCoqQlZWFsxmc6C7ExT8eqyif//+LOJERHTRKYqC6dOn85C7l4u+xE56evrF3gUREfVALOa+/PLT2LdvX4fvtba2+mMXREREBlVVecj9e/xS0EeMGIHU1FS0t4psfX39ebdXWVmJe++9F/X19YiOjsb69etx1VVX+WyzdetWPPzww2hpaYEsy5g1axaefPJJSLwwnPxAgH9HIYe/0pCiAMhyOjlK9+KXn0RKSgpKS0uRkJDQ5r2kpKTzbm/hwoXIzc1FTk4ONm7ciPnz5+Pzzz/32aZfv34oLCzEoEGDYLfbccMNN6CwsBB33XXXBedBRETdhCRBVVUWdC9+OYc+c+ZMHD58uN33Zs2adV5t1dXVYffu3Zg3bx4AYM6cObBaraiqqvLZLiMjA4MGDQIAhIeHY/To0R32gYiIQosaHo7i4mKoqhrorgQNvxT0P/zhD5gwYUK7761du/a82rLZbEhISDC+dUmShOTk5HPe7OXYsWPYuHEjsrKyOtzG4XCgubnZ5wEAmqYZ/20vVlXVJ/YsnuMdh4erkGVP7DLiiAgXZFkYsSQJAAIRES4AApLkiQFZ9o51hId7x+4/WJNJh8XijhXFO9YQFuYdu/trNmswm91xWJgGRfHEqhFbLCoURTdik4k5SZKAAOCKiIAAICQJrogIAICQZSPWZRmu8HAjVj2xyQTVYnHHimLEmqJADQszYs0Tm83QvjsHqIWFQfvub1/1ji0W6N7xd+tXq+Hh0L+7faTLO46IgPCOJYk5MaeQykl2uTBjxgyYzeYOP787+1keKoLyRrLfPw9+rju8Njc349Zbb8XSpUtxzTXXdLjdypUrERUVZTw8pwL27t0LAKioqEBFRQUAYM+ePaisrAQAlJeXw2q1AgB27txpXGdfVlaG2tpaAMDq1SUYOdI9V2Dt2q1IS2sEABQUFCMxsQUAUFhYhJgYOyIiVBQWFiEiQkVMjB2FhUUAgMTEFhQUFAMA0tIasXbtVgDAyJH1WL26BAAwblwt8vLKAACTJ9uwbJl7jfysLCuWLCkHAGRnV+L++/cAAObNq8C8ee6c7r9/D7Kz3TktWVKOrCx3TsuW7cTkye6c8vLKMG4cc4qJsUONiEBRYSHUiAjYY2JQVFgIAGhJTERxQQEAoDEtDVu/+8JaP3IkSlavBgDUjhuHsrw8AIBt8mTsXLYMAGDNykL5kiUAgMrsbOy5/34AQMW8eaj47ojUnvvvR2V2NgCgfMkSWL/7krpz2TLYJk8GAJTl5aF23DgAQMnq1agfORIAsHXtWjSmpQEAigsK0JKYCAAoKiyEPSaGOTGn0Mppxgzs3LkTQghUVlZizx73Z8T5fpbv2LEDIUMEmePHj4vIyEjhcrmEEELoui4GDBggrFZrm22bm5vFj3/8Y/HEE0/8YLt2u100NTUZD5vNJgCIhoYGIYQQqqoKVVXbxC6XyyfWNM0nBoQID3cJWfbETiOOiHAKWdaNWJJ0AegiIsIpAF1IkicWQpa9Y02Eh3vHLgEIYTJpwmJxx4riHasiLMw7VgUghNmsCrPZHYeFqUJRPLHLiC0Wl1AUzYhNJuYkSbrQAeGMiBA6IHRJEs6ICCEAocuyEWuyLJzh4Ubs8sQmk3BZLO5YUYxYVRThCgszYtUTm81CNZvdcViYUBVFCEC4vGOLRWjescnkjsPDhSbLQgDC6R1HRAjdO5Yk5sScQiqns337infeeUc4nc4OP78781l+8uRJAUA0NTWdf8EKMpIQwXcPosmTJyMnJ8eYFLdmzRps377dZ5vW1lbcdNNNmD59OlasWHHe+/DXTe05qT40cZY7UTfgh/Llr1oQDILykHt+fj7y8/ORnp6OVatWoeC7QzILFizA5s2bAbjP2+/cuRObNm3C6NGjMXr0aDz11FOB7DYREV0iuiyjoaHB56ZgPV1QjtAvBY7Q6Vw4QicKbq7wcGx96y1MnTq1SwvLhNIInRfwERFRt2O223HTTTcFuhtBJSgPuRMREZ2LLsuoq6vjIXcvLOhERNTt6GFh2Lt3Lwu6Fx5yJyKibkex2zF16tRAdyOocIRORETdjm4y4ejRoxyhe2FBJyKibkdXFBw6dIgF3QsPuRMRUbejOByYNGlSoLsRVDhCJyKibkdXFBw5coQjdC8s6ERE1O3wHHpbPORORETdjuJwIDMzM9DdCCocoRMRUbejKQoOHjxo3OOcWNCJiKgbErKMU6dOoYfejqRdPORORETdjuJ0YsyYMYHuRlDhCJ2IiLodTVGwf/9+HnL3woJORETdjyzj7Nmzge5FUOEhdyIi6nZMTicyMjIC3Y2gEpQj9MrKSmRmZiI9PR1jx47Fvn372t2uoKAAaWlpGDx4MHJzc6Gq6iXuKRERBYJmNmPv3r085O4lKAv6woULkZubiwMHDmDp0qWYP39+m22sViuWL1+O0tJSHDx4EMeOHUNBQUEAektERBR4QVfQ6+rqsHv3bsybNw8AMGfOHFitVlRVVflst3HjRsyePRsDBgyAJElYtGgRCgsLA9BjIiK61EwuF0aMGAGTyRTorgSNoDuHbrPZkJCQAEVxd02SJCQnJ6O6uhqpqanGdtXV1UhJSTGep6amorq6usN2HQ4HHA6H8bypqQkAcOrUKQAwDtuYTCafWFVVSJJkxLIsQ5ZlIwZkWCwqnE4ZQsiwWFxwOk0QQkZ4uAsOhwIhJISHu2C3u3MKD1e/F5shSQIWiyfWERamweHwxDocDgWyrENRdDidCkwmHSaTJ9YgywIulycGXC4TFMWdh6qaYDZr0HVA00wwm1XougRNMyEsTIWmydA0GWFhKlRVhq4zpyYAang4FLvdvb/wcJjtdghJgmqxwGy3Q5ckaGFhMDsc0CUJelgYFIcDuixDVxQoTid0kwm6yQTF6YRmMkHIMhSXC5rJBMgyTC4XtO/+1k2qCs1sBnQdJk2DajZD8sRhYZA1DbInVlXIug7VYoHsdEIWAi6LBSZPHB4OxeGA5Im98mBOzCkUcnJGRODrTz/FyJEjjc/1739+d+azvKGhAQBC4nr2oCvogLuIe+voB+293Q/9MlauXIm8vLw2r3t/SbhQXt8TfOLv/nY7FQvhG3va8Y51HXA63bGmuR/nir2nFLhc7cee9r4f9/ScokMxKebEnEIpp7NngcmT4S8tLS2IioryW3uBEHQFPSkpCTU1NVBVFYqiQAgBm82G5ORkn+2Sk5N9DsMfOXKkzTbeHn74YTz44IPGc13X0dDQgNjY2DZfIIiIKLg1NzcjKSkJNpsNkZGRF9yOEAItLS1ISEjwY+8CI+gKelxcHDIyMvDqq68iJycHb775JlJTU9uMpOfMmYMJEybgscceQ1xcHNatW4e5c+d22K7FYoHFYvF5LTo6+iJkQEREl0pkZGSXCjqAbj8y9wi6SXEAkJ+fj/z8fKSnp2PVqlXG7PUFCxZg8+bNAIBBgwYhLy8P48ePx+DBgxEXF9fubHgiIqKeQBKhMBOAiIh6lObmZkRFRaGpqanLI/RQEZQjdCIionOxWCxYsWJFm1OpPRlH6ERERCGAI3QiIqIQwIJOREQUAljQiYiIQgALOhERUQhgQSciIgoBLOhEREQhgAWdiIgoBLCgExERhQAWdCIiohAQdAX9V7/6FVJTUyFJEvbu3dvhdgUFBUhLS8PgwYORm5sL1fu+vkRERD1M0BX07OxslJaWIiUlpcNtrFYrli9fjtLSUhw8eBDHjh0z7shGRETUEwVdQZ80aRIGDhx4zm02btyI2bNnY8CAAZAkCYsWLUJhYeEl6iEREVHwUQLdgQtRXV3tM4JPTU1FdXX1Of+Nw+GAw+Ewnuu6joaGBsTGxkKSpIvWVyIiCl5CCLS0tCAhIQGyHHRj3PPSLQs6AJ8i3Jkbxq1cuRJ5eXkXs0tERNRN2Wy2Hzw6HOy6ZUFPTk5GVVWV8fzIkSNITk4+5795+OGH8eCDDxrPm5qajHb69esHTdMAACaTySdWVRWSJBmxLMuQZbnD2OVywWQyGbGiKJAkyYgBQFVVn9hsNkMIYcS6rkPTNCPWdR2KonQYa5oGIYQRt5cHc2JOzIk5hVJODocDu3btwnXXXWcM8C4kp4aGBlxxxRXo27cvurtuWdDnzJmDCRMm4LHHHkNcXBzWrVuHuXPnnvPfWCwWWCyWNq/369cPkZGRF6urRER0Eei6jlGjRiE6Otovh8pD4dRr0J0w+OUvf4mBAweipqYGN9xwA4YMGQIAWLBgATZv3gwAGDRoEPLy8jB+/HgMHjwYcXFxmD9/fiC7TUREl5Asy0hMTOz25739SRKdOQEdgpqbmxEVFYWmpiaO0ImIuhlVVVFSUoJJkyYZh/IvRCjVAn61ISKibkeWZYwYMYIjdC/d8hw6ERH1bLIsIy4uLtDdCCr8akNERN2Oy+XCBx98AJfLFeiuBA0WdCIi6nZMJhPGjBkDk8kU6K4EDR5yJyKibkeWZcTExAS6G0GFI3SiIPH444/jtttuC3Q3MHz4cLz77rvG8//7v/9DfHw8+vTpg/Ly8jbvEwWCy+XCli1beMjdCws6UQe++eYb3HrrrbjssssQGRmJYcOG4ZlnnvFL2+vXr8fo0aO71Mbjjz8ORVHQp08fREZGYsSIEXj11Ve73Levv/4aP/nJTwC4PzSXLFmCv/3tb2htbUVGRobP++frueeeQ3p6Ovr27Yv+/fvjhhtu8Fn18ULl5OTg17/+dZfboe5DURRMnDixS5eshRoWdKIOzJgxA6NGjUJ1dTVOnTqFN998E4MGDQp0t3z85Cc/QWtrKxobG/HYY48hJycHFRUVfmv/+PHjOHv2LEaOHNnltl599VW88MIL+Mc//oGWlhZUVlYiNzc3KFboUlU10F2g8yRJEiIjI4Pi7ydYsKATtaO+vh6HDh3CwoUL0atXL5hMJgwfPhy33367sc3x48dxxx13oH///khOTsZvf/tbozC0NwIfPXo01q9fj/LycixatAhfffUV+vTpgz59+hh3C9Q0DYsXL0Z0dDSSk5Pxt7/9rVP9lWUZd9xxB6Kjo7Fv3z4UFxfj2muvRVRUFOLj4/HAAw/g7NmzxvbNzc1YvHgxkpOTERkZiTFjxsBmswFw373wrbfeQnl5OYYOHQoAGDhwIAYPHuzzvseHH36IcePGITo6GvHx8Vi5cmW7fdy+fTumTZuGESNGAACio6Nxxx13+Nw58Z///CfGjh2L6OhoDB8+3FgdEnAv9fk///M/GDZsGPr27Yu0tDS8//77+J//+R+89tpr+OMf/4g+ffpg+PDhAICWlhbk5uYiPj4e8fHxWLRoEU6fPg0AqKqqgiRJePnllzFkyBAkJiZ26udMwcPlcuHtt9/mIXcvLOhE7YiNjcWwYcPw85//HG+88QaOHDnSZpu77roLZrMZVqsV27Ztw1tvvYXVq1f/YNsZGRlYt24drr76arS2tqK1tdW4udAHH3yA8ePH4+TJk3jyySexYMECtLS0/GCbmqbhr3/9K5qamjBy5EhERETg//7v/9DQ0IDPPvsMH3/8MX7/+98b2+fk5ODgwYPYvn07Ghsb8b//+7+IiIho08+vv/4aAFBTU4NDhw612W95eTlmzZqFpUuX4sSJE9i/fz+mTJnSbh8nTJiAN954A0899RQ+++wz2O12n/f37NmD22+/HatWrUJDQwPy8/Nxzz334JtvvgEArF27Fv/93/+N1157Dc3Nzfjoo4+QkpKCX/3qV7j77rvxwAMPoLW11ejzkiVLcPDgQezduxdfffUV9u/fj9/85jc++9y8eTO++OILWK3WH/wZU3BRFAXTp0/nIXdvoodqamoSAERTU1Ogu0JBqra2Vjz44IPiqquuErIsiyuvvFIUFxcLIYSoqakRAERtba2x/WuvvSbS0tKEEEK8/PLLYtSoUT7tjRo1Srz88ssdvr9ixQoxbtw447mu6yIsLEx88cUX7fZvxYoVQlEUERUVJWJjY8W1114rNm7c2O62zz//vLjhhhuEEEIcO3ZMABBHjhxpd9uUlBSxadMmIYQQVqtVABCnTp1q9/1FixaJn//85+22056///3vIisrS0RFRYlevXqJBQsWiNbWViGEEA888ID49a9/7bP9XXfdJZ544gkhhBDDhg0TGzZsaLfde++9VyxZssR4rmmasFgsYvv27cZrn332mbBYLELTNCOv8vLyTvedgouu68LpdApd17vUTijVAo7QiTpw+eWX47nnnsPXX3+NEydO4JZbbsHs2bPR0NCAmpoahIeH4/LLLze2HzRoEGpqarq8Tw9JkhAREXHOEfqMGTPQ2NiI+vp67Nq1C3PmzAEA7Nq1CzfccAMGDBiAyMhIPPLII6ivrwfgvt2wxWL5wVsOd8aRI0eQlpbW6e2zs7OxZcsWnDp1Ch988AGKi4vx1FNPAXAfBl+3bh2io6ONx9tvv41vv/32vPd14sQJOBwOpKamGq8NGjQIDofD+DkA8MvPgAJDVVUUFRVx/oMXFnSiToiJicHjjz+O06dPw2q1YuDAgbDb7Th+/Lixjed1AOjTpw/OnDnj08axY8eM+GKvP33nnXdiypQpOHz4MJqbm/H0009DfHcfppSUFDgcDuOceVekpKTg4MGD5/3vJEnChAkTkJ2dja+++goAkJSUhCVLlqCxsdF4tLa24qWXXvrBfX3/59m/f3+EhYX5zKC3Wq2wWCy47LLLOvx31H0oioKsrCwecvfCv2aidpw6dQqPPvoo9u/fD03TcObMGfz+979HTEwMhg0bhsTEREyZMgX/9V//hdOnT6O6uhpPP/007r33XgDuCXCHDx/Gtm3boKoqVq9ejZMnTxrtDxgwALW1tT4T1fypubkZ0dHR6N27NyoqKoyi6Nn3rFmzsGjRItTW1kLXdZSXl/v0r7Puv/9+FBYWYtOmTVBVFU1NTdi+fXu727788st4++230djYCADYu3cv3n77bWRmZgIAFi5ciJdffhkff/wxNE2Dw+HA559/bszaX7hwIfLy8vDll19CCIHq6mrjvQEDBuDw4cPGvmRZxl133YXf/va3aGhowMmTJ/Hb3/4W99xzD4t4COHo3Bf/sonaERYWhqNHjyIrKwtRUVFITk7GZ599hvfffx+9e/cGALz++us4e/YsUlJSMH78eMyYMQNLly4FAAwZMgSrV69GdnY24uPj4XA4jNnXADB16lRcd911SExMRHR0tDHL3V/y8/OxZs0a9OnTB4sWLcLcuXN93t+wYQOSkpJw7bXXIjo6GosWLbqgLxfXXHMN3nzzTTz11FOIiYnBlVdeiU8//bTdbaOjo/Hcc89h0KBB6Nu3L2677Tbceeedxs8sIyMDhYWFePTRR9G/f38kJiZi+fLlcDgcAIBf/epX+MUvfoE77rgDffv2xQ033GD83BYsWICjR4+iX79+xiV2f/jDH5CamoqrrroKw4cPx5AhQ3wmBlL3pqoqiouLWdS98H7oIXAPXCIiujChVAuCcoReWVmJzMxMpKenY+zYsdi3b1+bbYQQeOihhzB8+HCMHDkSU6ZMuaBzeURE1P0IIdDc3IweOiZtV1AW9IULFyI3NxcHDhzA0qVLMX/+/DbbbN68GSUlJfjyyy+xZ88eTJs2DY888kgAektERJeaqqrGHBVyC7qCXldXh927d2PevHkAgDlz5sBqtba73rPD4YDdbje+qXlmGBMRUWgzm82YMWMGzGZzoLsSNIKuoNtsNiQkJBiXIkiShOTk5DaThm699VZMmTIFl19+OeLj4/HRRx/hiSee6LBdh8OB5uZmnwfgXmHL89/2YlVVfWJd188Zu1wun9hzOMgTCyHaxAB8Yl3XfWLPN9COYk3TfGLmxJyYE3MK9ZxcLhdOnDgBXde7nFOoCLqCDqDNYvvtnSPZvXs39u/fj6NHj+Lbb7/FtGnTsHjx4g7bXLlyJaKiooxHUlISAPelMwBQUVFhXAKzZ88eVFZWAnAvbelZFnLnzp3GtbtlZWWora0FAJSUlBiLVWzdutW4LKe4uNhYFKSoqAh2u91nMQS73Y6ioiIA7nWni4uLAQCNjY3YunUrAPea4iUlJQCA2tpalJWVAXB/8dm5cycA9/W15eXlANzzD/bs2cOcmBNzYk4hndPhw4exfft2aJrWpZx27NiBUBF0s9zr6uqQlpaGkydPQlEUCCEQHx+P7du3+6z65LmxhOeSl6+//hpZWVntrrkNuEfonstfAPfMxqSkJDQ0NKBfv37GNzeTyeQTq6oKSZKMWJZlyLLcYexyuWAymYxYURRIkmTEgPsboXdsNpshhDBizzdOT6zrOhRF6TDWNA1CCCNuLw/mxJyYE3NiTm1zamhoQGxsbEjMcvdrQX/33Xcv+D7J3iZPnoycnBzk5ORg48aNWLNmTZvFKn7/+9/jgw8+wLvvvguz2YxVq1Zh27Zt2LJlS6f2EUqXKhAR9TS6rqO+vh6XXXZZlxYLCqVa0OWCfuONN0KSJAghcODAAQwdOtQ4hHKhvvnmG+Tk5ODkyZOIjIzEhg0bMHz4cCxYsAAzZ87EzJkz4XA4sHjxYmzbtg1hYWGIj49Hfn6+zyj+XELpl0hE1NOoqoqSkhJMmjSpS8u/hlIt6HJBX758OX70ox/htttuw29+8xs8//zz/urbRRVKv0QiIrowoVQLujwp7ne/+x1UVcUjjzwCp9Ppjz4RERGdk67rOHr0qDFbnfw0yz07Oxv33Xcfhg4d6o/miIiIzknXdRw6dIgF3UvQzXK/VPx1mOV7V9gRUZDqmZ909ENC6ZC7X28kW1FRgaeeegqHDx/2uVjfc90gERGRP+i6DpvNhqSkJN4S9zt+Leh33HEHfvazn+G+++6DyWTyZ9NEREQGzzn0xMREFvTv+LWgm81mPPTQQ/5skoiIqA1FUZCZmRnobgQVv36tufnmm/H+++/7s0kiIqI2NE3DwYMHjdXgyM8j9GnTpmHWrFkwmUywWCwQQkCSJNTV1flzN0RE1MMJIXDq1KlOLybWE/i1oC9cuBDr16/HNddcw3PoRER00SiKgjFjxgS6G0HFrwU9NjYW2dnZ/mySiIioDc9d1tLS0jiA/I5fz6HPnj0b69atQ0NDA86cOWM8iIiI/O3s2bOB7kJQ8evCMt6XDnhu2CJJUlBOWuDCMkQ9CxeWofaE0sIyfh2he+5V67mvree/RERE/qRpGvbu3csa48WvBd1ut7d57cSJE/7cBREREbXDrwX9zjvv9Hne2NiIm2++2Z+7ICIigslkwogRIzghzotfC/rQoUOxZMkSAEBrayuysrLwi1/8wp+7ICIigqZpKC8v5yF3L34t6KtWrcLx48fxzDPPYNasWbjjjjuwYMGC826nsrISmZmZSE9Px9ixY7Fv374223zyySfo1asXRo8ebTw445GIqOeIiIgIdBeCil+uQ/e+NO3FF1/ELbfcgmnTpiE3NxdnzpxBr169zqu9hQsXIjc3Fzk5Odi4cSPmz5+Pzz//vM12V111Fb744osu95+IiLoXk8mEYcOGBbobQcUvI/Q+ffqgb9++6NOnD+Li4vDFF1/gmWeeMV4/H3V1ddi9ezfmzZsHAJgzZw6sViuqqqr80VUiIgoBqqpi165dPrfq7un8UtC/f5na9y9fOx82mw0JCQlQFPfBA0mSkJycjOrq6jbbfvPNN7jmmmswZswY/PGPfzxnuw6HA83NzT4PAEb/NE1rN1ZV1SfWdb1NHB6uQpY9scuIIyJckGVhxJIkAAhERLgACEiSJwZk2TvWER7uHbv/YE0mHRaLO1YU71hDWJh37O6v2azBbHbHYWEaFMUTq0ZssahQFN2ITSbmxJxCNydd140C0FGsaZpP7I/PCO/Y5XL5xJ6lQDyxEKJNDMAn1nXdJ+6JOem6jqioKGOtk67kFCr8UtBPnz5txCdPnuxye9L3Vmtpb+2ba665BjU1Ndi9ezc2bdqEdevW4Y033uiwzZUrVyIqKsp4JCUlAQD27t0LAKioqEBFRQUAYM+ePaisrAQAlJeXw2q1AgB27twJm80GACgrK0NtbS0AYPXqEowcWQ8AWLt2K9LSGgEABQXFSExsAQAUFhYhJsaOiAgVhYVFiIhQERNjR2FhEQAgMbEFBQXFAIC0tEasXbsVADByZD1Wry4BAIwbV4u8vDIAwOTJNixbthMAkJVlxZIl5QCA7OxK3H//HgDAvHkVmDfPndP99+9BdrY7pyVLypGV5c5p2bKdmDzZnVNeXhnGjWNOzCl0c6qvr0dJiTun2tpalJW5c7LZbNi5052T1WpFebk7p8rKSuzZ486pK58RJSUlqK9357R161Y0NrpzKi4uRkuLO6eioiLY7XaoqoqioiKoqgq73Y6iIndOLS0tKC5259TY2IitW3t2TtXV1WhqaoLJZOpSTjt27EDIEF20ePFiMXPmTLFs2TIhhBC/+MUvutTe8ePHRWRkpHC5XEIIIXRdFwMGDBBWq/Wc/+7pp58Wixcv7vB9u90umpqajIfNZhMARENDgxBCCFVVhaqqbWKXy+UTa5rmEwNChIe7hCx7YqcRR0Q4hSzrRixJugB0ERHhFIAuJMkTCyHL3rEmwsO9Y5cAhDCZNGGxuGNF8Y5VERbmHasCEMJsVoXZ7I7DwlShKJ7YZcQWi0soimbEJhNzYk6hmZMQQmiaZny2dBSrquoTt/e5cD6fEd+PnU6nT6zruk+s63qb2PNZ6Ik1TfOJe2JOdrtdlJaWGn290JxOnjwpAIimpibR3XV56dd77rkHr7zyCt577z3s2rULx44d+8HD3z9k8uTJyMnJMSbFrVmzBtu3b/fZpra2FgMGDIAsy2hpacHNN9+M+fPn47777uvUPrj0K1HPwqVfQ4uu67DZbEhKSvJZdvx8celXLxaLBQBwyy23ID4+Hlu2bOlyp/Lz85Gfn4/09HSsWrUKBQUFAIAFCxZg8+bNAIA333wTV199NUaNGoXrrrsON954I37+8593ed9ERBT8ZFlGSkpKl4p5qOnyCL2kpASTJk0ynv/jH//Af/zHf3S5YxcbR+hEPQtH6KFFVVWUlZUhMzPTmER9IThC9+JdzAEgIyOjq00SERGdkyzLGDx4MEfoXvz+k3j22Wf93SQREZEPWZaRmJjIgu6lyyvFpaSkYOjQoQDcl5d98803XZ4UR0REdC6qqhqnfLtyyD2UdPmncOONN+JPf/qT8Zw3YyEiootNlmWMGDGCI3QvXZ4U19jYiOjoaD9159LhpDiinoWT4qg9nBTnxbuYV1dXo7S0FKWlpe0u1UpEROQPLpcLH3zwgbFcLPnpbmv79+/HfffdB6vViuTkZAghYLPZcMUVV6CgoABXXnmlP3ZDREQEwH23tTFjxsBkMgW6K0HDLwU9JycHDz30EObMmePz+saNG3Hvvfcaa+8SERH5gyzLiImJCXQ3gopfZhOcOnWqTTEHgOzsbDQ1NfljF0RERAaXy4UtW7bwkLsXvxT0yy67DK+88opxOzrAvc7uhg0bEBsb649dEBERGRRFwcSJE3nJmhe//CQ2bNiAhQsXYsmSJUhISIAkSaipqUFGRgbWr1/vj10QEREZJEnq9rPS/c0vBX3IkCH46KOPcOLECeMes0lJSejfv78/miciIvLhcrlQVFSErKwsmM3mQHcnKPj1WEX//v1ZxImI6KJTFAXTp0/nIXcvF32JnfT09Iu9CyIi6oFYzH355aexb9++Dt9rbW31xy6IiIgMqqrykPv3+KWgjxgxAqmpqWhvFdn6+vrzbq+yshL33nsv6uvrER0djfXr1+Oqq67y2Wbr1q14+OGH0dLSAlmWMWvWLDz55JOQuBYrEVHIUxQFWVlZHKV78ctPIiUlBaWlpUhISGjzXlJS0nm3t3DhQuTm5iInJwcbN27E/Pnz8fnnn/ts069fPxQWFmLQoEGw2+244YYbUFhYiLvuuuuC8yAiou5DVVUWdC9+OYc+c+ZMHD58uN33Zs2adV5t1dXVYffu3Zg3bx4AYM6cObBaraiqqvLZLiMjA4MGDQIAhIeHY/To0R32gYiIQouqqiguLoaqqoHuStDwS0H/wx/+gAkTJrT73tq1a8+rLZvNhoSEBONblyRJSE5OPufNXo4dO4aNGzciKyurw20cDgeam5t9HgCgaZrx3/ZiVVV9Ys/iOd5xeLgKWfbELiOOiHBBloURS5IAIBAR4QIgIEmeGJBl71hHeLh37P6DNZl0WCzuWFG8Yw1hYd6xu79mswaz2R2HhWlQFE+sGrHFokJRdCM2mZgTcwrdnHRdNwpAR7GmaT6xPz4jvGOXy+UTe05VemIhRJsYgE+s67pP3BNzkmUZM2bMgNls7nJOoSIobyT7/fPg57rDa3NzM2699VYsXboU11xzTYfbrVy5ElFRUcbDcypg7969AICKigpUVFQAAPbs2YPKykoAQHl5OaxWKwBg586dxnX2ZWVlqK2tBQCsXl2CkSPdcwXWrt2KtLRGAEBBQTESE1sAAIWFRYiJsSMiQkVhYREiIlTExNhRWFgEAEhMbEFBQTEAIC2tEWvXbgUAjBxZj9WrSwAA48bVIi+vDAAwebINy5a518jPyrJiyZJyAEB2diXuv38PAGDevArMm+fO6f779yA7253TkiXlyMpy57Rs2U5MnuzOKS+vDOPGMSfmFLo51dfXo6TEnVNtbS3Kytw52Ww2454TVqsV5eXunCorK7FnjzunrnxGlJSUGPOJtm7disZGd07FxcVoaXHnVFRUBLvdbkz2UlUVdrsdRUXunFpaWlBc7M6psbERW7cyp507d0II0aWcduzYgZAhgszx48dFZGSkcLlcQgghdF0XAwYMEFartc22zc3N4sc//rF44oknfrBdu90umpqajIfNZhMARENDgxBCCFVVhaqqbWKXy+UTa5rmEwNChIe7hCx7YqcRR0Q4hSzrRixJugB0ERHhFIAuJMkTCyHL3rEmwsO9Y5cAhDCZNGGxuGNF8Y5VERbmHasCEMJsVoXZ7I7DwlShKJ7YZcQWi0soimbEJhNzYk6hmZMQQmiaZny2dBSrquoTt/e5cD6fEd+PnU6nT6zruk+s63qb2PNZ6Ik1TfOJe2JOZ8+eFe+8845wOp1dyunkyZMCgGhqahLdnSTEOYa/ATJ58mTk5OQYk+LWrFmD7du3+2zT2tqKm266CdOnT8eKFSvOex/+uqk9J9UTdQ/B90lHwcBftSAYBOUh9/z8fOTn5yM9PR2rVq1CQUEBAGDBggXYvHkzAPd5+507d2LTpk0YPXo0Ro8ejaeeeiqQ3SYioktE13U0NDT43BSspwvKEfqlwBE6Uc/SMz/pQpfL5cLWrVsxderULi0sE0ojdF7AR0RE3Y7ZbMZNN90U6G4ElaA85E5ERHQuuq6jrq6Oh9y9sKATEVG3o+s69u7dy4LuhYfciYio21EUBVOnTg10N4IKR+hERNTt6LqOo0ePcoTuhQWdiIi6HV3XcejQIRZ0LzzkTkRE3Y6iKJg0aVKguxFUOEInIqJuR9d1HDlyhCN0LyzoRETU7fAcels85E5ERN2OoijIzMwMdDeCCkfoRETU7WiahoMHDxr3OCcWdCIi6oaEEDh16hR66O1I2sVD7kTtEOBdd0IOf6UhRQEwhsXcB0foRETU7WiKgv379/OQuxcWdCIi6n5kGWfPng10L4IKD7kTEVG3Y3I6kZGREehuBJWgHKFXVlYiMzMT6enpGDt2LPbt29fudgUFBUhLS8PgwYORm5sLVVUvcU+JiCgQNLMZe/fu5SF3L0FZ0BcuXIjc3FwcOHAAS5cuxfz589tsY7VasXz5cpSWluLgwYM4duwYCgoKAtBbIiKiwAu6gl5XV4fdu3dj3rx5AIA5c+bAarWiqqrKZ7uNGzdi9uzZGDBgACRJwqJFi1BYWBiAHhMR0aVmcrkwYsQImEymQHclaATdOXSbzYaEhAQoirtrkiQhOTkZ1dXVSE1NNbarrq5GSkqK8Tw1NRXV1dUdtutwOOBwOIznTU1NAIBTp04BgHHYxmQy+cSqqkKSJCOWZRmyLBsxIMNiUeF0yhBChsXigtNpghAywsNdcDgUCCEhPNwFu92dU3i4+r3YDEkSsFg8sY6wMA0OhyfW4XAokGUdiqLD6VRgMukwmTyxBlkWcLk8MeBymaAo7jxU1QSzWYOuA5pmgtmsQtclaJoJYWEqNE2GpskIC1OhqjJ0nTk1AVDDw6HY7e79hYfDbLdDSBJUiwVmux26JEELC4PZ4YAuSdDDwqA4HNBlGbqiQHE6oZtM0E0mKE4nNJMJQpahuFzQTCZAlmFyuaB997duUlVoZjOg6zBpGlSzGZInDguDrGmQPbGqQtZ1qBYLZKcTshBwWSwweeLwcCgOByRP7JUHc2JOoZCTMyICX3/6KUaOHGl8rn//87szn+UNDQ0AEBLXswddQQfcRdxbRz9o7+1+6JexcuVK5OXltXnd+0vChfL6nuATf/e326lYCN/Y0453rOuA0+mONc39OFfsPaXA5Wo/9rT3/bin5xQdikkxJ+YUSjmdPQtMngx/aWlpQVRUlN/aC4SgK+hJSUmoqamBqqpQFAVCCNhsNiQnJ/tsl5yc7HMY/siRI2228fbwww/jwQcfNJ7ruo6GhgbExsa2+QJBRETBrbm5GUlJSbDZbIiMjLzgdoQQaGlpQUJCgh97FxhBV9Dj4uKQkZGBV199FTk5OXjzzTeRmpraZiQ9Z84cTJgwAY899hji4uKwbt06zJ07t8N2LRYLLBaLz2vR0dEXIQMiIrpUIiMju1TQAXT7kblH0E2KA4D8/Hzk5+cjPT0dq1atMmavL1iwAJs3bwYADBo0CHl5eRg/fjwGDx6MuLi4dmfDExER9QSSCIWZAERE1KM0NzcjKioKTU1NXR6hh4qgHKETERGdi8ViwYoVK9qcSu3JOEInIiIKARyhExERhQAWdCIiohDAgk5ERBQCWNCJiIhCAAs6ERFRCAi6leKIiIja09jYiA8++ABHjx6FJEmIj4/HTTfdhH79+gW6a0GBI3QiIgp6BQUFGDt2LLZv3w5d16FpGrZv347rrrvOWE20p+N16EREFPSGDh2Kf/3rX+jTp4/P6y0tLfjRj36EAwcOBKhnwYMjdCIiCnqSJKG1tbXN662trbxj5nd4Dp2IiILemjVrcP3112PEiBFITEwEANTU1ODrr7/Gc889F+DeBQceciciom5B0zTs3LkT3377LYQQSExMxNixY2EymQLdtaDAgk5ERN3S2rVrsXjx4kB3I2jwHDoREXVLf/7znwPdhaDCgk5ERN0SDzD74iF3IiLqllwuF8xmc6C7ETQ4Qiciom7JU8yXLVsW4J4EB47QiYgo6J05c6bd14UQGDZsGGw22yXuUfDhdehERBT0+vbti5SUFJ/z5pIkQQiB48ePB7BnwYMFnYiIgt7gwYPx4YcfIiUlpc17SUlJAehR8OE5dCIiCnr/3//3/7W79CsA5OXlXeLeBCeeQyciIgoBHKETEVG3NH369EB3IaiwoBMRUbd04sSJQHchqLCgExFRt3TzzTcHugtBhefQiYiIQgAvWyMioqA3aNAgn+dCCOM6dEmScPjw4QD1LHiwoBMRUdAbOnQo6uvrcdttt+H2229HYmJioLsUdHjInYiIuoVTp05h06ZN2LhxIxwOB2bPno25c+fisssuC3TXggILOhERdStOpxOvv/46/t//+39YsWIFfvWrXwW6S0GBh9yJiCjoqaqK4uJivPHGG6ioqMD06dOxdetWjBo1KtBdCxocoRMRUdCLiYlBUlIS7rjjDowePRqSJPm8n5WVFaCeBQ8WdCIiCno5OTltiriHJEn485//fIl7FHxY0ImIiEIAV4ojIqKg98477+DIkSPG8xUrVmDkyJG49dZbcejQoQD2LHiwoBMRUdD77W9/i/79+wMANm3ahNdffx1//vOfMXv2bCxcuDDAvQsOLOhERBT0ZFlGr169ALgLem5uLq699lrcd999aGhoCHDvggMLOhERBT1ZltHQ0ACHw4EPP/zQ59apdrs9gD0LHrwOnYiIgt6KFSuQkZEBXddx0003Gdefb9u2DampqYHtXJDgLHciIuoWVFVFS0sL+vXrZ7x2+vRpCCHQp0+fAPYsOHCETkRE3cLXX38NSZLQr18/7Nu3D++99x6GDRuGGTNmBLprQYEjdCIiCnpPPvkkioqK4HK5cMMNN6C8vBxTp05FcXExJk2ahMceeyzQXQw4FnQiIgp6V199Nfbs2QO73Y7LL78c3377LXr37g2Hw4ExY8Zgz549ge5iwHGWOxERBT2TyQRJkhAREYERI0agd+/eAACLxQJZZikDWNCJiKgbiImJQWtrKwDgs88+M14/ceIEzGZzoLoVVHjInYiIuq2WlhY0NTVh4MCBge5KwHGETkREQa+wsNCIvUfoffv2xVtvvRWAHgUfjtCJiCjoXXPNNdi9e3ebuL3nPRVH6EREFPS8x57fH4dyXOrGgk5EREFPkqR24/ae91Q85E5EREFPURTExMRACIHGxkZj+VchBJqamuB0OgPcw8BjQSciIgoBPOROREQUAljQiYiIQgALOhERUQhgQSciIgoBLOhEREQhgAWdiIgoBLCgExERhQAWdCIiohDAgk5ERBQCWNCJiIhCAAs6ERFRCGBBJyIiCgEs6ERERCGABZ2IiCgEsKATERGFABZ0IiKiEMCCTkREFAJY0ImIiEIACzoREVEIYEEnIiIKASzoREREIYAFnYiIKASwoBMREYUAFnQiIqIQwIJOREQUAljQiYiIQgALOhERUQhgQSciIgoBLOhEREQhgAWdiIgoBLCgExERhQAWdCIiohDAgk5ERBQCWNCJiIhCAAs6ERFRCGBBJyIiCgEs6ERERCGABZ2IiCgEsKATERGFABZ0IiKiEMCCTkREFAL+f4v6xF4xtjWiAAAAAElFTkSuQmCC" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "a = Image(\"sea_ice_demo/ex1/MSE_bar_chart.png\")\n", - "display_png(a)" - ] - }, - { - "cell_type": "markdown", - "id": "a9b323ec", - "metadata": {}, - "source": [ - "## Working with multiple realizations" - ] - }, - { - "cell_type": "markdown", - "id": "0c427a07", - "metadata": {}, - "source": [ - "The sea ice driver can generate metrics based on an average of all available realizations. To do so, provide an asterisk \\* as the value to the --realization argument on the command line. Options passed on the command line will supercede arguments in the parameter file. \n", - "\n", - "In addition, we set the --case_id value to 'ex2' to save results in a new directory." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "5f8174e1", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-01-25 11:40:29,821 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['E3SM-1-0']\n", - "Find all realizations: True\n", - "OBS: Arctic\n", - "Converting units by multiply 0.01\n", - "OBS: Antarctic\n", - "Converting units by multiply 0.01\n", - "Model list: ['E3SM-1-0']\n", - "\n", - "=================================\n", - "model, runs: E3SM-1-0 ['r1i2p2f1', 'r2i2p2f1', 'r3i2p2f1', 'r4i2p2f1']\n", - "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n", - "Converting units by multiply 1e-06\n", - "\n", - "-----------------------\n", - "model, run, variable: E3SM-1-0 r1i2p2f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_185001-185912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_186001-186912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_187001-187912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_188001-188912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_189001-189912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_190001-190912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_191001-191912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_192001-192912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_193001-193912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_194001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_195001-195912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_196001-196912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_197001-197912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_198001-198912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_199001-199912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_200001-200912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_201001-201112.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: E3SM-1-0 r2i2p2f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_185001-185912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_186001-186912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_187001-187912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_188001-188912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_189001-189912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_190001-190912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_191001-191912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_192001-192912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_193001-193912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_194001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_195001-195912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_196001-196912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_197001-197912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_198001-198912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_199001-199912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_200001-200912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_201001-201312.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: E3SM-1-0 r3i2p2f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_185001-185912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_186001-186912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_187001-187912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_188001-188912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_189001-189912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_190001-190912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_191001-191912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_192001-192912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_193001-193912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_194001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_195001-195912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_196001-196912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_197001-197912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_198001-198912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_199001-199912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_200001-200912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_201001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: E3SM-1-0 r4i2p2f1 siconc\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO::2024-01-25 11:43::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n", - "2024-01-25 11:43:28,092 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex2/sea_ice_metrics.json\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_185001-185912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_186001-186912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_187001-187912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_188001-188912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_189001-189912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_190001-190912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_191001-191912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_192001-192912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_193001-193912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_194001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_195001-195912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_196001-196912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_197001-197912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_198001-198912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_199001-199912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_200001-200912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_201001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-------------------------------------------\n", - "Calculating model regional average metrics \n", - "for E3SM-1-0\n", - "--------------------------------------------\n", - "arctic\n", - "ca\n", - "na\n", - "np\n", - "antarctic\n", - "sp\n", - "sa\n", - "io\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[WARNING] yaksa: 10 leaked handle pool objects\n", - "\n", - "real\t4m0.717s\n", - "user\t4m12.255s\n", - "sys\t1m20.539s\n" - ] - } - ], - "source": [ - "%%bash\n", - "time python ice_driver.py -p demo_param_file.py --realization '*' --case_id \"ex2\"" - ] - }, - { - "cell_type": "markdown", - "id": "cadb1306", - "metadata": {}, - "source": [ - "Since we have averaged four different realizations, the resulting statistics are different than seen in example 1. The bar chart now contains markers showing the overall spread among the realizations." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d6cb5f07", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "a = Image(\"sea_ice_demo/ex2/MSE_bar_chart.png\")\n", - "display_png(a)" - ] - }, - { - "cell_type": "markdown", - "id": "499d3935", - "metadata": {}, - "source": [ - "# Working with multiple models" - ] - }, - { - "cell_type": "markdown", - "id": "51b008db", - "metadata": {}, - "source": [ - "Along with using multiple realizations, we can include multiple models in a single analysis. The model data must all follow a single filename template. All model inputs must use the same name and units for the sea ice variable.\n", - "\n", - "The example below shows how to use three models in the analysis, with all available realizations. The models are listed as inputs to the --test_data_set flag.\n", - "\n", - "Want to add more models? Six other model sea ice datasets are available in the directories linked in the notebook introduction." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "679d7289", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-01-25 11:44:23,351 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "2024-01-25 11:45:05,516 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['E3SM-1-0', 'CanESM5', 'MIROC6']\n", - "Find all realizations: True\n", - "OBS: Arctic\n", - "Converting units by multiply 0.01\n", - "OBS: Antarctic\n", - "Converting units by multiply 0.01\n", - "Model list: ['CanESM5', 'E3SM-1-0', 'MIROC6']\n", - "\n", - "=================================\n", - "model, runs: CanESM5 ['r2i1p1f1', 'r1i1p1f1', 'r3i1p1f1']\n", - "/p/user_pub/pmp/demo/sea-ice/links_area/CanESM5/*.nc\n", - "Converting units by multiply 1e-06\n", - "\n", - "-----------------------\n", - "model, run, variable: CanESM5 r2i1p1f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/CanESM5/historical/r2i1p1f1/siconc/siconc_SImon_CanESM5_historical_r2i1p1f1_gn_185001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: CanESM5 r1i1p1f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/CanESM5/historical/r1i1p1f1/siconc/siconc_SImon_CanESM5_historical_r1i1p1f1_gn_185001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: CanESM5 r3i1p1f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/CanESM5/historical/r3i1p1f1/siconc/siconc_SImon_CanESM5_historical_r3i1p1f1_gn_185001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-------------------------------------------\n", - "Calculating model regional average metrics \n", - "for CanESM5\n", - "--------------------------------------------\n", - "arctic\n", - "ca\n", - "na\n", - "np\n", - "antarctic\n", - "sp\n", - "sa\n", - "io\n", - "\n", - "=================================\n", - "model, runs: E3SM-1-0 ['r1i2p2f1', 'r2i2p2f1', 'r3i2p2f1', 'r4i2p2f1']\n", - "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n", - "Converting units by multiply 1e-06\n", - "\n", - "-----------------------\n", - "model, run, variable: E3SM-1-0 r1i2p2f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_185001-185912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_186001-186912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_187001-187912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_188001-188912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_189001-189912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_190001-190912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_191001-191912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_192001-192912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_193001-193912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_194001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_195001-195912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_196001-196912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_197001-197912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_198001-198912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_199001-199912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_200001-200912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r1i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r1i2p2f1_gr_201001-201112.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: E3SM-1-0 r2i2p2f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_185001-185912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_186001-186912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_187001-187912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_188001-188912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_189001-189912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_190001-190912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_191001-191912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_192001-192912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_193001-193912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_194001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_195001-195912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_196001-196912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_197001-197912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_198001-198912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_199001-199912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_200001-200912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r2i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r2i2p2f1_gr_201001-201312.