From 573bf3940e9b57beabf33b0a8c2c74dac514bc52 Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 18 Nov 2020 15:54:05 +0100 Subject: [PATCH 01/80] Update file format for monthly 5x5 spherical harmonic coefficients from SLR --- gravity_toolkit/read_CSR_monthly_6x1.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/gravity_toolkit/read_CSR_monthly_6x1.py b/gravity_toolkit/read_CSR_monthly_6x1.py index 3452ac11..845a5ce4 100644 --- a/gravity_toolkit/read_CSR_monthly_6x1.py +++ b/gravity_toolkit/read_CSR_monthly_6x1.py @@ -32,6 +32,7 @@ convert_calendar_decimal.py: converts from calendar dates to decimal years UPDATE HISTORY: + Updated 11/2020: following new format without geocenter coefficient Updated 07/2020: added function docstrings Updated 07/2019: following new format with mean field in header and no C6,0 Updated 10/2018: using future division for python3 Compatibility @@ -75,12 +76,13 @@ def read_CSR_monthly_6x1(input_file, HEADER=True): file_lines = len(file_contents) #-- spherical harmonic degree range (full 5x5 with 6,1) - LMIN = 1 + LMIN = 2 LMAX = 6 - n_harm = (LMAX**2 + 3*LMAX - LMIN**2 - LMIN)//2 - 5 + n_harm = (LMAX**2 + LMAX - LMIN**2 - LMIN)//2 + 1 #-- counts the number of lines in the header count = 0 + indice = 0 #-- Reading over header text while HEADER: #-- file line at count @@ -102,7 +104,7 @@ def read_CSR_monthly_6x1(input_file, HEADER=True): mean_Ylms['slm'] = np.zeros((LMAX+1,LMAX+1)) mean_Ylm_error['clm'] = np.zeros((LMAX+1,LMAX+1)) mean_Ylm_error['slm'] = np.zeros((LMAX+1,LMAX+1)) - for i in range(n_harm+1): + for i in range(n_harm): #-- split the line into individual components line = file_contents[indice+i].split() #-- degree and order for the line From dd6d335e16eba8cd02d083162fdebaad2f01d90c Mon Sep 17 00:00:00 2001 From: hulecom Date: Thu, 26 Nov 2020 09:48:14 +0100 Subject: [PATCH 02/80] Debug mean function from spatial.py Add C0,0 for JPL GSM data to be able to compare them with CSR and GFZ --- gravity_toolkit/read_GRACE_harmonics.py | 5 +++++ gravity_toolkit/spatial.py | 2 +- 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/gravity_toolkit/read_GRACE_harmonics.py b/gravity_toolkit/read_GRACE_harmonics.py index 98a72b0f..6d3a4504 100644 --- a/gravity_toolkit/read_GRACE_harmonics.py +++ b/gravity_toolkit/read_GRACE_harmonics.py @@ -173,6 +173,11 @@ def read_GRACE_harmonics(input_file, LMAX, MMAX=None, POLE_TIDE=False): drift_c[l1,m1] = np.float(line_contents[3]) drift_s[l1,m1] = np.float(line_contents[4]) + #-- Adding 0,0 coefficient for JPl + #-- to be able to compare it with CSR and GFZ + if (PRC == 'JPLEM') and (DSET == 'GSM'): + grace_L2_input['clm'][0, 0] = 1 + #-- Adding drift rates to clm and slm for RL04 #-- if drift rates exist at any time, will add to harmonics #-- Will convert the secular rates into a stokes contribution diff --git a/gravity_toolkit/spatial.py b/gravity_toolkit/spatial.py index 52c82e56..27a279f7 100644 --- a/gravity_toolkit/spatial.py +++ b/gravity_toolkit/spatial.py @@ -639,7 +639,7 @@ def mean(self, apply=False): Option: apply to remove the mean field from the input data """ #-- output spatial object - temp = spatial(nlon=self.shape[0],nlat=self.shape[1], + temp = spatial(nlon=self.data.shape[0],nlat=self.data.shape[1], fill_value=self.fill_value) #-- copy dimensions temp.lon = self.lon.copy() From 666ed57b5c9549385514a9642c8210296100592e Mon Sep 17 00:00:00 2001 From: hulecom Date: Fri, 27 Nov 2020 16:41:26 +0100 Subject: [PATCH 03/80] Update for CNES RL04 and RL05 --- gravity_toolkit/grace_date.py | 6 ++++-- gravity_toolkit/read_GRACE_harmonics.py | 4 +++- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/gravity_toolkit/grace_date.py b/gravity_toolkit/grace_date.py index 567ecb6a..2bdae121 100644 --- a/gravity_toolkit/grace_date.py +++ b/gravity_toolkit/grace_date.py @@ -34,6 +34,7 @@ convert_julian.py: converts a Julian date into a calendar date UPDATE HISTORY: + Updated 10/2020: updated for CNES RL04 & RL05 (monthly fields) Updated 10/2020: use argparse to set command line parameters Updated 07/2020: added function docstrings Updated 03/2020: for public release @@ -140,8 +141,9 @@ def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): #-- GFZOP: GFZ German Research Center for Geosciences (RL06+GRACE-FO) #-- JPLEM: NASA Jet Propulsion Laboratory (harmonic solutions) #-- JPLMSC: NASA Jet Propulsion Laboratory (mascon solutions) + #-- GRGS: CNES Groupe de Recherche de Géodésie Spatiale regex_pattern = (r'(.*?)-2_(\d+)-(\d+)_(.*?)_({0})_(.*?)_(\d+)(.*?)' - r'(\.gz|\.gfc)?$').format(r'UTCSR|EIGEN|GFZOP|JPLEM|JPLMSC') + r'(\.gz|\.gfc|\.txt)?$').format(r'UTCSR|EIGEN|GFZOP|JPLEM|JPLMSC|GRGS') rx = re.compile(regex_pattern, re.VERBOSE) #-- Output GRACE date ascii file @@ -269,7 +271,7 @@ def main(): parser.add_argument('--center','-c', metavar='PROC', type=str, nargs='+', default=['CSR','GFZ','JPL'], - choices=['CSR','GFZ','JPL'], + choices=['CSR','GFZ','JPL', 'CNES'], help='GRACE/GRACE-FO Processing Center') #-- GRACE/GRACE-FO data release parser.add_argument('--release','-r', diff --git a/gravity_toolkit/read_GRACE_harmonics.py b/gravity_toolkit/read_GRACE_harmonics.py index 6d3a4504..f6d967c0 100644 --- a/gravity_toolkit/read_GRACE_harmonics.py +++ b/gravity_toolkit/read_GRACE_harmonics.py @@ -31,6 +31,7 @@ PyYAML: YAML parser and emitter for Python (https://github.com/yaml/pyyaml) UPDATE HISTORY: + Updated 10/2020: Change parse function to work with GRGS data Updated 08/2020: flake8 compatible regular expression strings input file can be "diskless" bytesIO object Updated 07/2020: added function docstrings @@ -247,8 +248,9 @@ def parse_file(input_file): #-- GFZOP: GFZ German Research Center for Geosciences (RL06+GRACE-FO) #-- JPLEM: NASA Jet Propulsion Laboratory (harmonic solutions) #-- JPLMSC: NASA Jet Propulsion Laboratory (mascon solutions) + # -- GRGS: CNES Groupe de Recherche de Géodésie Spatiale regex_pattern = (r'(.*?)-2_(\d+)-(\d+)_(.*?)_({0})_(.*?)_(\d+)(.*?)' - r'(\.gz|\.gfc)?$').format('UTCSR|EIGEN|GFZOP|JPLEM|JPLMSC') + r'(\.gz|\.gfc|\.txt)?$').format('UTCSR|EIGEN|GFZOP|JPLEM|JPLMSC|GRGS') rx = re.compile(regex_pattern, re.VERBOSE) #-- extract parameters from input filename if isinstance(input_file, io.IOBase): From 6edaaf3f4f32534d934145616eac0b70dc0ce853 Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 1 Dec 2020 11:31:32 +0100 Subject: [PATCH 04/80] Addition of the C2,1/S2,1 and C2,2/S2,2 correction when reading GRACE data --- gravity_toolkit/grace_input_months.py | 63 +++++++++++++++- gravity_toolkit/read_SLR_CS2.py | 104 ++++++++++++++++++++++++++ 2 files changed, 164 insertions(+), 3 deletions(-) create mode 100644 gravity_toolkit/read_SLR_CS2.py diff --git a/gravity_toolkit/grace_input_months.py b/gravity_toolkit/grace_input_months.py index 8e62952b..db367399 100644 --- a/gravity_toolkit/grace_input_months.py +++ b/gravity_toolkit/grace_input_months.py @@ -69,6 +69,7 @@ read_GRACE_harmonics.py: reads an input GRACE data file and calculates date UPDATE HISTORY: + Updated 11/2020: added C/S2,1 and C/S2,2 correction from John Ries Updated 08/2020: flake8 compatible regular expression strings Updated 07/2020: added function docstrings Updated 06/2020: set relative time to mean of input within regress_model @@ -112,6 +113,7 @@ from gravity_toolkit.grace_date import grace_date from gravity_toolkit.read_SLR_C20 import read_SLR_C20 from gravity_toolkit.read_SLR_C30 import read_SLR_C30 +from gravity_toolkit.read_SLR_CS2 import read_SLR_CS2 from gravity_toolkit.read_tellus_geocenter import read_tellus_geocenter from gravity_toolkit.read_SLR_geocenter import aod_corrected_SLR_geocenter from read_GRACE_geocenter.read_GRACE_geocenter import read_GRACE_geocenter @@ -119,7 +121,8 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, missing, SLR_C20, DEG1, MMAX=None, SLR_C30='', - MODEL_DEG1=False, DEG1_GIA='', ATM=False, POLE_TIDE=False): + SLR_21='', SLR_22='', MODEL_DEG1=False, DEG1_GIA='', ATM=False, + POLE_TIDE=False): """ Reads GRACE/GRACE-FO files for a spherical harmonic degree and order and a date range @@ -156,6 +159,12 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, None: use original values CSR: use values from CSR (5x5 with 6,1) GSFC: use values from GSFC (TN-14) + SLR_21: replaces C21 and S21 with SLR values + None: use original values + CSR: use values from CSR (5x5 with 6,1) + SLR_22: replaces C22 and S22 with SLR values + None: use original values + CSR: use values from CSR (5x5 with 6,1) POLE_TIDE: correct GSM data with pole tides following Wahr et al (2015) ATM: correct data with ECMWF "jump" corrections GAE, GAF and GAG MODEL_DEG1: least-squares model missing degree 1 coefficients (True/False) @@ -215,11 +224,29 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, else: C30_str = '' + # -- Replacing C2,1 with SLR C2,1 + # -- Running function read_SLR_CS2.py + if (SLR_21 == 'CSR'): + SLR_file = os.path.join(base_dir, 'C21_S21_RL06.txt') + CS21_input = read_SLR_CS2(SLR_file) + CS21_str = '_wCSR_21' + else: + CS21_str = '' + + # -- Replacing C2,2 with SLR C2,2 + # -- Running function read_SLR_CS2.py + if (SLR_22 == 'CSR'): + SLR_file = os.path.join(base_dir, 'C22_S22_RL06.txt') + CS22_input = read_SLR_CS2(SLR_file) + CS22_str = '_wCSR_22' + else: + CS22_str = '' + #-- Correcting for Degree 1 (geocenter variations) #-- reading degree 1 file for given release if specified if (DEG1 == 'Tellus'): #-- Tellus (PO.DAAC) degree 1 - if DREL in ('RL04','RL05'): + if DREL in ('RL04','RL05') and PROC in ('JPL', 'CSR', 'GFZ'): DEG1_file = os.path.join(base_dir,'geocenter', 'deg1_coef_{0}.txt'.format(DREL)) JPL = False @@ -261,7 +288,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, #-- pole tide flag if correcting for pole tide drift (Wahr et al. 2015) pt_str = '_wPT' if POLE_TIDE else '' #-- full output string (C20, C30, geocenter and atmospheric flags) - out_str = C20_str + C30_str + DEG1_str + atm_str + pt_str + out_str = C20_str + C30_str + CS21_str + CS22_str + DEG1_str + atm_str + pt_str #-- Range of months from start_mon to end_mon (end_mon+1 to include end_mon) #-- Removing the missing months and months not to consider @@ -319,6 +346,36 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, k, = np.nonzero(C30_input['month'] == grace_month) grace_clm[3,0,i] = C30_input['data'][k] + # -- Replace CS21 with SLR coefficients for single-accelerometer months + if (SLR_21 == 'CSR'): + # -- verify that there are replacement CS21 months for specified range + months_test = sorted(set(mon[mon > 176]) - set(CS21_input['month'])) + if months_test: + gm = ','.join('{0:03d}'.format(gm) for gm in months_test) + raise IOError('No Matching CS21 Months ({0})'.format(gm)) + # -- replace CS21 with SLR coefficients + for i, grace_month in enumerate(months): + count = np.count_nonzero(CS21_input['month'] == grace_month) + if (count != 0) and (grace_month > 176): + k, = np.nonzero(CS21_input['month'] == grace_month) + grace_clm[2, 1, i] = CS21_input['datac'][k] + grace_slm[2, 1, i] = CS21_input['datas'][k] + + # -- Replace CS22 with SLR coefficients for single-accelerometer months + if (SLR_22 == 'CSR'): + # -- verify that there are replacement CS22 months for specified range + months_test = sorted(set(mon[mon > 176]) - set(CS22_input['month'])) + if months_test: + gm = ','.join('{0:03d}'.format(gm) for gm in months_test) + raise IOError('No Matching CS22 Months ({0})'.format(gm)) + # -- replace CS22 with SLR coefficients + for i, grace_month in enumerate(months): + count = np.count_nonzero(CS22_input['month'] == grace_month) + if (count != 0) and (grace_month > 176): + k, = np.nonzero(CS22_input['month'] == grace_month) + grace_clm[2, 2, i] = CS22_input['datac'][k] + grace_slm[2, 2, i] = CS22_input['datas'][k] + #-- Use Degree 1 coefficients #-- Tellus: Tellus Degree 1 (PO.DAAC following Sun et al., 2016) #-- SLR: CSR Satellite Laser Ranging (SLR) Degree 1 - GRACE AOD diff --git a/gravity_toolkit/read_SLR_CS2.py b/gravity_toolkit/read_SLR_CS2.py new file mode 100644 index 00000000..7579aab2 --- /dev/null +++ b/gravity_toolkit/read_SLR_CS2.py @@ -0,0 +1,104 @@ +#!/usr/bin/env python +u""" +read_SLR_CS2.py +Written by Hugo Lecomte (11/2020) + +Reads monthly degree 2,x spherical harmonic data files from SLR + +Dataset distributed by CSR + http://download.csr.utexas.edu/pub/slr/degree_2/ + C21_S21_RL06.txt or C22_S22_RL06.txt + +REFERENCE: + Dahle, C., Murböck, M., Flechtner, F. , Dobslaw, H., Michalak, G., + Neumayer, K. H., Abrykosov, O., Reinhold, A., König, R., Sulzbach, R. + and Förste C., "The GFZ GRACE RL06 Monthly Gravity Field Time Series: + Processing Details,and Quality Assessment", Remote Sensing, 11(18), 2116, 2019. + https://doi.org/10.3390/rs11182116 + +CALLING SEQUENCE: + SLR_2x = read_SLR_CS2(SLR_file) + +INPUTS: + SLR_file: + CSR 2,1: C21_S21_RL06.txt + CSR 2,2: C22_S22_RL06.txt + +OUTPUTS: + datac: SLR degree 2 order x cosine stokes coefficients (C2x) + datas: SLR degree 2 order x sine stokes coefficients (S2x) + errorc: SLR degree 2 order x cosine stokes coefficient error (eC2x) + errors: SLR degree 2 order x sine stokes coefficient error (eS2x) + month: GRACE/GRACE-FO month of measurement (Apr. 2002 = 004) + time: date of SLR measurement + +PYTHON DEPENDENCIES: + numpy: Scientific Computing Tools For Python (https://numpy.org) + +UPDATE HISTORY: + Written 11/2020 +""" +import os +import re +import numpy as np + +#-- PURPOSE: read Degree 2,x data from Satellite Laser Ranging (SLR) +def read_SLR_CS2(SLR_file): + """ + Reads CS2,x spherical harmonic coefficients from SLR measurements + + Arguments + --------- + SLR_file: Satellite Laser Ranging file + + Returns + ------- + datac: SLR degree 2 order x cosine stokes coefficients (C2x) + datas: SLR degree 2 order x sine stokes coefficients (S2x) + errorc: SLR degree 2 order x cosine stokes coefficient error (eC2x) + errors: SLR degree 2 order x sine stokes coefficient error (eS2x) + month: GRACE/GRACE-FO month of measurement + time: date of SLR measurement + """ + + #-- check that SLR file exists + if not os.access(os.path.expanduser(SLR_file), os.F_OK): + raise IOError('SLR file not found in file system') + #-- output dictionary with input data + dinput = {} + + if bool(re.search('C2\d_S2\d_RL',SLR_file)): + + #-- SLR 2x RL06 file from CSR + #-- automatically skip the header denoted with '#' + content = np.genfromtxt(os.path.expanduser(SLR_file)) + + #-- number of months within the file + n_mon = content.shape[0] + date_conv = content[:,0] + #-- remove the monthly mean of the AOD model + C2x_input = content[:,1] - content[:,5]*10**-10 + eC2x_input = content[:,3]*10**-10 + # -- remove the monthly mean of the AOD model + S2x_input = content[:,2] - content[:,6]*10**-10 + eS2x_input = content[:,4]*10**-10 + mon = np.zeros((n_mon),dtype=np.int) + + #-- for every line convert the date into month number: + for t in range(content.shape[0]): + # -- GRACE/GRACE-FO month of SLR solutions + mon[t] = 1 + t + + #-- convert to output variables and truncate if necessary + dinput['time'] = date_conv + dinput['datac'] = C2x_input + dinput['errorc'] = eC2x_input + dinput['datas'] = S2x_input + dinput['errors'] = eS2x_input + dinput['month'] = mon + + else: + raise FileNotFoundError("Invalid file given to read_SLR_2x:", SLR_file) + + #-- return the input CS2x data, year-decimal date, and GRACE/GRACE-FO month + return dinput From 9de32634970fbbf96b0d019a2167f3cdb5fd3153 Mon Sep 17 00:00:00 2001 From: hulecom Date: Thu, 3 Dec 2020 19:46:15 +0100 Subject: [PATCH 05/80] Rework grace_date and add GRAZ data --- gravity_toolkit/grace_date.py | 189 ++++++++++++++++++++++------------ 1 file changed, 123 insertions(+), 66 deletions(-) diff --git a/gravity_toolkit/grace_date.py b/gravity_toolkit/grace_date.py index 2bdae121..dde95574 100644 --- a/gravity_toolkit/grace_date.py +++ b/gravity_toolkit/grace_date.py @@ -11,7 +11,7 @@ base_dir: Working data directory for GRACE/GRACE-FO data OPTIONS: - PROC: GRACE data processing center (CSR/CNES/JPL/GFZ) + PROC: GRACE data processing center (CSR/CNES/JPL/GFZ/GRAZ) DREL: GRACE/GRACE-FO Data Release (RL03 for CNES) (RL06 for CSR/GFZ/JPL) DSET: GRACE dataset (GAA/GAB/GAC/GAD/GSM) GAA is the non-tidal atmospheric correction @@ -34,7 +34,7 @@ convert_julian.py: converts a Julian date into a calendar date UPDATE HISTORY: - Updated 10/2020: updated for CNES RL04 & RL05 (monthly fields) + Updated 11/2020: updated for CNES RL04 & RL05 and GRAZ 2018 (monthly fields) Updated 10/2020: use argparse to set command line parameters Updated 07/2020: added function docstrings Updated 03/2020: for public release @@ -98,7 +98,8 @@ def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): CSR: University of Texas Center for Space Research GFZ: German Research Centre for Geosciences (GeoForschungsZentrum) JPL: Jet Propulsion Laboratory - CNES: French Centre National D'Etudes Spatiales + CNES: French Centre Natnp.int(month)ional D'Etudes Spatiales + GRAZ: Institute of Geodesy from GRAZ University of Technology DREL: GRACE/GRACE-FO data release DSET: GRACE/GRACE-FO dataset GAA: non-tidal atmospheric correction @@ -134,16 +135,24 @@ def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): tdec = np.zeros((n_files))#-- tdec is the date in decimal form mon = np.zeros((n_files,),dtype=np.int)#-- GRACE/GRACE-FO month number - #-- compile numerical expression operator for parameters from files - #-- will work with previous releases and releases for GRACE-FO - #-- UTCSR: The University of Texas at Austin Center for Space Research - #-- EIGEN: GFZ German Research Center for Geosciences (RL01-RL05) - #-- GFZOP: GFZ German Research Center for Geosciences (RL06+GRACE-FO) - #-- JPLEM: NASA Jet Propulsion Laboratory (harmonic solutions) - #-- JPLMSC: NASA Jet Propulsion Laboratory (mascon solutions) - #-- GRGS: CNES Groupe de Recherche de Géodésie Spatiale - regex_pattern = (r'(.*?)-2_(\d+)-(\d+)_(.*?)_({0})_(.*?)_(\d+)(.*?)' - r'(\.gz|\.gfc|\.txt)?$').format(r'UTCSR|EIGEN|GFZOP|JPLEM|JPLMSC|GRGS') + if PROC in ('CSR', 'GFZ', 'JPL', 'CNES'): + #-- compile numerical expression operator for parameters from files + #-- will work with previous releases and releases for GRACE-FO + #-- UTCSR: The University of Texas at Austin Center for Space Research + #-- EIGEN: GFZ German Research Center for Geosciences (RL01-RL05) + #-- GFZOP: GFZ German Research Center for Geosciences (RL06+GRACE-FO) + #-- JPLEM: NASA Jet Propulsion Laboratory (harmonic solutions) + #-- JPLMSC: NASA Jet Propulsion Laboratory (mascon solutions) + #-- GRGS: CNES Groupe de Recherche de Géodésie Spatiale + regex_pattern = (r'(.*?)-2_(\d+)-(\d+)_(.*?)_({0})_(.*?)_(\d+)(.*?)' + r'(\.gz|\.gfc|\.txt)?$').format(r'UTCSR|EIGEN|GFZOP|JPLEM|JPLMSC|GRGS') + elif PROC == 'GRAZ': + # -- GRAZ: Institute of Geodesy from GRAZ University of Technology + regex_pattern = (r'(.*?)-({0})_(.*?)_(\d+)-(\d+)' + r'(\.gz|\.gfc|\.txt)').format(r'Grace_operational|Grace2018') + else: + raise ValueError("Unknown PROC value:", PROC) + rx = re.compile(regex_pattern, re.VERBOSE) #-- Output GRACE date ascii file @@ -160,58 +169,64 @@ def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): #-- for each data file for t, infile in enumerate(input_files): #-- extract parameters from input filename - PFX,start_date,end_date,AUX,PRC,F1,DRL,F2,SFX = rx.findall(infile).pop() - #-- find start date, end date and number of days - start_yr[t] = np.float(start_date[:4]) - end_yr[t] = np.float(end_date[:4]) - start_day[t] = np.float(start_date[4:]) - end_day[t] = np.float(end_date[4:]) - #-- end_day (will be changed if the month crosses 2 years) - end_plus = np.copy(end_day[t]) - - #-- calculate mid-month date taking into account if measurements are - #-- on different years - if ((start_yr[t] % 4) == 0):#-- Leap Year (% = modulus) - dpy = 366.0 - else:#-- Standard Year - dpy = 365.0 - #-- For data that crosses years - if (start_yr[t] != end_yr[t]): - #-- end_yr - start_yr should be 1 - end_plus = (end_yr[t]-start_yr[t])*dpy + end_day[t] - #-- Calculation of Mid-month value - mid_day[t] = np.mean([start_day[t], end_plus]) - - #-- Calculation of the Julian date from start_yr and mid_day - JD[t] = np.float(367.0*start_yr[t] - - np.floor(7.0*(start_yr[t] + np.floor(10.0/12.0))/4.0) - - np.floor(3.0*(np.floor((start_yr[t] - 8.0/7.0)/100.0) + 1.0)/4.0) + - np.floor(275.0/9.0) + mid_day[t] + 1721028.5) - #-- convert the julian date into calendar dates (hour, day, month, year) - cal_date = convert_julian(JD[t]) - - #-- Calculating the mid-month date in decimal form - tdec[t] = start_yr[t] + mid_day[t]/dpy - - #-- Calculation of total days since start of campaign - count = 0 - n_yrs = np.int(start_yr[t]-2002) - #-- for each of the GRACE years up to the file year - for iyr in range(n_yrs): - #-- year i - year = 2002 + iyr - #-- number of days in year i (if leap year or standard year) - if ((year % 4) == 0): - #-- Leap Year - dpm=[31,29,31,30,31,30,31,31,30,31,30,31] - else: - #-- Standard Year - dpm=[31,28,31,30,31,30,31,31,30,31,30,31] - #-- add all days from prior years to count - count += np.sum(dpm) - - #-- calculating the total number of days since 2002 - tot_days[t] = np.mean([count+start_day[t], count+end_plus]) + if PROC in ('CSR', 'GFZ', 'JPL', 'CNES'): + PFX,start_date,end_date,AUX,PRC,F1,DRL,F2,SFX = rx.findall(infile).pop() + + #-- find start date, end date and number of days + start_yr[t] = np.float(start_date[:4]) + end_yr[t] = np.float(end_date[:4]) + start_day[t] = np.float(start_date[4:]) + end_day[t] = np.float(end_date[4:]) + #-- end_day (will be changed if the month crosses 2 years) + end_plus = np.copy(end_day[t]) + + #-- Calculation of total days since start of campaign + count, dpm, dpy = day_count(start_yr[t]) + + #-- For data that crosses years + if (start_yr[t] != end_yr[t]): + #-- end_yr - start_yr should be 1 + end_plus = (end_yr[t] - start_yr[t]) * dpy + end_day[t] + #-- Calculation of Mid-month value + mid_day[t] = np.mean([start_day[t], end_plus]) + + #-- Calculation of the Julian date from start_yr and mid_day + JD[t] = np.float(367.0 * start_yr[t] - + np.floor(7.0 * (start_yr[t] + np.floor(10.0 / 12.0)) / 4.0) - + np.floor(3.0 * (np.floor((start_yr[t] - 8.0 / 7.0) / 100.0) + 1.0) / 4.0) + + np.floor(275.0 / 9.0) + mid_day[t] + 1721028.5) + #-- convert the julian date into calendar dates (hour, day, month, year) + cal_date = convert_julian(JD[t]) + month = cal_date['month'] + + #-- Calculating the mid-month date in decimal form + tdec[t] = start_yr[t] + mid_day[t] / dpy + + #-- calculating the total number of days since 2002 + tot_days[t] = np.mean([count + start_day[t], count + end_plus]) + + elif PROC == 'GRAZ': + PFX,SAT,trunc,year,month,SFX = rx.findall(infile).pop() + #-- find start year, end year + start_yr[t] = np.float(year) + end_yr[t] = np.float(year) + + #-- Calculation of total days since start of campaign + #-- Get information on the current year (day per month and day per year) + count, dpm, dpy = day_count(start_yr[t]) + + #-- find start day, end day + start_day[t] = np.sum(dpm[:np.int(month) - 1]) + 1 + end_day[t] = np.sum(dpm[:np.int(month)]) + + #-- Calculation of Mid-month value + mid_day[t] = np.mean([start_day[t], end_day[t]]) + + #-- Calculating the mid-month date in decimal form + tdec[t] = start_yr[t] + mid_day[t] / dpy + + #-- calculating the total number of days since 2002 + tot_days[t] = np.mean([count + start_day[t], count + end_day[t]]) #-- Calculates the month number (or 10-day number for CNES RL01,RL02) if ((PROC == 'CNES') and (DREL in ('RL01','RL02'))): @@ -220,7 +235,7 @@ def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): #-- calculate the GRACE/GRACE-FO month (Apr02 == 004) #-- https://grace.jpl.nasa.gov/data/grace-months/ #-- Notes on special months (e.g. 119, 120) below - mon[t] = 12*(cal_date['year']-2002) + cal_date['month'] + mon[t] = 12*(start_yr[t]-2002) + np.int(month) #-- The 'Special Months' (Nov 2011, Dec 2011 and April 2012) with #-- Accelerometer shutoffs make this relation between month number @@ -252,6 +267,48 @@ def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): #-- return the python dictionary that maps GRACE months with GRACE files return grace_files +def day_count(input_year): + """ + Count the number of days since the begining of the campaign + Return useful information on the current year + + Arguments + --------- + input_year: year of interest + + Returns + ------- + count: total days since start of campaign + dpm: list of the day per month + dpy: day per month this year + """ + # -- Calculation of total days since start of campaign + count = 0 + n_yrs = np.int(input_year - 2002) + # -- for each of the GRACE years up to the file year + for iyr in range(n_yrs): + # -- year i + year = 2002 + iyr + # -- number of days in year i (if leap year or standard year) + if ((year % 4) == 0): + # -- Leap Year + dpm = [31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + else: + # -- Standard Year + dpm = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + # -- add all days from prior years to count + count += np.sum(dpm) + + if ((input_year % 4) == 0): + dpy = 366.0 + dpm = [31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + else: + dpy = 365.0 + dpm = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + + return count, dpm, dpy + + #-- PURPOSE: program that calls grace_date() with set parameters def main(): #-- command line parameters From 532fd5c67950d2677332d7d4cedd1ec37ca8702d Mon Sep 17 00:00:00 2001 From: hulecom Date: Fri, 4 Dec 2020 15:27:04 +0100 Subject: [PATCH 06/80] Add GRAZ coefficient reading for dataset ITSG 2018/2016/2014 --- gravity_toolkit/grace_input_months.py | 8 +++- gravity_toolkit/read_ICGEM_harmonics.py | 60 +++++++++++++++++++++++-- 2 files changed, 62 insertions(+), 6 deletions(-) diff --git a/gravity_toolkit/grace_input_months.py b/gravity_toolkit/grace_input_months.py index db367399..f7f0103b 100644 --- a/gravity_toolkit/grace_input_months.py +++ b/gravity_toolkit/grace_input_months.py @@ -118,6 +118,7 @@ from gravity_toolkit.read_SLR_geocenter import aod_corrected_SLR_geocenter from read_GRACE_geocenter.read_GRACE_geocenter import read_GRACE_geocenter from gravity_toolkit.read_GRACE_harmonics import read_GRACE_harmonics +from gravity_toolkit.read_ICGEM_harmonics import read_ICGEM_harmonics def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, missing, SLR_C20, DEG1, MMAX=None, SLR_C30='', @@ -135,7 +136,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, Arguments --------- base_dir: Working data directory for GRACE/GRACE-FO data - PROC: (CSR/CNES/JPL/GFZ) data processing center + PROC: (CSR/CNES/JPL/GFZ/GRAZ) data processing center DREL: (RL01,RL02,RL03,RL04,RL05,RL06) data release DSET: (GAA/GAB/GAC/GAD/GSM) data product LMAX: Upper bound of Spherical Harmonic Degrees @@ -312,7 +313,10 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, for i,grace_month in enumerate(months): #-- Effects of Pole tide drift will be compensated if soecified infile = grace_files[grace_month] - Ylms = read_GRACE_harmonics(infile,LMAX,MMAX=MMAX,POLE_TIDE=POLE_TIDE) + if PROC in ('CSR', 'GFZ', 'JPL', 'CNES', 'JPLMSC'): + Ylms = read_GRACE_harmonics(infile,LMAX,MMAX=MMAX,POLE_TIDE=POLE_TIDE) + elif PROC == 'GRAZ': + Ylms = read_ICGEM_harmonics(infile) grace_clm[:,:,i] = Ylms['clm'][0:LMAX+1,0:MMAX+1] grace_slm[:,:,i] = Ylms['slm'][0:LMAX+1,0:MMAX+1] tdec[i] = Ylms['time'] diff --git a/gravity_toolkit/read_ICGEM_harmonics.py b/gravity_toolkit/read_ICGEM_harmonics.py index 6851d61a..6434cc8b 100644 --- a/gravity_toolkit/read_ICGEM_harmonics.py +++ b/gravity_toolkit/read_ICGEM_harmonics.py @@ -31,22 +31,24 @@ numpy: Scientific Computing Tools For Python (https://numpy.org) UPDATE HISTORY: + Updated 12/2020: added GRAZ information extraction Updated 07/2020: added function docstrings Updated 07/2017: include parameters to change the tide system Written 12/2015 """ import os import re +import io import numpy as np #-- PURPOSE: read spherical harmonic coefficients of a gravity model def read_ICGEM_harmonics(model_file, FLAG='gfc'): """ - Extract gravity model spherical harmonics from GFZ ICGEM gfc files + Extract gravity model spherical harmonics from GFZ/GRAZ ICGEM gfc files Arguments --------- - model_file: GFZ ICGEM gfc spherical harmonic data file + model_file: GFZ/GRAZ ICGEM gfc spherical harmonic data file Keyword arguments ----------------- @@ -66,12 +68,42 @@ def read_ICGEM_harmonics(model_file, FLAG='gfc'): norm: normalization of the spherical harmonics tide_system: tide system of gravity model """ + #-- python dictionary with model input and headers + model_input = {} + if 'ITSG' in model_file: + #-- parse filename + PFX, SAT, trunc, year, month, SFX = parse_file(model_file) + #-- convert string to integer + year, month = int(year), int(month) + + #-- calculate mid-month date taking into account if measurements are + #-- on different years + if (year % 4) == 0: #-- Leap Year + dpy = 366.0 + dpm = [31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + else: #-- Standard Year + dpy = 365.0 + dpm = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + + start_day = np.sum(dpm[:month - 1]) + 1 + end_day = np.sum(dpm[:month]) + + # -- Calculation of Mid-month value + mid_day = np.mean([start_day, end_day]) + # -- Calculating the mid-month date in decimal form + model_input['time'] = year + mid_day / dpy + # -- Calculating the Julian dates of the start and end date + model_input['start'] = np.float(367.0 * year - np.floor(7.0 * year / 4.0) - + np.floor(3.0 * (np.floor((year - 8.0 / 7.0) / 100.0) + 1.0) / 4.0) + + np.floor(275.0 / 9.0) + start_day + 1721028.5) + model_input['end'] = np.float(367.0 * year - np.floor(7.0 * year / 4.0) - + np.floor(3.0 * (np.floor((year - 8.0 / 7.0) / 100.0) + 1.0) / 4.0) + + np.floor(275.0 / 9.0) + end_day + 1721028.5) #-- read input data with open(os.path.expanduser(model_file),'r') as f: file_contents = f.read().splitlines() - #-- python dictionary with model input and headers - model_input = {} + #-- extract parameters from header header_parameters = ['modelname','earth_gravity_constant','radius', 'max_degree','errors','norm','tide_system'] @@ -104,3 +136,23 @@ def read_ICGEM_harmonics(model_file, FLAG='gfc'): model_input['eslm'][l1,m1] = np.float(line_contents[6]) #-- return the spherical harmonics and parameters return model_input + +#-- PURPOSE: extract parameters from filename +def parse_file(input_file): + """ + Extract parameters from filename + + Arguments + --------- + input_file: GRACE/GRACE-FO Level-2 spherical harmonic data file + """ + #-- compile numerical expression operator for parameters from files + # -- GRAZ: Institute of Geodesy from GRAZ University of Technology + regex_pattern = (r'(.*?)-({0})_(.*?)_(\d+)-(\d+)' + r'(\.gz|\.gfc|\.txt)').format(r'Grace_operational|Grace2018') + rx = re.compile(regex_pattern, re.VERBOSE) + #-- extract parameters from input filename + if isinstance(input_file, io.IOBase): + return rx.findall(input_file.filename).pop() + else: + return rx.findall(os.path.basename(input_file)).pop() \ No newline at end of file From 50958689cbe18d34ee8e252e40a26a82d1c4d3eb Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 8 Dec 2020 14:20:17 +0100 Subject: [PATCH 07/80] Tide transformation on C2,0 for comparison between CNES/GFZ and other centers --- gravity_toolkit/grace_input_months.py | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/gravity_toolkit/grace_input_months.py b/gravity_toolkit/grace_input_months.py index f7f0103b..1f7fe9ed 100644 --- a/gravity_toolkit/grace_input_months.py +++ b/gravity_toolkit/grace_input_months.py @@ -69,6 +69,7 @@ read_GRACE_harmonics.py: reads an input GRACE data file and calculates date UPDATE HISTORY: + Updated 12/2020: gestion of tide free and zero tide convention (IERS 2010) Updated 11/2020: added C/S2,1 and C/S2,2 correction from John Ries Updated 08/2020: flake8 compatible regular expression strings Updated 07/2020: added function docstrings @@ -119,6 +120,8 @@ from read_GRACE_geocenter.read_GRACE_geocenter import read_GRACE_geocenter from gravity_toolkit.read_GRACE_harmonics import read_GRACE_harmonics from gravity_toolkit.read_ICGEM_harmonics import read_ICGEM_harmonics +from gravity_toolkit.read_love_numbers import read_love_numbers +from gravity_toolkit.utilities import get_data_path def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, missing, SLR_C20, DEG1, MMAX=None, SLR_C30='', @@ -145,6 +148,8 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, missing: missing months to not consider in analysis SLR_C20: Replaces C20 with SLR values N: use original values + TideFree: add a bias for CSR, GFZ or GRAZ to convert C2,0 to tide free convention + ZeroTide: add a bias for CNES or JPL to convert C2,0 to zero tide convention CSR: use values from CSR (TN-07,TN-09,TN-11) GSFC: use values from GSFC (TN-14) DEG1: Use Degree 1 coefficients @@ -336,6 +341,23 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, k, = np.nonzero(C20_input['month'] == grace_month) grace_clm[2,0,i] = C20_input['data'][k] + elif SLR_C20 == 'TideFree' and PROC in ('CSR', 'JPL', 'GRAZ'): + # -- k2,0 nominal from IERS 2010 convention + k2 = 0.3190 + + # -- apply conversion formula from IERS 2010 Notes + grace_clm[2, 0, :] += 4.4228e-8 * 0.31460 * k2 + + elif SLR_C20 == 'ZeroTide' and PROC in ('CNES', 'GFZ'): + #-- k2,0 nominal from IERS 2010 convention + if PROC == 'CNES': + k2 = 0.3 + elif PROC == 'GFZ': + k2 = 0.3190 + + #-- apply conversion formula from IERS 2010 Notes + grace_clm[2, 0, :] -= 4.4228e-8 * 0.31460 * k2 + #-- Replace C30 with SLR coefficients for single-accelerometer months if SLR_C30 in ('CSR','GSFC','LARES'): #-- verify that there are replacement C30 months for specified range From d98226f2d13fc2978492e5a9a9f45040d741842e Mon Sep 17 00:00:00 2001 From: hulecom Date: Fri, 18 Dec 2020 14:11:55 +0100 Subject: [PATCH 08/80] Add SWARM data reading --- gravity_toolkit/grace_date.py | 28 +++++++--- gravity_toolkit/grace_input_months.py | 11 ++-- gravity_toolkit/read_ICGEM_harmonics.py | 72 +++++++++++++++---------- 3 files changed, 71 insertions(+), 40 deletions(-) diff --git a/gravity_toolkit/grace_date.py b/gravity_toolkit/grace_date.py index dde95574..ac878f91 100644 --- a/gravity_toolkit/grace_date.py +++ b/gravity_toolkit/grace_date.py @@ -11,7 +11,7 @@ base_dir: Working data directory for GRACE/GRACE-FO data OPTIONS: - PROC: GRACE data processing center (CSR/CNES/JPL/GFZ/GRAZ) + PROC: GRACE data processing center (CSR/CNES/JPL/GFZ/GRAZ) or SWARM for SWARM data DREL: GRACE/GRACE-FO Data Release (RL03 for CNES) (RL06 for CSR/GFZ/JPL) DSET: GRACE dataset (GAA/GAB/GAC/GAD/GSM) GAA is the non-tidal atmospheric correction @@ -34,6 +34,7 @@ convert_julian.py: converts a Julian date into a calendar date UPDATE HISTORY: + Updated 12/2020: Add SWARM data compilance Updated 11/2020: updated for CNES RL04 & RL05 and GRAZ 2018 (monthly fields) Updated 10/2020: use argparse to set command line parameters Updated 07/2020: added function docstrings @@ -75,7 +76,6 @@ """ from __future__ import print_function -import sys import os import re import argparse @@ -100,6 +100,8 @@ def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): JPL: Jet Propulsion Laboratory CNES: French Centre Natnp.int(month)ional D'Etudes Spatiales GRAZ: Institute of Geodesy from GRAZ University of Technology + + SWARM: gravity data from SWARM satellite DREL: GRACE/GRACE-FO data release DSET: GRACE/GRACE-FO dataset GAA: non-tidal atmospheric correction @@ -150,6 +152,10 @@ def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): # -- GRAZ: Institute of Geodesy from GRAZ University of Technology regex_pattern = (r'(.*?)-({0})_(.*?)_(\d+)-(\d+)' r'(\.gz|\.gfc|\.txt)').format(r'Grace_operational|Grace2018') + elif PROC == 'SWARM': + # -- SWARM: data from SWARM satellite + regex_pattern = (r'({0})_(.*?)_(EGF_SHA_2)__(.*?)_(.*?)_(.*?)' + r'(\.gz|\.gfc|\.txt)').format(r'SW') else: raise ValueError("Unknown PROC value:", PROC) @@ -205,11 +211,18 @@ def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): #-- calculating the total number of days since 2002 tot_days[t] = np.mean([count + start_day[t], count + end_plus]) - elif PROC == 'GRAZ': - PFX,SAT,trunc,year,month,SFX = rx.findall(infile).pop() - #-- find start year, end year - start_yr[t] = np.float(year) - end_yr[t] = np.float(year) + elif PROC == 'GRAZ' or PROC == 'SWARM': + if PROC == 'GRAZ': + PFX,SAT,trunc,year,month,SFX = rx.findall(infile).pop() + #-- find start year, end year + start_yr[t] = np.float(year) + end_yr[t] = np.float(year) + elif PROC == 'SWARM': + SAT, tmp, PROD, start_date, end_date, RL, SFX = rx.findall(os.path.basename(infile)).pop() + + start_yr[t] = int(start_date[:4]) + end_yr[t] = int(end_date[:4]) + month = int(start_date[4:6]) #-- Calculation of total days since start of campaign #-- Get information on the current year (day per month and day per year) @@ -252,6 +265,7 @@ def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): #-- add file to python dictionary mapped to GRACE/GRACE-FO month grace_files[mon[t]] = os.path.join(grace_dir,infile) + #-- print to GRACE DATES ascii file (NOTE: tot_days will be rounded up) if OUTPUT: print(('{0:13.8f} {1:03d} {2:8.0f} {3:03.0f} {4:8.0f} {5:03.0f} ' diff --git a/gravity_toolkit/grace_input_months.py b/gravity_toolkit/grace_input_months.py index 1f7fe9ed..2be005c4 100644 --- a/gravity_toolkit/grace_input_months.py +++ b/gravity_toolkit/grace_input_months.py @@ -70,6 +70,7 @@ UPDATE HISTORY: Updated 12/2020: gestion of tide free and zero tide convention (IERS 2010) + added SWARM gestion Updated 11/2020: added C/S2,1 and C/S2,2 correction from John Ries Updated 08/2020: flake8 compatible regular expression strings Updated 07/2020: added function docstrings @@ -139,7 +140,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, Arguments --------- base_dir: Working data directory for GRACE/GRACE-FO data - PROC: (CSR/CNES/JPL/GFZ/GRAZ) data processing center + PROC: (CSR/CNES/JPL/GFZ/GRAZ/SWARM) data processing center DREL: (RL01,RL02,RL03,RL04,RL05,RL06) data release DSET: (GAA/GAB/GAC/GAD/GSM) data product LMAX: Upper bound of Spherical Harmonic Degrees @@ -148,7 +149,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, missing: missing months to not consider in analysis SLR_C20: Replaces C20 with SLR values N: use original values - TideFree: add a bias for CSR, GFZ or GRAZ to convert C2,0 to tide free convention + TideFree: add a bias for CSR, GFZ, GRAZ or SWARM to convert C2,0 to tide free convention ZeroTide: add a bias for CNES or JPL to convert C2,0 to zero tide convention CSR: use values from CSR (TN-07,TN-09,TN-11) GSFC: use values from GSFC (TN-14) @@ -320,7 +321,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, infile = grace_files[grace_month] if PROC in ('CSR', 'GFZ', 'JPL', 'CNES', 'JPLMSC'): Ylms = read_GRACE_harmonics(infile,LMAX,MMAX=MMAX,POLE_TIDE=POLE_TIDE) - elif PROC == 'GRAZ': + elif PROC in ('GRAZ', 'SWARM'): Ylms = read_ICGEM_harmonics(infile) grace_clm[:,:,i] = Ylms['clm'][0:LMAX+1,0:MMAX+1] grace_slm[:,:,i] = Ylms['slm'][0:LMAX+1,0:MMAX+1] @@ -348,9 +349,9 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, # -- apply conversion formula from IERS 2010 Notes grace_clm[2, 0, :] += 4.4228e-8 * 0.31460 * k2 - elif SLR_C20 == 'ZeroTide' and PROC in ('CNES', 'GFZ'): + elif SLR_C20 == 'ZeroTide' and PROC in ('CNES', 'GFZ', 'SWARM'): #-- k2,0 nominal from IERS 2010 convention - if PROC == 'CNES': + if PROC in ('CNES', 'SWARM'): k2 = 0.3 elif PROC == 'GFZ': k2 = 0.3190 diff --git a/gravity_toolkit/read_ICGEM_harmonics.py b/gravity_toolkit/read_ICGEM_harmonics.py index 6434cc8b..bd959d9f 100644 --- a/gravity_toolkit/read_ICGEM_harmonics.py +++ b/gravity_toolkit/read_ICGEM_harmonics.py @@ -3,13 +3,21 @@ read_ICGEM_harmonics.py Written by Tyler Sutterley (07/2020) -Read gfc files and extract gravity model spherical harmonics from the GFZ ICGEM +Read gfc files and extract gravity model spherical harmonics from the GFZ/GRAZ/SWARM ICGEM GFZ International Centre for Global Earth Models (ICGEM) http://icgem.gfz-potsdam.de/ +GRAZ: https://www.tugraz.at/institute/ifg/downloads/gravity-field-models +data can be downloaded from this ftp server: + ftp://ftp.tugraz.at/outgoing/ITSG/GRACE/ + +SWARM: https://earth.esa.int/eogateway/missions/swarm +data can be downloaded from this ftp server: + ftp://swarm-diss.eo.esa.int/Level2longterm/EGF/ + INPUTS: - model_file: GFZ ICGEM gfc spherical harmonic data file + model_file: GFZ/GRAZ/SWARM ICGEM gfc spherical harmonic data file OPTIONS: FLAG: string denoting data lines (default gfc) @@ -71,11 +79,37 @@ def read_ICGEM_harmonics(model_file, FLAG='gfc'): #-- python dictionary with model input and headers model_input = {} if 'ITSG' in model_file: - #-- parse filename - PFX, SAT, trunc, year, month, SFX = parse_file(model_file) + # -- compile numerical expression operator for parameters from files + # -- GRAZ: Institute of Geodesy from GRAZ University of Technology + regex_pattern = (r'(.*?)-({0})_(.*?)_(\d+)-(\d+)' + r'(\.gz|\.gfc|\.txt)').format(r'Grace_operational|Grace2018') + rx = re.compile(regex_pattern, re.VERBOSE) + # -- extract parameters from input filename + if isinstance(model_file, io.IOBase): + PFX, SAT, trunc, year, month, SFX = rx.findall(model_file.filename).pop() + else: + PFX, SAT, trunc, year, month, SFX = rx.findall(os.path.basename(model_file)).pop() + #-- convert string to integer year, month = int(year), int(month) + elif 'SW_' in model_file: + # -- compile numerical expression operator for parameters from files + # -- SWARM: data from SWARM satellite + regex_pattern = (r'({0})_(.*?)_(EGF_SHA_2)__(.*?)_(.*?)_(.*?)' + r'(\.gz|\.gfc|\.txt)').format(r'SW') + rx = re.compile(regex_pattern, re.VERBOSE) + # -- extract parameters from input filename + if isinstance(model_file, io.IOBase): + SAT, tmp, PROD, start_date, end_date, RL, SFX = rx.findall(model_file.filename).pop() + else: + SAT, tmp, PROD, start_date, end_date, RL, SFX = rx.findall(os.path.basename(model_file)).pop() + # -- convert string to integer + + year = int(start_date[:4]) + month = int(start_date[4:6]) + + if 'ITSG' in model_file or 'SW_' in model_file: #-- calculate mid-month date taking into account if measurements are #-- on different years if (year % 4) == 0: #-- Leap Year @@ -118,8 +152,9 @@ def read_ICGEM_harmonics(model_file, FLAG='gfc'): #-- allocate for each Coefficient model_input['clm'] = np.zeros((LMAX+1,LMAX+1)) model_input['slm'] = np.zeros((LMAX+1,LMAX+1)) - model_input['eclm'] = np.zeros((LMAX+1,LMAX+1)) - model_input['eslm'] = np.zeros((LMAX+1,LMAX+1)) + if model_input['errors'] != 'no': + model_input['eclm'] = np.zeros((LMAX+1,LMAX+1)) + model_input['eslm'] = np.zeros((LMAX+1,LMAX+1)) #-- reduce file_contents to input data using data marker flag input_data = [l for l in file_contents if re.match(FLAG,l)] #-- for each line of data in the gravity file @@ -132,27 +167,8 @@ def read_ICGEM_harmonics(model_file, FLAG='gfc'): #-- read spherical harmonic coefficients model_input['clm'][l1,m1] = np.float(line_contents[3]) model_input['slm'][l1,m1] = np.float(line_contents[4]) - model_input['eclm'][l1,m1] = np.float(line_contents[5]) - model_input['eslm'][l1,m1] = np.float(line_contents[6]) + if model_input['errors'] != 'no': + model_input['eclm'][l1,m1] = np.float(line_contents[5]) + model_input['eslm'][l1,m1] = np.float(line_contents[6]) #-- return the spherical harmonics and parameters return model_input - -#-- PURPOSE: extract parameters from filename -def parse_file(input_file): - """ - Extract parameters from filename - - Arguments - --------- - input_file: GRACE/GRACE-FO Level-2 spherical harmonic data file - """ - #-- compile numerical expression operator for parameters from files - # -- GRAZ: Institute of Geodesy from GRAZ University of Technology - regex_pattern = (r'(.*?)-({0})_(.*?)_(\d+)-(\d+)' - r'(\.gz|\.gfc|\.txt)').format(r'Grace_operational|Grace2018') - rx = re.compile(regex_pattern, re.VERBOSE) - #-- extract parameters from input filename - if isinstance(input_file, io.IOBase): - return rx.findall(input_file.filename).pop() - else: - return rx.findall(os.path.basename(input_file)).pop() \ No newline at end of file From f05083b654be91d32957050ec8ae12ab07d594e9 Mon Sep 17 00:00:00 2001 From: tsutterley Date: Tue, 16 Feb 2021 16:45:37 -0800 Subject: [PATCH 09/80] spatial: added replace_masked to replace masked values in data --- doc/source/user_guide/spatial.rst | 5 +++++ gravity_toolkit/spatial.py | 15 ++++++++++++--- 2 files changed, 17 insertions(+), 3 deletions(-) diff --git a/doc/source/user_guide/spatial.rst b/doc/source/user_guide/spatial.rst index 64637267..146baa01 100644 --- a/doc/source/user_guide/spatial.rst +++ b/doc/source/user_guide/spatial.rst @@ -404,3 +404,8 @@ General Attributes and Methods Replace the masked values with a new fill_value Option: `mask` to update the current mask + + + .. method:: object.replace_masked() + + Replace the masked values with fill_value diff --git a/gravity_toolkit/spatial.py b/gravity_toolkit/spatial.py index 23adebee..dd7271c7 100644 --- a/gravity_toolkit/spatial.py +++ b/gravity_toolkit/spatial.py @@ -1,7 +1,7 @@ #!/usr/bin/env python u""" spatial.py -Written by Tyler Sutterley (01/2021) +Written by Tyler Sutterley (02/2021) Data class for reading, writing and processing spatial data @@ -19,6 +19,7 @@ hdf5_read.py: reads spatial data from HDF5 UPDATE HISTORY: + Updated 02/2021: added replace_masked to replace masked values in data Updated 01/2021: added scaling factor and scaling factor error function from Lander and Swenson (2012) https://doi.org/10.1029/2011WR011453 Updated 12/2020: added transpose function, can calculate mean over indices @@ -457,7 +458,7 @@ def copy(self): temp.update_spacing() temp.update_extents() temp.update_dimensions() - temp.update_mask() + temp.replace_masked() return temp def zeros_like(self): @@ -479,7 +480,7 @@ def zeros_like(self): temp.update_spacing() temp.update_extents() temp.update_dimensions() - temp.update_mask() + temp.replace_masked() return temp def expand_dims(self): @@ -880,3 +881,11 @@ def replace_invalid(self, fill_value, mask=None): #-- replace invalid values with new fill value self.data[self.mask] = self.fill_value return self + + def replace_masked(self): + """ + Replace the masked values with fill_value + """ + if self.fill_value is not None: + self.data[self.mask] = self.fill_value + return self \ No newline at end of file From fa54ab092ef822604905d4e8ffb4765c418b8a20 Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 17 Feb 2021 13:32:09 +0100 Subject: [PATCH 10/80] Read grid and produce harmonics objects --- gravity_toolkit/read_grid_to_harmonics.py | 224 ++++++++++++++++++++++ 1 file changed, 224 insertions(+) create mode 100644 gravity_toolkit/read_grid_to_harmonics.py diff --git a/gravity_toolkit/read_grid_to_harmonics.py b/gravity_toolkit/read_grid_to_harmonics.py new file mode 100644 index 00000000..7cb7652a --- /dev/null +++ b/gravity_toolkit/read_grid_to_harmonics.py @@ -0,0 +1,224 @@ +#!/usr/bin/env python +u""" +read_grid_to_harmonics.py +Written by Hugo Lecomte (12/2020) + +Reads netCDF file with grid data and extracts spherical harmonic from those data +Correct data for drift in pole tide following Wahr et al. (2015) +Parses date of GRACE/GRACE-FO data from filename + +Design for JPL MASCON netCDF data available on +https://podaac-tools.jpl.nasa.gov/drive/files +In the folder /allData/tellus/retired/L3/mascon/RL06/JPL/v02 + +INPUTS: + input_file: GRACE/GRACE-FO Level-3 netCDF grid data file + LMAX: Maximum degree of spherical harmonics (degree of truncation) + +OPTIONS: + MMAX: Maximum order of spherical harmonics (order of truncation) + default is the maximum spherical harmonic degree + POLE_TIDE: correct GSM data for pole tide drift following Wahr et al. (2015) + +OUTPUTS: + time: mid-month date in year-decimal + start: start date of range as Julian day + end: end date of range as Julian day + clm: cosine spherical harmonics of input data (LMAX,MMAX) + slm: sine spherical harmonics of input data (LMAX,MMAX) + eclm: cosine spherical harmonic uncalibrated standard deviations (LMAX,MMAX) + eslm: sine spherical harmonic uncalibrated standard deviations (LMAX,MMAX) + +PYTHON DEPENDENCIES: + numpy: Scientific Computing Tools For Python (https://numpy.org) + +UPDATE HISTORY: + Written 12/2020 +""" +import os +import re +import io +import numpy as np +from gravity_toolkit.ncdf_read import ncdf_read +from gravity_toolkit.hdf5_read import hdf5_read +from gravity_toolkit.utilities import get_data_path +from gravity_toolkit.read_love_numbers import read_love_numbers +from gravity_toolkit.gen_stokes import gen_stokes + +#-- PURPOSE: read Level-3 GRACE and GRACE-FO netCDF files +def read_grid_to_harmonics(input_file, VARNAME, LMAX, MMAX=None, LONNAME='lon', + LATNAME='lat', TIMENAME='time', UNITS=1, POLE_TIDE=False): + """ + Reads netCDF or HDF5 file with grid data and extracts spherical harmonic from those data + Correct data prior to Release 6 for pole tide drift + Parses date of GRACE/GRACE-FO data from filename + + Arguments + --------- + input_file: GRACE/GRACE-FO Level-3 netCDF grid data file + VARNAME: z variable name in the file + LMAX: Maximum degree of spherical harmonics (degree of truncation) + + Keyword arguments + ----------------- + MMAX: Maximum order of spherical harmonics + LONNAME: longitude variable name in the file + LATNAME: latitude variable name in the file + TIMENAME: time variable name in the file + UNITS: input data units + 1: cm of water thickness + 2: Gtons of mass + 3: kg/m^2 + POLE_TIDE: correct for pole tide drift following Wahr et al. (2015) + + Returns + ------- + clm: GRACE/GRACE-FO cosine spherical harmonics + slm: GRACE/GRACE-FO sine spherical harmonics + time: time of each GRACE/GRACE-FO measurement (mid-month) + month: GRACE/GRACE-FO months of input datasets + l: spherical harmonic degree to LMAX + m: spherical harmonic order to MMAX + title: string denoting low degree zonals replacement, geocenter usage and corrections + directory: directory of exact GRACE/GRACE-FO product + """ + + #-- parse filename + pfx,center,time,realm,release,v_id,sfx = parse_file(input_file) + + #-- read file content + if input_file[-3:] == '.nc': + file_contents = ncdf_read(input_file, DATE=True, VARNAME=VARNAME, LONNAME=LONNAME, + LATNAME=LATNAME, TIMENAME=TIMENAME, ATTRIBUTES=True, + TITLE=True, COMPRESSION=sfx) + elif input_file[-4:] == '.hdf' or input_file[-3:] == '.h5' or input_file[-5:] == '.hdf5': + file_contents = hdf5_read(input_file, DATE=True, VARNAME=VARNAME, LONNAME=LONNAME, + LATNAME=LATNAME, TIMENAME=TIMENAME, ATTRIBUTES=True, + TITLE=True, COMPRESSION=sfx) + + #-- load love numbers + hl, kl, ll = read_love_numbers(get_data_path(['data', 'love_numbers']), REFERENCE='CF') + + #-- set maximum spherical harmonic order + MMAX = np.copy(LMAX) if (MMAX is None) else MMAX + + #-- number of dates in data + n_time = file_contents['time'].shape[0] + #-- Spherical harmonic coefficient matrices to be filled from data file + grace_clm = np.zeros((LMAX + 1, MMAX + 1, n_time)) + grace_slm = np.zeros((LMAX + 1, MMAX + 1, n_time)) + #-- Time matrix to fill + tdec = np.zeros((n_time)) + month = np.zeros((n_time)) + #-- output dimensions + lout = np.arange(LMAX + 1) + mout = np.arange(MMAX + 1) + + #-- for each date, conversion to spherical harmonics + for i in range(n_time): + harmo = gen_stokes(file_contents['data'][i, :, :], + file_contents['lon'][:], file_contents['lat'][:], + LMAX=LMAX, MMAX=MMAX, UNITS=UNITS, LOVE=(hl, kl, ll)) + + grace_clm[:, :, i] = harmo['clm'] + grace_slm[:, :, i] = harmo['slm'] + + #-- extract GRACE date information from input file name + start_yr = np.float(time[:4]) + + #-- variables initialization for date conversion + current_year = start_yr + current_month = 1 + cmp_past_dpm = 0 + cmp_past_dpy = 0 + if (start_yr % 4) == 0:#-- Leap Year (% = modulus) + dpy = 366.0 + dpm = [31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + else:#-- Standard Year + dpy = 365.0 + dpm = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + + #-- for each date, conversion to month and decimal year + for i in range(n_time): + #-- Month iteration + while file_contents['time'][i] - cmp_past_dpm > dpm[(current_month - 1)%12]: + current_month += 1 + cmp_past_dpm += dpm[(current_month - 1)%12] + + #-- Year iteration + while file_contents['time'][i] - cmp_past_dpy > dpy: + current_year += 1 + cmp_past_dpy += dpy + if (current_year % 4) == 0: #-- Leap Year (% = modulus) + dpy = 366.0 + dpm = [31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + else: #-- Standard Year + dpy = 365.0 + dpm = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + + tdec[i] = current_year + (file_contents['time'][i] - cmp_past_dpy)/dpy + month[i] = current_month + + #-- The 'Special Months' (Nov 2011, Dec 2011 and April 2012) with + #-- Accelerometer shutoffs make this relation between month number + #-- and date more complicated as days from other months are used + #-- May15 (month 161) is centered in Apr15 (160) + if (month[i] == 160) and (month[i] == month[i - 1]): + month[i] = month[i - 1] + 1 + + #-- extract GRACE and GRACE-FO file informations + title = file_contents['attributes'] + + #-- Correct Pole Tide following Wahr et al. (2015) 10.1002/2015JB011986 + if POLE_TIDE: + for i in range(n_time): + #-- time since 2000.0 + dt = tdec[i] - 2000.0 + + #-- JPL Pole Tide Correction + #-- values for IERS mean pole [2010] + if tdec[i] < 2010.0: + a = np.array([0.055974,1.8243e-3,1.8413e-4,7.024e-6]) + b = np.array([-0.346346,-1.7896e-3,1.0729e-4,0.908e-6]) + elif tdec[i] >= 2010.0: + a = np.array([0.023513,7.6141e-3,0.0,0.0]) + b = np.array([-0.358891,0.6287e-3,0.0,0.0]) + #-- calculate m1 and m2 values + m1 = np.copy(a[0]) + m2 = np.copy(b[0]) + for x in range(1,4): + m1 += a[x]*dt**x + m2 += b[x]*dt**x + #-- pole tide values for JPL + #-- JPL remove the IERS mean pole from m1 and m2 + #-- before computing their harmonic solutions + C21_PT = -1.551e-9*(m1 - 0.62e-3*dt) - 0.012e-9*(m2 + 3.48e-3*dt) + S21_PT = 0.021e-9*(m1 - 0.62e-3*dt) - 1.505e-9*(m2 + 3.48e-3*dt) + #-- correct GRACE spherical harmonics for pole tide + #-- note: -= means grace_xlm = grace_xlm - PT + grace_clm[2, 1, i] -= C21_PT + grace_clm[2, 1, i] -= S21_PT + + #-- return the GRACE data, GRACE date (mid-month in decimal), and the + #-- start and end days as Julian dates + return {'clm': grace_clm, 'slm': grace_slm, 'time': tdec, 'month': month, + 'l': lout, 'm': mout, 'title': title, 'directory': os.path.split(input_file)[0]} + +#-- PURPOSE: extract parameters from filename +def parse_file(input_file): + """ + Extract parameters from filename + + Arguments + --------- + input_file: GRACE/GRACE-FO Level-2 spherical harmonic data file + """ + #-- compile numerical expression operator for parameters from files + #-- JPLMSC: NASA Jet Propulsion Laboratory (mascon solutions) + regex_pattern = r'(.*?)\.(.*?)\.(.*?)\.(.*?)\.(.*?)\.(.*?)\.(\w{2,})' + rx = re.compile(regex_pattern, re.VERBOSE) + #-- extract parameters from input filename + if isinstance(input_file, io.IOBase): + return rx.findall(input_file.filename).pop() + else: + return rx.findall(os.path.basename(input_file)).pop() From dcdfadb3ee090e0c3a714d596d1eeaa1d4552818 Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 17 Feb 2021 13:33:25 +0100 Subject: [PATCH 11/80] Add plot function and wavelets for wavelets analysis --- gravity_toolkit/harmonics.py | 381 +++++++++++++++++++++++++++++++++++ gravity_toolkit/wavelets.py | 219 ++++++++++++++++++++ 2 files changed, 600 insertions(+) create mode 100644 gravity_toolkit/wavelets.py diff --git a/gravity_toolkit/harmonics.py b/gravity_toolkit/harmonics.py index 9a16362c..8979b126 100644 --- a/gravity_toolkit/harmonics.py +++ b/gravity_toolkit/harmonics.py @@ -7,6 +7,10 @@ PYTHON DEPENDENCIES: numpy: Scientific Computing Tools For Python (https://numpy.org) + scipy: Scientific Numerical Routines For Python + (https://www.scipy.org/) + matplotlib.pyplot: Visualizations Tools For Python + (https://matplotlib.org/) netCDF4: Python interface to the netCDF C library (https://unidata.github.io/netcdf4-python/netCDF4/index.html) h5py: Pythonic interface to the HDF5 binary data format. @@ -21,6 +25,7 @@ destripe_harmonics.py: filters spherical harmonics for correlated errors UPDATE HISTORY: + Updated 11/2020: added plotting functions for visualization Updated 08/2020: added compression options for ascii, netCDF4 and HDF5 files Updated 07/2020: added class docstring and using kwargs for output to file added case_insensitive_filename function to search directories @@ -38,7 +43,11 @@ import re import gzip import zipfile +import matplotlib import numpy as np +import scipy as sc +import matplotlib.pyplot as plt +import gravity_toolkit.wavelets as wv from gravity_toolkit.ncdf_stokes import ncdf_stokes from gravity_toolkit.hdf5_stokes import hdf5_stokes from gravity_toolkit.ncdf_read_stokes import ncdf_read_stokes @@ -822,3 +831,375 @@ def destripe(self, **kwargs): temp.update_dimensions() #-- return the destriped field return temp + + def gap_fill(self, apply=False): + """ + Fill the missing months with a linear interpolation, the interpolation is made on month number, it's imprecise + Options: apply to the object if True, else return a new instance + """ + temp = self.copy() + missing_month = self.month[-1] - self.month[0] - len(self.month) + 1 + + temp.clm = np.zeros((self.lmax + 1, self.mmax + 1, len(self.time) + missing_month)) + temp.slm = np.zeros((self.lmax + 1, self.mmax + 1, len(self.time) + missing_month)) + temp.time = np.zeros(len(self.time) + missing_month) + temp.month = np.arange(self.month[0], self.month[-1] + 1) + + # initialize index and count variables + index = 0 + cmp = 0 + for i in range(int(self.month[0]), int(self.month[-1]) + 1): + if i in self.month: # if month in original object, copy time and data + cmp_miss_mon = 0 # variable for following missing months + temp.time[index] = self.time[index - cmp] + temp.clm[:, :, index] = self.clm[:, :, index - cmp] + temp.slm[:, :, index] = self.slm[:, :, index - cmp] + else: # fill values with a linear interpolation + cmp += 1 + cmp_miss_mon += 1 + # y(t) = (y2 - y1)/(x2 - x1)*t + y1 + temp.time[index] = (self.time[index - cmp + 1] - self.time[index - cmp]) / ( + self.month[index - cmp + 1] - self.month[index - cmp]) * cmp_miss_mon + self.time[index - cmp] + temp.clm[:, :, index] = (self.clm[:, :, index - cmp + 1] - self.clm[:, :, index - cmp]) / \ + (self.month[index - cmp + 1] - self.month[index - cmp]) * cmp_miss_mon \ + + self.clm[:, :, index - cmp] + temp.slm[:, :, index] = (self.slm[:, :, index - cmp + 1] - self.slm[:, :, index - cmp]) / \ + (self.month[index - cmp + 1] - self.month[index - cmp]) * cmp_miss_mon \ + + self.slm[:, :, index - cmp] + + index += 1 + + # -- assign ndim and shape attributes + temp.update_dimensions() + + if apply: + self.clm = temp.clm + self.slm = temp.slm + self.time = temp.time + self.month = temp.month + + self.update_dimensions() + + return temp + + def plot_correlation(self, l, m, save_path=False): + """ + Plot correlation between spherical harmonic coefficients of the object + Inputs: + l first degree of spherical harmonics + m second degree of spherical harmonics + + Options: + save_path : if not False, give a path to save the figure + """ + mat_c = np.zeros((self.lmax, self.lmax)) + if m: + mat_s = np.zeros((self.lmax, self.lmax)) + for i in range(self.lmax): + for j in range(i+1): + mat_c[i, i - j] = abs(np.mean((self.clm[l, m]-np.mean(self.clm[l, m]))*(self.clm[i, j]-np.mean(self.clm[i, j])))/\ + np.sqrt(np.mean((self.clm[l, m]-np.mean(self.clm[l, m]))**2))/\ + np.sqrt(np.mean((self.clm[i, j]-np.mean(self.clm[i, j]))**2))) + + if j: + mat_c[i - j, i] = abs(np.mean((self.clm[l, m]-np.mean(self.clm[l, m]))*(self.slm[i, j]-np.mean(self.slm[i, j])))/\ + np.sqrt(np.mean((self.clm[l, m]-np.mean(self.clm[l, m]))**2))/\ + np.sqrt(np.mean((self.slm[i, j]-np.mean(self.slm[i, j]))**2))) + + if m: + mat_s[i, i - j] = abs(np.mean( + (self.slm[l, m] - np.mean(self.slm[l, m])) * (self.clm[i, j] - np.mean(self.clm[i, j]))) / \ + np.sqrt(np.mean((self.slm[l, m] - np.mean(self.slm[l, m]))**2)) / \ + np.sqrt(np.mean((self.clm[i, j] - np.mean(self.clm[i, j]))**2))) + + if j: + mat_s[i - j, i] = abs(np.mean( + (self.slm[l, m] - np.mean(self.slm[l, m])) * (self.slm[i, j] - np.mean(self.slm[i, j]))) / \ + np.sqrt(np.mean((self.slm[l, m] - np.mean(self.slm[l, m]))**2)) / \ + np.sqrt(np.mean((self.slm[i, j] - np.mean(self.slm[i, j]))**2))) + + plt.figure() + plt.matshow(mat_c) + plt.colorbar() + plt.title('Correlation of each spherical harmonics with $C_{' + str(l) + ',' + str(m)+ '}$') + + if save_path: + if os.path.isdir(save_path): + plt.savefig(os.path.join(save_path, 'C' + str(l) + str(m) + '_correlation.png')) + else: + plt.savefig(save_path[:-3] + 'c' + save_path[-3:]) + + if m: + plt.figure() + plt.matshow(mat_s) + plt.colorbar() + plt.title('Correlation of each spherical harmonics with $S_{' + str(l) + ',' + str(m) + '}$') + + if save_path: + if os.path.isdir(save_path): + plt.savefig(os.path.join(save_path, 'S' + str(l) + str(m) + '_correlation.png')) + else: + plt.savefig(save_path[:-3] + 's' + save_path[-3:]) + plt.show() + + + def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], save_path=False): + """ + Plot Cl,m and Sl,m harmonic coefficients + Inputs: + l first degree of spherical harmonics + m second degree of spherical harmonics + Options: + dates: list with limits of the xaxis in year + ylms: list of Harmonics objects to plot with the instance + label: list of label for each Harmonics objects with element 0 representing the current Harmonics object + save_path : if not False, give a path to save the figure + """ + #-- figure for Cl,m + plt.figure() + plt.title("Normalized spherical harmonics coefficient $C_{" + str(l) + "," + str(m) + "}$") + if len(ylms): + plt.plot(self.time, self.clm[l, m, :], 'r', label=label[0]) + else: + plt.plot(self.time, self.clm[l, m, :], 'r', label="$C_{" + str(l) + "," + str(m) + "}$") + + try: + for i in range(len(ylms)): + plt.plot(ylms[i].time, ylms[i].clm[l, m, :], label=label[i+1]) + except IndexError: + raise IndexError("The list of labels is incomplete for correct plotting") + + plt.xlabel("Time (year)") + plt.legend() + if dates: + plt.xlim(dates) + plt.grid() + + if save_path: + if os.path.isdir(save_path): + plt.savefig(os.path.join(save_path, 'C' + str(l) + str(m) + '_coefficient.png')) + else: + plt.savefig(save_path[:-3] + 'c' + save_path[-3:]) + + if m: + #-- figure for Sl,m + plt.figure() + plt.title("Normalized spherical harmonics coefficient $S_{" + str(l) + "," + str(m) + "}$") + if len(ylms): + plt.plot(self.time, self.slm[l, m, :], 'r', label=label[0]) + else: + plt.plot(self.time, self.slm[l, m, :], 'r', label="$S_{" + str(l) + "," + str(m) + "}$") + + try: + for i in range(len(ylms)): + plt.plot(ylms[i].time, ylms[i].slm[l, m, :], label=label[i + 1]) + except IndexError: + raise IndexError("The list of labels is incomplete for correct plotting") + + plt.xlabel("Time (year)") + plt.legend() + if dates: + plt.xlim(dates) + plt.grid() + + if save_path: + if os.path.isdir(save_path): + plt.savefig(os.path.join(save_path, 'S' + str(l) + str(m) + '_coefficient.png')) + else: + plt.savefig(save_path[:-3] + 's' + save_path[-3:]) + + plt.show() + + def plot_fft(self, l, m, save_path=False): + """ + Plot Cl,m and Sl,m harmonic coefficients fast fourrier transform + Inputs: + l first degree of spherical harmonics + m second degree of spherical harmonics + + Options: + save_path : if not False, give a path to save the figure + """ + #-- compute fft and create x monthly frequency + N = len(self.time) + cf = sc.fft.fft(self.clm[l, m, :]) + sf = sc.fft.fft(self.slm[l, m, :]) + xf = np.linspace(0.0, 12/2, N // 2) + + # -- figure for Cl,m and Sl,m + plt.figure() + plt.title("Fourier transform of the normalized spherical harmonics coefficients $C_{" + str(l) + "," + str( + m) + "}$ et $S_{" + str( + l) + "," + str(m) + "}$") + plt.plot(xf, 2.0 / N * np.abs(cf[0:N // 2]), label="$C_{" + str(l) + "," + str(m) + "}$") + if m: + plt.plot(xf, 2.0 / N * np.abs(sf[0:N // 2]), label="$S_{" + str(l) + "," + str(m) + "}$") + + + plt.xlabel("Frequency ($year^{-1}$)") + plt.ylabel("Power") + plt.grid() + plt.legend() + + if save_path: + if os.path.isdir(save_path): + plt.savefig(os.path.join(save_path, 'CS' + str(l) + str(m) + '_fft.png')) + else: + plt.savefig(save_path) + + plt.show() + + def plot_wavelets(self, l, m, s0=0, pad=1, lag1=0, plot_coi=True, mother='MORLET', param=-1, func_plot=np.abs, save_path=False): + """ + Plot Cl,m and Sl,m wavelet analysis based on (Torrence and Compo, 1998) + + Inputs: + l first degree of spherical harmonics + m second degree of spherical harmonics + + Options: + s0 : minimal period of the wavelets, should be higher than 2*dt + pad : boolean for the zero padding of the series + lag1 : caracteristic of the noise: 0 for a white noise (default), 0.72 for a red noise + plot_coi : boolean to display the cone of interest in the figure + mother : name of the wavelet, can be MORLET, DOG or PAUL + param : param of the wavelet, -1 is the default value for each wavelet + func_plot : funtion for reducing the wave, can be np.abs, np.angle, np.real or np.imag + save_path : if not False, give a path to save the figure + """ + # len of the data + ndata = self.time.shape[0] + # compute the mean time delta of the object + dt = np.mean((self.time[1:] - self.time[:-1])) + + # resolution of the wavelet + dj = 0.005 + + if not s0: + s0 = 4 * dt # min scale of the wavelets + # max resolution of the wavelet, fixed for GRACE + j1 = 4.5 / dj + + siglvl = 0.95 + + # compute wavelets analysis of Cl,m and Sl,m + wavec = wv.wavelet(self.clm[l,m], dt, pad, dj, s0, j1, mother, param)[0] + waves, period, scale, coi = wv.wavelet(self.slm[l,m], dt, pad, dj, s0, j1, mother, param) + + # compute significativity of the wavelets + signifc = wv.wave_signif(self.clm[l,m], dt, scale, lag1=lag1, siglvl=siglvl, mother=mother, param=param) + signifs = wv.wave_signif(self.slm[l,m], dt, scale, lag1=lag1, siglvl=siglvl, mother=mother, param=param) + + # compute wavelet significance test at a level of confidence siglvl% + sig95c = np.abs(wavec**2) / [s * np.ones(ndata) for s in signifc] + sig95s = np.abs(waves**2) / [s * np.ones(ndata) for s in signifs] + + # Wavelet spectrum for fft plot + global_wsc = (np.sum(np.abs(wavec ** 2).conj().transpose(), axis=0) / ndata) + global_wss = (np.sum(np.abs(waves ** 2).conj().transpose(), axis=0) / ndata) + + # compute fft of the signal + fft_sigc = np.fft.fft(self.clm[l,m]) + sxxc = np.abs((fft_sigc * np.conj(fft_sigc)) / ndata)[int(np.ceil(ndata / 2)):] + fft_sigs = np.fft.fft(self.slm[l, m]) + sxxs = np.abs((fft_sigs * np.conj(fft_sigs)) / ndata)[int(np.ceil(ndata / 2)):] + + # compute frequency + f = -np.fft.fftfreq(ndata)[int(np.ceil(ndata / 2)):] + + # prepare yticks + yticks = [] + for i in [0.5, 1, 2, 4, 6, 10, 15]: + if np.min(period) <= i <= np.max(period): + yticks.append(i) + + # create figure Cl,m + fig = plt.figure(constrained_layout=True, figsize=(12, 6), dpi=200) + spec = matplotlib.gridspec.GridSpec(ncols=2, nrows=1, wspace=0.02, width_ratios=[3, 1]) + ax0 = fig.add_subplot(spec[0]) + ax1 = fig.add_subplot(spec[1], sharey=ax0) + axs = [ax0, ax1] + plt.setp(axs[1].get_yticklabels(), visible=False) + + # plot wavelet + im = axs[0].contourf(self.time, period, func_plot(wavec), 100) + axs[0].contour(self.time, period, sig95c, levels=[1], linewidths=2) + + # plot cone of interest of the wavelet + if plot_coi: + axs[0].fill(np.concatenate((self.time[:1] - 0.0001, self.time, self.time[-1:] + 0.0001, + self.time[-1:] + 0.0001, self.time[:1] - 0.0001, self.time[:1] - 0.0001)), + np.concatenate(([np.min(period)], coi, [np.min(period)], period[-1:], period[-1:], + [np.min(period)])), 'r', alpha=0.2, hatch='/') + axs[0].plot(self.time, coi, 'r--', lw=1.4) + + fig.colorbar(im, ax=axs[0], location='left') + axs[0].invert_yaxis() + axs[0].set_yscale('log', base=2) + axs[0].set_ylabel('Period (year)') + axs[0].set_yticks(yticks) + axs[0].set_ylim(np.max(period), np.min(period)) + axs[0].set_xlabel('Time (year)') + axs[0].get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) + axs[0].set_title('Wavelet Power Spectrum') + + # plot fft analysis at the right of the figure + axs[1].plot(sxxc, 1 / f * dt, 'gray', label='Fourier spectrum') + axs[1].plot(global_wsc, period, 'b', label='Wavelet spectrum') + axs[1].plot(np.array(signifc), period, 'g--', label='95% confidence spectrum') + axs[1].set_xlabel('Power') + axs[1].set_title('Global Wavelet Spectrum') + + plt.legend() + + if save_path: + if os.path.isdir(save_path): + plt.savefig(os.path.join(save_path, 'C' + str(l) + str(m) + '_wavelet.png')) + else: + plt.savefig(save_path[:-3] + 'c' + save_path[-3:]) + + # create figure Sl,m + fig = plt.figure(constrained_layout=True, figsize=(12, 6), dpi=200) + spec = matplotlib.gridspec.GridSpec(ncols=2, nrows=1, wspace=0.02, width_ratios=[3, 1]) + ax0 = fig.add_subplot(spec[0]) + ax1 = fig.add_subplot(spec[1], sharey=ax0) + axs = [ax0, ax1] + plt.setp(axs[1].get_yticklabels(), visible=False) + + # plot wavelet + im = axs[0].contourf(self.time, period, np.abs(waves), 100) + axs[0].contour(self.time, period, sig95s, levels=[1], linewidths=2) + + # plot cone of interest of the wavelet + if plot_coi: + axs[0].fill(np.concatenate((self.time[:1] - 0.0001, self.time, self.time[-1:] + 0.0001, + self.time[-1:] + 0.0001, self.time[:1] - 0.0001, self.time[:1] - 0.0001)), + np.concatenate(([s0], coi, [s0], period[-1:], period[-1:], [s0])), 'r', alpha=0.2, hatch='/') + axs[0].plot(self.time, coi, 'r--', lw=1.4) + + fig.colorbar(im, ax=axs[0], location='left') + axs[0].invert_yaxis() + axs[0].set_yscale('log', base=2) + axs[0].set_ylabel('Period (year)') + axs[0].set_ylim(np.max(period), np.min(period)) + axs[0].set_yticks(yticks) + axs[0].set_xlabel('Time (year)') + axs[0].get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) + axs[0].set_title('Wavelet Power Spectrum') + + # plot fft analysis at the right of the figure + axs[1].plot(sxxs, 1 / f * dt, 'gray', label='Fourier spectrum') + axs[1].plot(global_wss, period, 'b', label='Wavelet spectrum') + axs[1].plot(np.array(signifs) * np.var(self.clm[l, m]), period, 'g--', label='95% confidence spectrum') + axs[1].set_xlabel('Power') + axs[1].set_title('Global Wavelet Spectrum') + + plt.legend() + + if save_path: + if os.path.isdir(save_path): + plt.savefig(os.path.join(save_path, 'C' + str(l) + str(m) + '_wavelet.png')) + else: + plt.savefig(save_path[:-3] + 's' + save_path[-3:]) + + plt.show() \ No newline at end of file diff --git a/gravity_toolkit/wavelets.py b/gravity_toolkit/wavelets.py new file mode 100644 index 00000000..8153de78 --- /dev/null +++ b/gravity_toolkit/wavelets.py @@ -0,0 +1,219 @@ +#!/usr/bin/env python +u""" +wavelets.py +Written by Hugo Lecomte (02/2021) + +Function to apply a wavelets analysis, code based on (Torrence and Compo, 1998) +""" +import numpy as np +import scipy.special + +def wave_bases(mother, k, scale, param=-1): + """Computes the wavelet function as a function of Fourier frequency + used for the CWT in Fourier space (Torrence and Compo, 1998) + + Arguments + --------- + mother: str equal to 'MORLET' or 'DOG' to choose the wavelet type + k: vector of the Fourier frequencies + scale: wavelet scales + param: nondimensional parameter for the wavelet function + + Returns + ------- + daughter: the wavelet function + fourier_factor: the ratio of Fourier period to scale + coi: cone-of-influence size at the scale + dofmin: degrees of freedom for each point in the wavelet power (Morlet = 2) + """ + mother = mother.upper() + n = len(k) # length of Fourier frequencies + k = np.array(k) # turn k to array + + if mother == 'MORLET': # choose the wavelet function, in this case Morlet + if param == -1: + param = 6 # For Morlet this is k0 (wavenumber), default is 6 + + expnt = -(scale*k - param)**2/2*(k > 0) # table 1 Torrence and Compo (1998) + norm = np.sqrt(scale*k[1])*(np.pi** -0.25)*np.sqrt(len(k)) + + daughter = [] # define daughter as a list + for ex in expnt: # for each value scale (equal to next pow of 2) + daughter.append(norm*np.exp(ex)) + daughter = np.array(daughter) # transform in array + + daughter = daughter*(k > 0) # Heaviside step function + fourier_factor = (4*np.pi)/(param + np.sqrt(2 + param * param)) # scale --> Fourier period + coi = fourier_factor/np.sqrt(2) # cone-of-influence + dofmin = 2 # degrees of freedom + + elif mother == 'DOG': # DOG Wavelet + if param == -1: + param = 2 # For DOG this is m (wavenumber), default is 2 + m = param + + expnt = -(scale*k)**2/2 + pws = np.array((scale*k)**m) + # gamma(m+0.5) = 1.3293 + norm = np.sqrt(scale*k[1]/1.3293*np.sqrt(n)) + + daughter = [] + for ex in expnt: + daughter.append(-norm* 1j**m * np.exp(ex)) + daughter = np.array(daughter) + daughter = daughter[:]*pws + + fourier_factor = 2*np.pi/np.sqrt(m + .5) + coi = fourier_factor/np.sqrt(2) + dofmin = 1 + + elif mother == 'PAUL': # Paul Wavelet + if param == -1: + param = 4 + m = param + + expnt = -(scale*k)*(k > 0) + norm = np.sqrt(scale*k[1]) *(2**m /np.sqrt(m*(np.math.factorial(2*m - 1))))*np.sqrt(n) + pws = np.array((scale*k)**m) + + daughter = [] + for ex in expnt: + daughter.append(norm*np.exp(ex)) + daughter = np.array(daughter) + daughter = daughter[:]*pws + + daughter = daughter*(k > 0) # Heaviside step function + fourier_factor = 4*np.pi/(2*m + 1) + coi = fourier_factor*np.sqrt(2) + dofmin = 2 + + return daughter, fourier_factor, coi, dofmin + + +def wavelet(Y, dt, pad=1, dj=.25, s0=-1, J1=-1, mother='MORLET', param=-1): + """Computes the wavelet continuous transform of the vector Y, + by definition: + W(a,b) = sum(f(t)*psi[a,b](t) dt) a dilate/contract + psi[a,b](t) = 1/sqrt(a) psi(t-b/a) b displace + The wavelet basis is normalized to have total energy = 1 at all scales + + Arguments + --------- + Y: time series + dt: sampling rate + pad: bool for zero padding or not + dj: spacing between discrete scales + s0: smallest scale of the wavelet + J1: total number of scales + mother: the mother wavelet function + param: the mother wavelet parameter + + Returns + ------- + wave: wavelet transform of Y + period: the vector of "Fourier" periods (in time units) that correspond to the scales + scale: vector of scale indices, given by S0*2(j*DJ), j =0 ...J1 + coi: cone of influence + """ + n1 = len(Y) # time series length + + if s0 == -1: # define s0 as 2 times dt (Shannon criteria) if s0 is not given + s0 = 2 * dt + if J1 == -1: # define J1 if not provide + J1 = int((np.log(n1*dt/s0) / np.log(2))/dj) + + x = Y - np.mean(Y) # remove mean of the time serie + + if pad: # if zero padding, add zeros to x + base2 = int(np.log(n1)/np.log(2) + 0.4999) + x = np.concatenate((x, np.zeros(2**(base2 + 1) - n1))) + + n = len(x) #update length of x + + k = np.arange(0, int(n/2)) + k = k*(2*np.pi) / (n*dt) + k = np.concatenate((k, -k[int((n - 1)/2)::-1])) # be careful for parity + + f = np.fft.fft(x) # fft on the padded time series + + scale = s0 * 2**(np.arange(0, J1 + 1, 1)*dj) + # define wavelet array + wave = np.zeros((int(J1 + 1), n)) + wave = wave + 1j * wave # make it complex + + for a1 in range(0, int(J1 + 1)): + daughter, fourier_factor, coi, dofmin = wave_bases(mother, k, scale[a1], param) + wave[a1, :] = np.fft.ifft(f * daughter) + + period = fourier_factor * scale + + # cone-of-influence, differ for uneven len of timeseries: + if n1%2: # uneven + coi = coi * dt * np.concatenate((np.arange(0, n1/2 - 1), np.arange(0, n1/2)[::-1])) + else: # even + coi = coi * dt * np.concatenate((np.arange(0, n1/2), np.arange(0, n1/2)[::-1])) + + # cut zero padding + wave = wave[:, :n1] + + return wave, period, scale, coi + +def wave_signif(Y, dt, scale, dof=-1, lag1=0, siglvl=0.95, mother='MORLET', param=-1): + """Computes the wavelet significance test at a level of confidence siglvl% + + Arguments + --------- + Y: time series + dt: sampling rate + scale: scales of the wavelet decomposition + dof: degrees of freedom + lag1: assuming lag-1 autocorrelation of the serie (0 for white noise RECOMMENDED, 0.72 for red noise) + siglvl: percentage of the confidence level + mother: the mother wavelet function + param: the mother wavelet parameter + + Returns + ------- + wave: wavelet transform of Y + period: the vector of "Fourier" periods (in time units) that correspond to the scales + scale: vector of scale indices, given by S0*2(j*DJ), j =0 ...J1 + coi: cone of influence + """ + mother = mother.upper() + variance = np.var(Y) + + # define default param and fourier factor for the wavelet + if mother == 'MORLET': + if param == -1: + param = 6 # For Morlet this is k0 (wavenumber), default is 6 + if dof == -1: + dof = 2 + + fourier_factor = float(4 * np.pi) / (param + np.sqrt(2 + param**2)) + + if mother == 'DOG': + if param == -1: + param = 2 # For DOG, default param is 2 + if dof == -1: + dof = 1 + + fourier_factor = float(2 * np.pi / (np.sqrt(param + 0.5))) + + if mother == 'PAUL': + if param == -1: + param = 4 # For PAUL, default param is 4 + if dof == -1: + dof = 2 + + fourier_factor = float(4 * np.pi / (2 * param + 1)) + + # compute period from scale + period = [e * fourier_factor for e in scale] + + # compute theoretical fft associated to the theoretical noise of the data given by lag1 + freq = [dt / p for p in period] + fft_theor = [variance*((1 - lag1**2) / (1 - 2*lag1*np.cos(f * 2 * np.pi) + lag1**2)) for f in freq] + + chisquare = scipy.special.gammaincinv(dof/2.0, siglvl)*2.0/dof + signif = [ft * chisquare for ft in fft_theor] + return signif \ No newline at end of file From 17c6218f98d7447f1c2fbbd69588ed3d505b6bda Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 17 Feb 2021 13:55:11 +0100 Subject: [PATCH 12/80] Update gitignore --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index 31b89c20..8ecd26b7 100644 --- a/.gitignore +++ b/.gitignore @@ -81,3 +81,6 @@ None*.png ####################### .ipynb_checkpoints Untitled.ipynb +# Personal notebooks # +######################## +/notebooks/ From c051936f146975d80c203347bbe0b2679954b8c4 Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 17 Feb 2021 17:11:14 +0100 Subject: [PATCH 13/80] Update time.py to add a day per month function --- gravity_toolkit/time.py | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/gravity_toolkit/time.py b/gravity_toolkit/time.py index 751bc85d..6159ef3c 100644 --- a/gravity_toolkit/time.py +++ b/gravity_toolkit/time.py @@ -410,3 +410,25 @@ def convert_julian(JD, ASTYPE=None, FORMAT='dict'): return (YEAR, MONTH, DAY, HOUR, MINUTE, SECOND) elif (FORMAT == 'zip'): return zip(YEAR, MONTH, DAY, HOUR, MINUTE, SECOND) + +def dpm_count(input_year): + """ + Return the number of days per months on the current year + + Arguments + --------- + input_year: year of interest + + Returns + ------- + dpm: list of the day per month + """ + # -- Calculation of total days since start of campaign + if (input_year % 4) == 0: + # -- Leap Year + dpm = [31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + else: + # -- Standard Year + dpm = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + + return dpm \ No newline at end of file From 3388a2269fa2e3c168964891909e8f9d155ceed2 Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 17 Feb 2021 18:21:37 +0100 Subject: [PATCH 14/80] Adjust default argument of aod_corrected_SLR_geocenter --- gravity_toolkit/read_SLR_geocenter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gravity_toolkit/read_SLR_geocenter.py b/gravity_toolkit/read_SLR_geocenter.py index 5b7f5cf5..44286bc9 100644 --- a/gravity_toolkit/read_SLR_geocenter.py +++ b/gravity_toolkit/read_SLR_geocenter.py @@ -186,7 +186,7 @@ def read_SLR_geocenter(geocenter_file, RADIUS=None, HEADER=0, #-- special function for outputting AOD corrected SLR geocenter values #-- need to run aod1b_geocenter.py to calculate the monthly geocenter dealiasing def aod_corrected_SLR_geocenter(geocenter_file, DREL, RADIUS=None, HEADER=0, - COLUMNS=[]): + COLUMNS=['time','X','Y','Z','X_sigma','Y_sigma','Z_sigma']): """ Reads monthly geocenter files from satellite laser ranging corrected for non-tidal ocean and atmospheric variation From 4feb745293c958095104e90306ee2099743fcc5c Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 17 Feb 2021 19:22:44 +0100 Subject: [PATCH 15/80] Debug grace_input_months.py and read_grid_to_harmonics.py --- gravity_toolkit/grace_input_months.py | 2 +- gravity_toolkit/read_grid_to_harmonics.py | 14 ++++++-------- 2 files changed, 7 insertions(+), 9 deletions(-) diff --git a/gravity_toolkit/grace_input_months.py b/gravity_toolkit/grace_input_months.py index 4c1571f2..7951bfb4 100644 --- a/gravity_toolkit/grace_input_months.py +++ b/gravity_toolkit/grace_input_months.py @@ -289,7 +289,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, #-- new file of degree-1 mass variations from Minkang Cheng #-- http://download.csr.utexas.edu/outgoing/cheng/gct2est.220_5s DEG1_file = os.path.join(base_dir,'geocenter','gct2est.220_5s') - DEG1_input = aod_corrected_SLR_geocenter(DEG1_file,HEADER=15, + DEG1_input = aod_corrected_SLR_geocenter(DEG1_file, DREL=DREL, HEADER=15, RADIUS=6.378136e9,COLUMNS=['MJD','time','X','Y','Z','XM','YM','ZM', 'X_sigma','Y_sigma','Z_sigma','XM_sigma','YM_sigma','ZM_sigma']) DEG1_str = '_w{0}_DEG1'.format(DEG1) diff --git a/gravity_toolkit/read_grid_to_harmonics.py b/gravity_toolkit/read_grid_to_harmonics.py index 7cb7652a..7ce159ab 100644 --- a/gravity_toolkit/read_grid_to_harmonics.py +++ b/gravity_toolkit/read_grid_to_harmonics.py @@ -89,12 +89,10 @@ def read_grid_to_harmonics(input_file, VARNAME, LMAX, MMAX=None, LONNAME='lon', #-- read file content if input_file[-3:] == '.nc': file_contents = ncdf_read(input_file, DATE=True, VARNAME=VARNAME, LONNAME=LONNAME, - LATNAME=LATNAME, TIMENAME=TIMENAME, ATTRIBUTES=True, - TITLE=True, COMPRESSION=sfx) + LATNAME=LATNAME, TIMENAME=TIMENAME, COMPRESSION=sfx) elif input_file[-4:] == '.hdf' or input_file[-3:] == '.h5' or input_file[-5:] == '.hdf5': file_contents = hdf5_read(input_file, DATE=True, VARNAME=VARNAME, LONNAME=LONNAME, - LATNAME=LATNAME, TIMENAME=TIMENAME, ATTRIBUTES=True, - TITLE=True, COMPRESSION=sfx) + LATNAME=LATNAME, TIMENAME=TIMENAME, COMPRESSION=sfx) #-- load love numbers hl, kl, ll = read_love_numbers(get_data_path(['data', 'love_numbers']), REFERENCE='CF') @@ -108,8 +106,8 @@ def read_grid_to_harmonics(input_file, VARNAME, LMAX, MMAX=None, LONNAME='lon', grace_clm = np.zeros((LMAX + 1, MMAX + 1, n_time)) grace_slm = np.zeros((LMAX + 1, MMAX + 1, n_time)) #-- Time matrix to fill - tdec = np.zeros((n_time)) - month = np.zeros((n_time)) + tdec = np.zeros(n_time) + month = np.zeros(n_time) #-- output dimensions lout = np.arange(LMAX + 1) mout = np.arange(MMAX + 1) @@ -120,8 +118,8 @@ def read_grid_to_harmonics(input_file, VARNAME, LMAX, MMAX=None, LONNAME='lon', file_contents['lon'][:], file_contents['lat'][:], LMAX=LMAX, MMAX=MMAX, UNITS=UNITS, LOVE=(hl, kl, ll)) - grace_clm[:, :, i] = harmo['clm'] - grace_slm[:, :, i] = harmo['slm'] + grace_clm[:, :, i] = harmo.clm + grace_slm[:, :, i] = harmo.slm #-- extract GRACE date information from input file name start_yr = np.float(time[:4]) From ffce29189f836fe01a9de3e193dafce549d9f6a1 Mon Sep 17 00:00:00 2001 From: hulecom Date: Thu, 25 Feb 2021 10:07:47 +0100 Subject: [PATCH 16/80] Modify aod1b_geocenter.py to be compatible with RL06 and 3-hour interval of the AOD1B data --- scripts/aod1b_geocenter.py | 23 +++++++++++++++-------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/scripts/aod1b_geocenter.py b/scripts/aod1b_geocenter.py index aee2cb1f..2208d6e3 100644 --- a/scripts/aod1b_geocenter.py +++ b/scripts/aod1b_geocenter.py @@ -9,7 +9,7 @@ glo: global atmospheric and oceanic loading oba: ocean bottom pressure from OMCT/MPIOM -Creates monthly files of geocenter variations at 6-hour intervals +Creates monthly files of geocenter variations at 6-hour or 3-hour intervals NOTE: this reads the GFZ AOD1B files downloaded from PO.DAAC https://podaac-uat.jpl.nasa.gov/drive/files/allData/grace/L1B/GFZ/AOD1B/RL06/ @@ -69,7 +69,7 @@ def aod1b_geocenter(base_dir, DREL='', DSET='', CLOBBER=False, MODE=0o775, VERBOSE=False): """ - Creates monthly files of geocenter variations at 6-hour intervals from + Creates monthly files of geocenter variations at 6-hour or 3-hour intervals from GRACE/GRACE-FO level-1b dealiasing data files Arguments @@ -88,6 +88,13 @@ def aod1b_geocenter(base_dir, DREL='', DSET='', CLOBBER=False, MODE=0o775, MODE: Permission mode of directories and files VERBOSE: Output information for each output file """ + #-- set number of hours in a file + if DREL in ['RL01', 'RL02', 'RL03', 'RL04', 'RL05']: + hourly_max = 4 # for 00, 06, 12 and 18 + elif DREL == 'RL06': + hourly_max = 8 # for 00, 03, 06, 09, 12, 15, 18 and 21 + else: + raise ValueError('Invalid DREL value') #-- compile regular expressions operators for file dates #-- will extract the year and month from the tar file (.tar.gz) @@ -168,15 +175,15 @@ def aod1b_geocenter(base_dir, DREL='', DSET='', CLOBBER=False, MODE=0o775, else: fid = tar.extractfile(member) #-- degree 1 spherical harmonics for day and hours - C10 = np.zeros((4)) - C11 = np.zeros((4)) - S11 = np.zeros((4)) - hours = np.zeros((4),dtype=np.int) + C10 = np.zeros((hourly_max)) + C11 = np.zeros((hourly_max)) + S11 = np.zeros((hourly_max)) + hours = np.zeros((hourly_max),dtype=np.int) #-- create counter for hour in dataset c = 0 #-- while loop ends when dataset is read - while (c < 4): + while (c < hourly_max): #-- read line file_contents = fid.readline().decode('ISO-8859-1') #-- find file header for data product @@ -219,7 +226,7 @@ def main(): #-- Read the system arguments listed after the program parser = argparse.ArgumentParser( description="""Creates monthly files of geocenter variations - at 6-hour intervals + at 6-hour or 3-hour intervals """ ) #-- command line parameters From f251cfe1174c8294f1a3a74ad54e680ef7649150 Mon Sep 17 00:00:00 2001 From: hulecom Date: Thu, 25 Feb 2021 10:08:25 +0100 Subject: [PATCH 17/80] Add skip comment functionnality to harmonics.from_ascii() --- gravity_toolkit/harmonics.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/gravity_toolkit/harmonics.py b/gravity_toolkit/harmonics.py index 92c79f56..5904fe00 100644 --- a/gravity_toolkit/harmonics.py +++ b/gravity_toolkit/harmonics.py @@ -97,7 +97,7 @@ def case_insensitive_filename(self,filename): self.filename = os.path.join(directory,f.pop()) return self - def from_ascii(self, filename, date=True, compression=None, verbose=False): + def from_ascii(self, filename, date=True, compression=None, verbose=False, skip_comment=''): """ Read a harmonics object from an ascii file Inputs: full path of input ascii file @@ -105,6 +105,7 @@ def from_ascii(self, filename, date=True, compression=None, verbose=False): ascii file contains date information ascii file is compressed or streaming as bytes verbose output of file information + skip_comment skip lines that contains a particular string """ #-- set filename self.case_insensitive_filename(filename) @@ -126,6 +127,10 @@ def from_ascii(self, filename, date=True, compression=None, verbose=False): #-- read input ascii file (.txt, .asc) and split lines with open(self.filename,'r') as f: file_contents = f.read().splitlines() + + if skip_comment: + file_contents = [line for line in file_contents if not(skip_comment in line)] + #-- compile regular expression operator for extracting numerical values #-- from input ascii files of spherical harmonics regex_pattern = r'[-+]?(?:(?:\d*\.\d+)|(?:\d+\.?))(?:[EeD][+-]?\d+)?' From 68456e99166b1349654ea15806a0157ac62e344b Mon Sep 17 00:00:00 2001 From: hulecom Date: Thu, 25 Feb 2021 16:37:35 +0100 Subject: [PATCH 18/80] Debug grace_date.py after the merge --- gravity_toolkit/grace_date.py | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/gravity_toolkit/grace_date.py b/gravity_toolkit/grace_date.py index 9b6fbe35..e637252e 100644 --- a/gravity_toolkit/grace_date.py +++ b/gravity_toolkit/grace_date.py @@ -178,19 +178,6 @@ def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): start_day[t] = np.float(start_date[4:]) end_day[t] = np.float(end_date[4:]) - #-- number of days in the starting year for leap and standard years - dpy = gravity_toolkit.time.calendar_days(start_yr[t]).sum() - #-- end date taking into account measurements taken on different years - end_cyclic = (end_yr[t]-start_yr[t])*dpy + end_day[t] - #-- calculate mid-month value - mid_day[t] = np.mean([start_day[t], end_cyclic]) - - #-- calculate Modified Julian Day from start_yr and mid_day - MJD = gravity_toolkit.time.convert_calendar_dates(start_yr[t], - 1.0,mid_day[t],epoch=(1858,11,17,0,0,0)) - #-- convert from Modified Julian Days to calendar dates - cal_date = gravity_toolkit.time.convert_julian(MJD+2400000.5) - elif PROC == 'GRAZ' or PROC == 'SWARM': if PROC == 'GRAZ': PFX,SAT,trunc,year,month,SFX = rx.findall(infile).pop() @@ -212,8 +199,20 @@ def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): start_day[t] = np.sum(dpm[:np.int(month) - 1]) + 1 end_day[t] = np.sum(dpm[:np.int(month)]) - #-- Calculation of Mid-month value - mid_day[t] = np.mean([start_day[t], end_day[t]]) + # -- number of days in the starting year for leap and standard years + dpy = gravity_toolkit.time.calendar_days(start_yr[t]).sum() + + # -- end date taking into account measurements taken on different years + end_cyclic = (end_yr[t] - start_yr[t]) * dpy + end_day[t] + + #-- Calculation of Mid-month value + mid_day[t] = np.mean([start_day[t], end_cyclic]) + + # -- calculate Modified Julian Day from start_yr and mid_day + MJD = gravity_toolkit.time.convert_calendar_dates(start_yr[t], + 1.0, mid_day[t], epoch=(1858, 11, 17, 0, 0, 0)) + # -- convert from Modified Julian Days to calendar dates + cal_date = gravity_toolkit.time.convert_julian(MJD + 2400000.5) #-- Calculating the mid-month date in decimal form tdec[t] = start_yr[t] + mid_day[t] / dpy @@ -248,6 +247,7 @@ def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): #-- For JPL: Dec 2011 (120) is centered in Jan 2012 (121) #-- For all: May 2015 (161) is centered in Apr 2015 (160) mon = gravity_toolkit.time.adjust_months(mon) + print(PROC, DREL, DSET) #-- Output GRACE/GRACE-FO date ascii file if OUTPUT: From 78b708b42886a10c2a306124ecf9353470bcb9f7 Mon Sep 17 00:00:00 2001 From: hulecom Date: Fri, 5 Mar 2021 10:10:34 +0100 Subject: [PATCH 19/80] Remove a debug feature in grace_date.py --- gravity_toolkit/grace_date.py | 1 - 1 file changed, 1 deletion(-) diff --git a/gravity_toolkit/grace_date.py b/gravity_toolkit/grace_date.py index e637252e..4fd80e51 100644 --- a/gravity_toolkit/grace_date.py +++ b/gravity_toolkit/grace_date.py @@ -247,7 +247,6 @@ def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): #-- For JPL: Dec 2011 (120) is centered in Jan 2012 (121) #-- For all: May 2015 (161) is centered in Apr 2015 (160) mon = gravity_toolkit.time.adjust_months(mon) - print(PROC, DREL, DSET) #-- Output GRACE/GRACE-FO date ascii file if OUTPUT: From 7a91398e737774ae56cb871a79440427a833ea38 Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 13 Jul 2021 09:19:16 +0200 Subject: [PATCH 20/80] Implement COSTG reading for GRACE time series --- gravity_toolkit/grace_date.py | 11 ++++++----- gravity_toolkit/grace_input_months.py | 12 ++++++------ 2 files changed, 12 insertions(+), 11 deletions(-) diff --git a/gravity_toolkit/grace_date.py b/gravity_toolkit/grace_date.py index 4fd80e51..f9aebec2 100644 --- a/gravity_toolkit/grace_date.py +++ b/gravity_toolkit/grace_date.py @@ -11,7 +11,7 @@ base_dir: Working data directory for GRACE/GRACE-FO data OPTIONS: - PROC: GRACE data processing center (CSR/CNES/JPL/GFZ) + PROC: GRACE data processing center (CSR/CNES/JPL/GFZ/GRAZ/COSTG/SWARM) DREL: GRACE/GRACE-FO Data Release (RL03 for CNES) (RL06 for CSR/GFZ/JPL) DSET: GRACE dataset (GAA/GAB/GAC/GAD/GSM) GAA is the non-tidal atmospheric correction @@ -106,6 +106,7 @@ def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): JPL: Jet Propulsion Laboratory CNES: French Centre National D'Etudes Spatiales GRAZ: Institute of Geodesy from GRAZ University of Technology + COSTG: International Combination Service for Time-variable Gravity Fields SWARM: gravity data from SWARM satellite DREL: GRACE/GRACE-FO data release @@ -142,7 +143,7 @@ def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): tdec = np.zeros((n_files))#-- tdec is the date in decimal form mon = np.zeros((n_files,),dtype=np.int)#-- GRACE/GRACE-FO month number - if PROC in ('CSR', 'GFZ', 'JPL', 'CNES'): + if PROC in ('CSR', 'GFZ', 'JPL', 'CNES', 'COSTG'): #-- compile numerical expression operator for parameters from files #-- will work with previous releases and releases for GRACE-FO #-- UTCSR: The University of Texas at Austin Center for Space Research @@ -152,7 +153,7 @@ def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): #-- JPLMSC: NASA Jet Propulsion Laboratory (mascon solutions) #-- GRGS: CNES Groupe de Recherche de Géodésie Spatiale regex_pattern = (r'(.*?)-2_(\d+)-(\d+)_(.*?)_({0})_(.*?)_(\d+)(.*?)' - r'(\.gz|\.gfc|\.txt)?$').format(r'UTCSR|EIGEN|GFZOP|JPLEM|JPLMSC|GRGS') + r'(\.gz|\.gfc|\.txt)?$').format(r'UTCSR|EIGEN|GFZOP|JPLEM|JPLMSC|GRGS|COSTG') elif PROC == 'GRAZ': # -- GRAZ: Institute of Geodesy from GRAZ University of Technology regex_pattern = (r'(.*?)-({0})_(.*?)_(\d+)-(\d+)' @@ -169,7 +170,7 @@ def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): #-- for each data file for t, infile in enumerate(input_files): #-- extract parameters from input filename - if PROC in ('CSR', 'GFZ', 'JPL', 'CNES'): + if PROC in ('CSR', 'GFZ', 'JPL', 'CNES', 'COSTG'): PFX,start_date,end_date,AUX,PRC,F1,DRL,F2,SFX = rx.findall(infile).pop() #-- find start date, end date and number of days @@ -297,7 +298,7 @@ def main(): parser.add_argument('--center','-c', metavar='PROC', type=str, nargs='+', default=['CSR','GFZ','JPL'], - choices=['CSR','GFZ','JPL', 'CNES'], + choices=['CSR','GFZ','JPL', 'CNES','GRAZ','SWARM', 'COSTG'], help='GRACE/GRACE-FO Processing Center') #-- GRACE/GRACE-FO data release parser.add_argument('--release','-r', diff --git a/gravity_toolkit/grace_input_months.py b/gravity_toolkit/grace_input_months.py index b5a6b325..d9c8d18b 100644 --- a/gravity_toolkit/grace_input_months.py +++ b/gravity_toolkit/grace_input_months.py @@ -130,9 +130,9 @@ from gravity_toolkit.read_SLR_geocenter import aod_corrected_SLR_geocenter from read_GRACE_geocenter.read_GRACE_geocenter import read_GRACE_geocenter from gravity_toolkit.read_GRACE_harmonics import read_GRACE_harmonics -from gravity_toolkit.read_ICGEM_harmonics import read_ICGEM_harmonics from gravity_toolkit.read_love_numbers import read_love_numbers from gravity_toolkit.utilities import get_data_path +import geoid_toolkit.read_ICGEM_harmonics def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, missing, SLR_C20, DEG1, MMAX=None, SLR_C30='', @@ -348,8 +348,8 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, infile = grace_files[grace_month] if PROC in ('CSR', 'GFZ', 'JPL', 'CNES', 'JPLMSC'): Ylms = read_GRACE_harmonics(infile,LMAX,MMAX=MMAX,POLE_TIDE=POLE_TIDE) - elif PROC in ('GRAZ', 'SWARM'): - Ylms = read_ICGEM_harmonics(infile) + elif PROC in ('GRAZ', 'SWARM', 'COSTG'): + Ylms = geoid_toolkit.read_ICGEM_harmonics(infile) grace_clm[:,:,i] = Ylms['clm'][0:LMAX+1,0:MMAX+1] grace_slm[:,:,i] = Ylms['slm'][0:LMAX+1,0:MMAX+1] tdec[i] = Ylms['time'] @@ -376,11 +376,11 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, # -- apply conversion formula from IERS 2010 Notes grace_clm[2, 0, :] += 4.4228e-8 * 0.31460 * k2 - elif SLR_C20 == 'ZeroTide' and PROC in ('CNES', 'GFZ', 'SWARM'): + elif SLR_C20 == 'ZeroTide' and PROC in ('CNES', 'GFZ', 'SWARM', 'COSTG'): #-- k2,0 nominal from IERS 2010 convention - if PROC in ('CNES', 'SWARM'): + if PROC in ('CNES', 'SWARM', 'COSTG'): k2 = 0.3 - elif PROC == 'GFZ': + elif PROC in ('GFZ'): k2 = 0.3190 #-- apply conversion formula from IERS 2010 Notes From bfb17c305136d5d9139943af7f28679e99dac34e Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 13 Jul 2021 09:38:01 +0200 Subject: [PATCH 21/80] Homogeneous plot and improve read_grid_to_harmonics.py --- gravity_toolkit/harmonics.py | 8 ++++---- gravity_toolkit/read_grid_to_harmonics.py | 10 +++------- 2 files changed, 7 insertions(+), 11 deletions(-) diff --git a/gravity_toolkit/harmonics.py b/gravity_toolkit/harmonics.py index bf5cfd67..eb435476 100644 --- a/gravity_toolkit/harmonics.py +++ b/gravity_toolkit/harmonics.py @@ -1200,9 +1200,9 @@ def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], save_path=False) plt.figure() plt.title("Normalized spherical harmonics coefficient $C_{" + str(l) + "," + str(m) + "}$") if len(ylms): - plt.plot(self.time, self.clm[l, m, :], 'r', label=label[0]) + plt.plot(self.time, self.clm[l, m, :], label=label[0]) else: - plt.plot(self.time, self.clm[l, m, :], 'r', label="$C_{" + str(l) + "," + str(m) + "}$") + plt.plot(self.time, self.clm[l, m, :], label="$C_{" + str(l) + "," + str(m) + "}$") try: for i in range(len(ylms)): @@ -1227,9 +1227,9 @@ def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], save_path=False) plt.figure() plt.title("Normalized spherical harmonics coefficient $S_{" + str(l) + "," + str(m) + "}$") if len(ylms): - plt.plot(self.time, self.slm[l, m, :], 'r', label=label[0]) + plt.plot(self.time, self.slm[l, m, :], label=label[0]) else: - plt.plot(self.time, self.slm[l, m, :], 'r', label="$S_{" + str(l) + "," + str(m) + "}$") + plt.plot(self.time, self.slm[l, m, :], label="$S_{" + str(l) + "," + str(m) + "}$") try: for i in range(len(ylms)): diff --git a/gravity_toolkit/read_grid_to_harmonics.py b/gravity_toolkit/read_grid_to_harmonics.py index 7ce159ab..46fb1840 100644 --- a/gravity_toolkit/read_grid_to_harmonics.py +++ b/gravity_toolkit/read_grid_to_harmonics.py @@ -82,17 +82,13 @@ def read_grid_to_harmonics(input_file, VARNAME, LMAX, MMAX=None, LONNAME='lon', title: string denoting low degree zonals replacement, geocenter usage and corrections directory: directory of exact GRACE/GRACE-FO product """ - - #-- parse filename - pfx,center,time,realm,release,v_id,sfx = parse_file(input_file) - #-- read file content if input_file[-3:] == '.nc': file_contents = ncdf_read(input_file, DATE=True, VARNAME=VARNAME, LONNAME=LONNAME, - LATNAME=LATNAME, TIMENAME=TIMENAME, COMPRESSION=sfx) + LATNAME=LATNAME, TIMENAME=TIMENAME) elif input_file[-4:] == '.hdf' or input_file[-3:] == '.h5' or input_file[-5:] == '.hdf5': file_contents = hdf5_read(input_file, DATE=True, VARNAME=VARNAME, LONNAME=LONNAME, - LATNAME=LATNAME, TIMENAME=TIMENAME, COMPRESSION=sfx) + LATNAME=LATNAME, TIMENAME=TIMENAME) #-- load love numbers hl, kl, ll = read_love_numbers(get_data_path(['data', 'love_numbers']), REFERENCE='CF') @@ -122,7 +118,7 @@ def read_grid_to_harmonics(input_file, VARNAME, LMAX, MMAX=None, LONNAME='lon', grace_slm[:, :, i] = harmo.slm #-- extract GRACE date information from input file name - start_yr = np.float(time[:4]) + start_yr = np.int(file_contents['time'][0]) #-- variables initialization for date conversion current_year = start_yr From cb913e16d8d1b31272132970e18f3a4dee67a954 Mon Sep 17 00:00:00 2001 From: hulecom Date: Thu, 15 Jul 2021 13:43:57 +0200 Subject: [PATCH 22/80] Some debug coming after the merge --- gravity_toolkit/grace_input_months.py | 5 ++++- gravity_toolkit/read_grid_to_harmonics.py | 9 ++++++--- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/gravity_toolkit/grace_input_months.py b/gravity_toolkit/grace_input_months.py index 0dcd12cc..88899826 100644 --- a/gravity_toolkit/grace_input_months.py +++ b/gravity_toolkit/grace_input_months.py @@ -259,7 +259,10 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, for i,grace_month in enumerate(months): #-- Effects of Pole tide drift will be compensated if soecified infile = grace_files[grace_month] - Ylms = read_GRACE_harmonics(infile,LMAX,MMAX=MMAX,POLE_TIDE=POLE_TIDE) + if PROC in ('CSR', 'GFZ', 'JPL', 'CNES', 'JPLMSC'): + Ylms = read_GRACE_harmonics(infile,LMAX,MMAX=MMAX,POLE_TIDE=POLE_TIDE) + elif PROC in ('GRAZ', 'SWARM', 'COSTG'): + Ylms = geoid_toolkit.read_ICGEM_harmonics(infile) grace_clm[:,:,i] = Ylms['clm'][0:LMAX+1,0:MMAX+1] grace_slm[:,:,i] = Ylms['slm'][0:LMAX+1,0:MMAX+1] tdec[i] = Ylms['time'] diff --git a/gravity_toolkit/read_grid_to_harmonics.py b/gravity_toolkit/read_grid_to_harmonics.py index 46fb1840..8ff07fb2 100644 --- a/gravity_toolkit/read_grid_to_harmonics.py +++ b/gravity_toolkit/read_grid_to_harmonics.py @@ -9,7 +9,7 @@ Design for JPL MASCON netCDF data available on https://podaac-tools.jpl.nasa.gov/drive/files -In the folder /allData/tellus/retired/L3/mascon/RL06/JPL/v02 +In the folder /allData/tellus/L3/mascon/RL06/JPL/v02 INPUTS: input_file: GRACE/GRACE-FO Level-3 netCDF grid data file @@ -45,7 +45,7 @@ from gravity_toolkit.read_love_numbers import read_love_numbers from gravity_toolkit.gen_stokes import gen_stokes -#-- PURPOSE: read Level-3 GRACE and GRACE-FO netCDF files +#-- PURPOSE: read Level-3 GRACE and GRACE-FO netCDF or hdf5 files def read_grid_to_harmonics(input_file, VARNAME, LMAX, MMAX=None, LONNAME='lon', LATNAME='lat', TIMENAME='time', UNITS=1, POLE_TIDE=False): """ @@ -82,6 +82,9 @@ def read_grid_to_harmonics(input_file, VARNAME, LMAX, MMAX=None, LONNAME='lon', title: string denoting low degree zonals replacement, geocenter usage and corrections directory: directory of exact GRACE/GRACE-FO product """ + # -- parse filename to extract begin date of the file + pfx, center, time, realm, release, v_id, sfx = parse_file(input_file) + #-- read file content if input_file[-3:] == '.nc': file_contents = ncdf_read(input_file, DATE=True, VARNAME=VARNAME, LONNAME=LONNAME, @@ -118,7 +121,7 @@ def read_grid_to_harmonics(input_file, VARNAME, LMAX, MMAX=None, LONNAME='lon', grace_slm[:, :, i] = harmo.slm #-- extract GRACE date information from input file name - start_yr = np.int(file_contents['time'][0]) + start_yr = np.float(time[:4]) #-- variables initialization for date conversion current_year = start_yr From 7e7cd756cc1875b4f85bfd516b7df5b44eec7684 Mon Sep 17 00:00:00 2001 From: hulecom Date: Thu, 15 Jul 2021 14:15:40 +0200 Subject: [PATCH 23/80] Uniformization before pull request --- gravity_toolkit/grace_input_months.py | 8 +++----- gravity_toolkit/harmonics.py | 2 +- gravity_toolkit/read_SLR_CS2.py | 2 +- gravity_toolkit/read_SLR_monthly_6x1.py | 4 ++-- gravity_toolkit/time.py | 2 +- 5 files changed, 8 insertions(+), 10 deletions(-) diff --git a/gravity_toolkit/grace_input_months.py b/gravity_toolkit/grace_input_months.py index 88899826..b9fff2be 100644 --- a/gravity_toolkit/grace_input_months.py +++ b/gravity_toolkit/grace_input_months.py @@ -153,8 +153,6 @@ from gravity_toolkit.read_gravis_geocenter import read_gravis_geocenter from read_GRACE_geocenter.read_GRACE_geocenter import read_GRACE_geocenter from gravity_toolkit.read_GRACE_harmonics import read_GRACE_harmonics -from gravity_toolkit.read_love_numbers import read_love_numbers -from gravity_toolkit.utilities import get_data_path import geoid_toolkit.read_ICGEM_harmonics def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, @@ -172,7 +170,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, Arguments --------- base_dir: Working data directory for GRACE/GRACE-FO data - PROC: (CSR/CNES/JPL/GFZ/GRAZ/SWARM) data processing center + PROC: (CSR/CNES/JPL/GFZ/GRAZ/COST-G/SWARM) data processing center DREL: (RL01/RL02/RL03/RL04/RL05/RL06) data release DSET: (GAA/GAB/GAC/GAD/GSM) data product LMAX: Upper bound of Spherical Harmonic Degrees @@ -181,8 +179,8 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, missing: missing months to not consider in analysis SLR_C20: Replaces C20 with SLR values N: use original values - TideFree: add a bias for CSR, GFZ, GRAZ or SWARM to convert C2,0 to tide free convention - ZeroTide: add a bias for CNES or JPL to convert C2,0 to zero tide convention + TideFree: add a bias for CSR, JPL, GRAZ to convert C2,0 to tide free convention + ZeroTide: add a bias for CNES, GFZ, SWARM or COST-G to convert C2,0 to zero tide convention CSR: use values from CSR (TN-07,TN-09,TN-11) GFZ: use values from GFZ GSFC: use values from GSFC (TN-14) diff --git a/gravity_toolkit/harmonics.py b/gravity_toolkit/harmonics.py index c2ee654b..dba52414 100644 --- a/gravity_toolkit/harmonics.py +++ b/gravity_toolkit/harmonics.py @@ -1451,4 +1451,4 @@ def plot_wavelets(self, l, m, s0=0, pad=1, lag1=0, plot_coi=True, mother='MORLET else: plt.savefig(save_path[:-3] + 's' + save_path[-3:]) - plt.show() \ No newline at end of file + plt.show() diff --git a/gravity_toolkit/read_SLR_CS2.py b/gravity_toolkit/read_SLR_CS2.py index 65d9d373..fffacdbd 100644 --- a/gravity_toolkit/read_SLR_CS2.py +++ b/gravity_toolkit/read_SLR_CS2.py @@ -15,7 +15,7 @@ Dataset from GSFC provided by Bryant Loomis CALLING SEQUENCE: - SLR_2x = read_SLR_CS2(SLR_file) + SLR_2m = read_SLR_CS2(SLR_file) INPUTS: SLR_file: diff --git a/gravity_toolkit/read_SLR_monthly_6x1.py b/gravity_toolkit/read_SLR_monthly_6x1.py index de290cee..cdc5f98c 100644 --- a/gravity_toolkit/read_SLR_monthly_6x1.py +++ b/gravity_toolkit/read_SLR_monthly_6x1.py @@ -1,7 +1,7 @@ #!/usr/bin/env python u""" -read_CSR_monthly_6x1.py -Written by Tyler Sutterley (12/2020) +read_SLR_monthly_6x1.py +Written by Tyler Sutterley (05/2021) Reads in monthly 5x5 spherical harmonic coefficients with 1 coefficient from degree 6 all calculated from SLR measurements diff --git a/gravity_toolkit/time.py b/gravity_toolkit/time.py index 6b7ff2f4..8fa7a09c 100644 --- a/gravity_toolkit/time.py +++ b/gravity_toolkit/time.py @@ -479,4 +479,4 @@ def dpm_count(input_year): # -- Standard Year dpm = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] - return dpm \ No newline at end of file + return dpm From 1b158f0d26e3eef5c6f3e735f7082fcb786f1073 Mon Sep 17 00:00:00 2001 From: hulecom Date: Fri, 20 Aug 2021 15:51:34 +0200 Subject: [PATCH 24/80] Units with mm Geoid Height for spatial format --- gravity_toolkit/gen_stokes.py | 4 ++++ gravity_toolkit/units.py | 2 ++ 2 files changed, 6 insertions(+) diff --git a/gravity_toolkit/gen_stokes.py b/gravity_toolkit/gen_stokes.py index 4d97691b..013b1720 100755 --- a/gravity_toolkit/gen_stokes.py +++ b/gravity_toolkit/gen_stokes.py @@ -155,6 +155,10 @@ def gen_stokes(data, lon, lat, LMIN=0, LMAX=60, MMAX=None, UNITS=1, #-- Input in kg/m^2 (mm w.e.) dfactor = factors.mmwe int_fact[:] = np.sin(th)*dphi*dth + elif (UNITS == 4): + #-- Inputs in mmGH + dfactor = factors.mmGH + int_fact[:] = np.sin(th) * dphi * dth else: #-- default is cm w.e. (g/cm^2) dfactor = factors.cmwe diff --git a/gravity_toolkit/units.py b/gravity_toolkit/units.py index d439996d..35a572c6 100644 --- a/gravity_toolkit/units.py +++ b/gravity_toolkit/units.py @@ -98,6 +98,8 @@ def spatial(self, hl, kl, ll): self.cmwe=3.0*(1.0+kl[self.l])/(1.0+2.0*self.l)/(4.0*np.pi*self.rad_e*self.rho_e) # mmwe, millimeters water equivalent [kg/m^2] self.mmwe=3.0*(1.0+kl[self.l])/(1.0+2.0*self.l)/(40.0*np.pi*self.rad_e*self.rho_e) + # mmGH, millimeters geoid height + self.mmGH=np.ones((self.lmax+1))/(4.0*np.pi*self.rad_e) # return the degree dependent unit conversions return self From 0b6241461447f02fc0ff3cf9e423a73c1f2fc31f Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 15 Sep 2021 10:34:35 +0200 Subject: [PATCH 25/80] Option to include Eath oblateness in grid format as describe in Ditmar 2018 --- gravity_toolkit/harmonic_summation.py | 18 ++++++++++++++---- gravity_toolkit/units.py | 4 ++++ 2 files changed, 18 insertions(+), 4 deletions(-) diff --git a/gravity_toolkit/harmonic_summation.py b/gravity_toolkit/harmonic_summation.py index 2fdc5899..c0677b2f 100755 --- a/gravity_toolkit/harmonic_summation.py +++ b/gravity_toolkit/harmonic_summation.py @@ -27,14 +27,16 @@ plm_holmes.py: Computes fully-normalized associated Legendre polynomials UPDATE HISTORY: + Updated 09/2020: added influence of Earth oblateness in option Updated 07/2020: added function docstrings Updated 05/2015: added parameter MMAX for MMAX != LMAX. Written 05/2013 """ import numpy as np from gravity_toolkit.plm_holmes import plm_holmes +from gravity_toolkit.units import units -def harmonic_summation(clm1,slm1,lon,lat,LMIN=0,LMAX=0,MMAX=None,PLM=None): +def harmonic_summation(clm1,slm1,lon,lat,LMIN=0,LMAX=0,MMAX=None,PLM=None,ellps=False): """ Converts data from spherical harmonic coefficients to a spatial field @@ -51,6 +53,7 @@ def harmonic_summation(clm1,slm1,lon,lat,LMIN=0,LMAX=0,MMAX=None,PLM=None): LMAX: Upper bound of Spherical Harmonic Degrees MMAX: Upper bound of Spherical Harmonic Orders PLM: Fully-normalized associated Legendre polynomials + ellps: Consideration of the Earth oblateness Returns ------- @@ -70,6 +73,9 @@ def harmonic_summation(clm1,slm1,lon,lat,LMIN=0,LMAX=0,MMAX=None,PLM=None): th = (90.0 - np.squeeze(lat))*np.pi/180.0 thmax = len(th) + #-- create a unit object to get constants + unit = units(lmax=LMAX) + #-- Calculate fourier coefficients from legendre coefficients d_cos = np.zeros((MMAX+1,thmax))#-- [m,th] d_sin = np.zeros((MMAX+1,thmax))#-- [m,th] @@ -91,9 +97,13 @@ def harmonic_summation(clm1,slm1,lon,lat,LMIN=0,LMAX=0,MMAX=None,PLM=None): #-- Final signal recovery from fourier coefficients m = np.arange(0,MMAX+1)[:,np.newaxis] - #-- Calculating cos(m*phi) and sin(m*phi) - ccos = np.cos(np.dot(m,phi)) - ssin = np.sin(np.dot(m,phi)) + # -- Calculating cos(m*phi) and sin(m*phi) in the case of Earth oblateness consideration + if ellps: + ccos = np.cos(np.dot(m, phi))*(np.sqrt(1 - (2*unit.flat - unit.flat**2)*np.sin(th)**2)/(1 - unit.flat))**(unit.l + 2) + ssin = np.sin(np.dot(m, phi))*(np.sqrt(1 - (2*unit.flat - unit.flat**2)*np.sin(th)**2)/(1 - unit.flat))**(unit.l + 2) + else: #-- Calculating cos(m*phi) and sin(m*phi) + ccos = np.cos(np.dot(m,phi)) + ssin = np.sin(np.dot(m,phi)) #-- summation of cosine and sine harmonics s = np.dot(np.transpose(ccos),d_cos) + np.dot(np.transpose(ssin),d_sin) diff --git a/gravity_toolkit/units.py b/gravity_toolkit/units.py index 35a572c6..0538562f 100644 --- a/gravity_toolkit/units.py +++ b/gravity_toolkit/units.py @@ -9,6 +9,7 @@ numpy: Scientific Computing Tools For Python (https://numpy.org) UPDATE HISTORY: + Updated 09/2020: added influence of Earth oblateness with attribute cmweEl Updated 08/2020: made semi-major axis and ellipsoidal flattening arguments Updated 07/2020: added class docstring Updated 04/2020: include earth parameters as attributes @@ -47,6 +48,7 @@ def __init__(self, lmax=None, a_axis=6.378137e8, flat=1.0/298.257223563): self.microGal=None self.mbar=None self.Pa=None + self.cmweEl=None self.lmax=lmax self.l=np.arange(self.lmax+1) if self.lmax else None @@ -85,6 +87,8 @@ def harmonic(self, hl, kl, ll): self.mbar=self.g_wmo*self.rho_e*self.rad_e*(2.0*self.l+1.0)/(1.0+kl[self.l])/3e3 # Pa, pascals equivalent surface pressure self.Pa=self.g_wmo*self.rho_e*self.rad_e*(2.0*self.l+1.0)/(1.0+kl[self.l])/30.0 + # cmwe, centimeters water equivalent [g/cm^2] considering Earth oblateness + self.cmweEl = self.rho_e*self.rad_e*(2.0*self.l+1.0)/(1.0+kl[self.l])/3.0 *(1 - self.flat) # return the degree dependent unit conversions return self From 7aef15ab5ff8de92197d7c847be522f0f4d9012e Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 15 Sep 2021 10:57:48 +0200 Subject: [PATCH 26/80] debug option to include Eath oblateness in grid format as describe in Ditmar 2018 --- gravity_toolkit/harmonic_summation.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/gravity_toolkit/harmonic_summation.py b/gravity_toolkit/harmonic_summation.py index c0677b2f..0d5d0b37 100755 --- a/gravity_toolkit/harmonic_summation.py +++ b/gravity_toolkit/harmonic_summation.py @@ -92,18 +92,18 @@ def harmonic_summation(clm1,slm1,lon,lat,LMIN=0,LMAX=0,MMAX=None,PLM=None,ellps= slm[LMIN:LMAX+1,mm] = slm1[LMIN:LMAX+1,mm] for k in range(0,thmax): #-- summation over all spherical harmonic degrees - d_cos[:,k] = np.sum(PLM[:,mm,k]*clm[:,mm],axis=0) - d_sin[:,k] = np.sum(PLM[:,mm,k]*slm[:,mm],axis=0) + if ellps: + d_cos[:,k] = np.sum(PLM[:,mm,k]*clm[:,mm],axis=0)*(np.sqrt(1 - (2*unit.flat - unit.flat**2)*np.sin(th[k])**2)/(1 - unit.flat))**(unit.l + 2) + d_sin[:,k] = np.sum(PLM[:,mm,k]*slm[:,mm],axis=0)*(np.sqrt(1 - (2*unit.flat - unit.flat**2)*np.sin(th[k])**2)/(1 - unit.flat))**(unit.l + 2) + else: + d_cos[:, k] = np.sum(PLM[:, mm, k] * clm[:, mm], axis=0) + d_sin[:, k] = np.sum(PLM[:, mm, k] * slm[:, mm], axis=0) #-- Final signal recovery from fourier coefficients m = np.arange(0,MMAX+1)[:,np.newaxis] - # -- Calculating cos(m*phi) and sin(m*phi) in the case of Earth oblateness consideration - if ellps: - ccos = np.cos(np.dot(m, phi))*(np.sqrt(1 - (2*unit.flat - unit.flat**2)*np.sin(th)**2)/(1 - unit.flat))**(unit.l + 2) - ssin = np.sin(np.dot(m, phi))*(np.sqrt(1 - (2*unit.flat - unit.flat**2)*np.sin(th)**2)/(1 - unit.flat))**(unit.l + 2) - else: #-- Calculating cos(m*phi) and sin(m*phi) - ccos = np.cos(np.dot(m,phi)) - ssin = np.sin(np.dot(m,phi)) + # -- Calculating cos(m*phi) and sin(m*phi) + ccos = np.cos(np.dot(m, phi)) + ssin = np.sin(np.dot(m, phi)) #-- summation of cosine and sine harmonics s = np.dot(np.transpose(ccos),d_cos) + np.dot(np.transpose(ssin),d_sin) From bbaad71de57260f029f2772e7177934661e42d15 Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 19 Apr 2022 14:45:25 +0200 Subject: [PATCH 27/80] Create toolbox.py with usual manipulation function --- gravity_toolkit/toolbox.py | 650 +++++++++++++++++++++++++++++++++++++ 1 file changed, 650 insertions(+) create mode 100644 gravity_toolkit/toolbox.py diff --git a/gravity_toolkit/toolbox.py b/gravity_toolkit/toolbox.py new file mode 100644 index 00000000..24f01f82 --- /dev/null +++ b/gravity_toolkit/toolbox.py @@ -0,0 +1,650 @@ +from gravity_toolkit.gauss_weights import gauss_weights +from gravity_toolkit.gen_stokes import gen_stokes +from gravity_toolkit.harmonics import harmonics +from gravity_toolkit.harmonic_summation import harmonic_summation +from gravity_toolkit.plm_holmes import plm_holmes +from gravity_toolkit.read_love_numbers import read_love_numbers +from gravity_toolkit.spatial import spatial +from gravity_toolkit.units import units +from gravity_toolkit.utilities import get_data_path + +import numpy as np +import scipy.signal as sg +import matplotlib +import matplotlib.pyplot as plt +import matplotlib.colors as colors +import matplotlib.animation as animation +import cartopy.crs as ccrs +from IPython.display import HTML + + +def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, dlat=0.5, bounds=None): + """ + Function to convert a harmonic object to grid format + + Parameters + ---------- + Ylms : harmonics object to convert to grid format + lmax : maximum degree of spherical harmonics used + rad : radius of the gaussian filter. If set to 0, no gaussian filter is apply + destripe : boolean to apply or not the destripe method of harmonics + unit : unit of the grid in ['cmwe', 'geoid', 'cmwe_ne', 'microGal'] + dlon : output longitude spacing + dlat : output latitude spacing + bounds : list with [lon_max, lon_min, lat_max, lat_min] + + Returns + ------- + grid : spatial object with the grid converted from the original harmonics object + """ + # Output spatial data + grid = spatial() + grid.time = np.copy(Ylms.time) + grid.month = np.copy(Ylms.month) + + # Output Degree Interval + if bounds is None: + grid.lon = np.arange(-180 + dlon / 2.0, 180 + dlon / 2.0, dlon) + grid.lat = np.arange(90.0 - dlat / 2.0, -90.0 - dlat / 2.0, -dlat) + else: + grid.lon = np.arange(-bounds[1] + dlon / 2.0, bounds[0] + dlon / 2.0, dlon) + grid.lat = np.arange(bounds[2] - dlat / 2.0, -bounds[3] - dlat / 2.0, -dlat) + + nlon = len(grid.lon) + nlat = len(grid.lat) + + # update spacing and dimensions + grid.update_spacing() + grid.update_extents() + grid.update_dimensions() + + # Computing plms for converting to spatial domain + theta = (90.0 - grid.lat) * np.pi / 180.0 + if lmax is None: + PLM, dPLM = plm_holmes(Ylms.lmax, np.cos(theta)) + else: + PLM, dPLM = plm_holmes(lmax, np.cos(theta)) + + # read load love numbers file + love_numbers_file = get_data_path(['data', 'love_numbers']) + # LMAX of load love numbers from Han and Wahr (1995) is 696. + # from Wahr (2007) linearly interpolating kl worksand ll Love Numbers + hl, kl, ll = read_love_numbers(love_numbers_file, REFERENCE='CF') + + if unit == 'cmwe': + dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).cmwe + elif unit == 'geoid': + dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).mmGH + elif unit == 'cmwe_ne': + dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).cmwe_ne + elif unit == 'microGal': + dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).microGal + else: + raise ValueError("Unit not accepted, should be either 'cmwe' or 'cmwe_ne' or 'geoid' or 'microGal'") + + # converting harmonics to truncated, smoothed coefficients in units + # combining harmonics to calculate output spatial fields + # output spatial grid + if not (type(Ylms.month) in [list, np.array]) and len(Ylms.month) == 1: + grid.data = np.zeros((nlat, nlon)) + + if destripe: + tmp = Ylms.destripe() + else: + tmp = Ylms + + if rad != 0: + wt = 2.0 * np.pi * gauss_weights(rad, lmax) + tmp.convolve(dfactor * wt) + else: + tmp.convolve(dfactor) + # convert spherical harmonics to output spatial grid + if lmax is None: + grid.data[:, :] = harmonic_summation(tmp.clm, tmp.slm, + grid.lon, grid.lat, LMAX=Ylms.lmax, MMAX=Ylms.mmax, PLM=PLM).T + else: + grid.data[:, :] = harmonic_summation(tmp.clm, tmp.slm, + grid.lon, grid.lat, LMAX=lmax, MMAX=lmax, PLM=PLM).T + + else: + grid.data = np.zeros((nlat, nlon, len(Ylms.month))) + for i, grace_month in enumerate(Ylms.month): + # GRACE/GRACE-FO harmonics for time t + # convert to output units + if destripe: + tmp = Ylms.index(i).destripe() + else: + tmp = Ylms.index(i) + + if rad != 0: + wt = 2.0 * np.pi * gauss_weights(rad, lmax) + tmp.convolve(dfactor * wt) + else: + tmp.convolve(dfactor * np.ones((Ylms.lmax + 1))) + # convert spherical harmonics to output spatial grid + if lmax is None: + grid.data[:, :, i] = harmonic_summation(tmp.clm, tmp.slm, + grid.lon, grid.lat, LMAX=Ylms.lmax, MMAX=Ylms.mmax, PLM=PLM).T + else: + grid.data[:, :, i] = harmonic_summation(tmp.clm, tmp.slm, + grid.lon, grid.lat, LMAX=lmax, MMAX=lmax, PLM=PLM).T + + grid.mask = np.zeros(grid.data.shape) + return grid + + +def grid_to_hs(grid, lmax, mmax=None, unit='cmwe'): + """ + Function to convert spatial object (grid) to harmonics object (spherical harmonics) + + Parameters + ---------- + grid : spatial object to convert to harmonics + lmax : maximal degree of the harmonics object to create + mmax : maximal order of the harmonics object to create + unit : unit of the grid in ['cmwe', 'geoid', 'cmwe_ne', 'microGal'] + + Returns + ------- + harmonics : harmonics object + """ + # -- load love numbers + hl, kl, ll = read_love_numbers(get_data_path(['data', 'love_numbers']), REFERENCE='CF') + + # -- set maximum spherical harmonic order + mmax = np.copy(lmax) if (mmax is None) else mmax + + # -- number of dates in data + if type(grid.time) in [list, np.array] or len(grid.time) != 1: + n_time = len(grid.time) + else: + n_time = 1 + # -- Spherical harmonic coefficient matrices to be filled from data file + grace_clm = np.zeros((lmax + 1, mmax + 1, n_time)) + grace_slm = np.zeros((lmax + 1, mmax + 1, n_time)) + # -- output dimensions + lout = np.arange(lmax + 1) + mout = np.arange(mmax + 1) + + # -- Test to attribute UNITS number + if unit == 'cmwe': + UNITS = 1 + elif unit == 'geoid': + UNITS = 4 + elif unit == 'cmwe_ne': + UNITS = 6 + elif unit == 'microGal': + UNITS = 5 + else: + raise ValueError("Unit not accepted, should be either 'cmwe' or 'cmwe_ne' or 'geoid' or 'microGal'") + + # -- for each date, conversion to spherical harmonics + if n_time != 1: + for i in range(n_time): + harmo = gen_stokes(grid.data[:, :, i], + grid.lon[:], grid.lat[:], + LMAX=lmax, MMAX=mmax, UNITS=UNITS, LOVE=(hl, kl, ll)) + + grace_clm[:, :, i] = harmo.clm + grace_slm[:, :, i] = harmo.slm + + else: + print('mono grid_to_hs') + harmo = gen_stokes(grid.data[:, :], + grid.lon[:], grid.lat[:], + LMAX=lmax, MMAX=mmax, UNITS=UNITS, LOVE=(hl, kl, ll)) + + grace_clm[:, :, 0] = harmo.clm + grace_slm[:, :, 0] = harmo.slm + + # -- return the GRACE data, GRACE date (mid-month in decimal), and the + # -- start and end days as Julian dates + result_dict = {'clm': grace_clm, 'slm': grace_slm, 'time': grid.time, 'month': grid.month, + 'l': lout, 'm': mout, 'title': '', 'directory': ''} + + return harmonics().from_dict(result_dict) + + +def diff_grid(grid1, grid2): + """ + Create a grid resulting from the difference between the two given grids + + Parameters + ---------- + grid1 : spatial object + grid2 : spatial object to substract to the first + + Returns + ------- + grid : spatial object with the difference between both grid + """ + exclude1 = set(grid1.month) - set(grid2.month) + + # Output spatial data + grid = spatial() + grid.month = np.array(list(sorted(set(grid1.month) - exclude1))) + grid.time = np.array([grid1.time[i] for i in range(len(grid1.time)) if not (grid1.month[i] in exclude1)]) + + # Output Degree Interval + grid.lon = grid1.lon + grid.lat = grid1.lat + + # update spacing and dimensions + grid.update_spacing() + grid.update_extents() + grid.update_dimensions() + + grid.data = np.zeros((grid.lat.shape[0], grid.lon.shape[0], len(grid.month))) + cmp = 0 + for i in range(len(grid1.month)): + for j in range(len(grid2.month)): + if grid1.month[i] == grid2.month[j]: + grid.data[:, :, cmp] = grid1.data[:, :, i] - grid2.data[:, :, j] + cmp += 1 + + return grid + + +def filt_Ylms(ylms, filt='low', filt_param=None): + """ + Apply a temporal filter on harmonics object + + Parameters + ---------- + ylms : harmonics object to filter + filt : choice of the filter in ['low', 'band', 'fft'] + filt_param : cut frequency of the filter. For band filter, a list with (f_max, f_min) + + Returns + ------- + filtered_ylms : temporally filtered harmonics object + + """ + filtered_ylms = ylms.copy() + + # len of the data + ndata = filtered_ylms.time.shape[0] + # compute the mean time delta of the object + dt = float(np.mean((filtered_ylms.time[1:] - filtered_ylms.time[:-1]))) + + if filt_param is not None and type(filt_param) != list: + filt_param = [filt_param] + + if filt == 'low': + if filt_param is None: + b, a = sg.butter(10, 0.5, analog=False, fs=1 / dt) + else: + b, a = sg.butter(10, filt_param[0], analog=False, fs=1 / dt) + + for i in range(filtered_ylms.clm.shape[0]): + for j in range(filtered_ylms.clm.shape[1]): + filtered_ylms.clm[i, j] = sg.filtfilt(b, a, filtered_ylms.clm[i, j]) + filtered_ylms.slm[i, j] = sg.filtfilt(b, a, filtered_ylms.slm[i, j]) + + elif filt == 'band': + if filt_param is None: + b, a = sg.butter(6, 0.3, analog=False, fs=1 / dt) + b2, a2 = sg.butter(6, 0.04, btype='highpass', analog=False, fs=1 / dt) + else: + b, a = sg.butter(6, filt_param[0], analog=False, fs=1 / dt) + b2, a2 = sg.butter(6, filt_param[1], btype='highpass', analog=False, fs=1 / dt) + + for i in range(filtered_ylms.clm.shape[0]): + for j in range(filtered_ylms.clm.shape[1]): + filtered_ylms.clm[i, j] = sg.filtfilt(b, a, filtered_ylms.clm[i, j]) + filtered_ylms.clm[i, j] = sg.filtfilt(b2, a2, filtered_ylms.clm[i, j]) + filtered_ylms.slm[i, j] = sg.filtfilt(b, a, filtered_ylms.slm[i, j]) + filtered_ylms.slm[i, j] = sg.filtfilt(b2, a2, filtered_ylms.slm[i, j]) + + elif filt == 'fft': + # zero pad + n2 = 0 + while ndata > 2 ** n2: + n2 += 1 + n2 += 1 + + fc = np.fft.fft(filtered_ylms.clm, n=2 ** n2, axis=2) + fs = np.fft.fft(filtered_ylms.slm, n=2 ** n2, axis=2) + freq = np.fft.fftfreq(2 ** n2, d=dt) + if filt_param is None: + to_zero = np.logical_or(freq > 0.5, freq < -0.5) + else: + to_zero = np.logical_or(freq > filt_param[0], freq < filt_param[0]) + fc[:, :, to_zero] = 0 + fs[:, :, to_zero] = 0 + filtered_ylms.clm = np.real(np.fft.ifft(fc, axis=2))[:, :, :ndata] + filtered_ylms.slm = np.real(np.fft.ifft(fs, axis=2))[:, :, :ndata] + + return filtered_ylms + + +def filt_grid(grid, f_cut=0.5): + """ + Temporally filter a grid with a truncation in fft at 2 years + + Parameters + ---------- + grid : spatial object to filter + f_cut : cutting frequency + + Returns + ------- + filtered_grid : spatial object filtered + + """ + filtered_grid = grid.copy() + time = grid.time + ndata = grid.time.shape[0] + + # zero pad + n2 = 0 + while ndata > 2 ** n2: + n2 += 1 + n2 += 1 + + # compute the mean time delta of the object + dt = float(np.mean((time[1:] - time[:-1]))) + + f = np.fft.fft(grid.data, n=2 ** n2, axis=2) + freq = np.fft.fftfreq(2 ** n2, d=dt) + + to_zero = np.logical_or(freq > f_cut, freq < -f_cut) + f[:, :, to_zero] = 0 + filtered_grid.data = np.real(np.fft.ifft(f, axis=2))[:, :, :ndata] + + return filtered_grid + +def save_gif(grid, path, unit='cmwe', bound=None, mask=None, color='viridis'): + """ + Create a gif of the spatial object + + Parameters + ---------- + grid : spatial object to convert to gif + path : path of the future gif (mandatory to end in .gif) + unit : unit of the grid in ['cmwe', 'mmwe', 'geoid', 'cmwe_ne', 'microGal', 'secacc'] + bound : list with minimal value and maximal value of the colorbar. Default value is None + mask : np.array corresponding to the mask + color : matplotlib cmap color of the gif (Recommended: viridis, plasma, RdBu_r) + """ + matplotlib.rcParams['animation.embed_limit'] = 2**128 + + if mask is None: + data_to_set = grid.data + else: + data_to_set = grid.data*mask + + fig, ax1 = plt.subplots(num=1, nrows=1, ncols=1, figsize=(10.375,6.625), + subplot_kw=dict(projection=ccrs.PlateCarree())) + + # levels and normalization for plot range + print(np.min(data_to_set), np.max(data_to_set)) + if bound is None: + vmin, vmax = int(np.min(data_to_set)), int(np.ceil(np.max(data_to_set))) + else: + vmin, vmax = bound + + if vmax - vmin >= 3: + levels = np.arange(vmin, vmax, max(1, int((vmax - vmin)/10))) + elif vmax - vmin >= 0.3: + levels = np.arange(vmin, vmax, max(0.1, float('%.1f'%((vmax - vmin)/10)))) + elif vmax - vmin >= 0.03: + levels = np.arange(vmin, vmax, max(0.1, float('%.2f'%((vmax - vmin)/10)))) + else: + raise ValueError("The range of data to plot is too small") + + norm = colors.Normalize(vmin=vmin,vmax=vmax) + cmap = plt.cm.get_cmap(color) + im = ax1.imshow(np.zeros((np.int(180.0 + 1.0),np.int(360.0 + 1.0))), interpolation='nearest', + norm=norm, cmap=cmap, transform=ccrs.PlateCarree(), + extent=grid.extent, origin='upper', animated=True) + ax1.coastlines('50m') + + # add date label + time_text = ax1.text(0.025, 0.025, '', transform=fig.transFigure, + color='k', size=24, ha='left', va='baseline') + + # Add horizontal colorbar and adjust size + # extend = add extension triangles to upper and lower bounds + # options: neither, both, min, max + # pad = distance from main plot axis + # shrink = percent size of colorbar + # aspect = lengthXwidth aspect of colorbar + cbar = plt.colorbar(im, ax=ax1, extend='both', extendfrac=0.0375, + orientation='horizontal', pad=0.025, shrink=0.85, + aspect=22, drawedges=False) + # rasterized colorbar to remove lines + cbar.solids.set_rasterized(True) + # Add label to the colorbar + if unit == "cmwe": + cbar.ax.set_xlabel('Equivalent Water Thickness', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('cm', fontsize=24, rotation=0) + elif unit == "cmwe_ne": + cbar.ax.set_xlabel('Non elastic Equivalent Water Thickness', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('cm', fontsize=24, rotation=0) + elif unit == "mmwe": + cbar.ax.set_xlabel('Equivalent Water Thickness', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('mm', fontsize=24, rotation=0) + elif unit == "geoid": + cbar.ax.set_xlabel('Geoid Height', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('mm', fontsize=24, rotation=0) + elif unit == "microGal": + cbar.ax.set_xlabel('Acceleration', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('$\mu Gal$', fontsize=24, rotation=0) + elif unit == "secacc": + cbar.ax.set_xlabel('Secular Acceleration', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('$nT.y^{-2}$', fontsize=24, rotation=0) + + cbar.ax.yaxis.set_label_coords(1.045, 0.1) + # Set the tick levels for the colorbar + cbar.set_ticks(levels) + if vmax - vmin >= 3: + cbar.set_ticklabels(['{0:d}'.format(ct) for ct in levels]) + elif vmax - vmin >= 0.3: + cbar.set_ticklabels(['{.1f}'.format(ct) for ct in levels]) + elif vmax - vmin >= 0.03: + cbar.set_ticklabels(['{.2f}'.format(ct) for ct in levels]) + # ticks lines all the way across + cbar.ax.tick_params(which='both', width=1, length=26, labelsize=24, + direction='in') + + # stronger linewidth on frame + ax1.spines['geo'].set_linewidth(2.0) + ax1.spines['geo'].set_capstyle('projecting') + # adjust subplot within figure + fig.subplots_adjust(left=0.02,right=0.98,bottom=0.05,top=0.98) + + # animate frames + def animate_frames(i): + # set image + im.set_data(data_to_set[:,:,i]) + # add date label + time_text.set_text('{:.2f}'.format(grid.time[i])) + + # set animation + anim = animation.FuncAnimation(fig, animate_frames, frames=len(grid.month)) + HTML(anim.to_jshtml()) + + anim.save(path, writer='imagemagick', fps=10) + + +def plot_rms_map(grid, path=False, proj=ccrs.PlateCarree(), unit='cmwe', bound=None, mask=None, color='viridis'): + """ + Create a rms map of the spatial object + + Parameters + ---------- + grid : spatial object to convert into a rms map + path : path to save the figure if needed + proj : projection of the map (Recommended: ccrs.PlateCarree(), ccrs.Mollweide()) + unit : unit of the grid in ['cmwe', 'mmwe', 'geoid', 'cmwe_ne', 'microGal', 'secacc'] + bound : list with minimal value and maximal value of the colorbar. Default value is None + mask : np.array corresponding to the mask + color : matplotlib cmap color of the gif (Recommended: viridis, plasma, RdBu_r, OrRd, Blues) + """ + data_to_set = np.sqrt(np.sum(grid.data ** 2, axis=2) / grid.time.shape[0]) + + if mask is not None: + data_to_set *= mask + + plt.figure() + matplotlib.rcParams['animation.embed_limit'] = 2 ** 128 + + fig, ax1 = plt.subplots(num=1, nrows=1, ncols=1, figsize=(10.375, 6.625), + subplot_kw=dict(projection=proj)) + + if bound is None: + vmin, vmax = int(np.min(data_to_set)), int(np.ceil(np.max(data_to_set))) + else: + vmin, vmax = bound + + if vmax - vmin >= 3: + levels = np.arange(vmin, vmax, max(1, int((vmax - vmin) / 10))) + elif vmax - vmin >= 0.3: + levels = np.arange(vmin, vmax, max(0.1, float('%.1f' % ((vmax - vmin) / 10)))) + elif vmax - vmin >= 0.03: + levels = np.arange(vmin, vmax, max(0.1, float('%.2f' % ((vmax - vmin) / 10)))) + else: + raise ValueError("The range of data to plot is too small") + + norm = colors.Normalize(vmin=vmin, vmax=vmax) + cmap = plt.cm.get_cmap(color) + im = ax1.imshow(data_to_set, interpolation='nearest', + norm=norm, cmap=cmap, transform=ccrs.PlateCarree(), + extent=grid.extent, origin='upper') + ax1.coastlines('50m') + + # Add horizontal colorbar and adjust size + # extend = add extension triangles to upper and lower bounds + # options: neither, both, min, max + # pad = distance from main plot axis + # shrink = percent size of colorbar + # aspect = lengthXwidth aspect of colorbar + cbar = plt.colorbar(im, ax=ax1, extend='both', extendfrac=0.0375, + orientation='horizontal', pad=0.025, shrink=0.85, + aspect=22, drawedges=False) + # rasterized colorbar to remove lines + cbar.solids.set_rasterized(True) + # Add label to the colorbar + if unit == "cmwe": + cbar.ax.set_xlabel('Equivalent Water Thickness', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('cm', fontsize=24, rotation=0) + elif unit == "cmwe_ne": + cbar.ax.set_xlabel('Non elastic Equivalent Water Thickness', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('cm', fontsize=24, rotation=0) + elif unit == "mmwe": + cbar.ax.set_xlabel('Equivalent Water Thickness', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('mm', fontsize=24, rotation=0) + elif unit == "geoid": + cbar.ax.set_xlabel('Geoid Height', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('mm', fontsize=24, rotation=0) + elif unit == "microGal": + cbar.ax.set_xlabel('Acceleration', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('$\mu Gal$', fontsize=24, rotation=0) + elif unit == "secacc": + cbar.ax.set_xlabel('Secular Acceleration', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('$nT.y^{-2}$', fontsize=24, rotation=0) + + cbar.ax.yaxis.set_label_coords(1.045, 0.1) + # Set the tick levels for the colorbar + cbar.set_ticks(levels) + if vmax - vmin >= 3: + cbar.set_ticklabels(['{0:d}'.format(ct) for ct in levels]) + elif vmax - vmin >= 0.3: + cbar.set_ticklabels(['{.1f}'.format(ct) for ct in levels]) + elif vmax - vmin >= 0.03: + cbar.set_ticklabels(['{.2f}'.format(ct) for ct in levels]) + # ticks lines all the way across + cbar.ax.tick_params(which='both', width=1, length=26, labelsize=24, + direction='in') + + # stronger linewidth on frame + ax1.spines['geo'].set_linewidth(2.0) + ax1.spines['geo'].set_capstyle('projecting') + # adjust subplot within figure + fig.subplots_adjust(left=0.02, right=0.98, bottom=0.05, top=0.98) + + if path: + plt.savefig(path, bbox_inches='tight') + else: + plt.show() + plt.close() + + +def calc_rms_grid(grid, mask=None): + """ + Compute Root Mean Square (RMS) value of a spatial object + + Parameters + ---------- + grid : spatial object + mask : mask to applied before rms computation + + Returns + ------- + rms : rms of the grid + + """ + if mask in None: + rms = np.sqrt(np.sum( + [np.sum(np.cos(lat * np.pi / 180) ** 2 * line ** 2) for lat, line in zip(grid.lat, grid.data)]) / np.sum( + [np.sum(np.cos(lat * np.pi / 180) ** 2 * line.size) for lat, line in zip(grid.lat, grid.data)])) + + else: + rms = np.sqrt(np.sum( + [np.sum(np.cos(lat * np.pi / 180) ** 2 * (line * np.swapaxes(np.tile(line_mask, (line.shape[1], 1)), 0, 1)) ** 2) + for lat, line, line_mask in zip(grid.lat, grid.data, mask)]) / np.sum( + [np.sum(np.cos(lat * np.pi / 180) ** 2 * np.tile(line_mask, (line.shape[1], 1))) + for lat, line, line_mask in zip(grid.lat, grid.data, mask)])) + # attention au cut dans les deux listes + return rms + + +def plot_rms_grid(grid, path=False, mask=None, unit='cmwe'): + """ + Create a figure with rms of the grid spatial object in function of time + + Parameters + ---------- + grid : spatial object + path : path to save the figure if needed + mask : mask to apply on data if needed + unit : unit of the grid + """ + l_rms = [] + for i in range(len(grid.time)): + if mask is None: + rms = np.sqrt(np.sum([np.sum(np.cos(lat * np.pi / 180) ** 2 * line ** 2) for lat, line in + zip(grid.lat, grid.data[:, :, i])]) / np.sum( + [np.sum(np.cos(lat * np.pi / 180) ** 2 * line.size) for lat, line in + zip(grid.lat, grid.data[:, :, i])])) + else: + rms = np.sqrt(np.sum([np.sum(np.cos(lat * np.pi / 180) ** 2 * (line * line_mask) ** 2) + for lat, line, line_mask in zip(grid.lat, grid.data[:, :, i], mask)]) + / np.sum([np.sum(np.cos(lat * np.pi / 180) ** 2 * line_mask) + for lat, line_mask in zip(grid.lat, mask)])) + + l_rms.append(rms) + + plt.figure() + plt.plot(grid.time, l_rms) + plt.xlabel('Time (y)') + if unit == "cmwe": + plt.ylabel('cm EWH') + elif unit == "cmwe_ne": + plt.ylabel('Non elastic cm EWH') + elif unit == "mmwe": + plt.ylabel('mm EWH') + elif unit == "geoid": + plt.ylabel('mm Geoid Height') + elif unit == "microGal": + plt.ylabel('$\mu Gal$') + elif unit == "secacc": + plt.ylabel('$nT.y^{-2}$') + plt.ylabel('Power (cm EWH)') + + if path: + plt.savefig(path, bbox_inches='tight') + else: + plt.show() + plt.close() \ No newline at end of file From 6e62b4b8cb5f0c7f0610a360d2521fda81fc47da Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 19 Apr 2022 14:46:21 +0200 Subject: [PATCH 28/80] Requirements for toolbox.py and precise versions --- requirements.txt | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/requirements.txt b/requirements.txt index c912f117..c0e371e6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,10 +1,16 @@ -numpy -scipy -pyyaml -lxml +numpy~=1.19.2 +scipy~=1.5.2 +pyyaml~=5.3.1 +lxml~=4.6.1 future -matplotlib -python-dateutil +matplotlib~=3.3.2 +python-dateutil~=2.8.1 cartopy --no-binary=cartopy netCDF4 h5py + +datetime~=4.3 +cartopy~=0.18.0 +ipython~=7.19.0 +yaml~=0.2.5 +setuptools~=50.3.0 \ No newline at end of file From f77e4bcb6504f7776dfeb49ebf70f16b69045807 Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 19 Apr 2022 14:48:27 +0200 Subject: [PATCH 29/80] Debug and new features improvements --- gravity_toolkit/grace_date.py | 14 +-- gravity_toolkit/grace_input_months.py | 8 ++ gravity_toolkit/harmonics.py | 133 ++++++++++++++++++++------ gravity_toolkit/read_SLR_C20.py | 12 +-- 4 files changed, 123 insertions(+), 44 deletions(-) diff --git a/gravity_toolkit/grace_date.py b/gravity_toolkit/grace_date.py index ff5beaf9..0dd27d15 100644 --- a/gravity_toolkit/grace_date.py +++ b/gravity_toolkit/grace_date.py @@ -87,7 +87,7 @@ import re import argparse import numpy as np -import gravity_toolkit.time +import gravity_toolkit.time as time def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): """ @@ -195,24 +195,24 @@ def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): #-- Calculation of total days since start of campaign #-- Get information on the current year (day per month and day per year) - dpm = gravity_toolkit.time.dpm_count(start_yr[t]) + dpm = time.dpm_count(start_yr[t]) #-- find start day, end day start_day[t] = np.sum(dpm[:np.int(month) - 1]) + 1 end_day[t] = np.sum(dpm[:np.int(month)]) # -- number of days in the starting year for leap and standard years - dpy = gravity_toolkit.time.calendar_days(start_yr[t]).sum() + dpy = time.calendar_days(start_yr[t]).sum() #-- end date taking into account measurements taken on different years end_cyclic = (end_yr[t]-start_yr[t])*dpy + end_day[t] #-- calculate mid-month value mid_day[t] = np.mean([start_day[t], end_cyclic]) #-- calculate Modified Julian Day from start_yr and mid_day - MJD = gravity_toolkit.time.convert_calendar_dates(start_yr[t], + MJD = time.convert_calendar_dates(start_yr[t], 1.0,mid_day[t],epoch=(1858,11,17,0,0,0)) #-- convert from Modified Julian Days to calendar dates - cal_date = gravity_toolkit.time.convert_julian(MJD+2400000.5) + cal_date = time.convert_julian(MJD+2400000.5) #-- Calculating the mid-month date in decimal form tdec[t] = start_yr[t] + mid_day[t]/dpy @@ -226,7 +226,7 @@ def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): year = 2002 + iyr #-- add all days from prior years to count #-- number of days in year i (if leap year or standard year) - count += gravity_toolkit.time.calendar_days(year).sum() + count += time.calendar_days(year).sum() #-- calculating the total number of days since 2002 tot_days[t] = np.mean([count+start_day[t], count+end_cyclic]) @@ -246,7 +246,7 @@ def grace_date(base_dir, PROC='', DREL='', DSET='', OUTPUT=True, MODE=0o775): #-- For CSR and GFZ: Nov 2011 (119) is centered in Oct 2011 (118) #-- For JPL: Dec 2011 (120) is centered in Jan 2012 (121) #-- For all: May 2015 (161) is centered in Apr 2015 (160) - mon = gravity_toolkit.time.adjust_months(mon) + mon = time.adjust_months(mon) #-- Output GRACE/GRACE-FO date ascii file if OUTPUT: diff --git a/gravity_toolkit/grace_input_months.py b/gravity_toolkit/grace_input_months.py index b9fff2be..625abff7 100644 --- a/gravity_toolkit/grace_input_months.py +++ b/gravity_toolkit/grace_input_months.py @@ -411,6 +411,9 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, #-- Running function read_gravis_geocenter.py DEG1_input = read_gravis_geocenter(DEG1_file) FLAGS.append('_w{0}_DEG1'.format(DEG1)) + elif (DEG1 == 'NoneCNES'): + #-- degree 1 coefficients set to None for CNES + FLAGS.append('_w{0}_DEG1'.format(DEG1)) #-- atmospheric flag if correcting ECMWF "jumps" (using GAE/GAF/GAG files) if ATM: @@ -549,6 +552,11 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, grace_clm[1,1,i] = DEG1_input['C11'][k] grace_slm[1,1,i] = DEG1_input['S11'][k] + if DEG1 == 'NoneCNES': + grace_clm[1, 0] = 0 + grace_clm[1, 1] = 0 + grace_slm[1, 1] = 0 + #-- read and add/remove the GAE and GAF atmospheric correction coefficients if ATM: #-- read ECMWF correction files from Fagiolini et al. (2015) diff --git a/gravity_toolkit/harmonics.py b/gravity_toolkit/harmonics.py index dba52414..1baec8e8 100644 --- a/gravity_toolkit/harmonics.py +++ b/gravity_toolkit/harmonics.py @@ -389,16 +389,16 @@ def from_file(self, filename, format=None, date=True, **kwargs): #-- set default verbosity kwargs.setdefault('verbose',False) #-- read from file - if (format == 'ascii'): + if format == 'ascii': #-- ascii (.txt) return harmonics().from_ascii(filename, date=date, **kwargs) - elif (format == 'netCDF4'): + elif format == 'netCDF4': #-- netcdf (.nc) return harmonics().from_netCDF4(filename, date=date, **kwargs) - elif (format == 'HDF5'): + elif format == 'HDF5': #-- HDF5 (.H5) return harmonics().from_HDF5(filename, date=date, **kwargs) - elif (format == 'gfc'): + elif format == 'gfc': #-- ICGEM gravity model (.gfc) return harmonics().from_HDF5(filename, **kwargs) @@ -513,13 +513,13 @@ def to_index(self, filename, file_list, format=None, date=True, **kwargs): #-- index harmonics object at i h = self.index(i, date=date) #-- write to file - if (format == 'ascii'): + if format == 'ascii': #-- ascii (.txt) h.to_ascii(f, date=date, **kwargs) - elif (format == 'netCDF4'): + elif format == 'netCDF4': #-- netcdf (.nc) h.to_netCDF4(f, date=date, **kwargs) - elif (format == 'HDF5'): + elif format == 'HDF5': #-- HDF5 (.H5) h.to_HDF5(f, date=date, **kwargs) #-- close the index file @@ -537,13 +537,13 @@ def to_file(self, filename, format=None, date=True, **kwargs): #-- set default verbosity kwargs.setdefault('verbose',False) #-- write to file - if (format == 'ascii'): + if format == 'ascii': #-- ascii (.txt) self.to_ascii(filename, date=date, **kwargs) - elif (format == 'netCDF4'): + elif format == 'netCDF4': #-- netcdf (.nc) self.to_netCDF4(filename, date=date, **kwargs) - elif (format == 'HDF5'): + elif format == 'HDF5': #-- HDF5 (.H5) self.to_HDF5(filename, date=date, **kwargs) @@ -563,14 +563,14 @@ def to_masked_array(self): for m in range(-self.mmax,self.mmax+1): mm = np.abs(m) for l in range(mm,self.lmax+1): - if (m < 0): + if m < 0: Ylms.data[l,self.lmax+m,:] = self.slm[l,mm,:] Ylms.mask[l,self.lmax+m,:] = False else: Ylms.data[l,self.lmax+m,:] = self.clm[l,mm,:] Ylms.mask[l,self.lmax+m,:] = False #-- reshape to previous - if (self.ndim != ndim_prev): + if self.ndim != ndim_prev: self.squeeze() #-- return the triangular matrix return Ylms @@ -603,8 +603,24 @@ def add(self, temp): self.clm[:l1,:m1,i] += temp.clm[:l1,:m1] self.slm[:l1,:m1,i] += temp.slm[:l1,:m1] else: - self.clm[:l1,:m1,:] += temp.clm[:l1,:m1,:] - self.slm[:l1,:m1,:] += temp.slm[:l1,:m1,:] + old_month = self.month + exclude1 = set(self.month) - set(temp.month) + + self.month = np.array(list(sorted(set(self.month) - exclude1))) + self.time = np.array([self.time[i] for i in range(len(self.time)) if not (old_month[i] in exclude1)]) + + for i in range(len(old_month)): + for j in range(len(temp.month)): + if old_month[i] == temp.month[j]: + self.clm[:l1,:m1,i] += temp.clm[:l1,:m1,j] + self.slm[:l1,:m1,i] += temp.slm[:l1,:m1,j] + + to_keep = [] + for i in range(len(old_month)): + if not(old_month[i] in exclude1): + to_keep.append(i) + self.clm = self.clm[:,:,to_keep] + self.slm = self.slm[:, :, to_keep] return self def subtract(self, temp): @@ -625,8 +641,24 @@ def subtract(self, temp): self.clm[:l1,:m1,i] -= temp.clm[:l1,:m1] self.slm[:l1,:m1,i] -= temp.slm[:l1,:m1] else: - self.clm[:l1,:m1,:] -= temp.clm[:l1,:m1,:] - self.slm[:l1,:m1,:] -= temp.slm[:l1,:m1,:] + old_month = self.month + exclude1 = set(self.month) - set(temp.month) + + self.month = np.array(list(sorted(set(self.month) - exclude1))) + self.time = np.array([self.time[i] for i in range(len(self.time)) if not (old_month[i] in exclude1)]) + + for i in range(len(old_month)): + for j in range(len(temp.month)): + if old_month[i] == temp.month[j]: + self.clm[:l1,:m1,i] -= temp.clm[:l1,:m1,j] + self.slm[:l1,:m1,i] -= temp.slm[:l1,:m1,j] + + to_keep = [] + for i in range(len(old_month)): + if not(old_month[i] in exclude1): + to_keep.append(i) + self.clm = self.clm[:,:,to_keep] + self.slm = self.slm[:, :, to_keep] return self def multiply(self, temp): @@ -647,8 +679,24 @@ def multiply(self, temp): self.clm[:l1,:m1,i] *= temp.clm[:l1,:m1] self.slm[:l1,:m1,i] *= temp.slm[:l1,:m1] else: - self.clm[:l1,:m1,:] *= temp.clm[:l1,:m1,:] - self.slm[:l1,:m1,:] *= temp.slm[:l1,:m1,:] + old_month = self.month + exclude1 = set(self.month) - set(temp.month) + + self.month = np.array(list(sorted(set(self.month) - exclude1))) + self.time = np.array([self.time[i] for i in range(len(self.time)) if not (old_month[i] in exclude1)]) + + for i in range(len(old_month)): + for j in range(len(temp.month)): + if old_month[i] == temp.month[j]: + self.clm[:l1, :m1, i] *= temp.clm[:l1, :m1, j] + self.slm[:l1, :m1, i] *= temp.slm[:l1, :m1, j] + + to_keep = [] + for i in range(len(old_month)): + if not (old_month[i] in exclude1): + to_keep.append(i) + self.clm = self.clm[:, :, to_keep] + self.slm = self.slm[:, :, to_keep] return self def divide(self, temp): @@ -674,8 +722,24 @@ def divide(self, temp): self.clm[lc,mc,i] /= temp.clm[lc,mc] self.slm[ls,ms,i] /= temp.slm[ls,ms] else: - self.clm[lc,mc,:] /= temp.clm[lc,mc,:] - self.slm[ls,ms,:] /= temp.slm[ls,ms,:] + old_month = self.month + exclude1 = set(self.month) - set(temp.month) + + self.month = np.array(list(sorted(set(self.month) - exclude1))) + self.time = np.array([self.time[i] for i in range(len(self.time)) if not (old_month[i] in exclude1)]) + + for i in range(len(old_month)): + for j in range(len(temp.month)): + if old_month[i] == temp.month[j]: + self.clm[:l1, :m1, i] /= temp.clm[:l1, :m1, j] + self.slm[:l1, :m1, i] /= temp.slm[:l1, :m1, j] + + to_keep = [] + for i in range(len(old_month)): + if not (old_month[i] in exclude1): + to_keep.append(i) + self.clm = self.clm[:, :, to_keep] + self.slm = self.slm[:, :, to_keep] return self def copy(self): @@ -1081,10 +1145,12 @@ def amplitude(self, mmax=None): #-- return the harmonics object with degree amplitudes return self - def gap_fill(self, apply=False): + def gap_fill(self, apply=False, interpolate=1): """ Fill the missing months with a linear interpolation, the interpolation is made on month number, it's imprecise - Options: apply to the object if True, else return a new instance + Options: + apply: apply to the object if True, else return a new instance + interpolate: 0 = fill gap with 0, 1 = linear interpolation """ temp = self.copy() missing_month = self.month[-1] - self.month[0] - len(self.month) + 1 @@ -1109,12 +1175,18 @@ def gap_fill(self, apply=False): # y(t) = (y2 - y1)/(x2 - x1)*t + y1 temp.time[index] = (self.time[index - cmp + 1] - self.time[index - cmp]) / ( self.month[index - cmp + 1] - self.month[index - cmp]) * cmp_miss_mon + self.time[index - cmp] - temp.clm[:, :, index] = (self.clm[:, :, index - cmp + 1] - self.clm[:, :, index - cmp]) / \ - (self.month[index - cmp + 1] - self.month[index - cmp]) * cmp_miss_mon \ - + self.clm[:, :, index - cmp] - temp.slm[:, :, index] = (self.slm[:, :, index - cmp + 1] - self.slm[:, :, index - cmp]) / \ - (self.month[index - cmp + 1] - self.month[index - cmp]) * cmp_miss_mon \ - + self.slm[:, :, index - cmp] + + if interpolate == 1: + temp.clm[:, :, index] = (self.clm[:, :, index - cmp + 1] - self.clm[:, :, index - cmp]) / \ + (self.month[index - cmp + 1] - self.month[index - cmp]) * cmp_miss_mon \ + + self.clm[:, :, index - cmp] + temp.slm[:, :, index] = (self.slm[:, :, index - cmp + 1] - self.slm[:, :, index - cmp]) / \ + (self.month[index - cmp + 1] - self.month[index - cmp]) * cmp_miss_mon \ + + self.slm[:, :, index - cmp] + + elif interpolate == 0: + temp.clm[:, :, index] = 0 + temp.clm[:, :, index] = 0 index += 1 @@ -1191,7 +1263,6 @@ def plot_correlation(self, l, m, save_path=False): plt.savefig(save_path[:-3] + 's' + save_path[-3:]) plt.show() - def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], save_path=False): """ Plot Cl,m and Sl,m harmonic coefficients @@ -1233,7 +1304,7 @@ def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], save_path=False) if m: #-- figure for Sl,m plt.figure() - plt.title("Normalized spherical harmonics coefficient $S_{" + str(l) + "," + str(m) + "}$") + plt.title("Normalized spherical harmonic coefficient $S_{" + str(l) + "," + str(m) + "}$") if len(ylms): plt.plot(self.time, self.slm[l, m, :], label=label[0]) else: @@ -1277,7 +1348,7 @@ def plot_fft(self, l, m, save_path=False): # -- figure for Cl,m and Sl,m plt.figure() - plt.title("Fourier transform of the normalized spherical harmonics coefficients $C_{" + str(l) + "," + str( + plt.title("Fourier transform of the normalized spherical harmonic coefficients $C_{" + str(l) + "," + str( m) + "}$ et $S_{" + str( l) + "," + str(m) + "}$") plt.plot(xf, 2.0 / N * np.abs(cf[0:N // 2]), label="$C_{" + str(l) + "," + str(m) + "}$") diff --git a/gravity_toolkit/read_SLR_C20.py b/gravity_toolkit/read_SLR_C20.py index ed03e070..ea0152dc 100644 --- a/gravity_toolkit/read_SLR_C20.py +++ b/gravity_toolkit/read_SLR_C20.py @@ -108,7 +108,7 @@ import os import re import numpy as np -import gravity_toolkit.time +import gravity_toolkit.time as time #-- PURPOSE: read oblateness data from Satellite Laser Ranging (SLR) def read_SLR_C20(SLR_file, HEADER=True, AOD=True): @@ -319,10 +319,10 @@ def read_SLR_C20(SLR_file, HEADER=True, AOD=True): #-- modified julian date for line MJD = np.float64(line_contents[0]) #-- converting from MJD into month, day and year - YY,MM,DD,hh,mm,ss = gravity_toolkit.time.convert_julian( + YY,MM,DD,hh,mm,ss = time.convert_julian( MJD+2400000.5, FORMAT='tuple') #-- converting from month, day, year into decimal year - dinput['time'][t] = gravity_toolkit.time.convert_calendar_decimal( + dinput['time'][t] = time.convert_calendar_decimal( YY, MM, day=DD, hour=hh) #-- Spherical Harmonic data for line dinput['data'][t] = np.float64(line_contents[2]) @@ -378,10 +378,10 @@ def read_SLR_C20(SLR_file, HEADER=True, AOD=True): #-- modified julian date for line MJD = np.float64(line_contents[0]) #-- converting from MJD into month, day and year - YY,MM,DD,hh,mm,ss = gravity_toolkit.time.convert_julian( + YY,MM,DD,hh,mm,ss = time.convert_julian( MJD+2400000.5, FORMAT='tuple') #-- converting from month, day, year into decimal year - date_conv[t] = gravity_toolkit.time.convert_calendar_decimal( + date_conv[t] = time.convert_calendar_decimal( YY, MM, day=DD, hour=hh) #-- Spherical Harmonic data for line C20_input[t] = np.float64(line_contents[2]) @@ -434,7 +434,7 @@ def read_SLR_C20(SLR_file, HEADER=True, AOD=True): #-- For JPL: Dec 2011 (120) is centered in Jan 2012 (121) #-- For all: May 2015 (161) is centered in Apr 2015 (160) #-- For GSFC: Oct 2018 (202) is centered in Nov 2018 (203) - dinput['month'] = gravity_toolkit.time.adjust_months(dinput['month']) + dinput['month'] = time.adjust_months(dinput['month']) #-- return the SLR-derived oblateness solutions return dinput \ No newline at end of file From ea0f7c6533d1237fe0b755207ecd5e37b080b115 Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 19 Apr 2022 14:48:52 +0200 Subject: [PATCH 30/80] Add new units --- gravity_toolkit/gen_stokes.py | 6 ++++++ gravity_toolkit/units.py | 15 ++++++++++++++- 2 files changed, 20 insertions(+), 1 deletion(-) diff --git a/gravity_toolkit/gen_stokes.py b/gravity_toolkit/gen_stokes.py index 013b1720..9d5bab9b 100755 --- a/gravity_toolkit/gen_stokes.py +++ b/gravity_toolkit/gen_stokes.py @@ -159,6 +159,12 @@ def gen_stokes(data, lon, lat, LMIN=0, LMAX=60, MMAX=None, UNITS=1, #-- Inputs in mmGH dfactor = factors.mmGH int_fact[:] = np.sin(th) * dphi * dth + elif (UNITS == 5): + dfactor = factors.microGal + int_fact[:] = np.sin(th) * dphi * dth + elif (UNITS == 6): + dfactor = factors.cmwe_ne + int_fact[:] = np.sin(th) * dphi * dth else: #-- default is cm w.e. (g/cm^2) dfactor = factors.cmwe diff --git a/gravity_toolkit/units.py b/gravity_toolkit/units.py index 0538562f..87f8a805 100644 --- a/gravity_toolkit/units.py +++ b/gravity_toolkit/units.py @@ -42,6 +42,7 @@ def __init__(self, lmax=None, a_axis=6.378137e8, flat=1.0/298.257223563): self.norm=None self.cmwe=None self.mmwe=None + self.cmwe_ne=None self.mmGH=None self.mmCU=None self.mmCH=None @@ -71,6 +72,8 @@ def harmonic(self, hl, kl, ll): self.cmwe=self.rho_e*self.rad_e*(2.0*self.l+1.0)/(1.0+kl[self.l])/3.0 # mmwe, millimeters water equivalent [kg/m^2] self.mmwe=10.0*self.rho_e*self.rad_e*(2.0*self.l+1.0)/(1.0+kl[self.l])/3.0 + # cmwe_ne, centimeters water equivalent none elastic [g/cm^2] + self.cmwe_ne = self.rho_e * self.rad_e * (2.0 * self.l + 1.0) / 3.0 # mmGH, millimeters geoid height self.mmGH=np.ones((self.lmax+1))*(10.0*self.rad_e) # mmCU, millimeters elastic crustal deformation (uplift) @@ -97,13 +100,23 @@ def spatial(self, hl, kl, ll): Calculates degree dependent factors for converting spatial units Inputs: hl, kl, ll load love numbers to degree lmax """ + # WGS84 Gravitational Constant of the Earth [cm^3/s^2] + GM_e=3986004.418e14 + # Gravitational Constant of the Earth's atmosphere + GM_atm=3.5e14 + # Gravitational Constant of the Earth (w/o atm) + GM=GM_e-GM_atm # degree dependent coefficients # cmwe, centimeters water equivalent [g/cm^2] - self.cmwe=3.0*(1.0+kl[self.l])/(1.0+2.0*self.l)/(4.0*np.pi*self.rad_e*self.rho_e) + self.cmwe = 3.0 * (1.0 + kl[self.l]) / (1.0 + 2.0 * self.l) / (4.0 * np.pi * self.rad_e * self.rho_e) + # cmwe_ne, centimeters water equivalent none elastic [g/cm^2] + self.cmwe_ne = 3.0 / (1.0 + 2.0*self.l) / (4.0*np.pi*self.rad_e*self.rho_e) # mmwe, millimeters water equivalent [kg/m^2] self.mmwe=3.0*(1.0+kl[self.l])/(1.0+2.0*self.l)/(40.0*np.pi*self.rad_e*self.rho_e) # mmGH, millimeters geoid height self.mmGH=np.ones((self.lmax+1))/(4.0*np.pi*self.rad_e) + # microGal, microGal gravity perturbations + self.microGal = (self.rad_e ** 2.0)/(4.0*np.pi*1.e6 * GM)/(self.l + 1.0) # return the degree dependent unit conversions return self From daeb850b1c910ef5bcd06c430b9a685858a0d053 Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 20 Apr 2022 15:06:50 +0200 Subject: [PATCH 31/80] Revert version in requirements.txt --- requirements.txt | 22 ++++++++++------------ 1 file changed, 10 insertions(+), 12 deletions(-) diff --git a/requirements.txt b/requirements.txt index c0e371e6..c290b258 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,16 +1,14 @@ -numpy~=1.19.2 -scipy~=1.5.2 -pyyaml~=5.3.1 -lxml~=4.6.1 +numpy +scipy +pyyaml +lxml future -matplotlib~=3.3.2 -python-dateutil~=2.8.1 +matplotlib +python-dateutil cartopy --no-binary=cartopy netCDF4 h5py - -datetime~=4.3 -cartopy~=0.18.0 -ipython~=7.19.0 -yaml~=0.2.5 -setuptools~=50.3.0 \ No newline at end of file +datetime +cartopy +ipython +setuptools \ No newline at end of file From 59b72b6c1f471bf8d84772fa0a341b7d74279278 Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 18 May 2022 11:03:10 +0200 Subject: [PATCH 32/80] Update and correct writing of function --- gravity_toolkit/harmonics.py | 356 +++++++++++++++++------------------ gravity_toolkit/toolbox.py | 81 ++++---- setup.py | 3 +- 3 files changed, 221 insertions(+), 219 deletions(-) diff --git a/gravity_toolkit/harmonics.py b/gravity_toolkit/harmonics.py index 1baec8e8..ba77312a 100644 --- a/gravity_toolkit/harmonics.py +++ b/gravity_toolkit/harmonics.py @@ -83,8 +83,8 @@ def __init__(self, lmax=None, mmax=None): self.month=None self.lmax=lmax self.mmax=mmax - self.l=np.arange(self.lmax+1) if self.lmax else None - self.m=np.arange(self.mmax+1) if self.mmax else None + self.l=np.arange(self.lmax + 1) if self.lmax else None + self.m=np.arange(self.mmax + 1) if self.mmax else None self.shape=None self.ndim=None self.filename=None @@ -100,7 +100,7 @@ def case_insensitive_filename(self,filename): #-- tilde-expand input filename self.filename = os.path.expanduser(filename) #-- check if file presently exists with input case - if not os.access(self.filename,os.F_OK): + if not os.access(self.filename, os.F_OK): #-- search for filename without case dependence basename = os.path.basename(filename) directory = os.path.dirname(os.path.expanduser(filename)) @@ -108,7 +108,7 @@ def case_insensitive_filename(self,filename): if not f: errmsg = '{0} not found in file system'.format(filename) raise FileNotFoundError(errmsg) - self.filename = os.path.join(directory,f.pop()) + self.filename = os.path.join(directory, f.pop()) return self def from_ascii(self, filename, date=True, **kwargs): @@ -122,22 +122,22 @@ def from_ascii(self, filename, date=True, **kwargs): #-- set filename self.case_insensitive_filename(filename) #-- set default parameters - kwargs.setdefault('verbose',False) - kwargs.setdefault('compression',None) - kwargs.setdefault('skip_comment','') + kwargs.setdefault('verbose', False) + kwargs.setdefault('compression', None) + kwargs.setdefault('skip_comment', '') #-- open the ascii file and extract contents print(self.filename) if kwargs['verbose'] else None - if (kwargs['compression'] == 'gzip'): + if kwargs['compression'] == 'gzip': #-- read input ascii data from gzip compressed file and split lines with gzip.open(self.filename,'r') as f: file_contents = f.read().decode('ISO-8859-1').splitlines() - elif (kwargs['compression'] == 'zip'): + elif kwargs['compression'] == 'zip': #-- read input ascii data from zipped file and split lines base,_ = os.path.splitext(self.filename) with zipfile.ZipFile(self.filename) as z: file_contents = z.read(base).decode('ISO-8859-1').splitlines() - elif (kwargs['compression'] == 'bytes'): + elif kwargs['compression'] == 'bytes': #-- read input file object and split lines file_contents = self.filename.read().splitlines() else: @@ -158,19 +158,19 @@ def from_ascii(self, filename, date=True, **kwargs): #-- for each line in the file for line in file_contents: if date: - l1,m1,clm1,slm1,time = rx.findall(line) + l1, m1, clm1, slm1, time = rx.findall(line) else: - l1,m1,clm1,slm1 = rx.findall(line) + l1, m1, clm1, slm1 = rx.findall(line) #-- convert line degree and order to integers - l1,m1 = np.array([l1,m1],dtype=np.int64) + l1,m1 = np.array([l1, m1],dtype=np.int64) self.lmax = np.copy(l1) if (l1 > self.lmax) else self.lmax self.mmax = np.copy(m1) if (m1 > self.mmax) else self.mmax #-- output spherical harmonics dimensions array - self.l = np.arange(self.lmax+1) - self.m = np.arange(self.mmax+1) + self.l = np.arange(self.lmax + 1) + self.m = np.arange(self.mmax + 1) #-- output spherical harmonics data - self.clm = np.zeros((self.lmax+1,self.mmax+1)) - self.slm = np.zeros((self.lmax+1,self.mmax+1)) + self.clm = np.zeros((self.lmax + 1,self.mmax + 1)) + self.slm = np.zeros((self.lmax + 1,self.mmax + 1)) #-- if the ascii file contains date variables if date: self.time = np.float64(time) @@ -181,14 +181,14 @@ def from_ascii(self, filename, date=True, **kwargs): #-- for each line in the file for line in file_contents: if date: - l1,m1,clm1,slm1,time = rx.findall(line) + l1, m1, clm1, slm1, time = rx.findall(line) else: - l1,m1,clm1,slm1 = rx.findall(line) + l1, m1, clm1, slm1 = rx.findall(line) #-- convert line degree and order to integers - ll,mm = np.array([l1,m1],dtype=np.int64) + ll, mm = np.array([l1, m1], dtype=np.int64) #-- convert fortran exponentials if applicable - self.clm[ll,mm] = np.float64(clm1.replace('D','E')) - self.slm[ll,mm] = np.float64(slm1.replace('D','E')) + self.clm[ll, mm] = np.float64(clm1.replace('D', 'E')) + self.slm[ll, mm] = np.float64(slm1.replace('D', 'E')) #-- assign shape and ndim attributes self.update_dimensions() return self @@ -204,8 +204,8 @@ def from_netCDF4(self, filename, date=True, **kwargs): #-- set filename self.case_insensitive_filename(filename) #-- set default parameters - kwargs.setdefault('verbose',False) - kwargs.setdefault('compression',None) + kwargs.setdefault('verbose', False) + kwargs.setdefault('compression', None) #-- read data from netCDF4 file Ylms = ncdf_read_stokes(self.filename, DATE=date, COMPRESSION=kwargs['compression'], @@ -236,8 +236,8 @@ def from_HDF5(self, filename, date=True, **kwargs): #-- set filename self.case_insensitive_filename(filename) #-- set default parameters - kwargs.setdefault('verbose',False) - kwargs.setdefault('compression',None) + kwargs.setdefault('verbose', False) + kwargs.setdefault('compression', None) #-- read data from HDF5 file Ylms = hdf5_read_stokes(self.filename, DATE=date, COMPRESSION=kwargs['compression'], @@ -305,23 +305,23 @@ def from_index(self, filename, format=None, date=True, sort=True): #-- removes empty lines (if there are extra empty lines) parser = re.compile(r'^(?!