From 3858c05227df58233e20e94a36df38810ff6fff2 Mon Sep 17 00:00:00 2001 From: Timothy Nunn Date: Thu, 14 Nov 2024 16:06:13 +0000 Subject: [PATCH 1/7] Convert PROCESS and module variable initialisation to Python --- CMakeLists.txt | 1 + process/init.py | 252 ++++++++ process/main.py | 9 +- scripts/vardes.py | 3 +- source/fortran/init_module.f90 | 910 ++++++++++++++++++++++++----- source/fortran/initial.f90 | 977 -------------------------------- tests/integration/test_vmcon.py | 9 +- tests/unit/conftest.py | 5 +- tests/unit/test_availability.py | 12 +- tests/unit/test_input.py | 3 +- 10 files changed, 1042 insertions(+), 1139 deletions(-) create mode 100644 process/init.py delete mode 100755 source/fortran/initial.f90 diff --git a/CMakeLists.txt b/CMakeLists.txt index a2be73910..000b25dde 100755 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -117,6 +117,7 @@ LIST(APPEND PROCESS_SRCS stellarator_variables.f90 stellarator.f90 stellarator_configuration.f90 + input.f90 ) PREPROCESS() diff --git a/process/init.py b/process/init.py new file mode 100644 index 000000000..f58a51c79 --- /dev/null +++ b/process/init.py @@ -0,0 +1,252 @@ +import process.fortran as fortran + + +def init_process(): + """Routine that calls the initialisation routines + author: P J Knight, CCFE, Culham Science Centre + None + This routine calls the main initialisation routines that set + the default values for the global variables, reads in data from + the input file, and checks the run parameters for consistency. + """ + # Initialise error handling + fortran.error_handling.initialise_error_list() + + # Initialise the program variables + initialise_iterative_variables() + + # Initialise the Fortran file specifiers + # (creating and opening the files in the process) + fortran.init_module.open_files() + + # Input any desired new initial values + fortran.process_input.input() + + # Initialise the Stellarator + fortran.stellarator_module.stinit() + + # Check input data for errors/ambiguities + fortran.init_module.check() + + fortran.main_module.run_summary() + + +def init_all_module_vars(): + """Initialise all module variables + This is vital to ensure a 'clean' state of Process before a new run starts, + otherwise components of the previous run's state can persist into the new + run. This matters ever since Process is used as a shared library, rather + than a 'run-once' executable. + """ + fortran.numerics.init_numerics() + fortran.process_input.init_input() + fortran.buildings_variables.init_buildings_variables() + fortran.cost_variables.init_cost_variables() + fortran.divertor_variables.init_divertor_variables() + fortran.error_handling.init_error_handling() + fortran.fwbs_variables.init_fwbs_variables() + fortran.global_variables.init_global_variables() + fortran.ccfe_hcpb_module.init_ccfe_hcpb_module() + fortran.heat_transport_variables.init_heat_transport_variables() + fortran.ife_variables.init_ife_variables() + fortran.impurity_radiation_module.init_impurity_radiation_module() + fortran.pfcoil_module.init_pfcoil_module() + fortran.physics_module.init_physics_module() + fortran.physics_variables.init_physics_variables() + fortran.scan_module.init_scan_module() + fortran.sctfcoil_module.init_sctfcoil_module() + fortran.stellarator_module.init_stellarator_module() + fortran.stellarator_variables.init_stellarator_variables() + fortran.tfcoil_variables.init_tfcoil_variables() + fortran.times_variables.init_times_variables() + fortran.constants.init_constants() + fortran.current_drive_variables.init_current_drive_variables() + fortran.primary_pumping_variables.init_primary_pumping_variables() + fortran.pfcoil_variables.init_pfcoil_variables() + fortran.structure_variables.init_structure_variables() + fortran.vacuum_variables.init_vacuum_variables() + fortran.pf_power_variables.init_pf_power_variables() + fortran.build_variables.init_build_variables() + fortran.constraint_variables.init_constraint_variables() + fortran.pulse_variables.init_pulse_variables() + fortran.rebco_variables.init_rebco_variables() + fortran.reinke_variables.init_reinke_variables() + fortran.define_iteration_variables.init_define_iteration_variables() + fortran.reinke_module.init_reinke_module() + fortran.water_usage_variables.init_watuse_variables() + fortran.cs_fatigue_variables.init_cs_fatigue_variables() + fortran.blanket_library.init_blanket_library() + fortran.dcll_module.init_dcll_module() + + fortran.init_module.init_fortran_modules() + + +def initialise_iterative_variables(): + """Initialise each of the iteration variables""" + fortran.define_iteration_variables.init_itv_1() + fortran.define_iteration_variables.init_itv_2() + fortran.define_iteration_variables.init_itv_3() + fortran.define_iteration_variables.init_itv_4() + fortran.define_iteration_variables.init_itv_5() + fortran.define_iteration_variables.init_itv_6() + fortran.define_iteration_variables.init_itv_7() + fortran.define_iteration_variables.init_itv_8() + fortran.define_iteration_variables.init_itv_9() + fortran.define_iteration_variables.init_itv_10() + fortran.define_iteration_variables.init_itv_11() + fortran.define_iteration_variables.init_itv_12() + fortran.define_iteration_variables.init_itv_13() + fortran.define_iteration_variables.init_itv_14() + fortran.define_iteration_variables.init_itv_15() + fortran.define_iteration_variables.init_itv_16() + fortran.define_iteration_variables.init_itv_17() + fortran.define_iteration_variables.init_itv_18() + fortran.define_iteration_variables.init_itv_19() + fortran.define_iteration_variables.init_itv_20() + fortran.define_iteration_variables.init_itv_21() + + fortran.define_iteration_variables.init_itv_23() + + fortran.define_iteration_variables.init_itv_25() + fortran.define_iteration_variables.init_itv_26() + fortran.define_iteration_variables.init_itv_27() + fortran.define_iteration_variables.init_itv_28() + fortran.define_iteration_variables.init_itv_29() + fortran.define_iteration_variables.init_itv_30() + fortran.define_iteration_variables.init_itv_31() + fortran.define_iteration_variables.init_itv_32() + fortran.define_iteration_variables.init_itv_33() + fortran.define_iteration_variables.init_itv_34() + fortran.define_iteration_variables.init_itv_35() + fortran.define_iteration_variables.init_itv_36() + fortran.define_iteration_variables.init_itv_37() + fortran.define_iteration_variables.init_itv_38() + fortran.define_iteration_variables.init_itv_39() + fortran.define_iteration_variables.init_itv_40() + fortran.define_iteration_variables.init_itv_41() + fortran.define_iteration_variables.init_itv_42() + + fortran.define_iteration_variables.init_itv_44() + fortran.define_iteration_variables.init_itv_45() + fortran.define_iteration_variables.init_itv_46() + fortran.define_iteration_variables.init_itv_47() + fortran.define_iteration_variables.init_itv_48() + fortran.define_iteration_variables.init_itv_49() + fortran.define_iteration_variables.init_itv_50() + fortran.define_iteration_variables.init_itv_51() + + fortran.define_iteration_variables.init_itv_53() + fortran.define_iteration_variables.init_itv_54() + + fortran.define_iteration_variables.init_itv_56() + fortran.define_iteration_variables.init_itv_57() + fortran.define_iteration_variables.init_itv_58() + fortran.define_iteration_variables.init_itv_59() + fortran.define_iteration_variables.init_itv_60() + fortran.define_iteration_variables.init_itv_61() + fortran.define_iteration_variables.init_itv_62() + fortran.define_iteration_variables.init_itv_63() + fortran.define_iteration_variables.init_itv_64() + fortran.define_iteration_variables.init_itv_65() + fortran.define_iteration_variables.init_itv_66() + fortran.define_iteration_variables.init_itv_67() + fortran.define_iteration_variables.init_itv_68() + fortran.define_iteration_variables.init_itv_69() + fortran.define_iteration_variables.init_itv_70() + fortran.define_iteration_variables.init_itv_71() + fortran.define_iteration_variables.init_itv_72() + fortran.define_iteration_variables.init_itv_73() + fortran.define_iteration_variables.init_itv_74() + fortran.define_iteration_variables.init_itv_75() + + fortran.define_iteration_variables.init_itv_79() + + fortran.define_iteration_variables.init_itv_81() + fortran.define_iteration_variables.init_itv_82() + fortran.define_iteration_variables.init_itv_83() + + fortran.define_iteration_variables.init_itv_85() + fortran.define_iteration_variables.init_itv_86() + + fortran.define_iteration_variables.init_itv_89() + fortran.define_iteration_variables.init_itv_90() + fortran.define_iteration_variables.init_itv_91() + fortran.define_iteration_variables.init_itv_92() + fortran.define_iteration_variables.init_itv_93() + fortran.define_iteration_variables.init_itv_94() + fortran.define_iteration_variables.init_itv_95() + fortran.define_iteration_variables.init_itv_96() + fortran.define_iteration_variables.init_itv_97() + fortran.define_iteration_variables.init_itv_98() + + fortran.define_iteration_variables.init_itv_103() + fortran.define_iteration_variables.init_itv_104() + fortran.define_iteration_variables.init_itv_105() + fortran.define_iteration_variables.init_itv_106() + fortran.define_iteration_variables.init_itv_107() + fortran.define_iteration_variables.init_itv_108() + fortran.define_iteration_variables.init_itv_109() + fortran.define_iteration_variables.init_itv_110() + fortran.define_iteration_variables.init_itv_111() + fortran.define_iteration_variables.init_itv_112() + fortran.define_iteration_variables.init_itv_113() + fortran.define_iteration_variables.init_itv_114() + fortran.define_iteration_variables.init_itv_115() + fortran.define_iteration_variables.init_itv_116() + fortran.define_iteration_variables.init_itv_117() + fortran.define_iteration_variables.init_itv_118() + fortran.define_iteration_variables.init_itv_119() + fortran.define_iteration_variables.init_itv_120() + fortran.define_iteration_variables.init_itv_121() + fortran.define_iteration_variables.init_itv_122() + fortran.define_iteration_variables.init_itv_123() + fortran.define_iteration_variables.init_itv_124() + fortran.define_iteration_variables.init_itv_125() + fortran.define_iteration_variables.init_itv_126() + fortran.define_iteration_variables.init_itv_127() + fortran.define_iteration_variables.init_itv_128() + fortran.define_iteration_variables.init_itv_129() + fortran.define_iteration_variables.init_itv_130() + fortran.define_iteration_variables.init_itv_131() + fortran.define_iteration_variables.init_itv_132() + fortran.define_iteration_variables.init_itv_133() + fortran.define_iteration_variables.init_itv_134() + fortran.define_iteration_variables.init_itv_135() + fortran.define_iteration_variables.init_itv_136() + fortran.define_iteration_variables.init_itv_137() + fortran.define_iteration_variables.init_itv_138() + fortran.define_iteration_variables.init_itv_139() + fortran.define_iteration_variables.init_itv_140() + fortran.define_iteration_variables.init_itv_141() + fortran.define_iteration_variables.init_itv_142() + fortran.define_iteration_variables.init_itv_143() + fortran.define_iteration_variables.init_itv_144() + fortran.define_iteration_variables.init_itv_145() + fortran.define_iteration_variables.init_itv_146() + fortran.define_iteration_variables.init_itv_147() + fortran.define_iteration_variables.init_itv_148() + fortran.define_iteration_variables.init_itv_149() + fortran.define_iteration_variables.init_itv_152() + fortran.define_iteration_variables.init_itv_153() + fortran.define_iteration_variables.init_itv_154() + fortran.define_iteration_variables.init_itv_155() + fortran.define_iteration_variables.init_itv_156() + fortran.define_iteration_variables.init_itv_157() + fortran.define_iteration_variables.init_itv_158() + fortran.define_iteration_variables.init_itv_159() + fortran.define_iteration_variables.init_itv_160() + fortran.define_iteration_variables.init_itv_161() + fortran.define_iteration_variables.init_itv_162() + fortran.define_iteration_variables.init_itv_163() + fortran.define_iteration_variables.init_itv_164() + fortran.define_iteration_variables.init_itv_165() + fortran.define_iteration_variables.init_itv_166() + fortran.define_iteration_variables.init_itv_167() + fortran.define_iteration_variables.init_itv_168() + fortran.define_iteration_variables.init_itv_169() + fortran.define_iteration_variables.init_itv_170() + fortran.define_iteration_variables.init_itv_171() + fortran.define_iteration_variables.init_itv_172() + fortran.define_iteration_variables.init_itv_173() + fortran.define_iteration_variables.init_itv_174() + fortran.define_iteration_variables.init_itv_175() diff --git a/process/main.py b/process/main.py index be7052a46..94cabae03 100644 --- a/process/main.py +++ b/process/main.py @@ -73,6 +73,7 @@ from process.current_drive import CurrentDrive from process.impurity_radiation import initialise_imprad from process.caller import write_output_files +import process.init as init import process @@ -298,8 +299,8 @@ def run(self): config = RunProcessConfig(self.config_file) config.setup() - fortran.init_module.init_all_module_vars() - fortran.init_module.init() + init.init_all_module_vars() + init.init_process() neqns, itervars = get_neqns_itervars() lbs, ubs = get_variable_range(itervars, config.factor) @@ -399,7 +400,7 @@ def init_module_vars(): This "resets" all module variables to their initialised values, so each new run doesn't have any side-effects from previous runs. """ - fortran.init_module.init_all_module_vars() + init.init_all_module_vars() def set_filenames(self): """Validate the input filename and create other filenames from it.""" @@ -455,7 +456,7 @@ def initialise(): """Run the init module to call all initialisation routines.""" initialise_imprad() # Reads in input file - fortran.init_module.init() + init.init_process() # Order optimisation parameters (arbitrary order in input file) # Ensures consistency and makes output comparisons more straightforward diff --git a/scripts/vardes.py b/scripts/vardes.py index ab3cdf883..baee244c3 100644 --- a/scripts/vardes.py +++ b/scripts/vardes.py @@ -7,6 +7,7 @@ import numpy as np import jinja2 +from process.init import init_all_module_vars from process import fortran @@ -81,7 +82,7 @@ def get_input_output_variables(variables: List[FortranVariable]): except AttributeError: continue - fortran.init_module.init_all_module_vars() + init_all_module_vars() for var in variables: current_values_entry = f"{var.module}.{var.name}" diff --git a/source/fortran/init_module.f90 b/source/fortran/init_module.f90 index 1b49da3b0..ee4d42462 100644 --- a/source/fortran/init_module.f90 +++ b/source/fortran/init_module.f90 @@ -1,133 +1,31 @@ -#ifndef INSTALLDIR -#error INSTALLDIR not defined! -#endif - module init_module +#ifndef dp +use, intrinsic :: iso_fortran_env, only: dp=>real64 +#endif + implicit none contains - subroutine init_all_module_vars - !! Initialise all module variables - !! This is vital to ensure a 'clean' state of Process before a new run starts, - !! otherwise components of the previous run's state can persist into the new - !! run. This matters ever since Process is used as a shared library, rather - !! than a 'run-once' executable. - use numerics, only: init_numerics - use process_input, only: init_input - use buildings_variables, only: init_buildings_variables - use cost_variables, only: init_cost_variables - use divertor_variables, only: init_divertor_variables - use error_handling, only: init_error_handling + subroutine init_fortran_modules + !! Temporary routine to call initialisation routines for Fortran modules + !! that are not wrapped by f2py and thus cannot be called from Python. + use fson_library, only: init_fson_library - use fwbs_variables, only: init_fwbs_variables - use global_variables, only: init_global_variables - use ccfe_hcpb_module, only: init_ccfe_hcpb_module - use heat_transport_variables, only: init_heat_transport_variables - use ife_variables, only: init_ife_variables - use impurity_radiation_module, only: init_impurity_radiation_module - use pfcoil_module, only: init_pfcoil_module - use physics_module, only: init_physics_module - use physics_variables, only: init_physics_variables - use scan_module, only: init_scan_module - use sctfcoil_module, only: init_sctfcoil_module - use stellarator_module, only: init_stellarator_module - use stellarator_variables, only: init_stellarator_variables - use tfcoil_variables, only: init_tfcoil_variables - use times_variables, only: init_times_variables - use constants, only: init_constants - use current_drive_variables, only: init_current_drive_variables - use primary_pumping_variables, only: init_primary_pumping_variables - use pfcoil_variables, only: init_pfcoil_variables - use structure_variables, only: init_structure_variables - use vacuum_variables, only: init_vacuum_variables - use pf_power_variables, only: init_pf_power_variables - use build_variables, only: init_build_variables - use constraint_variables, only: init_constraint_variables - use pulse_variables, only: init_pulse_variables - use rebco_variables, only: init_rebco_variables - use reinke_variables, only: init_reinke_variables - use define_iteration_variables, only: init_define_iteration_variables - use reinke_module, only: init_reinke_module - use water_usage_variables, only: init_watuse_variables - use CS_fatigue_variables, only: init_CS_fatigue_variables - use blanket_library, only: init_blanket_library - use dcll_module, only: init_dcll_module - - call init_numerics - call init_input - call init_buildings_variables - call init_cost_variables - call init_divertor_variables - call init_error_handling - call init_fson_library - call init_fwbs_variables - call init_global_variables - call init_ccfe_hcpb_module - call init_heat_transport_variables - call init_ife_variables - call init_impurity_radiation_module - call init_pfcoil_module - call init_physics_module - call init_physics_variables - call init_scan_module - call init_sctfcoil_module - call init_stellarator_module - call init_stellarator_variables - call init_tfcoil_variables - call init_times_variables - call init_constants - call init_current_drive_variables - call init_primary_pumping_variables - call init_pfcoil_variables - call init_structure_variables - call init_vacuum_variables - call init_pf_power_variables - call init_build_variables - call init_constraint_variables - call init_pulse_variables - call init_rebco_variables - call init_reinke_variables - call init_define_iteration_variables - call init_reinke_module - call init_watuse_variables - call init_CS_fatigue_variables - call init_blanket_library - call init_dcll_module - end subroutine init_all_module_vars - - subroutine init - - !! Routine that calls the initialisation routines - !! author: P J Knight, CCFE, Culham Science Centre - !! None - !! This routine calls the main initialisation routines that set - !! the default values for the global variables, reads in data from - !! the input file, and checks the run parameters for consistency. - use global_variables, only: verbose, fileprefix, output_prefix - use main_module, only: run_summary - use constants, only: opt_file, vfile, nout, nplot, mfile, sig_file - use error_handling, only: initialise_error_list - use numerics, only: ixc , lablxc, nvar - use process_input, only: nin, input - use stellarator_module, only: stinit implicit none - ! Arguments - - ! Local variables - integer :: i - - ! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + call init_fson_library - ! Initialise error handling + end subroutine init_fortran_modules - call initialise_error_list + subroutine open_files + use global_variables, only: verbose, fileprefix, output_prefix + use constants, only: nout, mfile + use process_input, only: nin - ! Initialise the program variables - call initial + implicit none ! Open the input/output external files if (trim(fileprefix) == "") then @@ -135,36 +33,11 @@ subroutine init else open(unit=nin,file=trim(fileprefix),status='old') end if - ! open(unit=nin,file=trim(fileprefix)//'IN.DAT',status='old') open(unit=nout ,file=trim(output_prefix)//'OUT.DAT' ,status='unknown') open(unit=mfile ,file=trim(output_prefix)//'MFILE.DAT' ,status='unknown') - ! Input any desired new initial values - call input - - ! Initialise stellarator parameters if necessary - ! This overrides some of the bounds of the tokamak parameters - call stinit - - ! Check input data for errors/ambiguities - call check - - ! Write to the output file certain relevant details about this run - call run_summary - - ! Open verbose diagnostics file - if (verbose == 1) then - open(unit=vfile,file=trim(output_prefix)//'VFILE.DAT',status='unknown') - write(vfile,'(a80)') 'nviter = number of VMCON iterations.' - write(vfile,'(a80)') '(1-mod(ifail,7))=1 indicates that there has '// & - 'been an escape from a failed line search.' - write(vfile,'(a80)') 'odd/even is a convenient plotting bit.' - write(vfile,'(100a13)') 'nviter','escape', 'odd/even', 'te','coe','rmajor', & - 'fusion_power','bt','t_burn','sqsumsq', (lablxc(ixc(i)),i=1,nvar) - end if - - end subroutine init + end subroutine open_files subroutine open_idempotence_files ! Open new output file and mfile to write output to @@ -224,4 +97,755 @@ subroutine finish if (verbose == 1) close(unit = vfile) end subroutine finish + subroutine check + + !! Routine to reset specific variables if certain options are + !! being used + !! author: P J Knight, CCFE, Culham Science Centre + !! None + !! This routine performs a sanity check of the input variables + !! and ensures other dependent variables are given suitable values. + + !! ! + ! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + + use build_variables, only: blnkith, bore, gapoh, ohcth, precomp, iprecomp, & + i_r_cp_top, r_cp_top, vgaptop, vgap_xpoint_divertor, shldtth, shldlth, d_vv_top, d_vv_bot, tf_in_cs + use buildings_variables, only: esbldgm3, triv + use current_drive_variables, only: gamcd, iefrf, irfcd + use error_handling, only: errors_on, idiags, fdiags, report_error + use fwbs_variables, only: breeder_multiplier, iblanket, vfcblkt, vfpblkt, & + iblnkith + use global_variables, only: icase + use heat_transport_variables, only: trithtmw + use ife_variables, only: ife + use impurity_radiation_module, only: nimp, impurity_arr_frac, fimp + use numerics, only: ixc, icc, ioptimz, neqns, nineqns, nvar, boundl, & + boundu + use pfcoil_variables, only: ipfres, ngrp, pfclres, ipfloc, ncls, isumatoh + use physics_variables, only: aspect, f_deuterium, fgwped, f_helium3, & + fgwsep, f_tritium, i_bootstrap_current, i_single_null, i_plasma_current, idivrt, ishape, & + iradloss, isc, ipedestal, ilhthresh, itart, nesep, rhopedn, rhopedt, & + rnbeam, neped, te, tauee_in, tesep, teped, itartpf, ftar, i_diamagnetic_current + use pulse_variables, only: lpulse + use reinke_variables, only: fzactual, impvardiv + use tfcoil_variables, only: casthi, casthi_is_fraction, casths, i_tf_sup, & + tcoolin, tcpav, tfc_sidewall_is_fraction, tmargmin, tmargmin_cs, & + tmargmin_tf, eff_tf_cryo, eyoung_ins, i_tf_bucking, i_tf_shape, & + n_tf_graded_layers, n_tf_stress_layers, tlegav, i_tf_stress_model, & + i_tf_sc_mat, i_tf_wp_geom, i_tf_turns_integer, tinstf, thwcndut, & + tfinsgap, rcool, dhecoil, thicndut, i_cp_joints, t_turn_tf_is_input, & + t_turn_tf, tftmp, t_cable_tf, t_cable_tf_is_input, tftmp, tmpcry, & + i_tf_cond_eyoung_axial, eyoung_cond_axial, eyoung_cond_trans, & + i_tf_cond_eyoung_trans, i_str_wp + use stellarator_variables, only: istell + use sctfcoil_module, only: initialise_cables + use vacuum_variables, only: vacuum_model + use, intrinsic :: iso_fortran_env, only: dp=>real64 + + implicit none + + ! Local variables + + integer :: i,j,k,imp + real(dp) :: fsum + + real(dp) :: dr_tf_wp_min + !! Minimal WP or conductor layer thickness [m] + ! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! + + errors_on = .true. + + ! Check that there are sufficient iteration variables + if (nvar < neqns) then + idiags(1) = nvar ; idiags(2) = neqns + call report_error(137) + end if + + ! Check that sufficient elements of ixc and icc have been specified + if ( any(ixc(1:nvar) == 0) ) then + idiags(1) = nvar + call report_error(139) + end if + + + if ( any(icc(1:neqns+nineqns) == 0) ) then + idiags(1) = neqns ; idiags(2) = nineqns + call report_error(140) + end if + + ! Deprecate constraints 3 and 4 + if ( any(icc(1:neqns+nineqns) == 3) ) then + call report_error(162) + write(*,*) 'PROCESS stopping' + stop 1 + end if + + if ( any(icc(1:neqns+nineqns) == 4) ) then + call report_error(163) + write(*,*) 'PROCESS stopping' + stop 1 + end if + + + ! MDK Report error if constraint 63 is used with old vacuum model + if (any(icc(1:neqns+nineqns) == 63).and.(vacuum_model.ne.'simple') ) then + write(*,*) 'Constraint 63 is requested without the correct vacuum model ("simple").' + write(*,*) 'vacuum_model = ', vacuum_model + write(*,*) 'PROCESS stopping' + stop 1 + end if + + if ( any(icc(1:neqns+nineqns) == 74) ) then + write(*,*)'Constraint 74 (TF coil quench temperature for Croco HTS conductor) is not yet implemented' + write(*,*) 'PROCESS stopping' + stop 1 + end if + + ! Fuel ion fractions must add up to 1.0 + if (abs(1.0D0 - f_deuterium - f_tritium - f_helium3) > 1.0D-6) then + fdiags(1) = f_deuterium; fdiags(2) = f_tritium ; fdiags(3) = f_helium3 + call report_error(36) + end if + + if (f_tritium < 1.0D-3) then ! tritium fraction is negligible + triv = 0.0D0 + trithtmw = 0.0D0 + end if + + if (fimp(2) .ne. 0.1D0) then + write(*,*)'The thermal alpha/electron density ratio should be controlled using ralpne (itv 109) and not fimp(2).' + write(*,*)'fimp(2) should be removed from the input file, or set to the default value 0.1D0.' + stop 1 + end if + + ! Impurity fractions + do imp = 1,nimp + impurity_arr_frac(imp) = fimp(imp) + end do + + ! The 1/R B field dependency constraint variable is being depreciated + ! Stop the run if the constraint 10 is used + if ( any( icc == 10 ) ) then + call report_error(236) + stop 1 + end if + + ! Stop the run if oacdcp is used as an optimisation variable + ! As the current density is now calculated from bt without constraint 10 + if ( any( ixc == 12 ) ) then + call report_error(236) + stop 1 + end if + + ! Warn if ion power balance equation is being used with the new radiation model + if (any(icc == 3)) then + call report_error(138) + end if + + ! Plasma profile consistency checks + if (ife /= 1) then + if (ipedestal == 1) then + + ! Temperature checks + if (teped < tesep) then + fdiags(1) = teped ; fdiags(2) = tesep + call report_error(146) + end if + + if ((abs(rhopedt-1.0D0) <= 1.0D-7).and.((teped-tesep) >= 1.0D-7)) then + fdiags(1) = rhopedt ; fdiags(2) = teped ; fdiags(3) = tesep + call report_error(147) + end if + + ! Core temperature should always be calculated (later) as being + ! higher than the pedestal temperature, if and only if the + ! volume-averaged temperature never drops below the pedestal + ! temperature. Prevent this by adjusting te, and its lower bound + ! (which will only have an effect if this is an optimisation run) + if (te <= teped) then + fdiags(1) = te ; fdiags(2) = teped + te = teped*1.001D0 + call report_error(149) + end if + + if ((ioptimz >= 0).and.(any(ixc == 4)).and.(boundl(4) < teped*1.001D0)) then + call report_error(150) + boundl(4) = teped*1.001D0 + boundu(4) = max(boundu(4), boundl(4)) + end if + + ! Density checks + ! Case where pedestal density is set manually + ! --------------- + if ( (fgwped < 0) .or. (.not.any(ixc==145)) ) then + + ! Issue #589 Pedestal density is set manually using neped but it is less than nesep. + if ( neped < nesep ) then + fdiags(1) = neped ; fdiags(2) = nesep + call report_error(151) + end if + + ! Issue #589 Pedestal density is set manually using neped, + ! but pedestal width = 0. + if ( (abs(rhopedn-1.0D0) <= 1.0D-7).and.((neped-nesep) >= 1.0D-7) ) then + fdiags(1) = rhopedn ; fdiags(2) = neped ; fdiags(3) = nesep + call report_error(152) + end if + end if + + ! Issue #862 : Variable ne0/neped ratio without constraint eq 81 (ne0>neped) + ! -> Potential hollowed density profile + if ( (ioptimz >= 0) .and. (.not.any(icc==81)) ) then + if ( any(ixc == 145 )) call report_error(154) + if ( any(ixc == 6 )) call report_error(155) + end if + end if + end if + ! --------------- + + + ! Cannot use Psep/R and PsepB/qAR limits at the same time + if(any(icc == 68) .and. any(icc == 56)) then + call report_error(178) + endif + + if ((any(ixc==145)) .and. (boundl(145) < fgwsep)) then !if lower bound of fgwped < fgwsep + fdiags(1) = boundl(145); fdiags(2) = fgwsep + call report_error(186) + end if + + if (any(icc == 78)) then + + !If Reinke criterion is used tesep is calculated and cannot be an + !iteration variable + if (any(ixc == 119)) then + call report_error(219) + endif + + !If Reinke criterion is used need to enforce LH-threshold + !using Martin scaling for consistency + if (.not. ilhthresh == 6) then + call report_error(218) + endif + if (.not. any(icc==15) .and. (ipedestal .ne. 3)) then + call report_error(218) + endif + + + endif + + if (any(icc == 78)) then + + !If Reinke criterion is used tesep is calculated and cannot be an + !iteration variable + if (any(ixc == 119)) then + call report_error(219) + endif + + !If Reinke criterion is used need to enforce LH-threshold + !using Martin scaling for consistency + if (.not. ilhthresh == 6) then + call report_error(218) + endif + if (.not. any(icc==15) .and. (ipedestal .ne. 3)) then + call report_error(218) + endif + + + endif + + if (i_single_null == 0) then + idivrt = 2 + vgaptop = vgap_xpoint_divertor + shldtth = shldlth + d_vv_top = d_vv_bot + call report_error(272) + else ! i_single_null == 1 + idivrt = 1 + end if + + + ! Tight aspect ratio options (ST) + ! -------------------------------- + if ( itart == 1 ) then + + icase = 'Tight aspect ratio tokamak model' + + ! Disabled Forcing that no inboard breeding blanket is used + ! Disabled iblnkith = 0 + + ! Check if the choice of plasma current is addapted for ST + ! 2 : Peng Ip scaling (See STAR code documentation) + ! 9 : Fiesta Ip scaling + if (i_plasma_current /= 2 .and. i_plasma_current /= 9) then + idiags(1) = i_plasma_current ; call report_error(37) + end if + + !! If using Peng and Strickler (1986) model (itartpf == 0) + ! Overwrite the location of the TF coils + ! 2 : PF coil on top of TF coil + ! 3 : PF coil outside of TF coil + if (itartpf == 0) then + ipfloc(1) = 2 + ipfloc(2) = 3 + ipfloc(3) = 3 + end if + + ! Water cooled copper magnets initalisation / checks + if ( i_tf_sup == 0 ) then + ! Check if the initial centrepost coolant loop adapted to the magnet technology + ! Ice cannot flow so tcoolin > 273.15 K + if ( tcoolin < 273.15D0 ) call report_error(234) + + ! Temperature of the TF legs cannot be cooled down + if ( abs(tlegav+1.0D0) > epsilon(tlegav) .and. tlegav < 273.15D0 ) call report_error(239) + + ! Check if conductor upper limit is properly set to 50 K or below + if ( any(ixc == 20 ) .and. boundu(20) < 273.15D0 ) call report_error(241) + + ! Call a lvl 3 error if superconductor magnets are used + else if ( i_tf_sup == 1 ) then + call report_error(233) + + ! Aluminium magnets initalisation / checks + ! Initialize the CP conductor temperature to cryogenic temperature for cryo-al magnets (20 K) + else if ( i_tf_sup == 2 ) then + + ! Call a lvl 3 error if the inlet coolant temperature is too large + ! Motivation : ill-defined aluminium resistivity fit for T > 40-50 K + if ( tcoolin > 40.0D0 ) call report_error(235) + + ! Check if the leg average temperature is low enough for the resisitivity fit + if ( tlegav > 50.0D0 ) call report_error(238) + + ! Check if conductor upper limit is properly set to 50 K or below + if ( any(ixc == 20 ) .and. boundu(20) > 50.0D0 ) call report_error(240) + + ! Otherwise intitialise the average conductor temperature at + tcpav = tcoolin + + end if + + ! Check if the boostrap current selection is addapted to ST + if (i_bootstrap_current == 1) call report_error(38) + + ! Check if a single null divertor is used in double null machine + if (i_single_null == 0 .and. (ftar == 1.0 .or. ftar == 0.0)) then + call report_error(39) + end if + + ! Set the TF coil shape to picture frame (if default value) + if ( i_tf_shape == 0 ) i_tf_shape = 2 + + ! Warning stating that the CP fast neutron fluence calculation + ! is not addapted for cryoaluminium calculations yet + if ( i_tf_sup == 2 .and. any( icc == 85 ) .and. itart == 1 ) then + call report_error(260) + end if + + ! Setting the CP joints default options : + ! 0 : No joints for superconducting magents (i_tf_sup = 1) + ! 1 : Sliding joints for resistive magnets (i_tf_sup = 0, 2) + if ( i_cp_joints == -1 ) then + if ( i_tf_sup == 1 ) then + i_cp_joints = 0 + else + i_cp_joints = 1 + end if + end if + + ! Checking the CP TF top radius + if ( ( abs(r_cp_top) > epsilon(r_cp_top) .or. any(ixc(1:nvar) == 174) ) & + .and. i_r_cp_top /= 1 ) then + call report_error(267) + end if + ! -------------------------------- + + + ! Conventionnal aspect ratios specific + ! ------------------------------------ + else + + if (i_plasma_current == 2 .or. i_plasma_current == 9) call report_error(40) + + ! Set the TF coil shape to PROCESS D-shape (if default value) + if ( i_tf_shape == 0 ) i_tf_shape = 1 + + ! Check PF coil configurations + j = 0 ; k = 0 + do i = 1, ngrp + if ((ipfloc(i) /= 2).and.(ncls(i) /= 2)) then + idiags(1) = i ; idiags(2) = ncls(i) + call report_error(41) + end if + + if (ipfloc(i) == 2) then + j = j + 1 + k = k + ncls(i) + end if + end do + + if (k == 1) call report_error(42) + if (k > 2) call report_error(43) + if ((i_single_null == 1).and.(j < 2)) call report_error(44) + + ! Constraint 10 is dedicated to ST designs with demountable joints + if ( any(icc(1:neqns+nineqns) == 10 ) ) call report_error(259) + + end if + ! ------------------------------------ + + ! Pulsed power plant model + if (lpulse == 1) then + icase = 'Pulsed tokamak model' + else + esbldgm3 = 0.0D0 + end if + + ! Ensure minimum cycle time constraint is turned off + ! (not currently available, as routine thrmal has been commented out) + if ( any(icc == 42) ) then + call report_error(164) + end if + + + + ! TF coil + ! ------- + ! TF stress model not defined of r_tf_inboard = 0 + ! Unless i_tf_stress_model == 2 + ! -> If bore + gapoh + ohcth = 0 and fixed and stress constraint is used + ! Generate a lvl 3 error proposing not to use any stress constraints + if ( ( .not. ( any(ixc == 16 ) .or. any(ixc == 29 ) .or. any(ixc == 42 ) ) ) & ! No bore,gapoh, ohcth iteration + .and. ( abs(bore + gapoh + ohcth + precomp) < epsilon(bore) ) & ! bore + gapoh + ohcth = 0 + .and. ( any(icc == 31) .or. any(icc == 32) ) & ! Stress constraints (31 or 32) is used + .and. ( i_tf_stress_model /= 2 ) ) then ! TF stress model can't handle no bore + + call report_error(246) + stop 1 + end if + + ! Make sure that plane stress model is not used for resistive magnets + if ( i_tf_stress_model == 1 .and. i_tf_sup /= 1 ) call report_error(253) + + ! bucking cylinder default option setting + ! - bucking (casing) for SC i_tf_bucking ( i_tf_bucking = 1 ) + ! - No bucking for copper magnets ( i_tf_bucking = 0 ) + ! - Bucking for aluminium magnets ( i_tf_bucking = 1 ) + if ( i_tf_bucking == -1 ) then + if ( i_tf_sup == 0 ) then + i_tf_bucking = 0 + else + i_tf_bucking = 1 + end if + end if + + ! Ensure that the TF isnt placed against the + ! CS which is now outside it + if ( i_tf_bucking >= 2 .and. tf_in_cs == 1 ) then + call report_error(281) + end if + ! Ensure that no pre-compression structure + ! is used for bucked and wedged design + if ( i_tf_bucking >= 2 .and. iprecomp == 1 ) then + call report_error(252) + end if + + ! Number of stress calculation layers + ! +1 to add in the inboard TF coil case on the plasma side, per Issue #1509 + n_tf_stress_layers = i_tf_bucking + n_tf_graded_layers + 1 + + ! If TFC sidewall has not been set by user + if ( casths < 0.1d-10 ) tfc_sidewall_is_fraction = .true. + + ! If inboard TF coil case plasma side thickness has not been set by user + if( casthi < 0.1d-10 ) casthi_is_fraction = .true. + + ! Setting the default cryo-plants efficiencies + !-! + if ( abs(eff_tf_cryo + 1.0D0) < epsilon(eff_tf_cryo) ) then + + ! The ITER cyoplant efficiency is used for SC + if ( i_tf_sup == 1 ) then + eff_tf_cryo = 0.13D0 + + ! Strawbrige plot extrapolation is used for Cryo-Al + else if ( i_tf_sup == 2 ) then + eff_tf_cryo = 0.40D0 + end if + + ! Cryo-plane efficiency must be in [0-1.0] + else if ( eff_tf_cryo > 1.0D0 .or. eff_tf_cryo < 0.0D0 ) then + call report_error(248) + stop 1 + end if + !-! + + ! Integer turns option not yet available for REBCO taped turns + !-! + if ( i_tf_sc_mat == 6 .and. i_tf_turns_integer == 1 ) then + call report_error(254) + stop 1 + end if + !-! + + + ! Setting up insulation layer young modulae default values [Pa] + !-! + if ( abs(eyoung_ins - 1.0D8 ) < epsilon(eyoung_ins) ) then + + ! Copper magnets, no insulation material defined + ! But use the ITER design by default + if ( i_tf_sup == 0 ) then + eyoung_ins = 20.0D9 + + ! SC magnets + ! Value from DDD11-2 v2 2 (2009) + else if ( i_tf_sup == 1 ) then + eyoung_ins = 20.0D9 + + ! Cryo-aluminum magnets (Kapton polymer) + else if ( i_tf_sup == 2 ) then + eyoung_ins = 2.5D9 + end if + end if + !-! + + !-! Setting the default WP geometry + !-! + if ( i_tf_wp_geom == -1 ) then + if ( i_tf_turns_integer == 0 ) i_tf_wp_geom = 1 + if ( i_tf_turns_integer == 1 ) i_tf_wp_geom = 0 + end if + !-! + + !-! Setting the TF coil conductor elastic properties + !-! + if ( i_tf_cond_eyoung_axial == 0 ) then + ! Conductor stiffness is not considered + eyoung_cond_axial = 0 + eyoung_cond_trans = 0 + else if ( i_tf_cond_eyoung_axial == 2 ) then + ! Select sensible defaults from the literature + select case (i_tf_sc_mat) + case (1,4,5) + ! Nb3Sn: Nyilas, A et. al, Superconductor Science and Technology 16, no. 9 (2003): 1036–42. https://doi.org/10.1088/0953-2048/16/9/313. + eyoung_cond_axial = 32D9 + case (2) + ! Bi-2212: Brown, M. et al, IOP Conference Series: Materials Science and Engineering 279 (2017): 012022. https://doi.org/10.1088/1757-899X/279/1/012022. + eyoung_cond_axial = 80D9 + case (3,7) + ! NbTi: Vedrine, P. et. al, IEEE Transactions on Applied Superconductivity 9, no. 2 (1999): 236–39. https://doi.org/10.1109/77.783280. + eyoung_cond_axial = 6.8D9 + case (6,8,9) + ! REBCO: Fujishiro, H. et. al, Physica C: Superconductivity, 426–431 (2005): 699–704. https://doi.org/10.1016/j.physc.2005.01.045. + eyoung_cond_axial = 145D9 + end select + + if ( i_tf_cond_eyoung_trans == 0) then + ! Transverse stiffness is not considered + eyoung_cond_trans = 0 + else + ! Transverse stiffness is significant + eyoung_cond_trans = eyoung_cond_axial + end if + end if + !-! + + ! Check if the WP/conductor radial thickness (dr_tf_wp) is large enough + ! To contains the insulation, cooling and the support structure + ! Rem : Only verified if the WP thickness is used + if ( any(ixc(1:nvar) == 140) ) then + + ! Minimal WP thickness + if ( i_tf_sup == 1 ) then + dr_tf_wp_min = 2.0D0 * ( tinstf + tfinsgap + thicndut + dhecoil ) + + ! Steel conduit thickness (can be an iteration variable) + if ( any(ixc(1:nvar) == 58 ) ) then + dr_tf_wp_min = dr_tf_wp_min + 2.0D0 * boundl(58) + else + dr_tf_wp_min = dr_tf_wp_min + 2.0D0 * thwcndut + end if + + ! Minimal conductor layer thickness + else if ( i_tf_sup == 0 .or. i_tf_sup == 2 ) then + dr_tf_wp_min = 2.0D0 * ( thicndut + tinstf ) + 4.0D0 * rcool + end if + + if ( boundl(140) < dr_tf_wp_min ) then + fdiags(1) = dr_tf_wp_min + call report_error(255) + end if + end if + + ! Setting t_turn_tf_is_input to true if t_turn_tf is an input + if ( abs(t_turn_tf) < epsilon(t_turn_tf) ) then + t_turn_tf_is_input = .false. + else + t_turn_tf_is_input = .true. + end if + + ! Impossible to set the turn size of integer turn option + if ( t_turn_tf_is_input .and. i_tf_turns_integer == 1 ) then + call report_error(269) + end if + + if ( i_tf_wp_geom /= 0 .and. i_tf_turns_integer == 1 ) then + call report_error(283) + end if + + if ( i_bootstrap_current == 5 .and. i_diamagnetic_current /= 0 ) then + call report_error(284) + end if + + ! Setting t_cable_tf_is_input to true if t_cable_tf is an input + if ( abs(t_cable_tf) < epsilon(t_cable_tf) ) then + t_cable_tf_is_input = .false. + else + t_cable_tf_is_input = .true. + end if + + ! Impossible to set the cable size of integer turn option + if ( t_cable_tf_is_input .and. i_tf_turns_integer == 1 ) then + call report_error(269) + end if + + ! Impossible to set both the TF coil turn and the cable dimension + if ( t_turn_tf_is_input .and. t_cable_tf_is_input ) then + call report_error(271) + end if + + ! Checking the SC temperature for LTS + if ( ( i_tf_sc_mat == 1 .or. & + i_tf_sc_mat == 3 .or. & + i_tf_sc_mat == 4 .or. & + i_tf_sc_mat == 5 ) .and. tftmp > 10.0D0 ) then + call report_error(270) + end if + ! ------- + + + + ! PF coil resistivity is zero if superconducting + if (ipfres == 0) pfclres = 0.0D0 + + ! If there is no NBI, then hot beam density should be zero + if (irfcd == 1) then + if ((iefrf /= 5).and.(iefrf /= 8)) rnbeam = 0.0D0 + else + rnbeam = 0.0D0 + end if + + ! Set inboard blanket thickness to zero if no inboard blanket switch + ! used (Issue #732) + if (iblnkith == 0) blnkith = 0.0D0 + + ! Solid breeder assumed if ipowerflow=0 + + !if (ipowerflow == 0) blkttype = 3 + + ! Set coolant fluid type + + !if ((blkttype == 1).or.(blkttype == 2)) then + ! coolwh = 2 ! water + !else + ! coolwh = 1 ! helium + !end if + + ! But... set coolant to water if blktmodel > 0 + ! Although the *blanket* is by definition helium-cooled in this case, + ! the shield etc. are assumed to be water-cooled, and since water is + ! heavier (and the unit cost of pumping it is higher), the calculation + ! for coolmass is better done with coolwh=2 if blktmodel > 0 to give + ! slightly pessimistic results. + + !if (blktmodel > 0) then + ! secondary_cycle = 0 + ! blkttype = 3 ! HCPB + ! coolwh = 2 + !end if + + ! Ensure that blanket material fractions allow non-zero space for steel + ! CCFE HCPB Model + + if (istell == 0) then + if ((iblanket == 1).or.(iblanket == 3)) then + fsum = breeder_multiplier + vfcblkt + vfpblkt + if (fsum >= 1.0D0) then + idiags(1) = iblanket + fdiags(2) = breeder_multiplier + fdiags(3) = vfcblkt + fdiags(4) = vfpblkt + fdiags(5) = fsum + call report_error(165) + end if + end if + end if + + ! Initialise superconductor cable parameters + if(i_tf_sup==1)then + call initialise_cables() + end if + + ! Check that the temperature margins are not overdetermined + if(tmargmin>0.0001d0)then + ! This limit has been input and will be applied to both TFC and CS + if(tmargmin_tf>0.0001d0)then + write(*,*)'tmargmin_tf and tmargmin should not both be specified in IN.DAT.' + write(*,*)'tmargmin_tf has been ignored.' + end if + if(tmargmin_cs>0.0001d0)then + write(*,*)'tmargmin_cs and tmargmin should not both be specified in IN.DAT.' + write(*,*)'tmargmin_cs has been ignored.' + end if + tmargmin_tf = tmargmin + tmargmin_cs = tmargmin + end if + + if (tauee_in.ge.1.0D-10.and.isc.ne.48) then + ! Report error if confinement time is in the input + ! but the scaling to use it is not selected. + call report_error(220) + end if + + if (aspect.gt.1.7D0.and.isc.eq.46) then + ! NSTX scaling is for A<1.7 + call report_error(221) + end if + + if (i_plasma_current.eq.2.and.isc.eq.42) then + call report_error(222) + end if + + ! Cannot use temperature margin constraint with REBCO TF coils + if(any(icc == 36) .and. ((i_tf_sc_mat == 8).or.(i_tf_sc_mat == 9))) then + call report_error(265) + endif + + ! Cannot use temperature margin constraint with REBCO CS coils + if(any(icc == 60) .and. (isumatoh == 8)) then + call report_error(264) + endif + + ! Cold end of the cryocooler should be colder than the TF + if(tmpcry > tftmp) then + call report_error(273) + endif + + ! Cannot use TF coil strain limit if i_str_wp is off: + if(any(icc == 88) .and. (i_str_wp == 0)) then + call report_error(275) + endif + + errors_on = .false. + + ! Disable error logging only after all checks have been performed. + ! (CPSS #1582: Why is error logging disabled at all?) + errors_on = .false. + + + end subroutine check + end module init_module diff --git a/source/fortran/initial.f90 b/source/fortran/initial.f90 deleted file mode 100755 index 8b75f5f7e..000000000 --- a/source/fortran/initial.f90 +++ /dev/null @@ -1,977 +0,0 @@ -! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! - -subroutine initial - - !! Routine to initialise - !! author: P J Knight, CCFE, Culham Science Centre - !! None - !! ! - ! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! - - use define_iteration_variables, only: init_itv_1, init_itv_2, init_itv_3, & - init_itv_4, init_itv_5, init_itv_6, init_itv_7, init_itv_8, init_itv_9, & - init_itv_10, init_itv_11, init_itv_12, init_itv_13, init_itv_14, init_itv_15, & - init_itv_16, init_itv_17, init_itv_18, init_itv_19, init_itv_20, init_itv_21, & - init_itv_23, init_itv_25, init_itv_26, init_itv_27, init_itv_28, init_itv_29, & - init_itv_30, init_itv_31, init_itv_32, init_itv_33, init_itv_34, init_itv_35, & - init_itv_36, init_itv_37, init_itv_38, init_itv_39, init_itv_40, init_itv_41, & - init_itv_42, init_itv_44, init_itv_45, init_itv_46, init_itv_47, init_itv_48, & - init_itv_49, init_itv_50, init_itv_51, init_itv_53, init_itv_54, & - init_itv_56, init_itv_57, init_itv_58, init_itv_59, init_itv_60, init_itv_61, & - init_itv_62, init_itv_63, init_itv_64, init_itv_65, init_itv_66, init_itv_67, & - init_itv_68, init_itv_69, init_itv_70, init_itv_71, init_itv_72, init_itv_73, & - init_itv_74, init_itv_75, init_itv_79, init_itv_81, init_itv_82, init_itv_83, & - init_itv_84, init_itv_85, init_itv_86, init_itv_89, init_itv_90, init_itv_91, & - init_itv_92, init_itv_93, init_itv_94, init_itv_95, init_itv_96, init_itv_97, & - init_itv_98, init_itv_103, init_itv_104, init_itv_105, & - init_itv_106, init_itv_107, init_itv_108, init_itv_109, init_itv_110, & - init_itv_111, init_itv_112, init_itv_113, init_itv_114, init_itv_115, & - init_itv_116, init_itv_117, init_itv_118, init_itv_119, init_itv_120, & - init_itv_121, init_itv_122, init_itv_123, init_itv_124, init_itv_125, & - init_itv_126, init_itv_127, init_itv_128, init_itv_129, init_itv_130, & - init_itv_131, init_itv_132, init_itv_133, init_itv_134, init_itv_135, & - init_itv_136, init_itv_137, init_itv_138, init_itv_139, init_itv_140, & - init_itv_141, init_itv_142, init_itv_143, init_itv_144, init_itv_145, & - init_itv_146, init_itv_147, init_itv_148, init_itv_149, & - init_itv_152, init_itv_153, init_itv_154, init_itv_155, & - init_itv_156, init_itv_157, init_itv_158, init_itv_159, init_itv_160, & - init_itv_161, init_itv_162, init_itv_163, init_itv_164, init_itv_165, & - init_itv_166, init_itv_167, init_itv_168, init_itv_169, init_itv_170, & - init_itv_171, init_itv_172, init_itv_173, init_itv_174, init_itv_175 -#ifndef dp - use, intrinsic :: iso_fortran_env, only: dp=>real64 -#endif - - implicit none - - ! Arguments - - ! Local variables - - ! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! - - !! boundl(ipnvars) /../ : lower bounds on iteration variables - !! boundu(ipnvars) /../ : upper bounds on iteration variables - - ! Issue #287 The initialization subroutines for the iteration variables are called - call init_itv_1 - call init_itv_2 - call init_itv_3 - call init_itv_4 - call init_itv_5 - call init_itv_6 - call init_itv_7 - call init_itv_8 - call init_itv_9 - call init_itv_10 - call init_itv_11 - call init_itv_12 - call init_itv_13 - call init_itv_14 - call init_itv_15 - call init_itv_16 - call init_itv_17 - call init_itv_18 - call init_itv_19 - call init_itv_20 - call init_itv_21 - - call init_itv_23 - - call init_itv_25 - call init_itv_26 - call init_itv_27 - call init_itv_28 - call init_itv_29 - call init_itv_30 - call init_itv_31 - call init_itv_32 - call init_itv_33 - call init_itv_34 - call init_itv_35 - call init_itv_36 - call init_itv_37 - call init_itv_38 - call init_itv_39 - call init_itv_40 - call init_itv_41 - call init_itv_42 - - call init_itv_44 - call init_itv_45 - call init_itv_46 - call init_itv_47 - call init_itv_48 - call init_itv_49 - call init_itv_50 - call init_itv_51 - - call init_itv_53 - call init_itv_54 - - call init_itv_56 - call init_itv_57 - call init_itv_58 - call init_itv_59 - call init_itv_60 - call init_itv_61 - call init_itv_62 - call init_itv_63 - call init_itv_64 - call init_itv_65 - call init_itv_66 - call init_itv_67 - call init_itv_68 - call init_itv_69 - call init_itv_70 - call init_itv_71 - call init_itv_72 - call init_itv_73 - call init_itv_74 - call init_itv_75 - - call init_itv_79 - - call init_itv_81 - call init_itv_82 - call init_itv_83 - - call init_itv_85 - call init_itv_86 - - call init_itv_89 - call init_itv_90 - call init_itv_91 - call init_itv_92 - call init_itv_93 - call init_itv_94 - call init_itv_95 - call init_itv_96 - call init_itv_97 - call init_itv_98 - !Not used - call init_itv_103 - call init_itv_104 - call init_itv_105 - call init_itv_106 - call init_itv_107 - call init_itv_108 - call init_itv_109 - call init_itv_110 - call init_itv_111 - call init_itv_112 - call init_itv_113 - call init_itv_114 - call init_itv_115 - call init_itv_116 - call init_itv_117 - call init_itv_118 - call init_itv_119 - call init_itv_120 - call init_itv_121 - call init_itv_122 - call init_itv_123 - call init_itv_124 - call init_itv_125 - call init_itv_126 - call init_itv_127 - call init_itv_128 - call init_itv_129 - call init_itv_130 - call init_itv_131 - call init_itv_132 - call init_itv_133 - call init_itv_134 - call init_itv_135 - call init_itv_136 - call init_itv_137 - call init_itv_138 - call init_itv_139 - call init_itv_140 - call init_itv_141 - call init_itv_142 - call init_itv_143 - call init_itv_144 - call init_itv_145 - call init_itv_146 - call init_itv_147 - call init_itv_148 - call init_itv_149 - call init_itv_152 - call init_itv_153 - call init_itv_154 - call init_itv_155 - call init_itv_156 - call init_itv_157 - call init_itv_158 - call init_itv_159 - call init_itv_160 - call init_itv_161 - call init_itv_162 - call init_itv_163 - call init_itv_164 - call init_itv_165 - call init_itv_166 - call init_itv_167 - call init_itv_168 - call init_itv_169 - call init_itv_170 - call init_itv_171 - call init_itv_172 - call init_itv_173 - call init_itv_174 - call init_itv_175 - - -end subroutine initial - -subroutine check - - !! Routine to reset specific variables if certain options are - !! being used - !! author: P J Knight, CCFE, Culham Science Centre - !! None - !! This routine performs a sanity check of the input variables - !! and ensures other dependent variables are given suitable values. - - !! ! - ! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! - - use build_variables, only: blnkith, bore, gapoh, ohcth, precomp, iprecomp, & - i_r_cp_top, r_cp_top, vgaptop, vgap_xpoint_divertor, shldtth, shldlth, d_vv_top, d_vv_bot, tf_in_cs - use buildings_variables, only: esbldgm3, triv - use current_drive_variables, only: gamcd, iefrf, irfcd - use error_handling, only: errors_on, idiags, fdiags, report_error - use fwbs_variables, only: breeder_multiplier, iblanket, vfcblkt, vfpblkt, & - iblnkith - use global_variables, only: icase - use heat_transport_variables, only: trithtmw - use ife_variables, only: ife - use impurity_radiation_module, only: nimp, impurity_arr_frac, fimp - use numerics, only: ixc, icc, ioptimz, neqns, nineqns, nvar, boundl, & - boundu - use pfcoil_variables, only: ipfres, ngrp, pfclres, ipfloc, ncls, isumatoh - use physics_variables, only: aspect, f_deuterium, fgwped, f_helium3, & - fgwsep, f_tritium, i_bootstrap_current, i_single_null, i_plasma_current, idivrt, ishape, & - iradloss, isc, ipedestal, ilhthresh, itart, nesep, rhopedn, rhopedt, & - rnbeam, neped, te, tauee_in, tesep, teped, itartpf, ftar, i_diamagnetic_current - use pulse_variables, only: lpulse - use reinke_variables, only: fzactual, impvardiv - use tfcoil_variables, only: casthi, casthi_is_fraction, casths, i_tf_sup, & - tcoolin, tcpav, tfc_sidewall_is_fraction, tmargmin, tmargmin_cs, & - tmargmin_tf, eff_tf_cryo, eyoung_ins, i_tf_bucking, i_tf_shape, & - n_tf_graded_layers, n_tf_stress_layers, tlegav, i_tf_stress_model, & - i_tf_sc_mat, i_tf_wp_geom, i_tf_turns_integer, tinstf, thwcndut, & - tfinsgap, rcool, dhecoil, thicndut, i_cp_joints, t_turn_tf_is_input, & - t_turn_tf, tftmp, t_cable_tf, t_cable_tf_is_input, tftmp, tmpcry, & - i_tf_cond_eyoung_axial, eyoung_cond_axial, eyoung_cond_trans, & - i_tf_cond_eyoung_trans, i_str_wp - use stellarator_variables, only: istell - use sctfcoil_module, only: initialise_cables - use vacuum_variables, only: vacuum_model - use, intrinsic :: iso_fortran_env, only: dp=>real64 - - implicit none - - ! Local variables - - integer :: i,j,k,imp - real(dp) :: fsum - - real(dp) :: dr_tf_wp_min - !! Minimal WP or conductor layer thickness [m] - ! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! - - errors_on = .true. - - ! Check that there are sufficient iteration variables - if (nvar < neqns) then - idiags(1) = nvar ; idiags(2) = neqns - call report_error(137) - end if - - ! Check that sufficient elements of ixc and icc have been specified - if ( any(ixc(1:nvar) == 0) ) then - idiags(1) = nvar - call report_error(139) - end if - - - if ( any(icc(1:neqns+nineqns) == 0) ) then - idiags(1) = neqns ; idiags(2) = nineqns - call report_error(140) - end if - - ! Deprecate constraints 3 and 4 - if ( any(icc(1:neqns+nineqns) == 3) ) then - call report_error(162) - write(*,*) 'PROCESS stopping' - stop 1 - end if - - if ( any(icc(1:neqns+nineqns) == 4) ) then - call report_error(163) - write(*,*) 'PROCESS stopping' - stop 1 - end if - - - ! MDK Report error if constraint 63 is used with old vacuum model - if (any(icc(1:neqns+nineqns) == 63).and.(vacuum_model.ne.'simple') ) then - write(*,*) 'Constraint 63 is requested without the correct vacuum model ("simple").' - write(*,*) 'vacuum_model = ', vacuum_model - write(*,*) 'PROCESS stopping' - stop 1 - end if - - if ( any(icc(1:neqns+nineqns) == 74) ) then - write(*,*)'Constraint 74 (TF coil quench temperature for Croco HTS conductor) is not yet implemented' - write(*,*) 'PROCESS stopping' - stop 1 - end if - - ! Fuel ion fractions must add up to 1.0 - if (abs(1.0D0 - f_deuterium - f_tritium - f_helium3) > 1.0D-6) then - fdiags(1) = f_deuterium; fdiags(2) = f_tritium ; fdiags(3) = f_helium3 - call report_error(36) - end if - - if (f_tritium < 1.0D-3) then ! tritium fraction is negligible - triv = 0.0D0 - trithtmw = 0.0D0 - end if - - if (fimp(2) .ne. 0.1D0) then - write(*,*)'The thermal alpha/electron density ratio should be controlled using ralpne (itv 109) and not fimp(2).' - write(*,*)'fimp(2) should be removed from the input file, or set to the default value 0.1D0.' - stop 1 - end if - - ! Impurity fractions - do imp = 1,nimp - impurity_arr_frac(imp) = fimp(imp) - end do - - ! The 1/R B field dependency constraint variable is being depreciated - ! Stop the run if the constraint 10 is used - if ( any( icc == 10 ) ) then - call report_error(236) - stop 1 - end if - - ! Stop the run if oacdcp is used as an optimisation variable - ! As the current density is now calculated from bt without constraint 10 - if ( any( ixc == 12 ) ) then - call report_error(236) - stop 1 - end if - - ! Warn if ion power balance equation is being used with the new radiation model - if (any(icc == 3)) then - call report_error(138) - end if - - ! Plasma profile consistency checks - if (ife /= 1) then - if (ipedestal == 1) then - - ! Temperature checks - if (teped < tesep) then - fdiags(1) = teped ; fdiags(2) = tesep - call report_error(146) - end if - - if ((abs(rhopedt-1.0D0) <= 1.0D-7).and.((teped-tesep) >= 1.0D-7)) then - fdiags(1) = rhopedt ; fdiags(2) = teped ; fdiags(3) = tesep - call report_error(147) - end if - - ! Core temperature should always be calculated (later) as being - ! higher than the pedestal temperature, if and only if the - ! volume-averaged temperature never drops below the pedestal - ! temperature. Prevent this by adjusting te, and its lower bound - ! (which will only have an effect if this is an optimisation run) - if (te <= teped) then - fdiags(1) = te ; fdiags(2) = teped - te = teped*1.001D0 - call report_error(149) - end if - - if ((ioptimz >= 0).and.(any(ixc == 4)).and.(boundl(4) < teped*1.001D0)) then - call report_error(150) - boundl(4) = teped*1.001D0 - boundu(4) = max(boundu(4), boundl(4)) - end if - - ! Density checks - ! Case where pedestal density is set manually - ! --------------- - if ( (fgwped < 0) .or. (.not.any(ixc==145)) ) then - - ! Issue #589 Pedestal density is set manually using neped but it is less than nesep. - if ( neped < nesep ) then - fdiags(1) = neped ; fdiags(2) = nesep - call report_error(151) - end if - - ! Issue #589 Pedestal density is set manually using neped, - ! but pedestal width = 0. - if ( (abs(rhopedn-1.0D0) <= 1.0D-7).and.((neped-nesep) >= 1.0D-7) ) then - fdiags(1) = rhopedn ; fdiags(2) = neped ; fdiags(3) = nesep - call report_error(152) - end if - end if - - ! Issue #862 : Variable ne0/neped ratio without constraint eq 81 (ne0>neped) - ! -> Potential hollowed density profile - if ( (ioptimz >= 0) .and. (.not.any(icc==81)) ) then - if ( any(ixc == 145 )) call report_error(154) - if ( any(ixc == 6 )) call report_error(155) - end if - end if - end if - ! --------------- - - - ! Cannot use Psep/R and PsepB/qAR limits at the same time - if(any(icc == 68) .and. any(icc == 56)) then - call report_error(178) - endif - - if ((any(ixc==145)) .and. (boundl(145) < fgwsep)) then !if lower bound of fgwped < fgwsep - fdiags(1) = boundl(145); fdiags(2) = fgwsep - call report_error(186) - end if - - if (any(icc == 78)) then - - !If Reinke criterion is used tesep is calculated and cannot be an - !iteration variable - if (any(ixc == 119)) then - call report_error(219) - endif - - !If Reinke criterion is used need to enforce LH-threshold - !using Martin scaling for consistency - if (.not. ilhthresh == 6) then - call report_error(218) - endif - if (.not. any(icc==15) .and. (ipedestal .ne. 3)) then - call report_error(218) - endif - - - endif - - if (any(icc == 78)) then - - !If Reinke criterion is used tesep is calculated and cannot be an - !iteration variable - if (any(ixc == 119)) then - call report_error(219) - endif - - !If Reinke criterion is used need to enforce LH-threshold - !using Martin scaling for consistency - if (.not. ilhthresh == 6) then - call report_error(218) - endif - if (.not. any(icc==15) .and. (ipedestal .ne. 3)) then - call report_error(218) - endif - - - endif - - if (i_single_null == 0) then - idivrt = 2 - vgaptop = vgap_xpoint_divertor - shldtth = shldlth - d_vv_top = d_vv_bot - call report_error(272) - else ! i_single_null == 1 - idivrt = 1 - end if - - - ! Tight aspect ratio options (ST) - ! -------------------------------- - if ( itart == 1 ) then - - icase = 'Tight aspect ratio tokamak model' - - ! Disabled Forcing that no inboard breeding blanket is used - ! Disabled iblnkith = 0 - - ! Check if the choice of plasma current is addapted for ST - ! 2 : Peng Ip scaling (See STAR code documentation) - ! 9 : Fiesta Ip scaling - if (i_plasma_current /= 2 .and. i_plasma_current /= 9) then - idiags(1) = i_plasma_current ; call report_error(37) - end if - - !! If using Peng and Strickler (1986) model (itartpf == 0) - ! Overwrite the location of the TF coils - ! 2 : PF coil on top of TF coil - ! 3 : PF coil outside of TF coil - if (itartpf == 0) then - ipfloc(1) = 2 - ipfloc(2) = 3 - ipfloc(3) = 3 - end if - - ! Water cooled copper magnets initalisation / checks - if ( i_tf_sup == 0 ) then - ! Check if the initial centrepost coolant loop adapted to the magnet technology - ! Ice cannot flow so tcoolin > 273.15 K - if ( tcoolin < 273.15D0 ) call report_error(234) - - ! Temperature of the TF legs cannot be cooled down - if ( abs(tlegav+1.0D0) > epsilon(tlegav) .and. tlegav < 273.15D0 ) call report_error(239) - - ! Check if conductor upper limit is properly set to 50 K or below - if ( any(ixc == 20 ) .and. boundu(20) < 273.15D0 ) call report_error(241) - - ! Call a lvl 3 error if superconductor magnets are used - else if ( i_tf_sup == 1 ) then - call report_error(233) - - ! Aluminium magnets initalisation / checks - ! Initialize the CP conductor temperature to cryogenic temperature for cryo-al magnets (20 K) - else if ( i_tf_sup == 2 ) then - - ! Call a lvl 3 error if the inlet coolant temperature is too large - ! Motivation : ill-defined aluminium resistivity fit for T > 40-50 K - if ( tcoolin > 40.0D0 ) call report_error(235) - - ! Check if the leg average temperature is low enough for the resisitivity fit - if ( tlegav > 50.0D0 ) call report_error(238) - - ! Check if conductor upper limit is properly set to 50 K or below - if ( any(ixc == 20 ) .and. boundu(20) > 50.0D0 ) call report_error(240) - - ! Otherwise intitialise the average conductor temperature at - tcpav = tcoolin - - end if - - ! Check if the boostrap current selection is addapted to ST - if (i_bootstrap_current == 1) call report_error(38) - - ! Check if a single null divertor is used in double null machine - if (i_single_null == 0 .and. (ftar == 1.0 .or. ftar == 0.0)) then - call report_error(39) - end if - - ! Set the TF coil shape to picture frame (if default value) - if ( i_tf_shape == 0 ) i_tf_shape = 2 - - ! Warning stating that the CP fast neutron fluence calculation - ! is not addapted for cryoaluminium calculations yet - if ( i_tf_sup == 2 .and. any( icc == 85 ) .and. itart == 1 ) then - call report_error(260) - end if - - ! Setting the CP joints default options : - ! 0 : No joints for superconducting magents (i_tf_sup = 1) - ! 1 : Sliding joints for resistive magnets (i_tf_sup = 0, 2) - if ( i_cp_joints == -1 ) then - if ( i_tf_sup == 1 ) then - i_cp_joints = 0 - else - i_cp_joints = 1 - end if - end if - - ! Checking the CP TF top radius - if ( ( abs(r_cp_top) > epsilon(r_cp_top) .or. any(ixc(1:nvar) == 174) ) & - .and. i_r_cp_top /= 1 ) then - call report_error(267) - end if - ! -------------------------------- - - - ! Conventionnal aspect ratios specific - ! ------------------------------------ - else - - if (i_plasma_current == 2 .or. i_plasma_current == 9) call report_error(40) - - ! Set the TF coil shape to PROCESS D-shape (if default value) - if ( i_tf_shape == 0 ) i_tf_shape = 1 - - ! Check PF coil configurations - j = 0 ; k = 0 - do i = 1, ngrp - if ((ipfloc(i) /= 2).and.(ncls(i) /= 2)) then - idiags(1) = i ; idiags(2) = ncls(i) - call report_error(41) - end if - - if (ipfloc(i) == 2) then - j = j + 1 - k = k + ncls(i) - end if - end do - - if (k == 1) call report_error(42) - if (k > 2) call report_error(43) - if ((i_single_null == 1).and.(j < 2)) call report_error(44) - - ! Constraint 10 is dedicated to ST designs with demountable joints - if ( any(icc(1:neqns+nineqns) == 10 ) ) call report_error(259) - - end if - ! ------------------------------------ - - ! Pulsed power plant model - if (lpulse == 1) then - icase = 'Pulsed tokamak model' - else - esbldgm3 = 0.0D0 - end if - - ! Ensure minimum cycle time constraint is turned off - ! (not currently available, as routine thrmal has been commented out) - if ( any(icc == 42) ) then - call report_error(164) - end if - - - - ! TF coil - ! ------- - ! TF stress model not defined of r_tf_inboard = 0 - ! Unless i_tf_stress_model == 2 - ! -> If bore + gapoh + ohcth = 0 and fixed and stress constraint is used - ! Generate a lvl 3 error proposing not to use any stress constraints - if ( ( .not. ( any(ixc == 16 ) .or. any(ixc == 29 ) .or. any(ixc == 42 ) ) ) & ! No bore,gapoh, ohcth iteration - .and. ( abs(bore + gapoh + ohcth + precomp) < epsilon(bore) ) & ! bore + gapoh + ohcth = 0 - .and. ( any(icc == 31) .or. any(icc == 32) ) & ! Stress constraints (31 or 32) is used - .and. ( i_tf_stress_model /= 2 ) ) then ! TF stress model can't handle no bore - - call report_error(246) - stop 1 - end if - - ! Make sure that plane stress model is not used for resistive magnets - if ( i_tf_stress_model == 1 .and. i_tf_sup /= 1 ) call report_error(253) - - ! bucking cylinder default option setting - ! - bucking (casing) for SC i_tf_bucking ( i_tf_bucking = 1 ) - ! - No bucking for copper magnets ( i_tf_bucking = 0 ) - ! - Bucking for aluminium magnets ( i_tf_bucking = 1 ) - if ( i_tf_bucking == -1 ) then - if ( i_tf_sup == 0 ) then - i_tf_bucking = 0 - else - i_tf_bucking = 1 - end if - end if - - ! Ensure that the TF isnt placed against the - ! CS which is now outside it - if ( i_tf_bucking >= 2 .and. tf_in_cs == 1 ) then - call report_error(281) - end if - ! Ensure that no pre-compression structure - ! is used for bucked and wedged design - if ( i_tf_bucking >= 2 .and. iprecomp == 1 ) then - call report_error(252) - end if - - ! Number of stress calculation layers - ! +1 to add in the inboard TF coil case on the plasma side, per Issue #1509 - n_tf_stress_layers = i_tf_bucking + n_tf_graded_layers + 1 - - ! If TFC sidewall has not been set by user - if ( casths < 0.1d-10 ) tfc_sidewall_is_fraction = .true. - - ! If inboard TF coil case plasma side thickness has not been set by user - if( casthi < 0.1d-10 ) casthi_is_fraction = .true. - - ! Setting the default cryo-plants efficiencies - !-! - if ( abs(eff_tf_cryo + 1.0D0) < epsilon(eff_tf_cryo) ) then - - ! The ITER cyoplant efficiency is used for SC - if ( i_tf_sup == 1 ) then - eff_tf_cryo = 0.13D0 - - ! Strawbrige plot extrapolation is used for Cryo-Al - else if ( i_tf_sup == 2 ) then - eff_tf_cryo = 0.40D0 - end if - - ! Cryo-plane efficiency must be in [0-1.0] - else if ( eff_tf_cryo > 1.0D0 .or. eff_tf_cryo < 0.0D0 ) then - call report_error(248) - stop 1 - end if - !-! - - ! Integer turns option not yet available for REBCO taped turns - !-! - if ( i_tf_sc_mat == 6 .and. i_tf_turns_integer == 1 ) then - call report_error(254) - stop 1 - end if - !-! - - - ! Setting up insulation layer young modulae default values [Pa] - !-! - if ( abs(eyoung_ins - 1.0D8 ) < epsilon(eyoung_ins) ) then - - ! Copper magnets, no insulation material defined - ! But use the ITER design by default - if ( i_tf_sup == 0 ) then - eyoung_ins = 20.0D9 - - ! SC magnets - ! Value from DDD11-2 v2 2 (2009) - else if ( i_tf_sup == 1 ) then - eyoung_ins = 20.0D9 - - ! Cryo-aluminum magnets (Kapton polymer) - else if ( i_tf_sup == 2 ) then - eyoung_ins = 2.5D9 - end if - end if - !-! - - !-! Setting the default WP geometry - !-! - if ( i_tf_wp_geom == -1 ) then - if ( i_tf_turns_integer == 0 ) i_tf_wp_geom = 1 - if ( i_tf_turns_integer == 1 ) i_tf_wp_geom = 0 - end if - !-! - - !-! Setting the TF coil conductor elastic properties - !-! - if ( i_tf_cond_eyoung_axial == 0 ) then - ! Conductor stiffness is not considered - eyoung_cond_axial = 0 - eyoung_cond_trans = 0 - else if ( i_tf_cond_eyoung_axial == 2 ) then - ! Select sensible defaults from the literature - select case (i_tf_sc_mat) - case (1,4,5) - ! Nb3Sn: Nyilas, A et. al, Superconductor Science and Technology 16, no. 9 (2003): 1036–42. https://doi.org/10.1088/0953-2048/16/9/313. - eyoung_cond_axial = 32D9 - case (2) - ! Bi-2212: Brown, M. et al, IOP Conference Series: Materials Science and Engineering 279 (2017): 012022. https://doi.org/10.1088/1757-899X/279/1/012022. - eyoung_cond_axial = 80D9 - case (3,7) - ! NbTi: Vedrine, P. et. al, IEEE Transactions on Applied Superconductivity 9, no. 2 (1999): 236–39. https://doi.org/10.1109/77.783280. - eyoung_cond_axial = 6.8D9 - case (6,8,9) - ! REBCO: Fujishiro, H. et. al, Physica C: Superconductivity, 426–431 (2005): 699–704. https://doi.org/10.1016/j.physc.2005.01.045. - eyoung_cond_axial = 145D9 - end select - - if ( i_tf_cond_eyoung_trans == 0) then - ! Transverse stiffness is not considered - eyoung_cond_trans = 0 - else - ! Transverse stiffness is significant - eyoung_cond_trans = eyoung_cond_axial - end if - end if - !-! - - ! Check if the WP/conductor radial thickness (dr_tf_wp) is large enough - ! To contains the insulation, cooling and the support structure - ! Rem : Only verified if the WP thickness is used - if ( any(ixc(1:nvar) == 140) ) then - - ! Minimal WP thickness - if ( i_tf_sup == 1 ) then - dr_tf_wp_min = 2.0D0 * ( tinstf + tfinsgap + thicndut + dhecoil ) - - ! Steel conduit thickness (can be an iteration variable) - if ( any(ixc(1:nvar) == 58 ) ) then - dr_tf_wp_min = dr_tf_wp_min + 2.0D0 * boundl(58) - else - dr_tf_wp_min = dr_tf_wp_min + 2.0D0 * thwcndut - end if - - ! Minimal conductor layer thickness - else if ( i_tf_sup == 0 .or. i_tf_sup == 2 ) then - dr_tf_wp_min = 2.0D0 * ( thicndut + tinstf ) + 4.0D0 * rcool - end if - - if ( boundl(140) < dr_tf_wp_min ) then - fdiags(1) = dr_tf_wp_min - call report_error(255) - end if - end if - - ! Setting t_turn_tf_is_input to true if t_turn_tf is an input - if ( abs(t_turn_tf) < epsilon(t_turn_tf) ) then - t_turn_tf_is_input = .false. - else - t_turn_tf_is_input = .true. - end if - - ! Impossible to set the turn size of integer turn option - if ( t_turn_tf_is_input .and. i_tf_turns_integer == 1 ) then - call report_error(269) - end if - - if ( i_tf_wp_geom /= 0 .and. i_tf_turns_integer == 1 ) then - call report_error(283) - end if - - if ( i_bootstrap_current == 5 .and. i_diamagnetic_current /= 0 ) then - call report_error(284) - end if - - ! Setting t_cable_tf_is_input to true if t_cable_tf is an input - if ( abs(t_cable_tf) < epsilon(t_cable_tf) ) then - t_cable_tf_is_input = .false. - else - t_cable_tf_is_input = .true. - end if - - ! Impossible to set the cable size of integer turn option - if ( t_cable_tf_is_input .and. i_tf_turns_integer == 1 ) then - call report_error(269) - end if - - ! Impossible to set both the TF coil turn and the cable dimension - if ( t_turn_tf_is_input .and. t_cable_tf_is_input ) then - call report_error(271) - end if - - ! Checking the SC temperature for LTS - if ( ( i_tf_sc_mat == 1 .or. & - i_tf_sc_mat == 3 .or. & - i_tf_sc_mat == 4 .or. & - i_tf_sc_mat == 5 ) .and. tftmp > 10.0D0 ) then - call report_error(270) - end if - ! ------- - - - - ! PF coil resistivity is zero if superconducting - if (ipfres == 0) pfclres = 0.0D0 - - ! If there is no NBI, then hot beam density should be zero - if (irfcd == 1) then - if ((iefrf /= 5).and.(iefrf /= 8)) rnbeam = 0.0D0 - else - rnbeam = 0.0D0 - end if - - ! Set inboard blanket thickness to zero if no inboard blanket switch - ! used (Issue #732) - if (iblnkith == 0) blnkith = 0.0D0 - - ! Solid breeder assumed if ipowerflow=0 - - !if (ipowerflow == 0) blkttype = 3 - - ! Set coolant fluid type - - !if ((blkttype == 1).or.(blkttype == 2)) then - ! coolwh = 2 ! water - !else - ! coolwh = 1 ! helium - !end if - - ! But... set coolant to water if blktmodel > 0 - ! Although the *blanket* is by definition helium-cooled in this case, - ! the shield etc. are assumed to be water-cooled, and since water is - ! heavier (and the unit cost of pumping it is higher), the calculation - ! for coolmass is better done with coolwh=2 if blktmodel > 0 to give - ! slightly pessimistic results. - - !if (blktmodel > 0) then - ! secondary_cycle = 0 - ! blkttype = 3 ! HCPB - ! coolwh = 2 - !end if - - ! Ensure that blanket material fractions allow non-zero space for steel - ! CCFE HCPB Model - - if (istell == 0) then - if ((iblanket == 1).or.(iblanket == 3)) then - fsum = breeder_multiplier + vfcblkt + vfpblkt - if (fsum >= 1.0D0) then - idiags(1) = iblanket - fdiags(2) = breeder_multiplier - fdiags(3) = vfcblkt - fdiags(4) = vfpblkt - fdiags(5) = fsum - call report_error(165) - end if - end if - end if - - ! Initialise superconductor cable parameters - if(i_tf_sup==1)then - call initialise_cables() - end if - - ! Check that the temperature margins are not overdetermined - if(tmargmin>0.0001d0)then - ! This limit has been input and will be applied to both TFC and CS - if(tmargmin_tf>0.0001d0)then - write(*,*)'tmargmin_tf and tmargmin should not both be specified in IN.DAT.' - write(*,*)'tmargmin_tf has been ignored.' - end if - if(tmargmin_cs>0.0001d0)then - write(*,*)'tmargmin_cs and tmargmin should not both be specified in IN.DAT.' - write(*,*)'tmargmin_cs has been ignored.' - end if - tmargmin_tf = tmargmin - tmargmin_cs = tmargmin - end if - - if (tauee_in.ge.1.0D-10.and.isc.ne.48) then - ! Report error if confinement time is in the input - ! but the scaling to use it is not selected. - call report_error(220) - end if - - if (aspect.gt.1.7D0.and.isc.eq.46) then - ! NSTX scaling is for A<1.7 - call report_error(221) - end if - - if (i_plasma_current.eq.2.and.isc.eq.42) then - call report_error(222) - end if - - ! Cannot use temperature margin constraint with REBCO TF coils - if(any(icc == 36) .and. ((i_tf_sc_mat == 8).or.(i_tf_sc_mat == 9))) then - call report_error(265) - endif - - ! Cannot use temperature margin constraint with REBCO CS coils - if(any(icc == 60) .and. (isumatoh == 8)) then - call report_error(264) - endif - - ! Cold end of the cryocooler should be colder than the TF - if(tmpcry > tftmp) then - call report_error(273) - endif - - ! Cannot use TF coil strain limit if i_str_wp is off: - if(any(icc == 88) .and. (i_str_wp == 0)) then - call report_error(275) - endif - - errors_on = .false. - - ! Disable error logging only after all checks have been performed. - ! (CPSS #1582: Why is error logging disabled at all?) - errors_on = .false. - - -end subroutine check diff --git a/tests/integration/test_vmcon.py b/tests/integration/test_vmcon.py index 00022265a..9147f40e2 100644 --- a/tests/integration/test_vmcon.py +++ b/tests/integration/test_vmcon.py @@ -5,14 +5,15 @@ Expected answers for tests 1 to 3 are given in VMCON documentation ANL-80-64 """ -from process.evaluators import Evaluators -from process.fortran import init_module -from process.fortran import error_handling + import pytest import numpy as np import logging from abc import ABC, abstractmethod from process.solver import get_solver +from process.init import init_all_module_vars +from process.evaluators import Evaluators +from process.fortran import error_handling # Debug-level terminal output logging logger = logging.getLogger(__name__) @@ -24,7 +25,7 @@ @pytest.fixture(autouse=True) def reinit(): """Re-initialise Fortran module variables before each test is run.""" - init_module.init_all_module_vars() + init_all_module_vars() class Case: diff --git a/tests/unit/conftest.py b/tests/unit/conftest.py index 3fa07185b..bd3ff4d84 100644 --- a/tests/unit/conftest.py +++ b/tests/unit/conftest.py @@ -2,9 +2,10 @@ Define fixtures that will be shared across unit test modules. """ + import pytest -from process import fortran from pathlib import Path +from process.init import init_all_module_vars @pytest.fixture(scope="module", autouse=True) @@ -17,7 +18,7 @@ def reinit_fix(): the module variable values. autouse ensures that this fixture is used automatically by any test function in the unit directory. """ - fortran.init_module.init_all_module_vars() + init_all_module_vars() @pytest.fixture() diff --git a/tests/unit/test_availability.py b/tests/unit/test_availability.py index 6c08ae364..6c7df164a 100644 --- a/tests/unit/test_availability.py +++ b/tests/unit/test_availability.py @@ -10,6 +10,7 @@ from process.fortran import times_variables as tv from process.fortran import ife_variables as ifev from process.fortran import divertor_variables as dv +from process.init import init_all_module_vars import pytest from pytest import approx @@ -81,7 +82,7 @@ def test_avail_1(monkeypatch, availability): :type availability: tests.unit.test_availability.availability (functional fixture) """ # Initialise fortran variables to keep test isolated from others - fortran.init_module.init_all_module_vars() + init_all_module_vars() # Mock module vars monkeypatch.setattr(cv, "iavail", 1) @@ -104,7 +105,7 @@ def test_avail_1(monkeypatch, availability): assert pytest.approx(cfactr_exp) == cfactr_obs # Initialise fortran variables again to reset for other tests - fortran.init_module.init_all_module_vars() + init_all_module_vars() def test_calc_u_unplanned_hcd(availability): @@ -486,6 +487,7 @@ def test_avail_2(monkeypatch, availability): :param availability: fixture containing an initialised `Availability` object :type availability: tests.unit.test_availability.availability (functional fixture) """ + # Mock return values for for functions called in avail_2 def mock_calc_u_planned(*args, **kwargs): return 0.01 @@ -567,8 +569,7 @@ def test_avail_st(monkeypatch, availability): :type availability: tests.unit.test_availability.availability (functional fixture) """ # Initialise fortran variables to keep test isolated from others - fortran.init_module.init_all_module_vars() - + init_all_module_vars() monkeypatch.setattr(cv, "tmain", 1.0) monkeypatch.setattr(cv, "tlife", 30.0) monkeypatch.setattr(cv, "u_unplanned_cp", 0.05) @@ -588,9 +589,6 @@ def test_avail_st(monkeypatch, availability): assert pytest.approx(cv.cfactr) == 0.27008858 assert pytest.approx(cv.cpfact, abs=1.0e-8) == 0.00015005 - # Initialise fortran variables again to reset for other tests - fortran.init_module.init_all_module_vars() - @pytest.mark.parametrize("i_tf_sup, exp", ((1, 6.337618), (0, 4))) def test_cp_lifetime(monkeypatch, availability, i_tf_sup, exp): diff --git a/tests/unit/test_input.py b/tests/unit/test_input.py index 96ddbd865..8756a02cc 100644 --- a/tests/unit/test_input.py +++ b/tests/unit/test_input.py @@ -1,6 +1,7 @@ import pytest from process import fortran from process.utilities.f2py_string_patch import string_to_f2py_compatible +import process.init as init def _create_input_file(directory, content: str): @@ -57,6 +58,6 @@ def test_parse_real(epsvmc, expected, tmp_path): fortran.global_variables.fileprefix, _create_input_file(tmp_path, f"epsvmc = {epsvmc}"), ) - fortran.init_module.init() + init.init_process() assert fortran.numerics.epsvmc.item() == expected From 9c7a2bdf16a55694555513a4d63b657e8c951a79 Mon Sep 17 00:00:00 2001 From: Timothy Nunn Date: Thu, 14 Nov 2024 17:16:30 +0000 Subject: [PATCH 2/7] Add new error classes for PROCESS-raised errors --- process/exceptions.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) create mode 100644 process/exceptions.py diff --git a/process/exceptions.py b/process/exceptions.py new file mode 100644 index 000000000..b7d945d11 --- /dev/null +++ b/process/exceptions.py @@ -0,0 +1,19 @@ +class ProcessException(Exception): + def __init__(self, *args, **kwargs): + super().__init__(*args) + self._diagnostics = kwargs + + def __str__(self): + exception_message = super().__str__() + diagnostics_message = "\n".join( + [f"\t{d}: {repr(v)}" for d, v in self._diagnostics.items()] + ) + + if diagnostics_message: + return f"{exception_message}\n{diagnostics_message}" + + return exception_message + + +class ProcessValueError(ProcessException, ValueError): + pass From 54d72c351b65108d1c4795c9a1ed25927d0b5ef3 Mon Sep 17 00:00:00 2001 From: Timothy Nunn Date: Thu, 21 Nov 2024 17:08:47 +0000 Subject: [PATCH 3/7] Convert process input checking routines to Python --- process/exceptions.py | 10 + process/init.py | 842 ++++++++++++++++++++++++++++++++- source/fortran/init_module.f90 | 752 ----------------------------- 3 files changed, 851 insertions(+), 753 deletions(-) diff --git a/process/exceptions.py b/process/exceptions.py index b7d945d11..f087bb129 100644 --- a/process/exceptions.py +++ b/process/exceptions.py @@ -1,4 +1,6 @@ class ProcessException(Exception): + """A base Exception to derive other PROCESS exceptions from""" + def __init__(self, *args, **kwargs): super().__init__(*args) self._diagnostics = kwargs @@ -15,5 +17,13 @@ def __str__(self): return exception_message +class ProcessValidationError(ProcessException): + """Exception raised when validating PROCESS input. + + E.g. initial values, constraint/variable combinations, switch combinations""" + + pass + + class ProcessValueError(ProcessException, ValueError): pass diff --git a/process/init.py b/process/init.py index f58a51c79..346082818 100644 --- a/process/init.py +++ b/process/init.py @@ -1,4 +1,6 @@ +from warnings import warn import process.fortran as fortran +from process.exceptions import ProcessValidationError def init_process(): @@ -26,7 +28,7 @@ def init_process(): fortran.stellarator_module.stinit() # Check input data for errors/ambiguities - fortran.init_module.check() + check_process() fortran.main_module.run_summary() @@ -250,3 +252,841 @@ def initialise_iterative_variables(): fortran.define_iteration_variables.init_itv_173() fortran.define_iteration_variables.init_itv_174() fortran.define_iteration_variables.init_itv_175() + + +def check_process(): + """Routine to reset specific variables if certain options are + being used + author: P J Knight, CCFE, Culham Science Centre + None + This routine performs a sanity check of the input variables + and ensures other dependent variables are given suitable values. + """ + # error_handling.errors_on = True + + # Check that there are sufficient iteration variables + if fortran.numerics.nvar < fortran.numerics.neqns: + raise ProcessValidationError( + "Insufficient iteration variables to solve the problem! NVAR < NEQNS", + nvar=fortran.numerics.nvar, + neqns=fortran.numerics.neqns, + ) + + # Check that sufficient elements of ixc and icc have been specified + if (fortran.numerics.ixc[: fortran.numerics.nvar] == 0).any(): + raise ProcessValidationError( + "The number of iteration variables specified is smaller than the number stated in ixc", + nvar=fortran.numerics.nvar, + ) + + if ( + fortran.numerics.icc[: fortran.numerics.neqns + fortran.numerics.nineqns] == 0 + ).any(): + raise ProcessValidationError( + "The number of constraints specified is smaller than the number stated in neqns+nineqns", + neqns=fortran.numerics.neqns, + nineqns=fortran.numerics.nineqns, + ) + + # Deprecate constraints + for depcrecated_constraint in [3, 4, 10, 74, 42]: + if ( + fortran.numerics.icc[: fortran.numerics.neqns + fortran.numerics.nineqns] + == depcrecated_constraint + ).any(): + raise ProcessValidationError( + "Constraint equation is no longer available", icc=depcrecated_constraint + ) + + # MDK Report error if constraint 63 is used with old vacuum model + if ( + fortran.numerics.icc[: fortran.numerics.neqns + fortran.numerics.nineqns] == 63 + ).any() and fortran.vacuum_variables.vacuum_model != "simple": + raise ProcessValidationError( + "Constraint 63 is requested without the correct vacuum model (simple)" + ) + + # Fuel ion fractions must add up to 1.0 + if ( + abs( + 1.0 + - fortran.physics_variables.f_deuterium + - fortran.physics_variables.f_tritium + - fortran.physics_variables.f_helium3 + ) + > 1e-6 + ): + raise ProcessValidationError( + "Fuel ion fractions do not sum to 1.0", + f_deuterium=fortran.physics_variables.f_deuterium, + f_tritium=fortran.physics_variables.f_tritium, + f_helium3=fortran.physics_variables.f_helium3, + ) + + if fortran.physics_variables.f_tritium < 1.0e-3: # tritium fraction is negligible + fortran.buildings_variables.triv = 0.0 + fortran.heat_transport_variables.trithtmw = 0.0 + + if fortran.impurity_radiation_module.fimp[1] != 0.1: + raise ProcessValidationError( + "The thermal alpha/electron density ratio should be controlled using ralpne (itv 109) and not fimp(2)." + "fimp(2) should be removed from the input file, or set to the default value 0.1D0." + ) + + # Impurity fractions + for imp in range(fortran.impurity_radiation_module.nimp): + fortran.impurity_radiation_module.impurity_arr_frac[ + imp + ] = fortran.impurity_radiation_module.fimp[imp] + + # Stop the run if oacdcp is used as an optimisation variable + # As the current density is now calculated from bt without constraint 10 + + if (fortran.numerics.ixc[: fortran.numerics.nvar] == 12).any(): + raise ProcessValidationError( + "The 1/R toroidal B field dependency constraint is being depreciated" + ) + + # Plasma profile consistency checks + if fortran.ife_variables.ife != 1: + if fortran.physics_variables.ipedestal == 1: + + # Temperature checks + if fortran.physics_variables.teped < fortran.physics_variables.tesep: + raise ProcessValidationError( + "Pedestal temperature is lower than separatrix temperature", + teped=fortran.physics_variables.teped, + tesep=fortran.physics_variables.tesep, + ) + + if (abs(fortran.physics_variables.rhopedt - 1.0) <= 1e-7) and ( + (fortran.physics_variables.teped - fortran.physics_variables.tesep) + >= 1e-7 + ): + warn( + f"Temperature pedestal is at plasma edge, but teped " + f"({fortran.physics_variables.teped}) differs from tesep " + f"({fortran.physics_variables.tesep})" + ) + + # Core temperature should always be calculated (later) as being + # higher than the pedestal temperature, if and only if the + # volume-averaged temperature never drops below the pedestal + # temperature. Prevent this by adjusting te, and its lower bound + # (which will only have an effect if this is an optimisation run) + if fortran.physics_variables.te <= fortran.physics_variables.teped: + warn( + f"Volume-averaged temperature ({fortran.physics_variables.te}) has been " + f"forced to exceed input pedestal height ({fortran.physics_variables.teped}). " + "Changing to te = teped*1.001" + ) + fortran.physics_variables.te = fortran.physics_variables.teped * 1.001 + + if ( + fortran.numerics.ioptimz >= 0 + and (fortran.numerics.ixc[: fortran.numerics.nvar] == 4).any() + and fortran.numerics.boundl[3] < fortran.physics_variables.teped * 1.001 + ): + warn( + "Lower limit of volume averaged electron temperature (te) has been raised to ensure te > teped" + ) + fortran.numerics.boundl[3] = fortran.physics_variables.teped * 1.001 + fortran.numerics.boundu[3] = max( + fortran.numerics.boundu[3], fortran.numerics.boundl[3] + ) + + # Density checks + # Case where pedestal density is set manually + if ( + fortran.physics_variables.fgwped < 0 + or not (fortran.numerics.ixc[: fortran.numerics.nvar] == 145).any() + ): + + # Issue #589 Pedestal density is set manually using neped but it is less than nesep. + if fortran.physics_variables.neped < fortran.physics_variables.nesep: + raise ProcessValidationError( + "Density pedestal is lower than separatrix density", + neped=fortran.physics_variables.neped, + nesep=fortran.physics_variables.nesep, + ) + + # Issue #589 Pedestal density is set manually using neped, + # but pedestal width = 0. + if ( + abs(fortran.physics_variables.rhopedn - 1.0) <= 1e-7 + and ( + fortran.physics_variables.neped + - fortran.physics_variables.nesep + ) + >= 1e-7 + ): + warn( + "Density pedestal is at plasma edge " + f"({fortran.physics_variables.rhopedn = }), but neped " + f"({fortran.physics_variables.neped}) differs from " + f"nesep ({fortran.physics_variables.nesep})" + ) + + # Issue #862 : Variable ne0/neped ratio without constraint eq 81 (ne0>neped) + # -> Potential hollowed density profile + if ( + fortran.numerics.ioptimz >= 0 + and not ( + fortran.numerics.icc[ + : fortran.numerics.neqns + fortran.numerics.nineqns + ] + == 81 + ).any() + ): + if (fortran.numerics.ixc[: fortran.numerics.nvar] == 145).any(): + warn("neped set with fgwped without constraint eq 81 (neped 273.15 K + if fortran.tfcoil_variables.tcoolin < 273.15: + raise ProcessValidationError( + "Coolant temperature (tcoolin) cannot be < 0 C (273.15 K) for water cooled copper magents" + ) + + # Temperature of the TF legs cannot be cooled down + if ( + fortran.tfcoil_variables.tlegav > 0 + and fortran.tfcoil_variables.tlegav < 273.15 + ): + raise ProcessValidationError( + "TF legs conductor temperature (tlegav) cannot be < 0 C (273.15 K) for water cooled magents" + ) + + # Check if conductor upper limit is properly set to 50 K or below + if ( + fortran.numerics.ixc[: fortran.numerics.nvar] == 20 + ).any() and fortran.numerics.boundu[19] < 273.15: + raise ProcessValidationError( + "Too low CP conductor temperature (tcpav). Lower limit for copper > 273.15 K" + ) + + # Call a lvl 3 error if superconductor magnets are used + elif fortran.tfcoil_variables.i_tf_sup == 1: + warn( + "Joints res not cal. for SC (itart = 1) TF (fortran.tfcoil_variables.i_tf_sup = 1)" + ) + + # Aluminium magnets initalisation / checks + # Initialize the CP conductor temperature to cryogenic temperature for cryo-al magnets (20 K) + elif fortran.tfcoil_variables.i_tf_sup == 2: + + # Call a lvl 3 error if the inlet coolant temperature is too large + # Motivation : ill-defined aluminium resistivity fit for T > 40-50 K + if fortran.tfcoil_variables.tcoolin > 40.0: + raise ProcessValidationError( + "Coolant temperature (tcoolin) should be < 40 K for the cryo-al resistivity to be defined" + ) + + # Check if the leg average temperature is low enough for the resisitivity fit + if fortran.tfcoil_variables.tlegav > 50.0: + raise ProcessValidationError( + "TF legs conductor temperature (tlegav) should be < 40 K for the cryo-al resistivity to be defined" + ) + + # Check if conductor upper limit is properly set to 50 K or below + if ( + fortran.numerics.ixc[: fortran.numerics.nvar] == 20 + ).any() and fortran.numerics.boundu[19] > 50.0: + raise ProcessValidationError( + "Too large CP conductor temperature (tcpav). Upper limit for cryo-al < 50 K" + ) + + # Otherwise intitialise the average conductor temperature at + fortran.tfcoil_variables.tcpav = fortran.tfcoil_variables.tcoolin + + # Check if the boostrap current selection is addapted to ST + if fortran.physics_variables.i_bootstrap_current == 1: + raise ProcessValidationError( + "Invalid boostrap current law for ST, do not use i_bootstrap_current = 1" + ) + + # Check if a single null divertor is used in double null machine + if fortran.physics_variables.i_single_null == 0 and ( + fortran.physics_variables.ftar == 1.0 + or fortran.physics_variables.ftar == 0.0 + ): + warn("Operating with a single null in a double null machine") + + # Set the TF coil shape to picture frame (if default value) + if fortran.tfcoil_variables.i_tf_shape == 0: + fortran.tfcoil_variables.i_tf_shape = 2 + + # Warning stating that the CP fast neutron fluence calculation + # is not addapted for cryoaluminium calculations yet + if ( + fortran.tfcoil_variables.i_tf_sup == 2 + and ( + fortran.numerics.icc[ + : fortran.numerics.neqns + fortran.numerics.nineqns + ] + == 85 + ).any() + and fortran.physics_variables.itart == 1 + ): + raise ProcessValidationError( + "Al TF coil fluence not calculated properly for Al CP, do not use constraint 85" + ) + + # Setting the CP joints default options : + # 0 : No joints for superconducting magents (fortran.tfcoil_variables.i_tf_sup = 1) + # 1 : Sliding joints for resistive magnets (fortran.tfcoil_variables.i_tf_sup = 0, 2) + if fortran.tfcoil_variables.i_cp_joints == -1: + if fortran.tfcoil_variables.i_tf_sup == 1: + fortran.tfcoil_variables.i_cp_joints = 0 + else: + fortran.tfcoil_variables.i_cp_joints = 1 + + # Checking the CP TF top radius + if ( + abs(fortran.build_variables.r_cp_top) > 0 + or (fortran.numerics.ixc[: fortran.numerics.nvar] == 174).any() + ) and fortran.build_variables.i_r_cp_top != 1: + raise ProcessValidationError( + "To set the TF CP top value, you must use i_r_cp_top = 1" + ) + + # Conventionnal aspect ratios specific + else: + + if ( + fortran.physics_variables.i_plasma_current == 2 + or fortran.physics_variables.i_plasma_current == 9 + ): + raise ProcessValidationError( + "i_plasma_current=2,9 is not a valid option for a non-TART device" + ) + + # Set the TF coil shape to PROCESS D-shape (if default value) + if fortran.tfcoil_variables.i_tf_shape == 0: + fortran.tfcoil_variables.i_tf_shape = 1 + + # Check PF coil configurations + j = 0 + k = 0 + for i in range(fortran.pfcoil_variables.ngrp): + if ( + fortran.pfcoil_variables.ipfloc[i] != 2 + and fortran.pfcoil_variables.ncls[i] != 2 + ): + raise ProcessValidationError( + "ncls(i) .ne. 2 is not a valid option except for (ipfloc = 2)" + ) + + if fortran.pfcoil_variables.ipfloc[i] == 2: + j = j + 1 + k = k + fortran.pfcoil_variables.ncls[i] + + if k == 1: + raise ProcessValidationError( + "Only 1 divertor coil (ipfloc = 2) is not a valid configuration" + ) + if k > 2: + raise ProcessValidationError( + "More than 2 divertor coils (ipfloc = 2) is not a valid configuration" + ) + if fortran.physics_variables.i_single_null == 1 and j < 2: + raise ProcessValidationError( + "If snull=1, use 2 individual divertor coils (ipfloc = 2, 2; ncls = 1, 1)" + ) + + # Constraint 10 is dedicated to ST designs with demountable joints + if ( + fortran.numerics.icc[: fortran.numerics.neqns + fortran.numerics.nineqns] + == 10 + ).any(): + raise ProcessValidationError( + "Constraint equation 10 (CP lifetime) to used with ST desing (itart=1)" + ) + + # Pulsed power plant model + if fortran.pulse_variables.lpulse == 1: + fortran.global_variables.icase = "Pulsed tokamak model" + else: + fortran.buildings_variables.esbldgm3 = 0.0 + + # TF coil + # ------- + # TF stress model not defined of r_tf_inboard = 0 + # Unless i_tf_stress_model == 2 + # -> If bore + gapoh + ohcth = 0 and fixed and stress constraint is used + # Generate a lvl 3 error proposing not to use any stress constraints + if ( + ( + not ( + (fortran.numerics.ixc[: fortran.numerics.nvar] == 16).any() + or (fortran.numerics.ixc[: fortran.numerics.nvar] == 29).any() + or (fortran.numerics.ixc[: fortran.numerics.nvar] == 42).any() + ) + ) # No bore,gapoh, ohcth iteration + and ( + abs( + fortran.build_variables.bore + + fortran.build_variables.gapoh + + fortran.build_variables.ohcth + + fortran.build_variables.precomp + ) + <= 0 + ) # bore + gapoh + ohcth = 0 + and ( + ( + fortran.numerics.icc[ + : fortran.numerics.neqns + fortran.numerics.nineqns + ] + == 31 + ).any() + or ( + fortran.numerics.icc[ + : fortran.numerics.neqns + fortran.numerics.nineqns + ] + == 32 + ).any() + ) # Stress constraints (31 or 32) is used + and ( + fortran.tfcoil_variables.i_tf_stress_model != 2 + ) # TF stress model can't handle no bore + ): + raise ProcessValidationError( + "Invalid stress model if bore + gapoh + ohcth = 0. Don't use constraint 31" + ) + + # Make sure that plane stress model is not used for resistive magnets + if ( + fortran.tfcoil_variables.i_tf_stress_model == 1 + and fortran.tfcoil_variables.i_tf_sup != 1 + ): + raise ProcessValidationError( + "Use generalized plane strain for resistive magnets (i_tf_stress_model = 0 or 2)" + ) + + # bucking cylinder default option setting + # - bucking (casing) for SC i_tf_bucking ( i_tf_bucking = 1 ) + # - No bucking for copper magnets ( i_tf_bucking = 0 ) + # - Bucking for aluminium magnets ( i_tf_bucking = 1 ) + if fortran.tfcoil_variables.i_tf_bucking == -1: + if fortran.tfcoil_variables.i_tf_sup == 0: + fortran.tfcoil_variables.i_tf_bucking = 0 + else: + fortran.tfcoil_variables.i_tf_bucking = 1 + + # Ensure that the TF isnt placed against the + # CS which is now outside it + if ( + fortran.tfcoil_variables.i_tf_bucking >= 2 + and fortran.build_variables.tf_in_cs == 1 + ): + raise ProcessValidationError("Cannot have i_tf_bucking >= 2 when tf_in_cs = 1") + + # Ensure that no pre-compression structure + # is used for bucked and wedged design + if ( + fortran.tfcoil_variables.i_tf_bucking >= 2 + and fortran.build_variables.iprecomp == 1 + ): + raise ProcessValidationError( + "No CS precompression structure for bucked and wedged, use iprecomp = 0" + ) + + # Number of stress calculation layers + # +1 to add in the inboard TF coil case on the plasma side, per Issue #1509 + fortran.tfcoil_variables.n_tf_stress_layers = ( + fortran.tfcoil_variables.i_tf_bucking + + fortran.tfcoil_variables.n_tf_graded_layers + + 1 + ) + + # If TFC sidewall has not been set by user + if fortran.tfcoil_variables.casths < 0.1e-10: + fortran.tfcoil_variables.tfc_sidewall_is_fraction = True + + # If inboard TF coil case plasma side thickness has not been set by user + if fortran.tfcoil_variables.casthi < 0.1e-10: + fortran.tfcoil_variables.casthi_is_fraction = True + + # Setting the default cryo-plants efficiencies + if abs(fortran.tfcoil_variables.eff_tf_cryo + 1) < 1e-6: + + # The ITER cyoplant efficiency is used for SC + if fortran.tfcoil_variables.i_tf_sup == 1: + fortran.tfcoil_variables.eff_tf_cryo = 0.13 + + # Strawbrige plot extrapolation is used for Cryo-Al + elif fortran.tfcoil_variables.i_tf_sup == 2: + fortran.tfcoil_variables.eff_tf_cryo = 0.40 + + # Cryo-plane efficiency must be in [0-1.0] + elif ( + fortran.tfcoil_variables.eff_tf_cryo > 1.0 + or fortran.tfcoil_variables.eff_tf_cryo < 0.0 + ): + raise ProcessValidationError( + "TF cryo-plant efficiency `eff_tf_cryo` must be within [0-1]" + ) + + # Integer turns option not yet available for REBCO taped turns + + if ( + fortran.tfcoil_variables.i_tf_sc_mat == 6 + and fortran.tfcoil_variables.i_tf_turns_integer == 1 + ): + raise ProcessValidationError( + "Integer turns (i_tf_turns_integer = 1) not supported for REBCO (i_tf_sc_mat = 6)" + ) + + # Setting up insulation layer young modulae default values [Pa] + + if fortran.tfcoil_variables.eyoung_ins <= 1.0e8: + + # Copper magnets, no insulation material defined + # But use the ITER design by default + if fortran.tfcoil_variables.i_tf_sup == 0: + fortran.tfcoil_variables.eyoung_ins = 20.0e9 + + # SC magnets + # Value from DDD11-2 v2 2 (2009) + elif fortran.tfcoil_variables.i_tf_sup == 1: + fortran.tfcoil_variables.eyoung_ins = 20.0e9 + + # Cryo-aluminum magnets (Kapton polymer) + elif fortran.tfcoil_variables.i_tf_sup == 2: + fortran.tfcoil_variables.eyoung_ins = 2.5e9 + + # Setting the default WP geometry + + if fortran.tfcoil_variables.i_tf_wp_geom == -1: + if fortran.tfcoil_variables.i_tf_turns_integer == 0: + fortran.tfcoil_variables.i_tf_wp_geom = 1 + if fortran.tfcoil_variables.i_tf_turns_integer == 1: + fortran.tfcoil_variables.i_tf_wp_geom = 0 + + # Setting the TF coil conductor elastic properties + + if fortran.tfcoil_variables.i_tf_cond_eyoung_axial == 0: + # Conductor stiffness is not considered + fortran.tfcoil_variables.eyoung_cond_axial = 0 + fortran.tfcoil_variables.eyoung_cond_trans = 0 + elif fortran.tfcoil_variables.i_tf_cond_eyoung_axial == 2: + # Select sensible defaults from the literature + if fortran.tfcoil_variables.i_tf_sc_mat in [1, 4, 5]: + # Nb3Sn: Nyilas, A et. al, Superconductor Science and Technology 16, no. 9 (2003): 1036–42. https://doi.org/10.1088/0953-2048/16/9/313. + fortran.tfcoil_variables.eyoung_cond_axial = 32e9 + elif fortran.tfcoil_variables.i_tf_sc_mat == 2: + # Bi-2212: Brown, M. et al, IOP Conference Series: Materials Science and Engineering 279 (2017): 012022. https://doi.org/10.1088/1757-899X/279/1/012022. + fortran.tfcoil_variables.eyoung_cond_axial = 80e9 + elif fortran.tfcoil_variables.i_tf_sc_mat in [3, 7]: + # NbTi: Vedrine, P. et. al, IEEE Transactions on Applied Superconductivity 9, no. 2 (1999): 236–39. https://doi.org/10.1109/77.783280. + fortran.tfcoil_variables.eyoung_cond_axial = 6.8e9 + elif fortran.tfcoil_variables.i_tf_sc_mat in [6, 8, 9]: + # REBCO: Fujishiro, H. et. al, Physica C: Superconductivity, 426–431 (2005): 699–704. https://doi.org/10.1016/j.physc.2005.01.045. + fortran.tfcoil_variables.eyoung_cond_axial = 145e9 + + if fortran.tfcoil_variables.i_tf_cond_eyoung_trans == 0: + # Transverse stiffness is not considered + fortran.tfcoil_variables.eyoung_cond_trans = 0 + else: + # Transverse stiffness is significant + fortran.tfcoil_variables.eyoung_cond_trans = ( + fortran.tfcoil_variables.eyoung_cond_axial + ) + + # Check if the WP/conductor radial thickness (dr_tf_wp) is large enough + # To contains the insulation, cooling and the support structure + # Rem : Only verified if the WP thickness is used + if (fortran.numerics.ixc[: fortran.numerics.nvar] == 140).any(): + + # Minimal WP thickness + if fortran.tfcoil_variables.i_tf_sup == 1: + dr_tf_wp_min = 2.0 * ( + fortran.tfcoil_variables.tinstf + + fortran.tfcoil_variables.tfinsgap + + fortran.tfcoil_variables.thicndut + + fortran.tfcoil_variables.dhecoil + ) + + # Steel conduit thickness (can be an iteration variable) + if (fortran.numerics.ixc[: fortran.numerics.nvar] == 58).any(): + dr_tf_wp_min = dr_tf_wp_min + 2.0 * fortran.numerics.boundl[57] + else: + dr_tf_wp_min = dr_tf_wp_min + 2.0 * fortran.tfcoil_variables.thwcndut + + # Minimal conductor layer thickness + elif ( + fortran.tfcoil_variables.i_tf_sup == 0 + or fortran.tfcoil_variables.i_tf_sup == 2 + ): + dr_tf_wp_min = ( + 2.0 + * (fortran.tfcoil_variables.thicndut + fortran.tfcoil_variables.tinstf) + + 4.0 * fortran.tfcoil_variables.rcool + ) + + if fortran.numerics.boundl[139] < dr_tf_wp_min: + raise ProcessValidationError( + "The TF coil WP thickness (dr_tf_wp) must be at least", + dr_tf_wp_min=dr_tf_wp_min, + ) + + # Setting t_turn_tf_is_input to true if t_turn_tf is an input + fortran.tfcoil_variables.t_turn_tf_is_input = ( + abs(fortran.tfcoil_variables.t_turn_tf) > 0 + ) + + # Impossible to set the turn size of integer turn option + if ( + fortran.tfcoil_variables.t_turn_tf_is_input + and fortran.tfcoil_variables.i_tf_turns_integer == 1 + ): + raise ProcessValidationError( + "Impossible to set the TF turn/cable size with the integer turn option (i_tf_turns_integer: 1)" + ) + + if ( + fortran.tfcoil_variables.i_tf_wp_geom != 0 + and fortran.tfcoil_variables.i_tf_turns_integer == 1 + ): + raise ProcessValidationError( + "Can only have i_tf_turns_integer = 1 with i_tf_wp_geom = 0" + ) + + if ( + fortran.physics_variables.i_bootstrap_current == 5 + and fortran.physics_variables.i_diamagnetic_current != 0 + ): + raise ProcessValidationError( + "i_diamagnetic_current = 0 should be used with the Sakai plasma current scaling" + ) + + # Setting t_cable_tf_is_input to true if t_cable_tf is an input + fortran.tfcoil_variables.t_cable_tf_is_input = ( + abs(fortran.tfcoil_variables.t_cable_tf) > 0 + ) + + # Impossible to set the cable size of integer turn option + if ( + fortran.tfcoil_variables.t_cable_tf_is_input + and fortran.tfcoil_variables.i_tf_turns_integer == 1 + ): + raise ProcessValidationError( + "Impossible to set the TF turn/cable size with the integer turn option (i_tf_turns_integer: 1)" + ) + + # Impossible to set both the TF coil turn and the cable dimension + if ( + fortran.tfcoil_variables.t_turn_tf_is_input + and fortran.tfcoil_variables.t_cable_tf_is_input + ): + raise ProcessValidationError( + "Impossible to set the TF coil turn and cable size simultaneously" + ) + + # Checking the SC temperature for LTS + if ( + fortran.tfcoil_variables.i_tf_sc_mat in [1, 3, 4, 5] + and fortran.tfcoil_variables.tftmp > 10.0 + ): + raise ProcessValidationError( + "The LTS conductor temperature (tftmp) has to be lower than 10" + ) + + # PF coil resistivity is zero if superconducting + if fortran.pfcoil_variables.ipfres == 0: + fortran.pfcoil_variables.pfclres = 0.0 + + # If there is no NBI, then hot beam density should be zero + if fortran.current_drive_variables.irfcd == 1: + if ( + fortran.current_drive_variables.iefrf != 5 + and fortran.current_drive_variables.iefrf != 8 + ): + fortran.physics_variables.rnbeam = 0.0 + else: + fortran.physics_variables.rnbeam = 0.0 + + # Set inboard blanket thickness to zero if no inboard blanket switch + # used (Issue #732) + if fortran.fwbs_variables.iblnkith == 0: + fortran.build_variables.blnkith = 0.0 + + # Ensure that blanket material fractions allow non-zero space for steel + # CCFE HCPB Model + + if fortran.stellarator_variables.istell == 0: + if fortran.fwbs_variables.iblanket == 1 or fortran.fwbs_variables.iblanket == 3: + fsum = ( + fortran.fwbs_variables.breeder_multiplier + + fortran.fwbs_variables.vfcblkt + + fortran.fwbs_variables.vfpblkt + ) + if fsum >= 1.0: + raise ProcessValidationError( + "Blanket material fractions do not sum to 1.0", + iblanket=fortran.fwbs_variables.iblanket, + breeder_multiplier=fortran.fwbs_variables.breeder_multiplier, + vfcblkt=fortran.fwbs_variables.vfcblkt, + vfpblkt=fortran.fwbs_variables.vfpblkt, + fsum=fsum, + ) + + # Initialise superconductor cable parameters + if fortran.tfcoil_variables.i_tf_sup == 1: + fortran.sctfcoil_module.initialise_cables() + + # Check that the temperature margins are not overdetermined + if fortran.tfcoil_variables.tmargmin > 0.0001: + # This limit has been input and will be applied to both TFC and CS + if fortran.tfcoil_variables.tmargmin_tf > 0.0001: + warn( + "tmargmin_tf and tmargmin should not both be specified in IN.DAT " + "tmargmin_tf has been ignored" + ) + if fortran.tfcoil_variables.tmargmin_cs > 0.0001: + warn( + "tmargmin_cs and tmargmin should not both be specified in IN.DAT " + "tmargmin_cs has been ignored" + ) + + fortran.tfcoil_variables.tmargmin_tf = fortran.tfcoil_variables.tmargmin + fortran.tfcoil_variables.tmargmin_cs = fortran.tfcoil_variables.tmargmin + + if ( + fortran.physics_variables.tauee_in > 1e-10 + and fortran.physics_variables.isc != 48 + ): + # Report error if confinement time is in the input + # but the scaling to use it is not selected. + warn("tauee_in is for use with isc=48 only") + + if fortran.physics_variables.aspect > 1.7 and fortran.physics_variables.isc == 46: + # NSTX scaling is for A<1.7 + warn("NSTX scaling is for A<1.7") + + if ( + fortran.physics_variables.i_plasma_current == 2 + and fortran.physics_variables.isc == 42 + ): + raise ProcessValidationError( + "Lang 2012 confinement scaling cannot be used for i_plasma_current=2 due to wrong q" + ) + + # Cannot use temperature margin constraint with REBCO TF coils + if ( + fortran.numerics.icc[: fortran.numerics.neqns + fortran.numerics.nineqns] == 36 + ).any() and ( + fortran.tfcoil_variables.i_tf_sc_mat == 8 + or fortran.tfcoil_variables.i_tf_sc_mat == 9 + ): + raise ProcessValidationError( + "turn off TF temperature margin constraint icc = 36 when using REBCO" + ) + + # Cannot use temperature margin constraint with REBCO CS coils + if ( + fortran.numerics.icc[: fortran.numerics.neqns + fortran.numerics.nineqns] == 60 + ).any() and fortran.pfcoil_variables.isumatoh == 8: + raise ProcessValidationError( + "turn off CS temperature margin constraint icc = 60 when using REBCO" + ) + + # Cold end of the cryocooler should be colder than the TF + if fortran.tfcoil_variables.tmpcry > fortran.tfcoil_variables.tftmp: + raise ProcessValidationError("tmpcry should be lower than tftmp") + + # Cannot use TF coil strain limit if i_str_wp is off: + if ( + fortran.numerics.icc[: fortran.numerics.neqns + fortran.numerics.nineqns] == 88 + ).any() and fortran.tfcoil_variables.i_str_wp == 0: + raise ProcessValidationError("Can't use constraint 88 if i_strain_tf == 0") diff --git a/source/fortran/init_module.f90 b/source/fortran/init_module.f90 index ee4d42462..d2b00d683 100644 --- a/source/fortran/init_module.f90 +++ b/source/fortran/init_module.f90 @@ -96,756 +96,4 @@ subroutine finish close(unit = opt_file) if (verbose == 1) close(unit = vfile) end subroutine finish - - subroutine check - - !! Routine to reset specific variables if certain options are - !! being used - !! author: P J Knight, CCFE, Culham Science Centre - !! None - !! This routine performs a sanity check of the input variables - !! and ensures other dependent variables are given suitable values. - - !! ! - ! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! - - use build_variables, only: blnkith, bore, gapoh, ohcth, precomp, iprecomp, & - i_r_cp_top, r_cp_top, vgaptop, vgap_xpoint_divertor, shldtth, shldlth, d_vv_top, d_vv_bot, tf_in_cs - use buildings_variables, only: esbldgm3, triv - use current_drive_variables, only: gamcd, iefrf, irfcd - use error_handling, only: errors_on, idiags, fdiags, report_error - use fwbs_variables, only: breeder_multiplier, iblanket, vfcblkt, vfpblkt, & - iblnkith - use global_variables, only: icase - use heat_transport_variables, only: trithtmw - use ife_variables, only: ife - use impurity_radiation_module, only: nimp, impurity_arr_frac, fimp - use numerics, only: ixc, icc, ioptimz, neqns, nineqns, nvar, boundl, & - boundu - use pfcoil_variables, only: ipfres, ngrp, pfclres, ipfloc, ncls, isumatoh - use physics_variables, only: aspect, f_deuterium, fgwped, f_helium3, & - fgwsep, f_tritium, i_bootstrap_current, i_single_null, i_plasma_current, idivrt, ishape, & - iradloss, isc, ipedestal, ilhthresh, itart, nesep, rhopedn, rhopedt, & - rnbeam, neped, te, tauee_in, tesep, teped, itartpf, ftar, i_diamagnetic_current - use pulse_variables, only: lpulse - use reinke_variables, only: fzactual, impvardiv - use tfcoil_variables, only: casthi, casthi_is_fraction, casths, i_tf_sup, & - tcoolin, tcpav, tfc_sidewall_is_fraction, tmargmin, tmargmin_cs, & - tmargmin_tf, eff_tf_cryo, eyoung_ins, i_tf_bucking, i_tf_shape, & - n_tf_graded_layers, n_tf_stress_layers, tlegav, i_tf_stress_model, & - i_tf_sc_mat, i_tf_wp_geom, i_tf_turns_integer, tinstf, thwcndut, & - tfinsgap, rcool, dhecoil, thicndut, i_cp_joints, t_turn_tf_is_input, & - t_turn_tf, tftmp, t_cable_tf, t_cable_tf_is_input, tftmp, tmpcry, & - i_tf_cond_eyoung_axial, eyoung_cond_axial, eyoung_cond_trans, & - i_tf_cond_eyoung_trans, i_str_wp - use stellarator_variables, only: istell - use sctfcoil_module, only: initialise_cables - use vacuum_variables, only: vacuum_model - use, intrinsic :: iso_fortran_env, only: dp=>real64 - - implicit none - - ! Local variables - - integer :: i,j,k,imp - real(dp) :: fsum - - real(dp) :: dr_tf_wp_min - !! Minimal WP or conductor layer thickness [m] - ! !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! - - errors_on = .true. - - ! Check that there are sufficient iteration variables - if (nvar < neqns) then - idiags(1) = nvar ; idiags(2) = neqns - call report_error(137) - end if - - ! Check that sufficient elements of ixc and icc have been specified - if ( any(ixc(1:nvar) == 0) ) then - idiags(1) = nvar - call report_error(139) - end if - - - if ( any(icc(1:neqns+nineqns) == 0) ) then - idiags(1) = neqns ; idiags(2) = nineqns - call report_error(140) - end if - - ! Deprecate constraints 3 and 4 - if ( any(icc(1:neqns+nineqns) == 3) ) then - call report_error(162) - write(*,*) 'PROCESS stopping' - stop 1 - end if - - if ( any(icc(1:neqns+nineqns) == 4) ) then - call report_error(163) - write(*,*) 'PROCESS stopping' - stop 1 - end if - - - ! MDK Report error if constraint 63 is used with old vacuum model - if (any(icc(1:neqns+nineqns) == 63).and.(vacuum_model.ne.'simple') ) then - write(*,*) 'Constraint 63 is requested without the correct vacuum model ("simple").' - write(*,*) 'vacuum_model = ', vacuum_model - write(*,*) 'PROCESS stopping' - stop 1 - end if - - if ( any(icc(1:neqns+nineqns) == 74) ) then - write(*,*)'Constraint 74 (TF coil quench temperature for Croco HTS conductor) is not yet implemented' - write(*,*) 'PROCESS stopping' - stop 1 - end if - - ! Fuel ion fractions must add up to 1.0 - if (abs(1.0D0 - f_deuterium - f_tritium - f_helium3) > 1.0D-6) then - fdiags(1) = f_deuterium; fdiags(2) = f_tritium ; fdiags(3) = f_helium3 - call report_error(36) - end if - - if (f_tritium < 1.0D-3) then ! tritium fraction is negligible - triv = 0.0D0 - trithtmw = 0.0D0 - end if - - if (fimp(2) .ne. 0.1D0) then - write(*,*)'The thermal alpha/electron density ratio should be controlled using ralpne (itv 109) and not fimp(2).' - write(*,*)'fimp(2) should be removed from the input file, or set to the default value 0.1D0.' - stop 1 - end if - - ! Impurity fractions - do imp = 1,nimp - impurity_arr_frac(imp) = fimp(imp) - end do - - ! The 1/R B field dependency constraint variable is being depreciated - ! Stop the run if the constraint 10 is used - if ( any( icc == 10 ) ) then - call report_error(236) - stop 1 - end if - - ! Stop the run if oacdcp is used as an optimisation variable - ! As the current density is now calculated from bt without constraint 10 - if ( any( ixc == 12 ) ) then - call report_error(236) - stop 1 - end if - - ! Warn if ion power balance equation is being used with the new radiation model - if (any(icc == 3)) then - call report_error(138) - end if - - ! Plasma profile consistency checks - if (ife /= 1) then - if (ipedestal == 1) then - - ! Temperature checks - if (teped < tesep) then - fdiags(1) = teped ; fdiags(2) = tesep - call report_error(146) - end if - - if ((abs(rhopedt-1.0D0) <= 1.0D-7).and.((teped-tesep) >= 1.0D-7)) then - fdiags(1) = rhopedt ; fdiags(2) = teped ; fdiags(3) = tesep - call report_error(147) - end if - - ! Core temperature should always be calculated (later) as being - ! higher than the pedestal temperature, if and only if the - ! volume-averaged temperature never drops below the pedestal - ! temperature. Prevent this by adjusting te, and its lower bound - ! (which will only have an effect if this is an optimisation run) - if (te <= teped) then - fdiags(1) = te ; fdiags(2) = teped - te = teped*1.001D0 - call report_error(149) - end if - - if ((ioptimz >= 0).and.(any(ixc == 4)).and.(boundl(4) < teped*1.001D0)) then - call report_error(150) - boundl(4) = teped*1.001D0 - boundu(4) = max(boundu(4), boundl(4)) - end if - - ! Density checks - ! Case where pedestal density is set manually - ! --------------- - if ( (fgwped < 0) .or. (.not.any(ixc==145)) ) then - - ! Issue #589 Pedestal density is set manually using neped but it is less than nesep. - if ( neped < nesep ) then - fdiags(1) = neped ; fdiags(2) = nesep - call report_error(151) - end if - - ! Issue #589 Pedestal density is set manually using neped, - ! but pedestal width = 0. - if ( (abs(rhopedn-1.0D0) <= 1.0D-7).and.((neped-nesep) >= 1.0D-7) ) then - fdiags(1) = rhopedn ; fdiags(2) = neped ; fdiags(3) = nesep - call report_error(152) - end if - end if - - ! Issue #862 : Variable ne0/neped ratio without constraint eq 81 (ne0>neped) - ! -> Potential hollowed density profile - if ( (ioptimz >= 0) .and. (.not.any(icc==81)) ) then - if ( any(ixc == 145 )) call report_error(154) - if ( any(ixc == 6 )) call report_error(155) - end if - end if - end if - ! --------------- - - - ! Cannot use Psep/R and PsepB/qAR limits at the same time - if(any(icc == 68) .and. any(icc == 56)) then - call report_error(178) - endif - - if ((any(ixc==145)) .and. (boundl(145) < fgwsep)) then !if lower bound of fgwped < fgwsep - fdiags(1) = boundl(145); fdiags(2) = fgwsep - call report_error(186) - end if - - if (any(icc == 78)) then - - !If Reinke criterion is used tesep is calculated and cannot be an - !iteration variable - if (any(ixc == 119)) then - call report_error(219) - endif - - !If Reinke criterion is used need to enforce LH-threshold - !using Martin scaling for consistency - if (.not. ilhthresh == 6) then - call report_error(218) - endif - if (.not. any(icc==15) .and. (ipedestal .ne. 3)) then - call report_error(218) - endif - - - endif - - if (any(icc == 78)) then - - !If Reinke criterion is used tesep is calculated and cannot be an - !iteration variable - if (any(ixc == 119)) then - call report_error(219) - endif - - !If Reinke criterion is used need to enforce LH-threshold - !using Martin scaling for consistency - if (.not. ilhthresh == 6) then - call report_error(218) - endif - if (.not. any(icc==15) .and. (ipedestal .ne. 3)) then - call report_error(218) - endif - - - endif - - if (i_single_null == 0) then - idivrt = 2 - vgaptop = vgap_xpoint_divertor - shldtth = shldlth - d_vv_top = d_vv_bot - call report_error(272) - else ! i_single_null == 1 - idivrt = 1 - end if - - - ! Tight aspect ratio options (ST) - ! -------------------------------- - if ( itart == 1 ) then - - icase = 'Tight aspect ratio tokamak model' - - ! Disabled Forcing that no inboard breeding blanket is used - ! Disabled iblnkith = 0 - - ! Check if the choice of plasma current is addapted for ST - ! 2 : Peng Ip scaling (See STAR code documentation) - ! 9 : Fiesta Ip scaling - if (i_plasma_current /= 2 .and. i_plasma_current /= 9) then - idiags(1) = i_plasma_current ; call report_error(37) - end if - - !! If using Peng and Strickler (1986) model (itartpf == 0) - ! Overwrite the location of the TF coils - ! 2 : PF coil on top of TF coil - ! 3 : PF coil outside of TF coil - if (itartpf == 0) then - ipfloc(1) = 2 - ipfloc(2) = 3 - ipfloc(3) = 3 - end if - - ! Water cooled copper magnets initalisation / checks - if ( i_tf_sup == 0 ) then - ! Check if the initial centrepost coolant loop adapted to the magnet technology - ! Ice cannot flow so tcoolin > 273.15 K - if ( tcoolin < 273.15D0 ) call report_error(234) - - ! Temperature of the TF legs cannot be cooled down - if ( abs(tlegav+1.0D0) > epsilon(tlegav) .and. tlegav < 273.15D0 ) call report_error(239) - - ! Check if conductor upper limit is properly set to 50 K or below - if ( any(ixc == 20 ) .and. boundu(20) < 273.15D0 ) call report_error(241) - - ! Call a lvl 3 error if superconductor magnets are used - else if ( i_tf_sup == 1 ) then - call report_error(233) - - ! Aluminium magnets initalisation / checks - ! Initialize the CP conductor temperature to cryogenic temperature for cryo-al magnets (20 K) - else if ( i_tf_sup == 2 ) then - - ! Call a lvl 3 error if the inlet coolant temperature is too large - ! Motivation : ill-defined aluminium resistivity fit for T > 40-50 K - if ( tcoolin > 40.0D0 ) call report_error(235) - - ! Check if the leg average temperature is low enough for the resisitivity fit - if ( tlegav > 50.0D0 ) call report_error(238) - - ! Check if conductor upper limit is properly set to 50 K or below - if ( any(ixc == 20 ) .and. boundu(20) > 50.0D0 ) call report_error(240) - - ! Otherwise intitialise the average conductor temperature at - tcpav = tcoolin - - end if - - ! Check if the boostrap current selection is addapted to ST - if (i_bootstrap_current == 1) call report_error(38) - - ! Check if a single null divertor is used in double null machine - if (i_single_null == 0 .and. (ftar == 1.0 .or. ftar == 0.0)) then - call report_error(39) - end if - - ! Set the TF coil shape to picture frame (if default value) - if ( i_tf_shape == 0 ) i_tf_shape = 2 - - ! Warning stating that the CP fast neutron fluence calculation - ! is not addapted for cryoaluminium calculations yet - if ( i_tf_sup == 2 .and. any( icc == 85 ) .and. itart == 1 ) then - call report_error(260) - end if - - ! Setting the CP joints default options : - ! 0 : No joints for superconducting magents (i_tf_sup = 1) - ! 1 : Sliding joints for resistive magnets (i_tf_sup = 0, 2) - if ( i_cp_joints == -1 ) then - if ( i_tf_sup == 1 ) then - i_cp_joints = 0 - else - i_cp_joints = 1 - end if - end if - - ! Checking the CP TF top radius - if ( ( abs(r_cp_top) > epsilon(r_cp_top) .or. any(ixc(1:nvar) == 174) ) & - .and. i_r_cp_top /= 1 ) then - call report_error(267) - end if - ! -------------------------------- - - - ! Conventionnal aspect ratios specific - ! ------------------------------------ - else - - if (i_plasma_current == 2 .or. i_plasma_current == 9) call report_error(40) - - ! Set the TF coil shape to PROCESS D-shape (if default value) - if ( i_tf_shape == 0 ) i_tf_shape = 1 - - ! Check PF coil configurations - j = 0 ; k = 0 - do i = 1, ngrp - if ((ipfloc(i) /= 2).and.(ncls(i) /= 2)) then - idiags(1) = i ; idiags(2) = ncls(i) - call report_error(41) - end if - - if (ipfloc(i) == 2) then - j = j + 1 - k = k + ncls(i) - end if - end do - - if (k == 1) call report_error(42) - if (k > 2) call report_error(43) - if ((i_single_null == 1).and.(j < 2)) call report_error(44) - - ! Constraint 10 is dedicated to ST designs with demountable joints - if ( any(icc(1:neqns+nineqns) == 10 ) ) call report_error(259) - - end if - ! ------------------------------------ - - ! Pulsed power plant model - if (lpulse == 1) then - icase = 'Pulsed tokamak model' - else - esbldgm3 = 0.0D0 - end if - - ! Ensure minimum cycle time constraint is turned off - ! (not currently available, as routine thrmal has been commented out) - if ( any(icc == 42) ) then - call report_error(164) - end if - - - - ! TF coil - ! ------- - ! TF stress model not defined of r_tf_inboard = 0 - ! Unless i_tf_stress_model == 2 - ! -> If bore + gapoh + ohcth = 0 and fixed and stress constraint is used - ! Generate a lvl 3 error proposing not to use any stress constraints - if ( ( .not. ( any(ixc == 16 ) .or. any(ixc == 29 ) .or. any(ixc == 42 ) ) ) & ! No bore,gapoh, ohcth iteration - .and. ( abs(bore + gapoh + ohcth + precomp) < epsilon(bore) ) & ! bore + gapoh + ohcth = 0 - .and. ( any(icc == 31) .or. any(icc == 32) ) & ! Stress constraints (31 or 32) is used - .and. ( i_tf_stress_model /= 2 ) ) then ! TF stress model can't handle no bore - - call report_error(246) - stop 1 - end if - - ! Make sure that plane stress model is not used for resistive magnets - if ( i_tf_stress_model == 1 .and. i_tf_sup /= 1 ) call report_error(253) - - ! bucking cylinder default option setting - ! - bucking (casing) for SC i_tf_bucking ( i_tf_bucking = 1 ) - ! - No bucking for copper magnets ( i_tf_bucking = 0 ) - ! - Bucking for aluminium magnets ( i_tf_bucking = 1 ) - if ( i_tf_bucking == -1 ) then - if ( i_tf_sup == 0 ) then - i_tf_bucking = 0 - else - i_tf_bucking = 1 - end if - end if - - ! Ensure that the TF isnt placed against the - ! CS which is now outside it - if ( i_tf_bucking >= 2 .and. tf_in_cs == 1 ) then - call report_error(281) - end if - ! Ensure that no pre-compression structure - ! is used for bucked and wedged design - if ( i_tf_bucking >= 2 .and. iprecomp == 1 ) then - call report_error(252) - end if - - ! Number of stress calculation layers - ! +1 to add in the inboard TF coil case on the plasma side, per Issue #1509 - n_tf_stress_layers = i_tf_bucking + n_tf_graded_layers + 1 - - ! If TFC sidewall has not been set by user - if ( casths < 0.1d-10 ) tfc_sidewall_is_fraction = .true. - - ! If inboard TF coil case plasma side thickness has not been set by user - if( casthi < 0.1d-10 ) casthi_is_fraction = .true. - - ! Setting the default cryo-plants efficiencies - !-! - if ( abs(eff_tf_cryo + 1.0D0) < epsilon(eff_tf_cryo) ) then - - ! The ITER cyoplant efficiency is used for SC - if ( i_tf_sup == 1 ) then - eff_tf_cryo = 0.13D0 - - ! Strawbrige plot extrapolation is used for Cryo-Al - else if ( i_tf_sup == 2 ) then - eff_tf_cryo = 0.40D0 - end if - - ! Cryo-plane efficiency must be in [0-1.0] - else if ( eff_tf_cryo > 1.0D0 .or. eff_tf_cryo < 0.0D0 ) then - call report_error(248) - stop 1 - end if - !-! - - ! Integer turns option not yet available for REBCO taped turns - !-! - if ( i_tf_sc_mat == 6 .and. i_tf_turns_integer == 1 ) then - call report_error(254) - stop 1 - end if - !-! - - - ! Setting up insulation layer young modulae default values [Pa] - !-! - if ( abs(eyoung_ins - 1.0D8 ) < epsilon(eyoung_ins) ) then - - ! Copper magnets, no insulation material defined - ! But use the ITER design by default - if ( i_tf_sup == 0 ) then - eyoung_ins = 20.0D9 - - ! SC magnets - ! Value from DDD11-2 v2 2 (2009) - else if ( i_tf_sup == 1 ) then - eyoung_ins = 20.0D9 - - ! Cryo-aluminum magnets (Kapton polymer) - else if ( i_tf_sup == 2 ) then - eyoung_ins = 2.5D9 - end if - end if - !-! - - !-! Setting the default WP geometry - !-! - if ( i_tf_wp_geom == -1 ) then - if ( i_tf_turns_integer == 0 ) i_tf_wp_geom = 1 - if ( i_tf_turns_integer == 1 ) i_tf_wp_geom = 0 - end if - !-! - - !-! Setting the TF coil conductor elastic properties - !-! - if ( i_tf_cond_eyoung_axial == 0 ) then - ! Conductor stiffness is not considered - eyoung_cond_axial = 0 - eyoung_cond_trans = 0 - else if ( i_tf_cond_eyoung_axial == 2 ) then - ! Select sensible defaults from the literature - select case (i_tf_sc_mat) - case (1,4,5) - ! Nb3Sn: Nyilas, A et. al, Superconductor Science and Technology 16, no. 9 (2003): 1036–42. https://doi.org/10.1088/0953-2048/16/9/313. - eyoung_cond_axial = 32D9 - case (2) - ! Bi-2212: Brown, M. et al, IOP Conference Series: Materials Science and Engineering 279 (2017): 012022. https://doi.org/10.1088/1757-899X/279/1/012022. - eyoung_cond_axial = 80D9 - case (3,7) - ! NbTi: Vedrine, P. et. al, IEEE Transactions on Applied Superconductivity 9, no. 2 (1999): 236–39. https://doi.org/10.1109/77.783280. - eyoung_cond_axial = 6.8D9 - case (6,8,9) - ! REBCO: Fujishiro, H. et. al, Physica C: Superconductivity, 426–431 (2005): 699–704. https://doi.org/10.1016/j.physc.2005.01.045. - eyoung_cond_axial = 145D9 - end select - - if ( i_tf_cond_eyoung_trans == 0) then - ! Transverse stiffness is not considered - eyoung_cond_trans = 0 - else - ! Transverse stiffness is significant - eyoung_cond_trans = eyoung_cond_axial - end if - end if - !-! - - ! Check if the WP/conductor radial thickness (dr_tf_wp) is large enough - ! To contains the insulation, cooling and the support structure - ! Rem : Only verified if the WP thickness is used - if ( any(ixc(1:nvar) == 140) ) then - - ! Minimal WP thickness - if ( i_tf_sup == 1 ) then - dr_tf_wp_min = 2.0D0 * ( tinstf + tfinsgap + thicndut + dhecoil ) - - ! Steel conduit thickness (can be an iteration variable) - if ( any(ixc(1:nvar) == 58 ) ) then - dr_tf_wp_min = dr_tf_wp_min + 2.0D0 * boundl(58) - else - dr_tf_wp_min = dr_tf_wp_min + 2.0D0 * thwcndut - end if - - ! Minimal conductor layer thickness - else if ( i_tf_sup == 0 .or. i_tf_sup == 2 ) then - dr_tf_wp_min = 2.0D0 * ( thicndut + tinstf ) + 4.0D0 * rcool - end if - - if ( boundl(140) < dr_tf_wp_min ) then - fdiags(1) = dr_tf_wp_min - call report_error(255) - end if - end if - - ! Setting t_turn_tf_is_input to true if t_turn_tf is an input - if ( abs(t_turn_tf) < epsilon(t_turn_tf) ) then - t_turn_tf_is_input = .false. - else - t_turn_tf_is_input = .true. - end if - - ! Impossible to set the turn size of integer turn option - if ( t_turn_tf_is_input .and. i_tf_turns_integer == 1 ) then - call report_error(269) - end if - - if ( i_tf_wp_geom /= 0 .and. i_tf_turns_integer == 1 ) then - call report_error(283) - end if - - if ( i_bootstrap_current == 5 .and. i_diamagnetic_current /= 0 ) then - call report_error(284) - end if - - ! Setting t_cable_tf_is_input to true if t_cable_tf is an input - if ( abs(t_cable_tf) < epsilon(t_cable_tf) ) then - t_cable_tf_is_input = .false. - else - t_cable_tf_is_input = .true. - end if - - ! Impossible to set the cable size of integer turn option - if ( t_cable_tf_is_input .and. i_tf_turns_integer == 1 ) then - call report_error(269) - end if - - ! Impossible to set both the TF coil turn and the cable dimension - if ( t_turn_tf_is_input .and. t_cable_tf_is_input ) then - call report_error(271) - end if - - ! Checking the SC temperature for LTS - if ( ( i_tf_sc_mat == 1 .or. & - i_tf_sc_mat == 3 .or. & - i_tf_sc_mat == 4 .or. & - i_tf_sc_mat == 5 ) .and. tftmp > 10.0D0 ) then - call report_error(270) - end if - ! ------- - - - - ! PF coil resistivity is zero if superconducting - if (ipfres == 0) pfclres = 0.0D0 - - ! If there is no NBI, then hot beam density should be zero - if (irfcd == 1) then - if ((iefrf /= 5).and.(iefrf /= 8)) rnbeam = 0.0D0 - else - rnbeam = 0.0D0 - end if - - ! Set inboard blanket thickness to zero if no inboard blanket switch - ! used (Issue #732) - if (iblnkith == 0) blnkith = 0.0D0 - - ! Solid breeder assumed if ipowerflow=0 - - !if (ipowerflow == 0) blkttype = 3 - - ! Set coolant fluid type - - !if ((blkttype == 1).or.(blkttype == 2)) then - ! coolwh = 2 ! water - !else - ! coolwh = 1 ! helium - !end if - - ! But... set coolant to water if blktmodel > 0 - ! Although the *blanket* is by definition helium-cooled in this case, - ! the shield etc. are assumed to be water-cooled, and since water is - ! heavier (and the unit cost of pumping it is higher), the calculation - ! for coolmass is better done with coolwh=2 if blktmodel > 0 to give - ! slightly pessimistic results. - - !if (blktmodel > 0) then - ! secondary_cycle = 0 - ! blkttype = 3 ! HCPB - ! coolwh = 2 - !end if - - ! Ensure that blanket material fractions allow non-zero space for steel - ! CCFE HCPB Model - - if (istell == 0) then - if ((iblanket == 1).or.(iblanket == 3)) then - fsum = breeder_multiplier + vfcblkt + vfpblkt - if (fsum >= 1.0D0) then - idiags(1) = iblanket - fdiags(2) = breeder_multiplier - fdiags(3) = vfcblkt - fdiags(4) = vfpblkt - fdiags(5) = fsum - call report_error(165) - end if - end if - end if - - ! Initialise superconductor cable parameters - if(i_tf_sup==1)then - call initialise_cables() - end if - - ! Check that the temperature margins are not overdetermined - if(tmargmin>0.0001d0)then - ! This limit has been input and will be applied to both TFC and CS - if(tmargmin_tf>0.0001d0)then - write(*,*)'tmargmin_tf and tmargmin should not both be specified in IN.DAT.' - write(*,*)'tmargmin_tf has been ignored.' - end if - if(tmargmin_cs>0.0001d0)then - write(*,*)'tmargmin_cs and tmargmin should not both be specified in IN.DAT.' - write(*,*)'tmargmin_cs has been ignored.' - end if - tmargmin_tf = tmargmin - tmargmin_cs = tmargmin - end if - - if (tauee_in.ge.1.0D-10.and.isc.ne.48) then - ! Report error if confinement time is in the input - ! but the scaling to use it is not selected. - call report_error(220) - end if - - if (aspect.gt.1.7D0.and.isc.eq.46) then - ! NSTX scaling is for A<1.7 - call report_error(221) - end if - - if (i_plasma_current.eq.2.and.isc.eq.42) then - call report_error(222) - end if - - ! Cannot use temperature margin constraint with REBCO TF coils - if(any(icc == 36) .and. ((i_tf_sc_mat == 8).or.(i_tf_sc_mat == 9))) then - call report_error(265) - endif - - ! Cannot use temperature margin constraint with REBCO CS coils - if(any(icc == 60) .and. (isumatoh == 8)) then - call report_error(264) - endif - - ! Cold end of the cryocooler should be colder than the TF - if(tmpcry > tftmp) then - call report_error(273) - endif - - ! Cannot use TF coil strain limit if i_str_wp is off: - if(any(icc == 88) .and. (i_str_wp == 0)) then - call report_error(275) - endif - - errors_on = .false. - - ! Disable error logging only after all checks have been performed. - ! (CPSS #1582: Why is error logging disabled at all?) - errors_on = .false. - - - end subroutine check - end module init_module From 4fa879785717a25f19775c5d4459cdadf1f26efb Mon Sep 17 00:00:00 2001 From: ajpearcey <78224916+ajpearcey@users.noreply.github.com> Date: Wed, 8 Jan 2025 14:19:42 +0000 Subject: [PATCH 4/7] =?UTF-8?q?=F0=9F=90=9B=20fix=20miss=20labelled=20cs?= =?UTF-8?q?=20coil=20currents=20(#3381)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: apearce --- process/pfcoil.py | 28 ++++++++++++++-------------- process/power.py | 2 +- tests/integration/test_pfcoil_int.py | 28 ++++++++++++++-------------- tests/unit/test_pfcoil.py | 14 +++++++------- tests/unit/test_power.py | 8 ++++---- 5 files changed, 40 insertions(+), 40 deletions(-) diff --git a/process/pfcoil.py b/process/pfcoil.py index 64ab92055..e3f67e30d 100644 --- a/process/pfcoil.py +++ b/process/pfcoil.py @@ -510,7 +510,7 @@ def pfcoil(self): pf.ccls[nng] = 1.0e6 * pfv.ccls_ma[nng] # Beginning of pulse: t = tv.t_precharge - pfv.curpfs[ncl] = 1.0e-6 * pf.ccl0[nng] + pfv.curpfb[ncl] = 1.0e-6 * pf.ccl0[nng] # Beginning of flat-top: t = tv.t_precharge+tv.t_current_ramp_up pfv.curpff[ncl] = 1.0e-6 * ( @@ -518,7 +518,7 @@ def pfcoil(self): ) # End of flat-top: t = tv.t_precharge+tv.t_current_ramp_up+tv.t_fusion_ramp+tv.t_burn - pfv.curpfb[ncl] = 1.0e-6 * ( + pfv.curpfs[ncl] = 1.0e-6 * ( pf.ccls[nng] - (pf.ccl0[nng] * (1.0e0 / pfv.fcohbop)) ) @@ -526,9 +526,9 @@ def pfcoil(self): # Current in Central Solenoid as a function of time # N.B. If the Central Solenoid is not present then ioheof is zero. - pfv.curpfs[ncl] = -1.0e-6 * ioheof * pfv.fcohbop + pfv.curpfb[ncl] = -1.0e-6 * ioheof * pfv.fcohbop pfv.curpff[ncl] = 1.0e-6 * ioheof * pfv.fcohbof - pfv.curpfb[ncl] = 1.0e-6 * ioheof + pfv.curpfs[ncl] = 1.0e-6 * ioheof # Set up coil current waveforms, normalised to the peak current in # each coil @@ -1268,11 +1268,11 @@ def peakb(self, i, ii, it): kk = 0 else: # Check different times for maximum current - if abs(pfv.curpfs[i - 1] - pfv.ric[i - 1]) < 1.0e-12: + if abs(pfv.curpfb[i - 1] - pfv.ric[i - 1]) < 1.0e-12: it = 2 elif abs(pfv.curpff[i - 1] - pfv.ric[i - 1]) < 1.0e-12: it = 4 - elif abs(pfv.curpfb[i - 1] - pfv.ric[i - 1]) < 1.0e-12: + elif abs(pfv.curpfs[i - 1] - pfv.ric[i - 1]) < 1.0e-12: it = 5 else: eh.idiags[0] = it @@ -2700,10 +2700,10 @@ def waveform(self): for ic in range(pfv.nohc): # Find where the peak current occurs # Beginning of pulse, t = t_precharge - if (abs(pfv.curpfs[ic]) >= abs(pfv.curpfb[ic])) and ( - abs(pfv.curpfs[ic]) >= abs(pfv.curpff[ic]) + if (abs(pfv.curpfb[ic]) >= abs(pfv.curpfs[ic])) and ( + abs(pfv.curpfb[ic]) >= abs(pfv.curpff[ic]) ): - pfv.ric[ic] = pfv.curpfs[ic] + pfv.ric[ic] = pfv.curpfb[ic] # Beginning of flat-top, t = t_precharge + t_current_ramp_up if (abs(pfv.curpff[ic]) >= abs(pfv.curpfb[ic])) and ( @@ -2712,17 +2712,17 @@ def waveform(self): pfv.ric[ic] = pfv.curpff[ic] # End of flat-top, t = t_precharge + t_current_ramp_up + t_fusion_ramp + t_burn - if (abs(pfv.curpfb[ic]) >= abs(pfv.curpfs[ic])) and ( - abs(pfv.curpfb[ic]) >= abs(pfv.curpff[ic]) + if (abs(pfv.curpfs[ic]) >= abs(pfv.curpfs[ic])) and ( + abs(pfv.curpfs[ic]) >= abs(pfv.curpff[ic]) ): - pfv.ric[ic] = pfv.curpfb[ic] + pfv.ric[ic] = pfv.curpfs[ic] # Set normalized current waveforms pfv.waves[ic, 0] = 0.0e0 - pfv.waves[ic, 1] = pfv.curpfs[ic] / pfv.ric[ic] + pfv.waves[ic, 1] = pfv.curpfb[ic] / pfv.ric[ic] pfv.waves[ic, 2] = pfv.curpff[ic] / pfv.ric[ic] pfv.waves[ic, 3] = pfv.curpff[ic] / pfv.ric[ic] - pfv.waves[ic, 4] = pfv.curpfb[ic] / pfv.ric[ic] + pfv.waves[ic, 4] = pfv.curpfs[ic] / pfv.ric[ic] pfv.waves[ic, 5] = 0.0e0 def superconpf( diff --git a/process/power.py b/process/power.py index 9ec86321b..6479b0f25 100644 --- a/process/power.py +++ b/process/power.py @@ -130,7 +130,7 @@ def pfpwr(self, output: bool): cktr[ig] = pfcr[ig] + pfbusr[ig] # total resistance of circuit (ohms) cptburn = ( pfcoil_variables.cptdin[ic] - * pfcoil_variables.curpfb[ic] + * pfcoil_variables.curpfs[ic] / pfcoil_variables.ric[ic] ) rcktvm[ig] = abs(cptburn) * cktr[ig] # peak resistive voltage (V) diff --git a/tests/integration/test_pfcoil_int.py b/tests/integration/test_pfcoil_int.py index aca17a9de..1bea82a6c 100644 --- a/tests/integration/test_pfcoil_int.py +++ b/tests/integration/test_pfcoil_int.py @@ -2492,13 +2492,13 @@ def test_peakb(monkeypatch: pytest.MonkeyPatch, pfcoil: PFCoil): "curpfb", np.array( [ - 0.067422231232391661, - -2.9167273287450968, - -8.1098913365453491, - -8.1098913365453491, - -5.5984385047179153, - -5.5984385047179153, - -186.98751599968148, + 14.742063826112622, + 20.032681634901664, + 0.58040662653667285, + 0.58040662653667285, + 0.42974674788703021, + 0.42974674788703021, + 174.22748790786324, 0, 0, 0, @@ -2552,13 +2552,13 @@ def test_peakb(monkeypatch: pytest.MonkeyPatch, pfcoil: PFCoil): "curpfs", np.array( [ - 14.742063826112622, - 20.032681634901664, - 0.58040662653667285, - 0.58040662653667285, - 0.42974674788703021, - 0.42974674788703021, - 174.22748790786324, + 0.067422231232391661, + -2.9167273287450968, + -8.1098913365453491, + -8.1098913365453491, + -5.5984385047179153, + -5.5984385047179153, + -186.98751599968148, 0, 0, 0, diff --git a/tests/unit/test_pfcoil.py b/tests/unit/test_pfcoil.py index 6919f903d..a3fb892d6 100644 --- a/tests/unit/test_pfcoil.py +++ b/tests/unit/test_pfcoil.py @@ -1194,13 +1194,13 @@ def test_waveform(monkeypatch, pfcoil): ) waves_exp = np.array( [ - [0.0, 1.0, 0.00457346, 0.00457346, 0.00457346, 0.0], - [0.0, 1.0, -0.14559845, -0.14559845, -0.14559845, 0.0], - [0.0, -0.07156774, 1.0, 1.0, 1.0, 0.0], - [0.0, -0.07156774, 1.0, 1.0, 1.0, 0.0], - [0.0, -0.07676189, 1.0, 1.0, 1.0, 0.0], - [0.0, -0.07676189, 1.0, 1.0, 1.0, 0.0], - [0.0, -0.93176, 1.0, 1.0, 1.0, 0.0], + [0.0, 0.00457346, 0.00457346, 0.00457346, 1.0, 0.0], + [0.0, -0.14559845, -0.14559845, -0.14559845, 1.0, 0.0], + [0.0, 1.0, 1.0, 1.0, -0.07156774, 0.0], + [0.0, 1.0, 1.0, 1.0, -0.07156774, 0.0], + [0.0, 1.0, 1.0, 1.0, -0.07676189, 0.0], + [0.0, 1.0, 1.0, 1.0, -0.07676189, 0.0], + [0.0, 1.0, 1.0, 1.0, -0.93176, 0.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], diff --git a/tests/unit/test_power.py b/tests/unit/test_power.py index d53dc6967..ab4ea93ea 100644 --- a/tests/unit/test_power.py +++ b/tests/unit/test_power.py @@ -209,7 +209,7 @@ class PfpwrParam(NamedTuple): cptdin: Any = None - curpfb: Any = None + curpfs: Any = None sxlg: Any = None @@ -497,7 +497,7 @@ class PfpwrParam(NamedTuple): ), order="F", ).transpose(), - curpfb=numpy.array( + curpfs=numpy.array( numpy.array( ( 0.067422231232391661, @@ -1239,7 +1239,7 @@ class PfpwrParam(NamedTuple): ), order="F", ).transpose(), - curpfb=numpy.array( + curpfs=numpy.array( numpy.array( ( 0.019288882290113718, @@ -1812,7 +1812,7 @@ def test_pfpwr(pfpwrparam, monkeypatch, power): monkeypatch.setattr(pfcoil_variables, "cptdin", pfpwrparam.cptdin) - monkeypatch.setattr(pfcoil_variables, "curpfb", pfpwrparam.curpfb) + monkeypatch.setattr(pfcoil_variables, "curpfs", pfpwrparam.curpfs) monkeypatch.setattr(pfcoil_variables, "sxlg", pfpwrparam.sxlg) From b2d5938cb12c44f14bc5b9127af3f219cf9d136f Mon Sep 17 00:00:00 2001 From: Christopher Ashe <91618944+chris-ashe@users.noreply.github.com> Date: Wed, 8 Jan 2025 15:06:10 +0000 Subject: [PATCH 5/7] Add inductive plasma heating doc section (#3456) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * :memo: Update mkdocs.yml to restructure Inductive Current section and add Plasma Resistive Heating * 🔄 Rename pohm to plasma_ohmic_heating in documentation and code for clarity * :memo: Update mkdocs.yml and enhance plasma_ohmic_heating function documentation * 🔄 Rename pohm to p_plasma_ohmic_mw for clarity and consistency across the codebase * 🔄 Rename pohmpv to pden_plasma_ohmic_mw for clarity and consistency in the codebase * 🔄 Rename rplas to res_plasma for clarity and consistency across the codebase * :memo: Enhance documentation for resistive plasma heating and clarify references to ITER Physics Design Guidelines * 🔄 Correct formula notation for plasma resistive heating in documentation --- .../proc-pages/physics-models/error.txt | 8 +- .../physics-models/plasma_confinement.md | 6 +- .../plasma_resistive_heating.md | 25 ++++ .../physics-models/plasma_overview.md | 2 +- .../data/csv_output_large_tokamak_MFILE.DAT | 8 +- examples/data/large_tokamak_1_MFILE.DAT | 8 +- examples/data/large_tokamak_2_MFILE.DAT | 8 +- examples/data/large_tokamak_3_MFILE.DAT | 8 +- examples/data/large_tokamak_4_MFILE.DAT | 8 +- examples/data/scan_MFILE.DAT | 72 +++++------ mkdocs.yml | 4 +- process/current_drive.py | 4 +- process/io/plot_radial_build.py | 2 +- process/io/sankey_funcs.py | 24 ++-- process/objectives.py | 2 +- process/physics.py | 122 +++++++++++------- process/power.py | 22 ++-- process/pulse.py | 2 +- process/stellarator.py | 8 +- process/utilities/errorlist.json | 2 +- source/fortran/constraint_equations.f90 | 14 +- source/fortran/physics_variables.f90 | 14 +- .../data/large_tokamak_1_MFILE.DAT | 8 +- .../data/large_tokamak_2_MFILE.DAT | 8 +- .../data/large_tokamak_3_MFILE.DAT | 8 +- .../data/large_tokamak_4_MFILE.DAT | 8 +- .../integration/data/large_tokamak_MFILE.DAT | 8 +- tests/integration/data/scan_2D_MFILE.DAT | 120 ++++++++--------- tests/integration/data/scan_MFILE.DAT | 72 +++++------ tests/integration/ref_dicts.json | 20 +-- tests/unit/data/large_tokamak_MFILE.DAT | 8 +- tests/unit/test_current_drive.py | 10 +- tests/unit/test_physics.py | 75 ++++++----- tests/unit/test_power.py | 20 +-- tests/unit/test_pulse.py | 8 +- 35 files changed, 409 insertions(+), 337 deletions(-) create mode 100644 documentation/proc-pages/physics-models/plasma_current/plasma_resistive_heating.md diff --git a/documentation/proc-pages/physics-models/error.txt b/documentation/proc-pages/physics-models/error.txt index ecc546bc4..8c122a973 100644 --- a/documentation/proc-pages/physics-models/error.txt +++ b/documentation/proc-pages/physics-models/error.txt @@ -994,18 +994,18 @@ is derived directly from the energy confinement scaling law. \texttt{iradloss\ =\ 0} -- Total power lost is scaling power plus radiation -\texttt{pscaling\ +\ pradpv\ =\ f_alpha_plasma*alpha_power_density\ +\ charged_power_density\ +\ pohmpv\ +\ pinjmw/plasma_volume} +\texttt{pscaling\ +\ pradpv\ =\ f_alpha_plasma*alpha_power_density\ +\ charged_power_density\ +\ pden_plasma_ohmic_mw\ +\ pinjmw/plasma_volume} \texttt{iradloss\ =\ 1} -- Total power lost is scaling power plus core radiation only -\texttt{pscaling\ +\ pcoreradpv\ =\ f_alpha_plasma*alpha_power_density\ +\ charged_power_density\ +\ pohmpv\ +\ pinjmw/plasma_volume} +\texttt{pscaling\ +\ pcoreradpv\ =\ f_alpha_plasma*alpha_power_density\ +\ charged_power_density\ +\ pden_plasma_ohmic_mw\ +\ pinjmw/plasma_volume} \texttt{iradloss\ =\ 2} -- Total power lost is scaling power only, with no additional allowance for radiation. This is not recommended for power plant models. -\texttt{pscaling\ =\ f_alpha_plasma*alpha_power_density\ +\ charged_power_density\ +\ pohmpv\ +\ pinjmw/plasma_volume} +\texttt{pscaling\ =\ f_alpha_plasma*alpha_power_density\ +\ charged_power_density\ +\ pden_plasma_ohmic_mw\ +\ pinjmw/plasma_volume} \subsection{Plasma Core Power Balance}\label{plasma-core-power-balance} @@ -1365,7 +1365,7 @@ Effects}\label{neo-classical-correction-effects} Switch \texttt{ires} controls whether neo-classical (trapped particle) effects are included in the calculation of the plasma resistance and -ohmic heating power in routine \texttt{pohm}, which is called by routine +ohmic heating power in routine \texttt{plasma_ohmic_heating}, which is called by routine \texttt{physics}. If \texttt{ires\ =\ 1}, these effects are included. Note that the scaling used is only valid for aspect ratios between 2.5 and 4, and it is possible for the plasma resistance to be wrongly diff --git a/documentation/proc-pages/physics-models/plasma_confinement.md b/documentation/proc-pages/physics-models/plasma_confinement.md index ec079e85b..a5762459d 100644 --- a/documentation/proc-pages/physics-models/plasma_confinement.md +++ b/documentation/proc-pages/physics-models/plasma_confinement.md @@ -78,17 +78,17 @@ derived directly from the energy confinement scaling law. `iradloss = 0` -- Total power lost is scaling power plus radiation: -`pscaling + pradpv = f_alpha_plasma*alpha_power_density_total + charged_power_density + pohmpv + pinjmw/plasma_volume` +`pscaling + pradpv = f_alpha_plasma*alpha_power_density_total + charged_power_density + pden_plasma_ohmic_mw + pinjmw/plasma_volume` `iradloss = 1` -- Total power lost is scaling power plus radiation from a region defined as the "core": -`pscaling + pcoreradpv = f_alpha_plasma*alpha_power_density_total + charged_power_density + pohmpv + pinjmw/plasma_volume` +`pscaling + pcoreradpv = f_alpha_plasma*alpha_power_density_total + charged_power_density + pden_plasma_ohmic_mw + pinjmw/plasma_volume` `iradloss = 2` -- Total power lost is scaling power only, with no additional allowance for radiation. This is not recommended for power plant models. -`pscaling = f_alpha_plasma*alpha_power_density_total + charged_power_density + pohmpv + pinjmw/plasma_volume` +`pscaling = f_alpha_plasma*alpha_power_density_total + charged_power_density + pden_plasma_ohmic_mw + pinjmw/plasma_volume` ## L-H Power Threshold Scalings diff --git a/documentation/proc-pages/physics-models/plasma_current/plasma_resistive_heating.md b/documentation/proc-pages/physics-models/plasma_current/plasma_resistive_heating.md new file mode 100644 index 000000000..751f05ad4 --- /dev/null +++ b/documentation/proc-pages/physics-models/plasma_current/plasma_resistive_heating.md @@ -0,0 +1,25 @@ +# Resistive Plasma Heating + +The ohmic component of the plasma heating is given by that from the ITER 1989 Physics Design Guidelines[^1] + +Using the resistive loop voltage for a reference profile of parabolic shape with: + +$$ +\alpha_n \approx 0.5, \alpha_T \approx 1.0, \alpha_J \approx 1.5 +$$ + + +$$ +\Omega_{\text{plasma}} \approx 2.15 \times 10^{-3} Z_{\text{eff}}\langle \gamma_{\text{NC}} \rangle \frac{R_0}{\kappa a^2} \frac{1}{T_{10}^{1.5}} +$$ + +The neoclassical (avergae) resisitivity enhancement factor $\left(\langle \gamma_{\text{NC}} \rangle \right)$ is given by an empirical fit: + +$$ +\langle \gamma_{\text{NC}} \rangle = 4.3 -0.6A +$$ + +where $A$ is valid in the range of 2.5 - 4.0. + + +[^1]: N.A. Uckan and ITER Physics Group, 'ITER Physics Design Guidelines: 1989', \ No newline at end of file diff --git a/documentation/proc-pages/physics-models/plasma_overview.md b/documentation/proc-pages/physics-models/plasma_overview.md index cdd80f070..af1c6a9c1 100644 --- a/documentation/proc-pages/physics-models/plasma_overview.md +++ b/documentation/proc-pages/physics-models/plasma_overview.md @@ -22,7 +22,7 @@ More detail is given in [^1], but this webpage is more up to date. Neo-classical trapped particle effects are included in the calculation of the plasma resistance and ohmic heating power in -subroutine `pohm`, which is called by routine `physics`. The scaling used is only valid for aspect +subroutine `plasma_ohmic_heating()`, which is called by routine `physics`. The scaling used is only valid for aspect ratios between 2.5 and 4, and it is possible for the plasma resistance to be incorrect or even negative if the aspect ratio is outside this range. An error is reported if the calculated plasma resistance is negative. diff --git a/examples/data/csv_output_large_tokamak_MFILE.DAT b/examples/data/csv_output_large_tokamak_MFILE.DAT index 8bf3945c0..22e0aa409 100644 --- a/examples/data/csv_output_large_tokamak_MFILE.DAT +++ b/examples/data/csv_output_large_tokamak_MFILE.DAT @@ -452,7 +452,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.2724E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.8311E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.6172E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.6172E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.2139E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.7861E-01 @@ -522,7 +522,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.9627E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 4.0562E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 4.0562E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 3.0010E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.3992E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -1141,13 +1141,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.7935E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.0224E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2322E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.6172E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.6172E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 7.5213E+01 Total_(MW)______________________________________________________________ ______________________________ 3.7935E+02 Fusion_power_(MW)_______________________________________________________ (powfmw)______________________ 1.5926E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.0312E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 7.5213E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.6172E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.6172E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6306E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1347E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8043E+03 diff --git a/examples/data/large_tokamak_1_MFILE.DAT b/examples/data/large_tokamak_1_MFILE.DAT index e250bdeba..b349fc60a 100644 --- a/examples/data/large_tokamak_1_MFILE.DAT +++ b/examples/data/large_tokamak_1_MFILE.DAT @@ -449,7 +449,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3368E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9237E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.2284E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.2284E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1828E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8172E-01 @@ -518,7 +518,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.7450E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 3.9755E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 3.9755E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 3.0619E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.4181E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -1136,13 +1136,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8950E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.0746E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2659E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.2284E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.2284E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 8.0143E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8950E+02 Fusion_power_(MW)_______________________________________________________ (powfmw)______________________ 1.6202E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.0836E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 8.0143E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.2284E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.2284E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6576E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1751E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8339E+03 diff --git a/examples/data/large_tokamak_2_MFILE.DAT b/examples/data/large_tokamak_2_MFILE.DAT index 337d0d715..7f1e03643 100644 --- a/examples/data/large_tokamak_2_MFILE.DAT +++ b/examples/data/large_tokamak_2_MFILE.DAT @@ -449,7 +449,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3368E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9237E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.2284E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.2284E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1828E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8172E-01 @@ -518,7 +518,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.7450E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 3.9755E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 3.9755E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 3.0619E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.4181E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -1136,13 +1136,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8950E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.0746E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2659E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.2284E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.2284E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 8.0143E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8950E+02 Fusion_power_(MW)_______________________________________________________ (powfmw)______________________ 1.6202E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.0836E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 8.0143E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.2284E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.2284E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6576E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1751E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8339E+03 diff --git a/examples/data/large_tokamak_3_MFILE.DAT b/examples/data/large_tokamak_3_MFILE.DAT index 1e0218ba9..18cfa7ccf 100644 --- a/examples/data/large_tokamak_3_MFILE.DAT +++ b/examples/data/large_tokamak_3_MFILE.DAT @@ -449,7 +449,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3368E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9237E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.2284E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.2284E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1828E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8172E-01 @@ -518,7 +518,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.7450E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 3.9755E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 3.9755E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 3.0619E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.4181E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -1136,13 +1136,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8950E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.0746E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2659E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.2284E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.2284E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 8.0143E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8950E+02 Fusion_power_(MW)_______________________________________________________ (powfmw)______________________ 1.6202E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.0836E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 8.0143E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.2284E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.2284E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6576E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1751E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8339E+03 diff --git a/examples/data/large_tokamak_4_MFILE.DAT b/examples/data/large_tokamak_4_MFILE.DAT index 1fb138d12..422a338b7 100644 --- a/examples/data/large_tokamak_4_MFILE.DAT +++ b/examples/data/large_tokamak_4_MFILE.DAT @@ -449,7 +449,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3368E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9237E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.2284E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.2284E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1828E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8172E-01 @@ -518,7 +518,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.7450E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 3.9755E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 3.9755E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 3.0619E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.4181E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -1136,13 +1136,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8950E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.0746E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2659E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.2284E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.2284E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 8.0143E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8950E+02 Fusion_power_(MW)_______________________________________________________ (powfmw)______________________ 1.6202E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.0836E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 8.0143E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.2284E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.2284E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6576E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1751E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8339E+03 diff --git a/examples/data/scan_MFILE.DAT b/examples/data/scan_MFILE.DAT index ea7813056..c3be39f2a 100644 --- a/examples/data/scan_MFILE.DAT +++ b/examples/data/scan_MFILE.DAT @@ -305,7 +305,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.4266E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 2.0529E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 5.7803E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 5.7803E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1857E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8143E-01 @@ -375,7 +375,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.1974E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 2.8865E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 2.8865E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 4.8432E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.6558E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -963,13 +963,13 @@ Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.8174E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.6226E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 5.1000E+01 Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Fusion_power_(MW)_______________________________________________________ (powfmw.)_____________________ 2.0117E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.8286E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 5.1000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 2.0393E+02 Total_(MW)______________________________________________________________ ______________________________ 2.6500E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 2.2576E+03 @@ -1300,7 +1300,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.4266E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 2.0529E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 5.7803E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 5.7803E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1857E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8143E-01 @@ -1370,7 +1370,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.1974E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 2.8865E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 2.8865E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 4.8432E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.6558E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -1958,13 +1958,13 @@ Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.8174E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.6226E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 5.1000E+01 Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Fusion_power_(MW)_______________________________________________________ (powfmw.)_____________________ 2.0117E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.8286E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 5.1000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 2.0393E+02 Total_(MW)______________________________________________________________ ______________________________ 2.6500E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 2.2576E+03 @@ -2295,7 +2295,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.4266E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 2.0529E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 5.7803E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 5.7803E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1857E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8143E-01 @@ -2365,7 +2365,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.1974E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 2.8865E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 2.8865E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 4.8432E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.6558E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -2953,13 +2953,13 @@ Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.8174E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.6226E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 5.1000E+01 Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Fusion_power_(MW)_______________________________________________________ (powfmw.)_____________________ 2.0117E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.8286E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 5.1000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 2.0393E+02 Total_(MW)______________________________________________________________ ______________________________ 2.6500E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 2.2576E+03 @@ -3290,7 +3290,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.4266E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 2.0529E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 5.7803E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 5.7803E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1857E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8143E-01 @@ -3360,7 +3360,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.1974E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 2.8865E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 2.8865E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 4.8432E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.6558E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -3948,13 +3948,13 @@ Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.8174E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.6226E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 5.1000E+01 Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Fusion_power_(MW)_______________________________________________________ (powfmw.)_____________________ 2.0117E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.8286E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 5.1000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 2.0393E+02 Total_(MW)______________________________________________________________ ______________________________ 2.6500E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 2.2576E+03 @@ -4285,7 +4285,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.4266E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 2.0529E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 5.7803E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 5.7803E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1857E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8143E-01 @@ -4355,7 +4355,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.1974E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 2.8865E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 2.8865E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 4.8432E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.6558E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -4943,13 +4943,13 @@ Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.8174E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.6226E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 5.1000E+01 Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Fusion_power_(MW)_______________________________________________________ (powfmw.)_____________________ 2.0117E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.8286E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 5.1000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 2.0393E+02 Total_(MW)______________________________________________________________ ______________________________ 2.6500E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 2.2576E+03 @@ -5280,7 +5280,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.4266E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 2.0529E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 5.7803E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 5.7803E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1857E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8143E-01 @@ -5350,7 +5350,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.1974E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 2.8865E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 2.8865E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 4.8432E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.6558E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -5938,13 +5938,13 @@ Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.8174E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.6226E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 5.1000E+01 Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Fusion_power_(MW)_______________________________________________________ (powfmw.)_____________________ 2.0117E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.8286E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 5.1000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 2.0393E+02 Total_(MW)______________________________________________________________ ______________________________ 2.6500E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 2.2576E+03 @@ -6275,7 +6275,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.4266E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 2.0529E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 5.7803E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 5.7803E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1857E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8143E-01 @@ -6345,7 +6345,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.1974E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 2.8865E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 2.8865E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 4.8432E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.6558E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -6933,13 +6933,13 @@ Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.8174E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.6226E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 5.1000E+01 Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Fusion_power_(MW)_______________________________________________________ (powfmw.)_____________________ 2.0117E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.8286E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 5.1000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 2.0393E+02 Total_(MW)______________________________________________________________ ______________________________ 2.6500E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 2.2576E+03 @@ -7270,7 +7270,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.4266E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 2.0529E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 5.7803E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 5.7803E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1857E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8143E-01 @@ -7340,7 +7340,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.1974E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 2.8865E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 2.8865E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 4.8432E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.6558E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -7928,13 +7928,13 @@ Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.8174E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.6226E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 5.1000E+01 Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Fusion_power_(MW)_______________________________________________________ (powfmw.)_____________________ 2.0117E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.8286E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 5.1000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 2.0393E+02 Total_(MW)______________________________________________________________ ______________________________ 2.6500E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 2.2576E+03 @@ -8265,7 +8265,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.4266E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 2.0529E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 5.7803E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 5.7803E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1857E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8143E-01 @@ -8335,7 +8335,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.1974E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 2.8865E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 2.8865E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 4.8432E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.6558E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -8923,13 +8923,13 @@ Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.8174E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.6226E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 5.1000E+01 Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Fusion_power_(MW)_______________________________________________________ (powfmw.)_____________________ 2.0117E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.8286E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 5.1000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 2.0393E+02 Total_(MW)______________________________________________________________ ______________________________ 2.6500E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 2.2576E+03 diff --git a/mkdocs.yml b/mkdocs.yml index 4ff76e2de..3e038e171 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -62,7 +62,9 @@ nav: - Bootstrap Current: physics-models/plasma_current/bootstrap_current.md - Diamagnetic Current: physics-models/plasma_current/diamagnetic_current.md - Pfirsch-Schlüter Current: physics-models/plasma_current/pfirsch_schlüter_current_drive.md - - Inductive Current: physics-models/plasma_current/inductive_plasma_current.md + - Inductive Current: + - Inductive Current: physics-models/plasma_current/inductive_plasma_current.md + - Plasma Resistive Heating: physics-models/plasma_current/plasma_resistive_heating.md - Confinement time: physics-models/plasma_confinement.md - Plasma Core Power Balance: physics-models/plasma_power_balance.md - Pulsed Plant Operation: physics-models/pulsed-plant.md diff --git a/process/current_drive.py b/process/current_drive.py index 17b35ccf8..298b04ad4 100644 --- a/process/current_drive.py +++ b/process/current_drive.py @@ -702,7 +702,7 @@ def cudriv(self, output: bool): abs( current_drive_variables.pinjmw + current_drive_variables.porbitlossmw - + physics_variables.pohmmw + + physics_variables.p_plasma_ohmic_mw ) < 1.0e-6 ): @@ -711,7 +711,7 @@ def cudriv(self, output: bool): current_drive_variables.bigq = physics_variables.fusion_power / ( current_drive_variables.pinjmw + current_drive_variables.porbitlossmw - + physics_variables.pohmmw + + physics_variables.p_plasma_ohmic_mw ) if not output: diff --git a/process/io/plot_radial_build.py b/process/io/plot_radial_build.py index 09a708ddc..38722e81a 100644 --- a/process/io/plot_radial_build.py +++ b/process/io/plot_radial_build.py @@ -227,7 +227,7 @@ def main(args=None): "", "fvs", # actaully lower bound fvs "vburn", - "rplas", + "res_plasma", ] # "plasma_res_factor" diff --git a/process/io/sankey_funcs.py b/process/io/sankey_funcs.py index 6ae38492c..69a7b44b0 100644 --- a/process/io/sankey_funcs.py +++ b/process/io/sankey_funcs.py @@ -24,8 +24,12 @@ def plot_full_sankey( # Used in [PLASMA] fusion_power = m_file.data["fusion_power"].get_scan(-1) # Fusion power (MW) pinjmw = m_file.data["pinjmw"].get_scan(-1) # Total auxiliary injected power (MW) - pohmmw = m_file.data["pohmmw"].get_scan(-1) # Ohmic heating power (MW) - totalplasma = fusion_power + pinjmw + pohmmw # Total Power in plasma (MW) + p_plasma_ohmic_mw = m_file.data["p_plasma_ohmic_mw"].get_scan( + -1 + ) # Ohmic heating power (MW) + totalplasma = ( + fusion_power + pinjmw + p_plasma_ohmic_mw + ) # Total Power in plasma (MW) neutron_power_total = m_file.data["neutron_power_total"].get_scan( -1 ) # Neutron fusion power (MW) @@ -33,7 +37,7 @@ def plot_full_sankey( -1 ) # Non-alpha charged particle power (MW) pcharohmmw = ( - non_alpha_charged_power + pohmmw + non_alpha_charged_power + p_plasma_ohmic_mw ) # The ohmic and charged particle power (MW) alpha_power_total = m_file.data["alpha_power_total"].get_scan( -1 @@ -119,7 +123,7 @@ def plot_full_sankey( PLASMA = [ fusion_power, pinjmw, - pohmmw, + p_plasma_ohmic_mw, -pcharohmmw, -palpinjmw, -neutron_power_total, @@ -430,7 +434,7 @@ def plot_full_sankey( t.set_position((pos[0]-0.5*(pinjmw/totalplasma)-0.05,pos[1])) if t == diagrams[0].texts[2]: # Ohmic t.set_horizontalalignment('left') - t.set_position((pos[0]+0.5*(pohmmw/totalplasma)+0.05,pos[1])) + t.set_position((pos[0]+0.5*(p_plasma_ohmic_mw/totalplasma)+0.05,pos[1])) if t == diagrams[0].texts[3]: # Neutrons t.set_horizontalalignment('right') t.set_position((pos[0]-0.2,pos[1])) @@ -478,8 +482,12 @@ def plot_sankey(mfilename="MFILE.DAT"): # Plot simplified power flow Sankey Dia # Used in [PLASMA] fusion_power = m_file.data["fusion_power"].get_scan(-1) # Fusion Power (MW) pinjmw = m_file.data["pinjmw"].get_scan(-1) # Total auxiliary injected Power (MW) - pohmmw = m_file.data["pohmmw"].get_scan(-1) # Ohmic heating Power (MW) - totalplasma = fusion_power + pinjmw + pohmmw # Total Power in plasma (MW) + p_plasma_ohmic_mw = m_file.data["p_plasma_ohmic_mw"].get_scan( + -1 + ) # Ohmic heating Power (MW) + totalplasma = ( + fusion_power + pinjmw + p_plasma_ohmic_mw + ) # Total Power in plasma (MW) # Used in [DEPOSITION] pradmw = m_file.data["pradmw"].get_scan(-1) # Total radiation Power (MW) @@ -598,7 +606,7 @@ def plot_sankey(mfilename="MFILE.DAT"): # Plot simplified power flow Sankey Dia # --------------------------------------- PLASMA - 0 -------------------------------------- # Fusion power, Injected power + ohmic power, - total plasma power - PLASMA = [fusion_power, pinjmw + pohmmw, -totalplasma] + PLASMA = [fusion_power, pinjmw + p_plasma_ohmic_mw, -totalplasma] sankey.add( flows=PLASMA, orientations=[0, -1, 0], # [right(in), down(in), right(out)] diff --git a/process/objectives.py b/process/objectives.py index e27039cd5..d1bfba79c 100644 --- a/process/objectives.py +++ b/process/objectives.py @@ -55,7 +55,7 @@ def objective_function(minmax: int) -> float: objective_metric = physics_variables.fusion_power / ( current_drive_variables.pinjmw + current_drive_variables.porbitlossmw - + physics_variables.pohmmw + + physics_variables.p_plasma_ohmic_mw ) case 6: objective_metric = cost_variables.coe / 100.0 diff --git a/process/physics.py b/process/physics.py index d5786b091..026927e79 100644 --- a/process/physics.py +++ b/process/physics.py @@ -64,7 +64,7 @@ def vscalc( gamma, kappa, rmajor, - rplas, + res_plasma, plasma_current, t_fusion_ramp, t_burn, @@ -81,7 +81,7 @@ def vscalc( plasma_current: input real : plasma current (A) rli : input real : plasma normalised inductivity rmajor : input real : plasma major radius (m) - rplas : input real : plasma resistance (ohm) + res_plasma : input real : plasma resistance (ohm) t_fusion_ramp : input real : heating time (s) t_burn : input real : burn time (s) phiint : output real : internal plasma volt-seconds (Wb) @@ -125,7 +125,7 @@ def vscalc( # Include enhancement factor in flattop V-s requirement # to account for MHD sawtooth effects. - vburn = plasma_current * rplas * inductive_current_fraction * csawth + vburn = plasma_current * res_plasma * inductive_current_fraction * csawth # N.B. t_burn on first iteration will not be correct # if the pulsed reactor option is used, but the value @@ -2153,11 +2153,11 @@ def physics(self): # Calculate ohmic power ( - physics_variables.pohmpv, - physics_variables.pohmmw, + physics_variables.pden_plasma_ohmic_mw, + physics_variables.p_plasma_ohmic_mw, physics_variables.rpfac, - physics_variables.rplas, - ) = self.pohm( + physics_variables.res_plasma, + ) = self.plasma_ohmic_heating( physics_variables.inductive_current_fraction, physics_variables.kappa95, physics_variables.plasma_current, @@ -2199,7 +2199,7 @@ def physics(self): physics_variables.f_alpha_plasma * physics_variables.alpha_power_total + physics_variables.non_alpha_charged_power + pinj - + physics_variables.pohmmw + + physics_variables.p_plasma_ohmic_mw - physics_variables.pradmw ) @@ -2221,7 +2221,9 @@ def physics(self): # Resistive diffusion time = current penetration time ~ mu0.a^2/resistivity physics_variables.res_time = res_diff_time( - physics_variables.rmajor, physics_variables.rplas, physics_variables.kappa95 + physics_variables.rmajor, + physics_variables.res_plasma, + physics_variables.kappa95, ) # Power transported to the first wall by escaped alpha particles @@ -2313,7 +2315,7 @@ def physics(self): physics_variables.gamma, physics_variables.kappa, physics_variables.rmajor, - physics_variables.rplas, + physics_variables.res_plasma, physics_variables.plasma_current, times_variables.t_fusion_ramp, times_variables.t_burn, @@ -2487,7 +2489,7 @@ def physics(self): physics_module.total_loss_power = 1e6 * ( physics_variables.f_alpha_plasma * physics_variables.alpha_power_total + physics_variables.non_alpha_charged_power - + physics_variables.pohmmw + + physics_variables.p_plasma_ohmic_mw + current_drive_variables.pinjmw ) physics_module.rad_fraction_lcfs = ( @@ -2971,23 +2973,47 @@ def phyaux( return burnup, dntau, figmer, fusrat, qfuel, rndfuel, taup @staticmethod - def pohm( - inductive_current_fraction, - kappa95, - plasma_current, - rmajor, - rminor, - ten, - plasma_volume, - zeff, - ): - # Density weighted electron temperature in 10 keV units + def plasma_ohmic_heating( + inductive_current_fraction: float, + kappa95: float, + plasma_current: float, + rmajor: float, + rminor: float, + ten: float, + plasma_volume: float, + zeff: float, + ) -> Tuple[float, float, float, float]: + """ + Calculate the ohmic heating power and related parameters. - t10 = ten / 10.0 + Args: + inductive_current_fraction (float): Fraction of plasma current driven inductively. + kappa95 (float): Plasma elongation at 95% surface. + plasma_current (float): Plasma current (A). + rmajor (float): Major radius (m). + rminor (float): Minor radius (m). + ten (float): Density weighted average electron temperature (keV). + plasma_volume (float): Plasma volume (m^3). + zeff (float): Plasma effective charge. - # Plasma resistance, from loop voltage calculation in IPDG89 + Returns: + Tuple[float, float, float, float]: Tuple containing: + - pden_plasma_ohmic_mw (float): Ohmic heating power per unit volume (MW/m^3). + - p_plasma_ohmic_mw (float): Total ohmic heating power (MW). + - rpfac (float): Neo-classical resistivity enhancement factor. + - res_plasma (float): Plasma resistance (ohm). - rplas = ( + Notes: + + References: + - ITER Physics Design Guidelines: 1989 [IPDG89], N. A. Uckan et al, + + """ + # Density weighted electron temperature in 10 keV units + t10 = ten / 10.0 + + # Plasma resistance, from loop voltage calculation in ITER Physics Design Guidelines: 1989 + res_plasma = ( physics_variables.plasma_res_factor * 2.15e-9 * zeff @@ -2996,36 +3022,32 @@ def pohm( ) # Neo-classical resistivity enhancement factor - # Taken from N. A. Uckan et al, Fusion Technology 13 (1988) p.411. - # The expression is valid for aspect ratios in the range 2.5--4. - + # Taken from ITER Physics Design Guidelines: 1989 + # The expression is valid for aspect ratios in the range 2.5 to 4.0 rpfac = 4.3 - 0.6 * rmajor / rminor - rplas = rplas * rpfac + res_plasma = res_plasma * rpfac # Check to see if plasma resistance is negative # (possible if aspect ratio is too high) - - if rplas <= 0.0: - error_handling.fdiags[0] = rplas + if res_plasma <= 0.0: + error_handling.fdiags[0] = res_plasma error_handling.fdiags[1] = physics_variables.aspect error_handling.report_error(83) # Ohmic heating power per unit volume - # Corrected from: pohmpv = (inductive_current_fraction*plasma_current)**2 * ... - - pohmpv = ( + # Corrected from: pden_plasma_ohmic_mw = (inductive_current_fraction*plasma_current)**2 * ... + pden_plasma_ohmic_mw = ( inductive_current_fraction * plasma_current**2 - * rplas + * res_plasma * 1.0e-6 / plasma_volume ) # Total ohmic heating power + p_plasma_ohmic_mw = pden_plasma_ohmic_mw * plasma_volume - pohmmw = pohmpv * plasma_volume - - return pohmpv, pohmmw, rpfac, rplas + return pden_plasma_ohmic_mw, p_plasma_ohmic_mw, rpfac, res_plasma @staticmethod def calculate_plasma_current( @@ -4411,7 +4433,7 @@ def outplas(self): tot_power_plasma = ( physics_variables.f_alpha_plasma * physics_variables.alpha_power_total + physics_variables.non_alpha_charged_power - + physics_variables.pohmmw + + physics_variables.p_plasma_ohmic_mw + current_drive_variables.pinjmw ) po.ovarre( @@ -4632,8 +4654,8 @@ def outplas(self): po.ovarre( self.outfile, "Ohmic heating power (MW)", - "(pohmmw)", - physics_variables.pohmmw, + "(p_plasma_ohmic_mw)", + physics_variables.p_plasma_ohmic_mw, "OP ", ) po.ovarrf( @@ -5481,15 +5503,15 @@ def outplas(self): "Loop voltage during burn (V)", "(vburn)", physics_variables.plasma_current - * physics_variables.rplas + * physics_variables.res_plasma * physics_variables.inductive_current_fraction, "OP ", ) po.ovarre( self.outfile, "Plasma resistance (ohm)", - "(rplas)", - physics_variables.rplas, + "(res_plasma)", + physics_variables.res_plasma, "OP ", ) @@ -6430,7 +6452,7 @@ def fhz(self, hhh): - physics_variables.f_alpha_plasma * physics_variables.alpha_power_density_total - physics_variables.charged_power_density - - physics_variables.pohmpv + - physics_variables.pden_plasma_ohmic_mw ) # Take into account whether injected power is included in tau_e @@ -6555,7 +6577,7 @@ def pcond( powerht = ( physics_variables.f_alpha_plasma * alpha_power_total + non_alpha_charged_power - + physics_variables.pohmmw + + physics_variables.p_plasma_ohmic_mw ) # If the device is not ignited, add the injected auxiliary power @@ -7429,17 +7451,17 @@ def calculate_poloidal_beta(btot, bp, beta): return beta * (btot / bp) ** 2 -def res_diff_time(rmajor, rplas, kappa95): +def res_diff_time(rmajor, res_plasma, kappa95): """Calculates resistive diffusion time Author: James Morris (UKAEA) :param rmajor: plasma major radius (m) - :param rplas: plasma resistivity (Ohms) + :param res_plasma: plasma resistivity (Ohms) :param kappa95: plasma elongation at 95% flux surface """ - return 2 * constants.rmu0 * rmajor / (rplas * kappa95) + return 2 * constants.rmu0 * rmajor / (res_plasma * kappa95) def pthresh(dene, dnla, bt, rmajor, rminor, kappa, sarea, aion, aspect, plasma_current): diff --git a/process/power.py b/process/power.py index 6479b0f25..fabeeeeee 100644 --- a/process/power.py +++ b/process/power.py @@ -296,8 +296,8 @@ def pfpwr(self, output: bool): # PF wall plug power dissipated in power supply for ohmic heating (MW) # This is additional to that required for moving stored energy around - # pfwpmw = physics_variables.pohmmw / pfcoil_variables.etapsu - wall_plug_ohmicmw = physics_variables.pohmmw * ( + # pfwpmw = physics_variables.p_plasma_ohmic_mw / pfcoil_variables.etapsu + wall_plug_ohmicmw = physics_variables.p_plasma_ohmic_mw * ( 1.0e0 / pfcoil_variables.etapsu - 1.0e0 ) # Total mean wall plug power dissipated in PFC and CS power supplies. Issue #713 @@ -1652,14 +1652,14 @@ def power2(self, output: bool): po.ovarrf( self.outfile, "Ohmic heating (MW)", - "(pohmmw.)", - physics_variables.pohmmw, + "(p_plasma_ohmic_mw.)", + physics_variables.p_plasma_ohmic_mw, "OP ", ) # if (physics_variables.ignite == 1) : - # po.ovarrf(self.outfile,'Total (MW)','',f_alpha_plasma*physics_variables.alpha_power_total+physics_variables.non_alpha_charged_power+pohmmw, 'OP ') + # po.ovarrf(self.outfile,'Total (MW)','',f_alpha_plasma*physics_variables.alpha_power_total+physics_variables.non_alpha_charged_power+p_plasma_ohmic_mw, 'OP ') # po.oblnkl(self.outfile) - # if (abs(sum - (physics_variables.f_alpha_plasma*physics_variables.alpha_power_total+physics_variables.non_alpha_charged_power+physics_variables.pohmmw)) > 5.0e0) : + # if (abs(sum - (physics_variables.f_alpha_plasma*physics_variables.alpha_power_total+physics_variables.non_alpha_charged_power+physics_variables.p_plasma_ohmic_mw)) > 5.0e0) : # write(*,*) 'WARNING: Power balance across separatrix is in error by more than 5 MW.' # po.ocmmnt(self.outfile,'WARNING: Power balance across separatrix is in error by more than 5 MW.') # @@ -1677,7 +1677,7 @@ def power2(self, output: bool): "", physics_variables.f_alpha_plasma * physics_variables.alpha_power_total + physics_variables.non_alpha_charged_power - + physics_variables.pohmmw + + physics_variables.p_plasma_ohmic_mw + pinj, "OP ", ) @@ -1689,7 +1689,7 @@ def power2(self, output: bool): physics_variables.f_alpha_plasma * physics_variables.alpha_power_total + physics_variables.non_alpha_charged_power - + physics_variables.pohmmw + + physics_variables.p_plasma_ohmic_mw + pinj ) ) @@ -1725,8 +1725,8 @@ def power2(self, output: bool): po.ovarrf( self.outfile, "Ohmic power (MW)", - "(pohmmw.)", - physics_variables.pohmmw, + "(p_plasma_ohmic_mw.)", + physics_variables.p_plasma_ohmic_mw, "OP ", ) po.ovarrf( @@ -1741,7 +1741,7 @@ def power2(self, output: bool): + fwbs_variables.emultmw + pinj + self.htpmw_mech - + physics_variables.pohmmw + + physics_variables.p_plasma_ohmic_mw ) po.ovarrf(self.outfile, "Total (MW)", "", sum, "OP ") po.oblnkl(self.outfile) diff --git a/process/pulse.py b/process/pulse.py index b24c09d8a..8beb6978c 100755 --- a/process/pulse.py +++ b/process/pulse.py @@ -150,7 +150,7 @@ def burn(self, output: bool): vburn = ( physics_variables.plasma_current - * physics_variables.rplas + * physics_variables.res_plasma * physics_variables.inductive_current_fraction * physics_variables.csawth ) diff --git a/process/stellarator.py b/process/stellarator.py index 7944aadfd..8e638f3aa 100644 --- a/process/stellarator.py +++ b/process/stellarator.py @@ -4317,7 +4317,7 @@ def stphys(self, output): powht = ( physics_variables.f_alpha_plasma * physics_variables.alpha_power_total + physics_variables.non_alpha_charged_power - + physics_variables.pohmmw + + physics_variables.p_plasma_ohmic_mw - physics_variables.pradpv * physics_variables.plasma_volume ) powht = max( @@ -4389,7 +4389,7 @@ def stphys(self, output): physics_variables.rad_fraction_total = physics_variables.pradmw / ( physics_variables.f_alpha_plasma * physics_variables.alpha_power_total + physics_variables.non_alpha_charged_power - + physics_variables.pohmmw + + physics_variables.p_plasma_ohmic_mw + current_drive_variables.pinjmw ) @@ -4925,7 +4925,7 @@ def stheat(self, output: bool): abs( current_drive_variables.pinjmw + current_drive_variables.porbitlossmw - + physics_variables.pohmmw + + physics_variables.p_plasma_ohmic_mw ) < 1e-6 ): @@ -4934,7 +4934,7 @@ def stheat(self, output: bool): current_drive_variables.bigq = physics_variables.fusion_power / ( current_drive_variables.pinjmw + current_drive_variables.porbitlossmw - + physics_variables.pohmmw + + physics_variables.p_plasma_ohmic_mw ) if output: diff --git a/process/utilities/errorlist.json b/process/utilities/errorlist.json index 5bce49d12..1b2a59f57 100644 --- a/process/utilities/errorlist.json +++ b/process/utilities/errorlist.json @@ -423,7 +423,7 @@ { "no": 83, "level": 2, - "message": "POHM: Negative plasma resistance rplas" + "message": "POHM: Negative plasma resistance res_plasma" }, { "no": 84, diff --git a/source/fortran/constraint_equations.f90 b/source/fortran/constraint_equations.f90 index 6ae3089ed..109b499d0 100755 --- a/source/fortran/constraint_equations.f90 +++ b/source/fortran/constraint_equations.f90 @@ -455,13 +455,13 @@ subroutine constraint_eqn_002(tmp_cc, tmp_con, tmp_err, tmp_symbol, tmp_units) !! f_alpha_plasma : input real : fraction of alpha power deposited in plasma !! alpha_power_density_total : input real : alpha power per volume (MW/m3) !! charged_power_density : input real : non-alpha charged particle fusion power per volume (MW/m3) - !! pohmpv : input real : ohmic heating power per volume (MW/m3) + !! pden_plasma_ohmic_mw : input real : ohmic heating power per volume (MW/m3) !! pinjmw : input real : total auxiliary injected power (MW) !! plasma_volume : input real : plasma volume (m3) use physics_variables, only: iradloss, ignite, ptrepv, ptripv, pradpv, & pcoreradpv, f_alpha_plasma, alpha_power_density_total, charged_power_density, & - pohmpv, plasma_volume + pden_plasma_ohmic_mw, plasma_volume use current_drive_variables, only: pinjmw implicit none @@ -488,10 +488,10 @@ subroutine constraint_eqn_002(tmp_cc, tmp_con, tmp_err, tmp_symbol, tmp_units) ! if plasma not ignited include injected power if (ignite == 0) then - pdenom = f_alpha_plasma*alpha_power_density_total + charged_power_density + pohmpv + pinjmw/plasma_volume + pdenom = f_alpha_plasma*alpha_power_density_total + charged_power_density + pden_plasma_ohmic_mw + pinjmw/plasma_volume else ! if plasma ignited - pdenom = f_alpha_plasma*alpha_power_density_total + charged_power_density + pohmpv + pdenom = f_alpha_plasma*alpha_power_density_total + charged_power_density + pden_plasma_ohmic_mw end if tmp_cc = 1.0D0 - pnumerator / pdenom @@ -1032,10 +1032,10 @@ subroutine constraint_eqn_017(tmp_cc, tmp_con, tmp_err, tmp_symbol, tmp_units) !! plasma_volume : input real : plasma volume (m3) !! alpha_power_density_total : input real : alpha power per volume (MW/m3) !! charged_power_density : input real : non-alpha charged particle fusion power per volume (MW/m3) - !! pohmpv : input real : ohmic heating power per volume (MW/m3) + !! pden_plasma_ohmic_mw : input real : ohmic heating power per volume (MW/m3) !! fradpwr : input real : f-value for core radiation power limit !! pradpv : input real : total radiation power per volume (MW/m3) - use physics_variables, only: f_alpha_plasma, plasma_volume, alpha_power_density_total, charged_power_density, pohmpv, pradpv + use physics_variables, only: f_alpha_plasma, plasma_volume, alpha_power_density_total, charged_power_density, pden_plasma_ohmic_mw, pradpv use current_drive_variables, only: pinjmw use constraint_variables, only: fradpwr implicit none @@ -1048,7 +1048,7 @@ subroutine constraint_eqn_017(tmp_cc, tmp_con, tmp_err, tmp_symbol, tmp_units) real(dp) :: pradmaxpv !! Maximum possible power/plasma_volume that can be radiated (local) - pradmaxpv = pinjmw/plasma_volume + alpha_power_density_total*f_alpha_plasma + charged_power_density + pohmpv + pradmaxpv = pinjmw/plasma_volume + alpha_power_density_total*f_alpha_plasma + charged_power_density + pden_plasma_ohmic_mw tmp_cc = 1.0D0 - fradpwr * pradmaxpv / pradpv tmp_con = pradmaxpv * (1.0D0 - tmp_cc) tmp_err = pradpv * tmp_cc diff --git a/source/fortran/physics_variables.f90 b/source/fortran/physics_variables.f90 index a229830ed..3f05cd789 100644 --- a/source/fortran/physics_variables.f90 +++ b/source/fortran/physics_variables.f90 @@ -689,10 +689,10 @@ module physics_variables real(dp) :: neutron_power_density_plasma !! neutron fusion power per volume just from plasma (MW/m3) - real(dp) :: pohmmw + real(dp) :: p_plasma_ohmic_mw !! ohmic heating power (MW) - real(dp) :: pohmpv + real(dp) :: pden_plasma_ohmic_mw !! ohmic heating power per volume (MW/m3) real(dp) :: powerht @@ -831,9 +831,9 @@ module physics_variables !! n_oxygen / n_e real(dp) :: rpfac - !! neo-classical correction factor to rplas + !! neo-classical correction factor to res_plasma - real(dp) :: rplas + real(dp) :: res_plasma !! plasma resistance (ohm) real(dp) :: res_time @@ -1088,8 +1088,8 @@ subroutine init_physics_variables neutron_power_total = 0.0D0 neutron_power_density_total = 0.0D0 neutron_power_density_plasma = 0.0D0 - pohmmw = 0.0D0 - pohmpv = 0.0D0 + p_plasma_ohmic_mw = 0.0D0 + pden_plasma_ohmic_mw = 0.0D0 powerht = 0.0D0 fusion_power = 0.0D0 pperim = 0.0D0 @@ -1128,7 +1128,7 @@ subroutine init_physics_variables rnfene = 0.0D0 rnone = 0.0D0 rpfac = 0.0D0 - rplas = 0.0D0 + res_plasma = 0.0D0 res_time = 0.0D0 sarea = 0.0D0 sareao = 0.0D0 diff --git a/tests/integration/data/large_tokamak_1_MFILE.DAT b/tests/integration/data/large_tokamak_1_MFILE.DAT index dd355f23b..7c23dd5a5 100644 --- a/tests/integration/data/large_tokamak_1_MFILE.DAT +++ b/tests/integration/data/large_tokamak_1_MFILE.DAT @@ -448,7 +448,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3368E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9237E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.2284E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.2284E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1828E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8172E-01 @@ -517,7 +517,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.7450E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 3.9755E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 3.9755E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 3.0619E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.4181E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -1135,13 +1135,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8950E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.0746E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2659E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.2284E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.2284E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 8.0143E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8950E+02 Fusion_power_(MW)_______________________________________________________ (fusion_power)______________________ 1.6202E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.0836E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 8.0143E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.2284E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.2284E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6576E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1751E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8339E+03 diff --git a/tests/integration/data/large_tokamak_2_MFILE.DAT b/tests/integration/data/large_tokamak_2_MFILE.DAT index 83bf51a35..6c875d018 100644 --- a/tests/integration/data/large_tokamak_2_MFILE.DAT +++ b/tests/integration/data/large_tokamak_2_MFILE.DAT @@ -449,7 +449,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3368E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9237E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.2284E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.2284E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1828E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8172E-01 @@ -518,7 +518,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.7450E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 3.9755E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 3.9755E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 3.0619E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.4181E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -1136,13 +1136,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8950E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.0746E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2659E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.2284E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.2284E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 8.0143E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8950E+02 Fusion_power_(MW)_______________________________________________________ (fusion_power)______________________ 1.6202E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.0836E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 8.0143E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.2284E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.2284E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6576E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1751E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8339E+03 diff --git a/tests/integration/data/large_tokamak_3_MFILE.DAT b/tests/integration/data/large_tokamak_3_MFILE.DAT index 5ec8fe752..2b7b73626 100644 --- a/tests/integration/data/large_tokamak_3_MFILE.DAT +++ b/tests/integration/data/large_tokamak_3_MFILE.DAT @@ -449,7 +449,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3368E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9237E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.2284E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.2284E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1828E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8172E-01 @@ -518,7 +518,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.7450E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 3.9755E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 3.9755E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 3.0619E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.4181E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -1136,13 +1136,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8950E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.0746E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2659E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.2284E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.2284E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 8.0143E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8950E+02 Fusion_power_(MW)_______________________________________________________ (fusion_power)______________________ 1.6202E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.0836E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 8.0143E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.2284E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.2284E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6576E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1751E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8339E+03 diff --git a/tests/integration/data/large_tokamak_4_MFILE.DAT b/tests/integration/data/large_tokamak_4_MFILE.DAT index 543d038f6..7a8900d00 100644 --- a/tests/integration/data/large_tokamak_4_MFILE.DAT +++ b/tests/integration/data/large_tokamak_4_MFILE.DAT @@ -449,7 +449,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3368E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9237E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.2284E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.2284E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1828E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8172E-01 @@ -518,7 +518,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.7450E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 3.9755E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 3.9755E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 3.0619E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.4181E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -1136,13 +1136,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8950E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.0746E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2659E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.2284E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.2284E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 8.0143E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8950E+02 Fusion_power_(MW)_______________________________________________________ (fusion_power)______________________ 1.6202E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.0836E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 8.0143E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.2284E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.2284E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6576E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1751E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8339E+03 diff --git a/tests/integration/data/large_tokamak_MFILE.DAT b/tests/integration/data/large_tokamak_MFILE.DAT index bae67cdc7..eb6e95fbc 100644 --- a/tests/integration/data/large_tokamak_MFILE.DAT +++ b/tests/integration/data/large_tokamak_MFILE.DAT @@ -445,7 +445,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3458E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9366E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.0733E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.0733E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1742E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8258E-01 @@ -515,7 +515,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.6762E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 3.9470E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 3.9470E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 3.0839E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.4245E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -1139,13 +1139,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8856E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.0698E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2675E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.0733E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.0733E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 7.9710E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8856E+02 Fusion_power_(MW)_______________________________________________________ (fusion_power)______________________ 1.6176E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.0787E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 7.9710E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.0733E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.0733E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6536E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1712E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8294E+03 diff --git a/tests/integration/data/scan_2D_MFILE.DAT b/tests/integration/data/scan_2D_MFILE.DAT index 0154a5bd5..6d55532d0 100644 --- a/tests/integration/data/scan_2D_MFILE.DAT +++ b/tests/integration/data/scan_2D_MFILE.DAT @@ -450,7 +450,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3216E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9018E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.8062E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.8062E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.2311E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.7689E-01 @@ -520,7 +520,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 4.1078E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 4.0689E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 4.0689E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 2.9916E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.4126E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -1137,13 +1137,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8038E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.0347E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2307E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.8062E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.8062E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 7.5000E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8038E+02 Fusion_power_(MW)_______________________________________________________ (powfmw)______________________ 1.5991E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.0435E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 7.5000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.8062E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.8062E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6323E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1423E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8059E+03 @@ -1613,7 +1613,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3272E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9099E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.7249E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.7249E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.2311E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.7689E-01 @@ -1683,7 +1683,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 4.0431E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 4.0820E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 4.0820E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 2.9820E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.4036E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -2300,13 +2300,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8317E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.0626E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2420E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.7249E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.7249E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 7.5000E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8317E+02 Fusion_power_(MW)_______________________________________________________ (powfmw)______________________ 1.6138E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.0715E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 7.5000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.7249E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.7249E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6474E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1613E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8226E+03 @@ -2776,7 +2776,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3173E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.8956E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.7110E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.7110E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.2311E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.7689E-01 @@ -2846,7 +2846,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 4.0227E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 4.1144E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 4.1144E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 2.9585E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.3947E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -3463,13 +3463,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8521E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.0773E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2480E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.7110E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.7110E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 7.5559E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8521E+02 Fusion_power_(MW)_______________________________________________________ (powfmw)______________________ 1.6215E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.0862E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 7.5559E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.7110E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.7110E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6571E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1721E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8334E+03 @@ -3939,7 +3939,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3201E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.8996E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.6372E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.6372E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.2187E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.7813E-01 @@ -4009,7 +4009,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.9960E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 4.0884E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 4.0884E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 2.9773E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.3964E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -4626,13 +4626,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8517E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.0826E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2549E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.6372E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.6372E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 7.5000E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8517E+02 Fusion_power_(MW)_______________________________________________________ (powfmw)______________________ 1.6243E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.0915E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 7.5000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.6372E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.6372E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6593E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1751E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8358E+03 @@ -5102,7 +5102,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3329E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9181E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.6412E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.6412E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.2187E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.7813E-01 @@ -5172,7 +5172,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.9998E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 4.0680E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 4.0680E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 2.9923E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.4042E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -5789,13 +5789,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8567E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.0875E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2569E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.6412E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.6412E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 7.5000E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8567E+02 Fusion_power_(MW)_______________________________________________________ (powfmw)______________________ 1.6269E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.0964E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 7.5000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.6412E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.6412E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6607E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1783E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8374E+03 @@ -6265,7 +6265,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3417E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9307E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.5587E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.5587E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.2187E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.7813E-01 @@ -6335,7 +6335,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.9635E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 4.0504E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 4.0504E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 3.0053E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.4131E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -6952,13 +6952,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8468E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.0777E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2529E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.5587E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.5587E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 7.5000E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8468E+02 Fusion_power_(MW)_______________________________________________________ (powfmw)______________________ 1.6218E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.0866E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 7.5000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.5587E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.5587E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6544E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1715E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8303E+03 @@ -7428,7 +7428,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3456E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9364E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.4910E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.4910E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.2063E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.7937E-01 @@ -7498,7 +7498,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.9388E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 4.0293E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 4.0293E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 3.0210E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.4147E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -8115,13 +8115,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8530E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.0839E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2604E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.4910E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.4910E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 7.5000E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8530E+02 Fusion_power_(MW)_______________________________________________________ (powfmw)______________________ 1.6251E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.0929E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 7.5000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.4910E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.4910E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6575E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1757E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8337E+03 @@ -8591,7 +8591,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3356E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9219E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.5510E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.5510E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.2063E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.7937E-01 @@ -8661,7 +8661,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.9711E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 4.0529E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 4.0529E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 3.0034E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.4067E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -9278,13 +9278,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8574E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.0882E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2621E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.5510E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.5510E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 7.5000E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8574E+02 Fusion_power_(MW)_______________________________________________________ (powfmw)______________________ 1.6273E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.0972E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 7.5000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.5510E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.5510E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6609E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1788E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8375E+03 @@ -9754,7 +9754,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3258E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9079E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.5992E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.5992E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.2063E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.7937E-01 @@ -9824,7 +9824,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 4.0073E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 4.0706E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 4.0706E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 2.9903E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.3998E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -10441,13 +10441,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8458E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.0767E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2574E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.5992E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.5992E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 7.5000E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8458E+02 Fusion_power_(MW)_______________________________________________________ (powfmw)______________________ 1.6212E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.0856E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 7.5000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.5992E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.5992E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6554E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1710E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8315E+03 @@ -10917,7 +10917,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3317E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9163E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.5632E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.5632E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1939E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8061E-01 @@ -10987,7 +10987,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.9907E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 4.0501E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 4.0501E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 3.0054E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.4003E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -11604,13 +11604,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8659E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.0966E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2705E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.5632E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.5632E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 7.5000E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8659E+02 Fusion_power_(MW)_______________________________________________________ (powfmw)______________________ 1.6318E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.1056E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 7.5000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.5632E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.5632E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6660E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1846E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8432E+03 @@ -12080,7 +12080,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3366E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9234E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.5331E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.5331E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1939E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8061E-01 @@ -12150,7 +12150,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.9599E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 4.0408E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 4.0408E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 3.0124E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.4067E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -12767,13 +12767,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8842E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.1149E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2780E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.5331E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.5331E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 7.5000E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8842E+02 Fusion_power_(MW)_______________________________________________________ (powfmw)______________________ 1.6414E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.1240E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 7.5000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.5331E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.5331E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6758E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1970E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8540E+03 @@ -13243,7 +13243,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3458E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9366E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.4852E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.4852E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1939E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8061E-01 @@ -13313,7 +13313,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.9340E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 4.0207E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 4.0207E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 3.0274E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.4146E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -13930,13 +13930,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8830E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.1137E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2775E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.4852E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.4852E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 7.5000E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8830E+02 Fusion_power_(MW)_______________________________________________________ (powfmw)______________________ 1.6408E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.1228E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 7.5000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.4852E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.4852E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6742E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1961E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8522E+03 @@ -14406,7 +14406,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3510E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9442E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.4429E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.4429E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1815E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8185E-01 @@ -14476,7 +14476,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.9155E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 3.9990E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 3.9990E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 3.0439E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.4153E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -15093,13 +15093,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8998E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.1305E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2895E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.4429E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.4429E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 7.5000E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8998E+02 Fusion_power_(MW)_______________________________________________________ (powfmw)______________________ 1.6496E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.1396E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 7.5000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.4429E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.4429E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6830E+02 Total_(MW)______________________________________________________________ ______________________________ 2.2075E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8620E+03 @@ -15569,7 +15569,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3450E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9355E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.5500E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.5500E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1815E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8185E-01 @@ -15639,7 +15639,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.9658E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 4.0183E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 4.0183E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 3.0292E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.4063E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -16256,13 +16256,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.9165E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.1469E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2963E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.5500E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.5500E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 7.5000E+01 Total_(MW)______________________________________________________________ ______________________________ 3.9165E+02 Fusion_power_(MW)_______________________________________________________ (powfmw)______________________ 1.6583E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.1561E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 7.5000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.5500E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.5500E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6928E+02 Total_(MW)______________________________________________________________ ______________________________ 2.2188E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8730E+03 @@ -16732,7 +16732,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3387E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9265E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.6591E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.6591E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1815E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8185E-01 @@ -16802,7 +16802,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 4.0161E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 4.0375E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 4.0375E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 3.0148E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.3971E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -17419,13 +17419,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.9340E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.1643E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.3034E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.6591E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.6591E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 7.5000E+01 Total_(MW)______________________________________________________________ ______________________________ 3.9340E+02 Fusion_power_(MW)_______________________________________________________ (powfmw)______________________ 1.6674E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.1735E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 7.5000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.6591E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.6591E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.7032E+02 Total_(MW)______________________________________________________________ ______________________________ 2.2308E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8845E+03 diff --git a/tests/integration/data/scan_MFILE.DAT b/tests/integration/data/scan_MFILE.DAT index 102f0a6db..8904bf744 100644 --- a/tests/integration/data/scan_MFILE.DAT +++ b/tests/integration/data/scan_MFILE.DAT @@ -305,7 +305,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.4266E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 2.0529E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 5.7803E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 5.7803E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1857E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8143E-01 @@ -375,7 +375,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.1974E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 2.8865E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 2.8865E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 4.8432E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.6558E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -963,13 +963,13 @@ Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.8174E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.6226E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 5.1000E+01 Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Fusion_power_(MW)_______________________________________________________ (powfmw.)