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: E3SM-1-0 r3i2p2f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_185001-185912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_186001-186912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_187001-187912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_188001-188912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_189001-189912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_190001-190912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_191001-191912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_192001-192912.nc\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-01-25 11:48:07,932 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "INFO::2024-01-25 11:52::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n", - "2024-01-25 11:52:30,866 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/pcmdi_metrics/sea_ice/sea_ice_demo/ex3/sea_ice_metrics.json\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_193001-193912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_194001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_195001-195912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_196001-196912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_197001-197912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_198001-198912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_199001-199912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_200001-200912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_201001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: E3SM-1-0 r4i2p2f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_185001-185912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_186001-186912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_187001-187912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_188001-188912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_189001-189912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_190001-190912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_191001-191912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_192001-192912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_193001-193912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_194001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_195001-195912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_196001-196912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_197001-197912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_198001-198912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_199001-199912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_200001-200912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r4i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r4i2p2f1_gr_201001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-------------------------------------------\n", - "Calculating model regional average metrics \n", - "for E3SM-1-0\n", - "--------------------------------------------\n", - "arctic\n", - "ca\n", - "na\n", - "np\n", - "antarctic\n", - "sp\n", - "sa\n", - "io\n", - "\n", - "=================================\n", - "model, runs: MIROC6 ['r2i1p1f1', 'r1i1p1f1', 'r4i1p1f1', 'r3i1p1f1']\n", - "/p/user_pub/pmp/demo/sea-ice/links_area/MIROC6/*.nc\n", - "Converting units by multiply 1e-06\n", - "\n", - "-----------------------\n", - "model, run, variable: MIROC6 r2i1p1f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r2i1p1f1/siconc/siconc_SImon_MIROC6_historical_r2i1p1f1_gn_185001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r2i1p1f1/siconc/siconc_SImon_MIROC6_historical_r2i1p1f1_gn_195001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: MIROC6 r1i1p1f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r1i1p1f1/siconc/siconc_SImon_MIROC6_historical_r1i1p1f1_gn_185001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r1i1p1f1/siconc/siconc_SImon_MIROC6_historical_r1i1p1f1_gn_195001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: MIROC6 r4i1p1f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r4i1p1f1/siconc/siconc_SImon_MIROC6_historical_r4i1p1f1_gn_185001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r4i1p1f1/siconc/siconc_SImon_MIROC6_historical_r4i1p1f1_gn_195001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-----------------------\n", - "model, run, variable: MIROC6 r3i1p1f1 siconc\n", - "test_data (model in this case) full_path:\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r3i1p1f1/siconc/siconc_SImon_MIROC6_historical_r3i1p1f1_gn_185001-194912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/MIROC6/historical/r3i1p1f1/siconc/siconc_SImon_MIROC6_historical_r3i1p1f1_gn_195001-201412.nc\n", - "Converting units by multiply 0.01\n", - "\n", - "-------------------------------------------\n", - "Calculating model regional average metrics \n", - "for MIROC6\n", - "--------------------------------------------\n", - "arctic\n", - "ca\n", - "na\n", - "np\n", - "antarctic\n", - "sp\n", - "sa\n", - "io\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[WARNING] yaksa: 10 leaked handle pool objects\n", - "\n", - "real\t9m2.167s\n", - "user\t10m50.119s\n", - "sys\t2m40.216s\n" - ] - } - ], - "source": [ - "%%bash\n", - "time python ice_driver.py -p demo_param_file.py \\\n", - "--test_data_set \"E3SM-1-0\" \"CanESM5\" \"MIROC6\" \\\n", - "--realization '*' \\\n", - "--case_id \"ex3\"" - ] - }, - { - "cell_type": "markdown", - "id": "9a17ffee", - "metadata": {}, - "source": [ - "The output JSON now includes metrics for all three models." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "b07dbb8b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"DIMENSIONS\": {\n", - " \"index\": {\n", - " \"monthly_clim\": \"Monthly climatology of extent\",\n", - " \"total_extent\": \"Sum of ice coverage where concentration > 15%\"\n", - " },\n", - " \"json_structure\": [\n", - " \"model\",\n", - " \"realization\",\n", - " \"obs\",\n", - " \"region\",\n", - " \"index\",\n", - " \"statistic\"\n", - " ],\n", - " \"model\": [\n", - " \"CanESM5\",\n", - " \"E3SM-1-0\",\n", - " \"MIROC6\"\n", - " ],\n", - " \"region\": {},\n", - " \"statistic\": {\n", - " \"mse\": \"Mean Square Error (10^12 km^4)\"\n", - " }\n", - " },\n", - " \"RESULTS\": {\n", - " \"CanESM5\": {\n", - " \"antarctic\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"5.1043444982100254\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"4.816687734317558\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"3.8203905542158574\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"3.551903219690635\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"5.408472768567934\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"5.10073354794071\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"6.255511442006537\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"5.95826796378333\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"arctic\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"2.6739701578200408\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"2.5552395000296997\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.9601839559074323\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.8071711277770932\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"2.8686219657630323\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"2.7646000598233362\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"3.306431955856059\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"3.1987918127469728\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"ca\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.12445176930055403\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.0752818530752368\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.06261975386075735\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.04065017565672462\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.18876746901985617\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.11135594838591391\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.14126431682827864\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.08283366771548334\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"io\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.3902350350581252\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.3411097649596542\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.27378096542718267\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.23052580580517984\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.39222062635127936\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.34482149125771394\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.5287500850978069\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.4689404665141464\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"na\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.8575586124643404\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.6617817141384847\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.5264155067552119\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.3050483466111835\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.7640984838802416\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.5843839089856835\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"2.3388720869665747\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"2.1497244832528395\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"np\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.0063419603431157535\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.001033088420302666\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.005949894526659108\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.7412310294637067e-06\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.01271835367014484\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.004687148872326894\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.005275638631907463\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.0008574285177782025\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"sa\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.4618851114415225\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.21877947801248515\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.3604933562263525\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.16135560774807228\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.48465097876034335\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.18590427961007985\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.565345935295451\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.32530874506699275\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"sp\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.3466206749703824\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.3264114860024545\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.0412143157666585\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.0233677214618289\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.5844213803502167\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.5597222118436824\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.4483893228104396\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"1.4270545631414926\"\n", - " }\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"E3SM-1-0\": {\n", - " \"antarctic\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.7772427941035078\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.512854523904\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.4635192339671928\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.139646926848\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.7917153708317476\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.5296078848\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.9431708933066041\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.709116624896\"\n", - " }\n", - " }\n", - " },\n", - " \"r4i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"1.1123064886611145\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.8482918891519999\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"arctic\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"5.271005131039172\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"3.602193842176\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"5.476181000101471\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"3.628078727168\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"4.798813326297904\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"3.0712725504\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"5.695229471419496\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"4.135149109248\"\n", - " }\n", - " }\n", - " },\n", - " \"r4i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"5.16787172788022\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"3.6138642309119997\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"ca\": {\n", - " \"model_mean\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.06682122096680175\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.014511187968\"\n", - " }\n", - " }\n", - " },\n", - " \"r1i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.05045644169895609\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.007755424768\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.04953964308899206\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.007533873664\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.09545969211386617\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.026321457152\"\n", - " }\n", - " }\n", - " },\n", - " \"r4i2p2f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"0.08158619730649973\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"0.020952242176\"\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"io\": {\n", - " \"model_mean\": {\n", - " 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\"mse\": \"15.90437524234963\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"13.40484878336\"\n", - " }\n", - " }\n", - " },\n", - " \"r2i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"16.338122498955823\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"13.727326797824\"\n", - " }\n", - " }\n", - " },\n", - " \"r3i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"16.167353763780575\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"13.60793174016\"\n", - " }\n", - " }\n", - " },\n", - " \"r4i1p1f1\": {\n", - " \"OSI-SAF\": {\n", - " \"monthly_clim\": {\n", - " \"mse\": \"16.22520331188068\"\n", - " },\n", - " \"total_extent\": {\n", - " \"mse\": \"13.644895092736\"\n", - " }\n", - " }\n", - " }\n", - " }\n", - " }\n", - " },\n", - " \"json_structure\": [\n", - " \"model\",\n", - " \"realization\",\n", - " \"obs\",\n", - " \"region\",\n", - " \"index\",\n", - " \"statistic\"\n", - " ],\n", - " \"json_version\": 3.0,\n", - " \"model_year_range\": {\n", - " \"CanESM5\": [\n", - " \"1981\",\n", - " \"2010\"\n", - " ],\n", - " \"E3SM-1-0\": [\n", - " \"1981\",\n", - " \"2010\"\n", - " ],\n", - " \"MIROC6\": [\n", - " \"1981\",\n", - " \"2010\"\n", - " ]\n", - " },\n", - " \"provenance\": {\n", - " \"commandLine\": \"ice_driver.py -p demo_param_file.py --test_data_set E3SM-1-0 CanESM5 MIROC6 --realization * --case_id ex3\",\n", - " \"conda\": {\n", - " \"Platform\": \"linux-64\",\n", - " \"PythonVersion\": \"3.8.15.final.0\",\n", - " \"Version\": \"23.1.0\",\n", - " \"buildVersion\": \"not installed\"\n", - " },\n", - " \"date\": \"2024-01-25 11:52:17\",\n", - " \"openGL\": {\n", - " \"GLX\": {\n", - " \"client\": {},\n", - " \"server\": {}\n", - " }\n", - " },\n", - " \"osAccess\": false,\n", - " \"packages\": {\n", - " \"PMP\": \"v3.0.2-11-g06b151f\",\n", - " \"PMPObs\": \"See 'References' key below, for detailed obs provenance information.\",\n", - " \"blas\": \"0.3.24\",\n", - " \"cdat_info\": \"8.2.1\",\n", - " \"cdms\": \"3.1.5\",\n", - " \"cdp\": \"1.7.0\",\n", - " \"cdtime\": \"3.1.4\",\n", - " \"cdutil\": \"8.2.1\",\n", - " \"clapack\": null,\n", - " \"esmf\": \"0.8.2\",\n", - " \"esmpy\": \"8.4.2\",\n", - " \"genutil\": \"8.2.1\",\n", - " \"lapack\": \"3.9.0\",\n", - " \"matplotlib\": null,\n", - " \"mesalib\": null,\n", - " \"numpy\": \"1.22.4\",\n", - " \"python\": \"3.10.13\",\n", - " \"scipy\": \"1.11.3\",\n", - " \"uvcdat\": null,\n", - " \"vcs\": null,\n", - " \"vtk\": null,\n", - " \"xarray\": \"2023.10.1\",\n", - " \"xcdat\": \"0.5.0\"\n", - " },\n", - " \"platform\": {\n", - " \"Name\": \"gates.llnl.gov\",\n", - " \"OS\": \"Linux\",\n", - " \"Version\": \"3.10.0-1160.71.1.el7.x86_64\"\n", - " },\n", - " \"userId\": \"ordonez4\"\n", - " }\n", - "}\n" - ] - } - ], - "source": [ - "with open(\"sea_ice_demo/ex3/sea_ice_metrics.json\") as f:\n", - " print(f.read())" - ] - }, - { - "cell_type": "markdown", - "id": "f48b3856", - "metadata": {}, - "source": [ - "Now the resulting bar chart shows three different models with their spread." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "41aa14a3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "a = Image(\"sea_ice_demo/ex3/MSE_bar_chart.png\")\n", - "display_png(a)" - ] - }, - { - "cell_type": "markdown", - "id": "bc43281a", - "metadata": {}, - "source": [ - "# Further exploration" - ] - }, - { - "cell_type": "markdown", - "id": "19abc98d", - "metadata": {}, - "source": [ - "Maybe you want to compare more models, or take a closer look at the model data? Here are links to the data for further exploration.\n", - "\n", - "As a reminder, data for nine models is available here:\n", - "```\n", - "/p/user_pub/pmp/demo/sea-ice/links_siconc \n", - "/p/user_pub/pmp/demo/sea-ice/links_area\n", - "```\n", - "\n", - "The observational time series can be found at:\n", - "```\n", - "/p/user_pub/pmp/demo/sea-ice/EUMETSAT\n", - "```\n", - "\n", - "For some example plotting code using xcdat and matplotlib, see the scripts that were used to generate the introductory figures:\n", - "\n", - "```\n", - "create_sector_plots.py\n", - "make_demo_sea_ice_plots.py\n", - "```\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f1161f29", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:pmp_si] *", - "language": "python", - "name": "conda-env-pmp_si-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/doc/jupyter/Demo/sea_ice_line_plots.py b/doc/jupyter/Demo/sea_ice_line_plots.py index 3bf820142..c3c01132a 100644 --- a/doc/jupyter/Demo/sea_ice_line_plots.py +++ b/doc/jupyter/Demo/sea_ice_line_plots.py @@ -30,7 +30,7 @@ ] ) plt.legend(loc="upper right", fontsize=9) -plt.savefig("E3SM_arctic_tseries.png") +plt.savefig("Arctic_tseries.png") plt.close() # Climatology plot @@ -57,5 +57,5 @@ plt.ylabel("Extent (km${^2}$)") plt.xlim([1, 12]) plt.legend(loc="upper right", fontsize=9) -plt.savefig("E3SM_arctic_clim.png") +plt.savefig("Arctic_clim.png") plt.close() From 7b0457d9ae7bb26597eeb0f8c0ccb0af07e00d51 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 13:58:19 -0800 Subject: [PATCH 60/69] fix param --- doc/jupyter/Demo/sea_ice_param.py | 52 +++++++++++++++++++++++++++++++ 1 file changed, 52 insertions(+) diff --git a/doc/jupyter/Demo/sea_ice_param.py b/doc/jupyter/Demo/sea_ice_param.py index e69de29bb..fcff3da45 100644 --- a/doc/jupyter/Demo/sea_ice_param.py +++ b/doc/jupyter/Demo/sea_ice_param.py @@ -0,0 +1,52 @@ +# Sea ice metrics parameter file + +# List of models to include in analysis +test_data_set = [ + "E3SM-1-0", +] + +# realization can be a single realization, a list of realizations, or "*" for all realizations +realization = "r1i2p2f1" + +# test_data_path is a template for the model data parent directory +test_data_path = "/p/user_pub/pmp/demo/sea-ice/links_siconc/%(model)/historical/%(realization)/siconc/" + +# filename_template is a template for the model data file name +# combine it with test_data_path to get complete data path +filename_template = "siconc_SImon_%(model)_historical_%(realization)_*_*.nc" + +# The name of the sea ice variable in the model data +var = "siconc" + +# Start and end years for model data +msyear = 1981 +meyear = 2010 + +# Factor for adjusting model data to decimal rather than percent units +ModUnitsAdjust = (True, "multiply", 1e-2) + +# Template for the grid area file +area_template = "/p/user_pub/pmp/demo/sea-ice/links_area/%(model)/*.nc" + +# Area variable name; likely 'areacello' or 'areacella' for CMIP6 +area_var = "areacello" + +# Factor to convert area units to km-2 +AreaUnitsAdjust = (True, "multiply", 1e-6) + +# Directory for writing outputs +case_id = "ex1" +metrics_output_path = "sea_ice_demo/%(case_id)/" + +# Settings for the observational data +reference_data_path_nh = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_nh_ease2-250_cdr-v3p0_198801-202012.nc" +reference_data_path_sh = "/p/user_pub/pmp/demo/sea-ice/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/ice_conc_sh_ease2-250_cdr-v3p0_198801-202012.nc" +ObsUnitsAdjust = (True, "multiply", 1e-2) +reference_data_set = "OSI-SAF" +osyear = 1988 +oeyear = 2020 +obs_var = "ice_conc" +ObsAreaUnitsAdjust = (False, 0, 0) +obs_area_template = None +obs_area_var = None +obs_cell_area = 625 # km 2 From cfa990cd9857947f057b6268838e6052c85cc469 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 13:58:40 -0800 Subject: [PATCH 61/69] move file --- pcmdi_metrics/sea_ice/lib/__init.py__ | 1 - 1 file changed, 1 deletion(-) delete mode 100644 pcmdi_metrics/sea_ice/lib/__init.py__ diff --git a/pcmdi_metrics/sea_ice/lib/__init.py__ b/pcmdi_metrics/sea_ice/lib/__init.py__ deleted file mode 100644 index 06f0911d4..000000000 --- a/pcmdi_metrics/sea_ice/lib/__init.py__ +++ /dev/null @@ -1 +0,0 @@ -from .sea_ice_parser import create_sea_ice_parser From 803a36273e6dcf73442e984675a5cd9a6d3d45dc Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 13:58:51 -0800 Subject: [PATCH 62/69] add param --- pcmdi_metrics/sea_ice/lib/sea_ice_parser.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/pcmdi_metrics/sea_ice/lib/sea_ice_parser.py b/pcmdi_metrics/sea_ice/lib/sea_ice_parser.py index 785227abc..bc5ae5f6c 100644 --- a/pcmdi_metrics/sea_ice/lib/sea_ice_parser.py +++ b/pcmdi_metrics/sea_ice/lib/sea_ice_parser.py @@ -59,10 +59,18 @@ def create_sea_ice_parser(): ) parser.add_argument( - "--reference_data_path", + "--reference_data_path_nh", default=None, - dest="reference_data_path", - help="Path for the reference climitologies", + dest="reference_data_path_nh", + help="Path for the reference climatologies for southern hemisphere", + required=False, + ) + + parser.add_argument( + "--reference_data_path_sh", + default=None, + dest="reference_data_path_sh", + help="Path for the reference climatologies for northern hemisphere", required=False, ) From 429815ec314a1bc3d8e75cbd64e48fdae2cabe55 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 14:00:50 -0800 Subject: [PATCH 63/69] update README --- pcmdi_metrics/sea_ice/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pcmdi_metrics/sea_ice/README.md b/pcmdi_metrics/sea_ice/README.md index 40d555362..92983bf2e 100644 --- a/pcmdi_metrics/sea_ice/README.md +++ b/pcmdi_metrics/sea_ice/README.md @@ -3,5 +3,5 @@ Sea ice metrics driver Example command: ``` -sea_ice_driver.py -p parameter_file.py +sea_ice_driver.py -p param/parameter_file.py ``` \ No newline at end of file From fd28409f9f17f1e6c5fc6314a418627a4d724618 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 14:04:05 -0800 Subject: [PATCH 64/69] run pc --- pcmdi_metrics/sea_ice/sea_ice_parallel.py | 138 +++++++++++++--------- 1 file changed, 82 insertions(+), 56 deletions(-) diff --git a/pcmdi_metrics/sea_ice/sea_ice_parallel.py b/pcmdi_metrics/sea_ice/sea_ice_parallel.py index 5afacffcc..4565b711d 100644 --- a/pcmdi_metrics/sea_ice/sea_ice_parallel.py +++ b/pcmdi_metrics/sea_ice/sea_ice_parallel.py @@ -22,75 +22,101 @@ if mod is None: pathDict = xs.findPaths(exp, var, frq, mip_era=mip.upper()) else: - pathDict = xs.findPaths( - exp, var, frq, mip_era=mip.upper(), model=mod - ) + pathDict = xs.findPaths(exp, var, frq, mip_era=mip.upper(), model=mod) # Get which area variable needed print("Reading external variable attribute") -#pathDB = xs.addAttribute(pathDict, 'external_variables') -areacello = xs.findPaths("historical","areacello","fx",cmipTable="Ofx") -areacella = xs.findPaths("historical","areacella","fx") +# pathDB = xs.addAttribute(pathDict, 'external_variables') +areacello = xs.findPaths("historical", "areacello", "fx", cmipTable="Ofx") +areacella = xs.findPaths("historical", "areacella", "fx") path_list = sorted(list(pathDict.keys())) print("Number of datasets:", len(path_list)) cmd_list = [] log_list = [] -model_list = xs.getGroupValues(pathDict,'model') +model_list = xs.getGroupValues(pathDict, "model") print(model_list) -for model in model_list[7:11]: - path = xs.getValuesForFacet(pathDict,'model',model)[0] - basename = os.path.basename(glob.glob(os.path.join(path,"*"))[0]) +for model in model_list: + skip = False + path = xs.getValuesForFacet(pathDict, "model", model)[0] + basename = os.path.basename(glob.glob(os.path.join(path, "*"))[0]) - dir_template = "/".join(path.split("/")[0:9]) + "/%(realization)/" + "/".join(path.split("/")[10:13]) +"/*/" - file_template = "_".join(basename.split("_")[0:4]) + "_%(realization)_" + basename.split("_")[5] + "_*-*.nc" + dir_template = ( + "/".join(path.split("/")[0:9]) + + "/%(realization)/" + + "/".join(path.split("/")[10:13]) + + "/*/" + ) + file_template = ( + "_".join(basename.split("_")[0:4]) + + "_%(realization)_" + + basename.split("_")[5] + + "_*-*.nc" + ) - single=xs.getValuesForFacet(pathDict,'model',model) + single = xs.getValuesForFacet(pathDict, "model", model) empty = [{} for item in single] - d1=zip(single,empty) - db=dict(d1) - db = xs.addAttribute(db, 'external_variables') + d1 = zip(single, empty) + db = dict(d1) + db = xs.addAttribute(db, "external_variables") - #area_var = pathDB[path]["external_variables"] - try: - area_var = db[single[0]]['external_variables'] - except: - print("No external variables") - print("Guessing areacello") - area_var = 'areacello' - if area_var == "areacello": # Same for all realizations - try: - apath = xs.getValuesForFacet(areacello,'model',model) - abase = os.path.basename(glob.glob(os.path.join(apath[0],'*'))[0]) - area_path = os.path.join(apath[0],abase) - except: - print("No areacello for model ",model) - print(apath) - continue - ## Make filename template - #area_path = "/".join(apath[0].split("/")[0:9]) + "/%(realization)/" + "/".join(apath[0].split("/")[10:]) - elif area_var == "areacella": # Different for each realization - apath = xs.getValuesForFacet(areacella,'model',model) - abase = os.path.basename(glob.glob(os.path.join(apath[0],'*'))[0]) - abase = "_".join(abase.split("_")[0:4]) + "_%(realization)_" + "_".join(abase.split("_")[5:]) - # Make filename template - area_dir = "/".join(apath[0].split("/")[0:9]) + "/%(realization)/" + "/".join(apath[0].split("/")[10:]) - area_path = os.path.join(area_dir,abase) + # area_var = pathDB[path]["external_variables"] + # try: + area_var = db[single[0]]["external_variables"] + # except: + # print("No external variables") + # print("Guessing areacello") + # area_var = "areacello" + if area_var == "areacello": # Same for all realizations + # try: + apath = xs.getValuesForFacet(areacello, "model", model) + abase = os.path.basename(glob.glob(os.path.join(apath[0], "*"))[0]) + area_path = os.path.join(apath[0], abase) + # except: + # print("No areacello for model ", model) + # print(apath) + # skip = True + ## Make filename template + # area_path = "/".join(apath[0].split("/")[0:9]) + "/%(realization)/" + "/".join(apath[0].split("/")[10:]) + elif area_var == "areacella": # Different for each realization + apath = xs.getValuesForFacet(areacella, "model", model) + abase = os.path.basename(glob.glob(os.path.join(apath[0], "*"))[0]) + abase = ( + "_".join(abase.split("_")[0:4]) + + "_%(realization)_" + + "_".join(abase.split("_")[5:]) + ) + # Make filename template + area_dir = ( + "/".join(apath[0].split("/")[0:9]) + + "/%(realization)/" + + "/".join(apath[0].split("/")[10:]) + ) + area_path = os.path.join(area_dir, abase) else: "Area variable not found." - continue + skip = True + + if not skip: + cmd_list.append( + "python -u ice_driver.py -p parameter_file.py --case_id " + + model + + " --test_data_set '" + + model + + "' --test_data_path '" + + dir_template + + "' --filename_template '" + + file_template + + "' --area_template '" + + area_path + + "' --area_var " + + area_var + ) + log_list.append("log_" + mip + "_" + var + "_" + model) - cmd_list.append( - "python -u ice_driver.py -p parameter_file.py --case_id " + model + \ - " --test_data_set '" + model + "' --test_data_path '" + \ - dir_template + "' --filename_template '" + file_template + \ - "' --area_template '" + area_path + "' --area_var " + area_var - ) - #log_list.append("log_" + mip + "_" + var + "_" + mymodel) -print(cmd_list) -#parallel_submitter( -# cmd_list, -# log_dir="./log", -# logfilename_list=log_list, -# num_workers=num_cpus, -#) +parallel_submitter( + cmd_list, + log_dir="./log", + logfilename_list=log_list, + num_workers=num_cpus, +) From b1f50d62e599e61840ae9e51b7ce00f24a9e17a5 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 15:10:48 -0800 Subject: [PATCH 65/69] add init --- pcmdi_metrics/sea_ice/lib/__init__.py | 1 + 1 file changed, 1 insertion(+) create mode 100644 pcmdi_metrics/sea_ice/lib/__init__.py diff --git a/pcmdi_metrics/sea_ice/lib/__init__.py b/pcmdi_metrics/sea_ice/lib/__init__.py new file mode 100644 index 000000000..06f0911d4 --- /dev/null +++ b/pcmdi_metrics/sea_ice/lib/__init__.py @@ -0,0 +1 @@ +from .sea_ice_parser import create_sea_ice_parser From 05e3216049da34cb01268c401caa89ac04be2023 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 15:53:43 -0800 Subject: [PATCH 66/69] comments --- pcmdi_metrics/sea_ice/param/parameter_file.py | 20 +------------------ 1 file changed, 1 insertion(+), 19 deletions(-) diff --git a/pcmdi_metrics/sea_ice/param/parameter_file.py b/pcmdi_metrics/sea_ice/param/parameter_file.py index 5ea1037da..367619106 100644 --- a/pcmdi_metrics/sea_ice/param/parameter_file.py +++ b/pcmdi_metrics/sea_ice/param/parameter_file.py @@ -1,21 +1,3 @@ -# CMIP5 -# ========= -# Model settings -# test_data_set=["ACCESS1-3","GISS-E2-H","NorESM1-M"] -# test_data_set=["ACCESS1-3"] -# realization=["*"] -# test_data_path = "/p/user_pub/pmp/pmp_results/pmp_v1.1.2/additional_xmls/latest/v20231104/cmip5/historical/seaIce/mon/sic/" -# filename_template= "cmip5.historical.%(model).%(realization).mon.sic.xml" -# var="sic" -# msyear=1981 -# meyear=2010 -# ModUnitsAdjust=(True,"multiply",1e-2) - -# Model area file -# area_template="/p/user_pub/hoang1-backups/ARCHIVE/ivanova2/IceMetrics/CMIP5/AREACELLO/areacello_fx_%(model)_historical_r0i0p0.nc" -# area_var = "areacello" -# AreaUnitsAdjust = (True, "multiply", 1e-6) - # CMIP6 # ======= case_id = "cmip6_osi-saf" @@ -44,7 +26,7 @@ metrics_output_path = "demo/%(case_id)/" -# Reference is hard coded currently so this is a placeholder +# OSI-SAF data reference_data_path_nh = ( "/p/user_pub/PCMDIobs/obs4MIPs_input/EUMETSAT/OSI-SAF-450-a-3-0/v20231201/*nh*" ) From bf9af85e23edd3376bfd7564339367c6bf342048 Mon Sep 17 00:00:00 2001 From: Ana Ordonez Date: Thu, 25 Jan 2024 16:47:26 -0800 Subject: [PATCH 67/69] add parameters --- pcmdi_metrics/sea_ice/README.md | 62 ++++++++++++++++++++++++++++++--- 1 file changed, 58 insertions(+), 4 deletions(-) diff --git a/pcmdi_metrics/sea_ice/README.md b/pcmdi_metrics/sea_ice/README.md index 92983bf2e..947570024 100644 --- a/pcmdi_metrics/sea_ice/README.md +++ b/pcmdi_metrics/sea_ice/README.md @@ -1,7 +1,61 @@ -Sea ice metrics driver +# Sea Ice -Example command: +## Summary +## Demo + +* Link to notebook + +## Inputs + +### Sectors + +## Run + +The PMP sea ice metrics can be controlled via an input parameter file, the command line, or both. With the command line only it is executed via: + +``` +sea_ice_driver.py -p parameter_file.py +``` + +or as a combination of an input parameter file and the command line, e.g.: + +``` +sea_ice_driver.py -p parameter_file.py --msyear 1991 --meyear 2020 ``` -sea_ice_driver.py -p param/parameter_file.py -``` \ No newline at end of file + +## Outputs + +The driver produces a JSON file containing mean square error metrics for all input models and realizations relative to the reference data set. It also produces a bar chart displaying these metrics. + +## Parameters + +* **case_id**: Save JSON and figure files into this subdirectory so that results from multiple tests can be readily organized. +* **test_data_set**: List of model names. +* **realization**: List of realizations. +* **test_data_path**: File path to directory containing model/test data. +* **filename_template**: File name template for test data, e.g., "CMIP5.historical.%(model_version).r1i1p1.mon.%(variable).19810-200512.AC.v2019022512.nc" where "model_version" and "variable" will be analyzed for each of the entries in test_data_set and vars. +* **var**: Name of model sea ice variable +* **msyear**: Start year for test data set. +* **meyear**: End year for test data set. +* **ModUnitsAdjust**: Factor to convert model sea ice data to fraction of 1. Uses format (flag (bool), operation (str), value (float)). Operation can be "add", "subtract", "multiply", or "divide". For example, use (True, 'multiply', 1e-2) to convert from percent concentration to decimal concentration. +* **area_template**: File path of model grid area data. +* **area_var**: Name of model area variable, e.g. "areacello" +* **AreaUnitsAdjust**: Factor to convert model area data to units of km2. Uses format (flag (bool), operation (str), value (float)). Operation can be "add", "subtract", "multiply", or "divide". For example, use (True, 'multiply', 1e6) to convert from m2 to km2. +* **metrics_output_path**: Directory path for metrics output in JSON files, e.g., '~/demo_data/PMP_metrics/'. The %(case_id) variable can be used here. If exists, should be empty before run. +* **reference_data_path_nh**: The reference data file path for the northern hemisphere. If data is global, provide same path for nh and sh. +* **reference_data_path_sh**: The reference data file path for the southern hemisphere. If data is global, provide same path for nh and sh. +* **ObsUnitsAdjust**: Factor to convert reference sea ice data to fraction of 1. Uses format (flag (bool), operation (str), value (float)). Operation can be "add", "subtract", "multiply", or "divide". For example, use (True, 'multiply', 1e-2) to convert from percent concentration to decimal concentration. +* **reference_data_set**: A short name describing the reference dataset, e.g. "OSI-SAF". +* **osyear**: Start year for reference data set. +* **oeyear**: End year for reference data set. +* **obs_var**: Name of reference sea ice variable. +* **ObsAreaUnitsAdjust**: Factor to convert model area data to units of km2. Uses format (flag (bool), operation (str), value (float)). Operation can be "add", "subtract", "multiply", or "divide". For example, use (True, 'multiply', 1e6) to convert from m2 to km2. +* **obs_area_template**: File path of grid area data. If unavailalbe, skip and use "obs_cell_area". +* **obs_area_var**: Name of reference area variable, if available. If unavailable, skip and use "obs_cell_area". +* **obs_cell_area**: For equal area grids, the area of a single grid cell in units of km2. + + +## Reference + +Ivanova, D. P., P. J. Gleckler, K. E. Taylor, P. J. Durack, and K. D. Marvel, 2016: Moving beyond the Total Sea Ice Extent in Gauging Model Biases. J. Climate, 29, 8965–8987, https://doi.org/10.1175/JCLI-D-16-0026.1. \ No newline at end of file From 2aae7c3aa3e340849b2afc098acfd06a5ef1243b Mon Sep 17 00:00:00 2001 From: lee1043 Date: Thu, 25 Jan 2024 22:19:52 -0800 Subject: [PATCH 68/69] add nc-time-axis that is needed for plot in sea-ice notebook --- conda-env/dev.