\#|\%|$)', re.VERBOSE) #-- Read index file of input spherical harmonics - with open(self.filename,'r') as f: + with open(self.filename, 'r') as f: file_list = [l for l in f.read().splitlines() if parser.match(l)] #-- create a list of harmonic objects h = [] #-- for each file in the index for i,f in enumerate(file_list): - if (format == 'ascii'): + if format == 'ascii': #-- ascii (.txt) h.append(harmonics().from_ascii(os.path.expanduser(f),date=date)) - elif (format == 'netCDF4'): + elif format == 'netCDF4': #-- netcdf (.nc) h.append(harmonics().from_netCDF4(os.path.expanduser(f),date=date)) - elif (format == 'HDF5'): + elif format == 'HDF5': #-- HDF5 (.H5) h.append(harmonics().from_HDF5(os.path.expanduser(f),date=date)) #-- create a single harmonic object from the list - return self.from_list(h,date=date,sort=sort) + return self.from_list(h, date=date, sort=sort) def from_list(self, object_list, date=True, sort=True, clear=False): """ @@ -346,18 +346,18 @@ def from_list(self, object_list, date=True, sort=True, clear=False): self.l = np.arange(self.lmax+1) self.m = np.arange(self.mmax+1) #-- create output harmonics - self.clm = np.zeros((self.lmax+1,self.mmax+1,n)) - self.slm = np.zeros((self.lmax+1,self.mmax+1,n)) + self.clm = np.zeros((self.lmax + 1, self.mmax + 1, n)) + self.slm = np.zeros((self.lmax + 1, self.mmax + 1, n)) #-- create list of files self.filename = [] #-- output dates if date: - self.time = np.zeros((n)) - self.month = np.zeros((n),dtype=np.int64) + self.time = np.zeros((n,)) + self.month = np.zeros((n,), dtype=np.int64) #-- for each indice for t,i in enumerate(list_sort): - self.clm[:,:,t] = object_list[i].clm[:self.lmax+1,:self.mmax+1] - self.slm[:,:,t] = object_list[i].slm[:self.lmax+1,:self.mmax+1] + self.clm[:, :, t] = object_list[i].clm[:self.lmax + 1, :self.mmax + 1] + self.slm[:, :, t] = object_list[i].slm[:self.lmax + 1, :self.mmax + 1] if date: self.time[t] = np.atleast_1d(object_list[i].time) self.month[t] = np.atleast_1d(object_list[i].month) @@ -387,7 +387,7 @@ def from_file(self, filename, format=None, date=True, **kwargs): #-- set filename self.case_insensitive_filename(filename) #-- set default verbosity - kwargs.setdefault('verbose',False) + kwargs.setdefault('verbose', False) #-- read from file if format == 'ascii': #-- ascii (.txt) @@ -408,7 +408,7 @@ def from_dict(self, d): Inputs: dictionary object to be converted """ #-- assign dictionary variables to self - for key in ['l','m','clm','slm','time','month']: + for key in ['l', 'm', 'clm', 'slm', 'time', 'month']: try: setattr(self, key, d[key].copy()) except (AttributeError, KeyError): @@ -456,10 +456,10 @@ def to_netCDF4(self, filename, date=True, **kwargs): """ self.filename = os.path.expanduser(filename) #-- set default verbosity and parameters - kwargs.setdefault('verbose',False) - kwargs.setdefault('units','Geodesy_Normalization') - kwargs.setdefault('time_units','years') - kwargs.setdefault('time_longname','Date_in_Decimal_Years') + kwargs.setdefault('verbose', False) + kwargs.setdefault('units', 'Geodesy_Normalization') + kwargs.setdefault('time_units', 'years') + kwargs.setdefault('time_longname', 'Date_in_Decimal_Years') #-- copy keyword arguments to uppercase KWARGS = {} for key,val in kwargs.items(): @@ -478,10 +478,10 @@ def to_HDF5(self, filename, date=True, **kwargs): """ self.filename = os.path.expanduser(filename) #-- set default verbosity and parameters - kwargs.setdefault('verbose',False) - kwargs.setdefault('units','Geodesy_Normalization') - kwargs.setdefault('time_units','years') - kwargs.setdefault('time_longname','Date_in_Decimal_Years') + kwargs.setdefault('verbose', False) + kwargs.setdefault('units', 'Geodesy_Normalization') + kwargs.setdefault('time_units', 'years') + kwargs.setdefault('time_longname', 'Date_in_Decimal_Years') #-- copy keyword arguments to uppercase KWARGS = {} for key,val in kwargs.items(): @@ -503,13 +503,13 @@ def to_index(self, filename, file_list, format=None, date=True, **kwargs): """ #-- Write index file of output spherical harmonics self.filename = os.path.expanduser(filename) - fid = open(self.filename,'w') + fid = open(self.filename, 'w') #-- set default verbosity - kwargs.setdefault('verbose',False) + kwargs.setdefault('verbose', False) #-- for each file to be in the index for i,f in enumerate(file_list): #-- print filename to index - print(f.replace(os.path.expanduser('~'),'~'), file=fid) + print(f.replace(os.path.expanduser('~'), '~'), file=fid) #-- index harmonics object at i h = self.index(i, date=date) #-- write to file @@ -556,19 +556,19 @@ def to_masked_array(self): #-- verify dimensions and get shape ndim_prev = self.ndim self.expand_dims() - l1,m1,nt = self.shape + l1, m1, nt = self.shape #-- create single triangular matrices with harmonics - Ylms = np.ma.zeros((self.lmax+1,2*self.lmax+1,nt)) - Ylms.mask = np.ones((self.lmax+1,2*self.lmax+1,nt),dtype=bool) + Ylms = np.ma.zeros((self.lmax + 1, 2*self.lmax + 1, nt)) + Ylms.mask = np.ones((self.lmax + 1, 2*self.lmax + 1, nt),dtype=bool) for m in range(-self.mmax,self.mmax+1): mm = np.abs(m) for l in range(mm,self.lmax+1): if m < 0: - Ylms.data[l,self.lmax+m,:] = self.slm[l,mm,:] - Ylms.mask[l,self.lmax+m,:] = False + Ylms.data[l, self.lmax+m, :] = self.slm[l, mm, :] + Ylms.mask[l, self.lmax+m, :] = False else: - Ylms.data[l,self.lmax+m,:] = self.clm[l,mm,:] - Ylms.mask[l,self.lmax+m,:] = False + Ylms.data[l, self.lmax+m, :] = self.clm[l, mm, :] + Ylms.mask[l, self.lmax+m, :] = False #-- reshape to previous if self.ndim != ndim_prev: self.squeeze() @@ -581,8 +581,8 @@ def update_dimensions(self): """ self.ndim = self.clm.ndim self.shape = self.clm.shape - self.l = np.arange(self.lmax+1) if self.lmax else None - self.m = np.arange(self.mmax+1) if self.mmax else None + self.l = np.arange(self.lmax + 1) if self.lmax else None + self.m = np.arange(self.mmax + 1) if self.mmax else None return self def add(self, temp): @@ -595,13 +595,13 @@ def add(self, temp): temp.update_dimensions() l1 = self.lmax+1 if (temp.lmax > self.lmax) else temp.lmax+1 m1 = self.mmax+1 if (temp.mmax > self.mmax) else temp.mmax+1 - if (self.ndim == 2): - self.clm[:l1,:m1] += temp.clm[:l1,:m1] - self.slm[:l1,:m1] += temp.slm[:l1,:m1] + if self.ndim == 2: + self.clm[:l1, :m1] += temp.clm[:l1, :m1] + self.slm[:l1, :m1] += temp.slm[:l1, :m1] elif (self.ndim == 3) and (temp.ndim == 2): for i,t in enumerate(self.time): - self.clm[:l1,:m1,i] += temp.clm[:l1,:m1] - self.slm[:l1,:m1,i] += temp.slm[:l1,:m1] + self.clm[:l1, :m1, i] += temp.clm[:l1, :m1] + self.slm[:l1, :m1, i] += temp.slm[:l1, :m1] else: old_month = self.month exclude1 = set(self.month) - set(temp.month) @@ -612,14 +612,14 @@ def add(self, temp): for i in range(len(old_month)): for j in range(len(temp.month)): if old_month[i] == temp.month[j]: - self.clm[:l1,:m1,i] += temp.clm[:l1,:m1,j] - self.slm[:l1,:m1,i] += temp.slm[:l1,:m1,j] + self.clm[:l1, :m1, i] += temp.clm[:l1, :m1, j] + self.slm[:l1, :m1, i] += temp.slm[:l1, :m1, j] to_keep = [] for i in range(len(old_month)): if not(old_month[i] in exclude1): to_keep.append(i) - self.clm = self.clm[:,:,to_keep] + self.clm = self.clm[:, :, to_keep] self.slm = self.slm[:, :, to_keep] return self @@ -633,13 +633,13 @@ def subtract(self, temp): temp.update_dimensions() l1 = self.lmax+1 if (temp.lmax > self.lmax) else temp.lmax+1 m1 = self.mmax+1 if (temp.mmax > self.mmax) else temp.mmax+1 - if (self.ndim == 2): - self.clm[:l1,:m1] -= temp.clm[:l1,:m1] - self.slm[:l1,:m1] -= temp.slm[:l1,:m1] + if self.ndim == 2: + self.clm[:l1, :m1] -= temp.clm[:l1, :m1] + self.slm[:l1, :m1] -= temp.slm[:l1, :m1] elif (self.ndim == 3) and (temp.ndim == 2): for i,t in enumerate(self.time): - self.clm[:l1,:m1,i] -= temp.clm[:l1,:m1] - self.slm[:l1,:m1,i] -= temp.slm[:l1,:m1] + self.clm[:l1, :m1, i] -= temp.clm[:l1, :m1] + self.slm[:l1, :m1, i] -= temp.slm[:l1, :m1] else: old_month = self.month exclude1 = set(self.month) - set(temp.month) @@ -650,14 +650,14 @@ def subtract(self, temp): for i in range(len(old_month)): for j in range(len(temp.month)): if old_month[i] == temp.month[j]: - self.clm[:l1,:m1,i] -= temp.clm[:l1,:m1,j] - self.slm[:l1,:m1,i] -= temp.slm[:l1,:m1,j] + self.clm[:l1, :m1, i] -= temp.clm[:l1, :m1, j] + self.slm[:l1, :m1, i] -= temp.slm[:l1, :m1, j] to_keep = [] for i in range(len(old_month)): if not(old_month[i] in exclude1): to_keep.append(i) - self.clm = self.clm[:,:,to_keep] + self.clm = self.clm[:, :, to_keep] self.slm = self.slm[:, :, to_keep] return self @@ -669,15 +669,15 @@ def multiply(self, temp): #-- reassign shape and ndim attributes self.update_dimensions() temp.update_dimensions() - l1 = self.lmax+1 if (temp.lmax > self.lmax) else temp.lmax+1 - m1 = self.mmax+1 if (temp.mmax > self.mmax) else temp.mmax+1 - if (self.ndim == 2): - self.clm[:l1,:m1] *= temp.clm[:l1,:m1] - self.slm[:l1,:m1] *= temp.slm[:l1,:m1] + l1 = self.lmax + 1 if (temp.lmax > self.lmax) else temp.lmax+1 + m1 = self.mmax + 1 if (temp.mmax > self.mmax) else temp.mmax+1 + if self.ndim == 2: + self.clm[:l1, :m1] *= temp.clm[:l1, :m1] + self.slm[:l1, :m1] *= temp.slm[:l1, :m1] elif (self.ndim == 3) and (temp.ndim == 2): for i,t in enumerate(self.time): - self.clm[:l1,:m1,i] *= temp.clm[:l1,:m1] - self.slm[:l1,:m1,i] *= temp.slm[:l1,:m1] + self.clm[:l1, :m1, i] *= temp.clm[:l1, :m1] + self.slm[:l1, :m1, i] *= temp.slm[:l1, :m1] else: old_month = self.month exclude1 = set(self.month) - set(temp.month) @@ -707,20 +707,20 @@ def divide(self, temp): #-- reassign shape and ndim attributes self.update_dimensions() temp.update_dimensions() - l1 = self.lmax+1 if (temp.lmax > self.lmax) else temp.lmax+1 - m1 = self.mmax+1 if (temp.mmax > self.mmax) else temp.mmax+1 + l1 = self.lmax + 1 if (temp.lmax > self.lmax) else temp.lmax+1 + m1 = self.mmax + 1 if (temp.mmax > self.mmax) else temp.mmax+1 #-- indices for cosine spherical harmonics (including zonals) lc,mc = np.tril_indices(l1, m=m1) #-- indices for sine spherical harmonics (excluding zonals) m0 = np.nonzero(mc != 0) - ls,ms = (lc[m0],mc[m0]) - if (self.ndim == 2): - self.clm[lc,mc] /= temp.clm[lc,mc] - self.slm[ls,ms] /= temp.slm[ls,ms] + ls,ms = (lc[m0], mc[m0]) + if self.ndim == 2: + self.clm[lc, mc] /= temp.clm[lc, mc] + self.slm[ls, ms] /= temp.slm[ls, ms] elif (self.ndim == 3) and (temp.ndim == 2): for i,t in enumerate(self.time): - self.clm[lc,mc,i] /= temp.clm[lc,mc] - self.slm[ls,ms,i] /= temp.slm[ls,ms] + self.clm[lc, mc, i] /= temp.clm[lc, mc] + self.slm[ls, ms, i] /= temp.slm[ls, ms] else: old_month = self.month exclude1 = set(self.month) - set(temp.month) @@ -782,9 +782,9 @@ def expand_dims(self): self.time = np.atleast_1d(self.time) self.month = np.atleast_1d(self.month) #-- output harmonics with a third dimension - if (self.ndim == 2): - self.clm = self.clm[:,:,None] - self.slm = self.slm[:,:,None] + if self.ndim == 2: + self.clm = self.clm[:, :, None] + self.slm = self.slm[:, :, None] #-- reassign ndim and shape attributes self.update_dimensions() return self @@ -807,8 +807,8 @@ def flatten(self, date=True): Flatten harmonics matrices into arrays Options: harmonics objects contain date information """ - n_harm = (self.lmax**2 + 3*self.lmax - (self.lmax-self.mmax)**2 - - (self.lmax-self.mmax))//2 + 1 + n_harm = (self.lmax**2 + 3*self.lmax - (self.lmax - self.mmax)**2 - + (self.lmax - self.mmax))//2 + 1 #-- restructured degree and order temp = harmonics(lmax=self.lmax, mmax=self.mmax) temp.l = np.zeros((n_harm,), dtype=np.int32) @@ -818,25 +818,25 @@ def flatten(self, date=True): temp.time = np.copy(self.time) temp.month = np.copy(self.month) #-- restructured spherical harmonic arrays - if (self.clm.ndim == 2): - temp.clm = np.zeros((n_harm)) - temp.slm = np.zeros((n_harm)) + if self.clm.ndim == 2: + temp.clm = np.zeros((n_harm,)) + temp.slm = np.zeros((n_harm,)) else: n = self.clm.shape[-1] - temp.clm = np.zeros((n_harm,n)) - temp.slm = np.zeros((n_harm,n)) + temp.clm = np.zeros((n_harm, n)) + temp.slm = np.zeros((n_harm, n)) #-- create counter variable lm lm = 0 - for m in range(0,self.mmax+1):#-- MMAX+1 to include MMAX - for l in range(m,self.lmax+1):#-- LMAX+1 to include LMAX + for m in range(0,self.mmax + 1):#-- MMAX+1 to include MMAX + for l in range(m,self.lmax + 1):#-- LMAX+1 to include LMAX temp.l[lm] = np.int64(l) temp.m[lm] = np.int64(m) - if (self.clm.ndim == 2): - temp.clm[lm] = self.clm[l,m] - temp.slm[lm] = self.slm[l,m] + if self.clm.ndim == 2: + temp.clm[lm] = self.clm[l, m] + temp.slm[lm] = self.slm[l, m] else: - temp.clm[lm,:] = self.clm[l,m,:] - temp.slm[lm,:] = self.slm[l,m,:] + temp.clm[lm, :] = self.clm[l, m, :] + temp.slm[lm, :] = self.slm[l, m, :] #-- add 1 to lm counter variable lm += 1 #-- assign ndim and shape attributes @@ -849,8 +849,8 @@ def expand(self, date=True): Expand flattened harmonics into matrices Options: harmonics objects contain date information """ - n_harm = (self.lmax**2 + 3*self.lmax - (self.lmax-self.mmax)**2 - - (self.lmax-self.mmax))//2 + 1 + n_harm = (self.lmax**2 + 3*self.lmax - (self.lmax - self.mmax)**2 - + (self.lmax - self.mmax))//2 + 1 #-- restructured degree and order temp = harmonics(lmax=self.lmax, mmax=self.mmax) #-- copy date variables if applicable @@ -858,23 +858,23 @@ def expand(self, date=True): temp.time = np.copy(self.time) temp.month = np.copy(self.month) #-- restructured spherical harmonic matrices - if (self.clm.ndim == 1): - temp.clm = np.zeros((self.lmax+1,self.mmax+1)) - temp.slm = np.zeros((self.lmax+1,self.mmax+1)) + if self.clm.ndim == 1: + temp.clm = np.zeros((self.lmax + 1, self.mmax + 1)) + temp.slm = np.zeros((self.lmax + 1, self.mmax + 1)) else: n = self.clm.shape[-1] - temp.clm = np.zeros((self.lmax+1,self.mmax+1,n)) - temp.slm = np.zeros((self.lmax+1,self.mmax+1,n)) + temp.clm = np.zeros((self.lmax + 1, self.mmax + 1, n)) + temp.slm = np.zeros((self.lmax + 1, self.mmax + 1, n)) #-- create counter variable lm for lm in range(n_harm): l = self.l[lm] m = self.m[lm] - if (self.clm.ndim == 1): - temp.clm[l,m] = self.clm[lm] - temp.slm[l,m] = self.slm[lm] + if self.clm.ndim == 1: + temp.clm[l, m] = self.clm[lm] + temp.slm[l, m] = self.slm[lm] else: - temp.clm[l,m,:] = self.clm[lm,:] - temp.slm[l,m,:] = self.slm[lm,:] + temp.clm[l, m, :] = self.clm[lm, :] + temp.slm[l, m, :] = self.slm[lm, :] #-- assign ndim and shape attributes temp.update_dimensions() #-- return the expanded harmonics object @@ -887,10 +887,10 @@ def index(self, indice, date=True): Options: harmonics objects contain date information """ #-- output harmonics object - temp = harmonics(lmax=np.copy(self.lmax),mmax=np.copy(self.mmax)) + temp = harmonics(lmax=np.copy(self.lmax), mmax=np.copy(self.mmax)) #-- subset output harmonics - temp.clm = self.clm[:,:,indice].copy() - temp.slm = self.slm[:,:,indice].copy() + temp.clm = self.clm[:, :, indice].copy() + temp.slm = self.slm[:, :, indice].copy() #-- subset output dates if date: temp.time = self.time[indice].copy() @@ -921,19 +921,19 @@ def subset(self, months): m = ','.join(['{0:03d}'.format(m) for m in months_check]) raise IOError('GRACE/GRACE-FO months {0} not Found'.format(m)) #-- indices to sort data objects - months_list = [i for i,m in enumerate(self.month) if m in months] + months_list = [i for i, m in enumerate(self.month) if m in months] #-- output harmonics object - temp = harmonics(lmax=np.copy(self.lmax),mmax=np.copy(self.mmax)) + temp = harmonics(lmax=np.copy(self.lmax), mmax=np.copy(self.mmax)) #-- create output harmonics - temp.clm = np.zeros((temp.lmax+1,temp.mmax+1,n)) - temp.slm = np.zeros((temp.lmax+1,temp.mmax+1,n)) - temp.time = np.zeros((n)) - temp.month = np.zeros((n),dtype=np.int64) + temp.clm = np.zeros((temp.lmax + 1, temp.mmax + 1, n)) + temp.slm = np.zeros((temp.lmax + 1, temp.mmax + 1, n)) + temp.time = np.zeros((n,)) + temp.month = np.zeros((n,), dtype=np.int64) temp.filename = [] #-- for each indice for t,i in enumerate(months_list): - temp.clm[:,:,t] = self.clm[:,:,i].copy() - temp.slm[:,:,t] = self.slm[:,:,i].copy() + temp.clm[:,:, t] = self.clm[:,:, i].copy() + temp.slm[:,:, t] = self.slm[:,:, i].copy() temp.time[t] = self.time[i].copy() temp.month[t] = self.month[i].copy() #-- subset filenames if applicable @@ -966,18 +966,18 @@ def truncate(self, lmax, lmin=0, mmax=None): l1 = self.lmax+1 if (temp.lmax > self.lmax) else temp.lmax+1 m1 = self.mmax+1 if (temp.mmax > self.mmax) else temp.mmax+1 #-- create output harmonics - if (temp.ndim == 3): + if temp.ndim == 3: #-- number of months n = temp.clm.shape[-1] - self.clm = np.zeros((self.lmax+1,self.mmax+1,n)) - self.slm = np.zeros((self.lmax+1,self.mmax+1,n)) - self.clm[lmin:l1,:m1,:] = temp.clm[lmin:l1,:m1,:].copy() - self.slm[lmin:l1,:m1,:] = temp.slm[lmin:l1,:m1,:].copy() + self.clm = np.zeros((self.lmax+1, self.mmax+1, n)) + self.slm = np.zeros((self.lmax+1, self.mmax+1, n)) + self.clm[lmin:l1, :m1,:] = temp.clm[lmin:l1, :m1,:].copy() + self.slm[lmin:l1, :m1,:] = temp.slm[lmin:l1, :m1,:].copy() else: - self.clm = np.zeros((self.lmax+1,self.mmax+1)) - self.slm = np.zeros((self.lmax+1,self.mmax+1)) - self.clm[lmin:l1,:m1] = temp.clm[lmin:l1,:m1].copy() - self.slm[lmin:l1,:m1] = temp.slm[lmin:l1,:m1].copy() + self.clm = np.zeros((self.lmax + 1, self.mmax + 1)) + self.slm = np.zeros((self.lmax + 1, self.mmax + 1)) + self.clm[lmin:l1, :m1] = temp.clm[lmin:l1, :m1].copy() + self.slm[lmin:l1, :m1] = temp.slm[lmin:l1, :m1].copy() #-- reassign ndim and shape attributes self.update_dimensions() #-- return the truncated or expanded harmonics object @@ -990,23 +990,23 @@ def mean(self, apply=False, indices=Ellipsis): apply to remove the mean field from the input harmonics indices of input harmonics object to compute mean """ - temp = harmonics(lmax=np.copy(self.lmax),mmax=np.copy(self.mmax)) + temp = harmonics(lmax=np.copy(self.lmax), mmax=np.copy(self.mmax)) #-- allocate for mean field - temp.clm = np.zeros((temp.lmax+1,temp.mmax+1)) - temp.slm = np.zeros((temp.lmax+1,temp.mmax+1)) + temp.clm = np.zeros((temp.lmax + 1, temp.mmax + 1)) + temp.slm = np.zeros((temp.lmax + 1, temp.mmax + 1)) #-- Computes the mean for each spherical harmonic degree and order - for m in range(0,temp.mmax+1):#-- MMAX+1 to include l - for l in range(m,temp.lmax+1):#-- LMAX+1 to include LMAX + for m in range(0,temp.mmax + 1):#-- MMAX+1 to include l + for l in range(m,temp.lmax + 1):#-- LMAX+1 to include LMAX #-- calculate mean static field - temp.clm[l,m] = np.mean(self.clm[l,m,indices]) - temp.slm[l,m] = np.mean(self.slm[l,m,indices]) + temp.clm[l, m] = np.mean(self.clm[l, m, indices]) + temp.slm[l, m] = np.mean(self.slm[l, m, indices]) #-- calculating the time-variable gravity field by removing #-- the static component of the gravitational field if apply: - self.clm[l,m,:] -= temp.clm[l,m] - self.slm[l,m,:] -= temp.slm[l,m] + self.clm[l, m, :] -= temp.clm[l, m] + self.slm[l, m, :] -= temp.slm[l, m] #-- calculate mean of temporal variables - for key in ['time','month']: + for key in ['time', 'month']: try: val = getattr(self, key) setattr(temp, key, np.mean(val[indices])) @@ -1028,19 +1028,19 @@ def scale(self, var): temp.time = np.copy(self.time) temp.month = np.copy(self.month) #-- multiply by a single constant or a time-variable scalar - if (np.ndim(var) == 0): + if np.ndim(var) == 0: temp.clm = var*self.clm temp.slm = var*self.slm elif (np.ndim(var) == 1) and (self.ndim == 2): - temp.clm = np.zeros((temp.lmax+1,temp.mmax+1,len(var))) - temp.slm = np.zeros((temp.lmax+1,temp.mmax+1,len(var))) + temp.clm = np.zeros((temp.lmax + 1, temp.mmax + 1, len(var))) + temp.slm = np.zeros((temp.lmax + 1, temp.mmax + 1, len(var))) for i,v in enumerate(var): - temp.clm[:,:,i] = v*self.clm - temp.slm[:,:,i] = v*self.slm + temp.clm[:, :, i] = v*self.clm + temp.slm[:, :, i] = v*self.slm elif (np.ndim(var) == 1) and (self.ndim == 3): for i,v in enumerate(var): - temp.clm[:,:,i] = v*self.clm[:,:,i] - temp.slm[:,:,i] = v*self.slm[:,:,i] + temp.clm[:, :, i] = v*self.clm[:, :, i] + temp.slm[:, :, i] = v*self.slm[:, :, i] #-- assign ndim and shape attributes temp.update_dimensions() return temp @@ -1070,15 +1070,15 @@ def convolve(self, var): #-- reassign shape and ndim attributes self.update_dimensions() #-- check if a single field or a temporal field - if (self.ndim == 2): - for l in range(0,self.lmax+1):#-- LMAX+1 to include LMAX + if self.ndim == 2: + for l in range(0, self.lmax+1):#-- LMAX+1 to include LMAX self.clm[l,:] *= var[l] self.slm[l,:] *= var[l] else: for i,t in enumerate(self.time): - for l in range(0,self.lmax+1):#-- LMAX+1 to include LMAX - self.clm[l,:,i] *= var[l] - self.slm[l,:,i] *= var[l] + for l in range(0, self.lmax+1):#-- LMAX+1 to include LMAX + self.clm[l,:, i] *= var[l] + self.slm[l,:, i] *= var[l] #-- return the convolved field return self @@ -1089,24 +1089,24 @@ def destripe(self, **kwargs): """ #-- reassign shape and ndim attributes self.update_dimensions() - temp = harmonics(lmax=np.copy(self.lmax),mmax=np.copy(self.mmax)) + temp = harmonics(lmax=np.copy(self.lmax), mmax=np.copy(self.mmax)) temp.time = np.copy(self.time) temp.month = np.copy(self.month) #-- check if a single field or a temporal field - if (self.ndim == 2): + if self.ndim == 2: Ylms = destripe_harmonics(self.clm, self.slm, LMIN=1, LMAX=self.lmax, MMAX=self.mmax, **kwargs) temp.clm = Ylms['clm'].copy() temp.slm = Ylms['slm'].copy() else: n = self.shape[-1] - temp.clm = np.zeros((self.lmax+1,self.mmax+1,n)) - temp.slm = np.zeros((self.lmax+1,self.mmax+1,n)) + temp.clm = np.zeros((self.lmax+1, self.mmax+1, n)) + temp.slm = np.zeros((self.lmax+1, self.mmax+1, n)) for i in range(n): - Ylms = destripe_harmonics(self.clm[:,:,i], self.slm[:,:,i], + Ylms = destripe_harmonics(self.clm[:, :, i], self.slm[:, :, i], LMIN=1, LMAX=self.lmax, MMAX=self.mmax, **kwargs) - temp.clm[:,:,i] = Ylms['clm'].copy() - temp.slm[:,:,i] = Ylms['slm'].copy() + temp.clm[:, :, i] = Ylms['clm'].copy() + temp.slm[:, :, i] = Ylms['slm'].copy() #-- assign ndim and shape attributes temp.update_dimensions() #-- return the destriped field @@ -1123,25 +1123,25 @@ def amplitude(self, mmax=None): if mmax is not None: temp.truncate(self.lmax, mmax=mmax) #-- check if a single field or a temporal field - if (self.ndim == 2): + if self.ndim == 2: #-- allocate for degree amplitudes - self.amp = np.zeros((self.lmax+1)) - for l in range(self.lmax+1): + self.amp = np.zeros((self.lmax + 1)) + for l in range(self.lmax + 1): #-- truncate at mmax - m = np.arange(l,temp.mmax+1) + m = np.arange(l, temp.mmax + 1) #-- degree amplitude of spherical harmonic degree - self.amp[l] = np.sqrt(np.sum(temp.clm[l,m] + temp.slm[l,m])) + self.amp[l] = np.sqrt(np.sum(temp.clm[l, m] + temp.slm[l, m])) else: #-- allocate for degree amplitudes n = self.shape[-1] - self.amp = np.zeros((self.lmax+1,n)) - for l in range(self.lmax+1): + self.amp = np.zeros((self.lmax + 1, n)) + for l in range(self.lmax + 1): #-- truncate at mmax - m = np.arange(l,temp.mmax+1) + m = np.arange(l, temp.mmax + 1) #-- degree amplitude of spherical harmonic degree - var = temp.clm[l,m,:] + temp.slm[l,m,:] - self.amp[l,:] = np.sqrt(np.sum(var,axis=0)) + var = temp.clm[l, m, :] + temp.slm[l, m, :] + self.amp[l,:] = np.sqrt(np.sum(var, axis=0)) #-- return the harmonics object with degree amplitudes return self diff --git a/gravity_toolkit/toolbox.py b/gravity_toolkit/toolbox.py index 24f01f82..f52f26b3 100644 --- a/gravity_toolkit/toolbox.py +++ b/gravity_toolkit/toolbox.py @@ -354,6 +354,7 @@ def filt_grid(grid, f_cut=0.5): return filtered_grid + def save_gif(grid, path, unit='cmwe', bound=None, mask=None, color='viridis'): """ Create a gif of the spatial object @@ -466,6 +467,7 @@ def animate_frames(i): HTML(anim.to_jshtml()) anim.save(path, writer='imagemagick', fps=10) + plt.clf() def plot_rms_map(grid, path=False, proj=ccrs.PlateCarree(), unit='cmwe', bound=None, mask=None, color='viridis'): @@ -498,15 +500,6 @@ def plot_rms_map(grid, path=False, proj=ccrs.PlateCarree(), unit='cmwe', bound=N else: vmin, vmax = bound - if vmax - vmin >= 3: - levels = np.arange(vmin, vmax, max(1, int((vmax - vmin) / 10))) - elif vmax - vmin >= 0.3: - levels = np.arange(vmin, vmax, max(0.1, float('%.1f' % ((vmax - vmin) / 10)))) - elif vmax - vmin >= 0.03: - levels = np.arange(vmin, vmax, max(0.1, float('%.2f' % ((vmax - vmin) / 10)))) - else: - raise ValueError("The range of data to plot is too small") - norm = colors.Normalize(vmin=vmin, vmax=vmax) cmap = plt.cm.get_cmap(color) im = ax1.imshow(data_to_set, interpolation='nearest', @@ -528,32 +521,25 @@ def plot_rms_map(grid, path=False, proj=ccrs.PlateCarree(), unit='cmwe', bound=N # Add label to the colorbar if unit == "cmwe": cbar.ax.set_xlabel('Equivalent Water Thickness', labelpad=10, fontsize=24) - cbar.ax.set_ylabel('cm', fontsize=24, rotation=0) + cbar.ax.set_ylabel('cm', fontsize=24, rotation=0, labelpad=10) elif unit == "cmwe_ne": cbar.ax.set_xlabel('Non elastic Equivalent Water Thickness', labelpad=10, fontsize=24) - cbar.ax.set_ylabel('cm', fontsize=24, rotation=0) + cbar.ax.set_ylabel('cm', fontsize=24, rotation=0, labelpad=10) elif unit == "mmwe": cbar.ax.set_xlabel('Equivalent Water Thickness', labelpad=10, fontsize=24) - cbar.ax.set_ylabel('mm', fontsize=24, rotation=0) + cbar.ax.set_ylabel('mm', fontsize=24, rotation=0, labelpad=10) elif unit == "geoid": cbar.ax.set_xlabel('Geoid Height', labelpad=10, fontsize=24) - cbar.ax.set_ylabel('mm', fontsize=24, rotation=0) + cbar.ax.set_ylabel('mm', fontsize=24, rotation=0, labelpad=10) elif unit == "microGal": cbar.ax.set_xlabel('Acceleration', labelpad=10, fontsize=24) - cbar.ax.set_ylabel('$\mu Gal$', fontsize=24, rotation=0) + cbar.ax.set_ylabel('$\mu Gal$', fontsize=24, rotation=0, labelpad=10) elif unit == "secacc": cbar.ax.set_xlabel('Secular Acceleration', labelpad=10, fontsize=24) - cbar.ax.set_ylabel('$nT.y^{-2}$', fontsize=24, rotation=0) + cbar.ax.set_ylabel('$nT.y^{-2}$', fontsize=24, rotation=0, labelpad=10) - cbar.ax.yaxis.set_label_coords(1.045, 0.1) + cbar.ax.yaxis.set_label_coords(1.1, -0.4) # Set the tick levels for the colorbar - cbar.set_ticks(levels) - if vmax - vmin >= 3: - cbar.set_ticklabels(['{0:d}'.format(ct) for ct in levels]) - elif vmax - vmin >= 0.3: - cbar.set_ticklabels(['{.1f}'.format(ct) for ct in levels]) - elif vmax - vmin >= 0.03: - cbar.set_ticklabels(['{.2f}'.format(ct) for ct in levels]) # ticks lines all the way across cbar.ax.tick_params(which='both', width=1, length=26, labelsize=24, direction='in') @@ -585,7 +571,7 @@ def calc_rms_grid(grid, mask=None): rms : rms of the grid """ - if mask in None: + if mask is None: rms = np.sqrt(np.sum( [np.sum(np.cos(lat * np.pi / 180) ** 2 * line ** 2) for lat, line in zip(grid.lat, grid.data)]) / np.sum( [np.sum(np.cos(lat * np.pi / 180) ** 2 * line.size) for lat, line in zip(grid.lat, grid.data)])) @@ -600,34 +586,46 @@ def calc_rms_grid(grid, mask=None): return rms -def plot_rms_grid(grid, path=False, mask=None, unit='cmwe'): +def plot_rms_grid(grid, path=False, labels=None, mask=None, unit='cmwe'): """ Create a figure with rms of the grid spatial object in function of time Parameters ---------- - grid : spatial object + grid : spatial object or list of spatial object path : path to save the figure if needed mask : mask to apply on data if needed unit : unit of the grid """ - l_rms = [] - for i in range(len(grid.time)): - if mask is None: - rms = np.sqrt(np.sum([np.sum(np.cos(lat * np.pi / 180) ** 2 * line ** 2) for lat, line in - zip(grid.lat, grid.data[:, :, i])]) / np.sum( - [np.sum(np.cos(lat * np.pi / 180) ** 2 * line.size) for lat, line in - zip(grid.lat, grid.data[:, :, i])])) - else: - rms = np.sqrt(np.sum([np.sum(np.cos(lat * np.pi / 180) ** 2 * (line * line_mask) ** 2) - for lat, line, line_mask in zip(grid.lat, grid.data[:, :, i], mask)]) - / np.sum([np.sum(np.cos(lat * np.pi / 180) ** 2 * line_mask) - for lat, line_mask in zip(grid.lat, mask)])) + if type(grid) != list: + grid = [grid] + + plot_rms = [] + for g in grid: + l_rms = [] + for i in range(len(g.time)): + if mask is None: + rms = np.sqrt(np.sum([np.sum(np.cos(lat * np.pi / 180) ** 2 * line ** 2) for lat, line in + zip(g.lat, g.data[:, :, i])]) / np.sum( + [np.sum(np.cos(lat * np.pi / 180) ** 2 * line.size) for lat, line in + zip(g.lat, g.data[:, :, i])])) + else: + rms = np.sqrt(np.sum([np.sum(np.cos(lat * np.pi / 180) ** 2 * (line * line_mask) ** 2) + for lat, line, line_mask in zip(g.lat, g.data[:, :, i], mask)]) + / np.sum([np.sum(np.cos(lat * np.pi / 180) ** 2 * line_mask) + for lat, line_mask in zip(g.lat, mask)])) - l_rms.append(rms) + l_rms.append(rms) + plot_rms.append(l_rms) plt.figure() - plt.plot(grid.time, l_rms) + if not(type(labels) == list): + for g, rms in zip(grid, plot_rms): + plt.plot(g.time, rms) + else: + for g, rms, l in zip(grid, plot_rms, labels): + plt.plot(g.time, rms, label=l) + plt.xlabel('Time (y)') if unit == "cmwe": plt.ylabel('cm EWH') @@ -643,6 +641,9 @@ def plot_rms_grid(grid, path=False, mask=None, unit='cmwe'): plt.ylabel('$nT.y^{-2}$') plt.ylabel('Power (cm EWH)') + if type(labels) == list: + plt.legend() + if path: plt.savefig(path, bbox_inches='tight') else: diff --git a/setup.py b/setup.py index 2ca94f40..4b9b837f 100644 --- a/setup.py +++ b/setup.py @@ -18,11 +18,12 @@ else: # get install requirements with open('requirements.txt') as fh: - install_requires = [line.split().pop(0) for line in fh.read().splitlines()] + install_requires = [line.split()[0] for line in fh.read().splitlines()] # dependency links dependency_links = ['https://github.com/tsutterley/read-GRACE-geocenter/tarball/main', 'https://github.com/tsutterley/geoid-toolkit/tarball/tarball/main'] +print(install_requires) # get version with open('version.txt') as fh: version = fh.read() From 0533e4c11c7d1f35ba2728423222bd2013228de3 Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 18 May 2022 11:03:31 +0200 Subject: [PATCH 33/80] New plot eof on spatial grid with normalization --- gravity_toolkit/spatial.py | 125 +++++++++++++++++++++++++++++++++++++ 1 file changed, 125 insertions(+) diff --git a/gravity_toolkit/spatial.py b/gravity_toolkit/spatial.py index 01786497..24fb461a 100644 --- a/gravity_toolkit/spatial.py +++ b/gravity_toolkit/spatial.py @@ -52,12 +52,18 @@ import gzip import zipfile import numpy as np +import scipy as sc +import matplotlib.pyplot as plt +import matplotlib +import cartopy.crs as ccrs + from gravity_toolkit.time import adjust_months from gravity_toolkit.ncdf_write import ncdf_write from gravity_toolkit.hdf5_write import hdf5_write from gravity_toolkit.ncdf_read import ncdf_read from gravity_toolkit.hdf5_read import hdf5_read + class spatial(object): """ Data class for reading, writing and processing spatial data @@ -1033,3 +1039,122 @@ def replace_masked(self): if self.fill_value is not None: self.data[self.mask] = self.fill_value return self + + def plot_eof(self, number, path_folder, cmap='viridis', mode='full', unit='cmwe', mask=None, normalize=False, weight=False): + import gravity_toolkit.toolbox as tb + mat_svd = np.copy(self.data) + if mask is None: + mat_svd = np.reshape(mat_svd, (self.lat.shape[0] * self.lon.shape[0], self.time.shape[0])) + lat = self.lat.repeat(self.lon.shape[0]) + else: + mat_svd = np.reshape(mat_svd[mask], (np.sum(mask), self.time.shape[0])) + lat = self.lat.repeat(np.sum(mask, axis=0)) + + mat_svd_original = np.copy(mat_svd) + if normalize: + mat_svd = (mat_svd - np.mean(mat_svd, axis = 1).repeat(self.time.shape[0]).reshape(mat_svd.shape)) / np.std(mat_svd, axis=1).repeat(self.time.shape[0]).reshape(mat_svd.shape) + if weight: + mat_svd = mat_svd*np.cos(np.radians(lat).repeat(self.time.shape[0]).reshape(mat_svd.shape)) + + + c_svd = mat_svd.T@mat_svd/(mat_svd.shape[0] - 1) + w, v = sc.linalg.eigh(c_svd) + + v = v[:, ::-1] + w = w[::-1] + s = np.sqrt(w*(mat_svd.shape[0] - 1)) + us = mat_svd_original@v + + eof_grid = spatial() + eof_grid.lat, eof_grid.lon = self.lat, self.lon + eof_grid.time = np.array([0]) + + if not os.path.isdir(path_folder): + os.mkdir(path_folder) + + if mode == 'ts': + plt.figure() + plt.xlabel('Time (year)') + + for k in number: + power = s[k]**2/np.nansum(s**2) + eof = us[:, k]/np.sqrt(mat_svd.shape[1] - 1) + sort_eof = np.sort(eof) + scale_eof = 2*eof/(sort_eof[-int(len(sort_eof)*0.01)] - sort_eof[int(len(sort_eof)*0.01)]) + + pc = v.T[k] * np.sqrt(mat_svd.shape[1] - 1) /2*(sort_eof[-int(len(sort_eof)*0.01)] - sort_eof[int(len(sort_eof)*0.01)]) + + if mask is None: + eof_grid.data = np.reshape(scale_eof, (self.lat.shape[0], self.lon.shape[0], 1)) + else: + eof_grid.data = np.zeros((self.lat.shape[0], self.lon.shape[0], 1)) + eof_grid.data[mask] = scale_eof + eof_grid.data[np.logical_not(mask)] = None + + if mode == 'map': + tb.plot_rms_map(eof_grid, path=os.path.join(path_folder, 'map_eof_'+str(k)+'.png'), unit=unit, mask=mask) + + elif mode == 'full': + npow2 = 1 if len(self.time) == 0 else 2 ** (len(self.time) - 1).bit_length() + f = np.fft.fft(pc, npow2) + xf = np.fft.fftfreq(npow2, d=np.mean(self.time[1:] - self.time[:-1])) + + fig = plt.figure(constrained_layout=True, figsize=(12, 6), dpi=200) + spec = matplotlib.gridspec.GridSpec(ncols=4, nrows=12, wspace=0.03, width_ratios=[8, 1, 1, 1]) + axmap = fig.add_subplot(spec[:, 0], projection=ccrs.PlateCarree()) + + cmap = plt.cm.get_cmap(cmap) + + immap = axmap.imshow(eof_grid.data, cmap=cmap, transform=ccrs.PlateCarree(), extent=self.extent, + origin='upper', vmin=-1.15, vmax=1.15) + axmap.coastlines('50m') + # stronger linewidth on frame + axmap.spines['geo'].set_linewidth(2.0) + axmap.spines['geo'].set_capstyle('projecting') + + cbar = plt.colorbar(immap, ax=axmap, extend='both', extendfrac=0.0375, + orientation='horizontal', pad=0.025, shrink=0.85, + aspect=22, drawedges=False) + + # ticks lines all the way across + cbar.ax.tick_params(which='both', width=1, length=24, labelsize=18, + direction='in') + + power_str = '\nPower: '+str("%1.2f"%power) + cbar.ax.set_xlabel(power_str, labelpad=10, fontsize=18) + + axplot = fig.add_subplot(spec[1:5, 1:], box_aspect=0.5) + axplot.plot(self.time, pc) + axplot.yaxis.tick_right() + axplot.yaxis.set_label_position("right") + axplot.set_xlabel('Time (year)') + + if unit == "cmwe": + axplot.set_ylabel('Equivalent Water\nThickness\ncm', labelpad=50, fontsize=12, rotation='horizontal') + elif unit == "cmwe_ne": + axplot.set_ylabel('Non elastic\n Equivalent Water\nThickness\ncm', labelpad=50, fontsize=12, rotation='horizontal') + elif unit == "mmwe": + axplot.set_ylabel('Equivalent Water\n Thickness\nmm', labelpad=50, fontsize=12, rotation='horizontal') + elif unit == "geoid": + axplot.set_ylabel('Geoid Height\nmm', labelpad=45, fontsize=12, rotation='horizontal') + elif unit == "microGal": + axplot.set_ylabel('Acceleration\n$\mu Gal$', labelpad=40, fontsize=12, rotation='horizontal') + elif unit == "secacc": + axplot.set_ylabel('Secular\n Acceleration\n$nT.y^{-2}$', labelpad=40, fontsize=12, rotation='horizontal') + + axfft = fig.add_subplot(spec[6:10, 1:], box_aspect=0.5) + plt.plot(1/xf[:len(xf)//2][1/xf[:len(xf)//2] < 10], 2.0/len(self.time) * np.abs(f[:len(xf)//2][1/xf[:len(xf)//2] < 10])) + axfft.yaxis.tick_right() + axfft.set_xlim(0, 10) + axfft.set_ylim(0,) + axfft.set_xlabel('Period (year)') + + plt.savefig(os.path.join(path_folder, 'eof_pc_'+str(k)+'.png'), bbox_inches='tight') + plt.close() + + elif mode == 'ts': + plt.plot(self.time, pc, label=str(k)) + + if mode == 'ts': + plt.savefig(os.path.join(path_folder, 'pc_'+'-'.join([str(i) for i in number])+'.png')) + plt.legend() \ No newline at end of file From b66d4054fb08f32dd9c90cd962e492125f83b5a2 Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 18 May 2022 15:56:53 +0200 Subject: [PATCH 34/80] Add unit cmweEl for ellipsoidal EWH to create_grid --- gravity_toolkit/toolbox.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/gravity_toolkit/toolbox.py b/gravity_toolkit/toolbox.py index f52f26b3..687db527 100644 --- a/gravity_toolkit/toolbox.py +++ b/gravity_toolkit/toolbox.py @@ -28,7 +28,7 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d lmax : maximum degree of spherical harmonics used rad : radius of the gaussian filter. If set to 0, no gaussian filter is apply destripe : boolean to apply or not the destripe method of harmonics - unit : unit of the grid in ['cmwe', 'geoid', 'cmwe_ne', 'microGal'] + unit : unit of the grid in ['cmwe', 'cmweEl', 'geoid', 'cmwe_ne', 'microGal'] dlon : output longitude spacing dlat : output latitude spacing bounds : list with [lon_max, lon_min, lat_max, lat_min] @@ -73,6 +73,8 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d if unit == 'cmwe': dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).cmwe + elif unit == 'cmweEl': + dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).cmweEL elif unit == 'geoid': dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).mmGH elif unit == 'cmwe_ne': @@ -80,7 +82,7 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d elif unit == 'microGal': dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).microGal else: - raise ValueError("Unit not accepted, should be either 'cmwe' or 'cmwe_ne' or 'geoid' or 'microGal'") + raise ValueError("Unit not accepted, should be either 'cmwe' pr 'cmweEl' or 'cmwe_ne' or 'geoid' or 'microGal'") # converting harmonics to truncated, smoothed coefficients in units # combining harmonics to calculate output spatial fields From 16316647355bb3dbecbde8e2dbc70cd32d84293d Mon Sep 17 00:00:00 2001 From: hulecom Date: Mon, 23 May 2022 11:16:09 +0200 Subject: [PATCH 35/80] Debug toolbox and add new function hs_to_grid_amp --- gravity_toolkit/toolbox.py | 53 +++++++++++++++++++++++++++++++++----- 1 file changed, 46 insertions(+), 7 deletions(-) diff --git a/gravity_toolkit/toolbox.py b/gravity_toolkit/toolbox.py index 687db527..4625fb3f 100644 --- a/gravity_toolkit/toolbox.py +++ b/gravity_toolkit/toolbox.py @@ -1,3 +1,5 @@ +import os.path + from gravity_toolkit.gauss_weights import gauss_weights from gravity_toolkit.gen_stokes import gen_stokes from gravity_toolkit.harmonics import harmonics @@ -74,7 +76,7 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d if unit == 'cmwe': dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).cmwe elif unit == 'cmweEl': - dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).cmweEL + dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).cmweEl elif unit == 'geoid': dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).mmGH elif unit == 'cmwe_ne': @@ -498,7 +500,7 @@ def plot_rms_map(grid, path=False, proj=ccrs.PlateCarree(), unit='cmwe', bound=N subplot_kw=dict(projection=proj)) if bound is None: - vmin, vmax = int(np.min(data_to_set)), int(np.ceil(np.max(data_to_set))) + vmin, vmax = np.floor(np.min(data_to_set)), np.ceil(np.max(data_to_set)) else: vmin, vmax = bound @@ -521,7 +523,7 @@ def plot_rms_map(grid, path=False, proj=ccrs.PlateCarree(), unit='cmwe', bound=N # rasterized colorbar to remove lines cbar.solids.set_rasterized(True) # Add label to the colorbar - if unit == "cmwe": + if unit == "cmwe" or unit == "cmweEl": cbar.ax.set_xlabel('Equivalent Water Thickness', labelpad=10, fontsize=24) cbar.ax.set_ylabel('cm', fontsize=24, rotation=0, labelpad=10) elif unit == "cmwe_ne": @@ -552,11 +554,11 @@ def plot_rms_map(grid, path=False, proj=ccrs.PlateCarree(), unit='cmwe', bound=N # adjust subplot within figure fig.subplots_adjust(left=0.02, right=0.98, bottom=0.05, top=0.98) - if path: + if path and os.path.isdir(os.path.dirname(str(path))): plt.savefig(path, bbox_inches='tight') + plt.close() else: plt.show() - plt.close() def calc_rms_grid(grid, mask=None): @@ -629,7 +631,7 @@ def plot_rms_grid(grid, path=False, labels=None, mask=None, unit='cmwe'): plt.plot(g.time, rms, label=l) plt.xlabel('Time (y)') - if unit == "cmwe": + if unit == "cmwe" or unit == "cmweEl": plt.ylabel('cm EWH') elif unit == "cmwe_ne": plt.ylabel('Non elastic cm EWH') @@ -650,4 +652,41 @@ def plot_rms_grid(grid, path=False, labels=None, mask=None, unit='cmwe'): plt.savefig(path, bbox_inches='tight') else: plt.show() - plt.close() \ No newline at end of file + plt.close() + + +def hs_to_grid_amp(amplitude, l, m, unit='cmwe', map=False): + """ + Create a grid corresponding to a particular spherical harmonic coefficient in a unit. + Return the amplitude of the grid create by this coefficient in the given unit and the map signal + + Parameters + ---------- + amplitude : amplitude of the Spherical harmonic coefficient + l : degree + m : order + unit : unit of the grid + map : Default to False, True to print a map of the coefficent and give a path to save the map + + Returns + ------- + max, min : bound value of the grid create with the given amplitude in the asked unit + """ + ylms = harmonics(lmax=l, mmax=np.abs(m)) + ylms.time = np.array([0]) + ylms.month = np.array([0]) + + ylms.clm = np.zeros((l + 1, l + 1)) + ylms.slm = np.zeros((l + 1, l + 1)) + if m >= 0: + ylms.clm[l, np.abs(m)] = amplitude + else: + ylms.slm[l, np.abs(m)] = amplitude + + grid = create_grid(ylms, l, unit=unit) + + if map: + grid.data = grid.data[:, :, np.newaxis] + plot_rms_map(grid, path=map, unit=unit) + + return np.max(grid.data), np.min(grid.data) \ No newline at end of file From 39a73600264af53cc01f138b80303254bf5a94a3 Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 15 Nov 2022 14:42:44 +0100 Subject: [PATCH 36/80] Update and debug various function --- gravity_toolkit/gen_stokes.py | 16 ++-- gravity_toolkit/harmonics.py | 136 +++++++++++++++++++--------------- gravity_toolkit/spatial.py | 22 ++++-- gravity_toolkit/toolbox.py | 88 ++++++++++------------ gravity_toolkit/units.py | 2 + gravity_toolkit/wavelets.py | 2 +- 6 files changed, 145 insertions(+), 121 deletions(-) diff --git a/gravity_toolkit/gen_stokes.py b/gravity_toolkit/gen_stokes.py index 9d5bab9b..9a823ca8 100755 --- a/gravity_toolkit/gen_stokes.py +++ b/gravity_toolkit/gen_stokes.py @@ -142,29 +142,33 @@ def gen_stokes(data, lon, lat, LMIN=0, LMAX=60, MMAX=None, UNITS=1, #-- Multiplying sin(th) with differentials of theta and phi #-- to calculate the integration factor at each latitude int_fact = np.zeros((nlat)) - if (UNITS == 1): + if UNITS == 1: #-- Default Parameter: Input in cm w.e. (g/cm^2) dfactor = factors.cmwe int_fact[:] = np.sin(th)*dphi*dth - elif (UNITS == 2): + elif UNITS == 2: #-- Input in gigatonnes (Gt) dfactor = factors.cmwe #-- rad_e: Average Radius of the Earth [cm] int_fact[:] = 1e15/(factors.rad_e**2) - elif (UNITS == 3): + elif UNITS == 3: #-- Input in kg/m^2 (mm w.e.) dfactor = factors.mmwe int_fact[:] = np.sin(th)*dphi*dth - elif (UNITS == 4): + elif UNITS == 4: #-- Inputs in mmGH dfactor = factors.mmGH int_fact[:] = np.sin(th) * dphi * dth - elif (UNITS == 5): + elif UNITS == 5: dfactor = factors.microGal int_fact[:] = np.sin(th) * dphi * dth - elif (UNITS == 6): + elif UNITS == 6: dfactor = factors.cmwe_ne int_fact[:] = np.sin(th) * dphi * dth + elif UNITS == 7: + #-- Inputs in units with no dfactor + dfactor = factors.norm + int_fact[:] = np.sin(th) * dphi * dth else: #-- default is cm w.e. (g/cm^2) dfactor = factors.cmwe diff --git a/gravity_toolkit/harmonics.py b/gravity_toolkit/harmonics.py index ba77312a..c37c06a6 100644 --- a/gravity_toolkit/harmonics.py +++ b/gravity_toolkit/harmonics.py @@ -1263,7 +1263,7 @@ def plot_correlation(self, l, m, save_path=False): plt.savefig(save_path[:-3] + 's' + save_path[-3:]) plt.show() - def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], save_path=False): + def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], color=[], save_path=False): """ Plot Cl,m and Sl,m harmonic coefficients Inputs: @@ -1277,20 +1277,28 @@ def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], save_path=False) """ #-- figure for Cl,m plt.figure() + ax = plt.gca() plt.title("Normalized spherical harmonics coefficient $C_{" + str(l) + "," + str(m) + "}$") if len(ylms): - plt.plot(self.time, self.clm[l, m, :], label=label[0]) + if len(color): + plt.plot(self.time, self.clm[l, m, :], label=label[0], color=color[0]) + else: + plt.plot(self.time, self.clm[l, m, :], label=label[0]) else: plt.plot(self.time, self.clm[l, m, :], label="$C_{" + str(l) + "," + str(m) + "}$") try: for i in range(len(ylms)): - plt.plot(ylms[i].time, ylms[i].clm[l, m, :], label=label[i+1]) + if len(color): + plt.plot(ylms[i].time, ylms[i].clm[l, m, :], label=label[i + 1], color=color[i + 1]) + else: + plt.plot(ylms[i].time, ylms[i].clm[l, m, :], label=label[i + 1]) except IndexError: raise IndexError("The list of labels is incomplete for correct plotting") plt.xlabel("Time (year)") plt.legend() + ax.yaxis.offsetText.set_horizontalalignment('right') if dates: plt.xlim(dates) plt.grid() @@ -1299,25 +1307,33 @@ def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], save_path=False) if os.path.isdir(save_path): plt.savefig(os.path.join(save_path, 'C' + str(l) + str(m) + '_coefficient.png')) else: - plt.savefig(save_path[:-3] + 'c' + save_path[-3:]) + plt.savefig(save_path[:-4] + 'c' + save_path[-4:]) if m: #-- figure for Sl,m plt.figure() + ax = plt.gca() plt.title("Normalized spherical harmonic coefficient $S_{" + str(l) + "," + str(m) + "}$") if len(ylms): - plt.plot(self.time, self.slm[l, m, :], label=label[0]) + if len(color): + plt.plot(self.time, self.slm[l, m, :], label=label[0], color=color[0]) + else: + plt.plot(self.time, self.slm[l, m, :], label=label[0]) else: plt.plot(self.time, self.slm[l, m, :], label="$S_{" + str(l) + "," + str(m) + "}$") try: for i in range(len(ylms)): - plt.plot(ylms[i].time, ylms[i].slm[l, m, :], label=label[i + 1]) + if len(color): + plt.plot(ylms[i].time, ylms[i].slm[l, m, :], label=label[i + 1], color=color[i + 1]) + else: + plt.plot(ylms[i].time, ylms[i].slm[l, m, :], label=label[i + 1]) except IndexError: raise IndexError("The list of labels is incomplete for correct plotting") plt.xlabel("Time (year)") plt.legend() + ax.yaxis.offsetText.set_horizontalalignment('right') if dates: plt.xlim(dates) plt.grid() @@ -1326,11 +1342,11 @@ def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], save_path=False) if os.path.isdir(save_path): plt.savefig(os.path.join(save_path, 'S' + str(l) + str(m) + '_coefficient.png')) else: - plt.savefig(save_path[:-3] + 's' + save_path[-3:]) + plt.savefig(save_path[:-4] + 's' + save_path[-4:]) plt.show() - def plot_fft(self, l, m, save_path=False): + def plot_fft(self, l, m, save_path=False, fmax=6): """ Plot Cl,m and Sl,m harmonic coefficients fast fourrier transform Inputs: @@ -1339,11 +1355,12 @@ def plot_fft(self, l, m, save_path=False): Options: save_path : if not False, give a path to save the figure + fmax : maximal frequency (default to 6 for period > 2 months) """ #-- compute fft and create x monthly frequency N = len(self.time) - cf = sc.fft.fft(self.clm[l, m, :]) - sf = sc.fft.fft(self.slm[l, m, :]) + cf = sc.fft.fft(self.clm[l, m, :])[0:N // 2] + sf = sc.fft.fft(self.slm[l, m, :])[0:N // 2] xf = np.linspace(0.0, 12/2, N // 2) # -- figure for Cl,m and Sl,m @@ -1351,9 +1368,9 @@ def plot_fft(self, l, m, save_path=False): plt.title("Fourier transform of the normalized spherical harmonic coefficients $C_{" + str(l) + "," + str( m) + "}$ et $S_{" + str( l) + "," + str(m) + "}$") - plt.plot(xf, 2.0 / N * np.abs(cf[0:N // 2]), label="$C_{" + str(l) + "," + str(m) + "}$") + plt.plot(xf[xf <= fmax], 2.0 / N * np.abs(cf[xf <= fmax]), label="$C_{" + str(l) + "," + str(m) + "}$") if m: - plt.plot(xf, 2.0 / N * np.abs(sf[0:N // 2]), label="$S_{" + str(l) + "," + str(m) + "}$") + plt.plot(xf[xf <= fmax], 2.0 / N * np.abs(sf[xf <= fmax]), label="$S_{" + str(l) + "," + str(m) + "}$") plt.xlabel("Frequency ($year^{-1}$)") @@ -1369,7 +1386,7 @@ def plot_fft(self, l, m, save_path=False): plt.show() - def plot_wavelets(self, l, m, s0=0, pad=1, lag1=0, plot_coi=True, mother='MORLET', param=-1, func_plot=np.abs, save_path=False): + def plot_wavelets(self, l, m, s0=0, j1=None, pad=1, lag1=0, plot_coi=True, mother='MORLET', param=-1, func_plot=np.abs, save_path=False): """ Plot Cl,m and Sl,m wavelet analysis based on (Torrence and Compo, 1998) @@ -1397,8 +1414,10 @@ def plot_wavelets(self, l, m, s0=0, pad=1, lag1=0, plot_coi=True, mother='MORLET if not s0: s0 = 4 * dt # min scale of the wavelets + # max resolution of the wavelet, fixed for GRACE - j1 = 4.5 / dj + if j1 is None: + j1 = np.log2(11/s0)/dj siglvl = 0.95 @@ -1470,56 +1489,57 @@ def plot_wavelets(self, l, m, s0=0, pad=1, lag1=0, plot_coi=True, mother='MORLET axs[1].set_xlabel('Power') axs[1].set_title('Global Wavelet Spectrum') - plt.legend() + plt.legend(loc='upper right') if save_path: if os.path.isdir(save_path): plt.savefig(os.path.join(save_path, 'C' + str(l) + str(m) + '_wavelet.png')) else: - plt.savefig(save_path[:-3] + 'c' + save_path[-3:]) - - # create figure Sl,m - fig = plt.figure(constrained_layout=True, figsize=(12, 6), dpi=200) - spec = matplotlib.gridspec.GridSpec(ncols=2, nrows=1, wspace=0.02, width_ratios=[3, 1]) - ax0 = fig.add_subplot(spec[0]) - ax1 = fig.add_subplot(spec[1], sharey=ax0) - axs = [ax0, ax1] - plt.setp(axs[1].get_yticklabels(), visible=False) - - # plot wavelet - im = axs[0].contourf(self.time, period, np.abs(waves), 100) - axs[0].contour(self.time, period, sig95s, levels=[1], linewidths=2) - - # plot cone of interest of the wavelet - if plot_coi: - axs[0].fill(np.concatenate((self.time[:1] - 0.0001, self.time, self.time[-1:] + 0.0001, - self.time[-1:] + 0.0001, self.time[:1] - 0.0001, self.time[:1] - 0.0001)), - np.concatenate(([s0], coi, [s0], period[-1:], period[-1:], [s0])), 'r', alpha=0.2, hatch='/') - axs[0].plot(self.time, coi, 'r--', lw=1.4) + plt.savefig(save_path[:-4] + 'c' + save_path[-4:]) - fig.colorbar(im, ax=axs[0], location='left') - axs[0].invert_yaxis() - axs[0].set_yscale('log', base=2) - axs[0].set_ylabel('Period (year)') - axs[0].set_ylim(np.max(period), np.min(period)) - axs[0].set_yticks(yticks) - axs[0].set_xlabel('Time (year)') - axs[0].get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) - axs[0].set_title('Wavelet Power Spectrum') - - # plot fft analysis at the right of the figure - axs[1].plot(sxxs, 1 / f * dt, 'gray', label='Fourier spectrum') - axs[1].plot(global_wss, period, 'b', label='Wavelet spectrum') - axs[1].plot(np.array(signifs) * np.var(self.clm[l, m]), period, 'g--', label='95% confidence spectrum') - axs[1].set_xlabel('Power') - axs[1].set_title('Global Wavelet Spectrum') - - plt.legend() + if m: + # create figure Sl,m + fig = plt.figure(constrained_layout=True, figsize=(12, 6), dpi=200) + spec = matplotlib.gridspec.GridSpec(ncols=2, nrows=1, wspace=0.02, width_ratios=[3, 1]) + ax0 = fig.add_subplot(spec[0]) + ax1 = fig.add_subplot(spec[1], sharey=ax0) + axs = [ax0, ax1] + plt.setp(axs[1].get_yticklabels(), visible=False) + + # plot wavelet + im = axs[0].contourf(self.time, period, np.abs(waves), 100) + axs[0].contour(self.time, period, sig95s, levels=[1], linewidths=2) + + # plot cone of interest of the wavelet + if plot_coi: + axs[0].fill(np.concatenate((self.time[:1] - 0.0001, self.time, self.time[-1:] + 0.0001, + self.time[-1:] + 0.0001, self.time[:1] - 0.0001, self.time[:1] - 0.0001)), + np.concatenate(([s0], coi, [s0], period[-1:], period[-1:], [s0])), 'r', alpha=0.2, hatch='/') + axs[0].plot(self.time, coi, 'r--', lw=1.4) + + fig.colorbar(im, ax=axs[0], location='left') + axs[0].invert_yaxis() + axs[0].set_yscale('log', base=2) + axs[0].set_ylabel('Period (year)') + axs[0].set_ylim(np.max(period), np.min(period)) + axs[0].set_yticks(yticks) + axs[0].set_xlabel('Time (year)') + axs[0].get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) + axs[0].set_title('Wavelet Power Spectrum') + + # plot fft analysis at the right of the figure + axs[1].plot(sxxs, 1 / f * dt, 'gray', label='Fourier spectrum') + axs[1].plot(global_wss, period, 'b', label='Wavelet spectrum') + axs[1].plot(np.array(signifs) * np.var(self.clm[l, m]), period, 'g--', label='95% confidence spectrum') + axs[1].set_xlabel('Power') + axs[1].set_title('Global Wavelet Spectrum') + + plt.legend(loc='upper right') - if save_path: - if os.path.isdir(save_path): - plt.savefig(os.path.join(save_path, 'C' + str(l) + str(m) + '_wavelet.png')) - else: - plt.savefig(save_path[:-3] + 's' + save_path[-3:]) + if save_path: + if os.path.isdir(save_path): + plt.savefig(os.path.join(save_path, 'S' + str(l) + str(m) + '_wavelet.png')) + else: + plt.savefig(save_path[:-4] + 's' + save_path[-4:]) plt.show() diff --git a/gravity_toolkit/spatial.py b/gravity_toolkit/spatial.py index 24fb461a..77c01829 100644 --- a/gravity_toolkit/spatial.py +++ b/gravity_toolkit/spatial.py @@ -1129,25 +1129,33 @@ def plot_eof(self, number, path_folder, cmap='viridis', mode='full', unit='cmwe' axplot.yaxis.set_label_position("right") axplot.set_xlabel('Time (year)') + axfft = fig.add_subplot(spec[6:10, 1:], box_aspect=0.5) + plt.plot(1 / xf[:len(xf) // 2][1 / xf[:len(xf) // 2] < 10], + 2.0 / len(self.time) * np.abs(f[:len(xf) // 2][1 / xf[:len(xf) // 2] < 10])) + axfft.yaxis.tick_right() + axfft.set_xlim(0, 10) + axfft.set_ylim(0, ) + axfft.set_xlabel('Period (year)') + axfft.yaxis.set_label_position("right") + if unit == "cmwe": axplot.set_ylabel('Equivalent Water\nThickness\ncm', labelpad=50, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$cm^2$', labelpad=50, fontsize=12, rotation='horizontal') elif unit == "cmwe_ne": axplot.set_ylabel('Non elastic\n Equivalent Water\nThickness\ncm', labelpad=50, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$cm^2$', labelpad=50, fontsize=12, rotation='horizontal') elif unit == "mmwe": axplot.set_ylabel('Equivalent Water\n Thickness\nmm', labelpad=50, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$mm^2$', labelpad=50, fontsize=12, rotation='horizontal') elif unit == "geoid": axplot.set_ylabel('Geoid Height\nmm', labelpad=45, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$mm^2$', labelpad=50, fontsize=12, rotation='horizontal') elif unit == "microGal": axplot.set_ylabel('Acceleration\n$\mu Gal$', labelpad=40, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$\mu Gal^2$', labelpad=50, fontsize=12, rotation='horizontal') elif unit == "secacc": axplot.set_ylabel('Secular\n Acceleration\n$nT.y^{-2}$', labelpad=40, fontsize=12, rotation='horizontal') - - axfft = fig.add_subplot(spec[6:10, 1:], box_aspect=0.5) - plt.plot(1/xf[:len(xf)//2][1/xf[:len(xf)//2] < 10], 2.0/len(self.time) * np.abs(f[:len(xf)//2][1/xf[:len(xf)//2] < 10])) - axfft.yaxis.tick_right() - axfft.set_xlim(0, 10) - axfft.set_ylim(0,) - axfft.set_xlabel('Period (year)') + axfft.set_ylabel('Power\n$nT^2.y^{-4}$', labelpad=50, fontsize=12, rotation='horizontal') plt.savefig(os.path.join(path_folder, 'eof_pc_'+str(k)+'.png'), bbox_inches='tight') plt.close() diff --git a/gravity_toolkit/toolbox.py b/gravity_toolkit/toolbox.py index 4625fb3f..60870eff 100644 --- a/gravity_toolkit/toolbox.py +++ b/gravity_toolkit/toolbox.py @@ -52,6 +52,11 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d grid.lon = np.arange(-bounds[1] + dlon / 2.0, bounds[0] + dlon / 2.0, dlon) grid.lat = np.arange(bounds[2] - dlat / 2.0, -bounds[3] - dlat / 2.0, -dlat) + if lmax is None: + lmax = Ylms.lmax + else: + Ylms.lmax = lmax + nlon = len(grid.lon) nlat = len(grid.lat) @@ -62,10 +67,7 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d # Computing plms for converting to spatial domain theta = (90.0 - grid.lat) * np.pi / 180.0 - if lmax is None: - PLM, dPLM = plm_holmes(Ylms.lmax, np.cos(theta)) - else: - PLM, dPLM = plm_holmes(lmax, np.cos(theta)) + PLM, dPLM = plm_holmes(lmax, np.cos(theta)) # read load love numbers file love_numbers_file = get_data_path(['data', 'love_numbers']) @@ -74,15 +76,17 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d hl, kl, ll = read_love_numbers(love_numbers_file, REFERENCE='CF') if unit == 'cmwe': - dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).cmwe + dfactor = units(lmax=lmax).harmonic(hl, kl, ll).cmwe elif unit == 'cmweEl': - dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).cmweEl + dfactor = units(lmax=lmax).harmonic(hl, kl, ll).cmweEl elif unit == 'geoid': - dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).mmGH + dfactor = units(lmax=lmax).harmonic(hl, kl, ll).mmGH elif unit == 'cmwe_ne': - dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).cmwe_ne + dfactor = units(lmax=lmax).harmonic(hl, kl, ll).cmwe_ne elif unit == 'microGal': - dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).microGal + dfactor = units(lmax=lmax).harmonic(hl, kl, ll).microGal + elif unit == 'none': + dfactor = units(lmax=lmax).harmonic(hl, kl, ll).norm else: raise ValueError("Unit not accepted, should be either 'cmwe' pr 'cmweEl' or 'cmwe_ne' or 'geoid' or 'microGal'") @@ -93,9 +97,9 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d grid.data = np.zeros((nlat, nlon)) if destripe: - tmp = Ylms.destripe() + tmp = Ylms.copy().destripe() else: - tmp = Ylms + tmp = Ylms.copy() if rad != 0: wt = 2.0 * np.pi * gauss_weights(rad, lmax) @@ -103,11 +107,7 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d else: tmp.convolve(dfactor) # convert spherical harmonics to output spatial grid - if lmax is None: - grid.data[:, :] = harmonic_summation(tmp.clm, tmp.slm, - grid.lon, grid.lat, LMAX=Ylms.lmax, MMAX=Ylms.mmax, PLM=PLM).T - else: - grid.data[:, :] = harmonic_summation(tmp.clm, tmp.slm, + grid.data[:, :] = harmonic_summation(tmp.clm, tmp.slm, grid.lon, grid.lat, LMAX=lmax, MMAX=lmax, PLM=PLM).T else: @@ -124,13 +124,9 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d wt = 2.0 * np.pi * gauss_weights(rad, lmax) tmp.convolve(dfactor * wt) else: - tmp.convolve(dfactor * np.ones((Ylms.lmax + 1))) + tmp.convolve(dfactor * np.ones((lmax + 1))) # convert spherical harmonics to output spatial grid - if lmax is None: - grid.data[:, :, i] = harmonic_summation(tmp.clm, tmp.slm, - grid.lon, grid.lat, LMAX=Ylms.lmax, MMAX=Ylms.mmax, PLM=PLM).T - else: - grid.data[:, :, i] = harmonic_summation(tmp.clm, tmp.slm, + grid.data[:, :, i] = harmonic_summation(tmp.clm, tmp.slm, grid.lon, grid.lat, LMAX=lmax, MMAX=lmax, PLM=PLM).T grid.mask = np.zeros(grid.data.shape) @@ -179,6 +175,8 @@ def grid_to_hs(grid, lmax, mmax=None, unit='cmwe'): UNITS = 6 elif unit == 'microGal': UNITS = 5 + elif unit == 'norm': + UNITS = 7 else: raise ValueError("Unit not accepted, should be either 'cmwe' or 'cmwe_ne' or 'geoid' or 'microGal'") @@ -216,7 +214,7 @@ def diff_grid(grid1, grid2): Parameters ---------- grid1 : spatial object - grid2 : spatial object to substract to the first + grid2 : spatial object to subtract to the first Returns ------- @@ -389,15 +387,6 @@ def save_gif(grid, path, unit='cmwe', bound=None, mask=None, color='viridis'): else: vmin, vmax = bound - if vmax - vmin >= 3: - levels = np.arange(vmin, vmax, max(1, int((vmax - vmin)/10))) - elif vmax - vmin >= 0.3: - levels = np.arange(vmin, vmax, max(0.1, float('%.1f'%((vmax - vmin)/10)))) - elif vmax - vmin >= 0.03: - levels = np.arange(vmin, vmax, max(0.1, float('%.2f'%((vmax - vmin)/10)))) - else: - raise ValueError("The range of data to plot is too small") - norm = colors.Normalize(vmin=vmin,vmax=vmax) cmap = plt.cm.get_cmap(color) im = ax1.imshow(np.zeros((np.int(180.0 + 1.0),np.int(360.0 + 1.0))), interpolation='nearest', @@ -441,16 +430,8 @@ def save_gif(grid, path, unit='cmwe', bound=None, mask=None, color='viridis'): cbar.ax.set_ylabel('$nT.y^{-2}$', fontsize=24, rotation=0) cbar.ax.yaxis.set_label_coords(1.045, 0.1) - # Set the tick levels for the colorbar - cbar.set_ticks(levels) - if vmax - vmin >= 3: - cbar.set_ticklabels(['{0:d}'.format(ct) for ct in levels]) - elif vmax - vmin >= 0.3: - cbar.set_ticklabels(['{.1f}'.format(ct) for ct in levels]) - elif vmax - vmin >= 0.03: - cbar.set_ticklabels(['{.2f}'.format(ct) for ct in levels]) # ticks lines all the way across - cbar.ax.tick_params(which='both', width=1, length=26, labelsize=24, + cbar.ax.tick_params(which='both', width=1, length=26, labelsize=20, direction='in') # stronger linewidth on frame @@ -468,7 +449,7 @@ def animate_frames(i): # set animation anim = animation.FuncAnimation(fig, animate_frames, frames=len(grid.month)) - HTML(anim.to_jshtml()) + #HTML(anim.to_jshtml()) anim.save(path, writer='imagemagick', fps=10) plt.clf() @@ -560,6 +541,8 @@ def plot_rms_map(grid, path=False, proj=ccrs.PlateCarree(), unit='cmwe', bound=N else: plt.show() + return np.sqrt(np.sum(grid.data ** 2, axis=2) / grid.time.shape[0]) + def calc_rms_grid(grid, mask=None): """ @@ -672,16 +655,23 @@ def hs_to_grid_amp(amplitude, l, m, unit='cmwe', map=False): ------- max, min : bound value of the grid create with the given amplitude in the asked unit """ - ylms = harmonics(lmax=l, mmax=np.abs(m)) + ylms = harmonics(lmax=np.max(l), mmax=np.max(l)) ylms.time = np.array([0]) ylms.month = np.array([0]) - ylms.clm = np.zeros((l + 1, l + 1)) - ylms.slm = np.zeros((l + 1, l + 1)) - if m >= 0: - ylms.clm[l, np.abs(m)] = amplitude - else: - ylms.slm[l, np.abs(m)] = amplitude + ylms.clm = np.zeros((np.max(l) + 1, np.max(l) + 1)) + ylms.slm = np.zeros((np.max(l) + 1, np.max(l) + 1)) + try: + for amp, i, j in zip(amplitude, l, m): + if j >= 0: + ylms.clm[i, np.abs(j)] = amp + else: + ylms.slm[i, np.abs(j)] = amp + except TypeError: + if m >= 0: + ylms.clm[l, np.abs(m)] = amplitude + else: + ylms.slm[l, np.abs(m)] = amplitude grid = create_grid(ylms, l, unit=unit) diff --git a/gravity_toolkit/units.py b/gravity_toolkit/units.py index 87f8a805..742f8900 100644 --- a/gravity_toolkit/units.py +++ b/gravity_toolkit/units.py @@ -107,6 +107,8 @@ def spatial(self, hl, kl, ll): # Gravitational Constant of the Earth (w/o atm) GM=GM_e-GM_atm # degree dependent coefficients + # norm, fully normalized spherical harmonics + self.norm = np.ones((self.lmax + 1)) # cmwe, centimeters water equivalent [g/cm^2] self.cmwe = 3.0 * (1.0 + kl[self.l]) / (1.0 + 2.0 * self.l) / (4.0 * np.pi * self.rad_e * self.rho_e) # cmwe_ne, centimeters water equivalent none elastic [g/cm^2] diff --git a/gravity_toolkit/wavelets.py b/gravity_toolkit/wavelets.py index 8153de78..6fb13e0d 100644 --- a/gravity_toolkit/wavelets.py +++ b/gravity_toolkit/wavelets.py @@ -136,7 +136,7 @@ def wavelet(Y, dt, pad=1, dj=.25, s0=-1, J1=-1, mother='MORLET', param=-1): f = np.fft.fft(x) # fft on the padded time series - scale = s0 * 2**(np.arange(0, J1 + 1, 1)*dj) + scale = s0 * 2**(np.arange(0, J1, 1)*dj) # define wavelet array wave = np.zeros((int(J1 + 1), n)) wave = wave + 1j * wave # make it complex From c613d32d6fdab467bd49b3b041f0e82121e9b72a Mon Sep 17 00:00:00 2001 From: hulecom Date: Mon, 14 Aug 2023 21:43:50 +0200 Subject: [PATCH 37/80] Debug units, spatial, harmonics and run_grace_date script. Create an change information reference in the README --- README.rst | 7 ++++--- gravity_toolkit/harmonics.py | 7 ++++++- gravity_toolkit/spatial.py | 9 ++++----- gravity_toolkit/units.py | 4 ++-- scripts/run_grace_date.py | 18 +++++++++++++++++- 5 files changed, 33 insertions(+), 12 deletions(-) diff --git a/README.rst b/README.rst index d2f5b387..1536f5ad 100644 --- a/README.rst +++ b/README.rst @@ -25,6 +25,9 @@ read-GRACE-harmonics Python tools for obtaining and working with Level-2 spherical harmonic coefficients from the NASA/DLR Gravity Recovery and Climate Experiment (GRACE) and the NASA/GFZ Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) missions +This repository is **forked** from the original one created by Tyler Sutterley. It contains additions made by Hugo Lecomte, especially for plotting purpose on harmonics and spatial objects. The additions have been developed as part of my PhD work at ITES. +I specially thank Tyler for this tool and I am glad to have been able to contribute to it. + Resources ######### @@ -91,10 +94,8 @@ Data Repositories Download ######## -| The program homepage is: +| The original program homepage is: | https://github.com/tsutterley/read-GRACE-harmonics -| A zip archive of the latest version is available directly at: -| https://github.com/tsutterley/read-GRACE-harmonics/archive/main.zip Disclaimer ########## diff --git a/gravity_toolkit/harmonics.py b/gravity_toolkit/harmonics.py index c37c06a6..a37ccc2e 100644 --- a/gravity_toolkit/harmonics.py +++ b/gravity_toolkit/harmonics.py @@ -411,7 +411,9 @@ def from_dict(self, d): for key in ['l', 'm', 'clm', 'slm', 'time', 'month']: try: setattr(self, key, d[key].copy()) - except (AttributeError, KeyError): + except AttributeError: + setattr(self, key, d[key]) + except KeyError: pass #-- maximum degree and order self.lmax = np.max(d['l']) @@ -1212,6 +1214,9 @@ def plot_correlation(self, l, m, save_path=False): Options: save_path : if not False, give a path to save the figure + + TODO: Refaire ça avec une matrice carrée sur tous les coeffs test: C20, C21, C22, S21, S22, C30, ... + ou C20, C21, S21, C22, S22, C30, ... """ mat_c = np.zeros((self.lmax, self.lmax)) if m: diff --git a/gravity_toolkit/spatial.py b/gravity_toolkit/spatial.py index 77c01829..d9fc1640 100644 --- a/gravity_toolkit/spatial.py +++ b/gravity_toolkit/spatial.py @@ -254,7 +254,7 @@ def from_HDF5(self, filename, date=True, **kwargs): #-- copy variables to spatial object self.data = data['data'].copy() if '_FillValue' in data['attributes']['data'].keys(): - self.fill_value = data['attributes']['_FillValue'] + self.fill_value = data['attributes']['data']['_FillValue'] self.mask = np.zeros(self.data.shape, dtype=bool) self.lon = data['lon'].copy() self.lat = data['lat'].copy() @@ -1085,9 +1085,9 @@ def plot_eof(self, number, path_folder, cmap='viridis', mode='full', unit='cmwe' pc = v.T[k] * np.sqrt(mat_svd.shape[1] - 1) /2*(sort_eof[-int(len(sort_eof)*0.01)] - sort_eof[int(len(sort_eof)*0.01)]) if mask is None: - eof_grid.data = np.reshape(scale_eof, (self.lat.shape[0], self.lon.shape[0], 1)) + eof_grid.data = np.reshape(scale_eof, (self.lat.shape[0], self.lon.shape[0])) else: - eof_grid.data = np.zeros((self.lat.shape[0], self.lon.shape[0], 1)) + eof_grid.data = np.zeros((self.lat.shape[0], self.lon.shape[0])) eof_grid.data[mask] = scale_eof eof_grid.data[np.logical_not(mask)] = None @@ -1103,8 +1103,7 @@ def plot_eof(self, number, path_folder, cmap='viridis', mode='full', unit='cmwe' spec = matplotlib.gridspec.GridSpec(ncols=4, nrows=12, wspace=0.03, width_ratios=[8, 1, 1, 1]) axmap = fig.add_subplot(spec[:, 0], projection=ccrs.PlateCarree()) - cmap = plt.cm.get_cmap(cmap) - + cmap = matplotlib.colormaps.get_cmap(cmap) immap = axmap.imshow(eof_grid.data, cmap=cmap, transform=ccrs.PlateCarree(), extent=self.extent, origin='upper', vmin=-1.15, vmax=1.15) axmap.coastlines('50m') diff --git a/gravity_toolkit/units.py b/gravity_toolkit/units.py index 742f8900..48e2b118 100644 --- a/gravity_toolkit/units.py +++ b/gravity_toolkit/units.py @@ -108,7 +108,7 @@ def spatial(self, hl, kl, ll): GM=GM_e-GM_atm # degree dependent coefficients # norm, fully normalized spherical harmonics - self.norm = np.ones((self.lmax + 1)) + self.norm = np.ones((self.lmax + 1))/(4.0 * np.pi) # cmwe, centimeters water equivalent [g/cm^2] self.cmwe = 3.0 * (1.0 + kl[self.l]) / (1.0 + 2.0 * self.l) / (4.0 * np.pi * self.rad_e * self.rho_e) # cmwe_ne, centimeters water equivalent none elastic [g/cm^2] @@ -116,7 +116,7 @@ def spatial(self, hl, kl, ll): # mmwe, millimeters water equivalent [kg/m^2] self.mmwe=3.0*(1.0+kl[self.l])/(1.0+2.0*self.l)/(40.0*np.pi*self.rad_e*self.rho_e) # mmGH, millimeters geoid height - self.mmGH=np.ones((self.lmax+1))/(4.0*np.pi*self.rad_e) + self.mmGH=np.ones((self.lmax+1))/(4.0*np.pi*10*self.rad_e) # microGal, microGal gravity perturbations self.microGal = (self.rad_e ** 2.0)/(4.0*np.pi*1.e6 * GM)/(self.l + 1.0) diff --git a/scripts/run_grace_date.py b/scripts/run_grace_date.py index efa7602b..b922ee6a 100755 --- a/scripts/run_grace_date.py +++ b/scripts/run_grace_date.py @@ -86,6 +86,22 @@ def run_grace_date(base_dir, PROC, DREL, VERBOSE=False, MODE=0o775): 'RL05':['GAA', 'GAB', 'GAC', 'GAD', 'GSM'], 'RL06':['GAA', 'GAB', 'GAC', 'GAD', 'GSM']} VALID['JPL'] = ['RL04','RL05','RL06'] + # -- CNES RL04/5 at LMAX 90 + DSET['CNES'] = {'RL04': ['GSM'], + 'RL05': ['GSM'],} + VALID['CNES'] = ['RL04', 'RL05'] + # -- GRAZ/ITSG RL14/16/18 at LMAX 120 + DSET['GRAZ'] = {'RL14': ['GSM'], + 'RL16': ['GSM'], + 'RL18': ['GSM']} + VALID['GRAZ'] = ['RL14', 'RL16', 'RL18'] + # -- Swarm RL01 at LMAX 40 + DSET['SWARM'] = {'RL01': ['GSM'],} + VALID['SWARM'] = ['RL01'] + # -- COSTG RL01 at LMAX 90 + DSET['COSTG'] = {'RL01': ['GSM'], + 'RL06': ['GSM']} + VALID['COSTG'] = ['RL01', 'RL06'] #-- for each processing center for p in PROC: @@ -121,7 +137,7 @@ def main(): parser.add_argument('--center','-c', metavar='PROC', type=str, nargs='+', default=['CSR','GFZ','JPL'], - choices=['CSR','GFZ','JPL'], + choices=['CSR','GFZ','JPL', 'CNES','GRAZ','SWARM', 'COSTG'], help='GRACE/GRACE-FO Processing Center') #-- GRACE/GRACE-FO data release parser.add_argument('--release','-r', From e90d65c146e4f8945c2cfcc14fa8f25fd1e8bd00 Mon Sep 17 00:00:00 2001 From: hulecom Date: Mon, 14 Aug 2023 21:44:19 +0200 Subject: [PATCH 38/80] Debug toolbox --- gravity_toolkit/toolbox.py | 42 ++++++++++++++++++++------------------ 1 file changed, 22 insertions(+), 20 deletions(-) diff --git a/gravity_toolkit/toolbox.py b/gravity_toolkit/toolbox.py index 60870eff..42206d1e 100644 --- a/gravity_toolkit/toolbox.py +++ b/gravity_toolkit/toolbox.py @@ -93,24 +93,7 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d # converting harmonics to truncated, smoothed coefficients in units # combining harmonics to calculate output spatial fields # output spatial grid - if not (type(Ylms.month) in [list, np.array]) and len(Ylms.month) == 1: - grid.data = np.zeros((nlat, nlon)) - - if destripe: - tmp = Ylms.copy().destripe() - else: - tmp = Ylms.copy() - - if rad != 0: - wt = 2.0 * np.pi * gauss_weights(rad, lmax) - tmp.convolve(dfactor * wt) - else: - tmp.convolve(dfactor) - # convert spherical harmonics to output spatial grid - grid.data[:, :] = harmonic_summation(tmp.clm, tmp.slm, - grid.lon, grid.lat, LMAX=lmax, MMAX=lmax, PLM=PLM).T - - else: + if type(Ylms.time) in [list, np.array, np.ndarray] and len(Ylms.time) != 1: grid.data = np.zeros((nlat, nlon, len(Ylms.month))) for i, grace_month in enumerate(Ylms.month): # GRACE/GRACE-FO harmonics for time t @@ -128,6 +111,25 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d # convert spherical harmonics to output spatial grid grid.data[:, :, i] = harmonic_summation(tmp.clm, tmp.slm, grid.lon, grid.lat, LMAX=lmax, MMAX=lmax, PLM=PLM).T + else: + grid.data = np.zeros((nlat, nlon)) + + if destripe: + tmp = Ylms.copy().destripe() + else: + tmp = Ylms.copy() + if len(tmp.clm.shape) == 3: + tmp.clm = tmp.clm.reshape(tmp.clm.shape[:-1]) + tmp.slm = tmp.slm.reshape(tmp.slm.shape[:-1]) + + if rad != 0: + wt = 2.0 * np.pi * gauss_weights(rad, lmax) + tmp.convolve(dfactor * wt) + else: + tmp.convolve(dfactor * np.ones((lmax + 1))) + # convert spherical harmonics to output spatial grid + grid.data[:, :] = harmonic_summation(tmp.clm, tmp.slm, + grid.lon, grid.lat, LMAX=lmax, MMAX=lmax, PLM=PLM).T grid.mask = np.zeros(grid.data.shape) return grid @@ -155,7 +157,7 @@ def grid_to_hs(grid, lmax, mmax=None, unit='cmwe'): mmax = np.copy(lmax) if (mmax is None) else mmax # -- number of dates in data - if type(grid.time) in [list, np.array] or len(grid.time) != 1: + if type(grid.time) in [list, np.array, np.ndarray] and len(grid.time) != 1: n_time = len(grid.time) else: n_time = 1 @@ -311,7 +313,7 @@ def filt_Ylms(ylms, filt='low', filt_param=None): if filt_param is None: to_zero = np.logical_or(freq > 0.5, freq < -0.5) else: - to_zero = np.logical_or(freq > filt_param[0], freq < filt_param[0]) + to_zero = np.logical_or(freq > filt_param[0], freq < -filt_param[0]) fc[:, :, to_zero] = 0 fs[:, :, to_zero] = 0 filtered_ylms.clm = np.real(np.fft.ifft(fc, axis=2))[:, :, :ndata] From b8538ff8f8a65712cd9ff8a4b9d1d35b92f06206 Mon Sep 17 00:00:00 2001 From: hulecom Date: Mon, 14 Aug 2023 21:49:43 +0200 Subject: [PATCH 39/80] =?UTF-8?q?Add=20Notebook=20for=20"Gravitational=20c?= =?UTF-8?q?onstraints=20on=20the=20Earth=E2=80=99s=20inner=20core=20differ?= =?UTF-8?q?ential=20rotation"=20GRL=20article?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../GRL_Gravitational_Lecomte2023b.ipynb | 790 ++++++++++++++++++ 1 file changed, 790 insertions(+) create mode 100644 notebooks/GRL_Gravitational_Lecomte2023b.ipynb diff --git a/notebooks/GRL_Gravitational_Lecomte2023b.ipynb b/notebooks/GRL_Gravitational_Lecomte2023b.ipynb new file mode 100644 index 00000000..e3a6d3c1 --- /dev/null +++ b/notebooks/GRL_Gravitational_Lecomte2023b.ipynb @@ -0,0 +1,790 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "69ec460e", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-14T16:26:53.611538Z", + "start_time": "2023-08-14T16:26:52.366902Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import scipy as sc\n", + "import matplotlib.pyplot as plt\n", + "import scipy.signal as sg\n", + "\n", + "from gravity_toolkit.harmonics import harmonics\n", + "from gravity_toolkit.grace_find_months import grace_find_months\n", + "from gravity_toolkit.grace_input_months import grace_input_months\n", + "from gravity_toolkit.spatial import spatial\n", + "\n", + "from gravity_toolkit.toolbox import create_grid, grid_to_hs, filt_Ylms\n", + "\n", + "# maximal degree to load for the Stokes coefficients\n", + "n_harmo = 3\n", + "\n", + "# Base directory with all the dataset (see read-GRACE-harmonics installation)\n", + "base_dir = '/home/hugo/Documents/GRACE_DATA'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e637b560", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-14T16:27:28.613989Z", + "start_time": "2023-08-14T16:26:54.776331Z" + } + }, + "outputs": [], + "source": [ + "# Read CSR, GRAZ and COST-G data from the GRACE mission from the april 2002 to end of 2022\n", + "\n", + "total_months = grace_find_months(base_dir, 'CSR', 'RL06', DSET='GSM')\n", + "start_mon = np.min(total_months['months'])\n", + "end_mon = 251 # end of 2022\n", + "settmp = set(np.arange(start_mon,end_mon+1)) - set(total_months['months'])\n", + "missing = sorted(settmp)\n", + "Ylms = grace_input_months(base_dir, 'CSR', 'RL06', 'GSM',\n", + " n_harmo, start_mon, end_mon, missing, SLR_C20='GSFC', DEG1='', SLR_C30='GSFC')\n", + "# create harmonics object and remove mean\n", + "GRACE_Ylms = harmonics().from_dict(Ylms)\n", + "\n", + "total_months = grace_find_months(base_dir, 'GRAZ', 'RL18', DSET='GSM')\n", + "settmp = set(np.arange(start_mon,end_mon+1)) - set(total_months['months'])\n", + "missing = sorted(settmp)\n", + "Ylms = grace_input_months(base_dir, 'GRAZ', 'RL18', 'GSM',\n", + " n_harmo, start_mon, end_mon, missing, SLR_C20='GSFC', DEG1='', SLR_C30='GSFC')\n", + "# create harmonics object and remove mean\n", + "GRAZ_Ylms = harmonics().from_dict(Ylms)\n", + "\n", + "total_months = grace_find_months(base_dir, 'COSTG', 'RL06', DSET='GSM')\n", + "settmp = set(np.arange(start_mon,end_mon+1)) - set(total_months['months'])\n", + "missing = sorted(settmp)\n", + "Ylms = grace_input_months(base_dir, 'COSTG', 'RL06', 'GSM',\n", + " n_harmo, start_mon, end_mon, missing, SLR_C20='GSFC', DEG1='', SLR_C30='GSFC')\n", + "# create harmonics object and remove mean\n", + "COSTG_Ylms = harmonics().from_dict(Ylms)\n", + "\n", + "# remove mean to talk in gravity anomalies\n", + "GRACE_Ylms.mean(apply=True)\n", + "GRAZ_Ylms.mean(apply=True)\n", + "COSTG_Ylms.mean(apply=True)\n", + "\n", + "# Temporal filtering with a 3 year low pass filter\n", + "GRACE_filt_Ylms = filt_Ylms(GRACE_Ylms.copy(), filt='fft', filt_param=[1/3])\n", + "GRAZ_filt_Ylms = filt_Ylms(GRAZ_Ylms.copy(), filt='fft', filt_param=[1/3])\n", + "COSTG_filt_Ylms = filt_Ylms(COSTG_Ylms.copy(), filt='fft', filt_param=[1/3])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5da6ed08", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-14T16:28:27.907563Z", + "start_time": "2023-08-14T16:28:08.860868Z" + } + }, + "outputs": [], + "source": [ + "# remove trend to remove GIA effects and have a better spectral analysis\n", + "detrend=True\n", + "\n", + "# list of IGG-SLR file\n", + "files = os.listdir(os.path.join(base_dir, 'IGG/IGG_SLR_HYBRID'))\n", + "files.sort()\n", + "\n", + "# create a harmonics object to fill with IGG-SLR data\n", + "ylms_slr = harmonics(lmax=n_harmo, mmax=n_harmo)\n", + "ylms_slr.time = np.zeros(len(files))\n", + "ylms_slr.month = np.zeros(len(files))\n", + "ylms_slr.clm = np.zeros((n_harmo+1, n_harmo+1, len(files)))\n", + "ylms_slr.slm = np.zeros((n_harmo+1, n_harmo+1, len(files)))\n", + "\n", + "ylms_slr.update_dimensions()\n", + "\n", + "# fill the harmonics object\n", + "for i, f in enumerate(files):\n", + " ylms_tmp = harmonics().from_gfc(os.path.join(base_dir, 'IGG/IGG_SLR_HYBRID', f))\n", + " ylms_slr.time[i] = int(f[23:27]) + int(f[28:30])/12 - 1/24\n", + " ylms_slr.clm[:,:, i] = ylms_tmp.clm[:n_harmo+1, :n_harmo+1]\n", + " ylms_slr.slm[:,:, i] = ylms_tmp.slm[:n_harmo+1, :n_harmo+1]\n", + "\n", + "# convert decimal year to GRACE month equivalent\n", + "ylms_slr.month = np.floor((ylms_slr.time - 2002)*12)\n", + "# remove mean to talk in gravity anomalies\n", + "ylms_slr.mean(apply=True)\n", + "\n", + "\n", + "# Read ISBA data in m EWH after index 170 (= start of IGG-SLR product)\n", + "grid_isba_slr = spatial().from_HDF5(os.path.join(base_dir, 'HYDRO/ISBA-CTRIP_erai_gpcc_monthly_tws_1979-2019.nc'), date=True, timename='time_counter', lonname='lon', latname='lat', varname='tws')\n", + "grid_isba_slr.data[grid_isba_slr.mask] = 0 # Set masked data to 0\n", + "# swap axes to get (lon, lat, time)\n", + "grid_isba_slr.data = np.swapaxes(grid_isba_slr.data[170:, :, :], 0,2)/1000 #divide by rho_water\n", + "grid_isba_slr.data = np.swapaxes(grid_isba_slr.data, 0,1)*100 #go to cm EWH\n", + "grid_isba_slr.mask = np.swapaxes(grid_isba_slr.mask[170:, :, :], 0,2)\n", + "grid_isba_slr.mask = np.swapaxes(grid_isba_slr.mask, 0,1)\n", + "\n", + "# concert time from day to decimal year\n", + "grid_isba_slr.time = 1979 + grid_isba_slr.time[170:]/365.25\n", + "# convert decimal year to GRACE month equivalent\n", + "grid_isba_slr.month = np.floor((grid_isba_slr.time - 2002)*12)\n", + "\n", + "# remove mean to talk in gravity anomalies\n", + "grid_isba_slr.mean(apply=True)\n", + "\n", + "# from grid to harmonics\n", + "isba_Ylms_long = grid_to_hs(grid_isba_slr, n_harmo)\n", + "\n", + "# remove trend to remove GIA effects and have a better spectral analysis\n", + "if detrend:\n", + " isba_Ylms_long.slm[2,2] = sg.detrend(isba_Ylms_long.slm[2,2])\n", + " ylms_slr.slm[2,2] = sg.detrend(ylms_slr.slm[2,2])\n", + "\n", + "# Temporal filtering with a 3 year low pass filter \n", + "isba_filt_Ylms_long = filt_Ylms(isba_Ylms_long.copy(), filt='fft', filt_param=[1/3])\n", + "SLR_filt_Ylms = filt_Ylms(ylms_slr.copy(), filt='fft', filt_param=[1/3])\n", + "\n", + "# create IGG-SLR - ISBA + temporal filtering\n", + "SLR_filt_isba_Ylms = filt_Ylms(ylms_slr.copy().subtract(isba_filt_Ylms_long), filt='fft', filt_param=[1/3])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "384d9ab9", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-14T16:28:29.654159Z", + "start_time": "2023-08-14T16:28:28.728511Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time length of IGG-SLR : 28.083333333333258 yr\n", + "Time length of IGG-SLR - ISBA : 25.75 yr\n" + ] + }, + { + "data": { + "image/png": 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", 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000/ccsst3HvvvWzZsoW0tLTIuampqdxwww0AeDwe1q5dyxdffMEXX3zBO++8w4UXXgiBAIQSYsNekk2bNrFo0SIkSWLZsmWsXLmScePGtbBL0uu55PTTOfLII9HabG3avn37dl555RW0Wi1FRUW98ecRCAQCQV9F2YdpaGhQAKWhoaHD49xut/L7778rbrc7Rpb1Pe644w4FUA488EBl8+bNbR6zePFi5aijjlLKysoijwFKUVFRq2P/+9//KoBSUFCgKIqiBJ1OxbV6teJet67VnrfeeqsCKDfeeGOrdQJNTYpvV6UScDjatMntdisHHXSQAiiPP/54V15yryPeVwKBQNA9Ovv9rSiKIinKvlsX2tjYiN1up6GhgcTExHaP83g8lJSUMGjQIEwmUwwt7Bts2rSJESNGkJqaytq1a1t4S/ZEURRkWUar1QJq6KeoqIj169e3Oi4xMRGHw0FVVRXJZguBnTtAp8M4cCDBYJABAwYgyzJlZWUMGTIEh8PBjh07MHZhaOH06dN5/fXXueCCC/jvf//bvT9AL9Hf31cCgUDQXTr7/Q0iR6VfMGvWLILBINdcc02HIgVUYRIWKXsjrHF1Oh1aqwXjkCEYBw4E4Msvv2Tnzp1cdNFF6PV6LrnkEmpra/n44487bfezzz7L66+/zvjx43nllVc6fZ5AIBAI9h+EUOkH/PzzzwBMnTo1amu+/fbbOJ1ORo0aRVJSUqvnw8Li0ksvBeDyyy9v8fjemDdvHrfddhupqal8/PHHWCyW6BguEAgEgn2Kfp9MqygKLlmOtxl7xaLRIElSt86tqKgAICcnp9Vz8+bNY+HChS0eO/bYY5k8eXLk9+rqau6//35ADXesWbOGL7/8EovFwsyZM1utuWvXLr744gtGjRrFAQccAMCwYcM45JBD+O6779i2bRsFBQWR44MNDSh+P9rkZCStlq1bt3LeeecB8P777zMw5KURCAQCQf+j3wsVlyxTuHB1vM3YK8VHjMHayZDMnnSUhjRv3jwefvjhFo+ZTKYWQqWmpoYHHnigxTFWq5U5c+Zw+OGHq3sEAqDVIkkSr7/+OoFAIOJNCXPZZZfx66+/8tprr0WED4B/xw6UYBCNzYbb6+WMM86gpqaGZ555hqOPPrpbr1kgEAgE+wci9NMPyMzMBKC8vLzVcw899JDaA0VReO2119o8v6ioKHJMXV0dr732GsFgkLPPPjuypre4GM/atcguN6+99hoajYaLL764xToXXHABBoOB1157DXk3L1ZkirLfz5VXXsnKlSu59NJL+fOf/xyNly8QCASCfZh+71GxaDQUHzEm3mbsFYum+5ry8MMPZ8GCBXz//fc99lAkJSUxffp0gsEgf/jDH/jTn/7Exx9/HOks++PiXyMVQgMGDGhzjdLSUubOncu0adPUB/R68HiY8cQTvPfee0ycOJEXX3yxR3YKBAKBYP+g3wsVSZK6HVLZV7j88suZMWMGL774IjfffPNeK386w5VXXsnMmTP55JNP+GnRIiakpADw6uuvA3DiiSe2mRNTU1PD7NmzeeWVVyJCRdLpmPPDD9zz8MNkZGTw8ccfi3JfgUAgEAB9QKg8/vjj/O1vfwPU6pRDDz00zhbtfxQVFfGXv/yFJ598khNPPJF3332XwsLCVsfV19d3ek1Jkrjvvvs4/fTTufe++/j8X//C6fHwwQcfYLVaef/997G10Wk2EAiQm5vL7NmzqampITU1lc2lpUy//Xa0Wi0ffPBBu54YgUAgEPQ/4ipU1q1bx7333ovVasXpdMbTlP2exx57DL/fz3PPPUdRURFHHnkkY8eOjbTQX7FiBUuXLiUxMZGxY8d2as3TTjuNiRMnMm/+fBYtWULxjh04nU6uuOKKNkUKqD1XLrnkEp5++mneeustbr75Zs694grqm5oYN3Ik8+bNY968ee3uOX78eM4444zu/AkEAoFAsA8St860wWCQww47DEmSGDZsGG+99VaXPSqiM23XWbZsGS+88AILFy6kvLwcn89HSkoKY8aM4aSTTuLSSy9tERpqrzNtmM8//5xTTz2VSRMmEFAUfl2+nEWLFrWoGtqTNWvWMGbMGMaMGcOqVau6VHZ9+eWX95nBhOJ9JRAIBN2jK51p4+ZRmTFjBitXruS3337jiSeeiJcZ/Y6JEyfy0ksvdfr4venYU045BX9lJf5du9AmJWHIy9vrmqNHj26xbsDhwFdSgmQwYBo2rNO2CQQCgWD/Jy7lyWvWrOGBBx7g7rvvZtSoUfEwQRBFwhU/4anJXSV8XngdgUAgEAjCxFyoBAIBpk+fzogRI7jjjjtivb2gF4iWUJEkCWUf6BIsEAgEgtgR89DPI488wsqVK/n111/Rhxp9dRav14vX64383tjYGG3zBN1A8Yc8Id0VKlotppEjkXrQK0YgEAgE+ycx/WZYuXIlDz30ELfeeisTJkzo8vmPPvoodrs98iPKWPsGuswM9Lm5aHowOFCIFIFAIBC0RUy/HS6//HIKCwtbzHnpCnfeeScNDQ2Rn7KysugaKOgWWqsVXXIyGoMh3qYIBAKBYD8jpqGflStXArRbynnYYYcB8PHHH7fZK8NoNGI0GnvNPoFAIBAIBH2LmAqVq666qs3HFy5cyKZNmzjttNNIT09n4MCBsTRL0AcIVFURqK1Fm5SMPjMj3uYIBAKBoI8QU6Hy8ssvt/n49OnT2bRpE3feeadoob+PIXu9BGvrkExGdMnJ3V5HURQUvx/F74+idQKBQLDv4XW50JuMaDT79xy6ziIyGAU9QvF4CNRUE6yr69E6kdLmoOilIhAI+i87Nq7j//54Ea/cdDWbFv8Ub3P6BEKoCHpE2AMidbHUfE9E0zeBQCCAnz/8L8FAgMaqXXz+7Ayc9T27Cdwf6BNCZdasWSiKIsI++yA9bfYWQau6OJVgsKcmCQQCwT7JrpJitq78DQCD2YIcDFK87Nc4WxV/+oRQEey7RDwgPRQqEaEjPCoCgaCfsub7OQAMn3QkB512NgCbF/8cT5P6BEKoCHpEtDwqUtijIsuijb5AIOiX7NioTqkfctBhDD34cABK16zE63LF06y4I4SKoGeEc1R0PctRUUM/EiDCPwKBoP/h93mpLt0KQPbQYaTmDSA5O4dgIEDZ76vja1ycEUKln7Fs2TKuuuoqhg4ditVqxWw2U1hYyKWXXsq3337b6niPx8Nzzz3HlClTSE1NxWg0kpeXx3nnnce8efOaPSr6lh4Vl8vFI488woQJE7DZbJhMJvLy8pgyZQp33nknxcXFgFqaLkkSGo0Gy5jRWMaMQWswIElSmz+zZs3q1Ot0u93MnDmTadOmkZWVhcFgICEhgTFjxnDNNdcwf/78Hv0dBQKBIJpUlmxBDgax2JNISE0HIKtwGAA1ZdviaVrciflQQkF8kGWZW2+9lWeeeQadTsfRRx/Naaedhl6vZ8uWLXzxxRe89dZbPPjgg9xzzz0AbN68mZNPPpmNGzcyePBgzjvvPJKSkiLHf/DBB1x5zjk8+/e/Y9ot9NPU1MTkyZNZtWoVQ4YM4ZJLLiEpKYmysjLWrl3LY489RmFhIYWFhZxxxhmRBn+BmhqUYJAvfviBlatXc/nll7dq/jd+/Pi9vtaVK1dy5plnUlJSwoABAzj++OPJzc3F4/GwceNG3nnnHV588UXuuOMOHn300Wj9iQUCgaDb7PphIUN31pKksxKsrUWXmkpa/kAAqkKelv6KECr9hLvvvptnnnmG8ePH8+GHH1JYWNjiebfbzfPPP09NTQ2gTqY+4YQTKC4u5p577uG+++5Dq21uPrRjxw7OOP10Xv3wQ+wJCTz1yiuR55599llWrVrFVVddxUsvvYQkSS32KikpiUzBPuOMMyLjEsJCZftf/8rK1auZPn06Rx11VJde5/bt25k2bRo1NTU8++yz3HDDDS3sBnA4HLzwwgts3769S2sLBAJBb+DdUoLhuX8z1OeHyjq233wzBW++SdqAAkB4VIRQ6Qds3ryZxx9/nNTUVL7++msyMzNbHWM2m7ntttsiAuKJJ56guLiYiy++mAcffLDV8Tk5OXzyv/8xetw4nnv9da6/6y6GDBkCwM8/q1nqN9xwQyuRAjBo0KA27dSlpgLNibXd4c4776SyspIHH3yQm2++uc1jbDYbt956KwFRYSQQCOKM4vez/cYb0fr8OIx6ElJSybrnXiRJIi1fFSq1O7YTDPjR9jQXcB9F5Kj0A2bNmkUwGOSaa65pU6TsTnjo42uvvQYQCQO1RXZ+Pn+85hpkWW6RO5KSkgKoAimWuFwu3nvvPSwWC7fccstej9f1tPeLQCAQ9JCmed/jKy7Gq9PyS2EO2e+/i6lIzU1JSE2P9FOp27kjzpbGj34vVBRFIRh09fkfRVG6/Rp//PFHAI4++uhOHb9t2zbKy8vJzc2lqKiow2OPOeYYoNmLAnDuuecC6hDKO+64g3nz5lHXwxb7nWHp0qX4/X4OPPBAbDZbr+8nEAgEPaX+vXcBKEtJIGgxkxhKpAVUr0oo/FPdj/NU+v0tpSy7mb9gTLzN2CtHHbkardbSrXMrKioAyMvL69LxAwYM2Oux4WN27twZeez000/n8ccf58EHH2TGjBnMmDEDgMLCQk444QRuvvlmhg4d2mot2e9Hcbm6PZgwbHdOTk7rtWW5VQhLp9Nx9913d2svgUAg6Cm+bdtw/vQzSBKlqYkkZWYjaZr9B/7KStKzctixcR0120vjaGl86fdCRdB9FFlGDjVn2zMX5bbbbuPaa6/l66+/5qeffmLp0qX8+uuv/Pvf/+aVV17hvffe47TTTmu5nsuFr6wM2eNpc78VK1Ywe/bsFo8NHDiQ6dOnq+d34HWSZZkHHnigxWNGo1EIlT6Aoiht5jIJBPs7TXO/A0AuGorHECQ3KzvyXPmtt9H4+eekXHw+AA2Vu+JiY1+g3wsVjcbMUUf2/WY6Go252+dmZWWxfv16ysvL9xrKCR8PUFZW1uFx/h072PLDjy3O2Z2EhATOPffcSCiooaGBu+66i5kzZ3LVVVdRXl6OwWBoPkEbeju2IzhWrFjRSmwceeSREaESzr8pLy9vda5Op2shZAYOHBjxwAjiQ6XXz83rS/ml3kmuSc/zIwoYn9g9r6FAsC/iDIXlHXk5UFVGUmazUNFlZgBgLi4BoLG6MvYG9hH6fY6KJElotZY+/9OTO85JkyYB8N1333Xq+IKCAnJycigvL2fDhg3tHqcEAsz/9RcADjvssL2ua7fbef755ykoKKC6uprVq1sKREkXqvZpR6hMnz4dRVFa/OzeuO2ggw5Cr9ezbNkympqa9mqPIH64gjKXrS5hfc06pgef4TTn/Tz425usaHTG2zSBIGboc3PRpqVRnaAK9KSs5rB1wjHHqv+zeg2SotBYVRUPE/sE/V6o9AemT5+OVqvlxRdfpGovb/ZweXLYS/Hwww+3e2zVrl3M+ugjNBoNl19+eadskSQJi6Xtu+bIvKBuzvqxWq2ce+65uFwunnnmmW6tIYgNz26twNG4gke5lcksZCJL+ZP8OB+v+WePEscFgn2J7H88yNBFC6n0qAI9abfQj3ncWLRpaeB0kez04KitIdhPWyoIodIPGDJkCLfffjvV1dWceOKJlJSUtDrG4/Hw9NNPc//99wNqjsmgQYN48803efDBBwnuMX+noqKCs6+5hpr6ev6yR3Lsf/7zH5YsWdKmLR999BHr168nKSmJ0aNHt3yyB/1TwjzyyCOkpaXx4IMP8s9//rOV3aA2t/P5fD3eS9A9GgNB3tpezjX8Cz1+kpIOISX7EgAmeV7l863z42ugQBBDFEWmoVINQyfvJlQkrRZryFOd5vKiKDKO2pq42Bhv+n2OSn/hoYcewuPx8Mwzz1BUVMTRRx/N6NGj0ev1lJSUMHfuXGpqanjooYcASEpK4uuvv+bkk0/mvvvu44033uD444/HbrdHWug7HA6uOPtsHg6dE+arr77i2muvZciQIUyaNImcnBwcDgcrVqxg0aJFaDQaZs6cGenZEkaSpB41ewM1bPXNN99w1llncfPNN/Pkk08ydepUcnNzcbvdlJeX880339DY2Mixxx7bo70E3eP18mqOCH5CDjswGDIZO+b/0OkS+bCxkhTnHOpLn4JBU+NtpkAQE5z1dQQDATRabWTGTxjLgQfS+NlnpHsCbETNU7FndNwLa39ECJV+gkaj4emnn+aiiy7i//7v/1i4cCELFy5ElmWys7OZNm0aV1xxBccdd1zknGHDhrFq1SpeeOEFPvzwQ9555x2cTifp6emccPzxXHHCCUw99FD0JlOLvWbMmMGkSZP49ttvWbhwYaR0OTc3l8svv5wbb7yRiRMntm1oFJqwTZgwgd9//51XXnmF2bNn89VXX1FXV4fJZCI/P59zzz2Xiy++mKlTxZdhrFEUhXd37OIWvgZgyJDb0evtABw+8h5WL5lHTnAdv+/6lZGZh8TTVIGg11BkGeeiRZhGj454SazJKWj2uFGzHHQgAAmNTWjkVBqr+mdCraTswwHhxsZG7HY7DQ0NJCYmtnucx+OhpKSEQYMGYdrjS1XQPWSvF++mTUgaDaaRI6O2rnfLFmSXC8OAAWjt9qit2xuI91XXWedwc8fiV7mJp9Hr05g8aREaTXPl18wfr6fI+w21lqM499BXOlhJINh38W7ZwpaTTkYym5FmPsfnz84ge9hwLvrHky2OUxSFTZOnEKyp4echOYy44g8cdvaFcbI6unT2+xtEjoqgmyiBUO5HlNvQhxNqlX6aNLa/82VVA0fzLQB5uRe2ECkAeXnTAUh0/YDP1xBr8wSCmOAJVTyaRozAWa927U5ISYs87/NVoyiyWnxwoOpVSXF4+m3ljxAqgu4RVIVET3NKWhHqpaK0kQQr2Pf5vmo7I1gLQFbWma2ePyZvIuUMQEeAdTu/ibV5AkFMcK9RPwPmMaNpqqkGICE0lLWqai4//DiJpcvOw+9vxHzAePV5t7ff9lIRQkXQLcIeDynKHhV9ViamkSPRZ2REdV1B/Kny+TE4fkKLjMkyFIuloNUxVq2WnaYjANhW8VWsTRQIYoJnzRqAFjkqtuRUfL4a1q2/E0UJ0Ni4nJWr/kjiySdjfu5pVhRk9tuqHyFUBN1CY7WqzYpCk5KjhaTVtph1Idh/+LneyUTUsvWs9PYrrlLTTwDA6PyZQMARE9sEglihKArejRsBMA0fHvGo2FLT2LbtP/j9tVgsg9FoTDQ0LMVjqiZh1CiQJFz1vT/ctS8ivhEE3UJjNKJLTkabkBBvUwT7CD/X1TOO5QCkpbUvVA7PGs8uMtHhp7L211iZJxDEhEBFBbLTCTodhoICHHUhj0pKKjW1i9SDsq4nMelQAGpqF2JNUm8IPU4HgW4Obd2XEUJFIBDEhJLa1Zhxo2hsJCaObfe4ETYzWzTjANhUuTBW5gkEMcG7uRgAw8AC0OtxhDwqpkQNTqfqabl0SypvO4YDUFOzAKPVilanA0Xpl14VIVQEfQrZ58NbXIy3uDjepgiiSLUvgNG9CoBE+wFIUvuXHkmSCNoOAsBR/3NM7BMIYoV382YAjIVDcDc1Rtri+1FFyjYG4pAS+dY3BoCGhmU0/TyfwzduZ8K2XZEqof6EECqCbhGoriFQXY0cbTekJCG73chuj5j5sh+xrNHJMNYDkJ504F6Pz0lVB2mafMV4fdW9apsgvni3bMG3fXuLx+ref5+GTz+Nk0W9i7c4LFQKI8mxFnsSDY1q/tZaRnNWZjI1UhY7yUZRAji9xSQ0ObG7PEKoCASdJVBdhb+iAqLc76S53FkBUaK837Cs3hERKvakdroS78bE1Dy2MRCA2lrhVdlfCToclF1zLVvPPS9SsutavpyK++5n5733qdeY/Qx/aRkAxqFDmhNpU1KprlOFyjpGc19hDgckWihhMACeTBcAZn8QZ3lZHKyOL0KoCLqMoijNfU6i3fBNo4lU/YheKvsPG+q3kkItClrsieP2evwYm4VN0igASmsW97Z5gjhR88IL+MvK0JjN6HPUgXzmceMwH3AAisdD1bPPxdnC6JM/6zWGfDcX65QpEe+ILSUJj1sdFmu1jSTTqOeolATKyQfAxTYCdrV7a7hiqD8hhIqg6wSDEArLRL3hG4imb/sZQUXB51CrfQzW4Wi1lr2eo9dIBC1qjL6u4bdetU8QH3zbt1P7+hsAZN59N7pQqwNJoyHzjr8B0PDJJ/i2bo2Xib2CpNGorR1sNlwN9QBY0kAiiBszh6erXpSjUhLZzgAAHI5NyNlZAARLtsbD7LgihIqgy4QFhKTpnZ4nki4kfkQb/f2CjU4PBfI6ADKSD+r0eSl2NUSk9WwkGHT1im2C+FHz0ssofj+Www7FNvWoFs+Zx47FesQUUBTqP54dD/NigquxHgCj3QPADnI5KlWdcXZAgoV6nepRcbiK0QxU/5/ynTG3M94IoSLoMpE5PLpe8KYAhLw0wqOyf7Cs0cVQNgCQ3In8lDDDkwZSSwoaZBobV/eWeYI4EKiro+GTTwBIu+46JElqdUzSWWcB0DB79n57LXDV1wPgs6lCfAd5jLCqA051Gon8xEJ86EH2oBuSCYC+qv8llwuhIug6vdQ+P0wknLSfXpz6G2sbqslnGwBJ9s4LlbGJVjZRBEBtvQj/7E/Uv/c+iseDceQILAe17WWzTZ2Kxm4nsGsXzp9/ibGFvUPVv//NltNOp+799wFwNaqDNz2GWgC8+oGYtM1fy8NsVnaQB4BmqBUZkIOBflcRKYRKP2Hr1q1IksQJJ5zQ6rlAIMCbb77JaaedRm5uLkajEavVSlFREZdddhmfffZZiw9GJPSj06EoCp9++innnXceBQUFmM1mzGYzgwcP5txzz+Wdd97B38USZrfPx+MvvcRBRx+NzWbDZDKRl5fHlClTuPPOOyneo8fKUUcdhSRJVHSiQkCSpBY/Op2OzMxMTjnlFObOndslOwWdo75hORpkZH0ORmNmp88baDZQqlGFys66Zb1lniDGKLJM/QcfAJBy6WVtelNA7X6dePzxADi+/z5m9vUm3o2b8G7ciOJ2A0RyVBRpFwBma2GL44dbTZE8FTnHz5wxg/lpaF7sDO4j9M4tsWCfYdu2bZx55pksX76c9PR0jjnmGAoKCggGg2zZsoUvvviCN998kwsuuID//ve/QHPop7axkUunTWPu3LkkJiZyzDHHUFhYiEajoaysjPnz5/Phhx/yr3/9i59/7lyJaVNTE1NOO43Vv/9O4cCBXHLJJSQlJVFWVsbatWt57LHHKCwspLCwcO+LtUNqaio33HADAB6Ph7Vr1/LFF1/wxRdf8M4773DhhRd2e21BS4KKgs6thm1siQd06VyNJKG1jAYHuBxre8M8QRxwLV2Kv7wcjdVK4gnHd3is7cgjqH//fRyLFsXIut7FV1oKgD4/VM3TUA+SgkkuByAjsajF8cOtZhagVkPJmhpkjQSBAF6nE5PNFjvD44wQKv2YxsZGjj/+eDZs2MCdd97Jvffei8lkanGM1+vlrbfeaultCAQIBAKce911/PDLL0yfPp1nn30Wu93e4lxZlvn444954YUXOm3Ts88+y+rff+eKCy/kxZkz0SUltXi+pKQEr9fb5de6O2lpadx///0tHnv33Xe58MILufPOO4VQiSJbXF7yFNUDlp08vsvnZyaNRnZo0AYq8XorMRrFVO19nYbZam5KwoknoDGbOzzWcsihoNfjLy3Ft20bhoLWE7f3FRRFwb9NDYEaCgYiB4O4HU3orQF0+AigY2jS4BbnDLUaqSYdAId3BwazGZ/bjbupoV8JFRH66cc88cQTbNiwgSuvvJJHHnmklUgBMBqNXHXVVbz55pvND0oSb3/+OT/88gvHHHMMr776aiuRAqDRaDj77LP56quvOm1T2PNy0+23txIpAIMGDWL48OGdXq+znH/++dhsNrZt20Z1df9LVust1jrcDETtD2FPGN3l80fb0ygnF4CmpjVRtU0QGwKywganhwW1TTiDQVIuv4yUyy8j+Zxz9nqu1mYl89a/kjdzJrrMzocN+yLBmhpklws0GvR5uWp+iqKgS1LD6tWkMSqhpfiwarVoDTkAON3bMSeovVRcjY2xNT7OCI9KP+a1114D4O9///tej9Xtljirz87mzZD4uOuuu9qNMbd17t5ICfVS2Lx5M+PHj+/0edEgnIfTFXsFHbOuYQcHowq/hIQRXT5/pM3EfylkAGU0NK4iLe3oaJso6AW8sswzW3cxv7aJ9U43Hln9bOUa9fxrRAGH33lnp9dKufzy3jIzpkTCPtnZaAwGXDvq1Sdy1Qn09VI6mYbW155k6wDwgeyrwJw4nobKXbhDSbj9BXFFDiG7OtenQdLrkfT6yO+K34/SyWRRjaVloyvZ7Y40TuvKedGgtLSU8vJy8vPzGTx48N5P2I1AIMCSJUvQ6/VMmjQpqnade+65vP3221x11VUsXbqUadOmccABB5CcnBzVffbk7bffxul0MmrUKJLa8OQIukdNg+oFCegHoNMldPn8QrOJUmkIKPOprF9F9zOTBLHk+W2VPLttV+R3i1aDSSNR7vVz5ZoSFh82ksTeam/QR4kIlQFqcmw4kVaTrn6f+LWZbd705SXkIddp0Cg+rClGANxNwqPSL9kwoXNlk5n33E3KxRdHfq97/312/eOhTp07Yv26Fr+XnHsuvs17nxK853nRIFwhk5OT0+bzTz/9NI17uBdvvfVWbDYbtbW1+P1+srKyMBqNrc599dVXKQ19KMP84Q9/IC9v79nqp59+OjNmzOAf//gHM2bMYMaMGQAUFhZywgkncPPNNzN06NBOvcb2qK6ujuSoeDwe1qxZw5dffonFYmHmzJk9WlvQEtmt9k8x20a1e4zX5cLVWE9Cajq63W4CQO1QK5tHggucDhH62RcodXv5V6kqUu4anM0p6UkMNBtwyzInLN3IJpeX/5RVctug7DhbGlv85WrCrD5PDWWGS5N1CaG+VIa2/x4DzBZqSSGNaszJSotz+wtCqPRT9laH//TTT1Me+mCFufbaa7Farciy3OG5r776Kj/++GOLx0444QTy8vKor6/n2WefbXXO7smtt/31r0w/8ki+/eEHlpSVsWzZMn799Vf+/e9/88orr/Dee+9x2mmndfwCO6CmpoYHHnigxWNWq5U5c+Zw+OGHd3tdQUvq/QFSA5sAyEoa0+p5RZZZ/MmH/Dr7A/weN3qTmaOnX83oqce1OC41YTiyS0ITqMHrq8ZoSIuJ/YLuMbOsCo+sMCnJxo35Gc1egl9/5dmZL/Dk2EN5QTuZ0zKSKbK2zotri8Zvv6Xp629IOOF4Eo87bu8n9EH8O3YAYMgNCZXQnB+9KTRw0Jzb5nkDTAZ+J500qjEkqq0hROinn1L0W+f6NEh73PEln3ceSWee2a09B33wQadCP71BZigxbU8xEmb7bmPXjzrqKBYsWKD+Egxi3bULnU5HdXU1Xq+3lVflhx9+iPz/9OnTef311yO/19fXtxIJ0FKooNGQYLVx1vHHc2FRERq9noaGBu666y5mzpzJVVddRXl5OQaDoasvG4CioiLWr18fsWf27Nlcd911nH322SxdupTc3LYvGIKuscHpIZ+tAKQmtvao/PTB2/zy0XsAaLQ6/B4337zwHM6Geg4549zIcUWJyezalUU2O3E6NmBMEUKlr+KTZT6tVL+AbyzIaBHKcCxchGXpEk60JfH9QYdzwcpiPp0wlAGmvX+O3StW0PjFF2pJ874qVMpVoaIPebHDXhGzTv2v3TygzfMGmA0sJB1Yh86qttrvb0JFVP2E0FgsnfrZU6hIen2nz221p9ncrfOiwcCBA8nJyaGsrKxVA7WOUAIBdFotE0ePJhAItBAlnd1XUZRWP7sjSVKr7rR2u53nn3+egoICqqurWb06Oi3Vk5KSmD59Os8//zwVFRX86U9/isq6AljfVE8WaojRZmvZH6J42eKISJk6/Rpufut/HHaOWhb+43tvUrG5eULsKJuZstAUWYdjQyxMF3ST+bVN1PqDZBh0TE5qmZPk/FG9Vhx78jSGWozs9Po5f0UxVb695/hZDlB78LiX77sdipPOPIOU6dMxjRwJhIWKQoKkdqXNSshv87xco4FqVHEeNKrel/6WoyKESj9m+vTpADz88MOdPkcJqMLhsrPPBuDRRx/tnXbO4Xk/geY2+pIkYekl4XbllVcyYcIEPvnkE3766ade2aO/Ud6wEQ0yfo0dgyE98ngwEGDBmy8DcMCJpzLhxFPRaLQcds5FDDtsCoos880Lz6GEQoxFVhNlDASgvin6+VqC6PG/Xao35czMZHSaZm+Kv6IC76bNoNGQOWUy740rJNeoZ4vby0Urt9AY6HhchmnsWAC8xVvUIoR9EPvpp5N5x98whnLsPI4mtCYZg+RDRmJgYtseFbNWg0+nTk72aVWB0t/Kk4VQ6cfcfvvtDBkyhNdee4277roLj8fT6hi/349r94qooJr4ddm553L44Yfz3XffceWVV7ZKvAU1D6atxzviP//5D0uWLGmeoBxsnqD80UcfsX79epKSkhg9uus9OTpCkiTuu+8+AO65556ort1faQp7P0xDWoQA1nz/LXU7d2BOtDP5/Esjj0uSxLFXXYfRYqW6bBublqg9ddL0Omq0gwCoa1ofuxcg6BJBRWFBbRMAp6YntXjO+aMq/k1jRqNNSiLHZOD98YWk6nWsdri5eV3pnsu1QJ+RgTYtDWQZ78aNHR67r+BubCSQoWZf1JNMrrn9Bm66UKKtjCoE3U39K/QjclT6MXa7nTlz5nDGGWfw6KOP8vLLL0da6AcCAXbu3MncuXOprKxk/Pjx2Gw2lFBXWIPZzCeffMJ5553HrFmz+OijjzjmmGMYMmRIZO7OggUL2LZtG4MHD263umhPvvrqK6699loKBw7ksLFjyR08GFcwyIoVK1i0aBEajYaZM2e2WW108803Y26n0+XMmTP36o057bTTmDhxIvPmzWPBggUceeSRnbJZ0DaSR02kTdgt7KPIMks++x8Ah551PgZzy38Tc0IiB5x4Gr/877/88uF/GXrQYUgaDQbLMGgCv7sYWQ6g0YhLV19jZZOL+kCQRJ2G8Qkt/13DYR/bbu0MCi0m3hwziJN+28S3NQ24gjIWbfv3zqbhw3H+8AOedesxjxvXOy8ihribGglmqfk5Dikdvab9flQ2Uya4AKUeMOPuZx4V8Wnv5wwaNIilS5fyzjvv8P777zN//nxqamrQ6/Xk5uZy3HHHcf7553PyySej0WjwO0PeFZ2OtLQ0vvvuO2bPns1bb73FkiVL+PLLL5EkiaysLCZOnMgjjzzCOeec0+nE1xkzZjBp0iS++ewzfli2jIo5cwDIzc3l8ssv58Ybb2TixLZLyd8PTSRti2effbZTYaP777+fU089lXvuuYeFCxd2ymZBa2p8AdLlrQBkJzU3etu2ZiUNuyowWqyMOXpam+dOOOk0ln0xm6rSrZT9vob80WPJsBXgaTJiUry43duwWkVHlb7GwpA3ZXJSQouwjxIMRjwq1smTW5xzQKKFLIOeCp+flU0uDktq36tgGjEiJFT2vfCfZ8MGPGt/xzRiOKYR6ufB3dSIMlz1HPt16R2dToolC2pBJzeAlIXf68Hv86I3tL5h2x8RQqWfEE5ibQu9Xs/ll1/O5Z3oAKmEQjHhZFdJkjjzzDM5s5uVT3tSVFTEbbfdxp8vuYRATQ26tDT0WVkdnjN//vxOr7+3fJpTTjml341Q7w02uzwMQHXnp+7WkXbVXLWj8YgpU9Eb2y5NNdsSGDHpSFZ99zVrvp9D/uixDLVa2E4+Q9iEw7FeCJU+yII6VagckdIyidazdi3BhgY0NhvmUK5JGEmSmGi38EVVA8sanHsRKuroDE8v9JXqbRzz5lH13D+xn3kmOY8+ghwM4nE60FnVGzhJ33ElW7YlnSAatMgYbOBrUit/9Gn9Y/ZVzHNUysvLefbZZ5k2bRr5+fkYDAaysrI4++yz+fXXX2NtjqCrhCYnS73dZl6rrq/sJclO0DcpbtpFciiebrWGkgedDoqXqp/xscee0OH5o49WS1A3/vojHoeDIVYTZagD6RxOUfnT19jm9rK4wQnAUSl7VvuoPZXMhxzC4s8+4uuZz/DTB+/QUKlWhE1MtAKwrLHj7uDGkCfCu2EjSnDfui74d+wEmkuTPU6HOufHol5P9YaOPSq5ZhMNJAGQkK6Km/4U/om5R+Vf//oXM2bMoLCwkOOOO46MjAw2bdrE7NmzmT17Nv/9738577zzYm2WoJMoMRIqbSXTCvYdKhvWkQm4tdnodOpdcsnypcjBIKl5+aTnD+zw/KzCYaQNKKC6bBsbfl7E0MlH80ZIqDQ5REJtX+OFsiqCChyVnMBAc8twhOMHVahs9btY8u4bkcd//vAd8seMJ2PqSYCFpY1OFEVpd3aYoaAAyWJBcbnwbduGsYujP+KJf5cqyvTZqnc4XF5sNvnU/xo7HriYbdSzhWRSqMWSrKcGf7/qThtzoXLwwQezcOFCpkyZ0uLxRYsWccwxx3Dddddx+umnt5ksKYg/kTsZbe/O6dAmJqKxWnvfcxMH6vwB5lQ3ssHpQUGhwGxkakoCBeb95z3vdIYqM8xDIo9tDnlTCg88ZK/nS5LEiClTWfTOLDb+8gNnH3sCuzQDQYYGUfnTp6j2BXh3Zw0ANxS0DkWkXn8ta5//J6urytGYTRx8+jns3LyRbauWU7p6Bf7f16C96h6qfFDm8ZHfzudA0miwn3oqSDHw6EaZQIU6UiA8ATosVCwG1YuUZO5YqGQa9NSHPCr6JDUQ4nE09YapfZKY/2ufddZZbT4+ZcoUpk6dypw5c1i9ejUHHnhgjC0TdAZdejqKz4fUy0JS0un2uYvR3vDJMk+UVPDy9irccus8mGmpify9MKfTbcX7MlrPZgBsVrXiJ+D3s3XFUgCGHHhop9YYdsgkFr0zi7LfV+NpakRvGQYOCHjLCQSaujXkUBB9Xgm9n8clmJm0R46J29HEl3M/Z7vsQjIZOfH6Wxgx+SgAGiorWPP9tyz/5nNSaiupSstmvdPTrlAByH7g/l58Jb1HYFfbQsWmUf+baul47lGKXksj6mR5OVH1OHmcjl6xtS/Sp74J9KGur7r97Atqf0InJgt3ixpfgItWFbOySW1WNcJq4vAkGzqNxMpGF782OJlT08j3tU1cmJ3CZTmpjE7oneZ2vY1XlkkKlgCQbVe7cG5fuwqf2401OYWsws4NlUzKyiZjYCGVW4vZvOQX8rKKqHWkkEItDudGkuydGyQq6D2cgSCvlVcDcEN+y+m/9RU7+eix+6nbWY7BbOaUP9/BoPHN/2b2jCwmnX+pGg6sq6IqLZsNTg/T0uwxfx29iezxEGxQwzT6sFBpbETWSNgk1SuSbe24YECSJALaFAgCFtWrLTwqcaC0tJS5c+eSlZXFmDGtB5gJBPsqDf4A568sZo3DTbJOy5PDB3BSmr3FRX2zy8MDm3fwbU0jb+yo4Y0dNRxqt/J/owrINnZvplG8KHF5yAtV/OSGSpMjYZ8JByNpOp/DP+zQSapQWfwTQy8YRxkFqlBxbBBCpQ/wv1111AeCDDYbOSm9pcD49uV/U7eznIS0dM78233t5iUVHngIaR9/AcB6R8cJtfsiYW+KZDajSUwEVI+KP8WIBoUgGjKtnaje0aepQsWgNub09iOPSp/oTOv3+7n00kvxer08/vjjaNvJf/B6vTQ2Nrb4EeyfKIqCb1sp3i1b9rkM/91RFIVb1pexxuEmTa/jkwlDOTk9qVXC4BCLiTfGDOLD8YWclpGEXpL4pcHJGb9tZrvHFyfru8eWhhLMeAigw2IZhKIoFC8LCZWD9p6fsjtDDlLDRKVrVzHYoInM/HE69o/upPs6v4Yqfc7OTEa7x3vasX4dE0p2Mm3kxA6Tp7OHFJHjUb90f6/tOEFUkWU8v/9Ow2ef7TPXBX8oP0Wf0Tyk0eNoIpCiRhAcJKHT7D3nz2BUxYxG5wyt0X+EStw9KrIsc+WVV7Jw4UL++Mc/cumll7Z77KOPPtrm5N3OIvpj9AzZ50NuakIyGNAm9G5+gCRJBB0OUGSUYLB5SGEfYm/vp2pfgEe37ODL6gb0ksSbYwczrIP8E0UJcqDZwZi8em62l/FSyVokdyUfLm7gMKuLoK8Cv78BjcaAzTYce+IBpKYeid0+PsqvrGdU1K+jAHDoCtBo9FQUb8JRW4PeaCJ/VNc6iqbkDsCekUlD5S6sZSW7DScUCbV9gZVNqgdkfGLLMKXf5yVhZyVZjS6Un36BP93Q7hqSRsOY1GTeB7b4ZWRFQdNO5Q+KwtYLLkTx+TCPHYuhoCBaL6XXCFSG8lN26wflbmxETlKvaW5taqfWMRtUoaKTmgCzyFGJFYqi8Mc//pG33nqLSy65hBdeeKHD4++8807+8pe/RH5vbGxkwIC2BzntTthD4/f7222xLtg7ituNf+dONBZLrwsVUJvKKQEZAkHog9EPv1+d+rqnB9ATlHlpexX/3LaLpqA6WO+BITkcELqY+/311NX9isO5EadzIx73drzeXXh9VYAcWefU8P8EwLHHjWZtbRW1tYso2fpP0tNPYHjRPzAYUnrjZXaZcJ8TxaRW/Gz5bQkAA8dPQNfJDsVhJEli8MSDWf7VZ/hXLWb7kFCJsnNjh6Wsgt6nKRCk2KWO1Bib0PK66qipJi0kYhKOPGKva40bVIA2GMCr1VHm8bVbASdptRgGD8a7fj3e4uJ9QqgEGxpBo0GX2RzecTc1ICWr791gJ4WK3ayeb5QagHThUYkFsizzhz/8gddee40LL7yQWbNmodlL7NpoNHarbFmv12M0GmloaCAhIUFc3LpJrHqohJF0WpSAP9INty+hKAoNDQ0YjcZIEjjAVreXc1cUUxYK14y1mbl3SA6TkxNoaFjB1m3/R03N9yhK225rSdJhMKRjMmZhNGbh0KQyq1JDpZLCSdnDuDB/MHLQTWPjKurqF1NV9TVVVV/T1LSaceNewWbtXKJqb7JnxU/pmpUADBw3oVvrDZ6gCpWy35agHzOFoFsDgQa8vl2YjB0nIQp6j9VNbhQg16gn3aBv8Vx9+XZSHWriuG2PtvltkV80kpTlxWpCrcPdYam+MSRUfFu2wNFH9+g1xIKUSy4m+YLzkT3eyGPupka0eepNyd660oZJDpUwawmgNcgimba32V2knH/++bz55pvt5qVEi7S0NMrLy9m+fTt2ux29Xi8ESxfxu90EZRkNaiZ7b+NDfa8E3W50ev1ej48FiqLg9/tpaGjA4XCQm5vb4vmHi3dS5vGRbdRz5+BszslMRpF9bNh4P9u3vxk5zmodSmLCGKy2YVjMBRhDwsRgSEWSWn4WSlJq+Mv6MhbvgkPzchmTaCExcSx5eZfQ1LSO1Wuux+0uZcXyy5k48X3M5ryY/C3aQlEU7IEtAGQljcDv8bBzk+ph6WrYJ0zeiNHo9AacdbXkavVUkE0u5TgdG4VQiSPhsM+4NqrTdn07B7usEDSbMA4fvte10gcOJm3BEqrSslm1cxfT9pi+vDuGgaoXxbdtW/cMjwOSTofW1vx163E4MJjVm5m9daUNk2W20oQFCy505kC/SqaNuVCRZZmrrrqKWbNmce655/LWW2/1ukgBSAxlW1dXV1NeXt7r++2PBOvrkV0uNG43Wqez1/cL1NaieDxovT40Nmuv79cVjEYjubm5kfcVwAanh8+r6gG4befvJK3+jk8dOzAOnI8huQqAjPTTGDz4T1itQ9patk0uzErh+5omPquq58/rS/l6YlFk0mpCwggOOvAjlv12IU7nJlatupoDD/wYrTY+zeMqPC4y2AHA4ORRlK9fixwMkJiegT2ze6JCp9eTM3wkpatXkNZQQ5k2n1zKcTg3kJq697CCoHdY1YFQcf/6K3ZAGjO6U1VeWp2OATpYB2yorunw2HC4x7d13xEqe+JxNGE0qELF1MmQbaZBTzmJIaESxFPb+9fgvkLMhcqDDz7IrFmzsNlsDBs2jIceeqjVMWeccQbjx4+P+t6JiYkkJibi9/sJ7iMZ432JiocfwfnDD6Redx1Jp5269xN6SOXnX9D05ZckX3wxKZdc3Ov7dRatVtsi3BPmqU1lKMDwLStxbXoFx7AGEsc6kLQQ9GnYOjeXza467Nc7sY7u/H6SJPHIsFx+rG9ircPDa+VVXD2gOd6t1yczfvwsFi8+FYdzA8XFjzNs2D1ReKVdZ1Pt72iRcWMlwZzD8jXfADBg1NgeeTDzR4+jdPUKLNtLKCso4FB+xuEQM3/iyZpQaGf0HvkpPrcL87btAKQe1/aE7LYYHLoZKdlLlVtEqOxDHpXdURQFj9OBVa+GgqzGzoV+sox6GrGTRQWSRSFQ4SXg83U572tfJOZCZevWrQA4HA4efvjhNo8ZOHBgrwiVMHq9vs0vGkHHSCUlaHbuxGKzYjL1fvdUk1aLc+dONBUVMdmvJ8iyjPzbzxyc6+D63H+hH9R8sdVLgzH7TkbyrKKpppIPHrqbaVffyJijO38RTzfo+fvgHP66oYwnt1ZwVmYKaYbmj6/JmMXIEY+zctUfKNv+OllZZ5CYGPt+RLsa1pECNOgGI0kSZWtXAVAwunthnzAFo8fxAyCtX0lZgZr7IkqU44cnKLPFrX7RjrK1FCrbf/0Ze+i5tBM6Hj65O8PTUyEIO+nYw64PCZVAZaXq4bX03caISiDApqOmoktLo+D1WWjtdnxuN4osY9GG2uebOidUknRaHKgeXCVR/ex7nA5sfSSJvjeJeR+VWbNmoShKhz/Tp0+PtVmCThCoU6fhalNi88HQhrrgBuvrY7JfT9BoNDw09WD+FPw3eoMPoyaD/AF/5JBDvuaIqd9y+Gl/ZvqTMxl15LGgKMz5zz/57avPurTHBdkpjLaZaQzIPLW1otXzaWlTyco8HVDYuOkfcSnHD1f8yKZC3I4mdpUUA6pHpSdkDC7EaLGSsLOM7eESZeemdpOSBb3LJpeHoALJOi2Zhpb3u1XfqF40X1oquvTO5V8AjM1XKzhrjVa8gfYT6HXJyWjsanM5X2lpV02PKYHqaoLV1Xg3b0ZjU8cLeJ0OFMAmqTkmKebO/Y0kScKvTQJAm2yIrNUf6BMN3wT7BsHaWgC0yTEWKg37xpRQ2bgKc7mXjAdNpF5bj+amhUirayPP600mjr/uZiaefAYA38/6D0s/+6jT62slifuHqGPi39pRQ6nb2+qYwsLb0GjMNDQso7r62569oG4ghSp+rNYitv++GhSFlNwB2FI6V4LZHhqNlryRYzB73XiUNDwYURQfLte+6f7f1/ndoSbTj7CZW4X0gstXAKCbML5Law4bkIcu4EfRaFi3veM8wn0lTyVQod5Q6NLTI72gPE4HXoMxIlTSOylUAILaZAC0Cerf3N1PKn+EUBF0CiUYjHg2dKmxESqGgQXYjj0Gy8TulbXGmuofPyL1eR26CrXs0FdSQumVV+L48cfIMZIkceSlV3Ho2RcAsOCtV1n13ded3mNycgJTkm34FYVntu1q9bzJlE3+gOkAlJQ8H3OvSqJfnfGTZR8RKUvOH90zb0qY8DqpjgbKUe++wx4cQWxZ52yeWbU7shyk2KSlJM1OysknA+DzVVNZ9Q1bt/2Hhobl7b4ndVodaR41QXR12fYO9w9X/oSbqfVV/LsqgeYZP6Am0nqTTGhCPZNsps6LeI0uJFQs6rnCoyIQ7Ibc1ITGbAZJing6ehvLhAkMeP550q69Nib79QQlEMA6y4HGK2E57BAKv51DwvHHYznwQIxDWvY2kSSJSeddwsGnnwPAty/9mw0/L+r0XncMUietflhRx05v68TDAQOuRKu10ORYS3XNvB68qq7R6KnDjlqxMSRlNKVr1PyU/B7mp4QJr5Ows0y00o8z60IelZF75KfUbC+j0qhlc+EAlDFufvr5GBb9cAirV19PcfHjLF12DmvW3tSuWMmR1FDe+r1U/mTecQdFvy0j5bLLovBqeo/ArpBHZXeh4nQg29UcSTdWNJrO50vq9OpNot6ofu77S9M3IVQEnUKblETRb8soWrE8Zg3f9iUaZs/GV1yM1m4n77l/YhgwgJwnHif/tVfRZ7Y9cGzyhZcz9tgTQFH48l9PsXXFsk7tNdFu5VC7Fb+i8Mr26lbPGwwp5OaqVVJlpa92/0V1keLaNQDUko7VB7XlZSBJ5I2MTlJval4+FnsSSbUVza30hUclLvzejkcl3DMna3AhW7Y8idu9FQCrdRhpacciSXoqK7+komJ2m+sOsqqJsRvrOg736lJS+nQSbRh/aCChPqulR0VOUL96vZquTYo2G9TEW73OE1pLCBWBoBWabnQG3t9RZJnqF/4DQOq116IN9VbRGAwdziiSJIljrrqOosOmIAcDfPLUI5Sv/71Te16Xr4qfN3ZU42yj1H5A3mWAhrr6X3A4N3XxFXWPHfWq7fW6QZT9vhqA9IJBmG3RGbcgSRL5o8eRWle928wfIVRiTZXPT5UvgAQU7SFUKopVD1fm8Ay8vl1Ikp4pk3/l0EO+YtzY/zB40M0AbNz0ALW1P+65NAcWqCG9rQYrDZWtE8b3NQKhgYS63XoIeRwOsKlfvX5NUpfWs4bCREZNaDChCP0IBPHHV1qKe+VKZF/fnSDs/u03/Nu3o7FaSb7g/HaPCza1TnzTaLSceMNfGDR+IgGfl49nPMBvX36Co7Zj1/dxqYkUmAw0BmQ+q6xv9bzJlEN6+rEAlG9/u2svqJs0V/wMYfvvqndlQJS8KWHyR48jpaGaMtQcBbd7G8GgO6p7CDpmfSjsU2A2YNU1C3HZ5cL6/kfk1TRiyVRzTOz2CRgMzeW3+fl/wG4/kECgiRUrr6C07LUWYaDJmeqxOzMH8Pvin2PxcnqVwK6wUGn2qnqcDjQW9TUroZyTzmI3qYm3JskJKHhdQqgIBHGn5Myz2Hr+Bfj7cDfhYH09upxsEo4/Xs3j2QPZ6aTsuuvZdORRbZZaa3V6Tv3LneQOH4XX5eT711/iP9dP54OH7o640vdEI0lclK3eXb2zs7bNY/JyLwGgYtcnyHLrCqGoE6r4sViHRTwqeSO70NmuE+SPHou9sQ6HbKOBREDB6dwc1T0EHRNOpB1pbfledy5eTMr2CoZW1uJiAQB5uRe1OEaj0XPA+DfIyjoTRQmyadNDrFt3O8Gg+v4cYjFiV4IEdHrmb+j433XbFVew6cij+vS1oTn00+xR8TocaM2qF1TTRaGSEuq5opWCaI0y3hh0CO8LCKEi6BTVL73E5qOPofo/L8Z033DirtyHS5QTjj2WIXPnknnXnW0+L1ks+CsqUFwu6mfPbvMYvdHE2Xc9wNTpV5M9bDgoCqWrV/DO3X/l/Qfu5LevPqPs99VUbN5IyYplrFv0PSN/X4JGUVjc4OSl/8zk+9dfYt2PCyJx6+TkwzAaswgEGqmumd9Lr15FUWQS/OqMnyTDoOb8lBHRFSr2jCyS09NJbqjZrZ+KCP/EkubS5JZhn/p5auJ2Y66egFyP0ZhFevrxrc7Xao2MHPEEQ4feDWjYWfERy1dchiwHkCSJQ+xqh9rlPgVXQ327dgR27CSwaxe+vZQyxwtFUXbzqLTMUdGb1D4xXZ14nm6y4ELNzdGZAnhdrihZ27cRWZGCThHYuRP/jh3Inti62bV2O/7y8j7fS0XSaNCGGjq1ek6SSD7/PCruf4D6994n5fLL22wnrzeamHDiaUw48TQaKiv4+cN3WbvwO8p+Xx3xUOzJ4OMvZvOgEcx2Kxw975PmdU46jUPOOp/MzFMpLX2JiopPyWjjSyNauNylGHHjQ495l1o6mZ4/MGr5KbuTP3ocKfXVlCXnM4o1ovInxjQn0u7hUflBzTlRDlR/z825sN2KFkmSyB9wBTbrMFatvo6GhqU0NC4nOekgJmekMKdxB9uzC9i89FfGHtP2+1afm4tv27a+61EJBEi/+Wb8uyrQZbQM/RhD7fPNXRUqBj2rQvN+tOagCP0IBLsTqFW70upi1OwtzL7UnbYjEk85FclkwldSgmft3hNm7RlZnHD9n/nj868w+YLLGDh+IsnZuSSkppNeMIj80WMpOmwKp4e+KzaOPZQxJ55OSk4efq+HXz9+n//+/a9Y9ZMAqK6eh9/f2Guvr7ROzUkpJ5/ABvX1RTvsE2ZAWKiE8lREQm3sCCoKG52tS5P95eUoO3YgA/JBai5WZubJe10vJWUSKcmHA9DUqJazH5qkCv7yrHw2Lf2l3XP1ocnlfVWoSHo9qVddSdZdd6HZbR6Px+HArFfFnq2Tc37CpOp1NKJWCsmJ2n4T+hEeFUGniHSljVGztzDaJPVD2Vc9Kk3zvsc8ZvReW4VrbVZsU4+i6auvafzyS8yjR3Vq/cS0DA458zwOaef5E2SFt3/+nQofuE48h+mX/4FNi39i7sszqSrdyuePv8Loiwbj9m6hquprcnLO69oL7CQ769cCUKcbjO539Qsn2om0YfJHjSXl62/ZiNoAzuEUHpVYUeL24pEVzBqJAnPzl2+4qaEzVQc2HzbbcCyWQZ1aMzFxLFXV39IYEiqjbGasEjiNZpZu38mpbhcGc+tSZH1eHtB3hUp7eJyOLs/5CaPXSPikBFBAtunw1vWP0I/wqAg6RbBOFSq6GM35CROe6RGs73tCJVBTw/brr2fTEUdG5iB1ROJJJwHQ+OWXKLIcFRt0Gonzs9V/k48r65AkiWGHTOLSGc+RnJNHU3U1O1eolRk72+ldEQ0cjnUA+PWF1GxX569EOz8ljDUpmcFGfaQ7rc9Xic/XdkKxILrMr1W9JWMSLGh3C1+Gwz6e4Wo1S0Z654cRJiSqgjMsVLSSxMHJasiwNCOP8g3r2jwv4lHZ3nEX275Gk9tNgqT+HVMtnW+fHyaoUf82skUjQj8Cwe6EQz+xGkgYpi+Hfpy/qG5pY1ERuuS9Z+/bjjgCjc1GoKIC94qVUbPjjIwkAL6vaaIxoFYTJKSkce49D2FLSaX8N/XLo75+MR7Pjqjtuzsaj+rVkH1qLD49fyDmhMRe2QtgQsEAPJKZCtRqirBQEvQun4dK4U9Jb25UpgQCEY9KcKLaRiAl9YhOr5mYoHre3J7SiOA81K6Gf8qyB9JUXdXmefpcde6Vb0ff9Ki4V6+m4bPP8Gxs9vjJcpB6JBJQw7D2LnpUANCqQkVjAa/LFbWbnr6MECqCvaLIMsHw5OROfCFHE62974Z+wmLDctBBnTpeYzRiO+ooABzfR6+1/XCriaEWIz5F4Zvq5r9TQkoap/z5DoJuE46dZkChsuqbqO0bxu9vwBxUqxvkSvWSEq1utO0xYtQYrM4mtqKGF5qa1vbqfgLY6fXxa4OaE3FKelLkcfeq1ShOJ36dhDwsiEZjIME2otPr6vV2zOZwAz9VcB6apFb+bM8uoLGmPaGielQCFbtQ/P4uv57epvHLr9hx2+00zP4k8pjX6cSbaEKPWvVj7GIyLYCkU6+JGpMMioLP44mOwX0YIVQEeyXY0AAh1d4Zz0E00dqTVBv6oEfFvVIVKuZxnZ9lY5t6FKDmtkQLSZI4LeRV+XSP5m+5RSM4+Ixzqd+iejd2VXwVtX3DOBzrAagiHWlDMdB7+Slh8kaMIrWhmq0MBoRQiQVfVDWgAAclWskxNeen1H07B4CGLD1oICFhNBqNoZ1V2sZmGwlAU0iojE+0oFNkXJYEShrbDm/o0tORjEaQ5Ui/kr5EoEoVWLq0Zq+J2j5frYTyY0Sr7foYAK1O/SzrjKHBhP0g/COEimCvhBNpNYmJSPrOD9CKBn01mVb2evGsUy+q5vFdECpTpoBOR7CmplN5LZ3l1JBQmV/bRIM/0OK5Q848H9zqYMTGxmX4fK3nA/WEmka1ymcbA9FsUKt/ckd0Llm4uxjMFnKVQLNQcQih0tuERXBYFIepMGgoTUnEd4j6pWtPPKDLayfYhgPNHhWjRkOGpIYst7vabokgSRL6HDX84++DvVQC1ernTJe+m1BxOpAT1Zyxrs75CaPXJQFgMKqf8/7QS0UIFcFeCQuVWCfSAlgOOICCt98i96knY753R3h+/x38frQpKREXdGfQJiYy6H8fMvSHRVH1Tg23mimymvArCl9XtyxD1un1TD77elyVJpBg+7ZPo7YvwM56VZxUUYDJ5yFtQAGWxO5dhLvCcLstEvpxuUoIBFqPKBBEh51eH4vDYZ+M5n9bWQ7yW/F6NgxPgmNU4Z2ccniX17eFQkWOpuZcoyy9+oVe4Qu0eQ5A0vnnkX7zTehzsru8Z28TqG7Lo+IAm5qEHNAmdWtdk0E9z6BXw13efjDvRwgVwV7RZWaS9qc/kXRe75S2doQ2KQnLxIkYCgpivndHeFapFQrmcePabN7WEaaiol6ZQH1aKG/gs6r6Vs8NnnAwQYfqfdiy4Z2o7utyqqEfp0/tvtnb+SlhDhiQS5Nkp05WBV9TKAQliD6fV6oezUPsVrKNzWGdkuXLaKzaRd7hdYAfu30iqSlHdnn9hAQ19ON0FUfGPeSY1c63VUGpxTyg3UmdPp20667DkJ/f5T17m2BVyKOS1tKjorWEkl+13btRsYSFilb9O3ld+38vFSFUBHvFkJ9P+o03kHrlFfE2pc/gXqV2ijWPGxtnS5oJu+QX1DZX/4SRJIkxB18PgGLcSk15dCYqy7IfjUdtne+qUsOCA0bFRqgcNKQQgBJJ/W9T05qY7Nsf+TqUpH3qHmGfbauXY053Yx+sDtEcNvTuLgt3AKMxG50uEUUJ4HSqeU55CWpCbYPZgrup95oV9gayzxcJV+/eY8njaEJrCs350XdPqNhCQsWkUUNiIvQjEAjaxLNBvXs3jeh8dcOeyE4n/orojbIfajUxxGLEryh8X9v6wl449kSC7kQ0WoUl3z0VlT1dri1o8OHGTKCkDknSkD+q8zk7PSHfakEvB3cTKiJPpTdQFIW1DvVL8bCk5jERit+P8b3/Mdy2E4KQlXUmiYndE+6SJDWHf0KesTyL6lFpstrbLVHuqwRD+Sno9ZFeUKAOJNQb1ZBNdyp+AOymJABMuABFhH4Egr7ArhmPs/3Pt+Df0Ts9QLqKEghE7paMw4d3a436//2PjYceRuXjT0TTNI5LVSsCvq1u+w40K0ttOuf0/UpjVWWP9wuLg20MJLWumqyhwzC1M/Mo2mgliTxkSkJ5Ko6mvY8mEHSdan+A+kAQDTDYbIw87vptOSklZWTMlZE0BgoLb+3RPlarmvAdnoadZVA9dA5rIk01bSeAyy4XjXPmUPfuuz3aO9pEEmnT0lp4mDzOJkyG8Jyf1G6tnWxSBY5GUtAa+scEZSFUBHvF+dNPNM6ZE7cSwKa5c2n6+mv8FX2jBFHS6Ri6cCFDFixoMWysKxgGDULx+3H8+CNKoP1kwa4yLU29e/uuppGA3DquP3Do+QAk5DlY9tX/erxfXaib6BYKSa2vZODYCT1esysMT0qIVP44nJsJBvf/nhKxZkNotk+B2YBZ2/yVUfvN1wB4R8nk5F2IyZjVo31sYaHiUoVKtnF3odK2R0V2uym/6WYqHngQ2efr0f7RZHehsjsehxOzTvVOJRi7J1RSjBa8qHlCGmMQr1uEfgQCqv/zIuU33Yxr8ZK47N/c9K0+Lvu3hSRJ6DMzuhWPBzUJV2u3Izc0RPqxRIODEq0k6bTUBYIsbWx9p5WQMBqtlIzWILNl3Ww8PXQbV9evAKA0MBCry8HAcTEWKsl26kjBGbAAQRxOMaAw2mxyqR6AYVZTi8edCxcC4BmlkJY6pcf7WK1D1HUdav5URKhYEmhox6OiTUlBMplAUQjs3NljG6JFeOTHnkLF6XRg0aqfyyRz19vnAyTpdDhRvZayVStCPwIB7DaQMCW2zd7CNLfR71u9VHqCpNVinTwZAMeChVFbV6eROCYU/pnTRvhHkjRkZk0DwJpdw6q5X3d7L1n24XOp+QR19XbMVhtZQ4Z2e73uMNRqAklie0Ct+hB5KtEnPC15mKVZqPjLy1G2l6NICt6RMnZ713un7Ek49OP2lBEMuskw6NEoCrJWy46GtkOZkiShz1ZLk/07o5fv1VOSzjqT4atXkfP4jBaP13p9JIba56d0U6iYtRpcYaFi04nQj0AAEIhjHxXoex4VJUouZtuR6jwUx8LoCRXYLU+lpm1hl5Z+DAD2giaWf/0pwUD32o87HBuQFD9N2KBGJn/MeDQabfeM7iZDLGrOxGat+iXXUB8975RAJSxUhu7mUXEsWgSAb7CCZMxEr0/q8T4GQyp6fQqg4HQVo9NIJO+l6RuALkstiw/s6jtCBUDS69EmJLR4rC4QjMz5MXUzmRbAJ4U9KhoR+hEIFFmOtK+P9UDCMH1t3s+mo6ZSfPwJ+Ho4tdU6ZQpIEt7166Oa/3N0aiI6SXXZl4Tc9ruTknw4Go0RQ0KAADvY8NOibu3TGMlPGUJqXTUDx8c27ANQGEru3KhXK0ZqqhbH3Ib9nY2u1h6VsBfQO0rGZoleOXqrhNpw0zdv+3lc+qy+51Fpj0bZjxn176mKsu7hD01QlqySCP0IBMGGBgiqdf+xnvMTpi9NUA7U1hKsrcVXWooutXvJcGF0ycmYx6rlnNH0qiTqtJHps3Pa8KpotWZSkicBYC9wsPTzj9ttqNURDY3LAShmCKl1VTFPpAWw6rTkGPVsRv2C8wXLCAT2/wt3rKj1B6gKdYYdGvJeyV4vzp9/BsAzWiE9a3LU9ovkqTjVPJWskBCtktUuuG0R9qj4+5hHpS1cOtUbK6NBp+v+dHFZGzrXJPqoCASRqcmahAQkQ9cGjUWL8LwfuQ94VHzFajMqfU4OGrO5x+tZw+GfBQt6vNbuHJemXsi+q2k7tp+WdjQA9kFOqraVULq66yGT+vqlAGxkBIUmHQmp3RhZHwWGWIzUSyk4PBYkSaG+bnlc7Ngf2RQK++SZ9Fh1qnfDtWQpisdD0K4QyFXIyD4iavuFpy431C8DINeqzg9ymKy42rlRCXtUAn3Io1Jy/vlsOessvKHrBUDA5yNoCg0SlBK6nYgPIIWEitasiM60AkG8E2lht9BPH/CoeIvVLqyGwsFRWc92pNpu3PXTz1Etrzw6Rb2Q/VLvxBlsfSeamjYVAEuaC505wNLPP+rS+h5vBR5PGTIatgQHcdDo0T03upuEq1F2+NQxC6Wbu58gLGhJm2Gfhaqo9oyWkf0mzObota9PSVFFT0Pjb/j9jWSG2vU7LbZ2e6noIx6VvtG+QFEUvOs34P19nTrdOYTH6QBLaM6PpvveFABNyBujNQZF6EcgCNSEEmmT45OfAn2r6sdXogoV4+DCqKxnGjECbXoaSBK+kpKorAmql2GAyYBPUfixrvWFzGTMIiFhNEhgH+Rg68rfqC7d2un1G0LelG0MxFLvYsSh0XP/d5URVtWzVWpSO+LWVv0aN1v2NyIVP7sl0tqOOBLnAWl4DpDRBgt65BnYE7M5F6t1KIoSpLbuBzKM6kwspyWh3V4quohHpW+UJ8sOB4pXzQ1rOZCwCSnkhFW0PRMqulDyss4QIBgIEOhDPWR6AyFUBB0SrAt7VOInVHQZGZjGje12F9hoEm2PiqTRUPD6Gwz75WdMRUVRWRPUss2jU9SEu3m1bU8Vzsw4GYCc8ervSz+f3en16xtUobKB4WR5HKTlD+yuqT1meOhLdFVIqAR1Zfj38wt3rNjobN1DxTZ5EpVnKnhHKiRYoz8uITXkVampnk9GqDttZzwqiiz3iaZvgSpVUGkSEtCYmv9uHqcDjVkN/Ui6nk0XN4bOj0xQ3s/DP0KoCDokHG7RpcZPqJhGjGDQe++R88jDcbMhjHeLGnM2Do6OUFHXGoSk10dtvTBHh8qU59U0tpksmxESKtqECnQWP+t+mI8jJEz3Rn292vxvAyMYmdSzeHtPKQp9ia6QByEHNOhMAdYv7nnXXUFz6Kdot9CPLAfRJqjjFzJyopefEiY19SgAamoXkB6q+unIo6JJTKRo2VKKFv+KJk55dLsTaGNqMoDH4UBvUMOwWl1Sj/Ywhycoh5Jze9q4sa8jhIqgQ1KvuYZhS5eScWvP5njsD8hOJ4EdqnvZEEWh0ltMTrJhkCRKPT62uFuXKZvNudjtEwCFgsMsyMEAy7/+bK/ren3VkcFx6xjFEUXDom16l7DptOSbDAQlPZ5QO/2SdR/H1ab9gcZAkJ1e9Y599x4qlduXoDMGkQMSOQOPjPq+SUkHotVa8fmqsfvV6h+n2UZDddseFUmS0FitUbejuwRCAxT3FCpepwNdaCChoYd9Z2xG9XyzVhWSvv288kcIFUGHSJKE1maNJLT2Z3ylpQBok5N7pVRb9njwRzHObtVpOSRJvYDPq2kv/HMKAKnDVNfxym+/xOPo+O6srvZHALYqA2mS7Bycnxctk7tNOPzjTFJzZXxspG5neTxN2ucJV/xkGfQk6rTILhc7/v53dn30Gsjgb0xGb7BEfV+NxkBK8uEAKI0/ACBrtexqbPs93NcIT07Wpe/pUWnCGPKAmEIeke6SYFCvPyaN2ghvf0+oFUJFsE8gu1z4d+5E9rb2DMQKX2kZAPr8AVFfu/Hrb9h46GHsvO++qK47NVT9M6+27TLljIyTAA0+uZiMoWl4nU4Wf/JBh2tWV6s9X1ZL47Eik2eMftiqq4ywqVmKW0xqLxdbtotlXwivSk/YEK74saqVK85ffqXhfx+hef9nkEAnD+q1vcPhn/raBSSFvqUq3PvGwMnwQELtnqEfpwOjTr1+WQ09u9FJMqnnmyU3SAoekaMiEMSfLWeeyeapR+NZsyZuNpjHjCb74YdJnT496msbCwejeDy4fl2M7G6/XXhXOTpVTaj9ud6BOyi33teYTnLyIQCMPDEXgN+++pSGyrZ7UiiKQlXlfADWMJbRiba45qeECXtUFvsGImFAbwmyecUXuPrI2IV9kT0rfiJlySNkkCAxoefzfdojNVUNKTU0rqRAr34eqgIywXYmjde9/z6bjz6Giofin8cWqAyFftJbzvJxOZowa9QQTaKxZ0Il1dycM6gxyiL0I+jflJx3Plsvuhh/eXzd6Fp7EhDfNvr6nBySzj6LxBNPjPrahiFD0OVko3i9OH+NXnltkcVErlGPR1b4qb5t93Bm5qkA+LRLGDBqDEG/nzn/+VebCbj19cuRqccX1LOB4YxKiL7rvzuEhcpaVzAivCzZ9Sz5rGv9YQTN7D6MUFGUSPdk7zj18czc6CfShjGZsrFYhgAyo3Vq2b7DYsPZXrK3Av4dO/D3cKxFNAh7VHRpLYVKrduDTVI9H3ZTz4oTkvVG3KheRNmmFcm0gv6LEgjgWb0a92+/tWhcFA8iTd/q6uNqR28hSRK2I6LfpVaSpEj45/t2wj+ZGSeh1VpwuYo59OLJ6AxGStesZNkXs1sdu+qn5wBY7xuFXzIyytbz7rzRoNBiRCdBU1BGlzQFgMR8B8u/+pSGyr7RCGxfY5OruTTZt3mzmkhu0OIbpuCuMZI1KHozftrCEmokl61Ru2M7LTYa26n80WdnAeCviH932sSTTiL50ksxDW/ZbqDO58eKKijMPQz9mHaboBy06fDt54MJhVARtEugthYUBTQatHGa8xMm0vStD7TR7y1sRx0FgOO7eShy6zBNdwmHf9pLqNXpEsjKPB2ABvfXHHHxdAAWvPUq635sFk3Fvy3GFVC9Pb+YjwVgZB8RKgaNhiGhEtoKw8EA2LI9KJKX+W+81K1ZRv0ZZzBImUdN/BxmNTV7U4aCYoCmkgGYE3rWtGxvGIwZAKSielHUEuW2K390mapQCfQBoZJ09llk/f0uTCNGtHi81h/EhvoZjMa0aU9ogrJi0+JxihwVQT8lnL2uTU1B0mrjaku8JygrPh+7Hn2U2rfeRvH7e2UP6+GHo0lIIFBZiXvZsqitOyU5AZ0EW9xetrZRpgyQm3sxAFVV31B0xFjGHXciKApf/vMJZj/xD758/inmvnkXhgQ/clDHT0xAQ3MPk75AOPyzPpCB2VyApJFJzPeweckvrF3wXZyt27fYHPKmpOl1pOh1kWnJ7lFe/E4dqUkn9LoNRqMqPuzUAOA0J9BU3Y5HJdT0LVhfj+zpm0m3jbIfE6GOtT3sowLNE5QVi0ZU/fRHZK+Xqn89T+mVV/WJTofxIlCjXiB0cRo2tzvxnqDs37GD2tffoPKpp0Cn65U9NAYDCcccA0DjV19Fbd0EnZaD7OEy5bbDPwkJI0hJmYKiBNm69d8cfcW1HHjqWSBJFC/9lXWLvid9rNrkS5N2Kn7JyGCLEYu271xCwq301zs9pKWpf8chR6qewHmvvkDl1i1xs21fY/dE2mBTE67ffgPAO1qmclUK4449tddtMBlV8WGVQ0Klg+60msREJIuaL9UXvCpt4daoIkWdnGzr8XrBkFDBjAj99Eckg4G6d9/F+dNPeFavjrc5caO9DovxIN4eFV+ZWppsyMvr1SqXxJPURN3Gb+agtDFMsLscHSlTbr8XxeDBtwCws+JjXO7NHHnJlVzyyDNMvuAyDjx7MvYCJ6Bhc8KFQN8J+4QZblM9KuscbjIz1f4wsnEjBePH4Pd6+PjxB6mv6BvzYPo6axxqpc1QixHnTz9DIIA/U8Fr06B1TyRz8JBet8EYEir6gHod6ij0I0kS+szQcMKK+OUkBaqrafj8C1zLW07wVhQFX6jdvV+yIUk9/+oNzwvSmBUR+umPSJKE5eCDAHAtXhxna+JHJHs9NTXOloA2KSxU6uOyf7jZmz4/epNi28J62GFo7XaCNTW4liyJ2rrHhNrp/1jnwNNGmTKAPXEc6WnHATJr195CMOglc/AQDjr9TCyD1NyUjIwTWelThesoa98SKiNCoZ9NLi8m62jM5nxk2c0hF08gJScPR0017953O2W/99+bj86gKArfVKs3BIcl2SJlyd5RMtVrUhgz9ZSY2BEO/UgB1ZOnelTaDv3A7gm18ROjnrVr2XHrrVT84x8tHve53YSKdAj2cHJyGE1o3o/WKIvQT3/FerCakOfsx0IlWNN2h8V4EO8Jyv6QUDEMiH6zt92R9HoSph2HZLFEtdRyhNVElkGPW5b5taH9u6+ion+g16ficG5g1epraGpax7p1d9DYtAqdLpGhQ+7g99Dd9khb38lPARhgMpCk0+JXFDa6vJGuu9W1X3DefY+Slj8QZ30d7z9wJ9+9+gL+PprLEG9+d3rY6vZh0kgcl5qI5cjDcB0k4xkn07Alm+GH915Z8u6EPSpyoB694sNrNFPbQei3OaE2vh4VaLt9vmRWE7qVHg4kDKPTqYJHZwjgFaGf/onlINWj4l6+ot/mqYRDP9q+kKNij7dHpfe60u5J+p//zLAffyDpnHOitqYkSUyNVP+0nacCagO40aOeQaMxUVu7iMVLTqFi12wARgx/FEWfxeZQx9K+FvqRJInRIZtWN7nJzj4HkKitXQT6Gi544HHGHHM8ACu++ZxZt/6JkuVL42hx3+TzynpA7Wps1WlRDkqj/ooAjiwtQycei8Ecm945Op0djUZti5ApqSXK1QGZQDvX477gUWkOl7fsoeJ2NKE1qZ5MTZSEikGvrqM3+PGK0E//xFBYiDYlBcXj6bd5Ku3dHcQDw8CB5D77DDmPPRaX/X1lYY9K74Z+QA21aczRFwFH76WdfpiUlEkcOPFD7PYJaLU2bNYiDhj/BhkZJ7DG4SagQLpBR04faJ2/J2MSQkLF4cZiKSA9TS2jLiubhdFiYdrVN3L2XQ+SkJZOY9UuPnrsfj5/dgbupo7/Jv0FRVH4rKoegFPS1S/CxoZVALiqzIyeelzMbJEkKeJVGaRX/32cFlu7SdHWww4j7YYbSDg2djbuSaCq7a60XqcDvVHtqquLklAx7TZB2e9xI0cxp62vIYRKO6h5Kmr4J5q5AvsSCdOmkXTuuRiH9n7i3N7QJiaSeMIJkZBcLFFkGX+ZGoYxxMCj0lsckWxDK6k5HOEeGe2RkDCCAyd+wFFHruSQQ74kJWUSAL81qnduExItfaJ1/p6MCXXKXd2kusIHDLgSgJ0V/8Pj2QHAwHETmP7UTCaefAaSpGHDz4t4+65bqC7dGheb+xIbXB42u7wYJInj0tQv1Jpq9frnqbGQMbAwpvaE81QGaMNN3xIoWd729dhy0EGk3/AnbJMnxcy+PWnv5s7jaMKgVz9zxij0UAGwhjwqRq267v4c/hFCpQMsBx0I9N+E2pRLLib7Hw9iGj483qbElUBVFYrXC1ot+pycmO2r+P00zpmDv7IyKuvZ9ToOTOy4THlv/NaoXgwnJFijYlO0GRMK/ax1eAgqCklJB5GUdAiy7KN4y1OR4wwmM0dd9gcufuRp7JlZNFTu4v0H76Jme2m8TO8TfFGp5oAdmZKA1eNh5/334130GyigYyDaXirNb4+wRyVLUw+Aw5JA8bK+ez0OtDc52enAEJqc3NOutGFsxiQATFo1FOvbjwcTxk2oLFmyhJNOOonk5GSsVisHH3ww77zzTrzMaZPw3bvrt+Uo/TRPRdCcSKvPzkbSxy7csf2WWyi/6Wbq330vamt2NvzTHst286j0RcK9XdyyzGaXF0mSGDrkDgAqKmZTU7OoxfGZg4dw8SPPkDl4CO6mRv73yH39Ogz0eSTsk4Rr8a/Uv/seto+c6hDCxNExtycsVNIktTuty5JA1bYSGttp/BZvAiG79vSouB0OTDpVUFh7OJAwjD20jklSk9u9+/FgwrgIlfnz5zN58mQWLVrEOeecw3XXXUd1dTUXX3wxjzzySDxMahPDkCFok5NRPB7ccZzaK1CpfecdKh55BO+W2DbuUgIBjMOGYSwq2vvBUcR+0kkA1L3zTtQmKh8TSqhdWNv2NOWOqPL52e7xIwHje1GoyF4vDZ99RrCx64JBK0mMDXlVwmGqxMSx5OZeBMDa32/B4djY4hyzLYGz73qQ5Owcmmqq+HrmM/2y5X6DP8C6UKO349IScSxSRZ13pIy71khaXuw9q5HutIra9E3JzgNgy29th3+avv+e2rfeJlBXFxsD9yDYTu+pepcLa2ggYaKxZwMJwySZkgCwSG4UScErPCrRIxAI8Ic//AFJkli4cCEvvfQSTz75JCtXrmTUqFHcd999bNq0KdZmtYkkSSRfeglpN92ILiMz3ubElEBNDU3ffYfn99/jbUqExk8+pe6NN/HFWKhYDzuMwZ9+woB/Px/TfROmTUOfl0ewvp76/0VnCvAom5lco1qmvKiu/eZvbfFLvXohHGY1kaCL/kgFRVFo/OorNh97LDtuux3PuvUtnq+ZNYtdMx7fa4v0CaHw1vLG5jvMoUPuJiFhDH5/HUuXncO20pfxeHYQDHrx+qqRNVVMvfZUEnJ9bN/0I+sWfR/119fXCYuUXKOeZJ0W56IfAPCMlHHuNJOePzDmNoU9KpZQd9pAejYAW5a1PWF818OPsOuhh/CVlMTGwN2QnU7kkFdDu0fVT7XbGxlIaIuSRyV5t3Vki3a/rvyJuVCZN28excXFXHTRRRxwwAGRxxMSErjnnnsIBAK89tprsTarXdKvv57066/HkJcbb1Niinv1arb/6QZ23n1PvE2JEO82+rFG0ulIufIKAGpeeikqXhVJkjg+lCQZburVWcLC5ojknrf/3hPF76fi3vsov+UvBKuq0WVlIe3Wnj/ocFI98/+ofe01tp53Pr4OesyEw1K/7SZUtFoj48e9QlLSIQSDTjZvfpQff5rC/AUj+eGHQ/jl12ls2fFnCk8pZuRFxZTW38DWkteQ5d6Z69QXCQuVkTYz/m3b8G/fjqIF31AFR4WFtHgIFYM6mFAfUEMqLpsauixdu6rNPji6UHfawK7Y91IJ1KrhKclsRmNt6XGs9fpICA0k1EUrmVZnwIPayyiQYBAelWgyf/58AKZNm9bqufBjC6I45l7QPSIDCftAs7cwzd1p998JynuSdPbZ6HNyCOzaRe3rb0RlzbBQmVPTSLALIY4Fofb7U5ITomJHGMXnY/uNN1H/wQcgSaRdfx2Fc77BcuCBkWM0Vgs5j89Am5qKd+NGtl10Md7Nm9tcLyxU1jnduHYLbxkMqRww/g2GFz1MQsIoJKnZK6TV2jCZBmAyDUCRJfQ2L8UlD7F8xWX4/f0jZ2VdqJHfCKsJxw8/AuAbIqOYAFcOtuTohCy6QnN32ipQFHbJEgnpmQT9fratXtHqeH2mKmz8cRAqhgEDKFq5gsKvv2pVEVcXCEY8KvoolSdLkoQH1XsYtGpFjko0CYd1hg4d2uq55ORk0tLS2g39eL1eGhsbW/wIeodAdd8ZSBgmXh6VQHU1ity1fI5ooTEaSb/lzwDUvPgivu3lPV7zsCQrdp2WKl+AX+o713p7m9vLNo8PnQSHJ0XPo6IEg5T/9VYc8+cjGY3k/fvfpN90ExqDocVxkiSRcNRRDProI4xDhxKorKT0yqvwl7f+e+SYDGQZ9AQVWNXU8uKt0ejIzb2Agw/6lKOOXMsRU5Zz9NSNHHXkSiYdPp9Jh88n2/hvtv+QSdCnpb5+McuXX0IwuP93sW3uOGzGGc5PGSHja9KTljsmLjYZjarwQPGTpmmixh/AcojaGXfLb62rf8Ih+sCu6FTKdRWN0RiZObQ79XIAM+p7SB8ljwqAT1I/i4pVK6p+oklD6G7Ybm9bVSYmJkaO2ZNHH30Uu90e+RnQy+3Mw3i3bKH6pZeiOtG2r9OXmr2F0YS708awjb4SCLDpiCNZP2585G8SaxJPPhnzxInILhc777yzx6LJoNFwcqiZ1yehLqR7Y2Eo7DMx0YotivkplU8+RdO33yLp9eT9+98kHD21w+P1mRnkv/E6xqFDVLFy9TUEHa0v0GGvytIOxgVoNHr0+sQWnhWAEYcfi796JJs+yUdSbDQ51rJp00PdeHX7DrKiREI/RQZNZHSIZ6SCY6eZrMJhcbFLozGg16uenMOtqpCqKBoLqAm1e34WmkM/fWuCsic0OVlBQqeLnkcyoAm1CTBLoo9KX+HOO++koaEh8lMWmmjb2zh//Imqp56m/sP/xWS/vkAgPOcnLf4DCcNE2ujH0KMSqKyE0MVQmxJ71zeApNGQ8+gjSBYLriVLqJzxeI/XPCNDTcT7vKoev7z38M9XVao4nJoSvYts41dfURvKR8t+9NFON+rSJScz4OWX0WVm4isuZuff/96qSueQJPUC3tFco/bQaLUceMpZeGpNVPw8FJAo3/Ffamt/6vJa+wplHh/OoIxBksj5fS2K242cpCOQq+CssJA1JD5CBZrDP4dY1S/iNaZE9CYzzvo6dm1pGf5rDv3Ex6PSHr5QUzZ1cnL0hH5kwKGF/XowYcyFStiT0p7XpLGxsV1vi9FoJDExscVPLIh0qF2+HMXfP5Lr2iuziyeR0E8Mc1T8O9W5IfqsLCRN/HS9IT+f7AcfBCDY1NQpr4qvtJS6d99j5z33UvqHP7b4Mj88yUaaXketP7jXnioN/gCL6tSL4MnpSd1/EbshO51U3P8AAKlXX439lJO7dL4+M5PcZ58BvZ6mb76h9rVZLZ4/NBSe+rXB0aU8nDAjj5iK3mRm52oPdsuJAGze/BiKEp8QYG8TDvsMtRrx/Kjmp3hGBEACR4WVrMLWofpYEa78GWlU36c/N7jIH6cWYhTvUaasywoNJoxDjkrl089Qcs65NHz+RYvH5WAQxaB+b0RrcnIEnfo+1xhFH5WoEs5NaSsPpa6ujurq6jbzV+KJcegQtElJKC4XnrVr421OTAjUqDkqfWEgYRitPQmIrUfFvyMkVLKzY7Zne9hPOZmCt98i++GHWoimYH09sseDr7SUhk8/Zec997L5uGkUTzueivvvp/6DD3D+8APybgJPp5F45JO3ufir2bxZsqPDfb+tacSvKAyzmBhqjc7EZI3VSt7z/yLh+ONJv/GGbq1hOeAAMu9Um7lVPvUU3i3NJamjrGasWg2NAZn1zq7nlxhMZkZMOhKAXcvS0WrVENCuXZ93y9a+zuqQUBlts2AePw7TsYfgHucn4NZitQzGaIlfJ+KwUEmX6kjUaWgIBAkccDgAW/boUhvJUamsjHkvHO+mTXjWrEHeo0zY43SgCU1OlqKUSBtGo1WFj9YYFFU/0eTII9UP/5w5c1o9F34sfExfQdJoIu30nYv7x9yf9lpBx5O4elT6gFABsEyc2KKiIFhfz8bDDmfD+AMonnY8O27/G/UffIC/rAx0OiwHHUTqNdeQ/dijSLslqPq2bmXoF5/yh0/f49K//onixe1PEQ7nsZySEd2LrOWgg8h77tkedftNvvBCki+6iJwZMzAOHhR5XKeRONiufrn+3MmE4T0JT1re9PNy8nIuB6C09OX9shnc2pBQGZNgJnHaNLj1ILxjFZwVZrKHxLbR4Z6YQqEfv3cXk5LU0OO2nIEgSVRuLcZZ39zcTZ+h9i9RfL7YJ92Hbu72vGZ6nQ50JnVgoDbKQiWc76I1BPEJj0r0OOaYYxg8eDDvvPMOK1asiDze1NTEP/7xD3Q6HdOnT4+1WXvFclAo/NMP5v7IXi9yU6jmP7Xv5KjokpPQZWfHVDT4d6reBl1O3xAqe+JeuRJCX5ySXo9pzBhSrrySvP+bybBffqHgzTfIuOXPJJ1xBhpLc28HfV4eOY89SlNyCvmVO3FfeQV1H3zQav1tbi9zQ3OBzsyITqOqaCJJEln33tNm6OhQu+oW/6mue0Ilc/AQkrNzCPr9+KpGoNGYaXKspa5u/8tVWdOkCpVRNjOBgIOyslkANJTa4pZIG8ZkUudreTzlTA718PnFHYiUSztqayLHSgYD2pQUJIuFYKivSaxor32+x+FAZ1AnJ0ez4kddzx76b0B4VKKJTqfj5ZdfRpZlpkyZwtVXX82tt97KuHHjWLt2Lffffz/DhsX3g9EWlkNCQmXZMuT9fO5PuIeKpNejiVEeUGfQ5+Yy9Pt5DHz3vzHbM9CHQj9tYTvySIavWsmwJYspWrWSQR+8T+btt5EwdSpaW/vuekmnw37aaQTf+5CF4w9GGwhQcc+9VP/nxRYeg1e2V6OgJtH2NOwTqKtj+4034otREvyUFPVLbVFdU6cShvdEkiSGHBwKMSxeRU7OOQCUbX89ekb2AWr9Acq9ag7FKJuZbaUv4ffX4m00UrsxieyhcfaomNRmm25POUeEevgsbnCis6vC2d3Y0sM6ZO63FC1birEwdpOeFUVpzuvb4+bO43Q0T042JEV1X1NIqBj0PiFUos3UqVP54YcfmDx5Mu+//z4zZ84kNTWVt956i7///e/xMGmvGIcORZuWhuJ24/5tebzN6VV0OTkMW/wrgz//rFXjov5Gc+gndlOTu4pkMKBNSOjWv9Vh+dl8/7d7ePOEMwCoeuYZKp98EkVRKPf4eHunerd6dV56B6vsHUVR2HnPPTR9O5fyv/y118Inwfp6ym+/Hd+2bYxPsJCi19IUlFnSjeofgKEHHwbAluVLyco4H4Camvl4vX1zKF53CCfSFpgMuP7vGXZ9/jL4YccvaWi1BtLyC+JqX1ioeL07KDTryTLo8coKO3PUUN+eQyQ1FkvMr1tyY2Ok0EK7p0fF6cCoU8uTLVGanBzGEhIqRq1XJNP2BgcffDBfffUV9fX1uFwulixZwsUXXxwvc/aKpNFgm6TeXTl//CHO1vQukiShTUzEUBDfC1RfICJU+mjoJxrcOSSH1884n5lnq5+/2ldeZdejj3Hb+lKcQZmJiRaO7GFZcv277+KY+x3o9WTdf1+vfZFUPPgPGj/9jPJbb0MKBJjaw2nRWYOHYktJxe9xU7fNTWLiAShKkIpds6NodXxZHQr7HOpzUvfvV0j6vyCGwCAaShLIGDgYrS52E8PbQi1P1iDLPvz+2oinrDhD7aPVF6Zdh3P6NImJaIzGFs81OJqwaNS/cUKUBhKGsRlCQkXjJeDzEgwEorp+X2Gf6qMSb6yTJwNE2ksL4oPi98ekTDzY1BTJ1dGHyh73R8YlWPjboGw+OPYUnrr4DwDUvfEGBa+/ilEj8ezwfDQ9EBaeDRvZ9dgMADL++hfMo0ZFxe62yLjtVjSJiXhWr6bq+X9zdEhgzavp3peZpNFQMFYthd22egU52WcDsHPn/tNTKZxIe8R69brmz1eo3lwISOQMGxFHy1Q0Gn2kQ63Hsz0S/tmQpD7WJ4RKB+0cqp0ebOGBhFEO/SSaVA+NOSSE9tfwjxAqXcA6aRKmsWNJmDq1V1qqK4qCe+VKmubOxbN+/d5P6IeUXnkV68eMpen73p9uKxmNDHj5ZbIfeQSNNX7lmbHghvwMLshK4fPJx/D0hVfiMJlZOXocTxUN6FFuSrC+nu033IDi9WKdMoWUyy6LotWt0Wdnk/2g2p+l5sUXOXzLBjTA704PpW5vt9YsGDMeUIVKZuYpSJIBp3MTDmffmPLeU9aEQz8rVU9xcEQKW34qR6PVccAJp8bTtAjh8I/HUx6ZNVVisOIxmFoJlab589l83DTKbuhe2Xt3aG6Q2Vqo1Hg92EIDCaOdTGs3quuZJRdBjWa/rfwRQqUL6FJSGPT+e6TfdGPUm3+516yl5LTT2Xr+BWy/4UZ23P63uM2XqXzqabZdcilNc+fGZf+OCJfYxqL0UGMwYJs8iaSzzuz1veKNRpJ4dkQ+r4weiPm88/jsP7N4/LxTOSer+65qJRCg/K+34i8rQ5+bS87jM2LSNC/xhBOwn3UWKAqOu+7iSJ2aD/NVF6dFh8kfPQ6Aqq1b8LlkUlLUDrqVlV9Hx+A44g7KbHJ5QFEwr9oCQE2C+hkbe+wJ2DNaz62JB2ZTHqAKlSyjnqEWI4okUZozCNceybSSVoe/rAx/aWyStqG5AKGtTt413kDzQMIoC5Vko+pRMeHFZ9p/JygLodIHaJo3j20XX4x30yYkiwXT6NFk/O32uHVC9axdi2vpUoJNfa8lczx6qfQnTk5P4p8jCnjwkLGMTmguZ5bd7i6vVfnU0zh//BHJbCbv38+jS45deXPW3+/CUFBAoKKCG159Ho0sR8YAdBVrUjLp+QMBKF29goyMEwCoqtz3Z39tcHoIKjCyrhpNtRtFq1DmltEZjRx61vnxNi9CuETZ7VHbBYS9KqV5ha08KvosVVzFcoKysaiI5IsuwjppcqvnagOBSOgn2kLFZGjOHQvY9EKoCHoH12/LKb/lLyheL7Yjj2To9/MY9OEH2CZ1bu5JbxCoUudk6DJ6VunRG8Rj3k9/x19ZSck551L51NOdrtap/s+LkTk+OY88jGn48N40sRUaq5Wcp55CMhhI+fkn/vDJu/za4GSXt3u5TfmRPJWVpKcdiyTpcDg34HKV7OXMvk04P+WEbesA8A1UcNRamHDiaViT+k7fnN1DPwBjE8wA1CWm4G5sKVTCgwnlhgZkT2ymXlsPPZSse+8h6eyzWj3XEAxgQQ3JRFuoSJIWN2pYWrbphFARNOMrLaXmlVdwLW2/m2dnCFRXs/2mm1SRMnUqef9+PvJFvDve4mKcv/zSo726QnigV1vjyuONNjkJiI1QqX37bSqffBLP77/3+l59Gcf38/EVF1Pz0kuU33QTwca9Jy/6K9RqqYzbbiXxxBN728Q2MY8eRfYjjwBw4ZzPmPbzAj7cVbeXs9qmOU9lOTqdnaSkgwCoqVkYFVvjRbh1/oQNasdtz0DQYOegU8+Oo1Wt2VOopBvUSiSX2da6PDkhAcmsCpl4zPzZE7emOTdKp4t+XyqfFBIqFu1+W6IshEo3qH3rLSqfeJK6d9/r9hqKorDjzrsIVldjHDqU3KeeRNLpWh3n/OUXtpxxJjtu/xuyt3vJgF1BdruRQ19EuoyMXt+vq0RCP/W9H/pp/PIral5+Bd/Wrb2+V18m+fzz1C98vZ6mb+ey5ZRTafj00w4rr7L+/ndyn3ma1KuuiqGlrbGfcjJp118HwJTli/nvjupu9XDJGz4KrU5HU3UV9RU7SE05AoCa2n1XqLiCMnNr1M9Rxjo1eb8uycjgAw7GZLPF07RW7C5UFEUhzaBeK10WVajs/m8qSRL6jPAU5fgLlUB4cjLWqE5ODuPXhP6trBp8wqMiCGM/WW3X3TRvHnI3FWzDRx/jXLQIyWgk56knW7Q33x3zAQegS0sjUFlJwyefdNvmzhKoVL0pktmMJqFnvTN6A22obXYs2mNH2uf30a60sSTprDMZ+M7b6PPzCVRWsuP2v7HpqKmU3XAD5bfextaLL2mRxyLpdHHzpOxJ2g03kHjLLTx59Z/Z7PaxuBvN3/QmU6RUd9uqFaSmqvPI6up+IRjs/RuI3uDZrRVs9/gZ7WxEX+lAkRSqZBsZg2LX0bWzhHNUgkEnfn8dafqQUDFZkWW5VcijeYpyZUzsa5o3r92u5bJOfUzW9k6Xb1kTuk6bweMUQkUQwjR2LPoBA1Bcrm6XyQaqKkGrJf2mmzB1MDJAYzSSOl0diFb7yqsowWC39uu0XZXN+Sl9sSutLkWNm/e2UFECgchFTp/Td7vSxhLzmDEM/uxT0v98M9rUVII1NTjmfkfj55/jXraMhk8+jbeJbSJpNOReczUnDVBDmc+XqpN1u+pZCfdTKV27Eqt1GEZDJrLsob5+35v/tc7hZmaZ+v6+I13CPVbGW6RQV5FAZh8UKlqtGYtF7URbV/9LxKMia7V42yhR1mWqHpXAropet00JBtl+w41su/iSViFpv8+Lxqg2YYv25OTI/lpVqEhG8LmFUBGEkCSJxJNPAqDh0+5dnNOuvZZBH31EyuV77yuRdM45aOx2fNu24VjYu65mf0io6PtIWeKeaFNUj0qgrnu5Bp0lUFUFwSDodG32RuivaIxG0q69lqHfzyP/jdfJvOsuMm67jdxnnyXh2GPibV6H3JCfiQb4tqaRta+8xo6/3hqZeNsZcoePBGDHBjXxNCV13wz/yIrC7Ru2E1DgxDQ7owvqqLs2QPnFWgIufZ/0qACkpanvr+qquRg1GhJ16teX02JrNe8nnF/nj4FHJVhXB7IMkoQupWU5v9fhQGtUby51UU6kDSOFhIrWFBQ5KoKWJJ1xBgDOhYvwbd/erTVMRcPazEvZE43VGtmv4aOPu7VXZwl7Efpifgo0C5XdZ2v0BpHW+ZmZSNrox5X3dSSDAevBB5Ny2aWkXnUliScc3+cF3WCLkTMzk0lurEd+7jkav/yS4hNPou7dd1E60Xo8s3AoGq0WZ30djVWVkfDPvpZQ+/bOGpY0OrFqNTw0NDcitBrLbCRlZWO09M3mhmlpxwJQXfM9suwnXd9+Qq0udKMVi2TacPt8bUpKq+u5x+lAH5qcbND3ThWVNpSgqzW0DoHtLwih0k0MAwdinTQJFIX69zqXVCs7nZ26ILaFPdR0rGn+/F71JoQ/2Lo+WPEDanly5p13kPPEE9BLg+0A/H18arKge9w2KAtXUjI3/eUePEOHITc2UnH/AxRPO57aN95E7iDGrzcYI96GHRvXkZI8CUnS4nJtxu0uj9VL6BGVXj8PFavv7TsGZZNj1FFbuwiApjIrA0aNjad5HZJkn4Ben0wg0EhDw7LmhFqztVWJsnnCAaRdfx3203q/s25H7fN3n5xsjnL7/DCG0GBCvd4vPCqC1iRfdCEA9e9/QNCx9+ZoFQ8+yNYLL8JbXNzlvUxFRZhGjgS/n8bPPu/y+Z3FPH4c9rPOwjLhgF7boydIGg0pl1+O/dRTIl1qe4NIIu1+PIywPzLQbOTPBZmsHziES/76APrbbkebnIx/xw52PfIIm6Yeza7Hn8Bf3rbwCCfU7ti4Dr0+kcTE8QDU7iPhnxe3V9EQCDI2wcyVeWnUrf4WVtWgOLU4KywMGjcx3ia2iyRpSUudCkBV9dyIUHG24VExjxpF+k03kXDssb1uV6C6faHiaHJg1qpJ5tYoT04OY9SrHhW9zo9XJNMK9sR21FEYBg0i2NBA7azXOzy2ad48Gj75FM/atZFBd13FfvppADTO+aZb53eGxBNPJOeRh2PyAe/LBCJTk0Ui7f7Gn/IzGJ9goUaBP445jLQ5c8i6/z70BfnIjY3Uvvoqm4+bRvntt7dKuM0Zpjau27FRLefd18qUf6lXb6j+kJeOVpKoefd10v6px/KWHtCSP2ZcfA3cC2np6nWpqmouaTo1JOsyW+M6mDBY0377/CqnM9KV1mrsHaFiCXlUjDqvSKYVtEbSakm/+Sb+v707D4+qvB44/r139pnsJCEEQth3FJQdFKnUrW4tVot1q0Ld6lq12tatrdW21lq1dVfca9W2/txFERVkERCQfQ1LEgjZk9mX+/vjzgyJWUjCTGaSnM/z8BRn7sx95/aSOXnf854DUPOf/7S4I8dfVkbpXXcD0Ovyn2EbN65D50v9/vcBcK9eE43iRXwcXvqRQKW7Masqz40ZQI7ZyGanhx9t3k/9uT9i8Acf0O/xf+KYNhVCIVSLtcnOt8iMyqE9u/F53GRlTQOgqmolmpaY3lxt5Q2FogXeJqQ58PrK8XzzLQAVNn37dbLmp0RkZc5AVc14PPvop+i9fBIdqESWfgzNzKgccrmjDQktcUqmdZj1QMWqemXpRzQv9ZRTyLzoIgpffaXZpMuQ283+X1wXLuw2hOzrruvwuUz5+aSfczbZ114LCeoDlAycy5dT/tTTuNasids5jH3yMA8ahLmgX9zOIRIn32rmjXGD6W02ssXp4Xsrt7CgtBLrzJPo/9xzDPy/t+l15ZVNXpfaK5vUXjlooRAHdmwnNXUsqmojEKjGmeTdlDfWufGGNLJMBgpMPtatvAzDXj0hvcSZycBxybvsE2E0OsjM1NuL5HuXAc0n0wI4v/qKqtdew38gvluUDy/9NG05Uu71xq0hYUSaNdyYUHXjkWRa0RxFVcn77W8whQsMNRSoqGDvzy7Hs349hvR0+v3jH6gWy1GdL/9PfyLnF9c22QYXC8HaWuoWLcKzdVvM3zuWaj/8kEMPPYRzydK4naPP3Xcz+P33cEybFrdziMQa4bDxznFDmZjmoC4Y4o5t+5mxYjNvHajENHQo5n59o8dqoRAlv/0trtWrGyz/bEZVTWSk61/wVdUrEvI52mpVrf4lNiHVzIYN1+LZugUlqOAzGahXLAw49rgEj7BtcsLblNOc+nKby5bSpIMyQNnfHubAvb/Ds3FjXMfTWo5Kpdcft4aEEZkW/X3tiguPBoE47oZMFAlU4qDs4YfZf8ON7Pz+KbjXrkVNS6Pf449j7t8/0UNrlWfzFvZfcy3FN9yQ6KG0yhitpRL/6rSie+tvs/Df8UP449C+5JiN7PH4uHbzXr739VbeLasmFM5Rqf73G9S8+Rb7rryKvna9bkXJNr2eSkbmJP2YquQu/Laq1oWihTjH8zcqq5ZiLdJ/aaqyWbBnZJI7YFCCR9g2kXoqqnsjGVoVLpsDTzMzKpGib/Euox8NVHKaCVT8wWigYjRmxOX8KeGlHwdOvBZrtyyjL4FKjAVra6l8fgF1H31EyOXCOmoUA155OWl30TQUCDeSM/ZpOjuUTKJl9KuqEzsQ0S0YVYXL++WwfMpIbh+YR7rRwFanh3kbi/jhNzvY7vSQfu452CdOJFRfj+3FVzEHgpRu24IWCpGZMRnQZ1Q60keoM2iaxtc1Tk7hAzKcC1EUI9mV+gxKtd3KgGPGo3SR5WSLJZe0ND3pdzyr9F0/zTTKNEVrqcS36NvAN/7N4IUfYzu2aSJybSiIAz1wMJniU5k28r5GAnjtlm5ZRr9r3JldiGI0knfXneTe/isKnn6KAW++gWXo0Jiew19cTMWzzx1VU8Rm37e0aySQGuJcRj9YX0+goiJpv3REfDgMBm4ckMfKKSO5qbA3doPKihonp6zaxod1Hvo9/k/MAweilVcwbt8hPPV1VJYWk5Y2FlW14PdX4nTtSPTHaFaR20ep188JfA7AkMG34d+wF4Bqu4UB4yckcnjtFplVOZ6v8Vms1Hs8hL6zmeFwv5/4zqiodjvmggLUcMfmhtx4Do8nTiX0DQYHwfBXechhlBkVcWSq3U7GnDn0uuwyUk44IS6/pbg3bKTsL3+h8qWXYvq+0Z0uzeTbJJPI0k8wTks/te++y/bpMyi+PrmXwER8pJuM/GpQHz6fNIIZGSm4QyGu2FDEf+p99Hvk7yhWK9m1TvpV1nFw1w5U1UJ6enh2IkmXf5ZV15OllTOQnYCCqXIYoYNlaEDqhOMZNrlr5WLlhKvUjmE9Fs2Dy+rAU9+47IMp0u+nLHEdlP1GPVAJYEdVj1yFvCMURcGn6B2UQw5Dt9z5I4FKF+SYOgUMBnw7d+IvKYnZ+/oPRGqHJHeRs2i/n8r4VOiNBGzJ2kZAdI4Cq5l/HTuYi/r0QgNu2LyXzzJyyQnncI0sqeDQ+rUAZDRY/klGX1XXczx6EJXiOIal9z0IgCcthbPuuBeD0ZTI4bWbwzEMozEdE36yKcNlb7pFOVJd238gcYGKZtCr0mpqfDvR+8OBCnYFbzespSKBShdkSEvDdoxe6rp+yZKYva+/OFyNNcnLxhsyw0s/1dVoodjXrogugSV5wCbiz6gq/Hl4P+b2ySIEXLN5Dwd+dB5aYX9MoRDq+3rxxcyMcEJt9cqkWzLUNI1l1fVMCAcq+1a6CVVVEjSo5Mz6HmabPcEjbD9FUaK7aBw49S3K38lT6Yx+P87lyym64Ccc/MtfmjynaRqqSd+BE6/OyRFBVQ9UNLvSLfv9SKDSRTlm6LUEYrVFV9M0/OHmiuaCgpi8Z7wYw4EKwSDBmqbbEo9WpHy+9PkRAKqi8OdhBZyYmYIrGGL+5n3YwvWQsnbuwbNjB2lp41BVMz7fIVyu3QkecWN7PT6qPZWMRN+mW7ouSNWwwRR8upD83/wmwaPrOFP4yz+Felw2B66673ZQ1mdEQ04nwfr4fHn79u7FvW4dvp27mj7ndmOw6L3dTHEqnx+hhTsoq1a6ZRl9CVS6qJQZMwBwLlvW4UaHDQUOHULzekFVk/4LWjGZUFP1f5jBODRoDESWfpL8OojOY1IVnhw9gHyLiV1uL48NGcuhjFRUoOThhzEYLKSl6rs+ampWJXaw3/FVdT3jWY2BEN5qO746M7OvuJr0vHwMaWmJHl6HGU2Rbbn1OG0p1HwnF0V1OKI/J+KVpxKsqNDH0szWZE99HebwjIo1zoGKEglULCHJURHJwzpmDIb0dEJ1dbjXrz/q94vMppj69EExJf96tTE3F0NONiGXO6bvqwWD0boL0udHNJRpMvLIyP4owCulVXz7vZPZnZ2O63szAUjP0Au/VdesTuAom2q47FO1047ZZqPwmK5R3K01kRkVfenHwcFdTXdcGXvnolgscfmFBhqUz+/VtM+Ps7YGi9ELgC3OgYrRqAecRnNQdv2I5KEYDNinTgX0ddKjFclPMSX5sk/EoHffYdiXX2IbMzqm7xs4dAiCQTAam600KXq2GZmpXJyvfyk9d9LZbCjozcFq/bfqSIXamiQLVFZWVTCWtQDU7E5lyKhjMCRZHk1HNJxRcdlSKNvdNFAZ+PrrDF/7Dfbj49MeIHBIr9HSXOL9odo6HIoeNDjMGXE5f4TRFAlUAjKjIpKLY4q+08C17OgDlfSzzmTYiuXk//G+o36vzvDdZnGxEt2i3bt3s72bhPj1oD70MhkpNtlYP/J4Du7Se/xEtii7XLvx+SoSOcSovW4vKZ5vseLF77bgrrAwcPsetk6YSNVrryV6eEfFFJ5FcODEaUuhqrSkSSKp6nDE7WcF6A1nAUzNBCoHnM5oQ8J4L/1YwkGbyeiXZFqRXBxTpgDgXruWkPvol0AM6ek9frlDEmnFkWSYjPxygL6j5KvjZ1G8fz/BQADVpWC3DwGgpiZ+DTPb418HKqOzKbV7rBhNFozFpWg+H8beyV0v6Ugazqh4U/W/H9y1s1PHEKl6G9kK3VCZ0x33Pj8R9vC1MBt9EqiI5GIqLCTlpJPIuuxSPRG2B4r1VtBAeGuyUbYmi1ZclN+LQqsZlz2VnX2GsmvOHIou/CkZaXqrjGTIUzng9fP43kOMZR0AdfsdDBw5Bv9ufVeS7dhjEjm8o2YK985x4MTl0JNJDzaz/BMvWiikLxXTwtKP53DnZGOcAxVHuN+P1eCRQEUkF0VRKHjicXJ/+UsMGRmJHk6nqn7rP2ybOo3S22+P6fumnXkmBU8+QdZFF8X0fUX3YlZVfjVID2YXTz4Z/+4ifDt34ijVf7NOhjyVP+8upU9wK4XsQdOgrtjB4F69QdMwFRRgbCYBtCuJ5GU4qKfeaEFDaZJQ61rzDTtPPY09F18S8/MHKyr0fDZVbfZaVvj90aUfU5zrqKSGOyjbVA+eGG8wSAYSqAhCHg87vn8Ke372s5gsIXUKg0qwqiqadR8rprw8UmbOjBbUE6Il5+Zm0N/vojo9k02jxwCgLNJ3z9XWbiAYTNws5+Z6N/8tKeZaHgagemcaIZ+ZLI++XbY73N+Hd/3UE1IUPBZrk4RaxWzGt2cPvqKimJ8/kp9i7NULxdi0PH6FPxSdUYn30k+GRc+BseOkNgblKpKNBCoC3549+Pftw7NpM4rVmujhtEmk308gTtsOhTgSVVG4OlzU9cXppwDg+vhLTFoWmuajru7bhI3tz7sPcClPk8cBVC2T/V/mkT9sBIHNWwCa7fTb1USa/KWGuxM77alNEmpNeeHqtOXlaH5/TM9vcDjI+MkFpJ1xRrPPV2uhaOfkeC/9WM2Ht2rXhGK/JJ5oEqh0A76iIipfehn32rUde/0uvaqiZeDAuGbIx5IhM9yYME4dlIVoi3OHDCS74gBfjxyLOyeXUF0dmbsKgcQt/zgDQZzl74Q7JavUb5tG0GdgwNjxuNfp+Sq2cV0/UDGFE0jt1IOm4ek/CGicUGvIygKTCTSNQHlsZ1/NAwbQ55576H1H88vPXsWHih4wRHYoxUukjoodFy6jkYCve+UsSqDSDVS88AIH77uPmnfe7dDrveFAxTxoUCyHFVfGrHC/n8rKmP32EHI6Kb7tNsr+9jDad1rGC9GcjN55TN/8NZqq8vG4iQBY1uj9pw4cfAdNi30vqiP5srKW87SXASjsfw1FKw4A0K93H4LV1SgmE5YRIzp9XLEWmVExEsCCF1fhUKBxQq2iqtGqsfHs+dOcQLjYW0ixoarmuJ4rci1UQgQcpm5XS0UClW7AMSVc+G1Fx+qp+HYXAWAeOCBGI4q/SAdlze8n5IzNP0p/aSm1//cOVa+9JjVURJsoisL3DH7S6qr5eJzemDC0cg+GkIP6+s0cPNixXx6OxtcHltGLCgKKnf3LHfjcLhwZmaRU6r1wrKNGoZrj+8XZGQwGO6pqASCNGiqy9eTm7ybUmsLNCf3hrcSdRTGHZzXUjLify2CwEECvKB6yG7rdzh8JVLoB+6SJoCj4duyMJni1R3TppwvNqKg2G4rNBkCwMjbFtfwlUkNFtF/+wMFMWL+UzQOGUJmZheZ00a9Cz1nZtetvhEK+ThtLSNMIVH0MQNA4gdXv/B8AJ8+7Bs96PWemOyz7gB4kms36bEk6NRTbwluUwwX4Iox5er2YWM+ouFavxvXNNwTr65s85/d5MZr1pFZjnIu9Rc8Z6aCcouKTGRWRbIyZmVhG6lO5rhUr2/VaLRTCG66rYB7YdQIVILolMFAeq0AlXJVWAhXRDr0HDWHs5lXYAj4WH6sv/9jW6F+ibs9eSkr+3WljWVZVx5ig3lH90GL938UxJ5/G0IlTyb3llxS+/BIZ55/faeOJt8OBSjX7QgoB1UD1gdLGCbXhLsr+gwdieu4Dv/s9e+ZeiPubb5o8566txWTWA1SrpXO2gQfDgQo2VWZURHKKLv8sX9au1wUOHkRzu8FoxFzQLx5Di5tIL55ARWyS5PzhYm8mKfYm2iFv0FDMAT/HblzBF+P15R/frj0MKPwFALuLHiUY7JzfcD/bt4QsKvFqVio2B8jM78dJl8wD9FlI+4QJWAYP7pSxdIZIoNLbUEtAA98APU+lfO+e6DHG8NJPIMZLP5EZmsj7N1RRW4tD1f8/t3dSoBJS9YRaxapJoCKSk2OqXk7ftWx5u5JLvTvDibQFBV2ia3JDhmz9B0AwRtn8kfL5RplREe2Qmp2DLTWNcd8uY/OQEcz7zQNUPf0cfftegM3aH5+vnIMH34/7OLyhEJ6qjwAoK85GC6qcfs1NmLpIyYGOMJv0nwGDTPryS02+vuPKXVcbPSZS3j6WSz8hn49gdbX+/rk5TZ4/UFdHKvoYbOasmJ23NapBX/oyWCRQEUnKftxxYDTiLynBv39/m1/n3bIZAMvw4fEaWtykn3U2ubfdhm38+Ji8XyC69NOz+x2J9lEUhd6Dh5LiqudEPOzsV8iTxeWoqpnc3NOBztmqvKi8mmNDXwHg26CQld+PPkO73r/r9ojMqPQ16BVgD2Xqyzzu+sOBinXUKHpdeWVMl7wCZXrpfMVsbrYq+IF6J6nhqrRmU+fkqKjGSKDS/TooS6DSTagOR7SIk3NZ25d/7BMn0uvqq0g77bR4DS1u0k49hV6X/wxrjLZaRpd++nTtZm2i8+UN0psRzty7CYB3D1Wzz+MjPV0Pomtqm+YxxNrn+/VlH1/ISv0+O0MmTY0+V3rX3eydNx/XqlVxH0dnigQqvRQ9MDmYqs9eeBokuFoGDST3phtJP+vMmJ03UBZZ9slttvZUmcsTnVGJd7G3CFO4pYDRFMQnMyoiWTkmTwbAtXxFm19jO/ZYcm+4gbTTTo3XsLoELRDAf0BPtjP161q5OiLxcsOBimXTWk7MTCGowdPrtuAIDQTA6dxBIFAXt/PXBYJQ8wkAFcVZaCGVoZOmRZ+vX/IlziVL0ALdqz5QJFBJ1aoBKAnv/PHUx+9aAwTKWu6aDHpDwsMzKp2z9GMJ11IxmfwyoyKSl2PaVCzDhmEe0n2S5TpL4OBBvcGYyYQxp+masxCtyRsUTuLcv5er8rO4+s2XmHvpBXz+wjtYrQWARk3turid/60DFUzQ9GUf1waV1F459A4HT/6DZfqypqpiHTMmbmNIBLNZz1ExhfQK1WUmKz6TOf6BSjSRtvmfFRX+QHRGxdRJSz+2cBl9s9GH19l0y3RXJoFKN2KfMIFB//c2Oddck+ihdIpgXR01b79N1WuvHfV7hXw+7FOnYJ9wPIoq/yxE+6Rk9cKenoEWCjGiuozRo/TckNrPP6fOogcH1VVtn+lsjw11Lv614xMyqcIfslK/38GQiVOiSxLu9XqAZBk6FEOKIy5jSJTIjErQX0GuWW8MWJ6Zi6eucaDiXreOqn+9jmfrtpicN1KvytTMjh+AqqAWnVHprEDFHg5UrKoHr1tmVEQ34Vy2jIoFC/Bs3ZrooXRIqLaWkl/dzsH7HzjqMvqWgQMpfP55Cp9/PkajEz2JoijkDQ6XcN+1nbPP0RvVjd69nddL9aXEisovY37eQz4/V63/lotD/wCgfndqeNnncH6KZ/16oHt0TP6uSKASCNQxxq5Xky7P6o3H2ThQqXzxJQ7ccw/OJUtict7IVmdjbm6zzzdsSGjqpF0/aZYMAGyqmzqPp1PO2VkkUOnBqv/7X8oe+BN1H32U6KF0iCFcR0Xz+QjVxXeqV4gjyR2oL7Uc3LUDc79+mAcPxhAKoWzWf9Ovq9uAzxeb4oSgd8j9+bc7meP9k94lWc1h/5IMrKlp9B0xOnqce233aUT4XUZjOoqil1UYY3UDeqDi/k61WFO+vpMvUn36aB0pR8Wn+FDR+zyZwrkj8ZZqzgDAjpNqf6BTztlZOi1QcTqdvPzyy5x//vkMGzYMm81GRkYGM2fO5LUYTN2Lw/wlJVS+/AqBVjoLa5qGa+XXgL7zpytSLRbUVD15LtadUYVor7zBhwMVgJSZMwEYu3E7exkAaFRWLo3Z+T6trKOw5mmOYR2KaoUDZxP0GBk0fgJquFeVFgjg3rABILorsDvRy+jreSqFkVoqqRlNclRMfWMbqBQ8/k8Gf/gBKSee0OzzoWhDQgeq2jn1qSLdpB04qQ10fjPMeOq0QOXLL7/k4osvZtGiRYwfP54bb7yROXPmsH79ei688EKuu+66zhpKt7f/uus5+Ic/UP/ZZy0e49+3j8CBA2AyYRs3rvMGF2PR6rSHji5Q8RUVEaypiVknZtHz9A7PqFTs34ff44l+iU3fvJ61IT1IqKj4PGbne3X3Ss7ifwCMHvknilbsBWDQcYd/8fDu2IHmdqOmpHSp7ujtYbX2BaBXSP/8TntqkxyVWM+oqA4H5gEDMKSlNf+8SV96UQ2dM5sCYAzXUXHgpLZ7xSmdF6j06dOHV155hdLSUl5//XXuv/9+nn32WbZs2UJhYSGPPfYYX3/9dWcNp1tLnX0yALXvf9DiMa6Vek8g2zHHoIab+3VF0UCl/NBRvc/eK+axbfIU3N+sjcGoRE+UktWLlMwsNC1EWdEu7Mcdh2q3k1pTTdV+/YuyrOIzQqGjn5ZfVeMktfZD/byZs7AwgcqS/SiqSuExhwsgRpd9jhnbbZPEI7VqHB595shpT8Xv9RDw+6PHxDpQaU0wEMBo1s/dWYm0AEajHjTZcFOnhLrVL12dducee+yxXHjhhZi+U6a9d+/eXHnllQB8/nnsftvoydLO1AsbOZcta7GbsjMcqNgndc1ln4jIVuJIpciOaFRDpa9UpRUdF6mncnD3DhSzGftUPal1SpGPOlIJBWqoqTm6omuapnHfzv1MQ0/OHdB3Dms/fg+AviNGYXWkRI91r9MDFWs3XPaJSE8/Tv+LS/+s9fYUNGi0RTcSqITq6gjGOZ+tprYWm1HPl7Fbs+N6roYigQpA0GbA7+0+CbVJEWJHghej0ZjgkXQP5oICvax8KETt+017jGg+H87PvwDAMXlKZw8vpmLRxyNSQ0WRGiriKEXqqRzcuR0Ax3S96Nqs7Zv5hgkAbCw+ur4/iyrrqK1eTjblqIZUvBV5rH7vfwBMOPOHjY7N+NEP6XX1VaSG82W6o/Q0PVDxubZj01yEDEbcVnujfj+q3R4tdX+0syrOlSspmnshZX99qNnni6troluTbZ3UkBBAVU0EFL2vk2bvXh2UEx6oBINBXnzxRRRFYfbs2a0e6/V6qa2tbfRHNC/97LMAqH7jTbRQ4wXL+iVLCNbUYMzJwT5xQiKGFzOR7YGRktYd4SsuBvTfurrr9LjoHL3DCbUHIgm1M2YAYCwtxpZ2EgDlhz6kzFXdoff3hkLctb2Y09FnUHpnn8HH/3wMNI3RJ81m8PGTGx1vnziR3Btu6NJ5aEdiseREi+qNM+4Ewnkq302ojSz/FB9doOLbXYT7m2/w7tjR7PMH6+qjxd46q89PREDV81SwKfi6UXXahP9UvvPOO/n222/52c9+xpgjVE28//77SU9Pj/4pKCjopFF2PWlnnomakoJv507qFy9u9FzNO+/ox5xxBkp4d0BXZeqtByotLXG1ReQHl6lv35iMSfRckYTaypL9+NwuzP37M+id/2PIp5/y05FnU0EOqVoFL668ia+r21c9NKRp/GnXAVyunYxnNaCwf6WVmrKDpOXkMuvSn8fhE3UNGeHln7GqPpNVb09tUkY+8u/7aGdUIl3WTS10WT/gdDYo9tY5NVQiQtFAhZ49o5KdnY2iKG3+s/g7X5INPfXUU9x///2MHz+ev//970c89x133EFNTU30z759+9o7/B7DkJpK5ty5AJQ/+WR0VsV/4AD1i/TdQGlnnZWw8cWKsXdvUBS0Bolz7RXpNi2BijhajoxMUnplg6ZRVrQL0CvCKopCgSON4SMfIojK+NAX/O2bJ/jgUHWb3rfE42Puul38c18Zp/MuAOkp0/n2w5WgKJx2zU1Y7PZ4fayklxZOqB2k6cUrnfbUJo35TPn5KCYTIefRfYFHAh1TfvOBSpnbQ0o0UMk4qnO1l2KIdFDW8B7l50wm7U4KmTt3LnXtSEbKy2u+E+3zzz/PVVddxdixY1m4cCEpKSnNHteQxWLBYrG0+dw9Xdall1D50kt41q2ncsEL9Lr8Zxizssj+xbW4VqzEOnpUood41GzHHsuI9etQTB2vVeCPLP1IoCJiIG/QEHZUlHNw1w76jWw8S3xsn2ls9dzM/t0PcpH2LPdtGsHACSczwtHyzrvVNU4uXL+LmkCQHKWWWXwBGvgPjQUWUzh2HAWjxjZ5XfkTT6BYrKSdcUZ05rG7isyo5AU2o2ghnM3MqOTcdCO5v7rtqJd3AyV6l3VjCzMq5V4/BdZwoGLu3KUfgzESqAS71YxKuwOVRx999KhP+txzzzF//nxGjRrFp59+Sq9enZdw1JMYs7PpffvtHLjnHtzfrEHTLkMxm8mePx9t3rxm25N3NUoMErAlUBGx1HvQUHZ8vZyidWs4/gfnRh8P1tSAwciwAVfirF5BVdWX/CD4GnPXDeStcUMYZG/6S9jmejcXhYOUcal2/pC6kJpiL2mpx7D7S712UMO6KRFaKETFc88Tqq3FPmlitw9UHI7hGAx2CDrpy37qHSlNvqhVqzUm5/KX6oGKqU/zOwQrA0FGRRsSdu7ST2Tnj9EU6FYdlDs9R+W5555j3rx5jBgxgkWLFpEjuyziKuOC80mf8yN6XXFFo8CkOwQpsSKBioil4dNOQFFVitatoXSHvhRRes89bJs6jdr33kNRVIYNvQOACazE7TnImWu28f6h6mjti5Cm8Z+DVZy1ZjtVgSDHp1r4W/r/qCl+AoA+vX9K8dbNAAwa3zRQ8e3aRai2FsVmwzp8eGd87IRSVSNpqXovo6Fs1WdU4tCYTwsG8Yd3GLa09FMZCEVzVDo7mdYSrk5rNPu71YxKpwYqzz77bKMgJbeFhk4idhRFIf+++7p11v/R0AKBwz94JFARMZCZl8+oE2YBsOyNV4Hw7rRQCOdSvYR+Sspw0tOPRyXET8yLqfQHuXxDEdNWbOan63YxZflmrtm0h/pgiFlpfu5W/0jJvicBKCi4nLq92YSCQTLz+5GR1/QL0/XNN4Be0DEWs45dQaSeylC2Um9Pa5KjEqFpWoeLoQXKyyEQAIOhxVIGNYRIQU+U7syCbwDWcAdli8GPrxt1UO60O3jRokXMnz8fTdM48cQTefzxx5scM27cOM4999zOGpLoJg7c90dqP/iA3JtuJGPOnPa9WFUZ+J+38O/fjzGn84ozie5tyo9+wqYvP2P32tWUbNtC5vTplD/yKM7ly9ECARSjkX59L6KmZjWzQu9i6ftTnj7gZbfbx263D4AUg8rVBbnMrLyJmuqVGAx2Ro64n969z+Ttt+8DYNjkac2eP1Jh2TZ+XGd83KTQOFC5EG950y/q3RdcgHf7Dga++SaWQQPbfY5oIm3v3i3umPQa9D4/GgrGTmpIGOEwpVMHWAweXD05mbaj9u7dG41in3zyyWaPufTSSyVQEe2meTwEy8vxlx5o92sVVcU6fHiPmB4XnScjrw+jTvweGxd/wrI3X+VHv7obNT2dUE0N7m+/xT5+PLm5Z1C05584nds5T3mLX0z7FUur6jnk99PPYub4dAeqZwcrdq9EUYxMOP5NUlKG43O7KFq7GoBhU2Y0e353eEbFPn58s893R5FS+vmUoNhDeJqZUdFcLjSXC39JyVEFKi0l0gJoRj3QRElBVTt3NislPKNiV1zUerydeu546rSln8suuyw65dbSnwULFnTWcEQ3EovqtELE2pQf/QTVYKBo3RoO7N6BI1xO37n0K0DPqxgy5HYA9u1/gZBrA6flpHNxfjazeqWRaoAdO/8EQHb2bFJS9GB655qvCfh9ZPbJJ6ew6ZdtoKoK3+7dQPfsmNwSkykDi7U/AHnGg9T6mpYsMEZ7/hR36Bzmvn3JOP98Umed1OzzoVAIg0U/b2fPpgDYLBmA3piwqpnP31UlvOCbEEfL1EffAh/JxhciGWT0zmPIJH1pZvc3q6Pl9J1LlkSP6ZU1k9yc09G0ABs33EggoCdhhkJeNm/5DRUVn6OqFgYOuCb6mm3L9B4/w6ac0GxSvHvtWgDMgwdHy8b3FFazvoPUQT3lzXQQNvfTi4T69+3v0Pvbxo2jz+/upde8ec0+X15fj13Vl5w6s3x+RGTXjx0n1YFgp58/XnpGlpXo1o6mM+qhRx/DX1JC5k8u6FG/fYrO0W/kaLYt+5LSHVuZeKnefNW9fj3B2loMaWkoisKIEfdRW7cet2cvK1aeicMxBJerCLe7CIBRI/9CaupoAHxuF7ujyz7Tmz1nT8xPiTCa9C9qB04qmvk93Fyoz7j49u6Ny/n3VFRFd/xYO7EhYYTJePjz1wabidS6KJlREV1ew9LY7c3mr//sM2r++18CFRXxGJro4fKHjgCgdPsWjHl5mAcN0nf/LF8ePcZkSmfM6IdRVRsez34qKhbjdhdhMmVy7DHP0Lv3D6LH7ly9kqDfT2afvs0u+0DPzE+JiCy32HFSpTYtAmnqH99Apbi2tkH5/M7d8QONZ1RqOraxKSnJjIro8kx5eXoZfY+HYGUlxjYWENQ0Dd+ePQCYCwvjOUTRQ2X3H4DRbMHrdFJZWoxj+nR8u3bhXLKUtFNOiR6Xnn4c06Ytpr5uE27PfhTFQG7O6ZjCMwSgF3Hb8NlCAIZPndFiLSTb+PGEvF69g3oPY2wwo1BuzCAUDKI22J1j7q//O/fv2YOmae2qJ6UFAtR98gmmfgVYR41stsJtSb0z2pAwMYFKeHsyPpx0nxkVCVREl6eYzRhzcwkcPIi/uLjNgUqwslLv+6EomPr1i/MoRU9kMBrpPWgIxVs2UrJtMwOmT6PqpZfwbNjQ5FiLORtLrxNbfK+l/36FvRvWoRqMjAzXaWlO7s03xWTsXZEpXELejhOnvR+ummpSsg7/PDD16wuqSsjlIlhRgTG77csz/gMHKL7xJhSzmeFrv2n2mANuL6lqIgOVFDQUFDR8phBaKNQtOsJ3/U8gBB3rjBqZTTH2yUOVHlIiTgpG6f1+itZ9g2PyZAa88QYD3nyjXe+x4bOFrPjv6wCccuV1ZOVLYN2chjMqTnsq9VWVjZ5XzeZo1+P2Lv/4w01wTf36tfjlX+b1k0YNAGZz5yfTKopKUNGbUyp2DZ/H3eljiAcJVES3EE2oLW77tkPfHv0HlSz7iHgadNwkAIrWrkYzmbCNHdOu33L3bljHwqcfA2DKjy5g9MyT4zLO7sBoOpyjUm9PxVld2eSYaELtnvYFKr5IoFLQcpBYEQiRHglUTInpYRcKd1DGCp76+oSMIdYkUBHdQtoZp5N7yy+xT57S5tf49hQBh9ethYiHvMFDsaWl43O7KN6yqd2v/+yFpwkFg4yYPpNp51/U4nGaz0fVa6/h3bmzwyXiu7omMyqVTQOVjPPPp/edv8U2rn27/CJbmiNbnJtTqSmkU60fZ05QpWuDfg1Uq4bH2T0CFclREd1C6ve+B9/7XrteE02kDe8EECIeFFVl0PiJbPz8E3atWUH/MXrzvGC9E9VqabUXj6u2hvK9RQDMuuznrSZ/utev58C9v8OQlcXQpUtaPK47a7g9V59R2dnkmLTTTuvQe0eWilqbUak2qNFk2kQFKmp4RsVgDuE9yjL6xR4fDxUdYIDNwnWFvWMxvA6RGRXRY/l27gLAPHhQgkciurvBx+vLP9uWf4UWCrH/hhvZNmUKrjVrWn1d8VZ9BqZXv/7Y01qvdOpcuRIA+6RJPbY7urFBMq3PYqWiujpm7+3bpf+8sAxsufS+3xjAiF5ozWzOitm52yNSS8ZoDuA9yhmVIreXV0oref1A05mpziSBiuiRtEAgWmbcMmRIgkcjuruB4ydgttmpqzhE8ZZNKCYTBALRcvot2bNO313Sd8SoI57DtUIPVByTJx39gLuohks/ACXO2CSTasHg4RnYQc3/YuMNhTBZ9D4/qpqCqiYmQd8cvgYmU+Col34O+QIA5JgTu/gigYroNuo+/ZTyJ54k0My69HeFPB7Szjkb+8SJ0URcIeLFaDYzNNzpePOSxThm6FVlnUuXtvgaj7OeTV8sAmDopOa7JEeEvN7Dhd4mT47FkLskUziZ1ooHgxagJPxF+121779P2UN/w9/G/mD+0lI0nw/FbG7x58W+mrrojh9LovJTAJs5AwCzyRezQCXX3LR4XmeSQEV0G2V/eZBDDz+Md+vWIx5rSEkh/w9/oPClF7tFnQGR/EbOOAmATUs+oz4vFwDPxo0EqqqaPX7Doo/xez306tefwmNaL97mXrsOzefDkJONuZWlie5On1HR/z2nUsfBFmqelT/1NBVPPYVnU9uSmyPLPubCQpQGBeQa2lVREd3xY7Hmtm/gMWQPL/1YVQ/1R5mjUhZubCgzKkLESOQHtK+oKLEDEaIZ/UcfQ+Ex4wl4vbz91CMYBg4ATcP5VdPlH7/Xw+r3/gfAcWecc8ScE9eKFQA4Jvbc/BQARTFE65dkUEW5aiIUatqczxJevvHtbJps2xx/id7wtLUgcG917eEdPwnamgyQ0qCDcrnbc1TvVSYzKkLElnnAAAC84dwTIZKJoqqc/ctf02focDzOeor8ev6Ec0nT5Z9vPnyX+qpK0nJyGXXikXez1Yc7MjumTY3toLsgi1mfzcigitqUdA6FyxA0FEmg94YT6o8k8ycXMHz1Knr/9jctHrO/3kVaorcmA6YG/Y4qwjMiHXUo/PpsmVERIjYigUpbZlTqlyzFs20bmv/o/iEL0R5mq40f3n4P2QWFlJj1H7/1n3+OFjz8W7/X5WLl23rl2mk//ilGU+u/zQYqKvB8+y0AjhNaLsHfU5gtOcDhQOVfd93GmvffRgsdXgeyDB4MgLeNMyoAqsOBKbflJZ0Sj5f06NbkxM2oRHb92HFR5W8+R6etJEdFiBgzDxwAgG93UavHaYEA+3/xC3affQ6+/fvjPzAhGrClpHLubXdSm5GOz6ASrKzEHd6mrGkaa95/G6/TSVZ+P0aecNIR38+5ZAloGpaRIzH1TlxuRLJoOKPi6dWbgM/LZy88zb9/92uqD+hLOJFAxbdjR6MA5mgc9IeSYkbl8M6neqqCR1f4L5KjkiszKkLERmRGxV9cTMjna/E4765daB4Pqt0u5fNFQqTn5jFxzvmUpel9WYoWPM/mJYt55dc389UbrwAw8ew5qGrziZuNqAYsQ4eQcqLMpkDDGZVqalMyOHneNZgsVvZv3sALt/2Cbz56F1NhIYrFQsjlwn+Enj9aKNSmSr/lKIfL5ydwRiVS9M6Ok+qjiFNCmka5X7YnCxFTxpwc1JQUCIVanVWJZPpbWmjVLkRnmHTOeSgTjgegetlXvP/ogxzctR2jyczxPzi3TbkpAOlnncmgd94h5/rr4jncLsNsPrz04wqFKDzpFC598DEKRo0l4PWy6LknePOBezCGE2qPtPPH+eWXbJ9xAqV339PqcZUGc+LL59NwRsVFzVF8xVf6g0QmZMqWfRmLoXWY/JQW3YaiKFhGDAfAu2Vzi8d5Nuo/mGyjR3fKuIRojsFo4qQH/krNxT9h/cSxmG12pvzoAub/83lOumQeagvbYFvS0rbZniay9JOtVAN6Gfj03Dx+fOd9fO9nV2K0WNi3cT27KvQaKu6NrQcqns1bCFZUEGplq29I06ixWMlA32puNiduCS4SqBgJ4DZqHe77FEmktbmdrPrvv2I2vo6QXj+iW7GOGoV71Wo8mzaRfs45zR4T+Q3KOurI1T6FiCejw8GU39zN5PCXSU/eWhwrlvDST1Y4aNhQ72ZMqh1FVRl/2lkMHDeBDx9/mOqKZRQAe997B9tll5CW03xw4dmyBQDryBEtnvOQ14ddcWMi0GgMiWAw2NEwohBAsYPf48Zss7f7fQ75Aji0Ok4MLiJ9QMtL6Z1BZlREt2IdNQpDZiYYmo/BtVAIz+bN0WOFSAaKorQ7SNH8fipffgV/aWmcRtU1RWYzUrUq0DRu2rKPX23dR3U43yIjrw8X3H0/Q8//CW6ziSqvmxduvZb1n37U7OyDN/LzYuTIFs+5o6KKTPSK2CZTZsLK54N+L2nhDsrY6HB12jKfn3yKucjxIukj1sVwhO0ngYroVtLPOouhXy2l9223Nvu8b9cuNJcLxWrt0RU8RfIJVldH+8m0Rf0XX3DwD39g9/nnx2znSncQyVFR8TM314gGvFBSwfQVW3i9tBJN0/TZlZ9fw4APP6Bi9kx8bjcLn3qU/9x/N3UV5dH3CtY7o12TLSNanlHZVlERDVQslsR1GY5QjRkAGKwhPPUdDVQC0ZYAipYSq6F1iAQqoltRDIZWfzONdJi1jRuHYpSVT5Ecat9/n+0zT+LAffe1+TXV//kvAOlnnS1J4Q0YDBaM4aJnvx9o5a1xgxlqt1DhD3DDlr2c+80ONtfrxfay8vtywb0PMPPiKzCYTBStW8MLt1zLhs8Womka7nVrQdMw5edjzGq5G/LO6rrDgUoC81MijKYMAExH0UH5kM8fDVQMSlqshtYhcneLHsW1PFxqfErPbdwmko91zBg0rxfnl0vaVNsnUF5O/eLFAGT86IdxHl3XY7HowYLPW8b0zFQ+nTic3w7qg01VWVHjZPaqrdy9o5j6QBBVNTDhzB9y8Z8eoc+Q4XhdTj564u/878+/o37ZMgDsEye2er49bi+Z4ZwYiyUvvh+uDSzh5owmix93XUcDlUC0gJ3JmBmzsXWEBCqiWwrWO5vddhgMN4DryR1mRfIx9++PY/p00DSqXnr5iMdXvfYvCAaxHnsMliFDOmGEXUtk+cfrO6T/t6ryi8LefDl5BD/ISSeowZP7DjFjxRbe2bmHus8/p1ffAn7y+z9zwoWXYTAa2bXmayo+/RQA+6RJrZ6vJKCREZ5RMVsSP6NiN+uzP3bVTZWzrkPvUebzHy5gZ0ncdmuQQEV0Q+5vv2Xb1Knsu+rqJslxhS+9yNAvv8A2dmyCRidE87IuuwyAqtdfJ1BR0eJxIZeLqlf0onC9Lr20M4bW5USWX3zeskaP97OaeXbMQF45ZhADbGaq6uvpf86Z7L/yKnbs2IWqGph0znmMO/VMDMEQyh49P8U+ufVA5YBqIiuJclSs5gwAHNRxyOnu0HvoMyr60o/VltjPJIGK6HYsw4ahGI0Eysrwbt3a5HljTo7kp4ik45gxHesxx6B5PFQ882yLx1X9+98Eq6sxFRSQesopnTjCriMyAxCZUfmuk3ulsXjiCK4bVsjOggEAPPDvd/jTrlJcwRBpOblkujwooRDG/D6Y+vZt8VyBkEaV2RadUUmGHBWzSV+qSaGeck/HOig3TKa1O/JjNraOkEBFdDuqxYIjPFVb/0ViKyoK0VaKopBz7TUAVL70Ep6t25ocEzh0iPLH/gFAr/nzJOBuQUszKg1ZDSq3DMxj7KnfB+DEr5fytz0HmblyC+szelORYmPHtOPJvvKqVhP0iz0eQqpKppY8MyrGcI5KCnUc8rS/8WogpFHpPxyoONJaDtQ6gwQqoltKmXUSANVvvIHm9+PdtRvPtqY/+IVIJo4TTyRl9skQCFD6m98QavDbsBYKceB3vyNUX491zBgy5sxJ4EiT23dzVFpTOEdPRp6wZQNjXLXs8/i43WPhP6ddxG6LkcwLzm/19VvKylG0IBnhfI5kCFRM4V0/KdRTEWh/B+UKfwANSNP0QCUlrSCGo2s/CVREt5R+9tkYevXCv28fVa/9i9Lf/pbdZ59D+RNPJnpoQrRIURTy7rwTNS0Nz4YN7L/uekIuFwD+khJcq1aDyUTePfdIyfxWRHf9tCFQMRcUYJtwPIqm8eK+Tfyify5GBXYMHMny/CEEWmlwCrCtvJJ0alAVDVAT2pAwwhSuo+KgnsoOlNgp8/lRtSAp6DuGLFZJphUi5lS7newrfw7AwT/+EfeaNagOB6mnypq+SG6m3r0pePyfKFYrvr17UEwmAMz9+lH46iv0feiv2MZIn6rWRKrTeltZ+mko44c/AqDu6Wf4paeKu1x6MnNZdj51Fa0HO1vrnPRCLxJnseSiKIkPIA/PqNRR3YGv+TJfgBTqUBUNTdOr7SaSLHCKbivjggtwr11L7YcfgcFA30f+jkWq0YouwH788fR/7jlCLlc0UAGwDBwo93AbRHrtBIP1BINuDAZbq8enn3M21W+9hXvNGorO+zEzVJUz5l7B6qHDqC0/RGaflnM0tnv85Fj0YMZq7Re7D3EUGi79VKvtD5wO+fzRTtBawJLw4EtmVES3pVos9H3oIYZ+vpghH31IyvTpiR6SEG1mP248KTPknu0IgyEFVdWDk9LS/6Bpra9/KEYj+X/+M8b8PqBpKCYTO/r1pyIrl71bWu+uvFcxkY0+c2NLskDFRACvSWl3B+VDDXb8EGx/Q8NYk0BFdHvGnBxM+YndXieE6DyKopCRMQGArdvuYtWqOVTXrG71NeZ+fRn84YcM/mQhgxctYk/hIAJGEx8v+oSSbZubfU1dIEiVxU5OOFCx2hK7OyZCVW1oij4Tp9k1/J721VIp8/mjVWlVEls+Xx+DEEII0c0ce8yTDBl8GwZDCrV161m9+nw2bLwRj6ekxdeoZjPmfv0wZ2UyPEWfSShLz+adh/+Eq7amyfEbK6sByAvqHayTZUZFURQM4YRaxUq7GxM2nFExqumxHl67SaAihBCi21FVC4WFVzJ1yifk9zkfUDh48B2WLf8+u3Y/QjDY+izDyBR96ai+cCj1FeV88I+HmnSpXlOsByi5WnhGxZocMyoApmi/nwDOZoKs1jQs9mZKgl1MEqgIIYTotiyWHEaOvJ+JE/9HRvpEQiEPu3f/nWXLv8+BA//XYv7G6BQrAEVjJ6NZ7RStXc2K/73R6JiNlTWgaWQZ9GJvyZJMC2AJ79RxqE7K2tmY8ID3cOfkSGJyIkmgIoQQottLSx3Dcce9xpgxj2K19sXrLWXjpptYveZ8amvXNzn+h70zyTEb2ekPsf6ym9GAr/79Cns3HD52vS9IGrUYFT+gYLX26bwPdASW8ExIGrWU1jvb/DpN0yhtEKjY7InvBi2BihBCiB5BURR6557BlMkfM2jQzRgMdmpq1vD1qh+yadOtjequ5JhNPDGqEBX4GCsVZ1+EpoV4/7EHCfj9OINBdpkd0R0/FktvVNWcoE/WVKTfTyp1HHS1PZm2NhDEHQpFGxLaUxO/EUECFSGEED2KwWBl4IBrmTrlE/Ly9BL6pQf+w7LlJ1NU9E+CQS8A0zNTuX2QPkvyar+RVPUbiLOqkor9e/libwlB1UB/TxGQXPkpACZzFgCp1FDm9bb5daU+vTdQulalvz49seXzQQIVIYQQPZTF0pvRox5kwoT/kJY2nmDQxc5df2X5ilMoK/sQgF/0z+XkrDS8IY3/nXw+XrOFiv17WbhrDwAjA0UA2GyJ/0JvKFJNNo1ayn3BNr/ugNcffR2AzZH45SwJVIQQQvRo6WnHMuH4Nxg96iEsljw8nv18u+FaKiq+RFUUHh3Vn74WE+W2VJYddxKH9u1hqUv/Qh9m1cvtO+xDEvkRmjCb9ByVFOooD7Q9UCn1+rFoHiyK3uPIbE5snx+QQEUIIYTQG0LmncPUKQvJ7vU9AKqqlgGQZTLywDB9R8/a0ZN4bcde9qRkYgj4ybeHczkcgxIz8BYcnlGpoUJT2vy6hjt+tKABg0Eq0wohhBBJw2Cwk5Wlty5wuYuij8/ulcYouxm/ycLb038AwBm+KrRgMQAO++BOH2trzNEclToq1ba39Wu440cLWFGUtgc58SKBihBCCNGAzT4AALerKPqYoij8arCeMKupBuzBAPecMJxg0ImiGLHZChMw0paZGuz6qTZZ2/y6hjMqipYSl7G1l3RPFkIIIRqw2wYA4HLvQdNCKIr+O/2p2eksnjSc3S4vwxxWbB69f5DN1h9VNbX0dglhMukzKkYC+K16fZS2zI4c8PrJDAcqqpL4Pj+Q4BmV5cuXYzAYUBSFBx54IJFDEUIIIQC9wqyiGAmFPHi9Bxs9N8Jh4/ScDAbbrTidOwCw25MrPwX0LdiRDtJmS4Cauro2va7U54/WUDEZMuM2vvZIWKDidru57LLLsNlsiRqCEEII0YSqGqN1URrmqXyX06UHKsmWnxJhDlenTaWWosqqIx7vC4Uob9Dnx5wEfX4ggYHKb37zG0pLS7n99tsTNQQhhBCiWZFZkv37XyIUCjR7TE3NWgBS08Z01rDa5XCeSi17a2qPeHyJ148GZIT0oMZq7x3P4bVZQgKVpUuX8ve//50HH3yQfv2Sp4mTEEIIAdC/4HIUxcShQx+xadMvmwQrgUAd9fVbAMhIPz4RQzyiyM6fNGoprncd8fh9br12SlZIb7LoSE2OarudHqi4XC4uu+wyTjrpJObPn9/ZpxdCCCGOKCtrGmPHPIaimDhY9i6bNt+Kph0unKbPpoSwWguwWJJj5uG7Gs6olLg9Rzx+n0cPVDI0PVBJzRwQt7G1R6cHKrfffjulpaU888wznX1qIYQQos1ycmYzdswjKIqRgwf/j02bbosGK9U1qwDIyEjO2RQ4XJ02jVoO+JtfvmooEqikq3qOiiM1OdoCdGqg8vnnn/PYY4/xxz/+kYEDB7b79V6vl9ra2kZ/hBBCiHjJyTmFMeFg5cDB/7F5y28AqK5eCUBG+oREDq9VkWTYNGooO3Kcwj6PD5PmxWrQmxhazMkxU9TuQCU7OxtFUdr8Z/HixQA4nU4uv/xypk6dyi9+8YsODfb+++8nPT09+qegIDmiPSGEEN1Xbs6pjBn9dxTFQGnpGxw8+G44UFHJypqR6OG1yGzOASCDaiqUI3/d7/f4yEBPpNWCBozG1LiOr63aXfBt7ty51LVxPzZAXl4eoO/yKSkp4f3330dVOzaRc8cdd3DzzTdH/7u2tlaCFSGEEHGXm3saGcWTqKpaxoaNNwDQu/cPkq5rckORQCWdKioM5iMev8/jI5NqALSgIynK50MHApVHH320Qydau3YtHo+HESNGNPv8HXfcwR133MENN9zAww8/3OwxFosFi8XSofMLIYQQRyMrc0a0USHAgMKrEziaIzNbIoFKNdUWO75QCHMLEwX+kEap188E9ERaVUuOqrTQiSX0f/CDHzBkSNM22Nu3b+eLL75g4sSJHHPMMUydOrWzhiSEEEK0WX7+eVTXfE0o5CM7+3ukpAxP9JBaZTFnA5Cq1aEqQYo9fgbam/9lv8TrIwT00ipAAZMhqxNH2rpOC1RuvfXWZh9fsGABX3zxBT/60Y+k+JsQQoikZTZnM+7YZxM9jDbTtycbUJUgqdSys66+xUAlsuOnT6AMTGC25HbiSFsn3ZOFEEKIbkhRDNGdPxlUs6OyusVjd7n0nT7ZgTIAbLb8uI+vrSRQEUIIIbopS4OE2t119S0et9OtByqZwUMAONKSJ0m405Z+WnLZZZdx2WWXJXoYQgghRLdjtmRDvT6jsjdcIr85O8MzKqlKNQBpme2vdRYvMqMihBBCdFOHtyhXs98favG4nS69xL7DpPcEsqckTx8+CVSEEEKIbsoc3vmTQTUHMDR7jC8UYq/Hh1nzYjb7geSpSgsSqAghhBDdViRQSaeKOoOR2kCwyTF73D6CGuSjJ9JqQVPSVKUFCVSEEEKIbiuSTNsrWAHAVmfTLsq7wom0QwPFACih9KSpSgsSqAghhBDdltms10PppZUDsKne3eSYHeFE2r6+/QAY1ZxOGl3bSKAihBBCdFNWq14PJU2tQdFCzQYqm8OP5Xr2NXpNskj49mQhhBBCxIfF0htQMKp6ddqNtfYmx6yt03f6ZIVnVBypAzpxhEcmMypCCCFEN6WqJiyR5R/K2eL0oGla9PnaQDC69JMWXh5Kz2zaly+RJFARQgghujGLtQ8AOaGD1Guw3+uPPrc+PJuSb1Sx2sNF39IHdf4gWyGBihBCCNGNRXJOCtx7ANgQDk4A1tbqfx+OD1OKv9HxyUICFSGEEKIbs1r0GZV+nr0ALK0+3PMnkp8yzL0P1QCapkR3CiULCVSEEEKIbiyy9NPHXwLA55V1APhDGkur9KCloGoLAEooDVVNrn02yTUaIYQQQsSU1aIv5WSoVaihENtdXvZ7fGx1eqgKBMkxG+lVuQFywGRIntL5ETKjIoQQQnRj1vCMisXho0+ZvgX5s8pa/ldWBcBZORkEwlVpHY7k6ZocIYGKEEII0Y1ZwsmxJnuAoXs2AvDn3Qd471ANAD/IsIFZ/3t61sjEDLIVEqgIIYQQ3ZjZ1AtVtaEoMHnPlwwwaBzyBXAFQ0xOdzCorhJLug+A9IzhCR5tUxKoCCGEEN2Yoig47HptlJRUJ7c6Sxlit/CTvCxeO3YwVSX7sYYDFZttQAJH2jxJphVCCCG6Obt9IHX1G7Fk+HDs3saS08+IPldZuhVDdghNA5utMIGjbJ7MqAghhBDdnN0xGABrhpeyPbsaPXfowFoADGRiMFg6e2hHJIGKEEII0c1Fln4sGT4q9u0lGNCr0GqhEPU124HkXPYBCVSEEEKIbs9u12dUbJk+QkE/Ffv3AVBRvA+DXS8Al4w7fkACFSGEEKLbs9sHAAoGSxCDNUhZkb78U7JtM7ZsDwCpqRKoCCGEECIBDAYbVmtfAKwZPoq3bAL0QMWeowcqaaljEza+1kigIoQQQvQADscQAGy9PGxfsRSvy8m+bUsxWoOAkZSU5KuhAhKoCCGEED1Cetp4/X/7h/C6nLz78J9QrWWAvuyjquZEDq9FEqgIIYQQPUB6+nEApPb1AlC0bg22HDcAaWnHJGxcRyKBihBCCNEDpKePQ1EMYKglI98BQFrfgP6/SZqfAlKZVgghhOgRDAY7KSkjqavbwA9+dTGqvz/fbv8RAJmZUxM8upbJjIoQQgjRQ6SnHw/AofKPcIeWARoZ6ROx2foldmCtkEBFCCGE6CHy+/wYgLKy99mx888A5OWdm8ARHZkEKkIIIUQPkZo6ktzcww0JDYYUcnNPT+CIjkxyVIQQQogeZPCgW3A6t2O19mPwoJswmdITPaRWSaAihBBC9CB2eyFTJn+Y6GG0mSz9CCGEECJpSaAihBBCiKQlgYoQQgghkpYEKkIIIYRIWhKoCCGEECJpSaAihBBCiKQlgYoQQgghkpYEKkIIIYRIWhKoCCGEECJpSaAihBBCiKQlgYoQQgghkpYEKkIIIYRIWhKoCCGEECJpSaAihBBCiKRlTPQAjoamaQDU1tYmeCRCCCGEaKvI93bke7w1XTpQqaurA6CgoCDBIxFCCCFEe9XV1ZGent7qMYrWlnAmSYVCIUpKSkhNTUVRlEQPJ6Zqa2spKChg3759pKWlJXo4SU2uVdvJtWo7uVZtJ9eqfeR66TMpdXV15Ofno6qtZ6F06RkVVVXp169foocRV2lpaT32Rm4vuVZtJ9eq7eRatZ1cq/bp6dfrSDMpEZJMK4QQQoikJYGKEEIIIZKWBCpJymKxcPfdd2OxWBI9lKQn16rt5Fq1nVyrtpNr1T5yvdqnSyfTCiGEEKJ7kxkVIYQQQiQtCVSEEEIIkbQkUBFCCCFE0pJA5Si9/PLLXHnllUyYMAGLxYKiKCxYsKDF41esWME555xDdnY2FouFYcOGcdddd+F2u5s9vqqqiltuuYUhQ4ZgsVjIycnhvPPOY+PGjc0ef9JJJ6EoSrN/TjvttFh85A4rLi7m4Ycf5pRTTqF///6YzWby8vKYM2cOK1asaPY1tbW13HzzzRQWFmKxWCgsLOTmm29utW3Cq6++yqRJk3A4HGRmZnLGGWewatWqFo/fvn07559/Pjk5OdhsNo455hgee+wxQqHQUX/mjkrGa5Ws91a8r5XL5eKvf/0rF154ISNGjEBVVRRFoaioqNVx9cT7qiPXqqfeV2vXruXOO+9kypQp5ObmYrFYGDRoENdccw3FxcUtjisZ76u408RRKSws1AAtOzs7+vfnn3++2WPfeustzWg0ahaLRbvwwgu1m2++WZs8ebIGaNOnT9c8Hk+j48vLy7WhQ4dqgDZ16lTt5ptv1ubOnauZzWbNbrdry5cvb3KOmTNnaoB29913N/nz0ksvxeMStNmvfvUrDdAGDx6sXX755drtt9+uzZkzRzMYDJqqqtrrr7/e6Pj6+npt3LhxGqB9//vf1371q19pp512mgZo48aN0+rr65uc47777tMArX///trNN9+s/fznP9fS0tI0s9msffbZZ02O37hxo5aenq6ZTCbtpz/9qXbbbbdpY8eO1QBt/vz58boUR5SM1ypZ7614X6vdu3drgAZohYWFWlZWlgZou3fvbnFMPfW+6si16qn31eTJkzVFUbRJkyZp1113nXbLLbdoJ5xwQvT7ZPPmzU3GlKz3VbxJoHKUFi5cqBUVFWmapmn3339/i4GKy+XSsrOzNZPJpK1atSr6eCgU0q699loN0O6///5Gr4k8fvPNNzd6/KuvvtIMBoM2atQoLRgMNnou8o8+Gb311lvaF1980eTxL774QjOZTFpWVlajYO2uu+7SAO22225rdHzk8bvuuqvR49u2bdOMRqM2bNgwrbq6Ovr4hg0bNLvdrg0ePFjz+/2NXnPiiSdqgPbee+9FH/P5fNrJJ5+sAdqiRYuO6jN3VDJeq2S9t+J9rerq6rSPP/5Yq6io0DRN00499dQjfvn21PuqI9eqp95Xjz76qLZjx44m7//AAw9ogHbGGWc0eS5Z76t4S767owtrLVBZuHChBmg//vGPmzxXVVUV/Q0kFApFH+/bt6+mqqpWV1fX5DXnnntuszdmsv6jP5JTTjlFA7Svv/5a0zQ9gMvPz9dSUlKa/Cbidru1zMxMrW/fvo2u1x133KEB2gsvvNDk/a+66ioN0D766KPoY1u3btUAbdasWU2OX758uQZoc+fOjdVHjJlEXCtN65r3Viyu1Xcd6cu3J99X39WVA5XWxONaRQQCAc1ut2sOh6PR4131vooFyVHpJAcPHgRg4MCBTZ7LyMggMzOTPXv2sGvXrkavyc7OJiUlpclrIu+zaNGiZs/3r3/9i/vvv59HHnmEZcuWxeIjxJXJZALAaNTbT23fvp2SkhKmT5+Ow+FodKzVauXEE0+kuLiYHTt2RB9fvHgxAKecckqT9z/11FMB+Pzzz9t0/KRJk8jIyGh0fLJIxLVqqCvdW7G4Vu3Vk++royH3lU5RFAwGQ/S9I7rqfRULXbopYVeSk5MDwO7du5s8V1NTQ1VVFQDbtm1j8ODB0dccPHiQ+vr6JsFK5H22bdvW7Pnmzp3b6L8nTpzI66+/3myglGh79+7lk08+IS8vj7FjxwL6P3yAoUOHNvuayOPbt29v9PeUlBTy8vJaPT6itXMoisKQIUNYtWoVLpcLu93e0Y8XU4m6Vg11lXsrVteqvXryfXU05L7Svfnmm9TV1fHjH/+40eNd8b6KFZlR6STTpk0jLS2N//3vf3zzzTeNnrvzzjujf6+uro7+/fTTTycUCnHvvfc2On7lypW8++67TY4HOPfcc/nggw8oLS3F6XSydu1aLrnkEr7++mtmz56Ny+WK7Qc7Sn6/n4svvhiv18uf//xnDAYDoAdv0HJ3zUjH0chxkb+39/j2niOREnmtoGvdW7G8Vu3Vk++rjpD76rB9+/Zx/fXXY7PZ+P3vf9/oua52X8WSzKh0kpSUFB566CHmzZvH1KlTOe+888jLy+Orr75i9erVjBgxgi1btkRvfIB7772XDz74gAcffJBly5YxZcoUSktLefPNNxk1ahTr169vdDzAjTfe2Oi/jz32WF544QUCgQCvvvoqzz//PNdee21nfOQjCoVCXH755XzxxRfMnz+fiy++ONFDSlrJcK26yr2VDNeqq0iGayX3la6yspIzzjiDsrIyXnzxRYYPHx7T9+/KZEalE11xxRW8//77TJ06lbfffpt//vOfGI1GPv30U4YMGQIcXiIC6NevH19//TVXXHEFu3fv5pFHHmH58uX87ne/49e//nWT4490boClS5fG+FN1jKZpzJ8/n5dffpmLLrqIJ554otHzkd8aWvrtIFKXoOFvF+np6e0+vi3niPymkijJcK1ak0z3VjyuVXv15PsqlnrSfVVVVcXs2bPZuHEjjz/+OBdddFGTY7rKfRUPMqPSyU4//XROP/30Jo9ffPHFqKrKcccd1+jxvn378swzzzQ5/p577gFgwoQJbTpvdnY2QFJMo4ZCIebNm8fzzz/P3LlzWbBgAaraOGY+Up5Ec+u1Q4cOZdmyZRw4cKBJ7kVLx7d0Dk3T2LFjB/n5+U2S4zpTslyr1iTLvRWva9VePfm+iqWecl9VVlYye/ZsvvnmG/7xj39w5ZVXNvseXeG+ipvEbTjqflrbntyaJUuWtLhvvjmBQEAbPny4ZjQateLi4ja95qmnntIA7YYbbmjX2GItGAxqP/vZzzRAu+CCC7RAINDscW3Z7pefn99ou9/tt9/erbYnJ9O1ak0y3FvxvFbf1dW3JyfTtWpNT7ivKioqtPHjx2uA9uijj7Y6lmS/r+JJApUYOlKgUlNT0+Sx4uJibcSIEZrRaNRWr17d6Dmfz6e5XK5GjwWDQe3GG2/UAO2mm25q9NzOnTu1kpKSJufYtGmTlpOTowHasmXL2vmpYicYDGqXXXZZtJ7MdwuKfVd7Cyht3bo1ZgXfZs+endACSsl2rZL53or3tfquoyn41t3vq+860rXqyfdVRUVFtJLt3//+9zaNKVnvq3hTNE3TYjtH07M888wzLFmyBIBvv/2WNWvWMH369GjOybnnnsu5554LwB/+8AdefvllZsyYQW5uLvv27ePtt9/G5XLx7LPPcumllzZ67/379zN69GhOOeUUBg4ciM/n46OPPmLLli384Ac/4K233sJisUSPX7BgAfPnz2fWrFkMHjyY1NRUtm/fznvvvYff7+euu+5qsoOoM91zzz3ce++9pKSkcMMNNzSpEwD69Ro3bhwATqeTGTNmsHbtWr7//e9z/PHHs27dOj744APGjRvHkiVLmkxz3nffffz2t7+lf//+nHfeeTidTl577TXcbjcfffQRs2bNanT8pk2bmDZtGm63m/PPP5/8/Hw+/PBD1q9fz7x583j66afjdj1ak2zXKpnvrc64Vrfccgvl5eUALFy4kJKSEubMmRMtG3D77bczYsSI6PE9+b5qz7XqyffVSSedxOeff86IESO44IILmh3DjTfeSEZGRvS/k/W+irtER0pd3aWXXqoR7m3R3J+77747euynn36qzZ49W8vNzdVMJpOWl5enXXDBBdqaNWuafe/a2lrt4osv1gYNGqRZrVYtNTVVmzp1qvb00083KZ2vaZq2bt067eKLL9ZGjhyppaena0ajUevdu7d29tlnt3kaP56OdK1oZjaqurpau+mmm7SCggLNZDJpBQUF2k033dRoFuC7Xn75ZW3ChAmazWbT0tPTtdNOO01buXJli8dv3bpVO++887RevXppFotFGz16tPbII480e407S7Jdq2S+tzrjWkX6eLX0p7neSD31vmrPterJ99WRrhMtzEQl430VbzKjIoQQQoikJduThRBCCJG0JFARQgghRNKSQEUIIYQQSUsCFSGEEEIkLQlUhBBCCJG0JFARQgghRNKSQEUIIYQQSUsCFSGEEEIkLQlUhBBCCJG0JFARooc56aSTUBQl0cNos/r6evr06cM111yT6KF02GeffYaiKLz//vuJHooQXY4EKkJ0YYqitOtPV/TnP/+ZyspK7rjjjkQPpcNmzZrFzJkzufXWWwkGg4kejhBdStN2kEKILuPuu+9u8ti9995Leno6N954Y7OvefHFF3G5XHEeWWxUV1fz0EMPMXfuXAoKChI9nKNyyy23cNZZZ/Haa69x0UUXJXo4QnQZ0pRQiG5GURQKCwspKipK9FCO2qOPPsr111/PJ598wsknn5zo4RyVQCBAfn4+w4YNY8mSJYkejhBdhiz9CNHDNJejsmDBAhRFYcGCBbzzzjtMnjwZu91O3759ufPOOwmFQgC88sorjB8/HpvNRv/+/XnwwQebPYemaTz33HNMnz6dtLQ07HY7EyZM4LnnnmvXWBcsWECvXr2YNWtW9LFQKMTAgQPp1asXXq+32ddNmjQJs9lMWVlZo8fffvttTj75ZDIzM7FarYwZM4YHH3ywyXJMTU0Nf/rTn5g5cyb5+fmYzWby8/O55JJL2LlzZ5Pz3XPPPSiKwuLFi3nhhRc4/vjjsdvtnHTSSdFjjEYj5557LkuXLmX79u3tug5C9GQSqAghov773/9y/vnnM2jQIK666ipSUlL4wx/+wF133cVf//pXrrnmGsaOHcvPf/5zQqEQt956K6+88kqj99A0jYsuuogrrriC8vJyLrzwQubNm4fT6eSKK67glltuadNYqqqq+Oabb5g0aRKqevhHlaqqzJ8/n8rKSt56660mr/v222/5+uuvOfvss8nNzY0+/utf/5pzzz2Xbdu2MWfOHK655hqsViu33norP/nJTxq9x+bNm7nrrruw2Wz88Ic/5MYbb2TChAm8+uqrTJo0iT179jQ75r/85S9cffXVDB06lOuvv54ZM2Y0en7q1KkALFq0qE3XQAgBaEKIbgXQCgsLW3x+5syZ2nf/6T///PMaoJlMJm3lypXRx2tra7Xc3FzNbrdreXl52s6dO6PP7d27VzObzdoxxxzT6L2eeuopDdCuuOIKze/3Rx/3er3aWWedpQHaqlWrjvg53nvvPQ3QfvOb3zR5rrS0VDMajdqsWbOaPHf99ddrgPbBBx9EH/v44481QDv99NM1p9MZfTwUCmlXXXWVBmhvvvlm9PHq6mqtoqKiyXsvWrRIU1VVmzdvXqPH7777bg3QHA6Htn79+hY/07p16zRAu+SSS1r/8EKIKJlREUJE/fSnP2XixInR/05NTeXMM8/E5XJx9dVXM2jQoOhzBQUFzJgxg40bNxIIBKKPP/bYYzgcDh577DGMxsP5+mazmfvuuw+A11577Yhj2b9/PwC9e/du8lxeXh5nn302ixcvbrQU4/V6efnll+nfvz+nnHJKozEBPPnkk9jt9ujjiqLwwAMPoChKozGlp6eTlZXV5LyzZs1i9OjRfPLJJ82O+ec//zljx45t8TNFPkvkswkhjkx2/QghosaPH9/ksT59+gAwbty4Zp8LBoMcPHiQvn374nK5+Pbbb8nPz+eBBx5ocrzf7wdgy5YtRxxLRUUFAJmZmc0+f+WVV/Kf//yHZ599lj/+8Y+AvnRVWVnJ9ddf32i5aPny5TgcDp599tlm38tmszUZ0+LFi3n44YdZsWIF5eXljYIxs9nc7PtMmjSp1c8UCX7Ky8tbPU4IcZgEKkKIqLS0tCaPRWZFWnsuEoBUVVWhaRrFxcXce++9LZ7H6XQecSw2mw0At9vd7PPf//73GThwIAsWLOD3v/89BoOBZ555BlVVufzyyxsdW1lZSSAQaPOY3njjDS644AJSUlI49dRTGTBgAHa7PZpw3FKOSnOzPw1FPkvDWR0hROskUBFCxEwkmDn++ONZtWrVUb1XTk4OoAcZzVEUhfnz5/PrX/+a9957j7Fjx7Jo0SJOP/30JjVX0tLSUBSlzTMZ99xzD1arldWrVzN06NBGz/3rX/9q8XVHKqoX+SyRzyaEODLJURFCxExqaiojR45k8+bNVFdXH9V7RXI9WtvKe/nll2MymXjmmWd47rnn0DSNefPmNTlu8uTJVFRUtHlb8M6dOxk5cmSTIKWkpKTZ7clttXXrVoBW81iEEI1JoCKEiKnrr78el8vF/Pnzm13i2b17d5uK0Y0dO5asrCxWrlzZ4jG9e/fm7LPP5v333+epp54iLy+Ps846q9kxgR7YRHJfGjpw4ACbN2+O/ndhYSE7duzg4MGD0cc8Hg9XX311o1yV9lqxYgUAM2fO7PB7CNHTSKAihIipK6+8kksvvZQ333yToUOHcskll3D77bfzs5/9jKlTpzJ48GCWL19+xPdRFIWzzz6bjRs3Ulpa2ur5gsEgZWVlXHrppY12GkWcdtpp3HnnnSxZsoQhQ4Ywd+5cbr/9dubPn8+sWbPo168fb7/9dvT46667jtraWsaPH8/1118frR+zceNGjj322I5dGGDhwoVkZmZy4okndvg9hOhpJFARQsRUJOH09ddfZ/To0bz77rs89NBDLFy4EKvVyoMPPsjs2bPb9F5XXnkloVCo1e3Ms2fPpm/fviiK0uyyT8Tvfvc7Fi5cyAknnMCnn37KQw89xLvvvovX6+Wee+7hpz/9afTYa6+9lieeeIKsrCyefvpp/vvf/zJz5ky++uorMjIy2nwtGtqzZw9Lly7l0ksvxWq1dug9hOiJpNePECKpTZs2jZqaGjZs2NBssmpJSQmFhYWccMIJSV3x9a677uKBBx5g8+bNDB48ONHDEaLLkBkVIURSe/DBB9m0aRNvvPFGs88//PDDBAIBrrrqqk4eWdtVV1fzyCOPcPXVV0uQIkQ7yfZkIURSmzZtGk888US0VgvoTQMff/xx9uzZw9NPP83o0aOZM2dOAkfZuqKiIm688Uauu+66RA9FiC5Hln6EEF1OUVERAwcOxGazMXnyZJ544gmGDx+e6GEJIeJAAhUhhBBCJC3JURFCCCFE0pJARQghhBBJSwIVIYQQQiQtCVSEEEIIkbQkUBFCCCFE0pJARQghhBBJSwIVIYQQQiQtCVSEEEIIkbQkUBFCCCFE0vp/JJRQLVUlHQAAAAAASUVORK5CYII=", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Time length of IGG-SLR :\", SLR_filt_Ylms.time[-1] - SLR_filt_Ylms.time[0], \" yr\")\n", + "print(\"Time length of IGG-SLR - ISBA :\", SLR_filt_isba_Ylms.time[-1] - SLR_filt_isba_Ylms.time[0], \" yr\")\n", + "\n", + "# Figure 2a of the paper\n", + "plt.figure()\n", + "plt.plot(SLR_filt_Ylms.time, SLR_filt_Ylms.slm[2,2], label='IGG-SLR', color='C3', linestyle=(0, (5,2)))\n", + "plt.plot(SLR_filt_isba_Ylms.time, SLR_filt_isba_Ylms.slm[2,2], label='IGG-SLR - ISBA', color='C2')\n", + "plt.plot(isba_filt_Ylms_long.time, isba_filt_Ylms_long.slm[2,2], label='ISBA', color='C0', linestyle='dashdot')\n", + "plt.legend(fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "\n", + "# Figure Supplementary Information S3a of the paper\n", + "plt.figure()\n", + "plt.plot(GRACE_filt_Ylms.time, GRACE_filt_Ylms.slm[2,2], label='CSR', color='C5')\n", + "plt.plot(GRAZ_filt_Ylms.time, GRAZ_filt_Ylms.slm[2,2], label='GRAZ', color='C9')\n", + "plt.plot(COSTG_filt_Ylms.time, COSTG_filt_Ylms.slm[2,2], label='COST-G', color='C8')\n", + "plt.plot(SLR_filt_Ylms.time, SLR_filt_Ylms.slm[2,2], label='IGG-SLR', color='C3', linestyle=(0, (5,2)))\n", + "plt.legend(fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "\n", + "# Figure 3 of the paper\n", + "plt.figure()\n", + "# plot S22/(2*Kappa*delta h)*180/pi (Equation 7b + conversion from radians to degree)\n", + "plt.plot(SLR_filt_isba_Ylms.time, SLR_filt_isba_Ylms.slm[2,2]/1.41e-11/49/np.pi*180, label=r'$\\alpha$ from $S_{2,2}$ ($\\delta h$ = 49m)', color='#4bce4b', linestyle='dashed')\n", + "plt.plot(SLR_filt_isba_Ylms.time, SLR_filt_isba_Ylms.slm[2,2]/1.41e-11/90/np.pi*180, label=r'$\\alpha$ from $S_{2,2}$ ($\\delta h$ = 90m)', color='C2')\n", + "plt.plot(SLR_filt_isba_Ylms.time, SLR_filt_isba_Ylms.slm[2,2]/1.41e-11/126/np.pi*180, label=r'$\\alpha$ from $S_{2,2}$ ($\\delta h$ = 126m)', color='#1c641c', linestyle='dashdot')\n", + "plt.plot(SLR_filt_Ylms.time, SLR_filt_Ylms.slm[2,2]/1.41e-11/90/np.pi*180, label=r'$\\alpha$ from uncorrected $S_{2,2}$ ($\\delta h$ = 90m)', color='#5a3730')\n", + "\n", + "plt.grid()\n", + "plt.ylabel(r'$\\alpha (\\circ)$', fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=13)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5789a5dc", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-14T16:28:32.072092Z", + "start_time": "2023-08-14T16:28:31.618839Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure 2b of the paper\n", + "windows = 48\n", + "\n", + "# windows creation to reduce the apodization effect\n", + "global_hann = sc.signal.windows.hamming(windows)[:windows//2]\n", + "slr_hann = np.concatenate((global_hann, np.ones(len(SLR_filt_Ylms.time)-windows), global_hann[::-1]))\n", + "slrisba_hann = np.concatenate((global_hann, np.ones(len(SLR_filt_isba_Ylms.time)-windows), global_hann[::-1]))\n", + "\n", + "# compute periodogram\n", + "w = np.linspace(0.449, 2.3, 5000)[::-1]\n", + "pgram = sg.lombscargle(SLR_filt_Ylms.time.copy(), SLR_filt_Ylms.slm[2,2]*slr_hann, w.copy(), normalize=False)\n", + "pgram_slr_isba = sg.lombscargle(SLR_filt_isba_Ylms.time.copy(), SLR_filt_isba_Ylms.slm[2,2]*slrisba_hann, w.copy(), normalize=False)\n", + "pgram_isba = sg.lombscargle(isba_filt_Ylms_long.time.copy(), isba_filt_Ylms_long.slm[2,2]*slrisba_hann, w.copy(), normalize=False)\n", + "\n", + "plt.figure()\n", + "plt.plot(2*np.pi/w, pgram, label='IGG-SLR', color='C3', linestyle=(0, (5,2)))\n", + "plt.plot(2*np.pi/w, pgram_slr_isba, label='IGG-SLR - ISBA', color='C2')\n", + "plt.plot(2*np.pi/w, pgram_isba, label='ISBA', color='C0', linestyle='dashdot')\n", + "\n", + "plt.xlabel('Period (yr)', labelpad=4, fontsize=14)\n", + "plt.ylabel('($yr^{-1}$)', labelpad=-2, fontsize=14)\n", + "plt.ylim(10**-22)\n", + "plt.legend(loc='upper right', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7cf82451", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-14T16:28:52.834253Z", + "start_time": "2023-08-14T16:28:52.406763Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure Supplementary Information S3b of the paper\n", + "windows = 48\n", + "\n", + "# windows creation to reduce the apodization effect\n", + "global_hann = sc.signal.windows.hamming(windows)[:windows//2]\n", + "slr_hann = np.concatenate((global_hann, np.ones(len(SLR_filt_Ylms.time)-windows), global_hann[::-1]))\n", + "grace_hann = np.concatenate((global_hann, np.ones(len(GRACE_filt_Ylms.time)-windows), global_hann[::-1]))\n", + "graz_hann = np.concatenate((global_hann, np.ones(len(GRAZ_filt_Ylms.time)-windows), global_hann[::-1]))\n", + "\n", + "# compute periodogram\n", + "w = np.linspace(0.63, 2.3, 5000)[::-1]\n", + "pgram = sg.lombscargle(SLR_filt_Ylms.time.copy(), SLR_filt_Ylms.slm[2,2]*slr_hann, w.copy(), normalize=False)\n", + "pgram_grace = sg.lombscargle(GRACE_filt_Ylms.time.copy(), GRACE_filt_Ylms.slm[2,2]*grace_hann, w.copy(), normalize=False)\n", + "pgram_graz = sg.lombscargle(GRAZ_filt_Ylms.time.copy(), GRAZ_filt_Ylms.slm[2,2]*graz_hann, w.copy(), normalize=False)\n", + "pgram_costg = sg.lombscargle(COSTG_filt_Ylms.time.copy(), COSTG_filt_Ylms.slm[2,2]*grace_hann, w.copy(), normalize=False)\n", + "\n", + "plt.figure()\n", + "plt.plot(2*np.pi/w, pgram_grace, label='CSR', color='C5')\n", + "plt.plot(2*np.pi/w, pgram_graz, label='GRAZ', color='C9')\n", + "plt.plot(2*np.pi/w, pgram_costg, label='COST-G', color='C8')\n", + "plt.plot(2*np.pi/w, pgram, label='IGG-SLR', color='C3', linestyle=(0, (5,2)))\n", + "\n", + "plt.xlabel('Period (yr)', labelpad=4, fontsize=14)\n", + "plt.ylabel('($yr^{-1}$)', labelpad=-2, fontsize=14)\n", + "plt.ylim(10**-22)\n", + "plt.legend(loc='upper right', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "18a5be29", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-14T16:28:55.710880Z", + "start_time": "2023-08-14T16:28:55.419863Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure 4 of the paper\n", + "\n", + "plt.figure()\n", + "# create alpha and delta h range\n", + "alpha = np.arange(0.08, 1.2, 0.005)\n", + "h = np.arange(45, 160, 0.01)\n", + "# rectangle for the inscription \"Above $S_{2,2}$ ...\"\n", + "h2 = np.arange(94.5, 158.2, 0.01) \n", + "\n", + "# three line for three alpha maximal value at delta h = 90m\n", + "plt.plot(h, 90*0.4/h, label=r'$S_{2,2} = 2 \\mathcal{K} \\times 90 \\times {0.4} \\frac{\\pi}{180} \\approx 9 \\times 10^{-12}$', lw=2, color='C4')\n", + "plt.plot(h, 90*0.3/h, label=r'$S_{2,2} = 2 \\mathcal{K} \\times 90 \\times {0.3} \\frac{\\pi}{180} \\approx 7 \\times 10^{-12}$', lw=2, color='C9', linestyle=(0,(3,1,1,1,1,1)))\n", + "plt.plot(h, 90*0.1/h, label=r'$S_{2,2} = 2 \\mathcal{K} \\times 90 \\times {0.1} \\frac{\\pi}{180} \\approx 2 \\times 10^{-12}$', lw=2, color='C1')\n", + "\n", + "# hatch couple values of alpha, delta h that cannot be reach\n", + "plt.fill_between(h, 90*0.4/h, 2*np.ones(h.shape), hatch='//', fc='w', alpha=0.8)\n", + "\n", + "# dashed line for delta h = 90m\n", + "plt.plot([90,90], [1.2,0], '--', lw=2, color='C5')\n", + "# rectangle for the inscription \"Above $S_{2,2}$ ...\"\n", + "plt.fill_between(h2, 0.455*np.ones(h2.shape), 0.51*np.ones(h2.shape), fc='w') \n", + "plt.text(95, 0.47, 'Above $S_{2,2}$ upper bound constraint', weight=\"bold\")\n", + "\n", + "plt.xlabel('$\\delta h$ (m)', fontsize=12)\n", + "plt.ylabel(r'$\\alpha~(°)$', fontsize=12)\n", + "plt.xticks([50, 70, 90, 110, 130, 150])\n", + "plt.xlim(45, 160)\n", + "plt.ylim(0, 0.95)\n", + "plt.legend(framealpha=0.9)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "80fb78ed", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-14T16:28:58.475906Z", + "start_time": "2023-08-14T16:28:58.472055Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Low Γ value : 0.18169617014874107\n", + "Large Γ value : 0.027254425522311162\n" + ] + } + ], + "source": [ + "# calculation of maximal value for alpha based on LOD change with an amplitude ms and a period y\n", + "ms = 1e-3\n", + "y = 20\n", + "\n", + "# equation 8\n", + "print(\"Low Γ value :\", 360/86400**2*7.129e37/3e19*ms/(y*31536000))\n", + "print(\"Large Γ value :\", 360/86400**2*7.129e37/2e20*ms/(y*31536000))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "eb22604d", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-14T16:29:01.336531Z", + "start_time": "2023-08-14T16:29:00.153024Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For a period of 30 yr, spectral resolution on C04 time series is between : 18.75 and 75.0\n", + "For a period of 30 yr, spectral resolution on C01 time series is between : 21.428571428571427 and 50.00000000000001\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure Supplementary Information S1a and S2a\n", + "p = 30 #period of 30 years\n", + "\n", + "l = 50 #length of the LOD time series for C04\n", + "pmin = p - np.abs(p - 1/(1/p + 1/l))\n", + "pmax = p + np.abs(p - 1/(1/p - 1/l))\n", + "print(\"For a period of 30 yr, spectral resolution on C04 time series is between : \", pmin, \"and \", pmax)\n", + "fmin, fmax = 1/pmax, 1/pmin\n", + "\n", + "# read C04 file\n", + "f = open(os.path.join(base_dir, \"LOD/lod_AOHSl.txt\"), 'r')\n", + "lines = f.readlines()\n", + "\n", + "# create a new LOD to remove trend and AAM, OAM, HAM, ...\n", + "lod = np.zeros((len(lines) - 7, 7))\n", + "for i, l in enumerate(lines[7:]):\n", + " lod[i, :-1] = np.array(l.split())\n", + " \n", + "lod[:,6] = lod[:,1] - lod[:,2]\n", + "\n", + "for i in range(1,7):\n", + " lod[:,i] = sg.detrend(lod[:,i])\n", + " \n", + "# temporally filter LOD\n", + "filt_lod = lod.copy()\n", + "\n", + "ndata = lod.shape[0]\n", + "# compute the mean time delta of the object\n", + "dt = float(np.mean((lod[1:, 0] - lod[:-1, 0])))\n", + "\n", + "# fft filtering with 2**n2 zero padding\n", + "for i in range(1,7):\n", + " s = lod[:,i].copy()\n", + "\n", + " # zero pad\n", + " n2 = 0\n", + " while ndata > 2 ** n2:\n", + " n2 += 1\n", + " n2 += 1\n", + "\n", + " f = np.fft.fft(s, n=2 ** n2)\n", + " freq = np.fft.fftfreq(2 ** n2, d=dt)\n", + " to_zero = np.logical_or(freq > fmax, freq < -fmax) | np.logical_and(freq < fmin, freq > -fmin)\n", + " f[to_zero] = 0\n", + " filt_lod[:,i] = np.real(np.fft.ifft(f))[:ndata]\n", + "\n", + "\n", + "l = 75 #length of the LOD time series for C01\n", + "pmin = p - np.abs(p - 1/(1/p + 1/l))\n", + "pmax = p + np.abs(p - 1/(1/p - 1/l))\n", + "print(\"For a period of 30 yr, spectral resolution on C01 time series is between : \", pmin, \"and \", pmax)\n", + "fmin, fmax = 1/pmax, 1/pmin\n", + "\n", + "# read C01\n", + "f = open(os.path.join(base_dir, \"LOD/lod_AAMncep1948-2023.dat\"), 'r')\n", + "lines = f.readlines()\n", + "\n", + "# create a new LOD to remove trend and AAM, OAM, HAM, ...\n", + "lod2 = np.zeros((len(lines) - 1, 4))\n", + "for i, l in enumerate(lines[1:]):\n", + " lod2[i, :-1] = np.array(l.split())\n", + " \n", + "lod2[:,3] = lod2[:,1] - lod2[:,2]\n", + "\n", + "for i in range(1,4):\n", + " lod2[:,i] = sg.detrend(lod2[:,i])\n", + " \n", + "# temporally filter LOD\n", + "filt_lod2 = lod2.copy()\n", + "\n", + "ndata = lod2.shape[0]\n", + "# compute the mean time delta of the object\n", + "dt = float(np.mean((lod2[1:, 0] - lod2[:-1, 0])))\n", + "\n", + "# fft filtering with 2**n2 zero padding\n", + "for i in range(1,4):\n", + " s = lod2[:,i].copy()\n", + "\n", + " # zero pad\n", + " n2 = 0\n", + " while ndata > 2 ** n2:\n", + " n2 += 1\n", + " n2 += 1\n", + "\n", + " f = np.fft.fft(s, n=2 ** n2)\n", + " freq = np.fft.fftfreq(2 ** n2, d=dt)\n", + " to_zero = np.logical_or(freq > fmax, freq < -fmax) | np.logical_and(freq < fmin, freq > -fmin)\n", + " f[to_zero] = 0\n", + " filt_lod2[:,i] = np.real(np.fft.ifft(f))[:ndata]\n", + " \n", + "plt.figure()\n", + "plt.plot(lod2[:,0], filt_lod2[:,1], label='C01', color='C1', linestyle='dashdot')\n", + "plt.plot(lod2[:,0], filt_lod2[:,3], label='C01 LOD-AAM', color='C2')\n", + "#plt.plot(lod[:,0], filt_lod[:,1], label='C04 LOD', color='C6')\n", + "plt.plot(lod[:,0], filt_lod[:,6], label='C04 LOD-AAM', color='C8', linestyle=(0, (5,2)))\n", + "\n", + "plt.title('')\n", + "plt.ylabel('(ms)', fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(loc=(0.6769, 0.77))\n", + "\n", + "dlod = (filt_lod[1:,6] - filt_lod[:-1,6]) / ((lod[1:,0] - lod[:-1,0])*31536000)/1e3\n", + "dlod2 = (filt_lod2[1:,3] - filt_lod2[:-1,3]) / ((lod2[1:,0] - lod2[:-1,0])*31536000)/1e3\n", + "\n", + "plt.figure()\n", + "plt.plot(lod2[:-1,0], -360/86400**2*7.129e37/3e19*dlod2, label=r'$\\alpha$ from C01, $\\Gamma=3.10^{19}$', color='C1', linestyle='dashdot')\n", + "plt.plot(lod2[:-1,0], -360/86400**2*7.129e37/2e20*dlod2, label=r'$\\alpha$ from C01, $\\Gamma=2.10^{20}$', color='C8')\n", + "plt.plot(lod[:-1,0], -360/86400**2*7.129e37/3e19*dlod, label=r'$\\alpha$ from C04, $\\Gamma=3.10^{19}$', color='C0', linestyle='dashdot')\n", + "plt.plot(lod[:-1,0], -360/86400**2*7.129e37/2e20*dlod, label=r'$\\alpha$ from C04, $\\Gamma=2.10^{20}$', color='C9')\n", + "\n", + "plt.ylabel(r'$\\alpha (\\circ)$', fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.grid()\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "9ada98ea", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-14T16:29:48.110486Z", + "start_time": "2023-08-14T16:29:47.207118Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For a period of 30 yr, spectral resolution on C04 time series is between : 5.357142857142858 and 6.818181818181818\n", + "0.27328662391793657\n", + "0.07190718419811173\n", + "For a period of 30 yr, spectral resolution on C01 time series is between : 5.555555555555555 and 6.521739130434783\n" + ] + }, + { + "data": { + "image/png": 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RQX9/P6+++iqnn346K1as4Oyzzx61ge/VV19NX1+f8Wfbtm2WzNPGxweGEhYYaMqr/1snaJVEn16JeMoNAxsjkwmpKgDpfJpMPlOx+RjhyLTssVc3C9Q89G4llBPzqLQ6ZzQUL0jAn2jfMhs2bOyd2OeUsInEj3/840H/P/roo/nrX//KiSeeyKpVq/j73//OGWecMez+Xq8Xr9dr9TRtfIyw670/ANCkOeHBpbD9DRoXLAEGFJdKwrCoKAyDRloIbRtYYEQz0YpVABpKWC4LigOqp0H3Zlh2BOGGRgj5Kk7CdLWrsK2UHpbUc8Vs2LBhA/ZBJUxXwIZTu/r7+4dVyayAw+Hg61//OgAvvTRxjZdtfAyRywyoKsFGiO2C2C5qZN/GSj/QU7kUGVWoS4Xu9AQbcAJBRazpKhn+G2QeG2oGpxsikwEIyRBlpcOROgnTQ5Aw0Fzc9gmzUQra29u5/PLLmTlzJl6vl6lTp3LWWWfxzDPPGGPS6TSXX3459fX1BINBzj77bLZv3z7oODfeeCPHHXccgUCA6urqks590kknFbVZ0tHd3c0VV1zB9OnT8Xg8TJo0ia9//ets3TrYu/DCCy80fDLdbjdNTU2ceuqp3HXXXahSOS8FBx54IB6Phx07hrflSSaT1NTUUFtbSzKZHPK67uH54IMPDnlt7ty5KIoyYe2d9jkSpueCFcv76unpobOzc1R7CrOh54KV4jNmw4aB7s30OoQFRW31TKg/QPw7LW4ylX6g62RG0SC4892BF4KCbIRk5WYlE+GNcKSqQlgkv+MJQKCOsLzRT5QSVucbUANtEmajVLS2trJw4UKeffZZbr75ZtasWcOKFStYvHgx3/nOd4xxV1xxBY8++igPPvggq1atIhaLceaZZ5LXi1SATCbDOeecw2WXXWbK3Lq7uznmmGN4+umnueOOO9i4cSMPPfQQmzZt4sgjj2Tz5s2Dxp922mm0tbXR2trK448/zuLFi/nud7/LmWeeSS6XG/V8q1atIpVKcc4554xIkv70pz8xb948Dj74YB555JGiY6ZOnTrEGeHVV1+lvb2dYDA4+pu3CPtcOPLEE0/kpptu4sknn+S8884b9NqTTz5pjKkkXnvtNUCwcRs2SkbPFnqcYp1U7a8R+U5ATVIQj4lwpwcIaiqKyzPwQlAsMsLALirrUB/NCoIVUlUITxp4oWoK4ahofF5pJUwP2VYXVJDq4chkLkkyl8Tv8ld0TjZEi6tkbqhKUgn4Xf6SPf2+/e1voygKr7/++iByMHfuXC666CJARHqWL1/OfffdxymnnALA/fffz9SpU3n66adZskSkLOiFXmapPD/60Y/YuXMnGzdupLlZLHqmTZvGE088wezZs/nOd77D448/boz3er3GuMmTJ3P44YdzzDHHcPLJJ3PPPfdw8cUXj3i+5cuXc/7553PiiSfyne98xzBQLzbuggsuQNM0li9fztKlS4eMWbp0Kbfccgvbtm1j6tSpANx1110sXbqU3/3ud2P+TMaLfY6EnXzyycycOZMHHniAf/mXf2HBggUARKNRfvrTn+JyuQZVL3Z2dtLZ2Ul9ff2o1YsjYd26dbS0tAyRfFetWsWvfvUrvF4vX/jCF8Z8fBufPGi92+hxCnWpxlsDVeLGUZ3sA6Xy4UidzAT9ddBw4MALOgnLq+CoLOlJZIW6HCpUwgAiUwj1fQBUVpmD4nlzIXcIt8NteIj5QzYJqzSSuSRHP3D0hJz7tfNfI+AOjDquu7ubFStWcOONNxZVZ/Tny5tvvkk2mx1kxdTS0sK8efN4+eWXDRJmJlRV5cEHH2Tp0qUGsdLh9/v59re/zbXXXkt3dze1tcN3qPj0pz/N/PnzeeSRR0YkYdFolD/84Q+89tprHHTQQcTjcZ5//nkWL148aNymTZt45ZVXeOSRR9A0jSuuuILNmzczc+bMQeOamppYsmQJ9957L9deey2JRIKHHnqIlStXTigJ2+fCkS6Xi9/+9reoqsoJJ5zAJZdcwlVXXcX8+fNZu3Yt119/PQcccIAxftmyZcyZM4dly5YNOdaFF15o/GlraxuyrbNzoNz84YcfpqWlhbPOOovLL7+cq666itNOO41FixaRzWZZtmwZ06ZNs/4DsLHPINH3EVm56qvx1Ri5TrUxcd1VOrQVzwglLOSrBW+BibEejsyLlkqVJD2GOqc4IVKghIUaCKmiWEBXyyqFYiRMURQ7Od/GqNi4cSOapnHQQQeNOK69vR2Px0NNTc2g7U1NTbS3W+NF19HRQW9vL3PmzCn6+pw5c9A0jY0bN456rIMOOojW1tYRxzz44IPMnj2buXPn4nQ6Oe+881i+fPmQcXfddRenn366kRN22mmncddddxU95kUXXcQ999yDpmn88Y9/ZNasWYZQM1HY55QwgMWLF7Nq1Squu+46Hn74YTKZDHPnzuWnP/1pUZlyONx7770jbrv++usN9Wzx4sWsX7+et956i5UrV5JKpWhqauLLX/4y3/ve9zjqqKPG/8ZsfKLQ0/cRAD7FKcJXVVMAqOnfBeEGetO9FW1dZChhnj1W6AHxGwhlM+D2VDQHS59T4Gt/g9qCdkDBRpEnRuVzwvQ2SXsa6db6atmd2E1Xqqui87Eh4Hf5ee381ybs3KVAtzEa6296IlqZFZ4bSpt74TwvvfRS7r//fuO1WEz8pvUQo44LLriARYsW0dvbayiC+Xyee++9l//8z/8cNO573/seP/nJT3DKSIKOM844g29961u88MIL3HXXXUZ4dyKxT5IwgKOOOmpQbHo4XH/99cP2ehzN16sQJ554YsVzzWzs2+iNtoETql2S9ERaAIWabAoQnlzJXLKkMIcZiPeLyqtgco+cLxmOjOTSgGdiwpHuEAzKU2swvMsmLBzpGUzC9Bwx/XUblYWiKBX7rYwVs2fPRlEU1q9fz+c+97lhxzU3N5PJZOjp6Rmkhu3evZvjjjvOkrk1NDRQXV3NunXrir7+/vvvoygKs2bNKvp6IdavX8+MGTMAuOGGG7jqqqsGvb5u3Tpee+013njjDX74wx8a2/P5PL///e+NQoMnnniCHTt28OUvf3nQ/vl8nieffJLTTz990HaXy8VXvvIVrrvuOl577TUeffTR0d+4xdjnwpE2bOwr6JZmrDV6grfTDeFm/JqG1+EWYyoYkoz3tgIQ6t3DRNhXBQ63QXoqqTwZ4Uj3HupcaICExbNxVK30kvjxIK/mjfcf8UYGvabbekxEz08bHw/U1tayZMkSbr/9duLx+JDXe3t7AVi4cCFut5unnnrKeK2trY333nvPMhLmcDg499xzeeCBB4aEPJPJJHfccQdLliwZMR8MRH/nNWvW8MUvfhGAxsZG9t9/f+MPCBVs0aJFvPPOO6xevdr484Mf/GBQSHL58uWcd955g8asXr2apUuXFg1dgghJrly5kn/6p38aEs6dCOyzSpgNGx9rqCq96R4I11ATKCgYCTejRNuocQVoz/TRk+phSnhKRaYUk7lMQadv8AuKAsF6Qqp4aFSKhGXzWcO3LPDYFfCFXwtCCBBsJCyJl4ZGPBs3GnpbiVg2hoZQ0PdUwnRSZpMwGyPhjjvu4LjjjuOoo47ihhtu4NBDDyWXy/HUU09x5513sn79eqqqqvjGN77BlVdeSV1dHbW1tVx11VUccsghRrUkwNatW+nu7mbr1q3k83lWr14NwP77708oFBp2Dh0dHcZYHc3Nzdx4440888wznHrqqdx8883MmzePLVu2cO2115LNZrn99tsH7ZNOp2lvbyefz7Nr1y5WrFjBTTfdxJlnnslXv/rVoufOZrPcd9993HDDDcybN2/QaxdffDE333wz77zzDi0tLfzlL3/hscceGzLua1/7GmeccQYdHR00NDQMem3OnDl0dnYSCOwdqqhNwmzY2BuR7KZHplZUF7QsItgIQJXDQzuVfaDHU70AhIrlt0w9mnDyI6CrYuFIXQUDCH6wAlwF5DDYgFcDj6aRURRimVhFSJgeagy4Aridbsgk4L0/QcOBA0pYemJJWCq1k81b/pODDrwRh8N+BOxtmDFjBm+99RY33ngjV155JW1tbTQ0NLBw4ULuvPNOY9wtt9yCy+Xi3HPPJZlMGrYPhXlQP/7xjwflMR922GEAPPfcc5x00knDzuGBBx7ggQceGLTtuuuu4/rrr+fVV1/lhhtu4Fvf+hZtbW3U1dVx2mmncf/99w8pPluxYgWTJk3C5XJRU1PD/PnzufXWW/na176Gw1E8EPfYY4/R1dXF5z//+SGvzZ49m0MOOYTly5ez3377EQwGOfnkk4eMW7x4MeFwmPvuu4/vf//7Q16vq6tMR49SYP8CbdjYGxHvNDzCavwF8r6sRIzITIJKkrCYVLiGhP4Azr2X4Oa/wotXDyJHViKeE+fxOdy4zroVXAUtv0LicwqoKhmnk0SuMkbJQyoj/3YlvPMANB9C5BiRZDxRSlh/pp8fvPCvnKa8RFhJ0tR0FnW1x0/IXGyMjEmTJrFs2bKiVfs6fD4ft912G7fddtuwY+65556yPcKef/75EV+vr6/n1ltv5dZbbx1x3FjODfDFL35xkOHsnnj33QGj6CuvvLLoGJfLRVfXQAHMaJWYeph3ImDnhNmwsTci3kGvXNEWmn7q5CIsU5wqm3+lV0cWV5SCsoCgUoRHT7gPeMKw8GuDX/RVg9NDUNpUVIoYDqqMjHfCuw9B/YFwwaNEAmL1PVEkbPma5by042XWJUQIt6fn1QmZhw0bNgZgkzAbNgrx/t/ofvNuLn3qW1z69KVG78aKo3oq/c1zgT2sDg44HU7/BZE60XqrouFIWYkY9FUXfV2vPNMrFq2GTvaKKnOKAt9+Fb/8nCquhHmqoHUVaHlwOCHUMOHhyN7233OYP8f2jLjt9/W9OSHzsGHDxgBsEmbDho41f4QHz2fZa/+Pl3a+zEs7XuKO1XdMzFxqptMfEa7Ug3KZph0NR19CuEaUd1eUhOWFNUbIW6T66YVfEPj9P4txlQpHyvOE8jloe3fogLpZBGUyfMWUMEnCIt4IfPSS2Dj9BADCbvE9ToQStq33fRb5u/hafYYdkoT1R9eXZcNjw4YN82GTMBs2dLx6J1ngyciA8vR46+Nk1eyETEcPt+kKSiH0bRU1RpWViKHCak0dmkYwKQhIxcKRulFr1xZ4uXh+SsBVWXVuUDiyfQ0Zt0J3YzX5Ry+h6iFRDTYRJGxd29MARFUPTbVHoWqg5qNks7ZxrA0bEwmbhNmwARDdBTv+wTqvh758kognQtAdJJqJ8kHPB5WfT8cGoknRnmiQEpZLw0evEO4RXl2VDG3FNZEsGwwUqSw6/KsELvizGFch1UknVkFNA38RdW7NHwl2bR401moYHmGeCPmuD/jHgmre7l/Oe763iCR6Bo2pJLr61gCQcdUzq2YO3XlRehuPb6r4XGzYsDEAm4TZsAGwTbQzWVs/HYD5DfM5rFGUc6/evbry83n2p0SjOwHpBq8j1Q93n0bkH3cDFVbCENUAQX8RJSzcTGDSoQDk1BzZvPXqoa4UBlUVihHDzc8R6BIko9LFAmGc7ArFSPpFcUWntwf3uSK0ncwlK/L5FCKRFC2w3N4pzKyeye6sIGGJxJaKzsOGDRuDYZMwGzYA2kVO0fqgCEUevPkV5nVuBZgQJUxzh4hKH51BSpi/Bur2J1IjWoNUjIRpGnHpWxaUXmV7Qg/9QWXUMN2iQpCwIkrY7CUEmhdUbD5Q0F8zHaWrxjPotZR3QLWsdEgyn+kAoDo4k/2r96c7L66tVHpnRedhw4aNwbBJmA0bYCR2b3AItWdObxszoyIcuLl3c8Wnkzr7FnKywe2gnDCnCy5/k/CZomFtpR7m+XSMpCSFoVDT0AGx3bhe+CVeRSg/lVCeBoUji5Gwg88mMGNRxeYDBcUCyX56qkVrqfp64WDe1/ePCUnO1zQNtybIelPVHKaGp9KTk0pYcnvF5mHDho2hsEmYDRsAu9aiAR/lxMNxxpJfMnPJLwHY3Le54lVkusLlVJz4izjUVzoxP17QozIYKKKEpfrg+ZsI5ESYrRLKk6E6qWrxnDAG7CsqlRNmFAukYxywOc5+mZlMmfIVAKJdrxHJ54DKkrDedC9VcnHRXHUwtb5aoqqbtAqpXLpi87Bhw8ZQ2CTMho1sCvq30+1wkMinUVCYPO8c9ms5EgWF/kx/RRtlwwC5CnvCKFIRK4QeouzP9FeEIMZdIrTmcXjwFDrT6/CLRrgB2TS7EsqT0bxb1cBfPXRANkUgLeZRKRIWz4g5hZN9NO9Os7/vZCLhQwBIa1FqZOsnfVwl0B7bTsQprpFQYBoOxcEu5zR+uMMPTRdWbB42Skd7ezuXX345M2fOxOv1MnXqVM466yyeeeYZY0w6nebyyy+nvr6eYDDI2WefzfbtxZXNdDrNggULUBRlSE/IPXHSSSdxxRVXDPt6d3c3V1xxBdOnT8fj8TBp0iS+/vWvs3Xr1kHjLrzwQhRFQVEU3G43TU1NnHrqqdx1112o8j5RCg488EA8Hg87duwYdkwymaSmpoba2lqSyeSQ16dPn46iKDz44INDXps7dy6KoozJ3d8M2CTMho0+UWm43S+ITWOgEa/Ti8/loyEgHOrb4m2Vm0+yl+iDwnNrUFK+jr9cQeS/Pw1AXsuTzA296ZgNQ3UqZowKRuPsgGyaXQklzAhHqupA4+5CbHuVwMqbxXxyFc4Jk5WQRCbjdlfR0nIeM3f7Ccnihkr11wRo738fhwI5TcHtFgUMk4ItgMLO2MTmhHV2Psu27b8jLz3obIgWOwsXLuTZZ5/l5ptvZs2aNaxYsYLFixfzne98xxh3xRVX8Oijj/Lggw+yatUqYrEYZ555ZtGWPz/4wQ9oaWkZ99y6u7s55phjePrpp7njjjvYuHEjDz30EJs2beLII49k8+bBqRunnXYabW1ttLa28vjjj7N48WK++93vcuaZZ5LL5UY936pVq0ilUpxzzjkjkqQ//elPzJs3j4MPPphHHnmk6JipU6dy9913D9r26quv0t7eTjA4zH2tArBJmA0bPa0AbIuIXKep4amw6Tl47v8yySVIUGVJWDfRPlHNVrTpdKoPX/92XBXsH6krW7or/hA43eAOGm2CktkKEEO9l6WmgbcICfPXEJAqYcWUMD0nrHZ/mLwQ6kQBxZyDbmRGegae3OBxlUB3bCt9eYWkEjRU1ZaQeCDvjE8cCevpeY133v0mmzf/B9HoexM2j70N3/72t1EUhddff50vfelLHHDAAcydO5fvf//7vPqqaDXV19fH8uXL+fd//3dOOeUUDjvsMO6//37WrFnD008/Peh4jz/+OE8++SS//OUvxz23H/3oR+zcuZOnn36az372s0ybNo1FixbxxBNP4Ha7B5FEAK/XS3NzM5MnT+bwww/nmmuu4c9//jOPP/54ScrT8uXLOf/88/nKV77CXXfdNazqv3z5ci644AIuuOACli9fXnTM0qVLWblyJdu2bTO23XXXXSxduhSXa+LaaNskzIYNnYQFRZ7V1PBUWP8YrPw5k7Iix6miikGy16iMLGbUir8aBYg4ROJ3JUhYcvNKcepk7/CDvGEjHFkJ5SmhkzBVBV+xz6lmYD4VID2qphrnSR48l62nfYVU0/SBAeFJYq5UVgnbkXNz3U4/6wLnGtuag6Ibw65Y+4S55m/f8T8A7D/rX6muPqJi583nEyX9UfcwaVbVbMn7jhXd3d2sWLGC73znO0XVmerqagDefPNNstksn/nMZ4zXWlpamDdvHi+//LKxbdeuXXzzm9/kvvvuIxAYZgFVIlRV5cEHH2Tp0qU0NzcPes3v9/Ptb3+bJ554gu7ukVM3Pv3pTzN//vxhFSsd0WiUP/zhD1xwwQWceuqpxOPxos3FN23axCuvvMK5557Lueeey8svvzxEkQNoampiyZIl3HvvvQAkEgkeeughLrroolHeubWYOPpnw8beAknCdrq9kIfJocmgiRvpJCmZt8fbKzefVG9xewodMv8qiJNuKqPyJOO7gYGcr6LwhglqIgxXkTnJc/gdHqHE7Ql/TYEyZz0JS+aSaIjzdbU/xI5kK+HQHHw+GQYKNxPaUTlSqEO/dpuCAw/OBn8Dl9anmJ16gJ6ez1Bb+6mKzQdAVXN0dQliHwodVNFzP7/ykJLGHXDA9UyVRRUAO3Y+yAcfXF/Svid/emwmuBs3bkTTNA46aOTPpL29HY/HQ01NzaDtTU1NtLeL71vTNC688EIuvfRSjjjiCFpbW8c0Jx0dHR309vYyZ86coq/PmTMHTdPYuHEjRx111IjHOuigg3j33SKtxgrw4IMPMnv2bObOFT10zzvvPJYvX87ixYsHjbvrrrs4/fTTjc/itNNO46677uJnP/vZkGNedNFFXHnllfzoRz/ij3/8I7NmzWLBggUjzsNq2EqYDRtN82DO2XR4RMJ5Y6ARqvcDYFJKPOgnSgkbmYQJVCT/qnmePPWM4Qd5w/grqDzpIVL/cHlqnhD62j9eAeVJL6ZwK04yKXG9+HyTAchme+nwJ2gJ6fOpHAnbldgFyOtaot5fj0MBp6KSyXRWbC46EolN5PMxnM4Qkcj8ip9/b4WuShYrxil1f33f2267jf7+fq6++mrT5jfauaG0uRfO89JLLyUUChl/dOghRh0XXHABjzzyCL29vca2fD7PvffeO2TcvffeWzQ37owzziAWi/HCCy9w1113TbgKBrYSZsMGLPhnWPDP7H7sC4B8WHmF31Vzsg9C0J6orBLWr3tyeYok5huViOKmV4nQVlLmTPiL2VPo8IYJpmQOVgWqI5N5Ya/gL/YZASgKQb13ZAWKF3Ri1ej2oGpR0MDrFepTPL6RdzOPMqXZAbsrG47sku2v6gs6HdT56ojK1kVpaeRaSURj6wGhgimKg1wuTiazm0BgBJJvEk46cU1J4xRlsLo6ueU8WiZ90YopGZg9ezaKorB+/Xo+97nPDTuuubmZTCZDT0/PIDVs9+7dHHfccQA8++yzvPrqq3i9g6uZjzjiCJYuXWqE5UpFQ0MD1dXVrFu3rujr77//PoqiMGvWrFGPtX79embMEN/1DTfcwFVXXTXo9XXr1vHaa6/xxhtv8MMf/tDYns/n+f3vf89ll10GwBNPPMGOHTv48pe/PGj/fD7Pk08+yemnnz5ou8vl4itf+QrXXXcdr732Go8++ujob9xi2EqYDRsSnQnxsGoINIA0JG2Ii/yGrmQFGx0ne4k5xANyJCUspIqVXkVCf5LEFPMsM+ANG9WRFZmTbCgeuGD4G2lAmqMmcinLc590YjXJJb47b96FQ+bt6SFJlwcUtIpaVJzi3cjPWhL4kgPhn3p/PVFVzDOTrrwSFouKB3k4PIf+/ndZ+cKhvPX2BaPsZQ6czkBJf/TvTofD4S5537GitraWJUuWcPvttxOPD71GdBVo4cKFuN1unnrqKeO1trY23nvvPYOE3XrrrbzzzjusXr2a1atX8/e//x2Ahx56iBtvvLHsuTkcDs4991weeOABI+SpI5lMcscdd7BkyRJqa4t79ul49tlnWbNmDV/8oiC0jY2N7L///sYfECrYokWLBs1/9erV/OAHPxiUeL98+XLOO++8QWNWr17N0qVLh03Qv+iii1i5ciX/9E//NCScOxGwlTAbn2xoGiS6yHrC9KRFPlODvwGcPgDq0oJ8dKW6BknoliI1WmK+DEfmsuCpkBLWthqAQC4z/CBvxMjBsloJy6pZcqrI1/N7i3xGEgFvGIiioZHMJYev7jQBOrFqDAaBTnyRA43XvN4mwIGiqAQdlVPCVE3FR4aQE8LegQdOnX9ACYunKlj5KxGLi1ZgoeBB+P3TAUin28nlorhcRRYenyDccccdHHfccRx11FHccMMNHHrooeRyOZ566inuvPNO1q9fT1VVFd/4xje48sorqauro7a2lquuuopDDjmEU04RHRqmTZs26Lh6qG/WrFlMmTJlxDl0dHQM8RNrbm7mxhtv5JlnnuHUU0/l5ptvZt68eWzZsoVrr72WbDbL7bffPmifdDpNe3s7+XyeXbt2sWLFCm666SbOPPNMvvrVrxY9dzab5b777uOGG25g3rx5g167+OKLufnmm3nnnXdoaWnhL3/5C4899tiQcV/72tc444wz6OjooKGhYdBrc+bMobOzc9yFCmbBVsJsfLKR6IZfzKLzF+KG5XK4qPZWg9sP3irqZF5BTs1VzuW81Jww6XZekfyrnW+JUyd7hh9UoIRZPadCb7SR1Dm/N4KiVYYY6sSqxiW+O29ouvGaojjxuMX3FnFqxCt0LfWl+wg5xPuvDgw8lD1OD1lFfG6JdAVD7RLJpLAJ8Af2w+2O4PUI5TkeH1tC+76EGTNm8NZbb7F48WKuvPJK5s2bx6mnnsozzzzDnXfeaYy75ZZb+NznPse5557Lpz71KQKBAH/5y19wOp3jnsMDDzzAYYcdNujPf/3Xf1FfX8+rr77K4sWL+da3vsXMmTM599xzmTlzJm+88QYzZ84cdJwVK1YwadIkpk+fzmmnncZzzz3Hrbfeyp///Odh5/nYY4/R1dXF5z//+SGvzZ49m0MOOYTly5fzu9/9jmAwyMknnzxk3OLFiwmHw9x3331Fz1FXV4ffP4KqX0HYSpiNTzbiIh9mtzRqbfA3DKhd4WY86T7CLj/RXJKuZBdVxfyozEaql5gkYUXNUaUxaTCbBjwVIWHJfAoU8Bczj9XhDRt5alaHI3UfMifgfuchOPwrRccp3giBjEZcUcScLLzv6t9DRD5bvJ7BK3CPt5FMtkuQsHRlSFhPqoewdMsP+Ab3/HS6a4B+0unK5oRpmkpKFi74fVPF34H9SGd2kUxupapqQUXnszdi0qRJLFu2jGXLlg07xufzcdttt3HbbbeVdMzp06eXFJIvZgNRiPr6em699VZuvfXWEcfdc889Y3Kh/+IXv1g0qV5HYVXllVdeWXSMy+Wiq2sghWS0ytDCZP9Kw1bCbHyy0XgQXNtB5+k/BzAc8gEIi4dWvczx6ExWKHcm2Utc5oQVJWEy/BaUOWGVIWF6EvwIoaKa/QhU7VeROaWkw7pPVVE6Pxh+oC9SMa8wXQkLZfsA8KQH+0x5PCIxPuLQiGUqE47sSrTjcww+vw6vR7jn53N9FZmLjnw+Tk31kQQCM4zCBT1nTidnNmx8UmArYTZsuDzsRuQ6NfoLqv9C4gFRp7jZgsgLqwhSvSScUglzFSFhniAoDiP/qhL5RYl8FlwQKNYeSMfhX8XfOB2e+Y7lrZSMQgFPGOacNfxAbwS/XP1bPSf9e/Dnxd+e5ODm2LoyFnZqxCvQUQCgN7ENB5DXFJzOwSqm110HGpCvXJEAgMsV5rDDfjdom27lkUoP3x/Qho19EbYSZsMG0JEQIZnCMn5DCdPEz2QilLCiieSKIuwg1ApWImpC1fGPEo7V87NSFvcCNEiYvxamjmAM6Yvgl2Q1lbN2TnpiviOv4sqqeAKTB73ukSQs4tSIVahXYn9SkJqM4h1SVBLwivkoZCe8d6NBwlI2CbPxyYJNwmxUHj0fwd2fhQeXQqqyoZAheP038Kdv0r1beAfV+esGXgsKVaxOkp2K2VQcfSlxpxCph22Y/el/I7hgKVCBcKSmkdRE6NPvG7mkWydhlith2RIsMwCOvhRf0yEVmZOuhHVugxNf6aa+YXDCsMc7oITl1ByZ/AiVpiYhnhJGrXll6OcU8tbztz43WzzHWj6P0eD3iWo9Oxxp45MGm4TZqDyqp8G8L8om2TdN7FxaV8Gah+mTbXlqCsr4qdkPJi2gTqpjlQpHZo66mJxsfzOspcJR3yR40NlABUhYNklSV+ZGImE7V+P//fmA9aqToYTlMjBSxWa4Gb+sJq1UdWQwLb+PwOAcLD0nK+iooMluRlyzimPodVTtq+GpfjcfqC04pSVLJZBOd5DN9gxKEh/ICTNfCZuo3pg29g1Yff3YJMxG5aEo0HgwZBPw9v0wkveU1UiIh1QPQump9lUPvHbwP8G3VlJ1sCiV7k33VmRKhaQq4Brey0Z307echGViJBRxq/AXfj57wuHE37cdsF51MloW7V4Pu4o7eOuoVIhUDwvrYWICdYNeb6g/hUXhq7h3lwegIoat6UwvAI4i3lv6td6XrqwaveGD63jhxSPYvuN+Y5vHI1TnfD5BLmcOOdUtEDKZCby/2PjYQ79+zLD+KAY7Md/GxGDq0RCsFxYRH70EsxaPvo8VkCpKryqSqKu91UOG6Nv6K2ErkE0Rl55cPqcPl2OYn2j3FgK7NwAVUFQyMUMJG7ZPI0DtLHz//AdYdQXpfBpVU3Eo1qzzDCVM1cA3vFkr3VvwdQnvqaTFyfCJXAIFTRQC+KrBOfi7czr9OB1+QqpKwuGoiBK2Leviw5ib02cPDTnq13VPagQl0QJkZJukQgsPlyvIoYf+Go+nHofDO9yuZcHlchEIBOjo6MDtduNw2JrDxw0VM8geBqqq0tHRQSAQwOWyhi7tsyTsjTfe4LrrruOVV14hk8kwd+5crrjiCs4///yS9t+9ezfLly/nzTff5M033zR8RkaTJsd73n0ez/4M3AGYfx7MOhnefRA+enniSJiuhEllZSQSVhElrHsTid9/GaZMGtnd/bkbCa1/BKZOtj4xPx0jKW+EI+ZgeQL49zsOVon/pnIpyxzq9XCnIDwjFAv07ySwax2EQxUpFpjlVXEf5ufNhIuFxQYdeAbBLb+H+M7KkLAMrI26+VL1CUNeq/JWMdOTZwZbiUbXEg7PtXw+MNAmaU/LjIb6oaab44GiKEyaNIktW7bw0UcfmXpsG9YjmokSy8YIuAKV8WccBg6Hg2nTpllGBvdJEvb888+zZMkSPB4P5513HlVVVTzyyCMsXbqU1tZWrrnmmlGPsW7dOq655hoURWH27NkEAgESiZEfdmacd59GPgev3gmZGBywBKYcIUiYVH4qDtmySAX6dLfzwpynTBzu/BRVmV5oDFeGhOUzJAKi99qwSfkAVVMJVs8AMqTyKXJqbnjVbLzIxA0SNhqp8joHVIxELmEZCUtmooBUwrwjeJdVT8PXcDCktlofIs0mqHVq4FDAMUzoItRAyF8H8Z0V8XfTuzxEirR2qvZWc0wox1HBDrq6V1WEhGmaZjQM93pHaAZvEjweD7Nnz7ZDkh8ztPa1cu2z1xr///minzOnbs6EzMXj8Viqou5zJCyXy3HxxRejKAovvPAChx12GADXXXcdxx57LNdddx3nnHMOs2fPHvE4c+bMYeXKlRx22GGEw2EOOuggNmzYYPl592moWVh8Dex8GxoOAj15e8dbghBVWnbOxCCfIepQyMt2O4OUMHcA+rZRrWhAmP50v/XyeMthxM+9G56+dGQSdsp1BBdfA/cfDoi8MKtWi/l0P2l5ExqtGtHxxnL8ipOklrc0Od8gYZoKIxnIVk/FP/szsOa3FSkWCMikew/FE93fXn0h5/jWsNOpWk4KQagJAGF3kZwwbzUJ2cQ7nem2fC4A+XwMVRXve08lzCo4HA58vsoVHtgYPx5f+zhtmYGepn/b9jcOm3zYBM7IOuxzQfJnn32WTZs2cf755xtECCAcDvNv//Zv5HI57r777lGP09TUxKJFiwiHS2sma9Z592m4/XDsd+CLvxVKQeNcUByQ7IbY7srPR4Yiez1CrQm6g3icnoHXFQUueoKqS14AIKflKhJC0hWSkZLyAdxONx6HZ9A+ViCpDji/j2oJsfJmfLLQwkqSkZT5eX7FaeRe5fPifB0dT/Pee9+lt/cfgMits3o+IJS/oLyjupTi310ivpmIkiDs1EhY7JqvaRoznN0cFcjhVwoIaOeHsOVFwp4wCVVMWLeysBqZjAhFOp0h2hLdvLnrTVS5AOrsfI7166+mre2RiszFxt6LV9teBeC06acB8PLOlydyOpZinyNhet+rz3zmM0Ne07etXLlynznvxxpun7CrAOj6sPLnT4jVf0+gGiieD8aUI/A1zTMe5JUISRokrIRQnq6WWUrCph0NgIIyKNxYFJ5gRcxRk5LA+BU3APH4Zl54cSHvrvk2uztWsGv3X+noeFKMke2dEhVoKq4rYW5ncRXT7a4GIOCARMpa9SmRS3BKOM35dRmUjGzS3b0F/vtE2P46DsWB5pCVo5nK2K/ofSoVVxVfeOwLXLjiQpa9LfojxmLr2dn2MD29r1VkLjb2TsQyMdZ3rQfgsvmXAbAtuq3iBSSVwj5Hwj78UDzMi4X9ampqqK+vN8bsbedNp9P09/cP+rNPYceb0LEB8gU99erk59U5cSSs1yfUzkEeYXtAD/VZXiH5zoMkVv07MEpO2KZn4Y7jCGZEnqKlJEwqSAF3YPRQrCckQoRYrITpLYKkctnX9w9UNU0220ttzafEtv7VYszKXwCQGslPbJzI5rPk1NwACXMVr9h0e2S+n0Mjmey1bD4grtWAvMMHpVEs/1gO2ThIexNFtjLKZCvzgNMrIzszGeP6+N263xHNRI2OAvoYG59MbOzdiIZGY6CRmdUzmVk1E4A1nWsmeGbWYJ8jYX19wvOmqqp4fkwkEjHG7G3nvemmm6iqqjL+TJ061fR5Tige+y7cfhRsfHpgW/0B4u+ujZWfjx6O9AqyU1Wsyu7Dp+DZG6mWiovlSlj3ZuK9opJrRBKWy8DutQTyOcDa1kWGJ9dooUgATxCfrCC2shoxKd+vXypz/dG1AEQihxKJzAcgGl2LqmbxyTFJC4mq/hkZJCxY/LerK2FBh0bC4mupP9OPX5+PW17bp/wELvw7zFgEgNMltudylVnw6eHIbcmBe2E6n+aF7S8YOWJ69aSNTyY+6PkAgNk1YoF+YM2BgCBn+yL2ORL2ccbVV19NX1+f8Wfbtm0TPSXzoKoDIUedeAFa3Sx2OZ1oHR9Ufk46CXOLh3RRJWzD3+GFm6nOCfXOchKWjg70jRwpJ0x6YwXUCqhOq/8HAH8uO8pIBoUjrXSoN3zCJDGMRt8DIByeSyAwHaczhKqmSCQ2E5Ch5JSVn5E8dtApvjv3/qcXHed2i2ss4NRIWKyq9qW78Tn080oS5nDC9E9B1VRY80fcCaEoahVq4p3N9gLQn1MJu8NcMOcCAP6x6x+GSpitkCpnY+/Ehz3iOXFAtXhOzKieAcCWvi0TNicrsc+RMF2JGk516u/vH1atmujzer1eIpHIoD/7DOIdohpScRh5YKqm8v1dz3LKtMlcnPmQnJqr7JySMifMJVSuojlhsuVNRBMP18qQMPGzHFEJk7YMfvmZWUp4+oULfoASqkILlTALc8J0lc3v8qNpeWIxkUMSCc9DURwEg2IVHU9swueyPjFf//xDemK+q7roOJ2EBR2QkJWLViGaHEi2d+0ZHs3E4U/fwNMl/bNU6ys1ASZN+jy9tRfwcszFIQ2HcGTzkQC8vettPPKzqVRo1MbeCV3x0pUwPRy5uW/zhM3JSuxzJEzPySqWf9XT00NnZ6clNhETdd6PDWQ7G8KTwClIz3Nbn+PprncBeN2Z5y+b/lLZOTUcBHO/QK9fPKBqivVFlCHKakksLG/xko4anlwjkzCphEl1ylKCMeskAPzBptEHe0LCQNXqOeVFhwO/K0Aq1YaqplEUD36/IPjBgFg9J+KbDZf/pKXhUZk3Z4T/qouOKwxHWhkeBYjLJPiM5kJRnLDqP+Av34Xt/4BwE1RNxZcVSqqipdFkLp+V8Pun8VYsx46sg0MbDuXguoMBaO1vRXOI70lVk+QtNta1sfdie0w8K6ZFxG95RpWthH2scOKJJwLw5JNPDnlN36aP2RfO+7FB31bxd9UUY9OjGx8FRNUdwP9u/N/KzumQL8E5d9MTFGGQokqY7K9XnRcVdpUgYboSVko4cqDyz0IlTJrH+v21ow/2BPFXIkQqSZjPEyKZ/EjOb6ogG0AgIFbPicQW/LLKNJW3zrBTV8Je7nYxa4cDb39v0XFuqZAFHZrlDcUTkoTlFGm7sv4v8OY9IHMOaVmAPye+Kw0n+QqFJFv7WgHYv3p/mgJNhD1h8lqerbEOFEXYjdghyU8msvksuxPCrmhyaDIAU0LimRHNRA3z4X0J+xwJO/nkk5k5cyYPPPAAq1evNrZHo1F++tOf4nK5uPDCC43tnZ2dvP/++3R2ji8ZtNzzfuKgK2FVImE5nU/zWpsoRb/t07cBsLpjtWEuWUno54x4ioR/pRIWlt5XlvuEFeaEjWRR4dHDkRUgPHvkX40IT9BQwiy1qHAJYuE/4SoSkoQF/PsZrweCOgnbjM8tKgCTqnUkTP+MNvfB9E27cfuKG5EOWFRoJCz2LdMNWFVFfm968UvDQcbfobzKldv8PO85B1eRJt9mQ9M0tvQLRWN6ZLroSFItowi9HxqfT6VJWGfnc2zdeldF1EAbw6O97yNUTcXr9FLnrQVNI+AOGPm6bbG2UY7w8cM+R8JcLhe//e1vUVWVE044gUsuuYSrrrqK+fPns3btWq6//noOOGAgMXzZsmXMmTOHZcuWDTnWhRdeaPxpa2sbsq2QuJV73k8cemWRgVTCVu9eTSqfojHQyCJvM/v56lE1lX+0/6Nyc0r1g5ofmYT5qwEIZQShsHwllo6SUErICXO6wOUjoFUgCX6H+E5KywkLGYn5lSCGAX8dyUQrAP7AAAnz+0QoI5najt+jkzDrcg51JTIwaQFcshIiU4qO8/un4shP462Ey1DzrEJaJsHj9As7lpT8f40I71B/AGFVJY9SscXP2g9+xon+LiIOlf0i4vuaWS0I85a+LUbOXKVJ2I6dD/LhxhvZtu2eip7XRgGSvWy/R3hqtjgDKMuOgC3CW3NSaBIAbfF9j4Ttc22LABYvXsyqVau47rrrePjhh41G2j/96U9ZunRpyce59957R9x2/fXXU18/sOI167z7JHQlrFooYWu7hKXAgoYFKM/ewILOVj4Kh1jbtZbF0yrUzPvO46BvG9E5ovVPuFj7G10JyyTB5yRmscs56ShxTwk5YSAJTwVywjY9Ax7wZ0tQtgoS862ak6ZpAw283X66jXDkAAnz+SZTXX00Pt8kfFExnxwqWTWL2+E2fU6GWuirgZYFw44LBGZQ5VrEM9FHmK6UUG06DmRy4lp1OIMDKlhkMsgOEYKEic+mUiRsV9tDnBLJsUWdZBRMTA2Le8KO2A7C9XNxuSIoDs9IhzEVqpqhu1t0xaipPU5cX6kd+P3FibQNi7DlBXaqKSDIZBXo3iTsjGaeREuwhXVd69gZ2znRszQd+yQJAzjqqKN4/PHHRx13/fXXc/311xd9TZMPEyvO+4lDn66EiRuu7ogsEnMbmRPfxJ+Js6F7+P6cpiMl8ruiUkUqTsKqxWvpGFBl/cMqHSVeJcjX6CQsSEATISdLVScZxvOPNh85J6vNWlP5FBrit+nf/jbTpn6dmuqjqKk+2hjjdkdYePgDAGTafmJsT+aSuD3mk7BELkG9S+UgZxudXc9TX3fSsGMDPpFbl9CsrQbekGvg19v9/NsxF0DXJrGxdubAgPrZhGU4O6qrZBZCVbNGFWZdaIAw6zk/O2I7mLvofsvnsSeisfWoaga3uxaXM8yql45FVVMsOuEtFGWfCxbtvThgCTvaz4dtTzC5ehbwDmwR5Lg52Azsm0qYfYXZqAz2IGHrutYBMKduDpz0Qw4683YA1nevr9yc/nUT+Ss3jEzCZDgyLKvfLCVhmgbpfuLyxj9q26KC0J+lZq1SbfOP1ChbR7AevyQZVuWEFZI7X7SdmppjmDbtG4RCxcP9bk8Ep8V5aslckmkelbnau2x974YRx/oDdQAkLM4/6s/0o6EQ8dYJVQGgbv+BAZ4gYU8VX6rJcF5wHbt2W7t4zObEokfVoCE4QMImh0UC9vbodkvPPxz6+94GoCqyAK+3kVyuj1wuSjK5dULm84mFy8tOt9CFWpoOFdt2rYVcmpZQC8A+qYTZJMyG9cimQG8ZE5lEIptga1Tc4A6uFSXqB9SIB+iuxC7rQ346XB5inoFk86I5YdIKIqQrBlkLSVgmjoZGQibmB12jKE/eythBpGQulU/mVo2Ig87Ad8pPLJ2TTqS8ihPnlCNGHa94Q5aHSBPZxIA9Re8ozbA9aeb48uQd6pjU9lKh5y+GPWHokRWRNdMHjQmHJhFyaNQ4c4abvVXQ87ySKjTJHB8YUMK6Ul2WLiaGQ780+o1EDsXhcBMKisKFqOzCYKNy6EyIa7ChZn/h0ajmYPd6QwnblahMo/lKwiZhNqxHXJQc4/SCr5qP+sUDodZXS7Ue7vOEqfMJheCj6EcVm5qubPldftzOImEqhxO8VUQkCYtlYqhWKRjpKGlFIa+UUB0JIhwp52VZYn4+SwpxDr+3NPNgv1s2hbbI68nIv/KEoXHOsONyuTix2AYSrrTlTcVF827xbxe+Ecd+1PlrvtWQptalkbGwYlOv5A27w9AvFYSqwXlO4aopJOXlnM1aa7+Sk8dPqArNgWZje5W3SswRoYblclEyFWooDqKCFjAMfsPhuQBEY+sqNoe9Ba/sfIUfrfoRL+98ubIn7t4CT15LR6/4LhoCDdAs1bD2NTT4RV/RzuS+19LKJmE2rEdMNuQNNYGiGCRrWlhUsNG1Cf5zPvtFxY33o74KkLCuTfCnbxJ96RYA4yFQFL4qQvIhrqFZt1p3eUkc9U3jvyP6hAEceh7+Q88DLFTCMjHSkhT6vKV1mvA7BQnTDUzNhv5efS4f8fhGtmxZRkfHU0PGbdt+D6+9/llaXR/gCzcP2tdsJHIFSphjZCsPt1t6vDksvJaAk31t/LA5CfG3ISpJWKRl0Jhw9XRSshtEUlpaWAVdCYurCk17GP8a1W/tj7DyhQWsXXelpXPRoWmaQcIC0uA3EJwFCI+5TxJ2xnbyL8/+C49teozLn7m8sqG/7W/Ay7fRIUlWg78BmuaJ13avo94vCuC6kl2WqscTAZuE2bAeUxbCj9rhYvGg3NovQpG6IzLeCPS0Mi0lVu4VUcL6tsGah4m2isTPovlgOprn4Z00H7c0krQsLyxQS/z4fwGEMud0OEceP//LBA77KmAhCUvHSErzWK+nhMT83q34H/+hpXMylDBNoa/nDTZvuYXt24cmdHs94kGfUaP4peJq2ZyySbyKeDg4RyFhLtk026dYZ9iqaRpVjgyT3BpeRRlQwvYgYd7q6WSE3y+pjLW2EHrfyLjKICUMoDHQCEB/Li/HVsaiIpPtIpeLAgp+/3RggIzp1iefFPzxgz8a6nVGzfDQhocqd/KODaQUhaj8DdX766FOkGG6N1PnF1GSVD5lvVdjhWGTMBuVgdsPUo3Qw5G6TxCBOlCc7JcVuUc6SbMUsjKyX5brj0jC/vn3KJesJCzDcVbmhenKSEnGqAXjLFNUMnFDCdMVrhGhOPHJSjyrwpF6SNHX8xHpqFAxvL7mIeM8XrF6Tmc6jc/JSiVMb5btco5MVl0ukVvnd0Ci15oFRyqfwieVuYDiF31bQbQNK0T9bDRVTNzwFbMI8bTI50kUUcJ0EtYl/fhy2co4oyfi4lr1+abgdHoBCEgylki2fqLMW5/6SCySF08VFkHPbn22cifv2UKnU1yHHodH5Ofqlbzdm/G7/ISk6fK+FpK0SZiNikMnYYYS5nBAqJFpWVGFty26zfpJ6PYUHnHjHZGESehjLFPCUv0ke1uBEkKRAIluAtIEtxLhSK/LO/r4YD3+038l52QNMUzr4UhNJZ0X36PXO7Svpdcj8kgy6V34Y51yTtapc4YSNorzvN5M2+/QSCaseaDEs3F8cj4BubghUA97foczT0KR7v4Zi3PC+hLCXiCLd4j9ik7COtPimsnmKkPCcvkYPt/kQZW1Pt8UFMWFqqZJp9srMo+JRmeyk9b+VhQUrjn6GhQUWvtb6Uh0VGYC3VvodArlv95fj6IoA0pYTyuoeSMkaZMwGzbKxWv/DY9eBpueAwaUrv3CA2XqhBqZJEMR7fEK3Ph0JUy2v4mUkHSu541ZRsLe/yvJh78CYBhZjoh/3IX/AZETlsglrMmVyMSMhuI+ZwlzcnnxzxIr6aRFSfAp+d15NY10TuQRer1FlDCdhGV78HcKs1KrEvMT2QIlzDVyFanbIGGQkBYoZiOejRtKmNtbB0dcJHqlFoFTKnf5nLUeeHFVY1dWQXUOzS3UE6/bU4J85XL9Fcn9aag/mU8d9wKHHvLfxjaHw4XfL6x0Ep+QkOS7He8CMKt6Fs3BZvavEVYm73a+W5kJ9BSQsIA0QI9MBqcH8hno226TMBs2xozNK+GdB6B7M4lsgp60yPeYEi6o1Ao10ZwXK/aOZAc5C1vMAANKmFPkeY2YmP/yMviPQwnFxY/fMhKWz5CUSkVJSligFr+8Yamaak2lXTpGWlpmlEQMC8alcilLHqSptHhQ+zRIZ8RK3VeEhLndtYACqERqRC5U0qKw7SAlTPY/HA6FSphVhq2xbAyf7DLlqpsDZ94Cp/+86FhduVMtbuDdHziWm9r9bHcNrWjVlbCdyV65RSWfr1zuj6IMbsnl8wrvslR63/OlKgadhM1vmA/AofWiMnFNxxrrT57shWQPHZKE6YQch3PAUqV78z5bIWmTMBvWY+HX4OTrYOrRtCeEyhVyhwaHAEON1OVVXDhQNdV6GVwnYTLpfMRwZDYJvR8RliTRMhK28EJSXxAr8pJywo64CP/3BsroLalGzMRJ6eFIZwnhSCDw/hOAqCRNW9AfMS0/f6/iJJ0ReUbFwpEOh8voRRiZsgCAlGpNv8ZELoFLJz2y2fBwGETCLArZxjP9eEpU5jwdIq9Os1gJ606J6staaeZbCJ2EtSU6UBShTucsns9ImD79MuYf+lvqak+csDlUEht6RKcS0cEEDqw9EIBNvZusP3mPqELt8Ivfha54AYa5N/07jOT8jmSFQqQVgk3CbFiPA5bACd+H5nlGqFE33zMQqMcBNDnEg14na5ZBrrij8sFZ1KhVx/zz4BtPEZ4kekxaWZ1TaL9QClwOFx7ZZ8+SB7qmkpIO/qXOyfv8/zP+bUUOVqqAhGWkrYIeehwyF7k95JB+ahYqYTe1+9nvlV4igYNGHOvSLSoUSHa8b8l8YqkBtcCZSolFxzCqpDPr4IWoix7V2l6JpZCwrmS3QVIrkReWzxe/PmtqjqG+fjFeb/Hral/Dlj5BhGZVizysmVUiKX5z32brT94tzt3lFwthQwkDOOLrcMavYOoxg2wq9iXYJMxGRbErLpSLPaujkK1cmmQ7U8vzwvScMGlEOqISVj0Vph5FWHoZWdm6yLBfKLE6EgZMXa0gPNr880hJtbCknDDA6QnisdAcNS07KoRcLpDfn6547QmdnAUUoYAls9aE3HRyF9A0FN/IfmouGf7zOzQSFpEw3fMrpzlw/PV78P+mwZv3FB3rnXEqj/R62B45yZK56OhN7gY0Q9EoRK2vFpfiQkNDcYrrOWdxoYCmabzw4hGsfGEBqdQnI+xYDKlcyvAEm1El7Dl0ErY9tt0SNXsQZEu7TrdYgA9SwuacBUd+A+r3N8h7T6oy9iWVgk3CbFiLbAo2Pg3tIrfAUML28AkiKH54zbIivFIkLCpzckpJzA/Jtj2WkbCX/pPkm3cDJZKw3e/DXafhTwtiYYXKk1WzRrPsUpUwPEG8eq9GC2wqUvJ9hmSfOZerGoejeFNu3abCv20VAOmo+Q/bnJoz8vECqgbekasj6+tO4s3u6dzd5SVhESlMSbuJnOKGtLxeg/VFxwYjIuQTt/hhe0T6Mf59SpJ6x9DFgkNxGN0zNOmzlrNYCcvlelHVFLlcFLd7KDH8pOCj/o/Q0Ih4ItTIUHq9v56wJ4yqqbT2tVo7gX5RNdsrc09rfMUXVNXeajEu3WvtfCoMm4TZsBZ92+D+L8LdnwUGwozDKWGTcsKmoi3eZu28dCVMPjxHVML62+CV2wm3iR5zlpGwHW+S7P4QKJGEqTnY+gqBnFR5rAj9FZCoUpUwPEH80l/JkpwwGXb1ul14PU34iuSD6Zi+37c58sg/k4qJzzNtAVEt/Nz99QeKnncjwOUK41CqSKiKZTYesbzKs/0u2h2z4MK/CrPk2UuKjtXtIqw2wXSRwalAla+x6OtGmLL2LA455A4ikfmWzieVEveYnOLnsmcuZ/Xu1cZruVyUTZt+yfr1V+9zDu17Yku/CAfOqJphFCgoisKMiFDFLLcMkgujHkR1/CASlklA60uw4XFj+76mhLkmegI29nHIikKdZA2rhOnhyHQKXLA7sdvaeelKmCQJI5KwaBs8cQ3h+ikQdlj3sMrEB+wgSlGdpIO9X80DDktywlKv/RcAThRcjhJvF54Q3qx14UhdCSMf5vjjnxnxIRmULWg8ih+IkrKAqCZzSWqcKv+nMc27tQdwZO2MUfcJyObsCYt8y/pVF4/1eTiv+RixwT08qQ+mY8zw5KnqeZVstt9oq2QmNC2PRxHEvCbQUnSMTsL6nFNobChOGM2E7gHWlk7xyq5X+KDnA/7+hb8TcAdQFBetH90JwP77/xD3KBWvH2fobeKmR6YP2t4SauHdznetb1+kK2FyQawrXoBYxN/zWfBGqP72i2KcrYTZsFEGEjKJck8SNiQxX9yA61JCZbI8+VInYfIhGBnpwSNDlUFZfRi3KIREJkFSJsGXpITJsJc/L1aQVihh6d0iZ8mrOIeU8Q8Lq8OR8phe6eBfyrx0Fc8KEpbKpQg4oM6lkUrtKGmfgAxtJyzyLdOvUT2EPhKCmSRfqctwTHgHiaQ1/RKzBV0m6gLFCwB0pUNP4Lca6bRY6PXnxfXTlepiResKAJxOPy5X9aBx+yr0qMMgyyAECQPYGbeYhDXNJdtyGFH5u64prC6OTIaaGTBpPjXStieWjZHNZ62dUwVhkzAb1iIpb6iShO1KiMT8ISQs1Ayn30z9saJ3YlfKQhKmqnDAEnIzF5OQ1VEjKmE+QcICMvfKMhKWjZOSeRElkTCphAUk4bGChCXnng2Az12Cb1nBvPwyMT+dsyIcKY7pK8XBX0JXFtMWkJ5BHmGjtCwCyOfTNHre47KGFBnVWhIWVFX4n3PgL98ddmww1Exaion5nDUqb0wSmYwKdYHi4eM6n7hHVCrc1CFbXsVUJ1+f93VgcKsevTIyk9m3LBH2hK50TQoObmnVEhQkbEestIXFmHHWf9D3lT8CIjdw0L3YG4LvroYL/0o4UI9DLlL3JTXMJmE2rEWBEhbNRI2HwxAS5gnA0d+ifq5w9bbUkM/hgHPuJnru3camERUDqTgFZZ6TdUrYQDiyJBLm8oHixK9aZ7+Qlq1DfO7RFRUDBUpYchgLgPEgJcMW3hI+o2RyO+vW/Svems5B+5o6n3xqwC2/Z3TVwOFw41V2cqBPJadZYLALkGnnIF+eSKYTPnwSPnxq2KGhUAtpVVx3eYv6onYltgOQ0hSjmndP6OHITOJDNm2+hbb2/7VkLjq29a0FIOhr5vTppwPwRvsbhsri8ch2ThnrzUF/t/Z3nP6n0/nVm7+qeA6aroQNIWG6EmZ1OBLoTfUCUOWpwulwFh3jUBxGqFI3/N4XYJMwG9bCIGG1hgFr2BMelmTo5evJXNK6ptQSMWl14Hf5R853cvnA4SKkWk3CEiTLUcIUBTwhS5UwI/RXhuqEO2CQMEuUsCrhZh5sirLyhYW0tz827FhVTdPW/gh4ewFI5S0gYbnUgBKWG90BX1EcaIhqzpxijWN+Y34LlzakiWRl25lhKiMBguFJpORzP5fcZcl8+mRBTnaENGQ9HOnIbKe1dRm7dz9uyVx0RJNC4WkMz+LA2gOp8laRyCX4oPcDYMDeJG2xEramYw2/+Mcv2B7bzt3v3V3RxtmaphkpInuSsMkh8TuzlISpedA0g1RVeUe2dzEqJCVp2xdgkzAb1iKhhyNrDXVrkBlfIdreJbDxOfzSmd2yvDA1D2reSLAfsWURCLLjDQv7AYTiZE2fxnh5OWEA3pAR+rMkMX/bqwD4lDJqeNx+/DoJs6I6UhVKhYMUuVwvDmlWW3Qqun+YkseBZuxr6nzyaby6EtZ0eEn7KA4RHtWUvOnzAdBUQcjdeTmxwAgkzBseUMKS1uQ/9acEucsz/Hdl+EBlxDVjtU9YVnqpNUUOwKE4mFMr2imt71oPFDSAt5iEPfzBw4P+/+CGBy09XyF6073GQmvPivVJ0hcxlo3Rl7bou/jgCfhZE73PXA8MY0/x1I/hlwfAa7+2lTAbNspGYiAnTCdhxcwaAXjiGpSHllIrE647UxaFAbashBtqif3vpQAEPaPn8eANi/waIKflzCcXmiZywsoJR8IgOwhLlLC3fgeATy2DLBSGIy1U5xTZW9DtGd7jye2uQvSPhIADkhb0akzmkkazbFeoNNd5h/TC0qSTv9lwaOL6dOuHDw7v/B5yhwwlLGMRCYunBZHRHMNX/eokrDMjFhNW+oQlc0lcmjjP9JpDAJhTN5iEGeHItHXhyKya5ZmPngHgZ5/6GQCvt79uHenZA3oost5fj8c5mCD7XX5DmbKsjVx0J+TT9Mjf5aDKSB3ZFMR2QbTNIGm2EmbDRqkoyAnTk+3rfcOsypvmwuQjqJcthCzLC5PmlXFZVBcqJd/JW2WE/cCCkGQ+A2rOCEeWbow6oIRZQ3hkEnypHmEAbj8+C5WwVEyGzHK9AHhGMNpUFKdhLxByaKQ185WnVC6FTw9Hukog9IDDKa85B2BBpZdOwnw5ycJGCEcG3AFDCUulrHnYpjK9cmLDF3joJKwjLch1NmcdEdnYs5G/9Lp5PFpNS80RABxcK/omvt8tKoI9FVDCPuj+gGg2StgT5qxZZzE9Mh1VU/lH++uWnbMQOgnTk/D3hB61sKxf42Ffhe++S+/+nwaGUcJC0lcutttWwmzYKBs6CfPXjq6Enf5z+OYz1NfOBiwMRx74WbhqI/FjvgUwbKLwIHjDOAG/3qfR7Hy1jCB1ZSXmgwhHWqg6pfOlJ8EPzCmCT4aULWlblI3jREPTxLE9IyhhgEHCgk5rSFg6nx5IzI+WtnBwyRC44mTA0d5EuKSy4MtI5W8EEuZ2uMlpkoRlrHm4dVLPg90e+rzzhh2jk7BuPRxpYQPv1v5W1qZcdPvm45PmsTOrRaueLf1b0DQNr1dstzIn7O3dbwOwoGEBDsXBUU2Hc3F9GjZdyoYN11uepD+sZZCE5STM5YGa/eiRv5+iSphOwuK7B5QwuzrSho0SUaCEjUrCJPRSdcuUMKcbQg3EnSLPqTQlTFZIyvY48ZzJSphOwhxl5oR5QvhkONISY1SZQ+UbwexzCOZ+Du9RguBa4hPmdBNyioeToriMhs/DQc8LCzo0kmimP9gGWVR0lNbwWO8fqTgVoYKaCE3TcMmEf39aEvMRcsLEPqIiLZ21JgS4O+/k1bgLgnOHHRN0B/E4PCQlIczn46gW5PABbI1uBWBaeJqxbVp4GgoK0UyUnnQPXm8TgcBM/P5pwx1m3FjdsRqABY0LAFjoizHPLxYK23fcR1f3SsvODbCrZxMATW1rITY0FN0QECTMavNsnVQN8gjTEdSVsF0DStg+5JpvkzAb1iGfM0xRB4Uj/SM/EPTXLbWpYKBNi962ZURIEhaSCep6ZaVpkMpa2UqY24/PqmbZmkZKkySsxDCbDj18aUl1JBoheedyu2tHNWt1u4XCEpT7ZEy2qUjlUmxIOdnRpVHlKB7WGTonQRwdDtBCw7ddGtN88ik8khQGU/KaCIzWG1Fc11nVmopkPcepyjN89ZuiKFT7qkkVpMnl89ZUIn/UL1zip0UGCJbP5TMqBFv7WgkG9+fYY55i/qH/bckcADZ0bwBgXv08NE0jkBBhyIxcJ2zffr9l50ZV6Xz/LwA0tL8Hf/i6yE0tgK6EWXYvfuEX8PT19MjWRXr/UB25XBRNz2eMdeyT/SNtEmbDOqR6QTZ/xl9jhBd1pWsINjwOt8yjbu2fAQsNW997BP52FTHZVLwcJSygCMXA9EpET5DsYReQK5eEHXAa/gOEx5HpqlM+g35Er6cMs1YGctrMnpOmaaTzaQIyEb6UdjKFShiYT1ZT+RSrky52tqnUemaXtI9Hqnc+RTM9by6ejeOVvDSQkiRmlH6WWzM13LDTh9I98gJprOjPCIUt4h1Ztaz2VqOigCLC/jmLzGP7oxs4NZxlP2WwO/9+kf2AAZJmJTL5jKHI7V+9P4nEZvKZneQ0uG23COd3d79kXVj2wyfokOHn+nwePloFrasGDbFcCXv7flh1C73yXl+ohG3ZchuvvfbZQeHIiAzj96etbe5eSdgkzIZ1CNbDj3bB998Hp8sgYSMqYX3bqEmKH5hlFUKtL8IbvyHeJ26AJSlhkclQO5OgVHhMT8yvmkLy9JuM/5ZMwg49F98RFwEWKGGZOGmdFJZj1trzEd437rZkTjphMUiYa2RfIRggamHpAWk6CZPH82mqQdZHQ1V4Lu8lHXTmHKbn8gkSJj+fhHyAj9pUPER33kE8ZQ3pacpt4cRQlggjf/Z6NZ7mECTECgKiaRqO9FbOqM7iS7w26LXpVdOBgabWVmJL3xZUTSXsCdPgb0DTcjQ0LKE1X8W2jAPcjWhaht7eN6yZwLsP0ekUP4q6GSeLbWv+MGiI5UqY7C3cK38DuhLWH32PzVv+g1B4DgTlol3NEZGVzjqp3xdgkzAb1sLtg8gk8mre6Ak3LAmTP8Bq2R7Isrj/HtWRJZGwE/8V/uVtgvUHAFjSxFt/mDsVJ26Ze1YK9NCf6Yn52aRRrektp22RlsfXtREwvzpygISJ/7vco5OwYGB/qquPpl/zWjInXe3zaVrJJGzqlPO5rzPAGwkXqff/aup84tm44VvmTMiH1SgkLCR/e4lw8QTt8eIgVxufr8ni13pHHKeHm3KOCD7fFDQLLEV60724EL+VkG+wQekUaTGiG5RqmkY220/egtzGzX0if3BW1SwURSEUOpBDD7mDD72fBhTiThHa7u170/Rzk03ChhV0SRJWf8Bnxfb3/zYoJGmpEpZJgEzr6JGdGnQlbPv2+wBwOHwo7oDxbIjkRCpBNGNd0UalYZMwGxVBb7qXvJZHQSlehgzgrwagWq7GLYv7yxL4uFQLSgpHSuiEzfTqyGyKZFzc6HwuX+nNsjNx/MlewIJwZDZhKGElW2YAhJrxWZSYn5Sf+/a0wuwp/4dJzZ8fdZ+Wli+xcO5/83bMGrKayqU4yJenOgA5dxnkWa7qk1FzHcnj2Tg/bfNxb/8MPGlZDSp/W8MhGBbu6LE5Z5g6Fx1uBJkKy0rEIciL1yPSnqY1cj6fOm4lkcghps9lZ3ynEZr2eQcvCPVWPW0xYd2wevWFvPDiYXR0PGn6PDb1iqT4WdWzBm3XiwVa1UZmzbySpsbPmn5uskmyh3+VHp2EzThZdAZJdELnB8awQiXM9ErNhFDBsk4PCfmbrPJWoWl5OjpEm60pky8QY2VeWCQrSFh/pr/i7Z2sgk3CbFiHjU/Do5fCW78z8rtqfDXDtwiSq50aSSr60n3kyzEJLRVSCYtJu4KSzFolgjJB3fRw5JqHSf7mJKCMUCTAm/fie+DLgAXhyEISVo5PmCeAd/rxlswpLVfO3RmFadMuorHxtBJ3jOJPCVXI7Cbe6XyaC2rTNOzvIeUsvZrPJ0Nvyf2OM3U+8WycvryDrLMGx8KvwyHnwihtp2pccEl9iqru35k6FxCGpB7ZGWAICYvthl+fBDc2wTM/NZQwK81Kd8V3GUqq0VFBQneJ3xkXxNglCygyWfPzU7f0iZDnjKoZg7brxQJrEjmmT/824fDwFaVjRqCW7hOvBITyXh1sgClHitcK8sJ0JSydT5sfAowJ24toaMBIOOgO0t//LrlcHy5XhKqqw0ml29ncovDhjACRrPjt5rW8JR1CJgI2CbNhHdrehXd+D1tfNXIKdC+gopCr9SrZf09Ds0Z2lseMy1BHSUrYRy/Df51AcLMoGTc9HJlJlF8ZCeAJ4pPeZebnhA3MqazekRRUR5od+pNO2V5Ng3JCpN4wXqlipk1WwoRjvvi3q1iJfRGoapomn496l0rKW17Rw2jQVdqAJwJn/Qd88Tej7hN0BTnYrxLMfoSmmuviH81E8UlhN+LboxL0b9+HnW+DmoOXb6MqK64XK0lYe7y9oLBjcDhbr47sTHaSyWeMytps1vzUCD3kOSU8hWRyG13dq8hmew0lbGv/VtPPWQi9I0mdrw6H4gB9MbB9IAfN6/Qa6r/p6SFxScKCsnrZHcTlcNHdLUhgbc2ncDhc5LL9bKnpZnuLH0+yx0jV2FeS820SZsM6zDgRTvkJzDm7tKR8tx9cPtxAWCpOljgj60qYtCooKScsn4H2dwnGRV6b6eHIo79FcukfgTJJ2OFfxXfFe4AI/amaiQ/QbIK0YwxKGODd/paYk8mfU1reeP2aJvzeSoCmqWSUDPV1IuSWNNmiIpNL4JIkw+UdzQpCoLf3H1wU2cg36tOmh0f145VzHXklGVFQUXe9a+p8elPdRo6at5D0dG+G9TIfbvG18L21VNWJ6tK+jCBhVoScdiV2FRR2VA96rcZbY1zr7fF2PFaSMKm2TQ5NprPzGVav/hrr37/aUMLa4m1kLGg4j6bBtjfo6t8OFPg2thwGtbMg2ICmaYZHm56nZfq9WCdhfnFNhD2y8rFfXH/V1aKTQTA4G5fmRnUqxJMbjZD1vpKcv8+SsDfeeIPPfvaz1NTUEAwGOeqoo3jggQfKOoaqqixbtoxDDz0Uv99PQ0MD5557Lh9++GHR8dOnT0dRlKJ/Lr30UjPe1scLUxbC8VfAgaeVRsJgIDlfEiNL8sL0xPx8GSSs+VBY+ieCh54r9jU7HKkoJGXeTFn5V4oy6GFrqvKUSxu9LMuaE+Bf9SvAAhImb7zzAio7d/6RVKpt1H1Sqe28uOoozvGvE8cwWZ3LFxj3OodrybUHdLNWnwKpra+aOp9MqpVLG1IcoayHZA+UoGwFPAMKXj5hbo5ab3KX8W9Xod/cmj8BGsw6WRS+hBqM6shZ+TU89/w8Nm/+lalzAUGugsNYnCiKYoQk2+JtuOXnkskMtrIYLxLZhFGo1BJqIZFoBSAQmEmdr46AK4CGxqbdz7Gl9XZzc9J6t8LyU+h89JtAwT35wNPhX94iu/j7vPnWuTz3/MFs3PjzgU4GKXM/A52E9fvEb8EgYVGxqAyHRXcFRVGI+AU5jzY2GTYn+woJGyY55+ON559/niVLluDxeDjvvPOoqqrikUceYenSpbS2tnLNNdeUdJxLL72U3/zmNxx88MFcfvnl7Nq1i4ceeognn3ySl19+mYMPPnjIPlVVVVxxxRVDth9xxBHjfVumIa/m2RHbQcgTGjk8aCL0nLBhPcJ0+Ksh1k6NK8A2LKqQ1BPz87JCqpRwZKAWZp9CEEngzCZhjE3BABEy0JHKpcref1gc9FlSH94D3evLV8KsCkdKAn1kdZ717/+QBfPvwrdHhduecEkbC7ei4kQjlTVXedJ0g9O8hlJidaRT9o70OjSSW56HY68ybT65TDcH+VTi+Xb4+XSYcxZ8eWTTz6AnTFoFrwPyjQeYNheAaEoUnOQ1BYejIKy98Wnx95yzjE26mWsql0Z1Jcnlza9C3pXYxZFGTlj1kNcnBSexpW8LO2M72S8iSJjZSpgeigx7wkQ8ETYlRH5YwD8DRVFoCbWwsXcjuztWkut8iKams2ho+Iw5J4/tglAzHeEwkB6yMN646Zf09Qkl+6Otv+ZA35G8ixXhSBEOjXoCkICwO0w6vZtMZjegEA4PPF/DTYvo/mgd/Ur3AFnbR8KR+xwJy+VyXHzxxSiKwgsvvMBhhx0GwHXXXcexxx7LddddxznnnMPs2SObKj733HP85je/4YQTTuCpp57C6xU3j69+9auceuqpXHbZZaxcObSlRHV1Nddff73p78tM/OsL/8pTHz3FD4/8IRccfIF1J9rxFigK1M02fsB7OiIPgXy9SuY5ma6E5XOQS6ICcUl6SlLCJPSxppOw139DarMIzZRForo24VxxNV4U0mgkc0lqKC0vqRQY9gtlKmGi12SWlMnhlJRc/fqk55erBLNWoTopgIbfAanujabOSVPFdaTkNSixyENXhHwK9JicYJyVxEVvRTSaPQWI6zqugRfIYW6roGiqEz+QwzmwMdkL22WT6v2lR9WOt6he8a/ggn5pRWCFT1h7vJ1g1fBmv3peWFu8DU/dfACyWXNVoMJQJEAiKUlYYDogejlu7N1In+YjCCQTJprHTj0KrtpA58s/gQ//OIiEZbN9tLX9CYCammOpqzuJF7duAtZaoIQJch51i3tLxBMhGl0LQCAwC6dzIFdSL06IRdcR8QgblX1FCdvnwpHPPvssmzZt4vzzzzcIGEA4HObf/u3fyOVy3H333aMe5ze/EcmsP/vZzwwCBnDyySezZMkSXnjhBT744IPhdt+rMT0yHYBNfZusPdH/fltUPu34x8i9wQohHxg1sj2Q6asvmZSfKLCACHlKUMLUPLx1H8FNzwEWkLCPXia5U/gB+Z1lkLBcCj58Ap9qTf9Ive1QuUqYX3fMVzOm5vWkZI9Nr5HTM7IDO4CiOHC5xHcccGhGhaVpUKW/m+aEEgm9roQ5FEibbONhuMz7auHa3bDkppF3QKjBaVX8JsxWnxIZoYKrSkEO3443QVOhZgZUy9ZBioOqnasB6JUJ+nmTHfNVTaUz0U5CVVAUz5DqSICmoCge2J3YbSTmmx2O3BHbAUBLsIV8PkUqJUhZICCaiOsNtXdnxXWeSJrv4N8l8+4KSVjHS99H07IEqebww+5nv2kXU+UXFa1WJebH3GLBHfaE8flamL7fZUyefN6goUGXIMbx2AcDrvn7CAnb55Sw559/HoDPfGaodKtvK6ZgFTtOMBjkU5/61JDXlixZwooVK1i5ciUHHDBYuk+n09x7773s2LGDmpoajjvuOObPn1/S3NPpNOn0QPimv9+ai0z3pdncW1qz4TFDVrLhqzaSOkdVwnSvME2sD0yvktKT8uXqy+Vw4ZGq28hQ4LHLCXrd0NJsPgnLxAeqI8tpli0rBH2qSp/DSTJvYqjtvT+RkqvVcqsjRSViFA1NWBQ4S/mMR0c6G8eBhnuEcFIxuFxV5HJR/A6NlMnfnSIT/Z01s8FZ2i3V6QygaUIoTmvmkrB8XihrisMnrClK+O4C7gApyZXzW56Bw440bT7xTD9BFdRCIr/zbfH35IUD25oPoeqMW2HNzSRlP1SzCWF3qpu0mucnbQHe/MqbOJWh35fujdWR7DBywnK5XjRNRVHM0S30cGRLqIVk8iNAw+WqMkhhc0CQsG2pFDOAXK6PbLa35Ou9FOgV60ZiPtDJNgAaEwOLG+tywsT5+x3iMw17woRCBxIKHThkaCAaR9E08qSoc4vvzCZheyn0pPli4caamhrq6+uHTazXEY/HaWtrY968eTidziGv68cudpz29nYuvPDCQdtOO+007rvvPurrR07avemmm/jJT34y4hgzoJOwjb0b0TStdGPQciH9vvBXG2RqVCVM9wqT6onpFTl6Ur5XKBFBd7C09+9wgCdIUBUk2XQSlk0Y7vTlWlQA+FUVcJqrhO1aS0rNgMNRnjoH+FwDoYRUPmUiCUvgL3gOukpQwkC0N0qxnYDD/GIBB2k5l9JNfxVFIY8LFzmymsmFApKEORylf2cDSphGfucbcNiou5SMXVqEX+0I8M15XxnYqJOwloITOZx4Dv8K/vW3kVIF+TI7HLkrIYoE6vx1w3alaAwI5acj0YFHkiJNy5PLRYdYWowVuhIm7CmEyhXw72fci3QlbGeiE2+giXRmF4nkR1SNl4TlMrDsCKiZTk9YeLfpJEvTNHodXaBC7bH/19jFMhLWsgA8QaIFJGw4OMIt+DMuEt489S4RLt9XcsL2uXBkX5942FdVFf+xRCIRY8x4jlE4TsdFF13E888/T0dHB/39/bz66qucfvrprFixgrPPPnvUsMzVV19NX1+f8Wfbtm0jjh8rpkem41Ac9Gf6rWuSnUuDXnrvqxrICZNmjMNizllw+i+obhEr8V5dTTMLTg/MXEy85VCgPLd8PCGC0gLCfCUsZlQilkXCdCVMM785tXbA6aQdYhFSrhLm8gRxWjCnVDZuVLa5XGEUZegiqeh8pOmm36GZ6hOWVbO4EA8zt6u0pHwdmt6kGnNb82gyPOro3wWPfAtko/qREHQHDSUslzX34aYvwCLegvupDDvSsmDI+CpvFSlZ0Gl2A2+9SltXu4pBD891JjtxOLzMmfNzDj3kv3A4ygvJjwSdDDYFmoxQpM8/xXhdz0trj7fjD4im4qbkhfV+JP5s/we9UknSK1KTyVayuV4cDg+RmsMBSGc6CUef4oLatPnhyH+6Hb7xJFGX0IJGImHUziQweTEAEUX8fvcVJWyfI2ETiR//+MeceOKJ1NfXEw6HOfroo/nrX//K8ccfzyuvvMLf//73Eff3er1EIpFBf6yAz+UzeqRZFpJM6QRVIecOGj+YUcOR0z8FR19CTfMCwAIlrH42fPV/iZ0oqtHKScrHGyIowyQZNUM2b2ICcyZBUq4Iy8q/spCE5VoWkEcct7ACsxQonoAwVGUgr8wMpHMp/AYJK12V0BUzv0MjaWIifDqX5s2Ek+t3+pi9qbzPXlNkk2qHNSTMFe+Fdx8UNhWjIOAOsCXt5J2EE1fC3PCoTsL0hz2JbpAeVTQfOnhwrIOqbIakJhYkeZPDkToJq/UPXxWuE7SuVBd5NU/LpC/R0HAqzjJ/AyOhIyHyoRoDjQMkzDtQ5asrYbsSu/D7RM5c0oy8sC6RB6zWzhi4J8uFcW+vyEkNhw81qlgVHOS7/8YRwTzxtDULdt2QO+Jys2377+jufqnouOrqI6irXYTXI74fm4TtpdDVq+HUrv7+/mEVrnKOUThuJDgcDr7+9a8D8NJLxS+uicDMapEAallyvh6K9EXok81ZFRTDaG806GTNqv6RupJVnhIWJFDguWSqGpaJG8UCZSlhDge4/PikQmdmTlhh38eybS/cgQFiaGLiearxgIGWM2WQMH1sQDGXFKbyKbKaQl9OIdzdXt7OUlnJOzSjd6IZUGTI3J2Ri4TRFj4IEvZM1M3dXV58PeZWtOoPWUPpSPXC1GOg+RDw7XE/yMSo7ttJ2lDCzA1HdqW6ODqY43OOlax/v7hVUa2vFofiQNVU80NwiOKAjmQBCUsLrztvgdWKXhyQzCVRPCI8mkya4KDfLe730dr9DGNnnYR5fc00N/0Tjela+PN3oO1dPJ5aXPL8PtXE/pEFxzGuD62fDz74CevW/7DoLvtN+yYLFtxNoEa0RLNJ2F6KkfK1enp66OzsHNWeIhgMMmnSJLZs2UI+P7R34Uh5Z8Wg54IlEntJr6uODUzPiButZa0xCpLy9ZVw2BMevm+ksV8/tL5EdYe4WVhFwmKyQq4sJcwTxg14ZTJvPGciCcvGB8KR5STmA3gC+FQLQn87xMpYQRk2f2ZYuC2aE5rhdu4qIz9nUDjSRO8y/b35nF6UEqoQC5GsPptlu720JRWjatcMODTx23Zn5PsswaLC7XDjkd9x0mT1+UBlE9dNSuLpl75gtTPhG0/ApauGDq6ZTgQHKamEqWoa1cQOB13JLkIODTcZNLW4ku10OA0/w93J3aadW0dvupecKkh3nb+OuQffwqeOe5FJzZ8zxnidXiMXK4lQu3WyNi5IJay3SjQqD7gCRr5mXe3xzJ37K6Z9uAPevh92SdPU0BwAml1ZolmTrtOdb8PPmuDXJxkkzKeJ50RQVogOwQNfhl/MJtIj0nTsnLC9FCeeeCIATz451GFY36aPGe048Xi8qHr1xBNPlHwcgNdeew0Qjvp7BVb/D1PXCk+q7dHt1pyjIClfDynW+Erwr+p4H+75LJFnfgqIVZKprXhe/S+4aSrxd/4HKJeEibEB/WFlpulnJm4k5pdrB4E7KNr4YDLheeEXYj4OV/nFGwXhSDOVsHQ+jUcBjfKUsEnNXyAaP5CX4y6SVpAwdxCmHV3Wvu7QHDamnURRDANhM7Az52Rt0oEvIUlGCSQMICDVzni6f5BSMV64tSQ1Lg03JRxTUaj21ZBSoSM/mwMOuN60eYAgYQMkvnrYcUZeWKKTrq4X2LT5V3R1vWjKHPRQZK2vFrfDjcPhwudrGWKXoc8hrvnwepqK2mmUDamE9cqm2UVzdKunir/7xLOhSnp0TXar5uWFJXuEvU4+ayharrw4tl96pRXdJ76bSFaQclsJ20tx8sknM3PmTB544AFWr15tbI9Go/z0pz/F5XINql7s7Ozk/fffp7Ozc9BxLrnkEgCuvfZaMpmBldgzzzzDE088waJFiwbZU6xbt47e3t4h81m1ahW/+tWv8Hq9fOELXzDnTY4XgTqmyCbZ26LWJP8XKmF6cv2oSfkAgTqo259I9QxASPemNstO9UG6n3g5fSN1yIpKv1TCEmblFuWzkM+QlOXvgYLKwpLgsSb0l5bvz1euCgbg9htzMlV56trIK3EXO9xf5eCDf1nyfqHQgTiVyXTmHKRNVFbS+TSnRTJcUN1NZ9fzZe2rf88pRQETvctWxrz8ptNHbXcGHO6SDWSr3T4aXSpRjwoZ81Reh/y8fbpyqQ6NLhSiKtCAikJrv5+pU76CoyQLmdLQleoqaN5dPew4o0Iy2UFX94u0tt5OT8/Lpsxhd2L3oHMMB52EdSu1HH/8yxwy77bxn7xL5AD3BaqBgjy9QlRJ37ZeESUJSef6FrdmHgmbfgJcsQbO/Z2hhDlykoT5JhffJ1BHxq3gTrfiUTT6M/2W9BatNPY5EuZyufjtb3+LqqqccMIJXHLJJVx11VXMnz+ftWvXcv311w8iT8uWLWPOnDksW7Zs0HEWL17MxRdfzIsvvshhhx3GD37wA772ta9xxhlnEIlEuPPOOweNf/jhh2lpaeGss87i8ssv56qrruK0005j0aJFZLNZli1bxrRp0yryGYyKQB1Ts4KEbY9tt+ZCNnLCqoyQYkkkrG4WXP4m3q89ZihCpsrOx1wK/+dN4tKfqNycMICArMgzjYTJB15yLDlhMCj0Z2Yz6JQ8lm8sCcnuID5NP46JxLBPLBp80XacZSqGum1Gapgw1FiQzCWZ5lGZ5UmSaX+jzPmI+ScVh6lKmN5c3q9qwnevRBXzsECGayal6N3PB0lzcqFUTcUpHfj9en/KO4+D/1wAbe8U3ScsKwOjCfMTwYUSJv69Z/PuQugEqCPRYSiu2WyvKXPQ88FGqtAsnIPu5zVuZFMgfz+9XnEv0+/JyeRWOjqeFkUCVbJKUyphweD+ADSaqYS5PFA9jWzNNOOepWXF+/QNS8JqeeOwarakH2KyWyWn5kxddE4U9jkSBoJArVq1iuOPP56HH36YO+64g7q6Ou6//35+9KMflXyc//7v/+bWW29FURRuvfVW/va3v3HWWWfx+uuvD+kbuXjxYs466yzef/997r33Xm699VbWrl3Ll7/8ZV5++WUuvvhis9/m2BGoozmXw6mJlbx+UzAVenVkQTiyJBJWAD2J31TZ2VcF9fsTk6G/YIkqAQAysTiA2Ne0cKR8aKZkdWT5OWFBS6oj9WN5x6JEHHMZ3hmLxHHMTMwPiAe5r2aYvJER4JOdEVKqeUnwqXwKr7yLura/W9a+zuQ6vlidYVpEEe7xJiCrZslKkhnQNPCWXmGtSFKbdyolVVSWgmQuiVcR12bAUyNU366N0LNFqN5FEKkS4TArwk3dqe5hm3cXolAJ08dlc+YYRxcqYX19b/P662fz/obrhowznYT1ClNYPGF6GZyU39H5DO+u+RYffPjTgnCkIGwB/zQ0DfwOiCZ3mDMXiViBApzNiMKWYUmYrxpfSqioda6h+39csc+Zteo46qijePzxx0cdd/311w/b69HhcHD55Zdz+eWXj3qcE088seQcsQlHoA430KzCDqcISY4mjZeNIuHIknLCChDxRtid3G3JzVgPcQZd5eeE+SUJM10JG4tFBYA7gF8zv22R6PvoLLtvJACKYrwPUy0qArUQ3Yx30iHl7ZfeDY51nFmVYV2feeGtVC6FT5IMp7s8nzAlvZUTwjnecofLzicbDslckkaXSk4Dr6aKRUeJcDiEUph3KMJGwgREM1GDpHo91eBwwffWQecHEG4puk+4RphJhzxxtm/7HbV1i4yeiuNBTs3Rm+4tKRxpKGHJDlxuYShrmhImc8IaAg0kkh8Rja01CkeKzUEnYfl8ClAH9VQsCzK8SM1+RnRCD0cmEq2A6NlIVUFOmKbhcHhJKgECJEjIRuPjxpo/Qvu7RKeIzzbsDojfKODzTSm+j78Gf5dKL9DsdUFCI5qN0sDIiuLejn1SCbMxCuQKdGpWrJgtSc4vSMzf8wc/Ku45E26ZR0TmXpkajvzHXfDMT4nHxKqrpL6ROmROWECG2UwjYS4vHPxPJJ0i96rscOT+J+NvEeFVM8OR6TE279ahG7yaqoTlUny9Lo17+41097xS8n65XIyM+i6fCuVI+cojSyPOp1AJK9G9X4dHhsIdmonh0WySf2lM8eOWFFm/c6gFxAhwOcUiQ3MqpoUj49m4oYS5XCERGg03wYwThL1KEYSrhDnpgTUaGz78Cf395SmMw6En1YOGRtBo/j78/UivjuxOdRthy5xZSpisuGzwN5BOC9NWr7d5yLjC9knr1v+Q51fOZefOh8d+Yp2EVU0dSBGR9iUpaX/h90+FyGRAEYnzsrVQxiEW0Nm0Sc+KD1bAS/9JtE10Tpjk9QEqDocHj6e4Qop/QAlrlH3L9gUlzCZhn0TIaqkpGfFwtCQ5/4x/hys3wBEXld68W0f/TujbRkSGwfoyJvaPXPNHePGXxORDpqzEfF81BOoIyHklzGp/Uz0Nzv3d2BzzAY7+Fr5DRcNbMwlPUoa1vOXOB2DXWnxbXxdzMtUxP0ajW4XMzrJCeG7DogLSmFdtO0gJ85TX0sYrSZvuuG8GErkEHpkC5sqXF440SJgD08KRhUqYfvzRUCWJQUJ38DfJK0zvDuI3csKG/2x0M9fuZLfRqshsJawx0EhWNjfXDUgLofd07Ep2GXlpurHrmCDDi1RNGZKnmzBI2DSRrxVuHrRPxjuT1QknfXmTWtzJ66tfLtQmecU91ettGb4/p68av2ylUOMUF4dNwmx8POGrBsVhVEjqzWRNhdsnfsj+mtKbdxvzEzfHiCKUIVOVMHmshCbee1mJ+Qu/Bj/YTGD6CeIYJjqvZ/NZcnJOZeeEUZDkbZYSls+R1gQ58JdbrQmQjuKTK28zqyPTPa0DidVl9NFzFbYUUs1V5nw6ySizr59PjncrefJv3mPKfBLZGB59PnmtLCXMLX8ListVksFrKShUwpzOIKx9FJ76MbQW8QiT0E1dY5p4I2Y18e5KduFAw2MocyOQsIJ+iUZOWNacxWBhODIjSZjbM9TBX1fCOpOdhpHruLzCZKI91VMN78ZqbzWalieVErleAb9QIQeS8wUJy1edwj1dXj7KmaQiy3C33rKoxu3B4fAOXxkJ4K/GmxEkLOwQ9ybTfMsmEDYJ+yTC4QB/Lc05cSG3J8p0+i4TRk5YqUqYVydhIm5gak6YbOAdy4/BokJCV6pMS8xX84NUtXKbZZPP4VNNzgnLJkjJ4gWvewwkrHYW3hknmjsnII2G33iIlpPv5EWRpF7Jm0eeU7kYLl15KvX6lvBK5cyrQKrbnM4VyUyv8W9nXiuLTHkkUVVcTjjkS6bMJ5qNDpBUVwg+eAJe+k/Y9vqw++gFOTG9dZGJSpgCrM7vz/Tp/wenc/gFmE7CErkEOdleSlWT5Me5oMireTpTIsTX6G8kkxUkzOMeGoLTc8Ji2RgO+XoqNQ4StvBC+MyNMOPEQUpYKtWGpmVRFA9er3DqN0hYv1ig66kkOnkbN2QkQm/e3eOawUknruXQQ/9r+H18NfjS4pkVVMT921bCbHx8EahjkiRhbTETnJj3xIqr4fEfQn9beRYVYCQTR+RN2FwSJn60cRm2GwsJC0hSYpoS9s6DJH8hqv1cigu3s0xfrlW34H/0MsBkEibDo74xfEaEGvBNPkLMycQQac6hGkqPuwzHfACnQ5Bbt0MzKgjHi2zBStzpHb4fYTHoSpjXoZGcc7Yp80lI1TmvgXLw52DS/JL39cqQrYOsabY18XSMp/rdfKjNEKRZz0uqHt6uJ2RUsYr/m9XEuyvZRR6FTt/hzJr5PRwjdO8IuUNGl4j+bBb9UTnevLCedA+qpuJQHNT6ag0lrFgeVNAdNIpbEpoI12Uy43Dwn348HPd/oGXBoHuy3pPS75+CIhe+hGQ4MiqeDToJM62DSUJcp1G5gAl7wiiKMnLRgb8ar+xn5VbyeBXNXA/JCcI+Wx1pYxQE6mju3QiIJrH6jcE0vPU7yMTIHfGN0pt369DDkdL7ytxwpK6ECRWrrHBk50b46xUEnIJUmJYTlk2QkjejsvPBwBqz1myCtLwexpqYb1RHmhiOdDr0nBTHiEpGMbhcYXL5fnyKRiqXwu0ZgwntHshKlSaf11A85YVqXLIy16tAsmpoYvZYkMr24gRyOFHOubesfX1SWVQAVU3hLFeRLYJYLs4T/W5c9UeI99ur5yVNHXYfl8NFwOkjJQsWcnmTlDDZvFtPuh8JiqJQ66tlV2IXPele/H6hDOXH2ZtVn0O1txqnw2nkhLmLkDBFUaj317M9tp3+vPgtptMdaJpWfgeLPVDYVD0pCx/8/oLvRM8Ji7Yb42qdKtVqO/l8cnzXRj4H8vz9Mj8zXMpvx1+DUwVXViXndlDt1Ayj148zbCXsk4pALQ25PA4UsmrW3Ea1mgaL/hWO/x59biHll9O8G7nqisi+naYl5ucykE+TAbLSK6osnzA1C60vEugRK0fT8q8O/yrJCx8Dxkh4jrwY34V/N3dOmQElzDsWs1Y1j1eqHkmTyKqay+CUy0anM1T2g8gtzUL9DvOIYSqfYXXCSTyqlexMr0Nf9XskKTRlPlKpyY9hfR3wVBv/zj/wRVPmoysVIXdIPHz7pc/UCEoYQMRXTUoV369ZSlhZ7dMYnBd23LHPcdyxzxEI7DeuOej32VpfLZqmkcmK/xcLR0KBa76sZNe0DLlcb/knTvbC+r9A27skc0nj+hdKWEFSvo6muTB7iaGkVnmq+G5Tii+HdxCPbyz//IXQ7YuAmCTaJZEwGSHR88KqnfuGEmaTsE8qAnW4XX7qZdK1qSFJRYHjr4BTrqdPVn6V1Lxbh1TCqvLiB2qaEibzB+IFpfFl+YRVTYEv3UVg4UWAuRYVSZmMPyYlzOXFJ29i5oUjkwPhyLEQQzWP7427AEhnzWmBk071FHg8lReKhIHkfJ/DPNLTr7q4p8tL90fZskmYXi3odUBy83OmzCcjw6Oq4gK1vCrQgCfEHbu9PLlVwdVnTp6onrMTcocguhO0PDg9EGoacb+wJ2yEI/MmkbDedC/1LpXG3EZ6eobPSdOhV0jq6pUZKCRhuVwUTZIQT5HEfBiokOxOR43ekbqfVlloXwMPXQB/uNBQwVyKi6A7aFRc+v0FBHP2qbD0YTj2O4Agaz05aQshw5djhu5B560imo0DGgf2/ZZXXj2FVHqE687hhCX/l4Mbv8WOqgvZlHbYSpiNjzHO+BVc286kGtGSwqrk/HJXn8BATpjZjVolmYsVEB6nw1n6/t4wzPsigSlHASaGIxlQsMZEwgr2My0cOflwUguE7UXZ5rEALg8+aWqbNulzSqf6CCg6CSu/mbHLqff+NFN5kl5q2liUsBA9ORftWYXk6vtMmY9BwlJJuKEGtpTedDroDvJB2snWqoNxLH3UlPmkMx0s8Oeop7sgFDllWI8wHRFPhJTMCc1lzfn996Z7meVVCfQ8zEdbfz3qeD1sqd/DzEAhCQOVKZO/QnPz53A4iqvNuhrXk+rBI20sxkTCFAUmHwGT5huth6q8VSiKwty5/8EJx79By6ThizHCnjDd0p6iL7a5/PMXQrc/CdQQzUTxKeBUYyQSW0a0DQHg2O8QOfoafKEDyaHsE4n5dk7YJxUyrtMcbOadjnfMVcJSfdC1CYL15TXv1qFXR2bFA848EiaVMJ94WI4lKR8GLCRMU8JWP0Bqywpx7LGQsI4N+J7/v4AgBWbkjOBwkpL5GmM2a5V+amaFSFOpHgKSM3vKtIMA8Pmn0JVVyGgK6a6NIBcg45qTVPn8avkkzO2O8Kfo/rwX28oyk0hhVto5GBZq3tLz1PRrL64ANeMLu+lwZdq4sD5DLv4MuOeJjZERbAgkwp4w3fI9qMlxJKMXoDfVS61RWTv652KEIwuMazVNHd7HqgQUkjC3u5oDD7x+xPH64rUr1YXX10Q8/gHpzK7yTzz9ePjmMwD07nxl0LEVRRlWiSMdBXcAp8NJTPMCCWKJcSph+ufpryWaiVLlHPhOSu0GoOfy2uFIGx97TJLNck1Vwra/Ab9ZDL//5/IrI2EgMV+SplgmRl41wdBST8qXD8uykvJ1vP83AluEx5FpStimZ0lsfAoYI+FJ9uJb/2cA8lqenEm9EY3ekWPJCQP8TkHC0iYRjHS6n/aswot9LhoaPlP2/gfMvpb7tvt4I+EiZVKeoZbrpcml4vX6YAxWHj7d7sSkz6ibBpbt9rK77p/gqg+h8eDRd5Iwqn5NVHh1Uqg4AxCT5CE8ehFCxBOhNeOg9d00x6hnmTKX3nQvPsfoHmE6CnPCNm3+Fc89fzCbN/9qXHPQVSj92KXOoSfVw7SpFzF37n9QU33suOZQmJQ/In55ANw0BXpaAUgrgrgmU+N0zdfDkf4a+jP9RCQJ83hGDlEDojhq03OE5ALdDkfa+Pii/T144Ms0b3pB/DduIgnTWxb5xti8WyphVSm5qsekBEw9J8wjiM6YlLA//x/8z98MmNk7MmH0jRxrdaRfHbAUSI6zgguALS+Sbls99jkxQN7SJoVIU5l+tmacPN/rYfLkfx7bnKRCmEqbc/Oe4tjF1ZNS+I4+RuSslAmfJD4pM74zoF/V2Jh2ogVmQahRuJ+XiKAryKJQllN8O+h/+nKIdYx7PvmcUAqdzuAACRslHwwECdNQiB73Xfj0teOeR07N0Z/pL3DLL0MJS3Wj4ERV0+Nu4q279tcqbmFc2zWyP5xBwtI91NUtornpLPz+0ZXEISiwHCl5Yay3dJPfW94hOweMlLdVCnQlLDBYCfN5SyBhz/yE+B+/iLLjdv65Jm0rYTY+xsil4IMVNHeI+L6pJEyvfvGPsXm3zAlzp6MGATAlOV8eIy7VpjEpYd4QARnrMa8SMTb2lkUA7gBuwKXbVJihquxeR0qGqMeqhHkNF39zKhHTkjjpuWZjgQ9BlFImraAdmnhvTtcYriXA7w7gRCOZN8e3TL8mA2PochBwBzjYn+fIYIb4Bw8NtLkZBzRVLFTcrtAAqQs1jrqfXi0XNYnA6ykNfscYwpEp81oXGeHIp39K/K/foOd3x5J+885hx+v3ze7x9vK88zj4z/nQ9u4gEhaNrueVVz/Dmvf+Zeg+X/sLXL0D9jtO/F9WcGrZcZLzYCNMOxYa5wwiYR7v6NcFtTPQ6meRoZV5/vw+kRNmk7BPKmpnwlm30nzsdwFoi5uYE1ZECSu5eTcID6HF18KJPzBsLUzJC/OEYfJCYmHxYx+TEuYJEZCqUzKXRC2jf+GwyCZIOsZBwmR4VfcKM4UcTjmClMzdGWtOmF/3CVMz458PkJbEyTuOnByf4sCJRsqkm7eiiffmKtOzTMci52v8+9QkWlAVFirjhB5K9K95BJ74UVn7BlwB0tIWIu1UTOkfqckWUW5XeEAJC47+sDX1d89A146wzIUtKRypV0emuoyWVLlxti7qTgi3/Np0jO3TIrw1P8z2NT+Dzg+Ljte7jIyrOEDThOLW0wq+yKBwZCq1g0Rik2HYOghVk8E7cF27ZbhQ0dLj6+c5/8tw0Qpyn/ouiVzCIGHeUpSwU2/A+9UnAQg6IZHtN81YeKJgk7BPKgK1sPBrNB38OUCUYZuVS1SohOk/+JJbFgEE6+DEf4UjLiIiQ5OmeIUd8Bn45rPEDzpdnGasJKzgR2+K6pSJkxynEgbgU01UwiYvJO2vFscdS3UkA+2OzCJhyUyM/b159vephtN4OejoeJIvT+vjsoa0abYZTp1k7Hh3bAdQRLgw63QY4fLxoDG/hS9UZwj1/kM0qy8DfpefjLy0ky6HKSRMkUqh110FMZlgX4YSdkTyr7z8xBxSXe+Nax66+hNyiUdeSUqYd0AJ01tkZcfi0VWA7oQgorVV08nM/SwA7kwOnrux6HjdoqI33Usy1UFr651s3vwf5Z003gn5NKBAuGWQEpZOi/l4vaPn6YV8dSTkmnNEK4kSoatYek6Y11OCEga4XNUoiiDTfiVrqhn0RMAmYZ9w1PpqcSpONDTz/HCK5YSNsSGw2StiGKioGRsJC+LTNCMgZkpeWCZOUhlPTpiuhJkbJtXtLryusYUjfTIkljKJ3KcbD+KsqiyfbYzT1/d22fs7HD4cighJpUwiYS7Ee/PEesd2AGlNkHcqRuHIeFCndbAonMMbcJTVvBvA6XAaJq9p1/iVsKyaxSmb0vvcVTBloQhDjeCWr0P/3QcdGZLuDLme98c1F514BKTiXIoSpocCc2qOLKK7wniaeCf7d5LQRIFR7eJ/M/LLPFkV1j0GfTuG7KNHEFRNpTe1i02bf0nrR/9dnvrTLxPpZY7gYBImyFRRFWrra/DY5aLXp5xLn7Sp0MnbeKAn1dfI5qulEEHQqzmFXUdkHzBstUnYJxmtL+FY+yj1MvehIzn+RFxAWFTA4JywMpsbs2sdtL5ElczbMrN1UVw+gMdEwrwhFCAg7RdMqSTLxI1m2WMiYQ4nOL3mti7q3mKE7MashHkECctqeVOqW1NO10BOz7jMWiFlUgWgWxHE1zvz1LEdQJKwnEsxRQlzIFRHd04zClzKgSqbnGec41fCEtmEUY3o89TA2bfBRSug4YBR99WVsJQmezb2jc+bSicevjIsKnwun3GPiOXFfuPJCeuRXnBuDUIHnjnQvLv2YGFi+95Q5dLtcBuENK6KfMayXfNlE27dGkS/J1d5qwqUsCIkrHeraD/3oajcrvZWc2+nl+cdn6Gm+ujSz78nfvNp+OWB9G97WRzXCEeWoIRtfh6WHYU3Jp4HEcfHv3WRTcI+yfjrFfDHi2iUrvG7E+b48RjhSF/12CwqAO77PNzz2YH+kWYoYSuuhlvmEZdVf3qj4LIg9wlIOdw8JWwc7vQAnqBRIWlKOHLlz0nJcvox+4QVkFwzQgbpfHrAMd81DhKmaKbYZqiaOkDCmg8f0zEU2VRcc2D42I0HDt2BPa+WrYSJCQlSmDEhJyyaieKVkrHHXd5cdBKWlIJPLjq+IgH9PuRRxGKgFCUMBpLz+3JC0cvnY6hjbP7eveEv4pjuEIrDMdC8+7BL4J8fhKO+NeIcejKxsbnmGySsBRj4LGp8NSOTsJBQm4iLxXnEE6E956Atk8XhGEff1f42iLUTlWkKb2Wb2W+/ywa3TRoOah46N+BJie8j4tQ+9sn5Ngn7JMMvftANshlrR8IkJUyGI3O+cPnNu3XUzoDaWYT16kgzSFi0Hfq2EZcP4LFUkOkkzC/Dh+NWwtQ85JLjywkDI0wK5uWppXViOEYlzOceUBvMUOfSu9YOWAyMQQnTKxh9Dkjlx0+e0/k0Xqms+D3lO/gDOORvT3MqYMKKXg+PenOqUWVc3oSkMmcCCYtn43h15dLhL6uNkh6GS8hCgXxsaKiuHOjqz5rQ1zlx0WoCgekl7aeHJPuyA3mNudwY7kXpGN2SINeGJqFpebJZ8fm6Z3wGDjwd3MV/Z4VVmmNyze+T4cgq0YS8MDFfN34tGgrUCyhkLp/+nYw7KvGNJ+GSlUQD4rPdpsxg/1lX4SnSxHwI5DPLk5Y9J50QzdpKmI2PK3QSJkNru01yptaVsD6nWC2V1bxbx0Ur4F/eIlw7C8Cc1c6pN8DFzxIPVANjVMJktVBA/nTGrYRJEjcunzAAd8DU6shsJk5unOqcwxPAI9W5tAk2Fend7+KUyoprLEqYU5BCpwIZE4hqKpfCJ++gvtjYCIvuEK45FVOUMJfs1erPq2MKRzqkMmcGCYtmoizv9PLr/tk09LvhZw1C4S4BQ5Sw5PhykAxF3leDyxVGUfbwdNvxFrz9P2KhVgCDAKX7DCV1THlh3hDdn75aHDPYKAlYaS24dCLYk+oxktczmbEpYTk1Z5CWwYn5RZSwoFTCkt2Qzw6QsPEuiKunQssCQwkr6z4si4W8SXE/sZUwGx9v6CRMeieZp4SJm1SvfGCW1bx7D+jkzZS4f/VUmLKQuEwWLqt5tw6ZBB+QqfnjJjwZScLGHY40l4SlC8jlmOd04g/xSiJghhKWC4ljqZoD5xjUOaczYHhW5rXxf0apXMpQwjwbXxjTMfQm3jgdMM4CBlVTcctWU/6sNqZwpCKVubxTGXA2HyPi2TgZTcHhqsGZ6BXvr0R7kYArgFNxGk28c+nOcc1lVKucHW/Cn78Ntx4OmwaaqRc61s+c+X0OPOAG3GNomQUFHmHeWiMU6XbX4HC4hBP809fDyl8M2a+QhHm8uhJWxr26X6qIkcmGCgYQdLoNVc9XTAkL1A58X/FOIp4IzS6VM/1beW/t90o//zDQ7+k64S4JMqLiSUkS5vj4J+aPu3dkMpnk9ddfZ/v27XR2dhIIBGhoaOCQQw5h1qxZZszRhlWQPjiNUq0wJTFfzYP8offKUERZRq17QDdUNTP5Uk/MD4yhzQzyhqHnX407HClXcUmnIMJjV8KC+DPiiWVG/lUqEwefUDE9jtJd1wfBE8Dn8hHNRk0JkearmyEOOcfYSKGiKGi4UMiRx4TPKJ/i151eajWVkyaVVl6/J1zOEBrgCNbCIcM3UC5pPrkUHvmbC+RUKMebT4czzM60Qk1GG78SJhWXoCcIh34ZZi4umWgqikLYEyalCrKcz/YLH7UyOgAUwrDKGe5eNP+f4Y3fQsf78IevwaUvQfVUo6CoO9XN1ClXj+nc5LOgqYbhaq2vkITJEFzfNlh1i0ieX3SVaLgtoRPBrlQXXmnvkS5LCRtKwsKeMPmsILZOZwBnMZ87hxMC9RDfDfEOqmqmoCgwx5emu3tV6ecvRO82eP3XUD2NfneKyW6VmcpO+vvfJRI5dPT9ZYg9kMjTSQPbsr3UfMwT88dEwpLJJL///e+5++67ef3118nJpMU9GwdPmjSJz3/+81xyySUccsgh5szYhnnQlTD5/ZmihKUGVlq9UnEqOykf4KVb4d2HCM8+HjAp7v/yMtBU4lJOH5Njvq6ESUll3OFIhwtmnkRS2wZkx5anBjD9eHw7EpDvNIXwpKSa5nW4xtUMfKB10fhJTz4vrgFVGSNRBTSHH7Qo+cjYSFMhUrkUWzNOUjkNp2cMhAdwuUJkGTB9HQ8SuYSRCB8YY2J+0jODm7e+w3e705AdZ05YRix2wu4wON3C/LMMhD1h0pogGzmnIohK3dgW9r3pXqZ68ri2/4z3on9l3rz/FCam+rXtDcG3XoS7T4cd/4BnfgJf/O0gFWrM2PgM/OFCumeJBua1/tqByki9cfbUo+Hwr8HkhWIh6xx4NBeqcR6vOEYmU+K9WlUHwpFVk+mVimK1txqH089+076FRn7433ioUZKw3USaDjYsKrLZbvL5NM5yu2n0bIGXb4X6A4ke+Tnm+vPMyrzEjh2TSyNhDif4qqjt6+ND5Xie7H+CmZ8kJSyTyXDLLbfw85//nN7eXoLBIMceeywLFy6kqamJ2tpakskk3d3dbNiwgddee43bb7+dO+64g09/+tP84he/YMGCBRa9FRtlQ8bXGzLiAWmKEuarhis3QKqPnp41wBhJWLwDdr1HeIq46ZiihL34S0j2ED9gDjB2iwocroGcsPEqYTX7wVf/TOrhxZDsHLsStvhqfG+4YN3vTOkdKaoHFXxjbFkEwPZ/4JMhLTPCkZrMxdEcYydhuEKQjaJ5xqjuFUB/Tz5NM8h5uQiE5/Ob9W40d4RLxjmfRDaORz5L3XltTIn5+iIg4ZA5YYVEpUxEM/18tzFFFa+RzfaVXUwR8URIZWRivlMRD/CxkrBUL5McoKa3k0xK4vPGb+HDJ2HRv8LUo4TKduav4L8XwZo/wAlXFuSEjSM0u+1VyCUHEvN9tTQ2fIaqY1eiyYUqngCcfWvR3Qtd842csFLDkYkuyGcABULN9PZvBMQ92edtZv/9fzDy/npeWKwDl8OF4gySUZN4HJDJ7CqtonHQfIr3jSzVIwwQz5hUHyGZ1/dxzwkri4QdcMD/z955h8lxlVn/V9U5T5RmlLNlOeecMNjGJphgE2wWbEww2MB+BOMlmGwvS9gFL8nLrk0GkxcvOGEbB+GcLclKVhxJkztVdVdX1ffHvVXdI03oUDUSMOd59Ejqqe6+06Hq3POe97wr2LFjBxdeeCGXXnop559/PqHQ5K2qmzZt4oc//CE/+MEPOPbYY7npppu47LLLWlr0DDyCVMJmlYqgCMndMA1CgRbaj1UVUj2Q6mFk1/1AkyRM7uJTkiC2TMJsG0p5bKDodEc2U45cdSGsupD4I9fD2p96NsTb8XE17b+qua93Sli86bmRAIxuJ6oNQyTiiTHf3vM0dAJG85ljdvt5/GztjwknW3ZiuO9ZxLarw44bRCq5gocKIboYFUrtyVc1vZ5iOcsOQyGuQMBsLifM+U4UYx2QOEjMmA01R3qLxiiHRCywd6M8dCPoRaH21JETBkIJ25pXMfIZ2keyMDzOaJ06YNkWo+VRlsRqMsJsGx65CQbWwfJzBAkD6D0CVr4K1v4BHvkeHUeJEvGwPkw2+wzZ7DMkEstpb28gJ+vs6+Cwixl66FowhuiIdqCqEWKxeXXdvXZ+ZCQym3B4Vv2+tEmCWuuCM92gIDskw22MmoN0qzZ6qQkS5szAjLWTL+fpaSQjzIHcXCSlv/MfKifszDPPZO3atdx666289rWvnZKAASxZsoTrrruOF198kZtuuglVnekFOGAgSVibNuoa5we01gywtWhqeLcD6WdJGeJC1/Jup1ICy6CsQEWmVjdVjlQUUBRXsfIirNW2bfeC3rQSBsRUD0mYKcpjsSbjKQDoOYxIZiGAJ+rc5lyAT+2IoQVPbvoxIrGj+GshyFCh9cgTXdvBm9tLnNxmNq2EOe+3Zhmwu7XRPLpl8tXdMf57Z4yARVPlyEQogYJNadUr4Yo7myZgAFq5WsILPPNrWH1jdX5kHUiH07xYCjBoLKenvyxmHzaBXDmHZVs1w7vl6/LG/4bTPiz8arU4QeZ1Pf1z2mV47ZA+RP/AXax78Tr29P+xsQUoCsxexWBFnMM6oxNEMZiG6NJ8/rdjbnbLkaVh2tqO5bRTV3P44d+p77nbFsJFN8M5XwBonIS5Slg1psJNzdebmDfsKGGxDrLlbHVkUT1zIx3ICk7K0pgXstDKI42v4wBCQ9vBm2++ueknCgQCMwrYgYa4+HIrxWG6u+fTV+hjj7aH3mRv84+543F45lboOay54d0O5G4nVRK+Et3UKZtlwoEmy0iSxOVrurOaUsKc+8qyTcudiM/cinHbh7HmiN+3aRJ29+eIPv096GxvnYRZFiXpUYq0oMzRuZRox1LYucsTJUyzLXKW4naINQO3PDrSnKpSi3J5FycmTbJhRDmpmfU46qUawD7mcpor/Ak4n8V4KAFLXwbJBko8EnHF4uvzNSj/Atv+wr5RDg2gZAiiaykhlLwsnyXrv9g6XXPZiCS4TZIwh3hkgiHAEEqYokDPoeLP3lh0GnStgIEX6dj2mHgMfaQ6P7KJiArbtl1fmUOq9sHwFrjpLAhGYeUFwkdXc/xIaQTTMgmoDbwn8Q44pBoL4r4WkQxDQw9i2xVSqUMnzuhK7BvYOiJLxE7GWENwmj3i7eT0PjJSnQw3pIS1AdDBX/hIj86fK02QwQMIM7LUPzKkEoY2THdcfNlaNuf3PQMPfxvW/qG54d0O5C4+UZOd1JLsLAMGi1KxiAVjqHW2y49Bvh9++lbiz9wqHq/VcmRpFK2m6aDpcmQgXJ0d2arqVBMeG22BqIK3xnxdKpiRJkt/AFF5QSs12WVXC0OSDNOi6XJkEIPTkganJssYc49saT3OZzHWsRTe9hvoWtbwY8Rqylxmi58jh4ShRNyO6XqGdztw42ni7XDsOwUxaQIO+cmExHs+5cgiRYHDLwagY+2fAKjYFSpymkClkdFFf/w4/PKd5Lc+hCGT9tuj7axd+0mefubdZLPPVo/tWCI2nxUddj/v3uwEXVu21XJGl3NObou0sXHT13nq6csZGXls4ju45UhJwiJpRqQSpreohOXLWVKqM7y7ASVMbtADtvwOm83P8zwQ4AsJW716NZ/4xCf4whe+wM6dO/14ihl4AYeEVTRmSYm85dFFPYfBqf8MK1/V2vBu6WcJlHLexFRIMleQYatNmfIBsGHdbcR3rwFAM1okPIe/Ce0yUd4IqkFCzY4DOfFKoq/8KuBBObJcrKblt1AepVIiKsu1XpRIS5Jk1ibxNwolanF60mBOugWvm4Rhis+UZdJ0OTKEwRvaDV7VZrSsqmpGngB28x22QCKcwXKy1L51nBgs3SSMivi+Koq8WAYiDTULpCNpwopNIJRj8KQL4Yg3N7UOh3ikg6LwEzQV+N374fnfTHynQ98AQPil+0k4qrclLpfO4O0pYdvw3K/guV8yVBAhsPGgiG0ZGl7NwMDdmFbN90JVoUd2CNaUpkNqyFUFHUJpWWUsq46O2g13w5r/FaOCqFpEphze7aBzORx0PiwQFoCWh3hrVWO+XRlFjMxVq12i9UCWI4OWOFcGrL9tY35LJOwjH/kI0WiUoaFq58gvf/lLTjvtNK6//no+/elPc/TRR7NjR2sjJ2bgEyJpUAIQStAtZ7u17Ambdyy8/DNw1CXND++Gqp9FH3VPQC2F8pXEBSEfEaSiKT8YCCn8VV8nftwVgAdKWCRFUcYltOIHI5ohJseMtEx4jCK6JGEtlSNzu4iuvwvwRgk7ocvgym6dIM2rtWbxWV7fbnBQuHVPWEV6fCyr+e7IqOwYDCmgrWvQa7QXjPwzfHW+xrmBx5t+jHg4QdkhYcVdriG7GVQkSVWRG4vk7IY6LVOhFG0BmyONu3nu+Q80vQ5nM5iQZbzg8E548kfw0DcnvlPHEhEXYVt0SF9YUQbH1j3Ee/fz4vULxRlqEyZ8p7RoOBEVob3KgD0yymnXWH9g7eiip595N/fcezB7+m+feg33fxV+filseRCoLUem3JiLSUnY/OPgLT+FMz4KCHWyJRImlbBKNEPQEufOUKijsbL38nPg5Z8hkhKdsmEPgpf3J1oiYffccw9nnXUWHR1VFvupT32KTCbDD37wA7785S8zODjIV7/61ZYXOgMfoCjwiT74xE5mtS0CPBziTfXk11R3pNPZpWer3pBWpHjpCSuGBdFp2g8WDMOxlxNf8jLxeB4Y8x3S1BIJo8Zf1GocRHou+sv+RTxmK8b8cIKIlFVaVgwti9lRi4OiFqFg852NkWAbACGa77B0l1QRfkXM5rsjnbFFAIV7P9fSegxJCtFG4GurmnqMeDBOyZaxEBf9Fxzy+qbXY8oNSsCxHicb8/KlI2l0OTuyUslj9z3TVICso4TFpAk8OLBZ/GDJWZPfUXqp2g3xfcpVBAur2xO2SSbvLzyZIbmB7Ih1YFllKlIl3EcBmn2I+HuvJg3nHDpcGkaVDTjlch0b5tmHwNxjxSxealVBVcZjqO48ynqQiWTYU1HZo8yjs+O0uu/nQr5/hVCsxpTfYGbf4tPh1H8m3nWEuL8Hwcv7Ey2RsK1bt7J8+XL3/+vXr2fdunV84AMf4NJLL+UjH/kI559/Pv/3f//X8kIbxaOPPsr5559Pe3s7iUSC448/np/85CcNPYZlWdx4440cfvjhxGIxuru7ufjii1m/fr2vzzutCIqyTFesC/CAhA1thqFNVEo5t3zYVDnSUcLMEkk5Xqi1cqS4b0H+vs2XIwXcVv5WlbDnf4P2sOh0aomE7X6e6KPfBzxQwgJBdGk0byUyg1DMHaVUajVQ0SgSduY0NnrSrkFEKk9hxcTMtWboNeWFFBNoktSragRTKk+63dpnySFhtgnufKYGkQglKEnFx+xc6DbvNANLpt0HZRmvEVM+CGO+7v4aNuZNp8Gmexteh6P+ROWIqWCfsBKwdAoStvICWPYKOlJCxRquiPJfpTKKbdcxjNwZf7TkLAZ1oXx1RDooG0INUpRgtVPTwWzZKLD7uTHv4dj5kYI0lesZ4n3+v8G77haqHtXXIqkIf1o43CXGJk0G2wY9C2aFTDjDLkPlQeswliz54NTPvzdkOTIbChNVbSp2EyRMIhGbA0BSNT1R2vcXWiJh+XyeZLK6A3zggQdQFIVXvvKV7m2rVq1i+/btrTxNw7j33ns59dRTuf/++3njG9/IlVdeycDAAJdccglf+tKX6n6c9773vVx99dWYpsnVV1/N+eefz+9//3uOO+44XnjhBd+ed39gVkx8EVoObP3DP8M3jmL0mZ8DTQ7vhjEZR2lJBFqKqXDKkV6QsC2ricmuqZZJ2Lo/oT0rXquWSFh2J1Hp3/EiGNXpZmxJCQvGRIYWUJKjopqGUSQqKxbRWONdfw5iUnmIqVBqcSwPpnjvFYuWohzKtjgN63apafIEUHHWk1kIl/+pqccQSpj4t2k2/57Zto0t/U5hqWY1YsoHUfoq21Q9aqlOaEJRdXxUWvRg5rSfR2xkWPjT5h03+R07lsClv6SjRygugyXne2W7StaEMHTY8pD499Kzqp2RsQ4Md2RRB8rezUHdK4VFRBuGmk3C2NT8JkYXId4TRwmLIn6XuqIhvrYKbpgPAy+6ne61MyjrhmW5SlguEOSJYpAvDy3ksEMnKQuPh3IBdjxBOi8+7+mA/TedFdYSCevt7WXdunXu///0pz+RTCY55phj3Nuy2SyRSOsm2HpRqVS44oorUBSFv/zlL9x000185Stf4emnn+aQQw7huuuum1TJcnDPPfdw0003cdppp/HEE0/w5S9/mVtuuYXbbruNbDbLlVde6cvzTjtW/yf8+GK69qwFPPCEybFFI7Jk1PTwbjXgzmlMqeLz44USVpTraomE/f4q4nd8Ujxeq+VIo+B2IrZEwkJxYvIC3rIStvsFtA13ABAJtvDdVVWi0uuht/g6WeUcUXm2ikVbiKgIC0UhqtiUpGexWdiKSDtXF5zRdKo8QAXxGmkBRGdck7AcEhZMikkMTSAeilOWpMl4/lZ46qdNPU7ZKrPHsHm8EKC9LDfqDSphYvOmuOXRyrvvhCPf2vBaXMLQdi4Hq6eTLJoilLXOz7abWF/OEpAD16f0hW1/BCqa+J1nrXKtGe2Rdndu5LixEKEodMnqUk2H5NjUfEcJm2LDbJmC+EjkjTwVmdAfsgTBrouERWQjTHHA3VA3ZQ0p50AqiDn5fUmHM2NK8nVhzxq46Syif/4aACnV/ptOzW+JhJ1xxhncdttt/Od//iff//73+e1vf8s555xDIFA12W3YsIF58+pLBvYCf/7zn9m4cSNvfetbOeqoo9zbU6kUn/rUp6hUKvzP//zPlI9z0003AfCFL3xhDIk8++yzOffcc/nLX/7Ciy++6PnzTjt2PQvrb6c7K3ZVI6URDNNo/vHkhW1EhvI25QdzIEuSSUUQJy88YQVpzk0EWyBh4QRxx+vUak5YuYCuekDCwnFXdWqZhA2so7TzSaBFJQyIym7PUouKYblYNQHHIxNkGtWBcLgNgJhqozezm6+FVHoCrXyWgIr0TJWCitvF2wxMScLUFsY6xUNVJUx//lZ4/OamHidXzrFGD/CjoShLRuRFvFElTKrhmuQRjtG/UYzxpu58Qtw495iJ77AX2uVlcqg4QCiYQVWjmFOtZeOfxd9LzgRFYUgXZbj2aA0JC01Q6nV8Ybuq8RVuar4+VKOETUHC1v0RvjgbfnYJUFOWDUSxZGNAXeOC3v57+JedsPh0VwmzjEGGRx5Flx2WdaFSFl2WvUeQs4TS7vh9G0KsHVJziEQFgUwEIC9/t79FtETCPvGJTxCLxfjABz7Au971LkKhENddd5378/7+fu69915OOeWUlhdaL+69914AzjnnnH1+5tx233331fU4iURi3LWfe+65+zyOV8877TjiLfCab9J20Ku9Sc3XRgAYkf6LptLyHZzwHjjrk6RkhllL3ZGRNHQuJx8WpCLRZDcbAOEkMbmjMyyjNdJaLqLJkkRLhCeUICqJoW7qWPV4ViZCx1JKMsSyJU8YEJHhuq0qYUVNlGY0C2LNdrYCwYA46UcU0PXWSFjBDrOzrKBOdDGtE5YkYeWAItSCJmHLC1tgZDs89+umHiMaiFKWypMWVKqRAg2iIMvPiVACVWZMkWiMhDkXaN3xqFWa+/47SlgmkhFh0lA/CbNtOlZ/G4ChkU2cdNLdnHXm86RSh0x+P4eELRUNPLVBrYb0hIUmCkh1zflVJay2HOkqYVORsOxOMTdSqk61r4PT2RitRwlL9bjdvw4Je3m8nyeeeDMD/XdPfX8HyW64/I/wnr+4VY2mSFjnUvjwGoKX3Y0pP6tZfXotT16iJRK2bNkyXnjhBf7jP/6Db3zjGzz33HMcemg1gXjLli28733vm9akfKfkV9sw4KC9vZ2urq4py4KFQoG+vj4WL148RtVz4Dx27eN48bylUolsNjvmj+9YcgYc/U8oPYfQHZOBrc36wmzbLUcOy2G1TcVTODjlg3DGR0mn5wItliNPvgqufoziLDm8uyUlLOkqYdCiL6xcU45swVdEuFqOhBYjIXoPR+sRF4JWlbCIKklYi+qcpouLhmYpLc2zDAYFgQuroLe4e36i0MWXd8cIbNnW0uNYMkfLCKgtKWHY4jUODm2HzX9p6iEUReFJPcVPhsIEs2ZT3YhQ9W8mw8nqqKIGy5EhNUQsGEN3ypF3fwpuPF4Gs9WPkdIIIcUmqq1jNCe7DuceXd+dFYWOuWKu5LBRQFXrCPnN90Pf0+LfsgNzXCVsIhLWLc5RDFStPm53pD7sGtkNY3jyrLCsjIaS58/akUV1xVOMA6ccOSLfglIjSljt0spZPjpb4xzlQQqFDU09hqIo3FFexb/uilK0Wp8Fu7/Q8sp7e3u56qrxh84ee+yxHHvssa0+RUMYHZVsPzN+KGA6nZ6yUaCex6g9zqvnvf766/nsZz876TF+ojvWTV+hr/nU/HJetmbBiDw5NDWyaC94EtYqUbtDbxrhBCEgpKgYtoVW0Zr/PY0CmhflyFC1HAmC9LTyeA6Ja1UJiwYigEGpxWaBYiIN/VC0lOY8hhIOCQPQ5MWwWejy8xgd3NTS41hKmJIFhopbNm8GilWGAIQqVlNzIx30WW3s0cpUSjZUhsXmqkHPW97IMytosTASoLLwOIKZeZCe0/Ba0uE0JUsQwcrQWhgoCHLRVt/gaMeM3h6wGdryebKHxDjjiYgw3deJ9tM+Bre92d1YTgmng3P2YZASJMdRwtqj7STUZXR3n0sqNc7IJIDug8TfA+sF4VQDY5SwYLANRQlj22XK5QGi0Qle10lI2BFHfI+yMURAreP7vfkv8MzPoedwYse/m6AaZLQiXoumssKAXCnL8pBNkFzjnrAaFEJz6TM2k2s2Aue374e+p+DsT8OKc5teRyuYGVt0AOHaa69ldHTU/bNtW2s77LpQHBKpypvuc2MqmlbCZCmSQJhhmaHUUjmyMAC7niNlCjPpAUPCZOp+XHrVCq10/tUoYa1mcgWAsOWBL6wwiC4vGq2oTuCQMNBbbCEvKYLUl+zmZxkCKEqAsiVKXHqLg3+1gHj/I6te29LjrAmfyTU74gyP0JISpsh5nyHTHtNd3Cic+JWCqoJVaYoY5st5XtdW5i2J9fSfeD5c9n/QNr/hx0lH0q4SZibkuaSBGZJaRcOwjOrw7ootSpENkMqOmFCshvVh7Hq6VzfKEt0yUYq0bdv1pXVEOujtfT2HH/YtensuHP/+7YuE76n7IPec6kZUyMeJhMW5ujRZTEVWTquR5Le2HKkoKpFw15hNyYQY2iTCbTf+GUVRyISbTM1/7L/hKyvgT9eilQcIyrdgQkVwMvzsErjxeJLy2tC0VSU5S7zWLW42W0HLSlg+n+f73/8+Tz/9NDt27MAw9t0tKIrC3Xc3UDtuAY4SVatS1SKbzU6oVjXyGLXHefW8kUhkWjtJAWFU/dEbYPZhdB91HtACCXO6zaJtY3ZdTeOeL8Jj/03qhHcAkDNaIGG/+CcY3EihR5RcW1PCxIkrpgQYpUVzfrmIFhZfw5aUsEAYlABR26JMoLX5kQ/+O/qWhyAWbV0JC8XAgpJZx4iVSWAF0jxWCJCltVmWAD/o6+I5q8iNc1oZlw02FRRsYotOb+lxYpL0aEprnrDH9QyPlItcnit4QsKKwQhQEiXJSGPenbyRd3PdnI7CZpAKpfjtYIhDD7qOOTt/AWwXg64X13d/h3gkpa3EJWENwCFAFbvChqc/TX/xAWbPvoClSz+y78G2XS0FSz9YzshRsSpjHmtSqAH42OYxRNG5n2EZFIwCS5Z+GAWFWGwSYjsqKy8Z0RjX9DnZ8fLVzo/UBflraIh3vl+Upo0i5bJ4X0wlhqo2cc0b2gwD60guF01wTXdHvvy6qY/xGS2RsMcff5zzzjuPoaGhSXcISgvt242i1q9VG5UBMDw8zMDAACeffPKkj5FIJOjt7WXz5s2YprmPL2w8/5cXz7tfEJOmYm3Y9YQ1bcx3jM7RjPuFb0kJi3dBopuUJAItKWED62HPCxS6TwK8IWFxKSQ3HVNh21DOo6niNWo6xR/ECTucIGrbZGlRCTM0t2OzZU9YMApl0OuZczcJ7GKAHw1FmNtCmc1dkxxDo1daUDCBd6XWEEhDyGxNoXUnHaitdUeuK6nsKYb4QLHSUjlyTshidspAIw75rFDL6yz/OcgbeTccNdBCt2Y6kmbUVMlaIQJtS4F7G1LCnPNQZzgOFJsiYZFAhEQgSsHUyT/3M7Q51sRdgYoC77kf1v0fLBDnGqcU6cyNrAt7XTNjwRixYAytojGsDzN/IhXNgWVVc8akEubOjWw0PDshI2HygoRlwhn6Co4S1oAn7Ph3wUGvhEgS88l/AQUINPk5lfMj5zDIpR0looWHgCsnvcuBipbKkVdffTXDw8PccMMNbN26FcMwsCxrnz+m2fqIkHpxxhlnAHDHHXfs8zPnNueYqR6nUCjw4IMP7vOz22+/fZ/H8ep5px3OEG9tiG7Zhdh0ar5Tjoy1jRkU2zRe9gn46AZSJ7wPaJGEve478LbfUEBcGFr1hAHEZZmkaWN+RQdsd05jq2OLPMsKM2oHeLdKwsRrpUsloFmUtq0Wj1dufUxUNCBJWAuPZVklAvI6GdNaW1MsIN53oYQ1T8IcRTZm2w0rV7WYEyzx2jYDu0P+gk2Y8/PlPGF598AP3gg/bTzfCxibS+VknzVBwtrDQm0JJuY2TMIA2mVJsqQL4j5pTliiE45+m5tDVusHAxgdfYJicTO2Xcd1sTY1XzY5DZXq6FgtDojOSBRI9QLV12KWkuWvD5/HmjXXTv04UB03VegH2x4zxLtSybnRKFMi3gG9hws/nik27EqoyU26HASfQuPYhEnc2Nrc47QQjuwVWiJhTz75JG9+85v56Ec/yrx588btJJxunH322SxZsoSf/OQnPPXUU+7tuVyOz3/+8wSDQd7xjne4tw8MDLB27VoGBsaqP+9+97sB+OQnP0m5XN3F33333dx+++2cfvrprFixounnPWDgkDCjSJcc6dK8EjYi/o62tTY3ci84bcwFo4DZYGeUi94jYOnLKMgLlSeeMPnfpsuRZXFC11QPIioAwvFqTEWLJMwd4N2qJ+w0UbIp7Z0M3iD0uPDARFqIp3DXJDvcSi2UbCs1cQmx9a1ZLbqMdXy6V2Nhr9pUIrwD53MYt1rzhAVU8d2wAi2QMCNPRPqwAqYJgeaKLg4Jy5VzwisFMLKl7vuPlp1ZieI9D84/ueEZllD1hRWcWZb1DvGm2hnZEe3ANIs89vhFrP7ryzEn+/xtexS+dTLcfIF7k6NgOaRuUoxKP3FyNshNh0PCUkqZQmE9ml6n59hRwioalPOkw2l0GyypKDdjzlelehzae4B5vZCvRcgSawhYTajalgVfmAX/uhgKrTXptIKWzoydnZ10dzefXu0HgsEg//Vf/4VlWZx22mm8+93v5iMf+QhHHHEEzz//PJ/5zGfGkKcbb7yRgw8+mBtvvHHM45x11llcccUV3H///Rx11FF87GMf4+1vfzsXXHAB6XSab3/72y097wGDSBrkBbJbXnCb7o6sLUc2K32Pg1SouqtvKSsMr7ojJQmTadRNlyNdEiY2L60rYQl3VmNLnrByEV1+JloeKi534a3Odit1z2JOyCKZaX5kkYPDksNc16uhBnY2/RgOCStZEAu31gEcUhU6gjaBjl44fRyfUR0wTINT4hqnJg1Cqt1SOTIo1UvbuTo0S8Ikhwu+40449/qm1pKOpFkWMZmX+w0bjPvFjQ0oYaPynJQKSk/Y3rMa60RHRNg2slL9NoxxXpPtj8N/nweP/teYm2uVsHJZEDJVjUzulYskYc/z0PeMq9bUzo8sFjezZct32bHjZ+Pff0QSrJoysjvIXGlgZBEI5d85Xxb6ZSe4QlmRKne9Jcm/fgfu/yqMbCUk56Q2GpHhQpYjoxXxvobsJs535ZxQC7Uht7qxP9CSJ+z1r389f/7zn7EsC1U9cBotzzrrLB544AGuu+46fvGLX1AulznkkEP4/Oc/zyWXXFL343z3u9/l8MMP57vf/S7f+MY3SCaTvPrVr+aLX/ziuITKq+edVqiq2FVoQ3TL8SlD+hAVq9J4FIAsRxrRNLmC2Om0lBO24wm441OE0nOIBqLopk6unGs8DqJShke+hxGKU5beJE+M+ZYFais5YTbMORo9PAoYrZOw+ccRHXwQ0DwrR7ashMlyZsWuYFgGIZmg3yjs/h/zsR6dh1qYZ+ggEonSrtoMptuafgzdEBe0kg3RFn1qIdmh5nQ3NoNipcir2gxCishAa6UcGQwkwKC6RW+GhJVyRBxjfmYRNNMBh1DCEqpNh7WD0bJUwAr9wjsXmVoVddSfhKlBAIJqc0qqW0oMZUijY+jjbFSfuAW2rhbE57gr3JvHjCwynLT8zsm90h1L4a23VuMqqBLB4ZIgYRs2fplU6hDmzn3zvvd3lLCajlTntYjYGhp1puU7SHTBSAHy/e4kA50oUeocJA7wyHdFp+WCk4nITLt4tMlNldzcxyo2NhClhG3bjfnPdZnDqYbqHmHlB1oiYV/60pd42ctexiWXXMJXvvIV5s6d69W6Wsbxxx/PH//4xymP+8xnPsNnPvOZcX+mqipXX301V199tefPe0Ah3gHaEB2mSUAJYNomg9ogsxMN7lJOej8c9kZGzRLcflfzw7sdVHTY8gB0LCXV04au6c0pYfoI3PEJMbJoofiMeuIJs0xBwppVwtoXwbvvofi718HIhtbCWgFe9XVid18N2+9tmYRpIY88Ydsfd/9dqpQIhZsjYUjyZbWQKeQi3A6VHdjh5k+8xZK4mOqWQjTaohImSVig3hyq8dZTziHfMqKmDS3k84VDKdBBUWRUfRMkTK+MuiSule7IdDhdnR1p6+Liq4/AyFaYvWrK+zvlyHhhF6QhmG9uAoBDwgY7VzCfZ6hYBWzLQqkVH87+NHStgIUnjblvbTnSHd49FSkNhmHF2OkrtUpYOCzyN8ulCawjI9IjldmXhKmmIB8NqVDJWaIMXNhDRiq/T9mruPbEnxCtl0gVxetgxjLEFfFZT8aa5AxSCUvqFrkMBBUb08wTDDaw+aip3LQy+7VVtCRfJZNJvvvd73L77bezYMECOjs7WbJkyT5/li5d6tV6Z+AHpC9M1UbojIqTQ1O+sFgbdB/ESERcKDORDAG1BZ+g42spZV1fWFPmfDm8uxCVI2sCkZYCP1l0KvxLH/HDxA60pcR8ql6elj1h1HTatRCOWjGKVLzILgMie6qp362sCXmxUXItznsEFEnkbKv5km2tEhZqZaMBhKUXM2AW4d5/beoxCuUqUQqYrRnzwyHx+6iqDZ3LXdWhEZScOa+2gnrX50SkQBNIh9NjxxY5vrA6S5JOCS4SEuQ/2H7QZIdPCCcstT8l4h5sBczn9ioFJrrEZI69jP/jp+U3PupqvPmRZWNgfIP/yFglrGyWq97VipM11gAJS1TN+Y4S1mfYxOML64uYsEyX9OSDEf5aCHJ3NkhX5qgp7jgB5MYnXS65s0UnzUwbD6XsmMfaX2iJhN19992ccsopjIyMEAwGicfj2La9zx+rZpL7DA5AuB2Sw3TFWwxsBe9M+U6ZR8+K8Sc0OcTbIWFhcfFtSQUDYXQNx6t5Si3ORXS72lotR1IlYa1kl5XK1ZJfpEWZXpl3LFFFEPFWfGGKasm/WyeqCuJ3UloIay1JY7ZhKS37SSLSpxTAhJfub+oxNLke0wYlEBVKSpOIhdrEehQL+6pH4YyPNvwYRkVuliwbZfV/Vpt2GkRtWGvFzDfcIemQMGPJxzjx+D8xa86rm1qHQ8IGKzqqtG0Y931WxDaMbheWhwlQOzeyOry7jvLs7ufhnuvh8VvGrGFYHyYc6gIUbNukPJ4/7exPw0W3wNKzgaoKFlACVAyxoWlICauJqXCUsGypgXOxPgqyMz0XCPBoMchdhQyZVHOk2NkYJEp5srJTc8pZmuOuiZb8k16gpXLkNddcg23b/OxnP+ONb3zjAeULm0EDqCFhs2KzeIEXmiNhf/02aCOMdIvdYsskzFHCzBIpWbJpKpTPIWGROFBunYRJOKSpaSXshd/D7Z9A6xDfm3iwxVLbHZ8iuuHnkIi0VI4U9xVkp1VPGPOPIxJOopdGKVWaI2GmqaM6mVMtlLYcBMpiHYrWZBQLVSWsYgLh1t63aChDEQgFFDjxfU09hiYJpWGDsvCU1tYTbgNAxca2yyhK45+BvnKJr+yK8qUhSRAaHN7tIBVKuUpYpVYJq7ND0iEfmfg8Esl95/rWi9pSYKijk1J5D0apn9h3ThVdg5kFcNHN0LVsn/uOMeZrYnbllOVIECTsvhtgwclwzNvHzI9U1SChUAeGMUi5tMdN0Hcxe9WYcq2zhrZImnJZGOmbImGuMb9KcOuCU9IOp8jJDWJtw1XDkOXIlJ4lZyrMDtkU9V005EDWDwwlrCUS9sILL3DppZdy8cUXe7WeGewP1CphCamENdMh+fjN0L+WkVd8DPCgMzKSRiT62aQlGWipHBmK4QkJKxfhtg8TL4oSS9OqU3EQe3QreofoYGrZE2ZViBkloDUSJuY8RomoYdQWoyWgSuSaLUcalRFAqDyhRjwfEyAQykAZ1EDzPhDDECfwigWEW1tTVCoLYcWGlec39Ri6VMIqhOBtv25pPXGphAGYZrGpRPNsWSNnqLSPSOKdaK6LPh1JU5KxELZdxlr2MtRYO8w/sa77O0Qh3UJkB1RVqCF9iLnzLsXK7yIc+zX0y1T6drV6Ht0LYzxho+LfdY3qcUz5/WvBtqtKmDO6KDJLkLA6FCDndeiNJmX5UiUcbuA9STqp+Xtcn69SGeaZZ9+HbVc44vDvTX5/6Qcj3u6ew53qRlNwlDBtlKwlVN+8tqOxx3CUsBY/G62iJRLW3d1NLNZ6CWUG+xm1qfldouuzKSXs8DfB6DZGgsJ/0VJnJIjOzUgKSlmSMtupKRIm1bNCSFxMWiZhahCe/gnxZAK6O5svRx78GkrdK7HvFZl0LXvCTvkg0XQC1v+8ef+Vbbv3jXrRMWToRGX5plliWJGqU9GCSKtqIRBKL4IBCISbf73t+KH8eDBMTK+0rITFpT8oqIBlGahNdJCWjCwqYLY+iY54OMWgBQoq5s2vJGSG4L31l0kt23IbaJKWJc4vTZZHnUwqB5W5hxFefGbd93fIR9ut74RL/7dazmwQtQRo0cL3iS685dfAhjvF3MFlL3fzuGph2/YYJWyP2x1ZhyesczmgiAiFQv8YNQ4g7M6P3OtcPboDXvidaBJY/nJ33QC9kah7X7URX2z3QbDyVTDvOFcJy1cK9PffjqIEsG0TRZnE/+soYbF2coWXOD5Roa2V04tUr0L6KLsrc1mnl+hRGzyvO0re37ISdskll3DrrbeiadoMGftbhrODK+fdId4DxSaM+af9PwCGH/0y4E1GGJG0MObLL3hT8yOld6EYDEPFAxIWDMMrPkdc2wk7/q95JSzRiRZY6f63ZU9YqoeoHFHStBJWKaFL70bEg0YB+p4mMrIFwuHmlTCp8hQthWi09XJkKOR4sJrvRjSCnTxaDHJIyWzZExYLVzcrxvrbiBx0YcOPUa7kiAKm4gEJC8V59444c+OzuWDHo6KF37br7iDTKhq2/AwlbVsEhjaJaDBKSI1QsjQiqihJ1jvw2bItRkujKNgMzc3xzLYbWJX6t/qGVu8Fd36kVSFn5IQaFEnCIa+b9H7FStGNxWmPtLO93u5IEOS+fREMb4Y9a2ifd5T7mCWzRCQszfnlvcrqfU/D7ddC75EuCXMbFMIdLFhwBarSIClecqb4A6Tk9IucqQCq8KWVB4lEJik5a1IJi3VQLDzHWzvK9NsNKle1iHfAqf8M0Tae2Pk7bh8d4KzUCY09hn5gkLCWag2f+cxnOPTQQzn33HN54IEHyOdbC9KcwX7C0f8En9wDb/gvZsXFF6kVY74nI4scSNNkSqoprZQj83Knmgh64Ak75YPED34N0Fp3pEPgwmq4tU5SCUdNa1oJC4TQXyeCiL1oFCAUIyZT/Jv1hDnlyKKlEPUgVDESbBNLo/lRSpoM/Y3YdjXIskkkwxn+nA1y+2gQ49bLmhqlUpabE1vLw+8/0NJ6HG9i0SzBpb+GK+5s6P65co4VEZNLO8oM9ESqpawmkQqnXHO+aeZhcKPwU+Yn9/TlyjksLCIK5NsC9A/cgaI0F5ESCUTczduQQyjqgFOKjAaixENxDj/sWxxzzC/IpOvsCpx1sPi7fy2pUMrt6h7Wh90OydLeJCzeAasuhKVnuTc56lkkOo/ly65l6dIP1/077I2gGiQZSmKhEJBjh6acIVmjhJWlcldpVLkas4gIvPwzcOqHSEo7QMPxRX8PnjBH/bJte9K5iIqiUKm0NjtuBj4iVFU8nCHeDXvCKmXIbh8zsqjlciS49XrHddMcCZPlSDk6JeFROnLL3ZEv3oG+Q8xEbNkPBrDrWaLrxQWzaSVMDaBnRHZPpMWMMADCCUFUaL47siL9V0UL0h6MLQoH0tyeC5KoKLzS0KCJ174kT+Axy269OzIQ4fejQpm41kJMUqgjiLQWZVOjbIFtWmA1r/BBVSkuGEVYdnbD9y8YBXpDFscmKoxkgswLtUbC0pE0a3WVU3qOQVVj8OsrYMfjovvvkAsnvJ/TvdeGcParaphAC40m7ZF2CkaBgewa4uV1RCI9ZNJHTHqfvedGxmLzicXmT3aXseheKYaB71mDoii0R9rp1/oZ0odIRWYTCnXuq2otOFH8qYHboNBCfhy2LaoK4RSZSIa8kUcJtoMxOPXoItcT1oFpyPiMgDfkxzH4N3xt+HvwhJ122mmNJdTO4ICHU44c1AcxLbN+dWb4JfjP48TIokNFd5anSphUU1pRwoqye9cTJWzPGuK714rHbbo78rdoL9wKc3s8yQijfx3RdX+CWV0edEe2HtQKQCjmkrBm1bnurnP4xksf5/FImKsWtW7Mj8Vm8buRMIvKhiA8TZAwK/cQZ6UMUobdctp2QA0QVsOUrTKaqtBeri8NvhaDwWV8YUeci+efA0d/qKX1OCSsbJWbmnKQK9ek5Zs2tDdfjgThC/tpf4QTDr+CRGKJyOGyLeHNnAQO8ehG5GgFAq19djqiHWzPb2d06G6eHfwNvb1vbJiENYwaJQyExaNf62dYH2bVvLcxf97b6noYd5B5sxtj24br5wl/7T+/QDqcZgc7MFVxfp6ShNUoYVSeBUAJNp6VNgYj26A4QDIQJqnaFArrgZfVf/8DJCesJRJ27733erSMGexXFIfgT9dCOU/nxbegoGDaJsOlYZeUTYma4d3uF77ZE08tHCVMZs21VI6UFwZPIip+dxXx3U/B/DktzI7MU1TFJsaT0l/Yg9mR2Z2Unvsl4E14LKG4uya9mXgRIESIXWWVXQGVSIudiABRqWCWVEWQsESdn/EahEtP89o2g6fbjvMkbTsWilEulcXg9FK+Kv3WCTdrLtU7ZlRNM4iH4rylvcTKmMX2xz/D4vIsYcrurC90u2AUiMhIkaBJy+VIpxvPzQg8/9/qut+oVPM7pT+toTT1ceCczwomJKh6FSdDbWdkU+iWntE9a0SHZGRsh+S4yO0W3ag1kVHOObnD6mNo6EFSqcNcb2RdUBQxrq2cHxPYWlbjhKinHFn1hKlSgQo20p05Hn55OWx/hI5jXsEX5mrYff+GddDl9Te2nPZhOPSNsKBBL5nHaN3FOYO/fSgKPCPSn4OWSUe0g0F9kAFtoAESVjO8W37hPVXCKqLE0lx3pIyokP/1hISFE2J2JOICaNlW43EO5QKa4iEJC8WISsWwaSVseAv6C7+F7s7WM8IAQvFqObJJEoahCcIERFtMp4eayAxFgWYJtCVeX7WRC9kkWBIBU62Qj6ju57URuCTMg7J2SA0RD6hkAibFl+6C59eKeYh1krCcsZcS1oIxH3CnZTQa1DyyWygu7TKKJNTk8G4HDpHKmRYJql27k6GWhOXya9m8+RskEytYsuRD9T1p1wpQVLHJze/Zp0NyH9g2fOMosCrw/oehYzFQ9ekmRv+XJwf+i6OP+gnt7Q2Sj/fcJ1SjUIzMi0I90olKEjaFEpbqhVmrID2HcLEASoOzK8dDuhdScwirCUwbAoqNYQzVn3+26FTxZz+joavGq171Kh5//PGpDxwHmqbxla98hW9/+9tN3X8GPiKSgVd8Dl5zIwDdcbFD2VNsIMzSGd4dy7gGSU9I2JIz4aSrSM05GmiyOzKchEQ3Rbk794SERVLEJbmwsZsjPeWiIAJ4RcISxGxBDJsmYfFOdOkn8aQcGQwTlT7zZpUwjIL7OkU8IBnRQJTFYZPlCYtScWdTj6HYwt8W8KK0DZyVyHJ5V5l8JuR6GBuBS8Jeegh2PdfyepyoC02O+2lkfmShXFXCBAlrXQmLKTZa8aWxcQymAZNMYxkdWANAJiLeI6+UsFFD+JudhpHJ4JYjI+1oxS3099/O0NAD9T9pKArtgkjRv2ZcEmZZFWz5vSe/B4yC8AXKTmlwlDAb5JqbIkCpHrd078ZUWOLzMSUJO/eL8L7V2KteSxRxbko0OzfSwcU/gA+vITnrUPIyS67UaGr+AYCGSNi2bds4/vjjOfvss7n55pvJZqfemTz22GN86EMfYuHChXz605+mq6tx6X8GPkNV4ZQPwtFvg2CkGlPRyPxIudMakV4WVVHdHWxLWPVaOPeLpJaJYbb5ch670e6x130HPrqBvFTVvFLCojXraMoXVs6jyZKBN+XImtJfsySsewX6KjHaxZNyJBCVsQklozDFkeNj47bvcNYsk/kh07PRTq9vL/PWboNs/oWmHkORSliwf1PL64Eq6TECiptr1wgWlR/lU70aHSP3w8C6qe8wBWxp9i6HpCe0ARKWN/JEZIU2YNpNp+U7SEfSvDJjsDz3A7Zv/4G48b9fCV/sBal2jYfRYfHepKNyQHqLJMwNbDUEATfqUMLcJqVoO2WjgXiKWji+sD1rx8yPBFj915dzz70HU5TB0QzJz2Nm3hiv4khphJgCyM1DqyqUm5ovRwbpU5EwCcMYIqDYWDYk4/NaWoODZDhJVo7PLO+dmTYZnvsVrL8Tmuza9goNlSOfeuop/ud//ofPfe5zXH755VxxxRWsXLmSo48+mtmzZ9Pe3o6maQwNDbF+/Xoee+wxRkdHUVWViy++mC9+8YssWrTIp19lBl6hqQ5JScKGw1EoQSbc4vDuveAQOtM20Sqa25nYCAqSBHhFwlQgpgTR7AqaoUGj/MAoelyOjLfuCYOasFZvSFhEGqj1JknYYPFpDknZPKB7MEYJMQ9Tt8QkhlK5fnJRi4BtgALBkfouPlPBckhPQGlKCQvaeTqDNiXFFsp2q5Ap+eWg3Kc73W11IG/kiahSCWtfIUpRLSAdTrPdGV1kytcmGBZqz47HoXccc7xtM5LbDrEAiXgG2OFZObK/VIQgVCoj2LY9aXPamLT8skjXrzfnzEX3QbD2D9C/ho7lJwO1SpgCWJTKe0gkllZJWEe1dGxYBnkjT29IvCehUHtzXaIv3i5CYOef4HrCBisKXV1nE48tqushHO9Y3oI5XnTPI7ojh0zxfa57fmSlJDxlANdsabm5phU0RMIUReHyyy/nHe94B7fddhs333wz9913Hz/60Y/2OVZVVQ4//HAuvPBCrrjiCubMmTPOI87ggEH/izC6FboPdsuRDWWFyXLkiEylb6kVuhaVMhT2EDUrBJUgFbtCtpxtioQ5apU3JEzsruNKAM2uNKmEFdA8NubHWvWElYvo0t/niScMRwmz0Zv0XxnywisS81tfUzQQlSQMdBme2Qgsq+TOsgzPOa7l9UBVeaoElKY8YYpVhgCETFtMmWgVclC6IcmU8/2uB/lyni7JS4JnfRoSDZKOvSBS82VOWEWSsLnHwqZ7BQk79vJ97zS4gRFTBxLEY2nQvCtH7tbzkATLKmNZOoHAxN/dQU18vjpjnZSLTwN1puXXYv4JcNAFMOcoNwDb8d2Gw90Ui5uqCtDQRvF3xxL37k5Qa7vcEzetgu1ZA0/9GKwKmcPOA2C3YU49sqhShq8fAvFOym/8HKYNI6ZKulU/5Zo/wIP/QbKrly1qg+XISgkWnSa8zF58X1pAU8Z8VVV59atfzatfLcoWa9asYfv27QwODhKLxeju7uaQQw4hk9m/rZ8zaAB3fBLW3w6v+WaVhDWhhA3JLK7OWGsnXheb7oGfXIzSeySpTIrh0rAY4t0Ij/rv8wCFfNxLJUySMFQGaXJ+ZLmAHvNHCTMsg4pVccMd68bj/0Pp0e9AJuXNmoBIIAyUKDU5WWBMYr4HJdKQGnJH4ZSMxszeAJVKVdGLzD2p5fUArvJkBtSmlDBVpv+HTNttZmlpOap4701FSlANliPnuAPXWx8zlQ6nq0O8HSVs7jHi7+0TeJRfup+sLPWHYgvojEdJJFe0tA6HhPXroyipELZtYBgj9ZOwkSbLkSvOFX+Ajl2PAlWFLbJ3YKurhFVJmGPK74lEAL15EuZ4+/J7yIQbGOJdHITCHigOkO48g49sjxFV4PWt5nPpI7D9EVKRE8mmxHm0biUsmoZ3/KG15/cInnRHHnzwwRx88MFePNQM9hfi8sRQHKK78zCgUU+Y+DIOyh1J0y3ZeyOSFmNT1ACpcIzh0nBj5nzbhq1/BWyKy8RJ2BtjvkPCxO/bcEyFbQslLC52YV6TMBDhqA2TsHIRXb6HnilhkoTpTZAwy6qIlHQECfNiTYqiYFhi0mLZbFx1ctZTsqpxFy1DjYIFZpOesIAtzOKRiuXJzj4QiIMNptoECSvn2VVR6Y7PJtSo6jMO0pE0JamEVSp7kbD+tSKCZu/fedezjARkLmDnuRw5/8yW19EZFefIodIwoVCGcnkAozJKlPHLrZZtMagL4tUV7WJHWQ7vDjW/QXVyvtwh3s7oopIkYYP7KmHOsbPCwkRfd/fg3nCGsBcG3HJkXR2riS5474Ogj5CvFLFR0GwPzsNSFUyWNTlCCcqlJsbt7We0NLZoBn9HiMuTZXGQrrgw5jdTjhySO2fPSNiCE+FT/fCuP5OU6lNDMRW2DZf+EvON/40mvU5eecIA4pLzNFyOrJTANt2cME/8V6o6Zt5jU+qcUe3Y9MoTFpXEqZmxRZVK9SSvWd6tqSIv6mWzcZ+ao4SVbIiWmh9ZVQtHebIDNOcJk4GkEdPyJAE8KLs+LWe0UwMkLGfk+MFghMojG0ndXl+m12SoVcLccmRqNmQWADZse3jfO13wNUblsG5PurQZOz9SlcGvlUmywkZKI5i2eF86Yh01xvwmzo22DaM7aDfFCzFaGqViVQhHBDEqlwdEp+jgBnF813L3ro5a1SH9fdFmlTCXhO1xs9tGS6OUSv2Mjj6Brk/QaRwIQc+hsOhU99ydCCUa3yDujVgbAEk9T9Z0ypENdPQfIJghYTMQcIZ4a0PMilXnR9bdieiUI+WO3Nk1tgxFccMwm8oLUlVY9nKKK85xb/K0HClb5BsmYWVxIffUmA8o4bibX9aUL8woUnJImEfdkZGjRKq3Hm1coalUxAVEs8DCGyUMwLSFQaZiNU6iLLuMbqkULIXopns9WY8qy3a22pwnLKiIi32sgvvZbAXVIdcOCavfmF+Qn+2UaYIHpLl2dqRbjgRYcrr4e+M9+95JURiVjSBpj8bSRAIRd65mz/Ivc8bpT9HWdvyExzulyLZIGyE1RFn6D8PhJhIC/veD8PVVtD3/WxSpvo+URlwlrFTeAyNbRO5dIFKNtaCqhGWC4rzQdDmyRgnLSD9XtpRlw4YbeOzxi9i16/dTPoRz7vakc14qYalSziVhdZcj1/wB/nUx3HpZ6+toETMkbAYCNeVIJ6KiYlVcA+iUcMqRUm3yzBNWA2f31Uxga16WeEJqiHAgPMXRdWBvEtZoOVJeIDTpofOKhI1JqG+ShGleK2EZoUjoduPzY2v9YEEl2PruWcKyxeNU7MbVuUz6CP6rr5N/3RUj6sEsS6jxTrXNgQu+1vD9w1KBjiuRMUnpzUINz+GubJA+ZZG4QRuue7C4kxOYvPjHcPanW16LUMLEZ9Ko1Hz3l8q5lhvuGnsH26ZiVVzbQtQaQdN2YLU4UxOqaljOUgkGUyiTBDQ7do6uWBeWVcEwBBlqioR1LgMlQEAbdpuehvVhwjJ1vlTqF8Z5EAGvger3xFHCEor4/aPRJpvkHBJmm6QtQfordgVVlldL5QlS87c8BH/5Cmy8h/71H+ZjszUWRT34HstxQwktS5+h8t3+CIcccmN999WGxJ9mw5o9xAwJm4GAW44cIhQIuRJ+3SVJTXzRh6Qi5Fk50rbhZ5fAza8ipQhPQ0MkLLcbnvgh+Q13AB7twMAtR8YsQSwaVsKsCnQsRXcexysS1nMYUTm2o6lZjeWqEuZFJ2Lt4zQzwLtKwrwjhQCWLLeZkeZ+x5IklFGPhsEHA87jGKJ80wAMyyAsuxHjAW9IYTg6jz+MhtnkkDCzXPcFK1/OEldtEqkeEfDZIhKhBIZULt1yJIggZ0UVvjDHC2WZ8J8nkP3Fpe5hG59/Hw+tPp1C4cWW1+Io/I7XazI4x3RGO7Esna6us0mnjyIcaiKa4djL4F92wHnXu+fmkdKIa8wvl3dDvyRhs8b6s504i43Jt3LKyQ+QyRzT+PODiAWRxCem59yZolZgivmRG++BP38ee83vMPWtzAnbRFuMCxGLaAMgZejotsIaPYASrXNk1wEyvBtmSNgMHMQkaZJlBzewtVin0fGKu+Dy2xmSfhnPSJiiwOb74aX7ScsvfbbUQDmyfw38/iryq8UOKemRcuGSMFOSsEZ3VB1L4ANPoM09SjyOVyTsLT8l2iaUp+Y8YZprzI9N0vXVCKK7xcVBb6Ck5S5HliO9MuW7CIsLoR1obu6jLr0+UY9IvR1byXU7ozyintLwfQvlLEH5ayQC3pBCJwKmYJZEYwzU7QsLWDm+NFdj57MXebIWRVHc8qhladjytSfeAUvOEv9+6ifi762rYWAdI9sfAUSGVEWqZ61GVABTjw2qQW1nZDCY5IjDv8txx/4SRWkiPzGSctPq3dBYfcgtLVYqOSp7ZHDtrJVj7upUMzLRdqLRXgKt2Axk8K5S7HcVubIiPnMlfQIlrCheByOeRLHLWDaojXaIjodwEpQAYSAsP6P5epta9ANjeDfMkLAZOHDLkeILMyte9YXVhe4VsOBEBmXrtKflSGd+pDx5NdQdKYd358LiBOaJHwxg9qHw4XXEjxR+p6ZywqgZN+MVCaPq5WquHFkzIsgjJSza9wwApVLjZeRQMEPI7GB7WSVaKXuyHoBhtZtP74ii93ygqfvr0ggf9SgPLxpOM2qqFHY8BXd9tqH7akaR20eD/CUbIBpu82Q9zmalUCkIk3fXirqSxQ3LAEuOdLKA0R2erCcYyvCd/gipxV8Y+4OjxfePx28W6saiU+E9f2H01A8C0B5JYznTDTxQXxwSVh5dzWOPX8yGjRM3HjjlSK+tGbVEMBhMsmzpNRyy6msoA+vFAbNWjTne01m+ri+s37WHaIog7Jq+ffz7yI28JkuQWVMhGfYgqFVRquZ86dWru0rizjqeUcJmcKDAKUdqw2BZrhLWSIekVtFcMuKZEgauZJxGkLCGlDDZaZYPCR+YZ+XIYBhSPcSlObRhJUzCDxLmPFZT5UhDq3ZHemXM7z1SrEdtXHXq6jqL6OgK/nc0TKTRAemTIKREyFoqeq7xbqrNm2/ko3MKnJs2PFPCXOKc3QbP/6ah+5Yskz9mw9w5FEL1qLwSD8Y4JFqhx9qK9d6/wFWP1jXAO1/OE3XS8ksaGM1PbqhFKpxhrR5AC84ZqyStfBV0LhcX+vu+LG7rPYLhOSJmZ1ZNM0jAg1Ktc17TykOMjj5OPr92wmMdJcw5l7aMJ38EN72M9hFBbB01buHCd9PT81oCl90Bl/1JdJTXYEjzcGOclCQsX1XCCnJUiGEMuarjGBTE66CHxMZlyFS8Ow9LJSsViHBywmD3S//K8PAjU99PXkM+MfoUp//sdH6zvrHvnJeYIWEzEHDKkbYF+khjo4uGNsM91zP8xC2AML97VvYDd7eSlm3qDXVHSvUlHxQkzNN1gdst1XDpb90f4dunoOf6AA9J2J/+hagMsGx2qHjJa2O+nPup0+DMTwknXyyietBQ4axJE58h/ckfNnzfUnmAdACCik3Uo3KG8/5rbQvEHNcG4Gx8YmoQug7yZD3xYJx3dZc5O7LZzUWrB3kj7/rTgqYtoiQ8gBuJUN4rHDQQgtd8U3Rhzq92KjoEpTsqlO9AIIHqQVOHQ8KGnSHe5YlL7LXG/FKpH03bjtnMxsiBnoUdj9MuNw5OYKuLcAIWnlTtdJcY0oc4LWnAts+zdet/N//8UJ0DWuivBrYaZTcPTtPGUcMcJSwgFNLBiuK+ny3DyQpTwyyLWBgj95HLTTxP1IVUwoZsg+HS8KSjp/zGDAmbgUAwDM7uRBtubHRR/1q47waGnhbjqzpjnd5+qOXuPmULFtaQMV/uePKyWyjpQfs+IBoG/vhx4s//FmiiHJnfDbufQ7NEic0z07k+QlSqD80pYUV0qTh55cGqNeY3PHwdKEkyGfWiq9VZk/TXlAKNX5jLMrtMtxSiHim+UVXlvV0653UOYB19SUP3ddXUzHw470uerCcZTlF2UuprJgRMhXw5T8RJy7dUz0bCuON6ZBTOGCw8Cd5zPyw/173JiWXokN42L/xgUC0FDpbF99bJ/hoPtcb8bdv+m4dWn8HGTV9p/skXCb9gx/A2oPo7TgbbthnUB+kJ2Zj6S67HsmnUZoVFqllhsZgwxGv61n3vIy0uuiI+R0MVD5UwpxypBBl2h4n3TX0/6QnLS39hKrT/RhfNkLAZVBGXO6jioKuE1ZWan54Dx17O4HzRdeNpKRKqSlhFfGEaUsKkUTMv2/Y9U8IUBR6/mfiW1QBigHcjWH4uvO03aHLIuWdK2GkfJrLkZUDzERW6l/MscWZHigTxitV4TIUuh5FHPCRh0cxC3tOlM2uRSXkSNWM8lA1xIdNtiHikhEVDCVbGLHoCBcwGA2SLeh+HxSosCpuerAXE96Qk+bL56HfgWyfDIzdNeT8xvFv8O0hjXZ6ToT3SzhlJg9jwr8jl1ux7QPcKCFU3Mo4S1h4WGwCvSJhzbtstQ3on++zUKmFuRliohdLk7MMg2kZbWXwfHEJaLG5hy5/fwo7b3wi7nx9zl2KlSMks0REQjDoWndf88wP0HAYHvxp6j3TVrGw5S0x2JWratrHHW5Y7/F03xXsyZKoeliPbAEihVknYRKGxtZBKWE5ugj3bnDeBGRI2gyrcDskRVwnbU6zDM9N7BLzq6wwtPRPwgYQ5Spgpcm6aKkc6JMzLL9vpHyZ+2JuAJpSwdC/WkjPR5UnAMxLWuZRYWuQANUPCTKOI4RjzvRpbtOHP7r+bUeecpH2vPGoA0VCMRRGLmNnnBsLWC0PGJJgmKBFvPk+xUBJDkp7KtofE0OM6oRfW8c6uMqdF6rj41Il4KO6OCjL0QdjzfHUu4STIlXNVJUz17v1qi7RxWMyko/wcRW3zlMc7JCwdFETQayWsTxefAcvSMM19v/umZbqG+M5YJ2VDELJwK12BqgoLT6FDZhMOlQS5KRY2soFH2F561CU8DtwOzZC0GETnNv/8ACvPhzf9CI57p+sJG6OEaXspYfoISLVJM2QZ1Usl7KDz4dR/JpnsZaQiU/P1OpSw0gwJm8GBiH/6LXyyH1acU42o0AbqLiHVyu+ewlHCDHExzpfzWLI0OSUcY768MHgqO5/+UeJHvBVozphfS5L86I5sJqKidNHN1cfxqEQaCqdQWgiQLZkel2wRBNMJAB3XTDwJnFFKtml7kk4P4v0vOaN5fvk2Ua6uE2VJCq1SEZ651ZP1JEIJdz3FRcfDpb+GE9475f0KRsFVwrwY3u2gLdrmDl0fkxU2ARyCkgoIpdkzJSwiNpi79GFUOXR9PDVsSB/Csi1URaUt0tZaWn4tlpxBuylIjUM0YxFBrLREFHvecfusQ8GuKmGxOnO06oBDwrLlLHPnXcpJJ97FiuWfGHtQQZBPO5JGL4lNwpCXnrDDL4KXf4Zk+2JGTPHBc55nUjjlSHmOnClHzuDAQKxdeMPALUeWzNLUkRCFASgOMShr/x0xf5SwtJThbez6fWFORAXiJJTwKFzTgZOn1LAStvkvaE/c7P7XM4LR9wzRXc8BzalO2pwj3H97pYQpkUQ1xb+JNTlqYcRLomrbaK7nqTES5pAA2wI8GuAdC8Rc5anRId6GTKi3TQs8SIUH8d6XbXF5KMTisOxskLMYJ8MYJcwj4gOiHFklzVO/Ng5BScgh3l7EU0C1w7BimQSCbcD4vjCngtAV7SKoBsVsR1pUwgBWvsqdHzmiD2PbNtHEAgBMpUKFsTEig/ogHQGboGKjquHm0/JrYdugDVfLkaUs0UgP8fhil5i6kKZ8O9HBsmUf51EtybDpIQmTSIaTbjmyXB7AsiaJU7FtKGUxgaI8H80oYTM44BANRl3JeMrA1ts+DF9ezNAOMUjXLyUsXMq5Kk/9JMxHA+boDmLDQn5vmIQ99VO0Oz8JCOVK9Sp+YfujRDf/BWhWdRInr0gg4t2aQjEikoQ1M8S7JIlFNOidshINRKvzCMv1D6cGsGT5yTYBj7LUapWwckBpaIi3M0/Rjs+GZa/wZD2KomDKSBitPFL3/cZ4wkLeXWjbom0uSa3U0a3p+KUyXS/nqCN/yMIF7/ZkHeFAuGq3kEO8x+uQ3FUUwaWzE7OxbdtVy1pWwjJz6ZhzLAAV2yRbzhIIxNzxRZq2Zczhg9ogs0LiuxeLLWouKLYWpTx8vhv+dREZSbj26VithdyYq/Fu5s19G7cOBbHwsBxpaDC0maRRpmCBKSmNPlFwLIhrgm2Rr4nMmVHCZnBgYMNd8Kt3wcPfBapq2B5tCl+YTNIetMXF0ntPmDQ/l7JjzKB1QSoKBWkI93TH8/urif9MjEdpZnakJkmOl6VIwgliVpOlv1IO/YkfAN6pYACEqkpYM6OLnJmTkbCHJCycQneUsNLUI2hqYdvidVUyi93h8i2vJxh1SYYebEwJczxJSihdzXHyAKYcE6br/fDED2H1f055n7yR59FCkM3bTGYlT/BsLUIJk+uqRwmTnYPdyWV0dJxMKrVqinvUj9lxEbtRUcT31ik11mJ3QZSTZ8VnUanksG2h5oZCrW9Qw0e/nYT0hY088zP43w8SDwvDfaGwYcyxQ/oQs+Tg7nh8ScvPTTgBspkoI9fgzKYcF7IcSbwT3dQpS1U741HIMS/eDt84ktRzvwYUirbYpJcm65AMhOG13yJ/5scBca4LNTgqzEv8XZKwXbt2ccUVV9Db20s0GmXFihV87nOfo1xuPHH79ttv58wzzySdTpNKpTjzzDO5/fbbxz32He94B4qijPtn5cqV497ngMLQZnj2F/DSAwD1Z4XJMMA98mLgnKQ8g9OBpmfdtuiGy5FSnvY0JyySJC7JhWEZIi28XpQLaB53IQJjB3g3WvrL70F/QLTQe+m/IhQjajW5JttGlwQ64tW0AyASTlTLW6X6uyNt2wKZwK6GvBt5Isp/4t96UG2OhHlohAewFWFNKJWG4PdXwe2fAHPy7tZ8Oc9OQ8UYNkm3HeXZWjKRzPhDvMeBVtFcP6RjpPcSsxPi/JYPLWRO78XEYgv2OcYpR86Oz3ZLkYFAkoAXm5vDL6ZNloqH7/okPH4ziazoqC0UN445dFAbpFsqYfH44tafW1HgA0/CJ3aTlpl0DgnbtOk/eOTR1zIweG/1+FAcZh0Cncvc44Jq0M1XbBnRDITiJJ2xRbazcZjEFxaKwVGXkD/0dYCHAd5NwoNR5gcWdu3axQknnMC2bdu48MILWbFiBQ888ADXXXcdq1ev5rbbbkNV6+OeP/7xj7n00kvp6uri7W9/O4qi8Itf/ILzzjuPH/3oR1xyyfh5Ph/84Adpa2sbc1tXl0epyX5iwYnwis+7Yy+64lVz/qTQRgDol8TI6az0DO2L4KhLoWMJqfzTQANKWCAMgTB5p/bvJQkLJ4lb1QYBraIRCte5oyoX3WR6b5WwKglr2JgfjFBafAaUX/S0E5Fwwi1H6o0qhqZBSYpNUQ/fu2iwphxp1N8dadsmRturuG/rn1Di3pFCRVEwCAJl9AbLkZYl3me1MCIMxx6NYnFImIEhBmXbligvTRLA6szuS1o2JL3bjLVF2tzIDF0OdZ8ITikypIa8G1NWA2eTuUVdxmsOvnrcY3YXhRI2OzHb9Yy17AdzEAjR1bGMHaMbGAgEYP6JJJa9HjbdMK4StkwqYQkvSBiISCIgY4pNSLFSxLAMNG0Ludxz5HMv0NV5pjj2iDfBEW9i585fsqvvVyRVm0g4412O5JIz4RN9JHc+BHe+h52VCKtmnViXB9DZyHsd4N0o/u5I2DXXXMPWrVv51re+xZVXXgmIwLrLLruMW265hVtuuYXLLrtsyscZHh7mqquuoquriyeeeIL580VXybXXXsvRRx/NVVddxfnnn097+747rQ996EMsWrTI099rWtBzmPgjMStW5/zI4hBFRSEv85ycuZOeoXsFvFaUQtJ3XwU0MLrofauxbZv8j44GPC5HhhOEgCAqFSyKRrF+w2k5j+ZxMj0wpvTXcDkyMw/tjA/Dne/xXAlzSVgjQbswdpalh+9dNBB1jflmA54nVQ1RsJbyh9EwZ1bqH+lVD0xJwsoNKmG23GAEBjfXNd+xXqxVVvKjHVn+ZcFZEP8jFPqhsGcKEiYvbJYFqR7P1hINRjGVMGBQmuL9cjoj2yPtbNn6PUqlXcyZ8yZSSW+qET0J8Xs5Jcfx4JKwGiWsZT9YDWa1LYbRDew54yNw3D+TGPkrAMW9lLAhfYiH8kGWdp9IKnWoZ88PYxWkbClLIrEcgHxh/T7Hbtr875RKfXQHI6hezLB0IM8NjqfrrnyKj53348nvM7IN+teSk76x/a2E/V2VI3O5HD//+c9ZsmQJ731vtZ1aURSuv/56VFXlppumDhwEuPXWWxkZGeHqq692CRhAb28vH/rQhxgZGeHWW71pBz9Q4c6PnKwcWSmBUaBftoLHg3Ffdp8OnC9MI6n5JbPkhoR6+oWTpCAuza4NmfONIprqgycsFCMm1bmmjPk+ZHIRjFU9YY3M/QQIRin1io7NmIckLBKsjahobE16Vszui+bqj5GoB7vNBE8WA1gl2y2j1wWphAUrtmcJ9QDhUBsFS6FQKVWT0vOT+0Pz5SynJQ1SS3rRFG/mRjoISiN8eYr3yw1qjbazZ89tbN/+g/qyo+qEo4Q5RGs8OOXIWfFZdLSfxNFH/ZRlSz/m2Rpcv24wCGqAeELM9dS0rVhW1XYzqA/ylBakbd6VJJPejLRizf/Cb95L4LlfueRntDzqkrDCXiTMMEZdj9YuQ/XOD1YDZ3Odr2fzsuFO+PEbyT/9E3Hf/ayE/V2RsNWrV1MqlXjFK16xj9zZ29vLYYcdxsMPP4yuT31xuvfeewE455xz9vnZueeK8Rj33XffuPe97bbbuOGGG/j617/O3XffjWl6l2TtK8wK7Hgc1t8Ftl3f6CJpyt8TFKKq5yoYiJZiPQuj20nLL30jga152cKvoHhuggeIy69RQ6n55YKrhHm9pqY9YZbplgs9VcJUlSgySLFRJSwUQ0+Li56XzQLRQJSdhsKavEI62FiKuBYTF5Fox9QDrRvBOrOHWwYj2CNmQ0qYYgviHLLUManxrcLZTBWMQs24msnVv1ylyGnJCkrbCLrR+HD0yaCGMvQZCmagbdLjakmYYYjzRMhD/55DwgaLfYyOPrXPwGjbtl2VrCfeQyjURnv78bS1HevZGpzzrHNujoRnEw53k04fgVFTXnfmS3raLLXrWXj6p7DlIdejW6uEFQobsZzJGN89ndyPzgTADLSj2Yq3JMy24ccXk7r1nYA410+ZIRlJQ8/h5BLiNdmf8RTwd1aOXL9eMPDly5eP+/Ply5fz9NNPs2nTJlatmrxbZrLHcm5zjtkbV1111Zj/r1ixgp/+9KccffTRkz5nqVSiVKqWE7LZBlWDVmGW4SYx8oaPbxsT2DohZEJzf0x8GT33g4H4ot2wALBJv/JfgDpJ2MhW+M2V5BLiS58MJb2LXQBXdYhLgtGQElYuoIX9NeY37Al7/jfot38Auju97Y4EIlIt1BsgFw4cdS7iURwECJL5vB5kTx6uWnZM3ffT9Z1E1J0sjZhEZh/i2XqcNQGCnDfgCbunMJ8XcxqfKjQ+EmoyjCFhSbm5mkIJKxgFonHx+QsGvL24lULz+ded2/jC0osnPa6WhDnTEIJBD0mYNOZT3sljj7+BSKSHU0950P15tpx1N0CzEj5sSqmSMEdxUxSFU095CKXm/FYyS64Z3tPzsvSEkesjk86wI79DjC6KHU4gkMQ08xQKL5JKHgz968jNVoAEeqAb2E6b1+XIrX8lUc7CovkiQ1LvR6n0k0ysHH9o+2FvhMPeSP6Z78GTz8yUI73E6Kj4wGUy43/h0un0mOOafaxEIkEgENjncc444wx+9atfsW3bNjRNY82aNXzoQx9i48aNnHPOOezcOXmS7/XXX08mk3H/1JZBpwXhODgKiDY05os+YWq+VML6o+KE7cjknkJVhdlYDZFShVm4LhJW6IctD1DoewrwYcfjKGHypak7psK2x3RHeqo6hePVTsRGy5FGtVnA0zUB0WZJWHGIklRfvCyROiRTV1Qo10+eR7NPM6fwR87PGN6SZ6pkXFPVhsqRuUqJEVMlFvDWBtDFMO/v1unO3wEOmZhCCcuXc0RkESIY9HY9zsXbGQc0Edzh3ZE2t9Qc9FAJc1WostgclMuDomtWYldhl7terzczDsYbK6fstcHcU9zDqUmDk5IQUzysxqQkCcvucD2wo6VRFEUlkz5S/H/0CXHMu+8ld7jY2I8i3gPPy5GxDFHbJqgEAJvHH34Zjz76WnR926R3c5tIZsqR+6Krq2vCqIfx/jilw/2Nyy67jNe//vXMmzePaDTKypUr+frXv84111zD4OAgX//61ye9/7XXXsvo6Kj7Z9u2yT9EviAuO3iKQ64BVatoE2fBOPEUIXGy8aUcCfDhdfCpftLtosOnLhLWvhje+D/kjr8c8IOEiceLSYJatxJWKYFt+pMTFkq469EbVcIMjZJDwrz0hAEROcS74e7IvqfQhoTi7HU5EhDDyo36B2Y7GVW6has4eoVkMEhPyKIUVd2Q4XqgSbLtpWcOIK6qLI9axMw91fyxSUiYZVsUjVzN2CJv1+PETThK10Rwft4Rrn6vQh4l5oP4vmYiGXIyod22DQyjuqbt+e0AzE2KcULrXvws6178bH2DpeuESwQn8evuKezhvLTBm9qLFItTz/2sG44Slt05ZnQRQCYjqj2jo0+ComB3r2S0Iq5j/ZYg5Z6TsGgGBUgGooBCMNwLQHGv4Nq94ax5fythB2Q58i1veQu5XP07wZ4eQRYc1Woipcsp702klNWi9rE6O8e2FhcKBUzTrOtxAN75znfypS99iQcffHDS4yKRCJGIPzunuhHrgOwO0IaIBCJ0x7rp1/rZkd9Bm5xYPwaOEhYMgu2TEgYi24UGjfnxDjj09eS33Akv+rDjcYz5lgmBBpQweZyjhHmWmQMQDBOV5E43dWzbrr8dvEad87L0BxBd+jLYdieleFtjdwxEKAUjgO2pOhcNRglg0xW2GdY2UG+alJPWXrIU2rc9BsdNcYcGsCCQ5cIenXIiBMP1x2ZopkPCvL2YRGTivWKXq0rYJOXIolEkXPNR80MJU2S5ybatfZQfB84M246w+LwEAnFUqaB7hd5EL2tLoxBIg5mlVNrtRlDsyInGjXkp4TXs6/s1ppln3ty3efb8Tud6zshRNIru+DTbNslmnyYeX0r/6NMkA1CxVdLpwz17bpeEFQfJBMU50NmkZzIiG25k9DFs20bXt6GXdqIoQbYa4j3wnoS1AZBUw4xQQAnPAn0zWnELjJcK8rNLYM8LZJcIO4HXI5QaxQFJwr75zW82db+pvFrr169HVVWWLJk6OXj58uU89thjrF+/fh8SNpX3bG84GWHFYuNDnqcdCfm7FsSJbE5yjkvCDukaxwMjPWF7VMD0UQmTqJ1XVi98k50jkoSZkoTVq4TJ9ejSr+B1WSsaEI9n2hYVq1J/GrRR9E0Ji7YthG3QcIDColMoJbpB2+OpEhYJRGgL2FzTo/NU5TecxQ113c+ZW6jbCjGP5kY6CATiUAE70wvn/Kju+725fRDdsggXvf0cRWQJL2AbdXnCxMgioQ4qSmjfOYItoj3cxlfmaQRKP6Nc/iCRyPjnGmfMWkcoio13cyNrMT81n7VDazGUOCEECXNS+R0lbF5yHpVKHlMS94nW2wwSoQSxYAytotGv9bMwJOZ6PvXU5QwNP8DBK/+V4uhqMsCI0uUtCY21C9tKRScji2kOCWtrOxZVjaDr28lv/i2jW34HQDp9OEN7hOLsqScMICYeLyXV9kpAXMOK2kvjHz+8BYY2kV0gqipOc8H+wgFZjmwWJ554IpFIhDvvvHMfD1NfXx/PPvssJ5xwAtHo1BeYM844A4A77rhjn585ifnOMVPh4YfFTMW/iewwtwtKnGwdSX1nfgIpXZ7wdiNMwb6RsL9+G352Cem+54E6y5G7X4AXfkd+SAQY+uYJkynidRvhTQMS3WiyXOK1/yrWvsj9t2Y2UJI0NOGR8mFNrgermQHe8j5eEsPasFaLSrWbawo4F1TdgqjXJEx1Hq/iXlimgmWVWRypcHDMIh72Nh0+FhZrCFCBlCjxkJu4pJYv54k6cyM9HN7toC3W7k4VMM2JS8gDujgnZWTHdshDU76D+Snh1y3IhPZSqRpXsT0nSVhqHqWSOI8GAkmCQe/OP4qiuF2atb6wjOzA7Ov7JeHikwAUIwd79rzyyV01LG0Jr5kzPzIQiLNo4ftYufJLRHdtZtfInwHo7j7H9fJlwj4pYfLcVVbF42vFl8Y/Xob55uSEk/2thP1dkbB0Os2b3vQmNm3axHe+8x33dtu2ufbaa7Esi3e9611j7lMsFlm7di1bt24dc/vFF19MJpPhm9/85hhvVl9fH//+7/9OW1sbF110kXv7rl272LhxbFAewI4dO/jABz4AiDLrAY+9yg4OCXN2d/vgpKswL/sTu+RYoDnJOf6sa8cTsPYPpLMibyZXzk3cLODghd/CL/6J/BYxhsmvcmTMFF/musuRnUvhoxvQlothy14rYcF330dAGuEbGphdrgajeq6EjYjvkD7JRXwiOPMmvSSGqqJiUlUHJruo16JWCfNyjBJAQJbvFLv+8Wq16fGJmLdTORJh0cIfwsSuKUFhjE+k80aeqOJPZyRAZ7SzJtttfDuCbdtuN3cyKC5vXpryHTgkbKgiDPmlcpUIuUpYah6lsiBnXqpgDsYz58/pfQOKEmJk9FFi1jCaBYG0dzM8XUhzfqYiNi+1lYnFi69i7pw3ESwM0z5iELRDzJ71qmrJ0nNjfhsASbmpKiiCVOULL45/vLTQZOUGdX+TsAOyHNkKbrjhBu655x7e//73c9ddd7FixQruv/9+HnzwQc4991ze/va3jzn+kUce4ayzzuKMM84YY/Bvb2/nxhtv5G1vextHH300b37zm1FVlZ///Ofs3r2bH/7wh2PS8teuXcvLXvYyTj31VFauXElHRwcvvfQSf/jDHygUCrz97W/n4osnb60+ILCXAdchVRMqYake+lWo2CZBJeifJ0yOYkkb4oJsWAa6qU9OYHTxpc/JUFTPlbDMPHj/I8Q3/Ape/GljERVUlTOvSZiiKEQCEYqVYmMdkoYmjOr4oIT1PQtAKddYaKb98PdcEuZ1p1koGKVsjRJWxUW9niypWmO+10b4sFSPVLMIf/h/cMFXpxwQXpDp8GXLBxIW6aQAqApY4TiB9FxRitJHILRvGn4+3+cqYQGP/WAgwqPXyn1XZYIh3tly1g1mTke6SCUPIRH3Ns8NYEFKzIvcXSqzJAqlkuiItGyr6glLzqNceByASMTjebqMP9s3Gp3DokXvZ/Pmfwfgf0dCvG7pQs+f21XCZGexo4TVQsn2sXRLkUXLP4wa6amWLL0uR8rZwilnoDgpMoj3xDCGCYXEddqyDBTLRJF2kKzcNM+QMI/R29vLww8/zCc/+Uluu+02/vCHP7BgwQI++9nPcs0119Q9NxJw50Zef/313HzzzQAcffTR3HLLLW5gq4OlS5fyzne+k0ceeYRf/vKX5HI5MpkMJ598Mu985zt505ve5OWv6R8SDZIwoK8gLqyzE7MJqAF/1iXr9vFykYASwLRNsqXsFCRM7M7y6tjRFp4hEILug4jvEifYupUwCYe0+TFhIBqMUqwUGyxHFvzzhHUsg77N6A0a/kvyggY+xGYEIug2hJlYWdkbjjFftxWiHhvhQ7JcpSom9mPfRznn827JeyIUHRJmQyjmYSAnkIp24mgsplUg8P9emPT4/MgWFECrQEfIozmJNeiOd0slzKZQGmS839YhJJlIht7Z59I7+9xxjmodjhK2XctBFLfsuKe4h7JVJqgE6Un0sH1IkLNI2HsS5uSV7SruGnP7ksVX09lxKlff8/94sjDAe/ywiDgkTBffm3E9uqPiuxvILCJn5DFtUbr0y5ifdkiYUSIWXYCmbyWXW0NHx8kA9PffwQsvfIRZByU5eF2enAzx3t+esL87EgaCiH3/+9+v69gzzzxz0rLWeeedx3nnnTfl48yfP7/ukUgHNPYqR85Lig6fnYWd43faPfDv7CiKVmDfSpHgKmFKKUcqnGKkNEKunKsGJ44HeWLIIr6cfu14nM6kupWw9XfC/V+jGBUnAU+7IwH++HFixWFQG8wKMzQ3xd/z7sjlr4C+Oyk1OFy6NlfMayUsalbQLYV0wHbJ1VRwyJpu4TkJc7oRVUXBOuOj1LOd0cqitFK2FE8HZgOkI22ULIioUDKyUw6gzuX7WKMHuH17hm+ec4unawHxPSnZAcBiVOtjvBRFxw/WFfVWFdwbsxOzCathhqSa7XjCNowI/+nC9EKCahBdlyOuYnM9X4Nzbt5Rs1FxkEofwfM5cf7zxafrDPEujgATeHSz0sKSmeeqYNFA1PPNFDGhdGUqBihClUumDkbTt5LPr3VJWDb3LJZdJmDa5KMZbMR1f39HVPxdecJm4AES8uQllbCeRA8KClpFc0dguLBtuPd6+p7/JSDatn2Ds1spZasdklOZ82U5Mit3YL7seO7/GvE1/wc0QMJGtsLWh1zlLO6xwZvRbUSlF6whElau6Y48QIz5zsDvIArB8dKvW1mTGkaTHiOzXiVMhn9qlkLU4x19tKYcap5y5ZQqGFRJmKGEYOX5nq4nFUq5Rvh8aXDK43N5ocikfQq/VBQFSxGfy6w2flnb8YN1xf0lYaqisiC9gE0lleDCL3LM0WIO4cYR4Qte2iZKoLouiEgs2thorHowmV93QBugbJVRFXXyjWqzcEhYXlwnRkujY8UMy4SsrJ7UkDBfzsFSCctIq8poaZRkUjQj1PrCstlnxBpyFXJx8V2LBCK+BerWixkSNoOxcFrRC/1gWYQDYdcAuiO/147LqsBRl7KzexlQPSn4Aln3Rx+tPyvMIWFyoK0vStgjNxFbfyfQwOzIpS+Di26mIANuvfaEcfpH3LmGDZEePyMqnHDUBku2JXl8xGMCBhBNdKO7HqP6SNjxx/2eL2+Lsrmkek/CgnFKMnjdNOt7nTQZElrxoagRCoR4Xo/waCGAZlrw1E/gP0+AOz417vFZaRBPj5cn6BEUafgvlMaPynDiKbpiXVQqOSzZAecHVnaspGQrrCtk3W5QRwlb1ibOiZomlbCo9+dG53y7I79jn2qOc67uifcQUuuMqGkEXStg5atILxWzlU3bFOOtHOT3iOuDokKyx51i4LkfDFxjfqYsznXZUpbengs57tjfcPDKLwJgWRVyOdFZn8pXyEbF+7W//WAwQ8JmsDccT5hVcVt5HRPqluxeCcSBEFzwVXbOXgH4rITVkLC6lTCnHCmJiC9fuGPeTvzg1wINKGEdi+GQ16HZwkDsuSdszlFEZVm5ofmRRtGfUUpA5EUR61IabWwKhEPaIor3F5JoMDZlt93eUNUwQ5ZCBYVozONIiGCsGsHQ/zxoI1PepywHVFs+vD4Ad2s9/HgoQklNQUWH/rUwuGHcY3NyekbKj/mxEgGZ+aWXx1fmXCUs2sUzz7yXe+5dye7df/BlLQe1HwTA2qG17m0bhsVr4yhhqdQqkslVxGLej6Cbk5wzYZXCicmYm/JpY9x9ELz5x0TP+hfC442Sy8oNe6oXAkF3fZ1R772CpOfC0W8ns/RsQJQjY7H5pNOHo8gu8WzuaUwzT1CJkSyYZCPinDtDwmZw4CEYkSOCBkTiPLA4I0LtNo9uHvcuzq7LVyWsthwZqbccKUmYJCK+SOFnfpz48e8BGjPmm5bpEiTPy5FUSVRD5chXfplSSpQuPFfC5O+o243NsCvJ1yjqw24+EoygS+Vpom67vWEbJbeDNOIxCYsGo3x/IMKtuxLEbn4TbLhryvuUZFeapRfqIm2NYozqvPwceNtv4bzrxz02W87yhrYyy2MPsXXb/3i+FoBwqA0Awxh/ooDjCeuOd2PI0nHAw3yuWhzUIUjYi8Oi5FU2y6wbXgfAwR2iHHboIV/nhOP/l1hsgefPP1mVYlrOyYgSsWO0HzPablSWSNPi+YckQe/wuHlEPEcvvOYbZI69Yt91SAwO3ieeX52PAlUStp9N+TBDwmYwHlI9QuWSmJCElfIYuV3urmth2odWaAeOobumHDlpar5tQymLARR8zoNp2Ji/ZTX6s7+o3t9rY/6uZ4llhVG4IRK29Cw3xd/zTkTZmVrCmuLIsXBJWMDbsTMAURvuzoUYGVjInDn1xceUahSHmMeRELFgjJfKAbYYIQIWdc2P1K0wTxYDZPMV8IHMjyFhmXmw9CyoCQN2YdtkK0USqo1K/TlnjUJNHMpPh8JsYvxzjdMd2RnrpFIRF+NQsM2XtazsWAnAImsDDz/2Bp7c+B0My6Aj2uGOLPIbrjl/AhLm/NwX2Dbk95CRHsAxg9UdJSwjSZj83nREfSBhEk4I7N4kTNd30tf3awC6KyJaJRsS57f9bcqHGRI2gzqwJCPGPO1Dwp7/Ndu+cQimbRIPxv0dWeSoDvoomVAdSphRBKviZoSBT184bYR4QZxg6i79Pfwdir9/HyAMvp4bQ9ffQbTvaaAJI7w83us1RSKShNmNkTBnCLkf5tmoGmJPRSW/cxPh8NQXB03bzhPPvo13dcnXqA7jfCNwvIG6DDx1lNzJkI0s45bBCH3pUyHoPVHtDIeYF7LIa1snP7DQT1ax3bFFfoS1AmRSK3m4EGRTafxL166CaA7oife4alk9+W/NoD3azrK2ZXQEbfLZp9g59AgAh3cdXv+81hbhkL0JlTC/ypEAv3kPfGU5ndIQ78zsBMRoIIC2BWN+5ks5EoRNRZ6Hi5Uihmlg2zbPPnsVDz50GqVSH6FQB91F8Z3Nye/KTDlyBgcmnvoJ/OoKWCO8FI4StiW3xQ1CBKAwwEshoZgtTC/098TjkDDbol1erByz57hwSpFS0UuEEp53jZQ89gAAYYdJREFU1wFw2/8jfvMFgChHTpniD1DOU5QjNuLBuPevWyhBVGbm1D9KqQJP/8z1YHlejpQkTFOo7zWSKJlluR4fSJj04pVsU3RzTQHDGELTNzMnZBNUAp5/nhz1UcMhYVMP8S5I0hzvmHoebjM4MrCdj/To2KP3ixvW/C/c8yUY3Gs6yPBL5FSVqPwo+xHWCsJwD1XvVy1s23ZJ2Ox4lztiyo/ZkQ5O7D2RwYr4pYdzoix54pwTAdC0rWjaDuwGNx6NwPHr7r1B3jIqSJCTZ+YL0nMBhU4ZpjKo1ZCwIfn5kA1Cvith/3MBqe+ejoJ4L0bLoyiKQkfHKfIAlYMO+iyBw94M53yRbEbmnM2QsBkckNjxBDx7K+wUs8d6Ej3EgjEqVsUtPQKShIkL0aLMIn/XFIyAvGhm5KDWEdk4MC4cU35U7Mh9+7KFk8QtcdG0setTnkp5itJX5HkpEiAcJyqJTt3lyHIO8zfvwZCeLc+N+TXei7JVf7lKc5Q5rztIwR07pKsKlKceW+SY9zULYj68b7FgjOPiFV7TVqS/PVRXOdIpgfvyOQLUgHiNDKdxYfW34L5/dc8NLoY2kVVV35UwJyW+dlSPuwR9iLJVRkGhM1x9PfwkYWfOP5OBiriMqqYgGi+b/zIANm36dx5afTpbtvqXH+l0Ya4fXu/eli1n2aOJ12dpxvtpAS5O+3/wyd10LT4L2IsYty+GWatEFyVVEtYZ80kJS3SihhKk5ebRsarMnfsWTj7pPk45+T5mzzofFp0KJ19VvS7MeMJmcEBi5QVwzhdghQipVRWVRelFAGwcrdkBF/pdJWxxerH/6zrsDXDU22iXu6kxHoS9US6AGvK/CyacdAkP1GnOL+cpyDKpH6Z8QnFiDgmrtxxp25SWnOn+13NPWE1reiM+NVcJ84GERUNJZgUtZs8L8OLGL015vFGbEeZ14CSChC2OmJyQrDCSCtalhFmj6+gJWiSzU5QLm4RDptzZmo7iNrRpzHH2njVkVbVmgLc/JGx2rJMzkgbHh/vQ9/JgOipYd6wbLLHeQCCB6kdEg8TxPceTiAt/WlfQ5qz5Z9KbFF3ihaI4Vybi/p0bl7ULErZ5dDOmVHM3jYj3ZnZ8tvej2moRSUEwMr46ecFX4H2rYeFJwDQoYZf8Ej6xk7TMh6sdoxSLzSMaHRsk7pC0GSVsBgcmlp4FJ18N849zb3I6gV4YrBldkt/FurA4wTkt2b7iNd+E195Ipl1cCCYlYXOPhk/1kz3vC4CPO55IEhWISUm+LnN+KUdRliB9IWHhBFGrQSUs3kHxouqUCa/LkaFImoAkhs4syClh2y4Ji/jUQRpVbbraVfqH7pvyeDeo1VaImpUpjm4csZrIDD2o1uUJW1B5hI/36nQOP+z5egBCsqHCdnLLOiUJ26scqe15AVNR3AHegYA/5chMpI3XtRucm6mwY3RsVIbrB0v0YMj8tFDIPyM4iO7Aj5/8FSwbYip87Mh3A2DbFsWiIENxH2ZXOpiXnEckEEE3ddcHtndWmd9wSFi/1j/uz23brnZH+kXCpO1kInO+iw13w/bHyMprx4wxfwZ/Mzik8xAAnh983r3NyPWxPiwMjqs6V03bWtqjwh82KQkDUBQ3nsL5cnoOac6OS49XvUpYUa16wjxHqFqObCQnrHaguOc+tXCciEPCKvWTMH3FKwCI+LCjjwQjFCXpMcypCU81LR8idZQvG0VQDVJBXExKQbWucqSKCCMNqf6UI51ICGz5OXKVsLEkLLtYeG8irhLmz8UtEAhTtsWT9OXGrsGZYduT6KEsSVg45G2MyHhY0XkIqeRyAKKWUINKpV2YZhFFCfoST+EgoAbcxiknKmPdkIjJmJaN8Z2fpuveLwM1njBDB6vqgysYBdeC4Jy7/cK4cRkOLBN+9Ab4r7PJyWvHjBI2gwMTlbLwhckkeIBDuw4F4PmB511j9UZtAENRSAUTvufRADJ2Ik+bDKYsGAXK5uT+IqeD0jclTJKwmDSE1kV6Snl3RqNvSlijnrCaYz1P8AcIxarEsA5yAYCqUpp3rFiTDyQsGoi6JMy09CnT1StGtRwZ8+liYsngy3JQqascqSIUuYhPylNUkjDVksS5W+RfsfuFMRfa0ZWvJIBNyDHm++QJAzAQr1F//qUxt9eSMEOOcwqF/SdhAMmkiKvI50Vwazb3LACJxHJfy6FQ3QA/3f/0mL8P6zrM1+cFYMcTdO1aA9SUIx/+Nlw/D+7+PFDtjIwH4/6cWwA23w8/voj00EvABCSsXIDeIyAzn6zcLM+QsBkcmNBH4Kaz4McXgSkuTCvaVxBUg4yURsSssnKB51RBgA7uOGh6WrJv+39w/VxSj/0AVSpPE6phT/0EfvpWsttFmcY/T5jY8celLWxKJaxSBrPkrzE/FCcmO7Lq9oS9eAfa94Xq5A8JS1SVsIlKBePAr8gMEOVIraZxzcmVmghGrRKW8afrzJazESt1liMDivABRXyKYYhHRPko4GR/dS2HYAyMwhg1LFvOuioYQCDgjzIHgFT9hgpjZybuzItZhb2JXtrbT+CQVV9j/rx3+LeOGiQTe5GwrCBh6ZT/ROiY2ccA8NiuxygaRVcRO6L7CN+fm1mr6DLFZ3CkNIJhGrBnjfh8yOaI6cgIQx+B9XeQkTEVtZ4wF9E0vOc++OfnGC6PAP4rc/VghoTNYF/Eu0ANATbkRehnOBDm0E6hhv2176+Q28UjUXHBOLrnuIkeyVvI0UVqadSdQTYhCet7BtbdRrYg59n5RsJkOVJ6sKb0hJVF27wbUeGLEhZv3BNWyqJV/ImnACAQqq5pqpmf7pry7pgjP0hYJBDBRqEs0ykmSmF3UDu827cdvSoe1wwqdZUjQ9KDFYv4c4FLRITfJygVN9QA9IjzADKLjv4XyQ2ux7ThEWMBixd/ENWPOBiJgCx15vSxQ7y35EQsw8L0QmKx+fT0vJbOztN9W0ctUilh1xjNPolt22Sz4rVJpf0nYcfJ8++aoTXcueVOTNukN9FLT6LH9+dm9ioyluVOLh3UB+G134L3PwJHvBWoKmS+dUaCO24vI+f3TugJAypWxTXm+zLLskHMkLAZ7AtVFTO/ALI73ZtPnXsqAA9sfwAru5OHY+JifULvidOzrtM+Av+yE879klv7nzCm4rCL4FX/TjYhLk6+kbCIKLvE683lKgkCUpRhgf4oYYnGPWHlArpUM30hGIpCVJZsS/WWI/esQX/ht76tyR0q7pCwysikx9ca82M+kEKoRkKYAUnCJskvs23bJWHxqD9BycmIuLiFaycd9EqFpe8p8fe915P940co2QpbAytZsvgDvqzFgePzKupVI7hlW2yVHaK+Tu6YAG1txzCr+5UsXPheTLPA6Ohj4vbMMb4/d0+ih4PaD8K0TT754CcBeNmCl01PdWLO0ahAZ0V8Pga0AQgExWzJtLiG7C6Ijfzs+Gz/1iG7IjO62EhONk0lW85iyyw+5zqyPzFDwmYwPtKypbeGhJ027zQAHtr5EHdtv4ehQICUrUyP7A2C8IQToCi0R8SJeMLA1nnHwLGXkQ2IrkX/uiNlOVJ2yxWMKQzbjhLmkDC/lDDXBF8/CdNks0As5I/KE+kU3Vp63Y9vozmeOz9ImDNf05kfaUxODjs6TiWnpdltKMScRHCP4XQV2gF5wyQXE8vSkVVtEvFeX9aTluQuotqYjmdu3vHi702yozQYcUORp8Njk5BrKparkQh7insomSWCSpA5yTkT3dU3BAJxDjvsRubNfSvDww9hWWUikV4SiRXT8vyvX/76Mf+/cNmF0/K8zD4EIhm6pG1lvBBdJ9PN14kqCaGyuUrYeOXIp34C/3EkI3d/DhCfVV8CvBvEDAmbwfgYh4Qd3HEwB7UfhG7qfHjTrQCcF+oiFPDXeDoeJu2CqYHzc/+UMLGOhGwQmJKElQQJK8jXzBclLBirKUc2QMLkztmXciQQSYqdsF7vWWf+8WjLRBCkH7lcTolTczokjZFJj18w/zK2DbXzUjngS1grVEtthCPwwaerg+vHQUVmd1k2JBP+zAhsi8/lG7sjfHlXlKK8wLFUhJGy6xnI7YbXfYfsqUL9mo6W//a4aAJSrCLDutiEvZR9CRBjfIJqkG3bbuGll76Dpm2f6GF8Q7k8BCjMnv2qaRtfdNFBF3HGvDMIKkGuPOJKd66l71ADsOBEukyxk+l/9udi2spLD7iH7C5OgxIWbQM1SEb608a9LozugOHNDMsh7weCHwxg/9PAGRyYcElYdSaZoihceeSVfOieDwEQtSwubzt8+tY0tBn+8hUIRmjvkEqYPoEStv4uCIQYkabQtmibP2uSg8WTRhmITE3CKjoEIhSDAaDijxKmqkTlCab+xPxqx6ZffieH3NUdUUF1/X6QMOcxCwQBY8pyJIAuuwRjfrxvQCiYYUdBoTPRg902+SgwvSQ+2yUb4il/1J9oMM62ShTDMihUiqQiaUh2w9xjYMfj8PjNcOY15Kwys4MWC+ztjIw+7msZLiZLpAnVZsPIBo7rOc4d2+OUIrdt/wGa9hJtbccSi03PMG0Hc+e+mVhsAfGEP6OkxkNIDXHj2Tdi2/a0ET8Xy1/B7IfF3MxdWx+AXVvdoG+YJiVMUSDeRcYQ34lxvcJ5kSM3EklC/sDwg8GMEjaDieCQsNxY8+vZC87mS6d+idcsPJfvHfsJ5p149fStydDgqR/BC7+d2pj/m/dg/+A1DEvPWIdPxmVHqUhIT1huKtP5kjPgU3sozhclHb/GzUTfdTcAWr0jgowimuozCZPdfnpu5xRHVuFmlwX884T1WTHa2k4gHO6aej1S8YyF/IlgCIXb+bfdMTYmXj/lxTRfFK+jbinEU/6UIxVFcdUtJ+4FgBOulP+w3Z8tj5rM1e5k27abfVmLg3Cok7IdxLSr6fBrBkVMghMqXQ1r3T9qR0fHyUQj02CM3wvTTsAADn0DvbKk31cpiHPiQee7P3ZI2OyEj0oYQGo2nVKRczoyxyAnSNhwSCjgjqVlf2OGhM1gfIxTjnTw6qWv5otnfoWjDnsrdE5DIKADZ4i3NkybDF8dl4TZNmjDFBQFwxZeLd+UsFAULr+d5InvA+ooR0poUuHxRQmjSqT0il7nUPECmuzY9EN1AogMiNb50l4jbybEUz9B3/0c4A8xjATFyfiuXIhjjv4JPbNfPeGxlmUwNPQgwaAG2L7kloEYNA+g7X4W7rwOtj828cFWnK/tjvCTgRChuH+dZ04pf4zZ+dA3wHFXwDO/AENntDRKzOegVge9vW9gTeY9/Gw4wvoRMTNx7ZCIhljVsQrLqrhxI/uLhP1DId7BnKXnArAzGIAzrnHjKWzbdsuRviphAKk5dMhypFbR9o0Lkp3+I0FRADxQypEzJGwG48Mpb4zumPy46USsTfxtW7RJFWNcY34pB7bJcEAazYMx/yIFABacSEKOUsob+bru4vfgZUflsbHrG5hdzotB1n6uSe6E9WCdHsKRrW5OmC/lSKc7sqJja5N7C8vlAZ586p84bq5U5nxq9HBe++Lgenjw30XJbwLoxVG2lgP0l1ThzfEJp8ZzXNpRYnT06eqNqgoXfBU+8ASEooyURojJTk0/B2aDUHsO6RKREE/ueRK9ortkbGXnypqyskLIp/y0GYzFnOPeC0BfWy+c9H739mw5644p85+E9RC3bSKK+C7so4blBAkblmKhbxvzBjFDwmYwPtrkqI3sDjew1UWlBHd8Eh7+HvgwQ29ChGIiKBJok1+00fFSxWUJcjgkLrLTUftPyvLUlErY0z+Hn7yZovQnOMqH14jc9Tn333X5wmqM+X4R1shCMdpGT9dZpqnxqfnpCbOxKf/04kmPdcpbJRNAIeZTa3s8FEfFppzswDrxPaL7bAIUZOk7rvhr7V0QLHBswqSgvTThMcP6MDFVkLCQzyQMqgGl64fXc8+2e6hYFWbHZzMnMQejLC6+oVAbiuIfOZ1BFb2yI3V3OUvFrsaqOPM82yPtvmT9jUF6DgrQIa3uY0iYbVc9YZjumg4EzJCwGYyPVI8gPLYJI1vH/mx0Ozz0TbjrOl934OMiLrxd7dKDMK4SponbRmLiYuA7CXv2lyTX/RGoQwnb8zy8+EeKNXMa/UBozwsEG8kKq8kJ86sc2bAxv6ZE6sfr5Dymgk3OGEXXJ/aqjSVhEPNpFx0Pxvnn2TovTz/N0HHnw6JTJzy22CYufPF2fw3gigyQ1coTNMEgbAHVcqT/JKwr1sXSzBJsLD67+rMAnDTnJBRFwTAcEnZgXGT/EdAd7yaoBjFtk/5iNb9tW06ELc9P+TNhYgxSYnPXId0XY0iYNgzSzzlkivPhjDF/Bgc2FAU6Fot/D20e+7NgBE58Pxx1qThuOiF9Ye0ygmFcA6YkYUMRoTL5Oi4D4IlbSD71c6AOJWzVhfDq/6AgXza/PGGc/pExvrApUc5Xc8L8MuYHHRJWb8dmwS2R+rGmoBokpIZYHLF4enk/Tz71TxMe68RXFCX5942EheKUnHmWlckJfWHoXt7SXmJVpP4h7c3ACZAtTRDhYZgGeSPvKmF+e8IA/vrwebw/vYauoO1+51656JVinSVhBA+Hu31fxwwEVEWlJy5I0M5CdTPjkLC5qWmYLSwtNI4vbMy1QfrBiLUzIr2NM56wGRz4aF8MahAK/WNvz8yD874E5//b9K9JkrCuiiiDjmvAdJSwsCxd+l37X34uiZXC1J0vT6GEzT0a6+h/QpM+Cb/8Vyw5k6jsaqtrfmS5QNHvcuRW0caub32oruMrpTyGz9llsWCMoimeozyJ0uOQsIIkSH4N8E6EEui2eI5KfjtMEgprFNdxQtKkJ1hnB2yTcEiVMztzbzjNMfFpVMIsq4SKyUI5EeP4nuM5cY6Y3FGWIa6RiM8epBmMgROSuyNf9RFPqxLWtQyOeQcdbUIZHkPCZGckyR63ejKjhM3gwMeF34JP7IYj37K/V1JFQsQIxPWsSxYGtcGxxxTFl29IGsB9r/2ffBXJVwgPVj3dkbXKlG9KGDWJ8PUoTy//DHrXsjH383w9qng/9DpjM2pnTPqV4h8PxclLYlWpjGBZ43scnRKXc6xfOWHxYNwdKl655zNw69snPNYcFmZ0pVSnstgkwiFBqszK+J9t56IWl0rqdChh4ZDoBr3uuH/me6/4Hv959n+iytJ1qTyjhO0PLM6IyomT2QbTTMLaF8Gr/4MO6T0dc12QSpidnOXe7ussywYwQ8JmMDFibWIO2N7YsxYK+46nmBbIQa1KcYDOqPgSDeh7raUovmTT2YrsGPN1U8ewjIkP3Hw/hQ23AxBQAr4pPAysJ1oRZKcuT9iq16LFhNncNyVMNiHoZp0kTBJaBYWwGvZlTbFAlIKFE3fler/2hqOE5Sx/1cJEKIHukMKA4qq648EyheqqWP6exiOhNgBsa3wS5sxvjavT0x0JEAqL734InZPmnDRm46AqIcLh7v2S0/WPjCUZoUBtHNno3jatJEzCuS6MUcJGxToK6R63MtAVmzoXcDowQ8Jm0Dh+8Tb4t6Ww8Z7pf+6ELDEU+t0v0T7zyiRBHJZ+It9lZ8skXqkSi33Ko7X408fJ/foKQFxwfQtXfOG3xIZFQ0W9qfmaz80CUZmtVbLr66jV5OsYVUO+vU6xYAwbBeQA4rIxOO5xDgnL+kzC4sE4uiSElaACxYlJmB0TpFZN+psIH4+Ii5pijf85EkqYTVgRr2Ew5D8Jc4J1y+V936+lSz/Maaf+lQUL3un7OmZQxRJZBtw0KnIAC0bBLU06BM13GBodZfE5HUPCZHNZf1J8bpKhpL+xRQ1ghoTNYGIYOvzmSvjOaVCWxMI0wAnb7Fo+/WuS5Ujyk5Ew4WEbRlwUfDfm//XbhP5tGVH5dZq0Q1LPUpBlk6RPqesAhFPuEO8pPWGmAev+iC4VDd9ImBx2Xpqg5Lc3NKmExfxSC6mWFR1eaIxzUQcwKoIMZW1/mxdiodhYJaw0OkkMjLg9GPXX+5SQ3qqAPX5Xq1DCFP5PuYDTT3uSSNh/L1ZYKmETvV8zmH4szYjg7m25bZTMEutluXxWbNb0meB/fBGdd3wa2IuEHXkJnPslBnpWAQeOCgYzJGwGkyEYgfV3iEG9/WIsCEObwKpAOAnpaeh42RuyHEmh363pT0TCBqX3yPcTgJwfmUBcPCc15+uj5KVClwj7kxEGQCTpkrApy5H6KPz0zWhF8Tr6Vo6Uyet6TY7QpMuSCl4s6F++kPO7mhXxWo2nrAAY0rRfMMfez2uIcqT4dzko1b8JSpKqLV6fSLjNl7U4yMTESKQwBqa173vnGp2j7YRCaRTF/8uK4wkrlfunOHIG04WuWBeZSAbLtlg/vJ4Xh8WEjOUd07hZ71hMhzqOV3jBiXDS+xlIdrprPVAwQ8JmMDEUBc79Irz5J9ApTNv0PSP+7l45/fEUAB1LYOWrYMmZdMcEIdvHmF/KYQMDssV/VsznnblUeJKyjDShEmZZUMpSUKdDCUsSl/MspyRhlglzj0GTmW9+K2G6VCinQjUt37+ygdOdKpttJyxHHn7491h22A/YVAoQUAKE1DpT/xtENBClJNW2clg+xwQkLCDzjiIBHz9HQHtiEVvLKjsNddzZqE535HS2/Eek36tU2jXmdtu2J2yumIG/UBSFI7qPAODx3Y/zdL+YsHBwx8HTt4jzv0rX+x8FhBK2tz/X2bDPkLAZ/O3giDfDygsgKhPCnTEqc4/ZP+uZvQre/GM4+1MTlyPfcx+5j6xDl1/ArrjPXzg5wiYps8sm7JAs5wGb3HSQsEiKuFTCJvWoAaRmY11xFyXJqX3rjpQXad1xwU8Gy3SHj8f8ivGgSjidENaJyluRSDd2/y40WyHmo0dNURQsVbz+Rkg2xRT3XZNdLhJUxKJjPncjtqVX8d2hTn40FBl3VuuwPv0t/9GoUOdKet+Y20ulPu65dyUPPHhKfTNTZ+Apjp51NCBI2KO7BBk6bvZx07eAYJiOWCdBNYiNzUBxAPQsvPA72PkU/ZpQTmdImM/YtWsXV1xxBb29vUSjUVasWMHnPvc5yuXG8nS++c1vctlll3H44YcTDAZRFIV77713Wp77gMUOOVB43rH7dx1Uv0jOF6sWA3I247QYMCVBTcpSzYTlSDliqSA7Tn0nYVIJc+ZUToZa875vfifpzdMVxBiRyWCZaKteC0DUxxgPl4S5Stg44b8S2kv3i/v4fHEfsVP8eDBMcli20Bf27HOMld+OrGqTSCzwdT1QJVjjkbAhfYjlEZM5QzexZs21vq8FIBKRJKy8B6tG7RBTD2wUxT+iPIOJ4WS13bPtHvoKfUQCEY6cdeS0rkFVVGbHxZzaXcVdsPs5+MU/wc/fJkgZIuH/QIG/Q8f2A3bt2sUJJ5zAtm3buPDCC1mxYgUPPPAA1113HatXr+a2225DVevjnh/4wAcA6O3tpbu7m127dk16vJfPfUBh032w8c9w5FthxxPitvkn7L/12DaUsnTJC8M+ShhVYjYtXzaphCUqFQiFJi5HyqTmvAyR9dUTFk66ZGFKJYyxRM23YNS4IBVFRcE2NJTwJOQqGEY/+Hx4cLW/JEzmj+3J2rx86VuJz79swmO1jsXQDzGf5n06sAJpHs0OUQnNBtZAfjwS1sdODeyAwoqI/3lHbZE2duR3uKpXLQa0ATIBG9XoQ9d3jHNv7xEOd7J40QekIlYtbzvPH43OmZZ1zGAsVnWsYkX7CtcP9oqFr/A1C3EfVMrwi7cxW+tjRxB2F3aDkoJ5x0Pb/ANSCfu7I2HXXHMNW7du5Vvf+hZXXnklIHwCl112Gbfccgu33HILl1028Ym2Fn/4wx845phj6Onp4b3vfS/f/e53p+25Dyjc+Wnoewo2/0XMkuxeCe0L9996vnkMDG2k89JbARjShrBsS4Q1Dr8Ef7yG/pjIlXJ8Y75CGvOTlTIQmrgc6ShhoQhgT4MSJknYVErY2v9Dv/1j0KYQC8b8i4OICSXMVBQMfYTwZCQM/yMzah97xFDotudCYt9W+nx+Hes3XE9WjsGK+UzsnYtWUX6uxiNhIU3j5l1h9gSD/Czifzhqd6yNtGoxqO27Ee3X+ukNyYywkD+DzfeGoqgsWfLBfW7X9O0AxKL+xnbMYHwoisKnT/o0H7rnQ7RF2vjg0fu+R74iGIbtj9GTsCGZEAPEDz0PrrgTgF2/EZNNHKXsQMDfoCwzMXK5HD//+c9ZsmQJ733ve93bFUXh+uuvR1VVbrrpprof74ILLqCnp77AP6+f+4DCkW8Vf++UKthhb9x/awG3/NdVMVAVlYpdqZrzR7fDi3+if8+z4pjp2PE4Spgs/02ohOlCCcsFBEH0l4RVjfmFcczUY1AcRMuLi6uvhKdG+XM6MSdEuYg+ut33NTnGfE1VYYLXSdO3MzR0P5XiWt/XU7umokOunLl3tSjsmZ4GD4lz1L/yubk6+dxzY24vm2VGS6MkZFBrSAa77i9UlbD90Lk9AwCO6D6CP1/0Z37z2t/Qk9gPgbk9hzK7Iqwhu4rVTYNt2/QVhIfQGbF0IODvioStXr2aUqnEK17xin128729vRx22GE8/PDD6Lr3Yz68eO5SqUQ2mx3z54DAUZdCp2wzzsyH467Yv+u55Fb4xC6CKy9gVlx0PrpDYzuWwKv/g/55wiA6LUpYKAZqkKQtSc9ESpgsRxZkkn8y7G9OmGvMn4qElXJoPg7KdhBSQ4Sc2AxtYu8VADufRF/9DcC/8ihUf19NUaA0/utULokShkVkzH38QiKU4NSkgRnZRCmk7Du7FTDzu10SlvC5PAqgqOI58vpYQuhsflIB8fkJBdt8X8tkEJ6wGRK2v7Ff/Xg9h9Mj2513F3a5FYhBfZCSWUJBcYeNHwj4uyJh69eLcLjly8fPJVm+fDmWZbFp06YD8rmvv/56MpmM+2f+/Okb9TApwgm44i5404/h3fe6Q7T3GxJdgvgAcxJiR9OXl11S6TlwzDsYkMnI0+IJUxSIpKtK2BTG/Px0XDwDQRKKIHvaVEPFSzl0nwdlO3A6HYvxtskPtAw0+R771a0p1iNJmAobrUd5Yc01mObY8m1ZZlEZe8RMvFh56vmgrSAeivOyVIWouh49GhhXCcvlthNTbBRsUmH/y5EBOYpI26t71PFjtoeEuhsKTd+5oVjczLbtP2T37tvc22Y8YTOg53B6TKmEZbfBDQvhO6fSlxPK+qz4LEIBfyJmmsHfFQkbHRUXuUxmfF9COp0ec9yB9tzXXnsto6Oj7p9t27Z5vs6mEWuDg19VTaw/QNCbFF1SrhIm4fy/N9E7PQuJpqeOqHA8YZLw+F1GikvyUpxqqHgpK5Qg/Fd5YrJ0q011ElxyJtoxb/d9TW7pTwmwLbiRvr5foutjfU+lsiAaZUOc2KM+K0+pUErMswQqr/43kdO3F3aYz3L9PI23dxq+zdWsRViWGcvlscZ8x+icDoqMueksR46OPsGLL36GHTt/CoBlGWiaOGfGYvvRszqD/Yt5x9IrlbAduW2ADWqQHbI0eSCVIuEANeZ3dXUxOFj/OIp77rmHM888078FTRMikQiRiH/p4H832PkkPPJfkO5lTpv4Qu3MSxK2ZTWYJfnlg7nJaSpLRNtIZoWBekJPmFSkcooga76WI4F4IAZYFKcKay3lhCeKaregX3AIVV2xGW5Y6zQoYdE0EVWhaA9RKvWRqDHoO0qYLklYzOf3LRlOolkKYGOkuyC1b+mkKGdZVuzgtJR+YuEuNMCsjLVIOEpYUm7np1MJi8fFmJxiQVQXNG0rtm0QCMRnlLB/ZHQsZmFqAWAwbGqMqiqZxae714hp25jXiQOShL3lLW8hl5vCx1IDxzzvqFATqU2Ox2oitaoV7M/n/odDYQCe+hHMPozes0X3jWO45J4vom95gIFFopQ7bbueWDvJkSnKkS//DJzxcQq/fTVUcv4rYW/+Cdz2ZopTJdSXcq4S5ns5UkZsaUOboGfyEMdp6Y6UpFNLzSLa1k1x+IF9UtjLUgkrliugQizs74DqVDjFqJwfaVRGxj2mbAl108B/FQwgGZuFBmAVq53IVElYVDXBnm4SJohyqbybSiVHobjBvX0mI+wfG/GDX8PsLT9ndzDI5lCQI1ddyEsv/RaAhekDSyU9IEnYN7/5zabu5/ixHH/W3li/fj2qqrJkifcT3ffnc//DIS2JVXaH6wlzlbDsTvpkaSQejE9fivfLP0Ni4Fl48suTD/AORclXxAXUb0N1XJKFKXPCSjmK0pjvd6ZPvCAu2tqOR2DVmyY+8PGb0Z1w1GmIqNAqGhGZwq7vRcIcUlaoKBCGWMTfjVQylKTPIWEv3Q0vvAgnvq/6ubcsyuhAEDswPRlMqWgv/UBMsRjWh925rbsK4rUJ2yKMejrLkaFQmnC4m3K5n0JxE5n0URyy6usoPo2UmsHfEE58H4s2/YLdQXhp/rEcOfdoNj39NQCWtB1Y1+C/K0/YiSeeSCQS4c4779xnZEVfXx/PPvssJ5xwAtGo97v9/fnc/3BwBodrQ8yJiOypHfkd2JYFuT52yu7DOck507cjnnMkqXnHA5AtT9zVatu26xnzWwlzVZ6KhmVPooaVcmjK9HTaOSqSNlXZc89adNlB6ac653rCjCLRkGjiqFXCbNt0/z9qSqLqcy5XMpyk6HjC+h6Fh74J/euqBxT6qTjDvQP+qnIOomGhcMVUe0w48s7CThRsKulT6e19I+Gw/8GxtUgmDgIgl32WSGQWPT2vYfasV07rGmZwACI5i8UHvx6Alw56ObZts2lElK2XZGZImG9Ip9O86U1vYtOmTXznO99xb7dtm2uvvRbLsnjXu9415j7FYpG1a9eydevWaX/uGTSJaAYkWZhnqwTVIFpFY9fwejCKbJMkbNr8YBIZqZBky9nx59bd/gm037zbJUS+e8Ke+jkANvaYsUT7oJyn4ChhPs5pBIjNPgSA4uxVkx9YWyKdDk+YUSD8Z7FT1mvmEZZKu7FtE4WgS8L8bl5Ih9MUHCWsayGcfDWkanwspRxWRJQhVZ/nRjoISoUrEagp/SMUaBuF7nnvZdXB/0pwmtbjINMmxqeNjD42rc87gwMfS7rFuWbd6AZ2F3eTM3KoijpTjvQbN9xwA/fccw/vf//7ueuuu1ixYgX3338/Dz74IOeeey5vf/vbxxz/yCOPcNZZZ3HGGWfsMxfyhhtuYO1aEdC4evVq97abb74ZgCuuuIJTTz216eeeQZNQFMjMhYEXCeV3syi9iA0jG9i46wl6gY0xQdCmdcfTv470+tsBqFgVtIq2b2lv3f+RH90CC+YSUAK++6+i/etQbBtbUShWihOXGktZitKY73c5srb8NylK2WnJLnMe21YUAnoZiO7jCeuZfSFWfiea+qjv6wGhkGqShFU65sERXxh7QNcy7EQazGECwenxmIZDQnFOqjbbZau/ZVv7PfyyLXMMAMPDq7GsCqr6d3dJm0GTOLz7cACe6X+Gp/qfAmBF+woigQOr+e3vSgkDEYz68MMPc9lll/Hggw/yta99jd27d/PZz36W3/3udw3NbvzTn/7kjht68UUxC+v22293b9uwYYNvzz2DKeD4Y0Z3uGRrY/8z4u+oIBJL25ZO33p2PEHsjk+5u5pxS5JnXkv+FDGPNBFK+F4qVQ9/EzGZzj+pL6yUmzYlzE2or6NjU5+G2Izax7Yv+gUgsqYcJTMancMhh3yVwzJvo6hMz2tUW46cyJivWuL9DE3TmKB4fClbYq/gx0NhtsnO4/5iPxWrQkAJuKHJ0422tmMJhToolwd46unLyGaf2S/rmMGBhxXtK4gGomTLWX754i8BOLL7yP27qHHwd7lt6O3t5fvf/35dx5555pnjl45gH2XM6+eeQQvIyCDbkS0sa1vGHVvuYMOwIMUbZXr3srZl07eeruUoh7yOdOl5hkyd0dLoviM7Dr+Y7J6nYNuvpiVgk0WnkHikjaLWP3EkhG0LY36qDZgGT9iwKPtra34PR1018YE1sRl+liMDaoCwGqZslbHiokxRqWQxjGHC4Y6a9WTdhHq/1cJUKEXeUsibCp2BJOR2gTYMsw4GhE9NtUsARKbJgxUKpUl1ns3GFx9kbl4oYU4W34pUJ6PDDxKLLSAeXzwt63GgqmEWLbyS9Ru+yMjIYyhKYFqffwYHLkJqiGN6juHBHQ/ycN/DAJzYe+J+XtW+mJFmZvC3iU5JsAbWs6J9BQDPFXawKxBgCLE7X5yZxgvCvGPhoptJS+I1kTnfuT3tc8yBA3cY9ERKmG3DeTdQ7Fo25ni/EJPlIm2yDlIQSpg6PbEZzu9csm3S6aNobz8J09xrffqo20HqN1FNhpNsKgX45M4YB7ddAV89CH58kfvzyq2X4Gio0Vqi6DPmJcVQbKccuXlUTBA4JhnmqacvZ8PGL0/bWmqxYMHlHHfsbzjpxDtJpQ7ZL2uYwYGJ1y59rfvvtkgbp847dZKj9w/+LpWwGfwDoEuOhxrcwFGzjwJgg5nj7oQoL63sWOk7oRgPaZkIvw8J07Ow/VFGs6KsnfE55gCA/B7ipkiOnlAJU1U4/l0UBv4C2k4SQZ9jM2RHqGaWJz3OLuUoJmXUyDT41EZKIxTv+QLHnfhp6D3c/dnw8F8Jh7uJacPTVo5MhBIoKNjY5BIdYmJldgdUyhAMo+5awyM6vBgLc+6c6csdnJ8S6vP23HZMy2TjyEYA5kSTYEA4vH9KkgDp9OFTHzSDfzics/AcHlnxCI/0PcJHjv3IAecHgxklbAZ/q3CUsMENdETaWZoR/q+vdohW+qNmHTX9a7JMMjK3KVvai4T1r4MfvZ7sw98CpkkJ2/IgsUFxoZwqK8whab4n5kuSWrSMSY8rlXJY00R63GaBdbfBwIvu7bZt88yz7+OvD59DobRl2gZmq4rqxpfkw1GIpMG2YFCU2wOX/Jr1qcN4rBicNkUVQMk9whvbLWYHNV7KvuSSsI6w2MtHwgfWSLMZzCCgBrjupOu47fW3cdaCs/b3csbFDAmbwd8m2heBoopRQPndvHy++IIZ8sL9ioWvmN71WCZ8vpv0hruBcZQwmXmVDYnSmqOY+YpohricZzmhEqaNwEsPUNBHgGnwhEXbxNPalYkPsqwx6/V9nqVDwhTFne8JIim/UhkFVKLH/vO0dZBCNb4kW8phz1opbtzzgvi7cynZgCA+06KoSgwM3M6pSZ35IYsXBl9g7ZDoHE+pYpxTONw9bWuZwQz+XjBDwmbwt4lgRBAxgD0vcHHnUaRM0VJ29Kyjpl8JUwMQSZKWaxgt7TW+ShODj7MhIYdPi4IRzRC3xHomHCq+61m4+QK0gph76Xt3ZFQolZptTnyQUaAoz0yxQJSA6q/Z2iVhqgr6KKapMTzyKNns02LN8SWU09XMOb+JKggS9r5unf7n3shwjyzz7X7e/floWXy+plMJc4JYUwH400t/YlAfJKSGiCCaBMKRGRI2gxk0ihlP2Az+dnHYxVDKQqqXWdmd/HxI44nO+Zx19o37Z3ZcrJ2MJcnW3kpYUSphwSBUpknBiLYRl52/E0ZCKCp0LqeglgHbfyVMjrvRsF2P0z4o5SgqzkBx/1WnWiXM0oe4/4ETMM0Cs7pF8nomc5RLYlVF9b1RAESHJDaASblNkptdz8CTP6J/9CFOCO3khYg9rUpYWJYbk6rN7dv/AsChXYdilEVZMjKjhM1gBg1jhoTN4G8XZ11b/fesg5n/4Y3MLxfA5yT6CRFrJ50fBCYpR8qS1vQoYW3VcuREQ8UXnULl/X+l9EOhHPruv3JImKoIAh0cx0dUO8vy/7d35+FNldkDx783XdJ9L5RC2QtIZVEQBFFAEUFFUVQGEZFNFkdExHGZUcAZBxwVHHXcEIUZXHDE7QcqgogOyCKKgggqe6Fs3dMmXfP+/rhJaEiKBdt7aXo+z9OH9t6b5D2F0pPzvve8dTweqHIHqUXDUmIjOjGD/PzNHD/xCQDxcRdR/N1iACKDww1J8KNCoyiy669TFuv6Hh3YAAWHOBGbSf/UcMqcxq4JcydhscEn478i7XLKTmz2Oi+EqDmZjhSBQ9PAalICBhCeQIxr+s9nYb57OlIvbxiUhMUQ4doiye5a8+VP1fVXdV4Jc/392DWL1/orL6FR2M8bAhiz/sods3tMjRsP8ZzTtFCSkgZg36RvRRZuMebuqqiQKGyurvll1hCISILyYjixC3uoPj1rc2rG9Jtzca/5ahfdGNBbVgxpdQVKlbvOSxImxJmSJEzUb5XlsO0dOPyt2SOByKSTSVg105EF6GuhDFmYHxRCpBaiv3xpfrWXue+cDLYEExIUUqdD8nTMt2jVJ2GxTXFcMNLreiPGVGTRwJ5Lk5ShREd3AqB1q7sJCYnF3rIXAJEGVVmjQ6OxufaqLCvPgU4n+4SVROtTkBVaRJ2vl6sqLEzfpSI2qIKVw1bywdAPCFH6NG1ISDwWi5+pZSHEacl0pKjfvn4WPn8MIhvBrUuh6YXmjSWqEbGV1SVh2fpxV2uG2FBj1vKEu7rN28ts/i/48h/Yf1oGEcYsOK+6/kqVFFDdxF5xhf7L3cg7EYstFrDnEhQUQfdu71JRUejpml/cYzysuZtIg6b/YkJj2O+uhJVnw2VPwomdUJxNeWQFVDhwBhm7WXaYVU/CSkuPkxKRjMUSgq1E75rvTtCEEGdGkjBRv/X6o94ItdwOiQbuFelPZKPqpyOLTujHnfqdZIZUwoCI4HCgtPo1YXn7sefthYgUQ6pO7iTMqWmUOXLwO7lXUoDdpm+ibcSY3D259CRMT5YtlmCvbYvcC/ONSFRBv3HDUwkry4bIRLj9QwCca/Wu8EHBcYaMxS00NBGLJRSns4zS0mOEhzcjMbE/l/bZTEXFb+yAIITwS5IwUb8FW+HK2WaPQhfVmFinPt1YWFaIUzmxuO7yo+gYJZpGqasSZti2RcGRQGn1LSoc+Z7Nu42shAE47NUkYd8uwr75SUiIN6QS5n6NIk0De46+lVPVxfcVZRSX5HldW9firHEUupKw0tLjJ4dSUQTOEgCCQozbsghA0yxYrU1wOA5QUnKY8PBmaJpGaGiip32FEOLMyJowIWpLVDJxrunISlWJzT0FWFkOjlzPnZEWzWJYRcW9hqm4umatJfknm5AaUHUKsgRh1fT3fvaU8/1fVF7iaVFheCWssgxKT5m63fcl9k8fBIyrhMVZ48jzVMJOUFmpV1BLSo/oY62ESKvxiU9U1HlER2WgcBr+2kIEIqmECVFbIhsRAsQ4FYUWjRxHjt7HqVifisxzdTmPs8adrJDVsShrLNihqLo+YSUFFLu3BzKoyhMeGkVpaT6OmBT/F/R7AHsEsHOJoXdHFge5FrnbcyCsSqXSkefZssiIpBAgLiyOYieUK40QTVFamkVERCtKXWuw8is1Q9tTuHXu9C/DX1OIQCaVMCFqS5R+635Chb4lT06J3jOMYn2dUU6kPn2UEGbcNFK0a5ugImc1G2Y7TlbCjKryeBbnV5cYAnbXOSOSHk8SZo2EdoO9pyIB7DmevmVGVsJA49PCCM7r8AQhIfpOAyWuJCyvUjO0Uas/Sik2bhrE9z+Mpaws19SxCFFfSRImRG2JSADNQoJrXVhuiesXU5PO8Jfj5F05E4DEMOOmkaIu/RMARaoS5eqe76UkH7tBG2W7hVv0NhiOIz9Ue427d5khd0e6N8u2RsOtb5/cDsut6BjF7ulRA9eEAXxeqEhqfB0hIfrXmhZMMdFkV1gMu8O2OiUlWRQX/0pu7tcEBxt7p6YQgUKSMCFqiyUIbn2HxLQ+QJUkDCDYSi56hSzetX+iEaIj9X0HK1Wlb+WpshzKik5OtRmUYERW6FW54m1v+b/g4z/hOPC1PiYjK2HV3bxQdMLwSlh0aLRnyjq/So+31NSb+bjyYj7IDyUh3NiF+W5KVWK376OoWN/AOyKiJRZL3faXEyJQSRImRG1Kv5KEBL1VhlcSVuVrI6cjw4PDCdL0tU5F5ae0EXA1Sy1yJRjuilBdi3R1eS8Or6aSs3s1xcXHAGM75pdUllBRWQ7OUzYXLz7uSVSNSsIsmsVTDcs/pdGuGf+O3JRS/G9dLzZsHEBW1jsAREd1NHwcQgQKScKEqGXuX465rv0i2fgivHcnecd3eJ03gnb8JyJds5BFp/YKcyVhthC9UUSUQd3gI2ObA1Cccb3/C0ryDd07smpiVTw3DVbP8r6g6JjhNy+A3iss0qI4nvUO+/e/4DnuXmtoRhKmaRox0Xqfsuzs1fo447obPg4hAoUkYULUpqPbSTjyI1ClErZ3LWxbSo5d7/dk5HQkjjyiy/X2BrbyU1ovOPIBKArWkzCj7rZzJz0+lTnQe3Q58k+2qDAg6QkNCiXUteVOsSrT746squiEoW083OKt8URYFKXHXmff/n9RWemgpOQ4uSYmYQAJiZd5fZ2YcKkp4xAiEEgSJkRt2rOGhO3vAVWSsIsmwIBZ5AbpP25GLswnsS2RrnVhvpUwvQGpzdWawajpSM9CeH9d/MuKQFUaWgmDKuvCbnsPrpl38oTTCcUnsLnGY2RbiFhrLNkVGkoLweksIevIf1n/dS+mJutr18xKwpqk3EiIq1FsUtIAwsPTTBmHEIFAkjAhalOTriQ0vwSokoSlD4A+95JXqXc6N3RBdXQKUXEtAT+VMNem4p4kzKjpSNcibvumF6HilNYZruqckZUwqJKERcRBSNjJEyX54CzH5qqEGZWogl4xVWiUWvSkPTPzdQCOlWtEh0QTGmTOhtkhIbF07/YOGR3nc37GfFPGIESgkCRMiNrUui8Jg58E4ITjhOewUopsh94vzOgKRrR7IXzZKXf/NT4fLn+EotBIr+vqWpS7d1llCTjyvE+WFKAAh8GVMHcC6jNFWnQcBRS5kjCjvkdwsmKaoyUD4HAcBGBfqcW0OyPdIiJakZJyHUFBxk3PChGIJAkTopalROid4IvLi7Hl7oGdyyk8us3TIqJxRGNDxxNVqbfGKLIf9z7RuCNcNoMiV2/S6BBjEoxI1+voG2afsv6qJJ9yoMLghfCedWrfLYb3J4M7YS08hEPTqHSNx8gkrFGEPo28r+LkFKhC40dHkGlTkUKI2iVJmBC1LCI4nFhXonF090pYOpJjH98L6E04w4LDTvfwWhd1YCMAtrx9PueUUp5pSsOmI12VtyKL5puE2XM8VSfw3vC7TsfkSsLse1bDD29Cob5HIwWHPFORQVqQYeMBSI7QK2C/OCpo3HgIAI6oSyl0Woi3GnhzhxCizkgSJkRt+/f1NCnSk4sjJ/S2FEfj9OpYSmQ1+yXWoWjXlF7RKX3LOPojJVnfUeHUK2WGTUe6N8zW/FTCirM9SVh4cDjBFmO2t/VUwsLj9AOFh/U/z7sO240vAfr3Rzt1S6M61Chcr4Qdd5wgo+N8+vfbyd7grgA0jjS2miqEqBuShAlR2+JbkuLaP/JogV59OhahVy6MnoqEkxUuW2mh94kV91H02gBAbw5q+J2I/qYj7Tme5rFGTY9ClcQwzPWaNlclLCKBouR2+ngMnIqEk5WwbHs2CoXFEsrR4qOAOcm8EKL2SRImRG1rfD4plXrX9aOF+mLqY1Y9wTGlEuZqq1BUdsrdkWGx2CKTAD0xMqrK42lRYdE8d2h6FJ/wVMKMmh6FKpWwUFcimp/pOVdYpievRt4ZCZAYnoiGRoWq8Nxp607CzEjmhRC1T5IwIWpbs240cVXCjlTaQQvisOsnzYwkLMrVHLao4pS7I0e+Q9GoZYCx/a9OWwkrzj7ZDsLAJMxd5bK5kmVyftX/XPsEtl8+BYz9HgGEWEI8C/BP2PU7bY/Z9e2cpBImRGCQJEyI2tbkAlIt+i/zg8HB0Kw7B4r1NUYtY1oaPpyocL3Vga2ixOeczVUdM7LKUzUJcxZne59s2YfiFr0MH1NsqL6PZUGwayPq7F+gpBDW/p2irYv18RiYFLq575A8WnwUpRTHiiUJEyKQSBImRG2zWGiTfjUAe0NDUBeOZn/hfgBaxLQwfDjR7o75znKfc0bfGXnqa9ntJ7xPXjQOW+eb9OuMTMKsehJW6J6Szf4VKsug5yRsKecDxq8JA2geo++zedB2kGxHNmXOMiyaxbNoXwhRvwVkEnb06FHGjx9PkyZNCAsLo127djz22GOUlZX99oOreO655xgzZgydO3cmODgYTdNYu3ZttdffcccdaJrm96NDhw6/MypRn7S84m8Eo1FksbCzWVdsZTY0NNKijd/iJSa6KQCFquLkwSPb4PmLKNrwL8DYRfChllCCNb1Lv88dm5zczsjIpMc91VjgLAVLsL59UrkdBj+BrcMgw8fj1jxaT8IOFB7wJPKpkamEBIUYPhYhRO0z5v5vAx09epSePXuSmZnJ0KFDadeuHevWrWPmzJls2LCBFStWYLHULPecOnUqAE2aNCE5OZmjR4/W6HH33HMPcXFxXseSkpLOKA5Rv4VYo2ge24q9BXv5v33LAUiLTjO8RxhAbEJbAAo1RWVlBUFBwVCQCdm/UBhugWCIsRq33knTNKKCw8kvL8LuqLImTCmwHaWotAAwqRJWZoOUTpC1FQ5uhLjmnilbIxNVt5axLQE4WHiQfa47bVvFtjJ8HEKIuhFwSdgDDzzAwYMHeeGFF5g8eTKgN6QcM2YMixcvZvHixYwZM6ZGz7V8+XK6detGSkoKkyZN4uWXX67R46ZNm0bLli3PNgQRIM5LPI+9BXtZsnMJAJ2SO5kyjthEvcWC0jQKCw8SH98aCrMAyLdGQqXdk4QYJTIkkvzyIorsOeCsBEuQvoXRvA4UJSZATJSnqasR3JWwwrJCaHGlnoR9MAU6XGtKZc7NPX29r3CfJwlzJ2ZCiPovoKYjbTYbS5cupXXr1kyaNMlzXNM05syZg8ViYcGCBTV+vmuuuYaUFFkAK85Orya9vL6+sNGFpowjxBpNtFMBkJ+7Wz/oSsIKQqyA3snfSJGupK/okj+CcuoHi0+AJYSiUL1aaGTlyV0JdFQ4KGuvTz/iLId/9TzZosKEhfltYttg0Swctx/ny0NfApAel274OIQQdSOgkrANGzZQWlrKlVde6dPzqEmTJnTq1IlNmzZRUuJ7l1htWrFiBXPnzmX+/Pl8/vnnVLp6RomGpU/TPoRaQgG9GerlzS83bSyx6GuwCvJdWxe5mpHmuabmjU7CPM1RW1wM7vVNye3hL8exteytX2NwiwoN/f+MwkYdoNsdEBIJl/+ZAtf0qNHfI9C/B+3i9Upmpk3vXda1UVfDxyGEqBsBNR356696b5/0dP/vFNPT0/nhhx/Yu3cvHTt2rLNx/PGPf/T6ul27drz11ltceOHpKyGlpaWUlpZ6vi4sLDzN1eJclxieyCO9HmHBtgWMzhhNUrh56wLjgqwcUg7y3J3g3ZUwTa+QmTEdCScX4XtYLBRX6jfQGFkJs2gWokOjKSwrpKCskKQh/4RrnwFNI2/3IsCcJAzgopSL2JW7C9BbU5jR5kQIUTcCqhJWUKC/Y42N9f8LJSYmxuu62ta3b1+WLVtGZmYmDoeDnTt3Mm3aNPbs2cPAgQPJyso67ePnzJlDbGys5yMtzfg76UTtGtp2KCtuXMEt7W8xdRxxjTsDkN+0i37AtZl3vuuOSaMTDPf0X2HWt3DiF69zReV6YmbkmjCoujjf9ebHVU3PL80HID7MnE2zb2l3i6ei+of2fzB0/0ohRN06J5OwpKSkals9+Ps4XdsII40ZM4Ybb7yRZs2aERYWRocOHZg/fz4PPPAAOTk5zJ8//7SPf+ihhygoKPB8ZGZmnvZ6IWoqzrUPYUFpAVSUQsEhAPIrHfp5g5MwT3PU7/8N297WD/7vaXjndmx2vYGr0XcjetpUlJ58k1ZeWU5xub7TgFmVsJaxLXnjmjd4pv8zjD1/rCljEELUjXNyOnLEiBHYbLbfvtDFvXjeXQGrrtLlnt6rrlJWV8aNG8ff//531q9ff9rrrFYrVqvVoFGJhsSdQOSV5kHeflBOVGg0ha7pQKOnIz2VsMhEiNQTRPZ9BXvXUtRGb6lh9EJ4n0oYJ6tg7ulKs3RI6ECHBOk1KESgOSeTsOeee+6sHudeC+ZeG3aqX3/9FYvFQuvWrc96bGfD3SPMbrcb+rpCuMUGhQNQsO1tCNf//RcltqJC5QMmVsLSB8DFeisZcvdSARQ7XWvCDE56/FXC8krzAP37Y9HOyYkDIUQ9FlD/q1x88cVYrVZWrVqFUsrr3JEjR9i+fTs9e/YkLMzYhpmbNm0CkN5hwjTxrj0I8x3Z8ON7+ufJesUpLCjM8CayPlWn8hLIz6SwSiNlozfMdo/JXf0CyC/RPzdrKlIIEdgCKgmLiYlh+PDh7N27l5deeslzXCnFQw89hNPpZMKECV6Psdvt7Nq1i4MHD/6u1z569Ch79uzxOX748GFP5/0RI0b8rtcQ4mzFhscBkJ+SAUXHAShIbKOfM3gqsuprFpQW6J3yc/cAioIw/Xh0SDTBFmML9Ylh+kbnuVW2UnInZJKECSHqwjk5Hfl7zJ07ly+++IK77rqL1atX065dO/73v/+xfv16rrrqKkaPHu11/ebNm+nfvz99+/b1WeA/d+5cdu3Sbw3fsGGD59iiRYsAGD9+PH369AFg165dXH755fTp04cOHTqQkJDA/v37Wb58OcXFxYwePZpbbjH3DjnRcLkTjBzNCUd+AOBEQhrs11tpGM0z9ZfzC8xpBl30NygFCWlAvimJofv7kO3I9hyTJEwIUZcCLglr0qQJmzZt4i9/+QsrVqxg+fLlNG/enNmzZ/PAAw/UeN9IgE8//ZQvv/zS69jKlSs9n/fr18+ThLVp04Zx48axefNm3n33XWw2G7GxsfTu3Ztx48YxfPjw2glQiLOQHK4vfj/hyIaxn8Lml8kOjQCgUXgjw8fjXphfQKW+WfY3+k4WBYmtoHCrKUmPOwnLdZyshLmrYnFhxo9HCBH4Ai4JAz0RW7hwYY2u7devn8/6MbczaX2RlpZ2RlsiCWGkZFeLiuLyYoqT2hB5/b/I/v5FAJIijG8i616YX4STSnD184f8uGZQuNWUSpi7mW7VSpj7czMb7QohAldArQkTQvgXGRLp6VJ/wn5C/9Oh/+mukhnJXQlTQJG7Oq0FkR+vNyg2OwlzvzFzf6/MqBYKIQKfJGFCNBAnpyS9kzAzqjwhlhBPUlhw4ShAg/4PUeAsB8xJwtzr5sqcZZ6u/Z5ENcL4RFUIEfgkCROigWjkalNx3K7fHZnt6kxvRiUMqizO7zkB/nIMLrvf1M2yw4LDPBuLu6ch3d8r9/dOCCFqkyRhQjQQ7mqOe4rtuOO413GjedpUlBVAsPXk55hTCQPvOySdykmOIwcwL1EVQgQ2ScKEaCDc1Zxj9mOUVpZ6krGmUU1NGU9CWALgvy+XWUlY44jGABwtPkpeSR4VqgINzZQ2HkKIwCdJmBANRLOoZgActB3kcNFhFIrIkEjTemD5uxvRXXlyr88yWrNo/Xt0qOgQR4qPAHoVzOjGsUKIhkGSMCEaiFaxrQDYX7CfQ7ZDgJ6YaZpmynj8NUd1J2FmtYRwJ6qHbIc4WKjvopEWk2bKWIQQgU+SMCEaiBYxLQA4XHSYfQX7gJOVHzMkhbkqYa4bBMoryz0bZpuWhEVXScJsehLWPLq5KWMRQgQ+ScKEaCCSw5OJCI6gUlXy2YHPAGgb19a08XimI0v0JCynRK+CBWvBpq0Jc1fCMm2ZZNoyAUiLlkqYEKJuSBImRAOhaRrtE9oDsO3ENgDOSzjPtPGcuibMPRWZEJ6ARTPnv6ZWsa3Q0DjhOMHXWV8D0Dq2tSljEUIEPknChGhAujfu7vV1p+ROJo3ENwk7F7YIigqN8iRd7vFkJGWYNh4hRGCTJEyIBuTy5pd7Pu+S3MXUJqTuhfm2MhslFSWevmVm79PYpVEXz+dNIpt42lYIIURtkyRMiAbk/KTzmdJ1Cp2TOvNwz4dNHUtMaAzhweEAHCk+QlZRFgCpkalmDoshrYd4Pr+m9TWm3T0qhAh80vxGiAZmcpfJTO4y2exhoGkaadFp/JL3C5m2TA7bDgPm3rEJ0D2lO7N7zybTlsmdne80dSxCiMAmSZgQwjRVk7BDRXrvMrM6+Fd1Y/qNZg9BCNEAyHSkEMI07vYPh2yHPA1kz4UkTAghjCBJmBDCNO4k7Ntj35JXmoeG5mkqK4QQgU6SMCGEaTomdgRgZ+5OAFrGtiQiJMLMIQkhhGEkCRNCmKZ9fHusQVbP1+cnnm/iaIQQwliShAkhTBMSFEKv1F6er/ul9TNvMEIIYTC5O1IIYap7LriHfQX7aBffzquZrBBCBDpJwoQQpmob35blNyw3exhCCGE4mY4UQgghhDCBJGFCCCGEECaQJEwIIYQQwgSShAkhhBBCmECSMCGEEEIIE0gSJoQQQghhAknChBBCCCFMIEmYEEIIIYQJJAkTQgghhDCBJGFCCCGEECaQJEwIIYQQwgSShAkhhBBCmECSMCGEEEIIE0gSJoQQQghhgmCzByCqp5QCoLCw0OSRCCGEEKKm3L+33b/HqyNJ2DnMZrMBkJaWZvJIhBBCCHGmbDYbsbGx1Z7X1G+lacI0TqeTrKwsoqOj0TTN1LEUFhaSlpZGZmYmMTExpo7FKBKzxByoJOaGETM0zLjPhZiVUthsNlJTU7FYql/5JZWwc5jFYqFZs2ZmD8NLTExMg/lBdpOYGwaJuWFoiDFDw4zb7JhPVwFzk4X5QgghhBAmkCRMCCGEEMIEkoSJGrFarcycOROr1Wr2UAwjMTcMEnPD0BBjhoYZd32KWRbmCyGEEEKYQCphQgghhBAmkCRMCCGEEMIEkoQJIYQQQphAkjAhhBBCCBNIEhZglixZwsSJE+nevTtWqxVN01i0aFG112/atInrr7+epKQkrFYr7dq149FHH8XhcPhcu3//fjRNq/bj7bff9vsav/76K7fccgvJycmEh4fTuXNnnn/+eZxO5zkfs1tZWRnz5s2je/fuREdHEx0dzfnnn89dd93l9/r6HPMdd9xx2r9nTdP461//GlAxAzgcDubNm8eFF15IfHw8cXFxdOnShccff5yCggK/j6nrmKHu487Ly2PGjBm0bdsWq9VKcnIyN910Ezt27Kj2Neoy7sOHD/PMM88wcOBAmjdvTmhoKCkpKQwbNoxNmzb5fUxhYSHTp0+nRYsWWK1WWrRowfTp00+77+6bb75Jjx49iIyMJD4+nquvvpotW7YEZMx2u52nn36aW2+9lQ4dOmCxWNA0jf379592XPU55u+//55HHnmEiy++mEaNGmG1WmndujVTpkzh8OHDpsTslxIBpUWLFgpQSUlJns9ff/11v9cuW7ZMBQcHK6vVqm699VY1ffp01bNnTwWoSy65RJWUlHhdv2/fPgWoLl26qJkzZ/p8bN++3ec1duzYoWJjY1VISIgaOXKk+tOf/qQ6deqkADVhwoRzPmallMrNzVU9evRQgOrdu7e677771H333aduvPFGlZiYGHAxv//++37/fmfOnKkiIyMVoDZt2hRQMZeVlXnOd+3aVd1zzz1q2rRpqkuXLgpQGRkZqri42PCY6zru7OxslZ6ergDVq1cvNX36dDVixAgVGhqqIiIi1MaNG31eo67jfuCBBxSg2rRpo8aOHasefPBBNWzYMBUUFKQsFotaunSp1/VFRUWqa9euClBXXnmleuCBB9SgQYM8f5dFRUU+r/H4448rQDVv3lxNnz5d3XnnnSomJkaFhoaqL774IuBidv/fDagWLVqohIQEBah9+/ZVO6b6HnPPnj2VpmmqR48e6u6771YzZsxQl156qednaefOnYbH7I8kYQFm1apVav/+/UoppebMmVPtf9h2u10lJSWpkJAQtWXLFs9xp9Op7rrrLgWoOXPmeD3G/YM8evToGo/nsssuU4BasWKF51hZWZm64oorFKDWrFlzZgH6UZcxK6XUDTfcoDRNU2+88YbPufLycp9jgRCzP1u2bFGA6tSpk8+5+h7z0qVLFaBuvPFGn+cbOnSoAtTixYu9jhsRs1J1G7f7+PTp072Of/311yooKEh17NhRVVZWep2r67iXLVumvvrqK5/jX331lQoJCVEJCQleyeSjjz6qAPWnP/3J63r38UcffdTr+C+//KKCg4NVu3btVH5+vuf4jz/+qCIiIlSbNm18fq7re8w2m0199tlnKicnRyml1FVXXfWbSVh9j/m5555Tu3fv9nn+uXPnKkBdffXVPueM+pmuSpKwAHa6/7BXrVqlAHXzzTf7nMvLy/O8Y3I6nZ7jZ5qE/fzzzwpQ/fv39zm3ceNGBagRI0bUOJ6aqO2Y3eMcNWpUjV4/EGKuzqRJkxSgnnnmGa/jgRCz+/kWLFjg85hXXnlFAerJJ5/0HDMj5qrjrK24mzZtqiwWi7LZbD6PcSefVX/xmBW328CBAxWgvvnmG6WUnmCmpqaqqKgon0qIw+FQ8fHxqmnTpl4xP/TQQ36TaqVO/htfuXKl51ggxHyq30rCAjFmt4qKChUREaEiIyO9jpsVs6wJa6COHTsGQKtWrXzOxcXFER8fz4EDB9i7d6/P+aysLF588UXmzJnD4sWLOXTokN/XWLt2LQADBw70OdejRw/i4uL48ssvf0cUZ+ZsYl66dCkAN998M9nZ2bz22mvMmTOHJUuWkJOT4/M8gRCzPw6Hg7feegur1cqoUaO8zgVCzBkZGQB8+umnPo/55JNP0DSNfv36eY6dazHD2cV97NgxkpKSiIqK8nmM+3nWrFnjOWZ23CEhIQAEBwcD+vqdrKwsLrnkEiIjI72uDQsL47LLLuPw4cPs3r3bc/x0MVx11VUAXjEEQsxnKpBj1jSNoKAgz3O7mRWzJGENVHJyMgD79u3zOVdQUEBeXh4Av/zyi8/5VatWMWXKFB5++GHuuOMOWrVqxX333eezcPHXX38FID093ec5NE2jbdu2ZGVlYbfbf3c8NXE2MbsX6u7evZu2bdsybtw4Hn74YUaNGkXLli09SZpbIMTsz7vvvktBQQE33HADCQkJXucCIeZrr72WIUOGsGzZMrp168b06dOZPn06F154IatXr+aFF16ge/funuvPtZjh7OJOTk4mOzuboqIin8e4n6fq9WbGffDgQVavXk1KSgqdOnX6zfFUPe6+zv15VFQUKSkpNb6+uteoLzGfqUCO+d1338Vms/kkW2bFLElYA9W7d29iYmL44IMP2Lp1q9e5Rx55xPN5fn6+5/OIiAhmzpzJ999/T2FhIcePH+ejjz4iPT2defPm8ec//9nredx3lMXGxvodQ0xMjNd1de1sYj5+/DgA999/P9dffz179uwhLy+PJUuWYLFYGDVqFNu2bfNcHwgx+7Nw4UIAxo8f73MuEGLWNI3333+fGTNmsHXrVubPn8/8+fPZunUrQ4cOZdCgQV7Pc67FDGcX9+DBg3E6ncyePdvr+s2bN7N8+XKf682Ku7y8nFGjRlFaWso//vEPgoKCzno8BQUFZ3z9mb5GbajNmM9UoMacmZnJ1KlTCQ8P97nD26yYJQlroKKiopg3bx7l5eX06tWL2267jRkzZtC7d29efvllOnToAOD5IQBo1KgRs2bNokuXLkRHR5OcnMyQIUNYs2YNiYmJzJs3z/Nu+1x0NjG7q3udO3dm0aJFtG7dmri4OEaOHMkTTzxBeXk5zz77rCnx1MTZxHyq3bt389VXX9GqVSsuv/xyo4Z+1s4mZofDwY033sh//vMf3nzzTbKzs8nJyeGdd95h1apVXHTRRezZs8eskGrkbOKePXs2TZo04amnnqJPnz7MmDGDkSNHcumll9KxY0ef683gdDoZO3YsX331FRMmTPCZDg9EEnPtx5ybm8vVV1/N8ePHeeWVV2jfvn2tPv/ZkiSsARs3bhwff/wxvXr14sMPP+SFF14gODiYzz//nLZt2wInpzhOJyUlhauvvpqysjK++eYbz3H3O4rq3jm4e7u432EY4Uxjdsdw7bXXomma13MNGTIEwKu3UCDEfKqFCxeilGLs2LE+3wMIjJjnzJnDRx99xCuvvMIf/vAHEhMTSUhI4Oabb+b1118nOzubxx57zHP9uRgznHnczZo145tvvmHcuHHs27ePZ599lo0bN/LYY4/x8MMP+1xvdNxKKSZMmMCSJUu47bbbeOmll7zO13Q8VasbsbGxZ3x9TV7jXI75TAVazHl5eQwYMIAdO3bw4osvctttt/lcY9bPdPBvXyIC2eDBgxk8eLDP8VGjRmGxWLjwwgtr9DxJSUkAXvPlp5unV0qxe/duUlNTfRZa1rUzibl9+/Zs2bKFuLg4n+vdx6o2wQyEmKuqrKxk8eLFBAUFMWbMGL/XBELMK1asAKB///4+1/fv3x9N0/j22289x87VmOHM/66bNm3Kq6++6nP9rFmzALzWwhkZt9PpZPz48bz++uuMGDGCRYsWYbF41w1+ay2Qv3U+6enpbNiwgaNHj/qsC6vu+upeo77EfKYCKebc3FwGDBjA1q1b+de//sXEiRP9PodZP9NSCRM+1q9fz/79+xk0aFCN301t3rwZgJYtW3qOue8m++yzz/xen5+fT9++fX/3eGtDdTG7p99++uknn8e4jwVazFV9/PHHHDlyhEGDBtG0aVO/1wRCzGVlZQCcOHHC5zHZ2dkopbBarZ5j9SlmOPOf6crKSt5++22Cg4MZNmyY57hRcVf9xTx8+HD+85//+J0WTU9PJzU1lfXr11NcXOx1rqSkhK+++orU1FRPFRDwjM9fDCtXrvS6BgIj5jMVKDFXTcCee+45pkyZUu1YTPuZrvWmF+KccbqeQkopVVBQ4HPs8OHDqkOHDio4OFh9++23Xuc2bdqkysrKfB7z9NNPK0B17NjRp09Ldc3vBgwYUCfN72o75oKCApWUlKTCwsLUtm3bPMdLS0vV4MGDFaBeffVVr8fU95iruv766xWg3nvvvdOOob7HPHHiRAWo22+/XVVUVHiOV1ZWqrFjxypA3XfffV6PMTpmpWo/7rKyMmW3272OVVZWqmnTpilA3XvvvT7PV9dxV1ZWqjvuuMPT88xfQ+SqzrSJ588//1xrzVrrS8yn+j3NWutLzDk5OZ4O+//85z9rNCYzfqY1pZSq/dROmOXVV19l3bp1AGzfvp3vvvuOSy65xPMOYejQoQwdOhSAv/3tbyxZsoQ+ffrQqFEjMjMz+fDDD7Hb7SxcuJDRo0d7PXe/fv3YtWsXffv2JS0tDYfDwYYNG9i6dSvx8fGsXr3aZ6rjp59+onfv3jgcDm655RZSU1P59NNP2bZtG+PHj2fBggXndMwAH3zwATfddBNWq5WbbrrJE+uOHTu4+uqr+eijj7zevQVCzKD3kGrWrBmJiYkcOnTIp69OVfU95szMTHr27MmRI0fIyMjg8ssvR9M0vvjiC7Zv307Lli3ZvHmz1/ooI2Ku67gPHTpERkYGAwcOpFWrVpSVlbFy5Up27drFNddcw7Jly7wqgEbEPWvWLGbPnk1UVBT33HOP3393Q4cOpWvXrgAUFxfTp08fvv/+e6688kq6devGDz/8wCeffELXrl1Zt26dzxTS448/zl/+8heaN2/OTTfdRHFxMW+99RYOh4OVK1f6TEsHQswzZswgOzsb0NsMZWVlMWzYME+PuAcffNBz80YgxNyvXz++/PJLOnTowPDhw/2OYdq0aV5LTYz6mfZS62mdMNXo0aMVUO3HzJkzPdd+/vnnasCAAapRo0YqJCREpaSkqOHDh6vvvvvO73MvWLBADRo0SDVr1kyFhYWpsLAw1b59e3XPPfeozMzMasf0888/q5tuukklJiYqq9WqMjIy1LPPPuuzHcq5GLPbunXr1KBBg1RcXJwKDQ1VGRkZ6oknnqj23VsgxPzEE0/4fedZnfoe85EjR9Tdd9+t2rZtq0JDQ5XValXt2rVT06dPV9nZ2abEXNdxFxYWqlGjRqnWrVursLAwFR0drXr16qUWLFhw2hjqMu7fihc/lcD8/Hx17733qrS0NBUSEqLS0tLUvffe61XpOtWSJUtU9+7dVXh4uIqNjVWDBg1SmzdvDtiY3fuOVvfhb8/M+hzzb8VLNZVAI36mq5JKmBBCCCGECWRhvhBCCCGECSQJE0IIIYQwgSRhQgghhBAmkCRMCCGEEMIEkoQJIYQQQphAkjAhhBBCCBNIEiaEEEIIYQJJwoQQQgghTCBJmBBCCCGECSQJE0IElH79+qFpmtnDqLGioiKaNGnClClTzB7KWfviiy/QNI2PP/7Y7KEIUa9IEiaEOGdpmnZGH/XRP/7xD3Jzc3nooYfMHspZ69+/P3379uX++++nsrLS7OEIUW/4blsuhBDniJkzZ/ocmz17NrGxsUybNs3vY/79739jt9vreGS1Iz8/n3nz5jFixAjS0tLMHs7vMmPGDIYMGcJbb73FbbfdZvZwhKgXZANvIUS9omkaLVq0YP/+/WYP5Xd77rnnmDp1KqtXr+aKK64wezi/S0VFBampqbRr145169aZPRwh6gWZjhRCBBR/a8IWLVqEpmksWrSI//u//6Nnz55ERETQtGlTHnnkEZxOJwBvvPEGF1xwAeHh4TRv3pynnnrK72sopXjttde45JJLiImJISIigu7du/Paa6+d0VgXLVpEYmIi/fv39xxzOp20atWKxMRESktL/T6uR48ehIaGcvz4ca/jH374IVdccQXx8fGEhYVx/vnn89RTT/lMERYUFPDEE0/Qt29fUlNTCQ0NJTU1ldtvv509e/b4vN6sWbPQNI21a9eyePFiunXrRkREBP369fNcExwczNChQ1m/fj2//vrrGX0fhGioJAkTQjQY77//PrfccgutW7dm0qRJREVF8be//Y1HH32Up59+milTptCpUyfuvPNOnE4n999/P2+88YbXcyiluO222xg3bhzZ2dnceuutjB8/nuLiYsaNG8eMGTNqNJa8vDy2bt1Kjx49sFhO/ldssViYMGECubm5LFu2zOdx27dv55tvvuG6666jUaNGnuMPP/wwQ4cO5ZdffmHYsGFMmTKFsLAw7r//fv7whz94PcfOnTt59NFHCQ8P54YbbmDatGl0796dN998kx49enDgwAG/Y37yySeZPHky6enpTJ06lT59+nid79WrFwBr1qyp0fdAiAZPCSFEPQKoFi1aVHu+b9++6tT/2l5//XUFqJCQELV582bP8cLCQtWoUSMVERGhUlJS1J49ezznDh48qEJDQ1Xnzp29nuuVV15RgBo3bpwqLy/3HC8tLVVDhgxRgNqyZctvxrFixQoFqD//+c8+544cOaKCg4NV//79fc5NnTpVAeqTTz7xHPvss88UoAYPHqyKi4s9x51Op5o0aZIC1Lvvvus5np+fr3Jycnyee82aNcpisajx48d7HZ85c6YCVGRkpNq2bVu1Mf3www8KULfffvvpgxdCKKWUkkqYEKLBGDlyJBdddJHn6+joaK699lrsdjuTJ0+mdevWnnNpaWn06dOHHTt2UFFR4Tn+/PPPExkZyfPPP09w8Ml7m0JDQ3n88ccBeOutt35zLIcOHQKgcePGPudSUlK47rrrWLt2rdf0YGlpKUuWLKF58+YMHDjQa0wAL7/8MhEREZ7jmqYxd+5cNE3zGlNsbCwJCQk+r9u/f38yMjJYvXq13zHfeeeddOrUqdqY3LG4YxNCnJ7cHSmEaDAuuOACn2NNmjQBoGvXrn7PVVZWcuzYMZo2bYrdbmf79u2kpqYyd+5cn+vLy8sB2LVr12+OJScnB4D4+Hi/5ydOnMh7773HwoUL+fvf/w7o06m5ublMnTrVawpz48aNREZGsnDhQr/PFR4e7jOmtWvX8swzz7Bp0yays7O9Es3Q0FC/z9OjR4/TxuRO7LKzs097nRBCJ0mYEKLBiImJ8Tnmrmad7pw7ucrLy0MpxeHDh5k9e3a1r1NcXPybYwkPDwfA4XD4PX/llVfSqlUrFi1axF//+leCgoJ49dVXsVgsjB071uva3NxcKioqajym//73vwwfPpyoqCiuuuoqWrZsSUREhOfmherWhPmr2lXljqVqNU4IUT1JwoQQoobciVq3bt3YsmXL73qu5ORkQE+g/NE0jQkTJvDwww+zYsUKOnXqxJo1axg8eLBPT7GYmBg0TatxBWrWrFmEhYXx7bffkp6e7nXu7bffrvZxv9UQ1x2LOzYhxOnJmjAhhKih6OhozjvvPHbu3El+fv7vei732qrTtXMYO3YsISEhvPrqq7z22msopRg/frzPdT179iQnJ6fGrSH27NnDeeed55OAZWVl+W1RUVM///wzwGnXjQkhTpIkTAghzsDUqVOx2+1MmDDB77Tjvn37atRItlOnTiQkJLB58+Zqr2ncuDHXXXcdH3/8Ma+88gopKSkMGTLE75hAT9rca82qOnr0KDt37vR83aJFC3bv3s2xY8c8x0pKSpg8ebLX2rAztWnTJgD69u171s8hREMiSZgQQpyBiRMnMnr0aN59913S09O5/fbbefDBBxkzZgy9evWiTZs2bNy48TefR9M0rrvuOnbs2MGRI0dO+3qVlZUcP36c0aNHe92R6TZo0CAeeeQR1q1bR9u2bRkxYgQPPvggEyZMoH///jRr1owPP/zQc/3dd99NYWEhF1xwAVOnTvX0R9uxYwddunQ5u28MsGrVKuLj47nsssvO+jmEaEgkCRNCiDPgXry+dOlSMjIyWL58OfPmzWPVqlWEhYXx1FNPMWDAgBo918SJE3E6nadtaTFgwACaNm2Kpml+pyLdHnvsMVatWsWll17K559/zrx581i+fDmlpaXMmjWLkSNHeq696667eOmll0hISGDBggW8//779O3bl6+//pq4uLgafy+qOnDgAOvXr2f06NGEhYWd1XMI0dDI3pFCCGGi3r17U1BQwI8//uh34XtWVhYtWrTg0ksvPac70T/66KPMnTuXnTt30qZNG7OHI0S9IJUwIYQw0VNPPcVPP/3Ef//7X7/nn3nmGSoqKpg0aZLBI6u5/Px8nn32WSZPniwJmBBnQFpUCCGEiXr37s1LL73k6UUG+gbbL774IgcOHGDBggVkZGQwbNgwE0d5evv372fatGncfffdZg9FiHpFpiOFEOIcs3//flq1akV4eDg9e/bkpZdeon379mYPSwhRyyQJE0IIIYQwgawJE0IIIYQwgSRhQgghhBAmkCRMCCGEEMIEkoQJIYQQQphAkjAhhBBCCBNIEiaEEEIIYQJJwoQQQgghTCBJmBBCCCGECf4fv6NFVOKTqU0AAAAASUVORK5CYII=", 