_____________________ 2.0117E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.8286E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 5.1000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 2.0393E+02 Total_(MW)______________________________________________________________ ______________________________ 2.6500E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 2.2576E+03 @@ -1300,7 +1300,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.4266E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 2.0529E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 5.7803E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 5.7803E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1857E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8143E-01 @@ -1370,7 +1370,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.1974E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 2.8865E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 2.8865E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 4.8432E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.6558E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -1958,13 +1958,13 @@ Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.8174E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.6226E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 5.1000E+01 Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Fusion_power_(MW)_______________________________________________________ (powfmw.)_____________________ 2.0117E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.8286E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 5.1000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 2.0393E+02 Total_(MW)______________________________________________________________ ______________________________ 2.6500E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 2.2576E+03 @@ -2295,7 +2295,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.4266E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 2.0529E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 5.7803E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 5.7803E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1857E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8143E-01 @@ -2365,7 +2365,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.1974E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 2.8865E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 2.8865E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 4.8432E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.6558E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -2953,13 +2953,13 @@ Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.8174E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.6226E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 5.1000E+01 Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Fusion_power_(MW)_______________________________________________________ (powfmw.)_____________________ 2.0117E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.8286E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 5.1000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 2.0393E+02 Total_(MW)______________________________________________________________ ______________________________ 2.6500E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 2.2576E+03 @@ -3290,7 +3290,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.4266E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 2.0529E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 5.7803E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 5.7803E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1857E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8143E-01 @@ -3360,7 +3360,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.1974E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 2.8865E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 2.8865E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 4.8432E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.6558E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -3948,13 +3948,13 @@ Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.8174E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.6226E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 5.1000E+01 Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Fusion_power_(MW)_______________________________________________________ (powfmw.)_____________________ 2.0117E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.8286E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 5.1000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 2.0393E+02 Total_(MW)______________________________________________________________ ______________________________ 2.6500E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 2.2576E+03 @@ -4285,7 +4285,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.4266E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 2.0529E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 5.7803E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 5.7803E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1857E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8143E-01 @@ -4355,7 +4355,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.1974E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 2.8865E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 2.8865E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 4.8432E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.6558E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -4943,13 +4943,13 @@ Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.8174E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.6226E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 5.1000E+01 Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Fusion_power_(MW)_______________________________________________________ (powfmw.)_____________________ 2.0117E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.8286E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 5.1000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 2.0393E+02 Total_(MW)______________________________________________________________ ______________________________ 2.6500E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 2.2576E+03 @@ -5280,7 +5280,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.4266E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 2.0529E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 5.7803E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 5.7803E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1857E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8143E-01 @@ -5350,7 +5350,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.1974E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 2.8865E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 2.8865E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 4.8432E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.6558E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -5938,13 +5938,13 @@ Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.8174E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.6226E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 5.1000E+01 Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Fusion_power_(MW)_______________________________________________________ (powfmw.)_____________________ 2.0117E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.8286E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 5.1000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 2.0393E+02 Total_(MW)______________________________________________________________ ______________________________ 2.6500E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 2.2576E+03 @@ -6275,7 +6275,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.4266E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 2.0529E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 5.7803E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 5.7803E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1857E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8143E-01 @@ -6345,7 +6345,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.1974E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 2.8865E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 2.8865E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 4.8432E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.6558E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -6933,13 +6933,13 @@ Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.8174E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.6226E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 5.1000E+01 Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Fusion_power_(MW)_______________________________________________________ (powfmw.)_____________________ 2.0117E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.8286E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 5.1000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 2.0393E+02 Total_(MW)______________________________________________________________ ______________________________ 2.6500E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 2.2576E+03 @@ -7270,7 +7270,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.4266E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 2.0529E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 5.7803E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 5.7803E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1857E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8143E-01 @@ -7340,7 +7340,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.1974E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 2.8865E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 2.8865E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 4.8432E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.6558E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -7928,13 +7928,13 @@ Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.8174E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.6226E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 5.1000E+01 Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Fusion_power_(MW)_______________________________________________________ (powfmw.)_____________________ 2.0117E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.8286E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 5.1000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 2.0393E+02 Total_(MW)______________________________________________________________ ______________________________ 2.6500E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 2.2576E+03 @@ -8265,7 +8265,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.4266E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 2.0529E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 5.7803E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 5.7803E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1857E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8143E-01 @@ -8335,7 +8335,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.1974E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 2.8865E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 2.8865E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 4.8432E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.6558E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -8923,13 +8923,13 @@ Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.8174E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.6226E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 5.1000E+01 Total_(MW)______________________________________________________________ ______________________________ 4.3494E+02 Fusion_power_(MW)_______________________________________________________ (powfmw.)_____________________ 2.0117E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.8286E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 5.1000E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 5.7803E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 5.7803E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 2.0393E+02 Total_(MW)______________________________________________________________ ______________________________ 2.6500E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 2.2576E+03 diff --git a/tests/integration/ref_dicts.json b/tests/integration/ref_dicts.json index 30a3c8646..703a4291a 100644 --- a/tests/integration/ref_dicts.json +++ b/tests/integration/ref_dicts.json @@ -3581,8 +3581,8 @@ "pnuctfi": 0.0, "pnuctfo": 0.0, "pnucvvplus": 0.0, - "pohmmw": 0.0, - "pohmpv": 0.0, + "p_plasma_ohmic_mw": 0.0, + "pden_plasma_ohmic_mw": 0.0, "poisson_al": 0.35, "poisson_cond_axial": 0.3, "poisson_cond_trans": 0.3, @@ -4264,7 +4264,7 @@ "rpf2": -1.63, "rpf2dewar": 0.5, "rpfac": 0.0, - "rplas": 0.0, + "res_plasma": 0.0, "rref": [ 7.0, 7.0, @@ -10343,8 +10343,8 @@ "pnuctfi": "Nuclear heating on IB TF coil [MW/m3]", "pnuctfo": "Nuclear heating on OB TF coil [MW/m3]", "pnucvvplus": "nuclear heating to vacuum vessel and beyond(MW)", - "pohmmw": "ohmic heating power (MW)", - "pohmpv": "ohmic heating power per volume (MW/m3)", + "p_plasma_ohmic_mw": "ohmic heating power (MW)", + "pden_plasma_ohmic_mw": "ohmic heating power per volume (MW/m3)", "poisson_al": "Aluminium Poisson's ratio.\n Source : https://www.engineeringtoolbox.com/poissons-ratio-d_1224.html", "poisson_cond_axial": "SC TF coil conductor Poisson's ratio in the parallel-transverse direction", "poisson_cond_trans": "SC TF coil conductor Poisson's ratio in the transverse-transverse direction", @@ -10574,8 +10574,8 @@ "rpf1": "offset (m) of radial position of `ipfloc=1` PF coils from being directly above\n the central solenoid", "rpf2": "offset (m) of radial position of `ipfloc=2` PF coils from being at\n rmajor (offset = rpf2triangrminor)", "rpf2dewar": "radial distance between outer edge of largest (`ipfloc=3`) PF coil (or stellarator\n modular coil) and cryostat (m)", - "rpfac": "neo-classical correction factor to rplas", - "rplas": "plasma resistance (ohm)", + "rpfac": "neo-classical correction factor to res_plasma", + "res_plasma": "plasma resistance (ohm)", "rref": "PF coil radial positioning adjuster:\n
    \n
  • for groups j with ipfloc(j) = 1; rref(j) is ignored
    • \n
  • for groups j with ipfloc(j) = 2; rref(j) is ignored
    • \n
  • for groups j with ipfloc(j) = 3; rref(j) is ignored
    • \n
  • for groups j with ipfloc(j) = 4; rref(j) is radius of\n the coil in units of minor radii from the major radius\n (r = rmajor + rref*rminor)
    • \n
", "rrin": "Input IFE repetition rate (Hz) (`ifedrv=3 only`; `itv 156`)", "rrmax": "maximum IFE repetition rate (Hz)", @@ -19209,8 +19209,8 @@ "neutron_power_total", "neutron_power_density_total", "neutron_power_density_plasma", - "pohmmw", - "pohmpv", + "p_plasma_ohmic_mw", + "pden_plasma_ohmic_mw", "powerht", "fusion_power", "pperim", @@ -19249,7 +19249,7 @@ "rnfene", "rnone", "rpfac", - "rplas", + "res_plasma", "res_time", "sarea", "sareao", diff --git a/tests/unit/data/large_tokamak_MFILE.DAT b/tests/unit/data/large_tokamak_MFILE.DAT index 81f2fdf06..bf20a8bbc 100644 --- a/tests/unit/data/large_tokamak_MFILE.DAT +++ b/tests/unit/data/large_tokamak_MFILE.DAT @@ -445,7 +445,7 @@ Fraction_of_power_incident_on_the_lower_outer_target____________________ (fLO)_________________________ 5.9000E-01 OP Power_incident_on_the_lower_inner_target_(MW)___________________________ (pLImw)_______________________ 1.3458E+01 OP Power_incident_on_the_lower_outer_target_(MW)___________________________ (pLOmw)_______________________ 1.9366E+01 OP - Ohmic_heating_power_(MW)________________________________________________ (pohmmw)______________________ 6.0733E-01 OP + Ohmic_heating_power_(MW)________________________________________________ (p_plasma_ohmic_mw)______________________ 6.0733E-01 OP Fraction_of_alpha_power_deposited_in_plasma_____________________________ (falpha)______________________ 9.5000E-01 Fraction_of_alpha_power_to_electrons____________________________________ (falpe)_______________________ 7.1742E-01 Fraction_of_alpha_power_to_ions_________________________________________ (falpi)_______________________ 2.8258E-01 @@ -515,7 +515,7 @@ Diamagnetic_fraction_(enforced)_________________________________________ (diaipf.)_____________________ 0.0000E+00 Pfirsch-Schlueter_fraction_(enforced)___________________________________ (ps_current_fraction.)______________________ 0.0000E+00 Loop_voltage_during_burn_(V)____________________________________________ (vburn)_______________________ 3.6762E-02 OP - Plasma_resistance_(ohm)_________________________________________________ (rplas)_______________________ 3.9470E-09 OP + Plasma_resistance_(ohm)_________________________________________________ (res_plasma)_______________________ 3.9470E-09 OP Resistive_diffusion_time_(s)____________________________________________ (res_time)____________________ 3.0839E+03 OP Plasma_inductance_(H)___________________________________________________ (rlp)_________________________ 1.4245E-05 OP Coefficient_for_sawtooth_effects_on_burn_V-s_requirement________________ (csawth)______________________ 1.0000E+00 @@ -1139,13 +1139,13 @@ Total_(MW)______________________________________________________________ ______________________________ 3.8856E+02 Alpha_power_deposited_in_plasma_(MW)____________________________________ (falpha*palpmw)_______________ 3.0698E+02 Power_from_charged_products_of_DD_and/or_D-He3_fusion_(MW)______________ (pchargemw.)__________________ 1.2675E+00 - Ohmic_heating_(MW)______________________________________________________ (pohmmw.)_____________________ 6.0733E-01 + Ohmic_heating_(MW)______________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.0733E-01 Injected_power_deposited_in_plasma_(MW)_________________________________ (pinjmw)______________________ 7.9710E+01 Total_(MW)______________________________________________________________ ______________________________ 3.8856E+02 Fusion_power_(MW)_______________________________________________________ (powfmw)______________________ 1.6176E+03 Power_from_energy_multiplication_in_blanket_and_shield_(MW)_____________ (emultmw)_____________________ 3.0787E+02 Injected_power_(MW)_____________________________________________________ (pinjmw.)_____________________ 7.9710E+01 - Ohmic_power_(MW)________________________________________________________ (pohmmw.)_____________________ 6.0733E-01 + Ohmic_power_(MW)________________________________________________________ (p_plasma_ohmic_mw.)_____________________ 6.0733E-01 Power_deposited_in_primary_coolant_by_pump_(MW)_________________________ (htpmw_mech)__________________ 1.6536E+02 Total_(MW)______________________________________________________________ ______________________________ 2.1712E+03 Heat_extracted_from_first_wall_and_blanket_(MW)_________________________ (pthermfw_blkt)_______________ 1.8294E+03 diff --git a/tests/unit/test_current_drive.py b/tests/unit/test_current_drive.py index ee4ea56e1..b9b295f86 100644 --- a/tests/unit/test_current_drive.py +++ b/tests/unit/test_current_drive.py @@ -167,7 +167,7 @@ class CudrivParam(NamedTuple): ignite: Any = None - pohmmw: Any = None + p_plasma_ohmic_mw: Any = None fusion_power: Any = None @@ -276,7 +276,7 @@ class CudrivParam(NamedTuple): ipedestal=1, aux_current_fraction=0.12364081253383186, ignite=0, - pohmmw=0, + p_plasma_ohmic_mw=0, fusion_power=0, inductive_current_fraction=0.59999999999999998, fvsbrnni=0.40000000000000002, @@ -366,7 +366,7 @@ class CudrivParam(NamedTuple): ipedestal=1, aux_current_fraction=0.12364081253383186, ignite=0, - pohmmw=0.76707314489379119, + p_plasma_ohmic_mw=0.76707314489379119, fusion_power=1051.6562748933977, inductive_current_fraction=0.59999999999999998, fvsbrnni=0.40000000000000002, @@ -566,7 +566,9 @@ def test_cudriv(cudrivparam, monkeypatch, current_drive): monkeypatch.setattr(physics_variables, "ignite", cudrivparam.ignite) - monkeypatch.setattr(physics_variables, "pohmmw", cudrivparam.pohmmw) + monkeypatch.setattr( + physics_variables, "p_plasma_ohmic_mw", cudrivparam.p_plasma_ohmic_mw + ) monkeypatch.setattr(physics_variables, "fusion_power", cudrivparam.fusion_power) diff --git a/tests/unit/test_physics.py b/tests/unit/test_physics.py index b53f7893c..9911690e5 100644 --- a/tests/unit/test_physics.py +++ b/tests/unit/test_physics.py @@ -1710,7 +1710,7 @@ class VscalcParam(NamedTuple): rmajor: Any = None - rplas: Any = None + res_plasma: Any = None t_burn: Any = None @@ -1741,7 +1741,7 @@ class VscalcParam(NamedTuple): plasma_current=18398455.678867526, rli=1.2064840230894305, rmajor=8, - rplas=3.7767895536275952e-09, + res_plasma=3.7767895536275952e-09, t_burn=1000, t_fusion_ramp=10, expected_phiint=111.57651734747576, @@ -1760,7 +1760,7 @@ class VscalcParam(NamedTuple): plasma_current=18398455.678867526, rli=1.2064840230894305, rmajor=8, - rplas=3.7767895536275952e-09, + res_plasma=3.7767895536275952e-09, t_burn=0, t_fusion_ramp=10, expected_phiint=111.57651734747576, @@ -1791,7 +1791,7 @@ def test_vscalc(vscalcparam): plasma_current=vscalcparam.plasma_current, rli=vscalcparam.rli, rmajor=vscalcparam.rmajor, - rplas=vscalcparam.rplas, + res_plasma=vscalcparam.res_plasma, t_burn=vscalcparam.t_burn, t_fusion_ramp=vscalcparam.t_fusion_ramp, rmu0=constants.rmu0, @@ -1969,13 +1969,13 @@ class PohmParam(NamedTuple): zeff: Any = None - expected_pohmpv: Any = None + expected_pden_plasma_ohmic_mw: Any = None - expected_pohmmw: Any = None + expected_p_plasma_ohmic_mw: Any = None expected_rpfac: Any = None - expected_rplas: Any = None + expected_res_plasma: Any = None @pytest.mark.parametrize( @@ -1992,16 +1992,16 @@ class PohmParam(NamedTuple): ten=12.626131115905864, plasma_volume=1888.1711539956691, zeff=2.0909945616489103, - expected_pohmpv=0.0004062519138005805, - expected_pohmmw=0.7670731448937912, + expected_pden_plasma_ohmic_mw=0.0004062519138005805, + expected_p_plasma_ohmic_mw=0.7670731448937912, expected_rpfac=2.5, - expected_rplas=3.7767895536275952e-09, + expected_res_plasma=3.7767895536275952e-09, ), ), ) def test_pohm(pohmparam, monkeypatch, physics): """ - Automatically generated Regression Unit Test for pohm. + Automatically generated Regression Unit Test for plasma_ohmic_heating. This test was generated using data from tests/regression/scenarios/large-tokamak/IN.DAT. @@ -2018,7 +2018,12 @@ def test_pohm(pohmparam, monkeypatch, physics): physics_variables, "plasma_res_factor", pohmparam.plasma_res_factor ) - pohmpv, pohmmw, rpfac, rplas = physics.pohm( + ( + pden_plasma_ohmic_mw, + p_plasma_ohmic_mw, + rpfac, + res_plasma, + ) = physics.plasma_ohmic_heating( inductive_current_fraction=pohmparam.inductive_current_fraction, kappa95=pohmparam.kappa95, plasma_current=pohmparam.plasma_current, @@ -2029,13 +2034,15 @@ def test_pohm(pohmparam, monkeypatch, physics): zeff=pohmparam.zeff, ) - assert pohmpv == pytest.approx(pohmparam.expected_pohmpv) + assert pden_plasma_ohmic_mw == pytest.approx( + pohmparam.expected_pden_plasma_ohmic_mw + ) - assert pohmmw == pytest.approx(pohmparam.expected_pohmmw) + assert p_plasma_ohmic_mw == pytest.approx(pohmparam.expected_p_plasma_ohmic_mw) assert rpfac == pytest.approx(pohmparam.expected_rpfac) - assert rplas == pytest.approx(pohmparam.expected_rplas) + assert res_plasma == pytest.approx(pohmparam.expected_res_plasma) class CalculateDensityLimitParam(NamedTuple): @@ -2140,7 +2147,7 @@ class PcondParam(NamedTuple): kappaa_ipb: Any = None - pohmmw: Any = None + p_plasma_ohmic_mw: Any = None f_alpha_plasma: Any = None @@ -2225,7 +2232,7 @@ class PcondParam(NamedTuple): tauee_in=0, pradpv=0.11824275660100725, kappaa_ipb=1.68145080681586, - pohmmw=0.63634001890069991, + p_plasma_ohmic_mw=0.63634001890069991, f_alpha_plasma=0.94999999999999996, iinvqd=1, isc=32, @@ -2269,7 +2276,7 @@ class PcondParam(NamedTuple): tauee_in=0, pradpv=0.11824275660100725, kappaa_ipb=1.68145080681586, - pohmmw=0.63634001890069991, + p_plasma_ohmic_mw=0.63634001890069991, f_alpha_plasma=0.94999999999999996, iinvqd=1, isc=33, @@ -2313,7 +2320,7 @@ class PcondParam(NamedTuple): tauee_in=0, pradpv=0.11824275660100725, kappaa_ipb=1.68145080681586, - pohmmw=0.63634001890069991, + p_plasma_ohmic_mw=0.63634001890069991, f_alpha_plasma=0.94999999999999996, iinvqd=1, isc=34, @@ -2357,7 +2364,7 @@ class PcondParam(NamedTuple): tauee_in=0, pradpv=0.11824275660100725, kappaa_ipb=1.68145080681586, - pohmmw=0.63634001890069991, + p_plasma_ohmic_mw=0.63634001890069991, f_alpha_plasma=0.94999999999999996, iinvqd=1, isc=35, @@ -2401,7 +2408,7 @@ class PcondParam(NamedTuple): tauee_in=0, pradpv=0.11824275660100725, kappaa_ipb=1.68145080681586, - pohmmw=0.63634001890069991, + p_plasma_ohmic_mw=0.63634001890069991, f_alpha_plasma=0.94999999999999996, iinvqd=1, isc=36, @@ -2445,7 +2452,7 @@ class PcondParam(NamedTuple): tauee_in=0, pradpv=0.11824275660100725, kappaa_ipb=1.68145080681586, - pohmmw=0.63634001890069991, + p_plasma_ohmic_mw=0.63634001890069991, f_alpha_plasma=0.94999999999999996, iinvqd=1, isc=37, @@ -2489,7 +2496,7 @@ class PcondParam(NamedTuple): tauee_in=0, pradpv=0.11824275660100725, kappaa_ipb=1.68145080681586, - pohmmw=0.63634001890069991, + p_plasma_ohmic_mw=0.63634001890069991, f_alpha_plasma=0.94999999999999996, iinvqd=1, isc=38, @@ -2533,7 +2540,7 @@ class PcondParam(NamedTuple): tauee_in=0, pradpv=0.11824275660100725, kappaa_ipb=1.68145080681586, - pohmmw=0.63634001890069991, + p_plasma_ohmic_mw=0.63634001890069991, f_alpha_plasma=0.94999999999999996, iinvqd=1, isc=39, @@ -2577,7 +2584,7 @@ class PcondParam(NamedTuple): tauee_in=0, pradpv=0.11824275660100725, kappaa_ipb=1.68145080681586, - pohmmw=0.63634001890069991, + p_plasma_ohmic_mw=0.63634001890069991, f_alpha_plasma=0.94999999999999996, iinvqd=1, isc=40, @@ -2621,7 +2628,7 @@ class PcondParam(NamedTuple): tauee_in=0, pradpv=0.11824275660100725, kappaa_ipb=1.68145080681586, - pohmmw=0.63634001890069991, + p_plasma_ohmic_mw=0.63634001890069991, f_alpha_plasma=0.94999999999999996, iinvqd=1, isc=41, @@ -2665,7 +2672,7 @@ class PcondParam(NamedTuple): tauee_in=0, pradpv=0.11824275660100725, kappaa_ipb=1.68145080681586, - pohmmw=0.63634001890069991, + p_plasma_ohmic_mw=0.63634001890069991, f_alpha_plasma=0.94999999999999996, iinvqd=1, isc=42, @@ -2709,7 +2716,7 @@ class PcondParam(NamedTuple): tauee_in=0, pradpv=0.11824275660100725, kappaa_ipb=1.68145080681586, - pohmmw=0.63634001890069991, + p_plasma_ohmic_mw=0.63634001890069991, f_alpha_plasma=0.94999999999999996, iinvqd=1, isc=43, @@ -2753,7 +2760,7 @@ class PcondParam(NamedTuple): tauee_in=0, pradpv=0.11824275660100725, kappaa_ipb=1.68145080681586, - pohmmw=0.63634001890069991, + p_plasma_ohmic_mw=0.63634001890069991, f_alpha_plasma=0.94999999999999996, iinvqd=1, isc=44, @@ -2797,7 +2804,7 @@ class PcondParam(NamedTuple): tauee_in=0, pradpv=0.11824275660100725, kappaa_ipb=1.68145080681586, - pohmmw=0.63634001890069991, + p_plasma_ohmic_mw=0.63634001890069991, f_alpha_plasma=0.94999999999999996, iinvqd=1, isc=45, @@ -2841,7 +2848,7 @@ class PcondParam(NamedTuple): tauee_in=0, pradpv=0.11824275660100725, kappaa_ipb=1.68145080681586, - pohmmw=0.63634001890069991, + p_plasma_ohmic_mw=0.63634001890069991, f_alpha_plasma=0.94999999999999996, iinvqd=1, isc=46, @@ -2885,7 +2892,7 @@ class PcondParam(NamedTuple): tauee_in=0, pradpv=0.11824275660100725, kappaa_ipb=1.68145080681586, - pohmmw=0.63634001890069991, + p_plasma_ohmic_mw=0.63634001890069991, f_alpha_plasma=0.94999999999999996, iinvqd=1, isc=47, @@ -2947,7 +2954,9 @@ def test_pcond(pcondparam, monkeypatch, physics): monkeypatch.setattr(physics_variables, "kappaa_ipb", pcondparam.kappaa_ipb) - monkeypatch.setattr(physics_variables, "pohmmw", pcondparam.pohmmw) + monkeypatch.setattr( + physics_variables, "p_plasma_ohmic_mw", pcondparam.p_plasma_ohmic_mw + ) monkeypatch.setattr(physics_variables, "f_alpha_plasma", pcondparam.f_alpha_plasma) diff --git a/tests/unit/test_power.py b/tests/unit/test_power.py index ab4ea93ea..5d8b76c5c 100644 --- a/tests/unit/test_power.py +++ b/tests/unit/test_power.py @@ -221,7 +221,7 @@ class PfpwrParam(NamedTuple): rpf: Any = None - pohmmw: Any = None + p_plasma_ohmic_mw: Any = None rmajor: Any = None @@ -858,7 +858,7 @@ class PfpwrParam(NamedTuple): ), order="F", ).transpose(), - pohmmw=0.61391840981850698, + p_plasma_ohmic_mw=0.61391840981850698, rmajor=8.8901000000000003, active_constraints=( True, @@ -1600,7 +1600,7 @@ class PfpwrParam(NamedTuple): ), order="F", ).transpose(), - pohmmw=0.61391840981850698, + p_plasma_ohmic_mw=0.61391840981850698, rmajor=8.8901000000000003, active_constraints=( True, @@ -1824,7 +1824,9 @@ def test_pfpwr(pfpwrparam, monkeypatch, power): monkeypatch.setattr(pfcoil_variables, "rpf", pfpwrparam.rpf) - monkeypatch.setattr(physics_variables, "pohmmw", pfpwrparam.pohmmw) + monkeypatch.setattr( + physics_variables, "p_plasma_ohmic_mw", pfpwrparam.p_plasma_ohmic_mw + ) monkeypatch.setattr(physics_variables, "rmajor", pfpwrparam.rmajor) @@ -2135,7 +2137,7 @@ class Power2Param(NamedTuple): idivrt: Any = None - pohmmw: Any = None + p_plasma_ohmic_mw: Any = None iradloss: Any = None @@ -2276,7 +2278,7 @@ class Power2Param(NamedTuple): pdivt=143.03180561618876, palpfwmw=19.833077403424262, idivrt=1, - pohmmw=0.61391840981850698, + p_plasma_ohmic_mw=0.61391840981850698, iradloss=1, fusion_power=1985.785106643267, non_alpha_charged_power=1.6064693283140403, @@ -2378,7 +2380,7 @@ class Power2Param(NamedTuple): pdivt=142.91368967092416, palpfwmw=19.826887164528632, idivrt=1, - pohmmw=0.61391840981850698, + p_plasma_ohmic_mw=0.61391840981850698, iradloss=1, fusion_power=1985.1653095257811, non_alpha_charged_power=1.6059679220663614, @@ -2564,7 +2566,9 @@ def test_power2(power2param, monkeypatch, power): monkeypatch.setattr(physics_variables, "idivrt", power2param.idivrt) - monkeypatch.setattr(physics_variables, "pohmmw", power2param.pohmmw) + monkeypatch.setattr( + physics_variables, "p_plasma_ohmic_mw", power2param.p_plasma_ohmic_mw + ) monkeypatch.setattr(physics_variables, "iradloss", power2param.iradloss) diff --git a/tests/unit/test_pulse.py b/tests/unit/test_pulse.py index fbe48165a..5694c9f21 100755 --- a/tests/unit/test_pulse.py +++ b/tests/unit/test_pulse.py @@ -71,7 +71,7 @@ class TohswgParam(NamedTuple): class BurnParam(NamedTuple): - rplas: Any = None + res_plasma: Any = None vsres: Any = None @@ -1275,7 +1275,7 @@ def test_tohswg(tohswgparam, monkeypatch, pulse): "burnparam", ( BurnParam( - rplas=3.2347283861249307e-09, + res_plasma=3.2347283861249307e-09, vsres=59.392760827339345, vsind=284.23601098215397, vsbn=0, @@ -1291,7 +1291,7 @@ def test_tohswg(tohswgparam, monkeypatch, pulse): expected_tburn=0, ), BurnParam( - rplas=3.2347283861249307e-09, + res_plasma=3.2347283861249307e-09, vsres=59.392760827339345, vsind=284.23601098215397, vstot=-718.9849676846776, @@ -1321,7 +1321,7 @@ def test_burn(burnparam, monkeypatch, initialise_error_module, pulse): :type monkeypatch: _pytest.monkeypatch.monkeypatch """ - monkeypatch.setattr(physics_variables, "rplas", burnparam.rplas) + monkeypatch.setattr(physics_variables, "res_plasma", burnparam.res_plasma) monkeypatch.setattr(physics_variables, "vsres", burnparam.vsres) From c3e22b3b5930074b2adffc9b35a280012c8a2d72 Mon Sep 17 00:00:00 2001 From: Timothy Nunn Date: Thu, 12 Dec 2024 11:07:47 +0000 Subject: [PATCH 6/7] Use ruff instead of black and flake8 --- .flake8 | 15 ----- .pre-commit-config.yaml | 12 ++-- .../proc-pages/development/ci-guide.md | 16 ++--- .../proc-pages/development/pre-commit.md | 67 ++++++------------- ruff.toml | 31 +++++++++ 5 files changed, 64 insertions(+), 77 deletions(-) delete mode 100644 .flake8 create mode 100644 ruff.toml diff --git a/.flake8 b/.flake8 deleted file mode 100644 index 022357b24..000000000 --- a/.flake8 +++ /dev/null @@ -1,15 +0,0 @@ -[flake8] -# this option is broken in Python 3.8 https://github.com/pycqa/flake8/issues/725 -output-file = flake8.txt -# black has a different default line length to flake8 -max-line-length = 88 -statistics = True -# E203 is disabled as per black docs -extend-ignore = E203, - # Black formats code therefore only comments/strings are flagged by the E501 error code which is unnecessary - E501 -exclude = - .venv # Do not use flake8 on virtual environmet- will cause it to be incredibly slow - build # Do not use flake8 on build directory- these files are in git ignore anyway - env # Do not use flake8 on virtual environmet- will cause it to be incredibly slow - .env # Do not use flake8 on virtual environmet- will cause it to be incredibly slow diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index b9d702a46..80d58c6a1 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -9,14 +9,12 @@ repos: - id: check-docstring-first - id: check-merge-conflict - id: debug-statements - - repo: https://github.com/psf/black - rev: 22.3.0 + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.8.0 hooks: - - id: black - - repo: https://github.com/PyCQA/flake8 - rev: 4.0.1 - hooks: - - id: flake8 + - id: ruff + args: [--fix] + - id: ruff-format - repo: https://github.com/jumanjihouse/pre-commit-hook-yamlfmt rev: 0.2.3 hooks: diff --git a/documentation/proc-pages/development/ci-guide.md b/documentation/proc-pages/development/ci-guide.md index 17ddfe180..8af3244c0 100644 --- a/documentation/proc-pages/development/ci-guide.md +++ b/documentation/proc-pages/development/ci-guide.md @@ -5,13 +5,11 @@ Our GitHub actions Continuous Integration (CI) pipeline serves to ensure each br | Name | Functionality | | ---- | ------------- | -| docker | Checks if the `process-ci` Docker container is up-to-date and builds it if not. Only runs on the **main** branch. | -| make-py38 | Builds and archives the PROCESS build artefacts | -| unit-py38 | Installs PROCESS and runs the unit tests. The job will fail if any of the unit tests fail. | -| integration-py38 | Installs PROCESS and runs the integration tests. The job will fail if any of the integration tests fail. | -| regression-py38 | Installs PROCESS and runs the regression tests with a 0% and 5% tolerance, respectively. The job will fail if any of the regression tests fail. | -| large-tokamak-py38 | Installs PROCESS and runs the `large-tokamak` input file, archiving the output MFILE. Only runs on the **main** branch. | -| flake8 | Runs the flake8 Python linter and fails if any lint errors occur. | -| black | Runs the black Python formatter and fails if any formatting issues are detected. | -| tracking | Collects MFILEs for input files of interest and creates a dashboard of changes in key values over time (one datapoint for each commit on main). | +| make | Builds and archives the PROCESS build artefacts | +| unit-test | Installs PROCESS and runs the unit tests. The job will fail if any of the unit tests fail. | +| integration-test | Installs PROCESS and runs the integration tests. The job will fail if any of the integration tests fail. | +| regression-test | Installs PROCESS and runs the regression tests with a 0% and 5% tolerance, respectively. The job will fail if any of the regression tests fail. | +| run-tracking-inputs | Installs PROCESS and runs the regression test input files, archiving the output MFILEs. Only runs on the **main** branch. | +| tracking | Collects MFILEs for input files of interest and creates a dashboard of changes in key values over time (one datapoint for each commit on main). Only runs on the **main** branch. | +| pre-commit-quality-check | ensures the pushed code meets our standards as defined in `.pre-commit-config.yaml`. | | docs | Builds and deploys the documentation onto GitHub pages. | \ No newline at end of file diff --git a/documentation/proc-pages/development/pre-commit.md b/documentation/proc-pages/development/pre-commit.md index 83aac78c6..1064eff4f 100755 --- a/documentation/proc-pages/development/pre-commit.md +++ b/documentation/proc-pages/development/pre-commit.md @@ -14,8 +14,8 @@ the commit will not be made. On a failure, one of two things can happen: 1. Pre-commit plugins will rectify the mistakes made. This will happen with code formatters (whose job it is to edit your files to the correct style). The files the plugins have changed can then be `git add`ed again and the `git commit` command re-issued. -2. A pre-commit plugin will identify the mistake but will NOT fix it. This will happen with - `flake8` which is a linter and warns of code mistakes but does not correct them. You will need +2. A pre-commit plugin will identify the mistake but will NOT fix it. This could happen with + `ruff` which is a linter and warns of code mistakes but does not correct them. You will need to fix these mistakes manually. Now, the changed files can be `git add`ed and the `git commit` command re-issued. !!! Info "VSCode GUI users" @@ -58,7 +58,7 @@ fixed and you will need to re-add the files that pre-commit has changed. !!! example "Adding two files" Consider that two files are being `git add`ed. - One of the files, `foo.py` has stylistic changes which **Black** objects to. + One of the files, `foo.py` has stylistic changes which **ruff** objects to. ``` > git add foo.py bar.py @@ -66,55 +66,36 @@ fixed and you will need to re-add the files that pre-commit has changed. Trim Trailing Whitespace.................................................Passed Check for merge conflicts................................................Passed Debug Statements (Python)................................................Passed - black....................................................................Failed - - hook id: black + ruff.....................................................................Failed + - hook id: ruff-format - exit code: 1 - files were modified by this hook Fixing foo.py Format YAML files....................................(no files to check)Skipped - > git add foo.py # since black has modified foo.py + > git add foo.py # since ruff has modified foo.py > git commit -m "Adding foo and bar" ``` - To avoid the need to re-add files a second time you could run `black .` which will do the - formatting (of Python code) that pre-commit would do. - -!!! example "black won't fix all flake8 issues" - Flake8 (as has been stressed on this documentation) is a linter and not a formatter. This means - flake8 will never make any changes to your Python source code. - - Consider the following file very simple file, `example.py`: - - ```python - from process.fortran import tfcoil_variables, fwbs_variables - - def get_whttf(): - return tfcoil_variables.whttf - ``` - - Flake8 will return the following trace for this file: - ``` - example.py:1:47 F401 Module fwbs_variables imported but never used - ``` - because `fwbs_variables` is imported, but never used. However, this is not a style issue, it is - a semantic issue. Therefore, `black` will not fix this issue. It is up to the developer to - rectify this issue manually, `git add` the fixed file, and finally re-do the `git commit` command. - ## Pre-commit and the `quality` CI stage The Process continuous integration system (used to check Process builds and passes tests) also has a `quality` stage. This is where several jobs will run to ensure the quality of your code. If all your commits pass through pre-commit, then these jobs should not fail as your code will be of a high quality. -## Using black with VSCode -Although not required, the `black` VSCode extension will ensure that all the Python files you save -will be black-compliant and, as such, won't need to modified by pre-commit. - -In VSCode, use `Ctrl+,` (`Command+,` for Mac users) to open the settings page. From here, use the -search bar to find **"Editor: Format On Save"** and tick the box. Next, search for -**"Python > Formatting: Provider"** and set it to `black`. Now, upon saving a Python file, the -black formatter will run over it. +## Using ruff with VSCode +Although not required, the `ruff` VSCode extension will ensure that all the Python files you save +will be ruff-compliant and, as such, won't need to modified by pre-commit. + +Open or create the file `.vscode/settings.sh` and add/modify the following settings: +```json +{ + "[python]": { + "editor.defaultFormatter": "charliermarsh.ruff", + "editor.formatOnSave": true + } +} +``` ## What does pre-commit check for? @@ -131,13 +112,7 @@ Pre-commit performs a few checks on each and every file you add, regardless of t Because Process is becoming increasingly Pythonised, pre-commit performs many Python style checks. -* [`black`](https://black.readthedocs.io/en/stable/) has already been discussed on this page. It is - an industry-standard Python code formatter that enforces a "one-way is right" style. This ensures - all of our Python is of the same, black-correct, style. **This plugin will automatically fix any mistakes it finds**. -* [`flake8`](https://flake8.pycqa.org/en/latest/) is the linter of choice on Process. A linter - checks common errors in code. Flake8, for instance, can check for `import` statements that are - unused or variables that are declared but never used. Black, a formatter, will not remove these - "mistakes" as it will never change the semantic meaning of code. **This plugin will NOT automatically fix any mistakes it finds**. +* [`ruff`](https://github.com/astral-sh/ruff) formats and lints code. It will identify formatting and stylistic errors in Python code. **This plugin will automatically fix any mistakes it finds**. * `check-docstring-first` will check that a function/class docstring comes before any of the body (ie the docstring must be at the top). **This plugin will NOT automatically fix any mistakes it finds**. * `debug-statements` will check that debug statements (those using the built-in `pdb` module) @@ -146,4 +121,4 @@ Because Process is becoming increasingly Pythonised, pre-commit performs many Py ### Other checks * [`yamlfmt`](https://github.com/jumanjihouse/pre-commit-hook-yamlfmt) formats YAML code (similar - to what `black` does for Python code). **This plugin will automatically fix any mistakes it finds**. + to what `ruff` does for Python code). **This plugin will automatically fix any mistakes it finds**. diff --git a/ruff.toml b/ruff.toml new file mode 100644 index 000000000..83e065094 --- /dev/null +++ b/ruff.toml @@ -0,0 +1,31 @@ +extend-exclude = ["env", ".env"] +target-version = "py310" + +[lint] +extend-select = [ + "I", + "INT", + "YTT", + "ASYNC", + "COM", + "T10", + "FA", + "LOG", + "PYI", + "Q", + "RSE", + "SLOT", + "TID", + "PGH", + "FLY", + "F", + "W", +] + +ignore = ["COM812", "FBT", "G004"] + +[lint.per-file-ignores] +"tests/*" = ["ARG"] + +[format] +preview = true From 661027a8ce1afaac42ab0c7aa98e67556b0f7cf8 Mon Sep 17 00:00:00 2001 From: Timothy Nunn Date: Wed, 8 Jan 2025 15:21:49 +0000 Subject: [PATCH 7/7] Format code and fix linting errors --- .../deuterium_branching_plot.py | 2 +- .../plotting_scripts/menard_beta_norm_plot.py | 1 - .../original_beta_norm_plot.py | 1 - .../profile_parabolic_plot.py | 1 - .../plotting_scripts/profile_pedestal_plot.py | 1 - .../proc-pages/scripts/sort_vardes.py | 8 +- examples/csv_output.ipynb | 1 + examples/examples.ipynb | 13 +- examples/plot_solutions.ipynb | 2707 ++++---- examples/scan.ipynb | 655 +- process/availability.py | 14 +- process/blanket_library.py | 35 +- process/build.py | 377 +- process/buildings.py | 27 +- process/caller.py | 15 +- process/coolprop_interface.py | 1 + process/costs.py | 40 +- process/costs_2015.py | 27 +- process/cs_fatigue.py | 6 +- process/current_drive.py | 91 +- process/dcll.py | 12 +- process/divertor.py | 20 +- process/evaluators.py | 10 +- process/exceptions.py | 6 +- process/final.py | 8 +- process/fw.py | 8 +- process/geometry/blanket_geometry.py | 33 +- process/geometry/cryostat_geometry.py | 1 + process/geometry/firstwall_geometry.py | 33 +- .../geometry/geometry_parameterisations.py | 2 + process/geometry/pfcoil_geometry.py | 3 + process/geometry/plasma_geometry.py | 4 +- process/geometry/shield_geometry.py | 33 +- process/geometry/tfcoil_geometry.py | 2 + process/geometry/utils.py | 2 + process/geometry/vacuum_vessel_geometry.py | 33 +- process/hcpb.py | 32 +- process/ife.py | 28 +- process/impurity_radiation.py | 11 +- process/init.py | 16 +- process/io/configuration.py | 14 +- process/io/costs_bar.py | 8 +- process/io/costs_pie.py | 4 +- process/io/in_dat.py | 13 +- process/io/mfile.py | 56 +- process/io/mfile2dict.py | 10 +- process/io/mfile_comparison.py | 27 +- process/io/mfile_to_csv.py | 3 +- process/io/plot_proc.py | 97 +- process/io/plot_radial_build.py | 13 +- process/io/plot_sankey.py | 7 +- process/io/plot_scans.py | 19 +- process/io/plot_solutions.py | 28 +- process/io/plot_stress_tf.py | 7 +- process/io/process_config.py | 18 +- process/io/process_funcs.py | 21 +- process/io/python_fortran_dicts.py | 4 +- process/io/sankey_funcs.py | 57 +- process/io/write_new_in_dat.py | 12 +- process/main.py | 78 +- process/objectives.py | 10 +- process/optimiser.py | 5 +- process/pfcoil.py | 90 +- process/physics.py | 227 +- process/physics_functions.py | 19 +- process/plasma_geometry.py | 25 +- process/plasma_profiles.py | 7 +- process/power.py | 86 +- process/profiles.py | 8 +- process/pulse.py | 21 +- process/scan.py | 15 +- process/sctfcoil.py | 113 +- process/solver.py | 20 +- process/stellarator.py | 298 +- process/stellarator_config.py | 4 +- process/structure.py | 17 +- process/superconductors.py | 19 +- process/tfcoil.py | 10 +- .../uncertainties/evaluate_uncertainties.py | 15 +- process/uncertainties/hdf_to_scatter_plot.py | 3 +- process/uncertainties/morris_plotting.py | 6 +- process/uncertainties/sobol_plotting.py | 5 +- process/utilities/f2py_string_patch.py | 3 +- process/vacuum.py | 15 +- process/water_use.py | 5 +- scripts/create_dicts.py | 73 +- scripts/document_fortran_interface.py | 1 - scripts/python_dicts.py | 3 +- scripts/time_numpy_baseline.py | 3 +- scripts/vardes.py | 13 +- setup.py | 5 +- tests/conftest.py | 6 +- tests/integration/conftest.py | 6 +- tests/integration/test_blanket_library_int.py | 11 +- tests/integration/test_examples.py | 5 +- tests/integration/test_main_int.py | 11 +- tests/integration/test_mfile_to_csv.py | 1 + tests/integration/test_pfcoil_int.py | 5686 ++++++++--------- tests/integration/test_plot_proc.py | 4 +- tests/integration/test_plot_radial_build.py | 1 + tests/integration/test_plot_sankey.py | 1 + tests/integration/test_plot_scans.py | 1 + tests/integration/test_plot_solutions.py | 4 +- .../test_uncertainties_evaluate.py | 15 +- tests/integration/test_utilities.py | 6 +- tests/integration/test_vmcon.py | 30 +- tests/integration/test_write_new_in_dat.py | 5 +- tests/regression/regression_test_assets.py | 9 +- tests/regression/test_process_input_files.py | 19 +- tests/unit/conftest.py | 4 +- tests/unit/test_availability.py | 13 +- tests/unit/test_blanket_library.py | 15 +- tests/unit/test_build.py | 25 +- tests/unit/test_buildings.py | 24 +- tests/unit/test_ccfe_hcpb.py | 21 +- tests/unit/test_costs_1990.py | 48 +- tests/unit/test_costs_2015.py | 27 +- tests/unit/test_cs_fatigue.py | 5 +- tests/unit/test_current_drive.py | 10 +- tests/unit/test_dcll.py | 11 +- tests/unit/test_divertor.py | 4 +- tests/unit/test_fw.py | 5 +- tests/unit/test_ife.py | 12 +- tests/unit/test_impurity_radiation.py | 165 +- tests/unit/test_input.py | 3 +- tests/unit/test_main.py | 17 +- tests/unit/test_maths_library.py | 2 + tests/unit/test_mfile2dict.py | 9 +- tests/unit/test_neoclassics.py | 5 +- tests/unit/test_pfcoil.py | 631 +- tests/unit/test_physics.py | 28 +- tests/unit/test_physics_functions.py | 7 +- tests/unit/test_plasma_geom.py | 7 +- tests/unit/test_plasma_profiles.py | 8 +- tests/unit/test_power.py | 39 +- tests/unit/test_pulse.py | 31 +- tests/unit/test_sctfcoil.py | 39 +- tests/unit/test_stellarator.py | 35 +- tests/unit/test_superconductors.py | 3 +- tests/unit/test_tfcoil.py | 10 +- tests/unit/test_vacuum.py | 4 +- tests/unit/test_water_usage.py | 3 +- tracking/git.py | 2 +- tracking/run_tracking_inputs.py | 2 +- tracking/tracking_data.py | 26 +- 145 files changed, 6472 insertions(+), 6609 deletions(-) diff --git a/documentation/proc-pages/scripts/plotting_scripts/deuterium_branching_plot.py b/documentation/proc-pages/scripts/plotting_scripts/deuterium_branching_plot.py index 2cbb43cf6..68c342cac 100644 --- a/documentation/proc-pages/scripts/plotting_scripts/deuterium_branching_plot.py +++ b/documentation/proc-pages/scripts/plotting_scripts/deuterium_branching_plot.py @@ -1,5 +1,5 @@ -import numpy as np import matplotlib.pyplot as plt +import numpy as np plt.style.use("ggplot") diff --git a/documentation/proc-pages/scripts/plotting_scripts/menard_beta_norm_plot.py b/documentation/proc-pages/scripts/plotting_scripts/menard_beta_norm_plot.py index 9a67a6022..df770df38 100644 --- a/documentation/proc-pages/scripts/plotting_scripts/menard_beta_norm_plot.py +++ b/documentation/proc-pages/scripts/plotting_scripts/menard_beta_norm_plot.py @@ -2,7 +2,6 @@ from bokeh.models import ColumnDataSource from bokeh.plotting import figure, output_file, save - x = np.linspace(1.0, 5, 500) y = 3.12 + 3.5 * (1 / x) ** 1.7 source = ColumnDataSource(data=dict(x=x, y=y)) diff --git a/documentation/proc-pages/scripts/plotting_scripts/original_beta_norm_plot.py b/documentation/proc-pages/scripts/plotting_scripts/original_beta_norm_plot.py index 6b3f011af..9b572e0d4 100644 --- a/documentation/proc-pages/scripts/plotting_scripts/original_beta_norm_plot.py +++ b/documentation/proc-pages/scripts/plotting_scripts/original_beta_norm_plot.py @@ -2,7 +2,6 @@ from bokeh.models import ColumnDataSource from bokeh.plotting import figure, output_file, save - x = np.linspace(1.0, 5, 500) y = 2.7 * (1 + 5 * (1 / x) ** 3.5) source = ColumnDataSource(data=dict(x=x, y=y)) diff --git a/documentation/proc-pages/scripts/plotting_scripts/profile_parabolic_plot.py b/documentation/proc-pages/scripts/plotting_scripts/profile_parabolic_plot.py index fb2c66836..cfac0f267 100644 --- a/documentation/proc-pages/scripts/plotting_scripts/profile_parabolic_plot.py +++ b/documentation/proc-pages/scripts/plotting_scripts/profile_parabolic_plot.py @@ -3,7 +3,6 @@ from bokeh.models import ColumnDataSource, CustomJS, Slider from bokeh.plotting import figure, output_file, save - x = np.linspace(0, 1, 500) y = 5.0 * (1 - x**2) ** 2.0 diff --git a/documentation/proc-pages/scripts/plotting_scripts/profile_pedestal_plot.py b/documentation/proc-pages/scripts/plotting_scripts/profile_pedestal_plot.py index 1bfaca7b5..a6a721c20 100644 --- a/documentation/proc-pages/scripts/plotting_scripts/profile_pedestal_plot.py +++ b/documentation/proc-pages/scripts/plotting_scripts/profile_pedestal_plot.py @@ -3,7 +3,6 @@ from bokeh.models import ColumnDataSource, CustomJS, Slider from bokeh.plotting import figure, output_file, save - T0 = Slider(start=0.1, end=10, value=10.0, step=0.1, title="Plasma centre value | T0") alpha = Slider( start=0.01, end=10, value=2.0, step=0.01, title="Profile Index | alphan" diff --git a/documentation/proc-pages/scripts/sort_vardes.py b/documentation/proc-pages/scripts/sort_vardes.py index 804f8d132..91fa276ff 100644 --- a/documentation/proc-pages/scripts/sort_vardes.py +++ b/documentation/proc-pages/scripts/sort_vardes.py @@ -1,10 +1,10 @@ """ - Script to tidy up vardes.md for the GitLab Page +Script to tidy up vardes.md for the GitLab Page - J. Morris - 10/08/19 - UKAEA +J. Morris +10/08/19 +UKAEA """ diff --git a/examples/csv_output.ipynb b/examples/csv_output.ipynb index a8166c404..1ad51dff5 100644 --- a/examples/csv_output.ipynb +++ b/examples/csv_output.ipynb @@ -46,6 +46,7 @@ ], "source": [ "from pathlib import Path\n", + "\n", "from process.io import mfile_to_csv\n", "\n", "# Project directory for example result file and default .json list;\n", diff --git a/examples/examples.ipynb b/examples/examples.ipynb index 4e0b822ea..6fee2977c 100644 --- a/examples/examples.ipynb +++ b/examples/examples.ipynb @@ -25,8 +25,8 @@ "%load_ext autoreload\n", "%autoreload 2\n", "from pathlib import Path\n", - "from tempfile import TemporaryDirectory\n", "from shutil import copy\n", + "from tempfile import TemporaryDirectory\n", "\n", "# Define project root dir; when running a notebook, the cwd is the dir the notebook is in\n", "PROJ_DIR = Path.cwd().parent\n", @@ -174,10 +174,12 @@ } ], "source": [ - "from process.io import plot_proc\n", - "from pdf2image import convert_from_path\n", "import subprocess\n", "\n", + "from pdf2image import convert_from_path\n", + "\n", + "from process.io import plot_proc\n", + "\n", "# plot_proc uses command line arguments of the current process. Jupyter adds command line arguments under the hood causing plot_proc to fail. running plot proc in its own process isolates it from the jupyter command line arguments\n", "subprocess.run([\"python\", plot_proc.__file__, \"-f\", str(single_run.mfile_path)])\n", "\n", @@ -238,8 +240,8 @@ } ], "source": [ - "from PIL import Image\n", "from IPython.display import display\n", + "from PIL import Image\n", "\n", "img1 = Image.open(\"plot_proc_1.png\")\n", "display(img1)\n", @@ -457,9 +459,10 @@ } ], "source": [ - "from process.main import VaryRun\n", "import os\n", "\n", + "from process.main import VaryRun\n", + "\n", "input_rel = script_dir / \"data/run_process.conf\"\n", "temp_dir, temp_input_path = copy_to_temp_dir(input_rel)\n", "\n", diff --git a/examples/plot_solutions.ipynb b/examples/plot_solutions.ipynb index b840de1ce..2be40c1f2 100644 --- a/examples/plot_solutions.ipynb +++ b/examples/plot_solutions.ipynb @@ -1,1356 +1,1357 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# `plot_solutions` Solution Comparison Tool\n", - "\n", - "This tool plots the solution vectors (i.e. final values of optimisation parameters) for different runs of PROCESS. This allows visual comparisons of different solution points.\n", - "\n", - "It can use different intra-solution optimisation parameter normalisations (e.g. initial value, parameter range) and inter-solution normalisations (e.g. normalise to a certain solution).\n", - "\n", - "### Known Limitations\n", - "\n", - "- The solution vectors (optimisation parameter values at the solution) currently plotted are normalised to the initial point (from the `IN.DAT`) of each solution: each element of the vector is the $x_{final}/x_{initial}$, the `xcmxxx` values in the `MFILE.DAT`. This allows all optimisation parameters to be plotted on the same axis, showing the relative changes from their initial values across multiple solutions.\n", - "- Solutions being plotted together must also have the same optimisation parameters.\n", - "- The solutions plotted in this example are fictitious." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Reload Process each time (keep editable install up-to-date)\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "from process.io.plot_solutions import RunMetadata, plot_mfile_solutions\n", - "from pathlib import Path" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot single solution\n", - "\n", - "Plot a single solution, showing optimisation parameters normalised to their initial values." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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7OxGANpePbuVW4rPPPhMDAgJEmUwmOjk5iQMHDhQvX76sc86QIUNEGxsbvfsua4u5R5WMXdp/pW07R0TmSRDFJzzyiIjICCIiIpCZmYlz586ZOhQiIqIycc0xERnd3bt3dY7PnTuHlJQU7SOCiYiIqivOHBOR0bm7uyMyMhJeXl74888/sWrVKqhUKvz6669l7mtMRERUHfALeURkdD169MCnn36K69evQyaToUOHDpgzZw4LYyIiqvY4c0xEREREVIxrjomIiIiIirE4JiIiIiIqxuKYiIiIiKgYi2MiIiIiomIsjomIiIiIirE4JiIiIiIqxn2OSauoqAhXr16FnZ0dBEEwdThERERERiOKIm7fvo369evDwqLs+WEWx6R19epVyOVyU4dBREREVGkuXbqE5557rszXWRyTlp2dHYAHbxp7e/tKG0etVmP37t0IDQ2FVCqttHHMGXNoOObQOJhHwzGHhmMOjcPc86hUKiGXy7X1TllYHJNWyVIKe3v7Si+Ora2tYW9vb5a/fFWBOTQcc2gczKPhmEPDMYfG8azk8UlLR/mFPCIiIiKiYiyOq1hQUBBiY2NNHQYRERERlYLFcQ2TnJwMR0dHU4dBREREZJa45piIiJ5ZuzKvY2Xaefx+Ix++9WwxumtjhPm7mTqsGkkURRQWFkKj0VT52Gq1GrVq1cK9e/dMMr65qOl5lEgkqFWrlsHb0bI4NoHCwkLExMRg48aNkEqlGDVqFN577z0IggCVSoVp06bh008/xa1bt/D8889j/vz5CAoKgkKhwFtvvQXg/xeTz5w5E/Hx8di4cSOWLl2KrKws2NjYIDg4GElJSXB1dTXlrRIRVVu7Mq9j5MYM7fGJy3mI3pSB1YNas0CuoPv37+PatWsoKCgwyfiiKMLNzQ2XLl3iPv0GMIc8Wltbw93dHZaWlk/dB4tjE9iwYQOGDRuGo0eP4tixY4iKioKHhwdGjBiBmJgYnD59Glu3bkX9+vXxzTffoEePHjh16hQ6duyIpKQkzJgxA1lZWQAAW1tbAA/+2ps1axb8/PyQm5uLuLg4REZGIiUlpcw4VCoVVCqV9lipVGr7UqvVlXb/JX1X5hjmjjk0HHNoHDU5jyv2ndNrE0VgRdo5BPs6V1kcNTmHwIMHSF24cAESiQTu7u6QSqVVXliJoog7d+7AxsamxhZ11UFNzqMoilCr1fjrr7/wxx9/oFGjRnoP+ijv75ggiqJYGUFS6YKCgpCbm4vMzEztG+/tt9/Gd999h507d8LLyws5OTmoX7++9pru3bujbdu2mDNnDpKTkxEbG4tbt249dpxjx46hTZs2uH37traAflR8fDwSEhL02rds2QJra+unv0kiohpg0hEJ7hfpFwCWFiIWtqt5HymbSq1ateDm5obnnnsOMpnM1OHQM06lUuHy5cu4du2a3tKQgoICDBgwAHl5eY/dspYzxybQvn17nb/IOnTogMTERJw6dQoajQa+vr4656tUKjg7P34WIyMjA/Hx8Thx4gRu3ryJoqIiAEBOTg6aNWtW6jVTp05FXFyc9rhkc+zQ0NBK3+c4NTUVISEhZr2PYmViDg3HHBpHTc7jupzDOHlFqdfepL4DwsPbV1kcNTmHAHDv3j1cunQJdnZ2qF27tkliKHkssJ2dXY2b8axOzCGP9+7dg5WVFbp06aL3fiz5hPxJWBxXI/n5+ZBIJMjIyIBEItF5razZXwC4c+cOwsLCEBYWhs2bN6Nu3brIyclBWFgY7t+/X+Z1Mpms1L/ypVJplfwPdFWNY86YQ8Mxh8ZRE/M4JtgH0Zsy8PDnp4IAxHT1Mcm91MQcAoBGo4EgCLCwsND7GLuqlEwIlcRBT8cc8mhhYQFBEEr9fSrv7xeLYxM4cuSIzvHhw4fh4+ODgIAAaDQa5Obm4qWXXir1WktLS72PCc6ePYt//vkH8+bNg1wuB/BgWQUREZUtzN8Nqwe1xkpFNs7duA2fenYYE+SNUH4Zj+iZVjP/LKjhcnJyEBcXh6ysLHz66adYvnw5xo8fD19fXwwcOBCDBw/G119/jQsXLuDo0aOYO3cuduzYAQDw9PREfn4+9u7di7///hsFBQXw8PCApaUlli9fjj/++APfffcdZs2aZeK7JCKq/sL83fDtmE44/V4PfDumEwvjZ4i5PpRLEARs27bN1GHoqUnPaWBxbAKDBw/G3bt30bZtW4wZMwbjx49HVFQUAGD9+vUYPHgwJkyYAD8/P0RERCA9PR0eHh4AgI4dOyI6Ohr9+/dH3bp1sWDBAtStWxfJycn44osv0KxZM8ybNw+LFi0y5S0SERGRAaprkVuVvv76a4SGhsLZ2RmCIOD48eNVMi6XVVQxhUKh/feqVav0XpdKpUhISCh1F4mHr3v02jfeeANvvPGGThs3IiEiopqipj2QRRRFaDQa1KrFUqqy3LlzBy+++CL69euHESNGVNm4nDkmIiIikyp5IMuJy3m4q9ZoH8iyK/N6lcWwceNGBAYGws7ODm5ubhgwYAByc3O1rysUCgiCgB9++AGtW7eGTCbDzz//jNu3b2PgwIGwsbGBu7s7lixZordkQ6VSYeLEiWjQoAFsbGzQrl07ncmyR3l6egIAXn75ZQiCoD0GHkyQeXt7w9LSEn5+fti4ceNj72vmzJlwd3fHyZMnAQBTpkyBr68vrK2t4eXlhenTp+vs/ztv3jy88MIL+Pjjj+Hh4QFbW1uMHj0aGo0GCxYsgJubG1xdXfH+++/rjLN48WI0b94cNjY2kMvlGD16NPLz88uM66+//kJgYCBefvllnWcuPOzNN9/EjBkz0L1798feo7GxOCYiIiKTWpl2Xq9NFIGViuwqi6HkYVonTpzAtm3bcPHiRURGRuqd9/bbb2PevHk4c+YMWrRogbi4OBw4cADfffcdUlNT8dNPP+GXX37RuSYmJgaHDh3C1q1bcfLkSbz22mvo0aMHzp3TfxANAKSnpwN4sNTy2rVr2uNvvvkG48ePx4QJE/Dbb79h5MiReOutt5CWlqbXhyiKGDt2LD755BP89NNPaNGiBQDAzs4OycnJOH36NJYuXYoPP/wQS5Ys0bk2OzsbP/zwA3bu3IlPP/0U69atQ8+ePXH58mXs378f8+fPx7vvvquzwYCFhQWWLVuGzMxMbNiwAfv27cPkyZNLvb9Lly7hpZdewvPPP48vv/yy2u2Pzc8CiIiIyKR+v1H6DOO5G7erLIahQ4dq/+3l5YVly5ahTZs2yM/P19lO9b333kNISAgA4Pbt29iwYQO2bNmCbt26AXhQ0D78IK+cnBysX79e5wFfEydOxM6dO7F+/XrMmTNHL5a6desCABwdHeHm9v9LSxYtWoTIyEiMHj0aABAXF4fDhw9j0aJF6Nq1q/a8wsJCDBo0CL/++it+/vlnNGjQQPvau+++q/23p6cnJk6ciK1bt+oUskVFRfj4449hZ2eHZs2aoWvXrsjKykJKSgosLCzg5+eH+fPnIy0tDe3atQMAnZlyT09PzJ49G9HR0Vi5cqXOvWVlZSEkJAQvv/wykpKSquV+yiyOiYiIyKR869nixOU8vXafenZVFkN5H6YVGBio/fcff/wBtVqNtm3batscHBzg5+enPTbkAV+POnPmjPYL/CU6deqEpUuX6rT973//g0wmw+HDh+Hi4qLz2meffYZly5YhOzsb+fn5KCws1Hvwl6enJ+zs/j/39erVg0Qi0dn7uF69ejrLTvbs2YO5c+fi7NmzUCqVKCwsxL1791BQUKB96u7du3fx0ksvYcCAAUhKSqrQvVclLqsgIiIikxrdtTEenUAUBGBMkHeVjF/yMC17e3ts3rwZ6enp+OabbwBA72FaNjY2Fer74Qd8HT9+XPvfmTNn9IpaYwkJCcGVK1ewa9cunfZDhw5h4MCBCA8Px/bt2/Hrr79i2rRpevf46MMySh6q8WhbyR8QFy9eRK9evdCiRQt89dVXyMjIwIoVKwDo5k8mk6F79+7Yvn07rly5YrT7NTYWx0RERGRSJQ9kaSl3hLWlBC3ljlgzqHWV7Tv98MO0XnrpJTRp0kRnVrQsXl5ekEql2jXBAJCXl4fff/9de/zwA74aN26s89/DSyYeJZVK9R761bRpUxw4cECn7cCBAzoz2wDw3//+F1u2bMHw4cOxdetWbfvBgwfRsGFDTJs2DYGBgfDx8cGff/75xPt8koyMDBQVFSExMRHt27eHr68vrl69qneehYUFNm7ciNatW6Nr166lnlMdcFmFiQQFBaFVq1bV+mMFIiKiqhLm72ayrdsefphWdHQ0fvvtt3I9TMvOzg5DhgzBpEmT4OTkBFdXV8ycOVP7CGMAOg/4SkxMREBAAP766y/s3bsXLVq0QM+ePUvt29PTE3v37kWnTp0gk8lQp04dTJo0Cf369UNAQAC6d++O77//Hl9//TX27Nmjd/3LL7+MjRs34s0330StWrXw6quvwsfHBzk5Odi6dSvatGmDHTt2aGfIDdG4cWOo1WosX74cvXv3xoEDB7B69epSz5VIJNi8eTPeeOMNBAcHQ6FQlPlHwr///oucnBxtEZ2VlQUAcHNze+wfFobizDERERE90wx5mNbixYvRoUMH9OrVC927d0enTp3QtGlT1K5dW3vOkx7wVZrExESkpqZCLpcjICAAABAREYGlS5di0aJF8Pf3x5o1a7B+/XoEBQWV2serr76KDRs24M0338TXX3+N//73v/jf//6HmJgYtGrVCgcPHsT06dPLn6gytGzZEosXL8b8+fPx/PPPY/PmzZg7d26Z59eqVQuffvop/P39ERwcXOYs/XfffYeAgADtHxCvv/46AgICyiy8jUUQ+aQIk6iOM8dKpRIODg7Iy8vTW5xvTGq1GikpKQgPD9dbw0Tlwxwajjk0DlPlsaY9MOJxavp78d69e7hw4QIaNWqkUxBWpaKiIiiVStjb2+t8acwU7ty5gwYNGiAxMRHDhg0zaSwVVZ3y+LQe934sb51TM++8hrlz5w4GDx4MW1tbuLu7IzExUef1J20OXvI88l27dqFp06awtbVFjx49cO3aNZ1+PvroI+1fq02aNNHbPoWIyBxUhwdGEJX49ddf8emnnyI7Oxu//PILBg4cCADo06ePiSOjp8U1x1Vg0qRJ2L9/P7799lu4urrinXfewS+//IJWrVoBeLA5+OnTp7F161bUr18f33zzDXr06IFTp07Bx8cHAFBQUIBFixZh48aNsLCwwKBBgzBx4kRs3rwZALB582bMmDEDH3zwAQICAvDrr79ixIgRsLGxwZAhQ0qNS6VS6TyVRqlUAngwi/Hw03KMraTvyhzD3DGHhmMOjcMUeVyxT//BCaIIrEg7h2Dfim2NVR3U9PeiWq2GKIooKirS7l5Q1Uo+BC+JoyoVFRVh0aJFyMrKgqWlJV544QXs378fTk5OJsvH0zJlHo2lqKgIoihCrVZDIpHovFbe3zEuq6hk+fn5cHZ2xqZNm/Daa68BeLDA/LnnnkNUVBTi4uLg5eWlszk4AHTv3h1t27bFnDlzkJycjLfeegvnz5+Ht/eDbW1WrlyJ9957D9evP5gpady4MWbNmoU33nhD28fs2bORkpKCgwcPlhpbfHw8EhIS9Nq3bNmi3ZOQiKi6mXREgvtF+g8OsLQQsbCdppQrqDLVqlULbm5ukMvlsLS0NHU49Iy7f/8+Ll26hOvXr6OwsFDntYKCAgwYMOCJyyo4c1zJsrOzcf/+fe0TZADAyclJu0F4eTcHt7a21hbGAODu7q5dwH7nzh1kZ2dj2LBhGDFihPacwsJCODg4lBnb1KlTERcXpz1WKpWQy+UIDQ2t9DXHqampCAkJqZHr66oD5tBwzKFxmCKP63IO4+QVpV57k/oOCA9vXyUxGFNNfy/eu3cPly5dgq2trcnWHIuiiNu3b8POzq5aPnGtpjCHPN67dw9WVlbo3LlzqWuOy4PFsYk9vDn4o9P/Dz+usrTNt0sm/fPzHzx288MPP9QpwgHo9fkwmUxW6vPMpVJplfwPdFWNY86YQ8Mxh8ZRlXkcE+yD6E0ZePhzT0EAYrr61OifZU19L2o0GgiCAEEQTPYlrpIlAKaMwRyYQx5L3oul/T6V9/eLxXEl8/b2hlQqxZEjR7Rbtty8eRO///47unTporM5+EsvvfRUY9SrVw/169fHH3/8of0iABGRuSp5YMRKRTbO3bgNn3p2GBPkXWUPjCBdJQVHQUEBrKysTBwNPesKCgoAlL8QLg2L40pma2uLYcOGYdKkSXB2doarqyumTZum/YvsaTcHf1RCQgLGjRsHBwcH9OjRAyqVCseOHcPNmzd1lk4QEZkDUz4wgnRJJBI4Ojpql/pZW1tX+UfyRUVFuH//Pu7du1djZzyrg5qcR1EUUVBQgNzcXDg6Oj72k/MnYXFcBRYuXIj8/Hz07t0bdnZ2mDBhAvLy8rSvr1+/HrNnz8aECRNw5coVuLi4oH379ujVq1e5xxg+fDisra2xcOFCTJo0CTY2NmjevDliY2Mr4Y6IiIj+X8nTysrzyOXKIIoi7t69Cysrqxq7VrY6MIc8Ojo6Gvz0PO5WQVp8CEjNwRwajjk0DubRcOaUQ41GY5It6dRqNX788Ud07ty5xufQlGp6HqVS6WNnjMtb53DmmIiIiIxCIpEY9HG2IeMWFhaidu3aNbKoqy6Yxwdq1oISIiIiIqJKxOKYiIiIiKgYi2MiIiIiomIsjomIiIiIirE4JiIiIiIqxuLYSERRRFRUFJycnCAIAo4fP27U/gVBwLZt2wAAFy9efOIYCoUCgiDg1q1bRo2DiIiIyJxxKzcj2blzJ5KTk6FQKODl5QUXFxdTh0REREREFcTi2Eiys7Ph7u6Ojh07mjoUMpJdmdexMu08fr+RD996thjdtTEfV0tERGTmuKzCCCIjIzF27Fjk5ORAEAS4uLjoPPo5KSkJgiBg586d2rbGjRvjo48+AgCkp6cjJCQELi4ucHBwQJcuXfDLL79UKIaUlBT4+vrCysoKXbt2xcWLF41yb8+qXZnXMXJjBk5czsNdtQYnLuchelMGdmVeN3VoREREVIk4c2wES5cuhbe3N9auXYv09HSkpqZi7Nix0Gg0kEgk2L9/P1xcXKBQKNCjRw9cuXIF2dnZCAoKAgDcvn0bQ4YMwfLlyyGKIhITExEeHo5z587Bzs7uieNfunQJffv2xZgxYxAVFYVjx45hwoQJT7xOpVJBpVJpj5VKJYAHj4+szMd/lvRtikeMlteKfef02kQRWJF2DsG+ziaISFdNyGF1xxwaB/NoOObQcMyhcZh7Hst7XyyOjcDBwQF2dnaQSCRwc3ND7969ERkZiV9//RWtW7fGjz/+iEmTJmm/UKdQKNCgQQM0btwYABAcHKzT39q1a+Ho6Ij9+/frzECXZdWqVfD29kZiYiIAwM/PD6dOncL8+fMfe93cuXORkJCg1757925YW1uX59YNkpqaWuljPK2z1yQABP32q3lISUmp+oDKUJ1zWFMwh8bBPBqOOTQcc2gc5prHgoKCcp3H4rgSODo6omXLllAoFLC0tISlpSWioqIwc+ZM5OfnY//+/ejSpYv2/Bs3buDdd9+FQqFAbm4uNBoNCgoKkJOTU67xzpw5g3bt2um0dejQ4YnXTZ06FXFxcdpjpVIJuVyO0NBQ2Nvbl/NuK06tViM1NRUhISHV9tnt63IO4+QVpV57k/oOCA9vb4KIdNWEHFZ3zKFxMI+GYw4Nxxwah7nnseQT8idhcVxJgoKCoFAoIJPJ0KVLFzg5OaFp06b4+eefsX//fp1lD0OGDME///yDpUuXomHDhpDJZOjQoQPu379fqTHKZDLIZDK9dqlUWiW/FFU1ztMYE+yD6E0ZEMX/bxMEIKarT7WKuTrnsKZgDo2DeTQcc2g45tA4zDWP5b0nfiGvknTp0gU///wz9u7dq11bHBQUhE8//RS///67tg0ADhw4gHHjxiE8PBz+/v6QyWT4+++/yz1W06ZNcfToUZ22w4cPG+M2nllh/m5YPag1WsodYW0pQUu5I9YMao1Q7lZBRERk1jhzXEk6d+6M27dvY/v27Zg3bx6AB8Xxq6++Cnd3d/j6+mrP9fHxwcaNGxEYGAilUolJkybBysqq3GNFR0cjMTERkyZNwvDhw5GRkYHk5GRj39IzJ8zfjVu3ERERPWM4c1xJ6tSpg+bNm6Nu3bpo0qQJgAcFc1FRkc56YwBYt24dbt68iRdeeAFvvvkmxo0bB1dX13KP5eHhga+++grbtm1Dy5YtsXr1asyZM8eo90NERET0LODMsZHExsYiNjZWp+3Rxzs7OTmhqKhI79qAgACkp6frtL366qs6x+JDi189PT11jgGgV69eejtbvPXWW+UNn4iIiIjAmWMiIiIiIi0Wx0RERERExVgcExEREREVY3FMRERERFSMxTERERERUTEWx0RERERExVgcl0IURURFRcHJyQmCIODXX3/VOX50izYiIiIiMg/c57gUO3fuRHJyMhQKBby8vJCenq5z7OLiYuoQiYiIiKgSsDguRXZ2Ntzd3dGxY0cAwJ9//qlzTPQ4uzKvY2Xaefx+Ix++9WwxumtjPoaaiIiohuCyikdERkZi7NixyMnJgSAI8PT01DsGHjylLikpSefaVq1aIT4+HsCDpRnx8fHw8PCATCZD/fr1MW7cOO25np6emDVrFt544w3Y2NigQYMGWLFihU5/OTk56NOnD2xtbWFvb49+/frhxo0bAIC8vDxIJBIcO3YMAFBUVAQnJye0b99ee/2mTZsgl8uNnCF6nF2Z1zFyYwZOXM7DXbUGJy7nIXpTBnZlXjd1aERERFQOnDl+xNKlS+Ht7Y21a9ciPT0dKpUKn3zyifZYIpGUq5+vvvoKS5YswdatW+Hv74/r16/jxIkTOucsXLgQ77zzDhISErBr1y6MHz8evr6+CAkJQVFRkbYw3r9/PwoLCzFmzBj0798fCoUCDg4OaNWqFRQKBQIDA3Hq1Cnt+uj8/HztdV26dCkzRpVKBZVKpT1WKpUAALVaDbVa/RTZK5+SvitzDFNZse+cXpsoAivSziHY19lo45hzDqsKc2gczKPhmEPDMYfGYe55LO99sTh+hIODA+zs7CCRSODm9uCj8EePyyMnJwdubm7o3r07pFIpPDw80LZtW51zOnXqhLfffhsA4OvriwMHDmDJkiUICQnB3r17cerUKVy4cEE7+/vJJ5/A398f6enpaNOmDYKCgqBQKDBx4kQoFAqEhITg7Nmz+Pnnn9GjRw8oFApMnjy5zBjnzp2LhIQEvfbdu3fD2tq63Pf6tFJTUyt9jKp29poEgKDffjUPKSkpRh/PHHNY1ZhD42AeDcccGo45NA5zzWNBQUG5zmNxXElee+01JCUlwcvLCz169EB4eDh69+6NWrX+P+UdOnTQuaZDhw7apRpnzpyBXC7XWRbRrFkzODo64syZM2jTpg26dOmCdevWQaPRYP/+/QgNDYWbmxsUCgVatGiB8+fPIygoqMwYp06diri4OO2xUqmEXC5HaGgo7O3tjZOIUqjVaqSmpiIkJARSqbTSxjGFdTmHcfKKUq+9SX0HhIe3L+WKp2POOawqzKFxMI+GYw4Nxxwah7nnseQT8idhcfyULCwsIIqiTtvD0/VyuRxZWVnYs2cPUlNTMXr0aCxcuBD79+832huuc+fOuH37Nn755Rf8+OOPmDNnDtzc3DBv3jy0bNkS9evXh4+PT5nXy2QyyGQyvXapVFolvxRVNU5VGhPsg+hNGXj4rSEIQExXn0q5V3PMYVVjDo2DeTQcc2g45tA4zDWP5b0nfiHvKdWtWxfXrl3THiuVSly4cEHnHCsrK/Tu3RvLli2DQqHAoUOHcOrUKe3rhw8f1jn/8OHDaNq0KQCgadOmuHTpEi5duqR9/fTp07h16xaaNWsGAHB0dESLFi3wwQcfQCqVokmTJujcuTN+/fVXbN++/bHrjalyhPm7YfWg1mgpd4S1pQQt5Y5YM6g1QrlbBRERUY3AmeOnFBwcjOTkZPTu3RuOjo6YMWOGzpf1kpOTodFo0K5dO1hbW2PTpk2wsrJCw4YNteccOHAACxYsQEREBFJTU/HFF19gx44dAIDu3bujefPmGDhwIJKSklBYWIjRo0ejS5cuCAwM1PYRFBSE5cuX49VXXwUAODk5oWnTpvjss8/0dr+gqhHm78at24iIiGoozhw/palTp6JLly7o1asXevbsiYiICHh7e2tfd3R0xIcffohOnTqhRYsW2LNnD77//ns4O///jgUTJkzAsWPHEBAQgNmzZ2Px4sUICwsDAAiCgG+//RZ16tRB586d0b17d3h5eeGzzz7TiaNLly7QaDQ6a4uDgoL02oiIiIjoyThzXIrY2FjExsaWeQwA9vb22Lp1q07bkCFDtP+OiIhARETEY8ext7fH559/XubrHh4e+Pbbbx/bR0REhN7a56SkJL09mImIiIjoyThzTERERERUjMUxEREREVExLqswkYsXL5o6BCIiIiJ6BGeOiYiIiIiKsTgmIiIiIirG4piIiIiIqBiLYyMRRRFRUVFwcnKCIAg4fvy43jnx8fFo1apVlcdGREREROXD4thIdu7cieTkZGzfvh3Xrl1DQEAAtm3bZuqwiIiIiKgCuFuFkWRnZ8Pd3R0dO3as8rHv378PS0vLKh+XiJ5tuzKvY8W+czh7TYJ1OYcxJtiHj04nohqPM8dGEBkZibFjxyInJweCIMDT0xMA8PLLL+scl1izZg3kcjmsra3Rr18/5OXlaV8LCgrSexpfREQEIiMjtceenp6YNWsWBg8eDHt7e0RFRSE5ORmOjo7YtWsXmjZtCltbW/To0QPXrl2rpLsmomfZrszrGLkxAyevKHG/SMDJK0pEb8rArszrpg6NiMggnDk2gqVLl8Lb2xtr165Feno6JBIJXF1dsX79evTo0QMSiUR77vnz5/H555/j+++/h1KpxLBhwzB69Ghs3ry5QmMuWrQIM2bMwMyZMwEAP/30EwoKCrBo0SJs3LgRFhYWGDRoECZOnFhm3yqVCiqVSnusVCoBAGq1Gmq1uqJpKLeSvitzDHPHHBqOOTTMin3n9NpEEViRdg7Bvs4miKjm4nvRcMyhcZh7Hst7XyyOjcDBwQF2dnaQSCRwc/v/jxQdHR11jgHg3r17+OSTT9CgQQMAwPLly9GzZ08kJibqnfs4wcHBmDBhgvb4p59+glqtxurVq+Ht7Q0AiImJwXvvvVdmH3PnzkVCQoJe++7du2FtbV3uWJ5WampqpY9h7phDwzGHT+fsNQkAQb/9ah5SUlKqPiAzwPei4ZhD4zDXPBYUFJTrPBbHVczDw0NbGANAhw4dUFRUhKysrAoVx4GBgXpt1tbW2sIYANzd3ZGbm1tmH1OnTkVcXJz2WKlUQi6XIzQ0FPb29uWOpaLUajVSU1MREhICqVRaaeOYM+bQcMyhYdblHMbJK0q99ib1HRAe3t4EEdVcfC8ajjk0DnPPY8kn5E/C4riasbCwgCiKOm2lfQxgY2Oj1/boG1kQBL2+HiaTySCTyUrtpyp+KapqHHPGHBqOOXw6Y4J9EL0pAw//T4wgADFdfZjPp8T3ouGYQ+Mw1zyW9574hbxKIpVKodFo9NpzcnJw9epV7fHhw4dhYWEBPz8/AEDdunV1vkSn0Wjw22+/VX7AREQVEObvhtWDWqPFc/awtBDR4jl7rBnUGqHcrYKIajgWx5XE09MTe/fuxfXr13Hz5k1te+3atTFkyBCcOHECP/30E8aNG4d+/fppl1QEBwdjx44d2LFjB86ePYtRo0bh1q1bJroLIqKyhfm74auR7bGwnQZfjWzPwpiIzAKL40qSmJiI1NRUyOVyBAQEaNsbN26Mvn37Ijw8HKGhoWjRogVWrlypfX3o0KEYMmQIBg8ejC5dusDLywtdu3Y1xS0QERERPXME8XGLUumZolQq4eDggLy8vEr/Ql5KSgrCw8PNck1TVWAODcccGgfzaDjm0HDMoXGYex7LW+dw5piIiIiIqBiLYyIiIiKiYiyOiYiIiIiKsTgmIiIiIirG4piIiIiIqBiLYyIiIiKiYs9scSyKIqKiouDk5ARBEODo6IjY2Fjt656enkhKSqpwv5GRkYiIiDBanOUVFBSkEz8RERERVdwzWxzv3LkTycnJ2L59O65du4bnn3++QtdfvHgRgiDg+PHjlRMgEREREVW5WqYOwFSys7Ph7u6Ojh07AgBq1TJdKu7fvw9LS0uTjU9ERERkCrsyr2Nl2nn8fiMfvvVsMbprY4SZ+FH0z+TMcWRkJMaOHYucnBwIggBPT89SzysoKMDQoUNhZ2cHDw8PrF27Vvtao0aNAAABAQEQBAFBQUE61y5atAju7u5wdnbGmDFjoFarta95enpi1qxZGDx4MOzt7REVFQUA+Pnnn/HSSy/BysoKcrkc48aNw507d7TXrVy5Ej4+Pqhduzbq1auHV199VWfMoqIiTJ48GU5OTnBzc0N8fLwBWSIiIiKqPLsyr2PkxgycuJyHu2oNTlzOQ/SmDOzKvG7SuJ7JmeOlS5fC29sba9euRXp6OiQSCV577TW98xITEzFr1iy88847+PLLLzFq1Ch06dIFfn5+OHr0KNq2bYs9e/bA399fZ+Y3LS0N7u7uSEtLw/nz59G/f3+0atUKI0aM0J6zaNEizJgxAzNnzgTwYCa7R48emD17Nj7++GP89ddfiImJQUxMDNavX49jx45h3Lhx2LhxIzp27Ih///0XP/30k068GzZsQFxcHI4cOYJDhw4hMjISnTp1QkhISKl5UKlUUKlU2mOlUgngweMjHy7mja2k78ocw9wxh4ZjDo2DeTQcc2g45tA4qjqPK/ad02sTRWBF2jkE+zobfbzy3pcgiqJo9NFrgKSkJCQlJeHixYsAHnyhrVWrVtov4Xl6euKll17Cxo0bATz4Ap+bmxsSEhIQHR2NixcvolGjRvj111/RqlUrbb+RkZFQKBTIzs6GRCIBAPTr1w8WFhbYunWrtu+AgAB888032uuGDx8OiUSCNWvWaNt+/vlndOnSBXfu3EFKSgreeustXL58GXZ2dnr3ExQUBI1Go1Mwt23bFsHBwZg3b16pOYiPj0dCQoJe+5YtW2BtbV2OLBIRERE9nUlHJLhfJOi1W1qIWNhOY/TxCgoKMGDAAOTl5cHe3r7M857JmePyatGihfbfgiDAzc0Nubm5T7zO399fWxgDgLu7O06dOqVzTmBgoM7xiRMncPLkSWzevFnbJooiioqKcOHCBYSEhKBhw4bw8vJCjx490KNHD7z88ss6RezD8ZaM+7h4p06diri4OO2xUqmEXC5HaGjoY980hlKr1UhNTUVISAikUmmljWPOmEPDMYfGwTwajjk0HHNoHFWdx3U5h3HyilKvvUl9B4SHtzf6eCWfkD8Ji+PHePSNIQgCioqKjHKdjY2NznF+fj5GjhyJcePG6fXn4eEBS0tL/PLLL1AoFNi9ezdmzJiB+Ph4pKenw9HR8anilclkkMlkpcZfFb8UVTWOOWMODcccGgfzaDjm0HDMoXFUVR7HBPsgelMGHl7DIAhATFefShm/vH2yOH5KJWuMNRrjTPu/8MILOH36NBo3blzmObVq1UL37t3RvXt3zJw5E46Ojti3bx/69u1rlBiIiIiIqkqYvxtWD2qNlYpsnLtxGz717DAmyBuhJt6tgsXxU3J1dYWVlRV27tyJ5557DrVr14aDg8NT9zdlyhS0b98eMTExGD58OGxsbHD69Gmkpqbigw8+wPbt2/HHH3+gc+fOqFOnDlJSUlBUVAQ/Pz8j3hURERFR1QnzdzP51m2Peia3cjOGWrVqYdmyZVizZg3q16+PPn36GNRfixYtsH//fvz+++946aWXEBAQgBkzZqB+/foAAEdHR3z99dcIDg5G06ZNsXr1anz66afw9/c3xu0QEREREZ7hmePY2Fidxy0rFAqd10t2sXjYo0/DGz58OIYPH67TlpycrHfdo4+hLq1vAGjTpg12795d6msvvviiXowPK+21bdu2lXk+EREREenjzDERERERUTEWx0RERERExVgcExEREREVY3FMRERERFSMxTERERERUTEWx0RERERExVgcExEREREVY3FsAqIoIioqCk5OThAEQW//5NJ4enrq7Zf8OBcvXix330RERET0wDP7EBBT2rlzJ5KTk6FQKODl5QUXF5cnXpOeng4bG5tyjyGXy3Ht2rVy9V2VdmVex4p953D2mgTrcg5jTLBPtXtsJBERET27OHNsAtnZ2XB3d0fHjh3h5uaGWrWe/DdK3bp1YW1tXe4xJBJJufuuKrsyr2PkxgycvKLE/SIBJ68oEb0pA7syr5s6NCIiIiIAnDmucpGRkdiwYQMAQBAENGzYEJ6ennj++ecBABs3boRUKsWoUaPw3nvvQRAEAA+WVTz8yGtBEPDhhx9ix44d2LVrFxo0aIDExET897//BfBgWUWjRo3w66+/olWrVqXGolKpoFKptMdKpRIAoFaroVarjX7vK/ad02sTRWBF2jkE+zobfTxzVvLzqYyf07OCOTQO5tFwzKHhmEPjMPc8lve+WBxXsaVLl8Lb2xtr165Feno6JBIJXnvtNWzYsAHDhg3D0aNHcezYMURFRcHDwwMjRowos6+EhAQsWLAACxcuxPLlyzFw4ED8+eefcHJyKlcsc+fORUJCgl777t27KzRLXV5nr0kACPrtV/OQkpJi9PGeBampqaYOocZjDo2DeTQcc2g45tA4zDWPBQUF5TqPxXEVc3BwgJ2dnXbZQwm5XI4lS5ZAEAT4+fnh1KlTWLJkyWOL48jISLzxxhsAgDlz5mDZsmU4evQoevToUa5Ypk6diri4OO2xUqmEXC5HaGgo7O3tn/IOy7Yu5zBOXlHqtTep74Dw8PZGH8+cqdVqpKamIiQkBFKp1NTh1EjMoXEwj4ZjDg3HHBqHueex5BPyJ2FxXE20b99eu4QCADp06IDExERoNBpIJJJSr2nRooX23zY2NrC3t0dubm65x5TJZJDJZHrtUqm0Un4pxgT7IHpTBkTx/9sEAYjp6mOWv4RVobJ+Vs8S5tA4mEfDMYeGYw6Nw1zzWN574hfyarBHf8iCIKCoqMhE0TxZmL8bVg9qjRbP2cPSQkSL5+yxZlBrhHK3CiIiIqomOHNcTRw5ckTn+PDhw/Dx8Slz1rimCvN3Q7CvM1JSUhAe3t4s/zIlIiKimoszx9VETk4O4uLikJWVhU8//RTLly/H+PHjTR0WERER0TOFM8fVxODBg3H37l20bdsWEokE48ePR1RUlKnDIiIiInqmsDg2gYf3Ky4hlUqRlJSEVatWlXrNxYsXdY7Fh7/VVuzWrVvaf5fsX2xra2tQrERERETPEi6rMEP//vsvvvzyS9jb20Mul5s6HCIiIqIagzPHZmjYsGHIyMjAqlWrSt2qjYiIiIhKx+K4GlAoFEbt75tvvjFqf0RERETPCi6rICIiIiIqxuKYiIiIiKgYi2MiIiIiomIsjitAFEVERUXByckJgiDg+PHjpg7psSIjIxEREWHqMIiIiIhqDH4hrwJ27tyJ5ORkKBQKeHl5wcXFpUrGjYyMxK1bt7Bt27YqGY+IiIjoWcXiuAKys7Ph7u6Ojh07mjoUIiKT25V5HSv2ncPZaxKsyzmMMcE+CPN3M3VYREQG4bKKcoqMjMTYsWORk5MDQRDg6emJoKAgxMTEICYmBg4ODnBxccH06dN1nl7n6emJOXPmYOjQobCzs4OHhwfWrl2r0/elS5fQr18/ODo6wsnJCX369NE+ES8+Ph4bNmzAt99+C0EQIAiCduu3x11HRFSZdmVex8iNGTh5RYn7RQJOXlEielMGdmVeN3VoREQG4cxxOS1duhTe3t5Yu3Yt0tPTIZFI8Nprr2HDhg0YNmwYjh49imPHjiEqKgoeHh4YMWKE9trExETMmjUL77zzDr788kuMGjUKXbp0gZ+fH9RqNcLCwtChQwf89NNPqFWrFmbPno0ePXrg5MmTmDhxIs6cOQOlUon169cDAJycnJ54naWl5RPvSaVSaR8zDQBKpRIAoFaroVarjZzB/1fSd2WOYe6YQ8Mxh4ZZse+cXpsoAivSziHY19kEEdVcfC8ajjk0DnPPY3nvi8VxOTk4OMDOzg4SiQRubv//saFcLseSJUsgCAL8/Pxw6tQpLFmyRKc4Dg8Px+jRowEAU6ZMwZIlS5CWlgY/Pz989tlnKCoqwkcffQRBEAAA69evh6OjIxQKBUJDQ2FlZQWVSqUz7qZNm5543ZPMnTsXCQkJeu27d++GtbX10yWqAlJTUyt9DHPHHBqOOXw6Z69JAAj67VfzkJKSUvUBmQG+Fw3HHBqHueaxoKCgXOexODZQ+/bttcUpAHTo0AGJiYnQaDSQSCQAgBYtWmhfFwQBbm5uyM3NBQCcOHEC58+fh52dnU6/9+7dQ3Z2dpnjPu11D5s6dSri4uK0x0qlEnK5HKGhobC3ty9XH09DrVYjNTUVISEhkEqllTaOOWMODcccGmZdzmGcvKLUa29S3wHh4e1NEFHNxfei4ZhD4zD3PJZ8Qv4kLI6rwKNvMEEQUFRUBADIz89H69atsXnzZr3r6tatW2afT3vdw2QyGWQyWanxVsUvRVWNY86YQ8Mxh09nTLAPojdl4KGvWEAQgJiuPsznU+J70XDMoXGYax7Le08sjg105MgRnePDhw/Dx8dHO2v8JC+88AI+++wzuLq6ljlba2lpCY1GU+HriIgqS5i/G1YPao0Vaedw9moemtR3QExXH4RytwoiquG4W4WBcnJyEBcXh6ysLHz66adYvnw5xo8fX+7rBw4cCBcXF/Tp0wc//fQTLly4AIVCgXHjxuHy5csAHux4cfLkSWRlZeHvv/+GWq0u13VERJUpzN8NX41sj4XtNPhqZHsWxkRkFlgcG2jw4MG4e/cu2rZtizFjxmD8+PGIiooq9/XW1tb48ccf4eHhgb59+6Jp06YYNmwY7t27p50RHjFiBPz8/BAYGIi6deviwIED5bqOiIiIiCqGyyoqIDY2FrGxsTptUqkUSUlJWLVqVanXlLbv8KOPnXZzc8OGDRvKHLdu3brYvXu3XvuTrktOTi7zNSIiIiLSx5ljIiIiIqJiLI6JiIiIiIpxWYUBSh7jTERERETmgTPHRERERETFWBwTERERERVjcUxEREREVIzF8SNEUURUVBScnJwgCAIcHR31tm8jIiIiIvPE4vgRO3fuRHJyMrZv345r167h+eefN7jP5ORkODo6Vvg6hUIBQRBw69Ytg2MgIiIioifjbhWPyM7Ohru7Ozp27AgAqFWr+qfo/v37sLS0NHUYRERauzKvY2Xaefx+Ix++9WwxumtjhPHx0kRUA3Dm+CGRkZEYO3YscnJyIAgCPD09AQCFhYWIiYmBg4MDXFxcMH36dIiiqL1OpVJh4sSJaNCgAWxsbNCuXTvtNm8KhQJvvfUW8vLyIAgCBEFAfHw8AGDjxo0IDAyEnZ0d3NzcMGDAAOTm5gJ48GS9rl27AgDq1KkDQRAQGRkJAAgKCkJMTAxiY2Ph4uKCsLAwDB06FL169dK5H7VaDVdXV6xbt67ykkZE9IhdmdcxcmMGTlzOw121Bicu5yF6UwZ2ZV43dWhERE9U/adFq9DSpUvh7e2NtWvXIj09HRKJBK+99ho2bNiAYcOG4ejRozh27BiioqLg4eGBESNGAABiYmJw+vRpbN26FfXr18c333yDHj164NSpU+jYsSOSkpIwY8YMZGVlAQBsbW0BPCheZ82aBT8/P+Tm5iIuLg6RkZFISUmBXC7HV199hVdeeQVZWVmwt7eHlZWVNtYNGzZg1KhROHDgAADgn3/+QefOnXHt2jW4u7sDALZv346CggL079+/1PtVqVRQqVTaY6VSqY1LrVYbObv/r6TvyhzD3DGHhmMOjaO0PK7Yd07vPFEEVqSdQ7Cvc5XFVlPwvWg45tA4zD2P5b0vQXx4CpSQlJSEpKQkXLx4EcCDWdrc3FxkZmZCEAQAwNtvv43vvvsOp0+fRk5ODry8vJCTk4P69etr++nevTvatm2LOXPmIDk5GbGxsU9cO3zs2DG0adMGt2/fhq2tLRQKBbp27YqbN2/qrFkOCgqCUqnEL7/8onO9v78/hgwZgsmTJwMA/vvf/8LZ2Rnr168vdbz4+HgkJCTotW/ZsgXW1tZPShURUakmHZHgfpGg125pIWJhO40JIiIiAgoKCjBgwADk5eXB3t6+zPM4c1wO7du31xbGANChQwckJiZCo9Hg1KlT0Gg08PX11blGpVLB2fnxMyQZGRmIj4/HiRMncPPmTRQVFQEAcnJy0KxZs8de27p1a7224cOHY+3atZg8eTJu3LiBH374Afv27Suzj6lTpyIuLk57rFQqIZfLERoa+tg3jaHUajVSU1MREhICqVRaaeOYM+bQcMyhcZSWx3U5h3HyilLv3Cb1HRAe3r6qQ6z2+F40HHNoHOaex5JPyJ+ExbGB8vPzIZFIkJGRAYlEovNayfKJ0ty5cwdhYWEICwvD5s2bUbduXeTk5CAsLAz3799/4rg2NjZ6bYMHD8bbb7+NQ4cO4eDBg2jUqBFeeumlMvuQyWSQyWR67VKptEp+KapqHHPGHBqOOTSOh/M4JtgH0Zsy8PDnkoIAxHT1Ya4fg+9FwzGHxmGueSzvPbE4LocjR47oHB8+fBg+Pj6QSCQICAiARqNBbm5umYWopaUlNBrdjxLPnj2Lf/75B/PmzYNcLgfwYFnFo9cB0Lu2LM7OzoiIiMD69etx6NAhvPXWW+W6jojImML83bB6UGusVGTj3I3b8KlnhzFB3gjlbhVEVAOwOC6HnJwcxMXFYeTIkfjll1+wfPlyJCYmAgB8fX0xcOBADB48GImJiQgICMBff/2FvXv3okWLFujZsyc8PT2Rn5+PvXv3omXLlrC2toaHhwcsLS2xfPlyREdH47fffsOsWbN0xm3YsCEEQcD27dsRHh4OKyurx85GAw+WVvTq1QsajQZDhgyptJwQET1OmL8bt24johqJW7mVw+DBg3H37l20bdsWY8aMwfjx4xEVFaV9ff369Rg8eDAmTJgAPz8/REREID09HR4eHgCAjh07Ijo6Gv3790fdunWxYMEC1K1bF8nJyfjiiy/QrFkzzJs3D4sWLdIZt0GDBkhISMDbb7+NevXqISYm5omxdu/eHe7u7ggLC9P5giARERERPRl3qzAz+fn5aNCgAdavX4++fftW6FqlUgkHB4cnfovTUGq1GikpKQgPDzfLNU1VgTk0HHNoHMyj4ZhDwzGHxmHueSxvncNlFWaiqKgIf//9NxITE+Ho6Ij//ve/pg6JiIiIqMZhcWwmcnJy0KhRIzz33HNITk6uEY+9JiIiIqpuWEGZCU9PT3CFDBEREZFh+IU8IiIiIqJiLI6JiIiIiIqxOCYiIiIiKsbiuIJEUURUVBScnJwgCAKOHz9eJeMGBQUhNja2zNcjIyMRERFRJbEQERERmSt+Ia+Cdu7cieTkZCgUCnh5ecHFxcXUIRERERGRkbA4rqDs7Gy4u7ujY8eOpg6FiGqQXZnXsTLtPH6/kQ/ferYY3bUxH69MRFQNcVlFBURGRmLs2LHIycmBIAjw9PREUFAQYmJiEBMTAwcHB7i4uGD69Ok626qpVCpMnDgRDRo0gI2NDdq1aweFQqF9/Z9//sEbb7yBBg0awNraGs2bN8enn3762Fh27NgBBwcHbN68Wac9ISEBdevWhb29PaKjo3H//n2j5oCIKm5X5nWM3JiBE5fzcFetwYnLeYjelIFdmddNHRoRET2CM8cVsHTpUnh7e2Pt2rVIT0+HRCLBa6+9hg0bNmDYsGE4evQojh07hqioKHh4eGDEiBEAgJiYGJw+fRpbt25F/fr18c0336BHjx44deoUfHx8cO/ePbRu3RpTpkyBvb09duzYgTfffBPe3t5o27atXhxbtmxBdHQ0tmzZgl69emnb9+7di9q1a0OhUODixYt466234OzsjPfff7/U+1GpVFCpVNpjpVIJ4MHjI9VqtTFTp6Ok78ocw9wxh4aryhyu2HdOr00UgRVp5xDs61zp41cmvhcNxxwajjk0DnPPY3nvSxD55IgKSUpKQlJSEi5evAjgwRflcnNzkZmZCUEQAABvv/02vvvuO5w+fRo5OTnw8vJCTk4O6tevr+2ne/fuaNu2LebMmVPqOL169UKTJk2waNEi7TitWrWCj48Ppk2bhm+//RZdunTRnh8ZGYnvv/8ely5dgrW1NQBg9erVmDRpEvLy8mBhof8hQXx8PBISEvTat2zZou2DiAw36YgE94sEvXZLCxEL22lMEBER0bOnoKAAAwYMQF5eHuzt7cs8jzPHRtC+fXttYQwAHTp0QGJiIjQaDU6dOgWNRgNfX1+da1QqFZydH8wYaTQazJkzB59//jmuXLmC+/fvQ6VS6RWoX375JXJzc3HgwAG0adNGL46WLVvqXNOhQwfk5+fj0qVLaNiwod75U6dORVxcnPZYqVRCLpcjNDT0sW8aQ6nVaqSmpiIkJARSqbTSxjFnzKHhqjKH63IO4+QVpV57k/oOCA9vX6ljVza+Fw3HHBqOOTQOc89jySfkT8LiuJLl5+dDIpEgIyMDEolE5zVbW1sAwMKFC7F06VIkJSWhefPmsLGxQWxsrN564YCAAPzyyy/4+OOPERgYqFOQPw2ZTAaZTKbXLpVKq+SXoqrGMWfMoeGqIodjgn0QvSkDD39OJwhATFcfs/n58b1oOObQcMyhcZhrHst7TyyOjeDIkSM6x4cPH4aPjw8kEgkCAgKg0WiQm5uLl156qdTrDxw4gD59+mDQoEEAgKKiIvz+++9o1qyZznne3t5ITExEUFAQJBIJPvjgA53XT5w4gbt378LKykobh62tLeRyubFulYieQpi/G1YPao2Vimycu3EbPvXsMCbIG6HcrYKIqNphcWwEOTk5iIuLw8iRI/HLL79g+fLlSExMBAD4+vpi4MCBGDx4MBITExEQEIC//voLe/fuRYsWLdCzZ0/4+Pjgyy+/xMGDB1GnTh0sXrwYN27c0CuOS/pLS0tDUFAQatWqhaSkJO1r9+/fx7Bhw/Duu+/i4sWLmDlzJmJiYkpdb0xEVSvM341btxER1QAsjo1g8ODBuHv3Ltq2bQuJRILx48cjKipK+/r69esxe/ZsTJgwAVeuXIGLiwvat2+v3Wni3XffxR9//IGwsDBYW1sjKioKERERyMvLK3U8Pz8/7Nu3TzuDXFKId+vWDT4+PujcuTNUKhXeeOMNxMfHV/r9ExEREZkLFscVFBsbq/cYZ6lUiqSkJKxatarUa6RSKRISEkrdGQIAnJycsG3btseO+/C+yADQtGlT3LhxQ3ucnJys/XdZ4xARERHR4/HzdiIiIiKiYiyOiYiIiIiKcVmFgR5d7kBERERENRdnjomIiIiIirE4JiIiIiIqxuKYiIiIiKgYi+MqFhkZiYiIiEofp6CgAK+88grs7e0hCAJu3bpV6WMSERER1XT8Qp6Z2rBhA3766SccPHgQLi4ucHBwMHVIRERERNUei2MzlZ2djaZNm+L55583dShEREQ6dmVex8q08/j9Rj5869lidNfGfLw6VRtcVvEUioqKsGDBAjRu3BgymQweHh54//33AQCnTp1CcHAwrKys4OzsjKioKOTn5+v1sWjRIri7u8PZ2RljxoyBWq3WvqZSqTBx4kQ0aNAANjY2aNeund6WcV999RX8/f0hk8ng6empfYQ0AAQFBSExMRE//vgjBEFAUFBQpeSBiIioonZlXsfIjRk4cTkPd9UanLich+hNGdiVed3UoREB4MzxU5k6dSo+/PBDLFmyBC+++CKuXbuGs2fP4s6dOwgLC0OHDh2Qnp6O3NxcDB8+HDExMTqPd05LS4O7uzvS0tJw/vx59O/fH61atcKIESMAADExMTh9+jS2bt2K+vXr45tvvkGPHj1w6tQp+Pj4ICMjA/369UN8fDz69++PgwcPYvTo0XB2dkZkZCS+/vprvP322/jtt9/w9ddfw9LSstT7UKlUUKlU2mOlUgkAUKvVOsW6sZX0XZljmDvm0HDMoXEwj4Z71nK4Yt85vTZRBFaknUOwr/NT9fms5bCymHsey3tfgiiKYiXHYlZu376NunXr4oMPPsDw4cN1Xvvwww8xZcoUXLp0CTY2NgCAlJQU9O7dG1evXkW9evUQGRkJhUKB7OxsSCQSAEC/fv1gYWGBrVu3IicnB15eXsjJyUH9+vW1fXfv3h1t27bFnDlzMHDgQPz111/YvXu39vXJkydjx44dyMzMBADExsbi+PHjj31ISXx8PBISEvTat2zZAmtr66fOERERUVkmHZHgfpGg125pIWJhO40JIqJnRUFBAQYMGIC8vDzY29uXeR5njivozJkzUKlU6NatW6mvtWzZUlsYA0CnTp1QVFSErKws1KtXDwDg7++vLYwBwN3dHadOnQLwYFmGRqOBr6+vTt8qlQrOzs7acfr06aPzeqdOnZCUlASNRqPT9+NMnToVcXFx2mOlUgm5XI7Q0NDHvmkMpVarkZqaipCQEEil0kobx5wxh4ZjDo2DeTTcs5bDdTmHcfKKUq+9SX0HhIe3f6o+n7UcVhZzz2PJJ+RPwuK4gqysrAzu49E3nCAIKCoqAgDk5+dDIpEgIyNDr8i1tbU1eOyHyWQyyGSyUuOril+KqhrHnDGHhmMOjYN5NNyzksMxwT6I3pSBhz+3FgQgpquPwff/rOSwsplrHst7T/xCXgX5+PjAysoKe/fu1XutadOmOHHiBO7cuaNtO3DgACwsLODn51eu/gMCAqDRaJCbm4vGjRvr/Ofm5qYd58CBAzrXHThwAL6+vuWeNSYiIjKFMH83rB7UGi3ljrC2lKCl3BFrBrVGKHeroGqCM8cVVLt2bUyZMgWTJ0+GpaUlOnXqhL/++guZmZkYOHAgZs6ciSFDhiA+Ph5//fUXxo4dizfffFO7pOJJfH19MXDgQAwePBiJiYkICAjAX3/9hb1796JFixbo2bMnJkyYgDZt2mDWrFno378/Dh06hA8++AArV66s5LsnIiIyXJi/G7duo2qLxfFTmD59OmrVqoUZM2bg6tWrcHd3R3R0NKytrbFr1y6MHz8ebdq0gbW1NV555RUsXry4Qv2vX78es2fPxoQJE3DlyhW4uLigffv26NWrFwDghRdewOeff44ZM2Zg1qxZcHd3x3vvvYfIyMhKuFsiIiKiZweL46dgYWGBadOmYdq0aXqvNW/eHPv27Svz2oe3dCuRlJSkcyyVSpGQkFDqThIlXnnlFbzyyitlvv5on0RERET0ZFxzTERERERUjMUxEREREVExFsdERERERMVYHBMRERERFWNxTERERERUjMUxEREREVExFsdVRBRFREVFwcnJCYIgICIiAhEREeW+XqFQQBAE3Lp1q9JiJCIiInrWcZ/jKrJz504kJydDoVDAy8sLVlZWEB9+sDwRERERmRyL4yqSnZ0Nd3d3dOzY0dShEJGZ2ZV5HSvTzuP3G/nwrWeL0V0b89G8RERPicsqqkBkZCTGjh2LnJwcCIIAT09PREZG6iyrUKlUGDduHFxdXVG7dm28+OKLSE9P1+srIyMDgYGBsLa2RseOHZGVlaXz+vfff482bdqgdu3acHFxwcsvv1zZt0dEJrQr8zpGbszAict5uKvW4MTlPERvysCuzOumDo2IqEbizHEVWLp0Kby9vbF27Vqkp6dDIpFg0qRJOudMnjwZX331FTZs2ICGDRtiwYIFCAsLw/nz5+Hk5KQ9b9q0aUhMTETdunURHR2NoUOH4sCBAwCAHTt24OWXX8a0adPwySef4P79+0hJSSkzLpVKBZVKpT1WKpUAALVaDbVabcwU6CjpuzLHMHfMoeHMJYcr9p3TaxNFYEXaOQT7Olf6+OaSR1NiDg3HHBqHueexvPcliFz4WiWSkpKQlJSEixcvAngwm3zr1i1s27YNd+7cQZ06dZCcnIwBAwYAePAD9PT0RGxsLCZNmgSFQoGuXbtiz5496NatGwAgJSUFPXv2xN27d1G7dm107NgRXl5e2LRpU7liio+PR0JCgl77li1bYG1tbZwbJ6JKNemIBPeLBL12SwsRC9tpTBAREVH1VFBQgAEDBiAvLw/29vZlnseZ42ogOzsbarUanTp10rZJpVK0bdsWZ86c0Tm3RYsW2n+7u7sDAHJzc+Hh4YHjx49jxIgR5R536tSpiIuL0x4rlUrI5XKEhoY+9k1jKLVajdTUVISEhEAqlVbaOOaMOTScueRwXc5hnLyi1GtvUt8B4eHtK318c8mjKTGHhmMOjcPc81jyCfmTsDiuYR5+swrCg9mioqIiAICVlVWF+pLJZJDJZKWOURW/FFU1jjljDg1X03M4JtgH0Zsy8PBngIIAxHT1qdL7qul5rA6YQ8Mxh8Zhrnks7z3xC3nVgLe3NywtLbVrh4EHf72lp6ejWbNm5e6nRYsW2Lt3b2WESETVVJi/G1YPao2WckdYW0rQUu6INYNaI5S7VRARPRXOHFcDNjY2GDVqFCZNmgQnJyd4eHhgwYIFKCgowLBhw8rdz8yZM9GtWzd4e3vj9ddfR2FhIVJSUjBlypRKjJ6ITC3M341btxERGQmL42pi3rx5KCoqwptvvonbt28jMDAQu3btQp06dcrdR1BQEL744gvMmjUL8+bNg729PTp37lyJURMRERGZFxbHVSQ2NhaxsbHaY5VKBVtbW+1x7dq1sWzZMixbtqzU64OCgvSeqNeqVSu9tr59+6Jv377GC5yIiIjoGcI1x1WssLAQp0+fxqFDh+Dv72/qcIiIiIjoISyOq9hvv/2GwMBA+Pv7Izo62tThEBEREdFDuKyiirVq1QoFBQWmDoOIiIiISsGZYyIiIiKiYiyOiYiIiIiKsTgmIiIiIirG4tgIRFFEVFQUnJycIAgCIiIiEBERUe7rFQoFBEHArVu3Ki1GIiIiInoyfiHPCHbu3Ink5GQoFAp4eXnByspKb//hyiaKImbOnIkPP/wQt27dQqdOnbBq1Sr4+PhUaRxERERENRlnjo0gOzsb7u7u6NixI9zc3ODg4ABHR8cqjWHBggVYtmwZVq9ejSNHjsDGxgZhYWG4d+9elcZBRMazK/M6+nzwM5pO34k+H/yMXZnXTR0SEZHZY3FsoMjISIwdOxY5OTkQBAGenp6IjIzUWVahUqkwbtw4uLq6onbt2njxxReRnp6u11dGRgYCAwNhbW2Njh07IisrS+f177//Hm3atEHt2rXh4uKCl19+GcCDWeOkpCS8++676NOnD1q0aIFPPvkEV69exbZt2yrz9omokuzKvI6RGzNw4nIe7qo1OHE5D9GbMlggExFVMi6rMNDSpUvh7e2NtWvXIj09HRKJBJMmTdI5Z/Lkyfjqq6+wYcMGNGzYEAsWLEBYWBjOnz8PJycn7XnTpk1DYmIi6tati+joaAwdOhQHDhwAAOzYsQMvv/wypk2bhk8++QT3799HSkoKAODChQu4fv06unfvru3LwcEB7dq1w6FDh/D666+XGrtKpYJKpdIeK5VKAIBarYZarTZOgkpR0ndljmHumEPDVfccrth3Tq9NFIEVaecQ7OtsgohKV93zWBMwh4ZjDo3D3PNY3vsSxKpeHGuGkpKSkJSUhIsXLwJ4MJt869YtbNu2DXfu3EGdOnWQnJyMAQMGAHjww/H09ERsbCwmTZoEhUKBrl27Ys+ePejWrRsAICUlBT179sTdu3dRu3ZtdOzYEV5eXti0aZPe+AcPHkSnTp1w9epVuLu7a9v79esHQRDw2WeflRp3fHw8EhIS9Nq3bNkCa2trQ9NCRAaYdESC+0WCXrulhYiF7TQmiIiIqGYrKCjAgAEDkJeXB3t7+zLP48xxJcvOzoZarUanTp20bVKpFG3btsWZM2d0zm3RooX23yVFbm5uLjw8PHD8+HGMGDHCqLFNnToVcXFx2mOlUgm5XI7Q0NDHvmkMpVarkZqaipCQEEil0kobx5wxh4ar7jlcl3MYJ68o9dqb1HdAeHh7E0RUuuqex5qAOTQcc2gc5p7Hkk/In4TFcTXy8BtREB7MGBUVFQEArKysyrzOzc0NAHDjxg2dmeMbN26gVatWZV4nk8kgk8lKjaMqfimqahxzxhwarrrmcEywD6I3ZeDhz/YEAYjp6lMt462ueaxJmEPDMYfGYa55LO898Qt5lczb2xuWlpbatcPAg7/M0tPT0axZs3L306JFC+zdu7fU1xo1agQ3Nzed15VKJY4cOYIOHTo8ffBEZDJh/m5YPag1WsodYW0pQUu5I9YMao1QfzdTh0ZEZNY4c1zJbGxsMGrUKEyaNAlOTk7w8PDAggULUFBQgGHDhpW7n5kzZ6Jbt27w9vbG66+/jsLCQqSkpGDKlCkQBAGxsbGYPXs2fHx80KhRI0yfPh3169ev0MNIiKh6CfN3QxiLYSKiKsXiuArMmzcPRUVFePPNN3H79m0EBgZi165dqFOnTrn7CAoKwhdffIFZs2Zh3rx5sLe3R+fOnbWvT548GXfu3EFUVBRu3bqFF198ETt37kTt2rUr45aIiIiIzBKLYyOIjY1FbGys9lilUsHW1lZ7XLt2bSxbtgzLli0r9fqgoCC9J+q1atVKr61v377o27dvqX0IgoD33nsP77333lPeBRERERFxzbERFRYW4vTp0zh06BD8/f1NHQ4RERERVRCLYyP67bffEBgYCH9/f0RHR5s6HCIiIiKqIC6rMKJWrVqhoKDA1GEQERER0VPizDERERERUTEWx0RERERExVgcExEREREVY3H8CFEUERUVBScnJwiCgOPHjxvUX2Rk5BMfxOHp6YmkpCSDxiEiIiIiw/ELeY/YuXMnkpOToVAo4OXlBRcXF1OHRERERERVhMXxI7Kzs+Hu7o6OHTuaOhQiomfKrszrWJl2Hr/fyIdvPVuM7tqYj88moirHZRUPiYyMxNixY5GTkwNBEODp6YmioiIsWLAAjRs3hkwmg4eHB95//33tNadOnUJwcDCsrKzg7OyMqKgo5Ofn6/W9aNEiuLu7w9nZGWPGjIFardZ5/fbt23jjjTdgY2ODBg0aYMWKFTqv5+TkoE+fPrC1tYW9vT369euHGzduaF+Pj49Hq1atsGbNGsjlclhbW6Nfv37Iy8szcpaIiIxvV+Z1jNyYgROX83BXrcGJy3mI3pSBXZnXTR0aET1jOHP8kKVLl8Lb2xtr165Feno6JBIJpk6dig8//BBLlizBiy++iGvXruHs2bMAgDt37iAsLAwdOnRAeno6cnNzMXz4cMTExCA5OVnbb1paGtzd3ZGWlobz58+jf//+aNWqFUaMGKE9Z+HChXjnnXeQkJCAXbt2Yfz48fD19UVISAiKioq0hfH+/ftRWFiIMWPGoH///lAoFNo+zp8/j88//xzff/89lEolhg0bhtGjR2Pz5s2l3q9KpYJKpdIeK5VKAIBardYr3o2ppO/KHMPcMYeGYw6Nw1h5XLHvnF6bKAIr0s4h2NfZoL6rO74XDcccGoe557G89yWIoihWciw1SlJSEpKSknDx4kXcvn0bdevWxQcffIDhw4frnfvhhx9iypQpuHTpEmxsbAAAKSkp6N27N65evYp69eohMjISCoUC2dnZkEgkAIB+/frBwsICW7duBfDgC3lNmzbFDz/8oO379ddfh1KpREpKClJTU/Gf//wHFy5cgFwuBwCcPn0a/v7+OHr0KNq0aYP4+HjMnj0bf/75Jxo0aADgwfrpnj174sqVK3Bz0/9oMj4+HgkJCXrtW7ZsgbW1tYGZJCIqv0lHJLhfJOi1W1qIWNhOY4KIiMjcFBQUYMCAAcjLy4O9vX2Z53Hm+DHOnDkDlUqFbt26lfl6y5YttYUxAHTq1AlFRUXIyspCvXr1AAD+/v7awhgA3N3dcerUKZ2+OnTooHdcsoPFmTNnIJfLtYUxADRr1gyOjo44c+YM2rRpAwDw8PDQFsYlfZTEUlpxPHXqVMTFxWmPlUol5HI5QkNDH/umMZRarUZqaipCQkIglUorbRxzxhwajjk0DmPlcV3OYZy8otRrb1LfAeHh7Q0Jsdrje9FwzKFxmHseSz4hfxIWx49hZWVllH4efYMJgoCioiKj9G0ImUwGmUym1y6VSqvkl6KqxjFnzKHhmEPjMDSPY4J9EL0pAw9/likIQExXn2fm58P3ouGYQ+Mw1zyW9574hbzH8PHxgZWVFfbu3Vvq602bNsWJEydw584dbduBAwdgYWEBPz+/Co11+PBhveOmTZtqx7l06RIuXbqkff306dO4desWmjVrpm3LycnB1atXdfp4mliIiKpamL8bVg9qjZZyR1hbStBS7og1g1ojlLtVEFEV48zxY9SuXRtTpkzB5MmTYWlpiU6dOuGvv/5CZmYmhg0bhoEDB2LmzJkYMmQI4uPj8ddff2Hs2LF48803tUsqyuvAgQNYsGABIiIikJqaii+++AI7duwAAHTv3h3NmzfHwIEDkZSUhMLCQowePRpdunRBYGCgTrxDhgzBokWLoFQqMW7cOPTr16/UJRVERNVNmL8bt24jIpNjcfwE06dPR61atTBjxgxcvXoV7u7uiI6OBgBYW1trd5Zo06YNrK2t8corr2Dx4sUVHmfChAk4duwYEhISYG9vj8WLFyMsLAzAg2UY3377LcaOHYvOnTvDwsICPXr0wPLly3X6aNy4Mfr27Yvw8HD8+++/6NWrF1auXGl4EoiIiIieESyOHxEbG4vY2FjtsYWFBaZNm4Zp06aVen7z5s2xb9++Mvt7eEu3Eo8+KvrixYtPjMvDwwPffvvtE88bNWoURo0a9cTziIiIiEgf1xwTERERERVjcUxEREREVIzFsZmIj4/H8ePHTR0GERERUY3G4piIiIiIqBiLYyIiIiKiYiyOiYiIiIiKsTiuJKIoIioqCk5OThAEAY6OjjpbxBmDp6en3rZwRERERPT0uM9xJdm5cyeSk5OhUCjg5eUFCwsLWFlZGXWM9PR02NjYGLVPIiIiomcZi+NKkp2dDXd3d3Ts2LHSxqhbt26l9U1EZC52ZV7HyrTz+P1GPnzr2WJ018Z8TDURlYnLKipBZGQkxo4di5ycHAiCAE9PTwQFBeksq7h58yYGDx6MOnXqwNraGv/5z39w7tw5nX6++uor+Pv7QyaTwdPTE4mJiTqvP7qs4tatWxg5ciTq1auH2rVr4/nnn8f27dsr81aJiKq1XZnXMXJjBk5czsNdtQYnLuchelMGdmVeN3VoRFRNcea4EixduhTe3t5Yu3Yt0tPTIZFI8Nprr+mcExkZiXPnzuG7776Dvb09pkyZgvDwcJw+fRpSqRQZGRno168f4uPj0b9/fxw8eBCjR4+Gs7MzIiMj9cYsKirCf/7zH9y+fRubNm2Ct7c3Tp8+DYlEUmacKpUKKpVKe6xUKgEAarUaarXaOMkoRUnflTmGuWMODcccGkd1z+OKfef02kQRWJF2DsG+ziaISF91z2FNwBwah7nnsbz3xeK4Ejg4OMDOzg4SiQRubvof3ZUUxQcOHNAuu9i8eTPkcjm2bduG1157DYsXL0a3bt0wffp0AICvry9Onz6NhQsXlloc79mzB0ePHsWZM2fg6+sLAPDy8npsnHPnzkVCQoJe++7du2FtbV3R266w1NTUSh/D3DGHhmMOjaO65vHsNQkAQb/9ah5SUlKqPqDHqK45rEmYQ+Mw1zwWFBSU6zwWxyZw5swZ1KpVC+3atdO2OTs7w8/PD2fOnNGe06dPH53rOnXqhKSkJGg0Gr0Z4ePHj+O5557TFsblMXXqVMTFxWmPlUol5HI5QkNDYW9v/zS3Vi5qtRqpqakICQmBVCqttHHMGXNoOObQOKp7HtflHMbJK0q99ib1HRAe3t4EEemr7jmsCZhD4zD3PJZ8Qv4kLI7NxNPshCGTySCTyfTapVJplfxSVNU45ow5NBxzaBzVNY9jgn0QvSkDovj/bYIAxHT1qXbxVtcc1iTMoXGYax7Le0/8Qp4JNG3aFIWFhThy5Ii27Z9//kFWVhaaNWumPefAgQM61x04cAC+vr6lriNu0aIFLl++jN9//71ygyciqkHC/N2welBrtJQ7wtpSgpZyR6wZ1Bqh3K2CiMrAmWMT8PHxQZ8+fTBixAisWbMGdnZ2ePvtt9GgQQPtUooJEyagTZs2mDVrFvr3749Dhw7hgw8+wMqVK0vts0uXLujcuTNeeeUVLF68GI0bN8bZs2chCAJ69OhRlbdHRFSthPm7ces2Iio3zhybyPr169G6dWv06tULHTp0gCiKSElJ0U75v/DCC/j888+xdetWPP/885gxYwbee++9Ur+MV+Krr75CmzZt8MYbb6BZs2aYPHkyNBpNFd0RERERUc3HmeNKEhsbq7OvsUKh0Hm9Tp06+OSTTx7bxyuvvIJXXnmlzNcvXryoc+zk5ISPP/64oqESERERUTHOHBMRERERFWNxTERERERUjMUxEREREVExFsdERERERMVYHBMRERERFWNxTERERERUjMUxEREREVExFsdlEEURUVFRcHJygiAIOH78eIX7iI+PR6tWrYweGxERERFVDj4EpAw7d+5EcnIyFAoFvLy84OLiYuqQiIiMZlfmdaxMO4/fb+TDt54tRndtzEcsExGBM8dlys7Ohru7Ozp27Ag3NzfUqsW/I4jIPOzKvI6RGzNw4nIe7qo1OHE5D9GbMrAr87qpQyMiMjlWfKWIjIzEhg0bAACCIMDd3R0AcPnyZVhY/P/fE3369IGzs7P2kc3z5s3DkiVLUFBQgH79+qFu3bo6/QYFBaFVq1ZISkrStkVERMDR0RHJyckAAE9PTwwfPhy///47vv76azg7O2P58uXo0KEDhg8fjr1798LLywsff/wxAgMDAQDJycmIjY1FcnIyJk2ahEuXLqFLly746KOPIJfLy7xPlUoFlUqlPVYqlQAAtVoNtVr9lNl7spK+K3MMc8ccGu5ZzuGKfef02kQRWJF2DsG+zhXq61nOo7Ewh4ZjDo3D3PNY3vsSRFEUKzmWGicvLw/Lli3D2rVrkZ6eDolEgueeew4pKSno1q0bAODff/+Fu7u7tu3zzz/H4MGDsWLFCrz44ovYuHEjli1bBi8vL+165fIWx7dv38acOXMQHByMJUuWYPPmzejYsSOGDh2Kli1bYsqUKcjKykJmZiYEQUBycjKioqLQsmVLLFu2DJaWlhg9ejRq1aqFAwcOlHmf8fHxSEhI0GvfsmULrK2tjZZPIqpeJh2R4H6RoNduaSFiYTuNCSIiIqp8BQUFGDBgAPLy8mBvb1/meZw5LoWDgwPs7OwgkUjg5vZgDd5//vMfbNmyRVscf/nll3BxcUHXrl0BAElJSRg2bBiGDRsGAJg9ezb27NmDe/fuVXj88PBwjBw5EgAwY8YMrFq1Cm3atMFrr70GAJgyZQo6dOiAGzduaONTq9X44IMP0K5dOwDAhg0b0LRpUxw9ehRt27YtdZypU6ciLi5Oe6xUKiGXyxEaGvrYN42h1Go1UlNTERISAqlUWmnjmDPm0HDPcg7X5RzGyStKvfYm9R0QHt6+Qn09y3k0FubQcMyhcZh7Hks+IX8SFsflNHDgQIwYMQIrV66ETCbD5s2b8frrr2uXWZw5cwbR0dE613To0AFpaWkVHqtFixbaf9erVw8A0Lx5c7223NxcbXFcq1YttGnTRntOkyZN4OjoiDNnzpRZHMtkMshkMr12qVRaJb8UVTWOOWMODfcs5nBMsA+iN2Xg4c8NBQGI6erz1Ll4FvNobMyh4ZhD4zDXPJb3nviFvHLq3bs3RFHEjh07cOnSJfz0008YOHBghfqwsLDAo6tYSlv/8vAPTxCEMtuKiooqND4REQCE+bth9aDWaCl3hLWlBC3ljlgzqDVCuVsFERFnjsurdu3a6Nu3LzZv3ozz58/Dz88PL7zwgvb1pk2b4siRIxg8eLC27fDhwzp91K1bF9euXdMeazQa/Pbbb9qlGYYoLCzEsWPHtLPEWVlZuHXrFpo2bWpw30RkfsL83bh1GxFRKVgcV8DAgQPRq1cvZGZmYtCgQTqvjR8/HpGRkQgMDESnTp2wefNmZGZmwsvLS3tOcHAw4uLisGPHDnh7e2Px4sW4deuWUWKTSqUYO3Ysli1bhlq1aiEmJgbt27cvc0kFEREREeljcVwBwcHBcHJyQlZWFgYMGKDzWv/+/ZGdnY3Jkyfj3r17eOWVVzBq1Cjs2rVLe87QoUNx4sQJDB48GLVq1cL//vc/o8waA4C1tTWmTJmCAQMG4MqVK3jppZewbt06o/RNRERE9KxgcVyG2NhYxMbG6rRZWFjg6tWrZV7zzjvv4J133tFpmz9/vvbfUqkUK1euxMqVK8vs4+LFi3ptj65T9vT01GsDgL59+6Jv375l9k1EREREj8cv5BERERERFWNxTERERERUjMWxGYiMjDTaF/uIiIiInmUsjomIiIiIirE4JiIiIiIqxuKYiIiIiKgYi2MTio+PR6tWrSp0jSAI2LZtW6XEQ0RERPSsY3FMRERERFSMDwEhInoG7cq8jpVp5/H7jXz41rPF6K6NEebvZuqwiIhMjjPHRrJ27VrUr18fRUVFOu19+vTB0KFDAQDz5s1DvXr1YGdnh2HDhuHevXs656anpyMkJAQuLi5wcHBAly5d8Msvv5Q55quvvoqYmBjtcWxsLARBwNmzZwEA9+/fh42NDfbs2WOs2yQiM7Ar8zpGbszAict5uKvW4MTlPERvysCuzOumDo2IyOQ4c2wkr732GsaOHYu0tDR069YNAPDvv/9i586dSElJweeff474+HisWLECL774IjZu3Ihly5bBy8tL28ft27cxZMgQLF++HKIoIjExEeHh4Th37hzs7Oz0xuzSpQvWrFmjPd6/fz9cXFygUCjQpEkTpKenQ61Wo2PHjqXGrFKpoFKptMdKpRIAoFaroVarjZKX0pT0XZljmDvm0HDPcg5X7Dun1yaKwIq0cwj2da5QX89yHo2FOTQcc2gc5p7H8t6XIIqiWMmxPDMiIiLg7OyMdevWAXgwm5yQkIBLly7hxRdfREBAAFasWKE9v3379rh37x6OHz9ean9FRUVwdHTEli1b0KtXLwAPvpD3zTffICIiAqdOnULLli1x48YN1KpVC25ubpg+fTp+++03bN26Fe+//z5SUlJw4MCBUvuPj49HQkKCXvuWLVtgbW1tYDaIqLqadESC+0WCXrulhYiF7TQmiIiIqPIVFBRgwIAByMvLg729fZnncebYiAYOHIgRI0Zg5cqVkMlk2Lx5M15//XVYWFjgzJkziI6O1jm/Q4cOSEtL0x7fuHED7777LhQKBXJzc6HRaFBQUICcnJxSx3v++efh5OSE/fv3w9LSEgEBAejVq5e2AN+/fz+CgoLKjHfq1KmIi4vTHiuVSsjlcoSGhj72TWMotVqN1NRUhISEQCqVVto45ow5NNyznMN1OYdx8opSr71JfQeEh7evUF/Pch6NhTk0HHNoHOaex5JPyJ+ExbER9e7dG6IoYseOHWjTpg1++uknLFmypNzXDxkyBP/88w+WLl2Khg0bQiaToUOHDrh//36p5wuCgM6dO0OhUEAmkyEoKAgtWrSASqXCb7/9hoMHD2LixIlljieTySCTyfTapVJplfxSVNU45ow5NNyzmMMxwT6I3pSBhz83FAQgpqvPU+fiWcyjsTGHhmMOjcNc81jee+IX8oyodu3a6Nu3LzZv3oxPP/0Ufn5+eOGFFwAATZs2xZEjR3TOP3z4sM7xgQMHMG7cOISHh8Pf3x8ymQx///33Y8fs0qULFAoFFAoFgoKCYGFhgc6dO2PhwoVQqVTo1KmTcW+SiGq8MH83rB7UGi3ljrC2lKCl3BFrBrVGKHerICLizLGxDRw4EL169UJmZiYGDRqkbR8/fjwiIyMRGBiITp06YfPmzcjMzNT5Qp6Pjw82btyIwMBAKJVKTJo0CVZWVo8dLygoCP/73/9gaWmJF198Uds2ceJEtGnTBjY2NpVzo0RUo4X5u3HrNiKiUnDm2MiCg4Ph5OSErKwsDBgwQNvev39/TJ8+HZMnT0br1q3x559/YtSoUTrXrlu3Djdv3sQLL7yAN998E+PGjYOrq+tjx2vevDkcHR3RqlUr2NraAnhQHGs0mseuNyYiIiIifZw5NjILCwtcvXq11NfeeecdvPPOOzpt8+fP1/47ICAA6enpOq+/+uqrOsePbi5iYWGBf//9V6etVatWeucRERER0ZNx5piIiIiIqBiLYyIiIiKiYiyOiYiIiIiKsTgmIiIiIirG4piIiIiIqBiLYyIiIiKiYiyOK4koioiKioKTkxMEQUBERAQiIiJMHRYRERERPQb3Oa4kO3fuRHJyMhQKBby8vGBlZVVlew97enoiNjYWsbGxVTIeERERkblgcVxJsrOz4e7ujo4dOxqtz/v378PS0tJo/RERPY1dmdexMu08fr+RDx9XG7SxFRBu6qCIiIyEyyoqQWRkJMaOHYucnBwIggBPT09ERkbqLKsICgpCTEwMYmJi4ODgABcXF0yfPl1ndtnT0xOzZs3C4MGDYW9vj6ioKADAV199BX9/f8hkMnh6eiIxMVGn3z///BP/+9//IAgCBEGosvsmIvO3K/M6Rm7MwInLebir1uDkFSU+zrJA6ulcU4dGRGQUnDmuBEuXLoW3tzfWrl2L9PR0SCQSTJo0Se+8DRs2YNiwYTh69CiOHTuGqKgoeHh4YMSIEdpzFi1ahBkzZmDmzJkAgIyMDPTr1w/x8fHo378/Dh48iNGjR8PZ2RmRkZH4+uuv0bJlS0RFRen0UxqVSgWVSqU9ViqVAAC1Wg21Wm2MVJSqpO/KHMPcMYeGYw6fzop95/TaRAhYtT8bIc1cTRBRzcf3ouGYQ+Mw9zyW975YHFcCBwcH2NnZQSKRwM3Nrczz5HI5lixZAkEQ4Ofnh1OnTmHJkiU6RW1wcDAmTJigPR44cCC6deuG6dOnAwB8fX1x+vRpLFy4EJGRkXBycoJEIoGdnd1jxwaAuXPnIiEhQa999+7dsLa2ruhtV1hqamqlj2HumEPDMYcVc/aaBID+J1JZ15VISUmp+oDMCN+LhmMOjcNc81hQUFCu81gcm1D79u11lj106NABiYmJ0Gg0kEgkAIDAwECda86cOYM+ffrotHXq1AlJSUk615XH1KlTERcXpz1WKpWQy+UIDQ2Fvb3909xSuajVaqSmpiIkJARSqbTSxjFnzKHhmMOnsy7nME5eUeq1+7nZIzy8gwkiqvn4XjQcc2gc5p7Hkk/In4TFcTVnY2NTaX3LZDLIZDK9dqlUWiW/FFU1jjljDg3HHFbMmGAfRG/KwMOb7wgQMTrIm3k0EN+LhmMOjcNc81jee+IX8kzoyJEjOseHDx+Gj4/PY2d/mzZtigMHDui0HThwAL6+vtrrLC0todFojB8wET3zwvzdsHpQa7SUO8LaUoIWz9ljmF8RujflemMiMg+cOTahnJwcxMXFYeTIkfjll1+wfPlynZ0nSjNhwgS0adMGs2bNQv/+/XHo0CF88MEHWLlypfYcT09P/Pjjj3j99dchk8ng4uJS2bdCRM+QMH83hPk/+E6DWq3mWmMiMissjk1o8ODBuHv3Ltq2bQuJRILx48drt2srywsvvIDPP/8cM2bMwKxZs+Du7o733nsPkZGR2nPee+89jBw5Et7e3lCpVFX28BEiIiKimo7FcSV59Al1KpUKtra2OudIpVIkJSVh1apVpfZx8eLFUttfeeUVvPLKK2WO3b59e5w4caLCMRMRERE967jmuJIVFhbi9OnTOHToEPz9/U0dDhERERE9RoWKY7VaDW9vb5w5c6ay4jE7v/32GwIDA+Hv74/o6GhTh0NEREREj1GhZRVSqRT37t2rrFjMUqtWrUrddFqhUFR9MERERET0WBVeVjFmzBjMnz8fhYWFlREPEREREZHJVPgLeenp6di7dy92796N5s2b6z2k4uuvvzZacEREREREVanCxbGjo+Njd0ogIiIiIqqpKlwcr1+/vjLiMDlRFDFy5Eh8+eWXuHnzJn799Ve0atXK1GE9tbVr12LWrFm4cuUKFi9erLOtHBERERGV7qm2cissLMSePXuwZs0a3L59GwBw9epV5OfnGzW4qrRz504kJydj+/btuHbtGgICArBt2zZTh/VEgiDoxalUKhETE4MpU6bgypUrT3ywCBERERE9UOGZ4z///BM9evRATk4OVCoVQkJCYGdnh/nz50OlUmH16tWVEWely87Ohru7Ozp27GjqUKDRaCAIAiwsnm4b6pycHKjVavTs2RPu7u5Gjo6InnW7Mq9jZdp5/H4jHz6uNmhjKyDc1EERERlJhauv8ePHIzAwEDdv3oSVlZW2/eWXX8bevXuNGlxViYyMxNixY5GTkwNBEODp6QngwT09fBwfH49WrVrh448/hoeHB2xtbTF69GhoNBosWLAAbm5ucHV1xfvvv6/T/+LFi7VfXpTL5Rg9erTOLHtycjIcHR3x3XffoVmzZpDJZMjJycG1a9fQs2dPWFlZoVGjRtiyZQs8PT2RlJQEAKXGmZycjObNmwMAvLy8IAhCmU/aIyKqqF2Z1zFyYwZOXM7DXbUGJ68o8XGWBVJP55o6NCIio6jwzPFPP/2EgwcPwtLSUqfd09MTV65cMVpgVWnp0qXw9vbG2rVrkZ6eDolEAldXV6xfvx49evSARCLRnpudnY0ffvgBO3fuRHZ2Nl599VX88ccf8PX1xf79+3Hw4EEMHToU3bt3R7t27QAAFhYWWLZsGRo1aoQ//vgDo0ePxuTJk7Fy5UptvwUFBZg/fz4++ugjODs7w9XVFX369MHff/8NhUIBqVSKuLg45Ob+//8Bpaen68Vpa2sLuVyO7t274+jRo5DL5ahbt26p961SqaBSqbTHSqUSwIOHvajVaqPm+GElfVfmGOaOOTQcc/h0Vuw7p9cmQsCq/dkIaeZqgohqPr4XDcccGoe557G891Xh4rioqAgajUav/fLly7Czs6tod9WCg4MD7OzsIJFI4Obmpm13dHTUOQYe3P/HH38MOzs7NGvWDF27dkVWVhZSUlJgYWEBPz8/zJ8/H2lpadri+OEvw3l6emL27NmIjo7WKY7VajVWrlyJli1bAgDOnj2LPXv2ID09HYGBgQCAjz76CD4+PtprSoreR+N0dnbWvv5o/A+bO3cuEhIS9Np3794Na2vrxyfNCFJTUyt9DHPHHBqOOayYs9ckAAS99qzrSqSkpFR9QGaE70XDMYfGYa55LO2hbKWpcHEcGhqKpKQkrF27FsCDL4Tl5+dj5syZCA83/1Vnnp6eOn8E1KtXDxKJRGd9cL169XRmePfs2YO5c+fi7NmzUCqVKCwsxL1791BQUKAtQi0tLdGiRQvtNVlZWahVqxZeeOEFbVvjxo1Rp04do93L1KlTERcXpz1WKpWQy+UIDQ2Fvb290cZ5lFqtRmpqKkJCQiCVSittHHPGHBqOOXw663IO4+QVpV67n5s9wsM7mCCimo/vRcMxh8Zh7nks+YT8SSpcHCcmJiIsLAzNmjXDvXv3MGDAAJw7dw4uLi749NNPKxxoTfPom0UQhFLbioqKAAAXL15Er169MGrUKLz//vtwcnLCzz//jGHDhuH+/fva4tjKygqCoD8bU5lkMhlkMpleu1QqrZJfiqoax5wxh4ZjDitmTLAPojdlQBT/v02AiNFB3syjgfheNBxzaBzmmsfy3lOFi+PnnnsOJ06cwGeffYYTJ04gPz8fw4YNw8CBA3W+oFfTSaXSUpePVFRGRgaKioqQmJionV3+/PPPn3idn58fCgsL8euvv6J169YAgPPnz+PmzZuVEicRUXmE+bth9aDWWKnIxrkbt9HY1QZtbW6ie1OuNyYi81Dh4vjHH39Ex44dMXDgQAwcOFDbXlhYiB9//BGdO3c2aoCm4unpib1796JTp06QyWRPvZyhcePGUKvVWL58OXr37o0DBw6Ua7u7Jk2aoHv37oiKisKqVasglUoxYcIEvRlmY8VJRFReYf5uCPN/8H0GtVrNtcZEZFYqvJVb165d8e+//+q15+XloWvXrkYJqjpITExEamoq5HI5AgICnrqfli1bYvHixZg/fz6ef/55bN68GXPnzi3XtZ988gnq1auHzp074+WXX8aIESNgZ2eH2rVrGz1OIiIiIgIEUXx45diTWVhY4MaNG3rbg/3+++8IDAws92JnqrjLly9DLpdjz5496Natm9H7VyqVcHBwQF5eXqV/IS8lJQXh4eFmuaapKjCHhmMOjYN5NBxzaDjm0DjMPY/lrXPKvayib9++AB582SwyMlLni1wajQYnT56sFk+XMyf79u1Dfn4+mjdvjmvXrmHy5Mnw9PQ0m6UrRERERNVNuYtjBwcHAIAoirCzs9P58p2lpSXat2+PESNGGD/CZ5harcY777yDP/74A3Z2dujYsSM2b95sln/NEREREVUH5S6O169fD+DBF8AmTpwIGxubSguKHggLC0NYWJipwyAiIiJ6ZlT4C3kzZ86ETCbDnj17sGbNGty+fRsAcPXqVeTn5xs9QCIiIiKiqlLhrdz+/PNP9OjRAzk5OVCpVAgJCYGdnR3mz58PlUpVrm3KiIiIiIiqowrPHI8fPx6BgYG4efOmzrrjl19+GXv37jVqcEREREREVanCxfFPP/2Ed999F5aWljrtnp6euHLlitECqyyiKCIqKgpOTk4QBAHHjx83dUhGER8fj1atWpk6DCIiIqIarcLFcVFRUamPK758+TLs7OyMElRl2rlzJ5KTk7F9+3Zcu3YNAQEB2LZtm6nDIiIiIqJqoMJrjkNDQ5GUlIS1a9cCeLDvcX5+PmbOnInw8HCjB2hs2dnZcHd3rxZ7Mms0GgiCAAuLCv+NQkRkMrsyr2Nl2nn8fiMfPq42aGMroPr/rz8RUflUuCpLTEzEgQMH0KxZM9y7dw8DBgzQLqmYP39+ZcRoNJGRkRg7dixycnIgCAI8PT0BPFgv/fBxyRKFjz/+GB4eHrC1tcXo0aOh0WiwYMECuLm5wdXVFe+//75O/4sXL0bz5s1hY2MDuVyO0aNH6+zgkZycDEdHR3z33Xdo1qwZZDIZcnJycO3aNfTs2RNWVlZo1KgRtmzZAk9PTyQlJWmvzcnJQZ8+fWBrawt7e3v069cPN27c0LvHjRs3wtPTEw4ODnj99de1u4kQERnDrszrGLkxAycu5+GuWoOTV5T4OMsCqadzTR0aEZFRVHjm+LnnnsOJEyewdetWnDx5Evn5+Rg2bBgGDhyo8wW96mjp0qXw9vbG2rVrkZ6eDolEAldXV6xfvx49evSARCLRnpudnY0ffvgBO3fuRHZ2Nl599VX88ccf8PX1xf79+3Hw4EEMHToU3bt3R7t27QA8eLT2smXL0KhRI/zxxx8YPXo0Jk+ejJUrV2r7LSgowPz58/HRRx/B2dkZrq6u6NOnD/7++28oFApIpVLExcUhN/f//4+mqKhIWxjv378fhYWFGDNmDPr37w+FQqET87Zt27B9+3bcvHkT/fr1w7x58/SK+BIqlQoqlUp7XPLob7VaDbVabZScl6ak78ocw9wxh4ZjDp/Oin3n9NpECFi1PxshzVxNEFHNx/ei4ZhD4zD3PJb3vipcHANArVq1MGjQoKe51KQcHBxgZ2cHiUQCNzc3bbujo6POMfCgIP34449hZ2eHZs2aoWvXrsjKykJKSgosLCzg5+eH+fPnIy0tTVscx8bGaq/39PTE7NmzER0drVMcq9VqrFy5Ei1btgQAnD17Fnv27EF6ejoCAwMBAB999BF8fHy01+zduxenTp3ChQsXIJfLAQCffPIJ/P39kZ6ejjZt2mhjTk5O1q79fvPNN7F3794yi+O5c+ciISFBr3337t2wtrYuX1INkJqaWuljmDvm0HDMYcWcvSYBIOi1Z11XIiUlpeoDMiN8LxqOOTQOc81jQUFBuc57quL46tWr+Pnnn5Gbm4uioiKd18aNG/c0XVY7np6eOl8wrFevHiQSic764Hr16unM8O7Zswdz587F2bNnoVQqUVhYiHv37qGgoEBbbFpaWqJFixbaa7KyslCrVi288MIL2rbGjRujTp062uMzZ85ALpdrC2MAaNasGRwdHXHmzBltcfxozO7u7jrxPWrq1KmIi4vTHiuVSsjlcoSGhsLe3r58iXoKarUaqampCAkJ4aOwnxJzaDjm8OmsyzmMk1eUeu1+bvYID+9ggohqPr4XDcccGoe557HkE/InqXBxnJycjJEjR8LS0hLOzs4QhP+fQRAEwWyK40ffFIIglNpW8sfBxYsX0atXL4waNQrvv/8+nJyc8PPPP2PYsGG4f/++tji2srLSyVllx/zoHy8Pk8lkkMlkpfZTFb8UVTWOOWMODcccVsyYYB9Eb8qAKP5/mwARo4O8mUcD8b1oOObQOMw1j+W9pwp/IW/6/7F353FVVfv/x18bBBQREEXBQnEAU1Q0zTETB8Dx5lBZakZZikOFpJV5M/Rmml0Sx2wUc8i6VubXSMQBK26SYmoXzZlI08xSjorhEfj9oZ6fhAN4DiDH9/Px6PFgr73PWp/9YXPvx3XW3vvll5k0aRJZWVlkZGRw6NAhy38HDx4sdqBlzcnJ6aqPpiuutLQ08vLyiI2NpW3btgQGBvLrr7/e8HMNGzbkwoUL/PDDD5a2/fv3c/LkSct2o0aN+OWXX/jll18sbbt27eLUqVM0btzY6thFRIoqPMiHBUNaEuzniauzI83udGdYwzy6NdJ6YxGxD8WeOc7Ozubhhx+2m8eP+fv7s379ejp06ICLi0uB5QzF0aBBA8xmM3PmzKFPnz6kpKQU6VXad911F926dWP48OG89dZbODk58dxzzxWYYe7WrRtNmzZl8ODBxMXFceHCBUaNGkWnTp0s65RFREpLeJAP4UEX79Mwm81aaywidqXYFe6wYcP4z3/+UxKxlInY2FiSkpLw8/OjRYsWN91PcHAwb775Jq+//jpNmjRh6dKlTJs2rUif/fDDD6lZsyb33Xcf/fr146mnnqJKlSpUrFgRuLg84osvvqBq1arcd999dOvWjXr16vHxxx/fdLwiIiIiUpiRn3/lyrEby83NpXfv3pw7d46mTZsWWr/x5ptv2jTA29Hhw4fx8/Nj3bp1dO3atdTGNZlMeHh4kJWVVeI35CUkJNCzZ0+7XNNUGpRD6ymHtqE8Wk85tJ5yaBv2nsei1jnFXlYxbdo0EhMTadiwIUChG/Kk+DZs2MCZM2do2rQpR48e5fnnn8ff35/77ruvrEMTERERua0UuziOjY3lgw8+ICIiogTCuT2ZzWZeeuklDh48SJUqVWjfvj1Lly61y3+1iYiIiNzKil0cu7i40KFDh5KI5bYVHh5OeHh4WYchIiIictsr9g15zz77LHPmzCmJWEREREREylSxZ46///57NmzYwOrVqwkKCir01f9nn31ms+BEREREREpTsYtjT09P+vfvXxKxiIiIiIiUqWIXxwsXLiyJOMpUfn4+I0aMYMWKFZw8eZIffviB5s2b23ycjIwM6tata+k/OTmZzp07c/LkSTw9PW+6X39/f6KiooiKirJZrCIiIiK3I/t4zZ2V1qxZQ3x8PKtXr+bo0aO0aNGClStXlvi47du35+jRo3h4eFjVz5YtWxg+fLhl2zCMUolfRERExN4Ue+YYYMWKFXzyySdkZmZy/vz5Avu2bdtmk8BK04EDB/D19aV9+/alOq6zszM+Pj5W9+Pt7W2DaERKT2L6MeZv3M/e384QWNONUZ0bWF5HLCIiUpaKPXM8e/ZsHn/8cWrWrMkPP/xA69atqVatGgcPHqRHjx4lEWOJioiI4OmnnyYzMxPDMPD39wegX79+BbYPHDjA/fffT82aNXFzc+Oee+5h3bp1Bfq62oytp6cn8fHxVx07OTkZwzA4deoUAPHx8Xh6erJ69WoaNmyIq6srDzzwANnZ2SxatAh/f3+qVq3KM888Q25urqUff39/4uLiLD9fLX6RW0Vi+jFGLE5jx+Eszplz2XE4i8glaSSmHyvr0ERERIo/czx//nzeeecdHnnkEeLj43n++eepV68ekyZN4s8//yyJGEvUrFmzqF+/Pu+88w5btmzB0dGRGjVqsHDhQrp3746joyMAZ86coWfPnkydOhUXFxc+/PBD+vTpw549e6hdu7bN4snOzmb27NksX76c06dP079/f/r164enpycJCQkcPHiQAQMG0KFDBwYOHFjo81u2bLlq/FeTk5NDTk6OZdtkMgEXX0piNpttdk5/d7nvkhzD3pXnHM7bsK9QW34+zNu4jy6B1UotjvKcw1uJ8mg95dB6yqFt2Hsei3pexS6OMzMzLcsPKlWqxOnTpwF49NFHadu2LXPnzi1ul2XKw8ODKlWq4OjoWGCJg6enZ4Ht4OBggoODLdv/+te/+Pzzz1m1ahVjxoyxWTxms5m33nqL+vXrA/DAAw+wePFifvvtN9zc3GjcuDGdO3dm48aNVy2OLy+x+Hv8VzNt2jQmT55cqH3t2rW4urra4GyuLykpqcTHsHflMYc/HXUECr9q/qdfs0hISCj1eMpjDm9FyqP1lEPrKYe2Ya95zM7OLtJxxS6OfXx8+PPPP6lTpw61a9dm8+bNBAcHc+jQIfLz84sdaHlx5swZYmJi+PLLLzl69CgXLlzg3LlzZGZm2nQcV1dXS2EMULNmTfz9/XFzcyvQdvz4cavHmjBhAtHR0ZZtk8mEn58fYWFhuLu7W93/tZjNZpKSkggNDdUrsm9Sec7h+5mb2XnEVKj9rloe9OzZttTiKM85vJUoj9ZTDq2nHNqGvefx8jfkN1Ls4rhLly6sWrWKFi1a8PjjjzN27FhWrFjB1q1b7fr5x+PGjSMpKYl///vfNGjQgEqVKvHAAw8UuCHRMIxC/0Ao7lcTf78YDcO4alteXl4xz6AwFxcXXFxcrhpDafxRlNY49qw85nB0lwAil6Rx5Z+KYcCYzgFlci7lMYe3IuXResqh9ZRD27DXPBb1nIpdHL/zzjuWwmz06NFUq1aN//73v/zjH/9gxIgRxe3uluTk5FTghjeAlJQUIiIi6NevH3BxJjkjI6PAMd7e3hw9etSyvW/fviJP4dvS1eIXuVWEB/mwYEhL5icfYN9vpwmoWYXRIfUJ09MqRETkFlCs4vjChQu89tprPPHEE9x5550APPzwwzz88MMlElxZ8ff3Z/369XTo0AEXFxeqVq1KQEAAn332GX369MEwDF5++eVCs7ddunRh7ty5tGvXjtzcXF544YUy+ZfX1eIXuZWEB/no0W0iInJLKtaj3CpUqMCMGTO4cOFCScVzS4iNjSUpKQk/Pz9atGgBwJtvvknVqlVp3749ffr0ITw8nLvvvrvQ5/z8/OjYsSODBg1i3LhxpXJjW1HiFxEREZEbM/KLeRfd/fffT//+/XnsscdKKiYpIyaTCQ8PD7Kyskr8hryEhAR69uxpl2uaSoNyaD3l0DaUR+sph9ZTDm3D3vNY1Dqn2GuOe/TowYsvvsiPP/5Iy5YtqVy5coH9//jHP4ofrYiIiIjILaDYxfGoUaOAi8sM/s4wDN0IJiIiIiLlVrGLY1s8QkxERERE5FZUrBvyRERERETsWbFnjgHOnj3Lpk2byMzMLPASDIBnnnnGJoGJiIiIiJS2YhfHP/zwAz179iQ7O5uzZ8/i5eXFiRMncHV1pUaNGiqORURERKTcKvayirFjx9KnTx9OnjxJpUqV2Lx5Mz///DMtW7bk3//+d7H6ys/PZ/jw4Xh5eWEYBtu3by9uOCIiIiIiNlPs4nj79u0899xzODg44OjoSE5ODn5+fsyYMYOXXnqpWH2tWbOG+Ph4Vq9ezdGjR2nSpElxwykXQkJCiIqKKuswREREROQGir2swsnJCQeHizV1jRo1yMzMpFGjRnh4ePDLL78Uq68DBw7g6+tL+/btixuG3cnPzyc3N5cKFW5qGbjdSkw/xvyN+9n72xkCa7oxqnMDvXZYRERESkyxZ45btGjBli1bAOjUqROTJk1i6dKlREVFFWvmNyIigqeffprMzEwMw8Df35+QkBCefvppoqKiqFq1KjVr1uTdd9/l7NmzPP7441SpUoUGDRrw1VdfWfrJzc1l2LBh1K1bl0qVKtGwYUNmzZpl2f/XX38RFBTE8OHDLW0HDhygSpUqfPDBB5hMJipVqlSgT4DPP/+cKlWqkJ2dTfv27XnhhRcK7P/9999xcnLi66+/BmD+/PkEBARQsWJFatasyQMPPGA5z02bNjFr1iwMw8AwDDIyMkhOTsYwDL766itatmyJi4sL3377LXl5eUybNs1yPsHBwaxYscIy7smTJxk8eDDe3t5UqlSJgIAAFi5cCMD58+cZM2YMvr6+VKxYkTp16jBt2rQi/05uNYnpxxixOI0dh7M4Z85lx+EsIpekkZh+rKxDExERETtV7GnK1157jdOnTwMwdepUhg4dysiRIwkICOCDDz4ocj+zZs2ifv36vPPOO2zZsgVHR0cefPBBFi1axPPPP8/333/Pxx9/zMiRI/n888/p168fL730EjNnzuTRRx8lMzMTV1dX8vLyuPPOO/nPf/5DtWrV+O9//8vw4cPx9fXloYceomLFiixdupQ2bdrQq1cvevfuzZAhQwgNDeWJJ54AoHfv3ixbtowePXpY4lu6dCl9+/bF1dWVwYMHM2PGDKZPn45hGAB8/PHH1KpVi44dO7J161aeeeYZFi9eTPv27fnzzz/55ptvLOe5d+9emjRpwpQpUwDw9vYmIyMDgBdffJF///vf1KtXj6pVqzJt2jSWLFnCggULCAgI4Ouvv2bIkCF4e3vTqVMnXn75ZXbt2sVXX31F9erV2b9/P+fOnQNg9uzZrFq1ik8++YTatWvzyy+/XHc2Pycnh5ycHMu2yWQCLr4+0mw2F/l3WVyX+77RGPM27CvUlp8P8zbuo0tgtRKJrbwoag7l2pRD21AeraccWk85tA17z2NRz8vIz8/PL+FYrikuLo64uDhLoRgSEkJubq6lsMzNzcXDw4P+/fvz4YcfAnDs2DF8fX357rvvaNu27VX7HTNmDMeOHSsw4/rGG28wY8YMHn74YT799FN+/PFHqlW7WGCtXLmSRx99lN9++w1XV1dMJhM1a9bk888/p3v37vz+++/UqlWLDRs20LFjRwDat2/Pfffdx/Tp0/nss894/PHHOXz4MFWqVCkUT0hICM2bNycuLs7SlpycTOfOnVm5ciX3338/cLFY9fLyYt26dbRr185y7JNPPkl2djbLli3jH//4B9WrV7/qP0SeeeYZ0tPTWbdunaWIv56YmBgmT55cqH3ZsmW4urre8PMlbXyqI+fzCp+Hs0M+b7TRmxhFRESk6LKzsxk0aBBZWVm4u7tf87ibXuB6/Phx9uzZA8Bdd92Ft7f3zXZVQLNmzSw/Ozo6Uq1aNZo2bWppq1mzpmX8y+bNm8cHH3xAZmYm586d4/z58zRv3rxAv8899xwrV65k7ty5fPXVV5bCGKBnz544OTmxatUqS/Hs7u5Ot27dgIszvWFhYSxdupSOHTty6NAhvvvuO95++20AQkNDqVOnDvXq1aN79+50796dfv36FanAbNWqleXn/fv3k52dTWhoaIFjzp8/T4sWLQAYOXIkAwYMYNu2bYSFhdG3b1/Lmu2IiAhCQ0Np2LAh3bt3p3fv3oSFhV1z7AkTJhAdHW3ZNplM+Pn5ERYWdt2Lxlpms5mkpCRCQ0NxcnK65nHvZ25m5xFTofa7annQs+fV/2F0uyhqDuXalEPbUB6tpxxaTzm0DXvP4+VvyG+k2MXx6dOnGTVqFMuXLyc39+LsnaOjIwMHDmTevHl4eHgUt8sC/v7LMAyjQNvlGdHLr7Fevnw548aNIzY2lnbt2lGlShXeeOMNUlNTC/Rz/Phx9u7di6OjI/v27aN79+6Wfc7OzjzwwAMsW7aMhx9+mGXLljFw4MACN8cNHjyYZ555hjlz5rBs2TKaNm1qKdqrVKnCtm3bSE5OZu3atUyaNImYmBi2bNmCp6fndc+3cuXKlp/PnDkDwJdffskdd9xR4DgXFxcAevTowc8//0xCQgJJSUl07dqV0aNH8+9//5u7776bQ4cO8dVXX7Fu3ToeeughunXrVmAG/e99Xu73Sk5OTqXyR3GjcUZ3CSBySRpXfrdhGDCmc4Bd/tHejNL6Xdkz5dA2lEfrKYfWUw5tw17zWNRzKvYNeU8++SSpqamsXr2aU6dOcerUKVavXs3WrVsZMWJEsQO1VkpKCu3bt2fUqFG0aNGCBg0acODAgULHPfHEEzRt2pRFixbxwgsvsHv37gL7Bw8ezJo1a0hPT2fDhg0MHjy4wP7777+fv/76izVr1rBs2bJC+ytUqEC3bt2YMWMGO3fuJCMjgw0bNgAXi+/L/5C4nsaNG+Pi4kJmZiYNGjQo8J+fn5/lOG9vbx577DGWLFlCXFwc77zzjmWfu7s7AwcO5N133+Xjjz/m008/5c8//7xxIm9B4UE+LBjSkmA/T1ydHQn28+TtIS0J09MqREREpIQUe+Z49erVJCYmcu+991rawsPDeffddwvMxpaWgIAAPvzwQxITE6lbty6LFy9my5Yt1K1b13LMvHnz+O6779i5cyd+fn58+eWXDB48mM2bN+Ps7AzAfffdh4+PD4MHD6Zu3bq0adOmwDiVK1emb9++vPzyy+zevZtHHnnEsm/16tUcPHiQ++67j6pVq5KQkEBeXh4NGzYEwN/fn9TUVDIyMnBzc8PLy+uq51KlShXGjRvH2LFjycvL49577yUrK4uUlBTc3d157LHHmDRpEi1btiQoKIicnBxWr15No0aNAHjzzTfx9fWlRYsWODg48J///AcfH58bzl7fysKDfPToNhERESk1xZ45rlat2lWXTnh4eFC1alWbBFUcI0aMoH///gwcOJA2bdrwxx9/MGrUKMv+n376ifHjxzN//nzL7Ov8+fM5ceIEL7/8suU4wzB45JFH2LFjR6FZ4csGDx7Mjh076NixI7Vr17a0e3p68tlnn9GlSxcaNWrEggUL+OijjwgKCgJg3LhxODo60rhxY7y9vcnMzLzm+fzrX//i5ZdfZtq0aTRq1Iju3bvz5ZdfWop9Z2dnJkyYQLNmzbjvvvtwdHRk+fLlwMXiesaMGbRq1Yp77rmHjIwMEhISLM+lFhEREZHrK/bTKt555x3+85//sHjxYnx8Ls7oHTt2jMcee4z+/fuXydIKsQ2TyYSHh8cN7+K0ltlsJiEhwXIjpBSfcmg95dA2lEfrKYfWUw5tw97zWNQ6p9jLKt566y32799P7dq1LbOnmZmZuLi48Pvvv1ue4ACwbdu2mwhdRERERKRsFLs47tu3bwmEISIiIiJS9opdHL/yyislEYeIiIiISJnTnVoiIiIiIpeoOBYRERERuUTFsYiIiIjIJSqORUREREQusdviOD8/n+HDh+Pl5YVhGGzfvv26xycnJ2MYBqdOnSqV+ERERETk1lPsp1Xk5uYSHx/P+vXrOX78OHl5eQX2b9iwwWbBWWPNmjXEx8eTnJxMvXr1qF69epnFEh8fT1RUlApvESmyxPRjzN+4n72/nSGwphujOjfQq9RFREpBsYvjZ599lvj4eHr16kWTJk0wDKMk4rLagQMH8PX1pX379mUdSpGdP38eZ2fnsg5DRMpYYvoxRixOs2zvOJxF5JI0FgxpqQJZRKSEFbs4Xr58OZ988gk9e/YsiXhsIiIigkWLFgFgGAZ16tRhz549jB8/nuXLl2MymWjVqhUzZ87knnvuKfDZtLQ0XnjhBXbt2kXz5s1ZuHAhDRs2vOGYO3bsICoqiq1bt2IYBgEBAbz99tucOXOGxx9/3BILXHxWdExMDP7+/gwbNox9+/axcuVK+vfvT3x8PN9++y0TJkxg69atVK9enX79+jFt2jQqV64MwPz585k5cya//PILHh4edOzYkRUrVgCwYsUKJk+ezP79+3F1daVFixZ88cUXls9eKScnh5ycHMu2yWQCLr4+0mw2FzftRXa575Icw94ph9a7lXM4b8O+Qm35+TBv4z66BFYrg4iu7VbOY3mhHFpPObQNe89jUc/LyM/Pzy9Ox7Vq1SI5OZnAwMCbCqw0ZGVlMXv2bN555x22bNmCo6Mjr776KitWrOC9996jTp06zJgxg1WrVrF//368vLxITk6mc+fOtGnThtdffx1vb28iIyPJzc0lJSXlhmM2adKEFi1aMHHiRBwdHdm+fTuBgYE0atSIt956i0mTJrFnzx4A3NzccHNzw9/fn5MnTzJp0qQCbx4MDg7m1VdfpVevXvz++++MGTOG4OBgFi5cyNatW2nbti2LFy+mffv2/Pnnn3zzzTc888wzHD16lNq1azNjxgz69evH6dOn+eabbxg6dChubm6FYo6JiWHy5MmF2pctW4arq+vN/wJExCrjUx05n1f4Wzlnh3zeaJNbBhGJiJR/2dnZDBo0iKysLNzd3a95XLGL49jYWA4ePMjcuXNv2SUVAHFxccTFxZGRkcHZs2epWrUq8fHxDBo0CLj4rwd/f3+ioqIYP368pThet24dXbt2BSAhIYFevXpx7tw5KlaseN3x3N3dmTNnDo899lihfddac+zv70+LFi34/PPPLW1PPvkkjo6OvP3225a2b7/9lk6dOnH27FkSEhJ4/PHHOXz4MFWqVCnQ37Zt22jZsiUZGRnUqVPnhjm62syxn58fJ06cuO5FYy2z2UxSUhKhoaE4OTmV2Dj2TDm03q2cwwELNrPziKlQe7M73fl0RNsyiOjabuU8lhfKofWUQ9uw9zyaTCaqV69+w+K42Msqvv32WzZu3MhXX31FUFBQoeR99tlnxY+2hB04cACz2UyHDh0sbU5OTrRu3Zrdu3cXOLZZs2aWn319fQE4fvw4tWvXvu4Y0dHRPPnkkyxevJhu3brx4IMPUr9+/RvG1qpVqwLbO3bsYOfOnSxdutTSlp+fT15eHocOHSI0NJQ6depQr149unfvTvfu3enXrx+urq4EBwfTtWtXmjZtSnh4OGFhYTzwwANUrVr1qmO7uLjg4uJSqN3JyalU/ihKaxx7phxa71bM4eguAUQuSePKqQvDgDGdA265WC+7FfNY3iiH1lMObcNe81jUcyr2o9w8PT3p168fnTp1onr16nh4eBT4r7y7MnGXZ8b//kSOq4mJiSE9PZ1evXqxYcMGGjduXGBG+Fr+vhb4zJkzjBgxgu3bt1v+27FjB/v27aN+/fpUqVKFbdu28dFHH+Hr68ukSZMIDg7m1KlTODo6kpSUxFdffUXjxo2ZM2cODRs25NChQ8XMgoiUpfAgHxYMaUmwnyeuzo4E+3ny9pCWhOlmPBGRElfsmeOFCxeWRBwlqn79+jg7O5OSkmJZbmA2m9myZQtRUVE2GycwMJDAwEDGjh3LI488wsKFC+nXrx/Ozs7k5hZtneDdd9/Nrl27aNCgwTWPqVChAt26daNbt2688soreHp6smHDBvr3749hGHTo0IEOHTowadIk6tSpw+eff050dLStTlNESkF4kI+eTCEiUgaKXRxf9vvvv1tuMGvYsCHe3t42C8rWKleuzMiRIxk/fjxeXl6Wm9ays7MZNmyY1f2fO3eO8ePH88ADD1C3bl0OHz7Mli1bGDBgAHBxbfGZM2dYv349wcHBuLq6XvOGtxdeeIG2bdsyZswYnnzySSpXrsyuXbtISkpi7ty5rF69moMHD3LfffdRtWpVEhISyMvLo2HDhqSmprJ+/XrCwsKoUaMGqamp/P777zRq1MjqcxQRERG5HRS7OD579ixPP/00H374oWW5gaOjI0OHDmXOnDm37FMOpk+fTl5eHo8++iinT5+mVatWJCYmXnM9bnE4Ojryxx9/MHToUH777TeqV69O//79LU+CaN++PZGRkQwcOJA//vjD8ii3q2nWrBmbNm1i4sSJdOzYkfz8fOrXr8/AgQOBi8taPvvsM2JiYvjrr78ICAjgo48+IigoiN27d/P1118TFxeHyWSiTp06xMbG0qNHD6vPUUREROR2UOynVYwYMYJ169Yxd+5cyw1u3377Lc888wyhoaG89dZbJRKolDyTyYSHh8cN7+K0ltlsJiEhgZ49e9rlgv/SoBxaTzm0DeXResqh9ZRD27D3PBa1zin2zPGnn37KihUrCAkJsbT17NmTSpUq8dBDD6k4FhEREZFyq9hPq8jOzqZmzZqF2mvUqEF2drZNgroVBQUFWV7e8ff/rnzsmoiIiIiUX8WeOW7Xrh2vvPIKH374oeXFGOfOnWPy5Mm0a9fO5gHeKhISEq752sGr/WNBRERERMqfYhfHs2bNIjw8nDvvvJPg4GDg4osrKlasSGJios0DvFUU5Y1zIiIiIlK+Fbs4btKkCfv27WPp0qX89NNPADzyyCMMHjyYSpUq2TxAEREREZHSclPPOXZ1deWpp56ydSy3jfz8fEaMGMGKFSs4efIkP/zwA82bNy/rsERERERue0UqjletWkWPHj1wcnJi1apV1z32H//4h00Cs2dr1qwhPj6e5ORk6tWrR/Xq1W3Sb3JyMtHR0aSnp+Pn58c///lPIiIibNK3iIiIyO2gSMVx3759OXbsGDVq1KBv377XPM4wjCK/Jvl2duDAAXx9fWnfvr3N+jx06BC9evUiMjKSpUuXsn79ep588kl8fX0JDw+32ThSthLTjzF/4372/nYab2dHnPyP0zP4jrIOS0RExG4UqTi+/Ca8v/8sxRcREcGiRYuAi/+YqFOnDv7+/jRr1oyKFSvy3nvv4ezsTGRkZIG36J06dYpx48bxxRdfkJOTQ6tWrZg5c6blpsgFCxZQt25dYmNjAWjUqBHffvstM2fOVHFsJxLTjzFicZplO9NsMHr5dhZUcCQ8yKcMIxMREbEfxV5z/OGHHzJw4EBcXFwKtJ8/f57ly5czdOhQmwVnj2bNmkX9+vV555132LJlC46Ojjz44IMsWrSI6OhoUlNT+e6774iIiKBDhw6EhoYC8OCDD1KpUiW++uorPDw8ePvtt+natSt79+7Fy8uL7777jm7duhUYKzw8nKioqGvGkpOTQ05OjmXbZDIBF9+Qc63H1tnC5b5Lcgx7NG/DvkJt+fkwb+M+ugRWK4OIyjddh7ahPFpPObSecmgb9p7Hop5XsV8f7ejoyNGjR6lRo0aB9j/++IMaNWpoWUURxMXFERcXR0ZGBgAhISHk5ubyzTffWI5p3bo1Xbp0Yfr06Xz77bf06tWL48ePF/hHSYMGDXj++ecZPnw4gYGBPP7440yYMMGyPyEhgV69epGdnX3VJ4nExMQwefLkQu3Lli3D1dXVhmcstjA+1ZHzeUahdmeHfN5oo787ERGR68nOzmbQoEG2f310fn4+hlH4/6APHz6Mh4dHcbuTS5o1a1Zg29fXl+PHjwMXnyN95swZqlUrODt47tw5Dhw4cNNjTpgwgejoaMu2yWTCz8+PsLCw61401jKbzSQlJREaGmqX724vKe9nbmbnEVOh9rtqedCzZ9syiKh803VoG8qj9ZRD6ymHtmHvebz8DfmNFLk4btGiBYZhYBgGXbt2pUKF///R3NxcDh06RPfu3YsfqQAUuggNw7Cs7z5z5gy+vr4kJycX+pynpycAPj4+/PbbbwX2/fbbb7i7u1/z+dMuLi6FlsdcjqU0/ihKaxx7MbpLAJFL0rjyux7DgDGdA5RHK+g6tA3l0XrKofWUQ9uw1zwW9ZyKXBxffkrF9u3bCQ8Px83NzbLP2dkZf39/BgwYULwopUjuvvtujh07RoUKFfD397/qMe3atSMhIaFAW1JSkl2/0vt2Ex7kw4IhLZmffIB9v53G2/kCL/6jBWG6GU9ERMRmilwcv/LKKwD4+/szcOBAKlasWGJBSUHdunWjXbt29O3blxkzZhAYGMivv/7Kl19+Sb9+/WjVqhWRkZHMnTuX559/nieeeIINGzbwySef8OWXX5Z1+GJD4UE+hAf5YDabSUhIoFujGjf+kIiIiBSZQ3E/8Nhjj6kwLmWGYZCQkMB9993H448/TmBgIA8//DA///wzNWvWBKBu3bp8+eWXJCUlERwcTGxsLO+9954e4yYiIiJSDMW+IS83N5eZM2fyySefkJmZyfnz5wvs//PPP20WnL2Kiooq8Ii1q60lXrlyZYHtKlWqMHv2bGbPnn3NfkNCQvjhhx9sFKWIiIjI7afYM8eTJ0/mzTffZODAgWRlZREdHU3//v1xcHAo8NIKEREREZHyptjF8dKlS3n33Xd57rnnqFChAo888gjvvfcekyZNYvPmzSURo4iIiIhIqSh2cXzs2DGaNm0KgJubG1lZWQD07t1bN3+JiIiISLlW7OL4zjvv5OjRowDUr1+ftWvXArBly5arPjNXRERERKS8KHZx3K9fP9avXw/A008/zcsvv0xAQABDhw7liSeesHmAIiIiIiKlpdhPq5g+fbrl54EDB1K7dm2+++47AgIC6NOnj02DExEREREpTcUujv+uXbt2egubiIiIiNiFYi+rWLRoUYEb755//nk8PT1p3749P//8s02Du1n5+fkMHz4cLy8vDMNg+/btZR0S8fHxeHp6lnUYIiIiInIdxS6OX3vtNSpVqgTAd999x9y5c5kxYwbVq1dn7NixNg/wZqxZs4b4+HhWr17N0aNHadKkSVmHJCJidxLTjzFgwWbGpzoyYMFmEtOPlXVIIiJWK/ayil9++YUGDRoAF9/i9sADDzB8+HA6dOhASEiIreO7KQcOHMDX15f27duXdSi3BLPZjJOTU1mHISJ2JDH9GCMWp13aMth5xETkkjQWDGlJeJBPmcYmImKNYhfHbm5u/PHHH9SuXZu1a9cSHR0NQMWKFTl37pzNAyyuiIgIFi1aBIBhGNSpUwd/f3/L7PHixYtxcnJi5MiRTJkyBcMwAJg/fz4zZ87kl19+wcPDg44dO7JixQoA8vLyeP3113nnnXc4duwYgYGBvPzyyzzwwAPAxdc/d+7cmdWrVzNhwgT27t1L8+bNee+99wrNWq9cuZLx48fzyy+/0KlTJ9577z38/Pws+6dPn87MmTPJzs7moYcewtvbmzVr1liWhmzZsoWXXnqJH374AbPZTPPmzZk5cyZ33323pQ/DMJg/fz5fffUV69evZ/z48Vd9e2FOTg45OTmWbZPJBFwsps1mszW/huu63HdJjmHvlEPrKYfWmbdhX6G2/HyYt3EfXQKrlUFE5ZeuResph7Zh73ks6nkZ+fn5+cXpePDgwfz000+0aNGCjz76iMzMTKpVq8aqVat46aWX+N///ndTAdtKVlYWs2fP5p133mHLli04Ojry4IMPkpaWxrBhwxg5ciRbt25l+PDhxMXF8dRTT7F161batm3L4sWLad++PX/++SfffPMNzzzzDABTp05lyZIlxMXFERAQwNdff01kZCSJiYl06tTJUhw3atSIWbNm4ePjY8nF3r17cXJyIj4+nuHDhxMcHMzs2bNxdnZm1KhRVKhQgZSUFAA++eQThg4dyrx587j33ntZvHgxs2fPpl69epbieMOGDfz666+0atWK/Px8YmNjWb16Nfv27aNKlSrAxeK4Ro0aTJ8+nU6dOlGhQgVq165dKFcxMTFMnjy5UPuyZctwdXUtod+QiNiD8amOnM8zCrU7O+TzRpvcMohIROT6srOzGTRoEFlZWbi7u1/zuGIXx6dOneKf//wnv/zyCyNHjqR79+4AvPLKKzg7OzNx4kTrIreBuLg44uLiyMjIACAkJITjx4+Tnp5umSl+8cUXWbVqFbt27eKzzz7j8ccf5/Dhw5YC87KcnBy8vLxYt25dgadyPPnkk2RnZ7Ns2TJLcbx8+XIGDhwIwJ9//smdd95JfHw8Dz30EPHx8Tz++ONs3ryZNm3aAPDTTz/RqFEjUlNTad26Ne3bt6dFixbMmzfPMk7btm3566+/rnlTYV5eHp6enixbtozevXsDF4vjqKgoZs6ced08XW3m2M/PjxMnTlz3orGW2WwmKSmJ0NBQLfe4Scqh9ZRD6wxYsJmdR0yF2pvd6c6nI9qWQUTll65F6ymHtmHveTSZTFSvXv2GxXGxl1V4enoyd+7cQu1Xm4G8lbRt29ZSGMPFR9DFxsaSm5tLaGgoderUoV69enTv3p3u3bvTr18/XF1d2b9/P9nZ2YSGhhbo7/z587Ro0aJA25XFs5eXFw0bNmT37t2WtgoVKnDPPfdYtu+66y48PT3ZvXs3rVu3Zvfu3URGRhbqc+PGjZbt3377jX/+858kJydz/PhxcnNzyc7OJjMzs8DnWrVqdcOcuLi4XPWthk5OTqXyR1Fa49gz5dB6yuHNGd0lgMglaVw5vWIYMKZzgPJ5k3QtWk85tA17zWNRz6lIxfHOnTtp0qQJDg4O7Ny587rHNmvWrEgD30qqVKnCtm3bSE5OZu3atUyaNImYmBi2bNnCmTNnAPjyyy+54447CnyuLF6X/dhjj/HHH38wa9Ys6tSpg4uLC+3ateP8+fMFjqtcuXKpxyYit4/wIB8WDGnJvI37+OnXLO6q5cGYzgGE6WY8ESnnilQcN2/enGPHjlGjRg2aN2+OYRhcuRrj8rZhGOTm3pprzVJTUwtsb968mYCAABwdHYGLs7rdunWjW7duvPLKK3h6erJhwwZCQ0NxcXEhMzOTTp06XXeMzZs3W9b2njx5kr1799KoUSPL/gsXLrB161Zat24NwJ49ezh16pTlmMtLLIYOHVqgzyulpKQwf/58evbsCVx8esiJEyduJiUiIlYJD/KhS2A1EhIS6NmzrV3ONInI7adIxfGhQ4fw9va2/FweZWZmEh0dzYgRI9i2bRtz5swhNjYWgNWrV3Pw4EHuu+8+qlatSkJCAnl5eTRs2JAqVaowbtw4xo4dS15eHvfeey9ZWVmkpKTg7u7OY489ZhljypQpVKtWjZo1azJx4kSqV69O3759LfudnJx4+umnmT17NhUqVGDMmDG0bdvWUiw/++yzRERE0KpVKzp06MDSpUtJT0+nXr16lj4CAgJYvHgxrVq1wmQyMX78eMtzp0VERETEOkUqjuvUqXPVn8uToUOHcu7cOVq3bo2joyPPPvssw4cPBy6uo/7ss8+IiYnhr7/+IiAggI8++oigoCAA/vWvf+Ht7c20adM4ePAgnp6e3H333bz00ksFxpg+fTrPPvss+/bto3nz5vzf//0fzs7Olv2urq688MILDBo0iCNHjtCxY0fef/99y/6BAwdy4MABnn/+ef766y8GDBjAyJEjSUxMtBzz/vvvM3z4cO6++278/Px47bXXGDduXEmmTkREROS2Uewb8gB+/fVXvv32W44fP05eXl6BfZcff1aWoqKiiIqKKtDm5OREXFwcb731VqHj7733XpKTk6/Zn2EYPPvsszz77LPXHffee++95qPsIiIiiIiIAKB///7X7OOll14qUHT//fnELVq0YMuWLQXaLj9v+bJiPoBERERERC4pdnEcHx/PiBEjcHZ2plq1agWeAGEYxi1RHIuIiIiI3IxiF8cvv/wykyZNYsKECTg4OJRETCIiIiIiZaLYxXF2djYPP/xwuSqMr7dkwhZCQkJKbClDTEzMVV/9LCIiIiK2V+wKd9iwYfznP/8piVhERERERMpUsWeOp02bRu/evVmzZg1NmzYt9FzLN99802bBiYiIiIiUppsqjhMTE2nYsCFAoRvyRERERETKq2IXx7GxsXzwwQeWx5LZo/z8fEaMGMGKFSs4efIkP/zwA82bNy/rsERERESkhBW7OHZxcaFDhw4lEcstY82aNcTHx5OcnEy9evWoXr36NY+NiIjg1KlTrFy5svQCFBEREZESUezi+Nlnn2XOnDnMnj27JOK5JRw4cABfX1/at29vsz7NZnOh9dkiIuVRYvox5m/cz97fzhBQozL3uBn0LOugRERspNhPq/j+++9ZtGgR9erVo0+fPvTv37/Af+VdREQETz/9NJmZmRiGgb+/PytWrKBp06ZUqlSJatWq0a1bN86ePUtMTAyLFi3iiy++wDAMDMMgOTmZjIwMDMPg448/plOnTlSsWJGlS5cC8MEHHxAUFISLiwu+vr6MGTPGMvabb75J06ZNqVy5Mn5+fowaNYozZ85Y9v/888/06dOHqlWrUrlyZYKCgkhISLDs/9///kePHj1wc3OjZs2aPProo5w4caL0kicidi8x/RgjFqex43AW58y57Dxi4oM9DiTtOl7WoYmI2ESxZ449PT3togi+llmzZlG/fn3eeecdtmzZgtlspl69esyYMYN+/fpx+vRpvvnmG/Lz8xk3bhy7d+/GZDKxcOFCALy8vPj1118BePHFF4mNjaVFixZUrFiRt956i+joaKZPn06PHj3IysoiJSXFMraDgwOzZ8+mbt26HDx4kFGjRvH8888zf/58AEaPHs358+f5+uuvqVy5Mrt27cLNzQ2AU6dO0aVLF5588klmzpzJuXPneOGFF3jooYfYsGHDVc81JyeHnJwcy7bJZAIuznKbzWbbJ/eSy32X5Bj2Tjm0nnJ4c+Zt2FeoLR+DtzYdILRxjTKIqPzTtWg95dA27D2PRT0vI7+k3l5RjsXFxREXF0dGRgbbtm2jZcuWZGRkUKdOnULHXm3NcUZGBnXr1iUuLo5nn33W0n7HHXfw+OOP8+qrrxYpjhUrVhAZGWmZ/W3WrBkDBgzglVdeKXTsq6++yjfffENiYqKl7fDhw/j5+bFnzx4CAwMLfSYmJobJkycXal+2bBmurq5FilFEbi/jUx05n1f4yUTODvm80Sa3DCISESma7OxsBg0aRFZWFu7u7tc8rtgzx7eb4OBgunbtStOmTQkPDycsLIwHHniAqlWr3vCzrVq1svx8/Phxfv31V7p27XrN49etW8e0adP46aefMJlMXLhwgb/++ovs7GxcXV155plnGDlyJGvXrqVbt24MGDCAZs2aAbBjxw42btxomUm+0oEDB65aHE+YMIHo6GjLtslkws/Pj7CwsOteNNYym80kJSURGhqqddg3STm0nnJ4c97P3MzOI6ZC7Q193OnZs10ZRFT+6Vq0nnJoG/aex8vfkN9IkYrju+++m/Xr11O1alVatGhx3ecZb9u2rWgRlhOOjo4kJSXx3//+l7Vr1zJnzhwmTpxIamoqdevWve5nK1eubPm5UqVK1z02IyOD3r17M3LkSKZOnYqXlxfffvstw4YN4/z587i6uvLkk08SHh7Ol19+ydq1a5k2bRqxsbE8/fTTnDlzhj59+vD6668X6tvX1/eqY7q4uODi4lKo3cnJqVT+KEprHHumHFpPOSye0V0CiFySxpXfORrkMyqkvvJoJV2L1lMObcNe81jUcypScXz//fdbiqj777//tnvZh2EYdOjQgQ4dOjBp0iTq1KnD559/TnR0NM7OzuTm3virxCpVquDv78/69evp3Llzof1paWnk5eURGxuLg8PF+yQ/+eSTQsf5+fkRGRlJZGQkEyZM4N133+Xpp5/m7rvv5tNPP8Xf358KFfSFgIiUjPAgHxYMacn85APs++00DWpUpnXlk3RrpPXGImIfilRFXbnGNSYmpqRiuSWlpqayfv16wsLCqFGjBqmpqfz+++80atQIAH9/fxITE9mzZw/VqlXDw8Pjmn3FxMQQGRlJjRo16NGjB6dPnyYlJYWnn36aBg0aYDabmTNnDn369CElJYUFCxYU+HxUVBQ9evQgMDCQkydPsnHjRksco0eP5t133+WRRx7h+eefx8vLi/3797N8+XLee+89HB0dSy5JInJbCQ/yITzIB7j4NeyVT80RESnviv0ot3r16vHHH38Uaj916hT16tWzSVC3End3d77++mt69uxJYGAg//znP4mNjaVHjx4APPXUUzRs2JBWrVrh7e1d4OkTf/fYY48RFxfH/PnzCQoKonfv3uzbd/HO7+DgYN58801ef/11mjRpwtKlS5k2bVqBz+fm5jJ69GgaNWpE9+7dCQwMtDzJolatWqSkpJCbm0tYWBhNmzYlKioKT09Py0y0iIiIiFxfsb9/z8jIuOoygpycHA4fPmyToMpaVFQUUVFRADRq1Ig1a9Zc81hvb2/Wrl1bqP1aDwEZMWIEI0aMuOq+sWPHMnbs2AJtjz76qOXnOXPmXDfugIAAPvvss+seIyIiIiLXVuTieNWqVZafExMTCywfyM3NZf369Te8QU1ERERE5FZW5OK4b9++wMWb0x577LEC+5ycnPD39yc2NtamwYmIiIiIlKYiF8d5eXkA1K1bly1btlC9evUSC0pEREREpCwUe83xoUOHSiIOEREREZEyd1OPMVi/fj29e/emfv361K9fn969e7Nu3TpbxyYiIiIiUqqKXRzPnz+f7t27U6VKFZ599lmeffZZ3N3d6dmzJ/PmzSuJGEVERERESkWxi+PXXnuNmTNn8tFHH/HMM8/wzDPPsGzZMmbOnMlrr71WEjGWmvz8fIYPH46XlxeGYbB9+/ayDskqISEhlkfSiYiIiMiNFbs4PnXqFN27dy/UHhYWRlZWlk2CKitr1qwhPj6e1atXc/ToUZo0aXLNYyMiIixP8BARERER+1Ds4vgf//gHn3/+eaH2L774gt69e9skqLJy4MABfH19ad++PT4+PlSoUOz7FQsxm802iExEbnWJ6ce4f+63NHp5DffP/ZbE9GNlHZKIiNyEYhfHjRs3ZurUqfTq1YtXX32VV199ld69ezN16lSaNGnC7NmzLf+VJxERETz99NNkZmZiGAb+/v6sWLGCpk2bUqlSJapVq0a3bt04e/YsMTExLFq0iC+++ALDMDAMg+TkZDIyMjAMg48//phOnTpRsWJFli5dCsAHH3xAUFAQLi4u+Pr6MmbMGMvYmZmZ3H///bi5ueHu7s5DDz3Eb7/9ZtkfExND8+bNWbx4Mf7+/nh4ePDwww9z+vRpyzFnz55l6NChuLm54evrq2dOi5SixPRjjFicxo7DWZwz57LjcBaRS9JUIIuIlEPFnhp9//33qVq1Krt27WLXrl2Wdk9PT95//33LtmEYPPPMM7aJshTMmjWL+vXr884777BlyxbMZjP16tVjxowZ9OvXj9OnT/PNN9+Qn5/PuHHj2L17NyaTiYULFwLg5eXFr7/+CsCLL75IbGwsLVq0oGLFirz11ltER0czffp0evToQVZWFikpKcDF50dfLow3bdrEhQsXGD16NAMHDiQ5OdkS34EDB1i5ciWrV6/m5MmTPPTQQ0yfPp2pU6cCMH78eDZt2sQXX3xBjRo1eOmll9i2bRvNmze/5jnn5OSQk5Nj2TaZTMDF2e6SnPG+3Ldm1W+ecmg9W+Zw3oZ9hdry82Hexn10Caxmdf+3Ml2L1lMOracc2oa957Go56XnHF/i4eFBlSpVcHR0xMfHh23btnHhwgX69+9PnTp1AGjatKnl+EqVKpGTk4OPj0+hvqKioujfv79l+9VXX+W5557j2WeftbTdc889wMXH4v34448cOnQIPz8/AD788EOCgoLYsmWL5bi8vDzi4+OpUqUKAI8++ijr169n6tSpnDlzhvfff58lS5bQtWtXABYtWsSdd9553XOeNm0akydPLtS+du1aXF1db5w0KyUlJZX4GPZOObSeLXL401FHwCjc/msWCQkJVvdfHuhatJ5yaD3l0DbsNY/Z2dlFOu6mF9WeOHECwG7flBccHEzXrl1p2rQp4eHhhIWF8cADD1C1atUbfrZVq1aWn48fP86vv/5qKVr/bvfu3fj5+VkKY7i4dMXT05Pdu3dbimN/f39LYQzg6+vL8ePHgYuzyufPn6dNmzaW/V5eXjRs2PC6cU6YMIHo6GjLtslkws/Pj7CwMNzd3W94njfLbDaTlJREaGgoTk5OJTaOPVMOrWfLHL6fuZmdR0yF2u+q5UHPnm2t6vtWp2vResqh9ZRD27D3PF7+hvxGilUcnzp1iokTJ/Lxxx9z8uRJAKpWrcrDDz/Mq6++iqenZ7EDvVU5OjqSlJTEf//7X9auXcucOXOYOHEiqamp1K1b97qfrVy5suXnSpUq2SSev1+khmFYXul9s1xcXHBxcbnqWKXxR1Fa49gz5dB6tsjh6C4BRC5JIz///7cZBozpHHDb/H50LVpPObSecmgb9prHop5TkW/I+/PPP2nTpg2LFi1iwIABxMbGEhsbS//+/YmPj6ddu3aWgtleGIZBhw4dmDx5Mj/88APOzs6WJ3U4OzuTm5t7wz6qVKmCv78/69evv+r+Ro0a8csvv/DLL79Y2nbt2sWpU6do3LhxkeKsX78+Tk5OpKamWtpOnjzJ3r17i/R5EbFOeJAPC4a0JNjPE1dnR4L9PHl7SEvCggovuxIRkVtbkWeOp0yZgrOzMwcOHKBmzZqF9oWFhTFlyhRmzpxp8yDLQmpqKuvXrycsLIwaNWqQmprK77//TqNGjYCLyxwSExPZs2cP1apVw8PD45p9xcTEEBkZSY0aNejRowenT58mJSWFp59+mm7dutG0aVMGDx5MXFwcFy5cYNSoUXTq1KnA8ozrcXNzY9iwYYwfP55q1apRo0YNJk6ciIPDTb0dXERuQniQD+EqhkVEyr0iV08rV67k3//+d6HCGMDHx4cZM2Zc9fnH5ZW7uztff/01PXv2JDAwkH/+85/ExsbSo0cPAJ566ikaNmxIq1at8Pb2tjx94moee+wx4uLimD9/PkFBQfTu3Zt9+y7e3W4YBl988QVVq1blvvvuo1u3btSrV4+PP/64WPG+8cYbdOzYkT59+tCtWzfuvfdeWrZsefMJEBEREbkNGfn5V66SuzYXFxcOHDhwzScgHD58mAYNGvDXX3/ZNEApPSaTCQ8PD7Kyskr8hryEhAR69uxpl2uaSoNyaD3l0DaUR+sph9ZTDm3D3vNY1DqnyDPH1atXJyMj45r7Dx06hJeXV7GCFBERERG5lRS5OA4PD2fixImcP3++0L6cnBxefvllunfvbtPgRERERERKU7FuyGvVqhUBAQGMHj2au+66i/z8fHbv3s38+fPJyclh8eLFJRmriIiIiEiJKnJxfOedd/Ldd98xatQoJkyYwOWlyoZhEBoayty5cwu8yEJEREREpLwp1ktA6taty1dffcXJkyctT1to0KCB1hqLiIiIiF24qddHV61aldatW9s6FhERERGRMqW3RJRDISEhREVFlXUYIiIiInZHxbEd8vf3Jy4urqzDEBERESl3bmpZhYiI3FoS048xf+N+9v52hsCabozq3ECvsxYRuQmaOS6nLly4wJgxY/Dw8KB69eq8/PLL5OfnExISws8//8zYsWMxDAPDMMo6VBEpYYnpxxixOI0dh7M4Z85lx+EsIpekkZh+rKxDExEpdzRzXE4tWrSIYcOG8f3337N161aGDx9O7dq1+eyzzwgODmb48OE89dRT1+0jJyeHnJwcy7bJZAIuvj7SbDaXWOyX+y7JMeydcmg9e8rhvA37CrXl58O8jfvoElitRMe2pzyWFeXQesqhbdh7Hot6Xkb+5QcWS7kREhLC8ePHSU9Pt8wMv/jii6xatYpdu3bh7+9PVFTUDW/ai4mJYfLkyYXaly1bhqura0mELiIlYHyqI+fzCn9L5OyQzxttcssgIhGRW092djaDBg0iKysLd3f3ax6nmeNyqm3btgWWTLRr147Y2Fhyc4v+f4QTJkwgOjrasm0ymfDz8yMsLOy6F421zGYzSUlJhIaG4uTkVGLj2DPl0Hr2lMP3Mzez84ipUPtdtTzo2bNtiY5tT3ksK8qh9ZRD27D3PF7+hvxGVBzfxlxcXHBxcSnU7uTkVCp/FKU1jj1TDq1nDzkc3SWAyCVpXPk9oGHAmM4BpXZu9pDHsqYcWk85tA17zWNRz0k35JVTqampBbY3b95MQEAAjo6OODs7F2sGWUTKt/AgHxYMaUmwnyeuzo4E+3ny9pCWhOlpFSIixaaZ43IqMzOT6OhoRowYwbZt25gzZw6xsbHAxeccf/311zz88MO4uLhQvXr1Mo5WREpaeJCPHt0mImIDKo7LqaFDh3Lu3Dlat26No6Mjzz77LMOHDwdgypQpjBgxgvr165OTk4PuuRQREREpGhXH5VBycrLl57feeqvQ/rZt27Jjx45SjEhERETEPmjNsYiIiIjIJSqORUREREQuUXEsIiIiInKJimMRERERkUtUHIuIiIiIXKLiWERERETkEhXH5YBhGKxcubKswxARERGxe3rOcTlw9OhRqlatWtZhiIiIiNg9zRzbyPnz50usbx8fH1xcXG768yUZm4jcvhLTjzFgwWbGpzoyYMFmEtOPlXVIIiJWU3F8k0JCQhgzZgxRUVFUr14dFxcXDMMgMTGRFi1aUKlSJbp06cLx48f56quvaNSoEe7u7gwaNIjs7GxLP2vWrOHee+/F09OTatWq0bt3bw4cOFBgrL8vq/jxxx/p0qULlSpVolq1agwfPpwzZ85Y9kdERNC3b1+mTp1KrVq1aNiwYYnnQ0RuL4npxxixOI2dR0yczzPYecRE5JI0FcgiUu5pWYUVFi1axMiRI0lJSSE5OZnIyEhiYmKYO3curq6uPPTQQzz00EO4uLiwbNkyzpw5Q79+/ZgzZw4vvPACAGfPniU6OppmzZpx5swZJk2aRL9+/di+fTsODoX/7XL27FnCw8Np164dW7Zs4fjx4zz55JOMGTOG+Ph4y3Hr16/H3d2dpKSka8afk5NDTk6OZdtkMgFgNpsxm802ylJhl/suyTHsnXJoPeXQOvM27CvUlp8P8zbuo0tgtTKIqPzStWg95dA27D2PRT0vIz8/P7+EY7FLISEhmEwmtm3bBkBycjKdO3dm3bp1dO3aFYDp06czYcIEDhw4QL169QCIjIwkIyODNWvWXLXfEydO4O3tzY8//kiTJk2AizPHn3/+OX379uXdd9/lhRde4JdffqFy5coAJCQk0KdPH3799Vdq1qxJREQEa9asITMzE2dn52ueQ0xMDJMnTy7UvmzZMlxdXW8+OSJi98anOnI+zyjU7uyQzxttcssgIhGR68vOzmbQoEFkZWXh7u5+zeM0c2yFli1bFmpr1qyZ5eeaNWvi6upqKYwvt33//feW7X379jFp0iRSU1M5ceIEeXl5AGRmZlqK4yvt3r2b4OBgS2EM0KFDB/Ly8tizZw81a9YEoGnTptctjAEmTJhAdHS0ZdtkMuHn50dYWNh1Lxprmc1mkpKSCA0NxcnJqcTGsWfKofWUQ+u8n7mZnUdMhdrvquVBz55tyyCi8kvXovWUQ9uw9zxe/ob8RlQcW+HKAvWyKy8mwzAKXVyGYVgKYIA+ffpQp04d3n33XWrVqkVeXh5NmjSx+ia6q8X2dy4uLle90c/JyalU/ihKaxx7phxaTzm8OaO7BBC5JI0rv3s0DBjTOUD5vEm6Fq2nHNqGveaxqOekG/LK0B9//MGePXv45z//SdeuXWnUqBEnT5687mcaNWrEjh07OHv2rKUtJSUFBwcH3XgnIqUmPMiHBUNa0uxOd5wd8ml2pztvD2lJWJBPWYcmImIVFcdlqGrVqlSrVo133nmH/fv3s2HDhgLLHK5m8ODBVKxYkccee4z//e9/bNy4kaeffppHH33UsqRCRKQ0hAf58OmItrzRJpdPR7RVYSwidkHFcRlycHBg+fLlpKWl0aRJE8aOHcsbb7xx3c+4urqSmJjIn3/+yT333MMDDzxA165dmTt3bilFLSIiImK/tOb4JiUnJxfYDgkJ4e8P/oiIiCAiIqJAW0xMDDExMZbtbt26sWvXrgLHXNnP5Uetubm5WdqaNm3Khg0brhnblY90ExEREZGiU3F8CzOZTHz22Wc4ODhw1113lXU4IiIiInZPxfEt7JVXXmHZsmW8/vrr3HnnnWUdjoiIiIjdU3F8C5s5cyYzZ84s6zBEREREbhu6IU9ERERE5BIVxyIiIiIil6g4FhERERG5RMWxiIiIiMglKo7LqZCQEKKioso6DBERERG7oqdViIhImUhMP8b8jfvZ+9sZAmu6MapzA8L1CmoRKWOaOS6HIiIi2LRpE7NmzcIwDAzDICMjg//973/06NEDNzc3atasyaOPPsqJEyfKOlwRkUIS048xYnEaOw5ncc6cy47DWUQuSSMx/VhZhyYitznNHJdDs2bNYu/evTRp0oQpU6YA4OTkROvWrXnyySeZOXMm586d44UXXuChhx665qumc3JyLK+nhotv5AMwm82YzeYSi/9y3yU5hr1TDq2nHNrGzeZx3oZ9hdry82Hexn10Caxmk9jKC12L1lMObcPe81jU8zLy8/PzSzgWKQEhISE0b96cuLg4AF599VW++eYbEhMTLcccPnwYPz8/9uzZQ2BgYKE+YmJimDx5cqH2ZcuW4erqWmKxi4iMT3XkfJ5RqN3ZIZ832uSWQUQiYu+ys7MZNGgQWVlZuLu7X/M4zRzbiR07drBx40bc3NwK7Ttw4MBVi+MJEyYQHR1t2TaZTPj5+REWFnbdi8ZaZrOZpKQkQkNDcXJyKrFx7JlyaD3l0DZuNo/vZ25m5xFTofa7annQs2dbW4Z4y9O1aD3l0DbsPY+XvyG/ERXHduLMmTP06dOH119/vdA+X1/fq37GxcUFFxeXQu1OTk6l8kdRWuPYM+XQesqhbRQ3j6O7BBC5JI0rv7s0DBjTOeC2/X3oWrSecmgb9prHop6TiuNyytnZmdzc///V4913382nn36Kv78/FSro1yoit7bwIB8WDGnJ/OQD7PvtNAE1qzA6pD5helqFiJQxVVHllL+/P6mpqWRkZODm5sbo0aN59913eeSRR3j++efx8vJi//79LF++nPfeew9HR8eyDllEpIDwIB89uk1Ebjl6lFs5NW7cOBwdHWncuDHe3t6cP3+elJQUcnNzCQsLo2nTpkRFReHp6YmDg37NIiIiIkWhmeNyKjAwkO+++65Q+2effVYG0YiIiIjYB00pioiIiIhcouJYREREROQSFcciIiIiIpeoOBYRERERuUTFsYiIiIjIJSqORUREREQuUXFcwkJCQoiKirrm/mPHjhEaGkrlypXx9PQstbhEREREpDAVx2Vs5syZHD16lO3bt7N3716b9HmjglxERERErk4vASljBw4coGXLlgQEBJR1KCIi5U5i+jHmb9zP3t/OEFjTjVGdG+iV1CJiFc0cl4K8vDyef/55vLy88PHxISYmBgB/f38+/fRTPvzwQwzDICIiAoA333yTpk2bUrlyZfz8/Bg1ahRnzpwp0GdKSgohISG4urpStWpVwsPDOXnyJBEREWzatIlZs2ZhGAaGYZCRkVG6JywiUgoS048xYnEaOw5ncc6cy47DWUQuSSMx/VhZhyYi5ZhmjkvBokWLiI6OJjU1le+++46IiAg6dOjAli1bGDp0KO7u7syaNYtKlSoB4ODgwOzZs6lbty4HDx5k1KhRPP/888yfPx+A7du307VrV5544glmzZpFhQoV2LhxI7m5ucyaNYu9e/fSpEkTpkyZAoC3t/dV48rJySEnJ8eybTKZADCbzZjN5hLLx+W+S3IMe6ccWk85tI2yzOO8DfsKteXnw7yN++gSWK3U47lZuhatpxzahr3nsajnZeTn5+eXcCy3tZCQEHJzc/nmm28sba1bt6ZLly5Mnz6dvn374unpSXx8/DX7WLFiBZGRkZw4cQKAQYMGkZmZybfffnvNMZs3b05cXNx1Y4uJiWHy5MmF2pctW4arq+uNT05EpAyNT3XkfJ5RqN3ZIZ832uSWQUQicivLzs5m0KBBZGVl4e7ufs3jNHNcCpo1a1Zg29fXl+PHj1/z+HXr1jFt2jR++uknTCYTFy5c4K+//iI7OxtXV1e2b9/Ogw8+aHVcEyZMIDo62rJtMpnw8/MjLCzsuheNtcxmM0lJSYSGhuLk5FRi49gz5dB6yqFtlGUe38/czM4jpkLtd9XyoGfPtqUaizV0LVpPObQNe8/j5W/Ib0TFcSn4+wVmGAZ5eXlXPTYjI4PevXszcuRIpk6dipeXF99++y3Dhg3j/PnzuLq6WpZfWMvFxQUXF5erxlsafxSlNY49Uw6tpxzaRlnkcXSXACKXpHHl95+GAWM6B5TL36muResph7Zhr3ks6jnphrxbTFpaGnl5ecTGxtK2bVsCAwP59ddfCxzTrFkz1q9ff80+nJ2dyc3VV4oiYt/Cg3xYMKQlwX6euDo7EuznydtDWhKmp1WIiBU0c3yLadCgAWazmTlz5tCnTx9SUlJYsGBBgWMmTJhA06ZNGTVqFJGRkTg7O7Nx40YefPBBqlevjr+/P6mpqWRkZODm5oaXlxcODvp3kIjYn/AgHz26TURsShXTLSY4OJg333yT119/nSZNmrB06VKmTZtW4JjAwEDWrl3Ljh07aN26Ne3ateOLL76gQoWL/9YZN24cjo6ONG7cGG9vbzIzM8viVERERETKHc0cl7Dk5ORCbStXrrzqz5eNHTuWsWPHFmh79NFHC2x36tSJlJSUq44ZGBjId999V+xYRURERG53mjkWEREREblExbGIiIiIyCUqjkVERERELlFxLCIiIiJyiYpjEREREZFLVByLiIiIiFyi4riMpaSk0LRpU5ycnOjbt+9N9ZGRkYFhGGzfvt2msYmIiIjcblQcl6KQkBCioqIKtEVHR9O8eXMOHTpEfHz8DfuIiIi46SJaRERERK5PxXEZO3DgAF26dOHOO+/E09OzrMMRESmyxPRjDFiwmfGpjgxYsJnE9GNlHZKIiNVUHJeSiIgINm3axKxZszAMw/LfH3/8wRNPPIFhGJaZ4/T0dHr37o27uztVqlShY8eOHDhwgJiYGBYtWsQXX3xh+fyVb+A7ePAgnTt3xtXVleDgYL0lT0RKTGL6MUYsTmPnERPn8wx2HjERuSRNBbKIlHt6fXQpmTVrFnv37qVJkyZMmTKF3NxcABo3bsyUKVMYOHAgHh4eHDlyhPvuu4+QkBA2bNiAu7s7KSkpXLhwgXHjxrF7925MJhMLFy4EwMvLi19//RWAiRMn8u9//5uAgAAmTpzII488wv79+6lQ4eq/5pycHHJycizbJpMJALPZjNlsLrFcXO67JMewd8qh9ZRD68zbsK9QW34+zNu4jy6B1cogovJL16L1lEPbsPc8FvW8VByXEg8PD5ydnXF1dcXHx8fSbhgGHh4elrZ58+bh4eHB8uXLcXJyAiAwMNByfKVKlcjJySnQx2Xjxo2jV69eAEyePJmgoCD279/PXXfdddWYpk2bxuTJkwu1r127FldX15s/2SJKSkoq8THsnXJoPeXw5vx01BEwCrf/mkVCQkLpB2QHdC1aTzm0DXvNY3Z2dpGOU3F8i9m+fTsdO3a0FMbF0axZM8vPvr6+ABw/fvyaxfGECROIjo62bJtMJvz8/AgLC8Pd3b3Y4xeV2WwmKSmJ0NDQmzpPUQ5tQTm0zvuZm9l5xFSo/a5aHvTs2bYMIiq/dC1aTzm0DXvP4+VvyG9ExfEtplKlSjf92SsvZMO4OKOTl5d3zeNdXFxwcXG5aj+l8UdRWuPYM+XQesrhzRndJYDIJWnk5///NsOAMZ0DlM+bpGvResqhbdhrHot6TrohrxQ5Oztb1hpfS7Nmzfjmm2+uuS6mKH2IiJS08CAfFgxpSbM73XF2yKfZne68PaQlYUGFl3yJiJQnKo5Lkb+/P6mpqWRkZHDixImrzuqOGTMGk8nEww8/zNatW9m3bx+LFy9mz549lj527tzJnj17OHHihN0umheRW194kA+fjmjLG21y+XREWxXGImIXVByXonHjxuHo6Ejjxo3x9vYmMzOz0DHVqlVjw4YNnDlzhk6dOtGyZUveffddy1cBTz31FA0bNqRVq1Z4e3uTkpJS2qchIiIiYre05rgUBQYGFnr28KlTpwod16xZMxITE6/ah7e3N2vXri3Unn/lwj/A09OzUJuIiIiIXJ9mjkVERERELlFxLCIiIiJyiYpjEREREZFLVByLiIiIiFyi4lhERERE5BIVxyIiIiIil6g4FhERERG5RMVxKUtOTsYwjKs+39ga8fHxeHp62rRPERERkduNiuNyyN/fn7i4uLIOQ0TktpOYfoz7535Lo5fXcP/cb0lMP1bWIYmIjak4FhERKYLE9GOMWJzGjsNZnDPnsuNwFpFL0lQgi9gZvT66BOTk5DB+/HiWL1+OyWSiVatWzJw5k3vuucdyTFpaGi+88AK7du2iefPmLFy4kIYNG1r2/9///R9Tpkzhxx9/xM3NjY4dO/L5558TEhLCzz//zNixYxk7dixQ8NXRiYmJREVF8csvv3DvvfeycOFCfH19rxlnTk6OZdtkMgFgNpsxm802zcmVLvddkmPYO+XQesqhbdxOeZy3YV+htvx8mLdxH10Cq910v7dTDkuKcmgb9p7Hop6XkX9lZSU28eyzz7JixQree+896tSpw4wZM1i1ahX79+9n586ddO7cmTZt2vD666/j7e1NZGQkubm5pKSkAPDll19y//33M3HiRB5++GHOnz9PQkICEyZM4M8//yQ4OJjhw4fz1FNPAeDj40N8fDzDhw+nU6dOTJs2DQcHB4YMGUKLFi1YunTpVeOMiYlh8uTJhdqXLVuGq6trySVIRKQcGp/qyPk8o1C7s0M+b7TJLYOIRKQ4srOzGTRoEFlZWbi7u1/zOBXHNnb27FmqVq1KfHw8gwYNAi7+S8Xf35+oqCjuueceOnfuzLp16+jatSsACQkJ9OrVi3PnzlGxYkXat29PvXr1WLJkyVXHuNxXVFSUpS0+Pp7HH3+c/fv3U79+fQDmz5/PlClTOHbs6l/5XW3m2M/PjxMnTlz3orGW2WwmKSmJ0NBQnJycSmwce6YcWk85tI3bKY8DFmxm5xFTofZmd7rz6Yi2N93v7ZTDkqIc2oa959FkMlG9evUbFsdaVmFjBw4cwGw206FDB0ubk5MTrVu3Zvfu3ZalFc2aNbPsv7zs4fjx49SuXZvt27dbZoWLw9XV1VIYX+73+PHj1zzexcUFFxeXQu1OTk6l8kdRWuPYM+XQesqhbdwOeRzdJYDIJWlcOaVkGDCmc4BNzv12yGFJUw5tw17zWNRz0g15ZeTKX5BhXPyaLi8vD4BKlSpZ3eflfvXFgIiIbYQH+bBgSEuC/TxxdXYk2M+Tt4e0JCzIp6xDExEb0syxjdWvXx9nZ2dSUlKoU6cOcPFrii1bthRYBnE9zZo1Y/369Tz++ONX3e/s7Exurta3iYiUtvAgH8JVDIvYNc0c21jlypUZOXIk48ePZ82aNezatYunnnqK7Oxshg0bVqQ+XnnlFT766CNeeeUVdu/ezY8//sjrr79u2e/v78/XX3/NkSNHOHHiREmdioiIiMhtR8VxCZg+fToDBgzg0Ucf5e6772b//v0kJiZStWrVIn0+JCSE//znP6xatYrmzZvTpUsXvv/+e8v+KVOmkJGRQf369fH29i6p0xARERG57WhZRQmoWLEis2fPZvbs2YX2hYSEFFoH3Lx580Jt/fv3p3///lftv23btuzYsaNAW0REBBEREQXa+vbtqzXHIiIiIsWgmWMRERERkUtUHIuIiIiIXKLiWERERETkEhXHIiIiIiKXqDgWEREREblExbGIiIiIyCUqjkVERERELtFzjkVEpMQlph9j/sb97P3tDIE13RjVuYFewywityTNHIuISIlKTD/GiMVp7DicxTlzLjsOZxG5JI3E9GNlHZqISCGaObYjZ8+eZeTIkXz22WdUqVKFcePG8X//9380b96cuLi4Qsfn5OSQk5Nj2TaZTACYzWbMZnOJxXm575Icw94ph9ZTDm2jKHmct2Ffobb8fJi3cR9dAquVWGzlha5F6ymHtmHveSzqeRn5er+w3Rg1ahRffvklH3zwATVq1OCll15i06ZNPPHEE1ctjmNiYpg8eXKh9mXLluHq6loKEYvI7WB8qiPn84xC7c4O+bzRJrcMIhKR21F2djaDBg0iKysLd3f3ax6n4thOnDlzhmrVqrFkyRIefPBBAP7880/uvPNOhg8fXuSZYz8/P06cOHHdi8ZaZrOZpKQkQkNDcXJyKrFx7JlyaD3l0DaKkscBCzaz84ipUHuzO935dETbkg7xlqdr0XrKoW3Yex5NJhPVq1e/YXGsZRV24sCBA5w/f542bdpY2ry8vGjYsOE1P+Pi4oKLi0uhdicnp1L5oyitceyZcmg95dA2rpfH0V0CiFySxpVTMYYBYzoHKPdX0LVoPeXQNuw1j0U9J92QJyIiJSo8yIcFQ1oS7OeJq7MjwX6evD2kJWF6WoWI3II0c2wn6tevj5OTE6mpqdSuXRuAkydPsnfvXjp16lTG0YnI7S48yEePbhORckHFsZ1wc3Nj2LBhjB8/nmrVqlGjRg0mTpyIg4O+HBAREREpKhXHduSNN97gzJkz9OnThypVqvDcc8+RlZVV1mGJiIiIlBuaVrQjbm5uLF68mLNnz3Ls2DHGjx9f1iGJiIiIlCsqjkVERERELlFxLCIiIiJyidYc27nk5OSyDkFERESk3NDMsYiIiIjIJSqORUREREQuUXEsIiIiInKJiuNSFhISQlRUVFmHISIiIiJXoeJYREREROQSPa2inMvNzcUwDL0mWkRKRWL6MeZv3M/e384QWNONER3rlnVIIiI2pYqqDFy4cIExY8bg4eFB9erVefnll8nPzwfg5MmTDB06lKpVq+Lq6kqPHj3Yt2+f5bPx8fF4enqyatUqGjdujIuLC5mZmeTk5DBu3DjuuOMOKleuTJs2bfQYNxGxqcT0Y4xYnMaOw1mcM+ey43AWo5dvZ+efRlmHJiJiM5o5LgOLFi1i2LBhfP/992zdupXhw4dTu3ZtnnrqKSIiIti3bx+rVq3C3d2dF154gZ49e7Jr1y6cnJwAyM7O5vXXX+e9996jWrVq1KhRgzFjxrBr1y6WL19OrVq1+Pzzz+nevTs//vgjAQEBV40jJyeHnJwcy7bJZALAbDZjNptL7Pwv912SY9g75dB6ymHxzduwr1Bbfj6sO+LAc8rjTdO1aD3l0DbsPY9FPS8j//KUpZSKkJAQjh8/Tnp6OoZxcbblxRdfZNWqVXzxxRcEBgaSkpJC+/btAfjjjz/w8/Nj0aJFPPjgg8THx/P444+zfft2goODAcjMzKRevXpkZmZSq1Yty1jdunWjdevWvPbaa1eNJSYmhsmTJxdqX7ZsGa6urrY+dREp58anOnI+r/AssbNDPm+0yS2DiEREii47O5tBgwaRlZWFu7v7NY/TzHEZaNu2raUwBmjXrh2xsbHs2rWLChUq0KZNG8u+atWq0bBhQ3bv3m1pc3Z2plmzZpbtH3/8kdzcXAIDAwuMk5OTQ7Vq1a4Zx4QJE4iOjrZsm0wm/Pz8CAsLu+5FYy2z2UxSUhKhoaGW2XApHuXQesph8b2fuZmdR0yF2n1dUR6toGvResqhbdh7Hi9/Q34jKo7LoUqVKhUors+cOYOjoyNpaWk4OjoWONbNze2a/bi4uODi4lKo3cnJqVT+KEprHHumHFpPOSy60V0CiFySxpXfNxoGhN6RpzzagHJoPeXQNuw1j0U9JxXHZSA1NbXA9ubNmwkICKBx48ZcuHCB1NTUAssq9uzZQ+PGja/ZX4sWLcjNzeX48eN07NixRGMXkdtXeJAPC4a0ZH7yAfb9dpqAmlWI7OjP+UNbyzo0ERGbUXFcBjIzM4mOjmbEiBFs27aNOXPmEBsbS0BAAPfffz9PPfUUb7/9NlWqVOHFF1/kjjvu4P77779mf4GBgQwePJihQ4cSGxtLixYt+P3331m/fj3NmjWjV69epXh2ImLPwoN8CA/ysWybzWYSDpVhQCIiNqbiuAwMHTqUc+fO0bp1axwdHXn22WcZPnw4AAsXLuTZZ5+ld+/enD9/nvvuu4+EhIQbfhWwcOFCXn31VZ577jmOHDlC9erVadu2Lb179y6NUxIRERGxCyqOS9mVzx5+6623Cu2vWrUqH3744TU/HxERQURERKF2JycnJk+efNWnT4iIiIhI0eglICIiIiIil6g4FhERERG5RMWxiIiIiMglKo5FRERERC5RcSwiIiIicomKYxERERGRS1Qc/01+fj7Dhw/Hy8sLwzDYvn37TfcVHx+Pp6enzWIrrpiYGJo3b15m44uIiIiUNyqO/2bNmjXEx8ezevVqjh49SpMmTco6pJs2btw41q9fX9ZhiIiIiJQbegnI3xw4cABfX1/at29fJuOfP38eZ2dnm/Tl5uaGm5ubTfoSEblVJaYfY/7G/ez97QyBNd0Y1blBgVdci4gUh2aOrxAREcHTTz9NZmYmhmFQq1YtatWqRV5eXoHj7r//fp544gkAduzYQefOnalSpQru7u60bNmSrVu3Fjh+5cqVBAQEULFiRcLDw/nll18s+y4vfXjvvfeoW7cuFStWBMAwDN577z369euHq6srAQEBrFq1yvK55ORkDMNg/fr1tGrVCldXV9q3b8+ePXsK9S0iYq8S048xYnEaOw5ncc6cy47DWUQuSSMx/VhZhyYi5ZRmjq8wa9Ys6tevzzvvvMOWLVtwdHTkzjvvZOPGjXTt2hWAP//8kzVr1pCQkADA4MGDadGiBW+99RaOjo5s374dJycnS5/Z2dlMnTqVDz/8EGdnZ0aNGsXDDz9MSkqK5Zj9+/fz6aef8tlnn+Ho6Ghpnzx5MjNmzOCNN95gzpw5DB48mJ9//hkvLy/LMRMnTiQ2NhZvb28iIyN54oknCvR9PTk5OeTk5Fi2TSYTAGazGbPZfBMZLJrLfZfkGPZOObSecmgbZZ3HeRv2FWrLz4d5G/fRJbBaGURUfGWdQ3ugHNqGveexqOel4vgKHh4eVKlSBUdHR3x8Ln4l16NHD5YtW2YpjlesWEH16tXp3LkzAJmZmYwfP5677roLgICAgAJ9ms1m5s6dS5s2bQBYtGgRjRo14vvvv6d169bAxaUUH374Id7e3gU+GxERwSOPPALAa6+9xuzZs/n+++/p3r275ZipU6fSqVMnAF588UV69erFX3/9ZZmBvp5p06YxefLkQu1r167F1dX1hp+3VlJSUomPYe+UQ+sph7ZRVnn86agjYBRu/zXLMolRXuhatJ5yaBv2msfs7OwiHafi+AYGDx7MU089xfz583FxcWHp0qU8/PDDODhcXJESHR3Nk08+yeLFi+nWrRsPPvgg9evXt3y+QoUK3HPPPZbtu+66C09PT3bv3m0pjuvUqVOoMAZo1qyZ5efKlSvj7u7O8ePHr3mMr68vAMePH6d27do3PLcJEyYQHR1t2TaZTPj5+REWFoa7u/sNP3+zzGYzSUlJhIaGFphll6JTDq2nHNpGWefx/czN7DxiKtR+Vy0PevZsW+rx3IyyzqE9UA5tw97zePkb8htRcXwDffr0IT8/ny+//JJ77rmHb775hpkzZ1r2x8TEMGjQIL788ku++uorXnnlFZYvX06/fv2KPEblypWv2v73C9MwjELrn688xjAuzp78/ZhrcXFxwcXF5arjlsYfRWmNY8+UQ+sph7ZRVnkc3SWAyCVp5Of//zbDgDGdA8rd71XXovWUQ9uw1zwW9Zx0Q94NVKxYkf79+7N06VI++ugjGjZsyN13313gmMDAQMaOHcvatWvp378/CxcutOy7cOFCgRv09uzZw6lTp2jUqFGpnYOIiL0KD/JhwZCWBPt54ursSLCfJ28PaUmYnlYhIjdJM8dFMHjwYHr37k16ejpDhgyxtJ87d47x48fzwAMPULduXQ4fPsyWLVsYMGCA5RgnJyeefvppZs+eTYUKFRgzZgxt27a1LKkQERHrhAf56NFtImIzKo6LoEuXLnh5ebFnzx4GDRpkaXd0dOSPP/5g6NCh/Pbbb1SvXp3+/fsXuMnN1dWVF154gUGDBnHkyBE6duzI+++/XxanISIiIiI3YOTnX7lSS25nJpMJDw8PsrKySvyGvISEBHr27GmXa5pKg3JoPeXQNpRH6ymH1lMObcPe81jUOkdrjkVERERELlFxLCIiIiJyiYpjEREREZFLVByLiIiIiFyi4lhERERE5BIVxyIiIiIil6g4LgHJyckYhsGpU6fKLIZ33nkHPz8/HBwciIuLK7M4RERERMoTFcfFFBISQlRUVFmHYWEYBitXrizQZjKZGDNmDC+88AJHjhxh+PDhZROciIiISDmjN+TZoczMTMxmM7169cLX17eswxEplsT0Y8zfuJ+9v50hsKYbozo30KuBRUSk1GjmuBgiIiLYtGkTs2bNwjAMDMMgIyODhIQEAgMDqVSpEp07dyYjI6PA5+Lj4/H09GT16tU0bNgQV1dXHnjgAbKzs1m0aBH+/v5UrVqVZ555htzcXMvn/P39+de//sUjjzxC5cqVueOOO5g3b16B/QD9+vXDMAz8/f2Jj4+nadOmANSrV88So0h5kJh+jBGL09hxOItz5lx2HM4ickkaienHyjo0ERG5TWjmuBhmzZrF3r17adKkCVOmTAEgJyeH/v37M3r0aIYPH87WrVt57rnnCn02Ozub2bNns3z5ck6fPk3//v3p168fnp6eJCQkcPDgQQYMGECHDh0YOHCg5XNvvPEGL730EpMnTyYxMZFnn32WwMBAQkND2bJlCzVq1GDhwoV0794dR0dH3Nzc8PPzo1u3bnz//ff4+fnh7e191fPJyckhJyfHsm0ymYCLr480m822TF0Bl/suyTHsnb3mcN6GfYXa8vNh3sZ9dAmsZtOx7DWHpU15tJ5yaD3l0DbsPY9FPS8Vx8Xg4eGBs7Mzrq6u+Phc/Jr3pZdeon79+sTGxgLQsGFDfvzxR15//fUCnzWbzbz11lvUr18fgAceeIDFixfz22+/4ebmRuPGjencuTMbN24sUBx36NCBF198EYDAwEBSUlKYOXMmoaGhlqLX09PTEg9AtWoXiwhvb+8C7X83bdo0Jk+eXKh97dq1uLq6Fjs/xZWUlFTiY9g7e8vhT0cdAaNw+69ZJCQklMiY9pbDsqI8Wk85tJ5yaBv2msfs7OwiHafi2Eq7d++mTZs2BdratWtX6DhXV1dLYQxQs2ZN/P39cXNzK9B2/Pjx6/bVrl07mz19YsKECURHR1u2TSYTfn5+hIWF4e7ubpMxrsZsNpOUlERoaChOTk4lNo49s9ccvp+5mZ1HTIXa76rlQc+ebW06lr3msLQpj9ZTDq2nHNqGvefx8jfkN6LiuJT8/SIzDOOqbXl5eaUWk4uLCy4uLoXanZycSuWPorTGsWf2lsPRXQKIXJJGfv7/bzMMGNM5oMTO095yWFaUR+sph9ZTDm3DXvNY1HPSDXnF5OzsXOCmuUaNGvH9998XOGbz5s02G+/vfW3evJlGjRpZtp2cnArEI1KehQf5sGBIS4L9PHF1diTYz5O3h7QkTE+rEBGRUqKZ42Ly9/cnNTWVjIwM3NzciIyMJDY2lvHjx/Pkk0+SlpZGfHy8zcZLSUlhxowZ9O3bl6SkJP7zn//w5ZdfFohn/fr1dOjQARcXF6pWrWqzsUXKQniQjx7dJiIiZUYzx8U0btw4HB0dady4Md7e3uTl5fHpp5+ycuVKgoODWbBgAa+99prNxnvuuefYunUrLVq04NVXX+XNN98kPDzcsj82NpakpCT8/Pxo0aKFzcYVERERuR1p5riYAgMD+e677wq0+fv707t37wJtjz/+uOXniIgIIiIiCuyPiYkhJiamQNvVZpzd3d355JNPrhlPnz596NOnT4G25s2bk3/lok0RERERKRLNHIuIiIiIXKLiWERERETkEi2ruIXptc8iIiIipUszxyIiIiIil6g4FhERERG5RMWxiIiIiMglKo5tJDk5GcMwOHXq1DWPiYmJoXnz5qUWk4iIiIgUj4rjmxQSEkJUVFRZhyEiIiIiNqSnVYiUgcT0Y8zfuJ+9v50hsKYbozo30CuTRUREbgGaOb4JERERbNq0iVmzZmEYBoZhWB67lpaWRqtWrXB1daV9+/bs2bPnmv0cOHCAevXqMWbMGMsb7VJSUggJCcHV1ZWqVasSHh7OyZMnAVizZg333nsvnp6eVKtWjd69e3PgwAFLf+fPn2fMmDH4+vpSsWJF6tSpw7Rp00ouEXJTEtOPMWJxGjsOZ3HOnMuOw1lELkkjMf1YWYcmIiJy29PM8U2YNWsWe/fupUmTJkyZMgWA9PR0ACZOnEhsbCze3t5ERkbyxBNPkJKSUqiPnTt3Eh4ezrBhw3j11VcB2L59O127duWJJ55g1qxZVKhQgY0bN5KbmwvA2bNniY6OplmzZpw5c4ZJkybRr18/tm/fjoODA7Nnz2bVqlV88skn1K5dm19++YVffvnlmueRk5NDTk6OZdtkMgFgNpsxm822SdZVXO67JMe4lc3bsK9QW34+zNu4jy6B1YrUx+2eQ1tQDm1DebSecmg95dA27D2PRT0vI//ylKUUS0hICM2bNycuLg64eENe586dWbduHV27dgUgISGBXr16ce7cOSpWrEhMTAwrV65k/vz59O7dm4kTJ/Lcc89Z+hw0aBCZmZl8++23RYrhxIkTeHt78+OPP9KkSROeeeYZ0tPTWbduHYZh3PDzMTExTJ48uVD7smXLcHV1LVIMUnzjUx05n1f49+PskM8bbXLLICIRERH7l52dzaBBg8jKysLd3f2ax2nm2MaaNWtm+dnX1xeA48ePU7t2bQAyMzMJDQ1l6tSphW7o2759Ow8++OA1+963bx+TJk0iNTWVEydOkJeXZ+mzSZMmREREEBoaSsOGDenevTu9e/cmLCzsmv1NmDCB6Ohoy7bJZMLPz4+wsLDrXjTWMpvNJCUlERoaipOTU4mNc6t6P3MzO4+YCrXfVcuDnj3bFqmP2z2HtqAc2obyaD3l0HrKoW3Yex4vf0N+IyqObezKi+ny7O3lIhbA29ubWrVq8dFHH/HEE08UKEIrVap03b779OlDnTp1ePfdd6lVqxZ5eXk0adKE8+fPA3D33Xdz6NAhvvrqK9atW8dDDz1Et27dWLFixVX7c3FxwcXF5arnUBp/FKU1zq1mdJcAIpekceV3NoYBYzoHFDsft2sObUk5tA3l0XrKofWUQ9uw1zwW9Zx0Q95NcnZ2tqwFLo5KlSqxevVqKlasSHh4OKdPn7bsa9asGevXr7/q5/744w/27NnDP//5T7p27UqjRo0sN+pdyd3dnYEDB/Luu+/y8ccf8+mnn/Lnn38WO04pOeFBPiwY0pJgP09cnR0J9vPk7SEtCdPTKkRERMqcZo5vkr+/P6mpqWRkZODm5lZgdvhGKleuzJdffkmPHj3o0aMHa9aswc3NjQkTJtC0aVNGjRpFZGQkzs7ObNy4kQcffBAvLy+qVavGO++8g6+vL5mZmbz44osF+n3zzTfx9fWlRYsWODg48J///AcfHx88PT1tfPZirfAgHz26TURE5BakmeObNG7cOBwdHWncuDHe3t5kZmYW6/Nubm589dVX5Ofn06tXL86ePUtgYCBr165lx44dtG7dmnbt2vHFF19QoUIFHBwcWL58OWlpaTRp0oSxY8fyxhtvFOizSpUqzJgxg1atWnHPPfeQkZFBQkICDg76NYuIiIgUhWaOb1JgYCDfffddgbaIiIgC282bN+fKh4HExMQQExNj2XZzcyv0mLdOnTpd9dFvAN26dWPXrl0F2q7s/6mnnuKpp54qzmmIiIiIyBU0pSgiIiIicomKYxERERGRS1Qci4iIiIhcouJYREREROQSFcciIiIiIpeoOBYRERERueS2Lo6Tk5MxDINTp06VdSjXFRISQlRUVFmHISIiImL3bqvi+HYpMjMyMjAMg+3bt5d1KCIiIiLlym1VHIuIiO0kph9jwILNjE91ZMCCzSSmHyvrkERErHbbFMcRERFs2rSJWbNmYRgGhmGQkZEBQFpaGq1atcLV1ZX27duzZ88eALKysnB0dGTr1q0A5OXl4eXlRdu2bS39LlmyBD8/P8v24cOHeeSRR/Dy8qJy5cq0atWK1NRU4OIb8po3b87ixYvx9/fHw8ODhx9+mNOnT1s+f/bsWYYOHYqbmxu+vr7ExsYWOhfDMFi5cmWBNk9PT+Lj4wGoW7cuAC1atMAwDEJCQqzKnYjI3yWmH2PE4jR2HjFxPs9g5xETkUvSVCCLSLl327w+etasWezdu5cmTZowZcoUANLT0wGYOHEisbGxeHt7ExkZyRNPPEFKSgoeHh40b96c5ORkWrVqxY8//ohhGPzwww+cOXMGNzc3Nm3aRKdOnQA4c+YMnTp14o477mDVqlX4+Piwbds28vLyLHEcOHCAlStXsnr1ak6ePMlDDz3E9OnTmTp1KgDjx49n06ZNfPHFF9SoUYOXXnqJbdu20bx58yKf6/fff0/r1q1Zt24dQUFBODs7X/W4nJwccnJyLNsmkwkAs9mM2WwuenKL6XLfJTmGvVMOraccWmfehn2F2vLzYd7GfXQJrFYGEZVfuhatpxzahr3nsajnddsUxx4eHjg7O+Pq6oqPjw8AP/30EwBTp061FLgvvvgivXr14q+//qJixYqEhISQnJzMuHHjSE5OJjQ0lJ9++olvv/2W7t27k5yczPPPPw/AsmXL+P3339myZQteXl4ANGjQoEAceXl5xMfHU6VKFQAeffRR1q9fz9SpUzlz5gzvv/8+S5YsoWvXrgAsWrSIO++8s1jn6u3tDUC1atUs53o106ZNY/LkyYXa165di6ura7HGvBlJSUklPoa9Uw6tpxzenJ+OOgJG4fZfs0hISCj9gOyArkXrKYe2Ya95zM7OLtJxt01xfD3NmjWz/Ozr6wvA8ePHqV27Np06deL9998nNzeXTZs2ERYWho+PD8nJyTRr1oz9+/dbli1s376dFi1aWArjq/H397cUxpfHO378OHBxVvn8+fO0adPGst/Ly4uGDRva8nQtJkyYQHR0tGXbZDLh5+dHWFgY7u7uJTImXPyXW1JSEqGhoTg5OZXYOPZMObSecmid9zM3s/OIqVD7XbU86Nmz7VU+Ideia9F6yqFt2HseL39DfiMqjqHABWAYF2dCLi+FuO+++zh9+jTbtm3j66+/5rXXXsPHx4fp06cTHBxMrVq1CAgIAKBSpUrFGuvyeFcuuygKwzDIz88v0HYzX4G4uLjg4uJy1RhL44+itMaxZ8qh9ZTDmzO6SwCRS9K48n+KDAPGdA5QPm+SrkXrKYe2Ya95LOo53TY35AE4OzuTm5tbrM94enrSrFkz5s6di5OTE3fddRf33XcfP/zwA6tXr7Ysx4CLM9Dbt2/nzz//vKn46tevj5OTk+UGPoCTJ0+yd+/eAsd5e3tz9OhRy/a+ffsKfFVweY1xcc9VRKSowoN8WDCkJc3udMfZIZ9md7rz9pCWhAVdeymXiEh5cFsVx/7+/qSmppKRkcGJEyeKPGMbEhLC0qVLLYWwl5cXjRo14uOPPy5QHD/yyCP4+PjQt29fUlJSOHjwIJ9++infffddkcZxc3Nj2LBhjB8/ng0bNvC///2PiIgIHBwK/pq6dOnC3Llz+eGHH9i6dSuRkZEF/jVUo0YNKlWqxJo1a/jtt9/Iysoq0vgiIsURHuTDpyPa8kabXD4d0VaFsYjYhduqOB43bhyOjo40btwYb29vMjMzi/S5Tp06kZubW+CRaCEhIYXanJ2dWbt2LTVq1KBnz540bdqU6dOn4+joWOQY33jjDTp27EifPn3o1q0b9957Ly1btixwTGxsLH5+fnTs2JFBgwYxbty4AjfQVahQgdmzZ/P2229Tq1Yt7r///iKPLyIiInI7M/L/vnhVblsmkwkPDw+ysrJK/Ia8hIQEevbsaZdrmkqDcmg95dA2lEfrKYfWUw5tw97zWNQ657aaORYRERERuR4VxyIiIiIil6g4FhERERG5RMWxiIiIiMglKo5FRERERC5RcSwiIiIicomK42LKz89n+PDheHl5YRgG27dvL5FxsrOzGTBgAO7u7hiGwalTp0pkHBERERH5/1QcF9OaNWuIj49n9erVHD16lCZNmljVX3x8PJ6enoXaFy1axDfffMN///tfjh49ioeHh1XjiIiIiMiNVSjrAMqbAwcO4OvrS/v27Ut8nEaNGlldfIuIlCeJ6ceYv3E/e387Q2BNN0Z1bkC4XkstIqVIM8fFEBERwdNPP01mZiaGYeDv709eXh4zZsygQYMGuLi4ULt2baZOnQpAcnJyoSUR27dvxzAMMjIySE5O5vHHHycrKwvDMDAMg5iYGEJCQoiNjeXrr7/GMAzLK6rnz59PQEAAFStWpGbNmjzwwAOWfv39/YmLiysQb/PmzYmJiSnhrIiI2EZi+jFGLE5jx+Eszplz2XE4i8glaSSmHyvr0ETkNqKZ42KYNWsW9evX55133mHLli04OjoyYcIE3n33XWbOnMm9997L0aNH+emnn4rUX/v27YmLi2PSpEns2bMHADc3N5555hlefPFF/ve///HZZ5/h7OzM1q1beeaZZ1i8eDHt27fnzz//5JtvvrHqfHJycsjJybFsm0wm4OLrI81ms1V9X8/lvktyDHunHFpPObQNW+Zx3oZ9hdry82Hexn10Caxmdf+3Kl2L1lMObcPe81jU81JxXAweHh5UqVIFR0dHfHx8OH36NLNmzWLu3Lk89thjANSvX5977723SP05Ozvj4eGBYRj4+BT82tDV1RVnZ2dLe3JyMpUrV6Z3795UqVKFOnXq0KJFC6vOZ9q0aUyePLlQ+9q1a3F1dbWq76JISkoq8THsnXJoPeXQNmyRx5+OOgJG4fZfs0hISLC6/1udrkXrKYe2Ya95zM7OLtJxKo6tsHv3bnJycujatWuJjxUaGkqdOnWoV68e3bt3p3v37vTr18+qInbChAlER0dbtk0mE35+foSFheHu7m6LsK/KbDaTlJREaGgoTk5OJTaOPVMOracc2oYt8/h+5mZ2HjEVar+rlgc9e7a1qu9bma5F6ymHtmHvebz8DfmNqDi2QqVKla6738Hh4pLu/Px8S9vNflVRpUoVtm3bRnJyMmvXrmXSpEnExMSwZcsWPD09cXBwKDBOUcZycXHBxcWlULuTk1Op/FGU1jj2TDm0nnJoG7bI4+guAUQuSePK/ykzDBjTOeC2+B3pWrSecmgb9prHop6TbsizQkBAAJUqVWL9+vVX3e/t7Q3A0aNHLW1/fy6ys7Mzubm5RRqvQoUKdOvWjRkzZrBz504yMjLYsGGDZawrxzGZTBw6dKg4pyMiUqbCg3xYMKQlwX6euDo7EuznydtDWhKmp1WISCnSzLEVKlasyAsvvMDzzz+Ps7MzHTp04Pfffyc9PZ1hw4bRoEED/Pz8iImJYerUqezdu5fY2NgCffj7+3PmzBnWr19PcHAwrq6uV10qsXr1ag4ePMh9991H1apVSUhIIC8vj4YNGwLQpUsX4uPj6dOnD56enkyaNAlHR8dSyYOIiK2EB/no0W0iUqY0c2yll19+meeee45JkybRqFEjBg4cyPHjx4GL0/cfffQRP/30E82aNeP111/n1VdfLfD59u3bExkZycCBA/H29mbGjBlXHcfT05PPPvuMLl260KhRIxYsWMBHH31EUFAQcHH9cKdOnejduze9evWib9++1K9fv2RPXkRERMTOaOa4mKKiooiKirJsOzg4MHHiRCZOnHjV4zt06MDOnTsLtP19bfBbb73FW2+9VaDt788svvfee0lOTr5mXO7u7ixfvrxA2+UnaIiIiIhI0WjmWERERETkEhXHIiIiIiKXqDgWEREREblExbGIiIiIyCUqjkVERERELlFxLCIiIiJyiYpjO5aSkkLTpk1xcnKib9++ZR2OiIiIyC1PxXE5kpycjGEYnDp1qkB7SEhIgWcvXxYdHU3z5s05dOgQ8fHxpRKjiIiISHmml4DYsQMHDhAZGcmdd95Z1qGIiIjctMT0Y8zfuJ+9v50hsKYbozo30GvGpcRo5riU5eXlMWPGDBo0aICLiwu1a9dm6tSpZGRkYBgGy5cvp3379lSsWJEmTZqwadMmADIyMujcuTMAVatWxTAMIiIiiIiIYNOmTcyaNQvDMDAMw9LXH3/8wRNPPIFhGJo5FhGRcikx/RgjFqex43AW58y57DicReSSNBLTj5V1aGKnNHNcyiZMmMC7777LzJkzuffeezl69Cg//fSTZf/48eOJi4ujcePGvPnmm/Tp04dDhw7h5+fHp59+yoABA9izZw/u7u5UqlQJgL1799KkSROmTJkCgLe3N0ePHqVhw4ZMmTKFgQMH4uHhUSiWnJwccnJyLNsmkwkAs9mM2WwusRxc7rskx7B3yqH1VQSuuAAAUfJJREFUlEPbUB6tpxxe37wN+wq15efDvI376BJYDVAObcXe81jU8zLy8/PzSzgWueT06dN4e3szd+5cnnzyyQL7MjIyqFu3LtOnT+eFF14A4MKFC9StW5enn36a559/nuTkZDp37szJkyfx9PS0fDYkJITmzZsTFxdXoE9PT0/i4uKIiIi4ajwxMTFMnjy5UPuyZctwdXW16lxFRERsYXyqI+fzjELtzg75vNEmtwwikvIqOzubQYMGkZWVhbu7+zWP08xxKdq9ezc5OTl07dr1mse0a9fO8nOFChVo1aoVu3fvLpF4JkyYQHR0tGXbZDLh5+dHWFjYdS8aa5nNZpKSkggNDcXJyanExrFnyqH1lEPbUB6tpxxe3/uZm9l5xFSo/a5aHvTs2RZQDm3F3vN4+RvyG1FxXIouL4O4Vbi4uODi4lKo3cnJqVT+KEprHHumHFpPObQN5dF6yuHVje4SQOSSNK78ntswYEzngEL5Ug5tw17zWNRz0g15pSggIIBKlSqxfv36ax6zefNmy88XLlwgLS2NRo0aAeDs7AxAbm7Br5GcnZ0LtYmIiNiD8CAfFgxpSbCfJ67OjgT7efL2kJaE6WkVUkI0c1yKKlasyAsvvMDzzz+Ps7MzHTp04Pfffyc9Pd2y1GLevHkEBATQqFEjZs6cycmTJ3niiScAqFOnDoZhsHr1anr27EmlSpVwc3PD39+f1NRUMjIycHNzw8vLCwcH/btHRETsQ3iQjx7dJqVGFVQpe/nll3nuueeYNGkSjRo1YuDAgRw/ftyyf/r06UyfPp3g4GC+/fZbVq1aRfXq1QG44447mDx5Mi+++CI1a9ZkzJgxAIwbNw5HR0caN26Mt7c3mZmZZXJuIiIiIuWdZo5LmYODAxMnTmTixIkF2jMyMgBo1KgRqamp1/z8yy+/zMsvv1ygLTAwkO+++67QsX9/k56IiIiIXJ9mjkVERERELlFxLCIiIiJyiZZV3CL8/f3R+1hEREREypZmjkVERERELlFxLCIiIiJyiYpjEREREZFLVByLiIiIiFyi4riUxcfH4+npWdZhiIiIiMhV6GkVIiLFlJh+jPkb97P3tzME1nRjVOcGerWtiIid0MyxDZ0/f76sQxCREpaYfowRi9PYcTiLc+ZcdhzOInJJGonpx8o6NBERsQHNHFshJCSEJk2aUKFCBZYsWULTpk3p06cPCxcu5ODBg3h5edGnTx9mzJiBm5vbVfuIiYlh5cqVjBw5kldffZU//viD3r178+677+Lh4QFAREQEp06d4t577yU2Npbz58/z8MMPExcXh5OTEwA5OTlMnDiRjz76iFOnTtGkSRNef/11QkJCrhl/Tk4OOTk5lm2TyQSA2WzGbDbbKEuFXe67JMewd8qh9W42h/M27CvUlp8P8zbuo0tgNZvEVp7oWrSecmg95dA27D2PRT0vFcdWWrRoESNHjiQlJQWAr776itmzZ1O3bl0OHjzIqFGjeP7555k/f/41+9i/fz+ffPIJ//d//4fJZGLYsGGMGjWKpUuXWo7ZuHEjvr6+bNy4kf379zNw4ECaN2/OU089BcCYMWPYtWsXy5cvp1atWnz++ed0796dH3/8kYCAgKuOO23aNCZPnlyofe3atbi6ulqTliJJSkoq8THsnXJoveLm8KejjoBRuP3XLBISEmwUVfmja9F6yqH1lEPbsNc8ZmdnF+k4I1+vZbtpISEhmEwmtm3bds1jVqxYQWRkJCdOnAAu3pAXFRXFqVOngIszx6+++io///wzd9xxBwBr1qyhV69eHDlyBB8fHyIiIkhOTubAgQM4OjoC8NBDD+Hg4MDy5cvJzMykXr16ZGZmUqtWLcvY3bp1o3Xr1rz22mtXje1qM8d+fn6cOHECd3d3q3JzPWazmaSkJEJDQy0z31I8yqH1bjaHAxZsZucRU6H2Zne68+mItrYMsVzQtWg95dB6yqFt2HseTSYT1atXJysr67p1jmaOrdSyZcsC2+vWrWPatGn89NNPmEwmLly4wF9//UV2dvY1Z2Nr165tKYwB2rVrR15eHnv27MHH5+JNPkFBQZbCGMDX15cff/wRgB9//JHc3FwCAwML9JuTk0O1atf+mtfFxQUXF5dC7U5OTqXyR1Fa49gz5dB6xc3h6C4BRC5J48ppBcOAMZ0Dbuvfha5F6ymH1lMObcNe81jUc1JxbKXKlStbfs7IyKB3796MHDmSqVOn4uXlxbfffsuwYcM4f/68VUsV/v4LNQyDvLw8AM6cOYOjoyNpaWkFCmjgmmudReTmhAf5sGBIS+YnH2Dfb6cJqFmF0SH1CdPTKkRE7IKKYxtKS0sjLy+P2NhYHBwuPgjkk08+ueHnMjMz+fXXXy1LIjZv3oyDgwMNGzYs0rgtWrQgNzeX48eP07Fjx5s/AREpkvAgHz26TUTETulRbjbUoEEDzGYzc+bM4eDBgyxevJgFCxbc8HMVK1bkscceY8eOHXzzzTc888wzPPTQQ5YlFTcSGBjI4MGDGTp0KJ999hmHDh3i+++/Z9q0aXz55ZfWnpaIiIjIbUPFsQ0FBwfz5ptv8vrrr9OkSROWLl3KtGnTbvi5Bg0a0L9/f3r27ElYWBjNmjW77tMtrmbhwoUMHTqU5557joYNG9K3b1+2bNlC7dq1b/Z0RERERG47WlZhheTk5EJtY8eOZezYsQXaHn30UcvPERERREREFPrcyJEjGTly5FXHiY+PL9QWFxdXYNvJyYnJkydf9dFsIiIiIlI0mjkWEREREblExbGIiIiIyCUqjstYTEwM27dvL+swRERERAQVxyIiIiIiFiqORUREREQuUXEsIiIiInLJLV0cJycnYxgGp06dKutQisXf37/Qo9au5dixY4SGhlK5cmU8/197dx6XU/r+AfzztJc27asiRbZCloopWbJFfO1bkrWYYrI2pqxh7KOxzFD4VgaDrx+RNUvWUjE0lVIYZacSlbp/f6gzHu09W8v1fr16vTz3uc99rnN1ytX93M856uoijYsQQgghhFSuThfHohQSElInitFNmzYhMzMT8fHxSE5OlnQ4pBGKvJ+FoduuwnLpaQzddhWR97MkHRIhhBAiMY22OK4rUlNT0blzZ5ibm0NHR6fcPoWFhWKOijQWkfezMGN/LBKevsfHwiIkPH2Pmf+NpQKZEEJIoyXxJ+Tl5+dj/vz5OHDgALKzs2FjY4NNmzahS5cuXJ/Y2FgsXLgQDx48gLW1NYKDg9GqVasqx05ISICPjw9iYmLA4/Fgbm6OnTt3Ijc3F+7u7gAAHo8HAPD390dAQADy8/Ph5+eH8PBwvHv3Du3atcPatWvh6OjIjXv16lUsXrwYMTEx0NLSwrBhwxAYGIgmTZrU6NxNTU2RkZEBANi3bx/c3NwQEhICHo+HX3/9FadOncL58+cxf/58LF26FNOnT8eFCxeQlZWFZs2awdPTE97e3nxj7tmzBxs2bMDDhw+hoaGB//znP9i2bVuFuc/Pz+deZ2dnA/hSjIuyIC8dm4r+2hNWDoMupJRpYwwIupgCJwtNgcau6+g6FA7Ko+Aoh4KjHApHQ89jdc+LxxhjIo6lUt7e3jh8+DB+//13mJiYYN26dTh+/DgePnyIu3fvolevXujWrRvWrl0LbW1tzJw5E0VFRYiOjq5y7Hbt2qFjx47w8/ODtLQ04uPjYWFhAUtLS2zfvh0//fQTkpKSAADKyspQVlbGtGnT8ODBA6xZswYGBgY4evQofvzxR9y7dw/m5uZITU2FlZUVVq5ciUGDBuHly5eYPXs2rKysEBwcDOBL0evj4wMfH59K43v58iUmTZoEVVVVbNmyBYqKilBTUwOPx4OOjg7WrFkDBwcHyMjIQF9fHytXroSLiws0NTVx7do1TJ8+HcHBwRg1ahQAYPv27Zg3bx7WrFmDAQMG4P3794iOjq4wjoCAgHIfNx0WFgYlJaUq80vqv/k3pVFQzCvTLifF8HO3IglERAghhIhGXl4exo0bh/fv30NVVbXCfhItjj98+ICmTZsiJCQE48aNA/Clqi8tLrt06YJevXrh3Llz6N27NwAgIiICgwYNwsePH6GgoFDp+Kqqqvjll1/g5uZWZltISAh8fHz4Puz3+PFjtGjRAo8fP4aBgQHX3qdPH3Tt2hWrV6/G1KlTIS0tjZ07d3Lbr169CgcHB3z48AEKCgrVLo4BwNXVFerq6ggJCeHaeDwefHx8sGnTpkr3nT17NrKysnD48GEAgKGhIdzd3bFy5coqjwuUP3NsbGyMV69eVXrRCKqwsBBnz55F3759ISsrK7LjNGTCyuF/dtzA3X+yy7R3MFLFnzO6CxJinUfXoXBQHgVHORQc5VA4Gnoes7OzoaWlVWVxLNFlFampqSgsLIS9vT3XJisri65duyIxMZFbWtGhQwduu76+PgDgxYsXaNasWaXjz5s3D1OnTsX+/fvRp08fjBw5EmZmZhX2v3fvHoqKimBhYcHXnp+fD03NL28xJyQk4O7duwgNDeW2M8ZQXFyMR48ewdLSsppnXzkbG5sybUFBQdizZw8eP36Mjx8/oqCgANbW1gC+5OPZs2fcHxHVIS8vD3l5+TLtsrKyYvmhENdxGjJBc+jlZI6Z/43F138i83jA7F7mjeZ7Q9ehcFAeBUc5FBzlUDgaah6re04SX3NcHV+fTOka4eLi4ir3CwgIwLhx43Dy5EmcOnUK/v7+OHDgAIYNG1Zu/9zcXEhLSyM2NhbS0tJ825SVlbk+M2bMwPfff19m/6qK9Zr4dv3ygQMH4Ovriw0bNsDW1hYqKir4+eefcfPmTQCAoqKi0I5NGg/ntnrYMaEzfo1KRcrzHJjrqsDL0Qz92upJOjRCCCFEIiRaHJuZmUFOTg7R0dEwMTEB8GVK//bt29VaklAdFhYWsLCwwNy5czF27FgEBwdj2LBhkJOTQ1ER/5rKjh07oqioCC9evEDPnj3LHa9Tp0548OABWrZsKZT4qis6Ohp2dnbw9PTk2lJTU7l/q6iowNTUFOfPn0evXr3EGhup35zb6sGZimFCCCEEgIRv5dakSRPMmjUL8+fPx+nTp/HgwQNMmzYNeXl58PDwEGjsjx8/Yvbs2YiKikJGRgaio6Nx+/ZtbtmDqakpcnNzcf78ebx69Qp5eXmwsLDA+PHjMWnSJBw5cgSPHj3CrVu3EBgYiJMnTwIAFi5ciGvXrmH27NmIj49HSkoK/ve//2H27NkC56My5ubmiImJQWRkJJKTk7F06VLcvn2br09AQAA2bNiArVu3IiUlBXfu3MEvv/wi0rgIIYQQQhoSiS+rWLNmDYqLizFx4kTk5OTAxsYGkZGRaNq0qUDjSktL4/Xr15g0aRKeP38OLS0tDB8+nLs7g52dHWbOnInRo0fj9evX3K3cgoODsXLlSvzwww/4559/oKWlhe7du2Pw4MEAvqx/vnTpEvz8/NCzZ08wxmBmZobRo0cLnIvKzJgxA3FxcRg9ejR4PB7Gjh0LT09PnDp1iuvj5uaGT58+YdOmTfD19YWWlhZGjBgh0rgIIYQQQhoSid/KjdQd2dnZUFNTq/JTnIIqLCxEREQEBg4c2CAX/IsD5VBwlEPhoDwKjnIoOMqhcDT0PFa3zqEn5BFCCCGEEFKiXhfHbdu25R7e8e3X17dak5TQ0NAK42vbtq2kwyOEEEIIId+Q+JpjQURERFT4KEBdXV0xR1PWkCFD0K1bt3K3NcS3KwghhBBC6rt6XRyX3v6trlJRUYGKioqkwyCEEEIIIdVUr5dVEEIIIYQQIkxUHBNCCCGEEFKi0RfHjDFMnz4dGhoa4PF4UFdXF9rT+arju+++Q1hYmFDHPH36NKytrav1iG1CCCGEEPKvRl8cnz59GiEhIThx4gQyMzORnJyMFStWiOXYx48fx/PnzzFmzBiubdeuXXB0dISqqip4PB7evXtXZr8hQ4agWbNmUFBQgL6+PiZOnIhnz55x2/v37w9ZWdk6cccOQgghhJD6pNEXx6mpqdDX14ednR309PSgo6Mjtg/Rbd26Fe7u7pCS+vfbkJeXh/79+2PJkiUV7terVy8cPHgQSUlJ+PPPP5GamlrmSXiTJ0/G1q1bRRY7IYQQQmom8n4Whm67CsulpzF021VE3s+SdEikHI26OJ48eTLmzJmDx48fg8fjwdTUFI6OjnzLKkxNTbFy5UpMmjQJysrKMDExwfHjx/Hy5UsMHToUysrK6NChA2JiYrh9QkJCoK6ujmPHjsHc3BwKCgpwdnbGkydPuD4vX77EhQsX4OLiwheTj48PFi1ahO7du1cY99y5c9G9e3eYmJjAzs4OixYtwo0bN/hua+fi4oKYmBikpqYKIVOEEEIIEUTk/SzM2B+LhKfv8bGwCAlP32Pmf2OpQK6D6vWt3AS1ZcsWmJmZYdeuXbh9+zakpaUxcuTIMv02bdqE1atXY+nSpdi0aRMmTpwIOzs7TJkyBT///DMWLlyISZMm4f79++DxeAC+zACvWrUK+/btg5ycHDw9PTFmzBhER0cDAK5evQolJSVYWloKdA5v3rxBaGgo7Ozs+O6d3KxZM+jq6uLKlSswMzMrd9/8/Hzk5+dzr7OzswF8eXxkRfePFobSsUV5jIaOcig4yqFwUB4FRzkUXH3IYdCFlDJtjAFBF1PgZKEpgYjKqg95FER1z6tRF8dqampQUVGBtLQ09PT0Kuw3cOBAzJgxAwDw008/Yfv27ejSpQtXSC9cuBC2trZ4/vw5N05hYSG2bdvGPQRk7969sLS0xK1bt9C1a1dkZGRAV1eXb0lFTSxcuBDbtm1DXl4eunfvjhMnTpTpY2BggIyMjArHCAwMxLJly8q0nzlzBkpKSrWKqybOnj0r8mM0dJRDwVEOhYPyKDjKoeDqcg7/zpQGwCvb/uw9IiIixB9QJepyHgWRl5dXrX6Nujiurg4dOnD/Ln3yXvv27cu0vXjxgiuOZWRk0KVLF65P69atoa6ujsTERHTt2hUfP36EgoJCrWOaP38+PDw8kJGRgWXLlmHSpEk4ceIEN3MNAIqKipVeCIsXL8a8efO419nZ2TA2Nka/fv2gqqpa69iqUlhYiLNnz6Jv3770pMBaohwKjnIoHJRHwVEOBVcfcrj78Q3c/Se7THtrAzUMHFjxUkpxqg95FETpO+RVoeK4Gr6+QEqLz/LaanLrNC0tLbx9+7bWMWlpaUFLSwsWFhawtLSEsbExbty4AVtbW67PmzdvoK2tXeEY8vLykJeXL9MuKysrlh8KcR2nIaMcCo5yKByUR8FRDgVXl3Po5WSOmf+NBWP/tvF4wOxe5nUu5rqcR0FU95wa9QfyROnz5898H9JLSkrCu3fvuDXGHTt2RFZWlkAFcqnSovzr9cOfPn1CamoqOnbsKPD4hBBCCBGMc1s97JjQGVbG6lCSk4aVsTp2TuiMfm0rXtZJJINmjkVEVlYWc+bMwdatWyEjI4PZs2eje/fu6Nq1K4AvxbGWlhaio6MxePBgbr+srCxkZWXh4cOHAIB79+5BRUUFzZo1g4aGBm7evInbt2+jR48eaNq0KVJTU7F06VKYmZnxzRrfuHED8vLyfG2EEEIIkRzntnpwpmK4zqOZYxFRUlLCwoULMW7cONjb20NZWRl//PEHt11aWhru7u5lHtSxY8cOdOzYEdOmTQPw5Ql6HTt2xPHjx7lxjxw5gt69e6NVq1bw8PBAhw4dcOnSJb4lEuHh4Rg/frxYPlhHCCGEENJQNPqZYx8fH777GkdFRfFtT09PL7MP+3rBEL7cC/nbNgAYPnw4hg8fXuGx586di7Zt2yIjIwMmJiYAgICAAAQEBFS4T/v27XHhwoUKtwPAq1evcPjwYb5lHYQQQgghpGo0cyxBenp62L17Nx4/fizUcdPT0/Hrr7+iefPmQh2XEEIIIaSha/Qzx5Lm6uoq9DFtbGxgY2Mj9HEJIYQQQho6mjkWgcmTJ+Pdu3eSDoMQQgghhNQQFceEEEIIIYSUoOKYEEIIIYSQElQcE0IIIYQQUoKK4wo4Ojry3eJNWAICAqCrqwsej4djx44JfXxCCCGEEFJ7VByLQHp6Ong8HuLj4/naExMTsWzZMuzcuROZmZkYMGCAZAIkhBBCCCHlolu51UJBQQHk5ORqvF9qaioAYOjQoeDxeMIOixBCJObsgxfYeeURkp/nwkJXGZ69WtJjcgkh9RLNHAP48OEDJk2aBGVlZejr62PDhg18201NTbFixQpMmjQJqqqqmD59eqXjlT58o2PHjuDxeHB0dERAQABcXFwAAFJSUlUWx3/99RekpKTw8uVLAMCbN28gJSWFMWPGcH1WrlyJHj16APjyZD8ej4eTJ0+iQ4cOUFBQQPfu3fHXX3/VLBmEEFJDd9/w4Bkej4Sn7/GxsAgJT99j5n9jEXk/S9KhEUJIjdHMMYD58+fj0qVL+N///gcdHR0sWbIEd+7cgbW1Nddn/fr1+Omnn+Dv71/leLdu3ULXrl1x7tw5tG3bFnJycpCTk4OpqSnc3d2RmZlZ5Rht27aFpqYmLl26hBEjRuDKlSvc61KXLl2Co6NjmXPZsmUL9PT0sGTJEri4uCA5ORmysrJljpGfn4/8/HzudXZ2NgCgsLAQhYWFVcZYW6Vji/IYDR3lUHCUQ+EoLCzE2adl51kYA4IupsDJQlMCUdUvdC0KjnIoHA09j9U9r0ZfHOfm5mL37t3473//i969ewMA9u7dCyMjI75+Tk5O+OGHH6o1pra2NgBAU1MTenr/vq2orq4OAHxtFeHxePjuu+8QFRWFESNGICoqCu7u7vj999/x999/w8zMDNeuXcOCBQv49vP390ffvn35zuPo0aMYNWpUmWMEBgZi2bJlZdrPnDkDJSWlap2rIM6ePSvyYzR0lEPBUQ4Fl/VRutz2v5+9R0REhJijqb/oWhQc5VA4Gmoe8/LyqtWv0RfHqampKCgoQLdu3bg2DQ0NtGrViq+fJB7H7ODggF27dgH4Mku8evVqJCcnIyoqCm/evEFhYSHs7e359rG1teX+XXoeiYmJ5Y6/ePFizJs3j3udnZ0NY2Nj9OvXD6qqqiI4oy8KCwtx9uxZ9O3bt9wZbVI1yqHgKIfCUVhYCL27F/D4Q9ltrQ3UMHBgd/EHVc/QtSg4yqFwNPQ8lr5DXpVGXxxXV5MmTcR+zNLbyaWkpODBgwfo0aMH/v77b0RFReHt27ewsbERaIZXXl4e8vLyZdplZWXF8kMhruM0ZJRDwVEOBdfXqBh7kqXB2L9tPB4wu5c55bYG6FoUHOVQOBpqHqt7To3+A3lmZmaQlZXFzZs3uba3b98iOTm51mOW3smiqKhIoNjat2+Ppk2bYuXKlbC2toaysjIcHR1x6dIlREVFlVlvDAA3btzg/l16HpaWlgLFQQghlemgwRA0xhpWxupQkpOGlbE6dk7ojH50twpCSD3U6GeOlZWV4eHhgfnz50NTUxM6Ojrw8/ODlFTt/27Q0dGBoqIiTp8+DSMjIygoKEBNTa3G45SuOw4NDYWvry8AoEOHDsjPz8f58+f5lkSUWr58OTQ1NaGrqws/Pz9oaWnB1dW11udCCCHV0beNDgZaGUo6DEIIEVijnzkGgJ9//hk9e/aEi4sL+vTpgx49eqBz5861Hk9GRgZbt27Fzp07YWBggKFDh9Z6LAcHBxQVFXGzxFJSUvjuu+/A4/HKrDcGgDVr1sDb2xudO3dGVlYW/u///q9W92QmhBBCCGmMGv3MMfBl9nj//v3Yv38/1zZ//nzu3+np6TUec+rUqZg6dSpfm6urK9jXi/KqwcfHp8xjrCt77HSPHj3o3saEEEIIIbVEM8eEEEIIIYSUoJnjWli9ejVWr15d7raePXvi1KlT1RpHWVm5wm2nTp1Cz549axVfbZXOalf3Vie1VVhYiLy8PGRnZzfIT8OKA+VQcJRD4aA8Co5yKDjKoXA09DyW1jdVvYvPYzV9n5/gzZs3ePPmTbnbFBUVYWhYvQ+lPHz4sMJthoaGUFRUrFV8tfX06VMYGxuL9ZiEEEIIIeL05MmTMg97+xoVx4RTXFyMZ8+eQUVFBTweT2THKX3YyJMnT0T6sJGGjHIoOMqhcFAeBUc5FBzlUDgaeh4ZY8jJyYGBgUGldyWjZRWEIyUlVelfUsKmqqraIH/4xIlyKDjKoXBQHgVHORQc5VA4GnIeq3NrXfpAHiGEEEIIISWoOCaEEEIIIaQEFcdE7OTl5eHv7w95eXlJh1JvUQ4FRzkUDsqj4CiHgqMcCgfl8Qv6QB4hhBBCCCElaOaYEEIIIYSQElQcE0IIIYQQUoKKY0IIIYQQQkpQcUwIIYQQQkgJKo6JSAQFBcHU1BQKCgro1q0bbt26Va39Dhw4AB6PB1dXV9EGWA/UNIfv3r2Dl5cX9PX1IS8vDwsLC0RERIgp2rqppjncvHkzWrVqBUVFRRgbG2Pu3Ln49OmTmKKtey5fvgwXFxcYGBiAx+Ph2LFjVe4TFRWFTp06QV5eHi1btkRISIjI46zraprHI0eOoG/fvtDW1oaqqipsbW0RGRkpnmDrqNpci6Wio6MhIyMDa2trkcVXH9Qmh/n5+fDz84OJiQnk5eVhamqKPXv2iD5YCaPimAjdH3/8gXnz5sHf3x937tyBlZUVnJ2d8eLFi0r3S09Ph6+vL3r27CmmSOuumuawoKAAffv2RXp6Og4fPoykpCT89ttvMDQ0FHPkdUdNcxgWFoZFixbB398fiYmJ2L17N/744w8sWbJEzJHXHR8+fICVlRWCgoKq1f/Ro0cYNGgQevXqhfj4ePj4+GDq1KmNvrCraR4vX76Mvn37IiIiArGxsejVqxdcXFwQFxcn4kjrrprmsNS7d+8wadIk9O7dW0SR1R+1yeGoUaNw/vx57N69G0lJSQgPD0erVq1EGGUdwQgRsq5duzIvLy/udVFRETMwMGCBgYEV7vP582dmZ2fHfv/9d+bm5saGDh0qhkjrrprmcPv27axFixasoKBAXCHWeTXNoZeXF3NycuJrmzdvHrO3txdpnPUFAHb06NFK+yxYsIC1bduWr2306NHM2dlZhJHVL9XJY3natGnDli1bJvyA6qGa5HD06NHsxx9/ZP7+/szKykqkcdUn1cnhqVOnmJqaGnv9+rV4gqpDaOaYCFVBQQFiY2PRp08frk1KSgp9+vTB9evXK9xv+fLl0NHRgYeHhzjCrNNqk8Pjx4/D1tYWXl5e0NXVRbt27bB69WoUFRWJK+w6pTY5tLOzQ2xsLLf0Ii0tDRERERg4cKBYYm4Irl+/zpdzAHB2dq70Z59Urbi4GDk5OdDQ0JB0KPVKcHAw0tLS4O/vL+lQ6qXjx4/DxsYG69atg6GhISwsLODr64uPHz9KOjSRk5F0AKRhefXqFYqKiqCrq8vXrquri7///rvcfa5evYrdu3cjPj5eDBHWfbXJYVpaGi5cuIDx48cjIiICDx8+hKenJwoLCxvlfwy1yeG4cePw6tUr9OjRA4wxfP78GTNnzmzUyypqKisrq9ycZ2dn4+PHj1BUVJRQZPXb+vXrkZubi1GjRkk6lHojJSUFixYtwpUrVyAjQ6VObaSlpeHq1atQUFDA0aNH8erVK3h6euL169cIDg6WdHgiRTPHRKJycnIwceJE/Pbbb9DS0pJ0OPVWcXExdHR0sGvXLnTu3BmjR4+Gn58fduzYIenQ6o2oqCisXr0av/76K+7cuYMjR47g5MmTWLFihaRDI41YWFgYli1bhoMHD0JHR0fS4dQLRUVFGDduHJYtWwYLCwtJh1NvFRcXg8fjITQ0FF27dsXAgQOxceNG7N27t8HPHtOfU0SotLS0IC0tjefPn/O1P3/+HHp6emX6p6amIj09HS4uLlxbcXExAEBGRgZJSUkwMzMTbdB1TE1zCAD6+vqQlZWFtLQ012ZpaYmsrCwUFBRATk5OpDHXNbXJ4dKlSzFx4kRMnToVANC+fXt8+PAB06dPh5+fH6SkaC6hKnp6euXmXFVVlWaNa+HAgQOYOnUqDh06VGa5CqlYTk4OYmJiEBcXh9mzZwP48v8KYwwyMjI4c+YMnJycJBxl3aevrw9DQ0OoqalxbZaWlmCM4enTpzA3N5dgdKJFv+2JUMnJyaFz5844f/4811ZcXIzz58/D1ta2TP/WrVvj3r17iI+P576GDBnCfdrd2NhYnOHXCTXNIQDY29vj4cOH3B8WAJCcnAx9ff1GVxgDtcthXl5emQK49I8Nxpjogm1AbG1t+XIOAGfPnq0w56Ri4eHhcHd3R3h4OAYNGiTpcOoVVVXVMv+vzJw5E61atUJ8fDy6desm6RDrBXt7ezx79gy5ublcW3JyMqSkpGBkZCTByMRAsp8HJA3RgQMHmLy8PAsJCWEPHjxg06dPZ+rq6iwrK4sxxtjEiRPZokWLKtyf7lZR8xw+fvyYqaiosNmzZ7OkpCR24sQJpqOjw1auXCmpU5C4mubQ39+fqaiosPDwcJaWlsbOnDnDzMzM2KhRoyR1ChKXk5PD4uLiWFxcHAPANm7cyOLi4lhGRgZjjLFFixaxiRMncv3T0tKYkpISmz9/PktMTGRBQUFMWlqanT59WlKnUCfUNI+hoaFMRkaGBQUFsczMTO7r3bt3kjoFiatpDr9Fd6uoeQ5zcnKYkZERGzFiBLt//z67dOkSMzc3Z1OnTpXUKYgNFcdEJH755RfWrFkzJicnx7p27cpu3LjBbXNwcGBubm4V7kvF8Rc1zeG1a9dYt27dmLy8PGvRogVbtWoV+/z5s5ijrltqksPCwkIWEBDAzMzMmIKCAjM2Nmaenp7s7du34g+8jrh48SIDUOarNG9ubm7MwcGhzD7W1tZMTk6OtWjRggUHB4s97rqmpnl0cHCotH9jVJtr8WtUHNcuh4mJiaxPnz5MUVGRGRkZsXnz5rG8vDzxBy9mPMbo/UJCCCGEEEIAWnNMCCGEEEIIh4pjQgghhBBCSlBxTAghhBBCSAkqjgkhhBBCCClBxTEhhBBCCCElqDgmhBBCCCGkBBXHhBBCCCGElKDimBBCCCGEkBJUHBNCGj1HR0f4+PhIOow6JyQkBOrq6tzrgIAAWFtbi/SYPB4Px44dq3C7KL5XUVFR4PF4ePfuXbX3mTx5MlxdXYU+rjiuxerETkhjRsUxIaTecnFxQf/+/cvdduXKFfB4PNy9e1fMUTVcvr6+OH/+vKTDEDo7OztkZmZCTU2t2vts2bIFISEh3OvyitrajEsIkTwqjgkh9ZaHhwfOnj2Lp0+fltkWHBwMGxsbdOjQQQKRiU9hYaHYjqWsrAxNTU2xHU9c5OTkoKenBx6PV+191NTU+GbVhTUuIUTyqDgmhNRbgwcPhra2Nt8MHgDk5ubi0KFD8PDwwOvXrzF27FgYGhpCSUkJ7du3R3h4eKXjlvfWvrq6Ot9xnjx5glGjRkFdXR0aGhoYOnQo0tPTKxyz9C328+fPw8bGBkpKSrCzs0NSUhJfv+3bt8PMzAxycnJo1aoV9u/fXya27du3Y8iQIWjSpAlWrVrFLXfYs2cPmjVrBmVlZXh6eqKoqAjr1q2Dnp4edHR0sGrVKr6xNm7ciPbt26NJkyYwNjaGp6cncnNzKzyHb5dVREVFoWvXrmjSpAnU1dVhb2+PjIwMbvv//vc/dOrUCQoKCmjRogWWLVuGz58/c9tTUlLw3XffQUFBAW3atMHZs2crPPbXPn/+jNmzZ0NNTQ1aWlpYunQpGGMAgOXLl6Ndu3Zl9rG2tsbSpUvLHe/b5Q+ly0kiIyNhaWkJZWVl9O/fH5mZmdw+Xy9NmDx5Mi5duoQtW7aAx+OBx+MhPT29zLi1uRa/lpycDB6Ph7///puvfdOmTTAzMwMAFBUVwcPDA82bN4eioiJatWqFLVu2VDquqakpNm/ezNdmbW2NgIAA7vW7d+8wdepUaGtrQ1VVFU5OTkhISKh27ITUJ1QcE0LqLRkZGUyaNAkhISFccQQAhw4dQlFREcaOHYtPnz6hc+fOOHnyJP766y9Mnz4dEydOxK1bt2p93MLCQjg7O0NFRQVXrlxBdHQ0V0AVFBRUuq+fnx82bNiAmJgYyMjIYMqUKdy2o0ePwtvbGz/88AP++usvzJgxA+7u7rh48SLfGAEBARg2bBju3bvH7Z+amopTp07h9OnTCA8Px+7duzFo0CA8ffoUly5dwtq1a/Hjjz/i5s2b3DhSUlLYunUr7t+/j7179+LChQtYsGBBtXLw+fNnuLq6wsHBAXfv3sX169cxffp0bpb0ypUrmDRpEry9vfHgwQPs3LkTISEhXIFeXFyM4cOHQ05ODjdv3sSOHTuwcOHCah177969kJGRwa1bt7BlyxZs3LgRv//+OwBgypQpSExMxO3bt7n+cXFxuHv3Ltzd3as1PgDk5eVh/fr12L9/Py5fvozHjx/D19e33L5btmyBra0tpk2bhszMTGRmZsLY2LhMP0GvRQsLC9jY2CA0NJSvPTQ0FOPGjQPwJa9GRkY4dOgQHjx4gJ9++glLlizBwYMHq33u5Rk5ciRevHiBU6dOITY2Fp06dULv3r3x5s0bgcYlpE5ihBBSjyUmJjIA7OLFi1xbz5492YQJEyrcZ9CgQeyHH37gXjs4ODBvb2/uNQB29OhRvn3U1NRYcHAwY4yx/fv3s1atWrHi4mJue35+PlNUVGSRkZHlHvPixYsMADt37hzXdvLkSQaAffz4kTHGmJ2dHZs2bRrffiNHjmQDBw7ki83Hx4evj7+/P1NSUmLZ2dlcm7OzMzM1NWVFRUVcW6tWrVhgYGC58THG2KFDh5impib3Ojg4mKmpqfEdx8rKijHG2OvXrxkAFhUVVe5YvXv3ZqtXr+Zr279/P9PX12eMMRYZGclkZGTYP//8w20/depUubn/moODA7O0tOTL/cKFC5mlpSX3esCAAWzWrFnc6zlz5jBHR8cKxyz93rx9+5Y7bwDs4cOHXJ+goCCmq6vLvXZzc2NDhw7li+vra6i8cctT1bX4rU2bNjEzMzPudVJSEgPAEhMTK9zHy8uL/ec//6kwdhMTE7Zp0ya+faysrJi/vz9jjLErV64wVVVV9unTJ74+ZmZmbOfOnRUel5D6imaOCSH1WuvWrWFnZ4c9e/YAAB4+fIgrV67Aw8MDwJe3mVesWIH27dtDQ0MDysrKiIyMxOPHj2t9zISEBDx8+BAqKipQVlaGsrIyNDQ08OnTJ6Smpla679droPX19QEAL168AAAkJibC3t6er7+9vT0SExP52mxsbMqMa2pqChUVFe61rq4u2rRpAykpKb620mMBwLlz59C7d28YGhpCRUUFEydOxOvXr5GXl1dVCqChoYHJkyfD2dkZLi4u2LJlC9+yg4SEBCxfvpzLj7KyMjezmpeXh8TERBgbG8PAwIDbx9bWtsrjAkD37t351vHa2toiJSUFRUVFAIBp06YhPDwcnz59QkFBAcLCwvhm6KtDSUmJW6oAfPlefZ272hDGtThmzBikp6fjxo0bAL7MGnfq1AmtW7fm+gQFBaFz587Q1taGsrIydu3aJfD1npubC01NTb7v56NHj6q83gmpj2QkHQAhhAjKw8MDc+bMQVBQEIKDg2FmZgYHBwcAwM8//4wtW7Zg8+bN3PpaHx+fSpc/8Hg8vmUaAP8H33Jzc9G5c+cyb28DgLa2dqWxysrK8h0H+PJWeE00adKk0nFLxy6vrfRY6enpGDx4MGbNmoVVq1ZBQ0MDV69ehYeHBwoKCqCkpFRlHMHBwfj+++9x+vRp/PHHH/jxxx9x9uxZdO/eHbm5uVi2bBmGDx9eZj8FBYWanG6Nubi4QF5eHkePHoWcnBwKCwsxYsSIGo1RXu6+vSZqqjbX4rf09PTg5OSEsLAwdO/eHWFhYZg1axa3/cCBA/D19cWGDRtga2sLFRUV/Pzzz3zLab4lJSVV5fWur6+PqKioMvtW9aFEQuojKo4JIfXeqFGj4O3tjbCwMOzbtw+zZs3iCs/o6GgMHToUEyZMAPClEE1OTkabNm0qHE9bW5tvFjQlJYVvNrVTp074448/oKOjA1VVVaGdh6WlJaKjo+Hm5sa1RUdHVxprbcXGxqK4uBgbNmzgZpdrsy61Y8eO6NixIxYvXgxbW1uuaOvUqROSkpLQsmXLcveztLTEkydPkJmZyc2gl86GVuXbQu/GjRswNzeHtLQ0gC9r0d3c3BAcHAw5OTmMGTMGioqKNT63mpCTk+NmritSm2uxPOPHj8eCBQswduxYpKWlYcyYMXzHsLOzg6enJ9dW1ezut9d7dnY2Hj16xL3u1KkTsrKyICMjA1NT0xrFSkh9RMsqCCH1nrKyMkaPHo3FixcjMzMTkydP5raZm5vj7NmzuHbtGhITEzFjxgw8f/680vGcnJywbds2xMXFISYmBjNnzuSbSRw/fjy0tLQwdOhQXLlyBY8ePUJUVBS+//77cm8rV13z589HSEgItm/fjpSUFGzcuBFHjhyp8INggmjZsiUKCwvxyy+/IC0tDfv378eOHTuqvf+jR4+wePFiXL9+HRkZGThz5gxSUlJgaWkJAPjpp5+wb98+LFu2DPfv30diYiIOHDiAH3/8EQDQp08fWFhYwM3NDQkJCbhy5Qr8/PyqdezHjx9j3rx5SEpKQnh4OH755Rd4e3vz9Zk6dSouXLiA06dP13hJRW2Ympri5s2bSE9Px6tXr8p9N6A212J5hg8fjpycHMyaNQu9evXiW5pibm6OmJgYREZGIjk5GUuXLuX7cGJ5nJycsH//fly5cgX37t2Dm5sb94cG8OV7ZWtrC1dXV5w5cwbp6em4du0a/Pz8EBMTU+P4CanrqDgmhDQIHh4eePv2LZydnfmKhR9//BGdOnWCs7MzHB0doaenV+XTwTZs2ABjY2P07NkT48aNg6+vL98yAyUlJVy+fBnNmjXD8OHDYWlpCQ8PD3z69EmgmWRXV1ds2bIF69evR9u2bbFz504EBwfD0dGx1mNWxMrKChs3bsTatWvRrl07hIaGIjAwsNr7Kykp4e+//8Z//vMfWFhYYPr06fDy8sKMGTMAAM7Ozjhx4gTOnDmDLl26oHv37ti0aRNMTEwAfHkr/+jRo/j48SO6du2KqVOnlrnVXEUmTZrE7efl5QVvb29Mnz6dr4+5uTns7OzQunVrdOvWrdrnVVu+vr6QlpZGmzZtoK2tXe4a39pci+VRUVGBi4sLEhISMH78eL5tM2bMwPDhwzF69Gh069YNr1+/5ptFLs/ixYvh4OCAwYMHY9CgQXB1deVbb83j8RAREYHvvvsO7u7usLCwwJgxY5CRkQFdXd0ax09IXcdjgi6iIoQQQuoYxhjMzc3h6emJefPmSTocQkg9QmuOCSGENCgvX77EgQMHkJWVVaN7GxNCCEDFMSGEkAZGR0cHWlpa2LVrF5o2bSrpcAgh9QwVx4QQQhoUWi1ICBEEfSCPEEIIIYSQElQcE0IIIYQQUoKKY0IIIYQQQkpQcUwIIYQQQkgJKo4JIYQQQggpQcUxIYQQQgghJag4JoQQQgghpAQVx4QQQgghhJSg4pgQQgghhJASVBwTQgghhBBSgopjQgghhBBCSlBxTAghhBBCSAkqjgkhhBBCCClBxTEhhBBCCCElqDgmhBBCCCGkhIykAyDVU1xcjGfPnkFFRQU8Hk/S4RBCCCGE1CuMMeTk5MDAwABSUhXPD1NxXE88e/YMxsbGkg6DEEIIIaRee/LkCYyMjCrcTsVxPaGiogLgyzdUVVVVwtEQQgghhNQv2dnZMDY25mqqilBxXE+ULqVQVVWl4pgQQgghpJaqWp5KH8gjhBBCCCGkBBXHhBBCCCGElKDimBBCCCGEkBJ1ojgOCAiAtbW1pMOo0uTJk+Hq6sq9dnR0hI+Pj8TiIYQQQgghwlUnPpDn6+uLOXPmSDqMGjty5AhkZWUlHQYhhEhc5P0s/HrxIZKf58JCVxmevVrCua2epMMihJAaqxMzx8rKytDU1BRojMLCQqH2qw4NDY0qbwdCCCENXeT9LMzYH4uEp+/xsbAICU/fY+Z/YxF5P0vSoRFCSI3VqDh2dHTEnDlz4OPjg6ZNm0JXVxe//fYbPnz4AHd3d6ioqKBly5Y4deoUt09RURE8PDzQvHlzKCoqolWrVtiyZQvfuN8uqyguLsby5cthZGQEeXl5WFtb4/Tp09z29PR08Hg8/PHHH3BwcICCggJCQ0PLjZnH42H79u0YMmQImjRpglWrVlUrpqKiIsybNw/q6urQ1NTEggULwBgrk4+vl1XweDwcO3aMr4+6ujpCQkIAAAUFBZg9ezb09fWhoKAAExMTBAYGlht3fn4+srOz+b4IIaQu+vXiwzJtjAG/RqVKIBpCCBFMjWeO9+7dCy0tLdy6dQtz5szBrFmzMHLkSNjZ2eHOnTvo168fJk6ciLy8PABfCl0jIyMcOnQIDx48wE8//YQlS5bg4MGDFR5jy5Yt2LBhA9avX4+7d+/C2dkZQ4YMQUpKCl+/RYsWwdvbG4mJiXB2dq5wvICAAAwbNgz37t3DlClTqhXThg0bEBISgj179uDq1at48+YNjh49WtN08dm6dSuOHz+OgwcPIikpCaGhoTA1NS23b2BgINTU1LgvejoeIaSuSn6eW257yvMcMUdCCCGCq/GaYysrK/z4448AgMWLF2PNmjXQ0tLCtGnTAAA//fQTtm/fjrt376J79+6QlZXFsmXLuP2bN2+O69ev4+DBgxg1alS5x1i/fj0WLlyIMWPGAADWrl2LixcvYvPmzQgKCuL6+fj4YPjw4VXGPG7cOLi7u/O1VRXT5s2bsXjxYm78HTt2IDIysspjVebx48cwNzdHjx49wOPxYGJiUmHfxYsXY968edzr0qe6EEJIXWOhq4yEp+/LtJvr0rIzQkj9U+OZ4w4dOnD/lpaWhqamJtq3b8+16erqAgBevHjBtQUFBaFz587Q1taGsrIydu3ahcePH5c7fnZ2Np49ewZ7e3u+dnt7eyQmJvK12djYVCvm8vpVFtP79++RmZmJbt26cf1lZGSqfbyKTJ48GfHx8WjVqhW+//57nDlzpsK+8vLy3NPw6Kl4hJC6zLNXS3z7wCkeD/ByNJNMQIQQIoAaF8ff3p2Bx+PxtZU+kq+4uBgAcODAAfj6+sLDwwNnzpxBfHw83N3dUVBQIEjcAIAmTZrUqp+oYuLxeGXWJX/9AcBOnTrh0aNHWLFiBT5+/IhRo0ZhxIgRAh2TEEIkzbmtHnZM6AwrY3UoyUnDylgdOyd0Rj+6WwUhpB4S+a3coqOjYWdnB09PT64tNbXiD2moqqrCwMAA0dHRcHBw4Buna9euYolJTU0N+vr6uHnzJr777jsAwOfPnxEbG4tOnTpVOK62tjYyMzO51ykpKdza61KqqqoYPXo0Ro8ejREjRqB///548+YNNDQ0hHJuhBAiCc5t9ejWbYSQBkHkxbG5uTn27duHyMhING/eHPv378ft27fRvHnzCveZP38+/P39YWZmBmtrawQHByM+Pr7CO1KIIiZvb2+sWbMG5ubmaN26NTZu3Ih3795VOq6TkxO2bdsGW1tbFBUVYeHChXyz6hs3boS+vj46duwIKSkpHDp0CHp6elBXVxfKeRFCCCGEEMGIvDieMWMG4uLiMHr0aPB4PIwdOxaenp58t3v71vfff4/379/jhx9+wIsXL9CmTRscP34c5ubmYovphx9+QGZmJtzc3CAlJYUpU6Zg2LBheP++7IdOSm3YsAHu7u7o2bMnDAwMsGXLFsTGxnLbVVRUsG7dOqSkpEBaWhpdunRBREQEpKTqxO2mCSGEEEIaPR77dpGsBCxevBhXrlzB1atXJR1KnZWdnQ01NTW8f/+ePpxHCCGEEFJD1a2lJDplyRhDamoqzp8/j7Zt20oyFEIIIYQQQiRbHL9//x5t2rSBnJwclixZIslQCCGEEEIIEf2a48qoq6sjPz9fkiHUG6WrX+gx0oQQQgghNVdaQ1W1oliixTGpvpycL49hpafkEUIIIYTUXk5ODtTU1CrcXic+kEeqVlxcjGfPnkFFRYV70IqolD6q+smTJ/ThP1A+vkX5+Bflgh/l41+UC36Uj39RLviJMx+MMeTk5MDAwKDSO4XRzHE9ISUlBSMjI7Eekx5bzY/ywY/y8S/KBT/Kx78oF/woH/+iXPATVz4qmzEuRTfYJYQQQgghpAQVx4QQQgghhJSg4piUIS8vD39/f8jLy0s6lDqB8sGP8vEvygU/yse/KBf8KB//olzwq4v5oA/kEUIIIYQQUoJmjgkhhBBCCClBxTEhhBBCCCElqDgmhBBCCCGkBBXHhBBCCCGElKDiuBG4fPkyXFxcYGBgAB6Ph2PHjlXaPyoqCjwer8xXVlYW1ycwMBBdunSBiooKdHR04OrqiqSkJBGfieBEkYuvrVmzBjweDz4+PsIPXgRElY9//vkHEyZMgKamJhQVFdG+fXvExMSI8EwEJ4pcFBUVYenSpWjevDkUFRVhZmaGFStWoD58Drqm+QCA/Px8+Pn5wcTEBPLy8jA1NcWePXv4+hw6dAitW7eGgoIC2rdvj4iICBGdgfCIIhe//fYbevbsiaZNm6Jp06bo06cPbt26JcKzEB5RXRulDhw4AB6PB1dXV+EGLgKiysW7d+/g5eUFfX19yMvLw8LCotH+rADA5s2b0apVKygqKsLY2Bhz587Fp0+fRHQW9IS8RuHDhw+wsrLClClTMHz48Grvl5SUxPe0Gh0dHe7fly5dgpeXF7p06YLPnz9jyZIl6NevHx48eIAmTZoINX5hEkUuSt2+fRs7d+5Ehw4dhBKrOIgiH2/fvoW9vT169eqFU6dOQVtbGykpKWjatKlQYxc2UeRi7dq12L59O/bu3Yu2bdsiJiYG7u7uUFNTw/fffy/U+IWtNvkYNWoUnj9/jt27d6Nly5bIzMxEcXExt/3atWsYO3YsAgMDMXjwYISFhcHV1RV37txBu3btRHUqAhNFLqKiojB27FjY2dlBQUEBa9euRb9+/XD//n0YGhqK6lSEQhT5KJWeng5fX1/07NlT2GGLhChyUVBQgL59+0JHRweHDx+GoaEhMjIyoK6uLqKzEB5R5CMsLAyLFi3Cnj17YGdnh+TkZEyePBk8Hg8bN24UzYkw0qgAYEePHq20z8WLFxkA9vbt22qP++LFCwaAXbp0SbAAxUiYucjJyWHm5ubs7NmzzMHBgXl7ewstTnERVj4WLlzIevToIdzgxExYuRg0aBCbMmUKX9vw4cPZ+PHjhRCl+FQnH6dOnWJqamrs9evXFfYZNWoUGzRoEF9bt27d2IwZM4QRplgIKxff+vz5M1NRUWF79+4VMELxEmY+Pn/+zOzs7Njvv//O3Nzc2NChQ4UXqBgIKxfbt29nLVq0YAUFBUKOULyElQ8vLy/m5OTE1zZv3jxmb28vjDDLRcsqSIWsra2hr6+Pvn37Ijo6utK+79+/BwBoaGiIIzSxqyoXXl5eGDRoEPr06SOB6MSvsnwcP34cNjY2GDlyJHR0dNCxY0f89ttvEopU9CrLhZ2dHc6fP4/k5GQAQEJCAq5evYoBAwZIIlSRKv2+r1u3DoaGhrCwsICvry8+fvzI9bl+/XqZnxFnZ2dcv35d3OGKVHVy8a28vDwUFhY2yN+h1c3H8uXLoaOjAw8PDwlFKnrVycXx48dha2sLLy8v6Orqol27dli9ejWKiookGLloVCcfdnZ2iI2N5ZYdpaWlISIiAgMHDhRZXLSsgpShr6+PHTt2wMbGBvn5+fj999/h6OiImzdvolOnTmX6FxcXw8fHB/b29nX6rdHaqE4uDhw4gDt37uD27dsSjlb0qpOPtLQ0bN++HfPmzcOSJUtw+/ZtfP/995CTk4Obm5uEz0B4qpOLRYsWITs7G61bt4a0tDSKioqwatUqjB8/XsLRC19aWhquXr0KBQUFHD16FK9evYKnpydev36N4OBgAEBWVhZ0dXX59tPV1a1wDX99VZ1cfGvhwoUwMDBokH9gVycfV69exe7duxEfHy/ZYEWsOrlIS0vDhQsXMH78eERERODhw4fw9PREYWEh/P39JXwGwlWdfIwbNw6vXr1Cjx49wBjD58+fMXPmTCxZskR0gYlsTprUSajG2xzl+e6779iECRPK3TZz5kxmYmLCnjx5ImB04iWMXDx+/Jjp6OiwhIQEbntDXlZRnm+vDVlZWWZra8vXZ86cOax79+6Chig2wspFeHg4MzIyYuHh4ezu3bts3759TENDg4WEhAgxWtGrTj769u3LFBQU2Lt377i2P//8k/F4PJaXl8cY+3JthIWF8e0XFBTEdHR0hB6zqAgrF18LDAxkTZs25fs9Ul8IIx/Z2dnM1NSURUREcNsb6rKK6lwb5ubmzNjYmH3+/Jnrs2HDBqanpyeSuEVFWPm4ePEi09XVZb/99hu7e/cuO3LkCDM2NmbLly8XWew0c0yqpWvXrrh69WqZ9tmzZ+PEiRO4fPkyjIyMJBCZ+H2di9jYWLx48YJvRr2oqAiXL1/Gtm3bkJ+fD2lpaUmFKhbfXhv6+vpo06YNXx9LS0v8+eef4g5N7L7Nxfz587Fo0SKMGTMGANC+fXtkZGQgMDCwQc2iA1++74aGhlBTU+PaLC0twRjD06dPYW5uDj09PTx//pxvv+fPn0NPT0/c4YpUdXJRav369VizZg3OnTtXrz7MWxNV5ePDhw9IT0+Hi4sLt730A1kyMjJISkqCmZmZ2OMWhepcG/r6+pCVleX7v8PS0hJZWVkoKCiAnJycJEIXierkY+nSpZg4cSKmTp0K4Mvv0Q8fPmD69Onw8/ODlJTwVwjTmmNSLfHx8dDX1+deM8Ywe/ZsHD16FBcuXEDz5s0lGJ14fZ2L3r174969e4iPj+e+bGxsMH78eMTHxzf4whgoe23Y29uXua1fcnIyTExMxB2a2H2bi7y8vDK/uKWlpcv9lH59Z29vj2fPniE3N5drS05OhpSUFPeHs62tLc6fP8+339mzZ2FrayvWWEWtOrkAgHXr1mHFihU4ffo0bGxsJBGqWFSVj9atW5f5PTpkyBD06tUL8fHxMDY2lmD0wlWda8Pe3h4PHz7k+z2RnJwMfX39BlUYA9XLR0W/RwGI7raYIpuTJnVGTk4Oi4uLY3FxcQwA27hxI4uLi2MZGRmMMcYWLVrEJk6cyPXftGkTO3bsGEtJSWH37t1j3t7eTEpKip07d47rM2vWLKampsaioqJYZmYm91XeW4Z1iShy8a36tKxCFPm4desWk5GRYatWrWIpKSksNDSUKSkpsf/+979iP7+aEEUu3NzcmKGhITtx4gR79OgRO3LkCNPS0mILFiwQ+/nVVE3zkZOTw4yMjNiIESPY/fv32aVLl5i5uTmbOnUq1yc6OprJyMiw9evXs8TERObv789kZWXZvXv3xH5+NSGKXKxZs4bJycmxw4cP8/0OzcnJEfv51ZQo8vGt+rKsQhS5ePz4MVNRUWGzZ89mSUlJ7MSJE0xHR4etXLlS7OdXU6LIh7+/P1NRUWHh4eEsLS2NnTlzhpmZmbFRo0aJ7DyoOG4ESm859e2Xm5sbY+zLLyEHBweu/9q1a5mZmRlTUFBgGhoazNHRkV24cIFvzPLGA8CCg4PFd2K1IIpcfKs+Fceiysf//d//sXbt2jF5eXnWunVrtmvXLjGdUe2JIhfZ2dnM29ubNWvWjCkoKLAWLVowPz8/lp+fL8Yzq52a5oMxxhITE1mfPn2YoqIiMzIyYvPmzSvzB/PBgweZhYUFk5OTY23btmUnT54U0xnVnihyYWJiUu6Y/v7+4juxWhLVtfG1+lIciyoX165dY926dWPy8vKsRYsWbNWqVXxrkOsqUeSjsLCQBQQEcL9vjY2NmaenZ41uN1tTPMbqwaOaCCGEEEIIEQNac0wIIYQQQkgJKo4JIYQQQggpQcUxIYQQQgghJag4JoQQQgghpAQVx4QQQgghhJSg4pgQQgghhJASVBwTQgghhBBSgopjQgghhBBCSlBxTAgh1WRqaorNmzcL3EdQISEhUFdXF+kxAODYsWNo2bIlpKWl4ePjI/LjVcbR0VHiMVSHuL43hBDRoeKYENLoPXnyBFOmTIGBgQHk5ORgYmICb29vvH79usZj3b59G9OnTxdabOUV26NHj0ZycrLQjlGRGTNmYMSIEXjy5AlWrFgh8uMBQFRUFHg8Ht69e8fXfuTIEbHFQAhp3Kg4JoQ0amlpabCxsUFKSgrCw8Px8OFD7NixA+fPn4etrS3evHlTo/G0tbWhpKQkomi/UFRUhI6OjkiPkZubixcvXsDZ2RkGBgZQUVER6fGqoqGhIfEYCCGNAxXHhJBGzcvLC3Jycjhz5gwcHBzQrFkzDBgwAOfOncM///wDPz8/vv45OTkYO3YsmjRpAkNDQwQFBfFt/3am9927d5g6dSq0tbWhqqoKJycnJCQk8O3zf//3f+jSpQsUFBSgpaWFYcOGAfiylCAjIwNz584Fj8cDj8cDwP/WfXJyMng8Hv7++2++MTdt2gQzMzPu9V9//YUBAwZAWVkZurq6mDhxIl69elVuTqKiorhC1MnJCTweD1FRUQgICIC1tTVf382bN8PU1JR7PXnyZLi6umL9+vXQ19eHpqYmvLy8UFhYyPXJz8/HwoULYWxsDHl5ebRs2RK7d+9Geno6evXqBQBo2rQpeDweJk+ezOXi62UVb9++xaRJk9C0aVMoKSlhwIABSElJ4baX5igyMhKWlpZQVlZG//79kZmZWe45FxcXw8jICNu3b+drj4uLg5SUFDIyMgAAGzduRPv27dGkSRMYGxvD09MTubm55Y75dT6+5uPjA0dHR75jBwYGonnz5lBUVISVlRUOHz5c4ZiEENGi4pgQ0mi9efMGkZGR8PT0hKKiIt82PT09jB8/Hn/88QcYY1z7zz//DCsrK8TFxWHRokXw9vbG2bNnKzzGyJEj8eLFC5w6dQqxsbHo1KkTevfuzc1Inzx5EsOGDcPAgQMRFxeH8+fPo2vXrgC+LCUwMjLC8uXLkZmZWW5hZ2FhARsbG4SGhvK1h4aGYty4cQC+FOhOTk7o2LEjYmJicPr0aTx//hyjRo0qN2Y7OzskJSUBAP78809kZmbCzs6uqnRyLl68iNTUVFy8eBF79+5FSEgIQkJCuO2TJk1CeHg4tm7disTEROzcuRPKysowNjbGn3/+CQBISkpCZmYmtmzZUu4xJk+ejJiYGBw/fhzXr18HYwwDBw7kK8Lz8vKwfv167N+/H5cvX8bjx4/h6+tb7nhSUlIYO3YswsLC+NpDQ0Nhb28PExMTrt/WrVtx//597N27FxcuXMCCBQuqnZvyBAYGYt++fdixYwfu37+PuXPnYsKECbh06ZJA4xJCaokRQkgjdePGDQaAHT16tNztGzduZADY8+fPGWOMmZiYsP79+/P1GT16NBswYAD32sTEhG3atIkxxtiVK1eYqqoq+/TpE98+ZmZmbOfOnYwxxmxtbdn48eMrjPHr8UoFBwczNTU17vWmTZuYmZkZ9zopKYkBYImJiYwxxlasWMH69evHN8aTJ08YAJaUlFTucd++fcsAsIsXL3Jt/v7+zMrKiq/fpk2bmImJCffazc2NmZiYsM+fP3NtI0eOZKNHj+aL7ezZs+Ue9+LFiwwAe/v2LV+7g4MD8/b2ZowxlpyczACw6OhobvurV6+YoqIiO3jwIGPsS44AsIcPH3J9goKCmK6ubrnHZYyxuLg4xuPxWEZGBmOMsaKiImZoaMi2b99e4T6HDh1impqa3Otvvzdubm5s6NChfPt4e3szBwcHxhhjnz59YkpKSuzatWt8fTw8PNjYsWMrPC4hRHRo5pgQ0uixr2aGq2Jra1vmdWJiYrl9ExISkJubC01NTSgrK3Nfjx49QmpqKgAgPj4evXv3rn3wAMaMGYP09HTcuHEDwJfZzk6dOqF169ZcHBcvXuSLoXRbaRzC1LZtW0hLS3Ov9fX18eLFCwBfzldaWhoODg61Hj8xMREyMjLo1q0b16apqYlWrVrxfS+UlJT4lpZ8HUd5rK2tYWlpyc0eX7p0CS9evMDIkSO5PufOnUPv3r1haGgIFRUVTJw4Ea9fv0ZeXl6tzuXhw4fIy8tD3759+b4/+/btE8n3hhBSNRlJB0AIIZLSsmVL8Hg8JCYmcut8v5aYmIimTZtCW1u7VuPn5uZCX18fUVFRZbaVrhn+djlHbejp6cHJyQlhYWHo3r07wsLCMGvWLL44XFxcsHbt2jL76uvrV/s4UlJSZf6Q+HoZQylZWVm+1zweD8XFxQCEc77VVV4cVf0hNH78eISFhWHRokUICwtD//79oampCQBIT0/H4MGDMWvWLKxatQoaGhq4evUqPDw8UFBQUO4HMavKWel65ZMnT8LQ0JCvn7y8fPVPlhAiNDRzTAhptDQ1NdG3b1/8+uuv+PjxI9+2rKwshIaGYvTo0dwH4QBws7Nfv7a0tCx3/E6dOiErKwsyMjJo2bIl35eWlhYAoEOHDjh//nyFMcrJyaGoqKjKcyldH339+nWkpaVhzJgxfHHcv38fpqamZeJo0qRJlWOX0tbWRlZWFl+xFx8fX+39AaB9+/YoLi6ucD2tnJwcAFR6zpaWlvj8+TNu3rzJtb1+/RpJSUlo06ZNjeL51rhx4/DXX38hNjYWhw8fxvjx47ltsbGxKC4uxoYNG9C9e3dYWFjg2bNnlY6nra1dZq341zlr06YN5OXl8fjx4zLfG2NjY4HOhRBSO1QcE0IatW3btiE/Px/Ozs64fPkynjx5gtOnT6Nv374wNDTEqlWr+PpHR0dj3bp1SE5ORlBQEA4dOgRvb+9yx+7Tpw9sbW3h6uqKM2fOID09HdeuXYOfnx9iYmIAAP7+/ggPD4e/vz8SExNx7949vhleU1NTXL58Gf/880+Fd5cAgOHDhyMnJwezZs1Cr169YGBgwG3z8vLCmzdvMHbsWNy+fRupqamIjIyEu7t7tQrvUo6Ojnj58iXWrVuH1NRUBAUF4dSpU9Xev/R83NzcMGXKFBw7dgyPHj1CVFQUDh48CAAwMTEBj8fDiRMn8PLly3LvBGFubo6hQ4di2rRpuHr1KhISEjBhwgQYGhpi6NChNYqnvPjs7Ozg4eGBoqIiDBkyhNvWsmVLFBYW4pdffkFaWhr279+PHTt2VDqek5MTYmJisG/fPqSkpMDf3x9//fUXt11FRQW+vr6YO3cu9u7di9TUVNy5cwe//PIL9u7dK9C5EEJqh4pjQkijZm5ujpiYGLRo0QKjRo2CmZkZpk+fjl69euH69evQ0NDg6//DDz8gJiYGHTt2xMqVK7Fx40Y4OzuXOzaPx0NERAS+++47uLu7w8LCAmPGjEFGRgZ0dXUBfCk4Dx06hOPHj8Pa2hpOTk64desWN8by5cuRnp4OMzOzSpd3qKiowMXFBQkJCXyznQBgYGCA6OhoFBUVoV+/fmjfvj18fHygrq4OKanq/zdgaWmJX3/9FUFBQbCyssKtW7cqvPtDZbZv344RI0bA09MTrVu3xrRp0/DhwwcAgKGhIZYtW4ZFixZBV1cXs2fPLneM4OBgdO7cGYMHD4atrS0YY4iIiCizlKI2xo8fj4SEBAwbNoxvGYiVlRU2btyItWvXol27dggNDUVgYGClYzk7O2Pp0qVYsGABunTpgpycHEyaNImvz4oVK7B06VIEBgbC0tIS/fv3x8mTJ9G8eXOBz4UQUnM8VpNPohBCCKmUvr4+VqxYgalTp0o6FEIIIbVAH8gjhBAhyMvLQ3R0NJ4/f462bdtKOhxCCCG1RMsqCCFECHbt2oUxY8bAx8enzO3eCCGE1B+0rIIQQgghhJASNHNMCCGEEEJICSqOCSGEEEIIKUHFMSGEEEIIISWoOCaEEEIIIaQEFceEEEIIIYSUoOKYEEIIIYSQElQcE0IIIYQQUoKKY0IIIYQQQkr8P1chcdQk432XAAAAAElFTkSuQmCC", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "data_dir = Path(\"data\")\n", - "runs_metadata = [\n", - " RunMetadata(data_dir / \"large_tokamak_1_MFILE.DAT\", \"large tokamak 1\"),\n", - "]\n", - "\n", - "# Figure and dataframe returned for optional further modification\n", - "fig1, df1 = plot_mfile_solutions(\n", - " runs_metadata=runs_metadata,\n", - " plot_title=\"Large tokamak solution 1\",\n", - ")\n", - "df1" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot two solutions\n", - "\n", - "Plot two MFILEs together, showing normalised values of the optimisation parameters at the solution points, as well as the objective function values." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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tagobjf_namenorm_objfitvar001_namexcm001itvar002_namexcm002itvar003_namexcm003itvar004_name...itvar041_namexcm041itvar042_namexcm042itvar043_namexcm043itvar044_namexcm044itvar045_namexcm045
0large tokamak 1major radius1.60beta1.1216dene1.0756fwalld0.50758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.0083
1large tokamak 2major radius1.63beta1.3216dene1.0756fwalld0.51758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.1083
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" - ], - "text/plain": [ - " tag objf_name norm_objf itvar001_name xcm001 \\\n", - "0 large tokamak 1 major radius 1.60 beta 1.1216 \n", - "1 large tokamak 2 major radius 1.63 beta 1.3216 \n", - "\n", - " itvar002_name xcm002 itvar003_name xcm003 itvar004_name ... \\\n", - "0 dene 1.0756 fwalld 0.50758 ffuspow ... \n", - "1 dene 1.0756 fwalld 0.51758 ffuspow ... \n", - "\n", - " itvar041_name xcm041 itvar042_name xcm042 itvar043_name xcm043 \\\n", - "0 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", - "1 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", - "\n", - " itvar044_name xcm044 itvar045_name xcm045 \n", - "0 fimp(13) 1.5039 dr_tf_wp 1.0083 \n", - "1 fimp(13) 1.5039 dr_tf_wp 1.1083 \n", - "\n", - "[2 rows x 93 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "runs_metadata = [\n", - " RunMetadata(data_dir / \"large_tokamak_1_MFILE.DAT\", \"large tokamak 1\"),\n", - " RunMetadata(data_dir / \"large_tokamak_2_MFILE.DAT\", \"large tokamak 2\"),\n", - "]\n", - "\n", - "fig2, df2 = plot_mfile_solutions(\n", - " runs_metadata=runs_metadata,\n", - " plot_title=\"2 large tokamak solutions\",\n", - ")\n", - "df2" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot one solution normalised to another\n", - "\n", - "Normalised differences, relative to the a given solution, can also be plotted:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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tagobjf_namenorm_objfitvar001_namexcm001itvar002_namexcm002itvar003_namexcm003itvar004_name...itvar041_namexcm041itvar042_namexcm042itvar043_namexcm043itvar044_namexcm044itvar045_namexcm045
0large tokamak 1major radius1.60beta1.1216dene1.0756fwalld0.50758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.0083
1large tokamak 2major radius1.63beta1.3216dene1.0756fwalld0.51758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.1083
\n", - "