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/conda-env/dev.yml b/conda-env/dev.yml index 0a14f0395..539bac09e 100644 --- a/conda-env/dev.yml +++ b/conda-env/dev.yml @@ -29,6 +29,7 @@ dependencies: - rasterio=1.3.6 - shapely=2.0.1 - numdifftools + - nc-time-axis # ================== # Testing # ================== From 8311c81d76c5f67a203d3ac23d759400370f5ef8 Mon Sep 17 00:00:00 2001 From: lee1043 Date: Thu, 25 Jan 2024 23:02:18 -0800 Subject: [PATCH 69/69] minor update: add time --- .../Demo/Demo_9_seaIceExtent_ivanova.ipynb | 318 +++++++++++------- 1 file changed, 190 insertions(+), 128 deletions(-) diff --git a/doc/jupyter/Demo/Demo_9_seaIceExtent_ivanova.ipynb b/doc/jupyter/Demo/Demo_9_seaIceExtent_ivanova.ipynb index b1e1ba9d0..bdf9775bf 100644 --- a/doc/jupyter/Demo/Demo_9_seaIceExtent_ivanova.ipynb +++ b/doc/jupyter/Demo/Demo_9_seaIceExtent_ivanova.ipynb @@ -16,8 +16,8 @@ "**Summary** \n", "The PCMDI Metrics sea ice driver produces metrics that compare modeled and observed sea ice extent. This notebook demonstrates how to run the PCMDI Metrics sea ice code.\n", "\n", - "**Demo author list** \n", - "Ana Ordonez, Jiwoo Lee, Paul Durack, Peter Gleckler\n", + "**Authors** \n", + "Ana Ordonez, Jiwoo Lee, Paul Durack, Peter Gleckler (PCMDI, Lawrence Livermore National Laboratory)\n", "\n", "**Reference** \n", "Ivanova, D. P., P. J. Gleckler, K. E. Taylor, P. J. Durack, and K. D. Marvel, 2016: Moving beyond the Total Sea Ice Extent in Gauging Model Biases. J. Climate, 29, 8965–8987, https://doi.org/10.1175/JCLI-D-16-0026.1. " @@ -53,7 +53,9 @@ "metadata": {}, "source": [ "## Sectors\n", - "This code block produces maps that show the different regions used in the analysis along with the mean observed sea ice concentration. The code to generate these figures can be found in the script `sea_ice_sector_plots.py`." + "This code block produces maps that show the different regions used in the analysis along with the mean observed sea ice concentration. The code to generate these figures can be found in the script `sea_ice_sector_plots.py`.\n", + "\n", + "Below process will take about 30 seconds." ] }, { @@ -67,18 +69,14 @@ "output_type": "stream", "text": [ "Creating Arctic map\n", - "Creating Antarctic map\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[WARNING] yaksa: 10 leaked handle pool objects\n" + "Creating Antarctic map\n", + "CPU times: user 8.72 ms, sys: 4.33 ms, total: 13 ms\n", + "Wall time: 27.3 s\n" ] } ], "source": [ + "%%time\n", "%%bash\n", "python sea_ice_sector_plots.py" ] @@ -102,14 +100,14 @@ "outputs": [ { "data": { - "image/png": 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" + "image/png": 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" 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" + "image/png": 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" }, "metadata": {}, "output_type": "display_data" @@ -138,7 +136,9 @@ "\n", "For this demo, we start the OSI-SAF satellite data in 1988 as that avoids missing data in earlier parts of the record.\n", "\n", - "The code to generate these figures can be found in `sea_ice_line_plots.py`." + "The code to generate these figures can be found in `sea_ice_line_plots.py`.\n", + "\n", + "Below process will take about 15 seconds." ] }, { @@ -151,11 +151,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-25 13:41:42,705 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + "2024-01-25 22:44:28,596 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 6.37 ms, sys: 1.71 ms, total: 8.08 ms\n", + "Wall time: 15.1 s\n" ] } ], "source": [ + "%%time\n", "%%bash\n", "python sea_ice_line_plots.py" ] @@ -168,14 +177,14 @@ "outputs": [ { "data": { - "image/png": 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" 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" 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -387,13 +396,6 @@ " (True, 'multiply', 1e-6) # m^2 to km^2 - (False, 0, 0)\n", " # No adjustment (default) (default: (False, 0, 0))\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[WARNING] yaksa: 10 leaked handle pool objects\n" - ] } ], "source": [ @@ -411,7 +413,9 @@ "The PMP sea ice metrics driver call follows the basic format:\n", "ice_driver.py -p parameter_file.py --additional arguments\n", "\n", - "The following cell runs the driver with the demo parameter file we saw above." + "The following cell runs the driver with the demo parameter file we saw above.\n", + "\n", + "Below process will take about 3 minutes." ] }, { @@ -420,30 +424,31 @@ "id": "d6ff0052", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-01-25 13:42:49,930 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "INFO::2024-01-25 13:43::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex1/sea_ice_metrics.json\n", - "2024-01-25 13:43:52,348 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex1/sea_ice_metrics.json\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ "['E3SM-1-0']\n", - "\n", - "Metrics output path not found.\n", - "Creating metrics output directory sea_ice_demo/ex1/\n", "Find all realizations: False\n", "OBS: Arctic\n", "Converting units by multiply 0.01\n", "OBS: Antarctic\n", "Converting units by multiply 0.01\n", "Model list: ['E3SM-1-0']\n", - "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n", + "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-25 22:45:49,104 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Converting units by multiply 1e-06\n", "\n", "-----------------------\n", @@ -486,11 +491,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "[WARNING] yaksa: 10 leaked handle pool objects\n" + "INFO::2024-01-25 22:46::pcmdi_metrics:: Results saved to a json file: /home/lee1043/git/pcmdi_metrics_20240125/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex1/sea_ice_metrics.json\n", + "2024-01-25 22:46:57,691 [INFO]: base.py(write:251) >> Results saved to a json file: /home/lee1043/git/pcmdi_metrics_20240125/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex1/sea_ice_metrics.json\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 43.3 ms, sys: 12.8 ms, total: 56.1 ms\n", + "Wall time: 2min 15s\n" ] } ], "source": [ + "%%time\n", "%%bash\n", "sea_ice_driver.py -p sea_ice_param.py" ] @@ -735,25 +750,36 @@ " ]\n", " },\n", " \"provenance\": {\n", - " \"commandLine\": \"/home/ordonez4/miniconda3/envs/pmp_si/bin/sea_ice_driver.py -p sea_ice_param.py\",\n", + " \"commandLine\": \"/home/lee1043/.conda/envs/pcmdi_metrics_dev_20231129/bin/sea_ice_driver.py -p sea_ice_param.py\",\n", " \"conda\": {\n", " \"Platform\": \"linux-64\",\n", - " \"PythonVersion\": \"3.8.15.final.0\",\n", - " \"Version\": \"23.1.0\",\n", + " \"PythonVersion\": \"3.10.12.final.0\",\n", + " \"Version\": \"23.3.1\",\n", " \"buildVersion\": \"not installed\"\n", " },\n", - " \"date\": \"2024-01-25 13:43:38\",\n", + " \"date\": \"2024-01-25 22:46:38\",\n", " \"openGL\": {\n", " \"GLX\": {\n", - " \"client\": {},\n", - " \"server\": {}\n", - " }\n", + " \"client\": {\n", + " \"vendor\": \"Mesa Project and SGI\",\n", + " \"version\": \"1.4\"\n", + " },\n", + " \"server\": {\n", + " \"vendor\": \"SGI\",\n", + " \"version\": \"1.4\"\n", + " },\n", + " \"version\": \"1.4\"\n", + " },\n", + " \"renderer\": \"llvmpipe (LLVM 7.0, 256 bits)\",\n", + " \"shading language version\": \"1.20\",\n", + " \"vendor\": \"VMware, Inc.\",\n", + " \"version\": \"2.1 Mesa 18.3.4\"\n", " },\n", " \"osAccess\": false,\n", " \"packages\": {\n", " \"PMP\": \"v3.0.2-11-g06b151f\",\n", " \"PMPObs\": \"See 'References' key below, for detailed obs provenance information.\",\n", - " \"blas\": \"0.3.24\",\n", + " \"blas\": \"0.3.25\",\n", " \"cdat_info\": \"8.2.1\",\n", " \"cdms\": \"3.1.5\",\n", " \"cdp\": \"1.7.0\",\n", @@ -764,15 +790,15 @@ " \"esmpy\": \"8.4.2\",\n", " \"genutil\": \"8.2.1\",\n", " \"lapack\": \"3.9.0\",\n", - " \"matplotlib\": null,\n", + " \"matplotlib\": \"3.7.1\",\n", " \"mesalib\": null,\n", - " \"numpy\": \"1.22.4\",\n", - " \"python\": \"3.10.13\",\n", - " \"scipy\": \"1.11.3\",\n", + " \"numpy\": \"1.23.5\",\n", + " \"python\": \"3.10.10\",\n", + " \"scipy\": \"1.11.4\",\n", " \"uvcdat\": null,\n", " \"vcs\": null,\n", " \"vtk\": null,\n", - " \"xarray\": \"2023.10.1\",\n", + " \"xarray\": \"2023.11.0\",\n", " \"xcdat\": \"0.5.0\"\n", " },\n", " \"platform\": {\n", @@ -780,7 +806,7 @@ " \"OS\": \"Linux\",\n", " \"Version\": \"3.10.0-1160.71.1.el7.x86_64\"\n", " },\n", - " \"userId\": \"ordonez4\"\n", + " \"userId\": \"lee1043\"\n", " }\n", "}\n" ] @@ -809,7 +835,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "sea_ice_demo/ex1/MSE_bar_chart.png\r\n" + "sea_ice_demo/ex1/MSE_bar_chart.png\n" ] } ], @@ -825,7 +851,7 @@ "outputs": [ { "data": { - "image/png": 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" + "image/png": 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zzTdd2vnyyy9RWVnpsi6aMmUKiAgrV66stly9Xl/t7z81NbUhHwu7TjzKvREJgoDHHnsMf//732E2m9GpU6cajxkCwJkzZ1BeXg4vL68aXy8pKQFgX7H2798f3t7eeO2119CpUyf4+PjAZDLhgQcewOXLl13e5+PjA29vb5fndDrdVVcczvR6Pe6//37cf//9AIDCwkKMHDkSH3zwAWbPno0uXbqgtLQU7du3r/betm3b1mkZNfn6668xYcIEPPjgg3j22WfRtm1baDQafPTRR/j444+rzX/l8cPS0lKIooj3338f77//fo3LcHymV+Pn51fr6N9r9eFqz9d2LDU0NFR5vS5t17UvZ86cAQA888wzeOaZZ2p8z9U+j4cffhhbtmzBwoUL0aNHDxgMBgiCgISEhGp/c/UxZcoUrFu3Dj/88AOGDx+OtLQ0WCwWlyJb1/8b9VVaWgqNRoOgoCCX5wVBQNu2bat9B84iIiIAAEaj8arLOHHiRLUfbSqVCgMGDMCAAQMA2E85mzp1Kr744gt8/PHHePzxx2tsa8iQIS6noE2ePFkZnwDY/1YfeughZfzOb7/9hnvvvRcvvvgipk+fDn9/fwD2Ee3e3t4YMWKEMlYgNjYWUVFRWLVqFZKTk6FWq136W9f/A+zG4oLeyJKSkvDSSy9h+fLleP3112udr3Xr1ggMDMTmzZtrfN1xmltmZiZOnTqFbdu2uZyKUpdBPO4QERGBGTNm4KmnnsJvv/2GLl26IDAwEKdPn642b03PeXt71zggr6SkBK1bt1Yer1mzBu3bt8cXX3zhsoVT22A+53kAoFWrVlCr1XjkkUfwxBNP1Piemn6ENMSVfbja84GBgSguLq72/KlTpwDA5bO4Wtt17Yujveeff95lsJmzzp071/h8RUUFNm7ciEWLFrmcVmWxWFBWVlavfl1p+PDhCA0NxcqVKzF8+HCsXLkSvXr1wh133OHS97r836ivwMBAiKKIc+fOuRR1IsLp06fRo0ePWt8bEhKCLl26ICMjA5cuXYKPj0+1eX7++WecOXNGGShYmxYtWuD555/HF198gby8vFrnW7FiBaqqqpTHV/6NXKlLly6YOHEi3n33XRw+fBg9e/bE4cOHsWPHDgD//6PkSt9//z0SEhKu2jZrHFzQG1lYWBieffZZHDx4EJMnT651vvvuuw///ve/IUkSevXqVet8jhW1TqdzeX7FihXu6fDvqqqqIAgCWrZsWe01xy5Wx9bkoEGDkJKSgv3797vsdv/888+rvTcqKgq5ubkuzx0+fBiHDh1yWUEJggAvLy+XwnT69OkaR7nXxMfHB4MGDUJOTg5iY2Nr3bprLEOGDMH69etx6tQp5XMEgE8++QQ+Pj7o3bu3W5fXuXNnxMTEYP/+/Vi8eHG93isIAoio2t/cv/71L0iS5PKcY566brU7fnS9++672L59O/bs2VPtb7mu/zdq49wnvV6vPD9kyBCkpKRgzZo1+Mtf/qI8v27dOly8eBFDhgy5arsvvvgiHn74YTzzzDP48MMPXV67ePEinnzySfj4+Li0XVxcXOPeliv/T9Wkth9cpaWl8PX1rfFv/ODBgy7tOnaV//Of/0THjh1d5r18+TLGjh2Ljz/+mAt6E8UFvQm48gpQNZk4cSI+++wzJCQkYO7cuejZsye0Wi2KioqwdetWjB07FuPGjUOfPn3QqlUrzJo1C4sWLYJWq8Vnn32G/fv3u7XPhw4dwvDhwzFx4kQMHDgQISEhOH/+PDZt2oR//OMfiI+PR58+fQAATz31FD7++GOMGjUKr732GoKDg/HZZ58pKxNnjzzyCCZNmoTHH38c48ePx4kTJ5CSklJtt6fjVK/HH38ciYmJMJlMePXVVxESEoKCgoI65fDee++hX79+6N+/P2bPno2oqChUVVXhyJEj+O6772q8WMaVysvLazzWrtPpEBcXV6d+1GTRokXYuHEjBg0ahJdeegkBAQH47LPPsGnTJqSkpMDPz++6267NihUrMHLkSAwfPhxJSUkICwtDWVkZ8vPzsXfvXnz11Vc1vs9gMGDAgAF488030bp1a0RFReGnn35CamqqshvXoWvXrgCAf/zjH/D19YW3tzfat2+PwMDAWvs1ZcoUvPHGG3j44Yeh1+vxxz/+0eX1uv7fqM2dd94JAHjjjTcwcuRIqNVqxMbGYujQoRg+fDiee+45VFZWom/fvsjNzcWiRYsQFxeHRx555Kqf50MPPYS9e/firbfewvHjxzFlyhQEBwfj0KFDeOedd3D06FF8/vnn6NChg/KeLl26YMiQIRg5ciSio6NhNpuxe/duvP322wgODq5xbMa1bN26FXPnzsWf/vQn9OnTB4GBgTh79izS0tKwefNmPProo2jXrh1EUcQnn3yC22+/HdOmTauxrdGjR2PDhg3V9lqwJqJxx+TdepxHuV9NTReWsdls9NZbb1G3bt3I29ubWrZsSbfddhvNnDmTCgoKlPl27txJ99xzD/n4+FBQUBBNmzaN9u7dW+30o9ouCuEYlX0158+fp9dee40GDx5MYWFh5OXlRS1atKDu3bvTa6+9Vu1UoQMHDtDQoUPJ29ubAgICaOrUqfTtt99WG/EsyzKlpKRQhw4dyNvbm/7whz9QZmZmjaPcly5dSlFRUaTT6ej222+nf/7znzX2HQA98cQTNeZhNBppypQpFBYWRlqtloKCgqhPnz702muvXTV/oquPcg8LC6v2eTpO37qyjdpGKv/66680evRo8vPzIy8vL+rWrZvL90f0/yPOv/rqq2v215EvAHrzzTdrfH3//v00YcIEatOmDWm1Wmrbti0NHjyYli9fXm2Zzt9bUVERjR8/nlq1akW+vr40YsQIysvLq/GshXfffZfat29ParXa5W/yylHuzvr06UMA6E9/+lONr9f1/0ZNLBYLTZs2jYKCgkgQBAKgnIZ5+fJleu655ygyMpK0Wi2FhITQ7Nmz6fz581dt01l6ejolJCRQYGAgabVaCgsLo0ceeYR+++23avOuWLGCHnjgAerQoQP5+PiQl5cXRUdH06xZs8hkMrnMW9dR7iaTif76179S3759qW3btqTRaMjX15d69epF77//vnKK2TfffEMA6N133621LcfI/7fffpuI+MIyTY1ARHRTf0Ew9rtt27Zh0KBB2Lp1q0df+5wxxm4GPm2NMcYY8wBc0BljjDEPwLvcGWOMMQ/AW+iMMcaYB+CCzhhjjHkALuiMMcaYB+CCzhhjjHkALuiMMcaYB+CCzhhjjHkALuiMMcaYB2iSBb2goAB9+vRBp06d0LNnTxw4cKDaPMePH0d8fHy97kfNGGOMeaomWdBnzpyJGTNm4PDhw5g/f36NdxgyGAx47bXXarwFJ2OMMXaraXIF/ezZs9i7dy8mTZoEABg/fjyMRiOOHz/uMl9AQAD69euHFi1aNEIvGWOMsaalyd0P3WQyITQ0FBqNvWuCICAiIgKFhYWIioq67nYtFgssFovyWJZllJWVITAwEIIgNLTbjDHGmiEiQlVVFUJDQ6FSNblt3HppcgUdQLUC647LzS9ZsgTJyckNbocxxpjnMZlMaNeuXWN3o0GaXEEPDw9HUVERRFGERqMBEcFkMiEiIqJB7T7//PN4+umnlccVFRWIiIjA8ePH0apVK0iSBABQq9UusSiKEARBiVUqFVQqVa2xzWaDWq1WYo1GA0EQlBiAkpsj1mq1ICIllmUZkiQpsSzL0Gg0tcaSJIGIlLimPDgnzolz4pw8KSeLxYJffvkFvXv3VjYCryensrIytG/fHr6+vmjumlxBb9OmDeLi4rBmzRokJSVh3bp1iIqKatDudgDQ6XTQ6XTVnm/VqhUMBkOD2maMMXZzybKMbt26wd/f3y27yj3h0GuTvH3qoUOHkJSUhNLSUhgMBqxevRpdunTBtGnTMGbMGIwZMwYWiwXR0dGwWCyoqKhAmzZt8Mgjj2DJkiV1WkZlZSX8/PxQUVHBBZ0x5hEkSYLNZmvsbjRpWq0WarVaeexJtaBJFvSbwZO+RMYYu3DhAoqKitwy5qg5ICJYLBbodLp6bV0LgoB27dqhZcuWADyrFjS5Xe6MMcbqR5IkFBUVwcfHB0FBQR6x+/haHMffHcfr6/qec+fOoaioCDExMS5b6p6ACzpjjDVzNpsNRISgoCDo9frG7k6TFhQUhOPHjysD+TxJ8z7pjjHGmOJmb5n36dMHixcvdmubH330EQYMGIB+/frhwQcfxIULF2qcT5ZlVFRUQJZlAPbLgScmJl6zfU/ee8Fb6IwxxurNZDIhMjISW7ZswQsvvOCWNn/44Qf897//xdatW6FWq5GTkwOr1VrjvIIgoEWLFh5doOuLt9AZY8zDCELDp2tZu3YtJk2ahOjoaBw5cgQA8PLLL+NPf/oTRowYgQEDBuDSpUs4fvw4+vTpg/HjxyM2NhY//vhjrW2mpaXhueeeU3aFx8XFwdfXF/369VPm+eMf/4hjx47hl19+waBBgxAfH4+3337bpZ09e/Zg0KBB6N+/P956663r+ASbJy7ojDVz8fHxePfddxu1Dy1btsSvv/7aqH1gN9eWLVswbNgwPPTQQ/jqq6+U5zt37ozNmzejf//+SvEuLS3FF198gXXr1uHDDz+stc3i4mKEhoa6PKfVahEXF4c9e/agsrISZWVl6NChA/7yl79gxYoV2Lp1K/7yl7+4vOe5557D119/je3bt+O///0vzpw548bMmy4u6IxdxY4dOzBy5Ei0atUK/v7+6NatG1JSUmrdDVgfL7/8Mu6///6Gd7IOLl68CIPBgF69ejW4raioKHzzzTcuz124cAF33nlnvdsSRREvvPACoqKi0LJlS4SEhOC+++5DVVVVg/vZFH7oeKqioiLk5uZi9OjRWLJkCTZu3Ki8FhcXB8B+1c/z588DALp27QqNRuPyXE1CQ0Nx8uTJas8/+uijWLNmDdatW4fx48cDAKxWKzp37gxBEKpdWObXX3/FuHHjEB8fj2PHjsFkMjU45+aACzpjtdi4cSNGjhyJ4cOHo6CgAOXl5fjiiy9w4MABFBcX35Q+iKLolna+/PJLqNVq/PLLL8jLy7spy6yLpUuXIiMjA1u3bsWFCxewf/9+PPDAAzdt+VdzMz+H5mbt2rV47733sHnzZmRkZOC2225Tdrs7H9N2nBNf03M1eeihh5CSkqJcsnX//v0oKytDjx49kJubi3//+9+YMGECAPvVP8+ePQtBEJSBcQ7dunXDt99+i23btmHv3r24++673ZN4E8cFnbEaEBGefPJJPPfcc3jqqafQunVrAMBtt92GVatWITIyEgBw9OhRjB49GkFBQYiMjMRrr72mrFxWrVqF7t2749VXX0WbNm0QHBysbDF+8803WLx4MTZu3IiWLVsqF7lISkrC1KlTMWHCBBgMBnz00UfIyclBv379EBAQgKCgIDz00EMoLS2tVz6pqal47LHHMGDAAKSmprq8Fh8fj/nz52PYsGFo0aIF/vOf/6CyshJz5sxBREQEDAYDevToAZPJhAcffBCFhYV46KGH0LJlS8yaNQuAfYW9b98+pc20tDR069YNBoMBkZGRWLVqVY392rVrF8aOHYv27dsDsF/6ecqUKS7X1f73v/+N2NhY+Pv7o0ePHti5c6fymtVqxUsvvYTo6Gj4+vrizjvvxN69ezFv3jxs374dzz33HFq2bImRI0cCAM6cOYMJEyYgKCgIERERePHFF5XCvW3bNvj7++Ojjz5CREQE7rnnnnp9xreSdevWYeDAgcrjIUOGuOx2r4ulS5fCaDS6PHfvvfeib9++iI+PR//+/bF48WJ4eXkBAIYOHQofHx8EBAQAAN566y3cf//9GDRoULU9MUuXLsUDDzyAQYMGISEhAWaz+TqybIboFlVRUUEAqKKiorG7wpqgQ4cOEQA6cuRIrfNcunSJIiMj6W9/+xtZLBY6ceIEdenShf71r38REdHKlStJo9FQSkoKWa1W2rp1K6nVaqXNRYsW0dixY13anDx5Mun1etq8eTNJkkQXL16kffv20fbt28lqtdLp06epf//+NG3aNOU9AwcOpHfeeafWfh48eJAA0P79++njjz+mwMBAslgsLu8PCgqi3bt3kyzLdOnSJRo3bhwNHz6cTp48SZIk0d69e+ncuXNERBQZGUnr1693WQYAysnJISKiDRs2UEBAAG3ZsoUkSaIzZ87Q3r17a+zbkiVLKDg4mN555x365ZdfyGazuby+adMmCgsLo//9738kSRKtW7eOAgICqKSkhIiI/vKXv9Ddd99Nhw8fJlmW6eDBg3T8+PFaP5fBgwfTww8/TFVVVXT8+HG644476PXXXycioq1bt5JKpaKZM2fSxYsX6eLFi7V+pk3N5cuX6cCBA3T58mUiIgIaPjU1S5cupa+//lp5LMsySZJEsizXq50rPytPqgVN8Gu7OTzpS2Tut2PHDgKg/KevyZdffkndu3d3ee4f//gHDR48mIjsBT04ONjl9Y4dO9LatWuJqPaCfuVzV1q/fj117NhReXytgv7ss88q/aysrCQfHx/68ssvXd4/d+5c5fHp06cJAJ04caLG9q5V0EeMGEHJyclXzcFBkiT65z//SYMHD6YWLVqQn58fPffccySKIhERJSQk0Lvvvuvynj59+tAnn3xCsiyTj48P/fTTTzW2feXnUlRURACouLhYee6zzz6jmJgYIrIXdAB0/vz5OvW9KbmySHma5ORkGjZsmMsPPi7o1TXaLveCggL06dMHnTp1Qs+ePXHgwIEa50tNTUVMTAyio6MxY8YMl+Nab731Frp27Yru3bujd+/e+OWXX25W95mHc+xir2mAjsPx48eRl5cHf39/ZZo3bx5Onz6tzNO2bVuX97Ro0eKaA76uvFXwkSNHMHbsWISGhsJgMGDSpEkoKSmpUx6iKOKTTz7B5MmTAQC+vr4YN25ctd3uzss8ceIEdDrddd+y+MSJE4iJianTvCqVCtOmTcOWLVtQXl6Ozz//HMuXL1f6d/z4cbzwwgsun/G+fftw8uRJnDt3DpcuXarzsoqKiuDt7e3ynXTo0AFFRUXKY19fX/j7+9c9WXZTvPTSS/j++++V268C9sNilZWVt8y16+ui0Qr6zJkzMWPGDBw+fBjz58/H1KlTq81jNBqxcOFC7NixA0eOHMHp06eV/+j79+/H+++/j127dmHfvn2YM2cOnnjiiZudBvNQnTp1QlRUFP7973/XOk94eDjuvvtulJeXK1NlZSV+++23Oi2jtls+Xvn8rFmzEBYWhgMHDqCyshJr1qyp80ps48aNOHPmDF599VW0bdsWbdu2xYYNG/DDDz+gsLCwxmVGRkbCYrHUOjL4WreqjIyMVAZI1YdGo0FCQgKGDBminAIXHh6Ot99+2+UzvnjxIhYsWICgoCD4+PjUuqwr+9muXTuYzWaXU5iMRiPatWtX59xY0yEIAgwGA19Yxkmj/PWePXsWe/fuxaRJkwAA48ePh9FoxPHjx13mW7t2LcaNG4fg4GAIgoBZs2YhLS1Ned1ms+HixYsAgPLycpf/mIw1hCAIeP/997F06VK8//77yiC0w4cPY+rUqThx4gTuu+8+nDlzBh9++CHMZjMkScKhQ4ewbdu2Oi0jODgYJ06cUEb01qayshK+vr4wGAwwmUx4880365xHamoqxowZg99++w379u3Dvn37cPjwYXTs2LHWgWrBwcEYO3YsZs2aheLiYsiyjJycHOUzCA4OxtGjR2td5syZM/Hee+/hp59+gizLOHv2LHJycmqc95133sGPP/6ICxcugIjw3//+F9u2bUOfPn0AAHPmzMGbb76J//3vfyAiXLp0CT/++COKioogCAKmT5+OefPm4ciRIyAiHDp0CCdOnKixn2FhYRg0aBCeeeYZXLx4EYWFhVi8eLGy94Kx5q5RCrrJZEJoaKiy+0QQBERERLhsMQBAYWGhMpoYsJ//6pinW7duePrpp9G+fXu0a9cO77zzDt5///1al2mxWFBZWekyAVBWppIk1RiLougSO0Yw1xbbbDaX2LEl5YiJqFoMwCWWZdkldhxmqC2WJMkl5pzck9Pw4cORnp6OTZs2ITo6Gv7+/khMTERMTAxCQkLg4+OD//znP9iyZQuioqIQGBiIhx9+GKdOnXI5NOSch2M5kiThgQcegMFgQOvWrZXdvI5lO+f0t7/9DRs3boTBYMDYsWNdzl13Xs6VOZ08eRL/+c9/8OSTTyI4OBjBwcEIDAxEcHAw5syZg5UrV7r0x/l7Wr16Ndq1a4c//OEP8Pf3x6xZs5RDBQsWLMCyZcvQqlUrzJ49u9r3dP/99+Ott97CE088AT8/P/To0QP79++v8XvS6/V44YUXEBYWBn9/f0yfPh0vvvgi/vjHPwIAhg8fjsWLF2P69Olo1aoV2rdvj/feew8WiwVEhKVLlyI+Ph733nsvDAYDEhMTUVZWBiLCnDlz8OOPP8Lf3x+jRo0CAKxZswaXLl1CZGQk+vbti5EjR2L+/PmQZdnl76qx//au9/+T89/PzYxlWb5qTETVYkcbtcV1Wb5jl/v19N35u/EY13PgvaH27NlDd9xxh8tzf/jDH6oNbpkzZw6lpKQoj/Py8qh9+/ZERHT8+HHq378/nTp1ioiI3n//fRo4cGCty1y0aBEBqDZlZWUREdGvv/5Kv/76KxER7d27l/Lz84mIKDs7mwoKCoiI6L///a8ygvann36ioqIiIiLasmULnTlzhoiINm/eTKWlpUREtHHjRmWgxTfffEOXLl0iq9VK33zzDVmtVrp06RJ98803RGQfmLFx40YiIiotLaXNmzcTEdGZM2doy5YtRGQf1OP4jI4fP07//e9/iYiooKCAsrOziYgoPz9fGVHMOXFOnNOtkdPWrVvpwIEDVFFRQZWVlUREZDabqaqqiojsA8EuXLhARPazMxwj+C9dukSXLl0iIqKLFy8q8YULF5RBY1VVVWQ2m4nIPqjScYZERUUFWa1WIiIqLy9XBqydP39eGdR4/vx5kiSJJEmqFhMRiaKoxDabjcrLy4mIyGq1Kp+LxWJxa06XL1+mnJwc5WyT9PR0jxkU1ygF/cyZM2QwGJQ/AFmWKTg4mIxGo8t8KSkp9PjjjyuPN23apBTtN998k2bPnq28duHCBRIEQflDupLZbKaKigplMplMBIDKysqIyP6H5Xivc2yz2VxiSZKuGlutVpfYMQLTEcuyXC12fAaOWJIkl9jxOdUWi6LoEteUB+fEOXFOnptTVVUVHThwgC5duqS053j/jYzvueceev31111Gm9cUO49Id8SONpzj1NRU8vHxoaqqKpJlmXbv3k0AKDc3t8bl22w25bm69v3y5cv022+/KT8ASktLPaagN8ou9zZt2iAuLg5r1qwBYL9IQVRUFKKiolzmGz9+PNavX48zZ86AiLB8+XJMnDgRgH106o4dO5Rb63333Xe4/fbba72/rU6ng8FgcJkAKPOr1eoaY41G4xI7Bs3UFmu1WpfYMWDDEQuCUC0G4BKrVCqX2HFoorZYrVa7xJwT58Q53Zo5Od4DQYCgUkFQqa4//r1fSptXxEVFRcrd1lRO89cUOy7P6hw72rsyvuOOO7B582YIgoC1a9eiR48eNS6fiJSxF7X18Wqx83fjKRptSOeKFSuwYsUKdOrUCUuXLlVGr0+bNg0bNmwAYC/aycnJ6Nu3L6Kjo9GmTRtlNPy4ceMwatQo/OEPf0C3bt2wbNky5QcCY4yxG+tG3G0NAMaOHavUgAMHDuCOO+4AYC/gf/7znzFo0CAMHToUp06dgr+/Px555BHEx8ejX79+yhiru+66C7NmzUKvXr2wZMmSG/gpNC0C0a15El9lZSX8/PxQUVGhbK0zxlhzZDabYTQa0b59e3h7e9ft/qfXco3ScN9992H9+vXYsWMHdu3aheeffx4vv/wyVCoVXnrpJbz44ovo1asXYmNjMXz4cPz2228wGo3KndBqsmrVKly4cAE//fQTnn76aaSnp8NkMuGZZ57B8ePHkZ2djVdeeQX/+9//8PHHHysDJFu0aIENGzZg9+7deP3119GhQwds3boV4eHhiIuLUwZl1vRZeVIt8Jx9DYwxxm4K57utybKMixcv4vnnnwfQsLutOQwbNgyPP/44Vq5cqVyn/cCBA1i/fj2ysrJARGjXrh0qKyvx0ksvYf/+/bBYLOjSpQsAoFWrVsoZUnq93t3pN1lc0BljjNWL425r48aNAwBMnTrVLXdbcxg3bhx++eUXdO/eXXnutttuw4QJE7Bw4UIA9tP8cnNzcfbsWWzfvh0bNmxQtvxv1YvN1PsYuvN9bxljjN16btTd1hxat26Nf/zjHy7PjR49GqWlpRg0aBAGDRqE1atXIzo6GsXFxRg6dOg1j83fCup0DH3o0KEQBAFEhMOHD6Nz587IyMi4Gf27YTzpuAlj7NZW7Rj6LYCIUFVVBV9f33ptkXvyMfQ6baH37t0bjz/+OH744Qc88MADzb6YM8YYa974Wu7V1amgv/rqqxBFES+88AKsVuuN7hNjjDF2VUQEq9XKd1tzUudBcYmJiejevTvS09NvZH8YY4xdp1utuFksFuUCO3XlyZ9RvUa5d+zYEU8++eSN6gtjjLHr4Liy3Llz5xAUFHTL7IbWarWwWCx1np+IcO7cOZer7nmSep+2lp+fj9dffx3Hjh1zuUtNdna2WzvGGGOsbtRqNdq1a4eioqJqt6H2VEQESZKgVqvr9QNGEAS0a9eu1suEN2f1LugTJkzAo48+iilTpnjkB8IYY81Ry5YtERMTo9xW1dOJoohff/0Vd955Z72ux67Vaj22dtW7oGu1Wjz77LM3oi+MMcYawPmmMbeCe+65p7G70KTU+8IyI0aMwObNmxu84IKCAvTp0wedOnVCz549ceDAgRrnS01NRUxMDKKjozFjxgyX3fyFhYUYPXo0OnfujNtuuw3vv/9+g/vFGGOs6ZMkCUeOHIEkSY3dlSaj3gV9yJAhSExMhJ+fH9q0aYOgoCC0adOm3gueOXMmZsyYgcOHD2P+/PnKXdScGY1GLFy4EDt27MCRI0dw+vRp5a5sRIRx48bh0UcfxaFDh5Cfn48HH3yw3v1gjDHW/BARzp8/79Gj1uur3ndb69ixI5YuXYq77rrLZdeO40L4dXH27Fl06tQJJSUl0Gg0ICKEhIRg165dLvdEf/PNN3H8+HF88MEHAID09HSkpKRg27Zt+PHHH/Hyyy9jx44d9em+wpOuDsQYY+z6eFItqPcWemBgIBITE9GhQwdERkYqU32YTCaEhoYqAxkEQUBERIRyL1uHwsJCl7ajoqKUeQ4cOICgoCBMnDgRcXFxGDduHI4dO1brMi0WCyorK10mAMruGkmSaoxFUXSJZVm+amyz2Vxix+8lR0xE1WIALrEsyy6x4zBDbbEkSS4x58Q5cU6ck6fnZLVaceDAAaXfDcnJU9S7oI8bNw7Lly9HWVkZLl26pEz1deVpBrXtKKjtLj02mw0//vgjFi5ciJycHIwcORITJ06sdXlLliyBn5+fMoWHhwMA8vLyANhPx8vPzwcA5ObmoqCgAACQk5Oj3EAgOzsbJpMJALBz504UFxcDALKyslBSUgIAyMzMRHl5OQAgIyMDVVVVAOx7F8xmM0RRRHp6OkRRhNlsVi7UU1VVpVxSt7y8HJmZmQCAkpISZGVlAQCKi4uxc+dOAPYfRY5TBY1GI3JycgDYxybk5uZyTpwT58Q5eXROJ06cQFFRUYNz2r17NzwG1ZMgCMqkUqmUf+vjzJkzZDAYyGazERGRLMsUHBxMRqPRZb6UlBR6/PHHlcebNm2igQMHEhHRV199Rf3791deu3jxIqlUKhJFscZlms1mqqioUCaTyUQAqKysjIiIRFFU3usc22w2l1iSpKvGVqvVJZZl2SWWZbla7PgMHLEkSS6x43OqLRZF0SWuKQ/OiXPinDgnzql6TqWlpQSAKioqqLmrd0F3l4EDB9LKlSuJyF6ce/XqVW2eo0ePUkhICJ0+fZpkWabRo0fTRx99REREFy5coA4dOlBRUREREa1bt45iY2PrvPyKigqP+RIZY+xWI4oi/frrr7VuxNWVJ9WCep+Hbjabq92ez3G5wfpYsWIFkpKSsHjxYhgMBqxevRoAMG3aNIwZMwZjxoxBhw4dkJycjL59+0KWZQwePFgZDd+iRQt8+OGHGDVqFIgI/v7++Pzzz+ubDmOMMeYR6j3Kfdy4cVi/fr3yuLy8HEOGDMH//vc/t3fuRvKkkY2MMcaujyfVgnoPiuvcuTPmzp0LALhw4QISEhIwe/Zst3eMMcYYq40kScjJyeELyzipd0FfunQpzpw5gzfeeANjx47FhAkTMG3atBvRN8YYY6xWer2+sbvQpNR5l7vzqWmXL1/GyJEjMWTIECxcuBAA4OPjc2N6eIN40m4Wxhhj18eTakGdC7pKpYIgCCAi5V+lEUFodrs9POlLZIyxW40oisjJyUFcXFy97rZ2JU+qBXX+FBxX1WGu6nEbXsZYI+JLfnsWQRDQqlWret0L3dPV+Rj6xYsXlbi0tPSGdIYxxhirC7VajY4dO95St4u9ljoV9D//+c94+OGH8fzzzwOActycMcYYawyiKGLnzp0edS32hqpTQS8vL8e3336LAQMG4JVXXrnRfWKMMcauSqVSISwsDCpVvU/W8lh1+iR0Oh0AYOTIkQgJCcGmTZtuaKcYY4yxq1GpVIiMjOSC7qROg+IeffRRJZ4+fToCAwNvWIcYY4yxa3Hscu/Tp0+DRrl7kjr9tBkwYIDL47i4uAYvuKCgAH369EGnTp3Qs2dPHDhwoMb5UlNTERMTg+joaMyYMaPa8RIiwpAhQ9C6desG94kxxljzoFKpEB0dzVvoTq7rk3jzzTcbvOCZM2dixowZOHz4MObPn6/cdMWZ0WjEwoULsWPHDhw5cgSnT59GamqqyzzLli1DVFRUg/vDGGOs+eBj6NXV6ZOIjIzEsGHDMGzYMAwdOhQbN25s0ELPnj2LvXv3YtKkSQCA8ePHw2g04vjx4y7zrV27FuPGjUNwcDAEQcCsWbOQlpamvF5QUIB///vfWLBgQYP6wxhjrHkRRRGZmZk8yt1JnQr60KFDkZGRgYyMDPzwww8YNWpUgxZqMpkQGhqqHPcQBAEREREoLCx0ma+wsBCRkZHK46ioKGUeWZYxffp0fPDBB9BqtddcpsViQWVlpcsEQLnCnSRJNcaiKLrEjgvsOMfe3iJUKkdsU2K93gaVipRYEAgAQa+3ASAIgiMGVCrnWIa3t3Ns/4NVq2XodPZYo3GOJXh5Ocf2/mq1ErRae+zlJUGjccSiEut0IjQaWYnVas6Jc/LcnGRZVgpAbbEkSS6xO9YRzrHNZnOJHVfddMREVC0G4BLLsuwS34o5ERFuv/12qFSqBufkKepU0N966y2Xxx999FGDF3zl1X1quwKt83zO87z11lsYMGAAunfvXqflLVmyBH5+fsoUHh4OAMjLywMA5OfnIz8/HwCQm5uLgoICAEBOTg6MRiMAIDs7GyaTCQCwc+dOFBcXAwBSUrIQG1sCAFi2LBMxMeUAgNTUDISFVQEA0tLSERBghl4vIi0tHXq9iIAAM9LS0gEAYWFVSE3NAADExJRj2bJMAEBsbAlSUrIAAL16FSM5eScAID7ehAULsgEACQlGzJ2bAwBITCzA9Om5AIBJk/IxaZI9p+nTc5GYaM9p7twcJCTYc1qwIBvx8fackpN3olcvzolz8tycSkpKkJVlz6m4uBg7d9pzMplMyM6252Q0GpGTY8+poKAAubn2nBqyjsjKykJJiT2nzMxMlJfbc8rIyEBVlT2n9PR0mM1miKKI9PR0iKIIs9mM9HR7TlVVVcjIsOdUXl6OzMxbO6cTJ06gqKgIKpWqQTnt3r0bHoPq6cSJE7R9+3bavn07nThxor5vJyKiM2fOkMFgIJvNRkREsixTcHAwGY1Gl/lSUlLo8ccfVx5v2rSJBg4cSEREo0aNovDwcIqMjKSwsDBSqVQUGRlJZWVlNS7TbDZTRUWFMplMJgKgzC+KIomiWC222WwusSRJLjFA5O1tI5XKEVuVWK+3kkolK7EgyATIpNdbCZBJEBwxkUrlHEvk7e0c2wggUqsl0unssUbjHIvk5eUciwQQabUiabX22MtLJI3GEduUWKezkUYjKbFazTlxTp6ZExGRJEnKeqe2WBRFl7im9UJ91hFXxlar1SWWZdkllmW5WuxYTzpiSZJc4lsxp8uXL9N//vMfslqtDcqptLSUAFBFRQU1d3Uu6Pn5+XTPPfdQ27ZtqWfPntSjRw9q27Yt3XPPPXTgwIF6L3jgwIG0cuVKIiL66quvqFevXtXmOXr0KIWEhNDp06dJlmUaPXo0ffTRR9XmMxqNFBgYWK/lV1RUuOVLtF8hmieeeGrqE/MskiRRaWmpUpivl7tqQVNQ5z/zXr160dq1a6s9/9VXX1GPHj3qveCDBw9S7969KSYmhu6++27Ky8sjIqKpU6fSt99+q8z3j3/8g6Kjo6l9+/Y0depU5RecMy7oPPHE07UmxmriSQW9zrdP7dy5Mw4dOlTv15oqd90yj2/0w1jzULc1HWsubDYbMjIyMGzYsDoNjK6NJ90+tc4n8LVu3Rqffvqpy21UZVnG6tWr+cpxjDHGbiqNRoP+/fvzVeKc1PmTWL16NWbOnIm5c+ciNDQUgiCgqKgIcXFxWLVq1Q3sImOMMeZKEIRmv0XtbnUu6B07dsSWLVtw7tw5Zbh/eHg4goKCbljnGGOMsZrYbDakp6cjISGhQbvcPUm991UEBQVxEWeMMdaoNBoNhg0bxrvcnbjlIridOnVyRzOMMcZYnXExd1XnT6O2u6EBwIULF9zSGcYYY6wuHFef413u/6/OBb1r166IiopCTWe5OS7/xxhjjN0MGo0GCQkJvJXupM6fRGRkJHbs2IHQ0NBqrzmui84YY4zdLKIockF3Uudj6GPGjMGxY8dqfG3s2LFu6xBjjDF2LaIoIiMjw6PultZQdb5SnKfhK8Uxdmu5Ndd07FpuySvFMcYYY00FEaGysrLGcV23qkYr6AUFBejTpw86deqEnj171jqKPjU1FTExMYiOjsaMGTOU3Su//vorBgwYgNtuuw133nknZsyYAYvFcjNTYIwx1khEUcT27dt5l7uTRivoM2fOxIwZM3D48GHMnz8fU6dOrTaP0WjEwoULsWPHDhw5cgSnT59GamoqAMDb2xvLli3DwYMHsW/fPlRUVODtt9++2WkwxhhrBFqtFqNGjeJT1pw0SkE/e/Ys9u7di0mTJgEAxo8fD6PRiOPHj7vMt3btWowbNw7BwcEQBAGzZs1CWloaACAmJgaxsbEAALVajR49etQ6aI8xxphnkWUZZWVlLjcMu9U1SkE3mUwIDQ1VTjcQBAEREREoLCx0ma+wsBCRkZHK46ioqGrzAMDFixfxr3/9C6NHj651mRaLBZWVlS4TAEiSpPxbUyyKokvs+ONxjr29RahUjtimxHq9DSoVKbEgEACCXm8DQBAERwyoVM6xDG9v59i+S0mtlqHT2WONxjmW4OXlHNv7q9VK0GrtsZeXBI3GEYtKrNOJ0GhkJVarOSfOyXNzkmVZ2UVbWyxJkkvsjnWEc2yz2VxixzFgR0xE1WIALrEsyy7xrZiT1WpFdna20u+G5OQpGm2Xu3DF8PDaBjY4z1fTPDabDX/84x8xbNiwq54+t2TJEvj5+SmT49z5vLw8AEB+fj7y8/MBALm5uSgoKAAA5OTkwGg0AgCys7OVG9Ps3LkTxcXFAICUlCzExtovrrNsWSZiYsoBAKmpGQgLqwIApKWlIyDADL1eRFpaOvR6EQEBZqSlpQMAwsKqkJqaAQCIiSnHsmWZAIDY2BKkpGQBAHr1KkZy8k4AQHy8CQsWZAMAEhKMmDs3BwCQmFiA6dNzAQCTJuVj0iR7TtOn5yIx0Z7T3Lk5SEiw57RgQTbi4+05JSfvRK9enBPn5Lk5lZSUICvLnlNxcTF27rTnZDKZkJ1tz8loNCInx55TQUEBcnPtOTVkHZGVlaVcgCszMxPl5facMjIyUFVlzyk9PR1ms1m5ApooijCbzUhPt+dUVVWFjAx7TuXl5cjMvLVzKioqQmBgILRabYNy2r17NzwGNYIzZ86QwWAgm81GRESyLFNwcDAZjUaX+VJSUujxxx9XHm/atIkGDhyoPLZarXT//ffTtGnTSJblqy7TbDZTRUWFMplMJgJAZWVlREQkiiKJolgtttlsLrEkSS4xQOTtbSOVyhFblVivt5JKJSuxIMgEyKTXWwmQSRAcMZFK5RxL5O3tHNsIIFKrJdLp7LFG4xyL5OXlHIsEEGm1Imm19tjLSySNxhHblFins5FGIymxWs05cU6emRMRkSRJynqntlgURZe4pvVCfdYRV8ZWq9Uldqy7HLEsy9Vix3rSEUuS5BLfijlZrVY6deoUSZLUoJxKS0sJAFVUVFBz1ygFnYho4MCBtHLlSiIi+uqrr6hXr17V5jl69CiFhITQ6dOnSZZlGj16NH300UdEZP9CHnjgAZoyZco1i3lNKioq3PIl2s9u5Yknnpr6xDyLzWajLVu2KAX+ermrFjQFjXZhmUOHDiEpKQmlpaUwGAxYvXo1unTpgmnTpmHMmDEYM2YMAOCf//wn3njjDciyjMGDB+Ojjz6CVqvFZ599hkmTJiE2NlbZLd+3b1988MEHdVo+X1iGsVtL46zpWFPnSReW4SvFcUFn7JZwa67pPJcsyyguLkZISAhUqusfDuZJBZ2vFMcYY6zZkWUZR48e5dPWnPBtahhjjDU7Go0GAwYMaOxuNCm8hc4YY6zZkWUZJ06c4C10J1zQGWOMNTuyLOPkyZNc0J3wLnfGGGPNjkajQZ8+fRq7G00Kb6EzxhhrdiRJwpEjR5TLuTIu6IwxxpohIsL58+dxi555XSPe5c5YDQh8gQGPw1+pR9EA6MHF3AVvoTPGGGt2JI0GBw8e5F3uTrigM8YYa35UKly+fLmxe9Gk8C53xhhjzY7aakVcXFxjd6NJaZJb6AUFBejTpw86deqEnj174sCBAzXOl5qaipiYGERHR2PGjBkedaN6xhhjtZO0WuTl5fEudydNsqDPnDkTM2bMwOHDhzF//nxMnTq12jxGoxELFy7Ejh07cOTIEZw+fRqpqamN0FvGGGOs8TW5gn727Fns3bsXkyZNAgCMHz8eRqMRx48fd5lv7dq1GDduHIKDgyEIAmbNmoW0tLRG6DFjjLGbTW2zoWvXrlCr1Y3dlSajyR1DN5lMCA0NhUZj75ogCIiIiEBhYSGioqKU+QoLCxEZGak8joqKQmFhYa3tWiwWWCwW5XFFRQUA4Pz58wCg7LZRq9UusSiKEARBiVUqFVQqlRIDKuh0IqxWFYhU0OlssFrVIFLB29sGi0UDIgHe3jaYzfacvL3FK2ItBIGg0zliGV5eEiwWRyzDYtFApZKh0ciwWjVQq2Wo1Y5YgkpFsNkcMWCzqaHR2PMQRTW0WgmyDEiSGlqtCFkWIElqeHmJkCQVJEkFLy8RoqiCLHNOFQBEb29ozGb78ry9oTWbQYIAUaeD1myGLAiQvLygtVggCwJkLy9oLBbIKhVkjQYaqxWyWg1ZrYbGaoWkVoNUKmhsNkhqNaBSQW2zQfr9b10tipC0WkCWoZYkiFotBEfs5QWVJEHliEURKlmGqNNBZbVCRQSbTge1I/b2hsZigeCInfLgnDgnT8jJqtfjt59+QmxsrLJev3L9XZd1eVlZGQB4xPnsTa6gA/Yi7qy2D9p5vmt9GUuWLEFycnK1551/JFwvp98JLvHvf7t1iolcY0c7zrEsA1arPZYk+3S12HlIgc1Wc+xo78r4Vs/J3xOT4pw4J0/K6fJlID4e7lJVVQU/Pz+3tdcYmlxBDw8PR1FREURRhEajARHBZDIhIiLCZb6IiAiX3fAnTpyoNo+z559/Hk8//bTyWJZllJWVITAwsNoPCMYYY01bZWUlwsPDYTKZYDAYrrsdIkJVVRVCQ0Pd2LvG0eQKeps2bRAXF4c1a9YgKSkJ69atQ1RUVLUt6fHjx6Nfv3546aWX0KZNGyxfvhwTJ06stV2dTgedTufynL+//w3IgDHG2M1iMBgaVNABNPstc4cmNygOAFasWIEVK1agU6dOWLp0qTJ6fdq0adiwYQMAoEOHDkhOTkbfvn0RHR2NNm3a1DganjHGGLsVCOQJIwEYY4zdUiorK+Hn54eKiooGb6F7iia5hc4YY4xdjU6nw6JFi6odSr2V8RY6Y4wx5gF4C50xxhjzAFzQGWOMMQ/ABZ0xxhjzAFzQGWOMMQ/ABZ0xxhjzAFzQGWOMMQ/ABZ0xxhjzAFzQGWOMMQ/ABZ0xxhjzAFzQGWOMMQ/ABZ0xxhjzAE2yoA8bNgyxsbHo3r07+vfvj3379tU4X2pqKmJiYhAdHY0ZM2ZAFMWb21HGGGOsiWiSN2cpLy+Hv78/AOCbb77BK6+8gr1797rMYzQa0bdvX+Tk5KBNmzYYO3YsRo0ahZkzZ9ZpGbIs49SpU/D19YUgCO5OgTHGWDNARKiqqkJoaChUqia5jVtnmsbuQE0cxRwAKioqavyQ165di3HjxiE4OBgAMGvWLKSkpNS5oJ86dQrh4eFu6S9jjLHmzWQyoV27do3djQZpkgUdAB599FFs3boVALB58+ZqrxcWFiIyMlJ5HBUVhcLCwlrbs1gssFgsymPHjonjx4+jVatWkCQJAKBWq11iURQhCIISq1QqqFSqWmObzQa1Wq3EGo0GgiAoMQCIougSa7VaEJESy7IMSZKUWJZlaDSaWmNJkkBESlxTHpwT58Q5cU6elJPFYsEvv/yC3r17K3tZryensrIytG/fHr6+vmjumuz+hU8++QQmkwmvvfYann322Rrncd5Vfq0jB0uWLIGfn58yRUREALD/MDAYDDh58iROnjwJg8GA48eP48yZMzAYDDh69ChKS0thMBhw8OBBVFRUwGAwIC8vDxcvXoTBYMC+fftgtVphMBiwZ88eyLIMg8GAXbt2QRAEGAwG7NixA15eXvDx8cGOHTvg4+MDLy8v7NixAwaDAYIgYNeuXTAYDJBlGXv27IHBYIDVasW+fftgMBhw8eJF5OXlwWAwoKKiAgcPHoTBYEBpaSmOHj0Kg8GAM2fO4Pjx45wT58Q5cU4enVN5eTlatmwJf3//BuWUn59frZ40V03yGPqV9Ho9ioqKEBgYqDz35ptv4vjx4/jggw8AAOnp6UhJScG2bdtqbOPKLfTKykqEh4ejrKyMt9A5J86Jc+KcbtGcysrKEBgYqBT45qzJFfTKykpcuHABoaGhAID169fjz3/+M0wmk8svqGPHjqFfv34ug+ISEhIwa9asOi/Hz8/PI75Exhi71YiiiKysLAwYMED5oXA9PKkWNLlj6BUVFRg/fjwuX74MlUqFoKAgbNy4EYIgYNq0aRgzZgzGjBmDDh06IDk5GX379oUsyxg8eDCmTp3a2N1njDF2E6hUKnTt2rXZj0x3pya3hX6zeNKvMsYYY9fHk2oB/7RhjDHW7NhsNnz//few2WyN3ZUmgws6Y4yxZketVqNHjx5Qq9WN3ZUmo8kdQ2eMMcauRaVSISAgoLG70aTwFjpjTUBUVBS++eabZr2MLl26YOPGjTesfcac2Ww2bNq0iXe5O+GCzlgt4uPjoVarkZubqzxXXl4OQRBw/PjxBrX77rvvNryDAAYPHgy9Xo/z58/fsGXUpKb2f/vtN9x3333X1d7bb7+NTp06wdfXF0FBQbj33nsb9Bk7JCUl4amnnmpwO6zp0Wg06N+/f4NOWfM0XNAZu4pWrVrh+eefd0tbRKRc6MIdjh07hm3btsHHxwefffaZ29q92dasWYP3338fX3/9NaqqqlBQUIAZM2Y0iSt38R0cmy7H1eiawt9JU8EFnbGrePzxx7Fz505kZWXV+DoR4e2330Z0dDQCAgIwYsQIHDt2THk9KioKS5YsQe/eveHj44MJEyZg+/bteO6559CyZUuMHDlSmffw4cPo3bs3fH19MXDgQJhMpqv27eOPP0b37t3x5z//Gampqcrz8+bNq3UZDoWFhRg6dCiCgoLQqlUrjBo1ymWLOCkpCdOnT8fEiRPh6+uLzp07K1dhrK39K3fp//DDD+jVqxf8/f0REhKCJUuW1JjHrl27MGTIEHTt2hWA/eZMEyZMcLlXw48//oiePXvC398fXbp0wYYNG5TXZFnG3//+d9x2223w9fVFTEwMNm/ejL///e/47LPP8OGHH6Jly5bo0qULAKCqqgozZsxASEgIQkJCMGvWLFy8eBGA/d4OgiBg5cqV6NixI8LCwq76HbDGY7PZ8O233/Iud2d0i6qoqCAAVFFR0dhdYU3UwIED6Z133qHFixfTPffcQ0RE58+fJwBkNBqJiGj16tUUGhpKubm5dPnyZXr66afp9ttvJ5vNRkREkZGR1KlTJzp48CCJokgWi0Vp11lkZCR16dKFjh49SpcvX6aRI0fS5MmTa+2bKIoUFhZG7733Hh09epQEQaD//e9/1fp+5TLWr19PRERGo5HS09Pp8uXLVFFRQYmJiXTvvfcq806ePJlatmxJW7ZsIVEU6dVXX6XIyMg6t793717S6/W0du1aslqtVF5eTj///HONuaSlpVHLli3ptddeox07dtDly5ddXt+/fz/5+/vTli1bSJIk2r59OxkMBjp48CAREb333nvUvn172rNnD8myTCdOnKADBw4oecydO9elvccee4wGDRpEJSUldO7cORo4cCBNnz5d+VwA0P3330/nz5+nixcv1vodsMYlyzJdunSJZFluUDueVAt4C52xa3jqqadw4sSJGgeUffrpp3jyySdx5513wtvbG4sXL0ZRURGys7OVeWbPno3OnTtDrVbDy8ur1uXMmTMHHTp0gLe3N/70pz/hf//7X63zfv/99zh79iweeughdOjQAX379nXZSr+WqKgojBw5Et7e3jAYDHjxxReRlZUFWZaVeUaNGoXBgwdDrVbjsccew4kTJ1BaWlqn9v/xj39g4sSJGD9+PLRaLfz8/NC7d+8a5504cSJWrlyJnTt3YtSoUQgMDMT06dOVreYVK1YgKSkJgwcPhkqlQr9+/XDffffhyy+/BAB89NFHePnll3H33XdDEARERETg9ttvr3FZsizj888/x5IlSxAYGIjWrVtj8eLF+OSTT1xyX7RoEfz9/eHj41OnfFnj4OPnrrigM3YNer0eixYtwgsvvFDtGHhRURGioqKUxzqdDqGhoSgqKlKec9zZ71ratm2rxC1atEBVVVWt86ampiIhIQFBQUEAgMmTJ+Pzzz/H5cuX67Ssc+fO4eGHH0Z4eDgMBgMGDBgAq9Xqsswr+wPgqn1yduLECcTExNRpXgBITEzEpk2bcP78eXz//ffIyMjA66+/DsC+G3z58uXw9/dXpm+//RanTp2q97LOnTsHi8Xi8p116NABFosFJSUlynN1/c5Y4xFFEenp6TzOwQkXdMbqYOrUqZBlGatXr3Z5vl27di7Hnq1WK06dOoV27dopz115remGXnv63Llz+O6777Blyxa0bdsWbdu2xYIFC1BeXo6vv/66Tst4/vnncenSJezduxeVlZXKGAGq45Wgr9V+ZGQkjhw5Uqe2nAmCgH79+iExMRG//vorACA8PBxz585FeXm5