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YQGJiIikpKZx11lmtqj6uohIxbod4N1n8XiKpxYroMSPgnapfP02fAFl9Ra2p9sDrUquxq6ionHKc1p6rtWvXMmnSJAwGAzfccAPJycl88MEH3HzzzRw6dIjHH388ou0cOXKE888/n59++okLL7yQSy65BKfTyf79+3n//feZO3dujI9E5YzF4xdXOhNk5goPVnu11wmIGOkUumyU7hMCK6leaQePU4xg1IcfyaOioqISS06hq6SyeDwe7rjjDiRJYt26dcHGwXPnzmXUqFHMnTuXa6+9lp49m8/N8Hq9XHPNNRQUFPDpp582qFkU2I+KSszwBFq7mERV9PZEowWDBQyJ7WtHAFmuS6SvXyn+5TFQ+iPSzR+2j10qKipnPKdtWPCzzz7jwIED3HTTTUFhBWCxWJgzZw4ej4c333yzxe3861//YtOmTfzqV79qIqwAdLrTVp+qnAoE282cAjlNpmTIyAdrh4bTS/dB0Q5w2eNrj+wD/GX66uegmTPFu700vvaoqKio+DltlUGgKOPEiRObzAtM++KLL1rcznvvvQfAtddey9GjR/nvf/9LRUUFPXr0YPLkySQlta6oo4pKi8g+0fvR5xKJ4247VJeInKf2KscQCp+n7hXX/QZyraSGDckTRMV+qbYCaIfmzRVHYddHQhAf+Qay+8F5D8ffDhUVlXbjtBVXgXYrocJ+qampZGRkRNSSZfPmzQCsX7+eBx98sEEj28zMTP7xj38wfvz4ZrfhdDobrFdVVQWIirPueiOa3G43sizj8/nw+Xwt2tZWAsX5A/s8E/jZHXO9RHHJ50VylCNrDciWDs2s1JSYHndyZ5EHpjVAPM+pz4MGkDVafLKMLMu43W4MxmQ0gNd2Eshu8BuLB9KPa9CtfqLOzJoSvKMeiMu+A8ca72NuT9RjPjM4VY450v2ftu1vJk6cyJo1a9i3bx/5+flN5vfo0YNjx441ED2hMJlMOJ1OtFotv/rVr7j33nsxmUz8/e9/51e/+hUJCQns3r2b3NzcsNuYN29eyFGF77zzDomJdfkrOp2OnJwc8vLyMBjaOb9G5ZRD8nkxeGvwSTrcOnPc9292FKH1Oak1pOPWtb/HVuutxeIswivpOanN4ujRoxQVFdHr8Fv0LFnBvqzJ7Op4Y9ztyq3YRF7Z17i0iXQp+xKXNpEVA5e030AEFRUVxbDb7dx0001q+5u2EnjKv/TSS3nmmWeC02fNmsXx48f5wx/+wBtvvMETTzwRbhM89thjwaa2IDxXeXl5TJw4sUlvwaNHj5KUlBSX3oKyLFNdXY3FYkE6Qy78P/9jFiGvaBvgKHHckrsYyecj0ZyEbGz/kXiSQwYnaHR6ksxJJCQkcN5555G45QcoWUG37BR2ARdddFGce5FNEW9eF/LCLhi8dqaMGQTJeTHfs9vtZs2aNe1wzO2HeszqMceTQOSpJU5bcZWcnAxAZWVlyPmB5ouRbKe0tJTLLrusybypU6fyhz/8IRg6DIfRaMRobJqQrNfrG3xJvF4vkiSh0Wji0lQ4IBwD+zwT+Fkdc3WRKCmQmAHW8J7RSFDkuNO6g8+LpNUj1d+GywbOajGiMSGlTXZGhb8Vj6TRodFokCQJvV6P1pwOgMZVBbqmv7O4oddDWjco/RF91VHIiF8tsHY75nZEPeYzg/Y+5kj3fYrfXVpPINcqVF5VeXk5paWlLZZhAOjduzcAKSkpTeYFptXW1rbeUBWVcHjdTZPE3bVQWyna4sQbrR70pqbV4V01onmyoyK+9gT7HDZ6RgwIvNryuJoDiM+suliUiQDRjgeg4kj8bVFRUWk3TltxNW7cOABWr17dZF5gWmCZ5jj//PMB2LVrV5N5gWldu3ZtrZkqKuGx5kJmb9GYOEDlcSj/CVzV7WdXYyS/2GpFpfSjR48yfvx4+vXrx6BBg/jnP/8ZnPfJJ5/Qu3dvevbsyeuvv9505cD+Gou9wGhBR2ivdUwp/RGe6wWLBon/g+LqcPxtUVFRaTdOW3F1wQUX0L17d9555x22bdsWnF5dXc1TTz2FTqdj+vTpwemlpaXs2bOH0tKGtXFuu+02jEYjf/7znzl+/HiD7SxYsACA6667LqbHonKGotGJUgy6eoMbtH6XtDfOI2Z8Xqg8BlWFdV6ZAAFxI0c/UlCn0/HCCy+wa9cu/ve///Hggw9is9nweDzMnj2bzz77jK1bt/KHP/yBsrKyRjZ5Gu4/gClFvLeH56q6ULwHctJUz5WKyhnJaSuudDodr7/+Oj6fj7Fjx3LXXXfxq1/9isGDB/PDDz8wb948evXqFVx+8eLF9O3bl8WLFzfYTrdu3Xj22WcpKSlh8ODB3Hnnndx7770MGjSIbdu2cdddd3HBBRfE+/BOG2RZ5q677iItLQ1JkhoIYZUQBEJg8e6n5/OK/K+a4qbz2uC5ys3NZciQIQBkZWWRlpZGWVkZGzdupH///nTs2BGLxcKUKVNYtWpVU5ugaTueQFiwPTxX1UXi3ZIj3lVxpaJyRnLaiiuACRMmsH79esaMGcM//vEPXnrpJdLT03n77bf5zW9+E/F2Zs2axccff0y/fv149913eeONN0hPT+fVV1/llVdeieERnP6sXLmSpUuX8sknn1BYWMiAAQPa2yQAioqKmDVrFt27d8doNJKXl8fUqVP59NNPGyz30ksv0a1bN0wmE8OGDePLL79sMH/dunVMnTqVDh06IEkSH330UeRGVBVAdRHTb70FSZLEK6UTUsezkFI6NfC8tpWnn36aESNGYLFYyMrK4oorrmDv3r11C9T3EvlHGwaPrXtvpI5n8dHypiF4aPkcBdi8eTM+n4+8vDwKCgro2LFjcF6nTp0aeI6b2FSfQFjQbUOKd2HTgOcqIK4CIwQrj8XXDhUVlXbltB0tGGDkyJGsWLGixeXmzZvHvHnzws6fOnUqU6dOVdAyFYADBw6Qm5vL6NGjwy7jcrniWvfr0KFDnHvuuaSkpLBw4UIGDRqE2+1m1apVzJw5kz179gCiev8DDzzASy+9xLnnnssrr7zC5MmT2bVrF507C4+FzWZj8ODB3HbbbVx99dWRGyH7GniJLr74YtGuyX5SiC6DhYSOfRU75i+++IKZM2cyYsQIPB4Pv/nNb5g4cSK7du3CbDaHTB4PHtu0m7j6+ptCFhCN5BwBnDx5kltuuSWYWxWq/F6TEhIpeSI8qjOBu57XzJgMSICMwWtr9TlpFUHPlX90Z5K/QnxNiQin/izLf6ioqETLae25Uok/GzduZPz48SQkJNCnTx82bdrEq6++GrKUxfTp05k1axZHjhxBkqTgwIDx48dz7733Mnv2bDIyMrjooosAUen+vvvuIysrC5PJxJgxY9i0aVNwe+PHj2fWrFk88MADpKamkp2dzauvvorNZuO2227DYrHQs2dP1qxZ0+wx3HPPPUiSxMaNG7nmmmvo1asX/fv3Z/bs2WzYsCG43PPPP8/tt9/OHXfcQd++fXnhhRfIy8tjyZIlwWUmT57M7373O6666qroTmT9EJukwWg0kpOTQ05uR3KyMsjJTI2olEikrFy5kunTp9O/f38GDx7Mm2++yZEjR9iyZUtDe+q1mQkeW1A0yk3ysSI5R06nkyuvvJLHHnssKLI7duzYwFN17NixpoV6dSYwWury0AJoNDD9E9y3f4ZLG+diq43DgklZ4t3rBGdk9XFUVFR+/qji6hRHlmW8XnsMX7Vh50VbvH/Dhg2MGzeOiy++mO3bt9OvXz/mzZvHs88+G7JC/aJFi5g/fz6dOnWisLCwgVBatmwZOp2Or776Khh6feSRR3j//fdZtmwZW7duJT8/n0mTJjVIdF62bBkZGRls3LiRWbNmcffdd3PttdcyevRotm7dysSJE5kxYwZ2e+gmw2VlZaxcuZKZM2cKj00jAuU3XC4XW7ZsadK7cuLEiXz99ddRnbeQBENejZzLLeRcLViwgKSkpCYvq9VKp06dsFqtYcNy9QnUh0tLSxMT5DAj86Au5wq5QVJ7JOdIlmWmT5/O+eefz7Rp04LLjBw5kp07d3L8+HGqq6tZvnw5kyZNatHuIF3HQM4g5MbnL9YEw4J+IahPAINF/F1zIr62qKiotBunfVjw547PV8vaLwa2y77Hj9uBVpvY8oJ+Zs+ezdVXX82vf/1rAG644QZuvPFGLr/8coYOHdpk+eTkZCwWC1qtlpycnAbz8vPzWbhwYfB/m83GkiVLWLp0KZMnTwbgtddeY82aNbzxxhs8/LBojDt48OBgtfzHHnuMZ555hoyMDO68804A5syZw8svv8z27dtDhiL379+PLMv06dOn2WMtLS3F6/WSnd2wMXB2djZFRUXNrhsR4fKJguIqdC7RjBkzQo5e9fl81NTUkJSURF5e85XCZVlm9uzZjBkzpi4HLhDyk0I8j9WfJvsAYXMk5+irr77ivffeY9CgQcF8tLfeeouBAwfy3HPPMWHCBHw+H4888gjp6fVKUsiyCJtqtJCQ1uzxxJXGniuAYbeKcKA+2rr6KioqP1dUcaWiCMeOHeObb77h2WefDU4zGAzIshzSa9USw4cPb/D/gQMHcLvdnHvuucFper2ekSNHsnv37uC0QYMGBf/WarWkp6czcGCdOA3c6EtKSkLuN+Cti7RFTOPlZFlWpq1OS54r2RsyhyctLa3O21R/cz4fVVVVWK3WFiu033vvvWzfvp3169fXTQx4rqRQnqt6Nvi8TcJ0zZ2jMWPGhG0kfdlll4UMJ/s3UuclCiWu9vwXTfFurLVNOyPEDF+9PLmkeuJq0u/jZ4OKisopgSquTnE0mgTGj9sRk22LG241Vqsl5A1Xo4n8STsgcOqLor179zJy5MgG4iZSGofkwomexmKmcWuCQEuU+v8DYW/oPXv2RJIkdu/ezRVXXBHWvoyMDLRabRMvVUlJSRNPTasIK660DZdpJGQWLFgQrL8WjhUrVjB27NiQ8wIjY9etW0enTp3qZgTCfaHCgvWR68KVsT1HshBVsi+0N+27t9HuXU5q3m1t3E8UOCvrPrf6hV9VVFTOONScq1McSZLQahNj+EoIOy8aD0xlZSVabd2Nt6ysjIULF4bsqdga8vPzMRgMDbwpbrebzZs307evcqPm0tLSmDRpEi+++CI2W9ORZhUVFYDwyg0bNqxJcvyaNWuaHfkYMeHElSTVqyvVNDQ4Y8YMtm3b1uS1detW1q1bx9atW5t4BUGI1HvvvZcPPviAzz77jG7dujWyp2lCe2i768RVTM+RRgupXUTvvlDf027j8A26EZtRAaEbKXZ/7p/eLNoEBfC46gqwqqionBGonisVRRgyZAher5eFCxdy7bXXcv/999OlSxd2797N4cOH6dKlS5u2bzabufvuu3n44YdJS0ujc+fOLFy4ELvdzu23367QUQA+Ly8teIzRF1/DyJEjmD//KQYNGoTH42HNmjUsWbIk6KWbPXs206ZNY/jw4YwaNYpXX32VI0eOMGPGjODmampq2L9/f/D/gwcPsm3btuAxNGcH0FRcgRAWXm/IpPbWhgVnzpzJO++8w7///W8sFkvQ25ScnExCQgLIPha/+S4frvmaT9d+GfrYjpWwbfsO0nLygscWyTmKCefMwOt2U7p8eWz3U59ARfjERud//fOw9mkYNh2mLoqfPSoqKu2GKq5UFCE/P5/58+ezaNEiFixYwPXXX8/f/vY3Jk2axIUXXhiygXa0PPPMM/h8PqZNm0Z1dTXDhw9n1apVpKamKnAEfmyldMu2sHXl2/z+xbd56KGHKCwsJDMzk2HDhjUoIXD99ddz8uRJ5s+fHyyAunz58gZCcvPmzUyYMCH4/+zZswG49dZbWbp0KUuXLuW2225rOjLT10yOk0YLXlrVbiYcgeMaP358g+lvvvmmKFYqeyktq+DAwboeeU2Obe4fYO4fgscGkZ2jViH7/DlnmlOndlTAc5XQ6PtozgSNvn2abdej2uHGqNNi0KkBCxWVWCPJ0Y63V2kzVVVVJCcnU1lZidVqDU53OBwcPHgwWM061kST5Hy60OIxl+wBT63/HwlyBracZ9QG5s2bx9q1a1m7dm3DGWU/ifYtyXlgzmg4z+eNWlS0+bMu3QeuGkjp0tQz0x44KsU50idCZu+mvx1Zxm2vZPWqFUycek2TXLyYUFsBJbvFZ9P57Lrprfi8Wovb7Wb58uVMmTKlwTHvOFbJTa9tQJLg/btH0zPbEnNb4kW4Yz6dUY+5/Y453P27MarnSkUlgNfTUFghg8sGpvA/oLayatUqFi0KESryNVNXKoZiLyw6k/AWNS7Y2V4059kD2PQ6+uW/YkjKCOCa+NiUkAJdRjWd3h6fVyPK7C46piaQnKDH6VHO46miohIaVVypqARw+wuL6ozCI1JbLqbFUFx98803oWe0JB7iTUrztbGoOSEaOyem1hXQjCXBoqZhvHBG4ZnRe2tDz48xNU4PdyzbRHGVkz/fOJQBHZWrpt8axvXK5LyewgOqSKkQFRWVZlHFlYpKgIDXSpcghtIbksSrPdD5w8KhEtpry0UIymhpGjJsL2SvaPHijVNeUbCoaRjx6RdXuniKqx9XQdlB6DqGZbtNbPhJ5GAt+M923kl5WXhBb/kofvY0QhVVKirxQxVXKioBPA7xHuhZZ2zHvJS0ruHneZzgqDglwk1BElLBYAZtnBpsN9eOB+rElc8RH3sAvv87/PAhXPwHVu4Ule17ZJpZMLUnvPaJWMbtaFimIQ4cLbNj0mvJtBhxe30cPmnHpNfQKTXy7gsqKirRoYorFZUAbv+NOM43v6gxWkSCdLzaqcgyFO8U+8zoDdoQlw2dUbzihdxMOx6oFxYM3UMyJuSdDbJMZXJvdhwXvRnfvWsUmWa9sFP2CVGsz2l+Owrz58/28Y/Nx3h8ch8KKh0s/foQvzyvO49NUa4+nIqKSkPOjCFiKiotIcvCIwR1ITlnNdhKRaL7qYTBDElZ8fOsyT5RsNTrOnXKHrSUk2YUeXJxDQueczdct4zdRtGCqWNKApkWo8gLM6WIZQK1sOLIoVJRDDd77UN0d4uSKAdO1MTdDhWVMwlVXKmoBLB2gKRs0Po9MBVHofJovRGEccLjhKIdYlj/qYCkgcw+kNErvKfI6xIJ7faT8bGpxYR2Ia70Poei9cAiYXdhFQB9c62UVDt4a8Nh3vSKZuPtIa4OFovcr25dutOjZz8AfjrRtPuAioqKcqjiSkUFhEfGnCEEVuCGbTTXheDiic8rPEUh2tsE5ztrhGctHkiSCEEazOE9V4EWL9XF8bEpwoR2QCSSxwNHJfi87CkUn0vfXAs/nbAx56OdLKv1t/uprYiPLX5qnB5O1IrPrGtmEt06i1GfR8rseH1qiUMVlVih5lypqIQjpY1VxFuLziQ8ReE8Ll4XnNwnhEXuoPjaFo6AAI2Xl6ilhHadEVmjR/K5hQhNinHhU58PnukCyOzP+BiAXtkWumeamdgvmx4l30MNcfdcHTkpcs7SqMLa9wLMVhNajYTHJ3Oi2klO8imeX6ii8jNF9VypqIAYKeisjl8pgebQaOo8RaEIeGtkr8gVizUeh2g63FzIL+DtC4ieWBPYTzivoiSB0V9GIx4ePlcNID6LY5XiO5SXlkiWxcSrtwzn0W4/ieXiLK6KioUnMVc6Cald0O78Fzkm4RE9XtE+NcBUVM4EVHGlogKiL9zJ/aHDWqdah6j63pp42OZ2QE0R2JoRV0HB54uPTS2FBUEUggUkdxzCgn4B55JMlNQIcdUxpd5ozkC/QUdF7G2pR9GxAwDkGF1CIH9wBx3coj9kYaUqrlRUYoUqrlRUQNyktY3KCThroHA7nNgTX1ucNVBdBI6q0PPre2vi4SkKhPqa60nYwKY4hAZbSmiHOs9fPHKunOKzKtR3RpbBqNOQkSRqfvl8Mic0mZTLSfH3XJWKZPZsswYye0PXsXRIFqKvQPVcqajEDFVcqagAWLIhu58ocRBAoxE38XCJ5bHCVQ3VhSJBOhSSVC/HKR7iqoUQXON58RBXad0htTtowvc6lPV+ceWOQ60rvxA+phMJ4x1TE4IV0X/78U5GrO3Pm55JcU9oL/Z7p3KsRtFwe/on5PYfA0BBRRwLrKqonGGo4kpFJRyBG7fPE9/QoC8Cr0wgHOaLg5CJpM9hA8EXnU1XXnklqampXHNNwwbLf/zjH+nfvz8DBgzg7bffbriS0QIJyc1Wqfdd9Du+yv81csfhUdnTKvyeq15mG4tuGMIDF/YKzsqyiKTxYtLiN8LTT5FNfHbZqXW9DbMswjtbWuOMqy0qKmcSqrhSUQlH/b5+8fReyRHkE8VzdF4wLNhCu51WetPuu+8+/vrXvzaYtmPHDt555x22bNnC5s2bWbJkCRUVFVFtV847m1JLP9EnMtb4vYyZZj2XD+nIZYM7BGflWIW4KpJT4y6uih3iO5ydWdeDMsOfCnaiuv3Elc8nc+SkHZ9aDkLlNEUVVyrtiizL3HXXXaSlpSFJEtu2bWsHI3xQtBNK9tR5aUB4YwICy+eOnz2+CMJwmnojBmNNS61mArTSmzZhwgQslobV5nfv3s3o0aMxmUyYTCaGDBnCypUr/dv3+AuWlkW1n5ji91wFipfWJ9tf7qDY1B36TImfTT4fRW4RGs3p0ElM+/J5Mj66CWhfz9UzK/dw3rOfc+/ftyKfagNGVFQUQBVXKu3KypUrWbp0KZ988gmFhYUMGDAg/kb4PEI8eRxBAVFUVMSsWbPofvYUjN3OJq9bL6ZOncqnn37aYNWXXnqJbt26YTKZGDZsGF9++WXY3Tz99NNIksQDDzzQvD2NPEXTp09HkqSGr6w+TH9gbkMx2AaWLFnCoEGDsFqtWK1WRo0axYoVKxra00hcrVu3jqlTp9KhQwckSeKjFZ81XN5PNOcowIABA/j888+pqKigoqKCzz77jOPHj4uZXrcoWFp1vNltSMc307X0M6Tjm1s+AW3Fn3P1paM7/9tV3MArlG0VYbhi0mH0rNjb4sdTXUIFohxFRk5nMTEhlUxJeNlKa9qv7Mjsi3rRv4OVLYfLKWlHD5qKSqxQi4iqtCsHDhwgNzeX0aNHh13G5XJhMBhiZ0Sgd6BGC5LEoUOHOPfcc0lJSWHh3EcY1DMPd0IWq9ZtYObMmezZI0YPvvfeezzwwAO89NJLnHvuubzyyitMnjyZXbt20blz5wa72LRpE6+++iqDBkVQ9DNEAvnFF1/Mm2++WbdM2WESNE7FwoKdOnXimWeeIT8/H4Bly5Zx+eWX891339E/19zEHgCbzcbgwYO57bbbuPrqq+vVuqqzKZpzVJ9+/fpx3333cf7555OcnMyIESPQ6fyXK0kCU3LzYVNA+uFDBh9divfHZOg6KsozEiV+z9X/FfZj61838/IvzuLiAblAXViw3O7G4fZi0rcQXlUIja2I9cb7KE/oQqrF7zGzdqCLVMTaji+TcdeHcbEjFCa9lj/dOJRu6WY0mlOkX6WKioKonisVRdm4cSPjx48nISGBPn36BEXFZZdd1mTZ6dOnM2vWLI4cOYIkSXTt2hWA8ePHc++99zJ79mwyMjK46KKLAHA6ndx3331kZWVhMpkYM2YMmzZtCm5v/PjxzJo1iwceeIDU1FSys7N59dVXsdls3HbbbVgsFnr27MmaNWsaGhLIp/InsN9zzz1IksTGjRu55sqp9OrRhf59ejB79mw2bNgQXO3555/n9ttv54477qBv37688MIL5OXlsWTJkgabr6mp4eabb+a1114jNTW15ZMYooaT0WgkJyen7pWbTbLVolhYcOrUqUyZMoVevXrRq1cvfv/735OUlCSON4znavLkyfzud7/jqquu8s/33yTr2RTpOQrFL3/5S7Zu3crnn3+OwWAICj90Jv9oweYr6MvZAyhIHoac0Tuyk9AW/LlUvS1uBnVKpkO9GlfJCXqMOnHuThQcjtvgCE1GTzrd8XcGXvdbtAEBY8nFIHnp6thNkrF9n617ZCapwkrltEUVVz8T7C5P1C+Pt86D4PH6sLs8ONwNb8a1Lm/IdVvDhg0bGDduHBdffDHbt2+nX79+zJs3j2effZYnn3yyyfKLFi1i/vz5dOrUicLCwgZCadmyZeh0Or766iteeeUVAB555BHef/99li1bxtatW8nPz2fSpEmUlZU1WC8jI4ONGzcya9Ys7r77bq699lpGjx7N1q1bmThxIjNmzMBurzc831fnuSorK2PlypXMnDkTs9lcl3Pl926lpKQAwpu2ZcsWJk6c2OCYJk6cyNdff91g2syZM7nkkku48MILIzuREZU+CJ3ftGDBApKSkpq8rFYrnTp1wmq1thiW83q9vPvuu9hsNkaNGhWZPQGbNHpA3DCjOUehKCkpAWDv3r1s3LiRSZMmtbhOfeTBN7Kp+/3IA6+Lar1W4Q8LPn1WBR/fO4ZBnVKCsyRJIsMshHvpG9eI8HM8MJghbyR0H183zepPtLedEA3C44zb6+P2pZv446q9Ta5FKiqnE2pY8GdCv9+uinqdF286i0sGidDEqh+KmfnOVs7ulsZ7v6wLkUxZspny2qZi6tAzl0S9v9mzZ3P11Vfz61//GoAbbriBG2+8kcsvv5yhQ4c2WT45ORmLxYJWqyUnJ6fBvPz8fBYuXBj832azsWTJEpYuXcrkyZMBeO2111izZg1vvPEGDz/8MACDBw/miSeeAOCxxx7jmWeeISMjgzvvvBOAOXPm8PLLL7N9+/a6UGQ9z9X+/fuRZZk+ffr4p+kaLuOntLQUr9dLdnZ2g+nZ2dkUFRUF/3/33XfZunVrA+HYIpGMzgsRggOYMWMG113XVEz4fD5qampISkoiLy8v5CZ37NjBqFGjcDgcJCUl8eGHH9KvXz84sVcs0JK4SsqCnLqcuUjP0aRJk9i6dSs2m41OnTrx4YcfMmLECK644goqKiowm828+eabdWHBgOcnXBPp9qCZhHaAtCQjxyudlEspokisPiHkckqy/VgF/91eSL8OVi4f0lFMTEwHrYG3nWPZ+Y8t3HRevwZCMNb8WFzNp3tK2HiojIcm9mLmO1vZcaySN28bQY/MpLjZoaISa1RxpaIIx44d45tvvuHZZ58NTjMYDMiyHNJr1RLDhzesTXTgwAHcbjfnnntucJper2fkyJHs3r07OK1+TpNWqyU9PZ2BAwcGpwVu9AGvCFAnnLS64MilQAHIcOIqgNToBi/LcnDa0aNHuf/++1m9ejUmUxQNcn0RjM5LyoGk7CZ5R2lpaaSlNW1S7PP5qKqqwmq1oglTP6t3795s27aNiooK3n//fW699Va++OIL+mVILdvTDM2dI4BVq0I/OIT1btlPQuVRMKVCWtfgZK9PprCilm459c61LIsEeH34YqOKMOE3yENvQcruF3J2mlkktZ+cugySMmNri5/tW77mlW9kLuqeUCeuJAksOaypHcYX28sZ1qs6ruJqT6EIn/bLtSJJEkdO2jlSZmd/SY0qrlROK1Rx9TNh1/zoQiIABm3dzXBS/2x2zZ+EptGNbvndw7FYLWFvuJESEDj1RdHevXsZOXJkA3ETKWZzw6bFTURPven1p+kb3UQlSWowLbCsr344Lei50tGzZ08kSWL37t1cccUV9cRVwxBGRkYGWq22gQcGhGgLCLgtW7ZQUlLCsGHDgvO9Xi/r1q1j8eLFOJ1OtNpG3ilZBiKocxXGq7VgwQIWLFgQfj1gxYoVjB07tsn0+nlNw4cPZ9OmTSxatIhXFjwq7NJEd7mI5By1imAOWN0kp9tLcZWD3/77W+6/qD/XjchD2vNfpm67DcrOhv+3svX7i4ScAXzn6sgv/u9bBnQs4B+/bJhAn2YWAzLKbPEbodfn5P+4Q+ugV3KjZP6kHC4/+TVnDRxE39zQnrZYcaRMhOO7ZYjfd49MMzuOV3LgRE1c7VBRiTWquPqZkGho20el02rQaZsKqASDlkSDrs3iqrKysoFQKCsrY+HChYqVVsjPz8dgMLB+/XpuuknU6XG73WzevLnl0gYt4a0TV2lpaUyaNIkXX3yR++67D7MukNsklqmoqCAlJQWDwcCwYcNYs2YNV155ZXBTa9as4fLLLwfgggsuYMeOHQ12ddttt9GnTx8effTRpsIKGiaot+IzaUtYsIkpsozT6YT07pHt3FktQohGK1hzIzpHrSJEgv1JmwufDMjw7Oq9XHVWR9AZ0OBDjkdvQaC02ond5cXpaTqCMyiu7PETV8P79WJ40jcwolHivzmDq7TLoefV0DE59Mox4mi5EFd5aaKpdsBbtb9EFVcqjTi+VXhaOzRNKfk5oIorFUUYMmQIXq+XhQsXcu2113L//ffTpUsXdu/ezeHDh+nSpfmRXS1hNpu5++67efjhh0lLS6Nz584sXLgQu93O7bff3jbj63muQNRlGj16NCNHjmT+vLkM6t0Nj+xlzXt/YsmSJUEv3ezZs5k2bRrDhw9n1KhRvPrqqxw5coQZM2YAYLFYmohLs9lMenp6eNEpy6BLEAKiuTCcu1YkJWv1YMkNTm5tWPDxxx9n8uTJ5OXlUV1dzbvvvsvatWvrCncCixcv5sMPPwzW+qqpqWH//v3B+QcPHmTbd3rScjrR2Z/r19I5ahWNxJVPlqlx1oVtT1Q7+e5oBUMNcewtuGUppQeTgAQyk5qWDQmKqx1roH8ldD4n9jadM0O8GhOoWG87GXsbGnG0rKG46pwu3o+Vq02kVerhrIb374CEVLjz05aXPwU57UcLbtq0iSlTppCamorZbGbkyJG88847rd6e2+1myJAhSJJUl/SsQn5+PvPnz2fRokUMHTqU3NxcVq9eTV5eXuSj5FrgmWee4eqrr2batGmcddZZ7N+/n1WrVkVW3qA5Gomrbt26sXXrViZMmMBDDz/CgBFjuOiSK/j0008blBC4/vrreeGFF5g/fz5Dhgxh3bp1LF++PCohuXTp0oahTq0esvqIJtLN4XWL3KPaMM2do6S4uJhp06bRu3dvLrjgAr799ltWrlwZLIMBIkH9wIEDwf83b97M0KFDg4MVZj82l6GTbuS3f3w5uIwS56gJjcRVrcuLLMtoJRjfW+Qzrd9XWte4OdaeK1mG/z5E6bZPAEj351fV58qhHfmo27952PYclB2MrT1+jpbZKa5y4PY28qSZM7DJRn48YY+7x+homRBRnf3iqqO/ZEVhpSquVOrhskP5ITi+GU4eaHHxU5HT2nO1du1aJk2ahMFg4IYbbiA5OZkPPviAm2++mUOHDvH4449Hvc2nnnqqwdO6Sh1z5sxhzpw5DaZt2bKl2XUeeOCBJmG9tWvXhlzWZDLxpz/9iT/96U8h54da79ChQ02mlZeXY7XWyzVpJK4AcnNzWbx4MYsXL27OfO655x7uueeeZpdpzsZDhw4xbty4ZtdZunRp04k6o/BYaZVJ1H7jjTdCz/D54MRukLTM++0c5s2bF5w1fvz4iFqXRHuOWqRRaQibv3SIQadhQIckoIidxythYJzElc8DfS6h7HA/OAlpITxXHVIS6JDqgMKK+PQXlH3c/dZGdhba+Mv04Zzfp16OW2IGq33DeXDzcMZU/MDbd5wde3sAh9tLUZUoQ5GXKkRVrl9cFVU68Plkte7VmU5VIRz5GjqNgC6j4dCXcHAdpPdob8ui5rT1XHk8Hu644w4kSWLdunW89tpr/PGPf+T777+nf//+zJ07l3379kW1za1bt/L000/z9NNPx8hqlbgj++pu1uESth2VwksUg+bNq1atalByImJ0RrDkxL4psewDrws8ta0eLag4jUZTOlzif4NOQ59cK0M7p9C/gxX0/tFnLltsC3dq9XDdX6nsLOp5pSaGEbyBMg1OZbyNzVJdSHnhIQDSEhuJPXMGKQiPVXkcc8AKKoR3KtGgDYZJsy1GNBK4vXK79jpUOUX46XP41/+DD+4SNdoAjsWhfVUMOEWulsrz2WefceDAAW666aYGNZYsFgtz5szB4/E0bCfSAi6Xi+nTp3POOedw7733xsJklfYiuZPwAoWrK1VxFCqOgEf5G9E333zDyJEj6yY4qqB4F5QfVnxfrUKjgfSekNaj5bpSXrdoplxbHlubGoUFHR4hjvVaDX1zrXx4z7nMnthbFNEEJNkrBGKMqfALleSEpuKqxunhtZI+PO++Oj6eq9pyyhDNsNMahym7jydlym8BqLDHryF5wGuVm2wKhsJ1Wg3Z/vZAxyvU0OAZj84oEtg7jxLeKxChwZ8hp21YMBB+aVwduv60L774IuLtzZs3j3379vH99983KQfQEk6nU4y68lNVJQoOut1u3O66i5vb7UaWZXw+X8NSATEiENIJ7PNMIOQxJ6QHZob0cEgGM/i8yNCkIrrSSF43kteJ7DUgt7QvjwNJ9tblFjVDmz5rvciPaenYJY8TqeIwstaAbIzdKDRJ9iEBsqRB9vmCo/O0Ggm32x0chemW9ARkjttWAYlNE/0VwX9uA+IqyaBp8LsGqHW4+f2BbkA37ratQeeOjagJ7NdeUUItQrSY9TS0x5ROUrfhwFdU1Lqa2BoriipEMntGkqHBPnOTTRRWOjh6soYBudHXugpsK17HcSpw2h5z78vEC6DqOHpAPrkfj8OO23/5ae9jjnT/p624CoT8evbs2WReamoqGRkZEYcFN23axMKFC1mwYAG9evWK2pann346ZCHN1atXk5iYGPxfp9ORk5NDTU0NLlf83PXV1XF4kg6D5PMgS5q4h5yiOmZNqvDx2l1AbD8XyQcaYw6gwesX4SGRZVJqDwFQldAZuYUmxgFi+VlrvE6sgOzzBh8gYkGSx40OsNkduJ1VZJugVoJSp4N169bh8Xhw+8Drg2skPVrZzeerP6HWkBETe1Jt+xj74+8od/0fkMWe77fgOdRwGZ8ME5KK6FK7k2OHDrJr+fKY2BLg2w3fACOQkFn32RoapzLZ3AA6bE4v//lkOSGqtCjOugIJ0OKuOsny+sdv0wAaPt3wHfKR1odvm/QMPQM4rY9Z9nGJxoDO5+KLf/8Vm0mMQG7vY27QOq0ZTltxVVkp8hqSk0M/QVutVo4dO9bidpxOJ9OnT2fo0KE89NBDrbLlscceY/bs2cH/q6qqyMvLY+LEiQ0Sqx0OB0ePHiUpKSm6it6tRJZlqqursVgsUXvjlEByVkH5MZAk5PR8UYIgxjQ5Zp8HyeNA1upB23SU16mMXCshIWNJSmoxsb3Vn7XXheSoBK0B2dSCN8rrhBMFSNBwwIDCSK5C8IE5KQnZIDwdDocDW0UC5513Hi99eZSXvzzIXed2waMxovW6mXDu2ZAZmwbO0k9rkX6UqUZ4ECdNGEvvHEuT5aZ2fBvdf/+KL2MiXadMiYktbrebNWvWkNc5D/aDRePi0kumNlxIlvF99xa/2ZyNjMSo8ReQkRT77/6OVT/C4UMM7t2NKZPrPovvpb1s/fowGZ26M+Xi6D+jwDFfdNFFTYoIn66clsfs8wINixVrC3pByU7GD+yEq+v5p8QxR/rgeNqKK6WYM2cO+/btY8uWLaGLPkaA0WjEaGx68dLr9Q2+JF6vF0mS0Gg0bS7qGQmB8FBgn3FH9oo8p6RsJH1iXHrFNTlmRw1UHEYyJEFGUy9nQ3vlU6ufnaQB2YsGX4sFR1v9WbucUF0A+kSkxBZKXvjE70OSfUix/D75c64kjTa4H41GE6zGn24xIctQUOXEqzWBtwa9zxm7Fjg+B7IMlT7xQJRuTQh98U9MEba6atDE+OZgt4v8JYvOG9qWT+di5TkqScLmlsmNw81qcF4qVw11M6xrWgObspLFQ1WZ3dOmm2bj6+mZwGl1zMd3wBsTodPwuo4KmT2hZCe6ikPI/uNs72OOdN+nrbgKeKwCHqzGVFVVhfVqBdi6dSvPP/88c+bMaVULF5UWSEwXReKg/USLpBEeK13T4fNBqougphgSMyC5Y2ztcVSBxymSsQ2JzS+r0YLX26R5s6KEqIYe3p7AMnLLRVAVsqmq1o3L60NXr7L9tcPzuHJoRywGCcfz/ocaVwzrObls2DDhQYjLlITQ3yWnNokq2YrZ4aSFT7bNVPlDF1ZDmDDbwKtJ2SxR6YxfUvvUwR2YOrhDk+mje6Tz+JQ+9MuNb7V4lVOMsp/A1+i7mC7acXHy51f+6LQdLRjItQqVV1VeXk5paWnIfKz6bN++Ha/Xy7x585AkqcELRO88SZJISUlR3P4zhnbIt2pAQooo2JnSQlFL2Qey8qUYmlBbDlXHwBmB6zlw3mIpriJpIt3YHoht6YPkzuLz0hqosLspqKjF7qoTV8kJetKTjEiShFfjF1exrNLuqqHSHxI06DSY9KHP1fTVPkY4X+Z/1ZG1H2oL1XYxgMYa7plh6iJSssSDQnkcRwziroV3b4Y/DRX1i4BBnVK467wejOkZm5w4lZ8JgeK6afXabSX7fyuVLafwnGqctp6rcePG8fTTT7N69WpuuOGGBvNWr14dXKY5evXqFba1yhtvvEFycjLXXHNNg6R0lQhxO4S3SNIIT429THivLDntbVlTAiUaGjVvjglBr0wEIeh4iKtoPFf1OynLPqB1YfQWSajzcCQatcjoMepCn4PduVczckh/dLHsT+aykYyNRT224hhya9icNmuCHvBQ6Yr9w0R1rRh4YTWF/wxS/CUjKuJU66qwspa0ip0YC76DquPw75lw37bwJVBUziwqDon31K510wKRgqqCeFvTZk5bcXXBBRfQvXt33nnnHe677z6GDBkCiNFSTz31FDqdjunTpweXLy0tpbS0lIyMDDIyxBPU6NGjGT16dMjtv/HGG+Tk5PD666/H+lBOP2QZTuwRf2f1FeKqpgi0hlNTXEnxFFeBgqZReIpOFXElSf48MF9sbapHRpKRjCQjDoejwfQFy3dz8EQNYxMHMqLPlNjlWwG4bCRJDi7vUAEjOoddLDnRANRS5Yn9ZbfKIbysVlOY45ZlrEapwbKxxOXxMerpzwDY+sh60v7UTdSOO/ot3rxR/FBQyckaF+N6ZapV2s9UqovEu7Ve6NjaSbxX/fw8V6dtWFCn0/H666/j8/kYO3Ysd911F7/61a8YPHgwP/zwA/PmzWtQVmHx4sX07du3xXYnKgrgcQD+5HCtAfT+UYJeV3wETH0qjkDJbqitCL9M4MlajoNtvlZ4rmJZeysqzxWxF3w+r/ByOpqvcr5iZyFrdpdQFo+i34H2Oobm641ZzcLDXZU+KNYWUe13RlkTw4wCXP4wll2ix2q1I/ZhwQq7C61GQquRSElJhUH+aMKPK/HJMpct/orblm6Ka8V4lXrYTsL7d8KHd4u8z/agqlC812tET1p3uPtruP/79rGpDZy2niuACRMmsH79eubOncs//vEPXC4X/fv356mnnuLmm29ub/POXNz+Ssy6BL/A0oNGL5IZPY4Wb1KK4nGKfTYnBtrDcxVNjtOp4rmqv1zMxJUHKg6DpMGXMwivT0YXwtORa03gaFktzupSpB8+gJz+kDMgNja5avjJl8P+8ly6FlfTK7tpGQaAZIsFKKSyY/PpCEpQFRBX5jApC0YLFkSopToOnqssq4l9c8ZQIxuFZ6rbWNj+LhzdhF6roXe2BZ1WotYd54crFcHKR2HHP8XfBjNc8sf421AdQlzpDJDdX/z9MyuYelqLK4CRI0eyYsWKFpebN29eg6a0LRFJw1qVMHj8IRx9vbpWOhO42kFc+VroKwj1PFdxCHUF9hFJHkpQyMTwhhRNmBLiI/gMSSBJ1Lq8HDhRg1GnpUtKw/BXpkV4bLSVB9F99Gc475EYiisbq33DeWZrZ67iAM9fNyTkYsn+noOVtTG+Scgy92vf40bDf0kb9mHoZRJSuVP3Or8YkEDqRfG5kWrW/xHrlqVw/hzo5heYBd+Bz8uqB8+Liw0qIbCdhB/qfU+2vQMXzW95tLKSuB1QWyb+PhVTQ1rBaS+uVE5BPP5Yja5eyEJnBFd13bx4ERQPzYiZeHqufFF4rjRnoOdKZwzWI/P4Q0jaEJ6rjCQxTK5YysLXZQya5E6xsQfAZSMNG0PS3PTIDN++JZD/VGV3iM85honcqdPeJtNdDR3CjExMSCFDqgJfARjjdBs48WNdODe9h3ig8tRC+SHxv0r7cPAL4RHO6idCglXH4MjXkH9h/Gyo8edb6Ux15XkC7PwADn+F1DM2hXdjxWmbc6VyChNOXNWfFy98/pBIczlO9es3xboHY1QJ5KeouJI0EZdiOHr0KOPHj6dfv34MGjSIf/7zn8F5n3zyCb1796Znz54hB464fWIfem0ocSW+T0f13fD+4iMYdmtk9rcGVw3X6b7go8keZk7ID7tYoKFz5cHv6gZ0xAJJQu40AnpNCl+/zZQi3h0VsbOjHp/uLuZuz2zeGr0S+l0uhGWGP+c1lueiBWRZ5vnVe7n25a/5fG9Ju9nRrvhLYtB9vHgBHFofXxuC+VY5TWseHvgUNr2OdHxTfG1qI6rnSiW+yLJokwIN2820h7iS5cjCcPWFl+wlZs8ksgzITfcZDikO4cpoxVVLVe4bodPpeOGFFxgyZAglJSWcddZZTJkyBaPRyOzZs/n888+xWq2cddZZXHXVVaSl1TVf9njFuQqVc5XhDwtWxyNNI9KEdr+4qiKxbp0Y8ZevDuFF4qqhnchJDtFKKyGVQ75s/l44jOS1+7lnfHhRqAR7iqpZsesESQmdIClLTMzqC0XboWQ3rxT34d1NR7lhRB6/HBc/L9bne0v402f7OatzCt0z4piOcCpxfIt47zwK7Cdh29tQGOcE8mC+VdMis/SaDJZc5Lxz4IfmB7KcSqjiSiW++Dx1N2ydAVmW+eUvf8m//vUvysvL+W71uwy5qE+cbKkX5mtWXElCyMhesU4LffxaTX2RFImYScwQLvTm8sXaSjSjF1tBbm4uubkigTUrK4u0tDTKyso4evQo/fv3p2NHUedmypQprFq1ihuvuBgqjoLRikcSN2ldiK7DAc9VtSsOw/ovnAfVxZA7uNnFkhPE51Rp6gidRsTMnARXKX9Zu4tih47zemaGEVcpFJPKKzVj6L7lWMzFVblNhHBTzfU8ael+IV52kOoEDwdLbRRU1MbUjsaMyc/kr/9vJG6vjy7pZ6C48nrgxF7xd86AulHThd/Ht91XdT3PVWP6Xgp9L0V2u+GH2DY8VxI1LKgSXwKeKa0oILpy5UqWLl3KJx//m8LvVjOgd/f4lWMIVFwPUSW+qKiIWbNm0b17d4xGI3nDJzL11vv59NP/NVjupZdeolu3bphMJoYNG8aXX34ZdndPP/00kiTxwAMPhLGnvriqu6hNnz69SYcASZKYfvsdwuOnQO7O008/zYgRI7BYLGRlZXHFFVewd+9esW2NLqTYW7duHVOnTqVDhw5IksRHH30UctuRnqPNmzfj8/nIy8ujoKAgKKwAOnXqxPHjx8V3QxYtf5r1XPlzruxOJ7rne8J7v4j2lEROt/O46ds8Rr+4m28OnAy7WNBz5fDgi+F4GGvtUa5y/5drk3aQZQ1TisGUQifpBLfrVnLTiNhXjC8/cRyAlMp6IcBUf1eEisOk+JP941otHlFR/7xemVzQNzuu+z1lcNtFyLz7eEjpKkbm3fofmLU1vi3JAuLKGsJz9TNFFVcq8cXrHyOuFTe/AwcOkJuby+gxY8nJzkan09Ut48flilHtmzBemUOHDjFs2DA+++wzFi5cyI4dO1j591eZMHoEM++fHVzuvffe44EHHuA3v/kN3333HWPHjmXy5MkcOXKkya42bdrEq6++yqBBzdQ4qh+Ca3Rhu/jiiyksLGzwWrRoUeuOOwRffPEFM2fOZMOGDaxZswaPx8PEiROxmXIgZyCYrE3WsdlsDB48uGFtOFup6ANmFyIj0nN08uRJbrnlFl599VVxKkLkbEmS1OAcefwKJZTnKjBasNJrAHt583XMFKCoykFBpaPZ+1EgoV2WocYVu/IHTp2Vhwc5ePYcB1mWEF4rgIRUOkonmaP7K3ecE/vRWRUVFQCklm+vm5jiL7hacYSURHE9aK86Vx6vjw0/neTNrw7ii6XyPdUwWWHKs3DLv0Vuqc4I3c4TbcHiydBb4Lq/wqDrm87zuuHkgfiHKtuIKq5UFGXjxo2MHz+ehIQE+vTpExQVl112mVignriaPn06s2bN4siRI0iSRNezJwMw/oKJ3HvvvcyePZuMjAwuuugiAJxOJ/fddx9ZWVmYTCbGjBnDpk11SY7jx49n1qxZPPDAA6SmppKdnc2rr76KzWbjtttuw2Kx0LNnT9asWSNWCFNT6p577kGSJDZu3Mg111xDr1696D/iPGY/+hs2fP1VcLnnn3+e22+/nTvuuIO+ffvywgsvkJeXx5IlSxpsr6amhptvvpnXXnuN1NRGI2EaIAvRqWkadjQajeTk5DR4JScaofK4aCrdRlauXMn06dPp378/gwcP5s033+TIkSNs2bIl7DqTJ0/md7/7HVdddVXdRI8TnNViaDWRnSOn08mVV17JY489FuyI0LFjR+Gp8nPs2DERPgzmyGnweMXfoT1XQly5ZQ3VJNTVVosFO/6FzSZyqJKaGXln0msxasWNu3L32piZU2HugfeqN0S4MhwGc104OcbCE6CiVojJVGu90ZQpXSB7IHQYQqo/ZBqvJtIAq34o4nef7OLrA6XIwC1/2ciT/9nF0fIY9qFUCU1mLzHQITfEw2fpj/Dns9C9e1387WoDqrg6xZFlGZvXG5OX3esLvkLNj7aW14YNGxg3bhwXX3wx27dvp1+/fsybN49nn32WJ598Uizk9V88tXoWLVrE/Pnz6dSpE4WFhWxa46+1IvtYtmwZOp2Or776ildeeQWARx55hPfff59ly5axdetW8vPzmTRpEmVlZUEbli1bRkZGBhs3bmTWrFncfffdXHvttYwePZqtW7cyceJEZsyYgd1uD5nMXlZWxsqVK5k5cyZmc70cjIQUSEwjJT0TEN60LVu2MHHixAbnYOLEiXz99dcNps2cOZNLLrmECy9sYWizziTc8tn9IjjbCKFqKxGeGT8LFiwgKSmpyctqtdKpUyesVmuzocsAlZUicbR+AnlEJKQIj0RCakTnSJZlpk+fzvnnn8+0adOCy4wcOZKdO3dy/PhxqqurWb58OZMmTQp+ZnI9z1Wo0YImvTYodE7IKbETV143vH87Nru4IZtbKGuwrNc3fGSYQ2b17tjYA3h8wpNmb847JklgSqFITuXHYyW4PLEdBVvuzwZISU6pm2jNhbvXw3V/JcUsxHBFbfw8V5/uLub19QfZcOAkeq2GPjmi+OvO4+1Uobw9qCoEZ03DaYe/gVW/ge/fbR+bGmMW11zsZUjx6JKhEGpC+ymO3eejx7od7bLvA+cNxKyNPJ9n9uzZXH311fz6178G4IYbbuDGG2/k8ssvZ+hQf+PcoLgykGxOxmKxoNVqycnJAbMEbhtIGvLz81m4cGFw2zabjSVLlrB06VImTxYertdee401a9bwxhtv8PDDDwMwePBgnnjiCQAee+wxnnnmGTIyMrjzzjsBmDNnDi+//DLbt29n9BB/4nw9z9X+/fuRZZk+fZpPqi8tLcXr9ZKd3TBXIzs7m6KiouD/7777Llu3bm3gYVMMnRHMWQ0S7GfMmMF11zV9wvP5fNTU1JCUlEReXvM5NrIsM3v2bMaMGcOAbIN4ckztFlkiv8EcHDVXWlDQ4jn66quveO+99xg0aFAwZ+utt95i4MCBPPfcc0yYMAGfz8cjjzxCeno6VIoeYz40+ORAzpWGULfkjCQDNU4PpSTTwx0jb4TXjdxtPLbdoiCu2dj87+WcDAccPADemmaXawuFNR7GPruODskmvn7sgvALJqRwQdlj2N4q4IuHe8U0obvCLW41qWmZIeen+nOuKmzx81ztKhQiql8HEfLu3yGZ7ccq2VlQySWDcptb9fThgzvh0Jdw9Rsw8BoxrXAbfLMY+l4Gg2+Ijx3b3hFFpfMvBGOjDgeJ6YCEhIzBE7vfjdKo4kpFEY4dO8Y333zDs88+G5xmMIjRgEGvFYgkVq879Ag3i/8mrNEyfPjwBrMOHDiA2+3m3HPPDU7T6/WMHDmS3bvrvAD1c5q0Wi3p6ekMHDgwOC1woy8pKQHZX2enXs5VwFsnNU6ecTtE9XidAfR1lYsbLyfLcnDa0aNHuf/++1m9ejUmU5jcl7agM9Z1jfeTlpYW0tvk8/moqqrCarWiaaHa+r333sv27dtZ/+WX4C5ts5nNnaMxY8bgC1M77LLLLqsLJwdXFst6ZPGZaSQpbKPfC/pksnvXTiw2O7hjVOLDkIj9hveR564Cmg8LiuX9AiaGpRiyiz8HJmP1VjS/oNGKBTs2EmLaAsfnk6nwie9/SkaI/C5ZJsUkPs9qpwe314c+RB6d0jb9WCxu1H1yrP53cVM/UPLzuYG3mUCdM2u960je2XDOPZA3Mn52fDJbFJS9//um4kqjFQLLXorB8/PxKqri6hQnUaPhwHkDW16wFcg+OXjDlULcoBIjbXkCQYFTXxTt3buXkSNHNhA3YvRZy96wBiE5woue+jdqEIKrPpIkNZgWWNbn8wmRZMltUMy0Z8+eSJLE7t27ueKKK+o2ZD8pQnDmLEhOJCMjA61W28BLBUK0BQTcli1bKCkpYdiwYcH5Xq+XdevWsXjxYpxOJ9r6nkFHlRg1YzBDKyuKL1iwgAULFjS7zIoVKxg7dmzIebNmzeLjjz9m3bp1dOrUCZxWIWgiHZHodYkQnKSN6BxFjf974PFnNIQKCQb49cW9+cL+Ff12HwF304R8pbA5hTDRSJCgb/48fVqRwwHPJZxXDrEqOOL0iHNk1rcQ1p+0AMu75RSVy1TFsHlztb0Wn//zSslq5DVdMxc2vkbymAeRpH7Issi7CgxIiBXF1Q5cHh86jUSnVOF17Oqvc3XoZGxrkJ1SzFgvqubr6rUi63iWeMULrxt6Xija8CRmhF7GnAn2Ukxutc6VikJIkhRVaC4afJIPj1ZDolbTojejJSorKxsIhbKyMhYuXMiAAVH2c5PlkNW98/PzMRgMrF+/nptuugkAt9vN5s2bw5c2aAlDYpP+WWlpaUyaNIkXX3yR++67r07k6YygT6Si2k5KsvDKDRs2jDVr1nDllVcG11+zZg2XX345ABdccAE7djQM6d5222306dOHRx99tKGwAtG42m2PvG6VLAsxI/tEvpYktTosKMsys2bN4sMPP2Tt2rV069ZNzDAlR2ZLAGeNaKxsSMKQ0bPFcxQ1Qc+V+L5qW/jeejX+ukqxCgsCNpfIAzEbdE09no1493gmazw3k1T+XezElT8txWxo4brRZRQWy1dQXhFTz1WgDEMiDozJjUS1RgtuG9qaYqymwVTWuqmsdcVcXB05Kb4PHVMTgqNNu6aLa8Hhk3Z8PjmsR/S0I9rfuNJo9XD9280vY86AE6ieK5UzjyFDhuD1elm4cCHXXnst999/P126dGH37t0cPnyYLl26CCFQXSRGxIUqFudxQMmekDdCs9nM3XffzcMPP0xaWhqdO3dm4cKF2O12br/9dkWP5aWXXmL06NGMHDmS+fPnM2jQIDweD2vWrGHJkiVBL93s2bOZNm0aw4cPZ9SoUbz66qscOXKEGTNmAGCxWJqIS7PZTHp6emjRabBAWvfIC3bKPijZJf7OGQyS1Oqw4MyZM3nnnXf497//jcViCXqbkpOTSUgQT7WLFy/mww8/5NNPPwXEKMj9+/cHt3Hw4EG2bU8nTVtN5649IjpHUeMXV2Y99MhMarEUjwMjNbKJJJ9DPCErXQD20FfY3n4EeLzFZHaAc7J9JJV8SWdt7DoRuPyJ/i3lfwFY/OUhYimuKk6KtjKpmtqmDcBH3AlDbgZLDqm7NlJZ645LrasjZeIa0zmt7uGqY0oCOo2E0+OjsMpBx5SEcKsrx8bXYPUc6Hw23PD3+DZLbo7qIjESOaNnyDIsccef1G78GYkrdbSgiiLk5+czf/58Fi1axNChQ8nNzWX16tXk5eXVjZLzuER4zR6m0KKkQ7R/Ce29euaZZ7j66quZNm0aZ511Fvv372fVqlUtlDdoBo8DXHZRpbge3bp1Y+vWrUyYMIGHHnqIAQMGcNFFF/Hpp582KCFw/fXX88ILLzB//nyGDBnCunXrWL58uRCSEbJ06dI6b4fOIJ4ijeGb/zagfgmJNrbAWbJkCZWVlYwfPz5YNT03N5f3/vpGcKh+aWkpBw4cCK6zefNmhg4dGhysMHv2bIaOOo/fPvty0B4lzlED5LryC2ajjkRDeEHz2vqD3L8lmfke/yjEWHivnNXBmlWRiJnbB2j5P8MSxhj3t7hsa3H4xPcp0diCkCzYRpJd1BurjmFYsLxcjGZN0YUYdmDNFU2bDea6Wle22I8YPFp0AoC8Y5/AT2sBUS8tILYOl8YhNFhxBFb+WuQa/bQWNr4S+33WZ/cn8PbV8O2rTectmwqvny+S22ONz9tyL1KzCBcaPdWxt0chVM+VimLMmTOHOXPmNJjWoE6SVi9ynOq5Gx544IG6sJ5GC1n9WfvVxpAVwU0mE3/605/405/+FHL/a9eubTLt0KFDTaaVl5djtVpF+MpRIZI5A/3O/OTm5rJ48eKGBTJDcM8993DPPfc0u0xzNh46dIhx48Y1u87SpUtDz5AkQEKIUS9t+TmHLLvhtovWGJVHISGFefPmMW/evODs8ePHN13PWQMn9zUQe9Geo+YNjbzXYSC5vEL2J8i6a5UPgbhqsMkJDfbXLHFIaHd6xbkxm8I0bQ6wdwXWwlLg/Jh6rjIo5yrNTjqmpDS7XKBKezxqXR05KW7Snd0/wb8/FBXJdQby0hL5qdQWn1pX379b1zge4Lu/wbkPxK8y+ondsP9/kBQiimDtIEYJVxXE3o7t/4CPZ0G/y+Cav4ReJkE8QOu9P598OFVcqcQPnTF0ODCAJAnvTbwItHaJJMfJZYOygyKkmdlLMRNWrVpVV2ndZRNFOPUJ4hUJkibYDkZxAsIp0qbN9ZeNskZaxPi3W+mWcHmcJJl0YZPILx+ci75wB9fteRXcxMZz5bJRgxgJF0lYEIMZp6zD43ARq8IHDp84H4ktjVDN6oslbR+ciK3naqChkOcNL0P+LU1numyw/gWoKea6sx7l3B4ZDMqLfQ7QUbsQcp0NNeLh4dA6yL+QDininBVUOGJuA/tWi/dJT8P/5oqHkrKfhCcvHlT7iw+HuiYHRg9WHW86T2nspSLftLl0iASR6qD3/HzElRoWVDlzSeksWrskRlgo0+cWLwX55ptvGDnSP+TZXia8adFUzA6KmViIq8i9REE0MbQHxI3A2olyp4bCylrszvAel0SDjkQdaAx+oRqLQqIuW9Bz1VyIMsBbeyV6O//Kr0ovVd4WAFnGIQs7zAktiKv+V2AZcgUQ25wrbCIEFywGWR+NHtYthK3LmJJv4s7zugdLI8SSQn+D6A75/mbbe/4LQG6y+CyLKmMsrpw1UPCd+LvPJdDRP6L48Nfh11Ga5polB3r8xcNzZfOXe0lMD7+M33NlUD1XKiohcDuoa/ES5inFViqeZhNST41EygCBp6pYNpUOipkowgIaDfg4dcRVLMUeBHuemWUnGo0HUwulDwC8589FJyFC0krjsjFcs5f53XbRYcTwFhdPSkgAqqjxxujS66nFLgvvb2Jiy8nRFpOwo7oZkdpWbKl90faYgjGrP02+2TqDGH5vLxU3e3MzN1iF8B3eQEm1EE85vc+GHxFVyYHcZL/nqjKG7ZIAjm8RIcHkPFH7b/xjYnqnEbHdb30CbbOSQpRFCYiryjh5rqD5zz4grtQioioqIag8Bq5q4TEK95TiskFtmQghnlLiqp5okOXY5EUExUwUpTfi4rmK4liDQkwW60cjzKJADNVvfrh+tcPDez9pWFXdlxdvHtZimYRW4bbTS3OcXl3KoF/LtbuSzMIzUu2LUfjbWY09EKZMbDnwGAhl2mIorp4uHsHbP2Rzf05PHgxVsi8pG+yllJcWcdjdCZNeE1PvVdny3+Hx3Y2ETEa+XxCf2APOajqmJJCRZMSaoPCo0sYU7xTvuX7PWffm8y5jQrW//lyoh47AtJqipvOUxu5vXxaB50ofw84GSqOGBVXiRyCkFqIxcZBA/pMvhmGKAKX7RNKmJ4Jh8Zp6ooFY5RO1RsxoG64bE3ta4bmqv76SOKpEY+gItq3TSHxdrGH5zuJgLSrFCYQaI8yRSzKL5PoafUZsvKDOKux+0ZnYUg5YwTaSPhZtoWIprgIhx4CXrAl+j8WqH6u44sWveHbl3pjZAuA1JnOZfhMX5iehS84R3iNkKPiO0fkZbH7iQl68KcZFNIv9JVSy+8d2P+GQ5TrPlSXEQ0EghBsI2cWSQBpEQjOjvnMG4J6xgXW9nwy/zCmG6rlSiR8BwdRcArk2juLKZUfE1CKgvjfJ54NYtOc41cJwrUlorx/4UdomWYayA6J2alZ/tDp9s94ok16DTpLxyBLlP3xGUo/+TdoFtRm3nT2+PCqq0ule5SDL2nyek8UqkrVrjNmRV72PAslZjU32e65aKiJqMGOWhSegxhm7cPf/Xdmb310xAG24opz+qtzpVNIxpQup5tgOasn+f3/jT/UHXHQYIpLai3ZAt/Niuu8ggWLB9cXVDx+JcOHIO4V3P5bUlou6gxA6LBgQVzUlsfPUB3D6a1cZm/FW6hMgPR+X7sfY2aEwqrhSiQ+yXCeYtM187QJeLW/sh2MHhVUkYThJQjh6fW0uexCWtoQFw/TnU8SeaBzckuQfweiLgeCTQZeAT4ZdxWLk34AOyWEraUuShFkHlW6o+OgR8q56BIbcpKxJ7lpe8lzGx9925YmMAu4Y273ZxQPlGmpi5SlyVvOs/hVOJg+gd+eLml/WaGWoZj//NTxO8i9ilEjtdaN5OheL3gwP7gRDiMEj/hpGFyUf5aJf3xYbOxpTXyxk+Ef/lu6Lz74Brn2zqcf8y+egaDt0HhV7cRVIZk9Ia9D+K4j/M8HrFJ7iWKZoOPziqr0rxSuMGhY8BQnXyPZnTX1PVHOeq3iFBeV6T+qRtv6J9Ui4tuQ4xTIsGG0bkFiVY5A0kNUHT3pPv1l1TZvD/Wb8pZOoSB3YtCGsErjtZEqVdLd4ImrZYq4nrnxe5T8zOXsAZfnXM+LSO0lryQNksmKRaumvOUSnpBh9pwMFgz21YEoJvUygn1w8QlCA3eXBU//cB8TVSVHY9bEPtjPu2c/5ct+J2BqiMzYUNv0uhxF31CWTx5JgvlWY0jgGM+j9OXu2GJ8Hh79fYD3Plc3poaxRMVnNVy8w6OgyqCqMrT0KoXquTiEMBgMajYaCggIyMzMxGAyxScL14/P5cLlcOByONvcWbBG3AzwyoAVnMxWYXV6xnNcNDuWHQweP2a5B45EBqXl76uMBfDLU2sEbg8/F5RHbd3mACI/d7RPny+kEffh1WvVZO11i225fdJ9F8DzVxuQ81bo8yB4XkkZDbW0tLpeLEydOoNFoMBgaCgqzTnzG5ef/AfrG4KbltjNH/wlzLh0Lg1sOOdbPO7Id2YKlm8KjwxLTKU4egtx9fMvL6kzCU+xzC+9BLMRnUjaP9/8Mj8vBA1VOOoRqKRMYJRauc4OS/Od+fv9dMn+vHcnDk/py9/ge4BfrlIqQU0GFg8Mn7RTGuhxDI3bl30VBRS0TcrKITTfZegTEVaiQYABzBlTYhLiKVe0tn68uLOj3XK3dW8KMt7fg88Fz1w1m6mDxu9Vse4tuFYfxVB2D9Bh79hRAFVenEBqNhm7dulFYWEhBgcL1RXwe8eRfL39GlmVqa2tJSEiIqYgDRKuZmhOiSrvtYPN2Vp0AJKhRfsRO8JiNOqTqEyIE15w99akuFXkKFRLoW6gh1Boqi4VHrUorylVEQm2FuDgZHZAQvgZMqz7r4LadkBDFjUbWACawFUe+ThQ43F5Ka1wYtBIam/gcEhMT6dy5cxPhmOi/wlXYY9RSJcqEdqNOgw4vHrTYbLXEQM7w6XGJsm+PcMPIriQ0l3clSTiNabxefQ41/zvEQ1fkBpsYK4Yk8Z+dpVQ7PcyYGCavy++5qq6q4hcvfkV1rZs1s8eFz9FqC6X7KHGcjU+WsCb4vxwZ+eK9phgclTx4US/uHt+DXtmx+HSAnR/ANy9Cnykw9iEA/rermDvf2owsw40j83j6qkGx2XeAgDeqUWeKBiRlibp7sfRcuWoIDhAyWfF4fTz2wQ4cbuFZ/O2/d3J+nyzMRh2+obeyf/f3dG/O5lMIVVydYhgMBjp37ozH48HrVSDJVJbh0/mw5z/CLX/ZnyGrLwBut5t169Zx3nnnodfHeOjxj6vgq99Ax+Fw5cvhl3PZ4NUbxd93fVHXLkQhAsc8rk8aupUPgaUD3PpxZCv/6/dQtA0u/gN0u0BRuwB4ZTq4bXDzB5Aa4ZNZdTE4zGIYczN1Ylr1WX/+DPzwL9Fct98vI1snlpQdhE8e4H+cw9MnRjE0L4U/XtcXrVaLTqcLKRrN/itcVW2McvjOfQAGXA05kd0MJUkiyWSgwuGlJi1E8+424j6yhY+PaPn4yB6uGtaZhJZ8IMYkni2/HjZVMGOyl+REZcWVzycHey8GmkQ3IdA3zlHM9wUVANQ4PCQnxuCaVH6Yl/Vfc/LGlZi6+j2ZpmToNFI8MNVWMCSvlX0vI6VkNxzfLAoYI87RguW7g1H0v288yu1jupGfFSNxB3VewkBINhTBEYMxFFeBkKBGDzoT6/aWUFjpICVRT4JeS2GlgxU7i7hmWCd8o+9jT8VyuqfE+PNRCFVcnYJIkoRer1dG8Hz/Hmx+SfxdcxT+MwPu/ho0WrRaLR6PB5PJFHtxZTsu9q8bDs215TAaobZEJFJ6a8CkbFHB4DF7atDVHIVEa/P2NKBWHIO7Iop1IkSWoWKf8FyZLZFv3xTZhaZVn7WvWnwWWp/yx9savDVQsIFSfVeOV3sZojVgasEuk19bVH31OmCF83+jrE29L+aKF7/C8fVRlvwii24ZLT8MJCUYqHDUUu1R3jMj7/4P12jd2NL6k9jSaEHAaDJzo/YzTH0ujElJsprda4KiIWwpBv8N3mAvJkGvpdbtpcrhVl5ceVxQXYBWksnq2A3qi7071ii7r+YYerMQVv4WM9uPV/JTqQ2zQcMgzw6+8fXnwy3HeHhy39jZkDcShv8/6DIq/DKXPAeXvtB8/am2EgwJWkGS+O92Ea68cmhHrCY9iz7dx0q/uPq5oSa0n87IMnz1gvh71L3iCe3EHtGsM94EW2C04NKVpLp2NLVlsbPH5S9GZ0yKfB1DUsN1lcTrqkuyN7RcWTsuXPIczCkR3plo+GoR/PM2OLReWXs8IgRXLoncjJQIbr4JOnFnr3J4YlYQcV9xNXuKqiPO+4/liEFTZlceTvuaP4+T0EcS4jMl87T+deYOtWMN51lqA9WHtwNgkLzhq+kHRqY5KrD6BVhlLDyNVcfEIA2dqdlwWEFFLW9tOMw/Nh9V3gaA1K6iSXEn0fLmf7tE+Hx87yxu0K3zT4tx25m+U+HS/xPv4bB2EDWwmhvd3VasHeG6v8IlzyHLMht+Eh61Cb2zuLCvyAf79uBJfD4ZnNUkOQrj0+9QAVRxdTpT+D2U7AJdApz3MAz2D0Pf+X78bbGViPdQ/cUaE3hSssdBXBmiEFdDboYpf4SuY5S3x10rRstIWtBHIa5KdsO6Z2Hb35W3KUC0+XiHv4YfPoCTB5S1w5/fVOHPVEpNbDkvLcF/P6+WE2PSW9C3/9NggdJIegsCWLwVANQc2aG8PWdNZ0P+w8iDb4xshcAIrcBweIWpqhF5gFZ9M6MRE1JF25deF2MxiltSVSwaSZcfxi4buc/7IPP+s6vhiMEAPh+HTtqY89FOXvlC4e9vGDYdEte583plcm6yGDG594SDSns8ytG0MwkpYpRk/ys5Vl7L8YpadBqJ4V1T6ZtrIdGgpdrh4ceSajTfLOaC3Y+i+fpP7W11RKhhwdOZfX5Xd/4F4ks8/DbofDb0iEG+UEsEhlknRSCuApV6YyiuJGcrPFe9JsbGGBCfz2NHoy/YV/wDfPY76DoWhkR4Q401Z90C3SeI0IOS+MVRuSw+s8g8V+K9itiIq9q//z9AhN2TWqqI7ueRjG+orfiSftKNwPmK2uPx+nB5xQCGiDBasMlGqitsJLu8zSfAt4JquzjnFn0z9mi0cIfwpltf+gqojU0j6YrDnJStfFw7EOPGI8yd2q9u3g8fwvKHIe9sMieInNDSmhgMgvC6YeNrYM2FPlPxSVp2Fwphe1bnVDJSkuleWsBPcge2HCnj/D4tt1RqFeWHRCTDlBK83pTbXMx8Zys7jlfyxCV9ub6LHbYsFdfj8Y/Gxo56eH0yN47Mw+uTgw8qQzun8NX+k+wurKKnv6+o5KiIuS1KoIqr05l9q8R7T38xwcze4tUeBMOCEYirrmNFaDBUWwalCIYFY5g02hqi9RKl9xBiJrOP8rasfEwkkZ/3cDCEERF9LlHeFqgTVz7h2UuJwnNVJSeKqthKIsvY0geATZQCM+kjCwSMSKsF7Q7QXKqsPcDmwxU8vFHHq4e+Zs3s8S2vYEximusxtv6vG6/knmBS/zB1j1pJtV2MMrUYIjs3gZ5+MRmAUHGEkwhPXbq5UZkbXYK4RlUcCdYrq6x14/R4MeoUFJzVRbDqMZHA/UQJGo3Ext9cyA8FlfTITILkjpyl2cdP3g5sO1IRO3H18liR73TvluBoyT+s3MPXB0RY7omPdnLOtQl0+XaJqAMWK3FVsgdKfoD0fLrmDm4ySvLJywaQkqgnI8mIZ3OKmFhbHhtbFEYVV6crtRVwbLP4O7+FSs3xYNqHwhMVibiKw1NSXVgwCnFVXQwn94knvpxQHWjbgQ5DxQjQWHD4KxFaHnF7bLYfLf6cqypZlD1IiaC5bqpRZkJHL72KdivvuZIkaq59D577ArMx9GjFkARGwCot9gDHyt8Cl5HoC1+WowFGC2bJAXJs+gtW+8N7YZPZG2H1e/+qYuG5qiqgXPaHlBsXWO0yCn65DpLzSE7Qo9dKuL0yJ2tcoWtztZZAZXRLbrAosUmvZVgXf56ptQPTtR9z1YB0+o2J0XXb664r8OvPb620u/nXlmMAwWN/6yczT4yZrXzLqPrs/a8YzT7kF3DFi01m52fViywEKrg7YxPCVhpVXJ2uFHwHyJDSpeGPo/wQbHtH1Ls696H42WNKPqXaG8idRwFe6Dw68pV2fwzLfwV9L4Pr31LWoOIfYPUTkNZdJJKfCox/XNT+yerX8rL1qTwGFUfE4IVADSEl8IujKq+4MUYymqyjGe4aqkf33t/BPVg5W/zY/D35Ig0JAuysTWOnZzz5J/QMV9geu18gJUboRWPANST9eBCOxEhc+c+PtSUh/MmD8P27WDNeAJJi47mqLqLMn6/XpHq9KRlyxfdDAtLNRoqqHJyoDlP4tLVU+RPVrbmh51s7MkBzGLS7IALPbKvQ6uHxY6L9jr+e3pEyOx1SEkjQa5k9sRe/fGsLq/bX8JuHfxvbGoiWDtBlDGT1YVdBFd0zzeEHPvijDNLPRFyd9gntmzZtYsqUKaSmpmI2mxk5ciTvvPNOxOuvX7+ehx56iGHDhpGeno7JZKJPnz48+uijVFRUxM7wtlL4vXjv2CicU3kcvvgDfPd2/G2KBp/P31g5Nsg9LoCJvxOF/CIlKUu4yMO1jGgLNcVw4DM4siG69Xw+4aWsKVHept4Xw7Bbo39y3fwXeHMybHxVWXsaiatIvSHBAp8xyLkKjPgzRyGuVpVm8GvPXfynOESfvTZicwuPRKIxwlBWzgASM0RNtVg0b65yCnssCS2U8pB94LZjkfyfcSwS2muKKZPDiKtGBEKDJ6qdzS4XNfU9V8Czq/bw+Ic72FXgFwyB1jdVMR4tCKL1jl84DeyUzBcPj+fvd53D2J4ZGHQajpbVcuBEDEZG12fIjXDbfykf/Eum/OlLBsxdhd3VUOQ/v3ovt725keNuvxcrUBvrFOe09lytXbuWSZMmYTAYuOGGG0hOTuaDDz7g5ptv5tChQzz++OMtbuOaa66htLSUMWPGcMsttyBJEmvXrmXhwoW8//77fP3112RlnYIVY0ffB72nNO05lzMAhv5CFD1UuvdbOBxVsGaOSIy8YG7LeUU734f374Bu4+CWj+JiYkT0u1y8YkFmX7jyleiLppb9BIuHgTEZHjsSG9uiRecXMx6FxYxH5O+8NuAHKof8krzUCEdV6hNwyVr0rloUfQY/eQDbPx8DbsUcRSJ4zxS4ULOFni23Iowau7/dUKIh8rIKSX4hprjnyuejyl/Ly2Ju4bMa9yic+wDW7xzw00+xSWivLqRMFuH8kOJqyzIxuvqsW4Lzy5Su7B/0XAkR9d/thRw6aefSgX5Plr/21Scl6fywcg/XDc+LqHaaEkiSFDzuYZ1T+eank2zetZ98t09cn2JYIqaoyoHFpCPJqGsy6nbVD8XsLa7m5n4d6QKikfTPgNNWXHk8Hu644w4kSWLdunUMHToUgLlz5zJq1Cjmzp3LtddeS8+ePZvdzoMPPsgtt9xCbm6dG1eWZWbOnMmSJUt48sknefHFprHidkejgcxeTaebkuFyv73uOA31tZ0Qo04MFrhwXsvLGyxCFMay11jFEdDpRG+tWLSyiRZrLgy+Ifr1ArYrLWQA9q4QIYQu50bc3kXYFPAUKdybze95OivdDX0ie6CRZRj0+kns7rfY5HqcCDL+IsdZhc0mPA7ReK4u6y5x2c7nIOViJa0BwOYXM4mmCENK1cWYq/YDemwuhQWNs5Jqf36cJakFgeAXG9akw0AMEto9Tqgtp8yf0J4WKuS2459w6EvoOIw0s7h2ltsUFleNPFcPXtSLH4ur6Z3jz/30e8WX1Yxk09oD9M21Ki+uDnwm2u90GQ1jH8Lt9aGt1wQdYEjnFL756STb1n7ADWv/BHd+Dh3PUtaOevTNtbJ97sRgWZP63D62G06Pj/xcYZ/ktou8MW2MC1+3kdM2LPjZZ59x4MABbrrppqCwArBYLMyZMwePx8Obb77Z4nYeffTRBsIKhMKfM2cOAF988YWyhp+OGC0if2f0vZEt320sPLQX7vg0ZiZpP7wTFg2CA7HbR1wI1MTyusCr8M3x3Zvh7aujH50TK8EXCOvpIhd6kkSwR12VW+HcEXctNr94iEZcBT8zV4RJ55Hi82L3Ci+UOVJxVbwT815R905xz1VtOX2lI1yo20av3JSIVgkUMo1JWPDqNyjLPBsIkdAOdeH+6qJgDTXlPVd+ceUXk5cP6cjDk/qQnuR3Y/oH/EzSbOKWEdnkpSqY7xWgdL8oJF2wDYBVPxQxeP5q5n38Q3CRIXkpAGzzdhMTYjVCb+mlsLAH7P9UtIYK8Tu6bnge087pQsfseo9GMarLpiSnredq7dq1AEyc2LQ2UWBaW4RRoIWITncKnsKjm2DTa8LjMOzWpvPdtVD6I5JL4XyCcCRlRTcCUJ8QnaekNWhFL6uownDlh+DvN4p1f7lOWXtK94uRiCldIDuKBPL658lTC1qFSkt43XUV43VRevZ0sfNclcpW/n00l8zvC7hscIeIVvvv/+tD8l9GY/HURl9HrFl77NgQ5yaahPaAuPK5apV9unXZsPvtSYy0XZElh6T0jlBcl5yvGLXlTNP9j2nWPTCwhbZDJw/Ad2/R153JrPMvVN5bozPCwGsoW/81UE56KHGV5C97UFNMmllc35X3XPnDgpYwCe06I5iSucOxHMbOhaxUZfcPdREBf2X8PYXVVDs8OD11n39AXP3ozsSh0WOKlbiynQB7KWgi+P1odHg0RnQ+Jzgrm+2leipwCioDZdi3bx9AyLBfamoqGRkZwWVaw1/+8hcgtHhrjNPpxOmsEzJVVUJ1u91u3DEIzWmObEC7/T18ThveQTc1mS/t+ADdx/cgdToHMu+JiQ2nKoFjddz4oRDIshx5eNTrQ1+yC1lnwqPwOdPs/BDt2t/hG3wz3ksXRb6irCXgHHfbq0AT+qYaOO6IP2tndd12JX1UIWRJo0cH+Nx2vAqeJ6n3pRykL099m0le4R4m92s+yBc41gyrmURJDI5w11ZFVwG/OXtqa6jxe64S9FLE53ZDsY7bHUvpcbSMj5X8Htkrscni8zfqdZHZk9YL07kz4IMfqHa4FL0WSNWl6ADZlNLi70UqP4pu/f/RI70n9824C4j8uxrNd7usRlyHLUZNk+U1iRloAV9VIckdxa3xZI1TuXMiy+iqCpEAd2Im+46XU1LtpEemmRxr3e9WZ85CclTiqSxATu0RclNR/57roakpQQt4jan43G5+ObYLF/bJwKTXBreXatKQkqCnotbNAbkDfWpK8cXgPqFzVCIBMz9zY1v7LbMv7Enf3IYPiE6Pj10FVRRW2JmoTUTnc+KuOQmWPMXtiYRIz/lpK64qK8WIguTk0MP/rVYrx44da9W2t23bxpNPPklWVhaPPPJIi8s//fTTPPnkk02mr169msRE5ZMEk+0+snKvpcaVQ+Hy5U3mp9hKGAe4i3ZDJqxZE9umpSZ3OXqPDYc+BbcugorossyA43/D4LGxPW8aHq3y56g1x6z31DAFkDwOVvz3P8iScsUF+xRspzdwqKCEHSE+s+a4RDKgk118vmYFtcbmBUekx210V3IxICOxfNWnUXl7cip/4Gyg4kQRX0Z5LC1R6OjCWek+zDobyyPc9povvqJX7jV4NAYOrVqDT6NMrkansm84V7sThzEduWosy5cfimi90pJjuOhKjUcb8TFEgtlRFPRcHflpH8vtP0a03t6TEqDlaFGpovak2vbT3XIWLl9ai99pa+0RJgDOymJWtdKG5r7bltpjmJ3FFFeMADT8sGUDJ3c3XKZTWRHDgJOHd3Gwegeg5afjJYqdE63XyaX+UPmq9d/x/tFEPi/UMD7Xx5Vd6wYeWTJvw5utp2xbOaWbltOpGSdea65jww/upCOw61AxPzU6tr31/p7WHUaceJ/elUf58ftv+bEkjLetDVxiK0cHfHHQTo3XyUhTEQcbHW+ZE57cqkMjyWy2JpFAORvX/Y9SSxxGVIbAbo9sFPtpK65ixcGDB7n00kvxer28++67ZGRktLjOY489xuzZs4P/V1VVkZeXx8SJE7FarbE0l6GhJjqq4Ll5mDyV6Ly1TLj4smCYMxZovngG7fo/4j3rNnyTn41oHd2z9yC5bOTc8H+i9pNCuN1u1qxZw0UXXRT9MXtdsOMeACafP1a0rFEIzZqvoRi65Pcl7/woykMA2j1JUFvGhDHnhK3AH/VxVxyGnYDOxJRLoqu4Lv2UCD+9QGqSkSlTojuWSIi0pGngmG3Zg1jq6c6UAdlc3EO5UIL0XSm6wy8zvEc3vNdNjni9PT98BwdOYMek7Pkp2s6y71cDMGzwAKYMi+zJ3rK/lL/8uBVjooUpU6Ko+xYB458bQkmZk/cuHcnAjs3UuasqgD1PoPPW0nv4edS4vAzqaI2oxlIk323N579H/urP2L2iBM3lF19Ql+fkRzqUBIdfJsPoYeJ557CieAf5HaxMmTIk4uNtlsqjsB1krZFJl17Jf979HgpLGDO0L1NGdWmwaLXDw1m//wyA7544v0nYuS3XMe3br0IF9B0xlj79m//+aT7fgvZrH73yMsmfqPBv2edB952DSjmRGn+u4E2XTWySv+jzySz4/n+4vVAoZZHGUc4e3Ac5mjI6ChKIPLXEaSuuAh6rgAerMVVVVWG9WuE4fPgwEyZM4MSJE7z//vtMmDAhovWMRiNGY9Nx13q9PqaiJiz6dFHg0VZCkrMw9nY4xWegTcpAG+l+TKngsqH31IDCtml8LkzvXo3GZIVrl0U+WlCvF0X3vC70slNZu7wiP0lrTIr8HAXtSoTaMvSyq0WbIv6sZZHcLOkTov9umIR3UvI4lP1eFe8SeWCpXaNqW7TxUCUffV9It8wkxvVRsEaZT+TjaIxmNFEcZ3JKGiDElaLnx+cMhgUtCYbItu3zkvzBzcBD2F1uxa8DNpcXt1fGkmBsftsW4XGt9em4+M9fA7DnqYvDF5QMQbPf7eRc7B1GcJnbTpmpMxnWRHTaRhlv/npukq2Ec/Kz+OYxhXuwusR1UEpMR28wUFglQpRdMixN7E7T60lJ1FNhd1NS4yE1KXQOaquu3f78KZ0li58qnLz25U8MyUvh+hGdmy7rz2vSOiuivy61hF2UVDgqi5G/GUkGUsIcZ25yAkfK7KzL/AW9LnkRfVoXxe8LkRLp+T5txVUg12rfvn0MG9awkGZ5eTmlpaWMHh35U9qhQ4eYMGECBQUF/POf/+TSS5XvC6YIjko4+KXoNddcdeyMnkJcOYpib1MgGTIhiuRMUzJUHYtJwTi9txbNEXEBD1QojhiD8BLhVLi4XmAkXGtqycSiSGZgpF9rBhYExKrSCe0f3oWzcDfam99F1yvy1iAWf3Xw6pIj4MxSrp+k284+X0fcrizyHG4spsguuuaMPGAvtbIer08OjmZsMy57XUK7IcJLu0ZLkqcCiEWdKy9fPDwBm9MTLMoZFn0CaI2YPQ6STVqSTAYcbm9U4qpZzv4liWf/kj81t0ygl6mjUvyWlB5U43VDSudgMvvxcvEb69i4AvzxrbDnE3INg6mw6yiorK0r1aAEtWXiPSGNXYVV/H3jUX4srmkirkprnLx3sANV7ht4LLCOkvgrrRdoxPloch7q0SHFxJEyOwVyBqTnt5uwiobTthTDuHHjAJHX1JjAtMAyLXHo0CHGjx/P8ePHee+997j88hgVklSCgm3w3s3wzrXNL5cuEiWTnIWxtyn4Y45CXAVCbrUVSluDzue/6evNwf5eEWPw54y5FBZXgWH5bREzSpY+CJY9aEUNsFgVEU1M53nNreT/xcUzK/ZEvJrVX8m9attHULK7+YWjwV3Lb9z/jynbx/LFjyciXq1+2KPWreAIPVcNv9e/wR8t7zZJCm6OziY7f9c/xbIrFS6G/O+ZJD+XR4cf30bf2EvUGEmChFQ0ksz3v8zlq1+fH1FjbkUxpYDWLwJripXfft5IeGAH3L4au8vDSf9IxI6Nyy0U74Qvn6ODV+QUFVQo/DsKXFMTUjhSJvKHuqQ1fairdXl5dmcib3on47PFQFz5yykUasWo35zk8NeaQAuicoUHb8aS01ZcXXDBBXTv3p133nmHbdu2BadXV1fz1FNPodPpmD59enB6aWkpe/bsobS0tMF26gurd999lyuvvDJOR9BKSv1JrJl9ml8uVdQvSXRGflNoNUHPVRTtPgJ9CB0Vipuj8/ovVsYIkusbE1hH6SrBATGjb8UQ9MDoNyU9V0F7WuNJi5Hn6pZ/Uz3oNgASovBoBMRVtT4TlKzR7q4lWbKRaXCRHEET6QBGnYaAs8qupLeo41kMvuJBOub1DF1qIAwJJhOjtLsZkKzwaDBHlRDYmgg/q8DDV4yG/Xu8Pry+ZrpSSFK9cgwl3PqXjUz449qYtIAJCCaLSdf0u5MzCEbeRa6/80dhhYK/I7ej7qEnIZWjfnGVF0Jc5SabuLaPgZm6j3DVxqAqut9zVajJ9u+vGc+Vf15tTQWatQtg18fK26Mwp21YUKfT8frrrzNp0iTGjh3LjTfeiNVq5YMPPuDgwYP87ne/o1evugrmixcv5sknn2Tu3LnMmzcvOH38+PEcPnyYc845h+3bt7N9+/Ym+6q/fLtT9pN4Tw89hDdIinABJ7hiWAU9QKvCginiPQZhwaDnytAKcRUUMgr3PXS3JQwXi7BgwLvXRs+VknWlqKvcbU2I/NIV6EFY1XUS5I1QzBbcdl4zLIbxj0HPyB+6JEnCjINqTNScLCDLqtCAjZTOyAOvo+RolKPbAg8MLmVvoKUXLeI57iWlMJGIqtwFvdUxEFd/7MV/XGfzYNUNXNg3m9dvDdMy25INlUeguogjZVYOltoorXbSI7MV14pmOO4XTCFDYR2GQIch5H6+Hw7spaBSwd914GFV0oDBEvRcdQ4hrnRaDc9O6QQvfQiOGNTb8l/bC2WR19UhpWXPVVWtG+1Xz8OQX0C/y5S3SUFOW3EFMGHCBNavX8/cuXP5xz/+gcvlon///jz11FPcfPPNEW3j8GHRjmHDhg1s2BC6qe6pJa4Oine/ZyosfnGV6CptfjklsPsvlomt8FzFIizoTx5vVe5NoOio0k2l3baG24+GWAi+toQFzRkwe3fr1m2BKn/POWuE+U1AMBeqWumq320QxGZqqcaE3a5clXavT+bdTcf48YTEhR5f5GkpBgvveM6nYoudmzu4SU5UJp/lpMfE378vJ91s49GpEazgf/ia942H79Z+xaMX92Z0j5ZHY7eI2wE1xVT5HxiMumYCNv4K6dhO8PRV56KRpKhCrM3y+QJRGX3kLyl2i3zfbGtzgkLMU9RzFbiempJBo6kTV+lhPNSBaENthWgSH20aRXMEwoK+FKAFz5X/XBT5kvGOuANt53OUsyNGnNbiCmDkyJGsWLGixeXmzZsXUiTJ8WpurBTlAXHVtfnlAp4rdxkerzt2CYJeT3C0YKtyrmLpuWqTuIpRQntrPFcTHodRMyG9+T6ZrbOnFWFBjTbY3kMxZBlem0BVwTQgB2sUYbhAWLBS6X51/a8UA0O6nBv1qokpmVDuwWZQrtuh/egO5nx8FNDySDTXLWMSz3qupvw7LReOdygmrmr8Ic+IWwP5rw/7K2S+L62guEohUeHP+bxJt5ZLH30LuTlPau4Qkf9ozuSc7gpXAD+xB45vAUclJTZxbFnhEv1tpeT6RN5XYSw8V6YU3F4fBX7hFspzBeDQWTnuy0WHly6OiugekFsikNDuEdfh3GZyrgIevlKPCd+kZ5QfuRgDTntxdUbh84kWLQBpLXiuzFnIWgOS1yWaiZpaCCO2lvo5U4FQXyQEw4IVzS3VKoI5V6dSWDDgCWuNuMoZqKwt0LawYCzwuqDgO6qcwuMcEEyREAgL2kqPw45/wcBrFDHJlz+RS1ckYv7BxRvT3VF508yJZiivxO5VzhPg2/4PJmp0lBo6Ne+daYwhiSnab3F2HBVVLltL2L5+DeiPOdLT4hdXSZIDMFPjUCgfzS7ElT7RSrqlhe9zNG26omXcr2HQDZDVh5Ivxe89rOfqz2fRwW4EXqCg0oEsyxHV/GqRhDQYfjskpFBY4cDrkzHoNGQmhRZ5r39zjD+6nuPqQek8F831OxIcVfhkiWK3uKbmNjNaMDCv1itR7fCQpoorlbhSUyxuipIWklsoIKjRQHInKPsJqfIoZMZIXAXyJ4zJoI3i6xbLsGDQc9UKcRWzsGBAXCncU621jLgDhk6r6y8YLWvmCmE84Teit2Rb8Z+fKllciKPxXJn9ZQmqvTqoVq70iM3lYVehePo2tDQarhGJBiFiahRMaE9Oz+Hlrh+wXTsouhuxMYnf6/8C/TtAGA9Ga7Dt+RzoT5I+Qlv83uokxMNPtVLnprYVaQnAroIqNh8uo0u6mXG9FPAwZvcL9g0trtoMQJY1jOcqMZ3s2iMAuDw+TtpcZIQRQFGR2QsufR6Ao/tFSkheagKaMOVAAonux6plZUOCAL0nU6rLwf0fMcAju5lyHUlGHWajFpvTS0lxAWkebcvRmXbmtB0teEYSCAmm5Inmwi0gJ+fhQyMaZ8aKYDJ7SnTrxTIs6G1DQnufS+D8J6DrGGWNaktY8PgW+PZVUd9MKSRJeK1akwMG8N3bsGUp2BT6bvlHHlYhLvaWKDxXSf5la0hAdikXYrEd+wEAraaFPJ4QmP2jdO2lRxWzh9H34r1tFYczIituHMTgD48rmdDudlDjFec90RThyMXUbpB3Nklm8RtQzHPlDwu+6ZzAb/+9k++ORJAwL8t8ue8Ev/33D/x723Fl7KhHSbUoIBo2LJiYjlHykJkgwruK5l35OdLMSMEAnfxlIo6VK1wOAiBnAIWdRTJelsXUtKhrIwLiq2zZL+C185W3R2FUcXU6URZhvpUf75Vv8MmQN5D7xrBul98lH3WsPpalGNriueo1Cc57GLqMUtaoxwvEqyWPYyh+XAUrHoZdHylrU1sYfa/wWiUqlLfiqcUh63EhbtTReK6SjMJL5ENDrVO5m1TNJ78BwKwj6pDNnfrlLNYvYrTpsGL2tDo/1GhBlkXPNKdHobpbzips/oKmSaYIPS4Dr4HbV2PpfjagoFfPfw36X20v/vrN4aCoCMnhr+EPXeGVsaT6y1mU2RQoriTLsOFl2P5P8DixO8V5zgoXFvT/bnJN4hwolndVWy7Oh88bzGlrLtcpL1UIr8IKO+7Dm5SxoR4ZFiMPXdSLaY3a/4Qi02JEL8lUkyiS4U/xfGg1LHg6Eci3ammkYICEFEWbD4ekNWUYACw50H1868RGC9R5rhSsetxWNJrWe4my+kG/y0V9HKX47m04uA76XgZ9W9GNYMyDytkC4K6lGvEULUmQFGkFckRNLA0yPiRqHG6UCnzZjCLcaTZE/4w6KqUKir8F440KWQNr957grrc208WsJaqWhcYk7nLPZs03w3k69zg3jgzRBiVaHHXiyhyFlxEI9tFTTFz5PVdVsvj+NJsbp08U1yydiRS/gFdkIISzClb687n6FrHqwfOodXnRacOIcr+4yjI4AH3Q09VmPn8aNr4CY2ZTUn0VAJnN5KFlJBnRS17cspYTh3fRoYuCpUwOraejx8Gsc4aIEcYt8Pq0s/hixUdM2rEZfAhvf2s6WsQJVVydTgTCgi0ls8eT/le2zsuT1h1u+bfy9gA6XxuKiNZWQMUREb7LUHB0Xlvof4V4KcnRjbD9PdFqojXiSmncDqpkIT4tRl3YHJFQSJLEeZk1aE7uU7RqvG3is/Dat5gT29KySLncPduaZ3B7L0DnibK8gyGJRESnBsVa4DgrsfnFTMSjBf0Ew7gKJ7RX+YQHrdkaaZl94J4NkJhBygnx4FlpV0Bc2f31BPXm4GefYGjmwdbv6f9ttx+Ze+uvwudmRUvg+5aQQslxIdiym9m2RiORlSBx3A6FCT1RdAzwmt+KlIYb/g4RNGE26bV4tSZkSYMk+4RgVcWVSlyItMZVgPJDDDv4ItqP/g3X/iU2NulNp1zi4QlLfzp0yUeTPSD6lXf/Bz6+F3pOhJv/qYxBNSWw4hExQnLqC8pss630v1IIq9bWk6k8LkK61g7Rey1D4akN5ltFExIMsPScEljzR5Cub7stfmz+0E604gHgoDeD3d6R5JTInKWQPYGaWSaNL7oVu44hsasVfgK7S6mwYHWd5ypSL2NVAbx+IUm2/sB0BT1Xwnte5RHfm+Y9VybI6gtAik3koFUo4bkKpkdEGCb3L9dZLlR0kAGXL4ZL/w9kHwNrj+D0eOme0fxDZk5mOscPl1NsUvihPb0n+2styK50Oro8kfXDlCRRQsdRKV4WBRuxK4yac3U64XUCUuRiRvbSqeJbpB9Xnrrxa59PvBTkSPo4vJe8AN0j6y3ZgIQU0SIjkBOmBPYy+OFD2NUGT53PBx6FQgcAPSbAufeJfmit4aO7Yclo2Pc/Zexx1wY9V9GUPAgSgyr2AS9PIKcrGlZV5HGP+wH+dki50LTdr0UM4UJN4cjoSWIHIShsLoUEjaOKGtkvriIVn/oEqDqOxVUCKJtzJctQ5RGfU6TiPLleWLDN9Q4DnqvENLYcLufWv2zk/9b8GH75YPHOGPT00+pBZ+T+C3vy1u1nM6pH84Iv0POvsFLhpPqrXuFJ8+Nc9E4py3e0PIp3x/FKXt+j4QnnrWKCvwjpqYrquTqdmLFejKqKYKQgAJYO7Oh4E31HTkCncJuSIFuWCo9a/yugw9Do1v3zMNHOZ8Z6yO6vvG2toe9U8VIScyZMXihaUrSGPcvh3Ruh00i4Y42ytrWWgJhRKgznrmWg5iBvdvg3+skLWmGPePqXXbXKdBf0OKlZ8wxwMeZISw3UIy/Jx0hpN92MytURs/kdLFGLK8DsD1EFEq3bjLMKmz9HLmLxaUyGu74g6YQO/n5Y0dGCdox4ZXFeWhTnG1+D0n0kD70dEJXva5yeYKX/VhEUV+n8dKKGL348ga85web3XB2tdPPOyj0YtBoevKhX+OVjSI5ZfH7FxcWAst6rBL2W5AQ9mc2UYQhgd3nZUa6hRtdTKJcYjCRXElVcnW5EU/RRn8BPWRfTp98U5WuYBPjhI/jpc+Fqj1ZcAcg+xWtdGd0V4oepTYvdcUeDOR3O/mXr19f5L0xK9hYs3C48YRn5rQvrBVrfKGWTu5Y0qZoJKcXQM/qWKPdusPA/x5v8vmw9Vytijx1blfAqJBmjv+lekufkkn2/gw63KGENAHavEA8GXZTiymUjsVqkFCgWFqyf0B6p50qjgQ5DSKIKOKxonasqhNdTr5Uw6Vv4zW97Bwq2Yuo+DpNeg8Pto8LuVkxcjeyWxsKrB5HWXHNtv7gqs7lYsvYAOVaTMuLqX37BeOF83OYcTBEUjc2p3QckUPTjRkDZtjOv3hKmx2MI8rOSuLabl8nO9VCG4r0wlUYVVyqxZcBVQlhl9Yt+3V98IG7SSg3n9zN+zxz0O+8THrFYVDePN0p7iQA+eRCOb4442TSsTUqJq8CxtaYdD+CTtDjQBr07bcZdi80f9kpsVZjSfxxKFaP1erD5hB36KAuaUl2E+bvXgP+HXamwoLOKvtIRXNZOwbBSpFiUTmi/YC6VR0vgM+G1arFsRmDkmv0kKQkdKXI7qKx106Zxy/XEVZd0M13SWxgZ7L/m5bkOMH1012bLJUSMLMPuj8Hr4sdBjzD5LyvplmHm81+Nb3a1HH/PvyKnQkn1IOrfvXSOSK+YuSmih9x0s4ExOTLjq8uEuHKq4krlFCbJUYi0dzlk9xXVe5XmrDY8mae2XPukNWjkQPykFaMFK4/B+3eIEN5ty5UxqLoYTu4XlcxbMwIxBvlEbW5/E/BceRTK03A72OLrycHK3gwoqqJPjjWq1eeMMvFYwS9JNyk03sldG71npj7BNkpKefZs2BE3P0M0nRAATCmi12GpkqMFq3lc/w4Mz4Ae0yJf77u/kVp8gOlDJpCUmqVM25e+l1JlKoPPvoks3yrRL65spaQkdqWoykFFW0cM1hNXEeFfLs1VwLxLekWe6tEc7lrRRgo44RLf3UiK3+akmAEbRW4FE+sdlWA7IWyKNnoQ6Al7iourUyAmotKe9Cz+GN2/boE9/2lvU+LGikEv4370OKS0QrzJMhz5Bo4pWFDvwKewdAqsaGVfs1j0OwxsS9eKivGgvODLHcSHmXfzq0MjIkp+bbJ6ahJ5mhMkehVKgnXb64pkRlFzK8DO6iSGO15i0p7JytjjsgU9aYYoq8VjTidx0lxAybCgPx/GFJ0IZts7JG14jnkDSvjVpN7K9NMDqvwj/iISV2a/ALKfDCa1V9S2sZBovWLK6/eVsu7HE1TYm9lmQgoEsgMDtQLbSmA7kpax/fL4/rcTeWN6y3WrctLE4J0ir6Xtif0BHJXs9uVxse1JHnj3u4hXO1wDH1f1pEBOO+XFleq5OsNx6P2jUhTsuRbE54UTe0XOjiUn+oT5vSvgp7XQ7TzRdkZJdMbW5VsFCn16XeD1RNcvMRwBIdPami2x8Fy5w3uuvF4nBYX/wG7/ifS088gI1W5Fac9Vl9Hkn5XLOPMJ8rOaehx9Pic1NT9iMnXEYAjRDSC5I4yeBWYF+hwCuGsZp/keq0nHsC5nR726zmiilERkj0KC2GXD7hd7zZVPCkei3/tmU0pcpXWDzqMiLwsTINiZQaFkZUcVHFxHVYEQeRE1/K7nuQqKKwU9V099sou9xdW8dftIxvYM07NQo4W7PoeEVMqxUFhQRU6yqfk8rZYIdLtISEHSaEhO1JCc2LLYzM1M51PDjeTobEjSFa3ff32cVRTK6ezx5qI7URPxah8f1rK/ajCL9L25XBVXKqcyDr0/WbmqQPmN28tgib+A6G/LREPpaDjyDXz7slhPaXHVWupXUXfbQKtASQZ32/KJgt4lj0OUZFAiST9MjpPXW8t3226hsnIrAMeO/ZWuXe6hR4+HGq4fA8E3/dxuTD+36c26snIbO3bcg9NVjCQZyO/xKzp3vr3BMlsrzazy3ky+MYlrlTDGbWeydhOT022Q/4eoVzcnmABXME+qzdTzXLWiMkRwtGCtUjlXYx9i4KcDMBzXsKKLI3ybl8b4xVVpRTVVJ2rITU5ovthmS5zcB+/dTJXuOuCKCD1XgZyrUnKTTXRMSUAXRdHakNQTVydtomRKi0LJPwDo/r9sZN2PJ1h4zSCuG96GzK/AwCBTSlSr6RJT6KEp9FdFd7Q+VaA+jkpOyMKOzCgaUlv1MiBRKief8p4rNSx4huPQp4g/YuG5CrihTcniSSxajP6QglPBeiaVRxn50/+hWfFw69bXGupEoivKStjhCCQ1hxFXpaWfsWPnfezZOweb7UDTBeo3e1Ywxwmo80D5+XHfU1RWbkWns5CTLXpSHjr8EidONKpnpbTnqqZEVMZ3NnzKdTgK2Pb9dL+w0iPLLvbtX0BRUcOaYXsKq3nli59YvatYGXva0mgbMFtSAHDIejxeBeq41fNcGVtxVU9ccR8AtlplPi+Xx0e1w8NJmwtjBCPSgvgbtl/9VSfOf+4LfihoowdL0kDH4WSnp3Jer0z6d4ggTFnPc/Xk5QP46tfnc0NbWwL5xZUvIY1yfzgw3RyZqAg0dz7R1hY49TxXr647wBMf7WDb0YqW1zMk1ZWJUarXq6OKEwghHUkZhgBJfm1cOvhuGP9rZWyJEarn6gynNuC5qi6Mwcb9eQatrdAdCBEoKK6kmhPkVn6HfKC0lRuQhPfKWaXcSC93eHH108E/c/DgC8H/i4o+YsjgN0lJqTeEuf4NXol+W7Jcz3NVt+2qqu0UFLwHwMCBS0hLHYXBkMGRo2+wb//vSU8fi0ZjbLieUp6r/z2J77u/obnwtzB2dnDy3h/n4fFUY7UMYujQtzl0eAmHDy9h74/zGTlibHC5JH//v5rqSmW8e247h3zZaOVssj2+BnlOsuzl+PG/c/LkFyQkdKZzlzsxGRtWkk7schawEgC724s12hF+jXHVJbQbtdHnxZidJ/y2KFOwV6eR+PKRCdhdXizRJPz7f/NWjQuLMQmXp432dBgKd37KZCDi7LZ6owUVQZbFcXlcVGmS8frE55NqbsGLtue/cGwTWb4xAJRUtVH41vNcrdlVzKZD5ZzTPZ0heSnNr6fR8JF0Ad+6unDxngLGjVCgKrqzqs5zFYW4sujFuSslVXR/OIVRPVdnOMGwYHWRyJFSkmDT5hA5MJEQGBWiZCVel9/z0ZqRggGCCeQKea7CeEFOnvwiKKw6dryJlOQReL12duycidtd75xotKAN1LpSQPDV9zbVs+nw4VcByMm+nLRUEe7t1u3+/8/eWYfJVZ7v/3PGfWfWfTfZuBEjwSFAAgR31wKFoqVIaXGHQmlxp0jxAsVDkAQIIUKEuG6y7jLu5/fHOzO7STa7I2dbvv3lvq69kmv3vO88R+a89/vI/aDT5ePz1dDU/PGu45TyXEkS+wUfZ9yc4axvEufudP5CW9vXSJKG0WMeQqMxM3TItZhMVYTDXdTWvZgYbtGIRdpTt0aZaxTycUHoJg7ceg6/1HUlfi3LUVatvpoNG2+nrf0bauv+wZIlx+P1btthuF6jQh0LNfmUyHMKebhV8xoPF84lPw1nWoFR5nntwzx/aOamAKiemEzZSxMZqapPqQ9knFx9NPJLVt15BPsNS13TLGPE+vopRq4kCa5eDn+qo12yA6I/pl4zgEdvw+fww6PkB7YB0OrO0HOVeB/bE42g8/tp2twbP8njeDNyKCtrlcuFi5OrZG0AsMb4aLsnwwKD/wD2kKv/zxHQZiFLKpAjQntESSS+zGl6rgYjLBgjV3Im5Cqed6WY5ypG0np5nKLREBs23glAael5jBp5NxMnvoTJNJRgsI3qbY/vOEc8D0IJT1HvOWL5XD5fLS2tcwCoqLis588aM2VlFwBQW/uPnmoipUVEj3+Cbk0e7rAq0atue83zABQUHIvFLCQsVCoNVUOFZ6ux8S1AJCJbYs2V3ZJFmTZBIR86Quil8A5SDDU1z9Pa+gUqlY4hlVdjNo8gGGxj1eqriEZ78pkkScIUyyVSRP6gcAIzjj2XEw49ILEApQKDwcBM9TL2y1Fow+BqAndzj8Bt0obYAZACyqpvp1TlFpdLCHlZvLGB459cwDUpVLT1h44YKci2JJGYXjUDpl9ObqHIs2pzZUgoYiE92eCgxRknV8ndn8Nt27lO8y775ilEavzdtKTluRL/tjXWwKLnlLFlkLCHXP1/DllSi/YrAC6Fk9p7lR+nhXgZ92B4rvS7kitZlmloeI+lS0/hp0VHsbX6caLRPhbiOAlSKueqj4T2xqb38fm2o9VmUzX0egDUahMjht8KQF3d6wSCvchwfKwSQqJxe1SaRDVkY9MHQJRsx/5YLDvqoZUUn45KpcPtXofbvS5mj7Keq2A4ii8kPDw2g5ZgsIPW1i8BKC+/eIdjc3MPR68vJBzuQqP5BQCLUSxmLnN5T6l9Jgh5+VJ/ExumfcHoIvGcBgItVG97AoDhw+/Ak3cJwSHPIqtzcbvXUl//z57xARfmkPh+eH0KkL2cKph2CfLo4/v88y8uL3durufuLQ2s9/TxjCisHbT9jHncN+ZjXlmbolcuUS3YpYgdfHkrPDqOc/76PuNvn8OcNUnkluptoBKreMjXzcraLtY1KvMOanfHyFUyVX/jToajHiCnSqQAtHky9Vx1AeDWZie+S/m2JMlVdhtXaz5gb7tCSeSBdHOuBElu73bBj48PcPR/F3vI1R6IRsQgxCyVxK/QcyXFF4+dPFeyLLNp832sW38T3c7leDwbqa7+GytWXrwrwdLGPFdKhQUTCe3GhC11ta8AUFlxGRpNT4VidvaB2GwTkeUg9XWv97JJwRynhICoKWFPPEG8sPDEXQ7Xau3k5Ih4UnM8NFh5IPzuJzj1lcztAVz+nlJ4i0FDS8vnyHIYq3UsVsuoHY5VqTSUFJ8BgEa7CBgE1e+d7hlATe2LRCJeQpYDuLxpEjOXbuS0NR1cp3qCDYxie81zPc+SWocpKoi+x5v5cxSKRPl4ZQPfbmglupOT5oVtmzhy6Qaerm3lyZoWZi7ZyPvNO2kn6Sx8EpnOPzeq6M5UdgDYFsnmuWUu3vo5xVzOGLl6q72Kc19cxFuLazIzxNkA3bV0ByVcgXByGmCSlPBejbb6eO7cKTx48oT0bdjyLTx3CHxxc8JzlZOCpEKeVRzbplBCe4skzs2i12BKVqMtTnqV0txKs1ow7rlqlbKRx5ygjC2DhD3kag+Q49o/nhZlJ04ktP8KPVc7kavGxveorX0JgKFDrmX0qPtRq810dv7Ips0P7DhHwnOldEK7IFHdzmW4PRtQqQwUFe0oHCBJEuVlFwLQ0PgushxL+D3qIdGqJlcBlf04QYuF9lyuVfh821CpDOTlzexzSGGBaGbd1PyxsMlgE22PskoytwdwfX4XABathFol0dT8Yexz+/bUFBaeAIBavYVgsD0RuvOFIspU55VPh/2vEaEbhERFQ8O7eDFyV/hqlji9GFQS2Vo1LWEtD0q3siFgoKnpIzFercOcI66NN5p5XZGzfiNXvbmcS1/fMXz1ae0ybq12EUVisryYCfIyQrLMNeu2s7S7F6nTW7gjdD5/XpVPQ3fmBD0e6jTvRkahOxTmF5cXV3gnz1ZsEd/uN/P9pjY2tSSvgdQnYu+gVw8N8M0fDmZaZZLvovJ9YOgMss16Zo0tZFJ5mhtEEF0dGpZD+2Y6kpVhAKGj52oiJyzey05/mMDO1ysVxIhRSzR1j1FI52BztJifG5UJC3p83kR1azphwaCsxn3wbYrYMljYQ672oEdY0f0r9VyFfRBRqClcYNecq0CghU2b7wWgaugfGDLkKoqLT2Pc2L8DUFf3Gk7nqp45dAp7rnZKaK+vfxMQuURa7a6l47m5M9FobAQCTXR2Cc8Mw2eKHoDphmD7tEe8/Fpb58Y+91A0mr5z1XJyDkGtthAINOJyrc7chp3grFsLgE0Hfn9jTGdLoqDgmMQxLYEQ230BZFnGaCzDYhmHJMm0tc/F3Ev8yVO7aufpU0Zbwf6cvvUoLl1SBEBT078Jh7t5T305WwNqivRa5k8bxZJ9x3Cgw0IAA09zNdvqRbUlkoTJKr4XHgWcadHl/2Rf1RqmZrmI5493e1u4eXM7MipmapfzeIWPP6oeY5r8IyEZ/rChlmA0RjR1Fg5SrWJWbntSzXz7RXcdnmXvAru2BpJlmYermxi/YA2zlm5k/ILVPLa9uScnKibFYIl0AQp4GmOpCdl2B0PzLMm3KjrtFTjvQygYm9nnAww7DM58Cw74fSIROzsZGYaaH+GRkWT968yEzlZHJoncQw6GcSfTqhWkPhVS0zjpGg4PPsw5S4cqotLe6hEk0aRJrX2UTt1D2Nvcv+6k9ozI1ddff82f//xnZsyYwfDhw3E4HJSUlDBx4kTOPfdcXnjhBZqaBkE/aQ8UhWyJ5Vy5W5Wd2JuhFEM8DwSU81714bnatv0pwmEXVut4Kip+m/h9bu6MmGdEZvOWh3rmiIcFFcu56gkxRaOBBJkpLu5b7lKt1pOfLwrLd9ZzUgTFE+EPG+FCIRXQ3j4fgNycPpTYEzYZyM7eH4C29nni3n97P8x7YLdjUoEzJBYXm0GVsMdmm4heX0CdP8gZK7Yw4cc1TP9pHQcsWs9PXW7yco8U9rR9iV6jRodYqN1dmT/nXd4Qi6o7+GmrqChravqQLQxjTlRcg8dHl1Nh1GNWq3l2bCXZGhV1UgUfuIpxezYBPYuKN5B5tWCe3cqbxe/y9sE9YZuHVn9EC3nkSN08Oe1UqqquYdJez/Abnscmd7PB4+etxth3VG/hr7qneW74YobkDtBUeCB0bsOz6XtAhJ564/bNDTy8rYmgLGNVq/BHZe7b2sj9W2Phw5jnyiqL76k702R/X4Z5n8DHKxt45cdtdPvS3ODZimHkUVCxH52phAVj702VvzPh6cooqX3/q+GUl2jRlQLJJ7MD5BZXAuALRTO/J0Dr2IsAyNspsT8S8bN580P88MN+zJu/F6tWX43fv2NoOSc2pr2tRXj3fqVImVy53W7uu+8+hgwZwqxZs7j//vuZP38+9fX1mM1m/H4/q1ev5p///CeXXnop5eXlnHLKKSxYsGAw7N8DJWDOF8KYSpXNxxH3XPXxYguH3WytfoylP5/Kz8vOZHvNC0QiO+UUqLU9idoK5V1JOyW0+/2N1Me8CcOG3YS0k4p8VdX1SJKGzs4fcTpFcjTDZ8J+V0PxZEVs6ml/Y6ajYwGRiBu9roAs26TdDiksOAGA1tYviEZDULMIVr4NbZsyt0etBWsBZJUQCDTjcq8BJHJyDup3WG7OIQC0t88T92v+A7Dg75nbAzhD4r5Y9WraO+Jk72C2+wIct2wT8zpdSIBWktjiC3Dqii2s0x4MQHf3EiIRL1a1WJjcCiSQe9rFC9+iVxMINNPVvZT3Y9rvpxQ4OMDRszHI1mq4uUpo8nzMiWyvF4TY5BNzeLoVKPk/+Ea4cgnRvUVyf1v3at5zVwFwQ4UDW2wz4XDsw5jy0zie9wB4vKaZUFQGXczeYIZhOAC/Ew/CC9vbY/h5axfP1Qlie2eJm9XTi7l/hFjoH6tp4cu2brHpyR+DJUdcL1emC7m3E5+s47YFIR75ckNCYyppyDJ3fryG2z9aQ0NX5uHSHs9V8uQKXye5sbykjJPa6REjTUUCwaTTJIhyxmKmQGuuaBmVb+8h8pGIl+UrzmV7zbMEgs1EIm5aWj5lydIT8HqrE8fFiWnbPy+Bru0Z2zJYSIlcPfPMMwwbNoxbbrkFu93OPffcwzfffIPT6cTr9VJXV0d7ezuhUIj169fzyiuvcPrpp/Pll19y0EEHcdJJJ1FdXT3wB+3BfxTRKRfCrW1w3GPKThwXrdvJc+XxbGHR4tlUV/+d7u5ldHUtZvPm+1n68yk7VsCB8kntO3mu6upfR5aD2O3TEtpNvWEwFCdCTzWxnCzGnwKz7oYhB+5yfFq4dB5ctx6KJ9HSIrxFefmzkKTdfz3t9ilotdmEwy66u3+Gn56CDy4VvRgVRHv7dwDYbBPQ6frXHMrJEWTG6fyFoDoKUy8SPwrAGRYvdqtBTUeH2KhZHQdx6ZptNARCDDfpWbjPaNYdMI7ZuVmEZJnfV4dpiw5FlkN0di7CrBKeB0XI1fdPAmCWPbS0fME2KlkhTUUFXFe5q8ji6YXZFGnDdEkO3mnuRJZlTve+xf2a59k/SyE9pV74x5bv6JYc5Ks8nF0xfoe/VVZcwUz1EmxyF7X+EJ+2diU2G7LfTTRVArIzAi7csVY8ce+cPxLllo1iITxa/pBhdefz48IDmRH+F5eUiufqT5vq8ESj8LuFmGf9CQC3P4N0gHAQgi46sfDqii6enb+VviS3qr0BFna5ae/d+mfhU/BABXx2Q+b9Bdd/JjY+3XX4Y1V6SUkxxN+b0RC55lgoLF1iI8vCmxwJ92hcxSoFw2EPW7f+jcWLj2PxkuPZtu1pIpGdNtqtG8lTC0+9EuRqWL6F62eN4JQppTHzZNatuznW/cHG+HFPMnXKewkpk5W//DZhU65Fj5GgEM1VUqZHYaRErq666iqOPPJIVq1axfLly7n55ps55JBDsFh2zMOQJIkRI0Zw7rnn8tprr9Hc3Mzzzz/PqlWreO211xQ9gT1QAGqdMv3odkYfCu1+fwPLlp+N31+PwVDG6FH3M3LEnWi12bjda1m54qIdPVjl+8DQQ4SNSqCXzlU0GqSh4R3xMbEk8b5QVnoBAK2tcwiFlNXfAUQoxFZEVKWitU20kcnP619PWpLUCTLT1vYNFI6DoTNEg+xMUbsYPrsBfn5FhPgQOVUDQa8vwGIZA8h0eFfDMY/CEfdmbg/giohF2qDxEIl40GpzeLkjj5UuH3aNmrf2qqLSqMeiUfPM2Ar2shpxRqK8jNDkam+fj0UtFjaXAuTKLYvn0axV0dLyOXM5AoDj8u0MNe0abtGpVPy2TNybz0NTcbs3cKCtmTM13zLCmrk9X61tZvLdc7nizRVIUhv/9ohF66ISO9qdGIVWa2N4+dkchgg/v9bQDjoLvw9ezvA15/HPTCv0Aj2eq7i345nqjdQHZbLlNk5TfYrJNJRoNMiWLQ9xmvwWJXotdf4QL9eJzVVc1T2jEFTMc+6UxfpkM2qQejWPb4iFk/ddtI4Tl29m4o9ruGNzPYFoVLR78XeBtw27Sdzrbl+aIbkfHhUbn4blvHvZfmy450gOSEYcVWtKvPdy9TFl8nTzjIIeeGgI3J1DS6yQId+qx+9vZMnSE6je9jgu9xpcrtVs2fowPy87nWCwF+nv2EqebyuggJhpJMQI12KuHNHN6TFy1dr6Jc0tnyBJGvaa8Dz5+UeSlTWJSZNeQ6fLx+vdwrbtfwPg76dPYF3ZA5yk/uFX3V8wpRV1/fr1/OMf/2Ds2NSS/IxGIxdddBHr16/n/PPPT2nsHvwfxuU/wqXzIUuI4EWjIVavvppgsBWLeSR7T32f4uLTKC09h6lT3kGrzcblXsPmzff3zHHaK3Dev0XlmRLoJcXQ2voloVAHel1BQkqgL1it47BYRhGNBoXUQNADndsVb3bd1bWEcLgbrTZ7x/Y2u0FurrC5rf1bOOgGkYA7+tjMDWleA4ufQ974BZ2dPwKQk91/SDCOuPevs3Nh5nbEEY3ijAjCopVEWClqP4KnasX/HxhRSomhh3zrVCoeG12BRoIV6irWMYb29vmMNXezt7QeIwp4rqaJXnymLAcN3WtYiGhRckHJ7hfN04sL0RGmRhrC/PqfeslnZF516vz2MTo8QbzODpp1m9kmVaEhwrnlI/s8vqTkTGZI85HkCAu63GxR2ZCAMGq8mYbi/N0Jz5VJp8Eb8vFsrah4O9ewgBn7zWGf6V8yYsTtALTUPc1lOeLvz9S24otEsSghnRHb3HXrhNSMzdCjrrq1u5ajl6xgXqcLDVCi1xKSZZ6pbeXSNdsIjT0ZrlgMxzyKPea5SjvnqlfTZgC9Ro1WrSIcdlFb+wpr197A5s0P4XZv2HGcJCU2ptdPN/LDTTO46IDK9GyI64apdbS4xTXNMcmsWHkhXu9W9PpCxox+mNGjHhTvYddqfln1O5FyAJA9lLwckd6RsefK2w6vnwQvzgRJIhLxs3GTqAauKL90h3efXpfL6FH3AdDQ8DqS1IZWrVJcl20wkBK5qqqqyujD1Go1FRUVGc2xB4OAsB/eOR9eOko5RW0AR4VIjtaIha+27h90O5ej0ViZMOEZdLqeXCyTaQhjxzwCiFBdt3Olcnb0gjzkEOrt05DtZdTVvwFAcfHpqFS7r1iRJImiolMAIX/Ayjfh7xPg8xszNygSgo+uhs//SEfbPECE13bO/eoLOdkHIkkavN6tO+QkZIzCCXDQDbhHTCccdqJWm7FaxyU11OHYB4DOrp9EGKK7LvOk07AfV8wTopYEoX0zNBNfVGZalpnj8+27DBlpNnBmgViY3uFsfP4a7hq+lHf1dzE9qyszewB3LAldq3KykP0JSEaGm/RMz9p9MrhDq2GWTRCpd1pDNEm5fB8Zx+oWBcieSxAJkybKwliF6YHWIDm70THS6XIZV7Ave7ECgPcowVS+FwDeTNvxBJyJMnuLXs3L6z6kExsOOrly8u/Q6bKRJImy0vOoqLgcgKrmmynTq2kLhXnjy2ew/FNsEjLKuYp7rnSiGtpq1Ao9u+qnOW/ZTzSGdRTJ9TwoX8nLlsd5cUwhBpXEnDYndzcFIW8kGB1kmTIMC+5ErgC6un/mp0VHsnHTXTQ2vc/2mmdZtPgYqquf2LEaL0auijUuSh2mgVvm7A5ZpSL147r1HDA8l4NH5BF2vozHswm9roCpU96lqOhEiotPYcrkN1GrLXR3L+3pBJE3grwRIk8qY3IVCbLOMYMN9oPxhiI0NL5DINCEXl9EZeXvABEmjF+H3NwZZGcfiCyH0elF2sT/HLnag/9bkGWZ7u5lbNx0Lyt/uZTVa66lrv4NwuGdklbVetjwmSj99ShcMRhDINBMdbX4og4f9meMxl27zOfkHBQTqZTZuPEuRUp+d0b04D+ydMiVBBwFdMVkDHZXldcbhQXHIUlqXK7VeNUB0RYmCQI0IIIeWPYKLHqaji7hJcrOPiCpoRqNFXuW2OXF85AUQekUOPQWuvLFiz0ra3K/5LM37Pa9ARU+Xw3+J8fDo2OhO8MwU9iPUxakRR2to51sPukWBOK2quIdQj29cXVZHhpZZqM0is0Mp0vvTsyXKeLeHY3cyvccAsDZRTm7tSWOs8qGAfBjaBQfeio5N/Qnnl6TecjbG6umVKvd/CiJRfDs2GftDiUlZ7IfIqfug65wL3KVqefKiSdGrnRqL6+3C6/juflqrIa8HQ4dOuQazObhyOFWTjL8DMA/NCMxe0V+ljsQTj8HLFat7NQIUpNl1LJ5ywP8vbqazQzHjI8HHfMoktpobZtLXs1lPDFSeLmeq2tlfodYuO0xdf+udDxXkXDCa9QYtnD+S4v5w1vfsmLFhQQCTRiNFTjKruMj4y3cxMMcVz2ccxd/yUpXzJsZ1whUQrxTrQVzDrcfO5a/nRgF9yuAxNixf8VgEAUEgWiUsK6SUSOFt2j79ufweEQ4MC7dkDG5spfzJ+0NHNF4Cd9tbGL79mcBqKy4nGXuKGev3MqQ736h8rtfOHn5Zr7vcDGs6gYANJrlLN26kd/UHsXNod/sIVd78J+H39/Ayl8uZunPp1Jb+xJtbV/T3PwxGzbcyo8LD02U+wPC/Xz0I3DKy+nLJuyM9i0w9zb4WSh0b97yEJGIB5ttIkVFJ+922LCqm1CpjDidK0RV2Nzb4IFy+P6vytgVQ1ub6JOXlTUl8WLpDzpdDnZ7bOeWo4FbmkTIMlOotTDjFoIH/A5XrHVMtmP/pIc7svcDoGPLP+H+cvj42sxtiqGra4n4DPu0pMdoNFZsMS9XpyPeTDpDMhPycYHmC+61P8v43FV8rz6RCLBPlpmp/XiKCvVa9o5pkX3OMXRqu2LzZR6G8yz/FwABqZVNklCIP6Fg4O/OwXlF2CU3bslKXY6ZUdJ2ivSZk724VlaHwUuXlI1FCjIrr/82PzbbJPY3NKOTA2zzh3DqxHLgydhz5cItC0/j9u4FVDMUFVEuHbZrwYhKpWXkCNFDc3z33zCpYJMmh3VnfgiIPOx4q5aUEQsLOtXivuhoYnnNB3zMSQA8NHoksyb9halT3o2FwlZR3no3FxaL6/anlb8Q/OyPZBnFxiItz1WCFEk0+vXM39jKvA2NRCIeHI790I16jwtbDuZt/yTqpApapQK+8hYwe+kGnq5pSbyPa9u6uf/zdTzy5Ybdf1aSkOVoQhi5pOQsHI59WO3yctbKLVR99wsjf1jN7K2VzDX9gaAss2XLQyDL5OnE+be6Mn9erQYt2WYdUuAnAoEmdLp83gsdwnHLNvF1hxN/VCYQlVnQ5ebUlVt4rMWB3X4AkiRT1zSHr50lLIqO/t9JaB8I0Wh0ULwNe5Aaup0rWbzkBNrb5yFJWgoLT2DkyLsZOuT3GI0VhELt/LLqMmrrXuwZNPk8GHfSjtpSmaB1gyjDX/YKXm91Qpl65Ijb+62C0+vzKC05C4Dq6seRo2HwdyvXdiHgAjlKa9vnABTkz056aFxbKt7AWBHozHDwDXSMFyTJYh6JXp83wKAeJHKcIjXIgW5ldnLORuTWjQmBUnsK5Ap6hQbtMW9Xpv0Ow34mqKrZt3glBZY2vo55ivrLb4rjsKB4+S5mX56tm8w0/5P8ZWPmSf/uTiG42xIr5tnbpqdQP3DHZLUkcbilC4BtuVl8ob+ZW4ZnXk7ujYjvVEMs+frgLBW6AYpUJEliSNFsJrEUgE0RUazhDWQo2Bvo8Vwti13/fa1RcndzfRyO6WRnH4hRdjFDL6RE3o5ko44l4qed1B73XKmEdlbYJ+QyApKB6VlmToqRYZttPBMmPIMkaWlt/YILTEvI1arZgpkXGjqwa4XQaloJ7fGQoNFOWa6Va/ZZyfFDP8ZkGop6yN85a3UdzcEww016nhtbyd9zf2K6/CMRJO7c0sDf7UJbrtvl5tn5W3lzcW1612Lz1/Debwj89AI1DZ/hcq1CrTYzdMjVvN7QzhE/b+SbDhfh2PLdEAjxD99+3MW9bG5bhMu1lrw5ImTX2lvZP028etE0fr7lcOwRsUGda/kzD2xrRQZOK3Qwb9pIfpg+KvEdf7ymhbfVojhFH3iPe0as5zbNq//7niun08k555yDxWLBYrFwySWX4PMpmLuzB0mjs3Mxy5adRSjUjsUyhunTPmXsmEcoLTmLIUOuZJ/pX1BaKooKqqv/glb7/eAYYi+Dfa6AsSeyfftzQJTcnEOx2Qbu0VVecSkqlR6ncwXd4w4SiaUHXpe5TeEA2oeHcMS6i3A6lwMSeflHJj08L3cmIOF0rsTvVzaZPR7WSzYkGIfVOh612kwYP26zWpmcue8fxvvSPoRCHahUemy28QOP6QW7Q3j4usyxN7UCniuALpuGpUyjI2oiT6dhdl7WgEPLoiGmWvVEJTXrzVW04KAj0H/oLhl4YtWLTTYR3j4mP/lm0CcVCVX3BdI4/JI28zZK4SAeWYcMNFnE3EcX7Bp27wuFBcezL+LZWxvxIgNef6Y97JwMkxqoynKyTi8KUU4pHtLvkKFDrgVgX5/Y8H3a2o3JLMiYK105hqpDYfbDOB2iAEulDfCjdAgAfx5atEMI1541haqhvwegqfpubqoQYefHy8/GoBLPb1qeq175VprwSibYXuSA0mWUj3yUS9Y14QxHmZ5l5ospIzgu386pYy/kZvNHnCGLqvr7DXvzRc7+lEht/OaAIVxyYP/XcbdoXgOr3+Pb1ds5+HGJvyy9goryS3i7Da7fUEtEhqPzsvhh+ii2HDSex0aX49Co2SoN417uYmX18+TpBcltzVQZ/Zd34PEpOOdejtu9lqXS/jzXKaoGb68q5rHRFYwyGxlmMvDAiFIeGSkKol7tMPJt9HjMmmYOqVzBIepf/vfJ1aWXXsqWLVv45ptv+Pzzz1myZAk33XSTElPvQQpwudax8pdLiEb9ZGcfyJTJb2E271iEoFLpGDniNoYMuQYAnf5DkXvUvgXWfSx6YCmBwvFw5H34J59MY9MHAFRWXp7UUL0uN9Ezrrbzs0RiacaIfRFbcsXu3p41FYM+eS+GXp+XyHFq/fxMePtcRWySG1clktlTJVcqlTaW5wSddm3mXiKAkI+uLEEebLaJqFTJKzkDZNkmAxI+fZSAVlLEc/VJdBrfesczRxbew3OKcgb0zMRxeoEgPk3Z5fyj8DZ+X5Rh+5toFHdUi6xT0WKIkZk8e9LDDyqcgINOfJKJucVTMw9Thjx4ZQOyWYNLZ0ctRzjEsWvbpL5gMlVwgFVGKwfoNDqQLZrMqwUDTp7U/Z1L9nubZnUxGmSOGoAIZ2VNxG6fxhB5E8PVnQRlmTEjVZy3b0XyzYV3RtEEmHYJ7SrhRWvIHkIQLRMsRvbuI5xcVvYbrJaxhMNOJnteYJi/gU5tFgtlQSYyIVeyKZstW0XBTnHxadzTYKHGH6TcoOPV8UMwxxLVVSo9o0bew7F8yBHyZwBcNepP+CIebj1mDL89OM2islje1zZJXEuTNsw2y2n8cWMdAFeW5/PC2EqGmQyY1WpOK8zm0ykjKNBK1Etl3Nk+EaNNbJbavZHUxVh7w9UI7ZtpiKyhhQKek64E4LKyPC4vz9/l8LOLc7g+ph33quosGimiUdoi/vi/TK5CoRAffvghL7/8Mvvssw8HHXQQf/vb33jnnXeUsC9jLFmyhNmzZ+NwODCbzUybNo033ngjpTmi0ShPPPEEEyZMwGg0kpeXx2mnncamTQqoYSsEn6+OFSsvIBJxY7dPY8L4Z9Bodp+PMqTyKvLzj0eSZNat/wOBFS/A2+fA8n8qatf2mueR5RAO+z5kZSWvaF5aKohLa+scAgGFeh6acgjdWMuGCpEjk1+QfEgwjty8wwBoi2xVRrCz/me8rx5MINSKJOkSRCkVOGKhwQ67ThnPVchHV5bwGqSSbxWHVmvDYhYNpLuztJl7rowObo78jr9vvZyNqjGogXOLk/cUHZNrwyCFaVYV0VrsIJ+uzOwJ+/FgJJJvAElivDFCqSH5pHSNWsu+BtEW7HrDdRyzPPV7vgOCHjzoieYLEjEq0oE1haqy8sJZjEd0H4jmG/FEMlwW/E467VrmR8YAcEi2Bbt2YIJUXvYbAPaJfAqAz+zmruPHUWw3ZmROU8dmZGCrQyTsX1ia22fhgUqlYfiIWwFobnyby50i/P9ZRIOsltKTYoiRqw67hl9qm1jTPoH1ugv4oKULFfDM2Aqydro2dvtU8vKO4Cz+wSi249JYuF63T2YpNzFB570r5/L4jBu5aoaG329sIyKLjgI7e/IAhpr0vDlxBHpCrJIm8vyQw5CIEpGh05u+92pVS4hZgQe5Y8PhvMwleGQd07LM/Hno7nNfr6ss4IAsM0FJwzPS1SzpMPO2+gBa3Ar1nB0EZEyuVCoVkiTtEAb0+Xyo1QpUUmWIefPmccABB/D9999zyimncPnll9PW1sbZZ5/Nfffdl/Q8l112GVdddRWRSISrrrqK2bNn89FHH7H33nuzdu3aQTyD5KHT5cT0mEaz14TnUKv7b20gSRLDh91BJFJEKNTGBs0K8Qelmjc7Gwm2r6GhQbSWiZfYJgurdQxZWVOR5TD1C66An57O3CZJwh/pQKWpAyTy85IPCcYR76/XZdcSCXtExm0mCHrpcAgiY7dPQa1OfSHJdoh8ra4sDVEFmknLYZ/wgkFaZA8gK6ZV02XTZk74cqoYUxLFXCVyX47My6I4BTJj0ag5IkskRf8752gYtntNs6QQ8nGAfgW2CrHAHFdYkPIUR2SLjU93bg7V/gx7+QW9ONVGInniO79XiutNft6RTEEUL0TyDZn3OswbSX1FPj8hCjNOKEiOCOfmHorRWMl0eR4qOcJiQwXbMxF83f4jrg1v0uVxEc034FQZydaqOSF/915wh31vcnMPR5YjjLH8QJW3BicqIuVmutIhFN52ZGCLrYmvag7mkZ8v5u5t4jt6dnEOk2193/thVTegleAi+RF0RPlGP5SXtrewpqE70Z8wJfi7cJnVdEmNmHUhPtbNpikYYohRx19Glu22ynWMxcgdZeLzXjMdy9O5j7HmVG+iHU86aHSF2SiXsdkwkl+kSegkiUdHle0idtsbKknikRElGOQomxnBM52Xc5Pnd6x2Zka8BxMZkyu1Ws3555/POeecw3vvvccbb7zBFVdc8V8XCw2Hw1x88cVIksR3333H888/z8MPP8zKlSsZO3Yst99+e1Kep2+//Zbnn3+eAw88kGXLlvHQQw/xyiuv8Omnn+J0Orn88uRCXYMNtdrIhPHPMGniq2g0ySWlq9VGAv6zkCQNrZGNIlymlBTDF3+k5uPDiUYD2Gx74YgRgFRQWnoOAA2Bpcg//0MRs3qqBKei1+/qgh4IJlMVBn0xUZVER5Yawhnmp4R6yFV2dnrtdCyWUWhUZiIaFS5N5r3h/FEnAb0aCRVZWbvvb9gf7FlTAOi2aRQJVV6z37f4CuwAXFCchLr1TjinTOSqzJf340l3hp6isI99ilfQZRZ2HFuQfAFCHEeUTkYv+8Ggxm02ZuaVCLppN9iQ7YJwTkjxmTQYijjIEkCSo8hZOrrlzMhVy3HPcdrGe2iUStBJcETuwLlxAJKkorzsIhx0Mi4qQrev17aln9D+yXVsW3ED3rCRSLkgMWcX5WBU97/sibJ/iQ5jF5c2iyhHpNKCzawjHImmZoO3g9ZcHS61E3coi2ihkXqimNUqbuijTVIcJtMQCgqOo4R6zjYtBuCOTQ3MfmoB321K4x3t66KmVBCRpqwLeKNZeJMfHlk24PU4f+g+7KP6hYik4Z5xl6MNZ9alos0bQVZLtAypBOCK8nyqTAP3OSzWazk+Ft5sryhF1qpoVbgdrpJQJOfq73//O8ceeyx//OMfuf322znnnHO46667lJg6bXzzzTds2bKFs846i0mTehYIq9XKrbfeSjgc5uWXXx5wnueffx6Ae+65B72+h60fdthhHHHEEXz33Xds3LhR+RNIAyqVdgdhzmQQjZZQWiqavW4YZiHkbVLElpC/lbpi8YWprPjdgPo/fSE/bxYalYWAXk27ritzoxpW0LbpGQDycvtvL7M7SJKU8F615WgzzpeJBt10ZsXJVfISDDvapMJhEkm7nYbMiUyXRlRmWrVlqNWmtObIiuWmuSwaIsHMyqXloJdPunT4JSND9HCAwzLwoJ2wf04edtlFSK3nmQ1tAw/oDyEfn+UdQFRSM0LnotKY+i7ebi5lL9YDEM434g+luGjvYI+X1pwCkCQqVAEcaZCjkYUHUx4RQrSdlswS/qvrv8CZKwjnYTlZKYUoi4pORKO2sK9KFNo8saGBt9Nsx+POK6AlV48j10U0x4AKOD+JClOzeViin+h4y0LKZA+yTs1vzh6PZgAisjNkbxtbKsR3yCcNITxc5MJdWZ5P/gDVpZUVlwMSh3geZahBIqSRCFfZaE8jodwfaqM5T88/N57MHe2iVdM5RTns7xh4I65SqbmzXIVV7mazfiiP++wpf35vNASChKushHQGyvUarq5I3vN7cMjFKJOOiEZHeJiVmgqF+rsOAhQhV3q9ngceeIDNmzezadMm7rrrLjSaNJMQFcK8efMAmDVr1i5/i/9u/vz5Sc1jNpvZf/9dF74jjjgi6Xl+zago/x0mfSlBnYpqhzKSB3WGeiIaFRZtSaJNS6pQqfQUZh8OQKM9cxVrX+tSnBonyJCbOzPteXLyxPm0Z+uQA5l5iroDm4loVGijGqyWMWnP47CIDUSnOfMchE6dOCeHoe/WKcnAYChGH9UjqyS6Q9sysse94inmRAWhvbC0MC2irpIkDtYKUtWdk1koQQ66mWMW9hyZnR75BJjhEN6qaL4BTwbCnT7vdpw5IvS2nyU5L9HOyM8/kr2iopjFk51+yAcg2PUuWZXCpdCXen5/UKtNlOSdwFQWoYqGkS1atobTe6a3jR0CEuRNKAHgyNyspHPjhlSKJOvOXA2/CQpJkqdqWghGUyPBTfoWvGYNGslIQ3YBsklDjlrNb8sG9pqbzVXk5xyOhjCXeoXQZqTczHpP6huoOnMbskpivuEInBo1OWo1t1YVJT1+bNnxXCD/A4BHIkN5YXmakhDAKp2VSIXYIN03snxAz1lvqIG7q0RuVqTMzC9KdhRRGP9dBjSIiIf8hg8fvsvfHA4Hubm5A4YFPR4PjY2NjBs3rs8csvjcA80TCAQIBHrIgdMpdvKhUIhQ6L+XkBf/7EhEoqryj6zacCV1hWry237GnDWwZMLuEIl4qLWLBbrMcSLhcARIL9SQn3s8dW0f0upQ4XU3otWnHhKKo8kt3Osmtx5Jsqd97S3mKagiMgG9mq7OlVgsyb+kdkZ7QDw7jnBuRtfJGiNXXWaZQMCDSrXjIhI/12TOucsgFkarYVRGz6ctkk2rqpHO0HasGczzfLOKeksZqkiEk/KSv287n/Nx2i7+HYZgtpEGt5e8JHSp+kJD10bWqIQ8xT6m4rSv0VH55TzYKSPbdKzvdDFdn95et6Z7GeFsUUBwbF42rrrk7nNvqNV5HBZdzkecgpytp93nx5ZGqxVX7eds8HvpUOVhUEnMyDKlbEtB0dnUNL7G3ixiEfsTLTL2O0dfz7bXu5Xm5k/wYuKboNi0nF/oSNoWna6CPPUoWiPrmRp9h3zdLBoCId6qb+PMwuQql6PRIFsdneAHW+lVdIQEqf9tvgNtNEIoOvB3vbTwfFra51LGFwwLX8RmjYY5BLk9GNzlnHeHSMRLvSNAtVyJp0xENv5YlocJOYV7Y+AEqYV33LU0W8p4vL2dc4IFqFPc6EQiARaWTgKVxIhAFwfb+r+3vRE/bopJx5hImLVqDYtslXg8zSlHbDJBsvb+z5Kr7m4RF87K6nsnZ7PZqKury3iO3sftDvfffz933nnnLr//8ssvMZnS3/kqhblz54IsUxkM0ZarZenS6/EErgHSCw9otfPQG8Doi7BmrRHvls/Stk0VDVKgCuGyavlu/n0EwoenPZdduwQMYHHaxDlngLIQdGbDilVv4VyVfkgnW7MBjBBt0/LZZ+lfJ32oE5MlSkin4ssvnyUa7btke6DzlqRuzJYoyDLrV3vp2pq+TeUeDRRCk2cLqzM4tw+1Il/K2trNgi9TF3CNn7PK04lkCiJn6bjvu/nMDKVX8bRStRksI5FcIRqXLOaz9DgaEEAbNRKym3hh2ULaSe852mAIEtZpyYt04ly+HYmB73NfGOnupKionkaphL9+8z3TwqmHvG2qF1loFl7+sQE38+Z8kfIcyDJDAiEOyvuWRezPO/WtTN2wfMDFqvc56w1voNXKfBs5C68ExZEgnT/OJ5WnsMA9BArX0W1oxbGtnpbiAu5dtx3LsoUkQzs12h8xGOqIRq08uHkEskGF5AyS2/EznyUrtC7L5EtmvFYPUxrnsLl4Ns06iQfn/cgUkrvPWs33aIwqno9cCRoJVZMXS/ciPvslSRtiKOvO4XTzazwVvZ5mtY6bvv6BQ4OpSSH8pO/AaZkE4SgH1VTz2Wep52/NnTuXvdol1paV4jRnc//377J3oCzledKF15vc9+J/llz9mnDzzTdz3XU9IphOp5OysjJmzZqVIGj/DYRCIebOncvMmTPRarWEnv0zHQ4fUV0NUydI5OelLlUQifhZsuQ+giGorPGSd84pYEgvVBFH0z+uwWXVYnesY/LUR9IKC/l8NSxZ6gRZxujKZebJ4pzTRfPbt9CJF0tOKwdMT/06AYTDTn78UXj4huVOZMxh6c0DgN/J+k9voSVfz+iRUSqG7DjXzvd6d2hp/Yz168HiiTD5oNmQn36o0vPtV3RQS8jk4agDj0iqGfXOaPQH2bxE5CYN9UaZfUry12jnc45EIqhf+olwlo5fbDk8Oj29ZP2XFvhAhvOlLzj1uJvSeh4BcNZj+WANnXYT2+12Zu+3T8pTRCIe3lwoer8dWZjLrKHTk7rPfSE490umsoiPOYnG0lJmj+m/N+GutvhZ+NNt/Bi5BiTY217I7BmpS3kAeJ67nvF5K7HTSZfKgW76ARyZ0/e7cuf77PNtZ8nSZUSRmC+Lz4/WhTj67NS+X9I2K+t+/pCWfD329kbIzaVbpyU8dj+OHSDcGYn4WbL0AYJB0FfeyPe1wttl2OzklKuOSumZcTqLWbHyTMr1P6OuPpDIMBsfZhUyur2a42ce3u99luUIS5Y8ynuBY9muGQKhKIU1Xo65LvV3jbTKTXT7zbill3iJy/jIlMM1B0ynLMlQa2cozA2LV4AMmq0ujpg0gX0mJR8h6X2frdUdvPvDFsIjsviXfgLXHTwtpdy+TBCPPA2E/1lyFfc27c6r5HQ6d+uRSmWO3sftDnq9fodk+Di0Wm1GC7xSiNuh1RdSUbuG6koz1dUPUZB/eL9aWX2hqfktgqE2DP4Iha0hVOZsSFLscXcodBrZHJXx+Kvx+9cnpfK+M+rrxQ7P0RUCLBlf+9xgFhvw4gxuRZZdabmlO7uWgiRj8oYxWwohk2dBsuLoDtGSr6fbuXi35zbQebtcywCwd4fQGiwZ2WSbei3qld8QwU8guA2rZVTKc7xWvZWopEbqDFCus6V1zxLPt1aLudVN98gsNoZ0bA1GGGkeuEqpN7qDfn6WhVfwVO8GdLoMmi6r1eS2NNNZVcKGiBkPUlJ6UL3R3rmEn+XJIMFwVXYi1zWd51trKKK4qRqK4JtOLxGVGkMK+TBt7Z+yIVpEpyoHwlG2rG1HOy090Ut71E5zvQWzrp2ufAcftDo5trB/SYf4OW/a/AIQZZtzPPVZORCKomrwpf7s2AoYut1LS56eI4rfoMByLx8F1TxR184pxbmo+iFIDQ0vEQy2YAipeXEDREygavWTHyDlZyYnZxoOx77YuhrQVLtQlZhpAf5lcHDKAPe5tXUeWwNR3uMMADQbuiky6tN795mzKW3wMSPrK36SDmVtdAQ3b2nirb2GJkUW791YjVM2onYHUG9zUzizPO3vc4HDinqbG7nUSKfJxl+3beae0eNSP6c0kKzNKa16xxxzDD///HNaBvl8Ph5++GGefloBvaIk0F8+VGdnJ21tbX3mY/WG2WymqKiI6upqIpFd4+P95XX9n4Qln4o6HwbJTiDQRHX131MaHon42L5dVONV1PpQ6e0ZEysQopR5bSJnraHxvbTmaG4RwoQFrQHCqtQW1L5gkLKwuMOATHt7egUNiZY3nSHQZqjXotbimPgHAJyuVUQi6RUAdMebNXdnbpMquyqhd9XdtTTl8YFolH82ibCDZrsHW6AlI3sAbCoJVZvIKXuvqSPl8R/VryWMlmK5nknFGUo6mPPI1WUhuUJEJRVz21K35/vGlbikLAhF+csbK9P3ogHoLHyyZgb4wvhlNd91phbyaWh4J6FtpWrxY0tXWR3AkIWmyUjrJlHNNre9m47QwEn/Xu92mpreF2MQFcGnFzp4/YI0PGjmfMz2cRR5sxmZvYWzDU9hVavY6PXzRdvuw1mhUDfbYu/B9sYKPjONQUIQm2xzGsUC4QCV+Wdi1bmQoqBbK4qOftBZWdDVf0HN1u0v8wxXEpJ0jO6oRl3vJd+aZsGCIYvs1jCtnUUc0vFv9JLM/E4XbyfxPZrf4eLtFhFOU69xIsmQm51+nlSuRY8kg2a9cHC83BRkk+fXpcuQ0spXW1vLtGnTOOyww/jHP/6RlHts6dKlXHvttVRUVHDbbbeRm5t+QnIqOPjggwGR17Qz4r+LHzPQPB6PhwULFuzytzlz5iQ9z/8JWPJRR2EkojdcTe3LOJ3JB+Zra18mEGjCoMmhqMkPJoWSDPU2ipsEWWhu/ohIJLUKEa+3Grd7LZIskdcWJDyAwGpS0JnJbRc5O23t36Q1RUfHD0CcXGWYeydJmKbdgE6XT1QO0u1clvIUoVAXbo9IBrErQK6gl5hod+qbso9auuiIaDCEPKhafNjC7ZkZE41giThRN4iX/L+a24mmqC/1cYtY2DRtKu7pSr/iFACtAaujFFWLeJ4/bkxNckCWZb7qEps+e3s7o4wZapzpLUyVNpHbLeRYPmqqT3qo11tNR9diFiH07NRNPsy6DMI0hiyKXU5U7jB6t4eQDB80D1zJvG3bE8hyBL9qGt9lTUeSZa4dVsyY4jTSLyx58NvvGDrz36hUOsLd33Fmjnh2/ra9ebfaZNu2P0047MRgHMHfqoRY9UE6AypPmBxzGp7Od87D8fxZlJnEOUTaApweayd09YZ6mnfTaLujcyFPOceyRRqBNRrg8LZGJCAvXXJVMA7f2Z/zpyV/5KXFZ3F6rC/tHZsbaOmn2XdbMMxV60Rz8gPD36DqCqImQpY1OT3GvpAdu45Sa4DxkeVEUHHThpqUv8+DiZTI1YoVK3j++efZsmULF110EdnZ2YwbN47zzjuPG264gfvuu49bb72VK664glmzZpGdnc306dN58sknmTlzJmvXruXUU08drHPZAYcddhhDhw7ljTfeYMWKFYnfu1wu7r77bjQaDRdccEHi921tbaxfv562th01cC699FIAbrnlFoLBngTYr7/+mjlz5nDQQQcxYsSIQT2X/xjMojw412OgoOBYIMq69X8iGh24OiIYbGPbdlEuXGWZjVoGjEqRKyuOrhAGlYNw2EVz86cpDY8f7wha0YVlRTxX6Ezkdojnob39O6LR1JKjfb5afL7tSHLcS5R5YYMkSTgcIm+ns/OnlMd3xbxLJn0Zuovmgz7DfMC2Tdjr62JzL05JKFOWZZ6pFZ6q0o6tSDJYdRl6QSUVtkArqlY/+qiPhkCEHwfY+feGOxxhoVcsak0bVfywOXPBXbNeg7pF7Li/d4IvBZFKl3sNiyMiFHK/73U+npGhZ09n5VnN37jeIZoGf9nuIZxkD7n6+jfZwGg6pRz0oQCqNj9mfQaeK6MdG4J0Gpq6AHi7sX8Pic+3jcamDwH4Rha9SQ+P1DLElJm0hMFQjNZ2ET81TsW6+TWMKolfXD7+1QfZc7s3UFsr9BOXOm5lbViLXaNm/1ij7+x0yJXRgQSM1ExArxbPynlWmeJIkJZQmAtWVeMM7xhZkWWZx9d/zxzpaACe2GsU/oKDAMi3pvn+M9iwDZuOVi28oxO7XmesSUVXOMJla7fj7+PZ9UeiXLy6mpZgmBK5llk+4eXP0UdRpagZ1htatQqHSYTnjgt9jV72s7jbwwpXhn06FURKZydJEhdddBFbt27l3//+N8cffzxNTU28/vrrPPLII9xyyy3ce++9PP3003zzzTdUVlZy++23s337dv75z39SWVk5SKexKzQaDS+88ALRaJQDDzyQSy+9lOuvv5699tqLNWvWcMcdd+xAip544glGjx7NE088scM8M2bM4OKLL+b7779n0qRJ3HjjjZx//vkcffTR2Gy2/1iY8z8CSz6otBAJMmL4LWg0dtzudWzb9tSAQzduupdIxI3VOp6CaKX4pVKeK0MWElCiEb3B6upfT3qoLEdpbPoXAAVukT8WTqPFzC4Yegi24WehVVmJRNwJYpIs4l4rW8CIJiIr4iWidjGOgNCPSY9cCakKe87+ULQXqDJMEG1cSdY3zyDJEAg04fcnr40zp83JGrcfg+wjt0UQNJshQ3skCYsqiBSFYT6RJP9eU/K6bl+0thJCgyPcjuQOZ+aZicHka0RyhjCF3fhkDd+nEIr7ueFHGqUSNEQ4/LyXYL8rMzNGL56dwzprsMhOnFEtC7sGticaDdDY9D4/IgQdK1vEpsGSCbmyl2N1CAVzVaMfjRziF7ePZd27b++0tfovQBSd40jel0Uy/gHudh75cgPfrM+srVeLfBLPrzqPrzdUcZ5VeHdv21y/Q6gyGg2zbv2fkeUwfsfJPNos3jd/HFqEzyuOS5dcAeQEbdgNYhO3Zes/udzbSpZGzXKXl5OXb2ZjLCzmjUS5bc0invYLPcdrSs0ckZtFi0v8Pd+WPtmUJClBzjwBC9db5mJRq/ixy82Fq6vp7nU9nOEIF66u5qduD2YpwNU8gskqerPmZiffG3R3yImIzUR+qJhLeZLH7B/utp3QfwNpUUeVSsWxxx7Lv/71L9ra2lizZg1z5szhjTfe4IMPPuCHH36gvb2dZcuWcdttt1FcvPuGjIOJGTNm8MMPP3DAAQfwzjvv8NRTT5GTk8Prr7/On//856TnefbZZ3nssceQJInHHnuMTz/9lGOPPZbFixczZkz61VS/Oky5EG5theOfQKfLZeSI2wCo3vZ4v3lFLS1f0Nz8EaBi1Mi7kGItCuIvhYwR86AUR4ciSTpcrlVJhyu7uhbj89WgVlsoiK2jiniuplyAdNzj5OaLF1hb+7cpDW/vEC717KFnwu8WQVWG/e4AProKx5dic+B0rkw5fNoVy7dKt5/gLnAMQT3mZGySWCQ7OxclNSwqy/x1mwhNzeIz8IsFyarPnMxY1GJxGuoWC+THrV14k/QWvRGTbhnh3YQEWNzbMrbn6JbnuEn/BuMjQsDz0+bkScDnrSJMurc5oEyllE6Qq6LOCFMlEVb+oGHzgMNaWubgC7lYLIl8q5JGcZ0y8lwdfgfW34jcqaBPz76I78uztY19Hq5Wr6O9/WskScNiy+/wSRrGuDfj7dLw+Deb+Xpdml69t8+BB8rJ7hRk3BMys1/XrVTpw3SEIlyzricUtWXLQzidywmpcvhr6Dz8UZkDNR7Oow5PrIVPWmHB2HtU8nVRaBdpNduallEobeatcRVka9Wscvs4ePF6Dly0jr0WrOb5VvGOO9u6mT9WlkEkTItLpFaknXMFsOhZcmXx3HUHbRjbXuWlMYUYVBLfdrg4aPF67t/ayL1bGjhw0Tq+7XBhVME10fsopZZJw2ZxwxEjOXN6efo2xJCrCWDGhyFayT78iL3rHUIhZUSwlUBGfvbPP/8cgNGjRzNz5kzOOOMMjj/+ePbbb78BK+j+U5g2bRqff/45XV1deL1elixZwtlnn73LcXfccQeyLHPHHXfs8jeVSsVVV13F6tWr8fv9tLW18e677/7vhAPjUGugV0JsYeHxlBSfCcisXnMtTueqXYa43OtZu+5GACoqLhWVfL6Y+16psGDOUCiehM5cSkG+KCGuq0vOe9XQ8C4ABQVHo/YLl3FICc9VDLm5YifW1pZ83lU0Gkh4rnKLj4X8UWC0Z25M/hiMORPRa3KQ5VBKeU7hsAeXew0AjrU/KdMou3QKnPISjoqTAOjsSs6b9nZTB7+4fRilMLP5GH9Q7Eatxswra80qschledrJk5vxRKLM6Sc5OY4Gf5CFbkFgxjlFLpJZgULfQ83b+J30CceYxbX/st2ZVCjO49nK/KBQ0B9BNgf/5Vtufj9F4aKdobdwa+gC9q69HodbLOiftgX7VSWXZZm6uldZyUTcmCmIuLF0iuuZEbmix/MVkSVmq1cC8HGrk3r/jiH4YLADvUE0iM8tvohXWsT1u7TuXVyyCLfb0n12wgHwd+OgCwB/NA8NYX4TuA29JDO33cn162tYv/lhampfxIuJp02Ps8YTJpsgf/vhfFTLXuau48ex8Z6juOSgoanbEN+kejsoyBL/dwZtGIxvMsoQYe7UkczKsSEDm7wBXJEoeXIz16mf48GJRyB9/zDcnUNLs9iw5KUbFgSYdz/5rrXiWlBFJOKmxPlPPpo8nAqDjuZgmL9vb+bxmhaag2EqjToeyfqUsawmL+9IxtgdXDG8i3NHZV7o9Nroxawx/Ibj8yNYLWOR5SANjf/KeF6lkNEZHn300ey77759Jo3H4fP9euXp92BgjBhxK1lZkwmHnSxfcS7NLZ8ncmfaO35g+fJziEQ8OOz7MHTItWLQflfDb7+D6ZcqY8RBN8Cl82DK+ZSWCmLc3PIxgUD/OS+hUCctrWIDUFx0GgRFfo0inqtIGHydZJvGIUlafL5teL3VSQ3t7FxEJOJBryvAalWwfPjUl5Eu+RZH7kGxz0k+NChyoiIYNHkYvnsafnxcMbN68sAW7ZB31dei3RIIcc8W4Z04Q/s1Vlz4Q+J+2ZLU0+kPpVo3I6RasvQ6DuA7AN5NotrpveZOZCRGy6uxO0TlmblEAa91LNfuYJMJi+ykM6JJqkpvcf18tktD0RBhxIoP2d7upbU185wrH3o6ZTPDdMOwy510ywY+a9y+2yFdXYvpdi5ngSTaAZ3gWYkXsXnJNGxq0qkTe70Dhv6W0fJqoqi4b+0PiWMiER/r1l+DSuXEZKxinv5sWoJhykPtnNTyFc5o/NlJk1wdcT9csQT7WOGhdgeN5OYcSoW8gYujf0NFlDeaOjmzZjTPcCU3aV5kkUePUaXi1cA8SgKtYBIhMJ1GhUGbxjWJkytfJ7cdO4bvrp/C4UOrUalaWb/hBop08OqEofy87xieLmvhHvl6HuFKLhh1HBqNFfzdyDK0BsX3JyPP1YTTycsXnmjJKASda2tfYpTey/fTR/HY6HLOLsrmnKIcHh9dzqdjVGR1/AOAIZW/g/UfwwuHwRd/TN+GGLTGWI/RgIvS0nOELTUvEY1m3ipNCWRErubOnYvRaOSoo45i//3356uvvtrlmPvuuw+HQ6Hw0B4MLsJB4QZ/8QgICi+PSqVn4l4vkZU1lXDYxerVV7Jw4aH8tOhIVqw4n1CoE5t1AuPHP4lKFXuBmXNFzo6jUnETbbZJ2GyTiEaD1NQ83++xdXWvE40GsFjGYLPtBUMPITrkEIKa9KtUElj8HDxYieaLW3HYRXVlS0ty6uGtbV8DkJM7A2n+Q/Dt/eDJsBKuF9JJak9ULmZNg2mXwoTTlTEmEibLMBxJ0hIINCbyrl6oa+XYZZto69VTzx+Jcvna7bSHwowyaTkkIBKDPeHYAmnKnFxdl7uIL/U3ccpwFwcgQt3zOly7rbgCiMgyrzcI4nIg84iqSwGwKED2OtXZrIhW4fXtxX6Ie/B248Ak6YMW4R3az+yF9lgYTp1B82cASz7mEtEAXCaXw/SiIf1rNbtv77Vt+9N0YWcpgnCe2vE9Hlncr4w8V1vnIz21LxaV8FKpDRO4slA8Kx90ZTFn9f3U1b3OkqUn0d29BFnWkzvsEZ6sFWGhGxr/hU4O44yId5LNmKYtucMgbwR2ux0AXyjKiFGPU1FxGftLi7hG/gsW2UmjVML30gw6IjqGGHV8OHkYU2Oh5zi5Shtxr7avi1KHifLcQsaPeRhZVtPR8S0/LzuLxsYP8NT+BVvNFQyhmorSc8jPP1KMO+I+gtdt4sSJhcwYmZd+tSDAUQ+SN1YQaU+kBKt1HJGIl02bH0CnUnFaYTaPjCrn4VFlnFLgYPvmu4EoeXmzsFrHstFtZJ1pb9zG0owuCQD62Hs84KKw8AT0+kICweZEUcN/GxmRq8MOO4y//vWvnH766SxcuJAjjjiCAw88kAceeICHH36YG2+8kSeffDIzob09+M9BrYVNc6H2J/D0vOA1GiuTJ71KZcXvUKn0+Pw1eDybkCQtpaXnMnnyP9Fq7f8REyVJYkjlFQDU1b9BMNg3KYlEfNTWvQpARfklQv/npOeInPUefiX6UOliiZPhAPkFIlTZ3PLxgMNkWaYtRq7ycg+HBX+H+Q9AIDnV32TgsO8LgMv1C+FwctVw7TFylVM0G2b/BQ6/PXNDWtbD3TmoH5+eEH7t7FyEMxzhb9uaWenycdyyTXzV7uTnbg9n/bKVBV1ujCoV9xc1oCGEyTQMd1QsBjaTAh7HWOGAQzOEQhoZrdpGFHi+bvde0M9bu6nxR7DILg43t+EPi/eZKcOwF8Ac/1hOCN7NY8sczDSI/KYv2py7VH/1htuzlXlBIch6akkF3hgvNKXrnYnDaMc0VGwUvMEI55WLtIeF/hxqXA27HN7RsYCOju+ZLx1OBBVTbCbGOdfjQdynjBLa5Si0rsMaqxh0B8KcNPp89jO0EpXU3NdSwbqNd+LxbESrzcHrvZR7G010hyNMsBg5qVE0unGGhQ1pe65isOo1qGJeNGcAhlXdwAH7L+CiCRfz1Xg1j44o4sYhhbwwtpJ500axl9UE3ti7yZTDb19byrVvLafdnYZXpZfnKg6bbRJ+38Wo1SaczuWsXXc9tbUvAVFKis9kxPBbe8arteiz8nnw9L15+cJp6XnPeiFOzlrdQUaOuAOQaGr6YJfNZV3963R2/YRKpWf4MJHjfPfWYRzV8XvmlF6VkQ0AP7nzuTB4A3dvKEGl0lFe9hsAtlU/kXK+6WAgo7fDCy+8wGWXXUY05t6XZZkFCxawYMGChJid0Wjk0UcfzdzSPRh8SBIc86gIVeyUL6VS6amq+gMVFZfidP5CNBrEZhuPTteHbtmPj0M0DBPOAFv6TY0T2Pw1fHKtaMVy1tvk5ByC1Toel2sVW7b+ldGj7t1lSE3Ni4RCHRgMpeTnH5W5DTtj4lmw15mg0ZEf6mbDhttxu9fjdm/AYhm522HdzmUEAo2o1WYcjn1h799A0KNM8v+Xt8KaDzAeeB0GQxl+fy1dXUvIzZ3R7zC/vwGvdzOgEjYpBW2MDIX9OOzT6e7+mc7OnyguPpUPJg3jjJVb2OoLcM4vWxNDjCoVr00Ygr35bbyAw7E/91v/hdPrJ9d2kwI2CXKVo6pEktTMjrzBOulPvFTfxuVl+eTsJHwpyzJPxSQhDmMOJbkH4F2yDMjG4msEUled7w27TqaEVhw6K/sWjKdkey31lPHvlk7OLe5bE/Cj6nm0SJMxEWB2YQmPR8Qe2dxHF4hUEQ/leYNhppUewuitn7AuWsaja7/m0ennJo6LRAJs3HQ3QbR8qzoBonB+SS4EnMp4ror2gnM/xPJ+CNpDuP1hJEni0YkHMWPxWjZEx/C+/mb+UNBNYcEZXPvDBr5od6GR4NERxag/FUTEGRTrUNo5Vy3rYc0HqCz52E3ldHiCdHlDFNgM6HQ55OUeRh5Q2dfYGLkK6rOZs0YUKtx27NjUbehFrra3e3hjUQ1aFQyPjGTqlM9paPwHTucv6HS5FBedOuD3PSNEwuTpBJtvdQXIytqf8rKLqKl9kTVrr0OS/k5u7mE0Nv2LTZvuBqBq6PUYY54qq0FDjlmXmfcsBhcWvo1W0NEtrnNJyZnU1r6MP9DAtm1PUlV1fcafkQky8lw9+OCD5OXlMXfuXDo7O/F4PLjdbt5++20qKyuRZZmbb76Z888/Xyl792CwMfEsGHsCGPrWONJorGRn709u7oy+iRUIcvXVHeDJXAdIQIauGugW4Q9Jkhg+XOyEGhre3kUGwevdxvaamObW0D+IcKUsix+loNaCRngwtNoscnKEkGxTc//eq+Ym8fe8vFmo1QaYdY8gtEoktPu7oLsWvO1kZ4vKrWSqGONK8TbbXmhlDbhbBOHLFJpY4UDIlyBt7R3fI8tRhpsNfDl1JBeV5FKo05KtVXNMXhZf7T2CAxzWhE25OQdyguZHztPMxWTMvMz6W18VhwX+wvU/2sjKmsIkfmak3oc3EuVv25t2Of6T1m6WOb3oCDCLz8nNPRx3l3iZm6OpKZj3hSPz2lhguIZHJrVRWHgMhyC8ms/WNPUpiCjLEd5oE6TlhOwQZpUKb4xcmYyZL1imUBcAHn8ASZK4slIsih96hrKh9l8xG6Js2Hg7Hs8mvlcfS2vUSLFey/F5WeB3JjxXGeVcmbKhagYWs7jnrli1XYVRz8OjKgH4IDCZKzpnc+YGF5/o7QDcNayEsZoe75AzKK6hzZAm0WvfLDzLK9/EHiNond4kNe28HYlzefjUvfjT7FGJOVJCnFyFPHS6PDz73Vb+tVx4EvX6AkYMv4WpU95hwvin+iZWX96C56Mb8TRvSf2zd8bH15D3oUgZaI1VH1ZV3UhOzsFEo35+WfVb5n83gXXrbkKWIxQWnkhZ2YWJ4U+dPYWfb53JQSPyMjZlXIGeBzXPcVOeSH9Qq40MH34LANu2P0t7+/cZf0YmyIhc1dXVcfbZZ3PYYYeRlZWF0WjEZDJx6qmnsnbtWn77299y22238eSTTypl7x78X8DEs2Cvs8CqgNcKoGQq/GYunPZq4lcO+94UFZ4MyKxafSU+nyBe4bCL1WuuIRLxYrdPi4mhAm2b4K5sNI9PVMamnVBYKEQLm5v+jSz3HdKJRkOJNjyFBccpb0RciDTkIy9PqIe3ts5FlvvPxWlrnwcgCNnq9+Hh4fDuBQrYEw/jydjN41CrLYRC7Thdouo0R6fhvhGlrNh/LGsPGM8L44ZQZTLg9W7H769FkrTY7dMg5N9pvvQRkgxskUvY7pbIzZmBBJyr/QKAF+va+LmXjlJ3KMwdm0Vl4DHyh+TrtNhsE/DEPUUK5Fwl7lnQg8U8nONtbRhlD5t9Yb5q3zVUvKhuHj9HRRHEZVUTIBLEExV2mI2ZXx/T4scA8LrFZ59YPpFKrQefZOahTatZu+4mlq84n8bGd/Fi5mPVmQBcXVGAPhqCaIgyqZVSux5rpmFKwBKbw+3vyc07qcDBX0eWYVRJrHT5WOL0opJl7hhSyEWleaDRw+yH4fA7cPqElyVtz1U8X8rbjj0mWtnl3X1+3g6Iea501lxOmVLKpQdVoVJJAwzqA/osQIwrMwT5zQFDuHC/iuTHr3yb1xfXM/bR9dz43srUP783DFnkxSonW90BZFlGpdIwYfwzlJf9BknSEYl4UakMDBlyDWNGP7RjS6aPr4XnD4XNu+Znp4oih4XTNfPYT7sx8bu8vFmieIkoq1b/DlessvG/gYzIVUVFBc270WXR6/U8/fTTHHzwwTz00EOZfMwe/CfRvgXWfAj16fWQBODwO+DEp0X7CCVgtEPZNMjZsQnsiBG3YzGPJBhsZfGS49mw8U6WLD0Zl2s1Go2dsWMe6fliB10ijwOFvFed2+BfF8NHVwOQm3MoWq0Df6CB1ta+XxytrXMIhTrQ6fJwOPaDSAi6apVLZtf2eIqyHfuiVlsIBltwOlfsdkgk4k1omOXlHg7hOJFRQK5C0zOHKhohJ1uITLa39e9Ni7cTysqajMuv4cvAaJZFh+0wX7qY6vDwpvZu/jK+ntxcoS021P0OJ+RZiAIXrq5mnduHKxzh4jXbqA+EKFa7OZp/k5t7KJKkwhNT27YoQB56CLEoIBlecjKHIp6fB7Y27CDLIMsyD21rQZZUHGhsYYTFBkEP3pinyGTM/PqYYp4iT1AQcpUkcfcoEcr6nGP4oXE5nZ0/gqTlE/uTtIZVDDHqOLMoO5E3+LH+Vn648VAKszIke8teZX/1Wk6ckEexfcdzO6s4hwXTR/PgiFLuHlrIPe56flMSI0J6K0y7hOh+1yY8XmnnXO1ArgSJ7UrGcxX0Qti34xzpQqVKeLZzVG5uPWZM8uRKlsHfRYcskr/tmRaFGLLIk0QxRTAcxRkjvSqVjuHD/8RBBy5m+rTPOejAJQwdcjWStBPFaFkn1pagAkrqvRLa45AkiZEj7yDbcQB2+3TM5vQahyuBjMjVGWecwTvvvMOnn+6+HcmECRN2S8D24FeIlW/Cu+fDijf+25YMCI3GzF57vYjVOpZwuIu6ulfxereg0+UyedJrGAy9xGsL94I/bCB87sBJ50kh6IVV78J68eyr1QZKikXn+Zral/ocEk+wLyk+E5VKAx1b4W/j4PHJytjUa6FWqfSJUGVL6+6lUkTrHh8GQ6mQhQjFFgQFiAxqLcRfriE/ObmHAAP3YmxtnQtAXt5M1jV2c2nwOm6SrksoiGeCbJOWfdXrGG7oxmSqwmQaiiwH+b39F0abDbQEwxy6ZAPjF6zm+043JpXEVTyKgQD5eUcA4I6KhVoJT1FD1M4JgTs58SehKJ6fP5uTtT9gll2s9QR2SLT/17af+DE8Gokot4yISXgEPT1hOAU8aeaj7gTAJ/Xc/5m5do7JyyIqqXlUfTcbcu/k8/z3+Xe3FQl4eGQZepUK/DFPm96qSMN2vryV31Zfw6Mzs9i3aleCUmzQcX5JLhcU55DTh7fYHQwnMgGs6YYF48TI343dKMKcXb4kPFfxZHa1jlq3im/Xt7C5JYPej30ktSeFkA8iQW7Wvsnqm/flihnD0rcBwJCFQQphjYnxxkODcWg0ViyWEajVu7bz2tLqZlb1Gfw2eO1u005Sgt7KwshoPuwetgPhVan0TJjwHOPHPYlKlXmoPF1k9A244YYbGDJkCMcddxxnn302ixbtqMBcW1vLBx98QE5O5lL3e/AfQqy/IO40NXNCPnA29CzSSiAagYVPCsmCnXY8BkMRU6e8y5jRD1NWegHDh/2ZfaZ/idW6kwaRWgPWQrBnrgwMgK4nnBNHaem5SJKW7u6lu6iRd3T8SHf3z0iSlpISQcLi3opE5WGm0MQW+1gYLT9PaPO0tHy+29BgS4vQAcvPP1J4+eL3TYEQHJLUQ9LCPnJzDgEkXK41+Hx9NwUOBtsTSvF5uTPRaTXsVWZn1KgxPTvVTFC0F4w5AfJGIUkSRYUnAuBueY93JlYlxBj9UZkqo57nKtsoD69Ap8snO/sAAA7RrOFo1U8U2zPvB6nSGlghD2eVy4wsy6jVesYPOY/TEJube7Y08HpDO581t3LjNjHmdMtW9sqObRyCHryxBHKTEu14Ygn9nuCOZOXRUeWMsxjpjOq5q30cr7eIxeyuYSXs74jdFzkCeaMgd3jGdgDpE4quGtj2A91N2wDQp6svBTGPkfB+27XiO5RUzlWcXBmz+Wp9Cxf+Ywl/nbshPRsATnpeaP0VTaDVFWBNgxN3MtFJf0wgV1JjsTnIylSI1yDEwedUvc+6u45kWH7yG57GLj8bw/lslYsz71sKoLdxU/hSrnWexZbWHYmrWq1Hrf7vESvIsFrQbDbz9ddfc9JJJ/Hmm2/y1ltv4XA4GDlyJGq1mmXLluHz+bjiiiuUsncPBhuWDMlVzUJ47UTIHwu/+1EZmyQVzL1NVCBOPreH2MSgUukpKjqRoqITlfm8ZBBrFULYJ8ifSo1eX0Bx8WnU1/+TjZvuZu+p76NS6YhGg2zafD8AJSVnoNcXiLFxoqhECK73PDHSlpt7KGq1Bb+/jq4+1NFDoW5a24SXKD+mfJ8IZSjhuYrbFPJAyI9ONxSHfTqdXT/R3PwRlZWX73J4S+scIIrVMhajsZSplfDvK/ZXxhbAP+4M3vXvj6s9zGVRmcLCE9iy9a90dS1iTKSJVycMpTEQxB2OUmXSs2rVo7QhcuokSSzQN+neBdxQeGG/n5UMTAZBjMKyimAkil6jpqT4dI6vf5vNnm/5XprB9RviPRmNjJY2cc9es3omCHrwIBYRky6j1zkA5liLIW8gvMPvrRo1/540jL9ub+abdicOrYbfledzeE6vRTJvJJtP/ZpLX1tKyYuLeO030zMzxmiHTgi6OwkHw8mf39p/w5e34Ky6GDg0/XwrEP01jQ7wdeDQBLEmWwEZJ1fmXDo8goyl1VcwjtKpif9e/Y+fWLi1nXOHJZG/FW9FZsjaoftG2oiRq+JIA6RI5hO9DaWuxDwZQWchByc1FNDq9Gc+n8LI2HdbUlLCTz/9xPvvv8/JJ5+MVqtl4cKF/PDDD0SjUS666CIefPBBJWzdg/8E4uTKkya5ilfIKNVXEMRLIb7T8e+a5JsUqr+Dz25EWqNQewRtL4IX6vGmDR1yLRpNFm73Otat/xPhsIcNG+/A7V6LRmOnsrJXc92El0gpItOT0A6gVpsSifaNTe/scnhT0wdCZNU8Ept1QmysgjlXveeJkbbCwhNi9ny4g1p7HI2N78WOE3YrXeUZlWVu/fcaHvpiA75QBIOhmGyHIG81tUK0tEivY7jZgM+7NdHWqLjo1B57FAydmnqFFr0B4S1SqXSMG/cov1O/ymny62TLbdjkbo7iM/45vhyLrtfCFHQncq7MCvReNP0iQteePjprmDVqbq0q5ttpo3h/0rAdiVUM3b4gW1s9bG9XIKfG6OCl8JGMeDXKze/v2nprt9CaIGc4Kmshe5XZGVOUoZckFhq8cnyEVXcewc1HjR54TELjKpv2BLlSxpMSlzFwJeO58nUhy3CZ73Jufn8V3ckm4+8OcVIU94ilgBaneKby6VSGXBkd5JaInKo2d5IVnP9BZL7VQSSRnXDCCZxwwgkAOJ1OvF4veXl5qNUKNBTdg/8cEmHBNGUU4i58k8Kq/Aab6FmYrthm/c+w+FlUE84EtQK6V1ojIlwgCw9ULGSl02UzdswjrPzlEpqaPqCp6YPYAIkxo+9H31u+IhQLKWoVCgv2SmiPo6T4DOrr/0lb25dI0sTE76PRYIJMFJec2ZP4H1aY8O0cqsw/kg0bb8fr3UxX91IcvZpEu1zrcDpXIkmaHnLVthGenAbWYvjDuozNMWrVqCSIykKY0qzXUFFxKR2dP9DQ8BYV5ZdgMIgq161bHwVkcnMPTyTGyuEgnqgWE1FUClwjzbTfoP/4CwLhKJ5gGEfMu2ExD2fvKW9h23gnJzqvxmwexvBht+CItd5JIORN6Eop4bkyeeuAKnyh9AjtiAIrb126Dwr4SMBgx4zYrPWuFhwQe/8G9v4No4F/K2GHKQfaNyHFe6Ymg0hIEAhzHh2xhT+tps1x1C6B7QugYBz5VhESjmt49Qt/F26MfOEfC4truPWYJIhhf4iRom+cJXz+7kr2HpLNaVPLkhra2iUSz/OlLmXCghoducUVUFdLuydD0jgIUCDrcFfYbDYKCwv3EKv/i4hX+AVd6VV0xMmVUk2b48jUcxUQMXlZl3lSNCC8afFcqeCO8f7c3BlMGP8UWq3Y8Wq1OYwb9xh5ebN2nCNOgnSZ5+6ID9rRSwRgtY6JNZeOotd/lvh9ff0b+P116HR5FBedsqtNGgVyrqAndys2r0ZjpTCW51Sz/bkdDt22XTSLzss7IqGh9vB3zezv/zsveg9UxBxp5ZtYZEFqXbEF2+HYj6ysqUSjAdatv5loNERLyxxaWj9HktQ9PTMBp9vFuMBLDA38k4CkTF5aXGzTu1Oek8UykimT32DGIWuZtvdHuxIrgKCHqzQf8vvcJRTYMrfHYdJzu+YV7hlZ3adnsV8sexXriwewz7ZnmD5UgTxbo53j1D+y8tDVPHfe1IGPHyz0qhhMGpPOhj/WwMkvKhMW3PINfHU7rPso4blyJum5apHtgEjqz5iAx8jVBr+Dd3+u46etyV+Tlm6xnuSp3QmNwEyRaxHXoi0d5ftBhiKeqz34H4LeJhbWsF+EBnWVqY3vJZynKOJu5HQ9V/FyXb0VFIhYACL8EHTvEBaMIy9vFrm5h+H3N6LX56NS9fEyiSfDaxUmVzsVEwwdeh1tbd+i0f5CXd3LOByT2bzlLwAMqbwStbqXB0bpUKVmV8JXUX4xDQ1v09b+DR0dC8jO3p/u7uW0tAjyV1n5u8SxzREb9eQR2PsyZexR67DixokZTyyvSJIkRo26hyVLjqOj43t+WjQLv18k3JeVXYjV2rPb93jFvdYSRq+AIjqIRPQODwl7UkLOMM4/aBQ4SsGsIxTKbAdvNJm5UDMHcktSz9FxNkDreihXSOXf6MAoBTGG2yEdfSilEHuX1bY5+fNLi1FL8PKFfRDdviBJtHvEwp+R56p4ouh4UTadPDkFcuXvolkWUQQlyHf8PbyP9As3HP5HxpUnT6JbYnlReXrlvEy5vmoA2rtSD1MONvaQqz3YEZIkQoPdNSI0mGrz5bjrXGnPVQaxfqDHu6SzKEeudGbwsFsPnySpE20f+kSclClGrnbUTIrDahlFRcVVbN/+d7ZWPwjifUR29oGUlJy14xxK6lxBL89VT8KpyTSE0tJzqKt7jTVrr6Nq6B+orn4ckCksPBGrpaeljDNGOKx2hSqOR87GnPcTtPpw9yIzFvNwxo97ilWrr8LnqwGgsOB4qobesMPwIkOI9frz8WhzQDo+c3taN2L2NQCOXTxXSaF4ovhRCnHPbiAN2YBJ57DKNJ3lXUZGbG1nn0y9Vwa7+DeelJ0sXjsR3C08U3I/r60Nc8beZVx1WAYVjHHPla+L7za2otOokGV5R3HMfpDwXFkyIFcjjhA/QN4mkbLhSiYs6OuiBUGu8hVoOYPOApKKSaotTJruAGvyWoatsfLGfH0az/lukLPpPeBE2rqU682qFPaQqz3YFZY8Qa7SSWpPhAUVzrmKhwXT9lzFxikVFoTdhgWTxmCFBfuQwSgvu4yNGzdjMs0jEvGRn38Uo0fdt6vIn9JhwRl/hn2vhMIJO/y6augf6Or6Gbd7LevW3wyA0Vi5Y8NZoDumKZRxCXkcOhMWox7wJcKCceTmzmC/fb+lq2sRBkMxNtukXRZQKezHIIUwKLVAhDyYAm2AIy3PVSAcYU2DE4tew4gCBaQq9BaWRYfhbLMyxR9KTWU9q5TvPH7+8s0GTnMaMydXRgetso2/bh6F/K9feODkCQOPASFU6WqkxRGlvsuHN5ThvYqRq7xIE385ZQKOZIQ4P7oKuuuIHPynhC5WRmHBXsi3iu/mf8VzpVKJd7G/S2x0rQVJD23xCimLfKNyBSq5RZXQBm3+/6JnczfYQ672YFckktrTEH8dtLCgQjlXCghRJhAnV32EBZOC0mFBazEcdnuffQolSSIUPIx9D30AjUa9ew2YRFhQIZvK9+nz1xqNlcmTXmXzlofp7v4Zm3U8VcNuQqvdsYqo2ynud1bN17DXuX1NlTLiyup9kRm9Po+CgmN2P1hrgrEnKqdNZq/AnFcBLbvmXCWDxoZ6TnpqFRa9mtV3Hpm5PToLVwavpqE6l4/aPEwotac0PH5NM2raHIfRTggNb3aMRNddnxy5kuXEBu+y/Us5dr+cRF5O2nBUQNFEDI4STk0yeZuaRdC2gc4p1ySKXZMiZf0h5Ad/N3lWQZY8YYlQJIq2P/7r66I5lnOVb1NI98mQRdTXzcb6dlq7Wtl3aA4adf/p295gOKHLlW9WLhc7d+Y1sOq7PeRqD/6PIKF1lUbF4GCFBTP2XMVyrpT0XPXqC5cWlCYy5hw48Lp+D1GptKjV/byNE2FBhTxX/UCrdTB61L39HuP0BgEVWQ3fAwqQK2cD1o41QP4OYcFksdxt5zX5WsZk27g4c2vAlI0ptwxamvEEU7cnvPhFyqRKTFoFvFYAeivDVRtwaCKoUs25Wv0vPLURwIxZgcpFjA4siO9IMBIlEI6g1wywMIe8iWc4P7+AfCWEZ8ccL35SweyHwNlIh3EIsJosoxbtAASkX3TXwaNjQaXF/ucWNCqJcFSmzR2k3NAPaSocR7MZcEKBVaHv9InPIssSs59tJyovZvGfDiN/AK9YXMndiB+LSaH3HT0J7U5/OLnn4z+IQakW3IP/48hE6yohxfAr81zFQ3dKvGzjSIQF0yVXsXFKhQWVwInPwPkfQ9FEZearXwbLXxf/poHumHxNlkGhV1XAhblzDUBa5Gprq4f3l9Uzf2OaUiV9IFEtGEjdczXM5OV7883MOaR24IOTgc7CK7qH+DTvKcaVpKhF9OMTeLYtBRTyXBnsmOnJ1UtKjiHuOVfrlN1IxbC4uoMPltfR0DVAB4qhh8DEM2mPChsySmaHns1qNIQq7CEnlr81YJXcflfRmi0qLRXzXFXsi7pyH3JixKbFNXClXvyYPKOENLofz3CKyDJq0cSKHeK5bb8W7CFXe7ArzPni5RRNcfGJRsDXJf6vdM5Vpgntg+G5Kt9HtFKxp9ChvjeCCie0yzI0LIftC4XOTjooHA9DDlKOHK96F/59Baz7KOWhoUgUd1i8orJ0Crn9tcaENyQdcuUNiOuqiGcGIBrF5GsEwONPo5x89l/glibY/1pl7ImHzdPJIww4E5pbFgUETTFlo7bmY5bEdUnqfiXEO3N49aftvPD9Vpq6lVPvfuiL9fz+7ZWsqO1K6nhFZBhAbMDieZDe9kRyejLEpjmmjK5IzlUvxG3Yub9gX2hximPy8wth/CkDHJ08pO8eJkcWhLrN9esiV3vCgnuwK6ZeBNMuSb0U29cFxBIMfnUJ7fGcKyvQqIxN+12V2fjDboPpl4E9yTyOZPDcIeLf6zf1eCD/m8gbBcNmQnbq3emdvRrk2owKufu1ph5ylUwD3p3g3r4c0GOu/wGYkrk9ksTBWx7GoR7CtKKbMppHEWRSLeh34kYUVSjiubIVw/Ubsd73NR6nf5cChD7RKy3h2flbqe/yMbUym8KsDIhF0ANP7w/eduxFooNAV39K594O2PgFWItw+cVzr0gyuzEbXA3g7UgQm2Zn/8RGDodojkkgKBYW3L4QGpaTpxFNoONtbfrDyEILNx45MvP8t50hSeTShVdtSWuzNJjYQ672YFeo03wsPLFQicEO/eX1pANzHjiGgLUo9bGyLERRYVBCBWkje4j4UQqS1ENiUvU6xrHwKXHv9jqzx4uRCaacL37SQLxS0IoXjVI5YBoDVilGrtLwFImEbT0WtULVgpLELMM6ZoWWQsmfUx7+xeomnvx2M/sNy0muLctA0Ft5OHQqHwf25ZKftnHOPpXJj+3luVJCLT4Oi0EDTpIjV70KarqbxfNjM2Roi9YE3bUQDSfXvLl1PXx4OWRXccbVyzh5SimBcN+N01OCKUaufB0UZYmNU79euWgU5z2V+ENCrFexsOC6j+CnpyjM/juQR2MSnsFh+VaGqVtADgiyqlRBiCGLD3U3oR1zNFSdoMycCmEPudoD5eCohN9+n34OUn+o3B+uWZHe2JAX5NjLTcmcK4BoFKIh0Px3O7AncHV6uU2AIKFzhCwCY45XhlxlgDi5suFRLsFeayRbclFEO1Zt8mXkcbjto4BaLKMPVcaemE2iuXXqVafNC15nVf1oyk1BQAFypbPQhYXtciGtXSl4r8JBCPvxEA8LKkiuYnOlEhYMG3MTx2cs4yFJcNGXYLCRsygIdNDeXy+7XqFJAK1alVkyexzxUL23k8sP3ofhoWpOP2zY7o/3d9EUtQNgN2oxaBXy/hZPgnEnUxTIggZo7Eoy7PrJ76F6Ppz0Akw4VRlb9Da0UiT9iMYgYg+52oNdEQ7AexeBuwXO+3fyCddaAxQlqUXzn0QkBEMOFqRPqfwmgIVPwpw/CS/Pic+kPv7nf4i8q7EniBDIfxvRMEw4XVQxKrWzzAAJjSvJo0iTZABUak7WLeJk9fdw+OqUh7tjPffMFuWIp1eTRXNUg7rVRXmKfM/TVguMxqSUJ01rxFw0CurAk4o0RGxx8yjYRBqA9y/F2jIOqMIdSCKMG/NcuXU9IXGbEhpppSIEnGvdCpBQXe8Tnjbxrzl398ekg3hSeyznyq4HdX/K9UYHhkvncuZ31WhMCjRKjmPCaTDhNIqW1sKqX2joHiC5H1hW04khXMxQXS6GPqRi0kZ8sxzPqf0VYQ+52oNdodbBprkQCaTXAufXBqMdzo8lVGfYHmQHqGN5FOmKiP7wN+ishpIpvw5ypdbCSc8NfFwq+OVd+PhqkSR/1tspDd2BXCkpDaE1QiTYIzuRAuI6Tkp6Zr4MT+ba4Ikc8G0Xr49Lbaw3IhZXs16hMLwkYR59GNRtxBNOwdsSKzTxYIrZo9D16ajGEioEqpKsFhReo26tIDYmnVoZr1EM8Sq9fivTvDFyZcrm/s/W0djt55IDhzK+NEOCE/dcJdtAWpKoKC3m/rMG591SnCU2PMkUDPzhnZVUtx3LW5fem7m4bG8YbHwXGc9Ldccy+ov13HTkqIHH/Iewh1ztwa6QJDj2byI/yZTCF2Hz19C4Air23614ZNoIB+GFw8RL/PIFyof30sFeZ4pqwXTDZ6OPAWejsonnH1wGTatEFVnFfsrNmy4kSYS70ggVjy/J4q7y5eQ2zgfNicrZpDWJ5yiNMJy7vR5QY+lYC1QqYo5FK2PFg16VurcwToCM/WkdpYg4MUpJMT7uuZLjniuFlpaZd2H52gkbw7hSCAs6NYKI2FJRmO8Pm+ZC7WJyNOI71dZvWDCe95XDvNWtbGh2cerUftpgJYuE56oDTyDMv7ep+OrdX3jszMlJt+JRDJEwRUZxP5LJucox63D6QglCphj0NrqwMC8wAu/2TmXnzhB7yNUe9I2JZw18zM7Y8DkseR4OukF5cqXWirYW0ZBYGH8N5EpvySwvadY9ytkSR8dWaF7dk/eRCqJR4dHR6JWrPuunJc9AGJpnYahjLbQsBu2ZytgD1FDEtYHfon23ibev3iulsW63C7BjdlUrZs/hjmZWdV8CB72Y2kBZxhMR4TeLUTnPnjlWTenxpeDZ83cTlNUEY0uKRamE9op9seStgY3bkvNcxbw6TpUdAJtRITs2fAZLXyJn0p3AcNr705eKhwVNufx+5nBqO3zKtCZK9DjsQKtW8U2jChqbuOO4YEJzagds/5HmpR9hLp+AZe803ue7w/Yf4eWjKMoeC/wZdyCM0x/ql8i+d/kgbfQMNiarNvGQ/mUqZynsdc8QCC5DewAAeWtJREFUe8jVHiiH0r2Fh6J4svJzSxKc/a7wOphSzGXY8AV88FvhUTvlFeVt+zUhroUTSj3kRctaeGZ/sBTA9RuVtSeNEJwYF+91qNyOV9IaWCaPwNiSekVl3FNkzbQCrTcSDbdTJKBhPx5ZLKomo3LXx7zkceAY3N1Jhp8A/E786CjWuHBrHJiUyrkCrKkktE+9CCoPoFs9BGhSridljNjkRFqB4XR4grtv3hyvmjbncuS4NKqbd2tDT86VTqNiVkmUKeNH7b71TMMKrlqay+IlWTxlaGT2eIVsiW1sTcE2soxaun0hGrv82Ar7udZBL7xyrNArPPNN5QqA9DZKpTZOYy6U/wo23L2wh1ztQd9o2wxNvwiBzNIk9Xz2Ol38DBaqZqQ3zt8tGo2m2wNwd+jcDj8+JpK/Z96V2lhZFqEUrTl96Yu+kFio0zjXOAFSsvIxXeIArK7vxtOdTZVsI1fBnKs8fZhntX/Femj/rYL6QtxTZDYo04QX6OXdS/GeBT144wnkJuXIlSWWv+UNp9BgN+DEJvn4cdR7cM57itlCyzqGeZZzWJmD4flJeIljbWqci2uAJuXCgjFylR1qAiAclXH6wmSZ+pjfHetsYUm9GrVf9AoLAhxdHmX2/pVod9dc0NeJEwWbNsfRS9C5KMtAty9EQ7ePkYX9kBt/N9QvBUndk6uqBOL6hyC6d5gVzOfKEHsU2vegb/zyFrx3Iax8479tSeYYNRuuWAxHP6LsvP4uWPIC/PJOemMfKIe7c9JXU+8LGYThEou7gl6iRCJ6Gp6rx77exOl1J/GF7TRFFyqDXs8R6qXsl5e6Ta6wIMJKkqt2ycG5wT9yxvwUVfGD7l45TsrZYz7qDgA8Ugoh73hbKoOt/+NSxdZ5HLfycl7Mf5tz961MepjTH5PxUNhzpfe3JryWbburGIw1vG9X5fD1umbWNSokE5BIaE8yt8jXwRf6m1l16EomZJpM3xtxchX2U2yLteHpR6X9nSW1HPr0Kv4aOlmMVTI/TK0RG1SAQJrdOwYJezxXe9A3zPHmzSn0F+yuFy8ArcJJi3Fs/gpaN8LQg6FgbPLj9FbIGyn+r2S1YPxLHUzDSxQfo9IqK7iaiecqHkpU8v5p0id7+TY9Q3PN5B95A1QUKmdT8URQaVIr1ojhIEst3W4vdvNwxcxRaQ18H50AHRCORHcf5tkZQY/y0geICjtIsT2QWgvW4kH31vSLSAjqFoIph26vOAflwoI9Iblcix6XP0y7O0hV3s42hBP5jiu7Tfzm7aWML8ni46sOyNyGvJFw7odCUBnwhmFVfTcWo77vnK7YNbNa7aBgxSQ6KyABMg8fU4bJsXe/Glrb2j1s7QzRrbYoT75BzBnypN93dpCwh1ztQd+wpEiuolH42zgh1vmHDWBVcDGMY+nLsP4T4YFKhVwNFhKNm90izJfKjixONpRu2qyE50pJchX3XKVhzz0njFfOjt6YdQ9frG6krSXIUfmBvpOBd4Mn8j+G4BKwKufRNfXygnlDEWwpkSsF283EYEmnWnDaJfxccAp3f7KW0e+v4v6TFLp38TCPt51IVO5f18ndLPJ61Dqc478BFFBnj8PUY0eOWUe7O4A32Mf18bYBMkgqWkPiucqzKpVfZO1JjQiFWNQicfMzizh6QhFPntVHnquvR61eUahUIhwX6CZb5YUBxEnj1YRFUnuP10tJ6K3gavzVCYnuIVd70Dfi5MqTJLnydfaooKfhEUgKiVh/il+idR8LeYKhM6B4qnL2xImRHOmpsksWoZg0gZKiptArDJcGuQoPoucq7EudgA4i7vtsPTUdXkYX2VIiVwmSqOA10ulNaAgTRoM3EEk+Tyjo7klo1ynnuTJv/hQw4w2GiUZlVP0Rml5odflZUdvVPwFKFaZclkerOHP79RQ+Mo95N/STdxn2Q+4IUGmxm3QMyTVTkElPwR3s6CFXb1+7D+rdEeBYSBBzHq0xuYY8pfvpxZAbO7Wa9r691F+05vJm8EYO35bFuakVxQ4MQ5YIw/kHDsXVd4nvzKCRq8NuF5qMeb8ejSvYQ672YHdIhAVbkzs+Lpw3GH0F40i3efOGz2HFP8WCqCS50vbSJQp6UiNX8bCg4uQq/QTyxJjByLkCofyfamL638aLcRd9AdlDFTPLnEoFWgyyLEPQhwTKVi8ecC2m+fNx+sN4+vKG7A69PFdKipqavTXAaGQkfKFI0l6xyeUOnj9vKmYFiR6mHEwE8KPDOZAUQ84wuHIJAH8A/jBrpKJ2ABAJog57QL2b5O2gR4QyLfm0xvKQFPNcgRDl7a6FMaeQZxAFB9vaPX1WLq72ZjE/OpEy1yCQO0MWdENTexd//WklnmCkb+8ZPeSvXGoBvQItmnbG6GOUn1MB/E8ntDc1NXHxxRdTVFSEwWBgxIgR3HXXXQSD/QjA7YRNmzZx3333cdBBB1FcXIxOp6OsrIzzzjuP9evXD6L1/2VYYskEQVdyC3Wv8uNBQzxen6rnKt4aQemmzWoNqGMvrlRFMkODRa4yCQvGvTJKqqH3Or8UvGmhSJT9H/iGI1uuwO3qFjlSSuHb+7C2/AyQnHZSDGsanFQ13slhgb8oHjqNExhvIIWWM0EPl6g/5eKcVTjMyiW0G40Wfq95l1tKVyTvhfroavLfPY6Zxg3sN0zBd4A5lyFSI9/rruHbKwdB4iVZaE09siL9achV7Ac3VcOl82l1DwK5+u4v8PWdSB2byDEIR7DLH6bTu2su6baAIIAVuYPQIzTmgVIHnbyztI7PVzUSCO/67PpDEZqcwiNeIbWIzff/J/if9Vw1NTUxffp0amtrOeGEExgxYgQ//PADt99+OwsXLuTTTz9FpRqYW9566628/fbbjBs3juOPPx6bzcaqVat47bXXeO+995gzZw4HHnjgf+CM/sPQ28TuPOwDVxNkD+n/+AS52jnDU2GbIClX9A6Ikyv9ICRT6kzgC6SeQB4/XvGcq0ykGJQPeaHWivJrOSIS5pOcutsXioUTyjD+di5YFMzhi4axyOKZSCWvyBMIE0WFjKQ4KY6H9VLyXDmG8Pt9rJBrBaUkBwDJYOEazQdgnzlgPk0CTaugYVl6xR39QWtEpzNQFmoFuRv4L5XaS5LwXjnr+W59Ay+sa2FMkY0/HrWbUJRKPTieqxFHQOlUZGMOWpWbQpuBxm4/29o9ZPcm2CE/2yOC5FYUDMI1i5GrXLq5ftYBlGWbkPtQ7qjrFM+DRR3BgWtwwoKtG6B1PTiG/Kp62/7PkqubbrqJmpoannrqKS6//HJAuPUvvPBCXnnlFV555RUuvPDCAec58sgjufnmm9lrrx2D1m+99RZnnnkml112GWvWrBmUc/ivQpLAViQUv12NA5MrVyzXQOlqod6IfzFTDQvGe/9loqa+O2jNIt8s1f6CgxUWzEREND5GybAgCLIWdKfkueqK7cStBg3qYoVfmNMvw1K3AdY5k2upEsPkCgeLTb8nGImA9gjl7GlYgdlbBzj6TpLeHcqnix+lEffwpvJMH/UgK6qbqG4vZ3STk1GFCm5kzDnQ5QFPe7+hYdWip4WEzKSzOX7ZRELhKE+dPZnKXIWakJuywVlPV7eT7zZGCIT69zIOCrmadbf4NxQCtlORbaSx28/2dg+Tyx2Jw2RPK9tk8S6uLBqEDW/sXSwFurny0N1Xzm6PhwQNHqQIg1MtuOxVWPgE7Hvlr4pc/U+GBV0uF2+//TZDhw7lsssuS/xekiTuv/9+VCoVzz//fFJzXXDBBbsQK4AzzjiDESNGsHbtWtra2hSz/VcFW4n419kw8LFuIa43uOQqw7DgYLTM0aUpxzBoYcFMpBjiNikYFgQ45lE46YWUlPUTTZuVKqXvDUs+ZqsdSC0sqJUgP9pMqdSm7H3ztGLyie+YJ4WwoD8UYVOzK6nGuSlBb2FrtJCfnVl0eZNMoSibxoddVfz+o2o+XpnE+yIVmHJ4Mnw8N89tobajn+e6sxpa1oCvkw1NTtY2OpVNrrcKhfNJplYeOmUCNxzRR07X3Nvg1eNh01c95GqQEtoBKnLEc7itbcfr0tHWggvxbirPUYhc9kYvIdH+ECdXFdq4DtogeK5yhkHZdMhSoH+jgvif9FwtXLiQQCDAzJkzd0nyKyoqYvz48SxatAi/34/BkP5CElfG1Wj6v4yBQIBAoEdkzekUD1ooFCKkpO5Sioh/9u5sUFsKUQGRzhqiA9ipdjaKY015Ax6bLiSNGQ0g+7sJp/AZmoALCQirDAOec6pQa42ogLCvGzmFOVV+F2ogqjEQUfJ6ZQ9Htd81yNlVO9iTzHmrgl7UQESlV/Yeju7VdDnJeVud4qWcHWkjMv8RovtdnfLH9nfOJq14Lzh9geSfhXAA9dBDIeQlImmV00xzVGHK7YAWcKVgz/ptjZzw4ioKbHp+uOFgILn7PBAktZFrQ1fwS1MVz25t49CRyXk+XH5BxIwalaLvNbUxh39FDmTrRji6zUWhdUfCHf8s2RVTTzfk8tqFU3H5wziMasVsUeWNRnK3UpSl58QxhTt8dsLWuqWoti+ge/SZeIJiM2c3KHs9CPkI+cSGsTRLELfNLa4dPmNdrajyLlV3oSZKKBRV7vMBlc4i3hXeTlo73axucGLQqpk+ZEfZh+o24f0sU4s8tbDWktJ7sjd2+2zvdY74EX9Ma+507BgI/5PkatOmTQAMH963u3L48OGsXLmSrVu3MmbMmLQ+Y/HixaxZs4a9994bu93e77H3338/d9555y6///LLLzGZFPZcpIG5c+f2+fsxLV6GA9tWLWR1Z1W/c+xbvZp8YOXWZmq7P1PeSMDu2crBgK+rmbmfJf8Zsz2daIF5Py3DY2gEdn/OqWJ/l59cYPniBTRsSt7rMKJpBaOBmqY2VqZwLslhCtQD9bvO2995T9q+iXJgw5btbPIMzj1MFj+1SICabPcmovMf47OuYWnPtfM523w1hKrdwATWbarms+iWpOZZ1yWxtvMiqmwyE+fOS9uevtAVEe+BJStWYW7+Jakxms2fYOcwjL5uPtvpGcrk+bb66iiW2unAxrKlS/Fv6b8NjiSHqWj/jvqmyUAu2zav5zP3urQ/f2dM6vRhQxSMzFuwiPZ1fdvTXb+JHGDZxloaW38E4OtNipkBTIHCKbAN2Nb398OhOxRzxTjWrfcDVnQqmflff6mYBeXt85lU8yJttklQ9Xvc9ZsANT9vbuSzz+oSx/28rRUookzr3OXZUAIWfy6moX/A68tn7rvf8NZWNaPtUS4bvSOJW7peBajI94lG5z+v3kRTXWb2KPXuThdeb3JRgf9JctXdLVyVWVl9uyBtNtsOx6Uz//nnn49KpeKhhx4a8Pibb76Z667r6WPmdDopKytj1qxZCVv+GwiFQsydO5eZM2f22Z9KtaQBvvyUIdk6ymfP7ncuzfMPggsm7DeT8VWHDo7B7Zth4x0YVSFmD2BPArKMZoUImxw882hChpx+zzlVqN9+DTavZ9LYkUycmKRNgOrbZdAIZUNHUTIr+XHpYqB7DUBzOeGuGobnDmd4jnIK5FLdYnC3IpdMToRWBkLd99WwZRPZuNAYrcnf717Y3TlL6/5N9ep3gQk4CoqZPTu5PI1NX2/mu3VbKS0vZfbs9DZlu8OC4BqWtddTWTWS2YckJzmh/uhTjnb9lsjhdxOdfiqQ5H0eCM5GDl0/HlmlIXxW48DaZJ5WtH+7CF2wAMhl2qS9mD2pOL3P7gOqr37C1uQFGYaNmcDsySU7/D1+zg6tCPFOPvAI5PJ9Ffv8vrCouoPaTh8zRuaR00elpq+6A9YspcRhZvZsBdTZY5A2ADUvkh+L9J151IE8u/5HWvwSh848IqGU/uO/10JjHROnHczsmcp9l/uCvrqDt7YuxS2ZmT17x+KuB9d+B/gZP3EaEcnK5KlnQG569ijybCuAeORpIPyqyVVubi7t7f2Uve6Eb7/9lkMOOWTwDAL8fj8nnXQS69ev5957703q8/R6PXr9rnF3rVb7X31IBrTDUQZqPSqVhGogO2PieRp7MQzWOVlE1YsUcKFVq0CVRCVT0JMQN9WaHaLdDApe+5LJIEfQZBWldt4RQfjUejNqJa9XOAjOOtGGI2/ELn/u97xLJ4kfpfH1HVC3GE5/HbLLkxri9AsvoENyIWmMGd2rXc5Zb8GCSK73BiNJz+2LNTK2GfXKfm/DQSxh0S/OH5GTn/vk5+D4J1ADas2OYzJ6vq2x71k0jJbQjnpufSESu5aSOM5m0il7fbKKydJ0QRDcwehu55a8omK5Wcrjs4W1FNuNHD0hOTKfEmSZOz5Zz+YWN29cPJ1C+67Xp9UjQkdF9sye3V2QJc5HFdMVLHaYyTbr6PAEqe7wM6HUDsCmFhGOG11iH/Q1ZlSx+My6Lh/BqJSQFXH5QzTE8gFHzbwAtVGLEgpouzzbdUvhrbNFV5DfzlfgEwb+/GTwqyZXZ555Ji6XK+njCwtFHDzusdqdZyrOPHfn2dodAoEAJ554It988w0333wzf/rTn1Ia/38OI4+CW5oH3rlGQj0iokqWzO+M3lIKARcY7QOPiSezSyqRfB5OoRorGcxI8xnY+2IYdjjYkyMbSaNjKzw1XQgZ3lSt7NzpIt6qKIWCgg6PyN/JlpzKJ9hrjVgkQQhSSSD3dIo8FsvKl+CogT3WSSPgZNrae4moxzC57ObUxmqU07dKQGsSumLRsEhY1g1ArmJJzW5JhDaVbMUDwH5XkdWyCn6qwenbTX5oNIAUq27c5DFy72drGFtsU5ZcdW4TyerhIAVZL7O5xZ3QcAKEZM3GOZA9lG5vKSoJirIUrryNS914WiEmHDq6yMqCze2sa3QyodROKBJlTYNY48YUDVJkxNMOm+YAErkTzyTfqqfFFWB9k4spFaJqUatW8cw5k6np8A5OYUocKo0oqPqVdH+I41dNrh5//PG0xsVzreK5Vztj06ZNqFQqhg5NXvHZ7/dzwgknMGfOHG688Ubuu+++tGz7P4VkPEPQ038wzWa4SUNrEGEljb6nVctAiFcW6m2/ri9f7vC03eP9QmsUpfQDLYh9YcMXgoxWHiBkOJTCsX9LeUhnrEotG1ePvIRS0JoSnqtUpBhcfrGwm6PJb/iStedI9RKOVC+BYfcmPezjlQ28taSGQ0bkc8lByqnXI0m8xmzeDkznmO+3c9nRA4T4YtIoHln5PodxxBfn3am060Ox77nGQHdYfH7SbYSShSFLECygoETMvQO5alwJH18NheO54LIfOGefCvxhZRPJ423JpLAfTVR89tjiLJZu68QVuzZrG5wEwlHskpuq5i8h/yRlbQBw1sOHl4vN9MQzGV1ko8XVyrpGZ4JcGbRqjhxXJDbfbZvB6OjpFakk0q0iH2T8qslVuthnn33Q6/XMnTt3l7YAjY2NrFq1iunTpyddKdibWF1//fU8+OCDg2X6/01Y8uHyH0UX9iSEWTPCH1JUxY+XCg+GvsqvEY4K+FN9emPn3ScWiLPfU5ZcpYH2mOfKIbmUFTUF0BqwSD7Mkh+D1p70MI/KCrRj2e9ihe3pdX4hX9Ievm0/vs+C7UMoN4YABckV0K7KY7U8hAm76Vu3A+KeK1m8T62DQK7iRKl7N54rfTj2PTfnJ9TKHWalyZUdLvwcbMUU/iTIeXNvGQxn7HtnE5IAGrUKS7JNuJOFzizCtCEP+pA4598dUsUfZo1ArxGb4aXbRYh5srQRSbe3sp8fhzlPeN5jbdJGF9mYv7GVtY19EJzuWnhiirD7zwrLdADoYxGokEekQ6h/HbTmf1Lnymazcfrpp7N161aeeeaZxO9lWebmm28mGo1yySWX7DDG6/Wyfv16ampqdvi93+/n+OOPZ86cOVx33XX85S9/+Y+cw68Gn/wenj8UGlbs/hi1VoR+hvwKlepVKiieBAXjBmf+hU/BfaXw8bWpjVv/KSx/HbpqBj72P4XSvWHIwYOrsp8kOhNhwcHxXE1WbWaN7Vo++N3+SQ9zB4UXwpKjXLI2AJJESGOlTbbR0pF8kY2ndTsAJpXCoW7AUiyUxz1yEgTF70SWwS2LEKXFoPDi5moma9lTQD/kKkY0sOQlyJXdpHDIVJJEextHJQWxcN8OnqvuGLnKKuljsIKItSaLE0q7SZcgVgCzxhRw42EVHH/U0VCZ/POdEmxFcM6/4MSnARhTLDavq+t7nt9/LKjm2w0t+L0uETkYrKhG741zqgLTg4hfB8UbBDzwwAN8++23XHHFFXz11VeMGDGC77//ngULFnDEEUdw/vnn73D84sWLmTFjBgcffDDz5s1L/P6yyy7jyy+/pLCwEKvVyh133LHLZ11wwQVUVlYO7gn9t9C0Gup/Fu7w4on/bWtSR8kUuHTe4H5G0JX6l/qHv/UkeSudd5Uujn5kcOb95l5Y/hrscznsf01SQxI5V7hAO0B3gFSRULFPTWg13uR5MMJeXzGNywO/Ye8Pt/Pu1cmFiz0RsTc2G5UXqTSNPRI2rko0hu4XAScBtIRkscBblQ7HafRkdawEDsXpDfR5iD4c+/6Z8xPCpw7T4OX5FNjEM9Ts7GVPwnNVwkX/WIJFr+HWY8Yoq9AOwlvUua3nnGOQZZl2T5CybBO/mzlIm8ndYGosFLi6vptuXwi1SuKuT9YSlWHRnw7DcHMtRFPom5kK1NqeVm0Bp1DS/xXgf5ZcFRUVsWjRIm655RY+/fRTPvnkE8rLy7nzzju56aabkuorCLBt2zZA9CrsS6sK4JBDDvnfJVcH3wSRAJRM3f0x6z6B1nUw9FAonTK49vy/9s47PKoy++OfO5PJTHoPSUgILaE3QbAhoHRFsVdEBKyrIuJa9qeouy64KuvalhUbim0V2woWEBBBqiIivdeE9N4mmfv74507ScgkmYS5d5Lwfp5nnknufefe80793nPOe873/wcHV8OwR6C7/iUMGqXf9aLfV0BE42Nr0mGIyOEI9bIXBODDG0So5uq3IUTHivmeUlEkWiiV5Hj8kD+P7U7O7p9pdyBXB8+VUzA4KkU+iNmzH+HiElFrKej4Gki9opHRTUPTAVUOz3N0iquEmNFDXAVZnb0OPUn4L8un0CnCFAUCPe1H6Cm2MMIuvBt+gPz6cq4qqz1XWuukCG97rgAOrYV9y4nzPwswcbKW50rUmSoLbM+KXSIP9a+X6yBynHlXLm8dsC+jiD998CsmReF/917g3cr0DVFlBxQSwgPoFB3EwaxiNh7M4awO4Vx3dhI70gpdQtTjHN7mYAuFotIWlXfVZsUVCIH15ptvejR2+PDhqG46T9b0Yp2RpIxsfMyOL2Hbf8Hsr7+4yj0k8oIKmplX5G0CI5t3pTT6b963RePwWiGuygtahrjSxIynixCAm89JhsrP4WCpLi2CVBVutf+ZovnreXPKEI9CSEVldsBMyLFVgHfF1dCg4xwouQnTZV969oAqO8UOocgCA7yckwYEO38Ziss8eM1K8yhUxWsU7O+Hyds/7IpCaM+L4Yc15Je6F1c2e42cqyydwoIAh9bAmnnE95oKXMzJgjLsVQ5RGsb5naSEJ/DqjV05lltCaIAOP7FBWliwWkgkRYr3wMGsYkqyjhCy/gXRh/GCGd4/v8YbI+HYJpj0BXQZwfldoziYVcx329MZ1bMdc67s6/Y3VResoaIcUAsKC7bJnCuJwXQeBv1vhoSz9D/X+TPgxk9EmQhPWP0cvNhHhOHOFJrbX/Dv7WFuB7Gk3JtojaDtnjdurjXe2wntflYURWGTozu/HC2goJ4f7FMprhSiQY+woMk/EJOiev6aVRRTgvAIBOvQ5SHoj0UAFOV50De1NJcy/AnxcxCq05L76tWC7nOuToQPomrYY9DlIn3Dgs4G9jGFu7H6mXCocDy3VCRSO/MnrdGduKRvPHcM61Kn/ZpXcOO5svqZ+WD6OTw8thsF6Qfg14Uip1NPzE6PaYmoRXl5f5Fr9t0f6WQUClGuKIporPzelfDLQv1s0crylObpd44m0qY9VxIvUJYP+1eI+4G3uh8z4GZxM4LEBsKT7ihIE196zWlk7Al5R+CXd8QqnqEPev44VdWvNIQrp6gJDX2r7CJ8B8ID6U20OlUeeq6yi8rZm1FEXH4VHcH74kpRwBLAs1WvY7nkOSKDG59veWUVFQ5xLRrspiDwaeNqAF7s2fiKYoqdq/MCbTqEBQPFc15c6cH1d2kuPU1H2DYxD3XApV63BSA8fwcDoyoICw6myqHWCXtlhfTCccF4zBYLuSUrxGP0EFcRHQEw5R0iOSqQPSeLOJxTQkclXYSZ/WwQokOovyZOcWWrzKu1OTLIn1vP7wRbN4oNeq/41Tz2pWJ14qDkCPomhrE/o4j9GcXEhjg/9yd3wP4fIN6zTgjNQkvLKMvT7xxNRHquJA1TnAWf3ArfPCIEQWvjwlkwdTn0v0mf4xdlwE8vwOZ3PH+MqsLfYuGZeCH+vE1zPFc1vUreDsM1MYF8w8Ecrn99PQ/t6Q6xPfXJS/OzMcG8nrGdLQR74IkqrJHrE6xDuKnQHM49Ffdx6w9mHA4PPmf2Eoo1z5W3E8iBoMGTgOqq6w3i/HElIEIfTw0Qsnsxi4tv5a0uqxvNJ8or1jEsGOFcXFFwnA4R4vk/kl0MOc6CvZGdWXcwl++2p9fOx/ImocJDFFCR635/ofM7xcNWU81GW/3n9FwpisJL1w9gQIcI5v+4nwqtxleN94du2MJrn6sFID1XkoYJSxLVzStLhZA4NYfHXgoFJ8QH3tuVtN2Re0gklQZEeJbQHpqgz4+zhuZxsHvocQCoqqi++evQuNvSjDCcy6ukiCKtutjj2Y+N2aTQJSaI5A6JcM0679qiEd9XJL8qniXZOhwqF4RlYS/IwKzDa2b2D2SJ4xxIh7LKKgL9G/lqrihyiatAf+8nCgdrnquKyjq1AuvgZxWC3JOOCc0lxNn5wV3IWnUQU/A7ZHbGHtPDVRhWl4T2oGhRpLeiiOQgcZ7D2SVgcjb/juzMGz8d4IddGfxtYm+RO+htEgdTecMnbP7tIMPc7feRuALoGB3EomlDao8zQlxpx25B4kp6riQN4+fvKoqnVSeuRdpWePkseHWwMfYc2wxf3g3rXjXmfI2heXk8DeecOraxvm3NwZVA3gRxpXmV/GzeD1f6NS0sOKZXHD88OJznr+nnXTtqcsuXbB33OV+dCOZAZlGjw2NDbSxKXcPH1r9537MH2Gw2FMSVvkcr9CqKda2Irh3ToUKpvRF7pixlyYRfmfSDhTd+OuB1W4BqoVCYVjdJuugk5+1/Hr8FF5LvLNWgKOjTckVRXN6rZEs+YQEW8XHJdoqrqC4czhGfpeQoHS6cAIKiUDuPoNhWT6sxH4ort5Q6VwkH6FgioQXmXElxJWmcCOfVV66bXnXO5ceEJRlji/Yh8jS2vuF1IcT0CL+BuIoFIRw8reOi5Tb52fSpJtwcz5XmVfJ2fhPUCFM2MaFdZ15ffYD7PtzCT3s9SNqGagGqg+fKZAshECEMSio8SLCvKKYY4WHUQ1wFFB5CQYiYIg9aBB3ILOKnvVnsy2hcqDaLkDgmV/yZ7runsmp3Zu19FcXk25IguhtlDoXO0UEkRwbqV44gtgcAN0XsYOvs0fzlkp6QvU+YEt6ZQ1ni4qlLTLA+528M7btO95wrT8WVAZ6r1DFw6T9FaZwWggwLShonoiMc+sm95yrHeaVqVCHMprp/1/wTCk9Ah3P1+bKp+UNbUexZmx3Nc9Wc/n+e0KywoE4r86DJCe0uFk+HtN9gzN8hZZTXzdKWyddX9bsO2uumg+eKC2cRuGkTxUV2jzxX9rIiKpxhwSAdwoKmyjKCKaWQQArLKoltpCPPmN5xJIQH0EEvb01IPJX4Uab6k1daUXtfVFdW9XiG8ePHk2ixsGLWcH1s0GjXC7aBOWN79TZrMFhD2e/XjUpHDiE2P+LD9EuTUHYvJTX9C8hKgfietXdqoVPDPFeN1K8zQly1HyhuLQgpriSN41wh40rarInzio3orsbY0lRxpdU9sYXpY4+fDVAA5zL6liCumlP6QBvr7YKdzbBnytsbSS8o52+mcgZm74FK91W5T4sv7iH0j0BgOAUeiKv/bj7K33ZNYoySwnN6iKuACIKsFjKL7B55rqrKi7nVvIqS0C4EWT0sS9IUbKFM9VuKqvgRamug1l3BCfjkVlKD25F63Xvet0MjJI6/+72BCZWYlF/0O48naK20TtYQV9e+C6rK7i3HgBy6x4XoltwPYNr8Bj3SVlN54uLa4srhMDAs6PwubkhcORzGiKsWiBRXksaJcgqnrD1192XtdY7xrGXHaaOtCrGXiB/dhpKvqyqrQ3Da47yNojgTXAubsIzeaZO/TmGDZoUF9Qt5uTxXHtqzO72QE/llmG54AMJuh5ju3repJJvQCiG8PfFc5ZfYKXDYqDBZ9PFcgSuJvbiicc+VLTKRJ/uvgHgVvN0cGMAWxgy/z5x/v1z/uOJMOLoBgnUuVmsNIdlWIj47pSchWKeLJU9o10vcZ+/jhaXb+Gp7FjNHpXJ5//bsOim+A1LbedZ8u7moXUdyuFCh/akRg5IscIiq6bq/Jp6EBcvzQXWuGtRTXFUUi3zcqgpdvNzNQYorSePEOq+MMneJKxGtdZCq1kjkNMhzZQvD5SkqzWu4AnnNar2eeJSai39gE8WVJmT0CgueRikGPYSDn+cJ9qqqklkkPFWxyd0hXIcwJcDIJwmNyIQfC+otTFmT6wcnMWLTnVjz94O/DrWc0n4nqPgIEE6JBzlOdLlI3PTCPwTX56y8oP6VwOEd4Nr3WJemUrLzJH0Sw6rrG3mb8GTI2C5qy8Wkujabl8xgxK6VKJ0dfFg6kIU/H2JCvwTuGaHTd1JovPDm5x4iL/MYh7Md7DhRwOX927saF/eI1/H7BnAMuZvfsjuS0OHc2jtyDzttTBCLkfREE1eVpeI7zd2FWbEzn9Eapu9q8vxj8O5l4vfhkSP6nacJyIR2SeNEdhbVeO0ltZPaizPFlQmKGGMEJnN1iK+x0GCZs4KxJdDj/nHNoqliRvecq6Z5ioBqwaeHuAppJ9r9jHyy0aF5JXbsVSKROtqD4p7NJrY7YQldADyq0B5is9CVoySZsvR5jvKPEVR0CPDMc1Vmr+JkQZlnye/NwWQixxLPXkd7srIb8EwEREDPy5i3N4apCzfzyyH9lsLvsPVnrv163t18svaOjB2Elh0HVI7klLArvZDMQh1CyTXpdCEAVwf+xntxnzBjz2TsB9fzy2FnQc2OPgqBaXmxWiqHnvgHVxccLq0nNFjsXHwQFK2vLYFRwsMd26vF1GOUnitJ45j9IKYbpP8OGTshSvwouUKC4UnG1LjSCAgXqwUbWzGoea6s+l5FusJ7FR6ulNI7LNh+EAy6DTqc4/lj7Dp60wIi4Lx7PRqqea3CAy1YNy8QYrrvtbrkzIXampjQHttdzEUPL2hMN4JicuCkZ6sFN+w7yeSFW+gZH8rS+4d63x7gKftNfFkxgP/7PYNpyT0aHKsVWQ226feTctCvC/OrunP2oQJuqbFdcS6qUSM6c1NiB87pHEW7UB2q6Neky0VQkEa/Ht1gz0tQWcYvZTGU2rMJC7CQ2tgKAC9gclSInNe4Gq9N3iFxH65Dfa1TURQhagrTRGgwLLHumCLRwFrrh6gbQdFwzwZ9z9FEpLiSeEa7Xk5xtQN6OMMi6dvEfWzP+h+nBwER4grNU8+VXsnsGpo7vKKFeK66j/eswGpNNHGlx2rBJpBRIMRVTLAVls8WKwxTx3j/NTyynrC9vwFJHoUFP9p4hBPt5jJuZDw9InUQV1FdCEwsgpPHPFotWLr2P5gYRHBFhvdtcRJhqSK8rBAqG1iNmLYVsvdRWCIuFEJ0qBavERkpwlDZZTU8EyU5KNpFVmQnEgMDSYzQacViTXpdIW4AXUfCsY38b4dYDXthaoz3m1efSmEal26dDtv84C/p1SVdIjpBypimtwlrLpf+E0yW+j1lRnmuWiBSXEk8QxNQmqACOLFF3CcMMNYWT1cMlum8UlDDVaW9hYir5tDlYrj8NeGF9DaqKpJNK0sh6ZwGc0HSnS1DYkOsoLUP0SMMt+8HQje+B7zg0WrBL347zvoDOaS0C9Etn0arV+WJ52ps0B72W1+kYti/dLEF4Mn2m3jy0Dzo+mb9g/5YDGv/RVHlQsCzVkLNJTImHqgk115DwDlzPkstEfjptNCgMbIrrczfncA7P4uUiavOaq//SYPbUaVY8HNUQN7h6mhCn6vFzSi6NbJSNbYHnD0d4nobY08LQooriWe0Hyha3NRsJZP2m7g3Wly5+kjlNTzO5bnSOSwY3U0IOU/Dj66woE7iqsruFJaq51eMsd3FTS/eGi1WDc3c1WC9sRN5Ik+sfZgFjjs36lJ7K4BQRYjcwvJKHA61QW+DlpcVolfYy1FFYJW4GCgq8yCP6uq3UcoLserpafRkNVhpHqoKRVXiedHt+QEi2yUBB8lzBFQ3b84R4qrIGkc48N76w9j8TIzuFadPhXY3WPxMrHQWNh3Vsx3DUnUOgQEoJoptcYSVHhHpGZq4aml0vEDcjODjSXBsE1wxHzoPN+acDSDFlcQzks+DmTuq/y/Lh8zd4u/4/sba4rHnKk/c61WGQWPc3KaN73sdxPfT7wtx1xL4ZDJ0OA9u+0afczQFRRECVHVUL8uuB01cJQTXCEX56SSuEJ5GVRUCq6Ef48JS4UULWXIXdFvsfXsqiun96xNMNA2i76lFId1hsemf5xjobFfSUB2jsjxKsVKlCmGqp7iKSOwGHERFIbekguhgq6vOXrE1jjBV5ZklOyizOxjSKcowcRVqs/D1vRdwNKeELjHButa3qkmR1SmusvcCY0VpmrICCDZA3Glk7hZe6fAkV5K/zyjNFflf2gpFHyNXC0o849QvjJPbRcHJqJSGyyHoQVC06FOlNPL21X4UtB+JlkJcb+G618vjp4VHHB4magMc/xX2fCeWuevBPevhTxshrOGQyXGXuHJu8LNVl/7wJpYArEolNkV4iRoLDWoJ22HFh71vC4B/EOPNG3nR/zWu6NH4QodF6w9z53u/sOR3ndo6ARvLO3BjxWP8ZWtU/YNKcylCiF+TAgEW71eL1/DzMxMeKARTTrGzSrszTaHQ1p6SiirK7EK8R+m50tQNNouZlHYh+uda1aDI5vQAa/UHj/8Cz3eF/xgocnZ/I3q9blnkfn/uYSjOFiV89MbVX7BlNG+WnitJ03BUicT25PPg4YOQd9R4G0Y8Jm6NcYZWBqbrxfBEjlhp5ynrXhH5M2PmwLl362dbI2ieq8QgZ9KyXmEvpwANM5djNlkpbiDPSVVVCiuEPSHXNFBQ83QwmYVNdmehTBr2Pvy+bhnfnoynT4wZ+upTibvIHM7PjlgKChr4sSrJoUAVz2Ww1U93r01koD95JXZyCsugXQic+A2AvMCOBDoFl81iIlCHlkAtjQKbc3Ve2u/iPu8IhhQPrUlsD5GvqRVWPZVFVwrv4q1L9A8PahEKT/vO6owUVxLPqSiBV86GgmNw2/fQYUitYn4tjoBwUX9L7zYQv74Lq54VK/TGP9f4+P0rxQ9o4tkQUk9n+9OhKaJKIzxZeNJq5tQZjKqq1Z6rAKfY0StJ2dnmZ23Ht/Cb9m2DQ0sqqqhyOMVV57P1sQfAP5iKinJKCwoIa8TZWph9HIgnxKKfRyAkNhkopVBpoKxAcRb5iP1hgfqH4SJNRRxAIWf5PGh3PxSloyom8gOSsRYJcRUdbDUsNOdLcoOcaQUn/xA17fpdDz0uq841NYLUMeJWHw7n51jvUgzgWRjbQGRYUOI5/oEiru4fDBEG1FE5XS5+Au7bAoOm6HueynIhOLWGqY2x8u/w8c0iV6GlMHI23L4Kek3U5/iLp8OrQ+DQWgAcDpWjOSXkl1SH43JL7K6wTpzVGfbR2XPlV9l4VX0tJOhnUnQNe22mB6nl73H5hycaHlhVSZFDhL2Cg/RbcRrSWSznL6QegauqUJJNgSpsMCLHKTJYiOLsjBNwWLyXiO5GldlKttNzFRWsc42rFkKpJQo1KFYImLStYqN/oD4N6pvL/Vvh8Sxj2qMFOhfvyJwrSatk/D/gmnf08bh4SuZuWDgBPrrJdzbUpMdlMH0FjPm7Z+Pb9YKkIfq574uz4JMp8Mmt+hy/OeQdFu2TnJWcb1u4iaH/WMl3O6oFqdXPxPPX9GPW6FRsqtaORy9x5UwGryxrdKhWByvEXImy4wt97AGCrUK4FZU34o2qKKRQFc+LruLKWbOqsMyO6q7qdVk+OOwUOMVXqI41rjSiIoUHJGfIQ7DnewAczjZAmriKMTjfymcoCmqSs1Dwnoa9r7pjL6s/r8ps0Sdv8lQ075hWW8vHSHElaRrWEN83xlRVOLi6+srV14S0E6UqPK0RNeFFmPo9JOkUYqqyw/bPYMdXLaYVhBaGwy7ETHKk+EHen1Fd1T7I6sfVAxP500Up+vY6BJdoey+/H1Pe3shXW+v3FmnJ7iGVWbD5LX3sAboGlbPVOp0NV1U0PLC8yOVNCgnUrxSDtvLPXqVSXunmh9NZoiHfJHIajfBcRYeK91FWiQr5It9TTRW1lrSWN1FBZ4bnCsCh1Zla8094eRBs+9RYA1QVnkuBZ9pBwfHGx+uJS1xJz5VE0jzCk+CK1+GqBoobArx6DswfCgWNhFnaGpq3R60SQssT3hgJL/ZxJQjrZpOz0GrXWLEibl8NcVULu96eKyFO9lZEsXJ3JvtOFtY7NNcZuoygCCz6eYr8rIGEKcWYGwtVlhdS5PRc6Vn6IFgtRUGIqoKCgroDnD9ipf7GiatYp7hKLyyHaxZC/5tRE4cAcNIprtqFGdiKy8eo3S6BEGeeZPbexldQextFAT+nmC06pefjvh9g4WWw2oM8VG+g1fQraRniSia0S1of/kHQ77qGx1RWQOZO8befzl+2xVnw2/vi7/Pv1/dcnlDT22MvabAiuou8o1CUXrfkhtdscookZxiuiyauMqvF1ardGfibTfRODCPUrmMjaXC9Jy4z/Uzvq/9E74T6q/jnlQhPUrhSqG97IKuzBEN5Iz0qywtd5Q/0rIhusoUQTCmFBFGQn0Ns1Cmrbp0/YnfE7mTao69gr9J/uX3n6CDO6hAuxHlwDEx8FexC/J50VvSPCz1zxBWWQLj2XfjhKbEaT2vJYyTB7YQX8dSc08zdcPBH40rhaOKqOFN41Hy8qEGKK0nbxGSGaT+Icgx6FxEtzYNlT4A1rHFx5aiCZzuJxNO71+lTJsJsAcUsPFf2kur6Lw3h8hTp5JnRRJKz9Y/muTqaU0KZvQqbxczfl+5kz8ki3p5yNiPM/qIjgF49yZz2DFK3MWhgYoNfxHk1PVf+Ook9AP9g/ma/ifQNoTzWvZSEcPdCrqqsgGL091yhKISGRVCYX0Ghn5sfSC38EhiN2aRgbs4q1SZyftdozu/q/j2Rni88V3FhZ05YEBDpBbd+7bvzhyaIbgqnhgW1/0MNaAcE1QntjkpRjsHHJXikuJK0Tg6tgZyDYvWiu5WLJrNxzUs1j0NFYeNXTBXFUJ4vbnpUHgdxfv9gcQ5Pm0nbtX6HeuU4OY/r9EjFBFuJDPInp7iCnWkF9E8Kp1tcKMXlVfSMD4XQG6H/jfrYAsIDFdpe3FfZG/TuhQVa6BtaTKeSNLDE6meTfzDfVHXneFoY0wrL6xVXRcVFgHO1oJ7iCggJsEJ+hWvFZC2qKsTr2kKa8p4s1DxXvm0+fsYR3kHcn1qAuNBZ4FbvUjgaFptoQVZeIIS/j8WVzLmStE5WzYWv/iR6Sfkaq7MOkOpovHmz1rRZMVfnKuiBJpIqGgkxgQihavVo9Ap7uewRz4+iKJzVIRyAXw7noigKL98wgLWPXEQ7I8I6tlCYuYOC6ev58UA+K3dl1Dv02kFJfHXWFu73+1z3sGCIIjyIhWX158oVFYn3kL9ShdVPX2+R5hlzK64GT4e/pDHH8ifu+eBXth7N09WWmjgcKg5H9WKNSgfkFIvnLO4MyrlqEbjE1SndC7RcVyNr59UMDfoYKa4krZPGPkSZu+HnV1zLtXXFElidSNpYvowmrvyD9c0J0JpCNyb2Th2jW1hQs6c6WfusZHFl+cth37WrOJBZzOS3NvJ/X/zR8EDX66ZfQjv+QYTibCbdQPPmwhIhwELMHjR4Pk1CykWScuHBX+ods3pfNkt+TyO3pJFVjl7iitfW0u3xb9h2vLpYZmkVpMQGERNiJcKAYqaSGtTnuco/Ju7DEo2zZegsmPAviOhk3DnrQYYFJa2TxpbdHt0I3/8FUkZD6mh9bVEU8A9xhvsKG+61qHmS9PyRrnn8ikZWnkG1uFLMIl9LF3tqe64AhnQSeTxr9mWxM62AbjV7s61+TvQ6HHSbruHByEARXnP1qqsPvUtDAAyYRMiOP+BAcYO9DotKRPgr2K9KP1uchFVmA/HkZ9W/4vb+i7uSll9Gt7gGKrl7EYcqykOkF5TRz7ktxAJL7z0fi0UKK8NxiasardAqy6vFlZFCZ0ALqX2IFFeS1kpgI54rbbsRbRdA5F2V54u8q4ZwiSsdf6Sh2lPkSVhQEw7+QTquFqydcwUwICmCdqFWThaUM+5fP9Eu1Mr3DwwTS/qz94uQb48J+tgD8P61RORlArMotVdRWlFFgJuedNfOX0faiZG8yBYG6hkWDIomJDQMKG7QcxWm5nOV6UciY/Sven1hvIPwnG/obXGT0P7fyVBZxthRf4XexrXB+ue1/bBazMSGnGGJ6y2VMGd9v9IccXFpDRENm1HFRWcLyckzGhkWlLROXDVNst3vN1xcOa/ayxsRV9p+a6i+9rg8Vx6EBTXvlp7CwY0nzWRSmD60s+v/AUkR1bWSzr0Hrv8Aul+qn02ZOwnO+BWL81uwvrDWsdwSjlYE40eV7h7HmlXR6yOlQwIv9NjLX87TWaADV3QPYLblPc43ba+788BKZ2VwYwvVdo4Jpn14ABaz/PlqEdhCq1dk5zrzrnIOiPvIjsaWRCg8CQd+hBNbjDtnPUjPlaR10ljOldHiyt/zGkVAtRjTzZ5mhAX1DHm5PFeltTZPOV+EDDILy7l7eNfqHXF9xE1PLnsFRa0i4sNKMors5BRXuF2h9/70c8j56G66Zh7X9znKO0LIyQ1AHAUNeK4YdJu4GYG20qvwlAKRqgpXvE5ZXhpr022EFecwqKNB9Yzc8N8DJl59+WfuH5nKJX1bUG+9M4XoVDi2UbS4iutdQ1x1bvhx3mb75/Dtw9BzIly70Nhzn4IUV5LWSWN9pIoyao/TG489VwW1x+uFf1PCgiW1H6MHMd3hwocgomOtzWaTwrShBn8Ba3QeBkBk8GqXuHJHp+ggOvntB6VcX3FVnEno0RXAjQ2GBYvLK1EUCLCYUXT2ClQGtSNHDac8r5xazZ0UBbqN5WR2MVOfW0Wgv5kdT4/V1RaNjIIy3vn5ECUVVTx5WS8A0koUDhQW4Wgp7Z7ONGK7V4srqBZXRieWh3cQQs/IFYr10Kb9qunp6UybNo34+HhsNhupqak8/fTTVFSc3qqWu+++G0VRUBSF9PT0xh8g8T4ucVVfWNCZ6B5sYM4VNJ5zZVRYMPFs6H21+KJpDC10qGdYMCYVLvo/GHCzZ+N3fg1bP65b9VkHIoNEUnuDq91C4iE0EWz1V3I/bULbE9JxINBwWPBfy/fQ84nvmPPNLv1scfJzVgCDy19jesEUUbLjFLQCq+EGtL7RKK908Nqq/Xyw4QhVznIMN3Wt4q1bzmJIZ995z85oYnqI+wxnV4z0beI+tqexdnQfD3/aBGPnGHteN7RZz1V6ejpDhgzh6NGjTJw4kdTUVNasWcPs2bNZt24dS5YswdSMTt0//PAD8+fPJygoiOJiD0IuEn0IjBL35fliZcqpNaMMz7lyiiWPc6509lwNmiJunmDESrimsvIZyNgBt3wJIXH6nOPAj5B7kAizuLp257lKzy/jgw2HiU95gRtu6qCPHRohcYScfT3s20JBA+KqYMvnQH9CS48DPXQ1KTw8ChMOQIHijOpl9Zl7IO03corEcxIZ7EGLJS+REB6AxaxQUeUgLb+UdsEWom0wNCVarhb0Fd0vERdQsb1EF4r038X2hP4+NcuXtFnP1cMPP8yRI0d49dVX+eyzz5g7dy4//fQTkydP5ttvv2XhwqbHYwsLC5k6dSpXXHEFgwYZVP1b4h5bOJic1wanhgYdVdXNO8/UnKumEN8Pxs71XIw1hyo7ZO2tvqJtDCPywDa+Dv+7n8hKkU+U60ZcHcwq5qUV+1jw0wH97KhBg0U7ncwJ+og/rLcxpb/+76HeieHsi32Mb62P1PYi7lsGn00n5/dvAIgINE5cmU0KnaJFCHt3eiMXMxJjiEiGriMhNF581kc9DQNvhaiujT60rdImxVVhYSEff/wxnTt35s4773RtVxSFOXPmYDKZWLBgQZOP++CDD1JYWMhrr73mTXMlzcFkqk62LUirva80V1RLh+qSDXoT1l6E4BoLGxkprqoq6ySQuyUmFc65S9+mr0UZ8MogeH24Z+Nd3jQdQ5XOY0dYhJcox01YUPNmRRokHkJVZxHRBupcKbevJPjeNQR16Ku7PSaTginUWbetprhytjbJNYuLFy20ahR92ocDsPVYPqv2ZLL0qIlfj+QZaoOkHiw2Ub1/wr9EGzKjef9a0b/16Ebjz12DNimu1q1bR3l5OaNGjaqT8BkfH0+fPn3YsGEDZWVlHh/z+++/Z8GCBbz44ou0a9dAkUiJcWhJi6c2DNWS2QMiwGxQ5Pv8+0Ws//z7Gh434Ga45AXoMkJfe359D/4aBZ/o6I1qCv5BorF1UIy4sm0MI0KVTnEVaRLnyi2ua1ems19dTMbPsOBiEYLWkdgvb+Ai068MTWzgRykoGqJT9BWeNdHCsgU1Cok6l9xnm0SOk9Hiqm+iuIj5/VgeX2xJ47tjJtburyf/UmIMRZnww9Pw7aO+taMsX9Tc0oqY+og2mXO1d+9eAFJS3BfZS0lJYevWrRw4cICePRtPuCsoKGDatGmMHz+eSZMmNdme8vJyysurv5QLCsSKMbvdjt3uwQ+NTmjn9qUNp4M5JB4lMJqq8mLUGnNQ8o7hB6jBcVSeMjefz7n9EHETRuh2GsVkwQ9wlBdSVeN95nbeOQdQijNRw5L0W2XjFwSz9ou/HYCj4bn72UtQADt+zX6eGnutTX4BmIEwRYRys4vK6ow9WSCEV0zFUdTjv1DpUHR93RID7bxV+jyVQ4bWa/f/fbkdhwr3juhC/Cl99PR4f/9f+nCOVvTh6aMnSD5LHNcv5yAKkK2KXMMwm5+hn6le8SIMv/lQLlpR/0FJoa32u6yp+Px7zB2Fmfj9NA+1y8VUlZd53Wvl6ZzNIfGYgKrcIzh0eH48fc7bpLjKzxc9p8LC3IdoQkNDa41rjBkzZpCfn89//vOfZtkzZ84cnnrqqTrbv//+ewIDfZ9EvGzZMl+b0Dz8J0K3K+EocHSpa3OH7B8ZAGSUWVi/dKnbh7baOXuIyWHG3Oc1qkxWHDWeA3fz7n3sfbpkfsfe2EvY0f46I810i+Ko5DJnI+nvV62l0u/0SkTU91p3T0unG1CVsQ9IIT0zh6WnvF+27DcBJkqi+rIh+gFOfvPNadnSGENLVSKBX35eSfr2ut9P/vYCvvolhFLVQmrVYWLrcV558/29NiuEY454du3/iO1Ll4KqMj5zLxZgb2YpEMjxA7tZWqL/6kUNhwphFjP55eJ9YjWrZO7axNI9hpnQImhp32NJHabRIX012z57nYLAZF3O0dice2aVkwIc+n0tf+R4vxRESYkHhZlp4eIqOjqa7GzPXb0rV65k+PDhXrXhm2++4e2332b+/PkkJjavAeWjjz7KzJkzXf8XFBSQlJTE6NGjXULPF9jtdpYtW8aoUaPa1Cob5fcC1KLORHcezPgx42vt02vOytH1mL/9M2pEJ6qurn+xhHJwNSgKanx/Q5PaG5q36aftqJV76NzvXDoOHl/PEQykNBe2ij9HXzKx2f0OG3utTev2Q/qXTEiuYOzdF2O11L3S/mLRr5CRRf9zL2LgIP0b0Jrz34YD++ndPYW+fcfid0oVcvvxrZRuFgn4l48bWSeRXI/397sHV3AsvRK6jmL8+PFQnIXlNxEuNYfGQX4BFw45i7G9jE2X2OO/l3+vPgjAObEq48a0re+xhmi5393i++MCHY7s6ZxNm9Pgu6V0ijDTYbz3v8+0yFNjtGhxdcMNN1BY6PlqkLg4kRugeazq80xpT059ni2NkpISpk+fzogRI7j99ts9tuNUrFYrVmvdPlgWi6VFfDBaih1eY+AkGDgJM1CfY9rrc1ZUyNiBojowNXTcr+6GonS44ycI1j8h+VTczvuix+Cixxp8vrzCp1Mh7whc/qpIoq8PLbHc7I/Fdvqe3Xpf6wBxYWOtKsYaaKu7H8h25mG1Cws05jMSEM7o8mfZ81kEi9uVMDA5otbugpLq1ahRIYGYTcqpRwC8+/6OiIiC9JMUJA4Xxyxy5jiGJJBTJppHx4QGGP4dcv+oblSqUGavorfjQNv7HvMAOWc3OHtumnIPNvxdfBrn94QWLa5efvnlZj1Oy7XScq9OZe/evZhMJjp3brgydEZGBsePH+f48eP11sSKjxcr1rZs2UL//v2bZa+kmeQchP/dL1YG3vq1b22J6wOTvoDARooYxjhXFAaE62tPUSas+rv4+9J/6nsuTzmxBXL2198PUkNr2aNzHz/8nZ7DBqrYZxWKXMmYo9+BJQFSRuprkzUUK0LQ5blZvZhXUAiYCDGV1yusvE1kkPgxyS5y5o1mOb9XIzuTe9S5mtLghHYAm8XMXy7pid1uZ+lSY0plSFoBUV1F3UFbuGjTZGRvwxq0aHHVXM455xysVivLli1DVdVaKwbT0tLYtm0bQ4YMwWZzf7WqERISwtSpU93uW7JkCenp6dx4440EBAQQFRXl1TlIPMDsDwd/FPWuHA5RnsFXBER4tgJw8v/0twWgsgw2vwVma8sRV/5af8FGiu+6xFWwvvZYq2uTPfvtLv44ns/DY7vTu73waKuqSqZTUMT8/CSk9dRfXNnCeMP/eWxnTyakW92QRn5hIRBGmPn0ukw0hZgQ4XXPysqAgiA4+QcAVbG9yNsjhKAvxJVE4pbwDvDIEZ+JKo02Ka5CQ0O57rrrePfdd5k/fz533XUXIL4sH330URwOB9OnT6/1mJKSEo4cOUJgYCAdOoiqw1FRUbzxxhtuzzF8+HDS09N54YUXXOFIicEEt4Mr/uNc4ebsKaaqop6SfxBc/0F1RekzDc3rU1Uu6l01xH9vEb3Axj0HyefqZ5NF63fYSEKoJq70rhivibeKIn45nMvGgzlcd3axS1zll9qxV4n3VRQF+nvSAGyhtFPyoCoH3Him8otKgTDCLFX62+IkOliIq8yt30GszVUItiCyDwrik2dk+xuJpEF8LKo02qS4Apg7dy4rV67knnvuYfny5aSmpvLTTz+xdu1axowZw+TJk2uN37hxIyNGjGDYsGGsWrXKN0ZLmobZD/pdX3tbSQ5k7xN/BxrsTfz1XZGMPeg231dgrykE7MVgbkCoZO4WDVerdPaGuDxXHoor3cOCmrgqZtrwTlw3KIn+SeGu3VlOr1WInwObYtffkwYilAHVDb5PIb9E2BRmMa5BsctzRTiUpsGxTQBEdBrAvmd6kV9qr5N4L5Gc6bTZT0R8fDwbNmxgypQprF27lnnz5nHy5Emeeuopvvzyy2b1FZS0AmxhcNfPcON/jSuyqPHto7DsCSg86X5/5m6Y1wveGqe/LWb/6vZAFR6G4aw6iwfNE9WYPWqVEDJ6C1RXWLCQ0b3iuGpgIokR1SL0RJ5YERdvc4pOIzxX1lB+rurJYwf78MGGI3V25zvzsMLqro/RjRjNcxUxAHpMEOI4KAZie2IyKUTIkKBEUoc267kCIbDefPNNj8YOHz4cVfX8alB6t1oI2fvh4GpRRbrbOOHNatdL3IzGFi6So8vqqZ9WmgcFx5pdWqBJKIoIw5XnCzET0ECPRa0lj96eGU2cNCauul8Cjx0XIV49sYaI56ieeZtNCgM6hNOpYi/kYYw30hbGPrU9H+T2ZNzeTG4cUrtZdHapCAdGBhh3cah5rjKLKsAUAu16Q8IA3+Y4SiQtnDYtriRnAAdWwpIHIXWsEFe+JCBciKeyXPf7NdFlM6i2mX8NcVUfqlq9Wk5vcWXxMCyooXfuRGgC/EW0dCkss/Pb0TyKy6sY21vkUJ7fNZrzu0bD14thM4blXIU7K8bnuGkknV0mnpOoIONynDRxVVReSWn8EALuXAMVxXy+5RjLdpxkTK84Lu/f3jB7JJLWgBRXktZNtLNeUpazNPP6f4vedT0vF53ajUTLlynNc7/fJa7CDTCG6hynhsRVVQU4q6HrHhb01HPlAw5kFjPpzY20C7W6xJULo1YvAtjCiEB4EvNK6rbZyKkQlci0JHMjCLb6YbOYKLM7yCoqJykyEKzB/HbkEEu3pdMp2gDRKZG0MqRfV9K6ie4m7nMPifDW+n/DssfF6jej0WpXleW5369ttzVcvNZreCJmymvUeLLo/CPpqedq89vw3pWi+bRBdIwScz9ZUE5JhRCbVQ5nWFLz7OktPgEiOxNxxXMA5Lqpc5VdITxWkaHGCRpFUVxiLqOwukfqZf3bM3tCTy7qHmuYLRJJa0F6riStm5B2ENYB8o/A3u8h77DYnjDAeFs0j1RDOVegfwFRDc3T0lBdKU04+AWIfDVd7dE8aY2Iq8xdsP8HY17DxdMg/xhhl71CeKCFvBI7R3JK6B4XyoX/WInFrPBGmJmuYIznyhJAROcBwArySux16vSdY9pBqCmTjrET9LelBpf3T8BepRIdXJ28PjA5ok4FeYlEIpDiStL6SRosxNXqF8T/UV2NEzA10c5Zb1jQud2osKAnq/Nc+VYGeEI0z1hjRUT7XQ/x/SG2h+4mcWyT8HqWZJEcFUReSR6HskpIigjkeF4pANEhzoryRogrINLZL7CiykFReSUhNmd+laoyMzUTirZD57sMsUXjoTHdDT2fRNLakeJK0vrpOhL++BQytov/U8b4xg4t3NdYWNAwz5UHRTuNKsMAnnuuEgYY53kc83eRoxeVQseoI2w9msfBrGLG9GrHxscu5kBWMeHfPC3GGiFAgYDtHxFsDqGoykxmYXm1uFIUuPFjQ2xojCqHypJtaXSMCqR3Qhgmg1rxSCStBZlzJWn99Li0dh5T32t9Y0djCe3adsNyrqorkNeLqwyDAWUGYnvA4NtFqYWWQvdLoNdECIqiW5x4Dv44kY+iKMSG2jinc5SxAhRg0wJiHaJW2smC6hynSqcnqyklY7yFw6GSUVDGvgzxXjqRV8p9H27h6n+vw3hrJJKWj/RcSVo/1hC4ZiGsfg56XwkJ/X1jhyuhvZ6cK6NXC8b1hq6jRK+t+jCqGjpA+4Hi1hgHfhS9ERPOguAG6nN5mb7twwHYduyU188/SIhPIwQoQI8JxGQHcCAfMgrLXJv3nCxi/Es/kRBm4+dHLzbGFidr9mVxy1sbSW0XzPcPDONwtvA+JkUGGNZAWiJpTUhxJWkbdBnhWeNkPXEltOe53290Qvs5d4kbgL3usn7A2JVwnrLsCUj7TVTZT9U5xJu2VbRLiu1Jn8SuABzJKeGO9zbTNTaYSed0JO6eDfracCpDHyT22BbYeoLMGqvzMnetBSCs/ISx9gCJEQGYFNAWUB7KFqJcW2UpkUhqI8OCEom3aDSh3WDPlSe06w3DH4U+1+h/rqpK0Roor25bl1popRqM8KZtfB0+vQ12LSEswEIfZ9Pm77af5N+r9vvMKxMbUrf0wbCIHHZab+Wd5O8Mt6djVBC7/zaO5TOHAXBYE1eyxpVE4hbpuZJIvEVAJATFQlC0+/1GrxbUaChHJ76vuBlBzn54dbCY/yOH6x+nhSotDTSb9hZaqM/pwbtiQHu2HRci+IKUGFd1ckNxVNHOJtrcZBRUhwXpdQUBSUMI0LtyvRtMJgUT1ec9kKl5rgx4jSSSVogUVxKJt4juCg/trX//yCehNFfU5jKCHV/C53eJUhU3fGLMORuiZikDVa2/vY2RFdFdzZuFuLrpnA5sP1HA0dwSnr6sF+Qfg0+miNfsukX62wPwyzuk/vgOI0Im0SuhRjkK/0DxHvMxqqryu1OA9og3qJWTRNLKkOJKIjGKs6caez6zv6gpVV5Q/5i8I0LMhMRBgM4FIUMT4Incxhv+Gplk7ypXIcSV1c/MC9f2q95/Yi8c2wgh8frbohEYxXDz7wyP/wwuvN21+YXvd3OyoIxbzu1I7/YGrTitweo9mbz0w16CrH5kFpZjNin0SjDeDomkNSDFlUTSVuk4FO7b0nAYctWz8NsiuHg2DJ2prz2K0ngz5soKcDiT7/2NCAtqnqtC9/sjkoXHysjyB4FR4r4ku9bmZZt3sKvAn0uSHdDe+A4E/n4mNh+ubkrerV0IAf5mw+2QSFoDUlxJJN5k8TRI/wOumF+7JERxtihyGhIP0SnG2GINrg571bda0GITP+ZG1d5qjJo1ufTudQiijAfUX8U+IAJ6GNtqxiWuijMpKLNj8zNjMSscL3QA0J5MY+1xMjA5grAAC/ml4r00sofsKSiR1IdcLSiReJPsfZC5EwrTam8/sg4WToDP7/SNXfVxyQvw5wPGhSz/N0M0Zc7a536/q9ehDfz83Y/xJpq4qs9z5QtC4gCYkPcgfZ/8ni1HcskurqBQtaHgIDHewBBlDSxmE/eM6AJARKCFm85J9okdEklrQHquJBJvMuqv4KiE+H61t5vMEN0NoroYZ4u9FH58VgiHkX8z7rwNcWgNZO+FopPuk7M1kWM1qGCnq2VRPYVf0/8QjaSjU41bVRkQAX42wiuKQIW0/DL8EKsHE8jGFnGuMXa4YfrQzgzuFEVCuI3YEJvP7JBIWjpSXEkk3qTTUPfbu40TNyNRTLDmn+LvoY8Ye+76sDbSkqeliaud/4Mf58LAKTDhRWNsUhQIieN5+3yCbv6Q4JT2fPrT7wAkmzJEyQ8foSgK/ZPCfXZ+iaS1IMOCEklbxc8KZmedpop6wl4fXA/vXFp/mM7bNJZA3tLElbbS0uictJAE2il5BJeL8PLOYyK5PcXqwWpLiUTic6TnSiLxJtn7RegrKAa6j/e1NUKklJTXL2aOrBPFTVWHQfY46yLVK64Kao/TG20lZVU52MtEgn9NyjRxZXA9p1BnXlVhOgDbTxYDCr2DG2jCLZFIWgzyEkgi8SZHN8D/7oNNC2pv/3QqvHYe7FturD1OUaC4q3WlqjU8MwaJh8bCghXO1jdGea78g0X4FNz3hNS2GSX2NJx1teb8auHyV9awPl2UsOgdaZAIlkgkp4X0XEkk3iTQ2fqmOKv29qzdohSDw+Afx4ZWw1UUVXusjBIPja3OO2sS9L8RqiqMscdkEnOvqhBV2k/VdC7xGW6MPRqhCQD8muvP1iIRsowmn24JOhd6lUgkXkGKK4nEmwTVI66KnQUhg6KMtadWGK6ekJfJApYAY+zxr91uxi0mM5gMsgfgoX1gtrjf56uwYEQnAK63bWBT0aUAXG3+EVPk2cbaIZFImoUMC0ok3iQoRtwXZ1Z7qVQVSpxiS/NsGYXTU+Q2LKglcdtCG6+c7mV7GmzJYzT1CSuo8RwZnNDuLNkxsfx/PDQ6ldtDfuY+v89doksikbRspOdKIvEmIXGAIlq4lGRBcKxoY6KFuYzsUQeneK5Oqajti5Vwroro9Xiu1r0GxzaJ0GDKKOPsqg+jE+w1IjrBrUsxR3XlnqAY+PkNUMohUooriaQ1ID1XEok3MVtcFbbJPybuC46L+6BYY6qO18TlKXIjZjSvjJHCobGcq6PrYftnkHvIMJNYPx/ev1bUtKqJqvrOc+XnDx3Ph5B2kHsQKstEWY2wJGPtkEgkzUKKK4nE24S2F/eauMp3iitnkrKhNBSG80U+UWM5V2dNhrHPQgcDq5Cf/AP2fgeZu2tvt5eKavtgfM5VTRyV0HMidBvbcAhTIpG0GGRYUCLxNmGJcHxztcdKuw9LNN6WmqUYTv20l/vAK9NYKYauF4ubkfS7HpIGQ/uBtbdrXivFVC0KjSRrH2x5F8z+cO1C488vkUiajRRXEom30USUKyx4Qtz7wnMV4Fy6X5YHp+oDV1jQQHEVlgR9r4PwFtT0t+MF4nYqpTniPiDSuIT/mpQXwNp/QVQKXPhn40PKEomk2UhxJZF4mzriyodhwfAOkDgYNaorlJ+yr8wHCe3RKXDl6/XvP/CjaNsT38+48hD1UV4E/iEQaHD5DI2EAZB4Noz4ixRWEkkrQ4oricTbnJpzlXtY3PsiGbnLRdDlIhx2OyxdWntfzVIMLYWPbhJ9EO/91VWOQHeKsyHtNxF+q9l4u8MQeOwYVFUaY8epKApM+ty4avUSicRrSHElkXibiI7iPme/WHGWtUf8H53iM5Pc0mUEWAJFvpGRVNmhNA8CI0XBUA2Ho7rBtJGC4vhm+OBa4S27Y3Xd/WYffk1KYSWRtEqkuJJIvE10qgi1RXaBvMPO3B1F5M60JHpeLm5Goqrw9wRR92vGHxBew5tXM8ndSFERECnuS3KNO6dEImnTSHElkXgbiw0ePizCOuVFcOUCkdTuH2i8LfYyeHkgfqW5+PWYZ/z5T0VRRJ++4oy65SG0/01+4Ger81DdcLUsyhTiT0teX/cq7F8pCpr2vtI4eyQSSaunTde5Sk9PZ9q0acTHx2Oz2UhNTeXpp5+moqLpTWEdDgdvvfUWF1xwAeHh4QQGBpKamsqUKVMoLKynIKLkzEX7gbYGQ99r4YIZvrHDzwrFGSj2YixVxbX3Ze6BgjRwVBlr0582wRM50K5X7e2leeI+IMLY1XnBzsr1laVQUeM5OvEb7FtWvSBBIpFIPKTNeq7S09MZMmQIR48eZeLEiaSmprJmzRpmz57NunXrWLJkCSaTZ9qyvLycq6++mq+//pq+ffty6623YrVaOXLkCEuXLuWvf/0rISEyN0LihpIckVvkKxQFpi3Hbg6g7Oc/au9bcJHxyeMAAeHut5c6w3K2evbrhX+QyD2zlwjvlVaLa/Dt0HkYJJxlrD0SiaTV02bF1cMPP8yRI0d47bXXuOuuuwBQVZUpU6awcOFCFi5cyJQpUzw61qOPPsrXX3/N3Llzefjhh2vtc2jNeSWSmpQVwNvjRML2Jf+ExIGNP0Yv4vuB3Y6q7KzeVlUpvFr24upaWL6mLE/c+8KeoGjIOyLElda/L+lscZNIJJIm0ibDgoWFhXz88cd07tyZO++807VdURTmzJmDyWRiwYIFHh3r+PHjvPzyywwdOrSOsAIwmUwee8AkZxC2UFGjKG0rrP2nr62pi9kP/rwfHs82XsxsWQSf3Arbv6i9XfNc1efZ0pOgGHFfnGn8uSUSSZujTXqu1q1bR3l5OaNGjUI5JXcjPj6ePn36sGHDBsrKyrDZGk6cXbx4MZWVlVxzzTUUFhby1VdfceTIEdq1a8eYMWNo3759o/aUl5dTXl5dwbGgQCTu2u127HZ7M2boHbRz+9IGozF0zmP+AUPuEfWtfPgcK7uXwrFNRBaFYreP8pkdGqbjv2He/jlVYR1xpF5Svb04GzPgsIZR5YXnqymvtTkgChNQWZCOareDqqJs+y8Et0NNPk/UwGoFyM/0mYGcs+/taIw2Ka727t0LQEqK+6XvKSkpbN26lQMHDtCzZ88Gj7V582YA8vPz6datG2lpaa59/v7+zJ07lwceeKDBY8yZM4ennnqqzvbvv/+ewEAfrCA7hWXLlvnaBMMxds47DDxXXfofXkByzk9ExV/TIl7rbmmZdAeO7NnK76XVhU17nPiFVODgyXz+OLXg6WngyZz755SRDOzd8jN70qLxqyzmkm33APB1vwVUmaxes8cIWsLrbDRyzmcGvp5zSUmJR+PapLjKzxeVp8PC3Lf1CA0NrTWuITIyMgB48sknGTVqFMuXLycpKYnVq1dz++23M3PmTLp168b48ePrPcajjz7KzJkzXf8XFBSQlJTE6NGjXbb4ArvdzrJlyxg1ahQWi8VndhjJmThn04rNsO4nrJX5rnkrh1ZjWvMCauIQHMMfM9aejUcg/QuSY0NJrPG5MX2zAk5Cx+796HBh/Z8nT2nKa21a+Qv8vJrUxEi6jh4PmbtgG6i2MMZcesVp22IUZ+L7W85ZztlItMhTY7RocRUdHU12drbH41euXMnw4cO9aoOWsB4bG8vixYtdnqZLLrmEN998k3HjxjFv3rwGxZXVasVqrXvla7FYWsQHo6XYYSRn1JxD4wGw2vOr551/BA6vBWsoZqOfB2ddKVN5Aaaa5y4XFzvmoGiv2uTRax3STpy7NFucu1TkXikhCa3yfXJGvb+dyDmfGfh6zp6eu0WLqxtuuKFJNaTi4uKAao9VfZ4pTXnW59mqiTZm5MiRdUJ4o0ePxmq1ukKHEkmLxCkcbJU1Pg9FJ2vtMxQtYV2ra6XhqnMVbpwtGsHO56Egrfa9U5hKJBJJU2jR4urll19u1uO0XCst9+pU9u7di8lkonPnzo0eq1u3bgCEh4fX2WcymQgJCfHYTSiR+ASncLDZ86q3FabX2mcoWh2r0lPazdy8WDSTNrI6u0Z4srgvd17MFZ4Q9yEJxtsikUhaPW2yhsA555yD1Wpl2bJlqKpaa19aWhrbtm1jyJAhja4UBLjooosA2LGjblJyZmYmWVlZdOzY0St2SyS64BRQVrsbz5UvxJXmmdLqWmmYzKLgqi/aBCX0h0ePw11rxP/ScyWRSE6DNimuQkNDue666zhw4ADz5893bVdVlUcffRSHw8H06dNrPaakpIRdu3Zx5MiRWtuHDRtGjx49+OGHH2qtUlBVlcceE4nA1157rY6zkUhOE2d7F4ujVFQhhxphwTjj7dEaJZfmGd96pz7MlurK7AD5x8R9qPRcSSSSptOiw4Knw9y5c1m5ciX33HMPy5cvJzU1lZ9++om1a9cyZswYJk+eXGv8xo0bGTFiBMOGDWPVqlWu7WazmbfffpuLLrqI8ePHc8UVV5CUlMSaNWvYuHEjZ511Fo888ojBs5NImoA1FNXPhlJZBkUZEBgGhZrnygfiKjAKUAAVSrKF+Csvgi/uEn+P+4fwYvmSnP3iPrLx1AGJRCI5lTbpuQJRLHTDhg1MmTKFtWvXMm/ePE6ePMlTTz3Fl19+2aSq6kOGDGHjxo1cfvnlrFixgpdffpns7GweffRRfvzxR4KCgnSciURymiiKK/ynFGeAqvo2od3s5xRYVNtRnAE7v4LfPvSdsNrwH3h3IvyxGHIPiW2RBvZclEgkbYY267kCIbDefPNNj8YOHz68Tn5WTXr16sWnn37qLdMkEkNRg2JR8g6LRPaSHHA4qwwHxfrGoOB2UJIlPGkgktzHPQeVZb6xByDnIBxYCYoJHJXgFwChjXdgkEgkklNp0+JKIpE4CU+C45uEwMo7JLYFtwM/H7V1CY6BDKp7+QVGwpDbfWOLRp9roF0vyDsM+38QDa9l31CJRNIMpLiSSM4A1IhOACg5ByCig9joy3yiziOEuGtJnqHEgeL25Z+c/w/yrT0SiaTVIsWVRHIGoLbrTU5gF8LCk0X4C3wrri6YUfv/zN0iXBnVxbW60Wd0vxR+ex9SfN/kWiKRtE6kz1siOQNQu0/gp26zcZw/A3IOiI2RnXxqUy02/AfeHgsbX/e1JdBtLFz3PnS80NeWSCSSVor0XEkkZxp9rhY1nRIH+9aOygpRiiE0HgqOi20tpa5U99NvHC2RSM5cpLiSSM4kKoogYQB0vdi3dhz/Bd4YCaGJ8MA2yDsqtod18K1dEolE4gVkWFAiOUNITf8Svxd7wpb3fG2K6NmnOqA0R1Rpz3eKq/Ak39olkUgkXkB6riSSM4QCWyIERkOnYb42RbTdeXCPSF4vy4NyZ/PzsESfmiWRSCTeQIorieQMIT1sAI7kEMy/vgtxfX1bw0lRqqvDZ+0T98Fx4C+7HUgkktaPFFcSyZmCYsJx/gzMFouvLalN5i5xH9PNt3ZIJBKJl5DiSiKR+IbDP8P612Dn/8T/7Xr51h6JRCLxEjKhXSKR+IbyomphBZB8nu9skUgkEi8ixZVEIvENnYaCpUaOVfL5vrNFIpFIvIgUVxKJxDdYAmDcXFHbavzzonmzRCKRtAFkzpVEIvEdZ90ibhKJRNKGkJ4riUQikUgkEi8ixZVEIpFIJBKJF5HiSiKRSCQSicSLSHElkUgkEolE4kWkuJJIJBKJRCLxIlJcSSQSiUQikXgRKa4kEolEIpFIvIgUVxKJRCKRSCReRIoriUQikUgkEi8ixZVEIpFIJBKJF5HiSiKRSCQSicSLSHElkUgkEolE4kWkuJJIJBKJRCLxIlJcSSQSiUQikXgRP18bcCaiqioABQUFPrXDbrdTUlJCQUEBFovFp7YYxZk4Zzgz5y3nLOfcVpFz9t2ctd9t7Xe8PqS48gGFhYUAJCUl+dgSiUQikUgkTaWwsJCwsLB69ytqY/JL4nUcDgcnTpwgJCQERVF8ZkdBQQFJSUkcPXqU0NBQn9lhJGfinOHMnLecs5xzW0XO2XdzVlWVwsJCEhISMJnqz6ySnisfYDKZSExM9LUZLkJDQ8+YD6jGmThnODPnLed8ZiDnfGbQEubckMdKQya0SyQSiUQikXgRKa4kEolEIpFIvIgUV2cwVquV2bNnY7VafW2KYZyJc4Yzc95yzmcGcs5nBq1tzjKhXSKRSCQSicSLSM+VRCKRSCQSiReR4koikUgkEonEi0hxJZFIJBKJROJFpLiSSCQSiUQi8SJSXLUSFi1axB133MGgQYOwWq0oisI777xT7/gNGzZw+eWXEx0djdVqJTU1lSeeeILS0tI6Yw8dOoSiKPXePvroI7fn2Lt3L9deey0xMTEEBATQt29fXnnlFRwOR4ufs0ZFRQXz5s1j0KBBhISEEBISQu/evbnnnnvcjm/Nc7711lsbfJ0VReGvf/1rm5ozQGlpKfPmzeOss84iIiKC8PBw+vXrxzPPPEN+fr7bx7T2Oefm5jJr1iy6du2K1WolJiaGq6++mu3bt9d7Dr3nfPz4cV588UVGjx5Nhw4d8Pf3Jy4ujquuuooNGza4fUxBQQEzZ84kOTkZq9VKcnIyM2fObLAv6wcffMDgwYMJCgoiIiKC8ePHs3nz5nrH6zlvvedcUlLCCy+8wI033kj37t0xmUwoisKhQ4catKs1z/m3337j8ccf55xzziE2Nhar1Urnzp25++67OX78uE/m7BZV0ipITk5WATU6Otr199tvv+127OLFi1U/Pz/VarWqN954ozpz5kx1yJAhKqCef/75allZWa3xBw8eVAG1X79+6uzZs+vctm3bVucc27dvV8PCwlSLxaLedNNN6p///Ge1T58+KqBOnz69xc9ZVVU1JydHHTx4sAqo5513nvrggw+qDz74oHrllVeqUVFRbW7On3/+udvXd/bs2WpQUJAKqBs2bGhTc66oqHDt79+/v3r//ferM2bMUPv166cCaq9evdTi4uI2NeesrCw1JSVFBdRzzz1XnTlzpnrDDTeo/v7+amBgoLp+/fo65zBizg8//LAKqF26dFFvu+029ZFHHlGvuuoq1Ww2qyaTSf34449rjS8qKlL79++vAuqoUaPUhx9+WB07dqzrtSwqKqpzjmeeeUYF1A4dOqgzZ85Ub7/9djU0NFT19/dXV65cafi89Z6z9t0NqMnJyWpkZKQKqAcPHqzXptY+5yFDhqiKoqiDBw9W7733XnXWrFnq0KFDXZ+nnTt3Gj5nd0hx1UpYtmyZeujQIVVVVXXOnDn1fhmXlJSo0dHRqsViUTdv3uza7nA41HvuuUcF1Dlz5tR6jPYBnTx5ssf2XHjhhSqgLlmyxLWtoqJCvfjii1VAXbFiRdMm6AY956yqqnrFFVeoiqKo77//fp19dru9zra2MGd3bN68WQXUPn361NnX2uf88ccfq4B65ZVX1jnexIkTVUBduHBhre2tfc7a9pkzZ9ba/vPPP6tms1nt2bOnWlVVVWufEXNevHixunr16jrbV69erVosFjUyMrKWUHziiSdUQP3zn/9ca7y2/Yknnqi1fc+ePaqfn5+ampqq5uXlubb/8ccfamBgoNqlS5c6n2u95633nAsLC9Xvv/9ezc7OVlVVVceMGdOouGrtc3755ZfVffv21Tn+3LlzVUAdP358nX1GvL9PRYqrVkhDX8bLli1TAfWaa66psy83N9d1heNwOFzbmyqudu/erQLqiBEj6uxbv369Cqg33HCDx/PxBG/PWbNz0qRJHp2/Lcy5Pu68804VUF988cVa29vCnLXjLViwoM5jXn/9dRVQn3vuOde2tjDn9u3bqyaTSS0sLKzzGE1Q1vwx8cWcT2X06NEqoG7atElVVSEeExIS1ODg4Dqei9LSUjUiIkJt3759rXk/+uijbsWyqla/x7/77jvXNl/P2xtzPpXGxFVbnLNGZWWlGhgYqAYFBdXa7qs5y5yrNsbJkycB6NSpU5194eHhREREcPjwYQ4cOFBn/4kTJ/j3v//NnDlzWLhwIceOHXN7jlWrVgEwevToOvsGDx5MeHg4P/7442nMomk0Z84ff/wxANdccw1ZWVm89dZbzJkzh0WLFpGdnV3nOG1hzu4oLS3lww8/xGq1MmnSpFr72sKce/XqBcC3335b5zHffPMNiqIwfPhw17a2MOeTJ08SHR1NcHBwncdox1mxYoVrW0uYs8ViAcDPzw8Q+TEnTpzg/PPPJygoqNZYm83GhRdeyPHjx9m3b59re0PzGDNmDECtefh63t6Yc1Npy3NWFAWz2ew6toav5izFVRsjJiYGgIMHD9bZl5+fT25uLgB79uyps3/ZsmXcfffdPPbYY9x666106tSJBx98sE7C3969ewFISUmpcwxFUejatSsnTpygpKTktOfjCc2Zs5bgum/fPrp27crUqVN57LHHmDRpEh07dnSJL422MGd3fPrpp+Tn53PFFVcQGRlZa19bmPOll17KhAkTWLx4MQMHDmTmzJnMnDmTs846i+XLl/Paa68xaNAg1/i2MOeYmBiysrIoKiqq8xjtODXH+3rOR44cYfny5cTFxdGnT59Gbaq5XRun/R0cHExcXJzH4+s7h97z9tacm0pbnvOnn35KYWFhHRHlqzlLcdXGOO+88wgNDeWLL75gy5YttfY9/vjjrr/z8vJcfwcGBjJ79mx+++03CgoKyMjI4KuvviIlJYV58+bxl7/8pdZxtBVWYWFhbm0IDQ2tNU5vmjPnjIwMAB566CEuv/xy9u/fT25uLosWLcJkMjFp0iR+//131/i2MGd3vPnmmwBMmzatzr62MGdFUfj888+ZNWsWW7Zs4Z///Cf//Oc/2bJlCxMnTmTs2LG1jtMW5jxu3DgcDgdPPfVUrfEbN27k66+/rjPel3O22+1MmjSJ8vJy/vGPf2A2m5ttU35+fpPHN/Uc3sCbc24qbXXOR48e5b777iMgIKDOimdfzVmKqzZGcHAw8+bNw263c+6553LzzTcza9YszjvvPP7zn//QvXt3ANebGyA2NpYnn3ySfv36ERISQkxMDBMmTGDFihVERUUxb9481xVyS6Q5c9a8cX379uWdd96hc+fOhIeHc9NNN/Hss89it9t56aWXfDIfT2jOnE9l3759rF69mk6dOnHRRRcZZXqzac6cS0tLufLKK3nvvff44IMPyMrKIjs7m//+978sW7aMs88+m/379/tqSo3SnDk/9dRTxMfH8/zzz3PBBRcwa9YsbrrpJoYOHUrPnj3rjPcVDoeD2267jdWrVzN9+vQ6Yem2iJyz9+eck5PD+PHjycjI4PXXX6dbt25ePX5zkeKqDTJ16lSWLl3Kueeey5dffslrr72Gn58fP/zwA127dgWqww0NERcXx/jx46moqGDTpk2u7doVQH1KX6tNol0RGEFT56zN4dJLL0VRlFrHmjBhAkCt2jhtYc6n8uabb6KqKrfddlud5wDaxpznzJnDV199xeuvv871119PVFQUkZGRXHPNNbz99ttkZWXx9NNPu8a3hTknJiayadMmpk6dysGDB3nppZdYv349Tz/9NI899lid8b6Ys6qqTJ8+nUWLFnHzzTczf/78Wvs9tammNyIsLKzJ4z05h7fmrcecm0pbm3Nubi4jR45k+/bt/Pvf/+bmm2+uM8ZXn2m/xodIWiPjxo1j3LhxdbZPmjQJk8nEWWed5dFxoqOjAWrFoxuKg6uqyr59+0hISKiToKg3TZlzt27d2Lx5M+Hh4XXGa9tqFmhsC3OuSVVVFQsXLsRsNjNlyhS3Y9rCnJcsWQLAiBEj6owfMWIEiqLwyy+/uLa1hTkDtG/fnjfeeKPO+CeffBKgVp6Z0XN2OBxMmzaNt99+mxtuuIF33nkHk6n2dX5juTbu8mhSUlJYt24d6enpdfKu6htf3zm8PW+95txU2tKcc3JyGDlyJFu2bOHVV1/ljjvucHsMX32mpefqDGLt2rUcOnSIsWPHenz1s3HjRgA6duzo2qatrvr+++/djs/Ly2PYsGGnba83qG/OWhhsx44ddR6jbWtrc67J0qVLSUtLY+zYsbRv397tmLYw54qKCgAyMzPrPCYrKwtVVbFara5tbWHO9VFVVcVHH32En58fV111lWu7kXOu+YN73XXX8d5777kNUaakpJCQkMDatWspLi6uta+srIzVq1eTkJDg8twBLhvdzeO7776rNQaMm7eec24qbWXONYXVyy+/zN13312vLT77THu9uINEdxqqi6Oqqpqfn19n2/Hjx9Xu3burfn5+6i+//FJr34YNG9SKioo6j3nhhRdUQO3Zs2edOiP1FWUbOXKkLkXZvD3n/Px8NTo6WrXZbOrvv//u2l5eXq6OGzdOBdQ33nij1mNa+5xrcvnll6uA+tlnnzVoQ2uf8x133KEC6i233KJWVla6tldVVam33XabCqgPPvhgrce09jlXVFSoJSUltbZVVVWpM2bMUAH1gQceqHM8I+ZcVVWl3nrrra66Xe4K9dakqcUld+/e7bUiot6at95zPpXTKSLaWuacnZ3tquj+r3/9yyObjP5Mq6qqKqqqqt6XbBJv88Ybb7BmzRoAtm3bxq+//sr555/vUvQTJ05k4sSJAPztb39j0aJFXHDBBcTGxnL06FG+/PJLSkpKePPNN5k8eXKtYw8fPpxdu3YxbNgwkpKSKC0tZd26dWzZsoWIiAiWL19eJ+ywY8cOzjvvPEpLS7n22mtJSEjg22+/5ffff2fatGksWLCgRc8Z4IsvvuDqq6/GarVy9dVXu+a6fft2xo8fz1dffVXraqstzBlEHaTExESioqI4duxYnbowNWntcz569ChDhgwhLS2NXr16cdFFF6EoCitXrmTbtm107NiRjRs31spBau1zPnbsGL169WL06NF06tSJiooKvvvuO3bt2sUll1zC4sWLa3nrjJrzk08+yVNPPUVwcDD333+/2/fdxIkT6d+/PwDFxcVccMEF/Pbbb4waNYqBAweydetWvvnmG/r378+aNWvqhHKeeeYZ/u///o8OHTpw9dVXU1xczIcffkhpaSnfffddnfCw3vM2Ys6zZs0iKysLEOV0Tpw4wVVXXeWqc/bII4+4Fj60hTkPHz6cH3/8ke7du3Pddde5tWHGjBm1Uj6MeH/XwetyTaILkydPVoF6b7Nnz3aN/eGHH9SRI0eqsbGxqsViUePi4tTrrrtO/fXXX90ee8GCBerYsWPVxMRE1WazqTabTe3WrZt6//33q0ePHq3Xpt27d6tXX321GhUVpVqtVrVXr17qSy+9VKe1Rkucs8aaNWvUsWPHquHh4aq/v7/aq1cv9dlnn633aqstzPnZZ591e6VYH619zmlpaeq9996rdu3aVfX391etVquampqqzpw5U83Kympzcy4oKFAnTZqkdu7cWbXZbGpISIh67rnnqgsWLGjQfl/PGTfeu7y8PPWBBx5Qk5KSVIvFoiYlJakPPPBALc/UqSxatEgdNGiQGhAQoIaFhaljx45VN27c6JN5GzFnrTdlfTd3PRVb85wbmy/1eO70fn+fivRcSSQSiUQikXgRmdAukUgkEolE4kWkuJJIJBKJRCLxIlJcSSQSiUQikXgRKa4kEolEIpFIvIgUVxKJRCKRSCReRIoriUQikUgkEi8ixZVEIpFIJBKJF5HiSiKRSCQSicSLSHElkUgkEolE4kWkuJJIJC2e4cOHoyiKr83wmKKiIuLj47n77rt9bUqzWblyJYqisHTpUl+bIpG0OqS4kkgkhqIoSpNurZF//OMf5OTk8Oijj/ralGYzYsQIhg0bxkMPPURVVZWvzZFIWhV121VLJBKJjsyePbvOtqeeeoqwsDBmzJjh9jHvvvsuJSUlOlvmHfLy8pg3bx433HADSUlJvjbntJg1axYTJkzgww8/5Oabb/a1ORJJq0E2bpZIJD5HURSSk5M5dOiQr005bV5++WXuu+8+li9fzsUXX+xrc06LyspKEhISSE1NZc2aNb42RyJpNciwoEQiafG4y7l65513UBSFd955h//9738MGTKEwMBA2rdvz+OPP47D4QDg/fffZ8CAAQQEBNChQweef/55t+dQVZW33nqL888/n9DQUAIDAxk0aBBvvfVWk2x95513iIqKYsSIEa5tDoeDTp06ERUVRXl5udvHDR48GH9/fzIyMmpt//LLL7n44ouJiIjAZrPRu3dvnn/++Tqhuvz8fJ599lmGDRtGQkIC/v7+JCQkcMstt7B///4653vyySdRFIVVq1axcOFCBg4cSGBgIMOHD3eN8fPzY+LEiaxdu5a9e/c26XmQSM5kpLiSSCStms8//5xrr72Wzp07c+eddxIcHMzf/vY3nnjiCV544QXuvvtu+vTpw+23347D4eChhx7i/fffr3UMVVW5+eabmTp1KllZWdx4441MmzaN4uJipk6dyqxZszyyJTc3ly1btjB48GBMpuqvV5PJxPTp08nJyWHx4sV1Hrdt2zY2bdrEZZddRmxsrGv7Y489xsSJE9mzZw9XXXUVd999NzabjYceeojrr7++1jF27tzJE088QUBAAFdccQUzZsxg0KBBfPDBBwwePJjDhw+7tfm5557jrrvuIiUlhfvuu48LLrig1v5zzz0XgBUrVnj0HEgkEkCVSCQSHwOoycnJ9e4fNmyYeurX1dtvv60CqsViUTdu3OjaXlBQoMbGxqqBgYFqXFycun//fte+I0eOqP7+/mrfvn1rHev1119XAXXq1Kmq3W53bS8vL1cnTJigAurmzZsbnceSJUtUQP3LX/5SZ19aWprq5+enjhgxos6+++67TwXUb775xrXt+++/VwF13LhxanFxsWu7w+FQ77zzThVQP/30U9f2vLw8NTs7u86xV6xYoZpMJnXatGm1ts+ePVsF1KCgIPX333+vd05bt25VAfWWW25pePISicSF9FxJJJJWzU033cTZZ5/t+j8kJIRLL72UkpIS7rrrLjp37uzal5SUxAUXXMD27duprKx0bX/llVcICgrilVdewc+vep2Pv78/zzzzDAAffvhho7YcO3YMgHbt2tXZFxcXx2WXXcaqVatqhenKy8tZtGgRHTp0YPTo0bVsAvjPf/5DYGCga7uiKMydOxdFUWrZFBYWRmRkZJ3zjhgxgl69erF8+XK3Nt9+++306dOn3jlpc9HmJpFIGkeuFpRIJK2aAQMG1NkWHx8PQP/+/d3uq6qq4uTJk7Rv356SkhK2bdtGQkICc+fOrTPebrcDsGvXrkZtyc7OBiAiIsLt/jvuuIPPPvuMN998k7///e+ACGvm5ORw33331Qolrl+/nqCgIN588023xwoICKhj06pVq3jxxRfZsGEDWVlZtQSkv7+/2+MMHjy4wTlpgi0rK6vBcRKJpBopriQSSasmNDS0zjbN+9TQPk005ebmoqoqx48f56mnnqr3PMXFxY3aEhAQAEBpaanb/aNGjaJTp0688847/PWvf8VsNvPGG29gMpm47bbbao3NycmhsrLSY5s++eQTrrvuOoKDgxkzZgwdO3YkMDDQlfRfX86VOy9bTbS51PSeSSSShpHiSiKRnNFoAmzgwIFs3rz5tI4VExMDCGHkDkVRmD59Oo899hhLliyhT58+rFixgnHjxtWpiRUaGoqiKB57jJ588klsNhu//PILKSkptfZ99NFH9T6usUKt2ly0uUkkksaROVcSieSMJiQkhB49erBz507y8vJO61ha7lJDZQtuu+02LBYLb7zxBm+99RaqqjJt2rQ644YMGUJ2drbHJRD2799Pjx496girEydOuC3F4Cm7d+8GaDAvSyKR1EaKK4lEcsZz3333UVJSwvTp092G/w4ePOhRgdM+ffoQGRnJxo0b6x3Trl07LrvsMpYuXcrrr79OXFwcEyZMcGsTCDGm5XLVJD09nZ07d7r+T05OZt++fZw8edK1raysjLvuuqtW7lVT2bBhAwDDhg1r9jEkkjMNKa4kEskZzx133MHkyZP59NNPSUlJ4ZZbbuGRRx5hypQpnHvuuXTp0oX169c3ehxFUbjsssvYvn07aWlpDZ6vqqqKjIwMJk+eXGuFosbYsWN5/PHHWbNmDV27duWGG27gkUceYfr06YwYMYLExES+/PJL1/h7772XgoICBgwYwH333eeq77V9+3b69evXvCcGWLZsGREREVx44YXNPoZEcqYhxZVEIjnj0ZK+P/74Y3r16sXXX3/NvHnzWLZsGTabjeeff56RI0d6dKw77rgDh8PRYOmGkSNH0r59exRFcRsS1Hj66adZtmwZQ4cO5YcffmDevHl8/fXXlJeX8+STT3LTTTe5xt5zzz3Mnz+fyMhIFixYwOeff86wYcP4+eefCQ8P9/i5qMnhw4dZu3YtkydPxmazNesYEsmZiOwtKJFIJF7mvPPOIz8/nz/++MNtwviJEydITk5m6NChLbry+RNPPMHcuXPZuXMnXbp08bU5EkmrQXquJBKJxMs8//zz7Nixg08++cTt/hdffJHKykruvPNOgy3znLy8PF566SXuuusuKawkkiYiSzFIJBKJlznvvPOYP3++q5YWiMbK//73vzl8+DALFiygV69eXHXVVT60smEOHTrEjBkzuPfee31tikTS6pBhQYlEIjGAQ4cO0alTJwICAhgyZAjz58+nW7duvjZLIpHogBRXEolEIpFIJF5E5lxJJBKJRCKReBEpriQSiUQikUi8iBRXEolEIpFIJF5EiiuJRCKRSCQSLyLFlUQikUgkEokXkeJKIpFIJBKJxItIcSWRSCQSiUTiRaS4kkgkEolEIvEi/w8aJlAzyYUXmAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure Supplementary Information S1b and S2b\n", + "p = 6 #period of 6 years\n", + "l = 50\n", + "\n", + "pmin = p - np.abs(p - 1/(1/p + 1/l))\n", + "pmax = p + np.abs(p - 1/(1/p - 1/l))\n", + "print(\"For a period of 30 yr, spectral resolution on C04 time series is between : \", pmin, \"and \", pmax)\n", + "fmin, fmax = 1/pmax, 1/pmin\n", + "\n", + "filt_lod = lod.copy()\n", + "\n", + "ndata = lod.shape[0]\n", + "# compute the mean time delta of the object\n", + "dt = float(np.mean((lod[1:, 0] - lod[:-1, 0])))\n", + "\n", + "for i in range(1,7):\n", + " s = lod[:,i].copy()\n", + "\n", + " # zero pad\n", + " n2 = 0\n", + " while ndata > 2 ** n2:\n", + " n2 += 1\n", + " n2 += 1\n", + "\n", + " f = np.fft.fft(s, n=2 ** n2)\n", + " freq = np.fft.fftfreq(2 ** n2, d=dt)\n", + " to_zero = np.logical_or(freq > fmax, freq < -fmax) | np.logical_and(freq < fmin, freq > -fmin)\n", + " f[to_zero] = 0\n", + " filt_lod[:,i] = np.real(np.fft.ifft(f))[:ndata]\n", + "\n", + "print(np.max(filt_lod[:,6]) - np.min(filt_lod[:,6]))\n", + "print(np.std(filt_lod[:,6]))\n", + "\n", + "l = 75 #length of the LOD time series for C01\n", + "pmin = p - np.abs(p - 1/(1/p + 1/l))\n", + "pmax = p + np.abs(p - 1/(1/p - 1/l))\n", + "print(\"For a period of 30 yr, spectral resolution on C01 time series is between : \", pmin, \"and \", pmax)\n", + "fmin, fmax = 1/pmax, 1/pmin\n", + "\n", + "filt_lod2 = lod2.copy()\n", + "\n", + "ndata = lod2.shape[0]\n", + "# compute the mean time delta of the object\n", + "dt = float(np.mean((lod2[1:, 0] - lod2[:-1, 0])))\n", + "\n", + "for i in range(1,4):\n", + " s = lod2[:,i].copy()\n", + "\n", + " # zero pad\n", + " n2 = 0\n", + " while ndata > 2 ** n2:\n", + " n2 += 1\n", + " n2 += 1\n", + "\n", + " f = np.fft.fft(s, n=2 ** n2)\n", + " freq = np.fft.fftfreq(2 ** n2, d=dt)\n", + " to_zero = np.logical_or(freq > fmax, freq < -fmax) | np.logical_and(freq < fmin, freq > -fmin)\n", + " f[to_zero] = 0\n", + " filt_lod2[:,i] = np.real(np.fft.ifft(f))[:ndata]\n", + "\n", + "plt.figure()\n", + "plt.plot(lod2[:,0], filt_lod2[:,1], label='C01', color='C1', linestyle='dashdot')\n", + "plt.plot(lod2[:,0], filt_lod2[:,3], label='C01 LOD-AAM', color='C2')\n", + "#plt.plot(lod[:,0], filt_lod[:,1], label='C04 LOD', color='C6')\n", + "plt.plot(lod[:,0], filt_lod[:,6], label='C04 LOD-AAM', color='C8', linestyle=(0, (5,2)))\n", + "\n", + "plt.title('')\n", + "plt.ylabel('(ms)', fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(loc=(0.6769, 0.77))\n", + "\n", + "dlod = (filt_lod[1:,6] - filt_lod[:-1,6]) / ((lod[1:,0] - lod[:-1,0])*31536000)/1e3\n", + "dlod2 = (filt_lod2[1:,3] - filt_lod2[:-1,3]) / ((lod2[1:,0] - lod2[:-1,0])*31536000)/1e3\n", + "\n", + "plt.figure()\n", + "plt.plot(lod2[:-1,0], -360/86400**2*7.129e37/3e19*dlod2, label=r'$\\alpha$ from C01, $\\Gamma=3.10^{19}$', color='C1', linestyle='dashdot')\n", + "plt.plot(lod2[:-1,0], -360/86400**2*7.129e37/2e20*dlod2, label=r'$\\alpha$ from C01, $\\Gamma=2.10^{20}$', color='C8')\n", + "plt.plot(lod[:-1,0], -360/86400**2*7.129e37/3e19*dlod, label=r'$\\alpha$ from C04, $\\Gamma=3.10^{19}$', color='C0', linestyle='dashdot')\n", + "plt.plot(lod[:-1,0], -360/86400**2*7.129e37/2e20*dlod, label=r'$\\alpha$ from C04, $\\Gamma=2.10^{20}$', color='C9')\n", + "\n", + "plt.ylabel(r'$\\alpha (\\circ)$', fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.grid()\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + "metadata": { + "hide_input": false, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 4a39b0f13493a2f2949c31ec184b126eddd51798 Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 18 Nov 2020 15:54:05 +0100 Subject: [PATCH 40/80] Update file format for monthly 5x5 spherical harmonic coefficients from SLR --- gravity_toolkit/read_CSR_monthly_6x1.py | 171 ++++++++++++++++++++++++ 1 file changed, 171 insertions(+) create mode 100644 gravity_toolkit/read_CSR_monthly_6x1.py diff --git a/gravity_toolkit/read_CSR_monthly_6x1.py b/gravity_toolkit/read_CSR_monthly_6x1.py new file mode 100644 index 00000000..845a5ce4 --- /dev/null +++ b/gravity_toolkit/read_CSR_monthly_6x1.py @@ -0,0 +1,171 @@ +#!/usr/bin/env python +u""" +read_CSR_monthly_6x1.py +Written by Tyler Sutterley (07/2020) + +Reads in monthly 5x5 spherical harmonic coefficients with 1 + coefficient from degree 6 all calculated from SLR measurements + +Dataset distributed by UTCSR + ftp://ftp.csr.utexas.edu/outgoing/cheng/slrgeo.5d561_187_naod + +OPTIONS: + HEADER: file contains header text to be skipped (default: True) + +OUTPUTS: + clm: Cosine spherical harmonic coefficients + slm: Sine spherical harmonic coefficients + error/clm: Cosine spherical harmonic coefficient uncertainty + error/slm: Sine spherical harmonic coefficients uncertainty + MJD: output date as Modified Julian Day + time: output date in year-decimal + +REFERENCE: + Cheng, M., J. C. Ries, and B. D. Tapley, 'Variations of the Earth's Figure + Axis from Satellite Laser Ranging and GRACE', J. Geophys. Res., 116, B01409, + 2011, DOI:10.1029/2010JB000850. + +PYTHON DEPENDENCIES: + numpy: Scientific Computing Tools For Python (https://numpy.org) + +PROGRAM DEPENDENCIES: + convert_calendar_decimal.py: converts from calendar dates to decimal years + +UPDATE HISTORY: + Updated 11/2020: following new format without geocenter coefficient + Updated 07/2020: added function docstrings + Updated 07/2019: following new format with mean field in header and no C6,0 + Updated 10/2018: using future division for python3 Compatibility + Updated 10/2017: include the 6,0 and 6,1 coefficients in output Ylms + Written 10/2017 +""" +from __future__ import print_function, division + +import os +import re +import numpy as np +from gravity_toolkit.convert_calendar_decimal import convert_calendar_decimal + +#-- PURPOSE: read low degree harmonic data from Satellite Laser Ranging (SLR) +def read_CSR_monthly_6x1(input_file, HEADER=True): + """ + Reads in monthly low degree and order spherical harmonic coefficients + from Satellite Laser Ranging (SLR) measurements + + Arguments + --------- + input_file: input satellite laser ranging file from CSR + + Keyword arguments + ----------------- + HEADER: file contains header text to be skipped + + Returns + ------- + clm: Cosine spherical harmonic coefficients + slm: Sine spherical harmonic coefficients + error/clm: Cosine spherical harmonic coefficient uncertainty + error/slm: Sine spherical harmonic coefficients uncertainty + MJD: output date as Modified Julian Day + time: output date in year-decimal + """ + + #-- read the file and get contents + with open(os.path.expanduser(input_file),'r') as f: + file_contents = f.read().splitlines() + file_lines = len(file_contents) + + #-- spherical harmonic degree range (full 5x5 with 6,1) + LMIN = 2 + LMAX = 6 + n_harm = (LMAX**2 + LMAX - LMIN**2 - LMIN)//2 + 1 + + #-- counts the number of lines in the header + count = 0 + indice = 0 + #-- Reading over header text + while HEADER: + #-- file line at count + line = file_contents[count] + #-- find end within line to set HEADER flag to False when found + HEADER = not bool(re.match(r'end\sof\sheader',line)) + if bool(re.match(80*r'=',line)): + indice = count + 1 + #-- add 1 to counter + count += 1 + + #-- number of dates within the file + n_dates = (file_lines - count)//(n_harm + 1) + + #-- read mean fields from the header + mean_Ylms = {} + mean_Ylm_error = {} + mean_Ylms['clm'] = np.zeros((LMAX+1,LMAX+1)) + mean_Ylms['slm'] = np.zeros((LMAX+1,LMAX+1)) + mean_Ylm_error['clm'] = np.zeros((LMAX+1,LMAX+1)) + mean_Ylm_error['slm'] = np.zeros((LMAX+1,LMAX+1)) + for i in range(n_harm): + #-- split the line into individual components + line = file_contents[indice+i].split() + #-- degree and order for the line + l1 = np.int(line[0]) + m1 = np.int(line[1]) + #-- fill mean field Ylms + mean_Ylms['clm'][l1,m1] = np.float(line[2].replace('D','E')) + mean_Ylms['slm'][l1,m1] = np.float(line[3].replace('D','E')) + mean_Ylm_error['clm'][l1,m1] = np.float(line[4].replace('D','E')) + mean_Ylm_error['slm'][l1,m1] = np.float(line[5].replace('D','E')) + + #-- output spherical harmonic fields + Ylms = {} + Ylms['error'] = {} + Ylms['MJD'] = np.zeros((n_dates)) + Ylms['time'] = np.zeros((n_dates)) + Ylms['clm'] = np.zeros((LMAX+1,LMAX+1,n_dates)) + Ylms['slm'] = np.zeros((LMAX+1,LMAX+1,n_dates)) + Ylms['error']['clm'] = np.zeros((LMAX+1,LMAX+1,n_dates)) + Ylms['error']['slm'] = np.zeros((LMAX+1,LMAX+1,n_dates)) + #-- input spherical harmonic anomalies and errors + Ylm_anomalies = {} + Ylm_anomaly_error = {} + Ylm_anomalies['clm'] = np.zeros((LMAX+1,LMAX+1,n_dates)) + Ylm_anomalies['slm'] = np.zeros((LMAX+1,LMAX+1,n_dates)) + Ylm_anomaly_error['clm'] = np.zeros((LMAX+1,LMAX+1,n_dates)) + Ylm_anomaly_error['slm'] = np.zeros((LMAX+1,LMAX+1,n_dates)) + #-- for each date + for d in range(n_dates): + #-- split the date line into individual components + line_contents = file_contents[count].split() + #-- modified Julian date of the middle of the month + Ylms['MJD'][d] = np.mean(np.array(line_contents[5:7],dtype=np.float)) + #-- date of the mid-point of the arc given in years + YY,MM = np.array(line_contents[3:5]) + Ylms['time'][d] = convert_calendar_decimal(YY,MM) + #-- add 1 to counter + count += 1 + + #-- read the anomaly field + for i in range(n_harm): + #-- split the line into individual components + line = file_contents[count].split() + #-- degree and order for the line + l1 = np.int(line[0]) + m1 = np.int(line[1]) + #-- fill anomaly field Ylms (variations and sigmas scaled by 1.0e10) + Ylm_anomalies['clm'][l1,m1,d] = np.float(line[2])*1e-10 + Ylm_anomalies['slm'][l1,m1,d] = np.float(line[3])*1e-10 + Ylm_anomaly_error['clm'][l1,m1,d] = np.float(line[6])*1e-10 + Ylm_anomaly_error['slm'][l1,m1,d] = np.float(line[7])*1e-10 + #-- add 1 to counter + count += 1 + + #-- calculate full coefficients and full errors + Ylms['clm'][:,:,d] = Ylm_anomalies['clm'][:,:,d] + mean_Ylms['clm'][:,:] + Ylms['slm'][:,:,d] = Ylm_anomalies['slm'][:,:,d] + mean_Ylms['slm'][:,:] + Ylms['error']['clm'][:,:,d]=np.sqrt(Ylm_anomaly_error['clm'][:,:,d]**2 + + mean_Ylm_error['clm'][:,:]**2) + Ylms['error']['slm'][:,:,d]=np.sqrt(Ylm_anomaly_error['slm'][:,:,d]**2 + + mean_Ylm_error['slm'][:,:]**2) + + #-- return spherical harmonic fields and date information + return Ylms From 0ca86c62aec77faade20fced6db6f8c711000631 Mon Sep 17 00:00:00 2001 From: hulecom Date: Thu, 26 Nov 2020 09:48:14 +0100 Subject: [PATCH 41/80] Debug mean function from spatial.py Add C0,0 for JPL GSM data to be able to compare them with CSR and GFZ --- gravity_toolkit/spatial.