2 rows × 93 columns

\n", - "
" - ], - "text/plain": [ - " tag objf_name norm_objf itvar001_name xcm001 \\\n", - "0 large tokamak 1 major radius 1.60 beta 1.1216 \n", - "1 large tokamak 2 major radius 1.63 beta 1.3216 \n", - "\n", - " itvar002_name xcm002 itvar003_name xcm003 itvar004_name ... \\\n", - "0 dene 1.0756 fwalld 0.50758 ffuspow ... \n", - "1 dene 1.0756 fwalld 0.51758 ffuspow ... \n", - "\n", - " itvar041_name xcm041 itvar042_name xcm042 itvar043_name xcm043 \\\n", - "0 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", - "1 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", - "\n", - " itvar044_name xcm044 itvar045_name xcm045 \n", - "0 fimp(13) 1.5039 dr_tf_wp 1.0083 \n", - "1 fimp(13) 1.5039 dr_tf_wp 1.1083 \n", - "\n", - "[2 rows x 93 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig3, df3 = plot_mfile_solutions(\n", - " runs_metadata=runs_metadata,\n", - " plot_title=\"Large tokamak 2 solution, relative to large tokamak 1\",\n", - " normalising_tag=\"large tokamak 1\",\n", - ")\n", - "df3" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot multiple solutions normalised by one\n", - "\n", - "Plot two MFILEs, normalised by a third MFILE." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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tagobjf_namenorm_objfitvar001_namexcm001itvar002_namexcm002itvar003_namexcm003itvar004_name...itvar041_namexcm041itvar042_namexcm042itvar043_namexcm043itvar044_namexcm044itvar045_namexcm045
0large tokamak 1major radius1.60beta1.1216dene1.0756fwalld0.50758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.0083
1large tokamak 2major radius1.63beta1.3216dene1.0756fwalld0.51758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.1083
2large tokamak 3major radius1.50beta1.1216dene1.0756fwalld0.50758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.0083
\n", - "

3 rows × 93 columns

\n", - "
" - ], - "text/plain": [ - " tag objf_name norm_objf itvar001_name xcm001 \\\n", - "0 large tokamak 1 major radius 1.60 beta 1.1216 \n", - "1 large tokamak 2 major radius 1.63 beta 1.3216 \n", - "2 large tokamak 3 major radius 1.50 beta 1.1216 \n", - "\n", - " itvar002_name xcm002 itvar003_name xcm003 itvar004_name ... \\\n", - "0 dene 1.0756 fwalld 0.50758 ffuspow ... \n", - "1 dene 1.0756 fwalld 0.51758 ffuspow ... \n", - "2 dene 1.0756 fwalld 0.50758 ffuspow ... \n", - "\n", - " itvar041_name xcm041 itvar042_name xcm042 itvar043_name xcm043 \\\n", - "0 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", - "1 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", - "2 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", - "\n", - " itvar044_name xcm044 itvar045_name xcm045 \n", - "0 fimp(13) 1.5039 dr_tf_wp 1.0083 \n", - "1 fimp(13) 1.5039 dr_tf_wp 1.1083 \n", - "2 fimp(13) 1.5039 dr_tf_wp 1.0083 \n", - "\n", - "[3 rows x 93 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "runs_metadata = [\n", - " RunMetadata(data_dir / \"large_tokamak_1_MFILE.DAT\", \"large tokamak 1\"),\n", - " RunMetadata(data_dir / \"large_tokamak_2_MFILE.DAT\", \"large tokamak 2\"),\n", - " RunMetadata(data_dir / \"large_tokamak_3_MFILE.DAT\", \"large tokamak 3\"),\n", - "]\n", - "\n", - "fig4, df4 = plot_mfile_solutions(\n", - " runs_metadata,\n", - " \"2 large tokamak solutions, relative to large tokamak 1\",\n", - " normalising_tag=\"large tokamak 1\",\n", - ")\n", - "df4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## RMS Errors\n", - "\n", - "Plot RMS errors of multiple solutions relative to a reference solution." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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tagobjf_namenorm_objfitvar001_namexcm001itvar002_namexcm002itvar003_namexcm003itvar004_name...itvar041_namexcm041itvar042_namexcm042itvar043_namexcm043itvar044_namexcm044itvar045_namexcm045
0large tokamak 1major radius1.60beta1.1216dene1.0756fwalld0.50758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.0083
1large tokamak 2major radius1.63beta1.3216dene1.0756fwalld0.51758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.1083
2large tokamak 3major radius1.50beta1.1216dene1.0756fwalld0.50758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.0083
3large tokamak 4major radius1.52beta1.1216dene1.0756fwalld0.50758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.0083
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4 rows × 93 columns

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" - ], - "text/plain": [ - " tag objf_name norm_objf itvar001_name xcm001 \\\n", - "0 large tokamak 1 major radius 1.60 beta 1.1216 \n", - "1 large tokamak 2 major radius 1.63 beta 1.3216 \n", - "2 large tokamak 3 major radius 1.50 beta 1.1216 \n", - "3 large tokamak 4 major radius 1.52 beta 1.1216 \n", - "\n", - " itvar002_name xcm002 itvar003_name xcm003 itvar004_name ... \\\n", - "0 dene 1.0756 fwalld 0.50758 ffuspow ... \n", - "1 dene 1.0756 fwalld 0.51758 ffuspow ... \n", - "2 dene 1.0756 fwalld 0.50758 ffuspow ... \n", - "3 dene 1.0756 fwalld 0.50758 ffuspow ... \n", - "\n", - " itvar041_name xcm041 itvar042_name xcm042 itvar043_name xcm043 \\\n", - "0 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", - "1 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", - "2 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", - "3 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", - "\n", - " itvar044_name xcm044 itvar045_name xcm045 \n", - "0 fimp(13) 1.5039 dr_tf_wp 1.0083 \n", - "1 fimp(13) 1.5039 dr_tf_wp 1.1083 \n", - "2 fimp(13) 1.5039 dr_tf_wp 1.0083 \n", - "3 fimp(13) 1.5039 dr_tf_wp 1.0083 \n", - "\n", - "[4 rows x 93 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "runs_metadata = [\n", - " RunMetadata(data_dir / \"large_tokamak_1_MFILE.DAT\", \"large tokamak 1\"),\n", - " RunMetadata(data_dir / \"large_tokamak_2_MFILE.DAT\", \"large tokamak 2\"),\n", - " RunMetadata(data_dir / \"large_tokamak_3_MFILE.DAT\", \"large tokamak 3\"),\n", - " RunMetadata(data_dir / \"large_tokamak_4_MFILE.DAT\", \"large tokamak 4\"),\n", - "]\n", - "\n", - "fig5, df5 = plot_mfile_solutions(\n", - " runs_metadata,\n", - " \"3 large tokamak solutions with RMS errors normalised to large tokamak 1\",\n", - " normalising_tag=\"large tokamak 1\",\n", - " rmse=True,\n", - ")\n", - "df5" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solutions normalised by range\n", - "\n", - "Use `nitvar` values instead; the solution optimisation parameters are normalised to the range of their upper and lower bounds." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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tagobjf_namenorm_objfitvar001_namenitvar001itvar002_namenitvar002itvar003_namenitvar003itvar004_name...itvar041_namenitvar041itvar042_namenitvar042itvar043_namenitvar043itvar044_namenitvar044itvar045_namenitvar045
0large tokamak 1major radius1.60beta0.032681dene0.071381fwalld0.50709ffuspow...cpttf0.99182ralpne0.67908oh_steel_frac0.5455fimp(13)0.057148dr_tf_wp0.0651
1large tokamak 2major radius1.63beta0.042681dene0.071381fwalld0.70709ffuspow...cpttf0.99182ralpne0.67908oh_steel_frac0.5455fimp(13)0.057148dr_tf_wp0.0651
2large tokamak 3major radius1.50beta0.022681dene0.071381fwalld0.50709ffuspow...cpttf0.99182ralpne0.67908oh_steel_frac0.5455fimp(13)0.057148dr_tf_wp0.0651
3large tokamak 4major radius1.52beta0.032681dene0.071381fwalld0.40709ffuspow...cpttf0.99182ralpne0.67908oh_steel_frac0.5455fimp(13)0.057148dr_tf_wp0.0651
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4 rows × 93 columns

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" - ], - "text/plain": [ - " tag objf_name norm_objf itvar001_name nitvar001 \\\n", - "0 large tokamak 1 major radius 1.60 beta 0.032681 \n", - "1 large tokamak 2 major radius 1.63 beta 0.042681 \n", - "2 large tokamak 3 major radius 1.50 beta 0.022681 \n", - "3 large tokamak 4 major radius 1.52 beta 0.032681 \n", - "\n", - " itvar002_name nitvar002 itvar003_name nitvar003 itvar004_name ... \\\n", - "0 dene 0.071381 fwalld 0.50709 ffuspow ... \n", - "1 dene 0.071381 fwalld 0.70709 ffuspow ... \n", - "2 dene 0.071381 fwalld 0.50709 ffuspow ... \n", - "3 dene 0.071381 fwalld 0.40709 ffuspow ... \n", - "\n", - " itvar041_name nitvar041 itvar042_name nitvar042 itvar043_name nitvar043 \\\n", - "0 cpttf 0.99182 ralpne 0.67908 oh_steel_frac 0.5455 \n", - "1 cpttf 0.99182 ralpne 0.67908 oh_steel_frac 0.5455 \n", - "2 cpttf 0.99182 ralpne 0.67908 oh_steel_frac 0.5455 \n", - "3 cpttf 0.99182 ralpne 0.67908 oh_steel_frac 0.5455 \n", - "\n", - " itvar044_name nitvar044 itvar045_name nitvar045 \n", - "0 fimp(13) 0.057148 dr_tf_wp 0.0651 \n", - "1 fimp(13) 0.057148 dr_tf_wp 0.0651 \n", - "2 fimp(13) 0.057148 dr_tf_wp 0.0651 \n", - "3 fimp(13) 0.057148 dr_tf_wp 0.0651 \n", - "\n", - "[4 rows x 93 columns]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "runs_metadata = [\n", - " RunMetadata(data_dir / \"large_tokamak_1_MFILE.DAT\", \"large tokamak 1\"),\n", - " RunMetadata(data_dir / \"large_tokamak_2_MFILE.DAT\", \"large tokamak 2\"),\n", - " RunMetadata(data_dir / \"large_tokamak_3_MFILE.DAT\", \"large tokamak 3\"),\n", - " RunMetadata(data_dir / \"large_tokamak_4_MFILE.DAT\", \"large tokamak 4\"),\n", - "]\n", - "\n", - "fig6, df6 = plot_mfile_solutions(\n", - " runs_metadata,\n", - " \"4 large tokamak solutions normalised to the range of the optimisation parameters\",\n", - " normalisation_type=\"range\",\n", - ")\n", - "df6" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Actual values" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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tagobjf_namenorm_objfitvar001_nameitvar001itvar002_nameitvar002itvar003_nameitvar003itvar004_name...itvar041_nameitvar041itvar042_nameitvar042itvar043_nameitvar043itvar044_nameitvar044itvar045_nameitvar045
0large tokamak 1major radius1.60beta0.033648dene8.066700e+19fwalld0.50758ffuspow...cpttf89795.0ralpne0.083954oh_steel_frac0.51868fimp(13)0.000571dr_tf_wp0.50416
1large tokamak 2major radius1.63beta0.034648dene8.056700e+19fwalld0.50258ffuspow...cpttf89795.0ralpne0.083954oh_steel_frac0.51868fimp(13)0.000571dr_tf_wp0.50416
2large tokamak 3major radius1.50beta0.033648dene8.066700e+19fwalld0.50758ffuspow...cpttf88795.0ralpne0.081954oh_steel_frac0.52868fimp(13)0.000531dr_tf_wp0.57416
3large tokamak 4major radius1.52beta0.037648dene8.366700e+19fwalld0.55758ffuspow...cpttf89795.0ralpne0.083954oh_steel_frac0.51868fimp(13)0.000571dr_tf_wp0.50416
\n", - "

4 rows × 93 columns

\n", - "
" - ], - "text/plain": [ - " tag objf_name norm_objf itvar001_name itvar001 \\\n", - "0 large tokamak 1 major radius 1.60 beta 0.033648 \n", - "1 large tokamak 2 major radius 1.63 beta 0.034648 \n", - "2 large tokamak 3 major radius 1.50 beta 0.033648 \n", - "3 large tokamak 4 major radius 1.52 beta 0.037648 \n", - "\n", - " itvar002_name itvar002 itvar003_name itvar003 itvar004_name ... \\\n", - "0 dene 8.066700e+19 fwalld 0.50758 ffuspow ... \n", - "1 dene 8.056700e+19 fwalld 0.50258 ffuspow ... \n", - "2 dene 8.066700e+19 fwalld 0.50758 ffuspow ... \n", - "3 dene 8.366700e+19 fwalld 0.55758 ffuspow ... \n", - "\n", - " itvar041_name itvar041 itvar042_name itvar042 itvar043_name itvar043 \\\n", - "0 cpttf 89795.0 ralpne 0.083954 oh_steel_frac 0.51868 \n", - "1 cpttf 89795.0 ralpne 0.083954 oh_steel_frac 0.51868 \n", - "2 cpttf 88795.0 ralpne 0.081954 oh_steel_frac 0.52868 \n", - "3 cpttf 89795.0 ralpne 0.083954 oh_steel_frac 0.51868 \n", - "\n", - " itvar044_name itvar044 itvar045_name itvar045 \n", - "0 fimp(13) 0.000571 dr_tf_wp 0.50416 \n", - "1 fimp(13) 0.000571 dr_tf_wp 0.50416 \n", - "2 fimp(13) 0.000531 dr_tf_wp 0.57416 \n", - "3 fimp(13) 0.000571 dr_tf_wp 0.50416 \n", - "\n", - "[4 rows x 93 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "runs_metadata = [\n", - " RunMetadata(data_dir / \"large_tokamak_1_MFILE.DAT\", \"large tokamak 1\"),\n", - " RunMetadata(data_dir / \"large_tokamak_2_MFILE.DAT\", \"large tokamak 2\"),\n", - " RunMetadata(data_dir / \"large_tokamak_3_MFILE.DAT\", \"large tokamak 3\"),\n", - " RunMetadata(data_dir / \"large_tokamak_4_MFILE.DAT\", \"large tokamak 4\"),\n", - "]\n", - "\n", - "fig7, df7 = plot_mfile_solutions(\n", - " runs_metadata,\n", - " \"4 large tokamak solutions normalised to the range of the optimisation parameters\",\n", - " normalisation_type=None,\n", - ")\n", - "df7" - ] - } + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# `plot_solutions` Solution Comparison Tool\n", + "\n", + "This tool plots the solution vectors (i.e. final values of optimisation parameters) for different runs of PROCESS. This allows visual comparisons of different solution points.\n", + "\n", + "It can use different intra-solution optimisation parameter normalisations (e.g. initial value, parameter range) and inter-solution normalisations (e.g. normalise to a certain solution).\n", + "\n", + "### Known Limitations\n", + "\n", + "- The solution vectors (optimisation parameter values at the solution) currently plotted are normalised to the initial point (from the `IN.DAT`) of each solution: each element of the vector is the $x_{final}/x_{initial}$, the `xcmxxx` values in the `MFILE.DAT`. This allows all optimisation parameters to be plotted on the same axis, showing the relative changes from their initial values across multiple solutions.\n", + "- Solutions being plotted together must also have the same optimisation parameters.\n", + "- The solutions plotted in this example are fictitious." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# Reload Process each time (keep editable install up-to-date)\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "from pathlib import Path\n", + "\n", + "from process.io.plot_solutions import RunMetadata, plot_mfile_solutions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot single solution\n", + "\n", + "Plot a single solution, showing optimisation parameters normalised to their initial values." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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1 rows × 93 columns

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" ], - "metadata": { - "kernelspec": { - "display_name": "process", - "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": "3.10.12" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 + "text/plain": [ + " tag objf_name norm_objf itvar001_name xcm001 \\\n", + "0 large tokamak 1 major radius 1.6 beta 1.1216 \n", + "\n", + " itvar002_name xcm002 itvar003_name xcm003 itvar004_name ... \\\n", + "0 dene 1.0756 fwalld 0.50758 ffuspow ... \n", + "\n", + " itvar041_name xcm041 itvar042_name xcm042 itvar043_name xcm043 \\\n", + "0 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", + "\n", + " itvar044_name xcm044 itvar045_name xcm045 \n", + "0 fimp(13) 1.5039 dr_tf_wp 1.0083 \n", + "\n", + "[1 rows x 93 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data_dir = Path(\"data\")\n", + "runs_metadata = [\n", + " RunMetadata(data_dir / \"large_tokamak_1_MFILE.DAT\", \"large tokamak 1\"),\n", + "]\n", + "\n", + "# Figure and dataframe returned for optional further modification\n", + "fig1, df1 = plot_mfile_solutions(\n", + " runs_metadata=runs_metadata,\n", + " plot_title=\"Large tokamak solution 1\",\n", + ")\n", + "df1" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot two solutions\n", + "\n", + "Plot two MFILEs together, showing normalised values of the optimisation parameters at the solution points, as well as the objective function values." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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tagobjf_namenorm_objfitvar001_namexcm001itvar002_namexcm002itvar003_namexcm003itvar004_name...itvar041_namexcm041itvar042_namexcm042itvar043_namexcm043itvar044_namexcm044itvar045_namexcm045
0large tokamak 1major radius1.60beta1.1216dene1.0756fwalld0.50758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.0083
1large tokamak 2major radius1.63beta1.3216dene1.0756fwalld0.51758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.1083
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" + ], + "text/plain": [ + " tag objf_name norm_objf itvar001_name xcm001 \\\n", + "0 large tokamak 1 major radius 1.60 beta 1.1216 \n", + "1 large tokamak 2 major radius 1.63 beta 1.3216 \n", + "\n", + " itvar002_name xcm002 itvar003_name xcm003 itvar004_name ... \\\n", + "0 dene 1.0756 fwalld 0.50758 ffuspow ... \n", + "1 dene 1.0756 fwalld 0.51758 ffuspow ... \n", + "\n", + " itvar041_name xcm041 itvar042_name xcm042 itvar043_name xcm043 \\\n", + "0 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", + "1 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", + "\n", + " itvar044_name xcm044 itvar045_name xcm045 \n", + "0 fimp(13) 1.5039 dr_tf_wp 1.0083 \n", + "1 fimp(13) 1.5039 dr_tf_wp 1.1083 \n", + "\n", + "[2 rows x 93 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "runs_metadata = [\n", + " RunMetadata(data_dir / \"large_tokamak_1_MFILE.DAT\", \"large tokamak 1\"),\n", + " RunMetadata(data_dir / \"large_tokamak_2_MFILE.DAT\", \"large tokamak 2\"),\n", + "]\n", + "\n", + "fig2, df2 = plot_mfile_solutions(\n", + " runs_metadata=runs_metadata,\n", + " plot_title=\"2 large tokamak solutions\",\n", + ")\n", + "df2" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot one solution normalised to another\n", + "\n", + "Normalised differences, relative to the a given solution, can also be plotted:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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tagobjf_namenorm_objfitvar001_namexcm001itvar002_namexcm002itvar003_namexcm003itvar004_name...itvar041_namexcm041itvar042_namexcm042itvar043_namexcm043itvar044_namexcm044itvar045_namexcm045
0large tokamak 1major radius1.60beta1.1216dene1.0756fwalld0.50758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.0083
1large tokamak 2major radius1.63beta1.3216dene1.0756fwalld0.51758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.1083
\n", + "

2 rows × 93 columns

\n", + "
" + ], + "text/plain": [ + " tag objf_name norm_objf itvar001_name xcm001 \\\n", + "0 large tokamak 1 major radius 1.60 beta 1.1216 \n", + "1 large tokamak 2 major radius 1.63 beta 1.3216 \n", + "\n", + " itvar002_name xcm002 itvar003_name xcm003 itvar004_name ... \\\n", + "0 dene 1.0756 fwalld 0.50758 ffuspow ... \n", + "1 dene 1.0756 fwalld 0.51758 ffuspow ... \n", + "\n", + " itvar041_name xcm041 itvar042_name xcm042 itvar043_name xcm043 \\\n", + "0 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", + "1 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", + "\n", + " itvar044_name xcm044 itvar045_name xcm045 \n", + "0 fimp(13) 1.5039 dr_tf_wp 1.0083 \n", + "1 fimp(13) 1.5039 dr_tf_wp 1.1083 \n", + "\n", + "[2 rows x 93 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig3, df3 = plot_mfile_solutions(\n", + " runs_metadata=runs_metadata,\n", + " plot_title=\"Large tokamak 2 solution, relative to large tokamak 1\",\n", + " normalising_tag=\"large tokamak 1\",\n", + ")\n", + "df3" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot multiple solutions normalised by one\n", + "\n", + "Plot two MFILEs, normalised by a third MFILE." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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tagobjf_namenorm_objfitvar001_namexcm001itvar002_namexcm002itvar003_namexcm003itvar004_name...itvar041_namexcm041itvar042_namexcm042itvar043_namexcm043itvar044_namexcm044itvar045_namexcm045
0large tokamak 1major radius1.60beta1.1216dene1.0756fwalld0.50758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.0083
1large tokamak 2major radius1.63beta1.3216dene1.0756fwalld0.51758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.1083
2large tokamak 3major radius1.50beta1.1216dene1.0756fwalld0.50758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.0083
\n", + "

3 rows × 93 columns

\n", + "
" + ], + "text/plain": [ + " tag objf_name norm_objf itvar001_name xcm001 \\\n", + "0 large tokamak 1 major radius 1.60 beta 1.1216 \n", + "1 large tokamak 2 major radius 1.63 beta 1.3216 \n", + "2 large tokamak 3 major radius 1.50 beta 1.1216 \n", + "\n", + " itvar002_name xcm002 itvar003_name xcm003 itvar004_name ... \\\n", + "0 dene 1.0756 fwalld 0.50758 ffuspow ... \n", + "1 dene 1.0756 fwalld 0.51758 ffuspow ... \n", + "2 dene 1.0756 fwalld 0.50758 ffuspow ... \n", + "\n", + " itvar041_name xcm041 itvar042_name xcm042 itvar043_name xcm043 \\\n", + "0 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", + "1 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", + "2 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", + "\n", + " itvar044_name xcm044 itvar045_name xcm045 \n", + "0 fimp(13) 1.5039 dr_tf_wp 1.0083 \n", + "1 fimp(13) 1.5039 dr_tf_wp 1.1083 \n", + "2 fimp(13) 1.5039 dr_tf_wp 1.0083 \n", + "\n", + "[3 rows x 93 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "runs_metadata = [\n", + " RunMetadata(data_dir / \"large_tokamak_1_MFILE.DAT\", \"large tokamak 1\"),\n", + " RunMetadata(data_dir / \"large_tokamak_2_MFILE.DAT\", \"large tokamak 2\"),\n", + " RunMetadata(data_dir / \"large_tokamak_3_MFILE.DAT\", \"large tokamak 3\"),\n", + "]\n", + "\n", + "fig4, df4 = plot_mfile_solutions(\n", + " runs_metadata,\n", + " \"2 large tokamak solutions, relative to large tokamak 1\",\n", + " normalising_tag=\"large tokamak 1\",\n", + ")\n", + "df4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RMS Errors\n", + "\n", + "Plot RMS errors of multiple solutions relative to a reference solution." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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tagobjf_namenorm_objfitvar001_namexcm001itvar002_namexcm002itvar003_namexcm003itvar004_name...itvar041_namexcm041itvar042_namexcm042itvar043_namexcm043itvar044_namexcm044itvar045_namexcm045
0large tokamak 1major radius1.60beta1.1216dene1.0756fwalld0.50758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.0083
1large tokamak 2major radius1.63beta1.3216dene1.0756fwalld0.51758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.1083
2large tokamak 3major radius1.50beta1.1216dene1.0756fwalld0.50758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.0083
3large tokamak 4major radius1.52beta1.1216dene1.0756fwalld0.50758ffuspow...cpttf1.3815ralpne0.83954oh_steel_frac0.64835fimp(13)1.5039dr_tf_wp1.0083
\n", + "