Ml24cAEfffTRNZd1ZT+DgoLg5eXl8p0ZjUbodDq0bt26zvmxxqfRaJCQkMBb6U74r5axOlCr1Xj99dexePFil+cnTZqEZcuW4cCBA7BYLPjrX/+KsLAw9OzZs9a2goODcfTo0evuyyeffIKAgAAcPHgQ+/btw759+5CXl4ekpCRlt/u1llFZWQkfHx/4+/ujtLQUycnJ9erDtdqfPn060tLSsH79eoiiiIqKCuzatavGeVeuXIlvv/0W5eXlAIC8vDx8++236NOnDwBg5syZWLlyJbZu3QpJkmCxWPDzzz8r97GeOXMmkpOTsW/fPhARCgsLldeCg4NdBimqVCo8/PDDePHFF1FWVobS0lK8+OKLeOSRR7iIN0O8de6K/4IZq6Px48ejY8eOLs89+uij+POf/4z77rsPbdu2xf79+/Hdd99ddavhqaeewo8//gh/f//rOm87NTUVs2fPRlhYmLKF3rZtW8ybNw/btm3D0aNHr7mM5ORkHDlyBK1atULfvn1rHAl/Nddq/6677sK6devw+uuvIyAgALfffjt++umnGtvy9/fH22+/jQ4dOsDX1xf3338/HnroIcyfPx8AEBcXh7S0NPz1r39FUFAQwsLCsHDhQlgsFgDAk08+idmzZ2PChAnw9fXFvffei8LCQgDAtGnTcPLkSbRq1QqxsbEAgPfeew9RUVG444470KVLF3Ts2BF/+9vf6pU/a3yiKCIjI4OLuhO+25oH3GGHMcbY9fGkWsBb6IwxxpodIkJlZWWdx33cCppcQTebzbj//vvRqVMndO/eHSNGjKjxEpCZmZno1asX7rjjDnTt2hUvvvgif7GMMXaLEEUR27dv513uTprcLnez2YzMzEyMHDkSgiBg2bJl2LBhAzIyMlzmy8nJgZ+fHzp06ACz2Yx7770Xjz/+OB5++OE6Lcddu1n4qoOMNQ9Na03Hmgre5X4DeXt7IyEhQbk+b+/evV1GqTrExcWhQ4cOynu6d+9e43yMMcY8jyzLKCsrc7kg0K2uyRX0K/3973/H6NGjrzrP6dOnsXbtWiQkJNQ6j8ViQWVlpcsEQLlQiCRJNcaiKLrEjj8e59jbW4RK5YhtSqzX26BSkRILAgEg6PU2AARBcMSASuUcy/D2do7tu5TUahk6nT3WaJxjCV5ezrG9v1qtBK3WHnt5SdBoHLGoxDqdCI1GVmK1mnPinDw3J1mWlV20tcWSJLnE7lhHOMc2m80lduwkdcREVC0G4BLLsuwS34o5Wa1WZGdnK/1uSE6eokkX9MWLF6OgoEC5YlRNKisrMXr0aMyfPx933XVXrfMtWbIEfn5+yhQeHg7Afs4rAOTn5yvnrubm5qKgoACAfde+0WgEAGRnZys3zNi5cyeKi4sBACkpWYiNtV9latmyTMTElAMAUlMzEBZmv7JWWlo6AgLM0OtFpKWlQ68XERBgRlpaOgAgLKwKqan2wwoxMeVYtiwTABAbW4KUFPtFP3r1KkZy8k4AQHy8CQsW2C8vmpBgxNy5OQCAxMQCTJ9uv93npEn5mDTJntP06blITLTnNHduDhIS7DktWJCN+Hh7TsnJO9GrF+fEOXluTiUlJcpFdIqLi7Fzpz0nk8mkXK7XaDQiJ8eeU0FBgXL73IasI7KyspQr0WVmZirn3GdkZChX30tPT4fZbHa5AprZbEZ6uj2nqqoq5dBjeXk5MjNv7ZyKiooQGBgIrVbboJx2794Nj+HOC8N/9913bmvrzTffpLvvvpvOnz9f6zyVlZV0zz330CuvvHLN9sxmM1VUVCiTyWQiAFRWVkZE9ptdiKJYLbbZbC6xJEkuMUDk7W0jlcoRW5VYr7eSSiUrsSDIBMik11sJkEkQHDGRSuUcS+Tt7RzbCCBSqyXS6eyxRuMci+Tl5RyLBBBptSJptfbYy0skjcYR25RYp7ORRiMpsVrNOXFOnpkTEZEkScqNc2qLRVF0iWtaL9RnHXFlbLVaXWLHzUUcsSzL1WIicoklSXKJb8WcrFYrnTp1iiRJalBOpaWlHnNzlgYPihs6dCgEQQAR4fDhw+jcuXO1AWz19be//Q2fffYZfvzxR7Rq1arGeS5cuIDhw4dj2LBhWLRoUb2XwYPiGLu18KA4zyKKIrKysjBgwIAGXf6VB8U56d27Nx5//HH88MMPeOCBBxpczIuKijBv3jyUl5dj0KBB6N69O3r16gXAftUnx32Q33vvPWRnZ2P9+vXo3r07unfvftVd84wxxjyHRqPB4MGD+VruTtxy2tratWuxd+9eVFRU4IMPPnBHv2443kJn7NbCW+ieRZZlFBcXIyQkpEHX4ect9CskJiZiypQp6Ny5szuaY4wxxq5KlmUcPXqUT1tz0uQuLHOz8BY6Y7eWW3NNx67Fk7bQ3XrwIT8/H6+//jqOHTvmcm6f4zQDxhhjzB1kWYbJZEJ4eDjf+vZ3bi3oEyZMwKOPPoopU6ZArVa7s2nGGGNMIcsyTp48ibCwMC7ov3NrQddqtXj22Wfd2SRjjDFWjUajQZ8+fRq7G02KW3/WjBgxAps3b3Znk4wxxlg1kiThyJEjyuVcmZu30IcMGYKxY8dCrVZDp9OBiCAIAs6ePevOxTDGGLvFERHOnz+PqKioxu5Kk+HWgj5z5kysWrUKd911Fx9DZ4wxdsNoNBr06NGjsbvRpLi1oAcGBiIxMdGdTTLGGGPVSJKEgoICxMTE8Abk79x6DH3cuHFYvnw5ysrKcOnSJWVijDHG3O3y5cuN3YUmxa0XlnE+dcBxwxZBEJrkoAW+sAxjtxa+sAyriSddWMatW+iyLCuTJEnKv4wxxpg7SZKEvLw8rjFO3FrQzWZztefOnTvnzkUwxhhjrAZuLegPPfSQy+Py8nKMGDHCnYtgjDHGoFar0bVrVx4Q58StBb1z586YO3cuAODChQtISEjA7Nmz3bkIxhhjDJIkIScnh3e5O3FrQV+6dCnOnDmDN954A2PHjsWECRMwbdq0erXx5JNPIioqCoIgIC8vr8Z5tm3bBh8fH3Tv3l2ZeLQjY4zdWvR6fWN3oUlxy3nozqemffDBBxg5ciSGDBmCGTNm4NKlS/Dx8alzW4mJiZg/fz769et31fnuuOMO7Nmz57r7zBhjrPlSq9W47bbbGrsbTYpbCnrLli1dTlMjIuzZswdvvPFGvU9bGzBggDu6xFiDEPh8RI/DX6lHEb28kLNjB+Li4qDRuPUaac2WW3a5X3ma2pWnr90Ihw4dwl133YUePXrgww8/vOb8FosFlZWVLhMApX+SJNUYi6LoEsuyXC329hahUjlimxLr9TaoVKTEgkAACHq9DQBBEBwxoFI5xzK8vZ1j+73l1WoZOp091micYwleXs6xvb9arQSt1h57eUnQaByxqMQ6nQiNRlZitZpzEgQCAbDp9SAAJAiw/b5rj1QqJZZVKti8vZVYdMRqNUSdzh5rNEosaTQQvbyUWHLEWi0krdYee3lB+n3lJDrHOh1k5/j3gUCitzfk36//YHOO9XqQcywInBPn5FE5ySoV/Pz8lI3GhqzLPYVbCvrFixeVuLS01B1NXtVdd92FoqIi7N27F+vXr8fy5cvx5ZdfXvU9S5YsgZ+fnzKFh4cDgHKcPj8/H/n5+QCA3NxcFBQUAABycnJgNBoBANnZ2TCZTACAnTt3ori4GACQkpKF2NgSAMCyZZmIiSkHAKSmZiAsrAoAkJaWjoAAM/R6EWlp6dDrRQQEmJGWlg4ACAurQmpqBgAgJqYcy5ZlAgBiY0uQkpIFAOjVqxjJyTsBAPHxJixYkA0ASEgwYu7cHABAYmIBpk/PBQBMmpSPSZPsOU2fnovERHtOc+fmICHBntOCBdmIj7fnlJy8E716cU4BAWaIej3S09Ig6vUwBwQgPS0NAFAVFoaM1FQAQHlMDDKXLQMAlMTGIislBQBQ3KsXdiYnAwBM8fHIXrAAAGBMSEDO74NGCxITkTt9OgAgf9Ik5E+aBADInT4dBb9fPjln7lwYExIAANkLFsAUHw8A2JmcjOJevQAAWSkpKImNBQBkLluG8pgYAEBGaiqqwsIAAOlpaTAHBHBOnJNH5VQ4bBgqKiqgVqtRUFCA3Fz7OqK+6/Ldu3fDY1ADzZkzh8aMGUMLFiwgIqLZs2c3tEkiIoqMjKRff/21TvMuXryY5syZc9V5zGYzVVRUKJPJZCIAVFZWRkREoiiSKIrVYpvN5hJLkuQSA0Te3jZSqRyxVYn1eiupVLISC4JMgEx6vZUAmQTBEROpVM6xRN7ezrGNACK1WiKdzh5rNM6xSF5ezrFIAJFWK5JWa4+9vETSaByxTYl1OhtpNJISq9WckyDIJANk1etJBkgWBLLq9UQAySqVEksqFVm9vZXY5ojVarLpdPZYo1FiUaMhm5eXEouOWKslUau1x15eJGo0RADZnGOdjiTnWK22x97eJKlURABZnWO9nmTnWBA4J87Jo3Iyt2hBO3bsUNbRNa2/67IuLy0tJQBUUVFRt+LUhDX40q+PPPIIPv30U/znP//BL7/8gtOnT9dpF/i1REVFYePGjejatWu114qLixEcHAyVSoWqqiqMGDECU6dOxZQpU+rcPl/6lV0NH0NnrGmTNRqYjhxBeHi4y2XH64sv/epE9/vxjJEjRyIkJASbNm1qUHtPPPEE2rVrh6KiItx7773o2LEjAGDatGnYsGEDAGDdunW488470a1bN/Tu3RtDhw7FY4891rBEGGOMNRsqUURkZGSDirmnafAWelZWlsvI9K+//hoPPPBAgzt2o/EWOrsa3kJnrGkTdTrszMhAnz59GjTKnbfQnVx5mllcXFxDm2SMMcauSiWKiI6O5i10J27/JN588013N8kYY4y5UEkSwsLCuKA7afDZ+JGRkejcuTMAgIhw6NAhtwyKY4wxxmojensjKzMTAwYM4AvL/K7Bn8LQoUPxr3/9S3nMN2NhjDF2o6msVnTt2pW30J00eFBceXk5/P393dSdm4cHxbGr4UFxjDUDDStfAHhQnAvnYl5YWIgdO3Zgx44dKCwsbGjTjDHGWI1s3t74/vvvYbPZGrsrTYZbDjwcPHgQU6ZMgdFoREREBIgIJpMJ7du3R2pqKm6//XZ3LIYxxhgDAKitVvTo0QPq368tz9xU0JOSkvDss89i/PjxLs+vXbsWkydPRnZ2tjsWwxhjjAEAVLKMgICAxu5Gk+KW0QTnz5+vVswB+73NKyoq3LEIxhhjTGHT67Fp0ybe5e7ELQW9devW+PTTT5Xb0QH2W6quXr0agYGB7lgEY4wxptBYLOjfvz+fsubELZ/E6tWrMXPmTMydOxehoaEQBAFFRUWIi4vDqlWr3LEIxhhjTCHIcrMfle5ubinoHTt2xJYtW3Du3DnlHrPh4eEICgpyR/OMMcaYC5tej/Rvv0VCQgK0Wm1jd6dJcOu+iqCgIC7ijDHGbjiN2Yxhw4bxLncnN/wSO506dbrRi2CMMXarIeJifgW3fBoHDhyo9bULFy64YxGMMcaYQtTrkZ6ezrvcnbiloHft2hVRUVGo6SqyJSUl9W6voKAAkydPRklJCfz9/bFq1SrccccdLvMQEebPn4/09HSo1WoEBgbin//8Jzp27HjdeTDGGGseNJcvIyEhgbfSnbhll3tkZCR27NgBo9FYbQoODq53ezNnzsSMGTNw+PBhzJ8/H1OnTq02z4YNG5CVlYV9+/YhNzcXQ4YMwQsvvOCOdBhjjDV1ggBRFBu7F02KWwr6mDFjcOzYsRpfGzt2bL3aOnv2LPbu3YtJkyYBAMaPHw+j0Yjjx49Xm9discBsNoOIUFlZiXbt2tW774wxxpof0dsbGRkZXNSduKWgv/fee+jXr1+Nry1btqxebZlMJoSGhiq7UQRBQERERLWbvYwePRqDBg1C27ZtERISgi1btuCVV16ptV2LxYLKykqXCQAkSVL+rSkWRdEldlw8xzn29hahUjlimxLr9TaoVKTEgkAACHq9DQBBEBwxoFI5xzK8vZ1j+x+sWi1Dp7PHGo1zLMHLyzm291erlaDV2mMvLwkajSMWlVinE6HRyEqsVnNOgkAg2E+LIQAkCLDp9QAAUqmUWFapYPP2VmLREavVEHU6e6zRKLGk0UD08lJiyRFrtZB+PwYoeXlB+v1vX3SOdTrIzvHv168Wvb0h/377SJtzrNeDnGNB4Jw4J4/KSWWzYdSoUdBqtbWuv+u6LvcUTfJGssIV9ySt6dj83r17cfDgQZw8eRKnTp3CkCFDMGfOnFrbXLJkCfz8/JQpPDwcAJCXlwcAyM/PR35+PgAgNzcXBQUFAICcnBwYjUYAQHZ2tnKe/c6dO1FcXAwASEnJQmysfazAsmWZiIkpBwCkpmYgLKwKAJCWlo6AADP0ehFpaenQ60UEBJiRlpYOAAgLq0JqagYAICamHMuWZQIAYmNLkJKSBQDo1asYyck7AQDx8SYsWGC/Rn5CghFz5+YAABITCzB9ei4AYNKkfEyaZM9p+vRcJCbac5o7NwcJCfacFizIRny8Pafk5J3o1YtzCggw2wfcpKVB1OthDghAeloaAKAqLAwZqakAgPKYGGT+/oO1JDYWWSkpAIDiXr2wMzkZAGCKj0f2ggUAAGNCAnLmzgUAFCQmInf6dABA/qRJyP99j1Tu9OkoSEwEAOTMnQtjQgIAIHvBApji4wEAO5OTUdyrFwAgKyUFJbGxAIDMZctQHhMDAMhITUVVWBgAID0tDeaAAM6Jc/KsnEaNQnZ2NogIBQUFyM21ryPquy7fvXs3PAY1MWfOnCGDwUA2m42IiGRZpuDgYDIajS7zPfHEE/TGG28oj/Py8igiIqLWds1mM1VUVCiTyWQiAFRWVkZERKIokiiK1WKbzeYSS5LkEgNE3t42UqkcsVWJ9XorqVSyEguCTIBMer2VAJkEwRETqVTOsUTe3s6xjQAitVoinc4eazTOsUheXs6xSACRViuSVmuPvbxE0mgcsU2JdTobaTSSEqvVnJMgyCQDZNXrSQZIFgSy6vVEAMkqlRJLKhVZvb2V2OaI1Wqy6XT2WKNRYlGjIZuXlxKLjlirJVGrtcdeXiRqNEQA2ZxjnY4k51ittsfe3iSpVEQAWZ1jvZ5k51gQOCfOyaNyuuzrS9999x1ZrdZa1991WZeXlpYSAKqoqLjOqtV0CERuuEO8m8XHxyMpKQlJSUlYu3Yt3nrrLezatctlnr/97W/4/vvvsXHjRmi1WixduhTbt2/Hpk2b6rQMd93U/oqdCcxDEPiLZazJc0P5clctaAqa5Hj/FStWICkpCYsXL4bBYMDq1asBANOmTcOYMWMwZswYPPHEE8jPz8edd94JLy8vhISEYMWKFY3cc8YYYzeDrFKhvKwM/v7+UKma5NHjm65JbqHfDLyFzq6Gt9AZa9ps3t7I/OYbDB48uEEXluEtdMYYY6wRac1mDB8+vLG70aTwfgrGGGPNjqxS4ezZs8rpZ4wLOmOMsWZI9vJCXl4eF3QnvMudMcZYs6MxmzF48ODG7kaTwlvojDHGmh1ZrcbJkyd5C90JF3TGGGPNjqzR4OjRo1zQnfAud8YYY82OxmLBgAEDGrsbTQpvoTPGGGt2ZI0GJ06c4C10J1zQGWOMNTt8DL063uXOGGOs2dFYLOjTp09jd6NJ4S10xhhjzY6k0eDIkSPKPc4ZF3TGGGPNEKlUOH/+PG7R25HUiHe5M8YYa3Y0Vit69OjR2N1oUngLnTHGWLMjaTQ4ePAg73J3wgWdMcZY86NS4fLly43diyaFd7kzxhhrdtRWK+Li4hq7G01Kk9xCLygoQJ8+fdCpUyf07NkTBw4cqHG+1NRUxMTEIDo6GjNmzIAoije5p4wxxhqDpNUiLy+Pd7k7aZIFfebMmZgxYwYOHz6M+fPnY+rUqdXmMRqNWLhwIXbs2IEjR47g9OnTSE1NbYTeMsYYY42vyRX0s2fPYu/evZg0aRIAYPz48TAajTh+/LjLfGvXrsW4ceMQHBwMQRAwa9YspKWlNUKPGWOM3Wxqmw1du3aFWq1u7K40GU3uGLrJZEJoaCg0GnvXBEFAREQECgsLERUVpcxXWFiIyMhI5XFUVBQKCwtrbddiscBisSiPKyoqAADnz58HAGW3jVqtdolFUYQgCEqsUqmgUqmUGFBBpxNhtapApIJOZ4PVqgaRCt7eNlgsGhAJ8Pa2wWy25+TtLV4RayEIBJ3OEcvw8pJgsThiGRaLBiqVDI1GhtWqgVotQ612xBJUKoLN5ogBm00NjcaehyiqodVKkGVAktTQakXIsgBJUsPLS4QkqSBJKnh5iRBFFWSZc6oAIHp7Q2M225fn7Q2t2QwSBIg6HbRmM2RBgOTlBa3FAlkQIHt5QWOxQFapIGs00FitkNVqyGo1NFYrJLUapFJBY7NBUqsBlQpqmw3S73/ralGEpNUCsgy1JEHUaiE4Yi8vqCQJKkcsilDJMkSdDiqrFSoi2HQ6qB2xtzc0FgsER+yUB+fEOXlCTla9Hr/99BNiY2OV9fqV6++6rMvLysoAwCPOZ29yBR2wF3FntX3QzvNd68tYsmQJkpOTqz3v/CPhejn9TnCJf//brVNM5Bo72nGOZRmwWu2xJNmnq8XOQwpstppjR3tXxrd6Tv6emBTnxDl5Uk6XLwPx8XCXqqoq+Pn5ua29xtDkCnp4eDiKioogiiI0Gg2ICCaTCRERES7zRUREuOyGP3HiRLV5nD3//PN4+umnlceyLKOsrAyBgYHVfkAwxhhr2iorKxEeHg6TyQSDwXDd7RARqqqqEBoa6sbeNY4mV9DbtGmDuLg4rFmzBklJSVi3bh2ioqKqbUmPHz8e/fr1w0svvYQ2bdpg+fLlmDhxYq3t6nQ66HQ6l+f8/f1vQAaMMcZuFoPB0KCCDqDZb5k7NLlBcQCwYsUKrFixAp06dcLSpUuV0evTpk3Dhg0bAAAdOnRAcnIy+vbti+joaLRp06bG0fCMMcbYrUAgTxgJwBhj7JZSWVkJPz8/VFRUNHgL3VM0yS10xhhj7Gp0Oh0WLVpU7VDqrYy30BljjDEPwFvojDHGmAfggs4YY4x5AC7ojDHGmAfggs4YY4x5AC7ojDHGmAfggs4YY4x5AC7ojDHGmAfggs4YY4x5AC7ojDHGmAdocgX9ySefRFRUFARBQF5eXq3zpaamIiYmBtHR0ZgxYwZE5/v6MsYYY7eYJlfQExMTsWPHDkRGRtY6j9FoxMKFC7Fjxw4cOXIEp0+fVu7IxhhjjN2KmlxBHzBgANq1a3fVedauXYtx48YhODgYgiBg1qxZSEtLu0k9ZIwxxpoeTWN34HoUFha6bMFHRUWhsLDwqu+xWCywWCzKY1mWUVZWhsDAQAiCcMP6yhhjrOkiIlRVVSE0NBQqVZPbxq2XZlnQAbgU4brcMG7JkiVITk6+kV1ijDHWTJlMpmvuHW7qmmVBj4iIwPHjx5XHJ06cQERExFXf8/zzz+Ppp59WHldUVCjttGrVCpIkAQDUarVLLIoiBEFQYpVKBZVKVWtss9mgVquVWKPRQBAEJQYAURRdYq1WCyJSYlmWIUmSEsuyDI1GU2ssSRKISIlryoNz4pw4J87Jk3KyWCz45Zdf0Lt3b2UD73pyKisrQ/v27eHr64vmrlkW9PHjx6Nfv3546aWX0KZNGyxfvhwTJ0686nt0Oh10Ol2151u1agWDwXCjusoYY+wGkGUZ3bp1g7+/v1t2lXvCodcmd8DgiSeeQLt27VBUVIR7770XHTt2BABMmzYNGzZsAAB06NABycnJ6Nu3L6Kjo9GmTRtMnTq1MbvNGGPsJlKpVAgLC2v2x73dSaC6HID2QJWVlfDz80NFRQVvoTPGWDMjiiKysrIwYMAAZVf+9fCkWsA/bRhjjDU7KpUKXbt25S10J83yGDpjjLFbm0qlQps2bRq7G00K/7RhjDHW7NhsNnz//few2WyN3ZUmgws6Y4yxZketVqNHjx5Qq9WN3ZUmgws6Y01AVFQUvvnmm0btw/bt210urGE2mzFu3Dj4+/ujZ8+e1V5nrDGpVCoEBATwMXQn/EkwVov4+Hio1Wrk5uYqz5WXl0MQBJcLG11Pu++++26D+hYVFQW9Xo+WLVuidevWSEhIQEFBQYPa7N+/P4qKipTH69atw6FDh3DmzBlkZ2dXe70+iouL8fDDD6Nt27bw9fVFhw4d8Je//KVB/XUQBAH79u1zS1us+bDZbNi0aRPvcnfCBZ2xq2jVqhWef/55t7RFRMqVq9whLS0NFy5cwLFjx+Dr64vJkye7rW3AflfDTp061XhBpvp65JFH4O3tjYMHD6KiogI//PADunfv3vBOugHferl50mg06N+/f4NOWfM0XNAZu4rHH38cO3fuRFZWVo2vExHefvttREdHIyAgACNGjMCxY8eU16OiorBkyRL07t0bPj4+mDBhArZv347nnnsOLVu2xMiRI5V5Dx8+jN69e8PX1xcDBw6EyWSqUx8NBgMeeeQR/PrrrwCA+fPnIzIyEr6+vrjjjjvw1Vdfucz/v//9D4MHD0ZAQACCgoLw5z//GQCwbds2+Pv7AwDmzZuHV155BRs3bkTLli2xaNEil9cBwGq14qWXXkJ0dDR8fX1x5513Yu/evTX2cdeuXXjssceUq3pFR0e7/ACx2WxKW4GBgRgzZgxOnTqlvH769GlMmjQJoaGh8Pf3x4ABA3D58mX07NkTANCnTx+0bNkSixcvBgDs2bMHffv2hb+/P+644w6XuzG+/PLLuO+++zB79mwEBATgueeeq9PnzJoWQRBgMBg84gpvbkO3qIqKCgJAFRUVjd0V1kQNHDiQ3nnnHVq8eDHdc889RER0/vx5AkBGo5GIiFavXk2hoaGUm5tLly9fpqeffppuv/12stlsREQUGRlJnTp1ooMHD5IoimSxWJR2nUVGRlKXLl3o6NGjdPnyZRo5ciRNnjy51r5FRkbS+vXrlT49+OCDNGDAACIiWrNmDZ05c4ZEUaS0tDTS6XR07NgxIiIqKioig8FAH3zwAV2+fJkuXrxIWVlZRES0detW8vPzU5axaNEiGjt2rPL4ytf/8pe/0N13302HDx8mWZbp4MGDdPz48Rr7O3z4cLrrrrto9erVdOjQoWqvP/vsszR48GA6deoUWSwWmjdvHvXv35+IiCRJoh49etDkyZOprKyMbDYbbd++ncxmMxERAaCcnBylrfPnz1NgYCD9/e9/J6vVStu2baMWLVrQjh07lLzUajWtXLmSbDYbXbx4sdbPmTVdVquVvvnmG7JarQ1qx5NqARd0D/gS2Y3hKLyXLl2i0NBQWr9+fbWCfu+999LSpUuV95jNZvL19aX//ve/RGQvvFcW79oK+kcffaQ8XrNmDXXt2rXWvkVGRpKPjw/5+/tTaGgojR8/vtZi2q1bN1qzZg0RES1dupQGDRpU43z1KeiyLJOPjw/99NNPtfbRWUVFBS1atIji4uJIo9FQREQEffbZZ0pbLVq0oH379inzX758mVQqFRUWFtKuXbuoRYsWdOnSpRrbvrKgr1mzhm677TaXeaZPn07Tp09X8urWrVud+s2aLlmW6dKlSyTLcoPa8aRawLvcGbsGvV6PRYsW4YUXXqh2DLyoqAhRUVHKY51Oh9DQUJfBY9e6E6BD27ZtlbhFixaoqqq66vyfffYZzp8/j5MnT2Lt2rWIjIwEALzzzjvo0qUL/Pz84O/vj7y8PJSUlACw35kwJiamTv25mnPnzuHSpUt1bstgMODll1/G3r17cf78eTz55JN49NFHkZ+fj5KSEly8eBEDBgyAv78//P390bZtW3h5ecFkMuHEiRMICwuDXq+v07Ku/E4A+/0fruc7YU0bHz93xQWdsTqYOnUqZFnG6tWrXZ5v166dy4h3q9WKU6dOuZzedeVpNTfyNJsdO3bg5ZdfxieffILz58+jvLwcXbt2Bf1+y4bIyEgcOXKkwcsJCgqCj4/PdbXVsmVLzJs3D35+fjhw4AACAwPh4+OD3bt3o7y8XJkuX76MPn36IDIyEidPnsTly5drbO/KY6hXfieAfYDf1b4T1vyIooj09HQe1OiE/6oZqwO1Wo3XX39dGXTlMGnSJCxbtgwHDhyAxWLBX//6V4SFhSmDtWoSHByMo0eP3pB+VlZWQqPRICgoCLIs4+OPP0ZeXp7y+p/+9CdkZ2dj+fLlsFgsuHTpErZv317v5QiCgOnTp2PevHk4cuQIiAiHDh3CiRMnapz/2Wefxb59+2C1WmG1WvGvf/0LFy9exN133w2VSoVZs2Zh3rx5ykDA0tJSfPHFFwCAHj16oHPnznjiiSdQXl4OURSxY8cOWCwWANU/z4SEBJw9exYffvghRFHE9u3b8fnnn+PRRx+td56s6dJoNEhISOCtdCdc0Bmro/Hjxyu383V49NFH8ec//xn33Xcf2rZti/379+O777676krmqaeewo8//gh/f3/cd999bu3jiBEjMH78eNx5550IDQ3Fb7/9hr59+yqvt2vXDj/++CM+//xzBAcHIyoqCmvXrr2uZb3xxhsYMmQI7r33XhgMBjz44IMoKyurcV6LxYKJEyciMDAQbdu2xcqVK/Htt98qu8aXLFmCe+65B4MHD4avry/uvvtuZGRkALBvTX/33Xe4dOkSOnfujNatW+Ovf/0rZFkGALz66qt48skn0apVKyxduhStWrXCf/7zH6xZswaBgYGYMWMGPvroI/Tr1++68mRNF2+du+Lbp3rALfMYY+xWY7PZkJ6ejoSEBGi12utux5NqQZPcQi8oKECfPn3QqVMn9OzZEwcOHKg2DxHh2WefRZcuXRAbG4tBgwa55dggY4yxpk+r1WLs2LENKuaepkkW9JkzZ2LGjBk4fPgw5s+fj6lTp1abZ8OGDcjKysK+ffuQm5uLIUOG4IUXXmiE3jLGGLvZiAiVlZW4RXcy16jJFfSzZ89i7969mDRpEgD7cUuj0VjjtbMtFgvMZrPyxfKNIxhj7NbgGPDIx9H/X5Mr6CaTCaGhocqgIkEQEBERgcLCQpf5Ro8ejUGDBqFt27YICQnBli1b8Morr9TarsViQWVlpcsEQDmvWJKkGmNRFF1ix0Cc2mKbzeYSO349OmIiqhYDcIllWXaJHX+wtcWSJLnEnBPnxDlxTp6ek0qlwvDhw6HVahuck6docgUdqH5eaU27VPbu3YuDBw/i5MmTOHXqFIYMGYI5c+bU2uaSJUvg5+enTOHh4QCgnNKTn5+P/Px8AEBubq5y56qcnBwYjUYAQHZ2tnJazc6dO1FcXAwAyMrKUi7ckZmZifLycgBARkaGcnGQ9PR0mM1ml3MnzWYz0tPTAQBVVVXKqN7y8nJkZmYCAEpKSpTriBcXF2Pnzp0A7D98srOzAdjPsc3JyQFgH3/guDsY58Q5cU6ck6fmdOzYMezevRuyLDcop927d8NTNLlR7mfPnkVMTAxKS0uh0WhARAgJCcGuXbtcrv40Z84cREREYP78+QCA3377DQkJCbWeB2uxWJTzVgH7yMbw8HCUlZWhVatWyi83tVrtEouiCEEQlFilUkGlUtUa22w2qNVqJdZoNBAEQYkB+y9C51ir1YKIlFiWZUiSpMSyLEOj0dQaS5IEIlLimvLgnDgnzolz8qSczGYztm3bhiFDhigXCrqenMrKyhAYGOgRo9zdWtA3btzolvNq4+PjkZSUhKSkJKxduxZvvfUWdu3a5TLP3/72N3z//ffYuHEjtFotli5diu3bt2PTpk11WoYnnarAGGPs+nhSLWhwQR86dCgEQQAR4fDhw+jcubOyC+V6HTp0CElJSSgtLYXBYMDq1avRpUsXTJs2DWPGjMGYMWNgsVgwZ84cbN++HV5eXggJCcGKFSuqXcO5Np70JTLG2K1GlmWUlJSgdevWDbqUryfVggYX9IULF+Luu+/G/fffj7/85S9455133NW3G8qTvkTGGLvViKKIrKwsDBgwoEGXf/WkWtDgQXGvvvoqRFHECy+8AKvV6o4+McYYY1el0WgwePBgvpa7E7eMck9MTMSUKVPQuXNndzTHGGOMXZUsyzh58qRy+hlz42lrHTt2xJNPPumu5hhjjLFaybKMo0ePckF34tZ9Ffn5+Xj99ddx7Ngxl5P1HecNMsYYY+6g0WgwYMCAxu5Gk+LWgj5hwgQ8+uijmDJlCtRqtTubZowxxhSyLMNkMiE8PLxBo9w9iVsLularxbPPPuvOJhljjLFqHMfQw8LCuKD/zq2fwogRI7B582Z3NskYY4xVo9Fo0KdPHx7l7sStn8SQIUMwduxYqNVq6HQ6EBEEQcDZs2fduRjGGGO3OEmSYDQa0b59ez7E+zu3FvSZM2di1apVuOuuu26ZD/iK+8gwxpqopnXXCtZQRITz58/X+eqgtwK3FvTAwEAkJia6s0nGGGOsGo1Ggx49ejR2N5oUtx5DHzduHJYvX46ysjJcunRJmRhjjDF3kiQJBw8eVO6oxtx8tzXnkYaOG7YIgtAkP3B3Xb+Xd7kz1jzwLnfPIkkScnNzERsb26BDvJ50LXe37nLnK/Ywxhi7GdRqNeLi4hq7G02KW3e5m83mas+dO3fOnYtgjDHGIEkS8vLymuQe4Mbi1oL+0EMPuTwuLy/HiBEj3LkIxhhjjNXArQW9c+fOmDt3LgDgwoULSEhIwOzZs925CMYYYwxqtRpdu3a9ZU6Rrgu3FvSlS5fizJkzeOONNzB27FhMmDAB06ZNq3c7BQUF6NOnDzp16oSePXviwIED1ebZtm0bfHx80L17d2W6fPmyO9JgjDHWxEmShJycHN7l7sQtg+KcT0374IMPMHLkSAwZMgQzZszApUuX4OPjU6/2Zs6ciRkzZiApKQlr167F1KlT8fPPP1eb74477sCePXsa3H/GGGPNj16vb+wuNCluOW1NpVK5nKbm3GR9T1s7e/YsOnXqhJKSEmg0GhARQkJCsGvXLpcrAm3btg3PPPPMdRd0Pm2NsVsLn7bGauJJp625ZZe7LMuQJMnlX8dU390hJpMJoaGhygX3BUFAREQECgsLq8176NAh3HXXXejRowc+/PDDq7ZrsVhQWVnpMgFQ+idJUo2xKIousePUPOfY21uESuWIbUqs19ugUpESCwIBIOj1NgAEQXDEgErlHMvw9naO7feWV6tl6HT2WKNxjiV4eTnH9v5qtRK0Wnvs5SVBo3HEohLrdCI0GlmJ1WrOiXPy3JxkWYYoileNJUlyid2xjnCObTabS+zYAHLERFQtBuASy7LsEt+KOVksFuzevVvpa0Ny8hRuKegXL15U4tLS0ga3J1yx2VvTToS77roLRUVF2Lt3L9avX4/ly5fjyy+/rLXNJUuWwM/PT5nCw8MBAHl5eQCA/Px85OfnAwByc3NRUFAAAMjJyYHRaAQAZGdnw2QyAQB27tyJ4uJiAEBKShZiY0sAAMuWZSImphwAkJqagbCwKgBAWlo6AgLM0OtFpKWlQ68XERBgRlpaOgAgLKwKqakZAICYmHIsW5YJAIiNLUFKShYAoFevYiQn7wQAxMebsGBBNgAgIcGIuXNzAACJiQWYPj0XADBpUj4mTbLnNH16LhIT7TnNnZuDhAR7TgsWZCM+3p5TcvJO9OrFOXFOnptTSUkJsrLsORUXF2PnTntOJpMJ2dn2nIxGI3Jy7DkVFBQgN9eeU0PWEVlZWSgpseeUmZmJ8nJ7ThkZGaiqsueUnp4Os9kMURSRnp4OURRhNpuRnm7PqaqqChkZ9pzKy8uRmXlr51RYWIhLly5BEIQG5bR79254DGqgOXPm0JgxY2jBggVERDR79uwGtXfmzBkyGAxks9mIiEiWZQoODiaj0XjV9y1evJjmzJlT6+tms5kqKiqUyWQyEQAqKysjIiJRFEkUxWqxzWZziSVJcokBIm9vG6lUjtiqxHq9lVQqWYkFQSZAJr3eSoBMguCIiVQq51gib2/n2EYAkVotkU5njzUa51gkLy/nWCSASKsVSau1x15eImk0jtimxDqdjTQaSYnVas6Jc/LMnIiIJElS1i21xaIousQ1rRfqs464MrZarS6xLMsusSzL1WLHutARS5LkEnNO159TaWkpAaCKigpq7hpc0CdNmkREROnp6ZScnNzggk5ENHDgQFq5ciUREX311VfUq1evavOcOnVK+UIqKyupT58+lJqaWudlVFRUuOVLtB+Z44knnpr6xDyLzWaj//73v0pRv17uqgVNQYN3uet0OgDAyJEjERISgk2bNjW0SaxYsQIrVqxAp06dsHTpUqSmpgIApk2bhg0bNgAA1q1bhzvvvBPdunVD7969MXToUDz22GMNXjZjjLGmT6VSISwszOUeIre6Bo9yz8rKwoABA5THX3/9NR544IEGd+xG41HujN1aGramY56KR7k7cS7mAPhi+Ywxxm44URSRlZXlUaPUG8rt+yrefPNNdzfJGGOMuVCpVIiOjuZd7k4afKW4yMhIdO7cGQBARDh06NA1zwlnjDHGGsJxDJ39vwYX9KFDh+Jf//qX8phvxsIYY+xGc+xyHzBggHIhsltdgwfFlZeXw9/f303duXl4UBxjtxYeFOdZZFlGSUkJWrdu3aDd7p40KK7BP2uci3lhYaFyidaIiAhEREQ0tHnGGGOsGpVKhTZt2jR2N5oUt+ynOHjwIKZMmQKj0YiIiAgQEUwmE9q3b4/U1FTcfvvt7lgMY4wxBsB+nfjMzEwMHjwYWq22sbvTJLiloCclJeHZZ5/F+PHjXZ5fu3YtJk+erFx7lzHGGHMHtVqNHj16QK1WN3ZXmgy3jPc/f/58tWIOAImJiaioqHDHIhhjjDGFSqVCQEAAn7bmxC2fROvWrfHpp58qt6MD7AMWVq9ejcDAQHcsgjHGGFPYbDZs2rRJueUqc9Mu99WrV2PmzJmYO3cuQkNDIQgCioqKEBcXh1WrVrljEYwxxphCo9Ggf//+fMqaE7d8Eh07dsSWLVtw7tw55R6z4eHhCAoKckfzjDHGmAtBEJr9aWbu5tafNkFBQVzEGWOM3XA2mw3p6elISEjgUe6/u+GjCTp16nSjF8EYY+wWo9FoMGzYMN7l7sQtn8SBAwdqfe3ChQvuWARjjDHmgou5K7d8Gl27dkVUVBRquopsSUmJOxbBGGOMKURR5F3uV3BLQY+MjMSOHTsQGhpa7bXw8PB6t1dQUIDJkyejpKQE/v7+WLVqFe644w6XeTIzM/H888+jqqoKKpUKY8eOxWuvvQaBL67O3IDAf0ceh79Sj6IBkGC18la6E7ccQx8zZgyOHTtW42tjx46td3szZ87EjBkzcPjwYcyfPx9Tp06tNk+rVq2QlpaGAwcOYM+ePfjpp5+QlpZW72UxxhhrhgQBoig2di+alAbfbc3dzp49i06dOqGkpAQajQZEhJCQEOzatQtRUVG1vm/OnDlo27Yt/vrXv9ZpOXy3NXY1vIXOWNNm0+uRnpbW4F3unnS3tSZ3zTyTyYTQ0FBlN4ogCIiIiFDu4laT06dPY+3atUhISKh1HovFgsrKSpcJACRJUv6tKRZF0SV2XA3POfb2FqFSOWKbEuv1NqhUpMSCQAAIer0NAEEQHDGgUjnHMry9nWP7r1C1WoZOZ481GudYgpeXc2zvr1YrQau1x15eEjQaRywqsU4nQqORlVit5pwEgUCwrzAIAAkCbHo9AIBUKiWWVSrYvL2VWHTEajVEnc4eazRKLGk0EL28lFhyxFotpN9XSJKXF6Tf//ZF51ing+wc/379atHbG/Lvl760Ocd6Pcg5FgTOiXPyqJxUNhtGjRoFrVZb6/q7rutyT9HkCjqAasfBr7YTobKyEqNHj8b8+fNx11131TrfkiVL4Ofnp0yOY/t5eXkAgPz8fOTn5wMAcnNzUVBQAADIycmB0WgEAGRnZysXztm5cyeKi4sBACkpWYiNtQ/+W7YsEzEx5QCA1NQMhIVVAQDS0tIREGCGXi8iLS0der2IgAAz0tLSAQBhYVVITc0AAMTElGPZskwAQGxsCVJSsgAAvXoVIzl5JwAgPt6EBQvsN71JSDBi7twcAEBiYgGmT88FAEyalI9Jk+w5TZ+ei8REe05z5+YgIcGe04IF2YiPt+eUnLwTvXpxTgEBZoi///oX9XqYAwKQ/vvhnKqwMGSkpgIAymNikLlsGQCgJDYWWSkpAIDiXr2wMzkZAGCKj0f2ggUAAGNCAnLmzgUAFCQmInf6dABA/qRJyJ80CQCQO306ChITAQA5c+fC+PuP1OwFC2CKjwcA7ExORnGvXgCArJQUlMTGAgAyly1DeUwMACAjNRVVYWEAgPS0NJgDAjgnzsmzcho1CtnZ2SAiFBQUIDfXvo6o77p89+7d8BjUxJw5c4YMBgPZbDYiIpJlmYKDg8loNFabt7Kyku655x565ZVXrtmu2WymiooKZTKZTASAysrKiIhIFEUSRbFabLPZXGJJklxigMjb20YqlSO2KrFebyWVSlZiQZAJkEmvtxIgkyA4YiKVyjmWyNvbObYRQKRWS6TT2WONxjkWycvLORYJINJqRdJq7bGXl0gajSO2KbFOZyONRlJitZpzEgSZZICsej3JAMmCQFa9ngggWaVSYkmlIqu3txLbHLFaTTadzh5rNEosajRk8/JSYtERa7UkarX22MuLRI2GCCCbc6zTkeQcq9X22NubJJWKCCCrc6zXk+wcCwLnxDl5VE6XfX3pu+/+r71/j4+ivPvH/9fMzmYTDklIJJiEHAQSUBCIFbDhIAdFDRXkJlpUrKlgoJa7tH5u+aIVMVYFEevdW6zkvpsK9ZDWYlGUqLGihhg5WGIRCRJgQzYYCCHkBOxhZq7fH+vOb9ckGMjCbjav5+OxD9+7O1xzvZO4771mrrnmHeF0Ojv8/O7MZ/nJkycFANHU1HT+BSvIBN05dACYPHkycnJykJOTg40bN2LNmjXYvn27zzatra246aabMH36dKxYseK898Fz6HQuPIdO1A34oXzxHPpFlp+fj/z8fKSnp2PVqlUo+O6QzIIFC7B582YAwB/+8Afs3LkTmzZtwujRozF69Gg89dRTgew2ERFdIroso6Ghwecunz1dUI7QLwWO0OlcOEInCm6u8HBsfestTJ06lbPcv8Mr8omIqNsx2+246aabAt2NoBKUh9yJiIjORZdl1NXV8ZC7FxZ0IiLqdvSwMOzdu5cF3QsPuRMRUbej2O2YOnVqoLsRVDhCJyKibkc3mXD06FGO0L2woBMRUbejKwoOHTrEgu6Fh9yJiKjbURwOTJo0KdDdCCocoRMRUbejKwqOHDnCEboXFnQiIup2eA69LR5yJyKibkdxOJCZmRnobgQVjtCJiKjb0RQFBw8eNO5xTizoRETUDQlZxqlTp9BDb0fSLh5yJyKibkdxOjFmzJhAdyOocIRORETdjqYo2L9/Pw+5e2FBJyKi7keWcfbs2UD3IqjwkDsREXU7JqcTGRkZge5GUAnKEXplZSUyMzORnp6OsWPHYt++fe1uV1BQgLS0NAwePBi5ublQVfUS95SIiAJBM5uxd+9eHnL3EpQFfeHChcjNzcWBAwewdOlSzJ8/v802VqsVy5cvR2lpKQ4ePIhjx46hoKAgAL0lIiIKvKAr6HV1ddi9ezfmzZsHAJgzZw6sViuqqqp8ttu4cSNmz56NAQMGQJIkLFq0CIWFhQHoMRERXWomlwsjRoyAyWQKdFeCRtCdQ7fZbEhISICiuLsmSRKSk5NRXV2N1NRUY7vq6mqkpKQYz1NTU1FdXd1huw6HAw6Hw3je1NQEADh16hQAGIdtTCaTT6yqKiRJMmJZliHLshEDMiwWFU6nDCFkWCwuOJ0mCCEjPNwFh0OBEBLCw12w2905hYer34vNkCQBi8UT6wgL0+BweGIdDocCWdahKDqcTgUmkw6TyRNrkGUBl8sTAy6XCYrizkNVTTCbNeg6oGkmmM0qdF2CppkQFqZC02RomoywMBWqKkPXmVMTADU8HIrd7t5feDjMdjuEJEG1WGC226FLErSwMJgdDuiSBD0sDIrDAV2WoSsKFKcTuskE3WSC4nRCM5kgZBmKywXNZAJkGSaXC9p3f+smVYVmNgO6DpOmQTWbIXnisDDImgbZE6sqZF2HarFAdjohCwGXxQKTJw4Ph+JwQPLEXnkwJ+YUCjk5IyLw9aefYuTIkcbn+vc/vzvzWd7Q0AAAIXE9e9AVdMBdxL119IP23u6HfhkrV65EXl5em9e9vyRcKK/vCT7xd3+7nYqF8I097XjHug44ne5Y09yPc8XeUwpcrvZjT3vfj3t6TtGhmBRzYk6hlNPZs8DkyfCXlpYWREVF+a29QAi6gp6UlISamhqoqgpFUSCEgM1mQ3Jyss92ycnJPofhjxw50mYbbw8//DAefPBB47mu62hoaEBsbGybLxBERBTcmpubkZSUBJvNhsjIyAtuRwiBlpYWJCQk+LF3gRF0BT0uLg4ZGRl49dVXkZOTgzfffBOpqaltRtJz5szBhAkT8NhjjyEuLg7r1q3D3LlzO2zXYrHAYrH4vBYdHX0RMiAiokslMjKySwUdQLcfmXsE3aQ4AMjPz0d+fj7S09OxatUqY/b6ggULsHnzZgDAoEGDkJeXh/Hjx2Pw4MGIi4trdzY8ERFRTyCJUJgJQEREPUpzczOioqLQ1NTU5RF6qAjKEToREdG5WCwWrFixos2p1J6MI3QiIqIQwBE6ERFRCGBBJyIiCgEs6ERERCGABZ2IiCgEsKATERGFABZ0IiKiEMCCTkREFAJY0ImIiEJAUBb0yspKZGZmIj09HWPHjsW+ffvabFNVVYXJkycjKioK1157bQB6SUREFDyCsqAvXLgQubm5OHDgAJYuXdruTVciIyPx5JNP4vXXXw9AD4mIiIJL0BX0uro67N69G/PmzQPgvk2q1Wr1ufc5AMTExGDChAno3bt3AHpJREQUXILufug2mw0JCQlQFHfXJElCcnIyqqur29wT/Xw4HA44HA7jua7raGhoQGxsLCRJ6mq3iYioGxJCoKWlBQkJCZDloBvjnpegK+gA2hRYf9w/ZuXKlcjLy+tyO0REFHpsNhsGDhwY6G50SdAV9KSkJNTU1EBVVSiKAiEEbDYbkpOTu9Tuww8/jAcffNB43tTUhOTkZFRVVaFfv37QNA0AYDKZfGJVVSFJkhHLsgxZljuMXS4XTCaTESuKAkmSjBiAkZsnNpvNEEIYsa7r0DTNiHVdh6IoHcaapkEIYcTt5cGcmBNzYk6hlJPD4cCuXbtw3XXXGYPAC8mpoaEBV1xxBfr27YvuLugKelxcHDIyMvDqq68iJycHb775JlJTU7t0uB1w3zu3vfvm9uvXD5GRkV1qm4iILi1d1zFq1ChER0f75VB5KJx6Dcr7oX/zzTfIycnByZMnERkZiQ0bNmD48OFYsGABZs6ciZkzZ8LhcGDw4MFwOBxoampCXFwc7rnnHqxcubJT+2hubkZUVBSamppY0ImIeqhQqgVBWdAvhVD6JRIR9TSqqqKkpASTJk0yDuVfiFCqBd17Sh8REfVIsixjxIgR3X5muj8F3Tl0IiKiHyLLMuLi4gLdjaDCrzZERNTtuFwufPDBB3C5XIHuStBgQSciom7HZDJhzJgxMJlMge5K0OAhdyIi6nZkWUZMTEyguxFUOEInukhGjx6N9evXAwBee+01ZGZmBrZDRCHE5XJhy5YtPOTuhQWdqAOTJ0/Gf//3f/ulrbvvvhtlZWV+aas9LpcLeXl5GDx4MCIiIpCUlITf/OY3aG1tvWj77IodO3ZgypQp6NevH6KjozFy5Ejjy09XfPLJJ4iOju5yOxT8FEXBxIkTu3TJWqhhQScKAXfddRc2bdqEN954A62trfjoo4/w73//G9OnTw+6EUxLSwtuvvlm/PSnP0VdXR1OnDiBgoKCoJmxrKpqoLtAnSBJEiIjI0NihTd/YUEn6gTPyO9Pf/oTkpKSEBsbi6VLl/pss3btWuO93/72tz7vrV+/HqNHjzae//73v0daWhr69u2LwYMHY+3atcZ7VVVVkCQJr7zyCoYMGYLo6Gjk5OR0WJg/+eQTbN68GZs2bcKPfvQjmEwmpKenY9OmTThw4ABee+01Y9sPP/wQ48aNQ3R0NOLj431WVvznP/+JsWPHIjo6GsOHD8fmzZuN94qLi3HttdciKioK8fHxeOCBB3D27Fnj/dTUVKxevRrXXXcd+vbti+uvvx42m63d/n7zzTc4ffo0cnNzYTabYTabMWbMGGRlZRnb1NXV4e6770ZCQgISEhLw61//2uduif/6178wdepUxMTEoH///vjP//xPnDx5ErfccguamprQp08f9OnTB9u2bQMAvPrqq7jyyisRHR2NCRMmoLy83Ghr8uTJWLp0KaZPn47evXvjvffea7ffFFxcLhfefvvtoPvCGlCih2pqahIARFNTU6C7QkHq+uuvF88//7wQQoiPP/5YyLIsfvWrX4mzZ8+Kffv2iV69eomPP/5YCCHERx99JCIjI0VZWZlwOBzikUceESaTSbz88stCCCFefvllMWrUKKPtjRs3iurqaqHruti6dasIDw8XpaWlQgghrFarACB++tOfiqamJnH06FGRmJhotPV9y5YtExMnTmz3vXnz5ok777xTCCHE7t27RUREhNi4caNwOp2isbFRfP7550IIIf7973+L6Oho8dFHHwlN08S2bdtEZGSk2L9/vxBCiJKSErF7926hqqo4dOiQGDZsmHjyySeN/aSkpIjhw4eLQ4cOibNnz4pbbrlF3Hvvve32qbm5WfTv31/cfvvt4q233hK1tbU+7+u6LsaNGycefPBBcfr0aVFfXy8mT54sHn30USGEEDU1NSIyMlK8+OKL4uzZs+L06dOipKTE+D1FRUX5tFdSUiL69OkjPv30U+F0OsXzzz8v+vfvLxobG4UQ7t9z//79xY4dO4Su6+LMmTPt9puCi+d3pet6l9oJpVrAETpRJwkhsHLlSoSHh+PKK69EZmYm/vWvfwFwT3q7++678eMf/xhhYWF4/PHH0bt37w7bmjNnDpKSkiBJEqZMmYKbbroJn3zyic82jz/+OCIjI5GQkIBbbrnF2Nf31dfXIyEhod33EhIScOLECQDA//7v/2Lu3LmYM2cOzGYzoqKicN111wEA8vPzkZOTg6lTp0KWZUyYMAE/+clP8MYbbwAAJk6ciIyMDJhMJgwaNAgLFy5s09/Fixdj0KBBCA8Px913391hf/v27YuysjLExMTgwQcfREJCAsaNG4fdu3cDAL744gtUVlbi2WefRa9evRAbG4tHHnkEr7/+OgD3aPtHP/oRHnjgAYSHh6NXr16YOHFihz/rv/zlL5g3bx4mTZoEs9mMX//61+jXrx+2bNlibHPXXXdh7NixkCQJERERHbZFwYXnz30FrKBXVlYiMzMT6enpGDt2LPbt29fudgUFBUhLS8PgwYORm5vrc35rzZo1GDFiBEaPHo3rrrsOu3btulTdpx4oMjISvXr1Mp737t0bLS0tAIBvv/0WKSkpxntmsxnx8fEdtvXaa6/hmmuuMSaFFRUVob6+3mebyy+/vN19fd9ll12Gb7/9tt33vv32W/Tv3x8AcOTIEaSlpbW7XVVVFdatW4fo6Gjj8fbbbxvt7tq1CzfccAMGDBiAyMhIPPLIIxfcXwAYMmQI1q1bh0OHDqGmpgZDhgzBzJkzIYRAVVUVGhsbERMTY/QlOzsbx48f/8E82lNTU9Pmbo1XXHEFampqjOddvT0zXXqqqqKoqIhzHrwErKAvXLgQubm5OHDgAJYuXYr58+e32cZqtWL58uUoLS3FwYMHcezYMRQUFAAA/v3vf+OFF17A9u3b8eWXX2Lx4sX45S9/eanTIALgHgkfOXLEeO5yuVBbW9vuttXV1bj33nuxevVqnDhxAo2NjcjKyoK4wPsk3XjjjdixYwesVqvP683NzXjvvfdw4403AgBSUlJw8ODBdttISkrCkiVL0NjYaDxaW1vx0ksvAQDuvPNOTJkyBYcPH0ZzczOefvrpC+7v9yUkJGDZsmU4evQoGhoakJSUhLi4OJ++NDU1GTP2z5VHe+t6Dxw4EFVVVT6vVVVVYeDAgef8dxTcFEVBVlYWR+leAvJXXFdXh927d2PevHkA3IcfrVZrm//pNm7ciNmzZ2PAgAGQJAmLFi1CYWGh8b7L5cLp06cBAI2NjT7/gxJdSnfeeSdee+017NixA06nE0888YTxt/l9ra2tEEIgLi4OsiyjqKgIxcXFF7zvqVOnIisrC7Nnz8bu3buhaRoOHDiA2bNnY/Dgwbj77rsBAPfffz8KCwuxadMmqKqKpqYmbN++HYD7C/bLL7+Mjz/+GJqmweFw4PPPP0dFRQUA95eD6Oho9O7dGxUVFUahvxD79+/HM888g6qqKui6jsbGRqxduxbp6emIjY3FmDFjkJycjEcffRQtLS0QQuDIkSPGZLW7774bO3fuxLp16+BwOHDmzBlj8tuAAQPQ0tJinGYAgHnz5uG1117DZ599BlVV8cILL+DkyZM+k/Coe+Lo3FdACrrNZkNCQoLxzUqSJCQnJ6O6utpnu+rqap/DmKmpqcY2o0aNwoMPPogrrrgCAwcOxPPPP48XXnihw306HA40Nzf7PABA0zTjv+3Fqqr6xLqunzN2uVw+sWcU44mFEG1iAD6xrus+seePtqNY0zSfmDn5Jyfvh6cv38/Ps8+pU6fi8ccfx5w5cxAfHw9VVTFixAifnDx9uOqqq/Dwww9j6tSpiI2NxV//+lfceuut58xJ13Wjj+3l9Le//Q0/+clPkJ2djd69e2PKlCkYPnw4PvzwQ0iSBCEEMjIy8Le//Q1PPfUUYmJicOWVV+LTTz+FEAIjRoxAYWEhHn30UfTv3x+JiYlYvny58aXkpZdewpo1a9CnTx8sWrQId9xxR5vfk67rPv31+H5OvXr1Qnl5OSZOnIjIyEgMHToUdXV1ePvtt42f69tvv42jR4/iyiuvRFRUFGbMmIFvvvkGQggMHDgQ77//Pl5//XUMGDAAqamp+Pvf/w4hBAYNGoT58+cbM9pLS0sxceJEPP/885g/fz5iY2NRWFiI9957D5GRkT6/12D62wvF/5/8nZPD4UBxcbHR167kFDIuylS7H/DFF1+Iq666yue1a6+9Vnz66ac+ry1evFisXr3aeL53715xxRVXCCGEqKqqEhMnThTffvutEEKIF154QVx//fUd7nPFihUCQJuHZ3bsV199Jb766ishhHs2cEVFhRBCiJ07d4rKykohhBCfffaZqKqqEkII8emnn4qamhohhHuG8/Hjx4UQQrz//vvi5MmTQggh3n33XWPm5FtvvSXOnDkjnE6neOutt4TT6RRnzpwRb731lhDCPdPy3XffFUIIcfLkSfH+++8LIYQ4fvy4+Oijj4QQ7tm9np9RVVWV+Oyzz4QQQlRWVoqdO3cKIYSoqKgQu3fvZk7MiTkxJ+bUiZyKiopCZpa7JISfToSdh7q6OqSlpeHkyZNQFAVCCMTHx2P79u0+k1eeffZZVFVV4cUXXwQAFBUVYfXq1fjkk0+wZs0aHD58GH/84x8BAKdPn0bfvn3hcrnaXazf4XD4XMfa3NyMpKQkNDQ0oF+/fsY3N5PJ5BOrqgpJkoxYlmXIstxh7Nm/J1YUBZIkGTHg/kboHZvNZmOEYDabjZGOJ9Z1HYqidBhrmgYhhBG3lwdzYk7MiTmFUk6qqqKlpQXR0dHGaPtCcmpoaEBsbCyampoQGRmJbu3Sf4dwu/76643rav/+97+LcePGtdnm0KFDIj4+Xhw7dkzoui5uvfVW8dJLLwkhhHjzzTfF1VdfLVpaWoQQQhQWFrYZ9Z9LKF17SETU0zidTvHuu+8Kp9PZpXZCqRYEbHqg57rXp59+GpGRkdiwYQMAYMGCBZg5cyZmzpyJQYMGIS8vD+PHj4eu65g6daoxG3727NnYtWsXrr32WlgsFvTt2xevvvpqoNIhIqJLyGw2Y8aMGYHuRlAJyCH3YNDc3IyoqKjQOMxCRNTDeK6QiI6O7tJlh6FUC3jxJRERdTuapmHXrl3GeXICeEU+ERF1O2azGTfddFOguxFUznuE/u67716MfhAREXWaruuoq6vzWfOgp+vUCP3GG280Fqc4cOAA/ud//qdLK1sRERF1ha7r2Lt3LyZNmsSle7/TqZ/CddddhwceeAAffvgh/uM//oPFnIiIAkpRFEydOpVruXvpVEH/3e9+B1VV8cgjj8DpdF7sPhEREZ2Trus4evQoD7l76fRxiuzsbNx3330YOnToxewPERHRD9J1HYcOHWJB98Lr0EPg2kMiIrowoVQLzvvkQ0VFBZ566ikcPnzY5y41O3fu9GvHiIiIOqLrOmw2G5KSkjgp7jvnXdDvuOMO/OxnP8N9993X7k1QiIiILjbPOfTExEQW9O+cd0E3m8146KGHLkZfiIiIOkVRFGRmZga6G0HlvL/W3HzzzXj//fe7vOPKykpkZmYiPT0dY8eOxb59+9rdrqCgAGlpaRg8eDByc3N9DvNXV1fj1ltvxdChQzFs2DC88MILXe4XEREFP03TcPDgQS796uW8C/q0adOQnZ2NqKgoxMXFoX///oiLizvvHS9cuBC5ubk4cOAAli5datxFzZvVasXy5ctRWlqKgwcP4tixYygoKAAACCEwe/Zs/OxnP8M333yDiooK3H777efdDyIi6n6EEDh16hR66Lzudp33LPchQ4Zg1apVuOaaa3zOoaekpHS6jbq6OqSnp6O+vh6KokAIgfj4eGzfvh2pqanGds8++yyqqqrw4osvAgCKioqwevVqfPLJJ/jnP/+Jxx9/HKWlpefTfUMozWwkIqILE0q14LxH6LGxscjOzsagQYOQkpJiPM6HzWZDQkKCscKPJElITk5GdXW1z3bV1dU+baemphrb7Nu3D/3798fcuXORkZGB2bNn4/Dhwx3u0+FwoLm52ecBwDhco2lau7Gqqj6x55rHjmKXy+UTe74veWIhRJsYgE+s67pP7DnN0FGsaZpPzJyYE3NiTqGek9PpxL59+4x+dyWnUHHeBX327NlYt24dGhoacObMGeNxviRJ8nne0YEC7+28t3G5XPjnP/+J5cuXo7y8HLfccgvmzp3b4f5WrlyJqKgo45GUlAQA2Lt3LwD35XgVFRUAgD179qCyshIAUF5eDqvVCsB9aZ7NZgMAlJWVoba2FgBQUlKC+vp6AMDWrVvR2NgIACguLkZLSwsA99EFu90OVVVRVFQEVVVht9tRVFQEAGhpaTGW1G1sbMTWrVsBAPX19SgpKQEA1NbWoqysDID7S5HnUkGr1Yry8nIA7rkJe/bsYU7MiTkxp5DO6ciRI6ipqelyTjt27EDIEOdJkiTjIcuy8d/zcfz4cREZGSlcLpcQQghd18WAAQOE1Wr12W716tXigQceMJ5v2bJFXH/99UIIIf7+97+LiRMnGu+dPn1ayLIsVFVtd592u100NTUZD5vNJgCIhoYGIYQQqqoa/9Y7drlcPrGmaeeMnU6nT6zruk+s63qb2PMz8MSapvnEnp9TR7Gqqj5xe3kwJ+bEnJgTc2qb08mTJwUA0dTUJLq78y7o/nL99deLl19+WQjhLs7jxo1rs82hQ4dEfHy8OHbsmNB1Xdx6663ipZdeEkII0draKgYNGiRqamqEEEK8+eabYuTIkZ3ef1NTU8j8EomIehpVVcVXX33V4SCus0KpFpz3deh2ux3h4eE+r504cQL9+/c/r3by8/ORk5ODp59+GpGRkdiwYQMAYMGCBZg5cyZmzpyJQYMGIS8vD+PHj4eu65g6daoxG75379744x//iBkzZkAIgejoaLz++uvnmw4REVFIOO9Z7rNnz8amTZuM542NjZg2bRr+9a9/+b1zF1MozWwkIqILE0q14LwnxQ0dOhRLliwBALS2tiIrKwu/+MUv/N4xIiKijmiahvLyci4s4+W8C/qqVatw/PhxPPPMM5g1axbuuOMOLFiw4GL0jYiIqEMRERGB7kJQ6fQhd+9L086ePYtbbrkF06ZNw/LlywEAvXr1ujg9vEhC6TALERFdmFCqBZ0u6LIsQ5IkCCGM/xqNSFK3O+wRSr9EIqKeRlVVlJeXIyMjw1ik7EKEUi3o9E/Bs6oOERFRoEmShH79+rVZpKwn63RBP336NHr37g0AOHnyJGJjYy9ap7oT/i0RdQ+8h0doMZlMGDJkSKC7EVQ6NSnuP//zP3HXXXfh4YcfBgDjvDkREVEgqKqKsrKykFqLvas6VdAbGxvx9ttvY9KkSXjiiScudp+IiIjOSZZlJCYmQpbP+2KtkNWpn4TFYgEA3HLLLYiPj8eWLVsuaqeIiIjORZZlpKSksKB76dQ59J/97GdGfP/99/P8ORERBZTnkHtmZmaXZrmHkk59tZk0aZLP84yMjIvSGSIios6QZRmDBw/mCN3LBf0knn322S7vuLKyEpmZmUhPT8fYsWOxb9++drcrKChAWloaBg8ejNzc3DYTIIQQmDZtGi677LIu94mIiLoHnkNvq1M/iZSUFEyfPh3Tp0/HjTfeiHfffbfLO164cCFyc3Nx4MABLF261LiLmjer1Yrly5ejtLQUBw8exLFjx1BQUOCzzdq1a5Gamtrl/hARUfehqiq2bt3KWe5eOlXQb7zxRhQXF6O4uBgffvghZsyY0aWd1tXVYffu3Zg3bx4AYM6cObBaraiqqvLZbuPGjZg9ezYGDBgASZKwaNEiFBYWGu9XVlbir3/9K5YtW9al/hARUfciyzJGjBjBEbqXTv0k1qxZ4/P8pZde6tJObTYbEhISjIkMkiQhOTkZ1dXVPttVV1cjJSXFeJ6ammpso+s67r//frz44oswm80/uE+Hw4Hm5mafBwBjyVpN09qNVVX1iT0r5nnH4eEqZNkTu4w4IsIFWRZGLEkCgEBEhAuAgCR5YkCWvWMd4eHesfsbqMmkw2Jxx4riHWsIC/OO3f01mzWYze44LEyDonhi1YgtFhWKohuxycScmFPo5qTrujGi6yjWNM0n9sdnhHfscrl8Ys8y2p5YCNEmBuAT67ruE/fEnIQQiImJgSzLXc4pVHSqoEdHRxtxdXU1SktLUVpa2qYAn4/vL9fX0ZLy3tt5b7NmzRpMmjQJo0eP7tT+Vq5ciaioKOORlJQEANi7dy8AoKKiAhUVFQCAPXv2oLKyEgBQXl4Oq9UKANi5cydsNhsAoKysDLW1tQCA1atLMHJkPQBg7dqtSEtrBAAUFBQjMbEFAFBYWISYGDsiIlQUFhYhIkJFTIwdhYVFAIDExBYUFBQDANLSGrF27VYAwMiR9Vi9ugQAMG5cLfLyygAAkyfbsGzZTgBAVpYVS5aUAwCysytx//17AADz5lVg3jx3TvffvwfZ2e6cliwpR1aWO6dly3Zi8mR3Tnl5ZRg3jjkxp9DNqb6+HiUl7pxqa2tRVubOyWazYedOd05WqxXl5e6cKisrsWePO6eufEaUlJSgvt6d09atW9HY6M6puLgYLS3unIqKimC326GqKoqKiqCqKux2O4qK3Dm1tLSguNidU2NjI7Zu7dk5HTx4EO+99x5cLleXctqxYwdChuikiooK8eMf/1hcfvnlYuzYsWLMmDHi8ssvFz/+8Y/Fvn37OtuMEEKI48ePi8jISOFyuYQQQui6LgYMGCCsVqvPdqtXrxYPPPCA8XzLli3i+uuvF0IIMWPGDJGUlCRSUlJEYmKikGVZpKSkiIaGhnb3abfbRVNTk/Gw2WwCgLG9qqpCVdU2scvl8ok1TfOJASHCw11Clj2x04gjIpxClnUjliRdALqIiHAKQBeS5ImFkGXvWBPh4d6xSwBCmEyasFjcsaJ4x6oIC/OOVQEIYTarwmx2x2FhqlAUT+wyYovFJRRFM2KTiTkxp9DMSQghNE0zPnc6ilVV9Ynb+1w4n8+I78dOp9Mn1nXdJ9Z1vU3s+Zz0xJqm+cQ9MSen0ynq6uqEpmldyunkyZMCgGhqahLdXacL+rhx48TGjRvbvP73v/9djBkz5rx3fP3114uXX37ZaGPcuHFttjl06JCIj48Xx44dE7qui1tvvVW89NJLbbazWq0iNjb2vPbf1NTkl1+ie4VoPvjgI9gfRO3xVy0IBp2eTXDq1CnMmTOnzevZ2dloamo67yMD+fn5yM/PR3p6OlatWmXMXl+wYAE2b94MABg0aBDy8vIwfvx4DB48GHFxce3Ohiciop7F5XJhy5Ytxnl3Oo/7oY8fPx6LFi3C3Xffbcwq1HUdr7zyCvLz841zHN2Fv+6By7utEXUPnfuko+5CCIGWlhb07du3S7dQ7ZH3Q9+wYQMWLlyIJUuWICEhAZIkoaamBhkZGVi/fv1F7CIREZEvSZK6fQH2t04X9CFDhuCjjz7CiRMnjNmBSUlJ6N+//0XrHBERUXtcLheKioqQlZXVqUuXe4LzXtG+f//+LOJERBRQiqJg+vTpvDGLF78ssZOenu6PZoiIiDqNxdxXp38aHd08BQBaW1v90hkiIqLO8CxWw0Pu/3+dLugjRoxAamoq2psU71ktiIiI6FJQFAVZWVkcpXvp9E8iJSUFpaWlSEhIaPOeZxlVIiKiS0VVVRZ0L50+hz5z5kwcPny43fdmzZrltw4RERH9EFVVUVxcHFI3V+mqTi8sE2q4sAxRz9IzP+noh4TSwjK8kSwREXU7Qgg0Nze3O6+rp2JBJyKibkdVVWzbto2H3L0ErKBXVlYiMzMT6enpGDt2bIeXxRUUFCAtLQ2DBw9Gbm6u8cv76quvMGnSJAwbNgxXX301cnNz4XA4LmUKREQUIGazGTNmzOAla14CVtAXLlyI3NxcHDhwAEuXLm33LmpWqxXLly9HaWkpDh48iGPHjhl3ZQsPD8fatWuxf/9+fPnll2hqasJzzz13qdMgIqIA0HUdDQ0N0HU90F0JGgEp6HV1ddi9ezfmzZsHAJgzZw6sViuqqqp8ttu4cSNmz56NAQMGQJIkLFq0CIWFhQCAtLQ0jBw5EgBgMpkwZsyYDmfhExFRaNE0Dbt27YKmaYHuStAISEG32WxISEgwrh+UJAnJycmorq722a66uhopKSnG89TU1DbbAMDp06fxpz/9CbfeemuH+3Q4HGhubvZ5ADD+GDRNazdWVdUn9nwb9I7Dw1XIsid2GXFEhAuyLIxYkgQAgYgIFwABSfLEgCx7xzrCw71j92kGk0mHxeKOFcU71hAW5h27+2s2azCb3XFYmAZF8cSqEVssKhRFN2KTiTkxp9DNSdd147RdR7GmaT6xPz4jvGOXy+UTeyZ1eWIhRJsYgE+s67pP3BNzkmUZ06ZNg9ls7nJOoSJgh9y/f//ajmYqem/X3jYulws//elPMX369HNeD79y5UpERUUZD89iOHv37gUAVFRUoKKiAgCwZ88eVFZWAgDKy8thtVoBADt37jTuNFdWVoba2loAwOrVJRg50r1a3tq1W5GW1ggAKCgoRmJiCwCgsLAIMTF2RESoKCwsQkSEipgYOwoLiwAAiYktKCgoBgCkpTVi7dqtAICRI+uxenUJAGDcuFrk5bnvOz95sg3Llu0EAGRlWbFkSTkAIDu7EvffvwcAMG9eBebNc+d0//17kJ3tzmnJknJkZblzWrZsJyZPdueUl1eGceOYE3MK3Zzq6+tRUuLOqba2FmVl7pxsNht27nTnZLVaUV7uzqmyshJ79rhz6spnRElJibGi5tatW9HY6M6puLgYLS3unIqKimC3240lTVVVhd1uR1GRO6eWlhYUF7tzamxsxNatPTunw4cP4/PPP4eu613KaceOHQgZIgCOHz8uIiMjhcvlEkIIoeu6GDBggLBarT7brV69WjzwwAPG8y1btojrr7/eeO50OsVtt90mFixYIHRdP+c+7Xa7aGpqMh42m00AEA0NDUIIIVRVFaqqtoldLpdPrGmaTwwIER7uErLsiZ1GHBHhFLKsG7Ek6QLQRUSEUwC6kCRPLIQse8eaCA/3jl0CEMJk0oTF4o4VxTtWRViYd6wKQAizWRVmszsOC1OFonhilxFbLC6hKJoRm0zMiTmFZk5CCKFpmvG501GsqqpP3N7nwvl8Rnw/