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/gravity_toolkit/spatial.py b/gravity_toolkit/spatial.py index 1c90df3c..401f7748 100644 --- a/gravity_toolkit/spatial.py +++ b/gravity_toolkit/spatial.py @@ -17,6 +17,10 @@ https://www.h5py.org/ PROGRAM DEPENDENCIES: + ncdf_write.py: writes output spatial data to COARDS-compliant netCDF4 + hdf5_write.py: writes output spatial data to HDF5 + ncdf_read.py: reads spatial data from COARDS-compliant netCDF4 + hdf5_read.py: reads spatial data from HDF5 time.py: utilities for calculating time operations UPDATE HISTORY: @@ -1485,7 +1489,7 @@ def mean(self, apply=False, indices=Ellipsis): indices of input ``spatial`` object to compute mean """ # output spatial object - temp = spatial(nlon=self.shape[0],nlat=self.shape[1], + temp = spatial(nlon=self.data.shape[0],nlat=self.data.shape[1], fill_value=self.fill_value) # copy dimensions temp.lon = self.lon.copy() From 20f4d4049cf0714475aa6e220440a3d08a21f348 Mon Sep 17 00:00:00 2001 From: hulecom Date: Fri, 27 Nov 2020 16:41:26 +0100 Subject: [PATCH 42/80] Update for CNES RL04 and RL05 --- gravity_toolkit/grace_date.py | 1 + 1 file changed, 1 insertion(+) diff --git a/gravity_toolkit/grace_date.py b/gravity_toolkit/grace_date.py index 6201292f..4fb9f359 100644 --- a/gravity_toolkit/grace_date.py +++ b/gravity_toolkit/grace_date.py @@ -302,6 +302,7 @@ def arguments(): parser.add_argument('--center','-c', metavar='PROC', type=str, nargs='+', default=['CSR','GFZ','JPL'], + choices=['CSR','GFZ','JPL', 'CNES'], help='GRACE/GRACE-FO Processing Center') # GRACE/GRACE-FO data release parser.add_argument('--release','-r', From 394a3c6422545d02cc7c2e988ad9311e4da07c91 Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 1 Dec 2020 11:31:32 +0100 Subject: [PATCH 43/80] Addition of the C2,1/S2,1 and C2,2/S2,2 correction when reading GRACE data --- gravity_toolkit/read_SLR_CS2.py | 104 ++++++++++++++++++++++++++++++++ 1 file changed, 104 insertions(+) create mode 100644 gravity_toolkit/read_SLR_CS2.py diff --git a/gravity_toolkit/read_SLR_CS2.py b/gravity_toolkit/read_SLR_CS2.py new file mode 100644 index 00000000..7579aab2 --- /dev/null +++ b/gravity_toolkit/read_SLR_CS2.py @@ -0,0 +1,104 @@ +#!/usr/bin/env python +u""" +read_SLR_CS2.py +Written by Hugo Lecomte (11/2020) + +Reads monthly degree 2,x spherical harmonic data files from SLR + +Dataset distributed by CSR + http://download.csr.utexas.edu/pub/slr/degree_2/ + C21_S21_RL06.txt or C22_S22_RL06.txt + +REFERENCE: + Dahle, C., Murböck, M., Flechtner, F. , Dobslaw, H., Michalak, G., + Neumayer, K. H., Abrykosov, O., Reinhold, A., König, R., Sulzbach, R. + and Förste C., "The GFZ GRACE RL06 Monthly Gravity Field Time Series: + Processing Details,and Quality Assessment", Remote Sensing, 11(18), 2116, 2019. + https://doi.org/10.3390/rs11182116 + +CALLING SEQUENCE: + SLR_2x = read_SLR_CS2(SLR_file) + +INPUTS: + SLR_file: + CSR 2,1: C21_S21_RL06.txt + CSR 2,2: C22_S22_RL06.txt + +OUTPUTS: + datac: SLR degree 2 order x cosine stokes coefficients (C2x) + datas: SLR degree 2 order x sine stokes coefficients (S2x) + errorc: SLR degree 2 order x cosine stokes coefficient error (eC2x) + errors: SLR degree 2 order x sine stokes coefficient error (eS2x) + month: GRACE/GRACE-FO month of measurement (Apr. 2002 = 004) + time: date of SLR measurement + +PYTHON DEPENDENCIES: + numpy: Scientific Computing Tools For Python (https://numpy.org) + +UPDATE HISTORY: + Written 11/2020 +""" +import os +import re +import numpy as np + +#-- PURPOSE: read Degree 2,x data from Satellite Laser Ranging (SLR) +def read_SLR_CS2(SLR_file): + """ + Reads CS2,x spherical harmonic coefficients from SLR measurements + + Arguments + --------- + SLR_file: Satellite Laser Ranging file + + Returns + ------- + datac: SLR degree 2 order x cosine stokes coefficients (C2x) + datas: SLR degree 2 order x sine stokes coefficients (S2x) + errorc: SLR degree 2 order x cosine stokes coefficient error (eC2x) + errors: SLR degree 2 order x sine stokes coefficient error (eS2x) + month: GRACE/GRACE-FO month of measurement + time: date of SLR measurement + """ + + #-- check that SLR file exists + if not os.access(os.path.expanduser(SLR_file), os.F_OK): + raise IOError('SLR file not found in file system') + #-- output dictionary with input data + dinput = {} + + if bool(re.search('C2\d_S2\d_RL',SLR_file)): + + #-- SLR 2x RL06 file from CSR + #-- automatically skip the header denoted with '#' + content = np.genfromtxt(os.path.expanduser(SLR_file)) + + #-- number of months within the file + n_mon = content.shape[0] + date_conv = content[:,0] + #-- remove the monthly mean of the AOD model + C2x_input = content[:,1] - content[:,5]*10**-10 + eC2x_input = content[:,3]*10**-10 + # -- remove the monthly mean of the AOD model + S2x_input = content[:,2] - content[:,6]*10**-10 + eS2x_input = content[:,4]*10**-10 + mon = np.zeros((n_mon),dtype=np.int) + + #-- for every line convert the date into month number: + for t in range(content.shape[0]): + # -- GRACE/GRACE-FO month of SLR solutions + mon[t] = 1 + t + + #-- convert to output variables and truncate if necessary + dinput['time'] = date_conv + dinput['datac'] = C2x_input + dinput['errorc'] = eC2x_input + dinput['datas'] = S2x_input + dinput['errors'] = eS2x_input + dinput['month'] = mon + + else: + raise FileNotFoundError("Invalid file given to read_SLR_2x:", SLR_file) + + #-- return the input CS2x data, year-decimal date, and GRACE/GRACE-FO month + return dinput From a5c20248d8e8a39b52c30926e6824a85ed533768 Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 17 Feb 2021 13:32:09 +0100 Subject: [PATCH 44/80] Read grid and produce harmonics objects --- gravity_toolkit/read_grid_to_harmonics.py | 224 ++++++++++++++++++++++ 1 file changed, 224 insertions(+) create mode 100644 gravity_toolkit/read_grid_to_harmonics.py diff --git a/gravity_toolkit/read_grid_to_harmonics.py b/gravity_toolkit/read_grid_to_harmonics.py new file mode 100644 index 00000000..7cb7652a --- /dev/null +++ b/gravity_toolkit/read_grid_to_harmonics.py @@ -0,0 +1,224 @@ +#!/usr/bin/env python +u""" +read_grid_to_harmonics.py +Written by Hugo Lecomte (12/2020) + +Reads netCDF file with grid data and extracts spherical harmonic from those data +Correct data for drift in pole tide following Wahr et al. (2015) +Parses date of GRACE/GRACE-FO data from filename + +Design for JPL MASCON netCDF data available on +https://podaac-tools.jpl.nasa.gov/drive/files +In the folder /allData/tellus/retired/L3/mascon/RL06/JPL/v02 + +INPUTS: + input_file: GRACE/GRACE-FO Level-3 netCDF grid data file + LMAX: Maximum degree of spherical harmonics (degree of truncation) + +OPTIONS: + MMAX: Maximum order of spherical harmonics (order of truncation) + default is the maximum spherical harmonic degree + POLE_TIDE: correct GSM data for pole tide drift following Wahr et al. (2015) + +OUTPUTS: + time: mid-month date in year-decimal + start: start date of range as Julian day + end: end date of range as Julian day + clm: cosine spherical harmonics of input data (LMAX,MMAX) + slm: sine spherical harmonics of input data (LMAX,MMAX) + eclm: cosine spherical harmonic uncalibrated standard deviations (LMAX,MMAX) + eslm: sine spherical harmonic uncalibrated standard deviations (LMAX,MMAX) + +PYTHON DEPENDENCIES: + numpy: Scientific Computing Tools For Python (https://numpy.org) + +UPDATE HISTORY: + Written 12/2020 +""" +import os +import re +import io +import numpy as np +from gravity_toolkit.ncdf_read import ncdf_read +from gravity_toolkit.hdf5_read import hdf5_read +from gravity_toolkit.utilities import get_data_path +from gravity_toolkit.read_love_numbers import read_love_numbers +from gravity_toolkit.gen_stokes import gen_stokes + +#-- PURPOSE: read Level-3 GRACE and GRACE-FO netCDF files +def read_grid_to_harmonics(input_file, VARNAME, LMAX, MMAX=None, LONNAME='lon', + LATNAME='lat', TIMENAME='time', UNITS=1, POLE_TIDE=False): + """ + Reads netCDF or HDF5 file with grid data and extracts spherical harmonic from those data + Correct data prior to Release 6 for pole tide drift + Parses date of GRACE/GRACE-FO data from filename + + Arguments + --------- + input_file: GRACE/GRACE-FO Level-3 netCDF grid data file + VARNAME: z variable name in the file + LMAX: Maximum degree of spherical harmonics (degree of truncation) + + Keyword arguments + ----------------- + MMAX: Maximum order of spherical harmonics + LONNAME: longitude variable name in the file + LATNAME: latitude variable name in the file + TIMENAME: time variable name in the file + UNITS: input data units + 1: cm of water thickness + 2: Gtons of mass + 3: kg/m^2 + POLE_TIDE: correct for pole tide drift following Wahr et al. (2015) + + Returns + ------- + clm: GRACE/GRACE-FO cosine spherical harmonics + slm: GRACE/GRACE-FO sine spherical harmonics + time: time of each GRACE/GRACE-FO measurement (mid-month) + month: GRACE/GRACE-FO months of input datasets + l: spherical harmonic degree to LMAX + m: spherical harmonic order to MMAX + title: string denoting low degree zonals replacement, geocenter usage and corrections + directory: directory of exact GRACE/GRACE-FO product + """ + + #-- parse filename + pfx,center,time,realm,release,v_id,sfx = parse_file(input_file) + + #-- read file content + if input_file[-3:] == '.nc': + file_contents = ncdf_read(input_file, DATE=True, VARNAME=VARNAME, LONNAME=LONNAME, + LATNAME=LATNAME, TIMENAME=TIMENAME, ATTRIBUTES=True, + TITLE=True, COMPRESSION=sfx) + elif input_file[-4:] == '.hdf' or input_file[-3:] == '.h5' or input_file[-5:] == '.hdf5': + file_contents = hdf5_read(input_file, DATE=True, VARNAME=VARNAME, LONNAME=LONNAME, + LATNAME=LATNAME, TIMENAME=TIMENAME, ATTRIBUTES=True, + TITLE=True, COMPRESSION=sfx) + + #-- load love numbers + hl, kl, ll = read_love_numbers(get_data_path(['data', 'love_numbers']), REFERENCE='CF') + + #-- set maximum spherical harmonic order + MMAX = np.copy(LMAX) if (MMAX is None) else MMAX + + #-- number of dates in data + n_time = file_contents['time'].shape[0] + #-- Spherical harmonic coefficient matrices to be filled from data file + grace_clm = np.zeros((LMAX + 1, MMAX + 1, n_time)) + grace_slm = np.zeros((LMAX + 1, MMAX + 1, n_time)) + #-- Time matrix to fill + tdec = np.zeros((n_time)) + month = np.zeros((n_time)) + #-- output dimensions + lout = np.arange(LMAX + 1) + mout = np.arange(MMAX + 1) + + #-- for each date, conversion to spherical harmonics + for i in range(n_time): + harmo = gen_stokes(file_contents['data'][i, :, :], + file_contents['lon'][:], file_contents['lat'][:], + LMAX=LMAX, MMAX=MMAX, UNITS=UNITS, LOVE=(hl, kl, ll)) + + grace_clm[:, :, i] = harmo['clm'] + grace_slm[:, :, i] = harmo['slm'] + + #-- extract GRACE date information from input file name + start_yr = np.float(time[:4]) + + #-- variables initialization for date conversion + current_year = start_yr + current_month = 1 + cmp_past_dpm = 0 + cmp_past_dpy = 0 + if (start_yr % 4) == 0:#-- Leap Year (% = modulus) + dpy = 366.0 + dpm = [31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + else:#-- Standard Year + dpy = 365.0 + dpm = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + + #-- for each date, conversion to month and decimal year + for i in range(n_time): + #-- Month iteration + while file_contents['time'][i] - cmp_past_dpm > dpm[(current_month - 1)%12]: + current_month += 1 + cmp_past_dpm += dpm[(current_month - 1)%12] + + #-- Year iteration + while file_contents['time'][i] - cmp_past_dpy > dpy: + current_year += 1 + cmp_past_dpy += dpy + if (current_year % 4) == 0: #-- Leap Year (% = modulus) + dpy = 366.0 + dpm = [31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + else: #-- Standard Year + dpy = 365.0 + dpm = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + + tdec[i] = current_year + (file_contents['time'][i] - cmp_past_dpy)/dpy + month[i] = current_month + + #-- The 'Special Months' (Nov 2011, Dec 2011 and April 2012) with + #-- Accelerometer shutoffs make this relation between month number + #-- and date more complicated as days from other months are used + #-- May15 (month 161) is centered in Apr15 (160) + if (month[i] == 160) and (month[i] == month[i - 1]): + month[i] = month[i - 1] + 1 + + #-- extract GRACE and GRACE-FO file informations + title = file_contents['attributes'] + + #-- Correct Pole Tide following Wahr et al. (2015) 10.1002/2015JB011986 + if POLE_TIDE: + for i in range(n_time): + #-- time since 2000.0 + dt = tdec[i] - 2000.0 + + #-- JPL Pole Tide Correction + #-- values for IERS mean pole [2010] + if tdec[i] < 2010.0: + a = np.array([0.055974,1.8243e-3,1.8413e-4,7.024e-6]) + b = np.array([-0.346346,-1.7896e-3,1.0729e-4,0.908e-6]) + elif tdec[i] >= 2010.0: + a = np.array([0.023513,7.6141e-3,0.0,0.0]) + b = np.array([-0.358891,0.6287e-3,0.0,0.0]) + #-- calculate m1 and m2 values + m1 = np.copy(a[0]) + m2 = np.copy(b[0]) + for x in range(1,4): + m1 += a[x]*dt**x + m2 += b[x]*dt**x + #-- pole tide values for JPL + #-- JPL remove the IERS mean pole from m1 and m2 + #-- before computing their harmonic solutions + C21_PT = -1.551e-9*(m1 - 0.62e-3*dt) - 0.012e-9*(m2 + 3.48e-3*dt) + S21_PT = 0.021e-9*(m1 - 0.62e-3*dt) - 1.505e-9*(m2 + 3.48e-3*dt) + #-- correct GRACE spherical harmonics for pole tide + #-- note: -= means grace_xlm = grace_xlm - PT + grace_clm[2, 1, i] -= C21_PT + grace_clm[2, 1, i] -= S21_PT + + #-- return the GRACE data, GRACE date (mid-month in decimal), and the + #-- start and end days as Julian dates + return {'clm': grace_clm, 'slm': grace_slm, 'time': tdec, 'month': month, + 'l': lout, 'm': mout, 'title': title, 'directory': os.path.split(input_file)[0]} + +#-- PURPOSE: extract parameters from filename +def parse_file(input_file): + """ + Extract parameters from filename + + Arguments + --------- + input_file: GRACE/GRACE-FO Level-2 spherical harmonic data file + """ + #-- compile numerical expression operator for parameters from files + #-- JPLMSC: NASA Jet Propulsion Laboratory (mascon solutions) + regex_pattern = r'(.*?)\.(.*?)\.(.*?)\.(.*?)\.(.*?)\.(.*?)\.(\w{2,})' + rx = re.compile(regex_pattern, re.VERBOSE) + #-- extract parameters from input filename + if isinstance(input_file, io.IOBase): + return rx.findall(input_file.filename).pop() + else: + return rx.findall(os.path.basename(input_file)).pop() From 95a9bd23781bc65d280e64512a5a8caefe3315cc Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 17 Feb 2021 13:33:25 +0100 Subject: [PATCH 45/80] Add plot function and wavelets for wavelets analysis --- gravity_toolkit/harmonics.py | 382 +++++++++++++++++++++++++++++++++++ gravity_toolkit/wavelets.py | 219 ++++++++++++++++++++ 2 files changed, 601 insertions(+) create mode 100644 gravity_toolkit/wavelets.py diff --git a/gravity_toolkit/harmonics.py b/gravity_toolkit/harmonics.py index 83b24f12..17f2fe60 100644 --- a/gravity_toolkit/harmonics.py +++ b/gravity_toolkit/harmonics.py @@ -73,6 +73,7 @@ can calculate spherical harmonic mean over a range of time indices will also calculate the mean time and month of a harmonics object can create a harmonics object from an open file-like object + Updated 11/2020: added plotting functions for visualization Updated 08/2020: added compression options for ascii, netCDF4 and HDF5 files Updated 07/2020: added class docstring and using kwargs for output to file added case_insensitive_filename function to search directories @@ -98,9 +99,18 @@ import pathlib import zipfile import warnings +import matplotlib import numpy as np import gravity_toolkit.version from gravity_toolkit.time import adjust_months,calendar_to_grace +import scipy as sc +import matplotlib.pyplot as plt +import gravity_toolkit.wavelets as wv +from gravity_toolkit.ncdf_stokes import ncdf_stokes +from gravity_toolkit.hdf5_stokes import hdf5_stokes +from gravity_toolkit.ncdf_read_stokes import ncdf_read_stokes +from gravity_toolkit.hdf5_read_stokes import hdf5_read_stokes +from gravity_toolkit.read_ICGEM_harmonics import read_ICGEM_harmonics from gravity_toolkit.destripe_harmonics import destripe_harmonics from gravity_toolkit.read_gfc_harmonics import read_gfc_harmonics from gravity_toolkit.read_GRACE_harmonics import read_GRACE_harmonics @@ -1914,3 +1924,375 @@ def __next__(self): # add to index self.__index__ += 1 return temp + + def gap_fill(self, apply=False): + """ + Fill the missing months with a linear interpolation, the interpolation is made on month number, it's imprecise + Options: apply to the object if True, else return a new instance + """ + temp = self.copy() + missing_month = self.month[-1] - self.month[0] - len(self.month) + 1 + + temp.clm = np.zeros((self.lmax + 1, self.mmax + 1, len(self.time) + missing_month)) + temp.slm = np.zeros((self.lmax + 1, self.mmax + 1, len(self.time) + missing_month)) + temp.time = np.zeros(len(self.time) + missing_month) + temp.month = np.arange(self.month[0], self.month[-1] + 1) + + # initialize index and count variables + index = 0 + cmp = 0 + for i in range(int(self.month[0]), int(self.month[-1]) + 1): + if i in self.month: # if month in original object, copy time and data + cmp_miss_mon = 0 # variable for following missing months + temp.time[index] = self.time[index - cmp] + temp.clm[:, :, index] = self.clm[:, :, index - cmp] + temp.slm[:, :, index] = self.slm[:, :, index - cmp] + else: # fill values with a linear interpolation + cmp += 1 + cmp_miss_mon += 1 + # y(t) = (y2 - y1)/(x2 - x1)*t + y1 + temp.time[index] = (self.time[index - cmp + 1] - self.time[index - cmp]) / ( + self.month[index - cmp + 1] - self.month[index - cmp]) * cmp_miss_mon + self.time[index - cmp] + temp.clm[:, :, index] = (self.clm[:, :, index - cmp + 1] - self.clm[:, :, index - cmp]) / \ + (self.month[index - cmp + 1] - self.month[index - cmp]) * cmp_miss_mon \ + + self.clm[:, :, index - cmp] + temp.slm[:, :, index] = (self.slm[:, :, index - cmp + 1] - self.slm[:, :, index - cmp]) / \ + (self.month[index - cmp + 1] - self.month[index - cmp]) * cmp_miss_mon \ + + self.slm[:, :, index - cmp] + + index += 1 + + # -- assign ndim and shape attributes + temp.update_dimensions() + + if apply: + self.clm = temp.clm + self.slm = temp.slm + self.time = temp.time + self.month = temp.month + + self.update_dimensions() + + return temp + + def plot_correlation(self, l, m, save_path=False): + """ + Plot correlation between spherical harmonic coefficients of the object + Inputs: + l first degree of spherical harmonics + m second degree of spherical harmonics + + Options: + save_path : if not False, give a path to save the figure + """ + mat_c = np.zeros((self.lmax, self.lmax)) + if m: + mat_s = np.zeros((self.lmax, self.lmax)) + for i in range(self.lmax): + for j in range(i+1): + mat_c[i, i - j] = abs(np.mean((self.clm[l, m]-np.mean(self.clm[l, m]))*(self.clm[i, j]-np.mean(self.clm[i, j])))/\ + np.sqrt(np.mean((self.clm[l, m]-np.mean(self.clm[l, m]))**2))/\ + np.sqrt(np.mean((self.clm[i, j]-np.mean(self.clm[i, j]))**2))) + + if j: + mat_c[i - j, i] = abs(np.mean((self.clm[l, m]-np.mean(self.clm[l, m]))*(self.slm[i, j]-np.mean(self.slm[i, j])))/\ + np.sqrt(np.mean((self.clm[l, m]-np.mean(self.clm[l, m]))**2))/\ + np.sqrt(np.mean((self.slm[i, j]-np.mean(self.slm[i, j]))**2))) + + if m: + mat_s[i, i - j] = abs(np.mean( + (self.slm[l, m] - np.mean(self.slm[l, m])) * (self.clm[i, j] - np.mean(self.clm[i, j]))) / \ + np.sqrt(np.mean((self.slm[l, m] - np.mean(self.slm[l, m]))**2)) / \ + np.sqrt(np.mean((self.clm[i, j] - np.mean(self.clm[i, j]))**2))) + + if j: + mat_s[i - j, i] = abs(np.mean( + (self.slm[l, m] - np.mean(self.slm[l, m])) * (self.slm[i, j] - np.mean(self.slm[i, j]))) / \ + np.sqrt(np.mean((self.slm[l, m] - np.mean(self.slm[l, m]))**2)) / \ + np.sqrt(np.mean((self.slm[i, j] - np.mean(self.slm[i, j]))**2))) + + plt.figure() + plt.matshow(mat_c) + plt.colorbar() + plt.title('Correlation of each spherical harmonics with $C_{' + str(l) + ',' + str(m)+ '}$') + + if save_path: + if os.path.isdir(save_path): + plt.savefig(os.path.join(save_path, 'C' + str(l) + str(m) + '_correlation.png')) + else: + plt.savefig(save_path[:-3] + 'c' + save_path[-3:]) + + if m: + plt.figure() + plt.matshow(mat_s) + plt.colorbar() + plt.title('Correlation of each spherical harmonics with $S_{' + str(l) + ',' + str(m) + '}$') + + if save_path: + if os.path.isdir(save_path): + plt.savefig(os.path.join(save_path, 'S' + str(l) + str(m) + '_correlation.png')) + else: + plt.savefig(save_path[:-3] + 's' + save_path[-3:]) + plt.show() + + + def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], save_path=False): + """ + Plot Cl,m and Sl,m harmonic coefficients + Inputs: + l first degree of spherical harmonics + m second degree of spherical harmonics + Options: + dates: list with limits of the xaxis in year + ylms: list of Harmonics objects to plot with the instance + label: list of label for each Harmonics objects with element 0 representing the current Harmonics object + save_path : if not False, give a path to save the figure + """ + #-- figure for Cl,m + plt.figure() + plt.title("Normalized spherical harmonics coefficient $C_{" + str(l) + "," + str(m) + "}$") + if len(ylms): + plt.plot(self.time, self.clm[l, m, :], 'r', label=label[0]) + else: + plt.plot(self.time, self.clm[l, m, :], 'r', label="$C_{" + str(l) + "," + str(m) + "}$") + + try: + for i in range(len(ylms)): + plt.plot(ylms[i].time, ylms[i].clm[l, m, :], label=label[i+1]) + except IndexError: + raise IndexError("The list of labels is incomplete for correct plotting") + + plt.xlabel("Time (year)") + plt.legend() + if dates: + plt.xlim(dates) + plt.grid() + + if save_path: + if os.path.isdir(save_path): + plt.savefig(os.path.join(save_path, 'C' + str(l) + str(m) + '_coefficient.png')) + else: + plt.savefig(save_path[:-3] + 'c' + save_path[-3:]) + + if m: + #-- figure for Sl,m + plt.figure() + plt.title("Normalized spherical harmonics coefficient $S_{" + str(l) + "," + str(m) + "}$") + if len(ylms): + plt.plot(self.time, self.slm[l, m, :], 'r', label=label[0]) + else: + plt.plot(self.time, self.slm[l, m, :], 'r', label="$S_{" + str(l) + "," + str(m) + "}$") + + try: + for i in range(len(ylms)): + plt.plot(ylms[i].time, ylms[i].slm[l, m, :], label=label[i + 1]) + except IndexError: + raise IndexError("The list of labels is incomplete for correct plotting") + + plt.xlabel("Time (year)") + plt.legend() + if dates: + plt.xlim(dates) + plt.grid() + + if save_path: + if os.path.isdir(save_path): + plt.savefig(os.path.join(save_path, 'S' + str(l) + str(m) + '_coefficient.png')) + else: + plt.savefig(save_path[:-3] + 's' + save_path[-3:]) + + plt.show() + + def plot_fft(self, l, m, save_path=False): + """ + Plot Cl,m and Sl,m harmonic coefficients fast fourrier transform + Inputs: + l first degree of spherical harmonics + m second degree of spherical harmonics + + Options: + save_path : if not False, give a path to save the figure + """ + #-- compute fft and create x monthly frequency + N = len(self.time) + cf = sc.fft.fft(self.clm[l, m, :]) + sf = sc.fft.fft(self.slm[l, m, :]) + xf = np.linspace(0.0, 12/2, N // 2) + + # -- figure for Cl,m and Sl,m + plt.figure() + plt.title("Fourier transform of the normalized spherical harmonics coefficients $C_{" + str(l) + "," + str( + m) + "}$ et $S_{" + str( + l) + "," + str(m) + "}$") + plt.plot(xf, 2.0 / N * np.abs(cf[0:N // 2]), label="$C_{" + str(l) + "," + str(m) + "}$") + if m: + plt.plot(xf, 2.0 / N * np.abs(sf[0:N // 2]), label="$S_{" + str(l) + "," + str(m) + "}$") + + + plt.xlabel("Frequency ($year^{-1}$)") + plt.ylabel("Power") + plt.grid() + plt.legend() + + if save_path: + if os.path.isdir(save_path): + plt.savefig(os.path.join(save_path, 'CS' + str(l) + str(m) + '_fft.png')) + else: + plt.savefig(save_path) + + plt.show() + + def plot_wavelets(self, l, m, s0=0, pad=1, lag1=0, plot_coi=True, mother='MORLET', param=-1, func_plot=np.abs, save_path=False): + """ + Plot Cl,m and Sl,m wavelet analysis based on (Torrence and Compo, 1998) + + Inputs: + l first degree of spherical harmonics + m second degree of spherical harmonics + + Options: + s0 : minimal period of the wavelets, should be higher than 2*dt + pad : boolean for the zero padding of the series + lag1 : caracteristic of the noise: 0 for a white noise (default), 0.72 for a red noise + plot_coi : boolean to display the cone of interest in the figure + mother : name of the wavelet, can be MORLET, DOG or PAUL + param : param of the wavelet, -1 is the default value for each wavelet + func_plot : funtion for reducing the wave, can be np.abs, np.angle, np.real or np.imag + save_path : if not False, give a path to save the figure + """ + # len of the data + ndata = self.time.shape[0] + # compute the mean time delta of the object + dt = np.mean((self.time[1:] - self.time[:-1])) + + # resolution of the wavelet + dj = 0.005 + + if not s0: + s0 = 4 * dt # min scale of the wavelets + # max resolution of the wavelet, fixed for GRACE + j1 = 4.5 / dj + + siglvl = 0.95 + + # compute wavelets analysis of Cl,m and Sl,m + wavec = wv.wavelet(self.clm[l,m], dt, pad, dj, s0, j1, mother, param)[0] + waves, period, scale, coi = wv.wavelet(self.slm[l,m], dt, pad, dj, s0, j1, mother, param) + + # compute significativity of the wavelets + signifc = wv.wave_signif(self.clm[l,m], dt, scale, lag1=lag1, siglvl=siglvl, mother=mother, param=param) + signifs = wv.wave_signif(self.slm[l,m], dt, scale, lag1=lag1, siglvl=siglvl, mother=mother, param=param) + + # compute wavelet significance test at a level of confidence siglvl% + sig95c = np.abs(wavec**2) / [s * np.ones(ndata) for s in signifc] + sig95s = np.abs(waves**2) / [s * np.ones(ndata) for s in signifs] + + # Wavelet spectrum for fft plot + global_wsc = (np.sum(np.abs(wavec ** 2).conj().transpose(), axis=0) / ndata) + global_wss = (np.sum(np.abs(waves ** 2).conj().transpose(), axis=0) / ndata) + + # compute fft of the signal + fft_sigc = np.fft.fft(self.clm[l,m]) + sxxc = np.abs((fft_sigc * np.conj(fft_sigc)) / ndata)[int(np.ceil(ndata / 2)):] + fft_sigs = np.fft.fft(self.slm[l, m]) + sxxs = np.abs((fft_sigs * np.conj(fft_sigs)) / ndata)[int(np.ceil(ndata / 2)):] + + # compute frequency + f = -np.fft.fftfreq(ndata)[int(np.ceil(ndata / 2)):] + + # prepare yticks + yticks = [] + for i in [0.5, 1, 2, 4, 6, 10, 15]: + if np.min(period) <= i <= np.max(period): + yticks.append(i) + + # create figure Cl,m + fig = plt.figure(constrained_layout=True, figsize=(12, 6), dpi=200) + spec = matplotlib.gridspec.GridSpec(ncols=2, nrows=1, wspace=0.02, width_ratios=[3, 1]) + ax0 = fig.add_subplot(spec[0]) + ax1 = fig.add_subplot(spec[1], sharey=ax0) + axs = [ax0, ax1] + plt.setp(axs[1].get_yticklabels(), visible=False) + + # plot wavelet + im = axs[0].contourf(self.time, period, func_plot(wavec), 100) + axs[0].contour(self.time, period, sig95c, levels=[1], linewidths=2) + + # plot cone of interest of the wavelet + if plot_coi: + axs[0].fill(np.concatenate((self.time[:1] - 0.0001, self.time, self.time[-1:] + 0.0001, + self.time[-1:] + 0.0001, self.time[:1] - 0.0001, self.time[:1] - 0.0001)), + np.concatenate(([np.min(period)], coi, [np.min(period)], period[-1:], period[-1:], + [np.min(period)])), 'r', alpha=0.2, hatch='/') + axs[0].plot(self.time, coi, 'r--', lw=1.4) + + fig.colorbar(im, ax=axs[0], location='left') + axs[0].invert_yaxis() + axs[0].set_yscale('log', base=2) + axs[0].set_ylabel('Period (year)') + axs[0].set_yticks(yticks) + axs[0].set_ylim(np.max(period), np.min(period)) + axs[0].set_xlabel('Time (year)') + axs[0].get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) + axs[0].set_title('Wavelet Power Spectrum') + + # plot fft analysis at the right of the figure + axs[1].plot(sxxc, 1 / f * dt, 'gray', label='Fourier spectrum') + axs[1].plot(global_wsc, period, 'b', label='Wavelet spectrum') + axs[1].plot(np.array(signifc), period, 'g--', label='95% confidence spectrum') + axs[1].set_xlabel('Power') + axs[1].set_title('Global Wavelet Spectrum') + + plt.legend() + + if save_path: + if os.path.isdir(save_path): + plt.savefig(os.path.join(save_path, 'C' + str(l) + str(m) + '_wavelet.png')) + else: + plt.savefig(save_path[:-3] + 'c' + save_path[-3:]) + + # create figure Sl,m + fig = plt.figure(constrained_layout=True, figsize=(12, 6), dpi=200) + spec = matplotlib.gridspec.GridSpec(ncols=2, nrows=1, wspace=0.02, width_ratios=[3, 1]) + ax0 = fig.add_subplot(spec[0]) + ax1 = fig.add_subplot(spec[1], sharey=ax0) + axs = [ax0, ax1] + plt.setp(axs[1].get_yticklabels(), visible=False) + + # plot wavelet + im = axs[0].contourf(self.time, period, np.abs(waves), 100) + axs[0].contour(self.time, period, sig95s, levels=[1], linewidths=2) + + # plot cone of interest of the wavelet + if plot_coi: + axs[0].fill(np.concatenate((self.time[:1] - 0.0001, self.time, self.time[-1:] + 0.0001, + self.time[-1:] + 0.0001, self.time[:1] - 0.0001, self.time[:1] - 0.0001)), + np.concatenate(([s0], coi, [s0], period[-1:], period[-1:], [s0])), 'r', alpha=0.2, hatch='/') + axs[0].plot(self.time, coi, 'r--', lw=1.4) + + fig.colorbar(im, ax=axs[0], location='left') + axs[0].invert_yaxis() + axs[0].set_yscale('log', base=2) + axs[0].set_ylabel('Period (year)') + axs[0].set_ylim(np.max(period), np.min(period)) + axs[0].set_yticks(yticks) + axs[0].set_xlabel('Time (year)') + axs[0].get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) + axs[0].set_title('Wavelet Power Spectrum') + + # plot fft analysis at the right of the figure + axs[1].plot(sxxs, 1 / f * dt, 'gray', label='Fourier spectrum') + axs[1].plot(global_wss, period, 'b', label='Wavelet spectrum') + axs[1].plot(np.array(signifs) * np.var(self.clm[l, m]), period, 'g--', label='95% confidence spectrum') + axs[1].set_xlabel('Power') + axs[1].set_title('Global Wavelet Spectrum') + + plt.legend() + + if save_path: + if os.path.isdir(save_path): + plt.savefig(os.path.join(save_path, 'C' + str(l) + str(m) + '_wavelet.png')) + else: + plt.savefig(save_path[:-3] + 's' + save_path[-3:]) + + plt.show() \ No newline at end of file diff --git a/gravity_toolkit/wavelets.py b/gravity_toolkit/wavelets.py new file mode 100644 index 00000000..8153de78 --- /dev/null +++ b/gravity_toolkit/wavelets.py @@ -0,0 +1,219 @@ +#!/usr/bin/env python +u""" +wavelets.py +Written by Hugo Lecomte (02/2021) + +Function to apply a wavelets analysis, code based on (Torrence and Compo, 1998) +""" +import numpy as np +import scipy.special + +def wave_bases(mother, k, scale, param=-1): + """Computes the wavelet function as a function of Fourier frequency + used for the CWT in Fourier space (Torrence and Compo, 1998) + + Arguments + --------- + mother: str equal to 'MORLET' or 'DOG' to choose the wavelet type + k: vector of the Fourier frequencies + scale: wavelet scales + param: nondimensional parameter for the wavelet function + + Returns + ------- + daughter: the wavelet function + fourier_factor: the ratio of Fourier period to scale + coi: cone-of-influence size at the scale + dofmin: degrees of freedom for each point in the wavelet power (Morlet = 2) + """ + mother = mother.upper() + n = len(k) # length of Fourier frequencies + k = np.array(k) # turn k to array + + if mother == 'MORLET': # choose the wavelet function, in this case Morlet + if param == -1: + param = 6 # For Morlet this is k0 (wavenumber), default is 6 + + expnt = -(scale*k - param)**2/2*(k > 0) # table 1 Torrence and Compo (1998) + norm = np.sqrt(scale*k[1])*(np.pi** -0.25)*np.sqrt(len(k)) + + daughter = [] # define daughter as a list + for ex in expnt: # for each value scale (equal to next pow of 2) + daughter.append(norm*np.exp(ex)) + daughter = np.array(daughter) # transform in array + + daughter = daughter*(k > 0) # Heaviside step function + fourier_factor = (4*np.pi)/(param + np.sqrt(2 + param * param)) # scale --> Fourier period + coi = fourier_factor/np.sqrt(2) # cone-of-influence + dofmin = 2 # degrees of freedom + + elif mother == 'DOG': # DOG Wavelet + if param == -1: + param = 2 # For DOG this is m (wavenumber), default is 2 + m = param + + expnt = -(scale*k)**2/2 + pws = np.array((scale*k)**m) + # gamma(m+0.5) = 1.3293 + norm = np.sqrt(scale*k[1]/1.3293*np.sqrt(n)) + + daughter = [] + for ex in expnt: + daughter.append(-norm* 1j**m * np.exp(ex)) + daughter = np.array(daughter) + daughter = daughter[:]*pws + + fourier_factor = 2*np.pi/np.sqrt(m + .5) + coi = fourier_factor/np.sqrt(2) + dofmin = 1 + + elif mother == 'PAUL': # Paul Wavelet + if param == -1: + param = 4 + m = param + + expnt = -(scale*k)*(k > 0) + norm = np.sqrt(scale*k[1]) *(2**m /np.sqrt(m*(np.math.factorial(2*m - 1))))*np.sqrt(n) + pws = np.array((scale*k)**m) + + daughter = [] + for ex in expnt: + daughter.append(norm*np.exp(ex)) + daughter = np.array(daughter) + daughter = daughter[:]*pws + + daughter = daughter*(k > 0) # Heaviside step function + fourier_factor = 4*np.pi/(2*m + 1) + coi = fourier_factor*np.sqrt(2) + dofmin = 2 + + return daughter, fourier_factor, coi, dofmin + + +def wavelet(Y, dt, pad=1, dj=.25, s0=-1, J1=-1, mother='MORLET', param=-1): + """Computes the wavelet continuous transform of the vector Y, + by definition: + W(a,b) = sum(f(t)*psi[a,b](t) dt) a dilate/contract + psi[a,b](t) = 1/sqrt(a) psi(t-b/a) b displace + The wavelet basis is normalized to have total energy = 1 at all scales + + Arguments + --------- + Y: time series + dt: sampling rate + pad: bool for zero padding or not + dj: spacing between discrete scales + s0: smallest scale of the wavelet + J1: total number of scales + mother: the mother wavelet function + param: the mother wavelet parameter + + Returns + ------- + wave: wavelet transform of Y + period: the vector of "Fourier" periods (in time units) that correspond to the scales + scale: vector of scale indices, given by S0*2(j*DJ), j =0 ...J1 + coi: cone of influence + """ + n1 = len(Y) # time series length + + if s0 == -1: # define s0 as 2 times dt (Shannon criteria) if s0 is not given + s0 = 2 * dt + if J1 == -1: # define J1 if not provide + J1 = int((np.log(n1*dt/s0) / np.log(2))/dj) + + x = Y - np.mean(Y) # remove mean of the time serie + + if pad: # if zero padding, add zeros to x + base2 = int(np.log(n1)/np.log(2) + 0.4999) + x = np.concatenate((x, np.zeros(2**(base2 + 1) - n1))) + + n = len(x) #update length of x + + k = np.arange(0, int(n/2)) + k = k*(2*np.pi) / (n*dt) + k = np.concatenate((k, -k[int((n - 1)/2)::-1])) # be careful for parity + + f = np.fft.fft(x) # fft on the padded time series + + scale = s0 * 2**(np.arange(0, J1 + 1, 1)*dj) + # define wavelet array + wave = np.zeros((int(J1 + 1), n)) + wave = wave + 1j * wave # make it complex + + for a1 in range(0, int(J1 + 1)): + daughter, fourier_factor, coi, dofmin = wave_bases(mother, k, scale[a1], param) + wave[a1, :] = np.fft.ifft(f * daughter) + + period = fourier_factor * scale + + # cone-of-influence, differ for uneven len of timeseries: + if n1%2: # uneven + coi = coi * dt * np.concatenate((np.arange(0, n1/2 - 1), np.arange(0, n1/2)[::-1])) + else: # even + coi = coi * dt * np.concatenate((np.arange(0, n1/2), np.arange(0, n1/2)[::-1])) + + # cut zero padding + wave = wave[:, :n1] + + return wave, period, scale, coi + +def wave_signif(Y, dt, scale, dof=-1, lag1=0, siglvl=0.95, mother='MORLET', param=-1): + """Computes the wavelet significance test at a level of confidence siglvl% + + Arguments + --------- + Y: time series + dt: sampling rate + scale: scales of the wavelet decomposition + dof: degrees of freedom + lag1: assuming lag-1 autocorrelation of the serie (0 for white noise RECOMMENDED, 0.72 for red noise) + siglvl: percentage of the confidence level + mother: the mother wavelet function + param: the mother wavelet parameter + + Returns + ------- + wave: wavelet transform of Y + period: the vector of "Fourier" periods (in time units) that correspond to the scales + scale: vector of scale indices, given by S0*2(j*DJ), j =0 ...J1 + coi: cone of influence + """ + mother = mother.upper() + variance = np.var(Y) + + # define default param and fourier factor for the wavelet + if mother == 'MORLET': + if param == -1: + param = 6 # For Morlet this is k0 (wavenumber), default is 6 + if dof == -1: + dof = 2 + + fourier_factor = float(4 * np.pi) / (param + np.sqrt(2 + param**2)) + + if mother == 'DOG': + if param == -1: + param = 2 # For DOG, default param is 2 + if dof == -1: + dof = 1 + + fourier_factor = float(2 * np.pi / (np.sqrt(param + 0.5))) + + if mother == 'PAUL': + if param == -1: + param = 4 # For PAUL, default param is 4 + if dof == -1: + dof = 2 + + fourier_factor = float(4 * np.pi / (2 * param + 1)) + + # compute period from scale + period = [e * fourier_factor for e in scale] + + # compute theoretical fft associated to the theoretical noise of the data given by lag1 + freq = [dt / p for p in period] + fft_theor = [variance*((1 - lag1**2) / (1 - 2*lag1*np.cos(f * 2 * np.pi) + lag1**2)) for f in freq] + + chisquare = scipy.special.gammaincinv(dof/2.0, siglvl)*2.0/dof + signif = [ft * chisquare for ft in fft_theor] + return signif \ No newline at end of file From 0e75f0a09b52797ceb7217cd674cdb239cb34739 Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 17 Feb 2021 13:55:11 +0100 Subject: [PATCH 46/80] Update gitignore --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index b75bc92a..74cbae5f 100644 --- a/.gitignore +++ b/.gitignore @@ -86,6 +86,9 @@ None*.png ####################### .ipynb_checkpoints Untitled.ipynb +# Personal notebooks # +######################## +/notebooks/ # Large data files # #################### *-complete.dat From 1f713879be57fddabc78f4bc0194a739d8ac0df4 Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 17 Feb 2021 17:11:14 +0100 Subject: [PATCH 47/80] Update time.py to add a day per month function --- gravity_toolkit/time.py | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/gravity_toolkit/time.py b/gravity_toolkit/time.py index 137eddeb..f5267369 100644 --- a/gravity_toolkit/time.py +++ b/gravity_toolkit/time.py @@ -1155,3 +1155,25 @@ def update_leap_seconds(timeout=20, verbose=False, mode=0o775): pass else: return + +def dpm_count(input_year): + """ + Return the number of days per months on the current year + + Arguments + --------- + input_year: year of interest + + Returns + ------- + dpm: list of the day per month + """ + # -- Calculation of total days since start of campaign + if (input_year % 4) == 0: + # -- Leap Year + dpm = [31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + else: + # -- Standard Year + dpm = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] + + return dpm \ No newline at end of file From 956372dc226ddffed41b4c5a42728ecf2572a8d4 Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 17 Feb 2021 19:22:44 +0100 Subject: [PATCH 48/80] Debug grace_input_months.py and read_grid_to_harmonics.py --- gravity_toolkit/read_grid_to_harmonics.py | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/gravity_toolkit/read_grid_to_harmonics.py b/gravity_toolkit/read_grid_to_harmonics.py index 7cb7652a..7ce159ab 100644 --- a/gravity_toolkit/read_grid_to_harmonics.py +++ b/gravity_toolkit/read_grid_to_harmonics.py @@ -89,12 +89,10 @@ def read_grid_to_harmonics(input_file, VARNAME, LMAX, MMAX=None, LONNAME='lon', #-- read file content if input_file[-3:] == '.nc': file_contents = ncdf_read(input_file, DATE=True, VARNAME=VARNAME, LONNAME=LONNAME, - LATNAME=LATNAME, TIMENAME=TIMENAME, ATTRIBUTES=True, - TITLE=True, COMPRESSION=sfx) + LATNAME=LATNAME, TIMENAME=TIMENAME, COMPRESSION=sfx) elif input_file[-4:] == '.hdf' or input_file[-3:] == '.h5' or input_file[-5:] == '.hdf5': file_contents = hdf5_read(input_file, DATE=True, VARNAME=VARNAME, LONNAME=LONNAME, - LATNAME=LATNAME, TIMENAME=TIMENAME, ATTRIBUTES=True, - TITLE=True, COMPRESSION=sfx) + LATNAME=LATNAME, TIMENAME=TIMENAME, COMPRESSION=sfx) #-- load love numbers hl, kl, ll = read_love_numbers(get_data_path(['data', 'love_numbers']), REFERENCE='CF') @@ -108,8 +106,8 @@ def read_grid_to_harmonics(input_file, VARNAME, LMAX, MMAX=None, LONNAME='lon', grace_clm = np.zeros((LMAX + 1, MMAX + 1, n_time)) grace_slm = np.zeros((LMAX + 1, MMAX + 1, n_time)) #-- Time matrix to fill - tdec = np.zeros((n_time)) - month = np.zeros((n_time)) + tdec = np.zeros(n_time) + month = np.zeros(n_time) #-- output dimensions lout = np.arange(LMAX + 1) mout = np.arange(MMAX + 1) @@ -120,8 +118,8 @@ def read_grid_to_harmonics(input_file, VARNAME, LMAX, MMAX=None, LONNAME='lon', file_contents['lon'][:], file_contents['lat'][:], LMAX=LMAX, MMAX=MMAX, UNITS=UNITS, LOVE=(hl, kl, ll)) - grace_clm[:, :, i] = harmo['clm'] - grace_slm[:, :, i] = harmo['slm'] + grace_clm[:, :, i] = harmo.clm + grace_slm[:, :, i] = harmo.slm #-- extract GRACE date information from input file name start_yr = np.float(time[:4]) From af8b749fad6d34628e1b91c22e6d1daaacef59f2 Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 13 Jul 2021 09:38:01 +0200 Subject: [PATCH 49/80] Homogeneous plot and improve read_grid_to_harmonics.py --- gravity_toolkit/harmonics.py | 8 ++++---- gravity_toolkit/read_grid_to_harmonics.py | 10 +++------- 2 files changed, 7 insertions(+), 11 deletions(-) diff --git a/gravity_toolkit/harmonics.py b/gravity_toolkit/harmonics.py index 17f2fe60..a8dc1f69 100644 --- a/gravity_toolkit/harmonics.py +++ b/gravity_toolkit/harmonics.py @@ -2052,9 +2052,9 @@ def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], save_path=False) plt.figure() plt.title("Normalized spherical harmonics coefficient $C_{" + str(l) + "," + str(m) + "}$") if len(ylms): - plt.plot(self.time, self.clm[l, m, :], 'r', label=label[0]) + plt.plot(self.time, self.clm[l, m, :], label=label[0]) else: - plt.plot(self.time, self.clm[l, m, :], 'r', label="$C_{" + str(l) + "," + str(m) + "}$") + plt.plot(self.time, self.clm[l, m, :], label="$C_{" + str(l) + "," + str(m) + "}$") try: for i in range(len(ylms)): @@ -2079,9 +2079,9 @@ def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], save_path=False) plt.figure() plt.title("Normalized spherical harmonics coefficient $S_{" + str(l) + "," + str(m) + "}$") if len(ylms): - plt.plot(self.time, self.slm[l, m, :], 'r', label=label[0]) + plt.plot(self.time, self.slm[l, m, :], label=label[0]) else: - plt.plot(self.time, self.slm[l, m, :], 'r', label="$S_{" + str(l) + "," + str(m) + "}$") + plt.plot(self.time, self.slm[l, m, :], label="$S_{" + str(l) + "," + str(m) + "}$") try: for i in range(len(ylms)): diff --git a/gravity_toolkit/read_grid_to_harmonics.py b/gravity_toolkit/read_grid_to_harmonics.py index 7ce159ab..46fb1840 100644 --- a/gravity_toolkit/read_grid_to_harmonics.py +++ b/gravity_toolkit/read_grid_to_harmonics.py @@ -82,17 +82,13 @@ def read_grid_to_harmonics(input_file, VARNAME, LMAX, MMAX=None, LONNAME='lon', title: string denoting low degree zonals replacement, geocenter usage and corrections directory: directory of exact GRACE/GRACE-FO product """ - - #-- parse filename - pfx,center,time,realm,release,v_id,sfx = parse_file(input_file) - #-- read file content if input_file[-3:] == '.nc': file_contents = ncdf_read(input_file, DATE=True, VARNAME=VARNAME, LONNAME=LONNAME, - LATNAME=LATNAME, TIMENAME=TIMENAME, COMPRESSION=sfx) + LATNAME=LATNAME, TIMENAME=TIMENAME) elif input_file[-4:] == '.hdf' or input_file[-3:] == '.h5' or input_file[-5:] == '.hdf5': file_contents = hdf5_read(input_file, DATE=True, VARNAME=VARNAME, LONNAME=LONNAME, - LATNAME=LATNAME, TIMENAME=TIMENAME, COMPRESSION=sfx) + LATNAME=LATNAME, TIMENAME=TIMENAME) #-- load love numbers hl, kl, ll = read_love_numbers(get_data_path(['data', 'love_numbers']), REFERENCE='CF') @@ -122,7 +118,7 @@ def read_grid_to_harmonics(input_file, VARNAME, LMAX, MMAX=None, LONNAME='lon', grace_slm[:, :, i] = harmo.slm #-- extract GRACE date information from input file name - start_yr = np.float(time[:4]) + start_yr = np.int(file_contents['time'][0]) #-- variables initialization for date conversion current_year = start_yr From f43a776aea1d506c38b8019671bfbbcebeeecff7 Mon Sep 17 00:00:00 2001 From: hulecom Date: Thu, 15 Jul 2021 13:43:57 +0200 Subject: [PATCH 50/80] Some debug coming after the merge --- gravity_toolkit/read_grid_to_harmonics.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/gravity_toolkit/read_grid_to_harmonics.py b/gravity_toolkit/read_grid_to_harmonics.py index 46fb1840..8ff07fb2 100644 --- a/gravity_toolkit/read_grid_to_harmonics.py +++ b/gravity_toolkit/read_grid_to_harmonics.py @@ -9,7 +9,7 @@ Design for JPL MASCON netCDF data available on https://podaac-tools.jpl.nasa.gov/drive/files -In the folder /allData/tellus/retired/L3/mascon/RL06/JPL/v02 +In the folder /allData/tellus/L3/mascon/RL06/JPL/v02 INPUTS: input_file: GRACE/GRACE-FO Level-3 netCDF grid data file @@ -45,7 +45,7 @@ from gravity_toolkit.read_love_numbers import read_love_numbers from gravity_toolkit.gen_stokes import gen_stokes -#-- PURPOSE: read Level-3 GRACE and GRACE-FO netCDF files +#-- PURPOSE: read Level-3 GRACE and GRACE-FO netCDF or hdf5 files def read_grid_to_harmonics(input_file, VARNAME, LMAX, MMAX=None, LONNAME='lon', LATNAME='lat', TIMENAME='time', UNITS=1, POLE_TIDE=False): """ @@ -82,6 +82,9 @@ def read_grid_to_harmonics(input_file, VARNAME, LMAX, MMAX=None, LONNAME='lon', title: string denoting low degree zonals replacement, geocenter usage and corrections directory: directory of exact GRACE/GRACE-FO product """ + # -- parse filename to extract begin date of the file + pfx, center, time, realm, release, v_id, sfx = parse_file(input_file) + #-- read file content if input_file[-3:] == '.nc': file_contents = ncdf_read(input_file, DATE=True, VARNAME=VARNAME, LONNAME=LONNAME, @@ -118,7 +121,7 @@ def read_grid_to_harmonics(input_file, VARNAME, LMAX, MMAX=None, LONNAME='lon', grace_slm[:, :, i] = harmo.slm #-- extract GRACE date information from input file name - start_yr = np.int(file_contents['time'][0]) + start_yr = np.float(time[:4]) #-- variables initialization for date conversion current_year = start_yr From 95508b01b860003a3914ca600bc8807dd7dedb4c Mon Sep 17 00:00:00 2001 From: hulecom Date: Thu, 15 Jul 2021 14:15:40 +0200 Subject: [PATCH 51/80] Uniformization before pull request --- gravity_toolkit/harmonics.py | 2 +- gravity_toolkit/read_SLR_CS2.py | 2 +- gravity_toolkit/time.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/gravity_toolkit/harmonics.py b/gravity_toolkit/harmonics.py index a8dc1f69..4bf4280b 100644 --- a/gravity_toolkit/harmonics.py +++ b/gravity_toolkit/harmonics.py @@ -2295,4 +2295,4 @@ def plot_wavelets(self, l, m, s0=0, pad=1, lag1=0, plot_coi=True, mother='MORLET else: plt.savefig(save_path[:-3] + 's' + save_path[-3:]) - plt.show() \ No newline at end of file + plt.show() diff --git a/gravity_toolkit/read_SLR_CS2.py b/gravity_toolkit/read_SLR_CS2.py index 7579aab2..ff8c8152 100644 --- a/gravity_toolkit/read_SLR_CS2.py +++ b/gravity_toolkit/read_SLR_CS2.py @@ -17,7 +17,7 @@ https://doi.org/10.3390/rs11182116 CALLING SEQUENCE: - SLR_2x = read_SLR_CS2(SLR_file) + SLR_2m = read_SLR_CS2(SLR_file) INPUTS: SLR_file: diff --git a/gravity_toolkit/time.py b/gravity_toolkit/time.py index f5267369..ea0a8d0d 100644 --- a/gravity_toolkit/time.py +++ b/gravity_toolkit/time.py @@ -1176,4 +1176,4 @@ def dpm_count(input_year): # -- Standard Year dpm = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] - return dpm \ No newline at end of file + return dpm From f5dc33cf8a170bc1268fe684b540ae6f65b6e704 Mon Sep 17 00:00:00 2001 From: hulecom Date: Fri, 20 Aug 2021 15:51:34 +0200 Subject: [PATCH 52/80] Units with mm Geoid Height for spatial format --- gravity_toolkit/gen_stokes.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/gravity_toolkit/gen_stokes.py b/gravity_toolkit/gen_stokes.py index c2029652..b750b98c 100755 --- a/gravity_toolkit/gen_stokes.py +++ b/gravity_toolkit/gen_stokes.py @@ -180,6 +180,10 @@ def gen_stokes(data, lon, lat, LMIN=0, LMAX=60, MMAX=None, UNITS=1, # Input in kg/m^2 (mm w.e.) dfactor = factors.spatial(*LOVE).mmwe int_fact[:] = np.sin(th)*dphi*dth + elif (UNITS == 4): + #-- Inputs in mmGH + dfactor = factors.mmGH + int_fact[:] = np.sin(th) * dphi * dth else: raise ValueError(f'Unknown units {UNITS}') From 7dedb53a52a371328bf663351cdf2609a9653c94 Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 15 Sep 2021 10:34:35 +0200 Subject: [PATCH 53/80] Option to include Eath oblateness in grid format as describe in Ditmar 2018 --- gravity_toolkit/units.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/gravity_toolkit/units.py b/gravity_toolkit/units.py index 3fd42bff..9377cabf 100644 --- a/gravity_toolkit/units.py +++ b/gravity_toolkit/units.py @@ -87,6 +87,7 @@ def __init__(self, self.microGal = None self.mbar = None self.Pa = None + self.cmweEl = None self.lmax = lmax # calculate spherical harmonic degree (0 is falsy) self.l = np.arange(self.lmax+1) if (self.lmax is not None) else None @@ -185,6 +186,8 @@ def harmonic(self, hl, kl, ll, **kwargs): self.mbar = self.g_wmo*self.rho_e*self.rad_e*(2.0*self.l+1.0)/fraction/3e3 # Pa, pascals equivalent surface pressure self.Pa = self.g_wmo*self.rho_e*self.rad_e*(2.0*self.l+1.0)/fraction/30.0 + # cmwe, centimeters water equivalent [g/cm^2] considering Earth oblateness + self.cmweEl = self.rho_e*self.rad_e*(2.0*self.l+1.0)/(1.0+kl[self.l])/3.0 *(1 - self.flat) # return the degree dependent unit conversions return self From 9e49144c44f6b5a8e7f945c373cfad970a77c445 Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 19 Apr 2022 14:45:25 +0200 Subject: [PATCH 54/80] Create toolbox.py with usual manipulation function --- gravity_toolkit/toolbox.py | 650 +++++++++++++++++++++++++++++++++++++ 1 file changed, 650 insertions(+) create mode 100644 gravity_toolkit/toolbox.py diff --git a/gravity_toolkit/toolbox.py b/gravity_toolkit/toolbox.py new file mode 100644 index 00000000..24f01f82 --- /dev/null +++ b/gravity_toolkit/toolbox.py @@ -0,0 +1,650 @@ +from gravity_toolkit.gauss_weights import gauss_weights +from gravity_toolkit.gen_stokes import gen_stokes +from gravity_toolkit.harmonics import harmonics +from gravity_toolkit.harmonic_summation import harmonic_summation +from gravity_toolkit.plm_holmes import plm_holmes +from gravity_toolkit.read_love_numbers import read_love_numbers +from gravity_toolkit.spatial import spatial +from gravity_toolkit.units import units +from gravity_toolkit.utilities import get_data_path + +import numpy as np +import scipy.signal as sg +import matplotlib +import matplotlib.pyplot as plt +import matplotlib.colors as colors +import matplotlib.animation as animation +import cartopy.crs as ccrs +from IPython.display import HTML + + +def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, dlat=0.5, bounds=None): + """ + Function to convert a harmonic object to grid format + + Parameters + ---------- + Ylms : harmonics object to convert to grid format + lmax : maximum degree of spherical harmonics used + rad : radius of the gaussian filter. If set to 0, no gaussian filter is apply + destripe : boolean to apply or not the destripe method of harmonics + unit : unit of the grid in ['cmwe', 'geoid', 'cmwe_ne', 'microGal'] + dlon : output longitude spacing + dlat : output latitude spacing + bounds : list with [lon_max, lon_min, lat_max, lat_min] + + Returns + ------- + grid : spatial object with the grid converted from the original harmonics object + """ + # Output spatial data + grid = spatial() + grid.time = np.copy(Ylms.time) + grid.month = np.copy(Ylms.month) + + # Output Degree Interval + if bounds is None: + grid.lon = np.arange(-180 + dlon / 2.0, 180 + dlon / 2.0, dlon) + grid.lat = np.arange(90.0 - dlat / 2.0, -90.0 - dlat / 2.0, -dlat) + else: + grid.lon = np.arange(-bounds[1] + dlon / 2.0, bounds[0] + dlon / 2.0, dlon) + grid.lat = np.arange(bounds[2] - dlat / 2.0, -bounds[3] - dlat / 2.0, -dlat) + + nlon = len(grid.lon) + nlat = len(grid.lat) + + # update spacing and dimensions + grid.update_spacing() + grid.update_extents() + grid.update_dimensions() + + # Computing plms for converting to spatial domain + theta = (90.0 - grid.lat) * np.pi / 180.0 + if lmax is None: + PLM, dPLM = plm_holmes(Ylms.lmax, np.cos(theta)) + else: + PLM, dPLM = plm_holmes(lmax, np.cos(theta)) + + # read load love numbers file + love_numbers_file = get_data_path(['data', 'love_numbers']) + # LMAX of load love numbers from Han and Wahr (1995) is 696. + # from Wahr (2007) linearly interpolating kl worksand ll Love Numbers + hl, kl, ll = read_love_numbers(love_numbers_file, REFERENCE='CF') + + if unit == 'cmwe': + dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).cmwe + elif unit == 'geoid': + dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).mmGH + elif unit == 'cmwe_ne': + dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).cmwe_ne + elif unit == 'microGal': + dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).microGal + else: + raise ValueError("Unit not accepted, should be either 'cmwe' or 'cmwe_ne' or 'geoid' or 'microGal'") + + # converting harmonics to truncated, smoothed coefficients in units + # combining harmonics to calculate output spatial fields + # output spatial grid + if not (type(Ylms.month) in [list, np.array]) and len(Ylms.month) == 1: + grid.data = np.zeros((nlat, nlon)) + + if destripe: + tmp = Ylms.destripe() + else: + tmp = Ylms + + if rad != 0: + wt = 2.0 * np.pi * gauss_weights(rad, lmax) + tmp.convolve(dfactor * wt) + else: + tmp.convolve(dfactor) + # convert spherical harmonics to output spatial grid + if lmax is None: + grid.data[:, :] = harmonic_summation(tmp.clm, tmp.slm, + grid.lon, grid.lat, LMAX=Ylms.lmax, MMAX=Ylms.mmax, PLM=PLM).T + else: + grid.data[:, :] = harmonic_summation(tmp.clm, tmp.slm, + grid.lon, grid.lat, LMAX=lmax, MMAX=lmax, PLM=PLM).T + + else: + grid.data = np.zeros((nlat, nlon, len(Ylms.month))) + for i, grace_month in enumerate(Ylms.month): + # GRACE/GRACE-FO harmonics for time t + # convert to output units + if destripe: + tmp = Ylms.index(i).destripe() + else: + tmp = Ylms.index(i) + + if rad != 0: + wt = 2.0 * np.pi * gauss_weights(rad, lmax) + tmp.convolve(dfactor * wt) + else: + tmp.convolve(dfactor * np.ones((Ylms.lmax + 1))) + # convert spherical harmonics to output spatial grid + if lmax is None: + grid.data[:, :, i] = harmonic_summation(tmp.clm, tmp.slm, + grid.lon, grid.lat, LMAX=Ylms.lmax, MMAX=Ylms.mmax, PLM=PLM).T + else: + grid.data[:, :, i] = harmonic_summation(tmp.clm, tmp.slm, + grid.lon, grid.lat, LMAX=lmax, MMAX=lmax, PLM=PLM).T + + grid.mask = np.zeros(grid.data.shape) + return grid + + +def grid_to_hs(grid, lmax, mmax=None, unit='cmwe'): + """ + Function to convert spatial object (grid) to harmonics object (spherical harmonics) + + Parameters + ---------- + grid : spatial object to convert to harmonics + lmax : maximal degree of the harmonics object to create + mmax : maximal order of the harmonics object to create + unit : unit of the grid in ['cmwe', 'geoid', 'cmwe_ne', 'microGal'] + + Returns + ------- + harmonics : harmonics object + """ + # -- load love numbers + hl, kl, ll = read_love_numbers(get_data_path(['data', 'love_numbers']), REFERENCE='CF') + + # -- set maximum spherical harmonic order + mmax = np.copy(lmax) if (mmax is None) else mmax + + # -- number of dates in data + if type(grid.time) in [list, np.array] or len(grid.time) != 1: + n_time = len(grid.time) + else: + n_time = 1 + # -- Spherical harmonic coefficient matrices to be filled from data file + grace_clm = np.zeros((lmax + 1, mmax + 1, n_time)) + grace_slm = np.zeros((lmax + 1, mmax + 1, n_time)) + # -- output dimensions + lout = np.arange(lmax + 1) + mout = np.arange(mmax + 1) + + # -- Test to attribute UNITS number + if unit == 'cmwe': + UNITS = 1 + elif unit == 'geoid': + UNITS = 4 + elif unit == 'cmwe_ne': + UNITS = 6 + elif unit == 'microGal': + UNITS = 5 + else: + raise ValueError("Unit not accepted, should be either 'cmwe' or 'cmwe_ne' or 'geoid' or 'microGal'") + + # -- for each date, conversion to spherical harmonics + if n_time != 1: + for i in range(n_time): + harmo = gen_stokes(grid.data[:, :, i], + grid.lon[:], grid.lat[:], + LMAX=lmax, MMAX=mmax, UNITS=UNITS, LOVE=(hl, kl, ll)) + + grace_clm[:, :, i] = harmo.clm + grace_slm[:, :, i] = harmo.slm + + else: + print('mono grid_to_hs') + harmo = gen_stokes(grid.data[:, :], + grid.lon[:], grid.lat[:], + LMAX=lmax, MMAX=mmax, UNITS=UNITS, LOVE=(hl, kl, ll)) + + grace_clm[:, :, 0] = harmo.clm + grace_slm[:, :, 0] = harmo.slm + + # -- return the GRACE data, GRACE date (mid-month in decimal), and the + # -- start and end days as Julian dates + result_dict = {'clm': grace_clm, 'slm': grace_slm, 'time': grid.time, 'month': grid.month, + 'l': lout, 'm': mout, 'title': '', 'directory': ''} + + return harmonics().from_dict(result_dict) + + +def diff_grid(grid1, grid2): + """ + Create a grid resulting from the difference between the two given grids + + Parameters + ---------- + grid1 : spatial object + grid2 : spatial object to substract to the first + + Returns + ------- + grid : spatial object with the difference between both grid + """ + exclude1 = set(grid1.month) - set(grid2.month) + + # Output spatial data + grid = spatial() + grid.month = np.array(list(sorted(set(grid1.month) - exclude1))) + grid.time = np.array([grid1.time[i] for i in range(len(grid1.time)) if not (grid1.month[i] in exclude1)]) + + # Output Degree Interval + grid.lon = grid1.lon + grid.lat = grid1.lat + + # update spacing and dimensions + grid.update_spacing() + grid.update_extents() + grid.update_dimensions() + + grid.data = np.zeros((grid.lat.shape[0], grid.lon.shape[0], len(grid.month))) + cmp = 0 + for i in range(len(grid1.month)): + for j in range(len(grid2.month)): + if grid1.month[i] == grid2.month[j]: + grid.data[:, :, cmp] = grid1.data[:, :, i] - grid2.data[:, :, j] + cmp += 1 + + return grid + + +def filt_Ylms(ylms, filt='low', filt_param=None): + """ + Apply a temporal filter on harmonics object + + Parameters + ---------- + ylms : harmonics object to filter + filt : choice of the filter in ['low', 'band', 'fft'] + filt_param : cut frequency of the filter. For band filter, a list with (f_max, f_min) + + Returns + ------- + filtered_ylms : temporally filtered harmonics object + + """ + filtered_ylms = ylms.copy() + + # len of the data + ndata = filtered_ylms.time.shape[0] + # compute the mean time delta of the object + dt = float(np.mean((filtered_ylms.time[1:] - filtered_ylms.time[:-1]))) + + if filt_param is not None and type(filt_param) != list: + filt_param = [filt_param] + + if filt == 'low': + if filt_param is None: + b, a = sg.butter(10, 0.5, analog=False, fs=1 / dt) + else: + b, a = sg.butter(10, filt_param[0], analog=False, fs=1 / dt) + + for i in range(filtered_ylms.clm.shape[0]): + for j in range(filtered_ylms.clm.shape[1]): + filtered_ylms.clm[i, j] = sg.filtfilt(b, a, filtered_ylms.clm[i, j]) + filtered_ylms.slm[i, j] = sg.filtfilt(b, a, filtered_ylms.slm[i, j]) + + elif filt == 'band': + if filt_param is None: + b, a = sg.butter(6, 0.3, analog=False, fs=1 / dt) + b2, a2 = sg.butter(6, 0.04, btype='highpass', analog=False, fs=1 / dt) + else: + b, a = sg.butter(6, filt_param[0], analog=False, fs=1 / dt) + b2, a2 = sg.butter(6, filt_param[1], btype='highpass', analog=False, fs=1 / dt) + + for i in range(filtered_ylms.clm.shape[0]): + for j in range(filtered_ylms.clm.shape[1]): + filtered_ylms.clm[i, j] = sg.filtfilt(b, a, filtered_ylms.clm[i, j]) + filtered_ylms.clm[i, j] = sg.filtfilt(b2, a2, filtered_ylms.clm[i, j]) + filtered_ylms.slm[i, j] = sg.filtfilt(b, a, filtered_ylms.slm[i, j]) + filtered_ylms.slm[i, j] = sg.filtfilt(b2, a2, filtered_ylms.slm[i, j]) + + elif filt == 'fft': + # zero pad + n2 = 0 + while ndata > 2 ** n2: + n2 += 1 + n2 += 1 + + fc = np.fft.fft(filtered_ylms.clm, n=2 ** n2, axis=2) + fs = np.fft.fft(filtered_ylms.slm, n=2 ** n2, axis=2) + freq = np.fft.fftfreq(2 ** n2, d=dt) + if filt_param is None: + to_zero = np.logical_or(freq > 0.5, freq < -0.5) + else: + to_zero = np.logical_or(freq > filt_param[0], freq < filt_param[0]) + fc[:, :, to_zero] = 0 + fs[:, :, to_zero] = 0 + filtered_ylms.clm = np.real(np.fft.ifft(fc, axis=2))[:, :, :ndata] + filtered_ylms.slm = np.real(np.fft.ifft(fs, axis=2))[:, :, :ndata] + + return filtered_ylms + + +def filt_grid(grid, f_cut=0.5): + """ + Temporally filter a grid with a truncation in fft at 2 years + + Parameters + ---------- + grid : spatial object to filter + f_cut : cutting frequency + + Returns + ------- + filtered_grid : spatial object filtered + + """ + filtered_grid = grid.copy() + time = grid.time + ndata = grid.time.shape[0] + + # zero pad + n2 = 0 + while ndata > 2 ** n2: + n2 += 1 + n2 += 1 + + # compute the mean time delta of the object + dt = float(np.mean((time[1:] - time[:-1]))) + + f = np.fft.fft(grid.data, n=2 ** n2, axis=2) + freq = np.fft.fftfreq(2 ** n2, d=dt) + + to_zero = np.logical_or(freq > f_cut, freq < -f_cut) + f[:, :, to_zero] = 0 + filtered_grid.data = np.real(np.fft.ifft(f, axis=2))[:, :, :ndata] + + return filtered_grid + +def save_gif(grid, path, unit='cmwe', bound=None, mask=None, color='viridis'): + """ + Create a gif of the spatial object + + Parameters + ---------- + grid : spatial object to convert to gif + path : path of the future gif (mandatory to end in .gif) + unit : unit of the grid in ['cmwe', 'mmwe', 'geoid', 'cmwe_ne', 'microGal', 'secacc'] + bound : list with minimal value and maximal value of the colorbar. Default value is None + mask : np.array corresponding to the mask + color : matplotlib cmap color of the gif (Recommended: viridis, plasma, RdBu_r) + """ + matplotlib.rcParams['animation.embed_limit'] = 2**128 + + if mask is None: + data_to_set = grid.data + else: + data_to_set = grid.data*mask + + fig, ax1 = plt.subplots(num=1, nrows=1, ncols=1, figsize=(10.375,6.625), + subplot_kw=dict(projection=ccrs.PlateCarree())) + + # levels and normalization for plot range + print(np.min(data_to_set), np.max(data_to_set)) + if bound is None: + vmin, vmax = int(np.min(data_to_set)), int(np.ceil(np.max(data_to_set))) + else: + vmin, vmax = bound + + if vmax - vmin >= 3: + levels = np.arange(vmin, vmax, max(1, int((vmax - vmin)/10))) + elif vmax - vmin >= 0.3: + levels = np.arange(vmin, vmax, max(0.1, float('%.1f'%((vmax - vmin)/10)))) + elif vmax - vmin >= 0.03: + levels = np.arange(vmin, vmax, max(0.1, float('%.2f'%((vmax - vmin)/10)))) + else: + raise ValueError("The range of data to plot is too small") + + norm = colors.Normalize(vmin=vmin,vmax=vmax) + cmap = plt.cm.get_cmap(color) + im = ax1.imshow(np.zeros((np.int(180.0 + 1.0),np.int(360.0 + 1.0))), interpolation='nearest', + norm=norm, cmap=cmap, transform=ccrs.PlateCarree(), + extent=grid.extent, origin='upper', animated=True) + ax1.coastlines('50m') + + # add date label + time_text = ax1.text(0.025, 0.025, '', transform=fig.transFigure, + color='k', size=24, ha='left', va='baseline') + + # Add horizontal colorbar and adjust size + # extend = add extension triangles to upper and lower bounds + # options: neither, both, min, max + # pad = distance from main plot axis + # shrink = percent size of colorbar + # aspect = lengthXwidth aspect of colorbar + cbar = plt.colorbar(im, ax=ax1, extend='both', extendfrac=0.0375, + orientation='horizontal', pad=0.025, shrink=0.85, + aspect=22, drawedges=False) + # rasterized colorbar to remove lines + cbar.solids.set_rasterized(True) + # Add label to the colorbar + if unit == "cmwe": + cbar.ax.set_xlabel('Equivalent Water Thickness', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('cm', fontsize=24, rotation=0) + elif unit == "cmwe_ne": + cbar.ax.set_xlabel('Non elastic Equivalent Water Thickness', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('cm', fontsize=24, rotation=0) + elif unit == "mmwe": + cbar.ax.set_xlabel('Equivalent Water Thickness', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('mm', fontsize=24, rotation=0) + elif unit == "geoid": + cbar.ax.set_xlabel('Geoid Height', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('mm', fontsize=24, rotation=0) + elif unit == "microGal": + cbar.ax.set_xlabel('Acceleration', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('$\mu Gal$', fontsize=24, rotation=0) + elif unit == "secacc": + cbar.ax.set_xlabel('Secular Acceleration', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('$nT.y^{-2}$', fontsize=24, rotation=0) + + cbar.ax.yaxis.set_label_coords(1.045, 0.1) + # Set the tick levels for the colorbar + cbar.set_ticks(levels) + if vmax - vmin >= 3: + cbar.set_ticklabels(['{0:d}'.format(ct) for ct in levels]) + elif vmax - vmin >= 0.3: + cbar.set_ticklabels(['{.1f}'.format(ct) for ct in levels]) + elif vmax - vmin >= 0.03: + cbar.set_ticklabels(['{.2f}'.format(ct) for ct in levels]) + # ticks lines all the way across + cbar.ax.tick_params(which='both', width=1, length=26, labelsize=24, + direction='in') + + # stronger linewidth on frame + ax1.spines['geo'].set_linewidth(2.0) + ax1.spines['geo'].set_capstyle('projecting') + # adjust subplot within figure + fig.subplots_adjust(left=0.02,right=0.98,bottom=0.05,top=0.98) + + # animate frames + def animate_frames(i): + # set image + im.set_data(data_to_set[:,:,i]) + # add date label + time_text.set_text('{:.2f}'.format(grid.time[i])) + + # set animation + anim = animation.FuncAnimation(fig, animate_frames, frames=len(grid.month)) + HTML(anim.to_jshtml()) + + anim.save(path, writer='imagemagick', fps=10) + + +def plot_rms_map(grid, path=False, proj=ccrs.PlateCarree(), unit='cmwe', bound=None, mask=None, color='viridis'): + """ + Create a rms map of the spatial object + + Parameters + ---------- + grid : spatial object to convert into a rms map + path : path to save the figure if needed + proj : projection of the map (Recommended: ccrs.PlateCarree(), ccrs.Mollweide()) + unit : unit of the grid in ['cmwe', 'mmwe', 'geoid', 'cmwe_ne', 'microGal', 'secacc'] + bound : list with minimal value and maximal value of the colorbar. Default value is None + mask : np.array corresponding to the mask + color : matplotlib cmap color of the gif (Recommended: viridis, plasma, RdBu_r, OrRd, Blues) + """ + data_to_set = np.sqrt(np.sum(grid.data ** 2, axis=2) / grid.time.shape[0]) + + if mask is not None: + data_to_set *= mask + + plt.figure() + matplotlib.rcParams['animation.embed_limit'] = 2 ** 128 + + fig, ax1 = plt.subplots(num=1, nrows=1, ncols=1, figsize=(10.375, 6.625), + subplot_kw=dict(projection=proj)) + + if bound is None: + vmin, vmax = int(np.min(data_to_set)), int(np.ceil(np.max(data_to_set))) + else: + vmin, vmax = bound + + if vmax - vmin >= 3: + levels = np.arange(vmin, vmax, max(1, int((vmax - vmin) / 10))) + elif vmax - vmin >= 0.3: + levels = np.arange(vmin, vmax, max(0.1, float('%.1f' % ((vmax - vmin) / 10)))) + elif vmax - vmin >= 0.03: + levels = np.arange(vmin, vmax, max(0.1, float('%.2f' % ((vmax - vmin) / 10)))) + else: + raise ValueError("The range of data to plot is too small") + + norm = colors.Normalize(vmin=vmin, vmax=vmax) + cmap = plt.cm.get_cmap(color) + im = ax1.imshow(data_to_set, interpolation='nearest', + norm=norm, cmap=cmap, transform=ccrs.PlateCarree(), + extent=grid.extent, origin='upper') + ax1.coastlines('50m') + + # Add horizontal colorbar and adjust size + # extend = add extension triangles to upper and lower bounds + # options: neither, both, min, max + # pad = distance from main plot axis + # shrink = percent size of colorbar + # aspect = lengthXwidth aspect of colorbar + cbar = plt.colorbar(im, ax=ax1, extend='both', extendfrac=0.0375, + orientation='horizontal', pad=0.025, shrink=0.85, + aspect=22, drawedges=False) + # rasterized colorbar to remove lines + cbar.solids.set_rasterized(True) + # Add label to the colorbar + if unit == "cmwe": + cbar.ax.set_xlabel('Equivalent Water Thickness', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('cm', fontsize=24, rotation=0) + elif unit == "cmwe_ne": + cbar.ax.set_xlabel('Non elastic Equivalent Water Thickness', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('cm', fontsize=24, rotation=0) + elif unit == "mmwe": + cbar.ax.set_xlabel('Equivalent Water Thickness', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('mm', fontsize=24, rotation=0) + elif unit == "geoid": + cbar.ax.set_xlabel('Geoid Height', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('mm', fontsize=24, rotation=0) + elif unit == "microGal": + cbar.ax.set_xlabel('Acceleration', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('$\mu Gal$', fontsize=24, rotation=0) + elif unit == "secacc": + cbar.ax.set_xlabel('Secular Acceleration', labelpad=10, fontsize=24) + cbar.ax.set_ylabel('$nT.y^{-2}$', fontsize=24, rotation=0) + + cbar.ax.yaxis.set_label_coords(1.045, 0.1) + # Set the tick levels for the colorbar + cbar.set_ticks(levels) + if vmax - vmin >= 3: + cbar.set_ticklabels(['{0:d}'.format(ct) for ct in levels]) + elif vmax - vmin >= 0.3: + cbar.set_ticklabels(['{.1f}'.format(ct) for ct in levels]) + elif vmax - vmin >= 0.03: + cbar.set_ticklabels(['{.2f}'.format(ct) for ct in levels]) + # ticks lines all the way across + cbar.ax.tick_params(which='both', width=1, length=26, labelsize=24, + direction='in') + + # stronger linewidth on frame + ax1.spines['geo'].set_linewidth(2.0) + ax1.spines['geo'].set_capstyle('projecting') + # adjust subplot within figure + fig.subplots_adjust(left=0.02, right=0.98, bottom=0.05, top=0.98) + + if path: + plt.savefig(path, bbox_inches='tight') + else: + plt.show() + plt.close() + + +def calc_rms_grid(grid, mask=None): + """ + Compute Root Mean Square (RMS) value of a spatial object + + Parameters + ---------- + grid : spatial object + mask : mask to applied before rms computation + + Returns + ------- + rms : rms of the grid + + """ + if mask in None: + rms = np.sqrt(np.sum( + [np.sum(np.cos(lat * np.pi / 180) ** 2 * line ** 2) for lat, line in zip(grid.lat, grid.data)]) / np.sum( + [np.sum(np.cos(lat * np.pi / 180) ** 2 * line.size) for lat, line in zip(grid.lat, grid.data)])) + + else: + rms = np.sqrt(np.sum( + [np.sum(np.cos(lat * np.pi / 180) ** 2 * (line * np.swapaxes(np.tile(line_mask, (line.shape[1], 1)), 0, 1)) ** 2) + for lat, line, line_mask in zip(grid.lat, grid.data, mask)]) / np.sum( + [np.sum(np.cos(lat * np.pi / 180) ** 2 * np.tile(line_mask, (line.shape[1], 1))) + for lat, line, line_mask in zip(grid.lat, grid.data, mask)])) + # attention au cut dans les deux listes + return rms + + +def plot_rms_grid(grid, path=False, mask=None, unit='cmwe'): + """ + Create a figure with rms of the grid spatial object in function of time + + Parameters + ---------- + grid : spatial object + path : path to save the figure if needed + mask : mask to apply on data if needed + unit : unit of the grid + """ + l_rms = [] + for i in range(len(grid.time)): + if mask is None: + rms = np.sqrt(np.sum([np.sum(np.cos(lat * np.pi / 180) ** 2 * line ** 2) for lat, line in + zip(grid.lat, grid.data[:, :, i])]) / np.sum( + [np.sum(np.cos(lat * np.pi / 180) ** 2 * line.size) for lat, line in + zip(grid.lat, grid.data[:, :, i])])) + else: + rms = np.sqrt(np.sum([np.sum(np.cos(lat * np.pi / 180) ** 2 * (line * line_mask) ** 2) + for lat, line, line_mask in zip(grid.lat, grid.data[:, :, i], mask)]) + / np.sum([np.sum(np.cos(lat * np.pi / 180) ** 2 * line_mask) + for lat, line_mask in zip(grid.lat, mask)])) + + l_rms.append(rms) + + plt.figure() + plt.plot(grid.time, l_rms) + plt.xlabel('Time (y)') + if unit == "cmwe": + plt.ylabel('cm EWH') + elif unit == "cmwe_ne": + plt.ylabel('Non elastic cm EWH') + elif unit == "mmwe": + plt.ylabel('mm EWH') + elif unit == "geoid": + plt.ylabel('mm Geoid Height') + elif unit == "microGal": + plt.ylabel('$\mu Gal$') + elif unit == "secacc": + plt.ylabel('$nT.y^{-2}$') + plt.ylabel('Power (cm EWH)') + + if path: + plt.savefig(path, bbox_inches='tight') + else: + plt.show() + plt.close() \ No newline at end of file From 840cb78ba205bda022f3775e25b7e2d7239fd05e Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 19 Apr 2022 14:46:21 +0200 Subject: [PATCH 55/80] Requirements for toolbox.py and precise versions --- requirements.txt | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/requirements.txt b/requirements.txt index 86591e4e..0e603ff6 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,3 +6,9 @@ numpy python-dateutil pyyaml scipy + +datetime~=4.3 +cartopy~=0.18.0 +ipython~=7.19.0 +yaml~=0.2.5 +setuptools~=50.3.0 \ No newline at end of file From 184c6630b25ba67f01f4865ec28b9b09985a3703 Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 19 Apr 2022 14:48:27 +0200 Subject: [PATCH 56/80] Debug and new features improvements --- gravity_toolkit/harmonics.py | 135 +++++++++++++++++++++++++++-------- 1 file changed, 104 insertions(+), 31 deletions(-) diff --git a/gravity_toolkit/harmonics.py b/gravity_toolkit/harmonics.py index 4bf4280b..ccf79fba 100644 --- a/gravity_toolkit/harmonics.py +++ b/gravity_toolkit/harmonics.py @@ -727,19 +727,19 @@ def from_file(self, filename, format=None, date=True, **kwargs): # set default verbosity kwargs.setdefault('verbose',False) # read from file - if (format == 'ascii'): + if format == 'ascii': # ascii (.txt) return harmonics().from_ascii(filename, date=date, **kwargs) - elif (format == 'netCDF4'): + elif format == 'netCDF4': # netcdf (.nc) return harmonics().from_netCDF4(filename, date=date, **kwargs) - elif (format == 'HDF5'): + elif format == 'HDF5': # HDF5 (.H5) return harmonics().from_HDF5(filename, date=date, **kwargs) - elif (format == 'gfc'): + elif format == 'gfc': # ICGEM gravity model (.gfc) return harmonics().from_gfc(filename, **kwargs) - elif (format == 'SHM'): + elif format == 'SHM': # spherical harmonic model return harmonics().from_SHM(filename, self.lmax, **kwargs) @@ -1109,13 +1109,13 @@ def to_index(self, filename, file_list, format=None, date=True, **kwargs): # index harmonics object at i h = self.index(i, date=date) # write to file - if (format == 'ascii'): + if format == 'ascii': # ascii (.txt) h.to_ascii(f, date=date, **kwargs) - elif (format == 'netCDF4'): + elif format == 'netCDF4': # netcdf (.nc) h.to_netCDF4(f, date=date, **kwargs) - elif (format == 'HDF5'): + elif format == 'HDF5': # HDF5 (.H5) h.to_HDF5(f, date=date, **kwargs) # close the index file @@ -1145,13 +1145,13 @@ def to_file(self, filename, format=None, date=True, **kwargs): # set default verbosity kwargs.setdefault('verbose',False) # write to file - if (format == 'ascii'): + if format == 'ascii': # ascii (.txt) self.to_ascii(filename, date=date, **kwargs) - elif (format == 'netCDF4'): + elif format == 'netCDF4': # netcdf (.nc) self.to_netCDF4(filename, date=date, **kwargs) - elif (format == 'HDF5'): + elif format == 'HDF5': # HDF5 (.H5) self.to_HDF5(filename, date=date, **kwargs) @@ -1190,14 +1190,14 @@ def to_masked_array(self): for m in range(-self.mmax,self.mmax+1): mm = np.abs(m) for l in range(mm,self.lmax+1): - if (m < 0): + if m < 0: Ylms.data[l,self.lmax+m,:] = self.slm[l,mm,:] Ylms.mask[l,self.lmax+m,:] = False else: Ylms.data[l,self.lmax+m,:] = self.clm[l,mm,:] Ylms.mask[l,self.lmax+m,:] = False # reshape to previous - if (self.ndim != ndim_prev): + if self.ndim != ndim_prev: self.squeeze() # return the triangular matrix return Ylms @@ -1233,8 +1233,24 @@ def add(self, temp): self.clm[:l1,:m1,i] += temp.clm[:l1,:m1] self.slm[:l1,:m1,i] += temp.slm[:l1,:m1] else: - self.clm[:l1,:m1,:] += temp.clm[:l1,:m1,:] - self.slm[:l1,:m1,:] += temp.slm[:l1,:m1,:] + old_month = self.month + exclude1 = set(self.month) - set(temp.month) + + self.month = np.array(list(sorted(set(self.month) - exclude1))) + self.time = np.array([self.time[i] for i in range(len(self.time)) if not (old_month[i] in exclude1)]) + + for i in range(len(old_month)): + for j in range(len(temp.month)): + if old_month[i] == temp.month[j]: + self.clm[:l1,:m1,i] += temp.clm[:l1,:m1,j] + self.slm[:l1,:m1,i] += temp.slm[:l1,:m1,j] + + to_keep = [] + for i in range(len(old_month)): + if not(old_month[i] in exclude1): + to_keep.append(i) + self.clm = self.clm[:,:,to_keep] + self.slm = self.slm[:, :, to_keep] return self def subtract(self, temp): @@ -1259,8 +1275,24 @@ def subtract(self, temp): self.clm[:l1,:m1,i] -= temp.clm[:l1,:m1] self.slm[:l1,:m1,i] -= temp.slm[:l1,:m1] else: - self.clm[:l1,:m1,:] -= temp.clm[:l1,:m1,:] - self.slm[:l1,:m1,:] -= temp.slm[:l1,:m1,:] + old_month = self.month + exclude1 = set(self.month) - set(temp.month) + + self.month = np.array(list(sorted(set(self.month) - exclude1))) + self.time = np.array([self.time[i] for i in range(len(self.time)) if not (old_month[i] in exclude1)]) + + for i in range(len(old_month)): + for j in range(len(temp.month)): + if old_month[i] == temp.month[j]: + self.clm[:l1,:m1,i] -= temp.clm[:l1,:m1,j] + self.slm[:l1,:m1,i] -= temp.slm[:l1,:m1,j] + + to_keep = [] + for i in range(len(old_month)): + if not(old_month[i] in exclude1): + to_keep.append(i) + self.clm = self.clm[:,:,to_keep] + self.slm = self.slm[:, :, to_keep] return self def multiply(self, temp): @@ -1285,8 +1317,24 @@ def multiply(self, temp): self.clm[:l1,:m1,i] *= temp.clm[:l1,:m1] self.slm[:l1,:m1,i] *= temp.slm[:l1,:m1] else: - self.clm[:l1,:m1,:] *= temp.clm[:l1,:m1,:] - self.slm[:l1,:m1,:] *= temp.slm[:l1,:m1,:] + old_month = self.month + exclude1 = set(self.month) - set(temp.month) + + self.month = np.array(list(sorted(set(self.month) - exclude1))) + self.time = np.array([self.time[i] for i in range(len(self.time)) if not (old_month[i] in exclude1)]) + + for i in range(len(old_month)): + for j in range(len(temp.month)): + if old_month[i] == temp.month[j]: + self.clm[:l1, :m1, i] *= temp.clm[:l1, :m1, j] + self.slm[:l1, :m1, i] *= temp.slm[:l1, :m1, j] + + to_keep = [] + for i in range(len(old_month)): + if not (old_month[i] in exclude1): + to_keep.append(i) + self.clm = self.clm[:, :, to_keep] + self.slm = self.slm[:, :, to_keep] return self def divide(self, temp): @@ -1316,8 +1364,24 @@ def divide(self, temp): self.clm[lc,mc,i] /= temp.clm[lc,mc] self.slm[ls,ms,i] /= temp.slm[ls,ms] else: - self.clm[lc,mc,:] /= temp.clm[lc,mc,:] - self.slm[ls,ms,:] /= temp.slm[ls,ms,:] + old_month = self.month + exclude1 = set(self.month) - set(temp.month) + + self.month = np.array(list(sorted(set(self.month) - exclude1))) + self.time = np.array([self.time[i] for i in range(len(self.time)) if not (old_month[i] in exclude1)]) + + for i in range(len(old_month)): + for j in range(len(temp.month)): + if old_month[i] == temp.month[j]: + self.clm[:l1, :m1, i] /= temp.clm[:l1, :m1, j] + self.slm[:l1, :m1, i] /= temp.slm[:l1, :m1, j] + + to_keep = [] + for i in range(len(old_month)): + if not (old_month[i] in exclude1): + to_keep.append(i) + self.clm = self.clm[:, :, to_keep] + self.slm = self.slm[:, :, to_keep] return self def copy(self): @@ -1904,6 +1968,7 @@ def __iter__(self): self.__index__ = 0 return self + def __next__(self): """Get the next month of data """ @@ -1925,10 +1990,12 @@ def __next__(self): self.__index__ += 1 return temp - def gap_fill(self, apply=False): + def gap_fill(self, apply=False, interpolate=1): """ Fill the missing months with a linear interpolation, the interpolation is made on month number, it's imprecise - Options: apply to the object if True, else return a new instance + Options: + apply: apply to the object if True, else return a new instance + interpolate: 0 = fill gap with 0, 1 = linear interpolation """ temp = self.copy() missing_month = self.month[-1] - self.month[0] - len(self.month) + 1 @@ -1953,12 +2020,18 @@ def gap_fill(self, apply=False): # y(t) = (y2 - y1)/(x2 - x1)*t + y1 temp.time[index] = (self.time[index - cmp + 1] - self.time[index - cmp]) / ( self.month[index - cmp + 1] - self.month[index - cmp]) * cmp_miss_mon + self.time[index - cmp] - temp.clm[:, :, index] = (self.clm[:, :, index - cmp + 1] - self.clm[:, :, index - cmp]) / \ - (self.month[index - cmp + 1] - self.month[index - cmp]) * cmp_miss_mon \ - + self.clm[:, :, index - cmp] - temp.slm[:, :, index] = (self.slm[:, :, index - cmp + 1] - self.slm[:, :, index - cmp]) / \ - (self.month[index - cmp + 1] - self.month[index - cmp]) * cmp_miss_mon \ - + self.slm[:, :, index - cmp] + + if interpolate == 1: + temp.clm[:, :, index] = (self.clm[:, :, index - cmp + 1] - self.clm[:, :, index - cmp]) / \ + (self.month[index - cmp + 1] - self.month[index - cmp]) * cmp_miss_mon \ + + self.clm[:, :, index - cmp] + temp.slm[:, :, index] = (self.slm[:, :, index - cmp + 1] - self.slm[:, :, index - cmp]) / \ + (self.month[index - cmp + 1] - self.month[index - cmp]) * cmp_miss_mon \ + + self.slm[:, :, index - cmp] + + elif interpolate == 0: + temp.clm[:, :, index] = 0 + temp.clm[:, :, index] = 0 index += 1 @@ -2077,7 +2150,7 @@ def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], save_path=False) if m: #-- figure for Sl,m plt.figure() - plt.title("Normalized spherical harmonics coefficient $S_{" + str(l) + "," + str(m) + "}$") + plt.title("Normalized spherical harmonic coefficient $S_{" + str(l) + "," + str(m) + "}$") if len(ylms): plt.plot(self.time, self.slm[l, m, :], label=label[0]) else: @@ -2121,7 +2194,7 @@ def plot_fft(self, l, m, save_path=False): # -- figure for Cl,m and Sl,m plt.figure() - plt.title("Fourier transform of the normalized spherical harmonics coefficients $C_{" + str(l) + "," + str( + plt.title("Fourier transform of the normalized spherical harmonic coefficients $C_{" + str(l) + "," + str( m) + "}$ et $S_{" + str( l) + "," + str(m) + "}$") plt.plot(xf, 2.0 / N * np.abs(cf[0:N // 2]), label="$C_{" + str(l) + "," + str(m) + "}$") From d52b58e8a0a0caebcbf813207b78b609da51afc9 Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 19 Apr 2022 14:48:52 +0200 Subject: [PATCH 57/80] Add new units --- gravity_toolkit/gen_stokes.py | 6 ++++++ gravity_toolkit/units.py | 5 +++++ 2 files changed, 11 insertions(+) diff --git a/gravity_toolkit/gen_stokes.py b/gravity_toolkit/gen_stokes.py index b750b98c..2ae5945d 100755 --- a/gravity_toolkit/gen_stokes.py +++ b/gravity_toolkit/gen_stokes.py @@ -184,6 +184,12 @@ def gen_stokes(data, lon, lat, LMIN=0, LMAX=60, MMAX=None, UNITS=1, #-- Inputs in mmGH dfactor = factors.mmGH int_fact[:] = np.sin(th) * dphi * dth + elif (UNITS == 5): + dfactor = factors.microGal + int_fact[:] = np.sin(th) * dphi * dth + elif (UNITS == 6): + dfactor = factors.cmwe_ne + int_fact[:] = np.sin(th) * dphi * dth else: raise ValueError(f'Unknown units {UNITS}') diff --git a/gravity_toolkit/units.py b/gravity_toolkit/units.py index 9377cabf..5aeb48cf 100644 --- a/gravity_toolkit/units.py +++ b/gravity_toolkit/units.py @@ -170,6 +170,8 @@ def harmonic(self, hl, kl, ll, **kwargs): self.cmwe = self.rho_e*self.rad_e*(2.0*self.l+1.0)/fraction/3.0 # mmwe, millimeters water equivalent [kg/m^2] self.mmwe = 10.0*self.rho_e*self.rad_e*(2.0*self.l+1.0)/fraction/3.0 + # cmwe_ne, centimeters water equivalent none elastic [g/cm^2] + self.cmwe_ne = self.rho_e * self.rad_e * (2.0 * self.l + 1.0) / 3.0 # mmGH, millimeters geoid height self.mmGH = np.ones((self.lmax+1))*(10.0*self.rad_e) # mmCU, millimeters elastic crustal deformation (uplift) @@ -232,12 +234,15 @@ def spatial(self, hl, kl, ll, **kwargs): self.norm = np.ones((self.lmax+1)) # cmwe, centimeters water equivalent [g/cm^2] self.cmwe = 3.0*fraction/(1.0+2.0*self.l)/(4.0*np.pi*self.rad_e*self.rho_e) + # cmwe_ne, centimeters water equivalent none elastic [g/cm^2] + self.cmwe_ne = 3.0 / (1.0 + 2.0*self.l) / (4.0*np.pi*self.rad_e*self.rho_e) # mmwe, millimeters water equivalent [kg/m^2] self.mmwe = 3.0*fraction/(1.0+2.0*self.l)/(40.0*np.pi*self.rad_e*self.rho_e) # mmGH, millimeters geoid height self.mmGH = np.ones((self.lmax+1))/(4.0*np.pi*self.rad_e) # microGal, microGal gravity perturbations self.microGal = (self.rad_e**2.0)/(4.0*np.pi*1.e6*self.GM)/(self.l+1.0) + # return the degree dependent unit conversions return self From 4f3d767f0800dcedea929ef3adf5a41c6714c42b Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 20 Apr 2022 15:06:50 +0200 Subject: [PATCH 58/80] Revert version in requirements.txt --- requirements.txt | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/requirements.txt b/requirements.txt index 0e603ff6..b667528c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,8 +7,10 @@ python-dateutil pyyaml scipy -datetime~=4.3 -cartopy~=0.18.0 -ipython~=7.19.0 -yaml~=0.2.5 -setuptools~=50.3.0 \ No newline at end of file +matplotlib +python-dateutil +cartopy --no-binary=cartopy +datetime +cartopy +ipython +setuptools \ No newline at end of file From 89fc46e6b69d0b58ca1024d6e2ae5ccf0401ab8c Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 18 May 2022 11:03:10 +0200 Subject: [PATCH 59/80] Update and correct writing of function --- gravity_toolkit/harmonics.py | 291 ++++++++++++++++++----------------- gravity_toolkit/toolbox.py | 81 +++++----- 2 files changed, 188 insertions(+), 184 deletions(-) diff --git a/gravity_toolkit/harmonics.py b/gravity_toolkit/harmonics.py index ccf79fba..38b8c1e0 100644 --- a/gravity_toolkit/harmonics.py +++ b/gravity_toolkit/harmonics.py @@ -254,21 +254,21 @@ def from_ascii(self, filename, **kwargs): # set filename self.case_insensitive_filename(filename) # set default parameters - kwargs.setdefault('date',True) - kwargs.setdefault('verbose',False) - kwargs.setdefault('compression',None) + kwargs.setdefault('date', True) + kwargs.setdefault('verbose', False) + kwargs.setdefault('compression', None) # open the ascii file and extract contents logging.info(self.filename) - if (kwargs['compression'] == 'gzip'): + if kwargs['compression'] == 'gzip': # read input ascii data from gzip compressed file and split lines with gzip.open(self.filename, mode='r') as f: file_contents = f.read().decode('ISO-8859-1').splitlines() - elif (kwargs['compression'] == 'zip'): + elif kwargs['compression'] == 'zip': # read input ascii data from zipped file and split lines stem = self.filename.stem with zipfile.ZipFile(self.filename) as z: file_contents = z.read(stem).decode('ISO-8859-1').splitlines() - elif (kwargs['compression'] == 'bytes'): + elif kwargs['compression'] == 'bytes': # read input file object and split lines file_contents = self.filename.read().splitlines() else: @@ -333,15 +333,15 @@ def from_netCDF4(self, filename, **kwargs): # set filename self.case_insensitive_filename(filename) # set default parameters - kwargs.setdefault('date',True) - kwargs.setdefault('verbose',False) - kwargs.setdefault('compression',None) + kwargs.setdefault('date', True) + kwargs.setdefault('verbose', False) + kwargs.setdefault('compression', None) # Open the NetCDF4 file for reading - if (kwargs['compression'] == 'gzip'): + if kwargs['compression'] == 'gzip': # read as in-memory (diskless) netCDF4 dataset with gzip.open(self.filename, mode='r') as f: fileID = netCDF4.Dataset(uuid.uuid4().hex, memory=f.read()) - elif (kwargs['compression'] == 'zip'): + elif kwargs['compression'] == 'zip': # read zipped file and extract file into in-memory file object stem = self.filename.stem with zipfile.ZipFile(self.filename) as z: @@ -353,7 +353,7 @@ def from_netCDF4(self, filename, **kwargs): f,=[f for f in z.namelist() if re.search(r'\.nc(4)?$',f)] # read bytes from zipfile as in-memory (diskless) netCDF4 dataset fileID = netCDF4.Dataset(uuid.uuid4().hex, memory=z.read(f)) - elif (kwargs['compression'] == 'bytes'): + elif kwargs['compression'] == 'bytes': # read as in-memory (diskless) netCDF4 dataset fileID = netCDF4.Dataset(uuid.uuid4().hex, memory=filename.read()) else: @@ -423,11 +423,11 @@ def from_HDF5(self, filename, **kwargs): # set filename self.case_insensitive_filename(filename) # set default parameters - kwargs.setdefault('date',True) - kwargs.setdefault('verbose',False) - kwargs.setdefault('compression',None) + kwargs.setdefault('date', True) + kwargs.setdefault('verbose', False) + kwargs.setdefault('compression', None) # Open the HDF5 file for reading - if (kwargs['compression'] == 'gzip'): + if kwargs['compression'] == 'gzip': # read gzip compressed file and extract into in-memory file object with gzip.open(self.filename, mode='r') as f: fid = io.BytesIO(f.read()) @@ -437,7 +437,7 @@ def from_HDF5(self, filename, **kwargs): fid.seek(0) # read as in-memory (diskless) HDF5 dataset from BytesIO object fileID = h5py.File(fid, mode='r') - elif (kwargs['compression'] == 'zip'): + elif kwargs['compression'] == 'zip': # read zipped file and extract file into in-memory file object stem = self.filename.stem with zipfile.ZipFile(self.filename) as z: @@ -455,7 +455,7 @@ def from_HDF5(self, filename, **kwargs): fid.seek(0) # read as in-memory (diskless) HDF5 dataset from BytesIO object fileID = h5py.File(fid, mode='r') - elif (kwargs['compression'] == 'bytes'): + elif kwargs['compression'] == 'bytes': # read as in-memory (diskless) HDF5 dataset fileID = h5py.File(self.filename, mode='r') else: @@ -627,13 +627,13 @@ def from_index(self, filename, **kwargs): h = [] # for each file in the index for i,f in enumerate(file_list): - if (kwargs['format'] == 'ascii'): + if kwargs['format'] == 'ascii': # ascii (.txt) h.append(harmonics().from_ascii(f, date=kwargs['date'])) - elif (kwargs['format'] == 'netCDF4'): + elif kwargs['format'] == 'netCDF4': # netcdf (.nc) h.append(harmonics().from_netCDF4(f, date=kwargs['date'])) - elif (kwargs['format'] == 'HDF5'): + elif kwargs['format'] == 'HDF5': # HDF5 (.H5) h.append(harmonics().from_HDF5(f, date=kwargs['date'])) # create a single harmonic object from the list @@ -725,7 +725,7 @@ def from_file(self, filename, format=None, date=True, **kwargs): # set filename self.case_insensitive_filename(filename) # set default verbosity - kwargs.setdefault('verbose',False) + kwargs.setdefault('verbose', False) # read from file if format == 'ascii': # ascii (.txt) @@ -753,7 +753,7 @@ def from_dict(self, d, **kwargs): dictionary object to be converted """ # assign dictionary variables to self - for key in ['l','m','clm','slm','time','month']: + for key in ['l', 'm', 'clm', 'slm', 'time', 'month']: try: setattr(self, key, d[key].copy()) except (AttributeError, KeyError): @@ -1183,19 +1183,19 @@ def to_masked_array(self): # verify dimensions and get shape ndim_prev = np.copy(self.ndim) self.expand_dims() - l1,m1,nt = self.shape + l1, m1, nt = self.shape # create single triangular matrices with harmonics - Ylms = np.ma.zeros((self.lmax+1,2*self.lmax+1,nt)) - Ylms.mask = np.ones((self.lmax+1,2*self.lmax+1,nt),dtype=bool) - for m in range(-self.mmax,self.mmax+1): + Ylms = np.ma.zeros((self.lmax + 1, 2*self.lmax + 1, nt)) + Ylms.mask = np.ones((self.lmax + 1, 2*self.lmax + 1, nt),dtype=bool) + for m in range(-self.mmax, self.mmax + 1): mm = np.abs(m) - for l in range(mm,self.lmax+1): + for l in range(mm, self.lmax + 1): if m < 0: - Ylms.data[l,self.lmax+m,:] = self.slm[l,mm,:] - Ylms.mask[l,self.lmax+m,:] = False + Ylms.data[l, self.lmax+m, :] = self.slm[l, mm, :] + Ylms.mask[l, self.lmax+m, :] = False else: - Ylms.data[l,self.lmax+m,:] = self.clm[l,mm,:] - Ylms.mask[l,self.lmax+m,:] = False + Ylms.data[l, self.lmax+m, :] = self.clm[l,mm,:] + Ylms.mask[l, self.lmax+m, :] = False # reshape to previous if self.ndim != ndim_prev: self.squeeze() @@ -1207,8 +1207,8 @@ def update_dimensions(self): Update the dimension variables of the ``harmonics`` object """ # calculate spherical harmonic degree and order (0 is falsy) - self.l=np.arange(self.lmax+1) if (self.lmax is not None) else None - self.m=np.arange(self.mmax+1) if (self.mmax is not None) else None + self.l = np.arange(self.lmax + 1) if (self.lmax is not None) else None + self.m = np.arange(self.mmax + 1) if (self.mmax is not None) else None return self def add(self, temp): @@ -1225,13 +1225,13 @@ def add(self, temp): temp.update_dimensions() l1 = self.lmax+1 if (temp.lmax > self.lmax) else temp.lmax+1 m1 = self.mmax+1 if (temp.mmax > self.mmax) else temp.mmax+1 - if (self.ndim == 2): - self.clm[:l1,:m1] += temp.clm[:l1,:m1] - self.slm[:l1,:m1] += temp.slm[:l1,:m1] + if self.ndim == 2: + self.clm[:l1, :m1] += temp.clm[:l1, :m1] + self.slm[:l1, :m1] += temp.slm[:l1, :m1] elif (self.ndim == 3) and (temp.ndim == 2): for i,t in enumerate(self.time): - self.clm[:l1,:m1,i] += temp.clm[:l1,:m1] - self.slm[:l1,:m1,i] += temp.slm[:l1,:m1] + self.clm[:l1, :m1, i] += temp.clm[:l1, :m1] + self.slm[:l1, :m1, i] += temp.slm[:l1, :m1] else: old_month = self.month exclude1 = set(self.month) - set(temp.month) @@ -1242,14 +1242,14 @@ def add(self, temp): for i in range(len(old_month)): for j in range(len(temp.month)): if old_month[i] == temp.month[j]: - self.clm[:l1,:m1,i] += temp.clm[:l1,:m1,j] - self.slm[:l1,:m1,i] += temp.slm[:l1,:m1,j] + self.clm[:l1, :m1, i] += temp.clm[:l1, :m1, j] + self.slm[:l1, :m1, i] += temp.slm[:l1, :m1, j] to_keep = [] for i in range(len(old_month)): if not(old_month[i] in exclude1): to_keep.append(i) - self.clm = self.clm[:,:,to_keep] + self.clm = self.clm[:, :, to_keep] self.slm = self.slm[:, :, to_keep] return self @@ -1267,13 +1267,13 @@ def subtract(self, temp): temp.update_dimensions() l1 = self.lmax+1 if (temp.lmax > self.lmax) else temp.lmax+1 m1 = self.mmax+1 if (temp.mmax > self.mmax) else temp.mmax+1 - if (self.ndim == 2): - self.clm[:l1,:m1] -= temp.clm[:l1,:m1] - self.slm[:l1,:m1] -= temp.slm[:l1,:m1] + if self.ndim == 2: + self.clm[:l1, :m1] -= temp.clm[:l1, :m1] + self.slm[:l1, :m1] -= temp.slm[:l1, :m1] elif (self.ndim == 3) and (temp.ndim == 2): for i,t in enumerate(self.time): - self.clm[:l1,:m1,i] -= temp.clm[:l1,:m1] - self.slm[:l1,:m1,i] -= temp.slm[:l1,:m1] + self.clm[:l1, :m1, i] -= temp.clm[:l1, :m1] + self.slm[:l1, :m1, i] -= temp.slm[:l1, :m1] else: old_month = self.month exclude1 = set(self.month) - set(temp.month) @@ -1284,14 +1284,14 @@ def subtract(self, temp): for i in range(len(old_month)): for j in range(len(temp.month)): if old_month[i] == temp.month[j]: - self.clm[:l1,:m1,i] -= temp.clm[:l1,:m1,j] - self.slm[:l1,:m1,i] -= temp.slm[:l1,:m1,j] + self.clm[:l1, :m1, i] -= temp.clm[:l1, :m1, j] + self.slm[:l1, :m1, i] -= temp.slm[:l1, :m1, j] to_keep = [] for i in range(len(old_month)): if not(old_month[i] in exclude1): to_keep.append(i) - self.clm = self.clm[:,:,to_keep] + self.clm = self.clm[:, :, to_keep] self.slm = self.slm[:, :, to_keep] return self @@ -1307,15 +1307,15 @@ def multiply(self, temp): # assign degree and order fields self.update_dimensions() temp.update_dimensions() - l1 = self.lmax+1 if (temp.lmax > self.lmax) else temp.lmax+1 - m1 = self.mmax+1 if (temp.mmax > self.mmax) else temp.mmax+1 - if (self.ndim == 2): - self.clm[:l1,:m1] *= temp.clm[:l1,:m1] - self.slm[:l1,:m1] *= temp.slm[:l1,:m1] + l1 = self.lmax + 1 if (temp.lmax > self.lmax) else temp.lmax+1 + m1 = self.mmax + 1 if (temp.mmax > self.mmax) else temp.mmax+1 + if self.ndim == 2: + self.clm[:l1, :m1] *= temp.clm[:l1, :m1] + self.slm[:l1, :m1] *= temp.slm[:l1, :m1] elif (self.ndim == 3) and (temp.ndim == 2): for i,t in enumerate(self.time): - self.clm[:l1,:m1,i] *= temp.clm[:l1,:m1] - self.slm[:l1,:m1,i] *= temp.slm[:l1,:m1] + self.clm[:l1, :m1, i] *= temp.clm[:l1, :m1] + self.slm[:l1, :m1, i] *= temp.slm[:l1, :m1] else: old_month = self.month exclude1 = set(self.month) - set(temp.month) @@ -1349,20 +1349,20 @@ def divide(self, temp): # assign degree and order fields self.update_dimensions() temp.update_dimensions() - l1 = self.lmax+1 if (temp.lmax > self.lmax) else temp.lmax+1 - m1 = self.mmax+1 if (temp.mmax > self.mmax) else temp.mmax+1 + l1 = self.lmax + 1 if (temp.lmax > self.lmax) else temp.lmax+1 + m1 = self.mmax + 1 if (temp.mmax > self.mmax) else temp.mmax+1 # indices for cosine spherical harmonics (including zonals) lc,mc = np.tril_indices(l1, m=m1) # indices for sine spherical harmonics (excluding zonals) m0 = np.nonzero(mc != 0) - ls,ms = (lc[m0],mc[m0]) - if (self.ndim == 2): - self.clm[lc,mc] /= temp.clm[lc,mc] - self.slm[ls,ms] /= temp.slm[ls,ms] + ls,ms = (lc[m0], mc[m0]) + if self.ndim == 2: + self.clm[lc, mc] /= temp.clm[lc, mc] + self.slm[ls, ms] /= temp.slm[ls, ms] elif (self.ndim == 3) and (temp.ndim == 2): for i,t in enumerate(self.time): - self.clm[lc,mc,i] /= temp.clm[lc,mc] - self.slm[ls,ms,i] /= temp.slm[ls,ms] + self.clm[lc, mc, i] /= temp.clm[lc, mc] + self.slm[ls, ms, i] /= temp.slm[ls, ms] else: old_month = self.month exclude1 = set(self.month) - set(temp.month) @@ -1457,11 +1457,11 @@ def expand_dims(self, update_dimensions=True): self.month = np.atleast_1d(self.month) # output harmonics with a third dimension if (self.ndim == 2) and not self.flattened: - self.clm = self.clm[:,:,None] - self.slm = self.slm[:,:,None] + self.clm = self.clm[:, :, None] + self.slm = self.slm[:, :, None] elif (self.ndim == 1) and self.flattened: - self.clm = self.clm[:,None] - self.slm = self.slm[:,None] + self.clm = self.clm[:, None] + self.slm = self.slm[:, None] # assign degree and order fields if update_dimensions: self.update_dimensions() @@ -1503,7 +1503,7 @@ def flatten(self, date=True): ``harmonics`` objects contain date information """ n_harm = (self.lmax**2 + 3*self.lmax - (self.lmax-self.mmax)**2 - - (self.lmax-self.mmax))//2 + 1 + (self.lmax - self.mmax))//2 + 1 # restructured degree and order temp = harmonics(lmax=self.lmax, mmax=self.mmax) temp.l = np.zeros((n_harm,), dtype=np.int64) @@ -1521,20 +1521,20 @@ def flatten(self, date=True): temp.slm = np.zeros((n_harm)) else: n = self.clm.shape[-1] - temp.clm = np.zeros((n_harm,n)) - temp.slm = np.zeros((n_harm,n)) + temp.clm = np.zeros((n_harm, n)) + temp.slm = np.zeros((n_harm, n)) # create counter variable lm lm = 0 - for m in range(0,self.mmax+1):# MMAX+1 to include MMAX - for l in range(m,self.lmax+1):# LMAX+1 to include LMAX + for m in range(0,self.mmax + 1):# MMAX+1 to include MMAX + for l in range(m,self.lmax + 1):# LMAX+1 to include LMAX temp.l[lm] = np.int64(l) temp.m[lm] = np.int64(m) - if (self.clm.ndim == 2): - temp.clm[lm] = self.clm[l,m] - temp.slm[lm] = self.slm[l,m] + if self.clm.ndim == 2: + temp.clm[lm] = self.clm[l, m] + temp.slm[lm] = self.slm[l, m] else: - temp.clm[lm,:] = self.clm[l,m,:] - temp.slm[lm,:] = self.slm[l,m,:] + temp.clm[lm, :] = self.clm[l, m, :] + temp.slm[lm, :] = self.slm[l, m, :] # add 1 to lm counter variable lm += 1 # update flattened attribute @@ -1554,6 +1554,9 @@ def expand(self, date=True): # number of harmonics n_harm = len(self.l) # restructured degree and order + #n_harm = (self.lmax**2 + 3*self.lmax - (self.lmax - self.mmax)**2 - + # (self.lmax - self.mmax))//2 + 1 + # restructured degree and order temp = harmonics(lmax=self.lmax, mmax=self.mmax) # get filenames if applicable if getattr(self, 'filename'): @@ -1563,23 +1566,23 @@ def expand(self, date=True): temp.time = np.copy(self.time) temp.month = np.copy(self.month) # restructured spherical harmonic matrices - if (self.clm.ndim == 1): - temp.clm = np.zeros((self.lmax+1,self.mmax+1)) - temp.slm = np.zeros((self.lmax+1,self.mmax+1)) + if self.clm.ndim == 1: + temp.clm = np.zeros((self.lmax + 1, self.mmax + 1)) + temp.slm = np.zeros((self.lmax + 1, self.mmax + 1)) else: n = self.clm.shape[-1] - temp.clm = np.zeros((self.lmax+1,self.mmax+1,n)) - temp.slm = np.zeros((self.lmax+1,self.mmax+1,n)) + temp.clm = np.zeros((self.lmax + 1,self.mmax + 1, n)) + temp.slm = np.zeros((self.lmax + 1,self.mmax + 1, n)) # create counter variable lm for lm in range(n_harm): l = self.l[lm] m = self.m[lm] - if (self.clm.ndim == 1): - temp.clm[l,m] = self.clm[lm] - temp.slm[l,m] = self.slm[lm] + if self.clm.ndim == 1: + temp.clm[l, m] = self.clm[lm] + temp.slm[l, m] = self.slm[lm] else: - temp.clm[l,m,:] = self.clm[lm,:] - temp.slm[l,m,:] = self.slm[lm,:] + temp.clm[l, m, :] = self.clm[lm, :] + temp.slm[l, m, :] = self.slm[lm, :] # update flattened attribute temp.flattened = False # assign degree and order fields @@ -1601,8 +1604,8 @@ def index(self, indice, date=True): # output harmonics object temp = harmonics(lmax=np.copy(self.lmax), mmax=np.copy(self.mmax)) # subset output harmonics - temp.clm = self.clm[:,:,indice].copy() - temp.slm = self.slm[:,:,indice].copy() + temp.clm = self.clm[:, :, indice].copy() + temp.slm = self.slm[:, :, indice].copy() # subset output dates if date: temp.time = self.time[indice].copy() @@ -1637,19 +1640,19 @@ def subset(self, months): m = ','.join([f'{m:03d}' for m in months_check]) raise IOError(f'GRACE/GRACE-FO months {m} not Found') # indices to sort data objects - months_list = [i for i,m in enumerate(self.month) if m in months] + months_list = [i for i, m in enumerate(self.month) if m in months] # output harmonics object temp = harmonics(lmax=np.copy(self.lmax), mmax=np.copy(self.mmax)) # create output harmonics - temp.clm = np.zeros((temp.lmax+1,temp.mmax+1,n)) - temp.slm = np.zeros((temp.lmax+1,temp.mmax+1,n)) + temp.clm = np.zeros((temp.lmax + 1, temp.mmax + 1, n)) + temp.slm = np.zeros((temp.lmax + 1, temp.mmax + 1, n)) temp.time = np.zeros((n)) - temp.month = np.zeros((n),dtype=np.int64) + temp.month = np.zeros((n), dtype=np.int64) temp.filename = [] # for each indice for t,i in enumerate(months_list): - temp.clm[:,:,t] = self.clm[:,:,i].copy() - temp.slm[:,:,t] = self.slm[:,:,i].copy() + temp.clm[:,:, t] = self.clm[:,:, i].copy() + temp.slm[:,:, t] = self.slm[:,:, i].copy() temp.time[t] = self.time[i].copy() temp.month[t] = self.month[i].copy() # subset filenames if applicable @@ -1688,18 +1691,18 @@ def truncate(self, lmax, lmin=0, mmax=None): l1 = self.lmax+1 if (temp.lmax > self.lmax) else temp.lmax+1 m1 = self.mmax+1 if (temp.mmax > self.mmax) else temp.mmax+1 # create output harmonics - if (temp.ndim == 3): + if temp.ndim == 3: # number of months n = temp.clm.shape[-1] - self.clm = np.zeros((self.lmax+1,self.mmax+1,n)) - self.slm = np.zeros((self.lmax+1,self.mmax+1,n)) - self.clm[lmin:l1,:m1,:] = temp.clm[lmin:l1,:m1,:].copy() - self.slm[lmin:l1,:m1,:] = temp.slm[lmin:l1,:m1,:].copy() + self.clm = np.zeros((self.lmax+1, self.mmax+1, n)) + self.slm = np.zeros((self.lmax+1, self.mmax+1, n)) + self.clm[lmin:l1, :m1,:] = temp.clm[lmin:l1, :m1,:].copy() + self.slm[lmin:l1, :m1,:] = temp.slm[lmin:l1, :m1,:].copy() else: - self.clm = np.zeros((self.lmax+1,self.mmax+1)) - self.slm = np.zeros((self.lmax+1,self.mmax+1)) - self.clm[lmin:l1,:m1] = temp.clm[lmin:l1,:m1].copy() - self.slm[lmin:l1,:m1] = temp.slm[lmin:l1,:m1].copy() + self.clm = np.zeros((self.lmax + 1, self.mmax + 1)) + self.slm = np.zeros((self.lmax + 1, self.mmax + 1)) + self.clm[lmin:l1, :m1] = temp.clm[lmin:l1, :m1].copy() + self.slm[lmin:l1, :m1] = temp.slm[lmin:l1, :m1].copy() # assign degree and order fields self.update_dimensions() # return the truncated or expanded harmonics object @@ -1718,19 +1721,19 @@ def mean(self, apply=False, indices=Ellipsis): """ temp = harmonics(lmax=np.copy(self.lmax), mmax=np.copy(self.mmax)) # allocate for mean field - temp.clm = np.zeros((temp.lmax+1,temp.mmax+1)) - temp.slm = np.zeros((temp.lmax+1,temp.mmax+1)) + temp.clm = np.zeros((temp.lmax + 1, temp.mmax + 1)) + temp.slm = np.zeros((temp.lmax + 1, temp.mmax + 1)) # Computes the mean for each spherical harmonic degree and order - for m in range(0,temp.mmax+1):# MMAX+1 to include l - for l in range(m,temp.lmax+1):# LMAX+1 to include LMAX + for m in range(0, temp.mmax + 1):# MMAX+1 to include l + for l in range(m, temp.lmax + 1):# LMAX+1 to include LMAX # calculate mean static field - temp.clm[l,m] = np.mean(self.clm[l,m,indices]) - temp.slm[l,m] = np.mean(self.slm[l,m,indices]) + temp.clm[l, m] = np.mean(self.clm[l, m, indices]) + temp.slm[l, m] = np.mean(self.slm[l, m, indices]) # calculating the time-variable gravity field by removing # the static component of the gravitational field if apply: - self.clm[l,m,:] -= temp.clm[l,m] - self.slm[l,m,:] -= temp.slm[l,m] + self.clm[l, m, :] -= temp.clm[l, m] + self.slm[l, m, :] -= temp.slm[l, m] # calculate mean of temporal variables for key in ['time','month']: try: @@ -1761,19 +1764,19 @@ def scale(self, var): if getattr(self, 'filename'): temp.filename = copy.copy(self.filename) # multiply by a single constant or a time-variable scalar - if (np.ndim(var) == 0): + if np.ndim(var) == 0: temp.clm = var*self.clm temp.slm = var*self.slm elif (np.ndim(var) == 1) and (self.ndim == 2): - temp.clm = np.zeros((temp.lmax+1,temp.mmax+1,len(var))) - temp.slm = np.zeros((temp.lmax+1,temp.mmax+1,len(var))) + temp.clm = np.zeros((temp.lmax + 1, temp.mmax + 1, len(var))) + temp.slm = np.zeros((temp.lmax + 1, temp.mmax + 1, len(var))) for i,v in enumerate(var): - temp.clm[:,:,i] = v*self.clm - temp.slm[:,:,i] = v*self.slm + temp.clm[:, :, i] = v*self.clm + temp.slm[:, :, i] = v*self.slm elif (np.ndim(var) == 1) and (self.ndim == 3): for i,v in enumerate(var): - temp.clm[:,:,i] = v*self.clm[:,:,i] - temp.slm[:,:,i] = v*self.slm[:,:,i] + temp.clm[:, :, i] = v*self.clm[:, :, i] + temp.slm[:, :, i] = v*self.slm[:, :, i] # assign degree and order fields temp.update_dimensions() return temp @@ -1847,15 +1850,15 @@ def convolve(self, var): # assign degree and order fields self.update_dimensions() # check if a single field or a temporal field - if (self.ndim == 2): - for l in range(0,self.lmax+1):# LMAX+1 to include LMAX - self.clm[l,:] *= var[l] - self.slm[l,:] *= var[l] + if self.ndim == 2: + for l in range(0, self.lmax + 1):# LMAX+1 to include LMAX + self.clm[l, :] *= var[l] + self.slm[l, :] *= var[l] else: for i,t in enumerate(self.time): - for l in range(0,self.lmax+1):# LMAX+1 to include LMAX - self.clm[l,:,i] *= var[l] - self.slm[l,:,i] *= var[l] + for l in range(0, self.lmax + 1):# LMAX+1 to include LMAX + self.clm[l, :, i] *= var[l] + self.slm[l, :, i] *= var[l] # return the convolved field return self @@ -1885,20 +1888,20 @@ def destripe(self, **kwargs): if getattr(self, 'filename'): temp.filename = copy.copy(self.filename) # check if a single field or a temporal field - if (self.ndim == 2): + if self.ndim == 2: Ylms = destripe_harmonics(self.clm, self.slm, LMIN=1, LMAX=self.lmax, MMAX=self.mmax, **kwargs) temp.clm = Ylms['clm'].copy() temp.slm = Ylms['slm'].copy() else: n = self.shape[-1] - temp.clm = np.zeros((self.lmax+1,self.mmax+1,n)) - temp.slm = np.zeros((self.lmax+1,self.mmax+1,n)) + temp.clm = np.zeros((self.lmax+1, self.mmax+1, n)) + temp.slm = np.zeros((self.lmax+1, self.mmax+1, n)) for i in range(n): - Ylms = destripe_harmonics(self.clm[:,:,i], self.slm[:,:,i], + Ylms = destripe_harmonics(self.clm[:, :, i], self.slm[:, :, i], LMIN=1, LMAX=self.lmax, MMAX=self.mmax, **kwargs) - temp.clm[:,:,i] = Ylms['clm'].copy() - temp.slm[:,:,i] = Ylms['slm'].copy() + temp.clm[:, :, i] = Ylms['clm'].copy() + temp.slm[:, :, i] = Ylms['slm'].copy() # assign degree and order fields temp.update_dimensions() # return the destriped field @@ -1912,24 +1915,24 @@ def amplitude(self): # temporary matrix for squared harmonics temp = self.power(2) # check if a single field or a temporal field - if (self.ndim == 2): + if self.ndim == 2: # allocate for degree amplitudes - amp = np.zeros((self.lmax+1)) - for l in range(self.lmax+1): + amp = np.zeros((self.lmax + 1)) + for l in range(self.lmax + 1): # truncate at mmax - m = np.arange(0,temp.mmax+1) + m = np.arange(0, temp.mmax + 1) # degree amplitude of spherical harmonic degree - amp[l] = np.sqrt(np.sum(temp.clm[l,m] + temp.slm[l,m])) + amp[l] = np.sqrt(np.sum(temp.clm[l, m] + temp.slm[l, m])) else: # allocate for degree amplitudes n = self.shape[-1] - amp = np.zeros((self.lmax+1,n)) - for l in range(self.lmax+1): + amp = np.zeros((self.lmax + 1, n)) + for l in range(self.lmax + 1): # truncate at mmax - m = np.arange(0,temp.mmax+1) + m = np.arange(0, temp.mmax + 1) # degree amplitude of spherical harmonic degree - var = temp.clm[l,m,:] + temp.slm[l,m,:] - amp[l,:] = np.sqrt(np.sum(var, axis=0)) + var = temp.clm[l, m, :] + temp.slm[l, m, :] + amp[l, :] = np.sqrt(np.sum(var, axis=0)) # return the degree amplitudes return amp diff --git a/gravity_toolkit/toolbox.py b/gravity_toolkit/toolbox.py index 24f01f82..f52f26b3 100644 --- a/gravity_toolkit/toolbox.py +++ b/gravity_toolkit/toolbox.py @@ -354,6 +354,7 @@ def filt_grid(grid, f_cut=0.5): return filtered_grid + def save_gif(grid, path, unit='cmwe', bound=None, mask=None, color='viridis'): """ Create a gif of the spatial object @@ -466,6 +467,7 @@ def animate_frames(i): HTML(anim.to_jshtml()) anim.save(path, writer='imagemagick', fps=10) + plt.clf() def plot_rms_map(grid, path=False, proj=ccrs.PlateCarree(), unit='cmwe', bound=None, mask=None, color='viridis'): @@ -498,15 +500,6 @@ def plot_rms_map(grid, path=False, proj=ccrs.PlateCarree(), unit='cmwe', bound=N else: vmin, vmax = bound - if vmax - vmin >= 3: - levels = np.arange(vmin, vmax, max(1, int((vmax - vmin) / 10))) - elif vmax - vmin >= 0.3: - levels = np.arange(vmin, vmax, max(0.1, float('%.1f' % ((vmax - vmin) / 10)))) - elif vmax - vmin >= 0.03: - levels = np.arange(vmin, vmax, max(0.1, float('%.2f' % ((vmax - vmin) / 10)))) - else: - raise ValueError("The range of data to plot is too small") - norm = colors.Normalize(vmin=vmin, vmax=vmax) cmap = plt.cm.get_cmap(color) im = ax1.imshow(data_to_set, interpolation='nearest', @@ -528,32 +521,25 @@ def plot_rms_map(grid, path=False, proj=ccrs.PlateCarree(), unit='cmwe', bound=N # Add label to the colorbar if unit == "cmwe": cbar.ax.set_xlabel('Equivalent Water Thickness', labelpad=10, fontsize=24) - cbar.ax.set_ylabel('cm', fontsize=24, rotation=0) + cbar.ax.set_ylabel('cm', fontsize=24, rotation=0, labelpad=10) elif unit == "cmwe_ne": cbar.ax.set_xlabel('Non elastic Equivalent Water Thickness', labelpad=10, fontsize=24) - cbar.ax.set_ylabel('cm', fontsize=24, rotation=0) + cbar.ax.set_ylabel('cm', fontsize=24, rotation=0, labelpad=10) elif unit == "mmwe": cbar.ax.set_xlabel('Equivalent Water Thickness', labelpad=10, fontsize=24) - cbar.ax.set_ylabel('mm', fontsize=24, rotation=0) + cbar.ax.set_ylabel('mm', fontsize=24, rotation=0, labelpad=10) elif unit == "geoid": cbar.ax.set_xlabel('Geoid Height', labelpad=10, fontsize=24) - cbar.ax.set_ylabel('mm', fontsize=24, rotation=0) + cbar.ax.set_ylabel('mm', fontsize=24, rotation=0, labelpad=10) elif unit == "microGal": cbar.ax.set_xlabel('Acceleration', labelpad=10, fontsize=24) - cbar.ax.set_ylabel('$\mu Gal$', fontsize=24, rotation=0) + cbar.ax.set_ylabel('$\mu Gal$', fontsize=24, rotation=0, labelpad=10) elif unit == "secacc": cbar.ax.set_xlabel('Secular Acceleration', labelpad=10, fontsize=24) - cbar.ax.set_ylabel('$nT.y^{-2}$', fontsize=24, rotation=0) + cbar.ax.set_ylabel('$nT.y^{-2}$', fontsize=24, rotation=0, labelpad=10) - cbar.ax.yaxis.set_label_coords(1.045, 0.1) + cbar.ax.yaxis.set_label_coords(1.1, -0.4) # Set the tick levels for the colorbar - cbar.set_ticks(levels) - if vmax - vmin >= 3: - cbar.set_ticklabels(['{0:d}'.format(ct) for ct in levels]) - elif vmax - vmin >= 0.3: - cbar.set_ticklabels(['{.1f}'.format(ct) for ct in levels]) - elif vmax - vmin >= 0.03: - cbar.set_ticklabels(['{.2f}'.format(ct) for ct in levels]) # ticks lines all the way across cbar.ax.tick_params(which='both', width=1, length=26, labelsize=24, direction='in') @@ -585,7 +571,7 @@ def calc_rms_grid(grid, mask=None): rms : rms of the grid """ - if mask in None: + if mask is None: rms = np.sqrt(np.sum( [np.sum(np.cos(lat * np.pi / 180) ** 2 * line ** 2) for lat, line in zip(grid.lat, grid.data)]) / np.sum( [np.sum(np.cos(lat * np.pi / 180) ** 2 * line.size) for lat, line in zip(grid.lat, grid.data)])) @@ -600,34 +586,46 @@ def calc_rms_grid(grid, mask=None): return rms -def plot_rms_grid(grid, path=False, mask=None, unit='cmwe'): +def plot_rms_grid(grid, path=False, labels=None, mask=None, unit='cmwe'): """ Create a figure with rms of the grid spatial object in function of time Parameters ---------- - grid : spatial object + grid : spatial object or list of spatial object path : path to save the figure if needed mask : mask to apply on data if needed unit : unit of the grid """ - l_rms = [] - for i in range(len(grid.time)): - if mask is None: - rms = np.sqrt(np.sum([np.sum(np.cos(lat * np.pi / 180) ** 2 * line ** 2) for lat, line in - zip(grid.lat, grid.data[:, :, i])]) / np.sum( - [np.sum(np.cos(lat * np.pi / 180) ** 2 * line.size) for lat, line in - zip(grid.lat, grid.data[:, :, i])])) - else: - rms = np.sqrt(np.sum([np.sum(np.cos(lat * np.pi / 180) ** 2 * (line * line_mask) ** 2) - for lat, line, line_mask in zip(grid.lat, grid.data[:, :, i], mask)]) - / np.sum([np.sum(np.cos(lat * np.pi / 180) ** 2 * line_mask) - for lat, line_mask in zip(grid.lat, mask)])) + if type(grid) != list: + grid = [grid] + + plot_rms = [] + for g in grid: + l_rms = [] + for i in range(len(g.time)): + if mask is None: + rms = np.sqrt(np.sum([np.sum(np.cos(lat * np.pi / 180) ** 2 * line ** 2) for lat, line in + zip(g.lat, g.data[:, :, i])]) / np.sum( + [np.sum(np.cos(lat * np.pi / 180) ** 2 * line.size) for lat, line in + zip(g.lat, g.data[:, :, i])])) + else: + rms = np.sqrt(np.sum([np.sum(np.cos(lat * np.pi / 180) ** 2 * (line * line_mask) ** 2) + for lat, line, line_mask in zip(g.lat, g.data[:, :, i], mask)]) + / np.sum([np.sum(np.cos(lat * np.pi / 180) ** 2 * line_mask) + for lat, line_mask in zip(g.lat, mask)])) - l_rms.append(rms) + l_rms.append(rms) + plot_rms.append(l_rms) plt.figure() - plt.plot(grid.time, l_rms) + if not(type(labels) == list): + for g, rms in zip(grid, plot_rms): + plt.plot(g.time, rms) + else: + for g, rms, l in zip(grid, plot_rms, labels): + plt.plot(g.time, rms, label=l) + plt.xlabel('Time (y)') if unit == "cmwe": plt.ylabel('cm EWH') @@ -643,6 +641,9 @@ def plot_rms_grid(grid, path=False, mask=None, unit='cmwe'): plt.ylabel('$nT.y^{-2}$') plt.ylabel('Power (cm EWH)') + if type(labels) == list: + plt.legend() + if path: plt.savefig(path, bbox_inches='tight') else: From 3a7c3d1d01115d72eafeb298f650f2066eb89bf2 Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 18 May 2022 11:03:31 +0200 Subject: [PATCH 60/80] New plot eof on spatial grid with normalization --- gravity_toolkit/spatial.py | 124 +++++++++++++++++++++++++++++++++++++ 1 file changed, 124 insertions(+) diff --git a/gravity_toolkit/spatial.py b/gravity_toolkit/spatial.py index 401f7748..fdb8af73 100644 --- a/gravity_toolkit/spatial.py +++ b/gravity_toolkit/spatial.py @@ -99,6 +99,11 @@ warnings.warn("netCDF4 not available", ImportWarning) # ignore warnings warnings.filterwarnings("ignore") +import scipy as sc +import matplotlib.pyplot as plt +import matplotlib +import cartopy.crs as ccrs + class spatial(object): """ @@ -2008,3 +2013,122 @@ def update_mask(self): if getattr(self, 'magnitude') is not None: self.magnitude[self.mask] = self.fill_value return self + + def plot_eof(self, number, path_folder, cmap='viridis', mode='full', unit='cmwe', mask=None, normalize=False, weight=False): + import gravity_toolkit.toolbox as tb + mat_svd = np.copy(self.data) + if mask is None: + mat_svd = np.reshape(mat_svd, (self.lat.shape[0] * self.lon.shape[0], self.time.shape[0])) + lat = self.lat.repeat(self.lon.shape[0]) + else: + mat_svd = np.reshape(mat_svd[mask], (np.sum(mask), self.time.shape[0])) + lat = self.lat.repeat(np.sum(mask, axis=0)) + + mat_svd_original = np.copy(mat_svd) + if normalize: + mat_svd = (mat_svd - np.mean(mat_svd, axis = 1).repeat(self.time.shape[0]).reshape(mat_svd.shape)) / np.std(mat_svd, axis=1).repeat(self.time.shape[0]).reshape(mat_svd.shape) + if weight: + mat_svd = mat_svd*np.cos(np.radians(lat).repeat(self.time.shape[0]).reshape(mat_svd.shape)) + + + c_svd = mat_svd.T@mat_svd/(mat_svd.shape[0] - 1) + w, v = sc.linalg.eigh(c_svd) + + v = v[:, ::-1] + w = w[::-1] + s = np.sqrt(w*(mat_svd.shape[0] - 1)) + us = mat_svd_original@v + + eof_grid = spatial() + eof_grid.lat, eof_grid.lon = self.lat, self.lon + eof_grid.time = np.array([0]) + + if not os.path.isdir(path_folder): + os.mkdir(path_folder) + + if mode == 'ts': + plt.figure() + plt.xlabel('Time (year)') + + for k in number: + power = s[k]**2/np.nansum(s**2) + eof = us[:, k]/np.sqrt(mat_svd.shape[1] - 1) + sort_eof = np.sort(eof) + scale_eof = 2*eof/(sort_eof[-int(len(sort_eof)*0.01)] - sort_eof[int(len(sort_eof)*0.01)]) + + pc = v.T[k] * np.sqrt(mat_svd.shape[1] - 1) /2*(sort_eof[-int(len(sort_eof)*0.01)] - sort_eof[int(len(sort_eof)*0.01)]) + + if mask is None: + eof_grid.data = np.reshape(scale_eof, (self.lat.shape[0], self.lon.shape[0], 1)) + else: + eof_grid.data = np.zeros((self.lat.shape[0], self.lon.shape[0], 1)) + eof_grid.data[mask] = scale_eof + eof_grid.data[np.logical_not(mask)] = None + + if mode == 'map': + tb.plot_rms_map(eof_grid, path=os.path.join(path_folder, 'map_eof_'+str(k)+'.png'), unit=unit, mask=mask) + + elif mode == 'full': + npow2 = 1 if len(self.time) == 0 else 2 ** (len(self.time) - 1).bit_length() + f = np.fft.fft(pc, npow2) + xf = np.fft.fftfreq(npow2, d=np.mean(self.time[1:] - self.time[:-1])) + + fig = plt.figure(constrained_layout=True, figsize=(12, 6), dpi=200) + spec = matplotlib.gridspec.GridSpec(ncols=4, nrows=12, wspace=0.03, width_ratios=[8, 1, 1, 1]) + axmap = fig.add_subplot(spec[:, 0], projection=ccrs.PlateCarree()) + + cmap = plt.cm.get_cmap(cmap) + + immap = axmap.imshow(eof_grid.data, cmap=cmap, transform=ccrs.PlateCarree(), extent=self.extent, + origin='upper', vmin=-1.15, vmax=1.15) + axmap.coastlines('50m') + # stronger linewidth on frame + axmap.spines['geo'].set_linewidth(2.0) + axmap.spines['geo'].set_capstyle('projecting') + + cbar = plt.colorbar(immap, ax=axmap, extend='both', extendfrac=0.0375, + orientation='horizontal', pad=0.025, shrink=0.85, + aspect=22, drawedges=False) + + # ticks lines all the way across + cbar.ax.tick_params(which='both', width=1, length=24, labelsize=18, + direction='in') + + power_str = '\nPower: '+str("%1.2f"%power) + cbar.ax.set_xlabel(power_str, labelpad=10, fontsize=18) + + axplot = fig.add_subplot(spec[1:5, 1:], box_aspect=0.5) + axplot.plot(self.time, pc) + axplot.yaxis.tick_right() + axplot.yaxis.set_label_position("right") + axplot.set_xlabel('Time (year)') + + if unit == "cmwe": + axplot.set_ylabel('Equivalent Water\nThickness\ncm', labelpad=50, fontsize=12, rotation='horizontal') + elif unit == "cmwe_ne": + axplot.set_ylabel('Non elastic\n Equivalent Water\nThickness\ncm', labelpad=50, fontsize=12, rotation='horizontal') + elif unit == "mmwe": + axplot.set_ylabel('Equivalent Water\n Thickness\nmm', labelpad=50, fontsize=12, rotation='horizontal') + elif unit == "geoid": + axplot.set_ylabel('Geoid Height\nmm', labelpad=45, fontsize=12, rotation='horizontal') + elif unit == "microGal": + axplot.set_ylabel('Acceleration\n$\mu Gal$', labelpad=40, fontsize=12, rotation='horizontal') + elif unit == "secacc": + axplot.set_ylabel('Secular\n Acceleration\n$nT.y^{-2}$', labelpad=40, fontsize=12, rotation='horizontal') + + axfft = fig.add_subplot(spec[6:10, 1:], box_aspect=0.5) + plt.plot(1/xf[:len(xf)//2][1/xf[:len(xf)//2] < 10], 2.0/len(self.time) * np.abs(f[:len(xf)//2][1/xf[:len(xf)//2] < 10])) + axfft.yaxis.tick_right() + axfft.set_xlim(0, 10) + axfft.set_ylim(0,) + axfft.set_xlabel('Period (year)') + + plt.savefig(os.path.join(path_folder, 'eof_pc_'+str(k)+'.png'), bbox_inches='tight') + plt.close() + + elif mode == 'ts': + plt.plot(self.time, pc, label=str(k)) + + if mode == 'ts': + plt.savefig(os.path.join(path_folder, 'pc_'+'-'.join([str(i) for i in number])+'.png')) + plt.legend() \ No newline at end of file From 5fa2525fabbb182e3a4e43cff7472a7ce8b03e8a Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 18 May 2022 15:56:53 +0200 Subject: [PATCH 61/80] Add unit cmweEl for ellipsoidal EWH to create_grid --- gravity_toolkit/toolbox.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/gravity_toolkit/toolbox.py b/gravity_toolkit/toolbox.py index f52f26b3..687db527 100644 --- a/gravity_toolkit/toolbox.py +++ b/gravity_toolkit/toolbox.py @@ -28,7 +28,7 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d lmax : maximum degree of spherical harmonics used rad : radius of the gaussian filter. If set to 0, no gaussian filter is apply destripe : boolean to apply or not the destripe method of harmonics - unit : unit of the grid in ['cmwe', 'geoid', 'cmwe_ne', 'microGal'] + unit : unit of the grid in ['cmwe', 'cmweEl', 'geoid', 'cmwe_ne', 'microGal'] dlon : output longitude spacing dlat : output latitude spacing bounds : list with [lon_max, lon_min, lat_max, lat_min] @@ -73,6 +73,8 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d if unit == 'cmwe': dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).cmwe + elif unit == 'cmweEl': + dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).cmweEL elif unit == 'geoid': dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).mmGH elif unit == 'cmwe_ne': @@ -80,7 +82,7 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d elif unit == 'microGal': dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).microGal else: - raise ValueError("Unit not accepted, should be either 'cmwe' or 'cmwe_ne' or 'geoid' or 'microGal'") + raise ValueError("Unit not accepted, should be either 'cmwe' pr 'cmweEl' or 'cmwe_ne' or 'geoid' or 'microGal'") # converting harmonics to truncated, smoothed coefficients in units # combining harmonics to calculate output spatial fields From bc7ff8cb820b408b06c10abfbd0f0bb3800006ae Mon Sep 17 00:00:00 2001 From: hulecom Date: Mon, 23 May 2022 11:16:09 +0200 Subject: [PATCH 62/80] Debug toolbox and add new function hs_to_grid_amp --- gravity_toolkit/toolbox.py | 53 +++++++++++++++++++++++++++++++++----- 1 file changed, 46 insertions(+), 7 deletions(-) diff --git a/gravity_toolkit/toolbox.py b/gravity_toolkit/toolbox.py index 687db527..4625fb3f 100644 --- a/gravity_toolkit/toolbox.py +++ b/gravity_toolkit/toolbox.py @@ -1,3 +1,5 @@ +import os.path + from gravity_toolkit.gauss_weights import gauss_weights from gravity_toolkit.gen_stokes import gen_stokes from gravity_toolkit.harmonics import harmonics @@ -74,7 +76,7 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d if unit == 'cmwe': dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).cmwe elif unit == 'cmweEl': - dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).cmweEL + dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).cmweEl elif unit == 'geoid': dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).mmGH elif unit == 'cmwe_ne': @@ -498,7 +500,7 @@ def plot_rms_map(grid, path=False, proj=ccrs.PlateCarree(), unit='cmwe', bound=N subplot_kw=dict(projection=proj)) if bound is None: - vmin, vmax = int(np.min(data_to_set)), int(np.ceil(np.max(data_to_set))) + vmin, vmax = np.floor(np.min(data_to_set)), np.ceil(np.max(data_to_set)) else: vmin, vmax = bound @@ -521,7 +523,7 @@ def plot_rms_map(grid, path=False, proj=ccrs.PlateCarree(), unit='cmwe', bound=N # rasterized colorbar to remove lines cbar.solids.set_rasterized(True) # Add label to the colorbar - if unit == "cmwe": + if unit == "cmwe" or unit == "cmweEl": cbar.ax.set_xlabel('Equivalent Water Thickness', labelpad=10, fontsize=24) cbar.ax.set_ylabel('cm', fontsize=24, rotation=0, labelpad=10) elif unit == "cmwe_ne": @@ -552,11 +554,11 @@ def plot_rms_map(grid, path=False, proj=ccrs.PlateCarree(), unit='cmwe', bound=N # adjust subplot within figure fig.subplots_adjust(left=0.02, right=0.98, bottom=0.05, top=0.98) - if path: + if path and os.path.isdir(os.path.dirname(str(path))): plt.savefig(path, bbox_inches='tight') + plt.close() else: plt.show() - plt.close() def calc_rms_grid(grid, mask=None): @@ -629,7 +631,7 @@ def plot_rms_grid(grid, path=False, labels=None, mask=None, unit='cmwe'): plt.plot(g.time, rms, label=l) plt.xlabel('Time (y)') - if unit == "cmwe": + if unit == "cmwe" or unit == "cmweEl": plt.ylabel('cm EWH') elif unit == "cmwe_ne": plt.ylabel('Non elastic cm EWH') @@ -650,4 +652,41 @@ def plot_rms_grid(grid, path=False, labels=None, mask=None, unit='cmwe'): plt.savefig(path, bbox_inches='tight') else: plt.show() - plt.close() \ No newline at end of file + plt.close() + + +def hs_to_grid_amp(amplitude, l, m, unit='cmwe', map=False): + """ + Create a grid corresponding to a particular spherical harmonic coefficient in a unit. + Return the amplitude of the grid create by this coefficient in the given unit and the map signal + + Parameters + ---------- + amplitude : amplitude of the Spherical harmonic coefficient + l : degree + m : order + unit : unit of the grid + map : Default to False, True to print a map of the coefficent and give a path to save the map + + Returns + ------- + max, min : bound value of the grid create with the given amplitude in the asked unit + """ + ylms = harmonics(lmax=l, mmax=np.abs(m)) + ylms.time = np.array([0]) + ylms.month = np.array([0]) + + ylms.clm = np.zeros((l + 1, l + 1)) + ylms.slm = np.zeros((l + 1, l + 1)) + if m >= 0: + ylms.clm[l, np.abs(m)] = amplitude + else: + ylms.slm[l, np.abs(m)] = amplitude + + grid = create_grid(ylms, l, unit=unit) + + if map: + grid.data = grid.data[:, :, np.newaxis] + plot_rms_map(grid, path=map, unit=unit) + + return np.max(grid.data), np.min(grid.data) \ No newline at end of file From b24c739447ed28ea65ba6668346ff1be8f936c2c Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 15 Nov 2022 14:42:44 +0100 Subject: [PATCH 63/80] Update and debug various function --- gravity_toolkit/gen_stokes.py | 24 +++--- gravity_toolkit/harmonics.py | 136 +++++++++++++++++++--------------- gravity_toolkit/spatial.py | 22 ++++-- gravity_toolkit/toolbox.py | 88 ++++++++++------------ gravity_toolkit/units.py | 2 +- gravity_toolkit/wavelets.py | 2 +- 6 files changed, 148 insertions(+), 126 deletions(-) diff --git a/gravity_toolkit/gen_stokes.py b/gravity_toolkit/gen_stokes.py index 2ae5945d..523618c3 100755 --- a/gravity_toolkit/gen_stokes.py +++ b/gravity_toolkit/gen_stokes.py @@ -167,29 +167,33 @@ def gen_stokes(data, lon, lat, LMIN=0, LMAX=60, MMAX=None, UNITS=1, # custom units dfactor = np.copy(UNITS) int_fact[:] = np.sin(th)*dphi*dth - elif (UNITS == 1): + elif UNITS == 1: # Default Parameter: Input in cm w.e. (g/cm^2) dfactor = factors.spatial(*LOVE).cmwe int_fact[:] = np.sin(th)*dphi*dth - elif (UNITS == 2): + elif UNITS == 2: # Input in gigatonnes (Gt) dfactor = factors.spatial(*LOVE).cmwe # rad_e: Average Radius of the Earth [cm] int_fact[:] = 1e15/(factors.rad_e**2) - elif (UNITS == 3): + elif UNITS == 3: # Input in kg/m^2 (mm w.e.) dfactor = factors.spatial(*LOVE).mmwe int_fact[:] = np.sin(th)*dphi*dth - elif (UNITS == 4): + elif UNITS == 4: #-- Inputs in mmGH dfactor = factors.mmGH int_fact[:] = np.sin(th) * dphi * dth - elif (UNITS == 5): + elif UNITS == 5: dfactor = factors.microGal int_fact[:] = np.sin(th) * dphi * dth - elif (UNITS == 6): + elif UNITS == 6: dfactor = factors.cmwe_ne int_fact[:] = np.sin(th) * dphi * dth + elif UNITS == 7: + #-- Inputs in units with no dfactor + dfactor = factors.norm + int_fact[:] = np.sin(th) * dphi * dth else: raise ValueError(f'Unknown units {UNITS}') @@ -213,12 +217,12 @@ def gen_stokes(data, lon, lat, LMIN=0, LMAX=60, MMAX=None, UNITS=1, plm[:,m,j] = PLM[:,m,j]*int_fact[j] # Initializing preliminary spherical harmonic matrices - yclm = np.zeros((LMAX+1, MMAX+1)) - yslm = np.zeros((LMAX+1, MMAX+1)) + yclm = np.zeros((LMAX + 1, MMAX + 1)) + yslm = np.zeros((LMAX + 1, MMAX + 1)) # Initializing output spherical harmonic matrices Ylms = gravity_toolkit.harmonics(lmax=LMAX, mmax=MMAX) - Ylms.clm = np.zeros((LMAX+1, MMAX+1)) - Ylms.slm = np.zeros((LMAX+1, MMAX+1)) + Ylms.clm = np.zeros((LMAX + 1, MMAX + 1)) + Ylms.slm = np.zeros((LMAX + 1, MMAX + 1)) # Multiplying gridded data with sin/cos of m#phis # This will sum through all phis in the dot product # output [m,theta] diff --git a/gravity_toolkit/harmonics.py b/gravity_toolkit/harmonics.py index 38b8c1e0..c7880baa 100644 --- a/gravity_toolkit/harmonics.py +++ b/gravity_toolkit/harmonics.py @@ -2112,7 +2112,7 @@ def plot_correlation(self, l, m, save_path=False): plt.show() - def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], save_path=False): + def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], color=[], save_path=False): """ Plot Cl,m and Sl,m harmonic coefficients Inputs: @@ -2126,20 +2126,28 @@ def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], save_path=False) """ #-- figure for Cl,m plt.figure() + ax = plt.gca() plt.title("Normalized spherical harmonics coefficient $C_{" + str(l) + "," + str(m) + "}$") if len(ylms): - plt.plot(self.time, self.clm[l, m, :], label=label[0]) + if len(color): + plt.plot(self.time, self.clm[l, m, :], label=label[0], color=color[0]) + else: + plt.plot(self.time, self.clm[l, m, :], label=label[0]) else: plt.plot(self.time, self.clm[l, m, :], label="$C_{" + str(l) + "," + str(m) + "}$") try: for i in range(len(ylms)): - plt.plot(ylms[i].time, ylms[i].clm[l, m, :], label=label[i+1]) + if len(color): + plt.plot(ylms[i].time, ylms[i].clm[l, m, :], label=label[i + 1], color=color[i + 1]) + else: + plt.plot(ylms[i].time, ylms[i].clm[l, m, :], label=label[i + 1]) except IndexError: raise IndexError("The list of labels is incomplete for correct plotting") plt.xlabel("Time (year)") plt.legend() + ax.yaxis.offsetText.set_horizontalalignment('right') if dates: plt.xlim(dates) plt.grid() @@ -2148,25 +2156,33 @@ def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], save_path=False) if os.path.isdir(save_path): plt.savefig(os.path.join(save_path, 'C' + str(l) + str(m) + '_coefficient.png')) else: - plt.savefig(save_path[:-3] + 'c' + save_path[-3:]) + plt.savefig(save_path[:-4] + 'c' + save_path[-4:]) if m: #-- figure for Sl,m plt.figure() + ax = plt.gca() plt.title("Normalized spherical harmonic coefficient $S_{" + str(l) + "," + str(m) + "}$") if len(ylms): - plt.plot(self.time, self.slm[l, m, :], label=label[0]) + if len(color): + plt.plot(self.time, self.slm[l, m, :], label=label[0], color=color[0]) + else: + plt.plot(self.time, self.slm[l, m, :], label=label[0]) else: plt.plot(self.time, self.slm[l, m, :], label="$S_{" + str(l) + "," + str(m) + "}$") try: for i in range(len(ylms)): - plt.plot(ylms[i].time, ylms[i].slm[l, m, :], label=label[i + 1]) + if len(color): + plt.plot(ylms[i].time, ylms[i].slm[l, m, :], label=label[i + 1], color=color[i + 1]) + else: + plt.plot(ylms[i].time, ylms[i].slm[l, m, :], label=label[i + 1]) except IndexError: raise IndexError("The list of labels is incomplete for correct plotting") plt.xlabel("Time (year)") plt.legend() + ax.yaxis.offsetText.set_horizontalalignment('right') if dates: plt.xlim(dates) plt.grid() @@ -2175,11 +2191,11 @@ def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], save_path=False) if os.path.isdir(save_path): plt.savefig(os.path.join(save_path, 'S' + str(l) + str(m) + '_coefficient.png')) else: - plt.savefig(save_path[:-3] + 's' + save_path[-3:]) + plt.savefig(save_path[:-4] + 's' + save_path[-4:]) plt.show() - def plot_fft(self, l, m, save_path=False): + def plot_fft(self, l, m, save_path=False, fmax=6): """ Plot Cl,m and Sl,m harmonic coefficients fast fourrier transform Inputs: @@ -2188,11 +2204,12 @@ def plot_fft(self, l, m, save_path=False): Options: save_path : if not False, give a path to save the figure + fmax : maximal frequency (default to 6 for period > 2 months) """ #-- compute fft and create x monthly frequency N = len(self.time) - cf = sc.fft.fft(self.clm[l, m, :]) - sf = sc.fft.fft(self.slm[l, m, :]) + cf = sc.fft.fft(self.clm[l, m, :])[0:N // 2] + sf = sc.fft.fft(self.slm[l, m, :])[0:N // 2] xf = np.linspace(0.0, 12/2, N // 2) # -- figure for Cl,m and Sl,m @@ -2200,9 +2217,9 @@ def plot_fft(self, l, m, save_path=False): plt.title("Fourier transform of the normalized spherical harmonic coefficients $C_{" + str(l) + "," + str( m) + "}$ et $S_{" + str( l) + "," + str(m) + "}$") - plt.plot(xf, 2.0 / N * np.abs(cf[0:N // 2]), label="$C_{" + str(l) + "," + str(m) + "}$") + plt.plot(xf[xf <= fmax], 2.0 / N * np.abs(cf[xf <= fmax]), label="$C_{" + str(l) + "," + str(m) + "}$") if m: - plt.plot(xf, 2.0 / N * np.abs(sf[0:N // 2]), label="$S_{" + str(l) + "," + str(m) + "}$") + plt.plot(xf[xf <= fmax], 2.0 / N * np.abs(sf[xf <= fmax]), label="$S_{" + str(l) + "," + str(m) + "}$") plt.xlabel("Frequency ($year^{-1}$)") @@ -2218,7 +2235,7 @@ def plot_fft(self, l, m, save_path=False): plt.show() - def plot_wavelets(self, l, m, s0=0, pad=1, lag1=0, plot_coi=True, mother='MORLET', param=-1, func_plot=np.abs, save_path=False): + def plot_wavelets(self, l, m, s0=0, j1=None, pad=1, lag1=0, plot_coi=True, mother='MORLET', param=-1, func_plot=np.abs, save_path=False): """ Plot Cl,m and Sl,m wavelet analysis based on (Torrence and Compo, 1998) @@ -2246,8 +2263,10 @@ def plot_wavelets(self, l, m, s0=0, pad=1, lag1=0, plot_coi=True, mother='MORLET if not s0: s0 = 4 * dt # min scale of the wavelets + # max resolution of the wavelet, fixed for GRACE - j1 = 4.5 / dj + if j1 is None: + j1 = np.log2(11/s0)/dj siglvl = 0.95 @@ -2319,56 +2338,57 @@ def plot_wavelets(self, l, m, s0=0, pad=1, lag1=0, plot_coi=True, mother='MORLET axs[1].set_xlabel('Power') axs[1].set_title('Global Wavelet Spectrum') - plt.legend() + plt.legend(loc='upper right') if save_path: if os.path.isdir(save_path): plt.savefig(os.path.join(save_path, 'C' + str(l) + str(m) + '_wavelet.png')) else: - plt.savefig(save_path[:-3] + 'c' + save_path[-3:]) - - # create figure Sl,m - fig = plt.figure(constrained_layout=True, figsize=(12, 6), dpi=200) - spec = matplotlib.gridspec.GridSpec(ncols=2, nrows=1, wspace=0.02, width_ratios=[3, 1]) - ax0 = fig.add_subplot(spec[0]) - ax1 = fig.add_subplot(spec[1], sharey=ax0) - axs = [ax0, ax1] - plt.setp(axs[1].get_yticklabels(), visible=False) - - # plot wavelet - im = axs[0].contourf(self.time, period, np.abs(waves), 100) - axs[0].contour(self.time, period, sig95s, levels=[1], linewidths=2) - - # plot cone of interest of the wavelet - if plot_coi: - axs[0].fill(np.concatenate((self.time[:1] - 0.0001, self.time, self.time[-1:] + 0.0001, - self.time[-1:] + 0.0001, self.time[:1] - 0.0001, self.time[:1] - 0.0001)), - np.concatenate(([s0], coi, [s0], period[-1:], period[-1:], [s0])), 'r', alpha=0.2, hatch='/') - axs[0].plot(self.time, coi, 'r--', lw=1.4) + plt.savefig(save_path[:-4] + 'c' + save_path[-4:]) - fig.colorbar(im, ax=axs[0], location='left') - axs[0].invert_yaxis() - axs[0].set_yscale('log', base=2) - axs[0].set_ylabel('Period (year)') - axs[0].set_ylim(np.max(period), np.min(period)) - axs[0].set_yticks(yticks) - axs[0].set_xlabel('Time (year)') - axs[0].get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) - axs[0].set_title('Wavelet Power Spectrum') - - # plot fft analysis at the right of the figure - axs[1].plot(sxxs, 1 / f * dt, 'gray', label='Fourier spectrum') - axs[1].plot(global_wss, period, 'b', label='Wavelet spectrum') - axs[1].plot(np.array(signifs) * np.var(self.clm[l, m]), period, 'g--', label='95% confidence spectrum') - axs[1].set_xlabel('Power') - axs[1].set_title('Global Wavelet Spectrum') - - plt.legend() + if m: + # create figure Sl,m + fig = plt.figure(constrained_layout=True, figsize=(12, 6), dpi=200) + spec = matplotlib.gridspec.GridSpec(ncols=2, nrows=1, wspace=0.02, width_ratios=[3, 1]) + ax0 = fig.add_subplot(spec[0]) + ax1 = fig.add_subplot(spec[1], sharey=ax0) + axs = [ax0, ax1] + plt.setp(axs[1].get_yticklabels(), visible=False) + + # plot wavelet + im = axs[0].contourf(self.time, period, np.abs(waves), 100) + axs[0].contour(self.time, period, sig95s, levels=[1], linewidths=2) + + # plot cone of interest of the wavelet + if plot_coi: + axs[0].fill(np.concatenate((self.time[:1] - 0.0001, self.time, self.time[-1:] + 0.0001, + self.time[-1:] + 0.0001, self.time[:1] - 0.0001, self.time[:1] - 0.0001)), + np.concatenate(([s0], coi, [s0], period[-1:], period[-1:], [s0])), 'r', alpha=0.2, hatch='/') + axs[0].plot(self.time, coi, 'r--', lw=1.4) + + fig.colorbar(im, ax=axs[0], location='left') + axs[0].invert_yaxis() + axs[0].set_yscale('log', base=2) + axs[0].set_ylabel('Period (year)') + axs[0].set_ylim(np.max(period), np.min(period)) + axs[0].set_yticks(yticks) + axs[0].set_xlabel('Time (year)') + axs[0].get_yaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter()) + axs[0].set_title('Wavelet Power Spectrum') + + # plot fft analysis at the right of the figure + axs[1].plot(sxxs, 1 / f * dt, 'gray', label='Fourier spectrum') + axs[1].plot(global_wss, period, 'b', label='Wavelet spectrum') + axs[1].plot(np.array(signifs) * np.var(self.clm[l, m]), period, 'g--', label='95% confidence spectrum') + axs[1].set_xlabel('Power') + axs[1].set_title('Global Wavelet Spectrum') + + plt.legend(loc='upper right') - if save_path: - if os.path.isdir(save_path): - plt.savefig(os.path.join(save_path, 'C' + str(l) + str(m) + '_wavelet.png')) - else: - plt.savefig(save_path[:-3] + 's' + save_path[-3:]) + if save_path: + if os.path.isdir(save_path): + plt.savefig(os.path.join(save_path, 'S' + str(l) + str(m) + '_wavelet.png')) + else: + plt.savefig(save_path[:-4] + 's' + save_path[-4:]) plt.show() diff --git a/gravity_toolkit/spatial.py b/gravity_toolkit/spatial.py index fdb8af73..73974019 100644 --- a/gravity_toolkit/spatial.py +++ b/gravity_toolkit/spatial.py @@ -2103,25 +2103,33 @@ def plot_eof(self, number, path_folder, cmap='viridis', mode='full', unit='cmwe' axplot.yaxis.set_label_position("right") axplot.set_xlabel('Time (year)') + axfft = fig.add_subplot(spec[6:10, 1:], box_aspect=0.5) + plt.plot(1 / xf[:len(xf) // 2][1 / xf[:len(xf) // 2] < 10], + 2.0 / len(self.time) * np.abs(f[:len(xf) // 2][1 / xf[:len(xf) // 2] < 10])) + axfft.yaxis.tick_right() + axfft.set_xlim(0, 10) + axfft.set_ylim(0, ) + axfft.set_xlabel('Period (year)') + axfft.yaxis.set_label_position("right") + if unit == "cmwe": axplot.set_ylabel('Equivalent Water\nThickness\ncm', labelpad=50, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$cm^2$', labelpad=50, fontsize=12, rotation='horizontal') elif unit == "cmwe_ne": axplot.set_ylabel('Non elastic\n Equivalent Water\nThickness\ncm', labelpad=50, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$cm^2$', labelpad=50, fontsize=12, rotation='horizontal') elif unit == "mmwe": axplot.set_ylabel('Equivalent Water\n Thickness\nmm', labelpad=50, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$mm^2$', labelpad=50, fontsize=12, rotation='horizontal') elif unit == "geoid": axplot.set_ylabel('Geoid Height\nmm', labelpad=45, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$mm^2$', labelpad=50, fontsize=12, rotation='horizontal') elif unit == "microGal": axplot.set_ylabel('Acceleration\n$\mu Gal$', labelpad=40, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$\mu Gal^2$', labelpad=50, fontsize=12, rotation='horizontal') elif unit == "secacc": axplot.set_ylabel('Secular\n Acceleration\n$nT.y^{-2}$', labelpad=40, fontsize=12, rotation='horizontal') - - axfft = fig.add_subplot(spec[6:10, 1:], box_aspect=0.5) - plt.plot(1/xf[:len(xf)//2][1/xf[:len(xf)//2] < 10], 2.0/len(self.time) * np.abs(f[:len(xf)//2][1/xf[:len(xf)//2] < 10])) - axfft.yaxis.tick_right() - axfft.set_xlim(0, 10) - axfft.set_ylim(0,) - axfft.set_xlabel('Period (year)') + axfft.set_ylabel('Power\n$nT^2.y^{-4}$', labelpad=50, fontsize=12, rotation='horizontal') plt.savefig(os.path.join(path_folder, 'eof_pc_'+str(k)+'.png'), bbox_inches='tight') plt.close() diff --git a/gravity_toolkit/toolbox.py b/gravity_toolkit/toolbox.py index 4625fb3f..60870eff 100644 --- a/gravity_toolkit/toolbox.py +++ b/gravity_toolkit/toolbox.py @@ -52,6 +52,11 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d grid.lon = np.arange(-bounds[1] + dlon / 2.0, bounds[0] + dlon / 2.0, dlon) grid.lat = np.arange(bounds[2] - dlat / 2.0, -bounds[3] - dlat / 2.0, -dlat) + if lmax is None: + lmax = Ylms.lmax + else: + Ylms.lmax = lmax + nlon = len(grid.lon) nlat = len(grid.lat) @@ -62,10 +67,7 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d # Computing plms for converting to spatial domain theta = (90.0 - grid.lat) * np.pi / 180.0 - if lmax is None: - PLM, dPLM = plm_holmes(Ylms.lmax, np.cos(theta)) - else: - PLM, dPLM = plm_holmes(lmax, np.cos(theta)) + PLM, dPLM = plm_holmes(lmax, np.cos(theta)) # read load love numbers file love_numbers_file = get_data_path(['data', 'love_numbers']) @@ -74,15 +76,17 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d hl, kl, ll = read_love_numbers(love_numbers_file, REFERENCE='CF') if unit == 'cmwe': - dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).cmwe + dfactor = units(lmax=lmax).harmonic(hl, kl, ll).cmwe elif unit == 'cmweEl': - dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).cmweEl + dfactor = units(lmax=lmax).harmonic(hl, kl, ll).cmweEl elif unit == 'geoid': - dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).mmGH + dfactor = units(lmax=lmax).harmonic(hl, kl, ll).mmGH elif unit == 'cmwe_ne': - dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).cmwe_ne + dfactor = units(lmax=lmax).harmonic(hl, kl, ll).cmwe_ne elif unit == 'microGal': - dfactor = units(lmax=Ylms.lmax).harmonic(hl, kl, ll).microGal + dfactor = units(lmax=lmax).harmonic(hl, kl, ll).microGal + elif unit == 'none': + dfactor = units(lmax=lmax).harmonic(hl, kl, ll).norm else: raise ValueError("Unit not accepted, should be either 'cmwe' pr 'cmweEl' or 'cmwe_ne' or 'geoid' or 'microGal'") @@ -93,9 +97,9 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d grid.data = np.zeros((nlat, nlon)) if destripe: - tmp = Ylms.destripe() + tmp = Ylms.copy().destripe() else: - tmp = Ylms + tmp = Ylms.copy() if rad != 0: wt = 2.0 * np.pi * gauss_weights(rad, lmax) @@ -103,11 +107,7 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d else: tmp.convolve(dfactor) # convert spherical harmonics to output spatial grid - if lmax is None: - grid.data[:, :] = harmonic_summation(tmp.clm, tmp.slm, - grid.lon, grid.lat, LMAX=Ylms.lmax, MMAX=Ylms.mmax, PLM=PLM).T - else: - grid.data[:, :] = harmonic_summation(tmp.clm, tmp.slm, + grid.data[:, :] = harmonic_summation(tmp.clm, tmp.slm, grid.lon, grid.lat, LMAX=lmax, MMAX=lmax, PLM=PLM).T else: @@ -124,13 +124,9 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d wt = 2.0 * np.pi * gauss_weights(rad, lmax) tmp.convolve(dfactor * wt) else: - tmp.convolve(dfactor * np.ones((Ylms.lmax + 1))) + tmp.convolve(dfactor * np.ones((lmax + 1))) # convert spherical harmonics to output spatial grid - if lmax is None: - grid.data[:, :, i] = harmonic_summation(tmp.clm, tmp.slm, - grid.lon, grid.lat, LMAX=Ylms.lmax, MMAX=Ylms.mmax, PLM=PLM).T - else: - grid.data[:, :, i] = harmonic_summation(tmp.clm, tmp.slm, + grid.data[:, :, i] = harmonic_summation(tmp.clm, tmp.slm, grid.lon, grid.lat, LMAX=lmax, MMAX=lmax, PLM=PLM).T grid.mask = np.zeros(grid.data.shape) @@ -179,6 +175,8 @@ def grid_to_hs(grid, lmax, mmax=None, unit='cmwe'): UNITS = 6 elif unit == 'microGal': UNITS = 5 + elif unit == 'norm': + UNITS = 7 else: raise ValueError("Unit not accepted, should be either 'cmwe' or 'cmwe_ne' or 'geoid' or 'microGal'") @@ -216,7 +214,7 @@ def diff_grid(grid1, grid2): Parameters ---------- grid1 : spatial object - grid2 : spatial object to substract to the first + grid2 : spatial object to subtract to the first Returns ------- @@ -389,15 +387,6 @@ def save_gif(grid, path, unit='cmwe', bound=None, mask=None, color='viridis'): else: vmin, vmax = bound - if vmax - vmin >= 3: - levels = np.arange(vmin, vmax, max(1, int((vmax - vmin)/10))) - elif vmax - vmin >= 0.3: - levels = np.arange(vmin, vmax, max(0.1, float('%.1f'%((vmax - vmin)/10)))) - elif vmax - vmin >= 0.03: - levels = np.arange(vmin, vmax, max(0.1, float('%.2f'%((vmax - vmin)/10)))) - else: - raise ValueError("The range of data to plot is too small") - norm = colors.Normalize(vmin=vmin,vmax=vmax) cmap = plt.cm.get_cmap(color) im = ax1.imshow(np.zeros((np.int(180.0 + 1.0),np.int(360.0 + 1.0))), interpolation='nearest', @@ -441,16 +430,8 @@ def save_gif(grid, path, unit='cmwe', bound=None, mask=None, color='viridis'): cbar.ax.set_ylabel('$nT.y^{-2}$', fontsize=24, rotation=0) cbar.ax.yaxis.set_label_coords(1.045, 0.1) - # Set the tick levels for the colorbar - cbar.set_ticks(levels) - if vmax - vmin >= 3: - cbar.set_ticklabels(['{0:d}'.format(ct) for ct in levels]) - elif vmax - vmin >= 0.3: - cbar.set_ticklabels(['{.1f}'.format(ct) for ct in levels]) - elif vmax - vmin >= 0.03: - cbar.set_ticklabels(['{.2f}'.format(ct) for ct in levels]) # ticks lines all the way across - cbar.ax.tick_params(which='both', width=1, length=26, labelsize=24, + cbar.ax.tick_params(which='both', width=1, length=26, labelsize=20, direction='in') # stronger linewidth on frame @@ -468,7 +449,7 @@ def animate_frames(i): # set animation anim = animation.FuncAnimation(fig, animate_frames, frames=len(grid.month)) - HTML(anim.to_jshtml()) + #HTML(anim.to_jshtml()) anim.save(path, writer='imagemagick', fps=10) plt.clf() @@ -560,6 +541,8 @@ def plot_rms_map(grid, path=False, proj=ccrs.PlateCarree(), unit='cmwe', bound=N else: plt.show() + return np.sqrt(np.sum(grid.data ** 2, axis=2) / grid.time.shape[0]) + def calc_rms_grid(grid, mask=None): """ @@ -672,16 +655,23 @@ def hs_to_grid_amp(amplitude, l, m, unit='cmwe', map=False): ------- max, min : bound value of the grid create with the given amplitude in the asked unit """ - ylms = harmonics(lmax=l, mmax=np.abs(m)) + ylms = harmonics(lmax=np.max(l), mmax=np.max(l)) ylms.time = np.array([0]) ylms.month = np.array([0]) - ylms.clm = np.zeros((l + 1, l + 1)) - ylms.slm = np.zeros((l + 1, l + 1)) - if m >= 0: - ylms.clm[l, np.abs(m)] = amplitude - else: - ylms.slm[l, np.abs(m)] = amplitude + ylms.clm = np.zeros((np.max(l) + 1, np.max(l) + 1)) + ylms.slm = np.zeros((np.max(l) + 1, np.max(l) + 1)) + try: + for amp, i, j in zip(amplitude, l, m): + if j >= 0: + ylms.clm[i, np.abs(j)] = amp + else: + ylms.slm[i, np.abs(j)] = amp + except TypeError: + if m >= 0: + ylms.clm[l, np.abs(m)] = amplitude + else: + ylms.slm[l, np.abs(m)] = amplitude grid = create_grid(ylms, l, unit=unit) diff --git a/gravity_toolkit/units.py b/gravity_toolkit/units.py index 5aeb48cf..a1502b90 100644 --- a/gravity_toolkit/units.py +++ b/gravity_toolkit/units.py @@ -231,7 +231,7 @@ def spatial(self, hl, kl, ll, **kwargs): fraction += kl[self.l] # degree dependent coefficients # norm, fully normalized spherical harmonics - self.norm = np.ones((self.lmax+1)) + self.norm = np.ones((self.lmax + 1)) # cmwe, centimeters water equivalent [g/cm^2] self.cmwe = 3.0*fraction/(1.0+2.0*self.l)/(4.0*np.pi*self.rad_e*self.rho_e) # cmwe_ne, centimeters water equivalent none elastic [g/cm^2] diff --git a/gravity_toolkit/wavelets.py b/gravity_toolkit/wavelets.py index 8153de78..6fb13e0d 100644 --- a/gravity_toolkit/wavelets.py +++ b/gravity_toolkit/wavelets.py @@ -136,7 +136,7 @@ def wavelet(Y, dt, pad=1, dj=.25, s0=-1, J1=-1, mother='MORLET', param=-1): f = np.fft.fft(x) # fft on the padded time series - scale = s0 * 2**(np.arange(0, J1 + 1, 1)*dj) + scale = s0 * 2**(np.arange(0, J1, 1)*dj) # define wavelet array wave = np.zeros((int(J1 + 1), n)) wave = wave + 1j * wave # make it complex From e07a8b15fc14ffbccd6391f643b289e246a6849c Mon Sep 17 00:00:00 2001 From: hulecom Date: Mon, 14 Aug 2023 21:43:50 +0200 Subject: [PATCH 64/80] Debug units, spatial, harmonics and run_grace_date script. Create an change information reference in the README --- README.rst | 15 ++++++++------- gravity_toolkit/harmonics.py | 7 ++++++- gravity_toolkit/spatial.py | 7 +++---- gravity_toolkit/units.py | 4 ++-- scripts/run_grace_date.py | 18 +++++++++++++++++- 5 files changed, 36 insertions(+), 15 deletions(-) diff --git a/README.rst b/README.rst index 5b4185a8..a3105f04 100644 --- a/README.rst +++ b/README.rst @@ -1,6 +1,6 @@ -=============== -gravity-toolkit -=============== +==================== +read-GRACE-harmonics +==================== |Language| |License| @@ -25,6 +25,9 @@ gravity-toolkit Python tools for obtaining and working with Level-2 spherical harmonic coefficients from the NASA/DLR Gravity Recovery and Climate Experiment (GRACE) and the NASA/GFZ Gravity Recovery and Climate Experiment Follow-On (GRACE-FO) missions +This repository is **forked** from the original one created by Tyler Sutterley. It contains additions made by Hugo Lecomte, especially for plotting purpose on harmonics and spatial objects. The additions have been developed as part of my PhD work at ITES. +I specially thank Tyler for this tool and I am glad to have been able to contribute to it. + Resources ######### @@ -90,10 +93,8 @@ Data Repositories Download ######## -| The program homepage is: -| https://github.com/tsutterley/gravity-toolkit -| A zip archive of the latest version is available directly at: -| https://github.com/tsutterley/gravity-toolkit/archive/main.zip +| The original program homepage is: +| https://github.com/tsutterley/read-GRACE-harmonics Disclaimer ########## diff --git a/gravity_toolkit/harmonics.py b/gravity_toolkit/harmonics.py index c7880baa..9feb603e 100644 --- a/gravity_toolkit/harmonics.py +++ b/gravity_toolkit/harmonics.py @@ -756,7 +756,9 @@ def from_dict(self, d, **kwargs): for key in ['l', 'm', 'clm', 'slm', 'time', 'month']: try: setattr(self, key, d[key].copy()) - except (AttributeError, KeyError): + except AttributeError: + setattr(self, key, d[key]) + except KeyError: pass # maximum degree and order self.lmax = np.max(d['l']) @@ -2060,6 +2062,9 @@ def plot_correlation(self, l, m, save_path=False): Options: save_path : if not False, give a path to save the figure + + TODO: Refaire ça avec une matrice carrée sur tous les coeffs test: C20, C21, C22, S21, S22, C30, ... + ou C20, C21, S21, C22, S22, C30, ... """ mat_c = np.zeros((self.lmax, self.lmax)) if m: diff --git a/gravity_toolkit/spatial.py b/gravity_toolkit/spatial.py index 73974019..35454464 100644 --- a/gravity_toolkit/spatial.py +++ b/gravity_toolkit/spatial.py @@ -2059,9 +2059,9 @@ def plot_eof(self, number, path_folder, cmap='viridis', mode='full', unit='cmwe' pc = v.T[k] * np.sqrt(mat_svd.shape[1] - 1) /2*(sort_eof[-int(len(sort_eof)*0.01)] - sort_eof[int(len(sort_eof)*0.01)]) if mask is None: - eof_grid.data = np.reshape(scale_eof, (self.lat.shape[0], self.lon.shape[0], 1)) + eof_grid.data = np.reshape(scale_eof, (self.lat.shape[0], self.lon.shape[0])) else: - eof_grid.data = np.zeros((self.lat.shape[0], self.lon.shape[0], 1)) + eof_grid.data = np.zeros((self.lat.shape[0], self.lon.shape[0])) eof_grid.data[mask] = scale_eof eof_grid.data[np.logical_not(mask)] = None @@ -2077,8 +2077,7 @@ def plot_eof(self, number, path_folder, cmap='viridis', mode='full', unit='cmwe' spec = matplotlib.gridspec.GridSpec(ncols=4, nrows=12, wspace=0.03, width_ratios=[8, 1, 1, 1]) axmap = fig.add_subplot(spec[:, 0], projection=ccrs.PlateCarree()) - cmap = plt.cm.get_cmap(cmap) - + cmap = matplotlib.colormaps.get_cmap(cmap) immap = axmap.imshow(eof_grid.data, cmap=cmap, transform=ccrs.PlateCarree(), extent=self.extent, origin='upper', vmin=-1.15, vmax=1.15) axmap.coastlines('50m') diff --git a/gravity_toolkit/units.py b/gravity_toolkit/units.py index a1502b90..dbd9efb0 100644 --- a/gravity_toolkit/units.py +++ b/gravity_toolkit/units.py @@ -231,7 +231,7 @@ def spatial(self, hl, kl, ll, **kwargs): fraction += kl[self.l] # degree dependent coefficients # norm, fully normalized spherical harmonics - self.norm = np.ones((self.lmax + 1)) + self.norm = np.ones((self.lmax + 1))/(4.0 * np.pi) # cmwe, centimeters water equivalent [g/cm^2] self.cmwe = 3.0*fraction/(1.0+2.0*self.l)/(4.0*np.pi*self.rad_e*self.rho_e) # cmwe_ne, centimeters water equivalent none elastic [g/cm^2] @@ -239,7 +239,7 @@ def spatial(self, hl, kl, ll, **kwargs): # mmwe, millimeters water equivalent [kg/m^2] self.mmwe = 3.0*fraction/(1.0+2.0*self.l)/(40.0*np.pi*self.rad_e*self.rho_e) # mmGH, millimeters geoid height - self.mmGH = np.ones((self.lmax+1))/(4.0*np.pi*self.rad_e) + self.mmGH = np.ones((self.lmax+1))/(4.0*np.pi*10*self.rad_e) # microGal, microGal gravity perturbations self.microGal = (self.rad_e**2.0)/(4.0*np.pi*1.e6*self.GM)/(self.l+1.0) diff --git a/scripts/run_grace_date.py b/scripts/run_grace_date.py index 0a0fb12f..151e7fad 100755 --- a/scripts/run_grace_date.py +++ b/scripts/run_grace_date.py @@ -99,6 +99,22 @@ def run_grace_date(base_dir, PROC, DREL, VERBOSE=0, MODE=0o775): 'RL05':['GAA', 'GAB', 'GAC', 'GAD', 'GSM'], 'RL06':['GAA', 'GAB', 'GAC', 'GAD', 'GSM']} VALID['JPL'] = ['RL04','RL05','RL06'] + # -- CNES RL04/5 at LMAX 90 + DSET['CNES'] = {'RL04': ['GSM'], + 'RL05': ['GSM'],} + VALID['CNES'] = ['RL04', 'RL05'] + # -- GRAZ/ITSG RL14/16/18 at LMAX 120 + DSET['GRAZ'] = {'RL14': ['GSM'], + 'RL16': ['GSM'], + 'RL18': ['GSM']} + VALID['GRAZ'] = ['RL14', 'RL16', 'RL18'] + # -- Swarm RL01 at LMAX 40 + DSET['SWARM'] = {'RL01': ['GSM'],} + VALID['SWARM'] = ['RL01'] + # -- COSTG RL01 at LMAX 90 + DSET['COSTG'] = {'RL01': ['GSM'], + 'RL06': ['GSM']} + VALID['COSTG'] = ['RL01', 'RL06'] # for each processing center for p in PROC: @@ -132,7 +148,7 @@ def arguments(): parser.add_argument('--center','-c', metavar='PROC', type=str, nargs='+', default=['CSR','GFZ','JPL'], - choices=['CSR','GFZ','JPL'], + choices=['CSR','GFZ','JPL', 'CNES','GRAZ','SWARM', 'COSTG'], help='GRACE/GRACE-FO Processing Center') # GRACE/GRACE-FO data release parser.add_argument('--release','-r', From 7c673a0cbfa50e6d72078b6b1308f824680b14bd Mon Sep 17 00:00:00 2001 From: hulecom Date: Mon, 14 Aug 2023 21:44:19 +0200 Subject: [PATCH 65/80] Debug toolbox --- gravity_toolkit/toolbox.py | 42 ++++++++++++++++++++------------------ 1 file changed, 22 insertions(+), 20 deletions(-) diff --git a/gravity_toolkit/toolbox.py b/gravity_toolkit/toolbox.py index 60870eff..42206d1e 100644 --- a/gravity_toolkit/toolbox.py +++ b/gravity_toolkit/toolbox.py @@ -93,24 +93,7 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d # converting harmonics to truncated, smoothed coefficients in units # combining harmonics to calculate output spatial fields # output spatial grid - if not (type(Ylms.month) in [list, np.array]) and len(Ylms.month) == 1: - grid.data = np.zeros((nlat, nlon)) - - if destripe: - tmp = Ylms.copy().destripe() - else: - tmp = Ylms.copy() - - if rad != 0: - wt = 2.0 * np.pi * gauss_weights(rad, lmax) - tmp.convolve(dfactor * wt) - else: - tmp.convolve(dfactor) - # convert spherical harmonics to output spatial grid - grid.data[:, :] = harmonic_summation(tmp.clm, tmp.slm, - grid.lon, grid.lat, LMAX=lmax, MMAX=lmax, PLM=PLM).T - - else: + if type(Ylms.time) in [list, np.array, np.ndarray] and len(Ylms.time) != 1: grid.data = np.zeros((nlat, nlon, len(Ylms.month))) for i, grace_month in enumerate(Ylms.month): # GRACE/GRACE-FO harmonics for time t @@ -128,6 +111,25 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d # convert spherical harmonics to output spatial grid grid.data[:, :, i] = harmonic_summation(tmp.clm, tmp.slm, grid.lon, grid.lat, LMAX=lmax, MMAX=lmax, PLM=PLM).T + else: + grid.data = np.zeros((nlat, nlon)) + + if destripe: + tmp = Ylms.copy().destripe() + else: + tmp = Ylms.copy() + if len(tmp.clm.shape) == 3: + tmp.clm = tmp.clm.reshape(tmp.clm.shape[:-1]) + tmp.slm = tmp.slm.reshape(tmp.slm.shape[:-1]) + + if rad != 0: + wt = 2.0 * np.pi * gauss_weights(rad, lmax) + tmp.convolve(dfactor * wt) + else: + tmp.convolve(dfactor * np.ones((lmax + 1))) + # convert spherical harmonics to output spatial grid + grid.data[:, :] = harmonic_summation(tmp.clm, tmp.slm, + grid.lon, grid.lat, LMAX=lmax, MMAX=lmax, PLM=PLM).T grid.mask = np.zeros(grid.data.shape) return grid @@ -155,7 +157,7 @@ def grid_to_hs(grid, lmax, mmax=None, unit='cmwe'): mmax = np.copy(lmax) if (mmax is None) else mmax # -- number of dates in data - if type(grid.time) in [list, np.array] or len(grid.time) != 1: + if type(grid.time) in [list, np.array, np.ndarray] and len(grid.time) != 1: n_time = len(grid.time) else: n_time = 1 @@ -311,7 +313,7 @@ def filt_Ylms(ylms, filt='low', filt_param=None): if filt_param is None: to_zero = np.logical_or(freq > 0.5, freq < -0.5) else: - to_zero = np.logical_or(freq > filt_param[0], freq < filt_param[0]) + to_zero = np.logical_or(freq > filt_param[0], freq < -filt_param[0]) fc[:, :, to_zero] = 0 fs[:, :, to_zero] = 0 filtered_ylms.clm = np.real(np.fft.ifft(fc, axis=2))[:, :, :ndata] From 85b35f95102990a2278fa74e23d552cf7f62d7fb Mon Sep 17 00:00:00 2001 From: hulecom Date: Mon, 14 Aug 2023 21:49:43 +0200 Subject: [PATCH 66/80] =?UTF-8?q?Add=20Notebook=20for=20"Gravitational=20c?