4 rows × 93 columns

\n", + "
" + ], + "text/plain": [ + " tag objf_name norm_objf itvar001_name xcm001 \\\n", + "0 large tokamak 1 major radius 1.60 beta 1.1216 \n", + "1 large tokamak 2 major radius 1.63 beta 1.3216 \n", + "2 large tokamak 3 major radius 1.50 beta 1.1216 \n", + "3 large tokamak 4 major radius 1.52 beta 1.1216 \n", + "\n", + " itvar002_name xcm002 itvar003_name xcm003 itvar004_name ... \\\n", + "0 dene 1.0756 fwalld 0.50758 ffuspow ... \n", + "1 dene 1.0756 fwalld 0.51758 ffuspow ... \n", + "2 dene 1.0756 fwalld 0.50758 ffuspow ... \n", + "3 dene 1.0756 fwalld 0.50758 ffuspow ... \n", + "\n", + " itvar041_name xcm041 itvar042_name xcm042 itvar043_name xcm043 \\\n", + "0 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", + "1 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", + "2 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", + "3 cpttf 1.3815 ralpne 0.83954 oh_steel_frac 0.64835 \n", + "\n", + " itvar044_name xcm044 itvar045_name xcm045 \n", + "0 fimp(13) 1.5039 dr_tf_wp 1.0083 \n", + "1 fimp(13) 1.5039 dr_tf_wp 1.1083 \n", + "2 fimp(13) 1.5039 dr_tf_wp 1.0083 \n", + "3 fimp(13) 1.5039 dr_tf_wp 1.0083 \n", + "\n", + "[4 rows x 93 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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WAHvr1i0unpJcpwCwU6ZMkdpfeV1zZP1WWFhYsAMHDuSmGzZsyHbq1KmI7Mj/ufv5+bEA2IsXL3JlycnJrI6OjtQxyFLY92vv3r0sAHb27NkS5b169WIZhmEfPXpU5HYLXvNYtmS/D0pKSuydO3ckykePHs0yDMNeu3aNK3v//j2rp6cncaxpaWmsrq4uO2zYMIn1X79+zero6EiVF7Rjxw6Z1+eSXPOdnZ2578WuXbtYPp/PDhs2TGK9vLw8iXOfZT//9lSrVo0dPHgwV5b//TMwMOCuzSz733nasGFDVigUcuX9+vVjVVRUJM4dWefv8OHDWXV1dYnl5IlbHosWLZLr/KuMfshuFX5+foiOjkZYWBh++eUXiEQi5ObmftO2vv7LPCUlBe/evYOzszMeP36MlJQUiWVr1KgBd3d3ibLDhw/D1NQUXbp04cpUVVUxbNgwieWuX7/O3bJ9//493r17h3fv3iEjIwPt27fH6dOn5bqFVtDly5eRnJyMkSNHSvTl6tSpE2rVqoWDBw9KrXPy5Em4u7ujffv22L17t9Rf7F/n5OPHj0hJSYGTkxOuXr0qta327dtL/PWe36rZs2dPiVsz+eWPHz+WuZ+MjAy8e/cOLVu2BMuyuHbtWonjLkhNTQ0qKiqIiYnBx48fZS5z7Ngx5Obmws/PD0pK/31dhg0bBm1tbZn5K0sikQhHjx5Ft27dYGVlxZUbGxvDy8sLZ86cQWpqqsQ6Pj4+ErdRnZycIBKJ8PTpUwDgWuQiIyMhFArljsXAwAC1atXC6dOnAQBnz54Fj8fDxIkT8ebNGzx8+BDA55a81q1bf9cwiX379pVo5c2/O/L1+VKUwYMHw8DAACYmJujYsSNSUlIQHh7OdUeSxcvLCzExMXj9+jVOnDiB169fF9pKWrduXVy/fh2//fYbEhMTsWzZMnTr1g3VqlUr9iGtDx8+4MSJE+jTpw/S0tK47/779+/h7u6Ohw8fSnUtGDZsmMzWHYFAgEGDBkmURUVFwcjICP369ePK+Hw+xowZg/T0dJw6dUpi+Z49e8LAwECiTFdXF8+fP//mriyDBg2S6Ldc8PP7luvUH3/8IXNf5XnNKYquri7u3LnDfQ8KKsnnHhUVhebNm0vcPTQwMOC6k3yrqKgo8Hg87i5PvvHjx4NlWRw6dKjE2yzJ74OzszPq1KkjUXb48GG0aNFCokVVT09P6lijo6Px6dMn9OvXj8vdu3fvwOPx0KxZM5l3Z+XxLdf8rVu3om/fvhg+fDjWrl0rsR6Px+POfbFYjA8fPiAvLw+Ojo4yc9K7d2/o6Ohw0/nn6W+//QZlZWWJ8tzcXIlrw9e5zz+nnJyckJmZiXv37pUo7p/dD5mJWrVqwdXVFQMGDEBkZCTS09PRuXPnb+pDdfbsWbi6ukJDQwO6urowMDDg+vHJqhwX9PTpU1hbW0tVDGrWrCkxnX8BHThwIAwMDCT+27BhA3JycqT2J4/8CpCdnZ3UvFq1anHz82VnZ6NTp05wcHDAv//+K/NBnMjISDRv3hyqqqrQ09ODgYEBVq9eLTM+c3Nzien8L72ZmZnM8q8rqUlJSfD29oaenh40NTVhYGAAZ2dnANK5lyfuggQCARYsWIBDhw6hWrVqaNOmDRYuXCgxgkFh+VNRUYGVlZVU/sra27dvkZmZKfPzrF27NsRiMZ49eyZRXvAzyK9k5ufa2dkZPXv2RGBgIPT19dG1a1eEhIQgJyen2HicnJy4bhOxsbFwdHSEo6Mj9PT0EBsbi9TUVNy4cYOrDH2r4o6hODNmzEB0dDT27NmDAQMGICUlpdgfgvz+rNu3b0dERASaNGki9b39mq2tLcLDw/Hu3TvcvHkTc+fOhbKyMnx8fHDs2LFC13v06BFYlsX06dOlvvv5w8AVfKhP1rUGAExNTaXO/adPn8LGxkbqePNvIRc8h2Vte/LkydDU1ETTpk1hY2ODUaNGFfssxNeK+/xKep1SVlbm+loXt6+yuuYU5++//8anT59ga2uL+vXrY+LEibh58yY3vySfe/5nWJCsfJXE06dPYWJiItWHtLBzQx4l+X0o7DdT1vessN/Mdu3aSeXv6NGj3/wgbEmv+U+ePMFvv/2Gnj17YsWKFTIbAcLCwtCgQQOu77mBgQEOHjxY6r+Zd+7cQffu3aGjowNtbW0YGBjgt99+AyB9/soT98/sh+xzXFCvXr0wfPhwPHjwoEQXk4SEBLRv3x61atXCkiVLYGZmBhUVFURFRWHp0qVSLbnfMzJF/rYWLVok1QcpX3m86EIgEMDDwwP79u3D4cOH4enpKTE/NjYWXbp0QZs2bbBq1SoYGxuDz+cjJCRE5sM5hfVdKqw8/w8YkUgENzc3fPjwAZMnT0atWrWgoaGBFy9ewNvbWyr3xcVdGD8/P3Tu3Bl79+7FkSNHMH36dMybNw8nTpyAg4ODXNsoTGEXG3kejihNxeWaYRjs3LkTFy5cwIEDB3DkyBEMHjwYQUFBuHDhQpHnXevWrbF+/Xo8fvwYsbGxcHJyAsMwaN26NWJjY2FiYgKxWPzdlePijqE49evXh6urKwCgW7duyMzMxLBhw9C6dWupH518AoEAPXr0QFhYGB4/fiz3OLE8Hg/169dH/fr10aJFC7Rt2xYRERHc/gvKP5cnTJggdecpX8GKQWHXmtIYHUfWNmrXro379+8jMjIShw8fxq5du7Bq1SrMmDFDriHCvvfzK0ggEBT6x015XXOK06ZNGyQkJGDfvn04evQoNmzYgKVLl2LNmjUYOnToN33uFV1Jfx9K4zczPDwcRkZGUvO/bmUtS8bGxjA2NkZUVBQuX74MR0dHifmbN2+Gt7c3unXrhokTJ8LQ0BA8Hg/z5s2T+UDwt56/nz59grOzM7S1tfH3339z461fvXoVkydPljp/i4v7Z/dTVI7znzQu6V/+Bw4cQE5ODvbv3y/x11xJbtdYWFjg7t27YFlWorJU8Elta2trAIC2tnahP6JFKawilv/0/P3796UeFLp//77U0/UMwyAiIgJdu3ZF7969cejQIbi4uHDzd+3aBVVVVRw5ckSi20JISEiJYy7KrVu38ODBA4SFhWHAgAFcecEnveWNuyjW1tYYP348xo8fj4cPH8Le3h5BQUHYvHmzRP6+7saQm5uLJ0+eFPlZ5beOFXzCWVZrjLx/tRsYGEBdXR3379+Xmnfv3j0oKSkVWtkrTvPmzdG8eXPMmTMHW7ZsQf/+/bFt2zYMHTq00HXyK73R0dGIi4vDlClTAHyuGKxevRomJibQ0NBA48aNi9x3ebdazJ8/H3v27MGcOXOwZs2aQpfz8vLCpk2boKSkhF9//bXE+8n/wXn16lWhy+SfV3w+/5u++8WxsLDAzZs3IRaLJSqU+bdZZY2wIYuGhgb69u2Lvn37Ijc3Fz169MCcOXMwderU7x5+q6TXqbJQ0muOPPT09DBo0CAMGjQI6enpaNOmDQICAjB06NASfe4WFhYyu2fIug7IUtTvw7Fjx5CWlibRelzScyNfafw+WFhYyBzJpLDfTENDwzL7zZTnmq+qqorIyEi0a9cOHTt2xKlTp1C3bl1u/s6dO2FlZYXdu3dL7LO0Xw4UExOD9+/fY/fu3RIPPz958kTm8sXF/bP7obpVyLqNIhQK8c8//0BNTU2qb1Nx8v9S+7p1IyUlpURfdHd3d7x48UJiaJ7s7GypfoiNGzeGtbU1Fi9eLHPosYJDbxWUP8ZnwYqYo6MjDA0NsWbNGonb5IcOHUJ8fLzU08PA59tHu3fvRpMmTdC5c2dcunSJm8fj8cAwjETrZ2JiIvbu3VtkfCUlK/csy2LZsmWFrlNU3LJkZmYiOztbosza2hpaWlpcrlxdXaGiooLly5dLxLJx40akpKTIzF8+CwsL8Hg8rk9uvlWrVkktW9jnVxCPx0OHDh2wb98+iSGN3rx5gy1btqB169bQ1tYuchsFffz4UaoFL//uRXFdK2rUqAFTU1MsXboUQqEQrVq1AvC50pyQkICdO3eiefPmxbbiyHv8pcXa2ho9e/ZEaGiozBeB5Gvbti1mzZqFlStXymydyhcbGyuzv3ZUVBSAom9/GxoawsXFBWvXrpVZiS7uu18cDw8PvH79mhudBPg82sOKFSugqanJdRsoyvv37yWmVVRUUKdOHbAsW6J+6oX5lutUafuWa05RCuZMU1MTNWvW5I6vJJ+7h4cHLly4IHFNe/v2LSIiIuSKpbDvl4eHB0QiEVauXClRvnTpUjAMg19++UWu7ecrjd8Hd3d3nD9/XmL83A8fPkgdq7u7O7S1tTF37lyZ5+C3/mZ+yzVfR0cHR44cgaGhIdzc3CRahGWdVxcvXsT58+eLjK+kZO0nNzdX5u+NPHH/7H6oluPhw4cjNTUVbdq0gampKV6/fo2IiAjcu3cPQUFBJe6W0KFDB6ioqKBz584YPnw40tPTsX79ehgaGhbZElQwppUrV6Jfv34YO3YsjI2NJd6ylf+XpJKSEjZs2IBffvkFdevWxaBBg2BqaooXL17g5MmT0NbWLvI12NbW1tDV1cWaNWugpaUFDQ0NNGvWDDVq1MCCBQswaNAgODs7o1+/ftwQSZaWlhg3bpzM7ampqXF/Vf7yyy84deoU6tWrh06dOmHJkiXo2LEjvLy8kJycjP/973+oWbOmRH+671WrVi1YW1tjwoQJePHiBbS1tbFr165i+5gWFrcsDx48QPv27dGnTx/UqVMHysrK2LNnD968ecO1EhoYGGDq1KkIDAxEx44d0aVLF9y/fx+rVq1CkyZNuP5csujo6KB3795cfy5ra2tERkbK/CMuv2V1zJgxcHd3B4/HK7Slcvbs2YiOjkbr1q0xcuRIKCsrY+3atcjJycHChQuLzI8sYWFhWLVqFbp37w5ra2ukpaVh/fr10NbWlnqpjixOTk7Ytm0b6tevz7WWN2rUCBoaGnjw4IFcQ32V5PhLy8SJE/Hvv/8iODgY8+fPl7mMkpKSXG8nXLBgAa5cuYIePXqgQYMGAICrV6/in3/+gZ6eHvz8/Ipc/3//+x9at26N+vXrY9iwYbCyssKbN29w/vx5PH/+HDdu3Cjx8eXz8fHB2rVr4e3tjStXrsDS0hI7d+7E2bNnERwcLNeYpR06dICRkRFatWqFatWqIT4+HitXrkSnTp1KZcxTPp//Tdep0vSt15zC1KlTBy4uLmjcuDH09PRw+fJl7Ny5E76+vtwy8n7ukyZNQnh4ODp27IixY8dyQ7nl3xUoTmHfr86dO6Nt27b466+/kJiYiIYNG+Lo0aPYt28f/Pz8uNZZeZXG78OkSZOwefNmuLm5YfTo0dxQbubm5vjw4QP3m6mtrY3Vq1fj999/R6NGjfDrr7/CwMAASUlJOHjwIFq1aiVV6f+avb09eDweFixYgJSUFAgEAu6dBt9yzdfX1+euy66urjhz5gxMTU3h6emJ3bt3o3v37ujUqROePHmCNWvWoE6dOkWOwV9SLVu2RJUqVTBw4ECMGTMGDMMgPDy82K5LhcVdmJSUFKxYsQIAuOcOVq5cCV1dXejq6kqc35VauY2LUQ62bt3Kurq6stWqVWOVlZXZKlWqsK6uruy+ffvkWl/W8Dz79+9nGzRowKqqqrKWlpbsggUL2E2bNkkNX2JhYVHosD2PHz9mO3XqxKqpqbEGBgbs+PHj2V27drEA2AsXLkgse+3aNbZHjx5s1apVWYFAwFpYWLB9+vRhjx8/Xmz8+/btY+vUqcMqKytLDZW1fft21sHBgRUIBKyenh7bv39/9vnz5xLrfz2MVb53796xderUYY2MjNiHDx+yLMuyGzduZG1sbFiBQMDWqlWLDQkJkZk7AOyoUaMkymQNg8Wy/w35tGPHDq7s7t27rKurK6upqcnq6+uzw4YNY2/cuCF1bPLGXdC7d+/YUaNGsbVq1WI1NDRYHR0dtlmzZuy///4rtezKlSvZWrVqsXw+n61WrRr7xx9/sB8/fpTKX8Fhjd6+fcv27NmTVVdXZ6tUqcIOHz6cvX37ttQx5OXlsaNHj2YNDAxYhmEkcokCQ7mxLMtevXqVdXd3ZzU1NVl1dXW2bdu27Llz5ySWyR/2q+AQbQWHmLt69Srbr18/1tzcnBUIBKyhoSHr6enJXr58WWbeCvrf//7HAmD/+OMPiXJXV1cWgNS5K2sot8KOv7DzpbC8FCTrvPqai4sLq62tzQ2dJOtcKkhWTGfPnmVHjRrF1qtXj9XR0WH5fD5rbm7Oent7Swy5V5SEhAR2wIABrJGREcvn81lTU1PW09OT3blzJ7dMYZ8py0oOz1TQmzdv2EGDBrH6+vqsiooKW79+falhH4vK9dq1a9k2bdpw1yVra2t24sSJbEpKSpHHVFj+ZZ0DLPvt16l85XXNkWcot9mzZ7NNmzZldXV1WTU1NbZWrVrsnDlz2NzcXIn15PncWZZlb968yTo7O7OqqqqsqakpO2vWLHbjxo1yDaVV1PUlLS2NHTduHGtiYsLy+XzWxsaGXbRokcTwj4WRdc37nt+HfNeuXWOdnJxYgUDAVq9enZ03bx67fPlyFgD7+vVriWVPnjzJuru7szo6OqyqqiprbW3Nent7y3X9Wr9+PWtlZcXyeDypYd3kuebL+s49evSINTY2ZmvXrs2+ffuWFYvF7Ny5c1kLCwtWIBCwDg4ObGRkpFTuSnKesqzsa8HZs2fZ5s2bs2pqaqyJiQk7adIk9siRI1LHJk/chcmPU9Z/Bc+Fyoxh2W98IoJ8l+DgYIwbNw7Pnz8v8q80Qggh5Gfn5+eHtWvXIj09nV5SQcocVY7LQVZWlsRTudnZ2XBwcIBIJMKDBw8UGBkhhBBSsRT8zXz//j1sbW3RqFGj73pAkhB5/VB9jiuqHj16wNzcHPb29khJScHmzZtx7949uR+mIIQQQn4WLVq0gIuLC2rXro03b95g48aNSE1NxfTp0xUdGvlJUOW4HLi7u2PDhg2IiIiASCRCnTp1sG3bNvTt21fRoRFCCCEVioeHB3bu3Il169aBYRg0atQIGzdulBiijJCyRN0qCCGEEEII+eKHGueYEEIIIYSQ70GVY0IIIYQQQr6gyjEhhBBCCCFfUOWYEEIIIYSQL6hyTAghhBBCyBdUOSaEEEIIIeQLGueYcMRiMV6+fAktLS0wDKPocAghhBBCSg3LskhLS4OJiQmUlApvH6bKMeG8fPkSZmZmig6DEEIIIaTMPHv2DNWrVy90PlWOCUdLSwvA55NGW1tbwdF8P6FQiKNHj6JDhw7g8/mKDqdCo1zJj3IlP8qV/ChX8qNcyYfyJC01NRVmZmZcfacwVDkmnPyuFNra2j9M5VhdXR3a2tp0YSgG5Up+lCv5Ua7kR7mSH+VKPpSnwhXXdZQeyCOEEEIIIeQLqhyXMxcXF/j5+Sk6DEIIIYQQIgNVjiuZ0NBQ6OrqKjoMQgghhJAfEvU5JiXCsizy8vIgEokUHUqxhEIhlJWVkZ2dXSniVaTKlCsejwdlZWUabrACy854h3+PT0TM+xvgCcXgn74Aj3ZzAfrMCCGVAFWOFSAvLw++vr4IDw8Hn8/HH3/8gb///hsMwyAnJwd//fUXtm7dik+fPqFevXpYsGABXFxcEBMTg0GDBgH4rzP5zJkzERAQgPDwcCxbtgz379+HhoYG2rVrh+DgYBgaGpZa3Lm5uXj16hUyMzNLbZtliWVZGBkZ4dmzZ1SRKkZly5W6ujqMjY2hoqKi6FBIAXm5mRjxbwdcURJ+vjcpAC48j0T8jtsY3+eAosMjhFQgj9+mY+mxhzj36B101fno19Qcg1vVgJKSYn+HqHKsAGFhYRgyZAguXbqEy5cvw8fHB+bm5hg2bBh8fX1x9+5dbNu2DSYmJtizZw86duyIW7duoWXLlggODsaMGTNw//59AICmpiaAzy1/s2bNgp2dHZKTk+Hv7w9vb29ERUUVGkdOTg5ycnK46dTUVG5bQqFQYlmxWIwnT56Ax+PB2NgYfD6/wleiWJZFRkYGNDQ0KnysilZZcsWyLIRCId6+fYvHjx+jRo0aRQ7kXhbyvxsFvyPks2PnFnyuGBcQnvkEfZMuoppxIwVEVfHReSU/ypV8KnqektNy0GvNOXzI+Bzf+4xczD4Yj+cfMvCXR60y2ae8uWBYlmXLJAIik4uLC5KTk3Hnzh2uEjJlyhTs378fhw8fhpWVFZKSkmBiYsKt4+rqiqZNm2Lu3LkIDQ2Fn58fPn36VOR+Ll++jCZNmiAtLY2rQBcUEBCAwMBAqfItW7ZAXV1dokxZWRlGRkaoXr06BAJBCY+akNKVk5OD58+f49WrVxW+G8jP5kJyECJVPsqcN4JtjOpVupdzRISQiijqmRKOPJdu3FBmWAQ2FkGzDEafy8zMhJeXF1JSUoocspZajhWgefPmEq1zLVq0QFBQEG7dugWRSARbW1uJ5XNyclC1atUit3nlyhUEBATgxo0b+PjxI8RiMQAgKSkJderUkbnO1KlT4e/vz03nD47doUMHqZMmOzsbz549g5aWFlRVVUt0vIqS/5pIeh128SpbrrKzs6GmpgZnZ+dyPx+FQiGio6Ph5uZGY4fK8PbwXuDDZZnzmtq3g31dj/INqJKg80p+lCv5VPQ8RW65DiBZqjyPZVDToSUameuW+j7z75AXhyrHFUh6ejp4PB6uXLkCHo8nMa+w1l8AyMjIgLu7O9zd3REREQEDAwMkJSXB3d0dubm5ha4nEAhktgLz+XypL5JIJALDMFBSUir329jfKv8PhPy4SeEqW66UlJTAMIzMc7W8KHLfFVnX5pOxPrIXMgv0GbQT89DEfoCCoqo86LySH+VKPhU1TzWraSE6XrpyrKzEwMqwbF5cIu82K/6v4A/o4sWLEtMXLlyAjY0NHBwcIBKJkJycjJo1a0r8Z2RkBABQUVGRuo187949vH//HvPnz4eTkxNq1aqF5GTpE44QQsqagUEtrGryJ2qKP/+Bz7AsmjMaWNkpQsGREUIqkt+aW0BLVbqNtrdjdRhoKbb7JlWOFSApKQn+/v64f/8+tm7dihUrVmDs2LGwtbVF//79MWDAAOzevRtPnjzBpUuXMG/ePBw8eBAAYGlpifT0dBw/fhzv3r1DZmYmzM3NoaKighUrVuDx48fYv38/Zs2apeCjrBg8PT0xbtw4RYdR6hiGwd69exUdhhQah5sAQON6Xtgz6Dr2tw/BVI0xWNUvFkaGdRUdFiGkAjHVVcM2n+ZwtjWAshIDAy0BxrSrib+71lN0aFQ5VoQBAwYgKysLTZs2xahRozB27Fj4+PgAAEJCQjBgwACMHz8ednZ26NatG+Li4mBubg4AaNmyJUaMGIG+ffvCwMAACxcuhIGBAUJDQ7Fjxw7UqVMH8+fPx+LFixV5iEROFbWSW16EQiEmT56M+vXrQ0NDAyYmJhgwYABevnyp6NBIKaherSHUVaopOgxCSAVV10QHYYOb4tFcD8T95Qr/Dnbg8xRfNaU+x+UsJiaG+/fq1aul5vP5fAQGBsocReLr9Qqu269fP/Tr10+irCIORHLkzmusOvkID96kw7aaJka2rQn3ukaKDqtQLMtCJBJBWZm+KmUhMzMTV69exfTp09GwYUN8/PgRY8eORZcuXXD5suyHugghhJCypPjqOflpHLnzGsPDr+DG8xRkCUW48TwFIzZfwZE7r8sthvDwcDg6OkJLSwtGRkbw8vKS6J8dExMDhmFw6NAhNG7cGAKBAGfOnEFaWhr69+8PDQ0NGBsbY+nSpXBxcYGfnx+3bk5ODiZMmABTU1NoaGigWbNmEn8MFWRpaQkA6N69OxiG4aaBz38AWVtbQ0VFBXZ2dggPDy/yuGbOnAljY2PcvHkTADB58mTY2tpCXV0dVlZWmD59usT4jgEBAbC3t8emTZtgbm4ObW1tjB8/HiKRCAsXLoSRkREMDQ0xZ84cif0sWbKEa+U1MzPDyJEjkZ6eXmhcb9++haOjI7p37y4xpnY+HR0dREdHo0+fPrCzs0Pz5s2xcuVKXLlyBUlJSUUeMyGEEFIWqHJMys2qk4+kylgWWBWTUG4x5L8s5caNG9i7dy8SExPh7e0ttdyUKVMwf/58xMfHo0GDBvD398fZs2exf/9+REdHIzY2FlevXpVYx9fXF+fPn8e2bdtw8+ZN9O7dGx07dsTDhw9lxhIXFwfgc1eaV69ecdN79uzB2LFjMX78eNy+fRvDhw/HoEGDcPLkSaltsCyL0aNH459//kFsbCwaNGgAANDS0kJoaCju3r2LZcuWYf369Vi6dKnEugkJCTh06BAOHz6MiIgIbN68GZ6ennj+/DlOnTqFBQsWYNq0aRIPkCopKWH58uW4c+cOwsLCcOLECUyaNEnm8T179gxOTk6oV68edu7cKff42CkpKWAYhvouE0IIUQi6V0zKzYM3slsYH75JK7cYBg8ezP3bysoKy5cvR5MmTZCeni4xXN7ff/8NNzc3AEBaWhrCwsKwZcsWtG/fHsDnCu3XL2pJSkpCSEiIxAtcJkyYgMOHDyMkJARz586VisXAwAAAoKury41GAgCLFy+Gt7c3Ro4cCQDw9/fHhQsXsHjxYrRt25ZbLi8vD7/99huuXbuGM2fOwNTUlJs3bdo07t+WlpaYMGECtm3bJlGRFYvF2LRpE7S0tFCrVi04OTnhwYMHOHToEJSUlGBnZ4cFCxbg5MmTaNasGQBItJRbWlpi9uzZGDFiBFatWiVxbPfv34ebmxu6d++O4OBgucdOzs7OxuTJk9GvX78iB2gnhBBCygpVjkm5sa2miRvPU6TKbapplVsM8r4sxdHRkfv348ePIRQK0bRpU65MR0cHdnZ23PT3vMCloPj4eO4BzXytWrXCsmXLJMrGjRsHgUCACxcuQF9fX2Le9u3bsXz5ciQkJCA9PR15eXlSlU1LS0toaf2XewMDA6ioqEiMc1ytWjWJbifHjh3DvHnzcO/ePaSmpiIvLw/Z2dnIzMzk3qqYlZUFJycneHl5ITg4WO7jFgqF6NOnD1iWldkfnxBCCCkP1K2ClJuRbWuiYAMiwwCjXKzLZf/5L0vR1tZGREQE4uLisGfPHgCQelmKhoZGibb99Qtcrl+/zv0XHx8vVaktLW5ubnjx4gWOHDkiUX7+/Hn0798fHh4eiIyMxLVr1/DXX39JHWPBwdDzX6pRsCz/D4jExER4enqiQYMG2LVrF65cuYL//e9/ACTzJxAI4OrqisjISLx48UKuY8mvGD99+hTR0dHUakwIIURhqHJMyo17XSOs+a0xGprpQl2Fh4Zmulj7W2N0KKfRKr71ZSlWVlbg8/lcn2Dgc7/YBw8ecNPyvMBFFj6fL/VSl9q1a+Ps2bMSZWfPnpV6DXiXLl2wZcsWDB06FNu2bePKz507BwsLC/z1119wdHSEjY0Nnj59WuxxFufKlSsQi8UICgpC8+bNYWtrK3PINSUlJYSHh6Nx48Zo27ZtscOy5VeMHz58iGPHjpW4pZ1UXEKRGOKKN2gOIYQUibpVKIiLiwvs7e1LdNv5R+Be10hhQ7d9/bKUESNG4Pbt23K9LEVLSwsDBw7ExIkToaenB0NDQ8ycOZN7hTEAiRe4BAUFwcHBAW/fvsXx48fRoEEDdOrUSea2LS0tcfz4cbRq1QoCgQBVqlTBxIkT0adPHzg4OMDV1RUHDhzA7t27cezYMan1u3fvjvDwcPz+++9QVlZGr169YGNjg6SkJGzbtg1NmjTBwYMHuRby71GzZk0IhUKsWLECnTt3xtmzZ7FmzRqZy/J4PERERKBfv35o164dYmJiZP6RIBQK0atXL1y9ehWRkZEQiUR4/frz6CV6enpQUVH57rhJ+XuUnI7ZB+/i9IO34DE8nBfewbTOdaGtWvFeYUsIIQVRyzH5aXzPy1KWLFmCFi1awNPTE66urmjVqhVq164NVVVVbpniXuAiS1BQEKKjo2FmZgYHBwcAQLdu3bBs2TIsXrwYdevWxdq1axESEgIXFxeZ2+jVqxfCwsLw+++/Y/fu3ejSpQvGjRsHX19f2Nvb49y5c5g+fbr8iSpEw4YNsWTJEixYsAD16tVDREQE5s2bV+jyysrK2Lp1K+rWrYt27drJbKV/8eIF9u/fj+fPn8Pe3h7Gxsbcf+fOnfvumEn5S8kSou/6Uzj3bgcE5quhbLoJux9GYvg/VxQdGiGEyIVhK+KbIn4CFbHlODU1FTo6OkhJSZHq85mdnY0nT56gRo0aEhXCikwsFiM1NRXa2toSD5mVhoyMDJiamiIoKAhDhgwp1W0rQlnmqiwo8nwUCoWIioqCh4eHVB9tAmw88xDBt0YD6pL9zYUfWmJH74WoX11HQZFVbHReyY9yJR/Kk7Si6jlfq/i/gj+AjIwMDBgwAJqamjA2NkZQUJDE/OJeHhEaGgpdXV0cOXIEtWvXhqamJjp27IhXr15JbGfDhg1ca2atWrWkhtci3+7atWvYunUrEhIScPXqVfTv3x8A0LVrVwVHRkjFcvHlMamKMQCoVDmHa68eKyAiQggpGepzXA4mTpyIU6dOYd++fTA0NMSff/6Jq1evwt7eHsDnl0fcvXsX27Ztg4mJCfbs2YOOHTvi1q1bsLGxAfD5NbuLFy9GeHg4lJSU8Ntvv2HChAmIiIgAAERERGDGjBlYuXIlHBwccO3aNQwbNgwaGhoYOHCgzLhycnIk3lqWmpoK4PNfm1+/TS2/jGVZiMVibvSCii7/pkh+3N9DLBZj8eLFuH//PlRUVNCoUSOcOnUKenp6lSYfRSnNXJUHsVgMlmUhFArB4/HKdd/5342C3xHymXreKZnlLAMwORcgFNYr54gqBzqv5Ee5kg/lSZq8uaBuFWUsPT0dVatWxebNm9G7d28AwIcPH1C9enX4+PjA398fVlZWEi+PAABXV1c0bdoUc+fORWhoKAYNGoRHjx7B2vrzsGerVq3C33//zT28VLNmTcyaNQv9+vXjtjF79mxERUUV2nczICAAgYGBUuVbtmzhxqzNp6ysDCMjI5iZmdFDUkThcnNz8ezZM7x+/Rp5eXmKDod85cHrZfhH9a3MeX+yLaFexaOcIyKEkM8yMzPh5eVVbLcKajkuYwkJCcjNzeXeMAZ8fgo//wUS8r48Ql1dnasYA4CxsTH3gFNGRgYSEhIwZMgQDBs2jFsmLy8POjqF9++bOnUq/P39uenU1FSYmZmhQ4cOMvscP3v2DJqampWmzzHLskhLS4OWlpbcb2j7WVW2XGVnZ0NNTQ1t2rRRSJ/j6OhouLm5UT8+GVqcf4DdCaFIL9B3vU5ODnp1HAoY2ysmsAqOziv5Ua7kQ3mSln+HvDhUOVawr18eUfD28NevM5b1cob8Rv/09M+vZV6/fr1EJRxAkbecBQIBBAKBVDmfz5fan0gkAsMwUFJSqhQPbAHgugfkx00KV9lylT+Mnqxztbwoct8VmWGToVhzZT3maDCIF6hAiWXROisbAeo24Js3UXR4FR6dV/KjXMmH8vQfefNAleMyZm1tDT6fj4sXL3JDen38+BEPHjyAs7OzxMsjnJycvmkf1apVg4mJCR4/fsw9KEYIIQqhVgUNf4vEv0f/wqsnJ6HMKqFq/d5Q6jhX0ZERQohcqHJcxjQ1NTFkyBBMnDgRVatWhaGhIf766y+ude5bXx5RUGBgIMaMGQMdHR107NgROTk5uHz5Mj5+/CjRdYIQQsqcgS3Qfwf0szMRdfgIPDp5QolargghlQRVjsvBokWLkJ6ejs6dO0NLSwvjx49HSkoKNz8kJASzZ8/G+PHj8eLFC+jr66N58+bw9PSUex9Dhw6Furo6Fi1ahIkTJ0JDQwP169eHn59fGRwRIYTIgccHmIrfTYcQQr5GleNyoKmpifDwcISHh3NlEydO5P7N5/MRGBgoc+QIAPD29oa3t7dEWbdu3VBwoBEvLy94eXmVXuCEEEIIIT8Z+pOe/NA8PT0xbtw4RYdR6hiGwd69exUdhpT8F9YQQgghlRVVjglRoIpayS1PAQEBqFWrFjQ0NFClShW4urri4sWLig6LEELIT4oqx6R8xUcC69oCc4w//z8+UtERFYllWXrJRBmztbXFypUrcevWLZw5cwaWlpbo0KED3r6V/SIJQgghpCxR5ZiUn/hIYHt/4OVVQJj5+f/bfyvXCnJ4eDgcHR2hpaUFIyMjeHl5cS9TAYCYmBgwDINDhw6hcePGEAgEOHPmDNLS0tC/f39oaGjA2NgYS5cuhYuLi8QDjzk5OZgwYQJMTU2hoaGBZs2aISYmptBYLC0tAQDdu3cHwzDcNACsXr0a1tbWUFFRgZ2dnUR/dVlmzpwJY2Nj3Lx5EwAwefJk2NraQl1dHVZWVpg+fbrEazMDAgJgb2+PTZs2wdzcHNra2hg/fjxEIhEWLlwIIyMjGBoaYs6cORL7WbJkCerXrw8NDQ2YmZlh5MiR3Djbsrx9+xaOjo7o3r27xKvKv+bl5QVXV1dYWVmhbt26WLJkCVJTU7ljIYQQQsoTVY5J+YkNklHIAmeWlFsIQqEQs2bNwo0bN7B3714kJiZKPewIAFOmTMH8+fMRHx+PBg0awN/fH2fPnsX+/fsRHR2N2NhYXL16VWIdX19fnD9/Htu2bcPNmzfRu3dvdOzYEQ8fPpQZS1xcHIDPo5W8evWKm96zZw/Gjh2L8ePH4/bt2xg+fDgGDRqEkydPSm2DZVmMHj0a//zzD2JjY9GgQQMAgJaWFkJDQ3H37l0sW7YM69evx9KlSyXWTUhIwKFDh3D48GFERERg8+bN8PT0xPPnz3Hq1CksWLAA06ZNk+jioKSkhOXLl+POnTsICwvDiRMnMGnSJJnH9+zZMzg5OaFevXrYuXOnzBfOFJSbm4t169ZBR0cHDRs2LHZ5QgghpNSxhHyRkpLCAmBTUlKk5mVlZbF3795ls7Kyvn0Hs41Ydqa29H+zjb8j6sKJRCK2VatW7JgxYwpdJi4ujgXApqWlsSzLsidPnmQBsHv37uWWSU1NZfl8Prtjxw6u7NOnT6y6ujo7duxYlmVZ9unTpyyPx2NfvHghsf327duzU6dOLXT/ANg9e/ZIlLVs2ZIdNmyYRFnv3r1ZDw8PifV27NjBenl5sbVr12afP39e6D5YlmUXLVrENm7cmJueOXMmq66uzqamprIs+zlX7du3Zy0tLVmRSMQtZ2dnx86bN6/Q7e7YsYOtWrUqNx0SEsLq6Oiw9+7dY83MzNgxY8awYrG4yNhYlmUPHDjAamhosAzDsCYmJuylS5eKXL5UzsdvlJuby+7du5fNzc0t931XNpQr+VGu5Ee5kg/lSVpR9ZyvUctxKWFZFj4+PtDT0wPDMLh+/Xqpbv/rB7cSExOL3Ud+94BPnz6VahzfxaCW7HLDQsrLwJUrV9C5c2eYm5tDS0sLzs7OAICkpCSJ5RwdHbl/P378GEKhEE2bNuXKdHR0YGdnx03funULIpEItra20NTU5P47deoUEhISShRjfHw8WrVqJVHWqlUrxMfHS5SNGzcOFy9exOnTp2Fqaioxb/v27WjVqhWMjIygqamJadOmSR2jpaUltLS0uGkDAwPUrl1b4vXR1apVk+h2cuzYMbRv3x6mpqbQ0tLC77//jvfv3yMzM5NbJisrC05OTujRoweWLVsGhmGKPea2bdvi+vXrOHfuHDp27Ig+ffpI7JdUUsIsMGLqs08IqVyoclxKDh8+jNDQUERGRuLVq1eoV6+eokOqeJzGAyhYUWK+lJe9jIwMuLu7Q1tbGxEREYiLi8OePXsAfL6d/zUNDY0SbTs9PR08Hg9XrlzB9evXuf/i4+OxbNmyUjuGr7m5ueHFixc4cuSIRPn58+fRv39/eHh4IDIyEteuXcNff/0ldYwF3zHPMIzMMrFYDODzH2Wenp5o0KABdu3ahStXruB///sfAMn8CQQCuLq6IjIyEi9evJDrWDQ0NFCzZk00b94cGzduhLKyMjZu3ChfIkjFkxwPhHWB8kJzdLo5HLz9o4Csj2W+W5ZlcS7hHXZeeY6Hb9LKfH+EkB8TvQSklCQkJMDY2BgtW7ZUdCgVV21PoO/mz32Mk+99bjF2Gg/Uku8V2d/r3r17eP/+PebPnw8zMzMAwOXLl4tdz8rKCnw+H3FxcTA3NwcApKSk4MGDB2jTpg0AwMHBASKRCMnJyXBycpI7Jj6fD5FIJFFWu3ZtnD17FgMHDuTKzp49izp16kgs16VLF3Tu3BleXl7g8Xj49ddfAQDnzp2DhYUF/vrrL27Zp0+fyh1TYa5cuQKxWIygoCCudfnff/+VWk5JSQnh4eHw8vJC27ZtERMTAxMTkxLtSywWF/oAH6ngMj8AoZ5A5jswAHisELi1HUh9DgyKKrPdJqdlY1BIHO68TOXKujuYYnHvhuApFX/3ghBC8lHLcSnw9vbG6NGjkZSUBIZhoK+vL/Hq5+DgYDAMg8OHD3NlNWvWxIYNGwB8fjDLzc0N+vr60NHRgbOzs9TDXsWJioqCra0t1NTU0LZtWyQmJpbKsZW62p7AsBPAXy8//7+cKsYAYG5uDhUVFaxYsQKPHz/G/v37MWvWrGLX09LSwsCBAzFx4kScPHkSd+7cwZAhQ6CkpMR1GbC1tUX//v0xYMAA7N69G0+ePMGlS5cwb948HDx4sNBtW1pa4vjx43j9+jU+fvzcsjZx4kSEhoZi9erVePjwIZYsWYLdu3djwoQJUut3794d4eHhGDRoEHbu3AkAsLGxQVJSErZt24aEhAQsX76cayH/HjVr1oRQKOTyFx4ejjVr1shclsfjISIiAg0bNkS7du3w+vVrmctlZGTgzz//xIULF/D06VNcuXIFgwcPxosXL9C7d+/vjpkowM3tyM56j3+0tTDEyBCjqhngsIY68PQs8KJk17WSmL73tkTFGAD2XHuBzRe+/w9DQsjPhVqOS8GyZctgbW2NdevWIS4uDtHR0Rg9ejREIhF4PB5OnToFfX19xMTEoGPHjnjx4gUSEhLg4uICAEhLS8PAgQOxYsUKsCyLoKAgeHh44OHDhxJ9Qgvz7Nkz9OjRA6NGjYKPjw8uX76M8eOL76qQk5Mj0TqXmvr5h0UoFEoM+5VfxrIsxGIxd5u9omO/er22WCxG1apVsWnTJkybNg3Lly9Ho0aNsHDhQnTr1o07rvxjK3icixcvxh9//AFPT09oa2tj4sSJePbsGQQCAbfcxo0bMWfOHIwfPx4vXryAvr4+mjVrBg8Pj0JztmjRIkyYMAHr16+HqakpHj9+jC5dumDp0qVYvHgxxo4dixo1amDjxo1o06aNxHbyY+zRowfy8vLw+++/AwB69OgBPz8/+Pr6IicnBx4eHpg2bRoCAwO59fNzU3D667Kv8ygWi1G/fn0EBQVhwYIFmDp1KpycnDBnzhx4e3vLzJ+SkhIiIiLw66+/ol27djhx4gQMDQ0lts0wDOLj4xEWFoZ3796hatWqcHR0xKlTp1C7du1C8yYWi8GyLIRCIXg8nsxlykr+d6Pgd4R8Jn73CMONDHBVVZUrO62uhjufUjH23SOwhvVLfZ8ZOXk4Fi+7j/qea8/h1cRU5ryKhM4r+VGu5EN5kiZvLhj2619F8s2Cg4MRHByMxMREfPr0CVWrVsXFixfRuHFj6OvrY+LEidi7dy8uXLiAiIgITJ48Gc+fP5e5LbFYDF1dXWzZsoVrgWYYBnv27EG3bt2QmJiIGjVq4Nq1a7C3t8eff/6Jffv24c6dO9w2pkyZggULFuDjx4+Fvs43ICAAgYGBUuVbtmyBurq6RJmysjKMjIxgZmYGFRWVb8zSjyMjIwN16tTB7NmzuUopKT+5ubl49uwZXr9+TS9pqWBevd+J//GuS5XzWBZ/qXpDRc2m1PeZmQdMjZPd1lNdg8XEBiKZ8wghP5fMzEx4eXkhJSUF2trahS5HLcdlQFdXFw0bNkRMTAxUVFSgoqICHx8fzJw5E+np6Th16hQ3SgIAvHnzBtOmTUNMTAySk5MhEomQmZkpNbpAYeLj49GsWTOJshYtWhS73tSpU+Hv789Np6amwszMDB06dJA6abKzs/Hs2TNoampC9asWoYqMZVmkpaVBS0tLrhETinLt2jXcu3cPTZs2RUpKCmbNmgWGYdC3b98iv2CVRWnmqjxkZ2dDTU0Nbdq0KffzUSgUIjo6Gm5ublIPMBJgUdwN4OF1qXIRw0DdsTY6WHQok/3ufhuHuETph/56NK0Jj3bWZbLP0kTnlfwoV/KhPEnLv0NeHKoclxEXFxfExMRAIBDA2dkZenp6qF27Ns6cOYNTp05JdHsYOHAg3r9/j2XLlsHCwgICgQAtWrSQGl2gtAkEApkvZuDz+VJfJJFIBIZhoKSkJDHUV0WWf0s+P+7voaSkhCVLluD+/ftQUVFB48aNERsbK9VNoLIqzVyVh/z+3rLO1fKiyH1XZNW0Cu/CYKxlXGY5m92tPvpvuIB36f9dNx3MdTHcpSb4/MrzU0fnlfwoV/KhPP1H3jxUnitGJePs7IxNmzZBWVkZHTt2BPC5wrx161Y8ePCA628MfB6JYNWqVfDw8ADwuQ/xu3fv5N5X7dq1sX//fomyCxcufP9BEI6DgwOuXLmi6DAIqfC6WHfB+lvrkSHMkCivpVcLjao1KrP92hlp4cQEF+y7/hLPP2bCwUwXrrWrQZlX8f/YI4RULHTVKCNt2rRBWloaIiMjuYqwi4sLIiIiYGxsDFtbW25ZGxsbhIeHIz4+HhcvXkT//v2hpqYm975GjBiBhw8fYuLEibh//z62bNmC0NDQUj4iQggpnoG6AVa1X4WaujUBAAwYNDdqjhXtVpT5vrVV+fi9uQWm/lIbHesZU8WYEPJN6MpRRqpUqYL69evDwMAAtWp9fgNc/mgDX/c3Bj6PcvDx40c0atQIv//+O8aMGVOi2/Xm5ubYtWsX9u7di4YNG2LNmjWYO3duqR4PIYTIq1G1RtjTdQ/2d9mPSdqTsKrdKhhpGCk6LEIIkQt1qyglfn5+8PPzkygr+HpnPT09mUNTOTg4IC4uTqKsV69eEtNfDypiaWmJgoOMeHp6SoytDACDBg2SN3xCCCl11TWrQ0up+OEoCSGkIqGWY0IIIYQQQr6gyjEhhBBCCCFfUOWYEEIIIYSQL6hyTH5onp6eGDdunKLDKHUMw2Dv3r2KDkNKaGhooW9kJIQQQioDqhwTokAVtZKrKCNGjADDMAgODlZ0KIQQQn5SNFoFKVfHk45jw80NSEhJgLWONYY2GIr25u0VHVahWJaFSCSCsjJ9Vcranj17cOHCBZiYmCg6FEIIIT8xajmWgWVZ+Pj4QE9PDwzD4Nq1axLTBYdoI/I5nnQcfif9cPv9bWTlZeH2+9sYd3IcjicdL7cYwsPD4ejoCC0tLRgZGcHLywvJycnc/JiYGDAMg0OHDqFx48YQCAQ4c+YM0tLS0L9/f2hoaMDY2BhLly6Fi4uLxPB9OTk5mDBhAkxNTaGhoYFmzZohJiam0FgsLS0BAN27dwfDMNw0AKxevRrW1tZQUVGBnZ0dwsPDizyumTNnwtjYGDdv3gQATJ48Gba2tlBXV4eVlRWmT58OoVDILR8QEAB7e3ts2rQJ5ubm0NbWxvjx4yESibBw4UIYGRnB0NAQc+bMkdjPkiVLUL9+fWhoaMDMzAwjR45Eenp6oXG9ffsWjo6O6N69O3Jycgpd7sWLFxg9ejQiIiLoNaeEEEIUiirHMhw+fBihoaGIjIzEq1ev8Pz5c4npevXqKTrESmnDzQ1SZSxYbLy1sdxiEAqFmDVrFm7cuIG9e/ciMTER3t7eUstNmTIF8+fPR3x8PBo0aAB/f3+cPXsW+/fvR3R0NGJjY3H16lWJdXx9fXH+/Hls27YNN2/eRO/evdGxY0c8fPhQZiz5Y1uHhITg1atX3PSePXswduxYjB8/Hrdv38bw4cMxaNAgnDx5UmobLMti9OjR+OeffxAbG4sGDRoAALS0tBAaGoq7d+9i2bJlWL9+PZYuXSqxbkJCAg4dOoTDhw8jIiICmzdvhqenJ54/f45Tp05hwYIFmDZtGi5evMito6SkhOXLl+POnTsICwvDiRMnMGnSJJnH9+zZMzg5OaFevXrYuXMnBAKBzOXEYjF+//13TJw4EXXr1pW5DKmEslPA3DsIg9SbgEhY/PKEEFJB0L1iGRISEmBsbIyWLVsCAJ4+fSoxTb5NQkqCzPJHnx6VWwyDBw/m/m1lZYXly5ejSZMmSE9Ph6amJjfv77//hpubGwAgLS0NYWFh2LJlC9q3/9wFJCQkROL2f1JSEkJCQpCUlMSVT5gwAYcPH0ZISIjMNxYaGBgAAHR1dWFk9N/bwxYvXgxvb2+MHDkSAODv748LFy5g8eLFaNu2LbdcXl4efvvtN1y7dg1nzpyBqakpN2/atGncvy0tLTFhwgRs27ZNoiIrFouxadMmaGlpoVatWnBycsKDBw9w6NAhKCkpwc7ODgsWLMDJkyfRrFkzAJBoKbe0tMTs2bMxYsQIrFq1SuLY7t+/Dzc3N3Tv3h3BwcFgGEb2BwJgwYIFUFZWxpgxYwpdhlQy1zYDUROhLMxESwDsys3Ar1uA6o3LZHdpuWnY+2gv7r6/i+pa1dHTpie9kY8Q8s2o5bgAb29vjB49GklJSdyt7oLTwOeKQcGHhuzt7REQEADgc4teQEAAzM3NIRAIYGJiIvHjb2lpiVmzZqFfv37Q0NCAqakp/ve//0lsLykpCV27doWmpia0tbXRp08fvHnzBgCQkpICHo+Hy5cvA/hc0dHT00Pz5s259Tdv3gwzM7NSztC3s9axllleU7dmucVw5coVdO7cGebm5tDS0uJe5Z2UlCSxnKOjI/fvx48fQygUomnTplyZjo4O7OzsuOlbt25BJBLB1tYWmpqa3H+nTp1CQoLsPwoKEx8fj1atWkmUtWrVCvHx8RJl48aNw8WLF3H69GmJijEAbN++Ha1atYKRkRE0NTUxbdo0qWO0tLSEltZ/by8zMDBA7dq1oaT032WhWrVqEt1Ojh07hvbt28PU1BRaWlr4/fff8f79e2RmZnLLZGVlwcnJCT169MCyZcuKrBhfuXIFy5YtQ2hoaJHLkUrk7QOw+0cDwv/OCSb9Ndjt/cukBfld1jv0jeyLhXELEfk4EmturEH3fd1x+93tUt8XIeTnQC3HBSxbtgzW1tZYt24d4uLikJOTg3/++Yeb5vF4cm1n165dWLp0KbZt24a6devi9evXuHHjhsQyixYtwp9//onAwEAcOXIEY8eOha2tLdzc3CAWi7mK8alTp5CXl4dRo0ahb9++iImJgY6ODuzt7RETEwNHR0fcunWL6x+d3wp66tQprvInS05OjkQ/0NTUVACfux583T81v4xlWYjFYpmvwJbHkHpD4H/KHyz+e/U1AwZD6g355m0W5etXbIvFYmRkZMDd3R0dOnRAeHg4DAwMkJSUhF9++QXZ2dkSx6ampsb9++v/F4wzPyepqang8XgyzxFNTc0ij0/WdguW5R/L12Wurq7Ytm0bDh06hP79+3Pl58+fR//+/REQEIAOHTpAR0cH27dvx5IlS7j1WZYFn8+XmGYYRqIM+DyahkgkglgsRmJiIjw9PTFixAjMmjULenp6OHPmDIYNG4bs7GyoqqpCLBZDIBCgffv2iIyMxPjx46Uq7l87ffo0kpOTYW5uzpWJRCKMHz8ewcHBePz4caE5Y1kWQqFQ7u9kacn/bhT8jpAvrm0Fn5U+35m0V8h7eBysdek+gLvu+jo8S3smUZYuTMeCSwuwyW1Tqe6rLNF5JT/KlXwoT9LkzQVVjgvQ0dGBlpYWeDwed6u74LQ8kpKSYGRkBFdXV/D5fJibm0u0PAKfWwOnTJkCALC1tcXZs2exdOlSuLm54fjx47h16xaePHnCtf7+888/qFu3LuLi4tCkSRO4uLggJiYGEyZMQExMDNzc3HDv3j2cOXMGHTt2RExMTKH9QQFg3rx5CAwMlCo/evQo1NXVJcqUlZVhZGSE9PR05Obmyp2HrznqOmJ209nY/GAzEtMSYallid9tf0djncZcxbws5ObmIjU1FdevX8f79+/x559/onr16gCA2NhYAEBGRgZSU1O5FtC0tDSuBVVfXx98Ph+nT59Gly5dAHxuuX/w4AGaNWuG1NRU2NjYQCQS4cmTJzK73xR2fHw+H+np6RLzbWxscOrUKXTv3p0rO3XqFGxsbCSWc3V1Rfv27TFs2DDk5uaiZ8+eAICTJ0/CzMwMvr6+3LKPHj0Cy7Lc+jk5ORCJRFJx5eXlSZTl5eVx+Ttz5gzEYjFmzJjB5SYxMVEiX9nZ2WAYBitXrsSwYcPQtm1bHDhwAMbGxjKPv2vXrlyXjXy9evVCnz590L9//0Lzlpubi6ysLJw+fRp5eXkylylr0dHRCtlvRWfwMB6FdUA7FXsG6fcLfzDzW0Snyv4crr+9jr0H90KFUSnV/ZU1Oq/kR7mSD+XpP1/f5SwKVY7LSO/evREcHAwrKyt07NgRHh4e6Ny5s8SQYC1atJBYp0WLFlxXjfj4eJiZmUl0i6hTpw50dXURHx+PJk2awNnZGRs3boRIJMKpU6fQoUMHGBkZISYmBg0aNMCjR4/g4uJSaIxTp06Fv78/N52amgozMzN06NAB2traEstmZ2fj2bNn0NTUhKqq6jfnxVPbE552nt+8fknkt7aqqKhAW1sbtWvXhoqKCsLCwjB8+HDcvn0bS5YsAQBoaGhAW1ub+6NAS0uLy4G2tjYGDBiAgIAAmJqawtDQEAEBAVBSUoJAIIC2tjYaNWoELy8vjBo1CosWLYKDgwPevn2LEydOoH79+ujUqZPMGC0tLXH+/Hm4urpCIBCgSpUqmDx5Mn799Vc0adIErq6uiIyMxIEDB3D06FGJz0VNTQ3dunWDiooKBg4cCC0tLfTq1Qv16tXD8+fPERUVhSZNmiAqKgoHDx4EwzDc+gKBADwej5vOz5WysrLEPpSVlbn81a9fH0KhEP/88w88PT1x9uxZhIaGSuRLVVUVDMOgSpUq2LZtG7y8vNC9e3ecOHFC5h+X2traEqN05H9eFhYWaNy48P6p2dnZUFNTQ5s2bb7rfPwWQqEQ0dHRcHNzo5E1ZNi87RNaph+SKs9kBchu0A8ejWxLdX/bjmzDu/fvpMpVearo9Esn8JUqx2dE55X8KFfyoTxJk7chjirH30hJSUnitj0g2VxvZmaG+/fv49ixY4iOjsbIkSOxaNEinDp1qtRO0jZt2iAtLQ1Xr17F6dOnMXfuXBgZGWH+/Plo2LAhTExMYGNjU+j6AoFA5ggCfD5fKkaRSASGYaCkpCTRJ7Ui+7p7gJKSEqpVq4bQ0FD8+eefWLFiBRo1aoTFixejS5cu3HHlH1vB41y6dClGjBiBLl26QFtbG5MmTcLz58+hpqbGLRcaGorZs2dj4sSJePHiBfT19dG8eXN07ty50JwFBQXB398fGzZsgKmpKRITE7m+uosXL8a4ceNQo0YNhISEoF27dhLr5sfYp08fAMDAgQOhrKyMHj16YNy4cRgzZgxycnLQqVMnTJ8+navQA+D69+ZPF8zV1/I/dwcHByxZsgQLFy7En3/+iTZt2mDevHkYMGCAzPypqKhg27Zt6Nu3L1xdXRETEwNDQ0O5Prv8fRZGSUmJ6waiqIu+IvddkQktWmPr/bbop/zf6CoilkFA3gD0NTIu9Zx1t+mO2++l+xd7WntCXaAuY42Kjc4r+VGu5EN5+o/ceWCJlKVLl7IWFhaFTrMsyzZt2pSdOHEiN52SksKqqamxM2fOlLnNe/fusQDYK1eusCzLshYWFuwvv/wiscyvv/7KlR09epTl8XhsUlISN//OnTssADYuLo4rs7e3ZwcMGMAaGRmxLMuy79+/Z1VUVFgvLy+2X79+JTrulJQUFgCbkpIiNS8rK4u9e/cum5WVVaJtKpJIJGI/fvzIikSiUt92eno6q6Ojw27YsKHUt60IZZmrsqDI8zE3N5fdu3cvm5ubW+77rgzepmWz9WceZn+duoDd8FdfdvlfA9h2U9ayXVbElsn+xGIxu+DSAtb+H3u2Xmg9tl5oPdb3uC+bkZtRJvsrK3ReyY9yJR/Kk7Si6jlfo5bjb9SuXTuEhoaic+fO0NXVxYwZMyQeDAoNDYVIJEKzZs2grq6OzZs3Q01NDRYWFtwyZ8+excKFC9GtWzdER0djx44dOHjwIIDP/Unr16+P/v37Izg4GHl5eRg5ciScnZ0lRlJwcXHBihUr0KtXLwCAnp4eateuje3bt0uNfkG+3bVr13Dv3j00bdoUKSkp+PvvvwF87jNLCPmPvqYA4UOaYcY+Dcx6XhcMWLSvZYi5PRuUyf4YhsGkJpMwuN5gPPj4AKaaprDQtih+RUIIKUTluD9eAU2dOhXOzs7w9PREp06d0K1bN1hb/zdUma6uLtavX49WrVqhQYMGOHbsGA4cOICqVatyy4wfPx6XL1+Gg4MDZs+ejSVLlsDd3R3A5wv+vn37UKVKFbRp0waurq6wsrLC9u3bJeJwdnaGSCSS6Fvs4uIiVUa+3+LFi9GwYUO4uroiIyMDsbGx0NfXV3RYhFQ4Dc10sc+3Nc5NcsbcJiKs7u8AQ62y7Ruur6aPliYtqWJMCPluDMsW6DhLyoWlpSX8/PwkXqqgaKmpqdDR0UFKSorMB/KePHmCGjVqlPsDUN8qf4g1bW3tStNPWlEqW64UeT4KhUJERUXBw8OD+vEVg3IlP8qV/ChX8qE8SSuqnvO1iv8rSAghhBBCSDmhyjEpEbrRQCoCOg8JIYSUFXogT0HyX55QWeTfksnMzISampqCoyE/u/yB3OlWISGEkNJGlWMiFx6PB11dXSQnJwMA1NXVubFyKyqxWIzc3FxkZ2dXin60ilRZcsWyLDIzM5GcnAxdXd1yf3U0IYSQHx9Vjonc8t9wll9BruhYlkVWVhbU1NQqfEVe0SpbrnR1dUv0OndCCCFEXlQ5JnJjGAbGxsYwNDSUeBtgRSUUCnH69Gm0adOGbr8XozLlis/nU4sxIYSQMkOVY1JiPB6vUlROeDwe8vLyoKqqWuErfIpGufpBicVA/D7kxR8Ew1MBr0EvwLpd8et9p0+ZuVgdk4Dou2+Qm8VDcpWnGNzaGjylin9XghBCKm7nwkqGZVn4+PhAT08PDMPg+vXrUssEBATA3t6+3GMjhPycnu0agNGnJsAx/QKafjqNvw4Nwcfo6WW6z2yhCL+uu4C1px/j8bsMPM9gMCfqPibvulmm+yWEkNJCleNScvjwYYSGhiIyMhKvXr2Cg4MD9u7dq+iwCCE/qayEkxiSegUxGuoQMQxylRjs19LEqCf/Ap+eldl+D958hXuv06TKd119jifvMspsv4QQUlqoclxKEhISYGxsjJYtW5b7g0K5ubnluj9CSMV3+E44XilL95y7JVBB3K3wMtvvrRcpMstZFrhdyDxCCKlIqM9xKfD29kZYWBiAzw+tWVhYAAC6d+8OALCwsJAY13jt2rWYPXs23r9/D09PT6xfvx46OjoAABcXF9jb2yM4OJhbvlu3btDV1UVoaCiAz6+eHjJkCB4+fIi9e/eiR48ecHFxgZ+fH7Zv3w4/Pz88e/YMrVu3RkhICIyNjWXGnZOTg5ycHG46NTUVwOeHsyrDA3fFyT+GH+FYyhrlSn6VJVdP2cLjSxRlwb6M4jfVFRQ6z0RbpcLnTVEqy3lVEVCu5EN5kiZvLqhyXAqWLVsGa2trrFu3DnFxceDxeDA0NERISAg6duwo8fDao0eP8O+//+LAgQNITU3FkCFDMHLkSERERJRon4sXL8aMGTMwc+ZMAEBsbCwyMzOxePFihIeHQ0lJCb/99hsmTJhQ6LbnzZuHwMBAqfKjR49CXV29RPFUZNHR0YoOodKgXMmvoucqN8scwHWZ896/q4aoqKgy2a+GENBU5iE9T/LhO2stFs9vnsVz6npcpIp+XlUklCv5UJ7+k/8CqeJQ5bgU6OjoQEtLCzweT6JLhayxWLOzs/HPP//A1NQUALBixQp06tQJQUFBJeqO0a5dO4wfP56bjo2NhVAoxJo1a2BtbQ0A8PX1xd9//13oNqZOnQp/f39uOjU1FWZmZujQoQO0tbXljqWiEgqFiI6OhpubG43AUAzKlfwqS646iDsgbv8V3Mt8IVHuot8IQzoML9N92zdPx+xD93A+4QN4DItO9Y0x3bM2dNQqbr4UrbKcVxUB5Uo+lCdp+XfIi0OV43Jmbm7OVYwBoEWLFhCLxbh//36JKseOjo5SZerq6lzFGACMjY2LfGGHQCCAQCB9C5TP5/9QX6Qf7XjKEuVKfhU9V3zwsbHrv1h/Yy2OPzkMvpIyPGx6YFC9QeDzyjbuOtWrYMuwFkjNyEb00SPo4tmgQueqIqno51VFQrmSD+XpP/LmgSrHFYySkhJYlpUok9VHRkNDQ6qs4IfOMIzUtgghPw9tFW2MbzIR45tMVMj+1VR4UKbHvgkhlQxdtsoIn8+HSCSSKk9KSsLLly+56QsXLkBJSQl2dnYAAAMDA7x69YqbLxKJcPv27bIPmBBCCCGEUOW4rFhaWuL48eN4/fo1Pn78yJWrqqpi4MCBuHHjBmJjYzFmzBj06dOH61LRrl07HDx4EAcPHsS9e/fwxx9/4NOnTwo6CkIIIYSQnwtVjstIUFAQoqOjYWZmBgcHB668Zs2a6NGjBzw8PNChQwc0aNAAq1at4uYPHjwYAwcOxIABA+Ds7AwrKyu0bdtWEYdACCGEEPLTYVjqlEq+SE1NhY6ODlJSUn6Y0SqioqLg4eFBDyMUg3IlP8qV/ChX8qNcyY9yJR/KkzR56znUckwIIYQQQsgXVDkmhBBCCCHkC6ocE0IIIYQQ8gVVjgkhhBBCCPmCKseEEEIIIYR8QW/II4T8VD5kf8DeR3vxNPUpbKvYoqt1V2iqaCo6LEIIIRXET9tyzLIsfHx8oKenB4ZhoKurCz8/P26+paUlgoODS7xdb29vdOvWrdTilJeLi4tE/IQQaY8/PUb3fd2x9MpS7H64G/MvzUevA73wJuONokP78Tw+BaXov1D75Q7g7X1FR0MIIXL7aSvHhw8fRmhoKCIjI/Hq1SvUq1evROsnJiaCYRhcv369bAIkhJS6oCtB+JD9QaLsRfoLrL6xWkER/aD2jwb+6QLepbWwfXMAyuudgKv/KDoqQgiRy09bOU5ISICxsTFatmwJIyMjKCsrrodJbm6uwvZNyM+CZVmcfXFW5rzYF7HlHM0P7MlpqYoww4qRd3ASkPVJMTERQkgJ/JSVY29vb4wePRpJSUlgGAaWlpYyl8vMzMTgwYOhpaUFc3NzrFu3jptXo0YNAICDgwMYhoGLi4vEuosXL4axsTGqVq2KUaNGQSgUcvMsLS0xa9YsDBgwANra2vDx8QEAnDlzBk5OTlBTU4OZmRnGjBmDjIwMbr1Vq1bBxsYGqqqqqFatGnr16iWxT7FYjEmTJkFPTw9GRkYICAj4jiwR8mNhGAbqyuoy52nyqc9xaXkZt1dmubIoC5n3T5RvMIQQ8g1+ygfyli1bBmtra6xbtw5xcXHg8Xjo3bu31HJBQUGYNWsW/vzzT+zcuRN//PEHnJ2dYWdnh0uXLqFp06Y4duwY6tatCxUVFW69kydPwtjYGCdPnsSjR4/Qt29f2NvbY9iwYdwyixcvxowZMzBz5kwAn1uyO3bsiNmzZ2PTpk14+/YtfH194evri5CQEFy+fBljxoxBeHg4WrZsiQ8fPiA2VrK1KywsDP7+/rh48SLOnz8Pb29vtGrVCm5ubjLzkJOTg5ycHG46NTUVwOdXTn5dma+s8o/hRziWsvaz5MrTyhNb72+VLq/hKfex/yy5+lYJH/JgUsi8W29y0IjyJhOdV/KjXMmH8iRN3lwwLMuyZRxLhRQcHIzg4GAkJiYC+PxAm729PfcQnqWlJZycnBAeHg7g8y1ZIyMjBAYGYsSIEUhMTESNGjVw7do12Nvbc9v19vZGTEwMEhISwOPxAAB9+vSBkpIStm3bxm3bwcEBe/bs4dYbOnQoeDwe1q5dy5WdOXMGzs7OyMjIQFRUFAYNGoTnz59DS0tL6nhcXFwgEokkKsxNmzZFu3btMH/+fJk5CAgIQGBgoFT5li1boK4uu4WNkMosl83FjswdiBfGAwAYMHBQcUBXta7gMTwFR/djuPLkNWZ+nAweI/nT8pqtgjDLJailR3kmhChGZmYmvLy8kJKSAm1t7UKX+ylbjuXVoEED7t8Mw8DIyAjJycnFrle3bl2uYgwAxsbGuHXrlsQyjo6OEtM3btzAzZs3ERERwZWxLAuxWIwnT57Azc0NFhYWsLKyQseOHdGxY0d0795dohL7dbz5+y0q3qlTp8Lf35+bTk1NhZmZGTp06FDkSVNZCIVCREdHw83NDXw+X9HhVGg/U666oRsSUxORlJaEmjo1YaJZWDunbD9Trr6FTXI6pq56hJnKYdBgPt+Zes1WwRTlyVjVrxP4vJ+yN1+x6LySH+VKPpQnafl3yItDleMiFDyZGIaBWCwulfU0NDQkptPT0zF8+HCMGTNGanvm5uZQUVHB1atXERMTg6NHj2LGjBkICAhAXFwcdHV1vylegUAAgUAgM/4f6Yv0ox1PWfpZcmVT1QY2VW2+axs/S65Kqo5pFdh3GQ3nyOZoLLqFTAjwQM0BqwY0g7qq9PWGSKLzSn6UK/lQnv4jbx6ocvyN8vsYi0SiUtleo0aNcPfuXdSsWbPQZZSVleHq6gpXV1fMnDkTurq6OHHiBHr06FEqMRBCSGnwamYOj/pGOHWvKe7cvIY1fdtCQ40qxoSQyoEqx9/I0NAQampqOHz4MKpXrw5VVVXo6Oh88/YmT56M5s2bw9fXF0OHDoWGhgbu3r2L6OhorFy5EpGRkXj8+DHatGmDKlWqICoqCmKxGHZ2dqV4VIQQUjp01VXgUd8IeMZCRZm6UhBCKg+6Yn0jZWVlLF++HGvXroWJiQm6du36Xdtr0KABTp06hQcPHsDJyQkODg6YMWMGTEw+94fU1dXF7t270a5dO9SuXRtr1qzB1q1bUbdu3dI4HEIIIYQQgp+45djPz0/idcsxMTES8/NHsfhawbfhDR06FEOHDpUoCw0NlVqv4GuoZW0bAJo0aYKjR4/KnNe6dWupGL8ma97evXsLXZ4QQgghhEijlmNCCCGEEEK+oMoxIYQQQgghX1DlmBBCCCGEkC+ockwIIYQQQsgXVDkmhBBCCCHkC6ocE0JICdx9fRlPs28gKy9L0aEQQggpA1Q5VgCWZeHj4wM9PT0wDCM1RJwslpaWUkPCFSUxMVHubRNCipeUfBs9w5vhtxM+WJ+9Ax23t8KeC4sUHRYhhJBS9tOOc6xIhw8fRmhoKGJiYmBlZQV9ff1i14mLi4OGhobc+zAzM8OrV6/k2jYhpHjjDg3EA+Ry02kMEHAvDLWNHFHLsq0CIyOEEFKaqHKsAAkJCTA2NkbLli3lXsfAwKBE++DxeDAyMippaIQQGe4kHJaoGOcTMwz2XVlBleMCcvPE2H31OY7dfY0Pb5WgUfMtXOuaKDosQgiRC3WrKGfe3t4YPXo0kpKSwDAMLC0t4eLiAl9fX/j6+kJHRwf6+vqYPn06WJbl1ivYrYJhGGzYsAHdu3eHuro6bGxssH//fm4+dasgpPSkfXpS6LzU7E/lF0glIBKzGPrPZUzZfQvH7r3F1fdKGBp+DUujHyg6NEIIkQu1HJezZcuWwdraGuvWrUNcXBx4PB569+6NsLAwDBkyBJcuXcLly5fh4+MDc3NzDBs2rNBtBQYGYuHChVi0aBFWrFiB/v374+nTp9DT05MrlpycHOTk5HDTqampAAChUAihUPh9B1oB5B/Dj3AsZY1yVbQ6lr9A6+ZKpClJtye01HegvH3lWHwyTj94K1W+KuYR+jY2gYGWQAFRVXz0HZQf5Uo+lCdp8uaCKsflTEdHB1paWlLdHszMzLB06VIwDAM7OzvcunULS5cuLbJy7O3tjX79+gEA5s6di+XLl+PSpUvo2LGjXLHMmzcPgYGBUuVHjx6Furp6CY+s4oqOjlZ0CJUG5apwA4QWWK2SBDHDcGXNs1nkpTsiKipKgZFVLLufKEHWTUmhiMW6PSfgoM9Kr0Q49B2UH+VKPpSn/2RmZsq1HFWOK4jmzZuD+epHt0WLFggKCoJIJAKPx5O5ToMGDbh/a2hoQFtbG8nJyXLvc+rUqfD39+emU1NTYWZmhg4dOkBbW/sbjqJiEQqFiI6OhpubG/h8vqLDqdAoV/LwgFPcShx4sAPvs9PgbOSI9l3mQFmzZM8D/OgSYx7j1OtHMue5OjVDsxry3dn62dB3UH6UK/lQnqTl3yEvDlWOK7GCJzvDMBCLxXKvLxAIIBBI3+Lk8/k/1BfpRzueskS5KlrdluNg28QXUVFRcPfwoFzJ0LuJOVaffoxsoeS1qKahJlrZGEo0AhBp9B2UH+VKPpSn/8ibB3ogr4K4ePGixPSFCxdgY2NTaKsxIYRURCa6alj3uyOqV1HjyhpW18HGgY5UMSaEVArUclxBJCUlwd/fH8OHD8fVq1exYsUKBAUFKTosQggpsTa2Bjg9sS1uPf+AS+fOwLtnM2q5IoRUGlQ5riAGDBiArKwsNG3aFDweD2PHjoWPj4+iwyKEkG+ipMSgjrE2EtWKX5YQQioSqhwrgJ+fH/z8/CTK+Hw+goODsXr1apnrJCYmSkx/PQZyvk+fPnH/zh+iTVNT87tiJYQQQgj5mVCf4x/Qhw8fsHPnTmhra8PMzEzR4RBCCCGEVBrUcvwDGjJkCK5cuYLVq1fLHI2CEEIIIYTIRpXjCiAmJqZUt7dnz55S3R4hhBBCyM+CulUQQgghhBDyBVWOCSGEEEII+YIqx4QQQgghhHxBleMSYFkWPj4+0NPTA8MwuH79uqJDKpK3tze6deum6DAIIYQQQioNeiCvBA4fPozQ0FDExMTAysoK+vr65bJfb29vfPr0CXv37i2X/RFCSKlIeQYVYaqioyCEkBKhynEJJCQkwNjYGC1btlR0KIQQUnElXUTqwXG4lPYYGiwg3rIb6P4/QNtE0ZERQkixqFuFnLy9vTF69GgkJSWBYRhYWlrCxcUFvr6+8PX1hY6ODvT19TF9+nSJt9dZWlpi7ty5GDx4MLS0tGBubo5169ZJbPvZs2fo06cPdHV1oaenh65du3JvxAsICEBYWBj27dsHhmHAMAw39FtR6xFCiEKkvcHu3b/CVTUF46oZYJSRAX7Ju4cbW3sqOjJCCJELtRzLadmyZbC2tsa6desQFxcHHo+H3r17IywsDEOGDMGlS5dw+fJl+Pj4wNzcHMOGDePWDQoKwqxZs/Dnn39i586d+OOPP+Ds7Aw7OzsIhUK4u7ujRYsWiI2NhbKyMmbPno2OHTvi5s2bmDBhAuLj45GamoqQkBAAgJ6eXrHrqaioFHtMOTk53GumASA19fPtT6FQCKFQWMoZLH/5x/AjHEtZo1zJj3JVtMfnVyJQVwNihuHK3iorwx+fEPnoFJQt6M6bLHReyY9yJR/KkzR5c0GVYznp6OhAS0sLPB4PRkZGXLmZmRmWLl0KhmFgZ2eHW7duYenSpRKVYw8PD4wcORIAMHnyZCxduhQnT56EnZ0dtm/fDrFYjA0bNoD58mMSEhICXV1dxMTEoEOHDlBTU0NOTo7Efjdv3lzsesWZN28eAgMDpcqPHj0KdXX1b0tUBRQdHa3oECoNypX8KFeynXsRC7EGI1WerKyMjSfDYWb4qfyDqkTovJIf5Uo+lKf/ZGZmyrUcVY6/U/PmzbnKKQC0aNECQUFBEIlE4PF4AIAGDRpw8xmGgZGREZKTkwEAN27cwKNHj6ClpSWx3ezsbCQkJBS6329d72tTp06Fv78/N52amgozMzN06NAB2tracm2jIhMKhYiOjoabmxv4fL6iw6nQKFfyo1wV7UDENgDvZc7LMmsEj/Ye5RtQJUHnlfwoV/KhPEnLv0NeHKocl4OCJyXDMBCLxQCA9PR0NG7cGBEREVLrGRgYFLrNb13vawKBAAKBQGa8P9IX6Uc7nrJEuZIf5Uo2M+NeuPj6tlS5spiBU60ulLNi0HklP8qVfChP/5E3D1Q5/k4XL16UmL5w4QJsbGy4VuPiNGrUCNu3b4ehoWGhrbUqKioQiUQlXo8QQsrbWKfO2BdyEELNuP8KWcBA9DuaW1ZXXGCEECInGq3iOyUlJcHf3x/379/H1q1bsWLFCowdO1bu9fv37w99fX107doVsbGxePLkCWJiYjBmzBg8f/4cwOcRL27evIn79+/j3bt3EAqFcq1HCCHlTVddBdt6LYO1aAJyP7SG8F1btBDMxw4vP0WHRgghcqGW4+80YMAAZGVloWnTpuDxeBg7dix8fHzkXl9dXR2nT5/G5MmT0aNHD6SlpcHU1BTt27fnWoSHDRuGmJgYODo6Ij09HSdPnoSLi0ux6xFCiCLYVtPC3sEDkZbZF9FHjqCzZwe6rUsIqTSoclwCfn5+8PPzkyjj8/kIDg7G6tWrZa4ja9zhgq+dNjIyQlhYWKH7NTAwwNGjR6XKi1svNDS00HmEEFLWVPk88Oj+JCGkkqHLFiGEEEIIIV9Q5ZgQQgghhJAvqFvFd8h/jTMhhBBCCPkxUMsxIYQQQgghX1DlmBBCCCGEkC+ockwIIYQQQsgXVDkugGVZ+Pj4QE9PDwzDQFdXV2r4NkIIIYQQ8mOiynEBhw8fRmhoKCIjI/Hq1SvUq1fvu7cZGhoKXV3dEq8XExMDhmHw6dOn746BEELKU7ZQhDOP3uNxKiAWs4oOhxBC5EajVRSQkJAAY2NjtGzZEgCgrFzxU5SbmwsVFRVFh0EIIQCAAzdeYtre20jJEgJQxp5lZ7DmN0fUMaG3dxJCKj5qOf6Kt7c3Ro8ejaSkJDAMA0tLSwBAXl4efH19oaOjA319fUyfPh0s+19LSE5ODiZMmABTU1NoaGigWbNm3DBvMTExGDRoEFJSUsAwDBiGQUBAAAAgPDwcjo6O0NLSgpGREby8vJCcnAzg85v12rZtCwCoUqUKGIaBt7c3AMDFxQW+vr7w8/ODvr4+3N3dMXjwYHh6ekocj1AohKGhITZu3Fh2SSOEkK8kvsvAnJ0HYK8bCCubSahlPQlWvAX4I+wMRNSCTAipBCp+s2g5WrZsGaytrbFu3TrExcWBx+Ohd+/eCAsLw5AhQ3Dp0iVcvnwZPj4+MDc3x7BhwwAAvr6+uHv3LrZt2wYTExPs2bMHHTt2xK1bt9CyZUsEBwdjxowZuH//PgBAU1MTwOfK66xZs2BnZ4fk5GT4+/vD29sbUVFRMDMzw65du9CzZ0/cv38f2traUFNT42INCwvDH3/8gbNnzwIA3r9/jzZt2uDVq1cwNjYGAERGRiIzMxN9+/aVebw5OTnIycnhplNTU7m4hEJhKWe3/OUfw49wLGWNciU/ylXRdl28CV3z5biqooT89pcX+i/RIG0mTt9vhNY1qyo2wAqKziv5Ua7kQ3mSJm8uGPbrJlCC4OBgBAcHIzExEcDnVtrk5GTcuXMHDMMAAKZMmYL9+/fj7t27SEpKgpWVFZKSkmBiYsJtx9XVFU2bNsXcuXMRGhoKPz+/YvsOX758GU2aNEFaWho0NTURExODtm3b4uPHjxJ9ll1cXJCamoqrV69KrF+3bl0MHDgQkyZNAgB06dIFVatWRUhIiMz9BQQEIDAwUKp8y5YtUFdXLy5VhBAi5fTTLTiqc1fmvL45v6J+te9/joMQQr5FZmYmvLy8kJKSAm3twrt5UcuxHJo3b85VjAGgRYsWCAoKgkgkwq1btyASiWBrayuxTk5ODqpWLbqF5MqVKwgICMCNGzfw8eNHiMViAEBSUhLq1KlT5LqNGzeWKhs6dCjWrVuHSZMm4c2bNzh06BBOnDhR6DamTp0Kf39/bjo1NRVmZmbo0KFDkSdNZSEUChEdHQ03Nzfw+XxFh1OhUa7kR7kq2qWtq4FCmlzqWGXBw9mjfAOqJOi8kh/lSj6UJ2n5d8iLQ5Xj75Seng4ej4crV66Ax+NJzMvvPiFLRkYG3N3d4e7ujoiICBgYGCApKQnu7u7Izc0tdr8aGhpSZQMGDMCUKVNw/vx5nDt3DjVq1ICTk1Oh2xAIBBAIBFLlfD7/h/oi/WjHU5YoV/KjXMlW17Am9r95IXNebfNmlLNi0HklP8qVfChP/5E3D1Q5lsPFixclpi9cuAAbGxvweDw4ODhAJBIhOTm50IqoiooKRCKRRNm9e/fw/v17zJ8/H2ZmZgA+d6souB4AqXULU7VqVXTr1g0hISE4f/48Bg0aJNd6hBBSWjq3/BMbd8UgWYmRKG8BddS27aygqAghRH40WoUckpKS4O/vj/v372Pr1q1YsWIFxo4dCwCwtbVF//79MWDAAOzevRtPnjzBpUuXMG/ePBw8eBAAYGlpifT0dBw/fhzv3r1DZmYmzM3NoaKighUrVuDx48fYv38/Zs2aJbFfCwsLMAyDyMhIvH37Funp6cXGOnToUISFhSE+Ph4DBw4s/WQQQkgRtLRNsKn9WrTj6UKZZaEhZtFH1RxLex1QdGiEECIXqhzLYcCAAcjKykLTpk0xatQojB07Fj4+Ptz8kJAQDBgwAOPHj4ednR26deuGuLg4mJubAwBatmyJESNGoG/fvjAwMMDChQthYGCA0NBQ7NixA3Xq1MH8+fOxePFiif2ampoiMDAQU6ZMQbVq1eDr61tsrK6urjA2Noa7u7vEA4KEEFJeLMxbYdlvsbjodRVT9eZgSo+90NAwVHRYhBAiFxqt4geTnp4OU1NThISEoEePHiVaNzU1FTo6OsU+xVlZCIVCREVFwcPDg/pbFYNyJT/KlfwoV/KjXMmPciUfypM0ees51Of4ByEWi/Hu3TsEBQVBV1cXXbp0UXRIhBBCCCGVDlWOfxBJSUmoUaMGqlevjtDQ0Erx2mtCCCGEkIqGalA/CEtLS1APGUIIIYSQ70MP5BFCCCGEEPIFVY4JIYQQQgj5girHhBBCCCGEfEGV4xJiWRY+Pj7Q09MDwzC4fv16uezXxcUFfn5+hc739vZGt27dyiUWQggpikjMIvLmS0zceQvbE5Rw+elHRYdECCFyowfySujw4cMIDQ1FTEwMrKysoK+vr+iQCCGkwmBZFr5bruLQ7ddfSpRwbkMc/vSoBZ821gqNjRBC5EEtxyWUkJAAY2NjtGzZEkZGRjRkGiGkwmCfxOJcuAciVjdAXEQX4FlcuccQ8+DtVxXj/yw++gDv03PKPR5CCCkpqhyXgLe3N0aPHo2kpCQwDANLS0u4uLjA19cXvr6+0NHRgb6+PqZPny4xrFpOTg4mTJgAU1NTaGhooFmzZoiJieHmv3//Hv369YOpqSnU1dVRv359bN26tchYDh48CB0dHUREREiUBwYGwsDAANra2hgxYgRyc3NLNQeEkIop5e4+eEUPxXDxM8xXZzE47wkGRvVHRsKJco0j9sE7meW5eWJcePyhXGMhhJBvQc2eJbBs2TJYW1tj3bp1iIuLA4/HQ+/evREWFoYhQ4bg0qVLuHz5Mnx8fGBubo5hw4YBAHx9fXH37l1s27YNJiYm2LNnDzp27Ihbt27BxsYG2dnZaNy4MSZPngxtbW0cPHgQv//+O6ytrdG0aVOpOLZs2YIRI0Zgy5Yt8PT05MqPHz8OVVVVxMTEIDExEYMGDULVqlUxZ84cmceTk5ODnJz/WnJSU1MBfH7lpFAoLM3UKUT+MfwIx1LWKFfyq6i5WnphNm4LVCTKrqoKsOrMDPiZO5VbHJqCwttcNFWYCpe3iqKinlcVEeVKPpQnafLmgmHpzRElEhwcjODgYCQmJgL4/KBccnIy7ty5A4ZhAABTpkzB/v37cffuXSQlJcHKygpJSUkwMTHhtuPq6oqmTZti7ty5Mvfj6emJWrVqYfHixdx+7O3tYWNjg7/++gv79u2Ds7Mzt7y3tzcOHDiAZ8+eQV1dHQCwZs0aTJw4ESkpKVBSkv7BCggIQGBgoFT5li1buG0QQiqH+R/+RLqM73m1PBFG688rtzjeZwNzrvMgYhmJ8qoCFtMcRFBiClmREELKWGZmJry8vJCSkgJtbe1Cl6OW41LQvHlzrmIMAC1atEBQUBBEIhFu3boFkUgEW1tbiXVycnJQtWpVAIBIJMLcuXPx77//4sWLF8jNzUVOTo5UBXXnzp1ITk7G2bNn0aRJE6k4GjZsKLFOixYtkJ6ejmfPnsHCwkJq+alTp8Lf35+bTk1NhZmZGTp06FDkSVNZCIVCREdHw83NDXw+X9HhVGiUK/lV1FzNiZgms1zMU4aHh0e5xlKt1hvM2B+P9xmfu3VZG6hjxa/2sDHULNc4KpOKel5VRJQr+VCepOXfIS8OVY7LWHp6Ong8Hq5cuQIejycxT1Pz8w/FokWLsGzZMgQHB6N+/frQ0NCAn5+fVH9hBwcHXL16FZs2bYKjo6NEhfxbCAQCCAQCqXI+n/9DfZF+tOMpS5Qr+VW0XLlVqYv9n+5Ilxs2Kfc4OzWsDre6Joh78hbXLl3A8D6tK1SuKrKKdl5VZJQr+VCe/iNvHqhyXAouXrwoMX3hwgXY2NiAx+PBwcEBIpEIycnJcHKS3e/v7Nmz6Nq1K3777TcAgFgsxoMHD1CnTh2J5aytrREUFAQXFxfweDysXLlSYv6NGzeQlZUFNTU1Lg5NTU2YmZmV1qESQiqocR1W4s7enkjI/e+ht9pq1TCy/RKFxKOirISmlnp4d1chuyeEkG9GleNSkJSUBH9/fwwfPhxXr17FihUrEBQUBACwtbVF//79MWDAAAQFBcHBwQFv377F8ePH0aBBA3Tq1Ak2NjbYuXMnzp07hypVqmDJkiV48+aNVOU4f3snT56Ei4sLlJWVERwczM3Lzc3FkCFDMG3aNCQmJmLmzJnw9fWV2d+YEPJj0VfTx86+x3Ey6QSefHwAm6p10Ka6M3hKvOJXJoQQwqHKcSkYMGAAsrKy0LRpU/B4PIwdOxY+Pj7c/JCQEMyePRvjx4/HixcvoK+vj+bNm3MjTUybNg2PHz+Gu7s71NXV4ePjg27duiElJUXm/uzs7HDixAmuBTm/It6+fXvY2NigTZs2yMnJQb9+/RAQEFDmx08IqRiUlZThZtkBsOyg6FAIIaTSospxCfn5+Um9xpnP5yM4OBirV6+WuQ6fz0dgYKDMkSEAQE9PD3v37i1yv1+PiwwAtWvXxps3b7jp0NBQ7t+F7YcQQgghhBSN7rcTQgghhBDyBVWOCSGEEEII+YK6VXyngt0dCCGEEEJI5UUtx4QQQgghhHxBlWNCCCGEEEK+oMoxIYQQQgghX1DluJx5e3ujW7duZb6fzMxM9OzZE9ra2mAYBp8+fSrzfRJCCCGEVHb0QN4PKiwsDLGxsTh37hz09fWho6Oj6JAIIT+JvPQPeLxvDrKeRcMsTwlJSg9g3ckfoLf1EUIqAaoc/6ASEhJQu3Zt1KtXT9GhEEJ+IuLcbJxY3Q7rdbJwz1gFDJuH1s/Xov+6q2g1YquiwyOEkGJRt4pvIBaLsXDhQtSsWRMCgQDm5uaYM2cOAODWrVto164d1NTUULVqVfj4+CA9PV1qG4sXL4axsTGqVq2KUaNGQSgUcvNycnIwYcIEmJqaQkNDA82aNZMaMm7Xrl2oW7cuBAIBLC0tuVdIA4CLiwuCgoJw+vRpMAwDFxeXMskDIYQUdCF6NWbqC3FPoAIAYBkGsepqWKZ0FR+exSs4OkIIKR61HH+DqVOnYv369Vi6dClat26NV69e4d69e8jIyIC7uztatGiBuLg4JCcnY+jQofD19ZV4vfPJkydhbGyMkydP4tGjR+jbty/s7e0xbNgwAICvry/u3r2Lbdu2wcTEBHv27EHHjh1x69Yt2NjY4MqVK+jTpw8CAgLQt29fnDt3DiNHjkTVqlXh7e2N3bt3Y8qUKbh9+zZ2794NFRUVmceRk5ODnJwcbjo1NRUAIBQKJSrrlVX+MfwIx1LWKFfyo1wVLfpFNNIF0u0u8QIVHLm8Hb2M/lJAVBUfnVfyo1zJh/IkTd5cMCzLsmUcyw8lLS0NBgYGWLlyJYYOHSoxb/369Zg8eTKePXsGDQ0NAEBUVBQ6d+6Mly9folq1avD29kZMTAwSEhLA433uf9enTx8oKSlh27ZtSEpKgpWVFZKSkmBiYsJt29XVFU2bNsXcuXPRv39/vH37FkePHuXmT5o0CQcPHsSdO3cAAH5+frh+/XqRLykJCAhAYGCgVPmWLVugrq7+zTkihPy89r9chUvqL2XO6yJ0QVMD13KOiBBCPsvMzISXlxdSUlKgra1d6HLUclxC8fHxyMnJQfv27WXOa9iwIVcxBoBWrVpBLBbj/v37qFatGgCgbt26XMUYAIyNjXHr1i0An7tliEQi2NraSmw7JycHVatW5fbTtWtXifmtWrVCcHAwRCKRxLaLMnXqVPj7+3PTqampMDMzQ4cOHYo8aSoLoVCI6OhouLm5gc/nKzqcCo1yJT/KVdHSbqTh0p0FUuUMC/j0mAATLRMZaxE6r+RHuZIP5Ula/h3y4lDluITU1NS+exsFT1KGYSAWiwEA6enp4PF4uHLlilQlV1NT87v3/TWBQACBQCAzvh/pi/SjHU9ZolzJj3IlW99GfbEzYQcSsh9LlHuadYWFnoWCoqo86LySH+VKPpSn/8ibB6ocl5CNjQ3U1NRw/PhxqW4VtWvXRmhoKDIyMrjW47Nnz0JJSQl2dnZybd/BwQEikQjJyclwcnKSuUzt2rVx9uxZibKzZ8/C1tZW7lZjQggpC3wlPrb02Iot8VsR9egostOy4dNiELrZdlN0aIQQIheqHJeQqqoqJk+ejEmTJkFFRQWtWrXC27dvcefOHfTv3x8zZ87EwIEDERAQgLdv32L06NH4/fffuS4VxbG1tUX//v0xYMAABAUFwcHBAW/fvsXx48fRoEEDdOrUCePHj0eTJk0wa9Ys9O3bF+fPn8fKlSuxatWqMj56QggpnjpfHUMbDMHA2gMQFRUFjxoeig6JEELkRpXjbzB9+nQoKytjxowZePnyJYyNjTFixAioq6vjyJEjGDt2LJo0aQJ1dXX07NkTS5YsKdH2Q0JCMHv2bIwfPx4vXryAvr4+mjdvDk9PTwBAo0aN8O+//2LGjBmYNWsWjI2N8ffff8Pb27sMjpYQQggh5OdBleNvoKSkhL/++gt//SU9JFH9+vVx4sSJQtf9eki3fMHBwRLTfD4fgYGBMkeSyNezZ0/07Nmz0PkFt0kIIYQQQopHLwEhhBBCCCHkC6ocE0IIIYQQ8gVVjgkhhBBCCPmCKseEEEIIIYR8QZVjQgghhBBCvqDKMSGEEEIIIV9Q5ZgQQgipqNLfAsIsRUdByE+FKsflhGVZ+Pj4QE9PDwzDoFu3bujWrZvc68fExIBhGHz69KnMYiSEEFJBPDgKrGwKLK4JLLQCIv2pkkxIOaGXgJSTw4cPIzQ0FDExMbCysoKamhpYllV0WIQQQiqaVzeRs80LkeoquGxQFVVFIvS4EQYrYRbQfbWioyPkh0eV43KSkJAAY2NjtGzZUtGhEEJImXvwJg1Hb7/C41cMmqTlwESPr+iQKo3MS2swtJoebqkKuLIIbS0EPdqPdhmzAA19BUZHyI+PKsflwNvbG2FhYQAAhmFgYWEBFxcXfPr0CXv37gUA5OTkYOLEidi2bRtSU1Ph6OiIpUuXokmTJhLbunLlCiZPnoy7d+/C3t4eISEhsLOz4+YfOHAAf//9N27dugVNTU04OTlhz549MuPKyclBTk4ON52amgoAEAqFEAqFpZkChcg/hh/hWMoa5Up+lKviBUU/xJrTT75M8RAZFIslvevDvW41hcZVkX19Xu34dFeiYgwAeQyD+XraaPkhCTwVHUWEWGHQd1A+lCdp8uaCYenefplLSUnB8uXLsW7dOsTFxYHH42HixIkSleOxY8di586d2LBhAywsLLBw4ULs378fjx49gp6eHmJiYtC2bVs0a9YMCxYsgIGBAUaMGAGRSISzZ88CAA4ePIiuXbvir7/+wq+//orc3FxERUVh6tSpMuMKCAhAYGCgVPmWLVugrq5eZvkghPy4EtOApbel211UeSz+biyCgKeAoCqZne8X4DovTea8MRojYMivXs4REfJjyMzMhJeXF1JSUqCtrV3oclQ5LifBwcEIDg5GYmIigM+tyfmV44yMDFSpUgWhoaHw8vIC8PmvG0tLS/j5+WHixIlc5fjYsWNo3749ACAqKgqdOnVCVlYWVFVV0bJlS1hZWWHz5s1yxSSr5djMzAzv3r0r8qSpLIRCIaKjo+Hm5gY+n27pFoVyJT/KVdEWHHmADWcSZc5b1c8ebnUMyzegSuLr82rGuUk48uKUzOWiukbBSMOonKOrWOg7KB/Kk7TU1FTo6+sXWzmmbhUVQEJCAoRCIVq1asWV8fl8NG3aFPHx8RLLNmjQgPu3sbExACA5ORnm5ua4fv06hg0bJvd+BQIBBAKBVDmfz/+hvkg/2vGUJcqV/ChXsvGVC28aVuErU86Kwefz0aOOl8zKcUuTljDTNVNAVBUTfQflQ3n6j7x5oKHcKpmvP1iGYQAAYrEYAKCmpqaQmAghJF+n+sYyy6uo89Hahh4kk0dLk5aY4DgBasr/XdMbGTbCnNZzFBgVIT8PqhxXANbW1lBRUeH6DgOfb4fExcWhTp06cm+nQYMGOH78eFmESAghcqlnqoOpv9QCT4nhyjQFyljezwGqfOpwLK+BdQfiRO8TWN9hPXZ32Y2wX8Kgr0Z/XBBSHqhbRQWgoaGBP/74AxMnToSenh7Mzc2xcOFCZGZmYsiQIXJvZ+bMmWjfvj2sra3x66+/Ii8vD1FRUZg8eXIZRk8IIZKGO1ujc0MTRN95hYfxt+Hftx30tOjOVklpqmiiuXFzRYdByE+HKscVxPz58yEWi/H7778jLS0Njo6OOHLkCKpUqSL3NlxcXLBjxw7MmjUL8+fPh7a2Ntq0aVOGURNCiGwmumrwamqGqHe3oKVKPzWEkMqDrljlxM/PD35+ftx0Tk4ONDU1uWlVVVUsX74cy5cvl7m+i4uL1Bv17O3tpcp69OiBHj16lF7ghBBCCCE/EepzXM7y8vJw9+5dnD9/HnXr1lV0OIQQQggh5CtUOS5nt2/fhqOjI+rWrYsRI0YoOhxCCCGEEPIV6lZRzuzt7ZGZmanoMAghhBBCiAzUckwIIYQQQsgXVDkmhBBCCCHkC6ocE0IIKRspzyAQpig6CkIIKRGqHJcClmXh4+MDPT09MAyDbt26oVu3bnKvHxMTg/+zd+dhUZXtA8e/wzAMoOwiYKIoLrngiqZoigq4pK9ralpKabjkglSamoqluRSKa71aifrTyszMV3HBBTNyIbfKXRTR3DKVEdFhgPn9oU5NgzhjwADdn+vyujjP2e5zdyZunnnOcxQKBbdv3y60GIUQosik7YfFQagWNqT9r6NQftEbNJetHZUQQphFiuMCsGXLFuLi4ti4cSNXrlxh+fLlxMXFFWkMer2eyZMn4+Pjg4ODAyEhIZw5c6ZIYxBCCO5cg//rSe71Y5y0U3HBVonNuZ2wuo+1IxNCCLNIcVwAUlJS8PHxISgoCG9vb1xcXHB1dS3SGGbPns38+fP55JNP2L9/P2XKlKF9+/bcv3+/SOMQQvzLHf2CH5U6OlaswIvP+NDFtwL9fby4eOM4XNhr7eiEEOKJpDj+h8LDwxk5ciRpaWkoFAr8/PwIDw83Glah1WoZNWoU5cuXx97enpYtW5KcnGxyrIMHDxIYGIijoyNBQUGcOnXKaP3//vc/mjRpgr29PeXKlaN79+7Ag17j2NhY3n33Xbp27Uq9evVYsWIFly9fZv369YV5+UIIYeT4bycZXb4cl1V/zhT6s72aEV6eaG9dsmJkQghhHpnn+B+aN28e/v7+LFmyhOTkZJRKJW+//bbRNmPHjuWbb75h+fLlVK5cmdmzZ9O+fXvOnj2Lu7u7YbuJEycSExODp6cnQ4cO5bXXXiMpKQmATZs20b17dyZOnMiKFSvIysoiPj4egPPnz3P16lVCQkIMx3JxceG5555j79699O3bN8/YtVotWq3WsKzRaADQ6XTodLqCSZAVPbqG0nAthU1yZT7JVf7+q9Vy38a03+WcnYoVN7WES97yJPeV+SRX5pE8mTI3F1Ic/0MuLi44OTmhVCrx9vY2WX/37l0+/vhj4uLi6NixIwBLly4lISGBzz77zKiQnj59Oq1btwbgnXfe4YUXXuD+/fvY29szffp0+vbty9SpUw3b169fH4CrV68C4OXlZXRuLy8vw7q8zJgxw+h4j2zbtg1HR0dzU1DsJSQkWDuEEkNyZT7JVd6O37cFdd7rki9coPyd+KINqISR+8p8kivzSJ7+ZO5L2KQ4LmQpKSnodDpatGhhaFOpVDRt2pQTJ04YbVuvXj3Dzz4+PgBcv36dSpUqceTIEV5//fUCjW38+PFERUUZljUaDb6+voSFheHs7Fyg57IGnU5HQkICoaGhqFQqa4dTrEmuzCe5yt+1I3oWHN9nukKv5J3/DMDX2bQTQch9ZQnJlXkkT6YefUP+JFIcFyN/vXkVCgUAubm5ADg4ODx2v0c91teuXTMU1Y+WGzRo8Nj91Go1arVpF49KpSpVH6TSdj2FSXJlPslV3gY17sI3Z7/hctYRo/bQCv2o6uFrnaBKELmvzCe5Mo/k6U/m5kEeyCtk/v7+2NnZGcYOw4O/5pKTk6ldu7bZx6lXrx47duzIc12VKlXw9vY2Wq/RaNi/fz/Nmzd/+uCFEMJCShslG3t/zht1J+Dn0ATv7PpMbzaHOWFjrR2aEEKYRXqOC1mZMmUYNmwYb7/9Nu7u7lSqVInZs2eTmZnJoEGDzD7OlClTaNeuHf7+/vTt25fs7Gzi4+MZN24cCoWCyMhIpk2bRvXq1alSpQqTJk2iQoUKFr2MRAghCoJKqWJo45cYVK8X8fHxdKwabO2QhBDCbFIcF4GZM2eSm5vLK6+8wp07dwgMDGTr1q24ubmZfYzg4GC+/vpr3n//fWbOnImzszOtWrUyrB87dix3794lIiKC27dv07JlS7Zs2YK9vX1hXJIQQgghRKkkxXEBiIyMJDIy0rCs1WopW7asYdne3p758+czf/78PPcPDg5Gr9cbtTVo0MCkrUePHvTo0SPPYygUCt577z3ee++9p7wKIYQQQgghY44LUHZ2NsePH2fv3r3UqVPH2uEIIYQQQggLSXFcgH799VcCAwOpU6cOQ4cOtXY4QgghhBDCQjKsogA1aNDA7AmmhRBCCCFE8SM9x0IIIYQQQjwkxbEQQgghhBAPSXEshBCi4GXeRHF8PV7phyEny9rRCCGE2aQ4/hu9Xk9ERATu7u4oFAqOHDnyj44XHh7+xBdx+Pn5ERsb+4/OI4QQxcbBOJhTG9tvB9Ps3FxsFzSAiwesHZUQQphFiuO/2bJlC3FxcWzcuJErV65Qt25da4ckhBAlx/WTXNzyFlHuZWjk50tQpYrMUuvI/OplyNFZOzohhHgima3ib1JSUvDx8SEoKMjaoQghRImTcXQ14d6eXLd98OtFp1SwysWJ1Mx7fHIuEaqHWjdAIYR4Auk5/ovw8HBGjhxJWloaCoUCPz8/cnNzmT17NtWqVUOtVlOpUiWmT59u2OeXX36hbdu2ODg44OHhQUREBBkZGSbH/uijj/Dx8cHDw4M33ngDnc64B+XOnTu89NJLlClThmeeeYZFixYZrU9LS6Nr166ULVsWZ2dnevfuzbVr1wzro6OjadCgAf/973/x9fXF0dGR3r17k56eXsBZEkKIx9uYcdZQGP9VkqMDJ9NTrBCREEJYRnqO/2LevHn4+/uzZMkSkpOTUSqVjB8/nqVLlzJ37lxatmzJlStXOHnyJAB3796lffv2NG/enOTkZK5fv87gwYMZMWIEcXFxhuPu2rULHx8fdu3axdmzZ+nTpw8NGjTg9ddfN2zz4YcfMmHCBKZOncrWrVsZPXo0NWrUIDQ0lNzcXENhvHv3brKzs3njjTfo06cPiYmJhmOcPXuWNWvW8L///Q+NRsOgQYMYPnw4q1atyvN6tVotWq3WsKzRaADQ6XQmxXtJ9OgaSsO1FDbJlfkkV/k7qnR+7LqTDu74S97yJPeV+SRX5pE8mTI3Fwq9Xq8v5FhKlNjYWGJjY0lNTeXOnTt4enqycOFCBg8ebLLt0qVLGTduHBcvXqRMmTIAxMfH06VLFy5fvoyXlxfh4eEkJiaSkpKCUqkEoHfv3tjY2PDll18CDx7Iq1WrFps3bzYcu2/fvmg0GuLj40lISKBjx46cP38eX19fAI4fP06dOnU4cOAATZo0ITo6mmnTpnHhwgWeeeYZ4MH46RdeeIHffvsNb29vk/ijo6OZOnWqSfvq1atxdHT8h5kUQvwbfX7lIOccvs1zXSf9aILcPIs4IiGEeCAzM5N+/fqRnp6Os/Pj/5CXnuN8nDhxAq1WS7t27R67vn79+obCGKBFixbk5uZy6tQpvLy8AKhTp46hMAbw8fHhl19+MTpW8+bNTZYfzWBx4sQJfH19DYUxQO3atXF1deXEiRM0adIEgEqVKhkK40fHeBRLXsXx+PHjiYqKMixrNBp8fX0JCwvL96YpKXQ6HQkJCYSGhqJSqawdTrEmuTKf5Cp/hzY/w9lr32Nj94dRu04TQKtWHQir7WWlyIo3ua/MJ7kyj+TJ1KNvyJ9EiuN8ODg4FMhx/n5TKhQKcnNzC+TY/4RarUatVpu0q1SqUvVBKm3XU5gkV+aTXOWtc72qrPh0CHbltmPrdAJy7dClN8TuTgitn/VGpZJfO/mR+8p8kivzSJ7+ZG4e5IG8fFSvXh0HBwd27NiR5/patWpx9OhR7t69a2hLSkrCxsaGmjVrWnSuffv2mSzXqlXLcJ6LFy9y8eJFw/rjx49z+/ZtateubWhLS0vj8uXLRsd4mliEEOJpNfFz59Xn6qO92oO7ZyZyN+Vtcm+GMr17A8qqpTAWQhR/Uhznw97ennHjxjF27FhWrFhBSkoK+/bt47PPPgOgf//+2NvbM3DgQH799Vd27drFyJEjeeWVVwxDKsyVlJTE7NmzOX36NIsWLeLrr79m9OjRAISEhBAQEED//v05dOgQBw4cYMCAAbRu3ZrAwECjeAcOHMjRo0fZs2cPo0aNonfv3nkOqRBCiMIyuUttNoxowRvBVelSKYftY1rStcEzT95RCCGKAfkz/gkmTZqEra0tkydP5vLly/j4+DB06FAAHB0dDTNLNGnSBEdHR3r27MmcOXMsPs+bb77JTz/9xNSpU3F2dmbOnDm0b98eeDAM47vvvmPkyJG0atUKGxsbOnTowIIFC4yOUa1aNXr06EGnTp24efMmnTt3ZvHixf88CUIIYaF6FV2p5VWGeO1pnnEtmCFqQghRFKQ4/pvIyEgiIyMNyzY2NkycOJGJEyfmuX1AQAA7d+587PH+OqXbI39/VXRqauoT46pUqRLffffdE7cbNmwYw4YNe+J2QgghhBDClAyrEEIIIYQQ4iEpjoUQQgghhHhIiuNSIjo6miNHjlg7DCGEEEKIEk2KYyGEEEIIIR6S4lgIIYQQQoiHpDgWQgghhBDiISmOC4leryciIgJ3d3cUCgWurq5GU8QVBD8/P5Np4YQQQgghxNOTeY4LyZYtW4iLiyMxMZGqVatiY2ODg0PBToSfnJxMmTJlCvSYQgjxT2nu6/h28zb0pzaTkaXnWydvegU3xcZGYe3QhBDiiaQ4LiQpKSn4+PgQFBRUaOfw9PQstGMLIcTT0GbnsG7+IK7af09iOQcc9Ll0OLKRlReiGPjqaGuHJ4QQTyTDKgpBeHg4I0eOJC0tDYVCgZ+fH8HBwUbDKm7dusWAAQNwc3PD0dGRjh07cubMGaPjfPPNN9SpUwe1Wo2fnx8xMTFG6/8+rOL27dsMGTIELy8v7O3tqVu3Lhs3bizMSxVCCCPfJ27kW7cDrHJx4jeVLWft7Fjo4czRuwu5ePWGtcMTQognkp7jQjBv3jz8/f1ZsmQJycnJKJVKXnzxRaNtwsPDOXPmDBs2bMDZ2Zlx48bRqVMnjh8/jkql4uDBg/Tu3Zvo6Gj69OnDjz/+yPDhw/Hw8CA8PNzknLm5uXTs2JE7d+7wf//3f/j7+3P8+HGUSuVj49RqtWi1WsOyRqMBQKfTodPpCiYZVvToGkrDtRQ2yZX5JFf5O3xuOefUKpP2BCd7Wu9fgXenkVaIqviT+8p8kivzSJ5MmZsLKY4LgYuLC05OTiiVSry9vU3WPyqKk5KSDMMuVq1aha+vL+vXr+fFF19kzpw5tGvXjkmTJgFQo0YNjh8/zocffphncbx9+3YOHDjAiRMnqFGjBgBVq1bNN84ZM2YwdepUk/Zt27bh6Oho6WUXWwkJCdYOocSQXJlPcpW31Jybj133y/VjEB9fhNGUPHJfmU9yZR7J058yMzPN2k6KYys4ceIEtra2PPfcc4Y2Dw8PatasyYkTJwzbdO3a1Wi/Fi1aEBsbS05OjkmP8JEjR6hYsaKhMDbH+PHjiYqKMixrNBp8fX0JCwvD2dn5aS6tWNHpdCQkJBAaGopKZdqTJf4kuTKf5Cp/1388xp7UL/Jc958XhlHLs34RR1QyyH1lPsmVeSRPph59Q/4kUhyXEk8zE4ZarUatVpu0q1SqUvVBKm3XU5gkV+aTXOWtT/MR/F/aem7m3jNqb+xUnXoVAq0UVckh95X5JFfmkTz9ydw8yAN5VlCrVi2ys7PZv3+/oe2PP/7g1KlT1K5d27BNUlKS0X5JSUnUqFEjz3HE9erV49KlS5w+fbpwgxdCiHw42zkT95+vaOXVFBsU2OmVdPfrzILOy60dmhBCmEV6jq2gevXqdO3alddff53//ve/ODk58c477/DMM88YhlK8+eabNGnShPfff58+ffqwd+9eFi5cyOLFi/M8ZuvWrWnVqhU9e/Zkzpw5VKtWjZMnT6JQKOjQoUNRXp4Q4l+uiksVFnX4jHvae2zZvIXOQZ2l50oIUWJIz7GVLFu2jMaNG9O5c2eaN2+OXq8nPj7e8AukUaNGrFmzhi+//JK6desyefJk3nvvvTwfxnvkm2++oUmTJrz00kvUrl2bsWPHkpOTU0RXJIQQxmxtbLFRyK8ZIUTJIj3HhSQyMtJoXuPExESj9W5ubqxYsSLfY/Ts2ZOePXs+dn1qaqrRsru7O59//rmloQohhBBCiIfkT3ohhBBCCCEekuJYCCGEEEKIh6Q4FkIIIYQQ4iEpjoUQQgghhHhIimMhhBBCCCEekuJYCCGEEEKIh6Q4fgy9Xk9ERATu7u4oFAqOHDli8TGio6Np0KBBgccmhBDFmTY7h//bd4HBKw+x7JQNO05et3ZIQghhNpnn+DG2bNlCXFwciYmJVK1alXLlylk7JCGEKPZycvUMivuJ06nJ+Dt/jzpXzZtfaDh2JZA3w2paOzwhhHgiKY4fIyUlBR8fH4KCgqwdihBClBgJx6/B7WlkVTvPrwoFAGW9DrEnOYSXm83Cy9neyhEKIUT+ZFhFHsLDwxk5ciRpaWkoFAoqVKhAhQoVyM3NNdqua9euvPbaa4blmTNn4uXlhZOTE4MGDeL+/ftG2wcHBxu9NQ+gW7duRq+E9vPzY9q0aQwYMICyZctSuXJlNmzYwO+//07Xrl0pW7Ys9erV46effjLsExcXh6urK+vXr6d69erY29vTvn17Ll68WHBJEUIIMyT/so6jnqnkPCyMATJtbLjhs519Zy5YMTIhhDCP9BznYd68efj7+7NkyRKSk5NRKpVUrFiRXbt20a5dOwBu3rzJli1biI+PB2DNmjVER0ezaNEiWrZsycqVK5k/fz5Vq1a1+Pxz587lgw8+YNKkScydO5dXXnmFoKAgXnvtNT788EPGjRvHgAEDOHbsGIqHv4AyMzOZPn06K1aswM7OjuHDh9O3b1+SkpIeex6tVotWqzUsazQaAHQ6HTqdzuK4i5tH11AarqWwSa7MJ7nKX8a9TaA0bU9X2pB+9St0uvFFH1QJIPeV+SRX5pE8mTI3F1Ic58HFxQUnJyeUSiXe3t4AdOzYkdWrVxuK47Vr11KuXDnatGkDQGxsLIMGDWLQoEEATJs2je3bt5v0HpujU6dODBkyBIDJkyfz8ccf06RJE1588UUAxo0bR/Pmzbl27ZohPp1Ox8KFC3nuuecAWL58ObVq1eLAgQM0bdo0z/PMmDGDqVOnmrRv27YNR0dHi+MurhISEqwdQokhuTKf5CpvNtmZeRbHAJm/pxk6FETe5L4yn+TKPJKnP2VmZpq1nRTHZurfvz+vv/46ixcvRq1Ws2rVKvr27YuNzYORKSdOnGDo0KFG+zRv3pxdu3ZZfK569eoZfvby8gIgICDApO369euG4tjW1pYmTZoYtnn22WdxdXXlxIkTjy2Ox48fT1RUlGFZo9Hg6+tLWFgYzs7OFsdd3Oh0OhISEggNDUWlUlk7nGJNcmU+yVX+Kp3IYOPhmSbtTrm59OvxPg6O8nBzXuS+Mp/kyjySJ1OPviF/EimOzdSlSxf0ej2bNm2iSZMm7Nmzh7lz51p0DBsbG/R6vVFbXl38f72JHw2byKvt72OgLaVWq1Gr1XmevzR9kErb9RQmyZX5JFd5a1ivP6+f+ZalGacMbXZ6Pe/XeBlnFx8rRlYyyH1lPsmVeSRPfzI3D/JAnpns7e3p0aMHq1at4osvvqBmzZo0atTIsL5WrVrs37/faJ99+/YZLXt6enLlyhXDck5ODr/++muBxJednW30kN6pU6e4ffs2tWrVKpDjCyGEuUb1XMv6lh8xyrUhA7Orsqn9atq1kLHGQoiSQXqOLdC/f386d+7MsWPHePnll43WjR49mvDwcAIDA2nRogWrVq3i2LFjRg/ktW3blqioKDZt2oS/vz9z5szh9u3bBRKbSqVi5MiRzJ8/H1tbW0aMGEGzZs0eO6RCCCEKk79/eypVakt8fDwe5eSPdCFEySHFsQXatm2Lu7s7p06dol+/fkbr+vTpQ0pKCmPHjuX+/fv07NmTYcOGsXXrVsM2r732GkePHmXAgAHY2toyZswYwwN9/5SjoyPjxo2jX79+/Pbbbzz//PN89tlnBXJsIYQQQoh/CymOHyMyMtJkTmIbGxsuX7782H0mTJjAhAkTjNpmzZpl+FmlUrF48WIWL1782GOkpqaatP19nLKfn59JG0CPHj3o0aPHY48thBBCCCHyJ2OOhRBCCCGEeEiKYyGEEEIIIR6S4rgUCA8PL7AH+4QQQggh/s2kOBZCCCGEEOIhKY6FEEIIIYR4SIpjIYQQQgghHpLi2Iqio6Np0KCBRfsoFArWr19fKPEIIYSAP+79wY60HRy5fsTaoQghrEDmORZCCFHwrvyMzYlN+F9LBU0D8Khs7YjM8snRT1jy8xJ0uToAarjVYEHbBVQoW8HKkQkhior0HAshhChY296F/z6P8vuZ1L38JbaLGsOxb60d1RPtubSHRUcWGQpjgNO3TjN+z3grRiWEKGpSHBeQJUuWUKFCBXJzc43au3btymuvvQbAzJkz8fLywsnJiUGDBnH//n2jbZOTkwkNDaVcuXK4uLjQunVrDh069Nhz9urVixEjRhiWIyMjUSgUnDx5EoCsrCzKlCnD9u3bC+oyhRAifxcPwI8LjJoUuTr0340AbYaVgjLPhpQNebYfun6IS3cuFXE0QghrkWEVBeTFF19k5MiR7Nq1i3bt2gFw8+ZNtmzZQnx8PGvWrCE6OppFixbRsmVLVq5cyfz586latarhGHfu3GHgwIEsWLAAvV5PTEwMnTp14syZMzg5OZmcs3Xr1vz3v/81LO/evZty5cqRmJjIs88+S3JyMjqdjqCgoDxj1mq1aLVaw7JGowFAp9Oh0+ny3KckeXQNpeFaCpvkynySq/zlHF2HfR7tiqwMsk9vR//sC0Uek7nuZt197DrNfQ06+8L7by73lfkkV+aRPJkyNxcKvV6vL+RY/jW6deuGh4cHn332GfCgN3nq1KlcvHiRli1b0rBhQxYtWmTYvlmzZty/f58jR47kebzc3FxcXV1ZvXo1nTt3Bh48kPftt9/SrVs3fvnlF+rXr8+1a9ewtbXF29ubSZMm8euvv/Lll18yffp04uPjSUpKyvP40dHRTJ061aR99erVODo6/sNsCCH+jZzOfE3bjP/luS6+whh0Xg2LOCLz7dfu53/3TGN3s3FjjNMYbBTyZasQJVlmZib9+vUjPT0dZ2fnx24nPccFqH///rz++ussXrwYtVrNqlWr6Nu3LzY2Npw4cYKhQ4cabd+8eXN27dplWL527RrvvvsuiYmJXL9+nZycHDIzM0lLS8vzfHXr1sXd3Z3du3djZ2dHw4YN6dy5s6EA3717N8HBwY+Nd/z48URFRRmWNRoNvr6+hIWF5XvTlBQ6nY6EhARCQ0NRqVTWDqdYk1yZT3KVvyVrc2h7yrTA/EPvRHbDV+jUoJIVojJPSE4IVxKv8NO1nwxtaqWa959/n6AKeX8DV1DkvjKf5Mo8kidTj74hfxIpjgtQly5d0Ov1bNq0iSZNmrBnzx7mzp1r9v4DBw7kjz/+YN68eVSuXBm1Wk3z5s3JysrKc3uFQkGrVq1ITExErVYTHBxMvXr10Gq1/Prrr/z444+89dZbjz2fWq1GrVabtKtUqlL1QSpt11OYJFfmk1zlzbVqY97/9WXesf0ClSIHgHS9IyN0o5ji61msc6ZSqVgatpQdaTtIvpqMh70HXat1LdKZKuS+Mp/kyjySpz+ZmwcpjguQvb09PXr0YNWqVZw9e5aaNWvSqFEjAGrVqsX+/fsZMGCAYft9+/YZ7Z+UlMTixYvp1KkTABcvXuTGjRv5nrN169YsXboUtVrN9OnTsbGxoVWrVnz44YdotVpatGhRwFcphBCP163BM7RP7Mn/bjUnWHmEe3o123Mb0aJWZZ71Lv7fSNna2NLerz3t/dpbOxQhhJVIcVzA+vfvT+fOnTl27Bgvv/yyoX306NGEh4cTGBhIixYtWLVqFceOHTN6IK969eqsXLmSwMBANBoNb7/9Ng4ODvmeLzg4mDFjxmBnZ0fLli0NbW+99RZNmjShTJkyhXOhQgiRhzJqW9YMac5HW0+x+YQnNjk6XmlRhaiwZ60dmhBCmEWeLihgbdu2xd3dnVOnTtGvXz9De58+fZg0aRJjx46lcePGXLhwgWHDhhnt+9lnn3Hr1i0aNWrEK6+8wqhRoyhfvny+5wsICMDV1ZUGDRpQtmxZ4EFxnJOTk+94YyGEKCwVXB2Y06cBhya25b3AHN4Oq4G9SmntsIQQwizSc1zAbGxsuHz5cp7rJkyYwIQJE4zaZs2aZfi5YcOGJCcnG63v1auX0fLfJxexsbHh5s2bRm0NGjQw2U4IIYQQQjyZ9BwLIYQQQgjxkBTHQgghhBBCPCTFsRBCCCGEEA9JcSyEEEIIIcRDUhwLIYQQQgjxkBTHQgghhBBCPCTFcSHR6/VERETg7u6OQqGgW7dudOvWzdphCSGEEEKIfMg8x4Vky5YtxMXFkZiYSNWqVXFwcCiyuYf9/PyIjIwkMjKySM4nhChcGdpsDl64hZO9LQ19XVEoFNYOySz3s++To8+xdhhCCGERKY4LSUpKCj4+PgQFBRXYMbOysrCzsyuw4wkhir8vD6QxbdMJMrTZANTwKssnLzemqmdZK0f2eGdvnWXWgRnsv5qMEgU/7U1m3HPv4KJ2sXZoQgjxRDKsohCEh4czcuRI0tLSUCgU+Pn5ER4ebjSsIjg4mBEjRjBixAhcXFwoV64ckyZNMupd9vPz4/3332fAgAE4OzsTEREBwDfffEOdOnVQq9X4+fkRExNjdNwLFy4wZswYFApFielhEkKY+vW3dMZ/+4uhMAY4fS2Dof930IpR5S9dm86g+JfZd/UAevRkk8v/zm9k9NbXrR2aEEKYRXqOC8G8efPw9/dnyZIlJCcno1Qqefvtt022W758OYMGDeLAgQP89NNPREREUKlSJV5//c9fIh999BGTJ09mypQpABw8eJDevXsTHR1Nnz59+PHHHxk+fDgeHh6Eh4ezbt066tevT0REhNFx8qLVatFqtYZljUYDgE6nQ6fTFUQqrOrRNZSGaylskivzFWWu1iSnkddorNPXMjh4/gb1Kha/ntj1Rz/nZvZdk/aDt05w9MohapcLsEJUxZ98Bs0nuTKP5MmUubmQ4rgQuLi44OTkhFKpxNvb+7Hb+fr6MnfuXBQKBTVr1uSXX35h7ty5RkVt27ZtefPNNw3L/fv3p127dkyaNAmAGjVqcPz4cT788EPCw8Nxd3dHqVTi5OSU77kBZsyYwdSpU03at23bhqOjo6WXXWwlJCRYO4QSQ3JlvqLI1ckUGx73Bd+O73/kkmvRPMdgiWM3Nj72N0tiwn9JdelUtAGVMPIZNJ/kyjySpz9lZmaatZ0Ux1bUrFkzo2EPzZs3JyYmhpycHJRKJQCBgYFG+5w4cYKuXbsatbVo0YLY2Fij/cwxfvx4oqKiDMsajQZfX1/CwsJwdnZ+mksqVnQ6HQkJCYSGhqJSqawdTrEmuTJfUeZK/8tVDqz52aTdyd6Wob3a4WBn/ue9qOT+7ys237lu0q7Q6+lcqzGVGkpxnBf5DJpPcmUeyZOpR9+QP4kUx8VcmTJlCu3YarUatVpt0q5SqUrVB6m0XU9hklyZryhy1bn+M/zv56vsOPlnsWmjgCld6uBcxr5Qz/20OtV9hRW7Izn7t4eHu2Tex79uL5D7K1/yGTSf5Mo8kqc/mZsHKY6taP/+/UbL+/bto3r16vn2/taqVYukpCSjtqSkJGrUqGHYz87OjpwcmT5JiJLOVmnDkgGBbD12ld2nfsfZwZaejSvyrHfx/WZHXb0DcUea8OmVPSQ6OuCg19M54y79m70Dju7WDk8IIZ5IimMrSktLIyoqiiFDhnDo0CEWLFhgNPNEXt58802aNGnC+++/T58+fdi7dy8LFy5k8eLFhm38/Pz4/vvv6du3L2q1mnLlyhX2pQghConSRkGnAB86BfhYOxTzKBS49FrBm6c2MeZEPKmXr1Opx9so/ZpZOzIhhDCLFMdWNGDAAO7du0fTpk1RKpWMHj3aMF3b4zRq1Ig1a9YwefJk3n//fXx8fHjvvfcIDw83bPPee+8xZMgQ/P390Wq1RfbyESGEAMDGBmp1IadaB36Jj8f3mcbWjkgIIcwmxXEh+fsb6rRaLWXLGk/ar1KpiI2N5eOPP87zGKmpqXm29+zZk549ez723M2aNePo0aMWxyyEEEII8W8nLwEpZNnZ2Rw/fpy9e/dSp04da4cjhBBCCCHyIcVxIfv1118JDAykTp06DB061NrhCCGEEEKIfFg0rEKn0/Hss8+yceNGatWqVVgxlSoNGjTIc9LpxMTEog9GCCGEEELky6KeY5VKxf379wsrFiGEEEIIIazK4mEVb7zxBrNmzSI7O7sw4hFCCCGEEMJqLJ6tIjk5mR07drBt2zYCAgJM3uC2bt26AgtOCCGEEEKIomRxcezq6prvNGJCCFHYrt69SvLVZFzULgRVCMLWRmalLFZyc+DYtyhPbab+ld9RpLmCf2trRyWEEGax+DfKsmXLCiMOq9Pr9QwZMoS1a9dy69YtDh8+TIMGDawd1lNbsmQJ77//Pr/99htz5swxmnNZiJJsweEFfPbLZ+ToH7wivUKZCiwOWYy/q7+VIxMA5ObCmgFwciM2gB/Ayt0QMhVaRlo1NCGEMMdTTeWWnZ3N9u3b+e9//8udO3cAuHz5MhkZGQUaXFHasmULcXFxbNy4kStXrtCwYUPWr19v7bCeSKFQmMSp0WgYMWIE48aN47fffnviW/eEKCmSfktiyc9LDIUxwOW7lxn7/VgrRiWMnN0OJzeatu+aDndvFH08QghhIYt7ji9cuECHDh1IS0tDq9USGhqKk5MTs2bNQqvV8sknnxRGnIUuJSUFHx8fgoKCrB0KOTk5KBQKbGyebhrqtLQ0dDodL7zwAj4+PgUcnRDWs+ncpjzbT986zelbp6nhVqOIIxImzu3Kuz0nC1L3QJ3uRRuPEEJYyOLiePTo0QQGBnL06FE8PDwM7d27d+f1118v0OCKSnh4OMuXLwce9MRWrlwZeHBNAJUrVyY1NZXo6GjWr1/PqFGjiI6O5ubNmwwYMIAFCxYQExPDnDlzyM3NZfTo0UycONFw/Dlz5rBs2TLOnTuHu7s7Xbp0Yfbs2YbXScfFxREZGcmKFSt45513OH36NGfPnkWtVjN48GB27tyJt7c306dPZ8KECYZXU/v5+ZnEGR0dzauvvgpA1apVATh//rxh27/SarVotVrDskajAR7MZ63T6QoqvVbz6BpKw7UUtpKSq/vZj59KMlObWSTxl5RcWYuNnRPKx6zLVjmjl7zlSe4r80muzCN5MmVuLiwujvfs2cOPP/6InZ2dUbufnx+//fabpYcrFubNm4e/vz9LliwhOTkZpVJJ+fLlWbZsGR06dECp/PN/9SkpKWzevJktW7aQkpJCr169OHfuHDVq1GD37t38+OOPvPbaa4SEhPDcc88BYGNjw/z586lSpQrnzp1j+PDhjB07lsWLFxuOm5mZyaxZs/j000/x8PCgfPnydO3alRs3bpCYmIhKpSIqKorr168b9klOTjaJs2zZsvj6+hISEsKBAwfw9fXF09Mzz+ueMWMGU6dONWnftm0bjo6OBZVeq0tISLB2CCVGcc+Va5Zrnu0uChfO7zvPBcWFIouluOfKWrSa8nTV26JWGE/3eREvfjqmweZEvJUiKxnkvjKf5Mo8kqc/5fVStrxYXBzn5uaSk5Nj0n7p0iWcnJwsPVyx4OLigpOTE0qlEm9vb0O7q6ur0TI8uP7PP/8cJycnateuTZs2bTh16hTx8fHY2NhQs2ZNZs2axa5duwzF8V8fhvPz82PatGkMHTrUqDjW6XQsXryY+vXrA3Dy5Em2b99OcnIygYGBAHz66adUr17dsM+jovfvcT7q0ff09DSJ/6/Gjx9PVFSUYVmj0eDr60tYWBjOzs7mJa8Y0+l0JCQkEBoaikqlsnY4xVpJyVX73PbcTLrJjos7DG32SntmtJpBM59mRRJDScmVtSzclcKIE6P4QPUZnop0AE7m+vKGbhSTazUnyN/jCUf4d5L7ynySK/NInkw9+ob8SSwujsPCwoiNjWXJkiXAg2EIGRkZTJkyhU6dOll6uBLHz8/P6I8ALy8vlEql0fhgLy8vox7e7du3M2PGDE6ePIlGoyE7O5v79++TmZlp6KG1s7OjXr16hn1OnTqFra0tjRo1MrRVq1YNNze3ArsWtVqNWq02aVepVKXqg1TarqcwFfdcqVAxt81c9l3Zx74r+3BTu/FC1RfwdMz725FCjaWY58pa7mhzScgNZJe2AfUVKdxHzTG9HwAZWXrJ2RPIfWU+yZV5JE9/MjcPFj/xFRMTQ1JSErVr1+b+/fv069fPMKRi1qxZFgda0vw9sQqFIs+23NxcAFJTU+ncuTP16tXjm2++4eDBgyxatAiArKwswz4ODg4oFIpCjl6Ikk+hUNC8QnPGNB5DeN1wqxTG4vFaVn/QM5yNLQf1NQ2FsZ3ShuequlsxMiGEMI/FPccVK1bk6NGjfPXVVxw9epSMjAwGDRpE//79cXBwKIwYrUKlUuU5fMRSBw8eJDc3l5iYGEPv8po1a564X82aNcnOzubw4cM0btwYgLNnz3Lr1q1CiVMIIQpCcI3yhNb2IuH4NaP2MaE1KFfW9JsqIYQobiwujr///nuCgoLo378//fv3N7RnZ2fz/fff06pVqwIN0Fr8/PzYsWMHLVq0QK1WP/VwhmrVqqHT6ViwYAFdunQhKSnJrOnunn32WUJCQoiIiODjjz9GpVLx5ptvmvQwF1ScQghREGxsFHzycmM2/nyZhGNXuXHtMiM6N6VlDS9rhyaEEGaxeFhFmzZtuHnzpkl7eno6bdq0KZCgioOYmBgSEhLw9fWlYcOGT32c+vXrM2fOHGbNmkXdunVZtWoVM2bMMGvfFStW4OXlRatWrQxT5Tk5OWFvb1/gcQohREFR2ijo2uAZ5vaux0v+uTxXRYZTCCFKDoVer9dbsoONjQ3Xrl0zmR7s9OnTBAYGmv0koLDcpUuX8PX1Zfv27bRr167Aj6/RaHBxcSE9Pb3UzFYRHx9Pp06d5GGEJ5BcmU9yZT7JlfkkV+aTXJlH8mTK3DrH7GEVPXr0AB48DBMeHm40y0FOTg4///xzsXi7XGmyc+dOMjIyCAgI4MqVK4wdOxY/P79SM3RFCCGEEKK4Mbs4dnFxAUCv1+Pk5GT08J2dnR3NmjUrsW/IK650Oh0TJkzg3LlzODk5ERQUxKpVq+QvQCGEEEKIQmJ2cbxs2TLgwQNgb731FmXKlCm0oMQD7du3p3379tYOQwghhBDiX8PiB/KmTJmCWq1m+/bt/Pe//+XOnTsAXL58mYyMjAIPUAghhBBCiKJi8VRuFy5coEOHDqSlpaHVagkNDcXJyYlZs2ah1WrNmqZMCCGEEEKI4sjinuPRo0cTGBjIrVu3jMYdd+/enR07dhRocEIIIUquTF0mOr3O2mEIIYRFLC6O9+zZw7vvvoudnZ1R+6NXSBd3er2eiIgI3N3dUSgUHDlyxNohFYjo6GgaNGhg7TCEEILTt07z2tbXaPl1S6alT2PijxNJ16ZbOywhhDCLxcVxbm5unq8rvnTpEk5OTgUSVGHasmULcXFxbNy4kStXrtCwYUPWr19v7bCEEKJUuH3/Nq9uGUTy1WQAcshhc+pm3tg+0sqRCSGEeSwujsPCwoiNjTUsKxQKMjIymDJlCp06dSrI2ApFSkoKPj4+BAUF4e3tbdVYcnJyyM3NtWoMQghRkL4+tR5N1m2T9qM3DvPL778UfUBCCGEhi4vjmJgYkpKSqF27Nvfv36dfv36GIRWzZs0qjBgLTHh4OCNHjiQtLQ2FQoGfnx/wYLz0X5cfDVH4/PPPqVSpEmXLlmX48OHk5OQwe/ZsvL29KV++PNOnTzc6/pw5cwgICKBMmTL4+voyfPhwoxk84uLicHV1ZcOGDdSuXRu1Wk1aWhpXrlzhhRdewMHBgSpVqrB69Wr8/PyM/ghJS0uja9eulC1bFmdnZ3r37s21a9dMrnHlypX4+fnh4uJC3759DbOJCCFEUfgh9fRj1+1LO1OEkQghxNOxeLaKihUrcvToUb788kt+/vlnMjIyGDRoEP379zd6QK84mjdvHv7+/ixZsoTk5GSUSiXly5dn2bJldOjQAaVSadg2JSWFzZs3s2XLFlJSUujVqxfnzp2jRo0a7N69mx9//JHXXnuNkJAQnnvuOeDBq7Xnz59PlSpVOHfuHMOHD2fs2LEsXrzYcNzMzExmzZrFp59+ioeHB+XLl6dr167cuHGDxMREVCoVUVFRXL9+3bBPbm6uoTDevXs32dnZvPHGG/Tp04fExESjmNevX8/GjRu5desWvXv3ZubMmSZF/CNarRatVmtYfvTqb51Oh05X8h+ieXQNpeFaCpvkynySq/w5Z9jn2a7Q61He95K8PYbcV+aTXJlH8mTK3FxYXBwD2Nra8vLLLz/Nrlbl4uKCk5MTSqXSaEiFq6uryRCL3NxcPv/8c5ycnKhduzZt2rTh1KlTxMfHY2NjQ82aNZk1axa7du0yFMeRkZGG/f38/Jg2bRpDhw41Ko51Oh2LFy+mfv36AJw8eZLt27eTnJxMYGAgAJ9++inVq1c37LNjxw5++eUXzp8/j6+vLwArVqygTp06JCcn06RJE0PMcXFxhrHfr7zyCjt27HhscTxjxgymTp1q0r5t2zYcHR3NS2oJkJCQYO0QSgzJlfkkV3nrcvU0V5yyOKU2fmi7W8ZdfE7sIP7GDStFVjLIfWU+yZV5JE9/yszMNGu7pyqOL1++zA8//MD169dNxsyOGjXqaQ5Z7Pj5+Rk9YOjl5YVSqcTGxsao7a89vNu3b2fGjBmcPHkSjUZDdnY29+/fJzMz01Bs2tnZUa9ePcM+p06dwtbWlkaNGhnaqlWrhpubm2H5xIkT+Pr6GgpjgNq1a+Pq6sqJEycMxfHfY/bx8TGK7+/Gjx9PVFSUYVmj0eDr60tYWBjOzs7mJaoY0+l0JCQkEBoaKq/cfgLJlfkkV/nL3fojzx3ayDIXZ3Y7OmCv19M54y59NRnoe9ZF/2zxfzbFGuS+Mp/kyjySJ1OPviF/EouL47i4OIYMGYKdnR0eHh4oFArDOoVCUWqK47/fSAqFIs+2R38cpKam0rlzZ4YNG8b06dNxd3fnhx9+YNCgQWRlZRmKYwcHB6OcFXbM+T3wp1arUavVeR6nNH2QStv1FCbJlfkkV49RpzPqnz4h8lY6kbf+nL5Nr3JEUb0dSM7yJfeV+SRX5pE8/cncPFj8QN6kSZOYPHky6enppKamcv78ecO/c+fOWRyotalUqjynprPUwYMHyc3NJSYmhmbNmlGjRg0uX778xP1q1qxJdnY2hw8fNrSdPXuWW7duGZZr1arFxYsXuXjxoqHt+PHj3L59m9q1a//j2IUQosBUeR4ahxs16RU2KDrOBnsX68QkhBAWsLg4zszMpG/fvkbDC0oyPz8/duzYwdWrV40KUktVq1YNnU7HggULOHfuHCtXrjTrVdrPPvssISEhREREcODAAQ4fPkxERIRRD3NISAgBAQH079+fQ4cOceDAAQYMGEDr1q0N45SFEKLY6DIPwjeR89wwTnl3JTsiCRq9Yu2ohBDCLBZXuIMGDeLrr78ujFisIiYmhoSEBHx9fWnYsOFTH6d+/frMmTOHWbNmUbduXVatWsWMGTPM2nfFihV4eXnRqlUrunfvzuuvv46TkxP29g+e+lYoFHz33Xe4ubnRqlUrQkJCqFq1Kl999dVTxyuEEIXKryW5Ie9z0qcnlKv+5O2FEKKYUOj1er0lO+Tk5NC5c2fu3btHQECAyfiNOXPmFGiA/0aXLl3C19eX7du3065duyI7r0ajwcXFhfT09FLzQF58fDydOnWS8VZPILkyn+TKfJIr80muzCe5Mo/kyZS5dY7FD+TNmDGDrVu3UrNmTQCTB/KE5Xbu3ElGRgYBAQFcuXKFsWPH4ufnR6tWrawdmhBCCCHEv4rFxXFMTAyff/454eHhhRDOv5NOp2PChAmcO3cOJycngoKCWLVqlfylJ4QQQghRxCwujtVqNS1atCiMWP612rdvT/v27a0dhhBCCCHEv57FD+SNHj2aBQsWFEYsQgghhBBCWJXFPccHDhxg586dbNy4kTp16ph89b9u3boCC04IIYQQQoiiZHFx7OrqSo8ePQojFiGEEEIIIazK4uJ42bJlhRGHVen1eoYMGcLatWu5desWhw8fpkGDBgV+ntTUVKpUqWI4fmJiIm3atOHWrVu4uro+9XH9/PyIjIwkMjKywGIVQoh/zLKZQoUQolgoHa+5+4e2bNlCXFwcGzdu5MqVKzRs2JD169cX+nmDgoK4cuUKLi7/7JWqycnJREREGJYVCkWRxC+EEHn6IwW+6EfWrAqEHI3AZvPboL1j7aiEEMIsFvccA6xdu5Y1a9aQlpZGVlaW0bpDhw4VSGBFKSUlBR8fH4KCgor0vHZ2dnh7e//j43h6ehZANEIIUQDup3N65QvMts9hfyUfVHo97S9u4J0vU3AZ+D9rRyeEEE9kcc/x/PnzefXVV/Hy8uLw4cM0bdoUDw8Pzp07R8eOHQsjxkIVHh7OyJEjSUtLQ6FQ4OfnB0D37t2NllNSUujatSteXl6ULVuWJk2asH37dqNj5dVj6+rqSlxcXJ7nTkxMRKFQcPv2bQDi4uJwdXVl48aN1KxZE0dHR3r16kVmZibLly/Hz88PNzc3Ro0aRU5OjuE4fn5+xMbGGn7OK34hhCgK6YeWM9jZhv0O9gDoFAo2li3DqPsn4cpRK0cnhBBPZnHP8eLFi1myZAkvvfQScXFxjB07lqpVqzJ58mRu3rxZGDEWqnnz5uHv78+SJUtITk5GqVRSvnx5li1bRocOHVAqlQBkZGTQqVMnpk+fjlqtZsWKFXTp0oVTp05RqVKlAosnMzOT+fPn8+WXX3Lnzh169OhB9+7dcXV1JT4+nnPnztGzZ09atGhBnz59TPZPTk7OM/68aLVatFqtYVmj0QAPXkqi0+kK7Jqs5dE1lIZrKWySK/NJrvL3ZWoit/L4/84he3sOndtJQLnaVoiq+JP7ynySK/NInkyZmwuLi+O0tDTD8AMHBwfu3HkwjuyVV16hWbNmLFy40NJDWpWLiwtOTk4olUqjIQ6urq5Gy/Xr16d+/fqG5ffff59vv/2WDRs2MGLEiAKLR6fT8fHHH+Pv7w9Ar169WLlyJdeuXaNs2bLUrl2bNm3asGvXrjyL40dDLP4ef15mzJjB1KlTTdq3bduGo6NjAVxN8ZCQkGDtEEoMyZX5JFd5+zFdC4/538fqExd5/kZ80QZUwsh9ZT7JlXkkT3/KzMw0azuLi2Nvb29u3rxJ5cqVqVSpEvv27aN+/fqcP38efSl+MjkjI4Po6Gg2bdrElStXyM7O5t69e6SlpRXoeRwdHQ2FMYCXlxd+fn6ULVvWqO369ev/+Fzjx48nKirKsKzRaPD19SUsLAxnZ+d/fHxr0+l0JCQkEBoaKq/ifgLJlfkkV/nb+G0q3PvUpF2hh7oBfenUqGHRB1UCyH1lPsmVeSRPph59Q/4kFhfHbdu2ZcOGDTRs2JBXX32VMWPGsHbtWn766adSPf/xW2+9RUJCAh999BHVqlXDwcGBXr16GT2QqFAoTP5AsPTrjL/fwAqFIs+23NxcC6/AlFqtRq1W5xlDafoglbbrKUySK/NJrvL2SsMX2b9zA3p74z/g9Xca06N+Q8nZE8h9ZT7JlXkkT38yNw8WF8dLliwxFGZvvPEGHh4e/Pjjj/znP/9hyJAhlh6uWFKpVEYPvAEkJSURHh5O9+7dgQc9yampqUbbeHp6cuXKFcPymTNnzO7CL0h5xS+EEEWhVfUKhP0yjQ3nV2HrdAJy7cjWNOS9NhE428svaCFE8WdRcZydnc0HH3zAa6+9RsWKFQHo27cvffv2LZTgrMXPz48dO3bQokUL1Go1bm5uVK9enXXr1tGlSxcUCgWTJk0y6b1t27YtCxcupHnz5uTk5DBu3Dir/LWWV/xCCFFUPurRnBfP1WDbsStcunCeqJefp2YFV2uHJYQQZrFoKjdbW1tmz55NdnZ2YcVTLMTExJCQkICvry8NGz4YHzdnzhzc3NwICgqiS5cutG/fnkaNGpns5+vry/PPP0+/fv146623rPJgW17xCyFEUXquqgfvdKhJJ99cqnqWsXY4QghhNoXewqfounbtSo8ePRg4cGBhxSSsRKPR4OLiQnp6eql5IC8+Pp5OnTrJeKsnkFyZT3JlPsmV+SRX5pNcmUfyZMrcOsfiMccdO3bknXfe4ZdffqFx48aUKWPcI/Cf//zH8miFEEIIIYQoBiwujocPHw48GGbwdwqFQh4EE0IIIYQQJZbFxXFBTCEmhBBCCCFEcWTRA3lCCCGEEEKUZhb3HAPcvXuX3bt3k5aWZvQSDIBRo0YVSGBCCCGEEEIUNYuL48OHD9OpUycyMzO5e/cu7u7u3LhxA0dHR8qXLy/FsRBCCCGEKLEsHlYxZswYunTpwq1bt3BwcGDfvn1cuHCBxo0b89FHH1l0LL1eT0REBO7u7igUCo4cOWJpOEIIIYqhgxduEZNwhq2XFKTdLPo3hQohxNOyuDg+cuQIb775JjY2NiiVSrRaLb6+vsyePZsJEyZYdKwtW7YQFxfHxo0buXLlCnXr1rU0nBIhODiYyMhIa4chhBBFYvL6n+mzcinLznzIjqyNdPjvl3x7+JK1wxJCCLNYPKxCpVJhY/Ogpi5fvjxpaWnUqlULFxcXLl68aNGxUlJS8PHxISgoyNIwSh29Xk9OTg62tk81DFwIIYqFfSk3WJM2Cwffn/9sdN/LuzvTCKkVjZO9vIxACFG8Wdxz3LBhQ5KTkwFo3bo1kydPZtWqVURGRlrU8xseHs7IkSNJS0tDoVDg5+dHcHAwI0eOJDIyEjc3N7y8vFi6dCl3797l1VdfxcnJiWrVqrF582bDcXJychg0aBBVqlTBwcGBmjVrMm/ePMP6+/fvU6dOHSIiIgxtKSkpODk58fnnn6PRaHBwcDA6JsC3336Lk5MTmZmZBAUFMW7cOKP1v//+OyqViu+//x6AxYsXU716dezt7fHy8qJXr16G69y9ezfz5s1DoVCgUChITU0lMTERhULB5s2bady4MWq1mh9++IHc3FxmzJhhuJ769euzdu1aw3lv3bpF//798fT0xMHBgerVq7Ns2TIAsrKyGDFiBD4+Ptjb21O5cmVmzJhh9n8TIYT4p/7v522oXH42abd138i2k+esEJEQQljG4m7KDz74gDt37gAwffp0BgwYwLBhw6hevTqff/652ceZN28e/v7+LFmyhOTkZJRKJS+++CLLly9n7NixHDhwgK+++ophw4bx7bff0r17dyZMmMDcuXN55ZVXSEtLw9HRkdzcXCpWrMjXX3+Nh4cHP/74IxEREfj4+NC7d2/s7e1ZtWoVzz33HC+88AKdO3fm5ZdfJjQ0lNdeew2Azp07s3r1ajp27GiIb9WqVXTr1g1HR0f69+/P7NmzmTlzJgqFAoCvvvqKChUq8Pzzz/PTTz8xatQoVq5cSVBQEDdv3mTPnj2G6zx9+jR169blvffeA8DT05PU1FQA3nnnHT766COqVq2Km5sbM2bM4P/+7//45JNPqF69Ot9//z0vv/wynp6etG7dmkmTJnH8+HE2b95MuXLlOHv2LPfu3QNg/vz5bNiwgTVr1lCpUiUuXryYb2++VqtFq9UaljUaDfDglZM6nc7s/5bF1aNrKA3XUtgkV+aTXOXv7t0debbn2uRyIX0fOl3VIo6oZJD7ynySK/NInkyZmwuFXq/XF3IsjxUbG0tsbKyhUAwODiYnJ8dQWObk5ODi4kKPHj1YsWIFAFevXsXHx4e9e/fSrFmzPI87YsQIrl69atTj+uGHHzJ79mz69u3LN998wy+//IKHhwcA69ev55VXXuHatWs4Ojqi0Wjw8vLi22+/pUOHDvz+++9UqFCBnTt38vzzzwMQFBREq1atmDlzJuvWrePVV1/l0qVLODk5mcQTHBxMgwYNiI2NNbQlJibSpk0b1q9fT9euXYEHxaq7uzvbt2+nefPmhm0HDx5MZmYmq1ev5j//+Q/lypXL8w+RUaNGcezYMbZv324o4vMTHR3N1KlTTdpXr16No6PjE/cXQoi/O3hlHt86/J7nukh9EOXcOhVxREII8UBmZib9+vUjPT0dZ2fnx2731ANcr1+/zqlTpwB49tln8fT0fNpDGalXr57hZ6VSiYeHBwEBAYY2Ly8vw/kfWbRoEZ9//jlpaWncu3ePrKwsGjRoYHTcN998k/Xr17Nw4UI2b95sKIwBOnXqhEqlYsOGDYbi2dnZmZCQEOBBT29YWBirVq3i+eef5/z58+zdu5f//ve/AISGhlK5cmWqVq1Khw4d6NChA927dzerwAwMDDT8fPbsWTIzMwkNDTXaJisri4YNGwIwbNgwevbsyaFDhwgLC6Nbt26GMdvh4eGEhoZSs2ZNOnToQOfOnQkLC3vsucePH09UVJRhWaPR4OvrS1hYWL43TUmh0+lISEggNDQUlUrGOeZHcmU+yVX+6id8z/+ubyX7b3+gV9Bl83LzF7Cp0fExe/67yX1lPsmVeSRPph59Q/4kFhfHd+7cYfjw4Xz55Zfk5OQAD4rYPn36sGjRIlxcXCw9pJG//wdUKBRGbY96RB+9xvrLL7/krbfeIiYmhubNm+Pk5MSHH37I/v37jY5z/fp1Tp8+jVKp5MyZM3To0MGwzs7Ojl69erF69Wr69u3L6tWr6dOnj9HDcf3792fUqFEsWLCA1atXExAQYCjanZycOHToEImJiWzbto3JkycTHR1NcnIyrq6u+V5vmTJlDD9nZGQAsGnTJp555hmj7dRqNQAdO3bkwoULxMfHk5CQQLt27XjjjTf46KOPaNSoEefPn2fz5s1s376d3r17ExISYtSD/vdjPjruX6lUqlL1QSpt11OYJFfmk1zlza9RODNWrmaahxvpSiUAlXU6YjQ61DXbg63kLD9yX5lPcmUeydOfzM2DxQ/kDR48mP3797Nx40Zu377N7du32bhxIz/99BNDhgyxONB/KikpiaCgIIYPH07Dhg2pVq0aKSkpJtu99tprBAQEsHz5csaNG8eJEyeM1vfv358tW7Zw7Ngxdu7cSf/+/Y3Wd+3alfv377NlyxZWr15tst7W1paQkBBmz57Nzz//TGpqKjt37gQeFN+P/pDIT+3atVGr1aSlpVGtWjWjf76+vobtPD09GThwIP/3f/9HbGwsS5YsMaxzdnamT58+LF26lK+++opvvvmGmzdvPjmRQghRELwD6NDyXXZcusbSK9dYefkqG25kUrPb52Br+se4EEIUNxb3HG/cuJGtW7fSsmVLQ1v79u1ZunSpUW9sUalevTorVqxg69atVKlShZUrV5KcnEyVKlUM2yxatIi9e/fy888/4+vry6ZNm+jfvz/79u3Dzs4OgFatWuHt7U3//v2pUqUKzz33nNF5ypQpQ7du3Zg0aRInTpzgpZdeMqzbuHEj586do1WrVri5uREfH09ubi41a9YEwM/Pj/3795OamkrZsmVxd3fP81qcnJx46623GDNmDLm5ubRs2ZL09HSSkpJwdnZm4MCBTJ48mcaNG1OnTh20Wi0bN26kVq1aAMyZMwcfHx8aNmyIjY0NX3/9Nd7e3k/svRZCiALV/A3UdXoQeGorR46dIufFcdiUcbV2VEIIYRaLe449PDzyHDrh4uKCm5tbgQRliSFDhtCjRw/69OnDc889xx9//MHw4cMN60+ePMnbb7/N4sWLDb2vixcv5saNG0yaNMmwnUKh4KWXXuLo0aMmvcKP9O/fn6NHj/L8889TqVIlQ7urqyvr1q2jbdu21KpVi08++YQvvviCOnXqAPDWW2+hVCqpXbs2np6epKWlPfZ63n//fSZNmsSMGTOoVasWHTp0YNOmTYZi387OjvHjx1OvXj1atWqFUqnkyy+/BB4U17NnzyYwMJAmTZqQmppKfHy8YV5qIYQoMs4+6Bv05ze3ZmBX5snbCyFEMWHxbBVLlizh66+/ZuXKlXh7ewMPZpAYOHAgPXr0sMrQClEwNBoNLi4uT3yKs6TQ6XTEx8cbHrgUjye5Mp/kynySK/NJrswnuTKP5MmUuXWOxcMqPv74Y86ePUulSpUMvadpaWmo1Wp+//13wwwOAIcOHXqK0IUQQgghhLAOi4vjbt26FUIYQgghhBBCWJ/FxfGUKVMKIw4hhBBCCCGsTp7UEkIIIYQQ4iEpjoUQQgghhHhIimMhhBBCCCEeKrXFsV6vJyIiAnd3dxQKBUeOHMl3+8TERBQKBbdv3y6S+IQQQgghRPFTaovjLVu2EBcXx8aNG7ly5Qp169a1WixxcXHyljohhCipsrPAslcCCCFKMItnq8jJySEuLo4dO3Zw/fp1cnNzjdbv3LmzwIL7J1JSUvDx8SEoKMjaoZgtKyvL8DprIYQoye7rctif8gfn7zz4Jq9ESk2C7VPgUjI4uEHgaxA8HpTyQgUhSjOLe45Hjx7N6NGjycnJoW7dutSvX9/oX3EQHh7OyJEjSUtLQ6FQ4Ofnh1arZdSoUZQvXx57e3tatmxJcnKyyb4HDx4kMDAQR0dHgoKCOHXqlFnnPHr0KG3atMHJyQlnZ2caN27MTz/9RGJiIq+++irp6ekoFAoUCgXR0dEA+Pn58f777zNgwACcnZ2JiIgA4IcffuD555/HwcEBX19fRo0axd27dw3nWrx4MdWrV8fe3h4vLy969eplWLd27VoCAgJwcHDAw8ODkJAQo32FEKKwbfz5Ms1m7GBg3EFif7UlbF4SJ65orB2WZa6f5PrqXszOPEPvCt4Mc7Yl8afFsGW8tSMTQhQyi3uOv/zyS9asWUOnTp0KI54CMW/ePPz9/VmyZAnJyckolUrGjh3LN998w/Lly6lcuTKzZ8+mffv2nD17Fnd3d8O+EydOJCYmBk9PT4YOHcprr71GUlLSE8/Zv39/GjZsyMcff4xSqeTIkSOoVCqCgoKIjY1l8uTJhkK7bNmyhv0++ugjJk+ebJg/OiUlhQ4dOjBt2jQ+//xzfv/9d0aMGMGIESNYtmwZP/30E6NGjWLlypUEBQVx8+ZN9uzZA8CVK1d46aWXmD17Nt27d+fOnTvs2bPnsb02Wq0WrVZrWNZoHvzy0ul06HQ6C7Ne/Dy6htJwLYVNcmU+yVX+LtzMJPLLI2Tn/vn/ndQ/Mnl9eTLbxzyP0kZhxejMd+vHefT3cuWq7Z+/Jn9wdGD86a958flx4OBaoOeT+8p8kivzSJ5MmZsLhd7C77sqVKhAYmIiNWrUeKrAikpsbCyxsbGkpqZy9+5d3NzciIuLo1+/fsCDBPn5+REZGcnbb79NYmIibdq0Yfv27bRr1w6A+Ph4XnjhBe7du4e9vX2+53N2dmbBggUMHDjQZF1cXByRkZEmD/v5+fnRsGFDvv32W0Pb4MGDUSqVRq/h/uGHH2jdujV3794lPj6eV199lUuXLuHk5GR0vEOHDtG4cWNSU1OpXLnyE3MUHR3N1KlTTdpXr16No6PjE/cXQoi/23xRwZZLyjzXDauVw7OuJWOIxcnL7/F/jlkm7S45OUxyiOC+YxUrRCWE+CcyMzPp168f6enpODs7P3Y7i3uO33zzTebNm8fChQtRKEpGD0BKSgo6nY4WLVoY2lQqFU2bNuXEiRNG29arV8/ws4+PDwDXr1+nUqVK+Z4jKiqKwYMHs3LlSkJCQnjxxRfx9/d/YmyBgYFGy0ePHuXnn39m1apVhja9Xk9ubi7nz58nNDSUypUrU7VqVTp06ECHDh3o3r07jo6O1K9fn3bt2hEQEED79u0JCwujV69euLm55Xnu8ePHExUVZVjWaDT4+voSFhaW701TUuh0OhISEggNDUWlkjGC+ZFcmU9ylb8jm0/BpQt5rqtVryEd63oXcURPZ+PaeZD1u0l7ulKJb+vnqe5ZL4+9np7cV+aTXJlH8mTq0TfkT2JxcfzDDz+wa9cuNm/eTJ06dUwSvm7dOksPWaz89XoeFf9/f+gwL9HR0fTr149NmzaxefNmpkyZwpdffkn37t3z3a9MmTJGyxkZGQwZMoRRo0aZbFupUiXs7Ow4dOgQiYmJbNu2jcmTJxMdHU1ycjKurq4kJCTw448/sm3bNhYsWMDEiRPZv38/VaqY9nKo1WrUanWeOShNH6TSdj2FSXJlPslV3oKf9WLZj6bFsdrWhudreJWYnPl4N4S0bSbttijwdq9SaNch95X5JFfmkTz9ydw8WPxAnqurK927d6d169aUK1cOFxcXo3/Fkb+/P3Z2dkZjh3U6HcnJydSuXbvAzlOjRg3GjBnDtm3b6NGjB8uWLQPAzs6OnJwcs47RqFEjjh8/TrVq1Uz+PZrJwtbWlpCQEGbPns3PP/9MamqqYZYQhUJBixYtmDp1KocPH8bOzs5o2IYQQhSmVtXL0bmej0n7uA7P4lam5MzG07feIGwVpsNDOvl3wd3ePY89hBClhcU9x48KvpKkTJkyDBs2jLfffht3d3cqVarE7NmzyczMZNCgQf/4+Pfu3ePtt9+mV69eVKlShUuXLpGcnEzPnj2BB2OLMzIy2LFjB/Xr18fR0fGxY3rHjRtHs2bNGDFiBIMHD6ZMmTIcP36chIQEFi5cyMaNGzl37hytWrXCzc2N+Ph4cnNzqVmzJvv372fHjh2EhYVRvnx59u/fz++//06tWrX+8TUKIYQ5FAoF8/s25D/1K7Dt2FWuX77IqK7NCaxSztqhWaS2R23mtokl5qcYUjWp2Cvt6eLfhbFNxlo7NCFEIbO4OH7k999/N8y+ULNmTTw9PQssqMIwc+ZMcnNzeeWVV7hz5w6BgYFs3br1seNxLaFUKvnjjz8YMGAA165do1y5cvTo0cPwsFtQUBBDhw6lT58+/PHHH0yZMsUwndvf1atXj927dzNx4kSef/559Ho9/v7+9OnTB3jQc79u3Tqio6O5f/8+1atX54svvqBOnTqcOHGC77//ntjYWDQaDZUrVyYmJoaOHTv+42sUQghz2dgoCKvjTZsaHsTHX6B+xeL5reKTBPsGE+wbzO+Zv1PWriwOtg7WDkkIUQQsnq3i7t27jBw5khUrVhjG4iqVSgYMGMCCBQtkloMSTKPR4OLi8sSnOEsKnU5HfHw8nTp1kvFWTyC5Mp/kynySK/NJrswnuTKP5MmUuXWOxWOOo6Ki2L17N//73/+4ffs2t2/f5rvvvmP37t28+eab/yhoIYQQQgghrMni4vibb77hs88+o2PHjjg7O+Ps7EynTp1YunQpa9euLYwYi4U6depQtmzZPP/9ddo1IYQQQghRclk85jgzMxMvLy+T9vLly5OZmVkgQRVH8fHxj32zSl75EEIIIYQQJY/FxXHz5s2ZMmUKK1asMLw17t69e0ydOpXmzZsXeIDFhTlvnBNCCCGEECWbxcXxvHnzaN++PRUrVqR+/frAg7e62dvbs3Xr1gIPUAghhBBCiKJicXFct25dzpw5w6pVqzh58iQAL730Ev3798fBQaa5EUIIIYQQJddTzXPs6OjI66+/XtCx/Gvo9XqGDBnC2rVruXXrFocPH6ZBgwbWDksIIYQQ4l/PrOJ4w4YNdOzYEZVKxYYNG/Ld9j//+U+BBFaabdmyhbi4OBITE6latSrlyhXMm6MSExOJiori2LFj+Pr68u677xIeHl4gxy4IV+9eZe7Buey6uAs7pR2dqnRidKPRlFGVsXZoQgghhBCAmcVxt27duHr1KuXLl6dbt26P3U6hUJCTk1NQsZVaKSkp+Pj4EBQUVGDHPH/+PC+88AJDhw5l1apV7Nixg8GDB+Pj40P79u0L7DxP6372fV7d8iqXMi4BcC/7Hl+c/IJzt8/xaftPrRydEKKg/fpbOlt/vcyF3xQ0TL9PpXLyEgIhRMlg1jzHubm5lC9f3vDz4/5JYfxk4eHhjBw5krS0NBQKBX5+fgQHBzNq1CjGjh2Lu7s73t7eJq+Xvn37NoMHD8bT0xNnZ2fatm3L0aNHDes/+eQTqlSpQkxMDLVq1WLEiBH06tWLuXPnFvEV5m3z+c2Gwviv9l/dz8+//2yFiIQQhWXaxuOMWPg1d79fRJnfEuk5N54NRy9bOywhhDCLxWOOV6xYQZ8+fVCr1UbtWVlZfPnllwwYMKDAgiuN5s2bh7+/P0uWLCE5ORmlUsmLL77I8uXLiYqKYv/+/ezdu5fw8HBatGhBaGgoAC+++CIODg5s3rwZFxcX/vvf/9KuXTtOnz6Nu7s7e/fuJSQkxOhc7du3JzIy8rGxaLVatFqtYVmj0QAPXjn5uDmdn1bKrZTHrjtz8wy1XGsV6PkAwzUU9LWURpIr80mu8nfwwi0c98aw0+4bbBR6ACbrV/Lmukie94+irPqpHnUp9eS+Mp/kyjySJ1Pm5kKh1+v1lhxYqVRy5coVQ0/yI3/88Qfly5eX3mMzxMbGEhsbS2pqKgDBwcHk5OSwZ88ewzZNmzalbdu2zJw5kx9++IEXXniB69evG/1RUq1aNcaOHUtERAQ1atTg1VdfZfz48Yb18fHxvPDCC2RmZuY5k0h0dDRTp041aV+9ejWOjo4FeMVwOOsw32R+k+e6YWWH8YztMwV6PiGEdRw9m8rkO5NN2tP1jizynU8dTzsrRCWEEA9eZNevXz/S09NxdnZ+7HYW/wmv1+tRKBQm7ZcuXcLFxcXSw4mH6tWrZ7Ts4+PD9evXgQfzSGdkZODh4WG0zb1790hJeXyP7JOMHz+eqKgow7JGo8HX15ewsLB8b5qn0S6nHQc3HyRVk2rU3sKnBa+3KZyZT3Q6HQkJCYSGhqJSyXjH/EiuzCe5yl/5z8fAHdN2F0UmHStpqdumW5HHVBLIfWU+yZV5JE+mHn1D/iRmF8cNGzZEoVCgUCho164dtrZ/7pqTk8P58+fp0KGD5ZEKAJMbV6FQkJubC0BGRgY+Pj4kJiaa7Ofq6gqAt7c3165dM1p37do1nJ2dHzv/tFqtNhke8yiWgv4gqVQqlnVYxqIji9iZthM7pR0vVHmBofWHorIt3A9tYVxPaSW5Mp/kKm/VvZzgSt7r6lZ0lZw9gdxX5pNcmUfy9Cdz82B2cfxoloojR47Qvn17ypYta1hnZ2eHn58fPXv2tCxKYZZGjRpx9epVbG1t8fPzy3Ob5s2bEx8fb9SWkJBQrF7pXc6hHFOaT2FK8ynWDkUIUUjKNXkRjiw2ac+2c0FVvZ0VIhJCCMuYXRxPmfKgoPHz86NPnz7Y29sXWlDCWEhICM2bN6dbt27Mnj2bGjVqcPnyZTZt2kT37t0JDAxk6NChLFy4kLFjx/Laa6+xc+dO1qxZw6ZNm6wdvhDi3+SZRtBmIvpdH6DgwSMtepUDtr2WgkreoiqEKP4sHnM8cODAwohD5EOhUBAfH8/EiRN59dVX+f333/H29qZVq1Z4eXkBUKVKFTZt2sSYMWOYN28eFStW5NNPPy0WcxwLIf5lWo9FUbcnOSfj+eVkCnVenIDKufyT9xNCiGLA4uI4JyeHuXPnsmbNGtLS0sjKyjJaf/PmzQILrrSKjIw0mmItr7HE69evN1p2cnJi/vz5zJ8//7HHDQ4O5vDhwwUUpRBC/AMe/uQ2HcqFG/HUcXCzdjRCCGE2s14C8ldTp05lzpw59OnTh/T0dKKioujRowc2NjYmL64QQgghhBCiJLG4OF61ahVLly7lzTffxNbWlpdeeolPP/2UyZMns2/fvsKIUQghhBBCiCJhcXF89epVAgICAChbtizp6ekAdO7cWR7+EkIIIYQQJZrFxXHFihW5cuXBJJb+/v5s27YNgOTk5DznzBVCCCGEEKKksLg47t69Ozt27ABg5MiRTJo0ierVqzNgwABee+21Ag9QCCGEEEKIomLxbBUzZ840/NynTx8qVarE3r17qV69Ol26dCnQ4IQQQgghhChKFhfHf9e8efNi9RY2IYQQQgghnpbFwyqWL19u9ODd2LFjcXV1JSgoiAsXLhRocE9Lr9cTERGBu7s7CoWCI0eOWDsk4uLicHV1tXYYQgghhBAiHxYXxx988AEODg9eAbp3714WLlzI7NmzKVeuHGPGjCnwAJ/Gli1biIuLY+PGjVy5coW6detaOyQhhPjXuHv/Nos2D+OlVUGs+v191u+biV6vt3ZYQghhFouHVVy8eJFq1aoBD97i1qtXLyIiImjRogXBwcEFHd9TSUlJwcfHh6CgIGuHUizodDpUKpW1wxBC/AvodFoGftGOUzZZoABU8N65NRy9fJBpfdZbOzwhhHgii4vjsmXL8scff1CpUiW2bdtGVFQUAPb29ty7d6/AA7RUeHg4y5cvB0ChUFC5cmX8/PwMvccrV65EpVIxbNgw3nvvPRQKBQCLFy9m7ty5XLx4ERcXF55//nnWrl0LQG5uLrNmzWLJkiVcvXqVGjVqMGnSJHr16gU8eP1zmzZt2LhxI+PHj+f06dM0aNCATz/91KTXev369bz99ttcvHiR1q1b8+mnn+Lr62tYP3PmTObOnUtmZia9e/fG09OTLVu2GIaGJCcnM2HCBA4fPoxOp6NBgwbMnTuXRo0aGY6hUChYvHgxmzdvZseOHbz99tt5vr1Qq9Wi1WoNyxqNBnhQTOt0un/yn6FYeHQNpeFaCpvkynySq/x9nTjjQWH8N/+7d5bXLv2Er1d9K0RV/Ml9ZT7JlXkkT6bMzYVCb+F3Xf379+fkyZM0bNiQL774grS0NDw8PNiwYQMTJkzg119/faqAC0p6ejrz589nyZIlJCcno1QqefHFFzl48CCDBg1i2LBh/PTTT0RERBAbG8vrr7/OTz/9RLNmzVi5ciVBQUHcvHmTPXv2MGrUKACmT5/O//3f/xEbG0v16tX5/vvvGTp0KFu3bqV169aG4rhWrVrMmzcPb29vQy5Onz6NSqUiLi6OiIgI6tevz/z587Gzs2P48OHY2tqSlJQEwJo1axgwYACLFi2iZcuWrFy5kvnz51O1alVDcbxz504uX75MYGAger2emJgYNm7cyJkzZ3BycgIeFMfly5dn5syZtG7dGltbWypVqmSSq+joaKZOnWrSvnr1ahwdHQvpv5AQojTb9lsM35e5lee6PvcbE+DdvYgjEkKIBzIzM+nXrx/p6ek4Ozs/djuLi+Pbt2/z7rvvcvHiRYYNG0aHDh0AmDJlCnZ2dkycOPGfRV4AYmNjiY2NJTU1FYDg4GCuX7/OsWPHDD3F77zzDhs2bOD48eOsW7eOV199lUuXLhkKzEe0Wi3u7u5s377daFaOwYMHk5mZyerVqw3F8ZdffkmfPn0AuHnzJhUrViQuLo7evXsTFxfHq6++yr59+3juuecAOHnyJLVq1WL//v00bdqUoKAgGjZsyKJFiwznadasGffv33/sQ4W5ubm4urqyevVqOnfuDDwojiMjI5k7d26+ecqr59jX15cbN27ke9OUFDqdjoSEBEJDQ2VYyRNIrswnucrf1OV9+U51Os91Yz0G0bf9G0UcUckg95X5JFfmkTyZ0mg0lCtX7onFscXDKlxdXVm4cKFJe149kMVJs2bNDIUxPJiCLiYmhpycHEJDQ6lcuTJVq1alQ4cOdOjQge7du+Po6MjZs2fJzMwkNDTU6HhZWVk0bNjQqO2vxbO7uzs1a9bkxIkThjZbW1uaNGliWH722WdxdXXlxIkTNG3alBMnTjB06FCTY+7atcuwfO3aNd59910SExO5fv06OTk5ZGZmkpaWZrRfYGDgE3OiVqvzfKuhSqUqVR+k0nY9hUlyZT7JVd6eDxjNrmPD0CiNn/eucV9Pp9ZDJGdPIPeV+SRX5pE8/cncPJhVHP/888/UrVsXGxsbfv7553y3rVevnlknLk6cnJw4dOgQiYmJbNu2jcmTJxMdHU1ycjIZGRkAbNq0iWeeecZoP2u8LnvgwIH88ccfzJs3j8qVK6NWq2nevDlZWcZj/MqUKVPksQkhRGiT5/nhSC9O6r7mpL0SpV5Po7sKgvw+wMPJwdrhCSHEE5lVHDdo0ICrV69Svnx5GjRogEKhMJqW59GyQqEgJyen0IL9J/bv32+0vG/fPqpXr45SqQQe9OqGhIQQEhLClClTcHV1ZefOnYSGhqJWq0lLS6N169b5nmPfvn2Gsb23bt3i9OnT1KpVy7A+Ozubn376iaZNmwJw6tQpbt++bdjm0RCLAQMGGB3zr5KSkli8eDGdOnUCHswecuPGjadJiRBCFDgbGwXRr0ez6efBHDm4lT9uZdK7ax9aVPeydmhCCGEWs4rj8+fP4+npafi5JEpLSyMqKoohQ4Zw6NAhFixYQExMDAAbN27k3LlztGrVCjc3N+Lj48nNzaVmzZo4OTnx1ltvMWbMGHJzc2nZsiXp6ekkJSXh7OzMwIEDDed477338PDwwMvLi4kTJ1KuXDm6detmWK9SqRg5ciTz58/H1taWESNG0KxZM0OxPHr0aMLDwwkMDKRFixasWrWKY8eOUbVqVcMxqlevzsqVKwkMDESj0fD2228b5p0WQojiQGmj4D8NfOlYZyDx8fE09XO3dkhCCGE2s4rjypUr5/lzSTJgwADu3btH06ZNUSqVjB49moiICODBOOp169YRHR3N/fv3qV69Ol988QV16tQB4P3338fT05MZM2Zw7tw5XF1dadSoERMmTDA6x8yZMxk9ejRnzpyhQYMG/O9//8POzs6w3tHRkXHjxtGvXz9+++03nn/+eT777DPD+j59+pCSksLYsWO5f/8+PXv2ZNiwYWzdutWwzWeffUZERASNGjXC19eXDz74gLfeeqswUyeEEEII8a9h8QN5AJcvX+aHH37g+vXr5ObmGq17NP2ZNUVGRhIZGWnUplKpiI2N5eOPPzbZvmXLliQmJj72eAqFgtGjRzN69Oh8z9uyZcvHTmUXHh5OeHg4AD169HjsMSZMmGBUdP99fuKGDRuSnJxs1PZovuVH5E1UQgghhBBPx+LiOC4ujiFDhmBnZ4eHh4fRDBAKhaJYFMdCCCGEEEI8DYuL40mTJjF58mTGjx+PjY3Nk3cQQgghhBCihLC4OM7MzKRv374lqjDOb8hEQQgODi60oQzR0dF5vvpZCCGEEEIUPIsr3EGDBvH1118XRixCCCGEEEJYlcU9xzNmzKBz585s2bKFgIAAk7eNzJkzp8CCE0IIIYQQoig9VXG8detWatasCWDyQJ4QQgiRpytH4dYF8KkPbiVzWlAhROlncXEcExPD559/bpiWrDTS6/UMGTKEtWvXcuvWLQ4fPkyDBg2sHZYQohBcu3uN71K+48a9GzTyakRIpRBsbZ5qlkvxN3/czeJ+NnDvNqx6FVL3PFihsIFGA+CFuVCCnl8RQvw7WPwbQK1W06JFi8KIpdjYsmULcXFxJCYmUrVqVcqVK/fYbcPDw7l9+zbr168vugCFEAXiwJUDjNg5gnvZ9wD44uQXNPZqzCchn2Bva2/l6EquXy6lM+m7Xzly8TY2KKl0fijNM/b8uYE+Fw7GgXcANBlstTiFECIvFv/JPnr0aBYsWFAYsRQbKSkp+Pj4EBQUhLe3N7a2/7wXSafTFUBkQoiCotfreX/f+4bC+JGD1w7yzZlvrBRVyfdHhpaXP9vPkYu3AVCho9GdxLw3PvJFUYUlhBBms7g4PnDgAMuXL6dq1ap06dKFHj16GP0r6cLDwxk5ciRpaWkoFAr8/PxYu3YtAQEBODg44OHhQUhICHfv3iU6Oprly5fz3XffoVAoUCgUJCYmkpqaikKh4KuvvqJ169bY29uzatUqAD7//HPq1KmDWq3Gx8eHESNGGM49Z84cAgICKFOmDL6+vgwfPpyMjAzD+gsXLtClSxfc3NwoU6YMderUIT4+3rD+119/pWPHjpQtWxYvLy9eeeUVbty4UXTJE6IEuaC5QKomNc91iRcTizKUUmXdod9Iv/dnZ4AtOagV2XlvnHW3iKISQgjzWdwl6urqWiqK4MeZN28e/v7+LFmyhOTkZHQ6HVWrVmX27Nl0796dO3fusGfPHvR6PW+99RYnTpxAo9GwbNkyANzd3bl8+TIA77zzDjExMTRs2BB7e3s+/vhjoqKimDlzJh07diQ9PZ2kpCTDuW1sbJg/fz5VqlTh3LlzDB8+nLFjx7J48WIA3njjDbKysvj+++8pU6YMx48fp2zZsgDcvn2btm3bMnjwYObOncu9e/cYN24cvXv3ZufOnXleq1arRavVGpY1Gg3woJe7NPR0P7qG0nAthe3fmCvbfP73Z6+0f2wu/o25ssTFm8YF710c2J/7LM/ZnDTZNqdaKLmSR0DuK0tIrswjeTJlbi4U+sJ6e0UJFhsbS2xsLKmpqRw6dIjGjRuTmppK5cqmT1fnNeY4NTWVKlWqEBsby+jRow3tzzzzDK+++irTpk0zK461a9cydOhQQ+9vvXr16NmzJ1OmTDHZdtq0aezZs4etW7ca2i5duoSvry+nTp2iRo0aJvtER0czdepUk/bVq1fj6OhoVoxClGSfZ3zOuexzJu39HPtR2662FSIq+Q7eULDijNKorbYilVV2H+Cm+PObMI19RX6oPgGdbdmiDlEI8S+VmZlJv379SE9Px9nZ+bHbySPZT1C/fn3atWtHQEAA7du3JywsjF69euHm5vbEfQMDAw0/X79+ncuXL9OuXbvHbr99+3ZmzJjByZMn0Wg0ZGdnc//+fTIzM3F0dGTUqFEMGzaMbdu2ERISQs+ePalXrx4AR48eZdeuXYae5L9KSUnJszgeP348UVFRhmWNRoOvry9hYWH53jQlhU6nIyEhgdDQUJP5uIWxf2uuGmc2JnJ3JKdunQLAVmHLgNoDGFF/xGP3+bfmylwh2bkc+fQAP/+mMbQd1/uxuM5q3vE+CLfT0FdoiEPt7oSqHKwYafEi95X5JFfmkTyZevQN+ZOYVRw3atSIHTt24ObmRsOGDfOdz/jQoUPmRVhCKJVKEhIS+PHHH9m2bRsLFixg4sSJ7N+/nypVquS7b5kyZQw/Ozjk/0sgNTWVzp07M2zYMKZPn467uzs//PADgwYNIisrC0dHRwYPHkz79u3ZtGkT27ZtY8aMGcTExDBy5EgyMjLo0qULs2bNMjm2j49PnudUq9Wo1WqTdpVKVao+SKXtegrTvy1XFV0qsvY/azly/Qi/3/udBp4N8HT0NGvff1uuzKVSweqI5ny25zzbT1wlU5POa23r0K+ZHwpFa2uHV+zJfWU+yZV5JE9/MjcPZhXHXbt2NRRRXbt2/de97EOhUNCiRQtatGjB5MmTqVy5Mt9++y1RUVHY2dmRk5PzxGM4OTnh5+fHjh07aNOmjcn6gwcPkpubS0xMDDYP5/1cs2aNyXa+vr4MHTqUoUOHMn78eJYuXcrIkSNp1KgR33zzDX5+fgUyu4YQ/yYNyjewdgilSlm1LaNDqjO8tR/x8fF0Cqz4r/u9IYQoucyqov46xjU6OrqwYimW9u/fz44dOwgLC6N8+fLs37+f33//nVq1agHg5+fH1q1bOXXqFB4eHri4uDz2WNHR0QwdOpTy5cvTsWNH7ty5Q1JSEiNHjqRatWrodDoWLFhAly5dSEpK4pNPPjHaPzIyko4dO1KjRg1u3brFrl27DHG88cYbLF26lJdeeomxY8fi7u7O2bNn+fLLL/n0009RKpV5hSSEEEIIIf7C4qncqlatyh9//GHSfvv2bapWrVogQRUnzs7OfP/993Tq1IkaNWrw7rvvEhMTQ8eOHQF4/fXXqVmzJoGBgXh6ehrNPvF3AwcOJDY2lsWLF1OnTh06d+7MmTNngAdjm+fMmcOsWbOoW7cuq1atYsaMGUb75+Tk8MYbb1CrVi06dOhAjRo1DDNZVKhQgaSkJHJycggLCyMgIIDIyEhcXV0NPdFCCCGEECJ/Fn//npqamucwAq1Wy6VLlwokKGuLjIwkMjISgFq1arFly5bHbuvp6cm2bdtM2h83CciQIUMYMmRInuvGjBnDmDFjjNpeeeUVw89PevlK9erVWbduXb7bCCGEEEKIxzO7ON6wYYPh561btxoNH8jJyWHHjh1PfEBNCCGEEEKI4szs4rhbt27Ag4fTBg4caLROpVLh5+dHTExMgQYnhBBCCCFEUTK7OM7NzQWgSpUqJCcnU65cuUILSgghhBBCCGuweMzx+fPnCyMOIYQQQgghrO6ppjHYsWMHnTt3xt/fH39/fzp37sz27dsLOjYhhBBCCCGKlMXF8eLFi+nQoQNOTk6MHj2a0aNH4+zsTKdOnVi0aFFhxCiEEEIIIUSRsLg4/uCDD5g7dy5ffPEFo0aNYtSoUaxevZq5c+fywQcfFEaMRUav1xMREYG7uzsKhYIjR45YO6R/JDg42DAlnRBCFKnfDmGzeybVrm2E9IvWjkYIIcxmcXF8+/ZtOnToYNIeFhZGenp6gQRlLVu2bCEuLo6NGzdy5coV6tat+9htw8PDDTN4CCGE+Ist42FpG5Q/fESdy2uwXdwEfllr7aiEEMIsFhfH//nPf/j2229N2r/77js6d+5cIEFZS0pKCj4+PgQFBeHt7Y2trcXPK5rQ6XQFEJkQQpQQF/aStW8xa5zK8oaXJ1Hly7FbrSL3f6NBe8fa0QkhxBNZXBzXrl2b6dOn88ILLzBt2jSmTZtG586dmT59OnXr1mX+/PmGfyVJeHg4I0eOJC0tDYVCgZ+fH2vXriUgIAAHBwc8PDwICQnh7t27REdHs3z5cr777jsUCgUKhYLExERSU1NRKBR89dVXtG7dGnt7e1atWgXA559/Tp06dVCr1fj4+DBixAjDudPS0ujatStly5bF2dmZ3r17c+3aNcP66OhoGjRowMqVK/Hz88PFxYW+ffty586fv2ju3r3LgAEDKFu2LD4+PjLntBDCKjJ+/o43vD15v5w73zs6kFDGkZHenixyVMK5RGuHJ4QQT2Rx1+hnn32Gm5sbx48f5/jx44Z2V1dXPvvsM8OyQqFg1KhRBRNlEZg3bx7+/v4sWbKE5ORkdDodVatWZfbs2XTv3p07d+6wZ88e9Ho9b731FidOnECj0bBs2TIA3N3duXz5MgDvvPMOMTExNGzYEHt7ez7++GOioqKYOXMmHTt2JD09naSkJODB/NGPCuPdu3eTnZ3NG2+8QZ8+fUhMTDTEl5KSwvr169m4cSO3bt2id+/ezJw5k+nTpwPw9ttvs3v3br777jvKly/PhAkTOHToEA0aNHjsNWu1WrRarWFZo9EAD3q7S0OP96NrKA3XUtgkV+aTXOVv9e9p7HNwMGn/3NWZwCvXCawmecuL3Ffmk1yZR/JkytxcyDzHD7m4uODk5IRSqcTb25tDhw6RnZ1Njx49qFy5MgABAQGG7R0cHNBqtXh7e5scKzIykh49ehiWp02bxptvvsno0aMNbU2aNAEeTIv3yy+/cP78eXx9fQFYsWIFderUITk52bBdbm4ucXFxODk5AfDKK6+wY8cOpk+fTkZGBp999hn/93//R7t27QBYvnw5FStWzPeaZ8yYwdSpU03at23bhqOj45OTVkIkJCRYO4QSQ3JlPslV3jZmAaa1MdkKBcvOX+L6nfgij6kkkfvKfJIr80ie/pSZmWnWdk89qPbGjRsApfZNefXr16ddu3YEBATQvn17wsLC6NWrF25ubk/cNzAw0PDz9evXuXz5sqFo/bsTJ07g6+trKIzhwdAVV1dXTpw4YSiO/fz8DIUxgI+PD9evXwce9CpnZWXx3HPPGda7u7tTs2bNfOMcP348UVFRhmWNRoOvry9hYWE4Ozs/8TqLO51OR0JCAqGhoahUKmuHU6xJrswnucrfru1nOH/9lzzXdQ5qTcdqLYo4opJB7ivzSa7MI3ky9egb8iexqDi+ffs2EydO5KuvvuLWrVsAuLm50bdvX6ZNm4arq6vFgRZXSqWShIQEfvzxR7Zt28aCBQuYOHEi+/fvp0qVKvnuW6ZMGcPPDnl8vfg0/n5jKxQKwyu9n5ZarUatVud5rtL0QSpt11OYJFfmk1zl7a0W/dm57gv0iiyjdiebZ+jybGsUCoWVIisZ5L4yn+TKPJKnP5mbB7MfyLt58ybPPfccy5cvp2fPnsTExBATE0OPHj2Ii4ujefPmhoK5tFAoFLRo0YKpU6dy+PBh7OzsDDN12NnZkZOT88RjODk54efnx44dO/JcX6tWLS5evMjFi3/OA3r8+HFu375N7dq1zYrT398flUrF/v37DW23bt3i9OnTZu0vhBAFpaKzD/PazMdZ+eeQs4r2tfiq61IpjIUQJYLZPcfvvfcednZ2pKSk4OXlZbIuLCyM9957j7lz5xZ4kNawf/9+duzYQVhYGOXLl2f//v38/vvv1KpVC3gwzGHr1q2cOnUKDw8PXFxcHnus6Ohohg4dSvny5enYsSN37twhKSmJkSNHEhISQkBAAP379yc2Npbs7GyGDx9O69atjYZn5Kds2bIMGjSIt99+Gw8PD8qXL8/EiROxsXmqt4MLIcQ/0qZyC36otI2TN06yb88+Xu7ysvRcCSFKDLOrp/Xr1/PRRx+ZFMYA3t7ezJ49O8/5j0sqZ2dnvv/+ezp16kSNGjV49913iYmJoWPHjgC8/vrr1KxZk8DAQDw9PQ2zT+Rl4MCBxMbGsnjxYurUqUPnzp05c+YM8KB3+rvvvsPNzY1WrVoREhJC1apV+eqrryyK98MPP+T555+nS5cuhISE0LJlSxo3bvz0CRBCiH9AoVBQzbUa7kp3a4cihBAWUej1er05G6rValJSUh47A8KlS5eoVq0a9+/fL9AARdHRaDS4uLiQnp5eah7Ii4+Pp1OnTtJr9QSSK/NJrswnuTKf5Mp8kivzSJ5MmVvnmN1zXK5cOVJTUx+7/vz587i7Sw+BEEIIIYQoucwujtu3b8/EiRPJysoyWafVapk0aRIdOnQo0OCEEEIIIYQoShY9kBcYGEj16tV54403ePbZZ9Hr9Zw4cYLFixej1WpZuXJlYcYqhBBCCCFEoTK7OK5YsSJ79+5l+PDhjB8/nkdDlRUKBaGhoSxcuNDoRRZCCCGEEEKUNBa9BKRKlSps3ryZW7duGWZbqFatmow1FkIIIYQQpcJTvT7azc2Npk2bFnQsQgghhBBCWNVTFcfCuoKDg2nQoAGxsbHWDkUIIUykadKYc3AOuy/uRqlX8nPyz0Q1iaKMqoy1QxNCiCeSV6iVQn5+flI4CyGs4k7WHcI3vcyOtB1k67PRomXNmTWM2T7C2qEJIYRZpDgWQghRYL499gW/Z90yad97/SdO/HHCChEJIYRlpDguobKzsxkxYgQuLi6UK1eOSZMmodfrCQ4O5sKFC4wZMwaFQoFCobB2qEKIf5Gjx7Y8dt3hfNYJIURxIWOOS6jly5czaNAgDhw4wE8//URERASVKlVi3bp11K9fn4iICF5//fV8j6HVatFqtYZljUYDPHjlpE6nK9T4i8KjaygN11LYJFfmk1zlz/tuNtjnvc7xZpbk7THkvjKf5Mo8kidT5uZCoX80YbEoMYKDg7l+/TrHjh0z9Ay/8847bNiwgePHj+Pn50dkZCSRkZH5Hic6OpqpU6eatK9evRpHR8fCCF0IUcr9cW4P35bdSKqdyqg9LCOTemXewdWtvJUiE0L822VmZtKvXz/S09NxdnZ+7HbSc1xCNWvWzGjIRPPmzYmJiSEnJ8fsY4wfP56oqCjDskajwdfXl7CwsHxvmpJCp9ORkJBAaGgoKpXqyTv8i0muzCe5yt/tO615dtF2drjeJtHRAQe9ns4Zd6msDSIkItza4RVbcl+ZT3JlHsmTqUffkD+JFMf/Ymq1GrVabdKuUqlK1QeptF1PYZJcmU9ylTdPd1fSB26iztoP6H7hB+6h5nyFfgS9Pk7yZQa5r8wnuTKP5OlP5uZBiuMSav/+/UbL+/bto3r16iiVSuzs7CzqQRZCiIJUrdIzVItaxJ3M+yRs3Ur3zp3kl7MQosSQ2SpKqLS0NKKiojh16hRffPEFCxYsYPTo0cCDeY6///57fvvtN27cuGHlSIUQ/1b2KiVK+S0jhChhpOe4hBowYAD37t2jadOmKJVKRo8eTUREBADvvfceQ4YMwd/fH61WizxzKYQQQghhHimOS6DExETDzx9//LHJ+mbNmnH06NEijEgIIYQQonSQL7yEEEIIIYR4SIpjIYQQQgghHpLiWAghhBBCiIekOBZCCCGEEOIhKY6FEEIIIYR4SIpjIYQQQgghHpLiuARQKBSsX7/e2mEIIYQQQpR6Ms9xCXDlyhXc3NysHYYQQpgnN4fzB5eSeGYDmZpMbqUoKf9sZ2tHJUq4GxlaVu9P49hvt8m5bUP92/fw85TXkouCJ8VxAcnKysLOzq5Qju3t7f2P9i/M2IQQwkhuLotWh/FJzvUHyw6wbO84Prywgzbt51o3NlFiXbyZSc+Pf+T6He3DFhv2LdzLF683I6Cii1VjE6WPDKt4SsHBwYwYMYLIyEjKlSuHWq1GoVCwdetWGjZsiIODA23btuX69ets3ryZWrVq4ezsTL9+/cjMzDQcZ8uWLbRs2RJXV1c8PDzo3LkzKSkpRuf6+7CKX375hbZt2+Lg4ICHhwcRERFkZGQY1oeHh9OtWzemT59OhQoVqFmzZqHnQwghAH498vmfhfFDWhsbJl7exr30S1aKSpR0sdvP/KUwfiBDm83MLSesFJEozaTn+B9Yvnw5w4YNIykpicTERIYOHUp0dDQLFy7E0dGR3r1707t3b9RqNatXryYjI4Pu3buzYMECxo0bB8Ddu3eJioqiXr16ZGRkMHnyZLp3786RI0ewsTH92+Xu3bu0b9+e5s2bk5yczPXr1xk8eDAjRowgLi7OsN2OHTtwdnYmISHhsfFrtVq02j//Z6PRaADQ6XTodLoCypL1PLqG0nAthU1yZT7JVf62nt2QZ/sdGxt+OBpHcPNxRRxRySD3Vf5+OPt7nu0/pvyBVpuFjY2iiCMq/uSeMmVuLhR6vV5fyLGUSsHBwWg0Gg4dOgRAYmIibdq0Yfv27bRr1w6AmTNnMn78eFJSUqhatSoAQ4cOJTU1lS1btuR53Bs3buDp6ckvv/xC3bp1gQc9x99++y3dunVj6dKljBs3josXL1KmTBkA4uPj6dKlC5cvX8bLy4vw8HC2bNlCWlpavsMpoqOjmTp1qkn76tWrcXR0fPrkCCH+tZJvLOQ726t5rhtu04oKzmFFHJEoDWYdVXI507QAdlTqKnIZXgAAut5JREFUmdE0xwoRiZIoMzOTfv36kZ6ejrOz82O3k57jf6Bx48YmbfXq1TP87OXlhaOjo6EwftR24MABw/KZM2eYPHky+/fv58aNG+Tm5gKQlpZmKI7/6sSJE9SvX99QGAO0aNGC3NxcTp06hZeXFwABAQFPHGc8fvx4oqKiDMsajQZfX1/CwsLyvWlKCp1OR0JCAqGhoahU8tBGfiRX5pNc5c/vgj3fJb1l0u6cCy+/+AH2KvnDOy9yX+XvD/c03tt00qS9f/MqdGpfwwoRFX9yT5l69A35k0hx/A/8tUB95K83oEKhMLkhFQqFoQAG6NKlC5UrV2bp0qVUqFCB3Nxc6tatS1ZWVoHH9ndqtRq1Wm3SrlKpStUHqbRdT2GSXJlPcpW3+tXaM+pCAgsvbiFX8aCnz0EPM5tOwMlRHpx6Ermv8vZqy6pc0WhZvvcCWdm52KCnW8NneLtDLVS28vhUfuSe+pO5eZDi2Ir++OMPTp06xdKlS3n++ecB+OGHH/Ldp1atWsTFxXH37l1DAZyUlISNjY08eCeEKBZeb/cRHdNHsOvnlVy8cJWh3d/HvYy7tcMSJZhCoWDiC7UZFlyNU1duc+bwXl7qVlcKY1Eo5K6yIjc3Nzw8PFiyZAlnz55l586dRsMc8tK/f3/s7e0ZOHAgv/76K7t27WLkyJG88sorhiEVQghhbRVd/Ojb7B3qOIXhZOdk7XBEKeFexo7Aym64yOykohBJcWxFNjY2fPnllxw8eJC6desyZswYPvzww3z3cXR0ZOvWrdy8eZMmTZrQq1cv2rVrx8KFC4soaiGEEEKI0kuGVTylxMREo+Xg4GD+PvFHeHg44eHhRm3R0dFER0cblkNCQjh+/LjRNn89zqOp1sqWLWtoCwgIYOfOnY+N7a9TugkhhBBCCPNJcVyMaTQa1q1bh42NDc8++6y1wxFCCCGEKPWkOC7GpkyZwurVq5k1axYVK1a0djhCCCGEEKWeFMfF2Ny5c5k7d661wxBCCCGE+NeQB/KEEEIIIYR4SIpjIYQQQgghHpLiWAghhBBCiIekOBZCCCGEEOIhKY5LqODgYCIjI60dhhBCCCFEqSKzVQghhChQmVk65sfHcvzaNnJ1cOOH+wxq08faYQkhhFmk57gECg8PZ/fu3cybNw+FQoFCoSA1NZVff/2Vjh07UrZsWby8vHjllVe4ceOGtcMVQvyL6LJzGLysHavSV3DY/ipHna4SmzaNcSsjrB2aEEKYRXqOS6B58+Zx+vRp6taty3vvvQeASqWiadOmDB48mLlz53Lv3j3GjRtH7969H/uqaa1Wa3g9NTx4Ix+ATqdDp9MV/oUUskfXUBqupbBJrswnucrfsm3z+cX+lkn79uwfOX7hNNUrVLFCVMWf3Ffmk1yZR/JkytxcKPR6vb6QYxGFIDg4mAYNGhAbGwvAtGnT2LNnD1u3bjVsc+nSJXx9fTl16hQ1atQwOUZ0dDRTp041aV+9ejWOjo6FFrsQovTacmkuP5T9I891PTKb0qjCf4o4IiGEeCAzM5N+/fqRnp6Os7PzY7eTnuNS4ujRo+zatYuyZcuarEtJScmzOB4/fjxRUVGGZY1Gg6+vL2FhYfneNCWFTqcjISGB0NBQVCqVtcMp1iRX5pNc5S955WdA3sVxLb9qdArpVLQBlRByX5lPcmUeyZOpR9+QP4kUx6VERkYGXbp0YdasWSbrfHx88txHrVajVqtN2lUqVan6IJW26ylMkivzSa7y1rvpMDb99DbZCoVRu48ul15tR2ErOcuX3Ffmk1yZR/L0J3PzIMVxCWVnZ0dOTo5huVGjRnzzzTf4+flhayv/WYUQ1lGvbkeiTm1kSfoubiuVAFTS5TA5YBy26jJWjk4IIZ5MZqsoofz8/Ni/fz+pqancuHGDN954g5s3b/LSSy+RnJxMSkoKW7du5dVXXzUqooUQorC90nMRCd03M8uzG1G6FnzbN4nnnhto7bCEEMIsUhyXUG+99RZKpZLatWvj6elJVlYWSUlJ5OTkEBYWRkBAAJGRkbi6umJjI/+ZhRBFy96tMqGhk3H37IhCbfoshBBCFFfy/XsJVaNGDfbu3WvSvm7dOitEI4QQQghROkiXohBCCCGEEA9JcSyEEEIIIcRDUhwLIYQQQgjxkBTHQgghhBBCPCTFsRBCCCGEEA9JcSyEEEIIIcRDUhwXsuDgYCIjIx+7/urVq4SGhlKmTBlcXV2LLC4hhCgsubl6tvx6hXHrfuXrczYcvnjb2iEJIYTZZJ5jK5s7dy5XrlzhyJEjuLi4FMgxg4ODadCgAbGxsQVyPCGEMJder2f4F3vZeXkdtmVPoFer6PfVYca16M/rrfytHZ4QQjyRFMdWlpKSQuPGjalevbq1QxFCiH9s58krfH9nGuryFw1ttmVSmHP4Mr0az8etjJ0VoxNCiCeTYRVFIDc3l7Fjx+Lu7o63tzfR0dEA+Pn58c0337BixQoUCgXh4eEAzJkzh4CAAMqUKYOvry/Dhw8nIyPD6JhJSUkEBwfj6OiIm5sb7du359atW4SHh7N7927mzZuHQqFAoVCQmppatBcshPjX+uLYJpQOF03alS4/sunEMStEJIQQlpGe4yKwfPlyoqKi2L9/P3v37iU8PJwWLVqQnJzMgAEDcHZ2Zt68eTg4OABgY2PD/PnzqVKlCufOnWP48OGMHTuWxYsXA3DkyBHatWvHa6+9xrx587C1tWXXrl3k5OQwb948Tp8+Td26dXnvvfcA8PT0zDMurVaLVqs1LGs0GgB0Oh06na4wU1IkHl1DabiWwia5Mp/kKn8a/Zk82xUKPde0p9Dp6hZxRCWD3Ffmk1yZR/JkytxcKPR6vb6QY/lXCw4OJicnhz179hjamjZtStu2bZk5cybdunXD1dWVuLi4xx5j7dq1DB06lBs3bgDQr18/0tLS+OGHHx57TnPGHEdHRzN16lST9tWrV+Po6PjkixNCiL/54ea3bLE5mOe6CIeBVFLLEDIhhHVkZmbSr18/0tPTcXZ2fux20nNcBOrVq2e07OPjw/Xr1x+7/fbt25kxYwYnT55Eo9GQnZ3N/fv3yczMxNHRkSNHjvDiiy/+47jGjx9PVFSUYVmj0eDr60tYWFi+N01JodPpSEhIIDQ0FJVKZe1wijXJlfkkV/kLSviepKu53FEaj9qro9UyrFE19M92slJkxZvcV+aTXJlH8mTq0TfkTyLFcRH4+02pUCjIzc3Nc9vU1FQ6d+7MsGHDmD59Ou7u7vzwww8MGjSIrKwsHB0dDcMv/im1Wo1arc4z3tL0QSpt11OYJFfmk1zlzdPOmf9evc60cm4cV6ux0etplXmPKX/cxNbOHiRn+ZL7ynySK/NInv5kbh7kgbxi5uDBg+Tm5hITE0OzZs2oUaMGly9fNtqmXr167Nix47HHsLOzIycnp7BDFUIIU3V7EJCVxVeXr7Ez7RLfp11iwfUblFO7gX8ba0cnhBBPJMVxMVOtWjV0Oh0LFizg3LlzrFy5kk8++cRom/Hjx5OcnMzw4cP5+eefOXnyJB9//LFhTLKfnx/79+8nNTWVGzduPLaXWgghCpx3ALT/AGxs8czJxSVXj97eBXotA1vTb6qEEKK4keK4mKlfvz5z5sxh1qxZ1K1bl1WrVjFjxgyjbWrUqMG2bds4evQoTZs2pXnz5nz33XfY2j4YJfPWW2+hVCr5f/buPK6m9I8D+OdWt33fVKRIJUtCoixl38cydjNEYx1rssSgzNjXzBiGmZExluGHGWNvUEPImjWhkQzR2EoiV53fH+py3VsdqW7l83695jXd55zznO/zde7t23Ofe26NGjVgZWWFpKQkdQyFiD5W3l8C4y7hVcdlOO04Eq9GXwCq+qo7KiIiUbjmuJhFRkYqtf3+++8qf841fvx4jB8/XqHt888/V3js6+uL6Oholed0cXHB8ePH3ztWIqIiY2wLweMz3Lm7B3W0DdQdDRGRaJw5JiIiIiLKweKYiIiIiCgHi2MiIiIiohwsjomIiIiIcrA4JiIiIiLKweKYiIiIiCgHb+VGRERF7srdNBy4dAc370pQL+0F7C349bVEVDZw5ljNoqOjUbt2bUilUnTt2rVQfSQmJkIikSA2NrZIYyMiKoxV2/fjzsquGBXtg9n3huDIMn/sP3Nd3WEREYnC4rgE+fn5Ydy4cQptgYGB8PDwwM2bNxEeHl5gH/7+/oUuoomIitu567fQ7fxQtNY8Ay1JNgwlL9BP8y8Y7RyMZ5mv1B0eEVGBWByrWUJCAlq0aIFKlSrB1NRU3eEQEX2QlKProKmZhm8szNDa3g6dK9pitYkxGkgu4Pypv9UdHhFRgbjmuIT4+/sjKioKUVFRCAsLU9g2ePBgDB48GGvXroW/vz8uX76MyZMn4++//4YgCPDw8EB4eDjWr1+PdevWAQAkEgkA4PDhw3B0dAQA/PPPPxg/fjxiYmLg7OyMVatWwdvbO8+YMjMzkZmZKX+clpYGAJDJZJDJZEU5fLXIHUN5GEtxY67EY67yZ/giEYNsKyBR+80a42/NTXFdW4rPnv4DmaypGqMrvXhdicdcicM8KRObC4kgCEIxx0IAUlNT0b59e9SqVQuzZs1CVlYWAKBGjRqYNWsWevfuDRMTEzx69Aju7u7w8/NDcHAwjI2NER0dDR8fH1SsWBEBAQFIS0vD2rVrAQDm5ua4e/cuqlSpgurVq2PRokVwdnbGtGnTcOrUKdy4cQNaWqr/BgoJCUFoaKhS+8aNG6Gvr198ySCicuvy/Y3YpHNF5bZJ0j4wNqhVwhEREb2WkZGBfv36ITU1FcbGxnnux5njEmJiYgJtbW3o6+vDxsZG3i6RSGBiYiJvW7FiBUxMTLB582ZIpa9nXlxcXOT76+npITMzU6GPXEFBQejYsSMAIDQ0FDVr1sSNGzdQvXp1lTEFBwcjMDBQ/jgtLQ329vZo06ZNvhdNWSGTyRAREYHWrVvLc0mqMVfiMVf5u3wyFrihujg286qDtg5tSzagMoLXlXjMlTjMk7Lcd8gLwuK4lImNjUXTpk0LdSG7u7vLf7a1tQUApKSk5Fkc6+joQEdHR6ldKpWWqydSeRtPcWKuxGOuVHM0d85zm5OZE3NWAF5X4jFX4jBPb4jNAz+QV8ro6ekV+ti3/9Fz1yRnZ2d/cExERGJ1duoMaz1rpXZvW2+4WbipISIiovfD4rgEaWtry9ca58Xd3R1HjhzJc9G4mD6IiNTFSNsIP7f7Gc3tm0NTogltaKOnc08sbb5U3aEREYnC4rgEOTo6IiYmBomJiXjw4IHKWd1Ro0YhLS0Nffr0wenTp3H9+nWsX78e8fHx8j4uXLiA+Ph4PHjwgJ9CJaJSx8HYActbLMfJPicxw3QGghsEw0BqoO6wiIhEYXFcgoKCgqCpqYkaNWrAysoKSUlJSvtYWFjg0KFDSE9Ph6+vL+rXr481a9bIl0wMGTIErq6u8PT0hJWVFaKjo0t6GEREouQu7yIiKkv4gbwS5OLiguPHjyu0PXnyRGk/d3d37N+/X2UfVlZWOHDggFL7u3fkMzU1VWojIiIiovxx5piIiIiIKAeLYyIiIiKiHCyOiYiIiIhysDgmIiIiIsrB4piIiIiIKAeLYyIiIiKiHCyOS1hkZCQkEonKW7h9iPDwcJiamhZpn0REhSZ7DsnNKJilXwd4W0kiKkNYHJdBjo6OWLZsmbrDICJSSbi0HbKF1aG18VM0u/41sr9rANy7pO6wiIhEYXFMREXq6cunOJB4AIeTDiMzK1Pd4VBJe/QPsv/3BaQvn8ibdNISkR7eE8jOUl9cREQisTguBpmZmRgzZgysra2hq6uLJk2a4NSpUwr7nDlzBp6entDX14ePjw/i4+MVtv/5559o0KABdHV1YWlpiW7dugEA/Pz8cOvWLYwfPx4SiUTp61n3798PNzc3GBoaol27dkhOTi7ewRK95c+EP9Fya0tMiJqAMYfHoPXW1jh171TBB1K5kRQZDk0oF8GGL+7i2dVDaoiIiOj98Ouji8GkSZOwbds2rFu3Dg4ODliwYAHatm2LGzduyPeZNm0aFi9eDCsrKwwfPhyDBw9GdHQ0AGD37t3o1q0bpk2bhl9++QUvX77Enj17AADbt29HnTp1MHToUAwZMkThvBkZGVi0aBHWr18PDQ0NfPbZZwgKCsKGDRtUxpmZmYnMzDcze2lpaQAAmUwGmUxWpDlRh9wxlIexFLeiyNXd9LuYHj0dWcKbwuhx5mOMPzwee7rugZ6W3gfHWRrwusrfnXv3UDmPbXE3k1DHmXlThdeVeMyVOMyTMrG5kAgCPylRlJ49ewYzMzOEh4ejX79+AF7/Yzg6OmLcuHFo0KABmjdvjr/++gstW7YEAOzZswcdO3bE8+fPoaurCx8fH1StWhW//vqrynPk9jVu3Dh5W3h4OAYNGoQbN27AyckJAPD9999j1qxZuHfvnsp+QkJCEBoaqtS+ceNG6Ovrf0ga6CMU9SIKES8iVG7ro98HtbRrlXBEpA6JNy5h7NMFSu0vBClWOYShioWhGqIiIno9idivXz+kpqbC2Ng4z/04c1zEEhISIJPJ0LhxY3mbVCqFl5cX4uLi0KBBAwCAu7u7fLutrS0AICUlBZUrV0ZsbKzSrLAY+vr68sI4t9+UlJQ89w8ODkZgYKD8cVpaGuzt7dGmTZt8L5qyQiaTISIiAq1bt4ZUKlV3OKVaUeTq1oVbQB6fuapRpwY6VOnwARGWHryu8nfzv2b4c+URdNY8rtD+vdbnGNGvB7Q0uZpPFV5X4jFX4jBPynLfIS8Ii2M1eftCzV03nJ2dDQDQ0yvc28/vXvwSiQT5vTGgo6MDHR0dlf2UpydSeRtPcfqQXLV0aIk1l9Yo96khhW9l33L3b8DrSjUXOzNc6roao3auR5Osk3gOHRzVa4FxA/tCT1f59YYU8boSj7kSh3l6Q2we+Cd8EXNycoK2trZ8/TDw+q+3U6dOoUaNGqL6cHd3x8GDB/Pcrq2tjawsfuqbSpealjUxoMYAhTYJJJjcYDLMdc3VFBWpQ/f6lTFv6mSY9VmJBy6fYUXQYNSuZKLusIiIROHMcREzMDDAiBEjMHHiRJibm6Ny5cpYsGABMjIyEBAQgPPnzxfYx8yZM9GyZUs4OTmhT58+ePXqFfbs2YPJkycDeL3m+O+//0afPn2go6MDS0vL4h4WkSgTG0xEG8c2OJh0ENoa2mhfpT2cTJ0KPpDKHUMdLbRwtcKLBAGaGpKCDyAiKiVYHBeDefPmITs7G59//jmePn0KT09P7N+/H2ZmZqKO9/Pzw9atW/H1119j3rx5MDY2RrNmzeTbZ82ahWHDhsHJyQmZmZn5Lp0gKml1rOqgjlUddYdBRERUKCyOi4Guri6WL1+O5cuXK23z8/NTKmY9PDyU2rp3747u3bur7L9Ro0ZKM9D+/v7w9/dXaOvatSsLZyIiIqL3wDXHREREREQ5WBwTEREREeVgcUxERERElIPFMRERERFRDhbHREREREQ5WBwTEREREeVgcUxERERElIP3OSYioiIXe+swIi9vxKPHT+D+2BlVrGuoOyQiIlFYHBMRUZEK3dEX/0u79PqBBNi5pxdCq/ujS6Mg9QZGRCQCl1WUI8+ePcOAAQNgaGgIW1tbLF68GH5+fhg3bpy6QyOij8SJS1vfFMY5siQSzIkLR/rzh2qKiohIPM4clyMTJ05EVFQU/vjjD1hbW2Pq1Kk4e/YsPDw8VO6fmZmJzMxM+eO0tDQAgEwmg0wmK4mQi1XuGMrDWIobcyUec5W/38+sU9meoSHB7qPfo7vflBKOqGzgdSUecyUO86RMbC4kgiAIxRwLlYD09HRYWFjg119/Rc+ePQEAjx49QqVKlTB06FAsW7ZM6ZiQkBCEhoYqtW/cuBH6+vrFHTIRlUMHb4fhsNF/Krf1yvCGu13HEo6IiOi1jIwM9OvXD6mpqTA2Ns5zP84clxMJCQl4+fIlGjZsKG8zNzeHq6trnscEBwcjMDBQ/jgtLQ329vZo06ZNvhdNWSGTyRAREYHWrVtDKpWqO5xSjbkSj7nK33+77iAy9XsIEolCu/mrbHRsOQm1qzioKbLSjdeVeMyVOMyTstx3yAvC4vgjpqOjAx0dHaV2qVRarp5I5W08xYm5Eo+5Uq1nuyG4unInDpv9i5carwtks6wsNHnRFvVcqqk5utKP15V4zJU4zNMbYvPAD+SVE05OTpBKpYiJiZG3PX78GNeuXVNjVET0sTHRk2LE5/9De9kYNL9XBb53XdFGdyGmBCxQd2hERKJw5ricMDQ0REBAACZOnAgLCwtYW1tj2rRp0NDg3z9EVLKcrAzxzdCheJHpj31796JjxzacuSKiMoPFcTmycOFCpKeno3PnzjAyMsKECROQmpqq7rCI6COlqSHBO0uPiYhKPU4rliOGhoZYv349nj17hnv37mHixInqDomIiIioTGFxTERERESUg8UxEREREVEOrjku5yIjI9UdAhEREVGZwZljIiIiIqIcLI6JiIiIiHKwOCYiIiIiysE1xyXMz88PHh4eWLZsmbpDISIqHs8e4OnBOTj7z36YyAQIxvFAs/GAJr8IhIhKPxbHRERUdGTP8ftPLbHIUIZUM00AgOONnzHz9il4Dtiq5uCIiArGZRVlXFZWFrKzs9UdBhERACD27+8RapyFVE1NeVuithRfvbyEzHtX1BgZEZE4LI7V4NWrVxg1ahRMTExgaWmJ6dOnQxAEAMDjx48xYMAAmJmZQV9fH+3bt8f169flx4aHh8PU1BQ7d+5EjRo1oKOjg6SkJGRmZiIoKAgVK1aEgYEBGjZsyNu4EVGJ23bzL7xS8Z3Rd6Ra2HGaM8dEVPpxWYUarFu3DgEBATh58iROnz6NoUOHonLlyhgyZAj8/f1x/fp17Ny5E8bGxpg8eTI6dOiAK1euQCp9vV4vIyMD8+fPx48//ggLCwtYW1tj1KhRuHLlCjZv3gw7Ozvs2LED7dq1w8WLF+Hs7KwyjszMTGRmZsofp6WlAQBkMhlkMlnxJ6KY5Y6hPIyluDFX4jFX+XugoZvntnsSfeYtD7yuxGOuxGGelInNhUTInbKkEuHn54eUlBRcvnwZkpzZlSlTpmDnzp34448/4OLigujoaPj4+AAAHj58CHt7e6xbtw49e/ZEeHg4Bg0ahNjYWNSpUwcAkJSUhKpVqyIpKQl2dnbyc7Vq1QpeXl6YM2eOylhCQkIQGhqq1L5x40bo6+sX9dCJ6CNw9NEl7NPYrNQuzQa+NJoKSylfW4hIPTIyMtCvXz+kpqbC2Ng4z/04c6wGjRo1khfGAODt7Y3FixfjypUr0NLSQsOGDeXbLCws4Orqiri4OHmbtrY23N3d5Y8vXryIrKwsuLi4KJwnMzMTFhYWecYRHByMwMBA+eO0tDTY29ujTZs2+V40ZYVMJkNERARat24tn3Un1Zgr8Zir/LUX2iNu02XcwkWFdj/LzzGgXQ81RVX68boSj7kSh3lSlvsOeUFYHJdBenp6CsV1eno6NDU1cebMGWi+9SEYADA0NMyzHx0dHejo6Ci1S6XScvVEKm/jKU7MlXjMVd52DvgVK0/uxL6ECLxIf4lRjf3RpWZjdYdVJvC6Eo+5Eod5ekNsHlgcq0FMTIzC4xMnTsDZ2Rk1atTAq1evEBMTo7CsIj4+HjVq1Mizv7p16yIrKwspKSlo2rRpscZORFQQDYkGvmzYFUPrdcSePXvQwcVL3SEREYnGu1WoQVJSEgIDAxEfH49Nmzbh22+/xdixY+Hs7IwuXbpgyJAhOHr0KM6fP4/PPvsMFStWRJcuXfLsz8XFBf3798eAAQOwfft23Lx5EydPnsTcuXOxe/fuEhwZERERUdnGmWM1GDBgAJ4/fw4vLy9oampi7NixGDp0KABg7dq1GDt2LDp16oSXL1+iWbNm2LNnT4FvBaxduxbffPMNJkyYgDt37sDS0hKNGjVCp06dSmJIREREROUCi+MS9va9h1euXKm03czMDL/88kuex/v7+8Pf31+pXSqVIjQ0VOXdJ4iIiIhIHC6rICIiIiLKweKYiIiIiCgHi2MiIiIiohwsjomIiIiIcrA4JiIiIiLKweKYiIiIiCgHi+N3CIKAoUOHwtzcHBKJBLGxsYXuKzw8HKampkUW2/sKCQmBh4eH2s5PRB+nDFkGfr70MwZHDEZ4ejh23+SXERFR2cH7HL9j3759CA8PR2RkJKpWrQpLS0t1h1RoQUFBGD16tLrDIKKPiCxbhiEHhuLCg/PytunHpyP+STwme01WY2REROJw5vgdCQkJsLW1hY+PD2xsbKClVbJ/P7x8+bLI+jI0NISFhUWR9UdEVJCIxL8UCuNcG+I2Ijk9WQ0RERG9HxbHb/H398fo0aORlJQEiUQCOzs72NnZITs7W2G/Ll26YPDgwQCA8+fPo3nz5jAyMoKxsTHq16+P06dPK+z/+++/w9nZGbq6umjbti1u374t35a79OHHH39ElSpVoKurCwCQSCT48ccf0a1bN+jr68PZ2Rk7d+6UHxcZGQmJRIKDBw/C09MT+vr68PHxQXx8vFLfREQl5c+4IyrbBWTj76TTKrcREZUmXFbxlrCwMDg5OWH16tU4deoUNDU1UalSJRw+fBgtW7YEADx69Aj79u3Dnj17AAD9+/dH3bp1sXLlSmhqaiI2NhZSqVTeZ0ZGBmbPno1ffvkF2traGDlyJPr06YPo6Gj5Pjdu3MC2bduwfft2aGpqyttDQ0OxYMECLFy4EN9++y369++PW7duwdzcXL7PtGnTsHjxYlhZWWH48OEYPHiwQt/5yczMRGZmpvxxWloaAEAmk0EmkxUig6VL7hjKw1iKG3MlHnOVP92U5DynXVLvPIbMmXlThdeVeMyVOMyTMrG5YHH8FhMTExgZGUFTUxM2NjYAgPbt22Pjxo3y4vh///sfLC0t0bx5cwBAUlISJk6ciOrVqwMAnJ2dFfqUyWT47rvv0LBhQwDAunXr4ObmhpMnT8LLywvA66UUv/zyC6ysrBSO9ff3R9++fQEAc+bMwfLly3Hy5Em0a9dOvs/s2bPh6+sLAJgyZQo6duyIFy9eyGeg8zN37lyEhoYqtR84cAD6+voFHl9WREREqDuEMoO5Eo+5Us3vwROcsMxGuoZiheyW+RK2N65iT8YeNUVWNvC6Eo+5Eod5eiMjI0PUfiyOC9C/f38MGTIE33//PXR0dLBhwwb06dMHGjkv/IGBgfjiiy+wfv16tGrVCj179oSTk5P8eC0tLTRo0ED+uHr16jA1NUVcXJy8OHZwcFAqjAHA3d1d/rOBgQGMjY2RkpKS5z62trYAgJSUFFSuXLnAsQUHByMwMFD+OC0tDfb29mjTpg2MjY0LPL60k8lkiIiIQOvWrRVm80kZcyUec5W/ZwY34HjqKL6xMMdVHW1IBAFNnr9A6IOHMP2sM1DJU90hlkq8rsRjrsRhnpTlvkNeEBbHBejcuTMEQcDu3bvRoEEDHDlyBEuXLpVvDwkJQb9+/bB7927s3bsXM2fOxObNm9GtWzfR5zAwMFDZ/u7FLJFIlNY/v72PRCIBAKV98qKjowMdHR2V5y1PT6TyNp7ixFyJx1ypZurtD4OTy7H17j3c09SEjiDALDsbmbaekFbxVnd4pR6vK/GYK3GYpzfE5oEfyCuArq4uunfvjg0bNmDTpk1wdXVFvXr1FPZxcXHB+PHjceDAAXTv3h1r166Vb3v16pXCB/Ti4+Px5MkTuLm5ldgYiIhKjIEFpIN2QrBvBJusLJhkS5Dt1gU6n/2m7siIiEThzLEI/fv3R6dOnXD58mV89tln8vbnz59j4sSJ6NGjB6pUqYJ///0Xp06dwqeffirfRyqVYvTo0Vi+fDm0tLQwatQoNGrUSL6kgoio3LGtA0nAfsjSUrD/r8No27k7NDhzRURlBItjEVq0aAFzc3PEx8ejX79+8nZNTU08fPgQAwYMwP3792FpaYnu3bsrfMhNX18fkydPRr9+/XDnzh00bdoUP/30kzqGQURUsvTMkKVZ8IeDiYhKE4kgCIK6g6DSIS0tDSYmJkhNTS03H8jbs2cPOnTowPVWBWCuxGOuxGOuxGOuxGOuxGGelImtc7jmmIiIiIgoB4tjIiIiIqIcLI6JiIiIiHKwOCYiIiIiysHimIiIiIgoB4tjIiIiIqIcLI6LQWRkJCQSCZ48eaK2GFavXg17e3toaGhg2bJlaouDiD4+qZmpWHZmGXru7omVT1dic/xmZGVnqTssIiJRWBy/Jz8/P4wbN07dYchJJBL8/vvvCm1paWkYNWoUJk+ejDt37mDo0KHqCY6IPjovs14iYO9A/HTpJySkJuBO1h0sOLMAocdmqjs0IiJRWByXQ0lJSZDJZOjYsSNsbW2hr6+v7pCI6COxP+FPxKcmKLX/nvAHktKS1BAREdH7YXH8Hvz9/REVFYWwsDBIJBJIJBIkJiZiz549cHFxgZ6eHpo3b47ExESF48LDw2Fqaopdu3bB1dUV+vr66NGjBzIyMrBu3To4OjrCzMwMY8aMQVbWm7ceHR0d8fXXX6Nv374wMDBAxYoVsWLFCoXtANCtWzdIJBI4OjoiPDwctWvXBgBUrVpVHiMRUUm4fO1Ple0CgCuJB0s2GCKiQtBSdwBlSVhYGK5du4ZatWph1qxZAIDMzEx0794dX375JYYOHYrTp09jwoQJSsdmZGRg+fLl2Lx5M54+fYru3bujW7duMDU1xZ49e/DPP//g008/RePGjdG7d2/5cQsXLsTUqVMRGhqK/fv3Y+zYsXBxcUHr1q1x6tQpWFtbY+3atWjXrh00NTVhaGgIe3t7tGrVCidPnoS9vT2srKxUjiczMxOZmZnyx2lpaQBef+WkTCYrytSpRe4YysNYihtzJR5zlT+L9Kd5bqvw9DHzlgdeV+IxV+IwT8rE5oLF8XswMTGBtrY29PX1YWNjAwCYOnUqnJycsHjxYgCAq6srLl68iPnz5yscK5PJsHLlSjg5OQEAevTogfXr1+P+/fswNDREjRo10Lx5cxw+fFihOG7cuDGmTJkCAHBxcUF0dDSWLl2K1q1by4teU1NTeTwAYGFhAQCwsrJSaH/X3LlzERoaqtR+4MCBcrUUIyIiQt0hlBnMlXjMlWrSO5YwM4vDY01NhfZ6L17gwhUtJD3Yo6bIygZeV+IxV+IwT29kZGSI2o/F8QeKi4tDw4YNFdq8vb2V9tPX15cXxgBQoUIFODo6wtDQUKEtJSUl3768vb2L7O4TwcHBCAwMlD9OS0uDvb092rRpA2Nj4yI5hzrJZDJERESgdevWkEql6g6nVGOuxGOu8rd4pxUWxB3AGkstnNTThVQQ0OZZBpqnVEBm125oXyvvP9g/ZryuxGOuxGGelOW+Q14QFscl5N0LUyKRqGzLzs4usZh0dHSgo6Oj1C6VSsvVE6m8jac4MVfiMVeqedV0wtTTUzE1cwOWaZ7HK0ix91VjTBE+Q4SzNXNWAF5X4jFX4jBPb4jNA4vj96Stra3woTk3Nzfs3LlTYZ8TJ04U2fne7evEiRNwc3OTP5ZKpQrxEBGpk5+LNTZWr4sv4uygIctGNiQAJJjUzhWWhsp/jBMRlTa8W8V7cnR0RExMDBITE/HgwQMMHz4c169fx8SJExEfH4+NGzciPDy8yM4XHR2NBQsW4Nq1a1ixYgW2bt2KsWPHKsRz8OBB3Lt3D48fPy6y8xIRFYaGhgSrPquHsD4eaF/bDg2tBPw62BMj/aqpOzQiIlFYHL+noKAgaGpqokaNGrCyskJ2dja2bduG33//HXXq1MGqVaswZ86cIjvfhAkTcPr0adStWxfffPMNlixZgrZt28q3L168GBEREbC3t0fdunWL7LxERIWlpamBLh4VsayXO/pVy0bDKubqDomISDQuq3hPLi4uOH78uEKbo6MjOnXqpNA2aNAg+c/+/v7w9/dX2B4SEoKQkBCFNlUzzsbGxtiyZUue8XTu3BmdO3dWaPPw8IAgCPmMgoiIiIhU4cwxEREREVEOFsdERERERDm4rKIU49c+ExEREZUszhwTEREREeVgcUxERERElIPFMRERERFRDq45JiKiopcUA42ru+Fy7zbw2A2wdlF3REREonDmuIhERkZCIpHgyZMnee4TEhICDw+PEouJiEgtdgcBP7eB5rEwuCVvh9bKRsD5zeqOiohIFBbHheTn54dx48apOwwiotIlMRpZp9Zgv74eplpaYJaFGWK1NfHqz/HAizR1R0dEVCAuqyAioiJz79R2LLC2RISBvrxtq7ERJjx8jL7XDkLHvZsaoyMiKhiL40Lw9/dHVFQUoqKiEBYWBgBYu3YtAODMmTOYPHkyrly5Ag8PD6xduxaurq4q+0lISEDr1q3RoUMHfPvtt5BIJIiOjsa0adNw8uRJ6OjowMvLC5s3b4aZmRn27duHb775BpcuXYKmpia8vb0RFhYGJycnAMDLly8RGBiIbdu24fHjx6hQoQKGDx+O4OBglefPzMxEZmam/HFa2utZHZlMBplMVmT5UpfcMZSHsRQ35ko85ip/vz+5p1AY5/rWzBT2dx6imRvzpgqvK/GYK3GYJ2Vic8HiuBDCwsJw7do11KpVC7NmzQIAXL58GQAwbdo0LF68GFZWVhg+fDgGDx6M6OhopT4uXLiAtm3bIiAgAN988w0AIDY2Fi1btsTgwYMRFhYGLS0tHD58GFlZWQCAZ8+eITAwEO7u7khPT8eMGTPQrVs3xMbGQkNDA8uXL8fOnTuxZcsWVK5cGbdv38bt27fzHMfcuXMRGhqq1H7gwAHo6yv/ciurIiIi1B1CmcFcicdcqbYvS3X7Sw0J/ncvGel79pRsQGUMryvxmCtxmKc3MjIyRO3H4rgQTExMoK2tDX19fdjY2AAArl69CgCYPXs2fH19AQBTpkxBx44d8eLFC+jq6sqPP3bsGDp16oRp06ZhwoQJ8vYFCxbA09MT33//vbytZs2a8p8//fRThTh+/vlnWFlZ4cqVK6hVqxaSkpLg7OyMJk2aQCKRwMHBId9xBAcHIzAwUP44LS0N9vb2aNOmDYyNjd83LaWOTCZDREQEWrduDalUqu5wSjXmSjzmKn/njiUgIfGCym09/ZqhaSWfEo6obOB1JR5zJQ7zpCz3HfKCsDguYu7u7vKfbW1tAQApKSmoXLkyACApKQmtW7fG7NmzlT7QFxsbi549e+bZ9/Xr1zFjxgzExMTgwYMHyM7OlvdZq1Yt+Pv7o3Xr1nB1dUW7du3QqVMntGnTJs/+dHR0oKOjo9QulUrL1ROpvI2nODFX4jFXqn1Rrxd2JK5HNl4ptFvq2MHPsSk0JPwceH54XYnHXInDPL0hNg98lSpibydeIpEAgLyIBQArKyt4eXlh06ZNSn/B6Onp5dt3586d8ejRI6xZswYxMTGIiYkB8HqtMQDUq1cPN2/exNdff43nz5+jV69e6NGjR5GMi4hIjEpGlbDEbxFMdczlbY5GVfBju+9ZGBNRmcBXqkLS1taWrwV+H3p6eti1axd0dXXRtm1bPH36VL7N3d0dBw8eVHncw4cPER8fj6+++gotW7aEm5sbHj9+rLSfsbExevfujTVr1uC3337Dtm3b8OjRo/eOk4iosFo6tMShnn9hTas1GGE4Ats7b4OTqZO6wyIiEoXFcSE5OjoiJiYGiYmJCkscxDAwMMDu3buhpaWF9u3bIz09HcDrNcCnTp3CyJEjceHCBVy9ehUrV67EgwcPYGZmBgsLC6xevRo3btzAoUOHFNYLA8CSJUuwadMmXL16FdeuXcPWrVthY2MDU1PTohw6EVGBpJpS1Leuj4paFdUdChHRe2FxXEhBQUHQ1NREjRo1YGVlhaSkpPc63tDQEHv37oUgCOjYsSOePXsGFxcXHDhwAOfPn4eXlxe8vb3xxx9/QEtLCxoaGti8eTPOnDmDWrVqYfz48Vi4cKFCn0ZGRvIP9TVo0ACJiYnYs2cPNDT4z0xEREQkBj+QV0guLi44fvy4Qpu/v7/CYw8PDwiCIH8cEhKCkJAQ+WNDQ0Ol27z5+vqqvPUbALRq1QpXrlxRaHu7/yFDhmDIkCHvMwwiIiIiegunFImIiIiIcrA4JiIiIiLKweKYiIiIiCgHi2MiIiIiohwsjomIiIiIcrA4JiIiIiLK8VEXx5GRkZBIJHjy5Im6Q8mXn58fxo0bp+4wiIjey7PMV3gl/vuRiIhKhY+qOP5YiszExERIJBLExsaqOxQi+ghdvZeGWd+uxpZ5Abh7ZgcWbNyNJxkv1R0WEZEoH1VxTERExevxs5c48eMACJiD36qcw17HEzB7NAbrV81Wd2hERKJ8NN+Q5+/vj6ioKERFRSEsLAwAsHbtWgDAmTNnMHnyZFy5cgUeHh5Yu3YtXF1dkZqaCnNzc8TExMDT0xPZ2dmwtLSEi4sLTpw4AQD49ddfERwcjNu3bwMA/v33X0ycOBH79+9HZmYm3NzcsGLFCjRs2BAhISH4/fffMWHCBEyfPh2PHz9G+/btsWbNGhgZGQEAnj17hhEjRmD79u0wMjJCUFCQ0lgkEgl27NiBrl27yttMTU2xbNky+Pv7o0qVKgCAunXrAnj9rXuRkZFK/WRmZiIzM1P+OC0tDQAgk8kgk8k+JN2lQu4YysNYihtzJR5zlb9DB37D/2yu4pbUSN620twELdM34ly8P2pVraTG6EovXlfiMVfiME/KxObioymOw8LCcO3aNdSqVQuzZs0CAFy+fBkAMG3aNCxevBhWVlYYPnw4Bg8ejOjoaJiYmMDDwwORkZHw9PTExYsXIZFIcO7cOaSnp8PQ0BBRUVHw9fUFAKSnp8PX1xcVK1bEzp07YWNjg7NnzyI7+82iu4SEBPz+++/YtWsXHj9+jF69emHevHmYPfv1rMrEiRMRFRWFP/74A9bW1pg6dSrOnj0LDw8P0WM9efIkvLy88Ndff6FmzZrQ1tZWud/cuXMRGhqq1H7gwAHo6+uLPl9pFxERoe4QygzmSjzmSrVLSb/ilqlUqf2goS6q/RWGpErN1RBV2cHrSjzmShzm6Y2MjAxR+300xbGJiQm0tbWhr68PGxsbAMDVq1cBALNnz5YXuFOmTEHHjh3x4sUL6Orqws/PD5GRkQgKCkJkZCRat26Nq1ev4ujRo2jXrh0iIyMxadIkAMDGjRvx33//4dSpUzA3NwcAVKtWTSGO7OxshIeHy2eKP//8cxw8eBCzZ89Geno6fvrpJ/z6669o2bIlAGDdunWoVOn9ZlqsrKwAABYWFvKxqhIcHIzAwED547S0NNjb26NNmzYwNjZ+r3OWRjKZDBEREWjdujWkUuVf1vQGcyUec5W/6E1rAEH1NnNXC3Tw7VCyAZURvK7EY67EYZ6U5b5DXpCPpjjOj7u7u/xnW1tbAEBKSgoqV64MX19f/PTTT8jKykJUVBTatGkDGxsbREZGwt3dHTdu3ICfnx8AIDY2FnXr1pUXxqo4OjrKC+Pc86WkpAB4Pav88uVLNGzYUL7d3Nwcrq6uRTlcOR0dHejo6Ci1S6XScvVEKm/jKU7MlXjMlWq1XVti79WfVW6rV6cbc1YAXlfiMVfiME9viM0DP5AHxWRJJBIAkC+FaNasGZ4+fYqzZ8/i77//hp+fn3w2OSoqCnZ2dnB2dgYA6Onpvde5cs/39rILMSQSCQRBcWqGa4qIqDToWvcLWGkZKrX7mNdCdataaoiIiOj9fFTFsba2NrKyst7rGFNTU7i7u+O7776DVCpF9erV0axZM5w7dw67du2SL8cAXs9Ax8bG4tGjR4WKz8nJCVKpFDExMfK2x48f49q1awr7WVlZITk5Wf74+vXrCutoctcYv+9YiYg+lJG2EdZ13oJWFZtBS6IBPUjRu1pPLG33k7pDIyIS5aMqjh0dHRETE4PExEQ8ePBA9Iytn58fNmzYIC+Ezc3N4ebmht9++02hOO7bty9sbGzQtWtXREdH459//sG2bdtw/PhxUecxNDREQEAAJk6ciEOHDuHSpUvw9/eHhobiP1OLFi3w3Xff4dy5czh9+jSGDx+uMCNtbW0NPT097Nu3D/fv30dqaqqo8xMRFQV7Y3ssbbUCJ/uexjTTmZjsFQx9afn5kC8RlW8fVXEcFBQETU1N1KhRA1ZWVkhKShJ1nK+vL7KysuRri4HXBfO7bdra2jhw4ACsra3RoUMH1K5dG/PmzYOmpqboGBcuXIimTZuic+fOaNWqFZo0aYL69esr7LN48WLY29ujadOm6NevH4KCghTuLqGlpYXly5fjhx9+gJ2dHbp06SL6/EREREQfM4nw7uJV+milpaXBxMQEqamp5eZuFXv27EGHDh34YYQCMFfiMVfiMVfiMVfiMVfiME/KxNY5H9XMMRERERFRflgcExERERHlYHFMRERERJSDxTERERERUQ4Wx0REREREOVgcExERERHlYHH8ngRBwNChQ2Fubg6JRILY2NhiOU9GRgY+/fRTGBsbQyKR4MmTJ8VyHiIiIiJ6g8Xxe9q3bx/Cw8Oxa9cuJCcno1atWh/UX3h4OExNTZXa161bhyNHjuDYsWNITk6GiYnJB52HiIpWcuoLBG+/iKYLDqHzt0exIeYWeNt4RcnPkpGena7uMIiI3ouWugMoaxISEmBrawsfH59iP4+bm9sHF99EVPSeyYDea04iOfUFAOA2nuPijlQkPcxAcAc3NUenfrEpsfj6xNe49vgaJAD+Pvw3ZjWehQoGFdQdGhFRgThz/B78/f0xevRoJCUlQSKRwNHREdnZ2ViwYAGqVasGHR0dVK5cGbNnzwYAREZGKi2JiI2NhUQiQWJiIiIjIzFo0CCkpqZCIpFAIpEgJCQEfn5+WLx4Mf7++29IJBL5V1R///33cHZ2hq6uLipUqIAePXrI+3V0dMSyZcsU4vXw8EBISEgxZ4Xo43MsRSIvjN+29lgiHj17qYaISo8Hzx9g+IEhuPb4GgBAAHAs+RhGRwxXb2BERCJx5vg9hIWFwcnJCatXr8apU6egqamJ4OBgrFmzBkuXLkWTJk2QnJyMq1eviurPx8cHy5Ytw4wZMxAfHw8AMDQ0xJgxYzBlyhRcunQJ27dvh7a2Nk6fPo0xY8Zg/fr18PHxwaNHj3DkyJEPGk9mZiYyMzPlj9PS0gC8/spJmUz2QX2XBrljKA9jKW7MlXgymQz/PpOo3PbyVTbi7j6Gl6N5CUdVeuy4+CueZSn/4RCXegOn7p6Ch5VHyQdVBvA5KB5zJQ7zpExsLlgcvwcTExMYGRlBU1MTNjY2ePr0KcLCwvDdd99h4MCBAAAnJyc0adJEVH/a2towMTGBRCKBjY2NwjZ9fX1oa2vL2yMjI2FgYIBOnTrByMgIDg4OqFu37geNZ+7cuQgNDVVqP3DgAPT19T+o79IkIiJC3SGUGcyVOJY6qt90k0DA9XMn8OBKCQdUilx7sDvP3yxHD67DXeO7JRtQGcPnoHjMlTjM0xsZGRmi9mNx/AHi4uKQmZmJli1bFvu5WrduDQcHB1StWhXt2rVDu3bt0K1btw8qYoODgxEYGCh/nJaWBnt7e7Rp0wbGxsZFEbZayWQyREREoHXr1pBKpeoOp1RjrsSTyWR4+GcEjj+Q4tnLLIVtHWvbon83dzVFVjpI/9yKfU/vKbVrCgJ6VPeAjUcHNURV+vE5KB5zJQ7zpCz3HfKCsDj+AHp6evlu19B4Pbv09ifYC/v2hpGREc6ePYvIyEgcOHAAM2bMQEhICE6dOgVTU1NoaGgofVK+oHPp6OhAR0dHqV0qlZarJ1J5G09xYq7EsdAF1g3yxIID13Hy5iMY6mihp2clTG5XHVKpprrDU6vWzp9g87EYnNPVVWjvl5YOe+c2AK+vfPE5KB5zJQ7z9IbYPLA4/gDOzs7Q09PDwYMH8cUXXyhtt7KyAgAkJyfDzMwMAJTui6ytrY2srKx3D1VJS0sLrVq1QqtWrTBz5kyYmpri0KFD6N69O6ysrJCcnCzfNy0tDTdv3izkyIioIHUqmWDLMG9kvsqCVEMDGhqq1yF/bKQ1uuGHmNX47eFVROrrQVcQ0Dn9GTrWGgCYVlZ3eEREBWJx/AF0dXUxefJkTJo0Cdra2mjcuDH+++8/XL58GQEBAahWrRrs7e0REhKC2bNn49q1a1i8eLFCH46OjkhPT8fBgwdRp04d6Ovrq1wqsWvXLvzzzz9o1qwZzMzMsGfPHmRnZ8PV1RUA0KJFC4SHh6Nz584wNTXFjBkzoKn5cc9gEZUEHS0+zxRoSqE34A/4n1yNAVf34EFqBsxbjgTq9Vd3ZEREovBWbh9o+vTpmDBhAmbMmAE3Nzf07t0bKSkpAF5P32/atAlXr16Fu7s75s+fj2+++UbheB8fHwwfPhy9e/eGlZUVFixYoPI8pqam2L59O1q0aAE3NzesWrUKmzZtQs2aNQG8Xj/s6+uLTp06oWPHjujatSucnJyKd/BERKpoGwBNxiNr4B4crzYJQu1e6o6IiEg0zhy/p3HjxmHcuHHyxxoaGpg2bRqmTZumcv/GjRvjwoULCm3vrg1euXIlVq5cqdD27j2LmzRpgsjIyDzjMjY2xubNmxXacu+gQURERETicOaYiIiIiCgHi2MiIiIiohwsjomIiIiIcrA4JiIiIiLKweKYiIiIiCgHi2MiIiIiohwsjsux6Oho1K5dG1KpFF27dlV3OET0Ebn430V8f/57RL2Iwt30u+oOh4hINBbHZUhkZCQkEgmePHmi0O7n56dw7+VcgYGB8PDwwM2bNxEeHl4iMRIRzY+eiX57+uHHyz8i4kUEuu7sjN03dqo7LCIiUVgcl2MJCQlo0aIFKlWqBFNTU3WHQ0QfgbN3juPXG9sV2l5BwKzo6Xgme6amqIiIxGNxXMKys7OxYMECVKtWDTo6OqhcuTJmz56NxMRESCQSbN68GT4+PtDV1UWtWrUQFRUFAEhMTETz5s0BAGZmZpBIJPD394e/vz+ioqIQFhYGiUQCiUQi7+vhw4cYPHgwJBIJZ46JqET8dW61yvYMZONE/HaV24iIShN+fXQJCw4Oxpo1a7B06VI0adIEycnJuHr1qnz7xIkTsWzZMtSoUQNLlixB586dcfPmTdjb22Pbtm349NNPER8fD2NjY+jp6QEArl27hlq1amHWrFkAACsrKyQnJ8PV1RWzZs1C7969YWJiohRLZmYmMjMz5Y/T0tIAADKZDDKZrDjTUCJyx1AexlLcmCvxmKv8PXtwD5Co3vb0TgJkrsybKryuxGOuxGGelInNBYvjEvT06VOEhYXhu+++w8CBAwEATk5OaNKkCRITEwEAo0aNwqeffgoAWLlyJfbt24effvoJkyZNgrm5OQDA2tpaYZmEtrY29PX1YWNjI2+zsbGBRCKBiYmJQvvb5s6di9DQUKX2AwcOQF9fvyiGXCpERESoO4Qyg7kSj7lSzfihDSQWtyFIFCtk86ws3PnHBHte7FFTZGUDryvxmCtxmKc3MjIyRO3H4rgExcXFITMzEy1btsxzH29vb/nPWlpa8PT0RFxcXLHEExwcjMDAQPnjtLQ02Nvbo02bNjA2Ni6Wc5YkmUyGiIgItG7dGlKpVN3hlGrMlXjMVf42/V0ZX8b2xA8WupDlFMimWVnwTzaER/8BqFWx7L+2FAdeV+IxV+IwT8py3yEvCIvjEpS7DKK00NHRgY6OjlK7VCotV0+k8jae4sRcicdcqdbFuxYGHZuBr57+BA2DG9DK1sCD9Pr4u+JoDHK0UHd4pR6vK/GYK3GYpzfE5oEfyCtBzs7O0NPTw8GDB/Pc58SJE/KfX716hTNnzsDNzQ3A6+UTAJCVlaVwjLa2tlIbEZE6mOhLsWBYN/xReSEmPFqF8anf41LtECwd2EzdoRERicKZ4xKkq6uLyZMnY9KkSdDW1kbjxo3x33//4fLly/KlFitWrICzszPc3NywdOlSPH78GIMHDwYAODg4QCKRYNeuXejQoQP09PRgaGgIR0dHxMTEIDExEYaGhjA3N4eGBv/uISL1qGZthHWDvfDseSb279+Hzh1rcuaKiMoMVlAlbPr06ZgwYQJmzJgBNzc39O7dGykpKfLt8+bNw7x581CnTh0cPXoUO3fuhKWlJQCgYsWKCA0NxZQpU1ChQgWMGjUKABAUFARNTU3UqFEDVlZWSEpKUsvYiIjepq2lAc087lxBRFRacea4hGloaGDatGmYNm2aQnvu3Src3NwQExOT5/HTp0/H9OnTFdpcXFxw/PhxpX3f/SY9IiIiIsofZ46JiIiIiHKwOCYiIiIiysFlFaWEo6MjBEFQdxhEREREHzXOHBMRERER5WBxTERERESUg8UxEREVi+evnuOV8ErdYRARvRcWxyUsPDwcpqam6g6DiKjYXHt8DV/s/wKNtzTG16lf46tjXyE1M1XdYRERicIP5BERUZFJzUzFF/u/wOPMxwCALGRhT+Ie3Mu4h3Xt16k5OiKignHmuAi9fPlS3SEQEanVzoSd8sL4bWdTzuLSg0tqiIiI6P2wOP4Afn5+GDVqFMaNGwdLS0u0bdsWS5YsQe3atWFgYAB7e3uMHDkS6enpefYREhICDw8P/PDDD7C3t4e+vj569eqF1NQ3b0H6+/uja9euWLRoEWxtbWFhYYEvv/wSMplMvk9mZiaCgoJQsWJFGBgYoGHDhoiMjCzO4RMRKUl4dCvPbf8+/bcEIyEiKhwuq/hA69atw4gRIxAdHQ0A2Lt3L5YvX44qVargn3/+wciRIzFp0iR8//33efZx48YNbNmyBX/++SfS0tIQEBCAkSNHYsOGDfJ9Dh8+DFtbWxw+fBg3btxA79694eHhgSFDhgAARo0ahStXrmDz5s2ws7PDjh070K5dO1y8eBHOzs4qz5uZmYnMzEz547S0NACATCZTKLzLqtwxlIexFDfmSjzmKn8ad56qbJcIArIf60NWiXlThdeVeMyVOMyTMrG5kAj85olC8/PzQ1paGs6ePZvnPv/73/8wfPhwPHjwAMDrD+SNGzcOT548AfB65vibb77BrVu3ULFiRQDAvn370LFjR9y5cwc2Njbw9/dHZGQkEhISoKmpCQDo1asXNDQ0sHnzZiQlJaFq1apISkqCnZ2d/NytWrWCl5cX5syZozK2kJAQhIaGKrVv3LgR+vr6hcoJEX3cNK78inCLC7imo63Q3vVpOmoL/aFTuYGaIiOij11GRgb69euH1NRUGBsb57kfZ44/UP369RUe//XXX5g7dy6uXr2KtLQ0vHr1Ci9evEBGRkaeBWflypXlhTEAeHt7Izs7G/Hx8bCxsQEA1KxZU14YA4CtrS0uXrwIALh48SKysrLg4uKi0G9mZiYsLCzyjD04OBiBgYHyx2lpabC3t0ebNm3yvWjKCplMhoiICLRu3RpSqVTd4ZRqzJV4zFX+bjw7hp9vROBnE2NE6utDT8hG5/Rn6JOWjv86NYRFnXbqDrFU4nUlHnMlDvOkLPcd8oKwOP5ABgYG8p8TExPRqVMnjBgxArNnz4a5uTmOHj2KgIAAvHz58oNmY9+9sCUSCbKzswEA6enp0NTUxJkzZxQKaAAwNDTMs08dHR3o6OioPFd5eiKVt/EUJ+ZKPOZKNadWX0Dj2hqMf5yK8Y/ffHbiibQCbOq2BzT5ayc/vK7EY67EYZ7eEJsHvkoVoTNnziA7OxuLFy+Ghsbrzzpu2bKlwOOSkpJw9+5d+ZKIEydOQENDA66urqLOW7duXWRlZSElJQVNmzYt/ACIiD6QtrUzXnzyPbJ2T4BO1usPI6frVYTJ5xtZGBNRmcBXqiJUrVo1yGQyfPvtt+jcuTOio6OxatWqAo/T1dXFwIEDsWjRIqSlpWHMmDHo1auXfElFQVxcXNC/f38MGDAAixcvRt26dfHff//h4MGDcHd3R8eOHT90aEREounW6wPU6oxXCX/jxNkLaNhzHCTayu9SERGVRryVWxGqU6cOlixZgvnz56NWrVrYsGED5s6dW+Bx1apVQ/fu3dGhQwe0adMG7u7u+d7dQpW1a9diwIABmDBhAlxdXdG1a1ecOnUKlStXLuxwiIgKT9sAQrVWeGhYHZDwVw0RlR2cOf4Aqu4jPH78eIwfP16h7fPPP5f/7O/vD39/f6XjRowYgREjRqg8T3h4uFLbsmXLFB5LpVKEhoaqvPsEEREREYnDP+eJiIiIiHKwOCYiIiIiysHiWM1CQkIQGxur7jCIiIiICCyOiYiIiIjkWBwTEREREeVgcUxERERElKNUF8eRkZGQSCR48uSJukN5L46Ojkq3WsvLvXv30Lp1axgYGMDU1LRY4yIiKjHp/0FycQtsn5wCZM/VHQ0RkWilujguTuHh4aWiGF26dCmSk5MRGxuLa9euqTscIqIPln3iB2QtcYPWzpHwuvktEOYOJEarOywiIlE+2uK4tEhISED9+vXh7OwMa2trlfvIZLISjoqIqHCE+5eBfZOhmf3mdUua+RgZGz4DXr1UY2REROKovTjOzMzEmDFjYG1tDV1dXTRp0gSnTp1S2OfMmTPw9PSEvr4+fHx8EB8fL6rv8+fPo3nz5jAyMoKxsTHq16+P06dPIzIyEoMGDUJqaiokEgkkEglCQkLk8QQFBaFixYowMDBAw4YNlb4J7+jRo2jatCn09PRgb2+PMWPG4NmzZ+89dkdHR2zbtg2//PILJBKJ/JvzJBIJVq5ciU8++QQGBgaYPXs2srKyEBAQgCpVqkBPTw+urq4ICwtT6vPnn39GzZo1oaOjA1tbW4waNeq94yIiKqxbUeshgYDD+noIsTDHPHMzXNbWhr7sEdKuRKg7PCKiAqn966MnTZqEbdu2Yd26dXBwcMCCBQvQtm1b3LhxQ77PtGnTsHjxYlhZWWH48OEYPHgwoqMLfouuf//+qFu3LlauXAlNTU3ExsZCKpXCx8cHy5Ytw4wZM+SFtqGhIQBg1KhRuHLlCjZv3gw7Ozvs2LED7dq1w8WLF+Hs7IyEhAS0a9cO33zzDX7++Wf8999/GDVqFEaNGoW1a9e+19hPnTqFAQMGwNjYGGFhYdDT05NvCwkJwbx587Bs2TJoaWkhOzsblSpVwtatW2FhYYFjx45h6NChsLW1Ra9evQAAK1euRGBgIObNm4f27dsjNTU13zxlZmYiMzNT/jgtLQ3A65nq8jBbnTuG8jCW4sZcicdc5e/Og0f43soCew0N5G0bTIww5eEjON9OQV035k0VXlfiMVfiME/KxOZCIgiCUMyx5OnZs2cwMzNDeHg4+vXrB+B14I6Ojhg3bhwaNGiA5s2b46+//kLLli0BAHv27EHHjh3x/Plz6Orq5tu/sbExvv32WwwcOFBpW3h4OMaNG6fwYb+kpCRUrVoVSUlJsLOzk7e3atUKXl5emDNnDr744gtoamrihx9+kG8/evQofH198ezZM+jq6srjHzduXIE56Nq1K0xNTREeHi5vk0gkGDduHJYuXZrvsaNGjcK9e/fwv//9DwBQsWJFDBo0CN98802B5wVeF+ChoaFK7Rs3boS+vr6oPoiI3haZFIm/jP9SatfOFtBTMgFuZuZqiIqICMjIyEC/fv2QmpoKY2PjPPdT68xxQkICZDIZGjduLG+TSqXw8vJCXFwcGjRoAABwd3eXb7e1tQUApKSkoHLlyvn2HxgYiC+++ALr169Hq1at0LNnTzg5OeW5/8WLF5GVlQUXFxeF9szMTFhYWAB4vVTjwoUL2LBhg3y7IAjIzs7GzZs34ebmJnL0+fP09FRqW7FiBX7++WckJSXh+fPnePnyJTw8PAC8zsfdu3flf0SIERwcjMDAQPnjtLQ02Nvbo02bNvleNGWFTCZDREQEWrduDalUqu5wSjXmSjzmKn8nj10EEpXbX2pIULtxBbRxaF3iMZUFvK7EY67EYZ6U5b5DXhC1L6sQ4+1/VIlEAgDIzs4u8LiQkBD069cPu3fvxt69ezFz5kxs3rwZ3bp1U7l/eno6NDU1cebMGWhqaipsy112kZ6ejmHDhmHMmDFKxxdUrL8PAwMDhcebN29GUFAQFi9eDG9vbxgZGWHhwoWIiYkBAIUlGWLp6OhAR0dHqV0qlZarJ1J5G09xYq7EY65Uq2iS98ywmZ4pc1YAXlfiMVfiME9viM2DWotjJycnaGtrIzo6Gg4ODgBe/6Vz6tQpUUsSxHBxcYGLiwvGjx+Pvn37Yu3atejWrRu0tbWRlZWlsG/dunWRlZWFlJQUNG3aVGV/9erVw5UrV1CtWrUiiU+s6Oho+Pj4YOTIkfK2hIQE+c9GRkZwdHTEwYMH0bx58xKNjYgoV6eqnbDmwhq8El4ptNsZ2KGhbUM1RUVEJJ5a71ZhYGCAESNGYOLEidi3bx+uXLmCIUOGICMjAwEBAR/U9/PnzzFq1ChERkbi1q1biI6OxqlTp+TLHhwdHZGeno6DBw/iwYMHyMjIgIuLC/r3748BAwZg+/btuHnzJk6ePIm5c+di9+7dAIDJkyfj2LFjGDVqFGJjY3H9+nX88ccfxX5XCGdnZ5w+fRr79+/HtWvXMH36dKW7eoSEhGDx4sVYvnw5rl+/jrNnz+Lbb78t1riIiN5W2bgy5jadC2PtN0uzKhlWwvIWy6GpoZnPkUREpYPal1XMmzcP2dnZ+Pzzz/H06VN4enpi//79MDMz+6B+NTU18fDhQwwYMAD379+HpaUlunfvLv8Amo+PD4YPH47evXvj4cOHmDlzJkJCQrB27Vp88803mDBhAu7cuQNLS0s0atQInTp1AvB6/XNUVBSmTZuGpk2bQhAEODk5oXfv3h+ci/wMGzYM586dQ+/evSGRSNC3b1+MHDkSe/fule8zcOBAvHjxAkuXLkVQUBAsLS3Ro0ePYo2LiOhd7aq0g5+9H07dPYVzp85hWOdh0NFWXsJFRFQaqfVuFVS6pKWlwcTEpMBPcZYVMpkMe/bsQYcOHbjeqgDMlXjMlXjMlXjMlXjMlTjMkzKxdY7avwSEiIiIiKi0KNPFcc2aNWFoaKjyv7dvtaYuGzZsyDO+mjVrqjs8IiIiInqH2tccf4g9e/bk+W0nFSpUKOFolH3yySdo2FD1p7P5FgcRERFR6VOmi+Pc27+VVkZGRjAyMlJ3GEREREQkUpleVkFEREREVJRYHBMRERER5fjoi2NBEDB06FCYm5tDIpHA1NS0yL6dT4xmzZph48aNRdrnvn374OHhIeortomIiprs1SvMidqI9ptHYvG937H5YpS6QyIiEu2jL4737duH8PBw7Nq1C8nJybh27Rq+/vrrEjn3zp07cf/+ffTp00fetnr1avj5+cHY2BgSiQRPnjxROu6TTz5B5cqVoaurC1tbW3z++ee4e/eufHu7du0glUpLxR07iOjjkpWdhY6/9sOmxLm4n30Cj3VPY8HF8Rj5+2x1h0ZEJMpHXxwnJCTA1tYWPj4+sLGxgbW1dYl9iG758uUYNGgQNDTe/DNkZGSgXbt2mDp1ap7HNW/eHFu2bEF8fDy2bduGhIQEpW/C8/f3x/Lly4stdiIiVX449j8kS+KU2o8/2YyEh/fUEBER0fv5qItjf39/jB49GklJSZBIJHB0dISfn5/CsgpHR0d88803GDBgAAwNDeHg4ICdO3fiv//+Q5cuXWBoaAh3d3ecPn1afkx4eDhMTU3x+++/w9nZGbq6umjbti1u374t3+e///7DoUOH0LlzZ4WYxo0bhylTpqBRo0Z5xj1+/Hg0atQIDg4O8PHxwZQpU3DixAmF29p17twZp0+fRkJCQhFkiohInIvXf1PZ/koC7DrJd7OIqPQr07dy+1BhYWFwcnLC6tWrcerUKWhqaqJnz55K+y1duhRz5szB9OnTsXTpUnz++efw8fHB4MGDsXDhQkyePBkDBgzA5cuXIZFIALyeAZ49ezZ++eUXaGtrY+TIkejTpw+io6MBAEePHoW+vj7c3Nw+aAyPHj3Chg0b4OPjo3Dv5MqVK6NChQo4cuQInJycVB6bmZmJzMxM+eO0tDQAr79yMq/7R5cluWMoD2MpbsyVeMxV/qyznuc57VLh1VPmLQ+8rsRjrsRhnpSJzcVHXRybmJjAyMgImpqasLGxyXO/Dh06YNiwYQCAGTNmYOXKlWjQoIG8kJ48eTK8vb1x//59eT8ymQzfffed/EtA1q1bBzc3N5w8eRJeXl64desWKlSooLCk4n1MnjwZ3333HTIyMtCoUSPs2rVLaR87OzvcunUrzz7mzp2L0NBQpfYDBw5AX1+/UHGVRhEREeoOocxgrsRjrlSrm+2IP4XbkOVMFOSqLJPBPN0Ke/bsUVNkZQOvK/GYK3GYpzcyMjJE7fdRF8diubu7y3/O/ea92rVrK7WlpKTIi2MtLS00aNBAvk/16tVhamqKuLg4eHl54fnz59DV1S10TBMnTkRAQABu3bqF0NBQDBgwALt27ZLPXAOAnp5evhdCcHAwAgMD5Y/T0tJgb2+PNm3awNjYuNCxlRYymQwRERFo3bo1v5GwAMyVeMxVAWR+0PmxKRYYvMIDLU0AgPPLlwjRrgW3HkPVHFzpxetKPOZKHOZJWe475AVhcSzC2xdVbvGpqu19bp1maWmJx48fFzomS0tLWFpawsXFBW5ubrC3t8eJEyfg7e0t3+fRo0ewsrLKsw8dHR3o6OgotUul0nL1RCpv4ylOzJV4zFUepCZoP/gvND80Gxdu7IPklQT1GgyCZrMJgCbzVRBeV+IxV+IwT2+IzcNH/YG84vTq1SuFD+nFx8fjyZMn8jXGdevWxb179z6oQM6VW5S/vX74xYsXSEhIQN26dT+4fyKi92JoDd1PwlB39CXcdZ2P7CYsjImo7GBxXEykUilGjx6NmJgYnDlzBv7+/mjUqBG8vLwAvC6OLS0t5R/Qy3Xv3j3Exsbixo0bAICLFy8iNjYWjx49AgDExMTgu+++Q2xsLG7duoVDhw6hb9++cHJyUpg1PnHiBHR0dBTaiIiIiCh/LI6Lib6+PiZPnox+/fqhcePGMDQ0xG+/vbnFkaamJgYNGqT0RR2rVq1C3bp1MWTIEACvv0Gvbt262Llzp7zf7du3o2XLlnB1dUVAQADc3d0RFRWlsERi06ZN6N+/f7n6YB0RERFRcfvo1xyPGzdO4b7GkZGRCtsTExOVjhEEQeGxo6OjUhsAdO/eHd27d8/z3OPHj0fNmjVx69YtODg4AABCQkIQEhKS5zG1a9fGoUOH8twOAA8ePMD//vc/hWUdRERERFQwzhyrkY2NDX766SckJSUVab+JiYn4/vvvUaVKlSLtl4iIiKi8++hnjtWta9euRd6np6cnPD09i7xfIiIiovKOM8fFwN/fH0+ePFF3GERERET0nlgcExERERHlYHFMRERERJSDxTERERERUQ5+II+IiIqW7Dlwei004/eg4aOnkMTJAPce6o6KyrB/H2dg6+l/cS/1BTzsjSHNVndEVJ5x5jgPfn5+Cvc/LiohISGoUKECJBIJfv/99yLvn4hIrbJeAb9+iucHpuL4/VO4kxkHje0BwIGv1B0ZlVEn/nmI1kv+RtjB6/jt9G0E77iMZZc0kZ75St2hUTnF4rgYJCYmQiKRIDY2VqE9Li4OoaGh+OGHH5CcnIz27durJ0AiouJydRf2PjiHlvYVMdzGGv52FdCxki2unlkDpN5Rd3RUBs344xKey7IU2v59JsGvJ4r2OwKIcrE4LoSXL18W6riEhAQAQJcuXWBjY6Pwdc9EROXBrX8OYqqVBZ5qvvn18q9UijHW5niVdEyNkVFZdOfJc1y7n65yW+S1ByUcDX0suOYYwLNnzzBixAhs374dRkZGCAoKUtju6OiIgIAAXL9+Hb///ju6d++O8PDwPPvL/Wa6unXrAgB8fX3h5+eH0NBQAICGxutfGqq+cjrXpUuX4O7ujvv378PKygqPHj2CpaUlevXqhc2bNwMAvvnmG+zbtw9Hjx5FZGQkmjdvjl27diE4OBjXrl2Dh4cHfvzxR9SqVUvlOTIzM5GZmSl/nJaWBgCQyWSQyWT5paxMyB1DeRhLcWOuxGOu8rcz6zFeSSRK7claWjj+8hEaMW8q8bpSTSrJhoYEyFbx61JfW4P5ygevKWVic8HiGMDEiRMRFRWFP/74A9bW1pg6dSrOnj0LDw8P+T6LFi3CjBkzMHPmzAL7O3nyJLy8vPDXX3+hZs2a0NbWhra2NhwdHTFo0CAkJycX2EfNmjVhYWGBqKgo9OjRA0eOHJE/zhUVFQU/Pz+lsYSFhcHGxgZTp05F586dce3aNUilUqVzzJ07V16wv+3AgQPQ19cvMMayIiIiQt0hlBnMlXjMlWpxz/L+tfL3Pw/w6N89JRhN2cPrSllNUw1cfKz8RreTxn/Ys4fXU0F4Tb2RkZEhar+PvjhOT0/HTz/9hF9//RUtW7YEAKxbtw6VKlVS2K9FixaYMGGCqD6trKwAABYWFrCxsZG3m5qaAoBCW14kEgmaNWuGyMhI9OjRA5GRkRg0aBB+/PFHXL16FU5OTjh27BgmTZqkcNzMmTPRunVrhXHs2LEDvXr1UjpHcHAwAgMD5Y/T0tJgb2+PNm3awNjYWNRYSzOZTIaIiAi0bt1a5R8H9AZzJR5zlT/zZHMcOXxGqV1HQxvD2g+DiY6JGqIq/Xhd5a2h70uM2hSL07eeAACkmhI0t8nCpD6tmKt88JpSlvsOeUE++uI4ISEBL1++RMOGDeVt5ubmcHV1VdjP09OzpEODr68vVq9eDeD1LPGcOXNw7do1REZG4tGjR5DJZGjcuLHCMd7e3vKfc8cRFxensn8dHR2V656lUmm5eiKVt/EUJ+ZKPOZKtSb2TdChSgfsuflmRk8CCQI9J8DS0FKNkZUNvK6U2ZhK8b8RjXH5birup72AWwUDnPz7IHMlEvP0htg8fPTFsVgGBgYlfs7c28ldv34dV65cQZMmTXD16lVERkbi8ePH8PT0LFfLH4io7JNIJJjXdB46Vu2Ig7cO4t7te/iy+Zdwr+Cu7tCojKtpZ4KadiZcQ0vF7qO/W4WTkxOkUiliYmLkbY8fP8a1a9cK3ae2tjYAICsrq4A981e7dm2YmZnhm2++gYeHBwwNDeHn54eoqChERkYqrTcGgBMnTsh/zh2Hm5vbB8VBRPQ+JBIJmlVqhq+8vkIHvQ5wM+drEBGVHR99cWxoaIiAgABMnDgRhw4dwqVLl+Dv7y+/o0RhWFtbQ09PD/v27cP9+/eRmppaqH5y1x1v2LBBXgi7u7sjMzMTBw8ehK+vr9Ixs2bNwsGDB+XjsLS0RNeuXQs9FiIiIqKPyUdfHAPAwoUL0bRpU3Tu3BmtWrVCkyZNUL9+/UL3p6WlheXLl+OHH36AnZ0dunTpUui+fH19kZWVJS+ONTQ00KxZM0gkEqX1xgAwb948jB07FvXr18e9e/fw559/ymeyiYiIiCh/XHOM17PH69evx/r16+VtEydOlP+cmJj43n1+8cUX+OKLLxTaunbtmu+9jVUZN26c0tdY5/e1002aNMGlS5fe6xxERERE9BqLY5LLLdzF3uqktJPJZMjIyEBaWho/qVsA5ko85ko85ko85ko85koc5klZbn1T0EQli+NCmDNnDubMmaNyW9OmTbF3715R/RgaGua5be/evWjatGmh4iusp0+fAgDs7e1L9LxEREREJeXp06cwMcn7nusS4X3f5yc8evQIjx49UrlNT08PFStWFNXPjRs38txWsWJF6OnpFSq+wsrOzsbdu3dhZGQEiYqvfy1rcr/U5Pbt2+XiS02KE3MlHnMlHnMlHnMlHnMlDvOkTBAEPH36FHZ2dvneeIEzx4Vgbm4Oc3PzD+6nWrVqRRBN0dHQ0FD6ZsDywNjYmC8MIjFX4jFX4jFX4jFX4jFX4jBPivKbMc7Fu1UQEREREeVgcUxERERElIPFMZVbOjo6mDlzJnR0dNQdSqnHXInHXInHXInHXInHXInDPBUeP5BHRERERJSDM8dERERERDlYHBMRERER5WBxTERERESUg8UxEREREVEOFsdERERERDlYHFOZ9ujRI/Tv3x/GxsYwNTVFQEAA0tPT8z1m9erV8PPzg7GxMSQSCZ48eaK0j6OjIyQSicJ/8+bNK6ZRFL/iylNh+i3tCjOmFy9e4Msvv4SFhQUMDQ3x6aef4v79+wr7vHs9SSQSbN68uTiHUuRWrFgBR0dH6OrqomHDhjh58mS++2/duhXVq1eHrq4uateujT179ihsFwQBM2bMgK2tLfT09NCqVStcv369OIdQYoo6V/7+/krXT7t27YpzCCXmfXJ1+fJlfPrpp/LX6GXLln1wn2VJUecqJCRE6bqqXr16MY6gbGBxTGVa//79cfnyZURERGDXrl34+++/MXTo0HyPycjIQLt27TB16tR895s1axaSk5Pl/40ePbooQy9RxZWnwvRb2hVmTOPHj8eff/6JrVu3IioqCnfv3kX37t2V9lu7dq3CNdW1a9diGkXR++233xAYGIiZM2fi7NmzqFOnDtq2bYuUlBSV+x87dgx9+/ZFQEAAzp07h65du6Jr1664dOmSfJ8FCxZg+fLlWLVqFWJiYmBgYIC2bdvixYsXJTWsYlEcuQKAdu3aKVw/mzZtKonhFKv3zVVGRgaqVq2KefPmwcbGpkj6LCuKI1cAULNmTYXr6ujRo8U1hLJDICqjrly5IgAQTp06JW/bu3evIJFIhDt37hR4/OHDhwUAwuPHj5W2OTg4CEuXLi3CaNWnuPL0of2WRoUZ05MnTwSpVCps3bpV3hYXFycAEI4fPy5vAyDs2LGj2GIvbl5eXsKXX34pf5yVlSXY2dkJc+fOVbl/r169hI4dOyq0NWzYUBg2bJggCIKQnZ0t2NjYCAsXLpRvf/LkiaCjoyNs2rSpGEZQcoo6V4IgCAMHDhS6dOlSLPGq0/vm6m15vU5/SJ+lWXHkaubMmUKdOnWKMMrygTPHVGYdP34cpqam8PT0lLe1atUKGhoaiImJ+eD+582bBwsLC9StWxcLFy7Eq1evPrhPdSiuPBV3/tWhMGM6c+YMZDIZWrVqJW+rXr06KleujOPHjyvs++WXX8LS0hJeXl74+eefIZSR72B6+fIlzpw5ozBGDQ0NtGrVSmmMuY4fP66wPwC0bdtWvv/Nmzdx7949hX1MTEzQsGHDPPssC4ojV7kiIyNhbW0NV1dXjBgxAg8fPiz6AZSgwuRKHX2WBsU5ruvXr8POzg5Vq1ZF//79kZSU9KHhlnla6g6AqLDu3bsHa2trhTYtLS2Ym5vj3r17H9T3mDFjUK9ePZibm+PYsWMIDg5GcnIylixZ8kH9qkNx5ak4868uhRnTvXv3oK2tDVNTU4X2ChUqKBwza9YstGjRAvr6+jhw4ABGjhyJ9PR0jBkzpsjHUdQePHiArKwsVKhQQaG9QoUKuHr1qspj7t27p3L/3Jzk/j+/fcqi4sgV8HpJRffu3VGlShUkJCRg6tSpaN++PY4fPw5NTc2iH0gJKEyu1NFnaVBc42rYsCHCw8Ph6uqK5ORkhIaGomnTprh06RKMjIw+NOwyi8UxlTpTpkzB/Pnz890nLi6uWGMIDAyU/+zu7g5tbW0MGzYMc+fOLTXfU18a8lRWlIZcTZ8+Xf5z3bp18ezZMyxcuLBMFMekfn369JH/XLt2bbi7u8PJyQmRkZFo2bKlGiOjsqx9+/byn93d3dGwYUM4ODhgy5YtCAgIUGNk6sXimEqdCRMmwN/fP999qlatChsbG6UPIrx69QqPHj3K98MHhdGwYUO8evUKiYmJcHV1LdK+C0vdeSrJ/H+o4syVjY0NXr58iSdPnijMHt+/fz/fPDRs2BBff/01MjMzS80fXHmxtLSEpqam0h048hujjY1Nvvvn/v/+/fuwtbVV2MfDw6MIoy9ZxZErVapWrQpLS0vcuHGjzBbHhcmVOvosDUpqXKampnBxccGNGzeKrM+yiGuOqdSxsrJC9erV8/1PW1sb3t7eePLkCc6cOSM/9tChQ8jOzkbDhg2LNKbY2FhoaGgoveWuTurOU0nm/0MVZ67q168PqVSKgwcPytvi4+ORlJQEb2/vPGOKjY2FmZlZqS+MAUBbWxv169dXGGN2djYOHjyY5xi9vb0V9geAiIgI+f5VqlSBjY2Nwj5paWmIiYnJN2+lXXHkSpV///0XDx8+VPjDoqwpTK7U0WdpUFLjSk9PR0JCQpm+roqEuj8RSPQh2rVrJ9StW1eIiYkRjh49Kjg7Owt9+/aVb//3338FV1dXISYmRt6WnJwsnDt3TlizZo0AQPj777+Fc+fOCQ8fPhQEQRCOHTsmLF26VIiNjRUSEhKEX3/9VbCyshIGDBhQ4uMrKsWRJzH9lkWFydXw4cOFypUrC4cOHRJOnz4teHt7C97e3vLtO3fuFNasWSNcvHhRuH79uvD9998L+vr6wowZM0p0bB9i8+bNgo6OjhAeHi5cuXJFGDp0qGBqaircu3dPEARB+Pzzz4UpU6bI94+Ojha0tLSERYsWCXFxccLMmTMFqVQqXLx4Ub7PvHnzBFNTU+GPP/4QLly4IHTp0kWoUqWK8Pz58xIfX1Eq6lw9ffpUCAoKEo4fPy7cvHlT+Ouvv4R69eoJzs7OwosXL9QyxqLyvrnKzMwUzp07J5w7d06wtbUVgoKChHPnzgnXr18X3WdZVRy5mjBhghAZGSncvHlTiI6OFlq1aiVYWloKKSkpJT6+0oTFMZVpDx8+FPr27SsYGhoKxsbGwqBBg4SnT5/Kt9+8eVMAIBw+fFjeNnPmTAGA0n9r164VBEEQzpw5IzRs2FAwMTERdHV1BTc3N2HOnDll+pdQceRJTL9lUWFy9fz5c2HkyJGCmZmZoK+vL3Tr1k1ITk6Wb9+7d6/g4eEhGBoaCgYGBkKdOnWEVatWCVlZWSU5tA/27bffCpUrVxa0tbUFLy8v4cSJE/Jtvr6+wsCBAxX237Jli+Di4iJoa2sLNWvWFHbv3q2wPTs7W5g+fbpQoUIFQUdHR2jZsqUQHx9fEkMpdkWZq4yMDKFNmzaClZWVIJVKBQcHB2HIkCFlvtjL9T65yn3+vfufr6+v6D7LsqLOVe/evQVbW1tBW1tbqFixotC7d2/hxo0bJTii0kkiCGXkXkJERERERMWMa46JiIiIiHKwOCYiIiIiysHimIiIiIgoB4tjIiIiIqIcLI6JiIiIiHKwOCYiIiIiysHimIiIiIgoB4tjoiLi5+eHcePGqTuMUic8PBympqbyxyEhIfDw8CjWc0okEvz+++95bi+Of6vIyEhIJBI8efJE9DH+/v7o2rVrkfdbEteimNjpNUdHRyxbtkz+uKDr80OVxHMs17tjKw8SExMhkUgQGxur7lCUlOS/7ceMxTF99Dp37ox27dqp3HbkyBFIJBJcuHChhKMqv4KCgnDw4EF1h1HkfHx8kJycDBMTE9HHhIWFITw8XP5YVVFbmH6pdEtOTkb79u3Vdv7C/MFVFpXmIrckrV69Gn5+fjA2Nv4o/t2LAotj+ugFBAQgIiIC//77r9K2tWvXwtPTE+7u7mqIrOTIZLISO5ehoSEsLCxK7HwlRVtbGzY2NpBIJKKPMTExUZhVL6p+6f28fPmyRM9nY2MDHR2dEj1naZWVlYXs7Gx1h1GuZWRkoF27dpg6daq6QykzWBzTR69Tp06wsrJSmMEDgPT0dGzduhUBAQF4+PAh+vbti4oVK0JfXx+1a9fGpk2b8u1X1VunpqamCue5ffs2evXqBVNTU5ibm6NLly5ITEzMs8/cGZ+DBw/C09MT+vr68PHxQXx8vMJ+K1euhJOTE7S1teHq6or169crxbZy5Up88sknMDAwwOzZs+Vv1/3888+oXLkyDA0NMXLkSGRlZWHBggWwsbGBtbU1Zs+erdDXkiVLULt2bRgYGMDe3h4jR45Eenp6nmN4923ByMhIeHl5wcDAAKampmjcuDFu3bol3/7HH3+gXr160NXVRdWqVREaGopXr17Jt1+/fh3NmjWDrq4uatSogYiIiDzP/bZXr15h1KhRMDExgaWlJaZPnw5BEAAAs2bNQq1atZSO8fDwwPTp01X29+5sXO5ykv3798PNzQ2GhoZo164dkpOT5ce8vTTB398fUVFRCAsLg0QigUQiQWJiolK/hbkW33bt2jVIJBJcvXpVoX3p0qVwcnIC8LpgCQgIQJUqVaCnpwdXV1eEhYXl26+qt9c9PDwQEhIif/zkyRN88cUXsLKygrGxMVq0aIHz58+Ljh14Pbs+ZswYTJo0Cebm5rCxsVE4BwAkJSWhS5cuMDQ0hLGxMXr16oX79+/Lt+degz/++COqVKkCXV1dAK+fFz/88AM6deoEfX19uLm54fjx47hx4wb8/PxgYGAAHx8fJCQkyPtKSEhAly5dUKFCBRgaGqJBgwb466+/8h3D268NL1++xKhRo2BrawtdXV04ODhg7ty575WzefPmoUKFCjAyMkJAQABevHiR57kTExPRvHlzAICZmRkkEgn8/f0BAJmZmRgzZgysra2hq6uLJk2a4NSpU/mO5V0FvR7kPi927tyJGjVqQEdHB0lJSUhOTkbHjh2hp6eHKlWqYOPGjUrX1PteP1WqVAEA1K1bFxKJBH5+fgCA7OxszJo1C5UqVYKOjg48PDywb9++PPvJysrC4MGDUb16dSQlJYl6fuQ+t+fMmYMKFSrA1NQUs2bNwqtXrzBx4kSYm5ujUqVKWLt2rcJxkydPhouLC/T19VG1alVMnz4938mLhIQEVK1aFaNGjZK/fr1r3LhxmDJlCho1apRnP6SIxTF99LS0tDBgwACEh4crvLhs3boVWVlZ6Nu3L168eIH69etj9+7duHTpEoYOHYrPP/8cJ0+eLPR5ZTIZ2rZtCyMjIxw5cgTR0dHyAqqgmaxp06Zh8eLFOH36NLS0tDB48GD5th07dmDs2LGYMGECLl26hGHDhmHQoEE4fPiwQh8hISHo1q0bLl68KD8+ISEBe/fuxb59+7Bp0yb89NNP6NixI/79919ERUVh/vz5+OqrrxATEyPvR0NDA8uXL8fly5exbt06HDp0CJMmTRKVg1evXqFr167w9fXFhQsXcPz4cQwdOlQ+S3rkyBEMGDAAY8eOxZUrV/DDDz8gPDxcXqBnZ2eje/fu0NbWRkxMDFatWoXJkyeLOve6deugpaWFkydPIiwsDEuWLMGPP/4IABg8eDDi4uIUCoNz587hwoULGDRokKj+gdczNosWLcL69evx999/IykpCUFBQSr3DQsLg7e3N4YMGYLk5GQkJyfD3t5eab8PvRZdXFzg6emJDRs2KLRv2LAB/fr1A/A6r5UqVcLWrVtx5coVzJgxA1OnTsWWLVtEj12Vnj17IiUlBXv37sWZM2dQr149tGzZEo8ePXqvftatWwcDAwPExMRgwYIFmDVrlvyPouzsbHTp0gWPHj1CVFQUIiIi8M8//6B3794Kfdy4cQPbtm3D9u3bFd52//rrrzFgwADExsaievXq6NevH4YNG4bg4GCcPn0agiBg1KhR8v3T09PRoUMHHDx4EOfOnUO7du3QuXNnJCUliRrL8uXLsXPnTmzZsgXx8fHYsGEDHB0dRedsy5YtCAkJwZw5c3D69GnY2tri+++/z/N89vb22LZtGwAgPj4eycnJ8sJu0qRJ2LZtG9atW4ezZ8+iWrVqaNu27Xv9+4h5PcjIyMD8+fPx448/4vLly7C2tsaAAQNw9+5dREZGYtu2bVi9ejVSUlIUjnvf6yf3OfHXX38hOTkZ27dvB/D6ubZ48WIsWrQIFy5cQNu2bfHJJ5/g+vXrSn1kZmaiZ8+eiI2NxZEjR1C5cmXRz49Dhw7h7t27+Pvvv7FkyRLMnDkTnTp1gpmZGWJiYjB8+HAMGzZM4V1LIyMjhIeH48qVKwgLC8OaNWuwdOlSleO7cOECmjRpgn79+uG7777ju0tFSSAiIS4uTgAgHD58WN7WtGlT4bPPPsvzmI4dOwoTJkyQP/b19RXGjh0rfwxA2LFjh8IxJiYmwtq1awVBEIT169cLrq6uQnZ2tnx7ZmamoKenJ+zfv1/lOQ8fPiwAEP766y952+7duwUAwvPnzwVBEAQfHx9hyJAhCsf17NlT6NChg0Js48aNU9hn5syZgr6+vpCWliZva9u2reDo6ChkZWXJ21xdXYW5c+eqjE8QBGHr1q2ChYWF/PHatWsFExMThfPUqVNHEARBePjwoQBAiIyMVNlXy5YthTlz5ii0rV+/XrC1tRUEQRD2798vaGlpCXfu3JFv37t3r8rcv83X11dwc3NTyP3kyZMFNzc3+eP27dsLI0aMkD8ePXq04Ofnl2efuf82jx8/lo8bgHDjxg35PitWrBAqVKggfzxw4EChS5cuCnG9fQ2p6leVgq7Fdy1dulRwcnKSP46PjxcACHFxcXke8+WXXwqffvppnrE7ODgIS5cuVTimTp06wsyZMwVBEIQjR44IxsbGwosXLxT2cXJyEn744Yc8z/suX19foUmTJgptDRo0ECZPniwIgiAcOHBA0NTUFJKSkuTbL1++LAAQTp48KQjC62tQKpUKKSkpCv0AEL766iv54+PHjwsAhJ9++knetmnTJkFXVzffGGvWrCl8++238sfv5ubt63P06NFCixYtFK7FXGJy5u3tLYwcOVJhe8OGDeXPMVVUXVPp6emCVCoVNmzYIG97+fKlYGdnJyxYsCDPvlT9u79N1esBACE2Nlbelvv6e+rUKXnb9evXBQDyvgtz/dy8eVMAIJw7d06h3c7OTpg9e7ZCW4MGDeR5zD3uyJEjQsuWLYUmTZoIT548yXOMgqD6+eHg4KD02tm0aVP541evXgkGBgbCpk2b8ux34cKFQv369eWPc18/o6OjBTMzM2HRokX5xvU2Ma8l9BpnjokAVK9eHT4+Pvj5558BvJ5VOnLkCAICAgC8flvt66+/Ru3atWFubg5DQ0Ps379f9OyQKufPn8eNGzdgZGQEQ0NDGBoawtzcHC9evFB421aVt9dA29raAoB8liUuLg6NGzdW2L9x48aIi4tTaPP09FTq19HREUZGRvLHFSpUQI0aNaChoaHQ9vaMzl9//YWWLVuiYsWKMDIywueff46HDx8iIyOjoBTA3Nwc/v7+aNu2LTp37oywsDCFZQfnz5/HrFmz5PkxNDSUz6xmZGQgLi4O9vb2sLOzkx/j7e1d4HkBoFGjRgozLd7e3rh+/TqysrIAAEOGDMGmTZvw4sULvHz5Ehs3blSYoRdDX19fvlQBeP1v9e5s2PsqimuxT58+SExMxIkTJwC8njWuV68eqlevLt9nxYoVqF+/PqysrGBoaIjVq1d/8PWenp4OCwsLhX/PmzdvFni9v+vdzwC8ndfca+LtWfcaNWrA1NRU4Tng4OAAKyurfPuuUKECAKB27doKbS9evEBaWhqA1zPHQUFBcHNzg6mpKQwNDREXFyc6V/7+/oiNjYWrqyvGjBmDAwcOyLeJyVlcXBwaNmyo0KfY58DbEhISIJPJFF47pFIpvLy8lF478iPm9UBbW1shz/Hx8dDS0kK9evXkbdWqVYOZmZn8cVFdP2lpabh7966o18i+ffvi2bNnOHDggNIHYsU8P2rWrKn02vn2taSpqQkLCwuF14TffvsNjRs3ho2NDQwNDfHVV18p9ZuUlITWrVtjxowZmDBhguixk3ha6g6AqLQICAjA6NGjsWLFCqxduxZOTk7w9fUFACxcuBBhYWFYtmyZfD3duHHj8l3+IJFIlNaAvb12LD09HfXr11d6exuAyl/ab5NKpQrnAfDeH2oxMDDIt9/cvlW15Z4rMTERnTp1wogRIzB79myYm5vj6NGjCAgIwMuXL6Gvr19gHGvXrsWYMWOwb98+/Pbbb/jqq68QERGBRo0aIT09HaGhoejevbvScbnrRItL586doaOjgx07dkBbWxsymQw9evR4rz5U5e7da+J9FeZafJeNjQ1atGiBjRs3olGjRti4cSNGjBgh375582YEBQVh8eLF8Pb2hpGRERYuXKiwnOZdGhoaBV7vtra2iIyMVDq2oA8lviu/a1IsVdf/u33nPrfye74FBQUhIiICixYtQrVq1aCnp4cePXqI/veoV68ebt68ib179+Kvv/5Cr1690KpVK/zvf/8r0pyVBLGvB3p6eu+9BEAduejQoQN+/fVXHD9+HC1atJC3i31+vO/r6fHjx9G/f3+Ehoaibdu2MDExwebNm7F48WKFY6ysrGBnZ4dNmzZh8ODBMDY2LsphE1gcE8n16tULY8eOxcaNG/HLL79gxIgR8hfw6OhodOnSBZ999hmA178Yr127hho1auTZn5WVlcIs6PXr1xVmT+rVq4fffvsN1tbWRfri5ubmhujoaAwcOFDeFh0dnW+shXXmzBlkZ2dj8eLF8hmSwqxLrVu3LurWrYvg4GB4e3vLi7Z69eohPj4e1apVU3mcm5sbbt++jeTkZPkMeu5saEHe/UV24sQJODs7Q1NTE8DrtegDBw7E2rVroa2tjT59+kBPT++9x/Y+tLW15TPXeSnMtahK//79MWnSJPTt2xf//PMP+vTpo3AOHx8fjBw5Ut5W0Ozcu9d7Wloabt68KX9cr1493Lt3D1paWgpraota7jVx+/Zt+ezxlStX8OTJk2J5DkRHR8Pf3x/dunUD8LqIy+9DtaoYGxujd+/e6N27N3r06IF27drh0aNHonLm5uaGmJgYDBgwQN5W0HNAW1sbABSutdwP8EZHR8PBwQHA6z9uTp06Jfqe2YV9PXB1dcWrV69w7tw51K9fH8Drd+8eP34s36cw14+qcRobG8POzg7R0dHyyQ/g9b+jl5eXwvEjRoxArVq18Mknn2D37t3y/Qvz/BDj2LFjcHBwwLRp0+Rtb384OZeenh527dqFDh06oG3btjhw4IDCO3704bisgiiHoaEhevfujeDgYCQnJ8s/wQ0Azs7OiIiIwLFjxxAXF4dhw4YpfPpdlRYtWuC7777DuXPncPr0aQwfPlxh1qB///6wtLREly5dcOTIEdy8eRORkZEYM2aMytvKiTVx4kSEh4dj5cqVuH79OpYsWYLt27fn+UGwD1GtWjXIZDJ8++23+Oeff7B+/XqsWrVK9PE3b95EcHAwjh8/jlu3buHAgQO4fv063NzcAAAzZszAL7/8gtDQUFy+fBlxcXHYvHkzvvrqKwBAq1at4OLigoEDB+L8+fM4cuSIwi+W/CQlJSEwMBDx8fHYtGkTvv32W4wdO1Zhny+++AKHDh3Cvn373ntJRWE4OjoiJiYGiYmJePDggcrZ0MJci6p0794dT58+xYgRI9C8eXOFpSnOzs44ffo09u/fj2vXrmH69OkF3rWgRYsWWL9+PY4cOYKLFy9i4MCB8j80gNf/Vt7e3ujatSsOHDiAxMREHDt2DNOmTcPp06ffO/68tGrVCrVr10b//v1x9uxZnDx5EgMGDICvr6/KpUQfytnZWf6hvvPnz6Nfv37vNYu9ZMkSbNq0CVevXsW1a9ewdetW2NjYwNTUVFTOxo4di59//hlr167FtWvXMHPmTFy+fDnfczo4OEAikWDXrl3477//kJ6eDgMDA4wYMQITJ07Evn37cOXKFQwZMgQZGRny5WUFKezrQfXq1dGqVSsMHToUJ0+exLlz5zB06FCFGebCXD/W1tbQ09PDvn37cP/+faSmpgJ4/Ro5f/58/Pbbb4iPj8eUKVMQGxur9PwHgNGjR+Obb75Bp06dcPToUQCFe36I4ezsjKSkJGzevBkJCQlYvnw5duzYoXJfAwMD7N69G1paWmjfvn2+dwi6d+8eYmNjcePGDQDAxYsXERsb+94fhP2YsDgmektAQAAeP36Mtm3bKhQLX331FerVq4e2bdvCz88PNjY2BX472OLFi2Fvb4+mTZuiX79+CAoKUlhmoK+vj7///huVK1dG9+7d4ebmJr8N04fMJHft2hVhYWFYtGgRatasiR9++AFr166V38aoKNWpUwdLlizB/PnzUatWLWzYsEHhNlQF0dfXx9WrV/Hpp5/CxcUFQ4cOxZdffolhw4YBANq2bYtdu3bhwIEDaNCgARo1aoSlS5fKZ7Y0NDSwY8cOPH/+HF5eXvjiiy+UbjWXlwEDBsiP+/LLLzF27FgMHTpUYR9nZ2f4+PigevXqSus6i0NQUBA0NTVRo0YNWFlZqVy3WphrURUjIyN07twZ58+fR//+/RW2DRs2DN27d0fv3r3RsGFDPHz4UGGWTJXg4GD4+vqiU6dO6NixI7p27aqw3loikWDPnj1o1qwZBg0aBBcXF/Tp0we3bt2Sr+3N/dIGVW+diyWRSPDHH3/AzMwMzZo1Q6tWrVC1alX89ttvhe4zP0uWLIGZmRl8fHzQuXNntG3bVmHtbEGMjIywYMECeHp6okGDBkhMTMSePXugoaEhKme9e/fG9OnTMWnSJNSvXx+3bt1SWCKjSsWKFREaGoopU6agQoUK8rtvzJs3D59++ik+//xz1KtXDzdu3MD+/fsV1v7m50NeD3755RdUqFABzZo1Q7du3TBkyBAYGRkp3GavoFy8S0tLC8uXL8cPP/wAOzs7dOnSBQAwZswYBAYGYsKECahduzb27duHnTt3wtnZWWU/48aNQ2hoKDp06IBjx44V6vkhxieffILx48dj1KhR8PDwwLFjx/K8dSTwekJn7969EAQBHTt2xLNnz1Tut2rVKtStWxdDhgwBADRr1gx169bFzp07Pzjm8koifOgCOCKickoQBDg7O2PkyJEIDAxUdzjl3uHDh9G9e3f8888/ogsyKp/+/fdf2Nvbyz/gR1SSuOaYiEiF//77D5s3b8a9e/fe697GVHh79uzB1KlTWRh/hA4dOoT09HTUrl0bycnJmDRpEhwdHdGsWTN1h0YfIRbHREQqWFtbw9LSEqtXr2axVkIWLlyo7hBITWQyGaZOnYp//vkHRkZG8PHxwYYNG5Tu7kBUErisgoiIiIgoBz+QR0RERESUg8UxEREREVEOFsdERERERDlYHBMRERER5WBxTERERESUg8UxEREREVEOFsdERERERDlYHBMRERER5WBxTERERESUg8UxEREREVEOFsdERERERDlYHBMRERER5WBxTERERESUg8UxEREREVEOFsdERERERDlYHBMRERER5WBxTERERESUQ0vdAVDpkp2djbt378LIyAgSiUTd4RAREREVCUEQ8PTpU9jZ2UFDI+/5YRbHpODu3buwt7dXdxhERERExeL27duoVKlSnttZHJMCIyMjAK8vHGNjYzVHQ0RERFQ00tLSYG9vL6918sLimBTkLqUwNjZmcUxERETlTkHLRvmBPCIiIiKiHB9VcRwSEgIPDw91h1Egf39/dO3aVf7Yz88P48aNU1s8Re78b8CPrYCltYFtQ4AH19UdERERERGAj2xZRVBQEEaPHq3uMN7b9u3bIZVK1R1G0YgOAyJmvHl8MQm4EQEMjQLMHNQXFxERERE+suLY0NAQhoaGH9SHTCYTVaiK3U8Mc3PzIulH7WQvgCNLlNufPwZiVgHt5pZ8TGXMvWf3sObCGsTci4Gpjil6uPRA12pd1R0WERHRe0tJe4Hlh64jMv4/GOpooaenPQb5OEJDQ723ki2Vyyr8/PwwevRojBs3DmZmZqhQoQLWrFmDZ8+eYdCgQTAyMkK1atWwd+9e+TFZWVkICAhAlSpVoKenB1dXV4SFhSn0++6yiuzsbMyaNQuVKlWCjo4OPDw8sG/fPvn2xMRESCQS/Pbbb/D19YWuri42bNigMmaJRIKVK1fik08+gYGBAWbPni0qpqysLAQGBsLU1BQWFhaYNGkSBEFQysfbyyokEgl+//13hX1MTU0RHh4OAHj58iVGjRoFW1tb6OrqwsHBAXPnloLC80kS8OKJ6m3JF0o0lLLo0YtH+GzPZ9hybQtupd3C+f/OY3r0dKyIXaHu0IiIiN7L0xcy9Fh1HL+eSMK/j5/j6r2n+HrXFXz1xyV1h1Y6i2MAWLduHSwtLXHy5EmMHj0aI0aMQM+ePeHj44OzZ8+iTZs2+Pzzz5GRkQHgdaFbqVIlbN26FVeuXMGMGTMwdepUbNmyJc9zhIWFYfHixVi0aBEuXLiAtm3b4pNPPsH164prYKdMmYKxY8ciLi4Obdu2zbO/kJAQdOvWDRcvXsTgwYNFxbR48WKEh4fj559/xtGjR/Ho0SPs2LHjg3K3fPly7Ny5E1u2bEF8fDw2bNgAR0dHlftmZmYiLS1N4b9iY2QDaOmp3mauOj56Y0v8FtzPuK/Uvu7yOqS9LMZ/NyIioiK2/ewdJD3KUGr/7dRt3H3yXA0RvVFql1XUqVMHX331FQAgODgY8+bNg6WlJYYMGQIAmDFjBlauXIkLFy6gUaNGkEqlCA0NlR9fpUoVHD9+HFu2bEGvXr1UnmPRokWYPHky+vTpAwCYP38+Dh8+jGXLlmHFijezcePGjUP37t0LjLlfv34YNGiQQltBMS1btgzBwcHy/letWoX9+/cXeK78JCUlwdnZGU2aNIFEIoGDQ95reefOnasQY7HSNQbq+wMxKxXbNbWBhsNLJoYy7MrDKyrbn796jpupN1HHqk4JR0RERFQ4l++mqmzPyhZw9V4a7EzzmEwrAaV25tjd3V3+s6amJiwsLFC7dm15W4UKFQAAKSkp8rYVK1agfv36sLKygqGhIVavXo2kpCSV/aelpeHu3bto3LixQnvjxo0RFxen0Obp6SkqZlX75RdTamoqkpOT0bBhQ/n+Wlpaos+XF39/f8TGxsLV1RVjxozBgQMH8tw3ODgYqamp8v9u3779QecuUJtvgGaTAL2cddR29YD+/wNsaud/HKGSkepv89GUaMLWwLaEoyEiIio8BwuDQm0rCaW2OH73w2wSiUShLfcGztnZ2QCAzZs3IygoCAEBAThw4ABiY2MxaNAgvHz58oNjMTAQ94/07n7FFZNEIlFalyyTyeQ/16tXDzdv3sTXX3+N58+fo1evXujRo4fKvnR0dORf+FEiX/yhqQW0mAZM+gf4KgUYehio6lu85ywnerv2hp6KZSntq7SHtb61GiIiIiIqnF6e9jDVV75xQSu3CnCy+rCbJ3yoUlscv6/o6Gj4+Phg5MiRqFu3LqpVq4aEhIQ89zc2NoadnR2io6OV+qlRo0aJxGRiYgJbW1vExMTI2169eoUzZ87k26+VlRWSk5Plj69fvy5fe53L2NgYvXv3xpo1a/Dbb79h27ZtePToUZGMq0hIJICWjrqjKFMcjB2wqtUquFu+flfFQGqA/m79EeITot7AiIiI3pOVkQ42DWmEps6WkEgAA21NDPB2wPK+HuoOrfSuOX5fzs7O+OWXX7B//35UqVIF69evx6lTp1ClSpU8j5k4cSJmzpwJJycneHh4YO3atYiNjc3zjhTFEdPYsWMxb948ODs7o3r16liyZAmePHmSb78tWrTAd999B29vb2RlZWHy5MkKs+pLliyBra0t6tatCw0NDWzduhU2NjYwNTUtknGR+tSrUA8bOm5AhiwD2pra0NIoN09hIiL6yLjZGmN9QEPIsrKhpSEp8GudS0q5+c06bNgwnDt3Dr1794ZEIkHfvn0xcuRIhdu9vWvMmDFITU3FhAkTkJKSgho1amDnzp1wdnYusZgmTJiA5ORkDBw4EBoaGhg8eDC6deuG1FTVC9WB13e4GDRoEJo2bQo7OzuEhYUpzDYbGRlhwYIFuH79OjQ1NdGgQQPs2bMHGhrl5o2Cj56+VF/dIRARERUJqWbpqk8kwruLV8ux4OBgHDlyBEePHlV3KKVWWloaTExMkJqaWvzrj4mIiIhKiNgap3SV6sVEEAQkJCTg4MGDqFmzprrDISIiIqJSqtwsq8hPamoqatSogQYNGmDq1KnqDqdUy30joVi/DISIiIiohOXWNgUtmvgoimNTU1NkZmaqO4wy4enTpwAAe3t7NUdCREREVPSePn0KExOTPLd/VGuOqWDZ2dm4e/cujIyMivVTo2lpabC3t8ft27e5trkQmL/CY+4+DPNXeMzdh2H+Co+5e00QBDx9+hR2dnb53qTgo5g5JvE0NDRQqZLqb2IrDiXyxSPlGPNXeMzdh2H+Co+5+zDMX+Exd8h3xjjXR/GBPCIiIiIiMVgcExERERHlYHFMaqGjo4OZM2dCR4dfIV0YzF/hMXcfhvkrPObuwzB/hcfcvR9+II+IiIiIKAdnjomIiIiIcrA4JiIiIiLKweKYiIiIiCgHi2MiIiIiohwsjumD/f333+jcuTPs7OwgkUjw+++/57t/ZGQkJBKJ0n/37t1T2G/FihVwdHSErq4uGjZsiJMnTxbjKNSnOPI3d+5cNGjQAEZGRrC2tkbXrl0RHx9fzCMpecV17eWaN28eJBIJxo0bV/TBlwLFlb87d+7gs88+g4WFBfT09FC7dm2cPn26GEdS8oojd1lZWZg+fTqqVKkCPT09ODk54euvv0Z5/Nz8++YPADIzMzFt2jQ4ODhAR0cHjo6O+PnnnxX22bp1K6pXrw5dXV3Url0be/bsKaYRqE9x5G7NmjVo2rQpzMzMYGZmhlatWpXb37lisDimD/bs2TPUqVMHK1aseK/j4uPjkZycLP/P2tpavu23335DYGAgZs6cibNnz6JOnTpo27YtUlJSijp8tSuO/EVFReHLL7/EiRMnEBERAZlMhjZt2uDZs2dFHb5aFUfucp06dQo//PAD3N3diyrcUqc48vf48WM0btwYUqkUe/fuxZUrV7B48WKYmZkVdfhqVRy5mz9/PlauXInvvvsOcXFxmD9/PhYsWIBvv/22qMNXu8Lkr1evXjh48CB++uknxMfHY9OmTXB1dZVvP3bsGPr27YuAgACcO3cOXbt2RdeuXXHp0qXiGILaFEfuIiMj0bdvXxw+fBjHjx+Hvb092rRpgzt37hTHEEo/gagIARB27NiR7z6HDx8WAAiPHz/Ocx8vLy/hyy+/lD/OysoS7OzshLlz5xZRpKVTUeXvXSkpKQIAISoq6sMCLMWKMndPnz4VnJ2dhYiICMHX11cYO3ZskcVZWhVV/iZPniw0adKkaIMr5Yoqdx07dhQGDx6s0Na9e3ehf//+RRBl6SUmf3v37hVMTEyEhw8f5rlPr169hI4dOyq0NWzYUBg2bFhRhFkqFVXu3vXq1SvByMhIWLdu3QdGWDZx5pjUxsPDA7a2tmjdujWio6Pl7S9fvsSZM2fQqlUreZuGhgZatWqF48ePqyPUUimv/KmSmpoKADA3Ny+J0Eq9gnL35ZdfomPHjgrXIL2RX/527twJT09P9OzZE9bW1qhbty7WrFmjpkhLn/xy5+Pjg4MHD+LatWsAgPPnz+Po0aNo3769OkItVXKvqwULFqBixYpwcXFBUFAQnj9/Lt/n+PHjSs/Ztm3bfvS/N8Tk7l0ZGRmQyWQf7e8MLXUHQB8fW1tbrFq1Cp6ensjMzMSPP/4IPz8/xMTEoF69enjw4AGysrJQoUIFheMqVKiAq1evqinq0qOg/L0rOzsb48aNQ+PGjVGrVi01RFx6iMnd5s2bcfbsWZw6dUrN0ZY+YvL3zz//YOXKlQgMDMTUqVNx6tQpjBkzBtra2hg4cKCaR6A+YnI3ZcoUpKWloXr16tDU1ERWVhZmz56N/v37qzl69fvnn39w9OhR6OrqYseOHXjw4AFGjhyJhw8fYu3atQCAe/fuqfy9kddnCj4WYnL3rsmTJ8POzu7jnSBQ99Q1lS8Q8RaPKs2aNRM+++wzQRAE4c6dOwIA4dixYwr7TJw4UfDy8iqKMEutosjfu4YPHy44ODgIt2/f/sDoSreiyF1SUpJgbW0tnD9/Xr6dyyry9+61J5VKBW9vb4V9Ro8eLTRq1OhDQyy1iip3mzZtEipVqiRs2rRJuHDhgvDLL78I5ubmQnh4eBFGW/qIyV/r1q0FXV1d4cmTJ/K2bdu2CRKJRMjIyBAE4fW1t3HjRoXjVqxYIVhbWxd5zKVFUeXubXPnzhXMzMwUXgc/NlxWQaWCl5cXbty4AQCwtLSEpqYm7t+/r7DP/fv3YWNjo47wSr238/e2UaNGYdeuXTh8+DAqVaqkhshKv7dzd+bMGaSkpKBevXrQ0tKClpYWoqKisHz5cmhpaSErK0vN0ZY+7157tra2qFGjhsI+bm5uSEpKKunQSr13czdx4kRMmTIFffr0Qe3atfH5559j/PjxmDt3rhqjLB1sbW1RsWJFmJiYyNvc3NwgCAL+/fdfAICNjQ1/b6ggJne5Fi1ahHnz5uHAgQPl+sPIBWFxTKVCbGwsbG1tAQDa2tqoX78+Dh48KN+enZ2NgwcPwtvbW10hlmpv5w8ABEHAqFGjsGPHDhw6dAhVqlRRY3Sl29u5a9myJS5evIjY2Fj5f56enujfvz9iY2Ohqamp5mhLn3evvcaNGyvdNvDatWtwcHAo6dBKvXdzl5GRAQ0NxV/LmpqayM7OLunQSp3GjRvj7t27SE9Pl7ddu3YNGhoa8j/8vb29FX5vAEBERMRH/3tDTO4AYMGCBfj666+xb98+eHp6qiPUUoNrjumDpaenK8x+3Lx5E7GxsTA3N0flypURHByMO3fu4JdffgEALFu2DFWqVEHNmjXx4sUL/Pjjjzh06BAOHDgg7yMwMBADBw6Ep6cnvLy8sGzZMjx79gyDBg0q8fEVt+LI35dffomNGzfijz/+gJGRkXzNnYmJCfT09Ep2gMWoqHNnZGSktC7bwMAAFhYW5XK9dnFce+PHj4ePjw/mzJmDXr164eTJk1i9ejVWr15d4uMrTsWRu86dO2P27NmoXLkyatasiXPnzmHJkiUYPHhwiY+vuL1v/vr164evv/4agwYNQmhoKB48eICJEydi8ODB8te0sWPHwtfXF4sXL0bHjh2xefNmnD59+qO/9sTkbv78+ZgxYwY2btwIR0dH+e8MQ0NDGBoalvwg1U3NyzqoHMi9RdG7/w0cOFAQBEEYOHCg4OvrK99//vz5gpOTk6CrqyuYm5sLfn5+wqFDh5T6/fbbb4XKlSsL2tragpeXl3DixIkSGlHJKo78qeoPgLB27dqSG1gJKK5r723lec1xceXvzz//FGrVqiXo6OgI1atXF1avXl1CIyo5xZG7tLQ0YezYsULlypUFXV1doWrVqsK0adOEzMzMEhxZyXjf/AmCIMTFxQmtWrUS9PT0hEqVKgmBgYFKa2a3bNkiuLi4CNra2kLNmjWF3bt3l9CISk5x5M7BwUFlnzNnziy5gZUiEkEoh1+9Q0RERERUCFxzTERERESUg8UxEREREVEOFsdERERERDlYHBMRERER5WBxTERERESUg8UxEREREVEOFsdERERERDlYHBMRFSNHR0csW7bsg/f5UOHh4TA1/X979x7X4/3/D/zx7vB+d04lnaToQA5Lh0V8p4OsmFEjSirJme/CHD+bZYxo0cwMn33IYaWtia+JWVoNlUOtmkiUTrPIqXTQ+fX7Q10/16d3FNU7et5vt+t2c72u1/U6PLvw7NXrfdWrU/sAgOPHj8PY2BjS0tJYunRpp/f3Ivb29hIfQ1t01deGENI2lBwTQsgrKCoqwuzZs6GrqwuhUAgDAwMEBATg4cOH7W7rypUrmDdvXoeNTVyyPX36dNy8ebPD+mjN/PnzMXXqVBQVFWHjxo2d3h8AJCQkQCAQoLS0lFceHR3dZWMghLw9KDkmhJB2un37NqytrXHr1i0cOXIEOTk52LNnD+Li4mBra4tHjx61qz1NTU0oKCh00mifkZeXR58+fTq1j4qKCpSUlMDZ2Rm6urpQVlbu1P5eRl1dXeJjIIS8eSg5JoSQdlq8eDGEQiF+++032NnZoV+/fhg/fjzOnj2LO3fu4NNPP+XVLy8vh6enJxQVFaGnp4ddu3bxrv/3Sm9paSnmzJkDTU1NqKiowNHRERkZGbx7fvnlF7z77ruQk5ND79694ebmBuDZVoKCggIsW7YMAoEAAoEAAP9H9zdv3oRAIMCNGzd4bYaGhsLIyIg7z8zMxPjx46GkpAQtLS14e3vjwYMHYmOSkJDAJaKOjo4QCARISEjA+vXrMXz4cF7dr7/+GoaGhtz5rFmz4OrqipCQEOjo6EBDQwOLFy9GXV0dV6empgarV6+Gvr4+RCIRjI2NsW/fPuTn58PBwQEAoKamBoFAgFmzZnGxeH5bxePHj+Hj4wM1NTUoKChg/PjxuHXrFne9OUZnzpyBmZkZlJSU4OLiguLiYrFzbmxsRN++fbF7925eeVpaGqSkpFBQUAAA2L59O4YNGwZFRUXo6+tj0aJFqKioENvm8/F43tKlS2Fvb8/rOygoCP3794e8vDzMzc3x888/t9omIaTtKDkmhJB2ePToEc6cOYNFixZBXl6ed01bWxteXl748ccfwRjjyr/66iuYm5sjLS0Na9asQUBAAGJjY1vtw93dHSUlJTh9+jRSU1NhaWmJsWPHcivSMTExcHNzw4QJE5CWloa4uDjY2NgAeLaVoG/fvtiwYQOKi4vFJnampqawtrZGeHg4rzw8PBwzZswA8CxBd3R0hIWFBVJSUvDrr7/i3r17mDZtmtgxjxo1CtnZ2QCAo0ePori4GKNGjXpZODnx8fHIzc1FfHw8Dh48iAMHDuDAgQPcdR8fHxw5cgTffPMNsrKysHfvXigpKUFfXx9Hjx4FAGRnZ6O4uBg7duwQ28esWbOQkpKCEydOIDk5GYwxTJgwgZeEV1VVISQkBIcPH8a5c+dQWFiIFStWiG1PSkoKnp6eiIiI4JWHh4dj9OjRMDAw4Op98803uHbtGg4ePIjff/8dq1atanNsxAkKCsKhQ4ewZ88eXLt2DcuWLcPMmTPxxx9/vFa7hBAAjBBCSJtdvHiRAWDHjh0Te3379u0MALt37x5jjDEDAwPm4uLCqzN9+nQ2fvx47tzAwICFhoYyxhg7f/48U1FRYdXV1bx7jIyM2N69exljjNna2jIvL69Wx/h8e83CwsKYqqoqdx4aGsqMjIy48+zsbAaAZWVlMcYY27hxI3v//fd5bRQVFTEALDs7W2y/jx8/ZgBYfHw8VxYYGMjMzc159UJDQ5mBgQF37uvrywwMDFh9fT1X5u7uzqZPn84bW2xsrNh+4+PjGQD2+PFjXrmdnR0LCAhgjDF28+ZNBoAlJiZy1x88eMDk5eXZTz/9xBh7FiMALCcnh6uza9cupqWlJbZfxhhLS0tjAoGAFRQUMMYYa2hoYHp6emz37t2t3hMVFcU0NDS48//+2vj6+rLJkyfz7gkICGB2dnaMMcaqq6uZgoICS0pK4tXx9/dnnp6erfZLCGkbWjkmhJBXwJ5bGX4ZW1vbFudZWVli62ZkZKCiogIaGhpQUlLijry8POTm5gIA0tPTMXbs2FcfPAAPDw/k5+fj4sWLAJ6tdlpaWmLQoEHcOOLj43ljaL7WPI6ONGTIEEhLS3PnOjo6KCkpAfBsvtLS0rCzs3vl9rOysiAjI4MRI0ZwZRoaGhg4cCDva6GgoMDbWvL8OMQZPnw4zMzMuNXjP/74AyUlJXB3d+fqnD17FmPHjoWenh6UlZXh7e2Nhw8foqqq6pXmkpOTg6qqKowbN4739Tl06FCnfG0I6WlkJD0AQgh5kxgbG0MgECArK4vb5/u8rKwsqKmpQVNT85Xar6iogI6ODhISElpca94z/N/bOV6FtrY2HB0dERERgZEjRyIiIgILFy7kjePDDz/E1q1bW9yro6PT5n6kpKRafCPx/DaGZrKysrxzgUCAxsZGAB0z37YSN46XfSPk5eWFiIgIrFmzBhEREXBxcYGGhgYAID8/HxMnTsTChQuxadMmqKur48KFC/D390dtba3YD2K+LGbN+5VjYmKgp6fHqycSido+WUKIWLRyTAgh7aChoYFx48bhu+++w9OnT3nX7t69i/DwcEyfPp37IBwAbnX2+XMzMzOx7VtaWuLu3buQkZGBsbEx7+jduzcA4J133kFcXFyrYxQKhWhoaHjpXJr3RycnJ+P27dvw8PDgjePatWswNDRsMQ5FRcWXtt1MU1MTd+/e5SV76enpbb4fAIYNG4bGxsZW99MKhUIAeOGczczMUF9fj0uXLnFlDx8+RHZ2NgYPHtyu8fy3GTNmIDMzE6mpqfj555/h5eXFXUtNTUVjYyO2bduGkSNHwtTUFP/8888L29PU1GyxV/z5mA0ePBgikQiFhYUtvjb6+vqvNRdCCCXHhBDSbt9++y1qamrg7OyMc+fOoaioCL/++ivGjRsHPT09bNq0iVc/MTERwcHBuHnzJnbt2oWoqCgEBASIbdvJyQm2trZwdXXFb7/9hvz8fCQlJeHTTz9FSkoKACAwMBBHjhxBYGAgsrKycPXqVd4Kr6GhIc6dO4c7d+60+nYJAPjoo49QXl6OhQsXwsHBAbq6uty1xYsX49GjR/D09MSVK1eQm5uLM2fOwM/Pr02JdzN7e3vcv38fwcHByM3Nxa5du3D69Ok23988H19fX8yePRvHjx9HXl4eEhIS8NNPPwEADAwMIBAIcPLkSdy/f1/smyBMTEwwefJkzJ07FxcuXEBGRgZmzpwJPT09TJ48uV3jETe+UaNGwd/fHw0NDZg0aRJ3zdjYGHV1ddi5cydu376Nw4cPY8+ePS9sz9HRESkpKTh06BBu3bqFwMBAZGZmcteVlZWxYsUKLFu2DAcPHkRubi7+/PNP7Ny5EwcPHnytuRBCKDkmhJB2MzExQUpKCgYMGIBp06bByMgI8+bNg4ODA5KTk6Gurs6r/8knnyAlJQUWFhb48ssvsX37djg7O4ttWyAQ4NSpUxgzZgz8/PxgamoKDw8PFBQUQEtLC8CzhDMqKgonTpzA8OHD4ejoiMuXL3NtbNiwAfn5+TAyMnrh9g5lZWV8+OGHyMjI4K12AoCuri4SExPR0NCA999/H8OGDcPSpUvRq1cvSEm1/b8OMzMzfPfdd9i1axfMzc1x+fLlVt/+8CK7d+/G1KlTsWjRIgwaNAhz585FZWUlAEBPTw9ffPEF1qxZAy0tLSxZskRsG2FhYbCyssLEiRNha2sLxhhOnTrVYivFq/Dy8kJGRgbc3Nx420DMzc2xfft2bN26FUOHDkV4eDiCgoJe2JazszPWrVuHVatW4d1330V5eTl8fHx4dTZu3Ih169YhKCgIZmZmcHFxQUxMDPr37//acyGkpxOw9nyqhBBCSIfT0dHBxo0bMWfOHEkPhRBCejz6QB4hhEhIVVUVEhMTce/ePQwZMkTSwyGEEALaVkEIIRLz73//Gx4eHli6dGmL170RQgiRDNpWQQghhBBCSBNaOSaEEEIIIaQJJceEEEIIIYQ0oeSYEEIIIYSQJpQcE0IIIYQQ0oSSY0IIIYQQQppQckwIIYQQQkgTSo4JIYQQQghpQskxIYQQQgghTSg5JoQQQgghpAklx4QQQgghhDSh5Pg5CQkJEAgEKC0t7RbtEEIIIYSQrvXWJMezZs2CQCCAQCCArKws+vfvj1WrVqG6urpT+7W3t8fSpUt5ZaNGjUJxcTFUVVU7rV9KwAkhhBBCOp6MpAfQkVxcXBAWFoa6ujqkpqbC19cXAoEAW7du7dJxCIVCaGtrd2mfHaWxsRH//PMPlJWVIRAIJD0cQgghhJAOwRhDeXk5dHV1ISX1gvVh9pbw9fVlkydP5pV99NFHzMLCgjtvaGhgmzdvZoaGhkxOTo698847LCoqirseHx/PALDHjx8zxhh78OAB8/DwYLq6ukxeXp4NHTqURURE8PoEwDvy8vJ47ZSVlTE5OTl26tQp3tiio6OZkpISq6ysZIwxVlhYyNzd3ZmqqipTU1NjkyZNYnl5eWLnmpeX16JfX19fxhhj1dXV7H//93+ZpqYmE4lEbPTo0ezy5cttjmNRUVGLtumggw466KCDDjrelqOoqOiFudBbtXL8vMzMTCQlJcHAwIArCwoKwg8//IA9e/bAxMQE586dw8yZM6GpqQk7O7sWbVRXV8PKygqrV6+GiooKYmJi4O3tDSMjI9jY2GDHjh24efMmhg4dig0bNgAANDU1kZ+fz7WhoqKCiRMnIiIiAuPHj+fKw8PD4erqCgUFBdTV1cHZ2Rm2trY4f/48ZGRk8OWXX8LFxQV//fUXhEIhb1z6+vo4evQopkyZguzsbKioqEBeXh4AsGrVKhw9ehQHDx6EgYEBgoOD4ezsjJycHKirq780bsrKygCAoqIiqKiotD3ghBBCCCHd2JMnT6Cvr8/lOq1q85JiN+fr68ukpaWZoqIiE4lEDACTkpJiP//8M2Ps2YqqgoICS0pK4t3n7+/PPD09GWMtV47F+eCDD9gnn3zCndvZ2bGAgABenf9u59ixY7xV4ubV5NOnTzPGGDt8+DAbOHAga2xs5Nqoqalh8vLy7MyZM2LHIW6sFRUVTFZWloWHh3NltbW1TFdXlwUHB7c6p+eVlZUxAKysrKxN9QkhhBBC3gRtzXHeqpVjBwcH7N69G5WVlQgNDYWMjAymTJkCAMjJyUFVVRXGjRvHu6e2thYWFhZi22toaMDmzZvx008/4c6dO6itrUVNTQ0UFBTaNa4JEyZAVlYWJ06cgIeHB44ePQoVFRU4OTkBADIyMpCTk9PiO5nq6mrk5ua2uZ/c3FzU1dVh9OjRXJmsrCxsbGyQlZXVrjF3usZGoKEGkJWX9EgIIYR0Y3WNdbhTfgfq8upQEdJPNEnne6uSY0VFRRgbGwMA9u/fD3Nzc+zbtw/+/v6oqKgAAMTExEBPT493n0gkEtveV199hR07duDrr7/GsGHDoKioiKVLl6K2trZd4xIKhZg6dSoiIiLg4eGBiIgITJ8+HTIyz8JfUVEBKysrhIeHt7hXU1OzXX11ew31QMJm4Mo+oLoU0DEHnNYDRo6SHhkhhJBu5titY9iZthP3n96HrJQsPjT6EGtt1kJORk7SQyNvsbcqOX6elJQU/vWvf2H58uWYMWMGBg8eDJFIhMLCQrH7i8VJTEzE5MmTMXPmTADP3uRw8+ZNDB48mKsjFArR0NDw0ra8vLwwbtw4XLt2Db///ju+/PJL7pqlpSV+/PFH9OnTp837fJv3IT/ft5GREYRCIRITE7m91nV1dbhy5UqL181JzG+fApf2/P/z4gwgYjowJw7QeUdy4yKEENKtXCy+iM+TPufO6xrrEH0rGgIIsH7UeskNjHS4e0+qIScrDVV5WUkPBcBb9J5jcdzd3SEtLY1du3ZBWVkZK1aswLJly3Dw4EHk5ubizz//xM6dO3Hw4EGx95uYmCA2NhZJSUnIysrC/Pnzce/ePV4dQ0NDXLp0Cfn5+Xjw4AEaGxvFtjVmzBhoa2vDy8sL/fv3x4gRI7hrXl5e6N27NyZPnozz588jLy8PCQkJ+Pjjj/H333+Lbc/AwAACgQAnT57E/fv3UVFRAUVFRSxcuBArV67Er7/+iuvXr2Pu3LmoqqqCv7//K0axA1U/AVLFxLqhFri0t+vHQwghpNuKvBEptvyX3F9QWVfZxaMhneFy3iOM33EeIzbHwXJjLBaFp+JxZft+Ot8Z3urkWEZGBkuWLEFwcDAqKyuxceNGrFu3DkFBQTAzM4OLiwtiYmLQv39/sfd/9tlnsLS0hLOzM+zt7aGtrQ1XV1denRUrVkBaWhqDBw+GpqYmCgsLxbYlEAjg6emJjIwMeHl58a4pKCjg3Llz6NevHz766COYmZnB398f1dXVra4k6+np4YsvvsCaNWugpaWFJUuWAAC2bNmCKVOmwNvbG5aWlsjJycGZM2egpqbWzuh1gvK7QP1T8dce53fpUAghhHRv96vuiy2vbaxFWU1ZF4+GdLQ7pU8xK+wysoqfAAAaGhlOXb2LheGpEh4ZIGCMMUkPgkhOTU0NampquPPm15yUlZV1/Kvc6qqB7YOAp49bXhu5GHDZ3LH9EUIIeWNtS9mGA9cOtCjXU9LDqY9OQUrwVq/vvfVCY29iR9wtsddOB7wHM52O//DlkydPoKqq+tIch56sHi4oKAiqqqrcoa+v33mdycoB761oWS6vDoxc0Hn9EkIIeeP4DPaBloIWr0xKIIWllkspMX4LFJe18pPkl1zrCrRy3MN16cpxs6s/A1f+A5QXA/1GAWNWABpGndMXIYSQN9aDpw/ww/UfkH4/HVoKWvAc5InhfYZLelikA0ReLsSa6KstyoXSUkhc4whNZfFvEnsdbV05puSY8LT1wSGEEEIIeVXVdQ1w+y6J23PcbKG9EVa7DOqUPtua47y1r3IjhBBCCCHdk5ysNH6cPxL7L+QhPvs+lEUycLfui8nD9V5+cydr16Yde3v77vO+3A4kEAhw/PhxSQ+jhQMHDqBXr16SHgYhhBBCSIdTkZPFUidT/N/i0fhhzohukRgDb9kH8rprktuVoqOj8f7770NDQwMCgQDp6emSHhIhhBBCyBujS5Njxhjq6+u7sssep7KyEv/zP/+DrVu3SnoohBBCCCFvnNdKjg8fPgxra2soKytDW1sbM2bMQElJCXc9ISEBAoEAp0+fhpWVFUQiES5cuIDy8nJ4eXlBUVEROjo6CA0NbbFlo6amBitWrICenh4UFRUxYsQIJCQktDoWQ0NDAICbmxsEAgF3DgC7d+/mfrXywIEDcfjw4RfOKzAwEDo6Ovjrr78AAKtXr4apqSkUFBQwYMAArFu3DnV1dVz99evXY/jw4di/fz/69esHJSUlLFq0CA0NDQgODoa2tjb69OmDTZs28frZvn07hg0bBkVFRejr62PRokWoqKhodVz379+HtbU13NzceG+YeJ63tzc+//xzODk5vXCOhBBCCCGkpdf6QF5dXR02btyIgQMHoqSkBMuXL8esWbNw6tQpXr01a9YgJCQEAwYMgJqaGpYvX47ExEScOHECWlpa+Pzzz/Hnn39i+PDh3D1LlizB9evXERkZCV1dXRw7dgwuLi64evUqTExMWozlypUr6NOnD8LCwuDi4gJpaWkAwLFjxxAQEICvv/4aTk5OOHnyJPz8/NC3b184ODjw2mCM4eOPP8bJkydx/vx5GBsbAwCUlZVx4MAB6Orq4urVq5g7dy6UlZWxatUq7t7c3FycPn0av/76K3JzczF16lTcvn0bpqam+OOPP5CUlITZs2fDycmJ+9XRUlJS+Oabb9C/f3/cvn0bixYtwqpVq/Ddd9+1mF9RURHGjRuHkSNHYt++fdz8Olrzy0uePHnykpqEEEIIIW+O5tzmpS9qY+1gZ2fHAgICWr1+5coVBoCVl5czxhiLj49nANjx48e5Ok+ePGGysrIsKiqKKystLWUKCgpc2wUFBUxaWprduXOH1/7YsWPZ2rVrW+0fADt27BivbNSoUWzu3Lm8Mnd3dzZhwgTefVFRUWzGjBnMzMyM/f333632wRhjX331FbOysuLOAwMDmYKCAnvy5AlX5uzszAwNDVlDQwNXNnDgQBYUFNRqu1FRUUxDQ4M7DwsLY6qqquzGjRtMX1+fffzxx6yxsfGFY2uWl5fHALC0tLQ21W9WVFTEANBBBx100EEHHXS8lUdRUdELc6HXWjlOTU3F+vXrkZGRgcePH6OxsREAUFhYiMGDB3P1rK2tuT/fvn0bdXV1sLGx4cpUVVUxcOBA7vzq1atoaGiAqakpr7+amhpoaGi0a4xZWVmYN28er2z06NHYsWMHr2zZsmUQiUS4ePEievfuzbv2448/4ptvvkFubi4qKipQX1/f4v14hoaGUFZW5s61tLQgLS0NKSkpXtnz207Onj2LoKAg3LhxA0+ePEF9fT2qq6tRVVUFBQUFAMDTp0/x3nvvYcaMGfj666/bNfdXoauri6KiIigrK0MgEHR4+82/ZKSoqIjeo9yFKO6SQXGXDIq7ZFDcJYdi3zaMMZSXl0NXV/eF9V45Oa6srISzszOcnZ0RHh4OTU1NFBYWwtnZGbW1tby6ioqK7Wq7oqIC0tLSSE1NbbF9QElJ6VWH/ELjxo3DkSNHcObMGXh5eXHlycnJ8PLywhdffAFnZ2eoqqoiMjIS27Zt490vKyvLOxcIBGLLmr+ByM/Px8SJE7Fw4UJs2rQJ6urquHDhAvz9/VFbW8slxyKRiNsOsnLlSujpde5rTqSkpNC3b99O7QMAVFRU6C+wBFDcJYPiLhkUd8mguEsOxf7lVFVVX1rnlT+Qd+PGDTx8+BBbtmzBe++9h0GDBvFWRVszYMAAyMrK4sqVK1xZWVkZbt68yZ1bWFigoaEBJSUlMDY25h3a2tqtti0rK4uGhgZemZmZGRITE3lliYmJvJVtAJg0aRIiIiIwZ84cREZGcuVJSUkwMDDAp59+Cmtra5iYmKCgoOCl83yZ1NRUNDY2Ytu2bRg5ciRMTU3xzz//tKgnJSWFw4cPw8rKCg4ODmLrEEIIIYSQjvHKK8f9+vWDUCjEzp07sWDBAmRmZmLjxo0vvU9ZWRm+vr5YuXIl1NXV0adPHwQGBkJKSor7Mb6pqSm8vLzg4+ODbdu2wcLCAvfv30dcXBzeeecdfPDBB2LbNjQ0RFxcHEaPHg2RSAQ1NTWsXLkS06ZNg4WFBZycnPDLL78gOjoaZ8+ebXG/m5sbDh8+DG9vb8jIyGDq1KkwMTFBYWEhIiMj8e677yImJgbHjh171bBxjI2NUVdXh507d+LDDz9EYmIi9uzZI7autLQ0wsPD4enpCUdHRyQkJLT6TcKjR49QWFjIJdHZ2dkAAG1t7Rd+Y0EIIYQQQl5j5VhTUxMHDhxAVFQUBg8ejC1btiAkJKRN927fvh22traYOHEinJycMHr0aJiZmUFOTo6rExYWBh8fH3zyyScYOHAgXF1dceXKFfTr16/Vdrdt24bY2Fjo6+vDwsICAODq6oodO3YgJCQEQ4YMwd69exEWFgZ7e3uxbUydOhUHDx6Et7c3oqOjMWnSJCxbtgxLlizB8OHDkZSUhHXr1rU9UK0wNzfH9u3bsXXrVgwdOhTh4eEICgpqtb6MjAyOHDmCIUOGwNHRsdVV+hMnTsDCwoL7BsLDwwMWFhatJt5dTSQSITAwECKRSNJD6VEo7pJBcZcMirtkUNwlh2LfsQSMvex9Fp2vsrISenp62LZtG/z9/SU9HEIIIYQQ0kO91tsqXlVaWhpu3LgBGxsblJWVYcOGDQCAyZMnS2I4hBBCCCGEAJBQcgwAISEhyM7OhlAohJWVFc6fP9/iFWqEEEIIIYR0pW6xrYIQQgghhJDu4JU/kEcIIYQQQsjbhpJj0i67du2CoaEh5OTkMGLECFy+fPmF9aOiojBo0CDIyclh2LBhOHXqFO96dHQ03n//fWhoaEAgECA9Pb1FG9XV1Vi8eDE0NDSgpKSEKVOm4N69ex05rW5PEnG3t7eHQCDgHQsWLOjIaXV7HRn3uro6rF69GsOGDYOioiJ0dXXh4+PT4t3ljx49gpeXF1RUVNCrVy/4+/ujoqKiU+bXnUki9oaGhi2e+S1btnTK/Lqrjv63Zv369Rg0aBAUFRWhpqYGJycnXLp0iVeHnnnJxJ2e9xd44S+XJuQ5kZGRTCgUsv3797Nr166xuXPnsl69erF79+6JrZ+YmMikpaVZcHAwu379Ovvss8+YrKwsu3r1Klfn0KFD7IsvvmDff/89A8DS0tJatLNgwQKmr6/P4uLiWEpKChs5ciQbNWpUZ02z25FU3O3s7NjcuXNZcXExd5SVlXXWNLudjo57aWkpc3JyYj/++CO7ceMGS05OZjY2NszKyorXjouLCzM3N2cXL15k58+fZ8bGxszT07PT59udSCr2BgYGbMOGDbxnvqKiotPn2110xr814eHhLDY2luXm5rLMzEzm7+/PVFRUWElJCVenpz/zkop7T3/eX4SSY9JmNjY2bPHixdx5Q0MD09XVZUFBQWLrT5s2jX3wwQe8shEjRrD58+e3qJuXlyc2SSstLWWysrIsKiqKK8vKymIAWHJy8mvM5s0hibgz9iw5DggIeK2xv8k6M+7NLl++zACwgoICxhhj169fZwDYlStXuDqnT59mAoGA3blz53Wm80aRROwZe5YshIaGvt7g32BdEfeysjIGgJ09e5YxRs88Y5KJO2P0vL8IbasgbVJbW4vU1FQ4OTlxZVJSUnByckJycrLYe5KTk3n1AcDZ2bnV+uKkpqairq6O186gQYPQr1+/drXzppJU3JuFh4ejd+/eGDp0KNauXYuqqqp2t/Em6qq4l5WVQSAQoFevXlwbvXr1grW1NVfHyckJUlJSLX4k+raSVOybbdmyBRoaGrCwsMBXX32F+vr6V5/MG6Qr4l5bW4t///vfUFVVhbm5OddGT37mJRX3Zj31eX8Zib3KjbxZHjx4gIaGBmhpafHKtbS0cOPGDbH33L17V2z9u3fvtrnfu3fvQigUtvgPrL3tvKkkFXcAmDFjBgwMDKCrq4u//voLq1evRnZ2NqKjo9s3iTdQV8S9uroaq1evhqenJ1RUVLg2+vTpw6snIyMDdXX1HvG8A5KLPQB8/PHHsLS0hLq6OpKSkrB27VoUFxdj+/btrzmr7q8z437y5El4eHigqqoKOjo6iI2N5V7d2tOfeUnFHejZz/vLUHJMCBFr3rx53J+HDRsGHR0djB07Frm5uTAyMpLgyN58dXV1mDZtGhhj2L17t6SH06O8KPbLly/n/vzOO+9AKBRi/vz5CAoKol/L+xocHByQnp6OBw8e4Pvvv8e0adNw6dKlFkkx6Vgvizs9762jbRWkTXr37g1paekWb4m4d+8etLW1xd6jra3drvqttVFbW4vS0tLXaudNJam4izNixAgAQE5Ozmu18ybozLg3J2cFBQWIjY3lrVxqa2ujpKSEV7++vh6PHj3qEc87ILnYizNixAjU19cjPz+//RN5w3Rm3BUVFWFsbIyRI0di3759kJGRwb59+7g2evIzL6m4i9OTnveXoeSYtEnzbzKMi4vjyhobGxEXFwdbW1ux99ja2vLqA0BsbGyr9cWxsrKCrKwsr53s7GwUFha2q503laTiLk7z6950dHReq503QWfFvTk5u3XrFs6ePQsNDY0WbZSWliI1NZUr+/3339HY2Mh9c/K2k1TsxUlPT4eUlFSPWOHsyn9rGhsbUVNTw7XRk595ScVdnJ70vL+UpD8RSN4ckZGRTCQSsQMHDrDr16+zefPmsV69erG7d+8yxhjz9vZma9as4eonJiYyGRkZFhISwrKyslhgYGCL1808fPiQpaWlsZiYGAaARUZGsrS0NFZcXMzVWbBgAevXrx/7/fffWUpKCrO1tWW2trZdN3EJk0Tcc3Jy2IYNG1hKSgrLy8tj//d//8cGDBjAxowZ07WTl6COjnttbS2bNGkS69u3L0tPT+e9PqmmpoZrx8XFhVlYWLBLly6xCxcuMBMTkx71WivGJBP7pKQkFhoaytLT01lubi774YcfmKamJvPx8en6AEhIR8e9oqKCrV27liUnJ7P8/HyWkpLC/Pz8mEgkYpmZmVw7Pf2Zl0Tc6Xl/MUqOSbvs3LmT9evXjwmFQmZjY8MuXrzIXbOzs2O+vr68+j/99BMzNTVlQqGQDRkyhMXExPCuh4WFMQAtjsDAQK7O06dP2aJFi5iamhpTUFBgbm5uvOS5J+jquBcWFrIxY8YwdXV1JhKJmLGxMVu5cmWPes8xYx0b9+bX5ok74uPjuXoPHz5knp6eTElJiamoqDA/Pz9WXl7e2VPtdro69qmpqWzEiBFMVVWVycnJMTMzM7Z582ZWXV3dFdPtNjoy7k+fPmVubm5MV1eXCYVCpqOjwyZNmsQuX77Ma4Oe+a6POz3vLyZgjLGuW6cmhBBCCCGk+6I9x4QQQgghhDSh5JgQQgghhJAmlBwTQgghhBDShJJjQgghhBBCmlByTAghhBBCSBNKjgkhhBBCCGlCyTEhhBBCCCFNKDkmhBBCCCGkCSXHhBBCCCGENKHkmBBCeqhZs2ZBIBBAIBBAVlYW/fv3x6pVq1BdXc2r11zn4sWLvPKamhpoaGhAIBAgISGBK//jjz/g6OgIdXV1KCgowMTEBL6+vqitre2KaRFCyGuh5JgQQnowFxcXFBcX4/bt2wgNDcXevXsRGBjYop6+vj7CwsJ4ZceOHYOSkhKv7Pr163BxcYG1tTXOnTuHq1evYufOnRAKhWhoaOiwcbeWaNfV1b1Se696HyHk7UPJMSGE9GAikQja2trQ19eHq6srnJycEBsb26Ker68vIiMj8fTpU65s//798PX15dX77bffoK2tjeDgYAwdOhRGRkZwcXHB999/D3l5+VbHUVpaijlz5kBTUxMqKipwdHRERkYGd339+vUYPnw4/vOf/6B///6Qk5MD8GxVe/fu3Zg0aRIUFRWxadMmAMDu3bthZGQEoVCIgQMH4vDhw7z+WruPEEI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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "runs_metadata = [\n", + " RunMetadata(data_dir / \"large_tokamak_1_MFILE.DAT\", \"large tokamak 1\"),\n", + " RunMetadata(data_dir / \"large_tokamak_2_MFILE.DAT\", \"large tokamak 2\"),\n", + " RunMetadata(data_dir / \"large_tokamak_3_MFILE.DAT\", \"large tokamak 3\"),\n", + " RunMetadata(data_dir / \"large_tokamak_4_MFILE.DAT\", \"large tokamak 4\"),\n", + "]\n", + "\n", + "fig5, df5 = plot_mfile_solutions(\n", + " runs_metadata,\n", + " \"3 large tokamak solutions with RMS errors normalised to large tokamak 1\",\n", + " normalising_tag=\"large tokamak 1\",\n", + " rmse=True,\n", + ")\n", + "df5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solutions normalised by range\n", + "\n", + "Use `nitvar` values instead; the solution optimisation parameters are normalised to the range of their upper and lower bounds." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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tagobjf_namenorm_objfitvar001_namenitvar001itvar002_namenitvar002itvar003_namenitvar003itvar004_name...itvar041_namenitvar041itvar042_namenitvar042itvar043_namenitvar043itvar044_namenitvar044itvar045_namenitvar045
0large tokamak 1major radius1.60beta0.032681dene0.071381fwalld0.50709ffuspow...cpttf0.99182ralpne0.67908oh_steel_frac0.5455fimp(13)0.057148dr_tf_wp0.0651
1large tokamak 2major radius1.63beta0.042681dene0.071381fwalld0.70709ffuspow...cpttf0.99182ralpne0.67908oh_steel_frac0.5455fimp(13)0.057148dr_tf_wp0.0651
2large tokamak 3major radius1.50beta0.022681dene0.071381fwalld0.50709ffuspow...cpttf0.99182ralpne0.67908oh_steel_frac0.5455fimp(13)0.057148dr_tf_wp0.0651
3large tokamak 4major radius1.52beta0.032681dene0.071381fwalld0.40709ffuspow...cpttf0.99182ralpne0.67908oh_steel_frac0.5455fimp(13)0.057148dr_tf_wp0.0651
\n", + "