djqdPrHns8sT67reJvZ8TnpiTdN84p6Yk91uF//85z+Nvl5oTidPnhQARFNTk+juArawzOTJk5GTk4OcnBxs3LgRa9aswfbt2322OXz4MCZMmIDy8nLExcVh1qxZyMrKwqJFi6CqKn76058iOjoaf/rTn9qM+H8IF5Yh6ll4uTK1hwvL+EF+fj7y8/ORnp6OVatWGbPXFyxYgM2bNwMABg0ahLy8PIwfPx6DBw9GXFycMRv+b3/7G/7xj3/giy++QEZGBkaPHo1f/vKXgUqHiIguIV3XcfToUc5y98KlXzlCJ+oReuYnXehSVRVlZWXIzMzs0g1aQmmEztvUEBFRt6MoCiZNmhTobgQVLv1KRETdjq7rOHLkCA+5e2FBJyKibofn0NviIXciIup2FEVBZmZmoLsRVDhCJyKibkfTNBw8eJBLv3rhCJ2oHQK8fCHk8FcaUkRYGE6VliI1NTXQXQkaLOhERNTtKE4nxowZE+huBBUeciciom5HUxTs37+fh9y9sKATEVH3I8s4e/ZsoHsRVHjInYiIuh2T04mMjIxAdyOoBOUIvbKyEpmZmUhPT8fYsWOxb9++drcrKChAWloaBg8ejNzc3JC6ry0REXVMM5uxd+9eHnL3EpQFfeHChcjNzcWBAwewdOlS44Ys3qxWK5YvX47S0lIcPHgQx44dM27wQkRE1NMEXUGvq6vD7t27MW/ePADAnDlzYLVaUVVV5bPdxo0bMXv2bAwYMACSJGHRokUoLCwMQI+JiOhSM7lcGDFiBEwmU6C7EjSC7hy6zWZDQkKCcfccSZKQnJyM6upqn+sNq6urkZKSYjxPTU1FdXV1h+06HA44HA7jeVNTEwDg1KlTAGActjGZTD6xqqqQJMmIZVmGLMtGDMiwWFQ4nTKEkGGxuOB0miCEjPBwFxwOBUJICA93wW535xQern4vNkOSBCwWT6wjLEyDw+GJdTgcCmRZh6LocDoVmEw6TCZPrEGWBVwuTwy4XCYoijsPVTXBbNag64CmmWA2q9B1CZpmQliYCk2ToWkywsJUqKoMXWdOTQDU8HAodrt7f+HhMNvtEJIE1WKB2W6HLknQwsJgdjigSxL0sDAoDgd0WYauKFCcTugmE3STCYrTCc1kgpBlKC4XNJMJkGWYXC5o3/2tm1QVmtkM6DpMmgbVbIbkicPCIGsaZE+sqpB1HarFAtnphCwEXBYLTJ44PByKwwHJE3vlwZyYUyjk5IyIwNeffoqRI0can+vf//zuzGd5Q0MDACAUbjwadAUdcBdxbx39oL23+6FfxsqVK5GXl9fmdX8sSuD1PcEn/u5vt1OxEL6xpx3vWNcBp9Mda5r7ca7Ye0qBy9V+7Gnv+3FPzyk6FJNiTswplHI6exaYPBn+0tLSgqioKL+1FwhBV9CTkpJQU1MDVVWhKAqEELDZbEhOTvbZLjk52ecw/JEjR9ps4+3hhx/Ggw8+aDzXdR0NDQ2IjY1t8wWCiIiCW3NzM5KSkmCz2bp0H3MhBFpaWpCQkODH3gVG0BX0uLg4ZGRk4NVXX0VOTg7efPNNpKamthlJz5kzBxMmTMBjjz2GuLg4rFu3DnPnzu2wXYvFAovF4vNadHT0RciAiIgulcjIyC4VdADdfmTuEXST4gAgPz8f+fn5SE9Px6pVq4zZ6wsWLMDmzZsBAIMGDUJeXh7Gjx+PwYMHIy4urt3Z8ERERD2BJEJhJgAREfUozc3NiIqKQlNTU5dH6KEiKEfoRERE52KxWLBixYo2p1J7Mo7QiYiIQgBH6ERERCGABZ2IiCgEsKATERGFABZ0IiKiEMCCTkREFAJY0ImIiEIACzoREVEIYEEnIiIKASzoREREISDoCvqvfvUrpKamQpIk7N27t8PtCgoKkJaWhsGDByM3Nxeq9319iYiIepigK+jZ2dkoLS1FSkpKh9tYrVYsX74cpaWlOHjwII4dO2bckY2IiKgnCrqCPmnSJAwcOPCc22zcuBGzZ8/GgAEDIEkSFi1ahMLCwkvUQyIiouCjBLoDF6K6utpnBJ+amorq6upz/huHwwGHw2E813UdDQ0NiI2NhSRJF62vREQUvIQQaGlpQUJCAmQ56Ma456VbFnQAPkW4MzeMW7lyJfLy8i5ml4iIqJuy2Ww/eHQ42HXLgp6cnIyqqirj+ZEjR5CcnHzOf/Pwww/jwQcfNJ43NTUZ7fTr1w+apgEATCaTT6yqKiRJMmJZliHLcoexy+WCyWQyYkVRIEmSEQOAqqo+sdlshhDCiHVdh6ZpRqzrOhRF6TDWNA1CCCNuLw/mxJyYE3MKpZwcDgd27dqF6667zhjgXUhODQ0NuOKKK9C3b190d92yoM+ZMwcTJkzAY489hri4OKxbtw5z584957+xWCywWCxtXu/Xrx8iIyMvVleJiOgi0HUdo0aNQnR0tF8OlYfCqdegO2Hwy1/+EgMHDkRNTQ1uuOEGDBkyBACwYMECbN68GQAwaNAg5OXlYfz48Rg8eDDi4uIwf/78QHabiIguIVmWkZiY2O3Pe/uTJDpzAjoENTc3IyoqCk1NTRyhExF1M6qqoqSkBJMmTTIO5V+IUKoF/GpDRETdjizLGDFiBEfoXrrlOXQiIurZZFlGXFxcoLsRVPjVhoiIuh2Xy4UPPvgALpcr0F0JGizoRETU7ZhMJowZMwYmkynQXQkaPORORETdjizLiImJCXQ3ggpH6ERB4vHHH8dtt93Wrffx9NNP484777xo7RN5uFwubNmyhYfcvbCgE3Xgm2++wa233orLLrsMkZGRGDZsGJ555hm/tL1+/XqMHj3aL2395S9/gSRJeOmlly7aPtrTXvuPPPLIBd8oaceOHZgyZQr69euH6OhojBw5EuvXr+9yPz/55BNER0d3uR0KLoqiYOLEiV26ZC3UsKATdWDGjBkYNWoUqqurcerUKbz55psYNGhQoLvVRkFBAWJiYrr1LYRbWlpw880346c//Snq6upw4sQJFBQUBM0sZlVVA90F+h5JkhAZGRkSK7z5jeihmpqaBADR1NQU6K5QEDpx4oQAIKqrqzvc5tixY+L2228Xl112mUhKShKPPPKIcLlcQgghXn75ZTFq1Cif7UeNGiVefvllsXv3bmGxWIQsy6J3796id+/e4siRI2LFihXiJz/5ifjlL38poqKiRFJSkvjrX/96zn5WVlYKAOKtt94SkiSJL7/8UgghzrmPWbNmGf/+oYceEsnJyaJPnz7iyiuvFG+88Ybx3scffyyioqLE//3f/4mBAweKmJgY8dBDD51X+7W1teLuu+8W8fHxIioqSkycOFGcOXOmTR67du0SZrNZaJrWYa7Hjx8Xd911l4iPjxfx8fFiyZIlwm63G+9/8cUXYsqUKaJfv37isssuE4sXLxb19fUiPDxcADD6WVJSIoQQ4pVXXhHDhg0TUVFRYvz48WL37t1GW9dff7146KGHxI033ih69eolNm/efM7fA116TqdTvPXWW8LpdHapnVCqBRyhE7UjNjYWw4YNw89//nO88cYbOHLkSJtt7rrrLpjNZlitVmzbtg1vvfUWVq9e/YNtZ2RkYN26dbj66qvR2tqK1tZW4+ZCH3zwAcaPH4+TJ0/iySefxIIFC9DS0tJhWwUFBcjIyMCsWbMwceJEY5R+rn14GzVqFHbt2oXGxkY89thjuOeee2C1Wo33W1pa8NVXX6GyshKlpaV48cUX8cknn3SqfV3XMXPmTCiKgq+//hr19fV4+umn210IZOjQoYiOjsbcuXPx9ttv49ixYz7vCyEwc+ZMXH755Th48CC++uor/Pvf/8aTTz4JADh69CimTp2K7OxsfPvttzhy5AjuuOMOxMbG4r333kNUVJTRz4kTJ2Lbtm34xS9+gfz8fJw4cQLZ2dm46aab0NTUZOxz/fr1ePLJJ9Ha2oobbrjhh36tdIkpioLp06fzkLsXFnSidkiShI8//hijRo1CXl4eBg0ahKuuugoffvghAHcB2bp1K5577jn06dMHKSkp+O1vf9vlc77XXHMN7rzzTphMJtxzzz1wOp04cOBAu9tqmoYNGzbg3nvvBQD87Gc/w2uvvQaHw9Hp/d19992Ii4uDyWTC3LlzMWzYMJSVlRnvCyGwcuVKhIeH48orr0RmZib+9a9/dartXbt2Yd++fXjppZfQr18/KIqCCRMmtHuTpL59+6KsrAwxMTF48MEHkZCQgHHjxmH37t0AgC+++AKVlZV49tln0atXL8TGxuKRRx7B66+/DgB49dVX8aMf/QgPPPAAwsPD0atXL0ycOLHDvv3lL3/BvHnzMGnSJJjNZvz6179Gv379sGXLFmObu+66C2PHjoUkSYiIiOhUznRpsZj7YkEn6sDll1+O5557Dl9//TVOnDiBW265BbNnz0ZDQwNqamoQHh6Oyy+/3Nh+0KBBqKmp6fI+PTyFpKMRelFREerr63HXXXcBAG6//XacPXsWmzZt6vT+nn/+eQwfPhxRUVGIjo7G3r17UV9fb7wfGRmJXr16Gc979+59ziMG3o4cOYLExMROF8MhQ4Zg3bp1OHToEGpqajBkyBDMnDkTQghUVVWhsbERMTExiI6ORnR0NLKzs3H8+HFjX2lpaZ3Ou6amBqmpqT6vXXHFFT6/vx+6JTMFlqqqKCoq4vwGLyzoRJ0QExODxx9/HKdPn4bVasXAgQNht9uNggLAeB0A+vTpgzNnzvi04X0Y2R/rTxcUFEDXdVx99dW4/PLLkZ6eDpfLZRx2/6F9lJaW4vHHH8df/vIXnDp1Co2NjRgxYgREJ+/X9EPtp6Sk4OjRozh79mznEvKSkJCAZcuW4ejRo2hoaEBSUhLi4uLQ2NhoPJqamtDa2mrs6+DBg53u58CBA1FVVeXzWlVVlfH76+jfUfBQFAVZWVkcpXvhXyxRO06dOoVHH30U+/fvh6ZpOHPmDH7/+98jJiYGw4YNQ2JiIqZMmYL/+q//wunTp1FdXY2nn37aOPw9evRoHD58GNu2bYOqqli9ejVOnjxptD9gwADU1tZeULEDgOPHj2PLli34y1/+gi+//NJ4vPPOO/joo49QVVX1g/tobm6Goijo378/dF3Hn//8Z+zdu7fTffih9seMGYOhQ4fil7/8JRobG6GqKkpLS9s9JbB//34888wzqKqqgq7raGxsxNq1a5Geno7Y2FiMGTMGycnJePTRR9HS0gIhBI4cOYL33nsPgPvUwc6dO7Fu3To4HA6cOXMG27ZtM/rZ0tKCEydOGPubN28eXnvtNXz22WdQVRUvvPACTp48iaysrE7nT4HH0bkvFnSidoSFheHo0aPIyspCVFQUkpOT8dlnn+H9999H7969AQCvv/46zp49i5SUFIwfPx4zZszA0qVLAbgPH69evRrZ2dmIj4+Hw+HA8OHDjfanTp2K6667DomJiYiOjkZ1dfV59W/Dhg1ITk7G3LlzcfnllxuPm2++GT/60Y/w5z//+Qf3cfPNN2POnDm4+uqrkZCQgK+//hrjx4/vdB9+qH1ZlvHOO+/gzJkzGDp0KC677DI8+uij0HW9TVt9+/ZFeXk5Jk6ciMjISAwdOhQnTpzAO++8A8C9zOc777yDo0eP4sorr0RUVBRmzJhhjMoHDhyIf/7zn3j99dcxYMAApKamYuPGjQDcE+7mz5+PK6+8EtHR0SgtLcX111+PF154AfPnz0dsbCz++te/4r333uP16t2IqqooLi5mUffC+6GHwD1wiYjowoRSLQjKEXplZSUyMzORnp6OsWPHYt++fW22EULgoYcewvDhwzFy5EhMmTKlw3NoREQUWoQQaG5u7vScj54gKAv6woULkZubiwMHDmDp0qWYP39+m202b96MkpISfPnll9izZw+mTZuGRx55JAC9JSKiS01VVWOOCrkFXUGvq6vD7t27MW/ePADAnDlzYLVa28xIBQCHwwG73W58U/OeoUpERKHLbDZjxowZMJvNge5K0Ai6gm6z2ZCQkGBciiBJEpKTk9tMuLn11lsxZcoUXH755YiPj8dHH32EJ554osN2HQ4HmpubfR6Ae3EOz3/bi1VV9Yk9E3o6il0ul0/sORzkiYUQbWIAPrGu6z6x5xtoR7GmaT4xc2JOzIk5hXpOLpcLJ06cgK7rXc4pVARdQQfQZrH99s6R7N69G/v378fRo0fx7bffYtq0aVi8eHGHba5cuRJRUVHGIykpCQCMy3QqKipQUVEBANizZw8qKysBAOXl5cZSmDt37oTNZgMAlJWVoba2FgBQUlJiLMaxdetWNDY2AgCKi4uNRTiKiopgt9t9FkOw2+0oKioC4F5is7i4GADQ2NiIrVu3AgDq6+tRUlICAKitrTVW8bLZbNi5cycA9/XP5eXlANzzD/bs2cOcmBNzYk4hndPhw4exfft2aJrWpZx27NiBUBF0s9zr6uqQlpaGkydPQlEUCCEQHx+P7du3+6zstHjxYiQnJxuXCX399dfIyspqd81twD1C977+tbm5GUlJSWhoaEC/fv2Mb24mk8knVlUVkiQZsSzLkGW5w9jlcsFkMhmxoiiQJMmIAfc3Qu/YbDZDCGHEnm+cnljXdSiK0mGsaRqEEEbcXh7MiTkxJ+bEnNrm1NDQgNjY2JCY5e7Xgv7uu+/iJz/5SZfbmTx5MnJycpCTk4ONGzdizZo12L59u882v//97/HBBx/g3XffhdlsxqpVq7Bt2zaftZjPJZQuVSAi6ml0XUd9fT0uu+yyLq3qF0q1oMsF/cYbb4QkSRBC4MCBAxg6dKhxCOVCffPNN8jJycHJkycRGRmJDRs2YPjw4ViwYAFmzpyJmTNnwuFwYPHixdi2bRvCwsIQHx+P/Pz8NuszdySUfolERD2NqqooKSnBpEmTurT8ayjVgi4X9OXLl+NHP/oRbrvtNvzmN7/B888/76++XVSh9EskIqILE0q1oMuT4n73u99BVVU88sgjcDqd/ugTERHROem6jqNHj7a7lHBP5ZdZ7tnZ2bjvvvswdOhQfzRHRER0Trqu49ChQyzoXoJulvulEkqHWYiI6MKEUi3w641kKyoq8NRTT+Hw4cM+F+t7rhskIiLyB13XYbPZkJSUxHvXf8evBf2OO+7Az372M9x3330wmUz+bJqIiMjgOYeemJjIgv4dvxZ0s9mMhx56yJ9NEhERtaEoCjIzMwPdjaDi1681N998M95//31/NklERNSGpmk4ePCgsRoc+XmEPm3aNMyaNQsmkwkWiwVCCEiShLq6On/uhoiIejghBE6dOtXpxcR6Ar8W9IULF2L9+vW45ppreA6diIguGkVRMGbMmEB3I6j4taDHxsYiOzvbn00SERG14bnLWlpaGgeQ3/HrOfTZs2dj3bp1aGhowJkzZ4wHERGRv509ezbQXQgqfl1YxvvSAc8NWyRJCspJC6G0mAAREV2YUKoFfh2he+5V67mvree/RERE/qRpGvbu3csa48WvBd1ut7d57cSJE/7cBREREbXDrwX9zjvv9Hne2NiIm2++2Z+7ICIigslkwogRIzghzotfC/rQoUOxZMkSAEBrayuysrLwi1/8wp+7ICIigqZpKC8v5yF3L34t6KtWrcLx48fxzDPPYNasWbjjjjuwYMGC826nsrISmZmZSE9Px9ixY7Fv374223zyySfo1asXRo8ebTw445GIqOeIiIgIdBeCil+uQ/e+NO3FF1/ELbfcgmnTpiE3NxdnzpxBr169zqu9hQsXIjc3Fzk5Odi4cSPmz5+Pzz//vM12V111Fb744osu95+IiLoXk8mEYcOGBbobQcUvI/Q+ffqgb9++6NOnD+Li4vDFF1/gmWeeMV4/H3V1ddi9ezfmzZsHAJgzZw6sViuqqqr80VUiIgoBqqpi165dPrfq7un8UtC/f5na9y9fOx82mw0JCQlQFPfBA0mSkJycjOrq6jbbfvPNN7jmmmswZswY/PGPfzxnuw6HA83NzT4PAEb/NE1rN1ZV1SfWdf2cscvl8ok9l/l7YiFEmxiAT6zruk/s+YPtKNY0zSdmTsyJOTGnUM9J13VERUUZa510JadQ4ZeCfvr0aSM+efJkl9uTJMnneXtr31xzzTWoqanB7t27sWnTJqxbtw5vvPFGh22uXLkSUVFRxiMpKQkAsHfvXgBARUUFKioqAAB79uxBZWUlAKC8vBxWqxUAsHPnTthsNgBAWVkZamtrAQAlJSWor68HAGzduhWNjY0AgOLiYrS0tAAAioqKYLfboaoqioqKoKoq7HY7ioqKAAAtLS0oLi4G4L46YOvWrQCA+vp6lJSUAABqa2tRVlYGwP3FZ+fOnQAAq9WK8vJyAO75B3v27GFOzIk5MaeQzqm6uhpNTU0wmUxdymnHjh0IFV1eKe4///M/UV1djauuugorV67EAw888IOj5XOpq6tDWloaTp48CUVRIIRAfHw8tm/ffs676qxcuRLffvstXnjhhXbfdzgccDgcxvPm5mYkJSWhoaEB/fr1M765mUwmn1hVVUiSZMSyLEOW5Q5jl8sFk8lkxIqiQJIkIwbc3wi9Y7PZDCGEEXuObHhiXdehKEqHsaZpEEIYcXt5MCfmxJyYUyjl5HA48MUXX2DcuHHGIPBCcmpoaEBsbGxIrBTX5YJ+zz334JVXXsF7772HXbt24dixY10q6AAwefJk5OTkGJPi1qxZg+3bt/tsU1tbiwEDBkCWZbS0tODmm2/G/Pnzcd9993VqH6G03B8RUU+j6zpsNhuSkpJ8lh0/X6FUC7p8yN1isQAAbrnlFsTHx2PLli1d7lR+fj7y8/ORnp6OVatWoaCgAACwYMECbN68GQDw5ptv4uqrr8aoUaNw3XXX4cYbb8TPf/7zLu+biIiCnyzLSElJ6VIxDzVdHqGXlJRg0qRJxvN//OMf+I//+I8ud+xiC6VvZUREPY2qqigrK0NmZqZxKP9ChFIt6PJXG+9iDgAZGRldbZKIiOicZFnG4MGDOUL34vefxLPPPuvvJomIiHzIsozExEQWdC9dXikuJSUFQ4cOBeC+vOybb77p8qQ4IiKic1FV1Tjl25VD7qGkyz+FG2+8EX/605+M57wZCxERXWyyLGPEiBEcoXvp8qS4xsZGREdH+6k7l04oTYQgIqILE0q1oMtfbbyLeXV1NUpLS1FaWtruUq1ERET+4HK58MEHHxjLxZKf7ra2f/9+3HfffbBarUhOToYQAjabDVdccQUKCgpw5ZVX+mM3REREANyrv40ZMwYmkynQXQkafinoOTk5eOihhzBnzhyf1zdu3Ih7773XWHs3FH1v2XkiClJdO7lIwUaWZcTExAS6G0HFL7MJTp061aaYA0B2djaampr8sQsiIiKDy+XCli1beMjdi18K+mWXXYZXXnnFuB0d4F5nd8OGDYiNjfXHLoiIiAyKomDixIm8ZM2LX34SGzZswMKFC7FkyRIkJCRAkiTU1NQgIyMD69ev98cuiIiIDJIkdftZ6f7ml4I+ZMgQfPTRRzhx4oRxj9mkpCT079/fH80TERH5cLlcKCoqQlZWFsxmc6C7ExT8eqyif//+LOJERHTRKYqC6dOn85C7l4u+xE56evrF3gUREfVALOa+/PLT2LdvX4fvtba2+mMXREREBlVVecj9e/xS0EeMGIHU1FS0t4psfX39ebdXWVmJe++9F/X19YiOjsb69etx1VVX+WyzdetWPPzww2hpaYEsy5g1axaefPJJSLwwnPxAgH9HIYe/0pCiAMhyOjlK9+KXn0RKSgpKS0uRkJDQ5r2kpKTzbm/hwoXIzc1FTk4ONm7ciPnz5+Pzzz/32aZfv34oLCzEoEGDYLfbccMNN6CwsBB33XXXBedBRETdhCRBVVUWdC9+OYc+c+ZMHD58uN33Zs2adV5t1dXVYffu3Zg3bx4AYM6cObBaraiqqvLZLiMjA4MGDQIAhIeHY/To0R32gYiIQosaHo7i4mKoqhrorgQNvxT0P/zhD5gwYUK7761du/a82rLZbEhISDC+dUmShOTk5HPe7OXYsWPYuHEjsrKyOtzG4XCgubnZ5wEAmqYZ/20vVlXVJ/YsnuMdh4erkGVP7DLiiAgXZFkYsSQJAAIRES4AApLkiQFZ9o51hId7x+4/WJNJh8XijhXFO9YQFuYdu/trNmswm91xWJgGRfHEqhFbLCoURTdik4k5SZKAAOCKiIAAICQJrogIAICQZSPWZRmu8HAjVj2xyQTVYnHHimLEmqJADQszYs0Tm83QvjsHqIWFQfvub1/1ji0W6N7xd+tXq+Hh0L+7faTLO46IgPCOJYk5MaeQykl2uTBjxgyYzeYOP787+1keKoLyRrLfPw9+rju8Njc349Zbb8XSpUtxzTXXdLjdypUrERUVZTw8pwL27t0LAKioqEBFRQUAYM+ePaisrAQAlJeXw2q1AgB27txpXGdfVlaG2tpaAMDq1SUYOdI9V2Dt2q1IS2sEABQUFCMxsQUAUFhYhJgYOyIiVBQWFiEiQkVMjB2FhUUAgMTEFhQUFAMA0tIasXbtVgDAyJH1WL26BAAwblwt8vLKAACTJ9uwbJl7jfysLCuWLCkHAGRnV+L++/cAAObNq8C8ee6c7r9/D7Kz3TktWVKOrCx3TsuW7cTkye6c8vLKMG4cc4qJsUONiEBRYSHUiAjYY2JQVFgIAGhJTERxQQEAoDEtDVu/+8JaP3IkSlavBgDUjhuHsrw8AIBt8mTsXLYMAGDNykL5kiUAgMrsbOy5/34AQMW8eaj47ojUnvvvR2V2NgCgfMkSWL/7krpz2TLYJk8GAJTl5aF23DgAQMnq1agfORIAsHXtWjSmpQEAigsK0JKYCAAoKiyEPSaGOTGn0Mppxgzs3LkTQghUVlZizx73Z8T5fpbv2LEDIUMEmePHj4vIyEjhcrmEEELoui4GDBggrFZrm22bm5vFj3/8Y/HEE0/8YLt2u100NTUZD5vNJgCIhoYGIYQQqqoKVVXbxC6XyyfWNM0nBoQID3cJWfbETiOOiHAKWdaNWJJ0AegiIsIpAF1IkicWQpa9Y02Eh3vHLgEIYTJpwmJxx4riHasiLMw7VgUghNmsCrPZHYeFqUJRPLHLiC0Wl1AUzYhNJuYkSbrQAeGMiBA6IHRJEs6ICCEAocuyEWuyLJzh4Ubs8sQmk3BZLO5YUYxYVRThCgszYtUTm81CNZvdcViYUBVFCEC4vGOLRWjescnkjsPDhSbLQgDC6R1HRAjdO5Yk5sScQiqns337infeeUc4nc4OP78781l+8uRJAUA0NTWdf8EKMpIQwXcPosmTJyMnJ8eYFLdmzRps377dZ5vW1lbcdNNNmD59OlasWHHe+/DXTe05qT40cZY7UTfgh/Llr1oQDILykHt+fj7y8/ORnp6OVatWoeC7QzILFizA5s2bAbjP2+/cuRObNm3C6NGjMXr0aDz11FOB7DYREV0iuiyjoaHB56ZgPV1QjtAvBY7Q6Vw4QicKbq7wcGx96y1MnTq1SwvLhNIInRfwERFRt2O223HTTTcFuhtBJSgPuRMREZ2LLsuoq6vjIXcvLOhERNTt6GFh2Lt3Lwu6Fx5yJyKibkex2zF16tRAdyOocIRORETdjm4y4ejRoxyhe2FBJyKibkdXFBw6dIgF3QsPuRMRUbejOByYNGlSoLsRVDhCJyKibkdXFBw5coQjdC8s6ERE1O3wHHpbPORORETdjuJwIDMzM9DdCCocoRMRUbejKQoOHjxo3OOcWNCJiKgbErKMU6dOoYfejqRdPORORETdjuJ0YsyYMYHuRlDhCJ2IiLodTVGwf/9+HnL3woJORETdjyzj7Nmzge5FUOEhdyIi6nZMTicyMjIC3Y2gEpQj9MrKSmRmZiI9PR1jx47Fvn372t2uoKAAaWlpGDx4MHJzc6Gq6iXuKRERBYJmNmPv3r085O4lKAv6woULkZubiwMHDmDp0qWYP39+m22sViuWL1+O0tJSHDx4EMeOHUNBQUEAektERBR4QVfQ6+rqsHv3bsybNw8AMGfOHFitVlRVVflst3HjRsyePRsDBgyAJElYtGgRCgsLA9BjIiK61EwuF0aMGAGTyRTorgSNoDuHbrPZkJCQAEVxd02SJCQnJ6O6uhqpqanGdtXV1UhJSTGep6amorq6usN2HQ4HHA6H8bypqQkAcOrUKQAwDtuYTCafWFVVSJJkxLIsQ5ZlIwZkWCwqnE4ZQsiwWFxwOk0QQkZ4uAsOhwIhJISHu2C3u3MKD1e/F5shSQIWiyfWERamweHwxDocDgWyrENRdDidCkwmHSaTJ9YgywIulycGXC4TFMWdh6qaYDZr0HVA00wwm1XougRNMyEsTIWmydA0GWFhKlRVhq4zpyYAang4FLvdvb/wcJjtdghJgmqxwGy3Q5ckaGFhMDsc0CUJelgYFIcDuixDVxQoTid0kwm6yQTF6YRmMkHIMhSXC5rJBMgyTC4XtO/+1k2qCs1sBnQdJk2DajZD8sRhYZA1DbInVlXIug7VYoHsdEIWAi6LBSZPHB4OxeGA5Im98mBOzCkUcnJGRODrTz/FyJEjjc/1739+d+azvKGhAQBC4nr2oCvogLuIe+voB+293Q/9MlauXIm8vLw2r3t/SbhQXt8TfOLv/nY7FQvhG3va8Y51HXA63bGmuR/nir2nFLhc7cee9r4f9/ScokMxKebEnEIpp7NngcmT4S8tLS2IioryW3uBEHQFPSkpCTU1NVBVFYqiQAgBm82G5ORkn+2Sk5N9DsMfOXKkzTbeHn74YTz44IPGc13X0dDQgNjY2DZfIIiIKLg1NzcjKSkJNpsNkZGRF9yOEAItLS1ISEjwY+8CI+gKelxcHDIyMvDqq68iJycHb775JlJTU9uMpOfMmYMJEybgscceQ1xcHNatW4e5c+d22K7FYoHFYvF5LTo6+iJkQEREl0pkZGSXCjqAbj8y9wi6SXEAkJ+fj/z8fKSnp2PVqlXG7PUFCxZg8+bNAIBBgwYhLy8P48ePx+DBgxEXF9fubHgiIqKeQBKhMBOAiIh6lObmZkRFRaGpqanLI/RQEZQjdCIionOxWCxYsWJFm1OpPRlH6ERERCGAI3QiIqIQwIJOREQUAljQiYiIQgALOhERUQhgQSciIgoBLOhEREQhgAWdiIgoBLCgExERhQAWdCIiohAQdAX9V7/6FVJTUyFJEvbu3dvhdgUFBUhLS8PgwYORm5sL1fu+vkRERD1M0BX07OxslJaWIiUlpcNtrFYrli9fjtLSUhw8eBDHjh0z7shGRETUEwVdQZ80aRIGDhx4zm02btyI2bNnY8CAAZAkCYsWLUJhYeEl6iEREVHwUQLdgQtRXV3tM4JPTU1FdXX1Of+Nw+GAw+Ewnuu6joaGBsTGxkKSpIvWVyIiCl5CCLS0tCAhIQGyHHRj3PPSLQs6AJ8i3Jkbxq1cuRJ5eXkXs0tERNRN2Wy2Hzw6HOy6ZUFPTk5GVVWV8fzIkSNITk4+5795+OGH8eCDDxrPm5qajHb69esHTdMAACaTySdWVRWSJBmxLMuQZbnD2OVywWQyGbGiKJAkyYgBQFVVn9hsNkMIYcS6rkPTNCPWdR2KonQYa5oGIYQRt5cHc2JOzIk5hVJODocDu3btwnXXXWcM8C4kp4aGBlxxxRXo27cvurtuWdDnzJmDCRMm4LHHHkNcXBzWrVuHuXPnnvPfWCwWWCyWNq/369cPkZGRF6urRER0Eei6jlGjRiE6Otovh8pD4dRr0J0w+OUvf4mBAweipqYGN9xwA4YMGQIAWLBgATZv3gwAGDRoEPLy8jB+/HgMHjwYcXFxmD9/fiC7TUREl5Asy0hMTOz25739SRKdOQEdgpqbmxEVFYWmpiaO0ImIuhlVVVFSUoJJkyYZh/IvRCjVAn61ISKibkeWZYwYMYIjdC/d8hw6ERH1bLIsIy4uLtDdCCr8akNERN2Oy+XCBx98AJfLFeiuBA0WdCIi6nZMJhPGjBkDk8kU6K4EDR5yJyKibkeWZcTExAS6G0GFI3SiIPH444/jtttuC3Q3MHz4cLz77rvG8//7v/9DfHw8+vTpg/Ly8jbvEwWCy+XCli1beMjdCws6UQe++eYb3HrrrbjssssQGRmJYcOG4ZlnnvFL2+vXr8fo0aO71Mbjjz8ORVHQp08fREZGYsSIEXj11Ve73Levv/4aP/nJTwC4PzSXLFmCv/3tb2htbUVGRobP++frueeeQ3p6Ovr27Yv+/fvjhhtu8Fn18ULl5OTg17/+dZfboe5DURRMnDixS5eshRoWdKIOzJgxA6NGjUJ1dTVOnTqFN998E4MGDQp0t3z85Cc/QWtrKxobG/HYY48hJycHFRUVfmv/+PHjOHv2LEaOHNnltl599VW88MIL+Mc//oGWlhZUVlYiNzc3KFboUlU10F2g8yRJEiIjI4Pi7ydYsKATtaO+vh6HDh3CwoUL0atXL5hMJgwfPhy33367sc3x48dxxx13oH///khOTsZvf/tbozC0NwIfPXo01q9fj/LycixatAhfffUV+vTpgz59+hh3C9Q0DYsXL0Z0dDSSk5Pxt7/9rVP9lWUZd9xxB6Kjo7Fv3z4UFxfj2muvRVRUFOLj4/HAAw/g7NmzxvbNzc1YvHgxkpOTERkZiTFjxsBmswFw373wrbfeQnl5OYYOHQoAGDhwIAYPHuzzvseHH36IcePGITo6GvHx8Vi5cmW7fdy+fTumTZuGESNGAACio6Nxxx13+Nw58Z///CfGjh2L6OhoDB8+3FgdEnAv9fk///M/GDZsGPr27Yu0tDS8//77+J//+R+89tpr+OMf/4g+ffpg+PDhAICWlhbk5uYiPj4e8fHxWLRoEU6fPg0AqKqqgiRJePnllzFkyBAkJiZ26udMwcPlcuHtt9/mIXcvLOhE7YiNjcWwYcPw85//HG+88QaOHDnSZpu77roLZrMZVqsV27Ztw1tvvYXVq1f/YNsZGRlYt24drr76arS2tqK1tdW4udAHH3yA8ePH4+TJk3jyySexYMECtLS0/GCbmqbhr3/9K5qamjBy5EhERETg//7v/9DQ0IDPPvsMH3/8MX7/+98b2+fk5ODgwYPYvn07Ghsb8b//+7+IiIho08+vv/4aAFBTU4NDhw612W95eTlmzZqFpUuX4sSJE9i/fz+mTJnSbh8nTJiAN954A0899RQ+++wz2O12n/f37NmD22+/HatWrUJDQwPy8/Nxzz334JtvvgEArF27Fv/93/+N1157Dc3Nzfjoo4+QkpKCX/3qV7j77rvxwAMPoLW11ejzkiVLcPDgQezduxdfffUV9u/fj9/85jc++9y8eTO++OILWK3WH/wZU3BRFAXTp0/nIXdvoodqamoSAERTU1Ogu0JBqra2Vjz44IPiqquuErIsiyuvvFIUFxcLIYSoqakRAERtba2x/WuvvSbS0tKEEEK8/PLLYtSoUT7tjRo1Srz88ssdvr9ixQoxbtw447mu6yIsLEx88cUX7fZvxYoVQlEUERUVJWJjY8W1114rNm7c2O62zz//vLjhhhuEEEIcO3ZMABBHjhxpd9uUlBSxadMmIYQQVqtVABCnTp1q9/1FixaJn//85+22056///3vIisrS0RFRYlevXqJBQsWiNbWViGEEA888ID49a9/7bP9XXfdJZ544gkhhBDDhg0TGzZsaLfde++9VyxZssR4rmmasFgsYvv27cZrn332mbBYLELTNCOv8vLyTvedgouu68LpdApd17vUTijVAo7QiTpw+eWX47nnnsPXX3+NEydO4JZbbsHs2bPR0NCAmpoahIeH4/LLLze2HzRoEGpqarq8Tw9JkhAREXHOEfqMGTPQ2NiI+vp67Nq1C3PmzAEA7Nq1CzfccAMGDBiAyMhIPPLII6ivrwfgvt2wxWL5wVsOd8aRI0eQlpbW6e2zs7OxZcsWnDp1Ch988AGKi4vx1FNPAXAfBl+3bh2io6ONx9tvv41vv/32vPd14sQJOBwOpKamGq8NGjQIDofD+DkA8MvPgAJDVVUUFRVx/oMXFnSiToiJicHjjz+O06dPw2q1YuDAgbDb7Th+/Lixjed1AOjTpw/OnDnj08axY8eM+GKvP33nnXdiypQpOHz4MJqbm/H0009DfHcfppSUFDgcDuOceVekpKTg4MGD5/3vJEnChAkTkJ2dja+++goAkJSUhCVLlqCxsdF4tLa24qWXXvrBfX3/59m/f3+EhYX5zKC3Wq2wWCy47LLLOvx31H0oioKsrCwecvfCv2aidpw6dQqPPvoo9u/fD03TcObMGfz+979HTEwMhg0bhsTEREyZMgX/9V//hdOnT6O6uhpPP/007r33XgDuCXCHDx/Gtm3boKoqVq9ejZMnTxrtDxgwALW1tT4T1fypubkZ0dHR6N27NyoqKoyi6Nn3rFmzsGjRItTW1kLXdZSXl/v0r7Puv/9+FBYWYtOmTVBVFU1NTdi+fXu727788st4++230djYCADYu3cv3n77bWRmZgIAFi5ciJdffhkff/wxNE2Dw+HA559/bszaX7hwIfLy8vDll19CCIHq6mrjvQEDBuDw4cPGvmRZxl133YXf/va3aGhowMmTJ/Hb3/4W99xzD4t4COHo3Bf/sonaERYWhqNHjyIrKwtRUVFITk7GZ599hvfffx+9e/cGALz++us4e/YsUlJSMH78eMyYMQNLly4FAAwZMgSrV69GdnY24uPj4XA4jNnXADB16lRcd911SExMRHR0tDHL3V/y8/OxZs0a9OnTB4sWLcLcuXN93t+wYQOSkpJw7bXXIjo6GosWLbqgLxfXXHMN3nzzTTz11FOIiYnBlVdeiU8//bTdbaOjo/Hcc89h0KBB6Nu3L2677Tbceeedxs8sIyMDhYWFePTRR9G/f38kJiZi+fLlcDgcAIBf/epX+MUvfoE77rgDffv2xQ033GD83BYsWICjR4+iX79+xiV2f/jDH5CamoqrrroKw4cPx5AhQ3wmBlL3pqoqiouLWdS98H7oIXAPXCIiujChVAuCcoReWVmJzMxMpKenY+zYsdi3b1+bbYQQeOihhzB8+HCMHDkSU6ZMuaBzeURE1P0IIdDc3IweOiZtV1AW9IULFyI3NxcHDhzA0qVLMX/+/DbbbN68GSUlJfjyyy+xZ88eTJs2DY888kgAektERJeaqqrGHBVyC7qCXldXh927d2PevHkAgDlz5sBqtba73rPD4YDdbje+qXlmGBMRUWgzm82YMWMGzGZzoLsSNIKuoNtsNiQkJBiXIkiShOTk5DaThm699VZMmTIFl19+OeLj4/HRRx/hiSee6LBdh8OB5uZmnwfgXmHL89/2YlVVfWJd188Zu1wun9hzOMgTCyHaxAB8Yl3XfWLPN9COYk3TfGLmxJyYE3MK9ZxcLhdOnDgBXde7nFOoCLqCDqDNYvvtnSPZvXs39u/fj6NHj+Lbb7/FtGnTsHjx4g7bXLlyJaKiooxHUlISAPelMwBQUVFhXAKzZ88eVFZWAnAvbelZFnLnzp3GtbtlZWWora0FAJSUlBiLVWzdutW4LKe4uNhYFKSoqAh2u91nMQS73Y6ioiIA7nWni4uLAQCNjY3YunUrAPea4iUlJQCA2tpalJWVAXB/8dm5cycA9/W15eXlANzzD/bs2cOcmBNzYk4hndPhw4exfft2aJrWpZx27NiBUBF0s9zr6uqQlpaGkydPQlEUCCEQHx+P7du3+6z65LmxhOeSl6+//hpZWVntrrkNuEfonstfAPfMxqSkJDQ0NKBfv37GNzeTyeQTq6oKSZKMWJZlyLLcYexyuWAymYxYURRIkmTEgPsboXdsNpshhDBizzdOT6zrOhRF6TDWNA1CCCNuLw/mxJyYE3NiTm1zamhoQGxsbEjMcvdrQX/33Xcv+D7J3iZPnoycnBzk5ORg48aNWLNmTZvFKn7/+9/jgw8+wLvvvguz2YxVq1Zh27Zt2LJlS6f2EUqXKhAR9TS6rqO+vh6XXXZZlxYLCqVa0OWCfuONN0KSJAghcODAAQwdOtQ4hHKhvvnmG+Tk5ODkyZOIjIzEhg0bMHz4cCxYsAAzZ87EzJkz4XA4sHjxYmzbtg1hYWGIj49Hfn6+zyj+XELpl0hE1NOoqoqSkhJMmjSpS8u/hlIt6HJBX758OX70ox/htttuw29+8xs8//zz/urbRRVKv0QiIrowoVQLujwp7ne/+x1UVcUjjzwCp9Ppjz4RERGdk67rOHr0qDFbnfw0yz07Oxv33Xcfhg4d6o/miIiIzknXdRw6dIgF3UvQzXK/VPx1mOV7V9gRUZDqmZ909ENC6ZC7X28kW1FRgaeeegqHDx/2uVjfc90gERGRP+i6DpvNhqSkJN4S9zt+Leh33HEHfvazn+G+++6DyWTyZ9NEREQGzzn0xMREFvTv+LWgm81mPPTQQ/5skoiIqA1FUZCZmRnobgQVv36tufnmm/H+++/7s0kiIqI2NE3DwYMHjdXgyM8j9GnTpmHWrFkwmUywWCwQQkCSJNTV1flzN0RE1MMJIXDq1KlOLybWE/i1oC9cuBDr16/HNddcw3PoRER00SiKgjFjxgS6G0HFrwU9NjYW2dnZ/mySiIioDc9d1tLS0jiA/I5fz6HPnj0b69atQ0NDA86cOWM8iIiI/O3s2bOB7kJQ8evCMt6XDnhu2CJJUlBOWuDCMkQ9CxeWofaE0sIyfh2he+5V67mvree/RERE/qRpGvbu3csa48WvBd1ut7d57cSJE/7cBREREbXDrwX9zjvv9Hne2NiIm2++2Z+7ICIigslkwogRIzghzotfC/rQoUOxZMkSAEBrayuysrLwi1/8wp+7ICIigqZpKC8v5yF3L34t6KtWrcLx48fxzDPPYNasWbjjjjuwYMGC826nsrISmZmZSE9Px9ixY7Fv374223zyySfo1asXRo8ebTw445GIqOeIiIgIdBeCil+uQ/e+NO3FF1/ELbfcgmnTpiE3NxdnzpxBr169zqu9hQsXIjc3Fzk5Odi4cSPmz5+Pzz//vM12V111Fb744osu95+IiLoXk8mEYcOGBbobQcUvI/Q+ffqgb9++6NOnD+Li4vDFF1/gmWeeMV4/H3V1ddi9ezfmzZsHAJgzZw6sViuqqqr80VUiIgoBqqpi165dPrfq7un8UtC/f5na9y9fOx82mw0JCQlQFPfBA0mSkJycjOrq6jbbfvPNN7jmmmswZswY/PGPfzxnuw6HA83NzT4PAEb/NE1rN1ZV1SfWdb1NHB6uQpY9scuIIyJckGVhxJIkAAhERLgACEiSJwZk2TvWER7uHbv/YE0mHRaLO1YU71hDWJh37O6v2azBbHbHYWEaFMUTq0ZssahQFN2ITSbmxJxCNydd140C0FGsaZpP7I/PCO/Y5XL5xJ6lQDyxEKJNDMAn1nXdJ+6JOem6jqioKGOtk67kFCr8UtBPnz5txCdPnuxye9L3Vmtpb+2ba665BjU1Ndi9ezc2bdqEdevW4Y033uiwzZUrVyIqKsp4JCUlAQD27t0LAKioqEBFRQUAYM+ePaisrAQAlJeXw2q1AgB27twJm80GACgrK0NtbS0AYPXqEowcWQ8AWLt2K9LSGgEABQXFSExsAQAUFhYhJsaOiAgVhYVFiIhQERNjR2FhEQAgMbEFBQXFAIC0tEasXbsVADByZD1Wry4BAIwbV4u8vDIAwOTJNixbthMAkJVlxZIl5QCA7OxK3H//HgDAvHkVmDfPndP99+9BdrY7pyVLypGV5c5p2bKdmDzZnVNeXhnGjWNOzCl0c6qvr0dJiTun2tpalJW5c7LZbNi5052T1WpFebk7p8rKSuzZ486pK58RJSUlqK9357R161Y0NrpzKi4uRkuLO6eioiLY7XaoqoqioiKoqgq73Y6iIndOLS0tKC5259TY2IitW3t2TtXV1WhqaoLJZOpSTjt27EDIEF20ePFiMXPmTLFs2TIhhBC/+MUvutTe8ePHRWRkpHC5XEIIIXRdFwMGDBBWq/Wc/+7pp58Wixcv7vB9u90umpqajIfNZhMARENDgxBCCFVVhaqqbWKXy+UTa5rmEwNChIe7hCx7YqcRR0Q4hSzrRixJugB0ERHhFIAuJMkTCyHL3rEmwsO9Y5cAhDCZNGGxuGNF8Y5VERbmHasCEMJsVoXZ7I7DwlShKJ7YZcQWi0soimbEJhNzYk6hmZMQQmiaZny2dBSrquoTt/e5cD6fEd+PnU6nT6zruk+s63qb2PNZ6Ik1TfOJe2JOdrtdlJaWGn290JxOnjwpAIimpibR3XV56dd77rkHr7zyCt577z3s2rULx44d+8HD3z9k8uTJyMnJMSbFrVmzBtu3b/fZpra2FgMGDIAsy2hpacHNN9+M+fPn47777uvUPrj0K1HPwqVfQ4uu67DZbEhKSvJZdvx8celXLxaLBQBwyy23ID4+Hlu2bOlyp/Lz85Gfn4/09HSsWrUKBQUFAIAFCxZg8+bNAIA333wTV199NUaNGoXrrrsON954I37+8593ed9ERBT8ZFlGSkpKl4p5qOnyCL2kpASTJk0ynv/jH//Af/zHf3S5YxcbR+hEPQtH6KFFVVWUlZUhMzPTmER9IThC9+JdzAEgIyOjq00SERGdkyzLGDx4MEfoXvz+k3j22Wf93SQREZEPWZaRmJjIgu6lyyvFpaSkYOjQoQDcl5d98803XZ4UR0REdC6qqhqnfLtyyD2UdPmncOONN+JPf/qT8Zw3YyEiootNlmWMGDGCI3QvXZ4U19jYiOjoaD9159LhpDiinoWT4qg9nBTnxbuYV1dXo7S0FKWlpe0u1UpEROQPLpcLH3zwgbFcLPnpbmv79+/HfffdB6vViuTkZAghYLPZcMUVV6CgoABXXnmlP3ZDREQEwH23tTFjxsBkMgW6K0HDLwU9JycHDz30EObMmePz+saNG3Hvvfcaa+8SERH5gyzLiImJCXQ3gopfZhOcOnWqTTEHgOzsbDQ1NfljF0RERAaXy4UtW7bwkLsXvxT0yy67DK+88opxOzrAvc7uhg0bEBsb649dEBERGRRFwcSJE3nJmhe//CQ2bNiAhQsXYsmSJUhISIAkSaipqUFGRgbWr1/vj10QEREZJEnq9rPS/c0vBX3IkCH46KOPcOLECeMes0lJSejfv78/miciIvLhcrlQVFSErKwsmM3mQHcnKPj1WEX//v1ZxImI6KJTFAXTp0/nIXcvF32JnfT09Iu9CyIi6oFYzH355aexb9++Dt9rbW31xy6IiIgMqqrykPv3+KWgjxgxAqmpqWhvFdn6+vrzbq+yshL33nsv6uvrER0djfXr1+Oqq67y2Wbr1q14+OGH0dLSAlmWMWvWLDz55JOQuBYrEVHIUxQFWVlZHKV78ctPIiUlBaWlpUhISGjzXlJS0nm3t3DhQuTm5iInJwcbN27E/Pnz8fnnn/ts069fPxQWFmLQoEGw2+244YYbUFhYiLvuuuuC8yAiou5DVVUWdC9+OYc+c+ZMHD58uN33Zs2adV5t1dXVYffu3Zg3bx4AYM6cObBaraiqqvLZLiMjA4MGDQIAhIeHY/To0R32gYiIQouqqiguLoaqqoHuStDwS0H/wx/+gAkTJrT73tq1a8+rLZvNhoSEBONblyRJSE5OPufNXo4dO4aNGzciKyurw20cDgeam5t9HgCgaZrx3/ZiVVV9Ys/iOd5xeLgKWfbELiOOiHBBloURS5IAIBAR4QIgIEmeGJBl71hHeLh37P6DNZl0WCzuWFG8Yw1hYd6xu79mswaz2R2HhWlQFE+sGrHFokJRdCM2mZgTcwrdnHRdNwpAR7GmaT6xPz4jvGOXy+UTe05VemIhRJsYgE+s67pP3BNzkmUZM2bMgNls7nJOoSIobyT7/fPg57rDa3NzM2699VYsXboU11xzTYfbrVy5ElFRUcbDcypg7969AICKigpUVFQAAPbs2YPKykoAQHl5OaxWKwBg586dxnX2ZWVlqK2tBQCsXl2CkSPdcwXWrt2KtLRGAEBBQTESE1sAAIWFRYiJsSMiQkVhYREiIlTExNhRWFgEAEhMbEFBQTEAIC2tEWvXbgUAjBxZj9WrSwAA48bVIi+vDAAwebINy5a518jPyrJiyZJyAEB2diXuv38PAGDevArMm+fO6f779yA7253TkiXlyMpy57Rs2U5MnuzOKS+vDOPGMSfmFLo51dfXo6TEnVNtbS3Kytw52Ww2454TVqsV5eXunCorK7FnjzunrnxGlJSUGPOJtm7disZGd07FxcVoaXHnVFRUBLvdbkz2UlUVdrsdRUXunFpaWlBc7M6psbERW7cyp507d0II0aWcduzYgZAhgszx48dFZGSkcLlcQgghdF0XAwYMEFartc22zc3N4sc//rF44oknfrBdu90umpqajIfNZhMARENDgxBCCFVVhaqqbWKXy+UTa5rmEwNChIe7hCx7YqcRR0Q4hSzrRixJugB0ERHhFIAuJMkTCyHL3rEmwsO9Y5cAhDCZNGGxuGNF8Y5VERbmHasCEMJsVoXZ7I7DwlShKJ7YZcQWi0soimbEJhNzYk6hmZMQQmiaZny2dBSrquoTt/e5cD6fEd+PnU6nT6zruk+s63qb2PNZ6Ik1TfOJe2JOZ8+eFe+8845wOp1dyunkyZMCgGhqahLdnSTEOYa/ATJ58mTk5OQYk+LWrFmD7du3+2zT2tqKm266CdOnT8eKFSvOex/+uqk9J9UTdQ/B90lHwcBftSAYBOUh9/z8fOTn5yM9PR2rVq1CQUEBAGDBggXYvHkzAPd5+507d2LTpk0YPXo0Ro8ejaeeeiqQ3SYioktE13U0NDT43BSspwvKEfqlwBE6Uc/SMz/pQpfL5cLWrVsxderULi0sE0ojdF7AR0RE3Y7ZbMZNN90U6G4ElaA85E5ERHQuuq6jrq6Oh9y9sKATEVG3o+s69u7dy4LuhYfciYio21EUBVOnTg10N4IKR+hERNTt6LqOo0ePcoTuhQWdiIi6HV3XcejQIRZ0LzzkTkRE3Y6iKJg0aVKguxFUOEInIqJuR9d1HDlyhCN0LyzoRETU7fAcels85E5ERN2OoijIzMwMdDeCCkfoRETU7WiahoMHDxr3OCcWdCIi6oaEEDh16hR66O1I2sVD7kTtEOBdd0IOf6UhRQEwhsXcB0foRETU7WiKgv379/OQuxcWdCIi6n5kGWfPng10L4IKD7kTEVG3Y3I6kZGREehuBJWgHKFXVlYiMzMT6enpGDt2LPbt29fudgUFBUhLS8PgwYORm5sLVVUvcU+JiCgQNLMZe/fu5SF3L0FZ0BcuXIjc3FwcOHAAS5cuxfz589tsY7VasXz5cpSWluLgwYM4duwYCgoKAtBbIiKiwAu6gl5XV4fdu3dj3rx5AIA5c+bAarWiqqrKZ7uNGzdi9uzZGDBgACRJwqJFi1BYWBiAHhMR0aVmcrkwYsQImEymQHclaATdOXSbzYaEhAQoirtrkiQhOTkZ1dXVSE1NNbarrq5GSkqK8Tw1NRXV1dUdtutwOOBwOIznTU1NAIBTp04BgHHYxmQy+cSqqkKSJCOWZRmyLBsxIMNiUeF0yhBChsXigtNpghAywsNdcDgUCCEhPNwFu92dU3i4+r3YDEkSsFg8sY6wMA0OhyfW4XAokGUdiqLD6VRgMukwmTyxBlkWcLk8MeBymaAo7jxU1QSzWYOuA5pmgtmsQtclaJoJYWEqNE2GpskIC1OhqjJ0nTk1AVDDw6HY7e79hYfDbLdDSBJUiwVmux26JEELC4PZ4YAuSdDDwqA4HNBlGbqiQHE6oZtM0E0mKE4nNJMJQpahuFzQTCZAlmFyuaB997duUlVoZjOg6zBpGlSzGZInDguDrGmQPbGqQtZ1qBYLZKcTshBwWSwweeLwcCgOByRP7JUHc2JOoZCTMyICX3/6KUaOHGl8rn//87szn+UNDQ0AEBLXswddQQfcRdxbRz9o7+1+6JexcuVK5OXltXnd+0vChfL6nuATf/e326lYCN/Y0453rOuA0+mONc39OFfsPaXA5Wo/9rT3/bin5xQdikkxJ+YUSjmdPQtMngx/aWlpQVRUlN/aC4SgK+hJSUmoqamBqqpQFAVCCNhsNiQnJ/tsl5yc7HMY/siRI2228fbwww/jwQcfNJ7ruo6GhgbExsa2+QJBRETBrbm5GUlJSbDZbIiMjLzgdoQQaGlpQUJCgh97FxhBV9Dj4uKQkZGBV199FTk5OXjzzTeRmpraZiQ9Z84cTJgwAY899hji4uKwbt06zJ07t8N2LRYLLBaLz2vR0dEXIQMiIrpUIiMju1TQAXT7kblH0E2KA4D8/Hzk5+cjPT0dq1atMmavL1iwAJs3bwYADBo0CHl5eRg/fjwGDx6MuLi4dmfDExER9QSSCIWZAERE1KM0NzcjKioKTU1NXR6hh4qgHKETERGdi8ViwYoVK9qcSu3JOEInIiIKARyhExERhQAWdCIiohDAgk5ERBQCWNCJiIhCAAs6ERFRCAi6leKIiIja09jYiA8++ABHjx6FJEmIj4/HTTfdhH79+gW6a0GBI3QiIgp6BQUFGDt2LLZv3w5d16FpGrZv347rrrvOWE20p+N16EREFPSGDh2Kf/3rX+jTp4/P6y0tLfjRj36EAwcOBKhnwYMjdCIiCnqSJKG1tbXN662trbxj5nd4Dp2IiILemjVrcP3112PEiBFITEwEANTU1ODrr7/Gc889F+DeBQceciciom5B0zTs3LkT3377LYQQSExMxNixY2EymQLdtaDAgk5ERN3S2rVrsXjx4kB3I2jwHDoREXVLf/7znwPdhaDCgk5ERN0SDzD74iF3IiLqllwuF8xmc6C7ETQ4Qiciom7JU8yXLVsW4J4EB47QiYgo6J05c6bd14UQGDZsGGw22yXuUfDhdehERBT0+vbti5SUFJ/z5pIkQQiB48ePB7BnwYMFnYiIgt7gwYPx4YcfIiUlpc17SUlJAehR8OE5dCIiCnr/3//3/7W79CsA5OXlXeLeBCeeQyciIgoBHKETEVG3NH369EB3IaiwoBMRUbd04sSJQHchqLCgExFRt3TzzTcHugtBhefQiYiIQgAvWyMioqA3aNAgn+dCCOM6dEmScPjw4QD1LHiwoBMRUdAbOnQo6uvrcdttt+H2229HYmJioLsUdHjInYiIuoVTp05h06ZN2LhxIxwOB2bPno25c+fisssuC3TXggILOhERdStOpxOvv/46/t//+39YsWIFfvWrXwW6S0GBh9yJiCjoqaqK4uJivPHGG6ioqMD06dOxdetWjBo1KtBdCxocoRMRUdCLiYlBUlIS7rjjDowePRqSJPm8n5WVFaCeBQ8WdCIiCno5OTltiriHJEn485//fIl7FHxY0ImIiEIAV4ojIqKg98477+DIkSPG8xUrVmDkyJG49dZbcejQoQD2LHiwoBMRUdD77W9/i/79+wMANm3ahNdffx1//vOfMXv2bCxcuDDAvQsOLOhERBT0ZFlGr169ALgLem5uLq699lrcd999aGhoCHDvggMLOhERBT1ZltHQ0ACHw4EPP/zQ59apdrs9gD0LHrwOnYiIgt6KFSuQkZEBXddx0003Gdefb9u2DampqYHtXJDgLHciIuoWVFVFS0sL+vXrZ7x2+vRpCCHQp0+fAPYsOHCETkRE3cLXX38NSZLQr18/7Nu3D++99x6GDRuGGTNmBLprQYEjdCIiCnpPPvkkioqK4HK5cMMNN6C8vBxTp05FcXExJk2ahMceeyzQXQw4FnQiIgp6V199Nfbs2QO73Y7LL78c3377LXr37g2Hw4ExY8Zgz549ge5iwHGWOxERBT2TyQRJkhAREYERI0agd+/eAACLxQJZZikDWNCJiKgbiImJQWtrKwDgs88+M14/ceIEzGZzoLoVVHjInYiIuq2WlhY0NTVh4MCBge5KwHGETkREQa+wsNCIvUfoffv2xVtvvRWAHgUfjtCJiCjoXXPNNdi9e3ebuL3nPRVH6EREFPS8x57fH4dyXOrGgk5EREFPkqR24/ae91Q85E5EREFPURTExMRACIHGxkZj+VchBJqamuB0OgPcw8BjQSciIgoBPOROREQUAljQiYiIQgALOhERUQhgQSciIgoBLOhEREQhgAWdiIgoBLCgExERhQAWdCIiohDAgk5ERBQCWNCJiIhCAAs6ERFRCGBBJyIiCgEs6ERERCGABZ2IiCgEsKATERGFABZ0IiKiEMCCTkREFAJY0ImIiEIACzoREVEIYEEnIiIKASzoREREIYAFnYiIKASwoBMREYUAFnQiIqIQwIJOREQUAljQiYiIQgALOhERUQhgQSciIgoBLOhEREQhgAWdiIgoBLCgExERhQAWdCIiohDAgk5ERBQCWNCJiIhCAAs6ERFRCGBBJyIiCgEs6ERERCGABZ2IiCgEsKATERGFABZ0IiKiEMCCTkREFAL+f4v6xF4xtjWiAAAAAElFTkSuQmCC" }, "metadata": {}, "output_type": "display_data" @@ -851,7 +877,9 @@ "source": [ "The sea ice driver can generate metrics based on an average of all available realizations. To do so, provide an asterisk \\* as the value to the --realization argument on the command line. Options passed on the command line will supercede arguments in the parameter file. \n", "\n", - "In addition, we set the --case_id value to 'ex2' to save results in a new directory." + "In addition, we set the --case_id value to 'ex2' to save results in a new directory.\n", + "\n", + "Below process will take about 5 minutes." ] }, { @@ -860,21 +888,11 @@ "id": "5f8174e1", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-01-25 13:44:52,112 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ "['E3SM-1-0']\n", - "\n", - "Metrics output path not found.\n", - "Creating metrics output directory sea_ice_demo/ex2/\n", "Find all realizations: True\n", "OBS: Arctic\n", "Converting units by multiply 0.01\n", @@ -884,7 +902,20 @@ "\n", "=================================\n", "model, runs: E3SM-1-0 ['r1i2p2f1', 'r2i2p2f1', 'r3i2p2f1', 'r4i2p2f1']\n", - "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n", + "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-25 22:48:06,512 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Converting units by multiply 1e-06\n", "\n", "-----------------------\n", @@ -952,21 +983,7 @@ " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_200001-200912.nc\n", " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_201001-201412.nc\n", "Converting units by multiply 0.01\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO::2024-01-25 13:48::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex2/sea_ice_metrics.json\n", - "2024-01-25 13:48:09,795 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex2/sea_ice_metrics.json\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\n", "-----------------------\n", "model, run, variable: E3SM-1-0 r4i2p2f1 siconc\n", "test_data (model in this case) full_path:\n", @@ -1007,11 +1024,21 @@ "name": "stderr", "output_type": "stream", "text": [ - "[WARNING] yaksa: 10 leaked handle pool objects\n" + "INFO::2024-01-25 22:50::pcmdi_metrics:: Results saved to a json file: /home/lee1043/git/pcmdi_metrics_20240125/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex2/sea_ice_metrics.json\n", + "2024-01-25 22:50:52,832 [INFO]: base.py(write:251) >> Results saved to a json file: /home/lee1043/git/pcmdi_metrics_20240125/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex2/sea_ice_metrics.json\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 92.6 ms, sys: 36 ms, total: 129 ms\n", + "Wall time: 3min 55s\n" ] } ], "source": [ + "%%time\n", "%%bash\n", "sea_ice_driver.py -p sea_ice_param.py --realization '*' --case_id \"ex2\"" ] @@ -1032,7 +1059,7 @@ "outputs": [ { "data": { - "image/png": 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" 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" }, "metadata": {}, "output_type": "display_data" @@ -1060,7 +1087,9 @@ "\n", "The example below shows how to use three models in the analysis, with all available realizations. The models are listed as inputs to the --test_data_set flag.\n", "\n", - "Want to add more models? Six other model sea ice datasets are available in the directories linked in the notebook introduction." + "Want to add more models? Six other model sea ice datasets are available in the directories linked in the notebook introduction.\n", + "\n", + "Below process will take about 10 minutes." ] }, { @@ -1073,8 +1102,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "2024-01-25 13:49:18,281 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "2024-01-25 13:50:02,067 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + "bash: line 1: fg: no job control\n" ] }, { @@ -1082,9 +1110,6 @@ "output_type": "stream", "text": [ "['E3SM-1-0', 'CanESM5', 'MIROC6']\n", - "\n", - "Metrics output path not found.\n", - "Creating metrics output directory sea_ice_demo/ex3/\n", "Find all realizations: True\n", "OBS: Arctic\n", "Converting units by multiply 0.01\n", @@ -1094,7 +1119,20 @@ "\n", "=================================\n", "model, runs: CanESM5 ['r2i1p1f1', 'r1i1p1f1', 'r3i1p1f1']\n", - "/p/user_pub/pmp/demo/sea-ice/links_area/CanESM5/*.nc\n", + "/p/user_pub/pmp/demo/sea-ice/links_area/CanESM5/*.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-25 22:52:02,672 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Converting units by multiply 1e-06\n", "\n", "-----------------------\n", @@ -1130,7 +1168,20 @@ "\n", "=================================\n", "model, runs: E3SM-1-0 ['r1i2p2f1', 'r2i2p2f1', 'r3i2p2f1', 'r4i2p2f1']\n", - "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n", + "/p/user_pub/pmp/demo/sea-ice/links_area/E3SM-1-0/*.