= =?UTF-8?q?onstraints=20on=20the=20Earth=E2=80=99s=20inner=20core=20differ?= =?UTF-8?q?ential=20rotation"=20GRL=20article?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../GRL_Gravitational_Lecomte2023b.ipynb | 790 ++++++++++++++++++ 1 file changed, 790 insertions(+) create mode 100644 notebooks/GRL_Gravitational_Lecomte2023b.ipynb diff --git a/notebooks/GRL_Gravitational_Lecomte2023b.ipynb b/notebooks/GRL_Gravitational_Lecomte2023b.ipynb new file mode 100644 index 00000000..e3a6d3c1 --- /dev/null +++ b/notebooks/GRL_Gravitational_Lecomte2023b.ipynb @@ -0,0 +1,790 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "69ec460e", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-14T16:26:53.611538Z", + "start_time": "2023-08-14T16:26:52.366902Z" + } + }, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "import scipy as sc\n", + "import matplotlib.pyplot as plt\n", + "import scipy.signal as sg\n", + "\n", + "from gravity_toolkit.harmonics import harmonics\n", + "from gravity_toolkit.grace_find_months import grace_find_months\n", + "from gravity_toolkit.grace_input_months import grace_input_months\n", + "from gravity_toolkit.spatial import spatial\n", + "\n", + "from gravity_toolkit.toolbox import create_grid, grid_to_hs, filt_Ylms\n", + "\n", + "# maximal degree to load for the Stokes coefficients\n", + "n_harmo = 3\n", + "\n", + "# Base directory with all the dataset (see read-GRACE-harmonics installation)\n", + "base_dir = '/home/hugo/Documents/GRACE_DATA'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e637b560", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-14T16:27:28.613989Z", + "start_time": "2023-08-14T16:26:54.776331Z" + } + }, + "outputs": [], + "source": [ + "# Read CSR, GRAZ and COST-G data from the GRACE mission from the april 2002 to end of 2022\n", + "\n", + "total_months = grace_find_months(base_dir, 'CSR', 'RL06', DSET='GSM')\n", + "start_mon = np.min(total_months['months'])\n", + "end_mon = 251 # end of 2022\n", + "settmp = set(np.arange(start_mon,end_mon+1)) - set(total_months['months'])\n", + "missing = sorted(settmp)\n", + "Ylms = grace_input_months(base_dir, 'CSR', 'RL06', 'GSM',\n", + " n_harmo, start_mon, end_mon, missing, SLR_C20='GSFC', DEG1='', SLR_C30='GSFC')\n", + "# create harmonics object and remove mean\n", + "GRACE_Ylms = harmonics().from_dict(Ylms)\n", + "\n", + "total_months = grace_find_months(base_dir, 'GRAZ', 'RL18', DSET='GSM')\n", + "settmp = set(np.arange(start_mon,end_mon+1)) - set(total_months['months'])\n", + "missing = sorted(settmp)\n", + "Ylms = grace_input_months(base_dir, 'GRAZ', 'RL18', 'GSM',\n", + " n_harmo, start_mon, end_mon, missing, SLR_C20='GSFC', DEG1='', SLR_C30='GSFC')\n", + "# create harmonics object and remove mean\n", + "GRAZ_Ylms = harmonics().from_dict(Ylms)\n", + "\n", + "total_months = grace_find_months(base_dir, 'COSTG', 'RL06', DSET='GSM')\n", + "settmp = set(np.arange(start_mon,end_mon+1)) - set(total_months['months'])\n", + "missing = sorted(settmp)\n", + "Ylms = grace_input_months(base_dir, 'COSTG', 'RL06', 'GSM',\n", + " n_harmo, start_mon, end_mon, missing, SLR_C20='GSFC', DEG1='', SLR_C30='GSFC')\n", + "# create harmonics object and remove mean\n", + "COSTG_Ylms = harmonics().from_dict(Ylms)\n", + "\n", + "# remove mean to talk in gravity anomalies\n", + "GRACE_Ylms.mean(apply=True)\n", + "GRAZ_Ylms.mean(apply=True)\n", + "COSTG_Ylms.mean(apply=True)\n", + "\n", + "# Temporal filtering with a 3 year low pass filter\n", + "GRACE_filt_Ylms = filt_Ylms(GRACE_Ylms.copy(), filt='fft', filt_param=[1/3])\n", + "GRAZ_filt_Ylms = filt_Ylms(GRAZ_Ylms.copy(), filt='fft', filt_param=[1/3])\n", + "COSTG_filt_Ylms = filt_Ylms(COSTG_Ylms.copy(), filt='fft', filt_param=[1/3])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5da6ed08", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-14T16:28:27.907563Z", + "start_time": "2023-08-14T16:28:08.860868Z" + } + }, + "outputs": [], + "source": [ + "# remove trend to remove GIA effects and have a better spectral analysis\n", + "detrend=True\n", + "\n", + "# list of IGG-SLR file\n", + "files = os.listdir(os.path.join(base_dir, 'IGG/IGG_SLR_HYBRID'))\n", + "files.sort()\n", + "\n", + "# create a harmonics object to fill with IGG-SLR data\n", + "ylms_slr = harmonics(lmax=n_harmo, mmax=n_harmo)\n", + "ylms_slr.time = np.zeros(len(files))\n", + "ylms_slr.month = np.zeros(len(files))\n", + "ylms_slr.clm = np.zeros((n_harmo+1, n_harmo+1, len(files)))\n", + "ylms_slr.slm = np.zeros((n_harmo+1, n_harmo+1, len(files)))\n", + "\n", + "ylms_slr.update_dimensions()\n", + "\n", + "# fill the harmonics object\n", + "for i, f in enumerate(files):\n", + " ylms_tmp = harmonics().from_gfc(os.path.join(base_dir, 'IGG/IGG_SLR_HYBRID', f))\n", + " ylms_slr.time[i] = int(f[23:27]) + int(f[28:30])/12 - 1/24\n", + " ylms_slr.clm[:,:, i] = ylms_tmp.clm[:n_harmo+1, :n_harmo+1]\n", + " ylms_slr.slm[:,:, i] = ylms_tmp.slm[:n_harmo+1, :n_harmo+1]\n", + "\n", + "# convert decimal year to GRACE month equivalent\n", + "ylms_slr.month = np.floor((ylms_slr.time - 2002)*12)\n", + "# remove mean to talk in gravity anomalies\n", + "ylms_slr.mean(apply=True)\n", + "\n", + "\n", + "# Read ISBA data in m EWH after index 170 (= start of IGG-SLR product)\n", + "grid_isba_slr = spatial().from_HDF5(os.path.join(base_dir, 'HYDRO/ISBA-CTRIP_erai_gpcc_monthly_tws_1979-2019.nc'), date=True, timename='time_counter', lonname='lon', latname='lat', varname='tws')\n", + "grid_isba_slr.data[grid_isba_slr.mask] = 0 # Set masked data to 0\n", + "# swap axes to get (lon, lat, time)\n", + "grid_isba_slr.data = np.swapaxes(grid_isba_slr.data[170:, :, :], 0,2)/1000 #divide by rho_water\n", + "grid_isba_slr.data = np.swapaxes(grid_isba_slr.data, 0,1)*100 #go to cm EWH\n", + "grid_isba_slr.mask = np.swapaxes(grid_isba_slr.mask[170:, :, :], 0,2)\n", + "grid_isba_slr.mask = np.swapaxes(grid_isba_slr.mask, 0,1)\n", + "\n", + "# concert time from day to decimal year\n", + "grid_isba_slr.time = 1979 + grid_isba_slr.time[170:]/365.25\n", + "# convert decimal year to GRACE month equivalent\n", + "grid_isba_slr.month = np.floor((grid_isba_slr.time - 2002)*12)\n", + "\n", + "# remove mean to talk in gravity anomalies\n", + "grid_isba_slr.mean(apply=True)\n", + "\n", + "# from grid to harmonics\n", + "isba_Ylms_long = grid_to_hs(grid_isba_slr, n_harmo)\n", + "\n", + "# remove trend to remove GIA effects and have a better spectral analysis\n", + "if detrend:\n", + " isba_Ylms_long.slm[2,2] = sg.detrend(isba_Ylms_long.slm[2,2])\n", + " ylms_slr.slm[2,2] = sg.detrend(ylms_slr.slm[2,2])\n", + "\n", + "# Temporal filtering with a 3 year low pass filter \n", + "isba_filt_Ylms_long = filt_Ylms(isba_Ylms_long.copy(), filt='fft', filt_param=[1/3])\n", + "SLR_filt_Ylms = filt_Ylms(ylms_slr.copy(), filt='fft', filt_param=[1/3])\n", + "\n", + "# create IGG-SLR - ISBA + temporal filtering\n", + "SLR_filt_isba_Ylms = filt_Ylms(ylms_slr.copy().subtract(isba_filt_Ylms_long), filt='fft', filt_param=[1/3])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "384d9ab9", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-14T16:28:29.654159Z", + "start_time": "2023-08-14T16:28:28.728511Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time length of IGG-SLR : 28.083333333333258 yr\n", + "Time length of IGG-SLR - ISBA : 25.75 yr\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Time length of IGG-SLR :\", SLR_filt_Ylms.time[-1] - SLR_filt_Ylms.time[0], \" yr\")\n", + "print(\"Time length of IGG-SLR - ISBA :\", SLR_filt_isba_Ylms.time[-1] - SLR_filt_isba_Ylms.time[0], \" yr\")\n", + "\n", + "# Figure 2a of the paper\n", + "plt.figure()\n", + "plt.plot(SLR_filt_Ylms.time, SLR_filt_Ylms.slm[2,2], label='IGG-SLR', color='C3', linestyle=(0, (5,2)))\n", + "plt.plot(SLR_filt_isba_Ylms.time, SLR_filt_isba_Ylms.slm[2,2], label='IGG-SLR - ISBA', color='C2')\n", + "plt.plot(isba_filt_Ylms_long.time, isba_filt_Ylms_long.slm[2,2], label='ISBA', color='C0', linestyle='dashdot')\n", + "plt.legend(fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "\n", + "# Figure Supplementary Information S3a of the paper\n", + "plt.figure()\n", + "plt.plot(GRACE_filt_Ylms.time, GRACE_filt_Ylms.slm[2,2], label='CSR', color='C5')\n", + "plt.plot(GRAZ_filt_Ylms.time, GRAZ_filt_Ylms.slm[2,2], label='GRAZ', color='C9')\n", + "plt.plot(COSTG_filt_Ylms.time, COSTG_filt_Ylms.slm[2,2], label='COST-G', color='C8')\n", + "plt.plot(SLR_filt_Ylms.time, SLR_filt_Ylms.slm[2,2], label='IGG-SLR', color='C3', linestyle=(0, (5,2)))\n", + "plt.legend(fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "\n", + "# Figure 3 of the paper\n", + "plt.figure()\n", + "# plot S22/(2*Kappa*delta h)*180/pi (Equation 7b + conversion from radians to degree)\n", + "plt.plot(SLR_filt_isba_Ylms.time, SLR_filt_isba_Ylms.slm[2,2]/1.41e-11/49/np.pi*180, label=r'$\\alpha$ from $S_{2,2}$ ($\\delta h$ = 49m)', color='#4bce4b', linestyle='dashed')\n", + "plt.plot(SLR_filt_isba_Ylms.time, SLR_filt_isba_Ylms.slm[2,2]/1.41e-11/90/np.pi*180, label=r'$\\alpha$ from $S_{2,2}$ ($\\delta h$ = 90m)', color='C2')\n", + "plt.plot(SLR_filt_isba_Ylms.time, SLR_filt_isba_Ylms.slm[2,2]/1.41e-11/126/np.pi*180, label=r'$\\alpha$ from $S_{2,2}$ ($\\delta h$ = 126m)', color='#1c641c', linestyle='dashdot')\n", + "plt.plot(SLR_filt_Ylms.time, SLR_filt_Ylms.slm[2,2]/1.41e-11/90/np.pi*180, label=r'$\\alpha$ from uncorrected $S_{2,2}$ ($\\delta h$ = 90m)', color='#5a3730')\n", + "\n", + "plt.grid()\n", + "plt.ylabel(r'$\\alpha (\\circ)$', fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=13)\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5789a5dc", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-14T16:28:32.072092Z", + "start_time": "2023-08-14T16:28:31.618839Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure 2b of the paper\n", + "windows = 48\n", + "\n", + "# windows creation to reduce the apodization effect\n", + "global_hann = sc.signal.windows.hamming(windows)[:windows//2]\n", + "slr_hann = np.concatenate((global_hann, np.ones(len(SLR_filt_Ylms.time)-windows), global_hann[::-1]))\n", + "slrisba_hann = np.concatenate((global_hann, np.ones(len(SLR_filt_isba_Ylms.time)-windows), global_hann[::-1]))\n", + "\n", + "# compute periodogram\n", + "w = np.linspace(0.449, 2.3, 5000)[::-1]\n", + "pgram = sg.lombscargle(SLR_filt_Ylms.time.copy(), SLR_filt_Ylms.slm[2,2]*slr_hann, w.copy(), normalize=False)\n", + "pgram_slr_isba = sg.lombscargle(SLR_filt_isba_Ylms.time.copy(), SLR_filt_isba_Ylms.slm[2,2]*slrisba_hann, w.copy(), normalize=False)\n", + "pgram_isba = sg.lombscargle(isba_filt_Ylms_long.time.copy(), isba_filt_Ylms_long.slm[2,2]*slrisba_hann, w.copy(), normalize=False)\n", + "\n", + "plt.figure()\n", + "plt.plot(2*np.pi/w, pgram, label='IGG-SLR', color='C3', linestyle=(0, (5,2)))\n", + "plt.plot(2*np.pi/w, pgram_slr_isba, label='IGG-SLR - ISBA', color='C2')\n", + "plt.plot(2*np.pi/w, pgram_isba, label='ISBA', color='C0', linestyle='dashdot')\n", + "\n", + "plt.xlabel('Period (yr)', labelpad=4, fontsize=14)\n", + "plt.ylabel('($yr^{-1}$)', labelpad=-2, fontsize=14)\n", + "plt.ylim(10**-22)\n", + "plt.legend(loc='upper right', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7cf82451", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-14T16:28:52.834253Z", + "start_time": "2023-08-14T16:28:52.406763Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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jLSeYUH68hq4ue/jhh0lMTOTNN9/k0KFDDTq3rZBkSAjhMRW//AxAwGmnoQ0Pr9c5ilZL0JAhAJStloJwovXp3LkzDzzwAHl5eZx//vmkpaXVOsZsNvPyyy8zc+ZMysvLefHFF8nLy6t1nN1u5/nnnwfqvzrMZDLx4IMPYrPZ+M9//tO0xrRSMmdICOEx5RuqkqGggQMbdF7wsKGU/O9/lP30E7H33euN0ITwqSeffBKz2cwrr7xCt27dGDlyJL169UKv15OWlsby5cvJz8/nySefxGaz8dRTT/Hcc88xcOBATj/9dEJDQ8nOzub7778nMzOTDh068Pjjj9f7/rfeeivPPfcc77//Pg8//HCtCthtnSRDQgiPUFWV8p+rk6FBDUuGAs8+GwDLnj04iovRhoV5PD4hfEmj0fDyyy9z7bXX8t///pe1a9eydu1anE4nCQkJjBkzhhtvvJHRo0djt9v59NNP+emnn1i/fj2fffYZ+fn5BAYG0rVrV2699VbuuusuwhrwexIQEMBDDz3E9OnTmTVrFu+//74XW9vySDIkhPAIa9oh7Dk5KEYjpv79G3SuPjYWQ4cOWNPSqNiylZCRtbcgEKI1OOOMM5g3b95Jj9FoNIwePZorrrii3ivpUlNTa+xiX5c777yTO++8s96xtiUyZ0gI4RGGDql0Wr6M5DfeQNOIpciBZ54JQMWvv3o6NCGEOClJhoQQHqEoCobk5EbvQB9YvVdZfStXCyGEp8gwmRDCL4SMGE7ntWvQx8b6OhQhRBsjyZAQwi9ogoLQeGh3biGEaAgZJhNCNFnZT+s5ePHF5M5+3dehCCFEg0kyJIRossqtW7Hs2481I90j13OUlXnkOkIIUR+SDAkhmqzyt9+Aqq01msJ6+DD7R47iwJixp1wmLIQQniLJkBCiycx//AGA6bTTmnQdXXw8ttxcHAUFsqpMCNFsJBkSQjSJLScHR14eaDQYu3Vr0rU0RiMB3bsDyKatQohmI8mQEKJJLHv2AGDo0AGNydTk65n69AHAvHNnk68lhBD1IcmQEKJJzL9XDZEF9OjhkesF9O5V47pCCOFtkgwJIZrENV/IY8lQ9XXMe/bIJGohRLOQZEgI0SR/JUPdPXI9Y4cOKHo9ztJSmUQthGgWkgwJIRrNUVqK7cgRAIwe6hlSDAYMXToDfyVaQrQW27dvZ+rUqfTs2ZPQ0FAMBgMJCQmMGTOGV199lfz8/BrHK4pS402n0xEXF8dFF13E8uXLT3m/oUOHoigKZ5xxxgmPGT58eK37nOhtwYIFTf0S+CXZjkMI0XiKhviZj2NNT0cXEeGxywZ074Hl9z+w/LEHRo/22HWF8BWn08kDDzzASy+9hE6nY+jQoYwZM4bAwEBycnLYsGED99xzD//+97/Zv38/BoPBfW5UVBR33nknAGazmd27d7N06VKWLl3K4sWLueaaa+q85759+1i3bh2KorBlyxZ27NjB6XXUAps8eTLDhw8/YewZGRnMmzcPrVZLtyauGPVXkgwJIRpNGxxExNVXe/y6Ad27U4z0DInW45FHHuGll17ijDPO4OOPP6ZTp061jtm0aRMPPPAAZrO5RjIUHR3NzJkzaxz78ccfc8011/DQQw+dMBl67733ALjvvvt48cUXmTdvHrNnz6513OTJk08Yt9lsZujQoQA888wzDBw48FRNbZFkmEwI4XeChw8j8aUXifvXg74ORYgm27dvHy+88AKxsbF89913dSZCAGeeeSYrV64kISHhlNe86qqrCA4O5vDhw+Tl5dV63uFwsHDhQuLi4nj66adJSUnhww8/xGKxNCj2qVOnsmnTJq6++mpmzJjRoHNbEkmGhBB+x5CSQtiFF2Jo397XoQjRZAsWLMDhcHDbbbcRHR190mMVRUGr1dbruq7Vljpd7UGeb7/9lmPHjnHttdei1+u5/vrrKSgo4Msvv6x33K+++ioLFy6kb9++zJs3r97ntUSSDAkhGi33jTco/uornJWVvg5FCL/1888/AzBixAiPXfPDDz+kvLyc0047jfDw8FrPu5KXiRMnAjBp0qQaj5/KypUrmTFjBlFRUXz55ZcEBgZ6JnA/JXOGhBCNYi8sJO/1OQB0k0nOohFUVaXC6az7OadKhcOJzuFE8XG9qUCNBkVRGn1+VlYWAImJibWeW7lyJWvXrq3x2MiRI+lTXYkdIC8vzz1nyGw2s2vXLr799lsCAwN58803a10zOzubpUuXctppp9GvXz8Aunbtytlnn82KFSs4fPgw7U/S63ro0CGuvPJKAD799FNSU1Mb1N6WSJIhIUSjWPfvB0CflITGC/81VmzdRtEX/4ehfXuib7nF49cXvlfhdNJp7W++DuOUDgztTVA9h67qcrLioStXruSpp56q8ZjRaKyRDOXn5zNr1qwaxwQFBfHjjz8yaNCgWtdcuHAhdrvd3SvkcsMNN7Bx40bmz59fa0K2S0VFBePHjyc/P59XXnmFkSNHnqp5rYIMkwkhGsVSnQwZO3f2yvVtx45S/Pn/UbZipVeuL0RziYuLAyCzjiKiTz75JKqqoqoq8+fPr/P8bt26uY8pLCxk/vz5OBwOrrjiijqvOX/+fDQaDdddd12Nx6+++moMBgPz58/HeYIeuSlTprBjxw4mTpzI3Xff3cCWtlzSMySEaBTL/gMAGDrXvTKmqYydu1TfZ79sy9FKBWo0HBjau87nVKdKSUkJoaGhKJrGD1F5QqCmaf0GgwYNYs2aNaxatarJPS3h4eFMnjwZh8PBzTffzB133MGSJUvcz69fv5491Zsnt2vXrs5rHDlyhOXLlzNmzJgajz/77LN88sknDBgwgHfeeadJcbY0kgwJIRrFcqAqGXIlLZ5m6JAKWi3OsjLs2dkQFeWV+wjfURTlhMNPTsWJXashUKtB08RkxNcmTZrEc889xzvvvMNdd911yhVl9TFlyhTefPNNvvrqKzZs2OAeLnNNkD7//PPrnKOUn5/PkiVLmDdvXo1k6Pvvv+eRRx4hNjaWL7/8koCAgCbH2JJIMiSEaJS/hsm80zOkMRgwtG+P9eBBLPsPYJRkSLRQ3bp149577+XFF1/k/PPPP2HRxaKionpfU1EUHn/8cS699FIee+wxVqxYQVlZGZ9++ilBQUF8+umnBAcH1zrPbreTlJTEkiVLyM/PJyoqin379nHNNdeg1Wr57LPPTtij1JpJMiSEaDB7YSGO6kJvxo4dvXYfY6dO1cnQPoxnn+W1+wjhbc8++yw2m43XXnuNbt26MWzYMPr06ePejmP79u1s3ryZ0NBQeveue+jw7y655BIGDBjAypUrWbNmDfv27aO8vJwbb7yxzkQIqmoSXX/99bz88sssWrSIu+66i/Hjx1NUVETfvn1ZuXIlK1eeeJ5e3759GT9+fGO+BH5NkiEhRINZ0w4BoEtIQBMU5LX7GLt0pnTZMncvlBAtlVar5dVXX2XixIm89dZbrF27lo0bN2K1WomMjKR37968/PLLTJw4kcjISEpKSup13ZkzZ3LxxRfz2GOPYbPZgKohtJO58cYbefnll5k3bx533XUXv//+O1C1iez27dtPeu6kSZMkGRJCCADroUMAGFK9WyHatVLNuk+SIdE6DBgwgLlz5570mONXep1q8cBFF13U4AUGvXr1qnGOLFCQpfVCiMZQVfTJyRg7eme+kIuhOhmyHDggf7CFEF4jPUNCiAYLv+Jywq+43OsJijE1FXS6v1aUCSGEF0gyJIRotKZsUVCv6xsMhI4Zg2IwgN3u1XsJIdouSYaEEH4t6eWXAKomh+7c6eNohBCtkcwZEkI0iNNqxdGAeihCCOHvJBkSQjRI5bbt/HnOQNL+McHXoQghhEfIMJkQokGshw8BoI2MaJb7qQ4H1rQ0Ko8caZb7CSHaHukZEkI0iPXwYQAM7VOb5X6OkhIOXnQxx26/A8VqbZZ7CiHaFkmGhBANYj1UnQx5ueCiiy4iAm1YGAD6/PxmuacQom2RZEgI0SCuYbLm6hkCMKRW3cuQm9ts9xRCtB2SDAkh6k11OLAdSQear2cIwNChQ9V7SYaEEF7gN8lQZmYmr776KmPGjCElJQWDwUB8fDxXXHEFGzdubNC1nE4nc+bMoU+fPphMJmJiYrjyyivZt2+fl6IXom2wHctCtVpR9Hr0CQnNdl93MpSX12z3FEK0HX6TDL3++uvcc889HDx4kNGjR3Pfffdx7rnn8tVXXzFo0CA+/fTTel9r6tSpTJ8+HYfDwfTp07ngggv4+uuvOfPMM9278wohGs6WXrWiS5+cjKLVNtt9DR1Sq+6bK8mQEMLz/GZp/VlnncXatWsZMmRIjcfXrVvHqFGjmDZtGpdeeilGo/Gk11m1ahVz585lyJAhLFu2zH38DTfcwOjRo5k2bRpr1qzxWjuEaM2sGRkA6NslN+t9j58zJBu2CiE8zW96hi6//PJaiRDAkCFDGDFiBAUFBfz222+nvM7cuXMBePLJJ2skTqNGjWLs2LGsXbuWP//803OBC9GG2NKrkiFDcjMnQ+3bg6KgNZtx5Bc0672F8KQtW7Zw00030aVLF4KCgjCZTHTq1ImJEyeybNmyWsebzWZee+01hgwZQlRUFEajkeTkZK688kpWrlx5wvtUVFTw9NNP079/f4KDgwkICCA5OZkhQ4bw0EMPceDAAQAmT56Moij1fluwYEG92llZWcmbb77JmDFjiI+Px2AwEBISQu/evbnttttYvXp1Y758XuM3PUMno9frAdDpTh3u6tWrCQoKYvDgwbWeGzt2LN9//z1r1qyha9euHo9TiNYu/MorMZ3eB11cfLPeV2M0oktMxJ6Zie3wIUwJzXt/IZrK6XRy//3388orr6DT6Rg5ciSXXHIJer2egwcPsnTpUhYtWsQTTzzBI488AsD+/fu5+OKL+fPPP+nYsSNXXnkl4eHh7uM/++wzbr31Vt54440ar4+lpaWce+657Ny5k86dO3P99dcTHh5Oeno6u3fv5tlnn6VTp0506tSJ8ePHk1rd8+qyZMkSduzYwaRJk2o917dv31O2dceOHVx22WWkpaXRrl07xo4dS1JSEmazmT///JPFixfzzjvv8K9//YtnnnmmqV9aj/D7ZOjIkSMsX76c+Ph4evfufdJjy8vLOXbsGL169UJbx3yGLl26AMhEaiEayZCchCE5yTf3Tk2tSoaOpMM55/gkBiEa69FHH+WVV16hb9++fP7553Tq1KnG85WVlcyZM4f86lpaJSUlXHDBBRw4cIDHHnuMxx9/vMbr2tGjRxk/fjzvvPMOYWFhPP/88+7nXn31VXbu3MlNN93E3LlzURSlxr3S0tKwWCwAjB8/nvHjx9d4/tChQ+zYsYPJkyczfPjwBrUzIyODMWPGkJ+fz6uvvsqdd95Z6/W4rKyMt956i4zqYXd/4NfJkM1mY+LEiVgsFp5//vk6E5zjFRcXAxBWXaDt70JDQ2scdyIWi8X9gwJVP5SueGw2W73jbw6uePwtLm9rq+2Gttv2iIcfYuevv9L+ogvbVNtb+vfbZrOhqipOpxOn01nv81xzw1zntmT79+/n+eefJyoqim+//Za4uLhabTIajdx3331YLBZUVeX111/nwIEDXHvttcycOROgxjnx8fF89dVX9OrVi5deeombb76Zzp07A7BhwwYAbr/9dlRVrTXPrn379rWudzzX8Q39ngH861//Iicnh1mzZjF9+vQ67xMYGMi9996L3W6v8VxDvudOpxNVVbHZbCfNDer7e+O3yZDT6WTKlCmsXbuWW265hYkTJzbbvZ955hlmzZpV6/Eff/yRwMDAZoujIeoaa24L2mq7oY22PTCwbbablvv91ul0xMfHU1ZWhrUR26mUlpZ6Iarm9c477+BwOJg0aRImk8n9D/aJWCwWFi9eDMDdd999wuNNJhMTJ07ktdde45133uHRRx8FICQkBIDffvuNjh07NjheVwJRUVFxyliPV1FRwaeffkpgYCBTpkxp0LnHq8/33Gq1UllZydq1a7Hb7SeNqT78MhlSVZVbbrmFRYsWcf311/PWW2/V6zxXj9CJen5c35gT9Ry5PPTQQ9x77701zmvXrh1jxoxx9y75C5vNxrJlyxg9erR7blVb0FbbDb5ruy09g4K338bQpQsRk25otvu6799Gv+ctvd1ms5n09HT3JN7jVfUAVNZ5nqqqlJaWERISXGuYp7lpNKYmxbBlyxYAxo0bV6/XkEOHDnH06FGSkpIYMGDASY89//zzee2119i2bZv72tdccw2fffYZ//znP/n9998ZPXo0/fr1IyKifpsru37OAgMDG/Sat337dmw2GwMHDiQxMbHe57lUfc9LCQkJOeXX22w2YzKZGDp0aK2fq+PVNyHzu2TI6XRy8803M3/+fK655hoWLFiARlO/RW9BQUEkJCSQlpaGw+Go1XXmmivkmjt0Ikajsc4l/Hq93m//GPlzbN7UVtsNzd9286FDlH71FcaePYi9+aZmu+/ftdXveUttt8PhQFEUNBpNrb/lDkcFa9ed7qPI6m/4sN/QaBo/KpCVlQVASkpKvV7PsrOzAWjXrt0pj3cNeR07dsx97GWXXcbzzz/PE088wfPPP++eT9SpUyfGjRvHXXfdddLXQVciUtf37GRycnIASExMrHWe0+nkiSeeqPGYTqdz92a5jnHd/1T31Wg0KIpyyt+L+v7O+FUydHwidNVVV/HBBx+ccp7Q3w0bNoyPP/6Y9evXM3To0BrP/fDDD+5jhBANY8twLatv55P7q04nSfPe4/CcN0j9+CN0UVE+iUMIf+KaZ/P3npQZM2YwdepUvv/+ezZs2MDmzZvZuHEjb7zxBvPmzeOTTz7hkksuadC9tm/fzpIlS2o8lpqayuTJk2vEUhen01lr+onRaKyRDPmS3yRDTqeTm266iQULFjBhwgQWLVp00kQoLy+PvLw8oqOjiY6Odj9+66238vHHH/Poo4+yfPlyDAYDACtWrOCHH35g6NChsqxeiEawZVYXXGzmGkMuikaDIScHW1ER1sNHJBlqBTQaE8OH1V0/zul0UlJSSmhoSIN6J7xBozE16fz4+Hj27NlDZmYm3bp1q9fxAOnp6ac81rUiy3XO8UJCQpgwYQITJkwAqqaQPPzww7z55pvcdNNNZGZmul8j62P79u21Epphw4a5k6G4uDiganutv9PpdDWSpdTUVHePmT/wm2ToiSeeYMGCBQQHB9O1a1eefPLJWseMHz/eXeNgzpw5zJo1i8cff9w90x5gxIgR3Hzzzbz77rv069ePCy+8kOzsbD755BNCQ0P573//20wtEqJ1saa7kiHfLK0HsEVFoS8qqtoWpH8/n8UhPENRFLTauoefFMWJVmtHqw30eTLUVIMHD2b16tWsWLGCkSNHnvL49u3bk5CQQGZmJnv37j1pArVixQoABg4ceMrrhoWFMWfOHJYuXcrhw4f57bffTjkn6XiTJ092Jz51OfPMM9Hr9WzZssU996el8JufsEOHDgFV9QeeeuopZs2aVett+/bt9brW22+/zezZs1EUhdmzZ7N06VIuvvhifv31V3r27Om9RgjRiv01TOabniEAa3VvkPXwEZ/FIERDTZ48Ga1WyzvvvENubu5Jj3WVdbnmmmsAeOqpp054bG5uLu+++y4ajYZJkybVKxZFUby2KjooKIgJEyZQUVHBK6+84pV7eIvfJEMLFixw10M40dvxGenMmTNRVbVGr5CLRqNh+vTp7Nq1C7PZTF5eHp999pkMjwnRSKqqupMhvY/mDEFVzxCAtR7DB0L4i86dO/PAAw+Ql5fH+eefT1paWq1jzGYzL7/8svs17Z///CcdOnTggw8+4IknnsDhcNQ4Pisri0suuYT8/Hzuu+++GhOi3377bTZt2lRnLF988QV79uwhPDycXr16ea6R1Z5++mmio6N54oknmD17dq24oarAZGPKLHiT3wyTCSH8l6OwEGd1vQ59UsOXzHqKLSoSAOuRwz6LQYjGePLJJzGbzbzyyit069aNkSNH0qtXL/R6PWlpaSxfvpz8/Hz3FJGwsDC+/fZbLr74Yh5//HHef/99xo4dS1hYmHs7jrKyMm655RaefvrpGvf67rvvmDp1Kp07d2bw4MEkJiZSVlbG9u3bWbduHRqNhjfffPOUG583Rvv27fnhhx+4/PLLueuuu3jxxRcZMWIESUlJVFZWkpmZyQ8//EBJSQnnnXeex+/fWJIMCSFOydUrpIuNReOFP6D15Romsx2RniHRsmg0Gl5++WWuvfZa/vvf/7J27VrWrl2L0+kkISGBMWPGcOONNzJ69Gj3EvOuXbuyc+dO3nrrLT7//HMWL15MeXk5MTExjBs3jqlTpzJq1Kha93ruuecYPHgwy5YtY+3atRw7dgyApKQkJk2axPTp0xs0V6ih+vfvz++//868efNYsmQJ3333HYWFhQQEBJCSksKECRO47rrrGDFihNdiaChJhoQQp+QeImvnuyEy+GuYzFFYiKOkBK2fFUEV4lTOOOMM5s2bV+/jTSYT99xzD/fcc0+9z+nWrRszZsxgxowZjQmRBQsW1Ht3+hMJDAxk+vTp7i05/J3fzBkSQvgvxWTCdMYATF6YY9AQqtGI1jVvSHqHhBAeIj1DQohTChkxghA/6dLWp6TgyM/Hln4EU6/TfB2OEKIVkGRICNGihFx4AUFnnYWhQwdfhyKEaCUkGRJCtChhV13VIvfoEkL4L5kzJIQ4Jcu+fTjKyn0dhhBCeIX0DAkhTspRWsrBi6s2dOy2bSsaU9P2aRJCCH8jPUNCiJOyHT0KgDYiwi8SIdXppGzdOgo/+ginn1WxFUK0TNIzJIQ4KVcypE9I8HEk1RSFjLvuRq2oIPDsczB2lInUQoimkZ4hIcRJ2aqr1+oS/SMZUhTFvVmsLTPDx9EIIVoDSYaEECdlr06G9Am+25Ps7/SuZChDkiEhRNNJMiSEOCnbUVcy5B89QwD65CQArOmSDAkhmk6SISHESbmGyfR+MkwG/DVMJj1DQggPkGRICHFS7mTIr3qGJBkSQniOJENCiBNSbTbs2dkA6PwpGUqqSoasmZk+jkQI0RpIMiSEOCF7Tg44naDXo4uO9nU4bobqOUPO4mIcpaU+jkaIUzt06BCKojBu3Lhaz9ntdj744AMuueQSkpKSMJlMJCUl0aNHD2644Qa++eYbVFWt87qqqvL1119z5ZVX0r59e0wmEyaTiY4dOzJhwgQWL16MzWZrUKwVFRU8/fTT9O/fn+DgYAICAkhOTmbIkCE89NBDHDhwoMbxw4cPR1EUsrKyTnltRVFqvOl0OuLi4rjoootYvnx5g+L0JKkzJIQ4IW1MDKmffYajsABF4z//O2mCgtBGROAoLMSWkYG2Rw9fhyREoxw+fJjLLruMbdu2ERMTw6hRo0hJSaGiooLMzEyWLl3KBx98wNVXX81HH31U49yCggKuuuoqli9fTmhoKKNGjaJTp05oNBrS09NZvXo1n3/+Oa+//jo///xzveIpLS3l3HPPZefOnXTu3Jnrr7+e8PBw0tPT2b17N88++yydOnWiU6dOjW5zVFQUd955JwBms5ndu3ezdOlSli5dyty5c5kyZUqjr91YkgwJIU5IYzBg6t3L12HUSZ+cjLOiAnt+ga9DEaJRSkpKGDt2LHv37uWhhx7i3//+NwEBATidTkpKSggNDcVms7Fo0aJavSZ2u53x48ezbt06Jk+ezKuvvkpYWFiNY5xOJ19++SVvvfVWvWN69dVX2blzJzfddBNz585FUZQaz6elpWGxWBrfaCA6OpqZM2fWeOzjjz/mmmuu4YknnpBkSAgh6itl/ntogoJq/bEWoqV44YUX2Lt3L1OmTOHpp5+u8xij0chNN93EpEmTajy+cOFC1q1bx6hRo3jvvffq/D3QaDRcccUVXHrppfWOydWDdOedd9Z5zQ4dvFPx/aqrruKWW24hPT2dvLw8YmNjvXKfE/Gffm8hhGgAbXCwJEKiRZs/fz4AjzzyyCmP1elq9l289957ADz88MOn/D34+7knExkZCcD+/fvrfY6nuOZFNSReT5GeISHECeXOno09N4+Ia68hQOblCC9xVlTUfszpxFlZiVOng+r5aopej6LXu49RbTbUek4O1gQG1rx+ZSWcYFLyyc7zlCNHjpCZmUlKSgodO3Zs0Ll2u51Nmzah1+sZPHiwR+OaMGECH374ITfddBObN29mzJgx9OvXj4iICI/e5+8+/PBDysvL6d69O+Hh4V69V10kGRJCnFDpsuVY9u0jZOxYX4dyQqqqSg9RC7e3/4ATPpd93Mdxjz1K5HXXuT8v/PRTsv/zZL3u0WPPHzU+T5swAev+Ayc4+sTneYpr5VViYt3b3LzxxhtYrdYaP9v3338/wcHBFBQUYLPZiI+Px2g01jr3vffe48iRIzUeu/nmm0murs91MpdeeinPP/88TzzxBM899xzPPfccAJ06dWLcuHHcdddddOnSpd7trEteXp57zpDZbGbXrl18++23BAYG8uKLLzbp2o0lyZAQ4oT8sfq0iz03l8OTb8SRn0+XnzdIQiRalBMtlXd58803OXr0aI3Hpk6dSnBw8CnPfe+991i/fn2Nx8aNG0dycjJFRUW8+uqrtc45fkLzjBkzmDp1Kt9//z0bNmxg8+bNbNy4kTfeeIN58+bxySefcMkll5y8gSeRn5/PrFmzajwWFBTE999/T69evlmwIcmQEKJOjtJSnGVlAOjj430cTW3asDCsBw+CquLIy0MXE+PrkEQjddu6pdZjTqeTktJSQkNC0Bw3THa8iCuvJPyyyxp1zw6ffVavYTJviYuLAyDzBIVDd+/eTWhoKBqNhuHDh7NmzRr3c1FRUeh0OvLy8rBYLLV6h3766Sf3x5MnT2bhwoXuz4uKimolIkCt1V0hISFMmDCBCRMmAFBcXMzDDz/Mm2++yU033URmZiYGg6Fhja7WrVs39uzZ445nyZIlTJs2jQkTJrBixQpCQ0Mbdd2mkAnUQog62au78TVhYV6bN9EUisGArjpJs8q2HC2aJjCw7jeTqcbnf0+GFL3+xOf+7a3WPf927fqe5ympqakkJiaSnp5eq4jhqeh0Os4880zsdnuNxKe+91VVtdbbqYSFhTFnzhzat29PXl4ev/32W4PueyLh4eFMnjyZOXPmkJWVxYwZMzxy3YaSZEgIUSdbVtVsDX31f7D+6K8NW2VbDtHyTJ48GYCnnnqqwefeeOONADzzzDP1SmY8QVEUAr2UIE6ZMoX+/fvz7bffsmHDBq/c42QkGRJC1MmeU70nmR8nQ+4NWzOlZ0i0PA888ACdO3dm/vz5PPzww5jN5lrH2Gw2KupYbTd58mQGDRrEihUrmDJlCiUlJbWOUVW1zsdP5u2332bTpk11PvfFF1+wZ88ewsPDPT63R1EUHnvsMQAef/xxj167PmTOkBCiTrbqYTJ9vD8nQ1V7lMkwmWiJwsLC+PHHHxk/fjzPPPMM7777bo3tOPLz81mxYgU5OTn07duX4OBg97l6vZ6vvvqKK6+8kgULFvDFF18watQoOnfu7N4nbM2aNRw+fJiOHTuecNXa33333XdMnTqVzp07M3jwYBITEykrK2P79u2sW7cOjUbDm2++WecqtrvuuguTyVTndd98881T9ipdcskl9O3bl5UrV7JmzRqGDRtWr5g9QZIhIUSd7NXDZLpY/02G3MNk6ZIMiZapQ4cObN68mcWLF/Ppp5+yevVq8vPz0ev1JCUlMXr0aK666iouvPBC90Ryl+joaFasWMGSJUtYtGgRmzZt4ttvv0VRFOLj4xkwYABPP/00//jHP+o92fm5555j8ODBLFu2jLVr13KsekVpUlISkyZNYvr06QwYUHcphE8//fSE13311VfrNcT24IMPcs011/DYY4+xdu3aesXsCZIMCSHqZHMNk/l1z5BrzpAkQ8K/uSYu10Wv1zNp0iT3lhvH70329wTo7xRF4bLLLuOyRq6q+7tu3boxY8aMBk1kXr16db2PPdX8pnHjxuFwOE7Zbk+TZEgIUaeALl1wlpZhaN/e16GckDsZyspCtdtRfFDGXwjR8slfDiFEnWLvv9/XIZySLiYGxWBAtVqxZWW5h82EEKIhJBkSQrRYikZDzD+nowQGogkK8nU4QogWSpIhIUSLFnXzzb4OQQjRwkmdISFELY6SEixpaXXuJi6EEK2NJENCiFrK1qzh4PkXkD7tdl+HIoQQXifDZEKIWuzZ1Vtx+PGyehd7bi6l1Ut7I6o3lRRCiIaQniEhRC2ufcl0cf63W/3fWdMzyHrs3+T/9y1fhyJOorn2zxJtg6d/niQZEkLUYs+u2opDFxfr40hOTZ9UtSWHLTsb1W73cTTi77RaLVC1x5YQnuL6eXL9fDWVJENCiFps2TkA6OP9v2dIFxONoteDw+Ee3hP+Q6/XYzQaKS4ult4h4RGqqlJcXIzRaESv13vkmjJnSAhRi716k1Z/3pfMRdFo0CUmYDt8BGtmprunSPiP6OhoMjMzycjIICwsDL1ej6IoJz3H6XRitVoxm83NvjWDr7XVtp+q3aqqYrPZKC4upqysjCQP/q5LMiSEqEG127Hn5QEtYwI1gCEpCdvhI9iOHvV1KKIOoaGhAOTl5ZGZmVmvc1RVpbKyEpPJdMrEqbVpq22vb7uNRiNJSUnunytPkGRICFGDPS8PnE7Q6dBGRfk6nHrRJSYCYKvnC61ofqGhoYSGhmKz2XA4HKc83mazsXbtWoYOHeqxoZCWoq22vT7t1mq1XvmaSDIkhKjhryGyGJQW0kVvcE2ilp4hv6fX6+v1YqbVarHb7QQEBLSphADabtt92e6W8ZdOiGZkPXSIoiVLsGa0zV4G9+TpFrCs3sW9oixTkiEhRMNJz5AQxylduZKMu+4Gm43of04n5va2V4E5aNBAUj/+yNdhNIhehsmEEE0gyZAQ1eyFhRx94EGw2QgeOZLoqVN9HZJPaENCMPXt6+swGkSflAR6PYrBgKqqbWrSqRCi6SQZEqJawcKFOMvKMPboQfJrr7aY+TICdPHxdN++DcVDBdiEEG2LJENCULWcvOiTTwGIvn1aVRE/0WIoigKSCAkhGkn+9RUCKP9lI47CQrSRkYSMGOF+vHL7do4+8gjlGzb4MLrmdeyxxzg2cya26lVlQgjR2kkyJARQ+sP3AISMGY2i+6vDtPib/1H8f19QvHSpr0JrVqqqUrzkK4o+/gTVfupaMP5GVVVUq9XXYQghWhhJhoQAyjf8DEDIyJE1Hg8ZVfV52dq1bWJfJUdhIWr1Boj62BgfR9MwhR99xJ9nnkXWf/7j61CEEC2MJEOizbNmZFQtydbpCBwwoMZzpjPOQAkMxJGbh+WPP3wUYfNxbXSqjYpCMRh8HE3DKAEmnGVlUmtICNFgkgyJNs+8+3dQFEx9+qAJCqrxnMZgIOjsswEoW7/eF+E1K3tOVcFFXWysjyNpOKk1JIRoLFlNJtq80LFjCPp5A/b8/DqfDzzrLMpWraJy67Zmjqz52XNzAdDFRPs4kobTH7clh+p0SmkEIUS9yV8LIQBteDjGTp3qfC6wfz8AKrdtQ3U6mzOsZvdXMtSy5gsB6OPjQKtFtdmqNpsVQoh68qtkaNGiRdx2222cccYZGI1GFEVhwYIFDbrG6tWrURTlhG+//PKLd4IXrVZAjx4oAQE4ioqwpqX5OhyvcidDLXCYTNHp0MVVxS1DZUKIhvCrYbJHH32Uw4cPEx0dTUJCAocPH270tYYNG8bw4cNrPZ6cnNyECEVrU5/hFMVgwNSnDxW//krltm0n7EFqDWyuOUMtsGcIwJCYhP3osard6/v183U4QogWwq+SoXfffZcuXbrQvn17nn32WR566KFGX2v48OHMnDnTc8GJVqnww8Xkv/suEVdfRfS0aSc8LqB3Lyp+/RXz7783Y3TNryUPk0H1vKHNm2VFmRCiQfwqGTrvvPN8HYJoY8x//IE9OxvVZj/pcUHnnIM9JxfT35betzYBPXqgoGConozc0uiTZEWZEKLh/CoZ8qR9+/Yxe/ZsKioqaN++PaNHjyY6uuWtkBHeZTmwHwBj1y4nPS54yBCChwxpjpB8KqGF96Yev6JMCCHqq9UmQ4sXL2bx4sXuz00mE7NmzWLGjBk+jEr4E1VVsR44CNCq5wG1Jab+/Yl7+OFTJrdCCHG8VpcMxcTE8MILL3DRRReRkpJCUVERq1at4sEHH+SBBx4gNDSU22677aTXsFgsWCwW9+clJSUA2Gw2bNVbFfgLVzz+Fpe3eaLd9uxsnGVloNWiJCW1mK+hfM9P3G5NcjIh11x9yuNaEvl+t612Q9ttuzfaXd9rKaqfbrjkmkA9f/58Jk+e3OTr7dq1iwEDBhAREcHRo0fRnGQF0cyZM5k1a1atxxcvXkxgYGCTYxH+IXDfPpLfnYc1OppDM+4/5fHakhIC0tNxGo1Udu7cDBE2L01FBdrKSuwhIagtbCsOIYSoS0VFBddeey3FxcWEhoae8LhW1zN0Ir169eLss89m3bp17N+/n65du57w2Iceeoh7773X/XlJSQnt2rVjzJgxJ/1i+oLNZmPZsmWMHj0avV7v63CajSfaXfThYvKAiD596HnBBac8vviTT8h9/wMChwwh8Z//bNQ9PcFb3/OSJV+R8/wTmAYNIunttzx2XU+Rn3Vpd1vRVtvujXa7RnZOpc0kQ4B7AnVFRcVJjzMajRiNxlqP6/V6v/3B9OfYvKkp7bYfqiqgGNC5c72uYapOoG1paX7xtfb091wtKADAEBvrF+07kVO1u2zdOip37CR46BBMffo0Y2TeJb/jbU9bbbsn213f67SZZMhut7N161YURSElJcXX4Qg/4J483bl+k6eN1UNjtsxMnBUVaFrZkGlL3qT1eMVff0PJN9+gMQW0qmRICOE9frUdR0Pk5eWxZ88e8v62B9HPP//M36dB2e12ZsyYweHDhxk7diyRkZHNGarwU9bqCueG1NR6Ha+LjEQbEQGqiqUVbsvR0gsuusju9UKIhvKrnqF3332Xn376CYDffvvN/djq1asBGD9+POPHjwdgzpw5zJo1i8cff7xGpelrrrkGRVEYNGgQSUlJFBUVsXbtWvbu3UtKSgpvveV/cyGEb8TOuB/rkSP1Toagagl+xebNWA8exHTaad4Lzgda8r5kx3MVXrRKMiSEqCePJEMrVqxg5cqVbNiwgYyMDPLy8ggMDCQmJobevXszbNgwLrroIuLj4096nZ9++omFCxfWeGz9+vWsX78egNTUVHcydCLTpk3j+++/Z/Xq1eTl5aHT6ejcuTOPPPII9913HxEREU1qq2g9wi6+uMHnGDpXJUOW/Qe8EJFv2Vv4vmQursKLdim8KISop0YnQ2VlZcyePZu5c+dy5MgR99BUQEAAkZGRVFZWsmvXLnbu3MmHH36ITqfjkksu4Z577mHw4MF1XnPBggX13qV+5syZde499uCDD/Lggw82tllCnJSxY9X8Ilfl6tZCVdXjeoZaeDKU6OoZOoqqqiiK4uOIhBD+rlFzht566y06d+7Mo48+Snh4OE8++SQrV66kpKSEiooKMjIyyM/Px2azsWfPHhYuXMhVV13Fjz/+yNChQ7n88stJa4VzLkTr55ps7Zp83Vo4i4tRrVagFfQMVSdDakUFjqIi3wYjhGgRGpUMTZ8+nXHjxvHbb7+xbds2HnroIYYPH05wcHCN4xRFoWvXrkycOJEPPviA7Oxs5s6dy2+//cYHH3zgkQYI0RglP/5IwcKFWPbta9B5rvlF1owMVIfDC5H5hqtXSBMWhqaOshIticZodCd0snu9EKI+GjVMtmfPHjo1Yi8nk8nElClTmDRpEhkZGY25tRAeUbzkK8pWriT+8X9j7FL/fax08fEoBgOawEDs+fnoW/hkY5e/VpK1js2M9YmJ2HNzsWVmYurVuia6CyE8r1HJUGMSoeNptVrat2/fpGsI0RS29HQA9MntGnSeotHQZcN6tH/rBW3pAvr0of2Hi1pNb5c+KYnKHTtk93ohRL341dJ6IZqDqqpYq3smDe2SG3x+a0uEoKpNgQMG+DoMj9GntEOXmICikcnTQohT82jRxcLCQt5//31PXlIIj3Pk56NWVoKiuCfbitYl9u676bJyJZGTJvk6FCFEC+DRZOjIkSPceOONnrykEB5nq+4V0sXFoTRhd3bV6fRUSEIIIXyoQcNkR44cOenzR2V8XrQAtqwsAPQJCY06v3LXbjLvvhtNYCAdv/7Kk6H5TM4rr+IsLSHiuuswNnFOoBBCtDQNSoZSU1NPWsBMCpyJlsB2zJUMnbwi+olow8OwZWSgGAyoTieKpsVu8edW8t132I4cIfSCC3wdikc5ysrRBBhRdDI9UghxYg36CxEREcHTTz/N8OHD63z+jz/+4IorrvBEXEJ4jb26Z0gX37ieIX18POj1qFYr9qysFj/vSFXVVrNj/fEOXHAh1oMH6fDlFwT06OHrcIQQfqxBydCAAQPIzc2lW7dudT5vNptr7RgvhL9xD5PFxzXqfEWnw5CUhPXQIayHD7f4ZMhZVoZqNgMtv/r08TTVq/6sGRmSDAkhTqpB/fvTpk0j9SQ7fKekpDB//vymxiSEVykGA5rQUHSn2Dj4ZPTtquoT2VrBzuju6tMhIWhMJh9H4zmu3etlw1YhxKk0qGfosssuO+nzERERTJKlrMLPJb3wPECTejFdL7TW1pAMtZLd6v/OUL17vTWj5X+PhBDe1fJnfgrRSE2Z7K+vfqFtTT1DrS0Zak3fIyGEd0kyJEQjGNwvtC1/CKY1Tp4G0CdXVReXZEgIcSpNToa0Wu0p6w8J4S9Um80jxRJbU6+DPaeV9wxlZMjCDiHESTU5GZI/MqIlKfnxR/b0OZ2Mu+9p0nVcL7T27GxUq9UToflMqx0mq17l5ywvx1lc7ONohBD+TCqRiTbFnpUFdnuTi/Bpo6KIvvNO9ImJVcVGPRSfL+jbtSOgd28M7VN8HYpHaQIC0MZE48jNw5qZiSk83NchCSH8lCRDok1pavVpF0VRiLnzDk+E5HOx99wN99zt6zC8wpCYRGVuHraMTEynnebrcIQQfkqSIdGm2LNd1aeblgyJliFq6m2oVhumfn19HYoQwo9JMiTaFFtWNgD6uMZVnxYtS8iIEb4OQQjRAkgyJNoUe14e4JnJwtZDhyhb9xPa8HDCLr6oydfzBafZjD0vH11MNBqj0dfhCCGET0idIdFmqKrq0WSocvdusp96isJPPm7ytXylcudODpx3HmmXXOrrUIQQwmeanAw98sgjhMsqDdECOIqKwGYDQBcd3eTrtYbCi+4aQ62s4KKLs6KCvLfe5tisWVIGRAhxQk1Ohv7zn//QvXt3Zs6c6YFwhPAe1wu/NjwcxWBo8vVaQ62h1lpjyE2nI/e11yj66GMchYW+jkYI4ac8MkxWWlqKrfo/biH8ladf+LXR0VVJldOJrXpLi5bG/TVppT1DGoPB3bbWUC1cCOEdHkmGBgwYwNGjLXeoQLQNxs6dSHjyP0TdeotHrqcoCrrqVWn2rCyPXLO5tdYd6493/LYcQghRF48kQzNmzODzzz/n0KFDnricEF6hj48n/B//IOziiz16TfirmGNL81fPUBtIhqRnSAhxAh5Jho4cOcKQIUMYNmwYa9as8cQlhWgRdNWVrF3FHFuav3qGWucwGYA+uSoZskoyJIQ4AY/UGbrjjjtQFAVVVRk5ciT9+vXjoosu4swzz6Rfv34kVm+YKERro49PAKRnyJ+5V/1lSDIkhKibR5KhTz75hB07drB9+3a2bdvG1q1b2bp1K4pStX1lTEwM/fr1o3///jz11FOeuKUQDVb48SeoVisho89Dn5DgkWvq4qvmDNla4JwhZ0UFzrIyoI3MGZKeISHECXgkGZowYQITJkxwf56Xl8e2bdvYtm0b27dvZ/v27Sxbtowff/xRkiHhM/nvvYftyBECevbwWDKkj09AExSEomt5xdxdvUKKyYQmONjH0XjP8cmQqqruf9KEEMLFK3/Bo6OjGT16NKNHj3Y/VllZyc6dO71xOyFOSVVVr9TUCR4xnG5bNnvses1JFxtLyvsLcZaVteoEQR8fDxoNqsWCIy+vVfeCCSEaxyMTqF977TUcDsdJjzGZTJx99tmeuJ0QDeYsL0etrAQ8mwy15CRCYzIRdNZZhIwc6etQvEoxGAjo2RNT3744Kyp8HY4Qwg95JBm65557OP3001m2bJknLieEx7mqT2uCgtAEBvo4GtHcOnz+Gakff4ShfXtfhyKE8EMeSYb++9//kpOTw7hx4xg/fjwHDx70xGWF8BhvbzuhOp2odrtXri2EEMK7PJIM3Xbbbfz555/ccccdfPvtt5x22mk8/PDDlJeXe+LyQjSZN5OhzPtnsPf0vpR8953Hr+1NBYs+JOvJp6jcvt3XoQghhE95JBkCCA8PZ/bs2Wzbto1Bgwbx7LPP0rVrVz744ANP3UKIRvsrGWr6bvV/p2g1qDZbi6s1VLpiOYWLFmE9fNjXoTQLp9mM7dgxX4chhPBDHkuGXE477TRWrFjBZ599hsFgYPLkyQwcOJBNmzZ5+lZC1Js3e4Z01YUXW9r+ZK1+x/rjVGzZwt6+/Tgy+UZfhyKE8EMeT4ZcrrjiCvbs2cPMmTPZuXMnAwcO5MYbbySrhb1giNbBnue9F3599ZYcLa3woj03D2i9O9YfTxdX/T06ehTV6fRxNEIIf+PxZMjhcLBt2zbefvttpk2bxieffILFYsHpdLJw4UK6devGa6+95unbCnFSik6PJiQEbZTnh8l0rs1as1rOEIzTYsFZXAy0jZ4hfXwcaLWoNpu7R0wIIVw8UnTx448/ZuPGjfz6669s374ds9mMqqpAVQHGCy64gEGDBpGamsrLL7/MPffcw9dff80XX3xBWFiYJ0IQ4qQSn34Knn7K/XPpSa6d6+1Z2R6/tre4q08bDGhCQ30cjfcpOh36uDhsR49iy8xEHxfn65CEEH7EI8nQtddeC4BGo6Fnz54MGjSIQYMGMXDgQLp06VLj2Kuvvpo5c+Zw7733cs899/Dee+95IgQh6sUbRRJdyZCjoACnxYLGaPT4PTzNVXdJFxvbogtHNoQ+OdmdDNG/v6/DEUL4EY8kQ48//jiDBg3inHPOISQk5JTH33nnnWzdupVvvvnGE7cXwqc0YWEoJhNqZSX2rKwWUdivLU2edpENW4UQJ+KxZKihunbtSkFBgSduL8RJuYbGvNUDoigK+rg4rIcOYcvKbhnJUE4O0MaSoeSqZMiakeHjSIQQ/sZnW21PnDiROBm3F83AeugQaZeOx9A+hY5e6o3UVSdDriTD37l7htrASjIX6Rlqe1RVxZaRgfXIEVAh+NzBNZ6v3L0bfWIiuogIH0Uo/EWjkqGLLrqIWbNmMWDAgAafW1lZyRtvvEFQUBDTpk1rzO2FaBBHQQGq1YrTavXaPSKuu5bQCy/AdHofr93Dk3RRkQT07ImhQ6qvQ2k2BncydNTHkQhvsufmUrpiJWWrVlGxfbt71aShUyeCl/7PfZyqqhy+9jpUiwVdYgKm3n0IOncwwUOHoY9rO/8kiCqNSobS09M566yzGD58OBMnTuTyyy8n9BQrUjZv3syiRYtYvHgxZWVlLFy4sFEBC9FQ9vx8AHSRUV67R+iYMV67tjdETppE5KRJvg6jWemTkwGwHTuG6nCgaLU+jkh4UsXmzRR8sIjS5cvB4XA/ruj1GFLbY0jtUON4Z3kFuqgobEePYj96jNKjxyj94QcATKefTtgVlxN6wQVog4ObtR3CNxqVDG3fvp358+fzxBNPMGXKFG6++Wa6d+9O//79iYuLIyIigsrKSgoKCti3bx+bN2+muLgYjUbDlVdeyVNPPUVqaqqHmyJE3RwFhQBoIyN9HInwJV1sLIkvvoiheu6QaB0saWlkP/kU5evXux8L6NOHkFGjCBo8mICuXVAMhlrnaYOD6LxyBY6yMsy//07Fpk2UrV2LeedvVO7YQeWOHWQ/8yzhl11G9O3T0EV7vkaZ8B+NSoYURWHKlClMnjyZpUuXsmDBAtasWcOiRYtqHavRaOjTpw/jx4/n5ptvJjExsclBC9EQ9gJXz5AkQ22ZotUSdtGFvg5DeJii0VC+cSPo9YRfdhkR111HQLeu9T5fGxxM0FlnEXTWWcTccQf23FyKv/6Goi++wHrgAEWff070tKlebIHwB02aQK3RaLj44ou5+OKLAfjjjz/IyMggPz8fk8lETEwMp512mhRWFD7lyK9ataiN8l4y5CgqonjpUlSzmaibbvLafTxBdTiw5+Sgi45G0et9HY4QTWJo356E//yHwDMGYGjXrsnX08XEEHXTFCKn3EjFxo1YDhyoterSUVYmw2etjEdXk/Xo0YMePXp48pJCNNlfPUPemzPkKCsn+z9PohgMRE6Z4teFDO1ZWewfdR6KwUC3Hdv9OlYh/q7o/77AdHofjJ07ux8Lv2y8x++jKApB55xD0Dnn1Hi8bP16Mu+5l9h77yX8ygkoGq9t8SmakXwXRavXHD1Dutiq/xxVqxVHUZHX7uMJrmX12uioNpcIVe7cybFZs8ifN8/XoYgGUp1Osp95lmOPPELGndNxlJX7JI7iJV/hLCkha+ZMDt9wA5aDB30Sh/AsSYZEq+fuGYryXs+QxmBAGx5edb8c/94I1FadDOlj2t7yYVtmJkUffUzp8hW+DkU0gGq3c+yRRymoXoUcesnFaAJNPokl8dlniHv4IZTAQCo3byHt0vHkzZ2L6nT6JB7hGZIMiVbP3TPk5QnUuuoiovYc/96w9a+Ci22n+rSLvl0KANb0dB9HIupLdTjInDGD4i+/BK2WhGefIeb22302PKVotUTecAOd/vcNwcOGodps5L70MkdunIItK8snMYmmk2RItHoxd99N9D+no09I8Op9XNWc/b0KdVvcisPF0K6q1pAjLw9nRYWPoxGnpKrk/uc/lH73Pej1JL36CuHjx/s6KgD0iYkkv/VfEp56EiUwkIqNGzl46XhKli3zdWiiESQZEq1exFVXEnP77WjrsYlwU+jiWkgy1Aa34nDRhoWhqV7dak2XPcr8XdQPP1Dyf1+ARkPSCy8QOnq0r0OqQVEUwq+4go5f/B8BvXrhLC4mc/o/yX3zTV+HJhpIkiEhPERfnVzYslvIMFkb7BkCMLgqUWfIUJk/K/n6a6JWrQYgftZMQseN9W1AJ2FITSV18YdETpkCGg2m3r19HZJoIL9KhhYtWsRtt93GGWecgdFoRFEUFixY0ODrOJ1O5syZQ58+fdz1jq688kr27dvn+aCFqKaLdc0Z8u8J1K742moypE+pqkVjPSLJkL+q3L2b3FlPABBx661ETJjg44hOTTEYiHtgBh3/9z+ChwzxdTiigfwqGXr00Ud55513OHz4MAlNmN8xdepUpk+fjsPhYPr06VxwwQV8/fXXnHnmmfz+++8ejFj4O/PePylYvJjyX3/1+r1azJyhNjxMBmBIrkqGbDKJ2m8ZU1MJHDaUsp49iLzjdl+H0yDGjjX3QLPn51O2dq2PohH15VfJ0LvvvsuhQ4fIzc1l6tTGlT9ftWoVc+fOZciQIWzdupXnn3+ehQsXsnTpUkpKSpg2bZqHoxb+rGLjRrKf+A+FH33k9Xvp4mJRAgNRAoxev1djqXY7joKq1XVttmeoehK1rCjzX5qgIOJfeolj117boosaOs1m0m+/nfSp0yj4oPZ2VcJ/eLQCdVOdd955Tb7G3LlzAXjyyScxGv96URo1ahRjx47l+++/588//6Rr1/rvXSNaruaoPu0S0LMn3bZs9vtChinz38Oek4M2IsLXofiEIaVqeb30DPk3RVFQW/h2MYpWS0DXbph37CT7qaewZaQT+8ADKFqtr0MTf9NyU+4TWL16NUFBQQwePLjWc2PHVk3AW7NmTXOHJXykOapPuyiK4veJkKLTEXTOOYRdckmL/o+7KQwpKQT06kVAH5nk6k/Mf/xB0Rdfoqqqr0PxGEWvJ/6JWcTcdy8ABQvfJ/Puu3FaLD6OTPydX/UMNVV5eTnHjh2jV69eaOvIvLt06QJwyonUFosFy3E/rCUlJQDYbDZsNpsHI246Vzz+Fpe31bfdtrw8AJSw8FbzNZLveRPbHRND8keLPXOtZtAWvt+qzUbmQw9j3bMHW1Eh4RMntqp2h02ejCY+npxHHqV02XKO3HorCbNnowkMrPP41tT2hvBGu+t7rVaVDBUXFwMQVl1H5O9CQ0NrHHcizzzzDLNmzar1+I8//kjgCX54fW1ZGy30dap2tztwABOw41AaZd9+2zxBqSo4neDlrnD5nrctrbndEatXE7NnD47AQDYajTiO+11tTe02TZ5E0oKFVG78ld3/mEDmlBtxnuQ1pTW1vSE82e6KehZXbVXJkKc89NBD3Hvvve7PS0pKaNeuHWPGjHEnVP7CZrOxbNkyRo8ejb6Fj683RH3bffiNN7EBZ553Hqb+/b0eV85//kPpV18Tff99hF19tVfu0ZTvedkPP1K5YwdB555L4KCBXonPW+RnvXW223r4MOn/fhwVSHjkYbpdcgnQetttHj6co9Nux5SeTs/FH5H4ztvooqNrHNNa234q3mi3a2TnVFpVMuTqETpRz4/ri3KiniMXo9FYY/K1i16v99sfTH+OzZtO1W7XyiljbGyzfH20Oj2qxYIzL9/r92vM99y88ReKP/scfXgYYcOGeiky7/LEz7pqs2E7ehQAQ/v2ngjL61rj77iqqhz9z5OoFgtBgwYRefnltebdtbZ26/v3p/0H75N+081Y9+2j4OVXSHrh+bqPbWVtry9Ptru+12lVMyiDgoJISEggLS0Nh8NR63nXXCHX3CHRujktFpxlZYB3d6w/nr/XGrK59iVrozWGXAoWfciBsePIfe01X4fSppV+9x0VGzeiBAQQ/8Qsv1+A4CkBXbvSfvGHhIw+j/jHHvV1OIJWlgwBDBs2jPLyctavX1/ruR9++MF9jGj9XL1C6PVovLwvmctfO9f7ZzLkKriob+PJkMFda0j2J/MVZ2Ul2S++CEDUrbe4t0lpKwzt2pH8+uto/WzqRVvVYpOhvLw89uzZQ171aiGXW2+9FaiqZm21Wt2Pr1ixgh9++IGhQ4dKjaE2wlFSimIyoYuMbLb/OHWxVYUM7Tn+uT9ZW9+Kw0XfrroK9ZEjPo6k7cp/7z3sR4+hS0wgasoUX4fjF8o3bHAP34rm5Vdzht59911++uknAH777Tf3Y6tXrwZg/PjxjB8/HoA5c+Ywa9YsHn/8cWbOnOm+xogRI7j55pt599136devHxdeeCHZ2dl88sknhIaG8t///rc5myR8KKBbV7pv29qsNT301T1DNj/cn0y123HkVxehbOs9Q9W9EI7iYhwlJfLfeTOzHTtG/tx3AYibMQNNQICPI/K98g0bSL9tKrr4eBLfm+frcNocv0qGfvrpJxYuXFjjsfXr17uHvFJTU93J0Mm8/fbb9OnTh7fffpvZs2cTHBzMxRdfzFNPPSW9Qm2Qpo7J8N7iSjKcxcU4zWa/+iNvzy+oWvav1aKN9H4RSn+mCQpCGxWFIz8fa3o6ptNO83VIbYqjqAhDxw5oAgMJGTfO1+H4BUOHDuji47Glp5N54xS0k27wdUhtil8lQwsWLKj3LvUzZ86s0SN0PI1Gw/Tp05k+fbrnghOiHjQhISgmE2plJfacHPfWD/7ANY9JFx3dZqtPH8/Qrh2V+fnY0jMkGWpmAT160OHzz3GWlLSZSdOnok9IoP37Czl8wyRs6ekkz30Xx9ixbX5+X3ORv4hCeJCiKMfNG/KvSdTu3erb+HwhF9e8IWu6zBvyBUWjQRse7usw/Io+IYGU+fPRxsZizMnh6LTbcVSviBXeJcmQaLWOzZrF/tFjKP7qq2a9rz7GP5fX22VZfQ0G1yRqWVHWbFrTvmPeYkhOImnuO9iDgrDs3k3G1Gk4zWZfh9XqSTIkWi1bZia29HRUe+2aU94Ucd21xM98nIBevZr1vqeiCTRh7N4dY6eOvg7FL0jPUPPLvOtusp9/AUdRka9D8WuGjh3JvGkKmuBgKjZvJuOuu1CPWx0tPM+v5gyJls9ptVL85RJUu43wK67w6QTi5tyx/nih55/frPerr7BLLiGseqsDAYYU1/L6dB9H0jZUbt9O6Y8/glZLxFVXyhDZKViSkkiY8zpHp06jfM1ajv7rXyS++KLM9/MSSYaEx6iqSubd91C2ciUApcuWkzLvXRQvb1h6IvbqoovNVX1atCzGbt1JeX8hhtRUX4fSJuS9MxeoSspbyhYovmYaMIDk12eTfvsdGLt2BZls7jWSDAmPKVu1qioRqv6FrfjlF0r+9z/CLr202WNRVfWvmjptfBm5qJs2OIigs87ydRhtgvnPP91/G6JuucXX4bQowUOG0Gnp//xqZWprJP1twmMK5i8AIOrmm4i55x7Q67FU7wfX3Jzl5e4x9uauqWMvLKTw44/Jr2eZiOZiz81Ftdt9HYZog1wFFkPGjMHYsYOPo2l5JBHyPukZEh5hz82lYvNmACKuvRZtWBgRV07w2bwAV6+QJjAQjcnUrPd2FheTNXMWSmAgkZMm+UUdFdVuZ9+w4QB0WbsGXXS0bwMSbYY1PZ2SpUuBqj3IRNOodjtZTz1FyIgRBA8d6utwWg3pGRIeUbpiJagqAX36oE9IQBMY6NMJknb35Onmny/kWrquVlTgLC9v9vvXxZ5fAE4nANqICB9H4z/K1q0jfdrt5Mk2PV6TP28eOJ0EDRkixS09oHDxYoo++piMu++h8rddvg6n1ZBkSHhE+YYNAISMHOHjSKo4CquTocjmf+HXBAaiCQkB/KfWkLvgYnS0zya0+yN7Xj5lq1ZR/uuvvg6lVbLn51P8xZcAREuvkEdEXH01QYMGoVZUkD51KlbZbNgjJBkSHqEYDGgCAwkcMKDG46rdTsXWbTjKmreHxO6ePO2blWSu3iG/SYZcBRel+nQNrlVN1sOHfRxJ61T4ySeoVisBffpgOuMMX4fTKigGA0mzZ2Ps2QNHfj5HbrnFvXJWNJ4kQ8Ijkl58ga6/bsTUv3+Nxw9ddTWHr72Wyi2bmzWegB49ib59GqHjxjbrfV38bUsOd8+QVJ+uwZBalQzZj2XhtFh8HE3r4ywtA72eyIkT/WLuXGuhDQ4i5e230SclYTt8hIxpt0uV6iaSZEh4jKLT1RqCMXbuBEDljp3NGoupdy9i/vlPnyzrB9ybK9qys31y/7+TnqG6aSMiqoY0VRWbDDd4XNyDD9B5xXJCx47xdSitji4mhnZz30ETGkrljh0ce/hh1Op5gaLhJBkSXhXQpw8AlTubNxnyNV1sHAD2nFwfR1JF9iWrm6IoMlTmZfrYWBSDwddhtErGjh1Jnj0bdDpKvv2O3Ndf93VILZYkQ6LJrEeO4DzBvjmmPqcDUPnbb21qk0a/mzMkO9afkKsCtSRDoiUKOudsEmbNAiD/v281+8bUrYUkQ6JJVFXl0FVXs7dff8x//lnr+YBuXVEMBpzFxc06DFHy/feULl+Oo6Sk2e55PL9Lhtw9Q5IM/Z27Z+jQId8G0orkz3uP8l9+aVP/APlS+BWXE3Xrreji4zF26+brcFokSYZEkzjy8nAUFoKq1lklVTEYMHbuDFBnsuQtWf95kow7p2M7dqzZ7nk8XWwMitGIovOPuqYygfrEXJOorYekZ8gTbNk55Lz8Mkcm34j1wAFfh9NmxNx9Fx2+/IKA7t19HUqL5B9/qUWLZd5bleAYUlJOuEO9sVs3zL//jmXvnzB6tNdjUp3OqgQN3xUYNPXtS7ft2/xmBU3i889hy86WDTLrIHOGPKv4i/8DhwNT//7uf4SE9ykaDTopqNpokgyJJrFU9/YYu3Y94TGu5yzN1DPkKC52V1v21R8Hf0mCXIIGDvR1CH7LkJpK8MiRGFJTUZ1OFE3NDnOzw0mW1Uau1Y7F6cTsVFFVlUCthkCtllCdhgSjgUCtdLSrDgeFn30GQMRVV/o4mrbNsn8/+XPfJf4/T6CRCeynJMmQaBJrWhrw1xL6uhi7dgGaMRmqLrioDQtD0eub5Z6i5dKGhtLuzTcAKLDZWZdXzNaSCnaXVvJHuZl8W/02t43QaUkKMNA50EiPIBM9ggPoGWwiyaj3u+TYW8rXr8d+9BiasDBCxvqmxpcA1WrlyK23Yj96DHRaEp58ss38DDaWJEOiSVyl4E82/BJQ3TNkTU/HabGgMRq9GpMv9yX7O1VVQVVr9TYI/5FptvJFdiH/yy1iZ2kldU35NWkUYgx6TFoNRkUBBSodTiocTgrtDvf7wrJKdpVVsoQi97nxBj1nhgVxVlgQZ4YF0SvYhE7TOl+YCj/5FICwSy854bC58D7FYCBh1izSb5tK8f99QUC37kTeMNHXYfk1SYZEk7iSIX0dk6ddtNHRpH76CcZOnbyeCAE4ClxbcUR6/V4nc+yxxyhe+i3xDz9E+D/+4bM4yn/5hbK16wgc0J+QUaN8Foc/caoqKwtKeTs9h58Ky2okQN2DAhgYHkzvEBOnBZtoH2AgTKc94X/WqqpSYndw1GIj3Wxlb7mZPeVm/iir5M8KM1lWG9/kFvFNbhEAYTot50YEMyIylGGRIbQLaB1DGLbsbMpWrwYg4qqrfBuMIHjIEGLvv5+c558n+9lnMXTqSPDgwb4Oy29JMiQazWk2Y69ereWq1VIXRVEwVRdfbA7+0jOkqipqRQU2Hy+vr9i0mYL33sNZVtbmkyGnqrIkp4hXD2XzZ8Vf2xeM0KtcVZrPgNAg2p3Vt0HXVBSFML2OML2OHsEmxkSHuZ+rcDjZXlLBpuJyfi0uZ3NJOcV2B0tzi1maWwxAJ5OR4ZEhjIwKZVB4MKYWOveo6PPPqyZOnzEAY6cTD5uL5hN542Qsf/5J8ZIlZN5zLx0+/eSkf6vbMkmGRKPZ0tMB0ISEoA0P920wx3H3DEX5tmdI7ye1hqTgYpWfCkt5Yv9RdpZVAhCs1XB9YhRTkqIJX7WCzHvvw9GvHwz23GTzQK2GQRHBDIoIBsChquwoqWB1YSmrC0rZUlLOgUoLBzItzMvMI0CjMCg8mJFRoYyKDKVDoPd7Uj1BdTgo+vz/AIi4UiZO+wtFUYifNRNrWhqVO3aQfvsdpH7yMdqQEF+H5nckGRKN5p4vlJLiV5Pz3D1DPtqx3uWvwou+3ZKjrdcYyrPa+ff+TL7Iriq3EKzVcGdKLFOSYwjVVe2lV9lMy+u1ikL/sCD6hwVxb2o8JXYHPxWWsqqglJX5JWRabKwsKGVlQSmPkklHk5Hh4UEEaQMY4XDir+sByn/6CfuxY2hl4rTf0RiNJM95nbQJV2I9eJDM+++n3Ztv1tpHsq2TZEg0WtA555D68UeoDscpjzXv3Uv2M8+iGA2kvP22V+NyFBUBvu8Z8pcq1G15k9Yl2YU8vC+DApsDDXBDUjT3pcYRY6iZVRjapwLgKCjAUVyMNiys9sW8IFSn5YKYcC6ICUdVVfZWmFmRX5UYbSwu42ClhYOVFgiKY+7GPQwOD2FUVAijokJpb/KfXqPAM88k8blncVZWNsu8QNEwupgYkufM4fD111O+Zi05L79M3IwZvg7Lr0gyJBpNExSEqW/feh2r6PVU/PILSmAgqqp6tScp6dVXcJaV+fw/n782a5VhsuZW6XDyyL4MFh+r6iXsHhTAy93b0T80qM7jtcFB6GJjsefkYE1Lq/fPtScpikL3IBPdg0zckRJLqd3BusJSlucW892xXArRsaKghBUFJbAvk04mI6OiQhkZFcI5YcEE+HCukSYwkLBLL/XZ/cWpmXqdRuLTT1UNB+fl11lTqy2TZEg0C0NyMmi1qBUV2HNy0cd5b8hGURS/GBN37QNmz8tDdTh8kpypDgf2vLzqeNrGMNmBCjM37zrEH+VmFODu9nHckxqH4RR/+A2dOmLPycFy0DfJ0N+FVPcajQ4PYui+7XQcNpK1xRWsKChhU3H1XKOMXN7JyMWk0TA4IrgqOYoM8ateI+E/Qi+4AF18PKZ+/fxqaoM/kGRINAvFYECfnITt8BGshw55NRnyF7qoKNBowOnEnpfvkzbb8/OrqnFrND4fNmwOawtKuXl3GiV2J9F6HW/2bM/QyPolxsYOHan4+ResB/1vPy0F6BEUQJ/wEO5sH0dJda/RivwSVuaXkmW1sTy/hOX5VRsTdw40MioylOGRIZwRFkSIznuJuGq3+80efOLUAvv393UIfkl+gkWjqKpKxp3T0cXGEHvffWiDg095jiE11Z0MBZ19VjNE6VuKVosuOhp7Tg72nBzfJEPZ1fOFoqNb3AuWqqqU5uWSn3GEwqyjlOTlkr3rN77c+St2ixm71YrdasHpdKLV6tjS4TS+OG0wTo2GLhVF3F+STnDJAdISkohITCY0JgaN5sRJgaFjRwAsB9Oaq4mNFqrTcmFMOBdWzzX6vdzMyvwSVuSXsKmknP0VFvZX5PJ2Ri4K0C0ogDPDghgQGsgZYUF0Mhk90jNgOZjG4euuI+zSS4l98AHpbWhhVFWl5H//I2TMmDY/16tl/XUUfsNRUEDZihWgKMQ/9FC9zjGmplK+Zq17Cw9vsGXncGTSJHRxcaQsmO/zP86ueSj2XN/MG7JnZ1XFER/vk/s3hKWigqN7fydjz24y9+wm51AaNnNlreNK//a5Cqw/YxQ/9x4CQI99Oxi36gv2Ox3sP+44rV5PVHIKCZ27Et+5GwmduxGZlOz+GTF2qkqGrAcPeqF13qMoCqcFVxWInN4+jmKbnbWFZawsKGF9YRlHzFb2VBeC/OBoVdmJUJ2GnkEm93k9g010CwpocI2j4iVLcBQWYj10yOe/a55mdTqxOFXMTidWp4rFqWJxOrGqKhZH1Xurs2a98r9XL9cABo2CUaPBoFGqPlY07sdCdBqMPpy3k/3MMxS+/wFh6zeQ8MzTre572BCSDIlGsR2tKraoi4lBqecmgK4q1daMdK/FZc/LxXroEM7ycr/4xY6ceD2OsjKMXbr4JgCNFmOXLhirez38TXlRIfs3/cL+TT9zZNdOnI6a+4BptDoiEhKJTEomKDyS9Owc+p8zkMDgEHQGA1q9nlfK4OfyqhWNN+rtXNMlgcqY66goLqI0P4/CY5kUZh3FYbORk3aAnLQD7Fj2HQBB4RG0792X9qf3JykuCajaNka1Wuv9c+1vwvQ6Lo4N5+LYcAByLDY2l5SzubiCLSXlbC+toMTu5Jficn4pLnefpwGSAwx0NBlJDTTS0WSgg8lIislIglFPiFZT43dKdTgo/uqrqnuOH9+MLTw5m1Ol1OGg1O6gzOGkxF7741K7g1KHs/q9g1K7kzK7g5Lqj0vtDqxqXRuzeJ5RoxBcveFviE5LqFZLkEahLCCSnYeyiQswEG3QE63XEW3QEa3XEanXeWRLl5ARIyhc9CHFS5YQcNppRE683gMtapkkGRKNYjt2FAB9QkK9zzG0a1d1bnqGV2KCqh4r8H31aRdfr7AJGTmCkJEjfBrD36lOJ4d3bmPnih84sGUjzuNKM4TFxpHcozdJPXqS0LkbEQlJaKuH92w2G99++y09zh2OXq/Hqar8688MFpdX9XY83SWJKcl1r5hzOh2U5OSQnXaArAN/cmzfXrIP7qe8qJDf163i93WrABir06JRoWjP70T06evdL0QziTXq3cv3oarHY1+Fhd1llewuq+T36vcFNgdHzFaOmK1Q+Pf+t6oCkgkGPXFGPQlGPT1+28ao7GxsISH80PN0QvOKCdVpCdBoMGoUjBoFg0ZDgEbBUJ1Eqce/qa6P/+p1MTurel0qnU7KbTa260xYcoowo/yVvFQnMGX26uSm+mNXAlTp9HwSY1AUd89OgKuXp7qHx5WS1EhNqj9xqlT1JlX3LFnVqt4mq1PFVp1sVbXdTr7t7zcNYX1GXp3xKECMQUei0UBSgJ4ko4FEo57EAANJRj2JAXriDHo0p/iHMGjgQGJnzCDnuefIfvZZjN26EnRW65/CUBdJhkSjuLbh0CXWPxnSJ7uSoXSvLa+35/vHvmSiNofdzp71a9i45DMKj/6VEMd36kLnswbR+cxziEpqV69rqarK/XvTWXysAAV4qXs7rk04cQKs0WgJj08gPD6BbgPPBcBus3F07x8c3rmVQzu2kXPoAGu6JGHR61CfepTEbj3pNfw8ug08F4MpsElt9ycGjcY9POaiqio5VjsHKy2kVVhIq65vlFZhIcNipcRetSntgUoLByotADzyv/8BsLTfObx2IMs7wQbGwp+ZjTrVpFEIru5pCdZpCNVpCan1cXWPjFZLiE5LiLaqdyZEpyVYq3EnPqdKKhrDoarVvVHVvVLVb6UOJ4UWK7/u/p2oDp0ocDjJs9rJs9nJs9opsNlxAjlWOzlWO9tr561AVY9TSkBV716qyUh7018ftwswoK/uWYqcPAnz779T8s03ZN59Dx0+/wx9YqLH2+vvJBkSjeIaJtMn1P+XRp9UdayzogJHYaFXEhaHn+xLJv6iqip7f17HTx8tpDgnGwBjUBA9h46kz8ixRKekNvh6M/cfZfGxAjTAGz3bc1lcRIPj0un1pPTqQ0qvPgy5djIlebns27iBPzeu5+je391vqxa8Q9eB59J7xBgSu/Xwi+FXT1MUhThjVa/PwPDaiyHKHQ6yLXayLDayrTZyi4oZuHMzAJUXXMjwiBCKq1/MzdVzbVy9IJZ69NQYq3tcarxXFCqKi0iIiiJUryP4uKQl+Lik5e8JTEh1oqP3wDCSN2nde9rVfs5msxG9rYQLOiWg/1vZcYeqUmCzc9Ri46jZSqbFxlGzjaMWK0ctNjLNVrKsNixOlX0VFvZVWGpd//gh0S5BRrpN/Sen79kL+/4k/c7ppC7+EE1AgJda7p8kGRKNYjvmSobq3zOkMRqJuO66qn3MvPSCYveTHetd7AUFlC5fDg4HEddc0/z3LyxEGxbms+JqOYcOsmrBO2T8sQsAU2gYZ1x0GX3HXNDo3pbX0qtWSQG83L1doxKhuoRGxzDgwksZcOGllBbk8ce61exatYzCY5nsXr2c3auXE5OSSr8LLqHH4OHoWuicosYI0mrpGKilY/VeacWbNnDUYkHfPoVZl445aYKoqqp7/o0CKChoFNfHnPBc17DoBcMH1EoI2jKtohBj0BNj0HN6SN2/Q3anSqbFyqFKK4cqLdVvVvf7SqfTPSS6unpING7Snbz17COE//47i++8l633PUjnIBNdAo10CQqgXYABbSv8R8BFkiHRKO5kqAHDZADxjz3qjXDc/K1nyJ6bR9a/H0cbGemTZOjAqPNwWq10+u5b95yt5uCw2/l1yWf88sXHOB0OdAYjZ136D8646DL0TfiPc5U+hI+PVCVC/+mcxNUnGRpriuCIKM669B+ceckVHN37B7+t+pG9P68j98ghfnxrNusWL+T088Zx+pgLCY7wj8S7OZV8VzUBPfT880/ZU6YoCsZW/CLqj3QahfYmI+1NRoZRs86Wa0g0rdLCgQoL+yrM7Cu3sC/AwBM338ULs59mwE+r+DmhHU+MPN99XoBGoWtgAN2CA6orpQfQPSiARKO+VfSWSjIkGqUxPUPNwe4nO9a7uKpQOwoKcFqtaJqxN8FRVoazoqIqjmZMDouyjrF09vNkHdgHQJezBjF80s2ERjetztKy/FI+CajqBZqRGs8t7Ty/vYgtJ4eMabdjz8mh89o1KIpCUveeJHXvyfCJN/Pbyh/Y9sP/KM3L5ZcvPuHXr/6PHkOGc9al/yAyMdnj8fgjR2kp5WvXAhB6/gU+jkY01PFDouf8bUi08qzuHNRUwssvcet3X6K5+GJ2oeNgpQWzU2VnWSU7yyqBQvc5IVoN3YNM9AgOoFt1gtQ9yESUoWWlFy0rWuEXnBYLDtcWD36WDLl7hvxkmEwbHo6i16PabDhyc9EkJTXbve3ZVfNzNKGhaAKbZwJw2vYtLJ39PJbycoxBQYyaMo3ug4c1+T/H30oruHNvBqqicF18BPemxnko4pp04eGY9+wBhwN7djb64+ozBQQHc+YlVzDgwvHs3/QzW7/7msw9v1cNoa1ZQddzzuXs8ROITfXPMgaeUrlzJ6rTiaFTJ4xdfVQyQniFSauh5y03kVteRtjFF/F6585A1Tylw5VW9pRXumtW7Skzc6DSTKnDyaaScjaVlNe4VoxB5+49cvUkdQsKINiL1dCbQpIh0WBqZSUhY8diz8+rmv/TAI7iYiq2bkW12QgdM8bjsdmrl9Y3Z0/IySiKgi42FltmJracHPTNmAzZsqpW+DRH5WtVVdn8zResXbwAVJWELt24+J6HCImKbvK1j1ms3PBbGhVOJz3slfynY0+vdcsrBgOGlBSsaWlYDx6skQy5aLRaup5zLl3POZejf+7h168+48Dmjfz58zr+/HkdHfqdwdmXXUVStx5eidHXggcPpsu6tdiOHmsVwyOiJkVRiL3n7hqPaRWFjoFGOgYaueC4DlmL08nBCos7QfqjrCpZOmK2kmu1k2stY11hWY1rtQswHJckBdA92ETnQKNPi0+CJEOiEbTh4SS/9mqjzrXs30/GtNvRJyV5JRkKv+IK7NlZfjV850qG7Dm5zXpf91Ycsd7pRXFRnU5WfzCPrd9WFeDrPWosI2+cis4Dk14rHE4m7UzjmMVG10Ajt2Yd8foqIUPHjljT0rAcTCNo0KCTHpvYtTvjZzxG7pFD/LrkM/ZuWEfats2kbdtMcs9enHP51aT0Or3VJQ26yEi/WaQgvM9RWoomOLjWz7FRo6FHsIkex5VpACi3O9hbUZUg7S2r7kkqryTbaifdbCXdbGVZ9T56AFoFOpqMzEjx3Z6VkgyJZuWuNZSVhWqzoXh4lUjMnXd49HqeoIurSkbsOc27JYe9ehm76/7e4LDb+fGt19xFC4ffcAsDLvRMoUlVVXlgbzo7yyqJ0uuY3zOFXVl/euTaJ2Ps2IGyFQ3bliMmJZUL/zmDQVdex6av/4/dq1eQ8fsuPv/9URK6duecy6+iQ98zWl1SJFq/yt27yZg+ncgbbiBq8uR6nROk09I/NIj+oUE1Hi+w2dlTZnYPt+2t7lEqtjvYV2FBpyj8vfZkc5FkSDQrXUw0itGIarFgy8pq1hVOvuKaRN3cyZCtes6QPt47yZDT6eC7N15m74a1KBoN426/h55DPFftesHRfD7PLkSrwDuntSclwMAuj139xAwdOwFgOdDw3esj4hMZc+t0Bl5xDZu+/j9+W/EDx/7cw5fPziKuY2fOvvwqOg8422elDprKsn8/hk6dJKlrQyq3bsN+9Bg5L7xIQLduBA0c2OhrRep1DIoIZlDEXxO3VVUly2pjT5mZ00x6fvZE0I3QMn8jhU9Z09OxHj6M02xu8LmKRuOeN2NL994eZf5EH1vV9dvsPUNZ1T1DXhgmU51Olr0zh70b1qLR6rj0/kc8mghtKS7n3/uqKg8/2jGRwREhpzjDc4zVk0Yt+/ef4sgTC4mKZuSNt3HznHmccfHl6IxGsg/u5+sXn+L9B//J3p/X4XQ6Tn0hP2IvKODgpeM5MOo8HKUnKHssWp2I66+r2lbI4SDznnuxZjSuIviJKIpCgtHAiKhQIvS+65+RZEg0WM5LL3Ng7DiKPv20Uefr21UtQbZmeHaPMltmJqWrVmHZt6/Wc2aHk3fScxi/dR+Df/mDa3Yc4LOsAuxe2Mfo73TVyZCtetiqubhWk+m8MIF69Qfz2LVqGYqi4cJ/3k+nAWd77Nq5Vhs37z6ETVW5KCaMqV5YQn8yxk4dQVFw5Oe7t3dprKDwCIZdP4Vb5rzH2ZddicFkIu/IIf736nMsvO8Ofl+7ssbebP6s9McfweFAGxGBNqT5klPhW4qiED9rJgG9euEoKiJj+nSclZW+DsvjJBkSDWbPrZoI7HqRbyiDq2fIw/9hlK1fT8a028l5+ZUajx+qtDB6817+vf8ovxSXc6DSwqqCUqb/cYRLtu0j3Wz1aBx/p4uNRdE3f2EyW3VPlN7Dc4a2ff+Ne7L02Gl30fWccz12baeqcufvRzhmsdEl0Mir3VOa/eumCQxEXz18a9nX+N6h4wWGhnHu1Tdwy5z5DPzHtRiDgig4msF3b7zM/Hum8tvKH3HYfTVbon5Kvq0utHjB+ac4UrQ2moAAkl+fjTYqCssff3Ds0cdQVe//I9mcJBkSDeYa7mlsMqRPquoZsnm4Z+ivHev/WuWSYbZyydZ97KuwEGfQ8XSXJP6vbyf+1SGeUJ2GrSUVXLp1Hwfr2L/HUwLPOotuO3eQ8t57XrtHXRKfe5aEp55En9LeY9dM276FVQvmAnDuNZM4bdgoj10b4K30XNYUlmLSKLzbq4PPapIYu1TVz6mrl7EpAoKDGTThWm6ZM59zr5mEKSSUouxj/Pj2bObddSvbf/wWu9W7yXlj2HJyqNi0CYDQceN8HI3wBX1CAsmvvgI6HSVLl1Lw3nxfh+RRkgyJBlFVtenJUHL1MFmmZ5Mhe1519enq7RHMDieTf0sjx2qne1AAy87oxpTkGAZHhHB3ajwrzuxOl0AjRy02rtt5gCKb3aPxuCgajU8mnAYPHkz4FVegDQ469cH1kJ+Rzv9efRZVdXLasPM469J/eOS6LjtKK3jmYFVl8/90SaZbkO82igy/bDxxDz9E0MBzvHJ9Y2AgZ4+fwC1z3mPYxJsICo+gNC+XFfPeZN4/b2bL0q+wWRo+J89bSn/4EVQV0+mnN2utLOFfAs88k7iH/gVAzksvUfbTeh9H5DmSDIkGcZaWolqqelF0MY2by6FP9s4wmT2/uip2TFWhv9lHstlVvSx7UZ+OxBprLuNvF2Dgi36dSTLqSau0cvvvh1td16+n2MxmvnnlGayVlST36MXoW+/waIJXbncwbfdhbKrKhTFhXJfg2xo2IeedR+QNN7gnU3uLPiCAMy66jJtef5eRN95GcFQ0ZYUFrH5/Lu9Ov5lfv/ocqx/Mz3DvRSZDZG1exLXXEnbF5eB0knnffR6fUO0rkgyJBnH1CmnCwtAYjY26hqG6Z0g1m3FaPDc85ch1JUMx7K8w8/rhqlif7ZpMckDde4LFGPQs7N2BAI3CyoJSFh8r8Fg8dWmJyZaqqiyf9yb5GUcICo/gorsfRKvzbH2oR/ZlcrDSQqJRz4vd2rW5pdt6g5F+4y7mptfmMvrWOwmLjaOiuIh1ixew4J5bKdi1FUtF+akv5AW2Y8eo3LoVFIUQGSJr8xRFIf7xxzGdfjphF16APrZ5Fzh4iyRDokFcyVBTfgG0YWF0+XkDXTdvanRCVRf3xO7oaF5Iy8KmqpwXFcpFMWEnPa9XSCAPdqiqWP34/kyOWTw/Z+Pog/9i7xlnUrL0W49fuy5l634i58UXKVu3rsnX2rV6Gb+vXVm1cuyuBwgKj/BAhH/5JqeIj7MKUIA5Pdr7dHmtr+n0evqMGseNr7zNuNvvISIhCXNZGQU7tzD/7lv56eP3Ka3uAW0uJd//AIBpQH+PT8YXLZPGYCBlwXzi//1vlGbcfNqbJBkSDeJOOGKatlxbFxHh8f/+7dWbxx4ODOHrnCIAHu6YUK/73NouhgGhgZQ5nO55K56kOhw4y8qardZQ+c8/k//uPMqbOKZfcDSTle+9DcDgq66nXc/engjPLddq48E/q+pN/bN9XI1ibL5W9tN6cl57jcrfmqPUY01anY7Tho1i8stvMvb2ezGERWCtqGDjl58y984pfPPKs2T8satZehrdQ2TjZIhM/EVjMp36oBak7f4LJhrFtVy7sfOFvMVZUYGzvGoY4a1KJypwUUwYPYPr9wurVRSe7JLM+Vv+5NOsQm5KjuH0EM/t9K5r5sKLf9UYavx/8k6Hg+/feBm71UJK774enzCtqir/+jODApuDnkEB3OeFnehV1YHdXg443PdUFA1abRAazcmH+oq/+D9Kvv0ObVAQpt69PB5bfWg0WroNGsL+whK6x0axY9lSMn7fxZ+//MSfv/xETPsO9Bt3Md0HD0Vv9PyEc6fFUrVljlZL6FjP7yUoWgfV6ST/7bcxdulCyHnn+TqcRpFkSDSIa7PRxq4k8xZ3cbyAAL4orRrmmt6+YS+u/UID+UdcBJ9nF/LE/qP8Xz/PTZ79a0uO5im8aMuu3rG+CVtxbPr6/zi2fy8GUyBjp97l8S0kvsopYmluMToFXuuRgqER17dY8ygr/YPyiv2YzUcxmzMwmzOxWQux2UtwOMpOeK5GY0SrDUavD8NojMNojCfAGI8xIJHAwA5oO1ZN9Pf08vrGUBSFTmeeQ/dBQ8g9nMa2H/7HH+tWk3s4jR/fns3q99+l++Ch9BoxmvhOXT3W66oxGkn9cBH2ggLZmFWcUPGXX5L72mw0gYGkfvKxuzRFSyLJkGiQ6FtvIfT8cU3+w1i6YgU5z79AwGmnkfTyS02Oy149eboiIhIb0D80sFE9O//qmMBXOUWsLypjY1EZZ4d7ZthG765C3Uw9Q0erhvp0CQmNOj/3cBobPlsMwMgbbyM02rM9gblWGw/vqyqtcFf7OHrX43vldFrRaNI4kv42paVbKS39Has1t9ExOJ0WnE4LNls+FRW1N2UNsCpEoqdgx4+U7DUQGtKbkNA+BAV2RqPx3Z/OmPYdGHPrdIZcO5ldq5axY9m3FGdnsXP59+xc/j1RySn0GjGankNGEBgW7pF7SiIkTibskkso/vobKjZuJP3OO+nw6adow04+V9PfSDIkGkQXE+OZITKNBuvhwyiBnhmKMvXrS6efN3Dpum0ATE6KbtR1kgMMXJ0QyQdH83nlUDYf9/VMMvTXzvWNf/GuL9Xh+GuT1sTEBp/vdDj4/s1XcTrsdDrjbHoOHenZ+FSVB/dWDY/1CjZx10l68KzWPPLyVpKbu4yCwp8JDKrk0KHjj1AIDEwlKKgbJlMyAQFJBAQkYTBEo9eFotOFotMFoyg6QKm+vwOHowK7vQy7oxSbrRCLJRuLOQuLJYtKcwYVFQewJlbNZdJkVJKZvpjM6o4rjcZESEhPwkL7Eh5+FuHhZ6DXh3v0a1QfpuAQzrz4cs64cDzpv+9i1+pl7Nu4gfyMI6z5YB5rP5xPSq/T6TZwCJ3PGogpWLbQEN6h6PUkvfoKh674B7bDR8i89z7avfM2itY3RVMbQ5Ih4ROu5fW2TM/UqFAUhV/RsS0kknCdlotjwht9rekpsXx0LJ/VhaVsLS6nf1jTixYeP2eoat6K95aO23NzweEAnQ5ddMOTwq3ffU3OoQMEBAUz+pY7PR7rVzlFfJt34uExu72cnJzvOJb1fxQVbQL+miSsOoOIiRlEZOQ5hIb2JTi4K1ptwxJqRdGg0YSh15/8P1f7gHL2PzMILFba6a+kNOQwpaW7cTjKKC7eQnHxFo6kzwMUgoO7VSdGZxERfiYGQ+OS8cZQNBpSevUhpVcfLFOmsnfDOnatWsax/Xs5vHMbh3duY/m7b9C+d1+6DhxCx/5nEhh66v/ayzf+ijY8DGNXzw27iSqqquJ0VmKzl+B0VOJwWnA6zDidZhxOMzZrOTrdFo5lVaDVKKg4QVWr3ztrfg4oihaNokdRtCiKruZ7jQ6NokejMaLRBqDVmNBoAtBqA6rfm9BojChK04bBdRERJL8xh0PXXEv5+vXkvPwycTNm1OtrUVlaQnFOFiHRvlutKMmQ8AlXFVtnSQmOkhK0oaFNvuaS7CIALooJx6Rt/C92isnIP+Ii+TirgDlHcnivd4cmx+bqTVPNZpylpR5p74nYjlUNkeljYxv8n1lJXi4bPv0QgCHX3ejxZfSFNjuPVu9Gf3f7eE47boJ7aelu0jPeJyfnWxyOCvfjISG9iIkeTXjEcNau2cewYReh13u2zlFddIYgjF26Yt61i3jzCLoOG4OqOqmoOEhJyU6KirdQVLSJiooDlJXtoaxsDxkZ7wMQFNSFiPBziIg4h/DwszAYmmeYyRgYRJ/zxtHnvHEUZh3lz59/Yu/P68g9nEba9i2kbd8CikJC56507HcmHQecRUz7DnUmO9lPPoll3z6SXn6J0AsuaJb4WyJVVXE4yrBa87BY87Bac7Fa86rf8rHbS7DbSrDbS7DZi7HbS7HbS1DVk1e8DzBBc05X02iM7uSo6i0IrTYInS646r02CK0uCK02+LiPA9Fpq5/XBaFtH0TME/8i54GZFMx7j4DuPQi54HzKCvIpyc2hJC+Hkrzcqve5VR+X5uVit1bVm7vo3oebr8F/43fJ0KZNm3j88cf5+eefsVqtnHbaadx9991ce+219Tp/9erVjBgx4oTP//zzz5xzjndK7Ld2jpISjj78MPrYWOIefbRJE2o1gYFoIyNxFBRgy8hA27Nnk2KzOp38L7cIgPFx4U26FsC0lFg+zirg+7xijlRaSDE1rR6SJiAATVgYzuJi7Dk5Xk2G7NXJkC6x4fOFVi14G5vFTGK3nvQeMdrTofHkgaPk2ex0CTTyz/axqKpKQcE6Dh+ZS2HhBvdxJlMqiQn/ID7+UgICqob6bDYbcMDjMZ2MsVtVMmTZuwfGjkFRNAQFdSYoqDMJCZcDVZO4i4p+pajwV4qKfqWsfC/l5fsoL99HRuYHAAQHdSM84hwiI84hPPzsU/ZKeUJEfCJnX3YlZ192JQVHM9j78zr2bdxA7uE0ju3by7F9e1n/6SKCIyJJ6d2Xdj170+603oTGxGE9cKBq4rheT9DgwV6P1V+pqordXlI1Od9yFLP5KBbzUcyWY1UfW7KwWnNxOhtXm0xRtGg0purJ/NU9NZoAFEVPQWE5sTGJaLT66l4bTfV7BUXRoKABRQEVVNWOqjpqvHce/5jThsNZ3fPk+Ou9qv4Vt2sOnd1e3LQvWjCEjNES8qOWjIfvJyv9PsxxGhxWDU67BqdNg0PV4AzTYArUYEioekyvD8FsSQd8sw2PXyVDq1evZuzYsRgMBq6++mrCwsL44osvuO666zh06BAPP1z/rHHYsGEMHz681uPJ1cMzouHs2dmULV+BNiyM+H//u8nX0ycn4ygowJqRQUATk6Htj/ybx3b/wdJL/sHA4ac3ObZuQQEMiwhhTWEp72XmMbNz0/dj0sfGYCkuxpad7dVtHtw9Q/ENS4b2b97I/k2/oNFqGX3z7R5fPfZLURkfVlf4fqFbO0qLNnDgwAuUllbV8VEULbEx55OcPJGwsAF+MTQT0L0HxYD5jz0nPMZoiCYu9gLiYqt6T2y2QgoLf6Ww6GcKC3+hvHwfZeV7KSvfS0bGQkAhJLgnERGunqMz0em8O58nMjGZgVdcw8ArrqE0P4+0bZs5uG0Th3/bTllhAb+vXcnva1cCEBIVQ+9SC+GAvu/pEOSZve38ldNpx2zOoLLyMBWVh6msqHpftTLxKA5H/Sp/a7XBGAzR7jejIQa9IQq9PgydLvS4OWwh1e9D0WoD6/w5t9lsfPvtt/TqdYFXe0HtNguWimLMlYVYK0uwWkowVxRSWV6ApaIAi7kIm6UYm60Uu70Mh6Mcp1qJUzWjaG1o9U401W+uj7UGJ6WXONBnKgTs1hD7AeT9y4rzlJ3MOUR3MnBos9eae1J+kwzZ7XZuvvlmFEVh7dq19OvXD4DHH3+cgQMH8vjjjzNhwgS61HPJ3vDhw5k5c6YXI/YvFRt+pmjBfAK6diP2vnu9UhXU1sQNWv/OkJyEeedObJlHm3ytip076X9gH6UmHVoPvYjenBzNmsJSFh/LZ0ZqPEFN3EE9YuJEVIsVY2qqR+I7EcVkwtCpE4YO9b+P3Wpl1YKq4ooDLrqM6JT6n1sfFqeTGXurJiRPi8rFkPYS26t7grTaQBITr6Jd8o2YTP61CWhAzx4AmP/4o97n6PURxMaOJTZ2LFA1CbywcCOFRb9QWLiRiooDlJbtprRsd/WcIw0hIaf9lRyFnYFO573ikyFR0e6hNLvVSsae3WT8/hvpv+8ia/9eSvNy0FV/rzZlHeF/kycQ274jcZ06E9exC9HJKUQmt8MQ0HKK7jmdVszmTCoqDlUnPYeOS3oyTzlkpddHEhCQWPVmrHpvDEgkwBiPwRCDwRCNVuu9Hg2n04HdYsFqNmMzV7rf2yyWGp9bzWbsFvMpjnM9ZsZhb+jm1MbqtypanY7A8AiCwyMJioggKDwSU0QYgeHBmP7pRJ31KvrSMnqE34W2ZyIORwUOR3nVAgb3x+U47FUfBwQkAZ4velsffpMMrVy5kgMHDnDjjTe6EyGAkJAQHnvsMa6++mrmz5/P008/7cMo/ZM+P59j/34c1WKhcvMWAs8+i5CR9VsBZLMVsm/f0+QXrMMUkEynTjOIiDi7zmPdNYY8VHBRn1Q9iTqjabvX250q2oKqHocBHds3OS6XUVGhdDQZOVhp4ZOsAqYkN63dEVde6aHITi7y2muJrOewssu2H/5HSW4OwVHRDLziao/H9MaRHDLKC7hN8zHn5n1HISqKYiA5+TpS20/DYIjy+D09wditG7rYWIzduuK0WtE04p8MgyGauLgLiYu7EACLJYfCwl+qk6NfqKw8TGnpb5SW/saRI3NRFC0hIX3cyVFQYB9PN8tNZzCQ2qcfqX2q/ubazGYylv2Aeca/cGo0FCXE4LCYObZ/L8f2761xbkh0DFHJKUQltSMiIZHQmDhCo2MJjYnxSgHIU3E6bZjNGVRUHKKi8pA78amsOEylOQOqJxvXRaMxYjK1J9DUHlNge0ym9phMKZgCkjAa49Fq65f4qaqK3WrBZq5OSCyNS16sZjPFBQXM/d8n2MwW95wab9HqdOgDTOiNARiDgjCFhGIKDiEgJARTSBimkBACgkMwhYZiCg6tejw4FGNQ0El7cC3v9kXRaTGkpNQrjqqh8DaeDK1evRqAMWNqVzl1PbZmzZp6X2/fvn3Mnj2biooK2rdvz+jRo4luxMqaliBy1WpUiwV9Sgrt31+IPj6+XufZ7aVs2XoN5eVVs/Ss1ly2bb+B0/vMJSpqaO3jcz1bcNE1ibqpydDmolLCSooA6J3arqlhuWkUhSnJ0Ty6L5P3j+ZzY1K0XwzdeJq5rIxfv/wUgMFXXu/xF7IDFWY2pH3BC8wjwlkEQFzcJXTqeB8mk38PW2uDg+mytv5/d+rDaIwlPv4S4uMvAcBsPkZh0caqBKnwF8zmdEpKtlFSso3Dh/+LougxmZJJS/uDsPDTCQ3pRUBAsld+FvUBAQTtT8MMhI4cwdTXX6co+xhZB/aRfWAfOYcOUpCZTnlRIaXVk18Pbd9S6zqBYeGERscQGB5BUFg4ga630DACw8IxBgZhMJkwmAIxBAai0xvq1R5VdWA2H6OiIq26d+cQFZVpVFQcwmzOQFUdJzxXozFVJzjtMBraodcloFfi0BKDagvCbrNhs5ixF5spsFqwmfOxW49WJzRmbNVJjs1icSc8VY/VTHLw4BYpf++3URQNBpMJvdFYlbwEBGAIqOPzgAD0xgAMAQH1Ok6r804qYOzY9MUnzcVvkqF91dPm6xoGi4iIIDo62n1MfSxevJjFixe7PzeZTMyaNYsZ9VjqZ7FYsBy3m3pJSQlQlbVWZa7+w1JSQsiOHQDEzpwJUVH1jvHPfU9RXr4PjT6Grw3/JL78W85Qf2bHrns458zv0OtrDvJas6qqGmui63+Pk9EkVCVt1syMBl/PdbzNZmPVwXTGO6r+CGrCwjz6PRofFcKTBxT2lJv5taCE/qGe26KjsY5vuyf88uUnmMvLiEpOocvAcz369bPZSvhxy4NMU1cBVROjO3eeSUT4OdXP1/9enm63v9Bqo4mOupDoqKqeI7M5k6LijRQVbaS4+FcslmNodWmkZ8wlvfr/Bp0ujODg0wgJPo2goO4EBnbCZEpt8lCNqqqUfFu1mXDQmDHY7XaCo2LoHBVD57MGuY8zl5VSkJlOQWYGBZnpFOdmU5qXS3FONjZzJRXFRVQUF9X7vhqttrpnwojWCMYQO/pgCw5tMd8umoM+qBJtYDnagDIUzYl7eJwODfbyIGxlAVhLArAUGzAX6zEXarGUqP/f3nmGR1WmDfg+0ydl0ntCKoQQkF6kFxUsi7CIn11sYF97XRW7a1t7RZC1rQ11LSgoIkVp0nsIaZCQPkkmmUw934+TBGLaJCRMIO99mStyzlueN8nMPOepyG4ZOFz31bVo9YYGZaPR9/r//8u/dQYjGr0ercGASqNly7btjBk/HqOv31GlRavtdCXYLcu4T9BrynnkCI7DhzEOHdrs/a54jXu6VrdRhioqlAj2gBaqVppMJg55YEEICwvjueee47zzzqNXr16YzWZ+/fVX7r33Xu655x5MJhPz5s1rdY2nn36aRx99tMn1ZcuW4dNJRQI7C989e4ix23EEBfJrUSH84FlXdJXqMEafL5AkeMZ+M9uc/dCSwmMcppcrl+9W34u+dkajOVHbt+MP7CsuxuzhPq2hrqwkcNJE7GFh7OzgesuXL2dtpYsZQK2PL0uXLz9uuf7KIEMI63R+PLtpB1fUlnZ4HVVNDX67dqOy2zB3QobO8ubO6nKhtlpx+foqmSZt4Ki2kPvD/wDQJafxY12H8s5Apc5CZfiIPqoy3KiosJ+JpWoKxUVlQMf/fpo99ymHAZgAjEeSylBrMlCrclGpD6FSFeB0VmA2/47ZfDQDT5YlZDkYtzsCtzsctzsU2R2EWw5CdgdxbKxHS+gPHSI+Lw+3Vsua2lpkT16XIVGoQ6IIBAJkGbfDjtNShaPagstmxWW1Kt9rrbgc1UhSJWptDRq9FY3ehtbXgc7Pgc6kfNcYW7buALhdEvZKLTazDlulDlvF0S9H9dHimn+Z1fSSJKHSaJA0WlRqDZJGo/xbrUGl0Tbzb/XR63XjJbUGlVaLSqM9ek+rRVJrWlVanHVf1mMv1jqVrwolYNsQGs6fu1sO3D/Z0BYXE/f2O0gOB7k33YijFQ9DZ77Ga2pq2h4ESPKJaHvsAWeddRbLly8nIyODlGYybZKTkzl06FAji0172LlzJ0OHDiUoKIj8/HxUrWTKNGcZiouLo6SkBFMXpkR3hMKnnqbqk0/wmzWLyPmPAEoFYtveveiSk1EZmn9S3LP3LoqLv2OfZhyPuW7jND8DjydF8XHmMqZb7sOJhmHDf8FkOFoE69DlV1C7dSuR/34RPy8343M4HCxfvpw+4yZw56ff8cLLT6JOTibx6686fa8NFdXM2pGNUSXx54hU/DsYSG3PzSX33POQjAaS1q/v8BNe/dnPPPPMJpkm9qwscqefjzo4iEQP3Mo/v/Mqu1etIKZvOn9/8PFOeeqUZZn8/A/JPPgM4KKIcEoj5jOvz/FVsm7t3F2Nq6oKR04Ohv4nvmHrX8/tdtuprt6PpS4Iu6Z6PzXWgzidla2uo9EEYtBHo9UGodUGN3zXaAPRqP1Rq41Y3/6B2k+WoZ88itBnHgDq/tbr/i4k6gsG2nC7axt9d7msOF1VOB1mnM4KHM5KnM4K5f/tZdjsRbhcVR6dWSX5oZbCqDKrCQ5MRiOFonKHopEiUUvBSCo1ICGpJCQklP8kVBo1ao0WtVZ7zHfNX/6tRa2tU2i6qdvbm3/rXYVst3P42uuo3bIFbVwcsR9/hDowsNGYrjh3ZWUloaGhVFRUtPr53W0sQ/UWoXoL0V+prKxs0WrkCf3792fkyJGsXr2aAwcO0KdPnxbH6vV69PqmT1FarbZT/zBrarKprNpBgGlIh7NobBs3AOA3+vQG2Q7+fRa2jAx6LXwP39Gjm86xFVNSolgA/uOcjq9GxeLTkojS6+hrmsnna94nUd7L8gPvc9HgfzbMc9XFDOkjI7vNC3R1VS3BFeUA6MPDukSu0SEB9PbRk1Fj47syC1d0sNWHuq6sg2ytRVVdjSbo+AoaNvf3aC9RerSpg4Pb/FmU5GazZ/VKACZcdjW6TshAdLls7Nv3EAVHvgRgLeP4yXATP/cdivY4CmEeS2e/DtvCWVJC1thxoFaT+uemFh8wupqj59ai1w8mOPhoooksy9jtJVTXHKCmOpPqmkxqrUrTWmvtYVwuC06nGYvT3OoePioVPnEqypJWk7VpWpecQ6UyoteHoddFoNOHoddHYDDEYDTEYTDGYjTEoNH4N6SXDxvTtenl3ZkT/bfepWi1xL3+GtmzL8SRl0fhHXfS670FzWY+d+a5PV2n2yhD9bFCGRkZDP2LP7G8vJySkhJGN/PB3h7qA6g9NZt1Nfv2z6esbDWpqY8TG9O+7B8Al9mM/YBShM44YkTDdUO/NGwZGVRv2NCsMlRwZAmy7CBP1ZdsOYn7e0UQpVf+IAO0GkKir4TD96MvX0K18x58NTolS6IutV7bjTrWrzJXE2ZWMsm04V1Tyl2SJC6JCuHRzHw+LCjtsDKk0utRh4TgKi3FeeTIcStDzdFQYyiq7Z5kqz9ZjCy76TNyDFG9U497b7u9jG3b51JZuQVQ8Yl0Jd/J5/Je78TjqgjubdQhIQ0FQm0ZGRgHDPC2SE2QJElRMPRhBAed3uS+01mFtfYwttoCHI4y7I4yHPZy5bujDKfTohTiO8tK9ZRa3K5q1G4bSisUxXlwrBNBKRCoV4oFqgyo6goGajUmNNpAtNoAtJoA5f81AWi1Qej1Eej14ajVft3WIiPoWjTBwcS++QY5F19CzcaNFDz2GFGPd45F+rhl87YA9UyYMIGnn36aZcuWcdFFjVN7ly1b1jCmozidTjZv3owkSfTyMM2vq/H3T6esbDWWqt0dml+7W5lnDwlpZG40DhpExTf/o3bnrmbnFRV9D8BP7on4aFTMiWmc1nxeygx+OPw0Jir44eAyZvc5D9lmw3fcOJzFxag7KbUeoHbffqxbNqNLSMR3VPMp/S3hAtZXVDOnThnSeJhF1xFmRwbz1MECtldZ2WWxNmoj0R60kZG4SktxFBRgSEvrZCnBkV+vDLVecDFv9w4Obt6IpFIx5qIrjntfq/UwW7fNoabmIBpNID/43M93VSlMCPbnnNCTq3v1X5EkCUNaGtVr11K7e0+3VIbaQqPxx9+vL/5+fb0tiqCHY+jTh5gXXyDvhhup+OJL9MkphFw1x9ti0W0e16ZMmUJSUhIff/wxW7dubbheVVXF448/jkajYc6cOQ3XS0pK2Lt3LyV1boF6/vjjD/4aBuV0Orn77rvJyclh6tSpBAefmD5BbeHvp1RdrqpqXmlpC01YGAGXX07lMXWZAAzp6YCiLP31Z2G15lJVtQs3KjYykgsjgwnQNtaJtWodzgAlJujwkW8ApZ1E3Buvk/j5Zx2qtdISVT/9xJH5j1L5/fftnpuj1lHlcpOd0hfTjBkYBx5/5emWCNVpOCtU8Td/fqSsw+to61pk1FtwOhvHkXplqGXFUJZlVn/0PgCnTZlGcPTxFTq0WPbx55+zqak5iEEfjSN5IYurUtBI8ETvmG7x1He8GNIUJaJ2r+fFFwUCQfP4TZhAxL33AFD07LNU/fqrlyXqRsqQRqNhwYIFuN1uxo0bx9y5c7nrrrsYOHAgu3btYv78+Y3ifF577TXS0tJ47bXXGq1z8cUXk5SUxKWXXso999zD3Llz6d+/Py+99BK9evXirbfeOtFHaxF/f0UZslTvw+1ubyVQ0PfuTdg9d1N2ZuNgZn2fPqBW4yorw1mXDl9PUdFSAPbSH4tk4sLI5l01IxKUAoEpzt/ZU2Vut2yeoj2O7vV769KIXVOnEfPM0/hPbrknXWcwO0JRopcUluN0dyzvQFNnsXF2kTLU0JesFctQxobfKTiwD63ewOkXXHxc+1VV7eHPzZdgsxfi69ubAYP/y0N5io/+utgwevt6J76ms9HXWfFqd3XMitvdse7aRclbb2PPzfW2KIIeQtAVVxB44YUgy+TfeRe1+/Z7VZ5uowwBTJo0iTVr1jB27Fg+++wz3njjDUJCQvjwww958MEHPVrjhhtuICEhgZUrV/Lyyy/z0UcfodfrefDBB9m6dSvx8Z1Xofh4MRrjUav9cLtt1NR0XgNKlcHQ0Puq3pVWT0mpkmG0npH0MugY7N98qYDY4GHUqEIwUsvK3JWdJttf0cYqVgn74fYXXtyrUVxVY4O6rnXBsUwO8SdYq6bI7mR1uWdZMX+lvl9YvTursznqJms+ZsjldLLmk8WA0nbjeLrSWyz72LL1cpxOMybTQIYO+S+LizVkWe2E6zTckdB1bssTTb1rzLZnD7K9Y005uzMVS76i+KWXKHn9DW+LIughSJJE5EP/xGfkSNw1NZQfUxfQG3SbmKF6RowYwdKlS9scN3/+/GZ7j917773ce++9XSBZ53Ng03qclgAko4Wqqt34+R1/EGs9hrQ0bPv2Ubt3L/5TpgDgdFZTUbEZgB0MZHZ4YIsuDEmS0ASMh/KvKC39FXvh6eBwoAkLQ9VMpl1H0dVbhvILkF0uJLVnaetWl5tMtSLHuKCubXJZj06lYkZ4EAsPl/B5YTmTQtpfZqHBTfYXi11nILvdOPLzG+3zV3b+uozygnyMpgCG/21mh/eyWPazectlOBzlmPxPY/CgxZS7Dbyco1gW/pkc3eESBN0RbVwc6oAAXBUV1O7bj3HAiU+x7ypkl4vKZUp2qemcs70sjaAnIWm1xL78EuX//ZSQ667F6W65oGZX060sQz2N3b/9QskBJbOtytK+uCHHkSOYv1xC7Y4dzd7XpyQDYM882HDNbN6ALDsoIZxCItsMbB0QrTSaTHZuIPPNt8g840xK/uKWPF40ERGg0YDD0ZCt5gmbqmpwShLxLgeRG9adMBPrBXVuxaXFZizO1ovDNUd9q5SuiBlyFpcoVguVqtmWLPZaK79/rjx9nT7rInTGjhUQtVoPs2XrlTgcZfj7pzNo0PtoNP68mF2IxeXmND8jF0R0fqacN5EkqaHGUO3O5l9zJys1GzfhKi5BFRCA7+lNM9EEgq5EHRhI6PXzPH4Q7iqEMuRFolP7YS1VYiosVe0LzLRu2ULBgw9S8uxzzd7XJSvKkC3zqPutrHwtANs5jSCthoFttJaICh2LEx3hFFGcdwAATVjnptVLanVD5lN7epStNStVWs+tLOHQDTdw6IYbOlWulhjs70OKjx6rW+a7YnO752uiokGtRtJokDv5KchR52rURkYiNVNb48/vvqamwkxgRBSnndGxGjIOh5mt267Cbi/C17cPgwf9B602gMyaWj7IV5IZHk6JRnUKBE3/FcNpiqvMuv3UUoYq6yzx/mee0WzNF4GgJ9Dt3GQ9iZjUNDb+pLh6LNWe910DsOfkAKBtoUxAfcyQPTu7wf1UVqYoQzs5jfHB/qjb+MBSq404fIagqVkHRYr7QxPeeWn19WhjY3Dk5WE/fBif4cM9mrOhUrGoDbEqsTuaiKY1htxuJ0cKv+bIka+prs5AkjT4+/cnOmoWoaFndijLSZIkZkcE83RWAZ8fKeeiqPZ1W9eEh9F3+7YueQrSp6QQ987bzca01FSY2fjtEgDGXHQ5ak37C5q5XDa2bZ9HTU0men0kgwYuRKsNBODJzAKcMpwRYmLsCXJbnmga4ob2ezfQszORnU6q6kqXmM4WLjJBz0UoQ14kPDEFp8UXAIejFLu9DJ3Os7R/e11sRkvKkDYmhoT/foIuKQlJrcbhKKe6WnkT301/Lgr27AMrNnQMpbnr8Ck3A0o6f2eji42lBnAc8iyjrNblZluV0tUnpboCmabKkNV6iB07b6aqqvFTvM12hJKSnwkMHEm/tOc6VPl7VmQQT2cVsNZsIa/WTpzB86dpSZKgi8zBapMJv/Hjm733x5f/xVFrJSKpN6mjxrZ7bVl2s3v3nVRUbEKj8WfQwIUYDIpFb73Zwg8lFaiAh5LbLvZ4suIzYiSJ33yNvs7qeipQ/cc6XOXlqIOD8R3ZvjpfAsGphHCTeRGNVkt4fF9slcpTenU7rEP1KbDaXnHN3pdUKoyDBqGu68VSUbEVgHyiqZICmOihMpQUPhZkMFYo1oauUIa0MXVB1B66ybZX1WCXZfzdLkylSuNU7THKUE1NNpv+nE1V1Q40mgCSk+5i+LCvGTb0c+J7zUWlMmA2r2fjpplUVG5rt7yxBh1jApUMtiVHyts9/0RjPlLA9p8VV8j4S+cgtdKXryWysl+nqHgpkqTjtAFvNQT7y7LMY5lK0PYlUSGkniKp9M2h9vPFkJqKpDl1niHrO9Sbpk09pc4lELQXoQx5mcjUftSWK66ydilDDW4yz0oF1GeRZZBKslHf0H6jLfz903HafFE5lH93jTIUg6TTIbs9C0heX9fVOcVVi6su6LreMuRwmNmy9cqGmJaRI74jIeEGTKYBBAQMISXlXkaN/BE/vzQcjlK2br2yQ0Uv6wOpvygsa1LY0lNkV/sDsDvCmv/+B7fLRcKgofTq3/7ClMXFy8nKegmAvqmPExQ0quHet8UV/FlZg49axd2Jp04qfU/AbbdTVdcd3HTOOV6WRiDwLkIZ8iJPHyzgpuiBFNmVoGRP44ZclmpcdZW3tXGxHs05VhkaEejrsYySpEbnUIpD2o1aVMaOtaFoDdPZ00jduoWYZ5/1aPyGBmXIhrOwEABNRDiyLLNnz33U1h7CaOjF4MEfYDA0ddsYjXEMHfIpgQHDcTqr2LJ1DlZr+4o+nhcWiEElkVFjY7vF2q65Zf/5gH0jR1H45JPtmtcWR554kqJ/v4SzzloGcOTAfvb9sRokiXEXX9nuNS3VGezafScAsbFXEB19QcM9u9vNUwcVq9ANcWFE6E+RhpIe0FEFuDtRvXo1bosFTUQExiFDvC2OQOBVhF3UixTaHJTKEhnGVHpzkKrKvR7Nc+QpLjJ1UFCDG6w5rDt2Uvzvf6MKDKBiuuIO2k8q0wM8V4YAouRUZLZhN0kU2RyEd/KHXnvM825ZZmO9MuS04SxU6vVoo6I4cmQJxSXLkSQtQZob+O39TzAX5COpVATHxNF7xGji0gcoNZQ0vgwc+C6bN19KlWUXO3beyNAhn6FWe1ZDyV+jZmpoAN8UmfniSBkDWyhe2ex5DXrcFRXY25E91xay00n5J5+Ay0XQJUpVaVmWWfXx+wD0GzuR8ISkdq3pdFaxffv1uFzVBAaOpHfKA43uLz5cSnZdgcUb47pP896uxJ6dTf699+GyWEj+/jtvi3Nc6FNTCb3xBlS+fh1ynQoEpxLiFeBFzgsPBGBLmOJ2qLZ4lqVS7yLTtVlNW6b699+p3rAOt9tKDT7kE8vIgMYVm50OB/n795K/fw9Oh6PJKkEOxbqiDrDze10gtbfYX1OL2enCqJKIc1hxHlEsQ1JEABkH/gVAxb5kfnjhP+z45Sfydu8gd+c2tv70HZ8//gCfPHw3RdlK7SWNxp8BA95Eqw2iqmonGRmPt0uW+lo6XxWa29WeQxenxHk58jpPGXIcOQIuF5JO1+DKzN62mbxd21FrNIz5v8vbtZ5iZbsfqzUbgz6aAf1fRaU6qgRXOJy8mK0ooncnRuJ7ChVYbA11SAjW7duxZ2biLC72tjjHhS42lrBbbyXkmqu9LYpA4HWEMuRFxgX54a9WcUDfG1kGl1yJ3V7S5rz6TDJdfPOZZPXo6jLN3CXlSDbIoA8hOh2JxqPxQgc2ree9W67hk4fu4pOH7mbBLdeQseH3RuuoK5Q/EznAxfbirquxIssysrP1Hm0b6uoLDfH3QV9ZqSgAWi15lv/icJRiM+vJ+g10RiODp/2Nc269m2k33k7/SWeh0ekp2L+Xjx64gy0/KU/1RmMM/dNfBuBw/ieUlK70WN6JwSZCtBpKHE5WtqM9x7H92Dqr1lB98Lk2JgZJpUJ2u1n90SIABk09D1M760Mdzv+kLmBaQ/8Br6HTNS4h8EpuEeVOF7199Fwc2b7yAiczan9/pfcfULN5i5elEQgEnYVQhryIXqXizNAA7JKeCrtSDdrigXXIXVUJanWLafX1qAMCUAco66qLJQ7Qh5GBvg31dfas/pVvnn8CS3kZBn8TBn8T1eVl/O+Fp9j56/KGdQJn/p3iB3pjOdNNmfnPjh63VQoemc++IUMxf/VVq+Pq44WGm3zQliuZXJqoCPIOfwDAod/DSRg4nGteWcDkq+aRNmYC6ROmMPX6W7nmlXdJGT4Kt8vJioVvserj95FlmeDgMcTFXQXAnj3343CYPZJZq5KYGREIwJft6GSvjYwEtRrZbu8060KDMlSnaO1Zs5Li3Gz0Pr6MnHlhu9aqqtrTYCVLTr6bAFPjoOu8WjsLDilyP5wcjUZ16hVYbA3jkMEAWDd3zWtBIBCceIQy5GXqU9wPobhOPMkoC7/rLvpu3ULIVVe1OVZb50rTFEMWSYyoixcqPHiAn956GWSZ06ZMY96bi7n+rcUMPOtcAJa98yr5+5UYJk1ICGGjJ+OMlQlx7KTY3tSVdrxIahWy1dpmraH1xyhD1sREElevwnJTEuCg+oiR+JQZzLznYXxMTVuN+AUFM/3OBxlbF0i88ZsvWFVnPUlOugsfnyTs9iIyDjztsdyz6jrZ/1hS4XF7DkmrPdqWo5PihuwNylAMTrudNZ8qyuHw8y/A6O95DzWns5qdu27F7bYTEjKJXnFNXSj/OliAzS0zJtCPMzrQn+1kx2fIUODktQy5KivJuexyyv7zwQnLaBQIujtCGfIy4+uq9WbqewNQYfasLYek1aLyaTtoVxunFBXUFEvkkMQQky9ut4tl77yKy+kkedhIzrj2RjRaLWqNlilXX0+f08chu938+MaLOOw2ACKClcrQfdjLpjqFpDPxpNZQfq2dvFo7KmCIvxEkCYurjGLjCgDU1eM5a+6trQaDSpLEyBmzOXPuzQBs+nYJG79dglptoF+aEnNUUPAFZvMmj+Qe5G/sUHsObV3ckD0vz+M5rVGvROpiY9m67HuqSorxCw5hyDnT27XO/v3zqak5iF4fSb+0Z5Gkxj/L7VU1fFGoWOQeTonuUBXvkx2fOstQ7Z49uGtqvCxN+6n6+RdqNm3C/PlnXu8HJRB0F4Qy5GUi9Vr6+ho4XGcZqihvf82bVolSLEHuYj0VUjDpfkZ2/PITRVmZ6H19OfO6mxspD5Ikcea1N+EXFEx5QT5bln4LgMk0EBkVoZSwrTS7c2VEiXWB1pWhehdZfz8jfho1siyzftkDqDRu7GYTZ1z6nMdZMadNmcb4SxXL2qoPF3Jg4zoCAoYQHaW4lPbtfwS3u/X4JVB+XvWB1F+0owCjNrb+vO1L6W8Je12GIaFhrF/yKQCjZ1+KVudZdhxAUfFPFBxZAqhI7/fvJtXQZVnm0QNKKv2siKB2ZdCdSmiio5W6Vk7nSdmnrPL77wFRW0ggOBahDHUDJgT5NyhDtfbsVsfKbne7apw4QhUzuKtYT6qvEb3sYv1XnwMwevZl+AY27S5u8PNj3CVzAFj/1Wfk3nYbJc+8jNuppGYXd0HcUH2sS2vp5vXKUH2dJEtOBqrAnQAkp96E3qd9JQOGT5/FoKnnAbD09Rcoyz9EcvLdaDSBWCx7OVQXh9QWf69ThtaaLeTXNu0L1hy62PqMsk6yDGUrGYZ7MnZTW20hNC6e9IlTPJ5vt5exd+9DAMTHzyUoaESTMT+XVrLWbEGvkrgvKapT5D4ZkSTpaNzQls1elqZ9OEtLqV63DhDKkEBwLEIZ6gaMCvQlnxjcsgSqGuz20hbHVi3/mf0jR5H/wIMerV0bXAGArtjNQJMPu1f9SlVpMb5BwZw2ZWqL89LGTiQsPhGXxUL1jz9R/uGHBAYpHwBa6zZsndxxXZegxDa5SktxVVY2O6bePTfM5EutpYqULz4kciGoC31ISWtf6ng9E6+4lti0/titVr557glwGUhJuQeAgwdfavV3UU8vo55RAb7IwJJCz6xDDcrf4eOPGXKWl+OqUH7Pm/9UMgHHX3oVKpXnLpB9+x/B4SjF17c3SYm3Nt3DLfN4ZgEA18SEtasf26nIyRo3VPnjj+ByYUhP96A0h0DQcxDKUDdgeIAfdklPiaTUh6muPtDiWHtODu7KSmSnZ0HMFn/FDeNfXssgrYpNdZ3Lh503E42u5Q80SaVi1N//D31dqrtkNBIbezoAyfJedlS1r+pyW6j9/BpaatgyM5vcr3a52FWt7Dk8wJcNSz7FVGLHsFdFdOIFqFSeu4Ma7avRcN5t9+IXHEJZ/iFWLHqH6KjZ+Pun43JZOJj1ikfrXBCpuJS+KCz3yHJnHDSQ8HvvJfT6Gzok97HINTX4jhlDbUQYDrebXgMGkTBoqMfzCwu/p6joByRJTb+055r9Wf73SBn7a2oJ0qj5R3zPKLDYGkctQ1tOqiDkiv/9DwDT387zsiQCQfdCKEPdgFCdhiSDrsFVVlbccvNQe25dwUUPepLJsgsLGdSMcvHV2VOILDpMWf4htHoDAya3bBWqJ2XE6YT4KVlZLl9fgoOUIOp4sthU3nY9pPaiT1bccPaDB5vc21pZg0uGaL0WY1kxOau+QJJB1srEnXbNce3rGxjEubfejSSp2PXbz+z7fXVDteX8/E9aVU7r+VtYAHqVxN7qWnZ50J5DFxtLyFVz8Bs75rhkByXeSnf/PayINIEkMeGyqz0ObLbZS9i3/xEA4uOVHm5/pdrp4tksxSp0e0IEAVpRuN6Qmkr43XcT987bcJIEkduysqjdth3UagLOPdfb4ggE3QqhDHUTRgb5cRjFdVJa1LLp3eFhwUWAmpos3HItR67Q8Ob0q7BtVlwofceMR+9BJppKpSY1XakxY3E50OkicahDUeMmp6zz3QO6pGQAbJlNlaFNFUrWzlCTL6s/XkRYgOKOkiN9Mfp41p+tNWLT+jPy70rw9PJ3X0flSCQ09Axk2cWBusrWrRGg1XBmXZr5Fx66yjoLWZb57cOFAPQbN8njthuyLLNv30M4HOX4+fUlMeGmZse9mVdMkd1JvEHHnJjQTpP7ZEbSaAi55mp8hgw5aVpZ1FuFfMeM7pKGywLByczJ8SruAQwP8D1aa8jScq0hz1txQFXVbgBySaCPjw9Zf6wGYEArsUJ/JSpYedO0yC7y9+3G4F+XVly1tdObVdZbhmyZTS0xmyqVeKG+zhoObPyDIJ2iHOna2W+rNU6fdTFRffpit9bww6vPk5x4N5KkoaR0BWVla9ucf0FdzaGvCstxncBGngc2/MGhPTvRaHXtartRWPgtxcXLkCRNnXusqdu0yObgjbwiAB5IjkJ3knzwCxoju91U/k/JDA04/3wvSyMQdD/EO1s3YUSAb4ObzMmRZse4a2pwFikfTLo2qk8DVFmUNP0cEkmoqcDpsBMaF09kch+P5ZLLlMrKVq2GHb/8RFyI4iqLcu0h18PMKU/xGTmKiH/+k9Drr28sgyzzZ50ypFn3K75RVgwVSgC3b+9hnba/Sq3m3FvuQmf0IX//Hrb/uJ6YmEsAyDjwNLLcemzI5BB/grVqCu1OVnvQnqNm82aK33gDy5q2Fa2WcNhq2fDmK2icLoaeNxNTqGdP/DZbEfv2zwcgMeFm/P37NTvuuewj1LjcDDH5MD0ssMNyCryLdds2HIcPo/L1xX+K51mGAkFPQShD3YRkox6LSqk9o9JZsdubulrsdY09VQEBqAMD21yz3jJUUBNN/19/JLyimpQRo9tVKM9R1wi1Vqtm/7q1mPRpAPRmP5vMFo/X8QR9UiLBl12Kz+DBja4ftNooc7jQS2BbtZzg3mY0RcoZ9InJnSpDQHgkZ1x7IwDrvvwUo3MqGo0Ji2UPBQWttwrRqVRMD/e85pBlxQpKXnkVy4oVHZZ3w9efM2DDds7alc3ggZ4FTcuyzN69D+J0VuDvn058/PXNjttXXctH+Uo23SPJPbPAYluUffAhOXOuwrp9u7dFaRXjoEEkfP4ZkfPnozIYvC2OQNDtEMpQN0GSJNICQilBickoLmhaAdmekw145iKTZblBGZIP65n22aek5ZeQMmxku+RyHlGsVLqoKJwOO4e2FeFGh4lKdpW13TqkM9hYl1IfX21Gg53gPjWo65QhbULnpwenjZ1I2rhJyLKbn954h9joawHIPPgCLlfrFYdn19Uc+r64guo22nPoEhMBsGdndUhOc2EB25Z8htYtgyRhTPJMMTxyZAklpSuQJF2de0zb7LgnMvNxA2eHBjAy0K9DMp7q1GzcSM26dVSv7bh170QgSRLGAQMIEFlkAkGzCGWoGzEkwK/BVVaYt77JfUduXfC0By4ym60Ap9OMEzW7wpQMIR+Hk7DYtuc22rNQsQzFjR0PwN41a5F9+gJQXnFiGlX+WakoIIEZOzHFWVC5HGjKFWWoq2qlTLn6BgLCI6gsLmLv0ioMhjjs9iJyche0Om+IyYdEow6r283SkopWx9YrQ7as7A7JuPrDRRgsys9GGx2NSt92eYHa2gL21zVhTUr8B35+qc2OW1texfLSStQSPJjccwsstoXv6aMAqF7X9PUqEAhOHoQy1I0YbPJpyChrri1H+4KnlfmHiUVnMOKSJCQZnAUFHssjyzLGAf3R90sj6YypSJKK/P17CNAr8SU+tTvatH60l9rduyn690uYv/ii4Vq9ZSi6IIfoIRo0RxRFyOXjg8oDd2FH0Pv4cM4tdyOpVOxdswaD/WwAcnLewWYrbHGeJEkNzVvbcpXVK0POgoJ297iy5GWRtWUj/g7l5++ptXDv3gdwOqswmQbSq9e1zY5zyzKPZiptNy6PDiXFR7hVWsJnpKIMWbdswV1b62VpmsdZfmKzGwWCkxGhDHUjBvn7cKhOGapxNm3TEDDz74Tfey9+48e1uVa9iyyHREIOZ1GjV1wh9jrrkidIkkTsq6+StGQJAb370GuAkmZvz1OUkRT2saWqcxtV1u7ZQ+nbb1Px7XcAVDpd7KtWPmRiirMxhhfh9pXxmXM+5lGjujSOJbpPX06/4GIAfn9/I77G/rjdVg4efKnVeRdEKq6yVeVVFNpaLo6pCQpCHaDUcWrP76XWUkXxRsUtkxylWBL1vVPanJdf8BmlZatQqXT0S3sWlar5ekFLCsvZXmXFT63izoQIj+XqiegSE9BERiLb7dRs3OhtcZrgKCggY9x4cufOxW3v3IQHgeBUQihD3YhwvRarus6NpW9aydhnyGBCrpqDceDANteqshyjDB06iNWouFDsOZ5/6P6VtLETATi4VgnkjiWPzeUtW0k6giE9HVAsRLIss7myGhkIqCgjrb8Ot2xFExVB1B2PUjr1rE7duzlGzryQmL7p2K215K1SLD75BZ9TZdnb4pwEo57hJl/cKGn2rdEQN5TledzQ6o8W4aq1EhQdQ4haUXJ1Ka0rQ1brYTIyngIgKelOfH2bH291uXn6oGI9vDU+gjBd8/FEAgVJkvAbpzycWH5b5WVpmmJesgScTmRrLapWKs4LBD0doQx1M6IClHgcg6GW0vyOByjXW4aySSSi+DDauPrGoB1XhnqPOB2NTk9Jdgk2VTgqZA51cvFFfXIyklaLu6oKR14efxQrykRMYS6xw5U387BwxWV3IlCp1Jxzy53ofXzJ3VyMqjYdkDmQ8XSrdZZm1VmH2irAqEtIAJTqwJ6QvfVP9qz+FYAzrr0Ze12BSn0rypAsy+zZex8ul4WAgCH0iruqxbHvHirmsM1BjF7LdbGiMJ8n+E2cAIDlt986vfbW8SC7XJi//BKAwAtne1kagaB7I5Shbkb/oAjKUCwQhw+u6dAaDkc5NpsS83HYHUdoeRGBA04D2mcZchYX4ygoQK7rT6Yz+pAyXImRsFdHKmMsnVt8UdJq0acqQb21u3fzW45ihUq1m7HJWwEIDzu70/bzBFNoOGdcp1Rn3vWVHdBQVr6G0rLfWpxzfnggOklip8XKtlZciUczyrLblKOmsoIf33oZgIDU/oRHRuOsC3DXJ7ecSXb48MeUl/+OSmWgX9qzSFLzDVyL7Q5eyVHWeyApCqNavD14gu+oUUhaLY68vHZZ+Lqa6t9/x5lfgCogAP+zut6KKhCczIh3u27GIH+fhoyy0sKjVhfrzl0Uv/66RwX66q1ChUTgV1KBRobI0WOB9sWmlC5cxIFJkyl67vmGa2njJgJQvUepMRTr3kum1ebxmp5g6KcEaFs2b2aPrMS1nDdAi9NZiaE6GNcX27Bubbl/W1fQd/R4+k86E1ulhtLdSvmDAweewe12Njs+SKvh3DAlHuiDw6UtrqtLTADA3kZGmSzL/PjGv6kuLyM4Jo6QgcMberhpIiJQm0zNzqupySbjwNMAJCffhY9PYot7PJ91BIvLzUB/IzPrSgQI2kbl64vPcKUYaXdylZk/V5IQAv72N48yDQWCnoxQhroZA01Hg6jNtUefMmvWr6Pk1deo+Kr1wn/QOF4ooiSfmL798EtV3G+OvDyPu2w7C5UaQ5qoyIZr8QMGY/Q3UZGlVIBOYT+bzG1XW24PPsOU4oFHVq7EptOjc9hJCFcCyoPz0yh+4UVKnnu2U/f0hClX30BkSh/y15tw2bVUV2eQX/BZi+Mvj1aUpiVF5VS1kHWnT0lBHRyMJrz1TvCbf/gfWVs2odZqmXbznag0GuyZmQ1rNIcsu9i95x7cbiuBgSOJi72yxfX3V9fyYUF9gcUYVKLAYrs41lXWHXCWlFBVV8wzcLZwkQkEbSGUoW6GSaPGVhdEXasrw+1WPkTt7WjQ2jheKJ/koSPQRkWCVgsaDc7Sli0Vx1JffVobeVQZUms09B0zAWupAYesxZca9pbu8fyAHlD/lK3PO4SPtYZ0tZPS0p8BMGYrDWaNgwa3OL+r0Oh0TL/zAfTGEAo2Kq7MgwdfwulsXhk8PdCX3j56alxulrQQO6RLTKT32jXEvf5ai/vm7tzOqo+URqwTL7+W0Dgljd40fTpJ3/6P8Lvvan5e3kIqKv5Erfarc4+1/HJ/LDMflwzTQk2MDhIFFtuL3+TJhFw/j/Dbb/O2KACYv/oKnE4MA0/DkOp5+x2BoKcilKFuSJBJiZnx8a+mPP8wcLTGkNaTnmR1NYbqg6eTh41EUqvp/dtKUjf/ibYNK0Q9jrqaRJqIxunV6ROmgCxRbg4EoKJys0freYo2Kgo5LBSVLDMgcx/nhRfjcJSj1Qbh2qn8PAyDB3Xqnp7iHxzK9DsfxLw/lFqzDoejlKzsN5odK0kSl0eHAPBBfmmzsVWSJLVaHqD8SD7fvvgUbpeLvmMmMPCsc47O1WrR9+6NoW/fJvMslv1kZr4IQJ/eD2I0xra4x+qyKn4urUQjwUPJ0S2OE7SMLjaW8Ntu8yjTs6uRnU7KP/kEgKAL/8/L0ggEJwdCGeqGJAYr/b/89RbyD+wAjipD+rrso5ZwuWqoqVFiSfJcvehj1BMUpfQ80wQHe1yXR7bbj7biiG38QRqemExIbC9seUogrr9tF5WdWHzR7XZRpFfWHrFrG33dvwMQxljsGQeU1hNDhnTafu0lJjWNs2+6h/x1ipKYm7MAi2V/s2NnRwajVymB1O2tyVRtLuerZx6lttpCZEofzrr+Vo9+f263g9177kKW7YSETCIqqmU3idMt89ABRcG8IjqUZFFg8aSnasUKnPkFqIOCMJ13rrfFEQhOCoQy1A0ZGBSFmUAACg9tULrV1ykm2jYqDVssewGZcgLRl9bSZ+jwDsngKCgAWUYyGFCHhja6J0kS6ROm4DisBDf3Zi+b67rKdwb7/1jDPj9f/kztz+6kFDSVKwHw269YWYwDB6IODu60/TpCn1FjGXnWfZiz/EBys2HN3AaX5rEEaTVMDw8E4D+tBFK7q6sbZZRZLVV88eRDlBccxj8kjPPv+idanWdBsFnZr1JVtQuNJoC0vk+1qkC9n1/C3upagjRq7kqMbHGc4ORBttnQhIUROHu2CJwWCDxEKEPdkHQ/I/l1QdSHqzMbMsDUAQFoglrP8jm28nREST7JQ5s2ZvUkFd5+SElp18bENPthmjZ2IjVFvgBEk8/msvw21/QE2e1m/VefsanfAO667UH046JwOkrQaPxx/boPAL9Jkzplr+PltDOmERdxKy6HhKzL45fPbsbdTHD6lXWB1F8VlVNib5p9VvPnn+wbOozcefMAsJSV8vnjD1KSm41vYBCzH34Sv6DGyp/v3r3kXXIJJW+/0+h6WfkfZNe57fqmPo5e37JLtMTu5LksRcm+LymKYG3zFakFnlO5dCk5V1xJ5Q8/eE2GgL/9jZRffiZk7lyvySAQnGwIZagbolepqNUosUFVOjPWAweAowX6WuPYeKE4SzlRvY8GT1p37CTz3PPI9iCOwHFIcZ1oY2Oave8XHEJs6jAqapSU7oKyzokbOvDnekrycsiP6w3AeLXS4iC0YhjWTX+CWk3AjPM7Za/OYMR512DSzADA7f8LXz5zN9XmxsHSQ00+DPb3weaWef9wSZM16msNOXLzKNq7m48fuovi7IP4BARywT+fICiyaRyPITcP246dDe5TALu9lF277gBkoqMuJCKidRfJ0wfzqXC6GOBn5LK62CbB8VG7dx81GzZQuXSpV+WQdDrUfr5elUEgOJkQylA3xddPUWJ0QTbKNivd4XUJHjRotdRnkiUxPCIUlepogT2Vny/2zExsBw60aR1yHFaUIV1My4G3/SZMwVKkZHe5q7fhPs7ii7Iss37JpwAUJ/cDWSbWthrcoH1DSSM3nX022oju1S9rxKSn0ari0BhdSOEr+ODeW9i/bk3Dz1iSJObFKdWcFx0uodblbjRfExysuCJlmR/uu52qkmKComK4+PHnGzLH/oq+7vdTX5NJlt3s3nMPdnsRPj4p9OnzcKsyb62s4eOCMgCe7B2DWqTSdwqms6cBYFm1GpfF4mVpBAKBpwhlqJsSU9eWI8Cngqo9Sup6W5Yht9tBlUVxJeW54hif3r/RfV1MDKhUyFYrzuLiVtdy1LvJYltWhlKGj4ICJSYhXt7b0FC1o2Rt3UThwQPYTYHkao0kkonkOILv73pcWUfQhIe3mEbuTVQqLYOGvAqoCUquQhuaw7f/foYvn3qYQ3t2AnBeWCAxei2lDidfHpNmL7vdZG39k9I6ndW/spqkIcO5+PHnCIxoOYbH8BdlKDd3AaWlK1GpdAzo/wpqtbHFuW5Z5oGMQ8jABRFBjAgUqfSdhT41FV1yMrLNdsKtQ2X/+Q9lH3+Mu/b4XocCQU9EBAl0U9JD08nJgWB1GZXWMKIiItpUhqqrD4DsoBofpDJIHjeo0X1Jp0MbHY3j0CEcubmtptiH33M3gRfOblUZ0ur0xCZMQWYhyRxgU0UFaX4tfwi3hux2s+aT/yjrnjUDgKnajeAAv96DiHhgGqazz0YT1j37ZZlMA0hMvIWsrJdImFTGnkITOdu3kLN9C8HRsSQPG8n5vfrxBnpey8xjaPZujhzYR+am9ZgLC0iW3AQCqZFxpN/zcKtBz44jR9BUVYFajaFvKqWlqziQ+RwAvXs/hJ9faquyLs4vZXNlDb5qlUil72QkSSLw7zMpeu55Kr5cQtAJKnjoqqqi+JVXcVssaCMj8Z88+YTsKxCcKgjLUDcl2RRFFSZUksyqQcEk/fIz/tOmtTqnyqLEC+WQSF+3Da2haZq0rq5OUVs9yrSRkfiOGtUkrf6vDJp0BTaXDgO17D78Z6tjW2Pv76sozslCZ/TBnD4UZJkh7nUAhE6+mOArrui2ilA9CfHXY/I/DVS1DL/WyMAzp6LWainLP8TG/32J5p1n0dlryXLCS998w5/ff425sACd0YewiRMBMBSVtJk+X7tZic/Sp6ZSSxE7d/0DcBMddSEx0Re3Oje/1s6TmUqw+wNJUUToRVf6ziZg+nRQq7Fu3Yqtrkp4V1P+yX9xWyzoUpLxq/tbEggEniOUoW6KJElYNAkAOCIlirIy2/yQrKpUXDLZJDI6MrTZMQ1d0g92zpt0cHQcNbVRAFRWdEwZcjkdrP30AwBGnH8Bm2ocxJCHr+sQkqQjNGRip8ja1ahUWvr1ewG12hdLzRbiJ1Zzwzsfcc4tdzFg8lkk9Erg9CxFYd045hz6T5nKubfezby3FjPqzvtAknAcOoSzpGmQ9bHUbtkKgG5QOtt3XI/TWYnJNJjU1Pmt/o3Ide4xi8vNUJMPc2Ka/xsRHB+asDD8JijtOcxLlnT5fm6rlbL/KFbV0OuuQ1KJt3WBoL2IV003RuWjFF/0CbeRu2t7m+NLSpXMqwP0YVr/9GbH6PsoWVq2jIxOkhKig5VaRpHqAxwqK2v3/M0//I+KokJ8A4NIOvNctlfVMALFKhQSPBaNxr/TZO1qfH2TSEt7BoDc3HcxW34jbexEzpp3K5c8+QJvXnMlvmoVh/2Dcc28nL5jJqAzGFH7+6NPUTrPW7e13oTWukVp4FsUup7q6gx0unBOG/A6KlXrNWW+L67gx5JKtJLE86lxImi6Cwmc9XcAKr76GretcxsZ/5Xyjz7CVVKCNiYG0znntD1BIBA0QShD3ZjwwIGoLBCuKSZry6ZWx7pctVitShXkQnsvUkKbT5XW91Gy1GwZB1pcy/Lbbxy65RbKP//cIzn7ps0EIJ0dfLzyV4/m1FNZUsTvX3wMwNiLrmCj1YEbGKPaAEBY2NR2rdcdiAg/h169rgVg9+67MB9jMQvWarg2VnH3vZB9pFEGnnGwUlW7ZsOGFtd2VVZi36/8niuiDqDR+DNo0CL0+tYz7ErsTu7PUILib+4V3uHYLoFn+E2YgCY6CldZGZXffddl+7gqKyl5dwEAoTffjKQVbk+BoCMIZagb0y9sGH4/qRl1TwG6teuobSVV12LZjSS5KSeQaE1Qi+6S+g7nzoICXFXNNxi1bttG1fKfqd2+wyM5AwOH4ERPAJXsMO+npsLs0TxZllmx6B2cNhsxffuRPmEKv5VVESHnE+XOQpLUhIVN8Wit7kZy0t2EhEzE7a5l27brGrXruD4uDD+1il2WWr4pMjdc9x19OgDWrS1bhixrfgNZxhEhIwfqOG3A2/j7Ne1NdiyyLHP3vjyK7U5SfQ38I757lSY4FZE0GoIvvQwAy+o1XbZP6cKFuCsq0KUkEzD9b122j0BwqiOUoW5MlCkJVYGS8FcWYyJnx5YWx5YUKv27MunNuJiWM4TUJhOaui70LVmHbFlZAOiSkjySU6XSofIbBoApwsyqjxZ5NG/nr8vJ3LQOlVrNGdfciKRSsaq8itEoHx5BQaPRaluvuN1dUak0DOj/KibTYJzOCjZvuayhIGaQVsONvZRMvscz86mpqzvkO3YsCZ/+l/iPPmx2TafTQpb+EypmO7FMkenX7xWCgppWGP8rnxSUsbSkAq0k8Ua/eAxq8bI/EQTOvoBeC98j5t8vdsn6jsIiyv6jxNqF/eMfSGp1GzMEAkFLiHfFbowkSWgLlDc4c19/Dv7ZsvukIG8loMQLjY9u/ck/dN5cIh97tMXq0vaDdcpQYoLHssaHjwcg2ZjJH39uIndn63EvJXk5rHj/bQDG/N/lhPZKINdqI6vGxmhWAxAZMd3j/bsjarUPgwYuwN8vHYejlD83X0JpmaLo3RAXToxeS77NwRu5Rcp4Pz+MAwciaZpWvLBac9n05wWUSuuxTtFRNuRaQoIntinDwRob/6xrxHpvYiTpwj12wlCbTPiOHu1xc+T2UvTss8g1NRgHDsT/jDO6ZA+BoKcglKFujKuyEm250svKkQoZG9fhsDUtqCbLbqyykh12iBQG+vu0um7QxRcTdOGFzdYZkp3Ohoaheg8tQwDRoeMA6Msu8mIT+OHV57GUNd+Y1FJWypKn5+O02ejV/zSG/00JNl1VbiGeLKLJR6XSExZ2psf7d1e02kCGDPmIwMARuFwWtm6dQ+bBf6OXnDySoiijr+cWkmNtPshWlmXy8z9j/Ya/HRMs/T4uV59mxx9LtcvF1TuzqHG5GRXgyw29Wq4rJTi5kO123NXVIElEPPRQlylcAkFPQShD3RhbXU8yV6BMbGAeZknFgU3rm4xzVOxFr7fiQENY4BA0qo6/MdqzspBtNlS+vq0WXPwrvr59sKuC0WNHk+5PtbmcL596uIlCVHooj/8+cg9VpUrLifNuu68hFfjXssoGF1loyOSTKousNTQafwYNfJ/o6P8DZLKzX2P9hnMZKf/B2AAjVrfM7XvzGgVTO8vLKM5dxqY/Z7Nn7/24XBYCAoYwYvjXmEyD2txTiRM6xN7qWsJ1Gt5KTxDZY17EWVxMybvvIrvdbQ/2AEmnI+6tN0n8+muMLWSOCgQCzxEVqLsxtrqsIUe0TDT5bEgYws4Vy0gbM6HROKf5D4iDDFIZE3Z8T/+1da0/9H37tqteiSRJGAJH4y77jvCwIxiDginJy2HxPbcw9JzzCYqKIX/fbrb9vBSXw0FgRBSzHngUo7/S6LXW5WZlaQVP1ylDERGnVjCoWq0nre9TBAWdzv79j1NTc5Cdu27mZm04/RlAVnkvPtsTxxiThOW1L5C/zaByppPqyW5UKgOJkTfhfuAXKid+iunKK9vc7/XcIpYUlqOW4J30BCJFcUWvIdvtHJz5d1wlJWiCQxrS7jsDQ2rbFkKBQNA2wjLUjbHuVIooVscHKhd6a8nduY0jB45mJpXk5aDxVVKmdzGAcUGeWVMqly/nyBNPNqmQW7tLafRqSEtrt7ypUWcDkC6vp9+9jxPWK4HaqkrWfvoB3730DJuX/g+Xw0H8aYO5+PHnCAg/2ntrjdlCgms7IZSiVvsRcpIUWmwvkRF/Y/Tpv5CQcBNabTAuRxHj5F+4gkWEHXmM/fsfxWzYg+QCv180xEZczujTV+K/1oBt924qf1iKpG+9ntDnR8p44mABAI+mxDBK9B7zKpJOR8hVcwAoeu45nOXlrU9oheOZKxAIWkYoQ92Y2p1K9pG6/wAAgiMrcKlU/PbRQmS3G7fbxcrFb+MbawWgSDeINN+mLTiaw/zpZ5R/+CHV6xu73eotQx1RhiJCJ+BETzhFbKnN5tKnX2LqDbeRPGwU0X3SSBs3ib/fN59ZDzyGT0Bgo7k/lVQwiZ8BiIw8H7W69Q/8kxmNxp/kpDsYO2YNAwe+R69ec8nSjWcHA9mhOh2/C+agCjGhLoegH01IR2ooeeMNAEKuu7bVrKGfSiq4fa/SamVeXFhDTSOBdwm+4gr0ffrgMpspfOJJ5GNcop7iLCoia/r5HL7zLlyVlV0gpUDQcxFusm6K22ptqBKdMHIau4t+pZ+0nXW9xqDevZOf3n4Ft9NJRcWfhOvsWPAjPXy4x4GUxtNOo3rNGqxbtsIllyh72u1Yt2+vuz+g3TKr1UZk/1FQ9RtlJctRp02g/8Qz6D+x9UwXtyyztjiPh1EUs+joC9u998mISqUnNGQioSETCY93Mu3P/WRZ7SytNfKfBwZTduedlL77LqULFoAsYxw8mICZM3G6XM2u93VhOTftycElw8zwQB4RTVi7DZJWS9Tjj5F9yaVUfv89PiNHEHSh53/nksNBwe134CwuxrZ/H6hEGr1A0JkIy1A3pXbvXnC5UIeGEtb3XJySgVBK0EwdCsCulT+zZ81K/JOqAdjCUKaFNV91ujl8RigtNKp//70hqFO22QiecyV+EyagqyvO2F5617nKkh1ryKxumvnWHH+YLfSx/4IWJ35+6Zj8+3do75OZAK2GxQOSCNaq2VZl5arIFHz+8Q9QqUCWMaSnE/vKy83GcbllmeeyCrh+t6IIXRARxKtp8ahEwHS3wjhwIOG33wZA4eNPUP377x7Nc9tsRP/nA2zbt6MymYh97TXUfr5dKKlA0PMQylA3xXjaaST+7xui//UMGo0RXYCSuo60hbPufYSEQUNJGjKUkAGKuT1LO7pdsSE+Q4ag8vXFVVraECek9vcn/LbbiHv7rQ6n6vaKmIoTHXHksfzQHx7N+eJICWfyIwAxMRd1aN9TgT6+Bj4flEKgRs3myhr+PnAc5m9/IPGbr0n4/DM0YU1dXpk1tczemskL2YUAXBMTyitpvY4ro1DQdQRffTX+U6ciOxzk3XRzmwqRs7ycghtvxHf/fiSjgbg3XkcXH3+CpBUIeg5CGeqmSGo1hj598BszBoB+MUp21Uh5JevDYph1/6OMvXocWrmUKvwZEDW5XanTkk6H7+jRAFT9/HOnya3VmnCaJgFQVrikzdgIq8vNocJlRHIE1CYiI87vNFlORtL9jHw7pDfJRj2HbQ5m5pZzk0PHynIL1XXuMavLzR61gdv2HWLChr2sNVswqiRe7tuLJ/vECotQN0ZSqYh+7ll8x4xBtlrJvW4uJW+/02Sc7HZTuWwZWTP/jnXDRtw6HVGvvYbPsGFekFogOPURytBJQljYmTg1YQRRzu8H/0uJzcHmA68BsIaJXBoV1e41TecqHa7NX36J227vNFkHxyvWnf7OlWw0N194sZ7vi8qZ4v4agPjYy9BohPm/t6+BpcP6cEV0CBJKt/mLtx8kedUOEn/bRp8/9vCSbwRfFlfglGFysD8rR/Tl/6KCvS26wANUOh2xb7xOwIwZ4HLhttY0ul+6cBEHzzmXw7f+A+eRI2h79SL3ppvwGTHCOwILBD0AoQx1Q+RmAmRVKh194q8BYLprMe+suxWNdQe1GLDbJhKqa38svP+UKWgiInCVlJAxegwl776Ly1J93PJHhY6lWh2FLzWszfyoxXGyLPNzzo/0Zj9uSUdcXNv1c3oKJo2aZ1PjWDE8lSuiQ4iuqxNkdSuWNpPbyYXhgSwd2oePByYTbzx1s+9ORVR6PVFPP0XMyy8TOnduo3v2nBzs2dmofH0JuX4ecV98jj1SNNcVCLoSoQx1Q4pfeomDM/9O5fLlja7Hx81B65uOPxZGupYBsNnnaibaO+YWkbRaIv75IABui4XSdxfgMh9/HRNJUhEWczUA0ZUfc7imeQVrnbmSkTULAIiIvgy9LvS49z7VSPMz8mxqHH+e3o/dY/uzflQaO0f15VnLYV7oE8NgU+utVwTdF0mSME09C5VP499h4N9nEv388/Re9Rvht92Gyij6yQkEXY1QhroZsixT+eNP2Pbsgb9YiFQqLacPWUxwxAXYjANxxPyTuwbfzPEk2ZrOPJNeixcTeustJH72Kbp2tOBojRGJl1ItBRFKCd/tfbvJfVmWWbr3HXqRi0PyIy3ppk7Z91RFkiSCtRrijXoCNGpEVNCpi3HgQALOOxeVr3AZCwQnClFnyMu4KitxFhai790bUFLdHXl5SD4++I0f32S8VhvE4PR/Mbju3w6H47hl8B05At+RnRuPoFbr8Yu9GTnvcWLM77Gz9Fz6h6Q23P82dyOjrAsBiEu8E602sFP3FwgEAoHAU4RlyIs4CgvJufRScq66Gvuhw8huNyVvvAlA4KxZTcznJxuTUq6gSJOOkVp2bp9HWfVhAPaU7MCeeTN67FQYR9Av/nIvSyoQCASCnoywDHlAfXp4ZSeXwHe5XFS53NgLC9k1eza6vqnUbNiAZDQSfsEsj/ZzOBzU1NRQWVmJVtv9mnEOSHyGLVsvI4AcVqw4A5s+BaNtDzpc5KpiOHvAv6iqqmr3ut393F1JTz27OLc4d0+hp569K85d/znaVpkXSe5Ik5wexqFDh4iLi/O2GAKBQCAQCDpAXl4esa3ExAplyAPcbjf5+fn4+/t3uDJzV1FZWUlcXBx5eXmYTCZvi3PC6Knnhp57dnFuce6eQk89e1ecW5ZlqqqqiI6ORtVMO6N6hJvMA1QqVasaZXfAZDL1qBdNPT313NBzzy7O3bPoqeeGnnv2zj53QEBAm2NEALVAIBAIBIIejVCGBAKBQCAQ9GiEMnSSo9freeSRR9Dre1Y7hp56bui5ZxfnFufuKfTUs3vz3CKAWiAQCAQCQY9GWIYEAoFAIBD0aIQyJBAIBAKBoEcjlCGBQCAQCAQ9GqEMnWSYzWZuvfVWTj/9dCIjI9Hr9cTExDB58mS+/PLLNkuOn0o8++yzSJKEJEmsW7fO2+J0KQkJCQ1n/evX9ddf723xupyvvvqKM888k5CQEIxGI4mJiVx88cXk5eV5W7Qu4f3332/x913/NWXKFG+L2SXIssySJUuYNGkSUVFR+Pj4kJqayrx58zh48KC3xesy3G43r732GkOGDMHHxweTycSECRP43//+523ROoUPP/yQefPmMWzYMPR6PZIk8f7777c4vrKykjvuuIP4+Hj0ej3x8fHccccdnd4Wqx4RQH2SceDAAQYNGsSoUaNISUkhODiYoqIivv32W4qKirjuuut45513vC1ml7Nnzx4GDx6MRqOhurqaP/74g1GjRnlbrC4jISEBs9nMbbfd1uTesGHDOO+88068UCcAWZa5/vrreeedd0hOTmbq1Kn4+/uTn5/Pb7/9xkcffcTYsWO9LWans3XrVr7++utm733xxRfs2rWLf/3rX9xzzz0nVrATwJ133smLL75IVFQU559/PiaTiW3btrFs2TL8/Pz4/fff6d+/v7fF7FRkWWb27Nl8+eWXJCcnc/bZZ2Oz2fjmm28oKiri1Vdf5eabb/a2mMdFQkICOTk5hIaG4uvrS05ODosWLWLOnDlNxlZXVzN27Fi2bt3KmWeeyZAhQ9i2bRs//vgjgwYNYs2aNfj6+naugLLgpMLpdMoOh6PJ9crKSrlfv34yIO/cudMLkp04nE6nPHz4cHnEiBHyZZddJgPyH3/84W2xupT4+Hg5Pj7e22KccF5++WUZkG+66SbZ6XQ2ud/ca+FUxmazySEhIbJGo5GPHDnibXE6nYKCAlmlUskJCQlyRUVFo3v//ve/ZUC+6qqrvCRd1/H555/LgDxmzBi5pqam4XpxcbEcHx8v6/V6OSsry3sCdgLLly+Xs7OzZVmW5aeffloG5EWLFjU79uGHH5YB+Z577mn2+sMPP9zp8gk32UmGWq1Go2naRcXf35+pU6cCivXoVOZf//oX27ZtY+HChajVam+LI+girFYrjz76KElJSbz00kvN/q6bey2cynz11VeUlpZy3nnnERER4W1xOp3s7Gzcbjdjxoxp0o7h3HPPBaCoqMgbonUp9VbABx54AKPR2HA9NDSU22+/HZvNxqJFi7wkXedwxhlnEB8f3+Y4WZZZsGABfn5+PPzww43u3X///QQFBfHee+91ekiIUIZOEWpra1mxYgWSJNGvXz9vi9Nl7Ny5k0cffZR//vOfpKene1ucE4rNZmPx4sU89dRTvPnmm2zbts3bInUpy5cvp6ysjBkzZuByuViyZAnPPPMMb7311imv8LfEe++9B8C1117rZUm6ht69e6PT6Vi7di1VVVWN7v3www8ATJ482RuidSmFhYUAJCYmNrlXf23FihUnVCZvkZGRQX5+PmPGjGniCjMYDIwfP57Dhw93+ntAz3qsOoUwm8289NJLuN1uioqK+OGHH8jLy+ORRx6hd+/e3havS3A6ncyZM4e0tDTuu+8+b4tzwjly5EgT//q0adP44IMPCA0N9Y5QXcimTZsAxfozcOBA9u3b13BPpVJx++238/zzz3tLvBNOTk4Ov/zyCzExMUybNs3b4nQJISEhPPnkk9x9992kpaUxffp0/P392bFjBz///DNz587llltu8baYnU5YWBgAWVlZpKWlNbqXlZUFwP79+0+4XN4gIyMDoMXPsfrrGRkZnfpZJ5ShkxSz2cyjjz7a8G+tVstzzz3HnXfe6UWpupannnqKbdu2sX79erRarbfFOaFcffXVTJgwgfT0dPR6Pbt37+bRRx9l6dKlTJ8+nbVr1yJJkrfF7FTq3SEvvPACQ4YMYcOGDaSlpbFlyxbmzp3LCy+8QHJyMjfccIOXJT0xLFq0CLfbzVVXXXVKu4fvuusuoqOjmTdvHm+++WbD9dGjR3PZZZedkq/9s88+m08++YRnnnmGyZMnYzAYACgtLeWll14ClPf8nkBFRQXQcqf5evdp/bjOQrjJTlISEhKQZRmn00lWVhaPPfYYDz74ILNmzcLpdHpbvE5n27ZtPPHEE9x1110MGTLE2+KccB5++GEmTJhAaGgo/v7+jBw5ku+++46xY8fyxx9/NLgQTiXcbjcAOp2Or7/+muHDh+Pn58e4ceP44osvUKlUvPDCC16W8sTgdrtZtGgRkiRx9dVXe1ucLuWJJ55gzpw53H///eTl5WGxWFizZg1Op5NJkyaxZMkSb4vY6Vx88cVMmjSJ1atXM2DAAG655Rauv/560tPTGz78T2UFuDsglKGTHLVaTUJCAvfddx9PPPEEX331Fe+++663xep0rrzySpKTk5k/f763Rek2qFQqrrrqKgDWrl3rZWk6n/onw2HDhhEdHd3oXnp6OklJSWRmZvaIJ+bly5eTm5vL5MmTm40rOVVYsWIFDz30EDfffDMPPPAAsbGx+Pr6MmbMGL777juMRiO33367t8XsdDQaDUuXLmX+/PmoVCreeecdlixZwvnnn88XX3wBHHWlnerUv+5bsvzU1xlqyXLUUYQydApx1llnAbBy5UrvCtIFbNu2jb1792IwGBoVnlu8eDEAp59+OpIktVib5VSlPlaopqbGy5J0PqmpqQAEBgY2e7/+utVqPUESeY9TPXC6nu+//x6ASZMmNbkXFhbGgAEDyM3NpaSk5ESL1uXUd2zft28fNpuNoqIi3n77bQ4fPgwoDwU9gWNjgpqjrZiijiJihk4h8vPzgVMz3fiaa65p9vqqVavIyMhg+vTphIWFkZCQcGIF8zLr168HOCXPXf+BuGfPnib3HA4HBw4cwNfX95R/Yi4tLeWbb74hODiYmTNnelucLsVutwNQXFzc7P3663q9/oTJ5G0++ugjAC666CIvS3Ji6N27N9HR0axdu5bq6upGGWW1tbWsWrWK6OhoUlJSOnfjTq9cJOhStmzZIpvN5ibXS0tL5UGDBsmA/MEHH3hBMu9w5ZVXnvJFF3ft2iWXl5c3ub569WrZYDDIer1ezsnJOfGCnQDOOussGZDffffdRtcfe+wxGZAvu+wyL0l24qgvNnjrrbd6W5Qu55NPPpEBOT09vcn73Pvvvy8D8tChQ70kXdfy1yKTsqwUY1SpVPLw4cObLTp6stIdiy6KdhwnGbfddhsLFixg0qRJxMfHN5Q1//7777FYLMyaNYvPPvsMlapneEDnzJnD4sWLT+l2HPPnz+fZZ59lypQpJCQkoNfr2blzJ8uWLUOlUvHWW2+dsu6TzMxMRo8eTVFREeeeey59+/Zly5YtrFixgvj4eNatW0dkZKS3xexSBgwYwM6dO9m+fTsDBgzwtjhdisvl4owzzmDlypWEhYUxffp0goKC2LZtG8uXL0ev1/Pzzz+fki1Y0tLSiIuLIy0tDYPBwIYNG1i5ciVJSUkNf+8nMwsWLGDNmjUA7Nixg82bNzNmzJgGC8+MGTOYMWMG0LQdx9ChQ9m2bRtLly4V7TgECqtXr5bnzJkj9+3bVzaZTLJGo5HDw8PladOmyR9//LHsdru9LeIJpSdYhlauXClfeOGFckpKiuzv7y9rtVo5NjZWvuiii+T169d7W7wuJzc3V54zZ44cGRkpa7VaOS4uTr7pppvkwsJCb4vW5axfv14G5BEjRnhblBNGbW2t/K9//UseMmSI7OPjI2s0GjkmJka+5JJL5B07dnhbvC7jkUcekQcMGCD7+/vLBoNBTktLk//5z382azE6Gal/r27p65FHHmk03mw2y7fffrscFxfX8Lq//fbbm/WMdAbCMiQQCAQCgaBH0zN8KQKBQCAQCAQtIJQhgUAgEAgEPRqhDAkEAoFAIOjRCGVIIBAIBAJBj0YoQwKBQCAQCHo0QhkSCAQCgUDQoxHKkEAgEAgEgh6NUIYEAoFAIBD0aIQyJBAIBAKBoEcjlCGBQHBSkp2djSRJzJkzp8v2mDNnDpIkkZ2d7fEct9vNwIEDOeecc7pMLrPZTGBgIPfcc0+X7SEQ9CSEMiQQCDpMvUJy7JdOpyMuLo5LLrmE7du3e1vEE87777/P9u3bmT9/fpftERgYyD/+8Q9eeeWVdilqAoGgeURvMoFA0GGys7NJTEwkOTmZyy67DACLxcK6detYu3Yter2eFStWMHr06E7f2+FwkJmZSUBAAFFRUZ2+PiiWocWLF5OVlUVCQkKb410uF0lJSSQmJrJy5coukamesrIyoqKiuPzyy1mwYEGX7iUQnOoIy5BAIDhuUlJSmD9/PvPnz+f5559nzZo1PPjgg9hsNh588MEu2VOr1dK3b98uU4Q6wg8//EBubi6XX355l+8VHBzM2WefzSeffEJFRUWX7ycQnMoIZUggEHQJt9xyCwAbN25sdP2bb75hypQpBAUFYTAY6N+/P88//zwul6vRuPfffx9Jknj//ff5/vvvGTduHP7+/g0WmtZihnJzc7nmmmuIiYlBp9MRGxvLNddcQ15eXrOy7tq1i/POOw9/f38CAgI455xz2LlzZ7vPXC/zrFmzGl2fMGECWq2WgoKCZuddeOGFSJLEli1bAFi5ciWSJDF//nz++OMPpk6dSmBgIJIkNZlXU1PDZ5991m5ZBQLBUYQyJBAIuoS/fnADPPDAA8yYMYP9+/cza9YsbrzxRgwGA3fffTcXXXRRs+t8/vnnzJgxg9DQUG688cY2A5MzMjIYPnw4CxcuZOjQodx5550MGTKEhQsXMmzYMA4cONBo/M6dOxk9ejRLly5l2rRp3HTTTdjtdsaMGcPBgwc9Pq8sy6xcuZK+ffsSGBjY6N68efNwOp0sWrSoybySkhK++eYbhg4dyuDBgxvd+/3335kwYQIAc+fO5f/+7/8a3T/99NMBWLFihcdyCgSCZpAFAoGgg2RlZcmAPHXq1Cb3HnzwQRmQJ06cKMuyLC9btkwG5LPPPluurq5uGOd2u+Xrr79eBuQvvvii4fqiRYtkQJYkSV6+fHmLe1955ZWNrk+ePFkG5LfffrvR9bffflsG5ClTpjS6PmHCBBmQP/zww0bX77//fhmQATkrK6vNn8WuXbtkQL700kub3KutrZVDQkLk5ORk2e12N7r34osvyoD85ptvNlz79ddfG/Z+7733Wt03ODhY7tWrV5vyCQSClhHKkEAg6DD1CklycrL8yCOPyI888oh85513ymPGjJEB2WAwyL///rssy7I8ffp0GZBzc3ObrGM2m2VJkuRZs2Y1XKtXhmbOnNnq3scqQ7m5uTIg9+vXr4nS4Xa75bS0tEYy5OTkyIB82mmnNVm/qqpKDgwM9FgZ+umnn2RAvuOOO5q9f8cdd8iA/MsvvzS6np6eLvv4+MgVFRUN1+qVocGDB7e5b9++fWW1Wt3kvAKBwHM0J8gAJRAITmEyMzN59NFHASWwOSIigksuuYT77ruPAQMGALBu3Tp8fX157733ml3DaDSyd+/eJtdHjBjhsRz1MTcTJkxo4qaTJInx48ezZ88etm3bRlxcHNu2bQNg7NixTdby8/Nj0KBBHmeFlZaWAhAUFNTs/blz5/Liiy+yYMECJk+eDCg/k127djFnzhxMJlOTOZ6cPTg4GJfLhdlsbnFvgUDQOkIZEggEx83UqVP58ccfWx1TVlaG0+lsUJqao7q6usm1iIgIj+WorKxsdU5kZCRAQ/ZV/ffw8PBmx7dnb6PRCIDVam32fmpqKhMmTGDJkiWUlZURHBzckBJ/3XXXdXj/+v18fHw8llUgEDRGBFALBIITgslkIiQkBFlxzzf7lZWV1WRec4HYre0BUFhY2Oz9+uv14wICAgAoKipqdbwnhIWFAYrS1xLz5s3DZrPx4YcfYrFY+PTTT+nXr1+LdZg8OXtZWRn+/v7o9XqPZRUIBI0RypBAIDghjBw5ktLSUjIyMrpsj0GDBgGwatUq5L/Uk5VlmdWrVzcaN3DgQADWrFnTZC2LxcLWrVs93js9PR2VStXq+WbNmkVoaCgLFizg008/xWKxcO2113q8x1+pqanh0KFDDa5IgUDQMYQyJBAITgi33norAFdffXVDfM2xHDlyhD179hzXHr169WLSpEns2rWLhQsXNrq3cOFCdu3axeTJk4mLi2sYP378eLZv385HH33UaPxTTz2F2Wz2eO/AwEBOO+00Nm3a1EQRq0en03HllVeyY8cOHn74YXQ6HVdccUX7DnkMmzZtwuVyNaTfCwSCjiGUIYFAcEKYNm0aDz30EGvWrCElJYWLL76Y++67j+uuu45JkyYRGxvLN998c9z7vPnmm4SGhnLdddcxY8aMhtpG1113HWFhYbz55puNxr/++uuYTCauuOIKZs+ezQMPPMCZZ57J66+/zrhx49q194wZM6ioqGhSaPJY5s6dC0B+fj4zZ84kJCSk/YesY/ny5Q37CgSCjiOUIYFAcMJ47LHHWL58OePGjeOXX37hxRdf5LvvvsNmszF//nwuvfTS494jNTWVTZs2MWfOHDZs2MBzzz3Hhg0bmDNnDhs3bqRPnz6Nxvfv35+1a9cybdo0fvzxR1577TW0Wi1r164lKSmpXXtfe+21qNVqPvzwwxbH9OnTp6FYYkuB057y8ccfM2jQoHZl3AkEgqaIRq0CgUDQiVxyySUsW7aMnJwcfH19m9yvra0lJiaGwMBADhw40K4A8WNZsWIFU6ZMYfHixcflahMIBMIyJBAIBJ3Kk08+icVi4fXXX2/2/sKFCykrK2PevHkdVoRAsbINGjSIyy67rMNrCAQCBVFnSCAQCDqRxMREFi9eTElJSaPrzzzzDMXFxbz99tuEh4dz/fXXd3gPs9nMxIkT+dvf/oZKJZ5pBYLjRbjJBAKB4AQgSRI6nY6BAwfyyiuvMGrUKG+LJBAI6hCWIYFAIDgBiOdOgaD7IuyrAoFAIBAIejRCGRIIBAKBQNCjEcqQQCAQCASCHo1QhgQCgUAgEPRohDIkEAgEAoGgRyOUIYFAIBAIBD0aoQwJBAKBQCDo0QhlSCAQCAQCQY9GKEMCgUAgEAh6NP8P1rsIJIpswKIAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure Supplementary Information S3b of the paper\n", + "windows = 48\n", + "\n", + "# windows creation to reduce the apodization effect\n", + "global_hann = sc.signal.windows.hamming(windows)[:windows//2]\n", + "slr_hann = np.concatenate((global_hann, np.ones(len(SLR_filt_Ylms.time)-windows), global_hann[::-1]))\n", + "grace_hann = np.concatenate((global_hann, np.ones(len(GRACE_filt_Ylms.time)-windows), global_hann[::-1]))\n", + "graz_hann = np.concatenate((global_hann, np.ones(len(GRAZ_filt_Ylms.time)-windows), global_hann[::-1]))\n", + "\n", + "# compute periodogram\n", + "w = np.linspace(0.63, 2.3, 5000)[::-1]\n", + "pgram = sg.lombscargle(SLR_filt_Ylms.time.copy(), SLR_filt_Ylms.slm[2,2]*slr_hann, w.copy(), normalize=False)\n", + "pgram_grace = sg.lombscargle(GRACE_filt_Ylms.time.copy(), GRACE_filt_Ylms.slm[2,2]*grace_hann, w.copy(), normalize=False)\n", + "pgram_graz = sg.lombscargle(GRAZ_filt_Ylms.time.copy(), GRAZ_filt_Ylms.slm[2,2]*graz_hann, w.copy(), normalize=False)\n", + "pgram_costg = sg.lombscargle(COSTG_filt_Ylms.time.copy(), COSTG_filt_Ylms.slm[2,2]*grace_hann, w.copy(), normalize=False)\n", + "\n", + "plt.figure()\n", + "plt.plot(2*np.pi/w, pgram_grace, label='CSR', color='C5')\n", + "plt.plot(2*np.pi/w, pgram_graz, label='GRAZ', color='C9')\n", + "plt.plot(2*np.pi/w, pgram_costg, label='COST-G', color='C8')\n", + "plt.plot(2*np.pi/w, pgram, label='IGG-SLR', color='C3', linestyle=(0, (5,2)))\n", + "\n", + "plt.xlabel('Period (yr)', labelpad=4, fontsize=14)\n", + "plt.ylabel('($yr^{-1}$)', labelpad=-2, fontsize=14)\n", + "plt.ylim(10**-22)\n", + "plt.legend(loc='upper right', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "18a5be29", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-14T16:28:55.710880Z", + "start_time": "2023-08-14T16:28:55.419863Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure 4 of the paper\n", + "\n", + "plt.figure()\n", + "# create alpha and delta h range\n", + "alpha = np.arange(0.08, 1.2, 0.005)\n", + "h = np.arange(45, 160, 0.01)\n", + "# rectangle for the inscription \"Above $S_{2,2}$ ...\"\n", + "h2 = np.arange(94.5, 158.2, 0.01) \n", + "\n", + "# three line for three alpha maximal value at delta h = 90m\n", + "plt.plot(h, 90*0.4/h, label=r'$S_{2,2} = 2 \\mathcal{K} \\times 90 \\times {0.4} \\frac{\\pi}{180} \\approx 9 \\times 10^{-12}$', lw=2, color='C4')\n", + "plt.plot(h, 90*0.3/h, label=r'$S_{2,2} = 2 \\mathcal{K} \\times 90 \\times {0.3} \\frac{\\pi}{180} \\approx 7 \\times 10^{-12}$', lw=2, color='C9', linestyle=(0,(3,1,1,1,1,1)))\n", + "plt.plot(h, 90*0.1/h, label=r'$S_{2,2} = 2 \\mathcal{K} \\times 90 \\times {0.1} \\frac{\\pi}{180} \\approx 2 \\times 10^{-12}$', lw=2, color='C1')\n", + "\n", + "# hatch couple values of alpha, delta h that cannot be reach\n", + "plt.fill_between(h, 90*0.4/h, 2*np.ones(h.shape), hatch='//', fc='w', alpha=0.8)\n", + "\n", + "# dashed line for delta h = 90m\n", + "plt.plot([90,90], [1.2,0], '--', lw=2, color='C5')\n", + "# rectangle for the inscription \"Above $S_{2,2}$ ...\"\n", + "plt.fill_between(h2, 0.455*np.ones(h2.shape), 0.51*np.ones(h2.shape), fc='w') \n", + "plt.text(95, 0.47, 'Above $S_{2,2}$ upper bound constraint', weight=\"bold\")\n", + "\n", + "plt.xlabel('$\\delta h$ (m)', fontsize=12)\n", + "plt.ylabel(r'$\\alpha~(°)$', fontsize=12)\n", + "plt.xticks([50, 70, 90, 110, 130, 150])\n", + "plt.xlim(45, 160)\n", + "plt.ylim(0, 0.95)\n", + "plt.legend(framealpha=0.9)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "80fb78ed", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-14T16:28:58.475906Z", + "start_time": "2023-08-14T16:28:58.472055Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Low Γ value : 0.18169617014874107\n", + "Large Γ value : 0.027254425522311162\n" + ] + } + ], + "source": [ + "# calculation of maximal value for alpha based on LOD change with an amplitude ms and a period y\n", + "ms = 1e-3\n", + "y = 20\n", + "\n", + "# equation 8\n", + "print(\"Low Γ value :\", 360/86400**2*7.129e37/3e19*ms/(y*31536000))\n", + "print(\"Large Γ value :\", 360/86400**2*7.129e37/2e20*ms/(y*31536000))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "eb22604d", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-14T16:29:01.336531Z", + "start_time": "2023-08-14T16:29:00.153024Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For a period of 30 yr, spectral resolution on C04 time series is between : 18.75 and 75.0\n", + "For a period of 30 yr, spectral resolution on C01 time series is between : 21.428571428571427 and 50.00000000000001\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure Supplementary Information S1a and S2a\n", + "p = 30 #period of 30 years\n", + "\n", + "l = 50 #length of the LOD time series for C04\n", + "pmin = p - np.abs(p - 1/(1/p + 1/l))\n", + "pmax = p + np.abs(p - 1/(1/p - 1/l))\n", + "print(\"For a period of 30 yr, spectral resolution on C04 time series is between : \", pmin, \"and \", pmax)\n", + "fmin, fmax = 1/pmax, 1/pmin\n", + "\n", + "# read C04 file\n", + "f = open(os.path.join(base_dir, \"LOD/lod_AOHSl.txt\"), 'r')\n", + "lines = f.readlines()\n", + "\n", + "# create a new LOD to remove trend and AAM, OAM, HAM, ...\n", + "lod = np.zeros((len(lines) - 7, 7))\n", + "for i, l in enumerate(lines[7:]):\n", + " lod[i, :-1] = np.array(l.split())\n", + " \n", + "lod[:,6] = lod[:,1] - lod[:,2]\n", + "\n", + "for i in range(1,7):\n", + " lod[:,i] = sg.detrend(lod[:,i])\n", + " \n", + "# temporally filter LOD\n", + "filt_lod = lod.copy()\n", + "\n", + "ndata = lod.shape[0]\n", + "# compute the mean time delta of the object\n", + "dt = float(np.mean((lod[1:, 0] - lod[:-1, 0])))\n", + "\n", + "# fft filtering with 2**n2 zero padding\n", + "for i in range(1,7):\n", + " s = lod[:,i].copy()\n", + "\n", + " # zero pad\n", + " n2 = 0\n", + " while ndata > 2 ** n2:\n", + " n2 += 1\n", + " n2 += 1\n", + "\n", + " f = np.fft.fft(s, n=2 ** n2)\n", + " freq = np.fft.fftfreq(2 ** n2, d=dt)\n", + " to_zero = np.logical_or(freq > fmax, freq < -fmax) | np.logical_and(freq < fmin, freq > -fmin)\n", + " f[to_zero] = 0\n", + " filt_lod[:,i] = np.real(np.fft.ifft(f))[:ndata]\n", + "\n", + "\n", + "l = 75 #length of the LOD time series for C01\n", + "pmin = p - np.abs(p - 1/(1/p + 1/l))\n", + "pmax = p + np.abs(p - 1/(1/p - 1/l))\n", + "print(\"For a period of 30 yr, spectral resolution on C01 time series is between : \", pmin, \"and \", pmax)\n", + "fmin, fmax = 1/pmax, 1/pmin\n", + "\n", + "# read C01\n", + "f = open(os.path.join(base_dir, \"LOD/lod_AAMncep1948-2023.dat\"), 'r')\n", + "lines = f.readlines()\n", + "\n", + "# create a new LOD to remove trend and AAM, OAM, HAM, ...\n", + "lod2 = np.zeros((len(lines) - 1, 4))\n", + "for i, l in enumerate(lines[1:]):\n", + " lod2[i, :-1] = np.array(l.split())\n", + " \n", + "lod2[:,3] = lod2[:,1] - lod2[:,2]\n", + "\n", + "for i in range(1,4):\n", + " lod2[:,i] = sg.detrend(lod2[:,i])\n", + " \n", + "# temporally filter LOD\n", + "filt_lod2 = lod2.copy()\n", + "\n", + "ndata = lod2.shape[0]\n", + "# compute the mean time delta of the object\n", + "dt = float(np.mean((lod2[1:, 0] - lod2[:-1, 0])))\n", + "\n", + "# fft filtering with 2**n2 zero padding\n", + "for i in range(1,4):\n", + " s = lod2[:,i].copy()\n", + "\n", + " # zero pad\n", + " n2 = 0\n", + " while ndata > 2 ** n2:\n", + " n2 += 1\n", + " n2 += 1\n", + "\n", + " f = np.fft.fft(s, n=2 ** n2)\n", + " freq = np.fft.fftfreq(2 ** n2, d=dt)\n", + " to_zero = np.logical_or(freq > fmax, freq < -fmax) | np.logical_and(freq < fmin, freq > -fmin)\n", + " f[to_zero] = 0\n", + " filt_lod2[:,i] = np.real(np.fft.ifft(f))[:ndata]\n", + " \n", + "plt.figure()\n", + "plt.plot(lod2[:,0], filt_lod2[:,1], label='C01', color='C1', linestyle='dashdot')\n", + "plt.plot(lod2[:,0], filt_lod2[:,3], label='C01 LOD-AAM', color='C2')\n", + "#plt.plot(lod[:,0], filt_lod[:,1], label='C04 LOD', color='C6')\n", + "plt.plot(lod[:,0], filt_lod[:,6], label='C04 LOD-AAM', color='C8', linestyle=(0, (5,2)))\n", + "\n", + "plt.title('')\n", + "plt.ylabel('(ms)', fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(loc=(0.6769, 0.77))\n", + "\n", + "dlod = (filt_lod[1:,6] - filt_lod[:-1,6]) / ((lod[1:,0] - lod[:-1,0])*31536000)/1e3\n", + "dlod2 = (filt_lod2[1:,3] - filt_lod2[:-1,3]) / ((lod2[1:,0] - lod2[:-1,0])*31536000)/1e3\n", + "\n", + "plt.figure()\n", + "plt.plot(lod2[:-1,0], -360/86400**2*7.129e37/3e19*dlod2, label=r'$\\alpha$ from C01, $\\Gamma=3.10^{19}$', color='C1', linestyle='dashdot')\n", + "plt.plot(lod2[:-1,0], -360/86400**2*7.129e37/2e20*dlod2, label=r'$\\alpha$ from C01, $\\Gamma=2.10^{20}$', color='C8')\n", + "plt.plot(lod[:-1,0], -360/86400**2*7.129e37/3e19*dlod, label=r'$\\alpha$ from C04, $\\Gamma=3.10^{19}$', color='C0', linestyle='dashdot')\n", + "plt.plot(lod[:-1,0], -360/86400**2*7.129e37/2e20*dlod, label=r'$\\alpha$ from C04, $\\Gamma=2.10^{20}$', color='C9')\n", + "\n", + "plt.ylabel(r'$\\alpha (\\circ)$', fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.grid()\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "9ada98ea", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-14T16:29:48.110486Z", + "start_time": "2023-08-14T16:29:47.207118Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For a period of 30 yr, spectral resolution on C04 time series is between : 5.357142857142858 and 6.818181818181818\n", + "0.27328662391793657\n", + "0.07190718419811173\n", + "For a period of 30 yr, spectral resolution on C01 time series is between : 5.555555555555555 and 6.521739130434783\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure Supplementary Information S1b and S2b\n", + "p = 6 #period of 6 years\n", + "l = 50\n", + "\n", + "pmin = p - np.abs(p - 1/(1/p + 1/l))\n", + "pmax = p + np.abs(p - 1/(1/p - 1/l))\n", + "print(\"For a period of 30 yr, spectral resolution on C04 time series is between : \", pmin, \"and \", pmax)\n", + "fmin, fmax = 1/pmax, 1/pmin\n", + "\n", + "filt_lod = lod.copy()\n", + "\n", + "ndata = lod.shape[0]\n", + "# compute the mean time delta of the object\n", + "dt = float(np.mean((lod[1:, 0] - lod[:-1, 0])))\n", + "\n", + "for i in range(1,7):\n", + " s = lod[:,i].copy()\n", + "\n", + " # zero pad\n", + " n2 = 0\n", + " while ndata > 2 ** n2:\n", + " n2 += 1\n", + " n2 += 1\n", + "\n", + " f = np.fft.fft(s, n=2 ** n2)\n", + " freq = np.fft.fftfreq(2 ** n2, d=dt)\n", + " to_zero = np.logical_or(freq > fmax, freq < -fmax) | np.logical_and(freq < fmin, freq > -fmin)\n", + " f[to_zero] = 0\n", + " filt_lod[:,i] = np.real(np.fft.ifft(f))[:ndata]\n", + "\n", + "print(np.max(filt_lod[:,6]) - np.min(filt_lod[:,6]))\n", + "print(np.std(filt_lod[:,6]))\n", + "\n", + "l = 75 #length of the LOD time series for C01\n", + "pmin = p - np.abs(p - 1/(1/p + 1/l))\n", + "pmax = p + np.abs(p - 1/(1/p - 1/l))\n", + "print(\"For a period of 30 yr, spectral resolution on C01 time series is between : \", pmin, \"and \", pmax)\n", + "fmin, fmax = 1/pmax, 1/pmin\n", + "\n", + "filt_lod2 = lod2.copy()\n", + "\n", + "ndata = lod2.shape[0]\n", + "# compute the mean time delta of the object\n", + "dt = float(np.mean((lod2[1:, 0] - lod2[:-1, 0])))\n", + "\n", + "for i in range(1,4):\n", + " s = lod2[:,i].copy()\n", + "\n", + " # zero pad\n", + " n2 = 0\n", + " while ndata > 2 ** n2:\n", + " n2 += 1\n", + " n2 += 1\n", + "\n", + " f = np.fft.fft(s, n=2 ** n2)\n", + " freq = np.fft.fftfreq(2 ** n2, d=dt)\n", + " to_zero = np.logical_or(freq > fmax, freq < -fmax) | np.logical_and(freq < fmin, freq > -fmin)\n", + " f[to_zero] = 0\n", + " filt_lod2[:,i] = np.real(np.fft.ifft(f))[:ndata]\n", + "\n", + "plt.figure()\n", + "plt.plot(lod2[:,0], filt_lod2[:,1], label='C01', color='C1', linestyle='dashdot')\n", + "plt.plot(lod2[:,0], filt_lod2[:,3], label='C01 LOD-AAM', color='C2')\n", + "#plt.plot(lod[:,0], filt_lod[:,1], label='C04 LOD', color='C6')\n", + "plt.plot(lod[:,0], filt_lod[:,6], label='C04 LOD-AAM', color='C8', linestyle=(0, (5,2)))\n", + "\n", + "plt.title('')\n", + "plt.ylabel('(ms)', fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.legend(loc=(0.6769, 0.77))\n", + "\n", + "dlod = (filt_lod[1:,6] - filt_lod[:-1,6]) / ((lod[1:,0] - lod[:-1,0])*31536000)/1e3\n", + "dlod2 = (filt_lod2[1:,3] - filt_lod2[:-1,3]) / ((lod2[1:,0] - lod2[:-1,0])*31536000)/1e3\n", + "\n", + "plt.figure()\n", + "plt.plot(lod2[:-1,0], -360/86400**2*7.129e37/3e19*dlod2, label=r'$\\alpha$ from C01, $\\Gamma=3.10^{19}$', color='C1', linestyle='dashdot')\n", + "plt.plot(lod2[:-1,0], -360/86400**2*7.129e37/2e20*dlod2, label=r'$\\alpha$ from C01, $\\Gamma=2.10^{20}$', color='C8')\n", + "plt.plot(lod[:-1,0], -360/86400**2*7.129e37/3e19*dlod, label=r'$\\alpha$ from C04, $\\Gamma=3.10^{19}$', color='C0', linestyle='dashdot')\n", + "plt.plot(lod[:-1,0], -360/86400**2*7.129e37/2e20*dlod, label=r'$\\alpha$ from C04, $\\Gamma=2.10^{20}$', color='C9')\n", + "\n", + "plt.ylabel(r'$\\alpha (\\circ)$', fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.grid()\n", + "plt.legend()\n", + "plt.show()" + ] + } + ], + "metadata": { + "hide_input": false, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.16" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 50d9b1cd42aeeca2f029795076e71b22cf34276e Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 15 Aug 2023 10:53:28 +0200 Subject: [PATCH 67/80] Change os. reference to pathlib --- gravity_toolkit/harmonics.py | 33 ++++++++++++++------------------- gravity_toolkit/spatial.py | 11 ++++++----- 2 files changed, 20 insertions(+), 24 deletions(-) diff --git a/gravity_toolkit/harmonics.py b/gravity_toolkit/harmonics.py index 9feb603e..969203df 100644 --- a/gravity_toolkit/harmonics.py +++ b/gravity_toolkit/harmonics.py @@ -106,11 +106,6 @@ import scipy as sc import matplotlib.pyplot as plt import gravity_toolkit.wavelets as wv -from gravity_toolkit.ncdf_stokes import ncdf_stokes -from gravity_toolkit.hdf5_stokes import hdf5_stokes -from gravity_toolkit.ncdf_read_stokes import ncdf_read_stokes -from gravity_toolkit.hdf5_read_stokes import hdf5_read_stokes -from gravity_toolkit.read_ICGEM_harmonics import read_ICGEM_harmonics from gravity_toolkit.destripe_harmonics import destripe_harmonics from gravity_toolkit.read_gfc_harmonics import read_gfc_harmonics from gravity_toolkit.read_GRACE_harmonics import read_GRACE_harmonics @@ -2098,8 +2093,8 @@ def plot_correlation(self, l, m, save_path=False): plt.title('Correlation of each spherical harmonics with $C_{' + str(l) + ',' + str(m)+ '}$') if save_path: - if os.path.isdir(save_path): - plt.savefig(os.path.join(save_path, 'C' + str(l) + str(m) + '_correlation.png')) + if pathlib.Path(save_path).is_dir(): + plt.savefig(pathlib.Path(save_path) / 'C' + str(l) + str(m) + '_correlation.png') else: plt.savefig(save_path[:-3] + 'c' + save_path[-3:]) @@ -2110,8 +2105,8 @@ def plot_correlation(self, l, m, save_path=False): plt.title('Correlation of each spherical harmonics with $S_{' + str(l) + ',' + str(m) + '}$') if save_path: - if os.path.isdir(save_path): - plt.savefig(os.path.join(save_path, 'S' + str(l) + str(m) + '_correlation.png')) + if pathlib.Path(save_path).is_dir(): + plt.savefig(pathlib.Path(save_path) / 'S' + str(l) + str(m) + '_correlation.png') else: plt.savefig(save_path[:-3] + 's' + save_path[-3:]) plt.show() @@ -2158,8 +2153,8 @@ def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], color=[], save_p plt.grid() if save_path: - if os.path.isdir(save_path): - plt.savefig(os.path.join(save_path, 'C' + str(l) + str(m) + '_coefficient.png')) + if pathlib.Path(save_path).is_dir(): + plt.savefig(pathlib.Path(save_path) / 'C' + str(l) + str(m) + '_coefficient.png') else: plt.savefig(save_path[:-4] + 'c' + save_path[-4:]) @@ -2193,8 +2188,8 @@ def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], color=[], save_p plt.grid() if save_path: - if os.path.isdir(save_path): - plt.savefig(os.path.join(save_path, 'S' + str(l) + str(m) + '_coefficient.png')) + if pathlib.Path(save_path).is_dir(): + plt.savefig(pathlib.Path(save_path) / 'S' + str(l) + str(m) + '_coefficient.png') else: plt.savefig(save_path[:-4] + 's' + save_path[-4:]) @@ -2233,8 +2228,8 @@ def plot_fft(self, l, m, save_path=False, fmax=6): plt.legend() if save_path: - if os.path.isdir(save_path): - plt.savefig(os.path.join(save_path, 'CS' + str(l) + str(m) + '_fft.png')) + if pathlib.Path(save_path).is_dir(): + plt.savefig(pathlib.Path(save_path) / 'CS' + str(l) + str(m) + '_fft.png') else: plt.savefig(save_path) @@ -2346,8 +2341,8 @@ def plot_wavelets(self, l, m, s0=0, j1=None, pad=1, lag1=0, plot_coi=True, mothe plt.legend(loc='upper right') if save_path: - if os.path.isdir(save_path): - plt.savefig(os.path.join(save_path, 'C' + str(l) + str(m) + '_wavelet.png')) + if pathlib.Path(save_path).is_dir(): + plt.savefig(pathlib.Path(save_path) / 'C' + str(l) + str(m) + '_wavelet.png') else: plt.savefig(save_path[:-4] + 'c' + save_path[-4:]) @@ -2391,8 +2386,8 @@ def plot_wavelets(self, l, m, s0=0, j1=None, pad=1, lag1=0, plot_coi=True, mothe plt.legend(loc='upper right') if save_path: - if os.path.isdir(save_path): - plt.savefig(os.path.join(save_path, 'S' + str(l) + str(m) + '_wavelet.png')) + if pathlib.Path(save_path).is_dir(): + plt.savefig(pathlib.Path(save_path) / 'S' + str(l) + str(m) + '_wavelet.png') else: plt.savefig(save_path[:-4] + 's' + save_path[-4:]) diff --git a/gravity_toolkit/spatial.py b/gravity_toolkit/spatial.py index 35454464..ec425b5d 100644 --- a/gravity_toolkit/spatial.py +++ b/gravity_toolkit/spatial.py @@ -2043,8 +2043,8 @@ def plot_eof(self, number, path_folder, cmap='viridis', mode='full', unit='cmwe' eof_grid.lat, eof_grid.lon = self.lat, self.lon eof_grid.time = np.array([0]) - if not os.path.isdir(path_folder): - os.mkdir(path_folder) + if not pathlib.Path(path_folder).exists(): + pathlib.Path(path_folder).mkdir(exist_ok=True) if mode == 'ts': plt.figure() @@ -2066,7 +2066,7 @@ def plot_eof(self, number, path_folder, cmap='viridis', mode='full', unit='cmwe' eof_grid.data[np.logical_not(mask)] = None if mode == 'map': - tb.plot_rms_map(eof_grid, path=os.path.join(path_folder, 'map_eof_'+str(k)+'.png'), unit=unit, mask=mask) + tb.plot_rms_map(eof_grid, path=pathlib.Path(path_folder) / 'map_eof_'+str(k)+'.png', unit=unit, mask=mask) elif mode == 'full': npow2 = 1 if len(self.time) == 0 else 2 ** (len(self.time) - 1).bit_length() @@ -2130,12 +2130,13 @@ def plot_eof(self, number, path_folder, cmap='viridis', mode='full', unit='cmwe' axplot.set_ylabel('Secular\n Acceleration\n$nT.y^{-2}$', labelpad=40, fontsize=12, rotation='horizontal') axfft.set_ylabel('Power\n$nT^2.y^{-4}$', labelpad=50, fontsize=12, rotation='horizontal') - plt.savefig(os.path.join(path_folder, 'eof_pc_'+str(k)+'.png'), bbox_inches='tight') + plt.savefig(pathlib.Path(path_folder) / 'eof_pc_'+str(k)+'.png', bbox_inches='tight') plt.close() elif mode == 'ts': plt.plot(self.time, pc, label=str(k)) if mode == 'ts': - plt.savefig(os.path.join(path_folder, 'pc_'+'-'.join([str(i) for i in number])+'.png')) + + plt.savefig(pathlib.Path(path_folder) / 'pc_'+'-'.join([str(i) for i in number])+'.png') plt.legend() \ No newline at end of file From 497bf470d698291531207f3f54872f0b86596ff1 Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 15 Aug 2023 10:54:07 +0200 Subject: [PATCH 68/80] Change requirements.txt after fork from original repo --- requirements.txt | 3 --- 1 file changed, 3 deletions(-) diff --git a/requirements.txt b/requirements.txt index b667528c..6c5dc25c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,11 +6,8 @@ numpy python-dateutil pyyaml scipy - matplotlib -python-dateutil cartopy --no-binary=cartopy datetime -cartopy ipython setuptools \ No newline at end of file From d25a2ccc6dbf2c27e032bdf2ed4894b8ea847b89 Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 15 Aug 2023 11:46:11 +0200 Subject: [PATCH 69/80] Form change to grace_input_months.py --- gravity_toolkit/grace_input_months.py | 59 ++++++++++++++------------- 1 file changed, 31 insertions(+), 28 deletions(-) diff --git a/gravity_toolkit/grace_input_months.py b/gravity_toolkit/grace_input_months.py index 5c5675aa..8352c25c 100644 --- a/gravity_toolkit/grace_input_months.py +++ b/gravity_toolkit/grace_input_months.py @@ -439,12 +439,12 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, FLAGS = [] # Replacing C2,0 with SLR values - if (SLR_C20 == 'CSR'): - if (DREL == 'RL04'): + if SLR_C20 == 'CSR': + if DREL == 'RL04': SLR_file = base_dir.joinpath('TN-05_C20_SLR.txt') - elif (DREL == 'RL05'): + elif DREL == 'RL05': SLR_file = base_dir.joinpath('TN-07_C20_SLR.txt') - elif (DREL == 'RL06'): + elif DREL == 'RL06': # SLR_file = base_dir.joinpath('TN-11_C20_SLR.txt') SLR_file = base_dir.joinpath('C20_RL06.txt') # log SLR file if debugging @@ -453,7 +453,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C20_input = gravity_toolkit.SLR.C20(SLR_file) FLAGS.append('_wCSR_C20') attributes['SLR C20'] = ('CSR', SLR_file.name) - elif (SLR_C20 == 'GFZ'): + elif SLR_C20 == 'GFZ': SLR_file = base_dir.joinpath(f'GFZ_{DREL}_C20_SLR.dat') # log SLR file if debugging logging.debug(f'Reading SLR C20 file: {str(SLR_file)}') @@ -461,7 +461,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C20_input = gravity_toolkit.SLR.C20(SLR_file) FLAGS.append('_wGFZ_C20') attributes['SLR C20'] = ('GFZ', SLR_file.name) - elif (SLR_C20 == 'GSFC'): + elif SLR_C20 == 'GSFC': SLR_file = base_dir.joinpath('TN-14_C30_C20_GSFC_SLR.txt') # log SLR file if debugging logging.debug(f'Reading SLR C20 file: {str(SLR_file)}') @@ -471,7 +471,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, attributes['SLR C20'] = ('GSFC', SLR_file.name) # Replacing C2,1/S2,1 with SLR values - if (kwargs['SLR_21'] == 'CSR'): + if kwargs['SLR_21'] == 'CSR': SLR_file = base_dir.joinpath(f'C21_S21_{DREL}.txt') # log SLR file if debugging logging.debug(f'Reading SLR C21/S21 file: {str(SLR_file)}') @@ -479,7 +479,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C21_input = gravity_toolkit.SLR.CS2(SLR_file) FLAGS.append('_wCSR_21') attributes['SLR 21'] = ('CSR', SLR_file.name) - elif (kwargs['SLR_21'] == 'GFZ'): + elif kwargs['SLR_21'] == 'GFZ': GravIS_file = 'GRAVIS-2B_GFZOP_GRACE+SLR_LOW_DEGREES_0003.dat' SLR_file = base_dir.joinpath(GravIS_file) # log SLR file if debugging @@ -488,7 +488,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C21_input = gravity_toolkit.SLR.CS2(SLR_file) FLAGS.append('_wGFZ_21') attributes['SLR 21'] = ('GFZ GravIS', SLR_file.name) - elif (kwargs['SLR_21'] == 'GSFC'): + elif kwargs['SLR_21'] == 'GSFC': # calculate monthly averages from 7-day arcs SLR_file = base_dir.joinpath('gsfc_slr_5x5c61s61.txt') # log SLR file if debugging @@ -500,7 +500,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, attributes['SLR 21'] = ('GSFC', SLR_file.name) # Replacing C2,2/S2,2 with SLR values - if (kwargs['SLR_22'] == 'CSR'): + if kwargs['SLR_22'] == 'CSR': SLR_file = base_dir.joinpath(f'C22_S22_{DREL}.txt') # log SLR file if debugging logging.debug(f'Reading SLR C22/S22 file: {str(SLR_file)}') @@ -508,7 +508,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C22_input = gravity_toolkit.SLR.CS2(SLR_file) FLAGS.append('_wCSR_22') attributes['SLR 22'] = ('CSR', SLR_file.name) - elif (kwargs['SLR_22'] == 'GSFC'): + elif kwargs['SLR_22'] == 'GSFC': SLR_file = base_dir.joinpath('gsfc_slr_5x5c61s61.txt') # log SLR file if debugging logging.debug(f'Reading SLR C22/S22 file: {str(SLR_file)}') @@ -519,7 +519,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, attributes['SLR 22'] = ('GSFC', SLR_file.name) # Replacing C3,0 with SLR values - if (kwargs['SLR_C30'] == 'CSR'): + if kwargs['SLR_C30'] == 'CSR': SLR_file = base_dir.joinpath('CSR_Monthly_5x5_Gravity_Harmonics.txt') # log SLR file if debugging logging.debug(f'Reading SLR C30 file: {str(SLR_file)}') @@ -527,7 +527,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C30_input = gravity_toolkit.SLR.C30(SLR_file) FLAGS.append('_wCSR_C30') attributes['SLR C30'] = ('CSR', SLR_file.name) - elif (kwargs['SLR_C30'] == 'LARES'): + elif kwargs['SLR_C30'] == 'LARES': SLR_file = base_dir.joinpath('C30_LARES_filtered.txt') # log SLR file if debugging logging.debug(f'Reading SLR C30 file: {str(SLR_file)}') @@ -535,7 +535,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C30_input = gravity_toolkit.SLR.C30(SLR_file) FLAGS.append('_wLARES_C30') attributes['SLR_C30'] = ('CSR LARES', SLR_file.name) - elif (kwargs['SLR_C30'] == 'GFZ'): + elif kwargs['SLR_C30'] == 'GFZ': GravIS_file = 'GRAVIS-2B_GFZOP_GRACE+SLR_LOW_DEGREES_0003.dat' SLR_file = base_dir.joinpath(GravIS_file) # log SLR file if debugging @@ -544,7 +544,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C30_input = gravity_toolkit.SLR.C30(SLR_file) FLAGS.append('_wGFZ_C30') attributes['SLR C30'] = ('GFZ GravIS', SLR_file.name) - elif (kwargs['SLR_C30'] == 'GSFC'): + elif kwargs['SLR_C30'] == 'GSFC': SLR_file = base_dir.joinpath('TN-14_C30_C20_GSFC_SLR.txt') # log SLR file if debugging logging.debug(f'Reading SLR C30 file: {str(SLR_file)}') @@ -554,7 +554,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, attributes['SLR C30'] = ('GSFC', SLR_file.name) # Replacing C4,0 with SLR values - if (kwargs['SLR_C40'] == 'CSR'): + if kwargs['SLR_C40'] == 'CSR': SLR_file = base_dir.joinpath('CSR_Monthly_5x5_Gravity_Harmonics.txt') # log SLR file if debugging logging.debug(f'Reading SLR C40 file: {str(SLR_file)}') @@ -562,7 +562,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C40_input = gravity_toolkit.SLR.C40(SLR_file) FLAGS.append('_wCSR_C40') attributes['SLR C40'] = ('CSR', SLR_file.name) - elif (kwargs['SLR_C40'] == 'LARES'): + elif kwargs['SLR_C40'] == 'LARES': SLR_file = base_dir.joinpath('C40_LARES_filtered.txt') # log SLR file if debugging logging.debug(f'Reading SLR C40 file: {str(SLR_file)}') @@ -570,7 +570,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C40_input = gravity_toolkit.SLR.C40(SLR_file) FLAGS.append('_wLARES_C40') attributes['SLR C40'] = ('CSR LARES', SLR_file.name) - elif (kwargs['SLR_C40'] == 'GSFC'): + elif kwargs['SLR_C40'] == 'GSFC': SLR_file = base_dir.joinpath('gsfc_slr_5x5c61s61.txt') # log SLR file if debugging logging.debug(f'Reading SLR C40 file: {str(SLR_file)}') @@ -581,7 +581,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, attributes['SLR C40'] = ('GSFC', SLR_file.name) # Replacing C5,0 with SLR values - if (kwargs['SLR_C50'] == 'CSR'): + if kwargs['SLR_C50'] == 'CSR': SLR_file = base_dir.joinpath('CSR_Monthly_5x5_Gravity_Harmonics.txt') # log SLR file if debugging logging.debug(f'Reading SLR C50 file: {str(SLR_file)}') @@ -589,7 +589,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C50_input = gravity_toolkit.SLR.C50(SLR_file) FLAGS.append('_wCSR_C50') attributes['SLR C50'] = ('CSR', SLR_file.name) - elif (kwargs['SLR_C50'] == 'LARES'): + elif kwargs['SLR_C50'] == 'LARES': SLR_file = base_dir.joinpath('C50_LARES_filtered.txt') # log SLR file if debugging logging.debug(f'Reading SLR C50 file: {str(SLR_file)}') @@ -597,7 +597,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, C50_input = gravity_toolkit.SLR.C50(SLR_file) FLAGS.append('_wLARES_C50') attributes['SLR C50'] = ('CSR LARES', SLR_file.name) - elif (kwargs['SLR_C50'] == 'GSFC'): + elif kwargs['SLR_C50'] == 'GSFC': # SLR_file = base_dir.joinpath('GSFC_SLR_C20_C30_C50_GSM_replacement.txt') SLR_file = base_dir.joinpath('gsfc_slr_5x5c61s61.txt') # log SLR file if debugging @@ -610,7 +610,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, # Correcting for Degree 1 (geocenter variations) # reading degree 1 file for given release if specified - if (DEG1 == 'Tellus'): + if DEG1 == 'Tellus': # Tellus (PO.DAAC) degree 1 if DREL in ('RL04','RL05'): # old degree one files @@ -629,7 +629,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, DEG1_input = gravity_toolkit.geocenter().from_tellus(DEG1_file,JPL=JPL) FLAGS.append(f'_w{DEG1}_DEG1') attributes['geocenter'] = ('JPL Tellus', DEG1_file.name) - elif (DEG1 == 'SLR'): + elif DEG1 == 'SLR': # CSR Satellite Laser Ranging (SLR) degree 1 # # SLR-derived degree-1 mass variations # # ftp://ftp.csr.utexas.edu/pub/slr/geocenter/ @@ -672,7 +672,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, DEG1_input = gravity_toolkit.geocenter().from_UCI(DEG1_file) FLAGS.append(f'_w{DEG1}_DEG1') attributes['geocenter'] = ('UCI', DEG1_file.name) - elif (DEG1 == 'Swenson'): + elif DEG1 == 'Swenson': # degree 1 coefficients provided by Sean Swenson in mm w.e. default_geocenter = base_dir.joinpath('geocenter', f'gad_gsm.{DREL}.txt') @@ -683,7 +683,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, DEG1_input = gravity_toolkit.geocenter().from_swenson(DEG1_file) FLAGS.append(f'_w{DEG1}_DEG1') attributes['geocenter'] = ('Swenson', DEG1_file.name) - elif (DEG1 == 'GFZ'): + elif DEG1 == 'GFZ': # degree 1 coefficients provided by GFZ GravIS # http://gravis.gfz-potsdam.de/corrections default_geocenter = base_dir.joinpath('geocenter', @@ -717,7 +717,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, # replace C20 with SLR coefficients for i,grace_month in enumerate(months): count = np.count_nonzero(C20_input['month'] == grace_month) - if (count != 0): + if count != 0: k, = np.nonzero(C20_input['month'] == grace_month) grace_Ylms['clm'][2,0,i] = np.copy(C20_input['data'][k]) grace_Ylms['eclm'][2,0,i] = np.copy(C20_input['error'][k]) @@ -846,7 +846,7 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, # add files to lineage attribute attributes['lineage'].extend(atm_corr['files']) # Removing GAE/GAF/GAG from RL05 GSM Products - if (DSET == 'GSM'): + if DSET == 'GSM': for m in range(0,MMAX+1):# MMAX+1 to include l for l in range(m,LMAX+1):# LMAX+1 to include LMAX grace_Ylms['clm'][l,m,:] -= atm_corr['clm'][l,m,:] @@ -900,6 +900,9 @@ def read_ecmwf_corrections(base_dir, LMAX, months, MMAX=None): `doi: 10.1093/gji/ggv276 `_ """ + # directory of exact GRACE/GRACE-FO product + base_dir = pathlib.Path(base_dir).expanduser().absolute() + # correction files corr_file = {} corr_file['GAE'] = 'TN-08_GAE-2_2006032-2010031_0000_EIGEN_G---_0005.gz' @@ -951,7 +954,7 @@ def read_ecmwf_corrections(base_dir, LMAX, months, MMAX=None): elif (grace_month >= 98) & (grace_month <= 161): atm_corr['clm'][:,:,i] = atm_corr_clm['GAF'][:,:] atm_corr['slm'][:,:,i] = atm_corr_slm['GAF'][:,:] - elif (grace_month > 161): + elif grace_month > 161: atm_corr['clm'][:,:,i] = atm_corr_clm['GAG'][:,:] atm_corr['slm'][:,:,i] = atm_corr_slm['GAG'][:,:] From 6565f6e895028aa57e9e87dc5be5b16a08572c6c Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 15 Aug 2023 16:31:16 +0200 Subject: [PATCH 70/80] Update .rst file for local build --- doc/source/api_reference/aod1b_geocenter.rst | 2 +- doc/source/api_reference/aod1b_oblateness.rst | 2 +- doc/source/api_reference/calc_degree_one.rst | 2 +- doc/source/api_reference/calc_harmonic_resolution.rst | 2 +- doc/source/api_reference/calc_mascon.rst | 2 +- doc/source/api_reference/calc_sensitivity_kernel.rst | 2 +- doc/source/api_reference/cnes_grace_sync.rst | 2 +- doc/source/api_reference/combine_harmonics.rst | 2 +- doc/source/api_reference/convert_harmonics.rst | 2 +- doc/source/api_reference/dealiasing_global_uplift.rst | 2 +- doc/source/api_reference/dealiasing_monthly_mean.rst | 2 +- doc/source/api_reference/esa_costg_swarm_sync.rst | 2 +- doc/source/api_reference/gfz_icgem_costg_ftp.rst | 2 +- doc/source/api_reference/gfz_isdc_dealiasing_ftp.rst | 2 +- doc/source/api_reference/gfz_isdc_grace_ftp.rst | 2 +- doc/source/api_reference/grace_mean_harmonics.rst | 2 +- doc/source/api_reference/grace_spatial_error.rst | 2 +- doc/source/api_reference/grace_spatial_maps.rst | 2 +- doc/source/api_reference/itsg_graz_grace_sync.rst | 2 +- doc/source/api_reference/make_grace_index.rst | 2 +- doc/source/api_reference/mascon_reconstruct.rst | 2 +- doc/source/api_reference/monte_carlo_degree_one.rst | 2 +- doc/source/api_reference/piecewise_grace_maps.rst | 2 +- doc/source/api_reference/plot_AIS_GrIS_maps.rst | 2 +- doc/source/api_reference/plot_AIS_grid_3maps.rst | 2 +- doc/source/api_reference/plot_AIS_grid_4maps.rst | 2 +- doc/source/api_reference/plot_AIS_grid_maps.rst | 2 +- doc/source/api_reference/plot_AIS_grid_movie.rst | 2 +- doc/source/api_reference/plot_AIS_regional_maps.rst | 2 +- doc/source/api_reference/plot_AIS_regional_movie.rst | 2 +- doc/source/api_reference/plot_GrIS_grid_3maps.rst | 2 +- doc/source/api_reference/plot_GrIS_grid_maps.rst | 2 +- doc/source/api_reference/plot_GrIS_grid_movie.rst | 2 +- doc/source/api_reference/plot_global_grid_3maps.rst | 2 +- doc/source/api_reference/plot_global_grid_4maps.rst | 2 +- doc/source/api_reference/plot_global_grid_5maps.rst | 2 +- doc/source/api_reference/plot_global_grid_9maps.rst | 2 +- doc/source/api_reference/plot_global_grid_maps.rst | 2 +- doc/source/api_reference/plot_global_grid_movie.rst | 2 +- doc/source/api_reference/podaac_cumulus.rst | 2 +- doc/source/api_reference/quick_mascon_plot.rst | 2 +- doc/source/api_reference/quick_mascon_regress.rst | 2 +- doc/source/api_reference/regress_grace_maps.rst | 2 +- doc/source/api_reference/run_grace_date.rst | 2 +- doc/source/api_reference/run_sea_level_equation.rst | 2 +- doc/source/api_reference/scale_grace_maps.rst | 2 +- 46 files changed, 46 insertions(+), 46 deletions(-) diff --git a/doc/source/api_reference/aod1b_geocenter.rst b/doc/source/api_reference/aod1b_geocenter.rst index 0d2524d6..d100ae3b 100644 --- a/doc/source/api_reference/aod1b_geocenter.rst +++ b/doc/source/api_reference/aod1b_geocenter.rst @@ -18,7 +18,7 @@ Calling Sequence ################ .. argparse:: - :filename: aod1b_geocenter.py + :filename: ../scripts/aod1b_geocenter.py :func: arguments :prog: aod1b_geocenter.py :nodescription: diff --git a/doc/source/api_reference/aod1b_oblateness.rst b/doc/source/api_reference/aod1b_oblateness.rst index e6a7b87d..3e0f40a6 100644 --- a/doc/source/api_reference/aod1b_oblateness.rst +++ b/doc/source/api_reference/aod1b_oblateness.rst @@ -18,7 +18,7 @@ Calling Sequence ################ .. argparse:: - :filename: aod1b_oblateness.py + :filename: ../scripts/aod1b_oblateness.py :func: arguments :prog: aod1b_oblateness.py :nodescription: diff --git a/doc/source/api_reference/calc_degree_one.rst b/doc/source/api_reference/calc_degree_one.rst index 2bd076a3..b06f3c4b 100644 --- a/doc/source/api_reference/calc_degree_one.rst +++ b/doc/source/api_reference/calc_degree_one.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: calc_degree_one.py + :filename: ../scripts/calc_degree_one.py :func: arguments :prog: calc_degree_one.py :nodescription: diff --git a/doc/source/api_reference/calc_harmonic_resolution.rst b/doc/source/api_reference/calc_harmonic_resolution.rst index cabc22b6..b6f0f882 100644 --- a/doc/source/api_reference/calc_harmonic_resolution.rst +++ b/doc/source/api_reference/calc_harmonic_resolution.rst @@ -14,7 +14,7 @@ Calling Sequence ################ .. argparse:: - :filename: calc_harmonic_resolution.py + :filename: ../scripts/calc_harmonic_resolution.py :func: arguments :prog: calc_harmonic_resolution.py :nodescription: diff --git a/doc/source/api_reference/calc_mascon.rst b/doc/source/api_reference/calc_mascon.rst index ab25b5c3..841b4555 100644 --- a/doc/source/api_reference/calc_mascon.rst +++ b/doc/source/api_reference/calc_mascon.rst @@ -16,7 +16,7 @@ Calling Sequence ################ .. argparse:: - :filename: calc_mascon.py + :filename: ../scripts/calc_mascon.py :func: arguments :prog: calc_mascon.py :nodescription: diff --git a/doc/source/api_reference/calc_sensitivity_kernel.rst b/doc/source/api_reference/calc_sensitivity_kernel.rst index eeb1c800..224eaf8a 100644 --- a/doc/source/api_reference/calc_sensitivity_kernel.rst +++ b/doc/source/api_reference/calc_sensitivity_kernel.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: calc_sensitivity_kernel.py + :filename: ../scripts/calc_sensitivity_kernel.py :func: arguments :prog: calc_sensitivity_kernel.py :nodescription: diff --git a/doc/source/api_reference/cnes_grace_sync.rst b/doc/source/api_reference/cnes_grace_sync.rst index 15e2daf7..979244c0 100644 --- a/doc/source/api_reference/cnes_grace_sync.rst +++ b/doc/source/api_reference/cnes_grace_sync.rst @@ -13,7 +13,7 @@ Calling Sequence ################ .. argparse:: - :filename: cnes_grace_sync.py + :filename: ../scripts/cnes_grace_sync.py :func: arguments :prog: cnes_grace_sync.py :nodescription: diff --git a/doc/source/api_reference/combine_harmonics.rst b/doc/source/api_reference/combine_harmonics.rst index 6a670c84..89b51d0c 100644 --- a/doc/source/api_reference/combine_harmonics.rst +++ b/doc/source/api_reference/combine_harmonics.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: combine_harmonics.py + :filename: ../scripts/combine_harmonics.py :func: arguments :prog: combine_harmonics.py :nodescription: diff --git a/doc/source/api_reference/convert_harmonics.rst b/doc/source/api_reference/convert_harmonics.rst index c29c9eec..f79dd91c 100644 --- a/doc/source/api_reference/convert_harmonics.rst +++ b/doc/source/api_reference/convert_harmonics.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: convert_harmonics.py + :filename: ../scripts/convert_harmonics.py :func: arguments :prog: convert_harmonics.py :nodescription: diff --git a/doc/source/api_reference/dealiasing_global_uplift.rst b/doc/source/api_reference/dealiasing_global_uplift.rst index 6066ada4..581cc973 100644 --- a/doc/source/api_reference/dealiasing_global_uplift.rst +++ b/doc/source/api_reference/dealiasing_global_uplift.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: dealiasing_global_uplift.py + :filename: ../scripts/dealiasing_global_uplift.py :func: arguments :prog: dealiasing_global_uplift.py :nodescription: diff --git a/doc/source/api_reference/dealiasing_monthly_mean.rst b/doc/source/api_reference/dealiasing_monthly_mean.rst index 30f76257..4616976e 100644 --- a/doc/source/api_reference/dealiasing_monthly_mean.rst +++ b/doc/source/api_reference/dealiasing_monthly_mean.rst @@ -18,7 +18,7 @@ Calling Sequence ################ .. argparse:: - :filename: dealiasing_monthly_mean.py + :filename: ../scripts/dealiasing_monthly_mean.py :func: arguments :prog: dealiasing_monthly_mean.py :nodescription: diff --git a/doc/source/api_reference/esa_costg_swarm_sync.rst b/doc/source/api_reference/esa_costg_swarm_sync.rst index 2e63e482..250e16ce 100644 --- a/doc/source/api_reference/esa_costg_swarm_sync.rst +++ b/doc/source/api_reference/esa_costg_swarm_sync.rst @@ -13,7 +13,7 @@ Calling Sequence ################ .. argparse:: - :filename: esa_costg_swarm_sync.py + :filename: ../scripts/esa_costg_swarm_sync.py :func: arguments :prog: esa_costg_swarm_sync.py :nodescription: diff --git a/doc/source/api_reference/gfz_icgem_costg_ftp.rst b/doc/source/api_reference/gfz_icgem_costg_ftp.rst index df9f4e47..23e97852 100644 --- a/doc/source/api_reference/gfz_icgem_costg_ftp.rst +++ b/doc/source/api_reference/gfz_icgem_costg_ftp.rst @@ -13,7 +13,7 @@ Calling Sequence ################ .. argparse:: - :filename: gfz_icgem_costg_ftp.py + :filename: ../scripts/gfz_icgem_costg_ftp.py :func: arguments :prog: gfz_icgem_costg_ftp.py :nodescription: diff --git a/doc/source/api_reference/gfz_isdc_dealiasing_ftp.rst b/doc/source/api_reference/gfz_isdc_dealiasing_ftp.rst index 923466a8..487a45b6 100644 --- a/doc/source/api_reference/gfz_isdc_dealiasing_ftp.rst +++ b/doc/source/api_reference/gfz_isdc_dealiasing_ftp.rst @@ -13,7 +13,7 @@ Calling Sequence ################ .. argparse:: - :filename: gfz_isdc_dealiasing_ftp.py + :filename: ../scripts/gfz_isdc_dealiasing_ftp.py :func: arguments :prog: gfz_isdc_dealiasing_ftp.py :nodescription: diff --git a/doc/source/api_reference/gfz_isdc_grace_ftp.rst b/doc/source/api_reference/gfz_isdc_grace_ftp.rst index f6f14ccd..c0c868ea 100644 --- a/doc/source/api_reference/gfz_isdc_grace_ftp.rst +++ b/doc/source/api_reference/gfz_isdc_grace_ftp.rst @@ -16,7 +16,7 @@ Calling Sequence ################ .. argparse:: - :filename: gfz_isdc_grace_ftp.py + :filename: ../scripts/gfz_isdc_grace_ftp.py :func: arguments :prog: gfz_isdc_grace_ftp.py :nodescription: diff --git a/doc/source/api_reference/grace_mean_harmonics.rst b/doc/source/api_reference/grace_mean_harmonics.rst index 91ab32c3..abd8303f 100644 --- a/doc/source/api_reference/grace_mean_harmonics.rst +++ b/doc/source/api_reference/grace_mean_harmonics.rst @@ -13,7 +13,7 @@ Calling Sequence ################ .. argparse:: - :filename: grace_mean_harmonics.py + :filename: ../scripts/grace_mean_harmonics.py :func: arguments :prog: grace_mean_harmonics.py :nodescription: diff --git a/doc/source/api_reference/grace_spatial_error.rst b/doc/source/api_reference/grace_spatial_error.rst index 9e179790..49c02a3f 100644 --- a/doc/source/api_reference/grace_spatial_error.rst +++ b/doc/source/api_reference/grace_spatial_error.rst @@ -14,7 +14,7 @@ Calling Sequence ################ .. argparse:: - :filename: grace_spatial_error.py + :filename: ../scripts/grace_spatial_error.py :func: arguments :prog: grace_spatial_error.py :nodescription: diff --git a/doc/source/api_reference/grace_spatial_maps.rst b/doc/source/api_reference/grace_spatial_maps.rst index 04c5d1c0..be08c9e5 100644 --- a/doc/source/api_reference/grace_spatial_maps.rst +++ b/doc/source/api_reference/grace_spatial_maps.rst @@ -15,7 +15,7 @@ Calling Sequence ################ .. argparse:: - :filename: grace_spatial_maps.py + :filename: ../scripts/grace_spatial_maps.py :func: arguments :prog: grace_spatial_maps.py :nodescription: diff --git a/doc/source/api_reference/itsg_graz_grace_sync.rst b/doc/source/api_reference/itsg_graz_grace_sync.rst index 4862ffe0..3963b691 100644 --- a/doc/source/api_reference/itsg_graz_grace_sync.rst +++ b/doc/source/api_reference/itsg_graz_grace_sync.rst @@ -13,7 +13,7 @@ Calling Sequence ################ .. argparse:: - :filename: itsg_graz_grace_sync.py + :filename: ../scripts/itsg_graz_grace_sync.py :func: arguments :prog: itsg_graz_grace_sync.py :nodescription: diff --git a/doc/source/api_reference/make_grace_index.rst b/doc/source/api_reference/make_grace_index.rst index a3b77b82..061bab4c 100644 --- a/doc/source/api_reference/make_grace_index.rst +++ b/doc/source/api_reference/make_grace_index.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: make_grace_index.py + :filename: ../scripts/make_grace_index.py :func: arguments :prog: make_grace_index.py :nodescription: diff --git a/doc/source/api_reference/mascon_reconstruct.rst b/doc/source/api_reference/mascon_reconstruct.rst index d87c2fbf..1f300047 100644 --- a/doc/source/api_reference/mascon_reconstruct.rst +++ b/doc/source/api_reference/mascon_reconstruct.