4 rows × 93 columns

\n", + "
" + ], + "text/plain": [ + " tag objf_name norm_objf itvar001_name nitvar001 \\\n", + "0 large tokamak 1 major radius 1.60 beta 0.032681 \n", + "1 large tokamak 2 major radius 1.63 beta 0.042681 \n", + "2 large tokamak 3 major radius 1.50 beta 0.022681 \n", + "3 large tokamak 4 major radius 1.52 beta 0.032681 \n", + "\n", + " itvar002_name nitvar002 itvar003_name nitvar003 itvar004_name ... \\\n", + "0 dene 0.071381 fwalld 0.50709 ffuspow ... \n", + "1 dene 0.071381 fwalld 0.70709 ffuspow ... \n", + "2 dene 0.071381 fwalld 0.50709 ffuspow ... \n", + "3 dene 0.071381 fwalld 0.40709 ffuspow ... \n", + "\n", + " itvar041_name nitvar041 itvar042_name nitvar042 itvar043_name nitvar043 \\\n", + "0 cpttf 0.99182 ralpne 0.67908 oh_steel_frac 0.5455 \n", + "1 cpttf 0.99182 ralpne 0.67908 oh_steel_frac 0.5455 \n", + "2 cpttf 0.99182 ralpne 0.67908 oh_steel_frac 0.5455 \n", + "3 cpttf 0.99182 ralpne 0.67908 oh_steel_frac 0.5455 \n", + "\n", + " itvar044_name nitvar044 itvar045_name nitvar045 \n", + "0 fimp(13) 0.057148 dr_tf_wp 0.0651 \n", + "1 fimp(13) 0.057148 dr_tf_wp 0.0651 \n", + "2 fimp(13) 0.057148 dr_tf_wp 0.0651 \n", + "3 fimp(13) 0.057148 dr_tf_wp 0.0651 \n", + "\n", + "[4 rows x 93 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "runs_metadata = [\n", + " RunMetadata(data_dir / \"large_tokamak_1_MFILE.DAT\", \"large tokamak 1\"),\n", + " RunMetadata(data_dir / \"large_tokamak_2_MFILE.DAT\", \"large tokamak 2\"),\n", + " RunMetadata(data_dir / \"large_tokamak_3_MFILE.DAT\", \"large tokamak 3\"),\n", + " RunMetadata(data_dir / \"large_tokamak_4_MFILE.DAT\", \"large tokamak 4\"),\n", + "]\n", + "\n", + "fig6, df6 = plot_mfile_solutions(\n", + " runs_metadata,\n", + " \"4 large tokamak solutions normalised to the range of the optimisation parameters\",\n", + " normalisation_type=\"range\",\n", + ")\n", + "df6" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Actual values" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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tagobjf_namenorm_objfitvar001_nameitvar001itvar002_nameitvar002itvar003_nameitvar003itvar004_name...itvar041_nameitvar041itvar042_nameitvar042itvar043_nameitvar043itvar044_nameitvar044itvar045_nameitvar045
0large tokamak 1major radius1.60beta0.033648dene8.066700e+19fwalld0.50758ffuspow...cpttf89795.0ralpne0.083954oh_steel_frac0.51868fimp(13)0.000571dr_tf_wp0.50416
1large tokamak 2major radius1.63beta0.034648dene8.056700e+19fwalld0.50258ffuspow...cpttf89795.0ralpne0.083954oh_steel_frac0.51868fimp(13)0.000571dr_tf_wp0.50416
2large tokamak 3major radius1.50beta0.033648dene8.066700e+19fwalld0.50758ffuspow...cpttf88795.0ralpne0.081954oh_steel_frac0.52868fimp(13)0.000531dr_tf_wp0.57416
3large tokamak 4major radius1.52beta0.037648dene8.366700e+19fwalld0.55758ffuspow...cpttf89795.0ralpne0.083954oh_steel_frac0.51868fimp(13)0.000571dr_tf_wp0.50416
\n", + "

4 rows × 93 columns

\n", + "
" + ], + "text/plain": [ + " tag objf_name norm_objf itvar001_name itvar001 \\\n", + "0 large tokamak 1 major radius 1.60 beta 0.033648 \n", + "1 large tokamak 2 major radius 1.63 beta 0.034648 \n", + "2 large tokamak 3 major radius 1.50 beta 0.033648 \n", + "3 large tokamak 4 major radius 1.52 beta 0.037648 \n", + "\n", + " itvar002_name itvar002 itvar003_name itvar003 itvar004_name ... \\\n", + "0 dene 8.066700e+19 fwalld 0.50758 ffuspow ... \n", + "1 dene 8.056700e+19 fwalld 0.50258 ffuspow ... \n", + "2 dene 8.066700e+19 fwalld 0.50758 ffuspow ... \n", + "3 dene 8.366700e+19 fwalld 0.55758 ffuspow ... \n", + "\n", + " itvar041_name itvar041 itvar042_name itvar042 itvar043_name itvar043 \\\n", + "0 cpttf 89795.0 ralpne 0.083954 oh_steel_frac 0.51868 \n", + "1 cpttf 89795.0 ralpne 0.083954 oh_steel_frac 0.51868 \n", + "2 cpttf 88795.0 ralpne 0.081954 oh_steel_frac 0.52868 \n", + "3 cpttf 89795.0 ralpne 0.083954 oh_steel_frac 0.51868 \n", + "\n", + " itvar044_name itvar044 itvar045_name itvar045 \n", + "0 fimp(13) 0.000571 dr_tf_wp 0.50416 \n", + "1 fimp(13) 0.000571 dr_tf_wp 0.50416 \n", + "2 fimp(13) 0.000531 dr_tf_wp 0.57416 \n", + "3 fimp(13) 0.000571 dr_tf_wp 0.50416 \n", + "\n", + "[4 rows x 93 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "runs_metadata = [\n", + " RunMetadata(data_dir / \"large_tokamak_1_MFILE.DAT\", \"large tokamak 1\"),\n", + " RunMetadata(data_dir / \"large_tokamak_2_MFILE.DAT\", \"large tokamak 2\"),\n", + " RunMetadata(data_dir / \"large_tokamak_3_MFILE.DAT\", \"large tokamak 3\"),\n", + " RunMetadata(data_dir / \"large_tokamak_4_MFILE.DAT\", \"large tokamak 4\"),\n", + "]\n", + "\n", + "fig7, df7 = plot_mfile_solutions(\n", + " runs_metadata,\n", + " \"4 large tokamak solutions normalised to the range of the optimisation parameters\",\n", + " normalisation_type=None,\n", + ")\n", + "df7" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "process", + "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": "3.10.12" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/examples/scan.ipynb b/examples/scan.ipynb index fcb524de7..50696c191 100644 --- a/examples/scan.ipynb +++ b/examples/scan.ipynb @@ -1,329 +1,330 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Example scan\n", - "\n", - "Perform a parameter scan for a given input file and plot the results.\n", - "\n", - "## Scan details\n", - "\n", - "The input file is a scan-enabled version of the large tokamak `IN.DAT`, as found in the `tests` directory. The scan-relevant values are:\n", - "```\n", - "nsweep = 17 * bmxlim, maximum peak toroidal field (T) (`constraint equation 25`)\n", - "isweep = 11\n", - "sweep = 11., 11.2, 11.4, 11.6, 11.8, 12., 12.2, 12.4, 12.6, 12.8, 13.\n", - "```\n", - "\n", - "- `nsweep`: integer denoting the variable to scan (see `scan_module` for options). Here `17` corresponds to `bmxlim` being scanned\n", - "- `isweep`: the number of scan points to run\n", - "- `sweep`: array of values for the scanned variable to take; one for each run. Should be of length `isweep`" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The IN.DAT file does not contain any obsolete variables.\n", - " tmargmin_cs and tmargmin should not both be specified in IN.DAT.\n", - " tmargmin_cs has been ignored.\n", - " \n", - " **************************************************************************************************************\n", - " ************************************************** PROCESS ***************************************************\n", - " ************************************** Power Reactor Optimisation Code ***************************************\n", - " **************************************************************************************************************\n", - " \n", - " Program :\n", - " Version : 3.1.0 Release Date :: 2024-03-21\n", - " Tag No. : v3.1.0-84-g4c95269\n", - " Branch : 1120-scan-output-verbosity\n", - " Git log : Add blank line after Scan Convergence Summary and the first scan point result\n", - " Date/time : 10 Jun 2024 17:03:54 +01:00(hh:mm) UTC\n", - " User : clair\n", - " Computer : clair-Precision-3570\n", - " Directory : /home/clair/development/PROCESS/examples\n", - " Input : /home/clair/development/PROCESS/examples/data/scan_example_file_IN.DAT\n", - " Run title : Generic large tokamak\n", - " Run type : Reactor concept design: Pulsed tokamak model, (c) CCFE\n", - " \n", - " **************************************************************************************************************\n", - " \n", - " Equality constraints : 26\n", - " Inequality constraints : 00\n", - " Total constraints : 26\n", - " Iteration variables : 44\n", - " Max iterations : 200\n", - " Figure of merit : +01 -- minimise major radius\n", - " Convergence parameter : 1.00E-07\n", - " \n", - " **************************************************************************************************************\n", - "Starting scan point 1 of 11: Max_toroidal_field_(T), bmxlim = 1.100E+01\n", - "7 | Convergence Parameter: 1.125E-09\n", - " \n", - " ************************************* PROCESS found a feasible solution **************************************\n", - " \n", - "Starting scan point 2 of 11: Max_toroidal_field_(T), bmxlim = 1.120E+01\n", - "2 | Convergence Parameter: 3.299E-08\n", - " \n", - " ************************************* PROCESS found a feasible solution **************************************\n", - " \n", - "Starting scan point 3 of 11: Max_toroidal_field_(T), bmxlim = 1.140E+01\n", - "2 | Convergence Parameter: 3.245E-08\n", - " \n", - " ************************************* PROCESS found a feasible solution **************************************\n", - " \n", - "Starting scan point 4 of 11: Max_toroidal_field_(T), bmxlim = 1.160E+01\n", - "2 | Convergence Parameter: 3.134E-08\n", - " \n", - " ************************************* PROCESS found a feasible solution **************************************\n", - " \n", - "Starting scan point 5 of 11: Max_toroidal_field_(T), bmxlim = 1.180E+01\n", - "2 | Convergence Parameter: 3.027E-08\n", - " \n", - " ************************************* PROCESS found a feasible solution **************************************\n", - " \n", - "Starting scan point 6 of 11: Max_toroidal_field_(T), bmxlim = 1.200E+01\n", - "3 | Convergence Parameter: 1.368E-09\n", - " \n", - " ************************************* PROCESS found a feasible solution **************************************\n", - " \n", - "Starting scan point 7 of 11: Max_toroidal_field_(T), bmxlim = 1.220E+01\n", - "2 | Convergence Parameter: 2.809E-08\n", - " \n", - " ************************************* PROCESS found a feasible solution **************************************\n", - " \n", - "Starting scan point 8 of 11: Max_toroidal_field_(T), bmxlim = 1.240E+01\n", - "2 | Convergence Parameter: 2.715E-08\n", - " \n", - " ************************************* PROCESS found a feasible solution **************************************\n", - " \n", - "Starting scan point 9 of 11: Max_toroidal_field_(T), bmxlim = 1.260E+01\n", - "2 | Convergence Parameter: 2.630E-08\n", - " \n", - " ************************************* PROCESS found a feasible solution **************************************\n", - " \n", - "Starting scan point 10 of 11: Max_toroidal_field_(T), bmxlim = 1.280E+01\n", - "2 | Convergence Parameter: 2.548E-08\n", - " \n", - " ************************************* PROCESS found a feasible solution **************************************\n", - " \n", - "Starting scan point 11 of 11: Max_toroidal_field_(T), bmxlim = 1.300E+01\n", - "2 | Convergence Parameter: 2.470E-08\n", - " \n", - " ************************************* PROCESS found a feasible solution **************************************\n", - " \n", - " \n", - " ******************************************** Errors and Warnings *********************************************\n", - " \n", - " PROCESS status flag: Warning messages \n", - " \n", - " ID LEVEL MESSAGE\n", - "150 2 CHECK: Lower limit of volume averaged electron temperature (te) has been raised \n", - " \n", - "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", - " \n", - "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", - " \n", - "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", - " \n", - "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", - " \n", - "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", - " \n", - "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", - " \n", - "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", - " \n", - "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", - " \n", - "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", - " \n", - "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", - " \n", - "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", - " \n", - "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", - " \n", - "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", - " \n", - "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", - " \n", - "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", - " \n", - "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", - " \n", - "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", - " \n", - "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", - " \n", - "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", - " \n", - "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", - " \n", - "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", - " \n", - "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", - " \n", - " **************************************************************************************************************\n", - " \n", - " \n", - " ******************************************* End of PROCESS Output ********************************************\n", - " \n" - ] - } - ], - "source": [ - "from process.main import SingleRun\n", - "from pathlib import Path\n", - "\n", - "data_dir = Path(\"data\")\n", - "input_name = data_dir / \"scan_example_file_IN.DAT\"\n", - "# Perform a SingleRun on a scan-enabled input file\n", - "single_run = SingleRun(str(input_name), solver=\"vmcon_bounded\")\n", - "single_run.run()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Plot scan results\n", - "Use `plot_scans.py` to plot the resulting `MFILE.DAT`." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", 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p0iXS0tKAshV2hfT09OjXrx8HDhwgJibm8R+gFFxcXOjUqRPHjx9n7dq1dOzYUd17N3To0GKLqJI4OzszZ84cYmJiWLdunUYR/yiFbW1oaKi1pEhR0dHR6vXhil734Hp8Bw4cYOXKlUyZMkXjusJ/uBR688031UV0Ubp6IeRpKDoPtKRFnYXQNSnshKgi3n77bVasWEFOTg7jx4/n559/fugPi+joaBYsWMDChQvVx+bPn89ff/3FV199xfjx4zWuLzr3qmvXrly6dEnj88eOHUvbtm2ZPXu2VmGXkpJCeHg4FhYWALzyyiu0bt2ayZMns3Xr1id46uIVzvlq06aNVhuMGjWK5s2bo6enx8cff1xsnJ6ennrYtqj79+9rzVt7UGRkJKBdiJSHESNGcPz4cTZu3Mi7776r7rkrfLniQVlZWRgbG5f4eaampkDZFqwubLNnn332odMApkyZAhT02g0ZMkR9fPjw4RrXpaamsnLlSnx9fdW9dMV5/vnntWKrmsLeYRmGFU+TDMUKUUU8//zz6jW5Nm7ciK+vL3/88QfZ2dnqa+7evUtQUBBDhw7FxcVFq1dp48aNODk5MXbsWK3PL1ogmZqaqr/OzMwkISGB1NRUvL29+fvvv7XmaI0ePVpd1AG4ubnh7+/Pnj17yMnJefKHf8DD5sn16dOHefPm8cUXX2i9PVwY17x582LfLPb09GTSpEnq4q2orKws5s+fz/fffw/AgAEDnvg5HmXIkCEYGRmRkJCgXiakRYsWJfY2BgQEMHjwYH777TeNN1NTU1NZunQpa9asAeDFF18sdQ6FbVbcMGx+fj4nT57k5ZdfZt26dUDBkiPNmjUr9edXVzdu3FAvDSNLnYinSXrshKhCFi5ciImJCfPnz2f//v3s378fAwMDrK2tSUtL01i7S6VSaQ1lXb58GS8vr0cOZ2VnZ/PZZ5+xZs0aoqKitM7fv38fKysr9dfF7dTQvHlzdu3axa1bt9R7s+pCWloaly5dAsreE1LY+1RSYZSVlcWCBQtYsGABDg4OuLi4cOHCBS5dusTnn3+ubl9/f/+nshaara0tL730Eps3byY0NBQo+aUJKNhL+JdfflG/oWphYYGBgYHGDhZdunRh6tSppbp/0bbesmWLxvB/fn4+SUlJ6n9YmJiY8NlnnzFp0qQyPePD7n337l2NY+bm5upex8pOdpwQFUUKOyGqEH19fb788kveeustli9fzqFDh7h+/Tr379/H1NSUZ555hueee46uXbvSr1+/x96sfvz48SxZsoTXX38df39/7O3t0dfXZ+XKlaxfv15rr86n6cyZM+r7l+UHpqIojxwaO3nyJL/88gt79uwhPDyc06dPk5GRgUqlwtramvbt2/Paa6/x73//+6nN9RoxYgSbN28GCoZQHzY8GRgYiIeHBwcPHuTChQvcvn2b1NRUHBwcaN26NUOHDmXEiBGlzr1oW6elpannJ0LBnDtbW1tcXV3p3r07b775JvXr13+CJ9X0wQcfaO1gMWfOHK3h9cqq8I1YMzOzCt2iTtQ8UtgJUQW5urry1VdflTmuWbNmnD9//pG7Evz88894e3urh+4K/fjjj8Vef/HiRY0tnaDgRQVjY+OH7kTwODw9PR9rLTaVSqW1h+yDbG1tGTVqlMZuD97e3nh7e5fpTVJd6tevX6mf19nZmbFjxxY71P44HretdeH999/npZde0jhWlYZ4Z86cycyZMys6DVEDyRw7IWqQIUOGcPPmzWK3Yyr6A1xfX1+rV+7y5cts2bKl2M9dtmyZRm9OREQEe/bswc/PT9bdE4/l2WefpUePHhq/dDmkL0R1JYWdEDXIxIkTadeuHRMmTGDQoEEsWrSIhQsXMmzYMCZPnqy+rn///hw+fJjXXnuN77//nv/+97906NChxOUuLC0t6dy5M1999RXTp0/H29sbExOTJ9qh4c0331SvXRYUFPTYn/M0jR8/Xp3z6tWrKzodUQ7K8/tSvn+ELshQrBA1iKmpKX/88QezZ89m06ZNbN26FWtra1q3bs1bb72lvm7hwoWYmpqyefNmNm/ezLPPPsuSJUu4cOGCxmr6hb744gv279/PF198wb179/Dw8GDBggW4ubmVOcfatWtrbeBuYmJS9ofVkZEjR5Z6rqKVlZXWMijW1tblkJV42p7G96V8/whdUCkVNYFCCFHlrVq1ijfffJODBw8+dE0yIQotXbqUd955p8TvmT/++AMfHx+WLFnCmDFjnn6CQlRxMhQrhBBCCFFNSGEnhBBCCFFNyBw7IYQQT82YMWMeOsTq7e1dYUusCFEdyBw7IYQQQohqQoZihRBCCCGqCSnshBBCCCGqCZljV4Pl5+dz69YtLC0tUalUFZ2OEEIIUSUpikJKSgr16tVDT69i+8yksKvBbt26RYMGDSo6DSGEEKJaiImJoX79+hWagxR2NZilpSVQ8I1oZWVVwdk8npycHPbu3Yufnx+GhoYVnU6VJm2pG9KOuiHtqBvSjrrzsLZMTk6mQYMG6p+rFUkKuxqscPjVysqqShd2ZmZmWFlZyV9aT0jaUjekHXVD2lE3pB11pzRtWRmmNcnLE0IIIYQQ1US1KewuXbrEokWLGDlyJC1btsTAwACVSsVnn332yNh9+/bRu3dv7O3tMTU15dlnn2Xq1KmkpqY+NO7KlSuMHDmS+vXrY2xsTP369Rk5ciTXrl17aFxKSgpTpkyhefPmmJqaYm9vz4svvsiBAwceGpefn8+yZcvo0KEDlpaWWFpa0qFDB5YvXy4LegohhBCi+hR2S5YsYdy4caxevZqIiAjy8vJKFffVV1/h6+vL7t27ee655+jTpw9JSUnMnj2bdu3acffu3WLjjh07RuvWrVm9ejU2Nja8/PLL2NjYsHr1alq1asWJEyeKjYuLi6Ndu3bMmTOHlJQU+vTpw3PPPceuXbvo0aMHixYtKjYuLy+PQYMGMWbMGCIiIvDx8cHHx4dz584xevRohgwZQn5+fukaSwghhBDVUrUp7Nzc3Pjggw9Yt24dFy5c4PXXX39kzOnTp5k0aRL6+vrs3LmTQ4cOsWnTJq5evUr37t25dOlSsVvfpKenM3jwYNLT05k8eTIRERFs2LCBiIgIJk+eTFpaGoMHDyYjI0MrdtSoUfz99990796dK1eusGnTJg4dOsSOHTvQ09Nj/PjxnD17Vitu0aJFbN68GScnJyIiIti2bRvbtm3j/Pnz1KtXj19++YXFixc/XuMJIYQQolqoNoXdv//9b7788kuGDRvGs88+W6p1ZObMmYOiKLz55pv06tVLfdzMzIwffvgBPT09fvvtNy5evKgRt2rVKm7dusUzzzyjNdT72Wef8cwzzxATE8OaNWs0zkVGRrJ161b09fX54YcfMDMzU5/r3bs3I0eOJD8/nzlz5mjE5efn8/nnnwPw+eef06RJE/W5Jk2aqM/NmTNHeu2EEEKIGqzaFHZllZ2dzc6dOwEYNmyY1vlGjRrh6ekJwJYtWzTOFX796quvahWQenp6DBkyBIDNmzcXG+fp6UmjRo207lmYx/bt28nJyVEfP378OLdv38bY2JiBAwdqxQ0cOBAjIyNu3brFX3/99ZCnFkIIIUR1VmMLu7///pv09HQA2rVrV+w1hcdPnz6tcbzw6/KKS0tL4/Lly1pxzz33HCYmJlpxpqamPPfcc8XeUwghhBA1R40t7K5fvw6AjY1NiQsKFu7KUHgtFLzRmpCQAEDDhg0fGhcfH09aWprWPUuKK7qeXNF7PiqupFyFEEIIUbPU2AWKU1JSADA3Ny/xGgsLC6BgRekH4x4WWxhXGFt4XWnvmZycXOw9y5rrg7KyssjKytLIDQoWXSw69FuVFOZdVfOvTKQtdUPaUTekHXVD2vEfefkKoVH3iEvJwsHSmHaNbNHXK/2Cwg9ry8rUvjW2sKuJ5syZw/Tp07WO7927V+NFjqooODi4olOoNqQtdUPaUTekHXWjprdjeIKKzTf0uJ/9TyFnY6QwoHE+re3Ktg5scW1ZOLWrMqixhV3h8GvRodIHFS5QXHS7raLDtiXFFl3YuLjYx71nWeMeNHnyZCZOnKj+unBvOz8/vyq9pVhwcDC+vr6yXc4TkrbUDWlH3ZB21A1pR9hz/g4rj4fzYPmWlK1i5d/6LHq1Nf7P1Xnk5zysLR82Wva01djCrnHjxgDcv3+flJSUYufZxcTEaFwLBUVWrVq1SExMJDo6mtatW5cYZ29vrzF82rhxY06dOkV0dHSxORUdgi16z8LflxRXUq4PMjY2xtjYWOu4oaFhlf8DXx2eobKQttQNaUfdkHbUjZrajnn5CrN2XdIq6gAUQAXM2nWJXq2cSj0sW1xbVqa2rbEvTzRv3lw9/BgaGlrsNYXH3d3dNY4Xfl1ecebm5jzzzDNacefPnyczM1MrLiMjg/Pnzxd7TyGEEKKmCrmeSGyS9s/NQgoQm5RJyPXEp5dUOauxhZ2RkREvvvgiAOvXr9c6HxUVxZ9//gnAyy+/rHGu8OsNGzZoLQicn5/Pxo0bARgwYIDGuf79+wMF25EV1/tWmEefPn00qv9OnTpRt25dsrKy+O2337TifvvtN7Kzs6lXrx4dOnQo+aGFEEKIGiQupeSi7nGuqwpqbGEH8PHHH6NSqVi5ciW7d+9WH09PT+df//oXeXl5DBw4kGeffVYjbuTIkdSrV4+///6bwMBAjXOBgYH8/fff1K9fnxEjRmice+655+jXrx95eXn861//0thybNeuXaxatQo9PT0mT56sEaenp8dHH30EwEcffaS1FMrHH38MFMyhK82OG0IIIURN4GCpvfbrk1xXFVSbOXanTp3i3XffVX999epVAJYtW8aOHTvUx7ds2YKjoyNQMGw5f/58Jk6cSO/evenatSsODg4cOXKE2NhYmjdvztKlS7XuZWZmxqZNm/Dz82P27Nls27YNNzc3IiIiiIiIwNzcnF9++QVTU1Ot2OXLlxMZGcm+fftwdnbmhRdeIC4ujkOHDqEoCl9//TWtWrXSihs7diyHDx9my5YtuLm50aNHDwD27dtHeno6r7zyisbzCyGEEDXd801q4Whtwu2kzGLn2amAutYmPN+k1tNOrdxUm+6d5ORk/vrrL/Wvu3fvAvC///1P43jRddwAJkyYQHBwMP7+/pw9e5atW7diYWHB5MmTOXnyJPb29sXez9PTk/DwcEaMGEFiYiK//fYbiYmJjBgxgvDwcDp27FhsnIODA6GhoXz88cdYWFiwdetWzp49i7+/P/v27WPcuHHFxunr6/Prr7+ydOlSWrRowf79+9m/fz/PPfccS5cuZdOmTdJbJ4QQQgBhUff4cs9F9PVUfNKnBVBQxBVV+PUnfVqUaT27yq7a9Nh5e3ujKGVbi6ZQjx491D1gZeHi4sLq1avLHGdlZcWcOXOYM2dOmeL09PQYPXo0o0ePLvM9hRBCiJpgQ0g0gVsjyMlTeKaOJf3aOLFkuDvTt0dqvEhR19qET/q0oKebYwVmq3vVprATQgghRM2VnZvP9O3nWfdXwcuJPZ+rS3fXgvXpero54tuiLiHXE4lLycTBsmD4tTr11BWSwk4IIYQQVVpcSibv/nSK0Kh7qFQwyfcZ3vNxQaX6p3DT11PRydmuArN8OqSwE0IIIUSVdSbmPmPWhnE7ORNLEwO+frUN3Z599E4S1ZUUdkIIIYSosu6lZ3MnJRMXBwuWv+5B09oWFZ1ShZLCTgghhBBVlk9zB5a85o6niz2WJpVna6+KIutjCCGEEKLKiE/J4u01oUQnpKuP9XRzlKLu/0mPnRBCCCGqhPCY+4z5KYzYpEzup2ezaXQnjRckhBR2QgghhKgCfg37H1O2nCM7N5+mtc2ZM6CVFHXFkMJOCCGEEJVWTl4+s3ZeYNWfNwDo4erAgiFtsJKh12JJYSeEEEKISul+ejaj14bx1/VEAN7v3oz3uzdDrxouLKwrUtgJIYQQolIyMdQnMzcfC2MDFgxujd9zdSs6pUpPCjshhBBCVCqKoqBSqTAx1GfpcHfSsnJxcbCs6LSqBCnshBBCCFEp5OblM/v3i5ga6fGh/7MAOFqbVnBWVYsUdkIIIYSocAmpWQSsP83xawmoVNC/jRPN6kgvXVlJYSeEEEKIChVxM4nRa8O4eT8DcyN95g9uLUXdY5LCTgghhBAVJuj0TT767SxZufk0tjPj+xHtpKh7AlLYCSGEEKJCfLnnIt8dvAqAT/PaLHy1Ldamsj7dk5DCTgghhBAVotn/v+ka4OPCBN9n0Jf16Z6YFHZCCCGEeGpy8vIx1NcDoH9bJ5rXtcTV0aqCs6o+9Co6ASGEEELUDNvCb+H31WHiUjLVx6So0y0p7IQQQghRrgrWp7vAuJ9Pc/1uGiuP3ajolKotGYoVQgghRLm5n57N2J9Pc+TyXQDe8XbmA7/mFZxV9SWFnRBCCCGeWF6+Qsj1ROJSMnGwNOH5JrX4+04Ko9aGEpOYgamhPvMGtebFVo4VnWq1JoWdEEIIIZ7I7ohYpm+PJDbpn7lztcyNSM3MJTsvnwa1TFn+ejuZT/cUyBw7ICYmhoCAAJydnTE2Nsbe3h5/f3927txZYkxiYiKTJ0/G1dUVU1NTbG1t8fLyYu3atQ+9V3p6OnPmzKFNmzaYm5tjaWlJ+/btWbRoEXl5eSXG5ebmsnjxYrp06YKtrS2GhobY29vTvXt3Vq9eTX5+/mM/vxBCCPG4dkfE8s5PpzSKOoB7adlk5+Xj6mjJ9oAuUtQ9JTW+x+7kyZP07NmTxMREHB0d6dWrFwkJCRw8eJC9e/cybdo0pk+frhFz7do1unXrRlRUFHZ2dnTv3p2MjAxOnDjBkSNH2L9/PytXrkSl0lyPJzExkW7duhEeHo6lpSWenp7o6+tz4sQJxo0bx/bt29mxYwdGRkYacVlZWfj5+XH48GGMjIzo0qULtWvXJiYmhoMHD3LgwAGCgoLYvHmz1j2FEEKI8pKXrzB9eyRKMecKj91Pz8HSRBYdflpqdI9dZmYmAwcOJDExkSFDhnD16lWCgoI4cuQIx44dw87OjhkzZhAcHKwRN3ToUKKiovD29uby5cvs2LGD/fv3Ex4ejrOzM6tXr2bFihVa9xszZgzh4eG4ublx/vx59u7dy65du7h48SLPP/88wcHBWkUkwOLFizl8+DCNGjXi8uXL7N+/nw0bNnDs2DFCQkKwtLQkKCiIjRs3lltbCSGEEA8KuZ6o1VP3oNikTEKuJz6ljESNLuy2bNlCTEwMNjY2LF26FFNTU/W59u3bM23aNABmzJihPn78+HFCQkLQ19dnxYoV2Nraqs+5uLiwYMECAGbOnImi/PNvmFu3bvHrr78CsGjRIho0aKA+V6dOHb7//nsAvvrqK1JSUjTyPHDgAADvvfceDRs21DjXrl07Xn31VXVuQgghxNNSdD06XVwnnlyNLuxOnjwJgIeHBzY2Nlrne/ToAcCxY8e4ffu2Rkzjxo1xdnYuMSYmJoaQkBD18dDQUBRFwcjICC8vL624Vq1aUbt2bTIyMvj99981zpmYmJTqeezt7Ut1nRBCCKEL9hbGpbrOwbJ0P8fEk6vRhV1qaioAdnZ2xZ4vLJQUReHUqVOlijEzM1P3/IWFhWndy8bGBj294pu98H5F4wB69eoFwHfffUd0dLTGubCwMDZs2ICpqSmvv/56sZ8rhBBC6FpSeg7LD1996DUqwNG6YOkT8XTU6JcnHBwcgIKXIYpT9Pj169c1Ygq/ftDt27fJyMjQuqYwLi4ujtTUVCwsLDTi8vPziYqKKvazR44cyaFDh1izZg3NmjWjS5cuODg4EBMTw59//knLli1ZunQpjRs3LtVzCyGEEE8iOzefAUuOcTU+DUN9FTl5CirQeImi8FW+T/q0QF9PXux7Wmp0YdetWzdmzZpFWFgYp0+fpm3bthrnly5dqv59cnIyAD4+PqhUKuLj4wkKCqJ///6PjAHo0KEDZmZmpKens2LFCsaPH68Rt2bNGtLT07XiAPT09Fi1ahWtWrVi8uTJ6jl3UNBD2KNHj2KHhR+UlZVFVlaWVn45OTnk5OQ8Mr4yKsy7quZfmUhb6oa0o25IO+pGebWjChjSrj6rj0fx3dA2/O9eBp/9fpHbyf/8jKlrbczUXs/Svbl9tfj/+LC2rEzPp1KKzvCvgbp27crhw4epX78+ixcvxsvLi4SEBBYvXsyCBQswMDAgJyeHuXPn8tFHHwEwYsQI1q5di62tLd988w29evUiIyODdevWERgYCBT8Tx4zZgxLlixR32vatGnMnDkTExMTPv/8cwYNGoS+vj7btm1jwoQJZGVlkZOTQ8+ePdm1a5c6Ljk5maFDh7Jr1y7Gjx/PO++8g5OTE9euXWPOnDmsX7+eRo0aceTIEY2XMh706aefFvvW7fr16zEzM9NVkwohhKiG8hVIzQGr/1+RS1EgKw9MDP45fzVZRXIOWBmCs5VCTemoS09PZ9iwYSQlJWFlVbHr9dX4wi4uLo4BAwZw7NgxrXPjx4/n6NGjhIaGsnz5ct5++20A0tLSGD58OEFBQVoxgwcPJjs7m6CgIKZMmcKsWbPU5/Ly8ggICNDo1Svk5eWFq6sry5YtY9iwYaxbt0597u2332bFihW8++67fPfdd1qxPXv2ZM+ePYwYMYLVq1eX+KzF9dg1aNCAu3fvVvg34uPKyckhODgYX19fDA1lnaQnIW2pG9KOuiHtqBu6asfkjBwm/nqOmMR0fh3doUauS/ewtkxOTsbe3r5SFHY1eigWCua+HTlyhH379nHgwAESEhKoU6cO/fr1o127dtSrVw+Ali1bqmPMzc3ZsmULx48fZ/fu3cTGxlKrVi38/f3x8fGhc+fOWjEA+vr6LFmyhHfffZdt27YRHR2NhYUF3t7evPjiiwwfPlwrLi8vT72bxdChQ4t9hmHDhrFnzx727dv30Gc1NjbG2Fj7DSZDQ8Mq/xdndXiGykLaUjekHXVD2lE3nqQdL99JYdTaMK7fTcPYQI+Lcel0dq65qzAU15aV6Xu0xhd2ACqVCl9fX3x9fTWOX716ldjYWOzs7HB3d9eK69SpE506ddI4lpKSwpkzZzAwMMDHx6fY+7Vs2VKr6FMURd1rWDSPuLg4dS9bSf8KsLa2Bgp2thBCCCF0ZXfEbSZtOkNadh5ONqYse90DNyfrik5LPESNXu7kUebNmwfAqFGjtLb5KsnixYvJyMhg0KBB1KlTp9T32rRpE9HR0XTq1AkPDw/1cTs7O3Uv219//VVs7IkTJwBo0qRJqe8nhBBClCQ/X2H+3kuM+SmMtOw8OjW1Y/vYLlLUVQE1vrCLjIzUegs1NzeX2bNns2zZMlxcXJg6darG+atXrxIfH69xTFEUfvzxRwIDA6lVqxbz58/XutetW7eIiYnROr5jxw5GjRqFsbGx1vw7IyMj+vbtC0BgYCBnz57VOL9//34WLlwIFAzJCiGEEE/qq31/s+jAFQD+1aUJa//1PLXMS9fBISpWjR+KXb58OcuWLcPDwwMnJyeysrI4ceIEd+7cwcXFheDgYMzNzTVitm/fzocffoi7uzsNGzZEURRCQ0OJiorCwcGBXbt24ejoqHWvkJAQBgwYQOvWrWnSpAmGhoacPXuWixcvYmFhQVBQEK1atdKK++qrrwgLC+PatWu4u7vTsWNH9VuxoaGhQMHSLR988EH5NJIQQogaZUSnxuw4G8u47i683LZ+RacjyqDGF3a9e/fmxo0bnDp1itDQUIyNjWnevDmTJk0iICBAY//YQp6engwcOJCQkBAiIiJQqVQ0bdqUwMBAJk6cWOz2ZABubm6MGDGC48ePExwcTF5eHg0bNmTChAlMmjQJJyenYuOcnJw4c+YM33zzDVu3buXcuXOcOHECa2trunbtyrBhw/jXv/6Fvr6+LptGCCFEDXLpdgrN61oCUNvSmL0TvDDUr/EDe1VOjS/s/Pz88PPzK1NM+/bt2bBhQ5nv5eLiwqpVq8ocB2BpacnUqVO1hoWFEEKIJ5Gfr7Bw/2W+2X+ZBYNbM8C9oIdOirqqqcYXdkIIIURNlZyZw8SNZ9h3IQ6AS3dSKjgj8aSksBNCCCFqoCtxqYxaG8q1+DSMDPSY83JLBnrIfLqqTgo7IYQQooYJjrzDhI1nSM3KxdHahGWve9Cqvk1FpyV0QAo7IYQQoga5Gl/QU6co8HyTWnw3zJ3altq7EomqSQo7IYQQogZxrm3Be94upGTm8N+XWshLEtWMFHZCCCFENXctPg0rc2McrQuW8Jrk9wwqlaqCsxLlQcp0IYQQohqLSFQxcNlfjF4bRmZOHoAUddWY9NgJIYQQ1VB+vsKig1f5/pI+kIuJgT4Z2XmYGMpi9tWZFHZCCCFENZOSmcOkTeHsjbwDwPAODZjWxw0jAxmoq+6ksBNCCCGqkWvxqby9JpSr8WkY6qt4pXEun7zkiqEUdTWC/F8WQgghqpGPN5/janwada1M+Pnfz9PRQanolMRTJIWdEEIIUY3MH9SaHq512DbWk9b1rSs6HfGUSWEnhBBCVGGpWbnsjohVf92glhkr3miHg6VJBWYlKorMsRNCCCGqgLx8hZDricSlZOJgacLzTWoRnZjOqDWhXIlP5cc32uPzrENFpykqmBR2QgghRCW3OyKW6dsjiU3KVB+zNTMkIyePzJx8HCyNsTYzrMAMRWUhhZ0QQghRie2OiOWdn07x4CsQ99JzAGha25wNb3fEwUqGXoXMsRNCCCEqrbx8henbI7WKuqIysvOwszB+ajmJyk0KOyGEEKKSCrmeqDH8WpzYpExCric+pYxEZSeFnRBCCFFJxaU8vKgr63Wi+pPCTgghhKikHCxLN8QqS5uIQvLyhBBCCFEJpWXlsvrPGw+9RgXUtS5Y+kQIkMJOCCGEqHRu3E1j9NowLt1JQV8P8vILiriiL1Go/v+/n/Rpgb6eqphPETWRFHZCCCFEJfLHpTjG/Xya5Mxcalsas3S4O/EpWVrr2NW1NuGTPi3o6eZYgdmKykYKOyGEEKISUBSFJYeu8uWeSygKtG1ow9LhHtT5//XpfFvU1dp5QnrqxIPk5QkgJiaGgIAAnJ2dMTY2xt7eHn9/f3bu3FliTGJiIpMnT8bV1RVTU1NsbW3x8vJi7dq1D71Xeno6c+bMoU2bNpibm2NpaUn79u1ZtGgReXl5j8z18OHDvPrqq9SvX1+dq4eHBxMmTCAnJ6fMzy6EEKJySM3K5eeQaBQFhj7fgA2jOqqLOgB9PRWdnO3o18aJTs52UtSJYtX4HruTJ0/Ss2dPEhMTcXR0pFevXiQkJHDw4EH27t3LtGnTmD59ukbMtWvX6NatG1FRUdjZ2dG9e3cyMjI4ceIER44cYf/+/axcuRKVSvMPXWJiIt26dSM8PBxLS0s8PT3R19fnxIkTjBs3ju3bt7Njxw6MjIy08lQUhQkTJvD1119jaGhIhw4d8PLy4u7du1y4cIGFCxcyc+ZMDA1lSxkhhKiKLE0MWf56O05H32dYh4YVnY6oomp0YZeZmcnAgQNJTExkyJAhrFy5ElNTU6Cg4OvVqxczZsygS5cu+Pr6quOGDh1KVFQU3t7ebN68GVtbWwCuXLlCz549Wb16NZ6enrz99tsa9xszZgzh4eG4ubnx+++/06BBAwDu3LlD3759CQ4OZvr06cyaNUsr108//ZSvv/6azp07s379eho1aqRx/uTJk5iYyOvuQghRlRz6O574lCxe8agPgKujFa6OVhWclajKavRQ7JYtW4iJicHGxoalS5eqizqA9u3bM23aNABmzJihPn78+HFCQkLQ19dnxYoV6qIOwMXFhQULFgAwc+ZMFOWf95du3brFr7/+CsCiRYvURR1AnTp1+P777wH46quvSElJ0cjz0qVLzJ49mzp16rBz506toq4wXwODGl2nCyFElaEoCkv+uMqbK0OYvPks5/6XVNEpiWqiRhd2J0+eBMDDwwMbGxut8z169ADg2LFj3L59WyOmcePGODs7lxgTExNDSEiI+nhoaCiKomBkZISXl5dWXKtWrahduzYZGRn8/vvvGueWLFlCbm4ub7/9drF5CiGEqDrSsnIJWH+az3dfJF+Bge71eaauRUWnJaqJGl3YpaamAmBnZ1fseXt7e6DgX1anTp0qVYyZmZm65y8sLEzrXjY2NujpFd/shfcrGgewZ88eALy8vLh//z7Lli3jvffeY+zYsSxbtoy7d+8+4kmFEEJUBtEJ6Qxc8ic7z8ViqK9i1stuzB3YCmMD/YpOTVQTNXrszsHBASh4GaI4RY9fv35dI6bw6wfdvn2bjIwMrWsK4+Li4khNTcXCQvNfZ/n5+URFRWnFZWdnc+nSJfXx4cOHExcXpxH7wQcf8P333/Pqq68+7HGFEEJUoMN/xzP259MkZeRgb1GwPl27xrJjhNCtGl3YdevWjVmzZhEWFsbp06dp27atxvmlS5eqf5+cnAyAj48PKpWK+Ph4goKC6N+//yNjADp06ICZmRnp6emsWLGC8ePHa8StWbOG9PR0rbjExET1XL2AgABcXV3ZtGkT7u7u3Lp1i7lz57Jq1SqGDx+Ok5MTL7zwQonPm5WVRVZWllZ+OTk5VXaplMK8q2r+lYm0pW5IO+pGdWzH8OhEkjJyaF3fmm+HtqaulUm5P191bMeK8rC2rEztW+MLOy8vLw4fPkzfvn1ZvHgxXl5eJCQksHjxYtasWYOhoSE5OTnq4VNnZ2eGDx/O2rVreeutt0hNTaVXr15kZGSwbt06Zs+erRUDYGlpyaRJk5g5cyaTJ09GT0+PQYMGoa+vz7Zt25gwYUKxcUVfwDA1NWXfvn3Url0bgObNm7Ny5Uru3LnDrl27+PTTT9m/f3+JzztnzhytpVsA9u7di5mZ2RO3Z0UKDg6u6BSqDWlL3ZB21I3q1I4NFBjUREVHhwROHT3wVO9dndqxohXXloUdM5WBSilaOdRAcXFxDBgwgGPHjmmdGz9+PEePHiU0NJTly5erly9JS0tj+PDhBAUFacUMHjyY7OxsgoKCmDJlisbSJXl5eQQEBGj06hXy8vLC1dWVZcuWMWzYMNatWwcUzM2ztLQEYOTIkaxcuVIrdseOHfTp0wdDQ0NSU1OLXQcPiu+xa9CgAXfv3sXKqmq+Xp+Tk0NwcDC+vr6yht8TkrbUDWlH3agO7RidmM7C/Vf4rF8LzIwqph+lOrRjZfGwtkxOTsbe3p6kpKQK/3lao3vsoGDu25EjR9i3bx8HDhwgISGBOnXq0K9fP9q1a0e9evUAaNmypTrG3NycLVu2cPz4cXbv3k1sbCy1atXC398fHx8fOnfurBUDoK+vz5IlS3j33XfZtm0b0dHRWFhY4O3tzYsvvsjw4cO14iwsLKhduzbx8fE0bdq02GcoPJ6Tk8Pdu3fVOT/I2NgYY2NjreOGhoZV/g98dXiGykLaUjekHXWjqrbjkcvxBKwvmE9nZWrErJdbPjqoHFXVdqyMimvLytS2Nb6wA1CpVPj6+mosQgxw9epVYmNjsbOzw93dXSuuU6dOdOrUSeNYSkoKZ86cwcDAAB8fn2Lv17JlS62iT1EUda/hg3l4eHiwe/fuEt9+LXr8wZcyhBBCPD2KorDs8DW++P+lTFo3sCGgm0tFpyVqkBq93MmjzJs3D4BRo0aVOLz5oMWLF5ORkcGgQYOoU6dOqe+1adMmoqOj6dSpEx4eHhrnBg0aBMCBAwfIz8/Xii0c72/evHmFdwELIURNlZ6dy9ifTzN3V0FRN7hdfTaO6oijtemjg4XQkRpf2EVGRmq8hQqQm5vL7NmzWbZsGS4uLkydOlXj/NWrV4mPj9c4pigKP/74I4GBgdSqVYv58+dr3evWrVvExMRoHd+xYwejRo3C2Ni42Pl3w4cPx9nZmYiICKZNm6ZR3B08eFC928W4ceNK/+BCCCF0JiYxnQGL/2TH2VgM9FTM7Pccnw9shYmhrE8nnq4aPxS7fPlyli1bhoeHB05OTmRlZXHixAnu3LmDi4sLwcHBmJuba8Rs376dDz/8EHd3dxo2bIiiKISGhhIVFYWDgwO7du3C0dFR614hISEMGDCA1q1b06RJEwwNDTl79iwXL17EwsKCoKAgWrVqpRVnZGTE5s2b8fHxYdasWWzYsIE2bdpw8+ZNQkJCyM/P54033uCdd94pt3YSQghRMgN9FXdTs7C3MGLxax4830TWpxMVo8YXdr179+bGjRucOnWK0NBQjI2Nad68OZMmTSIgIEBj/9hCnp6eDBw4kJCQECIiIlCpVDRt2pTAwEAmTpxY4rZfbm5ujBgxguPHjxMcHExeXh4NGzZkwoQJTJo0CScnpxLzbNWqFREREcyaNYudO3eyfft2zMzM6Nq1K6NGjZLFiYUQogI5WpvywxvtcbAylqFXUaFqfGHn5+eHn59fmWLat2/Phg0bynwvFxcXVq1aVea4Qo6Ojnz77bd8++23j/0ZQgghnlxGdh4fbz5Lz+fq0qtlwQhN6wY2FZuUEEhhJ4QQQpRJTGI6o9aGcSE2mT8uxdOlmT2WJpVnuQtRs0lhJ4QQQpTS0ct3Cfj5FPfTc9Tz6aSoE5WJFHZCCCHEIyiKwooj15mz60LB+nT1rVky3IN6NjKfTlQuUtgJIYQQD5GXrzBx0xm2nrkFwCse9fmsv5ssZSIqJSnshBBCiIfQ11Nhb2GMgZ6KaX1a8HrHRqhUqopOS4hiSWEnhBCixsvLVwi5nkhcSiYOlibqdej09QoKuMm9nuXltk64OVlXZJpCPJIUdkIIIWq03RGxTN8eSWxSpvqYpYkBjtYm7Bj7AkYGehjo60lRJ6qEGr+lmBBCiJprd0Qs7/x0SqOoA0jJzOXvO6nM2RVZQZkJ8XiksBNCCFEj5eUrTN8eifKQa3ZH3CEv/2FXCFG5SGEnhBCiRgq5nqjVU/eg2KRMQq4nPqWMhHhyUtgJIYSokeJSHl7UlfU6ISoDKeyEEELUSA6WJjq9TojKQAo7IYQQNU5Gdh7PN6mFo7UJJa1IpwIcrf9Z+kSIqkAKOyGEEDXKn1fu8sIXB/nregKf9GkBoFXcFX79SZ8W6rXshKgKpLATQghRIxTs93qN4T/8xd3ULJYdukZPN0eWDHenrrXmcGtdaxOWDHenp5tjBWUrxOORBYqFEEJUexnZeUzefJag/9/vdYC7E7NfbglATzdHfFvU1dp5QnrqRFUkhZ0QQohqLSYxndFrw4iMTUZfT8V/X3RlZOfGGvu96uup6ORsV4FZCqEbZSrs3nrrrXJJwtramq+++qpcPlsIIUTNdet+Bn2/Pcq99BzszI347jV3OjaVAk5UX2Uq7FatWoVKpUJRdLcKt0qlok6dOlLYCSGE0DlHaxO8mztwJS6Vpa974GRjWtEpCVGuyjwUa2JiwuDBg3WWwOrVq3X2WUIIIURGdh65+flYmhiiUqmYM6BgLp2JoX4FZyZE+StzYWdtbc3KlSt1loAUdkIIIXSlcD5dPRsTlr/eDj09lRR0okaRlyeEEEJUC8eu3CVg/SnupedwJzmT/93LoKGdWUWnJcRTVabCbty4cVhbW+s0gfL4TCGEEDWHoij8cPQ6s3+/QL4CLZ2sZT6dqLHKVNgtXLhQ5wmUx2cKIYSoGUpan06GX0VNJTtPADExMQQEBODs7IyxsTH29vb4+/uzc+fOEmMSExOZPHkyrq6umJqaYmtri5eXF2vXrn3ovdLT05kzZw5t2rTB3NwcS0tL2rdvz6JFi8jLyyt1zuHh4RgZGaFSqXBxcSl1nBBCVCdjfz5F0Jlb6Oup+KRPC+YPai1FnajRavwcu5MnT9KzZ08SExNxdHSkV69eJCQkcPDgQfbu3cu0adOYPn26Rsy1a9fo1q0bUVFR2NnZ0b17dzIyMjhx4gRHjhxh//79rFy5UmPxSygoBrt160Z4eDiWlpZ4enqir6/PiRMnGDduHNu3b2fHjh0YGRk9NOfs7GxGjBhBbm6uzttDCCGqkrHdmhF5K5kFQ9rI+nRCUMN77DIzMxk4cCCJiYkMGTKEq1evEhQUxJEjRzh27Bh2dnbMmDGD4OBgjbihQ4cSFRWFt7c3ly9fZseOHezfv5/w8HCcnZ1ZvXo1K1as0LrfmDFjCA8Px83NjfPnz7N371527drFxYsXef755wkODtYqIoszY8YMzp49y3vvvaezthBCiKpAURSuxKWov27dwIaDH3pLUSfE/yvXwm7Tpk34+fnh6uqKt7d3mYcby9uWLVuIiYnBxsaGpUuXYmr6z0Tb9u3bM23aNKCgkCp0/PhxQkJC0NfXZ8WKFdja2qrPubi4sGDBAgBmzpypsZDzrVu3+PXXXwFYtGgRDRo0UJ+rU6cO33//PQBfffUVKSn//KX1oJMnTzJ37lwGDRrEwIEDn+TxhRCiSsnIzmP8xjO8+M1RIm4mqY8bG8jQqxCFHruw2717N15eXowYMaLY83PmzGHo0KHs37+fS5cucfjwYcaPH0/fvn11unPFkzh58iQAHh4e2NjYaJ3v0aMHAMeOHeP27dsaMY0bN8bZ2bnEmJiYGEJCQtTHQ0NDURQFIyMjvLy8tOJatWpF7dq1ycjI4Pfffy8238zMTN544w1sbW359ttvy/CkQghRtcUkpjNwyZ9sPXOL3HyFi7dL/gewEDXZYxd2W7du5dixY8VO3L927Zq6t8vHx4evv/6aUaNGoVKp2L17Nz/88MPjZ6xDqampANjZFd+Fb29vDxR0/Z86dapUMWZmZuqev7CwMK172djYoKdXfLMX3q9oXFGBgYFcuHCBb775BgcHh5IfTAghqpFjV+7S99ujRMYmY2duxLp/d+AVj/oVnZYQldJjvzxR2Bs1ZMgQrXPff/89eXl5eHp6EhwcrH6JoFmzZnz44Yf89NNP/Pvf/37cW+tMYXF07dq1Ys8XPX79+nWNmMKvH3T79m0yMjK0rimMi4uLIzU1FQsLC424/Px8oqKiSvzsP//8kwULFtCvXz+GDh366IcTQogqTlHgx2M3+HzP3+Qr0Kq+NUuHe1BP1qcTokSPXdjdunULIyMjmjdvrnVu7969qFQqxo8fr/Fm6NixYwkMDOTcuXOPe1ud6tatG7NmzSIsLIzTp0/Ttm1bjfNLly5V/z45ORko6IFUqVTEx8cTFBRE//79HxkD0KFDB8zMzEhPT2fFihWMHz9eI27NmjWkp6drxUHBEikjR47E2tqaJUuWPPbzZmVlkZWVpZVfTk4OOTk5j/25Fakw76qaf2Uibakb0o66kZOTw5kEFatO/A3Ay23rMaOPKyaG+tK2ZSDfj7rzsLasTO1bpsKu6EsE8fHxmJiYaBwrVFi4HTt2jPPnz2ucs7GxIS4uTiPO29u72Hln5a1bt254eXlx+PBh+vbty+LFi/Hy8iIhIYHFixezZs0aDA0NycnJUQ+fOjs7M3z4cNauXctbb71FamoqvXr1IiMjg3Xr1jF79mytGABLS0smTZrEzJkzmTx5Mnp6egwaNAh9fX22bdvGhAkTio0D+Pjjj7l8+TKrV6/G0dHxsZ93zpw5xb51u3fvXszMqva2Ow++uSwen7Slbkg7PrnWduB2N5/m1govGEdzIDi6olOqsuT7UXeKa8vCjpnKoEyF3cGDB9W/z8/PJz8/X+MYwL1798jNzcXS0pLTp09rfUZWVhaKomjENW7cuEIKO4BffvmFAQMGcOzYMfr27atxbvz48Rw9epTQ0FBq1aqlPr5kyRJSUlIICgri9ddf14gZPHgw2dnZBAUFacQAfPLJJ8THx7N06VLef/993n//ffU5Ly8vXF1dWbZsmUbcH3/8wbfffkvv3r1LfFGltCZPnszEiRPVXycnJ9OgQQP8/PywsrJ6os+uKDk5OQQHB+Pr64uhoWFFp1OlSVvqhrTjkzkVfZ/nHC3RI5/g4GA2BnR75NqeomTy/ag7D2vLB0faKtJjF3ZWVlZkZmby+++/aywT8t133zF27Fj8/f3ZtGmT1md06dKFM2fOaBWEFcXBwYEjR46wb98+Dhw4QEJCAnXq1KFfv360a9eOevXqAdCyZUt1jLm5OVu2bOH48ePs3r2b2NhYatWqhb+/Pz4+PnTu3FkrBkBfX58lS5bw7rvvsm3bNqKjo7GwsMDb25sXX3yR4cOHa8UFBQWhKArR0dF4e3trfN79+/cBuHnzpvrcwoULadOmTbHPamxsjLGxsdZxQ0PDKv8Hvjo8Q2Uhbakb0o5lU3S/14Hu9ZnVzxUAIyMjaUcdkO9H3SmuLStT2z72HLvmzZtz6tQpfvjhBwICAtTHV61ahUqlUi/78aArV66oi6XKQqVS4evri6+vr8bxq1evEhsbi52dHe7u7lpxnTp1olOnThrHUlJSOHPmDAYGBvj4+BR7v5YtW2oVfYqicOzYMQCtPAAiIiJKzD8zM5NDhw4B/xR7QghRVTy432u+Ann5lWNZLCGqmsde7mTgwIEoisLEiRN5//33Wbx4MT179iQsLAwLC4ti35a9cOECcXFxtG7d+omSflrmzZsHwKhRo0o9FLB48WIyMjIYNGgQderUKfW9Nm3aRHR0NJ06dcLDw0N9fOHChSiKUuyvwl5PZ2dn9bEHe/WEEKIyK1yfrnC/10/7tGDeoFYY6NfojZGEeGyP3WM3YcIEfvvtN8LCwtSL5RYuPDxv3jysra21Yn744QdUKhX+/v6Pe1udi4yMpH79+hpzzHJzc/niiy9YtmwZLi4uTJ06VSPm6tWrWFlZUbt2bfUxRVFYuXIlgYGB1KpVi/nz52vd69atW+Tl5WnsOgGwY8cORo0ahbGxscZbtUIIUZ39eeUu760/xb30HOzMjfjuNXfZGkyIJ/TYhZ2xsTGHDh1i/vz57Ny5k3v37tG0aVPee+89XnrpJa3rk5KS2LVrF02bNmXQoEFPlLQuLV++nGXLluHh4YGTkxNZWVmcOHGCO3fu4OLiQnBwMObm5hox27dv58MPP8Td3Z2GDRuiKAqhoaFERUXh4ODArl27in17NSQkhAEDBtC6dWuaNGmCoaEhZ8+e5eLFi1hYWBAUFESrVq2e1qMLIUSFSc/OZdyG09xLz5H16YTQoccu7KBgl4XAwEACAwMfea21tbXW0ieVQe/evblx4wanTp0iNDQUY2NjmjdvzqRJkwgICNB4MaSQp6cnAwcOJCQkhIiICFQqFU2bNiUwMJCJEycWuz0ZgJubGyNGjOD48eMEBweTl5dHw4YNmTBhApMmTcLJyamcn1YIIZ6uvHyFkOuJxKVk4mBpwvNNaqGvp8LMyICvhrRh25lbzOzvhomh7PcqhC48UWFXHfj5+eHn51emmPbt27Nhw4Yy38vFxYVVq1aVOa4k3t7elWbfXSGEeNDuiFimb48kNilTfayWuRGzX3ajp5sjLzSrzQvNaj/kE4QQZVXm2ant2rVj9OjRLFu2jNDQULKzs8sjLyGEEFXY7ohY3vnplEZRB5CYls2Yn06xOyK2gjITonorc4/dqVOnNBYeNjAwoEWLFnh4eODh4YG7uzutW7fGxMREp4kKIYSoGvLyFaZvj+Rh4wnTt0fi26Iu+nqqh1wlhCirJxqKVRSFnJwcwsPDOXv2LCtXrgQKFuJ1dXXF3d1dXfC1adOm2PlqQgghqpeQ64laPXUPik3KJOR6Ip2c5S1YIXSpzIWdSlXwr6v69evz5ptv0rBhQ8LCwjh16hRnz54lIyOD3Nxczp07R0REBGvWrAFAT0+P5s2bqwu9cePG6fZJhBBCVApxKQ8v6sp6nRCi9Mo8xy4sLAxPT09iYmKYOXMmGzdu5L333uP48eOkpKSoe+7Gjh1L586dMTMzQ1EU8vLyiIyMZO3atRr7lQohhKheHCxLNxWntNcJIUqvzD12bdq04fDhw2zYsIH//Oc/BAcH06ZNG959911mzJiBm5sbbm5uvPHGG0DBcO2lS5fUvXqhoaGEh4fr/EGEEEJUDs83qUVdK2NuJ2cVe14F1LUuWPpECKFbj71ny6uvvsqlS5f473//i4GBAYsWLcLFxYVly5ZpLMGhUql49tlnee2115g/fz6HDh2S/UyFEKKaiU3KYOaOSHLz8gu2Buv7HCoKiriiCr/+pE8LeXFCiHLwRJvxmZqaMmPGDC5cuMDLL7/M3bt3effdd3F3d+fw4cO6ylEIIUQlduzKXV785ig/HL3ONweuANDTzZElw92pa6053FrX2oQlw93p6aa9O48Q4snpZIHiRo0a8euvv3Lw4EHef/99wsPD8fHx4ZVXXuHLL7+kYcOGuriNEEKISiQ/X2HJoavM33uJfAVaOFox0P2fHXR6ujni26JusTtPCCHKxxP12D3Ix8eHM2fOsGjRImxsbPjll19wdXXl008/JSMjQ5e3EkIIUYGS0nMYtTaUL/cUFHWDPOqz+d3ONLLT3FtbX09FJ2c7+rVxopOznRR1QpQznRZ2ULCsyXvvvceVK1d49913yczMZObMmUyfPl3XtxJCCFEBLsQm0+fbo+y7EIeRgR6fD2zJl4Nay36vQlQCOtsrNiUlhYiICM6dO6f+FRERgaIoqFQq8vPzdXUrIYQQFUhPpSI+JYv6tqYsec2DlvWtKzolIcT/K3Nhl5uby8WLFzUKuHPnzhETE6O+puhbsfXr16dNmzZ07dpVNxkLIYR46gr/kQ7QvK4lK95ox3P1rLAxM6rgzIQQRZW5sDM3Nyc3NxfQLOAMDAxo3rw5bdq0oW3btrRp04Y2bdpQq5asUySEEFVZTGI6Y38+TeBLrng0Kvg73dPFvoKzEkIUp8yFXU5ODiqViiZNmtC7d29at25NmzZtcHNzw9jYuDxyFEIIUUEOXLzD+A1nSM7M5b9B5/l9XBd1z50QovJ57Dl2169f55dffuHq1avcuHGD6Oho3N3dadSokS7zE0IIUQHy8hUW7vubRf+/Ll3rBjYsfs1dijohKrkyF3YNGjRQz6e7c+cOu3btYvfu3erztra2tG3bFnd3d/WvZs2a6S5jIYQQ5SohNYv3N5zh6JW7AIzo1IipL7pibCBvvQpR2ZW5sIuKiiIhIYFTp06p938NCwvj+vXrACQmJrJ//34OHDigjrGwsKBNmzYaxd5zzz2nu6cQQgihE7eTMnl58TFikzIxNdRn7sCW9Gvj9OhAIUSl8FhDsXZ2dvj6+uLr66s+dv/+fU6dOqX+FRYWxpUrV1AUhZSUFI4cOcLRo0eBgv1jC1/AEEIIUXnUsTKmTQMbTA1TWPq6B8/UsazolIQQZaCzdexsbGzo1q0b3bp1Ux9LSUnh9OnTGr17ly5d0nibVgghRMVKz85FUcDc2ACVSsWXg1qjKAqWJoYVnZoQoox0VtgVx9LSEi8vL7y8vNTH0tPTOXPmTHneVgghRCldiUvlnZ/CeKauJd8ObYtKpcLCuFx/NAghytFT/9NrZmZG586dn/ZthRBCPGDn2Vj+82s4adl5JGXkcDs5E0dr04pOSwjxBOSfZUIIUcPk5OUzd9dFfjha8NJbhya1WDSsLQ6WJhWcmRDiSZWpsNu2bRsmJib4+fnpLIHy+EwhhBDFu5OcyXvrThEadQ+A0V2b8qFfcwz09So4MyGELpTpT3L//v158803dZpAeXxmWcXExBAQEICzszPGxsbY29vj7+/Pzp07S4xJTExk8uTJuLq6Ympqiq2tLV5eXqxdu/ah90pPT2fOnDm0adMGc3NzLC0tad++PYsWLSIvL6/YmEuXLrFw4UJ69+6Nk5MTRkZGWFlZ0b59e+bMmUNqauoTPb8QomZQFIU3V54kNOoelsYGLB3uweRerlLUCVGNlHkotjzeaK3It2RPnjxJz549SUxMxNHRkV69epGQkMDBgwfZu3cv06ZNY/r06Rox165do1u3bkRFRWFnZ0f37t3JyMjgxIkTHDlyhP3797Ny5UqtFdoTExPp1q0b4eHhWFpa4unpib6+PidOnGDcuHFs376dHTt2YGSkual29+7duXnzJiYmJrRr1w4vLy/u3LnD8ePHCQ0N5YcffuDAgQM0bNiw3NtLCFF1qVQqPu37HDN2nGfRUHea2JtXdEpCCB0rc2GXkZHBmjVryiOXpy4zM5OBAweSmJjIkCFDWLlyJaamBROHT548Sa9evZgxYwZdunTRWLNv6NChREVF4e3tzebNm7G1tQXgypUr9OzZk9WrV+Pp6cnbb7+tcb8xY8YQHh6Om5sbv//+Ow0aNAAKdvDo27cvwcHBTJ8+nVmzZmnENW/enBkzZjB48GAsLCzUx2/cuMFLL73E+fPnGTlypMai0EIIAZCcmUPkrWQ6NrUD4Pkmtdj2Xhf09GRrMCGqJaUMVCqVoqenp9NfKpVKcXR0LEsaOrN+/XoFUGxsbJR79+5pnf/6668VQOnSpYv62J9//qkAir6+vnLlyhWtmK1btyqA0qBBAyU/P199/ObNm4pKpVIA5eDBg1px4eHhCqCYmpoqycnJpX6GI0eOKIACKDExMaWOUxRFSUpKUgAlKSmpTHGVSXZ2thIUFKRkZ2dXdCpVnrSlblSmdoy8laR0/eKA4hq4S/n7dun/XqkMKlM7VmXSjrrzsLasTD9Py9Rj17Bhw3LZANrBwUHnn1kaJ0+eBMDDwwMbGxut8z169ADg2LFj3L59m7p166pjGjdujLOzc4kxMTExhISE0KFDBwBCQ0NRFAUjIyONdf0KtWrVitq1axMfH8/vv//OkCFDSvUMbdu2Vf8+JiaG+vXrlypOCFG9/Rb2P6YGnSMzJx8nG1OycvMrOiUhxFNQpsLuxo0b5ZRGxSh86cDOzq7Y8/b29kDBHMBTp07Ru3fvR8aYmZlhampKRkYGYWFh6sKuMM7GxgY9veInKtvb2xMfH09YWFipC7vLly+rf+/o6FiqGCFE9ZWZk8eMHZGs/ysaAK9navP1kDbYmhs9IlIIUR3U6HXsCnsKr127Vuz5osevX7+uEVP49YNu375NRkaG1jWFcXFxcaSmpmrMlQPIz88nKirqoZ9dnLlz5wLg7u5O48aNSx0nhKja8vIVQq4nEpeSiYOlCc83qcWt+xm8t/4UZ/+XhEoF73dvxthuzdCX+XRC1Bg1urDr1q0bs2bNIiwsjNOnT2sMawIsXbpU/fvk5GQAfHx8UKlUxMfHExQURP/+/R8ZA9ChQwfMzMxIT09nxYoVjB8/XiNuzZo1pKena8U9zKpVq9i4cSP6+vp8/fXXj7w+KyuLrKwsrfxycnLIyckp1T0rm8K8q2r+lYm0pW48jXbcc/4On/1+kdvJ//x5rmtlTNsGNpz9XxI2poYsGNSSF5rZk5+XS37xKylVavL9qBvSjrrzsLasTO1b4ws7Ly8vDh8+TN++fVm8eDFeXl4kJCSwePFi1qxZg6GhITk5OerhU2dnZ4YPH87atWt56623SE1NpVevXmRkZLBu3Tpmz56tFQMF++ZOmjSJmTNnMnnyZPT09Bg0aBD6+vps27aNCRMmFBtXkv379zN69GgAvvjiC7p06fLImDlz5mgt3QKwd+9ezMzMSttslVJwcHBFp1BtSFvqRnm1Y3iCih//Lvw74p+euNvJmew6f5sWNgqDmmaQcjmE3y8X/xlViXw/6oa0o+4U15aFHTOVgUpRKnARuUogLi6OAQMGcOzYMa1z48eP5+jRo4SGhrJ8+XL18iVpaWkMHz6coKAgrZjBgweTnZ1NUFAQU6ZM0Vi6JC8vj4CAAI1evUJeXl64urqybNkyhg0bxrp160rM+ejRo/Ts2ZO0tDQ++eQTPv3001I9a3E9dg0aNODu3btYWVmV6jMqm5ycHIKDg/H19cXQ0LCi06nSpC11ozzbMS9fwXv+YY2euqJUQF1rYw5O9Kryw6/y/agb0o6687C2TE5Oxt7enqSkpAr/eVqje+ygYO7bkSNH2LdvHwcOHCAhIYE6derQr18/2rVrR7169QBo2bKlOsbc3JwtW7Zw/Phxdu/eTWxsLLVq1cLf3x8fHx86d+6sFQOgr6/PkiVLePfdd9m2bRvR0dFYWFjg7e3Niy++yPDhw4uNK+rPP/+kd+/epKWlMXXq1FIXdQDGxsYYGxtrHTc0NKzyf+CrwzNUFtKWulEe7Rh6NaHEog4K1j2KTcri9P9S6ORc/AteVY18P+qGtKPuFNeWlalta3xhBwWrsfv6+mosQgxw9epVYmNjsbOzw93dXSuuU6dOdOrUSeNYSkoKZ86cwcDAAB8fn2Lv17JlS63iTVEUda/hg3kUOnHiBD179iQlJYUpU6bw2WeflfoZhRBVW0Z2HrsjYkt1bVxKZjlnI4SorKSwe4h58+YBMGrUKK1tvkqyePFiMjIyGDp0KHXq1Cn1vTZt2kR0dDSdOnXCw8ND63xISAj+/v7qou7B3SmEENVXXr6C97yD3HlIb11RDpYm5ZyREKKyqvE7P0dGRmq9hZqbm8vs2bNZtmwZLi4uTJ06VeP81atXiY+P1zimKAo//vgjgYGB1KpVi/nz52vd69atW8TExGgd37FjB6NGjcLY2LjY+XehoaH4+fmRnJwsRZ0Q1ZyiKJz9332+PXBZvY+2vp4KTxd7nGxMsDDWLzFWBThaFyx9IoSomWp8j93y5ctZtmwZHh4eODk5kZWVxYkTJ7hz5w4uLi4EBwdjbq65Ufb27dv58MMPcXd3p2HDhiiKQmhoKFFRUTg4OLBr165iFwsOCQlhwIABtG7dmiZNmmBoaMjZs2e5ePEiFhYWBAUF0apVK604Pz8/kpKSsLGx4ebNm4wcObLYZ/n444959tlnddIuQoinKyohja1nbhF0+ibX7qYB4N3cATcnawCm930OC2MD9py/zTs/nQIK5tQVKnxV4pM+Lar8ixNCiMdX4wu73r17c+PGDU6dOkVoaCjGxsY0b96cSZMmERAQgKmpqVaMp6cnAwcOJCQkhIiICFQqFU2bNiUwMJCJEycWuz0ZgJubGyNGjOD48eMEBweTl5dHw4YNmTBhApMmTcLJyanYuHv37gFw//59Vq9eXeKzjBw5Ugo7IaqQe2nZbD97iy2nb3I6+r76uImhHr4t6mKoX2TJJJOCydk93RxZMtyd6dsjiU36Zy5dXWsTPunTgp5usgONEDXZUy3s8vLyuHXrFlCw/ZWBQcXXlX5+fvj5+ZUppn379mzYsKHM93JxcWHVqlVljqvhK9IIUW2F/+8+07aeB0BPBZ4u9vRv44S/W10sjEv++7GnmyO+Lepq7TwhPXVCiKdSWV28eJHJkyezZ88e9TpqxsbG+Pv7M2vWLFq0aPE00hBCiAqRm5fPsasJbD19k4Z2Zozv8QwAXVzs8XqmNl2fqU2f1o5leulBX09VbZY0EULoTrkXdqGhoXTv3p20tDT8/f1p3rw5UFDsbd++nX379nHw4EHatWtX3qkIIcRToygK4TH32XL6JjvOxnI3teAftXWtTNT7txro67HmrecrOFMhRHVS7oXdxIkTMTY25s8//+S5557TOBcREYGPjw8TJ07k8OHD5Z2KEEI8FYdiVXz19TFuJPyzzVAtcyNeauVIvzZOyIipEKK8PJUeu48//lirqIOClwkCAgL4/PPPyzsNIYQoNwmpWdiaGaH3/xVbfIaKGwnpmBjq4deiLv3b1uOFZrU1XoYQQojyUO6Fna2t7UP3TbO2tsbW1ra80xBCiDLJy1ce+nJCenYuwZF32HL6Jkcu32X9vzvQoWnBnLfOdfPp26UVvVo5PfQlCCGE0LVy/xtn+PDhrF69mtGjR2stHZKens6qVasYMWJEeachhBCltjsiVms5EUdrE/77oivmxgYEnb7J3sg7pGfnqc+HXE9UF3b1zKB3m3oYGkpRJ4R4usr9b50ePXqwd+9eWrVqxZgxYzRenli2bBmWlpZ0795da46dl5dXeacmhBBadkfE8s5Pp3hwkaHYpEzeW39a41gjOzP6tXGif5t6NK1t8fSSFEKIEpR7Yefv76/+/YcffohKVTCUUXRttqLXKIqCSqUiL++ffwkLIcTTkJevMH17pFZRV5SeCl7r0JAB7vVp08BG/XeaEEJUBuVe2K1cubK8byGEEDoRcj1RY/i1OPkK9G5Zj7YNZW6wEKLyKffC7o033ijvWwghhE7EpTy8qCvrdUII8bQ91Zm9qamp3L9/n/z8fK1zDRs2fJqpCCGEhvx8hWNX7pbq2rLsECGEEE/TUyns1q5dy+zZs/n7779LvEbm1AkhKkpqVi6TNp1hz/k7D71OBdS1Llj6RAghKqNyXy1zw4YNvPHGG6hUKkaPHo2iKAwdOpQhQ4ZgbGxM27ZtmTZtWnmnIYQQJfoq+G/2nL+Dkb4er3dsiIqCIq6owq8/6dNCYz07IYSoTMq9x27BggW0bt2av/76i+TkZJYuXcpbb71Ft27duHz5Mh07dqRFixblnYYQQpRogu8z/H0nhYm+z9C2oS2eLvZa69jVtTbhkz4t6OnmWIGZCiHEw5V7YXf+/HlmzJiBkZERenoFHYSFw67NmjXjnXfeYe7cuQwaNKi8UxFCCKBgPt2e87fp6VYXlUqFhbEBa//VQX2+p5sjvi3qPnTnCSGEqIzKvbAzMDBQbylmbm4OwN27/0xQbty4MRcvXizvNIQQAoCUzBwmbgonOPIOU3u78rZX02Kv09dT0cnZ7ilnJ4QQT6bc59g1btyYGzduAGBsbEyjRo3Yu3ev+vwff/xBrVoyEVkIUf6uxKXS77tjBEcWzKezNjOs6JSEEEKnyr2w69atG7/99pv669dff53Vq1fTvXt3fHx8+PnnnxkwYEB5pyGEqOGCI+/Q/7tjXItPo66VCZvGdGJwuwYVnZYQQuhUuQ/F/uc//6Fnz55kZWVhbGxMYGAgd+/eZcOGDejr6/PWW28xZ86c8k5DCFFD5ecrfL3/Ml/vvwzA841r8d1r7tS2NK7gzIQQQvfKvbBzdHTE0fGft8gMDQ1ZvHgxixcvLu9bCyEEF2+n8N3BKwCM7NyYqS+6Yqhf7oMVQghRIZ7qzhNCCPG0tahnxSd9n8PEQI9BMvQqhKjmyv2frfv372fy5Mklnp8yZQoHDhwo7zSEEDXInvO3uXwnRf316x0bSVEnhKgRyr2wmz17NtevXy/xfFRUFHPnzi3vNIQQNUB+vsL8vZcYvTaMUWvDSM7MqeiUhBDiqSr3wu7s2bN06NChxPPPP/884eHh5Z2GEKKaS8rI4V+rT7LoQMF8Op/mDpga6ldwVkII8XSVe2GXmpqKkZFRief19fVJSkoq7zQeKiYmhoCAAJydnTE2Nsbe3h5/f3927txZYkxiYiKTJ0/G1dUVU1NTbG1t8fLyYu3atQ+9V3p6OnPmzKFNmzaYm5tjaWlJ+/btWbRokXpHjpJcuXKFkSNHUr9+fYyNjalfvz4jR47k2rVrj/XcQlQXf99Jod+3Rzl4KR5jAz2+GtKaaX1ayEsSQogap9z/1mvSpAlHjhwp8fzhw4dp1KhReadRopMnT9KmTRu+++47MjIy6NWrF66urhw8eJCXXnqJTz75RCvm2rVruLu7M3fuXOLj4+nevTvu7u6EhYUxYsQIRo4ciaIoWnGJiYl07tyZKVOmcO3aNTw9PenSpQtXrlxh3Lhx9OrVi+zs7GLzPHbsGK1bt2b16tXY2Njw8ssvY2Njw+rVq2nVqhUnTpzQedsIURXsjojl5e+OcSMhHScbU357pzMvt61f0WkJIUSFKPfCbvDgwfzyyy8sWrRIo9hRFIVvvvmG3377jcGDB5d3GsXKzMxk4MCBJCYmMmTIEK5evUpQUBBHjhzh2LFj2NnZMWPGDIKDgzXihg4dSlRUFN7e3ly+fJkdO3awf/9+wsPDcXZ2ZvXq1axYsULrfmPGjCE8PBw3NzfOnz/P3r172bVrFxcvXuT5558nODiY6dOna8Wlp6czePBg0tPTmTx5MhEREWzYsIGIiAgmT55MWloagwcPJiMjo9zaSojKSFEUVv8ZRVp2Hp2d7dgW4Imbk3VFpyWEEBVHKWfp6elKhw4dFD09PaVBgwZKz549lZ49eyoNGjRQVCqV0r59eyU9Pb280yjW+vXrFUCxsbFR7t27p3X+66+/VgClS5cu6mN//vmnAij6+vrKlStXtGK2bt2qAEqDBg2U/Px89fGbN28qKpVKAZSDBw9qxYWHhyuAYmpqqiQnJ2uc++677xRAeeaZZ5S8vDyNc3l5ecozzzyjAMrSpUvL9PxJSUkKoCQlJZUprjLJzs5WgoKClOzs7IpOpcqrqm0Zn5KpLAz+W8nJzXv0xU9BVW3HykbaUTekHXXnYW1ZmX6elnuPnampKYcOHWLGjBnY2tpy6NAhDh06hK2tLZ999hmHDx/G1NS0vNMo1smTJwHw8PDAxsZG63yPHj2AgmHQ27dva8Q0btwYZ2fnEmNiYmIICQlRHw8NDUVRFIyMjPDy8tKKa9WqFbVr1yYjI4Pff/9d49yWLVsAePXVV9HT0/xfpqenx5AhQwDYvHnzox9aiCruUpEFhwHsLYx5v0czDGQ+nRBCPN5Q7KVLlzh9+jS5ubmlut7Y2JipU6cSHh5Oeno66enphIeHM2XKFExMTB4nBZ1ITU0FwM7Ortjz9vb2QMFwz6lTp0oVY2Zmpi5Uw8LCtO5lY2OjVZw9eL+icQCnT58GoF27dsXGFR4vvE6I6mrn2VheXnyML/dcYlv4rYpORwghKp0yF3b5+fkMHDiQdu3a0b179/LI6alxcHAAKPGt0qLHC9fiK4wpaW2+27dvq+e6Fb2mMC4uLk5d5BWVn59PVFSUVlxKSgoJCQkANGzYsNh7NmhQsPBqfHw8aWlpxV4jRFWWl68wd9dF3lt/ivTsPLq42POCi31FpyWEEJVOmbcU27VrF5GRkZibm7N+/fryyOmp6datG7NmzSIsLIzTp0/Ttm1bjfNLly5V/z45ORkAHx8fVCoV8fHxBAUF0b9//0fGAHTo0AEzMzPS09NZsWIF48eP14hbs2YN6enpWnEpKf+snm9ubl7sc1hYWGjcs6TrsrKyyMrK0sovJyeHnJyquZBrYd5VNf/KpLK25f30HCb8cpajVwr+gfPvLo2Z1MMFA31VpcsVKm87VjXSjroh7ag7D2vLytS+ZS7sfvnlF1QqFWPHjsXJyanUcRMmTOB///sf3bt3Z8yYMWW9bbno1q0bXl5eHD58mL59+7J48WK8vLxISEhg8eLFrFmzBkNDQ3JyctTDp87OzgwfPpy1a9fy1ltvkZqaSq9evcjIyGDdunXMnj1bKwbA0tKSSZMmMXPmTCZPnoyenh6DBg1CX1+fbdu2MWHChGLjdGnOnDnFvnW7d+9ezMzMyuWeT8uDby6Lx1eZ2vJmGvxwSZ+ELBWGegrDnPNpmXeFvXuuPDq4glWmdqzKpB11Q9pRd4pry8KOmcpApSjFLLj2EM8++yyXL1/mzJkztGzZstRx0dHRNGvWjLy8PK5du1bisOLTFhcXx4ABAzh27JjWufHjx3P06FFCQ0NZvnw5b7/9NgBpaWkMHz6coKAgrZjBgweTnZ1NUFAQU6ZMYdasWepzeXl5BAQEaPTqFfLy8sLV1ZVly5YxbNgw1q1bBxT02FlZWQFw5swZWrdurRV7+vRp3N3dgYK5fGXpsWvQoAF3795V36OqycnJITg4GF9fXwwNDSs6nSqtMrblvgtxvLP+DPVtTVk8tA2ujpYVndIjVcZ2rIqkHXVD2lF3HtaWycnJ2Nvbk5SUVOE/T8vcY3fr1i309fXLVNRBwfywAQMGsGnTJrZu3crYsWPLeuty4eDgwJEjR9i3bx8HDhwgISGBOnXq0K9fP9q1a0e9evUANJ7X3NycLVu2cPz4cXbv3k1sbCy1atXC398fHx8fOnfurBUDBbtsLFmyhHfffZdt27YRHR2NhYUF3t7evPjiiwwfPlwrztLSklq1apGYmEh0dHSxhV1MTAxQ8PJFSUUdFLzEYmxsrHXc0NCwyv+Brw7PUFlUprbs1cqJhXng3bw2NmYl72BTGVWmdqzKpB11Q9pRd4pry8rUtmUu7HJzc7G0fLx/Nb/66qts3LiRQ4cOVZrCDkClUuHr64uvr6/G8atXrxIbG4udnZ26R6yoTp060alTJ41jKSkpnDlzBgMDA3x8fIq9X8uWLbWKPkVR1L2GD+bh7u7Ovn37CA0NpU+fPlqfFxoaqr5OiKrsXlo207ad5+Nez+JkU/B2ef+2pZ/yIYQQNV2ZJ3M5ODhw//59jSG90urSpQsA58+fL3NsRZg3bx4Ao0aNeuh+t0UtXryYjIwMBg0aRJ06dUp9r02bNhEdHU2nTp3w8PDQOPfyyy8DsGHDBvLz8zXO5efns3HjRgAGDBhQ6vsJUdmcv5VEn2+Psj38FhM3nil2Wz4hhBAPV+bCrvCFiYiIiDLfzM7ODhMTE27dqjzrT0VGRmq8hQoFvZKzZ89m2bJluLi4MHXqVI3zV69eJT4+XuOYoij8+OOPBAYGUqtWLebPn691r1u3bqmHTYvasWMHo0aNwtjYuNj5dyNHjqRevXr8/fffBAYGapwLDAzk77//pn79+owYMaLUzy1EZbL1zE0GLvmT/93LoJGdGdP7PYdKparotIQQosop81Bsjx49OH78OD/88INWz1JpmJubk5SUVOa48rJ8+XKWLVuGh4cHTk5OZGVlceLECe7cuYOLiwvBwcFa89a2b9/Ohx9+iLu7Ow0bNkRRFEJDQ4mKisLBwYFdu3bh6Oioda+QkBAGDBhA69atadKkCYaGhpw9e5aLFy9iYWFBUFAQrVq10oozMzNj06ZN+Pn5MXv2bLZt24abmxsRERFERERgbm7OL7/8UmE7eAjxuHLz8pm76yIrjhas3dj1mdp882pbrM0qz3wVIYSoSsrcY/fKK68AsHr16jLvdJCTk0NSUtJjz9ErD71798bf35/o6Gi2bt3KwYMHadiwIV988QVnz56lcePGWjGenp4MHDiQ+Ph4fv/9d3bv3o2VlRWBgYFcunSpxLlubm5ujBgxgvT0dIKDg9m+fTuKojBhwgQuXrxIz549S8zT09OT8PBwRowYQWJiIr/99huJiYmMGDGC8PBwOnbsqKsmEaJc5OUrHL+awNYzNzl+NYGE1CxG/BiiLure9Xbmx5HtpagTQognUOYeu5YtW/Lqq6+yYcMGXn75Zf74449ii5/i/Pnnn+Tm5tKkSZOy3rbc+Pn54efnV6aY9u3bs2HDhjLfy8XFhVWrVpU5rmj86tWrHzteiIqyOyKW6dsjiU3KVB+ra2WMgZ4eZkb6zBvUmt4ttXu5hRBClM1jrYQ7f/586tatS3R0NG3atCl1kfPZZ5+hUqno2rXr49xWCFEF7Y6I5Z2fTmkUdQB3krP43/0MPvBrLkWdEELoyGMVdo6Ojuzbtw97e3uSk5N57bXX6NKlC7/88ot6n9Si4uLiGDZsGPv370elUjFq1KgnTlwIUfnl5StM3x5Jce+3KoAK+P7INfLy5Q1YIYTQhTIPxRZq0aIFf/75J8OGDSM0NJTjx49z/PhxDA0NadGiBQ0aNMDIyIibN29y6tQp9T5qkyZNonnz5jp7ACFE5RVyPVGrp64oBYhNyiTkeiKdnO2eXmJCCFFNPXZhBwVzvv78808WLVrE/PnzuXXrFtnZ2Zw5c4bw8HD1dYXrUY0dO5a5c+c+WcZCiCojLqXkou5xrhNCCPFwT1TYARgYGDBhwgTee+89du7cye7duwkLC+P27dtkZWVRp04dOnfuzNtvv0379u11kbMQooq4EJtSquscLE3KORMhhKgZnriwK2RkZMTLL7+s3iVBCFGzzdoZyfdHrj/0GhVQ19qE55vUejpJCSFENfdYL08IIcSjNHMoWK/yxZaOqCgo4ooq/PqTPi3Q15NdJoQQQhd01mMnhBA5efkY6hf8e3Fw+wa0rG+Nq6NV8evYWZvwSZ8W9HSTpU6EEEJXpLATQujEppMxLD18lV/HdKaWuREAro5WAPR0c8S3RV1CricSl5KJg2XB8Kv01AkhhG5JYSeEeCLZufnM2HGen05EA/DTiSjGdW+mdZ2+nkqWNBFCiHImhZ0Q4rHFpWTy7k+nCI26h0oFE3o8Q4CPS0WnJYQQNZYUdkKIx3Iq+h7v/BTGneQsLI0NWPhqG7q71qnotIQQokaTwk4IUWYHL8Uxak0oOXkKzRwsWPa6B01rW1R0WkIIUeNJYSeEKDP3BrY4WpvSwtGKeYNbY2Esf5UIIURlIH8bCyFKJSkjBysTA1QqFdZmhvz6TidqWxijUsmbrUIIUVnIAsVCiEcKi0qkx4JDrPsrWn3MwdJEijohhKhkpLATQpRIURTW/RXFq8tPEJ+Sxc8h0eTm5Vd0WkIIIUogQ7FCiGJl5ebxydbzbDgZAxRsDfbFK60w0Jd/DwohRGUlhZ0QQsvtpEzG/BTGmZj76KngPz2fZbRXUxl6FUKISk4KOyGEhrSsXPp9d4w7yVlYmxqyaGhbvJ6pXdFpCSGEKAUZUxFCaDA3NmBk5yY8W9eS7QFdpKgTQogqRHrshBBk5uQRn5yp/npM16a86dkYE0P9CsxKCCFEWUlhJ0QNd+t+BmN+CiMzO49/Ny44plKppKgTQogqSAo7IWqw41cTCFh/ioS0bGzNDInPfHSMEEKIykvm2AExMTEEBATg7OyMsbEx9vb2+Pv7s3PnzhJjEhMTmTx5Mq6urpiammJra4uXlxdr16596L3S0tKYM2cO7dq1w8rKCkNDQ+rWrctLL73Etm3bSozLzc1l8eLFdOnSBVtbWwwNDbG3t6d79+6sXr2a/HxZW0yUnqIo/Hj0OsN/+IuEtGxaOFqxeUxH6ptXdGZCCCGeRI3vsTt58iQ9e/YkMTERR0dHevXqRUJCAgcPHmTv3r1MmzaN6dOna8Rcu3aNbt26ERUVhZ2dHd27dycjI4MTJ05w5MgR9u/fz8qVK7WWhkhISMDLy4vIyEgsLCzo3LkzNjY2XLlyhZ07d7Jz507GjRvH119/rRGXlZWFn58fhw8fxsjIiC5dulC7dm1iYmI4ePAgBw4cICgoiM2bN8tyFOKRMnPymLL5HJtP3wSgf5t6zBnQCgNVPmcrODchhBBPSKnBMjIylAYNGiiAMmTIECU9PV19LiQkRLGzs1MAZe/evRpxzz//vAIo3t7eSmJiovr45cuXFWdnZwVQli9frnW/cePGKYDi4eGhJCQkaJzbuXOnYmBgoADK8ePHNc4tWLBAAZRGjRopUVFRGudOnjypWFpaKoDy888/l+n5k5KSFEBJSkoqU1xlkp2drQQFBSnZ2dkVnUqV8fFv4Uqjj3YoTSfvVH44ck3Jz89XFEXaUlekHXVD2lE3pB1152FtWZl+ntboodgtW7YQExODjY0NS5cuxdTUVH2uffv2TJs2DYAZM2aojx8/fpyQkBD09fVZsWIFtra26nMuLi4sWLAAgJkzZ6Ioisb9Dhw4AMBHH31ErVq1NM717t0bHx8f9T2Ki3vvvfdo2LChxrl27drx6quvFhsnRHHG93iGZ+ta8tO/OvBWlybSyyuEENVIjS7sTp48CYCHhwc2NjZa53v06AHAsWPHuH37tkZM48aNcXZ2LjEmJiaGkJAQjXMmJialysve3l4ncUJAwXy6kOuJ6q/rWJnw+7gX6ORsV4FZCSGEKA81urBLTU0FwM6u+B9whYWSoiicOnWqVDFmZmbqnr+wsDCNc7169QLg888/JzExUePc77//zsGDB6lbty59+/YtNu67774jOjpa41xYWBgbNmzA1NSU119//SFPK2qijOw83t9whsHLjrPj7C31cT096aUTQojqqEa/POHg4AAUvAxRnKLHr1+/rhFT+PWDbt++TUZGRrHXfPTRR4SEhLBnzx4aNWqEp6en+uWJsLAwPD09+eGHH7C2ttaIGzlyJIcOHWLNmjU0a9aMLl264ODgQExMDH/++SctW7Zk6dKlNG7cuOyNIKqtmMR0Rq0N40JsMgZ6KpIycio6JSGEEOWsRhd23bp1Y9asWYSFhXH69Gnatm2rcX7p0qXq3ycnJwPg4+ODSqUiPj6eoKAg+vfv/8iYQubm5mzfvp0pU6Ywf/589uzZoz5nZ2dHjx49cHJy0spTT0+PVatW0apVKyZPnqyecwcFPYQ9evQodlj4QVlZWWRlZWnll5OTQ05O1fyhX5h3Vc2/vBy9ksCETWe5n5GDnbkR37zaiucb13poO0lb6oa0o25IO+qGtKPuPKwtK1P7qpQHZ/jXMF27duXw4cPUr1+fxYsX4+XlRUJCAosXL2bBggUYGBiQk5PD3Llz+eijjwAYMWIEa9euxdbWlm+++YZevXqRkZHBunXrCAwMBAr+J48ZM4YlS5ao7xUbG0u/fv04e/Ys06ZNY+jQoTg4OBAZGcl///tf9u7dS+vWrTly5AiWlpbquOTkZIYOHcquXbsYP34877zzDk5OTly7do05c+awfv16GjVqxJEjR2jQoEGJz/rpp59qLd0CsH79eszMzHTVpKICKQocuKVie7QeCioamiv8q3keNsYVnZkQQlRf6enpDBs2jKSkJKysrCo0lxpf2MXFxTFgwACOHTumdW78+PEcPXqU0NBQli9fzttvvw0ULDI8fPhwgoKCtGIGDx5MdnY2QUFBTJkyhVmzZqnP+fn5ERwczBdffMGHH36oEZeTk4OHhwfnzp3TWjvv7bffZsWKFbz77rt89913Wvfs2bMne/bsYcSIEaxevbrEZy2ux65BgwbcvXu3wr8RH1dOTg7BwcH4+vpiaGhY0ek8NXn5CqFR94hLycLB0ph2jWzR11MRGnWPoSsKXvAZ6F6P6S+5YlzKrcFqalvqmrSjbkg76oa0o+48rC2Tk5Oxt7evFIVdjR6KhYI5c0eOHGHfvn0cOHCAhIQE6tSpQ79+/WjXrh316tUDoGXLluoYc3NztmzZwvHjx9m9ezexsbHUqlULf39/fHx86Ny5s1bMzZs3CQ4OBmDo0KFaeRgaGvLKK69w7tw59u3bpy7s8vLy1LtZFBcHMGzYMPbs2cO+ffse+qzGxsYYG2t33RgaGlb5P/DV4RlKa3dELNO3RxKb9M/+X47WJnzSpwU93Rx5x9uZetYmDO/Y6LGWMqlJbVmepB11Q9pRN6Qddae4tqxMbVvjCzso2PDc19cXX19fjeNXr14lNjYWOzs73N3dteI6depEp06dNI6lpKRw5swZDAwM1OvSARpvs5ZUzRe+NFH0jdm4uDh1L1tZ4kT1tDsilnd+OsWD3eyxSZm889Mplgx356Oez1ZIbkIIISpejV7u5FHmzZsHwKhRozAyMipVzOLFi8nIyGDQoEHUqVNHfbzoSxF//fVXsbEnTpwAoEmTJupjdnZ26l62ssSJ6icvX2H69kitoq6o6dsjycuv0bMrhBCiRqvxhV1kZKTW26u5ubnMnj2bZcuW4eLiwtSpUzXOX716lfj4eI1jiqLw448/EhgYSK1atZg/f77G+YYNG9K+fXsA3n//fW7cuKFx/qeffmLjxo1AwdBqISMjI/W6doGBgZw9q7mb5/79+1m4cKFWnKh+Qq4nagy/PkihoOeu6GLEQgghapYaPxS7fPlyli1bhoeHB05OTmRlZXHixAnu3LmDi4sLwcHBmJuba8Rs376dDz/8EHd3dxo2bIiiKISGhhIVFYWDgwO7du3C0dFR614//vgjPj4+XLhwAVdXVzp27Ii9vT0XLlzg/PnzAAwfPpzXXntNI+6rr74iLCyMa9eu4e7uTseOHdVvxYaGhgIFS7d88MEH5dRKojKISym5qHuc64QQQlQ/Nb6w6927Nzdu3ODUqVOEhoZibGxM8+bNmTRpEgEBARr7xxby9PRk4MCBhISEEBERgUqlomnTpgQGBjJx4sRitycDcHNzIyIigq+++opdu3Zx8uRJsrKysLW1xd/fn7feeovBgwdrxTk5OXHmzBm++eYbtm7dyrlz5zhx4gTW1tZ07dqVYcOG8a9//Qt9/dK9/SiqJgfL0m0tV9rrhBBCVD81vrDz8/PDz8+vTDHt27dnw4YNj3W/OnXqMHfuXObOnVumOEtLS6ZOnao1LCxqDveGNhjoqcgtYQ6dCqhrbcLzTWo93cSEEEJUGjV+jp0QVYWxoT7/fckVKCjiiir8+pM+LdCXfWCFEKLGksJOiErsanwqv4TGqL8e2bkJS4e7U9dac7i1rrUJS4a709NNe26nEEKImqPGD8UKUVntPX+biZvCSc/OxcnGlM4u9gD0dHPEt0VdQq4nEpeSiYNlwfCr9NQJIYSQwk6ISiY/X2Hh/st8s/8yAM83qUWzOpYa1+jrqejkbFcR6QkhhKjEpLATohJJyshh4sYz7L8YB8DIzo2Z+qIrhvoya0IIIcSjSWEnRCVx+U4Ko9aGcf1uGsYGesx+uSUDPepXdFpCCCGqECnshKgkjl9L4PrdNJxsTFk63IOW9a0rOiUhhBBVjBR2QlQSr3dsRFZOPgPcnbCzMK7odIQQQlRBMnFHiAqSlJ7DlC3nSMrIAUClUvG2V1Mp6oQQQjw26bETogJcvJ3MqDVhRCemk5Sew3evuVd0SkIIIaoBKeyEeMp2nL3Fh7+cJSMnj/q2przn41LRKQkhhKgmpLAT4inJzcvnyz2XWHb4GgAvNLPnm1fbYmtuVMGZCSGEqC6ksBPiKbiXls3Yn09z9MpdAEZ3bcqHfs0xkPXphBBC6JAUdkI8BXmKwrX4VEwN9flyUCtealWvolMSQghRDUlhJ8RTYG9hzPIR7TDQV/FsXauKTkcIIUQ1JYWdEOUgNy+fObsu4upoxSv/v3uEm5MsOCyEEKJ8SWEnhI4lpGbx3vpTnLiWiLGBHl7N7HGwMqnotIQQQtQAUtgJoUPn/pfE6LWh3ErKxNxIn/mDW0tRJ4QQ4qmRwk4IHfk17H9M2XKO7Nx8mtibs/x1D5rVsazotIQQQtQgUtgJ8YQURWH69khW/XkDgO7POvDVq22wMjGs2MSEEELUOFLYCfGEVCoVVqYFRdz73Zvxfvdm6OmpKjgrIYQQNZEUdkI8pvx8RV3Aje/ejK7P2OPRqFYFZyWEEKImk2XvhXgMG09GM3jZcTJz8gDQ01NJUSeEEKLCSWEnRBlk5+Yzdcs5PvrtHKFR99h4MqaiUxJCCCHUpLADYmJiCAgIwNnZGWNjY+zt7fH392fnzp0lxiQmJjJ58mRcXV0xNTXF1tYWLy8v1q5d+9B7paWlMWfOHNq1a4eVlRWGhobUrVuXl156iW3btj0y18OHD/Pqq69Sv359da4eHh5MmDCBnJycMj+7KL245EyGfn+CdX9Fo1LBB37P8HrHRhWdlhBCCKFW4+fYnTx5kp49e5KYmIijoyO9evUiISGBgwcPsnfvXqZNm8b06dM1Yq5du0a3bt2IiorCzs6O7t27k5GRwYkTJzhy5Aj79+9n5cqVqFSaE+gTEhLw8vIiMjISCwsLOnfujI2NDVeuXGHnzp3s3LmTcePG8fXXX2vlqSgKEyZM4Ouvv8bQ0JAOHTrg5eXF3bt3uXDhAgsXLmTmzJkYGsqbmOUhLCqRd346RVxKFpYmBnzzalt8nnWo6LSEEEIIDTW6sMvMzGTgwIEkJiYyZMgQVq5ciampKVBQ8PXq1YsZM2bQpUsXfH191XFDhw4lKioKb29vNm/ejK2tLQBXrlyhZ8+erF69Gk9PT95++22N+82YMYPIyEg8PDzYu3cvtWr9Myfr999/p1+/fnzzzTcMHTqUjh07asR++umnfP3113Tu3Jn169fTqJFmT9HJkycxMZGFcMvD7+dieX/DaXLyFJ6pY8Gy19vRxN68otMSQgghtNToodgtW7YQExODjY0NS5cuVRd1AO3bt2fatGlAQUFW6Pjx44SEhKCvr8+KFSvURR2Ai4sLCxYsAGDmzJkoiqJxvwMHDgDw0UcfaRR1AL1798bHx0d9j6IuXbrE7NmzqVOnDjt37tQq6grzNTCo0XV6uWnpZI2FsQG9W9Zly7ueUtQJIYSotGp0JXDy5EkAPDw8sLGx0Trfo0cPAI4dO8bt27epW7euOqZx48Y4OzuXGBMTE0NISAgdOnRQnyttj5q9vb3G10uWLCE3N5e333672DzFk8nLVwi5nkhcSiYOliY836QW2bn5mBrpA9CglhnbArpQ39ZUa3hdCCGEqExqdGGXmpoKgJ2dXbHnCwssRVE4deoUvXv3fmSMmZkZpqamZGRkEBYWplHY9erVi9DQUD7//HO6d++uNRR78OBB6tatS9++fTU+c8+ePQB4eXlx//59Nm7cyNmzZ9HT08PNzY2BAwdqFYOidHZHxDJ9eySxSZnqY3bmRuTk5fPVkDZ0d60DFBR3QgghRGVXows7B4eCye/Xrl0r9nzR49evX9eIKfz6Qbdv3yYjI6PYaz766CNCQkLYs2cPjRo1wtPTU/3yRFhYGJ6envzwww9YW1urY7Kzs7l06ZL684YPH05cXJzG537wwQd8//33vPrqq6V+dlFQ1L3z0ymUB44npGUDMGfXRbo96yC9dEIIIaqMGl3YdevWjVmzZhEWFsbp06dp27atxvmlS5eqf5+cnAyAj48PKpWK+Ph4goKC6N+//yNjCpmbm7N9+3amTJnC/Pnz1T1xUNAD2KNHD5ycnDRiEhMT1XP1AgICcHV1ZdOmTbi7u3Pr1i3mzp3LqlWrGD58OE5OTrzwwgslPm9WVhZZWVla+eXk5FTZpVIK8y5r/nn5Cp9uO69V1BWVkplDVnYO+jVke7DHbUuhSdpRN6QddUPaUXce1paVqX1VyoMz/GuYrl27cvjwYerXr8/ixYvx8vIiISGBxYsXs2DBAgwMDMjJyWHu3Ll89NFHAIwYMYK1a9dia2vLN998Q69evcjIyGDdunUEBgYCBf+Tx4wZw5IlS9T3io2NpV+/fpw9e5Zp06YxdOhQHBwciIyM5L///S979+6ldevWHDlyBEtLS3VMvXr1ALCysuLKlSvUrl1b4xl69+7Nrl276NatG/v37y/xWT/99FOtpVsA1q9fj5lZzRpqvJyk4ttI/UdeF9Aij2bWNfqPiBBCiEdIT09n2LBhJCUlYWVlVaG51PjCLi4ujgEDBnDs2DGtc+PHj+fo0aOEhoayfPly9fIlaWlpDB8+nKCgIK2YwYMHk52dTVBQEFOmTGHWrFnqc35+fgQHB/PFF1/w4YcfasTl5OTg4eHBuXPnNNbOS01NVRd5I0eOZOXKlVr33LFjB3369MHQ0JDU1FSMjIyKfdbieuwaNGjA3bt3K/wb8XHl5OQQHByMr69vmdbw2342lom/nHvkdQsGtaRPK8cnSbHKeNy2FJqkHXVD2lE3pB1152FtmZycjL29faUo7Gr0UCwUzJk7cuQI+/bt48CBAyQkJFCnTh369etHu3bt1L1lLVu2VMeYm5uzZcsWjh8/zu7du4mNjaVWrVr4+/vj4+ND586dtWJu3rxJcHAwULAO3oMMDQ155ZVXOHfuHPv27VMXdhYWFtSuXZv4+HiaNm1a7DMUHs/JyeHu3bvqnB9kbGyMsbFxsfeu6n/gy/oMjjalW7LE0ca8yrdNWVWH74fKQNpRN6QddUPaUXeKa8vK1LY1vrADUKlU+Pr6aixCDHD16lViY2Oxs7PD3d1dK65Tp0506tRJ41hKSgpnzpzBwMBAvS4dQHR0tPr3JVXzhS9NJCYmahz38PBg9+7d3L17t9i4osctLCyKvUb8IzYpg6a1zXG0NuF2Umax8+xUQF3rgqVPhBBCiKqiRi9Q/Cjz5s0DYNSoUSUObz5o8eLFZGRkMGjQIOrUqaM+XvSliL/++qvY2BMnTgDQpEkTjeODBg0CChY4zs/P14or7Als3rx5hXcBV3aH/47nxW+OMmHjGQJfbAEUFHFFFX79SZ8WNebFCSGEENVDjS/sIiMjtd5ezc3NZfbs2SxbtgwXFxemTp2qcf7q1avEx8drHFMUhR9//JHAwEBq1arF/PnzNc43bNiQ9u3bA/D+++9z48YNjfM//fQTGzduBGDYsGEa54YPH46zszMRERFMmzZNo7g7ePCgereLcePGlfHpa468fIUFey/xxsoQEtOyuZ+ew/NNa7FkuDt1rTUXjq5rbcKS4e70dKsZc+uEEEJUHzV+KHb58uUsW7YMDw8PnJycyMrK4sSJE9y5cwcXFxeCg4MxN9ecj7V9+3Y+/PBD3N3dadiwIYqiEBoaSlRUFA4ODuzatQtHR+2i4Mcff8THx4cLFy7g6upKx44dsbe358KFC5w/fx4oKOJee+01jTgjIyM2b96Mj48Ps2bNYsOGDbRp04abN28SEhJCfn4+b7zxBu+88075NVQVFp+SxfsbTvPn1QQAhnVoyLSXWmBiqE9PN0d8W9TV2nlCeuqEEEJURTW+sOvduzc3btzg1KlThIaGYmxsTPPmzZk0aRIBAQEa+8cW8vT0ZODAgYSEhBAREYFKpaJp06YEBgYyceLEErf9cnNzIyIigq+++opdu3Zx8uRJsrKysLW1xd/fn7feeovBgwcXG9uqVSsiIiKYNWsWO3fuZPv27ZiZmdG1a1dGjRolixOX4MS1BMb+fJr4lCzMjPSZ/XJL+rfVXCtQX09FJ+fidxIRQgghqpIaX9j5+fnh5+dXppj27duzYcOGx7pfnTp1mPt/7d17VFTl3gfw73Ab7hdBUEERRMXU41GaMjEPNzWszDTvaGhpJ4X0TV1eqZOXtLTULO+JZtrtLOVkloaJJ7VMREVIU0AQUxAFiYtyG573D96Zl5HhNjMwzMz3s9asxTx7P3v/9m8xzI9n7/3sNWuwZs2aZvft2LEjPv74Y3z88cca7dvUyKsFYuJScbe4HN3d7bElYgD83B30HRYREVGLMfnCjoyXuZkEH03sjz2/ZOGt5x+DrRV/3YmIyLiZ/M0TZFwuZN/H1+duKt/36uiINWP+xqKOiIhMAr/tyCgIIbDrVCZW/3AFQgA9PBzw987O+g6LiIioVbGwI4P3sAqI/jIZRy/nAQBG9O2Abu2b9mQJIiIiY8LCjgza77eLsO6SOe6V58HSXIKlI3rh5UFdIZFwuhIiIjI9LOzIYH2VmI2Y//yOiioJOjlZ45PJA9C/i4u+wyIiItIbFnZksIrLqlBRVY3eLtXY/c+n0N7JVt8hERER6RULOzIo1dUCZv/3VIhXBvvA3d4S8hvn4WxrqefIiIiI9I/TnZDBOHD+T7zwyWmUllcBACQSCcL7dACf/kVERFSDhR21eWWVciw+cAlvfp2MlFt/Yc+vWfoOiYiIqE3iqVhq07LulWLWvvO4nFMEiQR4I6Q7XhvSTd9hERERtUks7KjN+iElBwv+fQkl5VVwtbPChgl/x9Pd2+s7LCIiojaLhR21Sft+u4GlB1MBALKuLtg0cQA6OFnrOSoiIqK2jdfYUZs09DEPuDtI8do/fLF/xkAWdURERE3AETtqM67mFqNnBwcAgLuDNeLf/AecbDiNCRERUVNxxI70rkpejfeO/IHhG37Gt8m3le0s6oiIiJqHhR3p1Z2iMkza+Ru2nMgAAFy+XaTniIiIiAwXT8WS3pxOv4c5X17AvZIK2EstsGZMXzz3t076DouIiMhgsbCjVlddLfBxQjrWH7sGIQD/Dg7YPHkAfNvb6zs0IiIig8bCjlrduRv38WH8NQDABFln/Gtkb1hbmus5KiIiIsPHwo5a3RM+7RAd4gdvVzu8FOCl73CIiIiMBgs7anFCCOz+JQvDe3dAJ2cbAMC8YT31HBUREZHxYWFHOiOvFjibWYC84jK4O1jjCZ92KCmrwvx/JyP+8h0cSr6Nr197ChbmvBmbiIioJbCwI504kpqDdw5dRs5fZco2V3srQAD5pRWwMjfDmAAvmJtJ9BglERGRcePQCYCbN28iKioK3bp1g1QqhZubG4YPH47Dhw/X26egoACLFy9Gr169YGNjAxcXFwwZMgR79+5tcF+lpaVYvXo1Hn/8cTg6OsLS0hIdOnTAc889h2+//bbJMScnJ8PKygoSiQR+fn5N7tcSjqTm4PXPz6sUdQCQX1KB/NIKuNlb4cCsQZj8pDckEhZ2RERELcXkR+wSExPxzDPPoKCgAB07dkR4eDjy8/ORkJCAH3/8EW+99RbeeecdlT7Xr19HSEgIbty4AVdXV4SGhuLhw4c4c+YMTp48iZ9++gmxsbF1ipj8/HwMGTIEly9fhr29PQYNGgRnZ2ekp6fj8OHDOHz4MN544w1s3LixwZgrKiowdepUVFVV6TwfzSWvFnjn0GWIBtYxN5OgV0fHVouJiIjIVJn0iF1ZWRnGjBmDgoICjB8/HhkZGYiLi8PJkydx+vRpuLq6Yvny5YiPj1fpN3HiRNy4cQNBQUFIS0vDd999h59++gnJycno1q0b9uzZg507d9bZ3/Lly3H58mUEBATgxo0bOHr0KL766iskJSXh8OHDsLCwwEcffYQzZ840GPfy5ctx6dIlzJ49W6f50MTZzII6I3WPulNUjrOZBa0UERERkeky6cLu4MGDuHnzJpydnbF161bY2Ngol8lkMrz11lsAagophV9//RVnz56Fubk5du7cCRcXF+UyPz8/fPjhhwCAFStWQAjVcazjx48DABYuXIh27dqpLBsxYgSCg4OV+6hPYmIi1qxZg7Fjx2LMmDGaHLZO5RU3XNQ1dz0iIiLSnEkXdomJiQCAgIAAODs711keFhYGADh9+jRyc3NV+nTt2hXdunWrt8/Nmzdx9uxZlWXW1tZNisvNzU1te1lZGV5++WW4uLjg448/btK2Wpq7Q9OOqanrERERkeZMurArKSkBALi6uqpdriiwhBA4f/58k/rY2toqR/6SkpJUloWHhwMA3nvvPRQUqJ6a/P7775GQkIAOHTpg5MiRarcdExODK1eu4KOPPoK7u3ujx9canvBph45O1qjvlggJgI5ONVOfEBERUcsy6ZsnFMXR9evX1S6v3Z6ZmanSR/H+Ubm5uXj48KHadRYuXIizZ8/i6NGj8Pb2RmBgoPLmiaSkJAQGBuLTTz+Fk5NTne3+8ssv+PDDD/HCCy9g4sSJzTzSlmNuJsHbzz+G1z8/DwmgchOFoth7+/nHOM0JERFRKzDpwi4kJASrVq1CUlISLly4gP79+6ss37p1q/LnoqIiAEBwcDAkEgnu3r2LuLg4jBo1qtE+CnZ2djh06BCWLFmCDz74AEePHlUuc3V1RVhYGDw9PevE+eDBA0RGRsLJyQlbtmzR+HjLy8tRXl5eJ77KykpUVlZqvN3Qnm7YNKEfVn7/B3KL/n/7HZykWBruj9CeblptvyGK7bbU9k0Jc6kbzKNuMI+6wTzqTkO5bEv5NfnCbsiQIfj5558xcuRIbN68GUOGDEF+fj42b96Mzz77DJaWlqisrISZWc1Z627duiEiIgJ79+7F9OnTUVJSgvDwcDx8+BD79u3Du+++W6ePQk5ODl544QVcunQJK1euxMSJE+Hu7o7Lly9j2bJleOedd5R35To4OCj7LVq0CGlpadizZw86duyo8fGuXr26ztQtAPDjjz/C1tZW4+0qLHwMyCiSoKgScLQEujmWQn4jCd/f0HrTjXr0zmXSHHOpG8yjbjCPusE86o66XD548EAPkagnEY/eumli8vLyMHr0aJw+fbrOsrlz5+LUqVM4d+4ctm/fjhkzZgComWQ4IiICcXFxdfqMGzcOFRUViIuLw5IlS7Bq1SrlsmHDhiE+Ph7vv/8+FixYoNKvsrISAQEBSElJUZk778SJEwgJCUF4eHidCZNPnDiB4OBgdOvWDenp6Y0eq7oRu86dO+PevXtwdDTMeeYqKysRHx+PoUOHwtLSUt/hGDTmUjeYR91gHnWDedSdhnJZVFQENzc3/PXXX3r/PjXpETug5pq5kydP4tixYzh+/Djy8/Ph4eGBF154AY8//jg6deoEAOjbt6+yj52dHQ4ePIhff/0VR44cQU5ODtq1a4fhw4cjODgYgwYNqtPn1q1byipf3TVylpaWeOmll5CSkoJjx44pC7u4uDgIIZCdnY2goCCVPoWFhcptK5Zt2LABf//739Ueq1QqhVQqVbtvQ//AG8MxtBXMpW4wj7rBPOoG86g76nLZlnJr8oUdAEgkEgwdOhRDhw5Vac/IyEBOTg5cXV0xYMCAOv2eeuopPPXUUyptxcXFuHjxIiwsLJTz0gFAdna28uf6qnnFTROP3jELAKmpqfXGX1ZWhv/+978A/r/YIyIiItNj0tOdNGbdunUAgJkzZ8LKyqpJfTZv3oyHDx9i7Nix8PDwULbXvinit99+U9tX8cQJHx8fZduGDRsghFD7SkhIAFBz3Z+i7dFRPSIiIjIdJl/YXb58uc7dq1VVVXj33Xexbds2+Pn5YenSpSrLMzIycPfuXZU2IQR27dqFmJgYtGvXDh988IHK8i5dukAmkwEA5syZg6ysLJXln3/+Ob766isAwKRJk3RxaERERGRiTP5U7Pbt27Ft2zYEBATA09MT5eXlOHPmDO7cuQM/Pz/Ex8fDzs5Opc+hQ4ewYMECDBgwAF26dIEQAufOncONGzfg7u6OH374Qe3dq7t27UJwcDCuXLmCXr16YeDAgXBzc8OVK1fw+++/AwAiIiIwefLkVjl2IiIiMi4mX9iNGDECWVlZOH/+PM6dOwepVIqePXti3rx5iIqKUnl+rEJgYCDGjBmDs2fPIjU1FRKJBL6+voiJicGbb76p9vFkANCnTx+kpqZi/fr1+OGHH5CYmIjy8nK4uLhg+PDhmD59OsaNG9fCR0xERETGyuQLu2HDhmHYsGHN6iOTyfDll19qtD8PDw+sWbMGa9as0ah/bUFBQTDx2WqIiIioFpO/xo6IiIjIWJj8iJ0pU4z2PXrziCGprKzEgwcPUFRU1KbmETJEzKVuMI+6wTzqBvOoOw3lUvE92hbOorGwM2HFxcUAgM6dO+s5EiIiIsNXXFysnJNWX0z+kWKmrLq6Grdv34aDgwMkEom+w9GI4rFoN2/e1PtjXAwdc6kbzKNuMI+6wTzqTkO5FEKguLgYnTp1qvOc+NbGETsTZmZmBi8vL32HoROOjo78o6UjzKVuMI+6wTzqBvOoO/XlUt8jdQq8eYKIiIjISLCwIyIiIjISLOzIoEmlUrz99tuQSqX6DsXgMZe6wTzqBvOoG8yj7hhKLnnzBBEREZGR4IgdERERkZFgYUdERERkJFjYkU5dvXoVmzZtQmRkJPr27QsLCwtIJBKsXLmy3j43b97Etm3bMHPmTAQEBEAqlUIikeDVV1/VOp6kpCSMHTsWHh4esLa2ho+PD6Kjo5GXl9dgvzt37iAqKgo+Pj6QSqXw8PDA2LFjcf78ea1jagpDz2N2dja2bduG0aNHw9vbG1KpFPb29ujXrx+WLFmCu3fvah1TUxl6LtW5desWXFxcIJFIYGHROrNWGVMek5OTMX36dPj4+MDa2houLi7o27cvXn/9deTn52sdW0OMIY9CCOzbtw9hYWFwc3ODpaUlnJ2dERgYiI8++ggVFRVax9WYtpLH/Px87N69G9HR0Rg0aBBsbW0hkUgQFhbWpP7p6emIjIyEl5cXpFIpvLy8EBkZievXr2scEwSRDs2ZM0cAqPNasWJFvX3Wr1+vts8rr7yiVSzffPONsLCwEACETCYT48aNE76+vgKA8PDwEGlpaWr7Xb16Vbi7uwsAwtfXV4wbN07IZDIBQFhYWIgDBw5oFVdTGHoeAwMDlfmSyWRi/PjxYujQocLR0VEAEO3btxcXLlzQKq6mMvRcqhMeHi4kEokAIMzNzbWKqamMJY9r164V5ubmwszMTMhkMjFhwgQRHh4u/Pz8BACRkpKiVWyNMYY8jh8/XgAQZmZmYvDgwWL8+PEiKChIWFpaCgBi4MCB4sGDB1rF1pi2kseDBw+q3WZoaGijfU+dOiVsbW0FANG7d28xfvx40bt3bwFA2NnZiV9//VWjmFjYkU7t2LFDzJ8/X+zbt09cuXJFTJkypdEPW1xcnIiOjhaxsbEiOTlZLF26VOsP261bt5QfmG3btinbq6qqREREhPIPWXV1tUq/6upq0b9/fwFATJkyRVRVVSmXbdu2TQAQ9vb2IicnR+PYmsLQ8zhu3Dixfv16ce/ePZX2vLw8ERQUJACI7t27q+S3pRh6LtUdDwARFRXVqoWdMeRx165dAoDo2bOn2gIuNTVVFBYWahxbUxh6Hg8cOCAACCcnJ3Hx4kWVZRkZGcLT01MAEKtXr9Y4tqZoK3n85ZdfxGuvvSa2bdsmEhMTxdatW5tU2JWWlopOnToJAGLx4sUqyxYvXiwAiM6dO2tUILOwoxb18ssvN/phe9Tbb7+t9YdtwYIFAoAICwurs6y4uFg4OTkJAOLIkSMqyw4fPiwACGdnZ1FcXFynb2hoqAAgFi1apHFsmjC0PDbk5s2byv9qT548qXFsmjLkXGZlZQkHBwcxcOBAkZGR0aqF3aMMLY8FBQXC0dFR2NjYiIyMDI33r2uGlkfFPxSzZs1Su91Vq1YJAGLkyJEax6YJfeXxUbGxsU0q7D755BMBQPTo0UPI5XKVZXK5XPTo0UMAEFu3bm12DLzGjozSwYMHAQCTJk2qs8ze3h4jR44EABw4cEBtv5EjR8Le3r5OX8X2Hu1nrDTNY0O8vLzg5uYGoOaaF1OhbS6FEJg+fToqKiqwa9cuvT+PUl80zeOePXtQVFSEMWPGwNfXt+UDbeM0zaO1tXWTtq/4jJN6ivxPmDChzmfZzMwM48ePB6DZd41p/mUgo1ZcXIz09HQAwOOPP652HUX7hQsXVNoV7xvrl5aWhtLSUp3E21Zpk8eG3Lt3D/fv3wcAdOzYUcsoDYMucrl582YcP34cb7/9Nnr16tUygbZx2uTx6NGjAIAhQ4bg4cOH2Lt3L9544w3Mnj0bGzZsMKl/MrTJY3h4OABg//79SE5OVll2/fp1bNmyBRKJBDNmzNB12Ealqd81zfnbqtA6t1MRtaKsrCzlz126dFG7TufOnQEAmZmZKu2K9431E0IgKysLvXv31jbcNkubPDZk3bp1kMvl6NixIwYNGqRVjIZC21xmZGRg4cKFCAgIwIIFC1okRkOgTR4vXboEoKao6dOnT527DhcuXIjVq1fjzTff1GHEbZM2eQwJCcHSpUuxatUqDBgwAIGBgfD09MSdO3dw6tQpdO7cGf/5z38wcODAFovf0BUXFyvvvm4s/3fv3kVpaSns7OyavH2O2JHRKS4uVv5c34dBcZq1qKhIbd/G+qnra2y0yWN9jh07hnXr1gEAPvjgA1hZWWkZpWHQJpfV1dWIjIxERUUFYmNjW216k7ZImzwqvkgXLVoEuVyOQ4cO4f79+8qiubKyEvPmzcMXX3zRQtG3Hdp+tleuXInPP/8ctra2OHnyJL788kskJCRACIGwsDCj/odXF5qTf6D53zUs7IioVaSkpGDs2LGQy+WIjo7GxIkT9R2SQdiwYQNOnTqFZcuWoW/fvvoOx2CJ/3t6ZnV1Nb7//ns899xzcHZ2hq+vL9asWYN//vOfAIBly5bpM8w2r7KyEtOnT0dERARGjRqFlJQUlJaW4tq1a4iKisKOHTsgk8lw8eJFfYdqsljYkdFxcHBQ/lzfdXAlJSUAAEdHR7V9G+unrq+x0SaPj/rjjz8QFhaGwsJCTJs2DRs3btRdoAZA01xevXoVS5cuRb9+/bB48eKWDdIA6OKz/fTTT+Oxxx6r02/WrFkAaq4Ta86lBYZImzy+//77iI2NxYgRI7B371706dMHtra26N69O9avX4+ZM2eioKAAc+bMabkDMHDNyT/Q/O8aFnZkdLy9vZU/Z2dnq11HcaF0165dVdoV7xvrJ5FIVPZjjLTJY23Xrl1DSEgI8vLyMHXqVOzcuRMSiUSnsbZ1mubyhx9+QFlZGUpLSzF06FAEBQUpXxMmTAAAyOVyZduRI0da7iDaAG1+JxV3wtZ3R2zt9pycHG3CbPO0yePu3bsBoN4Rd8VdtqdOnUJ5ebmWkRonBwcHtGvXDkDj+Xdzc2vW9XUACzsyQo6OjvDz8wMAnDt3Tu06ivYBAwaotCveN9ave/fuaqdDMSba5FEhLS0NwcHByMnJQUREBGJjY01ymg5tc5meno7//ve/Kq/ffvtNuVzRlpub2wLRtx3a5DEgIABAzV3Z6tRu52e7/jwqCpH6RpGcnJwA1JzyLiws1EW4Rqmp3zX1/W1tiOn9hSWT8OKLLwKouSX/USUlJTh06BAAYPTo0Wr7ffvtt2qHyBXbe7SfsdI0j0DNnZzBwcG4ffs2IiIisGfPHpMs6hQ0yeXcuXMhaiaSr/NSnC40NzdXtkVGRrb8geiZpr+TY8eOBQCcOXNG7Wc7Pj4eQE1RZwrTyWiaR09PTwBQ+ceitjNnzgCoGZXiXHb1U+T/yy+/RHV1tcqy6upqfPXVVwA0+64x3b+yZPAOHjwIf39/hIaG1lk2d+5c2Nra4tixY9ixY4eyXS6XY9asWSgsLIRMJsOwYcNU+oWHh6N///4oLCzErFmzIJfLlcu2b9+On376Cfb29kZ1/UhL5DEzMxPBwcG4desWpkyZYjJFXUvk0hS1RB5DQkLw9NNPIy8vD1FRUSqnCS9duqS8aeL111+HpaVlCx1Z62qJPL700ksAgPXr1+PEiRMqyy5evIiYmBgAwLhx42Bubq7jI9KPhvKoqcjISHTq1AnXrl1T5kwhJiYG165dg5eXF6ZOndrsbUuE4lYhIh04f/688iJkoGbU5t69e/Dy8lL+pwfUfFAUk9Pm5OQo/3sBgD///BO3bt1C+/btVa572bx5s8qw9O7duzFt2jR4e3urzMuk8M0332DixImQy+V48skn0bVrVyQmJuL69evw8PDAqVOnlKcjart69Sqefvpp3L17F76+vpDJZMjMzMTZs2dhYWGBr7/+WiXelmDoeRwwYAAuXLgAqVSKcePG1VvUvfrqqxg8eHDzktNMhp7L+mRlZcHHxwfm5uaoqqpqcj40ZQx5vHnzJoYMGYKsrCx4enpCJpOhoKAAZ86cQUVFBYYOHYpDhw5BKpVqlauGGHoei4uLERoaisTERACATCaDj48P/vzzT/z222+Qy+Xo27cvEhIS4Orqql2yGtCW8lh7zr67d+/i+vXrcHR0VBn5jYmJwbPPPqvS7/Tp0xg2bBgePHiAPn36oE+fPkhNTUVqairs7Oxw7NgxzeYDbPZDyIgakJCQoHwOaEOvzMxMZZ/MzMwm9UlISFDZl+KZfN7e3vXGc+7cOTF69GjRvn17YWVlJby9vcXs2bNFbm5ug8eRk5MjZs+eLby9vYWVlZVo3769GD16tEhKStIiO01n6Hn09vZuUiyxsbHaJ6sRhp7L+ihibK1nxRpLHgsLC8WiRYtEjx49hFQqVT57d8uWLaKqqkqLDDWNMeSxvLxcbNy4UQwePFi4uLgIc3Nz4ejoKAYOHCjWrl2r0YPrm6st5VGbv3VpaWli6tSpolOnTsLS0lJ06tRJTJ06VaSnp2ucG47YERERERkJ47/ohYiIiMhEsLAjIiIiMhIs7IiIiIiMBAs7IiIiIiPBwo6IiIjISLCwIyIiIjISLOyIiIiIjAQLOyIiIiIjwcKOiIiIyEiwsCMiIiIyEizsiIiaaPfu3ZBIJIiPj8fSpUvh6ekJOzs7DB8+HNnZ2QCATZs2wc/PD9bW1pDJZLhw4YLKNm7fvo3/+Z//wd/+9jc4OjrC1tYWMpkMX3zxhcp6lZWVkMlkcHNzw+3bt1WWzZgxA2ZmZjh+/HjLHjARGRwWdkREzbRw4UKcOHECCxcuRHR0NI4fP44XX3wR7733HrZv345Zs2Zh2bJl+OOPPzB69GhUVVUp+166dAmHDx/Gs88+i7Vr1+Kdd95BRUUFJk2ahD179ijXs7S0xL59+1BWVoapU6dC8VjvuLg47Ny5EwsWLEBISEiz4u7atSskEonKKy4uTic5UZg7d26dfURGRup0H0RUPwt9B0BE1Jq8vb2Vo2u1SSQS2Nvbw9fXF+Hh4Zg3bx7c3NzUbkMqleLnn3+Gubk5AEAul2PdunW4f/8+fv/9d9jY2AAAXFxcEBUVhR9//BEjRowAAPzjH//A1atXIZFIlNuLjo5G//798e677+Lll19Wtvfo0QPr16/HzJkzsW7dOkyePBmvvvoqAgICsHLlSo1z4OjoqIzR2tpaZdm0adOwe/dujbb7xBNPYPjw4fDw8AAA/PXXXygrK9M4TiJqPo7YEZHJuHfvnrKoc3FxgYeHh/JlY2OD4uJiJCcnY82aNZDJZLh//77a7cyYMUNZ1AFAYGAgAGDKlCnKgql2e0ZGhrLNxsZGWdSVlZUhPz8fJSUlCAoKwrVr11BUVFRnX6NGjcKyZcswcuRIPHz4EPv27YOlpaXGedi4cSNyc3ORm5uLZ555RmVZVlaWSl4UL3t7e+U66pZ7eHggKCgIy5cvV257/PjxGsdIRJrhiB0RmYzz588rf/7ll1/g7++vsjw7Oxvz5s3Dv//9b2RlZeHTTz/F/Pnz62zH29tb5b2zszMAoEuXLmrbCwoKlG0VFRVYuXIlPvvsM9y4caPOtgsLC+Ho6KjStnPnTvj5+SEpKQmbN29Gz549Gz9YDSUkJKhtnzlzJnbs2IHOnTurHfEkoraBI3ZEZDKSkpIAAE5OTmqLoy5dumD79u3K92lpaWq3U3u0rintiuvjgJpr0FasWIEhQ4bg888/x5EjRxAfH49JkyYBAKqrq+v0P336NAoLCwHUXKOnD4qieMCAAXrZPxE1DUfsiMhkKIqTgIAAlWvcarO3t4e5uTnkcjnc3d11HsMXX3yBoKAgfPbZZyrtu3btUrt+bm4uXnnlFfTr1w+DBw/GJ598ghEjRuD555/XeWz1qaqqQmpqKgAWdkRtHQs7IjIZihE7mUxW7zr79++HXC6HRCLBSy+9pPMYzM3N64zKpaWl4eDBg3XWFUIgMjISJSUl2L9/P7p164aTJ0/ilVdeQUpKivImhZb2+++/o7y8HADQv3//VtknEWmGp2KJyCTcv38fmZmZAOoWdkIIZGdnIyYmBq+99hrMzMzw/vvvo1+/fjqPY9SoUfj5558xefJk7NixA8uWLcOTTz6JXr161Vl348aNOHr0KNauXYvHHnsMUqkU+/fvR3FxMaZNm6bz2OpT+9pEjtgRtW0csSMik1C7OJk5cyZmz56tfF9YWIjy8nJYW1vjmWeeQXR0NEJDQ1skjg0bNsDGxgYHDhzAgQMH4O/vjy1btuDKlSsqkxmnpKRg0aJFCA8PR1RUlLK9d+/eWLduHaKiorBp0yZER0e3SJy1KeJyd3eHp6dni++PiDTHwo6ITELtwq72Xaq1lZeXo7i4GB07dlS7PDIyUu1ku0FBQSo3SCh07dq1Tru9vT02bdqETZs21Vn/X//6l/Lnvn371jsH3OzZs1UK05amyB1PwxK1fTwVS0QmQVGchIaGQgih8srLy8N3330Hf39/HD9+HIMHD0ZeXp6eI24bqqurkZycDICnYYkMAQs7IjIJihsn1F031759ezz77LP45ptvANRcj1d72hNTdu3aNZSUlABgYUdkCFjYEZHRKyoqQnp6OgD1hZ1C79694erqCgC4cuVKq8TW1tW+7o+FHVHbx8KOiIzehQsXlNe6NXanq4VF3UuPH32ofX2voKAgnDhxosF1qqqqWuQYW4riFLaTkxN8fHz0HA0RNYY3TxCR0VMUJ5aWlmqnFVHIzs7GnTt3AEBlvb1796qsd/z4ccTGxmLJkiUq69WeV27atGkICQmps4/6nk7RVtW+caK+SZ2JqO1gYUdERk9xfZ2/vz+srKzqXW/JkiUAakbtaj/APiIiQmW9kpISxMbGYujQoQgKClJZduLECQDAE088UaefIbp48SIAnoYlMhQ8FUtERk8x6qTuNGx1dTUSExPx4osvYt++fQBqph3p3r17q8bYFmVlZSmnhuFUJ0SGgSN2RGTUSktLcfXqVQDAwYMH0aFDB+Wy6upq/PXXX6ioqAAAWFtbY+XKlZg3b55O9nvv3j2VNjs7O9jY2Gi97dbCJ04QGR4WdkRk1C5evKh8NmtpaSlKS0uVyywtLeHi4oJevXohNDQU06ZNg5eXl072O3/+fMyfP1+lbfXq1Vi0aJFOtt8aFHfE2trawt/fX8/REFFTsLAjIqMWGBio9qkQLW3OnDl47rnnVNoM7fTuihUrsGLFCn2HQUTNwMKOiKgF+Pv7IywsTN9hEJGJ4c0TREQmZtq0acp59eLi4nS67blz5yq3vWfPHp1um4gaxxE7IiIT0b59e5SVlam0WVtb63Qfjo6OKvP5ATWTGxNR62BhR0RkIhITE1t8H8uXL8fy5ctbfD9EpB5PxRIREREZCRZ2REREREZCIvQxDwARERER6RxH7IiIiIiMBAs7IiIiIiPBwo6IiIjISLCwIyIiIjISLOyIiIiIjAQLOyIiIiIjwcKOiIiIyEiwsCMiIiIyEizsiIiIiIwECzsiIiIiI8HCjoiIiMhIsLAjIiIiMhL/C4m+dcyYU0gMAAAAAElFTkSuQmCC", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from process.io import plot_scans\n", - "\n", - "# Define working directory relative to project dir and input file name\n", - "mfile_name = data_dir / \"scan_example_file_MFILE.DAT\"\n", - "output_dir = data_dir\n", - "\n", - "plot_scans.main(\n", - " args=[\n", - " \"-f\",\n", - " str(mfile_name),\n", - " \"-yv\",\n", - " \"bt rmajor pnetelmw fusion_power capcost\",\n", - " \"--outputdir\",\n", - " str(output_dir),\n", - " ]\n", - ")" - ] - } - ], - "metadata": { - "celltoolbar": "Slideshow", - "interpreter": { - "hash": "95e8614a6e18ad6e528160ac32f08bcfa19db99daf3816cbd89c3976c3924301" - }, - "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": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "# Example scan\n", + "\n", + "Perform a parameter scan for a given input file and plot the results.\n", + "\n", + "## Scan details\n", + "\n", + "The input file is a scan-enabled version of the large tokamak `IN.DAT`, as found in the `tests` directory. The scan-relevant values are:\n", + "```\n", + "nsweep = 17 * bmxlim, maximum peak toroidal field (T) (`constraint equation 25`)\n", + "isweep = 11\n", + "sweep = 11., 11.2, 11.4, 11.6, 11.8, 12., 12.2, 12.4, 12.6, 12.8, 13.\n", + "```\n", + "\n", + "- `nsweep`: integer denoting the variable to scan (see `scan_module` for options). Here `17` corresponds to `bmxlim` being scanned\n", + "- `isweep`: the number of scan points to run\n", + "- `sweep`: array of values for the scanned variable to take; one for each run. Should be of length `isweep`" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The IN.DAT file does not contain any obsolete variables.\n", + " tmargmin_cs and tmargmin should not both be specified in IN.DAT.\n", + " tmargmin_cs has been ignored.\n", + " \n", + " **************************************************************************************************************\n", + " ************************************************** PROCESS ***************************************************\n", + " ************************************** Power Reactor Optimisation Code ***************************************\n", + " **************************************************************************************************************\n", + " \n", + " Program :\n", + " Version : 3.1.0 Release Date :: 2024-03-21\n", + " Tag No. : v3.1.0-84-g4c95269\n", + " Branch : 1120-scan-output-verbosity\n", + " Git log : Add blank line after Scan Convergence Summary and the first scan point result\n", + " Date/time : 10 Jun 2024 17:03:54 +01:00(hh:mm) UTC\n", + " User : clair\n", + " Computer : clair-Precision-3570\n", + " Directory : /home/clair/development/PROCESS/examples\n", + " Input : /home/clair/development/PROCESS/examples/data/scan_example_file_IN.DAT\n", + " Run title : Generic large tokamak\n", + " Run type : Reactor concept design: Pulsed tokamak model, (c) CCFE\n", + " \n", + " **************************************************************************************************************\n", + " \n", + " Equality constraints : 26\n", + " Inequality constraints : 00\n", + " Total constraints : 26\n", + " Iteration variables : 44\n", + " Max iterations : 200\n", + " Figure of merit : +01 -- minimise major radius\n", + " Convergence parameter : 1.00E-07\n", + " \n", + " **************************************************************************************************************\n", + "Starting scan point 1 of 11: Max_toroidal_field_(T), bmxlim = 1.100E+01\n", + "7 | Convergence Parameter: 1.125E-09\n", + " \n", + " ************************************* PROCESS found a feasible solution **************************************\n", + " \n", + "Starting scan point 2 of 11: Max_toroidal_field_(T), bmxlim = 1.120E+01\n", + "2 | Convergence Parameter: 3.299E-08\n", + " \n", + " ************************************* PROCESS found a feasible solution **************************************\n", + " \n", + "Starting scan point 3 of 11: Max_toroidal_field_(T), bmxlim = 1.140E+01\n", + "2 | Convergence Parameter: 3.245E-08\n", + " \n", + " ************************************* PROCESS found a feasible solution **************************************\n", + " \n", + "Starting scan point 4 of 11: Max_toroidal_field_(T), bmxlim = 1.160E+01\n", + "2 | Convergence Parameter: 3.134E-08\n", + " \n", + " ************************************* PROCESS found a feasible solution **************************************\n", + " \n", + "Starting scan point 5 of 11: Max_toroidal_field_(T), bmxlim = 1.180E+01\n", + "2 | Convergence Parameter: 3.027E-08\n", + " \n", + " ************************************* PROCESS found a feasible solution **************************************\n", + " \n", + "Starting scan point 6 of 11: Max_toroidal_field_(T), bmxlim = 1.200E+01\n", + "3 | Convergence Parameter: 1.368E-09\n", + " \n", + " ************************************* PROCESS found a feasible solution **************************************\n", + " \n", + "Starting scan point 7 of 11: Max_toroidal_field_(T), bmxlim = 1.220E+01\n", + "2 | Convergence Parameter: 2.809E-08\n", + " \n", + " ************************************* PROCESS found a feasible solution **************************************\n", + " \n", + "Starting scan point 8 of 11: Max_toroidal_field_(T), bmxlim = 1.240E+01\n", + "2 | Convergence Parameter: 2.715E-08\n", + " \n", + " ************************************* PROCESS found a feasible solution **************************************\n", + " \n", + "Starting scan point 9 of 11: Max_toroidal_field_(T), bmxlim = 1.260E+01\n", + "2 | Convergence Parameter: 2.630E-08\n", + " \n", + " ************************************* PROCESS found a feasible solution **************************************\n", + " \n", + "Starting scan point 10 of 11: Max_toroidal_field_(T), bmxlim = 1.280E+01\n", + "2 | Convergence Parameter: 2.548E-08\n", + " \n", + " ************************************* PROCESS found a feasible solution **************************************\n", + " \n", + "Starting scan point 11 of 11: Max_toroidal_field_(T), bmxlim = 1.300E+01\n", + "2 | Convergence Parameter: 2.470E-08\n", + " \n", + " ************************************* PROCESS found a feasible solution **************************************\n", + " \n", + " \n", + " ******************************************** Errors and Warnings *********************************************\n", + " \n", + " PROCESS status flag: Warning messages \n", + " \n", + " ID LEVEL MESSAGE\n", + "150 2 CHECK: Lower limit of volume averaged electron temperature (te) has been raised \n", + " \n", + "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", + " \n", + "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", + " \n", + "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", + " \n", + "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", + " \n", + "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", + " \n", + "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", + " \n", + "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", + " \n", + "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", + " \n", + "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", + " \n", + "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", + " \n", + "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", + " \n", + "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", + " \n", + "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", + " \n", + "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", + " \n", + "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", + " \n", + "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", + " \n", + "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", + " \n", + "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", + " \n", + "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", + " \n", + "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", + " \n", + "243 2 PHYSICS: Predicted plasma driven current is more than upper limit on non-inducti\n", + " \n", + "244 2 PHYSICS: Diamagnetic fraction is more than 1%, but not calculated. Consider usin\n", + " \n", + " **************************************************************************************************************\n", + " \n", + " \n", + " ******************************************* End of PROCESS Output ********************************************\n", + " \n" + ] + } + ], + "source": [ + "from pathlib import Path\n", + "\n", + "from process.main import SingleRun\n", + "\n", + "data_dir = Path(\"data\")\n", + "input_name = data_dir / \"scan_example_file_IN.DAT\"\n", + "# Perform a SingleRun on a scan-enabled input file\n", + "single_run = SingleRun(str(input_name), solver=\"vmcon_bounded\")\n", + "single_run.run()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Plot scan results\n", + "Use `plot_scans.py` to plot the resulting `MFILE.DAT`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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t/v77b3U8xW8UHBcXh2fPngEwrKhTMTMzw3/+8x8cOXIE9+7dK/8B6KFx48Z45ZVXEBMTgx9++AEdO3ZUj9oNGTJEawGlS6NGjbBgwQLcu3cPW7Zs0Sjgy6LKtVwuL3HbkOISExPV938r3u7f99s7cuQIwsPDMXPmTI12qj9aVEJCQtQFdHGmuvjjeSh+3qeuGzYTVUYs6ohENnbsWKxduxZKpRJTpkzB1q1bS/1FkZiYiK+//hpLly5VL1u8eDFOnz6NJUuWYMqUKRrti59r1a1bN8TFxWn0P3nyZLRr1w6ff/55iaIuMzMTFy9ehJ2dHQDgrbfeQps2bTBjxgzs3r3biKPWTnWOV9u2bUvkIDQ0FB4eHjAzM8PHH3+sdTszMzP1VG1xaWlpJc5T+7dr164BKFmEiGHEiBGIiYnBjz/+iAkTJqhH7FQXUvxbbm4uFAqFzv6sra0BGHYzalXOPD09S536nzlzJoCi0brBgwerlw8fPlyj3dOnTxEeHo6AgAD16Jw2L7/8coltqxrVqDCnXqmq4fQrkchefvll9T23fvzxRwQEBODYsWPIy8tTt0lNTcWuXbswZMgQNG7cuMRo0o8//oh69eph8uTJJfovXhxZW1urX+fk5ODRo0d4+vQpfH198ddff5U4J2vcuHHqgg4AWrZsiV69euHAgQNQKpXGH/y/lHZeXJ8+fbBo0SJ89dVXJa4SVm3n4eGh9Qrizp07Y/r06erCrbjc3FwsXrwYa9asAQAMGDDA6OMoy+DBg2FpaYlHjx6pbwXSvHlznaOMkyZNwqBBg/DLL79oXIH69OlTrFq1Cps2bQIAvP7663rHoMqZtqnXwsJCnDlzBv3798eWLVsAFN1WpEmTJnr3L1UJCQnq27/wdiZU1XCkjug5WLp0KaysrLB48WIcPnwYhw8fhoWFBRwdHfHs2TONe3PJZLIS01c3b97Eq6++WuYUVl5eHubPn49Nmzbh7t27JdanpaXBwcFB/VrbExg8PDywb98+3L9/X/2sVVN49uwZ4uLiABg+AqIaddJVFOXm5uLrr7/G119/jZo1a6Jx48a4fv064uLi8OWXX6rz26tXr+dyrzMnJye88cYb2LFjB86ePQtA9wUSQNGzgbdv366+EtXOzg4WFhYaT6bo0qULZs2apdf+i+d6586dGlP+hYWFSE9PV/9RYWVlhfnz52P69OkGHWNp+05NTdVYZmtrqx5trOz4JAmqyljUET0H5ubmWLhwIUaNGoXVq1fj+PHjuHPnDtLS0mBtbY2mTZuiRYsW6NatG/7zn/+U+8HzU6ZMwcqVKxEUFIRevXrB2dkZ5ubmCA8PR0RERIlnbz5PFy5cUO/fkF+WgiCUOR125swZbN++HQcOHMDFixcRGxuL7OxsyGQyODo6wsfHB8OGDcOYMWOe27ldI0aMwI4dOwAUTZuWNiU5Z84ceHl54ejRo7h+/ToePnyIp0+fombNmmjTpg2GDBmCESNG6B178Vw/e/ZMfT4iUHSOnZOTE5o1a4bu3bsjJCQE9evXN+JINb3//vslnkyxYMGCElPqlZXqylcbG5sKfewcUXmwqCN6jpo1a4YlS5YYvF2TJk1w9erVMp82sHXrVvj6+qqn61TWr1+vtf2NGzc0HtMEFF2UoFAoSn3CQHl07ty5XPdak8lkJZ4J+29OTk4IDQ3VeIqDr68vfH19Dbpi1JT+85//6H28jRo1wuTJk7VOr5dHeXNtCu+99x7eeOMNjWVVaVp33rx5mDdvXkWHQVQuPKeOqAoYPHgwkpKStD5iqfgvb3Nz8xKjcTdv3sTOnTu19vv9999rjOJcuXIFBw4cQM+ePXlfPSoXT09P9OjRQ+PLlNP4RKQbizqiKmDatGnw9vbG1KlTMXDgQCxfvhxLly7F0KFDMWPGDHW7fv36ITo6GsOGDcOaNWswe/ZsdOjQQectLezt7dGpUycsWbIEc+fOha+vL6ysrIx68kJISIj63mS7du0qdz/P05QpU9Qxb9y4saLDIRGI+b7k+4cqC06/ElUB1tbWOHbsGD7//HP89NNP2L17NxwdHdGmTRuMGjVK3W7p0qWwtrbGjh07sGPHDnh6emLlypW4fv26xl3yVb766iscPnwYX331FZ48eQIvLy98/fXXaNmypcEx1qhRo8TD2K2srAw/WBMJDg7W+9xEBweHErc6cXR0FCEqet6ex/uS7x+qLGRCRZ14QUQVZsOGDQgJCcHRo0dLvecYkcqqVavwzjvv6HzPHDt2DH5+fli5ciXGjx///AMkIk6/EhEREUkBizoiIiIiCeA5dUREVKbx48eXOq3q6+tbYbdRIaIiPKeOiIiISAI4/UpEREQkASzqiIiIiCSA59Q9R4WFhbh//z7s7e0hk8kqOhwiIiIyAUEQkJmZibp168LMrOLGy1jUPUf379+Hi4tLRYdBREREIrh37x7q169fYftnUfcc2dvbAyj6pjs4OFRwNKajVCpx8OBB9OzZE3K5vKLDkRzmV3zMsbiYX/Exx+IqK78ZGRlwcXFR/56vKCzqniPVlKuDg4PkijobGxs4ODjww0QEzK/4mGNxMb/iY47FpW9+K/rUKl4oQURERCQBLOqIiIiIJIBFHREREZEEsKgjIiIikgAWdUREREQSIKmiLjExEZMmTYKHhwesra1hZWUFNzc3jBw5EhcvXjS6/++++w4ymQwymQxjxowxQcREREREpiGZou706dNo2bIlvv32Wzx79gw9e/ZE7969IZPJsGnTJnh7e2P79u3l7v/27dv48MMPK/xyZSIiIiJtJFPUhYaGIjMzE6Ghobhz5w52796NHTt2ID4+HrNnz0Z+fj5CQ0ORk5NjcN+FhYUIDg6GTCbDiBEjRIieiIiIyDiSKOoePXqES5cuAQDmz5+vcWNAMzMzhIWFwdraGmlpabh+/brB/X/zzTc4ceIEvvzyS7i6upoqbCIiIiKTkURRp1Ao9G7r7OxsUN9xcXGYNWsWunXrhnfeecfQ0IiIiIieC0kUdXZ2dujatSsAYPbs2VAqlep1hYWFCAsLQ3Z2NgIDA+Hi4qJ3vwUFBRg5ciRkMhnWrVvH8+mIiIio0pLMs1/XrFmD3r17Y/Xq1YiMjIS3tzfMzc0RGxuLpKQkBAUFYcWKFQb1uXDhQpw+fRpLlixBo0aNRIqciIiIyHiSKeo8PDwQExODoKAgHDx4EElJSep1zZs3h6+vLxwcHPTu78qVK/j000/RqVMnvPvuu+WKKTc3F7m5uerXGRkZAIoeDFx8NLGqUx2LlI6pMmF+xccci4v5FR9zLK6y8ltZ8i6Zou7kyZMYMGAALCwsEBERAX9/f1haWuLkyZOYNm0aRo8ejZMnT2LdunVl9pWfn4+RI0fCzMwM69evh5lZ+WapFyxYgLlz55ZYfvDgQdjY2JSrz8osKiqqokOQNOZXfMyxuJhf8THH4tKV36ysrOcciXaSKOrS0tLQv39/pKamIiYmBh06dFCve+ONN9C8eXO0atUK69evx/Dhw+Hn51dqf//9739x/vx5fPnll/Dw8Ch3XDNmzMC0adPUrzMyMuDi4oKePXsaNGpY2SmVSkRFRSEgIEDjymMyDeZXfMyxuJhf8THH4iorv6qZuIomiaIuMjISKSkpaNSokUZBp+Lu7o4OHTrg6NGjOHToUJlF3c6dOwEAe/bswd69ezXWJSQkqPfp6+sLADh27JjWfhQKhdYrc+VyuSR/6KR6XJUF8ys+5lhczK/4mGNx6cpvZcm5JIq6xMREACh19MvR0REA8PjxY737/f3333Wue/jwIR4+fKh3X0RERERiksQtTerVqwcAuHHjBtLT00usVyqVOH/+PADAzc2tzP4uXLgAQRC0fn366acAgNGjR6uXEREREVU0SRR1gYGBsLW1RXZ2NsaOHYunT5+q1+Xl5WHq1KlITEyEXC7HW2+9pV63YsUKeHp68tFfREREVOVJYvq1Ro0aWLVqFUJCQrB9+3YcO3YMPj4+kMvlOHv2LJKSkmBmZoZly5bB3d1dvV1qairi4uJQu3btCoyeiIiIyHiSGKkDgOHDh+Ps2bMIDg6Gvb09Dh8+jH379sHCwgLDhg1DTEwMxo8fX9FhEhEREYlCEiN1Km3atEF4eLje7cPCwhAWFmbQPsqzDREREZHYJDNSR0RERPQiY1FHREREJAEs6oiIiIgkgEUdERERkQSwqCMiIiKSABZ1RERERBLAoo6IiIhIAljUEREREUkAizoiIiIiCWBRR0RERCQBLOqIiIiIJIBFHREREZEEsKgjIiIikgAWdUREREQSwKKOiIiISAJY1BERERFJAIs6IiIiIglgUUdEREQkASzqiIiIiCSARR0RERGRBLCoIyIiIpIAFnVEREREEsCijoiIiEgCJFXUJSYmYtKkSfDw8IC1tTWsrKzg5uaGkSNH4uLFiwb1tW/fPowZMwbe3t6oU6cOFAoF7O3t0bZtW8ycOROpqakiHQURERGR4SRT1J0+fRotW7bEt99+i2fPnqFnz57o3bs3ZDIZNm3aBG9vb2zfvl3v/rZs2YJ169YhPT0dLVu2xJtvvolOnTrh7t27WLBgAVq0aIGrV6+KeERERERE+pNMURcaGorMzEyEhobizp072L17N3bs2IH4+HjMnj0b+fn5CA0NRU5Ojl79vf/++3jw4AFu3ryJqKgoRERE4MCBA7h37x4GDhyI5ORkjBkzRuSjIiIiItKPJIq6R48e4dKlSwCA+fPnQy6Xq9eZmZkhLCwM1tbWSEtLw/Xr1/Xqs23btqhdu3aJ5XZ2dli8eDEA4I8//kBGRoYJjoCIiIjIOJIo6hQKhd5tnZ2djd6fhYUFgKKCsXgBSURERFRRJFHU2dnZoWvXrgCA2bNnQ6lUqtcVFhYiLCwM2dnZCAwMhIuLi1H7ys3NxcyZMwEAAQEBsLa2Nqo/IiIiIlOwqOgATGXNmjXo3bs3Vq9ejcjISHh7e8Pc3ByxsbFISkpCUFAQVqxYYXC/58+fx7JlyyAIAlJSUnDmzBmkpqbCx8cH69atE+FIiIiIiAwnmaLOw8MDMTExCAoKwsGDB5GUlKRe17x5c/j6+sLBwcHgfhMTE7Fx40aNZT169MD333+PevXqlbptbm4ucnNz1a9V598plUqN0cSqTnUsUjqmyoT5FR9zLC7mV3zMsbjKym9lybtMEAShooMwhZMnT2LAgAGwsLDAokWL4O/vD0tLS5w8eRLTpk3DzZs3MWrUqHKPrhUUFODvv//GoUOH8Omnn+Lx48fYtGkT3nrrLZ3bhIWFYe7cuSWWR0REwMbGplxxEBERUeWSlZWFoUOHIj09vVwDSKYiiaIuLS0NTZs2RWpqKmJiYtChQweN9bdv30arVq2QlZWFI0eOwM/Pz6j9JSQkoEWLFjAzM8PNmze1XiULaB+pc3FxQWpqaoV+001NqVQiKioKAQEBvHBEBMyv+JhjcTG/4mOOxVVWfjMyMuDs7FzhRZ0kpl8jIyORkpKCRo0alSjoAMDd3R0dOnTA0aNHcejQIaOLOldXV/j5+SEyMhJRUVEICgrS2k6hUGi9Mlcul0vyh06qx1VZML/iY47FxfyKjzkWl678VpacS+Lq18TERAAotTp2dHQEADx+/Ngk+7S1tQUAJCcnm6Q/IiIiImNIoqhTXbBw48YNpKenl1ivVCpx/vx5AICbm5vR+8vNzcXvv/8OAGjatKnR/REREREZSxJFXWBgIGxtbZGdnY2xY8fi6dOn6nV5eXmYOnUqEhMTIZfLNS5sWLFiBTw9PTFixAiN/pKTk7Fy5UqtT4tQ3R7l/v37cHV1RUBAgHgHRkRERKQnSZxTV6NGDaxatQohISHYvn07jh07Bh8fH8jlcpw9exZJSUkwMzPDsmXL4O7urt4uNTUVcXFxJS50yMrKwoQJEzBlyhS0bdsWrq6uEAQB9+7dw/nz55GXl4e6deti165dsLKyet6HS0RERFSCJIo6ABg+fDhatWqFpUuXIjo6GocPH4YgCKhTpw6GDRuGd999Fy+//LJefdWsWROLFy9GdHQ0rly5guvXryM7OxvVqlVDx44d0adPH4SGhkrqClYiIiKq2iRT1AFAmzZtEB4ernf7sLAwhIWFlVhuY2ODadOmYdq0aSaMjoiIiEg8kjinjoiIiOhFx6KOiIiISAJY1BERERFJAIs6IiIiIglgUUdEREQkASzqiIiIiCSARR0RERGRBLCoIyIiIpIAFnVEREREEsCijoiIiEgCWNQRERERSQCLOiIiIiIJYFFHREREJAEs6oiIiIgkgEUdERERkQSwqCMiIiKSABZ1RERERBLAoo6IiIhIAljUEREREUkAizoiIiIiCWBRR0RERCQBLOqIiIiIJEBSRV1iYiImTZoEDw8PWFtbw8rKCm5ubhg5ciQuXryodz+FhYU4deoUPvnkE3Tp0gXVq1eHXC6Hs7MzAgICsGXLFgiCIOKREBERERnGoqIDMJXTp08jICAAmZmZqFevHnr27Alzc3NcuHABmzZtQkREBCIiIjBw4MAy+7p9+zY6d+4MAHjppZfg7e0NJycn3L59G4cOHcKhQ4ewbds2/PLLL7C0tBT70IiIiIjKJJmRutDQUGRmZiI0NBR37tzB7t27sWPHDsTHx2P27NnIz89HaGgocnJyyuxLJpPB398f+/btQ3JyMg4cOIBt27bhzz//xLFjx2Bra4vffvsNX3zxxXM4MiIiIqKySaKoe/ToES5dugQAmD9/PuRyuXqdmZkZwsLCYG1tjbS0NFy/fr3M/ho1aoTDhw/jtddeg7m5uca6bt264eOPPwYAbNq0yYRHQURERFR+kijqFAqF3m2dnZ2N3l+7du0AAPfu3TO6LyIiIiJTkERRZ2dnh65duwIAZs+eDaVSqV5XWFiIsLAwZGdnIzAwEC4uLkbv7+bNmwCAOnXqGN0XERERkSlI5kKJNWvWoHfv3li9ejUiIyPh7e0Nc3NzxMbGIikpCUFBQVixYoXR+8nKysKyZcsAAG+++abR/RERERGZgmSKOg8PD8TExCAoKAgHDx5EUlKSel3z5s3h6+sLBwcHo/czYcIE3LlzB3Xr1sXMmTNLbZubm4vc3Fz164yMDACAUqnUGE2s6lTHIqVjqkyYX/Exx+JifsXHHIurrPxWlrzLBInccO3kyZMYMGAALCwssGjRIvj7+8PS0hInT57EtGnTcPPmTYwaNQrr1q0r9z7mzZuHTz75BFZWVjh06JD6tie6hIWFYe7cuSWWR0REwMbGptxxEBERUeWRlZWFoUOHIj093SQDSOUliaIuLS0NTZs2RWpqKmJiYtChQweN9bdv30arVq2QlZWFI0eOwM/Pz+B9fP3115g+fToUCgV27dqF1157rcxttI3Uubi4IDU1tUK/6aamVCoRFRWFgIAAjSuPyTSYX/Exx+JifsXHHIurrPxmZGTA2dm5wos6SUy/RkZGIiUlBY0aNSpR0AGAu7s7OnTogKNHj+LQoUMGF3XLly/H9OnTYWlpiV9++UWvgg4ouipX25W5crlckj90Uj2uyoL5FR9zLC7mV3zMsbh05bey5FwSV78mJiYCQKnVsaOjIwDg8ePHBvX97bff4t1331UXdK+//nr5AyUiIiISiSSKunr16gEAbty4gfT09BLrlUolzp8/DwBwc3PTu99Vq1Zh0qRJ6oLujTfeME3ARERERCYmiaIuMDAQtra2yM7OxtixY/H06VP1ury8PEydOhWJiYmQy+V466231OtWrFgBT09PjBgxokSfa9aswYQJE1jQERERUZUgiXPqatSogVWrViEkJATbt2/HsWPH4OPjA7lcjrNnzyIpKQlmZmZYtmwZ3N3d1dulpqYiLi4OtWvX1ujvwoULGDduHARBgLu7O37++Wf8/PPPWve9YcMGMQ+NiIiISC+SKOoAYPjw4WjVqhWWLl2K6OhoHD58GIIgoE6dOhg2bBjeffddvPzyy3r1lZaWBtVFwTdu3MCNGzd0tmVRR0RERJWBZIo6AGjTpg3Cw8P1bh8WFoawsLASy319fSGBO70QERHRC0QS59QRERERvehY1BERERFJAIs6IiIiIglgUUdEREQkASzqiIiIiCSARR0RERGRBLCoIyIiIpIAFnVEREREEsCijoiIiEgCWNQRERERSQCLOiIiIiIJYFFHREREJAEs6oiIiIgkgEUdERERkQSwqCMiIiKSABZ1RERERBJgYUjjUaNGiRKEo6MjlixZIkrfRERERC8Cg4q6DRs2QCaTQRAEkwUgk8lQq1YtFnVERERERjCoqAMAKysrDBo0yGQBbNy40WR9EREREb2oDC7qHB0dER4ebrIAWNQRERERGY8XShARERFJgEEjde+++y4cHR1NGoAYfRIRERG9aAwq6pYuXWryAMTok4iIiOhFI6np18TEREyaNAkeHh6wtraGlZUV3NzcMHLkSFy8eNGgvu7du4fvv/8eoaGh8PLygkKhgEwmw5gxY0SKnoiIiKj8DL5QorI6ffo0AgICkJmZiXr16qFnz54wNzfHhQsXsGnTJkRERCAiIgIDBw7Uq79ffvkFU6dOFTlqIiIiItOQzEhdaGgoMjMzERoaijt37mD37t3YsWMH4uPjMXv2bOTn5yM0NBQ5OTl69efm5obJkycjPDwcFy9exKxZs0Q+AiIiIqLyE7Wo++mnn9CzZ080a9YMvr6+WL58OQoKCky+n0ePHuHSpUsAgPnz50Mul6vXmZmZISwsDNbW1khLS8P169f16vM///kPli1bhuDgYLRu3RoWFpIZ1CQiIiIJKndRt3//frz66qsYMWKE1vULFizAkCFDcPjwYcTFxSE6OhpTpkxB3759TfpECgBQKBR6t3V2djbpvomIiIgqg3IXdbt378bJkyfRuHHjEutu376NTz75BADg5+eHb775BqGhoZDJZNi/fz/WrVtX/oi1sLOzQ9euXQEAs2fPhlKpVK8rLCxEWFgYsrOzERgYCBcXF5Pum4iIiKgyKPec4p9//gkAGDx4cIl1a9asQUFBATp37oyoqCjIZDIAQJMmTfDBBx9g8+bNJr+KdM2aNejduzdWr16NyMhIeHt7w9zcHLGxsUhKSkJQUBBWrFhh0n0SERERVRblLuru378PS0tLeHh4lFh38OBByGQyTJkyRV3QAcDkyZMxZ84cXL58uby71cnDwwMxMTEICgrCwYMHkZSUpF7XvHlz+Pr6wsHBweT7LU1ubi5yc3PVrzMyMgAASqVSYzSxqlMdi5SOqTJhfsXHHIuL+RUfcyyusvJbWfJuUFH32Wefqf+fkpICKysrjWUqqqLt5MmTuHr1qsa6atWqITk5WWM7X19fvPrqqwYF/m8nT57EgAEDYGFhgYiICPj7+8PS0hInT57EtGnTMHr0aJw8edLkU7+lWbBgAebOnVti+cGDB2FjY/Pc4nheoqKiKjoESWN+xccci4v5FR9zLC5d+c3KynrOkWhnUFF39OhR9f8LCwtRWFiosQwAnjx5gvz8fNjb2yM2NrZEH7m5uRAEQWM7V1dXo4q6tLQ09O/fH6mpqYiJiUGHDh3U69544w00b94crVq1wvr16zF8+HD4+fmVe1+GmDFjBqZNm6Z+nZGRARcXF/Ts2fO5jxqKSalUIioqCgEBARpXHpNpML/iY47FxfyKjzkWV1n5Vc3EVbRyF3UODg7IycnB3r17YW1trV7+7bffYvLkyejVqxd++umnEn106dIFFy5cKFEMGiMyMhIpKSlo1KiRRkGn4u7ujg4dOuDo0aM4dOjQcyvqFAqF1itz5XK5JH/opHpclQXzKz7mWFzMr/iYY3Hpym9lyXm5r3718PBAQUFBienMDRs2QCaToUePHlq3i4+PR926dcu7W60SExMBoNTRL0dHRwDA48ePTbpvIiIiosqg3BdKvPnmmzh37hymTZuGmzdvwsPDA7/++ivOnTsHe3t7rVfFXr9+HcnJyerbj5hKvXr1AAA3btxAenq6uoBTUSqVOH/+PICiJ0UQERERSU25R+qmTp0KLy8v5OfnY8WKFZg8eTIOHjwIAFi0aFGJwgoA1q1bB5lMhl69epU/Yi0CAwNha2uL7OxsjB07Fk+fPlWvy8vLw9SpU5GYmAi5XI633npLvW7FihXw9PTUeQNlIiIioqqi3CN1CoUCx48fx+LFixEZGYknT57A3d0dEydOxBtvvFGifXp6Ovbt2wd3d3cMHDjQqKD/rUaNGli1ahVCQkKwfft2HDt2DD4+PpDL5Th79iySkpJgZmaGZcuWwd3dXb1damoq4uLiULt27RJ9PnjwAP3791e//vvvvwEAv/76Kzp27Khe/t1336F9+/YmPR4iIiIiQxn1QFMbGxvMmTMHc+bMKbOto6NjidubmNLw4cPRqlUrLF26FNHR0Th8+DAEQUCdOnUwbNgwvPvuu3j55Zf17i83NxenT58usTwlJQUpKSnq15XlihciIiJ6sUnqKfVt2rRBeHi43u3DwsIQFhamdZ2rq6vJn1FLREREJBaDizpvb294eXmhffv28PLyQuvWrWFpaSlGbERERESkJ4OLuvPnz2vcVNjCwgLNmzeHl5eXuthr06YNrKysTBooEREREelm1PSrIAhQKpW4ePEiLl26pJ76NDc3R7NmzdSjeV5eXmjbtq3GTYqJiIiIyHQMLupkMhkAoH79+ggJCUGDBg1w7tw5nD9/HpcuXUJ2djby8/Nx+fJlXLlyBZs2bQIAmJmZwcPDQ13kvfvuu6Y9EiIiIqIXmMH3qTt37hw6d+6Me/fuYd68efjxxx8xceJExMTEIDMzUz1iN3nyZHTq1Ak2NjYQBAEFBQW4du0afvjhB43noRIRERGR8QweqWvbti2io6Oxbds2fPjhh4iKikLbtm0xYcIEfPbZZ2jZsiVatmyJkSNHAiiaoo2Li1OP5p09exYXL140+YEQERERvcjK/USJt99+G3FxcZg9ezYsLCywfPlyNG7cGN9//73GrUBkMhk8PT0xbNgwLF68GMePH0daWpopYiciIiKi/yl3UQcA1tbW+Oyzz3D9+nX0798fqampmDBhAtq3b4/o6GhTxUhEREREZTCqqFNp2LAhfv75Zxw+fBgtWrTAxYsX4efnh8GDByMxMdEUuyAiIiKiUpikqFPx8/PDhQsXsHz5clSrVg3bt29Hs2bNEBYWhuzsbFPuioiIiIiKMWlRBxTdumTixImIj4/HhAkTkJOTg3nz5mHu3Lmm3hURERER/Y/Jnv2amZmJK1eu4PLly+qvK1euQBAEyGQyFBYWmmpXRERERPQvBhd1+fn5uHHjhkbxdvnyZdy7d0/dpvjVr/Xr10fbtm3RrVs300RMRERERCUYXNTZ2toiPz8fgGbxZmFhAQ8PD7Rt2xbt2rVD27Zt0bZtW7z00kumi5aIiIiItDK4qFMqlZDJZHBzc0Pv3r3Rpk0btG3bFi1btoRCoRAjRiIiIiIqQ7nPqbtz5w62b9+OW7duISEhAYmJiWjfvj0aNmxoyviIiIiISA8GF3UuLi7q8+f++ecf7Nu3D/v371evd3JyQrt27dC+fXv1V5MmTUwXMRERERGVYHBRd/fuXTx69Ajnz59XP8/13LlzuHPnDgDg8ePHOHz4MI4cOaLexs7ODm3bttUo9Fq0aGG6oyAiIiJ6wZVr+rV69eoICAhAQECAellaWhrOnz+v/jp37hzi4+MhCAIyMzNx4sQJ/P777wCKngerutiCiIiIiIxnsvvUVatWDf7+/vD391cvy8zMRGxsrMaoXlxcnMZVs0RERERkPJMVddrY29vj1VdfxauvvqpelpWVhQsXLoi5WyIiIqIXjskfE1YWGxsbdOrU6XnvloiIiEjSnntRR0RERESmZ1BR9+uvv+LgwYMmDcCUfSYmJmLSpEnw8PCAtbU1rKys4ObmhpEjR+LixYvl6vPQoUPo3bs3nJ2dYW1tDU9PT8yaNQtPnz41ScxEREREpmBQUdevXz+EhISYNABT9Xn69Gm0bNkS3377LZ49e4aePXuid+/ekMlk2LRpE7y9vbF9+3aD+lyyZAkCAgKwf/9+tGjRAn369EF6ejo+//xzeHt7IzU11ei4iYiIiEzB4OlXMa5cNUWfoaGhyMzMRGhoKO7cuYPdu3djx44diI+Px+zZs5Gfn4/Q0FDk5OTo1V9sbCymT58Oc3NzREZG4vjx4/jpp59w69YtdO/eHXFxcRg/frzRcRMRERGZgsFXv2ZnZ2PTpk1ixFJujx49wqVLlwAA8+fPh1wuV68zMzNDWFgYFi9ejLS0NFy/fh3t2rUrs88FCxZAEASEhIQgMDBQvdzGxgbr1q2Du7s7fvnlF9y4cQOenp6mPygDFBQK+PPOYyRn5qCmvRVednsJ5mayCo3JUDyGileV469KsVelWIurinFXtZgZr3iqUqzGMLioy8jIMPkUrLEUCoXebZ2dnctsk5eXh8jISADA0KFDS6xv2LAhOnfujBMnTmDnzp2YMWOG/sGa2P4rDzB3zzU8SP//Ecg6jlb4tE9zvNayToXFZQgeQ8WryvFXpdirUqzFVcW4q1rMjFc8VSlWYxk0/dqgQQOTfzVs2BAuLi5GHYSdnR26du0KAJg9ezaUSqV6XWFhIcLCwpCdnY3AwEC99vXXX38hKysLAODt7a21jWp5bGysUbEbY/+VB3hn83mNNyoAPEzPwTubz2P/lQcVFJn+eAwVryrHX5Vir0qxFlcV465qMTNe8VSlWE3BoJG6hIQEkcIw3po1a9C7d2+sXr0akZGR8Pb2hrm5OWJjY5GUlISgoCCsWLFCr75Uz7GtVq0a7O3ttbZRFYeqts9bQaGAuXuuQdvZiAIAGYCwX6+hc2NnmJvJYCaTwUpurm6Tlaf7MW2GtjUv9jo7rwCC1qgAGWSwtvz/1k9z8vHpr1dLPYa5e64hoHltKAsKUVjKuZc2lv//Vs5RFjy3tgWFQpnHUPz7oGItN4dMVvQ6N78ABYW6YzAvts+8/ELkFxbqbGtlYQ6z/+2nrLYKi6LvhT7vI3/PWrC0KPobUFlQCGWB7n4tzc1gYW542/yCQuSV0lZubgZ5sbbZygK93j/+nrVKzYOFmRlU35mCQgHKUt7vFmZm6jwUFgrIyS/Qq60yv1Dv97qZDMhW6u7X3Eym/t4JglBqW2N/7vV5f8/dU/L9/e9+y/sZYUhb1c+nXj+TZcQs1ueJtp97U8RrZfH/ecjLL4RS0P19NvQzwvxfbU3xnqhsnxEBzWtLZipW1CdKPE8eHh6IiYlBUFAQDh48iKSkJPW65s2bw9fXFw4ODnr1lZmZCQCwtbXV2cbOzg5A0XS0Lrm5ucjNzVW/VrVVKpUao4nlcfrO4xJ/eRQnAHiYkYNWYUW3i+nW1Blrg9qr13vNO4RspfYfjpddnbBltI/6decvjuJJlvZ4W9VzwI+jvQAUHVfAshgkpWmPq3ENW+x7t7P6dc8l0fgnI1drW9UxPEjPQUx8Mr488BcuJ2nPtZONHH/O8FO/HrHuDP5MeKK1rbXcDJc+6aF+Pe6H8zj+l+6rmG/O66n+/5RtF7H/6j862+o6huLfB5U/PvZFdVtLAMBne65jy5/3dPYR9W5HAEX5XXw4DutO3tXZdu+kTmhSq+i9ufxIPJYfva2z7S/jOiBbWaDX+2jDyVsI6eQKANh8OhFzf7uhc5vVw9vBz6NG0T7OJ+HjnVd1tl02uDUCW9YGAOy78hDv/nhJZ9sv+rfAm+3rAQCOxqUgdHPpo+Sq98+Gk7fw+b6/dLb7sFcTBHeoDwC4mPgYg9ed09l2sp873vVvDAC4+c9T9F5xSmfb0Z0b4uPXPAAAv116oPd7vXFNO3T84pjOtv3b1cVXA1oCKCq82sw7orPtay1qYfnbbdSvm3+i+/ZRhnxG/DvuVxYcxtNc7cWlMZ8RfZafRHzKM61t61WzwrHp//+0ooGr/tD5GfHvmB+ml/yZVBHzM+LiHH91EfjxjivYGXvf6HgB4Oi0rqhlV9TvooNxCI/R/Xli6GdE6/qOAIC1v9/BVwdu6hXvgzLirWyfETHxyejg9lKpbVW/s3X97jb2d7qpSKaoO3nyJAYMGAALCwtERETA398flpaWOHnyJKZNm4bRo0fj5MmTWLdu3XOLacGCBZg7d26J5QcPHoSNjY1RfZ9LlQEaf/+WLiU5GXv37lW/LigwB6D9L5PHjx9rtM3L0902LS0dUVFRAICoqChkZ+tu+/TpU41+n2XpblvcwROnkZZmprNtXl6eRr+PH+vut6CgQKNtSrIZSjsLoXjbhw9Kb2uIw4cOwe5/1/PcvVt6vydO/I7qVkX5vZNQetvoE9G4+b+31s17pbc9eeokUnP0ex+dvXQDtdKuAQCuPix9m7NnzyL7VtHfxpeSS297PjYWQmJR29hHpbe9dOkSrB8W3W/y6hP93/9nL91AaXm4cf0GojKuAwBO/3kapX0s3rwZj705RQXigyyU2vbO7TvYu/cWAODEff3iPXjiNO46CqX2m/T339i7NxEAUFRH6W778MED7N2bVGyJ7raGfEb8m1KZr7OtMZ8RT5/qbpudna3RNi1N/3hLI+ZnxIEDB6H439sg6W/TfZ4cPXoU1a2K/n834W6p/Rr6GfF3Uf2HG0mG/c4pTWX7jDh44jQeXdfvLhyq9/K/qU7ZqmgyQYx7lDxnaWlpaNq0KVJTUxETE4MOHTporL99+zZatWqFrKwsHDlyBH5+fjp6KrJnzx707dsX1apVw5Mn2kd8lixZgmnTpsHb2xtnzpzR2kbbSJ2LiwtSU1P1HjXU5fSdxxi+/myZ7dYGtYOPqxPMZTIoRJt+LURUVBQCAgKQL5jpPV0S/VcqRv9wvsxj2DzKG23qO4oypZqrLECBEW3PJDzBmB/KPq9S9X1Q0ZyGKURBKVMg5ijE4UOHEBAQAEFmbtLp17N3n+j1PtoY7IVOjaoDqDzTrydvPdIr9xuDvdDWxVHnegszM8iEAkRFRcG/ew8UynT/kivv9GvMrUcYsUH3CKDK5lHeeNnVqYzpVzMo/tdvWdOvhvzca2tb3vd3ccZ8RpRn+tUUMZvyM6I4bT/3pojXysIcBQX5iIqKQjf/7pCZ6S5mjJ1+NUW8le0zYvMob71G6lTv4eJ32FDJyMiAs7Mz0tPTjf79bgxJjNRFRkYiJSUFjRo1KlHQAYC7uzs6dOiAo0eP4tChQ2UWda6urgCKisXMzEyt59Xdu3dPo602CoVC65W5crlc65vCEK80rok6jlZ4mJ6j9SNPBqC2oxX8mtXReq6AowH7L6utathZLpfDxoB+fZvV1usYXmlc06DzHQzJrbFt/ZpZoY7j9XJ/H4r6LX2/xfNrWLxlt9H3fdSlaS11/Ia8dQ1ta21AW79mdfTKffHYdVHl2EphaVCO9b3wvnPTWga91y0t9Q7BoLaG/tyb4v0NlP8zojw/n6aK2ZgY9Gtb9K/pcly0ztZKoXcc5flZNn1+DYtBjM8IQ37H6PocNvZ3uqlI4tmviYlFUxGlVceOjkV/qT9+/LjM/jw8PNTTo2fPah/FUC1v37691vViMzeT4dM+zQGUnHBQvf60T/NKffInj6HiVeX4q1LsVSnW4qpi3FUtZsYrnqoUq6lIoqirV6/oxMgbN24gPT29xHqlUonz54um+dzc3Mrsz9LSEq+//joAICIiosT6u3fv4tSpohOk+/fvX+64jfVayzpYObw9ajtaaSyv7WiFlcPbV4n77/AYKl5Vjr8qxV6VYi2uKsZd1WJmvOKpSrGahCABycnJgq2trQBAGDhwoJCZmalel5ubK0ycOFEAIMjlcuHWrVvqdcuXLxc8PDyEoKCgEn2eO3dOkMlkgrm5ubBv3z718mfPngndu3cXAAhvvvmmQXGmp6cLAIT09PRyHKVu+QWFwqn4VGFX7N/CqfhUIb+g0KT9lyUvL0/YtWuXkJeXV+4+KvoYTEGsYzBFfvVRlb8Hxsb+vHIsCFU3z8bE/TzzW1xVy3VVy3FVyq/YnxFi/X43lCTOqatRowZWrVqFkJAQbN++HceOHYOPjw/kcjnOnj2LpKQkmJmZYdmyZXB3d1dvl5qairi4ONSuXbtEn+3bt8fixYsxbdo09O7dG926dUPNmjVx4sQJPHjwAB4eHli1atXzPEydzM1keOV/J7FXVTyGileV469KsVelWIurinFXtZgZr3iqUqzGkMT0KwAMHz4cZ8+eRXBwMOzt7XH48GHs27cPFhYWGDZsGGJiYjB+/HiD+pw6dSqioqLQq1cvXLp0Cbt374adnR1mzJiBM2fO6PXIMSIiIqLnQRIjdSpt2rRBeHi43u3DwsIQFhZWapsePXqgR48epbYhIiIiqmjPtagrKCjA/ftFd9CuU6cOLCwkVVMSERERVZjnMv1648YN9O/fH/b29nB1dYWrqyscHBzQv39/XLt27XmEQERERCRpog+VnT17Ft27d8ezZ8/Qq1cveHgUPQvxxo0b2LNnDw4dOoSjR4/C29tb7FCIiIiIJEv0om7atGlQKBQ4deoUWrRoobHuypUr8PPzw7Rp0xAdHS12KERERESSJfr069mzZzFp0qQSBR0AtGzZEpMmTdL51AYiIiIi0o/oRZ2Tk1OZj+9yctL+0F8iIiIi0o/oRd3w4cOxceNGZGdnl1iXlZWFDRs2YMSIEWKHQURERCRpop9T16NHDxw8eBCtW7fG+PHjNS6U+P7772Fvb4/u3buXOKfu1VdfFTs0IiIiIskQvajr1auX+v8ffPABZDIZAEAQBK1tBEGATCZDQUGB2KERERERSYboRZ0hT3ggIiIiovIRvagbOXKk2LsgIiIieuE91+d0PX36FGlpaSgsLCyxrkGDBs8zFCIiIiJJeS5F3Q8//IDPP/8cf/31l842PIeOiIiIqPxEv6XJtm3bMHLkSMhkMowbNw6CIGDIkCEYPHgwFAoF2rVrh08++UTsMIiIiIgkTfSRuq+//hpt2rTB6dOnkZGRgVWrVmHUqFHw9/fHzZs30bFjRzRv3lzsMIiIiIgkTfSRuqtXr2L48OGwtLSEmVnR7lRTrU2aNME777yDL774QuwwiIiIiCRN9KLOwsJC/ZgwW1tbAEBqaqp6vaurK27cuCF2GERERESSJnpR5+rqioSEBACAQqFAw4YNcfDgQfX6Y8eO4aWXXhI7DCIiIiJJE72o8/f3xy+//KJ+HRQUhI0bN6J79+7w8/PD1q1bMWDAALHDICIiIpI00S+U+PDDD/Haa68hNzcXCoUCc+bMQWpqKrZt2wZzc3OMGjUKCxYsEDsMIiIiIkkTvairU6cO6tSpo34tl8vx3Xff4bvvvhN710REREQvDNGnX4mIiIhIfKIXdYcPH8aMGTN0rp85cyaOHDkidhhEREREkiZ6Uff555/jzp07OtffvXvX6PvUJSQkQCaT6fUVHR2td7+PHz/GjBkz0KxZM1hbW8PJyQmvvvoqfvjhB6PiJSIiIjI10c+pu3TpEmbOnKlz/csvv4zPP//cqH3Y2dlh5MiROtdfu3YNZ86cgb29Pby8vPTq8/bt2/D398fdu3dRvXp1dO/eHdnZ2fjjjz9w4sQJHD58GOHh4ZDJZEbFTkRERGQKohd1T58+haWlpc715ubmSE9PN2ofzs7O2LBhg871vXv3BgC8/fbb6hsgl2XIkCG4e/cufH19sWPHDjg5OQEA4uPj8dprr2Hjxo3o3Lkzxo4da1TsRERERKYg+vSrm5sbTpw4oXN9dHQ0GjZsKNr+k5KScODAAQDA6NGj9domJiYGf/75J8zNzbF27Vp1QQcAjRs3xtdffw0AmDdvHgRBMH3QRERERAYSvagbNGgQtm/fjuXLl2sUQIIgYNmyZfjll18waNAg0fa/YcMGFBYWokWLFujQoYNe25w5cwZA0dMwGjVqVGJ9jx49AAD37t3Dn3/+abpgiYiIiMpJ9OnXjz76CPv378eUKVOwcOFCtGjRAgBw9epV/P333/D29i71nDtjqaZl9R2lA4qmjAGgevXqWtfb2NjA2toa2dnZOHfunN7FIhEREZFYRB+ps7a2xvHjx/HZZ5/ByckJx48fx/Hjx+Hk5IT58+cjOjoa1tbWouz7+PHjiI+Ph6WlJYKCgvTermbNmgCg86rdhw8fIjs7u9Q2RERERM9TuUbq4uLikJWVhVatWsHCouwuFAoFZs2ahVmzZpVnd+W2fv16AEDfvn3h7Oys93Z+fn6QyWRISUnBrl270K9fP431q1atUv8/IyNDZz+5ubnIzc0t0VapVEKpVOodT2WnOhYpHVNlwvyKjzkWF/MrPuZYXGXlt7LkXSYYeKZ/YWEhWrdujevXr6NLly44fvy4WLEZJSMjA3Xq1EFWVhb27t2LwMBAg7YfMWIEfvjhBzg5OWHZsmUIDAxEdnY2tmzZgjlz5gAo+iaOHz8eK1eu1NpHWFgY5s6dW2J5REQEbGxsDD8oIiIiqnSysrIwdOhQpKenw8HBocLiMLioi4yMRJ8+fWBra4sbN26gXr16YsVmlNWrV2PcuHGoX78+7t69CzMzw2aanz17huHDh2PXrl0l1g0aNAh5eXnYtWsXZs6cif/+979a+9A2Uufi4oLU1NQK/aabmlKpRFRUFAICAiCXyys6HMlhfsXHHIuL+RUfcyyusvKbkZEBZ2fnCi/qDJ5+3b59O2QyGSZPnmxQQTd16lT8/fff6N69O8aPH2/obg2mmnoNDg42uKADAFtbW+zcuRMxMTHYv38/Hjx4gJdeegm9evWCn58fOnXqBABo1aqVzj4UCgUUCkWJ5XK5XJI/dFI9rsqC+RUfcywu5ld8zLG4dOW3suTc4KLujz/+AFB0c15DTJ06FU2aNMHOnTvRu3dvNGjQwNBd6+3atWs4ffo0ZDIZQkJCjOrrlVdewSuvvKKxLDMzExcuXICFhQX8/PyM6p+IiIjIFAwewrp//z7Mzc1LHaHSpkGDBhgwYAAEQcDu3bsN3a1B1q1bB6Doggd3d3eT9//dd98hOzsbAwcORK1atUzePxEREZGhDC7q8vPzYW9vX66dvf322xAEQdSLK5RKJTZv3gyg7HvTrVixAp6enhgxYkSJdbdu3UJKSorGMkEQsH79esyZMwcvvfQSFi9ebLrAiYiIiIxg8PRrzZo1ce/ePeTm5mo9X6w0Xbp0AVB042Gx/Pbbb0hOTka1atUwYMCAUtumpqYiLi4OtWvXLrFuz549+OCDD9C+fXs0aNAAgiDg7NmzuHv3LmrWrIl9+/ahTp06Yh0GERERkUEMHqlTXRxx5coVg3dWvXp1WFlZ4f79+wZvqy/VBRJDhw6FlZVVufvp3Lkz3nzzTaSkpGDv3r3Yv38/HBwcMGfOHMTFxaF9+/amCpmIiIjIaAaP1PXo0QMxMTFYt24dvLy8DN6hra0t0tPTDd5OX3v27NG7bVhYGMLCwrSu8/HxwbZt20wUFREREZG4DB6pe+uttwAAGzduRGxsrEHbKpVKpKenl/ucPCIiIiLSzuCirlWrVnj77beRnZ2N/v37IyEhQe9tT506hfz8fLi5uRm6WyIiIiIqheF35QWwePFi1K5dG4mJiWjbtq3e05Tz58+HTCZDt27dyrNbIiIiItKhXEVdnTp1cOjQITg7OyMjIwPDhg1Dly5dsH37dmRnZ5don5ycjKFDh+Lw4cOQyWQIDQ01OnAiIiIi+n8GXyih0rx5c5w6dQpDhw7F2bNnERMTg5iYGMjlcjRv3hwuLi6wtLREUlISzp8/D6VSCQCYPn06PDw8THYARERERGREUQcAjRs3xqlTp7B8+XIsXrwY9+/fR15eHi5cuICLFy+q2wmCAACYPHkyvvjiC+MiJiIiIqISjCrqAMDCwgJTp07FxIkTERkZif379+PcuXN4+PAhcnNzUatWLXTq1Aljx46Fj4+PKWImIiIion8xuqhTsbS0RP/+/dG/f39TdUlEREREeirXhRJEREREVLmwqCMiIiKSABZ1RERERBLAoo6IiIhIAljUEREREUkAizoiIiIiCWBRR0RERCQBLOqIiIiIJIBFHREREZEEsKgjIiIikgAWdUREREQSwKKOiIiISAJY1BERERFJAIs6IiIiIglgUUdEREQkAZIo6hISEiCTyfT6io6O1rvfR48eYcaMGWjVqhVsbW1haWmJ+vXrY+DAgQb1Q0RERCQ2i4oOwBTs7OwwcuRIneuvXbuGM2fOwN7eHl5eXnr1eevWLbz66qu4f/8+qlevDl9fX9jY2ODq1av4+eef8fPPP2Px4sWYNm2aqQ6DiIiIqNwkUdQ5Oztjw4YNOtf37t0bAPD222/D1tZWrz6nTZuG+/fv4/XXX8ePP/6osd3q1asxbtw4fPTRRxg0aBDq169vVPxERERExpLE9GtpkpKScODAAQDA6NGj9d7uyJEjAIBPP/20RCEYGhqKJk2aID8/H2fOnDFdsERERETlJPmibsOGDSgsLESLFi3QoUMHvbezsrLSq52zs3N5QyMiIiIymReiqAMMG6UDgMDAQADA3LlzkZWVpbFuzZo1uHnzJlq1aoVXXnnFJHESERERGUMS59Tpcvz4ccTHx8PS0hJBQUEGbbtw4UJcu3YNkZGRaNCgATp27Ki+UOLGjRt4/fXXsWbNGlhYSDqFREREVEVIuiJZv349AKBv374GT5PWqlULx44dwzvvvIPNmzcjMjJSvc7FxQX+/v6oUaNGqX3k5uYiNzdX/TojIwMAoFQqoVQqDYqnMlMdi5SOqTJhfsXHHIuL+RUfcyyusvJbWfIuEwRBqOggxJCRkYE6deogKysLe/fuVU+n6uvGjRvo06cPUlJSsGDBAvTp0wcODg6IjY3F+++/j7NnzyIgIAD79u2Dubm51j7CwsIwd+7cEssjIiJgY2NTruMiIiKiyiUrKwtDhw5Feno6HBwcKiwOyRZ1qtuO1K9fH3fv3oWZmf6nD+bn56NNmza4du0afvrpJwwcOFBj/ZMnT9C8eXM8fPgQ69evR0hIiNZ+tI3Uubi4IDU1tUK/6aamVCoRFRWFgIAAyOXyig5Hcphf8THH4mJ+xccci6us/GZkZMDZ2bnCizrJTr+qpl6Dg4MNKugA4PTp07h27RoUCgUGDBhQYr2TkxMCAwMRHh6OQ4cO6SzqFAoFFApFieVyuVySP3RSPa7KgvkVH3MsLuZXfMyxuHTlt7LkXJJXv167dg2nT5+GTCbTWXCVJjExEQBgY2Ojc2rV0dERAPD48ePyB0pERERkIpIs6tatWwcA8PPzg7u7u8Hb16tXD0DRNOvNmze1tjl9+jQAwM3NrZxREhEREZmO5Io6pVKJzZs3Ayj73nQrVqyAp6cnRowYobH8lVdeURd2Y8aMQUpKinpdYWEhvvjiC8TExAAAhgwZYsrwiYiIiMpFcufU/fbbb0hOTka1atW0ng9XXGpqKuLi4lC7dm2N5XK5HJs2bUKfPn0QHR2Nxo0bo0OHDrC3t8fFixdx69YtAMDMmTPRtWtX0Y6FiIiISF+SK+pUF0gMHTpU70d9aePv74/Lly/j66+/xuHDh/H7778jPz8fNWrUQP/+/fHOO+8gICDAVGETERERGUVyRd2ePXv0bhsWFoawsDCd693d3bFixQoTREVEREQkLsmdU0dERET0ImJRR0RERCQBLOqIiIiIJIBFHREREZEEsKgjIiIikgAWdUREREQSwKKOiIiISAJY1BERERFJAIs6IiIiIglgUUdEREQkASzqiIiIiCSARR0RERGRBLCoIyIiIpIAFnVEREREEsCijoiIiEgCWNQRERERSQCLOiIiIiIJYFFHREREJAEs6oiIiIgkgEUdERERkQSwqCMiIiKSABZ1RERERBLAoo6IiIhIAiRR1CUkJEAmk+n1FR0drVef+va3adMmkY+OiIiIqGwWFR2AKdjZ2WHkyJE611+7dg1nzpyBvb09vLy89OqztP4SExNx9OhRyGQydOvWzeB4iYiIiExNEkWds7MzNmzYoHN97969AQBvv/02bG1t9eqztP4mTJiAo0ePokePHmjYsKEhoRIRERGJQhLTr6VJSkrCgQMHAACjR482ur+cnBxs3brVZP0RERERmYLki7oNGzagsLAQLVq0QIcOHYzu75dffkFaWhpeeukl9OvXz/gAiYiIiEzghSjqANONqq1fvx4AMHz4cCgUCpP0SURERGQsSRd1x48fR3x8PCwtLREUFGR0fwkJCTh69CgATr0SERFR5SKJCyV0UY2q9e3bF87Ozkb3Fx4eDkEQ4O3tjdatW5fZPjc3F7m5uerXGRkZAAClUgmlUml0PJWF6likdEyVCfMrPuZYXMyv+JhjcZWV38qSd5kgCEJFByGGjIwM1KlTB1lZWdi7dy8CAwON6q+wsBBubm5ITEzEd999h3feeafMbcLCwjB37twSyyMiImBjY2NUPERERFQ5ZGVlYejQoUhPT4eDg0OFxSHZom716tUYN24c6tevj7t378LMzLiZ5oMHD6JXr16wtrbGgwcP4OjoWOY22kbqXFxckJqaWqHfdFNTKpWIiopCQEAA5HJ5RYcjOcyv+JhjcTG/4mOOxVVWfjMyMuDs7FzhRZ1kp19VU6/BwcFGF3TF+3vzzTf1KugAQKFQaL2YQi6XS/KHTqrHVVkwv+JjjsXF/IqPORaXrvxWlpxL8kKJa9eu4fTp05DJZAgJCTG6v8ePH2PXrl0AeIEEERERVU6SLOrWrVsHAPDz84O7u7vR/W3ZsgW5ublo1KgRHwtGRERElZLkijqlUonNmzcDKHtUbcWKFfD09MSIESNKbaeaeh01ahRkMplpAiUiIiIyIcmdU/fbb78hOTkZ1apVw4ABA0ptm5qairi4ONSuXVtnm9jYWFy4cAHm5uYIDg42cbREREREpiG5kTrVqNrQoUNhZWVlsv569eqFunXrGt0fERERkRgkN1K3Z88evduGhYUhLCys1DbLly/H8uXLjYyKiIiISFySG6kjIiIiehGxqCMiIiKSABZ1RERERBLAoo6IiIhIAljUEREREUkAizoiIiIiCWBRR0RERCQBLOqIiIiIJIBFHREREZEEsKgjIiIikgAWdUREREQSwKKOiIiISAJY1BERERFJAIs6IiIiIglgUUdEREQkASzqiIiIiCSARR0RERGRBLCoIyIiIpIAFnVEREREEsCijoiIiEgCWNQRERERSQCLOiIiIiIJYFFHREREJAGSKOoSEhIgk8n0+oqOjjao78LCQmzcuBE9evRAjRo1oFAoUKdOHfj7++O7774T6YiIiIiIDGNR0QGYgp2dHUaOHKlz/bVr13DmzBnY29vDy8tL737T09PRt29fREdHw8HBAZ06dUK1atWQlJSE2NhYZGRkYMKECaY4BCIiIiKjSKKoc3Z2xoYNG3Su7927NwDg7bffhq2trV59CoKAfv36ITo6GuPGjcOiRYtgZ2enXp+Xl4dLly4ZFTcRERGRqUiiqCtNUlISDhw4AAAYPXq03tuFh4fj2LFj6NWrF1atWlVivaWlJby9vU0WJxEREZExJHFOXWk2bNiAwsJCtGjRAh06dNB7u2XLlgEAPvjgA7FCIyIiIjIZyY/UqaZlDRml++eff3Dx4kWYm5ujU6dOuH37Nn766SckJCTAzs4OHTp0wH/+8x9YWlqKFDURERGRYSRd1B0/fhzx8fGwtLREUFCQ3tupzpWrXr061q5di+nTp0OpVGq0cXd3x86dO9G6dWuTxkxERERUHpIu6tavXw8A6Nu3L5ydnfXe7tGjRwCAx48f491338Vbb72FTz/9FK6urrhy5QqmTJmC06dP47XXXsPly5dRvXp1rf3k5uYiNzdX/TojIwMAoFQqSxSJVZnqWKR0TJUJ8ys+5lhczK/4mGNxlZXfypJ3mSAIQkUHIYaMjAzUqVMHWVlZ2Lt3LwIDA/XeduvWrRg6dCgA4JVXXsGpU6c01mdmZqJJkyb4559/8Nlnn2HOnDla+wkLC8PcuXNLLI+IiICNjY0BR0NERESVVVZWFoYOHYr09HQ4ODhUWBySHanbtm0bsrKyUL9+ffTq1cugbe3t7dX/HzdunNb1w4cPx+LFi3Ho0CGdRd2MGTMwbdo09euMjAy4uLigZ8+eFfpNNzWlUomoqCgEBARALpdXdDiSw/yKjzkWF/MrPuZYXGXlVzUTV9EkW9Sppl6Dg4NhZmbYRb7u7u5a/6+tzYMHD3T2o1AooFAoSiyXy+WS/KGT6nFVFsyv+JhjcTG/4mOOxaUrv5Ul55K8pcm1a9dw+vRpyGQyhISEGLx906ZN1aN1qampWtuolhe/ITERERFRRZFkUbdu3ToAgJ+fn86RttJYWFigX79+AIBDhw5pbRMVFQUAePnll8sXJBEREZEJSa6oUyqV2Lx5M4Cy7023YsUKeHp6YsSIESXWzZw5E3K5HGvWrMFvv/2msW7hwoX4/fffYW5ujokTJ5oueCIiIqJyktw5db/99huSk5NRrVo1DBgwoNS2qampiIuLQ+3atUus8/T0xJo1azBq1Cj06dMH3t7e6lua3LhxA+bm5li5ciVatWol1qEQERER6U1yI3WqCySGDh0KKysro/oaOXIk/vjjD7z55ptITEzE7t27kZaWhoEDB+LUqVMYO3asKUImIiIiMprkRur27Nmjd9uwsDCEhYWV2sbHxwc///yzkVERERERiUtyI3VERERELyIWdUREREQSwKKOiIiISAJY1BERERFJAIs6IiIiIglgUUdEREQkAZK7pUllJggCACAjI6OCIzEtpVKJrKwsZGRkVJqHGksJ8ys+5lhczK/4mGNxlZVf1e911e/5isKi7jnKzMwEALi4uFRwJERERGRqmZmZcHR0rLD9y4SKLitfIIWFhbh//z7s7e0hk8kqOhyTycjIgIuLC+7duwcHB4eKDkdymF/xMcfiYn7FxxyLq6z8CoKAzMxM1K1bF2ZmFXdmG0fqniMzMzPUr1+/osMQjYODAz9MRMT8io85FhfzKz7mWFyl5bciR+hUeKEEERERkQSwqCMiIiKSABZ1ZDSFQoFPP/0UCoWiokORJOZXfMyxuJhf8THH4qoq+eWFEkREREQSwJE6IiIiIglgUUdEREQkASzqJCYuLg7Lly9HcHAwWrVqBQsLC8hkMsyfP1/nNvfu3cP333+P0NBQeHl5QaFQQCaTYcyYMUbHc+7cOQwcOBC1atWClZUV3NzcMHnyZCQnJ5e63T///INJkybBzc0NCoUCtWrVwsCBA3H+/HmjYzJWVc9xYmIivv/+ewwYMAANGzaEQqGAnZ0d2rRpg5kzZyIlJcXomIxR1fOrTVJSEpycnCCTyWBhUfF3kpJSji9evIhRo0bBzc0NVlZWcHJyQqtWrfDOO+/g0aNHRsdWHlLIryAI2LJlC3r06AFnZ2fI5XJUq1YNnTt3xrJly5CXl2d0XMaoLDl+9OgRNmzYgMmTJ6NTp06wsbGBTCZDjx499No+Pj4ewcHBqF+/PhQKBerXr4/g4GDcvn27fAEJJCnvvfeeAKDE17x583Rus2TJEq3bjB492qhYtm/fLlhYWAgABB8fH2HQoEGCu7u7AECoVauWcPPmTa3bxcXFCTVr1hQACO7u7sKgQYMEHx8fAYBgYWEh7Nixw6i4jFXVc9y5c2d1Ln18fITBgwcLAQEBgoODgwBAqFGjhhAbG2tUXMao6vnVJjAwUJDJZAIAwdzc3KiYTEEqOV64cKFgbm4umJmZCT4+PsLbb78tBAYGCo0bNxYACJcvXzYqtvKSQn4HDx4sABDMzMyELl26CIMHDxZ8fX0FuVwuABA6duwoZGVlGRWbMSpLjnfu3Km1z+7du5e57e+//y7Y2NgIAIQWLVoIgwcPFlq0aCEAEGxtbYWYmBiD42FRJzFr1qwR3n//fWHLli3C9evXhaCgoDLf6Lt27RImT54shIeHCxcvXhRmzZpl9Bs9KSlJ/Wb9/vvv1cvz8/OF4cOHqz9gCgsLNbYrLCwU2rVrJwAQgoKChPz8fPW677//XgAg2NnZCQ8ePCh3bMaq6jkeNGiQsGTJEiE1NVVjeXJysuDr6ysAEJo0aaKR++epqudX2/EAECZNmlRpijop5Hj9+vUCAMHDw0Nr8XblyhUhLS2t3LEZo6rnd8eOHQIAwdHRUbhw4YLGulu3bgn16tUTAAgLFiwod2zGqiw5PnXqlDBu3Djh+++/F86cOSOsWrVKr6Lu2bNnQt26dQUAwowZMzTWzZgxQwAguLi4GFw4s6iTuJEjR5b5Rv+3Tz/91Og3+gcffCAAEHr06FFiXWZmpuDo6CgAEPbv36+xLjIyUgAgVKtWTcjMzCyxbffu3QUAwscff1zu2EytquW4NPfu3VP/pXnixIlyx2ZKVTm/CQkJgr29vdCxY0fh1q1blaao+7eqluPHjx8LDg4OgrW1tXDr1q1y7/95qWr5Vf0BMmHCBK39/ve//xUACH379i13bKZWUTn+t/DwcL2Kum+//VYAIDRt2lQoKCjQWFdQUCA0bdpUACCsWrXKoP3znDoSxc6dOwEAQ4cOLbHOzs4Offv2BQDs2LFD63Z9+/aFnZ1diW1V/f17uxdReXNcmvr168PZ2RlA0fknLzJj8ysIAkaNGoW8vDysX7++Qp8HWVmVN8cbN25ERkYG3nzzTbi7u4sfaBVV3vxaWVnp1b/qs4IMp/revP322yU+G8zMzDB48GAAhv+u46cMmVxmZibi4+MBAN7e3lrbqJbHxsZqLFe9Lmu7mzdv4tmzZyaJtyoyJselSU1NxZMnTwAAderUMTLKqssU+f3uu+9w5MgRfPrpp2jWrJk4gVZhxuT4wIEDAIBXX30V2dnZ+OGHH/Duu+9i4sSJWLp06Qv/BwlgXH4DAwMBABEREbh48aLGutu3b2PlypWQyWQYO3asqcN+Yej7u86Qz28AqPjLsEhyEhIS1P9v0KCB1jYuLi4AgDt37mgsV70uaztBEJCQkIAWLVoYG26VZEyOS7No0SIUFBSgTp066NSpk1ExVmXG5vfWrVv46KOP4OXlhQ8++ECUGKs6Y3J86dIlAEWFS8uWLUtcKfjRRx9hwYIFmDZtmgkjrlqMya+/vz9mzZqF//73v2jfvj06d+6MevXq4Z9//sHvv/8OFxcX7N69Gx07dhQtfinLzMxUX5ld1vcmJSUFz549g62trV59c6SOTC4zM1P9f11vRNXUakZGhtZty9pO27YvEmNyrMuhQ4ewaNEiAMDixYthaWlpZJRVlzH5LSwsRHBwMPLy8hAeHl4pbmFSGRmTY9UvxI8//hgFBQXYs2cPnjx5oi6mlUolpk+fjq1bt4oUfeVn7GfE/PnzsXnzZtjY2ODEiRPYtm0bjh49CkEQ0KNHjxf2D2pTMOR7Axj2u45FHRHh8uXLGDhwIAoKCjB58mQMGTKkokOqspYuXYrff/8ds2fPRqtWrSo6HEkS/vd0y8LCQuzduxdvvPEGqlWrBnd3d3zxxRcYP348AGD27NkVGWaVpVQqMWrUKAwfPhz9+vXD5cuX8ezZM/z111+YNGkS1qxZAx8fH1y4cKGiQ6V/YVFHJmdvb6/+v67z3p4+fQoAcHBw0LptWdtp2/ZFYkyO/+3GjRvo0aMH0tLSEBISgm+++cZ0gVZR5c1vXFwcZs2ahTZt2mDGjBniBlnFmeJzomvXrmjevHmJ7SZMmACg6PwvQ04/kBJj8vvVV18hPDwcvXv3xg8//ICWLVvCxsYGTZo0wZIlSxAaGorHjx/jvffeE+8AJMyQ7w1g2O86FnVkcg0bNlT/PzExUWsb1YnMrq6uGstVr8vaTiaTaeznRWNMjov766+/4O/vj+TkZIwYMQJr166FTCYzaaxVUXnzu2/fPuTk5ODZs2cICAiAr6+v+uvtt98GABQUFKiX7d+/X7yDqOSMeQ+rrnjVdeVr8eUPHjwwJswqy5j8btiwAQB0jtirrqb9/fffkZuba2SkLx57e3u89NJLAMr+3jg7O+t9Ph3Aoo5E4ODggMaNGwMAzp49q7WNann79u01lqtel7VdkyZNtN7y5EVhTI5Vbt68CT8/Pzx48ADDhw9HeHg4b7vxP8bmNz4+HsePH9f4On36tHq9atnDhw9FiL5qMCbHXl5eAIqu1tam+PIX9XPCmPyqCg1dI0SOjo4Aiqa/09LSTBHuC0ff33W6Pr914Sc4iaJ///4Aii6J/7enT59iz549AIABAwZo3e7XX3/VOiyt6u/f272IyptjoOjqTD8/P9y/fx/Dhw/Hxo0bWdD9S3nyO2XKFAhFN3Uv8aWaBjQ3N1cvCw4OFv9AKrHyvocHDhwIAPjjjz+0fk5ERUUBKCroXuTbyZQ3v/Xq1QMAjT9Eivvjjz8AFI048V515aP63mzbtg2FhYUa6woLC/Hjjz8CMPx3HT/Fqdx27twJT09PdO/evcS6KVOmwMbGBocOHcKaNWvUywsKCjBhwgSkpaXBx8cHPXv21NguMDAQ7dq1Q1paGiZMmICCggL1utWrV+Pw4cOws7N7Yc7lECPHd+7cgZ+fH5KSkhAUFPRCF3Ri5Jc0iZFjf39/dO3aFcnJyZg0aZLGFOClS5fUF0i88847kMvlIh1Z5SBGft966y0AwJIlS3Ds2DGNdRcuXMCcOXMAAIMGDYK5ubmJj6jyKS3H5RUcHIy6devir7/+UudTZc6cOfjrr79Qv359jBgxwqB+ZYLqMiKShPPnz6tPEgaKRmRSU1NRv3599V9fQNGbVHVz2QcPHqj/agCAv//+G0lJSahRo4bGuSnfffedxlDwhg0bEBISgoYNG2rcE0ll+/btGDJkCAoKCtChQwe4urrizJkzuH37NmrVqoXff/9dPT1QXFxcHLp27YqUlBS4u7vDx8cHd+7cwZ9//gkLCwv89NNPGvE+b1U9x+3bt0dsbCwUCgUGDRqks6AbM2YMunTpYlhyTKCq51eXhIQEuLm5wdzcHPn5+XrnQwxSyPG9e/fw6quvIiEhAfXq1YOPjw8eP36MP/74A3l5eQgICMCePXugUCiMylV5VPX8ZmZmonv37jhz5gwAwMfHB25ubvj7779x+vRpFBQUoFWrVjh69CiqV69uXLLKqTLluPj9+lJSUnD79m04ODhojBLPmTMHr7/+usZ2J0+eRM+ePZGVlYWWLVuiZcuWuHLlCq5cuQJbW1scOnTI8HsBGvRQMar0jh49qn52Z2lfd+7cUW9z584dvbY5evSoxr5Uz7hr2LChznjOnj0rDBgwQKhRo4ZgaWkpNGzYUJg4caLw8OHDUo/jwYMHwsSJE4WGDRsKlpaWQo0aNYQBAwYI586dMyI7plHVc9ywYUO9YgkPDzc+WeVQ1fOriyrGyvDsV6nkOC0tTfj444+Fpk2bCgqFQv2c3ZUrVwr5+flGZMg4Ushvbm6u8M033whdunQRnJycBHNzc8HBwUHo2LGjsHDhQoMfNG9qlSnHxnye3rx5UxgxYoRQt25dQS6XC3Xr1hVGjBghxMfHlysvHKkjIiIikoAX80QaIiIiIolhUUdEREQkASzqiIiIiCSARR0RERGRBLCoIyIiIpIAFnVEREREEsCijoiIiEgCWNQRERERSQCLOiIiIiIJYFFHREREJAEs6ohI8jZs2ACZTIaoqCjMmjUL9erVg62tLXr16oXExEQAwPLly9G4cWNYWVnBx8cHsbGxGn3cv38fU6dORevWreHg4AAbGxv4+Phg69atGu2USiV8fHzg7OyM+/fva6wbO3YszMzMcOTIEXEPmIheSCzqiOiF8dFHH+HYsWP46KOPMHnyZBw5cgT9+/fHl19+idWrV2PChAmYPXs2bty4gQEDBiA/P1+97aVLlxAZGYnXX38dCxcuxNy5c5GXl4ehQ4di48aN6nZyuRxbtmxBTk4ORowYAdXjtXft2oW1a9figw8+gL+/v0Fxu7q6QiaTaXzt2rXLJDlRmTJlSol9BAcHm3QfRCQui4oOgIhIHw0bNlSPqhUnk8lgZ2cHd3d3BAYGYvr06XB2dtbah0KhQHR0NMzNzQEABQUFWLRoEZ48eYKrV6/C2toaAODk5IRJkybh4MGD6N27NwCgW7duiIuLg0wmU/c3efJktGvXDp9//jlGjhypXt60aVMsWbIEoaGhWLRoEYYNG4YxY8bAy8sL8+fPL3cOHBwc1DFaWVlprAsJCcGGDRvK1e/LL7+MXr16oVatWgCA9PR05OTklDtOIqoYHKkjokovNTVVXdA5OTmhVq1a6i9ra2tkZmbi4sWL+OKLL+Dj44MnT55o7Wfs2LHqgg4AOnfuDAAICgpSF0vFl9+6dUu9zNraWl3Q5eTk4NGjR3j69Cl8fX3x119/ISMjo8S++vXrh9mzZ6Nv377Izs7Gli1bIJfLy52Hb775Bg8fPsTDhw/x2muvaaxLSEjQyIvqy87OTt1G2/patWrB19cXn332mbrvwYMHlztGIqo4HKkjokrv/Pnz6v+fOnUKnp6eGusTExMxffp0/Pzzz0hISMC6devw/vvvl+inYcOGGq+rVasGAGjQoIHW5Y8fP1Yvy8vLw/z587Fp0ybcvXu3RN9paWlwcHDQWLZ27Vo0btwY586dw3fffQcPD4+yD7acjh49qnV5aGgo1qxZAxcXF60jnUQkHRypI6JK79y5cwAAR0dHrYVRgwYNsHr1avXrmzdvau2n+CidPstV58MBReeczZs3D6+++io2b96M/fv3IyoqCkOHDgUAFBYWltj+5MmTSEtLA1B0Tl5FUBXE7du3r5D9E9Hzw5E6Iqr0VIWJl5eXxjltxdnZ2cHc3BwFBQWoWbOmyWPYunUrfH19sWnTJo3l69ev19r+4cOHGD16NNq0aYMuXbrg22+/Re/evdGnTx+Tx6ZLfn4+rly5AoBFHdGLgEUdEVV6qpE6Hx8fnW0iIiJQUFAAmUyGt956y+QxmJublxiNu3nzJnbu3FmirSAICA4OxtOnTxEREYFGjRrhxIkTGD16NC5fvqy+IEFsV69eRW5uLgCgXbt2z2WfRFRxOP1KRJXakydPcOfOHQAlizpBEJCYmIg5c+Zg3LhxMDMzw1dffYU2bdqYPI5+/fohOjoaw4YNw5o1azB79mx06NABzZo1K9H2m2++wYEDB7Bw4UI0b94cCoUCERERyMzMREhIiMlj06X4uYgcqSOSPo7UEVGlVrwwCQ0NxcSJE9Wv09LSkJubCysrK7z22muYPHkyunfvLkocS5cuhbW1NXbs2IEdO3bA09MTK1euxPXr1zVuVHz58mV8/PHHCAwMxKRJk9TLW7RogUWLFmHSpElYvnw5Jk+eLEqcxaniqlmzJurVqyf6/oioYrGoI6JKrXhRV/xq1OJyc3ORmZmJOnXqaF0fHBys9Ua6vr6+GhdDqLi6upZYbmdnh+XLl2P58uUl2oeFhan/36pVK533eJs4caJGUSo2Ve449Ur0YuD0KxFVaqrCpHv37hAEQeMrOTkZv/32Gzw9PXHkyBF06dIFycnJFRxx5VBYWIiLFy8C4NQr0YuCRR0RVWqqiyS0nSdXo0YNvP7669i+fTuAovPvit/a5EX2119/4enTpwBY1BG9KFjUEVGllZGRgfj4eADaizqVFi1aoHr16gCA69evP5fYKrvi5/mxqCN6MbCoI6JKKzY2Vn1uW1lXtFpYlDxF+N8PqNf15evri2PHjpXaJj8/X5RjFItq2trR0RFubm4VHA0RPQ+8UIKIKi1VYSKXy7XeOkQlMTER//zzDwBotPvhhx802h05cgTh4eGYOXOmRrvi940LCQmBv79/iX3oeupEZVX8IgldN2wmImlhUUdElZbqfDpPT09YWlrqbDdz5kwARaN1xR9GP3z4cI12T58+RXh4OAICAuDr66ux7tixYwCAl19+ucR2VdGFCxcAcOqV6EXC6VciqrRUo03apl4LCwtx5swZ9O/fH1u2bAFQdGuRJk2aPNcYK6OEhAT17V94OxOiFwdH6oioUnr27Bni4uIAADt37kTt2rXV6woLC5Geno68vDwAgJWVFebPn4/p06ebZL+pqakay2xtbWFtbW10388LnyRB9GJiUUdEldKFCxfUz1p99uwZnj17pl4nl8vh5OSEZs2aoXv37ggJCUH9+vVNst/3338f77//vsayBQsW4OOPPzZJ/8+D6spXGxsbeHp6VnA0RPS8sKgjokqpc+fOWp/2ILb33nsPb7zxhsayqjalO2/ePMybN6+iwyCi54xFHRFRMZ6enujRo0dFh0FEZDBeKEFEVEWEhISo75u3a9cuk/Y9ZcoUdd8bN240ad9E9HxwpI6IqJKrUaMGcnJyNJZZWVmZdB8ODg4a9+sDim5cTERVB4s6IqJK7syZM6Lv47PPPsNnn30m+n6ISDycfiUiIiKSABZ1RERERBIgEyringFEREREZFIcqSMiIiKSABZ1RERERBLAoo6IiIhIAljUEREREUkAizoiIiIiCWBRR0RERCQBLOqIiIiIJIBFHREREZEEsKgjIiIikgAWdUREREQSwKKOiIiISAJY1BERERFJwP8Bf6t8WIBfUGwAAAAASUVORK5CYII=", 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", 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XE46MjER6ejoA9Qo7OSMjI/Ts2ROnTp3C06dPS/4GiqFOnTpo2bIlLl26hB07dqBFixaK1rtBgwYVWEQVpnbt2li2bBmePn2K33//XamIL4o81yYmJipLiuT35MkTxfpw+Y97ez2+U6dOYcuWLZg7d67ScfI/XORGjRqlKKLz09SEkLKQfxxoYYs6E5U1FnZEeuLTTz/F5s2bkZOTg2nTpmHXrl3v/LB48uQJvv/+e6xdu1axbfXq1bh8+TLWrFmDadOmKR2ff+zVhx9+iMjISKXrT5kyBU2aNMHSpUtVCrvU1FSEh4fD0tISANC3b180atQIc+bMwZ9//lmKd10w+Zivxo0bq+Rg3LhxqFevHoyMjPDVV18VeJ6RkZGi2za/pKQklXFrb7t9+zYA1UJEG4YPH45Lly5hz549mDhxoqLlTj654m1ZWVmQSCSFXs/c3ByAegtWy3Pm6en5zmEAc+fOBZDXajdgwADF9qFDhyodl5aWhi1btqBjx46KVrqCNG/eXOXc8kbeOsxuWNIn7Iol0hPNmzdXrMm1Z88edOzYEWfOnEF2drbimISEBBw6dAiDBg1CnTp1VFqV9uzZg+rVq2PKlCkq189fIJmbmyteZ2Zm4tWrV0hLS4O/vz/u3bunMkZr/PjxiqIOALy9vdG5c2ccO3YMOTk5pX/zb3nXOLnu3btj1apV+O6771RmD8vPq1evXoEzi1u3bo0ZM2Yoirf8srKysHr1agQGBgIA+vTpU+r3UZQBAwbA1NQUr169UiwT4uXlVWhr4+TJk9G/f3/88ccfSjNT09LSsHHjRmzfvh0A0K1bt2LHIM9ZQd2wMpkMV69eRe/evfH7778DyFtyxMPDo9jXN1TR0dGKpWG41AnpE7bYEemRtWvXwszMDKtXr8bJkydx8uRJGBsbw8bGBunp6Uprd4lEIpWurPv37+ODDz4osjsrOzsbS5Yswfbt2/H48WOV/UlJSbC2tla8LuhJDfXq1cM///yDZ8+eKZ7Nqgnp6emIjIwEoH5LiLz1qbDCKCsrC99//z2+//57ODo6ok6dOrhz5w4iIyOxYsUKRX47d+5cJmuh2dnZ4eOPP8aBAwdw7do1AIVPmgDyniW8b98+xQxVS0tLGBsbKz3Bok2bNvj666+Ldf/8uT548KBS979MJkNycrLiDwszMzMsWbIEM2bMUOs9vuveCQkJStsqVaqkaHXUd3ziBOkrFnZEekQsFmPlypUYPXo0fv75Z5w9exZRUVFISkqCubk56tatiwYNGuDDDz9Ez549S/yw+mnTpiEgIADDhg1D586d4eDgALFYjC1btmDnzp0qz+osS2FhYYr7q/OBKQhCkV1jV69exb59+3Ds2DGEh4cjNDQUb968gUgkgo2NDfz8/DBkyBCMHTu2zMZ6DR8+HAcOHACQ14X6ru7J+fPno2nTpjh9+jTu3LmD58+fIy0tDY6OjmjUqBEGDRqE4cOHFzv2/LlOT09XjE8E8sbc2dnZoX79+mjfvj1GjRoFFxeXUrxTZTNnzlR5gsWyZctUutf1lXxGrIWFhU4fUUf0NhZ2RHqofv36WLNmjdrneXh44NatW0U+lWDXrl3w9/dXdN3J/frrrwUef/fuXaVHOgF5ExUkEsk7n0RQEq1bty7RWmwikUjlGbJvs7Ozw7hx45Se9uDv7w9/f3+1ZpJqUs+ePYv9fmvXro0pU6YU2NVeEiXNtSZ8/vnn+Pjjj5W2lacu3sWLF2Px4sW6DoNIBcfYERmQAQMGIDY2tsDHMeX/ABeLxSqtcvfv38fBgwcLvO6mTZuUWnMiIiJw7NgxdOrUievuUYl4enqiQ4cOSl+a7NInqqhY2BEZkOnTp6NZs2b44osv0K9fP6xfvx5r167F4MGDMWfOHMVxvXr1wrlz5zBkyBAEBgZi3rx5eO+99wpd7sLKygqtWrXCmjVrsHDhQvj7+8PMzKxUT2gYNWqUYu2yQ4cOlfg6ZWnatGmKmLdt26brcEgLtPlzyZ8fKgvsiiUyIObm5jhz5gyWLl2KvXv34s8//4SNjQ0aNWqE0aNHK45bu3YtzM3NceDAARw4cACenp4ICAjAnTt3lFbTl/vuu+9w8uRJfPfdd3j9+jWaNm2K77//Ht7e3mrHWKVKFZUHuJuZman/ZjVk5MiRxR6raG1trbIMio2NjRaiorJWFj+X/PmhsiASdDXAgoj03tatWzFq1CicPn36nWuSEclt3LgRn332WaE/M2fOnEHbtm0REBCACRMmlH2ARAaOXbFEREREBoKFHREREZGB4Bg7IiLSmAkTJryzi9Xf319nS6wQVQQcY0dERERkINgVS0RERGQgWNgRERERGQiDGWMXGRmJ48ePIyQkBCEhIbhz5w6kUikWL16MefPmvfNcmUyGHTt2YMeOHQgPD0dKSgoqV66M+vXro2/fvpg4caLS8aGhoQgKCsKJEycQERGBxMREWFpawtvbGwMHDsS4ceNgYmKicp/09HT8+eefihivX7+O1NRU1K5dGw8ePCjxe5fJZHj27BmsrKwgEolKfB0iIiJSnyAISE1NRbVq1WBkpNs2M4Mp7AICArBu3Tq1z0tOTkaPHj1w7tw5WFtbo1WrVrC1tUVsbCxCQ0ORkpKiVNjl5uYqHjBuaWkJPz8/ODk5ISYmBpcuXcKFCxewfft2HDt2DLa2tkr3un//PoYMGVKq91mQZ8+ewdXVVePXJSIiouJ7+vQpXFxcdBqDwRR23t7emDlzJpo0aQJfX18sXboUO3bseOc5giAoHq00fvx4rFq1CpaWlor92dnZuHHjhsp5TZs2xezZs9GjRw9IJBLF9ps3b6Jz5864cuUKpk+frvJAdSsrK4waNQq+vr5o0qQJkpKSVB6CXRJWVlYA8n6grK2tS309XcrJycHx48fRqVOnAls9ST3Mp+Yxp5rHnGoec6pZReUzJSUFrq6uis9jXTKYwm7s2LFKr4vTFLplyxacOXMGnTt3xsaNG1X2m5qaolmzZkrbjI2Nce3atQKv5+Pjg++++w7Dhg3D7t27sWnTJqUfgNq1aysVe2fOnCkyxuKQd79aW1sbRGFnYWEBa2tr/mekAcyn5jGnmsecah5zqlnFzac+DIeq0JMnfvjhBwDArFmzNHbNJk2aAADevHmDhIQEjV2XiIiIqCgG02KnrhcvXiA8PBxisRitWrXCo0ePsHfvXkRHR8PS0hLvvfceevbsCVNTU7Wue//+fQB5rX2VK1fWRuhEREREBaqwhZ187Jy9vT02b96MGTNmICcnR+kYd3d3HDx4EA0bNizWNQVBwHfffQcA+Pjjj5XG3xERERFpW4Ut7F69egUASExMxNSpU9G3b18sWLAAbm5uiIiIwLRp03D58mV06dIFN2/ehL29fZHXXLhwIS5dugRLS0ssX75ca7FnZWUhKytL8TolJQVA3hiAt4vT8kYef3l/H/qC+dQ85lTzmFPNY041q6h86lOeK2xhJ3+SWm5uLlq2bIl9+/Yp9rVo0QLBwcHw8PBAXFwcNmzYgPnz57/zetu3b8eiRYtgZGSEX3/9FR4eHlqLfdmyZVi4cKHK9uPHj8PCwkJr9y1LwcHBug7BoDCfmsecah5zqnnMqWYVls+MjIwyjqRwFbawyz8lefz48QXuHzp0KFavXo0TJ068s7Dbt28fRo8eDQAIDAxEv379NB9wPnPmzMH06dMVr+XTrDt16mQQs2KDg4PRsWNHzuTSAOZT85hTzWNONY851ayi8invOdMHFbawc3d3L/D7go6Ji4sr9DoHDhzA4MGDIZPJsGnTJkWBp00SiaTA8XsmJiYG8wtsSO9FHzCfmsecah5zqnnMqWYVlk99ynGFXe6kbt26ila7wpYlkW/Pv2hxfocOHcLAgQMhlUoREBCATz/9VDvBEhERERVDhS3sjI2N0atXLwDAiRMnCjxG3pfevHlzlX2HDx9G//79kZubi4CAgAK7c4mIiEg/SGUCLj18hT/DYnHp4StIZYKuQ9KKCtsVCwBz587F7t27ERgYiK5duyo93mvlypW4cOECxGIxJk2apHTe33//jb59+yI3NxcbN27EuHHjyjp0IiIiKqagiDgsPHwbccmZim3ONmZY0N0LXbyddRiZ5hlMYXf9+nVMnDhR8frhw4cAgE2bNuHIkSOK7QcPHoSzc94/oqenJwIDAzF69Gh0794dzZo1Uyx3cvfuXYjFYgQEBMDHx0dxfnx8PPr06YPs7Gy4uLjg4sWLuHjxYoExrVq1Cg4ODkrbevfurRizJx9sGRMTgxYtWiiOGTt2rMoj0oiIiEh9QRFx+Oy363i7fe55ciY+++06Aob6GlRxZzCFXUpKCi5fvqyyPSYmBjExMYrX+dd/A4ARI0bAy8sLK1aswPnz5xEeHg57e3v069cPM2fOVOmGzcjIUFwjJiYG27ZtKzSmb7/9VqWwCw0NxePHj5W2ZWVlKcXepUuXIt4tERERFUUqE7Dw8G2Vog4ABAAiAAsP30ZHr6oQG+n+Oa+aYDCFnb+/v2JtOnX5+flh//79xTrWzc2txPcBgOjo6BKfS0RERMV3JSpRqfv1bQKAuORMXIlKRMvaRT+IoDyosJMniIiIyLDFpxRe1Ckdl1q848oDFnZERERkkPZee1qs4xytzLQcSdlhYUdEREQGqYOX0zv3i5A3O7Z5rcplE1AZYGFHREREBuFNthQPX6YpXo9s5YZve3hBhLwiLj/56wXdvQxm4gTAwo6IiIgMwI2YJHRbfx6jtlxFWlYuAEAkEmFkq1oIGOqLqjbK3a1VbcwMbqkTwIBmxRIREVHFkyuVYePZh1h74j5yZQKcrCV4mpiB+s7WimO6eDujo1dVXIlKRHxqJhyt8rpfDamlTo6FHREREZVLT15l4Iu9YQh5/BoA0K2hM/7Xyxu2FqYqx4qNRAazpMm7sLAjIiKickUQBOy7FoOFh28hPVsKK4kxFvVqgF6Nq0MkMrxWOHWwsCMiIqJy5/jt50jPlqJ5rcr4vn8juNhZ6DokvcDCjoiIiMoFqUyA2EgEkUiE5Z80xJ9hzzCylZtBjpUrKc6KJSIiIr32JluK+YciMGtfuGKbg6UEY9rUYlH3FrbYERERkd66EZOEaXvC8OhlOgBgdJta8K5uo+Oo9BcLOyIiItI7BS1jsqpfIxZ1RWBhR0RERHrl7WVMPvKpiqW9fQpcxoSUsbAjIiIivSGVCRi55QoeJaTDUmKMhT0aoI8vlzEpLhZ2REREpDfERiJ8090LG848xOp+jeBamcuYqIOFHREREenUmch4ZOZIFc9t9a/niA/rVmErXQmwsCMiIiKdeJMtxbJ/7mD7pcewkhjDx8UW1W3NAYBFXQmxsCMiIqIydzMmGdP2hOLh/y1j0reZC+wrcXJEabGwIyIiojIjlQnYePYh1gTfU1rG5H2PKroOzSCwsCMiIqIykSOVYUjgZVyJTgSQt4zJ/3r5wI4tdRrDwo6IiIjKhInYCD4uNrgdl8JlTLSEhR0RERFpTWJ6NjJzpKj2f5MiZnWuh5Gt3LiMiZYY6ToAIiIiMkxnIuPRee05TN0VilypDABgZiJmUadFbLEjIiKiEpPKBFyJSkR8aiYcrczQvFZlZOfKsPyfO9h26TEAwMbcBAlp2ahqY6bjaA0fCzsiIiIqkaCIOCw8fBtxyZmKbQ6WphAbifAiJQsAMLKVG77q6gkzE7GuwqxQWNgRERGR2oIi4vDZb9chvLU9IS0bAGBtZoz1g33xYV0uY1KWOMaOiIiI1CKVCVh4+LZKUZefuakYbeo4lFlMlIeFHREREanlSlSiUvdrQV6kZOFKVGIZRURyLOyIiIhILfGp7y7q1D2ONIeFHREREanleRGtdXKOVpwFW9Y4eYKIiIiKJTNHiuX/3MXWi9HvPE4EoKpN3tInVLbYYkdERERFiohNxsfrLyiKug/rVoEIeUVcfvLXC7p7QWzEx4WVNbbYERER0TulZuZgcOB/SMnMRRUrCVb2bQj/eo4FrmNX1cYMC7p7oYu3sw4jrrhY2BEREdE7WZmZYFYXT1y4/xLL+jRE5UqmAIAu3s7o6FVV5ckTbKnTHRZ2REREpEQQBBwMjYVrZQv4ueWNkxv6Xg0Mfa8GRCLlok1sJELL2va6CJMKwMKOiIiIFJIysvH1wQgcvRkHFztz/PP5+7AyM1Ep6Eg/sbAjIiIiAMCF+wmYsS8ML1KyYGwkwkA/V5jzGa/lCgs7IiKiCi4zR4rvgiLx679RAAD3KpWwdkBjNHSx1W1gpDYWdkRERBXY6/RsDPj5Eu69SAMADGtRE3M/qg9zU7bUlUcs7IiIiCowWwsT1LSvhMT0HKzs2xBtPR11HRKVAgs7IiKiCibmdQaszExgY543KWLFJw0hCALsLSW6Do1KiU+eICIiqiAEQcCh0Fh0XXse3/wZodheuZIpizoDwRY7IiKiCiA5IwdfH7qJIzfiAABPEjOQnpWLShKWAoaE/5pEREQG7t8HCZixNxzPUzIhNhLh8/YemOhfG8ZidtwZGhZ2REREBiorR4rlx+7jlwt5y5jUcqiENQMao7GrrW4DI61hYUdERGSgMnKkOBz+DAAw5L0a+LpbfViY8qPfkPFfl4iIyIDIZAIEQQAA2FmYYu2AxsjMlaKdp5OOI6OywMKOiIjIQMQmvcGMvWHo07ga5HNcW9Vx0GlMVLZY2BERERmAP8NiMe9QBFIzc/HoZTpme+k6ItIFTochIiIqx5IzcjB1Vyg+3x2G1MxcNHa1xc4xfjDmJ3yFxBY7IiKicuriw7xlTOKS85YxmdKuDia3rQNBJsUtXQdHOsHCjoiIqBx68ioDw365AqlMgJu9BdYMaIwmNewAADkyqY6jI11hYUdERKSnpDIBV6ISEZ+aCUcrMzSvVRliIxEAoIa9BUa3dkNalhTzutXnEyQIAAs7IiIivRQUEYeFh28jLjlTsc3KzBhfdq6HYS3dAABzP6oPkUikowhJHxnM0MrIyEisX78eI0eOhI+PD4yNjSESibBkyZIiz5XJZNi2bRs6dOiAKlWqQCKRwNnZGe3atcOGDRtUjg8NDcWyZcvQvn17ODk5wcTEBHZ2dnj//ffx008/IScn5533e/DgAUaOHAkXFxdIJBK4uLhg5MiRePToUYnfPxERGY6giDh89tt1paIOAFIzczH/z1s4eiNv0WEWdfQ2g2mxCwgIwLp169Q+Lzk5GT169MC5c+dgbW2NVq1awdbWFrGxsQgNDUVKSgomTpyoOD43Nxe+vr4AAEtLS/j5+cHJyQkxMTG4dOkSLly4gO3bt+PYsWOwtbVVud+///6LTp06ISMjAw0aNECbNm0QERGBbdu2Yf/+/Thx4gRatGhR4jwQEVH5JpUJWHj4NoR3HLPk6B108XZWdMsSyRlMi523tzdmzpyJ33//HXfu3MGwYcOKPEcQBPTq1Qvnzp3D+PHjERsbi3/++Qe7du3CuXPn8OLFC2zcuFHlvKZNm2Lv3r1ISEjAqVOnsGvXLpw/fx6hoaFwdnbGlStXMH36dJXzMjIy0L9/f2RkZGDOnDmIiIjA7t27ERERgTlz5iA9PR39+/fHmzdvNJITIiIqf65EJaq01L0tLjkTV6ISyygiKk8MprAbO3YsVq5cicGDB8PT0xNGRkW/tS1btuDMmTPo3LkzNm7cCEtLS6X9pqamaNasmdI2Y2NjXLt2Df369YNEIlHa5+Pjg++++w4AsHv3bpUu2a1bt+LZs2eoW7euShfxkiVLULduXTx9+hTbt28v9vsmIiLDEp/67qJO3eOoYjGYwq4kfvjhBwDArFmzNHbNJk2aAADevHmDhIQEpX0HDx4EAAwcOFCl8DQyMsKAAQMAAAcOHNBYPEREVL44Wplp9DiqWAxmjJ26Xrx4gfDwcIjFYrRq1QqPHj3C3r17ER0dDUtLS7z33nvo2bMnTE1N1bru/fv3AeS19lWuXFlpX2hoKACotALKybfLjyMioorj4cs01Khsgea1KsPZxqzQ7lgRgKo2eUufEL2twhZ2N27cAADY29tj8+bNmDFjhkrXqbu7Ow4ePIiGDRsW65qCICi6Yj/++GOlrtrU1FS8evUKAFCjRo0Cz3d1dQUAvHz5Eunp6ahUqZJ6b4qIiModmUzA1ovRWB50F+M/cMeMTvWwoLsXPvvtOgAoTaKQT5VY0N2LEyeoQBW2sJMXWYmJiZg6dSr69u2LBQsWwM3NDREREZg2bRouX76MLl264ObNm7C3ty/ymgsXLsSlS5dgaWmJ5cuXK+1LTU1VfF9YwZZ/jF9KSkqhx2VlZSErK0vpWADIyckpcqkVfSePv7y/D33BfGoec6p5FTmnz1My8dWBW/j3Yd5n0q3YZGRlZaN9PQesH9gIS/6+i+cp////+6o2Enzd1RPt6zm8M18VOafaUFQ+9SnPFbawE4S8v4Fyc3PRsmVL7Nu3T7GvRYsWCA4OhoeHB+Li4rBhwwbMnz//ndfbvn07Fi1aBCMjI/z666/w8PDQWuzLli3DwoULVbYfP34cFhYWWrtvWQoODtZ1CAaF+dQ85lTzKlpOw16JsOehETKkIpgYCehZU4Y2dnEICopTHDPbC3iYIkJKDmBtAtS2Tof0cQj+fly8e1S0nGpbYfnMyMgo40gKV2ELOysrK8X348ePL3D/0KFDsXr1apw4ceKdhd2+ffswevRoAEBgYCD69ev3zvulp6cXeJ20tDTF99bW1oXeb86cOUrLqaSkpMDV1RWdOnV653nlQU5ODoKDg9GxY0eYmJjoOpxyj/nUPOZU8ypaTlMzc7D46F0cvJdXwHlXs8aqvj6oXUVzw28qWk61rah8ynvO9EGFLezc3d0L/L6gY+Li4grcD+TNYB08eDBkMhk2bdqkKPDeZmVlhcqVKyMxMRFPnjxBo0aNVI55+vQpAMDBweGd4+skEonKUisAYGJiYjC/wIb0XvQB86l5zKnmVZScJidnI+hWPIxEwET/Opja3gOmxtpZpKKi5LSsFJZPfcpxhV3upG7duopWtLeXJZGTb397fTu5Q4cOYeDAgZBKpQgICMCnn376znvKn1hx7dq1AvfLt8uPIyIiwyAf/gMAtRwqYUXfhtg7viVmdq6ntaKOKqYK+9NkbGyMXr16AQBOnDhR4DHyvvTmzZur7Dt8+DD69++P3NxcBAQEFNid+7bevXsDyFu8WCaTKe2TyWTYs2cPAKBPnz7Ffh9ERKTfHsSnoudP/+Lyo1eKbT0aVUMzNy5XQppXYQs7AJg7dy5MTEwQGBiII0eOKO1buXIlLly4ALFYjEmTJint+/vvv9G3b1/k5uZi48aNxSrqAGDkyJGoVq0a7t27pzJmb/78+bh37x5cXFwwfPjw0r0xIiLSOUEQsO1iNLr9cAE3YpKx+OhtpZY7Im0wmDF2169fx8SJExWvHz58CADYtGmTUtF28OBBODs7AwA8PT0RGBiI0aNHo3v37mjWrJliuZO7d+9CLBYjICAAPj4+ivPj4+PRp08fZGdnw8XFBRcvXsTFixcLjGnVqlVwcHBQvLawsMDevXvRqVMnLF26FH/99Re8vb0RERGBiIgIVKpUCfv27YO5ublGc0NERGUrPiUTs/bfwNl7LwEAH9StgpV9G0Ik4tpzpF0GU9ilpKTg8uXLKttjYmIQExOjeJ1//TcAGDFiBLy8vLBixQqcP38e4eHhsLe3R79+/TBz5kyVbtiMjAzFNWJiYrBt27ZCY/r222+VCjsAaN26NcLDw7F48WKcOHECf/zxB6pUqYLhw4fjm2++Qe3atdV+70REpD+CIuIw58BNvM7IgcTYCHM/qo/hLWuyqKMyYTCFnb+/f4mbuP38/LB///5iHevm5lbqpvQ6deq8syAkIqLy6UpUIib83xMjGlSzxtoBjeHhZFXEWUSaYzCFHRERka75udmhm48zatpbYFqHupzxSmWOhR0REVEJ5UhlCDz/CIOb14CthSlEIhHWD2oCIz7HlXSEhR0REVEJPHyZhi/2hOFGTDJuP0vBj4Pz1iBlUUe6xMKOiIhIDYIg4LfLT/C/o7eRmSODjbkJuno76zosIgAs7IiIiIotPjUTs/ffwOnIvGVM2tRxwKp+jVDVxkzHkRHlYWFHRERUDKFPXmPMtmtITM+GqbERvuriiZGt3Nj1SnqFhR0REVExuDtYwlRshPrO1lg3sDHqchkT0kMs7IiIiArxID4NtatUgkgkgo2FCX4b+x5cK5tDYizWdWhEBeICO0RERG/JkcrwffA9dFpzFvtD/v/Ti+o4WrKoI73Gwo6IiCifRy/T0DfgIn44eR8yAQh7mqTrkIiKjV2xREREyFvGZOeVJ1hy5A7e5EhhbWaMJb190KNRNV2HRlRsLOyIiKhCkcoEXIlKRHxqJhytzNC8VmUkpmfjqz9u4OTdeABAq9r2WNWvEarZmus4WiL1sLAjIqIKIygiDgsP30ZccqZim7ONGQY1r4FTkfEwFRvhyy71MLp1LS5jQuUSCzsiIqoQgiLi8Nlv1yG8tf15cibWBN9D/2auGNnaDfWdrXUSH5EmcPIEEREZPKlMwMLDt1WKOgCKbefuv+TadFTusbAjIiKDdyUqUan79W0CgLjkTFyJSiy7oIi0gIUdEREZvPjUwou6khxHpK9Y2BERkUETBAERscnFOtbRykzL0RBpFydPEBGRQTsUFovA81HvPEYEoKpN3tInROUZW+yIiMigfdywGprVtEMf3+oQIa+Iy0/+ekF3L4i5xAmVcyzsiIjIoLzJlmLDmQfIzpUBAEzERtgzviW+798YAUN9UdVGubu1qo0ZAob6oou3sy7CJdIodsUSEZHBuBGThGl7wvDoZTrSs3Ixq7MnACha4rp4O6OjV1WVJ0+wpY4MBQs7IiIq96QyAQFnHmDtifvIlQlwspagpbtDgceKjURoWdu+jCMkKhss7IiIqFx7mpiBL/aE4drj1wCAbj7O+F9vb9hamOo4MqKyx8KOiIjKrVN3X2DqrjCkZeXCUmKMRT0boHeT6hCJ2LVKFRMLOyIiKrfcHSwhEwQ0d6uM1f0bwbWyha5DItIpFnZERFSuRCekw82hEgDAzaES9k9ohXpVrTgBgghc7oSIiMqJzBwpvv3rFtqtPoNLD18ptntVs2ZRR/R/2GJHRER6LyI2GdP2hOFBfBoA4Fp0Ime2EhWAhR0REektqUzAxrMPsfbEPeRIBThaSbCyXyN8WLeKrkMj0kss7IiISC89TczA9L1huBqdt4xJlwZVsayPD+wqcRkTosKwsCMiIr10JSoRV6Nfo5KpGN/2aIC+TV24jAlREdQq7EaPHq2VIGxsbLBmzRqtXJuIiMoPQRAUxVsf3+p4+joDfZq4oIY9lzEhKg61CrutW7dCJBJBEASNBSASieDk5MTCjoiogrvw4BXWnnqIbaP8YGthCpFIhGkd6uo6LKJyRe2uWDMzM/Tv319jAWzbtk1j1yIiovInM0eKA1FGOHspBADww8kH+Ka7l46jIiqf1C7sbGxssGXLFo0FwMKOiKjiuv0sBZ/vvo778XnLqg5tUQMzO7OVjqikOHmCiIjKnFQmYPP5R1h1PBI5UgFWJgK+H+iLjg2q6To0onJNrcJu6tSpsLGx0WgA2rgmERHpt41nH2LlsUgAQAfPKmhrGQd/rk1HVGpqFXZr167VeADauCYREem3oe/VxMHQWHz6fi30blQV//wTp+uQiAwCnxVLRERal5yRg83nHylWVbCxMEHQ5+9jgF8Nrk1HpEEcY0dERFp18UECZuwLR1xyJqzMjDHArwYAwFjMtgUiTWNhR0REpSaVCbgSlYj41Ew4Wpmhea3KyJHKsOpYJDZfiAIAuNlboF5Vax1HSmTYtFrY7d27F5s3b8bTp0/h5OSETz75BBMnToRYLNbmbYmIqAwFRcRh4eHbiEvOVGxzsDSFxNgIsUl52wY1r4F53eqjkoTtCUTaVOLfsKCgICxduhRubm7Yvn27yv5ly5Zh3rx5APIeERMZGYnz588jKCgIR44c4ZgKIiIDEBQRh89+u463n0eUkJYNALCUGGPtgMbo4OVU9sERVUAlHuDw559/4t9//0WdOnVU9j169AjffPMNAKBt27ZYt24dxo0bB5FIhKCgIPzyyy8lj5iIiPSCVCZg4eHbKkVdfpUkYrT1dCyzmIgquhK32F25cgUAMGDAAJV9gYGBkEqlaN26NYKDgxWtcx4eHpg1axZ+++03jB07tqS3JiIiPXAlKlGp+7UgL1KycCUqES1r25dRVEQVW4lb7J49ewZTU1PUq1dPZd/x48fzHt48bZpSl+uUKVNgZmaGmzdvlvS2RESkJ16kvLuok4tPLd5xRFR6arXYLVq0SPH9y5cvYWZmprRNTl64/fvvv7h165bSPltbW8THxyud5+/vjw8++ECtwImISHcuPXyFtSfvFetYRyszLUdDRHJqFXanT59WfC+TySCTyZS2AcDr16+Rm5sLKysrhIaGqlwjKysLgiAonefm5sbCjoioHHj4Mg3L/r6DE3fiAQAioNAxdiIAVW3ylj4horJR4sLO2toamZmZ+Pvvv2Fubq7Y/tNPP2HKlCno3Lkz9u7dq3KNNm3aICwsTKUgJCIi/ZaRnYteP/2L1MxciI1EGNy8Bhq62ODL/TcAKBd48kE4C7p7QWzEVRCIykqJx9jVq1cPUqlUZYbr1q1bIRKJ0KFDhwLPe/DgAapVq1bS2xIRURnKkcoU31uYGmNMm1po7+mIY9Pex+Je3ujXzBUBQ31R1Ua5u7WqjRkChvqii7dzWYdMVKGVeFbsJ598gpCQEEyfPh33799HvXr18NdffyEkJARWVlYFzpa9c+cO4uPj8f7775cqaCIi0i5BEHD4Rhy+C7qLlX0bKWa1Tm3nAaO3WuC6eDujo1dVlSdPsKWOqOyVuLD74osv8McffyAkJAQ//vgjACge7rxq1SrY2NionPPLL79AJBKhc+fOJb0tERFpWcjjRCw+cgdhT5MAAIHnHykKu7eLOjmxkYhLmhDpgRIXdhKJBGfPnsXq1atx9OhRvH79Gu7u7pg0aRI+/vhjleOTk5Pxzz//wN3dHf369StV0EREpHmPX6VjRdBd/H3zOQDAwlSMCR/Wxtj3a+k4MiIqrlI9tM/CwgLz58/H/PnzizzWxsZGZekTIiLSDxvPPsTq45HIkQowEgH9m7liese6cLTmUiVE5UmJJ0/om8jISKxfvx4jR46Ej48PjI2NIRKJsGTJkiLPlclk2LZtGzp06IAqVapAIpHA2dkZ7dq1w4YNG1SOf/XqFbZu3YopU6agVatWsLCweOeEkfxyc3OxYcMGtGjRAtbW1rCwsICPjw8WL16MN2/elOi9ExGVlrONGXKkAt73cMDRqe9j+ScNWdQRlUNqt9g1a9YMTZs2ha+vL5o2bYqGDRvC1NRUG7GpJSAgAOvWrVP7vOTkZPTo0QPnzp2DtbU1WrVqBVtbW8TGxiI0NBQpKSmYOHGi0jnnz5/HqFGj1L5XVlYWPv74Y5w4cQISiURR3F2+fBnffPMN/vjjD5w5cwa2trZqX5uIqLgEQcCxWy8gEwR85JM3a7V7w2qoam2G99w5To6oPFO7sLt+/brSwsPGxsbw8vJC06ZNFQVfo0aNYGZWtn/peXt7Y+bMmWjSpAl8fX2xdOlS7Nix453nCIKAXr164dy5cxg/fjxWrVoFS0tLxf7s7GzcuHFD5TwnJyeMHz8evr6+8PX1RUhICCZMmFBkjPPnz8eJEydQvXp1BAUFwdvbGwCQmpqKQYMG4ejRo5g0aRJ+//13Nd89EVHxhD9Nwv+O3sGV6EQ4WErwQd0qsJQYw8hIxKKOyACUaoydIAjIyclBeHg4bty4gS1btgAAxGIx6tevr2jVa9q0KRo3bqy0kLGmjR07Vum1kVHRvcxbtmzBmTNn0LlzZ2zcuFFlv6mpKZo1a6ayvWXLlmjZsqXidURERJH3ysnJQUBAAABgyZIliqIOAKysrLB582a4u7tj165dWLhwIerUqVPkNYmIiis26Q2+C7qLP8OeAQAkxkYY6OcKrkhCZFjULuxEorz/BVxcXDBq1CjUqFEDISEhuH79Om7cuIE3b94gNzcXN2/eREREBLZv3w4gr9CqV6+eotCbOnWqZt9JCfzwww8AgFmzZmn9Xnfu3EFaWhoAFDgWr2rVqvD29sbVq1fxxx9/YPbs2VqPiYgMX2pmDjaceYhfLkQhOzdvseE+TapjZud6qGarvT+2iUg31C7sQkJCMHXqVFy4cAGLFy9Ghw4dsGbNGnh5eUEmk+H27duKQu/69esICwtDeno6pFIpbt++jdu3b+P333/XeWH34sULhIeHQywWo1WrVnj06BH27t2L6OhoWFpa4r333kPPnj01Nn5QXtQBgL19wd0dDg4OAPJyTESkCY9epiPgzEMAwHu1KmNeNy/4uKiuM0pEhkHtwq5x48Y4d+4cdu/ejS+//BLBwcFo3LgxJk6ciEWLFsHb2xve3t4YMWIEgLzu2sjISEWxd+3aNYSHh2v8jahLPnbO3t4emzdvxowZM5CTk6N0jLu7Ow4ePIiGDRuW+n6Ojo6K7x89eoQGDRqoHPPo0SMAQFRUVKnvR0QVkyAIeJSQjtpV8sYLN3K1xUT/2mjsaouOXk6KXhciMkwlHmM3cOBA9OzZE8uWLcOqVauwfv167Ny5E4sXL8a4ceMU/3mIRCJ4enrC09MTQ4YM0VjgpfXq1SsAQGJiIqZOnYq+fftiwYIFcHNzQ0REBKZNm4bLly+jS5cuuHnzZqGtbMVVp04d1KhRA0+ePEFgYCDWrl2rtP/MmTOIjIwEAKSkpLzzWllZWcjKylK8lh+fk5OjUpyWN/L4y/v70BfMp+bpc07vxKVieVAkrj5+jWOft4arnQUA4Iv2tQHkLbekj/Q5p+UVc6pZReVTn/JcqskT5ubmWLRoEcaMGYMZM2bgwIEDmDhxIjZu3Ih169bhgw8+0FScGid//Flubi5atmyJffv2Kfa1aNECwcHB8PDwQFxcHDZs2FCsRZiLsmDBAowZMwbr16+HtbU1xowZA2tra5w8eRKTJk2CiYkJcnJyipz4sWzZMixcuFBl+/Hjx2FhYVHqOPVBcHCwrkMwKMyn5ulTTpOzgaNPjHDlpQgCRBCLBGw9fBZNHQRdh6YWfcqpoWBONauwfGZkZJRxJIUrVWEnV7NmTezfvx+nT5/G559/jvDwcLRt2xZ9+/bFypUrUaNGDU3cRqOsrKwU348fP77A/UOHDsXq1atx4sQJjRR2o0ePRkxMDBYtWoTFixdj8eLFin3e3t4YO3Ysli5disqVK7/zOnPmzMH06dMVr1NSUuDq6opOnTrB2tq61HHqUk5ODoKDg9GxY0eYmJjoOpxyj/nUPH3KaUZ2LjZfiMbma9F4k5M3MaKbT1XM7OgBF7vyMzFCn3JqKJhTzSoqn0X1tJUljRR2cm3btkVYWBgCAgLwzTffYN++fThy5AhmzZqF2bNna3W5E3W5u7sX+H1Bx8TFxWnsvt988w2GDh2KAwcO4OHDhzA1NUWLFi3wySef4NtvvwUA+Pj4vPMaEokEEolEZbuJiYnB/AIb0nvRB8yn5pVFTqUyAVeiEhGfmglHKzM0r1UZ4v9bnyRXKkPPDRcQ/SqvpaBpTTvM61YfTWrYaTUmbeLPqeYxp5pVWD71KccaLeyAvGVNJk2ahMGDB2P+/PkICAjA4sWLkZmZieXLl2v6diVWt25dWFlZITU1FQkJCQUeI9+ef9FiTXB3d8fMmTNVtp8/fx4A0LFjR43ej4jKn6CIOCw8fBtxyZmKbc42ZljQ3QtdvJ1hLDZCz8bVcTA0Fl919URX76qcGEFEmivsUlNTERERgZs3byq+IiIiIAgCRCIRZDKZpm6lEcbGxujVqxd27NiBEydOoHfv3irHyPvSmzdvrvV4/vvvP1y4cAGurq7o2bOn1u9HRPorKCIOn/12HW+PkItLzsSE365j41BfdPF2xmf+tTGxbW1IjMU6iZOI9I/ahV1ubi7u3r2rVMDdvHkTT58+VRwjn5gA5C1k3LhxY3z44YeaiViD5s6di927dyMwMBBdu3bFxx9/rNi3cuVKXLhwAWKxGJMmTdLI/V6/fo34+HjUq1dPaft///2HTz75BCKRCD///DOMjTXekEpE5YRUJmDh4dsqRV1+Cw/fRkevqjAzYUFHRMrUriAqVaqkmDKfv4AzNjZGvXr10LhxYzRp0gSNGzdG48aNi5wIoCnXr1/HxIkTFa8fPsxbkHPTpk04cuSIYvvBgwfh7Jz30GtPT08EBgZi9OjR6N69O5o1a6ZY7uTu3bsQi8UICAgocMxbixYtFN+/fPkSAHD16lWl7fPnz0e3bt0Urx8/fowmTZqgfv36qFOnDipVqoTIyEiEhobCxMQEv/zyC7p06aKhjBBReXQlKlGp+7UgccmZuBKViJa1+WxXIlKmdmGXk5MDkUiEWrVq4aOPPkKjRo3QuHFjeHt7Fzigv6ykpKTg8uXLKttjYmIQExOjeJ1//TcAGDFiBLy8vLBixQqcP38e4eHhsLe3R79+/TBz5sxCu2ELutfbMcgLPrnq1atj/PjxuHDhAs6ePYusrCxUq1YNn376KWbMmKHSkkdEFU986ruLOnWPI6KKpcR9flFRUdi3bx8ePnyI6OhoPHnyBL6+vqhZs6Ym4ys2f39/pRZEdfj5+WH//v1qnVOSe1WpUgUbN25U+zwiqjgcrcw0ehwRVSxqF3aurq6K8XQvXrzAP//8g6CgIMV+Ozs7NGnSBL6+voovDw8PzUVMRGTAmteqjKrWEjxPySpwvwhAVZu8pU+IiN6mdmH3+PFjvHr1CtevX1c8/zUkJETxfNPExEScPHkSp06dUpxjaWmJxo0bKxV7BT0rlYioohMbifBtjwYFzoqVL2ayoLuXYj07IqL8StQVa29vj44dOyqtt5aUlITr168rvkJCQvDgwQMIgoDU1FScP38eFy5cAJD3/Fh9fWYhEZGuJGVkw8rMBF28nREw1FdlHbuq+daxIyIqiMbW1bC1tUW7du3Qrl07xbbU1FSEhoYqte5FRkaWeCwcEZGhypHKMGrrVViYirF2QBN08XZGR6+qhT55goioIFpdMM3KygoffPABPvjgA8W2jIwMhIWFafO2RETlzspjkQh9kgQrM2Nk5kgB5HXLckkTIlKHUVnf0MLCAq1atSrr2xIR6a2Td17g53OPAAAr+zaCa2ULHUdEROVVmRd2RET0/8UmvcGMfeEAgFGt3dDFu6qOIyKi8kytwu6vv/7C8ePHNRqANq5JRFQe5EhlmLLzOpIyctDIxQZzutbXdUhEVM6pNcauV69ecHZ2RmxsrMYC0MY1iYjKg++D7+H6/42r+3GwL0yN2YlCRKWj9v8i2pjRylmyRFQRdW9YDe4OlTiujog0Ru1ZsW/evMH27du1EQsRUYXiVc0aQdM+YEsdEWmM2oVdSkoKRo0apY1YiIgMXo5Uhvsv0uBVzRoAWNQRkUapVdjVqFEDIpHmF8d0dHTU+DWJiPTRquOR+PVCFBZ0b4ChLWrqOhwiMjBqFXbR0dFaCoOIyPCduvsCm87mrVfnYGmq42iIyBCxD4CIqAw8S3qD6Xvz1qsb2cqNz3slIq1gYUdEpGU5Uhmm7ApFUkYOGrrYYM5HnroOiYgMFAs7IiItW3U8EiGPX+etVzfIFxJjsa5DIiIDxcKOiEiLrj95rRhXt7JvQ9Sw53p1RKQ9ai93QkRExdfE1RbzutXH8+RMjqsjIq1jYUdEpEUikQhj33fXdRhEVEGwK5aISAsOhz9DelaursMgogqGhR0RkYadvPMCU3aFosePF1jcEVGZYmFHRKRBsUlvMGNf3np173tUQSUJR7wQUdlhYUdEpCE5Uhmm7LzO9eqISGfK9E9JqVSKZ8+eAQCcnZ1hbMy/ZInIcKw6FonrT5K4Xh0R6UyZtNjdvXsXvXv3hpWVFdzc3ODm5gZra2v07t0bt2/fLosQiIi06uSdF9h0juvVEZFuab3J7Nq1a2jfvj3S09PRuXNn1KtXD0BesXf48GGcOHECp0+fRrNmzbQdChGRVshkApb/cxcAnwNLRLql9cJu+vTpkEgkuHjxIho0aKC0LyIiAm3btsX06dNx7tw5bYdCRKQVRkYi/P7pe9hw+iHH1RGRTmm9K/batWuYPHmySlEHAN7e3pg8eTKuXbum7TCIiLTK0coM3/ZowHF1RKRTWi/s7OzsYG1tXeh+Gxsb2NnZaTsMIiKNOx0ZjyM3nuk6DCIiBa0XdkOHDsW2bdvw5s0blX0ZGRnYunUrhg8fru0wiIg0Ki45E1/sCcPknaE4eiNO1+EQEQEogzF2HTp0wPHjx9GwYUNMmDBBafLEpk2bYGVlhfbt26uMsfvggw+0HRoRUYlIZcC0vTeQlJEDn+o26ODlqOuQiIgAlEFh17lzZ8X3s2bNgkgkAgAIglDgMYIgQCQSQSqVajs0IqISOfrUCNefJcFKYoyfBnO9OiLSH1ov7LZs2aLtWxARlZkz917i5LO8USzfcb06ItIzWi/sRowYoe1bEBGViWdJb/DlHxEAgGEtaqCrD9erIyL9UqbP9EpLS0NSUhJkMpnKvho1apRlKEREajt5Nx6vM3LgWknA7M51dR0OEZGKMinsduzYgaVLl+LevXuFHsMxdUSk74a1qIkqFsaIvXMNEuMyeSIjEZFatP4/0+7duzFixAiIRCKMHz8egiBg0KBBGDBgACQSCZo0aYJvvvlG22EQEWlE+/qOcDDTdRRERAXTemH3/fffo1GjRggLC8OiRYsAAKNHj8bOnTsRHh6OqKgoeHl5aTsMIqISeZb0BmO3XcOzJNW1OImI9I3WC7tbt25h6NChMDU1hZFR3u3k3a4eHh747LPPsHz5cm2HQUSkthypDFN2heLEnReY/ccNXYdDRFQkrRd2xsbGikeKVapUCQCQkJCg2O/m5oa7d+9qOwwiIrWtPn4PIY9fw0pijCW9vHUdDhFRkbRe2Lm5uSE6OhoAIJFIULNmTRw/flyx/8yZM6hcubK2wyAiUsvpu/HYePYhAGBF34aoaV9JxxERERVN64Vdu3bt8McffyheDxs2DNu2bUP79u3Rtm1b7Nq1C3369NF2GERExfYs6Q2m7w0DAIxoWRMfcb06IiontL7cyZdffokuXbogKysLEokE8+fPR0JCAnbv3g2xWIzRo0dj2bJl2g6DiKhY5OPqXmfkwLu6NeZ2q6/rkIiIik3rhZ2zszOcnf//X7smJibYsGEDNmzYoO1bExGp7XV6NtIyc/kcWCIql8r0yRNERPrO0doMhya1RuSLVI6rI6JyR+tj7E6ePIk5c+YUun/u3Lk4deqUtsMgInonqUxQfG9uKkZjV1vdBUNEVEJaL+yWLl2KqKioQvc/fvyY69gRkU7lSGUYFPgffjx1H7J8BR4RUXmj9cLuxo0beO+99wrd37x5c4SHh2s7DCIiBalMwKWHr/BnWCwuPXyFlccicSUqEZvOPsLzlExdh0dEVGJaH2OXlpYGU1PTQveLxWIkJydrOwwiIgBAUEQcFh6+jbhk1QJuRd+GqGZrroOoiIg0Q+stdrVq1cL58+cL3X/u3DnUrFlT22EQESEoIg6f/Xa9wKIOAIxEZRwQEZGGab2w69+/P/bt24f169dDEP7/2BVBEPDDDz/gjz/+QP/+/bUdBhFVcFKZgIWHb6OwEXQiAAsP31aaREFEVN5ovSt29uzZCAoKwrRp07By5Uo0aNAAAHDr1i3ExMSgWbNmmDt3rrbDIKIK7kpUYqEtdQAgAIhLzsSVqES0rG1fdoEREWmQ1lvszM3NcfbsWSxatAh2dnY4e/Yszp49Czs7OyxZsgTnzp2DuTnHtBCRdsWnFm9SRHGPIyLSRyUq7CIjIxEaGorc3NxiHS+RSPD1118jPDwcGRkZyMjIQHh4OObOnQszM7OShFBgTOvXr8fIkSPh4+MDY2NjiEQiLFmypMhzZTIZtm3bhg4dOqBKlSqQSCRwdnZGu3btCnxCxqtXr7B161ZMmTIFrVq1goWFBUQiETp06FDkvXJzc7Fhwwa0adMGdnZ2MDExgYODA9q3b49t27ZBJpOV6P0T0bvZVyp8Eld+jlaa+T+JiEgX1O6Klclk+OSTT3Dnzh20adMGZ8+e1UZcagsICMC6devUPi85ORk9evTAuXPnYG1tjVatWsHW1haxsbEIDQ1FSkoKJk6cqHTO+fPnMWrUKLXvlZWVhU6dOuHcuXMwNTVFmzZtUKVKFTx9+hSnT5/GqVOncOjQIRw4cAAiEUdxE2lKVEI6Vh6LhAh45xi7qjZmaF6rchlGRkSkWWoXdv/88w9u376NSpUqYefOndqIqUS8vb0xc+ZMNGnSBL6+vli6dCl27NjxznMEQUCvXr1w7tw5jB8/HqtWrYKlpaVif3Z2Nm7cuKFynpOTE8aPHw9fX1/4+voiJCQEEyZMKDLGDRs2KGYBnzt3DjVq1FDsu3btGtq1a4dDhw5hz549GDhwoBrvnogKIggCdl99ikWHb+NNjhRmJkbIzJGpFHjyP6MWdPeCmFNjiagcU7uw27dvH0QiEaZMmYLq1asX+7wvvvgCMTExaN++fbGKIHWNHTtW6bWRUdG9zFu2bMGZM2fQuXNnbNy4UWW/qakpmjVrprK9ZcuWaNmypeJ1REREsWKUPzpt0qRJSkUdADRr1gwDBw5EYGAgLl26xMKOqJRepWVh9h83ceLOCwBAC/fKWN2/MW7GJKmsY1fVxgwLunuhi7ezrsIlItIItQu7//77DwAwaNAgtc774osv4OHhgYMHD+Kjjz5SKWx04YcffgAAzJo1q0zuV9zxhA4ODlqOhMiwnb4bj1n7byAhLQumYiPM6lwPY9rUgpGRCNVtzdHRqyquRCUiPjUTjlZ53a9sqSMiQ6D25Ilnz55BLBbDx8dHrfNq1KiBPn36QBAE/Pnnn+reVuNevHiB8PBwiMVitGrVCo8ePcLy5csxYcIEzJw5E/v27UN2drZG79m1a1cAwE8//YQnT54o7QsJCcHu3bthbm6OYcOGafS+RBXNqbvxSEjLQl0nSxya1BqffuAOo3yFm9hIhJa17dGzcXW0rG3Poo6IDIbaLXa5ubmwsrIq0c0GDhyIPXv24OzZs5gyZUqJrqEp8rFz9vb22Lx5M2bMmIGcnBylY9zd3XHw4EE0bNhQI/ccOXIkzp49i+3bt8PDwwNt2rSBo6Mjnj59iosXL8LHxwcbN26Em5ubRu5HVJHIZIKieJv7UX1UtTHDmDa1YGYi1nFkRERlR+3CTl6IZGVlQSKRqHVumzZtAOQtTqxrr169AgAkJiZi6tSp6Nu3LxYsWAA3NzdERERg2rRpuHz5Mrp06YKbN2/C3r70C5YaGRlh69ataNiwIebMmaMYcwcAFhYW6NChA2rXrl3kdbKyspCVlaV4nZKSAgDIyclRKU7LG3n85f196IuKkM9cqQw/n4/GlejX+GW4L8RGIhiLgHFtagKQISdHs0sIVYScljXmVPOYU80qKp/6lGe1C7vq1avj6dOniIiIQNOmTdU6197eHmZmZnj27Jm6t9U4+ePNcnNz0bJlS+zbt0+xr0WLFggODoaHhwfi4uKwYcMGzJ8/v9T3TElJwaBBg/DPP/9g2rRp+Oyzz1C9enU8evQIy5Ytw/fff48//vgD58+fh6ura6HXWbZsGRYuXKiy/fjx47CwsCh1nPogODhY1yEYFEPNZ0Im8NsDMaJS81rqVu4MQsPKZfNIMEPNqS4xp5rHnGpWYfnMyMgo40gKp3Zh16FDB1y6dAm//PKL2oUdAFSqVAnJyclqn6dp+buTx48fX+D+oUOHYvXq1Thx4oRGCrsZM2bg77//xsSJE/H9998rtnt7e+P333/Hq1evcOzYMcybNw/btm0r9Dpz5szB9OnTFa9TUlLg6uqKTp06wdrautRx6lJOTg6Cg4PRsWNHmJiY6Dqccs9Q8ykIAv4IfYbvj95FerYUlhJjfPuxJ3o0ctb6GpCGmlNdYk41jznVrKLyKe850wdqF3Z9+/bF4sWLsW3bNnz66ado0qRJsc/NyclBcnJyicfoaZK7u3uB3xd0TFxcXKnvJ5VKFevqFTajePDgwTh27BhOnDjxzmtJJJICu8FNTEwM5hfYkN6LPjCkfCamZ2PugZsIuvUcANDcrTJW928E18pl21ptSDnVF8yp5jGnmlVYPvUpx2rPivXx8cHAgQPx5s0b9O7dG9HR0cU+9+LFi8jNzUWtWrXUva3G1a1bV1FgJiQkFHiMfHv+RYtLKj4+XjEurrBWNRsbGwB54/6IqGCf7w5F0K3nMBGL8GWXetg1rkWZF3VERPqqRM+KXb16NapWrYonT56gcePG2L17d7HOW7JkCUQiET788MOS3FajjI2N0atXLwAotIVM3pfevHnzUt/P3t5e0cp2+fLlAo+RrxGoD4Uvkb6a07U+6jtb4+DE1pjoX4dLlRAR5VOiws7Z2RknTpyAg4MDUlJSMGTIELRp0wb79u3DmzdvVI6Pj4/H4MGDcfLkSYhEIowbN67UgWvC3LlzYWJigsDAQBw5ckRp38qVK3HhwgWIxWJMmjSp1PcyNTVFjx49AADz589XeVTZyZMnsXbtWgB5XbJElCciNhl7rv7/dR+9qlnj76lt4F3dRodRERHpJ7XH2Ml5eXnh4sWLGDx4MK5du4ZLly7h0qVLMDExgZeXF1xdXWFqaorY2Fhcv35dMRV4xowZqFevnsbegNz169cxceJExeuHDx8CADZt2qRUtB08eBDOznmPDfL09ERgYCBGjx6N7t27o1mzZorlTu7evQuxWIyAgIACF2Nu0aKF4vuXL18CAK5evaq0ff78+ejWrZvi9Zo1axASEoJHjx7B19cXLVq0UMyKvXbtGgCgXbt2mDlzpiZSQlSuSWUCfj73CN8HR0IQAC9nG/i45BVz2p4gQURUXpW4sAOAOnXq4OLFi1i/fj1Wr16NZ8+eITs7G2FhYQgPD1ccJ19aZMqUKVi+fHnpIi5ESkpKgV2cMTExiImJUbzOv/4bAIwYMQJeXl5YsWIFzp8/j/DwcNjb26Nfv36YOXNmod2wBd3r7RjkBZ9c9erVERYWhh9++AF//vknbt68if/++w82Njb48MMPMXjwYIwZMwZiMRdUpYot5nUGpu8Nx5WovPGmnRs4obqduY6jIiLSf6Uq7IC8sWpffPEFJk2ahKNHjyIoKAghISF4/vw5srKy4OTkhFatWuHTTz+Fn5+fJmIukL+/v6KAVJefnx/279+v1jklvZeVlRW+/vprfP311yU6n8iQCYKAQ2Gx+ObQLaRm5aKSqRgLejRAv6YubKUjIiqGUhd2cqampujduzd69+6tqUsSUQUza/8N7A/Ja2FvWtMOa/o3Rg17znglIioujRV2RESl5eVsDWMjET5v74HP/GvDWFyi+V1ERBUWCzsi0pnMHClepGSipn0lAMDIVm5438MBHk66X8SciKg84p/DRKQTd+JS0PPHfzHi1ytIz8oFABgZiVjUERGVAgs7IipTMpmAwHOP0PPHfxH5IhVpWbmISkjXdVhERAaBXbFEVGaeJb3BjL3huPToFQCgQ30nLP/EBw6Wqs8+JiIi9bGwI6Iy8Vf4M8w7eBMpmbkwNxHjm+5eGOjnymVMiIg0iIUdEWmdIAg4cD0GKZm5aORqi7UDGqOWQyVdh0VEZHBY2BGR1giCAJFIBJFIhO/6NsS+azEY94E7TLiMCRGRVrCwI6JSkcoEXIlKRHxqJhytzNC8VmXkymT4/vg9vM7Ixnd9GwEAHK3MMKltHR1HS0Rk2FjYEVGJBUXEYeHh24hLzlRsc7A0hcRYjNikNwCAYS3c4ONio6sQiYgqFBZ2RFQiQRFx+Oy363j7qckJadkAAEuJMVb3b8SijoioDHGgCxGpTSoTsPDwbZWiLr9KEjE61Hcqs5iIiIiFHRGVwJWoRKXu14K8SMnClajEMoqIiIgAFnZEVALxqe8u6tQ9joiINIOFHRGp7XV6drGOc7Qy03IkRESUHydPEFGxZefKsO7kPWw4/RAioNAxdiIAVW3ylj4hIqKyw8KOiIrlQXwavtgThpuxyQCAFrXscTkq75mv+Qs8+QPCFnT3gtiIjwsjIipLLOyI6J0EQcBv/z3G//6+g8wcGWzMTbCsjw8+8nEucB27qjZmWNDdC128nXUYNRFRxcTCjogKlSuVYdyOEJy6Gw8AaFPHAav6NUJVm7yxc128ndHRq6rKkyfYUkdEpBss7IioUMZiI9SobAFTYyN81cUTI1u5weitok1sJELL2vY6ipCIiPJjYUdEStKzcpGenauY0fpVV08Mea8GPJysdBwZEREVhcudEJFC6JPX6PbDeUzeGQqpLG9KhJmJmEUdEVE5wRY7IkKuVIYfTz/A+lMPIJUJyM6V4VnSG7hWttB1aEREpAYWdkQVXHRCOqbtCUPY0yQAQM/G1bCopzdszE10GxgREamNhR1RBSUIAvZcfYpFR24jI1sKKzNjLOnljZ6Nq+s6NCIiKiEWdkQVVI5UwNaL0cjIlqKFe2Ws7t8Y1W3NdR0WERGVAgs7ogpGEASIRCKYGhth7cDGOHfvJca2cVdZxoSIiMofFnZEFcSbbCmW/n0HjlYSTGnvAQDwrGoNz6rWOo6MiIg0hYUdUQUQEZuCGX/cxKOX6TARi9C3mQucbdjtSkRkaFjYERkwqUxAcKwIQZcvI1cmwMlaglX9GrGoIyIyUCzsiAzU08QMfLEnFNeeiAEI+MinKpb29oGthamuQyMiIi1hYUdkgN5kS9F7w79ISMuGRCxgcU8f9POrAZGIEySIiAwZCzsiA2RuKsbktnVwOPwZutknoHeTaizqiIgqAD4rlshAnL//EqFPXitej2jlht/H+MHeTIdBERFRmWKLHVE5l5kjxYqgu9jybzRq2lvg6NT3YSkxhkgkgphr0xERVSgs7IjKsdvPUjBtTyjuvUgDALzv4QAxu1yJiCosFnZE5ZBMJiDw/COsOh6JHKkAB0tTfNe3Idp5Ouk6NCIi0iEWdkTlTGpmDj7dfg3/PUoEAHSo74Tln/jAwVKi48iIiEjXWNgRlTOWEmNYmBrD3ESMb7p7YaCfK2e8EhERABZ2ROVC8pscGIkAKzMTiEQirPikIdKyclHLoZKuQyMiIj3C5U6I9IRUJuDSw1f4MywWlx6+glQmAAAuPkxA17XnsOCvW4pjq1hJWNQREZEKttgR6YGgiDgsPHwbccmZim1VrSXwcbHFiTsvIAhAyOPXSM7IgY2FiQ4jJSIifcbCjkjHgiLi8Nlv1yG8tf15Shae334BABjU3BXzunmhkoS/skREVDh+ShDpkFQmYOHh2ypFXX52FiZY0suHiw0TEVGROMaOSIeuRCUqdb8W5HVGDq5EJZZRREREVJ6xsCPSofjUdxd16h5HREQVGws7Ih2yLOaYOUcrMy1HQkREhoBj7Ih05EpUIuYfinjnMSIAVW3M0LxW5bIJioiIyjUWdkRlLDtXhjUn7mHj2YcQBMDe0hSv0rIhApQmUcinSizo7sWJE0REVCws7IjK0IP4VHy+Owy3nqUAAPo1dcE33b3w74ME1XXsbMywoLsXung76ypcIiIqZ1jYEZWR+JRMfLz+AjJzZLC1MMGy3j7o6pNXtHXxdkZHr6q4EpWI+NRMOFrldb+ypY6IiNTBwo6ojDham2Fw85q4H5+KVf0awclaeUKE2EiElrXtdRQdEREZAhZ2RFoUFPEcDapZw7WyBQBgzkeeEItEMGJLHBERaQGXOyHSgrSsXMzaF44Jv4Xgiz1hkMrypkWYiI1Y1BERkdYYTGEXGRmJ9evXY+TIkfDx8YGxsTFEIhGWLFlS5LkymQzbtm1Dhw4dUKVKFUgkEjg7O6Ndu3bYsGGDyvGvXr3C1q1bMWXKFLRq1QoWFhYQiUTo0KHDO+/j7+8PkUhU5Nfo0aNLnAfSvZDHifho3XnsC4mBSAQ0c6sMmfCuh4YRERFphsF0xQYEBGDdunVqn5ecnIwePXrg3LlzsLa2RqtWrWBra4vY2FiEhoYiJSUFEydOVDrn/PnzGDVqlNr36tKlC9zc3Arcl52djV27dgEA2rZtq/a1SfdypDL8cPI+fjr9ADIBqG5rju/7N8J77hw3R0REZcNgCjtvb2/MnDkTTZo0ga+vL5YuXYodO3a88xxBENCrVy+cO3cO48ePx6pVq2BpaanYn52djRs3bqic5+TkhPHjx8PX1xe+vr4ICQnBhAkTiozxq6++KnTf3r17sWvXLtjY2KBv375FXov0y4uUTHy6/RpuxCQDAPr4Vse3PRrA2sxEx5EREVFFYjCF3dixY5VeGxkV3cu8ZcsWnDlzBp07d8bGjRtV9puamqJZs2Yq21u2bImWLVsqXkdEvPvpAcXxyy+/AAAGDRoEc3PzUl+PypathQlypAJszE2wtLcPujXk2nNERFT2DKawK4kffvgBADBr1iydxvH06VOcOHECADBmzBidxkLFl5CWBVtzExiLjSAxFuOnwU1gYWqMqjZ8risREelGhS3sXrx4gfDwcIjFYrRq1QqPHj3C3r17ER0dDUtLS7z33nvo2bMnTE1NtR7L1q1bIZPJ0LBhwwJbCEn/HL/1HF8duInhLWtiWoe6AAD3KpZFnEVERKRdFbawk4+ds7e3x+bNmzFjxgzk5OQoHePu7o6DBw+iYcOGWotDEARs3boVAFvryoP0rFwsPnIbu68+BQCcvBOPSW3rwERsMBPMiYioHKuwhd2rV68AAImJiZg6dSr69u2LBQsWwM3NDREREZg2bRouX76MLl264ObNm7C3187MxjNnzuDRo0eQSCQYOnRosc7JyspCVlaW4nVKSt5zR3NyclSK0/JGHr8+vo/Qp0mYtT8CjxMzIBIBY1q7YVr7OoBMihyZVNfhFUif81leMaeax5xqHnOqWUXlU5/yXGELO+H/1hXLzc1Fy5YtsW/fPsW+Fi1aIDg4GB4eHoiLi8OGDRswf/58rcQhnzTRs2dPVK5cuVjnLFu2DAsXLlTZfvz4cVhYWGg0Pl0JDg7WdQgKUgE4HmOE4zEiyCCCramAoXVk8JA+wMnjD3QdXrHoUz4NBXOqecyp5jGnmlVYPjMyMso4ksJV2MLOyspK8f348eML3D906FCsXr0aJ06c0Ephl5ycjAMHDgBQrxt2zpw5mD59uuJ1SkoKXF1d0alTJ1hbW2s8zrKUk5OD4OBgdOzYESYm+rFUyONXGZj700XIIEOPhs5Y8LEnrM31I7ai6GM+yzvmVPOYU81jTjWrqHzKe870QYUt7Nzd3Qv8vqBj4uLitBLDrl278ObNG9SoUaPIp1bkJ5FIIJFIVLabmJgYzC+wPr2XOlVtsKiHN8xMxejRqJquwykRfcqnoWBONY851TzmVLMKy6c+5bjCjviuW7euotUuISGhwGPk2/MvWqxJv/76KwBg1KhRxVp3j8pGQloWPt1+DdeiExXb+vu5ltuijoiIKo4KW00YGxujV69eAKBYQ+5t8r705s2ba/z+ERERuHr1KkQiUYkeT0bacfLOC3RZew7Bt19g9h83IJXxGa9ERFR+VNjCDgDmzp0LExMTBAYG4siRI0r7Vq5ciQsXLkAsFmPSpEkav7d80kSHDh1Qs2ZNjV+f1JORnYu5B29izLZrSEjLhmdVK/w42BdiI5GuQyMiIio2gxljd/36dUycOFHx+uHDhwCATZs2KRVtBw8ehLNz3uOePD09ERgYiNGjR6N79+5o1qyZYrmTu3fvQiwWIyAgAD4+Pir3a9GiheL7ly9fAgCuXr2qtH3+/Pno1q2byrk5OTn47bffAHDtOn0Q9jQJX+wJQ1RCOgBgbJtamNm5HsxMxDqOjIiISD0GU9ilpKTg8uXLKttjYmIQExOjeJ1//TcAGDFiBLy8vLBixQqcP38e4eHhsLe3R79+/TBz5sxCu2ELutfbMcgLvrf99ddfSEhIQOXKlRXdwaQbEbHJ+CTgIqQyAVWtzbC6fyO0ruOg67CIiIhKxGAKO39/f8XadOry8/PD/v371TqnpPcCgE8++aRU55PmNKhmjbb1HGFuKsaSnt6wsdCfmU1ERETqMpjCjqg4BEHAwdBYtK/vBBtzE4hEIvw4uAm7XYmIyCBU6MkTVLG8SsvCuB0hmL43HAv+jFBsZ1FHRESGgi12ZHCkMgFXohIRn5oJRyszNK9VGefuvcSs/TeQkJYFE7EIns7WEAQBIhFnvRIRkeFgYUcGJSgiDgsP30ZccqZim4WpGBnZUgCAh6Ml1g5sjAbVbHQVIhERkdawsCODERQRh89+u463p6XIi7q2nlUQMKQpu16JiMhgcYwdGQSpTMDCw7dVirr87salwkTMH3kiIjJc/JQjg3AlKlGp+7UgccmZuBKV+M5jiIiIyjMWdmQQzt8veDHot8Wnvrv4IyIiKs84xo7KtfCnSfju2F38++BVsY53tDLTckRERES6w8KOyqUH8WlYfTwS/0Q8BwCYGIlgYmykmCjxNhGAqjZ5S58QEREZKnbFUrk058AN/BPxHCIR8ImvC07N9Mf3/RtBhLwiLj/56wXdvSA24rp1RERkuNhiR+XC6/RsmBgbwVKS9yM7o1M9bD4fhVmd66FeVSsAgGtlCwQM9VVZx66qjRkWdPdCF29nncRORERUVljYkV5Lz8rFrxei8PO5RxjRyg0zO9cDALRwt0cLd3uV47t4O6OjV1WVJ0+wpY6IiCoCFnakl7JzZdh5NRrrT91HQlo2AOC/R68gkwkwKqJIExuJ0LK2atFHRERk6FjYkV6RyQRceynCyh/+RczrNwCAGpUtMKNTXXRvWK3Ioo6IiKgiY2FHemXtqQfY8UAM4A2qWEkwtb0HBjRzhakx5/kQEREVhYUd6Vx2rkxRuA1o5oIdFx9h/IceGPNBbViY8keUiIiouPipSTpzJy4Fq45FQmJihA1DmgIAqtuaY6GvFD0+dIeJCX88iYiI1MFPTipzT15lYM2JezgUFgtBAIyNRIhNeoPqtuYAAPa6EhERlQwLOyozL1Oz8OOp+9h55QlypAIAoFtDZ8zoWFdR1BEREVHJsbCjMnElKhEjt1xRPPLrfQ8HfNnZEz4uNjqOjIiIyHCwsKMy4VPdBpYSY3g4WWF253poVcdB1yEREREZHBZ2pHG5UhkOXI/FsVvPETi8GYyMRDA3FePAxFaobmsOkYhr0REREWkDCzvSGEEQcOzWc6w8FomHL9MBAEdvxqF7o2oAABc7C12GR0REZPBY2JFGXHyQgBXHIhH+NAkAYGthgslt66Cjl5NuAyMiIqpAWNhRqSS/ycHknddx/n4CAMDCVIyxbWph7AfusDYz0XF0REREFQsLOyoVazNjpGTmwkQswpD3amJS2zqoYiXRdVhEREQVEgs7KpBUJuBKVCLiUzPhaGWG5rUqQ2wkwvPkTGw69xDTO9aFlZkJRCIRlvfxgaXEGK6VOYaOiIhIl1jYkYqgiDgsPHwbccmZim1O1hI0dLHFuXsvkZUrg5WZCaZ3rAsAqO9sratQiYiIKB8WdqQkKCIOn/12HcJb21+kZCH49gsAQLOadvjAg+vQERER6RsWdqQglQlYePi2SlGXX2ULE+we1wLGYj7QlYiISN/w05kUrkQlKnW/FiQxIwdXo1+XUURERESkDhZ2pBCf+u6iTt3jiIiIqGyxsCMFRyszjR5HREREZYuFHSk0r1UZzjZmKOxJriIAzjZ5S58QERGR/mFhRwpiIxEWdPcCAJXiTv56QXcviI0KK/2IiIhIl1jYkZIu3s4IGOqLqjbK3a1VbcwQMNQXXbyddRQZERERFYXLnZCKLt7O6OhVtcAnTxAREZH+YmFHBRIbidCytr2uwyAiIiI1sCuWiIiIyECwsCMiIiIyECzsiIiIiAwECzsiIiIiA8HCjoiIiMhAsLAjIiIiMhAs7IiIiIgMBAs7IiIiIgPBBYoNgCAIAICUlBQdR1J6OTk5yMjIQEpKCkxMTHQdTrnHfGoec6p5zKnmMaeaVVQ+5Z+/8s9jXWJhZwBSU1MBAK6urjqOhIiIqOJKTU2FjY2NTmMQCfpQXlKpyGQyPHv2DFZWVhCJyvfzXFNSUuDq6oqnT5/C2tpa1+GUe8yn5jGnmsecah5zqllF5VMQBKSmpqJatWowMtLtKDe22BkAIyMjuLi46DoMjbK2tuZ/RhrEfGoec6p5zKnmMaea9a586rqlTo6TJ4iIiIgMBAs7IiIiIgPBwo70ikQiwYIFCyCRSHQdikFgPjWPOdU85lTzmFPNKk/55OQJIiIiIgPBFjsiIiIiA8HCjoiIiMhAsLCjQkVGRmL9+vUYOXIkfHx8YGxsDJFIhCVLlhR6ztOnT7Fp0yaMGzcOTZs2hUQigUgkwtixY0sdT0hICPr16wcnJyeYmZmhVq1amDJlCuLj49953osXLzB58mTUqlULEokETk5O6NevH65fv17qmNRV3nP65MkTbNq0CX369EHNmjUhkUhgaWmJRo0aYe7cuXj58mWpY1JXec9pQWJjY2FnZweRSARj47JdlcqQ8hkeHo7Ro0ejVq1aMDMzg52dHXx8fPDZZ5/h1atXpY6tuAwhp4Ig4Pfff0eHDh3g4OAAExMT2NraonXr1vjhhx+QnZ1d6rjUoS85ffXqFbZu3YopU6agVatWsLCwgEgkQocOHYp1/oMHDzBy5Ei4uLhAIpHAxcUFI0eOxKNHj0ocEwSiQnz++ecCAJWvxYsXF3rOmjVrCjxnzJgxpYpl3759grGxsQBA8PPzE/r37y+4u7sLAAQnJyfh/v37BZ4XGRkpODo6CgAEd3d3oX///oKfn58AQDA2NhYOHDhQqrjUVd5z2rp1a0Xu/Pz8hAEDBggdO3YUrK2tBQBClSpVhNDQ0FLFpa7yntOCdO3aVRCJRAIAQSwWlyomdRlKPleuXCmIxWLByMhI8PPzEwYOHCh07dpVqFOnjgBAuHnzZqliU4ch5HTAgAECAMHIyEho06aNMGDAAMHf318wMTERAAgtWrQQMjIyShWbOvQlpwcPHizwmu3bty/y3AsXLggWFhYCAKFBgwbCgAEDhAYNGggAhEqVKgmXLl0qUUws7KhQgYGBwsyZM4Xff/9duHPnjjBs2LAif3EOHTokTJkyRdiyZYsQHh4ufP3116X+xYmNjVX88G/atEmxPTc3Vxg6dKjiPyiZTKZ0nkwmE5o0aSIAEIYNGybk5uYq9m3atEkAIFhaWgpxcXEljk1d5T2n/fv3F9asWSMkJCQobY+Pjxf8/f0FAIKHh4dSrrWtvOe0oPcDQJg8ebJOCjtDyOevv/4qABDq1atXYAEXEREhJCUllTg2dZX3nB44cEAAINjY2AhhYWFK+x4+fChUr15dACAsW7asxLGpS19yevHiRWH8+PHCpk2bhKtXrwobN24sVmGXnp4uVKtWTQAgzJkzR2nfnDlzBACCq6triYplFnZUbCNGjCjyF+dtCxYsKPUvzqxZswQAQocOHVT2paamCjY2NgIAISgoSGnf0aNHBQCCra2tkJqaqnJu+/btBQDCV199VeLYSqu85fRdnj59qvhr9fz58yWOrbTKc06jo6MFKysroUWLFsLDhw91Uti9rbzlMzExUbC2thbMzc2Fhw8flvj+2lTecir/I2PixIkFXvd///ufAEDo0aNHiWMrLV3l9G1btmwpVmH3008/CQCEunXrClKpVGmfVCoV6tatKwAQNm7cqHYMHGNHeu/gwYMAgMGDB6vss7S0RI8ePQAABw4cKPC8Hj16wNLSUuVc+fXePq8iKGlO38XFxQUODg4A8sayVDSlzakgCBg9ejSys7Px66+/6vx5k7pW0nxu27YNKSkp+OSTT+Du7q79QMuRkubUzMysWNeX//5T0eT/FgMHDlT5XTcyMsKAAQMAlOzzqWL/z0F6LzU1FQ8ePAAANGvWrMBj5NtDQ0OVtstfF3Xe/fv3kZ6erpF4y4PS5PRdEhIS8Pr1awCAs7NzKaMsXzSR0w0bNuDUqVNYsGAB6tevr51Ay4nS5PPYsWMAgA8++ABv3rzBjh07MHXqVEyaNAlr166tkH90AKXLadeuXQEAO3fuRHh4uNK+R48eISAgACKRCJ9++qmmwzZYxf18Uuf/YLmynW5FpKbo6GjF9zVq1CjwGFdXVwBAVFSU0nb566LOEwQB0dHRaNCgQWnDLRdKk9N3WbVqFaRSKZydndGqVatSxVjelDanDx8+xOzZs9G0aVPMmjVLKzGWJ6XJ540bNwDkFTLe3t4qswtnz56NZcuWYfr06RqMWP+VJqft2rXD119/jf/973/w9fVF69atUb16dbx48QIXLlyAq6sr/vzzT7Ro0UJr8RuS1NRUxazsov4tXr58ifT0dFSqVKnY12eLHem11NRUxfeF/WDLu1lTUlIKPLeo8wo615CVJqeFOXHiBFatWgUAWL16NUxNTUsZZflSmpzKZDKMHDkS2dnZ2LJlS5kvb6KPSpNP+QfmV199BalUisOHD+P169eK4jknJwczZszArl27tBS9firt7/2SJUvw22+/wcLCAufPn8fu3btx+vRpCIKADh06VJg/jDVBnX8LQP3PJxZ2RFQqN2/eRL9+/SCVSjFlyhQMGjRI1yGVK2vXrsWFCxcwb948+Pj46Dqcck/4v6dkymQy/P333/j4449ha2sLd3d3LF++HBMmTAAAzJs3T5dhlis5OTkYPXo0hg4dil69euHmzZtIT0/HvXv3MHnyZAQGBsLPzw9hYWG6DpXAwo70nJWVleL7wsbBpaWlAQCsra0LPLeo8wo615CVJqdvu3v3Ljp06ICkpCSMGjUK69at01yg5UhJcxoZGYmvv/4ajRo1wpw5c7QbZDmiid/7999/H15eXirnTZw4EUDe2DB1hhqUd6XJ6XfffYctW7bgo48+wo4dO+Dt7Q0LCwt4eHhgzZo1GDduHBITE/H5559r7w0YEHX+LQD1P59Y2JFeq1mzpuL7J0+eFHiMfDC0m5ub0nb566LOE4lESvcxdKXJaX737t1Du3btEB8fj+HDh2Pz5s0QiUQajbW8KGlO//nnH2RmZiI9PR0dO3aEv7+/4mvgwIEAAKlUqtgWFBSkvTehR0rzMyqfCVvYjNj82+Pi4koTZrlSmpxu3boVAAptjZfPsr1w4QKysrJKGanhs7KyQuXKlQEU/W/h4OCg1vg6gIUd6Tlra2vUqVMHAHDt2rUCj5Fv9/X1Vdouf13UeR4eHgUuh2KoSpNTufv376Nt27aIi4vD0KFDsWXLlgq9PEdpc/rgwQOcPXtW6evy5cuK/fJtz58/10L0+qc0+WzatCmAvFnaBcm/nb/3ygrLqbz4KKzlyMbGBkBe93dSUpImwjV4xf18Kuz/4HepuP8TU7nRu3dvAHlT7d+WlpaGw4cPAwD69OlT4Hl//fVXgc3d8uu9fV5FUNKcAnkzONu2bYtnz55h6NCh2LZtW4Uu6uRKktNp06ZByFsoXuVL3k0oFosV20aOHKn9N6InSvoz2q9fPwDAf//9V+DvfXBwMIC8oq6iLStT0pxWr14dAJT+2Mjvv//+A5DXEsW17IpH/m+xe/duyGQypX0ymQx79uwBULLPJ/5vTHrh4MGD8PT0RPv27VX2TZs2DRYWFjhx4gQCAwMV26VSKSZOnIikpCT4+fmhU6dOSud17doVTZo0QVJSEiZOnAipVKrY9/PPP+PkyZOwtLQ02HEh2shpVFQU2rZti9jYWAwbNqzCFXXayGlFpo18tmvXDu+//z7i4+MxefJkpa7BGzduKCZNfPbZZzAxMdHSO9MdbeS0b9++AIA1a9bgzJkzSvvCwsIwf/58AED//v0hFos1/I507105LamRI0eiWrVquHfvniJ/cvPnz8e9e/fg4uKC4cOHq31tkSCfQkT0luvXrysGGgN5LTUJCQlwcXFR/AUH5P3QyxekjYuLU/wlAgAxMTGIjY1FlSpVlMa2bNiwQamJeevWrRg1ahRq1qyptN6S3L59+zBo0CBIpVK89957cHNzw9WrV/Ho0SM4OTnhwoULim6G/CIjI/H+++/j5cuXcHd3h5+fH6KionDlyhUYGxtj7969SvFqW3nPqa+vL0JDQyGRSNC/f/9Ci7qxY8eiTZs26iWnhMp7TgsTHR2NWrVqQSwWIzc3t9j5KC1DyOfTp0/xwQcfIDo6GtWrV4efnx8SExPx33//ITs7Gx07dsThw4chkUhKlaviKu85TU1NRfv27XH16lUAgJ+fH2rVqoWYmBhcvnwZUqkUPj4+OH36NOzt7UuXrGLSp5zmX7/v5cuXePToEaytrZVahOfPn49u3bopnffvv/+iU6dOyMjIgLe3N7y9vREREYGIiAhUqlQJJ06cKNnagGo/hIwqjNOnTyue/fmur6ioKMU5UVFRxTrn9OnTSveSP1+vZs2ahcZz7do1oU+fPkKVKlUEU1NToWbNmsKkSZOE58+fv/N9xMXFCZMmTRJq1qwpmJqaClWqVBH69OkjhISElCI7JVPec1qzZs1ixbJly5bSJ6uYyntOCyOPsayfFWso+UxKShK++uoroW7duoJEIlE8gzcgIEDIzc0tRYbUZwg5zcrKEtatWye0adNGsLOzE8RisWBtbS20aNFCWLlyZYkeVl8a+pTT0vyfeP/+fWH48OFCtWrVBBMTE6FatWrC8OHDhQcPHpQ4N2yxIyIiIjIQFWdwDBEREZGBY2FHREREZCBY2BEREREZCBZ2RERERAaChR0RERGRgWBhR0RERGQgWNgRERERGQgWdkREREQGgoUdERERkYFgYUdERERkIFjYERH9n61bt0IkEiE4OBhff/01qlevjkqVKqFz58548uQJAGD9+vWoU6cOzMzM4Ofnh9DQUKVrPHv2DF988QUaNmwIa2trWFhYwM/PD7t27VI6LicnB35+fnBwcMCzZ8+U9n366acwMjLCqVOntPuGicjgsLAjInrL7NmzcebMGcyePRtTpkzBqVOn0Lt3b6xYsQI///wzJk6ciHnz5uHu3bvo06cPcnNzFefeuHEDR48eRbdu3bBy5UosXLgQ2dnZGDx4MLZt26Y4zsTEBL///jsyMzMxfPhwyB/bfejQIWzevBmzZs1Cu3bt1Irbzc0NIpFI6evQoUMayYnctGnTVO4xcuRIjd6DiErOWNcBEBFpUs2aNRWta/mJRCJYWlrC3d0dXbt2xYwZM+Dg4FDgNSQSCc6dOwexWAwAkEqlWLVqFV6/fo1bt27B3NwcAGBnZ4fJkyfj+PHj+OijjwAAH374ISIjIyESiRTXmzJlCpo0aYKlS5dixIgRiu1169bFmjVrMG7cOKxatQpDhgzB2LFj0bRpUyxZsqTEObC2tlbEaGZmprRv1KhR2Lp1a4mu27x5c3Tu3BlOTk4AgOTkZGRmZpY4TiLSPLbYEZHBSEhIUBR1dnZ2cHJyUnyZm5sjNTUV4eHhWL58Ofz8/PD69esCr/Ppp58qijoAaN26NQBg2LBhioIp//aHDx8qtpmbmyuKuszMTLx69QppaWnw9/fHvXv3kJKSonKvXr16Yd68eejRowfevHmD33//HSYmJiXOw7p16/D8+XM8f/4cXbp0UdoXHR2tlBf5l6WlpeKYgvY7OTnB398fixYtUlx7wIABJY6RiLSDLXZEZDCuX7+u+P7ixYvw9PRU2v/kyRPMmDED+/fvR3R0NH755RfMnDlT5To1a9ZUem1rawsAqFGjRoHbExMTFduys7OxZMkSbN++HY8fP1a5dlJSEqytrZW2bd68GXXq1EFISAg2bNiAevXqFf1mS+j06dMFbh83bhwCAwPh6upaYIsnEZUPbLEjIoMREhICALCxsSmwOKpRowZ+/vlnxev79+8XeJ38rXXF2S4fHwfkjUFbvHgxPvjgA/z2228ICgpCcHAwBg8eDACQyWQq5//7779ISkoCkDdGTxfkRbGvr69O7k9EmsEWOyIyGPLipGnTpkpj3PKztLSEWCyGVCqFo6OjxmPYtWsX/P39sX37dqXtv/76a4HHP3/+HGPGjEGjRo3Qpk0b/PTTT/joo4/QvXt3jcdWmNzcXERERABgYUdU3rGwIyKDIW+x8/PzK/SYnTt3QiqVQiQSoW/fvhqPQSwWq7TK3b9/HwcPHlQ5VhAEjBw5Emlpadi5cydq166N8+fPY8yYMbh586ZikoK23bp1C1lZWQCAJk2alMk9iUg72BVLRAbh9evXiIqKAqBa2AmCgCdPnmD+/PkYP348jIyM8N1336FRo0Yaj6NXr144d+4chgwZgsDAQMybNw/vvfce6tevr3LsunXrcOzYMaxcuRJeXl6QSCTYuXMnUlNTMWrUKI3HVpj8YxPZYkdUvrHFjogMQv7iZNy4cZg0aZLidVJSErKysmBmZoYuXbpgypQpaN++vVbiWLt2LczNzXHgwAEcOHAAnp6eCAgIwJ07d5QWM7558ya++uordO3aFZMnT1Zsb9CgAVatWoXJkydj/fr1mDJlilbizE8el6OjI6pXr671+xGR9rCwIyKDkL+wyz9LNb+srCykpqbC2dm5wP0jR44scLFdf39/pQkScm5ubirbLS0tsX79eqxfv17l+G+//VbxvY+PT6FrwE2aNEmpMNU2ee7YDUtU/rErlogMgrw4ad++PQRBUPqKj4/HkSNH4OnpiVOnTqFNmzaIj4/XccT6QSaTITw8HAC7YYkMAQs7IjII8okTBY2bq1KlCrp164Z9+/YByBuPl3/Zk4rs3r17SEtLA8DCjsgQsLAjonIvJSUFDx48AFBwYSfXoEED2NvbAwDu3LlTJrHpu/zj/ljYEZV/LOyIqNwLDQ1VjHUraqarsbHq0OK3H2pf2Je/vz/OnDnzzmNyc3O18h61Rd6FbWNjg1q1auk4GiIqLU6eIKJyT16cmJiYFLisiNyTJ0/w4sULAFA6bseOHUrHnTp1Clu2bMHcuXOVjsu/rtyoUaPQrl07lXsU9nQKfZV/4kRhizoTUfnBwo6Iyj35+DpPT0+YmpoWetzcuXMB5LXa5X+A/dChQ5WOS0tLw5YtW9CxY0f4+/sr7Ttz5gwAoHnz5irnlUdhYWEA2A1LZCjYFUtE5Z681amgbliZTIarV6+id+/e+P333wHkLTvi4eFRpjHqo+joaMXSMFzqhMgwsMWOiMq19PR0REZGAgAOHjyIqlWrKvbJZDIkJycjOzsbAGBmZoYlS5ZgxowZGrlvQkKC0rZKlSrB3Ny81NcuK3ziBJHhYWFHROVaWFiY4tms6enpSE9PV+wzMTGBnZ0d6tevj/bt22PUqFFwcXHRyH1nzpyJmTNnKm1btmwZvvrqK41cvyzIZ8RaWFjA09NTx9EQkSawsCOicq1169YFPhVC2z7//HN8/PHHStvKW/fu4sWLsXjxYl2HQUQaxMKOiKgEPD090aFDB12HQUSkhJMniIgMzKhRoxTr6h06dEij1542bZri2tu2bdPotYmo9NhiR0RkIKpUqYLMzEylbWZmZhq9h7W1tdJ6fkDe4sZEpB9Y2BERGYirV69q/R6LFi3CokWLtH4fIioZdsUSERERGQgWdkREREQGQiToYp0AIiIiItI4ttgRERERGQgWdkREREQGgoUdERERkYFgYUdERERkIFjYERERERkIFnZEREREBoKFHREREZGBYGFHREREZCBY2BEREREZCBZ2RERERAaChR0RERGRgWBhR0RERGQg/h+u+xQttvscegAAAABJRU5ErkJggg==", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from