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-25 22:52:53,710 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Converting units by multiply 1e-06\n", "\n", "-----------------------\n", @@ -1187,22 +1238,7 @@ " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_189001-189912.nc\n", " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_190001-190912.nc\n", " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_191001-191912.nc\n", - " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_192001-192912.nc\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-01-25 13:52:38,818 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n", - "INFO::2024-01-25 13:57::pcmdi_metrics:: Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex3/sea_ice_metrics.json\n", - "2024-01-25 13:57:06,766 [INFO]: base.py(write:251) >> Results saved to a json file: /home/ordonez4/git/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex3/sea_ice_metrics.json\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_192001-192912.nc\n", " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_193001-193912.nc\n", " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_194001-194912.nc\n", " /p/user_pub/pmp/demo/sea-ice/links_siconc/E3SM-1-0/historical/r3i2p2f1/siconc/siconc_SImon_E3SM-1-0_historical_r3i2p2f1_gr_195001-195912.nc\n", @@ -1251,7 +1287,20 @@ "\n", "=================================\n", "model, runs: MIROC6 ['r2i1p1f1', 'r1i1p1f1', 'r4i1p1f1', 'r3i1p1f1']\n", - "/p/user_pub/pmp/demo/sea-ice/links_area/MIROC6/*.nc\n", + "/p/user_pub/pmp/demo/sea-ice/links_area/MIROC6/*.nc\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-01-25 22:55:31,709 [WARNING]: dataset.py(open_dataset:109) >> \"No time coordinates were found in this dataset to decode. If time coordinates were expected to exist, make sure they are detectable by setting the CF 'axis' or 'standard_name' attribute (e.g., ds['time'].attrs['axis'] = 'T' or ds['time'].attrs['standard_name'] = 'time'). Afterwards, try decoding again with `xcdat.decode_time`.\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Converting units by multiply 1e-06\n", "\n", "-----------------------\n", @@ -1300,12 +1349,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "[WARNING] yaksa: 10 leaked handle pool objects\n" + "INFO::2024-01-25 22:59::pcmdi_metrics:: Results saved to a json file: /home/lee1043/git/pcmdi_metrics_20240125/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex3/sea_ice_metrics.json\n", + "2024-01-25 22:59:27,749 [INFO]: base.py(write:251) >> Results saved to a json file: /home/lee1043/git/pcmdi_metrics_20240125/pcmdi_metrics/doc/jupyter/Demo/sea_ice_demo/ex3/sea_ice_metrics.json\n" ] } ], "source": [ "%%bash\n", + "%%time\n", "sea_ice_driver.py -p sea_ice_param.py \\\n", "--test_data_set \"E3SM-1-0\" \"CanESM5\" \"MIROC6\" \\\n", "--realization '*' \\\n", @@ -2554,25 +2605,36 @@ " ]\n", " },\n", " \"provenance\": {\n", - " \"commandLine\": \"/home/ordonez4/miniconda3/envs/pmp_si/bin/sea_ice_driver.py -p sea_ice_param.py --test_data_set E3SM-1-0 CanESM5 MIROC6 --realization * --case_id ex3\",\n", + " \"commandLine\": \"/home/lee1043/.conda/envs/pcmdi_metrics_dev_20231129/bin/sea_ice_driver.py -p sea_ice_param.py --test_data_set E3SM-1-0 CanESM5 MIROC6 --realization * --case_id ex3\",\n", " \"conda\": {\n", " \"Platform\": \"linux-64\",\n", - " \"PythonVersion\": \"3.8.15.final.0\",\n", - " \"Version\": \"23.1.0\",\n", + " \"PythonVersion\": \"3.10.12.final.0\",\n", + " \"Version\": \"23.3.1\",\n", " \"buildVersion\": \"not installed\"\n", " },\n", - " \"date\": \"2024-01-25 13:56:52\",\n", + " \"date\": \"2024-01-25 22:59:08\",\n", " \"openGL\": {\n", " \"GLX\": {\n", - " \"client\": {},\n", - " \"server\": {}\n", - " }\n", + " \"client\": {\n", + " \"vendor\": \"Mesa Project and SGI\",\n", + " \"version\": \"1.4\"\n", + " },\n", + " \"server\": {\n", + " \"vendor\": \"SGI\",\n", + " \"version\": \"1.4\"\n", + " },\n", + " \"version\": \"1.4\"\n", + " },\n", + " \"renderer\": \"llvmpipe (LLVM 7.0, 256 bits)\",\n", + " \"shading language version\": \"1.20\",\n", + " \"vendor\": \"VMware, Inc.\",\n", + " \"version\": \"2.1 Mesa 18.3.4\"\n", " },\n", " \"osAccess\": false,\n", " \"packages\": {\n", " \"PMP\": \"v3.0.2-11-g06b151f\",\n", " \"PMPObs\": \"See 'References' key below, for detailed obs provenance information.\",\n", - " \"blas\": \"0.3.24\",\n", + " \"blas\": \"0.3.25\",\n", " \"cdat_info\": \"8.2.1\",\n", " \"cdms\": \"3.1.5\",\n", " \"cdp\": \"1.7.0\",\n", @@ -2583,15 +2645,15 @@ " \"esmpy\": \"8.4.2\",\n", " \"genutil\": \"8.2.1\",\n", " \"lapack\": \"3.9.0\",\n", - " \"matplotlib\": null,\n", + " \"matplotlib\": \"3.7.1\",\n", " \"mesalib\": null,\n", - " \"numpy\": \"1.22.4\",\n", - " \"python\": \"3.10.13\",\n", - " \"scipy\": \"1.11.3\",\n", + " \"numpy\": \"1.23.5\",\n", + " \"python\": \"3.10.10\",\n", + " \"scipy\": \"1.11.4\",\n", " \"uvcdat\": null,\n", " \"vcs\": null,\n", " \"vtk\": null,\n", - " \"xarray\": \"2023.10.1\",\n", + " \"xarray\": \"2023.11.0\",\n", " \"xcdat\": \"0.5.0\"\n", " },\n", " \"platform\": {\n", @@ -2599,7 +2661,7 @@ " \"OS\": \"Linux\",\n", " \"Version\": \"3.10.0-1160.71.1.el7.x86_64\"\n", " },\n", - " \"userId\": \"ordonez4\"\n", + " \"userId\": \"lee1043\"\n", " }\n", "}\n" ] @@ -2626,7 +2688,7 @@ "outputs": [ { "data": { - "image/png": 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" + "image/png": 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j66+/xnPPPYeIiAh8+eWXGDx4MN5++22b+Xz77bcoLy+3+SyaMWMGGGP49NNP7Zbr5eVll/8JCQmuvC3ESTTKvRFxHIcnnngC//jHP2AwGBATE+PwO0MAuHz5MkpLS+Hh4eHw9cLCQgCWD9aBAwfC09MTy5YtQ0xMDLy9vZGXl4eHHnoIVVVVNn/n7e0NT09Pm+f0ev1NPziq8/LywoQJEzBhwgQAQG5uLkaPHo0PPvgATz31FDp37oyioiK0adPG7m9btWpVp2U48v3332PKlCl4+OGH8cILL6BVq1bQarX46KOP8Mknn9hNX/P7w6KiIgiCgPfffx/vv/++w2VY39Ob8ff3r3X0763W4WbP1/ZdalhYmPx6XeZd13W5fPkyAOCvf/0r/vrXvzr8m5u9H48++ih27dqF1157Db1794afnx84jkNsbKxdztXHjBkzsHnzZvzyyy8YOXIkEhMTYTQabYpsXf836quoqAharRbBwcE2z3Mch1atWtltg+oiIyMBADk5OTddxvnz5+122niex6BBgzBo0CAAllPOZs6ciW+++QaffPIJnn76aYfzGj58uM0paNOnT5fHJwCWXJ06dao8fufPP//E/fffj1dffRWzZ89GQEAAAMuIdk9PT4waNUoeK9C1a1dER0fjs88+w9KlS6HRaGzWt67/A6RhUUFvZHFxcXj99dexfv16vPXWW7VO16JFCwQFBWHnzp0OX7ee5paSkoKLFy9iz549Nqei1GUQjxIiIyMxZ84cLFiwAH/++Sc6d+6MoKAgXLp0yW5aR895eno6HJBXWFiIFi1ayL9/+eWXaNOmDb755hubI5zaBvNVnwYAAgMDodFo8Nhjj+GZZ55x+DeOdkJcUXMdbvZ8UFAQCgoK7J6/ePEiANi8Fzebd13XxTq/l19+2WawWXUdO3Z0+HxZWRm2b9+OJUuW2JxWZTQaUVxcXK/1qmnkyJEICwvDp59+ipEjR+LTTz9F37590alTJ5t1r8v/Rn0FBQVBEARcvXrVpqgzxnDp0iX07t271r8NDQ1F586dkZycjMrKSnh7e9tN89tvv+Hy5cvyQMHaNGvWDC+//DK++eYbZGZm1jrdhg0bUFFRIf9eM0dq6ty5Mx555BGsXbsWp0+fRp8+fXD69Gns378fwP92Smr6+eefERsbe9N5k8ZBBb2RhYeH44UXXsDJkycxffr0WqcbM2YM/v3vf0MURfTt27fW6awf1Hq93ub5DRs2KLPCN1RUVIDjOPj4+Ni9Zu1itR5NDh06FKtXr8bvv/9u0+3+9ddf2/1tdHQ0MjIybJ47ffo0Tp06ZfMBxXEcPDw8bArTpUuXHI5yd8Tb2xtDhw5Feno6unbtWuvRXWMZPnw4tmzZgosXL8rvIwB8/vnn8Pb2xr333qvo8jp27IgOHTrg999/x/Lly+v1txzHgTFml3Mff/wxRFG0ec46TV2P2q07XWvXrsW+fftw5MgRu1yu6/9Gbaqvk5eXl/z88OHDsXr1anz55ZdYuHCh/PzmzZtx/fp1DB8+/KbzffXVV/Hoo4/ir3/9Kz788EOb165fv47nnnsO3t7eNvMuKChw2NtS83/Kkdp2uIqKiuDr6+swx0+ePGkzX2tX+b/+9S+0b9/eZtqqqiqMHz8en3zyCRV0N0UF3Q3UvAKUI4888gi++uorxMbGYv78+ejTpw90Oh3y8/Oxe/dujB8/HhMnTkS/fv0QGBiIJ598EkuWLIFOp8NXX32F33//XdF1PnXqFEaOHIlHHnkEgwcPRmhoKEpKSrBjxw7885//xJAhQ9CvXz8AwIIFC/DJJ5/gwQcfxLJly9CyZUt89dVX8odJdY899himTZuGp59+GpMmTcL58+exevVqu25P66leTz/9NCZPnoy8vDy8+eabCA0NRVZWVp3aEB8fjwEDBmDgwIF46qmnEB0djYqKCmRnZ+PHH390eLGMmkpLSx1+167X69GjR486rYcjS5Yswfbt2zF06FC8/vrraN68Ob766ivs2LEDq1evhr+/v9Pzrs2GDRswevRojBw5EnFxcQgPD0dxcTFOnDiBo0eP4rvvvnP4d35+fhg0aBDefvtttGjRAtHR0di7dy8SEhLkblyrLl26AAD++c9/wtfXF56enmjTpg2CgoJqXa8ZM2Zg1apVePTRR+Hl5YX/9//+n83rdf3fqM0999wDAFi1ahVGjx4NjUaDrl27YsSIERg5ciRefPFFlJeXo3///sjIyMCSJUvQo0cPPPbYYzd9P6dOnYqjR4/inXfewblz5zBjxgy0bNkSp06dwpo1a3DmzBl8/fXXaNu2rfw3nTt3xvDhwzF69Gi0a9cOBoMBBw8exLvvvouWLVs6HJtxK7t378b8+fPxl7/8Bf369UNQUBCuXLmCxMRE7Ny5E48//jhat24NQRDw+eef4+6778asWbMczmvs2LHYtm2bXa8FcRONOybvzlN9lPvNOLqwjNlsZu+88w7r1q0b8/T0ZD4+Puyuu+5ic+fOZVlZWfJ0aWlp7L777mPe3t4sODiYzZo1ix09etTu9KPaLgphHZV9MyUlJWzZsmVs2LBhLDw8nHl4eLBmzZqx7t27s2XLltmdKnT8+HE2YsQI5unpyZo3b85mzpzJtm7dajfiWZIktnr1ata2bVvm6enJ/u///o+lpKQ4HOW+cuVKFh0dzfR6Pbv77rvZv/71L4frDoA988wzDtuRk5PDZsyYwcLDw5lOp2PBwcGsX79+bNmyZTdtP2M3H+UeHh5u935aT9+qOY/aRir/8ccfbOzYsczf3595eHiwbt262Ww/xv434vy777675fpa2wuAvf322w5f//3339mUKVNYSEgI0+l0rFWrVmzYsGFs/fr1dsusvt3y8/PZpEmTWGBgIPP19WWjRo1imZmZDs9aWLt2LWvTpg3TaDQ2OVlzlHt1/fr1YwDYX/7yF4ev1/V/wxGj0chmzZrFgoODGcdxDIB8GmZVVRV78cUXWVRUFNPpdCw0NJQ99dRTrKSk5KbzrC4pKYnFxsayoKAgptPpWHh4OHvsscfYn3/+aTfthg0b2EMPPcTatm3LvL29mYeHB2vXrh178sknWV5ens20dR3lnpeXx/72t7+x/v37s1atWjGtVst8fX1Z37592fvvvy+fYvbDDz8wAGzt2rW1zss68v/dd99ljNGFZdwNxxhjt3UPgpAb9uzZg6FDh2L37t2qvvY5IYTcDnTaGiGEEKICVNAJIYQQFaAud0IIIUQF6AidEEIIUQEq6IQQQogKUEEnhBBCVIAKOiGEEKICVNAJIYQQFaCCTgghhKgAFXRCCCFEBdyuoD/33HOIjo4Gx3E3vVVgQkICOnTogHbt2mHOnDkQBOE2riUhhBDiXtyuoE+ePBn79+9HVFRUrdPk5OTgtddew/79+5GdnY1Lly7Jt/0jhBBC7kRuV9AHDRqE1q1b33SaTZs2YeLEiWjZsiU4jsOTTz6JxMTE27SGhBBCiPtpkvdDz83NtTmCj46ORm5u7k3/xmg0wmg0AgAYYygvL4fZbEZQUBA4jmvQ9SWEEOKeGGOoqKhAWFgYeN7tjnHrpUkWdAA2Rbgul6NfsWIFli5d2pCrRAghpInKy8u7Ze+wu2uSBT0yMhLnzp2Tfz9//jwiIyNv+jcvv/wynn/+eQCWHYCLFy+iU6dOOHfuHAIDAyGKIgBAo9HYxIIggOM4OeZ5HjzP1xqbzWZoNBo51mq14DhOjgFAEASbWKfTgTEmx5IkQRRFOZYkCVqtttZYFEUwxuTYUTsaqk0AcPDgQfTq1Quenp6qaJMat5O7tkmSJBw+fBi9evWCh4eHKtqkxu3kjm0yGo04fPgw7r33XvkAz5k2FRcXo02bNvD19a1ZNpqcJlnQJ02ahAEDBuD1119HSEgI1q9fj0ceeeSmf6PX66HX6+XfrQkQGBgIPz+/Bl1fNZMkCd26dUNwcHCT764it58kSejatStatGhB+UPqxfrZExAQoEjuqOGrV7e7feozzzyDrVu34tKlS2jRogV8fHyQnZ2NWbNmYdy4cRg3bhwA4F//+hdWrVoFSZIwbNgwfPTRR9DpdHVeTnl5Ofz9/VFWVkYFnRCiCqIoyj1nxDGdTgeNRiP/rqZa4HYF/XZR00ZsTIIgIDU1FYMGDZK70wipK8of5Vy7dg35+fl1GlOkBowxGI1G6PX6eh1dcxyH1q1bw8fHB4C6agH9BxGX8DyPLl26UHcpcQrljzJEUUR+fj68vb0RHBysiu7jW7F+/279vr6uf3P16lXk5+ejQ4cONkfqakAFnbiE53mEhIQ09mqQJoryRxlmsxmMMQQHB8PLy6uxV8etBQcH49y5c/JAPjWh3WLiErPZjJ9//pm+tyNOofxR1u0+Mu/Xrx+WL1+u6Dw/+ugjDBo0CAMGDMDDDz+Ma9euOZxOkiSUlZVBkiQAwLlz5zB58uRbzl/NvRd0hE5cotFo0Lt3b9Xt6ZLbg/Kn6crLy0NUVBR27dqFV155RZF5/vLLL/jPf/6D3bt3Q6PRID09HSaTyeG0HMehWbNmqi7Q9UVH6MQlPM+jefPm9B0ocQrlT8PgONcft7Jp0yZMmzYN7dq1Q3Z2NgDgjTfewF/+8heMGjUKgwYNQmVlJc6dO4d+/fph0qRJ6Nq1K3799dda55mYmIgXX3xR3sHr0aMHfH19MWDAAHma//f//h/Onj2Lw4cPY+jQoRgyZAjeffddm/kcOXIEQ4cOxcCBA/HOO+848Q42TfRfRFxiNpuxY8cO6jJtREOGDMHatWsbdR18fHzwxx9/1PvvKH+arl27duGBBx7A1KlT8d1338nPd+zYETt37sTAgQPl4l1UVIRvvvkGmzdvxocffljrPAsKChAWFmbznE6nQ48ePXDkyBGUl5ejuLgYbdu2xcKFC7Fhwwbs3r0bCxcutPmbF198Ed9//z327duH//znP7h8+bKCLXdfVNCJS7RaLQYOHKjaU47279+P0aNHIzAwEAEBAejWrRtWr15dazdgfbzxxhuYMGGC6ytZB9evX4efnx/69u3r8ryio6Pxww8/2Dx37do13HPPPU7NLyUlBR06dICPjw9CQ0MxZswYVFRUuLye7rCjo1b5+fnIyMjA2LFjsWLFCmzfvl1+rUePHgCAiIgIlJSUAAC6dOkCrVZr85wjYWFhuHDhgt3zjz/+OL788kts3rwZkyZNAgCYTCZ07NgRHMfZ9fD88ccfmDhxIoYMGYKzZ88iLy/P5TY3BVTQiUs4joOfn58qv8favn07Ro8ejZEjRyIrKwulpaX45ptvcPz4cRQUFNyWdRAEQZH5fPvtt9BoNDh8+DAyMzNvyzLrYtWqVdi7dy92796Na9eu4ffff8dDDz1025Z/M7fzfWhqNm3ahPj4eOzcuRPJycm466675G53R/fZqOu9N6ZOnYrVq1fLl2z9/fffUVxcjN69eyMjIwP//ve/MWXKFACWq39euXIFHMfJA+OsunXrhq1bt2LPnj04evQoevXqpUzD3RwVdOISs9mMrVu3qq7LlDGG5557Di+++CIWLFiAFi1aAADuuusufPbZZ/Ld/s6cOYOxY8ciODgYUVFRWLZsmfzh8tlnn6F79+548803ERISgpYtW8pHjD/88AOWL1+O7du3w8fHR77IRVxcHGbOnIkpU6bAz88PH330EdLT0zFgwAA0b94cwcHBmDp1KoqKiurVnoSEBDzxxBMYNGgQEhISbF4bMmQIFi9ejAceeADNmjXDTz/9hPLycsybNw+RkZHw8/ND7969kZeXh4cffhi5ubmYOnUqfHx88OSTTwKwfGAfO3ZMnmdiYiK6desGPz8/REVF4bPPPnO4Xr/99htiYmLkm2KEhIRgxowZNtfV/ve//42uXbsiICAAvXv3RlpamvyayWTC66+/jnbt2sHX1xf33HMPjh49ikWLFmHfvn148cUX4ePjg9GjRwMALl++jClTpiA4OBiRkZF49dVX5cK9Z88eBAQE4KOPPkJkZCTuu+++er3Hd5LNmzdj8ODB8u/Dhw+36Xavi5UrVyInJ8fmufvvvx/9+/fHkCFDMHDgQCxfvhweHh4AgBEjRsDb2xvNmzcHALzzzjuYMGEChg4datcTs3LlSjz00EMYOnQoYmNjYTAYnGhlE8TuUGVlZQwAKysra+xVadIkSWKVlZVMkqTGXhVFnTp1igFg2dnZtU5TWVnJoqKi2HvvvceMRiM7f/4869y5M/v4448ZY4x9+umnTKvVstWrVzOTycR2797NNBqNPM8lS5aw8ePH28xz+vTpzMvLi+3cuZOJosiuX7/Ojh07xvbt28dMJhO7dOkSGzhwIJs1a5b8N4MHD2Zr1qypdT1PnjzJALDff/+dffLJJywoKIgZjUabvw8ODmYHDx6Ut+fEiRPZyJEj2YULF5goiuzo0aPs6tWrjDHGoqKi2JYtW2yWAYClp6czxhjbtm0ba968Odu1axcTRZFdvnyZHT161OG6LV++nIWEhLD33nuPHT58mJnNZpvXd+zYwcLDw9l///tfJooi27x5M2vevDkrLCxkjDG2cOFC1qtXL3b69GkmSRI7efIkO3fuXK3vy7Bhw9ijjz7KKioq2Llz51inTp3YW2+9xRhjbPfu3YzneTZ37lx2/fp1dv369VrfU3dTVVXFjh8/zqqqqhhjjAGuP9zNypUr2ffffy//LkkSE0Wx3p89Nd8rNdUCN9xst4eaNmJjkiSJmUwm1RX0/fv3MwDyP70j3377LevevbvNc//85z/ZsGHDGGOWgt6yZUub19u3b882bdrEGKu9oNd8rqYtW7aw9u3by7/fqqC/8MIL8nqWl5czb29v9u2339r8/fz58+XfL126xACw8+fPO5zfrQr6qFGj2NKlS2/aBitBENj69evZsGHDWLNmzZi/vz978cUXmSAIjDHGYmNj2dq1a23+pl+/fuzzzz9nkiQxb29vtnfvXofzrvm+5OfnMwCsoKBAfu6rr75iHTp0YIxZCjoAVlJSUqd1dyc1i5TaLF26lD3wwAM2O3xU0O1RlztxiSAISEpKUt33jdYudkcDdKzOnTuHzMxMBAQEyI9Fixbh0qVL8jStWrWy+ZtmzZrdcsBXzVsBZ2dnY/z48QgLC4Ofnx+mTZuGwsLCOrVDEAR8/vnnmD59OgDA19cXEydOtOt2r77M8+fPQ6/X3/KWxLU5f/48OnToUKdpJUlCq1atsHPnTpSWluLrr7/G+vXr5fU7d+4cXnnlFZv3+NixY7hw4QKuXr2KysrKOi8rPz8fnp6eNtukbdu2yM/Pl3/39fVFQEBA3RtLbovXX38dP//8s83gW8YYysvL75hr19cFFXTiEq1Wi9jYWNWNco+JiUF0dDT+/e9/1zpNREQEevXqhdLSUvlRXl6OP//8s07LqO3c65rPP/nkkwgPD8fx48dRXl6OL7/8ss4fYtu3b8fly5fx5ptvolWrVmjVqhW2bduGX375Bbm5uQ6XGRUVBaPRWOvI4FudMx4VFSUPkLqV6vljjYcPHy6fAhcREYF3333X5j2+fv06XnrpJQQHB8Pb27vWZdVcz9atW8NgMNicwpSTkyN/f1+XthH3oeYBuc6i7CUuU9vROWD5sHj//fexcuVKvP/++/IgtNOnT2PmzJk4f/48xowZg8uXL+PDDz+EwWCAKIo4deoU9uzZU6dltGzZEufPn5dH9NamvLwcvr6+8PPzQ15eHt5+++06tyMhIQHjxo3Dn3/+iWPHjuHYsWM4ffo02rdvX+tAtZYtW2L8+PF48sknUVBQAEmSkJ6eLr8HLVu2xJkzZ2pd5ty5cxEfH4+9e/dCkiRcuXIF6enpDqdds2YNkpOTce3aNTDG8J///Ad79uxBv379AADz5s3D22+/jf/+979gjKGyshK//vor8vPzwXEcZs+ejUWLFiE7OxuMMZw6dQrnz593uJ7h4eEYOnQo/vrXv+L69evIzc3F8uXL5d4LQpo6KujEJYIgIDk5WZVFfcyYMfjpp5+wY8cOtGvXDgEBAZg8eTLuuusuhIaGwsfHB7/++it27dqF6OhoBAUF4dFHH7Xpcr+Zhx9+GH5+fmjRosVNu3nfe+89bN++HX5+fhg/frx8Hu6tXLx4ET/99BOef/55+ejc+nj22Wfx6aef1nqkv3HjRkREROD//u//EBAQgCeffBJVVVUAgFdeeQXr1q1DYGAgnn76abu/nTBhAt577z0888wz8Pf3R+/evWu96IyXlxcWLlyI1q1bIyAgALNnz8brr7+OqVOnArBsg5UrV2L27NkIDAxEmzZtEB8fL59JsGrVKgwfPhz3338//Pz88PDDD6O4uBgAsGDBAvz6668ICAjAmDFjAABff/01qqqqEBUVhf79++PBBx/E4sWL6/R+EvdCXe726H7oKrgHLiHkzmYwGJCTk4M2bdrA09OzsVfHrdV8r9RUC9zyCD0rKwv9+vVDTEwM+vTpg+PHj9tNwxjDCy+8gM6dO6Nr164YOnRonb+3I8qhvWTiCsqfpk3pu6199tlnaNasGa5fvw4AOHToEDiOc3gxJMYYRFGk3KnGLQv63LlzMWfOHJw+fRqLFy/GzJkz7abZtm0bUlNTcezYMWRkZGD48OGK3fGH1J0gCNi3b58qu9xJw6P8aSC34e4s1e+2pqROnTrhp59+AmC5Il3v3r0dTscYQ0VFBRX0atyuoF+5cgVHjx7FtGnTAACTJk1CTk4Ozp07Zzet0WiEwWCQ9/Krj1Ylt4dOp8ODDz4InU7X2KtCmiDKn6arIe62BgDjx4/Htm3bAADHjx9Hp06dAFgK+LPPPouhQ4dixIgRuHjxIgICAvDYY49hyJAhGDBggHzmRs+ePfHkk0+ib9++WLFiRQO+C+7F7Qp6Xl4ewsLC5NOgOI5DZGSkzSk2ADB27FgMHToUrVq1QmhoKHbt2oW///3vtc7XaDSivLzc5gFAHmEsiqLDWBAEm9g6GKe22Gw228TWvUdrzBiziwHYxJIk2cTWo5faYlEUbeLb2SZRFFFUVASj0aiaNqlxO7lrmwRBQHFxMUwmk2ra1Jjbyfo3SrDOp/o8q8e7du3CiBEjMHXqVHz77bfy8zExMfjpp58wcOBAJCcngzGGoqIiJCYmYtOmTfjwww/l9jPG7OKAgABUVVUhLS1NvtELYww7duxAQEAAUlJSsHLlSqxYsQJmsxn//Oc/sXv3brzwwgvYsGEDGGMoLS3Fyy+/jLS0NPnU05rtqL5t1MLtCjoAu/MKHSXo0aNHcfLkSVy4cAEXL17E8OHDMW/evFrnuWLFCvj7+8uPiIgIAJC/mzlx4gROnDgBAMjIyEBWVhYAID09Xb7e8KFDh+Rzc9PS0uQbdKSmpsoX+khJSUFpaSkAIDk5Wb6ISFJSEgwGg82FWAwGA5KSkgAAFRUVSE5OBgCUlpYiJSUFAFBYWIjU1FQAllsLWq9jnZeXh0OHDgGwnEtrPS0oKysLGRkZt61N169fx+HDh7Fz507VtEmN28ld25Sbm4vDhw/jt99+U02bGmM7HTlyBIBlp+DatWtQgtFoBGC5U5/17oLXrl2D2WxGfn4+fv/9d/lua9u2bZN3amJiYiBJEiIiIlBQUADGGDp37ozKykq0bt0aJSUl8gGVJEk2B1fWa64PHz4cTz75JCZNmgRJklBZWYnjx49jy5YtGDRoEJ5//nkUFxejvLwcL774IgYMGIA333wTFy9ehMFggL+/v3w9Bb1eDwCorKyU22QymeRtc/DgQUXeL7fg7CXmGsrly5eZn5+ffIk/SZJYy5YtWU5Ojs10zzzzDFu1apX8e2ZmJouMjKx1vgaDgZWVlcmPvLw8BoAVFxczxiyXoLRebrJ6bDabbWJRFG8am0wmm9h6WUJrXP1SqdbY2k5rLIqiTWx9L2qLBUGwiR21g9pEbaI2qbdNFRUV7Pjx4/+7r4ICF3O3rpd1ParHa9asYZs3b5aff+KJJ9jp06fZkiVL2NatW5kkSeyjjz5iCQkJ7OzZs2zSpElMFEVWWVnJBg8eLLffevlWa5yQkMDef/99duXKFfl+BdOnT2cZGRls69atbOnSpfIyjUYjO3z4MHv44YeZJEnshx9+YNOnT2eSJLFevXrJ8+zbt6/NuldVVbE///xTvlZ/UVERXfrVker3xHVWSEgIevTogS+//BKA5a4+0dHRiI6Otpmubdu22LVrl9zt9OOPP6JLly61zlev18PPz8/mAQAajUb+6SjWarU2sfVKUrXFOp3OJrb2NlhjjuPsYgA2Mc/zNrH164faYo1GYxPfzjYxxnD16lVoNBrVtEmN28ld2wRYxs3wPK+aNjXmdrL+jRKs86k+T2u8efNmDBkyRH7+/vvvx6ZNm+T1sT5/s3jlypU4d+6c/F5Uv695cHAw/vWvf9msy9ixY1FcXIxhw4Zh6NCh+Pzzz9G+fXsUFBTggQcekAfnVW9/zbh6O6pvG7Vw+Tz0ESNGgOM4MMZw+vRpdOzYUe4WctapU6cQFxeHoqIi+Pn5YePGjejcuTNmzZqFcePGYdy4cTAajZg3bx727dsHDw8PhIaGYsOGDXaFvzZqOvewMQmCgNTUVAwaNEhV/xjk9qD8UcadeB46uzHK3dfXt147MWo+D93lgv7aa6+hV69emDBhAhYuXIg1a9YotW4NSk0bkRByZ7sTC7qz1FzQXe5yf/PNNyEIAl555RV54AS5c0iShAsXLsgjRgmpD8of4izGGEwmE52HXo0i36FPnjwZM2bMQMeOHZWYHWlCJEnCmTNn6AOZOIXyR1l3WnGzjlqvDzW/R3QtdxV0sxBC7myiKCIrKwve3t4IDg6mW4rWwjqIt7KyEh06dIBGo1FVLVB0FMqJEyfw1ltv4ezZszYn61vP7yTqI0kS8vLyEBERQfeSJvVG+aMMjUaD1q1bIz8/3+FVNdWI3biWu/UMm7riOA6tW7eWz0xQE0UL+pQpU/D4449jxowZqnyziD3rd6Dh4eH0gUzqjfJHOT4+PujQoYN8Kq/aCYKAP/74A/fcc0+9zpDQ6XSqrU+Kdrn37NkTR48eVWp2DUpN3SyEEEKco6ZaoOgu8ahRo7Bz504lZ0ncnCiKyM7Oli/7SEh9UP4QZ1Hu2FO0oA8fPhyTJ0+Gv78/QkJCEBwcjJCQECUXQdwMYwwlJSWqHjlKGg7lD3EW5Y49Rbvc27dvj5UrV6Jnz54231FERUUptQjFqKmbhRBCiHPUVAsUHRQXFBSEyZMnKzlL4uasp8tYTwEhpD4of4izKHfsKdrlPnHiRKxfvx7FxcWorKyUH0TdqqqqGnsVSBNG+UOcRbljS9Eu9+qnnVhv2MJxnFsOWlBTNwshhBDnqKkWKHqELkmS/BBFUf5J1EsURWRmZtJ2Jk6h/CHOotyxp2hBNxgMds9dvXpVyUUQQgghxAFFC/rUqVNtfi8tLcWoUaOUXARxMxqNBl26dKFBKcQplD/EWZQ79hQt6B07dsT8+fMBANeuXUNsbCyeeuopJRdB3IwoikhPT6duL+IUyh/iLMode4oW9JUrV+Ly5ctYtWoVxo8fjylTpmDWrFn1nk9WVhb69euHmJgY9OnTB8ePH7ebZs+ePfD29kb37t3lB414bBxeXl6NvQqkCaP8Ic6i3LGlyHno1U9N++CDDzB69GgMHz4cc+bMQWVlJby9ves1v7lz52LOnDmIi4vDpk2bMHPmTPz2229203Xq1AlHjhxxef2J8zQaDe66667GXg3SRFH+EGdR7thT5Ajdx8cHvr6+8PHxQUhICI4cOYJVq1bJz9fHlStXcPToUUybNg0AMGnSJOTk5NwxtwRsagRBwOHDh21ul0tIXVH+EGdR7thTpKDXPE2t5ulr9ZGXl4ewsDD5dngcxyEyMhK5ubl20546dQo9e/ZE79698eGHH950vkajEeXl5TYPAPL6iaLoMBYEwSaWJOmmsdlstomtp/lbY8aYXQzAJpYkySa2JmxtsSiKNvHtbBMABAQEQBAE1bRJjdvJXdvEGENgYKD8uaGGNqlxO7ljmyRJgr+/v3ytE1fapBaKFPTr16/LcVFRkcvzq3mzekfXvunZsyfy8/Nx9OhRbNmyBevXr8e3335b6zxXrFgBf39/+REREQEAyMzMBACcOHECJ06cAABkZGQgKysLAJCeno6cnBwAwKFDh5CXlwcASEtLQ0FBAQAgNTUVhYWFAICUlBSUlpYCAJKTk1FRUQEASEpKgsFggCAISEpKgiAIMBgMSEpKAgBUVFQgOTkZgOXsgJSUFABAYWEhUlNTAQAFBQVIS0sDYNnxOXToEAAgJycH6enpACzjDzIyMm5bm8xmM6Kjo/Hzzz+rpk1q3E7u2qaLFy+iffv2OHjwoGrapMbt5I5tys3NRVlZGTQajUttOnjwINTC5SvFPfvss8jNzUWnTp2wYsUKPP3007c8Wr6ZK1euoEOHDigqKoJWqwVjDKGhoThw4ACio6Nr/bsVK1bg4sWLeP/99x2+bjQaYTQa5d/Ly8sRERGB4uJi+QgBsHwvUz0WBAEcx8kxz/Pgeb7W2Gw2Q6PRyLFWqwXHcXIMWPYIq8c6nQ6MMTm29mxYY0mSoNVqa41FUQRjTI4dtaOh2gRY/jl69uwJT09PVbRJjdvJXdskSRKOHDmCnj17wsPDQxVtUuN2csc2GY1GHDlyBH379pUPAp1pU3FxMYKCglRxpTiXC/pjjz2GL774Aj/99BMOHz6MS5cuuVTQAWDIkCGIi4uTB8W98847OHDggM00BQUFaNmyJXieR0VFBUaNGoWZM2dixowZdVqGmi7315gkSUJeXh4iIiJsLv1LSF1Q/hBnKZU7aqoFLv8H6fV6AMDo0aMRGhqKHTt2uLxSGzZswIYNGxATE4OVK1ciISEBADBr1ixs27YNALB582bcc8896NatG+69916MGDECTzzxhMvLJvXD8zyioqLow5g4hfKHOItyx57LR+ipqakYNGiQ/Pv333+Phx56yOUVa2hq2itrTIIgIC0tDf369ZO70wipK8of4iylckdNtcDlXZvqxRwAevTo4eosSRPC8zzatWtHe8nEKZQ/xFmUO/YUfyfefvttpWdJ3BjP8wgPD6d/KuIUyh/iLModey6/E1FRUXjggQfwwAMPYMSIEdi+fbsS60WaCEEQkJKSoqpzOcntQ/lDnEW5Y8/lL61GjBiBjz/+WP6dbsZyZ+F5Hl26dKG9ZOIUyh/iLModey4PiistLUVAQIBCq3P7qGkgBCGEEOeoqRa4vGtTvZjn5uZi//792L9/v8NLtRL1MZvN+Pnnn+WLzBBSH5Q/xFmUO/YUOU/k5MmTmDFjBnJychAZGQnGGPLy8tCmTRskJCTg7rvvVmIxxA1pNBr07t0bGo2msVeFNEGUP8RZlDv2FCnocXFxeOGFFzBp0iSb5zdt2oTp06fL194l6sPzPJo3b97Yq0GaKMof4izKHXuKjCYoKSmxK+YAMHnyZJSVlSmxCOKmzGYzduzYQd1exCmUP8RZlDv2FCnoLVq0wBdffCHfjg6wXGd348aNCAoKUmIRxE1ptVoMHDiQrvJFnEL5Q5xFuWPP5VHuAJCdnY25c+ciPT0dYWFh4DgO+fn56NGjB9avX4+YmBgl1lVRahrZSAghxDlqqgWK7Nq0b98eu3btwtWrV+V7zEZERCA4OFiJ2RM3ZjabkZSUhNjYWOh0usZeHdLEUP4QZ1Hu2FPkCL0pUtNeWWNijMFgMMDT01O+JzEhdUX5Q5ylVO6oqRY0+CV23LG7nSiLvsMirqD8Ic6i3LGlyLtx/PjxWl+7du2aEosgbkoQBOr2Ik6j/CHOotyxp0iXO8/ziI6OhqNZXbhwASaTqV7zy8rKwvTp01FYWIiAgAB89tln6NSpk800KSkpePnll1FRUQGe5zF+/HgsW7aszl0vaupmaUyMMQiCAK1WS12mpN4of4izlModNdUCRbrco6KisH//fuTk5Ng9WrZsWe/5zZ07F3PmzMHp06exePFizJw5026awMBAJCYm4vjx4zhy5Aj27t2LxMREJZpD6onudkRcQflDnEW5Y0uRgj5u3DicPXvW4Wvjx4+v17yuXLmCo0ePYtq0aQCASZMmIScnB+fOnbOZrkePHmjbti0AwNPTE927d691HUjDEQQBycnJ9I9FnEL5Q5xFuWNPkYIeHx+PAQMGOHxt3bp19ZpXXl4ewsLC5MEOHMchMjLypjd7uXTpEjZt2oTY2NhapzEajSgvL7d5AIAoivJPR7EgCDax9eI5tcVms9kmtn4NYY0ZY3YxAJtYkiSb2JqwtcWiKNrEt7NNWq0W48aNk9ughjapcTu5a5s0Gg3Gjx8PjuNU0yY1bid3bBPP83jwwQeh0+lcbpNauOWNZGt+H3Kzr/nLy8sxduxYLF68GD179qx1uhUrVsDf319+REREAAAyMzMBACdOnMCJEycAABkZGcjKygIApKenIycnBwBw6NAh+Tz7tLQ0FBQUAABSU1NRWFgIwPLdfmlpKQAgOTkZFRUVAICkpCQYDAZ5IIcgCDAYDEhKSgIAVFRUIDk5GYDllrQpKSkAgMLCQqSmpgIACgoKkJaWBsCy42O9Rn5OTg7S09MBWMYfZGRk3LY2VVVVoaSkRFVtUuN2ctc25ebmory8XFVtUuN2ctc2HTp0CIwxl9p08OBBqIXbnYd+5coVdOjQAUVFRdBqtWCMITQ0FAcOHEB0dLTNtBUVFRg5ciRGjx6N11577abzNRqNMBqN8u/l5eWIiIhAcXExAgMD5T03jUZjEwuCAI7j5JjnefA8X2tsNpuh0Wjk2DpgwxoDkAdyWGOdTicP8NDpdJAkCaIoyrEkSdBqtbXGoiiCMSbHjtrRUG1ijCE5ORnDhg2Dl5eXKtqkxu3krm0SRRG//vorhg0bBr1er4o2qXE7uWObDAYDfv31V4wcORI8zzvdpuLiYgQFBaliUJzbFXQAGDJkCOLi4hAXF4dNmzbhnXfewYEDB2ymuXbtGkaOHIkHHngAS5Ysqfcy3H1kY0VFBXx9fRt7NQghRNXcvRbUh1t2uW/YsAEbNmxATEwMVq5ciYSEBADArFmzsG3bNgCW7+0PHTqELVu2oHv37ujevTveeuutxlxtxcTHx8Pf3x/x8fGNvSq3JEkSiouLbW7MQ0hdUf4QZ1Hu2HPLI/TbwV33yuLj47FgwQL597Vr12L+/PmNt0K3YDabkZKSgmHDhtHFHUi9Uf4QZymVO+5aC5xBBd2NNmLNYm7l7kWdEEKaKnesBc6igu4mG7GiogL+/v4OR/RzHIeysjK3/E5dkiQUFhaiRYsW8sAUQuqK8oc4S6nccbda4Ar6D3ITvr6+WLNmjcPX1qxZ45bFHLD8U2VmZtL3WMQplD/EWZQ79ugI3c32yprad+iEENKUuWstcAYdoROXSJKECxcu0F4ycQrlD3EW5Y49KuhuxNGguAULFih2+pr16k1KkiQJZ86coX8q4hTKH+Isyh171OXuJt0sDT0oLj4+HgsXLsSaNWuoC58QUqs77aJW7lYLXEFH6G6iIQfFWY/8GWOKHvEDlr3k8+fP016yG2mInpiGQvnjXpraRa0od2xRQXcRxyn3WLBgPoC1NZawFgsWzHdhvg3bjU/fY7mX+Ph4+Pn5NYkPZIDyx5005I5/Q6DcsUdd7i52s9S4MZxC4gEsBLAGgCvd4xUA/AE0rXPbiXPoDAnirDv5olbU5U4a2HwA+XCtmAOALyw7BfaUOrddFEVkZ2fLdzUijaOhB1Q2FMqfxldRUYGFCxc6fG3hwoVu+xUO5Y49KuhuKR5A6xs/3RtjDCUlJTe9Zz1pWBUVFQ6PrgBLUXfXD2SA8scd+Pr6YsKECQ5fmzBhgtv24lHu2KMud7frco8HsKDa72vh/JE6dbnfCSoqKm6aw+Xl5bSdVUqZz58KADf7DCyHpbfPNe5aaajLnTSQmsUcABZgLTgwpx5+WOugmAPAGsbg6+fn8kg+UafDyZMnqdvLSUoMpvTz84X9YEqrtfDz81VkOQ1xpC+KIuVPo7vVdlVmu1+8eFGR+VhR7tijgu42KmAZCGdvIZz/l5oPYGKN5ybC9W/nZTyPqqoqpeZGnLa3ns/XV8ONnqf8aWxhsP+UsJp443VXPYTw8HA89NBDCszrfyh3bFFBdxs3GcAG5zu84gFsqfHcFij37bzGZEKPHj2g0WgUmiOpv4uw38pWW2687or/9RwpPdBOo9FQ/riF7+F41/97Beb9EKz5uWXLFsWKOuWOPbcs6FlZWejXrx9iYmLQp08fHD9+3OF0CQkJ6NChA9q1a4c5c+ZAEITbvKZKsz8PfS1c+wbd8TG/a0f91Yk6HTIzM6nbq1E15BGW/ddAShZ1URQpf9xG9aKufDG3UqqoU+7Yc8uCPnfuXMyZMwenT5/G4sWLMXPmTLtpcnJy8Nprr2H//v3Izs7GpUuXkJCQ0AhrqzRLUefgWjEHbnbM79pRP3FHDXGEVQH7MR0W7j56njjrewAXoEwxr73naMuWLYp/p07ccJT7lStXEBMTg8LCQmi1WjDGEBoaigMHDiA6Olqe7u2338a5c+fwwQcfAACSkpKwevVq7Nmzp07Lcd9R7hbl4BQruEqOm3fIvVKoSVE+f6xHREocYd189DONnm98DfH5w6DMTG/L2HkFPnvUNMpd29grUFNeXh7CwsKg1VpWjeM4REZGIjc316ag5+bmIioqSv49Ojoaubm5tc7XaDTCaDQCsJy/aN07LCkpAQC520aj0djEgiCA4zg55nkePM/LMcBDrxdgMvFgjIdeb4bJpAFjPDw9zTAatWCMg6enGQaDpU2enkKNWAeOY9DrrbEEk4ce5UYjJI6D5OEBrdEIiechabXQmkyQNBpIGg20JhNEjQaM56E1myFqNADPQ2M2Q7zxHj4hCDDqdHhVkrBMFPGYTocSSYJGFCF4eIAXRfDWWBDASxIEvR68yQSeMZj1emissacntEYjuBsxJ0nITE3FXXfdBb1eDwAQBAE6nQ6MMTmWJAmiKMqxJEnQarW1xqIogjEmx462TX22U83YbDZDo9HIsVarBcdxcmxtR/W4IdoEaKDVijeWoYFOJ0KSAFHUQKcTIEkcRFEDDw8BoshDFHl4eAgQBB6S5Cj3PsEJtgUtPH+C1sjL20lrMFiWUSPWGQxgHAdBr4fOYIDEcRA9PKAzGnGR49DdQw+j0Qie56HVamEymaDRaKDRaHDRzw8ht8g9jSBA1OkAa77pdOCq5R7jeRxPTsZdd90FnU7nttvJXXMPsHxeeHhIMBq14HkJWq0Ek0kLjUaCRmONRfA8g9lsjQGz2XHulYuw207OfEZcYwyenp4w3Mi3mnH5jXxzlHt1+dwzeXnhz7170bVrV/lz3pntVFxcLNeFps7tCjpgKeLV1fZGV5/uVhtjxYoVWLp0qd3z1XcSnHVjP8EuvpG7dYoZs41bWOfD2P9mKkmAyWSJRdHyuFlcfUyB2QwAeAnASzdiAP+bX824Po0aPBjEeQ42k11cn810F+Ba8llnVC2WJAmmGwsWRRGiKFqWU4/cq7VRgwaBOE/hjwj4V/8FUOYzApCLuTVubV35W+RerY2qqgKGDIFSrHe8bMrcrqBHREQgPz9f3kNljCEvLw+RkZE200VGRuLcuXPy7+fPn7ebprqXX34Zzz//PABL8S8vL4fZbEZQUJDdDgSpu/LyckRERCAvL6/Jd1eR24/yhzhLqdxhjKGiogJhYUqcnte43K6gh4SEoEePHvjyyy8RFxeHzZs3Izo62u5IetKkSRgwYABef/11hISEYP369XjkkUdqna9er5e7hAE0+T0xd+Pn50cfyMRplD/EWUrkjlrqgVuOct+wYQM2bNiAmJgYrFy5Uh69PmvWLGzbtg0A0LZtWyxduhT9+/dHu3btEBIS4nA0PCGEEHIncLtR7qRpUdMIUXL7Uf4QZ1Hu2HPLI3TSdOj1eixZssTm6wxC6oryhziLcsceHaETQgghKkBH6IQQQogKUEEnhBBCVIAKOiGEEKICVNAJIYQQFaCCTgghhKgAFXRCCCFEBaigE0IIISpABZ0QQghRASrohBBCiApQQSeEEEJUgAo6IYQQogJU0AkhhBAV0Db2CjQGxhjKy8tRUVEBX19fcBzX2KtECCGkETDGUFFRgbCwMPB80z7GvSMLekVFBQICAhp7NQghhLiJvLw8tG7durFXwyV3ZEH39fVFXl4eIiIikJeXBz8/v8ZepSZLEAQcPHgQffv2hVZ7R6YTcQHlD3GWUrlTXl6OiIgI+Pr6Krh2jeOO/A/iOE4u4n5+flTQXSBJErp27YqAgIAm311Fbj/KH+IspXNHDV+93pEFnSiH53mEh4c39mqQJoryhziLcseeW+4SP/DAA+jatSu6d++OgQMH4tixYw6nS0hIQIcOHdCuXTvMmTMHgiDc3hUlEAQBKSkp9N4Tp1D+EGdR7thzy4L+7bffIiMjA8eOHcOiRYswY8YMu2lycnLw2muvYf/+/cjOzsalS5eQkJDQCGt7Z+N5Hl26dKHuUuIUyh/iLMode275TlQfgV5WVuZwg23atAkTJ05Ey5YtwXEcnnzySSQmJt7GtSSA5Z8qJCSE/qmIUyh/iLMod+y57Tvx+OOPIyIiAn/729+wceNGu9dzc3MRFRUl/x4dHY3c3Nxa52c0GlFeXm7zAABRFOWfjmJBEGxiSZJuGpvNZpuYMWYTM8bsYgA2sSRJNrG1S6m2WBRFm/h2tslkMmHnzp2orKxUTZvUuJ3ctU1GoxE///wzqqqqVNMmNW4nd2yTwWDAzp07YTabXW6TWrhtQf/888+Rl5eHZcuW4YUXXnA4TfVRidYEqs2KFSvg7+8vPyIiIgAAmZmZAIATJ07gxIkTAICMjAxkZWUBANLT05GTkwMAOHToEPLy8gAAaWlpKCgoAACkpqaisLAQAJCSkoLS0lIAQHJyMioqKgAASUlJMBgMEAQBSUlJEAQBBoMBSUlJACznxicnJwMASktLkZKSAgAoLCxEamoqAKCgoABpaWkALOdMHjp0CIDl64f09HQAQFZWFjIyMm5bm8xmM3r27IlffvlFNW1S43Zy1zZdvHgRvXv3xuHDh1XTJjVuJ3dsU15eHnx9faHRaFxq08GDB6EWHLtVJXQDXl5eyM/PR1BQkPzc22+/jXPnzuGDDz4AYEmc1atXY8+ePQ7nYTQaYTQa5d+t5x4WFxcjMDBQ3nPTaDQ2sSAI4DhOjnmeB8/ztcZmsxkajUaOtVotOI6TY8CyR1g91ul0YIzJsSRJEEVRjiVJglarrTUWRRGMMTl21A5qE7WJ2kRtojbZt6m4uBhBQUEoKytr+qcwMzdTVlbGLly4IP/+/fffs/DwcCZJks10Z86cYaGhoezSpUtMkiQ2duxY9tFHH9VrOQBYWVmZYut+JzKZTGz79u3MZDI19qo0aVFRUWzLli1NehmdOnViP/74Y73+hvKHOEup3FFTLXC7LveysjJMmDAB99xzD7p164YPPvgA27dvB8dxmDVrFrZt2wYAaNu2LZYuXYr+/fujXbt2CAkJwcyZMxt57e88Wq0WAwcOVOVVvoYMGQKNRiN35QGWbkGO43Du3DmX5rt27VrXVxDAsGHD4OXlhZKSkgZbhiOO5v/nn39izJgx9ZqPNX/i4+MRExMDX19fBAcH4/7773fpPbaKi4vDggULXJ4PcT9q/uxxltu9ExEREfJ3JDV9/PHHNr/Pnj0bs2fPvh2rRWpR/ap7ahQYGIiXX34ZO3bscHlejDF5II4Szp49iz179iAwMBBfffUV5s2bp9i8bxeO47Bt2zasW7cO27dvR5cuXVBaWork5GS3uHJX9W5i4l7U/tnjDLc7QidNi9lsxtatW+XRqWrz9NNPIy0tTR6gUxNjDO+++y7atWuH5s2bY9SoUTh79qz8enR0NFasWIF7770X3t7emDJlCvbt24cXX3wRPj4+GD16tDzt6dOnce+998LX1xeDBw+WB+3U5pNPPkH37t3x7LPP2lyDYdGiRbUuwyo3NxcjRoxAcHAwAgMD8eCDD9ocEcfFxWH27Nl45JFH4Ovri44dO8rjU2qbf3R0NH744Qd5Hr/88gv69u2LgIAAhIaGYsWKFXbrYTabkZiYiKFDh6JLly4ALKetTpkyxeYsll9//RV9+vRBQEAAOnfuLPfUAZaRz//4xz9w1113wdfXFx06dMDOnTvxj3/8A1999RU+/PBD+Pj4oHPnzgAsA7HmzJmD0NBQhIaG4sknn8T169cBAOfOnQPHcfj000/Rvn17uhKZG1P7Z49TGrvPv7Go6XuTxiRJEqusrLQb46AGgwcPZmvWrGHLly9n9913H2OMsZKSEgaA5eTkMMYY27hxIwsLC2MZGRmsqqqKPf/88+zuu+9mZrOZMWb53jomJoadPHmSCYLAjEajPN/qoqKiWOfOndmZM2dYVVUVGz16NJs+fXqt6yYIAgsPD2fx8fHszJkzjOM49t///tdu3Wsuw/odek5ODktKSmJVVVWsrKyMTZ48md1///3ytNOnT2c+Pj5s165dTBAE9uabb7KoqKg6z//o0aPMy8uLbdq0iZlMJlZaWsp+++03u3ZIksQ2btzIfHx82LJly9j+/ftZVVWVzTS///47CwgIYLt27WKiKLJ9+/YxPz8/dvLkScYYY/Hx8axNmzbsyJEjTJIkdv78eXb8+HG5HfPnz7eZ3xNPPMGGDh3KCgsL2dWrV9ngwYPZ7Nmz5fcFAJswYQIrKSlh169fr3UbkMal1GePmmoBHaETl6m9S3LBggU4f/68zdGn1RdffIHnnnsO99xzDzw9PbF8+XLk5+fbfG301FNPoWPHjtBoNPDw8Kh1OfPmzUPbtm3h6emJv/zlL/jvf/9b67Q///wzrly5gqlTp6Jt27bo379/va6UGB0djdGjR8PT0xN+fn549dVXkZqaavOVwIMPPohhw4ZBo9HgiSeewPnz51FUVFSn+f/zn//EI488gkmTJkGn08Hf3x/33nuvw2mnTp2KTz75BGlpaXjwwQcRFBSE2bNny0fNGzZsQFxcHIYNGwae5zFgwACMGTMG3377LQDgo48+whtvvIFevXqB4zhERkbi7rvvdrgsSZLw9ddfY8WKFQgKCkKLFi2wfPlyfP755zZtX7JkCQICAuDt7V2n9pLGofbPnvqigk5cUv38UrXy8vLCkiVL8Morr8inwVjl5+cjOjpa/l2v1yMsLAz5+fnyc5GRkXVaTqtWreS4WbNm8rm8jiQkJCA2NhbBwcEAgOnTp+Prr79GVVVVnZZ19epVPProo4iIiICfnx8GDRoEk8lks8ya6wPgputU3fnz59GhQ4dbTmfNnwkTJmDHjh0oKSnBzz//jOTkZLz11lsALN3g69evR0BAgPzYunUrLl68WK9lWdttNBpttlnbtm1hNBrlc6qBum8z0njuhM+e+qKCTlyi1WoRGxur+j3lmTNnQpIku6sWtm7d2ua7Z5PJhIsXL6J169byczUvTenqpSqvXr2KH3/8Ebt27UKrVq3QqlUrvPTSSygtLcX3339fp2W8/PLLqKysxNGjR1FeXi6PEWB1vCzFreYfFRWF7OzsW86nZv5wHIcBAwZg8uTJ+OOPPwBYBsrOnz8fpaWl8uPatWv46KOPbrmsmusZHBwMDw8Pm22Wk5MDvV6PFi1a1Ll9pPHdKZ899UFZS1x2J+whazQavPXWW1i+fLnN89OmTcO6detw/PhxGI1G/O1vf0N4eDj69OlT67xatmyJM2fOOL0un3/+OZo3b46TJ0/i2LFjOHbsGDIzMxEXFyd3u99qGeXl5fD29kZAQACKioqwdOnSeq3DreY/e/ZsJCYmYsuWLRAEAWVlZThw4IDDaT/55BNs3bpVvtJYZmYmtm7din79+gEA5s6di08//RS7d++GKIowGo347bff5KuBzZ07F0uXLsWxY8fAGENubq78WsuWLW0GKfI8j0cffRSvvvoqiouLUVRUhFdffRWPPfYYFfEm6E747KkPymDiEkEQkJycfEf8Y02aNAnt27e3ee7xxx/Hs88+izFjxqBVq1b4/fff8eOPP970qGHBggX49ddfERAQUO/ztgFLd/tTTz2F8PBw+Qi9VatWWLRoEfbs2YPff//9lstYunQpsrOzERgYiP79+zscCX8zt5p/z549sXnzZrz11lto3rw57r77buzdu9duOkEQcO7cObz77rto27YtfH19MWHCBEydOhWLFy8GAPTo0QOJiYn429/+huDgYISHh+O1116Tr/z43HPP4amnnsKUKVPg6+uL+++/X76vw6xZs3DhwgUEBgaia9euAID4+HhER0ejU6dO6Ny5M9q3b4/33nuvXu0nje9O+uypqyZx6deGUF5eDn9/f3Vc7o+QG+Lj47Fw4UKsWbMG8+fPb+zVIcTtqakW0BE6cQljDOXl5XX+7pU0nPj4eCxYsACMMSxYsADx8fGNvUq3RPlDnEW5Y48KOnGJIAjYt28fdXs1Mmsxr64pFHXKH+Isyh171OWugm4WcmerqKiAv7+/wyMVjuNQVlYGX1/fRlgzQtyfmmoBHaG7qbqe79vYJElCcXGxotcoJ/Xj6+uLNWvWOHxtzZo1bl3MKX+Isyh37FFBd0Px8fHw9/d3++5SABBFEYcPH7a74Aq5vebPn29397O1a9e6/cA4yh/iLMode3RGvpup/l2o9ac7fyjrdDqMHDmysVeDNFGUP8RZlDv2FD1C3759u8vzMBgMmDBhAmJiYtC9e3eMGjXK4X2RU1JS0LdvX3Tq1AldunTBq6++2uRHOzbFgU2SJOHKlSvU7dXImmLuAJQ/xHmUO/ZcHhQ3YsQIcBwHxhhOnz6Njh07Ijk52en5GQwGpKSkYPTo0eA4DuvWrcO2bdvs5pmeng5/f3+0bdsWBoMB999/P55++mk8+uijdVqOuw2EaKoDmwRBQGpqKgYNGkSXYGwkTTV3AMof4jylcsfdaoErXD5Cv/fee/H000/jl19+wUMPPeRSMQcAT09PxMbGguM4ef7VL91o1aNHD7Rt21b+m+7duzucrqloqgObtFothg0bRh/Gjaip5g5A+UOcR7ljz+WC/uabb0IQBLzyyiswmUxKrJONf/zjHxg7duxNp7l06RI2bdqE2NjYWqcxGo0oLy+3eQCQB1SIougwFgTBJrZ279QWm81mm9h61GSNGWN2MWC5SMLTTz+NtWvXgud5eHp6ArAMbHrmmWcAWLqYrOdcVo9FUbSJa2tTWVmZ4m0SRRH5+fkwGo0O22SNJUmyiR21w5k2NcZ2csc2zZ8/H/Hx8fL1yL28vORBce7cJkEQcOHCBZhMpjtiO1GblGuT2WxGbm4uJElyuU1qoch36JMnT8aMGTPQsWNHJWYnW758ObKysuTbKDpSXl6OsWPHYvHixejZs2et061YsQL+/v7yIyIiAoDlRhAAcOLECfmGDhkZGcjKygJg6drPyckBABw6dAh5eXkAgLS0NBQUFAAAUlNT5VsvpqSkyDeZSE5Olk8/S0pKgsFgsLnln8FgQFJSEgBLt2lycjLmz5+PDz74AOvWrcPatWsxdepU+U5YBQUFSEtLAwDk5eXJ99zOyclBeno6ACArKwsZGRl2bUpMTMSTTz6J+Ph4RdtUWVmJM2fOYOfOnbW2CQBKS0uRkpICACgsLFSkTY25nZRqE8cBs2efwOzZJ8BxwPz5GZg2LQscB7zySjoeeigHHAcsW3YII0fmgeOAd99Nw8CBBeA44KOPUtGrVyE4DmjWrCM6dPgAAJCQ8BXeffcJcJylTcHBBjRrZmlTs2YCgoMtbeI4ICqqAps2JYPjgE6dSvH55yngOKBXr0J89FEqOA4YOLBhtlNubi7OnDmD3377za23kxpzTw1tyszMhCRJLrXp4MGDUA3mpt5++23Wq1cvVlJSUus05eXl7L777mN///vfbzk/g8HAysrK5EdeXh4DwIqLixljjAmCwARBsIvNZrNNLIriTWOTyWQTS5JkE0uSZBczxmxiURTl9RJFkZnN5pvGgiDYxDXbsXbtWubh4cG0Wi0DwOLj4xulTdVjV9vkLtvJ1TYBjOl0AtPpLLGHh8C0WmtslmO93sy0WlGONRpL7OlpZjxvjU2M59cygGNeXvGM5yUGMOblZWIcJzFAYl5eJgZIjOOsMWM8Xz0Wmadn9djMAMY0mjt7O1Gb1NumoqIiBoCVlZWxpk7RK8WdOHECb731Fs6ePWvTjWHdo6qr9957D1999RV+/fVXBAYGOpzm2rVrGDlyJB544AEsWbKk3uuqpoEQN+No9DOg3DnKkiQhLy8PERERdPtJJ9wYKqKQeAALqv2+FoBypzw2xEkklD/EWUrljppqgaIF/Z577sHjjz+OXr16QaPRyM8PHjy4zvPIz89HRESEfCtFANDr9Th48CBmzZqFcePGYdy4cXjrrbfwxhtvoHPnzvLfPvzww3j11VfrtBw1bcTa3I7Rz4Ig4NChQ+jTpw8NTnGCcgW9ZjG3WgulinpDFHTKH+IspXJHTbVA0YLes2dPHD16VKnZNSg1bcSbaegjdOIaZQp6BQB/AI7+lTkAZQBcH+nexC/zQIhDaqoFivZxjRo1Cjt37lRylsRFDX1JUFEUkZ2dTZdfbFS+AByftmZ53n1PW6P8Ic6i3LGnaEEfPnw4Jk+eDH9/f4SEhCA4OBghISFKLoI4wVrUOY5T/MicMYaSkpImf5W+pm8+gIk1npsIJb9DbwiUP8RZlDv2FO1yb9++PVauXImePXvafIceFRWl1CIUo6ZulrqqqKhw64uM3Inu9O/QCWlsaqoFih6hBwUFYfLkyWjbti2ioqLkB3EPDVHMRVHEyZMnqdurUVUAWFjLawtvvO6eKH+Isyh37Cla0CdOnIj169ejuLgYlZWV8oOoW1VVVWOvwh2u6X6HDlD+EOdR7thStMu9+rmA1hu2cBznlntQ7t7NQt3jd4amdB56eTnlJFEfd68F9aHoEbokSfJDFEX5J6mf+Ph4+Pv7u/2tLwFLt1dmZiZtZ7cwH5YizkHpYg40TE5S/hBnUe7YU/RKDgaDQb6piNXVq1cRHBys5GLcirJHWED1o6wFCxbAcgo5DWoidTUfwAwo281uyUnGIF/TgK5hQIj7UfQIferUqTa/l5aWYtSoUUouQuUcjVRecON596TRaNClSxebsxpIY1O+mFe3YMECxY7UKX+Isyh37Cla0Dt27CjvuV+7dg2xsbF46qmnlFyEijXNkcqiKCI9PZ26vVSp9pxcuHChfEctV1D+EGdR7thTtKCvXLkSly9fxqpVqzB+/HhMmTIFs2bNUnIRKtZ0Ryp7eXk19iqQBlF7Tq5Zs0axAXKUP8RZlDu2FBnlXv3UtKqqKowePRrDhw/Ha6+9BgDw9vZ2dRGKU2pkY0N+h26xFkp9h06jlN2P8vnTEGxzku4DQNSERrnX4OPjA19fX/j4+CAkJARHjhzBqlWr5OdJfVhGKlvHKTMsAAPn8mMtOPj7+SGe4yxVRKGHoNfj8OHDNrfLJWrzv8vKTpw4UdFiLggC5Q9xCuWOPUUKes3T1GqevlYfzz33HKKjo8FxHDIzMx1Os2fPHnh7e6N79+7yQ10XGJiPfCh30pH1+IpB+SF2nCQhMDAQXNM41CROiQewBQCwZcsWRU9d4ziO8oc4hXLHniIF/fr163JcVFTk0rwmT56M/fv33/KSsZ06dcKxY8fkh7q+S4lHayhTeBt63LxGENC+fXsaaapaDT/KnfKHOINyx57LBf3ZZ5/Fo48+ipdffhkA5O/NnTVo0CC0bt3a1dVqwm6c8wvXC+/tGDcv6PVIS0ujbi9VavhR7oIgUP4Qp1Du2HO5oJeWlmLr1q0YNGgQ/v73vyuxTnVy6tQp9OzZE71798aHH354y+mNRiPKy8ttHgDkrwREUXQYC4JgE0uSZBd7egrgeWtslmMvLzN4nskxxzEADF5eZgAMHGeNAZ5n8PL6B4AF4HlevkDP8zyPf9yIJY0Ggl5vibVaORa1WggeHnIs3oi9dTqs1ekAAB4eHtBqtXK8RquFLywFWbrxvKDXQ7qxtyt4ekK6cSlfc/XYywusWsxJEsLCwiCKIhhjYIzBbLa0qXosSZJNbP0nrC0WRdEmVmI7VY/NZrNNbB0bao2t63472qTTidDpxBvbRoRWa40FOdbrBWi1khxrNErnnjWW4OlpjZvB03MtAMvRkP5Gvmm1Wqxduxa+vr4ubycACA8Pl7+ic+ftpMbca8ptYowhNDQUPM+73Ca1cLmgW//JR48ejdDQUOzYscPllbqVnj17Ij8/H0ePHsWWLVuwfv16fPvttzf9mxUrVsDf319+REREAID8Pf2JEydw4sQJAEBGRgaysrIAAOnp6cjJyQEAHDp0CHl5eQCAtLQ0FBQUAABWr05F166FAIB161LQoUMpACAhIRnh4ZajmMTEJDRvboCXl4DExCR4eQlo3tyAxMQkAEB4+CUkJFiuqNehQwesW7cOANC1a1dwq1ejAkBB375IW7oUAJA3ZAgOvfQSACAnNhbpNwYqZU2ejIzZsy1tmjYNw6ZNw1oAs2fPxuTJkwEAn86fjwdjYy1teukl5A0ZYmnT0qUo6NsXAJC6ejUKu3YFAKSsW4fSDh0AAMkJCagIDwcAJCUmwuTnh/DwcOzcuROCIMBgMCApydKmiooKJCcnA7Ds+KWkpAAACgsLkZqaCgAoKChAWlqapU15eTh06JClTTk5SE9Pt7QpKwsZGRkub6fU1FQUFlq2U0pKCkpLLdspOTlZPtpMSkqCwWCAIAhISkq6LW2aNu0Epk2ztGn27AxMnmxp0/z56YiNtbTppZcOYcgQS5uWLk1D37615x4Dh+8SNqEkPAoMHBITk3C9eTBMXs2QmJgEk1czXG8ejMTEJDBwKAmPwncJm8DA4WqHTvhh3edg4FDQtRd2rOYwEUDfvn2x9EbuPT9kCP6vuBjgOOQ89BDSX3kF4DhkTZuGjPnzAY7DidmzcWL2bIDjkDF/PrKmTQM4DumvvIKchx4COA6Hli3DhdGjERUVhQMHDrj9dlJj7jXlNp0/fx6FhYXged6lNh08eBCqwVy0d+9em983b97s6iwZY4xFRUWxP/74o07TLl++nM2bN++m0xgMBlZWViY/8vLyGABWXFzMGGNMEAQmCIJdbDabbWJRFG1igDFPTzPjeWtskmMvLxPjeUmOOU5igMS8vEwMkBjHWWPGeF5iXl7xDADjeZ55enrKcbynJ2MAEzUaZtbrLbFWK8eCVsvMHh5yLFhjnY4JOh1jAIv38GA6rZatBZjZw4MJWi1jADPr9UysHms0ltjTk4k8zxjATNVjLy8mVYtNnp5sz549rLKykkmSxCRJYiaTiTHGbGJRFG1is9l801gQBJvY0bapz3aqGZtMJptYkiSb2LruDd0mgDGdTmA6nSX28BCYVmuNzXKs15uZVivKsUZTe+452k4SxzHJGgNM4jhm8vJiDGASz8uxyPPMZM23G7kHgGk0GqbX6xkAptVqWXw9ck+onm81cs/o7c327t3Lqqqq3Ho7qTH3mnqbDAYD27Nnj7yuzrapqKiIAWBlZWWsqVP0bmuAZU+wTZs2Ls8nOjoa27dvR5cuXexeKygoQMuWLcHzPCoqKjBq1CjMnDkTM2bMqPP8m8p56Guh3Gj3iwDCFJqXlaTRoOD8ebnri9RPQwzQZVBmphUA/GE5O6ImDkAZXL/cEeUPcZYkSSgoKHA5d+g89Jt4++23Xfr7Z555Bq1bt0Z+fj7uv/9+tG/fHgAwa9YsbNu2DQCwefNm3HPPPejWrRvuvfdejBgxAk888YTL6+4ebM9DV/LUNaVGzlfHiyLCw8Ppw1iFbse1Cyl/iLN4nqfcqcHlI/SoqCh07NgRgGWQwqlTp5Cbm6vIyjUk9z1CtygHp9jFXhvyLtmCpydSd+zAoEGD5EF3pO7c+QjdivKHuCNBEJCamupy7qjpCN3l/6ARI0bg448/ln+nm7Eoo6GKOar9rsSHMm8yoUuXLrSXTJxC+UOcxfM85U4NLh+hl5aWIiAgQKHVuX3c/QhdiaOs2/EdKAC60boL3P0I3dEOIaDsUTrlD2lMajpCd3nXpnoxz83Nxf79+7F///4m0e2udrfjO1Czpyd+/vln+fxRoh6348JElD/EWWazmXKnBkW+tDp58iRmzJiBnJwcREZGgjGGvLw8tGnTBgkJCbj77ruVWAxxgvUoakG159ZCuaMrjcmE3r170+UXVci6Q7jAwWtK7RBS/hBnaTQayp0aFCnocXFxeOGFFzBp0iSb5zdt2oTp06fLFwIgjcNavBfC8kGs5I0veUlC8+bNFZwjcScNvUNI+UOcxfM85U4NiowmKCkpsSvmgOVGK2VlZUosgrhoPizfmSt9F2uzlxd27NhB3V4qZjmREoqfSglQ/hDnmc1myp0aFCnoLVq0wBdffCFfGxewnPS/ceNGBAUFKbEIooCGuDO91mjEwIED6ZQjlWuoHULKH+IsrVZLuVODIleKy87Oxty5c5Geno6wsDBwHIf8/Hz06NED69evR0xMjBLrqqg7YZT7bUOjlJ3m7qPcbwvKH9KI1DTKXZFdm/bt22PXrl24evWqfMH7iIgIBAcHKzF74sbMXl5I2roVsbGx0N24sxshdUX5Q5xlNpuRlJREuVON4tdybyroCF0ZjONguH4dnp6e4BrqzVCxO/0InfKHOIsxBoPB4HLuqOkIvcEvseOO3e1EQYzRd1jEeZQ/xAWUO7YUeTeOHz9e62vXrl1TYhHETQleXtTtRZxG+UOcZb3HOuXO/yjS5c7zPKKjo+FoVhcuXIDJZHJ1EYqjLndlMACCyQStVktdpk6447vcQflDnMMYgyAILueOmrrcFTlCj4qKwv79+xEWZn+37YiICCUWQdwVx8n/VITUG+UPcQHlji1FvkMfN24czp496/C18ePH13t+WVlZ6NevH2JiYtCnTx+HXfqMMbzwwgvo3LkzunbtiqFDhyI7O7veyyKuETw9kZycDEEQGntVSBNE+UOcJQgC5U4NbjnKfdiwYXj88ccRFxeHTZs24d1338Vvv/1mM83WrVuxfPly7N+/HzqdDsuWLUNGRga+/fbbOi2DutwV5H4p1GTc6V3uACh/SKNSU5e7291I9sqVKzh69CimTZsGAJg0aRJycnJw7tw5u2mNRiMMBgMYYygvL0fr1q1v89oSxvMoLy93OH6CkFuh/CHOsn7uU+78j9sV9Ly8PISFhcnfi3Ach8jISLvbsY4dOxZDhw5Fq1atEBoail27duHvf/97rfM1Go0oLy+3eQCAKIryT0exIAg2sfXyttVjT08BPG+NzXLs5WUGzzM55jgGgMHLywyAgeOsMcDz1WMJZk9PAIDE8xCssUYDQa+3xFqtHItaLQQPDzkWrbFOB/HG6E/RwwPijfdUqB7r9ZCqxzfuXCR4ekLiLelhrh57eYFVi82enkhNTUVVVRUYY2CMyddWrh5LkmQTW7vJaotFUbSJldhO1WOz2WwTWz8UrLF13W9Hm3Q6ETqdJfbwEKHVWmNBjvV6AVqtJMcazc1zr+Z2YhwHZo1hOf/b7OVlaRPPy7HE87c190ze3ti3bx8MBoPbbyc15l5TbpPRaERqaqq8rq60SS3crqADsBux6GgP7OjRozh58iQuXLiAixcvYvjw4Zg3b16t81yxYgX8/f3lh3WwXmZmJgDgxIkTOHHiBAAgIyMDWVlZAID09HTk5OQAAA4dOiRfCS8tLQ0FBQUAgNWrU9G1ayEAYN26FHToUAoASEhIRni45a7RiYlJaN7cAC8vAYmJSfDyEtC8uQGJiUkAgPDwCiQkJAMAOnQoRcq6dQCAwq5dkbp6NQCgoG9fpC1dCgDIGzIEh156CQCQExuL9PmWq2xnTZ6MjNmzLW2aNg0nbvR0ZMyejazJky1tmj8fObGxlja99BLyhgyxtGnpUhT07QsASF29GoVduwIAUtatQ2mHDgCA5IQEVISHAwCSEhMhenlh5MiR+OWXXyAIAgwGA5KSLG2qqKhAcrKlTaWlpUhJSbG0qbAQqampljYVFCAtLc3Sprw8+c58OTk5SE9Pt7QpKwsZGRkub6fU1FQUFlq2U0pKCkpLLdspOTkZFRWW7ZSUlASDwSCfEnM72jRt2glMm2Zp0+zZGZg82dKm+fPTERtradNLLx3CkCGWNi1dmoa+fW+eezW3k6F5c8spYomJELy8YGjeHEmJiZY2hYcjOSHB0qYOHW5r7l269148+OCDOHz4sNtvJzXmXlNuU35+Plq2bAmdTudSmw4ePAjVYG7m8uXLzM/Pj5nNZsYYY5IksZYtW7KcnByb6Z555hm2atUq+ffMzEwWGRlZ63wNBgMrKyuTH3l5eQwAKy4uZowxJggCEwTBLjabzTaxKIo2McCYp6eZ8bw1Nsmxl5eJ8bwkxxwnMUBiXl4mBkiM46wxYzxfPRaZydOTMYCJPM/M1lijYWa93hJrtXIsaLXM7OEhx4I11umYoNNZYg8PJmi1jAHMXD3W65lYPdZoLLGnJxN5njGAmarHXl5MqhYLGg0rLCxkBoOBSZLEJEliJpNJ3nbWWBRFm9i6fWuLBUGwiR1tm/psp5qxyWSyiSVJsomt697QbQIY0+kEptNZYg8PgWm11tgsx3q9mWm1ohxrNLXnnqPtJHEck6wxwCSOYyYvL8YAJvG8HIs8f1tzz+zhwYqKipjRaHTr7aTG3GvqbTKZTOzKlStMFEWX2lRUVMQAsLKyMtbUuV1BZ4yxwYMHs08//ZQxxth3333H+vbtazfNu+++yx544AE5AVasWMFiY2PrvIyysjJFNqJlRI/yjwabscIPk6cn27lzp7wdSP3cyblD+UNcYTKZFMkdpWqBO3DLUe6nTp1CXFwcioqK4Ofnh40bN6Jz586YNWsWxo0bh3HjxsFoNGLevHnYt28fPDw8EBoaig0bNiA6OrpOy6BR7gpyvxRqMmiUOyh/SKNS0yh3tyzotwMVdGVIPI/CggK0aNECPO+WQzLc2p1e0Cl/iLMkSUJhYaHLuaOmgk7/QcQlkocHMjMz5RGjhNQH5Q9xliRJlDs10BE6HaG77s5MIUXc6UfoACh/SKOiI3RCbpA0Gly4cIH2kolTKH+IsyRJotypgQo6cYmk1eLMmTP0T0WcQvlDnCVJEuVODdTlTl3urrszU0gR1OUOyh/SqKjLnZAbJK0W58+fp71k4hTKH+IsSZIod2qggk5cQt+BEldQ/hBn0Xfo9qjLnbrcXXdnppAiqMsdlD+kUVGXOyE3iFotsrOz5bsYEVIflD/EWaIoUu7UQAWduITxPEpKSnCHdvQQF1H+uB/rndLcHWOMcqcGKujEJVqTCb1795bvX09IfVD+uJf4+Hj4+/sjPj5e8XkrvaOg1Wopd2qggk5cImq1OHnyJHV7EadQ/riP+Ph4LFiwAIwxLFiwQNGi3hA7CqIoUu7UQLs2xDU8j6qqqsZeC9JUUf64BWsxr876+/z58xWbt1LztKLcsUWj3GmUu+vuzBRSBI1yB+VPI6uoqLjpZ2B5eTl8fX2dmrejHQUAWLt2rWJF3VU0yr2BZWVloV+/foiJiUGfPn1w/Phxh9MlJCSgQ4cOaNeuHebMmQNBEG7zmhJRp0NmZiZ1exGnUP6oV0VFBRYuXOjwtYULF7r8nbooipQ7NbhlQZ87dy7mzJmD06dPY/HixZg5c6bdNDk5OXjttdewf/9+ZGdn49KlS0hISGiEtSWEkMbBca4/bnVQ6ufn7Hx9wdgEh/OcMGGC00f9pHZu1+V+5coVxMTEoLCwEFqtFowxhIaG4sCBA4iOjpane/vtt3Hu3Dl88MEHAICkpCSsXr0ae/bsqdNyqMtdQe6VQk0KdbmD8scFyuRPBYCbdLkDcKb03nyuzs/XhgK5o6Yud7cbFJeXl4ewsDD5VASO4xAZGYnc3Fybgp6bm4uoqCj59+joaOTm5tY6X6PRCKPRCMBy/uLFixcBACUlJQAgd9toNBqbWBAEcBwnxzzPg+d5OQZ46PUCTCYejPHQ680wmTRgjIenpxlGoxaMcfD0NMNgsLTJ01OoEevAcQx6vTWWUOShh85ohMRxkDw8oDUaIfE8JK0WWpMJkkYDSaOB1mSCqNGA8Ty0ZjNEjQbgeWjMZog33kONIEDU6QBJgkYUIeh04Kyxhwd4UQRvjQUBvCRB0OvBm0zgGYNZr4fGGnt6Qms0grsRc5KEzNRU3HXXXdDr9QAAQRCg0+nAGJNjSZIgiqIcS5IErVZbayyKIhhjcuxo29RnO9WMzWYzNBqNHGu1WnAcJ8fWdlSPG6JNgAZarXhjGRrodCIkCRBFDXQ6AZLEQRQ18PAQIIo8RJGHh4cAQeAhSY5zr5zBbjtpDQbLMmrEOoMBjOMg6PXQGQyQOA6ih8dtyz3G8zi+bx/uuusu6HQ6t91O7pp7gOXzwsNDgtGoBc9L0GolmExaaDQSNBprLILnGcxmawyYzdVzbyV0utfs3qu3RBFmDw+UOvEZcYkxeHp6wnAj32rGFw0GtHIh90xeXvhz71507dpV/px3ZjsVFxfLdaGpc7uCDliKeHW1vdHVp7vVxlixYgWWLl1q93z1nQRn3dhPsItv5G6dYsZs4xbW+TD2v5lKEmAyWWJRtDxuFlcfU2A2O46t86sZ16dRgweDOE/pzeQPuJZ81hndrtwbNAjEeQ2zmSy/vATgJSU+IwC5mFvju6wr72zuVVUBQ4ZAKRUVFfD391dsfo3B7Qp6REQE8vPz5T1Uxhjy8vIQGRlpM11kZCTOnTsn/37+/Hm7aap7+eWX8fzzzwOwFP/y8nKYzWYEBQXZ7UCQuisvL0dERATy8vKafHcVuf0of4izlModxhgqKioQFham4No1Drcr6CEhIejRowe+/PJLxMXFYfPmzYiOjrY7kp40aRIGDBiA119/HSEhIVi/fj0eeeSRWuer1+vlLmEATX5PzN34+fnRBzJxGuUPcZYSuaOWeuCWo9w3bNiADRs2ICYmBitXrpRHr8+aNQvbtm0DALRt2xZLly5F//790a5dO4SEhDgcDU8IIYTcCdxulDtpWtQ0QpTcfpQ/xFmUO/bc8gidNB16vR5Lliyx+TqDkLqi/CHOotyxR0fohBBCiArQETohhBCiAlTQCSGEEBWggk4IIYSoABV0QgghRAWooBNCCCEqQAWdEEIIUQEq6IQQQogKUEEnhBBCVIAKOiGEEKICblfQn3vuOURHR4PjOGRmZtY6XUJCAjp06IB27dphzpw5EKrf2JcQQgi5w7hdQZ88eTL279+PqKioWqfJycnBa6+9hv379yM7OxuXLl2S78hGCCGE3IncrqAPGjQIrVu3vuk0mzZtwsSJE9GyZUtwHIcnn3wSiYmJt2kNCSGEEPejbewVcEZubq7NEXx0dDRyc3Nv+jdGoxFGoxEAwBhDeXk5zGYzgoKCwHFcg64vIYQQ98QYQ0VFBcLCwsDzbneMWy9NsqADsCnCdblh3IoVK7B06dKGXCVCCCFNVF5e3i17h91dkyzokZGROHfunPz7+fPnERkZedO/efnll/H8888DsOwAXLx4EZ06dcK5c+cQGBgIURQBABqNxiYWBAEcx8kxz/Pgeb7W2Gw2Q6PRyLFWqwXHcXIMAIIg2MQ6nQ6MMTmWJAmiKMqxJEnQarW1xqIogjEmx47a0VBtAoCDBw+iV69e8PT0VEWb1Lid3LVNkiTh8OHD6NWrFzw8PFTRJjVuJ3dsk9FoxOHDh3HvvffKB3jOtKm4uBht2rSBr69vzbLR5DTJgj5p0iQMGDAAr7/+OkJCQrB+/Xo88sgjN/0bvV4PvV4v/25NgMDAQPj5+TXo+qqZJEno1q0bgoODm3x3Fbn9JElC165d0aJFC8ofUi/Wz56AgABFckcNX7263X/QM888g9atWyM/Px/3338/2rdvDwCYNWsWtm3bBgBo27Ytli5div79+6Ndu3YICQnBzJkzG3O171g8zyM8PJw+jIlTKH+Isyh37HGsLl9Aq1B5eTn8/f1RVlZGR+guEAQBqampGDRokNydRkhdUf4QZymVO2qqBbRrQ1zC8zy6dOlCe8nEKZQ/xFmUO/Zol5i4hOd5hISENPZqkCaK8oc4i3LHHu3aEJeYzWb8/PPP8oh3QuqD8oc4i3LHHhV04hKNRoPevXtDo9E09qqQJojyhziLcsceFXTiEp7n0bx5c/oey0XR0dH44YcfGnUd9u3bZ3NhDYPBgIkTJyIgIAB9+vSxe10JlD/EWZQ79uidIC4xm83YsWOHKru9hgwZAo1Gg4yMDPm50tJScBxnc2EjZ+a7du1al9YtOjoaXl5e8PHxQYsWLRAbG4usrCyX5jlw4EDk5+fLv2/evBmnTp3C5cuXcejQIbvX66OgoACPPvooWrVqBV9fX7Rt2xYLFy5UJH84jsOxY8ec/nvSNKn5s8dZVNCJS7RaLQYOHKjaU44CAwPx8ssvKzIvxph85SolJCYm4tq1azh79ix8fX0xffp0xeYNWO5qGBMTY3NBJmc99thj8PT0xMmTJ1FWVoZffvkF3bt3d4v8oVsvN03ukDvuhgo6cQnHcfDz81PFVZYcefrpp5GWlobU1FSHrzPG8O6776Jdu3Zo3rw5Ro0ahbNnz8qvR0dHY8WKFbj33nvh7e2NKVOmYN++fXjxxRfh4+OD0aNHy9OePn0a9957L3x9fTF48GDk5eXVaR39/Pzw2GOP4Y8//gAALF68GFFRUfD19UWnTp3w3Xff2Uz/3//+F8OGDUPz5s0RHByMZ599FgCwZ88eBAQEAAAWLVqEv//979i+fTt8fHywZMkSm9cBwGQy4fXXX0e7du3g6+uLe+65B0ePHnW4jgcOHMATTzwhX9WrXbt2mD59upw/giDI8woKCsK4ceNw8eJF+e8vXbqEadOmISwsDAEBARg0aBCqqqrQp08fAEC/fv3g4+OD5cuXAwCOHDmC/v37IyAgAJ06dbK5G+Mbb7yBMWPG4KmnnkLz5s3x4osv1ul9Ju5F7Z89TmF3qLKyMgaAlZWVNfaqNGkmk4n98MMPzGQyNfaqKG7w4MFszZo1bPny5ey+++5jjDFWUlLCALCcnBzGGGMbN25kYWFhLCMjg1VVVbHnn3+e3X333cxsNjPGGIuKimIxMTHs5MmTTBAEZjQa5flWFxUVxTp37szOnDnDqqqq2OjRo9n06dNrXbeoqCi2ZcsWeZ0efvhhNmjQIMYYY19++SW7fPkyEwSBJSYmMr1ez86ePcsYYyw/P5/5+fmxDz74gFVVVbHr16+z1NRUxhhju3fvZv7+/vIylixZwsaPHy//XvP1hQsXsl69erHTp08zSZLYyZMn2blz5xyu78iRI1nPnj3Zxo0b2alTp+Tnrfnz/PPPs2HDhrGLFy8yo9HIFi1axAYOHMgYY0wURda7d282ffp0VlxczMxmM9u3bx8zGAyMMcYAsPT0dHmeJSUlLCgoiP3jH/9gJpOJ7dmzhzVr1ozt379fbpdGo2GffvopM5vN7Pr167W+z8R9KfXZo6ZaQAVdBRuxMUmSxCorK5kkSY29KoqzFt7KykoWFhbGtmzZYlfQ77//frZy5Ur5bwwGA/P19WX/+c9/GGOWwluzeNdW0D/66CP59y+//JJ16dKl1nWLiopi3t7eLCAggIWFhbFJkybVWky7devGvvzyS8YYYytXrmRDhw51OF19CrokSczb25vt3bu31nWsrqysjC1ZsoT16NGDabVaFhkZyb766ismSRK7fv06a9asGTt27Jg8fVVVFeN5nuXm5rIDBw6wZs2ascrKSofzrlnQv/zyS3bXXXfZTDN79mw2e/ZsuV3dunWr03oT96XUZ4+aagF1uROXqf07LC8vLyxZsgSvvPKK3Xfg+fn5iI6Oln/X6/UICwuzGTx2qzsBWrVq1UqOmzVrhoqKiptO/9VXX6GkpAQXLlzApk2bEBUVBQBYs2YNOnfuDH9/fwQEBCAzMxOFhYUALHcm7NChQ53W52auXr2KysrKOs/Lz88Pb7zxBo4ePYqSkhI899xzePzxx3HixAmUlpbi+vXrGDRoEAICAhAQEIBWrVrBw8MDeXl5OH/+PMLDw+Hl5VWnZdXcJoDl/g/ObBPi3tT+2VNfVNCJSwRBQFJSkuoHFs2cOROSJGHjxo02z7du3dpmxLvJZMLFixdtTu+qeVpNQ55ms3//frzxxhv4/PPPUVJSgtLSUnTp0gXsxi0boqKikJ2d7fJygoOD4e3t7dS8fHx8sGjRIvj7++OPP/7AwYMH4e3tjYMHD6K0tFR+VFVVoV+/foiKisKFCxdQVVXlcH41v0OtuU0AywC/m20T0vTcKZ899UFZTVyi1WoRGxur+j1ljUaDt956Sx50ZTVt2jSsW7cOx48fh9FoxN/+9jeEh4fLg7UcadmyJc6cOdMg61leXg6tVovg4GBIkoRPPvkEmZmZ8ut/+ctfcOjQIaxfvx5GoxGVlZXYt29fvZfDcRxmz56NRYsWITs7G4wxnDp1CufPn3c4/QsvvIBjx47BZDLBZDLh448/xvXr19GnTx+MGTMGc+fOxaJFi+SBgEVFRfjmm28AAL1790bHjh3xzDPPoLS0FIIgYP/+/TAajQDs38/Y2FhcuXIFH374IQRBwL59+/D111/j8ccfr3c7ifu6Uz576oMKOnHZnbKHPGnSJPl2vlaPP/44nn32WYwZMwatWrXC77//jh9//PGmHzILFizAr7/+ioCAAIwZM0bRdRw1ahQmTZqEe+65B2FhYfjzzz/Rv39/+fXWrVvj119/xddff42WLVsiOjoamzZtcmpZq1atwvDhw3H//ffDz88PDz/8MIqLix1OazQa8cgjjyAoKAitWrXCp59+iq1btyI6OhqCIGDFihW47777MGzYMPj6+qJXr15ITk4GYDma/vHHH1FZWYmOHTuiRYsW+Nvf/gZJkgAAb775Jp577jkEBgZi5cqVCAwMxE8//YQvv/wSQUFBmDNnDj766CMMGDDAqXYS93WnfPbUFd0+VQW3zGtMZrMZSUlJiI2NhU6na+zVIU0M5Q9xllK5o6Za4JYFPSsrC9OnT0dhYSECAgLw2WefoVOnTjbTMMawePFiJCUlQaPRICgoCP/617/sjqBqo6aNSAghxDlqqgVu2eU+d+5czJkzB6dPn8bixYsxc+ZMu2m2bduG1NRUHDt2DBkZGRg+fDheeeWVRljbOxtjDOXl5XDD/ULSBFD+EGdR7thzu4J+5coVHD16FNOmTQNg+d4yJyfH4bWzjUYjDAaDvGGVvnEEuTXroCP6Los4g/KHOItyx57bFfS8vDyEhYXJg4o4jkNkZCRyc3Ntphs7diyGDh2KVq1aITQ0FLt27cLf//73WudrNBpRXl5u8wAgn1csiqLDWBAEm9g6EKe22Gw228TWvUdrzBiziwHYxJIk2cTWhK0tFkXRJr6dbbKONLW2QQ1tUuN2ctc2aTQaPPjgg+A4TjVtUuN2csc28TyPkSNHQqfTudwmtXC7gg7Yn1fqqEvl6NGjOHnyJC5cuICLFy9i+PDhmDdvXq3zXLFiBfz9/eVHREQEAMin9Jw4cQInTpwAAGRkZMh3rkpPT0dOTg4A4NChQ/JpNWlpaSgoKAAApKamyhfuSElJQWlpKQAgOTlZvjhIUlISDAaDzbmTBoMBSUlJAICKigp5VG9paSlSUlIAAIWFhfJ1xAsKCpCWlgbAsuNz6NAhAJZzbNPT0wFYxh9Y7w52O9pUWVmJq1evqqpNatxO7tqm8+fPo7i4WFVtUuN2csc2nT17FgcPHoQkSS616eDBg1ALtxsUd+XKFXTo0AFFRUXQarVgjCE0NBQHDhywufrTvHnzEBkZicWLFwMA/vzzT8TGxtZ6HqzRaJTPWwUsAyEiIiJQXFyMwMBAec9No9HYxIIggOM4OeZ5HjzP1xqbzWZoNBo51mq14DhOjgHLHmH1WKfTgTEmx5IkQRRFOZYkCVqtttZYFEUwxuTYUTsaqk2MMaSkpGDQoEHylbyaepvUuJ3ctU2iKGLPnj0YNGgQ9Hq9Ktqkxu3kjm0yGAzYs2cPhg8fLl8oyJk2FRcXIygoSBWD4hQt6Nu3b1fkvNohQ4YgLi4OcXFx2LRpE9555x0cOHDAZpr33nsPP//8M7Zv3w6dToeVK1di37592LFjR52WoaaRjYQQQpyjplrgckEfMWIEOI4DYwynT59Gx44d5S4UZ506dQpxcXEoKiqCn58fNm7ciM6dO2PWrFkYN24cxo0bB6PRiHnz5mHfvn3w8PBAaGgoNmzYYHcN59qoaSM2JkmSUFhYiBYtWtDlNEm9Uf4QZymVO2qqBS4X9Ndeew29evXChAkTsHDhQqxZs0apdWtQatqIjUkQBKSmpmLQoEF0CUZSb5Q/xFlK5Y6aaoEiXe6bNm3C0aNHUVZWhg8++ECJ9WpwatqIhBBCnKOmWqBIH9fkyZMxY8YMdOzYUYnZkSZEkiRcuHBBPgWEkPqg/CHOotyxp9iXVu3bt8dzzz2n1OxIEyFJEs6cOUP/VMQplD/EWZQ79hQd5X7ixAm89dZbOHv2rM3J+tbzBt2JmrpZCCGEOEdNtUDRUShTpkzB448/jhkzZkCj0Sg5a+KmJElCXl4eIiIiaJSym6ioqICvr29jr0adUP4QZ1Hu2FP0XdDpdHjhhRcwbNgwDB48WH4Q9aLvsdxLfHw8/P39ER8f39irUieUP8RZlDv2FC3oo0aNws6dO5WcJXFzWq0W/fr1o1OO3EB8fDwWLFgAxhgWLFjQJIo65Q9xFuWOPUUL+vDhwzF58mT4+/sjJCQEwcHBCAkJUXIRxM2Ioojs7Gz5EoukcViLeXVNoahT/hBnUe7YU7Sgz507F5999hnS09Nx+PBhHDlyBIcPH1ZyEcTNMMZQUlJC9yRuRBUVFVi4cKHD1xYuXCjfKMMdUf4QZ1Hu2FO0ryIoKAiTJ09WcpbEzWm1WvTu3buxV+OO5uvriwkTJmDLli12r02YMMGtB8hR/hBnUe7YU/QIfeLEiVi/fj2Ki4tRWVkpP4h6iaKIkydPUrdXI6qoqMAPP/zg8LUffvjBrY/QKX+Isyh37Cl6hP7KK68AAJ5++mn5hi0cx9EbrnJVVVWNvQpNFscpMRdfABMA2B+hMzYBfn7KHKE3VM8m5Q9xFuWOLbe7H/rtoqaLCZCmS5mCXgHAH4Cjf2UOQBksRd81d+YnBVE7NdUCRbvcDQaD3XNXr15VchHEzYiiiMzMTOqFaVTWI3RHJkCJYt5QKH+Isyh37Cla0KdOnWrze2lpKUaNGqXkIgghdioA/FDLaz/ceJ0QonaKFvSOHTti/vz5AIBr164hNjYWTz31lJKLIG5Go9GgS5cudKnfRuULYE0tr62BOx+hU/4QZ1Hu2FO0oK9cuRKXL1/GqlWrMH78eEyZMgWzZs2q93yysrLQr18/xMTEoE+fPjh+/LjdNHv27IG3tze6d+8uP2iAxO0niiLS09Op26vRzQewtsZza288774of4izKHfsKTLKvfqpaR988AFGjx6N4cOHY86cOaisrIS3t3e95jd37lzMmTMHcXFx2LRpE2bOnInffvvNbrpOnTrhyJEjLq8/cY2Xl1djrwIB8L/ivRCWI3P3LuZWlD/EWZQ7thQZ5c7zvM1patVnWd/T1q5cuYKYmBgUFhZCq9WCMYbQ0FAcOHAA0dHR8nR79uzBX//6V6cLuppGNpKmS5lR7jVVoCG62WmUO1EjNdUCRbrcJUmCKIo2P62P+naH5OXlISwsTL7gPsdxiIyMRG5urt20p06dQs+ePdG7d298+OGHN52v0WhEeXm5zQOAvH6iKDqMBUGwia139qktNpvNNrF158YaM8bsYgA2sSRJNrH13vK1xaIo2sS3s01msxmHDh1CVVWVatp0u7eTTidCp7PEHh4itFprLMixXi9Aq5XkWKOxxJ6eAnjeGptvxL7w8jKD5y1t8vIyg+MYAAYvLzMABo6zxgDPV48leHpWjy3rq9E0zHYymUw4fPgwDAaD228nNeZeU26T0WjEwYMH5XV1pU1qoUhBv379uhwXFRW5PD+uxmGLo06Enj17Ij8/H0ePHsWWLVuwfv16fPvtt7XOc8WKFfD395cfERERAIDMzEwAwIkTJ3DixAkAQEZGBrKysgAA6enpyMnJAQAcOnQIeXl5AIC0tDQUFBQAAFJTU1FYWAgASElJQWlpKQAgOTlZvkpXUlISDAYDBEFAUlISBEGAwWBAUlISAMvVvpKTkwFYzg5ISUkBABQWFiI1NRUAUFBQgLS0NACWHZ9Dhw4BAHJycpCeng7AMv4gIyPjtrXJaDTC398fycnJqmnT7d5O06adwLRpljbNnp2ByZMtbZo/Px2xsZY2vfTSIQwZYmnT0qVp6NvX0qbVq1PRtaulTevWpaBDB0ubEhKSER5uaVNiYhKaNzfAy0tAYmISvLwENG9uQGKipU3h4RVISLC0qUOHUqxbZ2lT166FWL3a0qa+fRtmO124cAGBgYE4ePCg228nNeZeU25Tbm4uKisrwXGcS206ePAg1MLlLvdnn30Wubm56NSpE1asWIGnn376lkfLN3PlyhV06NABRUVFN+1yr2nFihW4ePEi3n//fYevG41GGI1G+ffy8nJERESguLgYgYGB8p6bRqOxiQVBAMdxcszzPHierzU2m83QaDRyrNVqwXGcHAOWPcLqsU6nA2NMjq09G9ZYkiRotdpaY1EUwRiTY0ftoDa5Z5u0Wo18dG42a+DhIUKSAEHQwMNDgCRxEAQN9HoBoshDEHjo9QIEgYco8vD0FGAy8ZAkHp6eZphMGkgSDy8vM4xGLSSJg5eXGQaDFowBXl4Cqqq04DjL0X1VlQ48z6DXW2MJHh4iDAZrLMFg0EKjkWAw3Lnbidqk3jYVFxcjKChIFV3uLhf0xx57DF988QV++uknHD58GJcuXXKpoAPAkCFDEBcXJw+Ke+edd3DgwAGbaQoKCtCyZUvwPI+KigqMGjUKM2fOxIwZM+q0DDV9b9KYBEHAoUOH0KdPH7ovsRMa5jv0htEQ36FT/hBnKZU7aqoFLne56/V6AMDo0aMRGhqKHTt2uLxSGzZswIYNGxATE4OVK1ciISEBADBr1ixs27YNALB582bcc8896NatG+69916MGDECTzzxhMvLJvXD8zzCw8PB84qeAUnuEJQ/xFmUO/ZcPkJPTU3FoEGD5N+///57PPTQQy6vWENT014Zabru9CN0QhqbmmqBy7s21Ys5APTo0cPVWZImRBAEpKamqmqkKLl9KH+Isyh37CneV/H2228rPUvixnieR7t27ajbiziF8oc4i3LHnstd7lFRUejYsSMAy+llp06dcnjOuLtRUzcLabqoy52QxqWmWuDyrs2IESOQnJyM5ORk/PLLL3jwwQeVWC/SRAiCgJSUFOr2Ik6h/CHOotyx5/IRemlpKQICAhRandtHTXtljUmSJBQWFqJFixbU9eWEO/0InfKHOEup3FFTLXD5xM/qxTw3N1fubo+MjERkZKSrsydujud5hISENPZqkCaK8oc4i3LHniK7xCdPnkS/fv3Qt29fLFq0CM8//zz69u2Lfv36yZfgI+pkNpvx888/y9dgJqQ+KH+Isyh37Clyaaa4uDi88MILmDRpks3zmzZtwvTp0+Vr7xL10Wg06N27NzQaTWOvCmmCKH+Isyh37ClyhF5SUmJXzAFg8uTJKCsrU2IRxE3xPI/mzZvT95/EKZQ/xFmUO/YUeSdatGiBL774Qr4dHWAZsLBx40YEBQUpsQjipsxmM3bs2EHdXsQplD/EWZQ79lwe5Q4A2dnZmDt3LtLT0xEWFgaO45Cfn48ePXpg/fr1iImJUWJdFaWmkY2NiTGGiooK+Pr62t32ltxaU3rLGmKUO+UPcZZSuaOmWqBIQbe6evWqfI/ZiIgIBAcHKzVrxalpI5KmqynVMLqwDFEjNdUCRb98CA4ORs+ePdGzZ0+3LuZEOWazGVu3bqVurztARUWF4vOk/CHOotyx1+CjCdyxu50oR6vV4oEHHqB7WatePPz9/REfH6/oXCl/iLMod+wp8k4cP3681teuXbumxCKIG6N/KLWLB7AAjAELFiwAAMyfP1+xuVP+EGdR7thS5N3o0qULoqOj4ejr+MLCQiUWQdyUIAhISkpCbGwsdDpdY68OUZylmFenZFGn/CHOotyxp8iguDZt2uA///kPwsLC7F6LiIiQB8rVVVZWFqZPn47CwkIEBATgs88+Q6dOnWymSUlJwcsvv4yKigrwPI/x48dj2bJldR7tqKaBEI2JMQZBEKDVammUshPc+y2rAOAPwP4jguM4lJWVwdfX16UlUP4QZymVO2qqBYp8hz5u3DicPXvW4Wvjx4+v9/zmzp2LOXPm4PTp01i8eDFmzpxpN01gYCASExNx/PhxHDlyBHv37kViYmK9l0VcR3c7UitfABMcvjJhwgSXi7kV5Q9xFuWOLUVPW1PClStXEBMTg8LCQmi1WjDGEBoaigMHDiA6OrrWv5s3bx5atWqFv/3tb3Vajpr2yhqT2Wymbi8XNMRBKYMyM60AcLP/jHJYSr4rzF5eSEpMpPwh9abUZ4+aaoHbXTMvLy8PYWFh8mAHjuMQGRkp38XNkUuXLmHTpk2IjY2tdRqj0Yjy8nKbBwCIoij/dBQLgmATW6+GV1tsNpttYuv+kjVmjNnFAGxiSZJsYuteaG2xKIo28e1sk1arxbhx4+Q2qKFNt3s76XQidDpL7OEhQqu1xoIc6/UCtFpJjjUaS+zpKYDnrbFZjs1eXmA3Lolp9vIC4zgwawyAcRzMXl6WNvG8HEs8D7OnJwCgnOfheSPWaDTQ6/UALAOR9Ho9KgCIWi0EDw9Lm7RaiNZYp4N440NW9PCAeOP/Wage6/XQmM0YP348OI5z++2kxtxrym3ieR4PPvggdDqdy236/+zde1yUZf4//td9mAPKSfCQIEgqeIjwUIZbaWppSkeV2kq33Dy1nXDbT37ttGqfLZWt1NY2++y61nZw62fZQamoqNBIsZUiEjdUkAFJRYQZlZm5D9fvj2HuZTgozIEZbt7Px2MevpkZ7/u6uN/M+76vue771ouQK+gAWn0fcr5BBKvViptuugnLli3DuHHj2n3f6tWrERUVpT0SEhIAACUlJQCA0tJS7c5wxcXFKCsrAwAUFRWhvLwcAFBYWKjNBygoKEBNTQ0AID8/X5v8l5eXh/r6egBAbm6udu5uTk4O7Ha7NpFDlmXY7Xbk5OQAcJ3jm5ubC8B1j/m8vDwArkmF+fn5AICamhoUFBQAcO34uG96U15ejqKiIgCu+QfFxcVd1qfGxkacPn1aV33q6u00b14p5s1z9WnRomJkZrr6lJVVhIwMV5+WLy/E5MmuPq1aVYD0dFefsrPzkZbm6tPGjXlITm7q0+bNsMXHu/q0dSvsMTGQm46G5bAw2GNikNP0FZUtPh65mze7+pScjLyNGwEAhrQ0bMrOBgCkp6dj1apVAIDJkyfjheXLEQegPCMDRU2T48oyM1G8aJFrO82bh9J581zbadEilGVmurZTVhbKm3a8C5cvR+XUqbBard1iO+kx97p7nwoLC8EY86lPe/fuhW6wEHP8+HEWGRnJJElijDGmqiobMGAAKy8vb/Veq9XKfvWrX7Gnn376gsu12+2soaFBe1gsFgaA1dXVMcYYk2WZybLcKpYkySNWFOW8sdPp9IhVVfWIVVVtFbv76Y4VRfGI3b+L9mJZlj3itvoRqD45HA720UcfsbNnz+qmT125nQDGDAaZGQyu2GiUmSi6Y0mLTSaJiaKixYLgis1mifG8O3YynlcYA5gzLIypPP/fmOOY6o4BpnIcc4aFMQYwlee1WOF55jSbtVgym9ksgAmCwEwmEwPA5ogik0wmxgAmiyKTjEYtlt2xwcBkg8EVG41MFkXGACY1j00mZg8PZzt27GDnzp0L6e2kx9zr7n1qbGxkH330EXM6nT716dSpUwwAa2hoYN1dyH2HDriOAObPn4/58+dj27ZteO6557Bnzx6P95w5cwbXX389pk+fjhUrVnR6HXr63oR0X6H8HXpzswFsBzALwHv+XnjofQSRHkRPtSAkh9xfeeUVvPLKK0hJScGaNWuwuWk4cOHChfjwww8BABs2bEBhYSG2b9+OMWPGYMyYMXjmmWeC2eweSVVV1NXVedxpj+jPewCq4f9irvI85Q/xCn32tBaSR+hdQU97ZcEkSRLy8vIwdepUmqXshe5yhB4oktmMvPffp/whneavzx491QIq6DrYiKT76ukFHQANuZOg0lMtCMkhd9J9qKqKEydO0LAX8YrK85Q/xCv02dMaFXTiE1VVUVJSQn9UxCuq0Uj5Q7xCnz2t0ZC7DoZZSPdFQ+6gIfcQY7PZ/HZZ3+5AT7WAjtCJT1RVRXV1Ne0lE6+ogkD5E0I2bAjMfe8DgT57WqOCTnyiqioOHz5Mf1TEK6ooUv6EiA0bNmDp0qVgjGHp0qUhX9Tps6c1GnLXwTAL6b5oyB005B4C3MW8pfXr1/vlvvehTE+1gI7QiU9UVcXRo0dpL5l4RRVFyp8gs9lsbRZzAFi6dKl2XfZQQ589rVFBJz6h77GIL+g7dOIt+uxpjYbcdTDMQrovGnIHDbn7wD/5Y0Pg73wfuptZT7WAjtCJTxRFwaFDh7T7DBPSGYooUv4EXQRct91pyyz4o5gHAn32tEYFnfiEMYbTp0+f9571hLSH8TzlT9DZALzfzmvvN73uu2PHjvllOW702dMaFXTiE1EUMX78eIiiGOymkG5IdDopf4IuAsC6dl5bB/8coc9GfHw8Zs+e7YdludBnT2tU0IlPFEXBwYMHadiLeEURRcqfkJAFYH2L59Y3Pe+r2QC2AwC2b9/ut6JOnz2tUUEPUaF6qkhbGhsbg90E0l3xPOVPyHAXdQ6BKOZu/izqlDueaJZ7CM5sdF/kwd8Xdehp12juDmiWO0J3+nM3EIj8sYLzyyD7MQDx53m9GkCcryvxQ+6Eci3orJA8Qi8rK8OVV16JlJQUXHHFFThw4ECb79u8eTOSk5MxdOhQLF68GLIsd3FL/a/5FZv8efnFDRs2IDIy0u+Xc1QUBSUlJTTsRbyiGAyUPyGmu+zyU+60FpIFfcmSJVi8eDF+/vlnLFu2DAsWLGj1nvLycjz11FPYvXs3Dh06hF9++QWbN28OQmv9p63LL/qjqAdqJ4EQQtoTh/OfDOfz0TlpjYWY48ePs6ioKCZJEmOMMVVV2YABA1h5ebnH+7Kzs9n999+v/bxz5052zTXXdHg9DQ0NDABraGjwR7N9ZrVaGYB2H1ar1avlrl+/vs3lrV+/3s89IN5wjRn69xGQhQbyQbzWHfJnVovPnlkhljuhVgt8EXLz/S0WC+Li4rRTETiOQ2JiIiorK5GUlKS9r7KyEoMHD9Z+TkpKQmVlZbvLdTgccDgcAFwp6z4n8vTp0wCgDdsIguARy7IMjuO0mOd58DyvxX368DCZZDidPBjjYTJJcDoFMMbDbJbgcIhgjIPZLMFud/XJbJZbxAZw3DGYzWbY7XZwHAej0QiHw6HFxyIjMYDnoYoiRKcTqiBAFQSITicUQQDjeYiSBEUQAJ6HIEmoF0X8T1P/DQYDVFWFoigwGAz4wx/+gMylSxFmNIJXFPCKAtloBC/L4FUVsskE3ukEzxgkkwmCOzabIToc4JpiTlVR8tlnGDFiBEwmEwBAlmUYDAYwxrS4+bpVVYWqqhBFsd1YURQwxrS4rW3Tme3UMpYkCYIgaLEoiuA4Tovd/WgeB6JPgABRVJrWIcBgUKCqgKIIMBhkqCoHRRFgNMpQFB6KwsNolCHLPFS17dyzMrTaTqLd7lpHi9hgt4NxHGSTCQa7HSrHQTEaYXA4oHIcVKMRosMBtZO5pzT93gRZhmIwAKoKQVEgGwzg3LHRCMbzOLBrF0aMGAGDwRCy2ylUcw8wgONUGI0qHA4RPK9CFFU4nSIEQYUguGMFPM8gSe4YkKS2c8+qoNV28uUzYovdDgnA52YzrrPbsQXAKT/knjMsDD99/TXS0tK0z3lvtlNdXZ1WF7q7kCvogKuIN9feL7r5+y60MVavXo1Vq1a1er75ToK3mvYTWsVNn5sdihlrHjOPnQ+Hw4ERAKCqgNPpepOiuB7ni5vNKZAkqVU8CPjv8lrGnenUNdeAeK/51I9mm8kj7sxmigJ8Sb7/Lqh57EPuXbBTkyaBeM/fmymq+Q+Afz4jmuIdAKKbP+9L7jU2ApMnw19sNhuioqL8trxgCLmCnpCQgKqqKm0PlTEGi8WCxMREj/clJiaioqJC+/no0aOt3tPcY489hkceeQSAq0harVZIkoTY2NhWOxCk46xWKxISEmCxWLr9DFHS9Sh/iLf8lTuMMdhsNsTFdf9v9UOuoPfv3x9jx47FG2+8gfnz5+Pdd99FUlJSqyPpOXPm4Oqrr8Yf//hH9O/fH5s2bcIdd9zR7nJNJpM2JAyg2++JhZrIyEj6QCZeo/wh3vJH7uilHoTkLPdXXnkFr7zyClJSUrBmzRpt9vrChQvx4YcfAgCGDBmCVatW4aqrrsLQoUPRv3//NmfDE0IIIT1Bj72wDPEPPV2UgXQ9yh/iLcqd1kLyCJ10HyaTCStWrPD4OoOQjqL8Id6i3GmNjtAJIYQQHaAjdEIIIUQHqKATQgghOkAFnRBCCNEBKuiEEEKIDlBBJ4QQQnSACjohhBCiA1TQCSGEEB2ggk4IIYToABV0QgghRAeooBNCCCE6QAWdEEII0YGQux96V2CMwWq1wmazISIiAhzHBbtJhBBCgoAxBpvNhri4OPB89z7G7ZEF3WazITo6OtjNIIQQEiIsFgsGDRoU7Gb4pEcW9IiICFgsFiQkJMBisdC9dH0gyzL27t2L9PR0iGKPTCfiA8of4i1/5Y7VakVCQgIiIiL82Lrg6JF/QRzHaUU8MjKSCroPVFVFWloaoqOju/1wFel6lD/EW/7OHT189dojCzrxH57nER8fH+xmkG6K8od4i3KntZDcJZ4+fTrS0tIwZswYTJw4Ed9//32b79u8eTOSk5MxdOhQLF68GLIsd21DCWRZRl5eHv3uiVcof4i3KHdaC8mC/s4776C4uBjff/89/vCHP+Dee+9t9Z7y8nI89dRT2L17Nw4dOoRffvkFmzdvDkJrezae55GamkrDpcQrlD/EW5Q7rYXkb6L5DPSGhoY2N9i2bdswa9YsDBgwABzH4b777sPWrVu7sJUEcP1R9e/fn/6oiFcof4i3KHdaC9nfxN13342EhAQ8+eSTeO2111q9XllZicGDB2s/JyUlobKyst3lORwOWK1WjwcAKIqi/dtWLMuyR6yq6nljSZI8YsaYR8wYaxUD8IhVVfWI3UNK7cWKonjEXdknp9OJTz75BOfOndNNn/S4nUK1Tw6HA59++ikaGxt10yc9bqdQ7JPdbscnn3wCSZJ87pNehGxB/+c//wmLxYI//elPePTRR9t8T/NZie4Eas/q1asRFRWlPRISEgAAJSUlAIDS0lKUlpYCAIqLi1FWVgYAKCoqQnl5OQCgsLAQFosFAFBQUICamhoAQH5+PmprawEAeXl5qK+vBwDk5ubCZrMBAHJycmC32yHLMnJyciDLMux2O3JycgC4zo3Pzc0FANTX1yMvLw8AUFtbi/z8fABATU0NCgoKALjOmSwsLATg+vqhqKgIAFBWVobi4uIu65MkSRg3bhw+++wz3fRJj9spVPt07NgxjB8/Hvv27dNNn/S4nUKxTxaLBRERERAEwac+7d27F3rBsQtVwhAQFhaGqqoqxMbGas/9+c9/RkVFBV566SUArsTJzs7GV1991eYyHA4HHA6H9rP73MO6ujr06dNH23MTBMEjlmUZHMdpMc/z4Hm+3ViSJAiCoMWiKILjOC0GXHuEzWODwQDGmBarqgpFUbRYVVWIothurCgKGGNa3FY/qE/UJ+oT9Yn61LpPdXV1iI2NRUNDQ/c/hZmFmIaGBlZdXa39/N5777H4+HimqqrH+w4fPswGDhzIfvnlF6aqKrvpppvYyy+/3Kn1AGANDQ1+a3tP5HQ62Y4dO5jT6Qx2U0LO6NGj2ZYtWxhjjL3xxhvsV7/6VXAbFIIof4i3/JU7eqoFITfk3tDQgFtvvRWXXnopRo8ejZdeegk7duwAx3FYuHAhPvzwQwDAkCFDsGrVKlx11VUYOnQo+vfvjwULFgS59T2PKIqYOHGiLq/yNXnyZKxfv94vy5o7d642bBgIkiRh1apVGDp0KMLCwpCQkIDf//73OHPmTMDW6Yu9e/diypQp6N+/P+bOnYvLLrsMr776qs/L/eqrr+iyzj2Enj97vBVyv4mEhATtO5KW/v73v3v8vGjRIixatKgrmkXa0fyqeyR47rrrLpSVleGdd97BmDFjcPjwYdx3332YPn06vv76axgMhmA3UWOz2TBjxgysXr1a+/70+++/x8mTJ4PcMpfmQ8MkdNFnT2shd4ROuhdJkvDBBx9os1P1yn3k9/e//x0JCQmIjY3FsmXLPN6zceNG7bUnnnjC47VXX30VY8aM0X5+4YUXkJycjIiICAwdOhQbN27UXquoqADHcXj99dcxbNgwREdHY/78+e3+jr/66it8+OGH2L59Oy677DIIgoCUlBRs374dP//8M958803tvZ999hnS09MRHR2NgQMHYvXq1dprn3/+Oa644gpER0fjkksu0UbDANdEp8svvxxRUVEYOHAg7r//fjQ2NmqvJyUlITs7GxMmTEBERASuueYabdJRS//5z39w9uxZLF68GIBr/suYMWOQkZGhvefEiROYO3cu4uLiEBcXh6VLl3rMgfn3v/+NqVOnIiYmBv369cNDDz2EU6dOYebMmWhoaEB4eDjCw8Oxa9cuAMAbb7yBkSNHIjo6GldffbU2QQxwjcQsW7YM06dPR+/evfHxxx+32W4SWnrKZ0+nBHvMP1j09L1JMKmqys6dO9dqjoMeXHPNNWzdunWMMca+/PJLxvM8e/jhh1ljYyM7cOAA69WrF/vyyy8ZY4x98cUXLDIykhUUFDCHw8Eef/xxJgiC9h36li1b2OjRo7Vlb9u2jVVWVjJVVVleXh4zm81s9+7djDHGysvLGQD261//WptTEh8fry2rpeXLl7OJEye2+dq8efPYnXfeyRhjbP/+/SwsLIxt27aNOZ1OVl9fz7799lvGGGM//PADi46OZl988QVTFIXt2rWLRUZGsoMHDzLGGMvPz2f79+9nsiyzw4cPsxEjRrA//elP2noGDx7MLrnkEnb48GHW2NjIZs6cye65554222S1Wlm/fv3YbbfdxrZv386OHDnikT+qqrL09HT2yCOPsLNnz7La2lo2efJk9uSTTzLGGKuqqmKRkZHspZdeYo2Njezs2bMsPz9f205RUVEe68vPz2fh4eHs66+/Zk6nk61bt47169eP1dfXa9u5X79+bO/evVo+k9Dnr88ePdUCOkInPuspw5OMMaxevRpmsxkjR47ElVdeiX//+98AgDfffBNz587Fr371KxiNRqxcuRK9e/dud1lz5sxBQkICOI7DlClTcP3117c6Q2PlypWIjIxEXFwcZs6cqa2rpdraWsTFxbX5WlxcnDaU/X//93+44447MGfOHBgMBkRFRWHChAkAgFdeeQXz58/H1KlTwfM8rr76atx444145513AAATJ07E2LFjIQgChgwZgiVLlrRq74MPPoghQ4bAbDZj7ty57bY3IiICBQUFiImJwR/+8AcMHToUEyZMwP79+wEA3333HcrKyvDnP/8ZvXr1QmxsLB5//HG89dZbAFxH25dddhnuv/9+mM1m9OrVCxMnTmz3d/3Pf/4T8+bNw6RJk2AwGLB06VL06dMHO3fu1N5z11134YorrgDHcQgLC2t3WSS09JTPno6igk580vz8Ur2LjIxEr169tJ979+6tnW977NgxjwsdGQwGDBw4sN1lvfnmmxg3bhz69OmD6Oho5OTkaOf0ul100UVtrqulvn374tixY22+duzYMfTr1w8AcPToUSQnJ7f5voqKCmzatAnR0dHa44MPPtCWu2/fPlx33XUYMGAAIiMj8fjjj3vdXgAYNmwYNm3ahIMHD2Lz5s0YMmQIbr75ZjDGUFFRgfr6esTExGhtyczMxPHjxy/Yj7ZUVVUhKSnJ47mLL74YVVVV2s+JiYkdXh4JDT3ps6ejqKATn4iiiIyMjB6/pxwXF4ejR49qP0uSpF2Ao6XKykrcc889yM7OxsmTJ1FfX4+MjIwLXhypPdOmTcPevXu1i2a4Wa1WfPzxx5g2bRoAYPDgwTh06FCby0hISEBWVhbq6+u1x5kzZ/Dyyy8DAO68805MmTIFR44cgdVqxbPPPut1e5sTRRHz5s3DY489hurqatTV1SEhIQH9+/f3aEtDQ4M2Y/98/WjrMqCDBg1CRUWFx3MVFRUYNGjQef8fCW302dMaZTHxGe0huwrem2++ib1798LpdOLpp5/G2bNn23zvmTNnwBjTrkOdk5Ojzfb2xtSpU5GRkYFZs2Zh//79UBQFP//8M2bNmoWhQ4di7ty5AFxnhWzduhXbt2+HLMtoaGjAnj17AABLlizBli1b8OWXX0JRFDgcDnz77bfaFbesViuio6PRu3dvlJaWaoXeGwcPHsTatWtRUVEBVVVRW1uLjRs3IiUlBbGxsRg/fjwSExPx5JNPwmazgTGGo0ePapPV5s6di8LCQmzatAkOhwPnzp3TJr8NGDAANpvNY8b8vHnz8Oabb+Kbb76BLMv4y1/+glOnTnlMwiPdE332eKKCTnwiyzJyc3N7/B/Wddddh//93//FnDlzMHDgQKiqitTU1DbfO2rUKDzxxBOYOnUqYmNj8fbbb+Pmm2/2af1vv/02brnlFmRmZqJ3796YMmUKUlNT8dlnn8FoNAIAxo0bh3fffRfPPPMMYmJiMHLkSHz99dcAgLFjx2Lr1q148skn0a9fP8THx+Opp57SZpa/8soreO655xAeHo777rsPd9xxh9dtjYiIQFFRESZOnIioqCiMHDkSJ06cwEcffQTAdVWvjz76CNXV1Rg5ciSioqJwww03aEflgwYNwueff4633noLAwYMQFJSErZt2wYAGD58OBYsWKDNaN+9ezeuueYa/OUvf8GCBQsQGxuLf/3rX/j444/pfPV2tPf1Taihz57WusWlXwPBarUiKipKH5f7I4QQP5g9eza2b9+OWbNm4b333gt2c7qEnmoBHaETnzDGYLVa/fJ9Kul5KH9Ch7uYA8D27dsxe/bsILfo/Ch3WqOCTnwiyzJ27dpFw17EK5Q/oaF5MXcL9aJOudMaDbnrYJiFEEK8dezYMcTHx7f7enV1dbvXOdADPdUCOkInPlFVFXV1dVBVNdhNId0Q5Y9vOM73x3lqOQDX6/5Yj79R7rRGBZ34RFEU7Nu3T7vvMCGdQfkTCuIAzGrntVlNr4ceyp3WaMhdB8MshJCeyb9HvrMBNP8efRYA/810D9VKo6da4Ncj9B07dvi8DLvdjltvvRUpKSkYM2YMZsyY0eoqTwCQl5eH9PR0jBo1CqmpqXjiiSdotmMQqKqKEydO0LAX8QrlTyh5D/89UvdvMQ8Eyp3WfC7o06ZNw/Tp0zFt2jQ88MADmD59us+NWrx4Mf7zn//g+++/x4033qjdZrG5Pn36YOvWrThw4AC+++47fP3119i6davP6yado6oqSkpK6I+KeIXyJ9S8B6AaoV7MAcqdtvhc0CdMmID7778fn332GWbPnu3TJSwBwGw2IyMjA1zTWNKECRNw5MiRVu8bO3YshgwZov2fMWPGtPk+EliiKGLq1Kl0PWXiFcqfUBSa35m3RLnTms8F/X//938hyzIef/xxOJ1Of7TJw4svvoibbrrpvO/55ZdfsG3btvNem9nhcMBqtXo8AGgTKhRFaTOWZdkjdu8NthdLkuQRu78GcMeMsVYxAI9YVVWP2H2eZXuxoigecVf2SVEUVFVVweFw6KZPetxOodonWZZRXV0Np9Opmz519XbieRVms6u9gqDCZHLFotg8VmA0No9d7TUYFBgMrthoVCCK7ljWYpNJhiiqWiwIrthslsHz7ljS4rAwCTzPtJjjGAD/bydJklBZWQlVVX3eTnrhl+/QMzMzce+992L48OH+WJzm2WefRVlZGZ555pl232O1WnHTTTdh2bJlGDduXLvvW716NaKiorRHQkICAKCkpAQAUFpaqt2Iori4GGVlZQCAoqIi7S5WhYWFsFgsAICCggLtblr5+fnarSTz8vJQX18PAMjNzdVuIZmTkwO73e5xyz+73Y6cnBwAgM1m00Y36uvrkZeXB8B1r+v8/HwAQE1NDQoKCgAAFosFhYWFAIDy8nIUFRUBAMrKylBcXNxlfTp37hwOHz6MTz75RDd90uN2CtU+VVZW4vDhw/j2229106eu3k5pabXIznb1KT29BqtWufo0ebIFy5e7+pSRUY6sLFefMjPLsGiRq0/z5pVi3jxXnxYtKkZmpqtPWVlFyMhw9Wn58kJMnuzq06pVBUhPd/UpOzsfaWmuPm3cmIfkZFefNm/ORXy8q09bt+YgJsaOsLDAbCf3kLsv22nv3r3Qi5Cd5f7cc8/hX//6Fz7//PN2b6Jgs9lw/fXXY+bMmXjqqafOuzyHw6HdaAJw7QgkJCSgrq4Offr00fbcBEHwiGVZBsdxWszzPHiebzeWJAmCIGixKIrgOE6LAdceYfPYYDCAMabF7j1Od6yqKkRRbDdWFAWMMS1uqx/UJ+oT9Ul/fTIaDeB5FUajCrtdhCCoEEUVDocIUVQhCO5YAc8zOJ3uGHA6Be3oXJIEGI0KVBWQZQFGowxV5SDLAkwmGYrCQ5Z5mEwyZJmHovAwm2U4nTxUlYfZLMHpFKCqPMLCJDgcIlSVQ1iYBLtdBGOA0xma26murg6xsbG6mOXu14JeWlqKZ555BkeOHPEYxnDvUXXUCy+8gDfffBOff/45+vTp0+Z7zpw5g+uvvx7Tp0/HihUrOt1WPZ2qEEyqqsJisSAhIYHuKU06jfLHN4G4YEug+PvQ0V+5o6da4NfZBLfffjvuvvtu3HvvvRAEwatlVFVV4Q9/+AOGDBmCKVOmAABMJhP27t2LhQsX4uabb8bNN9+MDRs2oLCwEGfPntWuQXzbbbfhiSee8Ft/yIWpqorq6mrEx8fTBzLpNMof4i3Kndb8eoQ+btw47N+/31+LCyg97ZURQnqmnnyE7i96qgV+3a2ZMWMGPvnkE38ukoQ4RVFw6NAhuvwi8QrlD/EW5U5rfi3o1157LTIzMxEVFYX+/fujX79+6N+/vz9XQUIMYwynT5+mq/QRr1D+EG9R7rTm1yH3YcOGYc2aNRg3bpzHd+iDBw/21yr8Rk/DLISQnomG3H2np1rg10lxsbGxyMzM9OciSYhTFAVlZWVITk72eiIk6bkof4i3KHda8+uQ+6xZs7Bp0ybU1dXh3Llz2oPoW2NjY7CbQLoxyh/iLcodT34dcm9+6gDHcWCMgeO4kJy0oKdhFkJIz0RD7r7TUy3w6xG6+yo+7iv+uP8l+qUoCkpKSmg7E69Q/hBvUe605teCbrfbWz138uRJf66CEEIIIW3wa0G/8847PX6ur6/HjBkz/LkKEmIEQUBqaipNSiFeofwh3qLcac2vBX348OHIysoC4LrWekZGBn73u9/5cxUkxCiKgqKiIhr2Il6h/Ok5fv75Z78uj3KnNb8W9DVr1uD48eNYu3YtbrnlFtx+++1YuHChP1dBQlBYWFiwm0C6McqfniANw4cPR1paml+XSrnjyS+z3JufmtbY2IiZM2fi2muv1W5p2qtXL19X4Xd6mtlICOmZuscs9zQAP2o/XXrppdq9y0OBnmqBX47Qw8PDERERgfDwcPTv3x/fffcd1q5dqz1P9EuWZezbt8/jdrmEdBTlj955FnMA+PHHH/1ypE6505pfrhSnqqo/FkO6IY7j0KdPH3Dd41CBhBjKn9DD4J9t8TOA4e289uOPP+JnjkOKD8vnRBF9Skspd5rxyxH62bNntfjUqVM+Levhhx9GUlISOI5DSUlJm+/56quv0KtXL4wZM0Z70BWDgkMQBAwbNoxmmhKvUP7o10AfX78QQZYpd1rwuaA/9NBDuOuuu/DYY48BgPa9ubcyMzOxe/fuC97QZdSoUfj++++1B02OCA5ZllFQUEDDXsQrlD/6FQFgfTuvrW963ReyyUS504LPQ+719fX44IMP8PHHH+Ppp5/2uUGTJk3yeRmk6/A8j/j4eI/L/hLSUZQ/+pbV9O/SZs+tb/a8L3hFodxpweffhMlkAgDMnDkTAwcOxM6dO31uVEf85z//wbhx4zB+/Hj89a9/veD7HQ4HrFarxwOAdg6joihtxrIse8Tu+QLtxZIkecTukwjcMWOsVQzAI1ZV1SN274G2FyuK4hF3ZZ84jkNiYiIURdFNn/S4nUK1T4Dr9sruS0broU9dvZ14XoXZ7GqvIKgwmVyxKDaPFRiNzWNXew0GBQaDKzYaFYhiUz+MRiii63hPNpmgNo+bhrhlsxlqUzGVmsdhYWDN4oc5DuvhOsVsPYCHOQ5S04gq43ktVnkektmsxbI7FgTITXVGFUUtZoBW0H3dTnrhc0G/++67tXjRokVYt26dr4u8oHHjxqGqqgr79+/H9u3bsWnTJrzzzjvn/T+rV69GVFSU9khISAAA7Xv60tJSlJaWAgCKi4tRVlYGACgqKkJ5eTkAoLCwEBaLBQBQUFCAmpoaAEB+fj5qa2sBAHl5eaivrwcA5ObmwmazAQBycnJgt9shyzJycnIgyzLsdjtycnIAADabDbm5uQBcox55eXkAgNraWuTn5wMAampqUFBQAACwWCwoLCwEAJSXl6OoqAgAUFZWpp0S0hV9OnPmDPLz83XVJz1up1DtU0VFBfLz8/HNN9/opk9dvZ3S0mqRne3qU3p6DVatcvVp8mQLli939SkjoxxZWa4+ZWaWYdEiV5/mzSvFvHmuPi1aVIzMzKY+ZWWhPCPD1afly2GZPNnVp1WrUJOe7upTdjZqm2ar523ciPrkZFefNm+GLT7e1aetW2GPicH9YWHYunUr7g8Lgz0mBjlbt7r6FB+P3M2bXX1KTkbexo2u7ZSWhvzsbNd2Sk9HwapVru00eTIKly8HABy++Wbk5uZClmWfttPevXuhF3692xrg+mO4+OKLfV5OUlISduzYgdTU1Au+d/Xq1Th27Bj+8pe/tPseh8MBh8Oh/Wy1WpGQkIC6ujr06dNH23MTBMEjlmUZHMdpMc/z4Hm+3ViSJAiCoMWiKILjOC0GXHuEzWODwQDGmBa7b2rjjlVVhSiK7cbuo2N33FY/AtUnnudRU1ODfv36wWg06qJPetxOodonADh+/Dj69esHURR10aeu3E5GowE8r8JoVGG3ixAEFaKowuEQIYoqBMEdK+B5BqfTHQNOp6AdnUuSAKNRgaoCkixCNhrBqSoEWYZsMoFXFPDuWJbBKwpksxm80wleVSGZzRDccVgYRIcDnDu22wHGIIeFQWxsBDgOstkMQ2MjGM9DNplgaGyEyvNQjEYY7HaoPA/VaIRot0MVBKiiCNHhgCqKUAUBosMByWRCzc8/Y9CgQdoohzfbqa6uDrGxsbo4D93vBf3+++/v0BD4hZyvoNfU1GDAgAHgeR42mw0zZszAggULcO+993Z4+Xq6mAAhpGcKxBlb/jptrUv4oXzpqRb4POQ+ePBgTJ8+HdOnT8e0adOwY8cOn5b3wAMPYNCgQaiqqsJ1112HYcOGAQAWLlyIDz/8EADw7rvv4tJLL8Xo0aMxYcIETJs2Db/97W997QrxgizLyMvL09X3UKTrUP4Qb8lmM+VOCz4foS9cuBB///vftZ9/97vf4eWXX/a5YYGmp72yYFJVFbW1tejbty/NNiWdRvnjm558hK7yPGpranzOHT3VAp8Len19PaKjo/3UnK6jp41ICOmZenJBB0BD7i34vEvcvJhXVlZi9+7d2L17NyorK31dNOkGJEnCp59+qp1CQ0hnUP4Qb0lmM+VOC365lvvBgwdx7733ory8HImJiWCMwWKx4OKLL8bmzZsxcuRIf6yGhCBBEDB+/Hi6/CLxCuUP8ZbgdFLutOCXgj5//nw8+uijmDNnjsfz27Ztwz333KOd30n0h+d5xMTEBLsZpJui/CHe4lWVcqcFv8xCOX36dKtiDriuy97Q0OCPVZAQJUkSdu7cScNexCuUP8RbUlgY5U4Lfinoffv2xeuvv+5xG1VVVfHaa68hNjbWH6sgIUoURUycOFG7CAYhnUH5Q7wlOhyUOy345cIyhw4dwpIlS1BUVIS4uDhwHIeqqiqMHTsWmzZtQkqKL3e9DQw9zWwkhPRMNMudZrk355ddm2HDhuGLL77AyZMntevjJiQkoF+/fv5YPAlhkiQhJycHGRkZMBgMwW4O6WYof4i3pLAw5HzwAeVOM36/9Gt3oae9smBijMFut8NsNoMLxOEC0TXKH9/05CN0xnGwnz3rc+7oqRYE/NJMoTjcTvyLvsMivqD8IV5puoEO+S+//DYOHDjQ7mtnzpzxxypIiHLf6pGGvYg3KH+It+SwMMqdFvwy5M7zPJKSktDWoqqrq+F0On1dhd/paZglmNy3dHTfApKQzqD88U2PHnIHIDudPueOnmqBX47QBw8ejN27dyMuLq7VawkJCf5YBQlhze/dTEhnUf4Qr3Ac5U4LfvkO/eabb8aRI0fafO2WW27xxypIiJJlGbm5uXQLQ+IVyh/iLdlsptxpISRnuZeVleGee+5BbW0toqOj8eqrr2LUqFEe72GMYdmyZcjJyYEgCIiNjcXf/vY37f7pF6KnYRZCSM/Uk4fcAdB56C2E5A2IlyxZgsWLF+Pnn3/GsmXLsGDBglbv+fDDD5Gfn4/vv/8excXFuPbaa/H4448HobU9G2MMVqu1zfkThFwI5Q/xFuN5yp0WQq6gnzhxAvv378e8efMAAHPmzEF5eTkqKipavdfhcMBut2sfCoMGDeri1hJZlrFr1y4a9iJeofwh3pJNJsqdFkKuoFssFsTFxWkTHTiOQ2JiYqv7q990002YMmUKLrroIgwcOBBffPEFnn766XaX63A4YLVaPR4AoCiK9m9bsSzLHrH7evXtxZIkecTuvUd3zBhrFQPwiFVV9YjdCdterCiKR9yVfRJFERkZGVof9NAnPW6nUO2TIAi44YYbwHGcbvrU1duJ51WYza72CoIKk8kVi2LzWIHR2Dx2tddgUGAwuGKjUYEoNvXDaITS9Bksm0xQm8dNtyuVzWaovKuESM3jsDCw5jHHgbljuC4II4WFufrE81qs8jwks1mLZXcsCJBNJlcsilrMSxKuv/56GAwGn7eTXoRcQQfQ6hSEtoZU9u/fj4MHD6K6uhrHjh3DtddeiwcffLDdZa5evRpRUVHawz37vqSkBABQWlqK0tJSAEBxcTHKysoAAEVFRSgvLwcAFBYWape2LSgoQE1NDQAgPz8ftbW1AIC8vDzU19cDAHJzc2Gz2QAAOTk5sNvt2nm3sizDbrcjJycHAGCz2ZCbmwsAqK+vR15eHgCgtrYW+fn5AICamhoUFBQAcO34uG9LW15ejqKiIgCu+QfFxcVd1qdz587h5MmTuuqTHrdTqPbp6NGjqKur01Wfuno7paXVIjvb1af09BqsWuXq0+TJFixf7upTRkY5srJcfcrMLMOiRa4+zZtXinnzXH1atKgYmZlNfcrKQnnTjnrh8uWwTJ7s6tOqVahJT3f1KTsbtWlprj5t3Ij65GRXnzZvhi0+3tWnrVthj4lxnTO+dSvksDDYY2KQs3Wrq0/x8cjdvNnVp+Rk5G3c6NpOaWnIz852baf0dBSsWuXaTpMno3D5cgDAkRtuwN69e6Gqqk/bae/evdANFmKOHz/OIiMjmSRJjDHGVFVlAwYMYOXl5R7ve+CBB9jatWu1n0tKSlhiYmK7y7Xb7ayhoUF7WCwWBoDV1dUxxhiTZZnJstwqliTJI1YU5byx0+n0iFVV9YhVVW0Vu/vpjhVF8Yjdv4v2YlmWPeK2+hGoPjkcDvbxxx+zs2fP6qZPetxOodonu93OPvnkE3bu3Dnd9KkrtxPAGM8rzGyWGMCYICjMZHLFotg8lpnR2DyWGcCYwSAzg8EVG40yE0WZMYBJRiOTRdEVm0xMaR4Lgis2m5nC84wBzNk8DgtjavOY45jqjgGmchxzhoUxBjCV57VY4XnmNJu1WHLHgsAkk8kVi6IWN4aHs48//pg5nU6fttOpU6cYANbQ0MC6u5Cc5T558mTMnz8f8+fPx7Zt2/Dcc89hz549Hu954YUX8Omnn2LHjh0wGAxYs2YNdu3ahZ07d3ZoHaE+s9FmsyEiIiLYzSCEhDCa5U6z3JsLySH3V155Ba+88gpSUlKwZs0abG4aklm4cCE+/PBDAMADDzyAxMREXHrppUhLS8OXX36Jl156KZjN9psNGzYgKioKGzZsCHZTLkhVVZw4cUL7PoqQzqD8Id5SeZ5yp4WQPELvCqG6V7ZhwwYsXbpU+3n9+vXIysryy7IDcdQvyzLy8/MxadIkumIT6TTKH9/05CN02WxG/s6dPudOqNYCb4TkEXpP1bKYA8DSpUv9cqQeqKN+URQxdepU+jAmXqH8Id4S7XbKnRboCD1E9spsNhuioqLanNHPcRwaGhq8ProO5FG/qqqoqanBwIEDwfO0f0g6h/LHNz35CF0VBNQcPepz7oRaLfAF/QWFiIiICKxbt67N19atW+e3Yg7476gfcH0gHz58mL7HIl6h/CHeUkWRcqcFOkIPsb2y2bNnY/v27drPs2bNwnvvvefVsgJ51E8ICb6efIQOgGa5t0BH6CFkw4YNHsUcALZv3+710XSgjvqbU1UVR48epb1k4hXKH+ItVRQpd1qggu4jjvPXw4alS3/f5jqWLv09OM7m1XKXLs0CMMtjebNmzfLrd+jV1dX0R0W8QvlDvKUKAuVOC1TQQ0YEgLaPpl3Pe3s0vQGA/476WxJFEVdeeSXNNCVeofwh3hIdDsqdFqigh5Q2jqYBMCwFA9fphxUcOCxtc02/X7oUNj8MLSgGAw4dOqTd9ICQzlAUhfKHeEURRcqdFqigh5Q2jqabnvVGoI75m2M8j9OnT9M9iYlXGGOUP8Qr9NnTGhX0kGED0PZ36L9vejUUiU4nxo8fT8NeIcR9967uQBRFyh/iFfrsaY0Kesho/3ja26Pp9ncR/LeToIgiDh48SMNeIWLDhg2IjIzsFvcBAFxD7pQ/xBv02dMaFfSQkgVgvccz65ue9UZXDLmD59HY2OiPJREfNb+IkD8vHuQWqCN/yh/iFfrsaYUKeshxFXUOvhVzz6V58sdy3QSnE2PHjoUgCH5aIvFGoK8IGKgjf0EQKH+IV+izp7WQLOhlZWW48sorkZKSgiuuuAIHDhxo832bN29GcnIyhg4disWLF0OW5S5uaaBkoQH+K7ruou6vnYTmFIMBJSUlNOwVRDabrVUxd1u6dKnPR9aBPPJXFIXyh3iFPntaC8mCvmTJEixevBg///wzli1bhgULFrR6T3l5OZ566ins3r0bhw4dwi+//KLdN10P/H1B1izArzsJpGcI9JE/IcR/Qu5a7idOnEBKSgpqa2shiiIYYxg4cCD27NmDpKQk7X1//vOfUVFRgZdeegkAkJOTg+zsbHz11VcdWo+/rt8biGspAz3veso9lX/yxwbgfDlshffTKttfrtVqpXsBBBldy52u5d5cyM33t1gsiIuL005F4DgOiYmJqKys9CjolZWVGDx4sPZzUlISKisr212uw+GAw+EA4Dr39dixYwCA06dPA4A2bCMIgkcsyzI4jtNinufB87wWAzxMJhlOJw/GeJhMEpxOAYzxMJslOBwiGONgNkuw2119MpvlFrEBHMdgMrljFaeMJhgcDqgcB9VohOhwQOV5qKII0emEKghQBQGi0wlFEMB4HqIkQREEgOchSBKUpt+hIMtQDAZAVSEoCmSDAZw7NhrBKwp4dyzL4FUVsskE3ukEzxgkkwmCOzabIToc4JpiTlVRkp+PESNGwGQyAQBkWYbBYABjTItVVYWiKFqsqipEUWw3VhQFjDEtbmvbdGY7tYwlSYIgCFosiiI4jtNidz+ax4HoEyBAFJWmdQgwGBSoKqAoAgwGGarKQVEEGI0yFIWHovAwGmXIMg9VdefeWTAGmEwmOJ1OMMZgNpvhcDjAGEOFuT+i7XbXOsxmiM1ig90OxnGQTSYY7HaoHAfFaITB4UADxyHZaILD4QDP8xBFEU6nE4IgQBAEWCMjIfuYe4zncSA3FyNGjIDBYAjZ7RSquQe4Pi+MRhUOhwieVyGKKpxOEYKgQhDcsQKeZ5AkdwxIUtu5Z1Xg188I0Yvc68jnnjMsDD99/TXS0tK0z3lvtlNdXZ1WF7q7kCvogKuIN9feL7r5+y60MVavXo1Vq1a1er75ToK3mvYTWsVNuduhmDHPuK97OYz9d6GqCjidrlhRXI/zxc3nFEhS27F7eS3jznTqmmtAvOe/zfTfH+zNtlNSZ5PPvZxmsaqqcDatWFEUKIqCQa4ffM+9SZNAvOfvj4io5j8A/vmMaC/uQO6126nGRmDyZPiL++6U3VnIFfSEhARUVVVpe6iMMVgsFiQmJnq8LzExERUVFdrPR48ebfWe5h577DE88sgjAFzF32q1QpIkxMbGttqBIB1ntVqRkJAAi8XS7YerSNej/CHe8lfuMMZgs9kQFxfnx9YFR8gV9P79+2Ps2LF44403MH/+fLz77rtISkpqdSQ9Z84cXH311fjjH/+I/v37Y9OmTbjjjjvaXa7JZNKGhAF0+z2xUBMZGUkfyMRrlD/EW/7IHb3Ug5Cc5f7KK6/glVdeQUpKCtasWaPNXl+4cCE+/PBDAMCQIUOwatUqXHXVVRg6dCj69+/f5mx4QgghpCcIuVnupHvR0wxR0vUof4i3KHdaC8kjdNJ9mEwmrFixwuPrDEI6ivKHeItypzU6QieEEEJ0gI7QCSGEEB2ggk4IIYToABV0QgghRAeooBNCCCE6QAWdEEII0QEq6IQQQogOUEEnhBBCdIAKOiGEEKIDVNAJIYQQHaCCTgghhOgAFXRCCCFEB6igE0IIITogBrsBwcAYg9Vqhc1mQ0REBDiOC3aTCCGEBAFjDDabDXFxceD57n2M2yMLus1mQ3R0dLCbQQghJERYLBYMGjQo2M3wSY8s6BEREbBYLEhISIDFYkFkZGSwm9RtybKMvXv3Ij09HaLYI9OJ+IDyh3jLX7ljtVqRkJCAiIgIP7YuOHrkXxDHcVoRj4yMpILuA1VVkZaWhujo6G4/XEW6HuUP8Za/c0cPX732yIJO/IfnecTHxwe7GaSbovwh3qLcaY12iYlPZFlGXl4eZFkOdlNIN0T5Q7xFudMaFXTiE57nkZqaSsOlxCuUP8RblDut0ZA78QnP8+jfv3+wm0G6Kcof4i3KndZCftfm4YcfRlJSEjiOQ0lJifb85MmTMWTIEIwZMwZjxozBunXrgtjKnkuSJHz66aeQJCnYTSHdEOUP8RblTmshf4SemZmJZcuW4eqrr2712osvvogbb7wxCK0iboIgYPz48RAEIdhNId0Q5Q/xFuVOayFf0CdNmhTsJpDz4HkeMTExwW4G6aYof4i3KHdaC/kh9/N59NFHcemll+LXv/41jhw5ct73OhwOWK1WjwcAKIqi/dtWLMuyR6yq6nljSZI8YsaYR8wYaxUD8IhVVfWI3bM424sVRfGIu7JPTqcTO3bswLlz53TTp2Btp5UrV+KWW24JaJ+eeuop3HrrrQHr05/+9CfceeedHd5ODocDO3fuRGNjY7fZThfqU3fMve7YJ7vdjh07dkCSJJ/7pBfdtqC//vrrKC0tRXFxMSZOnHjBoffVq1cjKipKeyQkJACA9r18aWkpSktLAQDFxcUoKysDABQVFaG8vBwAUFhYCIvFAgAoKChATU0NACA/Px+1tbUAgLy8PNTX1wMAcnNzYbPZAAA5OTmw2+2QZRk5OTmQZRl2ux05OTkAXJejzc3NBQDU19cjLy8PAFBbW4v8/HwAQE1NDQoKCgC4LlNYWFgIACgvL0dRUREAoKysDMXFxV3WJ1mWceWVV+Kzzz7TTZ/c2+lvf/sbbrzxRvTr1w9RUVEYMWIEnn76ab/0KTs7G6NGjWrVJ6vV2uk+/fWvfwXHcXjkkUc8+rRp0yaMGTPGo09lZWVQVdUv2+nVV19Famqqx3a64YYbsHXr1g5vp5qaGkycOBH79u3Dzp07MWXKFPTp0wd9+vRBWloali9f7vPf044dO9CnT59ulXt6+owIVJ+qqqoQExMDURR96tPevXuhG6ybGDx4MPvxxx/bfd1kMrHa2tp2X7fb7ayhoUF7WCwWBoDV1dUxxhiTZZnJstwqliTJI1YU5byx0+n0iFVV9YhVVW0VM8Y8YkVRPGJJks4by7LsEbfVD+pT5/s0dOhQ9vjjj7MzZ86wxsZGVlJSwt5++22/9Gnz5s1s9OjRHv1YsWIFu/nmmzvdp0mTJrGYmBg2btw4jz794x//YKNHj/bo05NPPsluueUWv2ynLVu2sNGjR/tlO9XV1bHo6Gj28ssvs3PnzjG73c4KCwvZBx984HPuffHFFywqKsqn3HO3m/6e9NenU6dOMQCsoaGBdXfdsqBLksR++eUX7bVt27axxMTETi2voaFBNxsxmJxOJ3v//fe1P0S9OHnyJAPAKisr233PL7/8wm677TbWt29flpCQwB5//HHtA8Zd7JobPXo027JlC9u/fz8zmUyM53nWu3dv1rt3b3b06FG2YsUKduONN7IHHniARUVFsYSEBPavf/3rvO0sKytjANj777/POI5j33//PWOMnXcdt9xyi/b/H330UZaYmMjCw8PZyJEj2TvvvKO99uWXX7KoqCj2t7/9jQ0aNIjFxMSwRx99tFPLr6mpYXPnzmUDBw5kUVFRbOLEiezcuXPa6+78+fbbb5nBYNA+ZNty/Phxdtddd7GBAweygQMHsqysLGa327XXv/vuOzZlyhTWp08f1rdvX/bggw+y2tpaZjabGQCtnfn5+Ywxxl5//XU2YsQIFhUVxa666iq2f/9+bVnXXHMNe/TRR9m0adNYr1692Icffnje7UC6nr8+e/RUC0K+oN9///0sPj6eCYLABgwYwIYOHcrOnDnDLrvsMpaamsrS0tLY1KlTtQ+yjtLTRgwmVVXZuXPntD1yvVBVlY0YMYJde+217O2332YVFRWt3jN16lR21113MZvNxioqKtioUaPYM888wxg7f0Fv7/UVK1Ywg8HA3nrrLSbLMnvttddYeHg4s1qt7bZz+fLlbOzYsYwxxiZNmsQeeugh7bX21tG84L7xxhvs+PHjTJZltnXrVmYymdiRI0cYY66CzvM8e/jhh1ljYyM7cOAA69WrF/vyyy87tHxFUdj48ePZPffcw+rq6pgkSWzXrl0eRdidPw0NDaxfv37stttuY++//z6rqanxWK6qqiw9PZ098sgj7OzZs6y2tpZNnjyZPfnkk4wxxqqqqlhkZCR76aWXWGNjIzt79qxWuN07Js3l5+ez8PBw9vXXXzOn08nWrVvH+vXrx+rr6xljroLer18/tnfvXq2NJLT467NHT7Ug5At6oOhpIwZT86E0vampqWGPPPIIGzVqFON5no0cOZLl5uYyxlwFBIBH4XnzzTdZcnIyY8z7gp6enq79rKoqMxqN7LvvvmuzfbIss4EDB7L169czxhj7+9//zmJiYrSC2ZGC3tLo0aPZG2+8wRhzFUKO49jZs2e116+77jr23HPPdWj5e/bsYb179z5vMWyeP2VlZWzJkiVsyJAhjOM4dsUVV7B///vfjDHGCgsLWUxMjMcRfG5uLhsyZAhjjLE1a9awKVOmtLmOtgr6woUL2X333efxXEpKCnvzzTcZY66CnpWV1W67SfD567NHT7Wg206KI6Gh+WQXvbnooovw/PPP46effsLJkycxc+ZMzJo1C3V1daiqqoLZbMZFF12kvX/IkCGoqqryeZ1uHMchLCxMm2DUUk5ODmpra3HXXXcBAG677TY0NjZi+/btHV7funXrcMkllyAqKgrR0dEoKSnRJjoBrrsR9urVS/u5d+/e7banpaNHjyI+Ph5hYWHtvqd5/gwbNgybNm3C4cOHUVVVhWHDhuHmm28GYwwVFRWor69HTEwMoqOjER0djczMTBw/flxbV3Jycof7XVVVhaSkJI/nLr74Yo/tl5iY2OHlka6n588eb1FBJz4RRREZGRm6v5d1TEwMVq5cibNnz6K8vByDBg2C3W7XCgoA7XkACA8Px7lz5zyW8csvv2ixP64/vXnzZqiqiksvvRQXXXQRUlJSIEkSNm/e3KF17N69GytXrsQ///lPnD59GvX19UhNTdVOObqQCy1/8ODBqK6uRmNjY7vvaS9/4uLisHz5clRXV6Ourg4JCQno378/6uvrtUdDQwPOnDmjrevQoUMdbuegQYNQUVHh8VxFRYW2/TrSPxJcPeWzpzMoY4nP9LiHfPr0aTz55JM4ePAgFEXBuXPn8MILLyAmJgYjRoxAfHw8pkyZgv/5n//B2bNnUVlZiWeffRb33HMPAGDMmDE4cuQIdu3aBVmWkZ2djVOnTmnLHzBgAGpqas5b7M7n+PHj2LlzJ/75z3/i+++/1x4fffQRvvjiC1RUVFxwHVarFaIool+/flBVFf/4xz88Lq98IRda/vjx4zF8+HA88MADqK+vhyzL2L17NxwOh8f7ZFnGwYMHsXbtWlRUVEBVVdTX12Pjxo1ISUlBbGwsxo8fj8TERDz55JOw2WxgjOHo0aP4+OOPAQBz585FYWEhNm3aBIfDgXPnzmHXrl1aO202G06ePKmtc968eXjzzTfxzTffQJZl/OUvf8GpU6eQkZHR4f6T4NPjZ48vqKATn8iyjNzcXN39YRmNRlRXVyMjIwNRUVFITEzEN998g08++QS9e/cGALz11ltobGzE4MGDcdVVV+GGG27AsmXLAADDhg1DdnY2MjMzMXDgQDgcDlxyySXa8qdOnYoJEyYgPj4e0dHRqKys7FT7XnvtNSQmJuKOO+7ARRddpD1mzJiByy67DP/4xz8uuI4ZM2Zgzpw5uPTSSxEXF4effvoJV111VYfbcKHl8zyPjz76COfOncPw4cPRt29fPPnkk9oFPYD/5k9YWBiKioowceJEREZGYvjw4Th58iQ++ugjAK7LfH700Ueorq7GyJEjERUVhRtuuEE7Kh80aBA+//xzvPXWWxgwYACSkpKwbds2AMDw4cOxYMECjBw5EtHR0di9ezeuueYa/OUvf8GCBQsQGxuLf/3rX/j4448RHR3dqe2gR8eOHQt2EzpEr589vuBYR8fXdMZqtSIqKgoNDQ2IjIwMdnMIISToZs+eje3bt2PWrFl47733gt2cLqGnWkBH6MQnjDFYrdYOf+9KSHOUP6HDXcwBYPv27Zg9e3aQW3R+lDutUUEnPpFlWfuemJDOovwJDc2LuVuoF3XKndZoyF0HwyyEEOKtY8eOIT4+vt3Xq6urERcX14Ut6lp6qgV0hE58oqoq6urqPCY6EdJRlD++4TjfH/HxEeddR3x8hF/W42+UO61RQSc+URQF+/bt025LSEhnUP4Qb1HutEZD7joYZiGE9Ez+O/LdAGBpG8+vB5DllzWEaqXRUy0I6BH6jh07Arl4EgJUVcWJEydo2It4hfInVGTBVbybWw9/FfNAoNxpze8Ffdq0aZg+fTqmTZuGBx54ANOnT/f3KkgIUVUVJSUl9EdFvEL5E0rcRZ1DqBdzgHKnLX4fcn/qqadw2WWX4dZbb8Xvf/97rFu3zp+L9xs9DbMQQnqmQEw2A2wAzj9Rzhs05B54fj9C/9///V/IsozHH38cTqfT34snIUZVVVRXV9NeMvEK5U8o8n8xDwTKndYC8h16ZmYm7r33XgwfPjwQiychRFVVHD58mP6oiFcof4i3KHdao1nuOhhmIYT0TIEZcg+M6upjIXmBGj3VgoDOci8tLcW8efNw5ZVX4oorrtAeRD9UVcXRo0dpL5l4hfKnp5iN+Ph4v15KlnKntYDeGf7222/H3XffjXvvvReCIARyVSRI3N9jxcfHg+fpOkWkcyh/eoLZADxv+uKPO7lR7rQW0CH3cePGYf/+/YFavE/0NMxCCOmZQn/I/b/FvLlQuj2rnmpBQHdrZsyYgU8++cSnZTz88MNISkoCx3EoKSnRnj9x4gRmzJiB5ORkpKamYvfu3b42l3hBURQcOnSILr9IvEL5E3oYOL88qsGhrWIOuI7Uj/l4cXjFYKDcaSGgBf3aa69FZmYmoqKi0L9/f/Tr1w/9+/fv1DIyMzOxe/duDB482OP55cuXY8KECSgrK8OWLVswd+5cuo1eEDDGcPr0abonMfEK5Q/xFuN5yp0WAvod+pIlS/Dqq69i3LhxXn+HPmnSpDaff+edd1BeXg4AGD9+PAYMGIDdu3dj8uTJ3jaXeEEURYwfPz7YzSDdFOWPfsUBmIW2j9FnNb3uC9HppNxpIaBH6LGxscjMzMSQIUMwePBg7eGrU6dOQVVV9OvXT3suKSkJlZWV7f4fh8MBq9Xq8QCgDdcoitJmLMuyR+yeUdleLEmSR+zee3THjLFWMQCPWFVVj9g98tBerCiKR9yVfZJlGaWlpbDb7brpkx63U6j2SZIkHDx4EA6HQzd96urtxPMqzGZXewVBhcnkikWxeazAaGweu9prMCgwGFyx0ahAFJv6YTRCEV3He7LJBLV53HRwJpvNUJsmo0nN47AwsKb47bAwzGr6oj8sLAwAMIvj8HZTzHgeUlOs8jwks1mLZXcsCJBNJlcsilrsNJtx4MABbVv4sp30IqAFfdasWdi0aRPq6upw7tw57eEPXIvZIBcadlm9ejWioqK0R0JCAgBo38uXlpaitLQUAFBcXIyysjIAQFFRkTYSUFhYCIvFAgAoKChATU0NACA/Px+1tbUAgLy8PNTX1wMAcnNzYbPZAAA5OTmw2+2QZRk5OTmQZRl2ux05OTkAAJvNhtzcXABAfX098vLyAAC1tbXIz88HANTU1KCgoAAAYLFYUFhYCAAoLy9HUVERAKCsrAzFxcVd2qdz587h008/1VWf9LidQrFPVVVVaGxsxJ49e3TTp67eTmlptcjOdvUpPb0Gq1a5+jR5sgXLl7v6lJFRjqwsV58yM8uwaJGrT/PmlWLePFefFi0qRmZmU5+yslCekeHq0/LlsDSNfBasWoWa9HRXn7KzUZuW5urTxo2oT0529WnzZtji41192roVb8bE4PawMGzduhW3h4XhzZgY5Gzd6upTfDxyN2929Sk5GXkbN7q2U1oa8rOzXdspPR0Fq1a5ttPkyShcvhwAcHTmTFRVVfm8nfbu3Qu9COgs9+anEnAcB8YYOI7zahJDUlISduzYgdTUVABA7969UVFRoR2lX3HFFcjOzm53yN3hcMDhcGg/W61WJCQkoK6uDn369NHaJAiCRyzLMjiO02Ke58HzfLuxJEkQBEGLRVEEx3FaDLj2CJvHBoMBjDEtVlUViqJosaqqEEWx3VhRFDDGtLitflCfqE/UJ/31yWg0gOdVGI0q7HYRgqBCFFU4HCJEUYUguGMFPM/gdLpjwOkUtKNzSRJgNCpQVUCSRchGIzhVhSDLkE0m8IoC3h3LMnhFgWw2g3c6wasqJLMZgjsOC4PocIBzx3Y7wBiqwsIwqLER4DjIZjMMjY1gPA/ZZIKhsREqz0MxGmGw26HyPFSjEaLdDlUQoIoiRIcDqihCFQSIDgcUUQRrbPR5O9XV1SE2NlYXs9y7zZXiWhb0+fPnIykpCStXrsS+ffswZ84cHDlyRPsjuBA9naoQTIqioLS0FCNHjqRrDZBOo/zxTSBOW2MI+XPhAACKwYDS/ft9zh091YKADrnb7fZWz508ebJTy3jggQcwaNAgVFVV4brrrsOwYcMAAGvXrkVBQQGSk5Mxf/58vP766x0u5oQQQojeBPQIfdasWdi+/b9zHOvr63Httdfi3//+d6BW2WF62isjhPRMPfkIHYBf7smqp1oQ0CP04cOHIysrCwBw5swZZGRk4He/+10gV0m6mKIoKCoqoos7EK9Q/hBvKUYj5U4LAS3oa9aswfHjx7F27VrccsstuP3227Fw4cJArpIEgft0FEK8QflDvKKqlDstBGTIvfmpaY2NjZg5cyauvfZaPPXUUwCAXr16+XuVnaanYRZCSM9EQ+405N5cQI7Qw8PDERERgfDwcPTv3x/fffcd1q5dqz1P9EOWZezbt09XF2cgXYfyh3hLNhopd1oIyLRwuj9tz8FxHPr06dPqQj+EdATlD/EWp6qUOy0E5Aj97NmzWnzq1KlArIKECEEQMGzYMDqHmHiF8od4S5Blyp0W/F7QH3roIdx111147LHHAED73pzokyzLKCgooGEv4hXKH+It2WSi3GnB7wW9vr4eH3zwASZNmoSnn37a34snIYbnecTHx3tc5peQjqL8Id7iFYVypwW//yZMTXfCmTlzJgYOHIidO3f6exUkhPA8j8GDB9MfFfEK5Q/xFi/LlDst+P03cffdd2vxokWLsG7dOn+vgoQQWZaRn59Pw17EK5Q/xFuyyUS504LfC/qkSZM8fh47dqy/V0FCCM/zGDp0KO0lE69Q/hBv8bJMudNCwH8Tf/7znwO9ChJE9B0o8QXlD/EWfYfemt9/E4MHD8b06dMxffp0TJs2DTt27PD3KkgIkWUZeXl5NOxFvEL5Q7wlm82UOy34/cIy06ZNw9///nftZ7oZi77xPI/U1FTaSyZeofwh3uKdTsqdFvx+Lff6+npER0f7c5EBoafr9xJCeia6ljtdy705v+/aNC/mlZWV2L17N3bv3o3Kykp/r4qEAEmS8Omnn0KSpGA3hXRDlD/EW5LZTLnTQkCu5X7w4EHce++9KC8vR2JiIhhjsFgsuPjii7F582aMHDkyEKslQSAIAsaPH0+XXyReofwh3hKcTsqdFgJS0OfPn49HH30Uc+bM8Xh+27ZtuOeee1BYWBiI1ZIg4HkeMTExwW4G6aYof4i3eFWl3GkhILMJTp8+3aqYA0BmZiYaGhoCsUoSJJIkYefOnTTsRbxC+UO8JYWFUe60EJCC3rdvX7z++uset1FVVRWvvfYaYmNjA7FKEiSiKGLixIkQxYAM9hCdo/wh3hIdDsqdFvw+yx0ADh06hCVLlqCoqAhxcXHgOA5VVVUYO3YsNm3ahJSUFH+vstP0NLORENIz0Sx3muXeXEB2bYYNG4YvvvgCJ0+ehMViAQAkJCSgX79+fl1PUlISzGYzzGYzAOCxxx7Dr3/9a7+ug5yfJEnIyclBRkYGDAZDsJtDuhnKH+ItKSwMOR98QLnTTECO0LtKUlISduzYgdTU1E7/Xz3tlQUTYwx2ux1msxlcIA4XiK5R/vimJx+hM46D/exZn3NHT7Wgyy+xEwrD7cS/6Dss4gvKH+IVxih3WghIQT9w4EC7jzNnzvh1XXPnzsWll16KhQsX4uTJk+2+z+FwwGq1ejwAQFEU7d+2YlmWPWL3RL/2YkmSPGL3AIg7Zoy1igF4xKqqesTuaxW3FyuK4hF3ZZ/cQ6aNjY266ZMet1Oo9snpdCInJwd2u103ferq7cTzKsxmV3sFQYXJ5IpFsXmswGhsHrvaazAoMBhcsdGoQBSb+mE0QmkqlrLJBLV53HTet2w2Q2267KrUPA4LA2secxyYO4bryFoKC3P1iee1WOV5SE1fn6o8D9kdCwJkk8kVi6IWOyIikJOTo/3+fdlOehGQgp6amoobb7wRN9xwQ6tHbW2t39aTn5+PH374Afv370dsbCzuueeedt+7evVqREVFaY+EhAQAQElJCQCgtLQUpaWlAIDi4mKUlZUBAIqKilBeXg4AKCws1OYEFBQUoKamRmuHu195eXmor68HAOTm5sJmswGA9qEly7KWhHa7HTk5OQAAm82G3NxcAK7L5+bl5QEAamtrkZ+fDwCoqalBQUEBAMBisWjn85eXl6OoqAgAUFZWhuLi4i7rkyzLmDZtGj777DPd9EmP2ylU+1RTU4OMjAzs27dPN33q6u2UllaL7GxXn9LTa7BqlatPkydbsHy5q08ZGeXIynL1KTOzDIsWufo0b14p5s1z9WnRomJkZjb1KSsL5RkZrj4tXw7L5MmuPq1ahZr0dFefsrNRm5bm6tPGjahPTnb1afNm2OLjXX3auhX2mBjIYWHI2boVclgY7DExyNm61dWn+Hjkbt7s6lNyMvI2bnRtp7Q05Gdnu7ZTejoKVq1ybafJk1G4fDkAoGrKFFx00UUQRdGn7bR3717oRUC+Q7/44ovxzTffIC4urtVrCQkJ2i/Sn2pqapCSkqL9cbTkcDjgcDi0n61WKxISElBXV4c+ffpoe26CIHjEsiyD4zgt5nkePM+3G0uSBEEQtFgURXAcp8WAa4+weWwwGMAY02JVVaEoiharqgpRFNuNFUUBaxp+aq8fgeqTIAiw2+0QRVGbmNLd+6TH7RSqfeI4Dk6nE6Ioam3v7n3qyu1kNBrA8yqMRhV2uwhBUCGKKhwOEaKoQhDcsQKeZ3A63THgdAra0bkkCTAaFagqIMkiZKMRnKpCkGXIJhN4RQHvjmUZvKJANpvBO53gVRWS2QzBHYeFQXQ4wLljux1gDHJYGMTGRoDjIJvNMDQ2gvE8ZJMJhsZGqDwPxWiEwW6HyvNQjUaIdjtUQYAqihAdDqiiCFUQIDockA0G2Ovq0Lt3b+1o25vtVFdXh9jYWF18hx6Qgp6VlYXbbrsNV199davXHnzwQWxs2gvzxdmzZyFJknbt+BdeeAHvv/++tvd9IXqaCBFMNEuZ+ILyxzc9eVKc1HTU72vu6KkWdNtZ7keOHMGcOXO0vekhQ4Zgw4YNSEpK6tD/19NGJIT0TD25oAOg89Bb6LZTBIcMGaJ9z0WChzEGm82GiIgIOu2IdBrlD/EW43nYrFbKnWbozvDEJ7IsY9euXbqaKUq6DuUP8ZZsMlHutNBth9x9padhFkJIz0RD7jTk3hwdoROfqKqKuro6jxvxENJRlD/EW2rTDHXKnf+igk58oigK9u3bp50eQkhnUP4QbylGI+VOCzTkroNhFkJIz0RD7jTk3hwdoROfqKqKEydO0LAX8QrlD/GWyvOUOy1QQSc+UVUVJSUl9EdFvEL5Q7ylGo2UOy3QkLsOhlkIIT0TDbnTkHtzdIROfKKqKqqrq2kvmXiF8od4SxUEyp0WqKATn6iqisOHD9MfFfEK5Q/xliqKlDst0JC7DoZZCCE9Ew2505B7c3SETnyiqiqOHj1Ke8nEK5Q/xFuqKFLutEAFnfiEvgMlvqD8Id6i79BboyF3HQyzEEJ6JhpypyH35ugInfhEURQcOnSILr9IvEL5Q7yliCLlTgtU0IlPGGM4ffo0euhAD/ER5Q/xFuN5yp0WaMhdB8MshDRns9kQERER7GaQLkBD7jTk3hwdoYeoY8eOBbsJHaIoCg4ePEjDXiFiw4YNiIqKwoYNG/y+bJvN5vdlUv4QbymiSLnTAhX0EDR79mzEx8dj9uzZfl1uID6QAaCxsTEgyyWds2HDBixduhSMMSxdutSvRT2QOwqUP8QrPE+50xLroRoaGhgA1tDQEOymeJg1axYDoD1mzZrll+WuX7+ecRzH1q9f75flkdCyfv16j7xxP/yxvVsum3IodLjGnP37CMhCA/Xwg1CtBd7o1gX9559/Zr/61a9YcnIyGz9+PPvpp586/H9DcSO2LOb+KuqB/ECWZZn9+OOPTJZlvy2TdI7Vam0zb9wPq9Xq9bIDuaPAGOWPr3pyQZcNBr/kTijWAm+JAR4ACKglS5Zg8eLFmD9/PrZt24YFCxbg22+/7dI2+G9SyjEA29t8Zfv27eC4YwDivFjuBgBLPZ5ZutT1c1ZWlhfLI/4UiElNLXk/z8eGlrnjtnTpUtx77700+Y6QENJtZ7mfOHECKSkpqK2thSiKYIxh4MCB2LNnD5KSki74//01s9F/H8g2AOdrhxVAZz88z79Mq9VKH8hB5p/8OQYgvt1Xq+HdruDPAIaf5/X/AEjxYrmtdM+PoJBAs9x9zx09zXLvtkfoFosFcXFxEEVXFziOQ2JiIiorK9ss6A6HAw6HA4BrUMk9i/z06dMAoM2UFATBI5ZlGRzHaTHP8+B5XosBHiaTDKeTB2M8TCYJTqcAxniYzRIcDhGMcTCbJdjtrraazXKL2ACOYzCZ1sJu/3/gOA5GoxEOhwMcx2GN0Yj7HJFQeR6qKEJ0OqEKAlRBgOh0QhEEMJ6HKElQBAHgeQiShGpRRBpEyLIMg8EAVVWhKIoWH4uMRD+jEbyigFcUyEYjeFkGr6qQTSbwTid4xiCZTBDcsdkM0eEA1xRzqoqSzz7DiBEjYDKZAEBbH2Os3XWrqgpRFNuNFUUBY0yL29o2ndlOLWNJkiAIghaLogiO47TY3Y/mcSD6BAgQRaVpHQIMBgWqCiiKAINBhqpyUBQBRqMMReGhKDyMRhmyzENV3bnXC4zdCJPpMzidTjDGYDab4XA4cANjMJnNaLDbXeswmyE2iw12OxjHQTaZYLDboXIcFKMRBocD/TkOY4xGfO9wgOd5iKIIp9MJQRCQKgi4yOnE6XZyT2n6vQmyDMVgAFQVgqJANhjAuWOjEYzncWDXLowYMQIGgyFkt1Oo5h5gAMepMBpVOBwieF6FKKpwOkUIggpBcMcKeJ5BktwxIElt555VQavt5MtnhOhF7qkcB9VohOhwtPu55wwLw09ff420tDTtc96b7VRXV6fVhW4vKAP9fvDdd9+xUaNGeTx3+eWXs6+//rrN969YseK83zPSgx70oAc9eu7DYrF0RekKqG495J6cnIxTp051aMi95RG61WqFJEmIjY0F1xVfZOqU1WpFQkICLBZLtx+uIl2P8od4y1+5wxiDzWZDXFxc06hr99Vth9z79++PsWPH4o033sD8+fPx7rvvIikpqd3vz00mkzYkDABRUVFd1NKeITIykj6Qidcof4i3/JE7eqkH3bagA8Arr7yC+fPn49lnn0VkZCRee+21YDeJEEIICYpuXdCHDx/e5aepEUIIIaGoe39hQILOZDJhxYoVHl9nENJRlD/EW5Q7rXXbSXGEEEII+S86QieEEEJ0gAo6IYQQogNU0AkhhBAdoIJOCCGE6AAVdEIIIUQHqKATQgghOkAFnRBCCNEBKuiEEEKIDlBBJ4QQQnSACjohhBCiA1TQCSGEEB2ggk4IIYToQLe+faq3GGOwWq2w2WyIiIgAx3HBbhIhhJAgYIzBZrMhLi4OPN+9j3F7ZEG32WyIjo4OdjMIIYSECIvFgkGDBgW7GT7pkQU9IiICFosFCQkJsFgsiIyMDHaTui1ZlrF3716kp6dDFHtkOhEfUP4Qb/krd6xWKxISEhAREeHH1gVHSP8F2e123HHHHThw4AB69eqFiy66CJs2bUJSUhJOnDiBu+++G4cPH4bJZMKmTZtw9dVXd2i5HMdpRTwyMpIKug9UVUVaWhqio6O7/XAV6XqUP8Rb/s4dPXz1GvJ/QYsXL8Z//vMffP/997jxxhuxePFiAMDy5csxYcIElJWVYcuWLZg7dy5kWQ5ya3senucRHx9PH8bEK5Q/xFuUO62F9G/CbDYjIyND23OaMGECjhw5AgB455138MADDwAAxo8fjwEDBmD37t1Ba2tPJcsy8vLyaGeKeIXyh3iLcqe1kC7oLb344ou46aabcOrUKaiqin79+mmvJSUlobKyst3/63A4YLVaPR4AoCiK9m9bsSzLHrGqqueNJUnyiBljHjFjrFUMwCNWVdUjdidse7GiKB5xV/aJ4zhccsklUBRFN33S43YK1T4BQGpqKlRV1U2f9LidQrFPjDGMHDkSPM/73Ce96DYF/dlnn0VZWRmeeeYZAK2/73AnUHtWr16NqKgo7ZGQkAAAKCkpAQCUlpaitLQUAFBcXIyysjIAQFFREcrLywEAhYWFsFgsAICCggLU1NQAAPLz81FbWwsAyMvLQ319PQAgNzcXNpsNAJCTkwO73Q5ZlpGTkwNZlmG325GTkwPANfM+NzcXAFBfX4+8vDwAQG1tLfLz8wEANTU1KCgoAOCakVlYWAgAKC8vR1FREQCgrKwMxcXFXdYnp9OJmJgYfPLJJ7rpkx63U6j2qbq6Gv3798eePXt00yc9bqdQ7NPRo0dRVVUFnud96tPevXuhG6wb+POf/8wuu+wydvr0ae25Xr16sRMnTmg/jx8/nn355ZftLsNut7OGhgbtYbFYGABWV1fHGGNMlmUmy3KrWJIkj1hRlPPGTqfTI1ZV1SNWVbVVzBjziBVF8YglSTpvLMuyR9xWPwLVJ4fDwT7++GN29uxZ3fRJj9spVPtkt9vZJ598ws6dO6ebPulxO4VinxobG9nHH3/MnE6nT306deoUA8AaGhpYd8cxdoFD2yB74YUX8Oabb+Lzzz9Hnz59tOfnz5+PpKQkrFy5Evv27cOcOXNw5MiRDp++YLVaERUVhYaGBprl7gNVVVFfX0+zlIlXKH+It/yVO3qqBSFd0KuqqpCQkIAhQ4Zo5wiaTCbs3bsXx48fx29+8xuUl5fDaDTir3/9K6655poOL1tPG5EQQoh39FQLQnqXeNCgQWCM4fDhw/j+++/x/fffa993DBgwALm5uSgrK8NPP/3UqWJO/EeSJOzcuVObzEK8t3LlStx6663BbgYuueQS7NixQ/v5b3/7GwYOHIjw8HAUFRW1et0XlD/EW5Q7rYV0QSehTxRFTJw4UZdX+frPf/6Dm266CX379kVkZCRGjBiBtWvX+mXZr776KsaMGePTMlauXAlRFBEeHo7IyEikpqbijTfe8LltP/30E2688UYArg/NrKwsvP322zhz5gzGjh3r8XpnPf/880hJSUFERAT69euHmTNnIjEx0ef8mT9/PpYuXerTMkj3oufPHm9RQSc+cV91Tw9XWWrphhtuwOjRo1FZWYnTp0/j3XffxZAhQ4LdLA833ngjzpw5g/r6evzxj3/E/PnztRm+/nD8+HE0NjYiLS3N52W98cYb+Mtf/oL33nsPNpsNZWVlWLx4cUjkj55OXeop9PzZ4y0q6MQnkiThgw8+0N2wV21tLQ4fPowlS5agV69eEAQBl1xyCW677TbtPcePH8ftt9+Ofv36ITExEU888YRWGNo6Ah8zZgxeffVVFBUV4b777sOPP/6I8PBwhIeHa9dQUBQFDz74IKKjo5GYmIi33367Q+3leR633347oqOjceDAAeTm5uLyyy9HVFQUBg4ciPvvvx+NjY3a+61WKx588EEkJiYiMjIS48eP107jSUpKwvvvv4+ioiIMHz4cgOvrr6FDh3q87vbZZ58hPT0d0dHRGDhwIFavXt1mG/fs2YNrr70WqampAIDo6GjMmjUL33//vZY/n3/+Oa644gpER0fjkksuwYcffqj9f1VV8eKLL2LEiBGIiIhAcnIyPvnkE7z44ot488038de//hXh4eG45JJLALhOiVq8eDEGDhyIgQMH4r777sPZs2cBABUVFeA4Dlu2bMGwYcMQHx/fod8zCR16/ezxSVDn2AdRQ0ODbk5VCCZVVdm5c+e001T0QlVVNmLECHbttdeyt99+m1VUVLR6z9SpU9ldd93FbDYbq6ioYKNGjWLPPPMMY4yxLVu2sNGjR3u8f/To0WzLli3tvr5ixQpmMBjYW2+9xWRZZq+99hoLDw9nVqu1zTauWLGC3XLLLYwx1+k5W7duZaIosp9//pnl5+ez/fv3M1mW2eHDh9mIESPYn/70J+3/zpo1i11//fWsurqaKYrC9u/fz06ePMkYY2zw4MFs+/btjDHGysvLGQCPU0abv75//34WFhbGtm3bxpxOJ6uvr2fffvttm+3dunUrCw8PZ3/605/Y7t27WWNjo0f+/PDDDyw6Opp98cUXTFEUtmvXLhYZGckOHjzIGGNsw4YN7OKLL2bfffcdU1WVHT16lB04cIAxxtg999zDsrKyPNb329/+lk2ZMoXV1taykydPsmuuuYYtWrTIo1+33norO336NDt79mybbSahy1+fPXqqBVTQdbARg6n5+aV6U1NTwx555BE2atQoxvM8GzlyJMvNzWWMMVZVVcUAsJqaGu39b775JktOTmaMeV/Q09PTtZ9VVWVGo5F99913bbZvxYoVTBRFFhUVxWJjY9nll1/Otm3b1uZ7161bx6677jrGGGO//PILA8COHj3a5ns7U9Dvu+8+9tvf/rbN5bTl//v//j+WkZHBoqKiWK9evdiCBQvY6dOnmaqq7P7772dLly71eP9dd93Fnn76acYYYyNGjGCvvfZam8ttWdAVRWEmk4nt2bNHe+6bb75hJpOJKYqi9auoqKjDbSehxV+fPXqqBTTkTnzS/ApQenPRRRfh+eefx08//YSTJ09i5syZmDVrFurq6lBVVQWz2YyLLrpIe/+QIUNQVVXl8zrdOI5DWFiYdtWtttxwww2or69HbW2tdj0GANi3bx+uu+46DBgwAJGRkXj88ce1q3odPXoUJpMJiYmJPrXVvazk5OQOvz8zMxM7d+7E6dOn8emnn+Kzzz7DwoULIcsyKioqsGnTJkRHR2uPDz74AMeOHev0uk6ePAmHw4GkpCTtuSFDhsDhcGi/BwB++R2Q4NDzZ4+3qKATn4iiiIyMDN3PNI2JicHKlStx9uxZlJeXY9CgQbDb7Th+/Lj2HvfzABAeHo5z5855LOOXX37R4kBfROXOO+/ElClTcOTIEVitVjz77LPa5ZEHDx4Mh8OhfWfui8GDB+PQoUOd/n8cx+Hqq6/GnDlz0NjYCFEUkZCQgKysLNTX12uPM2fO4OWXX77gulr+Pvv16wej0YiKigrtufLycphMJvTt27fd/0e6j57y2dMZlM3EZ3rcQz59+jSefPJJHDx4EIqi4Ny5c3jhhRcQExODESNGID4+HlOmTMH//M//4OzZs6isrMSzzz6Le+65B4BrAtyRI0ewa9cuyLKM7OxsnDp1Slv+gAEDUFNT4zFRzZ+sViuio6PRu3dvlJaWakXRve5bbrkF9913H2pqaqCqKoqKijza11GLFi3C1q1bsX37dsiyjIaGBuzZs6fN927ZsgUffPCBds3vkpISfPjhh0hPTwcALFmyBFu2bMGXX34JRVHgcDjw7bffarP2lyxZglWrVuH7778HYwyVlZXaawMGDNDuxAi4CvVdd92FJ554AnV1dTh16hSeeOIJ/OY3v6EiriN6/OzxBWU28Yksy8jNzdXdH5bRaER1dTUyMjIQFRWFxMREfPPNN/jkk0/Qu3dvAMBbb72FxsZGDB48GFdddRVuuOEGLFu2DAAwbNgwZGdnIzMzEwMHDoTD4dBmXwPA1KlTMWHCBMTHxyM6Ovq8dwr0xiuvvILnnnsO4eHhuO+++3DHHXd4vP7aa68hISEBl19+OaKjo3Hfffd5tXMxbtw4vPvuu3jmmWcQExODkSNH4uuvv27zvdHR0Xj++ee1Kz/eeuutuP3225GamgpZljF27Fhs3boVTz75JPr164f4+Hg89dRTcDgcAICHH34Yv/vd73D77bcjIiIC1113nfZ7W7hwIaqrq9GnTx/tFLsNGzYgKSkJo0aNwiWXXIJhw4bhhRde6HQfexr3VxyhTq+fPb4I6Uu/BpKeLvdHCCH+MHv2bGzfvh2zZs3Ce++9F+zmdAk91QI6Qic+YYzBarVe8Pa1hLSF8id0uIs5AGzfvh2zZ88OcovOj3KnNSroxCeyLGvfExPSWZQ/oaF5MXcL9aJOudMaDbnrYJiFEEK8dezYsfNeKa+6uhpxcXFd2KKupadaQEfoxCeqqqKurg6qqga7KaQbovzxDcf5/oiPjzjvOuLjI/yyHn+j3Gkt5Av6ww8/jKSkJHAch5KSEu35yZMnY8iQIRgzZgzGjBmDdevWBbGVPZeiKNi3bx8URQl2U0g3RPlDvEW501rIn5GfmZmJZcuW4eqrr2712osvvuj1bRyJfxgMBlx//fXBbgbppih/QkEEgPUAlrbx2vqm10MP5U5rAT1C37Fjh8/LmDRpknb1LRJ6VFXFiRMnaNiLeIXyJ1RkwVW8m1vf9Hxootxpze8Ffdq0aZg+fTqmTZuGBx54ANOnT/f3KjSPPvooLr30Uvz617/2uEpUWxwOB6xWq8cDgDZcoyhKm7Esyx6xO3naiyVJ8ojdcw7dMWOsVQzAI1ZV1SN2z+JsL1YUxSPuyj4pioIff/wRDodDN33S43YK1T7JsoySkhI4nU7d9KmrtxPPqzCbXe0VBBUmkysWxeaxAqOxeexqr8GgwGBwxUbjgxDF9U3xBojigwAAk0mGKKpaLAiu2GyWwfPuWNLisDAJPM+0mOMYAP9vJ0mS8OOPP0JVVZ+3k174vaBPmDAB999/Pz777DPMnj0bubm5/l4FAOD1119HaWkpiouLMXHixAsOva9evRpRUVHaIyEhAQC07+VLS0u1y0gWFxejrKwMAFBUVITy8nIAQGFhoXb964KCAtTU1AAA8vPztRs+5OXlaZe2zM3N1W6skZOTA7vd7nFDAbvdjpycHACueze7f1f19fXIy8sD4Lovd35+PgCgpqYGBQUFAACLxYLCwkIArmtUFxUVAQDKyspQXFzcZX2SZRmTJk3Srtikhz7pcTuFap9qamowdepULdZDn7p6O6Wl1SI729Wn9PQarFrl6tPkyRYsX+7qU0ZGObKyXH3KzCzDokWuPs2bV4p581x9WrSoGJmZ1wOwIivrV8jIcPVp+fJCTJ7s6tOqVQVIT3f1KTs7H2lprj5t3JiH5GRXnzZvzkV8vKtPW7fmICbGjrAw/28ni8WCyMhIiKLo03bau3cv9CIgp61t27YN+/fvR0NDA1566SW/LDMpKQk7duxAampqm6+bzWZUV1cjNja2zdcdDod2CUnAdapCQkIC6urq0KdPH23PTRAEj1iWZXAcp8U8z4Pn+XZjSZIgCIIWi6IIjuO0GHDtETaPDQYDGGNa7N7jdMeqqkIUxXZjRVHAGNPitvoRqD7xPI+amhrtZhh66JMet1Oo9gkAjh8/jn79+kEURV30qSu3k9FoAM+rMBpV2O0iBEGFKKpwOESIogpBcMcKeJ7B6XTHgNMpaEfnkiTAaFSgqoAsCzAaZagqB1kWYDLJUBQesszDZJIhyzwUhYfZLMPp5KGqPMxmCU6nAFXlERYmweEQoaocwsIk2O0iGAOcTv9uJ0mSUFNTg0GDBmmjHN5sp7q6OsTGxuritLWATIrLzMzEmDFjtL0wf5NlGadOncKAAQMAAO+++y4GDBjQbjEHAJPJBJPJ1Op5QRA8/m0ZN7+TT0dig8HgVcxxnBa7E62jcXtt74o+ybKMI0eO4KKLLgLXdG5Kd+/T+WLqk3/7JMsyDh8+jAEDBmjL7O59ulDs7z6pKg+73RUriqvYAoAsu4qwK/5ve5vHkvTf2OlsHv+3vQ5H27Hd3jz+b3sbG9uOT548qZ3P7o/txHEcKioqEBcX5/H79WU7dXchf2GZBx54AB988AF++eUX9O3bF+Hh4fjhhx9wzTXXwOFwgOd59O3bFy+88AJGjx7d4eXq6WIChJCeKRDndwfGbACheY14PdWCgBb00tJSPPPMMzhy5IjHxAP3dyDBpKeNGEyqqsJisSAhIYFuS0k6jfLHN92joLuKuZu/irq/ckdPtSCgYw2333477r77btx7770eQx9EP1RVRXV1NeLj4+kDmXQa5Y/eeRZz4L/XiPe1qFPutBbQI/Rx48Zh//79gVq8T/S0V0YI6ZlC+wj9GIDQv0a8nmpBQHdrZsyYgU8++SSQqyBBpigKDh06RJdfJF6h/Ak9DJxfHtbzFHMAiIiP9+ni8IrBQLnTQkAL+rXXXovMzExERUWhf//+6NevH/r37x/IVZIuxhjD6dOn6Z7ExCuUP8RbjOcpd1oI6JD7sGHDsGbNGowbN87jO/TBgwcHapUdpqdhFkJIzxSIIXcG/y10A9q/QrxfLirrh/Klp1oQ0ElxsbGxyMzMDOQqSJApioKysjIkJyfTxEfSaZQ/+uYu2kubPbce/inmiiii7OBByp1mAjrkPmvWLGzatAl1dXU4d+6c9iD60tjYGOwmkG6M8kffmt/2ZT38eLsXnqfcaSGgQ+7NTyXgOA6MMXAcFxKTGPQ0zEII6ZlCfci9ORsCcCNWGnL3ENAjdPd1d93X6HX/S/RDURSUlJTQdiVeofzpOfxdzBWDgXKnhYAWdLvd3uq5kydPBnKVhBBCSI8U0IJ+5513evxcX1+PGTNmBHKVpIsJgoDU1FSalEK8QvlDvCVIEuVOCwEt6MOHD0dWlmsKxJkzZ5CRkYHf/e53gVwl6WKKoqCoqIiGvYhXKH+ItxSjkXKnhYAW9DVr1uD48eNYu3YtbrnlFtx+++1YuHBhIFdJgiAsLCzYTSDdGOUP8YqqUu60EJBZ7s1PTWtsbMTMmTNx7bXX4qmnngIA9OrVy9+r7DQ9zWwkhPRM3WmWe0DQLHcPATlCDw8PR0REBMLDw9G/f3989913WLt2rfZ8Zzz88MNISkoCx3EoKSnRnj9x4gRmzJiB5ORkpKamYvfu3f7uBukAWZaxb98+j9vjEtJRlD/EW7LRSLnTQkAKesvT1FqevtYZmZmZ2L17d6vLxS5fvhwTJkxAWVkZtmzZgrlz59KGDQKO49CnTx9woX3bJxKiKH+ItzhVpdxpISCXfj179ix69+4NADh16hRiY2O9XtakSZPafP6dd95BeXk5AGD8+PEYMGAAdu/ejcmTJ3u9LtJ5giBg2LBhwW4G6aYof4i3BFmm3GnB70foDz30EO666y489thjAKB9b+5Pp06dgqqq6Nevn/ZcUlISKisr2/0/DocDVqvV4wFAGzFQFKXNWJZlj1hV1fPGkiR5xO4pCu6YMdYqBuARq6rqEbtHHtqLFUXxiLuyT5Ik4ZtvvkFjY6Nu+qTH7RSqfXI6nSgoKIDdbtdNn7p6O/G8CrPZ1V5BUGEyuWJRbB4rMBqbx672GgwKDAZXbDQqEMWmfhiNUETX8Z5sMkFtHjedJiabzVCbrgYqNY/DwsCaxxwH5o4BMI6D1DSZjfG8Fqs8D8ls1mLZHQsCZJPJFYuiFjt698Y333yj/f592U564feCXl9fjw8++ACTJk3C008/7e/Fa1oOs1xobt/q1asRFRWlPRISEgBA+16+tLQUpaWlAIDi4mKUlZUBAIqKirSRgMLCQlgsFgBAQUEBampqAAD5+fmora0FAOTl5aG+vh4AkJubC5vNBgDIycmB3W6HLMvIycmBLMuw2+3IyckBANhsNuTm5gJw/Q7z8vIAALW1tcjPzwcA1NTUoKCgAABgsVhQWFgIACgvL0dRUREAoKysDMXFxV3WJ6fTiYEDByI3N1c3fdLjdgrVPlVXVyM+Ph6FhYW66VNXb6e0tFpkZ7v6lJ5eg1WrXH2aPNmC5ctdfcrIKEdWlqtPmZllWLTI1ad580oxb56rT4sWFSMzs6lPWVkoz8hw9Wn5cliaRj4LVq1CTXq6q0/Z2ahNS3P1aeNG1Ccnu/q0eTNs8a57oeds3Qp7TAzksDDkbN0KOSwM9pgY5Gzd6upTfDxyN2929Sk5GXkbN7q2U1oa8rOzXdspPR0Fq1a5ttPkyShcvhwAUDltGiRJAs/zPm2nvXv3QjeYny1YsECL/+///o8lJib6ZbmDBw9mP/74o/Zzr1692IkTJ7Sfx48fz7788st2/7/dbmcNDQ3aw2KxMACsrq6OMcaYLMtMluVWsSRJHrGiKOeNnU6nR6yqqkesqmqrmDHmESuK4hFLknTeWJZlj7itflCfqE/UJ/31CWCM5xVmNksMYEwQFGYyuWJRbB7LzGhsHssMYMxgkJnB4IqNRpmJoswYwCSjkcmi6IpNJqY0jwXBFZvNTOF5xgDmbB6HhTG1ecxxTHXHAFM5jjnDwhgDmMrzWqzwPHOazVosuWNBYJLJ5IpFUYtlUfTLdjp16hQDwBoaGlh35/fT1vLz8z2+937vvfcwe/Zsn5eblJSEHTt2IDU1FQAwf/58JCUlYeXKldi3bx/mzJmDI0eOQBQ7Ni1AT6cqBJMsyygoKMCVV17Z4d89IW6UP77pyaetySYTCnJzfc4dPdUCvw+5t5zENnbsWJ+W98ADD2DQoEGoqqrCddddp02CWLt2LQoKCpCcnIz58+fj9ddfpw+EIOB5HkOHDvW43ss0DQAAGV9JREFUsx4hHUX5Q7zFyzLlTgsBvX0qANx///3461//GshVeEVPe2WEkJ6pJx+hA6ALy7Tg912bwYMHY/r06Zg+fTqmTZuGHTt2+HsVJITIsoy8vDxdzRQlXYfyh3hLNpspd1rw+xj1tGnT8Pe//137mW7Gom88zyM1NZWGvYhXKH+It3ink3KnBb8PudfX1yM6OtqfiwwIPQ2zEEJ6JhpypyH35vy+a9O8mFdWVmL37t3YvXv3eS/6QrovSZLw6aefahe5IKQzKH+ItySzmXKnhYBMCz948CDuvfdelJeXIzExEYwxWCwWXHzxxdi8eTNGjhwZiNWSIBAEAePHj4fQdPUoQjqD8od4S3A6KXdaCEhBnz9/Ph599FHMmTPH4/lt27bhnnvu0a7IRLo/nucRExMT7GaQboryh3iLV1XKnRYCMpvg9OnTrYo54LpzWkNDQyBWSYJEkiTs3LmThr2IVyh/iLeksDDKnRYCUtD79u2L119/Xbv4PeC6mcBrr73m053XSOgRRRETJ06ki/oQr1D+EG+JDgflTgsBubDMoUOHsGTJEhQVFSEuLg4cx6Gqqgpjx47Fpk2bkJKS4u9VdpqeZjYSQnommuVOs9ybC8iuzbBhw/DFF1/g5MmT2h1tEhISPG53SvRBkiTk5OQgIyMDBoMh2M0h3QzlD/GWFBaGnA8+oNxpJuCXfg1VetorCybGGOx2O8xmc6tb2hJyIZQ/vunJR+iM42A/e9bn3NFTLejyS+yEwnA78S/6Dov4gvKHeIUxyp0WAvLbOHDgQLuvnTlzJhCrJEEiyzINmRKvUf4Qb8lhYZQ7LQRkyJ3neSQlJaGtRVdXV8PpdPp7lZ2mp2GWYGKMQZZliKJIQ6ak0yh/fNOjh9wByE6nz7mjp1oQkCP0wYMHY/fu3YiLi2v1WkJCQiBWSYLI/YFMiDcof4hXOI5yp4WAfId+880348iRI22+dsstt/htPUlJSRgxYgTGjBmDMWPG4O233/bbsknHyLKM3NxcuoUh8QrlD/GWbDZT7rTQrWe5JyUlYceOHUhNTe30/9XTMAshpGfqyUPuAOg89BboRrLEJ4wxWK3WNudLEHIhlD/EW4znKXda6PYFfe7cubj00kuxcOFCnDx5st33ORwOWK1WjwcAKIqi/dtWLMuyR+y+nG17sSRJHrE72dwxY6xVDMAjVlXVI3YPKbUXK4riEXdlnyRJQn5+PhobG3XTJz1up1Dtk9PpxK5du2C323XTp67eTjyvwmx2tVcQVJhMrlgUm8cKjMbmsau9BoMCg8EVG40KRLGpH0YjlKbvpmWTCWrzuOnuZrLZDJV3lRCpeRwWBtY85jgwdwzX+eNSWJirTzyvxSrPQzKbtVh2x4IA2WRyxaKoxY7evZGfn6/9/n3ZTnrRrQt6fn4+fvjhB+zfvx+xsbG455572n3v6tWrERUVpT3ck/NKSkoAAKWlpSgtLQUAFBcXo6ysDABQVFSE8vJyAEBhYaF25buCggLU1NRo7aitrQUA5OXlob6+HgCQm5sLm80GAMjJyYHdbtdO05FlGXa7HTk5OQAAm82G3NxcAEB9fT3y8vIAALW1tcjPzwcA1NTUoKCgAABgsVi0u9aVl5ejqKgIAFBWVobi4uIu65OiKLj++uvx2Wef6aZPetxOodqnX375BTfccAP27dunmz519XZKS6tFdrarT+npNVi1ytWnyZMtWL7c1aeMjHJkZbn6lJlZhkWLXH2aN68U8+a5+rRoUTEyM5v6lJWF8owMV5+WL4dl8mRXn1atQk16uqtP2dmoTUtz9WnjRtQnJ7v6tHkzbPHxrj5t3Qp7TIzrFLOtWyGHhcEeE4OcrVtdfYqPR+7mza4+JScjb+NG13ZKS0N+drZrO6Wno2DVKtd2mjwZhcuXAwCqpkzBgAEDYDAYfNpOe/fuhV506+/Qm6upqUFKSor2x9GSw+GAw+HQfrZarUhISEBdXR369Omj7bkJguARy7IMjuO0mOd58DzfbixJEgRB0GL3KRXuGPCc1SvLMgwGg3b6jsFggKqqUBRFi1VVhSiK7caKooA1XWShvX4Eqk88z6O+vh7h4eEwGo266JMet1Oo9sn9txgeHg5RFHXRp67cTkajATyvwmhUYbeLEAQVoqjC4RAhiioEwR0r4HkGp9MdA06noB2dS5IAo1GBqgKSLEI2GsGpKgRZhmwygVcU8O5YlsErCmSzGbzTCV5VIZnNENxxWBhEhwOcO7bbAcYgh4VBbGx0zU43m2FobATjecgmEwyNjVB5HorRCIPdDpXnoRqNEO12qIIAVRQhOhxQRRGqIEB0OCAZjaivqkJsbKw2yuHNdqqrq0NsbKwuvkPvtgX97NmzkCQJ0dHRAIAXXngB77//vrb3fSF6mggRTJIkIS8vD1OnTqWLO5BOo/zxTU+eFCeZzch7/32fc0dPtaDbFvQjR45gzpw52t70kCFDsGHDBiQlJXXo/+tpIxJCeqaeXNAB0Cz3FrrtGflDhgzRvuciwaOqKmpra9G3b1/wfLeekkGCgPKHeEvledSeOEG50wz9FohPVFVFSUmJNmOUkM6g/CHeUo1Gyp0Wuu2Qu6/0NMxCCOmZaMidhtyboyN04hNVVVFdXU17ycQrlD/EW6ogUO60QAWd+ERVVRw+fJj+qIhXKH+It1RRpNxpgYbcdTDMQgjpmWjInYbcm6MjdOITVVVx9OhR2ksmXqH8Id5SRZFypwUq6MQn9B0o8QXlD/EWfYfeGg25h+gwy7FjxxAXFxfyyySEBA8NudOQe3N0hB6CZs+ejfj4eMyePTuklwm47mZ06NAh7ZrJhHQG5Q/xliKKlDstUEEPMbNnz8b27dsBANu3b/dLAQ7EMt0YYzh9+jTdk5h4hfKHeIvxPOVOCzTkHkLDLM0Lb3OzZs3Ce++9FzLLJISEBhpypyH35ugIPUQcO3aszcILuI6qjx07FhLLbElRFBw8eJCGvUJIe7cQDkWUP8RbiihS7rRABd1HHOefR3x8HIBL21zHpQDi4uM7vdC4+Ph2luj9Mls9evVCY2NjgH67pLM2bNiAyMhIbNiwIdhN6TDKH+IVnqfcaYGG3H0cZvHfkJcNQBSA1puDA9AAIMJvS/R+mW3qmSkUcjZs2IClS5dqP69fvx5ZWVnBaxAJOBpypyH35rr1EXpZWRmuvPJKpKSk4IorrsCBAweC3SQfRABY1+Yr6+Bd4W1/id4vsyXFYEBJSQkNewVZy2IOAEuXLvXrkfr+/fv9tiw3RVEof4hX6LOntW5d0JcsWYLFixfj559/xrJly7BgwYJgN8lHWQDWezyzvulZ/y3R92WS0GKz2VoVc7elS5f65Tv1uLg4XHbZZXQdA0JCWLcdcj9x4gRSUlJQW1sLURTBGMPAgQOxZ88eJCUlXfD/h96Qe3MbwGEp1sF/hXcDgN8Dfl2mpnumUEjwT/78DGD4eV7/D4AUH5YfB6BG+2ngwIF+mVBJfEdD7jTk3pwY7AZ4y2KxIC4uDqLo6gLHcUhMTERlZWWbBd3hcMDhcABwnfvq/kA6ffo0AGjDNoIgeMSyLIPjOC3meR48z2sxwMNkkuF08mCMh8kkwekUwBgPs1mCwyGCMQ5mswS73dVWs1luERvAcQwmkzu+B+XG/4c+DgfqOQ6q0QjR4YDK81BFEaLTCVUQoAoCRKcTiiCA8TxESYIiCADPQ5AkKE2/G0GWcbfBgNmqiihFQZ3BAE5VISgKZKMRvKKAd8eyDF5VIZtM4J1O8IxBMpkguGOzGaLDAa4p5lQVJfn5GDFiBEwmEwBAlmUYDAYwxrRYVVUoiqLFqqpCFMV2Y0VRwBjT4ra2TWe2U8tYkiQIgqDFoiiC4zgtdvejeRyIPgECRFFpWocAg0GBqgKKIsBgkKGqHBRFgNEoQ1F4KAoPo1GGLPNQVXfu9Qdjo2AyHYbT6QRjDGazGQ6HA4yNhNkcC7u9oYO5p8JoVOBwuOPRcDhqwPM8RFGE0+nEiRMnkJiYqA13+rKdGGM4cOAARowYAYPBELLbKVRzD3BvJxUOhwieVyGKKpxOEYKgQhDcsQKeZ5AkdwxIUtu5Z1UA2Y+fEaLd7lpHi9hgt4NxHGSTCQa7HSrHQTEaYXA4oHbgc88ZFoafvv4aaWlp2ue8N9uprq5OqwvdHuumvvvuOzZq1CiP5y6//HL29ddft/n+FStWMLjmh9GDHvSgBz3o4fGwWCxdUboCqlsPuScnJ+PUqVMdGnJveYRutVohSRJiY2PBBWbcvEewWq1ISEiAxWLp9sNVpOtR/hBv+St3GGOw2WyIi4trGnXtvrrtkHv//v0xduxYvPHGG5g/fz7effddJCUltfv9uclk0oaEASAqKqqLWtozREZG0gcy8RrlD/GWP3JHL/Wg2xZ0AHjllVcwf/58PPvss4iMjMRrr70W7CYRQgghQdGtC/rw4cPx7bffBrsZhBBCSNB17y8MSNCZTCasWLHC4+sMQjqK8od4i3KntW47KY4QQggh/0VH6IQQQogOUEEnhBBCdIAKOiGEEKIDVNAJIYQQHaCCTgghhOgAFXRCSJeqr6/H22+/jRdeeAHr1q3Dv/71L+0mSYS0Z8OGDaitrQUAlJWV4eqrr8aAAQNwxRVX4Icffghy60IDFXTSYf/3f/+nxZWVlZg6dSr69++PX/3qVzh48GAQW0a6i82bN+OKK67Anj17tDuG7dmzBxMmTMDmzZuD3TwSwv72t7+hb9++AICHHnoIf/zjH3H8+HG89NJLuO+++4LcutBA56GTDhs3bhz2798PAJgzZw5mz56NuXPn4tNPP8WaNWvw5ZdfBrmFJNQNHz4c//73vxEeHu7xvM1mw2WXXYaff/45SC0joW7EiBE4cOAAeJ5Heno69u7dq7126aWX4scffwxi60IDHaETrxw5cgRz584FAFx//fVoaGgIcotId8BxHM6cOdPq+TNnztBdD8l5LVy4ELfccgsKCgqQkZGB5cuXo7CwEGvWrMEll1wS7OaFhG59LXfStcrLy3H77beDMYbq6mqcO3cOvXr1AgA4nc4gt450B8899xyuueYapKamIj4+HgBQVVWFn376Cc8//3yQW0dC2f/8z/9gzJgxWL9+PcrKyiBJEn744QfcdNNN+Oc//xns5oUEGnInHfb11197/HzZZZchPDwcJ06cwDvvvIMHH3wwSC0j3YmiKCgsLMSxY8fAGEN8fDyuuOIKCIIQ7KYR0q1RQSeEBNXGjRtpZ5Bc0MqVK5GQkIAFCxZ4PL9x40acPn0aTz31VJBaFjqooJMOmzp16nlfz8vL66KWED1pPtmSkPZccskl+PHHH8HznlO/FEXBmDFjaFIc6Dt00gk1NTUwGo349a9/jRkzZtBtC4lf0DEF6QjGWKtiDgCCIEBRlCC0KPTQLHfSYaWlpXjzzTdht9vxu9/9DmvXrkV5eTlSUlJolinxWmFhYbCbQLqByMhIFBcXt3r+hx9+QERERBBaFHpoyJ147Y033kBWVhaWL1+ORx99NNjNId3c8uXLsWbNmmA3g4SoXbt24Te/+Q0WLFiAMWPGgOM47N+/H1u2bMFrr72GSZMmBbuJQUcFnXTKgQMH8Pbbb+PTTz9FcnIyMjMzMXPmTBiNxmA3jXQD586da/N5xhhGjBgBi8XSxS0i3cmxY8fw8ssv48CBA2CMYdSoUbj//vsRFxcX7KaFBCropMNSU1MhCAJuv/12zJw5E2az2eP1UaNGBallpLsQBAGDBw/2+N6c4zjt2gZ0PQNyIY2NjTh8+DAAYOjQoQgLCwtyi0IHFXTSYZMnT9au5uX+EHbjOI5muZMLSklJwWeffYbBgwe3ei0hIYGO0Em7nE4nHn30Ubz11ltITEwEYwwWiwVz587F2rVraZIuaJY76YSvvvoq2E0g3dz/+3//r81LvwLAqlWrurg1pDt5+OGHYTAYUFFRgd69ewNwXTL4sccew0MPPeRx86ieio7QSYcVFhYiMTERF110EQBgy5Yt2L59OwYPHoyVK1ciNjY2yC0khOhVcnIyysrKWj3PGENKSkqbr/U0dNoa6bAlS5Zow1pffPEFHn/8cdx9992IjY1tdfUmQjpq+vTpwW4C6QbaO/Zs+fVfT0YFnXSYqqro06cPAOCdd97Bfffdh8zMTKxcuRIVFRXBbRzptk6ePBnsJpBu4LrrrsPDDz+MxsZG7blz587hoYceuuBVLHsKKuikUxwOBxhjyM3N9TiykiQpiK0i3dmMGTOC3QTSDWzYsAGAa/Lk5ZdfjssvvxyDBw8Gx3F48cUXg9y60EDfoZMO+9vf/oYNGzYgPDwc4eHh+PzzzwG4rtS0dOlSfPnll0FuISFE786ePetx2pp7ghyhgk46qbq6GidOnMDo0aO16yrX1NRAkiQkJiYGuXUk1A0ZMsTjZ8aY9h0ox3E4cuRIkFpGuqsvvvgCL7zwAnbu3BnspgQdDbmTDtu6dSvi4+MxduxYfPvtt9rzAwcOxIcffhjElpHuYvjw4dokyk8++QQlJSX48ccftX8JaU9eXh5SUlLQu3dv3Hnnnfjxxx9x2WWX4ZFHHsG9994b7OaFBDpCJx3W/DaXLW95SbfAJB11+vRpbN++Hdu2bYPD4cCsWbNwxx13oG/fvsFuGglhY8aMwfPPP4+rr74aO3bswN13343Vq1fj4YcfDnbTQgYdoZMOa77v13I/kPYLSUf16dMH9957L95//3385je/wYoVK/DWW28Fu1mkG7j22mthMpkwZ84cDBo0iIp5C3SlONJh7su+tozb+pmQtsiyjNzcXLzzzjsoLS3F9OnTkZeXh9GjRwe7aSTE2Ww25OTkaD8riuLxc0ZGRjCaFVJoyJ10mCiKiImJAWMM9fX12jnpjDE0NDTQjTXIBcXExCAhIQG33367dgvM5uhDmbTnt7/9bbuvcRyHf/zjH13YmtBEBZ0Q0mXmz5/f7mgOfSgT4hsq6MQrsizj+PHjUBRFe45OWyOEBMpf//rX875+//33d1FLQhd9h0467YUXXkB2djbi4+O1c9E5jkNhYWGQW0ZC3UcffYS0tDTt9qkrVqzQbvCzfv16DB06NMgtJKGKLhF8YXSETjotJSUF+/btQ1RUVLCbQrqZtLQ07NmzB7169cL27duxbNkybN26FcXFxXjrrbe0qw8SQjqPjtBJpyUlJWl3XSOkM3ieR69evQAA27dvx+LFi7Xrcm/cuDHIrSOh7Omnnz7v63/84x+7qCWhiwo66bQ+ffrg8ssv184JdcvOzg5iq0h3wPM86urq0Lt3b3z22Wf4wx/+oL1mt9uD2DIS6p5++mmkpqZi9uzZ6Nu3L137og1U0EmnZWRk0OlFxCsrVqzA2LFjoaoqrr/+eu388127diEpKSm4jSMhraqqCtu2bcP27dthNBpx2223YdasWdrps4S+QyeEdDFZlmGz2Tw+iM+ePQvGGMLDw4PYMtJd1NTU4F//+hfWrl2LtWvX4p577gl2k0ICHaGTTvvPf/6Dxx57DKWlpXA4HNrzdKcs0hE//fQTOI5Dnz59cODAAXz88ccYMWIEbrjhhmA3jYQ4xhi+/vprvP322ygsLMSdd96Jq666KtjNChlU0Emn/fa3v8Xzzz+P++67D1999RX++c9/4uzZs8FuFukG/vSnPyEnJweSJOG6665DUVERpk6dinXr1uHf//43TWwi7XrwwQexZ88eTJw4EXfffTdefvnlYDcp5NCQO+m0yy+/HN999x0uvfRS7ZaXV199NXbv3h3klpFQd+mll6K4uBh2ux0XXXQRjh07ht69e8PhcGD8+PEoLi4OdhNJiOJ5HjH/fzv3rwtNGAVw+EyWCKKZUlzBZldchFLjAsQVuARXoBStUKDQKJSU2tWp1TQGFWG+ysafzZfZKMaePE+zs7PNaSa/zLyzb1kOdxr8+KzrOoqiiLu7uzbH+xPcoTO2ubm5eH19jeXl5dje3o7FxcWoqqrtsZgAnU4niqKI2dnZ6PV6MT8/HxERMzMzw02KYJT39/e2R/jzXEE0VlVV3N7exsHBQby9vcXe3l50Op24uLiwBzeNlGUZz8/PERFxdXU1PH9/fx/T09NtjQUpeOROYxsbG7G5uRmrq6tfzp+fn8fp6WkcHh62NBmT7unpKaqqiqWlpbZHgYnlDp3Grq+vf8Q8ImJtbS0Gg0ELEzFpTk5Ohsef79AXFhbi7OyshYkgD0Gnsf/t5GWXL5rY2dkZHm9tbX35zbIN/I6g01i/34+jo6Mf54+Pj6Pb7bYwEZPm8wrf99U+q3/wO95yp7Hd3d1YX1+P/f39WFlZiaIoYjAYxOPjo8elNPLxV6Pvx6O+A+PxUhxju7y8jJubm6jrOrrd7sh1dRhlamoqyrKMuq7j4eFhuP1rXddRVVW8vLy0PCFMLkEHgASsoQNAAoIOAAkIOgAkIOgAkICgA0ACgg4ACQg6ACQg6ACQgKADQAKCDgAJCDoAJCDoAJCAoANAAoIOAAkIOgAkIOgAkICgA0ACgg4ACQg6ACQg6ACQgKADQAKCDgAJCDoAJCDoAJCAoANAAoIOAAkIOgAkIOgAkICgA0ACgg4ACQg6ACQg6ACQgKADQAKCDgAJCDoAJCDoAJCAoANAAoIOAAkIOgAkIOgAkICgA0AC/wDeOHoGpQormAAAAABJRU5ErkJggg==" }, "metadata": {}, "output_type": "display_data" @@ -2682,9 +2744,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python [conda env:pmp_si] *", + "display_name": "pcmdi_metrics_dev_20231129", "language": "python", - "name": "conda-env-pmp_si-py" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -2696,7 +2758,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.13" + "version": "3.10.10" } }, "nbformat": 4,