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: mascon_reconstruct.py + :filename: ../scripts/mascon_reconstruct.py :func: arguments :prog: mascon_reconstruct.py :nodescription: diff --git a/doc/source/api_reference/monte_carlo_degree_one.rst b/doc/source/api_reference/monte_carlo_degree_one.rst index 861c4d1f..6305cc30 100644 --- a/doc/source/api_reference/monte_carlo_degree_one.rst +++ b/doc/source/api_reference/monte_carlo_degree_one.rst @@ -13,7 +13,7 @@ Calling Sequence ################ .. argparse:: - :filename: monte_carlo_degree_one.py + :filename: ../scripts/monte_carlo_degree_one.py :func: arguments :prog: monte_carlo_degree_one.py :nodescription: diff --git a/doc/source/api_reference/piecewise_grace_maps.rst b/doc/source/api_reference/piecewise_grace_maps.rst index 611aef51..437e0a1f 100644 --- a/doc/source/api_reference/piecewise_grace_maps.rst +++ b/doc/source/api_reference/piecewise_grace_maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: piecewise_grace_maps.py + :filename: ../scripts/piecewise_grace_maps.py :func: arguments :prog: piecewise_grace_maps.py :nodescription: diff --git a/doc/source/api_reference/plot_AIS_GrIS_maps.rst b/doc/source/api_reference/plot_AIS_GrIS_maps.rst index 9d310fac..b1a62b89 100644 --- a/doc/source/api_reference/plot_AIS_GrIS_maps.rst +++ b/doc/source/api_reference/plot_AIS_GrIS_maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_AIS_GrIS_maps.py + :filename: ../scripts/plot_AIS_GrIS_maps.py :func: arguments :prog: plot_AIS_GrIS_maps.py :nodescription: diff --git a/doc/source/api_reference/plot_AIS_grid_3maps.rst b/doc/source/api_reference/plot_AIS_grid_3maps.rst index 4e072445..28d7fa58 100644 --- a/doc/source/api_reference/plot_AIS_grid_3maps.rst +++ b/doc/source/api_reference/plot_AIS_grid_3maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_AIS_grid_3maps.py + :filename: ../scripts/plot_AIS_grid_3maps.py :func: arguments :prog: plot_AIS_grid_3maps.py :nodescription: diff --git a/doc/source/api_reference/plot_AIS_grid_4maps.rst b/doc/source/api_reference/plot_AIS_grid_4maps.rst index 4ecf1ee8..85d5029d 100644 --- a/doc/source/api_reference/plot_AIS_grid_4maps.rst +++ b/doc/source/api_reference/plot_AIS_grid_4maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_AIS_grid_4maps.py + :filename: ../scripts/plot_AIS_grid_4maps.py :func: arguments :prog: plot_AIS_grid_4maps.py :nodescription: diff --git a/doc/source/api_reference/plot_AIS_grid_maps.rst b/doc/source/api_reference/plot_AIS_grid_maps.rst index 1400daee..5fe07791 100644 --- a/doc/source/api_reference/plot_AIS_grid_maps.rst +++ b/doc/source/api_reference/plot_AIS_grid_maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_AIS_grid_maps.py + :filename: ../scripts/plot_AIS_grid_maps.py :func: arguments :prog: plot_AIS_grid_maps.py :nodescription: diff --git a/doc/source/api_reference/plot_AIS_grid_movie.rst b/doc/source/api_reference/plot_AIS_grid_movie.rst index 43fecb5c..8a1d1dca 100644 --- a/doc/source/api_reference/plot_AIS_grid_movie.rst +++ b/doc/source/api_reference/plot_AIS_grid_movie.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_AIS_grid_movie.py + :filename: ../scripts/plot_AIS_grid_movie.py :func: arguments :prog: plot_AIS_grid_movie.py :nodescription: diff --git a/doc/source/api_reference/plot_AIS_regional_maps.rst b/doc/source/api_reference/plot_AIS_regional_maps.rst index 4bcd8b8f..d7071303 100644 --- a/doc/source/api_reference/plot_AIS_regional_maps.rst +++ b/doc/source/api_reference/plot_AIS_regional_maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_AIS_regional_maps.py + :filename: ../scripts/plot_AIS_regional_maps.py :func: arguments :prog: plot_AIS_regional_maps.py :nodescription: diff --git a/doc/source/api_reference/plot_AIS_regional_movie.rst b/doc/source/api_reference/plot_AIS_regional_movie.rst index fcd604bd..7439fa25 100644 --- a/doc/source/api_reference/plot_AIS_regional_movie.rst +++ b/doc/source/api_reference/plot_AIS_regional_movie.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_AIS_regional_movie.py + :filename: ../scripts/plot_AIS_regional_movie.py :func: arguments :prog: plot_AIS_regional_movie.py :nodescription: diff --git a/doc/source/api_reference/plot_GrIS_grid_3maps.rst b/doc/source/api_reference/plot_GrIS_grid_3maps.rst index 122c7fb8..a3181be4 100644 --- a/doc/source/api_reference/plot_GrIS_grid_3maps.rst +++ b/doc/source/api_reference/plot_GrIS_grid_3maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_GrIS_grid_3maps.py + :filename: ../scripts/plot_GrIS_grid_3maps.py :func: arguments :prog: plot_GrIS_grid_3maps.py :nodescription: diff --git a/doc/source/api_reference/plot_GrIS_grid_maps.rst b/doc/source/api_reference/plot_GrIS_grid_maps.rst index eaca7b98..e3ac1296 100644 --- a/doc/source/api_reference/plot_GrIS_grid_maps.rst +++ b/doc/source/api_reference/plot_GrIS_grid_maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_GrIS_grid_maps.py + :filename: ../scripts/plot_GrIS_grid_maps.py :func: arguments :prog: plot_GrIS_grid_maps.py :nodescription: diff --git a/doc/source/api_reference/plot_GrIS_grid_movie.rst b/doc/source/api_reference/plot_GrIS_grid_movie.rst index 89ed454f..8253904a 100644 --- a/doc/source/api_reference/plot_GrIS_grid_movie.rst +++ b/doc/source/api_reference/plot_GrIS_grid_movie.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_GrIS_grid_movie.py + :filename: ../scripts/plot_GrIS_grid_movie.py :func: arguments :prog: plot_GrIS_grid_movie.py :nodescription: diff --git a/doc/source/api_reference/plot_global_grid_3maps.rst b/doc/source/api_reference/plot_global_grid_3maps.rst index 6c0ba94c..16b3037c 100644 --- a/doc/source/api_reference/plot_global_grid_3maps.rst +++ b/doc/source/api_reference/plot_global_grid_3maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_global_grid_3maps.py + :filename: ../scripts/plot_global_grid_3maps.py :func: arguments :prog: plot_global_grid_3maps.py :nodescription: diff --git a/doc/source/api_reference/plot_global_grid_4maps.rst b/doc/source/api_reference/plot_global_grid_4maps.rst index 80dbf72d..2a63dbdb 100644 --- a/doc/source/api_reference/plot_global_grid_4maps.rst +++ b/doc/source/api_reference/plot_global_grid_4maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_global_grid_4maps.py + :filename: ../scripts/plot_global_grid_4maps.py :func: arguments :prog: plot_global_grid_4maps.py :nodescription: diff --git a/doc/source/api_reference/plot_global_grid_5maps.rst b/doc/source/api_reference/plot_global_grid_5maps.rst index 8b217506..20bb8396 100644 --- a/doc/source/api_reference/plot_global_grid_5maps.rst +++ b/doc/source/api_reference/plot_global_grid_5maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_global_grid_5maps.py + :filename: ../scripts/plot_global_grid_5maps.py :func: arguments :prog: plot_global_grid_5maps.py :nodescription: diff --git a/doc/source/api_reference/plot_global_grid_9maps.rst b/doc/source/api_reference/plot_global_grid_9maps.rst index de3c0543..82a07bda 100644 --- a/doc/source/api_reference/plot_global_grid_9maps.rst +++ b/doc/source/api_reference/plot_global_grid_9maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_global_grid_9maps.py + :filename: ../scripts/plot_global_grid_9maps.py :func: arguments :prog: plot_global_grid_9maps.py :nodescription: diff --git a/doc/source/api_reference/plot_global_grid_maps.rst b/doc/source/api_reference/plot_global_grid_maps.rst index af5419ac..f7fc602c 100644 --- a/doc/source/api_reference/plot_global_grid_maps.rst +++ b/doc/source/api_reference/plot_global_grid_maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_global_grid_maps.py + :filename: ../scripts/plot_global_grid_maps.py :func: arguments :prog: plot_global_grid_maps.py :nodescription: diff --git a/doc/source/api_reference/plot_global_grid_movie.rst b/doc/source/api_reference/plot_global_grid_movie.rst index 0461c659..48479a07 100644 --- a/doc/source/api_reference/plot_global_grid_movie.rst +++ b/doc/source/api_reference/plot_global_grid_movie.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: plot_global_grid_movie.py + :filename: ../scripts/plot_global_grid_movie.py :func: arguments :prog: plot_global_grid_movie.py :nodescription: diff --git a/doc/source/api_reference/podaac_cumulus.rst b/doc/source/api_reference/podaac_cumulus.rst index 2d680b18..42fe9779 100644 --- a/doc/source/api_reference/podaac_cumulus.rst +++ b/doc/source/api_reference/podaac_cumulus.rst @@ -14,7 +14,7 @@ Calling Sequence ################ .. argparse:: - :filename: podaac_cumulus.py + :filename: ../scripts/podaac_cumulus.py :func: arguments :prog: podaac_cumulus.py :nodescription: diff --git a/doc/source/api_reference/quick_mascon_plot.rst b/doc/source/api_reference/quick_mascon_plot.rst index 557f634c..f786eca8 100644 --- a/doc/source/api_reference/quick_mascon_plot.rst +++ b/doc/source/api_reference/quick_mascon_plot.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: quick_mascon_plot.py + :filename: ../scripts/quick_mascon_plot.py :func: arguments :prog: quick_mascon_plot.py :nodescription: diff --git a/doc/source/api_reference/quick_mascon_regress.rst b/doc/source/api_reference/quick_mascon_regress.rst index 75082f3e..a51fe1cf 100644 --- a/doc/source/api_reference/quick_mascon_regress.rst +++ b/doc/source/api_reference/quick_mascon_regress.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: quick_mascon_regress.py + :filename: ../scripts/quick_mascon_regress.py :func: arguments :prog: quick_mascon_regress.py :nodescription: diff --git a/doc/source/api_reference/regress_grace_maps.rst b/doc/source/api_reference/regress_grace_maps.rst index 309c3480..53de95be 100644 --- a/doc/source/api_reference/regress_grace_maps.rst +++ b/doc/source/api_reference/regress_grace_maps.rst @@ -12,7 +12,7 @@ Calling Sequence ################ .. argparse:: - :filename: regress_grace_maps.py + :filename: ../scripts/regress_grace_maps.py :func: arguments :prog: regress_grace_maps.py :nodescription: diff --git a/doc/source/api_reference/run_grace_date.rst b/doc/source/api_reference/run_grace_date.rst index 6f34775c..17ae4387 100644 --- a/doc/source/api_reference/run_grace_date.rst +++ b/doc/source/api_reference/run_grace_date.rst @@ -16,7 +16,7 @@ Calling Sequence ################ .. argparse:: - :filename: run_grace_date.py + :filename: ../scripts/run_grace_date.py :func: arguments :prog: run_grace_date.py :nodescription: diff --git a/doc/source/api_reference/run_sea_level_equation.rst b/doc/source/api_reference/run_sea_level_equation.rst index ee456d99..37dd55f5 100644 --- a/doc/source/api_reference/run_sea_level_equation.rst +++ b/doc/source/api_reference/run_sea_level_equation.rst @@ -14,7 +14,7 @@ Calling Sequence ################ .. argparse:: - :filename: run_sea_level_equation.py + :filename: ../scripts/run_sea_level_equation.py :func: arguments :prog: run_sea_level_equation.py :nodescription: diff --git a/doc/source/api_reference/scale_grace_maps.rst b/doc/source/api_reference/scale_grace_maps.rst index 22da42b1..625c2612 100644 --- a/doc/source/api_reference/scale_grace_maps.rst +++ b/doc/source/api_reference/scale_grace_maps.rst @@ -17,7 +17,7 @@ Calling Sequence ################ .. argparse:: - :filename: scale_grace_maps.py + :filename: ../scripts/scale_grace_maps.py :func: arguments :prog: scale_grace_maps.py :nodescription: From 3f0bda6cbbea8b50c42d9d3cc829f662f97ee685 Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 15 Aug 2023 16:31:29 +0200 Subject: [PATCH 71/80] Debug requirements.txt --- requirements.txt | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/requirements.txt b/requirements.txt index 6c5dc25c..c32c620c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,13 +1,13 @@ boto3 future lxml -matplotlib -numpy python-dateutil pyyaml -scipy matplotlib cartopy --no-binary=cartopy +matplotlib +scipy +numpy datetime ipython setuptools \ No newline at end of file From b931fef0090bc02c02e2ccaecfdc4cc3ab9003dc Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 15 Aug 2023 17:00:31 +0200 Subject: [PATCH 72/80] Debug pathlib and change old spatial called from before fork --- gravity_toolkit/harmonics.py | 14 +- gravity_toolkit/spatial.py | 257 ++++++++++++++++++----------------- gravity_toolkit/toolbox.py | 12 +- 3 files changed, 137 insertions(+), 146 deletions(-) diff --git a/gravity_toolkit/harmonics.py b/gravity_toolkit/harmonics.py index 969203df..3a40093f 100644 --- a/gravity_toolkit/harmonics.py +++ b/gravity_toolkit/harmonics.py @@ -2094,7 +2094,7 @@ def plot_correlation(self, l, m, save_path=False): if save_path: if pathlib.Path(save_path).is_dir(): - plt.savefig(pathlib.Path(save_path) / 'C' + str(l) + str(m) + '_correlation.png') + plt.savefig(pathlib.Path(save_path) / ('C' + str(l) + str(m) + '_correlation.png')) else: plt.savefig(save_path[:-3] + 'c' + save_path[-3:]) @@ -2106,7 +2106,7 @@ def plot_correlation(self, l, m, save_path=False): if save_path: if pathlib.Path(save_path).is_dir(): - plt.savefig(pathlib.Path(save_path) / 'S' + str(l) + str(m) + '_correlation.png') + plt.savefig(pathlib.Path(save_path) / ('S' + str(l) + str(m) + '_correlation.png')) else: plt.savefig(save_path[:-3] + 's' + save_path[-3:]) plt.show() @@ -2154,7 +2154,7 @@ def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], color=[], save_p if save_path: if pathlib.Path(save_path).is_dir(): - plt.savefig(pathlib.Path(save_path) / 'C' + str(l) + str(m) + '_coefficient.png') + plt.savefig(pathlib.Path(save_path) / ('C' + str(l) + str(m) + '_coefficient.png')) else: plt.savefig(save_path[:-4] + 'c' + save_path[-4:]) @@ -2189,7 +2189,7 @@ def plot_coefficient(self, l, m, dates=[], ylms=[], label=[''], color=[], save_p if save_path: if pathlib.Path(save_path).is_dir(): - plt.savefig(pathlib.Path(save_path) / 'S' + str(l) + str(m) + '_coefficient.png') + plt.savefig(pathlib.Path(save_path) / ('S' + str(l) + str(m) + '_coefficient.png')) else: plt.savefig(save_path[:-4] + 's' + save_path[-4:]) @@ -2229,7 +2229,7 @@ def plot_fft(self, l, m, save_path=False, fmax=6): if save_path: if pathlib.Path(save_path).is_dir(): - plt.savefig(pathlib.Path(save_path) / 'CS' + str(l) + str(m) + '_fft.png') + plt.savefig(pathlib.Path(save_path) / ('CS' + str(l) + str(m) + '_fft.png')) else: plt.savefig(save_path) @@ -2342,7 +2342,7 @@ def plot_wavelets(self, l, m, s0=0, j1=None, pad=1, lag1=0, plot_coi=True, mothe if save_path: if pathlib.Path(save_path).is_dir(): - plt.savefig(pathlib.Path(save_path) / 'C' + str(l) + str(m) + '_wavelet.png') + plt.savefig(pathlib.Path(save_path) / ('C' + str(l) + str(m) + '_wavelet.png')) else: plt.savefig(save_path[:-4] + 'c' + save_path[-4:]) @@ -2387,7 +2387,7 @@ def plot_wavelets(self, l, m, s0=0, j1=None, pad=1, lag1=0, plot_coi=True, mothe if save_path: if pathlib.Path(save_path).is_dir(): - plt.savefig(pathlib.Path(save_path) / 'S' + str(l) + str(m) + '_wavelet.png') + plt.savefig(pathlib.Path(save_path) / ('S' + str(l) + str(m) + '_wavelet.png')) else: plt.savefig(save_path[:-4] + 's' + save_path[-4:]) diff --git a/gravity_toolkit/spatial.py b/gravity_toolkit/spatial.py index ec425b5d..1e3d1918 100644 --- a/gravity_toolkit/spatial.py +++ b/gravity_toolkit/spatial.py @@ -1752,6 +1752,134 @@ def __next__(self): self.__index__ += 1 return temp + def plot_eof(self, number, path_folder, cmap='viridis', mode='full', unit='cmwe', mask=None, normalize=False, weight=False): + import gravity_toolkit.toolbox as tb + mat_svd = np.copy(self.data) + if mask is None: + mat_svd = np.reshape(mat_svd, (self.lat.shape[0] * self.lon.shape[0], self.time.shape[0])) + lat = self.lat.repeat(self.lon.shape[0]) + else: + mat_svd = np.reshape(mat_svd[mask], (np.sum(mask), self.time.shape[0])) + lat = self.lat.repeat(np.sum(mask, axis=0)) + + mat_svd_original = np.copy(mat_svd) + if normalize: + mat_svd = (mat_svd - np.mean(mat_svd, axis = 1).repeat(self.time.shape[0]).reshape(mat_svd.shape)) / np.std(mat_svd, axis=1).repeat(self.time.shape[0]).reshape(mat_svd.shape) + if weight: + mat_svd = mat_svd*np.cos(np.radians(lat).repeat(self.time.shape[0]).reshape(mat_svd.shape)) + + + c_svd = mat_svd.T@mat_svd/(mat_svd.shape[0] - 1) + w, v = sc.linalg.eigh(c_svd) + + v = v[:, ::-1] + w = w[::-1] + s = np.sqrt(w*(mat_svd.shape[0] - 1)) + us = mat_svd_original@v + + eof_grid = spatial() + eof_grid.lat, eof_grid.lon = self.lat, self.lon + eof_grid.time = np.array([0]) + + if not pathlib.Path(path_folder).exists(): + pathlib.Path(path_folder).mkdir(exist_ok=True) + + if mode == 'ts': + plt.figure() + plt.xlabel('Time (year)') + + for k in number: + power = s[k]**2/np.nansum(s**2) + eof = us[:, k]/np.sqrt(mat_svd.shape[1] - 1) + sort_eof = np.sort(eof) + scale_eof = 2*eof/(sort_eof[-int(len(sort_eof)*0.01)] - sort_eof[int(len(sort_eof)*0.01)]) + + pc = v.T[k] * np.sqrt(mat_svd.shape[1] - 1) /2*(sort_eof[-int(len(sort_eof)*0.01)] - sort_eof[int(len(sort_eof)*0.01)]) + + if mask is None: + eof_grid.data = np.reshape(scale_eof, (self.lat.shape[0], self.lon.shape[0])) + else: + eof_grid.data = np.zeros((self.lat.shape[0], self.lon.shape[0])) + eof_grid.data[mask] = scale_eof + eof_grid.data[np.logical_not(mask)] = None + + if mode == 'map': + tb.plot_rms_map(eof_grid, path=pathlib.Path(path_folder) / 'map_eof_'+str(k)+'.png', unit=unit, mask=mask) + + elif mode == 'full': + npow2 = 1 if len(self.time) == 0 else 2 ** (len(self.time) - 1).bit_length() + f = np.fft.fft(pc, npow2) + xf = np.fft.fftfreq(npow2, d=np.mean(self.time[1:] - self.time[:-1])) + + fig = plt.figure(constrained_layout=True, figsize=(12, 6), dpi=200) + spec = matplotlib.gridspec.GridSpec(ncols=4, nrows=12, wspace=0.03, width_ratios=[8, 1, 1, 1]) + axmap = fig.add_subplot(spec[:, 0], projection=ccrs.PlateCarree()) + + cmap = matplotlib.colormaps.get_cmap(cmap) + immap = axmap.imshow(eof_grid.data, cmap=cmap, transform=ccrs.PlateCarree(), extent=self.extent, + origin='upper', vmin=-1.15, vmax=1.15) + axmap.coastlines('50m') + # stronger linewidth on frame + axmap.spines['geo'].set_linewidth(2.0) + axmap.spines['geo'].set_capstyle('projecting') + + cbar = plt.colorbar(immap, ax=axmap, extend='both', extendfrac=0.0375, + orientation='horizontal', pad=0.025, shrink=0.85, + aspect=22, drawedges=False) + + # ticks lines all the way across + cbar.ax.tick_params(which='both', width=1, length=24, labelsize=18, + direction='in') + + power_str = '\nPower: '+str("%1.2f"%power) + cbar.ax.set_xlabel(power_str, labelpad=10, fontsize=18) + + axplot = fig.add_subplot(spec[1:5, 1:], box_aspect=0.5) + axplot.plot(self.time, pc) + axplot.yaxis.tick_right() + axplot.yaxis.set_label_position("right") + axplot.set_xlabel('Time (year)') + + axfft = fig.add_subplot(spec[6:10, 1:], box_aspect=0.5) + plt.plot(1 / xf[:len(xf) // 2][1 / xf[:len(xf) // 2] < 10], + 2.0 / len(self.time) * np.abs(f[:len(xf) // 2][1 / xf[:len(xf) // 2] < 10])) + axfft.yaxis.tick_right() + axfft.set_xlim(0, 10) + axfft.set_ylim(0, ) + axfft.set_xlabel('Period (year)') + axfft.yaxis.set_label_position("right") + + if unit == "cmwe": + axplot.set_ylabel('Equivalent Water\nThickness\ncm', labelpad=50, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$cm^2$', labelpad=50, fontsize=12, rotation='horizontal') + elif unit == "cmwe_ne": + axplot.set_ylabel('Non elastic\n Equivalent Water\nThickness\ncm', labelpad=50, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$cm^2$', labelpad=50, fontsize=12, rotation='horizontal') + elif unit == "mmwe": + axplot.set_ylabel('Equivalent Water\n Thickness\nmm', labelpad=50, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$mm^2$', labelpad=50, fontsize=12, rotation='horizontal') + elif unit == "geoid": + axplot.set_ylabel('Geoid Height\nmm', labelpad=45, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$mm^2$', labelpad=50, fontsize=12, rotation='horizontal') + elif unit == "microGal": + axplot.set_ylabel('Acceleration\n$\mu Gal$', labelpad=40, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$\mu Gal^2$', labelpad=50, fontsize=12, rotation='horizontal') + elif unit == "secacc": + axplot.set_ylabel('Secular\n Acceleration\n$nT.y^{-2}$', labelpad=40, fontsize=12, rotation='horizontal') + axfft.set_ylabel('Power\n$nT^2.y^{-4}$', labelpad=50, fontsize=12, rotation='horizontal') + + plt.savefig(pathlib.Path(path_folder) / ('eof_pc_'+str(k)+'.png'), bbox_inches='tight') + plt.close() + + elif mode == 'ts': + plt.plot(self.time, pc, label=str(k)) + + if mode == 'ts': + + plt.savefig(pathlib.Path(path_folder) / ('pc_'+'-'.join([str(i) for i in number])+'.png')) + plt.legend() + + # PURPOSE: additional routines for the spatial module # for outputting scaling factor data class scaling_factors(spatial): @@ -2012,131 +2140,4 @@ def update_mask(self): # replace fill values within scaling factor magnitudes if getattr(self, 'magnitude') is not None: self.magnitude[self.mask] = self.fill_value - return self - - def plot_eof(self, number, path_folder, cmap='viridis', mode='full', unit='cmwe', mask=None, normalize=False, weight=False): - import gravity_toolkit.toolbox as tb - mat_svd = np.copy(self.data) - if mask is None: - mat_svd = np.reshape(mat_svd, (self.lat.shape[0] * self.lon.shape[0], self.time.shape[0])) - lat = self.lat.repeat(self.lon.shape[0]) - else: - mat_svd = np.reshape(mat_svd[mask], (np.sum(mask), self.time.shape[0])) - lat = self.lat.repeat(np.sum(mask, axis=0)) - - mat_svd_original = np.copy(mat_svd) - if normalize: - mat_svd = (mat_svd - np.mean(mat_svd, axis = 1).repeat(self.time.shape[0]).reshape(mat_svd.shape)) / np.std(mat_svd, axis=1).repeat(self.time.shape[0]).reshape(mat_svd.shape) - if weight: - mat_svd = mat_svd*np.cos(np.radians(lat).repeat(self.time.shape[0]).reshape(mat_svd.shape)) - - - c_svd = mat_svd.T@mat_svd/(mat_svd.shape[0] - 1) - w, v = sc.linalg.eigh(c_svd) - - v = v[:, ::-1] - w = w[::-1] - s = np.sqrt(w*(mat_svd.shape[0] - 1)) - us = mat_svd_original@v - - eof_grid = spatial() - eof_grid.lat, eof_grid.lon = self.lat, self.lon - eof_grid.time = np.array([0]) - - if not pathlib.Path(path_folder).exists(): - pathlib.Path(path_folder).mkdir(exist_ok=True) - - if mode == 'ts': - plt.figure() - plt.xlabel('Time (year)') - - for k in number: - power = s[k]**2/np.nansum(s**2) - eof = us[:, k]/np.sqrt(mat_svd.shape[1] - 1) - sort_eof = np.sort(eof) - scale_eof = 2*eof/(sort_eof[-int(len(sort_eof)*0.01)] - sort_eof[int(len(sort_eof)*0.01)]) - - pc = v.T[k] * np.sqrt(mat_svd.shape[1] - 1) /2*(sort_eof[-int(len(sort_eof)*0.01)] - sort_eof[int(len(sort_eof)*0.01)]) - - if mask is None: - eof_grid.data = np.reshape(scale_eof, (self.lat.shape[0], self.lon.shape[0])) - else: - eof_grid.data = np.zeros((self.lat.shape[0], self.lon.shape[0])) - eof_grid.data[mask] = scale_eof - eof_grid.data[np.logical_not(mask)] = None - - if mode == 'map': - tb.plot_rms_map(eof_grid, path=pathlib.Path(path_folder) / 'map_eof_'+str(k)+'.png', unit=unit, mask=mask) - - elif mode == 'full': - npow2 = 1 if len(self.time) == 0 else 2 ** (len(self.time) - 1).bit_length() - f = np.fft.fft(pc, npow2) - xf = np.fft.fftfreq(npow2, d=np.mean(self.time[1:] - self.time[:-1])) - - fig = plt.figure(constrained_layout=True, figsize=(12, 6), dpi=200) - spec = matplotlib.gridspec.GridSpec(ncols=4, nrows=12, wspace=0.03, width_ratios=[8, 1, 1, 1]) - axmap = fig.add_subplot(spec[:, 0], projection=ccrs.PlateCarree()) - - cmap = matplotlib.colormaps.get_cmap(cmap) - immap = axmap.imshow(eof_grid.data, cmap=cmap, transform=ccrs.PlateCarree(), extent=self.extent, - origin='upper', vmin=-1.15, vmax=1.15) - axmap.coastlines('50m') - # stronger linewidth on frame - axmap.spines['geo'].set_linewidth(2.0) - axmap.spines['geo'].set_capstyle('projecting') - - cbar = plt.colorbar(immap, ax=axmap, extend='both', extendfrac=0.0375, - orientation='horizontal', pad=0.025, shrink=0.85, - aspect=22, drawedges=False) - - # ticks lines all the way across - cbar.ax.tick_params(which='both', width=1, length=24, labelsize=18, - direction='in') - - power_str = '\nPower: '+str("%1.2f"%power) - cbar.ax.set_xlabel(power_str, labelpad=10, fontsize=18) - - axplot = fig.add_subplot(spec[1:5, 1:], box_aspect=0.5) - axplot.plot(self.time, pc) - axplot.yaxis.tick_right() - axplot.yaxis.set_label_position("right") - axplot.set_xlabel('Time (year)') - - axfft = fig.add_subplot(spec[6:10, 1:], box_aspect=0.5) - plt.plot(1 / xf[:len(xf) // 2][1 / xf[:len(xf) // 2] < 10], - 2.0 / len(self.time) * np.abs(f[:len(xf) // 2][1 / xf[:len(xf) // 2] < 10])) - axfft.yaxis.tick_right() - axfft.set_xlim(0, 10) - axfft.set_ylim(0, ) - axfft.set_xlabel('Period (year)') - axfft.yaxis.set_label_position("right") - - if unit == "cmwe": - axplot.set_ylabel('Equivalent Water\nThickness\ncm', labelpad=50, fontsize=12, rotation='horizontal') - axfft.set_ylabel('Power\n$cm^2$', labelpad=50, fontsize=12, rotation='horizontal') - elif unit == "cmwe_ne": - axplot.set_ylabel('Non elastic\n Equivalent Water\nThickness\ncm', labelpad=50, fontsize=12, rotation='horizontal') - axfft.set_ylabel('Power\n$cm^2$', labelpad=50, fontsize=12, rotation='horizontal') - elif unit == "mmwe": - axplot.set_ylabel('Equivalent Water\n Thickness\nmm', labelpad=50, fontsize=12, rotation='horizontal') - axfft.set_ylabel('Power\n$mm^2$', labelpad=50, fontsize=12, rotation='horizontal') - elif unit == "geoid": - axplot.set_ylabel('Geoid Height\nmm', labelpad=45, fontsize=12, rotation='horizontal') - axfft.set_ylabel('Power\n$mm^2$', labelpad=50, fontsize=12, rotation='horizontal') - elif unit == "microGal": - axplot.set_ylabel('Acceleration\n$\mu Gal$', labelpad=40, fontsize=12, rotation='horizontal') - axfft.set_ylabel('Power\n$\mu Gal^2$', labelpad=50, fontsize=12, rotation='horizontal') - elif unit == "secacc": - axplot.set_ylabel('Secular\n Acceleration\n$nT.y^{-2}$', labelpad=40, fontsize=12, rotation='horizontal') - axfft.set_ylabel('Power\n$nT^2.y^{-4}$', labelpad=50, fontsize=12, rotation='horizontal') - - plt.savefig(pathlib.Path(path_folder) / 'eof_pc_'+str(k)+'.png', bbox_inches='tight') - plt.close() - - elif mode == 'ts': - plt.plot(self.time, pc, label=str(k)) - - if mode == 'ts': - - plt.savefig(pathlib.Path(path_folder) / 'pc_'+'-'.join([str(i) for i in number])+'.png') - plt.legend() \ No newline at end of file + return self \ No newline at end of file diff --git a/gravity_toolkit/toolbox.py b/gravity_toolkit/toolbox.py index 42206d1e..7e23b430 100644 --- a/gravity_toolkit/toolbox.py +++ b/gravity_toolkit/toolbox.py @@ -4,7 +4,7 @@ from gravity_toolkit.gen_stokes import gen_stokes from gravity_toolkit.harmonics import harmonics from gravity_toolkit.harmonic_summation import harmonic_summation -from gravity_toolkit.plm_holmes import plm_holmes +from gravity_toolkit.associated_legendre import plm_holmes from gravity_toolkit.read_love_numbers import read_love_numbers from gravity_toolkit.spatial import spatial from gravity_toolkit.units import units @@ -60,11 +60,6 @@ def create_grid(Ylms, lmax=None, rad=0, destripe=False, unit='cmwe', dlon=0.5, d nlon = len(grid.lon) nlat = len(grid.lat) - # update spacing and dimensions - grid.update_spacing() - grid.update_extents() - grid.update_dimensions() - # Computing plms for converting to spatial domain theta = (90.0 - grid.lat) * np.pi / 180.0 PLM, dPLM = plm_holmes(lmax, np.cos(theta)) @@ -233,11 +228,6 @@ def diff_grid(grid1, grid2): grid.lon = grid1.lon grid.lat = grid1.lat - # update spacing and dimensions - grid.update_spacing() - grid.update_extents() - grid.update_dimensions() - grid.data = np.zeros((grid.lat.shape[0], grid.lon.shape[0], len(grid.month))) cmp = 0 for i in range(len(grid1.month)): From d55d4454956cd4724507b063b57df2c461a21abd Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 15 Aug 2023 19:01:02 +0200 Subject: [PATCH 73/80] Debug units --- gravity_toolkit/gen_stokes.py | 8 +-- gravity_toolkit/read_gfc_harmonics.py | 3 +- .../GRL_Gravitational_Lecomte2023b.ipynb | 52 +++++++++---------- 3 files changed, 31 insertions(+), 32 deletions(-) diff --git a/gravity_toolkit/gen_stokes.py b/gravity_toolkit/gen_stokes.py index 523618c3..6c937b94 100755 --- a/gravity_toolkit/gen_stokes.py +++ b/gravity_toolkit/gen_stokes.py @@ -182,17 +182,17 @@ def gen_stokes(data, lon, lat, LMIN=0, LMAX=60, MMAX=None, UNITS=1, int_fact[:] = np.sin(th)*dphi*dth elif UNITS == 4: #-- Inputs in mmGH - dfactor = factors.mmGH + dfactor = factors.spatial(*LOVE).mmGH int_fact[:] = np.sin(th) * dphi * dth elif UNITS == 5: - dfactor = factors.microGal + dfactor = factors.spatial(*LOVE).microGal int_fact[:] = np.sin(th) * dphi * dth elif UNITS == 6: - dfactor = factors.cmwe_ne + dfactor = factors.spatial(*LOVE).cmwe_ne int_fact[:] = np.sin(th) * dphi * dth elif UNITS == 7: #-- Inputs in units with no dfactor - dfactor = factors.norm + dfactor = factors.spatial(*LOVE).norm int_fact[:] = np.sin(th) * dphi * dth else: raise ValueError(f'Unknown units {UNITS}') diff --git a/gravity_toolkit/read_gfc_harmonics.py b/gravity_toolkit/read_gfc_harmonics.py index f6eda4d5..59b5ddbf 100644 --- a/gravity_toolkit/read_gfc_harmonics.py +++ b/gravity_toolkit/read_gfc_harmonics.py @@ -201,9 +201,8 @@ def read_gfc_harmonics(input_file, TIDE=None, FLAG='gfc'): end_date = [int(year),int(month),dpm[int(month)-1],23,59,59] # python dictionary with model input and headers - ZIP = bool(re.search('ZIP', SFX, re.IGNORECASE)) model_input = read_ICGEM_harmonics(input_file, TIDE=TIDE, - FLAG=FLAG, ZIP=ZIP) + FLAG=FLAG) # start and end day of the year start_day = np.sum(dpm[:start_date[1]-1]) + start_date[2] + \ diff --git a/notebooks/GRL_Gravitational_Lecomte2023b.ipynb b/notebooks/GRL_Gravitational_Lecomte2023b.ipynb index e3a6d3c1..3165d294 100644 --- a/notebooks/GRL_Gravitational_Lecomte2023b.ipynb +++ b/notebooks/GRL_Gravitational_Lecomte2023b.ipynb @@ -6,8 +6,8 @@ "id": "69ec460e", "metadata": { "ExecuteTime": { - "end_time": "2023-08-14T16:26:53.611538Z", - "start_time": "2023-08-14T16:26:52.366902Z" + "end_time": "2023-08-15T15:14:38.646559Z", + "start_time": "2023-08-15T15:14:37.636094Z" } }, "outputs": [], @@ -38,8 +38,8 @@ "id": "e637b560", "metadata": { "ExecuteTime": { - "end_time": "2023-08-14T16:27:28.613989Z", - "start_time": "2023-08-14T16:26:54.776331Z" + "end_time": "2023-08-15T15:14:54.576984Z", + "start_time": "2023-08-15T15:14:40.166798Z" } }, "outputs": [], @@ -89,8 +89,8 @@ "id": "5da6ed08", "metadata": { "ExecuteTime": { - "end_time": "2023-08-14T16:28:27.907563Z", - "start_time": "2023-08-14T16:28:08.860868Z" + "end_time": "2023-08-15T15:15:13.402985Z", + "start_time": "2023-08-15T15:14:58.493686Z" } }, "outputs": [], @@ -163,8 +163,8 @@ "id": "384d9ab9", "metadata": { "ExecuteTime": { - "end_time": "2023-08-14T16:28:29.654159Z", - "start_time": "2023-08-14T16:28:28.728511Z" + "end_time": "2023-08-15T15:15:15.759726Z", + "start_time": "2023-08-15T15:15:14.924471Z" } }, "outputs": [ @@ -178,7 +178,7 @@ }, { "data": { - "image/png": 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", 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", 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000/ccsst3HvvvWzZsoW0tLTIuampqdxwww0AeDwe1q5dyxdffMEXX3zBO++8w4UXXgiBAIQSYsNekk2bNrFo0SIkSWLZsmWsXLmScePGtbBL0uu55PTTOfLII9HabG3avn37dl555RW0Wi1FRUW98ecRCAQCQV9F2YdpaGhQAKWhoaHD49xut/L7778rbrc7Rpb1Pe644w4FUA488EBl8+bNbR6zePFi5aijjlLKysoijwFKUVFRq2P/+9//KoBSUFCgKIqiBJ1OxbV6teJet67VnrfeeqsCKDfeeGOrdQJNTYpvV6UScDjatMntdisHHXSQAiiPP/54V15yryPeVwKBQNA9Ovv9rSiKIinKvlsX2tjYiN1up6GhgcTExHaP83g8lJSUMGjQIEwmUwwt7Bts2rSJESNGkJqaytq1a1t4S/ZEURRkWUar1QJq6KeoqIj169e3Oi4xMRGHw0FVVRXJZguBnTtAp8M4cCDBYJABAwYgyzJlZWUMGTIEh8PBjh07MHZhaOH06dN5/fXXueCCC/jvf//bvT9AL9Hf31cCgUDQXTr7/Q0iR6VfMGvWLILBINdcc02HIgVUYRIWKXsjrHF1Oh1aqwXjkCEYBw4E4Msvv2Tnzp1cdNFF6PV6LrnkEmpra/n44487bfezzz7L66+/zvjx43nllVc6fZ5AIBAI9h+EUOkH/PzzzwBMnTo1amu+/fbbOJ1ORo0aRVJSUqvnw8Li0ksvBeDyyy9v8fjemDdvHrfddhupqal8/PHHWCyW6BguEAgEgn2Kfp9MqygKLlmOtxl7xaLRIElSt86tqKgAICcnp9Vz8+bNY+HChS0eO/bYY5k8eXLk9+rqau6//35ADXesWbOGL7/8EovFwsyZM1utuWvXLr744gtGjRrFAQccAMCwYcM45JBD+O6779i2bRsFBQWR44MNDSh+P9rkZCStlq1bt3LeeecB8P777zMw5KURCAQCQf+j3wsVlyxTuHB1vM3YK8VHjMHayZDMnnSUhjRv3jwefvjhFo+ZTKYWQqWmpoYHHnigxTFWq5U5c+Zw+OGHq3sEAqDVIkkSr7/+OoFAIOJNCXPZZZfx66+/8tprr0WED4B/xw6UYBCNzYbb6+WMM86gpqaGZ555hqOPPrpbr1kgEAgE+wci9NMPyMzMBKC8vLzVcw899JDaA0VReO2119o8v6ioKHJMXV0dr732GsFgkLPPPjuypre4GM/atcguN6+99hoajYaLL764xToXXHABBoOB1157DXk3L1ZkirLfz5VXXsnKlSu59NJL+fOf/xyNly8QCASCfZh+71GxaDQUHzEm3mbsFYum+5ry8MMPZ8GCBXz//fc99lAkJSUxffp0gsEgf/jDH/jTn/7Exx9/HOks++PiXyMVQgMGDGhzjdLSUubOncu0adPUB/R68HiY8cQTvPfee0ycOJEXX3yxR3YKBAKBYP+g3wsVSZK6HVLZV7j88suZMWMGL774IjfffPNeK386w5VXXsnMmTP55JNP+GnRIiakpADw6uuvA3DiiSe2mRNTU1PD7NmzeeWVVyJCRdLpmPPDD9zz8MNkZGTw8ccfi3JfgUAgEAB9QKg8/vjj/O1vfwPU6pRDDz00zhbtfxQVFfGXv/yFJ598khNPPJF3332XwsLCVsfV19d3ek1Jkrjvvvs4/fTTufe++/j8X//C6fHwwQcfYLVaef/997G10Wk2EAiQm5vL7NmzqampITU1lc2lpUy//Xa0Wi0ffPBBu54YgUAgEPQ/4ipU1q1bx7333ovVasXpdMbTlP2exx57DL/fz3PPPUdRURFHHnkkY8eOjbTQX7FiBUuXLiUxMZGxY8d2as3TTjuNiRMnMm/+fBYtWULxjh04nU6uuOKKNkUKqD1XLrnkEp5++mneeustbr75Zs694grqm5oYN3Ik8+bNY968ee3uOX78eM4444zu/AkEAoFAsA8St860wWCQww47DEmSGDZsGG+99VaXPSqiM23XWbZsGS+88AILFy6kvLwcn89HSkoKY8aM4aSTTuLSSy9tERpqrzNtmM8//5xTTz2VSRMmEFAUfl2+nEWLFrWoGtqTNWvWMGbMGMaMGcOqVau6VHZ9+eWX95nBhOJ9JRAIBN2jK51p4+ZRmTFjBitXruS3337jiSeeiJcZ/Y6JEyfy0ksvdfr4venYU045BX9lJf5du9AmJWHIy9vrmqNHj26xbsDhwFdSgmQwYBo2rNO2CQQCgWD/Jy7lyWvWrOGBBx7g7rvvZtSoUfEwQRBFwhU/4anJXSV8XngdgUAgEAjCxFyoBAIBpk+fzogRI7jjjjtivb2gF4iWUJEkCWUf6BIsEAgEgtgR89DPI488wsqVK/n111/Rhxp9dRav14vX64383tjYGG3zBN1A8Yc8Id0VKlotppEjkXrQK0YgEAgE+ycx/WZYuXIlDz30ELfeeisTJkzo8vmPPvoodrs98iPKWPsGuswM9Lm5aHowOFCIFIFAIBC0RUy/HS6//HIKCwtbzHnpCnfeeScNDQ2Rn7KysugaKOgWWqsVXXIyGoMh3qYIBAKBYD8jpqGflStXArRbynnYYYcB8PHHH7fZK8NoNGI0GnvNPoFAIBAIBH2LmAqVq666qs3HFy5cyKZNmzjttNNIT09n4MCBsTRL0AcIVFURqK1Fm5SMPjMj3uYIBAKBoI8QU6Hy8ssvt/n49OnT2bRpE3feeadoob+PIXu9BGvrkExGdMnJ3V5HURQUvx/F74+idQKBQLDv4XW50JuMaDT79xy6ziIyGAU9QvF4CNRUE6yr69E6kdLmoOilIhAI+i87Nq7j//54Ea/cdDWbFv8Ub3P6BEKoCHpE2AMidbHUfE9E0zeBQCCAnz/8L8FAgMaqXXz+7Ayc9T27Cdwf6BNCZdasWSiKIsI++yA9bfYWQau6OJVgsKcmCQQCwT7JrpJitq78DQCD2YIcDFK87Nc4WxV/+oRQEey7RDwgPRQqEaEjPCoCgaCfsub7OQAMn3QkB512NgCbF/8cT5P6BEKoCHpEtDwqUtijIsuijb5AIOiX7NioTqkfctBhDD34cABK16zE63LF06y4I4SKoGeEc1R0PctRUUM/EiDCPwKBoP/h93mpLt0KQPbQYaTmDSA5O4dgIEDZ76vja1ycEUKln7Fs2TKuuuoqhg4ditVqxWw2U1hYyKWXXsq3337b6niPx8Nzzz3HlClTSE1NxWg0kpeXx3nnnce8efOaPSr6lh4Vl8vFI488woQJE7DZbJhMJvLy8pgyZQp33nknxcXFgFqaLkkSGo0Gy5jRWMaMQWswIElSmz+zZs3q1Ot0u93MnDmTadOmkZWVhcFgICEhgTFjxnDNNdcwf/78Hv0dBQKBIJpUlmxBDgax2JNISE0HIKtwGAA1ZdviaVrciflQQkF8kGWZW2+9lWeeeQadTsfRRx/Naaedhl6vZ8uWLXzxxRe89dZbPPjgg9xzzz0AbN68mZNPPpmNGzcyePBgzjvvPJKSkiLHf/DBB1x5zjk8+/e/Y9ot9NPU1MTkyZNZtWoVQ4YM4ZJLLiEpKYmysjLWrl3LY489RmFhIYWFhZxxxhmRBn+BmhqUYJAvfviBlatXc/nll7dq/jd+/Pi9vtaVK1dy5plnUlJSwoABAzj++OPJzc3F4/GwceNG3nnnHV588UXuuOMOHn300Wj9iQUCgaDb7PphIUN31pKksxKsrUWXmkpa/kAAqkKelv6KECr9hLvvvptnnnmG8ePH8+GHH1JYWNjiebfbzfPPP09NTQ2gTqY+4YQTKC4u5p577uG+++5Dq21uPrRjxw7OOP10Xv3wQ+wJCTz1yiuR55599llWrVrFVVddxUsvvYQkSS32KikpiUzBPuOMMyLjEsJCZftf/8rK1auZPn06Rx11VJde5/bt25k2bRo1NTU8++yz3HDDDS3sBnA4HLzwwgts3769S2sLBAJBb+DdUoLhuX8z1OeHyjq233wzBW++SdqAAkB4VIRQ6Qds3ryZxx9/nNTUVL7++msyMzNbHWM2m7ntttsiAuKJJ56guLiYiy++mAcffLDV8Tk5OXzyv/8xetw4nnv9da6/6y6GDBkCwM8/q1nqN9xwQyuRAjBo0KA27dSlpgLNibXd4c4776SyspIHH3yQm2++uc1jbDYbt956KwFRYSQQCOKM4vez/cYb0fr8OIx6ElJSybrnXiRJIi1fFSq1O7YTDPjR9jQXcB9F5Kj0A2bNmkUwGOSaa65pU6TsTnjo42uvvQYQCQO1RXZ+Pn+85hpkWW6RO5KSkgKoAimWuFwu3nvvPSwWC7fccstej9f1tPeLQCAQ9JCmed/jKy7Gq9PyS2EO2e+/i6lIzU1JSE2P9FOp27kjzpbGj34vVBRFIRh09fkfRVG6/Rp//PFHAI4++uhOHb9t2zbKy8vJzc2lqKiow2OPOeYYoNmLAnDuuecC6hDKO+64g3nz5lHXwxb7nWHp0qX4/X4OPPBAbDZbr+8nEAgEPaX+vXcBKEtJIGgxkxhKpAVUr0oo/FPdj/NU+v0tpSy7mb9gTLzN2CtHHbkardbSrXMrKioAyMvL69LxAwYM2Oux4WN27twZeez000/n8ccf58EHH2TGjBnMmDEDgMLCQk444QRuvvlmhg4d2mot2e9Hcbm6PZgwbHdOTk7rtWW5VQhLp9Nx9913d2svgUAg6Cm+bdtw/vQzSBKlqYkkZWYjaZr9B/7KStKzctixcR0120vjaGl86fdCRdB9FFlGDjVn2zMX5bbbbuPaa6/l66+/5qeffmLp0qX8+uuv/Pvf/+aVV17hvffe47TTTmu5nsuFr6wM2eNpc78VK1Ywe/bsFo8NHDiQ6dOnq+d34HWSZZkHHnigxWNGo1EIlT6Aoiht5jIJBPs7TXO/A0AuGorHECQ3KzvyXPmtt9H4+eekXHw+AA2Vu+JiY1+g3wsVjcbMUUf2/WY6Go252+dmZWWxfv16ysvL9xrKCR8PUFZW1uFx/h072PLDjy3O2Z2EhATOPffcSCiooaGBu+66i5kzZ3LVVVdRXl6OwWBoPkEbeju2IzhWrFjRSmwceeSREaESzr8pLy9vda5Op2shZAYOHBjxwAjiQ6XXz83rS/ml3kmuSc/zIwoYn9g9r6FAsC/iDIXlHXk5UFVGUmazUNFlZgBgLi4BoLG6MvYG9hH6fY6KJElotZY+/9OTO85JkyYB8N1333Xq+IKCAnJycigvL2fDhg3tHqcEAsz/9RcADjvssL2ua7fbef755ykoKKC6uprVq1sKREkXqvZpR6hMnz4dRVFa/OzeuO2ggw5Cr9ezbNkympqa9mqPIH64gjKXrS5hfc06pgef4TTn/Tz425usaHTG2zSBIGboc3PRpqVRnaAK9KSs5rB1wjHHqv+zeg2SotBYVRUPE/sE/V6o9AemT5+OVqvlxRdfpGovb/ZweXLYS/Hwww+3e2zVrl3M+ugjNBoNl19+eadskSQJi6Xtu+bIvKBuzvqxWq2ce+65uFwunnnmmW6tIYgNz26twNG4gke5lcksZCJL+ZP8OB+v+WePEscFgn2J7H88yNBFC6n0qAI9abfQj3ncWLRpaeB0kez04KitIdhPWyoIodIPGDJkCLfffjvV1dWceOKJlJSUtDrG4/Hw9NNPc//99wNqjsmgQYN48803efDBBwnuMX+noqKCs6+5hpr6ev6yR3Lsf/7zH5YsWdKmLR999BHr168nKSmJ0aNHt3yyB/1TwjzyyCOkpaXx4IMP8s9//rOV3aA2t/P5fD3eS9A9GgNB3tpezjX8Cz1+kpIOISX7EgAmeV7l863z42ugQBBDFEWmoVINQyfvJlQkrRZryFOd5vKiKDKO2pq42Bhv+n2OSn/hoYcewuPx8Mwzz1BUVMTRRx/N6NGj0ev1lJSUMHfuXGpqanjooYcASEpK4uuvv+bkk0/mvvvu44033uD444/HbrdHWug7HA6uOPtsHg6dE+arr77i2muvZciQIUyaNImcnBwcDgcrVqxg0aJFaDQaZs6cGenZEkaSpB41ewM1bPXNN99w1llncfPNN/Pkk08ydepUcnNzcbvdlJeX880339DY2Mixxx7bo70E3eP18mqOCH5CDjswGDIZO+b/0OkS+bCxkhTnHOpLn4JBU+NtpkAQE5z1dQQDATRabWTGTxjLgQfS+NlnpHsCbETNU7FndNwLa39ECJV+gkaj4emnn+aiiy7i//7v/1i4cCELFy5ElmWys7OZNm0aV1xxBccdd1zknGHDhrFq1SpeeOEFPvzwQ9555x2cTifp6emccPzxXHHCCUw99FD0JlOLvWbMmMGkSZP49ttvWbhwYaR0OTc3l8svv5wbb7yRiRMntm1oFJqwTZgwgd9//51XXnmF2bNn89VXX1FXV4fJZCI/P59zzz2Xiy++mKlTxZdhrFEUhXd37OIWvgZgyJDb0evtABw+8h5WL5lHTnAdv+/6lZGZh8TTVIGg11BkGeeiRZhGj454SazJKWj2uFGzHHQgAAmNTWjkVBqr+mdCraTswwHhxsZG7HY7DQ0NJCYmtnucx+OhpKSEQYMGYdrjS1XQPWSvF++mTUgaDaaRI6O2rnfLFmSXC8OAAWjt9qit2xuI91XXWedwc8fiV7mJp9Hr05g8aREaTXPl18wfr6fI+w21lqM499BXOlhJINh38W7ZwpaTTkYym5FmPsfnz84ge9hwLvrHky2OUxSFTZOnEKyp4echOYy44g8cdvaFcbI6unT2+xtEjoqgmyiBUO5HlNvQhxNqlX6aNLa/82VVA0fzLQB5uRe2ECkAeXnTAUh0/YDP1xBr8wSCmOAJVTyaRozAWa927U5ISYs87/NVoyiyWnxwoOpVSXF4+m3ljxAqgu4RVIVET3NKWhHqpaK0kQQr2Pf5vmo7I1gLQFbWma2ePyZvIuUMQEeAdTu/ibV5AkFMcK9RPwPmMaNpqqkGICE0lLWqai4//DiJpcvOw+9vxHzAePV5t7ff9lIRQkXQLcIeDynKHhV9ViamkSPRZ2REdV1B/Kny+TE4fkKLjMkyFIuloNUxVq2WnaYjANhW8VWsTRQIYoJnzRqAFjkqtuRUfL4a1q2/E0UJ0Ni4nJWr/kjiySdjfu5pVhRk9tuqHyFUBN1CY7WqzYpCk5KjhaTVtph1Idh/+LneyUTUsvWs9PYrrlLTTwDA6PyZQMARE9sEglihKArejRsBMA0fHvGo2FLT2LbtP/j9tVgsg9FoTDQ0LMVjqiZh1CiQJFz1vT/ctS8ivhEE3UJjNKJLTkabkBBvUwT7CD/X1TOO5QCkpbUvVA7PGs8uMtHhp7L211iZJxDEhEBFBbLTCTodhoICHHUhj0pKKjW1i9SDsq4nMelQAGpqF2JNUm8IPU4HgW4Obd2XEUJFIBDEhJLa1Zhxo2hsJCaObfe4ETYzWzTjANhUuTBW5gkEMcG7uRgAw8AC0OtxhDwqpkQNTqfqabl0SypvO4YDUFOzAKPVilanA0Xpl14VIVQEfQrZ58NbXIy3uDjepgiiSLUvgNG9CoBE+wFIUvuXHkmSCNoOAsBR/3NM7BMIYoV382YAjIVDcDc1Rtri+1FFyjYG4pAS+dY3BoCGhmU0/TyfwzduZ8K2XZEqof6EECqCbhGoriFQXY0cbTekJCG73chuj5j5sh+xrNHJMNYDkJ504F6Pz0lVB2mafMV4fdW9apsgvni3bMG3fXuLx+ref5+GTz+Nk0W9i7c4LFQKI8mxFnsSDY1q/tZaRnNWZjI1UhY7yUZRAji9xSQ0ObG7PEKoCASdJVBdhb+iAqLc76S53FkBUaK837Cs3hERKvakdroS78bE1Dy2MRCA2lrhVdlfCToclF1zLVvPPS9SsutavpyK++5n5733qdeY/Qx/aRkAxqFDmhNpU1KprlOFyjpGc19hDgckWihhMACeTBcAZn8QZ3lZHKyOL0KoCLqMoijNfU6i3fBNo4lU/YheKvsPG+q3kkItClrsieP2evwYm4VN0igASmsW97Z5gjhR88IL+MvK0JjN6HPUgXzmceMwH3AAisdD1bPPxdnC6JM/6zWGfDcX65QpEe+ILSUJj1sdFmu1jSTTqOeolATKyQfAxTYCdrV7a7hiqD8hhIqg6wSDEArLRL3hG4imb/sZQUXB51CrfQzW4Wi1lr2eo9dIBC1qjL6u4bdetU8QH3zbt1P7+hsAZN59N7pQqwNJoyHzjr8B0PDJJ/i2bo2Xib2CpNGorR1sNlwN9QBY0kAiiBszh6erXpSjUhLZzgAAHI5NyNlZAARLtsbD7LgihIqgy4QFhKTpnZ4nki4kfkQb/f2CjU4PBfI6ADKSD+r0eSl2NUSk9WwkGHT1im2C+FHz0ssofj+Www7FNvWoFs+Zx47FesQUUBTqP54dD/NigquxHgCj3QPADnI5KlWdcXZAgoV6nepRcbiK0QxU/5/ynTG3M94IoSLoMpE5PLpe8KYAhLw0wqOyf7Cs0cVQNgCQ3In8lDDDkwZSSwoaZBobV/eWeYI4EKiro+GTTwBIu+46JElqdUzSWWcB0DB79n57LXDV1wPgs6lCfAd5jLCqA051Gon8xEJ86EH2oBuSCYC+qv8llwuhIug6vdQ+P0wknLSfXpz6G2sbqslnGwBJ9s4LlbGJVjZRBEBtvQj/7E/Uv/c+iseDceQILAe17WWzTZ2Kxm4nsGsXzp9/ibGFvUPVv//NltNOp+799wFwNaqDNz2GWgC8+oGYtM1fy8NsVnaQB4BmqBUZkIOBflcRKYRKP2Hr1q1IksQJJ5zQ6rlAIMCbb77JaaedRm5uLkajEavVSlFREZdddhmfffZZiw9GJPSj06EoCp9++innnXceBQUFmM1mzGYzgwcP5txzz+Wdd97B38USZrfPx+MvvcRBRx+NzWbDZDKRl5fHlClTuPPOOyneo8fKUUcdhSRJVHSiQkCSpBY/Op2OzMxMTjnlFObOndslOwWdo75hORpkZH0ORmNmp88baDZQqlGFys66Zb1lniDGKLJM/QcfAJBy6WVtelNA7X6dePzxADi+/z5m9vUm3o2b8G7ciOJ2A0RyVBRpFwBma2GL44dbTZE8FTnHz5wxg/lpaF7sDO4j9M4tsWCfYdu2bZx55pksX76c9PR0jjnmGAoKCggGg2zZsoUvvviCN998kwsuuID//ve/QHPop7axkUunTWPu3LkkJiZyzDHHUFhYiEajoaysjPnz5/Phhx/yr3/9i59/7lyJaVNTE1NOO43Vv/9O4cCBXHLJJSQlJVFWVsbatWt57LHHKCwspLCwcO+LtUNqaio33HADAB6Ph7Vr1/LFF1/wxRdf8M4773DhhRd2e21BS4KKgs6thm1siQd06VyNJKG1jAYHuBxre8M8QRxwLV2Kv7wcjdVK4gnHd3is7cgjqH//fRyLFsXIut7FV1oKgD4/VM3TUA+SgkkuByAjsajF8cOtZhagVkPJmhpkjQSBAF6nE5PNFjvD44wQKv2YxsZGjj/+eDZs2MCdd97Jvffei8lkanGM1+vlrbfeaultCAQIBAKce911/PDLL0yfPp1nn30Wu93e4lxZlvn444954YUXOm3Ts88+y+rff+eKCy/kxZkz0SUltXi+pKQEr9fb5de6O2lpadx///0tHnv33Xe58MILufPOO4VQiSJbXF7yFNUDlp08vsvnZyaNRnZo0AYq8XorMRrFVO19nYbZam5KwoknoDGbOzzWcsihoNfjLy3Ft20bhoLWE7f3FRRFwb9NDYEaCgYiB4O4HU3orQF0+AigY2jS4BbnDLUaqSYdAId3BwazGZ/bjbupoV8JFRH66cc88cQTbNiwgSuvvJJHHnmklUgBMBqNXHXVVbz55pvND0oSb3/+OT/88gvHHHMMr776aiuRAqDRaDj77LP56quvOm1T2PNy0+23txIpAIMGDWL48OGdXq+znH/++dhsNrZt20Z1df9LVust1jrcDETtD2FPGN3l80fb0ygnF4CmpjVRtU0QGwKywganhwW1TTiDQVIuv4yUyy8j+Zxz9nqu1mYl89a/kjdzJrrMzocN+yLBmhpklws0GvR5uWp+iqKgS1LD6tWkMSqhpfiwarVoDTkAON3bMSeovVRcjY2xNT7OCI9KP+a1114D4O9///tej9Xtljirz87mzZD4uOuuu9qNMbd17t5ICfVS2Lx5M+PHj+/0edEgnIfTFXsFHbOuYQcHowq/hIQRXT5/pM3EfylkAGU0NK4iLe3oaJso6AW8sswzW3cxv7aJ9U43Hln9bOUa9fxrRAGH33lnp9dKufzy3jIzpkTCPtnZaAwGXDvq1Sdy1Qn09VI6mYbW155k6wDwgeyrwJw4nobKXbhDSbj9BXFFDiG7OtenQdLrkfT6yO+K34/SyWRRjaVloyvZ7Y40TuvKedGgtLSU8vJy8vPzGTx48N5P2I1AIMCSJUvQ6/VMmjQpqnade+65vP3221x11VUsXbqUadOmccABB5CcnBzVffbk7bffxul0MmrUKJLa8OQIukdNg+oFCegHoNMldPn8QrOJUmkIKPOprF9F9zOTBLHk+W2VPLttV+R3i1aDSSNR7vVz5ZoSFh82ksTeam/QR4kIlQFqcmw4kVaTrn6f+LWZbd705SXkIddp0Cg+rClGANxNwqPSL9kwoXNlk5n33E3KxRdHfq97/312/eOhTp07Yv26Fr+XnHsuvs17nxK853nRIFwhk5OT0+bzTz/9NI17uBdvvfVWbDYbtbW1+P1+srKyMBqNrc599dVXKQ19KMP84Q9/IC9v79nqp59+OjNmzOAf//gHM2bMYMaMGQAUFhZywgkncPPNNzN06NBOvcb2qK6ujuSoeDwe1qxZw5dffonFYmHmzJk9WlvQEtmt9k8x20a1e4zX5cLVWE9Cajq63W4CQO1QK5tHggucDhH62RcodXv5V6kqUu4anM0p6UkMNBtwyzInLN3IJpeX/5RVctug7DhbGlv85WrCrD5PDWWGS5N1CaG+VIa2/x4DzBZqSSGNaszJSotz+wtCqPRT9laH//TTT1Me+mCFufbaa7Farciy3OG5r776Kj/++GOLx0444QTy8vKor6/n2WefbXXO7smtt/31r0w/8ki+/eEHlpSVsWzZMn799Vf+/e9/88orr/Dee+9x2mmndfwCO6CmpoYHHnigxWNWq5U5c+Zw+OGHd3tdQUvq/QFSA5sAyEoa0+p5RZZZ/MmH/Dr7A/weN3qTmaOnX83oqce1OC41YTiyS0ITqMHrq8ZoSIuJ/YLuMbOsCo+sMCnJxo35Gc1egl9/5dmZL/Dk2EN5QTuZ0zKSKbK2zotri8Zvv6Xp629IOOF4Eo87bu8n9EH8O3YAYMgNCZXQnB+9KTRw0Jzb5nkDTAZ+J500qjEkqq0hROinn1L0W+f6NEh73PEln3ceSWee2a09B33wQadCP71BZigxbU8xEmb7bmPXjzrqKBYsWKD+Egxi3bULnU5HdXU1Xq+3lVflhx9+iPz/9OnTef311yO/19fXtxIJ0FKooNGQYLVx1vHHc2FRERq9noaGBu666y5mzpzJVVddRXl5OQaDoasvG4CioiLWr18fsWf27Nlcd911nH322SxdupTc3LYvGIKuscHpIZ+tAKQmtvao/PTB2/zy0XsAaLQ6/B4337zwHM6Geg4549zIcUWJyezalUU2O3E6NmBMEUKlr+KTZT6tVL+AbyzIaBHKcCxchGXpEk60JfH9QYdzwcpiPp0wlAGmvX+O3StW0PjFF2pJ874qVMpVoaIPebHDXhGzTv2v3TygzfMGmA0sJB1Yh86qttrvb0JFVP2E0FgsnfrZU6hIen2nz221p9ncrfOiwcCBA8nJyaGsrKxVA7WOUAIBdFotE0ePJhAItBAlnd1XUZRWP7sjSVKr7rR2u53nn3+egoICqqurWb06Oi3Vk5KSmD59Os8//zwVFRX86U9/isq6AljfVE8WaojRZmvZH6J42eKISJk6/Rpufut/HHaOWhb+43tvUrG5eULsKJuZstAUWYdjQyxMF3ST+bVN1PqDZBh0TE5qmZPk/FG9Vhx78jSGWozs9Po5f0UxVb695/hZDlB78LiX77sdipPOPIOU6dMxjRwJhIWKQoKkdqXNSshv87xco4FqVHEeNKrel/6WoyKESj9m+vTpADz88MOdPkcJqMLhsrPPBuDRRx/tnXbO4Xk/geY2+pIkYekl4XbllVcyYcIEPvnkE3766ade2aO/Ud6wEQ0yfo0dgyE98ngwEGDBmy8DcMCJpzLhxFPRaLQcds5FDDtsCoos880Lz6GEQoxFVhNlDASgvin6+VqC6PG/Xao35czMZHSaZm+Kv6IC76bNoNGQOWUy740rJNeoZ4vby0Urt9AY6HhchmnsWAC8xVvUIoR9EPvpp5N5x98whnLsPI4mtCYZg+RDRmJgYtseFbNWg0+nTk72aVWB0t/Kk4VQ6cfcfvvtDBkyhNdee4277roLj8fT6hi/349r94qooJr4ddm553L44Yfz3XffceWVV7ZKvAU1D6atxzviP//5D0uWLGmeoBxsnqD80UcfsX79epKSkhg9uus9OTpCkiTuu+8+AO65556ort1faQp7P0xDWoQA1nz/LXU7d2BOtDP5/Esjj0uSxLFXXYfRYqW6bBublqg9ddL0Omq0gwCoa1ofuxcg6BJBRWFBbRMAp6YntXjO+aMq/k1jRqNNSiLHZOD98YWk6nWsdri5eV3pnsu1QJ+RgTYtDWQZ78aNHR67r+BubCSQoWZf1JNMrrn9Bm66UKKtjCoE3U39K/QjclT6MXa7nTlz5nDGGWfw6KOP8vLLL0da6AcCAXbu3MncuXOprKxk/Pjx2Gw2lFBXWIPZzCeffMJ5553HrFmz+OijjzjmmGMYMmRIZO7OggUL2LZtG4MHD263umhPvvrqK6699loKBw7ksLFjyR08GFcwyIoVK1i0aBEajYaZM2e2WW108803Y26n0+XMmTP36o057bTTmDhxIvPmzWPBggUceeSRnbJZ0DaSR02kTdgt7KPIMks++x8Ah551PgZzy38Tc0IiB5x4Gr/877/88uF/GXrQYUgaDQbLMGgCv7sYWQ6g0YhLV19jZZOL+kCQRJ2G8Qkt/13DYR/bbu0MCi0m3hwziJN+28S3NQ24gjIWbfv3zqbhw3H+8AOedesxjxvXOy8ihribGglmqfk5Dikdvab9flQ2Uya4AKUeMOPuZx4V8Wnv5wwaNIilS5fyzjvv8P777zN//nxqamrQ6/Xk5uZy3HHHcf7553PyySej0WjwO0PeFZ2OtLQ0vvvuO2bPns1bb73FkiVL+PLLL5EkiaysLCZOnMgjjzzCOeec0+nE1xkzZjBp0iS++ewzfli2jIo5cwDIzc3l8ssv58Ybb2TixLZLyd8PTSRti2effbZTYaP777+fU089lXvuuYeFCxd2ymZBa2p8AdLlrQBkJzU3etu2ZiUNuyowWqyMOXpam+dOOOk0ln0xm6rSrZT9vob80WPJsBXgaTJiUry43duwWkVHlb7GwpA3ZXJSQouwjxIMRjwq1smTW5xzQKKFLIOeCp+flU0uDktq36tgGjEiJFT2vfCfZ8MGPGt/xzRiOKYR6ufB3dSIMlz1HPt16R2dToolC2pBJzeAlIXf68Hv86I3tL5h2x8RQqWfEE5ibQu9Xs/ll1/O5Z3oAKmEQjHhZFdJkjjzzDM5s5uVT3tSVFTEbbfdxp8vuYRATQ26tDT0WVkdnjN//vxOr7+3fJpTTjml341Q7w02uzwMQHXnp+7WkXbVXLWj8YgpU9Eb2y5NNdsSGDHpSFZ99zVrvp9D/uixDLVa2E4+Q9iEw7FeCJU+yII6VagckdIyidazdi3BhgY0NhvmUK5JGEmSmGi38EVVA8sanHsRKuroDE8v9JXqbRzz5lH13D+xn3kmOY8+ghwM4nE60FnVGzhJ33ElW7YlnSAatMgYbOBrUit/9Gn9Y/ZVzHNUysvLefbZZ5k2bRr5+fkYDAaysrI4++yz+fXXX2NtjqCrhCYnS73dZl6rrq/sJclO0DcpbtpFciiebrWGkgedDoqXqp/xscee0OH5o49WS1A3/vojHoeDIVYTZagD6RxOUfnT19jm9rK4wQnAUSl7VvuoPZXMhxzC4s8+4uuZz/DTB+/QUKlWhE1MtAKwrLHj7uDGkCfCu2EjSnDfui74d+wEmkuTPU6HOufHol5P9YaOPSq5ZhMNJAGQkK6Km/4U/om5R+Vf//oXM2bMoLCwkOOOO46MjAw2bdrE7NmzmT17Nv/9738577zzYm2WoJMoMRIqbSXTCvYdKhvWkQm4tdnodOpdcsnypcjBIKl5+aTnD+zw/KzCYaQNKKC6bBsbfl7E0MlH80ZIqDQ5REJtX+OFsiqCChyVnMBAc8twhOMHVahs9btY8u4bkcd//vAd8seMJ2PqSYCFpY1OFEVpd3aYoaAAyWJBcbnwbduGsYujP+KJf5cqyvTZqnc4XF5sNvnU/xo7HriYbdSzhWRSqMWSrKcGf7/qThtzoXLwwQezcOFCpkyZ0uLxRYsWccwxx3Dddddx+umnt5ksKYg/kTsZbe/O6dAmJqKxWnvfcxMH6vwB5lQ3ssHpQUGhwGxkakoCBeb95z3vdIYqM8xDIo9tDnlTCg88ZK/nS5LEiClTWfTOLDb+8gNnH3sCuzQDQYYGUfnTp6j2BXh3Zw0ANxS0DkWkXn8ta5//J6urytGYTRx8+jns3LyRbauWU7p6Bf7f16C96h6qfFDm8ZHfzudA0miwn3oqSDHw6EaZQIU6UiA8ATosVCwG1YuUZO5YqGQa9NSHPCr6JDUQ4nE09YapfZKY/2ufddZZbT4+ZcoUpk6dypw5c1i9ejUHHnhgjC0TdAZdejqKz4fUy0JS0un2uYvR3vDJMk+UVPDy9irccus8mGmpify9MKfTbcX7MlrPZgBsVrXiJ+D3s3XFUgCGHHhop9YYdsgkFr0zi7LfV+NpakRvGQYOCHjLCQSaujXkUBB9Xgm9n8clmJm0R46J29HEl3M/Z7vsQjIZOfH6Wxgx+SgAGiorWPP9tyz/5nNSaiupSstmvdPTrlAByH7g/l58Jb1HYFfbQsWmUf+baul47lGKXksj6mR5OVH1OHmcjl6xtS/Sp74J9KGur7r97Atqf0InJgt3ixpfgItWFbOySW1WNcJq4vAkGzqNxMpGF782OJlT08j3tU1cmJ3CZTmpjE7oneZ2vY1XlkkKlgCQbVe7cG5fuwqf2401OYWsws4NlUzKyiZjYCGVW4vZvOQX8rKKqHWkkEItDudGkuydGyQq6D2cgSCvlVcDcEN+y+m/9RU7+eix+6nbWY7BbOaUP9/BoPHN/2b2jCwmnX+pGg6sq6IqLZsNTg/T0uwxfx29iezxEGxQwzT6sFBpbETWSNgk1SuSbe24YECSJALaFAgCFtWrLTwqcaC0tJS5c+eSlZXFmDGtB5gJBPsqDf4A568sZo3DTbJOy5PDB3BSmr3FRX2zy8MDm3fwbU0jb+yo4Y0dNRxqt/J/owrINnZvplG8KHF5yAtV/OSGSpMjYZ8JByNpOp/DP+zQSapQWfwTQy8YRxkFqlBxbBBCpQ/wv1111AeCDDYbOSm9pcD49uV/U7eznIS0dM78233t5iUVHngIaR9/AcB6R8cJtfsiYW+KZDajSUwEVI+KP8WIBoUgGjKtnaje0aepQsWgNub09iOPSp/oTOv3+7n00kvxer08/vjjaNvJf/B6vTQ2Nrb4EeyfKIqCb1sp3i1b9rkM/91RFIVb1pexxuEmTa/jkwlDOTk9qVXC4BCLiTfGDOLD8YWclpGEXpL4pcHJGb9tZrvHFyfru8eWhhLMeAigw2IZhKIoFC8LCZWD9p6fsjtDDlLDRKVrVzHYoInM/HE69o/upPs6v4Yqfc7OTEa7x3vasX4dE0p2Mm3kxA6Tp7OHFJHjUb90f6/tOEFUkWU8v/9Ow2ef7TPXBX8oP0Wf0Tyk0eNoIpCiRhAcJKHT7D3nz2BUxYxG5wyt0X+EStw9KrIsc+WVV7Jw4UL++Mc/cumll7Z77KOPPtrm5N3OIvpj9AzZ50NuakIyGNAm9G5+gCRJBB0OUGSUYLB5SGEfYm/vp2pfgEe37ODL6gb0ksSbYwczrIP8E0UJcqDZwZi8em62l/FSyVokdyUfLm7gMKuLoK8Cv78BjcaAzTYce+IBpKYeid0+PsqvrGdU1K+jAHDoCtBo9FQUb8JRW4PeaCJ/VNc6iqbkDsCekUlD5S6sZSW7DScUCbV9gZVNqgdkfGLLMKXf5yVhZyVZjS6Un36BP93Q7hqSRsOY1GTeB7b4ZWRFQdNO5Q+KwtYLLkTx+TCPHYuhoCBaL6XXCFSG8lN26wflbmxETlKvaW5taqfWMRtUoaKTmgCzyFGJFYqi8Mc//pG33nqLSy65hBdeeKHD4++8807+8pe/RH5vbGxkwIC2BzntTthD4/f7222xLtg7ituNf+dONBZLrwsVUJvKKQEZAkHog9EPv1+d+rqnB9ATlHlpexX/3LaLpqA6WO+BITkcELqY+/311NX9isO5EadzIx73drzeXXh9VYAcWefU8P8EwLHHjWZtbRW1tYso2fpP0tNPYHjRPzAYUnrjZXaZcJ8TxaRW/Gz5bQkAA8dPQNfJDsVhJEli8MSDWf7VZ/hXLWb7kFCJsnNjh6Wsgt6nKRCk2KWO1Bib0PK66qipJi0kYhKOPGKva40bVIA2GMCr1VHm8bVbASdptRgGD8a7fj3e4uJ9QqgEGxpBo0GX2RzecTc1ICWr791gJ4WK3ayeb5QagHThUYkFsizzhz/8gddee40LL7yQWbNmodlL7NpoNHarbFmv12M0GmloaCAhIUFc3LpJrHqohJF0WpSAP9INty+hKAoNDQ0YjcZIEjjAVreXc1cUUxYK14y1mbl3SA6TkxNoaFjB1m3/R03N9yhK225rSdJhMKRjMmZhNGbh0KQyq1JDpZLCSdnDuDB/MHLQTWPjKurqF1NV9TVVVV/T1LSaceNewWbtXKJqb7JnxU/pmpUADBw3oVvrDZ6gCpWy35agHzOFoFsDgQa8vl2YjB0nIQp6j9VNbhQg16gn3aBv8Vx9+XZSHWriuG2PtvltkV80kpTlxWpCrcPdYam+MSRUfFu2wNFH9+g1xIKUSy4m+YLzkT3eyGPupka0eepNyd660oZJDpUwawmgNcgimba32V2knH/++bz55pvt5qVEi7S0NMrLy9m+fTt2ux29Xi8ESxfxu90EZRkNaiZ7b+NDfa8E3W50ev1ej48FiqLg9/tpaGjA4XCQm5vb4vmHi3dS5vGRbdRz5+BszslMRpF9bNh4P9u3vxk5zmodSmLCGKy2YVjMBRhDwsRgSEWSWn4WSlJq+Mv6MhbvgkPzchmTaCExcSx5eZfQ1LSO1Wuux+0uZcXyy5k48X3M5ryY/C3aQlEU7IEtAGQljcDv8bBzk+ph6WrYJ0zeiNHo9AacdbXkavVUkE0u5TgdG4VQiSPhsM+4NqrTdn07B7usEDSbMA4fvte10gcOJm3BEqrSslm1cxfT9pi+vDuGgaoXxbdtW/cMjwOSTofW1vx163E4MJjVm5m9daUNk2W20oQFCy505kC/SqaNuVCRZZmrrrqKWbNmce655/LWW2/1ukgBSAxlW1dXV1NeXt7r++2PBOvrkV0uNG43Wqez1/cL1NaieDxovT40Nmuv79cVjEYjubm5kfcVwAanh8+r6gG4befvJK3+jk8dOzAOnI8huQqAjPTTGDz4T1itQ9patk0uzErh+5omPquq58/rS/l6YlFk0mpCwggOOvAjlv12IU7nJlatupoDD/wYrTY+zeMqPC4y2AHA4ORRlK9fixwMkJiegT2ze6JCp9eTM3wkpatXkNZQQ5k2n1zKcTg3kJq697CCoHdY1YFQcf/6K3ZAGjO6U1VeWp2OATpYB2yorunw2HC4x7d13xEqe+JxNGE0qELF1MmQbaZBTzmJIaESxFPb+9fgvkLMhcqDDz7IrFmzsNlsDBs2jIceeqjVMWeccQbjx4+P+t6JiYkkJibi9/sJ7iMZ432JiocfwfnDD6Redx1Jp5269xN6SOXnX9D05ZckX3wxKZdc3Ov7dRatVtsi3BPmqU1lKMDwLStxbXoFx7AGEsc6kLQQ9GnYOjeXza467Nc7sY7u/H6SJPHIsFx+rG9ircPDa+VVXD2gOd6t1yczfvwsFi8+FYdzA8XFjzNs2D1ReKVdZ1Pt72iRcWMlwZzD8jXfADBg1NgeeTDzR4+jdPUKLNtLKCso4FB+xuEQM3/iyZpQaGf0HvkpPrcL87btAKQe1/aE7LYYHLoZKdlLlVtEqOxDHpXdURQFj9OBVa+GgqzGzoV+sox6GrGTRQWSRSFQ4SXg83U572tfJOZCZevWrQA4HA4efvjhNo8ZOHBgrwiVMHq9vs0vGkHHSCUlaHbuxGKzYjL1fvdUk1aLc+dONBUVMdmvJ8iyjPzbzxyc6+D63H+hH9R8sdVLgzH7TkbyrKKpppIPHrqbaVffyJijO38RTzfo+fvgHP66oYwnt1ZwVmYKaYbmj6/JmMXIEY+zctUfKNv+OllZZ5CYGPt+RLsa1pECNOgGI0kSZWtXAVAwunthnzAFo8fxAyCtX0lZgZr7IkqU44cnKLPFrX7RjrK1FCrbf/0Ze+i5tBM6Hj65O8PTUyEIO+nYw64PCZVAZaXq4bX03caISiDApqOmoktLo+D1WWjtdnxuN4osY9GG2uebOidUknRaHKgeXCVR/ex7nA5sfSSJvjeJeR+VWbNmoShKhz/Tp0+PtVmCThCoU6fhalNi88HQhrrgBuvrY7JfT9BoNDw09WD+FPw3eoMPoyaD/AF/5JBDvuaIqd9y+Gl/ZvqTMxl15LGgKMz5zz/57avPurTHBdkpjLaZaQzIPLW1otXzaWlTyco8HVDYuOkfcSnHD1f8yKZC3I4mdpUUA6pHpSdkDC7EaLGSsLOM7eESZeemdpOSBb3LJpeHoALJOi2Zhpb3u1XfqF40X1oquvTO5V8AjM1XKzhrjVa8gfYT6HXJyWjsanM5X2lpV02PKYHqaoLV1Xg3b0ZjU8cLeJ0OFMAmqTkmKebO/Y0kScKvTQJAm2yIrNUf6BMN3wT7BsHaWgC0yTEWKg37xpRQ2bgKc7mXjAdNpF5bj+amhUirayPP600mjr/uZiaefAYA38/6D0s/+6jT62slifuHqGPi39pRQ6nb2+qYwsLb0GjMNDQso7r62569oG4ghSp+rNYitv++GhSFlNwB2FI6V4LZHhqNlryRYzB73XiUNDwYURQfLte+6f7f1/ndoSbTj7CZW4X0gstXAKCbML5Law4bkIcu4EfRaFi3veM8wn0lTyVQod5Q6NLTI72gPE4HXoMxIlTSOylUAILaZAC0Cerf3N1PKn+EUBF0CiUYjHg2dKmxESqGgQXYjj0Gy8TulbXGmuofPyL1eR26CrXs0FdSQumVV+L48cfIMZIkceSlV3Ho2RcAsOCtV1n13ded3mNycgJTkm34FYVntu1q9bzJlE3+gOkAlJQ8H3OvSqJfnfGTZR8RKUvOH90zb0qY8DqpjgbKUe++wx4cQWxZ52yeWbU7shyk2KSlJM1OysknA+DzVVNZ9Q1bt/2Hhobl7b4ndVodaR41QXR12fYO9w9X/oSbqfVV/LsqgeYZP6Am0nqTTGhCPZNsps6LeI0uJFQs6rnCoyIQ7Ibc1ITGbAZJing6ehvLhAkMeP550q69Nib79QQlEMA6y4HGK2E57BAKv51DwvHHYznwQIxDWvY2kSSJSeddwsGnnwPAty/9mw0/L+r0XncMUietflhRx05v68TDAQOuRKu10ORYS3XNvB68qq7R6KnDjlqxMSRlNKVr1PyU/B7mp4QJr5Ows0y00o8z60IelZF75KfUbC+j0qhlc+EAlDFufvr5GBb9cAirV19PcfHjLF12DmvW3tSuWMmR1FDe+r1U/mTecQdFvy0j5bLLovBqeo/ArpBHZXeh4nQg29UcSTdWNJrO50vq9OpNot6ofu77S9M3IVQEnUKblETRb8soWrE8Zg3f9iUaZs/GV1yM1m4n77l/YhgwgJwnHif/tVfRZ7Y9cGzyhZcz9tgTQFH48l9PsXXFsk7tNdFu5VC7Fb+i8Mr26lbPGwwp5OaqVVJlpa92/0V1keLaNQDUko7VB7XlZSBJ5I2MTlJval4+FnsSSbUVza30hUclLvzejkcl3DMna3AhW7Y8idu9FQCrdRhpacciSXoqK7+komJ2m+sOsqqJsRvrOg736lJS+nQSbRh/aCChPqulR0VOUL96vZquTYo2G9TEW73OE1pLCBWBoBWabnQG3t9RZJnqF/4DQOq116IN9VbRGAwdziiSJIljrrqOosOmIAcDfPLUI5Sv/71Te16Xr4qfN3ZU42yj1H5A3mWAhrr6X3A4N3XxFXWPHfWq7fW6QZT9vhqA9IJBmG3RGbcgSRL5o8eRWle928wfIVRiTZXPT5UvgAQU7SFUKopVD1fm8Ay8vl1Ikp4pk3/l0EO+YtzY/zB40M0AbNz0ALW1P+65NAcWqCG9rQYrDZWtE8b3NQKhgYS63XoIeRwOsKlfvX5NUpfWs4bCREZNaDChCP0IBPHHV1qKe+VKZF/fnSDs/u03/Nu3o7FaSb7g/HaPCza1TnzTaLSceMNfGDR+IgGfl49nPMBvX36Co7Zj1/dxqYkUmAw0BmQ+q6xv9bzJlEN6+rEAlG9/u2svqJs0V/wMYfvvqndlQJS8KWHyR48jpaGaMtQcBbd7G8GgO6p7CDpmfSjsU2A2YNU1C3HZ5cL6/kfk1TRiyVRzTOz2CRgMzeW3+fl/wG4/kECgiRUrr6C07LUWYaDJmeqxOzMH8Pvin2PxcnqVwK6wUGn2qnqcDjQW9TUroZyTzmI3qYm3JskJKHhdQqgIBHGn5Myz2Hr+Bfj7cDfhYH09upxsEo4/Xs3j2QPZ6aTsuuvZdORRbZZaa3V6Tv3LneQOH4XX5eT711/iP9dP54OH7o640vdEI0lclK3eXb2zs7bNY/JyLwGgYtcnyHLrCqGoE6r4sViHRTwqeSO70NmuE+SPHou9sQ6HbKOBREDB6dwc1T0EHRNOpB1pbfledy5eTMr2CoZW1uJiAQB5uRe1OEaj0XPA+DfIyjoTRQmyadNDrFt3O8Gg+v4cYjFiV4IEdHrmb+j433XbFVew6cij+vS1oTn00+xR8TocaM2qF1TTRaGSEuq5opWCaI0y3hh0CO8LCKEi6BTVL73E5qOPofo/L8Z033DirtyHS5QTjj2WIXPnknnXnW0+L1ks+CsqUFwu6mfPbvMYvdHE2Xc9wNTpV5M9bDgoCqWrV/DO3X/l/Qfu5LevPqPs99VUbN5IyYplrFv0PSN/X4JGUVjc4OSl/8zk+9dfYt2PCyJx6+TkwzAaswgEGqmumd9Lr15FUWQS/OqMnyTDoOb8lBHRFSr2jCyS09NJbqjZrZ+KCP/EkubS5JZhn/p5auJ2Y66egFyP0ZhFevrxrc7Xao2MHPEEQ4feDWjYWfERy1dchiwHkCSJQ+xqh9rlPgVXQ327dgR27CSwaxe+vZQyxwtFUXbzqLTMUdGb1D4xXZ14nm6y4ELNzdGZAnhdrihZ27cRWZGCThHYuRP/jh3Inti62bV2O/7y8j7fS0XSaNCGGjq1ek6SSD7/PCruf4D6994n5fLL22wnrzeamHDiaUw48TQaKiv4+cN3WbvwO8p+Xx3xUOzJ4OMvZvOgEcx2Kxw975PmdU46jUPOOp/MzFMpLX2JiopPyWjjSyNauNylGHHjQ495l1o6mZ4/MGr5KbuTP3ocKfXVlCXnM4o1ovInxjQn0u7hUflBzTlRDlR/z825sN2KFkmSyB9wBTbrMFatvo6GhqU0NC4nOekgJmekMKdxB9uzC9i89FfGHtP2+1afm4tv27a+61EJBEi/+Wb8uyrQZbQM/RhD7fPNXRUqBj2rQvN+tOagCP0IBLsTqFW70upi1OwtzL7UnbYjEk85FclkwldSgmft3hNm7RlZnHD9n/nj868w+YLLGDh+IsnZuSSkppNeMIj80WMpOmwKp4e+KzaOPZQxJ55OSk4efq+HXz9+n//+/a9Y9ZMAqK6eh9/f2Guvr7ROzUkpJ5/ABvX1RTvsE2ZAWKiE8lREQm3sCCoKG52tS5P95eUoO3YgA/JBai5WZubJe10vJWUSKcmHA9DUqJazH5qkCv7yrHw2Lf2l3XP1ocnlfVWoSHo9qVddSdZdd6HZbR6Px+HArFfFnq2Tc37CpOp1NKJWCsmJ2n4T+hEeFUGniHSljVGztzDaJPVD2Vc9Kk3zvsc8ZvReW4VrbVZsU4+i6auvafzyS8yjR3Vq/cS0DA458zwOaef5E2SFt3/+nQofuE48h+mX/4FNi39i7sszqSrdyuePv8Loiwbj9m6hquprcnLO69oL7CQ769cCUKcbjO539Qsn2om0YfJHjSXl62/ZiNoAzuEUHpVYUeL24pEVzBqJAnPzl2+4qaEzVQc2HzbbcCyWQZ1aMzFxLFXV39IYEiqjbGasEjiNZpZu38mpbhcGc+tSZH1eHtB3hUp7eJyOLs/5CaPXSPikBFBAtunw1vWP0I/wqAg6RbBOFSq6GM35CROe6RGs73tCJVBTw/brr2fTEUdG5iB1ROJJJwHQ+OWXKLIcFRt0Gonzs9V/k48r65AkiWGHTOLSGc+RnJNHU3U1O1eolRk72+ldEQ0cjnUA+PWF1GxX569EOz8ljDUpmcFGfaQ7rc9Xic/XdkKxILrMr1W9JWMSLGh3C1+Gwz6e4Wo1S0Z654cRJiSqgjMsVLSSxMHJasiwNCOP8g3r2jwv4lHZ3nEX275Gk9tNgqT+HVMtnW+fHyaoUf82skUjQj8Cwe6EQz+xGkgYpi+Hfpy/qG5pY1ERuuS9Z+/bjjgCjc1GoKIC94qVUbPjjIwkAL6vaaIxoFYTJKSkce49D2FLSaX8N/XLo75+MR7Pjqjtuzsaj+rVkH1qLD49fyDmhMRe2QtgQsEAPJKZCtRqirBQEvQun4dK4U9Jb25UpgQCEY9KcKLaRiAl9YhOr5mYoHre3J7SiOA81K6Gf8qyB9JUXdXmefpcde6Vb0ff9Ki4V6+m4bPP8Gxs9vjJcpB6JBJQw7D2LnpUANCqQkVjAa/LFbWbnr6MECqCvaLIMsHw5OROfCFHE62974Z+wmLDctBBnTpeYzRiO+ooABzfR6+1/XCriaEWIz5F4Zvq5r9TQkoap/z5DoJuE46dZkChsuqbqO0bxu9vwBxUqxvkSvWSEq1utO0xYtQYrM4mtqKGF5qa1vbqfgLY6fXxa4OaE3FKelLkcfeq1ShOJ36dhDwsiEZjIME2otPr6vV2zOZwAz9VcB6apFb+bM8uoLGmPaGielQCFbtQ/P4uv57epvHLr9hx2+00zP4k8pjX6cSbaEKPWvVj7GIyLYCkU6+JGpMMioLP44mOwX0YIVQEeyXY0AAh1d4Zz0E00dqTVBv6oEfFvVIVKuZxnZ9lY5t6FKDmtkQLSZI4LeRV+XSP5m+5RSM4+Ixzqd+iejd2VXwVtX3DOBzrAagiHWlDMdB7+Slh8kaMIrWhmq0MBoRQiQVfVDWgAAclWskxNeen1H07B4CGLD1oICFhNBqNoZ1V2sZmGwlAU0iojE+0oFNkXJYEShrbDm/o0tORjEaQ5Ui/kr5EoEoVWLq0Zq+J2j5frYTyY0Sr7foYAK1O/SzrjKHBhP0g/COEimCvhBNpNYmJSPrOD9CKBn01mVb2evGsUy+q5vFdECpTpoBOR7CmplN5LZ3l1JBQmV/bRIM/0OK5Q848H9zqYMTGxmX4fK3nA/WEmka1ymcbA9FsUKt/ckd0Llm4uxjMFnKVQLNQcQih0tuERXBYFIepMGgoTUnEd4j6pWtPPKDLayfYhgPNHhWjRkOGpIYst7vabokgSRL6HDX84++DvVQC1ernTJe+m1BxOpAT1Zyxrs75CaPXJQFgMKqf8/7QS0UIFcFeCQuVWCfSAlgOOICCt98i96knY753R3h+/x38frQpKREXdGfQJiYy6H8fMvSHRVH1Tg23mimymvArCl9XtyxD1un1TD77elyVJpBg+7ZPo7YvwM56VZxUUYDJ5yFtQAGWxO5dhLvCcLstEvpxuUoIBFqPKBBEh51eH4vDYZ+M5n9bWQ7yW/F6NgxPgmNU4Z2ccniX17eFQkWOpuZcoyy9+oVe4Qu0eQ5A0vnnkX7zTehzsru8Z28TqG7Lo+IAm5qEHNAmdWtdk0E9z6BXw13efjDvRwgVwV7RZWaS9qc/kXRe75S2doQ2KQnLxIkYCgpivndHeFapFQrmcePabN7WEaaiol6ZQH1aKG/gs6r6Vs8NnnAwQYfqfdiy4Z2o7utyqqEfp0/tvtnb+SlhDhiQS5Nkp05WBV9TKAQliD6fV6oezUPsVrKNzWGdkuXLaKzaRd7hdYAfu30iqSlHdnn9hAQ19ON0FUfGPeSY1c63VUGpxTyg3UmdPp20667DkJ/f5T17m2BVyKOS1tKjorWEkl+13btRsYSFilb9O3ld+38vFSFUBHvFkJ9P+o03kHrlFfE2pc/gXqV2ijWPGxtnS5oJu+QX1DZX/4SRJIkxB18PgGLcSk15dCYqy7IfjUdtne+qUsOCA0bFRqgcNKQQgBJJ/W9T05qY7Nsf+TqUpH3qHmGfbauXY053Yx+sDtEcNvTuLgt3AKMxG50uEUUJ4HSqeU55CWpCbYPZgrup95oV9gayzxcJV+/eY8njaEJrCs350XdPqNhCQsWkUUNiIvQjEAjaxLNBvXs3jeh8dcOeyE4n/orojbIfajUxxGLEryh8X9v6wl449kSC7kQ0WoUl3z0VlT1dri1o8OHGTKCkDknSkD+q8zk7PSHfakEvB3cTKiJPpTdQFIW1DvVL8bCk5jERit+P8b3/Mdy2E4KQlXUmiYndE+6SJDWHf0KesTyL6lFpstrbLVHuqwRD+Sno9ZFeUKAOJNQb1ZBNdyp+AOymJABMuABFhH4Egr7ArhmPs/3Pt+Df0Ts9QLqKEghE7paMw4d3a436//2PjYceRuXjT0TTNI5LVSsCvq1u+w40K0ttOuf0/UpjVWWP9wuLg20MJLWumqyhwzC1M/Mo2mgliTxkSkJ5Ko6mvY8mEHSdan+A+kAQDTDYbIw87vptOSklZWTMlZE0BgoLb+3RPlarmvAdnoadZVA9dA5rIk01bSeAyy4XjXPmUPfuuz3aO9pEEmnT0lp4mDzOJkyG8Jyf1G6tnWxSBY5GUtAa+scEZSFUBHvF+dNPNM6ZE7cSwKa5c2n6+mv8FX2jBFHS6Ri6cCFDFixoMWysKxgGDULx+3H8+CNKoP1kwa4yLU29e/uuppGA3DquP3Do+QAk5DlY9tX/erxfXaib6BYKSa2vZODYCT1esysMT0qIVP44nJsJBvf/nhKxZkNotk+B2YBZ2/yVUfvN1wB4R8nk5F2IyZjVo31sYaHiUoVKtnF3odK2R0V2uym/6WYqHngQ2efr0f7RZHehsjsehxOzTvVOJRi7J1RSjBa8qHlCGmMQr1uEfgQCqv/zIuU33Yxr8ZK47N/c9K0+Lvu3hSRJ6DMzuhWPBzUJV2u3Izc0RPqxRIODEq0k6bTUBYIsbWx9p5WQMBqtlIzWILNl3Ww8PXQbV9evAKA0MBCry8HAcTEWKsl26kjBGbAAQRxOMaAw2mxyqR6AYVZTi8edCxcC4BmlkJY6pcf7WK1D1HUdav5URKhYEmhox6OiTUlBMplAUQjs3NljG6JFeOTHnkLF6XRg0aqfyyRz19vnAyTpdDhRvZayVStCPwIB7DaQMCW2zd7CNLfR71u9VHqCpNVinTwZAMeChVFbV6eROCYU/pnTRvhHkjRkZk0DwJpdw6q5X3d7L1n24XOp+QR19XbMVhtZQ4Z2e73uMNRqAklie0Ct+hB5KtEnPC15mKVZqPjLy1G2l6NICt6RMnZ713un7Ek49OP2lBEMuskw6NEoCrJWy46GtkOZkiShz1ZLk/07o5fv1VOSzjqT4atXkfP4jBaP13p9JIba56d0U6iYtRpcYaFi04nQj0AAEIhjHxXoex4VJUouZtuR6jwUx8LoCRXYLU+lpm1hl5Z+DAD2giaWf/0pwUD32o87HBuQFD9N2KBGJn/MeDQabfeM7iZDLGrOxGat+iXXUB8975RAJSxUhu7mUXEsWgSAb7CCZMxEr0/q8T4GQyp6fQqg4HQVo9NIJO+l6RuALkstiw/s6jtCBUDS69EmJLR4rC4QjMz5MXUzmRbAJ4U9KhoR+hEIFFmOtK+P9UDCMH1t3s+mo6ZSfPwJ+Ho4tdU6ZQpIEt7166Oa/3N0aiI6SXXZl4Tc9ruTknw4Go0RQ0KAADvY8NOibu3TGMlPGUJqXTUDx8c27ANQGEru3KhXK0ZqqhbH3Ib9nY2u1h6VsBfQO0rGZoleOXqrhNpw0zdv+3lc+qy+51Fpj0bZjxn176mKsu7hD01QlqySCP0IBMGGBgiqdf+xnvMTpi9NUA7U1hKsrcVXWooutXvJcGF0ycmYx6rlnNH0qiTqtJHps3Pa8KpotWZSkicBYC9wsPTzj9ttqNURDY3LAShmCKl1VTFPpAWw6rTkGPVsRv2C8wXLCAT2/wt3rKj1B6gKdYYdGvJeyV4vzp9/BsAzWiE9a3LU9ovkqTjVPJWskBCtktUuuG0R9qj4+5hHpS1cOtUbK6NBp+v+dHFZGzrXJPqoCASRqcmahAQkQ9cGjUWL8LwfuQ94VHzFajMqfU4OGrO5x+tZw+GfBQt6vNbuHJemXsi+q2k7tp+WdjQA9kFOqraVULq66yGT+vqlAGxkBIUmHQmp3RhZHwWGWIzUSyk4PBYkSaG+bnlc7Ngf2RQK++SZ9Fh1qnfDtWQpisdD0K4QyFXIyD4iavuFpy431C8DINeqzg9ymKy42rlRCXtUAn3Io1Jy/vlsOessvKHrBUDA5yNoCg0SlBK6nYgPIIWEitasiM60AkG8E2lht9BPH/CoeIvVLqyGwsFRWc92pNpu3PXTz1Etrzw6Rb2Q/VLvxBlsfSeamjYVAEuaC505wNLPP+rS+h5vBR5PGTIatgQHcdDo0T03upuEq1F2+NQxC6Wbu58gLGhJm2Gfhaqo9oyWkf0mzObota9PSVFFT0Pjb/j9jWSG2vU7LbZ2e6noIx6VvtG+QFEUvOs34P19nTrdOYTH6QBLaM6PpvveFABNyBujNQZF6EcgCNSEEmmT45OfAn2r6sdXogoV4+DCqKxnGjECbXoaSBK+kpKorAmql2GAyYBPUfixrvWFzGTMIiFhNEhgH+Rg68rfqC7d2un1G0LelG0MxFLvYsSh0XP/d5URVtWzVWpSO+LWVv0aN1v2NyIVP7sl0tqOOBLnAWl4DpDRBgt65BnYE7M5F6t1KIoSpLbuBzKM6kwspyWh3V4quohHpW+UJ8sOB4pXzQ1rOZCwCSnkhFW0PRMqulDyss4QIBgIEOhDPWR6AyFUBB0SrAt7VOInVHQZGZjGje12F9hoEm2PiqTRUPD6Gwz75WdMRUVRWRPUss2jU9SEu3m1bU8Vzsw4GYCc8ervSz+f3en16xtUobKB4WR5HKTlD+yuqT1meOhLdFVIqAR1Zfj38wt3rNjobN1DxTZ5EpVnKnhHKiRYoz8uITXkVampnk9GqDttZzwqiiz3iaZvgSpVUGkSEtCYmv9uHqcDjVkN/Ui6nk0XN4bOj0xQ3s/DP0KoCDokHG7RpcZPqJhGjGDQe++R88jDcbMhjHeLGnM2Do6OUFHXGoSk10dtvTBHh8qU59U0tpksmxESKtqECnQWP+t+mI8jJEz3Rn292vxvAyMYmdSzeHtPKQp9ia6QByEHNOhMAdYv7nnXXUFz6Kdot9CPLAfRJqjjFzJyopefEiY19SgAamoXkB6q+unIo6JJTKRo2VKKFv+KJk55dLsTaGNqMoDH4UBvUMOwWl1Sj/Ywhycoh5Jze9q4sa8jhIqgQ1KvuYZhS5eScWvP5njsD8hOJ4EdqnvZEEWh0ltMTrJhkCRKPT62uFuXKZvNudjtEwCFgsMsyMEAy7/+bK/ren3VkcFx6xjFEUXDom16l7DptOSbDAQlPZ5QO/2SdR/H1ab9gcZAkJ1e9Y599x4qlduXoDMGkQMSOQOPjPq+SUkHotVa8fmqsfvV6h+n2UZDddseFUmS0FitUbejuwRCAxT3FCpepwNdaCChoYd9Z2xG9XyzVhWSvv288kcIFUGHSJKE1maNJLT2Z3ylpQBok5N7pVRb9njwRzHObtVpOSRJvYDPq2kv/HMKAKnDVNfxym+/xOPo+O6srvZHALYqA2mS7Bycnxctk7tNOPzjTFJzZXxspG5neTxN2ucJV/xkGfQk6rTILhc7/v53dn30Gsjgb0xGb7BEfV+NxkBK8uEAKI0/ACBrtexqbPs93NcIT07Wpe/pUWnCGPKAmEIeke6SYFCvPyaN2ghvf0+oFUJFsE8gu1z4d+5E9rb2DMQKX2kZAPr8AVFfu/Hrb9h46GHsvO++qK47NVT9M6+27TLljIyTAA0+uZiMoWl4nU4Wf/JBh2tWV6s9X1ZL47Eik2eMftiqq4ywqVmKW0xqLxdbtotlXwivSk/YEK74saqVK85ffqXhfx+hef9nkEAnD+q1vcPhn/raBSSFvqUq3PvGwMnwQELtnqEfpwOjTr1+WQ09u9FJMqnnmyU3SAoekaMiEMSfLWeeyeapR+NZsyZuNpjHjCb74YdJnT496msbCwejeDy4fl2M7G6/XXhXOTpVTaj9ud6BOyi33teYTnLyIQCMPDEXgN+++pSGyrZ7UiiKQlXlfADWMJbRiba45qeECXtUFvsGImFAbwmyecUXuPrI2IV9kT0rfiJlySNkkCAxoefzfdojNVUNKTU0rqRAr34eqgIywXYmjde9/z6bjz6Giofin8cWqAyFftJbzvJxOZowa9QQTaKxZ0Il1dycM6gxyiL0I+jflJx3Plsvuhh/eXzd6Fp7EhDfNvr6nBySzj6LxBNPjPrahiFD0OVko3i9OH+NXnltkcVErlGPR1b4qb5t93Bm5qkA+LRLGDBqDEG/nzn/+VebCbj19cuRqccX1LOB4YxKiL7rvzuEhcpaVzAivCzZ9Sz5rGv9YQTN7D6MUFGUSPdk7zj18czc6CfShjGZsrFYhgAyo3Vq2b7DYsPZXrK3Av4dO/D3cKxFNAh7VHRpLYVKrduDTVI9H3ZTz4oTkvVG3KheRNmmFcm0gv6LEgjgWb0a92+/tWhcFA8iTd/q6uNqR28hSRK2I6LfpVaSpEj45/t2wj+ZGSeh1VpwuYo59OLJ6AxGStesZNkXs1sdu+qn5wBY7xuFXzIyytbz7rzRoNBiRCdBU1BGlzQFgMR8B8u/+pSGyr7RCGxfY5OruTTZt3mzmkhu0OIbpuCuMZI1KHozftrCEmokl61Ru2M7LTYa26n80WdnAeCviH932sSTTiL50ksxDW/ZbqDO58eKKijMPQz9mHaboBy06fDt54MJhVARtEugthYUBTQatHGa8xMm0vStD7TR7y1sRx0FgOO7eShy6zBNdwmHf9pLqNXpEsjKPB2ABvfXHHHxdAAWvPUq635sFk3Fvy3GFVC9Pb+YjwVgZB8RKgaNhiGhEtoKw8EA2LI9KJKX+W+81K1ZRv0ZZzBImUdN/BxmNTV7U4aCYoCmkgGYE3rWtGxvGIwZAKSielHUEuW2K390mapQCfQBoZJ09llk/f0uTCNGtHi81h/EhvoZjMa0aU9ogrJi0+JxihwVQT8lnL2uTU1B0mrjaku8JygrPh+7Hn2U2rfeRvH7e2UP6+GHo0lIIFBZiXvZsqitOyU5AZ0EW9xetrZRpgyQm3sxAFVV31B0xFjGHXciKApf/vMJZj/xD758/inmvnkXhgQ/clDHT0xAQ3MPk75AOPyzPpCB2VyApJFJzPeweckvrF3wXZyt27fYHPKmpOl1pOh1kWnJ7lFe/E4dqUkn9LoNRqMqPuzUAOA0J9BU3Y5HJdT0LVhfj+zpm0m3jbIfE6GOtT3sowLNE5QVi0ZU/fRHZK+Xqn89T+mVV/WJTofxIlCjXiB0cRo2tzvxnqDs37GD2tffoPKpp0Cn65U9NAYDCcccA0DjV19Fbd0EnZaD7OEy5bbDPwkJI0hJmYKiBNm69d8cfcW1HHjqWSBJFC/9lXWLvid9rNrkS5N2Kn7JyGCLEYu271xCwq301zs9pKWpf8chR6qewHmvvkDl1i1xs21fY/dE2mBTE67ffgPAO1qmclUK4449tddtMBlV8WGVQ0Klg+60msREJIuaL9UXvCpt4daoIkWdnGzr8XrBkFDBjAj99Eckg4G6d9/F+dNPeFavjrc5caO9DovxIN4eFV+ZWppsyMvr1SqXxJPURN3Gb+agtDFMsLscHSlTbr8XxeDBtwCws+JjXO7NHHnJlVzyyDNMvuAyDjx7MvYCJ6Bhc8KFQN8J+4QZblM9KuscbjIz1f4wsnEjBePH4Pd6+PjxB6mv6BvzYPo6axxqpc1QixHnTz9DIIA/U8Fr06B1TyRz8JBet8EYEir6gHod6ij0I0kS+szQcMKK+OUkBaqrafj8C1zLW07wVhQFX6jdvV+yIUk9/+oNzwvSmBUR+umPSJKE5eCDAHAtXhxna+JHJHs9NTXOloA2KSxU6uOyf7jZmz4/epNi28J62GFo7XaCNTW4liyJ2rrHhNrp/1jnwNNGmTKAPXEc6WnHATJr195CMOglc/AQDjr9TCyD1NyUjIwTWelThesoa98SKiNCoZ9NLi8m62jM5nxk2c0hF08gJScPR0017953O2W/99+bj86gKArfVKs3BIcl2SJlyd5RMtVrUhgz9ZSY2BEO/UgB1ZOnelTaDv3A7gm18ROjnrVr2XHrrVT84x8tHve53YSKdAj2cHJyGE1o3o/WKIvQT3/FerCakOfsx0IlWNN2h8V4EO8Jyv6QUDEMiH6zt92R9HoSph2HZLFEtdRyhNVElkGPW5b5taH9u6+ion+g16ficG5g1epraGpax7p1d9DYtAqdLpGhQ+7g99Dd9khb38lPARhgMpCk0+JXFDa6vJGuu9W1X3DefY+Slj8QZ30d7z9wJ9+9+gL+PprLEG9+d3rY6vZh0kgcl5qI5cjDcB0k4xkn07Alm+GH915Z8u6EPSpyoB694sNrNFPbQei3OaE2vh4VaLt9vmRWE7qVHg4kDKPTqYJHZwjgFaGf/onlINWj4l6+ot/mqYRDP9q+kKNij7dHpfe60u5J+p//zLAffyDpnHOitqYkSUyNVP+0nacCagO40aOeQaMxUVu7iMVLTqFi12wARgx/FEWfxeZQx9K+FvqRJInRIZtWN7nJzj4HkKitXQT6Gi544HHGHHM8ACu++ZxZt/6JkuVL42hx3+TzynpA7Wps1WlRDkqj/ooAjiwtQycei8Ecm945Op0djUZti5ApqSXK1QGZQDvX477gUWkOl7fsoeJ2NKE1qZ5MTZSEikGvrqM3+PGK0E//xFBYiDYlBcXj6bd5Ku3dHcQDw8CB5D77DDmPPRaX/X1lYY9K74Z+QA21aczRFwFH76WdfpiUlEkcOPFD7PYJaLU2bNYiDhj/BhkZJ7DG4SagQLpBR04faJ2/J2MSQkLF4cZiKSA9TS2jLiubhdFiYdrVN3L2XQ+SkJZOY9UuPnrsfj5/dgbupo7/Jv0FRVH4rKoegFPS1S/CxoZVALiqzIyeelzMbJEkKeJVGaRX/32cFlu7SdHWww4j7YYbSDg2djbuSaCq7a60XqcDvVHtqquLklAx7TZB2e9xI0cxp62vIYRKO6h5Kmr4J5q5AvsSCdOmkXTuuRiH9n7i3N7QJiaSeMIJkZBcLFFkGX+ZGoYxxMCj0lsckWxDK6k5HOEeGe2RkDCCAyd+wFFHruSQQ74kJWUSAL81qnduExItfaJ1/p6MCXXKXd2kusIHDLgSgJ0V/8Pj2QHAwHETmP7UTCaefAaSpGHDz4t4+65bqC7dGheb+xIbXB42u7wYJInj0tQv1Jpq9frnqbGQMbAwpvaE81QGaMNN3xIoWd729dhy0EGk3/AnbJMnxcy+PWnv5s7jaMKgVz9zxij0UAGwhjwqRq267v4c/hFCpQMsBx0I9N+E2pRLLib7Hw9iGj483qbElUBVFYrXC1ot+pycmO2r+P00zpmDv7IyKuvZ9ToOTOy4THlv/NaoXgwnJFijYlO0GRMK/ax1eAgqCklJB5GUdAiy7KN4y1OR4wwmM0dd9gcufuRp7JlZNFTu4v0H76Jme2m8TO8TfFGp5oAdmZKA1eNh5/334130GyigYyDaXirNb4+wRyVLUw+Aw5JA8bK+ez0OtDc52enAEJqc3NOutGFsxiQATFo1FOvbjwcTxk2oLFmyhJNOOonk5GSsVisHH3ww77zzTrzMaZPw3bvrt+Uo/TRPRdCcSKvPzkbSxy7csf2WWyi/6Wbq330vamt2NvzTHst286j0RcK9XdyyzGaXF0mSGDrkDgAqKmZTU7OoxfGZg4dw8SPPkDl4CO6mRv73yH39Ogz0eSTsk4Rr8a/Uv/seto+c6hDCxNExtycsVNIktTuty5JA1bYSGttp/BZvAiG79vSouB0OTDpVUFh7OJAwjD20jklSk9u9+/FgwrgIlfnz5zN58mQWLVrEOeecw3XXXUd1dTUXX3wxjzzySDxMahPDkCFok5NRPB7ccZzaK1CpfecdKh55BO+W2DbuUgIBjMOGYSwq2vvBUcR+0kkA1L3zTtQmKh8TSqhdWNv2NOWOqPL52e7xIwHje1GoyF4vDZ99RrCx64JBK0mMDXlVwmGqxMSx5OZeBMDa32/B4djY4hyzLYGz73qQ5Owcmmqq+HrmM/2y5X6DP8C6UKO349IScSxSRZ13pIy71khaXuw9q5HutIra9E3JzgNgy29th3+avv+e2rfeJlBXFxsD9yDYTu+pepcLa2ggYaKxZwMJwySZkgCwSG4UScErPCrRIxAI8Ic//AFJkli4cCEvvfQSTz75JCtXrmTUqFHcd999bNq0KdZmtYkkSSRfeglpN92ILiMz3ubElEBNDU3ffYfn99/jbUqExk8+pe6NN/HFWKhYDzuMwZ9+woB/Px/TfROmTUOfl0ewvp76/0VnCvAom5lco1qmvKiu/eZvbfFLvXohHGY1kaCL/kgFRVFo/OorNh97LDtuux3PuvUtnq+ZNYtdMx7fa4v0CaHw1vLG5jvMoUPuJiFhDH5/HUuXncO20pfxeHYQDHrx+qqRNVVMvfZUEnJ9bN/0I+sWfR/119fXCYuUXKOeZJ0W56IfAPCMlHHuNJOePzDmNoU9KpZQd9pAejYAW5a1PWF818OPsOuhh/CVlMTGwN2QnU7kkFdDu0fVT7XbGxlIaIuSRyV5t3Vki3a/rvyJuVCZN28excXFXHTRRRxwwAGRxxMSErjnnnsIBAK89tprsTarXdKvv57066/HkJcbb1Niinv1arb/6QZ23n1PvE2JEO82+rFG0ulIufIKAGpeeikqXhVJkjg+lCQZburVWcLC5ojknrf/3hPF76fi3vsov+UvBKuq0WVlIe3Wnj/ocFI98/+ofe01tp53Pr4OesyEw1K/7SZUtFoj48e9QlLSIQSDTjZvfpQff5rC/AUj+eGHQ/jl12ls2fFnCk8pZuRFxZTW38DWkteQ5d6Z69QXCQuVkTYz/m3b8G/fjqIF31AFR4WFtHgIFYM6mFAfUEMqLpsauixdu6rNPji6UHfawK7Y91IJ1KrhKclsRmNt6XGs9fpICA0k1EUrmVZnwIPayyiQYBAelWgyf/58AKZNm9bqufBjC6I45l7QPSIDCftAs7cwzd1p998JynuSdPbZ6HNyCOzaRe3rb0RlzbBQmVPTSLALIY4Fofb7U5ITomJHGMXnY/uNN1H/wQcgSaRdfx2Fc77BcuCBkWM0Vgs5j89Am5qKd+NGtl10Md7Nm9tcLyxU1jnduHYLbxkMqRww/g2GFz1MQsIoJKnZK6TV2jCZBmAyDUCRJfQ2L8UlD7F8xWX4/f0jZ2VdqJHfCKsJxw8/AuAbIqOYAFcOtuTohCy6QnN32ipQFHbJEgnpmQT9fratXtHqeH2mKmz8cRAqhgEDKFq5gsKvv2pVEVcXCEY8KvoolSdLkoQH1XsYtGpFjko0CYd1hg4d2uq55ORk0tLS2g39eL1eGhsbW/wIeodAdd8ZSBgmXh6VQHU1ity1fI5ooTEaSb/lzwDUvPgivu3lPV7zsCQrdp2WKl+AX+o713p7m9vLNo8PnQSHJ0XPo6IEg5T/9VYc8+cjGY3k/fvfpN90ExqDocVxkiSRcNRRDProI4xDhxKorKT0yqvwl7f+e+SYDGQZ9AQVWNXU8uKt0ejIzb2Agw/6lKOOXMsRU5Zz9NSNHHXkSiYdPp9Jh88n2/hvtv+QSdCnpb5+McuXX0IwuP93sW3uOGzGGc5PGSHja9KTljsmLjYZjarwQPGTpmmixh/AcojaGXfLb62rf8Ih+sCu6FTKdRWN0RiZObQ79XIAM+p7SB8ljwqAT1I/i4pVK6p+oklD6G7Ybm9bVSYmJkaO2ZNHH30Uu90e+RnQy+3Mw3i3bKH6pZeiOtG2r9OXmr2F0YS708awjb4SCLDpiCNZP2585G8SaxJPPhnzxInILhc777yzx6LJoNFwcqiZ1yehLqR7Y2Eo7DMx0YotivkplU8+RdO33yLp9eT9+98kHD21w+P1mRnkv/E6xqFDVLFy9TUEHa0v0GGvytIOxgVoNHr0+sQWnhWAEYcfi796JJs+yUdSbDQ51rJp00PdeHX7DrKiREI/RQZNZHSIZ6SCY6eZrMJhcbFLozGg16uenMOtqpCqKBoLqAm1e34WmkM/fWuCsic0OVlBQqeLnkcyoAm1CTBLoo9KX+HOO++koaEh8lMWmmjb2zh//Imqp56m/sP/xWS/vkAgPOcnLf4DCcNE2ujH0KMSqKyE0MVQmxJ71zeApNGQ8+gjSBYLriVLqJzxeI/XPCNDTcT7vKoev7z38M9XVao4nJoSvYts41dfURvKR8t+9NFON+rSJScz4OWX0WVm4isuZuff/96qSueQJPUC3tFco/bQaLUceMpZeGpNVPw8FJAo3/Ffamt/6vJa+wplHh/OoIxBksj5fS2K242cpCOQq+CssJA1JD5CBZrDP4dY1S/iNaZE9CYzzvo6dm1pGf5rDv3Ex6PSHr5QUzZ1cnL0hH5kwKGF/XowYcyFStiT0p7XpLGxsV1vi9FoJDExscVPLIh0qF2+HMXfP5Lr2iuziyeR0E8Mc1T8O9W5IfqsLCRN/HS9IT+f7AcfBCDY1NQpr4qvtJS6d99j5z33UvqHP7b4Mj88yUaaXketP7jXnioN/gCL6tSL4MnpSd1/EbshO51U3P8AAKlXX439lJO7dL4+M5PcZ58BvZ6mb76h9rVZLZ4/NBSe+rXB0aU8nDAjj5iK3mRm52oPdsuJAGze/BiKEp8QYG8TDvsMtRrx/Kjmp3hGBEACR4WVrMLWofpYEa78GWlU36c/N7jIH6cWYhTvUaasywoNJoxDjkrl089Qcs65NHz+RYvH5WAQxaB+b0RrcnIEnfo+1xhFH5WoEs5NaSsPpa6ujurq6jbzV+KJcegQtElJKC4XnrVr421OTAjUqDkqfWEgYRitPQmIrUfFvyMkVLKzY7Zne9hPOZmCt98i++GHWoimYH09sseDr7SUhk8/Zec997L5uGkUTzueivvvp/6DD3D+8APybgJPp5F45JO3ufir2bxZsqPDfb+tacSvKAyzmBhqjc7EZI3VSt7z/yLh+ONJv/GGbq1hOeAAMu9Um7lVPvUU3i3NJamjrGasWg2NAZn1zq7nlxhMZkZMOhKAXcvS0WrVENCuXZ93y9a+zuqQUBlts2AePw7TsYfgHucn4NZitQzGaIlfJ+KwUEmX6kjUaWgIBAkccDgAW/boUhvJUamsjHkvHO+mTXjWrEHeo0zY43SgCU1OlqKUSBtGo1WFj9YYFFU/0eTII9UP/5w5c1o9F34sfExfQdJoIu30nYv7x9yf9lpBx5O4elT6gFABsEyc2KKiIFhfz8bDDmfD+AMonnY8O27/G/UffIC/rAx0OiwHHUTqNdeQ/dijSLslqPq2bmXoF5/yh0/f49K//onixe1PEQ7nsZySEd2LrOWgg8h77tkedftNvvBCki+6iJwZMzAOHhR5XKeRONiufrn+3MmE4T0JT1re9PNy8nIuB6C09OX9shnc2pBQGZNgJnHaNLj1ILxjFZwVZrKHxLbR4Z6YQqEfv3cXk5LU0OO2nIEgSVRuLcZZ39zcTZ+h9i9RfL7YJ92Hbu72vGZ6nQ50JnVgoDbKQiWc76I1BPEJj0r0OOaYYxg8eDDvvPMOK1asiDze1NTEP/7xD3Q6HdOnT4+1WXvFclAo/NMP5v7IXi9yU6jmP7Xv5KjokpPQZWfHVDT4d6reBl1O3xAqe+JeuRJCX5ySXo9pzBhSrrySvP+bybBffqHgzTfIuOXPJJ1xBhpLc28HfV4eOY89SlNyCvmVO3FfeQV1H3zQav1tbi9zQ3OBzsyITqOqaCJJEln33tNm6OhQu+oW/6mue0Ilc/AQkrNzCPr9+KpGoNGYaXKspa5u/8tVWdOkCpVRNjOBgIOyslkANJTa4pZIG8ZkUudreTzlTA718PnFHYiUSztqayLHSgYD2pQUJIuFYKivSaxor32+x+FAZ1AnJ0ez4kddzx76b0B4VKKJTqfj5ZdfRpZlpkyZwtVXX82tt97KuHHjWLt2Lffffz/DhsX3g9EWlkNCQmXZMuT9fO5PuIeKpNejiVEeUGfQ5+Yy9Pt5DHz3vzHbM9CHQj9tYTvySIavWsmwJYspWrWSQR+8T+btt5EwdSpaW/vuekmnw37aaQTf+5CF4w9GGwhQcc+9VP/nxRYeg1e2V6OgJtH2NOwTqKtj+4034otREvyUFPVLbVFdU6cShvdEkiSGHBwKMSxeRU7OOQCUbX89ekb2AWr9Acq9ag7FKJuZbaUv4ffX4m00UrsxieyhcfaomNRmm25POUeEevgsbnCis6vC2d3Y0sM6ZO63FC1birEwdpOeFUVpzuvb4+bO43Q0T042JEV1X1NIqBj0PiFUos3UqVP54YcfmDx5Mu+//z4zZ84kNTWVt956i7///e/xMGmvGIcORZuWhuJ24/5tebzN6VV0OTkMW/wrgz//rFXjov5Gc+gndlOTu4pkMKBNSOjWv9Vh+dl8/7d7ePOEMwCoeuYZKp98EkVRKPf4eHunerd6dV56B6vsHUVR2HnPPTR9O5fyv/y118Inwfp6ym+/Hd+2bYxPsJCi19IUlFnSjeofgKEHHwbAluVLyco4H4Camvl4vX1zKF53CCfSFpgMuP7vGXZ9/jL4YccvaWi1BtLyC+JqX1ioeL07KDTryTLo8coKO3PUUN+eQyQ1FkvMr1tyY2Ok0EK7p0fF6cCoU8uTLVGanBzGEhIqRq1XJNP2BgcffDBfffUV9fX1uFwulixZwsUXXxwvc/aKpNFgm6TeXTl//CHO1vQukiShTUzEUBDfC1RfICJU+mjoJxrcOSSH1884n5lnq5+/2ldeZdejj3Hb+lKcQZmJiRaO7GFZcv277+KY+x3o9WTdf1+vfZFUPPgPGj/9jPJbb0MKBJjaw2nRWYOHYktJxe9xU7fNTWLiAShKkIpds6NodXxZHQr7HOpzUvfvV0j6vyCGwCAaShLIGDgYrS52E8PbQi1P1iDLPvz+2oinrDhD7aPVF6Zdh3P6NImJaIzGFs81OJqwaNS/cUKUBhKGsRlCQkXjJeDzEgwEorp+X2Gf6qMSb6yTJwNE2ksL4oPi98ekTDzY1BTJ1dGHyh73R8YlWPjboGw+OPYUnrr4DwDUvfEGBa+/ilEj8ezwfDQ9EBaeDRvZ9dgMADL++hfMo0ZFxe62yLjtVjSJiXhWr6bq+X9zdEhgzavp3peZpNFQMFYthd22egU52WcDsHPn/tNTKZxIe8R69brmz1eo3lwISOQMGxFHy1Q0Gn2kQ63Hsz0S/tmQpD7WJ4RKB+0cqp0ebOGBhFEO/SSaVA+NOSSE9tfwjxAqXcA6aRKmsWNJmDq1V1qqK4qCe+VKmubOxbN+/d5P6IeUXnkV68eMpen73p9uKxmNDHj5ZbIfeQSNNX7lmbHghvwMLshK4fPJx/D0hVfiMJlZOXocTxUN6FFuSrC+nu033IDi9WKdMoWUyy6LotWt0Wdnk/2g2p+l5sUXOXzLBjTA704PpW5vt9YsGDMeUIVKZuYpSJIBp3MTDmffmPLeU9aEQz8rVU9xcEQKW34qR6PVccAJp8bTtAjh8I/HUx6ZNVVisOIxmFoJlab589l83DTKbuhe2Xt3aG6Q2Vqo1Hg92EIDCaOdTGs3quuZJRdBjWa/rfwRQqUL6FJSGPT+e6TfdGPUm3+516yl5LTT2Xr+BWy/4UZ23P63uM2XqXzqabZdcilNc+fGZf+OCJfYxqL0UGMwYJs8iaSzzuz1veKNRpJ4dkQ+r4weiPm88/jsP7N4/LxTOSer+65qJRCg/K+34i8rQ5+bS87jM2LSNC/xhBOwn3UWKAqOu+7iSJ2aD/NVF6dFh8kfPQ6Aqq1b8LlkUlLUDrqVlV9Hx+A44g7KbHJ5QFEwr9oCQE2C+hkbe+wJ2DNaz62JB2ZTHqAKlSyjnqEWI4okUZozCNceybSSVoe/rAx/aWyStqG5AKGtTt413kDzQMIoC5Vko+pRMeHFZ9p/JygLodIHaJo3j20XX4x30yYkiwXT6NFk/O32uHVC9axdi2vpUoJNfa8lczx6qfQnTk5P4p8jCnjwkLGMTmguZ5bd7i6vVfnU0zh//BHJbCbv38+jS45deXPW3+/CUFBAoKKCG159Ho0sR8YAdBVrUjLp+QMBKF29goyMEwCoqtz3Z39tcHoIKjCyrhpNtRtFq1DmltEZjRx61vnxNi9CuETZ7VHbBYS9KqV5ha08KvosVVzFcoKysaiI5IsuwjppcqvnagOBSOgn2kLFZGjOHQvY9EKoCHoH12/LKb/lLyheL7Yjj2To9/MY9OEH2CZ1bu5JbxCoUudk6DJ6VunRG8Rj3k9/x19ZSck551L51NOdrtap/s+LkTk+OY88jGn48N40sRUaq5Wcp55CMhhI+fkn/vDJu/za4GSXt3u5TfmRPJWVpKcdiyTpcDg34HKV7OXMvk04P+WEbesA8A1UcNRamHDiaViT+k7fnN1DPwBjE8wA1CWm4G5sKVTCgwnlhgZkT2ymXlsPPZSse+8h6eyzWj3XEAxgQQ3JRFuoSJIWN2pYWrbphFARNOMrLaXmlVdwLW2/m2dnCFRXs/2mm1SRMnUqef9+PvJFvDve4mKcv/zSo726QnigV1vjyuONNjkJiI1QqX37bSqffBLP77/3+l59Gcf38/EVF1Pz0kuU33QTwca9Jy/6K9RqqYzbbiXxxBN728Q2MY8eRfYjjwBw4ZzPmPbzAj7cVbeXs9qmOU9lOTqdnaSkgwCoqVkYFVvjRbh1/oQNasdtz0DQYOegU8+Oo1Wt2VOopBvUSiSX2da6PDkhAcmsCpl4zPzZE7emOTdKp4t+XyqfFBIqFu1+W6IshEo3qH3rLSqfeJK6d9/r9hqKorDjzrsIVldjHDqU3KeeRNLpWh3n/OUXtpxxJjtu/xuyt3vJgF1BdruRQ19EuoyMXt+vq0RCP/W9H/pp/PIral5+Bd/Wrb2+V18m+fzz1C98vZ6mb+ey5ZRTafj00w4rr7L+/ndyn3ma1KuuiqGlrbGfcjJp118HwJTli/nvjupu9XDJGz4KrU5HU3UV9RU7SE05AoCa2n1XqLiCMnNr1M9Rxjo1eb8uycjgAw7GZLPF07RW7C5UFEUhzaBeK10WVajs/m8qSRL6jPAU5fgLlUB4cjLWqE5ODuPXhP6trBp8wqMiCGM/WW3X3TRvHnI3FWzDRx/jXLQIyWgk56knW7Q33x3zAQegS0sjUFlJwyefdNvmzhKoVL0pktmMJqFnvTN6A22obXYs2mNH2uf30a60sSTprDMZ+M7b6PPzCVRWsuP2v7HpqKmU3XAD5bfextaLL2mRxyLpdHHzpOxJ2g03kHjLLTx59Z/Z7PaxuBvN3/QmU6RUd9uqFaSmqvPI6up+IRjs/RuI3uDZrRVs9/gZ7WxEX+lAkRSqZBsZg2LX0bWzhHNUgkEnfn8dafqQUDFZkWW5VcijeYpyZUzsa5o3r92u5bJOfUzW9k6Xb1kTuk6bweMUQkUQwjR2LPoBA1Bcrm6XyQaqKkGrJf2mmzB1MDJAYzSSOl0diFb7yqsowWC39uu0XZXN+Sl9sSutLkWNm/e2UFECgchFTp/Td7vSxhLzmDEM/uxT0v98M9rUVII1NTjmfkfj55/jXraMhk8+jbeJbSJpNOReczUnDVBDmc+XqpN1u+pZCfdTKV27Eqt1GEZDJrLsob5+35v/tc7hZmaZ+v6+I13CPVbGW6RQV5FAZh8UKlqtGYtF7URbV/9LxKMia7V42yhR1mWqHpXAropet00JBtl+w41su/iSViFpv8+Lxqg2YYv25OTI/lpVqEhG8LmFUBGEkCSJxJNPAqDh0+5dnNOuvZZBH31EyuV77yuRdM45aOx2fNu24VjYu65mf0io6PtIWeKeaFNUj0qgrnu5Bp0lUFUFwSDodG32RuivaIxG0q69lqHfzyP/jdfJvOsuMm67jdxnnyXh2GPibV6H3JCfiQb4tqaRta+8xo6/3hqZeNsZcoePBGDHBjXxNCV13wz/yIrC7Ru2E1DgxDQ7owvqqLs2QPnFWgIufZ/0qACkpanvr+qquRg1GhJ16teX02JrNe8nnF/nj4FHJVhXB7IMkoQupWU5v9fhQGtUby51UU6kDSOFhIrWFBQ5KoKWJJ1xBgDOhYvwbd/erTVMRcPazEvZE43VGtmv4aOPu7VXZwl7Efpifgo0C5XdZ2v0BpHW+ZmZSNrox5X3dSSDAevBB5Ny2aWkXnUliScc3+cF3WCLkTMzk0lurEd+7jkav/yS4hNPou7dd1E60Xo8s3AoGq0WZ30djVWVkfDPvpZQ+/bOGpY0OrFqNTw0NDcitBrLbCRlZWO09M3mhmlpxwJQXfM9suwnXd9+Qq0udKMVi2TacPt8bUpKq+u5x+lAH5qcbND3ThWVNpSgqzW0DoHtLwih0k0MAwdinTQJFIX69zqXVCs7nZ26ILaFPdR0rGn+/F71JoQ/2Lo+WPEDanly5p13kPPEE9BLg+0A/H18arKge9w2KAtXUjI3/eUePEOHITc2UnH/AxRPO57aN95E7iDGrzcYI96GHRvXkZI8CUnS4nJtxu0uj9VL6BGVXj8PFavv7TsGZZNj1FFbuwiApjIrA0aNjad5HZJkn4Ben0wg0EhDw7LmhFqztVWJsnnCAaRdfx3203q/s25H7fN3n5xsjnL7/DCG0GBCvd4vPCqC1iRfdCEA9e9/QNCx9+ZoFQ8+yNYLL8JbXNzlvUxFRZhGjgS/n8bPPu/y+Z3FPH4c9rPOwjLhgF7boydIGg0pl1+O/dRTIl1qe4NIIu1+PIywPzLQbOTPBZmsHziES/76APrbbkebnIx/xw52PfIIm6Yeza7Hn8Bf3rbwCCfU7ti4Dr0+kcTE8QDU7iPhnxe3V9EQCDI2wcyVeWnUrf4WVtWgOLU4KywMGjcx3ia2iyRpSUudCkBV9dyIUHG24VExjxpF+k03kXDssb1uV6C6faHiaHJg1qpJ5tYoT04OY9SrHhW9zo9XJNMK9sR21FEYBg0i2NBA7azXOzy2ad48Gj75FM/atZFBd13FfvppADTO+aZb53eGxBNPJOeRh2PyAe/LBCJTk0Ui7f7Gn/IzGJ9goUaBP445jLQ5c8i6/z70BfnIjY3Uvvoqm4+bRvntt7dKuM0Zpjau27FRLefd18qUf6lXb6j+kJeOVpKoefd10v6px/KWHtCSP2ZcfA3cC2np6nWpqmouaTo1JOsyW+M6mDBY0377/CqnM9KV1mrsHaFiCXlUjDqvSKYVtEbSakm/+Sb+v707D4+qvB44/r139pnsJCEEQth3FJQdFKnUrW4tVot1q0Ld6lq12tatrdW21lq1dVfca9W2/txFERVkERCQfQ1LEgjZk9mX+/vjzgyJWUjCTGaSnM/z8BRn7sx95/aSOXnf854DUPOf/7S4I8dfVkbpXXcD0Ovyn2EbN65D50v9/vcBcK9eE43iRXwcXvqRQKW7Masqz40ZQI7ZyGanhx9t3k/9uT9i8Acf0O/xf+KYNhVCIVSLtcnOt8iMyqE9u/F53GRlTQOgqmolmpaY3lxt5Q2FogXeJqQ58PrK8XzzLQAVNn37dbLmp0RkZc5AVc14PPvop+i9fBIdqESWfgzNzKgccrmjDQktcUqmdZj1QMWqemXpRzQv9ZRTyLzoIgpffaXZpMuQ283+X1wXLuw2hOzrruvwuUz5+aSfczbZ114LCeoDlAycy5dT/tTTuNasids5jH3yMA8ahLmgX9zOIRIn32rmjXGD6W02ssXp4Xsrt7CgtBLrzJPo/9xzDPy/t+l15ZVNXpfaK5vUXjlooRAHdmwnNXUsqmojEKjGmeTdlDfWufGGNLJMBgpMPtatvAzDXj0hvcSZycBxybvsE2E0OsjM1NuL5HuXAc0n0wI4v/qKqtdew38gvluUDy/9NG05Uu71xq0hYUSaNdyYUHXjkWRa0RxFVcn77W8whQsMNRSoqGDvzy7Hs349hvR0+v3jH6gWy1GdL/9PfyLnF9c22QYXC8HaWuoWLcKzdVvM3zuWaj/8kEMPPYRzydK4naPP3Xcz+P33cEybFrdziMQa4bDxznFDmZjmoC4Y4o5t+5mxYjNvHajENHQo5n59o8dqoRAlv/0trtWrGyz/bEZVTWSk61/wVdUrEvI52mpVrf4lNiHVzIYN1+LZugUlqOAzGahXLAw49rgEj7BtcsLblNOc+nKby5bSpIMyQNnfHubAvb/Ds3FjXMfTWo5Kpdcft4aEEZkW/X3tiguPBoE47oZMFAlU4qDs4YfZf8ON7Pz+KbjXrkVNS6Pf449j7t8/0UNrlWfzFvZfcy3FN9yQ6KG0yhitpRL/6rSie+tvs/Df8UP449C+5JiN7PH4uHbzXr739VbeLasmFM5Rqf73G9S8+Rb7rryKvna9bkXJNr2eSkbmJP2YquQu/Laq1oWihTjH8zcqq5ZiLdJ/aaqyWbBnZJI7YFCCR9g2kXoqqnsjGVoVLpsDTzMzKpGib/Euox8NVHKaCVT8wWigYjRmxOX8KeGlHwdOvBZrtyyjL4FKjAVra6l8fgF1H31EyOXCOmoUA155OWl30TQUCDeSM/ZpOjuUTKJl9KuqEzsQ0S0YVYXL++WwfMpIbh+YR7rRwFanh3kbi/jhNzvY7vSQfu452CdOJFRfj+3FVzEHgpRu24IWCpGZMRnQZ1Q60keoM2iaxtc1Tk7hAzKcC1EUI9mV+gxKtd3KgGPGo3SR5WSLJZe0ND3pdzyr9F0/zTTKNEVrqcS36NvAN/7N4IUfYzu2aSJybSiIAz1wMJniU5k28r5GAnjtlm5ZRr9r3JldiGI0knfXneTe/isKnn6KAW++gWXo0Jiew19cTMWzzx1VU8Rm37e0aySQGuJcRj9YX0+goiJpv3REfDgMBm4ckMfKKSO5qbA3doPKihonp6zaxod1Hvo9/k/MAweilVcwbt8hPPV1VJYWk5Y2FlW14PdX4nTtSPTHaFaR20ep188JfA7AkMG34d+wF4Bqu4UB4yckcnjtFplVOZ6v8Vms1Hs8hL6zmeFwv5/4zqiodjvmggLUcMfmhtx4Do8nTiX0DQYHwfBXechhlBkVcWSq3U7GnDn0uuwyUk44IS6/pbg3bKTsL3+h8qWXYvq+0Z0uzeTbJJPI0k8wTks/te++y/bpMyi+PrmXwER8pJuM/GpQHz6fNIIZGSm4QyGu2FDEf+p99Hvk7yhWK9m1TvpV1nFw1w5U1UJ6enh2IkmXf5ZV15OllTOQnYCCqXIYoYNlaEDqhOMZNrlr5WLlhKvUjmE9Fs2Dy+rAU9+47IMp0u+nLHEdlP1GPVAJYEdVj1yFvCMURcGn6B2UQw5Dt9z5I4FKF+SYOgUMBnw7d+IvKYnZ+/oPRGqHJHeRs2i/n8r4VOiNBGzJ2kZAdI4Cq5l/HTuYi/r0QgNu2LyXzzJyyQnncI0sqeDQ+rUAZDRY/klGX1XXczx6EJXiOIal9z0IgCcthbPuuBeD0ZTI4bWbwzEMozEdE36yKcNlb7pFOVJd238gcYGKZtCr0mpqfDvR+8OBCnYFbzespSKBShdkSEvDdoxe6rp+yZKYva+/OFyNNcnLxhsyw0s/1dVoodjXrogugSV5wCbiz6gq/Hl4P+b2ySIEXLN5Dwd+dB5aYX9MoRDq+3rxxcyMcEJt9cqkWzLUNI1l1fVMCAcq+1a6CVVVEjSo5Mz6HmabPcEjbD9FUaK7aBw49S3K38lT6Yx+P87lyym64Ccc/MtfmjynaRqqSd+BE6/OyRFBVQ9UNLvSLfv9SKDSRTlm6LUEYrVFV9M0/OHmiuaCgpi8Z7wYw4EKwSDBmqbbEo9WpHy+9PkRAKqi8OdhBZyYmYIrGGL+5n3YwvWQsnbuwbNjB2lp41BVMz7fIVyu3QkecWN7PT6qPZWMRN+mW7ouSNWwwRR8upD83/wmwaPrOFP4yz+Felw2B66673ZQ1mdEQ04nwfr4fHn79u7FvW4dvp27mj7ndmOw6L3dTHEqnx+hhTsoq1a6ZRl9CVS6qJQZMwBwLlvW4UaHDQUOHULzekFVk/4LWjGZUFP1f5jBODRoDESWfpL8OojOY1IVnhw9gHyLiV1uL48NGcuhjFRUoOThhzEYLKSl6rs+ampWJXaw3/FVdT3jWY2BEN5qO746M7OvuJr0vHwMaWmJHl6HGU2Rbbn1OG0p1HwnF0V1OKI/J+KVpxKsqNDH0szWZE99HebwjIo1zoGKEglULCHJURHJwzpmDIb0dEJ1dbjXrz/q94vMppj69EExJf96tTE3F0NONiGXO6bvqwWD0boL0udHNJRpMvLIyP4owCulVXz7vZPZnZ2O63szAUjP0Au/VdesTuAom2q47FO1047ZZqPwmK5R3K01kRkVfenHwcFdTXdcGXvnolgscfmFBhqUz+/VtM+Ps7YGi9ELgC3OgYrRqAecRnNQdv2I5KEYDNinTgX0ddKjFclPMSX5sk/EoHffYdiXX2IbMzqm7xs4dAiCQTAam600KXq2GZmpXJyvfyk9d9LZbCjozcFq/bfqSIXamiQLVFZWVTCWtQDU7E5lyKhjMCRZHk1HNJxRcdlSKNvdNFAZ+PrrDF/7Dfbj49MeIHBIr9HSXOL9odo6HIoeNDjMGXE5f4TRFAlUAjKjIpKLY4q+08C17OgDlfSzzmTYiuXk//G+o36vzvDdZnGxEt2i3bt3s72bhPj1oD70MhkpNtlYP/J4Du7Se/xEtii7XLvx+SoSOcSovW4vKZ5vseLF77bgrrAwcPsetk6YSNVrryV6eEfFFJ5FcODEaUuhqrSkSSKp6nDE7WcF6A1nAUzNBCoHnM5oQ8J4L/1YwkGbyeiXZFqRXBxTpgDgXruWkPvol0AM6ek9frlDEmnFkWSYjPxygL6j5KvjZ1G8fz/BQADVpWC3DwGgpiZ+DTPb418HKqOzKbV7rBhNFozFpWg+H8beyV0v6Ugazqh4U/W/H9y1s1PHEKl6G9kK3VCZ0x33Pj8R9vC1MBt9EqiI5GIqLCTlpJPIuuxSPRG2B4r1VtBAeGuyUbYmi1ZclN+LQqsZlz2VnX2GsmvOHIou/CkZaXqrjGTIUzng9fP43kOMZR0AdfsdDBw5Bv9ufVeS7dhjEjm8o2YK985x4MTl0JNJDzaz/BMvWiikLxXTwtKP53DnZGOcAxVHuN+P1eCRQEUkF0VRKHjicXJ/+UsMGRmJHk6nqn7rP2ybOo3S22+P6fumnXkmBU8+QdZFF8X0fUX3YlZVfjVID2YXTz4Z/+4ifDt34ijVf7NOhjyVP+8upU9wK4XsQdOgrtjB4F69QdMwFRRgbCYBtCuJ5GU4qKfeaEFDaZJQ61rzDTtPPY09F18S8/MHKyr0fDZVbfZaVvj90aUfU5zrqKSGOyjbVA+eGG8wSAYSqAhCHg87vn8Ke372s5gsIXUKg0qwqiqadR8rprw8UmbOjBbUE6Il5+Zm0N/vojo9k02jxwCgLNJ3z9XWbiAYTNws5+Z6N/8tKeZaHgagemcaIZ+ZLI++XbY73N+Hd/3UE1IUPBZrk4RaxWzGt2cPvqKimJ8/kp9i7NULxdi0PH6FPxSdUYn30k+GRc+BseOkNgblKpKNBCoC3549+Pftw7NpM4rVmujhtEmk308gTtsOhTgSVVG4OlzU9cXppwDg+vhLTFoWmuajru7bhI3tz7sPcClPk8cBVC2T/V/mkT9sBIHNWwCa7fTb1USa/KWGuxM77alNEmpNeeHqtOXlaH5/TM9vcDjI+MkFpJ1xRrPPV2uhaOfkeC/9WM2Ht2rXhGK/JJ5oEqh0A76iIipfehn32rUde/0uvaqiZeDAuGbIx5IhM9yYME4dlIVoi3OHDCS74gBfjxyLOyeXUF0dmbsKgcQt/zgDQZzl74Q7JavUb5tG0GdgwNjxuNfp+Sq2cV0/UDGFE0jt1IOm4ek/CGicUGvIygKTCTSNQHlsZ1/NAwbQ55576H1H88vPXsWHih4wRHYoxUukjoodFy6jkYCve+UsSqDSDVS88AIH77uPmnfe7dDrveFAxTxoUCyHFVfGrHC/n8rKmP32EHI6Kb7tNsr+9jDad1rGC9GcjN55TN/8NZqq8vG4iQBY1uj9pw4cfAdNi30vqiP5srKW87SXASjsfw1FKw4A0K93H4LV1SgmE5YRIzp9XLEWmVExEsCCF1fhUKBxQq2iqtGqsfHs+dOcQLjYW0ixoarmuJ4rci1UQgQcpm5XS0UClW7AMSVc+G1Fx+qp+HYXAWAeOCBGI4q/SAdlze8n5IzNP0p/aSm1//cOVa+9JjVURJsoisL3DH7S6qr5eJzemDC0cg+GkIP6+s0cPNixXx6OxtcHltGLCgKKnf3LHfjcLhwZmaRU6r1wrKNGoZrj+8XZGQwGO6pqASCNGiqy9eTm7ybUmsLNCf3hrcSdRTGHZzXUjLify2CwEECvKB6yG7rdzh8JVLoB+6SJoCj4duyMJni1R3TppwvNqKg2G4rNBkCwMjbFtfwlUkNFtF/+wMFMWL+UzQOGUJmZheZ00a9Cz1nZtetvhEK+ThtLSNMIVH0MQNA4gdXv/B8AJ8+7Bs96PWemOyz7gB4kms36bEk6NRTbwluUwwX4Iox5er2YWM+ouFavxvXNNwTr65s85/d5MZr1pFZjnIu9Rc8Z6aCcouKTGRWRbIyZmVhG6lO5rhUr2/VaLRTCG66rYB7YdQIVILolMFAeq0AlXJVWAhXRDr0HDWHs5lXYAj4WH6sv/9jW6F+ibs9eSkr+3WljWVZVx5ig3lH90GL938UxJ5/G0IlTyb3llxS+/BIZ55/faeOJt8OBSjX7QgoB1UD1gdLGCbXhLsr+gwdieu4Dv/s9e+ZeiPubb5o8566txWTWA1SrpXO2gQfDgQo2VWZURHKKLv8sX9au1wUOHkRzu8FoxFzQLx5Di5tIL55ARWyS5PzhYm8mKfYm2iFv0FDMAT/HblzBF+P15R/frj0MKPwFALuLHiUY7JzfcD/bt4QsKvFqVio2B8jM78dJl8wD9FlI+4QJWAYP7pSxdIZIoNLbUEtAA98APU+lfO+e6DHG8NJPIMZLP5EZmsj7N1RRW4tD1f8/t3dSoBJS9YRaxapJoCKSk2OqXk7ftWx5u5JLvTvDibQFBV2ia3JDhmz9B0AwRtn8kfL5RplREe2Qmp2DLTWNcd8uY/OQEcz7zQNUPf0cfftegM3aH5+vnIMH34/7OLyhEJ6qjwAoK85GC6qcfs1NmLpIyYGOMJv0nwGDTPryS02+vuPKXVcbPSZS3j6WSz8hn49gdbX+/rk5TZ4/UFdHKvoYbOasmJ23NapBX/oyWCRQEUnKftxxYDTiLynBv39/m1/n3bIZAMvw4fEaWtykn3U2ubfdhm38+Ji8XyC69NOz+x2J9lEUhd6Dh5LiqudEPOzsV8iTxeWoqpnc3NOBztmqvKi8mmNDXwHg26CQld+PPkO73r/r9ojMqPQ16BVgD2Xqyzzu+sOBinXUKHpdeWVMl7wCZXrpfMVsbrYq+IF6J6nhqrRmU+fkqKjGSKDS/TooS6DSTagOR7SIk3NZ25d/7BMn0uvqq0g77bR4DS1u0k49hV6X/wxrjLZaRpd++nTtZm2i8+UN0psRzty7CYB3D1Wzz+MjPV0Pomtqm+YxxNrn+/VlH1/ISv0+O0MmTY0+V3rX3eydNx/XqlVxH0dnigQqvRQ9MDmYqs9eeBokuFoGDST3phtJP+vMmJ03UBZZ9slttvZUmcsTnVGJd7G3CFO4pYDRFMQnMyoiWTkmTwbAtXxFm19jO/ZYcm+4gbTTTo3XsLoELRDAf0BPtjP161q5OiLxcsOBimXTWk7MTCGowdPrtuAIDQTA6dxBIFAXt/PXBYJQ8wkAFcVZaCGVoZOmRZ+vX/IlziVL0ALdqz5QJFBJ1aoBKAnv/PHUx+9aAwTKWu6aDHpDwsMzKp2z9GMJ11IxmfwyoyKSl2PaVCzDhmEe0n2S5TpL4OBBvcGYyYQxp+masxCtyRsUTuLcv5er8rO4+s2XmHvpBXz+wjtYrQWARk3turid/60DFUzQ9GUf1waV1F459A4HT/6DZfqypqpiHTMmbmNIBLNZz1ExhfQK1WUmKz6TOf6BSjSRtvmfFRX+QHRGxdRJSz+2cBl9s9GH19l0y3RXJoFKN2KfMIFB//c2Oddck+ihdIpgXR01b79N1WuvHfV7hXw+7FOnYJ9wPIoq/yxE+6Rk9cKenoEWCjGiuozRo/TckNrPP6fOogcH1VVtn+lsjw11Lv614xMyqcIfslK/38GQiVOiSxLu9XqAZBk6FEOKIy5jSJTIjErQX0GuWW8MWJ6Zi6eucaDiXreOqn+9jmfrtpicN1KvytTMjh+AqqAWnVHprEDFHg5UrKoHr1tmVEQ34Vy2jIoFC/Bs3ZrooXRIqLaWkl/dzsH7HzjqMvqWgQMpfP55Cp9/PkajEz2JoijkDQ6XcN+1nbPP0RvVjd69nddL9aXEisovY37eQz4/V63/lotD/wCgfndqeNnncH6KZ/16oHt0TP6uSKASCNQxxq5Xky7P6o3H2ThQqXzxJQ7ccw/OJUtict7IVmdjbm6zzzdsSGjqpF0/aZYMAGyqmzqPp1PO2VkkUOnBqv/7X8oe+BN1H32U6KF0iCFcR0Xz+QjVxXeqV4gjyR2oL7Uc3LUDc79+mAcPxhAKoWzWf9Ovq9uAzxeb4oSgd8j9+bc7meP9k94lWc1h/5IMrKlp9B0xOnqce233aUT4XUZjOoqil1UYY3UDeqDi/k61WFO+vpMvUn36aB0pR8Wn+FDR+zyZwrkj8ZZqzgDAjpNqf6BTztlZOi1QcTqdvPzyy5x//vkMGzYMm81GRkYGM2fO5LUYTN2Lw/wlJVS+/AqBVjoLa5qGa+XXgL7zpytSLRbUVD15LtadUYVor7zBhwMVgJSZMwEYu3E7exkAaFRWLo3Z+T6trKOw5mmOYR2KaoUDZxP0GBk0fgJquFeVFgjg3rABILorsDvRy+jreSqFkVoqqRlNclRMfWMbqBQ8/k8Gf/gBKSee0OzzoWhDQgeq2jn1qSLdpB04qQ10fjPMeOq0QOXLL7/k4osvZtGiRYwfP54bb7yROXPmsH79ei688EKuu+66zhpKt7f/uus5+Ic/UP/ZZy0e49+3j8CBA2AyYRs3rvMGF2PR6rSHji5Q8RUVEaypiVknZtHz9A7PqFTs34ff44l+iU3fvJ61IT1IqKj4PGbne3X3Ss7ifwCMHvknilbsBWDQcYd/8fDu2IHmdqOmpHSp7ujtYbX2BaBXSP/8TntqkxyVWM+oqA4H5gEDMKSlNf+8SV96UQ2dM5sCYAzXUXHgpLZ7xSmdF6j06dOHV155hdLSUl5//XXuv/9+nn32WbZs2UJhYSGPPfYYX3/9dWcNp1tLnX0yALXvf9DiMa6Vek8g2zHHoIab+3VF0UCl/NBRvc/eK+axbfIU3N+sjcGoRE+UktWLlMwsNC1EWdEu7Mcdh2q3k1pTTdV+/YuyrOIzQqGjn5ZfVeMktfZD/byZs7AwgcqS/SiqSuExhwsgRpd9jhnbbZPEI7VqHB595shpT8Xv9RDw+6PHxDpQaU0wEMBo1s/dWYm0AEajHjTZcFOnhLrVL12dducee+yxXHjhhZi+U6a9d+/eXHnllQB8/nnsftvoydLO1AsbOZcta7GbsjMcqNgndc1ln4jIVuJIpciOaFRDpa9UpRUdF6mncnD3DhSzGftUPal1SpGPOlIJBWqoqTm6omuapnHfzv1MQ0/OHdB3Dms/fg+AviNGYXWkRI91r9MDFWs3XPaJSE8/Tv+LS/+s9fYUNGi0RTcSqITq6gjGOZ+tprYWm1HPl7Fbs+N6roYigQpA0GbA7+0+CbVJEWJHghej0ZjgkXQP5oICvax8KETt+017jGg+H87PvwDAMXlKZw8vpmLRxyNSQ0WRGiriKEXqqRzcuR0Ax3S96Nqs7Zv5hgkAbCw+ur4/iyrrqK1eTjblqIZUvBV5rH7vfwBMOPOHjY7N+NEP6XX1VaSG82W6o/Q0PVDxubZj01yEDEbcVnujfj+q3R4tdX+0syrOlSspmnshZX99qNnni6troluTbZ3UkBBAVU0EFL2vk2bvXh2UEx6oBINBXnzxRRRFYfbs2a0e6/V6qa2tbfRHNC/97LMAqH7jTbRQ4wXL+iVLCNbUYMzJwT5xQiKGFzOR7YGRktYd4SsuBvTfurrr9LjoHL3DCbUHIgm1M2YAYCwtxpZ2EgDlhz6kzFXdoff3hkLctb2Y09FnUHpnn8HH/3wMNI3RJ81m8PGTGx1vnziR3Btu6NJ5aEdiseREi+qNM+4Ewnkq302ojSz/FB9doOLbXYT7m2/w7tjR7PMH6+qjxd46q89PREDV81SwKfi6UXXahP9UvvPOO/n222/52c9+xpgjVE28//77SU9Pj/4pKCjopFF2PWlnnomakoJv507qFy9u9FzNO+/ox5xxBkp4d0BXZeqtByotLXG1ReQHl6lv35iMSfRckYTaypL9+NwuzP37M+id/2PIp5/y05FnU0EOqVoFL668ia+r21c9NKRp/GnXAVyunYxnNaCwf6WVmrKDpOXkMuvSn8fhE3UNGeHln7GqPpNVb09tUkY+8u/7aGdUIl3WTS10WT/gdDYo9tY5NVQiQtFAhZ49o5KdnY2iKG3+s/g7X5INPfXUU9x///2MHz+ev//970c89x133EFNTU30z759+9o7/B7DkJpK5ty5AJQ/+WR0VsV/4AD1i/TdQGlnnZWw8cWKsXdvUBS0Bolz7RXpNi2BijhajoxMUnplg6ZRVrQL0CvCKopCgSON4SMfIojK+NAX/O2bJ/jgUHWb3rfE42Puul38c18Zp/MuAOkp0/n2w5WgKJx2zU1Y7PZ4fayklxZOqB2k6cUrnfbUJo35TPn5KCYTIefRfYFHAh1TfvOBSpnbQ0o0UMk4qnO1l2KIdFDW8B7l50wm7U4KmTt3LnXtSEbKy2u+E+3zzz/PVVddxdixY1m4cCEpKSnNHteQxWLBYrG0+dw9Xdall1D50kt41q2ncsEL9Lr8Zxizssj+xbW4VqzEOnpUood41GzHHsuI9etQTB2vVeCPLP1IoCJiIG/QEHZUlHNw1w76jWw8S3xsn2ls9dzM/t0PcpH2LPdtGsHACSczwtHyzrvVNU4uXL+LmkCQHKWWWXwBGvgPjQUWUzh2HAWjxjZ5XfkTT6BYrKSdcUZ05rG7isyo5AU2o2ghnM3MqOTcdCO5v7rtqJd3AyV6l3VjCzMq5V4/BdZwoGLu3KUfgzESqAS71YxKuwOVRx999KhP+txzzzF//nxGjRrFp59+Sq9enZdw1JMYs7PpffvtHLjnHtzfrEHTLkMxm8mePx9t3rxm25N3NUoMErAlUBGx1HvQUHZ8vZyidWs4/gfnRh8P1tSAwciwAVfirF5BVdWX/CD4GnPXDeStcUMYZG/6S9jmejcXhYOUcal2/pC6kJpiL2mpx7D7S712UMO6KRFaKETFc88Tqq3FPmlitw9UHI7hGAx2CDrpy37qHSlNvqhVqzUm5/KX6oGKqU/zOwQrA0FGRRsSdu7ST2Tnj9EU6FYdlDs9R+W5555j3rx5jBgxgkWLFpEjuyziKuOC80mf8yN6XXFFo8CkOwQpsSKBioil4dNOQFFVitatoXSHvhRRes89bJs6jdr33kNRVIYNvQOACazE7TnImWu28f6h6mjti5Cm8Z+DVZy1ZjtVgSDHp1r4W/r/qCl+AoA+vX9K8dbNAAwa3zRQ8e3aRai2FsVmwzp8eGd87IRSVSNpqXovo6Fs1WdU4tCYTwsG8Yd3GLa09FMZCEVzVDo7mdYSrk5rNPu71YxKpwYqzz77bKMgJbeFhk4idhRFIf+++7p11v/R0AKBwz94JFARMZCZl8+oE2YBsOyNV4Hw7rRQCOdSvYR+Sspw0tOPRyXET8yLqfQHuXxDEdNWbOan63YxZflmrtm0h/pgiFlpfu5W/0jJvicBKCi4nLq92YSCQTLz+5GR1/QL0/XNN4Be0DEWs45dQaSeylC2Um9Pa5KjEqFpWoeLoQXKyyEQAIOhxVIGNYRIQU+U7syCbwDWcAdli8GPrxt1UO60O3jRokXMnz8fTdM48cQTefzxx5scM27cOM4999zOGpLoJg7c90dqP/iA3JtuJGPOnPa9WFUZ+J+38O/fjzGn84ozie5tyo9+wqYvP2P32tWUbNtC5vTplD/yKM7ly9ECARSjkX59L6KmZjWzQu9i6ftTnj7gZbfbx263D4AUg8rVBbnMrLyJmuqVGAx2Ro64n969z+Ttt+8DYNjkac2eP1Jh2TZ+XGd83KTQOFC5EG950y/q3RdcgHf7Dga++SaWQQPbfY5oIm3v3i3umPQa9D4/GgrGTmpIGOEwpVMHWAweXD05mbaj9u7dG41in3zyyWaPufTSSyVQEe2meTwEy8vxlx5o92sVVcU6fHiPmB4XnScjrw+jTvweGxd/wrI3X+VHv7obNT2dUE0N7m+/xT5+PLm5Z1C05584nds5T3mLX0z7FUur6jnk99PPYub4dAeqZwcrdq9EUYxMOP5NUlKG43O7KFq7GoBhU2Y0e353eEbFPn58s893R5FS+vmUoNhDeJqZUdFcLjSXC39JyVEFKi0l0gJoRj3QRElBVTt3NislPKNiV1zUerydeu546rSln8suuyw65dbSnwULFnTWcEQ3EovqtELE2pQf/QTVYKBo3RoO7N6BI1xO37n0K0DPqxgy5HYA9u1/gZBrA6flpHNxfjazeqWRaoAdO/8EQHb2bFJS9GB655qvCfh9ZPbJJ6ew6ZdtoKoK3+7dQPfsmNwSkykDi7U/AHnGg9T6mpYsMEZ7/hR36Bzmvn3JOP98Umed1OzzoVAIg0U/b2fPpgDYLBmA3piwqpnP31UlvOCbEEfL1EffAh/JxhciGWT0zmPIJH1pZvc3q6Pl9J1LlkSP6ZU1k9yc09G0ABs33EggoCdhhkJeNm/5DRUVn6OqFgYOuCb6mm3L9B4/w6ac0GxSvHvtWgDMgwdHy8b3FFazvoPUQT3lzXQQNvfTi4T69+3v0Pvbxo2jz+/upde8ec0+X15fj13Vl5w6s3x+RGTXjx0n1YFgp58/XnpGlpXo1o6mM+qhRx/DX1JC5k8u6FG/fYrO0W/kaLYt+5LSHVuZeKnefNW9fj3B2loMaWkoisKIEfdRW7cet2cvK1aeicMxBJerCLe7CIBRI/9CaupoAHxuF7ujyz7Tmz1nT8xPiTCa9C9qB04qmvk93Fyoz7j49u6Ny/n3VFRFd/xYO7EhYYTJePjz1wabidS6KJlREV1ew9LY7c3mr//sM2r++18CFRXxGJro4fKHjgCgdPsWjHl5mAcN0nf/LF8ePcZkSmfM6IdRVRsez34qKhbjdhdhMmVy7DHP0Lv3D6LH7ly9kqDfT2afvs0u+0DPzE+JiCy32HFSpTYtAmnqH99Apbi2tkH5/M7d8QONZ1RqOraxKSnJjIro8kx5eXoZfY+HYGUlxjYWENQ0Dd+ePQCYCwvjOUTRQ2X3H4DRbMHrdFJZWoxj+nR8u3bhXLKUtFNOiR6Xnn4c06Ytpr5uE27PfhTFQG7O6ZjCMwSgF3Hb8NlCAIZPndFiLSTb+PGEvF69g3oPY2wwo1BuzCAUDKI22J1j7q//O/fv2YOmae2qJ6UFAtR98gmmfgVYR41stsJtSb0z2pAwMYFKeHsyPpx0nxkVCVREl6eYzRhzcwkcPIi/uLjNgUqwslLv+6EomPr1i/MoRU9kMBrpPWgIxVs2UrJtMwOmT6PqpZfwbNjQ5FiLORtLrxNbfK+l/36FvRvWoRqMjAzXaWlO7s03xWTsXZEpXELejhOnvR+ummpSsg7/PDD16wuqSsjlIlhRgTG77csz/gMHKL7xJhSzmeFrv2n2mANuL6lqIgOVFDQUFDR8phBaKNQtOsJ3/U8gBB3rjBqZTTH2yUOVHlIiTgpG6f1+itZ9g2PyZAa88QYD3nyjXe+x4bOFrPjv6wCccuV1ZOVLYN2chjMqTnsq9VWVjZ5XzeZo1+P2Lv/4w01wTf36tfjlX+b1k0YNAGZz5yfTKopKUNGbUyp2DZ/H3eljiAcJVES3EE2oLW77tkPfHv0HlSz7iHgadNwkAIrWrkYzmbCNHdOu33L3bljHwqcfA2DKjy5g9MyT4zLO7sBoOpyjUm9PxVld2eSYaELtnvYFKr5IoFLQcpBYEQiRHglUTInpYRcKd1DGCp76+oSMIdYkUBHdQtoZp5N7yy+xT57S5tf49hQBh9ethYiHvMFDsaWl43O7KN6yqd2v/+yFpwkFg4yYPpNp51/U4nGaz0fVa6/h3bmzwyXiu7omMyqVTQOVjPPPp/edv8U2rn27/CJbmiNbnJtTqSmkU60fZ05QpWuDfg1Uq4bH2T0CFclREd1C6ve+B9/7XrteE02kDe8EECIeFFVl0PiJbPz8E3atWUH/MXrzvGC9E9VqabUXj6u2hvK9RQDMuuznrSZ/utev58C9v8OQlcXQpUtaPK47a7g9V59R2dnkmLTTTuvQe0eWilqbUak2qNFk2kQFKmp4RsVgDuE9yjL6xR4fDxUdYIDNwnWFvWMxvA6RGRXRY/l27gLAPHhQgkciurvBx+vLP9uWf4UWCrH/hhvZNmUKrjVrWn1d8VZ9BqZXv/7Y01qvdOpcuRIA+6RJPbY7urFBMq3PYqWiujpm7+3bpf+8sAxsufS+3xjAiF5ozWzOitm52yNSS8ZoDuA9yhmVIreXV0oref1A05mpziSBiuiRtEAgWmbcMmRIgkcjuruB4ydgttmpqzhE8ZZNKCYTBALRcvot2bNO313Sd8SoI57DtUIPVByTJx39gLuohks/ACXO2CSTasHg4RnYQc3/YuMNhTBZ9D4/qpqCqiYmQd8cvgYmU+Col34O+QIA5JgTu/gigYroNuo+/ZTyJ54k0My69HeFPB7Szjkb+8SJ0URcIeLFaDYzNNzpePOSxThm6FVlnUuXtvgaj7OeTV8sAmDopOa7JEeEvN7Dhd4mT47FkLskUziZ1ooHgxagJPxF+121779P2UN/w9/G/mD+0lI0nw/FbG7x58W+mrrojh9LovJTAJs5AwCzyRezQCXX3LR4XmeSQEV0G2V/eZBDDz+Md+vWIx5rSEkh/w9/oPClF7tFnQGR/EbOOAmATUs+oz4vFwDPxo0EqqqaPX7Doo/xez306tefwmNaL97mXrsOzefDkJONuZWlie5On1HR/z2nUsfBFmqelT/1NBVPPYVnU9uSmyPLPubCQpQGBeQa2lVREd3xY7Hmtm/gMWQPL/1YVQ/1R5mjUhZubCgzKkLESOQHtK+oKLEDEaIZ/UcfQ+Ex4wl4vbz91CMYBg4ATcP5VdPlH7/Xw+r3/gfAcWecc8ScE9eKFQA4Jvbc/BQARTFE65dkUEW5aiIUatqczxJevvHtbJps2xx/id7wtLUgcG917eEdPwnamgyQ0qCDcrnbc1TvVSYzKkLElnnAAAC84dwTIZKJoqqc/ctf02focDzOeor8ev6Ec0nT5Z9vPnyX+qpK0nJyGXXikXez1Yc7MjumTY3toLsgi1mfzcigitqUdA6FyxA0FEmg94YT6o8k8ycXMHz1Knr/9jctHrO/3kVaorcmA6YG/Y4qwjMiHXUo/PpsmVERIjYigUpbZlTqlyzFs20bmv/o/iEL0R5mq40f3n4P2QWFlJj1H7/1n3+OFjz8W7/X5WLl23rl2mk//ilGU+u/zQYqKvB8+y0AjhNaLsHfU5gtOcDhQOVfd93GmvffRgsdXgeyDB4MgLeNMyoAqsOBKbflJZ0Sj5f06NbkxM2oRHb92HFR5W8+R6etJEdFiBgzDxwAgG93UavHaYEA+3/xC3affQ6+/fvjPzAhGrClpHLubXdSm5GOz6ASrKzEHd6mrGkaa95/G6/TSVZ+P0aecNIR38+5ZAloGpaRIzH1TlxuRLJoOKPi6dWbgM/LZy88zb9/92uqD+hLOJFAxbdjR6MA5mgc9IeSYkbl8M6neqqCR1f4L5KjkiszKkLERmRGxV9cTMjna/E4765daB4Pqt0u5fNFQqTn5jFxzvmUpel9WYoWPM/mJYt55dc389UbrwAw8ew5qGrziZuNqAYsQ4eQcqLMpkDDGZVqalMyOHneNZgsVvZv3sALt/2Cbz56F1NhIYrFQsjlwn+Enj9aKNSmSr/lKIfL5ydwRiVS9M6Ok+qjiFNCmka5X7YnCxFTxpwc1JQUCIVanVWJZPpbWmjVLkRnmHTOeSgTjgegetlXvP/ogxzctR2jyczxPzi3TbkpAOlnncmgd94h5/rr4jncLsNsPrz04wqFKDzpFC598DEKRo0l4PWy6LknePOBezCGE2qPtPPH+eWXbJ9xAqV339PqcZUGc+LL59NwRsVFzVF8xVf6g0QmZMqWfRmLoXWY/JQW3YaiKFhGDAfAu2Vzi8d5Nuo/mGyjR3fKuIRojsFo4qQH/krNxT9h/cSxmG12pvzoAub/83lOumQeagvbYFvS0rbZniay9JOtVAN6Gfj03Dx+fOd9fO9nV2K0WNi3cT27KvQaKu6NrQcqns1bCFZUEGplq29I06ixWMlA32puNiduCS4SqBgJ4DZqHe77FEmktbmdrPrvv2I2vo6QXj+iW7GOGoV71Wo8mzaRfs45zR4T+Q3KOurI1T6FiCejw8GU39zN5PCXSU/eWhwrlvDST1Y4aNhQ72ZMqh1FVRl/2lkMHDeBDx9/mOqKZRQAe997B9tll5CW03xw4dmyBQDryBEtnvOQ14ddcWMi0GgMiWAw2NEwohBAsYPf48Zss7f7fQ75Aji0Ok4MLiJ9QMtL6Z1BZlREt2IdNQpDZiYYmo/BtVAIz+bN0WOFSAaKorQ7SNH8fipffgV/aWmcRtU1RWYzUrUq0DRu2rKPX23dR3U43yIjrw8X3H0/Q8//CW6ziSqvmxduvZb1n37U7OyDN/LzYuTIFs+5o6KKTPSK2CZTZsLK54N+L2nhDsrY6HB12jKfn3yKucjxIukj1sVwhO0ngYroVtLPOouhXy2l9223Nvu8b9cuNJcLxWrt0RU8RfIJVldH+8m0Rf0XX3DwD39g9/nnx2znSncQyVFR8TM314gGvFBSwfQVW3i9tBJN0/TZlZ9fw4APP6Bi9kx8bjcLn3qU/9x/N3UV5dH3CtY7o12TLSNanlHZVlERDVQslsR1GY5QjRkAGKwhPPUdDVQC0ZYAipYSq6F1iAQqoltRDIZWfzONdJi1jRuHYpSVT5Ecat9/n+0zT+LAffe1+TXV//kvAOlnnS1J4Q0YDBaM4aJnvx9o5a1xgxlqt1DhD3DDlr2c+80ONtfrxfay8vtywb0PMPPiKzCYTBStW8MLt1zLhs8Womka7nVrQdMw5edjzGq5G/LO6rrDgUoC81MijKYMAExH0UH5kM8fDVQMSlqshtYhcneLHsW1PFxqfErPbdwmko91zBg0rxfnl0vaVNsnUF5O/eLFAGT86IdxHl3XY7HowYLPW8b0zFQ+nTic3w7qg01VWVHjZPaqrdy9o5j6QBBVNTDhzB9y8Z8eoc+Q4XhdTj564u/878+/o37ZMgDsEye2er49bi+Z4ZwYiyUvvh+uDSzh5owmix93XUcDlUC0gJ3JmBmzsXWEBCqiWwrWO5vddhgMN4DryR1mRfIx9++PY/p00DSqXnr5iMdXvfYvCAaxHnsMliFDOmGEXUtk+cfrO6T/t6ryi8LefDl5BD/ISSeowZP7DjFjxRbe2bmHus8/p1ffAn7y+z9zwoWXYTAa2bXmayo+/RQA+6RJrZ6vJKCREZ5RMVsSP6NiN+uzP3bVTZWzrkPvUebzHy5gZ0ncdmuQQEV0Q+5vv2Xb1Knsu+rqJslxhS+9yNAvv8A2dmyCRidE87IuuwyAqtdfJ1BR0eJxIZeLqlf0onC9Lr20M4bW5USWX3zeskaP97OaeXbMQF45ZhADbGaq6uvpf86Z7L/yKnbs2IWqGph0znmMO/VMDMEQyh49P8U+ufVA5YBqIiuJclSs5gwAHNRxyOnu0HvoMyr60o/VltjPJIGK6HYsw4ahGI0Eysrwbt3a5HljTo7kp4ik45gxHesxx6B5PFQ882yLx1X9+98Eq6sxFRSQesopnTjCriMyAxCZUfmuk3ulsXjiCK4bVsjOggEAPPDvd/jTrlJcwRBpOblkujwooRDG/D6Y+vZt8VyBkEaV2RadUUmGHBWzSV+qSaGeck/HOig3TKa1O/JjNraOkEBFdDuqxYIjPFVb/0ViKyoK0VaKopBz7TUAVL70Ep6t25ocEzh0iPLH/gFAr/nzJOBuQUszKg1ZDSq3DMxj7KnfB+DEr5fytz0HmblyC+szelORYmPHtOPJvvKqVhP0iz0eQqpKppY8MyrGcI5KCnUc8rS/8WogpFHpPxyoONJaDtQ6gwQqoltKmXUSANVvvIHm9+PdtRvPtqY/+IVIJo4TTyRl9skQCFD6m98QavDbsBYKceB3vyNUX491zBgy5sxJ4EiT23dzVFpTOEdPRp6wZQNjXLXs8/i43WPhP6ddxG6LkcwLzm/19VvKylG0IBnhfI5kCFRM4V0/KdRTEWh/B+UKfwANSNP0QCUlrSCGo2s/CVREt5R+9tkYevXCv28fVa/9i9Lf/pbdZ59D+RNPJnpoQrRIURTy7rwTNS0Nz4YN7L/uekIuFwD+khJcq1aDyUTePfdIyfxWRHf9tCFQMRcUYJtwPIqm8eK+Tfyify5GBXYMHMny/CEEWmlwCrCtvJJ0alAVDVAT2pAwwhSuo+KgnsoOlNgp8/lRtSAp6DuGLFZJphUi5lS7newrfw7AwT/+EfeaNagOB6mnypq+SG6m3r0pePyfKFYrvr17UEwmAMz9+lH46iv0feiv2MZIn6rWRKrTeltZ+mko44c/AqDu6Wf4paeKu1x6MnNZdj51Fa0HO1vrnPRCLxJnseSiKIkPIA/PqNRR3YGv+TJfgBTqUBUNTdOr7SaSLHCKbivjggtwr11L7YcfgcFA30f+jkWq0YouwH788fR/7jlCLlc0UAGwDBwo93AbRHrtBIP1BINuDAZbq8enn3M21W+9hXvNGorO+zEzVJUz5l7B6qHDqC0/RGaflnM0tnv85Fj0YMZq7Re7D3EUGi79VKvtD5wO+fzRTtBawJLw4EtmVES3pVos9H3oIYZ+vpghH31IyvTpiR6SEG1mP248KTPknu0IgyEFVdWDk9LS/6Bpra9/KEYj+X/+M8b8PqBpKCYTO/r1pyIrl71bWu+uvFcxkY0+c2NLskDFRACvSWl3B+VDDXb8EGx/Q8NYk0BFdHvGnBxM+YndXieE6DyKopCRMQGArdvuYtWqOVTXrG71NeZ+fRn84YcM/mQhgxctYk/hIAJGEx8v+oSSbZubfU1dIEiVxU5OOFCx2hK7OyZCVW1oij4Tp9k1/J721VIp8/mjVWlVEls+Xx+DEEII0c0ce8yTDBl8GwZDCrV161m9+nw2bLwRj6ekxdeoZjPmfv0wZ2UyPEWfSShLz+adh/+Eq7amyfEbK6sByAvqHayTZUZFURQM4YRaxUq7GxM2nFExqumxHl67SaAihBCi21FVC4WFVzJ1yifk9zkfUDh48B2WLf8+u3Y/QjDY+izDyBR96ai+cCj1FeV88I+HmnSpXlOsByi5WnhGxZocMyoApmi/nwDOZoKs1jQs9mZKgl1MEqgIIYTotiyWHEaOvJ+JE/9HRvpEQiEPu3f/nWXLv8+BA//XYv7G6BQrAEVjJ6NZ7RStXc2K/73R6JiNlTWgaWQZ9GJvyZJMC2AJ79RxqE7K2tmY8ID3cOfkSGJyIkmgIoQQottLSx3Dcce9xpgxj2K19sXrLWXjpptYveZ8amvXNzn+h70zyTEb2ekPsf6ym9GAr/79Cns3HD52vS9IGrUYFT+gYLX26bwPdASW8ExIGrWU1jvb/DpN0yhtEKjY7InvBi2BihBCiB5BURR6557BlMkfM2jQzRgMdmpq1vD1qh+yadOtjequ5JhNPDGqEBX4GCsVZ1+EpoV4/7EHCfj9OINBdpkd0R0/FktvVNWcoE/WVKTfTyp1HHS1PZm2NhDEHQpFGxLaUxO/EUECFSGEED2KwWBl4IBrmTrlE/Ly9BL6pQf+w7LlJ1NU9E+CQS8A0zNTuX2QPkvyar+RVPUbiLOqkor9e/libwlB1UB/TxGQXPkpACZzFgCp1FDm9bb5daU+vTdQulalvz49seXzQQIVIYQQPZTF0pvRox5kwoT/kJY2nmDQxc5df2X5ilMoK/sQgF/0z+XkrDS8IY3/nXw+XrOFiv17WbhrDwAjA0UA2GyJ/0JvKFJNNo1ayn3BNr/ugNcffR2AzZH45SwJVIQQQvRo6WnHMuH4Nxg96iEsljw8nv18u+FaKiq+RFUUHh3Vn74WE+W2VJYddxKH9u1hqUv/Qh9m1cvtO+xDEvkRmjCb9ByVFOooD7Q9UCn1+rFoHiyK3uPIbE5snx+QQEUIIYTQG0LmncPUKQvJ7vU9AKqqlgGQZTLywDB9R8/a0ZN4bcde9qRkYgj4ybeHczkcgxIz8BYcnlGpoUJT2vy6hjt+tKABg0Eq0wohhBBJw2Cwk5Wlty5wuYuij8/ulcYouxm/ycLb038AwBm+KrRgMQAO++BOH2trzNEclToq1ba39Wu440cLWFGUtgc58SKBihBCCNGAzT4AALerKPqYoij8arCeMKupBuzBAPecMJxg0ImiGLHZChMw0paZGuz6qTZZ2/y6hjMqipYSl7G1l3RPFkIIIRqw2wYA4HLvQdNCKIr+O/2p2eksnjSc3S4vwxxWbB69f5DN1h9VNbX0dglhMukzKkYC+K16fZS2zI4c8PrJDAcqqpL4Pj+Q4BmV5cuXYzAYUBSFBx54IJFDEUIIIQC9wqyiGAmFPHi9Bxs9N8Jh4/ScDAbbrTidOwCw25MrPwX0LdiRDtJmS4Cauro2va7U54/WUDEZMuM2vvZIWKDidru57LLLsNlsiRqCEEII0YSqGqN1URrmqXyX06UHKsmWnxJhDlenTaWWosqqIx7vC4Uob9Dnx5wEfX4ggYHKb37zG0pLS7n99tsTNQQhhBCiWZFZkv37XyIUCjR7TE3NWgBS08Z01rDa5XCeSi17a2qPeHyJ148GZIT0oMZq7x3P4bVZQgKVpUuX8ve//50HH3yQfv2Sp4mTEEIIAdC/4HIUxcShQx+xadMvmwQrgUAd9fVbAMhIPz4RQzyiyM6fNGoprncd8fh9br12SlZIb7LoSE2OarudHqi4XC4uu+wyTjrpJObPn9/ZpxdCCCGOKCtrGmPHPIaimDhY9i6bNt+Kph0unKbPpoSwWguwWJJj5uG7Gs6olLg9Rzx+n0cPVDI0PVBJzRwQt7G1R6cHKrfffjulpaU888wznX1qIYQQos1ycmYzdswjKIqRgwf/j02bbosGK9U1qwDIyEjO2RQ4XJ02jVoO+JtfvmooEqikq3qOiiM1OdoCdGqg8vnnn/PYY4/xxz/+kYEDB7b79V6vl9ra2kZ/hBBCiHjJyTmFMeFg5cDB/7F5y28AqK5eCUBG+oREDq9VkWTYNGooO3Kcwj6PD5PmxWrQmxhazMkxU9TuQCU7OxtFUdr8Z/HixQA4nU4uv/xypk6dyi9+8YsODfb+++8nPT09+qegIDmiPSGEEN1Xbs6pjBn9dxTFQGnpGxw8+G44UFHJypqR6OG1yGzOASCDaiqUI3/d7/f4yEBPpNWCBozG1LiOr63aXfBt7ty51LVxPzZAXl4eoO/yKSkp4f3330dVOzaRc8cdd3DzzTdH/7u2tlaCFSGEEHGXm3saGcWTqKpaxoaNNwDQu/cPkq5rckORQCWdKioM5iMev8/jI5NqALSgIynK50MHApVHH320Qydau3YtHo+HESNGNPv8HXfcwR133MENN9zAww8/3OwxFosFi8XSofMLIYQQRyMrc0a0USHAgMKrEziaIzNbIoFKNdUWO75QCHMLEwX+kEap188E9ERaVUuOqrTQiSX0f/CDHzBkSNM22Nu3b+eLL75g4sSJHHPMMUydOrWzhiSEEEK0WX7+eVTXfE0o5CM7+3ukpAxP9JBaZTFnA5Cq1aEqQYo9fgbam/9lv8TrIwT00ipAAZMhqxNH2rpOC1RuvfXWZh9fsGABX3zxBT/60Y+k+JsQQoikZTZnM+7YZxM9jDbTtycbUJUgqdSys66+xUAlsuOnT6AMTGC25HbiSFsn3ZOFEEKIbkhRDNGdPxlUs6OyusVjd7n0nT7ZgTIAbLb8uI+vrSRQEUIIIbopS4OE2t119S0et9OtByqZwUMAONKSJ0m405Z+WnLZZZdx2WWXJXoYQgghRLdjtmRDvT6jsjdcIr85O8MzKqlKNQBpme2vdRYvMqMihBBCdFOHtyhXs98favG4nS69xL7DpPcEsqckTx8+CVSEEEKIbsoc3vmTQTUHMDR7jC8UYq/Hh1nzYjb7geSpSgsSqAghhBDdViRQSaeKOoOR2kCwyTF73D6CGuSjJ9JqQVPSVKUFCVSEEEKIbiuSTNsrWAHAVmfTLsq7wom0QwPFACih9KSpSgsSqAghhBDdltms10PppZUDsKne3eSYHeFE2r6+/QAY1ZxOGl3bSKAihBBCdFNWq14PJU2tQdFCzQYqm8OP5Xr2NXpNskj49mQhhBBCxIfF0htQMKp6ddqNtfYmx6yt03f6ZIVnVBypAzpxhEcmMypCCCFEN6WqJiyR5R/K2eL0oGla9PnaQDC69JMWXh5Kz2zaly+RJFARQgghujGLtQ8AOaGD1Guw3+uPPrc+PJuSb1Sx2sNF39IHdf4gWyGBihBCCNGNRXJOCtx7ANgQDk4A1tbqfx+OD1OKv9HxyUICFSGEEKIbs1r0GZV+nr0ALK0+3PMnkp8yzL0P1QCapkR3CiULCVSEEEKIbiyy9NPHXwLA55V1APhDGkur9KCloGoLAEooDVVNrn02yTUaIYQQQsSU1aIv5WSoVaihENtdXvZ7fGx1eqgKBMkxG+lVuQFywGRIntL5ETKjIoQQQnRj1vCMisXho0+ZvgX5s8pa/ldWBcBZORkEwlVpHY7k6ZocIYGKEEII0Y1ZwsmxJnuAoXs2AvDn3Qd471ANAD/IsIFZ/3t61sjEDLIVEqgIIYQQ3ZjZ1AtVtaEoMHnPlwwwaBzyBXAFQ0xOdzCorhJLug+A9IzhCR5tUxKoCCGEEN2Yoig47HptlJRUJ7c6Sxlit/CTvCxeO3YwVSX7sYYDFZttQAJH2jxJphVCCCG6Obt9IHX1G7Fk+HDs3saS08+IPldZuhVDdghNA5utMIGjbJ7MqAghhBDdnN0xGABrhpeyPbsaPXfowFoADGRiMFg6e2hHJIGKEEII0c1Fln4sGT4q9u0lGNCr0GqhEPU124HkXPYBCVSEEEKIbs9u12dUbJk+QkE/Ffv3AVBRvA+DXS8Al4w7fkACFSGEEKLbs9sHAAoGSxCDNUhZkb78U7JtM7ZsDwCpqRKoCCGEECIBDAYbVmtfAKwZPoq3bAL0QMWeowcqaaljEza+1kigIoQQQvQADscQAGy9PGxfsRSvy8m+bUsxWoOAkZSU5KuhAhKoCCGEED1Cetp4/X/7h/C6nLz78J9QrWWAvuyjquZEDq9FEqgIIYQQPUB6+nEApPb1AlC0bg22HDcAaWnHJGxcRyKBihBCCNEDpKePQ1EMYKglI98BQFrfgP6/SZqfAlKZVgghhOgRDAY7KSkjqavbwA9+dTGqvz/fbv8RAJmZUxM8upbJjIoQQgjRQ6SnHw/AofKPcIeWARoZ6ROx2foldmCtkEBFCCGE6CHy+/wYgLKy99mx888A5OWdm8ARHZkEKkIIIUQPkZo6ktzcww0JDYYUcnNPT+CIjkxyVIQQQogeZPCgW3A6t2O19mPwoJswmdITPaRWSaAihBBC9CB2eyFTJn+Y6GG0mSz9CCGEECJpSaAihBBCiKQlgYoQQgghkpYEKkIIIYRIWhKoCCGEECJpSaAihBBCiKQlgYoQQgghkpYEKkIIIYRIWhKoCCGEECJpSaAihBBCiKQlgYoQQgghkpYEKkIIIYRIWhKoCCGEECJpSaAihBBCiKRlTPQAjoamaQDU1tYmeCRCCCGEaKvI93bke7w1XTpQqaurA6CgoCDBIxFCCCFEe9XV1ZGent7qMYrWlnAmSYVCIUpKSkhNTUVRlEQPJ6Zqa2spKChg3759pKWlJXo4SU2uVdvJtWo7uVZtJ9eqfeR66TMpdXV15Ofno6qtZ6F06RkVVVXp169foocRV2lpaT32Rm4vuVZtJ9eq7eRatZ1cq/bp6dfrSDMpEZJMK4QQQoikJYGKEEIIIZKWBCpJymKxcPfdd2OxWBI9lKQn16rt5Fq1nVyrtpNr1T5yvdqnSyfTCiGEEKJ7kxkVIYQQQiQtCVSEEEIIkbQkUBFCCCFE0pJA5Si9/PLLXHnllUyYMAGLxYKiKCxYsKDF41esWME555xDdnY2FouFYcOGcdddd+F2u5s9vqqqiltuuYUhQ4ZgsVjIycnhvPPOY+PGjc0ef9JJJ6EoSrN/TjvttFh85A4rLi7m4Ycf5pRTTqF///6YzWby8vKYM2cOK1asaPY1tbW13HzzzRQWFmKxWCgsLOTmm29utW3Cq6++yqRJk3A4HGRmZnLGGWewatWqFo/fvn07559/Pjk5OdhsNo455hgee+wxQqHQUX/mjkrGa5Ws91a8r5XL5eKvf/0rF154ISNGjEBVVRRFoaioqNVx9cT7qiPXqqfeV2vXruXOO+9kypQp5ObmYrFYGDRoENdccw3FxcUtjisZ76u408RRKSws1AAtOzs7+vfnn3++2WPfeustzWg0ahaLRbvwwgu1m2++WZs8ebIGaNOnT9c8Hk+j48vLy7WhQ4dqgDZ16lTt5ptv1ubOnauZzWbNbrdry5cvb3KOmTNnaoB29913N/nz0ksvxeMStNmvfvUrDdAGDx6sXX755drtt9+uzZkzRzMYDJqqqtrrr7/e6Pj6+npt3LhxGqB9//vf1371q19pp512mgZo48aN0+rr65uc47777tMArX///trNN9+s/fznP9fS0tI0s9msffbZZ02O37hxo5aenq6ZTCbtpz/9qXbbbbdpY8eO1QBt/vz58boUR5SM1ypZ7614X6vdu3drgAZohYWFWlZWlgZou3fvbnFMPfW+6si16qn31eTJkzVFUbRJkyZp1113nXbLLbdoJ5xwQvT7ZPPmzU3GlKz3VbxJoHKUFi5cqBUVFWmapmn3339/i4GKy+XSsrOzNZPJpK1atSr6eCgU0q699loN0O6///5Gr4k8fvPNNzd6/KuvvtIMBoM2atQoLRgMNnou8o8+Gb311lvaF1980eTxL774QjOZTFpWVlajYO2uu+7SAO22225rdHzk8bvuuqvR49u2bdOMRqM2bNgwrbq6Ovr4hg0bNLvdrg0ePFjz+/2NXnPiiSdqgPbee+9FH/P5fNrJJ5+sAdqiRYuO6jN3VDJeq2S9t+J9rerq6rSPP/5Yq6io0DRN00499dQjfvn21PuqI9eqp95Xjz76qLZjx44m7//AAw9ogHbGGWc0eS5Z76t4S767owtrLVBZuHChBmg//vGPmzxXVVUV/Q0kFApFH+/bt6+mqqpWV1fX5DXnnntuszdmsv6jP5JTTjlFA7Svv/5a0zQ9gMvPz9dSUlKa/Cbidru1zMxMrW/fvo2u1x133KEB2gsvvNDk/a+66ioN0D766KPoY1u3btUAbdasWU2OX758uQZoc+fOjdVHjJlEXCtN65r3Viyu1Xcd6cu3J99X39WVA5XWxONaRQQCAc1ut2sOh6PR4131vooFyVHpJAcPHgRg4MCBTZ7LyMggMzOTPXv2sGvXrkavyc7OJiUlpclrIu+zaNGiZs/3r3/9i/vvv59HHnmEZcuWxeIjxJXJZALAaNTbT23fvp2SkhKmT5+Ow+FodKzVauXEE0+kuLiYHTt2RB9fvHgxAKecckqT9z/11FMB+Pzzz9t0/KRJk8jIyGh0fLJIxLVqqCvdW7G4Vu3Vk++royH3lU5RFAwGQ/S9I7rqfRULXbopYVeSk5MDwO7du5s8V1NTQ1VVFQDbtm1j8ODB0dccPHiQ+vr6JsFK5H22bdvW7Pnmzp3b6L8nTpzI66+/3myglGh79+7lk08+IS8vj7FjxwL6P3yAoUOHNvuayOPbt29v9PeUlBTy8vJaPT6itXMoisKQIUNYtWoVLpcLu93e0Y8XU4m6Vg11lXsrVteqvXryfXU05L7Svfnmm9TV1fHjH/+40eNd8b6KFZlR6STTpk0jLS2N//3vf3zzzTeNnrvzzjujf6+uro7+/fTTTycUCnHvvfc2On7lypW8++67TY4HOPfcc/nggw8oLS3F6XSydu1aLrnkEr7++mtmz56Ny+WK7Qc7Sn6/n4svvhiv18uf//xnDAYDoAdv0HJ3zUjH0chxkb+39/j2niOREnmtoGvdW7G8Vu3Vk++rjpD76rB9+/Zx/fXXY7PZ+P3vf9/oua52X8WSzKh0kpSUFB566CHmzZvH1KlTOe+888jLy+Orr75i9erVjBgxgi1btkRvfIB7772XDz74gAcffJBly5YxZcoUSktLefPNNxk1ahTr169vdDzAjTfe2Oi/jz32WF544QUCgQCvvvoqzz//PNdee21nfOQjCoVCXH755XzxxRfMnz+fiy++ONFDSlrJcK26yr2VDNeqq0iGayX3la6yspIzzjiDsrIyXnzxRYYPHx7T9+/KZEalE11xxRW8//77TJ06lbfffpt//vOfGI1GPv30U4YMGQIcXiIC6NevH19//TVXXHEFu3fv5pFHHmH58uX87ne/49e//nWT4490boClS5fG+FN1jKZpzJ8/n5dffpmLLrqIJ554otHzkd8aWvrtIFKXoOFvF+np6e0+vi3niPymkijJcK1ak0z3VjyuVXv15PsqlnrSfVVVVcXs2bPZuHEjjz/+OBdddFGTY7rKfRUPMqPSyU4//XROP/30Jo9ffPHFqKrKcccd1+jxvn378swzzzQ5/p577gFgwoQJbTpvdnY2QFJMo4ZCIebNm8fzzz/P3LlzWbBgAaraOGY+Up5Ec+u1Q4cOZdmyZRw4cKBJ7kVLx7d0Dk3T2LFjB/n5+U2S4zpTslyr1iTLvRWva9VePfm+iqWecl9VVlYye/ZsvvnmG/7xj39w5ZVXNvseXeG+ipvEbTjqflrbntyaJUuWtLhvvjmBQEAbPny4ZjQateLi4ja95qmnntIA7YYbbmjX2GItGAxqP/vZzzRAu+CCC7RAINDscW3Z7pefn99ou9/tt9/erbYnJ9O1ak0y3FvxvFbf1dW3JyfTtWpNT7ivKioqtPHjx2uA9uijj7Y6lmS/r+JJApUYOlKgUlNT0+Sx4uJibcSIEZrRaNRWr17d6Dmfz6e5XK5GjwWDQe3GG2/UAO2mm25q9NzOnTu1kpKSJufYtGmTlpOTowHasmXL2vmpYicYDGqXXXZZtJ7MdwuKfVd7Cyht3bo1ZgXfZs+endACSsl2rZL53or3tfquoyn41t3vq+860rXqyfdVRUVFtJLt3//+9zaNKVnvq3hTNE3TYjtH07M888wzLFmyBIBvv/2WNWvWMH369GjOybnnnsu5554LwB/+8AdefvllZsyYQW5uLvv27ePtt9/G5XLx7LPPcumllzZ67/379zN69GhOOeUUBg4ciM/n46OPPmLLli384Ac/4K233sJisUSPX7BgAfPnz2fWrFkMHjyY1NRUtm/fznvvvYff7+euu+5qsoOoM91zzz3ce++9pKSkcMMNNzSpEwD69Ro3bhwATqeTGTNmsHbtWr7//e9z/PHHs27dOj744APGjRvHkiVLmkxz3nffffz2t7+lf//+nHfeeTidTl577TXcbjcfffQRs2bNanT8pk2bmDZtGm63m/PPP5/8/Hw+/PBD1q9fz7x583j66afjdj1ak2zXKpnvrc64Vrfccgvl5eUALFy4kJKSEubMmRMtG3D77bczYsSI6PE9+b5qz7XqyffVSSedxOeff86IESO44IILmh3DjTfeSEZGRvS/k/W+irtER0pd3aWXXqoR7m3R3J+77747euynn36qzZ49W8vNzdVMJpOWl5enXXDBBdqaNWuafe/a2lrt4osv1gYNGqRZrVYtNTVVmzp1qvb00083KZ2vaZq2bt067eKLL9ZGjhyppaena0ajUevdu7d29tlnt3kaP56OdK1oZjaqurpau+mmm7SCggLNZDJpBQUF2k033dRoFuC7Xn75ZW3ChAmazWbT0tPTtdNOO01buXJli8dv3bpVO++887RevXppFotFGz16tPbII480e407S7Jdq2S+tzrjWkX6eLX0p7neSD31vmrPterJ99WRrhMtzEQl430VbzKjIoQQQoikJduThRBCCJG0JFARQgghRNKSQEUIIYQQSUsCFSGEEEIkLQlUhBBCCJG0JFARQgghRNKSQEUIIYQQSUsCFSGEEEIkLQlUhBBCCJG0JFARooc56aSTUBQl0cNos/r6evr06cM111yT6KF02GeffYaiKLz//vuJHooQXY4EKkJ0YYqitOtPV/TnP/+ZyspK7rjjjkQPpcNmzZrFzJkzufXWWwkGg4kejhBdStN2kEKILuPuu+9u8ti9995Leno6N954Y7OvefHFF3G5XHEeWWxUV1fz0EMPMXfuXAoKChI9nKNyyy23cNZZZ/Haa69x0UUXJXo4QnQZ0pRQiG5GURQKCwspKipK9FCO2qOPPsr111/PJ598wsknn5zo4RyVQCBAfn4+w4YNY8mSJYkejhBdhiz9CNHDNJejsmDBAhRFYcGCBbzzzjtMnjwZu91O3759ufPOOwmFQgC88sorjB8/HpvNRv/+/XnwwQebPYemaTz33HNMnz6dtLQ07HY7EyZM4LnnnmvXWBcsWECvXr2YNWtW9LFQKMTAgQPp1asXXq+32ddNmjQJs9lMWVlZo8fffvttTj75ZDIzM7FarYwZM4YHH3ywyXJMTU0Nf/rTn5g5cyb5+fmYzWby8/O55JJL2LlzZ5Pz3XPPPSiKwuLFi3nhhRc4/vjjsdvtnHTSSdFjjEYj5557LkuXLmX79u3tug5C9GQSqAghov773/9y/vnnM2jQIK666ipSUlL4wx/+wF133cVf//pXrrnmGsaOHcvPf/5zQqEQt956K6+88kqj99A0jYsuuogrrriC8vJyLrzwQubNm4fT6eSKK67glltuadNYqqqq+Oabb5g0aRKqevhHlaqqzJ8/n8rKSt56660mr/v222/5+uuvOfvss8nNzY0+/utf/5pzzz2Xbdu2MWfOHK655hqsViu33norP/nJTxq9x+bNm7nrrruw2Wz88Ic/5MYbb2TChAm8+uqrTJo0iT179jQ75r/85S9cffXVDB06lOuvv54ZM2Y0en7q1KkALFq0qE3XQAgBaEKIbgXQCgsLW3x+5syZ2nf/6T///PMaoJlMJm3lypXRx2tra7Xc3FzNbrdreXl52s6dO6PP7d27VzObzdoxxxzT6L2eeuopDdCuuOIKze/3Rx/3er3aWWedpQHaqlWrjvg53nvvPQ3QfvOb3zR5rrS0VDMajdqsWbOaPHf99ddrgPbBBx9EH/v44481QDv99NM1p9MZfTwUCmlXXXWVBmhvvvlm9PHq6mqtoqKiyXsvWrRIU1VVmzdvXqPH7777bg3QHA6Htn79+hY/07p16zRAu+SSS1r/8EKIKJlREUJE/fSnP2XixInR/05NTeXMM8/E5XJx9dVXM2jQoOhzBQUFzJgxg40bNxIIBKKPP/bYYzgcDh577DGMxsP5+mazmfvuuw+A11577Yhj2b9/PwC9e/du8lxeXh5nn302ixcvbrQU4/V6efnll+nfvz+nnHJKozEBPPnkk9jt9ujjiqLwwAMPoChKozGlp6eTlZXV5LyzZs1i9OjRfPLJJ82O+ec//zljx45t8TNFPkvkswkhjkx2/QghosaPH9/ksT59+gAwbty4Zp8LBoMcPHiQvn374nK5+Pbbb8nPz+eBBx5ocrzf7wdgy5YtRxxLRUUFAJmZmc0+f+WVV/Kf//yHZ599lj/+8Y+AvnRVWVnJ9ddf32i5aPny5TgcDp599tlm38tmszUZ0+LFi3n44YdZsWIF5eXljYIxs9nc7PtMmjSp1c8UCX7Ky8tbPU4IcZgEKkKIqLS0tCaPRWZFWnsuEoBUVVWhaRrFxcXce++9LZ7H6XQecSw2mw0At9vd7PPf//73GThwIAsWLOD3v/89BoOBZ555BlVVufzyyxsdW1lZSSAQaPOY3njjDS644AJSUlI49dRTGTBgAHa7PZpw3FKOSnOzPw1FPkvDWR0hROskUBFCxEwkmDn++ONZtWrVUb1XTk4OoAcZzVEUhfnz5/PrX/+a9957j7Fjx7Jo0SJOP/30JjVX0tLSUBSlzTMZ99xzD1arldWrVzN06NBGz/3rX/9q8XVHKqoX+SyRzyaEODLJURFCxExqaiojR45k8+bNVFdXH9V7RXI9WtvKe/nll2MymXjmmWd47rnn0DSNefPmNTlu8uTJVFRUtHlb8M6dOxk5cmSTIKWkpKTZ7clttXXrVoBW81iEEI1JoCKEiKnrr78el8vF/Pnzm13i2b17d5uK0Y0dO5asrCxWrlzZ4jG9e/fm7LPP5v333+epp54iLy+Ps846q9kxgR7YRHJfGjpw4ACbN2+O/ndhYSE7duzg4MGD0cc8Hg9XX311o1yV9lqxYgUAM2fO7PB7CNHTSKAihIipK6+8kksvvZQ333yToUOHcskll3D77bfzs5/9jKlTpzJ48GCWL19+xPdRFIWzzz6bjRs3Ulpa2ur5gsEgZWVlXHrppY12GkWcdtpp3HnnnSxZsoQhQ4Ywd+5cbr/9dubPn8+sWbPo168fb7/9dvT46667jtraWsaPH8/1118frR+zceNGjj322I5dGGDhwoVkZmZy4okndvg9hOhpJFARQsRUJOH09ddfZ/To0bz77rs89NBDLFy4EKvVyoMPPsjs2bPb9F5XXnkloVCo1e3Ms2fPpm/fviiK0uyyT8Tvfvc7Fi5cyAknnMCnn37KQw89xLvvvovX6+Wee+7hpz/9afTYa6+9lieeeIKsrCyefvpp/vvf/zJz5ky++uorMjIy2nwtGtqzZw9Lly7l0ksvxWq1dug9hOiJpNePECKpTZs2jZqaGjZs2NBssmpJSQmFhYWccMIJSV3x9a677uKBBx5g8+bNDB48ONHDEaLLkBkVIURSe/DBB9m0aRNvvPFGs88//PDDBAIBrrrqqk4eWdtVV1fzyCOPcPXVV0uQIkQ7yfZkIURSmzZtGk888US0VgvoTQMff/xx9uzZw9NPP83o0aOZM2dOAkfZuqKiIm688Uauu+66RA9FiC5Hln6EEF1OUVERAwcOxGazMXnyZJ544gmGDx+e6GEJIeJAAhUhhBBCJC3JURFCCCFE0pJARQghhBBJSwIVIYQQQiQtCVSEEEIIkbQkUBFCCCFE0pJARQghhBBJSwIVIYQQQiQtCVSEEEIIkbQkUBFCCCFE0vp/JJRQLVUlHQAAAAAASUVORK5CYII=", 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", 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", 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", 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" ] @@ -253,14 +253,14 @@ "id": "5789a5dc", "metadata": { "ExecuteTime": { - "end_time": "2023-08-14T16:28:32.072092Z", - "start_time": "2023-08-14T16:28:31.618839Z" + "end_time": "2023-08-15T15:15:22.158945Z", + "start_time": "2023-08-15T15:15:21.734259Z" } }, "outputs": [ { "data": { - "image/png": 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/Pp9//jnfffcdN998c9UvsArp6enMmTOnzGM+Pj6sXr2aQYMG1bpdUXvyaSyEAxUdOQKAZ5fOFT5/ftYsclf+QcT//h9+I0bUY8+EO+uw9y+7jlNdUZAy6I47CLz11lpds80PP5SZJsvJzcXfzw+1un6WkoaHhwOUC3isEhMTbf8/YsQINm3aZPs+JCQErVZLWloaxcXF5UaHtm7davv/KVOm8MUXX9i+z8rKKheIAOV2d/n5+TF+/HjGjx8PQHZ2Ns8++ywffPABU6dOJSkpCQ8PDztfbVkdOnTg2LFjtv4sW7aM6dOnc/vtt7Nnzx4iIyNr1a6oPVlALYQDFR4+DIBn54qDIQxGzPn5FB04WI+9Eu5O7e1t19eVwZBKp7P73HLX9PIqe8yV31dynqNER0cTERFBQkJCuSSG1dFqtfTt2xej0Vgm8LH3uoqilPuqTkBAAAsWLKB169akpaVx8KBjfoYDAwOZMmUKCxYs4MKFCzzyyCMOaVfUjARDQjiQbWSokmDIs3MnAIpPnqy3PgnhrqZMmQLA3Llza3zufffdB8Brr71mVzDjCCqVCm8nBYj3338/vXr14pdffmH79u1OuYaonARDQjiIMS0NU2oaqFR4duhQ4TH60oWXEgwJAU8//TTt2rVj0aJFPPvssxQVFZU7xmAwUFDBbrspU6YwaNAg1q1bx/33319usTVY1iVV9HhVPv74Y3bv3l3hcz/99BPHjh0jMDCQrl271qjd6qhUKl588UUAnn/+eYe2Laona4aEcJDiM2cA0EVGVjq9YA2GSuLjMRcVoa4gyZwQTUVAQACrV69m3LhxvPbaa3z22We2chxGo5Hk5GTWrl1LSkoKPXr0wNfX13auTqfjl19+4Y477mDx4sX89NNPjBw5knbt2qFSqbhw4QKbNm0iLi6Otm3bVrpr7UorV67k4Ycfpl27dgwePJiIiAjy8vLYv38/W7ZsQa1W88EHH1S4i+3xxx+vdOTogw8+qHZU6eabb6Z3796sX7+eTZs2MXz4cLv6LOpOgiEhHKTk7DkAPNq2qfQYTWgomsBATFlZFJ8+jVeXLvXUOyHcU5s2bdizZw9Lly7l+++/Z+PGjaSnp6PT6YiMjGTUqFFMmDCBG2+8sdzi7tDQUNatW8eyZctYsmQJu3fvZsWKFahUKpo3b07v3r159dVX+cc//mH3Yud58+YxePBg1qxZw+bNm0lOTgYgMjKSyZMnM2PGDHr3rjgVwg8//FBpu/Pnz7drim327NncdNNNPP/882wurXEonE+CISEcpKR0ZEjfpvIEciqVCn379hTs3k3xyZMSDIkmw7pwuSI6nY7JkyczefLkGrerUqm49dZbubWWu+qu1KFDB5566imeeuopu89Zv349OTk5+Pv7V7sbr7r1TWPHjq23NVDiElkzJISDFJ+1BEMe1WTT1cfGWo6XdUNCCOEWZGRICAcJfeABfAcPxrtv3yqPs06jlZyLq49uCSGEqIYEQ0I4iHffvtUGQgAeUa0BKIk75+QeCSGEsIcEQ0LUM89OHWn2zDPoY2pWnFIIIYRzSDAkRD3ThoYSct8UV3dDCCFEKQmGhHCAvK3bKNy3D+/+/fDp18/V3RFCCFEDEgwJ4QB5mzeR+eVXBBcVSjAkhBANjARDQjiAIdFSedujZUu7ji88fJi8TZvwaN2agBtvdGbXhBBCVEPyDAnhAIbERMBSisMehfv3k/bue+QsX+HMbgkhhLCDBENC1JGiKJeCITtHhqwjSIakJKf1SwghhH0kGBKijkxZWZhLq2rbOzKkKy0aaTh/3mn9EkIIYR8JhoSoI+t6IW1YGOoKKllXxBoMmXNzMeXmOq1vQgghqifBkBB1ZEiq2XohALWPD5rAQMv5MjokhBAuJcGQEHVkSLIEMzUJhuCyqbIkCYZE43bu3DlUKhVjxoyp8Hmj0chXX33FzTffTGRkJHq9Hh8fHzp06MC9997Lb7/9Vmkld0VR+PXXX7njjjto3bo1Xl5eeHl50bZtW8aPH8/SpUsxGAw16m9BQQGvvvoqvXr1wtfXF09PT1q2bMnQoUOZOXMmp0+fLnP8NddcQ1BQEBcuXKi2bZVKVeZLq9USHh7O2LFjWbt2bY36WRtV/VscOnSIyZMnEx0djV6vJyAggHbt2nHbbbfxzjvvlPk3sLZz5ZePjw/du3dnzpw55OXlVdmXhQsX2s47dOiQw19rTcjWeiHqyHDR8gGoa9G8RufpIiMoOnIEw/nzeDqjY0I0AHFxcdx6663s27ePsLAwRo4cSevWrTGZTJw5c4bly5fz1Vdfceedd/LNN9+UOTcjI4MJEyawdu1a/P39GTlyJDExMajVahISEti4cSM//vgj7733Hjt27LCrP7m5uQwZMoQDBw7Qrl07Jk2aRGBgIAkJCRw+fJjXX3+dmJgYYmJiav2aQ0JCePTRRwEoKiri8OHDLF++nOXLl7N06VImTpxY67Zra82aNYwdOxaj0cjIkSO59dZbAThz5gzbtm3j559/5pFHHkGrLRs2xMTEMGnSJMASmKamprJy5Upmz57NqlWr2LJlCxqNpsJrWoMhRVFYuHAhs2fPduprrIoEQ0LUkdrbG11ERB1GhmRHmWiacnJyGD16NMePH2fmzJm88MILeHqW/dOguLiYJUuWlBs1MRqNjBs3ji1btjBlyhTmz59PQEBAmWPMZjM///wzH330kd19mj9/PgcOHGDq1Kl8+umnqFSqMs+fPXuW4uLiGr7SskJDQ8v94v/222+ZOHEiM2fOdEkwNH36dEwmE2vXruXqq68u85yiKKxevbrCoKZdu3blXktxcTEDBw5kx44dbN68uVx7AMePH2fbtm2MHz+e3bt3s2TJEp599lmHvqaakGkyIeqo2eOP0279OoJq+AEmO8pEU/fmm29y/Phx7r//fl599dVygRCAXq9n6tSpfPXVV2Ue/+KLL9iyZQsjR45k4cKF5QIhALVaze23387KlSvt7pN1BOnRRx8tFwgBtGnTho4dO9rdnr0mTJiAr68vcXFxpKWlObz9qqSkpHD69Gm6du1aYeCiUqkYPXp0hfejInq93tZOampqhcd8/vnnANx7771MmjSJ9PR0VqxwXd41CYaEcBFdq1ZomzdH4+/v6q4I4RKLFi0C4Lnnnqv22CunZxYuXAjAs88+W+0v6SvPrUpwcDAAp06dsvscR7GuyalJfx0hICAAjUZDcnIy+fn5dW6vpKSEjRs3olKp6NGjR7nnjUYjX375JWFhYYwZM4Z7770XgCVLltT52rUl02RCuIjfNdfgd801ADVe4ClEQxcfH09SUhJRUVG0bdu2RucajUZ2796NTqdj8ODBDu3X+PHj+frrr5k6dSp79uzhuuuuo2fPngQFBTn0Olf6+uuvyc/Pp0uXLgSW7jStL3q9nptuuolly5YxZMgQHnzwQQYNGkTnzp3R6XRVnnvq1CnbNJmiKKSlpbFq1SqSkpJ44403iI2NLXfO77//zsWLF/nXv/6FVqulffv2DBw4kA0bNpCQkEDr1q2d8TKrJMGQEHWgGI0oxcWofXxc3RXRACmKQqGx0NXdwGw2U2gsRGvQolaXnzDw0nrZPUViL+vOq4jS6eIrvfXWW+Tk5JR57D//+Q++vr5kZGRgMBho3rw5+gpyey1cuJD4+Pgyj02bNo2WdmSIv+WWW3jjjTd46aWXmDdvHvPmzQMsC4XHjBnDY489Rvv27e16jZVJS0uzBRBFRUUcOnSIFStW4O3tzQcffFCntmvr008/xWAwsHz5cv75z38C4OHhQZ8+fZgwYQIPPPAAXl5e5c47ffo0c+bMKff4zTffzI2V1F20TpHdc889tscmTZrEjh07WLx4MS+++KIjXlKNSDAkRB0UHT3KufF34BETQ8zy313dHdHAFBoL6b+0v6u7Ua1dd+3CW+ft0DYr2ypv9dZbb5F0xeaChx9+GF9f32rPXbhwIdu2bSvz2JgxY2jZsiVZWVnMnz+/3DmXLwJ+6qmnePjhh/njjz/Yvn07e/bsYdeuXbz//vt8/vnnfPfdd9x8881Vv8AqpKenlwsgfHx8WL16NYMGDbKrjXPnzrF48eIyjwUGBvL444/Xqk+hoaH8/vvvnDhxglWrVvHnn3+yc+dOtm/fzvbt2/n000/ZtGmTbRrRavTo0fzxxx+271NSUli3bh3/+te/GDRoELt27SozOpScnMzKlSvp2LEjffr0sT0+YcIEnnzySRYvXswLL7zg8OC7OhIMCVEHxosXAeo0MmQuLMRsMjmqS0I0COHh4QDlAh6rxNJ6fwAjRoxg06ZNtu9DQkLQarWkpaVRXFxcbnRo69attv+fMmUKX3zxhe37rKysCkcyrtwR5efnx/jx4xk/fjwA2dnZPPvss3zwwQdMnTqVpKQkPDw87Hy1ZXXo0IFjx47Z+rNs2TKmT5/O7bffzp49e4i0Y2fquXPnyr2O1q1b1zoYsoqNjS0TvOzfv59JkyZx6NAh5syZwzvvvFPl+c2aNWPixIkUFhYydepUXn/9ddv6LrAsfDeZTGVGhQCCgoIYM2YMv/zyC+vXr2fkyJF1eh01JcGQEHVguGAJhnSlH+w1lfDwdPI2biT89degnv8SEq7npfVi1127XN0NzGYzubm5+Pn5VTpN5mjR0dFERESQkJDA6dOna5S3R6vV0rdvX3bs2MHWrVtr9IszOjq62pGligQEBLBgwQKWL19OXFwcBw8epHfv3jVu50qBgYFMmTIFk8nEtGnTeOSRR1i2bFm1540YMaJWr6OmevTowXvvvcc111zD+vXr7T6vX79+AOzdu7fM49bA6Lnnnqt04fznn38uwZAQDYl1ZEhby2BI7ednaSclBWrZhmi4VCqVw6efasNsNmPUGvHWeVcYDDnLlClTePXVV5k7d26Z0QN73HfffezYsYPXXnuNa665pl6mVVQqFd7ezvn3uv/++/nggw/45Zdf2L59u93TZfXBpxYj3xkZGYDlvWW1efNmTp48SUxMDCNGjChzvKIotjVLP//8M5mZmU5ftH452VovRB3Ysk83r10gowtvBpQGQ0I0MU8//TTt2rVj0aJFPPvssxQVFZU7xmAwUFBQUO7xKVOmMGjQINatW8f9999fbrE1WH7BVvR4VT7++GN2795d4XM//fQTx44dIzAwkK5du9ao3eqoVCrbwuHnn3/eoW1XJz8/n7lz51aY38hoNPLGG28AMGTIELvaM5vNvPfeewAMHTrU9rh14fSsWbP47LPPynx9+umnvPvuu9x7770UFRXx9ddf1/Vl1YiMDAlRB8aLliCmtiND2mbhl9rp5rBuCdEgBAQEsHr1asaNG8drr73GZ599ZivHYTQaSU5OZu3ataSkpNCjRw98fX1t5+p0On755RfuuOMOFi9ezE8//cTIkSNp164dKpWKCxcusGnTJuLi4mjbtm2lu9autHLlSh5++GHatWvH4MGDiYiIIC8vj/3797NlyxbUajUffPBBhbvYHn/88UpHjj744INqR5Vuvvlmevfuzfr169m0aRPDhw+3q891ZTAYmDVrFrNnz2bgwIFcddVV+Pv7c/HiRf744w+SkpJo06ZNhbu8Lt9aD5Ykixs2bODo0aO0atWKWbNmAZZs4z/++CO+vr62dVgVsWYT//zzz20lS+qDBENC1IGxdHuwNaipKWsQZZKRIdFEtWnThj179rB06VK+//57Nm7cSHp6OjqdjsjISEaNGsWECRO48cYby03hhYaGsm7dOpYtW8aSJUvYvXs3K1asQKVS0bx5c3r37s2rr77KP/7xD7sXO8+bN4/BgwezZs0aNm/eTHJyMgCRkZFMnjyZGTNmVLpW6Icffqi03fnz59s1xTZ79mxuuukmnn/+eTZv3mxXn+vK39+fFStWsGrVKrZu3coPP/xAeno63t7exMbG8uCDD/LYY49VmOX7yq31er2e6OhonnzySWbOnEloaCgA33zzDQUFBUydOrXKabdu3brRu3dv/vrrL/bu3UuvXr0c/4IroFLqYwVWA5eTk0NAQADZ2dn4S7bgChkMBlasWMENN9xQbZKuxuR4r96YCwpou3IF+jZtanx+4f79nLtzItoWLTjy+GNN7v45iru//4qKijh79ixt2rSpsOSEq5nNZnJycvD396/XNUONhdy/uqvpPbT3Z8re39/yryZELZkLCjCXrmXQhoXVqg3ryJAxNRUuW2gohBCi/kgwJEQtGdPTAVB5etY6z5A2NNSypd5oROOAmkBCCCFqTtYMCVFLmqAgIue/jbmgsNbbelU6HZrQEEypaWhruOtFCCGEY0gwJEQtaXx98R8zps7t6JqFW4Kh7GwH9EoIIURNSTAkhIuF/nM6hoJCTmdlurorQgjRJEkwJISL+Y0cicFgwLRihau7IoQQTZIEQ0LUUs6KFRSfO4fv0GF4dXNsNlohhBD1R4IhIWopZ+VKctesRRMYKMGQEEI0YLK1XohaMqZa6vhoSzOs1pYhOZmMTz4haONGB/RKCCFETcnIkBC1ZM0zVNdgyJieQcZ7CwiS7OZCCOESMjIkRC3ZgqGQkDq1ow2zBFOavDwUyUIthBD1zm2CoaSkJObPn891111HVFQUHh4eNG/enNtvv51du3bVqC2z2cyCBQvo3r07Xl5ehIWFcccdd3Dy5Ekn9V40Neb8fBRrKY46jgxpg4MBUJnNmLOy6to1IYQQNeQ2wdB7773HE088wZkzZxg1ahT//ve/GTJkCL/88guDBg3i+++/t7uthx9+mBkzZmAymZgxYwY33HADv/76K3379uXIkSNOfBWiqbCV4vDyqnUpDiuVToc6KMjSblpanfsmhBCiZtxmzVC/fv3YvHkzQ4cOLfP4li1bGDlyJNOnT+eWW25Br9dX2c6GDRv49NNPGTp0KGvWrLEdf++99zJq1CimT5/Opk2bnPY6RNNgDVrqOipkpQ0JoSQzE1NaukPaE0IIYT+3GRm67bbbygVCAEOHDuXqq68mIyODgwcPVtvOp59+CsArr7xSJnAaOXIko0ePZvPmzZw4ccJxHRdNki0YquN6IStNaVBlSpdgSDRO586dQ6VSMeaKEjaHDh1i8uTJREdHo9frCQgIoF27dtx222288847KIpSro0rv3x8fOjevTtz5swhLy+vyn4sXLjQdt6hQ4ec8lpFw+M2I0NV0el0AGi11Xd348aN+Pj4MHjw4HLPjR49mj/++INNmzYRGxvr8H6KpsMWDIU5aGSoNBgypss0mWg61qxZw9ixYzEajYwcOZJbb70VgDNnzrBt2zZ+/vlnHnnkkXKf/TExMUyaNAkARVFITU1l5cqVzJ49m1WrVrFlyxY0Gk2F17QGQ4qi8Pnnn/P2228790WKBsHtg6H4+HjWrl1L8+bN6datW5XH5ufnk5ycTNeuXSv8QWjfvj2ALKQWdaZSa9C2aIG2eQuHtKcJtYwwmWTNkGhCpk+fjslkYu3atVx99dVlnlMUhdWrV1f4Wd6uXTtmz55d5rHi4mIGDhzIjh072Lx5c7n2AI4fP862bdsYP348u3fv5quvvmLevHl4eHg49HWJhsetgyGDwcA999xDcXExb7zxRqWRvlV2adXvgICACp/3L83jkl1NdfDi4mKKi4tt3+fk5Nj6YzAY7O5/U2K9L03l/vjefhu+t98GOOg1BwZa2kpJbTL30JHc/f1nMBhQFAWz2YzZDdMnWKeirH10Bmu71mukpKRw+vRprrrqKoYPH17hdUeNGoWiKLb+XdnG5XQ6HSNGjGDfvn1cvHixwvY+++wzACZNmkRsbCxz587l559/Zvz48XV6bfVx/xq7mt5Ds9mMoigYDIYqYwN7PxPcNhgym83cf//9bN68mQceeIB77rmn3q792muvMWfOnHKPr169Gm9v73rrR0O0Zs0aV3ehQfK7mEJoQABJmZnsk4Ktteau7z+tVkvz5s3Jy8ujpKTE1d2pVG5urtPatq7lMZlM5OTkoFKp0Gg0nD9/nuTkZHzs2JV5ZRuXKykpYf369ahUKtq1a1fueaPRyJdffkloaCiDBg0iMjKSuXPn8umnnzJ69GiHvEZn3r+mwt57WFJSQmFhIZs3b8ZoNFZ6XEFpCpTquGUwpCgKDzzwAEuWLGHSpEl89NFHdp1nHRGqbOTH+sNR2ciR1cyZM3nyySfLnNeqVSuuu+462+iSKMtgMLBmzRpGjRplW+Ml7GcYNYo1Pa6S+1dL7v7+KyoqIiEhAV9fXzw9Pcs9X1BS+Yd5ZTw0arQayx4Yo8lMicmMWqXCU3fpr2R721UUhbzcPHz9fPHQatCVtmsyK2jUqhr3rSK+vr4AaDQa2+fo2LFj+eWXXxg7dizTpk1j0KBBdO7cudJ/Q2sb586ds631URSFtLQ0Vq9eTVJSEvPmzaNXr17lzl22bBkpKSnMmDGD4OBggoODGThwIBs2bCA7O5tWrVrV+rUpikJubi5+fn6oVI65X01NTe9hUVERXl5eDBs2rMKfKasrg+LKuF0wZDabmTZtGosWLWLixIksXrwYtdq+TW8+Pj60aNGCs2fPYjKZyg2dWdcKWdcOVUav11e4hV+n07nlB607aSr3SDEaUdmxoL+mmsr9cxZ3vX8mkwmVSoVara7w86zr7JqPaL1/Vy9u7G5Zs7bm0EUeWbqX/m2C+e6hgbZjhr25iYz8mo1EvXRLF+4dGA3ArrPpDIxxzI5J6+u23gewTFsZjUaWL1/Oo48+CoCHhwd9+vRhwoQJPPDAA3h5eZVr4/Tp07z00kvlrnHzzTczduzYCu/xokWLAEuaFevz9957Lzt27OCLL77ghRdeqPVrs07rXP7aRM3U9B6q1WpUKlW1P/P2fh641b/a5YHQhAkT+Oqrr6pdJ3Sl4cOHk5+fz7Zt28o9t2rVKtsxQtTFyRFXc7xvP4pPn3Z1V4RosEJDQ/n99985fvw47777LpMmTSIqKort27fz2GOP0a9fPzIyMsqdN3r0aNtaIkVRuHjxIkuXLmX79u0MGjSoXPqU5ORkVq5cSceOHenTp4/t8QkTJqDX61m0aFGZLfyi6XGbkSGz2czUqVNZvHgx48ePZ8mSJVUGQmlpaaSlpREaGkroZYnvHnzwQb799ltmzZrF2rVrbbsE1q1bx6pVqxg2bJhsqxd1opjNmDIzwWRC7evn2LZLSlBUKqeMOgn3deSlmq9Z8dBc+lt2dJdwjrw0GvUV0wtbnym/o6oiZrOZ3Jxc/Pz90Osuvff6tQmucb9qIzY2tszn8v79+5k0aRKHDh1izpw5vPPOO1We36xZMyZOnEhhYSFTp07l9ddfZ+HChbbnv/jiC0wmU7m1p0FBQdx00038+OOPrF+/npEjRzr2hYkGw20+cV966SUWL16Mr68vsbGxvPLKK+WOGTduHD169ABgwYIFzJkzhxdffLHMFsurr76aadOm8dlnn9GzZ09uvPFGLl68yHfffYe/vz8ffvhhPb0i0ViZc3LAZAJAGxTosHZbv/02p5/5L9E//IBXt64Oa1e4P2+Pun0Uay9bP1Sbds1mM0YPDd4e2jJTFI5aL1RTPXr04L333uOaa65h/fr1dp/Xr18/APbu3VvmcWtg9Nxzz/Hcc89VeO7nn38uwVAT5jbB0Llz5wDLboG5c+dWeEx0dLQtGKrKxx9/TPfu3fn4449599138fX15aabbmLu3LkyKiTqzJiZCYDa1xeVA/OTKFrL3LYxLdVhbQrRUNmzu+xK1im1y7dmb968mZMnTxITE8OIESMqPO/nn3/m559/JjMzk6DSOoGiaXGbYGjx4sUsXrzY7uNnz55dLumWlVqtZsaMGcyYMcMxnRPiMqbSYEgT7NgpBKOfZaeMMVWCIdH45efnM3/+fB566KEySx3Asg3+jTfeAGDIkCF2tWc2m3nvvfcAypR2+vzzzwGYNWsWU6ZMqfBcf39/3n77bb7++mvbQm7RtLhNMCREQ2ELhhw4RQZgKt02bKpgwagQjY3BYGDWrFnMnj2bgQMHctVVV+Hv78/Fixf5448/SEpKok2bNrz44ovlzj116lSZP4ZTU1PZsGEDR48epVWrVsyaNQuwbKv+8ccf8fX1rTKx4n333cfbb7/N559/LsFQEyXBkBA1ZCwNVrRBDh4ZKg2GjOkSDInGz9/fnxUrVrBq1Sq2bt3KDz/8QHp6Ot7e3sTGxvLggw/y2GOPVZgX7vTp02US4+r1eqKjo3nyySeZOXOmbaTpm2++oaCggKlTp1Y57datWzd69+7NX3/9xd69eyvMUyQaNwmGhKghU2YWABoHry0wlX5YS+V60RhFR0eX2b6uVqu5/vrruf7662vdRnUeeughHnroIbuO3bNnj93tisbHrfIMCdEQWKexHB4MWUeGZJpMCCHqlQRDQtSQKbN0mizY0SNDsmZICCFcQabJhKihgFtvw7NLF7x69XZouyZfyzSZjAwJIUT9kmBIiBryGdAfnwH9Hd6u8bLdZIrZjEpqHAkhRL2QYEgIN2Hy8SH06afwCGsGZjNIMCSEEPVCgiEh3IVGQ+A997hl1XUhhGjM5E9PIWrAXFJC2iefkvXjjyhGo6u7I4QQwgFkZEiIGjBlZJD61lug1RJw++2u7o5oYGqSI0cIUTlH/yzJyJAQNXApx1AgKpXjK3oX7NpF2iefUnBF1W3RsGk0GsBSgkIIUXfWnyXrz1ZdSTAkRA1YK9ZrA51T2Tpv9WpS33qL/G3bndK+cA2dToderyc7O1tGh4SoI0VRyM7ORq/XO2yNpUyTCVEDpgznVKy3srZrzJCSHI1NaGgoSUlJJCYmEhAQgE6nc8roYm2YzWZKSkooKipCLbsYa0zuX93Zcw8VRcFgMJCdnU1eXh6RkZEOu74EQ0LUwKWK9c4ZGdKUFn81SbHWRsff3x+AtLQ0kpKSXNybshRFobCwEC8vL7cJ0BoSuX91V5N7qNfriYyMtP1MOYIEQ0LUgCkrCwBNYPlK2o4gI0ONm7+/P/7+/hgMBkwmk6u7Y2MwGNi8eTPDhg2T1A61IPev7uy9hxqNxin3WIIhIWrgUjAU6JT2NSGlI0Ol03GicdLpdG71S1Oj0WA0GvH09HSrfjUUcv/qztX3UCY3hagBU3Y24LxgSBtsnSaTkSEhhKgvEgwJUQO2YCgg0CntW9cimbKzUWQbthBC1AsJhoSoKa3WaWuG1AEBtppk1m38QgghnEvWDAlRA1GffWrJE+OkXDEqjQZNUBCm9HRMGRnomjVzynWEEEJcIsGQEDWkUqnAidtntc2agVqFubDQadcQQghxiQRDQriZNj/9n+QqEUKIeiRrhoRwMxIICSFE/ZJgSAg7lcTFcWLQYM7e/g9Xd0UIIYQDyTSZEHYyZWVhyshA7elZL9dTFEVGiYQQoh7IyJAQdrLmGFI7aVu9Vc7KlZwYOIjEGTOceh0hhBAWEgwJYSdrMKR1UvZpG60WU2YmxtRU515HCCEEIMGQEHYzZWYBpYkRnchWkqP0ekIIIZxLgiEh7HSpFIdzgyFbSQ7JQC2EEPVCgiEh7OTsumRW1mDInJsr9cmEEKIeSDAkhJ3qbWRI6pMJIUS9kmBICDuZsrMA0Dh5AbVKrbZdQ9YNCSGE80kwJISdTFmlI0NO3loPsm5ICCHqkyRdFMJOQXeMp6R/f/Rt2zr9WpqgQABMmRlOv5YQQjR1EgwJYafAf9RfGQ5tkGV7vawZEkII55NgSAg35H/TWLx69MC7Z09Xd0UIIRo9CYaEcEP+113n6i4IIUSTIQuohbCDKTubrGXLyNuy1dVdEUII4WAyMiSEHUri4kj+70y0ES1ov369q7sjhBDCgSQYEsIO9ZV92sqYmUnepk0ABI4bVy/XFEKIpkqCISHsUJ85hgAM589bRqLCwyUYEkIIJ5M1Q0LYwZSVBdTfyJDWmnQxIwNFUerlmkII0VRJMCTczv6ELOavPcHeePfJsVNfdcmsrBmoFYMBc35BvVxTCCGaKgmGhFvZcjKV2z/czvy1Jxn/0Q7WH7vo6i4B9R8Mqb28UHl5Wa4tWahFHcnoohBVk2BIuA2jycyzPx/EZFbw89RiMis8+9MhigwmV3ftUpHWegqG4PKSHO4zQiYalh2n0+nzylpG/m9TmcffXnOCl38/wtojF8krNrqod0K4D1lALVyucP9+FKORzZ4tScgoJNjHg3VPDueGd7eg16pJzCygXTM/l/bRNjLk5Ir1l9MGBWM8n4wxQ0aGRPWyCkr4YnscrUO8GdczEoDoUG/S8oopKCkb8Kw5cpEjyTl8vvUsnjo113ZsRpRRxfUygiSaKAmGhEtl/76c8//5DwDf3fMq4MEdfVoR5OPB9w8NJDLQC7Va5dpOctkC6nraTQaXV67PqrdriobHaDLz5Y443lpzgrxiI/3aBNuCoeb+niz/1xDCfPUoioJKZflZ+ufVMWw7lc62U2nEZxTw+8ELgIaNH+5k+oh23NithVv83AlRXyQYEi7lN+paQh58kOTPPmdnpgJauLFbCwBaBXu7uHeXmLNzAND4+9fbNTXB1mBIpslExc6k5jHjm30cPm95f3Zq4c+9A1vbnlepVHSJKB/Aj+0ewdjuESiKwoHEbL7fHc8Pe+I5kpzLjG/28cnmMzw/tjP92gTX22sRwpUkGBIupdbrCXvsX2zYuJ9CrZ5QD+gSUTbgMJkV8oqMBHjrXNRLaPv7b5hyc9H4+NTbNW3b62UBtajAH4eS+c8PB8grNhLgpeOZMR25s2+rGo3oqFQqrmoVSOfmPnQxn+WCXwcWbY/jYFI2d3y8gxu7t2D2TV0I89M78ZUI4XqygFq4nEqj4fhVwwDoZ0ov82H+875EesxZzezfDruqewCotFq0QUGoPDzq7ZqaoCBQqTAXFNbbNUXDsHDrWR5estcyLRYdzOonhnFX/6g6TW356GDGNTFseGoEd/ePQq2C5QeSue7tTaw8mOzA3gvhfmRkSLiFY/4RkG6mfdyhMo838/Mkt9jIoaRsF/XMdYLvv5+QadNQaeXHVFzy9poTvLPuJABTBkUz68ZOaDWO+7s21FfP3Fu7MbFfFE/9eICjyTmk5BY7rH0h3JF8ygqXUBSF+Hsno4tqRei//82hPMtftO1O7sWUl4/G1zId1bt1EL/PGEKnFvW3VsddqOtxFEo0DB9vOm0LhJ4a3YF/joixLYp2tK6RAfzyyGB+3pfI+N6tbI+bzAoaWVwtGhmZJhMuYTx/noLdu8n+5VeMei/uHhhN76yzROckU3ziuO04T52GrpEBLv3wLT5zljO3jCPh0Udd1gchvt+TwGsrjwEw8/qOPHJ1O6cFQlYeWjUT+l6afssrNjL2va18vStOEjmKRkWCIeESRcctAY++XTu8fbx4ZkxH5iuH0Cpmio4edXHvyjJlpFN8/DglJ0+5uiuiiTqYmM3Mnw4C8NCwtjw0PMYl/fj2z3iOJufwztqT5BRKskbReMg0mXCJknNxAOjbtrE9pu/UkbxNmyg+drzMsadScvlg42k0KhVvjr+qXvsJYMqxbFtW12P2aQBTbi5xd0/ClJVFu3VrUelct5tOuFbnCH8eHNaW5KxC/nt9R5f1Y+qQNhjNCoNiQly6u1MIR5NgSLhESUI8ALpWUZxKySXER4/fiBGofXzw7tmzzLFGs8JPe5Pw1WuZd3v3ek8GZw2GNH71mwVb7e1N8cmToCiYsrPRhobW6/WF+9CoVTwzpiNms+L0qbGqqFQqHr5iVGr9sYu0CfWlTWj9pZ0QwtFkmky4hCHOEgx5REXx4Fd/0fPlNezzbUnoAw/g3adPmWPbhfniqVOTV2zkbHp+vffVbA2GAup3EbdKo7HVQpPEi03T1pNplBjNtu/dLSv0wcRs/vn1Xm77YBv74uU9KhoutwqGlixZwkMPPUSfPn3Q6/WoVCoWL15cozY2btyISqWq9Gvnzp3O6byokZKEBAA0rVpiNlsWYrYN863wWK1GTYfmlkDk+IXc+ungZUw5lmuq6zH7tJW1JIcxQ37RNDV/xWVy78Jd3PrBNrctphoeoKdDuB+ZBQbu+nQXG46nuLpLQtSKW02TzZo1i7i4OEJDQ2nRogVxcXG1bmv48OGMGDGi3OMtW7asQw+FIygGA4bz5wHwbN2ajU+FU1BixEunqfSc9s18+Tshi5MX86BbffXUwpRTWqTVz0XB0NmzMjLUBOUUGvD30tEh3A9fvVt9VNs08/Nk6QMDmP71XjafSOWBL/bwxj+6c1sv+ZwVDYtb/YR99tlntG/fntatW/P6668zc+bMWrc1YsQIZs+e7bjOCYcxJCeDyYRKr0cbFgaAt4flrZi/cxeFf/+Nz6CBeHW7FPW0b2YZNTqZUv8jQ+bSkaH6niaDy+uTSUmOpubqjs1Y/fgw9FX8keAOfPRaPp/ch6d/PMDP+5J48vu/Sc8r4YFhbV3dNSHs5lbB0LXXXuvqLoh6YLx4EQBt83BU6rIztVk//kjO77+j0mrKBkPhlmDoVEpe/XW0lG03mQtGhqz1yYwyMtQkNfP3dHUX7KLTqPnf+KsI8fHgs61nmbviKBkFJTw9uoNLF3wLYS+3CoYc6eTJk7z77rsUFBTQunVrRo0aRajsxnELav8AAv5xO5qAAP63+jh/ns3gvsHRjOnaAl0LS8V6Q/KFMue0b2bZyXUmNR+jyezQ8gPVsU2TuWJkKMhSNdyUmVXv1xb1z2xW+Ne3+7ixWwvGdG3eoAIJtVrFrLGdCfPT89rKY3y48TSFJSZeGNvZ7RZ+C3GlRhsMLV26lKVLl9q+9/LyYs6cOTz11FMu7JUA8OwQS8QrrwCw//Nd7Dqbwe29LWsMdBHWYKhsYcjIQC88dWqKDGbiMwoqXWztDH4jRuDRshUerVvX2zWtrAuoTRkyTdYU/Lg3kd8PJLP+WAp92wQT6tvwqsU/NDwGH72WWcsOsXj7OYoMJube2k1KeAi31uiCobCwMN58803Gjh1LVFQUWVlZbNiwgWeeeYann34af39/HnrooSrbKC4uprj4UmHCnNJpEoPBgMFgcGr/Gyrrfanp/YlPLwAgwt8Dg8GAqnQNkeF8Urm2YsJ8OHw+l6Pns2gVWH+/JPwnT7b9v7P+/Su9f/6WETFDRoa896pQ2/efOykoMfLmH5ZyG49e3ZYAvbreXo+j79+E3hF4qOG/Px/i290JFBQbmXdbl3od0a1PjeH952rOuof2tqdS3LTAjHUB9aJFi5gyZUqd2zt06BC9e/cmKCiI8+fPo1ZX/kM5e/Zs5syZU+7xpUuX4u3tXee+CAuzAv/ZpcGkqHixl5FgPXgkJxM9/x1M3t6cfvGFMsd/cULN3nQ1N0eZGBnplm9bh/M4f56Av/ZS3Lw5OX37VH+CaLBWJapYkaAhRK/wbA8T2kYQN+xLV/HlSTVmRcWdbU0MDG8aP7fCfRQUFHDXXXeRnZ2NfxXpURrdyFBlunbtSv/+/dmyZQunTp0iNja20mNnzpzJk08+afs+JyeHVq1acd1111V5M5syg8HAmjVrGDVqFLpqykYU7t2LSq8nLSQC087d6DQqJt5yPRq1ClNODmfnv4OmoIAxV1+N2svLdt5xj1Ps3XQG7/DW3HBDZ2e/pHpV5f2b5po+NSQ1ef+5o/S8Yp59eytgYtbN3RnbvUW9Xt9Z9+8GYMCxFDYcT2POTZ0a7dqhhv7+cwfOuofWmZ3qNJlgCLAtoC4oKKjyOL1ej15ffhpGp9PJG70a9tyjuOdmYUhM5OL8RUDpeiC9BwDa4GDU3t6YCwogLR3dZbXL2pYuok7ILKy3fwdzcTGFf/2F2j8Ar65dnH49eY/VTUO9fx9uPk5+iYlukQHc0rOVy4IGZ9y/Md0iGdMt0va9wWTGYDLb0mk0Jg31/edOHH0P7W2rEQzE2sdoNLJ3715UKhVRUVGu7k6TpSgKxhRLltrzasuUY6vgS1OPKpUKrW0R9fky517VMoCHhrVlQt/6+/czJicTf/9U4u+9t96uKZqWhIwCvt5lKU8z84aOjXb0BMBoMvP4d/uZvPBPcotkfY1wHw02GEpLS+PYsWOkpaWVeXzHjh1cuQzKaDTy1FNPERcXx+jRowkODq7ProrLmLKyUEpKALioWCL2yECvMsfowpsDYLxwsczj7cP9mHlDJ26+KqIeemrhqor1l8vfuYvs35fb+iIalw83ncZoVhjaPpRBMY07/Ud8RgGbT6SyPyGLw+fl/Szch1uNU3722Wds3boVgIMHD9oe27hxIwDjxo1j3LhxACxYsIA5c+bw4osvlsk0PXHiRFQqFYMGDSIyMpKsrCw2b97M8ePHiYqK4qOPPqrPlySuYCoNXjUBAaTkW/4ybB5QNrGcNjwcTWgoKOZy59c3a12y+q5Yf7nz//0vxgsXiP7h+zKJKEXDdz6rkB/2WOr0/Wtkexf3xvnahvnyzQMDSM4uYkDbEFd3RwgbhwRD69atY/369Wzfvp3ExETS0tLw9vYmLCyMbt26MXz4cMaOHUvz5s2rbGfr1q188cUXZR7btm0b27ZtAyA6OtoWDFVm+vTp/PHHH2zcuJG0tDS0Wi3t2rXjueee49///jdBpXlbhGtYC45qgoNJzi4CoPkVWXZbzH2l0mRzaXnFnE3Lp2WQFy0CvCo8xpHM1oSLLlw4rwkOwnjhgtQna4Q+2nQag0lhYNsQ+kY3jRHrrpEBdI28NNIal56Pt4eWML+Gl1NJNB61Doby8vJ49913+fTTT4mPj7dNTXl6ehIcHExhYSGHDh3iwIEDfP3112i1Wm6++WaeeOIJBg8eXGGbixcvtrtK/ezZsyusPfbMM8/wzDPP1PZlCSez1tjSBAdzwRoMXTEyVFXW3Vk/H+KPwxd48abO3De4TaXHOYorK9ZbaQODKAaMknixUbmQXcS3f1pGhWaMbOfi3rhGQkYBEz/ZiaeHhqXTBpT7LBCivtRqzdBHH31Eu3btmDVrFoGBgbzyyiusX7+enJwcCgoKSExMJD09HYPBwLFjx/jiiy+YMGECq1evZtiwYdx2222cPXvW0a9FNADWX+ja4CAu5lQcDFWlbZgPLYO86i2brXWdjmtHhqQkR2P06ZYzlJjM9I0OYmATnTIyKwoqlYozqfnc8fEOEjOr3ukrhLPUamRoxowZ3H333Tz11FN06VL5dmOVSkVsbCyxsbHcc889FBYW8s033/Daa6/x1Vdf8cILL1R6rmicTKXTZIbAEDILStcMVVKMUjGZUGnKVux+anQHnh7T0bmdvIw5tzQYckFdMispydE4TR3SBq1axbDYsAZVg8yRWof48N1DA7jr013EZxQw4eOdfD2tP9GhPq7ummhiahUMHTt2jJiYmBqf5+Xlxf3338/kyZNJTEyszaVFA2f9ha4OCmJW306k5hYT4FU2D4Th/HnOjr8DpaSEDrv/LPNcff/SMGW7rmK9lSYo0NKXLFkz1JhEBHox84ZOru6Gy7UM8ub7hwZy12c7bSNESx8YQLtm9Vd/UIhaTZPVJhC6nEajobULil4K1zOWrhnyCw1i2tC2zLyhU7kAR+0fgCk9HXNuLub8fFd008aU6/ppMm3pNJl18bkQjU3zAE++e3AgHcL9SMkt5s5PdnDsgmy9F/WnweYZEg1Ti9mzafv7b/jfeGOlx2h8fVCV1oAzXpFHymAyc+sH2+j/6lqyC52ftM1sHRnyd93Wek1g6TSZ7CZrFH77+zz3L97N7nMy7Xm5MD893zw4gC4R/qTllXDnJzs5mJjt6m6JJsKhwVBmZiZffvmlI5sUjYwmIAB9u3YkKp78nZBFZn5JhcdpS0unGFNTyzyu06g5l5bPxZxizmcVOr2/EW/Mo80vv+A7fLjTr1UZTbCsGWpMPt96lvXHUthxOt3VXXE7wT4eLJ02gB6tAskqMHDXZzv5K07+CBDO59BgKD4+nvvuu8+RTYpG6osd57jl/W18tPl0hc/bgqErRoYAIoMs+YWSMp0fDGlDQ/HsEIvWhfmprNeWkaHG4e0JPZg8sDV395eyQBUJ8Nbx1dR+9I0OIrfIyL2f72LXGQkchXPVaAF1fHx8lc+fP3++yueFsPLUaWju71muFIeVNiwMAGNKarnnIgO9OJSUQ1I9jAy5A4/oaNpt2mjbVSYatjahPsy5pauru+HW/Dx1fHF/P6Z9sYftp9OZvOhPvp42gN6t5WdAOEeNgqHo6Ogqd/MopTkjhKiIKTeXxEdnoAkO4un//Y9nqtgirw2x5F0xZpT/izCiNIBqKsGQSqdDFx7u6m4IUa+8PbQsnNKXh5f8RWZ+CbHhsrtMOE+NgqGgoCBeffVVRowYUeHzR48e5fbbb3dEv0QjZExLo2DXLtQ+PqjUVc/Q2hINVrCDKrKegiFzQQGJjz2Oxs+PiHmvo9Lpqj9JiEp8tuUM+xKyeGhYW7q3DHR1dxoET52Gj+/pTZHBjJ+n/PwJ56lRMNS7d29SU1Pp0KFDhc8XFRWVqxgvhJV1zYs10KmKbdFwZvlFwy3rac2QKSeH/C1bQKcj4n//z6nXEo2byaywePs5EjMLGREbJsFQDei1GvTaS8lXP9h4ikAvD+6SNVfCgWoUDE2fPp38KvK+REVFsWjRojp3SjRO1mDIFBzC8Dc3EOarZ8m0/njqNOWOteXWqWDRcGSgZdu9s0eGrAkXNX5+Lp/+vfjGmxTs3EnYY/9y6c42UTubT6aSmFmIv6eWsd0jXN2dBmvXmXTe+OM4AJ0j/OnRKtC1HRKNRo2CoVtvvbXK54OCgpg8eXKdOiQaL2twkR0QRlx6AclZRei1FU+XeXbuTLOn/oNHBck5IwIt5TtSc4spNprK/NXoSGY3SLhoZUiIp+jIEQyySaFB+npnHAD/6N0KLw/nvF+bgn5tgnn06naYFUUCIeFQta5aL0RNmXIsCdSy/SyjPqG+HpWOuHhERREydWqFzwX7eOCpU1NkMJOcVeS0OkbWIq2urFhvpQmyZqGWXEMNTVJWIeuPpQBw9wCZ2qkLlUrFf0Z3KLMcI7fIgF6rwaOSP6yEsIe8e0S9MZcGF5k+gQCE+Opr1Y5KpbLtKHNm4kXbNJlbBEPWxIuSa6ih+b+/EjErMLBtCDFhsiPKEax/RBUZTExdvIepX+wmv9jo4l6JhqzOwZBGo6k2/5AQcNk0macluAj19ah1W9ZK9xdyiuresUpcmiZzXSkOK20VC8qF+zKbFX78y1KU+o6+LV3cm8bn+IVcDiZls+VkGnd9touMSjLaC1GdOgdDsntM2Ms67ZSls0xrhVYzMpTzxx+kL1xUriQH1E8wZKtY7w4jQ1KstUH681wG8RkF+Oq1jOnSwtXdaXSuahXI0gf6E+it4++ELG7/cDvn0lxb3Fk0TDJNJuqNdc1QhsYSyIT6VR0Mpc5/h5Q33qD4zNlyz43rGcnLt3Th6g7NHN/RUraK9X5uEAwFWfMuychQQ2IdFRrbvYUsnHaSnlFB/PDQQCIDvTibls9tH26XemaixiQYEvXGq1t3fIYPI8vDvpEhW+LFCqaGhsWGcc/AaDq1cF6gYq1YrwlwfTBknSYzyjRZg5FfbGTFwWQAxveRKTJnah/ux8//HETXSH8y8kuY+OlO270Xwh4SDIl6E/boI0R9/DGZtmCo6jVDlxIvuuavPH1se3wGD8YjOtol17/cpcAwS6amG4jlB5MpKDHRNtSHXlFSU8vZmvl78t2DA7m2UzNKjGYeWbqXTzefkZ8XYRcJhkS9S8uzLHKsbmRIW8V28mKjiT/PZrDq8AXHd7BUyNSpRH3+GX7XXuu0a9jLVqTVaLTtyhPuzTpFdnvvli5P2tlU+Oi1fHxPHyYPbI2iwNwVR3nhl8MYTWZXd024OckzJOpdWl4xUINpsgoWDWcXGLjj4x2oVXDilevRahp3XK/W6wm45RbUvr4gf+m6vRKjGR8PDTqNitt6Rbq6O02KRq1i9s1diArx4ZXlR/hqZxxJWYW8O7Envnr5lScqJu8MUS8Uo5HiU6cw+/iRVWAAqp8mq2o7eYivnrahPoT66ckvMRHg1biDIYCIea+7ugvCTh5aNYvu60dWQQmB3rVPISFqR6VSMXVIGyIDPXns2/2sP5bCMz8e4P27e7m6a8JN1TkYeu655wgMDHRAV0RjZkxP5+y4W0n3DoTrZqFRqwiq5peEdWqoou3kGrWK9f8Z4YSeXlJ86hRqP3+0YaGo1I0/2BKOJ4GQa43p2oLvHvLimR8P8PSYiguMCwEOWDP08ssv07FjR2bPnu2A7ojGypRt2Vbv5ePFczd04olr26NWV72OwpXbyRWDgTNjb+LU8OG2vgthjwvZRSRnO7eIsLBfj1aBrHxsKK1DLpXtOXZB1t2Jshzy525ubi4Gg8ERTYlGyrroN9DbgweGteXRa9pXe46tBIULFgybcnMv9cPP9RmoAQwXLpC/60+Kz5xxdVdEFT7ZfIZBr6/n3XUnXd0VUeryP7w2Hk/hhne28PyyQ5jMsv5OWDgkGOrduzfnpZq2qIKt6GlAgN3naAIDQKWqdMHwJ5tP02/uWt7445hD+ng5a/Cm9vFBpXWPpXUZX31F/OTJZH33vau7IqpwMacIRYHOTsyBJWrvdGo+ZsWyyL2awWnRhDjkU/6pp57izjvv5MUXXyTaDXKyCPdjLW2RFhhOdkIWLQI8CS8tqVEZXWQkHQ8dRKWpOHOvWYGU3GKnFGu9FLy5zy80rbUkhyRedGvv392LZ7MKCatlIWLhXFOHtKFTCz/6tA62pTxQFEXSHzRxDhkZio+PZ+jQoQwfPpxNmzY5oknRyJiyswBY69+Oce9v4/+tOl7tOSqVqtJACJxbn8yUY5kmc4dSHFaX1lBJqQF3FxnohYdWFt27q0ExobZ/H5NZ4cGv/uLnfYku7pVwJYeMDD3yyCOoVCoUReGaa66hZ8+ejB07lr59+9KzZ08iIiIccRnRgFmnnfSeHkT6elU7KmSP5gGWNi7mFNe5rSuZS+uoadygSKuVJigQkPpk7qrEaCav2Eiwj+wga0h+2pvImiMXWXPkIvvis5h1Y2cJZJsghwRD3333HX///Tf79+9n37597N27l71799qGHcPCwujZsye9evVi7ty5jrikaGCs02STQot48vFrHNKmbWQou8jhw9zWkSF3qFhvZZsmy5KRIXe04XgKj3y9l9t7tWTeP7q7ujvCTrf1aklCZiHvrjvJlzviOJiUzYd397b9sSWaBoeEv+PHj+eVV17h999/JykpiZSUFFatWsVrr73GhAkTCA4OZs2aNbz+uiSNa6qsa3A0/vYvoAZIevppTg4bTu76DeWes35YFRpM5BQZ697Jy9gq1rtRMFRVRm7her/uP4/RrBDgrXN1V0QNaNQqnhwVy+eT++DvqWVffBZj39vCjtPpru6aqEdO2SYTGhrKqFGjGDVqlO2xwsJCDhw44IzLiQbAOk1W0wrw5uwcjCkpmDLKfzB56jQEeOnILjSQklNEgJfjfgnZ+uvvHtvq4dKaIaWoCHNBAWpvbxf3SFjlFRtZe/QiADdfJcsCGqKRncL5bcYQHl6yl6PJOUz6fBf/HdORaUPbyOLqJsAhI0PvvPMOJpOpymO8vLzo37+/Iy4nGqBmzzxN1OJFPJ4Wzrj3t3H4vH2JDDWBlpGkyhIfNvOz7NhJyXXsuiF3nCZT+3ij8rCsR6koK7dwnXVHL1JsNNM21IcuEe7znhE10zrEh5+mD+K2npGYzApzVxzloa/+IrtA8ug1dg4Jhp544gmuuuoq1qxZ44jmRCOkb9sWnwEDOJxWxP6ELLvP05SWejFlVXxOM39rMOTYHWUh902h1WefEXDjjQ5tty5UKtWlqTLZXu9WVhxMBuDG7i1kFKGB8/LQ8L87ruLlW7rgoVGz+shFbnh3C/vi5Q+QxswhwdCHH35ISkoKY8aMYdy4cZyRDLmiAoqi2P7CsrdmkzVJoymrspEhy7qhFAfvKPOIjsZ3yGA83CxvlsZWvFY+mN1FfrGRjcdTAbi+awsX90Y4gkql4p6B0fzf9EG0DvEmKauQ8R/t4LMtZ1AqSQIrGjaHBEMPPfQQJ06c4JFHHmHFihV06dKFZ599lvz8fEc0LxqJQoOJEpMZgEA71/doAlwzTeauIv/3P2LWrsVnwABXd0WUWn8shWKjmegQbzq1cJ81ZqLuurUM4LcZQ7ixWwuMZoUvd8SRX1L1khDRMDksmUJgYCDvvvsu+/btY9CgQbz++uvExsby1VdfOeoSooFSFIXzM5/l5OtvAaDTqPD2qDyZ4uWqmyYLa2LBkL5NGzxaRtrWDgnXW3nIMkV2QzeZImuM/D11LLirJy+P68qCu3riq3eP8jzCsRyeWapLly6sW7eOH374AQ8PD6ZMmcLAgQPZvXu3oy8lGgilsJDsn38m8fc/AAjw8rD7l4YmIBCoYmTI3zpN5tg1QxdemcvF117HKAkORRUKSoysP5YCWIIh0TipVCruGdCa7i0DbY8t2RnHx5tOY5Zir42C09Js3n777Rw7dozZs2dz4MABBg4cyH333ceFCxecdUnhpky5eQDkeFqmEIJqkIelummynq0CeWVcV/41sn0de3mJoihkfvcdGV98gVJS4rB2ReOz8XgqRQYzrYK9ZBdZExKfXsBLvx3htZXHWH3koqu7IxzA4cGQyWRi3759fPzxx0yfPp3vvvuO4uJizGYzX3zxBR06dOCdd95x9GWFGzPnWbap5/tbdkIF1iQYqmZrfatgbyYNaM3gdqF17OUlSlERGCwLvTV+7rUGJP/PP0l4eDoX33jT1V0RwPLSXWQ3dJUpsqakVbAXs2/uwo3dWjC6S7iruyMcwCGTn99++y27du3izz//ZP/+/RQVFdlW3IeGhnLDDTcwaNAgoqOjeeutt3jiiSf49ddf+emnnwgIqFlGYtHwmPMsI0N5PpZ/6wAv+9e7aENCCJ4yBU1gIIrZjErt/JpB1hxDaDSo3CyxoSkri7yNG/GS3WQuV1hiYoNMkTVJKpWKu/pHMbFfK1sQXGSE99af5p/XtMdTZ9+aSOE+HBIM3XXXXQCo1Wo6d+7MoEGDGDRoEAMHDqR9+7LTF3feeScLFizgySef5IknnmDhwoWO6IJwY9ZpMmswVJORIbW3N+H/fabKY/acyyApq5DhsWF2b9mvitlaisPPz+3+2rfVJ5NgyOWyCw2M6BDGiYt5dG8pf9Q1RZd/Pvx4Ts3u1NOsPprCuxN7EhvuXqPKomoOCYZefPFFBg0axIABA/CzY1rh0UcfZe/evfz222+OuLxwc7aRodI1Q/Zuq7fXf374m3PpBXz34AD6tw2pc3vWOmrulH3a6lLSRQmGXK15gCcf3N0bs9mxRYJFw9QzROF0gY5jF3K56b2tzBrbmUn9o+S90UA4ZM7hxRdfZNSoUXYFQlaxsbFkyE6dJsG6ZihXb5lyCvJx7LbwHq0C6d8mGK3GMVNol4rKumEwFGRJumjOzZXF3W5CrZZfdgK6BCn8/sgghsWGUWw08/yyQzzw5V9k5MvPaUPgsoQJ99xzD+HhsvCsKTCVjgzl6izBUE0LqhYeOEBJfAJeV3XHo1Wrcs/Pv7Nn3Tt5GXOuJXhzpyKtVpqAANBowGTCmJmFLryZq7vU4ChmM8XHj1N8+gymzExUHh7oIiPx7NIZbWmwWZ2zafkoikLbMF8n91Y0JGF+ehZP6cui7eeYt/IYa49eZMz8zbw9oYdDN3kIx6tVMDR27FjmzJlD7969a3xuYWEh77//Pj4+PkyfPr02lxcNjLl0zdAofTbdhwzkqstyddgj7f0PyNu0iRavvFxhMORotmkyP/cbGVKp1WgCAzGlp2PKzJBgqIYK//6bxBn/wpiSUv5JlQqvXr0IvO1WAm66qcrElh9tPM13exJ4/Nr2PH5trBN7LBoatVrF1CFtGNA2mMe+3c+plDwmfb6LB4a25d/XxaLXyuJqd1SreYWEhAT69evHyJEjWbx4MTmlvzyqsmfPHh5//HFat27NCy+8QGioRMlNhSY4CH3nTtzQ2of/jO5AtxouNq0uC7WVo2oGufPIEIDWWp9MpplrTB8bi2I0ovL2xqtPb/zGjMH36qstNegUhcK//iL5uVmcGnUdhX//XWk7xUYTOo2KvtHB9dd50aB0iQjgt0eHcHf/KBQFPtl8hlsWbOPYhep/X4r6V6uRof3797No0SJeeukl7r//fqZNm0bHjh3p1asX4eHhBAUFUVhYSEZGBidPnmTPnj1kZ2ejVqu54447mDt3LtFuVgBTOE/w3XcTfPfdtT6/ulxD645e5L8/HaRTC3++vL9fra9jpfbzQx8biy6yZZ3bstfuc5kcy1JxdYkJna7qaURNkOwos5diMqHSXPpLXO3lReslS9BFRqDW68sca7hwgZzly8n44ktQFDxiYiptd/6dPXlpXFe8ZQu1qIKXh4a5t3ZjeGwYM386yLELudz83jaeHtOB+we3kfVmbqRWwZBKpeL+++9nypQpLF++nMWLF7Np0yaWLFlS7li1Wk337t0ZN24c06ZNIyIios6dFg2Poij8nZhNoJeOVsHeaGrwIWDd1WXKrvgvKg+tmtTcYoK8Cx3S17oGb7Xxw1+J/HxUw+4v/+KHhwdVuQPFtqMsQ4KhqpgLC0l4eDoBN40l8B//sD2ub9umwuN1zZsTMnUqQffcgyEhAY1v1euB/D0duytSNF7XdWlOz6ggnvm/A6w/lsK6oyncP7ji96FwjTotoFar1dx0003cdNNNABw9epTExETS09Px8vIiLCyMLl26SGJFQZHBzLj3twFwaM7oGhU71PiXjgxVMh3bzK+0PlkDKtZq2Y59KU9JvzbBbDt2ntljO1W7Fde7Zw8wm9FFRtZDTxsmxWAg8fHHKdi1i+Ljx/EbM6ba4MZK7eGB/opRodx16zAkJRF8772k5RUT6quv5GwhKhbmp+fzyX34dncCw2LDbKNCRpPZYTthRe05dDdZp06d6NSpkyObFI2AKTeXXANEBnqRX2LEx86K9VbWtTvmnEqKtZZWrs8qMFBsNDWIBYrzVh0jp9DAy7d0RatR849ekXgl/02H5pfWKVX2IRk8eTLBkyfXZ3cbnAuvvkr+ps2oPD1p+f4CuwOhipQkJpL07/+gFBVxJiGdf+TG0isqkB8eHlSjEU4hVCoVE/tFlXlszm9HyCo08MotXQmoQUJa4VgSjgqnO3fnRNIH9WP11b7sf+G6Gichs02TWctkXCHQW4dHadCQ6oDRIVNePorJVOd2KrM3PpNPNp/hmz8T+PPcpUXQl9+WX/Yncc3/NpGSW+S0fjRW2b/9TtY334JKReTbb+Fdi12vl9NFRhL26CMArN5+FABPnUYCIVFn8ekFfPNnPL/9fZ5D5yv+Y0/UDwmGhNNZd2epfX1qdb7GFgxVPE2mUqkIKx0dcsRU2dnbbuNYl64U7t9f57auZDYrPPfzIRQFbusVyaCY8rsqjSYzn2w+Q3xGAf9bdcLhfWjMDElJJL/4IgCh0x/G7+qr69ymSqUiZNo0Wsydy87mXQDon3TQYbsXRdMVFeLNj9MH8d/rO5bJQyTvrfonwZBwOms5jtpOVViDIXMlu8mAS8FQTt2DIbM1z1AdplYqs/xgMkeTc/DTa3n+xs4VHqPVqHnplq4AfP9XAkfOlw0ClZISis+epfDwYYf3r0FTFFJffhmloACvPr0JfeQRx7Z//VgOh1nWEnX7dTFp73/g2PZFk9SjVSAPD7+0Ri0ho4DbP9zOYRkpqlcSDAmnUkwmzAUFbInozvhl55i/tuYjHWrrAurcXBSzucJjrOuGUus4raQoCibrSJaDky6azYrt9U8b2rbKsiS9WwdxY7cWKAq8v/FUmeeKz57jzPU3kDDtAYf2r6Hz27+fgm3bUXl40OKll8tsqXeEjcdTMaGind5Ei4IM0hYsIGvZModeQ4i5y4+yNz6Lce9v48ONpzGZZZSoPkgwJJzKnJ8PQLJPCH8n55GQUfPt79rQENpt3kSHfXtRqSt+yzbzd8w0mTm/AErXCzk66eKmE6mcTs3H31PL/UOiqz1+xsh2AKw8mExcer7tcVvSxawsp65takjMBQWELV8BQOg/p1e6fb4u1hy9CMCYQR0IeWAaAMnPv0D+zl0Ov5Zouube2pXrOodjMCnM++MYEz/ZSUJGgau71ehJMCSc6lLFesuUU2AtdkuoNBp0zZqVS5J3Odv2+jpOk5lzS6ekdDpUnp51autKX+44B8D4Pq3wsyNHTcfm/ozoEIZZgc+3nrU9rgkKsqy2VpRqs3I3FUUHDqAuLEQbGUHw/fc7vP0So5lNx1MBuLZzOGFPPIHf9WPAYCDxsccwVFTeQ4haCPHV8/E9vXnjH93x8dDw57kMrn9nC9/vSZC1RE7kVsHQkiVLeOihh+jTpw96vR6VSsXixYtr3I7ZbGbBggV0797dlu/ojjvu4OTJk47vtKiSqbQuWa63ZaoryElbR5vZFlDXbZrMumNN4+9f411vVYlLz2fjCcsv00kDWtt93rQhbQH4eW8ShSWWUSCVVmsrUWJMS3dYHxsy7wEDOPeffxP++uuoq6gpVlu7zqaTV2wkzE9P98gAVGo1Ea+/jlfPnoQ++ADasDCHX1M0XSqVijv6tGLlY8Po0zqIvGIjT/94gAe/+ou0vIaTT60hcatgaNasWXzyySfExcXRokWLWrfz8MMPM2PGDEwmEzNmzOCGG27g119/pW/fvhw5csSBPRbVMeeXjgx5WUaGArwd/4sKHDhNVprLSOPn2CmyH/9KRFFgaPtQ2oTav6tuUEwIrYK9yC02svJQsu1xTYg1C7UEQ1bGoCC8evRwSttrj1imyK7t1MyWLE+t19P6qy8JmTrVoYGzEFZRId5899BAnh7TAZ1GxZojFxn99mZWH77g6q41Om4VDH322WecO3eO1NRUHn744Vq1sWHDBj799FOGDh3K3r17eeONN/jiiy9Yvnw5OTk5TJ8+3cG9FlWxbqvP9bAEAIFetRsZuvDqq5y9/R/kbdtW4fOOykJtWzzt77jF04qi8Ovf5wH4R++a1TtTq1WM790KgO92J9ge14ZYtuHKyJDzKYrCGlswFF7mOZXWoXlrhShHo1bxzxHtWPbIYDqE+5GeX8KDX/3FUz/8TW6RwdXdazTcKhi69tprad3a/imEinz66acAvPLKK+gvW2MycuRIRo8ezebNmzlxQnK31BdT6ZqhHJ03AEG1HBkqOXeOosOHMV64WOHzUSHevHprN16/rVvtOlrKmsvIkSNDfydmE5degJdOU+6XqT3+0bslKhXsOptBfLplIaU2JAQAY3qaw/rZEOWsWEH+jh1OXUtxJDmH89lFeOrUZXLBXMlcXMzF1+dRfOaM0/oimq4uEQH8OmMwDw1vi0plqWd4/TtbyC6UgMgR3CoYcoSNGzfi4+PD4MGDyz03evRoADZt2lTf3WqyvHv0IOKNeeT7BgK1W0AN1dcn8/fUcVf/KEbWIti4nDnHOjLkuGDo1/2WUaFRncPxqUFNNquIQC8GxViCn+UHLVNlmtJgyJTedEeGzEVFXHhlLvH33U+BE3+m1x6xLI4e2j4Mzyqq1F98/XUyFi8m6T//QSkpcVp/RNOl12qYeX0nvntwIK2CvRgcE0pALUfbRVmNaow3Pz+f5ORkunbtiqaCHCPt27cHqHYhdXFxMcXFl6Zbckp/ARsMBgwGicIrYr0v5e5Ps2Z4X3892X+uBRR8dKpa3UNVaQJEQ1aWU/8N9P370+yVl9GGhzvsOl0jfOkbHcTYbpW3Wen9KzWmczjbTqXz+4Ekpg2OQhUUCEBJalqTfU/m/PobpowMtBER6Pr3hw0bnHIvzGYTQd46ro4NrbL9wGnTyFn5B8VHjnLh7fmEPvmEw/viLNW9/0TV6vv+9Wzpx6//HFjmmhdyikjPK6FLhGPzo9UXZ91De9trVMFQdmmG4oCAgAqf9y9dB5JdRSZjgNdee405c+aUe3z16tV4e3vXsZeN25o1a8o9VmKCYqPlrfbnlg0cqMW7LiTlIiHAmYMH2LViRYXHxOdBSqGKaD+F0LrsitfpICMDKrlOTWmASS2g8HQqK05XfWxF9w9AbYDmXhpaqrL5ffkKApOSaA4kHzvKXw7qZ0MT9ckneALJV3XnyIYNQOX3ry7aAi90B3Py36xY8XeVx/rcfBORX35F5uLFHNRqKGzXzuH9cSZn3L+mxFX3T1Hgo6NqTuSomBhjpl9Yw92C7+h7WFBgX46mRhUMOcrMmTN58sknbd/n5OTQqlUrrrvuOltAJcoyGAysWbOGUaNGodOVHbZNzi6CPzejVau49abra7XzJjM1lfT1G2gVFEzfG26o8JgHl+xlw6k0XrmlMzf0qdlCZVer6v5Z3XHLpf839u+HYdytaJuH06N583rqpfsoOnyExMRE0Ono99//Yvbzq/b+1YsbbiCloJCcH3+kzfIVRP30f6h9aleTrz7Z8/4TlXP1/SsoMbIi5xBn89O454YhxIS5/3vuSs66hzmVLK24UqMKhqwjQpWN/FhvSmUjR1Z6vb7M4msrnU4nHxTVuPIe5fzxB0knzwPhlurytcwB41GaV4e8vEr/Dbq3DKLQYCbUz9Nt/p1WHkymd3SQbbdbdex9j+latMCrDuknGrrUH38AwH/0aDwvm9J09M9oXHo+UcHeNQrgW8z8L4U7d2JITCTz3Xdp/sILDuuPs8lnXN246v4F6HR8fE8fzqTlExN2qabivvhMerQKbFCpHxx9D+1tq1EtoPbx8aFFixacPXsWUwVlCqxrhaxrh4TzZf/+O/Hf/QRQp4V+6moq1wM8MSqWbx8cyJiutQ8Ssn78kfTPF1KSkFD9wdW4kF3E9K/3MvC19WQXOGYevLDExNojF8nMb7oLdE25ueT8vhyAoIl3Ou06ecVGRr21mUGvrye9Bonu1D4+tHj5JQAyl35D/p9/OquLQtioVKoygdBfcRnc/uF2Ji/azYXsuiWjbQoaVTAEMHz4cPLz89lWQT6aVatW2Y4R9cOcl09oYRYPtII7+rSqdTvV7SZzlIyvl5Ly5puUnIurc1tpecVc1TKAHq0CCXBQ5u27P9vJtC/3sO5Y0y3/kLt6NUpRER4xMXj16uW06xy/kItGrUKvVRNcRVHdivgMHEjgHXcAkDzrecyFNa/JJ0RdxKUXoNOo2XwildHzN9tynYmKNdhgKC0tjWPHjpGWVjbPyoMPPghYslmXXLa9dd26daxatYphw4YRGxtbr31tysy5uUTkp/OvLj48NDym1u1Yi6aa7QiGzHWo8mxt3xFFWrtGBvDLo0NY+kD/OrdlNaRdKC2DvDCbFS6+Po+ERx/FcL5pfchl//obAAE33+zU4f/erYPY98IoPpvct1bXafbUf9A2b44hPp60jz92Qg+FqNxtvVqy/F9D6N4ygOxCA//6Zh+PLt1LVkHTHVWuilsFQ5999hlTpkxhypQp/PDDD+UeW7Zsme3YBQsW0KlTJxYsWFCmjauvvppp06axZcsWevbsydNPP83kyZO58cYb8ff358MPP6zPl9TkWQu11jWJoTYsDP8bb8S/ksXTACcv5tLnlbUMmbe+1texjjyp/Ry3UF6vrTw3TU09ck07tjx9NXf0bUXu+vXkrV3XpIIhQ3IyBaXTTgFjb3T69Tx1Gto1863+wApo/Pxo8fJL+N98E8GTJzu4Z0JUr10zP/5v+iAev7Y9GrWK3w8kc93bm9l4vOmOLFfGrRZQb926lS+++KLMY9u2bbNNeUVHRzNu3Lhq2/n444/p3r07H3/8Me+++y6+vr7cdNNNzJ07V0aF6pkpL49UrwBUJg9aFhvxrUXSQbAEQ5H/+39VHuPnqSMtrxiNWoXJrKBR1+yvecVsvhS81XFkKLvQgFatqlWSxapcHlhpQ0IwxMdjTM9w6DXcmSYwkIjXX6Po+Al0kZFOu47BZEanqfvfir5Dh+I7dKgDeiRE7eg0ah6/NparOzTjie/3cyY1nymLdnN3/yieu7ET3h5uFQa4jFuNDC1evBhFUSr9mj17tu3Y2bNnl3vMSq1WM2PGDA4dOkRRURFpaWn88MMPEgi5gDkvj6UdRjHmp3gWbT3r1GuF+nqgUoHJrJBRiwXG5rw8S8IO6l6bbMnOOK6as5p5fxyrUzuVMZrMZIRFWP6/CZXkUHt5EXDLLYQ//ZRTr/PmquNc+9Ymfj/QdEbdRON2VatAls8YypRB0QB8vSueG97Zwl9xma7tmJtwq2BINC5KSQlKcTFqRcHXQ1PrUhz20mrUhPhYUiJczKn57glTaSkOlacn6lqmALDacTodo1kh3K98ioa62nkmnZ4vreEZn34AmKRYq8OtPXKRUyl5qB24JklRFHJWrsSYmuqwNoWoCS8PDbNv7sLX0/rTIsCTc+kFjP9oO2+uOkaJ0ezq7rmUBEPCaUz5+QDM+Pv/OPjCtUwaULcivMVnzpD/558YMyv/Sybc3xJ8pNaier051zFFWosMJnafs0xdVVXYs7ZiwnzJLTZyUvEh28MbY4YEQ450OjWPM2n5eGjUDIsNc1i7Ka/PI+mJJ7n45psOa1OI2hjcLpQ/Hh/GbT0jMSvwyeYznEvPd3W3XEqCIeE01vU3Ki8vVFptnXf+nH/qaeLvnUzh35WXRGjmV/eRobpOke2Nz6TYaKaZn77Wi2+rEuanp0O4JWA7ENquyRRrTXjoYS689DKGi85d/LnmyEUABsSE1HqNW0X8x44FlYqcX38jf+cuh7UrRG0EeOl4a0IPPry7F8+P7UxsuOOKUzdEEgwJFIOBrP/7ibwtWxzbbkkJmtBQtMHBDmlPE2AJUqraXh/ub8n0nFKLkSFTjiVzeV1HhnactgQng2JCnLb1e1A7S9X6fWHtMTaBabKSxETyNm0i89tvUXk4d7p1bWkwNKpTM4e269Wtqy1J5IWXX0aRoqjCDVzfrQX3Doy2fX8oKZu7P9tJQoZ9Nb0aCwmGBKnvLSD5uedIeOBB8jZvdli7+pgYYrduYeb4l5i88M9aLWq+nHW7u3UEpyJ1GRnCZEYTFIQmKKhW/bOyTpENaBtSp3aqMqR0+m1/WPsmMU2Wu2YtAN59+6Kt479PVdLzivkr3jINO7JTuMPbD3v8cTRBQZScPk3mN986vH0h6kJRFGYtO8S2U+m8veaEq7tTryQYauLMxcVkfved7fuML79yaPuW9TOZbDqRik5Tt1ESja0kR8W15wDC6jAy5D9mNLE7ttPqww9q10EsW7L3J2QB0CfaMSNiFenXJhiNCpJ9Q0nKK196prHJLa1k7XfttU69zvpjKSgKdInwJyLQy+Hta/z9CXv8cQBSFyyocv2bEPVNpVLx7p09GdOlObPGdnZ1d+qVBENNnEqlIvLNNwi45WYA8nftwpTnuIV0WaU1uTRqVZ3XX9imybKrmCYrHRlKqc3IkAMcPp9DkcFMkLfOqZWj/Tx1dI+03I8jfa9FMRqddi1XM6amUrhvHwB+14506rXWHrVMkV3rhFEhq8B/3I6+Y0fMOTmkvfee064jRG1EhXjz0T29bSVoLKNFB1l1+IKLe+ZcEgw1cSoPD3yHDSNi3jy0LVqAwUDR4cMOaz+r0DI1Fuilq/P6Gds0WW4V02R1GBlyhD2lU2S9Wwc7vVL00FjLmpaj/Uej0jbexGm5GzeCouDZrRu6FrUvwludIoOJzScsOZtGdXZeMKTSaAifOROAzG+/o+h405qOEA3LmiMXWbIznoe++ot/f/83OUWNc62bBEPCxqt7dwAKD1S+W6sm0hcv5tDkBwAcUqjUOjJU1TTZ5Vvr61KjrLas64X6RDtvXYvVgBjLmqQ/z2agKPX/WutL/mbLwn5fJxdY3nE6nUKDieb+nnSJcFw5lor49O+H33XXgdnMxddea9T/fqJhG94hjOkjYlCr4P/2JnL9/C1sP934Er1KMCRsvLp1BaDoyBGHtGfKziarxJLIK9DLAcGQf/XTZKG+elQqMJoVMmpYkDDx8Sc4MWQoOStX1qp/iqLYsrn2rYdgqGerIHQaFRdyikjMbJxV0RWDgfwdOwDwHebcshZrrFNknZs5fVQPoNnTT6Hy8MCUlYUpK8vp1xOiNvRaDc+M6cj3Dw0kKtibpKxC7vp0Fy//foQiQ+NZryjBUBOmGAycf+a/pH34IeaSEjxiLFXlS86ec0j75rx8cj28AQjyrltGZ7BvmkynUfPmP67i88l98KlhzR1jWiqmtDSo5S/Cc+kFpOWV4KFV0zUyoFZt1ISXh4ZuLSxpAHYcinf69VyhYN8+zHl5aIKD8eza1WnXMZsV1pUGQ6M6N3fadS7n0bIlrb9eQpv/+9GpO+SEcIQ+0cGsfGwoE/tFAfD51rPc9N5WDiZWPlLfkEgw1ISVJCaS/csvpH3yKSqtFn3btpbHz51DMdc9Nbs5L49cnSUYqq9pMoB/9G7JyE7heHnUrFq82Zp0sZZ5hs6l5+Ol03BVywCHVqqvSudEyyje1s0H6uV69S2/NPeVz5DBqNTO+7i6mFuERqXCx0PDgLbO2wV4Ja9u3VBp6ue9IkRd+ei1vHZbNxZO6UOor56TKXnc+sE23l13EqOpYZfzaLyrLkW1Ss6dA8CjdWtUajW6yEhavPoq+pi2DmnfnH9pZCjQq+4jQ/p27Yj+vx/RBgbWua2KWEecNLXMQH11h2YcmH1dnfMp1URvf1iabSa3kS5qDJo0CV1UFPo2bZx6nRYBXmz77zUkZhbWWyArREN1TcdwVj8RxKxlB1lx8AJvrTnB2qMX+X/jr2qwmaxlZKgJK4mLAyzBEIBKqyXwtlvxuuoqh/wVbs7LuxQMOWBkSO3tjVeXLugiI6s87lRKLsv2JdW4GrM1s3VtgyGwTNNZs2DXh4EtPPl++fO8UvBXvV2zPunCwwkaPx7vPn2cfi2VSkWrYG+nX6ciptxcUt56m9R333XJ9YWoqWAfD96/qxfzJ/TA31PLgcRsxr67lT8OJbu6a7UiwVATZky2vGl1LasOLmrLlH9pmszZFesvt2zfeR7/bj/L9iXZfY5iNGIuLSxb19pk9cmrWSg+xmKMKVIJvbaKDCaXD/EX7t1L+iefkP7Z5xiS7H/fCuFKKpWKcT0jWfPkcEZ2bIa3XkOv1g1z/ZsEQ02YteClLtw5OVXMefnk2UaG6j5NZq/Y5n4MigkhOtT+pIeXL8rW+Na8uOr202lc9/Ym5v1xrMbn1oU2zFJV3Zia6pJUAo3Bt3/G02fuWj7YeMplffAZNgzv/v1RSkpIeecdl/VDiNoI9/fks8l9+O3RITTzuzQyvv7YRUwN5HNJgqEmzHjRsntG2+xSMFR44AAXXnqZ9MWL69y+OS+PHNuaIceMDF187XUSpv+T4tOnKz3m5qsiWPrAAKYOsX+dibk0GFJ5e6PS1byv+xOyOHExj/h6Lm6oDQvjnF9zHm0zjlve31av13YmxWzm3KRJXHhlLqZs5+5W2XEmnawCAzonLtCujkqlotlTTwGQ8+tvDktvIUR9uXKaedXhC9y/eA8TP9np8pFXe0gw1IQZLlrSq2vDL1XnLklIIHPpUvLWrqtz++a8PEYm/MVdXYJpHeKYtRj5O3aQt2EDhguOTQ1vLf5a24r1E/q04rN7+3DfoGgH9qp62rBmBJTkcTSwFYeSsskuaBwLqYtPnqRwz19k/fQTai/H1wi73Pt39eK7Bwdwc48Ip16nOl5du+A/diwAF998UxIxigatyGDCV6+ld3QQWo37hxqym6yJUsxm2zoTXfNLeVV0LSy/EAznz9f5GqGPPMJDebkE39oNja9j6nSp/S3BinWxc1UMJjNatcquBHrm0u36Gv/aBUMhvnqudWIJh8pofH0I1ph59s8vGTn/Zfy9GsePdMHOnQB49+6NysO5U6xajZr+bUOceg17hT3+GLmrVlGwYyf5W7fhO3SIq7skRK3c0iOSPtHBhPpe+vk9m5aPWgWtQ5xXt7G23D9cE05hysgAoxFUKrShobbHdZGlwdDFiyimumUXDbn/PsL+9a9arcGpjMbfkszQVEUWapNZoc8ra2n/3Eoy7Rwp0bVqRdiTTxJ0110O6Wd90oaFMvT8AVoUZ9dL5uT6kL/DEgz5DBjg4p7UL4+WLQm6+24AUt58s84/g0K4UmSgly1VhdFk5vFv9zFm/ha+3HHO7dY4SjDURJmLivHq0xvP7t3KrJHRhoWBVgsmE8bUuu1QKjaaOJOa59C8O9Zt76YqRoY0ahXm0imGi3ZWr/do1YrQBx8gaOLEGvdpX3wmb605wa4z6TU+1xEuX0TdGChGIwW7dwPgPaC/065jMJm5/p0tvPDLIbcqPhn68EOo/f0pPnGCnN9/d3V3hHCInCIjXh4aCg0mXvjlMHd/touEel5jWRUJhpooj5aRRC9ZQpvvvivzuEqtRhtimTKo6y/Xc2kFXPO/TVz71qY6tXM5axZqczVZqJv5WQq21kf1+vXHUnh33Ul+/CvR6deqSLN//5sWX3zJN5rWPLJ0L4YGsFixKkWHD2POz0cdEIBnx45Ou87ucxkcTc7h9wPJNS7d4kyawEBCpk0DIPW9BSgl9ZfEUwhnCfbxYOm0Acy5uQteOg07zqQzev5mvtoZ5xajRBIMiXIcMdJgTE0lZc16fLWOzTGkDqh+mgygWWniQ3tHhuri79LaPN1bBTr9WhXx7tkT/759+GBnEssPJHM0ufr1VO7MNkXWr69TS1WsPWJJLXFNx2Zo1O41vRg86W604eF49+2LubBxFuEVTY9arWLyoGj+eHwo/aKDKSgx8fyyQ9z12U7iXDxKJMGQKMcWDNUhkV/RiROEzv0vvx78lDVPDHdU1y6tGapmAXV46cjQhWz7gqH8nbvI/vVXis+erVF/FEXhQGIWAFe1dH5x1sqo1Sp6RQUC1DjztrvJ31W6eNqJ64UURWHNUcuOxGs71f/C9+qovb2JWbGciNdeRRPguveVEM7QOsSHbx8cwOybOuOl07DzTAYfbjrj0j5JMNREmbKyMOXlVbh91xEjQ+a80mzOvr4O/avb3mKtLQIt27GT7QyGMr/7lvNPP0P+lq016k98RgFZBQY8NGo6Nndt5urepZlfG3IwZC4upnDvPsC5i6dPpuSRkFGIh1bNsNjQ6k9wAbWP++24EcJR1GoVUwa3YdXjw7ixWwuevi7Wtf1x6dWFy1yY+yon+vQlY/EX5Z5zTDCUB4DaQVvqrawLqM3VTJNFBFimyS5k2zfFYG3PGmzZyzpF1inCHw+ta36cShITufDSy7TZthKAvQ04GCo6eBCluBhNaCgebR1TMLgia45YEo4OaReKtxutF6qK7CwTjVFUiDfv392LYJ/6q1JQEQmGmihTRgYAmqDAcs95REfj2bUruhbNyz1nL3N+Hr+3GcRTAYP5PwcuLNY2b453v354XtW9yuOalwZD9o4MWbMc17Qu2YGELMC1U2Tm/AIyly6l5R8/olGrOJ9dxPmshrnORBcVRfjzswh9YJpT0wRYgyF3nCK7UtGxY8TfP5XUd99zdVeEaLQaxp9EwuGMmZZgSBscXO65gJvGEnDT2Dq1b8rL43RABLvUIQzKdNwvZs8OHWj9ZfnRrCtFlE6T2RsUWNcgaQICa9Sfv0vXC3VvWbPzHEnbzDKS55GeSqdwXw4l57I3PtN2DxoSXbNmBJfm2XGWlNwi9pcGsSM7Nav6YDdgSEwkf/t2CvbtI/ieSWXyggkhHENGhpooU4ZlKkUTVD4YcgRzXr6tYn2QT/1VrLdqUToylFNkJL/YWO3xl4Ih+0eGjCYzh5Is5/Vo5bqRIU1gIJTmiuoZZlk4vjcuy2X9cXfrjlp2kV3VMoBwf89qjnY935Ej8ezeHaWwkLSPPnZ1d4RolCQYaoIURbFNk2mDg5xyDXNeHrmlRVoDHFSktSb8PHX46S0Dn9VNlSlms628h6YG02SnUvMoLK2/0zbUcVm2a0p1WRbxq3wsOYb+im+464aczTpFNsoF5VNqQ6VS0eyJxwHI/O47DElJru2QEI2QBENNkDm/wJbITRNUcTBkLinBkJRU64RvlwdDQd6OXRhnzMyk+OxZzMVVJ1S8tG6o6qkyc24ulO6qU9dgG/PfpVMtXSP9Ubs4T4110Xs3tWXh+uGkbIoMDWvBbc6q1Vx8800K9u1z2jXyi41sPZUGwKjOtV8TV998Bg60pBowGEhd8L6ruyNEoyPBUBNkKl0vpPL0RO1dcTX5UyOu5tTIa2ucd8d2jfw8ckqDIUcmXQQ4O+5Wzlx/A8UnTlZ5nL3b661TZCovL9Q1KApq3Ul2lYuSLV7OGgyF5abRzE+P0axwILHq9APuJuePlWR8vtBWpNUZtpxMpcRoJirYm9hw143m1YZ1dCj7l18oPnXKtZ0RopGRBdRNkG0nWRVTZJqgIEwZGbZja8qcl0+uv2VbvaNHhjQBARgvXqw219AjI2K4b3A0XSKqnvqyZrOuyRQZcFmyxcAanecM2jDLNJkpLZXerWNZeegCf8Vl0q+Nc9aEOZqiKBTu+QsAr969nXad/QmW98y1ncIbXFFbr6uuwnfkSPLWrSP13fdo+e47TruWwWQgsziTzKJMsouzKTAWUGQqothYTLHJ8mVWzKhVatQqNSpUqFVqPDQeeOu88dZ646PzwUfng5+HHyGeIXhq3X99lmi6JBhqgozW9UJVLJ7WBgVRctmxNRX++UJKZq8BHD8yZMs1VE0W6v5tQ+xqz5xrXTxds0XQ13dtQbifJz3caGTImJpK755BtmCooTAkJFjyWul0eHWvOm1CXfz3+o5M6NsKnaZhBUJWYY/9i7z168ldvZrCQ4fx6tqlxm0YzUaS8pJIyksiOS+Z8/nnScpJ4nDuYT769SOyirPIM+Q5vO8+Oh9CPEMI8QohxDOE5j7NaenXkgifCCL9Ion0jcRHJ4kmhWtIMNQE2XaSVbCt3kpTWqzVemxNZRVb1qto1Sp89Y59m9lbn8xePgMH0vHA35iLalbH7JGr2znk+o5weQmVoe3DeHBYMYPbNZwt2AXWUaGuXVF7OncEoU1ow/2F6xkbi//YseT89hup77xD1KefVHpsgaGAs9lnOZN9hrPZZ23/H58bj9FcyQ7Ly2IgtUpNoD6QAH0APlof9Fo9nhpP9Bo9eq0ejUqDWTGjKApmzJgVMyWmEvIN+eQb8ik0FpJvyCe7OJsS86XH43PjK+1zoD6Q1v6taRvQlpjAGNoEtKFtQFsifCNQq2RVh3AeCYaaIJ9BA4l87100fpVPC1mn0Kzri2oqM98AWEaFHD0dYR0Zqq4+WXaBgXXHLlJoMHF3/9ZVHqvy8EBTg/VC7sYjKgrPrl3xiI6mVXM/nr2hk6u7VCMFf+0BwLt3L6ddQ1GUBjc1VpGwRx+hYM8efIcPR1EUDGYDZ7LPcDLzJCezTnIq8xQns05yIf9CpW14ab2I8ImghW8LInwiCPcK58LJC4wZNIZmvs0I8gzCz8PPIQGIoijkGfJIL0wnvSid9MJ00grTSM5Pto1QJeUlkV2cTVZxFlmpWfyd+neZNjw1nrQJaENsUCwdgzvSMbgjHYI74OfhV+f+CQESDDVJuhYt0LVoUeUx2mDLyJAxvXbBUFaBZRdaoIPXC8Hl02RVrxlKzSvmye//xk+vrTYYqqmDidmE+eltO9ZczWfAANr8+IOru1Frzl4vpCgKN7y7lahgL164qQsRAZ4UGAswKSZbfT5vnTc6df2ngbCXyWwiKS+Jk5zi5Fv/4GT2Xk798j1xOXGYlIp3DoZ4hthGVy7/b7hPeJlAx2AwsCJuBT2b9USnc+w9UKlU+Hn44efhR3RAdKXH5ZXkkZiXyLnsc5zJPsPprNOcyT5DXE4cRaYijmYc5WjGUX45/YvtnJa+LW3BUcfgjnQO6UyYd5hD+y+aBgmGRIVsI0O1WDNkLi7m+IuvQKtRBHpqHN011NZirdlVB0MRgZ4MbhdCc38vDCYzOo3jhtkf+24fZ1Lz+fL+fgyLdb8P3/xiI3vjMyk2mLnWzfPpGNPTKYmLA8C7l2NHhjKLMvnr4l9sOXuSo8lRHLuQyRHNTHKNqRUGEHqNHl+dr2XRr1cIYV5hhHqFEuYdZvv/UK9QQrxCCNQHOnzqxmAykFKYQnJeMnE5ccTlxhGXHUdcThwJuQmUmCtOdeHv4U/7oPa0C2xHbFAs7QLbERMYQ4C+4VS89/XwtQU1lzOajSTmJnI66zTHMo9xLOMYxzOOk5yfTGJeIol5iayNX2s7vpl3M7qGdKVraFe6hHahS0iXBnUfhGtIMCQqZC3TYazFNJk5L4+M9GxoBYE+ekd3DY2/fWuGvD20fD2t+srnKf97i7ytWwm5bwoBN99c7fEGkxm9VoNGrap2p5qrbDmZxsNL/qJDuJ/bB0OFf1umRDzaxdR4EXtFzmWf449zf7Ambg0nMk8AljRS3tERmEvCyTJUPn1k3SmVXpTOuZxzVV5HrVITpA+yLQgO8QrBV+eLl84LL60X3lpvvLSW9A5mxbKmRkGhyFhEniGP3JJc8gx55BTnkFqYSkpBChlFVf+86TV62ga0pX1Qe9oHtqd9UHvaekcRHhCJWt0419Ro1VqiA6KJDohmZOuRtsezirI4nnmcYxnHbF9nss+QUpDC+oL1rE9Ybzs2yi+KrqFdbV8dgzva/m2EAAmGmqSMpUsxpaXhf8MN6NtVvAhYE1z7BdTmvDxa5aZwU/wuBtw6tU59rYi1ZEZ1a4bsVXLuHMVHj2LKs28HjU6jZuVjQyksMeHl4fiRr9pSSkowpKSiCQykT3QQLYO86Bzhj9msuDwpZFUK/z4AWLaO15bJbOKo4Sg/rf2JvSl7yzwXExBDj2Y9iAmMIdo/mmbel9bEaFVaUAEKFBgLyDPkkVeSR05JDumF6aQUpJBWmEZqYSqphamkFaSRXpROVnEWZsVsWQNTlF6Xl1+Oh9qDcJ9wovyjaO3Xmtb+rYn2j6Z1QGuaezdHo770nsv6eRmpbz+Lz6zn8L/uOof2w90FegbSv0V/+rfob3uswFDA0YyjHEo7xOG0wxxMO0hiXiLxufHE58az4uwKADQqDTGBMZbRo5AudA3tSvug9m49TSqcS4KhJij752UUHTyIZ9dulQZD1jIdpvSaf9Cb8vLoln6Gnpo82vd/qU59rYh19KC6PENWRQYTBpMZP8+KP+hsdcn8azYq4U6BEEDcvZMp3L+fyHffIfS669j6zDWu7pJd9LHt8R05Ep+Bg2p8rqIobE7czFt/vcWZ/DOQb/lFNzBiIGOixzC05VCCPe3LtRSgCbB7OsVgNpBZlFlmUXB6UTp5JXkUmYooNBZSaCykyGjZoWjNx6NGjU6jw8/DzzYd56vzJcw7jHDvcJp5NyNQH2j3Qu+S+DiMKSmkvvsufiNHotK413uyvnnrvOkd3pve4ZfWnmUVZXE4/TCH0g5xKP0Qh9IOkVaYxonME5zIPMFPJ38CLEFoh+AOdA7pTJeQLnQJ7ULbgLZo1fJrsimQf+UmyBpEVFWUVBcZSeRb/7ONENWEOS8fALWvczL8enbtSsuPPrRtJ6/K/1t1nAUbTvHw8Bj+e33HCo+paZFWdx1p0YZbpsOMFy66uCc1E3DjjQTceGONz4vPieelnS+xK3kXAJ4qTyZ2msjdne8m3OfS1OAv+5PYdCKV8b1bMTCm5u/niujUOpp5N6OZt2ur3ofcdx+ZS7+h5NRpcn7/nYBbbnFpf9xRoGcggyMHMzhyMGAJoC8WXORw2mFbcHQ47TC5hlwOph3kYNpB27meGk86Bne0rT3qEtKF1v6ty4zOicZBgqEmyJyTC4Dar/JtqWpvb/xvuKF27efnkebpj69/CCazgsbBgYM2OBi/ESPsOjbIx7KbLSGjoNJjTNlZgP1JF6/530YCvT14584etA5xn5w1uuaWAMBw8dKaGLNZISmrkFbBFZddaYjMipmlR5fyzt53KDIV4aH24K6OdxGZGMntPW4vtxtq2b4kNhxPJSbM12HBkLvQ+PsTMnUqqW+9ReqC9/G//npUDThFRH1QqVQ092lOc5/mtjVIiqKQkJvA4fTDHE47zOH0wxxJP0KBsYD9qfvZn7rfdr631rvM6FGXkC4092w4de5ExSQYamIURblsJKTiX/51zcdizsvjtb73cCSkDR8ducCYrlVv43emVkGWRZIJmZUHQ+YalONIySniXHoB6owCQn0dvzi8LrThlg9k68hQYmYBN7yzBZNZ4e8Xr0PrwN10rpJdnM0zW55hW9I2APo378/sQbMJ9wxnxfkV5Y7PLzay7bRlqrehVKmvqeBJd5Px5ZcYEhLI+ukngu6809VdanBUKhVR/lFE+UdxfZvrAUvQfS7nHIfTLIHR4fTDHMs4RoGxgD0X97Dn4h7b+b46X0LMIRzac4hOoZ3oENSBmMAYKUHSgEgw1MQoBQVgsmwptuXrKZ32OZWSy9trTxLopWPurd1qfQ1TXh6G0nl2Z+QZqomoEMuISHwlI0OKwYA5v3Raz46RIWtx1vbN/PBxcGbtutKGW6ZsjBctwVCLAC8UBfJLTBy7kEvXSPfbXpy7YQNqvR6vq65C7VP1KNuxjGM8vuFxkvKS8NR48lTfpxgfOx6VSoXBYKjwHGth1tYh3rRv1rAKs9pL7e1N6EMPcXHuXNI++JCAceOcnsW7KVCr1LQNaEvbgLbcFHMTYNnmfzb7rG0E6Uj6EY5lHLMsvCePuBNxYNnAiEalIdo/mtjgWDoEdaBDcAc6Bnck1KvhZIZvStzr01w4nW0Hlk6HqvQD88b3thIR4MmN3Vuw/EAynjo1s27sTMkfyyk8dAj/66/Hu2dPu69hzs3j3U3v4HPrbURG126qrTqp7y3AmJpK6KOPoGtW+bqNVkGWYCirwEBukaHcImpTbq7t/zVVTBta/Z2QBcBVrdwvsNA1t4wMGUqDIY1aRc/WQWw+kcre+Ey3DIZS33qL4pOnaPnB+/hdU/mC7+1J23l84+MUGgtp6duS+VfPp0Nwh2rbX33Yci8aYmHWmgiccAfpixZiPJ9M5jffEnLfFFd3qVHSqrWWtAZB7RnXbhxgWUx/Iu0EP2z6Ae8ob05kn+BExgkyizM5nX2a09mnWXl2pa2NYM9gOgR1sOWFsuaE8tY1nqnshkiCoSbm0s4pf1QqFadS8jianMOplFzm39mDf4+KZUj7UPRaNenr1pO7ahUeraJqGAxZrqH393P4eiGrrP/7P4wXLhA4fnyVwZCPXkuwjwcZ+SUkZBTSOeKKYKg0caPa1xeVtvofh79LK9V3d4NK9Ve6NE12wTbV2TvKEgz9FZfJvQOjXdvBK5jy8ig+dRqgyuKsy88sZ9bWWRgVI/1b9Od/w/9n166vEqOZtUctwdB1jXSKzErt4UHYI4+Q/Nws0j/5hMDx49H4us96tsZMp9YRGxRLT4+e3NDrBnQ6HYqikFqYyrGMY5zIPMHxjOMczzxOXE4cGUUZ7EjewY7kHWXaifCJICYwxhYctQtqR9uAtpIPqZ5IMNTEWCu9W0dB/jxrSfLWNzoYP08dM0a2tx1b2/pknl27EXD7bXj17OGAHldM4++P8cKFarNQA7QK9iYjv4T4jAI6X5Ek0Zxj/3ohRVFsI0PuUKn+Srpmlt11isGAKTMTbXAwvVtb/g33nHO/CvZFBw+CoqCLjEQbWvHUwc8nf+aF7S8AcH309cwdMhedxr5cMDvPpJNTZCTU14M+0fZtr2/IAm65hfRPP6Pk3DkyvvyCsH/+09VdarJUKpVtt+GwlsNsjxcaCzmVeYrjmcc5lXWKU1mnOJ11mrTCNM7nn+d8/nm2JG251A4qIn0jywRI7QLb0SagDXqNe61ZbOgkGGpirCND1pIW1l/uPaMCyx2rDSrNQl3D+mT5A4fxTFIgLfI8eaP2Xa2SvfXJwLKI+u+ELBIrWEStCQwk6J57UPtUP0R9Lr2AnCIjHlo1HZq7X4FIlYcHmpAQTOnpGC9cQBscTI+oQNQqSMoq5EJ2kdvUUoNLmacrS7b42+nfeHH7iwBM7DiR//b7b43KX/xx2LKr7rouzZ02QulOVFotoTMeJevHH/EdMsTV3REV8NJ60S2sG93Cyq7JzCrK4nT2aU5llgZI2ac5nXWajKIMW8mRjYkbbcerVWpa+bWyBUntA9vbkora+8eCKEuCoaZGpUbXsiW6FhHApWmfqy6b9ll+IJldZ9OZ7G/NQl2zYCglp5gtJ9OIcOIvXrUt8WL1Wait28orWkTt0bo1zZ971q5rHii9V10i/B1a58yRdOHhmNLTMVy8iGfnzvjqtXRs7s+R5Bz2xmdyQzfX7ey7UuH+0mCoR/lgaNW5VczaNgsFhQkdJjCz38warfkxmRVWlwZDY7o0nW3P/jfcUKucTcK1Aj0D6e1ZNlkkQHphOqezTttGkKyjSTklOZbadTlxrItfZzteq9LS2r91mVGkmMAYovyiJHlkNeTuNDF+11yN3zVXA5bMzCcuWhYQX3XZtM/8tSc4mZJHny4BtKfm9ckynVix3so6MlRdfTKAqNJgqKpcQ/bYb1087YbrhayaPfMMAJ4dLy0u7t06iCPJOew55z7BkKIolY4M/Zn8J//d8l/Mipnb29/Os/2frfHi57/iMknLK8HfU8uAto0rt1BVGvMi8aYoxMtS865fi362xxRFIa0wrcw0m/X/8w35tkXbq+NW287RqXW0CWhTZsF2+8D2RPpFOrzYcEMlwVATdiY1H7MCAV46mvldmn/uFhnAyZQ8jpu8aU/N65NdPHISwCkV661swZBd02RVb6+3lzvvJLPy6d+v3GN9ooP4amccf8W7z7ohQ0ICpsxMVDod+k6dbI+fyTrD4xsex2g2Mjp6NM8PeL5WH9YrDyUDcG3ncDy0TffD3pSVhSYw0NXdEA6kUqkI8w4jzDuMgREDbY9bM2ufyjplm247lXWKM9lnKDQW2sqPXM5T40mbgDa0D2pvW7zdMbgjYV5hTS6wlmCoCTuTZilMGhPmU+aN3znCn5/2JXG8QMVYaj5NFvfNj9D2WgLMJY7sbhnWNU9mO6bJWpfmGkrILCyXEbvo2DFMWVl4tGmLLrzyXWkGk5nD5y3XcueRoYr0irIsoj6clE2RwYSnzvWlBKzFWfWdO6EuzZicVpjGP9f9k1xDLj2b9WTukLm1KnugKAqrDjW9KbLLKSUlXHj5ZbJ/+522v/+OR8tIV3dJONnlmbWHRF5aM2ZWzJzPO19uJOlM1hmKTEUczTjK0YyjZdoK9gymU3AnOgZ3pGNIRzoFd6KVX6tGPYokwVATo5jNqNSWN/SZVEuywbZhZZPRWXdcHcsyApbt54rJZHcRyEyz5bhgf+etGbIWVbVnmiwi0AsPrZoSo5mkzEJbIkaAjEWLyf7lF8L+/SShDzxQaRvHL+RSbDTj56kl2o1KcNijZZAXzfz0pOQW83dCFv3dYNroyikyg8nAExueICkviSi/KN65+p1a75Y5lZLH+ewivD00DIutvn5dY6Ty8KAkMRGlqIi0998n4rVXXd0l4SJqlZqWfi1p6deSEa1G2B43mU0k5CaUmWY7mXmSszlnySjKYNv5bWw7v812vI/Ox5Y8slNwJzqFdKJdYLtGsxapcbwKYbfE6f8kf9cuWrz8EmfyWwLQNqzsL/cuLSyBRmJOMbk6L/wMhZiystCGVP9LVDEYyNJYgqCwAOclEbMWVbVnAbVGrWLRlL78//buOzqqcmvg8O9MzUx676TQe+i9i2BDrtiwAVcQxasoKoooxa6fYi+gIipiA6xIU4hI76FDAqQnJCG9TznfH5MEYtqkzgTeZy2Wy1PfOQwze96yt5+rAwFulQM0U3a25Zg6hhIOlg0x9WrjbpdFWssZUlK4+MUXYJbxe24+YPnF2CfEnfXHUjkQn2UXwRBmE5JeXxEMvbn/TQ6nH8ZZ7cyHYz7E3cG9wZdu7+vMnmfHcDIl1y56wWzF57HHiN11Jzm//ILnjOlow8Nt3STBjigVSkJdQwl1Da2o0QaW5f/RWdGcyjzFycyTnLp4iujsaAoMBRxMO8jBtIMVxzooHeji2YXuXpYVct29uuPv6N8qh9hEMHSVMeXlIRcXI2m0nIuz9Ay1/VfPkKteTaCbjqTsIhK79COiNB25uNiq65vz88nRWq7n6d585Q9U3t5owsIqsi7XZUi76vPYlAdDKvfav3zv7NeG7oGuyPVqZcszFxeT9dXXKPT6imAILJOoNxxPJTXHur/H5ua3YAG+8+eDycS6c+tYdWoVAK8Oe5VQ19BGX9/XxQHfZuyZbA10PXviNHo0+Vu2kP7++wS9/batmyS0AjqVjh7ePejhfSkRankZklOZpyr+nLh4gnxDfpUAydPBk+7e3enh1YNuXt3o7tUdJ439l8IRwdBVpnzCscLZifPplpVk4V5Vh306+DqRlF1E4ePPEj4wxPrr5+WRXRYMebk0X+ZUx4EDabu+amHO+jJmW3p86uoZ0qgU9GrT8N6KllIeHJoLCzHl5lZMNL+tbzC39Q3GVWc/OUgkpZLo3HMs3rUYgAd6PMCI4BE2btWVxXv2o+Rv3Ure+g0Uz5iBQ5cutm6S0ApdXoakvE6bWTYTmxPL0YyjHM04ypH0I0RnRXOx+CKRCZFEJkQClmG6ju4d6e3bm94+vent29su67OJYOgqU16hPd/BibwSSyAQ5F51OKudjxNbT6cTk5Zfv+vn5ZOjsQRXHo62LdJ6ueTsIn46lIQsy/xv9KUs26ZsS3B4pay4Ueh0KN3dMWVlYUhOrgiG7CkIKldoKGRO5ByKjEUMDhjMrJ6Nz5j8yd/n2BObzf3DwhjVseYJ8VcLh44dcbn+enLXrSP93fcIXvqJrZskXCEUkoJwt3DC3cK5ud3NABQbizmVeYoj6Uc4lnGMIxlHSMpPqpik/c3JbwAIcQmhl08vevv0po9vH4Kdg235UgA7DIb27dvHwoUL2bVrF6WlpXTt2pXHHnuMu+66y6rzIyMjGTVqVI37d+3axcCBA5uqua1OeWFSRw83vpjqR1peMTpN1XkV7coqfJ9Nr28wlHtpmMzJftLFX8wv5f82nsbLSVMRDMlG46VyHLUEQ9vOpLPheCpjO/syqpP9f8GqAwMtwVBSEg6dOtm6OVWUxsej9vPj/w78H7G5sfjofXht2GsNWjn2b+uOXeBUah43RwQ0QUuvDN6P/I/cDRvI//tvCg8eQt/b+jqDglAfDioHInwiiPCJqNh2oeACh9IOceDCAQ6mHSQ6K7oiYeTPMT8D4KXzoo9PH4KNtguK7CoYioyMZNy4cWg0Gu68805cXV1Zu3Ytd999N7GxsTz7rHWZggFGjBjByJEjq2wPCgpqwha3LubS0oq5P3p3V0a51pwvpyIYSstHNhiQDQYU+ronRJfk5pGvsRzn2cw9Q7IsYy4oRKFzqHOlW7i3I7f0CiTc27Fieb0pNxdkyywgZS3PYsupNFbtiUetkFpHMBQQQPGxYxiSkittjzydxkdbz9IlwIVFE7raqHUQN3Uqu9wyWH2zjITEq0NfbdSE6ct9OLknG0+mc03nK7swa31oQkNxu+U/ZP+4mvR33qHNlyta5QRXoXXydfRlfNh4xoeNByC3NJfDaYc5eMEy1+hYxjEyijLYGLeRO/R32KyddhMMGY1Gpk+fjiRJbNu2jV5lVdIXLlzIoEGDWLhwIbfddhvt27ev40oWI0eOZNGiRc3Y4pZnuHCB1MUvoAkLxeeJJyqWyFvLXF7UVJJQONdeW6t8UnVyTjGHIvoScMet+C14vs57ZGYVAHqUsrnZh2bODBiIOTeXths3oAmpfV6To1bFkjsiKm0rnzytcHZGUtfc1nFd/dCoFAxv3zqWaasDLL0ihuTKwZDBJLM3NpPMwubL/1QXQ1oaabkpfHKbEpCY2m1qpey6jdXGQ8+ske2a7HpXCq+HHiLn518o3LuXwl27cBw82NZNEq5SLhoXhgcNryhgW2Iq4Uj6EfYk78E9znbzMu0mGNqyZQtnz55l2rRpFYEQgLOzM88//zx33nknX3zxBa+8cvXmy7jw0svkb9kCgC4iApexY+t1fvkQmcLZmV3nM0nPKyEi2I2QavLmuOk1eDlpyMgvJcHZBx8rEy9KBflMOHsAwto2+xJ0hV6POTfXquX11bF2Wf2gtp4MamsHy9GtVFMw1D/Ugzdu7VFRyd4WCqMO89GNCvL0Ep09OvNIxCM2a8vVRB0QgMe0aWA2Vcr4LQi2plVq6efXjwjPCP5IaPyimIaym2AoMjISgGuvvbbKvvJtf//9t9XXi46O5r333qOwsJCQkBDGjh2Ll5f9zWC3lrmoiPzLXn/uuj/qHwyV9QwpXVxYtSee34+k8PyNXbh/aFi1x4/t4kfO+XjUZqPVWahDbruZ/xuTiaRt/vlCSldXjKmpViVeBDCbZeIyC8kvNtI9yPVSMFTHsvrWRl2WbdiQlFRpu6teze19bTtR8fszP3AkTIHWrOS14a81WYXt6LR8Pj2lgOBUbu5t+8mY9shnzuO2boIg2C27CYaioy31rKobBnN3d8fLy6viGGusWrWKVatWVfy/Tqdj8eLFPPXUU3WeW1JSQklJScX/55b1PBgMBgwGg9VtaEoFu3cjl14a3ijcu5fS0tJ6jf2XZllWjymcnWjn7ciAMHfCPB1qfE0v3NSJwl3ZJH+aijHTsdbXXr7P7OCAumzIqrmflVQ21FeaedGqe607mspjPxyhZ5Arq2cOQDtwICGbN4HBWOP5f59JR6VU0DvYrdqJ5k2l/P5N8cwkH8u8ptLkZJu9X6uTlJ/Epw77AJipHUuwPrjJ2vfb4SSOZSn46VAS13e/OktwNEZTvv+uRuL5NV5zPUNrr2c3wVBOWa+Faw0TWV1cXEhMTKzzOt7e3vzf//0fN954I23atCE7O5utW7fy9NNPM3fuXFxcXJg5c2at13j11VdZvHhxle2bNm1Cb8Uk4ubgvnUr3kBe166Y9XqKg4M4+fvvYGWJDABVZiaOE2/G7OBAWOEpwvwg70w6f5yp+RxNcjKhQGHqBf74o+4uzJ//2IwZcFRBcydq9i8uxhk4smMnOXLd6RDTigBUnEjO5vd1f1Ru39Ej1Z7z5hElCQUS97Yz0de7+VMubt68udHXUBQVE+zvh8HdnT9++63Se6TQCPvTJTJLJCaGmht9L2vJsswX+cspVpnpHC8T4NPOqveTddeGNYctc5CC5LQmu+4VzWxGlZuH0a3y521TvP+uZuL5NV5TP8PCQusKdEuybMW3SAu49tpr2bx5M9HR0bRrV3UCZNu2bUlMTKzUY1Mfx44do0+fPri7u5OcnIyilsnH1fUMBQcHk5GRgUtZ3paWlvr0M+T/8Qcesx/FY/r0FrtvSeoFDky4DZ/SPNoePFDjpG2DwcDmzZs5pgjn0x3x3DuwDQtuaN5l3WmLXyB39Wo8Zj2Ex0MP1Xm8ySzT++UtFJaaWP/I4IoVczXJLjTQ/7WtyDJsf2p4s2Y0Ln9+Y8eORV3LZO7GSs8rYfAbfyNJsG/eqBbLP7QmZg0v730ZjUHmrW+1DP5jZ70XANTkZEoeEz7ahUqS2Tl3OO5OzZfs80pQGhtL6lNzkQsLafPzT0hqdYu9/65U4vk1XnM9w9zcXLy8vMjJyan1+9tueobKe4TKe4j+LTc3t8ZeI2t069aNAQMG8M8//xATE0OHDh1qPFar1aKtZs6LWq222RvdcPYsAPpOnRrdBpNZRpZlVMrav4wu5BYz7JMjmMc+w8+/PYuisLDOshU5+w8iyZ64F+Y0+7NSl9VKk3PzrLqXGujk58zB+GzOpBfSObD213Ig4SKybEkzEORZ++q7ptLc77EADzXhXo6cyyjgcGIe13Rp/iXoqQWpvHPoHQDu/NtMeEhPNE04p2zDiTQAurjLuDvpxJdRHZQBAZjS0jBlZlLw+++43357xT5bfsZdCcTza7ymfobWXqtpfpo1gfK5QtXNC8rKyiIjI8PqZfU1KZ9AbW23mT1x6NYVXZ8+OHTs2OhrHU7Iov1z65nwwfZaj/N20qJQSChkmQwHV0xlc45q8+CRn/nt16eZ4m9qdDvronR3A7CqXeW6BVoC6sMJ2aS99RbxDzxAwa5d1R67PSYdgEH2UNi0CQ0I9wBgz/mLzX4vWZZZtGsRBYYCOhe5c/1+GYcePeo+sR7X//1ICgC9PO2ik9vuKRwd8XrQMlUg46OPMTewt10QriR2EwyNGGGpSbRp06Yq+8q3lR/TEEajkYMHDyJJEm3atGnwdWwl4OWXCf1mJeqAAIyZmWT//DPZq1fX6xp5kZFkffstSadjkWVQ1TGpR6GQiHxyFL8f+RjfoiyrVpSZcnNRymb07g3vxbNW+ZL4+gRDfUMtgcC+2EwKDx2iYNs/FavKLifLMn+dtPQ4jOrUOvILXU42mTCkpFRZUQYwsCy423PeuhWCjfHbud/YkbQDjULD//5xRCFTUam+KRxNyiE+sxCdWkFXdxEMWcvtjjtQ+flhTE0l+7vvbN0cQbA5uwmGxowZQ3h4OKtWreLw4cMV2/Py8njxxRdRqVRMnTq1YntGRganTp0iIyOj0nV27drFv6dBGY1GnnrqKeLi4hg3bhweHh7N+VKanSExkZRn5pH+3vv1Oi9nzVpSF79A4qlzAPg41z0Hxs/VAY275XkZrQiGymufKRoxpGmt8iG76oKZmvQLtZxzIjmX3NwCoPo8Q8eTc0nJKUanVjK4betLyZD55VfEjBpN2pKqlcoHhFmCoWNJOeQVN9/ql+zibN7c9yYAD0U8xNDPfyJk1Tfo+/VrsnuU9wqN6uiNtvkW+11xFFotXg9basFlfLK0IgeZIFyt7GbOkEql4rPPPmPcuHEMGzaMyZMn4+Liwtq1azl//jwvvfRSpXk+H3zwAYsXL2bhwoWVMk1PnjwZSZIYPHgwgYGBZGdns23bNk6fPk2bNm345JPWX6hQHWjJI2NMT0cuLUXSWFf2ojw5YZbSMsHUx8W6eRueD8zAnJ+Prnv3Wo8zG0080+Mu3EryWKJzorkzDWk7dcLvhcWo/a2vQ+XvqiPIXUdiVhHHzM70ovpgqLxXaGh7LxzUre9bVh3gD1RNvAiWADfEU0/cxUL2x2U1W0HTdw6+Q1ZJFu3c2jGl6xQUCjX63r2b7Ppms8yvhy2v7/pufpjiqvaCCTVz+89/yPxiBaXnzpH12efQseZ5lIJwpbObniGAUaNGsX37doYOHcoPP/zARx99hKenJytXrmT+/PlWXeOhhx4iNDSUyMhI3n33Xb755hu0Wi3z58/n8OHDhNRRtsEelSYkUBIdjblsrpPSw8NSPkKWMaSlW32d8mAoQ7ZMKPO2opDq6dQ85qW68VJJCGp//1qPLc4v5pBPByKDeuHYAsNkal9f3G+/HadhQ+t1Xv+yobIorSUIqC7p4qYTqQBc09n+a5FVpzxgrm6YDGBAWNm8oXPNM1R2KO0Qa6LXAPD8wOdRK5p+UunucxdJzS3GxUHFyI6tbyjT1iSVCp8nnwQgZ+VKVFYmVhWEK5FdBUMA/fv3Z/369WRnZ1NYWMi+ffu4++67qxy3aNEiy+TMf9Ufe/rpp9m6dStJSUmUlJRQUFBAVFQUL730Eu6tNNPwxc8+59xNE7j42WcASJKEys+SWM54IdXq65RXaM8wWf7arekZMpjM/H4khb9OpdV5bF6eZSKmq6EQjbZ5i7Q2xtD2lmGvPT6Wyej/7hmKvpDH8eRcVAqJsV1aZwK/8pIcxvT0aifIlg+VNcckaoPZwAu7XgDglva30Nu3d5Wh66bw0yFLoHdDjwC0Krv7KGsVnEaNRD9gAHJpKV4bN9q6OYJgM+ITpBUoH+oo/4IDUJcFQ4YU64Ohip6hUsvEaWvmDIV7W+qWZRaUklVQe4HPvALLCjJPo32v1hvdyQelBLGuAVxw90fhUPk5rC37kh3Z0QcPR/sN6mqj9PBAoddbeg+rm0RdVmvtSGLTzxtaeWIlMdkxuGndeLz348iyzNlrxxF//3SM6db3ZNamqNTE+mOW9/4tvQOb5JpXI0mS8H16LkgSLoejKDl92tZNEgSbEMFQK1BdMFTfniHZZMJcNkkyvcgStHg7190zpNeo8HeyDHEc/u6XWo/NLbZc10tuuaW62WvWkP7e+xhSUqw+x02vobe35bXvDO1TaV+J0cSaA5ZM55Na8ZesJEmoy1ZNlsbFVdkf6KYj1FOPySyzuwmHypLzk/k46mMAnuj7BG4ObpTGxmJISKBw/36UTTSx/nhyDqUmM0HuOvq0aZ09vvbCoUsX3GfMIPnuu9HUkn9NEK5kIhhqBYxpliEqle+lBHn17Rky5+cDYEIis8jSE2DtBOpQB8sQx4lt+2s9LrfUcpyXovlzDJXLXLGCjI8+qvYLvzbXeYNPYSaumsr/BH49nExaXgm+LlrGdG7+hITNSVMWDBkSEqrdXz5cuD26aXprAF7d+ypFxiL6+Pbh5rY3A1AUFQWAQ9euVk/2r0vfUA/2zb+Gj+/ug6K5675cBTwf+R/5PbrXq9ahIFxJ7GY1mVA9c0lJRY+OyuvSEm+Vf/16hsqHyHJcvTDLlrphno7WBUNtPXXsysgjzlz78VkqRzCBn3fLlSxRupUtr69HriGAm3R59N/0Ki5Dh1TaPr6bH9mFBlx0KjStfB6Kpo2lentpXHy1+4e282bl7nj+ic6odn99bYnfQmRCJCqFigUDF1R8sZYHQ02ZXwjAVaeme1DzT9QXBOHKJ4IhO2cqy6MkqdWkmtVEHU3hum5+qMt6iQwX6p7YDJcFQ56WIMrTSYvSyl/Ubf3d4HQe8UpnZFmu8dfjRb0blEDoNcOtum5TKJ/8bKxnMKTz98Ft/DgcOncGLCVKlAoJZwc1M4aHN3UzbaJimCyh+mBoUFtP7hsUwtB2XrX+vVqj0FDIq3tfBWBa12mEu116hk0dDBWUGHHUio+u5mLMzKTkzBkcBw60dVMEocWITxQ7Z7xoWe2j9PJCo1Iy65uD/N+tPbjR07NeFevloiIkrZZsV8sSZB8r5guVaxfiDSSQ4OiFOS8PZQ3F7nLKJmb71uPajaVsQOJFAKchQ3AaYukV+np3HO//Fc3vjw61alJ5a1ExTFZDz5CrTs0LN3drknstPbKU1IJUAp0CmdFjRsV2c2EhJafPAKCLaJpg6K5PdyMDr93Sgy4BtimcfKUqiY4m6b4poFDQduOGOmsRCsKVonWPA1wFjGU9QyovL9LyilEpJL7eHYdDjx50OnqEsB++t+o6+r596RR1GOnRJ4D6BUPtAywfiCmOnhSl1zykklm22CzAreWqhl8qyZFt9Tl5pXnE5caRVZzFxfwSPok8S1peCc//fKx5GmkjDl270mb55wR//nmz3udczjm+Ov4VAPP6z0OnuvT3X3z8OJhMqHx9K+a5NUZqTjEnUnI5kZxr9Zw3wXqa8HDUQUGYc3NJf/sdWzdHEFqM6Bmyc8b0S8FQGw89L03sxo09A5AUDYtjR3cNYLm7I05a65Pg+bpo0ZlKKVJqOJ+QQfe2VYeRig0m8g2WnqEgp5Z7W1X0DNUxTCbLMn+c/4OvT3zN8YvHK7aHuIRw7/iJRLheT0ef1ld2ozZKZ2ccBw+u9RiTWWZ/bCZ7zmfyv1Ht6j0ZWZZlXt3zKkbZyMigkYwIrlw/sKmHyPxcHdg9bwz747LwsiJpqFA/klKJ3/PPEXf3PWT/+CNut92GrnvT9B4Kgj0TPUN2zngxg0+6T+Alt/7EXSzkzv5tcGrEfAlfFwdGd/Klf5j19dkkSSLYaJlzdDYlu9pjkrKLAdAZilEc2NPg9tVXReX6WobJ8krzmPXXLJ7555mKQEivcEBCIi43jg+j3mXevrs5kLGtBVpsX8yyzH9X7GPJ5jOcSMmt9/mb4jaxO2U3GoWGuf3nVtnfHJOnPZ20jOvaOpNhtgb6Pn1wuekmkGUuvPQSstls6yYJQrMTwZCdc588mZ3dRrFR9ia/xGizdoRgCXbOZhRUuz85uwgA38IsVG4tN8+grsr1OSU5TNswje1J29EqtcyKmMXW27ey8nMnvlhiYGHIg4S6hJJRlMHjkY+zZP8STOaWSw1ga2qlgnHd/JjQMwC1sn4fB4WGQt7Y9wYA07tPJ9g5uNJ+WZYpLCu63BTzhUqMV8/fi635PPkkCr2eoqgocn6uPb+YIFwJRDBk57JUetKLTEgS9Axy42J+CW9uPM3c1VEkP/00ZwYOIndD3Wn00958k5hrx7Hyo9WsPZhIRn79EiOGOlh+HZ7PqT5bsa+LlptjdzAi6RBKt5Zb7qyqZZjMYDLweOTjnM46jaeDJ19d9xUP9XwITwdPTBcvoi+Bm9tPZM2ENUzrNg2AL45/wTP/PIPRbLvAsynlbd1K4mOPk/nV1zUes+T2CN6b3IuOfs71uvbSI0tJK0wj0Cmw4vlVIssEvPwyXrMewqFLl/o2vYoHvjrA7Ut3cSwpp9HXEmqn9vWpqGqf9tZboqq9cMUTwZCdO5VqGboI83REp1GiVEh8sDWGH/YnkltswpSdbVWJA0NyCob4eD5M0TDnhyjiLtavZMYAN4k7EnZyjVNxtfvbezjw4OGfuPPMFlQe1g/BNZbS3R2FoyMKR8cq+9479B77UvehV+lZdu0yunhavpDN+fnIBktQp/TwQKPUMKfPHN4Y/gYqhYoNsRuYu23uFdFDZEhKJm/DBgr37W3S6/570rSDquoqPEmhwGn4cLwffRSFrnGT6hOzCtkWnc7e85m4ODR90VehKo9770UTFobp4kUyPvjA1s0RhGYlgiE7d7JsHkcnf8uvdje9hsCy1Vrn3IMAy7yiupT/shvgCkPaeRLgVr8l5Nc8/SCvf/MyEx+7r/rrl1e8VqlQ1LD0vjlo2rSh44H9hP/2a6XtBy8c5MvjXwLwyrBX6OB+qcyAqSxdgUKvr/QlfV3Ydbw98m3UCjWb4zbz2t7XmqXAaEvShJTlGoqtPUO3LMucTs2rCL7rOra2SdPNYfWBRGQZBoV70sZT3+z3E0DSaPCdPx+AzJXfUHzmjI1bJAjNRwRDdu7AL38B0NH9UhmDDr5OACTqLT0w5cvva2PKtQwtvNjLkW+mD8TftWmXvx+NTiFb44jC3b3BK92aisFsYOHOhcjI3Nz2Zsa0GVNpv7EscFN6elY5d2TwSF4f/joSEt+d/o6vTnzVIm1uLpqwMMBSn0w21dzT9cnf5xj3zjbe/yumzmvWNWm6qRlNZn7YZykpcmf/4DqOFpqS09AhOI+9BhQKio9eWaknBOFyIhiyY+aCAs7lW+bqdPC9NJ8j1MsyJJSgtvTAmGrJ/VNxrVxLz1BNCROtkZZXzM6YDFJyiiptl2WZaX+mMfn6xST62T578w+nfyA2NxYPB49qv6zLE1nWNJw3NmQsT/S15GN6a/9b7Eze2XyNbWZqf38krRa5tBRDYmKNxw0qq2L/95l0So01rx6qa9J0OXNJCYmzH+PiihUVQ5IN9depNJJzinHXq8UqMhvwnT+f8J/W4jbpFls3RRCajQiG7JgxI4MLessE4RD/S1/c4eXBkNky1FX+5V4bU24uBkmJ0bF+k2Qv9+zaY9z12R7+OFq5HlpukRFXpRmV2UhwC+YYupwsy8hmM7mluXwS9QkAD0c8jIumavBnulhzz1C5+7rcxy3tb0FGZt4/87hQcKF5Gt7MJKWyoneo5Ny5Go/rEeiKl5OW/BIj+2JrrmJf56TpMsVHj5K3cSMXP/8cVI17T3y1KxaAO/u3wUFtfdZ1oWmo/fzQtmtn62YIQrMSwZAdy0lNJ1drGRILdL80rBXmZdkWV2JJkFfXMJksy5hyc9kZ0I0eX53lvyv21bstpvwCAjf8iH9BBkpT5ZVWrno1Pwensub359B7tnz6/uT58zndqzfZq1fz5fEvyS7Jpq1rW25pX/0vWWNmWc+QZ80TvSVJYl7/eXTy6ERmcSZPbXsKg7lxPRy2og239NaV1hIMKRQSoztZSrVsPlF94Hcuu+5J0+UKDxwEQN+7T6NqnsWk5bEj5iIKCe4e0KbB1xGaTm3DrYLQWolgyI4lJFm+tB3NpbjqLq2gCfMu6xnKN2KSFBgvXqx1oq9cXAwGA9laS6+QrgG/rhWOeu48sZHlm1/jrvCqX4KmzEw0ZiPKFlxJVkGSkIuLyclI5ttT3wIwK2IWKkX1PRIVPUMeNfcMATioHFgyYglOaicOpR3isyOfNW27W4imLGN4ydmagyGAa7tYhqA2HEvFbK78fpJlmVf2vmL1pOnCgwcA0Pfp3dBmA/DVLsvE72s6+xLkLiZO21rRkSOcn3QreVu32ropgtCkRDBkxxIvZAPgL1dezu7v4oBWpcBohgs6dzAYMOfUnHulvGJ9ls4yZOTdgEKqkiSh9vEBwHihas+B6113kTBjOi42mFeg8rSU0fildB95pXmEuYZxTcg1NR5fMWeolp6hcsEuwSwYtACAZUeWcTzjeB1n2J+KnqGzZ2s9blgHL5y1KlJzizkYXzlv06a4TexJ2WPVpGnZbKbo4CEAdL37NLjdecUG1hywzHO6b1Bog68jNJ3cjRspOXWK1BdfxFxQfQJWQWiNRDBkxxLLsjoHqCt3SysUUsWwWYZ3IFD7vCFTWaCU7WT58m9IMASg8rUEQ0WpF8grvjRkNHnZbqb9kUCMfzs04S0/gVrl6UmpEtY4nQLgv93+i0Kq+a3tN/9ZQr//Dudrr7Xq+teFXcf40PEYZSPPbn+WYmP1uZbslSa8LQAl58/X2oOoVSkZ28UXgHVHUyq2WztpulxJdAzmvDwkvR6HTh0b3O61B5MoKDUR7u3IkHa19+IJLcP74YdRBwVhTE4h7a0ltm6OIDQZEQzZsaR8y9wcf33VYa3yXEMF196Ez9y5KF1rzvqsdHXF88GZ5Idacu14OWlqPLY2ah9ffg4fyvCdZj6KtPQylBhN7I/LZM/5LBxsNLdV5eXJzi4SWepS/Bz9uCHshtqP9/ZG17Nnvaqozx8wH2+dN+dyzvH+ofcb2+QWpQkLxXHIEFxvvrnOlV3Xd/cH4I+jKRVDZdZOmi5XVD5EFtETqYGTp01mmc+3nwdg6uDQRs07EpqOQq/H/4XFAGStWkXBrl02bpEgNA0RDNmxvvkJ3HH6L4Z4V/1CCSrrGcru0R/P/05D5VVzxXW1ry8+jz1Gjpfly7/BPUM+PriUFlJgVrDtjCXr9ZHEHAwmGQ9HNR42KiKu9PLir56Wt/LtHW5HrWz6DMVuDm4sHmz5Evj6xNccTT/a5PdoLgqNhjaff4bf/GdRaGoPhMuHyi7klnAwPssyafqEdZOmy5VPnm7MENmGY6nEZxbirldza5+gBl9HaHqOgwfjNvlOwLJ4wZSfb+MWCULjiWDIjvVKi2bqyfWMDKva61M+mTQpq6jKvppk5JUC4O1Uv+zT5VS+vkSkRwNwPDmXtNziiqCod0483ut+x1xSv5pnTSFeV8jpYAmFGW5ud3Oz3WdY0DBuCr8JGZnFuxa32tVltbl8qOzXqGRe3P0iRrOREUEjrM403RSTp7sEuHBH32CmDwtHr7FNugahZr5PPok6ONgyXPb667ZujiA0mgiG7JhD167o+vZBHRBYZV+PIFcm9AygX6h1S9nNZrmiOKuXcwOHyXx98CjJo2uJJQD68UAi649Zcg71iPob9527kNQtXzfq15ztAPSOMeOlrL1IbGlCAgkPzeLC62806F5P9nsSV60rp7NOs/LEygZdw95NiAgAYPXBWPalHMJB6cC8AfOsOteQnIwxOQWUSnQ9ejS4DWFejrx+aw8eHiXy29gjhaMjAa+8DJJE9o+ryd+2zdZNEoRGEcGQnSo1mom760HUH3yKQ9eqFb+Htffmvcm9uMUPsn/+mfx//qnxWsUnT5K44U+MZXNAPB0bPkwGcH2KZaXQ/208TUxaPjqVxOCUoxhdXFq8FEepqZTfEzcCMCZKxlRHziVDQgL5W7eS/0/DPrw9HDx4oo8lO/VHhz8iMa/mrM72xFxaStGRI+T99Vedxw5r742Ps4bCEgljfmceiniIQKeqAXm1JAmPqVNxnTCh2uK5wpVD368fHvfdC0DKc89jys62bYMEoRFEMGSnErIKuWPZbsa9XfuXdsGePaQ8M4+sld/UeEz22p84sfBlANz0ajSqhv21qwMtX4hDT/xNx7L6aAD3BoKjsQRDLZO4m8tf8X+RXZKNR4GCiLNyndm4DWlpABVpAhpiYruJ9PPrR7GpmJf2vNQqirmWxsYSe/sdJM99Gtlcc7kNAKVCIig4GrXHNsJ9NNzb5V6r76P298f3macJePWVBrXzUHwWD686SPSFvAadL7Qs78ceQxMaijEtjZzf19m6OYLQYGIw3k7lFxsJ8dTj7KCqcSWN0WQmzdGDEoUKh1p6RExZWWSWJVz0dmr4LGeVry/ej81GHRTMlwN78enuRPxdHbgpZhuZgNEGwdCaM2sAuMllMIELhqOqY4WYMd0yxKfybngwJEkSzw98nkm/TmJH0g7+iv+r1rxG9kAbFoakVmMuKMCQlIQmuObl8ftT9xMtf4yDL7wy6mvUipYb+nz7z2i2nUlHp1by5m09W+y+QsModDoC3nqTkuho3CZOtHVzBKHBRDBkp7q5q9k0KRRlLYkBr317G+cyinnDvQ29asszlJ1NlkNZMNTAlWQAkkKB14MPAuAKPH+jZfjuwo4fgZYPhhJyE9iTugcJibsmPo+7U0Cd5xjTyoIhH+9G3TvMNYxp3aax7Mgy3tj3BkMCh6BT6eo+0UYktRpN+3aUnDhJ8alTNQZDBpOBF3e/CMCtHW4lwieiBVsJz17fCSetktlj2rfofYWG03Xtiq5rV1s3QxAaRQyT2anCvXs5d/31JNw/vcZj/FwdUCskcjX6WktymLKyKkpxeDWiZ6gmhhRLgj6ja9WiqM1pbcxaAAYHDCbAikAImqZnqNz07tPxd/QnpSCFz49+3ujrNTeHTp0BKDl1usZjvjj+BedyzuHh4MHsXrPZeTaDp36MwmCqfWgNoHDfPrK++47SxIbPo+rk58JHd/ch2EOU3mitZFmuM5+VINgbEQzZKVNFMdGaM+8uvbcPJxeMYUjKsVpLcpiysxmWFMWyEZ78d2hYk7e1NM5SP8rgWXOuo6ZmMBv4OeZnACZ1mGT1ecayOUOqRswZKqdT6Xiy75MAfHHsCxLyEhp9zeZUng26+NSpavfH58azNGopAE/1ewqdyplHvz3MjwcS2VC2arA22T/9TOqixWR/912921ZiFMU/rwSmnBySHnuc1BdesHVTBKFeRDBkpx45Do8Nf5STHiE1HuPsoEbloEXhYumRqWnysCkrC+/iHEZ29iMi2K1R7SqNi+Pi8i/I+tEyNCbLMqXx8ZZ9Xi1XMmFb4jYyijLwcPBgZNBIDBcukPXd92SvWVPreeW9WOWlRRprbMhYBvgPoNRcWlGywl5pO3YCoKSaYMgsm1m4cyGl5lIG+g/khrAb0KqUTB8Wxj0D29A9sO4h0MLduwHQDxhQr3YZTWYmvL+DeWuPkl1YWq9zBftSfOo0eZs2kf3j6orPCEFoDUQwZKdOFqs47dEGjRVDT+W9R8aMqsGQubQUc2EhAEp363IS1abk7DnS3niDrG9WWe6Zlo5cWAgKBYYmuL61yidO39zuZtRKNaWxcaQuWsTFZZ/WeI5sMFQUma0ud1NDSJLEvP7zUEkqIhMi2ZZov/lWynuGDElJFcV7y/14+kf2X9iPTqVjwaAFFZP2HxzRlpcmdifUq/Zl8qWJiRiSk0GlQt+7fskWV+2N5/SFPDYcSxFlN1o5xwH98Z49G4ALL7xI0ZEjNm6RIFhHBEN2qNhgIkO2rOAJ8nKq8bjYjAIe/uYgL7W/CQBjRnqVY0xZ2QBsCunPT9E5ZBY07pe3toOlvlnp2bPIpaXIpSU4jRyJrn9/aGAdqvpKLUhlR/IOACa1twyRqf0tq8gMFy7UOHfKcCENzGYktRqVd9MN6bV1a8vdne8G4PW9r1Nqss/eDaWrK+ogS2mL4mPHKran5Kew5ICl6Obs3rPrLMRanfJeIV2PHvXKL5RVUMqSzWcAmHNtR1x1LZ+0U2hang/MwOmaMcgGA4mPzq4z3YUg2AMRDNmhlBxLVXStsRQvn9qHntYdTWGnLhAZMFXzoVOeCG1F1+t5cvVRUnKsL99RHXVgAApnZ2SDgZLz59EEBxP8yccEfrqsUdetj5+if8Ism+nr25cQF8swosrXUkJCLi6uJfmbjPO4cTiNHNHkySEf7PkgXjov4vPiK2p52aPyrNDlv9hl2VJapNBYSC+fXkzuNLna844n5zDjq/3sOVf9F1vBTkvBTv2A/vVqz4vrTpBdaKCjrzOT+9U/CBPsj6RQEPDaa2jCwjCmppL0+BwxoVqweyIYskOJWZZhLd/CTNReNS+t93ez1BgrkZTkaJ2qLZhoyspCBgbkJzC0nRd+Lg2rS1ZOkiQcOlnmnhSfONmoazWEyWyqWEV2+cRphVaL0sPyrMqHwv5NExRE0LvvEPR+01edd9I4MafPHACWHVlGakHdE45tQdezByiVGDMzAfjl7C/sSN6BRqFh8eDFKKTqPxJW7Yln84kLvLnpdJWeN9lkomCHpafOaehQq9sSeTqNtQeTkCR4dVJ3VErxcXSlUDo5EfTB+yj0egr37iVl8eJWkZxUuHqJTx87VF581bcwC2UtK7S0KmVF3iD9rxvwnjWryjH6Af3puG8v7794LyunD8CzCZbWO5TlFCk6eLDR16qvnck7SS1IxUXjwtiQsZX2qcsSLpZPkm5pN4bfSC+fXhQZi+x2MrXrpFvpuH8ffs8+S1phWkU7H+71MGGuNa80/N/odjioFeyLzeLXqORK+4qOHMGUk4PCxQVdT+sSJeYUGZj/k2WobtrgMHq3abn5ZkLL0LZtS8Bbb4JCQc7qNVz89DNbN0kQaiSCITuUcLEAAJ/CLFR1rNAKdLMk+kvJq36eiiRJKJ2dUZcNIzUFxyGDAUtNtLw//8RcUNBk167LmuiyjNNtb0KrrBzYlWefNqbapldGkiTmD5iPUlKyOW4zO5J22KQdtVE6OaLQ6TDLZp7f8Tx5pXl09ezKfV3uq/U8f1cd/ysrmvryupPkFV8a9igoq4vnOGQwkhXzxmRZZu7qKJKyi2jjoefJcR0a8YoEe+Y8ahS+zz4LQPqSJeRu3GTjFgn2yB56DUUwZIcS0iz5gnyLslC6udV6bKC7JRhKzKp5LlCxwUSxoenyuOj79UOh14PBQOL/HiFh1sNNdu3aZBRl8HfC38ClidOXq+gZSq6+Z6j45ElK4+Kadf5CR4+OFfNuXtnzCiWmkma7V2N8e+pbdibvRKvU8srQV1Ap6g5ipg8LJ8RTT1peCS/+fqJie/7flhV0TsOGW3Xvz7efZ+PxC2iUCj68qzd6jUiEfyXzuOduPKbch7Z9e3Tdu9m6OYKdkM1mCvfvJ/WFFzl34002n1cmgiE7lFxgCVw63/UfJKWy1mPLe4Yizx/h0yOfcibrTJVjftifQKfnNzDn+8NN0j6FgwMeU6dU/H955erm9nPMzxhlIz28e9DevWq5BnUbywTc0oTqkx8mPvYYZ8eNp/DgoWZt58MRD+Ot8yY+L57lx5Y3670aIiYrhrcPvA3AE32fINwt3KrzHNRKXp/UA0mCH/Yn8ltUMrLZjL5vHzShoTgNq3u+0Mbjqbzyh2Wu2fwbOtM9qOXr2Qktz2fuXEK+/RZ1gHWZ4oUrkyzLFB09yoXXXidm1Gji7rmXrFWrKD17tmJFqq2In2R2KKlsNVm7IX3rPFartQxR7U44TZS8kvcPvc8TfZ9gSldLsHLh1dc4naAAl644OzTdX7fXrFlowtuidHHGafhwDM0c1Ztlc0VuoVvb31rtMZo2lpVl5RmxLyeXlmJITLIcFxraPI0s46Rx4ql+TzF321w+P/o5N4bf2KDl6s2h1FTK05tmU2IqoXeqjjs73lmv8weGe/LwyHZ8sDWGp1ZHEeSuo9e8efjOm1fnuTtjMnj020OYZZjcP5j7BtWcUFS4skhKJUqnyikXZJOpzh97QusnyzIlZ86Qu+4Pctevx3DZj1WFkxPOY8ficv31aPr0hs2bbdZO0TNkZ0qNZlJzLcFQkHvt9ZlySnJYc96SZFBR5EbPc2ZkZN7c/ya/n/sdgOITJ0jNLpuQ7dq4lWSXk1QqXG+8Aafh1g2NNNbu5N0k5ifirHZmfNj4ao/RhIYgqdXVfsCWJiaByYSk1ze6SKs1xoeOZ4DfAEpMJby29zW7GBMHeP/Q+5wpjse5UObBH/JqSUNQs8euac/Ijt4UG8z8d8U+ohLqvobJLPPC7ycoMZoZ08mHF2/uJhIsXsUK9+/n3ISba+zFFa4MstHI+f/cwvmbJ3Jx2TIMCQlIOh0u119P0Icf0H7HdgJefQWnYUOR1LbNMSaCITuTklOELINWpcDLSVPrsW8feJts03kAtEUezP/ezNQgy1yal3e/TFphGsaMDC46WLJYN3ZZvS39eMaS2v/GtjfWWB1eExpKx8OHCFtdtQxAaez5smNCWuRLWJIknh34LCqFim2J29iSsKXZ71mXrfFbWXF8BQD/O+yDWwEU7t1X7+uoyub69Ax2I6vQwB3LdvHt3njM5poDPqVC4vOp/bh3YAgf3dNbLKO/ismyzIXX36D07FnipkyptidXaJ1KE5OQzZeKOksqFWo/PySNBuex1xD49hI67NhO4JK3cB4zBoW26QuHN5T4RLIzCZmWXhx/Y0GtX1THM46zJnoNCnUWAHlqPcVKDTP04+ju1Z18Qz5v7X/LEgzpLPMyWmswlF6YztaErQDc1uG2Go+TFIoau91Lz8cCoA1t+kK1NQl3DWdq16mAJTjNKam+kG5LSMxLZP6O+QDc0/keRgeNBqBwT8PG6R21Kj7rrWaIs5Fig5l5a48y6ZOdFT1gGfklzP/pKIt+PV5xTqCbjhcndkOrEkMjVzNJkgj68AM0oaEYk1OIveceSqKjbd0soYEMFy6Q+eWXxN5xJ2evuaZKyhXf5+bTfucOgt5/H5frrrMsvrFDIhiyM219HHmy4AgTD/5KaVxsjcctO2LJ+HxT+2tw1lrmAqXp3JAzLvLcwOcAWH9+PXHaPDIcLMFQUw6TtaSfYn7CJJuI8I6oduK0NS71DIU2YcvqNrPHTEJdQkkvSuf1va+36L3LlZhKmBM5h7zSPHp492BOnznoB1qKqRbs3tPg65b++D3zVj7LbH0qeo2Szv4uFb1uJrPMN3vi+XJXLNEX8prkdQhXDrWPDyErv0bboQOm9Azi7r2P4hMn6j5RsAvGzEyyvvuOuHvvI2bkKC68+hpFUVGgUFB8/HilYzVBQSidai4rZS9EMGRn/F11XJ+0j3Hx+2rMDRSTFcOWhC1ISMzoPoOAshVlaXp3DMnJdPHswtiQscjIfDdER5HaEgT5tsKeIZPZxOozqwG4rWPNvUKXk81mTDmVe2FKomMA0IS1XM8QgIPKgReHvIhCUvDbud+ITIhs0fvLsswre17hZOZJ3LRuvDXiLdRKNY79+4NSSem5c5QmJtb7uuaiIvIjI1HKZh68uS+75o3hsWsuBao+zlqmDg7ly2n9Cfe2/w9CoeWpvLwI+epLHLp3x5SdTdx9UyjYudPWzRJqYMrNJXvNWuLvn070sOGkLlpM4b59IMvoevfG97nnaP93JB5TptR9MTskgiE7ZEyzFFxV+fhUu/+zY5ZMrteEXEO4Wzi39A5kilMW3kXZlsrhWGplAewJt/QKOWlVOGlb3+LBLQlbSClIwU3rxrUh19Z5fN7WrZzp15/E2Y9VbJNNJopPnwbAoUvn5mpqjSJ8IpjSxfIBsXjXYrKLs1vs3l+d+Iq10WtRSApeG/Yafo6WXExKV9eK6vL5W+o/nynvzz8xFxaiDgrCoXt3XHVqfJwvBduSJLFoQleGd/BGqRATpYXqKd3caPPFcvR9+2LOzyf+gZlkr15t62YJ1Uh67HFS5s+3lN4xmXDo1g2fuXNpt+UvQld9g8c9d6Pybv7FKc1FBEN2ZsvxZKJkF4qVmmqDoYS8BNafXw/AjO4zAJg5oi2Pd3IgJO9CRTDUwb0DA7QdMRstZQ7K8xG1Nl8dtxQ9va3DbTio6u7ZUvn4YC4ooPjkyYr5K6WxschFRUg6XYsPk5UrL3eRUZTBczuewyyb6z6pkSITInlr/1sAPNn3SYYEDqm032mMZd5Q3l/1D4ZyfvoZANebb27yorfC1UXp5ETw8s9xuekmMBpJee550t5+x9bNumqZi4vJ3bSJkvPnK213HjcObfv2eD82m7YbNxC2+kc8/zvtiskdJT7F7MzcNUd5atgsEl39ULpXrde0/NhyzLKZoYFD6ex5qZdD5e8PUBEMAdxS0g2zwVK8NMCt9pVp9uhI+hEOpx9GpVDVWE3937Tt24NKhTknB2PZsyifnOnQsaPN8ppolVreGP4GGoWGvxP/5svjXzbr/Y5nHOfpbU8jI3Nbh9u4p/M9VY5xHl02iXr//notsTekplKwy1Kl3nXizU3SXuHqptBoCHjjdbwetmSzV+ha35B+a2YuLSVv61aS5s4leshQkh6dXaWHzu22Wwn/7Ve8HnwQTciVlyOs9Y2bXMFMZpl2TgpU6RcJdFZX+cWdVpjGLzG/ADC9+/RK56U7e3PexZ/w5GRkWUaSJHola9Ab3CgBihWVi2u2Bl+f+BqA68Oux1tvXferQqNB264dJadOUXT0GOrAQFzGj0e3rTfmHNut5gLo5NGJZwY8wwu7XuDdg+/S3as7ff3qTqxZX9FZ0cz8cyaFxkIG+A9g3oB51aYT0LRpg7ZDB0rPn6fo+HGchgyp5mpVZa9ZY5kn0LcPmmD7SCYptH6SJOH9yP/Q9+uLvn9/WzfnimcuLqZg+3ZyN24if+tWzPn5FftUAf6oPCrXxbzSe4BFMGRHlAqJpe2KSF72KvoBA6rs//rE1xjMBnr59KKPb5+K7YcTspm0Khq/MY+wrn02mEygUuHYqze+O8+SBcQWHkCW/9NqEt3F58azOc6SjbSuIqL/pu/Xj5JTpyjYtQuX8eMAy+oVapiD1ZJubX8rBy4cYN25dTwe+ThfX/c1oa6hTXb9uNw4Htj8ADklOXT36s67o95Frag5mVnAa6+i8vdHVU0vZHXMpaVkffsdAO531C97tSBYw3HgwEr/by4uJvvH1bjfeYfNE/NdCQp27yH7h+/Ji/wbubCwYrvK1xfna6/F5frr0EVEtJrviqZyZYd6rVD5yh51UGCl7TklOfxw+gegcq8QQJC7DrVSQu/pjvvUqRWVw13Gj0ft3R2ALONZdqfYtvZLfSw9shSTbGJ40HA6enSs17mOQwYDULB9u91kfi4nSRILBi6gm2c3skuyeejPh7hYdLFJrn068zRT1k8hoyiD9u7t+fiaj3FUO9Z6jkOXLlYHQgC5f/yBKSMDlY9PRaApCM0p/d33uPDyy8ROvouSmBhbN6fVKz51ktw/1iMXFqIK8Mdj6lRCvl1Fu61b8Jv/LPpeva66QAhEMGRXjCYzhiRL/Sx1YOVg6PNjn1NoLKSDeweGBQ6rtM/HWcvpF69jy5MjUfxr5c57k/swftA5lPpYvj31bZO0U5ZlTmWeYmPsRv5O+JuMoowmuW65+Nx41p1bB8BDPR+q9/mO/fsj6fUYkpLI/GKFzash/5teref9Me8T6BRIYn4i92+8n/Si9EZdc2/KXqZtmMbF4ot0dO/IsrHLcNU2bRFUWZbJXP4FAO533SV+pQstwqFrVxSurhQfO8b5WyaR/t77mIuLbd0su2ZITSXru++If+ABMpYuq7TP5dpr8Zx+P6E//kC7v/7C95mnLQHQFT4MVhcxTGZH5q09yhZ5ANOHwt3tL+VsSc5P5psT3wDwaK9Hq0TtkiRRUyAf5uXI3JE3sOPnZfyd+DdJ+UkEOgVWf7AV/oz7k3cPvktsbmzFNoWkYETgCHqaejb4upf7KOojTLKJEUEj6ObVrd7nK/R6XCfcRPZ335P2xhtkr11Dm+XLLUNldsJL58XSsUv578b/cjbnLNM3T2eSNKne15FlmS+Pf8k7B9/BJJvo7dOb98e8j4vGpX7XMZvJ37oVXUQEKk/Pao+RJInAd97m4tJluN9l3YR2QWgs1xtvQN+vHynPP0fBtn/I+Ogjcn75Bd95z+A0ZsxV2Yvxb7IsU3LyJHlbtpK/ZUulBJbGlBS8Zj5Q8f/qgAB8nnzSFs20ayIYsiNn0/O5aFYS/OjDuPS4tFxxyYEllJpL6efXj+FBNRdGNWZkWFb5yDK63n0oPnYMh04dCQ8NZ5D/IHal7OL7098zp8+ceret1FTKK3teYU20pXK8g9KBzp6dyTfkE50VzdbErWxjG84xztze+fb6v/gyh9MOs+7cOiQkZkXMavB1vB95hIJ/tmNISkITFIzKy6vB12ouIS4hrBi/ghmbZpCQn8DHfIx/vD/XhV9n1Qd8Yl4iL+5+kZ3JlkR1N4bfyMJBC61KQfBvyc88Q+6vv+F+3734Pftsjcdpw8MJeP21el9fEBpD7etD8NKl5G3cxIXXX8eQlETi/x5B17cPPrNno+/Xz9ZNbHHmwkIK9u6lYNs28rZsxZiaemmnJKHr2ROn0aNxHjXSVk1sVUQwZCdkWeZsegEAbS/L2Ptn3J9sjN2IUlLyZN8na/ySXH80hY/WRdF+3zYezj+Kx7Rp/PneCk72HcPY2dOY3Gkyu1J2sTZ6LbN6zqrXF6ZZNjPvn3lsituEhMT93e9nevfpFfNRzmaf5bU9r7E7dTcv7n2R83nnearfUyik+nW7mmVzRcmKie0m0sWzS73Ov5zK05Pw33/DkJiIpm1bu+0CDnYO5uvrvmZO5BwOpx/m6e1P8/v533k44mG6enWt9pzUglS+OvEVP57+kWJTMRqFhif7PcmdHe9s8K9k1wk3k/vrb2St/AbXm29G17X6ewuCrUiShMv4cTgNH0bGJ0vJXLGCov0HiLv3PhyHDSP4k49tljrDFrK+/4G01y+V+JF0OhwHD8Z59CicRoywyx+A9kwEQ3YiPb+EnCIDkmQZ2gJIyU/hxd0vAvDfbv+tNTgoMZo5mm1Cdm9D6Yl1FOzezQHfTnyn70xRVDIvTRxOoFMgSflJrD+/nv+0/4/VbXvn4DtsituESqHi3VHvVumdauvWlg9GfcAzPz3Dn8V/svLkSvIN+SwatAilwvoPp29Pfcuxi8dwVDvyaO9HrT6vJgqdzpJ3yM55671ZOmYpT//0NNsN2/kn6R/+SfqHju4d6efXjyDnIAAuFFzgcPphDqUdqji3n18/Fg5aSIhL4/J+OA0dgvP48eRt2EDSo7MJWfUNal/fsnlCy9GEh+M8alSj7iEITUGh1+Mz53Hc75pMxtKlZK9eg8rH+4oLhGRZxpCQQOG+/RTs3o1Dly54Tptasd9xyGDUgYE4DhmC0+hROA4ciMJB5GdqKBEM2YnjSbkAhLtq0GKm0FDC7K2zySzOpJNHp4ryGjVp52PpTYpzC0CWZfI2bKCDX1duDlAypJ0nSoWSOzrewZIDS1h1ahUT2020qhfhh9M/8MUxy6TZFwa/UOMwnUJSMNJhJMN7DWfx7sX8HPMzBYYCXhv2Ghpl3Qkfz+ec550D7wCWeVFeuqvrV41aoeYa3TU8OvZRlp9YzobYDZzOOs3prNPVHt/Prx/Tu01nUMCgJpsz4bdwAcUnTmCIj+f8pFtxvXkCJWeiKfjnH1AoCFu7BodOnZrkXoLQWGo/P/wXLsTz/ulVJvPnRUaSvzUS1wk3oWslk4Nls5mSmBgK9++naP8BCvfvx5iWVrG/NC6uUjCkbd+etn9uFnOmmogIhuzEkURLQsCQo7s4/+JWXh5ygZOZJ3HXuvPuqHfrDCja+TghSZCnciBL64xHSR6DLp5hypRBKF0tq4puaX8LHx7+kFOZp9iXuo/+/rUnNtuetJ1X9rwCwKyIWdzU9qY6X8eNYTfionXhqW1PsTluMwWGAt4e+TZ6tb7Gc/JL85kTOYdiUzED/QdyZ6erN39NqEsorw57laf6PcWu5F0cyzjGhcILKCQFblo3Ont0ZkjgkIoaY01J5e5Om+Wfk/jQLEqio8n8fLllh1KJzxNPoO1YvxQHgtASNEFVF4Rk//Aj+Vu2kP3996gDAnAaORKnEcPR9++PQmdfpYlyfl9Hzto1FB07jjk3t/JOtRpdt27o+/XDadjQSrtEENS0RDBkJ44mWYKhwMIE5rWN50RqMo5qR94b/R4BTnXXfnFQK+no68yp1DxOeoczJDEK1+uvqwiEAFy1rkxsN5HvT3/PuwffZeX1K2v8B3U68zRPRD6BSTYxoe0EHuxRe8/U5caEjOHDMR8ye+tsdibvZObmmXww5oNql3oXGgp5PPJxYrJj8NH58NKQl+o91+hK5OHgwQ3hN3BD+A0tel9NUBChq38k9/d1FB09gsrDE5ebbkQbFtai7RCExvC47z6Urq7kbdyIITmZrFWryFq1CkmrRde9O7peEegiItD16oXKw6PZ2iHLMqbMTEpizlJ67iwlZ8/hMn4c+r6XMs8bU1Mo2GkpbyPpdOh7RaDr0wd9337oevYQQ18tRARDdsBkltkfmwnApqFJFJKMs8aZj8Z8RIRPhNXX6RPizqnUPBImz0QmmaxRY/ExmVEpLwUXD/Z8kF/P/sqRjCNsjtvMtaFVK8GnFqQy669ZFBoL6e/Xn0WDFtX7V8iggEEsG7uMWX/N4nD6YW759RYWDVrE0MChFdeKzYll3j/zOHbxGDqVjvfGvIevo2+97iM0PYVWi9ukW3CbdIutmyIIDeI4cACOAwdgXvA8Bbt2k7/tb/K3bcOYnELh/v0U7t8PgOeMGfg8cWl1bUlMDIX79qHy9kbp6YlC74hCr0Oh0yFpNMgmE5hMyGZzlVQdzocOk5mUhDktDWNKKoaUFAypqVV6e5Qe7pWCIaeRI1E4u+DQrSsOHTqI/F02IoIhG5MNBv45cpLsIgMoiinwTKKda1veG/0ebVza1Ota/UI9+GZPPPsLVGjb9WbZsn3c0TeY12/tUXGMl86LKV2n8EnUJ7yy5xX6+vXFw+HSL6Ockhxmbp5JWmEa4a7hvD3qbdTKhv3jjPCJYMX4FTwR+QSxubHM+msW7dza0dWzK+lF6exO2Y1ZNuOiceHDMR/S1VOsYBIEoekodDqcR4/CefQoZFm21OE7dIiiw4cpOnwYbft2lY4v2LOHCy++VOd1JbWaTkePVNrmtWEDmdUVPJYk1EFBaMPD0bRti75P5XqE2nbt0LZrV/U8oUWJYMiGMo7s55OVj7HKcyAwCpU+hv8k+zLv7lW1zrGpydD2XigVEseTczmebPk1MrxD1QKn07tP58+4P4nJjmH2ltl8dM1HOGucSchN4NGtj3Iu5xy+el8+ueaTeifv+7cO7h34/sbv+fDwh3x/+ntismOIyb6UUn940HCeG/Ac/k7+jbqPIAhCbSRJQhsejjY8HLdJ1Sc4Vfv74zRmDMb0dEwZGZiLijAXFyMXFf37YlXOLejcmWBvbzSBAaj9/S1//PxQBweLoa5WQARDViivb5X778ltjbzm9COLOB2ST+H5zsimQm4/sYcZt0/BWGQkt6j+99IAg4J1bDtjKY/h7KCkt7+22nYviFjAzD9nciD+ANd9cx0d3DtwMO0gpeZSvB28eXPAmziaHa1+zQaDgcLCQnJzc1FX0807s+NMbg+5nf2p+0kusMyH6u3Tm3C3cDA37bNtjep6fkLtxPNrHPH8yvTti2vfvlU2y2YzstFoWb6vUCBJUqXPLIPBQOy1Y+lw7bUVz88MlAAlpaVQWtpCL6D1aq73YPnfU111KiXZ3ipZ2qHExESCg4Nt3QxBEARBEBogISGBoKCgGveLYMgKZrOZ5ORknJ2dxXLGGuTm5hIcHExCQgIuLo0bWrsaiefXOOL5NY54fo0jnl/jNdczlGWZvLw8AgICUNSSb0oMk1lBoVDUGlEKl7i4uIgPg0YQz69xxPNrHPH8Gkc8v8Zrjmfo6lo1rcu/iYQugiAIgiBc1UQwJAiCIAjCVU0EQ0KT0Gq1LFy4EK1Wa+umtEri+TWOeH6NI55f44jn13i2foZiArUgCIIgCFc10TMkCIIgCMJVTQRDgiAIgiBc1UQwJAiCIAjCVU0EQ0KTeeONN5AkCUmS2L17t62b02r89NNPjB07Fk9PT3Q6HWFhYUyePJmEhARbN83uybLM2rVrGTVqFP7+/uj1ejp27MjMmTM5d+6crZtnF1auXMnMmTPp27cvWq0WSZJYsWJFjcfn5uYyZ84cQkJC0Gq1hISEMGfOnKu2ZI61z89gMLBmzRqmTp1K586dcXR0xNnZmQEDBvDRRx9hMplavvF2oL7vv8udP38eJycnJEniwQcfbNZ2iqSLQpM4efIkCxYswNHRkYKCAls3p1WQZZkHH3yQZcuW0bZtW+68806cnZ1JTk7m77//Ji4uTpSBqcOTTz7JkiVL8Pf3Z+LEibi4uBAVFcWnn37Kt99+y86dO+nWrZutm2lTzz33HHFxcXh5eeHv709cXFyNxxYUFDBixAgOHz7M2LFjmTx5MlFRUbz99tts3bqV7du34+jo2IKttz1rn9/Zs2e59dZbcXZ2ZvTo0UyYMIGcnBx+++03Hn74YTZs2MAvv/xy1VUxqM/773KyLDNt2rRmbl3lGwpCoxiNRrlfv35y//795XvuuUcG5F27dtm6WXbv3XfflQH54Ycflo1GY5X9BoPBBq1qPVJSUmSFQiGHhobKOTk5lfa9/fbbMiBPmzbNRq2zH5s3b5ZjY2NlWZblV199VQbkL774otpjFyxYIAPy3Llzq92+YMGC5m6u3bH2+SUmJsofffSRXFBQUGl7fn6+3LdvXxmQf/jhh5Zosl2pz/vvcu+++66sUqnkJUuWyIA8c+bMZm2nGCYTGu31118nKiqK5cuXo1Qqbd2cVqGoqIjFixcTHh7OO++8U+1zU6lEx21tYmNjMZvNDBkypEr6/htuuAGAtLQ0WzTNrlxzzTWEhITUeZwsy3z22Wc4OTmxYMGCSvvmzZuHu7s7n3/+eZ3Vv6801j6/wMBAHnroIfR6faXtjo6OzJkzB4C///67Wdpoz6x9fpeLiYlh3rx5zJ07l169ejVTyyoTwZDQKMeOHWPx4sU899xzdO3a1dbNaTU2b95MZmYmEydOxGQysXbtWl577TU++eQTYmJibN28VqF9+/ZoNBp27NhBXl5epX1//PEHAKNHj7ZF01ql6OhokpOTGTJkSJWhMAcHB4YPH05SUpJ4fzaAWq0GxA8ca5jNZqZNm0ZISEiVoLw5ib8ZocGMRmPFZMFnnnnG1s1pVfbv3w9YPhx79uzJ6dOnK/YpFAoef/xx3nzzTVs1r1Xw9PTk5Zdf5qmnnqJz585MmDABZ2dnjh49yp9//skDDzzAI488YutmthrR0dGAJcisTvn26OjoGo8Rqrd8+XIArr32Whu3xP6988477Ny5k+3bt7doNmoRDAkN9sorrxAVFcWePXsqfvkI1ikfvnnrrbfo3bs3e/fupXPnzhw6dIgHHniAt956i7Zt2/LQQw/ZuKX27cknnyQgIICZM2fy8ccfV2wfPHgw99xzj3hf1kNOTg5Qc4Xv8qHI8uME6yxbtoz169czevRorr/+els3x66dOXOG5557jtmzZzNo0KAWvbcYJhMaJCoqipdeeoknn3yS3r1727o5rY7ZbAZAo9Hw888/069fP5ycnBg2bBirV69GoVDw1ltv2biV9u+ll15i6tSpzJs3j4SEBPLz89m+fTtGo5FRo0axdu1aWzdRuIqtW7eO//3vf4SEhLBy5UpbN8eumc1mpk6dSkBAAC+99FKL318EQ0KDTJkyhbZt27Jo0SJbN6VVKv/13bdvXwICAirt69q1K+Hh4Zw9e5bs7GwbtK512LJlC88//zz/+9//ePbZZwkKCsLR0ZEhQ4bw+++/o9PpePzxx23dzFaj/D1ZU89PeZ6hmnqOhMo2btzIpEmT8PX1ZcuWLfj7+9u6SXbtvffeY/fu3Xz22WdVJqG3BBEMCQ0SFRXFqVOncHBwqEi0KEkSX375JQCDBg1CkiR+/vln2zbUTnXs2BEANze3aveXby8qKmqhFrU+69atA2DUqFFV9nl7e9O9e3fi4+PJyMho6aa1SpfPCapOXXOKhEs2bNjAxIkT8fLyYuvWrYSHh9u6SXbv8OHDyLLMqFGjKn2nlP/7Xrp0KZIkMXHixGa5v5gzJDTI/fffX+32bdu2ER0dzYQJE/D29iY0NLRlG9ZKlP8DP3nyZJV9BoOBmJgYHB0d8fb2bummtRqlpaUApKenV7u/fHtLTsJszdq3b09AQAA7duygoKCg0oqy4uJitm3bRkBAAO3atbNhK+1feSDk4eHB1q1bxfOy0ogRI6pdbZeSksIff/xBp06dGDJkSPMttW/WLEbCVWfKlCki6aKVrr32WhmQP/3000rbX3jhBRmQ77nnHhu1rHX49ttvZUDu2rWrnJ2dXWnfihUrZEDu06ePjVpnn0TSxcap6/mtX79e1mq1sp+fn3zq1KmWbVwrUJ+ki+W2bt3aIkkXJVm+yjJoCc1q6tSpfPnll+zatYuBAwfaujl27ezZswwePJi0tDRuuOEGOnXqxKFDh9iyZQshISHs3r0bPz8/WzfTbplMJq655hoiIyPx9vZmwoQJuLu7ExUVxebNm9Fqtfz5558MHTrU1k21qc8++4zt27cDcPToUQ4ePMiQIUMqeiwmTpxYMfRQUFDA0KFDK8px9OnTh6ioKNavX09ERMRVWY7D2ud36tQpIiIiKCkp4c4776wYCr9caGgoU6dObcnm21x93n/ViYyMZNSoUcycOZNPPvmk+RrarKGWcNURPUP1Ex8fL0+dOlX28/OT1Wq1HBwcLD/88MPyhQsXbN20VqG4uFh+/fXX5d69e8t6vV5WqVRyYGCgfNddd8lHjx61dfPsQvm/yZr+LFy4sNLx2dnZ8uOPPy4HBwdXvCcff/zxKr1vVwtrn195D0Ztf0aMGGHT12IL9X3//ZvoGRIEQRAEQWgBYjWZIAiCIAhXNREMCYIgCIJwVRPBkCAIgiAIVzURDAmCIAiCcFUTwZAgCIIgCFc1EQwJgiAIgnBVE8GQIAiCIAhXNREMCYIgCIJwVRPBkCAIgiAIVzURDAmC0CrFxsYiSVKz1nqaOnUqkiQRGxtr9Tlms5mePXty/fXXN1u7srOzcXNzY+7cuc12D0G4mohgSBCEBisPSC7/o9FoCA4O5q677uLIkSO2bmKLW7FiBUeOHGHRokXNdg83Nzdmz57Ne++9V69ATRCE6onaZIIgNFhsbCxhYWG0bduWe+65B4D8/Hx2797Njh070Gq1bNmyhcGDBzf5vQ0GA2fPnsXV1RV/f/8mvz5Yeoa+/PJLzp8/T2hoaJ3Hm0wmwsPDCQsLIzIyslnaVC4zMxN/f3/uvfdePvvss2a9lyBc6UTPkCAIjdauXTsWLVrEokWLePPNN9m+fTvz58+npKSE+fPnN8s91Wo1nTp1arZAqCH++OMP4uPjuffee5v9Xh4eHlx33XV8++235OTkNPv9BOFKJoIhQRCaxSOPPALAvn37Km3/5ZdfGDNmDO7u7jg4ONCtWzfefPNNTCZTpeNWrFiBJEmsWLGCdevWMWzYMJydnSt6aGqbMxQfH8/9999PYGAgGo2GoKAg7r//fhISEqpt6/Hjx7nxxhtxdnbG1dWV66+/nmPHjtX7NZe3edKkSZW2jxgxArVaTUpKSrXn3X777UiSxKFDhwCIjIxEkiQWLVrErl27GDduHG5ubkiSVOW8wsJCfvjhh3q3VRCES0QwJAhCs/j3FzfAs88+y8SJEzlz5gyTJk1i1qxZODg48NRTT3HnnXdWe50ff/yRiRMn4uXlxaxZs+qcmBwdHU2/fv1Yvnw5ffr04YknnqB3794sX76cvn37EhMTU+n4Y8eOMXjwYNavX8/48eN5+OGHKS0tZciQIZw7d87q1yvLMpGRkXTq1Ak3N7dK+2bOnInRaOSLL76ocl5GRga//PILffr0oVevXpX27dy5kxEjRgDwwAMPcMcdd1TaP2jQIAC2bNlidTsFQaiGLAiC0EDnz5+XAXncuHFV9s2fP18G5JEjR8qyLMubNm2SAfm6666TCwoKKo4zm83ygw8+KAPy6tWrK7Z/8cUXMiBLkiRv3ry5xntPmTKl0vbRo0fLgLx06dJK25cuXSoD8pgxYyptHzFihAzIK1eurLR93rx5MiAD8vnz5+t8FsePH5cB+e67766yr7i4WPb09JTbtm0rm83mSvuWLFkiA/LHH39csW3r1q0V9/78889rva+Hh4fcpk2bOtsnCELNRDAkCEKDlQckbdu2lRcuXCgvXLhQfuKJJ+QhQ4bIgOzg4CDv3LlTlmVZnjBhggzI8fHxVa6TnZ0tS5IkT5o0qWJbeTD0n//8p9Z7Xx4MxcfHy4DcpUuXKkGH2WyWO3fuXKkNcXFxMiD36NGjyvXz8vJkNzc3q4OhjRs3yoA8Z86cavfPmTNHBuS//vqr0vauXbvKer1ezsnJqdhWHgz16tWrzvt26tRJViqVVV6vIAjWU7VQB5QgCFews2fPsnjxYsAysdnX15e77rqLZ555hu7duwOwe/duHB0d+fzzz6u9hk6n49SpU1W29+/f3+p2lM+5GTFiRJVhOkmSGD58OCdPniQqKorg4GCioqIAGDp0aJVrOTk5ERERYfWqsIsXLwLg7u5e7f4HHniAJUuW8NlnnzF69GjA8kyOHz/O1KlTcXFxqXKONa/dw8MDk8lEdnZ2jfcWBKF2IhgSBKHRxo0bx4YNG2o9JjMzE6PRWBE0VaegoKDKNl9fX6vbkZubW+s5fn5+ABWrr8r/6+PjU+3x9bm3TqcDoKioqNr9HTt2ZMSIEaxdu5bMzEw8PDwqlsTPmDGjwfcvv59er7e6rYIgVCYmUAuC0CJcXFzw9PREtgzPV/vn/PnzVc6rbiJ2bfcAuHDhQrX7y7eXH+fq6gpAWlparcdbw9vbG7AEfTWZOXMmJSUlrFy5kvz8fL7//nu6dOlSYx4ma157ZmYmzs7OaLVaq9sqCEJlIhgSBKFFDBgwgIsXLxIdHd1s94iIiABg27ZtyP/KJyvLMv/880+l43r27AnA9u3bq1wrPz+fw4cPW33vrl27olAoan19kyZNwsvLi88++4zvv/+e/Px8pk+fbvU9/q2wsJDExMSKoUhBEBpGBEOCILSIRx99FID//ve/FfNrLpeamsrJkycbdY82bdowatQojh8/zvLlyyvtW758OcePH2f06NEEBwdXHD98+HCOHDnCN998U+n4V155hezsbKvv7ebmRo8ePdi/f3+VQKycRqNhypQpHD16lAULFqDRaLjvvvvq9yIvs3//fkwmU8Xye0EQGkYEQ4IgtIjx48fz/PPPs337dtq1a8fkyZN55plnmDFjBqNGjSIoKIhffvml0ff5+OOP8fLyYsaMGUycOLEit9GMGTPw9vbm448/rnT8hx9+iIuLC/fddx+33XYbzz77LGPHjuXDDz9k2LBh9br3xIkTycnJqZJo8nIPPPAAAMnJyfznP//B09Oz/i+yzObNmyvuKwhCw4lgSBCEFvPCCy+wefNmhg0bxl9//cWSJUv4/fffKSkpYdGiRdx9992NvkfHjh3Zv38/U6dOZe/evfzf//0fe/fuZerUqezbt48OHTpUOr5bt27s2LGD8ePHs2HDBj744APUajU7duwgPDy8XveePn06SqWSlStX1nhMhw4dKpIl1jRx2lqrVq0iIiKiXivuBEGoShRqFQRBaEJ33XUXmzZtIi4uDkdHxyr7i4uLCQwMxM3NjZiYmHpNEL/cli1bGDNmDF9++WWjhtoEQRA9Q4IgCE3q5ZdfJj8/nw8//LDa/cuXLyczM5OZM2c2OBACSy9bREQE99xzT4OvIQiChcgzJAiC0ITCwsL48ssvycjIqLT9tddeIz09naVLl+Lj48ODDz7Y4HtkZ2czcuRIbrrpJhQK8ZtWEBpLDJMJgiC0AEmS0Gg09OzZk/fee4+BAwfaukmCIJQRPUOCIAgtQPzuFAT7JfpXBUEQBEG4qolgSBAEQRCEq5oIhgRBEARBuKqJYEgQBEEQhKuaCIYEQRAEQbiqiWBIEARBEISrmgiGBEEQBEG4qolgSBAEQRCEq5oIhgRBEARBuKr9P6QNgusA0LcQAAAAAElFTkSuQmCC", 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", 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" ] @@ -305,14 +305,14 @@ "id": "7cf82451", "metadata": { "ExecuteTime": { - "end_time": "2023-08-14T16:28:52.834253Z", - "start_time": "2023-08-14T16:28:52.406763Z" + "end_time": "2023-08-15T15:15:24.733063Z", + "start_time": "2023-08-15T15:15:24.319356Z" } }, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -360,8 +360,8 @@ "id": "18a5be29", "metadata": { "ExecuteTime": { - "end_time": "2023-08-14T16:28:55.710880Z", - "start_time": "2023-08-14T16:28:55.419863Z" + "end_time": "2023-08-15T15:15:26.834519Z", + "start_time": "2023-08-15T15:15:26.544715Z" } }, "outputs": [ @@ -415,8 +415,8 @@ "id": "80fb78ed", "metadata": { "ExecuteTime": { - "end_time": "2023-08-14T16:28:58.475906Z", - "start_time": "2023-08-14T16:28:58.472055Z" + "end_time": "2023-08-15T15:15:28.791927Z", + "start_time": "2023-08-15T15:15:28.780071Z" } }, "outputs": [ @@ -445,8 +445,8 @@ "id": "eb22604d", "metadata": { "ExecuteTime": { - "end_time": "2023-08-14T16:29:01.336531Z", - "start_time": "2023-08-14T16:29:00.153024Z" + "end_time": "2023-08-15T15:15:31.957200Z", + "start_time": "2023-08-15T15:15:30.719904Z" } }, "outputs": [ @@ -603,12 +603,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "9ada98ea", "metadata": { "ExecuteTime": { - "end_time": "2023-08-14T16:29:48.110486Z", - "start_time": "2023-08-14T16:29:47.207118Z" + "end_time": "2023-08-15T15:15:35.365717Z", + "start_time": "2023-08-15T15:15:34.656324Z" } }, "outputs": [ From 2b5f280af10d3b50325204cdf400990108c6304a Mon Sep 17 00:00:00 2001 From: hulecom Date: Tue, 15 Aug 2023 21:05:40 +0200 Subject: [PATCH 74/80] Update notebook with documentation --- .../GRL_Gravitational_Lecomte2023b.ipynb | 58 +++++++++++++++---- 1 file changed, 48 insertions(+), 10 deletions(-) diff --git a/notebooks/GRL_Gravitational_Lecomte2023b.ipynb b/notebooks/GRL_Gravitational_Lecomte2023b.ipynb index 3165d294..8da3bbdc 100644 --- a/notebooks/GRL_Gravitational_Lecomte2023b.ipynb +++ b/notebooks/GRL_Gravitational_Lecomte2023b.ipynb @@ -1,13 +1,51 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "ec4ce1ef", + "metadata": {}, + "source": [ + "# Gravitational constraints on the Earth's inner core differential rotation\n", + "Python Jupyter notebook to reproduce figures from the article \"Gravitational constraints on the Earth's inner core differential rotation\"\n", + "\n", + "## Installation\n", + "You need to install the module gravity_toolkit from [https://github.com/hulecom/read-GRACE-harmonics](https://github.com/hulecom/read-GRACE-harmonics).\n", + "\n", + "Then follow installation instructions from the documentation [https://gravity-toolkit.readthedocs.io/en/latest/](https://gravity-toolkit.readthedocs.io/en/latest/).\n", + "\n", + "## Download data\n", + "With the \"Getting Started\" instruction, you need to download CSR, GRAZ and COST products.\n", + "\n", + "For this, use the scripts: podaac_cumulus.py, itsg_graz_grace_sync.py and esa_costg_swarm_sync.py\n", + "\n", + "You will also need IGG-SLR data from the link [http://icgem.gfz-potsdam.de/series/04_SLR/IGG_SLR_HYBRID](http://icgem.gfz-potsdam.de/series/04_SLR/IGG_SLR_HYBRID) ==> EnsMean dataset\n", + "\n", + "ISBA-CTRIP_erai_gpcc_monthly_tws_1979-2019.nc has been provided by Bertrand Descharmes\n", + "\n", + "LOD files are in-house treated solutions based on data from [https://hpiers.obspm.fr/](https://hpiers.obspm.fr/)\n", + "\n", + "## Folder organisation\n", + "These datasets will be organized in subfolders:\n", + "```python\n", + "base_dir/\n", + " CSR/RL06/GSM/...\n", + " GRAZ/RL18/GSM/...\n", + " COSTG/RL06/GSM/...\n", + " IGG/IGG_SLR_HYBRID/...\n", + " HYDRO/ISBA-CTRIP_erai_gpcc_monthly_tws_1979-2019.nc\n", + " LOD/lod_AAMncep1948-2023.dat\n", + " lod_AOHSl.txt\n", + "```" + ] + }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "69ec460e", "metadata": { "ExecuteTime": { - "end_time": "2023-08-15T15:14:38.646559Z", - "start_time": "2023-08-15T15:14:37.636094Z" + "end_time": "2023-08-15T18:19:11.901955Z", + "start_time": "2023-08-15T18:19:09.927781Z" } }, "outputs": [], @@ -26,7 +64,7 @@ "from gravity_toolkit.toolbox import create_grid, grid_to_hs, filt_Ylms\n", "\n", "# maximal degree to load for the Stokes coefficients\n", - "n_harmo = 3\n", + "n_harmo = 4\n", "\n", "# Base directory with all the dataset (see read-GRACE-harmonics installation)\n", "base_dir = '/home/hugo/Documents/GRACE_DATA'" @@ -34,12 +72,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "e637b560", "metadata": { "ExecuteTime": { - "end_time": "2023-08-15T15:14:54.576984Z", - "start_time": "2023-08-15T15:14:40.166798Z" + "end_time": "2023-08-15T18:19:27.829513Z", + "start_time": "2023-08-15T18:19:12.494469Z" } }, "outputs": [], @@ -52,7 +90,7 @@ "settmp = set(np.arange(start_mon,end_mon+1)) - set(total_months['months'])\n", "missing = sorted(settmp)\n", "Ylms = grace_input_months(base_dir, 'CSR', 'RL06', 'GSM',\n", - " n_harmo, start_mon, end_mon, missing, SLR_C20='GSFC', DEG1='', SLR_C30='GSFC')\n", + " n_harmo, start_mon, end_mon, missing, SLR_C20='', DEG1='', SLR_C30='')\n", "# create harmonics object and remove mean\n", "GRACE_Ylms = harmonics().from_dict(Ylms)\n", "\n", @@ -60,7 +98,7 @@ "settmp = set(np.arange(start_mon,end_mon+1)) - set(total_months['months'])\n", "missing = sorted(settmp)\n", "Ylms = grace_input_months(base_dir, 'GRAZ', 'RL18', 'GSM',\n", - " n_harmo, start_mon, end_mon, missing, SLR_C20='GSFC', DEG1='', SLR_C30='GSFC')\n", + " n_harmo, start_mon, end_mon, missing, SLR_C20='', DEG1='', SLR_C30='')\n", "# create harmonics object and remove mean\n", "GRAZ_Ylms = harmonics().from_dict(Ylms)\n", "\n", @@ -68,7 +106,7 @@ "settmp = set(np.arange(start_mon,end_mon+1)) - set(total_months['months'])\n", "missing = sorted(settmp)\n", "Ylms = grace_input_months(base_dir, 'COSTG', 'RL06', 'GSM',\n", - " n_harmo, start_mon, end_mon, missing, SLR_C20='GSFC', DEG1='', SLR_C30='GSFC')\n", + " n_harmo, start_mon, end_mon, missing, SLR_C20='', DEG1='', SLR_C30='')\n", "# create harmonics object and remove mean\n", "COSTG_Ylms = harmonics().from_dict(Ylms)\n", "\n", From 1bf444ddfdcfa79a94c8afa3eebca07ece63ad8a Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 23 Aug 2023 22:21:16 +0200 Subject: [PATCH 75/80] Debug Swarm reading --- gravity_toolkit/grace_input_months.py | 3 ++- gravity_toolkit/read_gfc_harmonics.py | 4 ++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/gravity_toolkit/grace_input_months.py b/gravity_toolkit/grace_input_months.py index 8352c25c..2fb0f2ab 100644 --- a/gravity_toolkit/grace_input_months.py +++ b/gravity_toolkit/grace_input_months.py @@ -415,7 +415,8 @@ def grace_input_months(base_dir, PROC, DREL, DSET, LMAX, start_mon, end_mon, # read GRACE/GRACE-FO/Swarm file if PROC in ('GRAZ','Swarm'): # Degree 2 zonals will be converted to a tide free state - Ylms = read_gfc_harmonics(infile, TIDE='tide_free') + flag = pathlib.Path(infile).suffix + Ylms = read_gfc_harmonics(infile, TIDE='tide_free', FLAG=flag) else: # Effects of Pole tide drift will be compensated if specified Ylms = read_GRACE_harmonics(infile, LMAX, MMAX=MMAX, diff --git a/gravity_toolkit/read_gfc_harmonics.py b/gravity_toolkit/read_gfc_harmonics.py index 59b5ddbf..d328f32f 100644 --- a/gravity_toolkit/read_gfc_harmonics.py +++ b/gravity_toolkit/read_gfc_harmonics.py @@ -164,8 +164,8 @@ def read_gfc_harmonics(input_file, TIDE=None, FLAG='gfc'): itsg_pattern = (r'(AOD1B_RL\d+|model|ITSG)[-_]({0})(_n\d+)?_' r'(\d+)-(\d+)(\.gfc)').format(r'|'.join(itsg_products)) # regular expression operators for Swarm data and models - swarm_data = r'(SW)_(.*?)_(EGF_SHA_2)__(.*?)_(.*?)_(.*?)(\.gfc|\.ZIP)' - swarm_model = r'(GAA|GAB|GAC|GAD)_Swarm_(\d+)_(\d{2})_(\d{4})(\.gfc|\.ZIP)' + swarm_data = r'(SW)_(.*?)_(EGF_SHA_2)__(.*?)_(.*?)_(.*?)(\.gfc|\.ZIP|\.zip)' + swarm_model = r'(GAA|GAB|GAC|GAD)_Swarm_(\d+)_(\d{2})_(\d{4})(\.gfc|\.ZIP|\.zip)' # extract parameters for each data center and product if re.match(itsg_pattern, input_file.name): # compile numerical expression operator for parameters from files From 546ee6b0a8235e53d532c59b8ee72cb44c48080f Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 23 Aug 2023 22:21:40 +0200 Subject: [PATCH 76/80] Update notebook with proofreading from Juliette Ortet --- .../GRL_Gravitational_Lecomte2023b.ipynb | 215 ++++++++++++------ 1 file changed, 151 insertions(+), 64 deletions(-) diff --git a/notebooks/GRL_Gravitational_Lecomte2023b.ipynb b/notebooks/GRL_Gravitational_Lecomte2023b.ipynb index 8da3bbdc..594d7fe0 100644 --- a/notebooks/GRL_Gravitational_Lecomte2023b.ipynb +++ b/notebooks/GRL_Gravitational_Lecomte2023b.ipynb @@ -16,13 +16,13 @@ "## Download data\n", "With the \"Getting Started\" instruction, you need to download CSR, GRAZ and COST products.\n", "\n", - "For this, use the scripts: podaac_cumulus.py, itsg_graz_grace_sync.py and esa_costg_swarm_sync.py\n", + "For this, download manually the data or use the scripts: podaac_cumulus.py, itsg_graz_grace_sync.py and esa_costg_swarm_sync.py\n", "\n", "You will also need IGG-SLR data from the link [http://icgem.gfz-potsdam.de/series/04_SLR/IGG_SLR_HYBRID](http://icgem.gfz-potsdam.de/series/04_SLR/IGG_SLR_HYBRID) ==> EnsMean dataset\n", "\n", "ISBA-CTRIP_erai_gpcc_monthly_tws_1979-2019.nc has been provided by Bertrand Descharmes\n", "\n", - "LOD files are in-house treated solutions based on data from [https://hpiers.obspm.fr/](https://hpiers.obspm.fr/)\n", + "LOD files are in-house solutions based on C01 and C04 data from [https://hpiers.obspm.fr/](https://hpiers.obspm.fr/). We correct the LOD time-series with zonal tides and for atmospheric, oceanic, hydrologic and sea level angular momentum obtained from the operational products of the Earth-System-Modelling group at GFZ\n", "\n", "## Folder organisation\n", "These datasets will be organized in subfolders:\n", @@ -40,12 +40,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "69ec460e", "metadata": { "ExecuteTime": { - "end_time": "2023-08-15T18:19:11.901955Z", - "start_time": "2023-08-15T18:19:09.927781Z" + "end_time": "2023-08-23T06:42:32.547480Z", + "start_time": "2023-08-23T06:42:30.197057Z" } }, "outputs": [], @@ -72,12 +72,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "e637b560", "metadata": { "ExecuteTime": { - "end_time": "2023-08-15T18:19:27.829513Z", - "start_time": "2023-08-15T18:19:12.494469Z" + "end_time": "2023-08-23T06:42:50.644999Z", + "start_time": "2023-08-23T06:42:36.326441Z" } }, "outputs": [], @@ -91,7 +91,7 @@ "missing = sorted(settmp)\n", "Ylms = grace_input_months(base_dir, 'CSR', 'RL06', 'GSM',\n", " n_harmo, start_mon, end_mon, missing, SLR_C20='', DEG1='', SLR_C30='')\n", - "# create harmonics object and remove mean\n", + "# create harmonics object\n", "GRACE_Ylms = harmonics().from_dict(Ylms)\n", "\n", "total_months = grace_find_months(base_dir, 'GRAZ', 'RL18', DSET='GSM')\n", @@ -99,7 +99,7 @@ "missing = sorted(settmp)\n", "Ylms = grace_input_months(base_dir, 'GRAZ', 'RL18', 'GSM',\n", " n_harmo, start_mon, end_mon, missing, SLR_C20='', DEG1='', SLR_C30='')\n", - "# create harmonics object and remove mean\n", + "# create harmonics object\n", "GRAZ_Ylms = harmonics().from_dict(Ylms)\n", "\n", "total_months = grace_find_months(base_dir, 'COSTG', 'RL06', DSET='GSM')\n", @@ -107,10 +107,10 @@ "missing = sorted(settmp)\n", "Ylms = grace_input_months(base_dir, 'COSTG', 'RL06', 'GSM',\n", " n_harmo, start_mon, end_mon, missing, SLR_C20='', DEG1='', SLR_C30='')\n", - "# create harmonics object and remove mean\n", + "# create harmonics object\n", "COSTG_Ylms = harmonics().from_dict(Ylms)\n", "\n", - "# remove mean to talk in gravity anomalies\n", + "# remove mean to consider in gravity anomalies\n", "GRACE_Ylms.mean(apply=True)\n", "GRAZ_Ylms.mean(apply=True)\n", "COSTG_Ylms.mean(apply=True)\n", @@ -127,8 +127,8 @@ "id": "5da6ed08", "metadata": { "ExecuteTime": { - "end_time": "2023-08-15T15:15:13.402985Z", - "start_time": "2023-08-15T15:14:58.493686Z" + "end_time": "2023-08-23T06:43:07.678119Z", + "start_time": "2023-08-23T06:42:51.320617Z" } }, "outputs": [], @@ -201,8 +201,8 @@ "id": "384d9ab9", "metadata": { "ExecuteTime": { - "end_time": "2023-08-15T15:15:15.759726Z", - "start_time": "2023-08-15T15:15:14.924471Z" + "end_time": "2023-08-23T06:44:03.287631Z", + "start_time": "2023-08-23T06:44:02.223259Z" } }, "outputs": [ @@ -224,6 +224,16 @@ "metadata": {}, "output_type": "display_data" }, + { + "data": { + "image/png": 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", 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", @@ -250,6 +260,7 @@ "print(\"Time length of IGG-SLR - ISBA :\", SLR_filt_isba_Ylms.time[-1] - SLR_filt_isba_Ylms.time[0], \" yr\")\n", "\n", "# Figure 2a of the paper\n", + "# Fig 2a. Time-series of S2,2 coefficient for IGG-SLR (red) product, IGG-SLR minus ISBA combination (green) and ISBA (blue).\n", "plt.figure()\n", "plt.plot(SLR_filt_Ylms.time, SLR_filt_Ylms.slm[2,2], label='IGG-SLR', color='C3', linestyle=(0, (5,2)))\n", "plt.plot(SLR_filt_isba_Ylms.time, SLR_filt_isba_Ylms.slm[2,2], label='IGG-SLR - ISBA', color='C2')\n", @@ -259,7 +270,20 @@ "plt.xticks(fontsize=14)\n", "plt.yticks(fontsize=14)\n", "\n", + "# Figure S4a of the paper\n", + "# Fig S4a. Time-series of C2,2 coefficient for IGG-SLR (red) product, IGG-SLR minus ISBA combination (green) and ISBA (blue).\n", + "plt.figure()\n", + "plt.plot(SLR_filt_Ylms.time, SLR_filt_Ylms.clm[2,2], label='IGG-SLR', color='C3', linestyle=(0, (5,2)))\n", + "plt.plot(SLR_filt_isba_Ylms.time, SLR_filt_isba_Ylms.clm[2,2], label='IGG-SLR - ISBA', color='C2')\n", + "plt.plot(isba_filt_Ylms_long.time, isba_filt_Ylms_long.clm[2,2], label='ISBA', color='C0', linestyle='dashdot')\n", + "plt.legend(fontsize=14)\n", + "plt.xlabel('Time (year)', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "\n", "# Figure Supplementary Information S3a of the paper\n", + "# Fig S3a. Time-series of S2,2 coefficient for GRACE CSR (brown), GRAZ (light blue) and COSTG (lime) products and for IGG-SLR (red) product\n", + "\n", "plt.figure()\n", "plt.plot(GRACE_filt_Ylms.time, GRACE_filt_Ylms.slm[2,2], label='CSR', color='C5')\n", "plt.plot(GRAZ_filt_Ylms.time, GRAZ_filt_Ylms.slm[2,2], label='GRAZ', color='C9')\n", @@ -271,6 +295,7 @@ "plt.yticks(fontsize=14)\n", "\n", "# Figure 3 of the paper\n", + "# Fig 3. α time-series reconstructed from Eq. (7b) based on the corrected S2,2 variations (green) for different choices of δh (49 m, 90 m, 126 m) and based on the S2,2 time-series uncorrected for hydrological loading with δh = 90 m (brown).\n", "plt.figure()\n", "# plot S22/(2*Kappa*delta h)*180/pi (Equation 7b + conversion from radians to degree)\n", "plt.plot(SLR_filt_isba_Ylms.time, SLR_filt_isba_Ylms.slm[2,2]/1.41e-11/49/np.pi*180, label=r'$\\alpha$ from $S_{2,2}$ ($\\delta h$ = 49m)', color='#4bce4b', linestyle='dashed')\n", @@ -287,12 +312,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "id": "5789a5dc", "metadata": { "ExecuteTime": { - "end_time": "2023-08-15T15:15:22.158945Z", - "start_time": "2023-08-15T15:15:21.734259Z" + "end_time": "2023-08-16T07:38:46.280642Z", + "start_time": "2023-08-16T07:38:45.875047Z" } }, "outputs": [ @@ -309,6 +334,7 @@ ], "source": [ "# Figure 2b of the paper\n", + "# Fig 2b. Lomb-Scargle periodogram of S2,2 coefficient for IGG-SLR (red) product, IGG-SLR minus ISBA combination (green) and ISBA (blue).\n", "windows = 48\n", "\n", "# windows creation to reduce the apodization effect\n", @@ -339,12 +365,65 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, + "id": "98f5283b", + "metadata": { + "ExecuteTime": { + "end_time": "2023-08-23T06:45:55.960549Z", + "start_time": "2023-08-23T06:45:55.576991Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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6668zZcqUCpcxbty4Eo9/9dVXqKrK0KFDS5yEGBUVVaxLVgghROB45pln+Pzzz5k3bx516tQpcSFNq9VKfglPKI4bN4758+fz448/MmHCBN555x0iIiKKnKOqarGJ6GX58MMP6dy5M9ddd12x95YtW8ahQ4eIioqiTZs2nM89D+CR+UeKojB16lRuvvlmXnzxRX788cdyXRcWFEatkFqk5KeQnJeMUWvEqJcn8yojoBKov/zlL14re86cOQDcf//9XqtDCCGE90RGRrJmzRpGjhzJjBkzmDNnjnsrF5vNxoULF1i3bh0pKSl07NiRsLA/n2zT6/V8/fXX3HHHHcyfP59ly5YxYMAAmjdvjqIoJCcns3HjRpKSkmjatOkVn/a73OrVq5k0aRLNmzenV69e1KtXj9zcXHbv3s3PP/+MRqNh9uzZFFBAga0AjaIhSON8Cm7KlCmEh4eX2Bs2e/bsIr1XJRkxYgRdunRh/fr1bNy4kb59+5Yr5jhjHIW2QkwWE2dzz9I0silaTeV7w65WAZVAecvZs2dZs2YNderU4aabbirxHLPZzIIFCzh37hzR0dH07NmTDh06+DhSIYQQpWnSpAk7duxgyZIlfP7552zYsIH09HT0ej3169dn4MCB3Hnnndx0003FJrfHxcXx448/smLFChYtWsT27dtZtWoViqJQp04dunTpwmuvvcbtt99OUFD5HvV/44036NWrF2vXrmXTpk1cuHABgPr16zN27Fgee+wxOnTqwLEs50Kb8SHx7oRp+fLlVyx35syZZSZQ4JzMPnz4cF588UU2bdpUrpgVRaFuWF3ys/Kx2C0k5ydTP6z0ieiiuKsigZo3bx4Oh4Nx48ZdcYXZ5OTkYsN/Q4YMYeHChcTFxfkgSiGEEC6uydsl0ev1jB07lrFjx1a4XEVRuOWWW7ilkk8iXq5Vq1Y8/fTTPP3001c8JzkvGbvDjkFnICY4hg0bNjifYjSZiIiIKPUpxrImrA8bNqxck9ovp9PoaBDegFPZp8gqzCJMH1ahVdDFVZBAqarqXv7/SsN3EyZMoG/fvrRp0waDwcCBAweYPn06q1evZsSIEWzevLnUCYdms7nIZpGuMXSr1Yq1nCv+Xo1cbSNtVDnSflUTyO1ntVpRVdW52rfDUfYFfuD60HbFKUpmc9jIKMwAoLaxNqjOdaACof2MWiOxxljSC9K5kHsBo86ITqkeaUFF28/hcLa51WpFq73ycGVFfh9Uj5aqgvXr13Py5En69u1L8+bNSzznpZdeKvK6e/fufPfdd/Tt25fExERWrVp1xaE/gBkzZpS4TsiaNWvK1QV7tVu7dq2/Q6jWpP2qJhDbT6fTUadOHXJzc7FYLP4Op1Q5OTn+DiGgZdozUVWVICUIW74Nk1J0krq/28+gGtAreqyqlTNZZ4jVxvo1nooqb/tZLBYKCgrYtGkTNpvtiueV9ADCldT4BMo1eXzixIkVuk6j0TB+/HgSExPZvHlzqQnUlClTePLJJ92vTSYTDRs2ZNCgQcWe8hB/slqtrF27loEDB6K/bK0aUTZpv6oJ5PYrLCzkzJkzhIWFFXvKLFCoqkpOTs4VJ0ELsDgs5Gc5P5Drhdcrsg9dILVfsD2Yk9knKVQLUYIVwoPC/RpPeVS0/QoLCzEajdxwww2l/kxV5CnMGp1AZWZmsnz5cqKiorjtttsqfL1r7lNZGanBYChxfyW9Xh9wv5gDkbRT1Uj7VU0gtp/d7txgVqPR+GSV78pwDZu44hTFpeWlARAeFE5oUNGNggOp/YwaI7EhsaTlp3Eh/wKhQaEBv9VLRdtPo9GgKEqZP+8V+V1Qo7/rFy1ahNlsZvTo0RiNFV/nYtu2bYBzMqMQQghRXma7mWxzNuB88i7QxRvjMWgN2B12LuaVtEW0uFy1TaDS0tI4dOgQaWlpVzzn448/Bkpf++nAgQNkZWUVO56YmMjbb7+NwWDg1ltvrXK8Qgghrh7pBemAc+HKS4fuApVG0VAvzLn2VZY5izxrnp8jCnwB1Uc3Z84c9z5Frn2D5syZw4YNGwAYOXIkI0eOBGDWrFlMnz6dqVOnlriC+G+//cbvv/9O586d6dSp0xXr/Pzzz3nzzTcZMGAACQkJGAwG9u3bx5o1a9BoNHzwwQc0atTIo/cphBCi5rLarWSZswDnopXVRYg+hOjgaDILM7mQd4Fmkc38Pj8rkAVUApWYmFhkF2yAzZs3s3nzZsA5lOZKoMri6n0qa/J4//79OXjwIDt37mTjxo0UFhZSu3Zt7rzzTp544gm6detW8RsRQghx1UovTEdVVYx6IyG66vUkdq2QWpjMJsw2MxmFGcQaq9dTeb4UUAnU/PnzmT9/frnOnTZtWql7182ePZvZs2eXWU7fvn3Lvfy9EEIIURq7w05mYSbgnFdU3XpwdBodtUJrcSH3Ain5KUQYItBrAushi0BRbedACSGEEIEmy5yFQ3UQpA0iTB9W9gUBKNoQTbAuGIfqICUvxd/hBCxJoIQQQggPUFXV3fsUExxT7XqfXBRFoW5oXcCZEBZYC/wcUWCSBEoIIYTwgDxrHma7GY2iIcoQ5e9wqiREH+LeGy85P7lS++3VdJJACSGEEB7g2vMu0hCJVnPl/daqi1ohtVAUhXxrPjkW2bLncpJACSGECCinTp1CURSGDBlS4vs2m42FCxcyYsQI6tevj8FgIDQ0lFatWjFmzBi+/fbbK/aYqKrKN998wx133EHjxo0xGo0YjUaaNm3KqFGjWLJkSYU3mM7Pz+eVf77CkN5DuK7xdTSNa0qDBg3o06cPU6ZM4fjx40XO79evH1qtlosXy16wUlGUIv90Oh21a9dm2LBhrFu3rkJxVlSQNoi8i3m0jW/LsKHDcKhFN+3dt28fY8eOdS8BFBkZSfPmzbn11lt55513inwNXF/Ty/+FhobSvn17pk+fTm5ubqnxzJ07133dvn37vHLPFRFQT+EJIYQQpUlKSuKWW25h165dxMfHM2DAABo3bozdbufEiROsXLmShQsXctddd7F06dIi12ZkZHDnnXeybt06IiIiGDBgAM2aNUOj0XDmzBk2bNjAl19+yX//+1+2bt1arnhycnLo3bs3e/bsoVGTRtxy5y00rNWQM2fOsH//fl5//XWaNWtGs2bNKn3PsbGxPProo4BzT7f9+/ezcuVKVq5cyZIlS7j77rsrXXZZYoJjAHCoDrIKs4gxOl+vXbuWYcOGYbPZGDBgALfccgsAJ06cYPPmzSxfvpxHHnkEna5omtGsWTPuvfdewJnMpqamsnr1aqZNm8YPP/zAzz//jFZbcu+dK4FSVZW5c+eW+iS+L0gCJYQQolowmUwMHjyYw4cPM2XKFF566aViG8OazWYWLVpUrHfGZrMxcuRIfv75Z8aNG8fMmTOJjIwsco7D4WD58uV88MEH5Y5p5syZ7Nmzh9vvvZ2pb0+lYURD99whgJMnT2I2mytxt3+Ki4srlix8+umn3H333UyZMsWrCdSlQ5EpBSnu4cmHH34Yu93OunXr6N+/f5FrVFVlzZo1JSZCzZs3L3YvZrOZHj16sHXrVjZt2lSsPIDDhw+zefNmRo0axfbt21m0aBHPPfecZ26ykmQITwghRLXw1ltvcfjwYSZMmMBrr71WLHkC5+bu999/PwsXLixyfMGCBfz8888MGDCAuXPnFkuewLnh7G233cbq1avLHZOrp+quCXeh0+oIDwov8n6TJk245ppryl1eed15552EhYWRlJRU6pZmnqJRNNgddtIK0khJSeH48eO0bdu2xGRHURQGDx5c7qcQDQaDu5zU1NQSz3Etjj1mzBjuvfde0tPTWbVqVSXvxjMkgRJCCFEtzJs3D4Dnn3++zHMvHzqaO3cuAM8991yZH+yXX1uamBjnkNbpk6eJNESiUXz3seqaY1SReCsrSBsEOCfKh4SHoNVquXDhAnl5Vd8zz2KxsGHDBhRFoWPHjsXet9lsfPLJJ8THxzNkyBDGjBkDwKJFi6pcd1XIEJ4QQoiAd/r0ac6dO0ejRo1o2rRpha612Wxs374dvV5Pr169PBrXrbfdyuLFi3lp8kucO3SOoUOG0qlTJ6Kjoz1az+UWL15MXl4ebdq0ISoqyqt1gXOFcqPOSIGtgBxHDsOHD2fFihX07t2bBx98kJ49e9K6dWv0+tJXLT927Jh7CE9VVdLS0vjhhx84d+4cb775Ji1btix2zXfffcfFixd5/PHH0el0tGjRgh49evDTTz9x5swZGjdu7I1bLpMkUEIIUY2oqkqBzf8LGzocDgpsBeisOjSa4r0uRp3RowtJJicnA1CvXr0S33/77bcxmUxFjj311FOEhYWRkZGB1WqlTp06GAyGYtfOnTuX06dPFzk2ceJEGjRoUGZcfYf05cmpT/Lhvz/k32/9m3+/9W/AOVl6yJAh/N///R8tWrQo1z1eSVpamjvpKCwsZN++faxatYqQkJBybVnmKbVCapFkSiKzMJP3PngPq9XKypUr+dvf/gZAUFAQXbt25c477+SBBx7AaDQWK+P48eNMnz692PERI0Zw0003lViva/juvvvucx+799572bp1K/Pnz2fq1KmeuL0KkwRKCCGqkQJbAd2XdPd3GGXads82QvSe20i3rIUc3377bc6dO1fk2KRJkwgLCyvz2rlz57o3rXcZMmQIDRo0ICsri5kzZxa7xpXQZJmzmPDoBB7722Ns37idLVu2sGPHDrZt28Z7773Hxx9/zGeffcaIESPKvskrSE9PL5Z0hIaGsmbNGnr27FmuMk6dOlVsr9moqCgmT55c7jhC9aGE6kPJs+ZBqLNn6MiRI/zwww/8+uuv/PLLL2zZsoUtW7bw0UcfsXHjRvcQp8vgwYP5/vvv3a9TUlL48ccfefzxx+nZsyfbtm0r0gt14cIFVq9ezTXXXEPXrl3dx++8806efPJJ5s+fz0svveSXVd8lgRJCCBHwateuDVAsSXI5e/as+//79evHxo0b3a9jY2PR6XSkpaVhNpuL9UIlJia6/3/cuHEsWLDA/TorK6vEHpNp06ZRaCuk0FaIoig0iGtAwqgERo0aBUB2djbPPfccs2fP5v777+fcuXMEBQVV4s6hVatWHDp0yB3PihUrePjhh7ntttvYsWMH9evXL7OMU6dOFbuPxo0bVyiBUhSFWiG1OJl9kszCTGKNsbRs2bJIwrN7927uvfde9u3bx/Tp03nnnXdKLbNWrVrcfffdFBQUcP/99/P666+756uBc/K/3W4v0vsEEB0dzZAhQ/j6669Zv349AwYMKPd9eIokUEIIUY0YdUa23bPN32HgcDjIyckhPDz8ikN4npSQkEC9evU4c+YMx48fr9C6Sjqdjuuuu46tW7eSmJhYoQ/bhISEK/ZgZZuzAQjTh6HTFP04jYyMZNasWaxcuZKkpCT27t1Lly5dyl3vlURFRTFu3DjsdjsTJ07kkUceYcWKFWVe169fP49sxxKiDyEsKIxcSy6p+ak0CC86zNmxY0f++9//cuONN7J+/fpyl9utWzcAdu7cWeS4K5l6/vnnr/jwwMcffywJlBBCiNIpiuLRobHKcjgc2HQ2QvQhJSZQ3jBu3Dhee+01Xn311SK9FOUxfvx4tm7dyowZM7jxxhurPOSjqirZFmcCdem6T5dSFIWQEO98rSZMmMDs2bP5+uuv2bJlS7mH8jyhVkgtci25ZJuziTPGEawrupxEaGhohcvMyHBug+Nw/Lna+aZNmzh69CjNmjWjX79+Rc5XVdU9B2v58uVkZmZ6feL+5WQZAyGEENXCM888Q/PmzZk3bx7PPfcchYWFxc6xWq3k5+cXOz5u3Dh69uzJjz/+yIQJE4pNOAfnh3JJx0tSYCvAarfyxYIvOPT7oRLPWbZsGYcOHSIqKoq2bduWq9zyUhTFPXn6xRdf9GjZZXGYHSx4dwGZ6Zmk5hddt8lms/Hmm28C0Lt37/KV53Dw3//+F4A+ffq4j7smj7/wwgvMmTOnyL+PPvqId999lzFjxlBYWMjixYs9cWsVIj1QQgghqoXIyEjWrFnDyJEjmTFjBnPmzHFv5WKz2bhw4QLr1q0jJSWFjh07EhYW5r5Wr9fz9ddfc8cddzB//nyWLVvGgAEDaN68OYqikJyczMaNG0lKSqJp06ZXfNrPxdX7tPWnrUx/ajrNmzenV69e1KtXj9zcXHbv3s3PP/+MRqNh9uzZJT79N2XKFMLDw0vsDZs9e3aZvVcjRoygS5curF+/no0bN9K3b9/yNGOVWa1W3nrlLf4z4z906NqB6zpfR0xUDBcvXuT777/n3LlzNGnSpMSn4y5dxgCcC2f+9NNPHDx4kIYNG/LCCy8AzlXnv/zyS8LCwtzzykriWlX+448/dm934zOq8Ljs7GwVULOzs/0dSkCzWCzqihUrVIvF4u9QqiVpv6oJ5PYrKChQDxw4oBYUFPg7lCuy2+1qZmamarfbPV72yZMnVUAdPHhwie9bLBZ1/vz56tChQ9U6deqoer1eDQkJUVu0aKGOHj1a/eabb64Yl8PhUJctW6beeuutasOGDVWDwaAGBwerCQkJ6q233qouXrxYNZvNpcbncDjUQ+mH1H2p+9Tf9vymvvnmm+rAgQPVJk2aqMHBwWpwcLDarFkzdezYseqOHTuKXd+3b18VKPVfZmamqqqqCqitWrW6YizffvutCqh9+vQpNebKKulrYbfb1VWrVqkTHp6gtu7QWo2vFa/qdDo1IiJC7dq1qzp9+nQ1KyurxHIu/2cwGNRWrVqpTz75pJqamuo+/4MPPlAB9f777y8xrku//7p06aIC6m+//XbF+yjvz1RFPr8VVfXArDJRhMlkIjIykuzsbCIiIvwdTsCyWq2sWrWKoUOHlrn4mihO2q9qArn9CgsLOXnyJE2aNClxu5JA4HA4MJlMRERE+GwOVKDIs+RxynQKjaKhVUyrSq0+XhPar9BWyPGs4wA0j2qOQVe8l81bKtp+5f2Zqsjnd/X8qgkhhBB+4hq+izBE+HTrlkATrAt27/2XWlDyHnY12dX7lRdCCCEqyKE6MFmcE80jg0p++u5qEh8SDziXdDDbzX6OxrckgRJCCCHKKd+aj91hR6vREqqv+OP6NY1RZ3T3QqXlp/k5Gt+SBEoIIYQoJ1fvU0RQhF+2DwlEccY4wLmtjcVu8XM0viMJlBBCCFEOqqqSY8kBcPe6iD9XJwdIK7h6eqEkgRJCCCHKocBWgM1hQ6NoZPjuMvFG51yorMKrpxdKEighhBCiHFzDd+FB4Vf103clCdGHEKoPRUUlvTDd3+H4hHwHCCGEEGWQ4buyueZCZRZmYnPY/ByN90kCJYQQQpTBbDdjsVtQFIUwfVjZF1yFQvWhBOuCUVWVjMIMf4fjdZJACSGEEGVwDd+F6cPQarR+jiYwKYringuVUZCB3WH3c0TeJQmUEEIIUQYZviuf8KBwgrRB2FU7meZMf4fjVZJACSGEEKWw2q0U2goBSaDKoiiKey5UekE6DtXh54i8RxIoIYQQohQ5Vmfvk1FvRKfR+TmawBdpiESn0WFz2Mg2Z/s7HK+RBEoIIYQoRa4lF4BwvfQ+lYdG0RBrjAWcC2uqqurniLwjoBKoRYsW8dBDD9G1a1cMBgOKojB//vwKlbFhwwYURbniv19++aXE67Zv387QoUOJjo4mNDSUbt26sWTJEg/clRBCiIo6deoUiqIwZMiQIsf37dvH2LFjSUhIwGAwEBkZSfPmzbn11lt55513inxYu8q4/F9oaCjt27dn+vTp5ObmlhrHnI/n0DiyMW3j25J0JMkr91oTRRui0SpaLHaLe/5YTRNQfZEvvPACSUlJxMXFUbduXZKSKv/N2rdvX/r161fseIMGDYod27BhA4MHDyYoKIi77rqLyMhIli1bxujRozl16hTPPfdcpeMQQgjhGWvXrmXYsGHYbDYGDBjALbfcAsCJEyfYvHkzy5cv55FHHkGnK/rR1qxZM+69917AuZ5Tamoqq1evZtq0afzwww/8/PPPaLUlP1n38dyPURQFVVVZNH8RXWd29e5N1hBajZYYYwyp+amkFqQSHhRe4/YODKgEas6cObRo0YLGjRvz+uuvM2XKlEqX1a9fP6ZNm1bmeTabjYkTJ6IoCps2baJTp04ATJ06lR49ejB16lRGjRpFixYtKh2LEEKIqnv44Yex2+2sW7eO/v37F3lPVVXWrFlTYiLUvHnzYp8HZrOZHj16sHXrVjZt2lSsPIDDhw/zy5ZfGDxiMAd+P8CiRYt48803CQoK8uh91VQxwTGkFaRRaCskz5rn3i+vpgioIby//OUvNG7c2Kd1rl+/nuPHj3PPPfe4kyeA8PBwXnzxRWw2G/PmzfNpTEIIIYpKSUnh+PHjtG3btsRkR1EUBg8eXO5eDoPB4C4nNTW1xHPmzJkDwPA7h3PX3XeRnp7O119/Xck7uProNDqiDdFAzdxkOKASKE86evQo7777Lq+//jpLly4lLa3kL96GDRsAGDRoULH3XMc2btzotTiFEEKULTIyEq1Wy4ULF8jLy6tyeRaLxT1ntmPHjsXet9lsLFy4kJi4GPoM6MOEcRMA+Pjjj6tc99XENZk8z5pHgbXAz9F4VkAN4XnSkiVLikwCNxqNTJ8+naeffrrIeUePHgUocYguOjqauLg49zlCCBEo8i0V32ssSKtBp3X+3WyzO7DYHWgUhWD9n8Ne5S3X4XBQYLGjs9gw6HXo/yjX7lDRajw/18VgMDB8+HBWrFhB7969efDBB+nZsyetW7dGr9eXeu2xY8fcQ3iqqpKWlsYPP/zAuXPnePPNN2nZsmWxa7777jsuXrzI6AdGE2mMpHHtxvTo0YO1a9dy5swZGjZs6PF7rImCtEFEGiLJNmeTVphGQ33Nabcal0DFx8fz1ltvMWzYMBo1akRWVhY//fQTzz77LM888wwRERE89NBD7vOzs51rVERGRpZYXkREBGfPni21TrPZjNlsdr82mZxL/lutVqxWa1VvqcZytY20UeVI+1VNILef1WpFVVUcDgcOR8kLEbZ+6YcKlzvr7o4MbVcXgO/3XeDRpbvp3iSGpQ90d5/T+/X1ZORXrE2mDW/NmB7O6RfbTqRzfdPYCsd2Odd9u9oB4MMPP8RisbBq1Sr+9re/ARAUFETXrl254447mDhxIkajsVgZx48fZ/r06cXqGD58OH/9619LbGP38N0dwwnVh+JwOLj33nvZunUrc+fO5cUXX6zS/bmeFrz0/mqq2OBYss3ZmMwmzEYzek3pCW95VLT9HA4HqqpitVqv+MAAVOz3QY1LoNq0aUObNm3cr0NCQhg9ejQdOnSgS5cuTJ06lQceeACNxnOjlzNmzCjxh3PNmjWEhIR4rJ6aau3atf4OoVqT9quaQGw/nU5HnTp1yM3NxWKxeKzc/IIC9x94+QXO4RSbzeY+BlRqzZ7CwkJ3GXl5+ZhMVf+AdC0vYLfb3WUHBQWxePFijh07xvr16/ntt9/YsWMHW7ZsYcuWLXz44YesXLmS6OjoImUMGDCAL7/80l12amoqGzdu5B//+Ae9evVi3bp1NG/e3P1+cnIy33//PU1aNKFtx7ZQCCaziaFDh2IwGJg7dy6PP/64R54qy8mpmY/4X86gGDCrZi5kXyBKG+WxcsvbfhaLhYKCAjZt2oTNduVe1vz8/HLXXeMSqCtp27Yt3bt35+eff+bYsWPuLltXz5OrJ+pyJpPpir1TLlOmTOHJJ58sck3Dhg0ZNGgQERERHrqDmsdqtbJ27VoGDhxYZhe8KE7ar2oCuf0KCws5c+YMYWFhBAcHl3jOvmkDK1zupUN4I7uEMbRjo2JDeD8/W3yCdklUVSU3J5ew8DCCdFr3EF6/NuEeGcILC3M+saXVaov9Hu3cuTOdO3d2v969ezdjxoxh//79/Oc//2HmzJmllhEREUGzZs0AeOCBB3jvvfeKzG16//33sdvtDB81nCBtEDGRMe7rhg0bxldffcWOHTsYMGBApe9PVVVycnIID695j/eXRGvVcjrnNPnkUz+8PlqlahsyV7T9CgsLMRqN3HDDDVf8mQKK/DFRlqsmgQKIi3Puz3Nphuma+3T06FG6dOlS5PzMzEzS0tLo2bNnqeUaDAYMBkOx43q9PuB+MQciaaeqkfarmkBsP7vdjqIoaDSaK/aWhwVX7VH6II2GoBJuu7zlOhwO7GYtoQZ9kRg91bnvKtPVDqXp3Lkz//3vf7nxxhv56aef3OeXVcb1118PwK5du4q873ry+t3X3uXd194tsc558+YxcGDFk1gX17BTee6vJggLCiNYF0yhrZAscxbxIfFVKq+i7afRaFAUpcyf94r8LrhqEiibzcbOnTtRFIVGjRq5j/ft25cZM2awZs0a7rrrriLXrFmzxn2OEEKIwBUaGlrhazIyMgCKzKHZtGkTR48epVGTRnTt2ZVQfShB2qJJ5fLly1m+fDmZmZnu4UJROkVRiA2O5VzuOTIKM4g1xqJRqnfiWG0TqLS0NNLS0oiLi3P3LAFs3bqV66+/vkiXns1m4+mnnyYpKYkhQ4YQExPjfm/AgAE0bdqUJUuW8Pjjj7sfZ83JyeGVV15Bp9Mxbtw4X92WEEKIEuTl5TFz5kweeuihIr/zwfk7/s033wSgd+/e5SrP4XDw3//+F4A+ffq4j7uG8h584kFG3j2Sa2KuQaspOtwUERHBf/7zHxYvXsyjjz5a6Xu62kQYIriYf9G9yXB0cPVOPgMqgZozZw6JiYkA7N27133MtVbTyJEjGTlyJACzZs1i+vTpTJ06tcgKs3fffTeKotCzZ0/q169PVlYWmzZt4vDhwzRq1IgPPvigSJ06nY45c+YwePBg+vTpw913301ERATLli3j5MmT/POf/yzxEVchhBC+Y7VaeeGFF5g2bRo9evSgQ4cOREREcPHiRb7//nvOnTtHkyZNmDp1arFrL13GAJyTyH/66ScOHjxIw4YNeeGFFwDn/Jcvv/yS0LBQBo0YRIg+pFjyBDB+/Hj+85//8PHHH0sCVQGuTYYv5l0krSCNKENUtZ7/FVAJVGJiIgsWLChybPPmzWzevBmAhIQEdwJ1JQ8//DDff/89GzZsIC0tDZ1OR/PmzXn++ef5+9//XmJ3a//+/UlMTGTq1Kl8/vnnWCwW2rRpwyuvvMLo0aM9dn9CCCEqJyIiglWrVvHDDz+QmJjIF198QXp6OiEhIbRs2ZIHH3yQ//u//yvxoZ/LlzEwGAwkJCTw5JNPMmXKFHeP1tKlS8nPz+euMXcREhpCqL7kYcF27drRpUsXfvvtN3bu3FlkQrsoXbQhmtT8VCx2C7nWXMKDwv0dUqUpamWeWRWlcj25l52dLU/hlcJqtbJq1SqGDh0acJN4qwNpv6oJ5PYrLCzk5MmTNGnSpNQnhvzJ4XBgMpmIiIioUZOgVVXlUMYhHKqDJpFNCNF7Zymamtp+5ZGcl0x6QToh+hCaRDapVBkVbb/y/kxV5PP76vqqCSGEEKUosBXgUB1oFA1GnbHsC0SFxQbHoigK+dZ88q3lX3cp0EgCJYQQQvwh1+pcfDMsKKxaz88JZHqtnsgg51BremG6n6OpPEmghBBCiD/kWZ0bFV9p/pPwDNcmwyazCYvdc6vt+5IkUEIIIQRgd9jdQ0ph+jA/R1OzBeuCCQtytnF6QfXshZIESgghhODP3qcgbVCxxTOF58UGO3uhMs2Z2BxX3p8uUEkCJYQQQiDDd74Wqg8lWBeMqqpkFGb4O5wKkwRKCCGEAPJszgRKhu98Q1EU4ozONbgyCjNwqI4yrggskkAJIUSAkmX6fMfqsGK2mQG8tvaTKC4iKAK9Ro/dYSfLnOW1erzxsyQJlBBCBBit1rl9iNVq9XMkVw/X5PFgXTA6TUBt0lGjKYrifiIvvSDda3802GzOOVY6nee+tpJACSFEgNHr9RgMBrKzs6UXykdc85+k98n3ogxRaBQNFruFHEuOV+owmUxotVr3HyeeIGm2EEIEoLi4OM6dO8fZs2eJjIxEr9cH1MKODocDi8VCYWFhjdiKxJRnwuFwEBQURGFhodfrq2ntV1XhSjiZlkxSTCkEhZf9BGR5209VVfLy8jCZTNStW9ejP0OSQAkhRABy7cOVlpbGuXPn/BxNcaqqUlBQgNFoDKjErjLsDjsX8y8CoAnVoFG8n9DUpPbzBLvDTmp+Kioqeca8MpeRqEj7KYpCVFRUiRtNV4UkUEIIEaAiIiKIiIjAarVit9v9HU4RVquVTZs2ccMNNwTcZswV9dPpn/jPsf/QIqoF/273b5/UWZPaz1NW7VzFuqR1XF/vep7r/lyp51ak/fR6vUeH7lwkgRJCiACn1+sD7kNWq9Vis9kIDg4OuNgqanPKZi5YLjCk1hCCg4N9UmdNaj9Pua31bSw8upAVp1YwoeMEEiITrnhuILSfDLwKIYS4aqmqyq/JvwLQvU53P0dzdWsW1Yx+DfqhorLgwAJ/h1MmSaCEEEJctc7mnuVC3gV0io5OtTr5O5yr3vi24wH45tg3pBWk+Tma0kkCJYQQ4qq1PXk7AO3i28kSBgGgU61OdIjvgMVhYcnBJf4Op1SSQAkhhLhqbbuwDYDr6lzn50gEOJ+Yc/VCfXr4U/f6XIFIEighhBBXJVVV3T1QMv8pcPRv2J+EiARyLDl8deQrf4dzRZJACSGEuCqdNJ0ktSCVIE0QHWp18Hc44g8aRcO4NuMA+OTAJ1gdgbmlkSRQQgghrko7kncA0KFWBwxag5+jEZca1mwYccY4LuZf5PuT3/s7nBJJAiWEEOKq9NvF3wDoWrurnyMRlzNoDYy+djQAc/fNDcg9ISWBEkIIcdVRVZUdF509UJ1rd/ZzNKIkd7S6gxBdCMeyjpF4LtHf4RQjCZQQQoirzrncc6Tkp6BTdLSPa+/vcEQJIoIiGNVyFADz9s/zczTFSQIlhBDiqrMzZScAreNay/pPAeze1veiU3RsT97O3tS9/g6nCEmghBBCXHVc85+61O7i50hEaeqE1mFo06FA4PVCSQIlhBDiqiMTyKsP15IG65LWcdp02r/BXEISKCGEEFeV1PxUkkxJKCh0rNXR3+GIMrSIbsENDW5wbjK8P3A2GZYESgghxFXltxRn71PL6JZEBEX4ORpRHuPbOLd3WXFsRcBsMiwJlBBCiKvKb8ky/6m66VK7C+3j2mNxWFh6aKm/wwEkgRJCCHGVcT2BJwlU9VFkk+FDn5JvzfdzRJJACSGEuIpkm7M5mnkUkAU0q5v+DfvTOKIxJouJZceX+TscSaCEEEJcPXal7EJFJSEigThjnL/DERWg1WjdT+QtOrgIm2rzazySQAkhhLhqyPpP1duIZiOIN8aTUpDCbstuv8YSUAnUokWLeOihh+jatSsGgwFFUZg/f36FykhMTOTvf/87Xbp0ITY2luDgYK655hqeffZZsrKySrwmISEBRVFK/Ddp0qSq35gQQoiAIAlU9RakDWJM6zEAJJoTcagOv8Wi81vNJXjhhRdISkoiLi6OunXrkpSUVOEybr/9dtLS0ujduzdjxoxBURQ2bNjAm2++yVdffcWWLVuoVatWsesiIyOZPHlyseNdu8oia0IIURPkW/M5mH4QkASqOhvVahSH0g/RKK0RGsV//UABlUDNmTOHFi1a0LhxY15//XWmTJlS4TKeeOIJxowZQ926dd3HVFXlkUce4f3332f69Om89957xa6Liopi2rRpVQlfCCFEAPs99Xdsqo26oXWpF1bP3+GISgrVh/JKz1dYtWqVX+MIqCG8v/zlLzRu3LhKZTz77LNFkidwPv744osvArBx48YqlS+EEKJ6cg3fydN3whMCqgfKm/R6PQA6Xcm3bDabWbBgAefOnSM6OpqePXvSoUMHX4YohBDCi2T+k/CkqyaBmjt3LgCDBg0q8f3k5GTGjRtX5NiQIUNYuHAhcXGlP+pqNpsxm83u1yaTCQCr1YrVaq1C1DWbq22kjSpH2q9qpP2qprq1n8VuYW/aXgA6xHTwe9zVrf0CjbfaryLlXRUJ1O7du5k+fTq1atXimWeeKfb+hAkT6Nu3L23atMFgMHDgwAGmT5/O6tWrGTFiBJs3b0ZRlCuWP2PGDKZPn17s+Jo1awgJCfHovdREa9eu9XcI1Zq0X9VI+1VNdWm/07bTmO1mQpQQDiQe4KBy0N8hAdWn/QKVp9svP7/8K5zX+ATq5MmTDBs2DLvdzqefflpib9JLL71U5HX37t357rvv6Nu3L4mJiaxatYqbbrrpinVMmTKFJ5980v3aZDLRsGFDBg0aRESEbFR5JVarlbVr1zJw4ED3EKsoP2m/qpH2q5rq1n4LDy6EXXBdveu4qe+Vf5/7SnVrv0DjrfZzjSCVR41OoJKSkujfvz+pqal89dVX9O/fv9zXajQaxo8fT2JiIps3by41gTIYDBgMhmLH9Xq9/GCUg7RT1Uj7VY20X9VUl/bbk74HgE61OwVUvNWl/QKVp9uvImUF1FN4nnTq1Cn69evH+fPn+fzzzxk2bFiFy3D1VlWkS08IIURgUVWV3Sm7AehUq5N/gxE1Ro3sgbo0efrss8+4+eabK1XOtm3bAOdK5UIIIaqnszlnSS9MR6fR0Tq2tb/DETVEte2BSktL49ChQ6SlpRU57kqezp07x6effsott9xSajkHDhwocYuXxMRE3n77bQwGA7feeqsnQxdCCOFDu1N3A9A6tjXBumD/BiNqjIDqgZozZw6JiYkA7N27131sw4YNAIwcOZKRI0cCMGvWLKZPn87UqVOLrCDer18/kpKSuP7669mzZw979uwpVs+l53/++ee8+eabDBgwgISEBAwGA/v27WPNmjVoNBo++OADGjVq5JX7FUII4X27UnYB0Clehu+E5wRUApWYmMiCBQuKHNu8eTObN28GnENprgTqSlz75/3yyy/88ssvJZ5zaQLVv39/Dh48yM6dO9m4cSOFhYXUrl2bO++8kyeeeIJu3bpV/oaEEEL4nasHqmOtjn6NQ9QsAZVAzZ8/n/nz55fr3GnTppW4d52qqhWqs2/fvvTt27dC1wghhKgeTBYTxzKPAZJACc+qtnOghBBCiLLsSd2DikqDsAbEGUvfVUKIipAESgghRI0lyxcIb5EESgghRI3lSqBk+E54miRQQgghaiSbw8aeNOeT2JJACU+TBEoIIUSNdCTzCAW2AsL0YTSLbObvcEQNIwmUEEKIGsk1fNchvgNajda/wYgaRxIoIYQQNZLMfxLeJAmUEEKIGkkW0BTeJAmUEEKIGic5L5kLeRfQKBrax7X3dziiBpIESgghRI3jGr5rFd2KEH2If4MRNZIkUEIIIWocGb4T3iYJlBBCiBpnV8ouADrGd/RvIKLGkgRKCCFEjZJvzedwxmFAtnAR3iMJlBBCiBplX9o+7Kqd2iG1qRtW19/hiBpKEighhBA1isx/Er4gCZQQQogaxTX/SYbvhDdJAiWEEKLGcKgOfk/9HZAJ5MK7JIESQghRY5zIOkGOJQejzkjLmJb+DkfUYJJACSGEqDFc85/axrVFr9H7NxhRo+k8UciPP/7I+vXr2bJlC2fPniUtLY2QkBDi4+Np164dffv2ZdiwYdSpU8cT1QkhhBAlkvWfhK9UOoHKzc3l3Xff5aOPPuL06dOoqgpAcHAwMTExFBQUsG/fPvbs2cPixYvR6XSMGDGCJ554gl69ennsBoQQQggX1xYuMoFceFulhvA++OADmjdvzgsvvEBUVBT//Oc/Wb9+PSaTifz8fM6ePUt6ejpWq5VDhw6xYMEC7rzzTtasWcMNN9zArbfeysmTJz19L0IIIa5i6QXpnM45DUD7eNlAWHhXpRKoxx57jCFDhrB371527drFlClT6NevH2FhYUXOUxSFli1bct9997Fw4UIuXrzIRx99xN69e1m4cKFHbkAIIYSAP+c/NY9qTqQh0r/BiBqvUkN4hw4dolmzZhW+zmg0MmHCBMaOHcvZs2crU7UQQghRot9TnMsXdIjv4OdIxNWgUj1QlUmeLqXVamncuHGVyhBCCCEuJQtoCl+SZQyEEEJUexa7hf3p+wHZwkX4hkcTqMzMTD755BNPFimEEEKU6UD6AawOKzHBMTQKb+TvcMRVwKMJ1OnTpxk/frwnixRCCCHK5Fq+oGN8RxRF8W8w4qpQoUnkp0+fLvX98+fPVykYIYQQojLcC2jK8J3wkQolUAkJCaVm9qqqSuYvhBDCp1RVdS9hIBPIA4tqtVKwdx8FO3/DfOIklqQkHKZsHPkFoNFQ/+1/Y2zXzn2+o6AAJTi4WuQSFUqgoqOjee211+jXr1+J7x88eJDbbrvNE3EJIYQQ5XIm5wwZhRnoNXqujb3W3+Fc9VSbjbwtW8j+5lty1q9Hzc+/8smaojOJLr75Jjnr1hF+4wAibhpKSNeuKJrAfN6tQglUly5dSE1NpVWrViW+X1hY6N7SRQghhPAF1/Bdm9g2GLQGP0cjzMeOcebBh9yvtVFRhFx3HYZrWhHUOAFdbAwaoxHVoWJo0qTItQW7dmNPTSPrs8/I+uwz9A0bEnPfvUTeehvasFBf30qpKpRAPfzww+Tl5V3x/UaNGjFv3rwqByWEEEKUl2v4TuY/BYbga64hYtgwtFFRRI4YTnDbtuXuRUr47FPyd+zAtGoVOWvWYj1zhouvzSB11nvETpxIzH33ojEavXwH5VOhfrFbbrmFe++994rvR0dHM3bs2EoHs2jRIh566CG6du2KwWBAURTmz59f4XIcDgezZs2iffv2GI1G4uPjueOOOzh69OgVr9m+fTtDhw4lOjqa0NBQunXrxpIlSyp9L0IIIXzD/QSeJFA+Zz13jtMTH8CSlFTkeP1/vUWdF57H2L59hYbgNAYDYb16Ue/VV2mxaSN1pk0jqGlTHCYTqW+/zfFBg8n68ktUh8PTt1JhATWw+MILL/C///2PpKQk6tatW+lyJk2axGOPPYbdbuexxx5j6NChfPPNN1x33XUcOHCg2PkbNmygd+/e/Pzzz9x+++08/PDDpKWlMXr0aF577bWq3JIQQggvMllMHMs6BjiXMBC+oaoqWcuWc2LEzeQlJpIyc6bH69AYjUTfdSdNv/2Gem+8jr5+fWypqVyYNh1rGasC+EJAJVBz5szh1KlTpKamMmnSpEqV8dNPP/HRRx/Rp08fdu7cyZtvvsmCBQtYuXIlJpOJhx9+uMj5NpuNiRMnoigKmzZt4qOPPuJf//oXv//+O23atGHq1Kml9lwJIYTwH9f+d43CGxFrjPVzNFcHh8XChRde4MJzz+HIy8PYqRO1nnjCa/UpWi2RN99M09WrqPXMM8Q9PImghASv1VdeVU6gtFptmetDlddf/vKXKu+R99FHHwHwz3/+E4Phz8mEAwYMYPDgwWzatIkjR464j69fv57jx49zzz330KnTn4+/hoeH8+KLL2Kz2WRelxBCBCiZ/+RbtrQ0To8dR/ZXy0CjIX7yZBovWkhQI++v/q4JCiJ2wnjiH3nE63WVR5UTqEB76m7Dhg2EhobSq1evYu8NHjwYgI0bNxY5H2DQoEHFzncdu/R8IYQQgUPmP/mO5cwZTt11NwW7dqEJD6fhhx8SN+khFK3W36H5RYWewgt0eXl5XLhwgbZt26It4QvaokULgCJDcq7/d713qejoaOLi4socwjObzZjNZvdrk8kEgNVqxWq1VvxGrhKutpE2qhxpv6qR9quaQGg/q8PKntQ9ALSLbletvpaB0H4VYTlxgnMPPIg9JQVdgwbUm/0eQU2a+C1+b7VfRcqrUQlUdnY2AJGRkSW+HxERUeS88l5z9uzZUuudMWMG06dPL3Z8zZo1hISElB34VW7t2rX+DqFak/arGmm/qvFn+52znaPQXkiwEszBzQc5rBz2WyyVVR2+//RpaTSc/T66vDzMtWtzfOwYDhw8CAcP+js0j7dffmmLfl6mRiVQ/jJlyhSefPJJ92uTyUTDhg0ZNGiQO2kTxVmtVtauXcvAgQPR6/X+DqfakfarGmm/qgmE9lt6eCn8Bl3qdGFY/2F+iaGyAqH9yku1WEjeuQvbhQs0+fAD2kRF+Tskr7WfawSpPGpUAuXqRbq0h+lSroa5tLepPNdcqXfKxWAwFJmw7qLX6wP+ByMQSDtVjbRf1Uj7VY0/229v+l4AOtfpXG2/htXi+0+vp+E7M3EUFKAND/d3NEV4uv0qUlZALWNQVaGhodStW5eTJ09it9uLvV/SfKeS5kW5ZGZmkpaWVuL8KCGEEP7l2sJF1n/yvMsfEFN0uoBLnvytRiVQAH379iUvL4/NmzcXe++HH35wn3Pp+eCcr3Q517FLzxdCCOF/yXnJXMy/iFbR0jaurb/DqVFUh4NzTz5J5tKl/g4loFU5gXr++eeJ8sN4aFpaGocOHSItLa3I8QcffBBwrmpusVjcx3/88Ud++OEHbrjhBlq2bOk+PmDAAJo2bcqSJUvYvXu3+3hOTg6vvPIKOp2OcePGefVehBBCVIyr96lVTCtC9PKwjielvvsuOau/5+JrM7CcOePvcAJWlROoV155hWuuuYZp06ZVOZg5c+Ywbtw4xo0bxxdffFHs2IoVK9znzpo1i2uvvZZZs2YVKaN///5MnDiRn3/+mU6dOvHMM88wduxYbrrpJiIiInj//feLnK/T6ZgzZw4Oh4M+ffrw4IMP8tRTT9GhQwf279/PtGnTiiRcQggh/M+VQHWq1amMM0VFmFavJv2DDwGo88rLBDVs6OeIApdHJpHn5OR4ZC2GxMREFixYUOTY5s2b3cNxCQkJjBw5ssxyPvzwQ9q3b8+HH37Iu+++S1hYGMOHD+fVV18tMRnq378/iYmJTJ06lc8//xyLxUKbNm145ZVXGD16dJXvSwghhGfJApqeZzl1igvPvwBA7MT7iSrH5+3VzCMJVJcuXTh//nyVy5k/fz7z588v17nTpk27Yq+XRqPhscce47HHHit33d26dWP16tXlPl8IIfwp+7uVYLcR1LgxwW3bouhq1EPVpcq35nMk07kll0wg9wyH2czZyU/gyM8npGtX4idP9ndIAc8jk8iffvppvvzyS06dOuWJ4oQQQpQh/cMPOf/sPzh1190c7XMDqe++iz03199h+cTetL3YVTt1Q+tSJ7SOv8OpES7OmIH50CG0MTHU+/e/r6qEvLI8kkCdPn2aPn360LdvX9k3TgghPMx84gSqzVbkWPjgwYT0uB5NZCT2zEzSZr/PiZuGkbdli5+i9B1ZvsCzctatI+vTz0BRqPfmm+hr1/J3SNWCR1LMRx55BEVRUFWVG2+8kU6dOjFs2DCuu+46OnXqRL169TxRjRBCXHXyfvmFM5MeJvruu6n97DPu4/GPOnekV202ctatI+Xt/2A9fZrTEx+g9j+eJWbMGH+F7HW7U3cDMv/JE2zp6Vx4aSoAsfdPIKx3Lz9HVH14JIH67LPP+P3339m9eze7du1i586d7Ny5E0VRAIiPj6dTp0507tyZV1991RNVCiFEjVewZw9n/vYIamEh5iNHUK1WlMtWSlZ0OiKGDCGsXz+Sp79M9vLlXHxtBo78AuImPeSnyL3HoTrYk+LcQFgSqKorPHAAR0EBhhYtiHv8cX+HU614JIEaNWoUo0aNcr9OS0tj165d7Nq1i927d7N7927Wrl3LmjVrJIESQohysKWnc/axx1Hz8wnt2ZMGs98rljxdShMcTN3XXiWocSNSZ75D6syZaCLCibnnHh9G7X3Hso6RY83BqDPSMlqWmKmqsD59aLpiOarFgiYoyN/hVCtemSUWFxfHwIEDGThwoPtYQUEBe/bs8UZ1QghRo6iqyvlnnsV28SJBTZvS4L/voilhv83LKYpC3KRJoKpkr/ia0B49fBCtb7mWL2gf3x6dRiY6e0JQ48b+DqFa8sgk8nfeeafEvecuZTQa6d69uyeqE0KIGi17xdfkbd6MYjDQ4J2ZaEJDK3R97KRJNFn2FYYmTbwUof+413+SCeRVol6yU4eoHI8kUE888QQdOnRg7dq1nihOCCGuWrbMTFLeeAOAuEcfwVCJzcwVRalw0lVduCaQywrklZez/idODB9B3tat/g6lWvNIAvX++++TkpLCkCFDGDlyJCdOnPBEsUIIcdVJffdd7FlZGFq1ItZD+3AW7N9PxsJFHinLn9IK0jiTcwYFhfbx7f0dTrXkyMsj+Z+vYElKIu+PXT5E5XgkgXrooYc4cuQIjzzyCKtWraJNmzY899xz5OXleaJ4IYS4KlhOnybriy8BqP38c6VOGi93mUlJnLrrbi6+9hoFe/dWuTx/+j3ldwCaRzcnPCjcz9FUT6mz3sN2/gL6+vWJ+9vf/B1OteaRBAogKiqKd999l127dtGzZ09ef/11WrZsycKFCz1VhRBC1Gh2Uw6GZs0I7dOH0G7dPFJmUOPGRAwZAqrKhZemFluQszpxbyAcL8N3lVF46BAZn3wCQJ2XXkQTEuLniKo3jyVQLm3atOHHH3/kiy++ICgoiHHjxtGjRw+2b9/u6aqEEKJGMbZtQ5Ply6j35hseLbf2P55FGxmJ+eBBMj6pvn/U7kr9YwVyWf+pwlRVJfmf/wS7nfDBgwnr29ffIVV7Hk+gXG677TYOHTrEtGnT2LNnDz169GD8+PEkJyd7q0ohhKj2FI0GXXS0R8vUxcZS65mnAUj973+xXrjg0fJ9wWw3cyD9ACAJVGXk/PADBTt+QwkOpvY/nvV3ODWCxxMou93Orl27+PDDD3n44Yf57LPPMJvNOBwOFixYQKtWrXjnnXc8Xa0QQohSRN56K8YuXVALCkj5z3/8HU6F7U/bj81hIzY4lgZhDfwdTrXiKCzk4ptvAhA7cSL6unX9HFHN4JFVyD799FO2bdvGr7/+yu7duyksLERVVcC5qObQoUPp2bMnCQkJvP322zzxxBN88803LFu2jMjISE+EIIQQ1VbGJ59gS88g5t7R6OLjvVKHoijU/sc/ODVqFKZvviXmvjEY27X1Sl3ecOnyBa5twkT5pM+di+38BXR16xJ7/wR/h1NjeCSBuuePrQI0Gg2tW7emZ8+e9OzZkx49etDisjVM7rrrLmbNmsWTTz7JE088wdy5cz0RghBCVEuqzUb6x3OxXbyIoXlzIocP81pdxnZtiRgxHNM335Lyxhs0WvhJtUlGXBPIZfiuYmwZGaT/7yMAaj/9FBqj0c8R1RweSaCmTp1Kz549uf766wkPL/vR0kcffZSdO3fy7bffeqJ6IYSotnITE7FdvIg2KorwwYO8Xl+tJ54g54c15O/YQe6PPxL+l794vc6qUlXVvYSBJFAVo4uJocF/38W0+nvC//pXf4dTo3gsgaqoli1bkpGR4YnqhRCi2nKt+xR5880+2cxVX7cuMePGkfXVVzgKCr1enyeczD5JpjmTYG0wrWNa+zucaiesTx/C+vTxdxg1jt92YrzvvvuoXbu2v6oXQgi/s6akkLthAwBRo273Wb1xDz5A3KSHqs1wzm8pvwHQLr4dem3VFxcVwhMq9RTesGHD+O233ypVYUFBAf/617/45ptvGD9+fKXKEEKImiD766/BbsfYqROG5s19Vq8mNLTaJE8AOy/uBKBzrc5+jqT6KDxyBOvFFH+HUaNVKoE6c+YM3bp1Y8CAAcyfPx+TyVTmNTt27GDy5Mk0btyYl156ibi4uMpULYQQNYbpu5UARN4y0r+BBDh3AlVbEqjyUB0OLkx5juODB5Pz00/+DqfGqtQQ3u7du5k3bx4vv/wyEyZMYOLEiVxzzTV07tyZ2rVrEx0dTUFBARkZGRw9epQdO3aQnZ2NRqPhjjvu4NVXXyUhIcHDtyKEENWH+fhxzIcPg05HxCDvTx4viWq1krVsOabVq2k05yMUnd9mdVxRcl4y5/POo1W0dIzv6O9wqoWcH36gcP9+NCEhGNvLpsveUqmfFkVRmDBhAuPGjWPlypXMnz+fjRs3smhR8d2+NRoN7du3Z+TIkUycOJF69epVOWghhKjuTKtWAxDaqyfaqCi/xKBaLKTOnIk9MxPT6tVEDh/ulzhK89tF53SRa2KuIUQve7eVRbVaSZk5E4CY+yegi431b0A1WJX+3NBoNAwfPpzhf/zQHTx4kLNnz5Keno7RaCQ+Pp42bdrIYplCCHEZ02pnAhU5dKjfYtCEhhIzdiypM2eS/r+PiBg2LODWhZLhu4rJ+vJLrEmn0cbGEjtunL/DqdE82l977bXXcu2113qySCGEqJEafvgBptXfEzZggF/jiL7nbtI/+gjz0aPkbthAeP/+fo3ncjtTZAJ5eTkKCkidPRuAuL89jCY01M8R1Wxe20xYCCHElQU1bEjcgw+gDQvzaxzaiAii774LgPSP5vg1lstlFWZxLOsY4NzCRZQu89PPsKemoa9fn+hRo/wdTo0nCZQQQlzloseMQdHrKdi5k/xKLlHjDa7tWxIiEog1ylye0jjy80mf40yA4/72MIoPFmW92kkCJYQQPuTaaD2Q6GvVIvKWWwDc+6YFAtfwXZfaXfwcSeDLXPop9vR09A0bEjlihL/DuSpIAiWEED6UuXAhJ269lawVK/wdShGx908AjYbcjRspPHzY3+EAMoG8vFSbjYyFCwGImzQJRS+rtfuCJFBCCOFDOet/wnzgII7sbH+HUkRQ48buzYwz5i/wczRQYCvgQPoBQCaQl0XR6UhYspjYBx8k8mbpffIVSaCEEMJH7CYT+Tt2ABAWYE+7AcSOHQuA9cIFVIfDr7HsTd2LTbVRK6QW9cPq+zWW6kBfrx61nnwiIBdDramkpYUQwkdyf/4ZbDaCmjcjqFEjf4dTjLFjR5p88zXBLVv6OxT3BsJdanUJuLWphADpgRJCCJ/J/WkDQMCttXSpQEie4M/5T51qy/IFV2LPzSPvl23+DuOqFXAJ1Pbt2xk6dCjR0dGEhobSrVs3lixZUu7r+/Xrh6Iopf5b+MdkO5eEhIQrnjtp0iRP36IQIsCZ7WZOZZ/iXO457A67R8pUHQ7yNm8GIKxvX4+UWVPZHDZ+T/0dkPlPpclcvJjT48Zx/rnn/R3KVSmghvA2bNjA4MGDCQoK4q677iIyMpJly5YxevRoTp06xXPPPVdmGePGjaNfv37FjlutVmbMmIFGo2FACSv/RkZGMnny5GLHu3btWplbEUJUQ6dNp3lv93v8dOYnCmwFAMQEx3BT05t4oN0DRAdHV7rswoMHsWdmogkNxdihg6dC9hrL6dOYVq4kdtIknw+hHco4RIGtgPCgcFpEt/Bp3dWFIz+fjHnzAAi9vrufo7k6BUwCZbPZmDhxIoqisGnTJjp1cnbbTp06lR49ejB16lRGjRpFixal/zCNu8LeP1999RWqqjJ06NASNzSOiopi2rRpVb0NIUQ19fWxr3l568tYHBYAQnQh2Bw2MgozWHhgIV8f+5oZfWZwQ4MbKlV+3uYtznK7dw/4x8wdhYWcvPU2HLm5BLdvT1ivXj6t37WBcKdandAoATdQEhCyvvgCe1YW+kaNiPDjfopXs4D5zly/fj3Hjx/nnnvucSdPAOHh4bz44ovYbDbm/ZFtV8acP1Zovf/++6scqxCiZll6aCkvbH4Bi8PC9XWvZ8nQJfxyzy9sG72N9wa8R6voVpgsJh798VG+OvJVpepwDd+F9urpydC9QhMcTOTIkQBkLPD9kgY7kp1PKnatLSMAJXFYLKTPdX4exk68X56885OAafUNGzYAMGjQoGLvuY5t3LixUmWfPXuWNWvWUKdOHW666aYSzzGbzSxYsIBz584RHR1Nz5496VANutmFEFWz/sx6ZmybAcD4tuOZ3Hmyu9dDp+i4ocEN9Kjbg9d+fY0vj3zJ9K3TCdIGMbzZ8HLX4cjPp2Cnc1J0aM/AT6AAYsbcR+bixeRt+hnz8eMYmjXzSb12h93dA9WtTjef1FndZH/9NbaLF9HVquVOdIXvBUwCdfToUYASh+iio6OJi4tzn1NR8+bNw+FwMG7cOHRXyNSTk5OLDf8NGTKEhQsXEhcXV2r5ZrMZs9nsfm0ymQDnvCur1VqpmK8GrraRNqocab+qsVqtZDmy+PCXD1FRGdViFI+2exS7zY6d4hPHp3SZgh49S48sZeqWqdQPqU+7uHblqkvV6WiwdCmFu3ai1K9fLb5mSt26hPbrR95PP5G2YAG1XnyxyPve+v47kHGAHGsOYfowmoY3rRZtVRmVbT/VZnNvtxM1dix2RcFeQ9uoNN76/qtIeYoaIBszDRo0iLVr13L06FGaN29e7P1mzZpx9uzZIolKeaiqSrNmzTh58uQVy3755Zfp27cvbdq0wWAwcODAAaZPn87q1avp0aMHmzdvLnUS5bRp05g+fXqx40uWLCEkJKRC8QohfENVVebmzeWk7SQNtA14IOwBtIq21GscqoOl+Us5aD1IuBLOY+GPEaKpuT/jxuPHafi/j3Do9Zx4bgoOH/w+SyxM5PvC72mla8V9Yfd5vb7qJnz379RduhR7SAgn/vEsqsHg75BqlPz8fO655x6ys7OJiIgo9dyA6YHylvXr13Py5En69u1bYvIE8NJLLxV53b17d7777jv69u1LYmIiq1atuuLQH8CUKVN48skn3a9NJhMNGzZk0KBBZX4BrmZWq5W1a9cycOBA9AE+qTYQSftVzaoTqzj5y0kMWgOzhs6iUXj5Frbsb+3PfT/cxynTKXZG7+S1Xq95OVL/UVWVMxs2YjlyhO4mE9G33+5+z1vffz9s+AHOw1/b/ZWh19bcydGVaT9VVTkz52MsQPz48bT6YwPoq5G3vv9cI0jlETAJVGRkJADZV9gfymQyuc+pCNfk8YkTJ1boOo1Gw/jx40lMTGTz5s2lJlAGgwFDCX8F6PV6+WArB2mnqpH2q7gCWwGz9swCYHzr8TSLKf/8nih9FK/1fo17V9/L90nfM6TJEAY0Lr40Sk0RO3YMF55/gexPPyP+/uITlj35/Wd32NmVuguA6+tdf1V8X1ek/XI3bsRy9CiakBDixtyH9ipon7J4+vdfRcoKmKfwXHOfSprnlJmZSVpaWplLGJR03fLly4mKiuK2226rcEyuuU/5+fkVvlYIEbi+OPwFyfnJRCqRjLl2TIWvbxffjvFtxgPw2rbXyLde+XeEac0aTt19D5lLl1Y6Xn+KGDYMbXQ0tgsXyFn3o1frOpR5iFxrLmH6MK6JucardVVHIT16UOeVl4n/v8fRVqJDQXhWwCRQff9YmXfNmjXF3nMd61vB1XsXLVqE2Wxm9OjRGI3GCse0bZtzifyEhIQKXyuECExmu5n5++cD0D+4P8G64EqV83DHh6kfVp+UghQ+3vfxFc/L27qVgl27sJw6Val6/E1jMBB15x0AXk8CXcsXdKndBa2m9PloVyNNUBDRo0YR88emz8K/AiaBGjBgAE2bNmXJkiXs3r3bfTwnJ4dXXnkFnU5X5Cm5tLQ0Dh06RFpa2hXL/Phj5y+10tZ+OnDgAFlZWcWOJyYm8vbbb2MwGLj11lsrfD9CiMC0/OhyUgtSqRNSh45BHStdjkFr4KmuTwGwYP8CzueeL/G8/F+3AxBy3XWVrsvfou++h7hHH6X+v//l1Xp+Tf4VgOvqVN+2ElePgEmgdDodc+bMweFw0KdPHx588EGeeuopOnTowP79+5k2bRotL9nkctasWVx77bXMmjWrxPJ+++03fv/9dzp37lxkYc7Lff7559SrV4/hw4fz2GOP8dRTTzFkyBBuuOEGrFYrs2bNolEA7pouhKg4h+pgwX7nwpBjrh2DTqnaNNABjQZwXZ3rMNvNvLPznWLv29LTsRw/DoCxS5cq1eVP+tq1iH/0EXRlLOlSFTaHzb2BcNc6soDmpawXU3AUFvo7DHGZgEmgAPr3709iYiK9e/fm888/Z/bs2cTGxrJo0SKef75imyW6ep/Kmjzev39/hg8fzqFDh1iwYAHvvvsu+/fv584772TLli0VnnwuhAhcW85v4WzuWcL14dzc7OYql6coCk93fRqA1SdXczzreJH387c7h6QMLVuii678PnpXg8MZh8m15hKuD+eaaJn/dKnkV17m2IC/kPPHgtMiMATMU3gu3bp1Y/Xq1WWeN23atFL3rps9ezazZ88us5y+fftWeG6VEKJ6+uzwZwDc3PxmjLqKz4ssybWx1/KXRn9h3el1fPD7B7zV9y33e/nbq//w3eVUVcV67jxK7VoeLXd7srOtZP5TUeajR8ld9yMoCkENG/o7HHGJgOqBEkIIbzmfe55NZzcBcEerOzxa9qQOkwD44dQPHM3880ni/F+dc3pCutWMLUksZ85wYvhwTt11F6rF4tGyt190JlAyfFdU2kfOVcfDBw702XY6onwkgRJCXBW+PvY1DtVB9zrdaRLZxKNlt4ppxcDGA1FR+eD3DwCwZWZi/mNZlpDrakZSoK9TB0dOLva0NHJ++MFj5V46/0kmkP/JcuYMppWrAIh96EE/RyMuJwmUEKLGU1WVlSdXAs7hO29w9UKtO72OMzlnyN/hnP8U1LwZupgYr9Tpa4peT/TddwOQvWgxeGgnMPf8p6BwWkW38kiZNUH6nI/Bbie0Tx+Mbdr4OxxxGUmghBA13r60fSSZkjDqjAxo5J1Vw1tGt6RX/V44VAef7P+kRs5/Aoi68w4UgwHzgQMEJyV5pMxtyc4192T+05+sF1PIXrYMgDjpfQpIkkAJIWo8V+9Tv4b9CNF7b0PccW3GAbDi2ArUm24k/u9PElnKNlDVkS46mojhwwCITtzskTK3nt8KwPV1r/dIeTVBxoIFqFYrxs6dCelaM4aAaxpJoIQQNZrNYWP1SeeTvcOaDvNqXd3rdOfamGsptBfyNbuJe+CBGvnhF3Ofc/ubsP37sV64UKWyCm2F7vlPPer2qHJsNYE9K4usTz8FIPbBB/wcjbgSSaCEEDXabxd/I6MwgyhDFD3qefcDWlEUxrZxbrOx9NBSzHazV+vzl+BWLTF274bicJC99NMqlbU7dTcWh4Vaxloen9xfXWUsXowjPx9Dq1aEyTI7AUsSKCFEjbb+9HoA+jfsj17j/d3rByUMok5oHTIKM/j+5Pder89fokaPBsD01Vc4qrDhunv4rt71KIrikdiqu7AbbiBswADiHnpQ2iSASQIlhKixVFVl/RlnAuWtyeOX02v0DEt3Lni4dO8nPqnTH0JuuAFLTAwOk4nsb76tdDm/XPgFkPlPlzK2a0fD92YRMXSov0MRpZAESghRYx3IOEByXjJGnZHudbv7pE7V4aDnkv3obCr7TUfYl7bPJ/X6mqLVktWzJ4a2bdHXrVOpMrIKsziYfhCQBEpUP5JACSFqLNfwXe/6vQnWBfukTsupU4RdNNHjqPPX69JDS31Srz9k9epJgyWLKz1PZ1vyNlRUmkc1Jz4k3sPRVT8Oc82cM1dTSQIlhKixLp3/5CsFu3YBcHNOcwC+P/k9mYWZPqvfpzSaKs3RkeG7P6l2OydvvY1zT/4da0qKv8MR5SAJlBCiRjpjOsOxrGNoFS03NLjBZ/Xm/5FAdWzai2tjrsXisLD82HKf1V+d/HLemUB5++nI6iBn7Vosx4+Tu3kzmpBQf4cjykESKCFEjZR4PhGAzrU7E2mI9Fm9Bbt2AxDSuTN3X+Pc9uTzw59jd9h9FoOvWZOTufjWW6S88065rzmTc4azuWfRKTq61O7ixegCn6qqpP3vfwDEjB6NNkwSqOpAEighRI205dwWAHrW6+mzOu1ZWViOHwfA2LEjf23yVyKCIjiXe46tF7b6LA5fs5w4QcbHc8lc8An2nJxyXeMavmsf355Q/dWdMOQlbsZ84CCK0Uj0fff6OxxRTpJACSFqHKvdyq/JvwLQq14vn9Wbv3s3AEFNmqCLjiZYF8zwZsMB+OrIVz6Lw9dCevTA0KI5jvx8sr4q331euv7T1S79j96n6DtGoYuO9nM0orwkgRJC1Di7U3eTb8snJjiGVjGtfFava/jO2KmT+9itLW4FYMOZDaQVpPksFl9SFIXoe+8DIHPRYlR76cOVNofN3QN1tW/fUrB7t3Pjab2emPHj/R2OqABJoIQQNc7mc85NbnvW64lG8d2vOdcTeMZOHd3HWka3pH1ce2yqjW+Of+OzWHwtcsRwtJGRWM+eJXfDhlLP3ZO6hxxLDpGGSNrFtfNNgAEqc84cACJvHoG+TuXW0xL+IQmUEKLG2XLe9/OfAEJ79iTk+usJ6VJ0UvRtLW8DYNnRZaiq6tOYfEVjNBJ1xygAMhaUvgJ74jnnBP+e9Xqi1Wi9HlugCrqQTP7GTaAoxN5/v7/DERUkCZQQokZJK0jjYIZzdWtfJ1Bxkx6i8fx5GJo2LXJ8SMIQQnQhJJmS2HFxh09j8qXoe+4BrZb8X3+l8PDhK57nSqD61O/jq9ACUux65zpl4UMGY2giGylXN5JACSFqFNfk5GtjriXWGOvnaJxC9CH8tclfAfjqaM2dTK6vW5fwgQMByPik5F6o1PxUvyW4gURVVcy1a6OJiCBu0sP+DkdUgiRQQogaZduFbUDgLc54e8vbAVh7ai3Z5mw/R+M9MWOck8lN336HLSOj2Puu3qe2sW0DJsH1B0VRyPjLABJ+XEdwq5b+DkdUgiRQQlQz+dZ8vjn+Df/b+z8OWw/X2Dk1leUaIutWp5tP683++mvyd+1CtVhKfL9NbBtaRrfE4rDw3YnvfBqbLxk7dSK4TRtUh4P8334r9r4rgerdoLevQwtImmDf7NEoPE/n7wCEEOV3POs4j61/jDM5Z9zHjvx0hJn9ZxIWFObHyALDudxznMs9h1bR0qlWp7Iv8BBHQQHnn3se7Haab/ipxKepFEXhtha3MePXGXx19CvuueaeKu0jF6gURaHuKy+jjY1FX7t2kfdsDpt7QdHe9a/eBEr+6KkZpAdKiGoiJT+FB9c8yJmcM9QOqc1fE/6KHj3bkrfx+E+P1+itQsprR7Kz96lNXBtC9CE+q7fw4EGw29HFx6O7LGm41E1Nb8KgNXA08yh70/b6LD5fC27duljyBH8uXxBliKJtbFs/ROZ/llOnOHnzSHJWrfJ3KKKKJIESohpQVZWXt75MSkEKzSKb8eXwL3m156s8EPYAIboQtidvZ+6+uf4O0++2J28H4Lra1/m03oI9ewAIbteu1F6lSEMkAxs7J1kvO7rMJ7EFkp/P/Qw456ddrcsXpH34P8xHjpCzUhKo6k4SKCGqgZ/O/MTGsxvRa/T8u9+/iQqOAqCerh7/6PoPAP63539czLvoxyj9zzX/6bo6vk2gCvfuA8DYvuxFIW9r4VwTatXJVeRZ87wal7+pqkrer79iPnYMgJ/POhOoq3X5Asvp02R/41xMNWbSQ36ORlSVJFBCBDhVVXn/9/cBGNtmLM2imhV5/6YmN9ExviOF9kI+2POBP0IMCP6a/wRQsNc5HBfcruwEqkvtLiREJFBgK2D1ydXeDs2vUt99l9NjxpL6zrucyz3H4czDaBTNVTv/Ke1//wO7ndA+fcr1vSICmyRQQgS4DWc2cCjjEKH6UMa2HlvsfUVRmNxlMgDfHPuG9IJ03wYYIPw1/8mWmYn19GkAjG3LntejKIp7f7yaPowXedNNAOSsXcu6nV8A0KlWJ6KDr74Ncy1nzpC94msA4v4m6z7VBJJACRHgPj38KQB3trrTPXR3uc61OtMurh0Wh4XPDn/mw+gCh7/mPxXucw7fBTVujDYyslzXjGg2Ap2iY2/aXg5nXHnF7urO0Ly5e2HNdXtXANC/YX8/RuQ/abNmgc1GaO/ehHTybQ+p8A5JoIQIYGdzzrr3dRvVctQVz1MUhftaOxcwXHZ02VX5RJ6/5j+5h+/aty/3NbHGWPo3ciYSNb0XKnbSQ+QGw56QNABubHijnyPyPfPRo2R/8y0A8ZMn+zcY4TGSQAkRwFwfrj3r9aRBeINSzx3QaADhQeFczL/Ir8m/+iK8gJGcl+ye/9SxVkef1l24x5lAGSs4p8U1mfzbE99SaCv0eFyBwtimDQduuhaHRqGxOZyGEQ39HZLPpb77X1BVwgcOxNi2jb/DER4iCZQQAUpVVVaeWAngnjNTmiBtEH9NcO639u3xb70aW6DZnbobgJbRLQnVh/q0btVhB72+XE/gXapHvR7UC61HjiWHtUlrvRRdYNh1nXPOU+ddOVjPn/dzNL5VsHcvOWvXgqIQ/3+P+zsc4UEBl0Bt376doUOHEh0dTWhoKN26dWPJkiXlvn7Dhg0oinLFf7/88otX6hXC0w6kH+B83nmMOiN9G/Qt1zUjmo8AYN3pdTW6V+Nye1Kd6zC1jy//MJqnNPrf/2i1YzvB5ZhAfimNouGWFrcANXsYz2K3sDXPOU/sukM20j++ytYrczgIbt2ayBHDMTRv7u9ohAcF1FYuGzZsYPDgwQQFBXHXXXcRGRnJsmXLGD16NKdOneK5554rd1l9+/alX79+xY43aFB8GMST9QrhKWuS1gBwQ4MbCNaVb7+s9nHtqRtalwt5F/jlwi/0a9jPixEGjt9TfwegQ3wHv9SvMRgqdd3I5iN5//f32XFxB6eyT5EQmeDZwALA9uTt5NvyidNG0vRCOllffEHsgw+UuFJ5TWTs0IGEr75Ezc/3dyjCwwImgbLZbEycOBFFUdi0aROd/nhKYerUqfTo0YOpU6cyatQoWrRoUa7y+vXrx7Rp03xerxCeoKqqe1jHtXJ1eSiKQv+G/VlyaAnrT6+/KhIoi93CwfSDgP8SqMqqE1qH3vV7s+nsJpYdXcaTXZ/0d0ge9+PpHwHo13QgoV2PUrDjNzKXLqXWVTSZWlEUlFDfDi0L7wuYIbz169dz/Phx7rnnHncSAxAeHs6LL76IzWZj3rx5NaZeIUpzJPMIZ3LOYNAaKrxq842NnE85bTiz4ap4Gu9gxkGsDivRhmgahvt2grL1/HlUh6NKZbgmk399/GusdqsnwgoYNoeNdUnrABiYMJDaTz1F3X++Qvyjj/o5Mu9TrTXraymKC5gEasOGDQAMGjSo2HuuYxs3bix3eUePHuXdd9/l9ddfZ+nSpaSlpfmkXiE8YfP5zQB0r9u9wotCdq7dmYigCDLNme7J1TXZ7ynO4bv28e1L3YfO01S7nRPDhnPkum5Yzp6tdDl9GvQhzhhHRmEGG85u8FyAAWDHxR1kmjOJMkTRrU43jB07EnX77Si6gBn88AqHxcKJkbdw8a23sOfW7O16rmYB81189OhRgBKHyqKjo4mLi3OfUx5LliwpMgncaDQyffp0nn76aY/XazabMZvN7tcmkwkAq9WKVf4KuSJX20gbFefaM+z62tdfsX1Ka79e9Xqx+tRqNp7eSPuYqk+sVq1W0Ol8mqCU1+6U3QC0jW1boe+lqn7/mY8dw5Gfj2I0Qlxclb6PhzcZzrwD8/ji8Bf0q9ev0uX4Unnab/UJ51Y1/Rv0R7WrNa6H7UoyP/kEy/HjZJtMRD34II4S2kh+/1WNt9qvIuUFTAKVnZ0NQOQVVvKNiIjgbDn+youPj+ett95i2LBhNGrUiKysLH766SeeffZZnnnmGSIiInjooT83cfREvTNmzGD69OnFjq9Zs4aQEN9tKVFdrV1bsx/hriizamZX9i4ALEcsrDpe+q7tJbVfiMX5fffD4R9odr5ZsffLRVWJ2LGD6E0/Y0hJwR4SgqljR9IHDcRhNFauTC/4Ndu55lXBsQJWJVV8h/vKfv9FbN9BHSCvbh1W//BDpcpwibJHAfDLhV9Y/N1iojXVZ6uTK7WfXbXzvel7ACKTI1m1qujXRjGbCT18mNwKLEBaHWjy8mjy3ntogXN9b+DgH6McVyK//6rG0+2XX4HJ/gGTQHlKmzZtaNPmz4XKQkJCGD16NB06dKBLly5MnTqVBx54AI3Gc6OXU6ZM4ckn/5z8aTKZaNiwIYMGDSIiIsJj9dQ0VquVtWvXMnDgQPR6vb/DCRgbz27EvslOg7AG3Dfsviv2+pTWftcVXMdXy7/igv0CPQf0JMoQVaEYVLudlBdeJOe779zHtPn5RG/ZQnzSKerPmYO+hCdafS0lP4XsFdloFA0Thk6o0HBnVb//UnbuxATUu+EG2g8dWuHrL7f5x838evFXTI1MjG4/usrleVtZ7bcteRv56/OJMkTxt+F/Q6f58+PGkZ9P0rDh2FNTaTB0aIVWcQ90qa+/TnZBIUEtW9LrhRdQtNoSz5Pff1XjrfZzjSCVR8AkUK4eIFeP0OVMJtMVe4nKo23btnTv3p2ff/6ZY8eO0bJlS4/VazAYMJTwGLNer5cfjHKQdipq28VtAPSq34ugoKAyzy+p/erp69E8qjnHso7xW9pvDEkYUqEYsn9Y40yedDri/+9xIkfcjPnIEZJffhnrmTOcf+BBEr74HF20f3tK9mfuB6BFVAsiQyr3+6Gy33+Wfc66Qzt29Mj37+2tbufXi7/yzYlveKTTI2g1JX/wBportd+6M87J439p/BeMhst6LCMjCevdm+zly0l/6180XrokIIeHK8p89CjZn30OQJ1/PEtQcNnLj8jvv6rxdPtVpKyAmUTumoNU0nyjzMxM0tLSqryUQFxcHFC0i84X9QpREa6973rV61WlcnrU6wHAL+dLXjy2NBE3DSXukUeo/6+3iHvgAfS1axHWpzeNFy9C37Ah1rNnufDCi6iqWqUYq8q1gKavly9wmM0UHnZuAlzRLVyuZECjAUQZoriYf9H9EEF1ZXVY3csXDE4YXOI58ZMnoxiNFOzejWlVxYdeA42qqiS/8k+w2Qi78UZCe/b0d0jCywImgerb17nS8po1a4q95zrmOqcybDYbO3fuRFEUGjVq5LN6haiI5LxkTuecRqNoqrwpbo+6zgRq6/mtFU50FEUh/rFHiRhStOdKX6sW9Wf+ByU4GF18HKrFUqUYq8q1gKavVyA3HzoENhvamBh09ep5pMwgbRDDmw0H4IvDX3ikTH/ZdmEbWeYsYoJj6Fq7a4nn6GvXIvaBiQCk/OvfOAqr98r5ppWryP/1VxSDgdqy+PJVIWASqAEDBtC0aVOWLFnC7t273cdzcnJ45ZVX0Ol0jBs3zn08LS2NQ4cOFVueYOvW4h8WNpuNp59+mqSkJAYPHkxMTEyl6xXCm367+BsA18ZcS1hQWJXK6lK7CzqNjvN55zmbW/nH7C9nbNOG5j+tp+60aZVegdsTrHYrB9IPAL7vgSq4ZANhTw49jWo5CnDOgzub47mvma+59mIcnDC4yNyny8WOH4+ubl1sFy6QMX++j6LzPHtuLilvvAFA3KSHCGpQ388RCV8ImARKp9MxZ84cHA4Hffr04cEHH+Spp56iQ4cO7N+/n2nTprnnLQHMmjWLa6+9llmzZhUp5+6776Zp06aMHj2aZ555hgcffJC2bdsyc+ZMGjVqxAcffFCleoXwJlcC1aV2lyqXFaIPoU2s84GKnRd3lnm+LS2N1HffxZ6TU+a5/p77BHAo4xAWh4UoQxSNIxr7tO6Cvc6hw2APDd+5NIlsQs96PVFR+ezwZx4t21dyLbmsP70egBHNRpR6rsZopNbf/w5A2v8+wpqc7PX4vCH9w/9hS01F37gRMRMm+Dsc4SMBk0AB9O/fn8TERHr37s3nn3/O7NmziY2NZdGiRTz//PPlKuPhhx8mISGBDRs28M4777B48WIMBgPPP/88u3fvpnHj4r9oPVGvEJ6w4+IOwDMJ1KXluBKz0mR8spC02e9z7omKbyfij7lQlw7f+XoCcuFe5+a4xvaeTaAA7rnmHsC5wXCBrcDj5Xvb2qS1FNoLaRLZxJ3AlybipqEYO3VCzc/n4quv+iBCz4sZP47I226lzvPP+7VXVvhWwDyF59KtWzdWr15d5nnTpk0rca+7Z599lmeffdZr9QrhLekF6ZzMPgl4NoGau29umQmUo6CArM+cPR5Rd95R7vLzd+4k9b//JWLwEKLvurNKsVaUO4GK8/0j8AlLl1Cwbz/Gjp4fOuxdvzcNwhpwNvcsK0+s5PaWt3u8Dm/69oRz+G5EsxHlSmwVRaHOtGmcvO02ctauI+fHHwkfMMDbYXqULiaGetU0+ROVF1A9UEJczXamOIfZWkS3INJQ+SU7LtWxVkcUFE7nnCY1P/WK52V/+y327Gz09esTfuON5S6/cN8+8rf+QsaCBT7vhXI/gVfL9xsIa6OiCOvdC21Y1eaplVi2Rstd19wFwJJDS/z+pGNFnM89z/bk7Sgo3NTkpnJfF9yqJbHjxwNQuP+At8ITwqMkgRIiQOxI/mP4rpZnep8AIoIiaBXTCoDfUkruhVJVlcxFiwGIvvfeKy78V5LIW29FCQnBcvIk+du3Vz3gckrNT+V83nkUFNrFeX4Yzd9GNh+JUWfkaOZR97BudfDdCefCq93qdKNuWN0KXRv3t4dpvHQJ8Y8/5o3QPK7w0CGyv/2uWiW4wrMkgRIiQLgnkNfxXAIF0LlWZ+DKE8kLDxzAfOQISlAQUbfdWqGytWFhRA4bBkDWHwsI+oJr+K55dHNC9aE+q9dXIg2R3NTU2YOz9NBSP0dTPg7VwfKjywHcyzFUhMZoJKRTJ0+H5RUOs5nzz/6D808/TcbHH/s7HOEnkkAJEQBMFhNHMo8AXHHdnMoqayJ59tdfAxD+lwFoK7H1kGvOlGnNGmwZGZWMsmL8tYAmwMlRd3D2scewXrzo1XruvuZuANafXs+53HNercsTfjn/C2dzzxKuD2dQwqAql6fa7TgKAnMSfcqbb2E+fBhtTAyRN9/s73CEn0gCJUQA2Ju6FxWVBmENiDPGebTszrWdPVBHM49ishTd50m1WjF9txKg0h8ExjZtCG7dGqxWTN9/X7Vgy8nVA+XrBMp68SKFe/eS8+N6tOHhXq2rZXRLrq97PXbVzif7P/FqXZ7wxRHn4p/Dmw3HqKvaZtOWs+dIGjuW5GnFN2n3t5z168lc7Bzyrvf6DHTx8X6OSPiLJFBCBABvToiOM8ZRP6w+Kir70vYVeS83MRF7Rgba2FhCe1V+65iIEc4hG1cy5k1Wu5X96c596Hy9AnnhXucCmoYWLdCElH/j4sqa0Na5ptCyo8vILMz0en2VlZKfwk9nfgL+XAy0KmwpKRTs3EX211+T7YPvqfKyXrjAhSnOVcZjxo0j7IYb/ByR8CdJoIQIAL+nefeRfFdPjStRczGtdO5BFnHTUBRd5Vc1ifjrUFAUCnbuxHLWu8NNhzMPY7abiQiKICEiwat1Xc61Anlwu7Y+qe/6utdzbcy1FNoLA3ou1PKjy7GrdjrV6kTz6OZVLi+kcydiH3oQgAsvvkjhkSNVLrOqHAUFnH3kUezZ2QS3bk38k0/4OyThZ5JACeFnDtXB3lTnB7O3hqRcPTWXJ1Ax48YR+8DEKs/j0NeuRUj37gBe3xj20gU0NYpvf4UV7nNt4eKbni9FUZjQztkLteTQEvKt+WVc4Xs2h40vj34JeKb3ySX+0UcJ7dkDtaCAc489Xq4V8r1FVVUuPP8ChQcOoI2Opv6776IJCvJbPCIwSAIlhJ8lmZIwWUwYtAZaRntn2yB3D1TaniKPXRvbtqHW3/+OsU3ZK0aXJXKY86mx3I0bq1xWafy1gbDqcFDgxRXIr2Rgo4E0DG9Itjmb5ceW+6ze8vrx9I8k5yUTExzjkcnjLopWS71//xtdvbpYkpI4//QzqDabx8qviPT/feT8w0Cno/47M2WvOwFIAiWE37l6hVrHtkav1XuljlbRrQjSBJFtzuZ0zmmv1BE+eDCN5s2l8fx5XinfxV9P4FlOJeHIyUExGDA0r/owVXlpNVrGtRkHwIL9C7DarT6ruyyqqrLw0EIA7rrmLgxaz25joouOpsE776IYDORu2EDyy6/4fN0lW0YG6XPmAFDnhecJ7dbNp/WLwCUJVA3kMJvJ/vY7rCkp/g5FlIMrIfDmliR6rZ7Wsa2L1Odp2vBwQnv0QNF7JwkESCtI41zuOb8soOkavgtu3dqr91iSEc1GEGeM40LehYDqhUqyJ3Eg4wBBmiDubOWdrXyM7dpS719vgUZD1uefk/bebK/UcyW6mBgaf7KA+P97nOi77vJp3SKwSQJVA1385z85//TTnLrtdhyFhf4OR5RhT9ofCZSXh6Rc5f+e+jv5v/1G0vjxZC1f4dU6Pc01fNcsqhnhQd5dRuByrgnkvhy+cwnWBfNAuwcA+PD3Dym0BcbP9RbzFsC5dEFMcIzX6okYOJA6L74AQNqsWWStWOG1ukoSfO21xD38sE/rFIFPEqgaxlFQQPa3zu0UbKmp5K5f7+eIRGnyrfnuBTR9lUDtSd1Dzpo15G/9hfxt27xSl6OgwCuLIPpr/SeAgr3ORDe4rX+2jrm95e3UCa1DSkGKe80lfzqRfYKD1oMA3Nf6Pq/XF3333cRPnoyxUyciBg70al3Z33yD5exZr9Yhqj9JoGqY/N92ol7S65T3669+jEaUZX/6fhyqg1ohtagTWserdbmSjiOZR0jb7JzoHda/v8frSfn3vznSoyemlZ5fv8efK5DHTpxIzIQJhHTp7PO6AYK0QUxqPwmAOXvn+P2JvI/2fYSKyo0Nb6RZVDOf1Bk36SEaL5iPJtQ72/eoNhsX33iT8888y+nxE7BnZ3ulHlEzSAJVw5gPH3b+zx9r+hT87p35LsIzfJkQ1AmtQ62QWthVO4fMSaDVEtqzh8fr0YSEoBYWkrPuR4+Wa3VY2Z/mnwU0wTmMVPuZp9HXq+fzul1GNB9Bg7AGZBRmsPjgYr/FcSzzGGuS1gDwYNsHfVq3ctnyAdnfrcR8/HiVy7UmJ3P6/olkzHM+BBFx01A0ldjaSFw9JIGqYcx/LDgXdctIou64g+g/9ikTgckXE8gv5UrUjtZTMHbs6JXtSMIGDAAgb8sWjw7jHck8QqG9kPCgcJpENvFYudWJXqPnkU6PAPDR3o9IyffPgyIf7vkQFZXW+tZeW3qjPPK2buX8s89y8pZbSfvfRzjM5gqXodpsZC5dyolhw8nftg0lJIT677xDrcmTURTFC1GLmkISqBrGfPQoAGF9+1L35eny1EgAU1XVZxPIXVyJ2tH6CmF9enulDkOLFujq1UW1WMj34BDypcmmrxfQDCRDmwylfVx7CmwFvLPzHZ/XfyjjED+c+gGA/sGeHwKuiKCmTQnt1RPVYiH17bc5PuSvZH76KfbcvDKvdRQUkLVsOSeGDSd5+ss4cnMxduhAky+/IGKw59azEjXX1ftbqIaKuutOYsaPJ9gDCyMK77qQd4G0gjR0io5rY6/1SZ1to5z1HK2nENq7j1fqUBSFsD7OPcJyN/3ssXL9OYE85T8zyVy6FLvJVPbJXqZRNDzb7VkAvjn+TbH9Db1JVVXe2v4WKiqDGg2irrauz+ouib52bRp++CF1Z8xAV7s2tgsXSJ42nWM33MDZ/5tMxpIl5P2yrdiSLtnfreToDX258NxzWE6dQhsdTe3nn6fxksUYmjb1092I6kYSqBom+o47qP3sM+jr+vcXmyibq0elZUzLKu9eX15NzlrQOFQywxWyGnvvsfOwG5zJWe7PHkygUvyzArk9N4/0//2P5Okvo1ZiiMgb2se3Z3hT5wbOr/7yKnaH3Sf1/nTmJ35N/pUgTRCPd3rcJ3WWRVEUom4ZSbMfvqf2lH8QlJCAIz+fnB9+4OLLr3B63DjSZhddO0oXE40jJwd9/frEP/EEzdauJea+e1G0Wj/dhaiOJIGqwSxnzpD93UrytsmTeIHIvSWJj+Y/Adi37KDRH3+M703f67V6QrpfD3o91tOnsZw6VeXy0gvSOZvrfKy8XbyPF9Dcvx9UFV3duuji431ad2kmd5lMuD6cfen7WHRwkdfrM9vN/HvHvwEY02YM9UL9N5m+JJrgYGLGjqXp6lUkfLqUuMcfI7RPH4IaN0YTUvSpPWOXLjRetJBma9cQ99CDaMO881SfqNkkgarBctau4/xTT5H1+ef+DkWUYG+aM4HxZY9KbuLPND+vFqnfG7RhoYR06eKs0wPDeK7eumaRzYgI8u2TUX9uIOyf9Z+upFZILf7e9e8AzNo1i9Mm72zR4/LB7x9wOuc08cZ4Jrab6NW6qkJRnA9IxP/tbzT66H/Onqlnni5yjsZgIKRrVxSNfASKypPvnhqkYP9+spYtp/DQIQD349bW8+f9GZYogdVu5WC6cxFCX25JUvuZZ+nU3Dl53FtburiE9fHcMJ6/NhAG/65AXpZbW9xK97rdKbQX8sLmF7A5vLPZ7sH0g8zb53y8/4XrXyBULz02QkgCVYPkrF3LheeeI/OzzwDQ13POg7JeuODPsEQJjmQeweKwEBEUQeOIxj6rN/T67vS+zzkB+WDGQa994MKf86AK9+xBtVZtA1zX04pX4wrkpVEUhWk9phGmD2NXyi5m7Zrl8ToKbc7kzK7aGZwwmBsb3ejxOoSojiSBqkFsF5IB0Net98d/nQmULSUF1ea9D0pRca7hs3Zx7Xy+1kxCZALh+nAKbAUcyzrmtXqCmjen0fx5NN+0sUqb79ocNveTZr5OoGxpadjOXwBFIbhtYD7Z2iC8AdN6TgPg430fs+nsJo+W/+b2NzmSeYSY4Bj+0e0fHi1biOpMEqgaxJaeDoAuLg4AbUwMaDTgcGDLyPBnaOIy7gTKxxOiwfkYfJs4ZzLgzWE8RVEIvf56NAZDlco5mnmUAlsBYfowmkb59hFz1/BdUNOmaMPCfFp3RQxOGMxdrZxrvj276Vn3/opV9fWxr/niyBcoKMzoM4M4Y5xHyhWiJpAEqgaxZ2YCoI2JBkDRatHFxgLOjYVF4HAlLr6a/1R44ABnHv4bWcuWF6nX2/OgPOHStvL1ApoFu3cDYOzg+6HDinr6uqfpXKszudZcHl73MMl5yVUq75cLvzBtyzQAHurwED3r9fRAlELUHJJA1SCuBEoXHe0+po13/sVoT0vzS0yiOJPFxCnTKQDaxrX1SZ25GzeS+9NP5P70E/DnZGxvPol3KdVqxXHJJtcV4V5As5Yf5j/97qzb2DHwE6ggbRDv3vguTSObkpKfwvjvx3M252ylytqRvIPJP03Gptr4a8JfebjDwx6OVojqTxKoGsSWlQWA9pIEyrVujfRABQ7XfJ4GYQ2ICfbeYpaXyv05EYDQ3s4n8Fw9UCezT5JjyfFq3SnvvMOR63uQvWJFpa73x3pZLsHXXIOhZUtCOnXyed2VEWmI5IO/fECDsAaczT3LmNVjKtzL+GPSj0xaN4k8ax7d63bnn73/eVVvnSPElchPRQ3hKCxEzc8HLkug/pgPZZMeqICxN/XPCeS+YDeZ3D0pYb17ARBrjKV+WH1UVK9vBaIxGHDk5ZGbmFjhazMKMzid41zfyB9LGNSe8g+afvM1hhYtfF53ZdUNq8uCvy6geVRzUgtSGbt6LHP3zcVqL/1JyHxrPm/8+gaTN0zGbDfTt0Ff3hvwHkHaIB9FLkT1IglUDeEavkOnQ3PJZFddnLMHym7ybi+DKD9XwuKrCeR5W38Bu52gZs3Q16/vPu7q0fH2MF5oL2evV/7WXyq8nIGr96RpZFMiDZEej62mqhVSi0/++gmDGg/Cptr4z2//4ZZvbuHTQ5+SVZhV5Ny0gjQW7F/A8BXD3Suaj2k9hv/0/w8GbdUeABCiJtP5OwDhGe4J5NFRRR6Lj3voQeIe+RuaIPkrMhCoqupe08hXPVB5ic6FLF29Ty7t4tux+tRqd4+YtwS3aY02Ohp7ZiYFu3cTct115b52d8puADrW6uid4Gqw8KBw/tX3X6w4toKZO2eSZEri1W2v8uq2V6kfVp+IoAiyzFlcyPtznbj6YfV5rvtz3NDgBj9GLkT1IAlUDWE35YCioIuKLnJcExLip4hESS7kXSCjMAOdouPa2Gu9Xp+qqpfMf+pT5D33k3hpe1BV1WvrUSkaDaG9emH67jtyEzdXKIFyTyD3wwKaprVrCW7VCn3Dhj5fq8tTFEXhlha3MLDxQJYfW86KYys4knmEc7nnOMc55zkotIltw60tb2VEsxHS6yREOUkCVUOEXt+da/btxfHHPCgRmFy9Ty1jWvrkg8py/Di25GQUg4GQ67oWee/a2GvRaXRkFGZwLvccDcIbeC2O0N7OBCovMRGemFyua6wOq3u4s2N8R6/FVhJbZibnHnscgJbbfkEbWb2HD8OCwriv9X3c1/o+MgszOZl9klxrLhFBETSJbCLDo0JUQsDNgdq+fTtDhw4lOjqa0NBQunXrxpIlS8p9fWJiIn//+9/p0qULsbGxBAcHc8011/Dss8+S9cdTapdLSEhAUZQS/02aNMlDd+Z9ilaLNjzc32FUWGZhJv/a/i+e3fQsW85v8Xc4XuXrCeSu3qeQ665DExxc5D2D1kCr6FbOuLw8Dyqsl3P4sHD/fveCr2U5knGEQnsh4UHhJEQmeDG64lzrPwU1bVrtk6fLRQdH07l2Z25ocAMda3WU5EmISgqoHqgNGzYwePBggoKCuOuuu4iMjGTZsmWMHj2aU6dO8dxzz5VZxu23305aWhq9e/dmzJgxKIrChg0bePPNN/nqq6/YsmULtWrVKnZdZGQkkydPLna8a9euxY5VJ6rFwql7RmNLS6Ppd98G3GrKBY4Cxq4Zy9lc53o1q06uYkafGQxrOszPkXmHq0fFV0+U5f3s3NbDtS/d5drHt2d/+n72pO7hr03+6rU4dPHxGK65BvOhQ+Rt2ULk8OFlXrM7dTfgHL7z/QKarvWfOvq0XiFE9REwCZTNZmPixIkoisKmTZvo9Me6K1OnTqVHjx5MnTqVUaNG0aKMx4mfeOIJxowZQ90/9oED5zyQRx55hPfff5/p06fz3nvvFbsuKiqKadOmefSeAoESFITlxAkc+fnY09ICLoFaWbCSs9az1A2tS9u4tqxNWssrW1+ha+2u1Amt4+/wPMrqsHIg/QDgux6o2IkTCWrenLC+fUt8v11cO5ay1CcLaob16e1MoBITy5VA/Z7iTGJ8PXwHl6xAXg0W0BRC+EfADOGtX7+e48ePc88997iTJ4Dw8HBefPFFbDYb8+bNK7OcZ599tkjyBM6JlC+++CIAGzdu9GzgAeLC9OmcGHEzph/WFHvPtRp5oK0FdSL7BL9bnR+S/+77b/7V9190iO9Avi2fD/d86OfoPO9Y5jHnkJQ+nMYRjX1SZ2jPntR57jmCGpdcn6sn7GD6wTLXCapyLH8sZ5C3fTuqqpZ5vr9WIFdtNgr2OhNK6YESQlxJwCRQGzZsAGDQoEHF3nMdq0ryo/9jN3idruRON7PZzIIFC3jttdd4//33+f2PhQerC8vJU5iPHEG1WIq951oLKtASqIUHF6KicmPDG2kX79znbHLnyQB8c+wb0gvKN1emunD18rSNaxswKzs3Cm9EpCESi8PC4czDXq0rpHMnGnzwPs2++67Mp9pS8lM4n3cejaLxWW+di/nYMdT8fDShoRiaNfNp3UKI6iMwfosDR48eBShxiC46Opq4uDj3OZUxd+5coOQEDSA5OZlx48bx/PPP87e//Y2OHTvy17/+lbQASzqu5M91oKKLvedejTw1cO6lwFbA2tNrAbin1T3u411qd6FdXDssDgvfnfjOX+F5xaUJVKBQFMUdj7c3FlaCggjv169cS2u4ep9aRLUgVB/q1bgul79zJwDGDu1RtFqf1i2EqD4CZg5UdnY24JzMXZKIiAjOnq3cxpi7d+9m+vTp1KpVi2eeeabY+xMmTKBv3760adMGg8HAgQMHmD59OqtXr2bEiBFs3ry51L+YzWYzZrPZ/dpkMgFgtVqxVnDl5cqy/ZFAqeFhxerUxDj3W7OkXPRZPGVZe2ot+bZ8ojXRtI1qWySu4U2GszdtL98e/5Z7Wt5TSinVy66LuwBoF9POI18HVxkllWU9c4a0//yH0L59ibj55lLLaRPdhs3nNvN7yu+Maj6qynF5ws5kZxLTPq69175nr9R+eb9uB8DQsVPA/LwEotK+/0TZpP2qxlvtV5HyAiaB8paTJ08ybNgw7HY7n376KXF/9MZc6qWXXiryunv37nz33Xf07duXxMREVq1axU033XTFOmbMmMH06dOLHV+zZg0hvljIUlVpnp6OBti0aze2U6eKvB2TkU4ccGrXbratWuX9eMphUa5zy4iO+o6sW7eu6JsO0KLlcOZh5n07j9ra2n6I0LPyHHkk5SQBcHHXRVb97rmvw9q1a4sdi9qyhVpr15F64gRn/xi+vpJCayEA205vY1WWj74/HA5QVbhCD8/GHOdwvXpOZVWqd2Mq0n6qStMtW9ABv9usFATIz0sgK+n7T5SftF/VeLr98iuwlmLAJFCunidXT9Tl/r+9+w6PqkofOP6dnt4LSQiQhB56FQKGACqiIooVW1gFdK3YVlERFF1dxfazoYiwiw0URaUrNUDohJYAgTTSe5/JlPv74zIDIQnJpEfP53nyGG87Z24mzJtz3vuekpKSOken6pKSkkJUVBS5ubn89NNPREVFNfhcpVLJjBkziImJYdeuXVcMoF588UWefvrpan0NDg7m2muvxc3Nza4+N4alvJxzZjMAE2+9BaWjY7X9JXo9ORs3EeDoyNDJk1u8P/WpMlex8MeFAPTV9OWaa66x5ahZxWyPYUf6DswhZib3a/s+N9X289thh7ym27QbpjXLNY1GI5s3b671/mX8vpYKIPjmmxlQz888whDBf3/6L/mWfCImRLR4XaC8d96l5Jdf8F/4Os61/E4azAYWrJL/ILlvwn0Euwa3SD9qu3/moiLS3NwwVVQQOXNmjdpZwkVXev8J9RP3r2la6v5ZZ5Aaot0EUNbcpzNnzjB06NBq+woLC8nLy2P06NENvl5ycjJRUVFkZGSwatUqbrzR/rpC1tGq+iJSnU6HTlezqrRGo2mVX4yqsjIAFA4O6GoJ2LT+8giOJT+/Xfyi7s/Zj96sx9fRl06qTrXep/FdxrMjfQcxGTE8MviRNupp8zlWIOc/DfYb3Ow/g8vvn8VgoHK/PA3lFjmu3vZ8ND50detKSkkKCcUJjAka06z9q8FYhaWkBH1sLB615CQeLzyO0WLEy8GLEM+QFl9G5dL7p/H1pccfmzEVFqLugEVp20Jr/Tv3VyXuX9M09/2z51rtJok88kKdmk2baj6Gb90WWUctm8slJyczbtw40tPT+eGHH7i5nhyQuuzduxeQK5W3Z1dKIAf7n8LL/eQTClZ80zydq8XOdHlx2zGBY+r8cBzbWS78eCzvGHmV7Sf5vbFac1HcithYJL0edadO6HpeuW6alW1dvBZOJAdwGSv/bMtidtW6/2D2QUB+oKCt1qBT1/G7JAiCYNVuAqgJEyYQGhrKt99+y5ELRewASktLef3111Gr1URHR9u25+XlkZCQUOMpuUuDp++//55bbrnliu2ePHmy1iVeYmJieO+999DpdNx6661NeWkt7mIA5VHrfk1QIJ733IPnvffWf62SEvK/Wkr2woVUHD7cnN20sQZQEYERdR7j5+RHX+++SEjsPL+zRfrRWoxmIyfyTwCtUxSy9M8tALiOj2pwAHLpwsItzWnESFCrMaamUpWSUmP/pQGUIAhCe9VupvDUajVLlizhuuuuY+zYsdx99924ubmxevVqkpKSWLhwIT179rQd//HHH7NgwQJeffXVahXEx40bR0pKCldddRVHjx7l6NGaHwiXHr9y5Ur+85//MGHCBLp164ZOp+P48eNs2rQJpVLJ559/TpcuXVrypTeZNYBSe9QxAuXpSadXXm7QtUo3b0aqqEDbPaxFighml2eTUpKCUqFkuP9wdh6rOzgaGzSWk/kn2ZOxh1t6XDkQbs/iC+IxmA146jxbvICmZLFQulUOoFzGT2jwedaCmsfzjiNJUouO/KhcnHEaPJiK/fspi4nB65Iin2aLmcM5cuDe2gGUpaICS0WFreyHIAjClbSbAAogKiqKmJgYXn31VVauXElVVRXh4eG8/vrr3HPPPQ26RsqFv2hjY2OJjY2t9ZhLA6ioqCji4+M5dOgQ27dvR6/X4+/vz5133smcOXMYMWJEk19XS9OGhuIVHY22a9MDvbIdckDjdt0kLOXlFK/+GcPZswQsmN/kawMcypEfT+/l2QtX7ZVzTK4KuIrFRxezN2tvi3+otyTr9N1Av4Et/hr0x45hzs1D6eKC84jhDT6vl2cvtEotxYZiUktTWzzQcx4zhor9+ymP2YXXJb/bpwpPUW4sx1XjSg+Phk0/NpeyHTtJf+opXCZMIPiTj1u1bUEQOp52FUABjBgxgvXr19d73Pz582tdu64hS0RcKjIyssG5Ve2VY//+OPZverVmyWSifPduQF63DBRkv/02mM34zJ6FJjCwyW3YMz0zwHcADioHCvQFnC06S3fP7k1uvy1YF8Vtzek7l6vHotBqG3yeRqWhj3cf4nLjOJp7tMUDKJexY8h9/30q9u5Fqqqy9dX6/hjkNwiVsnWLWFYcOACAptNfaw1GQRBaRrvJgRKaV0JWCR/+cbFyu/7UaUrWr8dw7lyd5xjOnsNSWorS2RmH/v1RuTjjEB4OQMWFp7qayvoBOcx/WL3HalVaBvvJ6yLuzdrbLO23NkmSWjWBvHTLn4B903dW1jyo1lhYWNe7NypvbywVFVQcPmLb3pb5T9YAymmYyL0SBKF+IoD6CzJbJB7/9jBf7jxHiV6uqpr/1RLS5zxN2dZtdZ5nSIgHQNent20JC6dhcqBTcajpCeVF+iISixIBGOw/uJ6jZSMC5CnUvZkdM4BKK00jtzIXjVJDuHd4i7YlSRL+zz2Hxx134HL1WLvPt+ZBtcaTeAqlEucIuSxJeYw8bSxJUpsFUKbCQgwJCQA4dYBpe0EQ2p4IoP4CynfvpixmF+YLTxOWVBrp5uOMWqXAYpGnNDM9A5G4cikD/Uk5gHLo3ce2zbGf/KGvj49vcj+t+U9h7mF4OXg16JyRnUYCcCDrAGaLucl9aG37s+SRuwG+A3BQt2xRRoVCgUtkJAGvLUDViAKu1hGoU4WnMJgN9RzddC5j5HpT5bv3AHCu+BxFhiIcVA4tHmxeruJCyRJdz56ovb1btW1BEDomEUD9BWS98SZpDz2EPuEUAJ7OWr68fxhbnhmHh5OWp384wu3F3Unw7HrlAOrCX+AOfS4GUA59+wJgSEhAauKaQ9bRhSH+Qxp8Th/vPrhoXCg1lhJf0PQgrrXtz5YDqOGdGp7Q3VaCXILwcvDCZDERn9/y99p57FiC/u8juiz7Grj4/hjoOxCNqnULC5bvkR84cR51Vau2KwhCxyUCqL+AugppejnLiblmSUJCwfbOAzHl5dZ6DUmSLgmgetu2a7p0QensjFRVheFcUpP62ZjpGbVSbcuX2pe1r0nttzZJkmwjUMP9238ApVAoGOg7ELj4s2pJak9P3K65BtWFit8HsuUcpLbIfyqPlUfBnEaKAEoQhIYRAVQHJ1kstqk7lacHmcWV5JToqx1z4wD56bk9Af3rHIGSDAbcrp+E4+DBaLtffNpNoVTaRqT0J040up/lxnISCuQAzd4PSOvoTUcLoFJLU8mpyEGj1Njyi1pK3uefc/6pOU3OVbPea2sw01okSWJfpvzzHdap/gcMmpMxMxNjSiqoVDjZUfpBEIS/NxFAdXCWkhJ5ZXtA7eHBFzvOMeLNP1m06ZTtmIju3miUCnKcPEktqz2PSOngQMD8+XT77luUlz3+rrsQQBlOn250P4/mHsUsmQl0DqSTs32PiVsTyQ9lH8Joado0YmtqrfwnSZIoWv0zpRs2YMzIaNK1rKN9h3MOY7KYmqN7DXKm4DT5+nwcVA62UbDWUhkr5z859uuHysWlVdsWBKHjEgFUB2e6MH2ndHFBodWy91wBAD38LxapdNKqGdL5wjSJrhNSVZVdbegujEgZEhMb3c+mPMrf07Mn7jp3Kk2VnMhr/ChYa7MGUCM6texTXfpDhzCmpqJwcsI1alyTrtXTsyeuGlfKjeWcKjhV/wnNIOu11/h9rrzM0FD/oWhVDa9f1RwMJ08C4CTynwRBsIMIoDo4c2ERIOc/FVcYic8qAeCqkOpPuY3p5Q/AYb+emAoK7GrDoW8fHIcMwaFf45+Msi7PYa3rZA+lQmkbGbEGJe2dJEkcyJKnwVo6gbzkp58AcJt8PUpn5yZdS6VU2ZL8W+teG7NziPOtAGBkwMhWafNSPnNfJHT9OjzvuqvV2xYEoeMSAVQHZy66mEB+NL0ISYIuXk74uVWfMhrd3ReAE94hGHNqJpKXbNiI/vRpJHPNKT7H/v3p9u03+D31VOP6aDHbFqltTAAFDc+DMuXlUfrHH1QeO253VfrmlFScRE5lDlql1lYeoCUoKysp27QZAM/bbmuWa7Z2HpRDxFWc7CIvcXNVQOuPAikUCnQhIaICuSAIdml3S7kI9rn4BJ4HJzPk0ad+QTVrAPULckNjMVOscyH5fB59L8lpNhcXk34hOOq5f5/tqajmcqboDOXGclw0LnT3aNxyLNZpsCM5R6gyV9U6zVO0+meyXn3VVm7BefQoAhctQu1Z+yLLLSkmPQaQg5GWzH9yPXwEyWBA16MHDgObJ3fIOtp3KPsQZou5xZdUServg/6QAtcK6K4NatG2BEEQmosYgergrNNxak8vTmbKAVTfgJoBlE6ton9ndwASOlVfpFUfLz8dp+ncudmDJ5A/iEGu79PYD+MgpxDUpRMpr3C1LTVSZbKw/XSubaTJacQINMHBaLt1Q6HTUb57D2kzZ2GprGyeF2KHXRm7AIgIimixNiSzGc9dcjsed9zRbAsV9/LqhbPGmVJjKacLG//gQEMdNMvlMfolW9Dvbd0nLZUVFa3aniAIfx0igOrgzAUXp/CsI1DhgXKgpDfpOZB1gLKqMgCGhsnTeAdTCqtdQ39hCZdL6z9dzlJVhf7UKfQXEm7t0dS14Paey2f8ou0Unp+IuayPbRpv26kcHli6j3u/2ovFIqHtHETIj6sIXb+OkB9XofL0RH/8ODnvLmpUu41Vaaq05T+1ZABVvm0b2rw8lG5ueNx6S7NdV61U26ZaWyMPKjZTLmLZP0WibGdMi7dnZS4uJuz1haTdcScWEUgJgmAnEUB1cH5znqL7ju04R8/gbK4cKPUNdKPYUMztv93OjI0zmPLLFM6XnmdoV3kq6/IAynBhBEp3SQXyyxWvWUPSzVNJWvQhizad4rbPdvPIioPsPZdfbx8P58oJ5H2LnLHY+QTgb3EZTF+yl6wSPZ4uFhTaPNuHelGFEUeNisHBniiV8uiL0slJzmnp0YPAd94BoPCbb5plLb+G2p+1nypLFYHOgYS4hbRIG5IkUfT1MgDc77yjycnjl7MuobMnc0+zXvdyxYZi4nLjABiQJFG65U+kC2U5WlrF7t0oLBYkYxVKJ6dWaVMQhL8OEUB1cAqtFo2fH2erNFgk8HbW4ueq4/2D75NckgxAbmUub+59kyFd5ADqVHYppfqL9ZSs69xdugbe5XTdu1Ooc+ERp1H835ZEDqQUsv54Fnd+EcuSnefqPC+rPIus8iyUFnB97E3O3XQTVampDXptWxKyeeqHI5gtElMGBrJidh80rieJy4nDYDZwx/BgNt3Tk+igi8niZQaTbUrPZUwE7tNuBSDnnXdaLal8V3r16TtLZWWzt23Oy8OUlYVFrcb97rub9doAo4PkhX4PZB1o0XXx9mTswSyZCXULoZPkhjk3j8pDh1qsvUtV7JQXMXYaa//Cy4IgCCKA+ouw5T8FupGvz+e3s78BsGD0ApQKJTvTd1KQc4ROxjIcTVUk58lTFha9HsPZswA4hPet8/qasDD+M3Q6yc6++LloeevW/tw2tDNqpQInbd3PIsTu+xmAbtkSDmgwpqRy/rHH661FlZJfzpPfy8HTrUOC+ODOQfT1CcPH0YcqSxVxOfKohfKrz8m97Rbylyyh3GDiniV7mfvzxSfwfJ94EoWDA5WHD1O2dWtDbmWTSJLEznT5g3lAoolTQ4ZyavAQTg0ZSuo/HqTol1/srsNVG7WvL11+/430f8xA7evb5OtdrodHD/wc/dCb9S26rMuO8zsAiAweh+v48YD8RGhLk8xmKi7kj4kAShCExhAB1F+ENf+pb4Abm5I3UWWpItw7nFu630Jk50gANuTF8J8/32flby/R113+0RtOnQKzGZW3N2o/vzqvvyq+iCN+PdGZqvhqjBt3jejCu7cP5M9nIpk+skud5x3VZgMwyK0P3f/4A5WXF4bTpyn8YWWd5xjNFh799hClehNDu3ry9rQBKJUKFApFtXIGpvx8StavB8DpqlHsSyrg2PkivtuXypvr4pEkCY2/H1733QdAecyuht7ORjtdeJq00jR0Kh0RPSYiXUhglyorKd+9m8wXXuTspOsp/eOPJreldHCgMiysydepjUKhsI1C7U7f3SJtmC1mW7A5tvNYXK+7FoCybdtafLSw4uBBzAWFmB0dcRw0qEXbEgThr0kEUB1c8t3TSX3wIU6kyrlIfQPd2Jomj7RM6jYJhULBDaE3ALAx/U/8nZSokGzTaNakcIe+fet8iquiysT7f8hPY0WfXEfnrIuLCnf1vph7Y7ZINT749uXIydRRNz2Kxt8P3yceByBv8WJbuYHLLd+dzPH0EjydNHwyfQga1cW3qbWcwf6s/ZSsXQcmEw79+uHYL5yo3n68NU2uz/DlziQ+3SaPrHlFP0DXb7+h07xXrnAnm06SJP5M/ROAUYGj8BkeQciva+i5fx8hv67B96knUfn6YMzI4Pxjj5P2z0cxZmfb3UZrsU5BWp8obG7H8o5RZCjCVePKIL9BOEdEEPj2W4Ss/qnZniisS+mF2lllffug0GhatC1BEP6aRADVgVn0enlqatcu8irkdcu6+qpsT4BFdYkCYGzQWNRKNefLzlPQLxgAw+kzQPUAqi4rYlPILTUQpKzihqQ96E/VXOJj55lcJn+4k40nsmzbssqzSClJQalQ2hYQ9pg2DbWfH+a8PMo2b65xncziSt7fLAdrL17fh07u1WsoWQOoo3lHyfldnh50v/lm2/47hgXz8g1yLtc7G0/xa1wGam9vnIYMqfP1NQdjdjbJ025j86nfAZjYZSIKlQqHnj1Rubri0LMnPg8/TPdNm/CePRs0Gsq2bOHcjTdRsnFTg9qQqqpImzWbguXLay142txGBYxCqVCSWJRIVnlW/SfYyTp9FxEUgUapQanV4n7zzajcapbhaE6SxULphfdeWb9+LdqWIAh/XSKA6sCsRTQVGg3bno9i79wJFJjiMUkmurl1o6tbVwCcNE62x9KP9XXikwG3cON+CwlZJba19Bz71/5BYrFIrIiVR6seCtGgkcwYTtWsDbQ/qYBT2aV8tl1OKJfMZvZmyI+nh3uH46p1tfXV4447ACj+4Yca11n4ezzlVWaGdvXktqGda+wPdg3G38kfk8VEXEkCqNW43TC52jEPjQ1l1tWhADy7Ko7DqYU1rtOcJLOZ9GeeISnzJIn6NFQKFeOCx9V6rNLREb85TxHy04849O+PpaysQTlMkslExssvU75zJ7n/93GTFw1uCHedO/285feFNTG+OVlHSq/ufHWzX/tK9EePYsrORuHkREWPHvWfIAiCUAsRQHVgtiKaHh4olUr83RyIyzsCYFvPzGp0oJzPctinlPMuvpy36DiYUkjwxx/TY/cunMeMqbWNnYl5pBZU4Oqg5ubRFxYVPnWqxlTS7MgwHovqzvIZco5S+Z5Y/lz2OlBzLTiP228HhQL9ocOoi4tt23eczmXtsUxUSgULp/azlSa4lEKhsI1CHeumwGXsWNReXjWO+9ek3kzo7UeVycKs/x0ko0jORao6n07xb7/X+lobq2D5f6k8cJC9/XUADOs0DHed+xXPcejZk27ffUuXr5fiNOTi8jaSJNUo9WDMySHtn/+k5NffQKUi6L1FaIODm/U11GVMZ/l9sS1tW7Ne91zxORKLElEr1UQGR9bYL5lMtuC+uZVcmL5zjrwaSUzfCYLQSCKA6sAuLaJpZSta6Tuo2rHWNcaOcp7bT29h4cH/cmMfHwDUXl4oHWpfbuSb2BQApg3pjGfPMNBosJSXY0yvPgLirFPz7HW98HCSl1gpXreWIwHy4+/WmkJWGn8/Av79Jl3W/o7J/WKg8eGf8rRi9Ohu9KmlmrqVNRg81F1RbfruUiqlgg/vHkzvTq7klhp4aPkBihKTOHvttWTMnYsxO6fO69vDcOYMuR98gATEjPEAYHLI5CueY6VQq3G+qvrab/qjR0kcezXpzzxL9jvvcP6JJzl7zbWU79iJQqul84cf4HJ1643YTOwyEYDdGbspN5Y323U3JcvTlqMCRuGmrf6zLtm8mTNRUeS89VaztXcpw4UpaJeJE1vk+oIg/D2IAKoDsy4k/E63a3ho+X72J+dyPO84UHPR3t5evXFUO1JiLsffPZehaUdRnzxWbxv/GBPCiO4qdpa+yfAfRvHxnc6UOUBVUlKd51gMBjYkHKHQVYGTUsewTsNqHOMxdSraLtWf3vvqgWHMujqUpyZeeVplaJYjSotEmq+CwmF1r63nolOz5IFh+LhoOZlZwouxBegGDwajkcIVK+p55fWTjEYy/vUCUlUV6ZMHkSrl46By4Nqu1zb6miXr1mMuLqZk7VoKvlpK6aZNSAYDDgMH0G3VKlxb+UO/u0d3urp1pcpSZXtirjlsTpFHga7pek2NfRo/P8y5eZRs3IS5rKzZ2rQKXvIl3VatxCmy5siXIAhCQ4kAqgMz5eYBsM8xkD/iczhbdI4qSxVeDl62/CcrtVLNAF/5CbWkSPnR94qD9df3MeniSdA8T47pKAazgR3BZbw1vzfKq2pPyi4zmLjjgy280WM2liovRgWNqXXh39p4OGmZO7kPrg5XnlaRfvuDPqnyFOKOnCs/Yt/Z04nF9w1Fq1Ky/ngWKyPvBaDw++8xlzVtRCXv88XoT55E6e5O7E3yPR3fZTwuWpdGX9Pv+efo8vVSfJ96Eq9//APfZ56m2w/f0+3773Ho1bNJ/W0MhULBhC4TAPgz5c9muWZycTKnC0+jVqgZ32V8jf0OAwagDQ1F0usp3dj8NaEUCgWO/fuj1Oma/dqCIPx9iACqAzPl5iIBb7imM/+mvhQjP1E30HdgrY+BD/GTg54z/b04FtiHd/48yx+f1D0SU24s56WYl5CQuCn0Jr669it8HH04XZzI/D3zaz3HWatCUZCHRaHGkHOd7UnAukgS7DqWZtfj+bpevRiZL09bNiQ3Z2hXL964RU6G/vycmd2DJmIpLaX4px8b3OblKo8dJ+/zzwFwf+VfbMiSE6KnhE1p9DUBFCoVzqNG4fPww/g//xw+M2fiOLD2n2drsU7j7Ti/o1mqkq9Plmt3jQwcWWuumEKhwP2WqQAU/bS6ye0JgiC0BBFAdWCmnBwUwPBAF6IjQjhRIK/3dvn0nZV1+9GKMxya8Rwre05gZ9DAWo/dmpDD/f/7lbxiJV3dujJ/9HxGBIzg/XHvo1KoWJ+0nq2pNSt7SwYDk09+A1gwlQ7EXaq9LwBVycmkfrOJ6JXxPLziYIODKO8Z0dy28DsADmYfpEhfVO85tw8LZtbVoYT6ODP0ejmHKH/5ciSTqUFtXsqi15PxwgtgNuN6/SR29DBSUlVCZ5fOtlyzv5Jwn3A6OXeiwlTB9rTtTbqWRbKwJnENADeG3ljnce5TbgaVispDh9AnJDSpTavKEycwZjV/OQZBEP6eRADVgZly5ERotZ8fkiTZFmWtK4Aa4DsAlUJFelk6Q8LlEZytp/NqDVy+2JnIwdNuGIuH8sTgJ2zTcIP8BnF/+P0ALNy7kNKq0mrnle3YQWLnNDTu8vTgW2uTMZhqr1mk9vfHWKFHbTHRU1Fh1yhLsFswvTx7YZbMbEppWB2lf03qzS+PRTDgzptQeXlhysikpBFTRGVbt1J19iwqXx/8X3mFb+K/AeCePvegUqrsvl57p1QouSFELsb669lfm3Stg9kHSS9Lx0XjYpsarI3G3w/Xa+X8qIJmyFcDyJq/gMQJExtcd0sQBOFKRADVgTlfPZaYG2ew1uLDoYxECvQFaJVa+nrXXhTTWeNML69eAGhdktCoFKQWVJCUVzMXaED3HFQuJwkOSmVi1+qJy9M2V9KpWEFORQ6LDiyqti9n4+9sHaBA67cBV0dIyCrlnQ01C2+CXBNpjFsFH277kNuT7a8zZB3BsK77Vx+VUoGbgwalgwOe90znhFc3Mpcut7u6t9v119P5888I/Pdb7Kk4zrniczhrnJnafaq9L6HDmNJdnpqMSY8hrzKv0df5+Yxc/HRSyCQc1Y5XPNa6BE/Jb783uaRBZVwc+mPHUKhUOA2v+VCDIAiCvUQA1YH5zJzJdwEjmLs7j02nTwDQz6ffFZO2rXlQJwoOMTLEG4A/4msuJ3K8YhVOwf9lev/rUCqqv020BhOzf5envn468xN7MvYAYCkv5+fC7ZQ6Keji7MI7t8kjYUtikli+O9l2vsFkJqdUD0DpwIGElmSi37iu3uraRT//Qv7XyzDly8vWTA6djFKh5EjuEdJK0q547uU2hU/g+TGP8G/HAZRtt39aynXcOJwiRvHx4Y8BuKPnHU1KHm/vQt1DGeAzALNkZu25tY26RrGh2Pb0XUOCTcfBg3EID0cyGCiqpeiqPQpWyKOEbpMn11o3TBAEwV4igOrAiiuNttGjQuSSBIP8Bl3xHGuBzQPZB7i+fycAVh04X20U5lTBKY7mHUWtUHNz95p1lpyGDCE8VWLyOTkBeG7MXFJLUknYv5GVo+Qg6KEhjzApPJAnJsglCV799QSPrDjI4u1nmfrJbqZ+vItzueWU9+iB0s0Nc24eFfv21dlvSZLI/+ILct5+m7Jt2wDwc/Kz5Rz9nPjzFV/35UKDfVEolTiaDOR8/kWDzrl8pGpzymbiC+Jx1jgzo98Mu9rviKzvhR9P/4hFsth9/qrTq9Cb9fTy7MUAnwH1Hq9QKPCKfgCAgq+XNbqkQdX587ZFpz3vuadR1xAEQbicCKA6sCNpRUgSdPV2IqF4P1B3/pPVcP/hKFCQWJRIRC8djhoVZ3LKiD0nVzVftOkUz67ehcXkzPgu4/Fx9KlxDeeICFAouGt1Pj1cQsmrzGPar9OITn8LvVbBIOde3NLjFgDmTOzBnIk9UShg/fEs/r0+gfjMEsqrzGQU60GtxuUaOdeleG3dIxuVR45QlZSEwtER1+sm2bbf3vN2AFaeXkmlqbLB925UmDerogczf7AbXT/7pN7ji9esIe2hmRgv5J2VG8t5Z/87ANzf9348HTyvdPpfwuSQybhoXEguSSYmPcauc41mI9/Fy4n/94ff3+B8N7frr0cbGorj8GFYyhtXdiJ/8WIwmXCOiKi2ZFFOqYGcSvkPEaviCiN7zuaz52w++5MLOJhSSFxaEcfTi4nPLOFMdilpBRXklxnQG82turizIAjti7qtOyA0jjE9nT0x8QD0D3Jme7Fc2PLyCuSX83DwoLdXb+IL4jlRcJBbh3Thm72pvLHuJI+P78Gn285itvjjGBzEbT1vq/Uaai8vHAcPhkOHeLtyMgt8d9oS2MO9w/lw4qe2aT+FQsGTE3swsa8fPx1MJ6dUT58AN+4aHoybTsm60+Ay+XpKfvqJ0o2bsMydi9LJqUabhdYpmOuuQ+XibNseFRxFkEsQ6WXp/Hb2N+7odUeD7+GQ3kHQ+zkATGYLf8Rnc114pxof7hWHD5P5yjykqiqKf/4Fn9mzeP/g+2RXZNPZpfPfYvQJwEXrwm09b2PZiWX898R/7VrDbn3yenIqc/Bx9OH6btc3+DyFWk3IqpUonZ1r3W+2SOSU6skpMZBXZiC/rIrcsovf5+SXkJsXTMXEF8DPnz2XnPvSLyfYdlqNQ5ds7hsdAsDR9CLu+6rukdDLKRXgpFWz7blx+LjIdaW+3ZvK7rN53DggkEn95FHecoOJfUkFeDhp8HXV4eOiw0Hz13vgQBD+TkQA1UGV/rmFvTF54NcTL89iyIEQ9xA8HDzqPfeqgKuIL4gnNjOWpyZO5Ne4DI6nlzD7f/KTc2q3w4QEVDIyYGSd13C95hoqDx1C9eMGlq/+kZP5JzFLZvr79K/1SbTwQHfCA6vX/DEa5b/8HYcORRMcjDEtjeI1a/C8++7qx2VmUrJhAwBe999XbZ9KqeK+vvfx1r63WHJsCVPCpuCgrn1ZmrqYLRKPfnuIjSeyib4qmHlT+tvW4as8coS0hx9BqqrCZfx4vB96kA1JG/jhlJyT88qoV+pNhv4rmd57Ov87+T/2Zu3lSM6ReqeMQR59+vTIp4D8pKJGZd/6c1lGJcln8wjzdcHfTf7Zbk3IYd6vx8ks0mOy1DMK5B4k/7fCjMlsQa2Sg3svZy2OKgnVJePwrg4auvvJuWwWi4TJImG+8CV/b6HSaEZvlKcwLZJcPNbxkmDoYEohvx/NpG/gxSVqkvLKmbFsf7VuuTmo8XNzwNdFh6+rDj9X+b/y9w74uuoI9XVGoxITBYLQHokAqoOJy41DkiS80s5z0qs3AAaNPBJlTRCvz8iAkXx94mtiM2N5bbSWr6OH8+T3R8gorsTbJwm910/c1vPxGsnjl/K49RZyP/oIQ0ICOa+9TteRI3GdNKlRBR8VKhVe991H9ptvUrD8v3jccQcK1cUPpPyvloLZjNOIETj0rfmE4bQe0/j6+NdklmeyIn4FD/V/yK72VUoFI0O82Xgim2WxaZw/lcy/J3ZB2rmNgq+WIhmNOA4cSNA7/2F3ViwvxbwEwEP9H7Kty/d3EeASwJSwKfyc+DPvH3yfZZOW1fszX3V6Fell6fg4+jC99/Rq+/RGMxlFlaQXVXK+sJL0wkryygy8Ne1ijtSLq4+x/XQu/5k2gDuGB2MuK0enVpJWIE/ZqpUK26iOj4sWbxf5e9fcdPjvV7iaDXR/bR7ePUJRXtLXt2/tR6RDKpOHdrZtGxTswR9P17/Ei9kiUWk0U1FlosJgxkl78f06bWgQfQPdGN6t+rRuvyA3CsuN5JYaqDJbKNGbKNGXkZhTd27X7hfGE+ghB+j/i01hd2IeNw8KZFK/ANv9S8mvwN9Nh7ujpk0LrgrC340IoDqQHxJ+YOHehQz2G8zUjC4YXLX4qC0klG4BYFTgqAZdZ4j/EBzVjmSVZ3Es7xjDug0g5l9RHMk+wf0bX0CjVNf7lJTK3R3vBx8k7+OPKfr+B4q+/wG/jEy8H/xHo16b+623kvvxx3JCeWEhah8598qQlETh998D4PPIw7We66B24MkhTzI3Zi6L4xYzPng8oR6hdrUfPSwAy2cf8VbQOP4oVBP/9RHm7vuVUKMRlwkT8H/7Tf6X/CMfHPoAk8XE+ODxPDbosUa91o7un4P+ybqkdRzKOcTmlM1c263utf/Ol2Tz/p6VmCr6MMDtLj7YnEJ6YSXniy4GS7V5+ca+uOjkf57CfF1IK6yA8lLOP/EkhtOn6fvN9/z48CiCPB3xc3VApaweOEiSRMq9C6k8fxivB+7Hf2ztBWMbS6VU4KJTy310rb5vdJgPo8Oq5w72C3Ln98fH2vpWUmkit0yeeswtM5BbaiCnVP6v/L2e3FKDbVoQ4FBKIeuPZzEw2MO2LTGnjBv/T85H06qV+Lnq8HdzwN9NHsXyc9Ph7+qAv9vF790c1SLQEoRmIAKoDmRC1wm8vf9tDuccRmWWczaGBarYVXIWpULZ4CrYjmpHooKjWJe0jvVJ6xngOwCFQsGqM98C8gKvXg71P+rt8/BsLGVllG7ahNPIkXhOv7vec+qicnGm23ffog0JQaGUR74sFRVkPP8vOQH46rE4j6o7QLwh9AZ+P/c7uzN288z2Z1g2aVmty4TURengwH1fvo7fkhUsSPUg3cWXx6PmEBWUz+AxRjZuvofkkmQAru92PW+MeeMvWTTzSiRJosxgwmx0Z3LQTFYlrGVh7BsM9R+Kt6M33+1LZeeZXKYOCuLa8E5IksSzGz8l9/RMANacBzhX47pOWhWdPR0J8nAkyNORIA+nasnZ827qyzz6YiosJGn+UUxZWZQtmMfQ99+rNlJ5KYVCQfCnn5D7ySf4PvFES9yORlMoFLg7aXB30tDdz7X+Ey64a3gwAzu7M6zbxd/NMoMJDycNRRVGqkwWzhfKI3lXolMr2fl8FH4XpkM3ncjibG45Ed29GdDZA7j4xKkItAShbiKA6kC8FC5co+7PWsMRdrn3AcA9OA8K5PXv7AkYJodMZl3SOn49+yuPDX6MnIoc1ibJT8E90PeBBl1DoVbj/8K/8H/hX/a/mFrowsKq/X/m/Pnojx1D6e5OwIIFVzxXqVCyMGIhd/5+J4lFiczYOIN3I98l1L3mSFReZR7Hco9xPP84J/JPkF6aTn5lPqXGUnAHqY8T6qxbMZX2488MX7b8VIrWOxgf/wqeHDab23ve3mYfLBaLRKneRGFFFXmllZwpVrDjTB5mSUGV2YLBaKHKbKHKdOHrwvdPTexh6/M3e1M4nFrE1EFBjOkhj5SczChh4dqTGK3nmiWMZov8ZbJQYTRTUmnkYrpRZ2A2BboFPLfjOT6b+BnH0otZdyyLHn6uXBveic/iPuNY0XYUqn6EeXsS6uNBZ0+nC0GSoy1o8nBq2NST2tOToPffI+W++yndtImMF14k4I2FKLW11z1TubvTae7cZrjr7cPIUG9GhnpX23ZVqDdH5l2L3mi2jVzllBjILtGTXWogp0Tell2iJ6fUIAdaZguezhfv2dpjmaw5ksHcyb1tAdTx9BJu+3y3PHJ1YVTLz03+r6+LDg8nDS5aJVkVkFdmwNtVhVYtcrWEvxcRQLUA619vJSUlzXpdc2kpIz7dz+qb+mOoUuFqzuNo8UrMlWbG+423q70BrgMIUgeRWpzKgq0LSC5JxlhhZEzgGIK1wc3e99oYjUYqKiooKSlBo6mZWFzp6EilqytBi96l0tmZynr6pEPHopGLeGzLYyRkJHDz9zczKmAU3T26Y5bMZJRncDLvJJkVmVe8jpPajG/wBszlyWSmjsBQ4UJlRRQ5WRPYoveD/NNE9vTDUdu0Eagqk4XiiiqKKo0UVRipNJq5uqevbf8XO85y9HwR0aNCGBYijzqsO5bJ8z8erX6hw/WXFLh/mJ8tGXnH8VTWH8+im5uCAf7yB2lOfgExJxtWjFSjUuDmoEanlaiqciI2KZaZa2ZyU+AcgscH0zdQzetbX+e7U3LZgtdvMzAlbETtFzPrKb1QVLVBwsJwfe01Ml96ibJffiHv+HG8ZsxAFxaGIfEM5txcvO6/v0GXqu/919G4q8HdU00PTzVQ+1OLeqOZgvIqKsvLsI5TDfDTYurtToir0vZ7fy4jl8ryMpLLy0i+8q8Lb+yTa2w5aZVsfCrSFpz9eCCNo+eLua5fJyK6y4F6YXkVMYm5OGrVuGjVOOlUOGtVOGnVOGnVOGpVaFSKv8XI11/t/dfaWur+WX8HGlKiRCGJQibN7vz58wQHB7d1NwRBEARBaIS0tDQ6d+58xWNEANUCLBYLGRkZuLq6/i3+kmqskpISgoODSUtLw83Nrf4ThGrE/Wsacf+aRty/phH3r2la6v5JkkRpaSmBgYEolVeelhZTeC1AqVTWG7kKF7m5uYl/QJpA3L+mEfevacT9axpx/5qmJe6fu3vD8olF1p8gCIIgCIKdRAAlCIIgCIJgJxFACW1Gp9Px6quvotPp6j9YqEHcv6YR969pxP1rGnH/mqY93D+RRC4IgiAIgmAnMQIlCIIgCIJgJxFACYIgCIIg2EkEUIIgCIIgCHYSAZTQZv7zn/+gUMjLNsTGxrZ1dzqUn3/+mWuuuQZvb28cHR0JCQnh7rvvJi2tYcux/F1JksTq1auJiooiICAAJycnevXqxezZszl3ruZCx39XK1asYPbs2QwbNgydTodCoWDZsmV1Hl9SUsLTTz9N165d0el0dO3alaeffrpVloRqjxp6/4xGIz/99BPR0dH06dMHZ2dnXF1dGTlyJJ9++ilms7n1O98O2Pv+u1RSUhIuLi4oFAoefvjhFu2nKKQptIn4+HjmzZuHs7Mz5eXlbd2dDkOSJB5++GG++OILwsLCuOuuu3B1dSUjI4Pt27eTkpIilhG6gmeffZb33nuPgIAApk6dipubG3FxcXz55Zd899137N69m379+rV1N9vcyy+/TEpKCj4+PgQEBJCSklLnseXl5URGRnLkyBGuueYa7r77buLi4nj//ffZunUrMTExODvXvjbfX1VD79/Zs2e57bbbcHV1Zfz48UyZMoXi4mJ+++03Hn30UTZs2MCaNWv+dita2PP+u5QkScyYMaOFe1e9QUFoVSaTSRo+fLg0YsQI6d5775UAac+ePW3drQ7hww8/lADp0UcflUwmU439RqOxDXrVMWRmZkpKpVLq1q2bVFxcXG3f+++/LwHSjBkz2qh37cvmzZul5ORkSZIk6d///rcESF9//XWtx86bN08CpOeff77W7fPmzWvp7rY7Db1/58+flz799FOpvLy82vaysjJp2LBhEiCtXLmyNbrcrtjz/rvUhx9+KKnVaum9996TAGn27Nkt2k8xhSe0urfffpu4uDiWLl2KSqVq6+50GJWVlSxYsIDQ0FA++OCDWu+dWi0GleuSnJyMxWIhIiKixtIPN9xwAwA5OTlt0bV2Z+LEiXTt2rXe4yRJYsmSJbi4uDBv3rxq+1588UU8PT356quvGrSy/V9JQ+9fUFAQjzzyCE5OTtW2Ozs78/TTTwOwffv2Fulje9bQ+3epxMREXnzxRZ5//nkGDx7cQj2rTgRQQqs6fvw4CxYs4OWXXyY8PLytu9OhbN68mYKCAqZOnYrZbGb16tW89dZbfP755yQmJrZ199q9Hj16oNVq2bVrF6WlpdX2rVu3DoDx48e3Rdc6rDNnzpCRkUFERESNaToHBweuvvpq0tPTxfuzETQaDSD+KGoIi8XCjBkz6Nq1a41AviWJn4zQakwmky1Z8oUXXmjr7nQ4Bw4cAOR/UAcOHMipU6ds+5RKJXPmzOHdd99tq+61e97e3rzxxhs899xz9OnThylTpuDq6sqxY8f4448/mDVrFo8//nhbd7NDOXPmDCAHp7Wxbj9z5kydxwi1W7p0KQDXXnttG/ek/fvggw/YvXs3MTExrVqZXARQQqt58803iYuLY+/evba/roSGs04vLVq0iCFDhrBv3z769OnD4cOHmTVrFosWLSIsLIxHHnmkjXvafj377LMEBgYye/ZsPvvsM9v20aNHc++994r3pZ2Ki4uBulevt06VWo8TGuaLL75g/fr1jB8/nsmTJ7d1d9q106dP8/LLL/Pkk08yatSoVm1bTOEJrSIuLo6FCxfy7LPPMmTIkLbuTodksVgA0Gq1/PLLLwwfPhwXFxfGjh3Ljz/+iFKpZNGiRW3cy/Zt4cKFREdH8+KLL5KWlkZZWRkxMTGYTCaioqJYvXp1W3dR+Jtbu3Ytjz32GF27dmXFihVt3Z12zWKxEB0dTWBgIAsXLmz19kUAJbSKBx54gLCwMObPn9/WXemwrH/lDxs2jMDAwGr7wsPDCQ0N5ezZsxQVFbVB79q/LVu28Morr/DYY48xd+5cOnfujLOzMxEREfz+++84OjoyZ86ctu5mh2J9T9Y1wmStA1XXCJVQ3caNG5k2bRr+/v5s2bKFgICAtu5Su/bRRx8RGxvLkiVLaiTitwYRQAmtIi4ujoSEBBwcHGzFMxUKBcuXLwdg1KhRKBQKfvnll7btaDvWq1cvADw8PGrdb91eWVnZSj3qWNauXQtAVFRUjX2+vr7079+f1NRU8vLyWrtrHdalOU61qS9HSrhow4YNTJ06FR8fH7Zu3UpoaGhbd6ndO3LkCJIkERUVVe1zxfo7vnjxYhQKBVOnTm2R9kUOlNAqHnzwwVq379ixgzNnzjBlyhR8fX3p1q1b63asA7H+oxAfH19jn9FoJDExEWdnZ3x9fVu7ax1CVVUVALm5ubXut25vzSTUjq5Hjx4EBgaya9cuysvLqz2Jp9fr2bFjB4GBgXTv3r0Ne9n+WYMnLy8vtm7dKu5XA0VGRtb6lGJmZibr1q2jd+/eREREtFxZgxatMiUI9XjggQdEIU07XHvttRIgffnll9W2v/baaxIg3XvvvW3Us/bvu+++kwApPDxcKioqqrZv2bJlEiANHTq0jXrXfolCmk1T3/1bv369pNPppE6dOkkJCQmt27kOwJ5CmlZbt25tlUKaCkn6m1U4E9qV6Oholi9fzp49e7jqqqvaujvt3tmzZxk9ejQ5OTnccMMN9O7dm8OHD7Nlyxa6du1KbGwsnTp1aututktms5mJEyeybds2fH19mTJlCp6ensTFxbF582Z0Oh1//PEHY8aMaeuutrklS5YQExMDwLFjxzh06BARERG2kZGpU6fapkXKy8sZM2aMbSmXoUOHEhcXx/r16xk0aNDfcimXht6/hIQEBg0ahMFg4K677rJN01+qW7duREdHt2b325w977/abNu2jaioKGbPns3nn3/ech1t0fBMEOohRqDsl5qaKkVHR0udOnWSNBqNFBwcLD366KNSdnZ2W3et3dPr9dLbb78tDRkyRHJycpLUarUUFBQkTZ8+XTp27Fhbd6/dsP5e1vX16quvVju+qKhImjNnjhQcHGx7T86ZM6fGSN/fRUPvn3Wk5EpfkZGRbfpa2oK977/LiREoQRAEQRCEdko8hScIgiAIgmAnEUAJgiAIgiDYSQRQgiAIgiAIdhIBlCAIgiAIgp1EACUIgiAIgmAnEUAJgiAIgiDYSQRQgiAIgiAIdhIBlCAIgiAIgp1EACUIgiAIgmAnEUAJgvC3kJycjEKhaNF1xaKjo1EoFCQnJzf4HIvFwsCBA5k8eXKL9auoqAgPDw+ef/75FmtDEP5uRAAlCEKrsQYxl35ptVqCg4OZPn06R48ebesutrply5Zx9OhR5s+f32JteHh48OSTT/LRRx/ZFdwJglA3sRaeIAitJjk5mZCQEMLCwrj33nsBKCsrIzY2ll27dqHT6diyZQujR49u9raNRiNnz57F3d2dgICAZr8+yCNQy5cvJykpiW7dutV7vNlsJjQ0lJCQELZt29YifbIqKCggICCA++67jyVLlrRoW4LwdyBGoARBaHXdu3dn/vz5zJ8/n3fffZeYmBheeuklDAYDL730Uou0qdFo6N27d4sFT42xbt06UlNTue+++1q8LS8vL66//nq+++47iouLW7w9QfirEwGUIAjtwuOPPw7A/v37q21fs2YNEyZMwNPTEwcHB/r168e7776L2WyudtyyZctQKBQsW7aMtWvXMnbsWFxdXW0jQVfKgUpNTeXBBx8kKCgIrVZL586defDBB0lLS6u1rydOnODGG2/E1dUVd3d3Jk+ezPHjx+1+zdY+T5s2rdr2yMhINBoNmZmZtZ53xx13oFAoOHz4MADbtm1DoVAwf/589uzZw3XXXYeHhwcKhaLGeRUVFaxcudLuvgqCUJ0IoARBaBcu/7AHmDt3LlOnTuX06dNMmzaNf/7znzg4OPDcc89x11131XqdVatWMXXqVHx8fPjnP/9Zb3L2mTNnGD58OEuXLmXo0KE888wzDBkyhKVLlzJs2DASExOrHX/8+HFGjx7N+vXrmTRpEo8++ihVVVVERERw7ty5Br9eSZLYtm0bvXv3xsPDo9q+2bNnYzKZ+Prrr2ucl5eXx5o1axg6dCiDBw+utm/37t1ERkYCMGvWLO68885q+0eNGgXAli1bGtxPQRDqIAmCILSSpKQkCZCuu+66GvteeuklCZDGjRsnSZIkbdq0SQKk66+/XiovL7cdZ7FYpIcfflgCpB9//NG2/euvv5YASaFQSJs3b66z7QceeKDa9vHjx0uAtHjx4mrbFy9eLAHShAkTqm2PjIyUAGnFihXVtr/44osSIAFSUlJSvffixIkTEiDdc889Nfbp9XrJ29tbCgsLkywWS7V97733ngRIn332mW3b1q1bbW1/9dVXV2zXy8tL6tKlS739EwThykQAJQhCq7EGMWFhYdKrr74qvfrqq9IzzzwjRURESIDk4OAg7d69W5IkSZoyZYoESKmpqTWuU1RUJCkUCmnatGm2bdYA6pZbbrli25cGUKmpqRIg9e3bt0agYrFYpD59+lTrQ0pKigRIAwYMqHH90tJSycPDo8EB1MaNGyVAevrpp2vd//TTT0uA9Oeff1bbHh4eLjk5OUnFxcW2bdYAavDgwfW227t3b0mlUtV4vYIg2EfdSgNdgiAINmfPnmXBggWAnNzt7+/P9OnTeeGFF+jfvz8AsbGxODs789VXX9V6DUdHRxISEmpsHzFiRIP7Yc0hioyMrDGFqFAouPrqq4mPjycuLo7g4GDi4uIAGDNmTI1rubi4MGjQoAY/TZefnw+Ap6dnrftnzZrFe++9x5IlSxg/fjwg35MTJ04QHR2Nm5tbjXMa8tq9vLwwm80UFRXV2bYgCPUTAZQgCK3uuuuuY8OGDVc8pqCgAJPJZAu0alNeXl5jm7+/f4P7UVJScsVzOnXqBGB7as36Xz8/v1qPt6dtR0dHACorK2vd36tXLyIjI1m9ejUFBQV4eXnZyg/MnDmz0e1b23NycmpwXwVBqEkkkQuC0C65ubnh7e2NJKca1PqVlJRU47zaktGv1AZAdnZ2rfut263Hubu7A5CTk3PF4xvC19cXkAPFusyePRuDwcCKFSsoKyvjhx9+oG/fvnXWyWrIay8oKMDV1RWdTtfgvgqCUJMIoARBaJdGjhxJfn4+Z86cabE2Bg0aBMCOHTuQLqspLEkSO3furHbcwIEDAYiJialxrbKyMo4cOdLgtsPDw1EqlVd8fdOmTcPHx4clS5bwww8/UFZWxkMPPdTgNi5XUVHB+fPnbdOkgiA0ngigBEFol5544gkA/vGPf9jyhS6VlZVFfHx8k9ro0qULUVFRnDhxgqVLl1bbt3TpUk6cOMH48eMJDg62HX/11Vdz9OhRvvnmm2rHv/nmmxQVFTW4bQ8PDwYMGMCBAwdqBG9WWq2WBx54gGPHjjFv3jy0Wi3333+/fS/yEgcOHMBsNttKHQiC0HgigBIEoV2aNGkSr7zyCjExMXTv3p27776bF154gZkzZxIVFUXnzp1Zs2ZNk9v57LPP8PHxYebMmUydOtVWe2rmzJn4+vry2WefVTv+k08+wc3Njfvvv5/bb7+duXPncs011/DJJ58wduxYu9qeOnUqxcXFNYqHXmrWrFkAZGRkcMstt+Dt7W3/i7xg8+bNtnYFQWgaEUAJgtBuvfbaa2zevJmxY8fy559/8t577/H7779jMBiYP38+99xzT5Pb6NWrFwcOHCA6Opp9+/bxzjvvsG/fPqKjo9m/fz89e/asdny/fv3YtWsXkyZNYsOGDXz88cdoNBp27dpFaGioXW0/9NBDqFQqVqxYUecxPXv2tBXArCt5vKG+/fZbBg0aZNeTioIg1E4sJiwIgtCGpk+fzqZNm0hJScHZ2bnGfr1eT1BQEB4eHiQmJtqVJH+pLVu2MGHCBJYvX96kaUBBEGRiBEoQBKENvfHGG5SVlfHJJ5/Uun/p0qUUFBQwe/bsRgdPII/mDRo0iHvvvbfR1xAE4SJRB0oQBKENhYSEsHz5cvLy8qptf+utt8jNzWXx4sX4+fnx8MMPN7qNoqIixo0bx0033YRSKf5uFoTmIKbwBEEQ2iGFQoFWq2XgwIF89NFHXHXVVW3dJUEQLiFGoARBENoh8betILRvYixXEARBEATBTiKAEgRBEARBsJMIoARBEARBEOwkAihBEARBEAQ7iQBKEARBEATBTiKAEgRBEARBsJMIoARBEARBEOwkAihBEARBEAQ7iQBKEARBEATBTv8P6vxglQBjnpkAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Figure S4b of the paper\n", + "# Fig S4b. Lomb-Scargle periodogram of C2,2 coefficient for IGG-SLR (red) product, IGG-SLR minus ISBA combination (green) and ISBA (blue).\n", + "windows = 48\n", + "\n", + "# windows creation to reduce the apodization effect\n", + "global_hann = sc.signal.windows.hamming(windows)[:windows//2]\n", + "slr_hann = np.concatenate((global_hann, np.ones(len(SLR_filt_Ylms.time)-windows), global_hann[::-1]))\n", + "slrisba_hann = np.concatenate((global_hann, np.ones(len(SLR_filt_isba_Ylms.time)-windows), global_hann[::-1]))\n", + "\n", + "# compute periodogram\n", + "w = np.linspace(0.449, 2.3, 5000)[::-1]\n", + "pgram = sg.lombscargle(SLR_filt_Ylms.time.copy(), SLR_filt_Ylms.clm[2,2]*slr_hann, w.copy(), normalize=False)\n", + "pgram_slr_isba = sg.lombscargle(SLR_filt_isba_Ylms.time.copy(), SLR_filt_isba_Ylms.clm[2,2]*slrisba_hann, w.copy(), normalize=False)\n", + "pgram_isba = sg.lombscargle(isba_filt_Ylms_long.time.copy(), isba_filt_Ylms_long.clm[2,2]*slrisba_hann, w.copy(), normalize=False)\n", + "\n", + "plt.figure()\n", + "plt.plot(2*np.pi/w, pgram, label='IGG-SLR', color='C3', linestyle=(0, (5,2)))\n", + "plt.plot(2*np.pi/w, pgram_slr_isba, label='IGG-SLR - ISBA', color='C2')\n", + "plt.plot(2*np.pi/w, pgram_isba, label='ISBA', color='C0', linestyle='dashdot')\n", + "\n", + "plt.xlabel('Period (yr)', labelpad=4, fontsize=14)\n", + "plt.ylabel('($yr^{-1}$)', labelpad=-2, fontsize=14)\n", + "plt.ylim(10**-22)\n", + "plt.legend(loc='upper right', fontsize=14)\n", + "plt.xticks(fontsize=14)\n", + "plt.yticks(fontsize=14)\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "id": "7cf82451", "metadata": { "ExecuteTime": { - "end_time": "2023-08-15T15:15:24.733063Z", - "start_time": "2023-08-15T15:15:24.319356Z" + "end_time": "2023-08-16T07:38:48.361525Z", + "start_time": "2023-08-16T07:38:47.945254Z" } }, "outputs": [ @@ -361,6 +440,7 @@ ], "source": [ "# Figure Supplementary Information S3b of the paper\n", + "# Fig S3b. Lomb-Scargle periodogram of S2,2 coefficient for GRACE CSR (brown), GRAZ (light blue) and COSTG (lime) products and for IGG-SLR (red) product\n", "windows = 48\n", "\n", "# windows creation to reduce the apodization effect\n", @@ -394,12 +474,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 13, "id": "18a5be29", "metadata": { "ExecuteTime": { - "end_time": "2023-08-15T15:15:26.834519Z", - "start_time": "2023-08-15T15:15:26.544715Z" + "end_time": "2023-08-16T07:38:50.128420Z", + "start_time": "2023-08-16T07:38:49.833504Z" } }, "outputs": [ @@ -416,6 +496,7 @@ ], "source": [ "# Figure 4 of the paper\n", + "# Fig 4. Upper bounds on the combination of α and δh for periods of 4 (orange curve), 5-6 (blue curve) and 8-12 (purple curve) years, based on the amplitudes of the corrected S2,2 signal \n", "\n", "plt.figure()\n", "# create alpha and delta h range\n", @@ -449,12 +530,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "id": "80fb78ed", "metadata": { "ExecuteTime": { - "end_time": "2023-08-15T15:15:28.791927Z", - "start_time": "2023-08-15T15:15:28.780071Z" + "end_time": "2023-08-16T07:38:52.214642Z", + "start_time": "2023-08-16T07:38:52.208644Z" } }, "outputs": [ @@ -479,12 +560,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 15, "id": "eb22604d", "metadata": { "ExecuteTime": { - "end_time": "2023-08-15T15:15:31.957200Z", - "start_time": "2023-08-15T15:15:30.719904Z" + "end_time": "2023-08-16T07:38:54.711705Z", + "start_time": "2023-08-16T07:38:53.615328Z" } }, "outputs": [ @@ -492,8 +573,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "For a period of 30 yr, spectral resolution on C04 time series is between : 18.75 and 75.0\n", - "For a period of 30 yr, spectral resolution on C01 time series is between : 21.428571428571427 and 50.00000000000001\n" + "For a period of 30 yr, spectral resolution on C04 time series is between : 18.014885607955826 and 89.62969719522997\n", + "For a period of 30 yr, spectral resolution on C01 time series is between : 21.323655559433195 and 50.58068555210291\n" ] }, { @@ -519,22 +600,25 @@ ], "source": [ "# Figure Supplementary Information S1a and S2a\n", - "p = 30 #period of 30 years\n", - "\n", - "l = 50 #length of the LOD time series for C04\n", - "pmin = p - np.abs(p - 1/(1/p + 1/l))\n", - "pmax = p + np.abs(p - 1/(1/p - 1/l))\n", - "print(\"For a period of 30 yr, spectral resolution on C04 time series is between : \", pmin, \"and \", pmax)\n", - "fmin, fmax = 1/pmax, 1/pmin\n", + "# Fig S1a. IERS EOP C01 LOD time-series with (green) and without (orange) removal of the Atmopheric Angular Momentum (AAM) and C04 LOD time-series\n", + "# Fig S2b. α estimated from C01 LOD - AAM time-series (orange and lime) and C04 LOD- AAM time-series (blue and light blue) for the range values of Γ\n", + "# The band-pass filters are between 21 & 50 years\n", + "p = 30 #looking for a signal with a period of 30 years\n", "\n", "# read C04 file\n", "f = open(os.path.join(base_dir, \"LOD/lod_AOHSl.txt\"), 'r')\n", "lines = f.readlines()\n", "\n", - "# create a new LOD to remove trend and AAM, OAM, HAM, ...\n", + "# create a new LOD to remove trend and AAM, OAM, HAM, Sea level AM\n", "lod = np.zeros((len(lines) - 7, 7))\n", "for i, l in enumerate(lines[7:]):\n", " lod[i, :-1] = np.array(l.split())\n", + " \n", + "l = lod[-1,0] - lod[0,0] #length of the LOD time series for C04\n", + "pmin = p - np.abs(p - 1/(1/p + 1/l))\n", + "pmax = p + np.abs(p - 1/(1/p - 1/l))\n", + "print(\"For a period of 30 yr, spectral resolution on C04 time series is between : \", pmin, \"and \", pmax)\n", + "fmin, fmax = 1/pmax, 1/pmin\n", " \n", "lod[:,6] = lod[:,1] - lod[:,2]\n", "\n", @@ -564,22 +648,21 @@ " f[to_zero] = 0\n", " filt_lod[:,i] = np.real(np.fft.ifft(f))[:ndata]\n", "\n", - "\n", - "l = 75 #length of the LOD time series for C01\n", - "pmin = p - np.abs(p - 1/(1/p + 1/l))\n", - "pmax = p + np.abs(p - 1/(1/p - 1/l))\n", - "print(\"For a period of 30 yr, spectral resolution on C01 time series is between : \", pmin, \"and \", pmax)\n", - "fmin, fmax = 1/pmax, 1/pmin\n", - "\n", "# read C01\n", "f = open(os.path.join(base_dir, \"LOD/lod_AAMncep1948-2023.dat\"), 'r')\n", "lines = f.readlines()\n", "\n", - "# create a new LOD to remove trend and AAM, OAM, HAM, ...\n", + "# create a new LOD to remove trend and AAM (OAM and other are not available for C01)\n", "lod2 = np.zeros((len(lines) - 1, 4))\n", "for i, l in enumerate(lines[1:]):\n", " lod2[i, :-1] = np.array(l.split())\n", - " \n", + "\n", + "l = lod2[-1, 0] - lod2[0, 0] #length of the LOD time series for C01\n", + "pmin = p - np.abs(p - 1/(1/p + 1/l))\n", + "pmax = p + np.abs(p - 1/(1/p - 1/l))\n", + "print(\"For a period of 30 yr, spectral resolution on C01 time series is between : \", pmin, \"and \", pmax)\n", + "fmin, fmax = 1/pmax, 1/pmin\n", + "\n", "lod2[:,3] = lod2[:,1] - lod2[:,2]\n", "\n", "for i in range(1,4):\n", @@ -641,12 +724,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 16, "id": "9ada98ea", "metadata": { "ExecuteTime": { - "end_time": "2023-08-15T15:15:35.365717Z", - "start_time": "2023-08-15T15:15:34.656324Z" + "end_time": "2023-08-16T07:39:01.807707Z", + "start_time": "2023-08-16T07:39:01.023957Z" } }, "outputs": [ @@ -654,15 +737,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "For a period of 30 yr, spectral resolution on C04 time series is between : 5.357142857142858 and 6.818181818181818\n", - "0.27328662391793657\n", - "0.07190718419811173\n", - "For a period of 30 yr, spectral resolution on C01 time series is between : 5.555555555555555 and 6.521739130434783\n" + "For a period of 6 yr, spectral resolution on C04 time series is between : 5.295404578162772 and 6.920877428589756\n", + "0.2851279081703251\n", + "0.07344308913742192\n", + "For a period of 6 yr, spectral resolution on C01 time series is between : 5.548477928692466 and 6.5315196960005\n" ] }, { "data": { - "image/png": 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", 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", 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", 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" ] @@ -683,12 +766,16 @@ ], "source": [ "# Figure Supplementary Information S1b and S2b\n", - "p = 6 #period of 6 years\n", - "l = 50\n", + "# Fig S1a. IERS EOP C01 LOD time-series with (green) and without (orange) removal of the Atmopheric Angular Momentum (AAM) and C04 LOD time-series\n", + "# Fig S2b. α estimated from C01 LOD - AAM time-series (orange and lime) and C04 LOD- AAM time-series (blue and light blue) for the range values of Γ\n", + "# The band-pass filters are between 5.5 & 6.5 years\n", + "\n", + "p = 6 #looking for a signal with a period of 6 years\n", + "l = lod[-1,0] - lod[0,0] #length of the LOD time series for C04\n", "\n", "pmin = p - np.abs(p - 1/(1/p + 1/l))\n", "pmax = p + np.abs(p - 1/(1/p - 1/l))\n", - "print(\"For a period of 30 yr, spectral resolution on C04 time series is between : \", pmin, \"and \", pmax)\n", + "print(\"For a period of 6 yr, spectral resolution on C04 time series is between : \", pmin, \"and \", pmax)\n", "fmin, fmax = 1/pmax, 1/pmin\n", "\n", "filt_lod = lod.copy()\n", @@ -715,10 +802,10 @@ "print(np.max(filt_lod[:,6]) - np.min(filt_lod[:,6]))\n", "print(np.std(filt_lod[:,6]))\n", "\n", - "l = 75 #length of the LOD time series for C01\n", + "l = lod2[-1, 0] - lod2[0, 0] #length of the LOD time series for C01\n", "pmin = p - np.abs(p - 1/(1/p + 1/l))\n", "pmax = p + np.abs(p - 1/(1/p - 1/l))\n", - "print(\"For a period of 30 yr, spectral resolution on C01 time series is between : \", pmin, \"and \", pmax)\n", + "print(\"For a period of 6 yr, spectral resolution on C01 time series is between : \", pmin, \"and \", pmax)\n", "fmin, fmax = 1/pmax, 1/pmin\n", "\n", "filt_lod2 = lod2.copy()\n", From 3e4cecc0b44fdf8de502960274ffe797471a0721 Mon Sep 17 00:00:00 2001 From: hulecom Date: Wed, 22 Nov 2023 15:03:21 +0100 Subject: [PATCH 77/80] Debug --- gravity_toolkit/read_CSR_monthly_6x1.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gravity_toolkit/read_CSR_monthly_6x1.py b/gravity_toolkit/read_CSR_monthly_6x1.py index 845a5ce4..9d3741e2 100644 --- a/gravity_toolkit/read_CSR_monthly_6x1.py +++ b/gravity_toolkit/read_CSR_monthly_6x1.py @@ -44,7 +44,7 @@ import os import re import numpy as np -from gravity_toolkit.convert_calendar_decimal import convert_calendar_decimal +from gravity_toolkit.time import convert_calendar_decimal #-- PURPOSE: read low degree harmonic data from Satellite Laser Ranging (SLR) def read_CSR_monthly_6x1(input_file, HEADER=True): From f4c35586874715356b371525704850ddf7353cbd Mon Sep 17 00:00:00 2001 From: lecomte Date: Mon, 27 Nov 2023 10:58:04 +0100 Subject: [PATCH 78/80] Debug time --- gravity_toolkit/read_CSR_monthly_6x1.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) mode change 100644 => 100755 gravity_toolkit/read_CSR_monthly_6x1.py diff --git a/gravity_toolkit/read_CSR_monthly_6x1.py b/gravity_toolkit/read_CSR_monthly_6x1.py old mode 100644 new mode 100755 index 845a5ce4..9d3741e2 --- a/gravity_toolkit/read_CSR_monthly_6x1.py +++ b/gravity_toolkit/read_CSR_monthly_6x1.py @@ -44,7 +44,7 @@ import os import re import numpy as np -from gravity_toolkit.convert_calendar_decimal import convert_calendar_decimal +from gravity_toolkit.time import convert_calendar_decimal #-- PURPOSE: read low degree harmonic data from Satellite Laser Ranging (SLR) def read_CSR_monthly_6x1(input_file, HEADER=True): From 1e6771310a5a5ac1ce31b6fca0ff5f05a9fffeff Mon Sep 17 00:00:00 2001 From: lecomte Date: Mon, 27 Nov 2023 11:01:44 +0100 Subject: [PATCH 79/80] set up --- .binder/environment.yml | 0 .dockerignore | 0 .gitattributes | 0 .github/workflows/auto-update-files.yml | 0 .github/workflows/python-publish.yml | 0 .github/workflows/python-request.yml | 0 .gitignore | 0 CONTRIBUTORS.rst | 0 Dockerfile | 0 LICENSE | 0 MANIFEST.in | 0 README.rst | 0 doc/Makefile | 0 doc/environment.yml | 0 doc/make.bat | 0 doc/source/_assets/geoid_height.svg | 0 doc/source/_static/style.css | 0 doc/source/_templates/layout.html | 0 doc/source/api_reference/SLR/C20.rst | 0 doc/source/api_reference/SLR/C30.rst | 0 doc/source/api_reference/SLR/C40.rst | 0 doc/source/api_reference/SLR/C50.rst | 0 doc/source/api_reference/SLR/CS2.rst | 0 doc/source/api_reference/aod1b_geocenter.rst | 0 doc/source/api_reference/aod1b_oblateness.rst | 0 doc/source/api_reference/associated_legendre.rst | 0 doc/source/api_reference/calc_degree_one.rst | 0 .../api_reference/calc_harmonic_resolution.rst | 0 doc/source/api_reference/calc_mascon.rst | 0 .../api_reference/calc_sensitivity_kernel.rst | 0 doc/source/api_reference/clenshaw_summation.rst | 0 doc/source/api_reference/cnes_grace_sync.rst | 0 doc/source/api_reference/combine_harmonics.rst | 0 doc/source/api_reference/convert_harmonics.rst | 0 .../api_reference/dealiasing_global_uplift.rst | 0 .../api_reference/dealiasing_monthly_mean.rst | 0 doc/source/api_reference/degree_amplitude.rst | 0 doc/source/api_reference/destripe_harmonics.rst | 0 doc/source/api_reference/esa_costg_swarm_sync.rst | 0 doc/source/api_reference/fourier_legendre.rst | 0 doc/source/api_reference/gauss_weights.rst | 0 doc/source/api_reference/gen_averaging_kernel.rst | 0 doc/source/api_reference/gen_disc_load.rst | 0 doc/source/api_reference/gen_harmonics.rst | 0 doc/source/api_reference/gen_point_load.rst | 0 doc/source/api_reference/gen_spherical_cap.rst | 0 doc/source/api_reference/gen_stokes.rst | 0 doc/source/api_reference/geocenter.rst | 0 doc/source/api_reference/gfz_icgem_costg_ftp.rst | 0 .../api_reference/gfz_isdc_dealiasing_ftp.rst | 0 doc/source/api_reference/gfz_isdc_grace_ftp.rst | 0 doc/source/api_reference/grace_date.rst | 0 doc/source/api_reference/grace_find_months.rst | 0 doc/source/api_reference/grace_input_months.rst | 0 doc/source/api_reference/grace_mean_harmonics.rst | 0 doc/source/api_reference/grace_months_index.rst | 0 doc/source/api_reference/grace_spatial_error.rst | 0 doc/source/api_reference/grace_spatial_maps.rst | 0 doc/source/api_reference/harmonic_gradients.rst | 0 doc/source/api_reference/harmonic_summation.rst | 0 doc/source/api_reference/harmonics.rst | 0 doc/source/api_reference/itsg_graz_grace_sync.rst | 0 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doc/source/index.rst | 0 doc/source/user_guide/Examples.rst | 0 gravity_toolkit/SLR/C20.py | 0 gravity_toolkit/SLR/C30.py | 0 gravity_toolkit/SLR/C40.py | 0 gravity_toolkit/SLR/C50.py | 0 gravity_toolkit/SLR/CS2.py | 0 gravity_toolkit/SLR/__init__.py | 0 gravity_toolkit/__init__.py | 0 gravity_toolkit/associated_legendre.py | 0 gravity_toolkit/clenshaw_summation.py | 0 gravity_toolkit/data/Load_Love2_CE.dat | 0 gravity_toolkit/data/PREM-LLNs-truncated.dat | 0 gravity_toolkit/data/PREMhard-LLNs-truncated.dat | 0 gravity_toolkit/data/PREMsoft-LLNs-truncated.dat | 0 gravity_toolkit/data/land_fcn_300km.nc | 0 gravity_toolkit/data/love_numbers | 0 gravity_toolkit/destripe_harmonics.py | 0 gravity_toolkit/gen_disc_load.py | 0 gravity_toolkit/gen_harmonics.py | 0 gravity_toolkit/gen_point_load.py | 0 gravity_toolkit/geocenter.py | 0 gravity_toolkit/grace_date.py | 0 gravity_toolkit/grace_find_months.py | 0 gravity_toolkit/grace_input_months.py | 0 gravity_toolkit/grace_months_index.py | 0 gravity_toolkit/harmonic_gradients.py | 0 gravity_toolkit/harmonics.py | 11 ++++++----- gravity_toolkit/legendre.py | 0 gravity_toolkit/mascons.py | 0 gravity_toolkit/ocean_stokes.py | 0 gravity_toolkit/read_GRACE_harmonics.py | 0 gravity_toolkit/read_SLR_CS2.py | 0 gravity_toolkit/read_SLR_harmonics.py | 0 gravity_toolkit/read_gfc_harmonics.py | 0 gravity_toolkit/read_grid_to_harmonics.py | 0 gravity_toolkit/sea_level_equation.py | 0 gravity_toolkit/spatial.py | 0 gravity_toolkit/time.py | 0 gravity_toolkit/time_series/__init__.py | 0 gravity_toolkit/time_series/fit.py | 0 gravity_toolkit/time_series/savitzky_golay.py | 0 gravity_toolkit/toolbox.py | 0 gravity_toolkit/tools.py | 0 gravity_toolkit/units.py | 3 +++ gravity_toolkit/utilities.py | 0 gravity_toolkit/version.py | 0 gravity_toolkit/wavelets.py | 0 notebooks/GRACE-Geostrophic-Maps.ipynb | 0 notebooks/GRACE-Harmonic-Plots.ipynb | 0 notebooks/GRACE-Spatial-Error.ipynb | 0 notebooks/GRACE-Spatial-Maps.ipynb | 0 notebooks/GRL_Gravitational_Lecomte2023b.ipynb | 0 readthedocs.yml | 0 requirements.txt | 0 scripts/aod1b_geocenter.py | 0 scripts/aod1b_oblateness.py | 0 scripts/calc_mascon.py | 0 scripts/calc_sensitivity_kernel.py | 0 scripts/combine_harmonics.py | 0 scripts/convert_harmonics.py | 0 scripts/dealiasing_global_uplift.py | 0 scripts/esa_costg_swarm_sync.py | 0 scripts/geocenter_compare_tellus.py | 0 scripts/geocenter_monte_carlo.py | 0 scripts/geocenter_ocean_models.py | 0 scripts/geocenter_processing_centers.py | 0 scripts/gfz_icgem_costg_ftp.py | 0 scripts/gfz_isdc_dealiasing_ftp.py | 0 scripts/gfz_isdc_grace_ftp.py | 0 scripts/grace_mean_harmonics.py | 0 scripts/make_grace_index.py | 0 scripts/mascon_reconstruct.py | 0 scripts/monte_carlo_degree_one.py | 0 scripts/plot_AIS_GrIS_maps.py | 0 scripts/plot_AIS_grid_3maps.py | 0 scripts/plot_AIS_grid_4maps.py | 0 scripts/plot_AIS_grid_maps.py | 0 scripts/plot_AIS_grid_movie.py | 0 scripts/plot_AIS_regional_maps.py | 0 scripts/plot_AIS_regional_movie.py | 0 scripts/plot_GrIS_grid_3maps.py | 0 scripts/plot_GrIS_grid_maps.py | 0 scripts/plot_GrIS_grid_movie.py | 0 scripts/plot_QML_grid_3maps.py | 0 scripts/plot_global_grid_3maps.py | 0 scripts/plot_global_grid_4maps.py | 0 scripts/plot_global_grid_5maps.py | 0 scripts/plot_global_grid_9maps.py | 0 scripts/plot_global_grid_maps.py | 0 scripts/plot_global_grid_movie.py | 0 scripts/podaac_cumulus.py | 0 scripts/quick_mascon_plot.py | 0 scripts/run_sea_level_equation.py | 0 scripts/scale_grace_maps.py | 0 setup.cfg | 0 setup.py | 0 test/__init__.py | 0 test/conftest.py | 0 test/out.combine.green_ice.0.5.2008.60.gz | Bin test/out.geoid.green_ice.0.5.2008.60.gz | Bin test/out.green_ice.grid.0.5.2008.cmh20.gz | Bin test/requirements.txt | 0 test/test_download_and_read.py | 0 test/test_gia.py | 0 test/test_legendre.py | 0 test/test_love_numbers.py | 0 test/test_point_masses.py | 0 test/test_time.py | 0 test/test_units.py | 0 version.txt | 0 232 files changed, 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b/gravity_toolkit/harmonics.py old mode 100644 new mode 100755 index 3a40093f..a6e33f1e --- a/gravity_toolkit/harmonics.py +++ b/gravity_toolkit/harmonics.py @@ -279,11 +279,12 @@ def from_ascii(self, filename, **kwargs): self.mmax = 0 # for each line in the file for line in file_contents: - l1,m1,clm1,slm1,*aux = rx.findall(line) - # convert line degree and order to integers - l1,m1 = np.array([l1,m1],dtype=np.int64) - self.lmax = np.copy(l1) if (l1 > self.lmax) else self.lmax - self.mmax = np.copy(m1) if (m1 > self.mmax) else self.mmax + if not '#' in line: + l1,m1,clm1,slm1,*aux = rx.findall(line) + # convert line degree and order to integers + l1,m1 = np.array([l1,m1],dtype=np.int64) + self.lmax = np.copy(l1) if (l1 > self.lmax) else self.lmax + self.mmax = np.copy(m1) if (m1 > self.mmax) else self.mmax # output spherical harmonics data self.clm = np.zeros((self.lmax+1,self.mmax+1)) self.slm = np.zeros((self.lmax+1,self.mmax+1)) diff --git a/gravity_toolkit/legendre.py b/gravity_toolkit/legendre.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/mascons.py b/gravity_toolkit/mascons.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/ocean_stokes.py b/gravity_toolkit/ocean_stokes.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/read_GRACE_harmonics.py b/gravity_toolkit/read_GRACE_harmonics.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/read_SLR_CS2.py b/gravity_toolkit/read_SLR_CS2.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/read_SLR_harmonics.py b/gravity_toolkit/read_SLR_harmonics.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/read_gfc_harmonics.py b/gravity_toolkit/read_gfc_harmonics.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/read_grid_to_harmonics.py b/gravity_toolkit/read_grid_to_harmonics.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/sea_level_equation.py b/gravity_toolkit/sea_level_equation.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/spatial.py b/gravity_toolkit/spatial.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/time.py b/gravity_toolkit/time.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/time_series/__init__.py b/gravity_toolkit/time_series/__init__.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/time_series/fit.py b/gravity_toolkit/time_series/fit.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/time_series/savitzky_golay.py b/gravity_toolkit/time_series/savitzky_golay.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/toolbox.py b/gravity_toolkit/toolbox.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/tools.py b/gravity_toolkit/tools.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/units.py b/gravity_toolkit/units.py old mode 100644 new mode 100755 index dbd9efb0..bf068e1f --- a/gravity_toolkit/units.py +++ b/gravity_toolkit/units.py @@ -225,10 +225,13 @@ def spatial(self, hl, kl, ll, **kwargs): """ # set default keyword arguments kwargs.setdefault('include_elastic', True) + kwargs.setdefault('include_ellipsoidal', False) fraction = np.ones((self.lmax+1)) # compensate for elastic deformation within the solid earth if kwargs['include_elastic']: fraction += kl[self.l] + if kwargs['include_ellipsoidal']: + fraction /= (1.0 - self.flat) # degree dependent coefficients # norm, fully normalized spherical harmonics self.norm = np.ones((self.lmax + 1))/(4.0 * np.pi) diff --git a/gravity_toolkit/utilities.py b/gravity_toolkit/utilities.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/version.py b/gravity_toolkit/version.py old mode 100644 new mode 100755 diff --git a/gravity_toolkit/wavelets.py b/gravity_toolkit/wavelets.py old mode 100644 new mode 100755 diff --git a/notebooks/GRACE-Geostrophic-Maps.ipynb b/notebooks/GRACE-Geostrophic-Maps.ipynb old mode 100644 new mode 100755 diff --git a/notebooks/GRACE-Harmonic-Plots.ipynb b/notebooks/GRACE-Harmonic-Plots.ipynb old mode 100644 new mode 100755 diff --git a/notebooks/GRACE-Spatial-Error.ipynb b/notebooks/GRACE-Spatial-Error.ipynb old mode 100644 new mode 100755 diff --git a/notebooks/GRACE-Spatial-Maps.ipynb b/notebooks/GRACE-Spatial-Maps.ipynb old mode 100644 new mode 100755 diff --git a/notebooks/GRL_Gravitational_Lecomte2023b.ipynb b/notebooks/GRL_Gravitational_Lecomte2023b.ipynb old mode 100644 new mode 100755 diff --git a/readthedocs.yml b/readthedocs.yml old mode 100644 new mode 100755 diff --git a/requirements.txt b/requirements.txt old mode 100644 new mode 100755 diff --git a/scripts/aod1b_geocenter.py b/scripts/aod1b_geocenter.py old mode 100644 new mode 100755 diff --git a/scripts/aod1b_oblateness.py b/scripts/aod1b_oblateness.py old mode 100644 new mode 100755 diff --git a/scripts/calc_mascon.py b/scripts/calc_mascon.py old mode 100644 new mode 100755 diff --git a/scripts/calc_sensitivity_kernel.py b/scripts/calc_sensitivity_kernel.py old mode 100644 new mode 100755 diff --git a/scripts/combine_harmonics.py b/scripts/combine_harmonics.py old mode 100644 new mode 100755 diff --git a/scripts/convert_harmonics.py b/scripts/convert_harmonics.py old mode 100644 new mode 100755 diff --git a/scripts/dealiasing_global_uplift.py b/scripts/dealiasing_global_uplift.py old mode 100644 new mode 100755 diff --git a/scripts/esa_costg_swarm_sync.py b/scripts/esa_costg_swarm_sync.py old mode 100644 new mode 100755 diff --git a/scripts/geocenter_compare_tellus.py b/scripts/geocenter_compare_tellus.py old mode 100644 new mode 100755 diff --git a/scripts/geocenter_monte_carlo.py b/scripts/geocenter_monte_carlo.py old mode 100644 new mode 100755 diff --git a/scripts/geocenter_ocean_models.py b/scripts/geocenter_ocean_models.py old mode 100644 new mode 100755 diff --git a/scripts/geocenter_processing_centers.py b/scripts/geocenter_processing_centers.py old mode 100644 new mode 100755 diff --git a/scripts/gfz_icgem_costg_ftp.py b/scripts/gfz_icgem_costg_ftp.py old mode 100644 new mode 100755 diff --git a/scripts/gfz_isdc_dealiasing_ftp.py b/scripts/gfz_isdc_dealiasing_ftp.py old mode 100644 new mode 100755 diff --git a/scripts/gfz_isdc_grace_ftp.py b/scripts/gfz_isdc_grace_ftp.py old mode 100644 new mode 100755 diff --git a/scripts/grace_mean_harmonics.py b/scripts/grace_mean_harmonics.py old mode 100644 new mode 100755 diff --git a/scripts/make_grace_index.py b/scripts/make_grace_index.py old mode 100644 new mode 100755 diff --git a/scripts/mascon_reconstruct.py b/scripts/mascon_reconstruct.py old mode 100644 new mode 100755 diff --git a/scripts/monte_carlo_degree_one.py b/scripts/monte_carlo_degree_one.py old mode 100644 new mode 100755 diff --git a/scripts/plot_AIS_GrIS_maps.py b/scripts/plot_AIS_GrIS_maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_AIS_grid_3maps.py b/scripts/plot_AIS_grid_3maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_AIS_grid_4maps.py b/scripts/plot_AIS_grid_4maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_AIS_grid_maps.py b/scripts/plot_AIS_grid_maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_AIS_grid_movie.py b/scripts/plot_AIS_grid_movie.py old mode 100644 new mode 100755 diff --git a/scripts/plot_AIS_regional_maps.py b/scripts/plot_AIS_regional_maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_AIS_regional_movie.py b/scripts/plot_AIS_regional_movie.py old mode 100644 new mode 100755 diff --git a/scripts/plot_GrIS_grid_3maps.py b/scripts/plot_GrIS_grid_3maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_GrIS_grid_maps.py b/scripts/plot_GrIS_grid_maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_GrIS_grid_movie.py b/scripts/plot_GrIS_grid_movie.py old mode 100644 new mode 100755 diff --git a/scripts/plot_QML_grid_3maps.py b/scripts/plot_QML_grid_3maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_global_grid_3maps.py b/scripts/plot_global_grid_3maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_global_grid_4maps.py b/scripts/plot_global_grid_4maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_global_grid_5maps.py b/scripts/plot_global_grid_5maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_global_grid_9maps.py b/scripts/plot_global_grid_9maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_global_grid_maps.py b/scripts/plot_global_grid_maps.py old mode 100644 new mode 100755 diff --git a/scripts/plot_global_grid_movie.py b/scripts/plot_global_grid_movie.py old mode 100644 new mode 100755 diff --git a/scripts/podaac_cumulus.py b/scripts/podaac_cumulus.py old mode 100644 new mode 100755 diff --git a/scripts/quick_mascon_plot.py b/scripts/quick_mascon_plot.py old mode 100644 new mode 100755 diff --git a/scripts/run_sea_level_equation.py b/scripts/run_sea_level_equation.py old mode 100644 new mode 100755 diff --git a/scripts/scale_grace_maps.py b/scripts/scale_grace_maps.py old mode 100644 new mode 100755 diff --git a/setup.cfg b/setup.cfg old mode 100644 new mode 100755 diff --git a/setup.py b/setup.py old mode 100644 new mode 100755 diff --git a/test/__init__.py b/test/__init__.py old mode 100644 new mode 100755 diff --git a/test/conftest.py b/test/conftest.py old mode 100644 new mode 100755 diff --git a/test/out.combine.green_ice.0.5.2008.60.gz b/test/out.combine.green_ice.0.5.2008.60.gz old mode 100644 new mode 100755 diff --git a/test/out.geoid.green_ice.0.5.2008.60.gz b/test/out.geoid.green_ice.0.5.2008.60.gz old mode 100644 new mode 100755 diff --git a/test/out.green_ice.grid.0.5.2008.cmh20.gz b/test/out.green_ice.grid.0.5.2008.cmh20.gz old mode 100644 new mode 100755 diff --git a/test/requirements.txt b/test/requirements.txt old mode 100644 new mode 100755 diff --git a/test/test_download_and_read.py b/test/test_download_and_read.py old mode 100644 new mode 100755 diff --git a/test/test_gia.py b/test/test_gia.py old mode 100644 new mode 100755 diff --git a/test/test_legendre.py b/test/test_legendre.py old mode 100644 new mode 100755 diff --git a/test/test_love_numbers.py b/test/test_love_numbers.py old mode 100644 new mode 100755 diff --git a/test/test_point_masses.py b/test/test_point_masses.py old mode 100644 new mode 100755 diff --git a/test/test_time.py b/test/test_time.py old mode 100644 new mode 100755 diff --git a/test/test_units.py b/test/test_units.py old mode 100644 new mode 100755 diff --git a/version.txt b/version.txt old mode 100644 new mode 100755 From b553c03f7c83ed5fce0764314db2b6a708c2a356 Mon Sep 17 00:00:00 2001 From: lecomte Date: Tue, 28 Nov 2023 16:24:19 +0100 Subject: [PATCH 80/80] Deal with '#' comments in ascii file --- gravity_toolkit/harmonics.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/gravity_toolkit/harmonics.py b/gravity_toolkit/harmonics.py index a6e33f1e..2c02afb1 100755 --- a/gravity_toolkit/harmonics.py +++ b/gravity_toolkit/harmonics.py @@ -279,7 +279,7 @@ def from_ascii(self, filename, **kwargs): self.mmax = 0 # for each line in the file for line in file_contents: - if not '#' in line: + if not '#' in line[:2]: l1,m1,clm1,slm1,*aux = rx.findall(line) # convert line degree and order to integers l1,m1 = np.array([l1,m1],dtype=np.int64) @@ -297,12 +297,13 @@ def from_ascii(self, filename, **kwargs): # extract harmonics and convert to matrix # for each line in the file for line in file_contents: - l1,m1,clm1,slm1,*aux = rx.findall(line) - # convert line degree and order to integers - ll,mm = np.array([l1,m1],dtype=np.int64) - # convert fortran exponentials if applicable - self.clm[ll,mm] = np.float64(clm1.replace('D','E')) - self.slm[ll,mm] = np.float64(slm1.replace('D','E')) + if not '#' in line[:2]: + l1,m1,clm1,slm1,*aux = rx.findall(line) + # convert line degree and order to integers + ll,mm = np.array([l1,m1],dtype=np.int64) + # convert fortran exponentials if applicable + self.clm[ll,mm] = np.float64(clm1.replace('D','E')) + self.slm[ll,mm] = np.float64(slm1.replace('D','E')) # assign degree and order fields self.update_dimensions() return self