process.io import plot_scans\n", + "\n", + "# Define working directory relative to project dir and input file name\n", + "mfile_name = data_dir / \"scan_example_file_MFILE.DAT\"\n", + "output_dir = data_dir\n", + "\n", + "plot_scans.main(\n", + " args=[\n", + " \"-f\",\n", + " str(mfile_name),\n", + " \"-yv\",\n", + " \"bt rmajor pnetelmw fusion_power capcost\",\n", + " \"--outputdir\",\n", + " str(output_dir),\n", + " ]\n", + ")" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "interpreter": { + "hash": "95e8614a6e18ad6e528160ac32f08bcfa19db99daf3816cbd89c3976c3924301" + }, + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } diff --git a/process/availability.py b/process/availability.py index 3109ec33e..3690d24af 100644 --- a/process/availability.py +++ b/process/availability.py @@ -1,18 +1,18 @@ -import math import logging +import math from process import fortran as ft +from process.fortran import constraint_variables as ctv from process.fortran import cost_variables as cv -from process.fortran import physics_variables as pv -from process.fortran import ife_variables as ifev -from process.fortran import fwbs_variables as fwbsv from process.fortran import divertor_variables as dv +from process.fortran import fwbs_variables as fwbsv +from process.fortran import ife_variables as ifev +from process.fortran import maths_library +from process.fortran import physics_variables as pv +from process.fortran import process_output as po from process.fortran import tfcoil_variables as tfv -from process.fortran import constraint_variables as ctv from process.fortran import times_variables as tv -from process.fortran import process_output as po from process.fortran import vacuum_variables as vacv -from process.fortran import maths_library logger = logging.getLogger(__name__) diff --git a/process/blanket_library.py b/process/blanket_library.py index c8a5004aa..c127a376e 100644 --- a/process/blanket_library.py +++ b/process/blanket_library.py @@ -5,24 +5,28 @@ import numpy as np +from process.coolprop_interface import FluidProperties from process.fortran import ( - constants, - fwbs_variables, - process_output as po, blanket_library, build_variables, - physics_variables, - primary_pumping_variables, - error_handling as eh, - heat_transport_variables, + buildings_variables, + constants, divertor_variables, + error_handling, + fwbs_variables, + heat_transport_variables, maths_library, pfcoil_variables, - buildings_variables, - error_handling, + physics_variables, + primary_pumping_variables, +) +from process.fortran import ( + error_handling as eh, +) +from process.fortran import ( + process_output as po, ) from process.utilities.f2py_string_patch import f2py_compatible_to_string -from process.coolprop_interface import FluidProperties # Acronyms for this module: # BB Breeding Blanket @@ -63,7 +67,6 @@ def component_volumes(self): # D-shaped blanket and shield if physics_variables.itart == 1 or fwbs_variables.fwbsshape == 1: - for icomponent in range(3): self.dshaped_component(icomponent) @@ -1087,7 +1090,6 @@ def blanket_mod_pol_height(self): if ( physics_variables.itart == 1 or fwbs_variables.fwbsshape == 1 ): # D-shaped machine - # Segment vertical inboard surface (m) blanket_library.bllengi = ( 2.0 * blanket_library.hblnkt @@ -1127,7 +1129,6 @@ def blanket_mod_pol_height(self): # shape defined by two half-ellipses else: - # Major radius where half-ellipses 'meet' (m) r1 = ( physics_variables.rmajor @@ -1222,7 +1223,6 @@ def liquid_breeder_properties(self, output: bool = False): # If the liquid metal is PbLi... if fwbs_variables.i_bb_liq == 0: - # PbLi from [Mar2019] # Constant pressure ~ 17 atmospheres ~ 1.7D6 Pa # Li content is ~ 17% @@ -1267,7 +1267,6 @@ def liquid_breeder_properties(self, output: bool = False): # If the liquid metal is Li... elif fwbs_variables.i_bb_liq == 1: - # Temporary - should be updated with information from Li reviews conducted at CCFE once completed # Li Properties from [Mal1995] at 300 Celcius # den_liq = 505 kg/m3 @@ -1619,7 +1618,7 @@ def thermo_hydraulic_model(self, output: bool): # First wall flow is just along the first wall, with no allowance for radial # pipes, manifolds etc. The outputs are mid quantities of inlet and outlet. # This subroutine recalculates cp and rhof. - (blanket_library.tpeakfwi, _, _, blanket_library.mffwpi,) = self.fw.fw_temp( + (blanket_library.tpeakfwi, _, _, blanket_library.mffwpi) = self.fw.fw_temp( output, fwbs_variables.afw, build_variables.fwith, @@ -1930,7 +1929,7 @@ def thermo_hydraulic_model(self, output: bool): self.outfile, "First wall coolant type", "(fwcoolant)", - f'"{fwbs_variables. fwcoolant}"', + f'"{fwbs_variables.fwcoolant}"', ) po.ovarre( self.outfile, @@ -2277,7 +2276,6 @@ def liquid_breeder_pressure_drop_mhd( # If have thin conducting walls... if fwbs_variables.ifci != 1: - # Caculate resistances of fluid and walls r_i = half_wth_b / (conduct_liq * half_wth_a) r_w = half_wth_b / ( @@ -2290,7 +2288,6 @@ def liquid_breeder_pressure_drop_mhd( # If have perfcetly insulating FCIs... else: - # Calculate pressure drop for (perfectly) insulating FCI [Mal1995] mhd_pressure_drop = ( vel * b_mag * l_channel * np.sqrt(conduct_liq * vsc / half_wth_a) diff --git a/process/build.py b/process/build.py index ef258ebc3..8d825755c 100755 --- a/process/build.py +++ b/process/build.py @@ -1,19 +1,23 @@ -from process.fortran import tfcoil_variables -from process.fortran import divertor_variables -from process.fortran import current_drive_variables -from process.fortran import physics_variables -from process.fortran import maths_library -from process.fortran import constants -from process.fortran import pfcoil_variables -from process.fortran import build_variables -from process.fortran import numerics -from process.fortran import fwbs_variables -from process.fortran import error_handling +import logging + +import numpy as np + +from process.fortran import ( + build_variables, + buildings_variables, + constants, + current_drive_variables, + divertor_variables, + error_handling, + fwbs_variables, + maths_library, + numerics, + pfcoil_variables, + physics_variables, + tfcoil_variables, +) from process.fortran import process_output as po -from process.fortran import buildings_variables from process.variables import AnnotatedVariable -import numpy as np -import logging logger = logging.getLogger(__name__) @@ -81,7 +85,6 @@ def portsz(self): g = np.sqrt(e * e + f * f - 2.0e0 * e * f * np.cos(phi)) # cosine rule if g > c: - h = np.sqrt(g * g - c * c) alpha = np.arctan(h / c) @@ -92,7 +95,6 @@ def portsz(self): current_drive_variables.rtanmax = f * np.cos(eps) - 0.5e0 * c else: # coil separation is too narrow for beam... - error_handling.fdiags[0] = g error_handling.fdiags[1] = c error_handling.report_error(63) @@ -1441,7 +1443,6 @@ def ripple_amplitude(self, ripmax: float, r_tf_outboard_mid: float) -> float: """ n = float(tfcoil_variables.n_tf) if tfcoil_variables.i_tf_sup == 1: - # Minimal inboard WP radius [m] r_wp_min = build_variables.r_tf_inboard_in + tfcoil_variables.thkcas @@ -1498,7 +1499,6 @@ def ripple_amplitude(self, ripmax: float, r_tf_outboard_mid: float) -> float: physics_variables.rmajor + physics_variables.rminor ) / ((0.01e0 * ripmax) ** (1.0e0 / n)) else: - # Winding pack to iter-coil at plasma centre toroidal lenth ratio x = t_wp_max * n / physics_variables.rmajor @@ -1633,7 +1633,6 @@ def calculate_radial_build(self, output: bool) -> None: # Issue #514 Radial dimensions of inboard leg # Calculate build_variables.tfcth if tfcoil_variables.dr_tf_wp is an iteration variable (140) if any(numerics.ixc[0 : numerics.nvar] == 140): - # SC TF coil thickness defined using its maximum (diagonal) if tfcoil_variables.i_tf_sup == 1: build_variables.tfcth = ( @@ -1666,7 +1665,6 @@ def calculate_radial_build(self, output: bool) -> None: # WP radial thickness [m] # Calculated only if not used as an iteration variable if not any(numerics.ixc[0 : numerics.nvar] == 140): - # SC magnets if tfcoil_variables.i_tf_sup == 1: tfcoil_variables.dr_tf_wp = ( @@ -1687,7 +1685,6 @@ def calculate_radial_build(self, output: bool) -> None: # Radius of the centrepost at the top of the machine if physics_variables.itart == 1 and tfcoil_variables.i_tf_sup != 1: - # build_variables.r_cp_top is set using the plasma shape if build_variables.i_r_cp_top == 0: build_variables.r_cp_top = ( @@ -1721,7 +1718,6 @@ def calculate_radial_build(self, output: bool) -> None: # User defined build_variables.r_cp_top elif build_variables.i_r_cp_top == 1: - # Notify user that build_variables.r_cp_top has been set to 1.01*build_variables.r_tf_inboard_out (lvl 2 error) if build_variables.r_cp_top < 1.01e0 * build_variables.r_tf_inboard_out: error_handling.fdiags[0] = build_variables.r_cp_top @@ -1760,7 +1756,6 @@ def calculate_radial_build(self, output: bool) -> None: ) + tfcoil_variables.drtop ): - error_handling.fdiags[0] = build_variables.r_cp_top error_handling.report_error(256) if build_variables.tf_in_cs == 1: @@ -1906,7 +1901,6 @@ def calculate_radial_build(self, output: bool) -> None: if (physics_variables.itart == 1) or ( fwbs_variables.fwbsshape == 1 ): # D-shaped - # Major radius to outer edge of inboard section r1 = ( physics_variables.rmajor @@ -1934,7 +1928,6 @@ def calculate_radial_build(self, output: bool) -> None: ) = maths_library.dshellarea(r1, r2, hfw) else: # Cross-section is assumed to be defined by two ellipses - # Major radius to centre of inboard and outboard ellipses # (coincident in radius with top of plasma) @@ -2000,7 +1993,6 @@ def calculate_radial_build(self, output: bool) -> None: # if output: - # Print out device build po.oheadr(self.outfile, "Radial Build") @@ -2076,16 +2068,14 @@ def calculate_radial_build(self, output: bool) -> None: - build_variables.gapoh ) - radial_build_data.append( - [ - "Machine bore wedge support cylinder", - "bore", - build_variables.bore - - build_variables.tfcth - - build_variables.gapoh, - radius, - ] - ) + radial_build_data.append([ + "Machine bore wedge support cylinder", + "bore", + build_variables.bore + - build_variables.tfcth + - build_variables.gapoh, + radius, + ]) elif build_variables.tf_in_cs == 1 and tfcoil_variables.i_tf_bucking < 2: radius = ( radius @@ -2093,201 +2083,238 @@ def calculate_radial_build(self, output: bool) -> None: - build_variables.tfcth - build_variables.gapoh ) - radial_build_data.append( - [ - "Machine bore hole", - "bore", - build_variables.bore - - build_variables.tfcth - - build_variables.gapoh, - radius, - ] - ) + radial_build_data.append([ + "Machine bore hole", + "bore", + build_variables.bore + - build_variables.tfcth + - build_variables.gapoh, + radius, + ]) else: radius = radius + build_variables.bore - radial_build_data.append( - ["Machine bore", "bore", build_variables.bore, radius] - ) + radial_build_data.append([ + "Machine bore", + "bore", + build_variables.bore, + radius, + ]) if build_variables.tf_in_cs == 1: radius += build_variables.tfcth - radial_build_data.append( - [ - "TF coil inboard leg (in bore)", - "tfcth", - build_variables.tfcth, - radius, - ] - ) + radial_build_data.append([ + "TF coil inboard leg (in bore)", + "tfcth", + build_variables.tfcth, + radius, + ]) radius += build_variables.gapoh - radial_build_data.append( - [ - "CS precompresion to TF coil radial gap", - "gapoh", - build_variables.gapoh, - radius, - ] - ) + radial_build_data.append([ + "CS precompresion to TF coil radial gap", + "gapoh", + build_variables.gapoh, + radius, + ]) radius = radius + build_variables.ohcth - radial_build_data.append( - ["Central solenoid", "ohcth", build_variables.ohcth, radius] - ) + radial_build_data.append([ + "Central solenoid", + "ohcth", + build_variables.ohcth, + radius, + ]) radius = radius + build_variables.precomp - radial_build_data.append( - ["CS precompression", "precomp", build_variables.precomp, radius] - ) + radial_build_data.append([ + "CS precompression", + "precomp", + build_variables.precomp, + radius, + ]) if build_variables.tf_in_cs == 0: radius = radius + build_variables.gapoh - radial_build_data.append( - [ - "CS precompresion to TF coil radial gap", - "gapoh", - build_variables.gapoh, - radius, - ] - ) + radial_build_data.append([ + "CS precompresion to TF coil radial gap", + "gapoh", + build_variables.gapoh, + radius, + ]) radius = radius + build_variables.tfcth - radial_build_data.append( - [ - "TF coil inboard leg", - "tfcth", - build_variables.tfcth, - radius, - ] - ) + radial_build_data.append([ + "TF coil inboard leg", + "tfcth", + build_variables.tfcth, + radius, + ]) radius = radius + build_variables.tftsgap - radial_build_data.append( - [ - "TF coil inboard leg insulation gap", - "tftsgap", - build_variables.tftsgap, - radius, - ] - ) + radial_build_data.append([ + "TF coil inboard leg insulation gap", + "tftsgap", + build_variables.tftsgap, + radius, + ]) radius = radius + build_variables.thshield_ib - radial_build_data.append( - [ - "Thermal shield, inboard", - "thshield_ib", - build_variables.thshield_ib, - radius, - ] - ) + radial_build_data.append([ + "Thermal shield, inboard", + "thshield_ib", + build_variables.thshield_ib, + radius, + ]) radius = radius + build_variables.gapds - radial_build_data.append( - [ - "Thermal shield to vessel radial gap", - "gapds", - build_variables.gapds, - radius, - ] - ) + radial_build_data.append([ + "Thermal shield to vessel radial gap", + "gapds", + build_variables.gapds, + radius, + ]) radius += build_variables.d_vv_in - radial_build_data.append( - [ - "Inboard vacuum vessel", - "d_vv_in", - build_variables.d_vv_in, - radius, - ] - ) + radial_build_data.append([ + "Inboard vacuum vessel", + "d_vv_in", + build_variables.d_vv_in, + radius, + ]) radius += build_variables.shldith - radial_build_data.append( - ["Inner radiation shield", "shldith", build_variables.shldith, radius] - ) + radial_build_data.append([ + "Inner radiation shield", + "shldith", + build_variables.shldith, + radius, + ]) radius = radius + build_variables.vvblgap - radial_build_data.append( - ["Gap", "vvblgap", build_variables.vvblgap, radius] - ) + radial_build_data.append([ + "Gap", + "vvblgap", + build_variables.vvblgap, + radius, + ]) radius = radius + build_variables.blnkith - radial_build_data.append( - ["Inboard blanket", "blnkith", build_variables.blnkith, radius] - ) + radial_build_data.append([ + "Inboard blanket", + "blnkith", + build_variables.blnkith, + radius, + ]) radius = radius + build_variables.fwith - radial_build_data.append( - ["Inboard first wall", "fwith", build_variables.fwith, radius] - ) + radial_build_data.append([ + "Inboard first wall", + "fwith", + build_variables.fwith, + radius, + ]) radius = radius + build_variables.scrapli - radial_build_data.append( - ["Inboard scrape-off", "scrapli", build_variables.scrapli, radius] - ) + radial_build_data.append([ + "Inboard scrape-off", + "scrapli", + build_variables.scrapli, + radius, + ]) radius = radius + physics_variables.rminor - radial_build_data.append( - ["Plasma geometric centre", "rminor", physics_variables.rminor, radius] - ) + radial_build_data.append([ + "Plasma geometric centre", + "rminor", + physics_variables.rminor, + radius, + ]) radius = radius + physics_variables.rminor - radial_build_data.append( - ["Plasma outboard edge", "rminor", physics_variables.rminor, radius] - ) + radial_build_data.append([ + "Plasma outboard edge", + "rminor", + physics_variables.rminor, + radius, + ]) radius = radius + build_variables.scraplo - radial_build_data.append( - ["Outboard scrape-off", "scraplo", build_variables.scraplo, radius] - ) + radial_build_data.append([ + "Outboard scrape-off", + "scraplo", + build_variables.scraplo, + radius, + ]) radius = radius + build_variables.fwoth - radial_build_data.append( - ["Outboard first wall", "fwoth", build_variables.fwoth, radius] - ) + radial_build_data.append([ + "Outboard first wall", + "fwoth", + build_variables.fwoth, + radius, + ]) radius = radius + build_variables.blnkoth - radial_build_data.append( - ["Outboard blanket", "blnkoth", build_variables.blnkoth, radius] - ) + radial_build_data.append([ + "Outboard blanket", + "blnkoth", + build_variables.blnkoth, + radius, + ]) radius = radius + build_variables.vvblgap - radial_build_data.append( - ["Gap", "vvblgap", build_variables.vvblgap, radius] - ) + radial_build_data.append([ + "Gap", + "vvblgap", + build_variables.vvblgap, + radius, + ]) radius += build_variables.shldoth - radial_build_data.append( - ["Outer radiation shield", "shldoth", build_variables.shldoth, radius] - ) + radial_build_data.append([ + "Outer radiation shield", + "shldoth", + build_variables.shldoth, + radius, + ]) radius += build_variables.d_vv_out - radial_build_data.append( - ["Outboard vacuum vessel", "d_vv_out", build_variables.d_vv_out, radius] - ) + radial_build_data.append([ + "Outboard vacuum vessel", + "d_vv_out", + build_variables.d_vv_out, + radius, + ]) radius = radius + build_variables.gapsto - radial_build_data.append( - ["Vessel to TF gap", "gapsto", build_variables.gapsto, radius] - ) + radial_build_data.append([ + "Vessel to TF gap", + "gapsto", + build_variables.gapsto, + radius, + ]) radius = radius + build_variables.thshield_ob - radial_build_data.append( - [ - "Ouboard thermal shield", - "thshield_ob", - build_variables.thshield_ob, - radius, - ] - ) + radial_build_data.append([ + "Ouboard thermal shield", + "thshield_ob", + build_variables.thshield_ob, + radius, + ]) radius = radius + build_variables.tftsgap - radial_build_data.append( - ["Gap", "tftsgap", build_variables.tftsgap, radius] - ) + radial_build_data.append([ + "Gap", + "tftsgap", + build_variables.tftsgap, + radius, + ]) radius = radius + build_variables.tfthko - radial_build_data.append( - ["TF coil outboard leg", "tfthko", build_variables.tfthko, radius] - ) + radial_build_data.append([ + "TF coil outboard leg", + "tfthko", + build_variables.tfthko, + radius, + ]) for description, variable, thickness, radius in radial_build_data: po.obuild( diff --git a/process/buildings.py b/process/buildings.py index 9eaadaf3a..f98d945f5 100644 --- a/process/buildings.py +++ b/process/buildings.py @@ -1,18 +1,21 @@ +import logging + import numpy -from process.fortran import current_drive_variables -from process.fortran import fwbs_variables -from process.fortran import buildings_variables -from process.fortran import physics_variables -from process.fortran import cost_variables -from process.fortran import pfcoil_variables -from process.fortran import tfcoil_variables -from process.fortran import build_variables -from process.fortran import divertor_variables -from process.fortran import heat_transport_variables -from process.fortran import constants +from process.fortran import ( + build_variables, + buildings_variables, + constants, + cost_variables, + current_drive_variables, + divertor_variables, + fwbs_variables, + heat_transport_variables, + pfcoil_variables, + physics_variables, + tfcoil_variables, +) from process.fortran import process_output as po -import logging logger = logging.getLogger(__name__) diff --git a/process/caller.py b/process/caller.py index b80d03a9b..d07fe2b45 100644 --- a/process/caller.py +++ b/process/caller.py @@ -1,14 +1,17 @@ from __future__ import annotations -from process import fortran as ft -import numpy as np + import logging +import warnings +from typing import TYPE_CHECKING, Tuple, Union + +import numpy as np +from tabulate import tabulate + +from process import fortran as ft from process.final import finalise -from process.objectives import objective_function from process.io.mfile import MFile +from process.objectives import objective_function from process.utilities.f2py_string_patch import f2py_compatible_to_string -from typing import Union, Tuple, TYPE_CHECKING -import warnings -from tabulate import tabulate if TYPE_CHECKING: from process.main import Models diff --git a/process/coolprop_interface.py b/process/coolprop_interface.py index 0cd12c848..50751bfea 100644 --- a/process/coolprop_interface.py +++ b/process/coolprop_interface.py @@ -1,5 +1,6 @@ import dataclasses from typing import Optional + from CoolProp.CoolProp import PropsSI diff --git a/process/costs.py b/process/costs.py index f7beb0297..1959b2a0a 100644 --- a/process/costs.py +++ b/process/costs.py @@ -1,22 +1,27 @@ -from process.fortran import constants, cost_variables +import numpy + +from process.fortran import ( + build_variables, + buildings_variables, + constants, + cost_variables, + current_drive_variables, + divertor_variables, + error_handling, + fwbs_variables, + heat_transport_variables, + ife_variables, + pf_power_variables, + pfcoil_variables, + physics_variables, + pulse_variables, + structure_variables, + tfcoil_variables, + times_variables, + vacuum_variables, +) from process.fortran import process_output as po -from process.fortran import ife_variables, fwbs_variables -from process.fortran import tfcoil_variables -from process.fortran import physics_variables -from process.fortran import buildings_variables -from process.fortran import build_variables -from process.fortran import structure_variables -from process.fortran import divertor_variables -from process.fortran import pfcoil_variables -from process.fortran import current_drive_variables -from process.fortran import vacuum_variables -from process.fortran import heat_transport_variables -from process.fortran import pf_power_variables -from process.fortran import pulse_variables -from process.fortran import times_variables -from process.fortran import error_handling from process.variables import AnnotatedVariable -import numpy class Costs: @@ -1443,7 +1448,6 @@ def acc2221(self): # Superconductor ($/m) if cost_variables.supercond_cost_model == 0: - costtfsc = ( cost_variables.ucsc[tfcoil_variables.i_tf_sc_mat - 1] * tfcoil_variables.whtconsc diff --git a/process/costs_2015.py b/process/costs_2015.py index 086a34e1e..24c78fe22 100644 --- a/process/costs_2015.py +++ b/process/costs_2015.py @@ -1,20 +1,23 @@ import logging + import numpy -from process.fortran import constants -from process.fortran import cost_variables -from process.fortran import heat_transport_variables + +from process.fortran import ( + build_variables, + constants, + cost_variables, + current_drive_variables, + fwbs_variables, + global_variables, + heat_transport_variables, + pf_power_variables, + pfcoil_variables, + physics_variables, + tfcoil_variables, +) from process.fortran import process_output as po -from process.fortran import global_variables -from process.fortran import fwbs_variables -from process.fortran import build_variables -from process.fortran import current_drive_variables -from process.fortran import pfcoil_variables -from process.fortran import tfcoil_variables -from process.fortran import pf_power_variables -from process.fortran import physics_variables from process.variables import AnnotatedVariable - logger = logging.getLogger(__name__) diff --git a/process/cs_fatigue.py b/process/cs_fatigue.py index 2022acad0..0e0012115 100644 --- a/process/cs_fatigue.py +++ b/process/cs_fatigue.py @@ -1,7 +1,8 @@ +import numpy from numba import njit + from process.fortran import constants from process.fortran import cs_fatigue_variables as csfv -import numpy class CsFatigue: @@ -205,8 +206,7 @@ def surface_stress_intensity_factor(hoop_stress, t, w, a, c, phi): H2 = ( 1.0e0 + (-2.11e0 + 0.77e0 * c_a) * a_t # G21 * a / t - + (0.55e0 - 0.72e0 * c_a**0.75e0 + 0.14e0 * c_a * 1.5e0) - * a_t_2 # G22 + + (0.55e0 - 0.72e0 * c_a**0.75e0 + 0.14e0 * c_a * 1.5e0) * a_t_2 # G22 ) # compute the unitless geometric correction diff --git a/process/current_drive.py b/process/current_drive.py index 298b04ad4..4a0f710c8 100644 --- a/process/current_drive.py +++ b/process/current_drive.py @@ -1,16 +1,19 @@ import numpy as np -from process.plasma_profiles import PlasmaProfile - from process.fortran import ( - heat_transport_variables, + constants, + cost_variables, current_drive_variables, + heat_transport_variables, physics_variables, - cost_variables, - constants, - process_output as po, +) +from process.fortran import ( error_handling as eh, ) +from process.fortran import ( + process_output as po, +) +from process.plasma_profiles import PlasmaProfile class CurrentDrive: @@ -241,10 +244,7 @@ def cudriv(self, output: bool): # X-mode case elif current_drive_variables.wave_mode == 1: f_cutoff = 0.5 * ( - fc - + np.sqrt( - current_drive_variables.harnum * fc**2 + 4 * fp**2 - ) + fc + np.sqrt(current_drive_variables.harnum * fc**2 + 4 * fp**2) ) # Plasma coupling only occurs if the plasma cut-off is below the cyclotron harmonic @@ -539,10 +539,7 @@ def cudriv(self, output: bool): # X-mode case elif current_drive_variables.wave_mode == 1: f_cutoff = 0.5 * ( - fc - + np.sqrt( - current_drive_variables.harnum * fc**2 + 4 * fp**2 - ) + fc + np.sqrt(current_drive_variables.harnum * fc**2 + 4 * fp**2) ) # Plasma coupling only occurs if the plasma cut-off is below the cyclotron harmonic @@ -2076,52 +2073,48 @@ def sigbeam(self, eb, te, ne, rnhe, rnc, rno, rnfe): for a hydrogen beam in a fusion plasma. Janev, Boley and Post, Nuclear Fusion 29 (1989) 2125 """ - a = np.array( + a = np.array([ + [ + [4.4, -2.49e-2], + [7.46e-2, 2.27e-3], + [3.16e-3, -2.78e-5], + ], + [ + [2.3e-1, -1.15e-2], + [-2.55e-3, -6.2e-4], + [1.32e-3, 3.38e-5], + ], + ]) + + b = np.array([ [ + [[-2.36, -1.49, -1.41, -1.03], [0.185, -0.0154, -4.08e-4, 0.106]], [ - [4.4, -2.49e-2], - [7.46e-2, 2.27e-3], - [3.16e-3, -2.78e-5], + [-0.25, -0.119, -0.108, -0.0558], + [-0.0381, -0.015, -0.0138, -3.72e-3], ], + ], + [ [ - [2.3e-1, -1.15e-2], - [-2.55e-3, -6.2e-4], - [1.32e-3, 3.38e-5], + [0.849, 0.518, 0.477, 0.322], + [-0.0478, 7.18e-3, 1.57e-3, -0.0375], ], - ] - ) - - b = np.array( - [ [ - [[-2.36, -1.49, -1.41, -1.03], [0.185, -0.0154, -4.08e-4, 0.106]], - [ - [-0.25, -0.119, -0.108, -0.0558], - [-0.0381, -0.015, -0.0138, -3.72e-3], - ], + [0.0677, 0.0292, 0.0259, 0.0124], + [0.0105, 3.66e-3, 3.33e-3, 8.61e-4], ], + ], + [ [ - [ - [0.849, 0.518, 0.477, 0.322], - [-0.0478, 7.18e-3, 1.57e-3, -0.0375], - ], - [ - [0.0677, 0.0292, 0.0259, 0.0124], - [0.0105, 3.66e-3, 3.33e-3, 8.61e-4], - ], + [-0.0588, -0.0336, -0.0305, -0.0187], + [4.34e-3, 3.41e-4, 7.35e-4, 3.53e-3], ], [ - [ - [-0.0588, -0.0336, -0.0305, -0.0187], - [4.34e-3, 3.41e-4, 7.35e-4, 3.53e-3], - ], - [ - [-4.48e-3, -1.79e-3, -1.57e-3, -7.43e-4], - [-6.76e-4, -2.04e-4, -1.86e-4, -5.12e-5], - ], + [-4.48e-3, -1.79e-3, -1.57e-3, -7.43e-4], + [-6.76e-4, -2.04e-4, -1.86e-4, -5.12e-5], ], - ] - ) + ], + ]) z = np.array([2.0, 6.0, 8.0, 26.0]) nn = np.array([rnhe, rnc, rno, rnfe]) diff --git a/process/dcll.py b/process/dcll.py index a179afd2f..7d2bf91df 100644 --- a/process/dcll.py +++ b/process/dcll.py @@ -1,13 +1,15 @@ from process.fortran import ( - constants, build_variables, - fwbs_variables, + constants, + current_drive_variables, dcll_module, + fwbs_variables, + heat_transport_variables, physics_variables, - current_drive_variables, - process_output as po, primary_pumping_variables, - heat_transport_variables, +) +from process.fortran import ( + process_output as po, ) diff --git a/process/divertor.py b/process/divertor.py index 831b05659..63b311e46 100644 --- a/process/divertor.py +++ b/process/divertor.py @@ -1,12 +1,12 @@ import math -from process.fortran import constants from process.fortran import build_variables as bv +from process.fortran import constants from process.fortran import divertor_variables as dv +from process.fortran import error_handling as eh from process.fortran import physics_variables as pv -from process.fortran import tfcoil_variables as tfv from process.fortran import process_output as po -from process.fortran import error_handling as eh +from process.fortran import tfcoil_variables as tfv class Divertor: @@ -911,17 +911,11 @@ def divwade( Bt_omp = -bt * rmajor / r_omp # Eich scaling for lambda_q - lambda_eich = ( - 1.35 * pdivt**-0.02 * rmajor**0.04 * bp**-0.92 * aspect**0.42 - ) + lambda_eich = 1.35 * pdivt**-0.02 * rmajor**0.04 * bp**-0.92 * aspect**0.42 # Spreading factor spread_fact = ( - 0.12 - * (nesep / 1e19) ** -0.02 - * pdivt**-0.21 - * rmajor**0.71 - * bp**-0.82 + 0.12 * (nesep / 1e19) ** -0.02 * pdivt**-0.21 * rmajor**0.71 * bp**-0.82 ) # SOL width @@ -934,9 +928,7 @@ def divwade( alpha_div = flux_exp * alpha_mid # Tilt of the separatrix relative to the target in the poloidal plane - theta_div = math.asin( - (1 + 1 / alpha_div**2) * math.sin(math.radians(beta_div)) - ) + theta_div = math.asin((1 + 1 / alpha_div**2) * math.sin(math.radians(beta_div))) # Wetted area area_wetted = ( diff --git a/process/evaluators.py b/process/evaluators.py index 35c68653e..3185a2514 100644 --- a/process/evaluators.py +++ b/process/evaluators.py @@ -1,12 +1,14 @@ +import logging +import math + +import numpy as np + from process.caller import Caller -from process.fortran import global_variables as gv from process.fortran import cost_variables as cv +from process.fortran import global_variables as gv from process.fortran import numerics from process.fortran import physics_variables as pv from process.fortran import times_variables as tv -import numpy as np -import math -import logging logger = logging.getLogger(__name__) diff --git a/process/exceptions.py b/process/exceptions.py index f087bb129..873343f85 100644 --- a/process/exceptions.py +++ b/process/exceptions.py @@ -7,9 +7,9 @@ def __init__(self, *args, **kwargs): def __str__(self): exception_message = super().__str__() - diagnostics_message = "\n".join( - [f"\t{d}: {repr(v)}" for d, v in self._diagnostics.items()] - ) + diagnostics_message = "\n".join([ + f"\t{d}: {repr(v)}" for d, v in self._diagnostics.items() + ]) if diagnostics_message: return f"{exception_message}\n{diagnostics_message}" diff --git a/process/final.py b/process/final.py index bd0775d01..c9f1ef42f 100644 --- a/process/final.py +++ b/process/final.py @@ -2,13 +2,15 @@ from tabulate import tabulate +from process import output as op from process.fortran import ( - process_output as po, constants, - numerics, constraints, + numerics, +) +from process.fortran import ( + process_output as po, ) -from process import output as op from process.objectives import objective_function from process.utilities.f2py_string_patch import f2py_compatible_to_string diff --git a/process/fw.py b/process/fw.py index 614d9955b..2255fa8e9 100644 --- a/process/fw.py +++ b/process/fw.py @@ -1,14 +1,18 @@ import numpy as np +from process.coolprop_interface import FluidProperties from process.fortran import ( constants, + error_handling, fwbs_variables, +) +from process.fortran import ( error_handling as eh, +) +from process.fortran import ( process_output as po, - error_handling, ) from process.utilities.f2py_string_patch import f2py_compatible_to_string -from process.coolprop_interface import FluidProperties class Fw: diff --git a/process/geometry/blanket_geometry.py b/process/geometry/blanket_geometry.py index 9256ccfec..37df275a6 100644 --- a/process/geometry/blanket_geometry.py +++ b/process/geometry/blanket_geometry.py @@ -1,10 +1,13 @@ """ Calculate radial and vertical coordinates for the geometry of the blanket """ + from typing import Tuple + import numpy as np -from process.geometry.utils import dh_vertices, dhgap_vertices + from process.geometry.geometry_parameterisations import ArbitraryGeometry +from process.geometry.utils import dh_vertices, dhgap_vertices def blanket_geometry_single_null( @@ -80,22 +83,18 @@ def blanket_geometry_single_null( divgap=divgap, ) - rs = np.concatenate( - [ - rs_upper_outboard, - rs_lower_inboard, - rs_upper_inboard[::-1], - rs_lower_outboard[::-1], - ] - ) - zs = np.concatenate( - [ - zs_upper_outboard, - zs_lower_inboard, - zs_upper_inboard[::-1], - zs_lower_outboard[::-1], - ] - ) + rs = np.concatenate([ + rs_upper_outboard, + rs_lower_inboard, + rs_upper_inboard[::-1], + rs_lower_outboard[::-1], + ]) + zs = np.concatenate([ + zs_upper_outboard, + zs_lower_inboard, + zs_upper_inboard[::-1], + zs_lower_outboard[::-1], + ]) return ArbitraryGeometry(rs=rs, zs=zs) diff --git a/process/geometry/cryostat_geometry.py b/process/geometry/cryostat_geometry.py index 35535998e..d33c24c19 100644 --- a/process/geometry/cryostat_geometry.py +++ b/process/geometry/cryostat_geometry.py @@ -1,6 +1,7 @@ """ Calculate cryostat geometries """ + from typing import List from process.geometry.geometry_parameterisations import RectangleGeometry diff --git a/process/geometry/firstwall_geometry.py b/process/geometry/firstwall_geometry.py index b47c54b1c..d3be88982 100644 --- a/process/geometry/firstwall_geometry.py +++ b/process/geometry/firstwall_geometry.py @@ -1,10 +1,13 @@ """ Calculate radial and vertical coordinates for the geometry of the first wall """ + from typing import Tuple + import numpy as np -from process.geometry.utils import dh_vertices, dhgap_vertices + from process.geometry.geometry_parameterisations import ArbitraryGeometry +from process.geometry.utils import dh_vertices, dhgap_vertices def first_wall_geometry_single_null( @@ -82,22 +85,18 @@ def first_wall_geometry_single_null( top_point=top_point, ) - rs = np.concatenate( - [ - rs_upper_outboard, - rs_lower_inboard, - rs_upper_inboard[::-1], - rs_lower_outboard[::-1], - ] - ) - zs = np.concatenate( - [ - zs_upper_outboard, - zs_lower_inboard, - zs_upper_inboard[::-1], - zs_lower_outboard[::-1], - ] - ) + rs = np.concatenate([ + rs_upper_outboard, + rs_lower_inboard, + rs_upper_inboard[::-1], + rs_lower_outboard[::-1], + ]) + zs = np.concatenate([ + zs_upper_outboard, + zs_lower_inboard, + zs_upper_inboard[::-1], + zs_lower_outboard[::-1], + ]) return ArbitraryGeometry(rs=rs, zs=zs) diff --git a/process/geometry/geometry_parameterisations.py b/process/geometry/geometry_parameterisations.py index 2e312a181..5a89eafb8 100644 --- a/process/geometry/geometry_parameterisations.py +++ b/process/geometry/geometry_parameterisations.py @@ -1,7 +1,9 @@ """ Module to hold geometry parameterisation dataclasses common to multiple reactor components """ + from dataclasses import dataclass + import numpy as np diff --git a/process/geometry/pfcoil_geometry.py b/process/geometry/pfcoil_geometry.py index 7d3e1974e..94ba73cd3 100644 --- a/process/geometry/pfcoil_geometry.py +++ b/process/geometry/pfcoil_geometry.py @@ -1,8 +1,11 @@ """ Calculate radial and vertical coordinates for the geometry of the pf coils and central coil """ + from typing import List, Tuple + import numpy as np + from process.geometry.geometry_parameterisations import RectangleGeometry diff --git a/process/geometry/plasma_geometry.py b/process/geometry/plasma_geometry.py index 070db744a..e7ba9c185 100644 --- a/process/geometry/plasma_geometry.py +++ b/process/geometry/plasma_geometry.py @@ -1,8 +1,10 @@ """ Calculate plasma elongation and radial and vertical coordinates for the geometry of the plasma """ -from dataclasses import dataclass + import math +from dataclasses import dataclass + import numpy as np diff --git a/process/geometry/shield_geometry.py b/process/geometry/shield_geometry.py index f31b93e43..dc5bf8229 100644 --- a/process/geometry/shield_geometry.py +++ b/process/geometry/shield_geometry.py @@ -1,10 +1,13 @@ """ Calculate radial and vertical coordinates for the geometry of the shield """ + from typing import Tuple + import numpy as np -from process.geometry.utils import dh_vertices + from process.geometry.geometry_parameterisations import ArbitraryGeometry +from process.geometry.utils import dh_vertices def shield_geometry_single_null( @@ -63,22 +66,18 @@ def shield_geometry_single_null( triang=triang, ) - rs = np.concatenate( - [ - rs_lower_inboard, - rs_lower_outboard[::-1], - rs_upper_outboard, - rs_upper_inboard[::-1], - ] - ) - zs = np.concatenate( - [ - zs_lower_inboard, - zs_lower_outboard[::-1], - zs_upper_outboard, - zs_upper_inboard[::-1], - ] - ) + rs = np.concatenate([ + rs_lower_inboard, + rs_lower_outboard[::-1], + rs_upper_outboard, + rs_upper_inboard[::-1], + ]) + zs = np.concatenate([ + zs_lower_inboard, + zs_lower_outboard[::-1], + zs_upper_outboard, + zs_upper_inboard[::-1], + ]) return ArbitraryGeometry( rs=rs, zs=zs, diff --git a/process/geometry/tfcoil_geometry.py b/process/geometry/tfcoil_geometry.py index 8fc30b5ca..1d52a7d81 100644 --- a/process/geometry/tfcoil_geometry.py +++ b/process/geometry/tfcoil_geometry.py @@ -1,7 +1,9 @@ """ Calculate radial and vertical coordinates for the geometry of the tf coils """ + from typing import List, Tuple + from process.geometry.geometry_parameterisations import RectangleGeometry from process.geometry.utils import ellips_fill_vertices diff --git a/process/geometry/utils.py b/process/geometry/utils.py index 506a948a0..b2dfd69b2 100644 --- a/process/geometry/utils.py +++ b/process/geometry/utils.py @@ -1,7 +1,9 @@ """ Module to hold plotting functions, used in plot_proc.py, which are common to multiple reactor components """ + from typing import List, Tuple + import numpy as np diff --git a/process/geometry/vacuum_vessel_geometry.py b/process/geometry/vacuum_vessel_geometry.py index 693699f0f..6a508082f 100644 --- a/process/geometry/vacuum_vessel_geometry.py +++ b/process/geometry/vacuum_vessel_geometry.py @@ -1,10 +1,13 @@ """ Calculate radial and vertical coordinates for the geometry of the vacuum vessel """ + from typing import Tuple + import numpy as np -from process.geometry.utils import dh_vertices + from process.geometry.geometry_parameterisations import ArbitraryGeometry +from process.geometry.utils import dh_vertices def vacuum_vessel_geometry_single_null( @@ -70,22 +73,18 @@ def vacuum_vessel_geometry_single_null( triang=triang, ) - rs = np.concatenate( - [ - rs_lower_inboard, - rs_lower_outboard[::-1], - rs_upper_outboard, - rs_upper_inboard[::-1], - ] - ) - zs = np.concatenate( - [ - zs_lower_inboard, - zs_lower_outboard[::-1], - zs_upper_outboard, - zs_upper_inboard[::-1], - ] - ) + rs = np.concatenate([ + rs_lower_inboard, + rs_lower_outboard[::-1], + rs_upper_outboard, + rs_upper_inboard[::-1], + ]) + zs = np.concatenate([ + zs_lower_inboard, + zs_lower_outboard[::-1], + zs_upper_outboard, + zs_upper_inboard[::-1], + ]) return ArbitraryGeometry( rs=rs, zs=zs, diff --git a/process/hcpb.py b/process/hcpb.py index 25d14fdc5..4360a5ebb 100644 --- a/process/hcpb.py +++ b/process/hcpb.py @@ -1,23 +1,27 @@ import numpy as np +from process.coolprop_interface import FluidProperties from process.fortran import ( - constants, - ccfe_hcpb_module, build_variables, + buildings_variables, + ccfe_hcpb_module, + constants, + constraint_variables, + cost_variables, + current_drive_variables, + divertor_variables, fwbs_variables, + heat_transport_variables, physics_variables, - process_output as po, + primary_pumping_variables, tfcoil_variables, - heat_transport_variables, - cost_variables, - divertor_variables, - buildings_variables, +) +from process.fortran import ( error_handling as eh, - current_drive_variables, - primary_pumping_variables, - constraint_variables, ) -from process.coolprop_interface import FluidProperties +from process.fortran import ( + process_output as po, +) class CCFE_HCPB: @@ -855,8 +859,7 @@ def st_cp_angle_fraction(self, h_cp_top, r_cp_mid, r_cp_top, rmajor): int_calc_3 = 0.0 int_calc_1 = 1.0 / np.sqrt( - h_cp_top**2 - + (rho_maj * np.cos(phy_cp_calc) - np.sqrt(int_calc_3)) ** 2 + h_cp_top**2 + (rho_maj * np.cos(phy_cp_calc) - np.sqrt(int_calc_3)) ** 2 ) phy_cp_calc = phy_cp_calc + d_phy_cp @@ -867,8 +870,7 @@ def st_cp_angle_fraction(self, h_cp_top, r_cp_mid, r_cp_top, rmajor): int_calc_3 = 0.0 int_calc_2 = 1.0 / np.sqrt( - h_cp_top**2 - + (rho_maj * np.cos(phy_cp_calc) - np.sqrt(int_calc_3)) ** 2 + h_cp_top**2 + (rho_maj * np.cos(phy_cp_calc) - np.sqrt(int_calc_3)) ** 2 ) cp_sol_angle = cp_sol_angle + d_phy_cp * 0.5 * (int_calc_1 + int_calc_2) diff --git a/process/ife.py b/process/ife.py index a91ef6a7e..3c7004604 100644 --- a/process/ife.py +++ b/process/ife.py @@ -6,18 +6,19 @@ """ import numpy as np + from process.fortran import ( - ife_variables, - constants, - process_output, build_variables, - physics_variables, - structure_variables, - fwbs_variables, + buildings_variables, + constants, cost_variables, error_handling, + fwbs_variables, heat_transport_variables, - buildings_variables, + ife_variables, + physics_variables, + process_output, + structure_variables, vacuum_variables, ) @@ -584,7 +585,6 @@ def sombld(self): # Material volumes for i in range(ife_variables.maxmat): - ife_variables.chmatv[i] = max( 0.0, ife_variables.chvol * ife_variables.chmatf[i] ) @@ -903,8 +903,7 @@ def bld2019(self): # Area acurt = np.pi * ( - (ife_variables.chrad + ife_variables.bldr) ** 2.0 - - ife_variables.chrad**2.0 + (ife_variables.chrad + ife_variables.bldr) ** 2.0 - ife_variables.chrad**2.0 ) # Mass Flow @@ -1481,7 +1480,6 @@ def ifephy(self, output: bool = False): # Wall load (assume total fusion power applies) if ife_variables.ifetyp == 1: - # OSIRIS-type build: First wall subtends a solid angle of 2 pi * SANG phi = 0.5 * np.pi + np.arctan(ife_variables.zl1 / ife_variables.r1) @@ -1491,7 +1489,6 @@ def ifephy(self, output: bool = False): ) elif ife_variables.ifetyp == 4: - # 2019 build only has first wall at the top which has a tube at # its centre. This calculates solid angle and removes tube. @@ -1624,17 +1621,14 @@ def lasdrv(self, edrive): # Would be better to prevent extrapolation if ie <= 1: - gain = gve[1] - 1.0e-6 * (edrive - 2.0e6) * (gve[0] - gve[1]) etadrv = eve[1] - 1.0e-6 * (edrive - 2.0e6) * (eve[0] - eve[1]) elif ie >= 9: - gain = gve[8] + 1.0e-6 * (edrive - 9.0e6) * (gve[9] - gve[8]) etadrv = eve[8] + 1.0e-6 * (edrive - 9.0e6) * (eve[9] - eve[8]) else: - gain = gve[ie - 1] + de * (gve[ie] - gve[ie - 1]) etadrv = eve[ie - 1] + de * (eve[ie] - eve[ie - 1]) @@ -1682,17 +1676,14 @@ def iondrv(self, edrive): # Would be better to prevent extrapolation if ie <= 1: - gain = gve[1] - 1.0e-6 * (edrive - 2.0e6) * (gve[0] - gve[1]) etadrv = eve[1] - 1.0e-6 * (edrive - 2.0e6) * (eve[0] - eve[1]) elif ie >= 9: - gain = gve[8] + 1.0e-6 * (edrive - 9.0e6) * (gve[9] - gve[8]) etadrv = eve[8] + 1.0e-6 * (edrive - 9.0e6) * (eve[9] - eve[8]) else: - gain = gve[ie - 1] + de * (gve[ie] - gve[ie - 1]) etadrv = eve[ie - 1] + de * (eve[ie] - eve[ie - 1]) @@ -1993,7 +1984,6 @@ def ifepw2(self, output: bool = False): # Calculate powers relevant to a power-producing plant if cost_variables.ireactor == 1: - # Gross electric power heat_transport_variables.pgrossmw = ( heat_transport_variables.pthermmw * heat_transport_variables.etath diff --git a/process/impurity_radiation.py b/process/impurity_radiation.py index 37045bb28..e79fdef88 100644 --- a/process/impurity_radiation.py +++ b/process/impurity_radiation.py @@ -1,15 +1,14 @@ -import numpy import dataclasses +import logging import re from importlib import resources -from typing import Optional, List from pathlib import Path -from scipy import integrate -from process.fortran import impurity_radiation_module -from process.fortran import error_handling +from typing import List, Optional +import numpy +from scipy import integrate -import logging +from process.fortran import error_handling, impurity_radiation_module logger = logging.getLogger(__name__) diff --git a/process/init.py b/process/init.py index 346082818..fc1d261b5 100644 --- a/process/init.py +++ b/process/init.py @@ -1,4 +1,5 @@ from warnings import warn + import process.fortran as fortran from process.exceptions import ProcessValidationError @@ -335,9 +336,9 @@ def check_process(): # Impurity fractions for imp in range(fortran.impurity_radiation_module.nimp): - fortran.impurity_radiation_module.impurity_arr_frac[ - imp - ] = fortran.impurity_radiation_module.fimp[imp] + fortran.impurity_radiation_module.impurity_arr_frac[imp] = ( + fortran.impurity_radiation_module.fimp[imp] + ) # Stop the run if oacdcp is used as an optimisation variable # As the current density is now calculated from bt without constraint 10 @@ -350,7 +351,6 @@ def check_process(): # Plasma profile consistency checks if fortran.ife_variables.ife != 1: if fortran.physics_variables.ipedestal == 1: - # Temperature checks if fortran.physics_variables.teped < fortran.physics_variables.tesep: raise ProcessValidationError( @@ -401,7 +401,6 @@ def check_process(): fortran.physics_variables.fgwped < 0 or not (fortran.numerics.ixc[: fortran.numerics.nvar] == 145).any() ): - # Issue #589 Pedestal density is set manually using neped but it is less than nesep. if fortran.physics_variables.neped < fortran.physics_variables.nesep: raise ProcessValidationError( @@ -468,7 +467,6 @@ def check_process(): if ( fortran.numerics.icc[: fortran.numerics.neqns + fortran.numerics.nineqns] == 78 ).any(): - # If Reinke criterion is used tesep is calculated and cannot be an # iteration variable if (fortran.numerics.ixc[: fortran.numerics.nvar] == 119).any(): @@ -503,7 +501,6 @@ def check_process(): # Tight aspect ratio options (ST) if fortran.physics_variables.itart == 1: - fortran.global_variables.icase = "Tight aspect ratio tokamak model" # Disabled Forcing that no inboard breeding blanket is used @@ -564,7 +561,6 @@ def check_process(): # Aluminium magnets initalisation / checks # Initialize the CP conductor temperature to cryogenic temperature for cryo-al magnets (20 K) elif fortran.tfcoil_variables.i_tf_sup == 2: - # Call a lvl 3 error if the inlet coolant temperature is too large # Motivation : ill-defined aluminium resistivity fit for T > 40-50 K if fortran.tfcoil_variables.tcoolin > 40.0: @@ -642,7 +638,6 @@ def check_process(): # Conventionnal aspect ratios specific else: - if ( fortran.physics_variables.i_plasma_current == 2 or fortran.physics_variables.i_plasma_current == 9 @@ -799,7 +794,6 @@ def check_process(): # Setting the default cryo-plants efficiencies if abs(fortran.tfcoil_variables.eff_tf_cryo + 1) < 1e-6: - # The ITER cyoplant efficiency is used for SC if fortran.tfcoil_variables.i_tf_sup == 1: fortran.tfcoil_variables.eff_tf_cryo = 0.13 @@ -830,7 +824,6 @@ def check_process(): # Setting up insulation layer young modulae default values [Pa] if fortran.tfcoil_variables.eyoung_ins <= 1.0e8: - # Copper magnets, no insulation material defined # But use the ITER design by default if fortran.tfcoil_variables.i_tf_sup == 0: @@ -887,7 +880,6 @@ def check_process(): # To contains the insulation, cooling and the support structure # Rem : Only verified if the WP thickness is used if (fortran.numerics.ixc[: fortran.numerics.nvar] == 140).any(): - # Minimal WP thickness if fortran.tfcoil_variables.i_tf_sup == 1: dr_tf_wp_min = 2.0 * ( diff --git a/process/io/configuration.py b/process/io/configuration.py index 08abd12a4..69c7daccb 100755 --- a/process/io/configuration.py +++ b/process/io/configuration.py @@ -12,7 +12,6 @@ class ConfigurationParser(object): - """Abstract parser class. Must be subclassed to be used. The parser should always put read-in data in the data property. @@ -39,11 +38,10 @@ def data_validate(self, value): """Check that value corresponds to a specific data format.""" logger.info("type of value: {}".format(type(value))) if not isinstance(value, dict) and value is not None: - raise ValueError("Configuration data must be specified as a " "dictionary") + raise ValueError("Configuration data must be specified as a dictionary") class JsonConfigParser(ConfigurationParser): - """JSON configuration parser.""" def __init__(self, filename): @@ -53,12 +51,11 @@ def __init__(self, filename): config_file_data = json.load(fh) self.data = config_file_data except FileNotFoundError: - logger.error("Cannot find configuration file " "{}".format(filename)) + logger.error("Cannot find configuration file {}".format(filename)) pass class Config(object): - """Generic configuration for PROCESS tools. Read-only.""" def __init__(self, config_file, parser=JsonConfigParser): @@ -94,7 +91,7 @@ def _search_config_for(self, config, *keys): if isinstance(config, dict) and len(keys) > 1: return self._search_config_for(value, *keys[1:]) elif not isinstance(value, dict) and len(keys) > 1: - raise KeyError("{} cannot be found in " "{}".format(search_key, value)) + raise KeyError("{} cannot be found in {}".format(search_key, value)) else: return self._lowercase(value) @@ -117,8 +114,9 @@ def get(self, *config_keys, default=None): return default else: logger.exception( - "Cannot find value or default for {} in " - "configuration".format(config_keys) + "Cannot find value or default for {} in configuration".format( + config_keys + ) ) except (IndexError, TypeError): raise diff --git a/process/io/costs_bar.py b/process/io/costs_bar.py index 023c62ffb..3abfdcaed 100644 --- a/process/io/costs_bar.py +++ b/process/io/costs_bar.py @@ -14,12 +14,14 @@ # Imported libraries import argparse -import process.io.mfile as mf -import matplotlib.pyplot as plt -import numpy as np import sys from typing import List +import matplotlib.pyplot as plt +import numpy as np + +import process.io.mfile as mf + def comp_orig(args, mfile_list: List[str], inflate: float) -> None: """ diff --git a/process/io/costs_pie.py b/process/io/costs_pie.py index 5d7984d11..dde9c82f3 100644 --- a/process/io/costs_pie.py +++ b/process/io/costs_pie.py @@ -12,9 +12,11 @@ # Imported libraries import argparse -import process.io.mfile as mf + import matplotlib.pyplot as plt +import process.io.mfile as mf + def orig_cost_model(m_file, args): """ diff --git a/process/io/in_dat.py b/process/io/in_dat.py index e224acdc0..239f1b57f 100644 --- a/process/io/in_dat.py +++ b/process/io/in_dat.py @@ -13,9 +13,10 @@ generation script imports, and inspects, process. """ -from re import sub import subprocess +from re import sub from sys import stderr + from process.io.python_fortran_dicts import get_dicts # ioptimz values @@ -800,8 +801,9 @@ def variable_constraint_type_check(item_number, var_type): # If not an integer warn of rounding and return rounded integer else: print( - "Value {0} for {1} not an integer. Value rounded to {2}. " - "Check!".format(item_number, var_type, int(item_number)) + "Value {0} for {1} not an integer. Value rounded to {2}. Check!".format( + item_number, var_type, int(item_number) + ) ) return int(item_number) @@ -863,8 +865,9 @@ def variable_bound_check(bound_number, bound_type): else: bound_number = int(bound_number) print( - "Bound number {0} not an integer. " - "Value rounded to {1}".format(bound_number, int(bound_number)) + "Bound number {0} not an integer. Value rounded to {1}".format( + bound_number, int(bound_number) + ) ) return bound_number, bound_type diff --git a/process/io/mfile.py b/process/io/mfile.py index f42d1ff01..8904e183d 100755 --- a/process/io/mfile.py +++ b/process/io/mfile.py @@ -1,34 +1,34 @@ """ - PROCESS MFILE.DAT IO library - - process.io.mfile. - - James Morris - CCFE - - Notes: - + 12/03/2014: Initial version - + 12/03/2014: Added MFILE variable class - + 12/03/2014: Added MFILE class for containing all info from file. - + 12/03/2014: Added ability to read MFILE.DAT into class - + 12/03/2014: Added ability write MFILE.DAT from class - + 12/05/2014: Fixed mfile issue with strings in MFILE.DAT with no scans - + 16/05/2014: Cleaned up MFileVariable - + 19/05/2014: Cleaned up MFile and put some functions outside class. - + 12/06/2014: Fixed error handling for "variable not in MFILE" errors - + 16/06/2014: Fixed library path error; fix in get_scans - + 24/11/2021: Global dictionary variables moved within the functions - to avoid cyclic dependencies. This is because the dicts - generation script imports, and inspects, process. - - Compatible with PROCESS version 286 +PROCESS MFILE.DAT IO library + +process.io.mfile. + +James Morris +CCFE + +Notes: + + 12/03/2014: Initial version + + 12/03/2014: Added MFILE variable class + + 12/03/2014: Added MFILE class for containing all info from file. + + 12/03/2014: Added ability to read MFILE.DAT into class + + 12/03/2014: Added ability write MFILE.DAT from class + + 12/05/2014: Fixed mfile issue with strings in MFILE.DAT with no scans + + 16/05/2014: Cleaned up MFileVariable + + 19/05/2014: Cleaned up MFile and put some functions outside class. + + 12/06/2014: Fixed error handling for "variable not in MFILE" errors + + 16/06/2014: Fixed library path error; fix in get_scans + + 24/11/2021: Global dictionary variables moved within the functions + to avoid cyclic dependencies. This is because the dicts + generation script imports, and inspects, process. + +Compatible with PROCESS version 286 """ -from collections import OrderedDict -import logging import json +import logging +from collections import OrderedDict from typing import List, Union logger = logging.getLogger(__name__) @@ -152,8 +152,7 @@ def get_error(self, *args, **kwargs): # Missing error_status key means Process exited prematurely, usually # due to a "STOP 1" raise KeyError( - "error_status not found in MFILE. Process probably " - "exited prematurely" + "error_status not found in MFILE. Process probably exited prematurely" ) else: return 0 @@ -283,7 +282,6 @@ class or create a new class if it is the first instance of it. self.mfile_modules[self.current_module] = list() else: - var_des = line[0] extracted_var_name = sort_brackets(line[1]) @@ -361,7 +359,7 @@ def write_to_json(self, keys_to_write=None, scan=-1, verbose=False): else: entry = data sub_dict[item] = entry - dict_to_write[f"scan-{i+1}"] = sub_dict + dict_to_write[f"scan-{i + 1}"] = sub_dict else: for item in keys_to_write: # Initialize dat_key properly based on the number of scans diff --git a/process/io/mfile2dict.py b/process/io/mfile2dict.py index d83b62192..73d18d777 100644 --- a/process/io/mfile2dict.py +++ b/process/io/mfile2dict.py @@ -11,13 +11,11 @@ # @date : last modified 2021-02-22 # # # ############################################################################### -from typing import Dict, List, Any -from collections import OrderedDict -from collections import abc -import re import logging import os - +import re +from collections import OrderedDict, abc +from typing import Any, Dict, List MFILE_END = "# Copy of PROCESS Input Follows #" VETO_LIST = [" # PROCESS found a feasible solution #"] @@ -229,7 +227,7 @@ def parse(self, mfile_addr: str) -> Dict: """ if not os.path.exists(mfile_addr): raise FileNotFoundError( - "Could not open MFILE '{}', " "file does not exist.".format(mfile_addr) + "Could not open MFILE '{}', file does not exist.".format(mfile_addr) ) self._logger.info("Parsing MFILE: %s", mfile_addr) diff --git a/process/io/mfile_comparison.py b/process/io/mfile_comparison.py index 2d7360da7..e816a2c1d 100755 --- a/process/io/mfile_comparison.py +++ b/process/io/mfile_comparison.py @@ -1,27 +1,29 @@ #!/usr/bin/env python """ - Python tool for comparing MFILE and outputting differences. - The tool does not work for MFiles that are not the result of - a full PROCESS run (ie if an error or exception occured). +Python tool for comparing MFILE and outputting differences. +The tool does not work for MFiles that are not the result of +a full PROCESS run (ie if an error or exception occured). - James Morris - 14/04/15 +James Morris +14/04/15 - CCFE +CCFE - Notes: - + 24/11/2021: Global dictionary variables moved within the functions - to avoid cyclic dependencies. This is because the dicts - generation script imports, and inspects, process. +Notes: + + 24/11/2021: Global dictionary variables moved within the functions + to avoid cyclic dependencies. This is because the dicts + generation script imports, and inspects, process. """ +import argparse import sys + import numpy -import argparse -import process.io.mfile as mf from numpy import isfinite + +import process.io.mfile as mf from process.io.python_fortran_dicts import get_dicts # Dictionary for parameter descriptions @@ -476,7 +478,6 @@ def main(arg): if __name__ == "__main__": - parser = argparse.ArgumentParser( description="Produce a comparison " "between two PROCESS " diff --git a/process/io/mfile_to_csv.py b/process/io/mfile_to_csv.py index f6165d89d..fe57ac177 100644 --- a/process/io/mfile_to_csv.py +++ b/process/io/mfile_to_csv.py @@ -22,13 +22,12 @@ # standard python modules import argparse import csv -from pathlib import Path, PurePath import json +from pathlib import Path, PurePath # PROCESS-specific modules from process.io.mfile import MFile - # == define functions == diff --git a/process/io/plot_proc.py b/process/io/plot_proc.py index ee33c92f9..d170e2857 100755 --- a/process/io/plot_proc.py +++ b/process/io/plot_proc.py @@ -14,43 +14,42 @@ """ -import os import argparse +import os from argparse import RawTextHelpFormatter -import matplotlib -import matplotlib.pyplot as plt from importlib import resources -from matplotlib.patches import Rectangle -from matplotlib.patches import Circle + +import matplotlib import matplotlib.backends.backend_pdf as bpdf -from matplotlib.path import Path import matplotlib.patches as patches +import matplotlib.pyplot as plt import numpy as np +from matplotlib.patches import Circle, Rectangle +from matplotlib.path import Path import process.io.mfile as mf - -from process.geometry.shield_geometry import ( - shield_geometry_single_null, - shield_geometry_double_null, -) -from process.geometry.plasma_geometry import plasma_geometry -from process.geometry.vacuum_vessel_geometry import ( - vacuum_vessel_geometry_single_null, - vacuum_vessel_geometry_double_null, -) from process.geometry.blanket_geometry import ( - blanket_geometry_single_null, blanket_geometry_double_null, + blanket_geometry_single_null, ) from process.geometry.cryostat_geometry import cryostat_geometry +from process.geometry.firstwall_geometry import ( + first_wall_geometry_double_null, + first_wall_geometry_single_null, +) +from process.geometry.pfcoil_geometry import pfcoil_geometry +from process.geometry.plasma_geometry import plasma_geometry +from process.geometry.shield_geometry import ( + shield_geometry_double_null, + shield_geometry_single_null, +) from process.geometry.tfcoil_geometry import ( - tfcoil_geometry_rectangular_shape, tfcoil_geometry_d_shape, + tfcoil_geometry_rectangular_shape, ) -from process.geometry.pfcoil_geometry import pfcoil_geometry -from process.geometry.firstwall_geometry import ( - first_wall_geometry_single_null, - first_wall_geometry_double_null, +from process.geometry.vacuum_vessel_geometry import ( + vacuum_vessel_geometry_double_null, + vacuum_vessel_geometry_single_null, ) from process.impurity_radiation import read_impurity_file from process.io.python_fortran_dicts import get_dicts @@ -901,24 +900,22 @@ def plot_radprofile(prof, mfile_data, scan, impp, demo_ranges) -> float: imp_data = read_imprad_data(2, impp) # find impurity densities - imp_frac = np.array( - [ - mfile_data.data["fimp(01)"].get_scan(scan), - mfile_data.data["fimp(02)"].get_scan(scan), - mfile_data.data["fimp(03)"].get_scan(scan), - mfile_data.data["fimp(04)"].get_scan(scan), - mfile_data.data["fimp(05)"].get_scan(scan), - mfile_data.data["fimp(06)"].get_scan(scan), - mfile_data.data["fimp(07)"].get_scan(scan), - mfile_data.data["fimp(08)"].get_scan(scan), - mfile_data.data["fimp(09)"].get_scan(scan), - mfile_data.data["fimp(10)"].get_scan(scan), - mfile_data.data["fimp(11)"].get_scan(scan), - mfile_data.data["fimp(12)"].get_scan(scan), - mfile_data.data["fimp(13)"].get_scan(scan), - mfile_data.data["fimp(14)"].get_scan(scan), - ] - ) + imp_frac = np.array([ + mfile_data.data["fimp(01)"].get_scan(scan), + mfile_data.data["fimp(02)"].get_scan(scan), + mfile_data.data["fimp(03)"].get_scan(scan), + mfile_data.data["fimp(04)"].get_scan(scan), + mfile_data.data["fimp(05)"].get_scan(scan), + mfile_data.data["fimp(06)"].get_scan(scan), + mfile_data.data["fimp(07)"].get_scan(scan), + mfile_data.data["fimp(08)"].get_scan(scan), + mfile_data.data["fimp(09)"].get_scan(scan), + mfile_data.data["fimp(10)"].get_scan(scan), + mfile_data.data["fimp(11)"].get_scan(scan), + mfile_data.data["fimp(12)"].get_scan(scan), + mfile_data.data["fimp(13)"].get_scan(scan), + mfile_data.data["fimp(14)"].get_scan(scan), + ]) if ipedestal == 0: # Intialise the radius @@ -946,9 +943,7 @@ def plot_radprofile(prof, mfile_data, scan, impp, demo_ranges) -> float: te = np.zeros(rho.shape[0]) for q in range(rho.shape[0]): if rho[q] <= rhopedn: - ne[q] = ( - neped + (ne0 - neped) * (1 - rho[q] ** 2 / rhopedn**2) ** alphan - ) + ne[q] = neped + (ne0 - neped) * (1 - rho[q] ** 2 / rhopedn**2) ** alphan else: ne[q] = nesep + (neped - nesep) * (1 - rho[q]) / ( 1 - min(0.9999, rhopedn) @@ -1668,7 +1663,7 @@ def plot_tf_wp(axis, mfile_data, scan: int) -> None: dr_tf_wp, wp_toridal_dxbig, color="darkgreen", - label=f"Insulation: \n{tinstf*1000} mm thickness \n", + label=f"Insulation: \n{tinstf * 1000} mm thickness \n", ), ) # Plots the WP inside the insulation @@ -1723,7 +1718,7 @@ def plot_tf_wp(axis, mfile_data, scan: int) -> None: (dr_tf_wp / 2) + (tinstf), wp_toridal_dxsmall + (tinstf), color="darkgreen", - label=f"Insulation: \n{tinstf*1000} mm thickness \n", + label=f"Insulation: \n{tinstf * 1000} mm thickness \n", ), ) @@ -1777,7 +1772,7 @@ def plot_tf_wp(axis, mfile_data, scan: int) -> None: patches.Polygon( xy=list(zip(x, y)), color="darkgreen", - label=f"Insulation: \n{tinstf*1000} mm thickness \n", + label=f"Insulation: \n{tinstf * 1000} mm thickness \n", ) ) @@ -2393,12 +2388,10 @@ def plot_magnetics_info(axis, mfile_data, scan): pf_info = [] for i in range(1, number_of_coils): if i % 2 != 0: - pf_info.append( - ( - mfile_data.data["ric[{:01}]".format(i)].get_scan(scan), - "PF {}".format(i), - ) - ) + pf_info.append(( + mfile_data.data["ric[{:01}]".format(i)].get_scan(scan), + "PF {}".format(i), + )) if len(pf_info) > 2: pf_info_3_a = pf_info[2][0] diff --git a/process/io/plot_radial_build.py b/process/io/plot_radial_build.py index 38722e81a..77fae18f8 100644 --- a/process/io/plot_radial_build.py +++ b/process/io/plot_radial_build.py @@ -9,15 +9,16 @@ """ -import matplotlib.pyplot as plt -import numpy as np import argparse from argparse import RawTextHelpFormatter from pathlib import Path -from process.io.variable_metadata import var_dicts as meta + +import matplotlib.pyplot as plt +import numpy as np # PROCESS libraries import process.io.mfile as mf +from process.io.variable_metadata import var_dicts as meta def parse_args(args): @@ -119,9 +120,9 @@ def get_radial_build(m_file): for ii in range(isweep): if m_file.data["ifail"].get_scan(ii + 1) == 1: - radial_build.append( - [m_file.data[rl].get_scan(ii + 1) for rl in radial_labels] - ) + radial_build.append([ + m_file.data[rl].get_scan(ii + 1) for rl in radial_labels + ]) radial_build = np.array(radial_build) diff --git a/process/io/plot_sankey.py b/process/io/plot_sankey.py index 1d0ee371d..76a54724e 100755 --- a/process/io/plot_sankey.py +++ b/process/io/plot_sankey.py @@ -9,9 +9,12 @@ MFILE.DAT """ -import matplotlib + import argparse -from pylab import show, savefig + +import matplotlib +from pylab import savefig, show + from process.io.sankey_funcs import plot_sankey matplotlib.use("Agg") diff --git a/process/io/plot_scans.py b/process/io/plot_scans.py index 72620f366..bddcce3af 100644 --- a/process/io/plot_scans.py +++ b/process/io/plot_scans.py @@ -22,16 +22,17 @@ - If the file is a folder, the contained MFILE is used as an input. """ -import matplotlib.pyplot as plt -import numpy as np -import os import argparse +import os from argparse import RawTextHelpFormatter from pathlib import Path -from process.io.variable_metadata import var_dicts as meta + +import matplotlib.pyplot as plt +import numpy as np # PROCESS libraries import process.io.mfile as mf +from process.io.variable_metadata import var_dicts as meta def parse_args(args): @@ -223,9 +224,9 @@ def main(args=None): nsweep_dict[14] = "fiooic" nsweep_dict[15] = "fjprot" nsweep_dict[16] = "rmajor" - nsweep_dict[ - 17 - ] = "bmaxtf" # bmxlim the maximum T field upper limit is the scan variable + nsweep_dict[17] = ( + "bmaxtf" # bmxlim the maximum T field upper limit is the scan variable + ) nsweep_dict[18] = "gammax" nsweep_dict[19] = "boundl(16)" nsweep_dict[20] = "cnstv.t_burn_min" @@ -779,9 +780,7 @@ def main(args=None): else: # Converged indexes, for normal 2D line plot - for ( - conv_j - ) in ( + for conv_j in ( conv_ij ): # conv_j is an array element containing the converged scan numbers # Scanned variables diff --git a/process/io/plot_solutions.py b/process/io/plot_solutions.py index 972f2028e..e6863a6a4 100644 --- a/process/io/plot_solutions.py +++ b/process/io/plot_solutions.py @@ -8,20 +8,20 @@ currently. """ -from process.io.mfile import MFile -from process.fortran import numerics -from process.utilities.f2py_string_patch import f2py_compatible_to_string +import logging +from dataclasses import asdict, dataclass from pathlib import Path -import pandas as pd +from typing import Dict, List, Optional, Sequence, Tuple, Union -import numpy as np -import matplotlib.pyplot as plt import matplotlib as mpl -import logging +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd import seaborn as sns -from dataclasses import dataclass, asdict -from typing import Optional, Sequence, List, Dict, Tuple, Union +from process.fortran import numerics +from process.io.mfile import MFile +from process.utilities.f2py_string_patch import f2py_compatible_to_string # Variables of interest in mfiles and subsequent dataframes # Be specific about exact names, patterns and regex @@ -358,12 +358,10 @@ def _plot_solutions( else: numerics.init_numerics() objf_list = list( - set( - [ - f2py_compatible_to_string(numerics.lablmm[int(abs(minmax)) - 1]) - for minmax in diffs_df["minmax"] - ] - ) + set([ + f2py_compatible_to_string(numerics.lablmm[int(abs(minmax)) - 1]) + for minmax in diffs_df["minmax"] + ]) ) if len(objf_list) != 1: diff --git a/process/io/plot_stress_tf.py b/process/io/plot_stress_tf.py index 7f093f951..6d20c5f02 100644 --- a/process/io/plot_stress_tf.py +++ b/process/io/plot_stress_tf.py @@ -10,20 +10,19 @@ SIG_TF.json """ +import argparse import json -import matplotlib import os -import argparse from argparse import RawTextHelpFormatter -import matplotlib.pyplot as plt from pathlib import Path +import matplotlib +import matplotlib.pyplot as plt matplotlib.use("Agg") def main(args=None): - # PARSING USER PARAMETERS # please execute 'python plot_stress_tf.py -h' for input information # Option definition diff --git a/process/io/process_config.py b/process/io/process_config.py index cdf7407cd..14e5fb77e 100755 --- a/process/io/process_config.py +++ b/process/io/process_config.py @@ -13,24 +13,26 @@ generation script imports, and inspects, process. """ +import logging import os import subprocess import sys +from pathlib import Path from sys import stderr from time import sleep -from numpy.random import seed, uniform, normal + from numpy import argsort, argwhere, logical_or -from pathlib import Path +from numpy.random import normal, seed, uniform + +from process.io.configuration import Config +from process.io.in_dat import InDat +from process.io.mfile import MFile from process.io.process_funcs import ( + check_in_dat, get_from_indat_or_default, set_variable_in_indat, - check_in_dat, ) -from process.io.in_dat import InDat -from process.io.mfile import MFile -from process.io.configuration import Config from process.io.python_fortran_dicts import get_dicts -import logging logger = logging.getLogger(__name__) @@ -728,7 +730,7 @@ def __init__(self, configfilename="config_evaluate_uncertainties.json"): ) # setup the output_vars for u_dict in self.uncertainties: - if not u_dict["varname"] in self.output_vars: + if u_dict["varname"] not in self.output_vars: self.output_vars += [u_dict["varname"]] # add normalised constraints/iteration variables to output diff --git a/process/io/process_funcs.py b/process/io/process_funcs.py index ebf0ef1df..96cf1ccce 100755 --- a/process/io/process_funcs.py +++ b/process/io/process_funcs.py @@ -10,18 +10,19 @@ generation script imports, and inspects, process. """ +import logging from os.path import join as pjoin +from pathlib import Path from sys import stderr +from time import sleep + +from numpy.random import uniform + +from process.fortran import numerics from process.io.in_dat import InDat from process.io.mfile import MFile -from process.fortran import numerics -from numpy.random import uniform -from time import sleep from process.io.python_fortran_dicts import get_dicts from process.utilities.f2py_string_patch import f2py_compatible_to_string -from pathlib import Path - -import logging logger = logging.getLogger(__name__) @@ -133,9 +134,7 @@ def get_variable_range(itervars, factor, wdir="."): if lbs[-1] > ubs[-1]: print( "Error: Iteration variable {0} has BOUNDL={1} >\ - BOUNDU={2}\n Update process_dicts or input file!".format( - varname, lbs[-1], ubs[-1] - ), + BOUNDU={2}\n Update process_dicts or input file!".format(varname, lbs[-1], ubs[-1]), file=stderr, ) @@ -203,9 +202,7 @@ def check_in_dat(): "Warning: boundu for", itervarname, "lies out of allowed input range!\n Reset boundu({}) \ -to".format( - itervarno - ), +to".format(itervarno), upperinputbound, file=stderr, ) diff --git a/process/io/python_fortran_dicts.py b/process/io/python_fortran_dicts.py index 5a5a09b66..ea8055ad0 100644 --- a/process/io/python_fortran_dicts.py +++ b/process/io/python_fortran_dicts.py @@ -6,10 +6,12 @@ Process Python can call process.io.python_fortran_dicts.get_dicts() to load the dicts from the JSON file created and saved at build-time and use them. """ -from pkg_resources import resource_filename + import json import logging +from pkg_resources import resource_filename + logger = logging.getLogger(__name__) diff --git a/process/io/sankey_funcs.py b/process/io/sankey_funcs.py index 69a7b44b0..4ee4f7b9e 100644 --- a/process/io/sankey_funcs.py +++ b/process/io/sankey_funcs.py @@ -7,10 +7,11 @@ Updated 13/09/2019: Adam Brown (adam.brown@ukaea.uk) """ +import matplotlib.pyplot as plt import numpy as np -from numpy import sqrt from matplotlib.sankey import Sankey -import matplotlib.pyplot as plt +from numpy import sqrt + from process.io.mfile import MFile @@ -756,9 +757,10 @@ def plot_sankey(mfilename="MFILE.DAT"): # Plot simplified power flow Sankey Dia t.set_position(pos) if t == diagrams[0].texts[0]: # Fusion Power t.set_horizontalalignment("left") - t.set_position( - (pos[0] - 0.35, pos[1] + 0.5 * (fusion_power / totalplasma) + 0.2) - ) + t.set_position(( + pos[0] - 0.35, + pos[1] + 0.5 * (fusion_power / totalplasma) + 0.2, + )) if t == diagrams[0].texts[2]: # Plasma t.set_horizontalalignment("right") t.set_position((pos[0] - 0.25, pos[1])) @@ -772,9 +774,10 @@ def plot_sankey(mfilename="MFILE.DAT"): # Plot simplified power flow Sankey Dia t.set_position((pos[0] - 0.25, pos[1])) if t == diagrams[3].texts[1]: # Gross Electric t.set_horizontalalignment("right") - t.set_position( - (pos[0] - 0.5 * (pgrossmw / totalplasma) - 0.1, pos[1] + 0.1) - ) + t.set_position(( + pos[0] - 0.5 * (pgrossmw / totalplasma) - 0.1, + pos[1] + 0.1, + )) if t == diagrams[3].texts[2]: # Losses t.set_horizontalalignment("right") t.set_position((pos[0] - 0.2, pos[1])) @@ -788,9 +791,10 @@ def plot_sankey(mfilename="MFILE.DAT"): # Plot simplified power flow Sankey Dia t.set_position((pos[0] + 0.2, pos[1])) if t == diagrams[4].texts[2]: # Recirc. Power if pnetelmw >= 1: - t.set_position( - (pos[0] + 0.15, pos[1] + 0.5 * (precircmw / totalplasma) + 0.2) - ) + t.set_position(( + pos[0] + 0.15, + pos[1] + 0.5 * (precircmw / totalplasma) + 0.2, + )) elif pnetelmw < 1: t.set_horizontalalignment("left") t.set_position((pos[0] + 0.2, pos[1])) @@ -798,24 +802,25 @@ def plot_sankey(mfilename="MFILE.DAT"): # Plot simplified power flow Sankey Dia t.set_position((pos[0], pos[1] - 0.2)) if t == diagrams[5].texts[2]: # Heating System if pnetelmw >= 1: - t.set_position( - (pos[0] + 0.15, pos[1] + 0.5 * (pinjwp / totalplasma) + 0.2) - ) + t.set_position(( + pos[0] + 0.15, + pos[1] + 0.5 * (pinjwp / totalplasma) + 0.2, + )) if pnetelmw < 1: - t.set_position( - (pos[0] + 0.15, pos[1] + 0.5 * (pinjwp / totalplasma) + 0.2) - ) + t.set_position(( + pos[0] + 0.15, + pos[1] + 0.5 * (pinjwp / totalplasma) + 0.2, + )) if t == diagrams[6].texts[1]: # Plasma Heating t.set_horizontalalignment("left") - t.set_position( - (pos[0] + 0.5 * (pinjmw / totalplasma) + 0.1, pos[1] - 0.05) - ) + t.set_position(( + pos[0] + 0.5 * (pinjmw / totalplasma) + 0.1, + pos[1] - 0.05, + )) if t == diagrams[6].texts[2]: # Losses t.set_horizontalalignment("left") - t.set_position( - ( - pos[0] + 0.15, - pos[1] - 0.5 * ((pinjwp - pinjmw) / totalplasma) - 0.2, - ) - ) + t.set_position(( + pos[0] + 0.15, + pos[1] - 0.5 * ((pinjwp - pinjmw) / totalplasma) - 0.2, + )) y += 1 diff --git a/process/io/write_new_in_dat.py b/process/io/write_new_in_dat.py index 81a77d4a4..6b42bd80c 100644 --- a/process/io/write_new_in_dat.py +++ b/process/io/write_new_in_dat.py @@ -1,14 +1,15 @@ """ - Modifies the PROCESS input file IN.DAT so all the iteration variables are - given their values from the output file MFILE.DAT. +Modifies the PROCESS input file IN.DAT so all the iteration variables are +given their values from the output file MFILE.DAT. - James Morris 30/04/2014 based on code by Michael Kovari 9/8/13 and - J C Rivas, 16/7/2013 +James Morris 30/04/2014 based on code by Michael Kovari 9/8/13 and +J C Rivas, 16/7/2013 """ import argparse import re + import process.io.mfile as mf from process.io.in_dat import InDat @@ -98,7 +99,6 @@ def replace_iteration_variables(iteration_vars, in_data): """ for variable_name, variable_value in iteration_vars.items(): - if (match := re.search(r"([a-zA-Z0-9_]+)\(([0-9]+)\)", variable_name)) is None: in_data.add_parameter(variable_name.lower(), variable_value) else: @@ -137,7 +137,7 @@ def main(args=None): metavar="o", type=str, default="new_IN.DAT", - help="File to write as new IN.DAT " '(default="new_IN.DAT")', + help='File to write as new IN.DAT (default="new_IN.DAT")', ) parser.add_argument( diff --git a/process/main.py b/process/main.py index 94cabae03..0f3a21d04 100644 --- a/process/main.py +++ b/process/main.py @@ -41,61 +41,58 @@ Box file T&M/PKNIGHT/PROCESS (from 24/01/12) """ +import argparse +import logging +import os +from pathlib import Path from typing import Protocol + +import process +import process.init as init from process import fortran +from process.availability import Availability +from process.blanket_library import BlanketLibrary +from process.build import Build from process.buildings import Buildings +from process.caller import write_output_files from process.costs import Costs -from process.io import plot_proc -from process.io import mfile -from process.plasma_geometry import PlasmaGeom -from process.pulse import Pulse -from process.scan import Scan -from process.stellarator import Stellarator, Neoclassics -from process.structure import Structure -from process.build import Build -from process.utilities.f2py_string_patch import string_to_f2py_compatible -import argparse -from process.pfcoil import PFCoil -from process.tfcoil import TFcoil -from process.divertor import Divertor -from process.availability import Availability -from process.ife import IFE from process.costs_2015 import Costs2015 -from process.power import Power from process.cs_fatigue import CsFatigue -from process.physics import Physics -from process.io import obsolete_vars as ov -from process.plasma_profiles import PlasmaProfile -from process.hcpb import CCFE_HCPB +from process.current_drive import CurrentDrive from process.dcll import DCLL -from process.blanket_library import BlanketLibrary +from process.divertor import Divertor from process.fw import Fw -from process.current_drive import CurrentDrive +from process.hcpb import CCFE_HCPB +from process.ife import IFE from process.impurity_radiation import initialise_imprad -from process.caller import write_output_files -import process.init as init - -import process - -from pathlib import Path -import os -import logging +from process.io import mfile, plot_proc +from process.io import obsolete_vars as ov # For VaryRun from process.io.process_config import RunProcessConfig from process.io.process_funcs import ( + check_input_error, get_neqns_itervars, get_variable_range, - check_input_error, - process_stopped, no_unfeasible_mfile, - vary_iteration_variables, + process_stopped, process_warnings, + vary_iteration_variables, ) +from process.pfcoil import PFCoil +from process.physics import Physics +from process.plasma_geometry import PlasmaGeom +from process.plasma_profiles import PlasmaProfile +from process.power import Power +from process.pulse import Pulse +from process.scan import Scan +from process.sctfcoil import Sctfcoil +from process.stellarator import Neoclassics, Stellarator +from process.structure import Structure +from process.tfcoil import TFcoil +from process.utilities.f2py_string_patch import string_to_f2py_compatible from process.vacuum import Vacuum from process.water_use import WaterUse -from process.sctfcoil import Sctfcoil - os.environ["PYTHON_PROCESS_ROOT"] = os.path.join(os.path.dirname(__file__)) @@ -351,8 +348,9 @@ def run(self): break else: print( - "WARNING: {} non-feasible point(s) in sweep! " - "Rerunning!".format(no_unfeasible) + "WARNING: {} non-feasible point(s) in sweep! Rerunning!".format( + no_unfeasible + ) ) else: print("PROCESS has stopped without finishing!") @@ -425,9 +423,9 @@ def set_input(self): else: print("-- Info -- run `process --help` for usage") raise FileNotFoundError( - "Input file not found on this path. There " "is no input file named", + "Input file not found on this path. There is no input file named", self.input_file, - "in the analysis " "folder", + "in the analysis folder", ) # Set the input file in the Fortran @@ -481,7 +479,7 @@ def call_solver(self): # Original call: # self.ifail = fortran.main_module.eqslv() raise NotImplementedError( - "HYBRD non-optimisation solver is not " "implemented" + "HYBRD non-optimisation solver is not implemented" ) def run_scan(self, solver): diff --git a/process/objectives.py b/process/objectives.py index d1bfba79c..b2205dc0f 100644 --- a/process/objectives.py +++ b/process/objectives.py @@ -1,14 +1,14 @@ import numpy as np from process.fortran import ( - physics_variables, - tfcoil_variables, - pf_power_variables, - current_drive_variables, cost_variables, + current_drive_variables, divertor_variables, - times_variables, heat_transport_variables, + pf_power_variables, + physics_variables, + tfcoil_variables, + times_variables, ) diff --git a/process/optimiser.py b/process/optimiser.py index dfcc63c1f..1c6969053 100644 --- a/process/optimiser.py +++ b/process/optimiser.py @@ -1,7 +1,6 @@ -from process.fortran import numerics -from process.solver import get_solver -from process.fortran import define_iteration_variables from process.evaluators import Evaluators +from process.fortran import define_iteration_variables, numerics +from process.solver import get_solver class Optimiser: diff --git a/process/pfcoil.py b/process/pfcoil.py index e3f67e30d..08183e914 100644 --- a/process/pfcoil.py +++ b/process/pfcoil.py @@ -1,27 +1,27 @@ -from process.fortran import pfcoil_module as pf -from process.fortran import pfcoil_variables as pfv -from process.fortran import times_variables as tv -from process.fortran import error_handling as eh +import logging +import math + +import numba +import numpy as np +from scipy import optimize + +import process.superconductors as superconductors +from process import fortran as ft from process.fortran import build_variables as bv -from process.fortran import physics_variables as pv -from process.fortran import tfcoil_variables as tfv -from process.fortran import fwbs_variables as fwbsv -from process.fortran import constants +from process.fortran import constants, numerics +from process.fortran import constraint_variables as ctv from process.fortran import cs_fatigue_variables as csfv +from process.fortran import error_handling as eh +from process.fortran import fwbs_variables as fwbsv from process.fortran import maths_library as ml +from process.fortran import pfcoil_module as pf +from process.fortran import pfcoil_variables as pfv +from process.fortran import physics_variables as pv from process.fortran import process_output as op -from process.fortran import numerics from process.fortran import rebco_variables as rcv -from process.fortran import constraint_variables as ctv - +from process.fortran import tfcoil_variables as tfv +from process.fortran import times_variables as tv from process.utilities.f2py_string_patch import f2py_compatible_to_string -from process import fortran as ft -import process.superconductors as superconductors -import math -import numpy as np -import numba -import logging -from scipy import optimize logger = logging.getLogger(__name__) @@ -1191,7 +1191,7 @@ def ohcalc(self): # Allowable coil overall current density at EOF # (superconducting coils only) - (jcritwp, pfv.jcableoh_eof, pfv.jscoh_eof, tmarg1,) = self.superconpf( + (jcritwp, pfv.jcableoh_eof, pfv.jscoh_eof, tmarg1) = self.superconpf( pfv.bmaxoh, pfv.vfohc, pfv.fcuohsu, @@ -1214,7 +1214,7 @@ def ohcalc(self): # Allowable coil overall current density at BOP - (jcritwp, pfv.jcableoh_bop, pfv.jscoh_bop, tmarg2,) = self.superconpf( + (jcritwp, pfv.jcableoh_bop, pfv.jscoh_bop, tmarg2) = self.superconpf( pfv.bmaxoh0, pfv.vfohc, pfv.fcuohsu, @@ -1605,10 +1605,7 @@ def axial_stress(self): # term 3 ek2b2_1, ek2b2_2 = ml.ellipke(k2b2) axial_term_3 = ( - 2.0e0 - * hl - * (math.sqrt(4.0e0 * b**2 + 4.0e0 * hl**2)) - * (ek2b2_1 - ek2b2_2) + 2.0e0 * hl * (math.sqrt(4.0e0 * b**2 + 4.0e0 * hl**2)) * (ek2b2_1 - ek2b2_2) ) # calculate axial force [N] @@ -1839,18 +1836,18 @@ def induct(self, output): for ig in range(pf.nef): op.write( self.outfile, - f"{ig}\t{pfv.sxlg[:pfv.ncirt,ig]}", + f"{ig}\t{pfv.sxlg[: pfv.ncirt, ig]}", ) if bv.iohcl != 0: op.write( self.outfile, - f"CS\t\t\t{pfv.sxlg[:pfv.ncirt,pfv.ncirt-2]}", + f"CS\t\t\t{pfv.sxlg[: pfv.ncirt, pfv.ncirt - 2]}", ) op.write( self.outfile, - f"Plasma\t{pfv.sxlg[:pfv.ncirt,pfv.ncirt-1]}", + f"Plasma\t{pfv.sxlg[: pfv.ncirt, pfv.ncirt - 1]}", ) def outpf(self): @@ -2343,7 +2340,7 @@ def outpf(self): for k in range(pf.nef): op.write( self.outfile, - f"PF {k}\t\t\t{pfv.rpf[k]:.2e}\t{pfv.zpf[k]:.2e}\t{pfv.rb[k]-pfv.ra[k]:.2e}\t{abs(pfv.zh[k]-pfv.zl[k]):.2e}\t{pfv.turns[k]:.2e}", + f"PF {k}\t\t\t{pfv.rpf[k]:.2e}\t{pfv.zpf[k]:.2e}\t{pfv.rb[k] - pfv.ra[k]:.2e}\t{abs(pfv.zh[k] - pfv.zl[k]):.2e}\t{pfv.turns[k]:.2e}", ) for k in range(pf.nef): @@ -2395,7 +2392,7 @@ def outpf(self): if bv.iohcl != 0: op.write( self.outfile, - f"CS\t\t\t\t{pfv.rpf[pfv.nohc-1]:.2e}\t{pfv.zpf[pfv.nohc-1]:.2e}\t{pfv.rb[pfv.nohc-1]-pfv.ra[pfv.nohc-1]:.2e}\t{abs(pfv.zh[pfv.nohc-1]-pfv.zl[pfv.nohc-1]):.2e}\t{pfv.turns[pfv.nohc-1]:.2e}\t{pfv.pfcaseth[pfv.nohc-1]:.2e}", + f"CS\t\t\t\t{pfv.rpf[pfv.nohc - 1]:.2e}\t{pfv.zpf[pfv.nohc - 1]:.2e}\t{pfv.rb[pfv.nohc - 1] - pfv.ra[pfv.nohc - 1]:.2e}\t{abs(pfv.zh[pfv.nohc - 1] - pfv.zl[pfv.nohc - 1]):.2e}\t{pfv.turns[pfv.nohc - 1]:.2e}\t{pfv.pfcaseth[pfv.nohc - 1]:.2e}", ) op.ovarre( self.mfile, @@ -2443,7 +2440,7 @@ def outpf(self): # Plasma op.write( self.outfile, - f"Plasma\t\t\t{pv.rmajor:.2e}\t0.0e0\t\t{2.0e0*pv.rminor:.2e}\t{2.0e0*pv.rminor*pv.kappa:.2e}\t1.0e0", + f"Plasma\t\t\t{pv.rmajor:.2e}\t0.0e0\t\t{2.0e0 * pv.rminor:.2e}\t{2.0e0 * pv.rminor * pv.kappa:.2e}\t1.0e0", ) op.osubhd(self.outfile, "PF Coil Information at Peak Current:") @@ -2464,7 +2461,7 @@ def outpf(self): if pfv.ipfres == 0: op.write( self.outfile, - f"PF {k}\t{pfv.ric[k]:.2e}\t{pfv.rjpfalw[k]:.2e}\t{pfv.rjconpf[k]:.2e}\t{pfv.rjconpf[k]/pfv.rjpfalw[k]:.2e}\t{pfv.wtc[k]:.2e}\t{pfv.wts[k]:.2e}\t{pfv.bpf[k]:.2e}", + f"PF {k}\t{pfv.ric[k]:.2e}\t{pfv.rjpfalw[k]:.2e}\t{pfv.rjconpf[k]:.2e}\t{pfv.rjconpf[k] / pfv.rjpfalw[k]:.2e}\t{pfv.wtc[k]:.2e}\t{pfv.wts[k]:.2e}\t{pfv.bpf[k]:.2e}", ) else: op.write( @@ -2478,12 +2475,12 @@ def outpf(self): # Issue #328 op.write( self.outfile, - f"CS\t\t{pfv.ric[pfv.nohc-1]:.2e}\t{pfv.rjpfalw[pfv.nohc-1]:.2e}\t{max(abs(pfv.cohbop),abs(pfv.coheof)):.2e}\t{max(abs(pfv.cohbop),abs(pfv.coheof))/pfv.rjpfalw[pfv.nohc-1]:.2e}\t{pfv.wtc[pfv.nohc-1]:.2e}\t{pfv.wts[pfv.nohc-1]:.2e}\t{pfv.bpf[pfv.nohc-1]:.2e}", + f"CS\t\t{pfv.ric[pfv.nohc - 1]:.2e}\t{pfv.rjpfalw[pfv.nohc - 1]:.2e}\t{max(abs(pfv.cohbop), abs(pfv.coheof)):.2e}\t{max(abs(pfv.cohbop), abs(pfv.coheof)) / pfv.rjpfalw[pfv.nohc - 1]:.2e}\t{pfv.wtc[pfv.nohc - 1]:.2e}\t{pfv.wts[pfv.nohc - 1]:.2e}\t{pfv.bpf[pfv.nohc - 1]:.2e}", ) else: op.write( self.outfile, - f"CS\t\t{pfv.ric[pfv.nohc-1]:.2e}\t-1.0e0\t{max(abs(pfv.cohbop)):.2e}\t{abs(pfv.coheof):.2e}\t1.0e0\t{pfv.wtc[pfv.nohc-1]:.2e}\t{pfv.wts[pfv.nohc-1]:.2e}\t{pfv.bpf[pfv.nohc-1]:.2e}", + f"CS\t\t{pfv.ric[pfv.nohc - 1]:.2e}\t-1.0e0\t{max(abs(pfv.cohbop)):.2e}\t{abs(pfv.coheof):.2e}\t1.0e0\t{pfv.wtc[pfv.nohc - 1]:.2e}\t{pfv.wts[pfv.nohc - 1]:.2e}\t{pfv.bpf[pfv.nohc - 1]:.2e}", ) # Miscellaneous totals @@ -2551,12 +2548,12 @@ def outvolt(self): for k in range(pf.nef): op.write( self.outfile, - f"\t{k}\t\t\t{pf.vsdum[k,0]:.3f}\t\t\t{pf.vsdum[k,1]:.3f}\t\t{pf.vsdum[k,2]:.3f}", + f"\t{k}\t\t\t{pf.vsdum[k, 0]:.3f}\t\t\t{pf.vsdum[k, 1]:.3f}\t\t{pf.vsdum[k, 2]:.3f}", ) op.write( self.outfile, - f"\tCS coil\t\t\t{pf.vsdum[pfv.nohc-1,0]:.3f}\t\t\t{pf.vsdum[pfv.nohc-1,1]:.3f}\t\t{pf.vsdum[pfv.nohc-1,2]:.3f}", + f"\tCS coil\t\t\t{pf.vsdum[pfv.nohc - 1, 0]:.3f}\t\t\t{pf.vsdum[pfv.nohc - 1, 1]:.3f}\t\t{pf.vsdum[pfv.nohc - 1, 2]:.3f}", ) op.oshead(self.outfile, "Waveforms") @@ -2580,12 +2577,12 @@ def outvolt(self): for k in range(pfv.ncirt - 1): line = f"\t{k}\t\t" for jj in range(6): - line += f"\t{pfv.cpt[k,jj]*pfv.turns[k]:.3e}" + line += f"\t{pfv.cpt[k, jj] * pfv.turns[k]:.3e}" op.write(self.outfile, line) line = "Plasma (A)\t\t" for jj in range(6): - line += f"\t{pfv.cpt[pfv.ncirt-1,jj]:.3e}" + line += f"\t{pfv.cpt[pfv.ncirt - 1, jj]:.3e}" op.write(self.outfile, line) @@ -2595,12 +2592,12 @@ def outvolt(self): op.write( self.outfile, ( - f"{k}\t\t\t{pfv.cpt[k,0]*pfv.turns[k]:.3e}\t" - f"{pfv.cpt[k,1]*pfv.turns[k]:.3e}\t" - f"{-pfv.cpt[k,1]*pfv.turns[k]*(pfv.fcohbof/pfv.fcohbop):.3e}\t" - f"{-pfv.cpt[k,1]*pfv.turns[k]*(pfv.fcohbof/pfv.fcohbop):.3e}\t" - f"{-pfv.cpt[k,1]*pfv.turns[k]*(1.0e0/pfv.fcohbop):.3e}\t" - f"{pfv.cpt[k,5]*pfv.turns[k]:.3e}" + f"{k}\t\t\t{pfv.cpt[k, 0] * pfv.turns[k]:.3e}\t" + f"{pfv.cpt[k, 1] * pfv.turns[k]:.3e}\t" + f"{-pfv.cpt[k, 1] * pfv.turns[k] * (pfv.fcohbof / pfv.fcohbop):.3e}\t" + f"{-pfv.cpt[k, 1] * pfv.turns[k] * (pfv.fcohbof / pfv.fcohbop):.3e}\t" + f"{-pfv.cpt[k, 1] * pfv.turns[k] * (1.0e0 / pfv.fcohbop):.3e}\t" + f"{pfv.cpt[k, 5] * pfv.turns[k]:.3e}" ), ) @@ -2611,9 +2608,9 @@ def outvolt(self): self.outfile, ( f"{k}\t\t\t{0.0:.3e}\t{0.0:.3e}\t" - f"{(pfv.cpt[k,2]+pfv.cpt[k,1]*pfv.fcohbof/pfv.fcohbop)*pfv.turns[k]:.3e}\t" - f"{(pfv.cpt[k,3]+pfv.cpt[k,1]*pfv.fcohbof/pfv.fcohbop)*pfv.turns[k]:.3e}\t" - f"{(pfv.cpt[k,4]+pfv.cpt[k,1]*1.0e0/pfv.fcohbop)*pfv.turns[k]:.3e}\t" + f"{(pfv.cpt[k, 2] + pfv.cpt[k, 1] * pfv.fcohbof / pfv.fcohbop) * pfv.turns[k]:.3e}\t" + f"{(pfv.cpt[k, 3] + pfv.cpt[k, 1] * pfv.fcohbof / pfv.fcohbop) * pfv.turns[k]:.3e}\t" + f"{(pfv.cpt[k, 4] + pfv.cpt[k, 1] * 1.0e0 / pfv.fcohbop) * pfv.turns[k]:.3e}\t" "0.0e0" ), ) @@ -2926,7 +2923,6 @@ def superconpf( or (isumat == 8) or (isumat == 9) ): # Find temperature at which current density margin = 0 - if isumat == 3: arguments = (isumat, jsc, bmax, strain, bc20m, tc0m, c0) else: diff --git a/process/physics.py b/process/physics.py index 026927e79..d62451a8d 100644 --- a/process/physics.py +++ b/process/physics.py @@ -1,34 +1,36 @@ import math from typing import Tuple + +import numba as nb import numpy as np import scipy -import numba as nb -from scipy.optimize import root_scalar -from process.utilities.f2py_string_patch import f2py_compatible_to_string - import scipy.integrate as integrate +from scipy.optimize import root_scalar -import process.physics_functions as physics_funcs import process.impurity_radiation as impurity_radiation +import process.physics_functions as physics_funcs from process.fortran import ( - constraint_variables, - reinke_variables, - reinke_module, - impurity_radiation_module, + build_variables, constants, - physics_variables, - physics_module, - pulse_variables, - times_variables, + constraint_variables, current_drive_variables, + divertor_variables, error_handling, fwbs_variables, - build_variables, - divertor_variables, + impurity_radiation_module, numerics, + physics_module, + physics_variables, + pulse_variables, + reinke_module, + reinke_variables, stellarator_variables, + times_variables, +) +from process.fortran import ( process_output as po, ) +from process.utilities.f2py_string_patch import f2py_compatible_to_string @nb.jit(nopython=True, cache=True) @@ -109,9 +111,7 @@ def vscalc( aeps = (1.0 + 1.81 * np.sqrt(eps) + 2.05 * eps) * np.log(8.0 / eps) - ( 2.0 + 9.25 * np.sqrt(eps) - 1.21 * eps ) - beps = ( - 0.73 * np.sqrt(eps) * (1.0 + 2.0 * eps**4 - 6.0 * eps**5 + 3.7 * eps**6) - ) + beps = 0.73 * np.sqrt(eps) * (1.0 + 2.0 * eps**4 - 6.0 * eps**5 + 3.7 * eps**6) rlpext = rmajor * rmu0 * aeps * (1.0 - eps) / (1.0 - eps + beps * kappa) rlp = rlpext + rlpint @@ -422,12 +422,7 @@ def calculate_current_coefficient_todd( base_scaling = ( (1.0 + 2.0 * eps**2) * ((1.0 + kappa95**2) / 0.5) - * ( - 1.24 - - 0.54 * kappa95 - + 0.3 * (kappa95**2 + triang95**2) - + 0.125 * triang95 - ) + * (1.24 - 0.54 * kappa95 + 0.3 * (kappa95**2 + triang95**2) + 0.125 * triang95) ) if model == 1: return base_scaling @@ -497,9 +492,9 @@ def calculate_current_coefficient_hastie( eprime = er * lamp1 / (1.0 + lamda / 3.0) # Delta primed in AEA FUS 172 - deltap = (0.5 * kap1 * eps * 0.5 * li) + ( - beta0 / (0.5 * kap1 * eps) - ) * lamp1**2 / (1.0 + nu) + deltap = (0.5 * kap1 * eps * 0.5 * li) + (beta0 / (0.5 * kap1 * eps)) * lamp1**2 / ( + 1.0 + nu + ) # Delta/R0 in AEA FUS 172 deltar = beta0 / 6.0 * (1.0 + 5.0 * lamda / 6.0 + 0.25 * lamda**2) + ( @@ -631,11 +626,7 @@ def _nevins_integral( # Compute average electron beta betae = ( - dene - * te - * 1.0e3 - * constants.electron_charge - / (bt**2 / (2.0 * constants.rmu0)) + dene * te * 1.0e3 * constants.electron_charge / (bt**2 / (2.0 * constants.rmu0)) ) nabla = rminor * np.sqrt(y) / rmajor @@ -1042,15 +1033,13 @@ def _calculate_l31_32_coefficient( # $f^{32\_ee}_{teff}(\nu_{e*})$, Eq.15d f32ee_teff = f_trapped / ( - ( - 1.0 - + 0.26 * (1.0 - f_trapped) * np.sqrt(electron_collisionality) - + ( - 0.18 - * (1.0 - 0.37 * f_trapped) - * electron_collisionality - / np.sqrt(charge_profile) - ) + 1.0 + + 0.26 * (1.0 - f_trapped) * np.sqrt(electron_collisionality) + + ( + 0.18 + * (1.0 - 0.37 * f_trapped) + * electron_collisionality + / np.sqrt(charge_profile) ) ) @@ -1074,11 +1063,7 @@ def _calculate_l31_32_coefficient( * (f32ee_teff - f32ee_teff**4) ) + ( - ( - f32ee_teff**2 - - f32ee_teff**4 - - 1.2 * (f32ee_teff**3 - f32ee_teff**4) - ) + (f32ee_teff**2 - f32ee_teff**4 - 1.2 * (f32ee_teff**3 - f32ee_teff**4)) / (1.0 + 0.22 * charge_profile) ) + (1.2 / (1.0 + 0.5 * charge_profile) * f32ee_teff**4) @@ -1095,11 +1080,7 @@ def _calculate_l31_32_coefficient( + ( 4.95 / (1.0 + 2.48 * charge_profile) - * ( - f32ei_teff**2 - - f32ei_teff**4 - - 0.55 * (f32ei_teff**3 - f32ei_teff**4) - ) + * (f32ei_teff**2 - f32ei_teff**4 - 0.55 * (f32ei_teff**3 - f32ei_teff**4)) ) - (1.2 / (1.0 + 0.5 * charge_profile) * f32ei_teff**4) ) @@ -1227,11 +1208,9 @@ def _calculate_l34_alpha_31_coefficient( # $\alpha(\nu_{i*})$, Eq.17b alpha = ( - ( - (alpha_0 + (0.25 * (1.0 - f_trapped**2)) * np.sqrt(ion_collisionality)) - / (1.0 + (0.5 * np.sqrt(ion_collisionality))) - + (0.315 * ion_collisionality**2 * f_trapped**6) - ) + (alpha_0 + (0.25 * (1.0 - f_trapped**2)) * np.sqrt(ion_collisionality)) + / (1.0 + (0.5 * np.sqrt(ion_collisionality))) + + (0.315 * ion_collisionality**2 * f_trapped**6) ) / (1.0 + (0.15 * ion_collisionality**2 * f_trapped**6)) # Corrections suggested by Fable, 15/05/2015 @@ -1470,8 +1449,7 @@ def _trapped_particle_fraction_sauter( # Similar to, but not quite identical to above return 1.0 - ( - ((1.0 - eps) ** 2) - / ((1.0 + 1.46 * sqeps_reduced) * np.sqrt(1.0 - eps**2)) + ((1.0 - eps) ** 2) / ((1.0 + 1.46 * sqeps_reduced) * np.sqrt(1.0 - eps**2)) ) elif fit == 2: @@ -1576,9 +1554,7 @@ def physics(self): # *************************** # physics_variables.beta_toroidal = ( - physics_variables.beta - * physics_variables.btot**2 - / physics_variables.bt**2 + physics_variables.beta * physics_variables.btot**2 / physics_variables.bt**2 ) # Calculate physics_variables.beta poloidal [-] @@ -2418,10 +2394,14 @@ def physics(self): else: # Single null configuration - including SoL radaition physics_variables.photon_wall = ( - 1.0e0 - fwbs_variables.fhcd - fwbs_variables.fdiv - ) * physics_variables.pradmw / build_variables.fwarea + ( - 1.0e0 - fwbs_variables.fhcd - fwbs_variables.fdiv - ) * physics_variables.rad_fraction_sol * physics_variables.pdivt / build_variables.fwarea + (1.0e0 - fwbs_variables.fhcd - fwbs_variables.fdiv) + * physics_variables.pradmw + / build_variables.fwarea + + (1.0e0 - fwbs_variables.fhcd - fwbs_variables.fdiv) + * physics_variables.rad_fraction_sol + * physics_variables.pdivt + / build_variables.fwarea + ) constraint_variables.peakradwallload = ( physics_variables.photon_wall * constraint_variables.peakfactrad @@ -2609,18 +2589,14 @@ def calculate_density_limit( # This applies to the density at the plasma edge, so must be scaled # to give the density limit applying to the average plasma density. - dlimit[0] = ( - 1.54e20 * p_perp**0.43 * bt**0.31 / (q95 * rmajor) ** 0.45 - ) / prn1 + dlimit[0] = (1.54e20 * p_perp**0.43 * bt**0.31 / (q95 * rmajor) ** 0.45) / prn1 # Borrass density limit model for ITER (I) # This applies to the density at the plasma edge, so must be scaled # to give the density limit applying to the average plasma density. # Borrass et al, ITER-TN-PH-9-6 (1989) - dlimit[1] = ( - 1.8e20 * p_perp**0.53 * bt**0.31 / (q95 * rmajor) ** 0.22 - ) / prn1 + dlimit[1] = (1.8e20 * p_perp**0.53 * bt**0.31 / (q95 * rmajor) ** 0.22) / prn1 # Borrass density limit model for ITER (II) # This applies to the density at the plasma edge, so must be scaled @@ -2628,9 +2604,7 @@ def calculate_density_limit( # This formula is (almost) identical to that in the original routine # denlim (now deleted). - dlimit[2] = ( - 0.5e20 * p_perp**0.57 * bt**0.31 / (q95 * rmajor) ** 0.09 - ) / prn1 + dlimit[2] = (0.5e20 * p_perp**0.57 * bt**0.31 / (q95 * rmajor) ** 0.09) / prn1 # JET edge radiation density limit model # This applies to the density at the plasma edge, so must be scaled @@ -4001,9 +3975,9 @@ def outplas(self): for imp in range(impurity_radiation_module.nimp): # MDK Update fimp, as this will make the ITV output work correctly. - impurity_radiation_module.fimp[ - imp - ] = impurity_radiation_module.impurity_arr_frac[imp] + impurity_radiation_module.fimp[imp] = ( + impurity_radiation_module.impurity_arr_frac[imp] + ) str1 = ( f2py_compatible_to_string( impurity_radiation_module.impurity_arr_label[imp] @@ -5782,41 +5756,37 @@ def bootstrap_fraction_wilson( # Square root of current profile index term saj = np.sqrt(aj) - a = np.array( - [ - 1.41 * (1.0 - 0.28 * saj) * (1.0 + 0.12 / z), - 0.36 * (1.0 - 0.59 * saj) * (1.0 + 0.8 / z), - -0.27 * (1.0 - 0.47 * saj) * (1.0 + 3.0 / z), - 0.0053 * (1.0 + 5.0 / z), - -0.93 * (1.0 - 0.34 * saj) * (1.0 + 0.15 / z), - -0.26 * (1.0 - 0.57 * saj) * (1.0 - 0.27 * z), - 0.064 * (1.0 - 0.6 * aj + 0.15 * aj * aj) * (1.0 + 7.6 / z), - -0.0011 * (1.0 + 9.0 / z), - -0.33 * (1.0 - aj + 0.33 * aj * aj), - -0.26 * (1.0 - 0.87 / saj - 0.16 * aj), - -0.14 * (1.0 - 1.14 / saj - 0.45 * saj), - -0.0069, - ] - ) + a = np.array([ + 1.41 * (1.0 - 0.28 * saj) * (1.0 + 0.12 / z), + 0.36 * (1.0 - 0.59 * saj) * (1.0 + 0.8 / z), + -0.27 * (1.0 - 0.47 * saj) * (1.0 + 3.0 / z), + 0.0053 * (1.0 + 5.0 / z), + -0.93 * (1.0 - 0.34 * saj) * (1.0 + 0.15 / z), + -0.26 * (1.0 - 0.57 * saj) * (1.0 - 0.27 * z), + 0.064 * (1.0 - 0.6 * aj + 0.15 * aj * aj) * (1.0 + 7.6 / z), + -0.0011 * (1.0 + 9.0 / z), + -0.33 * (1.0 - aj + 0.33 * aj * aj), + -0.26 * (1.0 - 0.87 / saj - 0.16 * aj), + -0.14 * (1.0 - 1.14 / saj - 0.45 * saj), + -0.0069, + ]) seps1 = np.sqrt(eps1) - b = np.array( - [ - 1.0, - alfpnw, - alftnw, - alfpnw * alftnw, - seps1, - alfpnw * seps1, - alftnw * seps1, - alfpnw * alftnw * seps1, - eps1, - alfpnw * eps1, - alftnw * eps1, - alfpnw * alftnw * eps1, - ] - ) + b = np.array([ + 1.0, + alfpnw, + alftnw, + alfpnw * alftnw, + seps1, + alfpnw * seps1, + alftnw * seps1, + alfpnw * alftnw * seps1, + eps1, + alfpnw * eps1, + alftnw * eps1, + alfpnw * alftnw * eps1, + ]) # Empirical bootstrap current fraction return seps1 * betpth * (a * b).sum() @@ -5958,8 +5928,7 @@ def bootstrap_fraction_sauter(plasma_profile: float) -> float: # inverse_q = 1/safety factor # Parabolic q profile assumed inverse_q = 1 / ( - physics_variables.q0 - + (physics_variables.q - physics_variables.q0) * roa**2 + physics_variables.q0 + (physics_variables.q - physics_variables.q0) * roa**2 ) # Create new array of average mass of fuel portion of ions amain = np.full_like(inverse_q, physics_variables.afuel) @@ -6746,11 +6715,7 @@ def pcond( * np.sqrt(kappa95) * denfac / powerht**0.4e0 - * ( - zeff**2 - * pcur**4 - / (rmajor * rminor * qstar**3 * kappa95**1.5e0) - ) + * (zeff**2 * pcur**4 / (rmajor * rminor * qstar**3 * kappa95**1.5e0)) ** 0.08e0 ) @@ -7205,9 +7170,7 @@ def pcond( # Table 4. (Issue #311) # Note that aspect ratio and M (afuel) do not appear, and B (bt) only # appears in the "saturation factor" h. - h = dnla19**0.448e0 / ( - 1.0e0 + np.exp(-9.403e0 * (bt / dnla19) ** 1.365e0) - ) + h = dnla19**0.448e0 / (1.0e0 + np.exp(-9.403e0 * (bt / dnla19) ** 1.365e0)) tauee = ( hfact * 0.0367e0 @@ -7529,36 +7492,20 @@ def pthresh(dene, dnla, bt, rmajor, rminor, kappa, sarea, aion, aspect, plasma_c # Snipes et al (2000) scaling with mass correction # Nominal, upper and lower - snipes_2000 = ( - 1.42 * dnla20**0.58 * bt**0.82 * rmajor * rminor**0.81 * (2.0 / aion) - ) + snipes_2000 = 1.42 * dnla20**0.58 * bt**0.82 * rmajor * rminor**0.81 * (2.0 / aion) snipes_2000_ub = ( - 1.547 - * dnla20**0.615 - * bt**0.851 - * rmajor**1.089 - * rminor**0.876 - * (2.0 / aion) + 1.547 * dnla20**0.615 * bt**0.851 * rmajor**1.089 * rminor**0.876 * (2.0 / aion) ) snipes_2000_lb = ( - 1.293 - * dnla20**0.545 - * bt**0.789 - * rmajor**0.911 - * rminor**0.744 - * (2.0 / aion) + 1.293 * dnla20**0.545 * bt**0.789 * rmajor**0.911 * rminor**0.744 * (2.0 / aion) ) # Snipes et al (2000) scaling (closed divertor) with mass correction # Nominal, upper and lower snipes_2000_cd = 0.8 * dnla20**0.5 * bt**0.53 * rmajor**1.51 * (2.0 / aion) - snipes_2000_cd_ub = ( - 0.867 * dnla20**0.561 * bt**0.588 * rmajor**1.587 * (2.0 / aion) - ) - snipes_2000_cd_lb = ( - 0.733 * dnla20**0.439 * bt**0.472 * rmajor**1.433 * (2.0 / aion) - ) + snipes_2000_cd_ub = 0.867 * dnla20**0.561 * bt**0.588 * rmajor**1.587 * (2.0 / aion) + snipes_2000_cd_lb = 0.733 * dnla20**0.439 * bt**0.472 * rmajor**1.433 * (2.0 / aion) # Hubbard et al. 2012 L-I threshold scaling hubbard_2012 = 2.11 * (plasma_current / 1e6) ** 0.94 * dnla20**0.65 diff --git a/process/physics_functions.py b/process/physics_functions.py index b51a76484..79049aafb 100644 --- a/process/physics_functions.py +++ b/process/physics_functions.py @@ -1,11 +1,12 @@ import logging +from dataclasses import dataclass + import numpy as np from scipy import integrate -from dataclasses import dataclass -from process.fortran import physics_variables, physics_module, constants -from process.plasma_profiles import PlasmaProfile -import process.impurity_radiation as impurity +import process.impurity_radiation as impurity +from process.fortran import constants, physics_module, physics_variables +from process.plasma_profiles import PlasmaProfile logger = logging.getLogger(__name__) @@ -722,10 +723,7 @@ def fusion_rate_integral( # Calculate a volume averaged fusion reaction integral that allows for fusion power to be scaled with # just the volume averged ion density. fusion_integral = ( - 2.0 - * plasma_profile.teprofile.profile_x - * sigv - * density_profile_normalised**2 + 2.0 * plasma_profile.teprofile.profile_x * sigv * density_profile_normalised**2 ) return fusion_integral @@ -971,7 +969,6 @@ def fast_alpha_beta( # Determine average fast alpha density if physics_variables.f_deuterium < 1.0: - beta_thermal = ( 2.0 * constants.rmu0 @@ -1508,9 +1505,7 @@ def _hot_beam_fusion_reaction_rate_integrand( beam_velcoity = critical_velocity * velocity_ratio # Calculate the beam kinetic energy per amu and normalise to keV - xvcs = ( - beam_velcoity**2 * constants.atomic_mass_unit / (constants.kiloelectron_volt) - ) + xvcs = beam_velcoity**2 * constants.atomic_mass_unit / (constants.kiloelectron_volt) # Calculate the fusion reaction cross-section from beam kinetic energy cross_section = _beam_fusion_cross_section(xvcs) diff --git a/process/plasma_geometry.py b/process/plasma_geometry.py index db5b6b8e8..be1c1af9d 100644 --- a/process/plasma_geometry.py +++ b/process/plasma_geometry.py @@ -1,8 +1,8 @@ import logging + import numpy -from process.fortran import constants -from process.fortran import build_variables -from process.fortran import physics_variables + +from process.fortran import build_variables, constants, physics_variables logger = logging.getLogger(__name__) @@ -39,7 +39,6 @@ def geomty(self): if ( physics_variables.ishape == 0 ): # Use input kappa, physics_variables.triang values - # Rough estimate of 95% values # ITER Physics Design Guidlines: 1989 (Uckan et al. 1990) # (close to previous estimate of (physics_variables.kappa - 0.04) / 1.1 @@ -51,7 +50,6 @@ def geomty(self): if ( physics_variables.ishape == 1 ): # ST scaling with physics_variables.aspect ratio [STAR Code] - physics_variables.qlim = 3.0e0 * ( 1.0e0 + 2.6e0 * physics_variables.eps**2.8e0 ) @@ -74,7 +72,6 @@ def geomty(self): if ( physics_variables.ishape == 2 ): # Zohm et al. ITER scaling for elongation, input physics_variables.triang - physics_variables.kappa = physics_variables.fkzohm * min( 2.0e0, 1.5e0 + 0.5e0 / (physics_variables.aspect - 1.0e0) ) @@ -86,7 +83,6 @@ def geomty(self): if ( physics_variables.ishape == 3 ): # Zohm et al. ITER scaling for elongation, input physics_variables.triang95 - physics_variables.kappa = physics_variables.fkzohm * min( 2.0e0, 1.5e0 + 0.5e0 / (physics_variables.aspect - 1.0e0) ) @@ -99,7 +95,6 @@ def geomty(self): if ( physics_variables.ishape == 4 ): # Use input kappa95, physics_variables.triang95 values - # ITER Physics Design Guidlines: 1989 (Uckan et al. 1990) physics_variables.kappa = 1.12e0 * physics_variables.kappa95 physics_variables.triang = 1.5e0 * physics_variables.triang95 @@ -107,7 +102,6 @@ def geomty(self): if ( physics_variables.ishape == 5 ): # Use input kappa95, physics_variables.triang95 values - # Fit to MAST data (Issue #1086) physics_variables.kappa = 0.91300e0 * physics_variables.kappa95 + 0.38654e0 physics_variables.triang = ( @@ -117,7 +111,6 @@ def geomty(self): if ( physics_variables.ishape == 6 ): # Use input kappa, physics_variables.triang values - # Fit to MAST data (Issue #1086) physics_variables.kappa95 = ( physics_variables.kappa - 0.38654e0 @@ -129,7 +122,6 @@ def geomty(self): if ( physics_variables.ishape == 7 ): # Use input kappa95, physics_variables.triang95 values - # Fit to FIESTA (Issue #1086) physics_variables.kappa = 0.90698e0 * physics_variables.kappa95 + 0.39467e0 physics_variables.triang = ( @@ -139,7 +131,6 @@ def geomty(self): if ( physics_variables.ishape == 8 ): # Use input kappa, physics_variables.triang values - # Fit to FIESTA (Issue #1086) physics_variables.kappa95 = ( physics_variables.kappa - 0.39467e0 @@ -151,7 +142,6 @@ def geomty(self): if ( physics_variables.ishape == 9 ): # Use input triang, physics_variables.rli values - # physics_variables.kappa found from physics_variables.aspect ratio and plasma internal inductance li(3) physics_variables.kappa = (1.09e0 + 0.26e0 / physics_variables.rli) * ( 1.5e0 / physics_variables.aspect @@ -161,7 +151,6 @@ def geomty(self): physics_variables.triang95 = physics_variables.triang / 1.50e0 if physics_variables.ishape == 10: - # physics_variables.kappa95 found from physics_variables.aspect ratio and stabilty margin # Based on fit to CREATE data. ref Issue #1399 # valid for EU-DEMO like machine - physics_variables.aspect ratio 2.6 - 3.6 @@ -196,7 +185,6 @@ def geomty(self): physics_variables.triang95 = physics_variables.triang / 1.50e0 if physics_variables.ishape == 11: - # See Issue #1439 # physics_variables.triang is an input # physics_variables.kappa found from physics_variables.aspect ratio scaling on p32 of Menard: @@ -249,7 +237,6 @@ def geomty(self): ) else: - # Poloidal perimeter physics_variables.pperim = 2.0e0 * (xo * thetao + xi * thetai) physics_variables.sf = physics_variables.pperim / ( @@ -456,9 +443,9 @@ def xsecta(self, xi, thetai, xo, thetao): F/MI/PJK/LOGBOOK14, p.41 """ - xsecta = xo**2 * ( - thetao - numpy.cos(thetao) * numpy.sin(thetao) - ) + xi**2 * (thetai - numpy.cos(thetai) * numpy.sin(thetai)) + xsecta = xo**2 * (thetao - numpy.cos(thetao) * numpy.sin(thetao)) + xi**2 * ( + thetai - numpy.cos(thetai) * numpy.sin(thetai) + ) return xsecta diff --git a/process/plasma_profiles.py b/process/plasma_profiles.py index 0322576da..4199e480a 100644 --- a/process/plasma_profiles.py +++ b/process/plasma_profiles.py @@ -1,8 +1,9 @@ import logging + import numpy as np import scipy as sp -import process.profiles as profiles +import process.profiles as profiles from process.fortran import ( constants, divertor_variables, @@ -322,7 +323,7 @@ def calculate_parabolic_profile_factors() -> None: * (1 - rho_te_max**2) ** physics_variables.alphat ) else: - raise ValueError(f"alphat is negative: { physics_variables.alphat}") + raise ValueError(f"alphat is negative: {physics_variables.alphat}") # Same for density if physics_variables.alphan > 1.0: @@ -357,7 +358,7 @@ def calculate_parabolic_profile_factors() -> None: * (1 - rho_ne_max**2) ** physics_variables.alphan ) else: - raise ValueError(f"alphan is negative: { physics_variables.alphan}") + raise ValueError(f"alphan is negative: {physics_variables.alphan}") # set normalized gradient length # te at rho_te_max diff --git a/process/power.py b/process/power.py index fabeeeeee..98525bc39 100644 --- a/process/power.py +++ b/process/power.py @@ -1,24 +1,28 @@ import logging import math + import numpy -from process.fortran import constants + +from process.fortran import ( + build_variables, + buildings_variables, + constants, + constraint_variables, + cost_variables, + current_drive_variables, + error_handling, + fwbs_variables, + heat_transport_variables, + numerics, + pf_power_variables, + pfcoil_variables, + physics_variables, + primary_pumping_variables, + structure_variables, + tfcoil_variables, + times_variables, +) from process.fortran import process_output as po -from process.fortran import physics_variables -from process.fortran import pfcoil_variables -from process.fortran import build_variables -from process.fortran import pf_power_variables -from process.fortran import times_variables -from process.fortran import heat_transport_variables -from process.fortran import numerics -from process.fortran import buildings_variables -from process.fortran import fwbs_variables -from process.fortran import primary_pumping_variables -from process.fortran import current_drive_variables -from process.fortran import tfcoil_variables -from process.fortran import structure_variables -from process.fortran import cost_variables -from process.fortran import constraint_variables -from process.fortran import error_handling from process.variables import AnnotatedVariable logger = logging.getLogger(__name__) @@ -165,7 +169,6 @@ def pfpwr(self, output: bool): jpf = jpf + 1 inductxcurrent[:] = 0.0e0 for ipf in range(0, pfcoil_variables.ncirt): - # Voltage in circuit jpf due to change in current from circuit ipf vpfij = ( pfcoil_variables.sxlg[jpf, ipf] @@ -281,7 +284,6 @@ def pfpwr(self, output: bool): pf_power_variables.spsmva = 0.0e0 for jpf in range(0, pfcoil_variables.ncirt - 1): - # Power supply MVA for each PF circuit psmva[jpf] = 1.0e-6 * abs(vpfi[jpf] * pfcoil_variables.cptdin[jpf]) @@ -384,7 +386,7 @@ def pfpwr(self, output: bool): if any(poloidalenergy < 0.0e0): po.oheadr(self.outfile, "ERROR Negative stored energy in poloidal field") - logger.error(f'{"ERROR Negative stored energy in poloidal field"}') + logger.error(f"{'ERROR Negative stored energy in poloidal field'}") po.ocmmnt(self.outfile, "Energy stored in poloidal magnetic field :") po.oblnkl(self.outfile) @@ -609,7 +611,6 @@ def power1(self): # Calculate total deposited power (MW), n.b. energy multiplication in pnucblkt already if fwbs_variables.primary_pumping == 2: - # Liquid metal breeder/coolant if fwbs_variables.icooldual == 2: self.pthermblkt_liq = ( @@ -653,7 +654,6 @@ def power1(self): ) elif fwbs_variables.primary_pumping == 3: - # First wall and blanket coolant combined self.pthermfw_blkt = ( fwbs_variables.pnucfw @@ -666,7 +666,6 @@ def power1(self): ) else: - # Total power deposited in first wall coolant (MW) self.pthermfw = ( fwbs_variables.pnucfw @@ -735,7 +734,7 @@ def power1(self): self.iprimdiv = 1 if abs(heat_transport_variables.pthermmw) < 1.0e-4: - logger.error(f'{"ERROR Primary thermal power is zero or negative"}') + logger.error(f"{'ERROR Primary thermal power is zero or negative'}") # #284 Fraction of total high-grade thermal power to divertor self.pdivfraction = self.pthermdiv / heat_transport_variables.pthermmw @@ -783,7 +782,6 @@ def power1(self): # Superconductors TF/PF cryogenic cooling if tfcoil_variables.i_tf_sup == 1 or pfcoil_variables.ipfres == 0: - # heat_transport_variables.helpow calculation heat_transport_variables.helpow = self.cryo( tfcoil_variables.i_tf_sup, @@ -895,7 +893,6 @@ def power2(self, output: bool): # Calculate powers relevant to a power-producing plant if cost_variables.ireactor == 1: - # Gross electric power # pgrossmw = (heat_transport_variables.pthermmw-hthermmw) * heat_transport_variables.etath if fwbs_variables.icooldual > 0 and fwbs_variables.primary_pumping == 2: @@ -1357,7 +1354,7 @@ def power2(self, output: bool): po.write( self.outfile, ( - f"{fwbs_variables.pnucshld*heat_transport_variables.iprimshld} {fwbs_variables.pnucshld*(1-heat_transport_variables.iprimshld)} {fwbs_variables.pnucshld}" + f"{fwbs_variables.pnucshld * heat_transport_variables.iprimshld} {fwbs_variables.pnucshld * (1 - heat_transport_variables.iprimshld)} {fwbs_variables.pnucshld}" ), ) po.write(self.outfile, "0.0e0 0.0e0 0.0e0") @@ -1365,7 +1362,7 @@ def power2(self, output: bool): po.write( self.outfile, ( - f"{heat_transport_variables.htpmw_shld*heat_transport_variables.iprimshld} {heat_transport_variables.htpmw_shld*(1-heat_transport_variables.iprimshld)} {heat_transport_variables.htpmw_shld}" + f"{heat_transport_variables.htpmw_shld * heat_transport_variables.iprimshld} {heat_transport_variables.htpmw_shld * (1 - heat_transport_variables.iprimshld)} {heat_transport_variables.htpmw_shld}" ), ) @@ -1387,25 +1384,25 @@ def power2(self, output: bool): po.write( self.outfile, ( - f"{fwbs_variables.pnucdiv*self.iprimdiv} {fwbs_variables.pnucdiv*(1-self.iprimdiv)} {fwbs_variables.pnucdiv}" + f"{fwbs_variables.pnucdiv * self.iprimdiv} {fwbs_variables.pnucdiv * (1 - self.iprimdiv)} {fwbs_variables.pnucdiv}" ), ) po.write( self.outfile, ( - f"{physics_variables.pdivt*self.iprimdiv} {physics_variables.pdivt*(1-self.iprimdiv)} {physics_variables.pdivt}" + f"{physics_variables.pdivt * self.iprimdiv} {physics_variables.pdivt * (1 - self.iprimdiv)} {physics_variables.pdivt}" ), ) po.write( self.outfile, ( - f"{fwbs_variables.praddiv*self.iprimdiv} {fwbs_variables.praddiv*(1-self.iprimdiv)} {fwbs_variables.praddiv}" + f"{fwbs_variables.praddiv * self.iprimdiv} {fwbs_variables.praddiv * (1 - self.iprimdiv)} {fwbs_variables.praddiv}" ), ) po.write( self.outfile, ( - f"{heat_transport_variables.htpmw_div*self.iprimdiv} {heat_transport_variables.htpmw_div*(1-self.iprimdiv)} {heat_transport_variables.htpmw_div}" + f"{heat_transport_variables.htpmw_div * self.iprimdiv} {heat_transport_variables.htpmw_div * (1 - self.iprimdiv)} {heat_transport_variables.htpmw_div}" ), ) @@ -1457,7 +1454,7 @@ def power2(self, output: bool): po.oblnkl(self.outfile) # write(self.outfile,'(t10,a)') repeat('-',88) - po.write(self.outfile, (f"{primsum} {secsum} {primsum+secsum}")) + po.write(self.outfile, (f"{primsum} {secsum} {primsum + secsum}")) # 10 format(t32,'neutrons',t50,f8.2,t70,f8.2,t90,f8.2) # 20 format(t14,'charged particle transport',t50,f8.2,t70,f8.2,t90,f8.2) # 30 format(t31,'radiation',t50,f8.2,t70,f8.2,t90,f8.2) @@ -1627,7 +1624,7 @@ def power2(self, output: bool): sum = physics_variables.pscalingmw else: logger.error( - f'{"The value of physics_variables.iradloss appears to be invalid."}' + f"{'The value of physics_variables.iradloss appears to be invalid.'}" ) po.ocmmnt( self.outfile, @@ -1696,7 +1693,7 @@ def power2(self, output: bool): > 5.0e0 ): logger.warning( - f'{"WARNING: Power balance across separatrix is in error by more than 5 MW."}' + f"{'WARNING: Power balance across separatrix is in error by more than 5 MW.'}" ) po.ocmmnt( self.outfile, @@ -1807,7 +1804,7 @@ def power2(self, output: bool): > 5.0e0 ): logger.warning( - f'{"WARNING: Power balance for reactor is in error by more than 5 MW."}' + f"{'WARNING: Power balance for reactor is in error by more than 5 MW.'}" ) po.ocmmnt( self.outfile, @@ -1925,7 +1922,7 @@ def power2(self, output: bool): po.oblnkl(self.outfile) if abs(sum - heat_transport_variables.pgrossmw) > 5.0e0: logger.warning( - f'{"WARNING: Electrical Power balance is in error by more than 5 MW."}' + f"{'WARNING: Electrical Power balance is in error by more than 5 MW.'}" ) po.ocmmnt( self.outfile, @@ -1994,7 +1991,7 @@ def power2(self, output: bool): > 5.0e0 ): logger.warning( - f'{"WARNING: Power balance for power plant is in error by more than 5 MW."}' + f"{'WARNING: Power balance for power plant is in error by more than 5 MW.'}" ) po.ocmmnt( self.outfile, @@ -2338,7 +2335,6 @@ def plant_thermal_efficiency(self, etath): New Power Module Harrington Cycle correlations Cycle correlations.xls """ if fwbs_variables.secondary_cycle == 0: - # CCFE HCPB Model (with or without TBR) if (fwbs_variables.iblanket == 1) or (fwbs_variables.iblanket == 3): # HCPB, efficiency taken from M. Kovari 2016 @@ -2355,11 +2351,10 @@ def plant_thermal_efficiency(self, etath): # Feedheat & reheat cycle assumed etath = 0.411e0 else: - logger.log(f'{"iblanket does not have a value in range 1-3."}') + logger.log(f"{'iblanket does not have a value in range 1-3.'}") # Etath from reference. Div power to primary elif fwbs_variables.secondary_cycle == 1: - # CCFE HCPB Model (with or without TBR) if (fwbs_variables.iblanket == 1) or (fwbs_variables.iblanket == 3): # HCPB, efficiency taken from M. Kovari 2016 @@ -2372,7 +2367,7 @@ def plant_thermal_efficiency(self, etath): elif fwbs_variables.iblanket == 2: etath = 0.411e0 - self.delta_eta else: - logger.log(f'{"iblanket does not have a value in range 1-3."}') + logger.log(f"{'iblanket does not have a value in range 1-3.'}") # User input used, etath not changed elif fwbs_variables.secondary_cycle == 2: @@ -2381,7 +2376,6 @@ def plant_thermal_efficiency(self, etath): # Steam Rankine cycle to be used elif fwbs_variables.secondary_cycle == 3: - # CCFE HCPB Model (with or without TBR) if (fwbs_variables.iblanket == 1) or (fwbs_variables.iblanket == 3): # If coolant is helium, the steam cycle is assumed to be superheated @@ -2425,7 +2419,7 @@ def plant_thermal_efficiency(self, etath): - self.delta_eta ) else: - logger.log(f'{"iblanket does not have a value in range 1-3."}') + logger.log(f"{'iblanket does not have a value in range 1-3.'}") # Supercritical CO2 cycle to be used elif fwbs_variables.secondary_cycle == 4: @@ -2450,7 +2444,7 @@ def plant_thermal_efficiency(self, etath): else: logger.log( - f'{"secondary_cycle does not appear to have a value within its range (0-4)"}' + f"{'secondary_cycle does not appear to have a value within its range (0-4)'}" ) return etath @@ -2556,7 +2550,6 @@ def tfpwr(self, output: bool): ) else: # Superconducting TF coil option - self.tfpwcall(output) return @@ -2826,7 +2819,6 @@ def tfcpwr(self, output: bool, itfka, rmajor, ntfc, vtfskv, ettfmj, rptfc): # Output section if output: - po.oheadr(self.outfile, "Superconducting TF Coil Power Conversion") po.ovarre(self.outfile, "TF coil current (kA)", "(itfka)", itfka, "OP ") po.ovarre(self.outfile, "Number of TF coils", "(ntfc)", ntfc) diff --git a/process/profiles.py b/process/profiles.py index 5ec5c4ab1..570d99302 100644 --- a/process/profiles.py +++ b/process/profiles.py @@ -1,9 +1,10 @@ -import numpy as np import logging -import scipy as sp from abc import ABC, abstractmethod -from process.fortran import physics_variables, error_handling +import numpy as np +import scipy as sp + +from process.fortran import error_handling, physics_variables logger = logging.getLogger(__name__) # Logging handler for console output @@ -171,7 +172,6 @@ def calculate_profile_y( def ncore( rhopedn: float, nped: float, nsep: float, nav: float, alphan: float ) -> float: - """ This routine calculates the core density of a pedestalised profile. The solution comes from integrating and summing the two separate density profiles for the core diff --git a/process/pulse.py b/process/pulse.py index 8beb6978c..1fcc4fbde 100755 --- a/process/pulse.py +++ b/process/pulse.py @@ -1,13 +1,15 @@ -from process.fortran import physics_variables -from process.fortran import times_variables -from process.fortran import pfcoil_variables -from process.fortran import constraint_variables -from process.fortran import constants -from process.fortran import pulse_variables -from process.fortran import numerics -from process.fortran import pf_power_variables +from process.fortran import ( + constants, + constraint_variables, + error_handling, + numerics, + pf_power_variables, + pfcoil_variables, + physics_variables, + pulse_variables, + times_variables, +) from process.fortran import process_output as po -from process.fortran import error_handling class Pulse: @@ -170,7 +172,6 @@ def burn(self, output: bool): # Output section if output: - po.osubhd(self.outfile, "Volt-second considerations:") po.ovarre( diff --git a/process/scan.py b/process/scan.py index 56689fd73..e5d066226 100644 --- a/process/scan.py +++ b/process/scan.py @@ -1,9 +1,8 @@ -from process.fortran import error_handling -from process.fortran import scan_module -from process.fortran import numerics -from process.optimiser import Optimiser import numpy as np + from process.caller import write_output_files +from process.fortran import error_handling, numerics, scan_module +from process.optimiser import Optimiser class Scan: @@ -110,13 +109,13 @@ def scan_1d(self): if scan_1d_ifail_dict[iscan] == 1: converged_count += 1 print( - f"Scan {iscan:02d}: {nsweep_var_name} = {sweep_values[iscan-1]} " + f"Scan {iscan:02d}: {nsweep_var_name} = {sweep_values[iscan - 1]} " + " " * offsets[iscan - 1] + "\u001b[32mCONVERGED \u001b[0m" ) else: print( - f"Scan {iscan:02d}: {nsweep_var_name} = {sweep_values[iscan-1]} " + f"Scan {iscan:02d}: {nsweep_var_name} = {sweep_values[iscan - 1]} " + " " * offsets[iscan - 1] + "\u001b[31mUNCONVERGED \u001b[0m" ) @@ -197,14 +196,14 @@ def scan_2d(self): if scan_2d_ifail_list[iscan_1][iscan_2] == 1: converged_count += 1 print( - f"Scan {scan_point:02d}: ({nsweep_var_name} = {sweep_1_values[iscan_1-1]}, {nsweep_2_var_name} = {sweep_2_values[iscan_2-1]}) " + f"Scan {scan_point:02d}: ({nsweep_var_name} = {sweep_1_values[iscan_1 - 1]}, {nsweep_2_var_name} = {sweep_2_values[iscan_2 - 1]}) " + " " * offsets[iscan_1 - 1][iscan_2 - 1] + "\u001b[32mCONVERGED \u001b[0m" ) scan_point += 1 else: print( - f"Scan {scan_point:02d}: ({nsweep_var_name} = {sweep_1_values[iscan_1-1]}, {nsweep_2_var_name} = {sweep_2_values[iscan_2-1]}) " + f"Scan {scan_point:02d}: ({nsweep_var_name} = {sweep_1_values[iscan_1 - 1]}, {nsweep_2_var_name} = {sweep_2_values[iscan_2 - 1]}) " + " " * offsets[iscan_1 - 1][iscan_2 - 1] + "\u001b[31mUNCONVERGED \u001b[0m" ) diff --git a/process/sctfcoil.py b/process/sctfcoil.py index 513bd222b..906468dfe 100644 --- a/process/sctfcoil.py +++ b/process/sctfcoil.py @@ -1,28 +1,28 @@ +import copy import json -import numpy import logging -import copy -import numba -from process.fortran import rebco_variables -from process.fortran import global_variables -from process.fortran import tfcoil_variables -from process.fortran import physics_variables -from process.fortran import build_variables -from process.fortran import constants -from process.fortran import sctfcoil_module -from process.fortran import process_output as po -from process.fortran import error_handling -from process.fortran import fwbs_variables -from process.fortran import pfcoil_variables -from process.fortran import numerics -from process.fortran import divertor_variables +import numba +import numpy +from scipy import optimize import process.superconductors as superconductors - +from process.fortran import ( + build_variables, + constants, + divertor_variables, + error_handling, + fwbs_variables, + global_variables, + numerics, + pfcoil_variables, + physics_variables, + rebco_variables, + sctfcoil_module, + tfcoil_variables, +) +from process.fortran import process_output as po from process.utilities.f2py_string_patch import f2py_compatible_to_string -from scipy import optimize - logger = logging.getLogger(__name__) @@ -46,7 +46,7 @@ def run(self, output: bool): ) if tfcoil_variables.i_tf_sc_mat == 6: - (tfcoil_variables.jwdgcrt, tfcoil_variables.tmargtf,) = self.supercon_croco( + (tfcoil_variables.jwdgcrt, tfcoil_variables.tmargtf) = self.supercon_croco( aturn, tfcoil_variables.bmaxtfrp, tfcoil_variables.cpttf, @@ -852,7 +852,6 @@ def supercon( or (isumat == 8) or (isumat == 9) ): # Find temperature at which current density margin = 0 - if isumat == 3: arguments = (isumat, jsc, bmax, strain, bc20m, tc0m, c0) else: @@ -1493,8 +1492,7 @@ def tf_global_geometry(self): if tfcoil_variables.i_tf_case_geom == 0: # Circular front case tfcoil_variables.tfareain = numpy.pi * ( - build_variables.r_tf_inboard_out**2 - - build_variables.r_tf_inboard_in**2 + build_variables.r_tf_inboard_out**2 - build_variables.r_tf_inboard_in**2 ) else: # Straight front case @@ -1974,9 +1972,9 @@ def cpost( """ yy_ins = numpy.zeros((101,)) # Exact conductor area (to be integrated) yy_cond = numpy.zeros((101,)) # Turn insulation area (to be integrated) - yy_gr_ins = numpy.zeros( - (101,) - ) # Outter ground insulation area (to be integrated) + yy_gr_ins = numpy.zeros(( + 101, + )) # Outter ground insulation area (to be integrated) yy_casout = numpy.zeros((101,)) # Outter case area (to be integrated) rtop = r_cp_top - cas_out_th - gr_ins_th @@ -3115,10 +3113,7 @@ def sc_tf_internal_geom(self, i_tf_wp_geom, i_tf_case_geom, i_tf_turns_integer): # ------------------- # Central helium channel down the conductor core [m2] tfcoil_variables.awphec = ( - 0.25e0 - * tfcoil_variables.n_tf_turn - * numpy.pi - * tfcoil_variables.dhecoil**2 + 0.25e0 * tfcoil_variables.n_tf_turn * numpy.pi * tfcoil_variables.dhecoil**2 ) # Total conductor cross-sectional area, taking account of void area @@ -3778,7 +3773,7 @@ def stresscl( # [EDIT: eyoung_cond is for the TF coil, not the CS coil] # Get transverse properties - (eyoung_trans[0], a_working, poisson_trans[0],) = eyoung_parallel( + (eyoung_trans[0], a_working, poisson_trans[0]) = eyoung_parallel( eyoung_steel, oh_steel_frac, poisson_steel, @@ -3944,7 +3939,7 @@ def stresscl( ) # Lateral casing correction (series-composition) - (eyoung_wp_trans_eff, a_working, poisson_wp_trans_eff,) = eyoung_series( + (eyoung_wp_trans_eff, a_working, poisson_wp_trans_eff) = eyoung_series( eyoung_wp_trans, numpy.double(t_wp_toroidal_av), poisson_wp_trans, @@ -3979,7 +3974,7 @@ def stresscl( poisson_member_array[4] = poisson_steel l_member_array[4] = awpc - acond - a_tf_ins - aswp # Compute the composite / smeared properties: - (eyoung_wp_axial, a_working, poisson_wp_axial,) = eyoung_parallel_array( + (eyoung_wp_axial, a_working, poisson_wp_axial) = eyoung_parallel_array( 5, eyoung_member_array, l_member_array, @@ -3988,7 +3983,7 @@ def stresscl( # Average WP Young's modulus in the vertical direction, now including the lateral case # Parallel-composite the steel and insulation, now including the lateral case (sidewalls) - (eyoung_wp_axial_eff, a_working, poisson_wp_axial_eff,) = eyoung_parallel( + (eyoung_wp_axial_eff, a_working, poisson_wp_axial_eff) = eyoung_parallel( eyoung_steel, a_wp_steel_eff - aswp, poisson_steel, @@ -4020,7 +4015,7 @@ def stresscl( # Effective conductor region young modulus in the vertical direction [Pa] # Parallel-composite conductor and insulator - (eyoung_wp_axial, a_working, poisson_wp_axial,) = eyoung_parallel( + (eyoung_wp_axial, a_working, poisson_wp_axial) = eyoung_parallel( eyoung_cond, (a_wp_eff - a_tf_ins) * (1.0e0 - fcoolcp), poisson_cond, @@ -4029,7 +4024,7 @@ def stresscl( poisson_ins, ) # Parallel-composite cooling pipes into that - (eyoung_wp_axial, a_working, poisson_wp_axial,) = eyoung_parallel( + (eyoung_wp_axial, a_working, poisson_wp_axial) = eyoung_parallel( 0e0, (a_wp_eff - a_tf_ins) * fcoolcp, poisson_cond, @@ -4125,7 +4120,7 @@ def stresscl( if i_tf_stress_model == 1: # Plane stress calculation (SC) [Pa] - (sig_tf_r, sig_tf_t, deflect, radial_array,) = plane_stress( + (sig_tf_r, sig_tf_t, deflect, radial_array) = plane_stress( nu=poisson_trans, rad=radtf, ey=eyoung_trans, @@ -4661,13 +4656,13 @@ def outtf(self, peaktfflag): po.ovarre( constants.mfile, f"TF coil arc point {ii} R (m)", - f"(xarc({ii+1}))", + f"(xarc({ii + 1}))", tfcoil_variables.xarc[ii], ) po.ovarre( constants.mfile, f"TF coil arc point {ii} Z (m)", - f"(yarc({ii+1}))", + f"(yarc({ii + 1}))", tfcoil_variables.yarc[ii], ) @@ -5061,13 +5056,13 @@ def outtf(self, peaktfflag): po.ovarre( self.outfile, - "Conductor axial Young" "s modulus", + "Conductor axial Youngs modulus", "(eyoung_cond_axial)", tfcoil_variables.eyoung_cond_axial, ) po.ovarre( self.outfile, - "Conductor transverse Young" "s modulus", + "Conductor transverse Youngs modulus", "(eyoung_cond_trans)", tfcoil_variables.eyoung_cond_trans, ) @@ -5966,7 +5961,7 @@ def table_format_arrays(a, mult=1, delim="\t\t"): ) po.ovarre( self.outfile, - "WP transverse Poisson" "s ratio", + "WP transverse Poissons ratio", "(poisson_wp_trans)", poisson_wp_trans, "OP ", @@ -5983,40 +5978,40 @@ def table_format_arrays(a, mult=1, delim="\t\t"): for ii in range(n_tf_bucking + 2): po.ovarre( constants.mfile, - f"Radial stress at maximum shear of layer {ii+1} (Pa)", - f"(sig_tf_r_max({ii+1}))", + f"Radial stress at maximum shear of layer {ii + 1} (Pa)", + f"(sig_tf_r_max({ii + 1}))", sig_tf_r_max[ii], ) po.ovarre( constants.mfile, - f"toroidal stress at maximum shear of layer {ii+1} (Pa)", - f"(sig_tf_t_max({ii+1}))", + f"toroidal stress at maximum shear of layer {ii + 1} (Pa)", + f"(sig_tf_t_max({ii + 1}))", sig_tf_t_max[ii], ) po.ovarre( constants.mfile, - f"Vertical stress at maximum shear of layer {ii+1} (Pa)", - f"(sig_tf_z_max({ii+1}))", + f"Vertical stress at maximum shear of layer {ii + 1} (Pa)", + f"(sig_tf_z_max({ii + 1}))", sig_tf_z_max[ii], ) po.ovarre( constants.mfile, - f"Von-Mises stress at maximum shear of layer {ii+1} (Pa)", - f"(sig_tf_vmises_max({ii+1}))", + f"Von-Mises stress at maximum shear of layer {ii + 1} (Pa)", + f"(sig_tf_vmises_max({ii + 1}))", sig_tf_vmises_max[ii], ) if tfcoil_variables.i_tf_tresca == 1 and tfcoil_variables.i_tf_sup == 1: po.ovarre( constants.mfile, - f"Maximum shear stress for CEA Tresca yield criterion {ii+1} (Pa)", - f"(sig_tf_tresca_max({ii+1}))", + f"Maximum shear stress for CEA Tresca yield criterion {ii + 1} (Pa)", + f"(sig_tf_tresca_max({ii + 1}))", sig_tf_tresca_max[ii], ) else: po.ovarre( constants.mfile, - f"Maximum shear stress for the Tresca yield criterion {ii+1} (Pa)", - f"(sig_tf_tresca_max({ii+1}))", + f"Maximum shear stress for the Tresca yield criterion {ii + 1} (Pa)", + f"(sig_tf_tresca_max({ii + 1}))", sig_tf_tresca_max[ii], ) @@ -6927,12 +6922,10 @@ def plane_stress(nu, rad, ey, j, nlayers, n_radial_array): area = numpy.zeros((nlayers,)) # Layer area - aa = numpy.zeros( - ( - 2 * nlayers, - 2 * nlayers, - ) - ) + aa = numpy.zeros(( + 2 * nlayers, + 2 * nlayers, + )) # Matrix encoding the integration constant cc coeficients bb = numpy.zeros((2 * nlayers,)) diff --git a/process/solver.py b/process/solver.py index 085b0d14a..198265af8 100644 --- a/process/solver.py +++ b/process/solver.py @@ -1,22 +1,24 @@ """An adapter for different solvers.""" +import importlib import logging -from process.fortran import numerics, global_variables -from process.utilities.f2py_string_patch import f2py_compatible_to_string -import numpy as np -from process.evaluators import Evaluators from abc import ABC, abstractmethod from typing import Optional, Union -import importlib + +import numpy as np from pyvmcon import ( AbstractProblem, - Result, - solve, + LineSearchConvergenceException, QSPSolverException, + Result, VMCONConvergenceException, - LineSearchConvergenceException, + solve, ) +from process.evaluators import Evaluators +from process.fortran import global_variables, numerics +from process.utilities.f2py_string_patch import f2py_compatible_to_string + logger = logging.getLogger(__name__) @@ -173,7 +175,7 @@ def _solver_callback(i: int, _result, _x, convergence_param: float): numerics.nviter = i + 1 global_variables.convergence_parameter = convergence_param print( - f"{i+1} | Convergence Parameter: {convergence_param:.3E}", + f"{i + 1} | Convergence Parameter: {convergence_param:.3E}", end="\r", flush=True, ) diff --git a/process/stellarator.py b/process/stellarator.py index 8e638f3aa..a189d2df5 100644 --- a/process/stellarator.py +++ b/process/stellarator.py @@ -1,39 +1,44 @@ import logging from copy import copy -import numpy as np from pathlib import Path +import numpy as np + +import process.physics_functions as physics_funcs +import process.superconductors as superconductors +from process.coolprop_interface import FluidProperties from process.fortran import ( + build_variables, constants, - stellarator_module as st, - process_output as po, - physics_variables, - physics_module, + constraint_variables, + cost_variables, current_drive_variables, - tfcoil_variables, - stellarator_configuration, - stellarator_variables, - numerics, - build_variables, - fwbs_variables, - heat_transport_variables, - structure_variables, divertor_variables, - cost_variables, error_handling, - constraint_variables, - rebco_variables, + fwbs_variables, + global_variables, + heat_transport_variables, + impurity_radiation_module, maths_library, neoclassics_module, - impurity_radiation_module, + numerics, + physics_module, + physics_variables, + rebco_variables, sctfcoil_module, - global_variables, + stellarator_configuration, + stellarator_variables, + structure_variables, + tfcoil_variables, +) +from process.fortran import ( + process_output as po, +) +from process.fortran import ( + stellarator_module as st, ) -import process.superconductors as superconductors -import process.physics_functions as physics_funcs -from process.stellarator_config import load_stellarator_config -from process.coolprop_interface import FluidProperties from process.physics import rether +from process.stellarator_config import load_stellarator_config from process.utilities.f2py_string_patch import f2py_compatible_to_string logger = logging.getLogger(__name__) @@ -195,9 +200,9 @@ def stigma(self): po.write( self.outfile, - f"{' '*5}scaling law{' '*30}confinement time (s){' '*55}H-factor for", + f"{' ' * 5}scaling law{' ' * 30}confinement time (s){' ' * 55}H-factor for", ) - po.write(self.outfile, f"{' '*34}for H = 2{' '*54}power balance") + po.write(self.outfile, f"{' ' * 34}for H = 2{' ' * 54}power balance") # Label stellarator scaling laws (update if more are added) @@ -2318,24 +2323,19 @@ def sctfcoil_nuclear_heating_iter90(self): ptfnuc = 0.0 else: - # TF coil nuclear heating coefficients in region i (first element), # assuming shield material j (second element where present) fact = np.array([8.0, 8.0, 6.0, 4.0, 4.0]) - coef = np.array( - [ - [10.3, 11.6, 7.08e5, 2.19e18, 3.33e-7], - [8.32, 10.6, 7.16e5, 2.39e18, 3.84e-7], - ] - ).T - - decay = np.array( - [ - [10.05, 17.61, 13.82, 13.24, 14.31, 13.26, 13.25], - [10.02, 3.33, 15.45, 14.47, 15.87, 15.25, 17.25], - ] - ).T + coef = np.array([ + [10.3, 11.6, 7.08e5, 2.19e18, 3.33e-7], + [8.32, 10.6, 7.16e5, 2.39e18, 3.84e-7], + ]).T + + decay = np.array([ + [10.05, 17.61, 13.82, 13.24, 14.31, 13.26, 13.25], + [10.02, 3.33, 15.45, 14.47, 15.87, 15.25, 17.25], + ]).T # N.B. The vacuum vessel appears to be ignored @@ -2609,8 +2609,8 @@ def stcoil(self, output: bool): tfcoil_variables.jwptf = ( coilcurrent * 1.0e6 / awptf ) # [A/m^2] winding pack current density - tfcoil_variables.n_tf_turn = awptf / ( - tfcoil_variables.t_turn_tf**2 + tfcoil_variables.n_tf_turn = ( + awptf / (tfcoil_variables.t_turn_tf**2) ) # estimated number of turns for a given turn size (not global). Take at least 1. tfcoil_variables.cpttf = ( coilcurrent * 1.0e6 / tfcoil_variables.n_tf_turn @@ -5043,74 +5043,70 @@ def init_neoclassics(self, r_effin, eps_effin, iotain): neoclassics_module.dr_densities, neoclassics_module.dr_temperatures, ) = self.init_profile_values_from_PROCESS(r_effin) - neoclassics_module.roots = np.array( - [ - 4.740718054080526184e-2, - 2.499239167531593919e-1, - 6.148334543927683749e-1, - 1.143195825666101451, - 1.836454554622572344, - 2.696521874557216147, - 3.725814507779509288, - 4.927293765849881879, - 6.304515590965073635, - 7.861693293370260349, - 9.603775985479263255, - 1.153654659795613924e1, - 1.366674469306423489e1, - 1.600222118898106771e1, - 1.855213484014315029e1, - 2.132720432178312819e1, - 2.434003576453269346e1, - 2.760555479678096091e1, - 3.114158670111123683e1, - 3.496965200824907072e1, - 3.911608494906788991e1, - 4.361365290848483056e1, - 4.850398616380419980e1, - 5.384138540650750571e1, - 5.969912185923549686e1, - 6.618061779443848991e1, - 7.344123859555988076e1, - 8.173681050672767867e1, - 9.155646652253683726e1, - 1.041575244310588886e2, - ] - ) - neoclassics_module.weights = np.array( - [ - 1.160440860204388913e-1, - 2.208511247506771413e-1, - 2.413998275878537214e-1, - 1.946367684464170855e-1, - 1.237284159668764899e-1, - 6.367878036898660943e-2, - 2.686047527337972682e-2, - 9.338070881603925677e-3, - 2.680696891336819664e-3, - 6.351291219408556439e-4, - 1.239074599068830081e-4, - 1.982878843895233056e-5, - 2.589350929131392509e-6, - 2.740942840536013206e-7, - 2.332831165025738197e-8, - 1.580745574778327984e-9, - 8.427479123056716393e-11, - 3.485161234907855443e-12, - 1.099018059753451500e-13, - 2.588312664959080167e-15, - 4.437838059840028968e-17, - 5.365918308212045344e-19, - 4.393946892291604451e-21, - 2.311409794388543236e-23, - 7.274588498292248063e-26, - 1.239149701448267877e-28, - 9.832375083105887477e-32, - 2.842323553402700938e-35, - 1.878608031749515392e-39, - 8.745980440465011553e-45, - ] - ) + neoclassics_module.roots = np.array([ + 4.740718054080526184e-2, + 2.499239167531593919e-1, + 6.148334543927683749e-1, + 1.143195825666101451, + 1.836454554622572344, + 2.696521874557216147, + 3.725814507779509288, + 4.927293765849881879, + 6.304515590965073635, + 7.861693293370260349, + 9.603775985479263255, + 1.153654659795613924e1, + 1.366674469306423489e1, + 1.600222118898106771e1, + 1.855213484014315029e1, + 2.132720432178312819e1, + 2.434003576453269346e1, + 2.760555479678096091e1, + 3.114158670111123683e1, + 3.496965200824907072e1, + 3.911608494906788991e1, + 4.361365290848483056e1, + 4.850398616380419980e1, + 5.384138540650750571e1, + 5.969912185923549686e1, + 6.618061779443848991e1, + 7.344123859555988076e1, + 8.173681050672767867e1, + 9.155646652253683726e1, + 1.041575244310588886e2, + ]) + neoclassics_module.weights = np.array([ + 1.160440860204388913e-1, + 2.208511247506771413e-1, + 2.413998275878537214e-1, + 1.946367684464170855e-1, + 1.237284159668764899e-1, + 6.367878036898660943e-2, + 2.686047527337972682e-2, + 9.338070881603925677e-3, + 2.680696891336819664e-3, + 6.351291219408556439e-4, + 1.239074599068830081e-4, + 1.982878843895233056e-5, + 2.589350929131392509e-6, + 2.740942840536013206e-7, + 2.332831165025738197e-8, + 1.580745574778327984e-9, + 8.427479123056716393e-11, + 3.485161234907855443e-12, + 1.099018059753451500e-13, + 2.588312664959080167e-15, + 4.437838059840028968e-17, + 5.365918308212045344e-19, + 4.393946892291604451e-21, + 2.311409794388543236e-23, + 7.274588498292248063e-26, + 1.239149701448267877e-28, + 9.832375083105887477e-32, + 2.842323553402700938e-35, + 1.878608031749515392e-39, + 8.745980440465011553e-45, + ]) neoclassics_module.kt = self.neoclassics_calc_KT() neoclassics_module.nu = self.neoclassics_calc_nu() @@ -5261,14 +5257,12 @@ def neoclassics_calc_KT(self): def neoclassics_calc_nu(self): """Calculates the collision frequency""" - mass = np.array( - [ - constants.electron_mass, - constants.proton_mass * 2.0, - constants.proton_mass * 3.0, - constants.proton_mass * 4.0, - ] - ) + mass = np.array([ + constants.electron_mass, + constants.proton_mass * 2.0, + constants.proton_mass * 3.0, + constants.proton_mass * 4.0, + ]) z = np.array([-1.0, 1.0, 1.0, 2.0]) * constants.electron_charge # transform the temperature back in eV @@ -5327,14 +5321,12 @@ def neoclassics_calc_nu_star(self): k = np.repeat(neoclassics_module.roots[:, np.newaxis], 4, axis=1) kk = (k * neoclassics_module.temperatures).T - mass = np.array( - [ - constants.electron_mass, - constants.proton_mass * 2.0, - constants.proton_mass * 3.0, - constants.proton_mass * 4.0, - ] - ) + mass = np.array([ + constants.electron_mass, + constants.proton_mass * 2.0, + constants.proton_mass * 3.0, + constants.proton_mass * 4.0, + ]) v = np.empty((4, self.no_roots)) v[0, :] = constants.speed_light * np.sqrt( @@ -5359,33 +5351,27 @@ def neoclassics_calc_nu_star(self): def neoclassics_calc_nu_star_fromT(self, iota): """Calculates the collision frequency""" temp = ( - np.array( - [ - physics_variables.te, - physics_variables.ti, - physics_variables.ti, - physics_variables.ti, - ] - ) + np.array([ + physics_variables.te, + physics_variables.ti, + physics_variables.ti, + physics_variables.ti, + ]) * KEV ) - density = np.array( - [ - physics_variables.dene, - physics_variables.deni * physics_variables.f_deuterium, - physics_variables.deni * (1 - physics_variables.f_deuterium), - physics_variables.dnalp, - ] - ) - - mass = np.array( - [ - constants.electron_mass, - constants.proton_mass * 2.0, - constants.proton_mass * 3.0, - constants.proton_mass * 4.0, - ] - ) + density = np.array([ + physics_variables.dene, + physics_variables.deni * physics_variables.f_deuterium, + physics_variables.deni * (1 - physics_variables.f_deuterium), + physics_variables.dnalp, + ]) + + mass = np.array([ + constants.electron_mass, + constants.proton_mass * 2.0, + constants.proton_mass * 3.0, + constants.proton_mass * 4.0, + ]) z = np.array([-1.0, 1.0, 1.0, 2.0]) * constants.electron_charge # transform the temperature back in eV @@ -5484,14 +5470,12 @@ def neoclassics_calc_vd(self): def neoclassics_calc_D11_plateau(self): """Calculates the plateau transport coefficients (D11_star sometimes)""" - mass = np.array( - [ - constants.electron_mass, - constants.proton_mass * 2.0, - constants.proton_mass * 3.0, - constants.proton_mass * 4.0, - ] - ) + mass = np.array([ + constants.electron_mass, + constants.proton_mass * 2.0, + constants.proton_mass * 3.0, + constants.proton_mass * 4.0, + ]) v = np.empty((4, self.no_roots)) v[0, :] = constants.speed_light * np.sqrt( diff --git a/process/stellarator_config.py b/process/stellarator_config.py index 7827ee6ed..0f23685a2 100644 --- a/process/stellarator_config.py +++ b/process/stellarator_config.py @@ -1,6 +1,6 @@ -from typing import Optional -from pathlib import Path import json +from pathlib import Path +from typing import Optional from process.fortran import stellarator_configuration diff --git a/process/structure.py b/process/structure.py index f8fc547a9..e259f3bd4 100644 --- a/process/structure.py +++ b/process/structure.py @@ -1,16 +1,17 @@ +import logging import math -from process.fortran import structure_variables as stv -from process.fortran import pfcoil_variables as pfv -from process.fortran import physics_variables as pv -from process.fortran import tfcoil_variables as tfv +import numpy as np + from process.fortran import build_variables as bv -from process.fortran import fwbs_variables as fwbsv +from process.fortran import constants from process.fortran import divertor_variables as divv +from process.fortran import fwbs_variables as fwbsv +from process.fortran import pfcoil_variables as pfv +from process.fortran import physics_variables as pv from process.fortran import process_output as po -from process.fortran import constants -import numpy as np -import logging +from process.fortran import structure_variables as stv +from process.fortran import tfcoil_variables as tfv logger = logging.getLogger(__name__) diff --git a/process/superconductors.py b/process/superconductors.py index 4514fb0e6..4f2b09997 100644 --- a/process/superconductors.py +++ b/process/superconductors.py @@ -1,10 +1,11 @@ import logging -import numpy as np - -from process.fortran import error_handling as eh, rebco_variables +import numpy as np from scipy import optimize +from process.fortran import error_handling as eh +from process.fortran import rebco_variables + logger = logging.getLogger(__name__) @@ -458,13 +459,9 @@ def hijc_rebco(thelium, bmax, strain, bc20max, t_c0): # giving a negative but real value of jcrit. if bcrit > bmax: - jcrit = ( - (A_t / bmax) * bcrit**b * (bmax / bcrit) ** p * (1 - bmax / bcrit) ** q - ) + jcrit = (A_t / bmax) * bcrit**b * (bmax / bcrit) ** p * (1 - bmax / bcrit) ** q else: - jcrit = ( - (A_t / bmax) * bcrit**b * (bmax / bcrit) ** p * (bmax / bcrit - 1) ** q - ) + jcrit = (A_t / bmax) * bcrit**b * (bmax / bcrit) ** p * (bmax / bcrit - 1) ** q # print("thelium = ", thelium, " bcrit = ", bcrit, " bmax = ", bmax, " 1 - bmax / bcrit = ", 1 - bmax / bcrit) @@ -539,9 +536,7 @@ def Bottura_scaling( # Strain function # 0.83 < s < 1.0, for -0.005 < strain < 0.005 - strfun = np.sqrt(epssh**2 + eps0a**2) - np.sqrt( - (strain - epssh) ** 2 + eps0a**2 - ) + strfun = np.sqrt(epssh**2 + eps0a**2) - np.sqrt((strain - epssh) ** 2 + eps0a**2) strfun = strfun * ca1 - ca2 * strain strfun = 1.0 + (1 / (1.0 - ca1 * eps0a)) * strfun diff --git a/process/tfcoil.py b/process/tfcoil.py index e9a77c1cb..dad5e9b4f 100644 --- a/process/tfcoil.py +++ b/process/tfcoil.py @@ -1,14 +1,15 @@ -import numpy as np import copy +import numpy as np + from process import fortran as ft from process.build import Build -from process.fortran import tfcoil_variables as tfv from process.fortran import build_variables as bv from process.fortran import constants -from process.fortran import fwbs_variables as fwbsv from process.fortran import error_handling as eh +from process.fortran import fwbs_variables as fwbsv from process.fortran import process_output as po +from process.fortran import tfcoil_variables as tfv from process.sctfcoil import Sctfcoil @@ -85,7 +86,6 @@ def cntrpst(self): # Water coollant # -------------- if tfv.i_tf_sup == 0: - # Water coolant physical properties coolant_density = constants.denh2o coolant_cp = constants.cph2o @@ -105,7 +105,6 @@ def cntrpst(self): # Helium coolant # -------------- elif tfv.i_tf_sup == 2: - # Inlet coolant density [kg/m3] coolant_density = self.he_density(tfv.tcoolin) @@ -117,7 +116,6 @@ def cntrpst(self): tcool_calc = copy.copy(tfv.tcoolin) # K for i in range(n_tcool_it): - # Thermal capacity Cp coolant_cp = self.he_cp(tcool_calc) diff --git a/process/uncertainties/evaluate_uncertainties.py b/process/uncertainties/evaluate_uncertainties.py index dfade43a9..ede4f016f 100644 --- a/process/uncertainties/evaluate_uncertainties.py +++ b/process/uncertainties/evaluate_uncertainties.py @@ -24,23 +24,23 @@ """ import argparse +from pathlib import Path -from SALib.analyze import sobol +import numpy as np +import pandas as pd from SALib.analyze import morris as morris_method +from SALib.analyze import sobol from SALib.sample import morris, saltelli -from pathlib import Path -import pandas as pd -import numpy as np import process.io.mfile as mf from process.io.in_dat import InDat from process.io.process_config import UncertaintiesConfig from process.io.process_funcs import ( + check_input_error, get_neqns_itervars, get_variable_range, - check_input_error, - process_stopped, no_unfeasible_mfile, + process_stopped, set_variable_in_indat, vary_iteration_variables, ) @@ -55,8 +55,7 @@ def parse_args(args): :rtype: Namespace """ parser = argparse.ArgumentParser( - description="Program to evaluate " - "uncertainties in a given PROCESS design point." + description="Program to evaluate uncertainties in a given PROCESS design point." ) parser.add_argument( diff --git a/process/uncertainties/hdf_to_scatter_plot.py b/process/uncertainties/hdf_to_scatter_plot.py index a9d868444..82aaf455f 100644 --- a/process/uncertainties/hdf_to_scatter_plot.py +++ b/process/uncertainties/hdf_to_scatter_plot.py @@ -8,6 +8,7 @@ """ import argparse + import pandas as pd from pylab import figure, savefig @@ -21,7 +22,7 @@ def parse_args(args): :rtype: Namespace """ parser = argparse.ArgumentParser( - description="Program to read and " "plot PROCESS hdf5 output." + description="Program to read and plot PROCESS hdf5 output." ) parser.add_argument( diff --git a/process/uncertainties/morris_plotting.py b/process/uncertainties/morris_plotting.py index c3c09e438..629f35138 100644 --- a/process/uncertainties/morris_plotting.py +++ b/process/uncertainties/morris_plotting.py @@ -16,10 +16,12 @@ morris method output """ + import argparse -import numpy as np -import matplotlib.pyplot as plt + import matplotlib.backends.backend_pdf as bpdf +import matplotlib.pyplot as plt +import numpy as np def parse_args(args): diff --git a/process/uncertainties/sobol_plotting.py b/process/uncertainties/sobol_plotting.py index f447316d3..c6be589db 100644 --- a/process/uncertainties/sobol_plotting.py +++ b/process/uncertainties/sobol_plotting.py @@ -17,9 +17,10 @@ """ import argparse -import numpy as np -import matplotlib.pyplot as plt + import matplotlib.backends.backend_pdf as bpdf +import matplotlib.pyplot as plt +import numpy as np def parse_args(args): diff --git a/process/utilities/f2py_string_patch.py b/process/utilities/f2py_string_patch.py index 81d37e165..a7ea24dd4 100644 --- a/process/utilities/f2py_string_patch.py +++ b/process/utilities/f2py_string_patch.py @@ -1,7 +1,8 @@ -import numpy as np import re import warnings +import numpy as np + def string_to_f2py_compatible( target: np.ndarray, string: str = None, except_length: bool = False diff --git a/process/vacuum.py b/process/vacuum.py index d5195dd0d..01678fd92 100644 --- a/process/vacuum.py +++ b/process/vacuum.py @@ -1,17 +1,17 @@ import logging import math -import numpy as np -from process.utilities.f2py_string_patch import f2py_compatible_to_string +import numpy as np +from process.fortran import build_variables as buv from process.fortran import constants +from process.fortran import error_handling as eh from process.fortran import physics_variables as pv -from process.fortran import vacuum_variables as vacv -from process.fortran import build_variables as buv +from process.fortran import process_output as po from process.fortran import tfcoil_variables as tfv from process.fortran import times_variables as tv -from process.fortran import process_output as po -from process.fortran import error_handling as eh +from process.fortran import vacuum_variables as vacv +from process.utilities.f2py_string_patch import f2py_compatible_to_string logger = logging.getLogger(__name__) @@ -121,7 +121,6 @@ def vacuum_simple(self, output) -> float: # Output section if output: - po.oheadr(self.outfile, "Vacuum System") po.ovarst( self.outfile, @@ -385,7 +384,6 @@ def vacuum( d = np.full(4, 1e-6) for i in range(4): - sss = nduct / ( 1.0e0 / sp[i] / pumpn + 1.0e0 / cmax * xmult[i] / xmult[imax] ) @@ -515,7 +513,6 @@ def vacuum( dimax = d[imax] if output: - # Output section po.oheadr(self.outfile, "Vacuum System") diff --git a/process/water_use.py b/process/water_use.py index 5715a47b9..44ec1299a 100644 --- a/process/water_use.py +++ b/process/water_use.py @@ -1,8 +1,6 @@ import numpy -from process.fortran import constants -from process.fortran import heat_transport_variables -from process.fortran import water_usage_variables +from process.fortran import constants, heat_transport_variables, water_usage_variables from process.fortran import process_output as po SECDAY = 86400e0 @@ -115,7 +113,6 @@ def cooling_water_body(self, wastetherm: float, output: bool): evapsum = 0.0e0 for icool in range(1, 4): - if icool == 1: # small pond as a cooling body # heat loading, MW/acre, based on estimations from US power plants diff --git a/scripts/create_dicts.py b/scripts/create_dicts.py index 6fbe5b1d2..fe399ab2d 100755 --- a/scripts/create_dicts.py +++ b/scripts/create_dicts.py @@ -16,19 +16,17 @@ information in the Process Fortran source code. """ -import re -import logging import argparse import json +import logging import pickle - -import numpy -import create_dicts_config +import re from pathlib import Path +import create_dicts_config +import numpy from python_dicts import get_python_variables - output_dict = {} # Dict of nested dicts e.g. output_dict['DICT_DESCRIPTIONS'] = # {descriptions_dict} @@ -867,7 +865,6 @@ def dict_ixc_full(): ixc_full[itv_num] = dict() for line in lines: - if "lablxc" in line and "=" in line: if "lablxc(i)" not in line and "lablxc(ixc(i))" not in line: labl_num = line.split("(")[1].split(")")[0] @@ -914,9 +911,7 @@ def dict_ixc_default(): if name in default: ixc_default[name] = default[name] else: - logging.warning( - "print_dict_ixc could not find %s" " in DICT_DEFAULT\n", name - ) + logging.warning("print_dict_ixc could not find %s in DICT_DEFAULT\n", name) return ixc_default @@ -951,34 +946,32 @@ def create_dicts(project): # Make dict objects # Some dicts depend on other dicts already existing in output_dicts, so # be careful if changing the order! - dict_objects.extend( - [ - VariableDescriptions(project, python_variables), - DefaultValues(project, python_variables), - Modules(project, python_variables), - HardcodedDictionary("DICT_TF_TYPE", create_dicts_config.DICT_TF_TYPE), - HardcodedDictionary("DICT_FIMP", create_dicts_config.DICT_FIMP), - HardcodedDictionary( - "DICT_OPTIMISATION_VARS", create_dicts_config.DICT_OPTIMISATION_VARS - ), - HardcodedDictionary("IFAIL_SUCCESS", create_dicts_config.IFAIL_SUCCESS), - HardcodedDictionary( - "PARAMETER_DEFAULTS", create_dicts_config.PARAMETER_DEFAULTS - ), - HardcodedDictionary("NON_F_VALUES", create_dicts_config.NON_F_VALUES), - SourceDictionary("DICT_INPUT_BOUNDS", dict_input_bounds), - SourceDictionary("DICT_NSWEEP2VARNAME", dict_nsweep2varname), - SourceDictionary("DICT_VAR_TYPE", dict_var_type), - SourceDictionary("DICT_ICC_FULL", dict_icc_full), - SourceDictionary("DICT_IXC2NSWEEP", dict_ixc2nsweep), - SourceDictionary("DICT_NSWEEP2IXC", dict_nsweep2ixc), - SourceDictionary("DICT_IXC_FULL", dict_ixc_full), - SourceDictionary("DICT_IXC_BOUNDS", dict_ixc_bounds), - SourceDictionary("DICT_IXC_DEFAULT", dict_ixc_default), - SourceDictionary("DICT_IXC_SIMPLE", dict_ixc_simple), - SourceDictionary("DICT_IXC_SIMPLE_REV", dict_ixc_simple_rev), - ] - ) + dict_objects.extend([ + VariableDescriptions(project, python_variables), + DefaultValues(project, python_variables), + Modules(project, python_variables), + HardcodedDictionary("DICT_TF_TYPE", create_dicts_config.DICT_TF_TYPE), + HardcodedDictionary("DICT_FIMP", create_dicts_config.DICT_FIMP), + HardcodedDictionary( + "DICT_OPTIMISATION_VARS", create_dicts_config.DICT_OPTIMISATION_VARS + ), + HardcodedDictionary("IFAIL_SUCCESS", create_dicts_config.IFAIL_SUCCESS), + HardcodedDictionary( + "PARAMETER_DEFAULTS", create_dicts_config.PARAMETER_DEFAULTS + ), + HardcodedDictionary("NON_F_VALUES", create_dicts_config.NON_F_VALUES), + SourceDictionary("DICT_INPUT_BOUNDS", dict_input_bounds), + SourceDictionary("DICT_NSWEEP2VARNAME", dict_nsweep2varname), + SourceDictionary("DICT_VAR_TYPE", dict_var_type), + SourceDictionary("DICT_ICC_FULL", dict_icc_full), + SourceDictionary("DICT_IXC2NSWEEP", dict_ixc2nsweep), + SourceDictionary("DICT_NSWEEP2IXC", dict_nsweep2ixc), + SourceDictionary("DICT_IXC_FULL", dict_ixc_full), + SourceDictionary("DICT_IXC_BOUNDS", dict_ixc_bounds), + SourceDictionary("DICT_IXC_DEFAULT", dict_ixc_default), + SourceDictionary("DICT_IXC_SIMPLE", dict_ixc_simple), + SourceDictionary("DICT_IXC_SIMPLE_REV", dict_ixc_simple_rev), + ]) # Make individual dicts within dict objects, process, then add to output_dict for dict_object in dict_objects: @@ -996,9 +989,7 @@ def create_dicts(project): # create_dicts.py. This module would benefit from more class structuring # Called from make; parse arguments from make - parser = argparse.ArgumentParser( - description="Create Fortran-Python " "dictionaries" - ) + parser = argparse.ArgumentParser(description="Create Fortran-Python dictionaries") parser.add_argument("fortran_source", help="Fortran source dir") parser.add_argument("ford_project", help="The pickled Ford project filename") parser.add_argument("dicts_filename", help="The output dicts filename") diff --git a/scripts/document_fortran_interface.py b/scripts/document_fortran_interface.py index 6a45a32fe..0dbc30552 100644 --- a/scripts/document_fortran_interface.py +++ b/scripts/document_fortran_interface.py @@ -13,7 +13,6 @@ │ ├─ module variable (python class attribute) """ - import inspect from pathlib import Path from typing import Any, List, NamedTuple, Set, Union diff --git a/scripts/python_dicts.py b/scripts/python_dicts.py index f267801ad..dfa90a212 100644 --- a/scripts/python_dicts.py +++ b/scripts/python_dicts.py @@ -6,13 +6,12 @@ still be included in the 'dictionaries'. """ +import importlib.util import inspect import pathlib -import importlib.util import sys from typing import Any, List, NamedTuple - # the directory which this script resides (scripts/) CURRENT_DIR = pathlib.Path(__file__).resolve().parent diff --git a/scripts/time_numpy_baseline.py b/scripts/time_numpy_baseline.py index 1305b9f09..d4ba05456 100644 --- a/scripts/time_numpy_baseline.py +++ b/scripts/time_numpy_baseline.py @@ -2,9 +2,10 @@ This can then be used to normalise other runtime calculations to mitigate bias introduced by hardware, system load, etc. The average runtime [s] will be printed to stdout.""" -import numpy as np import time +import numpy as np + def numpy_baseline_runtime(): """Runs a computational intense numeric baseline 10 times and diff --git a/scripts/vardes.py b/scripts/vardes.py index baee244c3..27e5a9f44 100644 --- a/scripts/vardes.py +++ b/scripts/vardes.py @@ -1,14 +1,15 @@ -from pathlib import Path -from typing import Any, List -from enum import Enum -import pickle import dataclasses +import pickle from copy import copy -import numpy as np +from enum import Enum +from pathlib import Path +from typing import Any, List + import jinja2 +import numpy as np -from process.init import init_all_module_vars from process import fortran +from process.init import init_all_module_vars class VariableTypes(str, Enum): diff --git a/setup.py b/setup.py index d2c7b97a2..7a1947d2a 100644 --- a/setup.py +++ b/setup.py @@ -1,7 +1,8 @@ -from setuptools import setup, find_packages -import site import os import platform +import site + +from setuptools import find_packages, setup MODULE_NAME = "process" _install_loc = os.path.join(site.getsitepackages()[0], MODULE_NAME) diff --git a/tests/conftest.py b/tests/conftest.py index 4ded8ca02..ed59662a2 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -4,10 +4,12 @@ """ import os -import pytest -from system_check import system_compatible import warnings + +import pytest from _pytest.fixtures import SubRequest +from system_check import system_compatible + from process.fortran import error_handling as eh diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py index 3fe93230e..4fef649ce 100644 --- a/tests/integration/conftest.py +++ b/tests/integration/conftest.py @@ -2,10 +2,12 @@ Define fixtures that will be shared across integration test modules. """ -import pytest + +import os from pathlib import Path from shutil import copy -import os + +import pytest @pytest.fixture diff --git a/tests/integration/test_blanket_library_int.py b/tests/integration/test_blanket_library_int.py index e7787aa8f..ca65cac71 100644 --- a/tests/integration/test_blanket_library_int.py +++ b/tests/integration/test_blanket_library_int.py @@ -1,10 +1,15 @@ import pytest + +from process.blanket_library import BlanketLibrary from process.fortran import ( - physics_variables as pv, - fwbs_variables as fwbs, build_variables as bv, ) -from process.blanket_library import BlanketLibrary +from process.fortran import ( + fwbs_variables as fwbs, +) +from process.fortran import ( + physics_variables as pv, +) from process.fw import Fw diff --git a/tests/integration/test_examples.py b/tests/integration/test_examples.py index 090cf21a7..cf4b86232 100644 --- a/tests/integration/test_examples.py +++ b/tests/integration/test_examples.py @@ -3,9 +3,10 @@ import os from pathlib import Path from shutil import copy, copytree -import pytest -import pandas + import numpy as np +import pandas +import pytest from testbook import testbook diff --git a/tests/integration/test_main_int.py b/tests/integration/test_main_int.py index cc18cf438..d1adaa1a0 100644 --- a/tests/integration/test_main_int.py +++ b/tests/integration/test_main_int.py @@ -1,8 +1,9 @@ """Integration tests for the main.py module.""" -from process import main -from shutil import copy import json +from shutil import copy + +from process import main def test_single_run(temp_data): @@ -103,9 +104,9 @@ def test_single_run_with_mfilejson(temp_data): # Assert that 'large_tokamak_once_through.MFILE.DAT.json' has been produced in the temp_data directory. expected_json = temp_data / "large_tokamak_once_through.MFILE.DAT.json" - assert ( - expected_json.exists() - ), "large_tokamak_once_through.MFILE.DAT.json was not found" + assert expected_json.exists(), ( + "large_tokamak_once_through.MFILE.DAT.json was not found" + ) # Check if the file contains valid JSON. try: diff --git a/tests/integration/test_mfile_to_csv.py b/tests/integration/test_mfile_to_csv.py index 74147d76a..4134f290e 100644 --- a/tests/integration/test_mfile_to_csv.py +++ b/tests/integration/test_mfile_to_csv.py @@ -1,4 +1,5 @@ """Integration tests for mfile_to_csv.py.""" + from process.io import mfile_to_csv diff --git a/tests/integration/test_pfcoil_int.py b/tests/integration/test_pfcoil_int.py index 1bea82a6c..bc9c5205d 100644 --- a/tests/integration/test_pfcoil_int.py +++ b/tests/integration/test_pfcoil_int.py @@ -10,21 +10,20 @@ """ import numpy as np -from numpy.testing import assert_array_almost_equal import pytest -from process.fortran import pfcoil_module as pf +from numpy.testing import assert_array_almost_equal + +from process.cs_fatigue import CsFatigue from process.fortran import build_variables as bv -from process.fortran import pfcoil_variables as pfv -from process.fortran import physics_variables as pv +from process.fortran import constants from process.fortran import error_handling as eh - from process.fortran import fwbs_variables as fwbsv +from process.fortran import pfcoil_module as pf +from process.fortran import pfcoil_variables as pfv +from process.fortran import physics_variables as pv from process.fortran import tfcoil_variables as tfv - from process.fortran import times_variables as tv -from process.fortran import constants -from process.pfcoil import PFCoil, mtrx, fixb -from process.cs_fatigue import CsFatigue +from process.pfcoil import PFCoil, fixb, mtrx @pytest.fixture @@ -149,32 +148,30 @@ def test_pfcoil(monkeypatch, pfcoil): pfcoil.pfcoil() assert pytest.approx(pv.bvert) == -0.65121393 - assert pytest.approx(pfv.zpf) == np.array( - [ - 4.86, - -4.86, - 7.2075, - -7.2075, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - ] - ) + assert pytest.approx(pfv.zpf) == np.array([ + 4.86, + -4.86, + 7.2075, + -7.2075, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + ]) def test_ohcalc(monkeypatch, reinitialise_error_module, pfcoil): @@ -290,42 +287,40 @@ def test_efc(pfcoil: PFCoil, monkeypatch: pytest.MonkeyPatch): lrow1 = 2 * nptsmx + ngrpmx lcol1 = ngrpmx npts = 32 - rpts = np.array( - [ - 6.0547741935483881, - 6.2407887617065567, - 6.4268033298647254, - 6.612817898022894, - 6.7988324661810626, - 6.9848470343392313, - 7.1708616024973999, - 7.3568761706555676, - 7.5428907388137372, - 7.7289053069719049, - 7.9149198751300744, - 8.1009344432882422, - 8.2869490114464099, - 8.4729635796045795, - 8.658978147762749, - 8.8449927159209167, - 9.0310072840790845, - 9.217021852237254, - 9.4030364203954235, - 9.5890509885535913, - 9.775065556711759, - 9.9610801248699286, - 10.147094693028098, - 10.333109261186266, - 10.519123829344434, - 10.705138397502601, - 10.891152965660771, - 11.07716753381894, - 11.263182101977108, - 11.449196670135276, - 11.635211238293445, - 11.821225806451615, - ] - ) + rpts = np.array([ + 6.0547741935483881, + 6.2407887617065567, + 6.4268033298647254, + 6.612817898022894, + 6.7988324661810626, + 6.9848470343392313, + 7.1708616024973999, + 7.3568761706555676, + 7.5428907388137372, + 7.7289053069719049, + 7.9149198751300744, + 8.1009344432882422, + 8.2869490114464099, + 8.4729635796045795, + 8.658978147762749, + 8.8449927159209167, + 9.0310072840790845, + 9.217021852237254, + 9.4030364203954235, + 9.5890509885535913, + 9.775065556711759, + 9.9610801248699286, + 10.147094693028098, + 10.333109261186266, + 10.519123829344434, + 10.705138397502601, + 10.891152965660771, + 11.07716753381894, + 11.263182101977108, + 11.449196670135276, + 11.635211238293445, + 11.821225806451615, + ]) zpts = np.full(nptsmx, 0.0) brin = np.full(nptsmx, 0.0) bzin = np.full(nptsmx, 0.0) @@ -451,14 +446,12 @@ def test_efc(pfcoil: PFCoil, monkeypatch: pytest.MonkeyPatch): ) assert pytest.approx(ssq) == 4.208729e-4 - assert pytest.approx(ccls[0:4]) == np.array( - [ - 12846165.42893886, - 16377261.02000236, - 579111.6216917, - 20660782.82356247, - ] - ) + assert pytest.approx(ccls[0:4]) == np.array([ + 12846165.42893886, + 16377261.02000236, + 579111.6216917, + 20660782.82356247, + ]) def test_mtrx(pfcoil: PFCoil): @@ -475,42 +468,40 @@ def test_mtrx(pfcoil: PFCoil): lcol1 = 10 nptsmx = 32 npts = 32 - rpts = np.array( - [ - 6.0547741935483881, - 6.2407887617065567, - 6.4268033298647254, - 6.612817898022894, - 6.7988324661810626, - 6.9848470343392313, - 7.1708616024973999, - 7.3568761706555676, - 7.5428907388137372, - 7.7289053069719049, - 7.9149198751300744, - 8.1009344432882422, - 8.2869490114464099, - 8.4729635796045795, - 8.658978147762749, - 8.8449927159209167, - 9.0310072840790845, - 9.217021852237254, - 9.4030364203954235, - 9.5890509885535913, - 9.775065556711759, - 9.9610801248699286, - 10.147094693028098, - 10.333109261186266, - 10.519123829344434, - 10.705138397502601, - 10.891152965660771, - 11.07716753381894, - 11.263182101977108, - 11.449196670135276, - 11.635211238293445, - 11.821225806451615, - ] - ) + rpts = np.array([ + 6.0547741935483881, + 6.2407887617065567, + 6.4268033298647254, + 6.612817898022894, + 6.7988324661810626, + 6.9848470343392313, + 7.1708616024973999, + 7.3568761706555676, + 7.5428907388137372, + 7.7289053069719049, + 7.9149198751300744, + 8.1009344432882422, + 8.2869490114464099, + 8.4729635796045795, + 8.658978147762749, + 8.8449927159209167, + 9.0310072840790845, + 9.217021852237254, + 9.4030364203954235, + 9.5890509885535913, + 9.775065556711759, + 9.9610801248699286, + 10.147094693028098, + 10.333109261186266, + 10.519123829344434, + 10.705138397502601, + 10.891152965660771, + 11.07716753381894, + 11.263182101977108, + 11.449196670135276, + 11.635211238293445, + 11.821225806451615, + ]) zpts = np.zeros(nptsmx) brin = np.zeros(nptsmx) bzin = np.zeros(nptsmx) @@ -569,84 +560,82 @@ def test_mtrx(pfcoil: PFCoil): order="F", ) alfa = 5.0e-10 - bfix = np.array( - [ - -4.163336342344337e-17, - 0, - 1.3877787807814457e-17, - -3.4694469519536142e-17, - -6.9388939039072284e-18, - 2.0816681711721685e-17, - -1.3877787807814457e-17, - 3.1225022567582528e-17, - 1.3877787807814457e-17, - 2.7755575615628914e-17, - 3.1225022567582528e-17, - 1.0408340855860843e-17, - -4.163336342344337e-17, - -3.4694469519536142e-18, - -1.7347234759768071e-17, - 1.3877787807814457e-17, - -2.0816681711721685e-17, - 1.7347234759768071e-17, - -2.4286128663675299e-17, - 0, - -3.4694469519536142e-18, - -1.0408340855860843e-17, - 0, - 0, - 1.7347234759768071e-18, - -1.0408340855860843e-17, - 1.7347234759768071e-18, - -6.9388939039072284e-18, - 6.9388939039072284e-18, - -6.9388939039072284e-18, - 3.4694469519536142e-18, - 5.2041704279304213e-18, - -0.3130693525427572, - -0.30317412503141067, - -0.29349361903088056, - -0.28403698539500122, - -0.27481156279537977, - -0.26582301409269415, - -0.25707546259584763, - -0.24857162691582502, - -0.24031295298976921, - -0.23229974199262643, - -0.22453127307719892, - -0.21700592011907616, - -0.20972126186523041, - -0.20267418508484636, - -0.19586098049525957, - -0.18927743138451436, - -0.18291889497602498, - -0.17678037668189961, - -0.17085659747167894, - -0.16514205464473081, - -0.15963107633981927, - -0.15431787014642362, - -0.14919656620141122, - -0.1442612551639135, - -0.13950602146198512, - -0.13492497219911381, - -0.13051226209776626, - -0.12626211484245656, - -0.12216884116737772, - -0.11822685401402291, - -0.11443068106371825, - -0.11077497492862906, - 1.244050972187992e-316, - 9.655957481515668e-97, - 0, - 6.9533558071276999e-310, - 6.9533558069559627e-310, - 6.9533474562307984e-310, - 0, - 0, - 1.2440161899665248e-316, - 9.655957481515668e-97, - ] - ) + bfix = np.array([ + -4.163336342344337e-17, + 0, + 1.3877787807814457e-17, + -3.4694469519536142e-17, + -6.9388939039072284e-18, + 2.0816681711721685e-17, + -1.3877787807814457e-17, + 3.1225022567582528e-17, + 1.3877787807814457e-17, + 2.7755575615628914e-17, + 3.1225022567582528e-17, + 1.0408340855860843e-17, + -4.163336342344337e-17, + -3.4694469519536142e-18, + -1.7347234759768071e-17, + 1.3877787807814457e-17, + -2.0816681711721685e-17, + 1.7347234759768071e-17, + -2.4286128663675299e-17, + 0, + -3.4694469519536142e-18, + -1.0408340855860843e-17, + 0, + 0, + 1.7347234759768071e-18, + -1.0408340855860843e-17, + 1.7347234759768071e-18, + -6.9388939039072284e-18, + 6.9388939039072284e-18, + -6.9388939039072284e-18, + 3.4694469519536142e-18, + 5.2041704279304213e-18, + -0.3130693525427572, + -0.30317412503141067, + -0.29349361903088056, + -0.28403698539500122, + -0.27481156279537977, + -0.26582301409269415, + -0.25707546259584763, + -0.24857162691582502, + -0.24031295298976921, + -0.23229974199262643, + -0.22453127307719892, + -0.21700592011907616, + -0.20972126186523041, + -0.20267418508484636, + -0.19586098049525957, + -0.18927743138451436, + -0.18291889497602498, + -0.17678037668189961, + -0.17085659747167894, + -0.16514205464473081, + -0.15963107633981927, + -0.15431787014642362, + -0.14919656620141122, + -0.1442612551639135, + -0.13950602146198512, + -0.13492497219911381, + -0.13051226209776626, + -0.12626211484245656, + -0.12216884116737772, + -0.11822685401402291, + -0.11443068106371825, + -0.11077497492862906, + 1.244050972187992e-316, + 9.655957481515668e-97, + 0, + 6.9533558071276999e-310, + 6.9533558069559627e-310, + 6.9533474562307984e-310, + 0, + 0, + 1.2440161899665248e-316, + 9.655957481515668e-97, + ]) nrws, gmat, bvec = mtrx( lrow1, @@ -665,1732 +654,1707 @@ def test_mtrx(pfcoil: PFCoil): int(pfv.nclsmx), ) - gmat_exp = np.array( + gmat_exp = np.array([ [ - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 2.92158969e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 3.04960151e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 3.18186728e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 3.31865171e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 3.46023678e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 3.60692320e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 3.75903204e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 3.91690643e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 4.08091348e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 4.25144640e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 4.42892676e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 4.61380701e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 4.80657328e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 5.00774837e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 5.21789513e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 5.43762013e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 5.66757773e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 5.90847452e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 6.16107431e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 6.42620349e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 6.70475709e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 6.99770541e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 7.30610130e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 7.63108823e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 7.97390922e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 8.33591662e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 8.71858292e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 9.12351264e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 9.55245534e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 1.00073199e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 1.04901901e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 1.10033415e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -3.30317878e-19, - -3.30317878e-19, - -6.60635757e-19, - 3.50145552e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -3.20472338e-19, - -3.20472338e-19, - -6.40944676e-19, - 3.51776252e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -3.11196728e-19, - -3.11196728e-19, - -6.22393456e-19, - 3.53472730e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -3.02442952e-19, - -3.02442952e-19, - -6.04885904e-19, - 3.55236630e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -2.94168179e-19, - -2.94168179e-19, - -5.88336358e-19, - 3.57069672e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -2.86334140e-19, - -2.86334140e-19, - -5.72668279e-19, - 3.58973648e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -2.78906536e-19, - -2.78906536e-19, - -5.57813071e-19, - 3.60950425e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -2.71854537e-19, - -2.71854537e-19, - -5.43709074e-19, - 3.63001946e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -2.65150356e-19, - -2.65150356e-19, - -5.30300712e-19, - 3.65130230e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -2.58768880e-19, - -2.58768880e-19, - -5.17537759e-19, - 3.67337373e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -2.52687355e-19, - -2.52687355e-19, - -5.05374710e-19, - 3.69625546e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -2.46885119e-19, - -2.46885119e-19, - -4.93770239e-19, - 3.71996994e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -2.41343366e-19, - -2.41343366e-19, - -4.82686732e-19, - 3.74454037e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -2.36044938e-19, - -2.36044938e-19, - -4.72089877e-19, - 3.76999062e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -2.30974156e-19, - -2.30974156e-19, - -4.61948311e-19, - 3.79634522e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -2.26116655e-19, - -2.26116655e-19, - -4.52233310e-19, - 3.82362931e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -2.21459257e-19, - -2.21459257e-19, - -4.42918515e-19, - 3.85186853e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -2.16989848e-19, - -2.16989848e-19, - -4.33979695e-19, - 3.88108893e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -2.12697269e-19, - -2.12697269e-19, - -4.25394538e-19, - 3.91131688e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -2.08571231e-19, - -2.08571231e-19, - -4.17142461e-19, - 3.94257887e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -2.04602225e-19, - -2.04602225e-19, - -4.09204451e-19, - 3.97490133e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -2.00781456e-19, - -2.00781456e-19, - -4.01562911e-19, - 4.00831037e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -1.97100769e-19, - -1.97100769e-19, - -3.94201538e-19, - 4.04283150e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -1.93552600e-19, - -1.93552600e-19, - -3.87105201e-19, - 4.07848927e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -1.90129919e-19, - -1.90129919e-19, - -3.80259839e-19, - 4.11530678e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -1.86826185e-19, - -1.86826185e-19, - -3.73652370e-19, - 4.15330516e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -1.83635302e-19, - -1.83635302e-19, - -3.67270604e-19, - 4.19250290e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -1.80551586e-19, - -1.80551586e-19, - -3.61103172e-19, - 4.23291505e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -1.77569727e-19, - -1.77569727e-19, - -3.55139453e-19, - 4.27455221e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -1.74684759e-19, - -1.74684759e-19, - -3.49369519e-19, - 4.31741941e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -1.71892037e-19, - -1.71892037e-19, - -3.43784075e-19, - 4.36151462e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - -1.69187206e-19, - -1.69187206e-19, - -3.38374412e-19, - 4.40682706e-08, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 5.00000000e-10, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 5.00000000e-10, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 1.00000000e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 1.00000000e-09, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - [ - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ], - ] - ) - - bvec_exp = np.array( - [ - 4.16333634e-17, - 0.00000000e00, - -1.38777878e-17, - 3.46944695e-17, - 6.93889390e-18, - -2.08166817e-17, - 1.38777878e-17, - -3.12250226e-17, - -1.38777878e-17, - -2.77555756e-17, - -3.12250226e-17, - -1.04083409e-17, - 4.16333634e-17, - 3.46944695e-18, - 1.73472348e-17, - -1.38777878e-17, - 2.08166817e-17, - -1.73472348e-17, - 2.42861287e-17, - 0.00000000e00, - 3.46944695e-18, - 1.04083409e-17, - 0.00000000e00, - 0.00000000e00, - -1.73472348e-18, - 1.04083409e-17, - -1.73472348e-18, - 6.93889390e-18, - -6.93889390e-18, - 6.93889390e-18, - -3.46944695e-18, - -5.20417043e-18, - 3.13069353e-01, - 3.03174125e-01, - 2.93493619e-01, - 2.84036985e-01, - 2.74811563e-01, - 2.65823014e-01, - 2.57075463e-01, - 2.48571627e-01, - 2.40312953e-01, - 2.32299742e-01, - 2.24531273e-01, - 2.17005920e-01, - 2.09721262e-01, - 2.02674185e-01, - 1.95860980e-01, - 1.89277431e-01, - 1.82918895e-01, - 1.76780377e-01, - 1.70856597e-01, - 1.65142055e-01, - 1.59631076e-01, - 1.54317870e-01, - 1.49196566e-01, - 1.44261255e-01, - 1.39506021e-01, - 1.34924972e-01, - 1.30512262e-01, - 1.26262115e-01, - 1.22168841e-01, - 1.18226854e-01, - 1.14430681e-01, - 1.10774975e-01, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ] - ) - - assert nrws == 68 - assert_array_almost_equal(gmat, gmat_exp) - assert_array_almost_equal(bvec, bvec_exp) - - -def test_solv(pfcoil: PFCoil): - """Test solv() with simple arguments. - - Running baseline_2019 results in 2D array args with 740 elements: unfeasible - for a unit test. Made-up simplified args are used instead. - - :param pfcoil: a PFCoil instance - :type pfcoil: process.pfcoil.PFCoil - """ - ngrpmx = 3 - ngrp = 3 - nrws = 3 - gmat = np.full((3, 3), 2.0, order="F") - bvec = np.full(3, 1.0) - - ccls, umat, vmat, sigma, work2 = pfcoil.solv(ngrpmx, ngrp, nrws, gmat, bvec) - - assert_array_almost_equal(ccls, np.array([0.16666667, 0.37079081, -0.03745748])) - assert_array_almost_equal( - umat, - np.array( - [ - [-0.81649658, -0.57735027, 0.0], - [0.40824829, -0.57735027, -0.70710678], - [0.40824829, -0.57735027, 0.70710678], - ] - ), - ) - assert_array_almost_equal( - vmat, - np.array( - [ - [-0.81649658, -0.57735027, 0.0], - [0.40824829, -0.57735027, -0.70710678], - [0.40824829, -0.57735027, 0.70710678], - ] - ), - ) - assert_array_almost_equal( - sigma, np.array([5.1279005e-16, 6.0000000e00, 0.0000000e00]) - ) - assert_array_almost_equal( - work2, np.array([-2.22044605e-16, -1.73205081e00, 0.00000000e00]) - ) - - -def test_fixb(pfcoil: PFCoil): - """Test fixb subroutine. - - fixb() requires specific arguments in order to work; these were discovered - using gdb to break on the first subroutine call when running the baseline - 2018 IN.DAT. - - :param pfcoil: a PFCoil instance - :type pfcoil: process.pfcoil.PFCoil - """ - nptsmx = 32 - lrow1 = 74 - npts = 32 - rpts = np.array( + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 2.92158969e-09, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], [ - 6.0223258064516134, - 6.2073434963579608, - 6.3923611862643082, - 6.5773788761706555, - 6.7623965660770038, - 6.9474142559833512, - 7.1324319458896985, - 7.3174496357960459, - 7.5024673257023942, - 7.6874850156087415, - 7.8725027055150889, - 8.0575203954214363, - 8.2425380853277836, - 8.427555775234131, - 8.6125734651404784, - 8.7975911550468275, - 8.9826088449531731, - 9.1676265348595223, - 9.3526442247658697, - 9.537661914672217, - 9.7226796045785644, - 9.9076972944849118, - 10.092714984391259, - 10.277732674297607, - 10.462750364203954, - 10.647768054110301, - 10.83278574401665, - 11.017803433922996, - 11.202821123829345, - 11.387838813735691, - 11.57285650364204, - 11.757874193548387, - ] - ) - zpts = np.zeros(nptsmx) - nfix = 14 - rfix = np.array( + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 3.04960151e-09, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], [ - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ) - zfix = np.array( + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 3.18186728e-09, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], [ - 0.58327007281470211, - 1.7498102184441064, - 2.9163503640735104, - 4.0828905097029144, - 5.2494306553323193, - 6.4159708009617233, - 7.5825109465911273, - -0.58327007281470211, - -1.7498102184441064, - -2.9163503640735104, - -4.0828905097029144, - -5.2494306553323193, - -6.4159708009617233, - -7.5825109465911273, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ) - cfix = np.array( + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 3.31865171e-09, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], [ - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ) - - bfix_exp = np.array( - [ - -2.77555756e-17, - -2.77555756e-17, - -2.08166817e-17, - -6.24500451e-17, - 4.85722573e-17, - 8.32667268e-17, - 6.93889390e-18, - 4.16333634e-17, - 2.08166817e-17, - 1.38777878e-17, - -2.77555756e-17, - -2.77555756e-17, - 1.38777878e-17, - 3.12250226e-17, - -1.38777878e-17, - -3.46944695e-18, - 6.93889390e-18, - -2.77555756e-17, - 3.46944695e-18, - 2.42861287e-17, - 4.51028104e-17, - 1.38777878e-17, - 1.04083409e-17, - -6.93889390e-18, - 1.38777878e-17, - 1.73472348e-17, - 2.77555756e-17, - -6.93889390e-18, - -1.73472348e-18, - 2.25514052e-17, - 1.04083409e-17, - 1.73472348e-18, - -3.53728301e-01, - -3.43046326e-01, - -3.32568315e-01, - -3.22305794e-01, - -3.12268396e-01, - -3.02463988e-01, - -2.92898791e-01, - -2.83577512e-01, - -2.74503461e-01, - -2.65678678e-01, - -2.57104045e-01, - -2.48779412e-01, - -2.40703694e-01, - -2.32874990e-01, - -2.25290668e-01, - -2.17947471e-01, - -2.10841592e-01, - -2.03968763e-01, - -1.97324322e-01, - -1.90903284e-01, - -1.84700401e-01, - -1.78710219e-01, - -1.72927125e-01, - -1.67345392e-01, - -1.61959222e-01, - -1.56762779e-01, - -1.51750219e-01, - -1.46915716e-01, - -1.42253492e-01, - -1.37757829e-01, - -1.33423089e-01, - -1.29243733e-01, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - 0.00000000e00, - ] - ) - - bfix = fixb(lrow1, npts, rpts, zpts, nfix, rfix, zfix, cfix) - - assert_array_almost_equal(bfix, bfix_exp) - - -def test_peakb(monkeypatch: pytest.MonkeyPatch, pfcoil: PFCoil): - """Test peakb subroutine. - - peakb() requires specific arguments in order to work; these were discovered - using gdb to break on the first subroutine call when running the baseline - 2018 IN.DAT. - :param monkeypatch: mocking fixture - :type monkeypatch: MonkeyPatch - :param pfcoil: a PFCoil instance - :type pfcoil: process.pfcoil.PFCoil - """ - # Mock module vars - monkeypatch.setattr(pf, "nfxf", 14) - monkeypatch.setattr( - pf, - "rfxf", - np.array( - ( - 6.2732560483870969, - 6.2732560483870969, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ) - ), - ) - monkeypatch.setattr( - pf, - "zfxf", - np.array( - ( - 9.606146709677418, - -11.141090021562032, - 2.9163503640735104, - 4.0828905097029144, - 5.2494306553323193, - 6.4159708009617233, - 7.5825109465911273, - -0.58327007281470211, - -1.7498102184441064, - -2.9163503640735104, - -4.0828905097029144, - -5.2494306553323193, - -6.4159708009617233, - -7.5825109465911273, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ) - ), - ) - monkeypatch.setattr( - pf, - "cfxf", - np.array( - ( - 15889161.548344759, - 18583102.707854092, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ) - ), - ) - monkeypatch.setattr(pf, "xind", np.zeros(64)) - monkeypatch.setattr(pf, "bpf2", np.zeros(22)) - - monkeypatch.setattr(bv, "iohcl", 1) - monkeypatch.setattr(bv, "hmax", 9.0730900215620327) - monkeypatch.setattr(bv, "ohcth", 0.55242000000000002) + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 3.46023678e-09, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 3.60692320e-09, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 3.75903204e-09, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 3.91690643e-09, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 4.08091348e-09, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 4.25144640e-09, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 4.42892676e-09, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + 0.00000000e00, + 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3.88108893e-08, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + -2.12697269e-19, + -2.12697269e-19, + -4.25394538e-19, + 3.91131688e-08, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + -2.08571231e-19, + -2.08571231e-19, + -4.17142461e-19, + 3.94257887e-08, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + -2.04602225e-19, + -2.04602225e-19, + -4.09204451e-19, + 3.97490133e-08, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + -2.00781456e-19, + -2.00781456e-19, + -4.01562911e-19, + 4.00831037e-08, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + -1.97100769e-19, + -1.97100769e-19, + -3.94201538e-19, + 4.04283150e-08, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 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0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + 0.00000000e00, + 0.00000000e00, + 1.00000000e-09, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 1.00000000e-09, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + ]) + + bvec_exp = np.array([ + 4.16333634e-17, + 0.00000000e00, + -1.38777878e-17, + 3.46944695e-17, + 6.93889390e-18, + -2.08166817e-17, + 1.38777878e-17, + -3.12250226e-17, + -1.38777878e-17, + -2.77555756e-17, + -3.12250226e-17, + -1.04083409e-17, + 4.16333634e-17, + 3.46944695e-18, + 1.73472348e-17, + -1.38777878e-17, + 2.08166817e-17, + -1.73472348e-17, + 2.42861287e-17, + 0.00000000e00, + 3.46944695e-18, + 1.04083409e-17, + 0.00000000e00, + 0.00000000e00, + -1.73472348e-18, + 1.04083409e-17, + -1.73472348e-18, + 6.93889390e-18, + -6.93889390e-18, + 6.93889390e-18, + -3.46944695e-18, + -5.20417043e-18, + 3.13069353e-01, + 3.03174125e-01, + 2.93493619e-01, + 2.84036985e-01, + 2.74811563e-01, + 2.65823014e-01, + 2.57075463e-01, + 2.48571627e-01, + 2.40312953e-01, + 2.32299742e-01, + 2.24531273e-01, + 2.17005920e-01, + 2.09721262e-01, + 2.02674185e-01, + 1.95860980e-01, + 1.89277431e-01, + 1.82918895e-01, + 1.76780377e-01, + 1.70856597e-01, + 1.65142055e-01, + 1.59631076e-01, + 1.54317870e-01, + 1.49196566e-01, + 1.44261255e-01, + 1.39506021e-01, + 1.34924972e-01, + 1.30512262e-01, + 1.26262115e-01, + 1.22168841e-01, + 1.18226854e-01, + 1.14430681e-01, + 1.10774975e-01, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ]) + + assert nrws == 68 + assert_array_almost_equal(gmat, gmat_exp) + assert_array_almost_equal(bvec, bvec_exp) + + +def test_solv(pfcoil: PFCoil): + """Test solv() with simple arguments. + + Running baseline_2019 results in 2D array args with 740 elements: unfeasible + for a unit test. Made-up simplified args are used instead. + + :param pfcoil: a PFCoil instance + :type pfcoil: process.pfcoil.PFCoil + """ + ngrpmx = 3 + ngrp = 3 + nrws = 3 + gmat = np.full((3, 3), 2.0, order="F") + bvec = np.full(3, 1.0) + + ccls, umat, vmat, sigma, work2 = pfcoil.solv(ngrpmx, ngrp, nrws, gmat, bvec) + + assert_array_almost_equal(ccls, np.array([0.16666667, 0.37079081, -0.03745748])) + assert_array_almost_equal( + umat, + np.array([ + [-0.81649658, -0.57735027, 0.0], + [0.40824829, -0.57735027, -0.70710678], + [0.40824829, -0.57735027, 0.70710678], + ]), + ) + assert_array_almost_equal( + vmat, + np.array([ + [-0.81649658, -0.57735027, 0.0], + [0.40824829, -0.57735027, -0.70710678], + [0.40824829, -0.57735027, 0.70710678], + ]), + ) + assert_array_almost_equal( + sigma, np.array([5.1279005e-16, 6.0000000e00, 0.0000000e00]) + ) + assert_array_almost_equal( + work2, np.array([-2.22044605e-16, -1.73205081e00, 0.00000000e00]) + ) + + +def test_fixb(pfcoil: PFCoil): + """Test fixb subroutine. + + fixb() requires specific arguments in order to work; these were discovered + using gdb to break on the first subroutine call when running the baseline + 2018 IN.DAT. + + :param pfcoil: a PFCoil instance + :type pfcoil: process.pfcoil.PFCoil + """ + nptsmx = 32 + lrow1 = 74 + npts = 32 + rpts = np.array([ + 6.0223258064516134, + 6.2073434963579608, + 6.3923611862643082, + 6.5773788761706555, + 6.7623965660770038, + 6.9474142559833512, + 7.1324319458896985, + 7.3174496357960459, + 7.5024673257023942, + 7.6874850156087415, + 7.8725027055150889, + 8.0575203954214363, + 8.2425380853277836, + 8.427555775234131, + 8.6125734651404784, + 8.7975911550468275, + 8.9826088449531731, + 9.1676265348595223, + 9.3526442247658697, + 9.537661914672217, + 9.7226796045785644, + 9.9076972944849118, + 10.092714984391259, + 10.277732674297607, + 10.462750364203954, + 10.647768054110301, + 10.83278574401665, + 11.017803433922996, + 11.202821123829345, + 11.387838813735691, + 11.57285650364204, + 11.757874193548387, + ]) + zpts = np.zeros(nptsmx) + nfix = 14 + rfix = np.array([ + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]) + zfix = np.array([ + 0.58327007281470211, + 1.7498102184441064, + 2.9163503640735104, + 4.0828905097029144, + 5.2494306553323193, + 6.4159708009617233, + 7.5825109465911273, + -0.58327007281470211, + -1.7498102184441064, + -2.9163503640735104, + -4.0828905097029144, + -5.2494306553323193, + -6.4159708009617233, + -7.5825109465911273, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]) + cfix = np.array([ + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]) + + bfix_exp = np.array([ + -2.77555756e-17, + -2.77555756e-17, + -2.08166817e-17, + -6.24500451e-17, + 4.85722573e-17, + 8.32667268e-17, + 6.93889390e-18, + 4.16333634e-17, + 2.08166817e-17, + 1.38777878e-17, + -2.77555756e-17, + -2.77555756e-17, + 1.38777878e-17, + 3.12250226e-17, + -1.38777878e-17, + -3.46944695e-18, + 6.93889390e-18, + -2.77555756e-17, + 3.46944695e-18, + 2.42861287e-17, + 4.51028104e-17, + 1.38777878e-17, + 1.04083409e-17, + -6.93889390e-18, + 1.38777878e-17, + 1.73472348e-17, + 2.77555756e-17, + -6.93889390e-18, + -1.73472348e-18, + 2.25514052e-17, + 1.04083409e-17, + 1.73472348e-18, + -3.53728301e-01, + -3.43046326e-01, + -3.32568315e-01, + -3.22305794e-01, + -3.12268396e-01, + -3.02463988e-01, + -2.92898791e-01, + -2.83577512e-01, + -2.74503461e-01, + -2.65678678e-01, + -2.57104045e-01, + -2.48779412e-01, + -2.40703694e-01, + -2.32874990e-01, + -2.25290668e-01, + -2.17947471e-01, + -2.10841592e-01, + -2.03968763e-01, + -1.97324322e-01, + -1.90903284e-01, + -1.84700401e-01, + -1.78710219e-01, + -1.72927125e-01, + -1.67345392e-01, + -1.61959222e-01, + -1.56762779e-01, + -1.51750219e-01, + -1.46915716e-01, + -1.42253492e-01, + -1.37757829e-01, + -1.33423089e-01, + -1.29243733e-01, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ]) + + bfix = fixb(lrow1, npts, rpts, zpts, nfix, rfix, zfix, cfix) + + assert_array_almost_equal(bfix, bfix_exp) + + +def test_peakb(monkeypatch: pytest.MonkeyPatch, pfcoil: PFCoil): + """Test peakb subroutine. + + peakb() requires specific arguments in order to work; these were discovered + using gdb to break on the first subroutine call when running the baseline + 2018 IN.DAT. + :param monkeypatch: mocking fixture + :type monkeypatch: MonkeyPatch + :param pfcoil: a PFCoil instance + :type pfcoil: process.pfcoil.PFCoil + """ + # Mock module vars + monkeypatch.setattr(pf, "nfxf", 14) + monkeypatch.setattr( + pf, + "rfxf", + np.array(( + 6.2732560483870969, + 6.2732560483870969, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + )), + ) + monkeypatch.setattr( + pf, + "zfxf", + np.array(( + 9.606146709677418, + -11.141090021562032, + 2.9163503640735104, + 4.0828905097029144, + 5.2494306553323193, + 6.4159708009617233, + 7.5825109465911273, + -0.58327007281470211, + -1.7498102184441064, + -2.9163503640735104, + -4.0828905097029144, + -5.2494306553323193, + -6.4159708009617233, + -7.5825109465911273, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + )), + ) + monkeypatch.setattr( + pf, + "cfxf", + np.array(( + 15889161.548344759, + 18583102.707854092, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + )), + ) + monkeypatch.setattr(pf, "xind", np.zeros(64)) + monkeypatch.setattr(pf, "bpf2", np.zeros(22)) + + monkeypatch.setattr(bv, "iohcl", 1) + monkeypatch.setattr(bv, "hmax", 9.0730900215620327) + monkeypatch.setattr(bv, "ohcth", 0.55242000000000002) monkeypatch.setattr( eh, "idiags", - np.array( - [-999999, -999999, -999999, -999999, -999999, -999999, -999999, -999999] - ), + np.array([ + -999999, + -999999, + -999999, + -999999, + -999999, + -999999, + -999999, + -999999, + ]), ) monkeypatch.setattr( pfv, "ra", - np.array( - [ - 5.6944236847973242, - 5.5985055619292972, - 17.819978201682968, - 17.819978201682968, - 16.385123084628962, - 16.385123084628962, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 5.6944236847973242, + 5.5985055619292972, + 17.819978201682968, + 17.819978201682968, + 16.385123084628962, + 16.385123084628962, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr(pfv, "nohc", 7) monkeypatch.setattr( pfv, "ric", - np.array( - [ - 14.742063826112622, - 20.032681634901664, - -8.1098913365453491, - -8.1098913365453491, - -5.5984385047179153, - -5.5984385047179153, - -186.98751599968148, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 14.742063826112622, + 20.032681634901664, + -8.1098913365453491, + -8.1098913365453491, + -5.5984385047179153, + -5.5984385047179153, + -186.98751599968148, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr(pfv, "ohhghf", 0.90000000000000002) monkeypatch.setattr( pfv, "rb", - np.array( - [ - 6.8520884119768697, - 6.9480065348448967, - 18.98258241468535, - 18.98258241468535, - 17.22166645654087, - 17.22166645654087, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 6.8520884119768697, + 6.9480065348448967, + 18.98258241468535, + 18.98258241468535, + 17.22166645654087, + 17.22166645654087, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr( pfv, "rpf", - np.array( - [ - 6.2732560483870969, - 6.2732560483870969, - 18.401280308184159, - 18.401280308184159, - 16.803394770584916, - 16.803394770584916, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 6.2732560483870969, + 6.2732560483870969, + 18.401280308184159, + 18.401280308184159, + 16.803394770584916, + 16.803394770584916, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr( pfv, @@ -2429,184 +2393,172 @@ def test_peakb(monkeypatch: pytest.MonkeyPatch, pfcoil: PFCoil): monkeypatch.setattr( pfv, "zpf", - np.array( - [ - 9.606146709677418, - -11.141090021562032, - 2.8677741935483869, - -2.8677741935483869, - 8.0297677419354834, - -8.0297677419354834, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 9.606146709677418, + -11.141090021562032, + 2.8677741935483869, + -2.8677741935483869, + 8.0297677419354834, + -8.0297677419354834, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr(pfv, "ncls", np.array([1, 1, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0])) monkeypatch.setattr( pfv, "zl", - np.array( - [ - 9.0273143460876444, - -10.466339535104233, - 2.2864720870471951, - -2.2864720870471951, - 7.6114960559795311, - -7.6114960559795311, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 9.0273143460876444, + -10.466339535104233, + 2.2864720870471951, + -2.2864720870471951, + 7.6114960559795311, + -7.6114960559795311, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr( pfv, "curpfb", - np.array( - [ - 14.742063826112622, - 20.032681634901664, - 0.58040662653667285, - 0.58040662653667285, - 0.42974674788703021, - 0.42974674788703021, - 174.22748790786324, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 14.742063826112622, + 20.032681634901664, + 0.58040662653667285, + 0.58040662653667285, + 0.42974674788703021, + 0.42974674788703021, + 174.22748790786324, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr( pfv, "curpff", - np.array( - [ - 0.067422231232391661, - -2.9167273287450968, - -8.1098913365453491, - -8.1098913365453491, - -5.5984385047179153, - -5.5984385047179153, - -186.98751599968148, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 0.067422231232391661, + -2.9167273287450968, + -8.1098913365453491, + -8.1098913365453491, + -5.5984385047179153, + -5.5984385047179153, + -186.98751599968148, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr( pfv, "curpfs", - np.array( - [ - 0.067422231232391661, - -2.9167273287450968, - -8.1098913365453491, - -8.1098913365453491, - -5.5984385047179153, - -5.5984385047179153, - -186.98751599968148, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 0.067422231232391661, + -2.9167273287450968, + -8.1098913365453491, + -8.1098913365453491, + -5.5984385047179153, + -5.5984385047179153, + -186.98751599968148, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr(pfv, "cohbop", 19311657.760000002) monkeypatch.setattr( pfv, "zh", - np.array( - [ - 10.184979073267192, - -11.815840508019832, - 3.4490763000495788, - -3.4490763000495788, - 8.4480394278914357, - -8.4480394278914357, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 10.184979073267192, + -11.815840508019832, + 3.4490763000495788, + -3.4490763000495788, + 8.4480394278914357, + -8.4480394278914357, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr(pv, "rmajor", 8.8901000000000003) monkeypatch.setattr(pv, "plasma_current", 17721306.969367817) @@ -2691,122 +2643,114 @@ def test_axial_stress(pfcoil: PFCoil, monkeypatch: pytest.MonkeyPatch): monkeypatch.setattr( pfv, "rb", - np.array( - [ - 6.8520884119768697, - 6.9480065348448967, - 18.98258241468535, - 18.98258241468535, - 17.22166645654087, - 17.22166645654087, - 2.88462, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 6.8520884119768697, + 6.9480065348448967, + 18.98258241468535, + 18.98258241468535, + 17.22166645654087, + 17.22166645654087, + 2.88462, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr( pfv, "ra", - np.array( - [ - 5.6944236847973242, - 5.5985055619292972, - 17.819978201682968, - 17.819978201682968, - 16.385123084628962, - 16.385123084628962, - 2.3321999999999998, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 5.6944236847973242, + 5.5985055619292972, + 17.819978201682968, + 17.819978201682968, + 16.385123084628962, + 16.385123084628962, + 2.3321999999999998, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr( pfv, "ric", - np.array( - [ - 14.742063826112622, - 20.032681634901664, - -8.1098913365453491, - -8.1098913365453491, - -5.5984385047179153, - -5.5984385047179153, - -186.98751599968145, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 14.742063826112622, + 20.032681634901664, + -8.1098913365453491, + -8.1098913365453491, + -5.5984385047179153, + -5.5984385047179153, + -186.98751599968145, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr( pfv, "zh", - np.array( - [ - 10.184979073267192, - -11.815840508019832, - 3.4490763000495788, - -3.4490763000495788, - 8.4480394278914357, - -8.4480394278914357, - 8.1657810194058289, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 10.184979073267192, + -11.815840508019832, + 3.4490763000495788, + -3.4490763000495788, + 8.4480394278914357, + -8.4480394278914357, + 8.1657810194058289, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) s_axial_exp = -7.468967e8 @@ -2834,107 +2778,115 @@ def test_induct(pfcoil: PFCoil, monkeypatch: pytest.MonkeyPatch): monkeypatch.setattr( eh, "fdiags", - np.array( - [-999999, -999999, -999999, -999999, -999999, -999999, -999999, -999999] - ), + np.array([ + -999999, + -999999, + -999999, + -999999, + -999999, + -999999, + -999999, + -999999, + ]), ) monkeypatch.setattr( eh, "idiags", - np.array( - [-999999, -999999, -999999, -999999, -999999, -999999, -999999, -999999] - ), + np.array([ + -999999, + -999999, + -999999, + -999999, + -999999, + -999999, + -999999, + -999999, + ]), ) monkeypatch.setattr(pfv, "nohc", 7) monkeypatch.setattr( pfv, "turns", - np.array( - [ - 349.33800535811901, - 474.70809561378354, - 192.17751982334951, - 192.17751982334951, - 130.19624429576547, - 130.19624429576547, - 4348.5468837135222, - 1, - 100, - 100, - 100, - 100, - 100, - 100, - 100, - 100, - 100, - 100, - 100, - 100, - 100, - 100, - ] - ), + np.array([ + 349.33800535811901, + 474.70809561378354, + 192.17751982334951, + 192.17751982334951, + 130.19624429576547, + 130.19624429576547, + 4348.5468837135222, + 1, + 100, + 100, + 100, + 100, + 100, + 100, + 100, + 100, + 100, + 100, + 100, + 100, + 100, + 100, + ]), ) monkeypatch.setattr( pfv, "zpf", - np.array( - [ - 9.606146709677418, - -11.141090021562032, - 2.8677741935483869, - -2.8677741935483869, - 8.0297677419354834, - -8.0297677419354834, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 9.606146709677418, + -11.141090021562032, + 2.8677741935483869, + -2.8677741935483869, + 8.0297677419354834, + -8.0297677419354834, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr( pfv, "rpf", - np.array( - [ - 6.2732560483870969, - 6.2732560483870969, - 18.401280308184159, - 18.401280308184159, - 16.803394770584916, - 16.803394770584916, - 2.6084100000000001, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 6.2732560483870969, + 6.2732560483870969, + 18.401280308184159, + 18.401280308184159, + 16.803394770584916, + 16.803394770584916, + 2.6084100000000001, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr(pfv, "sxlg", np.ones((22, 22), dtype=int)) monkeypatch.setattr(pfv, "rohc", 2.6084100000000001) @@ -2943,658 +2895,648 @@ def test_induct(pfcoil: PFCoil, monkeypatch: pytest.MonkeyPatch): monkeypatch.setattr( pfv, "zl", - np.array( - [ - 9.0273143460876444, - -10.466339535104233, - 2.2864720870471951, - -2.2864720870471951, - 7.6114960559795311, - -7.6114960559795311, - -8.1657810194058289, - -5.2996467096774191, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 9.0273143460876444, + -10.466339535104233, + 2.2864720870471951, + -2.2864720870471951, + 7.6114960559795311, + -7.6114960559795311, + -8.1657810194058289, + -5.2996467096774191, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr(pfv, "ncirt", 8) monkeypatch.setattr( pfv, "ra", - np.array( - [ - 5.6944236847973242, - 5.5985055619292972, - 17.819978201682968, - 17.819978201682968, - 16.385123084628962, - 16.385123084628962, - 2.3321999999999998, - 6.0223258064516134, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 5.6944236847973242, + 5.5985055619292972, + 17.819978201682968, + 17.819978201682968, + 16.385123084628962, + 16.385123084628962, + 2.3321999999999998, + 6.0223258064516134, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr( pfv, "zh", - np.array( - [ - 10.184979073267192, - -11.815840508019832, - 3.4490763000495788, - -3.4490763000495788, - 8.4480394278914357, - -8.4480394278914357, - 8.1657810194058289, - 5.2996467096774191, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 10.184979073267192, + -11.815840508019832, + 3.4490763000495788, + -3.4490763000495788, + 8.4480394278914357, + -8.4480394278914357, + 8.1657810194058289, + 5.2996467096774191, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr( pfv, "rb", - np.array( - [ - 6.8520884119768697, - 6.9480065348448967, - 18.98258241468535, - 18.98258241468535, - 17.22166645654087, - 17.22166645654087, - 2.88462, - 11.757874193548387, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 6.8520884119768697, + 6.9480065348448967, + 18.98258241468535, + 18.98258241468535, + 17.22166645654087, + 17.22166645654087, + 2.88462, + 11.757874193548387, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr(pv, "rmajor", 8.8901000000000003) monkeypatch.setattr(pv, "rlp", 1.6039223939491056e-05) - sxlg_exp = np.array( + sxlg_exp = np.array([ + [ + 2.49332453e00, + 4.46286166e-02, + 2.38094100e-01, + 1.57653632e-01, + 2.18695928e-01, + 6.62002005e-02, + 8.81068392e-01, + 8.15132226e-04, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + 4.46286166e-02, + 4.33166888e00, + 1.89207090e-01, + 2.93333228e-01, + 7.84212470e-02, + 2.83752898e-01, + 8.54403195e-01, + 8.60878436e-04, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + 2.38094100e-01, + 1.89207090e-01, + 3.12872133e00, + 1.10843611e00, + 7.24769254e-01, + 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0.00000000e00, + 0.00000000e00, + ], + [ + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + [ + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + 0.00000000e00, + ], + ]) pfcoil.induct(False) assert_array_almost_equal(pfv.sxlg, sxlg_exp) diff --git a/tests/integration/test_plot_proc.py b/tests/integration/test_plot_proc.py index e1204965b..f925e4724 100644 --- a/tests/integration/test_plot_proc.py +++ b/tests/integration/test_plot_proc.py @@ -1,7 +1,9 @@ """Integration tests for plot_proc.py.""" -from process.io import plot_proc + from shutil import copy +from process.io import plot_proc + def test_input_file(temp_data, mfile_name): """Run plot_proc on an input MFILE and check for an output. diff --git a/tests/integration/test_plot_radial_build.py b/tests/integration/test_plot_radial_build.py index 59e6d73bd..1067c4ff4 100644 --- a/tests/integration/test_plot_radial_build.py +++ b/tests/integration/test_plot_radial_build.py @@ -1,4 +1,5 @@ """Integration tests for plot_radial_build.py.""" + from process.io import plot_radial_build diff --git a/tests/integration/test_plot_sankey.py b/tests/integration/test_plot_sankey.py index a4dfc391f..1f310578a 100644 --- a/tests/integration/test_plot_sankey.py +++ b/tests/integration/test_plot_sankey.py @@ -1,4 +1,5 @@ """Integration tests for plot_sankey.py.""" + from process.io import plot_sankey diff --git a/tests/integration/test_plot_scans.py b/tests/integration/test_plot_scans.py index 1ff10724e..4c5aa76a6 100644 --- a/tests/integration/test_plot_scans.py +++ b/tests/integration/test_plot_scans.py @@ -1,4 +1,5 @@ """Integration tests for plot_scans.py.""" + from process.io import plot_scans diff --git a/tests/integration/test_plot_solutions.py b/tests/integration/test_plot_solutions.py index e69a3a98a..dcd993d60 100644 --- a/tests/integration/test_plot_solutions.py +++ b/tests/integration/test_plot_solutions.py @@ -1,8 +1,10 @@ """Test the plot_solutions tool.""" +from typing import List, Sequence + import pytest + from process.io.plot_solutions import RunMetadata, plot_mfile_solutions -from typing import List, Sequence @pytest.fixture diff --git a/tests/integration/test_uncertainties_evaluate.py b/tests/integration/test_uncertainties_evaluate.py index 29953d587..c92855d9b 100644 --- a/tests/integration/test_uncertainties_evaluate.py +++ b/tests/integration/test_uncertainties_evaluate.py @@ -1,12 +1,17 @@ """Integration tests for uncertainties/evaluate.py.""" -from process.uncertainties import evaluate_uncertainties -from shutil import move + import json -from process.uncertainties import morris_plotting -from process.uncertainties import sobol_plotting -from process.uncertainties import hdf_to_scatter_plot +from shutil import move + import pytest +from process.uncertainties import ( + evaluate_uncertainties, + hdf_to_scatter_plot, + morris_plotting, + sobol_plotting, +) + # Uncertainties tests currently take too long for a 1h CI job pytest.skip( "Uncertainties tests currently time out integration test jobs", diff --git a/tests/integration/test_utilities.py b/tests/integration/test_utilities.py index c86ace58b..4352f6781 100644 --- a/tests/integration/test_utilities.py +++ b/tests/integration/test_utilities.py @@ -4,10 +4,12 @@ on each of the regression test scenarios. """ -import pytest import logging -import process.io.mfile as mf + +import pytest + import process.io.in_dat as indat +import process.io.mfile as mf logger = logging.getLogger(__name__) diff --git a/tests/integration/test_vmcon.py b/tests/integration/test_vmcon.py index 9147f40e2..36562f7d3 100644 --- a/tests/integration/test_vmcon.py +++ b/tests/integration/test_vmcon.py @@ -6,14 +6,16 @@ VMCON documentation ANL-80-64 """ -import pytest -import numpy as np import logging from abc import ABC, abstractmethod -from process.solver import get_solver -from process.init import init_all_module_vars + +import numpy as np +import pytest + from process.evaluators import Evaluators from process.fortran import error_handling +from process.init import init_all_module_vars +from process.solver import get_solver # Debug-level terminal output logging logger = logging.getLogger(__name__) @@ -138,9 +140,10 @@ def fcnvmc1(self, n, m, x, ifail): :rtype: tuple(float, np.ndarray) """ objf = (x[0] - 2.0) ** 2 + (x[1] - 1.0) ** 2 - conf = np.array( - [x[0] - 2.0 * x[1] + 1.0, -0.25 * x[0] ** 2 - x[1] * x[1] + 1.0] - ) + conf = np.array([ + x[0] - 2.0 * x[1] + 1.0, + -0.25 * x[0] ** 2 - x[1] * x[1] + 1.0, + ]) return objf, conf @@ -200,9 +203,10 @@ def fcnvmc1(self, n, m, x, ifail): :rtype: tuple(float, np.ndarray) """ objf = (x[0] - 2.0) ** 2 + (x[1] - 1.0) ** 2 - conf = np.array( - [x[0] - 2.0 * x[1] + 1.0, -0.25 * x[0] ** 2 - x[1] * x[1] + 1.0] - ) + conf = np.array([ + x[0] - 2.0 * x[1] + 1.0, + -0.25 * x[0] ** 2 - x[1] * x[1] + 1.0, + ]) return objf, conf @@ -575,9 +579,9 @@ def get_case5(): case.solver_args.n = 1 case.solver_args.m = neqns + nineqns case.solver_args.meq = neqns - case.solver_args.x = np.array( - [5.0] - ) # Try different values, e.g. 5.0, 2.0, 1.0, 0.0... + case.solver_args.x = np.array([ + 5.0 + ]) # Try different values, e.g. 5.0, 2.0, 1.0, 0.0... # Expected values case.exp.x = np.array([3.0]) diff --git a/tests/integration/test_write_new_in_dat.py b/tests/integration/test_write_new_in_dat.py index 2b04cf92f..ca4499162 100644 --- a/tests/integration/test_write_new_in_dat.py +++ b/tests/integration/test_write_new_in_dat.py @@ -1,9 +1,10 @@ """Integration tests for write_new_in_dat.py.""" +from pytest import approx + from process.io import write_new_in_dat -from process.io.mfile import MFile from process.io.in_dat import InDat -from pytest import approx +from process.io.mfile import MFile def test_write_new_in_dat(temp_data, mfile_name): diff --git a/tests/regression/regression_test_assets.py b/tests/regression/regression_test_assets.py index 6ecb3e158..df3f75484 100644 --- a/tests/regression/regression_test_assets.py +++ b/tests/regression/regression_test_assets.py @@ -2,13 +2,14 @@ a remote data repository """ -import subprocess -import requests import dataclasses -import re import logging -from typing import Optional +import re +import subprocess from pathlib import Path +from typing import Optional + +import requests logger = logging.getLogger(__name__) diff --git a/tests/regression/test_process_input_files.py b/tests/regression/test_process_input_files.py index 26ae36aff..cde60d321 100644 --- a/tests/regression/test_process_input_files.py +++ b/tests/regression/test_process_input_files.py @@ -6,19 +6,18 @@ by changes made off of main. """ -from pathlib import Path -from dataclasses import dataclass -from typing import List -import shutil import logging import re +import shutil +from dataclasses import dataclass +from pathlib import Path +from typing import List import pytest -from process.main import main -from process.io.mfile import MFile - from regression_test_assets import RegressionTestAssetCollector +from process.io.mfile import MFile +from process.main import main logger = logging.getLogger(__name__) @@ -95,9 +94,9 @@ def compare( assert (ifail := mfile.data["ifail"].get_scan(-1)) == 1 or mfile.data[ "ioptimz" - ].get_scan( - -1 - ) == -2, f"ifail of {ifail} indicates PROCESS did not solve successfully" + ].get_scan(-1) == -2, ( + f"ifail of {ifail} indicates PROCESS did not solve successfully" + ) mfile_keys = set(mfile.data.keys()) reference_mfile_keys = set(reference_mfile.data.keys()) diff --git a/tests/unit/conftest.py b/tests/unit/conftest.py index bd3ff4d84..5d7c899de 100644 --- a/tests/unit/conftest.py +++ b/tests/unit/conftest.py @@ -3,8 +3,10 @@ Define fixtures that will be shared across unit test modules. """ -import pytest from pathlib import Path + +import pytest + from process.init import init_all_module_vars diff --git a/tests/unit/test_availability.py b/tests/unit/test_availability.py index 6c7df164a..d737337b5 100644 --- a/tests/unit/test_availability.py +++ b/tests/unit/test_availability.py @@ -1,18 +1,19 @@ """Unit tests for availability.f90.""" +import pytest +from pytest import approx + from process import fortran from process.availability import Availability +from process.fortran import constraint_variables as ctv from process.fortran import cost_variables as cv +from process.fortran import divertor_variables as dv +from process.fortran import fwbs_variables as fwbsv +from process.fortran import ife_variables as ifev from process.fortran import physics_variables as pv from process.fortran import tfcoil_variables as tfv -from process.fortran import constraint_variables as ctv -from process.fortran import fwbs_variables as fwbsv from process.fortran import times_variables as tv -from process.fortran import ife_variables as ifev -from process.fortran import divertor_variables as dv from process.init import init_all_module_vars -import pytest -from pytest import approx @pytest.fixture diff --git a/tests/unit/test_blanket_library.py b/tests/unit/test_blanket_library.py index 9dc19faf6..93d5a77d6 100644 --- a/tests/unit/test_blanket_library.py +++ b/tests/unit/test_blanket_library.py @@ -1,18 +1,19 @@ -import pytest +from typing import Any, NamedTuple + import numpy -from typing import NamedTuple, Any +import pytest from process.blanket_library import BlanketLibrary -from process.fw import Fw from process.fortran import ( - fwbs_variables, + blanket_library, build_variables, - physics_variables, + buildings_variables, divertor_variables, - blanket_library, + fwbs_variables, pfcoil_variables, - buildings_variables, + physics_variables, ) +from process.fw import Fw @pytest.fixture diff --git a/tests/unit/test_build.py b/tests/unit/test_build.py index ced5a7d05..c12d0faae 100755 --- a/tests/unit/test_build.py +++ b/tests/unit/test_build.py @@ -1,17 +1,15 @@ -import pytest -from typing import NamedTuple, Any -from process.build import Build - - -from process.fortran import build_variables - -from process.fortran import divertor_variables - -from process.fortran import physics_variables +from typing import Any, NamedTuple -from process.fortran import tfcoil_variables +import pytest -from process.fortran import current_drive_variables +from process.build import Build +from process.fortran import ( + build_variables, + current_drive_variables, + divertor_variables, + physics_variables, + tfcoil_variables, +) @pytest.fixture @@ -25,7 +23,6 @@ def build(): class DivgeomParam(NamedTuple): - rspo: Any = None plleno: Any = None @@ -62,7 +59,6 @@ class DivgeomParam(NamedTuple): class RippleAmplitudeParam(NamedTuple): - rminor: Any = None rmajor: Any = None @@ -310,7 +306,6 @@ def test_ripple_amplitude(rippleamplitudeparam, monkeypatch, build): class PortszParam(NamedTuple): - r_tf_outboard_mid: Any = None tfthko: Any = None diff --git a/tests/unit/test_buildings.py b/tests/unit/test_buildings.py index 00c144bf3..8846fa8de 100644 --- a/tests/unit/test_buildings.py +++ b/tests/unit/test_buildings.py @@ -1,15 +1,19 @@ +from typing import Any, NamedTuple + import pytest -from typing import NamedTuple, Any + from process.buildings import Buildings -from process.fortran import current_drive_variables -from process.fortran import fwbs_variables -from process.fortran import buildings_variables -from process.fortran import physics_variables -from process.fortran import cost_variables -from process.fortran import pfcoil_variables -from process.fortran import tfcoil_variables -from process.fortran import build_variables -from process.fortran import divertor_variables +from process.fortran import ( + build_variables, + buildings_variables, + cost_variables, + current_drive_variables, + divertor_variables, + fwbs_variables, + pfcoil_variables, + physics_variables, + tfcoil_variables, +) @pytest.fixture diff --git a/tests/unit/test_ccfe_hcpb.py b/tests/unit/test_ccfe_hcpb.py index 601171d10..1986077b0 100644 --- a/tests/unit/test_ccfe_hcpb.py +++ b/tests/unit/test_ccfe_hcpb.py @@ -1,22 +1,23 @@ +from typing import Any, NamedTuple + import pytest -from typing import NamedTuple, Any -from process.hcpb import CCFE_HCPB from process.blanket_library import BlanketLibrary -from process.fw import Fw from process.fortran import ( - fwbs_variables, build_variables, - global_variables, - tfcoil_variables, - physics_variables, ccfe_hcpb_module, - primary_pumping_variables, - current_drive_variables, - heat_transport_variables, constraint_variables, + current_drive_variables, divertor_variables, + fwbs_variables, + global_variables, + heat_transport_variables, + physics_variables, + primary_pumping_variables, + tfcoil_variables, ) +from process.fw import Fw +from process.hcpb import CCFE_HCPB @pytest.fixture diff --git a/tests/unit/test_costs_1990.py b/tests/unit/test_costs_1990.py index 5a0698d65..45f7ec592 100644 --- a/tests/unit/test_costs_1990.py +++ b/tests/unit/test_costs_1990.py @@ -1,30 +1,34 @@ """Unit tests for costs.f90.""" -from process.fortran import cost_variables -from process.fortran import fwbs_variables as fv -from process.fortran import heat_transport_variables as htv -from process.fortran import buildings_variables -from process.fortran import build_variables -from process.fortran import ife_variables -from process.fortran import fwbs_variables -from process.fortran import structure_variables -from process.fortran import divertor_variables -from process.fortran import tfcoil_variables -from process.fortran import physics_variables -from process.fortran import pfcoil_variables -from process.fortran import current_drive_variables -from process.fortran import vacuum_variables -from process.fortran import heat_transport_variables -from process.fortran import pf_power_variables -from process.fortran import pulse_variables -from process.fortran import times_variables -from process.fortran import error_handling as eh -from process import fortran -import pytest +from typing import Any, NamedTuple + import numpy +import pytest from pytest import approx + +from process import fortran from process.costs import Costs -from typing import NamedTuple, Any +from process.fortran import ( + build_variables, + buildings_variables, + cost_variables, + current_drive_variables, + divertor_variables, + fwbs_variables, + heat_transport_variables, + ife_variables, + pf_power_variables, + pfcoil_variables, + physics_variables, + pulse_variables, + structure_variables, + tfcoil_variables, + times_variables, + vacuum_variables, +) +from process.fortran import error_handling as eh +from process.fortran import fwbs_variables as fv +from process.fortran import heat_transport_variables as htv @pytest.fixture diff --git a/tests/unit/test_costs_2015.py b/tests/unit/test_costs_2015.py index 85c0bce68..386292281 100644 --- a/tests/unit/test_costs_2015.py +++ b/tests/unit/test_costs_2015.py @@ -1,19 +1,22 @@ """Unit tests for costs_2015.f90.""" -import pytest +from typing import Any, NamedTuple + import numpy -from typing import NamedTuple, Any -from process.costs_2015 import Costs2015 +import pytest -from process.fortran import pfcoil_variables -from process.fortran import heat_transport_variables -from process.fortran import cost_variables -from process.fortran import current_drive_variables -from process.fortran import tfcoil_variables -from process.fortran import fwbs_variables -from process.fortran import build_variables -from process.fortran import physics_variables -from process.fortran import pf_power_variables +from process.costs_2015 import Costs2015 +from process.fortran import ( + build_variables, + cost_variables, + current_drive_variables, + fwbs_variables, + heat_transport_variables, + pf_power_variables, + pfcoil_variables, + physics_variables, + tfcoil_variables, +) @pytest.fixture diff --git a/tests/unit/test_cs_fatigue.py b/tests/unit/test_cs_fatigue.py index 0591873c2..b4374cd93 100644 --- a/tests/unit/test_cs_fatigue.py +++ b/tests/unit/test_cs_fatigue.py @@ -1,5 +1,7 @@ +from typing import Any, NamedTuple + import pytest -from typing import NamedTuple, Any + from process.cs_fatigue import CsFatigue @@ -16,7 +18,6 @@ def cs_fatigue_python(): class NcycleParam(NamedTuple): - max_hoop_stress: Any = None residual_stress: Any = None diff --git a/tests/unit/test_current_drive.py b/tests/unit/test_current_drive.py index b9b295f86..224eec857 100644 --- a/tests/unit/test_current_drive.py +++ b/tests/unit/test_current_drive.py @@ -1,14 +1,14 @@ -import pytest -from typing import NamedTuple, Any +from typing import Any, NamedTuple +import pytest +from process.current_drive import CurrentDrive from process.fortran import ( - current_drive_variables, cost_variables, - physics_variables, + current_drive_variables, heat_transport_variables, + physics_variables, ) -from process.current_drive import CurrentDrive from process.plasma_profiles import PlasmaProfile diff --git a/tests/unit/test_dcll.py b/tests/unit/test_dcll.py index 0ed48ef6c..f363bd1c8 100644 --- a/tests/unit/test_dcll.py +++ b/tests/unit/test_dcll.py @@ -1,16 +1,17 @@ +from typing import Any, NamedTuple + import pytest -from typing import NamedTuple, Any -from process.dcll import DCLL from process.blanket_library import BlanketLibrary -from process.fw import Fw +from process.dcll import DCLL from process.fortran import ( + build_variables, current_drive_variables, + dcll_module, fwbs_variables, physics_variables, - build_variables, - dcll_module, ) +from process.fw import Fw @pytest.fixture diff --git a/tests/unit/test_divertor.py b/tests/unit/test_divertor.py index 1bfd919ea..4b5dff229 100644 --- a/tests/unit/test_divertor.py +++ b/tests/unit/test_divertor.py @@ -1,10 +1,10 @@ """Unit tests for divertor.f90 subroutines/functions""" -from process.divertor import Divertor import pytest -from process.fortran import tfcoil_variables as tfv +from process.divertor import Divertor from process.fortran import divertor_variables as dv +from process.fortran import tfcoil_variables as tfv @pytest.fixture diff --git a/tests/unit/test_fw.py b/tests/unit/test_fw.py index 36d8a6b0c..4f949f37d 100644 --- a/tests/unit/test_fw.py +++ b/tests/unit/test_fw.py @@ -1,8 +1,9 @@ +from typing import Any, NamedTuple + import pytest -from typing import NamedTuple, Any -from process.fw import Fw from process.fortran import fwbs_variables +from process.fw import Fw @pytest.fixture diff --git a/tests/unit/test_ife.py b/tests/unit/test_ife.py index f23f241aa..5535e1898 100644 --- a/tests/unit/test_ife.py +++ b/tests/unit/test_ife.py @@ -1,22 +1,22 @@ """Unit tests for ife.""" -from typing import NamedTuple, Any +from typing import Any, NamedTuple -import pytest import numpy +import pytest -from process.ife import IFE from process.availability import Availability from process.costs import Costs from process.fortran import ( build_variables, - ife_variables, + buildings_variables, cost_variables, fwbs_variables, - physics_variables, heat_transport_variables, - buildings_variables, + ife_variables, + physics_variables, ) +from process.ife import IFE @pytest.fixture diff --git a/tests/unit/test_impurity_radiation.py b/tests/unit/test_impurity_radiation.py index 397653e41..e9df9f06f 100644 --- a/tests/unit/test_impurity_radiation.py +++ b/tests/unit/test_impurity_radiation.py @@ -1,9 +1,12 @@ """Unit tests for the impurity_radiation.f90.py module.""" -import pytest -import numpy as np + from typing import NamedTuple -from process.fortran import impurity_radiation_module + +import numpy as np +import pytest + import process.impurity_radiation as impurity_radiation +from process.fortran import impurity_radiation_module @pytest.fixture(autouse=True) @@ -36,48 +39,42 @@ def test_pimpden(): """ pimden_parameters = PimpdenParam( imp_element_index=0, - ne=np.array( - [ - 9.42593370e19, - 9.37237672e19, - 9.21170577e19, - 8.94392086e19, - 8.56902197e19, - 8.08700913e19, - 7.49788231e19, - 6.80164153e19, - 5.99828678e19, - 3.28986749e19, - ] - ), - te=np.array( - [ - 27.73451868, - 27.25167194, - 25.82164396, - 23.50149071, - 20.39190536, - 16.64794796, - 12.50116941, - 8.31182764, - 4.74643357, - 0.1, - ] - ), - expected_pimpden=np.array( - [ - 25483.040634309407, - 24983.364799017138, - 23519.36229676814, - 21187.36013272842, - 18173.71029818293, - 14685.542994819023, - 11005.497709894435, - 7448.7783515380615, - 4440.090318064716, - 294.54192663787137, - ] - ), + ne=np.array([ + 9.42593370e19, + 9.37237672e19, + 9.21170577e19, + 8.94392086e19, + 8.56902197e19, + 8.08700913e19, + 7.49788231e19, + 6.80164153e19, + 5.99828678e19, + 3.28986749e19, + ]), + te=np.array([ + 27.73451868, + 27.25167194, + 25.82164396, + 23.50149071, + 20.39190536, + 16.64794796, + 12.50116941, + 8.31182764, + 4.74643357, + 0.1, + ]), + expected_pimpden=np.array([ + 25483.040634309407, + 24983.364799017138, + 23519.36229676814, + 21187.36013272842, + 18173.71029818293, + 14685.542994819023, + 11005.497709894435, + 7448.7783515380615, + 4440.090318064716, + 294.54192663787137, + ]), ) pimpden = impurity_radiation.pimpden( @@ -110,20 +107,18 @@ def test_fradcore(): rho=np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]), coreradius=0.75000000000000011, coreradiationfraction=0.60000000000000009, - expected_fradcore=np.array( - [ - 0.6, - 0.6, - 0.6, - 0.6, - 0.6, - 0.6, - 0.6, - 0.6, - 0.0, - 0.0, - ] - ), + expected_fradcore=np.array([ + 0.6, + 0.6, + 0.6, + 0.6, + 0.6, + 0.6, + 0.6, + 0.6, + 0.0, + 0.0, + ]), ) fradcore = impurity_radiation.fradcore( fradcoreparam.rho, fradcoreparam.coreradius, fradcoreparam.coreradiationfraction @@ -151,34 +146,30 @@ def test_zav_of_te(): """ zavofteparam = ZavofteParam( imp_element_index=0, - te=np.array( - [ - 27.73451868, - 27.25167194, - 25.82164396, - 23.50149071, - 20.39190536, - 16.64794796, - 12.50116941, - 8.31182764, - 4.74643357, - 0.1, - ] - ), - expected_zav_of_te=np.array( - [ - 1.00000000000001, - 1.00000000000001, - 1.00000000000001, - 1.00000000000001, - 1.00000000000001, - 1.00000000000001, - 1.00000000000001, - 1.00000000000001, - 1.00000000000001, - 1.00000000000001, - ] - ), + te=np.array([ + 27.73451868, + 27.25167194, + 25.82164396, + 23.50149071, + 20.39190536, + 16.64794796, + 12.50116941, + 8.31182764, + 4.74643357, + 0.1, + ]), + expected_zav_of_te=np.array([ + 1.00000000000001, + 1.00000000000001, + 1.00000000000001, + 1.00000000000001, + 1.00000000000001, + 1.00000000000001, + 1.00000000000001, + 1.00000000000001, + 1.00000000000001, + 1.00000000000001, + ]), ) zav_of_te = impurity_radiation.zav_of_te( zavofteparam.imp_element_index, zavofteparam.te diff --git a/tests/unit/test_input.py b/tests/unit/test_input.py index 8756a02cc..c0eea11db 100644 --- a/tests/unit/test_input.py +++ b/tests/unit/test_input.py @@ -1,7 +1,8 @@ import pytest + +import process.init as init from process import fortran from process.utilities.f2py_string_patch import string_to_f2py_compatible -import process.init as init def _create_input_file(directory, content: str): diff --git a/tests/unit/test_main.py b/tests/unit/test_main.py index b348d5808..24a29d62e 100644 --- a/tests/unit/test_main.py +++ b/tests/unit/test_main.py @@ -1,17 +1,16 @@ """Unit tests for the main.py module.""" -from process import main -from process.main import Process -from process.main import SingleRun -from process.main import VaryRun -from process import fortran +import argparse +import shutil +from pathlib import Path + +import pytest + +from process import fortran, main +from process.main import Process, SingleRun, VaryRun from process.utilities.f2py_string_patch import ( f2py_compatible_to_string, ) -import pytest -from pathlib import Path -import argparse -import shutil def test_main(monkeypatch): diff --git a/tests/unit/test_maths_library.py b/tests/unit/test_maths_library.py index a020758a0..010a53859 100644 --- a/tests/unit/test_maths_library.py +++ b/tests/unit/test_maths_library.py @@ -1,5 +1,7 @@ """Unit tests for maths_library.f90.""" + import pytest + from process import fortran diff --git a/tests/unit/test_mfile2dict.py b/tests/unit/test_mfile2dict.py index 33b1ba9aa..b3f7ae9ea 100644 --- a/tests/unit/test_mfile2dict.py +++ b/tests/unit/test_mfile2dict.py @@ -1,11 +1,12 @@ -import pytest -import os -import tempfile import json -import yaml +import os import pickle +import tempfile from pathlib import Path +import pytest +import yaml + from process.io import mfile2dict diff --git a/tests/unit/test_neoclassics.py b/tests/unit/test_neoclassics.py index 99909c57d..83563fc6e 100644 --- a/tests/unit/test_neoclassics.py +++ b/tests/unit/test_neoclassics.py @@ -1,6 +1,7 @@ -import pytest +from typing import Any, NamedTuple + import numpy -from typing import NamedTuple, Any +import pytest from process.fortran import neoclassics_module, physics_variables from process.stellarator import Neoclassics diff --git a/tests/unit/test_pfcoil.py b/tests/unit/test_pfcoil.py index a3fb892d6..22ef6fd5a 100644 --- a/tests/unit/test_pfcoil.py +++ b/tests/unit/test_pfcoil.py @@ -1,14 +1,17 @@ """Unit tests for pfcoil.f90.""" + +from typing import NamedTuple + +import numpy as np import pytest -from process.pfcoil import PFCoil, bfield, rsid +from numpy.testing import assert_array_almost_equal + +from process.cs_fatigue import CsFatigue +from process.fortran import build_variables as bv from process.fortran import pfcoil_module as pf from process.fortran import pfcoil_variables as pfv from process.fortran import tfcoil_variables as tfv -from process.fortran import build_variables as bv -import numpy as np -from numpy.testing import assert_array_almost_equal -from typing import NamedTuple -from process.cs_fatigue import CsFatigue +from process.pfcoil import PFCoil, bfield, rsid @pytest.fixture @@ -51,104 +54,100 @@ def test_rsid(pfcoil): bzin = np.zeros(nptsmx) nfix = 14 ngrp = 4 - ccls = np.array( - [ - 14742063.826112622, - 20032681.634901665, - 580406.62653667293, - 429746.74788703024, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ) + ccls = np.array([ + 14742063.826112622, + 20032681.634901665, + 580406.62653667293, + 429746.74788703024, + 0, + 0, + 0, + 0, + 0, + 0, + ]) brssq = 1.7347234759768071e-18 brnrm = 0 bzssq = 164.92735114640433 bznrm = -0.12924373312291032 ssq = 0 - bfix = np.array( - [ - -2.7755575615628914e-17, - -2.7755575615628914e-17, - -2.0816681711721685e-17, - -6.2450045135165055e-17, - 4.8572257327350599e-17, - 8.3266726846886741e-17, - 6.9388939039072284e-18, - 4.163336342344337e-17, - 2.0816681711721685e-17, - 1.3877787807814457e-17, - -2.7755575615628914e-17, - -2.7755575615628914e-17, - 1.3877787807814457e-17, - 3.1225022567582528e-17, - -1.3877787807814457e-17, - -3.4694469519536142e-18, - 6.9388939039072284e-18, - -2.7755575615628914e-17, - 3.4694469519536142e-18, - 2.4286128663675299e-17, - 4.5102810375396984e-17, - 1.3877787807814457e-17, - 1.0408340855860843e-17, - -6.9388939039072284e-18, - 1.3877787807814457e-17, - 1.7347234759768071e-17, - 2.7755575615628914e-17, - -6.9388939039072284e-18, - -1.7347234759768071e-18, - 2.2551405187698492e-17, - 1.0408340855860843e-17, - 1.7347234759768071e-18, - -0.3537283013510894, - -0.34304632621819631, - -0.33256831505329448, - -0.32230579414441957, - -0.31226839635096026, - -0.30246398750499448, - -0.29289879096799015, - -0.283577511993583, - -0.27450346141940818, - -0.26567867760304337, - -0.25710404545385829, - -0.24877941153731781, - -0.24070369440957809, - -0.23287498952924324, - -0.22529066827105398, - -0.21794747072660745, - -0.21084159211740575, - -0.20396876276560738, - -0.19732432166849023, - -0.1909032838051643, - -0.18470040136999577, - -0.17871021917825794, - -0.17292712452755193, - -0.16734539182494823, - -0.16195922230675014, - -0.15676277918619164, - -0.15175021856634768, - -0.1469157164516591, - -0.14225349218352654, - -0.13775782861391137, - -0.13342308931695324, - -0.12924373312291032, - 6.9533558060713876e-310, - 3.7299818735403947e-315, - 1.2461007517394582e-316, - 9.3633631912996395e-97, - 6.9533558060721781e-310, - 6.9533472776350595e-310, - 6.9533558069464767e-310, - 6.9533558071276999e-310, - 6.9533558060745496e-310, - 3.7299818735403947e-315, - ] - ) + bfix = np.array([ + -2.7755575615628914e-17, + -2.7755575615628914e-17, + -2.0816681711721685e-17, + -6.2450045135165055e-17, + 4.8572257327350599e-17, + 8.3266726846886741e-17, + 6.9388939039072284e-18, + 4.163336342344337e-17, + 2.0816681711721685e-17, + 1.3877787807814457e-17, + -2.7755575615628914e-17, + -2.7755575615628914e-17, + 1.3877787807814457e-17, + 3.1225022567582528e-17, + -1.3877787807814457e-17, + -3.4694469519536142e-18, + 6.9388939039072284e-18, + -2.7755575615628914e-17, + 3.4694469519536142e-18, + 2.4286128663675299e-17, + 4.5102810375396984e-17, + 1.3877787807814457e-17, + 1.0408340855860843e-17, + -6.9388939039072284e-18, + 1.3877787807814457e-17, + 1.7347234759768071e-17, + 2.7755575615628914e-17, + -6.9388939039072284e-18, + -1.7347234759768071e-18, + 2.2551405187698492e-17, + 1.0408340855860843e-17, + 1.7347234759768071e-18, + -0.3537283013510894, + -0.34304632621819631, + -0.33256831505329448, + -0.32230579414441957, + -0.31226839635096026, + -0.30246398750499448, + -0.29289879096799015, + -0.283577511993583, + -0.27450346141940818, + -0.26567867760304337, + -0.25710404545385829, + -0.24877941153731781, + -0.24070369440957809, + -0.23287498952924324, + -0.22529066827105398, + -0.21794747072660745, + -0.21084159211740575, + -0.20396876276560738, + -0.19732432166849023, + -0.1909032838051643, + -0.18470040136999577, + -0.17871021917825794, + -0.17292712452755193, + -0.16734539182494823, + -0.16195922230675014, + -0.15676277918619164, + -0.15175021856634768, + -0.1469157164516591, + -0.14225349218352654, + -0.13775782861391137, + -0.13342308931695324, + -0.12924373312291032, + 6.9533558060713876e-310, + 3.7299818735403947e-315, + 1.2461007517394582e-316, + 9.3633631912996395e-97, + 6.9533558060721781e-310, + 6.9533472776350595e-310, + 6.9533558069464767e-310, + 6.9533558071276999e-310, + 6.9533558060745496e-310, + 3.7299818735403947e-315, + ]) gmat = np.reshape( [ -7.5172758677351012e-09, @@ -923,81 +922,73 @@ def test_bfield(): :param pfcoil: PFCoil object :type pfcoil: process.pfcoil.PFCoil """ - rc = np.array( - [ - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - 2.6084100000000001, - ] - ) - zc = np.array( - [ - 0.58327007281470211, - 1.7498102184441064, - 2.9163503640735104, - 4.0828905097029144, - 5.2494306553323193, - 6.4159708009617233, - 7.5825109465911273, - -0.58327007281470211, - -1.7498102184441064, - -2.9163503640735104, - -4.0828905097029144, - -5.2494306553323193, - -6.4159708009617233, - -7.5825109465911273, - ] - ) - cc = np.array( - [ - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - 12444820.564847374, - ] - ) + rc = np.array([ + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + 2.6084100000000001, + ]) + zc = np.array([ + 0.58327007281470211, + 1.7498102184441064, + 2.9163503640735104, + 4.0828905097029144, + 5.2494306553323193, + 6.4159708009617233, + 7.5825109465911273, + -0.58327007281470211, + -1.7498102184441064, + -2.9163503640735104, + -4.0828905097029144, + -5.2494306553323193, + -6.4159708009617233, + -7.5825109465911273, + ]) + cc = np.array([ + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + 12444820.564847374, + ]) rp = 6.0223258064516134 zp = 0 - xc_exp = np.array( - [ - 2.36278088e-06, - 2.05233185e-06, - 1.62139434e-06, - 1.22298265e-06, - 9.08339602e-07, - 6.75290638e-07, - 5.06601647e-07, - 2.36278088e-06, - 2.05233185e-06, - 1.62139434e-06, - 1.22298265e-06, - 9.08339602e-07, - 6.75290638e-07, - 5.06601647e-07, - ] - ) + xc_exp = np.array([ + 2.36278088e-06, + 2.05233185e-06, + 1.62139434e-06, + 1.22298265e-06, + 9.08339602e-07, + 6.75290638e-07, + 5.06601647e-07, + 2.36278088e-06, + 2.05233185e-06, + 1.62139434e-06, + 1.22298265e-06, + 9.08339602e-07, + 6.75290638e-07, + 5.06601647e-07, + ]) br_exp = -2.7755575615628914e-17 bz_exp = -0.3537283013510894 psi_exp = 232.7112153010189 @@ -1078,120 +1069,112 @@ def test_waveform(monkeypatch, pfcoil): monkeypatch.setattr( pfv, "curpfb", - np.array( - [ - 0.067422231232391661, - -2.9167273287450968, - -8.1098913365453491, - -8.1098913365453491, - -5.5984385047179153, - -5.5984385047179153, - -186.98751599968148, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 0.067422231232391661, + -2.9167273287450968, + -8.1098913365453491, + -8.1098913365453491, + -5.5984385047179153, + -5.5984385047179153, + -186.98751599968148, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr( pfv, "curpff", - np.array( - [ - 0.067422231232391661, - -2.9167273287450968, - -8.1098913365453491, - -8.1098913365453491, - -5.5984385047179153, - -5.5984385047179153, - -186.98751599968148, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 0.067422231232391661, + -2.9167273287450968, + -8.1098913365453491, + -8.1098913365453491, + -5.5984385047179153, + -5.5984385047179153, + -186.98751599968148, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr( pfv, "curpfs", - np.array( - [ - 14.742063826112622, - 20.032681634901664, - 0.58040662653667285, - 0.58040662653667285, - 0.42974674788703021, - 0.42974674788703021, - 174.22748790786324, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 14.742063826112622, + 20.032681634901664, + 0.58040662653667285, + 0.58040662653667285, + 0.42974674788703021, + 0.42974674788703021, + 174.22748790786324, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) - ric_exp = np.array( - [ - 14.74206383, - 20.03268163, - -8.10989134, - -8.10989134, - -5.5984385, - -5.5984385, - -186.987516, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - ] - ) + ric_exp = np.array([ + 14.74206383, + 20.03268163, + -8.10989134, + -8.10989134, + -5.5984385, + -5.5984385, + -186.987516, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + ]) waves_exp = np.array( [ [0.0, 0.00457346, 0.00457346, 0.00457346, 1.0, 0.0], @@ -2001,62 +1984,58 @@ def test_hoop_stress(pfcoil, monkeypatch): monkeypatch.setattr( pfv, "rb", - np.array( - [ - 6.8520884119768697, - 6.9480065348448967, - 18.98258241468535, - 18.98258241468535, - 17.22166645654087, - 17.22166645654087, - 2.88462, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 6.8520884119768697, + 6.9480065348448967, + 18.98258241468535, + 18.98258241468535, + 17.22166645654087, + 17.22166645654087, + 2.88462, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr( pfv, "ra", - np.array( - [ - 5.6944236847973242, - 5.5985055619292972, - 17.819978201682968, - 17.819978201682968, - 16.385123084628962, - 16.385123084628962, - 2.3321999999999998, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - ] - ), + np.array([ + 5.6944236847973242, + 5.5985055619292972, + 17.819978201682968, + 17.819978201682968, + 16.385123084628962, + 16.385123084628962, + 2.3321999999999998, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + 0, + ]), ) monkeypatch.setattr(tfv, "poisson_steel", 0.29999999999999999) diff --git a/tests/unit/test_physics.py b/tests/unit/test_physics.py index 9911690e5..d69de1339 100644 --- a/tests/unit/test_physics.py +++ b/tests/unit/test_physics.py @@ -1,32 +1,34 @@ """Unit tests for physics.f90.""" from typing import Any, NamedTuple + +import numpy +import pytest + +from process.current_drive import CurrentDrive from process.fortran import ( constants, - physics_variables, - physics_module, current_drive_variables, impurity_radiation_module, + physics_module, + physics_variables, ) -import numpy -import pytest +from process.impurity_radiation import initialise_imprad from process.physics import ( Physics, - calculate_poloidal_field, - diamagnetic_fraction_scene, - diamagnetic_fraction_hender, - ps_fraction_scene, - calculate_plasma_current_peng, calculate_beta_limit, calculate_current_coefficient_hastie, - vscalc, - rether, + calculate_plasma_current_peng, calculate_poloidal_beta, + calculate_poloidal_field, + diamagnetic_fraction_hender, + diamagnetic_fraction_scene, + ps_fraction_scene, res_diff_time, + rether, + vscalc, ) from process.plasma_profiles import PlasmaProfile -from process.current_drive import CurrentDrive -from process.impurity_radiation import initialise_imprad @pytest.fixture diff --git a/tests/unit/test_physics_functions.py b/tests/unit/test_physics_functions.py index 257a9a4c7..80bac0743 100644 --- a/tests/unit/test_physics_functions.py +++ b/tests/unit/test_physics_functions.py @@ -1,13 +1,14 @@ """Unit tests for physics_functions.f90.""" - from typing import Any, NamedTuple -from process.fortran import physics_variables as pv -from process import physics_functions + import numpy as np import pytest from pytest import approx +from process import physics_functions +from process.fortran import physics_variables as pv + class SetFusionPowersParam(NamedTuple): f_alpha_plasma: Any = None diff --git a/tests/unit/test_plasma_geom.py b/tests/unit/test_plasma_geom.py index cc42b561f..e5fcb65cf 100644 --- a/tests/unit/test_plasma_geom.py +++ b/tests/unit/test_plasma_geom.py @@ -1,7 +1,9 @@ """Unit tests for plasma_geometry.f90.""" +from typing import Any, NamedTuple + import pytest -from typing import NamedTuple, Any + from process.plasma_geometry import PlasmaGeom @@ -18,7 +20,6 @@ def plasma(): class XparamParam(NamedTuple): - a: Any = None kap: Any = None @@ -258,7 +259,6 @@ def test_xsect0(a, kap, tri, expected_xsect0, plasma): class XsurfParam(NamedTuple): - rmajor: Any = None rminor: Any = None @@ -329,7 +329,6 @@ def test_xsurf(xsurfparam, monkeypatch, plasma): class SurfaParam(NamedTuple): - a: Any = None r: Any = None diff --git a/tests/unit/test_plasma_profiles.py b/tests/unit/test_plasma_profiles.py index e428d6356..382791d18 100644 --- a/tests/unit/test_plasma_profiles.py +++ b/tests/unit/test_plasma_profiles.py @@ -1,9 +1,11 @@ -from typing import NamedTuple, Any -import pytest +from typing import Any, NamedTuple + import numpy as np +import pytest + from process.fortran import divertor_variables, physics_variables from process.plasma_profiles import PlasmaProfile -from process.profiles import TProfile, NProfile +from process.profiles import NProfile, TProfile class ProfileParam(NamedTuple): diff --git a/tests/unit/test_power.py b/tests/unit/test_power.py index 5d8b76c5c..702e0cc5a 100644 --- a/tests/unit/test_power.py +++ b/tests/unit/test_power.py @@ -1,22 +1,24 @@ -from typing import NamedTuple, Any -import pytest -import numpy +from typing import Any, NamedTuple +import numpy +import pytest -from process.fortran import fwbs_variables -from process.fortran import heat_transport_variables -from process.fortran import pfcoil_variables -from process.fortran import numerics -from process.fortran import physics_variables -from process.fortran import build_variables -from process.fortran import pf_power_variables -from process.fortran import times_variables -from process.fortran import buildings_variables +from process.fortran import ( + build_variables, + buildings_variables, + constraint_variables, + cost_variables, + current_drive_variables, + fwbs_variables, + heat_transport_variables, + numerics, + pf_power_variables, + pfcoil_variables, + physics_variables, + tfcoil_variables, + times_variables, +) from process.fortran import primary_pumping_variables as ppv -from process.fortran import constraint_variables -from process.fortran import cost_variables -from process.fortran import current_drive_variables -from process.fortran import tfcoil_variables from process.power import Power @@ -31,7 +33,6 @@ def power(): class CryoParam(NamedTuple): - qnuc: Any = None inuclear: Any = None @@ -166,7 +167,6 @@ def test_cryo(cryoparam, monkeypatch, power): class PfpwrParam(NamedTuple): - iohcl: Any = None peakmva: Any = None @@ -1870,7 +1870,6 @@ def test_pfpwr(pfpwrparam, monkeypatch, power): class AcpowParam(NamedTuple): - efloor: Any = None baseel: Any = None @@ -2008,7 +2007,6 @@ def test_acpow(acpowparam, monkeypatch, power): class Power2Param(NamedTuple): - pnetelin: Any = None ipnet: Any = None @@ -2654,7 +2652,6 @@ def test_power2(power2param, monkeypatch, power): class Power3Param(NamedTuple): - etacd: Any = None htpmw: Any = None diff --git a/tests/unit/test_pulse.py b/tests/unit/test_pulse.py index 5694c9f21..3cec0de11 100755 --- a/tests/unit/test_pulse.py +++ b/tests/unit/test_pulse.py @@ -1,22 +1,17 @@ -import pytest -import numpy -from typing import NamedTuple, Any - - -from process.fortran import numerics - -from process.fortran import physics_variables - -from process.fortran import pulse_variables - -from process.fortran import pf_power_variables +from typing import Any, NamedTuple -from process.fortran import times_variables - -from process.fortran import constraint_variables - -from process.fortran import pfcoil_variables +import numpy +import pytest +from process.fortran import ( + constraint_variables, + numerics, + pf_power_variables, + pfcoil_variables, + physics_variables, + pulse_variables, + times_variables, +) from process.pulse import Pulse @@ -31,7 +26,6 @@ def pulse(): class TohswgParam(NamedTuple): - t_current_ramp_up_min: Any = None vpfskv: Any = None @@ -70,7 +64,6 @@ class TohswgParam(NamedTuple): class BurnParam(NamedTuple): - res_plasma: Any = None vsres: Any = None diff --git a/tests/unit/test_sctfcoil.py b/tests/unit/test_sctfcoil.py index 0a6e80064..4f7020b4e 100644 --- a/tests/unit/test_sctfcoil.py +++ b/tests/unit/test_sctfcoil.py @@ -1,16 +1,19 @@ -import pytest +from typing import Any, NamedTuple + import numpy -from typing import NamedTuple, Any - -from process.fortran import sctfcoil_module -from process.fortran import tfcoil_variables -from process.fortran import global_variables -from process.fortran import physics_variables -from process.fortran import build_variables -from process.fortran import fwbs_variables -from process.fortran import divertor_variables -from process.sctfcoil import Sctfcoil +import pytest + from process import sctfcoil as sctf +from process.fortran import ( + build_variables, + divertor_variables, + fwbs_variables, + global_variables, + physics_variables, + sctfcoil_module, + tfcoil_variables, +) +from process.sctfcoil import Sctfcoil @pytest.fixture @@ -6475,14 +6478,12 @@ class PlaneStressParam(NamedTuple): n_radial_array=100, nlayers=3, nu=numpy.array([0.3, 0.34006912702297704, 0.3]), - rad=numpy.array( - [ - 3.6732023601326333, - 3.7688101124061717, - 3.7649909451102674, - 3.8249909451102675, - ] - ), + rad=numpy.array([ + 3.6732023601326333, + 3.7688101124061717, + 3.7649909451102674, + 3.8249909451102675, + ]), ey=numpy.array([2.05000000e11, 21085960915.80571, 2.05000000e11]), j=numpy.array([0.00000000e00, -2245759961.294637, 0.00000000e00]), expected_sigr=[ diff --git a/tests/unit/test_stellarator.py b/tests/unit/test_stellarator.py index a642d7b98..105537dda 100644 --- a/tests/unit/test_stellarator.py +++ b/tests/unit/test_stellarator.py @@ -1,31 +1,32 @@ -import pytest -from typing import NamedTuple, Any +from typing import Any, NamedTuple + import numpy +import pytest +from process.availability import Availability +from process.blanket_library import BlanketLibrary +from process.buildings import Buildings +from process.costs import Costs +from process.current_drive import CurrentDrive from process.fortran import ( - physics_variables, - stellarator_configuration, - stellarator_module, build_variables, + cost_variables, fwbs_variables, heat_transport_variables, + impurity_radiation_module, + physics_variables, + stellarator_configuration, + stellarator_module, structure_variables, tfcoil_variables, - impurity_radiation_module, - cost_variables, ) -from process.power import Power -from process.stellarator import Stellarator, Neoclassics -from process.vacuum import Vacuum -from process.availability import Availability -from process.buildings import Buildings -from process.costs import Costs -from process.plasma_profiles import PlasmaProfile -from process.hcpb import CCFE_HCPB -from process.blanket_library import BlanketLibrary from process.fw import Fw -from process.current_drive import CurrentDrive +from process.hcpb import CCFE_HCPB from process.physics import Physics +from process.plasma_profiles import PlasmaProfile +from process.power import Power +from process.stellarator import Neoclassics, Stellarator +from process.vacuum import Vacuum @pytest.fixture diff --git a/tests/unit/test_superconductors.py b/tests/unit/test_superconductors.py index 5f35508f1..eeea2437d 100644 --- a/tests/unit/test_superconductors.py +++ b/tests/unit/test_superconductors.py @@ -1,5 +1,6 @@ +from typing import Any, NamedTuple + import pytest -from typing import NamedTuple, Any import process.superconductors as superconductors diff --git a/tests/unit/test_tfcoil.py b/tests/unit/test_tfcoil.py index 3eacf3851..83ace36a4 100644 --- a/tests/unit/test_tfcoil.py +++ b/tests/unit/test_tfcoil.py @@ -1,15 +1,15 @@ """Unit and Integration tests for tfcoil.f90.""" from typing import NamedTuple -from process.sctfcoil import Sctfcoil -import pytest -from process.tfcoil import TFcoil +import pytest -from process.fortran import tfcoil_variables as tfv +from process.build import Build from process.fortran import build_variables as bv from process.fortran import fwbs_variables as fwbsv -from process.build import Build +from process.fortran import tfcoil_variables as tfv +from process.sctfcoil import Sctfcoil +from process.tfcoil import TFcoil @pytest.fixture diff --git a/tests/unit/test_vacuum.py b/tests/unit/test_vacuum.py index d288284d0..85ed4475a 100644 --- a/tests/unit/test_vacuum.py +++ b/tests/unit/test_vacuum.py @@ -1,10 +1,10 @@ import pytest -from process.vacuum import Vacuum from process.fortran import physics_variables as pv -from process.fortran import vacuum_variables as vacv from process.fortran import tfcoil_variables as tfv from process.fortran import times_variables as tv +from process.fortran import vacuum_variables as vacv +from process.vacuum import Vacuum @pytest.fixture diff --git a/tests/unit/test_water_usage.py b/tests/unit/test_water_usage.py index 0ed52d615..96ecb31b9 100644 --- a/tests/unit/test_water_usage.py +++ b/tests/unit/test_water_usage.py @@ -1,5 +1,6 @@ +from typing import Any, NamedTuple + import pytest -from typing import NamedTuple, Any from process.fortran import water_usage_variables from process.water_use import WaterUse diff --git a/tracking/git.py b/tracking/git.py index 4ba66e582..98c6d6ae9 100644 --- a/tracking/git.py +++ b/tracking/git.py @@ -1,7 +1,7 @@ """Simple submodule to provide access to some git attributes about the current repository""" -from pathlib import Path import subprocess +from pathlib import Path def git_commit_message(directory=None) -> str: diff --git a/tracking/run_tracking_inputs.py b/tracking/run_tracking_inputs.py index 3a3ed3777..a75f593da 100644 --- a/tracking/run_tracking_inputs.py +++ b/tracking/run_tracking_inputs.py @@ -1,9 +1,9 @@ """Run the tracked files and move into tracking directory.""" import argparse -from pathlib import Path import shutil import subprocess +from pathlib import Path from tracking_data import ProcessTracker diff --git a/tracking/tracking_data.py b/tracking/tracking_data.py index 6ad261ab5..602bca535 100644 --- a/tracking/tracking_data.py +++ b/tracking/tracking_data.py @@ -31,32 +31,32 @@ e.g. FOO.bar says `bar`'s parent module is `FOO`. """ +import argparse import datetime +import inspect +import itertools +import json import logging +import math import pathlib -import json -import itertools + +import git import pandas as pd -import inspect -import argparse -import math -from bokeh.plotting import figure +from bokeh.embed import file_html +from bokeh.layouts import gridplot from bokeh.models import ( ColumnDataSource, - HoverTool, DatetimeTickFormatter, - Tabs, + HoverTool, TabPanel, + Tabs, ) -from bokeh.layouts import gridplot from bokeh.palettes import Category10 +from bokeh.plotting import figure from bokeh.resources import CDN -from bokeh.embed import file_html - -import git -from process.io import mfile as mf from process import fortran +from process.io import mfile as mf logging.basicConfig(level=logging.INFO, filename="tracker.log") logger = logging.getLogger("PROCESS Tracker")